From 030851b318918df503cee14605cae8f82873b114 Mon Sep 17 00:00:00 2001 From: shenyunhang Date: Wed, 29 Nov 2023 03:43:10 +0800 Subject: [PATCH] This is the 1st commit --- .asset/framework.png | Bin 0 -> 1400742 bytes .asset/head.png | Bin 0 -> 5100419 bytes .asset/radar.png | Bin 0 -> 424539 bytes .gitignore | 53 + README.md | 270 +++ ape/__init__.py | 5 + ape/checkpoint/__init__.py | 6 + ape/checkpoint/detection_checkpoint.py | 45 + ape/data/__init__.py | 20 + ape/data/build_copypaste.py | 255 +++ ape/data/build_multi_dataset.py | 741 ++++++++ ape/data/build_multi_dataset_copypaste.py | 805 ++++++++ ape/data/common_copypaste.py | 83 + ape/data/dataset_mapper.py | 41 + ape/data/dataset_mapper_copypaste.py | 499 +++++ ape/data/dataset_mapper_detr_instance.py | 288 +++ ape/data/dataset_mapper_detr_instance_exp.py | 232 +++ ape/data/dataset_mapper_detr_panoptic.py | 443 +++++ .../dataset_mapper_detr_panoptic_copypaste.py | 676 +++++++ ape/data/dataset_mapper_detr_semantic.py | 244 +++ 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file mode 100644 index 0000000..a2838ec --- /dev/null +++ b/.gitignore @@ -0,0 +1,53 @@ +# output dir +output +instant_test_output +inference_test_output + + +*.png +*.json +*.diff +*.jpg +!/projects/DensePose/doc/images/*.jpg + +# compilation and distribution +__pycache__ +_ext +*.pyc +*.pyd +*.so +*.dll +*.egg-info/ +build/ +dist/ +wheels/ + +# pytorch/python/numpy formats +*.pth +*.pkl +*.npy +*.ts +model_ts*.txt + +# ipython/jupyter notebooks +*.ipynb +**/.ipynb_checkpoints/ + +# Editor temporaries +*.swn +*.swo +*.swp +*~ + +# editor settings +.idea +.vscode +_darcs + +# project dirs +/ape/model_zoo/configs +/datasets/* +!/datasets/*.* +/projects/*/datasets +/models +/snippet diff --git a/README.md b/README.md new file mode 100644 index 0000000..5ac277a --- /dev/null +++ b/README.md @@ -0,0 +1,270 @@ +# APE + +APE: Aligning and Prompting Everything All at Once for Universal Visual Perception + +--- + + +[[`Paper`](https://arxiv.org/abs/2312.02153)] [[`Demo`](https://huggingface.co/spaces/shenyunhang/APE)] [[`BibTex`](#black_nib-citation)] + + +## :bulb: Highlight + +- **High Performance.** SotA (or competitive) performance on **160** datasets with only one model. +- **Perception in the Wild.** Detect and segment **everything** with thousands of vocabularies or language descriptions all at once. +- **Flexible.** Support both foreground objects and background stuff for instance segmentation and semantic segmentation. + + +## :label: TODO + +- [x] Release inference code and demo. +- [x] Release checkpoints. +- [x] Release training codes. +- [ ] Add clean docs. + + +## :hammer_and_wrench: Install + +1. Clone the APE repository from GitHub. + +```bash +git clone https://github.com/shenyunhang/APE +cd APE +``` + +2. Install the required dependencies and APE. + +```bash +pip3 install -r requirements.txt +python3 -m pip install -e . +``` + + +## :arrow_forward: Demo Localy + +**Web UI demo** +``` +pip3 install gradio +cd APE/demo +python3 app.py +``` +If you have GPUs, this demo will detect them and use one GPU. + + +## :books: Data Prepare +Following [here](https://github.com/shenyunhang/APE/blob/main/datasets/README.md) to prepare the following datasets: + +| | COCO | LVIS | Objects365 | Openimages | VisualGenome | SA-1B | RefCOCO | GQA | PhraseCut | Flickr30k | ODinW | SegInW | Roboflow100 | ADE20k | ADE-full | BDD10k | Cityscapes | PC459 | PC59 | VOC | D3 | +|:-----:|:-------:|:-------:|:----------:|:----------:|:------------:|:-------:|:-------:|:-------:|:---------:|:---------:|:-------:|:-------:|:-----------:|:-------:|:--------:|:-------:|:----------:|:-------:|:-------:|:-------:|:-------:| +| Train | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | +| Test | ✓ | ✓ | ✓ | ✓ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | + + +## :test_tube: Inference + +### Infer on 160+ dataset +We provide several scripts to evaluate all models. + +It is necessary to adjust the checkpoint location and GPU number in the scripts before running them. + +```bash +scripts/eval_all_D.sh +scripts/eval_all_C.sh +scripts/eval_all_B.sh +scripts/eval_all_A.sh +``` + +### Infer on images or videos + +APE-D +``` +python3.9 demo/demo_lazy.py \ +--config-file configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_16x4_1080k.py \ +--input image1.jpg image2.jpg image3.jpg \ +--output /path/to/output/dir \ +--confidence-threshold 0.1 \ +--text-prompt 'person,car,chess piece of horse head' \ +--with-box \ +--with-mask \ +--with-sseg \ +--opts \ +train.init_checkpoint=/path/to/APE-D/checkpoint \ +model.model_vision.select_box_nums_for_evaluation=500 \ +model.model_vision.text_feature_bank_reset=True \ +``` + + +## :train: Training + +### Prepare backbone and language models +```bash +git lfs install +git clone https://huggingface.co/QuanSun/EVA-CLIP models/QuanSun/EVA-CLIP/ +git clone https://huggingface.co/BAAI/EVA models/BAAI/EVA/ +git clone https://huggingface.co/Yuxin-CV/EVA-02 models/Yuxin-CV/EVA-02/ +``` + +Resize patch size: +```bash +python3.9 tools/eva_interpolate_patch_14to16.py --input models/QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt --output models/QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14to16_plus_s9B.pt --image_size 224 +python3.9 tools/eva_interpolate_patch_14to16.py --input models/QuanSun/EVA-CLIP/EVA01_CLIP_g_14_plus_psz14_s11B.pt --output models/QuanSun/EVA-CLIP/EVA01_CLIP_g_14_plus_psz14to16_s11B.pt --image_size 224 +python3.9 tools/eva_interpolate_patch_14to16.py --input models/QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14_s6B.pt --output models/QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14to16_s6B.pt --image_size 336 +``` + +### Train APE-D + +Single node: +```bash +python3.9 tools/train_net.py \ +--num-gpus 8 \ +--resume \ +--config-file configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_16x4_1080k_mdl.py \ +train.output_dir=output/APE/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_16x4_1080k_mdl_`date +'%Y%m%d_%H%M%S'` +``` + +Multiple nodes: +```bash +python3.9 tools/train_net.py \ +--dist-url="tcp://${MASTER_IP}:${MASTER_PORT}" \ +--num-gpus ${HOST_GPU_NUM} \ +--num-machines ${HOST_NUM} \ +--machine-rank ${INDEX} \ +--resume \ +--config-file configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_16x4_1080k_mdl.py \ +train.output_dir=output/APE/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_16x4_1080k_mdl_`date +'%Y%m%d_%H'`0000 +``` + +### Train APE-C + +Single node: +```bash +python3.9 tools/train_net.py \ +--num-gpus 8 \ +--resume \ +--config-file configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_1080k.py \ +train.output_dir=output/APE/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_1080k_`date +'%Y%m%d_%H%M%S'` +``` + +Multiple nodes: +```bash +python3.9 tools/train_net.py \ +--dist-url="tcp://${MASTER_IP}:${MASTER_PORT}" \ +--num-gpus ${HOST_GPU_NUM} \ +--num-machines ${HOST_NUM} \ +--machine-rank ${INDEX} \ +--resume \ +--config-file configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_1080k.py \ +train.output_dir=output/APE/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_1080k_`date +'%Y%m%d_%H'`0000 +``` + +### Train APE-B + +Single node: +```bash +python3.9 tools/train_net.py \ +--num-gpus 8 \ +--resume \ +--config-file configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_1080k.py \ +train.output_dir=output/APE/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_1080k_`date +'%Y%m%d_%H%M%S'` +``` + +Multiple nodes: +```bash +python3.9 tools/train_net.py \ +--dist-url="tcp://${MASTER_IP}:${MASTER_PORT}" \ +--num-gpus ${HOST_GPU_NUM} \ +--num-machines ${HOST_NUM} \ +--machine-rank ${INDEX} \ +--resume \ +--config-file configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_1080k.py \ +train.output_dir=output/APE/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_1080k_`date +'%Y%m%d_%H'`0000 +``` + +### Train APE-A + +Single node: +```bash +python3.9 tools/train_net.py \ +--num-gpus 8 \ +--resume \ +--config-file configs/LVISCOCOCOCOSTUFF_O365_OID_VG/ape_deta/ape_deta_vitl_eva02_lsj1024_cp_720k.py \ +train.output_dir=output/APE/configs/LVISCOCOCOCOSTUFF_O365_OID_VG/ape_deta/ape_deta_vitl_eva02_lsj1024_cp_720k_`date +'%Y%m%d_%H%M%S'` +``` + +Multiple nodes: +```bash +python3.9 tools/train_net.py \ +--dist-url="tcp://${MASTER_IP}:${MASTER_PORT}" \ +--num-gpus ${HOST_GPU_NUM} \ +--num-machines ${HOST_NUM} \ +--machine-rank ${INDEX} \ +--resume \ +--config-file configs/LVISCOCOCOCOSTUFF_O365_OID_VG/ape_deta/ape_deta_vitl_eva02_lsj1024_cp_720k.py \ +train.output_dir=output/APE/configs/LVISCOCOCOCOSTUFF_O365_OID_VG/ape_deta/ape_deta_vitl_eva02_lsj1024_cp_720k_`date +'%Y%m%d_%H'`0000 +``` + + + +## :luggage: Checkpoints + +``` +git lfs install +git clone https://huggingface.co/shenyunhang/APE +``` + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      nameCheckpointConfig
      1APE-AHF linklink
      2APE-BHF link + link
      3APE-CHF link + link
      4APE-DHF link + link
      + + +## :medal_military: Results + +radar + + +## :black_nib: Citation + +If you find our work helpful for your research, please consider citing the following BibTeX entry. + +```bibtex +@article{shen2023aligning, + title={Aligning and Prompting Everything All at Once for Universal Visual Perception}, + author={Yunhang Shen and Chaoyou Fu and Peixian Chen and Mengdan Zhang and Ke Li and Xing Sun and Yunsheng Wu and Shaohui Lin and Rongrong Ji}, + journal={arXiv preprint arXiv:2312.02153}, + year={2023} +} +``` \ No newline at end of file diff --git a/ape/__init__.py b/ape/__init__.py new file mode 100644 index 0000000..6f40cb4 --- /dev/null +++ b/ape/__init__.py @@ -0,0 +1,5 @@ +from .data import * + +# This line will be programatically read/write by setup.py. +# Leave them at the bottom of this file and don't touch them. +__version__ = "0.0" diff --git a/ape/checkpoint/__init__.py b/ape/checkpoint/__init__.py new file mode 100644 index 0000000..d69773d --- /dev/null +++ b/ape/checkpoint/__init__.py @@ -0,0 +1,6 @@ +# -*- coding: utf-8 -*- + + +from .detection_checkpoint import DetectionCheckpointer + +__all__ = ["DetectionCheckpointer"] diff --git a/ape/checkpoint/detection_checkpoint.py b/ape/checkpoint/detection_checkpoint.py new file mode 100644 index 0000000..a09ebe8 --- /dev/null +++ b/ape/checkpoint/detection_checkpoint.py @@ -0,0 +1,45 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +import os +import pickle +from collections import defaultdict +from typing import IO, Any, Dict, Iterable, List, NamedTuple, Optional, Tuple, cast + +import numpy as np +import torch + +from detectron2.checkpoint import DetectionCheckpointer as DetectionCheckpointer_d2 + + +class DetectionCheckpointer(DetectionCheckpointer_d2): + + # def __init__(self, skip_key="", **kwargs): + # super().__init__(**kwargs) + # self.skip_key = skip_key + + def _convert_ndarray_to_tensor(self, state_dict: Dict[str, Any]) -> None: + """ + In-place convert all numpy arrays in the state_dict to torch tensor. + Args: + state_dict (dict): a state-dict to be loaded to the model. + Will be modified. + """ + logger = logging.getLogger(__name__) + # model could be an OrderedDict with _metadata attribute + # (as returned by Pytorch's state_dict()). We should preserve these + # properties. + for k in list(state_dict.keys()): + + # if self.skip_key in k: + # if "model_language" in k: + # state_dict.pop(k) + # continue + + v = state_dict[k] + if not isinstance(v, np.ndarray) and not isinstance(v, torch.Tensor): + logger.warning("Unsupported type found in checkpoint! {}: {}".format(k, type(v))) + state_dict.pop(k) + continue + raise ValueError("Unsupported type found in checkpoint! {}: {}".format(k, type(v))) + if not isinstance(v, torch.Tensor): + state_dict[k] = torch.from_numpy(v) diff --git a/ape/data/__init__.py b/ape/data/__init__.py new file mode 100644 index 0000000..7ca826b --- /dev/null +++ b/ape/data/__init__.py @@ -0,0 +1,20 @@ +from . import datasets +from .build_copypaste import ( + build_detection_train_loader_copypaste, + get_detection_dataset_dicts_copypaste, +) +from .build_multi_dataset import ( + build_detection_train_loader_multi_dataset, + get_detection_dataset_dicts_multi_dataset, +) +from .build_multi_dataset_copypaste import ( + build_detection_train_loader_multi_dataset_copypaste, + get_detection_dataset_dicts_multi_dataset_copypaste, +) +from .dataset_mapper import DatasetMapper_ape +from .dataset_mapper_copypaste import DatasetMapper_copypaste +from .dataset_mapper_detr_instance import DatasetMapper_detr_instance +from .dataset_mapper_detr_instance_exp import DatasetMapper_detr_instance_exp +from .dataset_mapper_detr_panoptic import DatasetMapper_detr_panoptic +from .dataset_mapper_detr_panoptic_copypaste import DatasetMapper_detr_panoptic_copypaste +from .dataset_mapper_detr_semantic import DatasetMapper_detr_semantic diff --git a/ape/data/build_copypaste.py b/ape/data/build_copypaste.py new file mode 100644 index 0000000..46bf38f --- /dev/null +++ b/ape/data/build_copypaste.py @@ -0,0 +1,255 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import itertools +import logging + +import torch.utils.data as torchdata + +from detectron2.config import configurable +from detectron2.data.build import ( + build_batch_data_loader, + filter_images_with_few_keypoints, + filter_images_with_only_crowd_annotations, + get_detection_dataset_dicts, + load_proposals_into_dataset, + print_instances_class_histogram, +) +from detectron2.data.catalog import DatasetCatalog, MetadataCatalog +from detectron2.data.common import DatasetFromList +from detectron2.data.detection_utils import check_metadata_consistency +from detectron2.data.samplers import ( + RandomSubsetTrainingSampler, + RepeatFactorTrainingSampler, + TrainingSampler, +) +from detectron2.utils.logger import _log_api_usage + +from .common_copypaste import MapDataset_coppaste +from .dataset_mapper_copypaste import DatasetMapper_copypaste + +""" +This file contains the default logic to build a dataloader for training or testing. +""" + +__all__ = [ + "build_detection_train_loader_copypaste", +] + + +def get_detection_dataset_dicts_copypaste( + names, + filter_empty=True, + min_keypoints=0, + proposal_files=None, + check_consistency=True, + copypastes=[True], +): + """ + Load and prepare dataset dicts for instance detection/segmentation and semantic segmentation. + + Args: + names (str or list[str]): a dataset name or a list of dataset names + filter_empty (bool): whether to filter out images without instance annotations + min_keypoints (int): filter out images with fewer keypoints than + `min_keypoints`. Set to 0 to do nothing. + proposal_files (list[str]): if given, a list of object proposal files + that match each dataset in `names`. + check_consistency (bool): whether to check if datasets have consistent metadata. + + Returns: + list[dict]: a list of dicts following the standard dataset dict format. + """ + if isinstance(names, str): + names = [names] + assert len(names), names + dataset_dicts = [DatasetCatalog.get(dataset_name) for dataset_name in names] + for dataset_name, dicts in zip(names, dataset_dicts): + assert len(dicts), "Dataset '{}' is empty!".format(dataset_name) + + for copypaste, dicts in zip(copypastes, dataset_dicts): + for d in dicts: + d["copypaste"] = copypaste + + if proposal_files is not None: + assert len(names) == len(proposal_files) + # load precomputed proposals from proposal files + dataset_dicts = [ + load_proposals_into_dataset(dataset_i_dicts, proposal_file) + for dataset_i_dicts, proposal_file in zip(dataset_dicts, proposal_files) + ] + + if isinstance(dataset_dicts[0], torchdata.Dataset): + return torchdata.ConcatDataset(dataset_dicts) + + dataset_dicts = list(itertools.chain.from_iterable(dataset_dicts)) + + has_instances = "annotations" in dataset_dicts[0] + if filter_empty and has_instances: + dataset_dicts = filter_images_with_only_crowd_annotations(dataset_dicts) + if min_keypoints > 0 and has_instances: + dataset_dicts = filter_images_with_few_keypoints(dataset_dicts, min_keypoints) + + if check_consistency and has_instances: + try: + class_names = MetadataCatalog.get(names[0]).thing_classes + check_metadata_consistency("thing_classes", names) + print_instances_class_histogram(dataset_dicts, class_names) + except AttributeError: # class names are not available for this dataset + pass + + assert len(dataset_dicts), "No valid data found in {}.".format(",".join(names)) + return dataset_dicts + + +def _train_loader_from_config(cfg, mapper=None, *, dataset=None, sampler=None): + assert len(cfg.DATASETS.TRAIN) == len(cfg.DATASETS.COPYPASTE.COPYPASTE) + + if dataset is None: + dataset = get_detection_dataset_dicts_copypaste( + cfg.DATASETS.TRAIN, + filter_empty=cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS, + min_keypoints=cfg.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE + if cfg.MODEL.KEYPOINT_ON + else 0, + proposal_files=cfg.DATASETS.PROPOSAL_FILES_TRAIN if cfg.MODEL.LOAD_PROPOSALS else None, + copypastes=cfg.DATASETS.COPYPASTE.COPYPASTE, + ) + _log_api_usage("dataset." + cfg.DATASETS.TRAIN[0]) + + if True: + dataset_bg = get_detection_dataset_dicts( + cfg.DATASETS.COPYPASTE.BG, + filter_empty=cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS, + min_keypoints=cfg.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE + if cfg.MODEL.KEYPOINT_ON + else 0, + proposal_files=cfg.DATASETS.PROPOSAL_FILES_TRAIN if cfg.MODEL.LOAD_PROPOSALS else None, + ) + _log_api_usage("dataset." + cfg.DATASETS.TRAIN[0]) + + if mapper is None: + mapper = DatasetMapper_copypaste(cfg, True) + + if sampler is None: + sampler_name = cfg.DATALOADER.SAMPLER_TRAIN + logger = logging.getLogger(__name__) + logger.info("Using training sampler {}".format(sampler_name)) + if sampler_name == "TrainingSampler": + sampler = TrainingSampler(len(dataset)) + elif sampler_name == "RepeatFactorTrainingSampler": + repeat_factors = RepeatFactorTrainingSampler.repeat_factors_from_category_frequency( + dataset, cfg.DATALOADER.REPEAT_THRESHOLD + ) + sampler = RepeatFactorTrainingSampler(repeat_factors) + elif sampler_name == "RandomSubsetTrainingSampler": + sampler = RandomSubsetTrainingSampler(len(dataset), cfg.DATALOADER.RANDOM_SUBSET_RATIO) + else: + raise ValueError("Unknown training sampler: {}".format(sampler_name)) + + if True: + sampler_name = cfg.DATALOADER.COPYPASTE.SAMPLER_TRAIN + logger = logging.getLogger(__name__) + logger.info("Using training sampler {}".format(sampler_name)) + if sampler_name == "TrainingSampler": + sampler_bg = TrainingSampler(len(dataset_bg)) + elif sampler_name == "RepeatFactorTrainingSampler": + repeat_factors = RepeatFactorTrainingSampler.repeat_factors_from_category_frequency( + dataset_bg, cfg.DATALOADER.COPYPASTE.REPEAT_THRESHOLD + ) + sampler_bg = RepeatFactorTrainingSampler(repeat_factors) + elif sampler_name == "RandomSubsetTrainingSampler": + sampler_bg = RandomSubsetTrainingSampler( + len(dataset_bg), cfg.DATALOADER.COPYPASTE.RANDOM_SUBSET_RATIO + ) + else: + raise ValueError("Unknown training sampler: {}".format(sampler_name)) + + return { + "dataset": dataset, + "dataset_bg": dataset_bg, + "sampler": sampler, + "sampler_bg": sampler_bg, + "mapper": mapper, + "total_batch_size": cfg.SOLVER.IMS_PER_BATCH, + "aspect_ratio_grouping": cfg.DATALOADER.ASPECT_RATIO_GROUPING, + "num_workers": cfg.DATALOADER.NUM_WORKERS, + } + + +@configurable(from_config=_train_loader_from_config) +def build_detection_train_loader_copypaste( + dataset, + dataset_bg, + *, + mapper, + sampler=None, + sampler_bg=None, + total_batch_size, + aspect_ratio_grouping=True, + num_workers=0, + collate_fn=None, +): + """ + Build a dataloader for object detection with some default features. + This interface is experimental. + + Args: + dataset (list or torch.utils.data.Dataset): a list of dataset dicts, + or a pytorch dataset (either map-style or iterable). It can be obtained + by using :func:`DatasetCatalog.get` or :func:`get_detection_dataset_dicts`. + mapper (callable): a callable which takes a sample (dict) from dataset and + returns the format to be consumed by the model. + When using cfg, the default choice is ``DatasetMapper(cfg, is_train=True)``. + sampler (torch.utils.data.sampler.Sampler or None): a sampler that produces + indices to be applied on ``dataset``. + If ``dataset`` is map-style, the default sampler is a :class:`TrainingSampler`, + which coordinates an infinite random shuffle sequence across all workers. + Sampler must be None if ``dataset`` is iterable. + total_batch_size (int): total batch size across all workers. Batching + simply puts data into a list. + aspect_ratio_grouping (bool): whether to group images with similar + aspect ratio for efficiency. When enabled, it requires each + element in dataset be a dict with keys "width" and "height". + num_workers (int): number of parallel data loading workers + collate_fn: same as the argument of `torch.utils.data.DataLoader`. + Defaults to do no collation and return a list of data. + No collation is OK for small batch size and simple data structures. + If your batch size is large and each sample contains too many small tensors, + it's more efficient to collate them in data loader. + + Returns: + torch.utils.data.DataLoader: + a dataloader. Each output from it is a ``list[mapped_element]`` of length + ``total_batch_size / num_workers``, where ``mapped_element`` is produced + by the ``mapper``. + """ + if isinstance(dataset_bg, list): + dataset_bg = DatasetFromList(dataset_bg, copy=False) + + if isinstance(dataset_bg, torchdata.IterableDataset): + assert sampler_bg is None, "sampler must be None if dataset is IterableDataset" + else: + if sampler_bg is None: + sampler_bg = TrainingSampler(len(dataset)) + assert isinstance( + sampler_bg, torchdata.Sampler + ), f"Expect a Sampler but got {type(sampler)}" + + if isinstance(dataset, list): + dataset = DatasetFromList(dataset, copy=False) + if mapper is not None: + dataset = MapDataset_coppaste(dataset, mapper, dataset_bg, sampler_bg) + + if isinstance(dataset, torchdata.IterableDataset): + assert sampler is None, "sampler must be None if dataset is IterableDataset" + else: + if sampler is None: + sampler = TrainingSampler(len(dataset)) + assert isinstance(sampler, torchdata.Sampler), f"Expect a Sampler but got {type(sampler)}" + return build_batch_data_loader( + dataset, + sampler, + total_batch_size, + aspect_ratio_grouping=aspect_ratio_grouping, + num_workers=num_workers, + collate_fn=collate_fn, + ) diff --git a/ape/data/build_multi_dataset.py b/ape/data/build_multi_dataset.py new file mode 100644 index 0000000..8dd3468 --- /dev/null +++ b/ape/data/build_multi_dataset.py @@ -0,0 +1,741 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import itertools +import logging +import operator +import time +from collections import defaultdict +from typing import Callable, Optional + +import numpy as np +import torch +import torch.utils.data as torchdata +from termcolor import colored +from torch.utils.data.sampler import Sampler + +from detectron2.config import configurable +from detectron2.data.build import ( + filter_images_with_few_keypoints, + filter_images_with_only_crowd_annotations, + get_detection_dataset_dicts, + load_proposals_into_dataset, + trivial_batch_collator, + worker_init_reset_seed, +) +from detectron2.data.catalog import DatasetCatalog, MetadataCatalog +from detectron2.data.common import DatasetFromList, MapDataset, ToIterableDataset +from detectron2.data.detection_utils import check_metadata_consistency +from detectron2.data.samplers import ( + RandomSubsetTrainingSampler, + RepeatFactorTrainingSampler, + TrainingSampler, +) +from detectron2.utils import comm +from detectron2.utils.comm import get_world_size +from detectron2.utils.logger import _log_api_usage, log_first_n +from tabulate import tabulate + +from .dataset_mapper import DatasetMapper_ape +from .samplers import MultiDatasetTrainingSampler + +""" +This file contains the default logic to build a dataloader for training or testing. +""" + +__all__ = [ + "build_detection_train_loader_multi_dataset", +] + + +def print_instances_class_histogram(dataset_dicts, class_names): + """ + Args: + dataset_dicts (list[dict]): list of dataset dicts. + class_names (list[str]): list of class names (zero-indexed). + """ + num_classes = len(class_names) + hist_bins = np.arange(num_classes + 1) + histogram = np.zeros((num_classes,), dtype=np.int) + total_num_out_of_class = 0 + for entry in dataset_dicts: + annos = entry["annotations"] + classes = np.asarray( + [x["category_id"] for x in annos if not x.get("iscrowd", 0)], dtype=np.int + ) + if len(classes): + assert classes.min() >= 0, f"Got an invalid category_id={classes.min()}" + # assert ( + # classes.max() < num_classes + # ), f"Got an invalid category_id={classes.max()} for a dataset of {num_classes} classes" + histogram += np.histogram(classes, bins=hist_bins)[0] + + total_num_out_of_class += sum(classes >= num_classes) + + N_COLS = min(6, len(class_names) * 2) + + def short_name(x): + # make long class names shorter. useful for lvis + if len(x) > 13: + return x[:11] + ".." + return x + + data = list( + itertools.chain(*[[short_name(class_names[i]), int(v)] for i, v in enumerate(histogram)]) + ) + total_num_instances = sum(data[1::2]) + data.extend([None] * (N_COLS - (len(data) % N_COLS))) + if num_classes > 1: + data.extend(["total", total_num_instances]) + if total_num_out_of_class > 0: + data.extend(["total out", total_num_out_of_class]) + data = itertools.zip_longest(*[data[i::N_COLS] for i in range(N_COLS)]) + table = tabulate( + data, + headers=["category", "#instances"] * (N_COLS // 2), + tablefmt="pipe", + numalign="left", + stralign="center", + ) + log_first_n( + logging.INFO, + "Distribution of instances among all {} categories:\n".format(num_classes) + + colored(table, "cyan"), + key="message", + ) + + +def DatasetCatalog_get(dataset_name, reduce_memory, reduce_memory_size): + import os, psutil + + logger = logging.getLogger(__name__) + logger.info( + "Current memory usage: {} GB".format( + psutil.Process(os.getpid()).memory_info().rss / 1024**3 + ) + ) + + dataset_dicts = DatasetCatalog.get(dataset_name) + + # logger.info( + # "Current memory usage: {} GB".format( + # psutil.Process(os.getpid()).memory_info().rss / 1024**3 + # ) + # ) + # logger.info("Reducing memory usage...") + + # for d in dataset_dicts: + # # LVIS + # if "not_exhaustive_category_ids" in d.keys(): + # del d["not_exhaustive_category_ids"] + # if "neg_category_ids" in d.keys(): + # del d["neg_category_ids"] + # if "pos_category_ids" in d.keys(): + # del d["pos_category_ids"] + + # if "annotations" not in d.keys(): + # continue + # for anno in d["annotations"]: + # if "iscrowd" in anno.keys(): + # if anno["iscrowd"] == 0: + # del anno["iscrowd"] + + logger.info( + "Current memory usage: {} GB".format( + psutil.Process(os.getpid()).memory_info().rss / 1024**3 + ) + ) + + if not reduce_memory: + return dataset_dicts + if len(dataset_dicts) < reduce_memory_size: + return dataset_dicts + + logger.info("Reducing memory usage further...") + + for d in dataset_dicts: + if "annotations" not in d.keys(): + continue + + for anno in d["annotations"]: + + if "bbox" in anno.keys(): + del anno["bbox"] + + if "bbox_mode" in anno.keys(): + del anno["bbox_mode"] + + if "segmentation" in anno.keys(): + del anno["segmentation"] + + if "phrase" in anno.keys(): + del anno["phrase"] + + logger.info( + "Current memory usage: {} GB".format( + psutil.Process(os.getpid()).memory_info().rss / 1024**3 + ) + ) + + return dataset_dicts + + +def get_detection_dataset_dicts_multi_dataset( + names, + filter_empty=True, + min_keypoints=0, + proposal_files=None, + check_consistency=True, + filter_emptys=[True], + dataloader_id=None, + reduce_memory=False, + reduce_memory_size=1e6, +): + """ + Load and prepare dataset dicts for instance detection/segmentation and semantic segmentation. + + Args: + names (str or list[str]): a dataset name or a list of dataset names + filter_empty (bool): whether to filter out images without instance annotations + min_keypoints (int): filter out images with fewer keypoints than + `min_keypoints`. Set to 0 to do nothing. + proposal_files (list[str]): if given, a list of object proposal files + that match each dataset in `names`. + check_consistency (bool): whether to check if datasets have consistent metadata. + + Returns: + list[dict]: a list of dicts following the standard dataset dict format. + """ + if isinstance(names, str): + names = [names] + assert len(names), names + # dataset_dicts = [DatasetCatalog.get(dataset_name) for dataset_name in names] + dataset_dicts = [ + DatasetCatalog_get(dataset_name, reduce_memory, reduce_memory_size) + for dataset_name in names + ] + + if isinstance(dataset_dicts[0], torchdata.Dataset): + if len(dataset_dicts) > 1: + # ConcatDataset does not work for iterable style dataset. + # We could support concat for iterable as well, but it's often + # not a good idea to concat iterables anyway. + return torchdata.ConcatDataset(dataset_dicts) + return dataset_dicts[0] + + for dataset_name, dicts in zip(names, dataset_dicts): + assert len(dicts), "Dataset '{}' is empty!".format(dataset_name) + + for dataset_id, (dataset_name, dicts) in enumerate(zip(names, dataset_dicts)): + for d in dicts: + d["dataset_id"] = dataset_id + if dataloader_id is not None: + d["dataloader_id"] = dataloader_id + + has_instances = "annotations" in dicts[0] + if not check_consistency or not has_instances: + continue + try: + class_names = MetadataCatalog.get(dataset_name).thing_classes + check_metadata_consistency("thing_classes", [dataset_name]) + print_instances_class_histogram(dicts, class_names) + except AttributeError: # class names are not available for this dataset + pass + + assert proposal_files is None + if proposal_files is not None: + assert len(names) == len(proposal_files) + # load precomputed proposals from proposal files + dataset_dicts = [ + load_proposals_into_dataset(dataset_i_dicts, proposal_file) + for dataset_i_dicts, proposal_file in zip(dataset_dicts, proposal_files) + ] + + dataset_dicts = [ + filter_images_with_only_crowd_annotations(dicts) + if flag and "annotations" in dicts[0] + else dicts + for dicts, flag in zip(dataset_dicts, filter_emptys) + ] + + dataset_dicts = list(itertools.chain.from_iterable(dataset_dicts)) + + has_instances = "annotations" in dataset_dicts[0] + if filter_empty and has_instances and False: + dataset_dicts = filter_images_with_only_crowd_annotations(dataset_dicts) + if min_keypoints > 0 and has_instances: + dataset_dicts = filter_images_with_few_keypoints(dataset_dicts, min_keypoints) + + if check_consistency and has_instances and False: + try: + class_names = MetadataCatalog.get(names[0]).thing_classes + check_metadata_consistency("thing_classes", names) + print_instances_class_histogram(dataset_dicts, class_names) + except AttributeError: # class names are not available for this dataset + pass + + assert len(dataset_dicts), "No valid data found in {}.".format(",".join(names)) + return dataset_dicts + + +def build_batch_data_loader_multi_dataset( + dataset, + sampler, + total_batch_size, + total_batch_size_list, + *, + aspect_ratio_grouping=False, + num_workers=0, + collate_fn=None, + num_datasets=1, +): + """ + Build a batched dataloader. The main differences from `torch.utils.data.DataLoader` are: + 1. support aspect ratio grouping options + 2. use no "batch collation", because this is common for detection training + + Args: + dataset (torch.utils.data.Dataset): a pytorch map-style or iterable dataset. + sampler (torch.utils.data.sampler.Sampler or None): a sampler that produces indices. + Must be provided iff. ``dataset`` is a map-style dataset. + total_batch_size, aspect_ratio_grouping, num_workers, collate_fn: see + :func:`build_detection_train_loader`. + + Returns: + iterable[list]. Length of each list is the batch size of the current + GPU. Each element in the list comes from the dataset. + """ + world_size = get_world_size() + assert ( + total_batch_size > 0 and total_batch_size % world_size == 0 + ), "Total batch size ({}) must be divisible by the number of gpus ({}).".format( + total_batch_size, world_size + ) + batch_size = total_batch_size // world_size + + if len(total_batch_size_list) < num_datasets: + total_batch_size_list += [ + total_batch_size, + ] * (num_datasets - len(total_batch_size_list)) + assert all([x > 0 for x in total_batch_size_list]) and all( + [x % world_size == 0 for x in total_batch_size_list] + ), "Total batch size ({}) must be divisible by the number of gpus ({}).".format( + total_batch_size_list, world_size + ) + batch_size = [x // world_size for x in total_batch_size_list] + + if isinstance(dataset, torchdata.IterableDataset): + assert sampler is None, "sampler must be None if dataset is IterableDataset" + else: + dataset = ToIterableDataset(dataset, sampler) + + assert aspect_ratio_grouping + if aspect_ratio_grouping: + data_loader = torchdata.DataLoader( + dataset, + num_workers=num_workers, + collate_fn=operator.itemgetter(0), # don't batch, but yield individual elements + worker_init_fn=worker_init_reset_seed, + ) # yield individual mapped dict + # data_loader = AspectRatioGroupedDataset(data_loader, batch_size) + data_loader = MultiDatasetAspectRatioGroupedDataset( + data_loader, batch_size, num_datasets=num_datasets + ) + if collate_fn is None: + return data_loader + return MapDataset(data_loader, collate_fn) + else: + return torchdata.DataLoader( + dataset, + batch_size=batch_size, + drop_last=True, + num_workers=num_workers, + collate_fn=trivial_batch_collator if collate_fn is None else collate_fn, + worker_init_fn=worker_init_reset_seed, + ) + + +def _train_loader_from_config(cfg, mapper=None, *, dataset=None, sampler=None): + assert len(cfg.DATASETS.TRAIN) == len(cfg.MULTI_DATASET.NAMES) + assert len(cfg.DATASETS.TRAIN) == len(cfg.MULTI_DATASET.ENTITIES) + assert len(cfg.DATASETS.TRAIN) == len(cfg.MULTI_DATASET.NUM_CLASSES) + assert len(cfg.DATASETS.TRAIN) == len(cfg.MULTI_DATASET.RATIOS) + assert len(cfg.DATASETS.TRAIN) == len(cfg.MULTI_DATASET.USE_CAS) + assert len(cfg.DATASETS.TRAIN) == len(cfg.MULTI_DATASET.USE_RFS) + assert len(cfg.DATASETS.TRAIN) == len(cfg.MULTI_DATASET.FILTER_EMPTY_ANNOTATIONS) + # assert len(cfg.DATASETS.TRAIN) == len(cfg.SOLVER.IMS_PER_BATCH_LIST) + # assert len(cfg.DATASETS.TRAIN) == len(cfg.SOLVER.AUGMENT_TYPE) + + seed1 = comm.shared_random_seed() + seed2 = comm.shared_random_seed() + logger = logging.getLogger(__name__) + logger.info("rank {} seed1 {} seed2 {}".format(comm.get_local_rank(), seed1, seed2)) + + # Hard-coded 2 sequent group and 1200s time wait. + wait_group = 2 + wait_time = cfg.DATALOADER.GROUP_WAIT + wait = comm.get_local_rank() % wait_group * wait_time + logger.info("rank {} _train_loader_from_config sleep {}".format(comm.get_local_rank(), wait)) + time.sleep(wait) + + if dataset is None: + dataset = get_detection_dataset_dicts_multi_dataset( + cfg.DATASETS.TRAIN, + filter_empty=cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS, + min_keypoints=cfg.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE + if cfg.MODEL.KEYPOINT_ON + else 0, + proposal_files=cfg.DATASETS.PROPOSAL_FILES_TRAIN if cfg.MODEL.LOAD_PROPOSALS else None, + filter_emptys=cfg.MULTI_DATASET.FILTER_EMPTY_ANNOTATIONS, + ) + _log_api_usage("dataset." + cfg.DATASETS.TRAIN[0]) + + if mapper is None: + mapper = DatasetMapper_ape(cfg, True) + + if sampler is None: + sampler_name = cfg.DATALOADER.SAMPLER_TRAIN + logger = logging.getLogger(__name__) + if isinstance(dataset, torchdata.IterableDataset): + logger.info("Not using any sampler since the dataset is IterableDataset.") + sampler = None + else: + logger.info("Using training sampler {}".format(sampler_name)) + if sampler_name == "TrainingSampler": + sampler = TrainingSampler(len(dataset), seed=seed1) + elif sampler_name == "RepeatFactorTrainingSampler": + repeat_factors = RepeatFactorTrainingSampler.repeat_factors_from_category_frequency( + dataset, cfg.DATALOADER.REPEAT_THRESHOLD + ) + sampler = RepeatFactorTrainingSampler(repeat_factors, seed=seed1) + elif sampler_name == "RandomSubsetTrainingSampler": + sampler = RandomSubsetTrainingSampler( + len(dataset), + cfg.DATALOADER.RANDOM_SUBSET_RATIO, + seed_shuffle=seed1, + seed_subset=seed2, + ) + elif sampler_name == "MultiDatasetSampler": + raise ValueError("Despreted training sampler: {}".format(sampler_name)) + sizes = [0 for _ in range(len(cfg.DATASETS.TRAIN))] + for d in dataset: + sizes[d["dataset_id"]] += 1 + sampler = MultiDatasetSampler(cfg, dataset, sizes, seed=seed1) + elif sampler_name == "MultiDatasetTrainingSampler": + # sampler = MultiDatasetTrainingSampler(cfg, dataset, seed=seed1) + repeat_factors = MultiDatasetTrainingSampler.get_repeat_factors( + dataset, + len(cfg.DATASETS.TRAIN), + cfg.MULTI_DATASET.RATIOS, + cfg.MULTI_DATASET.USE_RFS, + cfg.MULTI_DATASET.USE_CAS, + cfg.MULTI_DATASET.REPEAT_THRESHOLD, + cfg.MULTI_DATASET.CAS_LAMBDA, + ) + sampler = MultiDatasetTrainingSampler(repeat_factors, seed=seed1) + else: + raise ValueError("Unknown training sampler: {}".format(sampler_name)) + + return { + "dataset": dataset, + "sampler": sampler, + "mapper": mapper, + "total_batch_size": cfg.SOLVER.IMS_PER_BATCH, + "total_batch_size_list": cfg.SOLVER.IMS_PER_BATCH_LIST, + "aspect_ratio_grouping": cfg.DATALOADER.ASPECT_RATIO_GROUPING, + "num_workers": cfg.DATALOADER.NUM_WORKERS, + "num_datasets": len(cfg.DATASETS.TRAIN), + } + + +@configurable(from_config=_train_loader_from_config) +def build_detection_train_loader_multi_dataset( + dataset, + *, + mapper, + sampler=None, + total_batch_size, + total_batch_size_list, + aspect_ratio_grouping=True, + num_workers=0, + collate_fn=None, + num_datasets=1, +): + """ + Build a dataloader for object detection with some default features. + + Args: + dataset (list or torch.utils.data.Dataset): a list of dataset dicts, + or a pytorch dataset (either map-style or iterable). It can be obtained + by using :func:`DatasetCatalog.get` or :func:`get_detection_dataset_dicts`. + mapper (callable): a callable which takes a sample (dict) from dataset and + returns the format to be consumed by the model. + When using cfg, the default choice is ``DatasetMapper(cfg, is_train=True)``. + sampler (torch.utils.data.sampler.Sampler or None): a sampler that produces + indices to be applied on ``dataset``. + If ``dataset`` is map-style, the default sampler is a :class:`TrainingSampler`, + which coordinates an infinite random shuffle sequence across all workers. + Sampler must be None if ``dataset`` is iterable. + total_batch_size (int): total batch size across all workers. + aspect_ratio_grouping (bool): whether to group images with similar + aspect ratio for efficiency. When enabled, it requires each + element in dataset be a dict with keys "width" and "height". + num_workers (int): number of parallel data loading workers + collate_fn: a function that determines how to do batching, same as the argument of + `torch.utils.data.DataLoader`. Defaults to do no collation and return a list of + data. No collation is OK for small batch size and simple data structures. + If your batch size is large and each sample contains too many small tensors, + it's more efficient to collate them in data loader. + + Returns: + torch.utils.data.DataLoader: + a dataloader. Each output from it is a ``list[mapped_element]`` of length + ``total_batch_size / num_workers``, where ``mapped_element`` is produced + by the ``mapper``. + """ + # wait = round(comm.get_local_rank() * 1.0 * len(dataset) / 60000) + # logger = logging.getLogger(__name__) + # logger.info("get_detection_dataset_dicts_multi_dataset sleep {}".format(wait)) + # time.sleep(wait) + + if isinstance(sampler, Callable): + sampler = sampler(dataset) + + if isinstance(dataset, list): + dataset = DatasetFromList(dataset, copy=False) + if mapper is not None: + dataset = MapDataset(dataset, mapper) + + if isinstance(dataset, torchdata.IterableDataset): + assert sampler is None, "sampler must be None if dataset is IterableDataset" + else: + if sampler is None: + sampler = TrainingSampler(len(dataset)) + assert isinstance(sampler, torchdata.Sampler), f"Expect a Sampler but got {type(sampler)}" + return build_batch_data_loader_multi_dataset( + dataset, + sampler, + total_batch_size, + total_batch_size_list, + aspect_ratio_grouping=aspect_ratio_grouping, + num_workers=num_workers, + collate_fn=collate_fn, + num_datasets=num_datasets, + ) + + +class MultiDatasetSampler(Sampler): + def __init__(self, cfg, dataset_dicts, sizes, seed: Optional[int] = None): + self.sizes = sizes + self.sample_epoch_size = cfg.MULTI_DATASET.SAMPLE_EPOCH_SIZE + assert self.sample_epoch_size % cfg.SOLVER.IMS_PER_BATCH == 0, ( + self.sample_epoch_size % cfg.SOLVER.IMS_PER_BATCH == 0 + ) + if seed is None: + seed = comm.shared_random_seed() + self._seed = int(seed) + + self._rank = comm.get_rank() + self._world_size = comm.get_world_size() + + dataset_ratio = cfg.MULTI_DATASET.RATIOS + assert len(dataset_ratio) == len( + sizes + ), "length of dataset ratio {} should be equal to number if dataset {}".format( + len(dataset_ratio), len(sizes) + ) + dataset_weight = [ + torch.ones(s) * max(sizes) / s * r / sum(dataset_ratio) + for i, (r, s) in enumerate(zip(dataset_ratio, sizes)) + ] + st = 0 + cas_factors = [] + for i, s in enumerate(sizes): + if cfg.MULTI_DATASET.USE_CAS[i]: + cas_factor = self._get_class_balance_factor_per_dataset( + dataset_dicts[st : st + s], l=cfg.MULTI_DATASET.CAS_LAMBDA + ) + cas_factor = cas_factor * (s / cas_factor.sum()) + else: + cas_factor = torch.ones(s) + cas_factors.append(cas_factor) + st = st + s + cas_factors = torch.cat(cas_factors) + dataset_weight = torch.cat(dataset_weight) + self.weights = dataset_weight * cas_factors + + def __iter__(self): + start = self._rank + yield from itertools.islice(self._infinite_indices(), start, None, self._world_size) + + def _infinite_indices(self): + g = torch.Generator() + g.manual_seed(self._seed) + while True: + ids = torch.multinomial( + self.weights, self.sample_epoch_size, generator=g, replacement=True + ) + yield from ids + + def _get_class_balance_factor_per_dataset(self, dataset_dicts, l=1.0): + ret = [] + category_freq = defaultdict(int) + for dataset_dict in dataset_dicts: # For each image (without repeats) + cat_ids = {ann["category_id"] for ann in dataset_dict["annotations"]} + for cat_id in cat_ids: + category_freq[cat_id] += 1 + for dataset_dict in dataset_dicts: + cat_ids = {ann["category_id"] for ann in dataset_dict["annotations"]} + ret.append(sum([1.0 / (category_freq[cat_id] ** l) for cat_id in cat_ids])) + return torch.tensor(ret).float() + + +# class MultiDatasetTrainingSampler(Sampler): +# def __init__(self, cfg, dataset_dicts, *, shuffle=True, seed=None): +# sizes = [0 for _ in range(len(cfg.DATASETS.TRAIN))] +# for d in dataset_dicts: +# sizes[d["dataset_id"]] += 1 + +# dataset_ratio = cfg.MULTI_DATASET.RATIOS +# assert len(dataset_ratio) == len( +# sizes +# ), "length of dataset ratio {} should be equal to number if dataset {}".format( +# len(dataset_ratio), len(sizes) +# ) +# dataset_weight = [ +# torch.ones(s) * max(sizes) / s * r for i, (r, s) in enumerate(zip(dataset_ratio, sizes)) +# ] + +# logger = logging.getLogger(__name__) +# logger.info( +# "Training sampler dataset weight: {}".format( +# str([max(sizes) / s * r for i, (r, s) in enumerate(zip(dataset_ratio, sizes))]) +# ) +# ) + +# st = 0 +# repeat_factors = [] +# for i, s in enumerate(sizes): +# assert cfg.MULTI_DATASET.USE_RFS[i] * cfg.MULTI_DATASET.USE_CAS[i] == 0 +# if cfg.MULTI_DATASET.USE_RFS[i]: +# repeat_factor = RepeatFactorTrainingSampler.repeat_factors_from_category_frequency( +# dataset_dicts[st : st + s], cfg.MULTI_DATASET.REPEAT_THRESHOLD +# ) +# elif cfg.MULTI_DATASET.USE_CAS[i]: +# repeat_factor = MultiDatasetTrainingSampler.get_class_balance_factor_per_dataset( +# dataset_dicts[st : st + s], l=cfg.MULTI_DATASET.CAS_LAMBDA +# ) +# repeat_factor = repeat_factor * (s / repeat_factor.sum()) +# else: +# repeat_factor = torch.ones(s) +# repeat_factors.append(repeat_factor) +# st = st + s +# repeat_factors = torch.cat(repeat_factors) +# dataset_weight = torch.cat(dataset_weight) +# repeat_factors = dataset_weight * repeat_factors + +# self._shuffle = shuffle +# if seed is None: +# seed = comm.shared_random_seed() +# self._seed = int(seed) + +# self._rank = comm.get_rank() +# self._world_size = comm.get_world_size() + +# # Split into whole number (_int_part) and fractional (_frac_part) parts. +# self._int_part = torch.trunc(repeat_factors) +# self._frac_part = repeat_factors - self._int_part + +# @staticmethod +# def get_class_balance_factor_per_dataset(dataset_dicts, l=1.0): +# rep_factors = [] +# category_freq = defaultdict(int) +# for dataset_dict in dataset_dicts: # For each image (without repeats) +# cat_ids = {ann["category_id"] for ann in dataset_dict["annotations"]} +# for cat_id in cat_ids: +# category_freq[cat_id] += 1 +# for dataset_dict in dataset_dicts: +# cat_ids = {ann["category_id"] for ann in dataset_dict["annotations"]} +# rep_factor = sum([1.0 / (category_freq[cat_id] ** l) for cat_id in cat_ids]) +# rep_factors.append(rep_factor) + +# return torch.tensor(rep_factors, dtype=torch.float32) + +# def _get_epoch_indices(self, generator): +# """ +# Create a list of dataset indices (with repeats) to use for one epoch. + +# Args: +# generator (torch.Generator): pseudo random number generator used for +# stochastic rounding. + +# Returns: +# torch.Tensor: list of dataset indices to use in one epoch. Each index +# is repeated based on its calculated repeat factor. +# """ +# # Since repeat factors are fractional, we use stochastic rounding so +# # that the target repeat factor is achieved in expectation over the +# # course of training +# rands = torch.rand(len(self._frac_part), generator=generator) +# rep_factors = self._int_part + (rands < self._frac_part).float() +# # Construct a list of indices in which we repeat images as specified +# indices = [] +# for dataset_index, rep_factor in enumerate(rep_factors): +# indices.extend([dataset_index] * int(rep_factor.item())) +# return torch.tensor(indices, dtype=torch.int64) + +# def __iter__(self): +# start = self._rank +# yield from itertools.islice(self._infinite_indices(), start, None, self._world_size) + +# def _infinite_indices(self): +# g = torch.Generator() +# g.manual_seed(self._seed) +# while True: +# # Sample indices with repeats determined by stochastic rounding; each +# # "epoch" may have a slightly different size due to the rounding. +# indices = self._get_epoch_indices(g) +# if self._shuffle: +# randperm = torch.randperm(len(indices), generator=g) +# yield from indices[randperm].tolist() +# else: +# yield from indices.tolist() + + +class MultiDatasetAspectRatioGroupedDataset(torch.utils.data.IterableDataset): + """ + Batch data that have similar aspect ratio together. + In this implementation, images whose aspect ratio < (or >) 1 will + be batched together. + This improves training speed because the images then need less padding + to form a batch. + + It assumes the underlying dataset produces dicts with "width" and "height" keys. + It will then produce a list of original dicts with length = batch_size, + all with similar aspect ratios. + """ + + def __init__(self, dataset, batch_size, num_datasets): + """ + Args: + dataset: an iterable. Each element must be a dict with keys + "width" and "height", which will be used to batch data. + batch_size (int): + """ + self.dataset = dataset + self.batch_size = batch_size + self._buckets = [[] for _ in range(2 * num_datasets)] + # Hard-coded two aspect ratio groups: w > h and w < h. + # Can add support for more aspect ratio groups, but doesn't seem useful + + def __iter__(self): + for d in self.dataset: + w, h = d["width"], d["height"] + bucket_id = 0 if w > h else 1 + bucket_id = d["dataset_id"] * 2 + bucket_id + bucket = self._buckets[bucket_id] + bucket.append(d) + if len(bucket) == self.batch_size[d["dataset_id"]]: + data = bucket[:] + # Clear bucket first, because code after yield is not + # guaranteed to execute + del bucket[:] + yield data diff --git a/ape/data/build_multi_dataset_copypaste.py b/ape/data/build_multi_dataset_copypaste.py new file mode 100644 index 0000000..b7dc12a --- /dev/null +++ b/ape/data/build_multi_dataset_copypaste.py @@ -0,0 +1,805 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import itertools +import logging +import operator +import time +from collections import defaultdict +from typing import Callable, Optional + +import numpy as np +import torch +import torch.utils.data as torchdata +from termcolor import colored +from torch.utils.data.sampler import Sampler + +from detectron2.config import configurable +from detectron2.data.build import ( + filter_images_with_few_keypoints, + filter_images_with_only_crowd_annotations, + get_detection_dataset_dicts, + load_proposals_into_dataset, + trivial_batch_collator, + worker_init_reset_seed, +) +from detectron2.data.catalog import DatasetCatalog, MetadataCatalog +from detectron2.data.common import DatasetFromList, MapDataset, ToIterableDataset +from detectron2.data.detection_utils import check_metadata_consistency +from detectron2.data.samplers import ( + RandomSubsetTrainingSampler, + RepeatFactorTrainingSampler, + TrainingSampler, +) +from detectron2.utils import comm +from detectron2.utils.comm import get_world_size +from detectron2.utils.logger import _log_api_usage, log_first_n +from tabulate import tabulate + +from .common_copypaste import MapDataset_coppaste +from .dataset_mapper_copypaste import DatasetMapper_copypaste +from .samplers import MultiDatasetTrainingSampler + +""" +This file contains the default logic to build a dataloader for training or testing. +""" + +__all__ = [ + "build_detection_train_loader_multi_dataset_copypaste", +] + + +def print_instances_class_histogram(dataset_dicts, class_names): + """ + Args: + dataset_dicts (list[dict]): list of dataset dicts. + class_names (list[str]): list of class names (zero-indexed). + """ + num_classes = len(class_names) + hist_bins = np.arange(num_classes + 1) + histogram = np.zeros((num_classes,), dtype=np.int) + total_num_out_of_class = 0 + for entry in dataset_dicts: + annos = entry["annotations"] + classes = np.asarray( + [x["category_id"] for x in annos if not x.get("iscrowd", 0)], dtype=np.int + ) + if len(classes): + assert classes.min() >= 0, f"Got an invalid category_id={classes.min()}" + # assert ( + # classes.max() < num_classes + # ), f"Got an invalid category_id={classes.max()} for a dataset of {num_classes} classes" + histogram += np.histogram(classes, bins=hist_bins)[0] + + total_num_out_of_class += sum(classes >= num_classes) + + N_COLS = min(6, len(class_names) * 2) + + def short_name(x): + # make long class names shorter. useful for lvis + if len(x) > 13: + return x[:11] + ".." + return x + + data = list( + itertools.chain(*[[short_name(class_names[i]), int(v)] for i, v in enumerate(histogram)]) + ) + total_num_instances = sum(data[1::2]) + data.extend([None] * (N_COLS - (len(data) % N_COLS))) + if num_classes > 1: + data.extend(["total", total_num_instances]) + if total_num_out_of_class > 0: + data.extend(["total out", total_num_out_of_class]) + data = itertools.zip_longest(*[data[i::N_COLS] for i in range(N_COLS)]) + table = tabulate( + data, + headers=["category", "#instances"] * (N_COLS // 2), + tablefmt="pipe", + numalign="left", + stralign="center", + ) + log_first_n( + logging.INFO, + "Distribution of instances among all {} categories:\n".format(num_classes) + + colored(table, "cyan"), + key="message", + ) + + +def DatasetCatalog_get(dataset_name, reduce_memory, reduce_memory_size): + import os, psutil + + logger = logging.getLogger(__name__) + logger.info( + "Current memory usage: {} GB".format( + psutil.Process(os.getpid()).memory_info().rss / 1024**3 + ) + ) + + dataset_dicts = DatasetCatalog.get(dataset_name) + + # logger.info( + # "Current memory usage: {} GB".format( + # psutil.Process(os.getpid()).memory_info().rss / 1024**3 + # ) + # ) + # logger.info("Reducing memory usage...") + + # for d in dataset_dicts: + # # LVIS + # if "not_exhaustive_category_ids" in d.keys(): + # del d["not_exhaustive_category_ids"] + # if "neg_category_ids" in d.keys(): + # del d["neg_category_ids"] + # if "pos_category_ids" in d.keys(): + # del d["pos_category_ids"] + + # if "annotations" not in d.keys(): + # continue + # for anno in d["annotations"]: + # if "iscrowd" in anno.keys(): + # if anno["iscrowd"] == 0: + # del anno["iscrowd"] + + logger.info( + "Current memory usage: {} GB".format( + psutil.Process(os.getpid()).memory_info().rss / 1024**3 + ) + ) + + if not reduce_memory: + return dataset_dicts + if len(dataset_dicts) < reduce_memory_size: + return dataset_dicts + + logger.info("Reducing memory usage further...") + + for d in dataset_dicts: + if "annotations" not in d.keys(): + continue + + for anno in d["annotations"]: + + if "bbox" in anno.keys(): + del anno["bbox"] + + if "bbox_mode" in anno.keys(): + del anno["bbox_mode"] + + if "segmentation" in anno.keys(): + del anno["segmentation"] + + if "phrase" in anno.keys(): + del anno["phrase"] + + logger.info( + "Current memory usage: {} GB".format( + psutil.Process(os.getpid()).memory_info().rss / 1024**3 + ) + ) + + return dataset_dicts + + +def get_detection_dataset_dicts_multi_dataset_copypaste( + names, + filter_empty=True, + min_keypoints=0, + proposal_files=None, + check_consistency=True, + filter_emptys=[True], + copypastes=[True], + dataloader_id=None, + reduce_memory=False, + reduce_memory_size=1e6, +): + """ + Load and prepare dataset dicts for instance detection/segmentation and semantic segmentation. + + Args: + names (str or list[str]): a dataset name or a list of dataset names + filter_empty (bool): whether to filter out images without instance annotations + min_keypoints (int): filter out images with fewer keypoints than + `min_keypoints`. Set to 0 to do nothing. + proposal_files (list[str]): if given, a list of object proposal files + that match each dataset in `names`. + check_consistency (bool): whether to check if datasets have consistent metadata. + + Returns: + list[dict]: a list of dicts following the standard dataset dict format. + """ + if isinstance(names, str): + names = [names] + assert len(names), names + # dataset_dicts = [DatasetCatalog.get(dataset_name) for dataset_name in names] + dataset_dicts = [ + DatasetCatalog_get(dataset_name, reduce_memory, reduce_memory_size) + for dataset_name in names + ] + + if isinstance(dataset_dicts[0], torchdata.Dataset): + if len(dataset_dicts) > 1: + # ConcatDataset does not work for iterable style dataset. + # We could support concat for iterable as well, but it's often + # not a good idea to concat iterables anyway. + return torchdata.ConcatDataset(dataset_dicts) + return dataset_dicts[0] + + for dataset_name, dicts in zip(names, dataset_dicts): + assert len(dicts), "Dataset '{}' is empty!".format(dataset_name) + + for dataset_id, (dataset_name, copypaste, dicts) in enumerate( + zip(names, copypastes, dataset_dicts) + ): + for d in dicts: + d["dataset_id"] = dataset_id + d["copypaste"] = copypaste + if dataloader_id is not None: + d["dataloader_id"] = dataloader_id + + has_instances = "annotations" in dicts[0] + if not check_consistency or not has_instances: + continue + try: + class_names = MetadataCatalog.get(dataset_name).thing_classes + check_metadata_consistency("thing_classes", [dataset_name]) + print_instances_class_histogram(dicts, class_names) + except AttributeError: # class names are not available for this dataset + pass + + assert proposal_files is None + if proposal_files is not None: + assert len(names) == len(proposal_files) + # load precomputed proposals from proposal files + dataset_dicts = [ + load_proposals_into_dataset(dataset_i_dicts, proposal_file) + for dataset_i_dicts, proposal_file in zip(dataset_dicts, proposal_files) + ] + + dataset_dicts = [ + filter_images_with_only_crowd_annotations(dicts) + if flag and "annotations" in dicts[0] + else dicts + for dicts, flag in zip(dataset_dicts, filter_emptys) + ] + + dataset_dicts = list(itertools.chain.from_iterable(dataset_dicts)) + + has_instances = "annotations" in dataset_dicts[0] + if filter_empty and has_instances and False: + dataset_dicts = filter_images_with_only_crowd_annotations(dataset_dicts) + if min_keypoints > 0 and has_instances: + dataset_dicts = filter_images_with_few_keypoints(dataset_dicts, min_keypoints) + + if check_consistency and has_instances and False: + try: + class_names = MetadataCatalog.get(names[0]).thing_classes + check_metadata_consistency("thing_classes", names) + print_instances_class_histogram(dataset_dicts, class_names) + except AttributeError: # class names are not available for this dataset + pass + + assert len(dataset_dicts), "No valid data found in {}.".format(",".join(names)) + return dataset_dicts + + +def build_batch_data_loader_multi_dataset( + dataset, + sampler, + total_batch_size, + total_batch_size_list, + *, + aspect_ratio_grouping=False, + num_workers=0, + collate_fn=None, + num_datasets=1, +): + """ + Build a batched dataloader. The main differences from `torch.utils.data.DataLoader` are: + 1. support aspect ratio grouping options + 2. use no "batch collation", because this is common for detection training + + Args: + dataset (torch.utils.data.Dataset): a pytorch map-style or iterable dataset. + sampler (torch.utils.data.sampler.Sampler or None): a sampler that produces indices. + Must be provided iff. ``dataset`` is a map-style dataset. + total_batch_size, aspect_ratio_grouping, num_workers, collate_fn: see + :func:`build_detection_train_loader`. + + Returns: + iterable[list]. Length of each list is the batch size of the current + GPU. Each element in the list comes from the dataset. + """ + world_size = get_world_size() + assert ( + total_batch_size > 0 and total_batch_size % world_size == 0 + ), "Total batch size ({}) must be divisible by the number of gpus ({}).".format( + total_batch_size, world_size + ) + batch_size = total_batch_size // world_size + + if len(total_batch_size_list) < num_datasets: + total_batch_size_list += [ + total_batch_size, + ] * (num_datasets - len(total_batch_size_list)) + assert all([x > 0 for x in total_batch_size_list]) and all( + [x % world_size == 0 for x in total_batch_size_list] + ), "Total batch size ({}) must be divisible by the number of gpus ({}).".format( + total_batch_size_list, world_size + ) + batch_size = [x // world_size for x in total_batch_size_list] + + if isinstance(dataset, torchdata.IterableDataset): + assert sampler is None, "sampler must be None if dataset is IterableDataset" + else: + dataset = ToIterableDataset(dataset, sampler) + + assert aspect_ratio_grouping + if aspect_ratio_grouping: + data_loader = torchdata.DataLoader( + dataset, + num_workers=num_workers, + collate_fn=operator.itemgetter(0), # don't batch, but yield individual elements + worker_init_fn=worker_init_reset_seed, + ) # yield individual mapped dict + # data_loader = AspectRatioGroupedDataset(data_loader, batch_size) + data_loader = MultiDatasetAspectRatioGroupedDataset( + data_loader, batch_size, num_datasets=num_datasets + ) + if collate_fn is None: + return data_loader + return MapDataset(data_loader, collate_fn) + else: + return torchdata.DataLoader( + dataset, + batch_size=batch_size, + drop_last=True, + num_workers=num_workers, + collate_fn=trivial_batch_collator if collate_fn is None else collate_fn, + worker_init_fn=worker_init_reset_seed, + ) + + +def _train_loader_from_config(cfg, mapper=None, *, dataset=None, sampler=None): + assert len(cfg.DATASETS.TRAIN) == len(cfg.MULTI_DATASET.NAMES) + assert len(cfg.DATASETS.TRAIN) == len(cfg.MULTI_DATASET.ENTITIES) + assert len(cfg.DATASETS.TRAIN) == len(cfg.MULTI_DATASET.NUM_CLASSES) + assert len(cfg.DATASETS.TRAIN) == len(cfg.MULTI_DATASET.RATIOS) + assert len(cfg.DATASETS.TRAIN) == len(cfg.MULTI_DATASET.USE_CAS) + assert len(cfg.DATASETS.TRAIN) == len(cfg.MULTI_DATASET.USE_RFS) + assert len(cfg.DATASETS.TRAIN) == len(cfg.MULTI_DATASET.FILTER_EMPTY_ANNOTATIONS) + # assert len(cfg.DATASETS.TRAIN) == len(cfg.SOLVER.IMS_PER_BATCH_LIST) + # assert len(cfg.DATASETS.TRAIN) == len(cfg.SOLVER.AUGMENT_TYPE) + assert len(cfg.DATASETS.TRAIN) == len(cfg.DATASETS.COPYPASTE.COPYPASTE) + + seed1 = comm.shared_random_seed() + seed2 = comm.shared_random_seed() + seed3 = comm.shared_random_seed() + seed4 = comm.shared_random_seed() + logger = logging.getLogger(__name__) + logger.info("rank {} seed1 {} seed2 {}".format(comm.get_local_rank(), seed1, seed2)) + logger.info("rank {} seed3 {} seed4 {}".format(comm.get_local_rank(), seed3, seed4)) + + # Hard-coded 2 sequent group and 1200s time wait. + wait_group = 2 + wait_time = cfg.DATALOADER.GROUP_WAIT + wait = comm.get_local_rank() % wait_group * wait_time + logger.info("rank {} _train_loader_from_config sleep {}".format(comm.get_local_rank(), wait)) + time.sleep(wait) + + if dataset is None: + dataset = get_detection_dataset_dicts_multi_dataset_copypaste( + cfg.DATASETS.TRAIN, + filter_empty=cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS, + min_keypoints=cfg.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE + if cfg.MODEL.KEYPOINT_ON + else 0, + proposal_files=cfg.DATASETS.PROPOSAL_FILES_TRAIN if cfg.MODEL.LOAD_PROPOSALS else None, + filter_emptys=cfg.MULTI_DATASET.FILTER_EMPTY_ANNOTATIONS, + copypastes=cfg.DATASETS.COPYPASTE.COPYPASTE, + ) + _log_api_usage("dataset." + cfg.DATASETS.TRAIN[0]) + + if True: + dataset_bg = get_detection_dataset_dicts( + cfg.DATASETS.COPYPASTE.BG, + filter_empty=cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS, + min_keypoints=cfg.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE + if cfg.MODEL.KEYPOINT_ON + else 0, + proposal_files=cfg.DATASETS.PROPOSAL_FILES_TRAIN if cfg.MODEL.LOAD_PROPOSALS else None, + ) + _log_api_usage("dataset." + cfg.DATASETS.COPYPASTE.BG[0]) + + if mapper is None: + mapper = DatasetMapper_copypaste(cfg, True) + + if sampler is None: + sampler_name = cfg.DATALOADER.SAMPLER_TRAIN + logger = logging.getLogger(__name__) + if isinstance(dataset, torchdata.IterableDataset): + logger.info("Not using any sampler since the dataset is IterableDataset.") + sampler = None + else: + logger.info("Using training sampler {}".format(sampler_name)) + if sampler_name == "TrainingSampler": + sampler = TrainingSampler(len(dataset), seed=seed1) + elif sampler_name == "RepeatFactorTrainingSampler": + repeat_factors = RepeatFactorTrainingSampler.repeat_factors_from_category_frequency( + dataset, cfg.DATALOADER.REPEAT_THRESHOLD + ) + sampler = RepeatFactorTrainingSampler(repeat_factors, seed=seed1) + elif sampler_name == "RandomSubsetTrainingSampler": + sampler = RandomSubsetTrainingSampler( + len(dataset), + cfg.DATALOADER.RANDOM_SUBSET_RATIO, + seed_shuffle=seed1, + seed_subset=seed2, + ) + elif sampler_name == "MultiDatasetSampler": + raise ValueError("Despreted training sampler: {}".format(sampler_name)) + sizes = [0 for _ in range(len(cfg.DATASETS.TRAIN))] + for d in dataset: + sizes[d["dataset_id"]] += 1 + sampler = MultiDatasetSampler(cfg, dataset, sizes, seed=seed1) + elif sampler_name == "MultiDatasetTrainingSampler": + # sampler = MultiDatasetTrainingSampler(cfg, dataset, seed=seed1) + repeat_factors = MultiDatasetTrainingSampler.get_repeat_factors( + dataset, + len(cfg.DATASETS.TRAIN), + cfg.MULTI_DATASET.RATIOS, + cfg.MULTI_DATASET.USE_RFS, + cfg.MULTI_DATASET.USE_CAS, + cfg.MULTI_DATASET.REPEAT_THRESHOLD, + cfg.MULTI_DATASET.CAS_LAMBDA, + ) + sampler = MultiDatasetTrainingSampler(repeat_factors, seed=seed1) + else: + raise ValueError("Unknown training sampler: {}".format(sampler_name)) + + if True: + sampler_name = cfg.DATALOADER.COPYPASTE.SAMPLER_TRAIN + logger = logging.getLogger(__name__) + if isinstance(dataset_bg, torchdata.IterableDataset): + logger.info("Not using any sampler since the dataset is IterableDataset.") + sampler = None + else: + logger.info("Using training sampler {}".format(sampler_name)) + if sampler_name == "TrainingSampler": + sampler_bg = TrainingSampler(len(dataset_bg), seed=seed3) + elif sampler_name == "RepeatFactorTrainingSampler": + repeat_factors = RepeatFactorTrainingSampler.repeat_factors_from_category_frequency( + dataset_bg, cfg.DATALOADER.COPYPASTE.REPEAT_THRESHOLD + ) + sampler_bg = RepeatFactorTrainingSampler(repeat_factors, seed=seed3) + elif sampler_name == "RandomSubsetTrainingSampler": + sampler_bg = RandomSubsetTrainingSampler( + len(dataset_bg), + cfg.DATALOADER.COPYPASTE.RANDOM_SUBSET_RATIO, + seed_shuffle=seed3, + seed_subset=seed4, + ) + else: + raise ValueError("Unknown training sampler: {}".format(sampler_name)) + + return { + "dataset": dataset, + "dataset_bg": dataset_bg, + "sampler": sampler, + "sampler_bg": sampler_bg, + "mapper": mapper, + "total_batch_size": cfg.SOLVER.IMS_PER_BATCH, + "total_batch_size_list": cfg.SOLVER.IMS_PER_BATCH_LIST, + "aspect_ratio_grouping": cfg.DATALOADER.ASPECT_RATIO_GROUPING, + "num_workers": cfg.DATALOADER.NUM_WORKERS, + "num_datasets": len(cfg.DATASETS.TRAIN), + } + + +@configurable(from_config=_train_loader_from_config) +def build_detection_train_loader_multi_dataset_copypaste( + dataset, + dataset_bg, + *, + mapper, + sampler=None, + sampler_bg=None, + total_batch_size, + total_batch_size_list, + aspect_ratio_grouping=True, + num_workers=0, + collate_fn=None, + num_datasets=1, +): + """ + Build a dataloader for object detection with some default features. + + Args: + dataset (list or torch.utils.data.Dataset): a list of dataset dicts, + or a pytorch dataset (either map-style or iterable). It can be obtained + by using :func:`DatasetCatalog.get` or :func:`get_detection_dataset_dicts`. + mapper (callable): a callable which takes a sample (dict) from dataset and + returns the format to be consumed by the model. + When using cfg, the default choice is ``DatasetMapper(cfg, is_train=True)``. + sampler (torch.utils.data.sampler.Sampler or None): a sampler that produces + indices to be applied on ``dataset``. + If ``dataset`` is map-style, the default sampler is a :class:`TrainingSampler`, + which coordinates an infinite random shuffle sequence across all workers. + Sampler must be None if ``dataset`` is iterable. + total_batch_size (int): total batch size across all workers. + aspect_ratio_grouping (bool): whether to group images with similar + aspect ratio for efficiency. When enabled, it requires each + element in dataset be a dict with keys "width" and "height". + num_workers (int): number of parallel data loading workers + collate_fn: a function that determines how to do batching, same as the argument of + `torch.utils.data.DataLoader`. Defaults to do no collation and return a list of + data. No collation is OK for small batch size and simple data structures. + If your batch size is large and each sample contains too many small tensors, + it's more efficient to collate them in data loader. + + Returns: + torch.utils.data.DataLoader: + a dataloader. Each output from it is a ``list[mapped_element]`` of length + ``total_batch_size / num_workers``, where ``mapped_element`` is produced + by the ``mapper``. + """ + # wait = round(comm.get_local_rank() * 1.0 * len(dataset) / 60000) + # logger = logging.getLogger(__name__) + # logger.info("get_detection_dataset_dicts_multi_dataset sleep {}".format(wait)) + # time.sleep(wait) + + if isinstance(sampler_bg, Callable): + sampler_bg = sampler_bg(dataset_bg) + if isinstance(sampler, Callable): + sampler = sampler(dataset) + + if isinstance(dataset_bg, list): + dataset_bg = DatasetFromList(dataset_bg, copy=False) + + if isinstance(dataset_bg, torchdata.IterableDataset): + assert sampler_bg is None, "sampler must be None if dataset is IterableDataset" + else: + if sampler_bg is None: + sampler_bg = TrainingSampler(len(dataset_bg)) + assert isinstance( + sampler_bg, torchdata.Sampler + ), f"Expect a Sampler but got {type(sampler)}" + + if isinstance(dataset, list): + dataset = DatasetFromList(dataset, copy=False) + if mapper is not None: + dataset = MapDataset_coppaste(dataset, mapper, dataset_bg, sampler_bg) + + if isinstance(dataset, torchdata.IterableDataset): + assert sampler is None, "sampler must be None if dataset is IterableDataset" + else: + if sampler is None: + sampler = TrainingSampler(len(dataset)) + assert isinstance(sampler, torchdata.Sampler), f"Expect a Sampler but got {type(sampler)}" + return build_batch_data_loader_multi_dataset( + dataset, + sampler, + total_batch_size, + total_batch_size_list, + aspect_ratio_grouping=aspect_ratio_grouping, + num_workers=num_workers, + collate_fn=collate_fn, + num_datasets=num_datasets, + ) + + +class MultiDatasetSampler(Sampler): + def __init__(self, cfg, dataset_dicts, sizes, seed: Optional[int] = None): + self.sizes = sizes + self.sample_epoch_size = cfg.MULTI_DATASET.SAMPLE_EPOCH_SIZE + assert self.sample_epoch_size % cfg.SOLVER.IMS_PER_BATCH == 0, ( + self.sample_epoch_size % cfg.SOLVER.IMS_PER_BATCH == 0 + ) + if seed is None: + seed = comm.shared_random_seed() + self._seed = int(seed) + + self._rank = comm.get_rank() + self._world_size = comm.get_world_size() + + dataset_ratio = cfg.MULTI_DATASET.RATIOS + assert len(dataset_ratio) == len( + sizes + ), "length of dataset ratio {} should be equal to number if dataset {}".format( + len(dataset_ratio), len(sizes) + ) + dataset_weight = [ + torch.ones(s) * max(sizes) / s * r / sum(dataset_ratio) + for i, (r, s) in enumerate(zip(dataset_ratio, sizes)) + ] + st = 0 + cas_factors = [] + for i, s in enumerate(sizes): + if cfg.MULTI_DATASET.USE_CAS[i]: + cas_factor = self._get_class_balance_factor_per_dataset( + dataset_dicts[st : st + s], l=cfg.MULTI_DATASET.CAS_LAMBDA + ) + cas_factor = cas_factor * (s / cas_factor.sum()) + else: + cas_factor = torch.ones(s) + cas_factors.append(cas_factor) + st = st + s + cas_factors = torch.cat(cas_factors) + dataset_weight = torch.cat(dataset_weight) + self.weights = dataset_weight * cas_factors + + def __iter__(self): + start = self._rank + yield from itertools.islice(self._infinite_indices(), start, None, self._world_size) + + def _infinite_indices(self): + g = torch.Generator() + g.manual_seed(self._seed) + while True: + ids = torch.multinomial( + self.weights, self.sample_epoch_size, generator=g, replacement=True + ) + yield from ids + + def _get_class_balance_factor_per_dataset(self, dataset_dicts, l=1.0): + ret = [] + category_freq = defaultdict(int) + for dataset_dict in dataset_dicts: # For each image (without repeats) + cat_ids = {ann["category_id"] for ann in dataset_dict["annotations"]} + for cat_id in cat_ids: + category_freq[cat_id] += 1 + for dataset_dict in dataset_dicts: + cat_ids = {ann["category_id"] for ann in dataset_dict["annotations"]} + ret.append(sum([1.0 / (category_freq[cat_id] ** l) for cat_id in cat_ids])) + return torch.tensor(ret).float() + + +# class MultiDatasetTrainingSampler(Sampler): +# def __init__(self, cfg, dataset_dicts, *, shuffle=True, seed=None): +# sizes = [0 for _ in range(len(cfg.DATASETS.TRAIN))] +# for d in dataset_dicts: +# sizes[d["dataset_id"]] += 1 + +# dataset_ratio = cfg.MULTI_DATASET.RATIOS +# assert len(dataset_ratio) == len( +# sizes +# ), "length of dataset ratio {} should be equal to number if dataset {}".format( +# len(dataset_ratio), len(sizes) +# ) +# dataset_weight = [ +# torch.ones(s) * max(sizes) / s * r for i, (r, s) in enumerate(zip(dataset_ratio, sizes)) +# ] + +# logger = logging.getLogger(__name__) +# logger.info( +# "Training sampler dataset weight: {}".format( +# str([max(sizes) / s * r for i, (r, s) in enumerate(zip(dataset_ratio, sizes))]) +# ) +# ) + +# st = 0 +# repeat_factors = [] +# for i, s in enumerate(sizes): +# assert cfg.MULTI_DATASET.USE_RFS[i] * cfg.MULTI_DATASET.USE_CAS[i] == 0 +# if cfg.MULTI_DATASET.USE_RFS[i]: +# repeat_factor = RepeatFactorTrainingSampler.repeat_factors_from_category_frequency( +# dataset_dicts[st : st + s], cfg.MULTI_DATASET.REPEAT_THRESHOLD +# ) +# elif cfg.MULTI_DATASET.USE_CAS[i]: +# repeat_factor = MultiDatasetTrainingSampler.get_class_balance_factor_per_dataset( +# dataset_dicts[st : st + s], l=cfg.MULTI_DATASET.CAS_LAMBDA +# ) +# repeat_factor = repeat_factor * (s / repeat_factor.sum()) +# else: +# repeat_factor = torch.ones(s) +# repeat_factors.append(repeat_factor) +# st = st + s +# repeat_factors = torch.cat(repeat_factors) +# dataset_weight = torch.cat(dataset_weight) +# repeat_factors = dataset_weight * repeat_factors + +# self._shuffle = shuffle +# if seed is None: +# seed = comm.shared_random_seed() +# self._seed = int(seed) + +# self._rank = comm.get_rank() +# self._world_size = comm.get_world_size() + +# # Split into whole number (_int_part) and fractional (_frac_part) parts. +# self._int_part = torch.trunc(repeat_factors) +# self._frac_part = repeat_factors - self._int_part + +# @staticmethod +# def get_class_balance_factor_per_dataset(dataset_dicts, l=1.0): +# rep_factors = [] +# category_freq = defaultdict(int) +# for dataset_dict in dataset_dicts: # For each image (without repeats) +# cat_ids = {ann["category_id"] for ann in dataset_dict["annotations"]} +# for cat_id in cat_ids: +# category_freq[cat_id] += 1 +# for dataset_dict in dataset_dicts: +# cat_ids = {ann["category_id"] for ann in dataset_dict["annotations"]} +# rep_factor = sum([1.0 / (category_freq[cat_id] ** l) for cat_id in cat_ids]) +# rep_factors.append(rep_factor) + +# return torch.tensor(rep_factors, dtype=torch.float32) + +# def _get_epoch_indices(self, generator): +# """ +# Create a list of dataset indices (with repeats) to use for one epoch. + +# Args: +# generator (torch.Generator): pseudo random number generator used for +# stochastic rounding. + +# Returns: +# torch.Tensor: list of dataset indices to use in one epoch. Each index +# is repeated based on its calculated repeat factor. +# """ +# # Since repeat factors are fractional, we use stochastic rounding so +# # that the target repeat factor is achieved in expectation over the +# # course of training +# rands = torch.rand(len(self._frac_part), generator=generator) +# rep_factors = self._int_part + (rands < self._frac_part).float() +# # Construct a list of indices in which we repeat images as specified +# indices = [] +# for dataset_index, rep_factor in enumerate(rep_factors): +# indices.extend([dataset_index] * int(rep_factor.item())) +# return torch.tensor(indices, dtype=torch.int64) + +# def __iter__(self): +# start = self._rank +# yield from itertools.islice(self._infinite_indices(), start, None, self._world_size) + +# def _infinite_indices(self): +# g = torch.Generator() +# g.manual_seed(self._seed) +# while True: +# # Sample indices with repeats determined by stochastic rounding; each +# # "epoch" may have a slightly different size due to the rounding. +# indices = self._get_epoch_indices(g) +# if self._shuffle: +# randperm = torch.randperm(len(indices), generator=g) +# yield from indices[randperm].tolist() +# else: +# yield from indices.tolist() + + +class MultiDatasetAspectRatioGroupedDataset(torch.utils.data.IterableDataset): + """ + Batch data that have similar aspect ratio together. + In this implementation, images whose aspect ratio < (or >) 1 will + be batched together. + This improves training speed because the images then need less padding + to form a batch. + + It assumes the underlying dataset produces dicts with "width" and "height" keys. + It will then produce a list of original dicts with length = batch_size, + all with similar aspect ratios. + """ + + def __init__(self, dataset, batch_size, num_datasets): + """ + Args: + dataset: an iterable. Each element must be a dict with keys + "width" and "height", which will be used to batch data. + batch_size (int): + """ + self.dataset = dataset + self.batch_size = batch_size + self._buckets = [[] for _ in range(2 * num_datasets)] + # Hard-coded two aspect ratio groups: w > h and w < h. + # Can add support for more aspect ratio groups, but doesn't seem useful + + def __iter__(self): + for d in self.dataset: + w, h = d["width"], d["height"] + bucket_id = 0 if w > h else 1 + bucket_id = d["dataset_id"] * 2 + bucket_id + bucket = self._buckets[bucket_id] + bucket.append(d) + if len(bucket) == self.batch_size[d["dataset_id"]]: + data = bucket[:] + # Clear bucket first, because code after yield is not + # guaranteed to execute + del bucket[:] + yield data diff --git a/ape/data/common_copypaste.py b/ape/data/common_copypaste.py new file mode 100644 index 0000000..57f85bb --- /dev/null +++ b/ape/data/common_copypaste.py @@ -0,0 +1,83 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +import random + +import numpy as np +import torch.utils.data as data + +from detectron2.data.common import _MapIterableDataset +from detectron2.utils.serialize import PicklableWrapper + +__all__ = ["MapDataset_coppaste"] + + +class MapDataset_coppaste(data.Dataset): + """ + Map a function over the elements in a dataset. + """ + + def __init__(self, dataset, map_func, dataset_bg, sampler_bg): + """ + Args: + dataset: a dataset where map function is applied. Can be either + map-style or iterable dataset. When given an iterable dataset, + the returned object will also be an iterable dataset. + map_func: a callable which maps the element in dataset. map_func can + return None to skip the data (e.g. in case of errors). + How None is handled depends on the style of `dataset`. + If `dataset` is map-style, it randomly tries other elements. + If `dataset` is iterable, it skips the data and tries the next. + """ + self._dataset = dataset + self._map_func = PicklableWrapper(map_func) # wrap so that a lambda will work + + self._rng = random.Random(42) + self._fallback_candidates = set(range(len(dataset))) + + self._dataset_bg = dataset_bg + self._sampler_bg = sampler_bg + self._sampler_bg_iter = None + + def __new__(cls, dataset, map_func, dataset_bg, sampler_bg): + is_iterable = isinstance(dataset, data.IterableDataset) + if is_iterable: + assert 0 + return _MapIterableDataset(dataset, map_func) + else: + return super().__new__(cls) + + def __getnewargs__(self): + return self._dataset, self._map_func, self._dataset_bg, self._sampler_bg + + def __len__(self): + return len(self._dataset) + + def __getitem__(self, idx): + retry_count = 0 + cur_idx = int(idx) + + if self._sampler_bg_iter: + pass + else: + self._sampler_bg._seed = np.random.randint(2**31) + self._sampler_bg_iter = iter(self._sampler_bg) + + while True: + cur_idx_bg = next(self._sampler_bg_iter) + data = self._map_func(self._dataset[cur_idx], self._dataset_bg[cur_idx_bg]) + if data is not None: + self._fallback_candidates.add(cur_idx) + return data + + # _map_func fails for this idx, use a random new index from the pool + retry_count += 1 + self._fallback_candidates.discard(cur_idx) + cur_idx = self._rng.sample(self._fallback_candidates, k=1)[0] + + if retry_count >= 3: + logger = logging.getLogger(__name__) + logger.warning( + "Failed to apply `_map_func` for idx: {}, retry count: {}".format( + idx, retry_count + ) + ) diff --git a/ape/data/dataset_mapper.py b/ape/data/dataset_mapper.py new file mode 100644 index 0000000..d92a5b4 --- /dev/null +++ b/ape/data/dataset_mapper.py @@ -0,0 +1,41 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging + +from detectron2.data import detection_utils as utils +from detectron2.data import transforms as T +from detectron2.data.dataset_mapper import DatasetMapper as DatasetMapper_d2 + +from . import detection_utils as utils_sota + +""" +This file contains the default mapping that's applied to "dataset dicts". +""" + +__all__ = ["DatasetMapper_ape"] + + +class DatasetMapper_ape(DatasetMapper_d2): + """ + A callable which takes a dataset dict in Detectron2 Dataset format, + and map it into a format used by the model. + + This is the default callable to be used to map your dataset dict into training data. + You may need to follow it to implement your own one for customized logic, + such as a different way to read or transform images. + See :doc:`/tutorials/data_loading` for details. + + The callable currently does the following: + + 1. Read the image from "file_name" + 2. Applies cropping/geometric transforms to the image and annotations + 3. Prepare data and annotations to Tensor and :class:`Instances` + """ + + def __init__(self, cfg, is_train: bool = True): + super().__init__(cfg, is_train) + augmentations = utils_sota.build_augmentation(cfg, is_train) + self.augmentations = T.AugmentationList(augmentations) + + logger = logging.getLogger(__name__) + mode = "training" if is_train else "inference" + logger.info(f"[DatasetMapper] Augmentations used in {mode}: {augmentations}") diff --git a/ape/data/dataset_mapper_copypaste.py b/ape/data/dataset_mapper_copypaste.py new file mode 100644 index 0000000..4f862b9 --- /dev/null +++ b/ape/data/dataset_mapper_copypaste.py @@ -0,0 +1,499 @@ +import copy +import logging +import os +import random +from typing import List, Optional, Union + +import cv2 +import numpy as np +import torch + +import detectron2.utils.comm as comm +from detectron2.config import configurable +from detectron2.data import MetadataCatalog +from detectron2.data import detection_utils as utils +from detectron2.data import transforms as T +from detectron2.data.dataset_mapper import DatasetMapper as DatasetMapper_d2 +from detectron2.data.detection_utils import convert_image_to_rgb +from detectron2.structures import BitMasks, Boxes, Instances + +from . import detection_utils as utils_sota +from . import mapper_utils + +""" +This file contains the default mapping that's applied to "dataset dicts". +""" + +__all__ = ["DatasetMapper_copypaste"] + + +class DatasetMapper_copypaste(DatasetMapper_d2): + """ + A callable which takes a dataset dict in Detectron2 Dataset format, + and map it into a format used by the model. + + This is the default callable to be used to map your dataset dict into training data. + You may need to follow it to implement your own one for customized logic, + such as a different way to read or transform images. + See :doc:`/tutorials/data_loading` for details. + + The callable currently does the following: + + 1. Read the image from "file_name" + 2. Applies cropping/geometric transforms to the image and annotations + 3. Prepare data and annotations to Tensor and :class:`Instances` + """ + + @configurable + def __init__( + self, + is_train: bool, + *, + augmentations: List[Union[T.Augmentation, T.Transform]], + augmentations_d2: List[Union[T.Augmentation, T.Transform]], + augmentations_aa: List[Union[T.Augmentation, T.Transform]], + augmentations_lsj: List[Union[T.Augmentation, T.Transform]], + augmentations_type: List[str], + image_format: str, + use_instance_mask: bool = False, + use_keypoint: bool = False, + instance_mask_format: str = "polygon", + keypoint_hflip_indices: Optional[np.ndarray] = None, + precomputed_proposal_topk: Optional[int] = None, + recompute_boxes: bool = False, + copypaste_prob: float = 0.5, + output_dir: str = None, + vis_period: int = 0, + dataset_names: tuple = (), + ): + """ + NOTE: this interface is experimental. + + Args: + is_train: whether it's used in training or inference + augmentations: a list of augmentations or deterministic transforms to apply + image_format: an image format supported by :func:`detection_utils.read_image`. + use_instance_mask: whether to process instance segmentation annotations, if available + use_keypoint: whether to process keypoint annotations if available + instance_mask_format: one of "polygon" or "bitmask". Process instance segmentation + masks into this format. + keypoint_hflip_indices: see :func:`detection_utils.create_keypoint_hflip_indices` + precomputed_proposal_topk: if given, will load pre-computed + proposals from dataset_dict and keep the top k proposals for each image. + recompute_boxes: whether to overwrite bounding box annotations + by computing tight bounding boxes from instance mask annotations. + """ + if recompute_boxes: + assert use_instance_mask, "recompute_boxes requires instance masks" + # fmt: off + self.is_train = is_train + self.augmentations = T.AugmentationList(augmentations) + self.augmentations_d2 = T.AugmentationList(augmentations_d2) + self.augmentations_aa = T.AugmentationList(augmentations_aa) + self.augmentations_lsj = T.AugmentationList(augmentations_lsj) + self.augmentations_type = augmentations_type + self.image_format = image_format + self.use_instance_mask = use_instance_mask + self.instance_mask_format = instance_mask_format + self.use_keypoint = use_keypoint + self.keypoint_hflip_indices = keypoint_hflip_indices + self.proposal_topk = precomputed_proposal_topk + self.recompute_boxes = recompute_boxes + # fmt: on + logger = logging.getLogger(__name__) + mode = "training" if is_train else "inference" + logger.info(f"[DatasetMapper] Augmentations used in {mode}: {augmentations}") + logger.info(f"[DatasetMapper] D2 Augmentations D2 used in {mode}: {augmentations_d2}") + logger.info(f"[DatasetMapper] AA Augmentations used in {mode}: {augmentations_aa}") + logger.info(f"[DatasetMapper] LSJ Augmentations used in {mode}: {augmentations_lsj}") + logger.info(f"[DatasetMapper] Type Augmentations used in {mode}: {augmentations_type}") + + if output_dir is not None: + self.output_dir = os.path.join(output_dir, "vis_mapper") + os.makedirs(self.output_dir, exist_ok=True) + + self.copypaste_prob = copypaste_prob + self.vis_period = vis_period + self.iter = 0 + self.dataset_names = dataset_names + + self.metatada_list = [] + for dataset_name in self.dataset_names: + metadata = MetadataCatalog.get(dataset_name) + self.metatada_list.append(metadata) + + @classmethod + def from_config(cls, cfg, is_train: bool = True): + augs = utils_sota.build_augmentation(cfg, is_train) + augs_d2 = utils.build_augmentation(cfg, is_train) + augs_aa = utils_sota.build_augmentation_aa(cfg, is_train) + augs_lsj = utils_sota.build_augmentation_lsj(cfg, is_train) + if cfg.INPUT.CROP.ENABLED and is_train: + raise NotImplementedError("cfg.INPUT.CROP.ENABLED is not supported yet") + augs.insert(0, T.RandomCrop(cfg.INPUT.CROP.TYPE, cfg.INPUT.CROP.SIZE)) + recompute_boxes = cfg.MODEL.MASK_ON + else: + recompute_boxes = False + + if cfg.INPUT.MASK_FORMAT == "polygon": + logger = logging.getLogger(__name__) + logger.warning("Using polygon is slow, use bitmask instead") + if cfg.INPUT.MASK_FORMAT == "bitmask": + logger = logging.getLogger(__name__) + logger.warning("Using bitmask may has bug, use polygon instead") + assert ( + cfg.INPUT.SEG_PAD_VALUE == 0 + ), "PadTransform should pad bitmask with value 0. Please setting cfg.INPUT.SEG_PAD_VALUE to 0. \nNoted that cfg.INPUT.SEG_PAD_VALUE is also used to pad semantic segmentation. If semantic segmentation is used, Please set cfg.INPUT.FORMAT to polygon." + + ret = { + "is_train": is_train, + "augmentations": augs, + "augmentations_d2": augs_d2, + "augmentations_aa": augs_aa, + "augmentations_lsj": augs_lsj, + "augmentations_type": cfg.INPUT.AUGMENT_TYPE, + "image_format": cfg.INPUT.FORMAT, + "use_instance_mask": cfg.MODEL.MASK_ON, + "instance_mask_format": cfg.INPUT.MASK_FORMAT, + "use_keypoint": cfg.MODEL.KEYPOINT_ON, + "recompute_boxes": recompute_boxes, + "output_dir": cfg.OUTPUT_DIR, + "copypaste_prob": cfg.DATASETS.COPYPASTE.PROB, + "vis_period": cfg.VIS_PERIOD, + "dataset_names": cfg.DATASETS.TRAIN, + } + + if cfg.MODEL.KEYPOINT_ON: + ret["keypoint_hflip_indices"] = utils.create_keypoint_hflip_indices(cfg.DATASETS.TRAIN) + + if cfg.MODEL.LOAD_PROPOSALS: + ret["precomputed_proposal_topk"] = ( + cfg.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TRAIN + if is_train + else cfg.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TEST + ) + return ret + + def _transform_annotations(self, dataset_dict, transforms, image_shape): + # USER: Modify this if you want to keep them for some reason. + for anno in dataset_dict["annotations"]: + if not self.use_instance_mask: + anno.pop("segmentation", None) + if not self.use_keypoint: + anno.pop("keypoints", None) + + copypaste = [ + obj.get("copypaste", 0) + for obj in dataset_dict["annotations"] + if obj.get("iscrowd", 0) == 0 + ] + + phrases = [ + obj.get("phrase", "") + for obj in dataset_dict["annotations"] + if obj.get("iscrowd", 0) == 0 + ] + + # USER: Implement additional transformations if you have other types of data + annos = [ + utils.transform_instance_annotations( + obj, transforms, image_shape, keypoint_hflip_indices=self.keypoint_hflip_indices + ) + for obj in dataset_dict.pop("annotations") + if obj.get("iscrowd", 0) == 0 + ] + instances = utils.annotations_to_instances( + annos, image_shape, mask_format=self.instance_mask_format + ) + + instances.copypaste = torch.tensor(copypaste) + + if sum([len(x) for x in phrases]) > 0: + instances.phrase_idxs = torch.tensor(range(len(phrases))) + + # After transforms such as cropping are applied, the bounding box may no longer + # tightly bound the object. As an example, imagine a triangle object + # [(0,0), (2,0), (0,2)] cropped by a box [(1,0),(2,2)] (XYXY format). The tight + # bounding box of the cropped triangle should be [(1,0),(2,1)], which is not equal to + # the intersection of original bounding box and the cropping box. + if self.recompute_boxes and instances.has("gt_masks"): + instances.gt_boxes = instances.gt_masks.get_bounding_boxes() + dataset_dict["instances"] = utils.filter_empty_instances(instances, box_threshold=10) + + if sum([len(x) for x in phrases]) > 0: + phrases_filtered = [] + for x in dataset_dict["instances"].phrase_idxs.tolist(): + phrases_filtered.append(phrases[x]) + dataset_dict["instances"].phrases = mapper_utils.transform_phrases( + phrases_filtered, transforms + ) + dataset_dict["instances"].remove("phrase_idxs") + # dataset_dict["instances"].gt_classes = torch.tensor(range(len(phrases_filtered))) + + def __call__(self, dataset_dict, dataset_dict_bg): + """ + Args: + dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format. + + Returns: + dict: a format that builtin models in detectron2 accept + """ + dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below + # USER: Write your own image loading if it's not from a file + try: + image = utils.read_image(dataset_dict["file_name"], format=self.image_format) + except Exception as e: + logger = logging.getLogger(__name__) + logger.error(f"read_image fails: {dataset_dict['file_name']}") + logger.error(f"read_image fails: {e}") + return None + utils.check_image_size(dataset_dict, image) + + # ------------------------------------------------------------------------------------ + if ( + self.is_train + and "annotations" in dataset_dict + and ( + len(dataset_dict["annotations"]) == 0 + or any(["bbox" not in anno for anno in dataset_dict["annotations"]]) + ) + ): + if "dataset_id" in dataset_dict: + dataset_id = dataset_dict["dataset_id"] + else: + dataset_id = 0 + metadata = self.metatada_list[dataset_id] + if "sa1b" in self.dataset_names[dataset_id]: + metadata = None + dataset_dict = mapper_utils.maybe_load_annotation_from_file(dataset_dict, meta=metadata) + + for anno in dataset_dict["annotations"]: + if "bbox" not in anno: + logger = logging.getLogger(__name__) + logger.warning(f"Box not found: {dataset_dict}") + return None + if "category_id" not in anno: + anno["category_id"] = 0 + # ------------------------------------------------------------------------------------ + + # ------------------------------------------------------------------------------------ + if dataset_dict["copypaste"] and self.copypaste_prob > random.uniform(0, 1): + image_cp, dataset_dict_cp = mapper_utils.copypaste( + dataset_dict, dataset_dict_bg, self.image_format, self.instance_mask_format + ) + + if dataset_dict_cp is None or image_cp is None: + pass + else: + for key in dataset_dict.keys(): + if key in dataset_dict_cp: + continue + dataset_dict_cp[key] = dataset_dict[key] + dataset_dict = dataset_dict_cp + image = image_cp + # ------------------------------------------------------------------------------------ + + # USER: Remove if you don't do semantic/panoptic segmentation. + if "sem_seg_file_name" in dataset_dict: + try: + sem_seg_gt = utils.read_image(dataset_dict.pop("sem_seg_file_name"), "L").squeeze(2) + except Exception as e: + logger = logging.getLogger(__name__) + logger.error(f"read_image fails: {e}") + logger.error(f"read_image fails: {dataset_dict}") + return None + + if "copypaste_mask" in dataset_dict: + # assume thing class is 0 + sem_seg_gt = sem_seg_gt.copy() + sem_seg_gt[dataset_dict["copypaste_mask"]] = 0 + else: + sem_seg_gt = None + + aug_input = T.AugInput(image, sem_seg=sem_seg_gt) + try: + if "dataset_id" not in dataset_dict or dataset_dict["dataset_id"] >= len( + self.augmentations_type + ): + transforms = self.augmentations(aug_input) + elif self.augmentations_type[dataset_dict["dataset_id"]] == "D2": + transforms = self.augmentations_d2(aug_input) + elif self.augmentations_type[dataset_dict["dataset_id"]] == "AA": + transforms = self.augmentations_aa(aug_input) + elif self.augmentations_type[dataset_dict["dataset_id"]] == "LSJ": + transforms = self.augmentations_lsj(aug_input) + else: + print("fall back to default augmentation") + transforms = self.augmentations(aug_input) + image, sem_seg_gt = aug_input.image, aug_input.sem_seg + except Exception as e: + logger = logging.getLogger(__name__) + logger.error(f"augment fails: {dataset_dict['file_name']}") + logger.error(f"augment fails: {e}") + return None + + image_shape = image.shape[:2] # h, w + # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory, + # but not efficient on large generic data structures due to the use of pickle & mp.Queue. + # Therefore it's important to use torch.Tensor. + dataset_dict["image"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1))) + if sem_seg_gt is not None: + dataset_dict["sem_seg"] = torch.as_tensor(sem_seg_gt.astype("long")) + + # USER: Remove if you don't use pre-computed proposals. + # Most users would not need this feature. + if self.proposal_topk is not None: + utils.transform_proposals( + dataset_dict, image_shape, transforms, proposal_topk=self.proposal_topk + ) + + if not self.is_train: + # USER: Modify this if you want to keep them for some reason. + dataset_dict.pop("annotations", None) + dataset_dict.pop("sem_seg_file_name", None) + return dataset_dict + + # seperate box and region + if "annotations" in dataset_dict: + annotations = [] + annotations_phrase = [] + for ann in dataset_dict.pop("annotations"): + if ann.get("isobject", 1) == 0: + annotations_phrase.append(ann) + else: + annotations.append(ann) + if len(annotations_phrase) > 0: + dataset_dict["annotations"] = annotations_phrase + self._transform_annotations(dataset_dict, transforms, image_shape) + dataset_dict["instances_phrase"] = dataset_dict.pop("instances") + dataset_dict["annotations"] = annotations + + if "annotations" in dataset_dict: + self._transform_annotations(dataset_dict, transforms, image_shape) + + # ------------------------------------------------------------------------------------ + if self.vis_period > 0 and self.iter % self.vis_period == 0: + self.visualize_training(dataset_dict) + # ------------------------------------------------------------------------------------ + self.iter += 1 + + return dataset_dict + + def visualize_training(self, dataset_dict, prefix="", suffix=""): + if self.output_dir is None: + return + if dataset_dict is None: + return + # if "instances" not in dataset_dict: + # return + from detectron2.utils.visualizer import Visualizer + from detectron2.data import MetadataCatalog + + if "dataset_id" in dataset_dict: + dataset_id = dataset_dict["dataset_id"] + else: + dataset_id = 0 + dataset_name = self.dataset_names[dataset_id] + metadata = MetadataCatalog.get(dataset_name) + class_names = metadata.get( + "thing_classes", + [ + "thing", + ], + ) + + img = dataset_dict["image"] + img = convert_image_to_rgb(img.permute(1, 2, 0), self.image_format) + image_shape = img.shape[:2] # h, w + vis = Visualizer(img, metadata=metadata) + if "instances" in dataset_dict: + vis = vis.overlay_instances( + boxes=dataset_dict["instances"].gt_boxes, + masks=dataset_dict["instances"].gt_masks + if dataset_dict["instances"].has("gt_masks") + else None, + labels=[class_names[i] for i in dataset_dict["instances"].gt_classes], + ) + else: + vis = vis.overlay_instances( + boxes=None, + masks=None, + labels=None, + ) + vis_gt = vis.get_image() + + if "instances_phrase" in dataset_dict: + vis = Visualizer(img, metadata=metadata) + vis = vis.overlay_instances( + boxes=dataset_dict["instances_phrase"].gt_boxes, + masks=dataset_dict["instances_phrase"].gt_masks + if dataset_dict["instances_phrase"].has("gt_masks") + else None, + labels=dataset_dict["instances_phrase"].phrases, + ) + vis_phrase = vis.get_image() + vis_gt = np.concatenate((vis_gt, vis_phrase), axis=1) + + if "captions" in dataset_dict: + vis = Visualizer(img, metadata=metadata) + vis = vis.overlay_instances( + boxes=Boxes( + np.array( + [ + [ + 0 + i * 20, + 0 + i * 20, + image_shape[1] - 1 - i * 20, + image_shape[0] - 1 - i * 20, + ] + for i in range(len(dataset_dict["captions"])) + ] + ) + ), + masks=None, + labels=dataset_dict["captions"], + ) + vis_cap = vis.get_image() + vis_gt = np.concatenate((vis_gt, vis_cap), axis=1) + + if "sem_seg" in dataset_dict: + vis = Visualizer(img, metadata=metadata) + vis = vis.draw_sem_seg(dataset_dict["sem_seg"], area_threshold=0, alpha=0.5) + vis_sem_gt = vis.get_image() + vis_gt = np.concatenate((vis_gt, vis_sem_gt), axis=1) + + concat = np.concatenate((vis_gt, img), axis=1) + + image_name = os.path.basename(dataset_dict["file_name"]).split(".")[0] + + save_path = os.path.join( + self.output_dir, + prefix + + str(self.iter) + + "_" + + image_name + + "_g" + + str(comm.get_rank()) + + suffix + + ".png", + ) + concat = cv2.cvtColor(concat, cv2.COLOR_RGB2BGR) + cv2.imwrite(save_path, concat) + + return + + import pickle + + save_path = os.path.join( + self.output_dir, + prefix + + str(self.iter) + + "_" + + str(dataset_dict["image_id"]) + + "_g" + + str(comm.get_rank()) + + suffix + + ".pkl", + ) + with open(save_path, "wb") as save_file: + pickle.dump(dataset_dict, save_file) diff --git a/ape/data/dataset_mapper_detr_instance.py b/ape/data/dataset_mapper_detr_instance.py new file mode 100644 index 0000000..0096a0c --- /dev/null +++ b/ape/data/dataset_mapper_detr_instance.py @@ -0,0 +1,288 @@ +import copy +import logging +from typing import List, Optional, Union + +import numpy as np +import torch + +from detectron2.config import configurable +from detectron2.data import MetadataCatalog +from detectron2.data import detection_utils as utils +from detectron2.data import transforms as T +from detectron2.layers import batched_nms + +from . import mapper_utils + +""" +This file contains the default mapping that's applied to "dataset dicts". +""" + +__all__ = ["DatasetMapper_detr_instance"] + + +class DatasetMapper_detr_instance: + """ + A callable which takes a dataset dict in Detectron2 Dataset format, + and map it into a format used by the model. + + This is the default callable to be used to map your dataset dict into training data. + You may need to follow it to implement your own one for customized logic, + such as a different way to read or transform images. + See :doc:`/tutorials/data_loading` for details. + + The callable currently does the following: + + 1. Read the image from "file_name" + 2. Applies cropping/geometric transforms to the image and annotations + 3. Prepare data and annotations to Tensor and :class:`Instances` + """ + + @configurable + def __init__( + self, + is_train: bool, + *, + augmentations: List[Union[T.Augmentation, T.Transform]], + augmentations_with_crop: List[Union[T.Augmentation, T.Transform]], + image_format: str, + use_instance_mask: bool = False, + use_keypoint: bool = False, + instance_mask_format: str = "polygon", + keypoint_hflip_indices: Optional[np.ndarray] = None, + precomputed_proposal_topk: Optional[int] = None, + recompute_boxes: bool = False, + dataset_names: tuple = (), + max_num_phrase: int = 0, + nms_thresh_phrase: float = 0.0, + ): + """ + NOTE: this interface is experimental. + + Args: + is_train: whether it's used in training or inference + augmentations: a list of augmentations or deterministic transforms to apply + image_format: an image format supported by :func:`detection_utils.read_image`. + use_instance_mask: whether to process instance segmentation annotations, if available + use_keypoint: whether to process keypoint annotations if available + instance_mask_format: one of "polygon" or "bitmask". Process instance segmentation + masks into this format. + keypoint_hflip_indices: see :func:`detection_utils.create_keypoint_hflip_indices` + precomputed_proposal_topk: if given, will load pre-computed + proposals from dataset_dict and keep the top k proposals for each image. + recompute_boxes: whether to overwrite bounding box annotations + by computing tight bounding boxes from instance mask annotations. + """ + if recompute_boxes: + assert use_instance_mask, "recompute_boxes requires instance masks" + # fmt: off + self.is_train = is_train + self.augmentations = T.AugmentationList(augmentations) + self.augmentations_with_crop = T.AugmentationList(augmentations_with_crop) + self.image_format = image_format + self.use_instance_mask = use_instance_mask + self.instance_mask_format = instance_mask_format + self.use_keypoint = use_keypoint + self.keypoint_hflip_indices = keypoint_hflip_indices + self.proposal_topk = precomputed_proposal_topk + self.recompute_boxes = recompute_boxes + # fmt: on + logger = logging.getLogger(__name__) + mode = "training" if is_train else "inference" + logger.info(f"[DatasetMapper] Augmentations used in {mode}: {augmentations}") + logger.info(f"[DatasetMapper] Augmentations used in {mode}: {augmentations_with_crop}") + + self.dataset_names = dataset_names + + self.metatada_list = [] + for dataset_name in self.dataset_names: + metadata = MetadataCatalog.get(dataset_name) + self.metatada_list.append(metadata) + + self.max_num_phrase = max_num_phrase + self.nms_thresh_phrase = nms_thresh_phrase + + @classmethod + def from_config(cls, cfg, is_train: bool = True): + raise NotImplementedError(self.__class__.__name__) + + def _transform_annotations(self, dataset_dict, transforms, image_shape): + # USER: Modify this if you want to keep them for some reason. + for anno in dataset_dict["annotations"]: + if not self.use_instance_mask: + anno.pop("segmentation", None) + if not self.use_keypoint: + anno.pop("keypoints", None) + + phrases = [ + obj.get("phrase", "") + for obj in dataset_dict["annotations"] + if obj.get("iscrowd", 0) == 0 + ] + + # USER: Implement additional transformations if you have other types of data + annos = [ + utils.transform_instance_annotations( + obj, transforms, image_shape, keypoint_hflip_indices=self.keypoint_hflip_indices + ) + for obj in dataset_dict.pop("annotations") + if obj.get("iscrowd", 0) == 0 + ] + instances = utils.annotations_to_instances( + annos, image_shape, mask_format=self.instance_mask_format + ) + + if sum([len(x) for x in phrases]) > 0: + instances.phrase_idxs = torch.tensor(range(len(phrases))) + + # After transforms such as cropping are applied, the bounding box may no longer + # tightly bound the object. As an example, imagine a triangle object + # [(0,0), (2,0), (0,2)] cropped by a box [(1,0),(2,2)] (XYXY format). The tight + # bounding box of the cropped triangle should be [(1,0),(2,1)], which is not equal to + # the intersection of original bounding box and the cropping box. + if self.recompute_boxes and instances.has("gt_masks"): + instances.gt_boxes = instances.gt_masks.get_bounding_boxes() + dataset_dict["instances"] = utils.filter_empty_instances(instances) + + if sum([len(x) for x in phrases]) > 0: + phrases_filtered = [] + for x in dataset_dict["instances"].phrase_idxs.tolist(): + phrases_filtered.append(phrases[x]) + dataset_dict["instances"].phrases = mapper_utils.transform_phrases( + phrases_filtered, transforms + ) + dataset_dict["instances"].remove("phrase_idxs") + # dataset_dict["instances"].gt_classes = torch.tensor(range(len(phrases_filtered))) + + def __call__(self, dataset_dict): + """ + Args: + dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format. + + Returns: + dict: a format that builtin models in detectron2 accept + """ + dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below + # USER: Write your own image loading if it's not from a file + try: + image = utils.read_image(dataset_dict["file_name"], format=self.image_format) + dataset_dict["width"] = image.shape[1] + dataset_dict["height"] = image.shape[0] + except Exception as e: + logger = logging.getLogger(__name__) + logger.error(f"read_image fails: {dataset_dict['file_name']}") + logger.error(f"read_image fails: {e}") + return None + utils.check_image_size(dataset_dict, image) + + # ------------------------------------------------------------------------------------ + if ( + self.is_train + and "annotations" in dataset_dict + and ( + len(dataset_dict["annotations"]) == 0 + or any(["bbox" not in anno for anno in dataset_dict["annotations"]]) + ) + ): + if "dataset_id" in dataset_dict: + dataset_id = dataset_dict["dataset_id"] + else: + dataset_id = 0 + metadata = self.metatada_list[dataset_id] + if "sa1b" in self.dataset_names[dataset_id]: + metadata = None + dataset_dict = mapper_utils.maybe_load_annotation_from_file(dataset_dict, meta=metadata) + + for anno in dataset_dict["annotations"]: + if "bbox" not in anno: + logger = logging.getLogger(__name__) + logger.warning(f"Box not found: {dataset_dict}") + return None + if "category_id" not in anno: + anno["category_id"] = 0 + # ------------------------------------------------------------------------------------ + + # USER: Remove if you don't do semantic/panoptic segmentation. + if "sem_seg_file_name" in dataset_dict: + sem_seg_gt = utils.read_image(dataset_dict.pop("sem_seg_file_name"), "L").squeeze(2) + else: + sem_seg_gt = None + + # ordinal numbers + disable_crop = False + if ( + "annotations" in dataset_dict + and len(dataset_dict["annotations"]) > 0 + and "phrase" in dataset_dict["annotations"][0] + ): + disable_crop = disable_crop or mapper_utils.has_ordinal_num( + [anno["phrase"] for anno in dataset_dict["annotations"]] + ) + if "expressions" in dataset_dict: + disable_crop = disable_crop or mapper_utils.has_ordinal_num(dataset_dict["expressions"]) + + if self.augmentations_with_crop is None or disable_crop: + augmentations = self.augmentations + else: + if np.random.rand() > 0.5: + augmentations = self.augmentations + else: + augmentations = self.augmentations_with_crop + + aug_input = T.AugInput(image, sem_seg=sem_seg_gt) + # transforms = self.augmentations(aug_input) + transforms = augmentations(aug_input) + image, sem_seg_gt = aug_input.image, aug_input.sem_seg + + image_shape = image.shape[:2] # h, w + # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory, + # but not efficient on large generic data structures due to the use of pickle & mp.Queue. + # Therefore it's important to use torch.Tensor. + dataset_dict["image"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1))) + if sem_seg_gt is not None: + dataset_dict["sem_seg"] = torch.as_tensor(sem_seg_gt.astype("long")) + + # USER: Remove if you don't use pre-computed proposals. + # Most users would not need this feature. + if self.proposal_topk is not None: + utils.transform_proposals( + dataset_dict, image_shape, transforms, proposal_topk=self.proposal_topk + ) + + if "expressions" in dataset_dict: + dataset_dict["expressions"] = mapper_utils.transform_expressions( + dataset_dict["expressions"], transforms + ) + + if not self.is_train: + # USER: Modify this if you want to keep them for some reason. + dataset_dict.pop("annotations", None) + dataset_dict.pop("sem_seg_file_name", None) + return dataset_dict + + if "annotations" in dataset_dict: + self._transform_annotations(dataset_dict, transforms, image_shape) + + if "instances" in dataset_dict and dataset_dict["instances"].has("phrases"): + num_instances = len(dataset_dict["instances"]) + + if self.nms_thresh_phrase > 0: + boxes = dataset_dict["instances"].gt_boxes.tensor + scores = torch.rand(num_instances) + classes = torch.zeros(num_instances) + keep = batched_nms(boxes, scores, classes, self.nms_thresh_phrase) + else: + keep = torch.randperm(num_instances) + + if self.max_num_phrase > 0: + keep = keep[: self.max_num_phrase] + + phrases = dataset_dict["instances"].phrases + phrases_filtered = [] + for x in keep: + phrases_filtered.append(phrases[x]) + + dataset_dict["instances"].remove("phrases") + dataset_dict["instances"] = dataset_dict["instances"][keep] + dataset_dict["instances"].phrases = phrases_filtered + + return dataset_dict diff --git a/ape/data/dataset_mapper_detr_instance_exp.py b/ape/data/dataset_mapper_detr_instance_exp.py new file mode 100644 index 0000000..9ce0619 --- /dev/null +++ b/ape/data/dataset_mapper_detr_instance_exp.py @@ -0,0 +1,232 @@ +import copy +import logging +from typing import List, Optional, Union + +import numpy as np +import torch + +from detectron2.config import configurable +from detectron2.data import MetadataCatalog +from detectron2.data import detection_utils as utils +from detectron2.data import transforms as T + +from . import mapper_utils + +""" +This file contains the default mapping that's applied to "dataset dicts". +""" + +__all__ = ["DatasetMapper_detr_instance_exp"] + + +class DatasetMapper_detr_instance_exp: + """ + A callable which takes a dataset dict in Detectron2 Dataset format, + and map it into a format used by the model. + + This is the default callable to be used to map your dataset dict into training data. + You may need to follow it to implement your own one for customized logic, + such as a different way to read or transform images. + See :doc:`/tutorials/data_loading` for details. + + The callable currently does the following: + + 1. Read the image from "file_name" + 2. Applies cropping/geometric transforms to the image and annotations + 3. Prepare data and annotations to Tensor and :class:`Instances` + """ + + @configurable + def __init__( + self, + is_train: bool, + *, + augmentations: List[Union[T.Augmentation, T.Transform]], + augmentations_with_crop: List[Union[T.Augmentation, T.Transform]], + image_format: str, + use_instance_mask: bool = False, + use_keypoint: bool = False, + instance_mask_format: str = "polygon", + keypoint_hflip_indices: Optional[np.ndarray] = None, + precomputed_proposal_topk: Optional[int] = None, + recompute_boxes: bool = False, + dataset_names: tuple = (), + ): + """ + NOTE: this interface is experimental. + + Args: + is_train: whether it's used in training or inference + augmentations: a list of augmentations or deterministic transforms to apply + image_format: an image format supported by :func:`detection_utils.read_image`. + use_instance_mask: whether to process instance segmentation annotations, if available + use_keypoint: whether to process keypoint annotations if available + instance_mask_format: one of "polygon" or "bitmask". Process instance segmentation + masks into this format. + keypoint_hflip_indices: see :func:`detection_utils.create_keypoint_hflip_indices` + precomputed_proposal_topk: if given, will load pre-computed + proposals from dataset_dict and keep the top k proposals for each image. + recompute_boxes: whether to overwrite bounding box annotations + by computing tight bounding boxes from instance mask annotations. + """ + if recompute_boxes: + assert use_instance_mask, "recompute_boxes requires instance masks" + # fmt: off + self.is_train = is_train + self.augmentations = T.AugmentationList(augmentations) + self.augmentations_with_crop = T.AugmentationList(augmentations_with_crop) + self.image_format = image_format + self.use_instance_mask = use_instance_mask + self.instance_mask_format = instance_mask_format + self.use_keypoint = use_keypoint + self.keypoint_hflip_indices = keypoint_hflip_indices + self.proposal_topk = precomputed_proposal_topk + self.recompute_boxes = recompute_boxes + # fmt: on + logger = logging.getLogger(__name__) + mode = "training" if is_train else "inference" + logger.info(f"[DatasetMapper] Augmentations used in {mode}: {augmentations}") + logger.info(f"[DatasetMapper] Augmentations used in {mode}: {augmentations_with_crop}") + + self.dataset_names = dataset_names + + self.metatada_list = [] + for dataset_name in self.dataset_names: + metadata = MetadataCatalog.get(dataset_name) + self.metatada_list.append(metadata) + + @classmethod + def from_config(cls, cfg, is_train: bool = True): + raise NotImplementedError(self.__class__.__name__) + + def _transform_annotations(self, dataset_dict, transforms, image_shape): + # USER: Modify this if you want to keep them for some reason. + for anno in dataset_dict["annotations"]: + if not self.use_instance_mask: + anno.pop("segmentation", None) + if not self.use_keypoint: + anno.pop("keypoints", None) + + # USER: Implement additional transformations if you have other types of data + annos = [ + utils.transform_instance_annotations( + obj, transforms, image_shape, keypoint_hflip_indices=self.keypoint_hflip_indices + ) + for obj in dataset_dict.pop("annotations") + if obj.get("iscrowd", 0) == 0 + ] + instances = utils.annotations_to_instances( + annos, image_shape, mask_format=self.instance_mask_format + ) + + # After transforms such as cropping are applied, the bounding box may no longer + # tightly bound the object. As an example, imagine a triangle object + # [(0,0), (2,0), (0,2)] cropped by a box [(1,0),(2,2)] (XYXY format). The tight + # bounding box of the cropped triangle should be [(1,0),(2,1)], which is not equal to + # the intersection of original bounding box and the cropping box. + if self.recompute_boxes and instances.has("gt_masks"): + instances.gt_boxes = instances.gt_masks.get_bounding_boxes() + dataset_dict["instances"] = utils.filter_empty_instances(instances) + + def __call__(self, dataset_dict): + """ + Args: + dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format. + + Returns: + dict: a format that builtin models in detectron2 accept + """ + dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below + # USER: Write your own image loading if it's not from a file + image = utils.read_image(dataset_dict["file_name"], format=self.image_format) + utils.check_image_size(dataset_dict, image) + + # ------------------------------------------------------------------------------------ + if ( + self.is_train + and "annotations" in dataset_dict + and ( + len(dataset_dict["annotations"]) == 0 + or any(["bbox" not in anno for anno in dataset_dict["annotations"]]) + ) + ): + if "dataset_id" in dataset_dict: + dataset_id = dataset_dict["dataset_id"] + else: + dataset_id = 0 + metadata = self.metatada_list[dataset_id] + if "sa1b" in self.dataset_names[dataset_id]: + metadata = None + dataset_dict = mapper_utils.maybe_load_annotation_from_file(dataset_dict, meta=metadata) + + for anno in dataset_dict["annotations"]: + if "bbox" not in anno: + logger = logging.getLogger(__name__) + logger.warning(f"Box not found: {dataset_dict}") + return None + if "category_id" not in anno: + anno["category_id"] = 0 + # ------------------------------------------------------------------------------------ + + # USER: Remove if you don't do semantic/panoptic segmentation. + if "sem_seg_file_name" in dataset_dict: + sem_seg_gt = utils.read_image(dataset_dict.pop("sem_seg_file_name"), "L").squeeze(2) + else: + sem_seg_gt = None + + # ordinal numbers + disable_crop = False + if ( + "annotations" in dataset_dict + and len(dataset_dict["annotations"]) > 0 + and "phrase" in dataset_dict["annotations"][0] + ): + disable_crop = disable_crop or mapper_utils.has_ordinal_num( + [anno["phrase"] for anno in dataset_dict["annotations"]] + ) + if "expressions" in dataset_dict: + disable_crop = disable_crop or mapper_utils.has_ordinal_num(dataset_dict["expressions"]) + + if self.augmentations_with_crop is None or disable_crop: + augmentations = self.augmentations + else: + if np.random.rand() > 0.5: + augmentations = self.augmentations + else: + augmentations = self.augmentations_with_crop + + aug_input = T.AugInput(image, sem_seg=sem_seg_gt) + # transforms = self.augmentations(aug_input) + transforms = augmentations(aug_input) + image, sem_seg_gt = aug_input.image, aug_input.sem_seg + + image_shape = image.shape[:2] # h, w + # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory, + # but not efficient on large generic data structures due to the use of pickle & mp.Queue. + # Therefore it's important to use torch.Tensor. + dataset_dict["image"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1))) + if sem_seg_gt is not None: + dataset_dict["sem_seg"] = torch.as_tensor(sem_seg_gt.astype("long")) + + # USER: Remove if you don't use pre-computed proposals. + # Most users would not need this feature. + if self.proposal_topk is not None: + utils.transform_proposals( + dataset_dict, image_shape, transforms, proposal_topk=self.proposal_topk + ) + + if "expressions" in dataset_dict: + dataset_dict["expressions"] = mapper_utils.transform_expressions( + dataset_dict["expressions"], transforms + ) + + if not self.is_train: + # USER: Modify this if you want to keep them for some reason. + dataset_dict.pop("annotations", None) + dataset_dict.pop("sem_seg_file_name", None) + return dataset_dict + + if "annotations" in dataset_dict: + self._transform_annotations(dataset_dict, transforms, image_shape) + + return dataset_dict diff --git a/ape/data/dataset_mapper_detr_panoptic.py b/ape/data/dataset_mapper_detr_panoptic.py new file mode 100644 index 0000000..b03b91d --- /dev/null +++ b/ape/data/dataset_mapper_detr_panoptic.py @@ -0,0 +1,443 @@ +import copy +import logging +import re +from typing import List, Optional, Union + +import numpy as np +import torch + +from detectron2.config import configurable +from detectron2.data import MetadataCatalog +from detectron2.data import detection_utils as utils +from detectron2.data import transforms as T +from detectron2.structures import BitMasks, Boxes, Instances, PolygonMasks + +from . import mapper_utils + +""" +This file contains the default mapping that's applied to "dataset dicts". +""" + +__all__ = ["DatasetMapper_detr_panoptic"] + + +class DatasetMapper_detr_panoptic: + """ + A callable which takes a dataset dict in Detectron2 Dataset format, + and map it into a format used by the model. + + This is the default callable to be used to map your dataset dict into training data. + You may need to follow it to implement your own one for customized logic, + such as a different way to read or transform images. + See :doc:`/tutorials/data_loading` for details. + + The callable currently does the following: + + 1. Read the image from "file_name" + 2. Applies cropping/geometric transforms to the image and annotations + 3. Prepare data and annotations to Tensor and :class:`Instances` + """ + + @configurable + def __init__( + self, + is_train: bool, + *, + augmentations: List[Union[T.Augmentation, T.Transform]], + augmentations_with_crop: List[Union[T.Augmentation, T.Transform]], + image_format: str, + use_instance_mask: bool = False, + use_keypoint: bool = False, + instance_mask_format: str = "polygon", + keypoint_hflip_indices: Optional[np.ndarray] = None, + precomputed_proposal_topk: Optional[int] = None, + recompute_boxes: bool = False, + ignore_label: int = 255, + stuff_classes_offset: int = 80, + stuff_classes_decomposition: bool = False, + dataset_names: tuple = (), + ): + """ + NOTE: this interface is experimental. + + Args: + is_train: whether it's used in training or inference + augmentations: a list of augmentations or deterministic transforms to apply + image_format: an image format supported by :func:`detection_utils.read_image`. + use_instance_mask: whether to process instance segmentation annotations, if available + use_keypoint: whether to process keypoint annotations if available + instance_mask_format: one of "polygon" or "bitmask". Process instance segmentation + masks into this format. + keypoint_hflip_indices: see :func:`detection_utils.create_keypoint_hflip_indices` + precomputed_proposal_topk: if given, will load pre-computed + proposals from dataset_dict and keep the top k proposals for each image. + recompute_boxes: whether to overwrite bounding box annotations + by computing tight bounding boxes from instance mask annotations. + """ + if recompute_boxes: + assert use_instance_mask, "recompute_boxes requires instance masks" + # fmt: off + self.is_train = is_train + self.augmentations = T.AugmentationList(augmentations) + self.augmentations_with_crop = T.AugmentationList(augmentations_with_crop) + self.image_format = image_format + self.use_instance_mask = use_instance_mask + self.instance_mask_format = instance_mask_format + self.use_keypoint = use_keypoint + self.keypoint_hflip_indices = keypoint_hflip_indices + self.proposal_topk = precomputed_proposal_topk + self.recompute_boxes = recompute_boxes + self.ignore_label = ignore_label + self.stuff_classes_offset = stuff_classes_offset + self.stuff_classes_decomposition = stuff_classes_decomposition + # fmt: on + logger = logging.getLogger(__name__) + mode = "training" if is_train else "inference" + logger.info(f"[DatasetMapper] Augmentations used in {mode}: {augmentations}") + logger.info(f"[DatasetMapper] Augmentations used in {mode}: {augmentations_with_crop}") + + self.dataset_names = dataset_names + + self.metatada_list = [] + for dataset_name in self.dataset_names: + metadata = MetadataCatalog.get(dataset_name) + self.metatada_list.append(metadata) + + @classmethod + def from_config(cls, cfg, is_train: bool = True): + raise NotImplementedError(self.__class__.__name__) + + def _transform_annotations(self, dataset_dict, transforms, image_shape): + # USER: Modify this if you want to keep them for some reason. + for anno in dataset_dict["annotations"]: + if not self.use_instance_mask: + anno.pop("segmentation", None) + if not self.use_keypoint: + anno.pop("keypoints", None) + + # USER: Implement additional transformations if you have other types of data + annos = [ + utils.transform_instance_annotations( + obj, transforms, image_shape, keypoint_hflip_indices=self.keypoint_hflip_indices + ) + for obj in dataset_dict.pop("annotations") + if obj.get("iscrowd", 0) == 0 + ] + instances = utils.annotations_to_instances( + annos, image_shape, mask_format=self.instance_mask_format + ) + + # After transforms such as cropping are applied, the bounding box may no longer + # tightly bound the object. As an example, imagine a triangle object + # [(0,0), (2,0), (0,2)] cropped by a box [(1,0),(2,2)] (XYXY format). The tight + # bounding box of the cropped triangle should be [(1,0),(2,1)], which is not equal to + # the intersection of original bounding box and the cropping box. + if self.recompute_boxes and instances.has("gt_masks"): + instances.gt_boxes = instances.gt_masks.get_bounding_boxes() + dataset_dict["instances"] = utils.filter_empty_instances(instances) + + def __call__(self, dataset_dict): + """ + Args: + dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format. + + Returns: + dict: a format that builtin models in detectron2 accept + """ + dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below + # USER: Write your own image loading if it's not from a file + image = utils.read_image(dataset_dict["file_name"], format=self.image_format) + utils.check_image_size(dataset_dict, image) + + # ------------------------------------------------------------------------------------ + if "dataset_id" in dataset_dict: + dataset_id = dataset_dict["dataset_id"] + else: + dataset_id = 0 + metadata = self.metatada_list[dataset_id] + if "sa1b" in self.dataset_names[dataset_id]: + metadata = None + if ( + self.is_train + and "annotations" in dataset_dict + and ( + len(dataset_dict["annotations"]) == 0 + or any(["bbox" not in anno for anno in dataset_dict["annotations"]]) + ) + ): + dataset_dict = mapper_utils.maybe_load_annotation_from_file(dataset_dict, meta=metadata) + + for anno in dataset_dict["annotations"]: + if "bbox" not in anno: + logger = logging.getLogger(__name__) + logger.warning(f"Box not found: {dataset_dict}") + return None + if "category_id" not in anno: + anno["category_id"] = 0 + # ------------------------------------------------------------------------------------ + + # USER: Remove if you don't do semantic/panoptic segmentation. + if "sem_seg_file_name" in dataset_dict: + sem_seg_gt = utils.read_image(dataset_dict.pop("sem_seg_file_name"), "L").squeeze(2) + else: + sem_seg_gt = None + + # ordinal numbers + disable_crop = False + if ( + "annotations" in dataset_dict + and len(dataset_dict["annotations"]) > 0 + and "phrase" in dataset_dict["annotations"][0] + ): + disable_crop = disable_crop or mapper_utils.has_ordinal_num( + [anno["phrase"] for anno in dataset_dict["annotations"]] + ) + if "expressions" in dataset_dict: + disable_crop = disable_crop or mapper_utils.has_ordinal_num(dataset_dict["expressions"]) + + if self.augmentations_with_crop is None or disable_crop: + augmentations = self.augmentations + else: + if np.random.rand() > 0.5: + augmentations = self.augmentations + else: + augmentations = self.augmentations_with_crop + + aug_input = T.AugInput(image, sem_seg=sem_seg_gt) + # transforms = self.augmentations(aug_input) + transforms = augmentations(aug_input) + image, sem_seg_gt = aug_input.image, aug_input.sem_seg + + image_shape = image.shape[:2] # h, w + # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory, + # but not efficient on large generic data structures due to the use of pickle & mp.Queue. + # Therefore it's important to use torch.Tensor. + dataset_dict["image"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1))) + if sem_seg_gt is not None: + dataset_dict["sem_seg"] = torch.as_tensor(sem_seg_gt.astype("long")) + + # USER: Remove if you don't use pre-computed proposals. + # Most users would not need this feature. + if self.proposal_topk is not None: + utils.transform_proposals( + dataset_dict, image_shape, transforms, proposal_topk=self.proposal_topk + ) + + if "expressions" in dataset_dict: + dataset_dict["expressions"] = mapper_utils.transform_expressions( + dataset_dict["expressions"], transforms + ) + + if not self.is_train: + # USER: Modify this if you want to keep them for some reason. + dataset_dict.pop("annotations", None) + dataset_dict.pop("sem_seg_file_name", None) + dataset_dict.pop("pan_seg_file_name", None) + dataset_dict.pop("segments_info", None) + return dataset_dict + + if "annotations" in dataset_dict: + self._transform_annotations(dataset_dict, transforms, image_shape) + + dataset_dict["instances"].is_thing = torch.tensor( + [True for _ in range(len(dataset_dict["instances"]))], dtype=torch.bool + ) + + # Prepare per-category binary masks + if sem_seg_gt is not None and not self.stuff_classes_decomposition: + instances = Instances(image_shape) + classes = np.unique(sem_seg_gt).astype(np.int64) + # remove ignored region + classes = classes[classes != self.ignore_label] + + if self.stuff_classes_offset > 0: + classes = classes[classes != 0] + instances.gt_classes = torch.tensor( + classes + self.stuff_classes_offset - 1, dtype=torch.int64 + ) + else: + instances.gt_classes = torch.tensor(classes, dtype=torch.int64) + + masks = [] + for class_id in classes: + masks.append(sem_seg_gt == class_id) + + if len(masks) == 0: + # # Some image does not have annotation (all ignored) + # instances.gt_masks = torch.zeros((0, sem_seg_gt.shape[-2], sem_seg_gt.shape[-1])) + masks = BitMasks(torch.zeros((0, sem_seg_gt.shape[-2], sem_seg_gt.shape[-1]))) + else: + masks = BitMasks( + torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks]) + ) + + instances.gt_masks = masks + instances.gt_boxes = masks.get_bounding_boxes() + + instances.is_thing = torch.tensor( + [False for _ in range(len(instances))], dtype=torch.bool + ) + + if "instances" in dataset_dict and dataset_dict["instances"].has("copypaste"): + instances.copypaste = torch.tensor([False for _ in range(len(instances))]) + + if len(instances) > 0: + if "instances" in dataset_dict and len(dataset_dict["instances"]) > 0: + dataset_dict["instances"] = Instances.cat( + [dataset_dict["instances"], instances] + ) + else: + dataset_dict["instances"] = instances + + # Prepare per-category binary masks + if sem_seg_gt is not None and self.stuff_classes_decomposition: + classes = np.unique(sem_seg_gt) + # remove ignored region + classes = classes[classes != self.ignore_label] + + if self.stuff_classes_offset > 0: + classes = classes[classes != 0] + + gt_masks = [] + gt_classes = [] + for class_id in classes: + bitmask = sem_seg_gt == class_id + pygmask, _ = mapper_utils.mask_to_polygons_2(bitmask) + for mask in pygmask: + gt_masks.append([mask]) + gt_classes.append(class_id) + + # if len(gt_masks) == 0: + # return None + + instances = Instances(image_shape) + instances.gt_classes = torch.tensor(gt_classes, dtype=torch.int64) + if self.stuff_classes_offset > 0: + instances.gt_classes += self.stuff_classes_offset - 1 + if self.instance_mask_format == "polygon": + instances.gt_masks = PolygonMasks(gt_masks) + else: + assert self.instance_mask_format == "bitmask", self.instance_mask_format + instances.gt_masks = BitMasks.from_polygon_masks( + gt_masks, image_shape[0], image_shape[1] + ) + instances.gt_boxes = instances.gt_masks.get_bounding_boxes() + + if self.instance_mask_format == "polygon": + area = instances.gt_masks.area() + else: + assert self.instance_mask_format == "bitmask", self.instance_mask_format + area = instances.gt_masks.tensor.sum((1, 2)) + instances = instances[area > 8 * 8] + + instances.is_thing = torch.tensor( + [False for _ in range(len(instances))], dtype=torch.bool + ) + + if "instances" in dataset_dict and dataset_dict["instances"].has("copypaste"): + instances.copypaste = torch.tensor([False for _ in range(len(instances))]) + + if len(instances) > 0: + if "instances" in dataset_dict and len(dataset_dict["instances"]) > 0: + dataset_dict["instances"] = Instances.cat( + [dataset_dict["instances"], instances] + ) + else: + dataset_dict["instances"] = instances + + if "pan_seg_file_name" in dataset_dict and not self.stuff_classes_decomposition: + pan_seg_gt = utils.read_image(dataset_dict.pop("pan_seg_file_name"), "RGB") + segments_info = dataset_dict["segments_info"] + + # apply the same transformation to panoptic segmentation + pan_seg_gt = transforms.apply_segmentation(pan_seg_gt) + + from panopticapi.utils import rgb2id + + pan_seg_gt = rgb2id(pan_seg_gt) + + instances = Instances(image_shape) + classes = [] + masks = [] + for segment_info in segments_info: + class_id = segment_info["category_id"] + if not segment_info["iscrowd"]: + classes.append(class_id) + masks.append(pan_seg_gt == segment_info["id"]) + + classes = np.array(classes) + instances.gt_classes = torch.tensor(classes, dtype=torch.int64) + if len(masks) == 0: + # Some image does not have annotation (all ignored) + instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1])) + instances.gt_boxes = Boxes(torch.zeros((0, 4))) + else: + masks = BitMasks( + torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks]) + ) + instances.gt_masks = masks.tensor + instances.gt_boxes = masks.get_bounding_boxes() + + if "instances" in dataset_dict and dataset_dict["instances"].has("copypaste"): + instances.copypaste = torch.tensor([False for _ in range(len(instances))]) + + dataset_dict["instances"] = instances + + if "pan_seg_file_name" in dataset_dict and self.stuff_classes_decomposition: + pan_seg_gt = utils.read_image(dataset_dict.pop("pan_seg_file_name"), "RGB") + segments_info = dataset_dict["segments_info"] + + # apply the same transformation to panoptic segmentation + pan_seg_gt = transforms.apply_segmentation(pan_seg_gt) + + from panopticapi.utils import rgb2id + + pan_seg_gt = rgb2id(pan_seg_gt) + + instances = Instances(image_shape) + classes = [] + masks = [] + for segment_info in segments_info: + class_id = segment_info["category_id"] + if not segment_info["iscrowd"]: + if class_id in metadata.thing_dataset_id_to_contiguous_id.values(): + classes.append(class_id) + masks.append(pan_seg_gt == segment_info["id"]) + else: + bitmask = pan_seg_gt == segment_info["id"] + pygmask, _ = mapper_utils.mask_to_polygons_2(bitmask) + for mask in pygmask: + mask = ( + BitMasks.from_polygon_masks( + [[mask]], image_shape[0], image_shape[1] + ) + .tensor[0, ...] + .numpy() + ) + classes.append(class_id) + masks.append(mask) + + classes = np.array(classes) + instances.gt_classes = torch.tensor(classes, dtype=torch.int64) + if len(masks) == 0: + # Some image does not have annotation (all ignored) + instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1])) + instances.gt_boxes = Boxes(torch.zeros((0, 4))) + else: + masks = BitMasks( + torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks]) + ) + instances.gt_masks = masks.tensor + instances.gt_boxes = masks.get_bounding_boxes() + + if "instances" in dataset_dict and dataset_dict["instances"].has("copypaste"): + instances.copypaste = torch.tensor([False for _ in range(len(instances))]) + + dataset_dict["instances"] = instances + + if "instances" in dataset_dict and len(dataset_dict["instances"]) > 0: + pass + else: + return None + + return dataset_dict diff --git a/ape/data/dataset_mapper_detr_panoptic_copypaste.py b/ape/data/dataset_mapper_detr_panoptic_copypaste.py new file mode 100644 index 0000000..61d6b98 --- /dev/null +++ b/ape/data/dataset_mapper_detr_panoptic_copypaste.py @@ -0,0 +1,676 @@ +import copy +import logging +import os +import random +from typing import List, Optional, Union + +import cv2 +import numpy as np +import torch + +import detectron2.utils.comm as comm +from detectron2.config import configurable +from detectron2.data import MetadataCatalog +from detectron2.data import detection_utils as utils +from detectron2.data import transforms as T +from detectron2.data.detection_utils import convert_image_to_rgb +from detectron2.layers import batched_nms +from detectron2.structures import BitMasks, Boxes, Instances, PolygonMasks + +from . import mapper_utils + +""" +This file contains the default mapping that's applied to "dataset dicts". +""" + +__all__ = ["DatasetMapper_detr_panoptic_copypaste"] + + +class DatasetMapper_detr_panoptic_copypaste: + """ + A callable which takes a dataset dict in Detectron2 Dataset format, + and map it into a format used by the model. + + This is the default callable to be used to map your dataset dict into training data. + You may need to follow it to implement your own one for customized logic, + such as a different way to read or transform images. + See :doc:`/tutorials/data_loading` for details. + + The callable currently does the following: + + 1. Read the image from "file_name" + 2. Applies cropping/geometric transforms to the image and annotations + 3. Prepare data and annotations to Tensor and :class:`Instances` + """ + + @configurable + def __init__( + self, + is_train: bool, + *, + augmentations: List[Union[T.Augmentation, T.Transform]], + augmentations_with_crop: List[Union[T.Augmentation, T.Transform]], + image_format: str, + use_instance_mask: bool = False, + use_keypoint: bool = False, + instance_mask_format: str = "polygon", + keypoint_hflip_indices: Optional[np.ndarray] = None, + precomputed_proposal_topk: Optional[int] = None, + recompute_boxes: bool = False, + ignore_label: int = 255, + stuff_classes_offset: int = 80, + stuff_classes_decomposition: bool = False, + copypaste_prob: float = 0.5, + output_dir: str = None, + vis_period: int = 0, + dataset_names: tuple = (), + max_num_phrase: int = 0, + nms_thresh_phrase: float = 0.0, + ): + """ + NOTE: this interface is experimental. + + Args: + is_train: whether it's used in training or inference + augmentations: a list of augmentations or deterministic transforms to apply + image_format: an image format supported by :func:`detection_utils.read_image`. + use_instance_mask: whether to process instance segmentation annotations, if available + use_keypoint: whether to process keypoint annotations if available + instance_mask_format: one of "polygon" or "bitmask". Process instance segmentation + masks into this format. + keypoint_hflip_indices: see :func:`detection_utils.create_keypoint_hflip_indices` + precomputed_proposal_topk: if given, will load pre-computed + proposals from dataset_dict and keep the top k proposals for each image. + recompute_boxes: whether to overwrite bounding box annotations + by computing tight bounding boxes from instance mask annotations. + """ + if recompute_boxes: + assert use_instance_mask, "recompute_boxes requires instance masks" + # fmt: off + self.is_train = is_train + self.augmentations = T.AugmentationList(augmentations) + self.augmentations_with_crop = T.AugmentationList(augmentations_with_crop) + self.image_format = image_format + self.use_instance_mask = use_instance_mask + self.instance_mask_format = instance_mask_format + self.use_keypoint = use_keypoint + self.keypoint_hflip_indices = keypoint_hflip_indices + self.proposal_topk = precomputed_proposal_topk + self.recompute_boxes = recompute_boxes + self.ignore_label = ignore_label + self.stuff_classes_offset = stuff_classes_offset + self.stuff_classes_decomposition = stuff_classes_decomposition + # fmt: on + logger = logging.getLogger(__name__) + mode = "training" if is_train else "inference" + logger.info(f"[DatasetMapper] Augmentations used in {mode}: {augmentations}") + logger.info(f"[DatasetMapper] Augmentations used in {mode}: {augmentations_with_crop}") + + if output_dir is not None: + self.output_dir = os.path.join(output_dir, "vis_mapper") + os.makedirs(self.output_dir, exist_ok=True) + + self.copypaste_prob = copypaste_prob + self.vis_period = vis_period + self.iter = 0 + self.dataset_names = dataset_names + + self.metatada_list = [] + for dataset_name in self.dataset_names: + metadata = MetadataCatalog.get(dataset_name) + self.metatada_list.append(metadata) + + self.max_num_phrase = max_num_phrase + self.nms_thresh_phrase = nms_thresh_phrase + + @classmethod + def from_config(cls, cfg, is_train: bool = True): + raise NotImplementedError(self.__class__.__name__) + + def _transform_annotations(self, dataset_dict, transforms, image_shape): + # USER: Modify this if you want to keep them for some reason. + for anno in dataset_dict["annotations"]: + if not self.use_instance_mask: + anno.pop("segmentation", None) + if not self.use_keypoint: + anno.pop("keypoints", None) + + copypaste = [ + obj.get("copypaste", 0) + for obj in dataset_dict["annotations"] + if obj.get("iscrowd", 0) == 0 + ] + + phrases = [ + obj.get("phrase", "") + for obj in dataset_dict["annotations"] + if obj.get("iscrowd", 0) == 0 + ] + + # USER: Implement additional transformations if you have other types of data + annos = [ + utils.transform_instance_annotations( + obj, transforms, image_shape, keypoint_hflip_indices=self.keypoint_hflip_indices + ) + for obj in dataset_dict.pop("annotations") + if obj.get("iscrowd", 0) == 0 + ] + instances = utils.annotations_to_instances( + annos, image_shape, mask_format=self.instance_mask_format + ) + + instances.copypaste = torch.tensor(copypaste) + + if sum([len(x) for x in phrases]) > 0: + instances.phrase_idxs = torch.tensor(range(len(phrases))) + + # After transforms such as cropping are applied, the bounding box may no longer + # tightly bound the object. As an example, imagine a triangle object + # [(0,0), (2,0), (0,2)] cropped by a box [(1,0),(2,2)] (XYXY format). The tight + # bounding box of the cropped triangle should be [(1,0),(2,1)], which is not equal to + # the intersection of original bounding box and the cropping box. + if self.recompute_boxes and instances.has("gt_masks"): + instances.gt_boxes = instances.gt_masks.get_bounding_boxes() + dataset_dict["instances"] = utils.filter_empty_instances(instances) + + if sum([len(x) for x in phrases]) > 0: + phrases_filtered = [] + for x in dataset_dict["instances"].phrase_idxs.tolist(): + phrases_filtered.append(phrases[x]) + dataset_dict["instances"].phrases = mapper_utils.transform_phrases( + phrases_filtered, transforms + ) + dataset_dict["instances"].remove("phrase_idxs") + # dataset_dict["instances"].gt_classes = torch.tensor(range(len(phrases_filtered))) + + def __call__(self, dataset_dict, dataset_dict_bg): + """ + Args: + dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format. + + Returns: + dict: a format that builtin models in detectron2 accept + """ + dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below + # USER: Write your own image loading if it's not from a file + try: + image = utils.read_image(dataset_dict["file_name"], format=self.image_format) + except Exception as e: + logger = logging.getLogger(__name__) + logger.error(f"read_image fails: {dataset_dict['file_name']}") + logger.error(f"read_image fails: {e}") + return None + utils.check_image_size(dataset_dict, image) + + # ------------------------------------------------------------------------------------ + if "dataset_id" in dataset_dict: + dataset_id = dataset_dict["dataset_id"] + else: + dataset_id = 0 + metadata = self.metatada_list[dataset_id] + if "sa1b" in self.dataset_names[dataset_id]: + metadata = None + if ( + self.is_train + and "annotations" in dataset_dict + and ( + len(dataset_dict["annotations"]) == 0 + or any(["bbox" not in anno for anno in dataset_dict["annotations"]]) + ) + ): + dataset_dict = mapper_utils.maybe_load_annotation_from_file(dataset_dict, meta=metadata) + + for anno in dataset_dict["annotations"]: + if "bbox" not in anno: + logger = logging.getLogger(__name__) + logger.warning(f"Box not found: {dataset_dict}") + return None + if "category_id" not in anno: + anno["category_id"] = 0 + # ------------------------------------------------------------------------------------ + + # ------------------------------------------------------------------------------------ + if dataset_dict["copypaste"] and self.copypaste_prob > random.uniform(0, 1): + image_cp, dataset_dict_cp = mapper_utils.copypaste( + dataset_dict, dataset_dict_bg, self.image_format, self.instance_mask_format + ) + + if dataset_dict_cp is None or image_cp is None: + pass + else: + for key in dataset_dict.keys(): + if key in dataset_dict_cp: + continue + dataset_dict_cp[key] = dataset_dict[key] + dataset_dict = dataset_dict_cp + image = image_cp + # ------------------------------------------------------------------------------------ + + # USER: Remove if you don't do semantic/panoptic segmentation. + if "sem_seg_file_name" in dataset_dict: + try: + sem_seg_gt = utils.read_image(dataset_dict.pop("sem_seg_file_name"), "L").squeeze(2) + except Exception as e: + logger = logging.getLogger(__name__) + logger.error(f"read_image fails: {e}") + logger.error(f"read_image fails: {dataset_dict}") + return None + + if "copypaste_mask" in dataset_dict: + # assume thing class is 0 + sem_seg_gt = sem_seg_gt.copy() + sem_seg_gt[dataset_dict["copypaste_mask"]] = 0 + else: + sem_seg_gt = None + + # ordinal numbers + disable_crop = False + if ( + "annotations" in dataset_dict + and len(dataset_dict["annotations"]) > 0 + and "phrase" in dataset_dict["annotations"][0] + ): + disable_crop = disable_crop or mapper_utils.has_ordinal_num( + [anno["phrase"] for anno in dataset_dict["annotations"]] + ) + if "expressions" in dataset_dict: + disable_crop = disable_crop or mapper_utils.has_ordinal_num(dataset_dict["expressions"]) + + if self.augmentations_with_crop is None or disable_crop: + augmentations = self.augmentations + else: + if np.random.rand() > 0.5: + augmentations = self.augmentations + else: + augmentations = self.augmentations_with_crop + + aug_input = T.AugInput(image, sem_seg=sem_seg_gt) + # transforms = self.augmentations(aug_input) + transforms = augmentations(aug_input) + image, sem_seg_gt = aug_input.image, aug_input.sem_seg + + image_shape = image.shape[:2] # h, w + # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory, + # but not efficient on large generic data structures due to the use of pickle & mp.Queue. + # Therefore it's important to use torch.Tensor. + dataset_dict["image"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1))) + if sem_seg_gt is not None: + dataset_dict["sem_seg"] = torch.as_tensor(sem_seg_gt.astype("long")) + + # USER: Remove if you don't use pre-computed proposals. + # Most users would not need this feature. + if self.proposal_topk is not None: + utils.transform_proposals( + dataset_dict, image_shape, transforms, proposal_topk=self.proposal_topk + ) + + if "expressions" in dataset_dict: + dataset_dict["expressions"] = mapper_utils.transform_expressions( + dataset_dict["expressions"], transforms + ) + + if not self.is_train: + # USER: Modify this if you want to keep them for some reason. + dataset_dict.pop("annotations", None) + dataset_dict.pop("sem_seg_file_name", None) + dataset_dict.pop("pan_seg_file_name", None) + dataset_dict.pop("segments_info", None) + return dataset_dict + + if "annotations" in dataset_dict: + self._transform_annotations(dataset_dict, transforms, image_shape) + + dataset_dict["instances"].is_thing = torch.tensor( + [True for _ in range(len(dataset_dict["instances"]))], dtype=torch.bool + ) + + if "instances" in dataset_dict and dataset_dict["instances"].has("phrases"): + num_instances = len(dataset_dict["instances"]) + + if self.nms_thresh_phrase > 0: + boxes = dataset_dict["instances"].gt_boxes.tensor + scores = torch.rand(num_instances) + classes = torch.zeros(num_instances) + keep = batched_nms(boxes, scores, classes, self.nms_thresh_phrase) + else: + keep = torch.randperm(num_instances) + + if self.max_num_phrase > 0: + keep = keep[: self.max_num_phrase] + + phrases = dataset_dict["instances"].phrases + phrases_filtered = [] + for x in keep: + phrases_filtered.append(phrases[x]) + + dataset_dict["instances"].remove("phrases") + dataset_dict["instances"] = dataset_dict["instances"][keep] + dataset_dict["instances"].phrases = phrases_filtered + + # Prepare per-category binary masks + if sem_seg_gt is not None and not self.stuff_classes_decomposition: + instances = Instances(image_shape) + classes = np.unique(sem_seg_gt).astype(np.int64) + # remove ignored region + classes = classes[classes != self.ignore_label] + + if self.stuff_classes_offset > 0: + classes = classes[classes != 0] + instances.gt_classes = torch.tensor( + classes + self.stuff_classes_offset - 1, dtype=torch.int64 + ) + else: + instances.gt_classes = torch.tensor(classes, dtype=torch.int64) + + masks = [] + for class_id in classes: + masks.append(sem_seg_gt == class_id) + + if len(masks) == 0: + # # Some image does not have annotation (all ignored) + # instances.gt_masks = torch.zeros((0, sem_seg_gt.shape[-2], sem_seg_gt.shape[-1])) + masks = BitMasks(torch.zeros((0, sem_seg_gt.shape[-2], sem_seg_gt.shape[-1]))) + else: + masks = BitMasks( + torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks]) + ) + + instances.gt_masks = masks + instances.gt_boxes = masks.get_bounding_boxes() + + instances.is_thing = torch.tensor( + [False for _ in range(len(instances))], dtype=torch.bool + ) + + if "instances" in dataset_dict and dataset_dict["instances"].has("copypaste"): + instances.copypaste = torch.tensor([False for _ in range(len(instances))]) + + if len(instances) > 0: + if "instances" in dataset_dict and len(dataset_dict["instances"]) > 0: + dataset_dict["instances"] = Instances.cat( + [dataset_dict["instances"], instances] + ) + else: + dataset_dict["instances"] = instances + + # Prepare per-category binary masks + if sem_seg_gt is not None and self.stuff_classes_decomposition: + classes = np.unique(sem_seg_gt) + # remove ignored region + classes = classes[classes != self.ignore_label] + + if self.stuff_classes_offset > 0: + classes = classes[classes != 0] + + gt_masks = [] + gt_classes = [] + for class_id in classes: + bitmask = sem_seg_gt == class_id + pygmask, _ = mapper_utils.mask_to_polygons_2(bitmask) + for mask in pygmask: + gt_masks.append([mask]) + gt_classes.append(class_id) + + # if len(gt_masks) == 0: + # return None + + instances = Instances(image_shape) + instances.gt_classes = torch.tensor(gt_classes, dtype=torch.int64) + if self.stuff_classes_offset > 0: + instances.gt_classes += self.stuff_classes_offset - 1 + if self.instance_mask_format == "polygon": + instances.gt_masks = PolygonMasks(gt_masks) + else: + assert self.instance_mask_format == "bitmask", self.instance_mask_format + instances.gt_masks = BitMasks.from_polygon_masks( + gt_masks, image_shape[0], image_shape[1] + ) + instances.gt_boxes = instances.gt_masks.get_bounding_boxes() + + if self.instance_mask_format == "polygon": + area = instances.gt_masks.area() + else: + assert self.instance_mask_format == "bitmask", self.instance_mask_format + area = instances.gt_masks.tensor.sum((1, 2)) + instances = instances[area > 8 * 8] + + instances.is_thing = torch.tensor( + [False for _ in range(len(instances))], dtype=torch.bool + ) + + if "instances" in dataset_dict and dataset_dict["instances"].has("copypaste"): + instances.copypaste = torch.tensor([False for _ in range(len(instances))]) + + if len(instances) > 0: + if "instances" in dataset_dict and len(dataset_dict["instances"]) > 0: + dataset_dict["instances"] = Instances.cat( + [dataset_dict["instances"], instances] + ) + else: + dataset_dict["instances"] = instances + + if "pan_seg_file_name" in dataset_dict and not self.stuff_classes_decomposition: + pan_seg_gt = utils.read_image(dataset_dict.pop("pan_seg_file_name"), "RGB") + segments_info = dataset_dict["segments_info"] + + # apply the same transformation to panoptic segmentation + pan_seg_gt = transforms.apply_segmentation(pan_seg_gt) + + from panopticapi.utils import rgb2id + + pan_seg_gt = rgb2id(pan_seg_gt) + + instances = Instances(image_shape) + classes = [] + masks = [] + for segment_info in segments_info: + class_id = segment_info["category_id"] + if not segment_info["iscrowd"]: + classes.append(class_id) + masks.append(pan_seg_gt == segment_info["id"]) + + classes = np.array(classes) + instances.gt_classes = torch.tensor(classes, dtype=torch.int64) + if len(masks) == 0: + # Some image does not have annotation (all ignored) + instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1])) + instances.gt_boxes = Boxes(torch.zeros((0, 4))) + else: + masks = BitMasks( + torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks]) + ) + instances.gt_masks = masks.tensor + instances.gt_boxes = masks.get_bounding_boxes() + + if "instances" in dataset_dict and dataset_dict["instances"].has("copypaste"): + instances.copypaste = torch.tensor([False for _ in range(len(instances))]) + + dataset_dict["instances"] = instances + + if "pan_seg_file_name" in dataset_dict and self.stuff_classes_decomposition: + pan_seg_gt = utils.read_image(dataset_dict.pop("pan_seg_file_name"), "RGB") + segments_info = dataset_dict["segments_info"] + + # apply the same transformation to panoptic segmentation + pan_seg_gt = transforms.apply_segmentation(pan_seg_gt) + + from panopticapi.utils import rgb2id + + pan_seg_gt = rgb2id(pan_seg_gt) + + instances = Instances(image_shape) + classes = [] + masks = [] + for segment_info in segments_info: + class_id = segment_info["category_id"] + if not segment_info["iscrowd"]: + if class_id in metadata.thing_dataset_id_to_contiguous_id.values(): + classes.append(class_id) + masks.append(pan_seg_gt == segment_info["id"]) + else: + bitmask = pan_seg_gt == segment_info["id"] + pygmask, _ = mapper_utils.mask_to_polygons_2(bitmask) + for mask in pygmask: + mask = ( + BitMasks.from_polygon_masks( + [[mask]], image_shape[0], image_shape[1] + ) + .tensor[0, ...] + .numpy() + ) + classes.append(class_id) + masks.append(mask) + + classes = np.array(classes) + instances.gt_classes = torch.tensor(classes, dtype=torch.int64) + if len(masks) == 0: + # Some image does not have annotation (all ignored) + instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1])) + instances.gt_boxes = Boxes(torch.zeros((0, 4))) + else: + masks = BitMasks( + torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks]) + ) + instances.gt_masks = masks.tensor + instances.gt_boxes = masks.get_bounding_boxes() + + if "instances" in dataset_dict and dataset_dict["instances"].has("copypaste"): + instances.copypaste = torch.tensor([False for _ in range(len(instances))]) + + dataset_dict["instances"] = instances + + if "instances" in dataset_dict and len(dataset_dict["instances"]) > 0: + pass + else: + return None + + # ------------------------------------------------------------------------------------ + if self.vis_period > 0 and self.iter % self.vis_period == 0: + self.visualize_training(dataset_dict) + # ------------------------------------------------------------------------------------ + self.iter += 1 + + return dataset_dict + + def visualize_training(self, dataset_dict, prefix="", suffix=""): + if self.output_dir is None: + return + if dataset_dict is None: + return + # if "instances" not in dataset_dict: + # return + from detectron2.utils.visualizer import Visualizer + from detectron2.data import MetadataCatalog + + if "dataset_id" in dataset_dict: + dataset_id = dataset_dict["dataset_id"] + else: + dataset_id = 0 + dataset_name = self.dataset_names[dataset_id] + metadata = MetadataCatalog.get(dataset_name) + class_names = ( + metadata.get("thing_classes", []) + metadata.get("stuff_classes", ["thing"])[1:] + ) + + if "instances" in dataset_dict and dataset_dict["instances"].has("phrases"): + labels = dataset_dict["instances"].phrases + elif "expressions" in dataset_dict: + labels = [dataset_dict["expressions"]] + else: + labels = [class_names[i] for i in dataset_dict["instances"].gt_classes] + + img = dataset_dict["image"] + img = convert_image_to_rgb(img.permute(1, 2, 0), self.image_format) + image_shape = img.shape[:2] # h, w + vis = Visualizer(img, metadata=metadata) + if "instances" in dataset_dict: + vis = vis.overlay_instances( + boxes=dataset_dict["instances"].gt_boxes, + masks=dataset_dict["instances"].gt_masks + if dataset_dict["instances"].has("gt_masks") + else None, + labels=labels, + ) + else: + vis = vis.overlay_instances( + boxes=None, + masks=None, + labels=None, + ) + vis_gt = vis.get_image() + + if "instances_phrase" in dataset_dict: + vis = Visualizer(img, metadata=metadata) + vis = vis.overlay_instances( + boxes=dataset_dict["instances_phrase"].gt_boxes, + masks=dataset_dict["instances_phrase"].gt_masks + if dataset_dict["instances_phrase"].has("gt_masks") + else None, + labels=dataset_dict["instances_phrase"].phrases, + ) + vis_phrase = vis.get_image() + vis_gt = np.concatenate((vis_gt, vis_phrase), axis=1) + + if "captions" in dataset_dict: + vis = Visualizer(img, metadata=metadata) + vis = vis.overlay_instances( + boxes=Boxes( + np.array( + [ + [ + 0 + i * 20, + 0 + i * 20, + image_shape[1] - 1 - i * 20, + image_shape[0] - 1 - i * 20, + ] + for i in range(len(dataset_dict["captions"])) + ] + ) + ), + masks=None, + labels=dataset_dict["captions"], + ) + vis_cap = vis.get_image() + vis_gt = np.concatenate((vis_gt, vis_cap), axis=1) + + if "sem_seg" in dataset_dict: + vis = Visualizer(img, metadata=metadata) + vis = vis.draw_sem_seg(dataset_dict["sem_seg"], area_threshold=0, alpha=0.5) + vis_sem_gt = vis.get_image() + vis_gt = np.concatenate((vis_gt, vis_sem_gt), axis=1) + + concat = np.concatenate((vis_gt, img), axis=1) + + image_name = os.path.basename(dataset_dict["file_name"]).split(".")[0] + + save_path = os.path.join( + self.output_dir, + prefix + + str(self.iter) + + "_" + + image_name + + "_g" + + str(comm.get_rank()) + + suffix + + ".png", + ) + concat = cv2.cvtColor(concat, cv2.COLOR_RGB2BGR) + cv2.imwrite(save_path, concat) + + return + + import pickle + + save_path = os.path.join( + self.output_dir, + prefix + + str(self.iter) + + "_" + + str(dataset_dict["image_id"]) + + "_g" + + str(comm.get_rank()) + + suffix + + ".pkl", + ) + with open(save_path, "wb") as save_file: + pickle.dump(dataset_dict, save_file) diff --git a/ape/data/dataset_mapper_detr_semantic.py b/ape/data/dataset_mapper_detr_semantic.py new file mode 100644 index 0000000..604d549 --- /dev/null +++ b/ape/data/dataset_mapper_detr_semantic.py @@ -0,0 +1,244 @@ +import copy +import logging +from typing import List, Optional, Union + +import cv2 +import numpy as np +import torch + +from detectron2.config import configurable +from detectron2.data import detection_utils as utils +from detectron2.data import transforms as T +from detectron2.projects.point_rend import ColorAugSSDTransform +from detectron2.structures import BitMasks, Instances, PolygonMasks + +from . import mapper_utils + +""" +This file contains the default mapping that's applied to "dataset dicts". +""" + +__all__ = ["DatasetMapper_detr_semantic"] + + +class DatasetMapper_detr_semantic: + """ + A callable which takes a dataset dict in Detectron2 Dataset format, + and map it into a format used by the model. + + This is the default callable to be used to map your dataset dict into training data. + You may need to follow it to implement your own one for customized logic, + such as a different way to read or transform images. + See :doc:`/tutorials/data_loading` for details. + + The callable currently does the following: + + 1. Read the image from "file_name" + 2. Applies cropping/geometric transforms to the image and annotations + 3. Prepare data and annotations to Tensor and :class:`Instances` + """ + + @configurable + def __init__( + self, + is_train: bool, + *, + augmentations: List[Union[T.Augmentation, T.Transform]], + augmentations_with_crop: List[Union[T.Augmentation, T.Transform]], + image_format: str, + use_instance_mask: bool = False, + use_keypoint: bool = False, + instance_mask_format: str = "polygon", + keypoint_hflip_indices: Optional[np.ndarray] = None, + precomputed_proposal_topk: Optional[int] = None, + recompute_boxes: bool = False, + ignore_label: int = 255, + stuff_classes_decomposition: bool = False, + ): + """ + NOTE: this interface is experimental. + + Args: + is_train: whether it's used in training or inference + augmentations: a list of augmentations or deterministic transforms to apply + image_format: an image format supported by :func:`detection_utils.read_image`. + use_instance_mask: whether to process instance segmentation annotations, if available + use_keypoint: whether to process keypoint annotations if available + instance_mask_format: one of "polygon" or "bitmask". Process instance segmentation + masks into this format. + keypoint_hflip_indices: see :func:`detection_utils.create_keypoint_hflip_indices` + precomputed_proposal_topk: if given, will load pre-computed + proposals from dataset_dict and keep the top k proposals for each image. + recompute_boxes: whether to overwrite bounding box annotations + by computing tight bounding boxes from instance mask annotations. + """ + if recompute_boxes: + assert use_instance_mask, "recompute_boxes requires instance masks" + # fmt: off + self.is_train = is_train + self.augmentations = T.AugmentationList(augmentations) + self.augmentations_with_crop = T.AugmentationList(augmentations_with_crop) + self.image_format = image_format + self.use_instance_mask = use_instance_mask + self.instance_mask_format = instance_mask_format + self.use_keypoint = use_keypoint + self.keypoint_hflip_indices = keypoint_hflip_indices + self.proposal_topk = precomputed_proposal_topk + self.recompute_boxes = recompute_boxes + self.ignore_label = ignore_label + self.stuff_classes_decomposition = stuff_classes_decomposition + # fmt: on + logger = logging.getLogger(__name__) + mode = "training" if is_train else "inference" + logger.info(f"[DatasetMapper] Augmentations used in {mode}: {augmentations}") + logger.info(f"[DatasetMapper] Augmentations used in {mode}: {augmentations_with_crop}") + + @classmethod + def from_config(cls, cfg, is_train: bool = True): + raise NotImplementedError(self.__class__.__name__) + + def _transform_annotations(self, dataset_dict, transforms, image_shape): + # USER: Modify this if you want to keep them for some reason. + for anno in dataset_dict["annotations"]: + if not self.use_instance_mask: + anno.pop("segmentation", None) + if not self.use_keypoint: + anno.pop("keypoints", None) + + # USER: Implement additional transformations if you have other types of data + annos = [ + utils.transform_instance_annotations( + obj, transforms, image_shape, keypoint_hflip_indices=self.keypoint_hflip_indices + ) + for obj in dataset_dict.pop("annotations") + if obj.get("iscrowd", 0) == 0 + ] + instances = utils.annotations_to_instances( + annos, image_shape, mask_format=self.instance_mask_format + ) + + # After transforms such as cropping are applied, the bounding box may no longer + # tightly bound the object. As an example, imagine a triangle object + # [(0,0), (2,0), (0,2)] cropped by a box [(1,0),(2,2)] (XYXY format). The tight + # bounding box of the cropped triangle should be [(1,0),(2,1)], which is not equal to + # the intersection of original bounding box and the cropping box. + if self.recompute_boxes: + instances.gt_boxes = instances.gt_masks.get_bounding_boxes() + dataset_dict["instances"] = utils.filter_empty_instances(instances) + + def __call__(self, dataset_dict): + """ + Args: + dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format. + + Returns: + dict: a format that builtin models in detectron2 accept + """ + dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below + # USER: Write your own image loading if it's not from a file + image = utils.read_image(dataset_dict["file_name"], format=self.image_format) + utils.check_image_size(dataset_dict, image) + + # USER: Remove if you don't do semantic/panoptic segmentation. + if "sem_seg_file_name" in dataset_dict: + # sem_seg_gt = utils.read_image(dataset_dict.pop("sem_seg_file_name")).astype("double") + sem_seg_gt = utils.read_image(dataset_dict.pop("sem_seg_file_name"), "L").squeeze(2) + else: + sem_seg_gt = None + + if self.augmentations_with_crop is None: + augmentations = self.augmentations + else: + if np.random.rand() > 0.5: + augmentations = self.augmentations + else: + augmentations = self.augmentations_with_crop + + aug_input = T.AugInput(image, sem_seg=sem_seg_gt) + # transforms = self.augmentations(aug_input) + transforms = augmentations(aug_input) + image, sem_seg_gt = aug_input.image, aug_input.sem_seg + + image_shape = image.shape[:2] # h, w + # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory, + # but not efficient on large generic data structures due to the use of pickle & mp.Queue. + # Therefore it's important to use torch.Tensor. + dataset_dict["image"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1))) + if sem_seg_gt is not None: + dataset_dict["sem_seg"] = torch.as_tensor(sem_seg_gt.astype("long")) + + # USER: Remove if you don't use pre-computed proposals. + # Most users would not need this feature. + if self.proposal_topk is not None: + utils.transform_proposals( + dataset_dict, image_shape, transforms, proposal_topk=self.proposal_topk + ) + + if not self.is_train: + # USER: Modify this if you want to keep them for some reason. + dataset_dict.pop("annotations", None) + dataset_dict.pop("sem_seg_file_name", None) + return dataset_dict + + if "annotations" in dataset_dict: + self._transform_annotations(dataset_dict, transforms, image_shape) + + # Prepare per-category binary masks + if sem_seg_gt is not None and not self.stuff_classes_decomposition: + instances = Instances(image_shape) + classes = np.unique(sem_seg_gt) + # remove ignored region + classes = classes[classes != self.ignore_label] + instances.gt_classes = torch.tensor(classes, dtype=torch.int64) + + masks = [] + for class_id in classes: + masks.append(sem_seg_gt == class_id) + + if len(masks) == 0: + # # Some image does not have annotation (all ignored) + # instances.gt_masks = torch.zeros((0, sem_seg_gt.shape[-2], sem_seg_gt.shape[-1])) + masks = BitMasks(torch.zeros((0, sem_seg_gt.shape[-2], sem_seg_gt.shape[-1]))) + else: + masks = BitMasks( + torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks]) + ) + + instances.gt_masks = masks + instances.gt_boxes = masks.get_bounding_boxes() + dataset_dict["instances"] = instances + + # Prepare per-category binary masks + if sem_seg_gt is not None and self.stuff_classes_decomposition: + classes = np.unique(sem_seg_gt) + # remove ignored region + classes = classes[classes != self.ignore_label] + + gt_masks = [] + gt_classes = [] + for class_id in classes: + bitmask = sem_seg_gt == class_id + pygmask, _ = mapper_utils.mask_to_polygons_2(bitmask) + for mask in pygmask: + gt_masks.append([mask]) + gt_classes.append(class_id) + + # if len(gt_masks) == 0: + # return None + + instances = Instances(image_shape) + instances.gt_classes = torch.tensor(gt_classes, dtype=torch.int64) + instances.gt_masks = PolygonMasks(gt_masks) + instances.gt_boxes = instances.gt_masks.get_bounding_boxes() + + area = instances.gt_masks.area() + instances = instances[area > 8 * 8] + + dataset_dict["instances"] = instances + + if "instances" in dataset_dict and len(dataset_dict["instances"]) > 0: + pass + else: + return None + + return dataset_dict diff --git a/ape/data/datasets/__init__.py b/ape/data/datasets/__init__.py new file mode 100644 index 0000000..e334b3f --- /dev/null +++ b/ape/data/datasets/__init__.py @@ -0,0 +1,19 @@ +from . import d_cube as _d_cube +from . import flickr30k as _flickr30k +from . import gqa as _gqa +from . import grit as _grit +from . import lvis_coco as _lvis_coco +from . import lvis_coco_panoptic as _lvis_coco_panoptic +from . import objects365 as _objects365 +from . import odinw_instance as _odinw_instance +from . import oid as _oid +from . import pascal_voc_external as _pascal_voc_external +from . import phrasecut as _phrasecut +from . import refcoco as _refcoco +from . import register_bdd100k_panoseg as _register_bdd100k_panoseg +from . import register_bdd100k_semseg as _register_bdd100k_semseg +from . import register_pascal_context as _register_pascal_context +from . import register_voc_seg as _register_voc_seg +from . import sa1b as _sa1b +from . import seginw_instance as _seginw_instance +from . import visualgenome as _visualgenome diff --git a/ape/data/datasets/coco.py b/ape/data/datasets/coco.py new file mode 100644 index 0000000..909b7d0 --- /dev/null +++ b/ape/data/datasets/coco.py @@ -0,0 +1,383 @@ +import contextlib +import io +import logging +import os + +import pycocotools.mask as mask_util + +from detectron2.data import DatasetCatalog, MetadataCatalog +from detectron2.structures import BoxMode +from detectron2.utils.file_io import PathManager +from fvcore.common.timer import Timer + +""" +This file contains functions to parse COCO-format annotations into dicts in "Detectron2 format". +""" + + +logger = logging.getLogger(__name__) + +__all__ = ["custom_load_coco_json", "custom_register_coco_instances"] + + +def custom_load_coco_json(json_file, image_root, dataset_name=None, extra_annotation_keys=None): + """ + Load a json file with COCO's instances annotation format. + Currently supports instance detection, instance segmentation, + and person keypoints annotations. + + Args: + json_file (str): full path to the json file in COCO instances annotation format. + image_root (str or path-like): the directory where the images in this json file exists. + dataset_name (str or None): the name of the dataset (e.g., coco_2017_train). + When provided, this function will also do the following: + + * Put "thing_classes" into the metadata associated with this dataset. + * Map the category ids into a contiguous range (needed by standard dataset format), + and add "thing_dataset_id_to_contiguous_id" to the metadata associated + with this dataset. + + This option should usually be provided, unless users need to load + the original json content and apply more processing manually. + extra_annotation_keys (list[str]): list of per-annotation keys that should also be + loaded into the dataset dict (besides "iscrowd", "bbox", "keypoints", + "category_id", "segmentation"). The values for these keys will be returned as-is. + For example, the densepose annotations are loaded in this way. + + Returns: + list[dict]: a list of dicts in Detectron2 standard dataset dicts format (See + `Using Custom Datasets `_ ) when `dataset_name` is not None. + If `dataset_name` is None, the returned `category_ids` may be + incontiguous and may not conform to the Detectron2 standard format. + + Notes: + 1. This function does not read the image files. + The results do not have the "image" field. + """ + from pycocotools.coco import COCO + + timer = Timer() + json_file = PathManager.get_local_path(json_file) + with contextlib.redirect_stdout(io.StringIO()): + coco_api = COCO(json_file) + if timer.seconds() > 1: + logger.info("Loading {} takes {:.2f} seconds.".format(json_file, timer.seconds())) + + id_map = None + if dataset_name is not None: + meta = MetadataCatalog.get(dataset_name) + cat_ids = sorted(coco_api.getCatIds()) + cats = coco_api.loadCats(cat_ids) + # The categories in a custom json file may not be sorted. + thing_classes = [c["name"] for c in sorted(cats, key=lambda x: x["id"])] + meta.thing_classes = thing_classes + + # In COCO, certain category ids are artificially removed, + # and by convention they are always ignored. + # We deal with COCO's id issue and translate + # the category ids to contiguous ids in [0, 80). + + # It works by looking at the "categories" field in the json, therefore + # if users' own json also have incontiguous ids, we'll + # apply this mapping as well but print a warning. + if not (min(cat_ids) == 1 and max(cat_ids) == len(cat_ids)): + if "coco" not in dataset_name: + logger.warning( + """ +Category ids in annotations are not in [1, #categories]! We'll apply a mapping for you. +""" + ) + id_map = {v: i for i, v in enumerate(cat_ids)} + meta.thing_dataset_id_to_contiguous_id = id_map + + cat_ids = cat_ids + list(range(max(cat_ids) + 1, 100000)) + id_map = {v: i for i, v in enumerate(cat_ids)} + + # sort indices for reproducible results + img_ids = sorted(coco_api.imgs.keys()) + # imgs is a list of dicts, each looks something like: + # {'license': 4, + # 'url': 'http://farm6.staticflickr.com/5454/9413846304_881d5e5c3b_z.jpg', + # 'file_name': 'COCO_val2014_000000001268.jpg', + # 'height': 427, + # 'width': 640, + # 'date_captured': '2013-11-17 05:57:24', + # 'id': 1268} + imgs = coco_api.loadImgs(img_ids) + # anns is a list[list[dict]], where each dict is an annotation + # record for an object. The inner list enumerates the objects in an image + # and the outer list enumerates over images. Example of anns[0]: + # [{'segmentation': [[192.81, + # 247.09, + # ... + # 219.03, + # 249.06]], + # 'area': 1035.749, + # 'iscrowd': 0, + # 'image_id': 1268, + # 'bbox': [192.81, 224.8, 74.73, 33.43], + # 'category_id': 16, + # 'id': 42986}, + # ...] + anns = [coco_api.imgToAnns[img_id] for img_id in img_ids] + total_num_valid_anns = sum([len(x) for x in anns]) + total_num_anns = len(coco_api.anns) + if total_num_valid_anns < total_num_anns: + logger.warning( + f"{json_file} contains {total_num_anns} annotations, but only " + f"{total_num_valid_anns} of them match to images in the file." + ) + + if "minival" not in json_file: + # The popular valminusminival & minival annotations for COCO2014 contain this bug. + # However the ratio of buggy annotations there is tiny and does not affect accuracy. + # Therefore we explicitly white-list them. + ann_ids = [ann["id"] for anns_per_image in anns for ann in anns_per_image] + assert len(set(ann_ids)) == len(ann_ids), "Annotation ids in '{}' are not unique!".format( + json_file + ) + + imgs_anns = list(zip(imgs, anns)) + logger.info("Loaded {} images in COCO format from {}".format(len(imgs_anns), json_file)) + + dataset_dicts = [] + + ann_keys = ["iscrowd", "bbox", "keypoints", "category_id"] + (extra_annotation_keys or []) + + ann_keys += ["phrase", "isobject"] + + num_instances_without_valid_segmentation = 0 + + for (img_dict, anno_dict_list) in imgs_anns: + record = {} + record["file_name"] = os.path.join(image_root, img_dict["file_name"]) + record["height"] = img_dict["height"] + record["width"] = img_dict["width"] + image_id = record["image_id"] = img_dict["id"] + if "neg_category_ids" in img_dict: + record["neg_category_ids"] = [id_map[x] for x in img_dict["neg_category_ids"]] + + objs = [] + for anno in anno_dict_list: + # Check that the image_id in this annotation is the same as + # the image_id we're looking at. + # This fails only when the data parsing logic or the annotation file is buggy. + + # The original COCO valminusminival2014 & minival2014 annotation files + # actually contains bugs that, together with certain ways of using COCO API, + # can trigger this assertion. + assert anno["image_id"] == image_id + + assert anno.get("ignore", 0) == 0, '"ignore" in COCO json file is not supported.' + + obj = {key: anno[key] for key in ann_keys if key in anno} + if "bbox" in obj and len(obj["bbox"]) == 0: + raise ValueError( + f"One annotation of image {image_id} contains empty 'bbox' value! " + "This json does not have valid COCO format." + ) + + segm = anno.get("segmentation", None) + if segm: # either list[list[float]] or dict(RLE) + if isinstance(segm, dict): + if isinstance(segm["counts"], list): + # convert to compressed RLE + segm = mask_util.frPyObjects(segm, *segm["size"]) + else: + # filter out invalid polygons (< 3 points) + segm = [poly for poly in segm if len(poly) % 2 == 0 and len(poly) >= 6] + if len(segm) == 0: + num_instances_without_valid_segmentation += 1 + continue # ignore this instance + obj["segmentation"] = segm + + keypts = anno.get("keypoints", None) + if keypts: # list[int] + for idx, v in enumerate(keypts): + if idx % 3 != 2: + # COCO's segmentation coordinates are floating points in [0, H or W], + # but keypoint coordinates are integers in [0, H-1 or W-1] + # Therefore we assume the coordinates are "pixel indices" and + # add 0.5 to convert to floating point coordinates. + keypts[idx] = v + 0.5 + obj["keypoints"] = keypts + + # phrase = anno.get("phrase", None) + # if phrase: + # obj["phrase"] = phrase + + # isobject = anno.get("isobject", None) + # if isobject: + # obj["isobject"] = isobject + + obj["bbox_mode"] = BoxMode.XYWH_ABS + if id_map: + annotation_category_id = obj["category_id"] + try: + obj["category_id"] = id_map[annotation_category_id] + except KeyError as e: + raise KeyError( + f"Encountered category_id={annotation_category_id} " + "but this id does not exist in 'categories' of the json file." + ) from e + objs.append(obj) + record["annotations"] = objs + dataset_dicts.append(record) + + if num_instances_without_valid_segmentation > 0: + logger.warning( + "Filtered out {} instances without valid segmentation. ".format( + num_instances_without_valid_segmentation + ) + + "There might be issues in your dataset generation process. Please " + "check https://detectron2.readthedocs.io/en/latest/tutorials/datasets.html carefully" + ) + return dataset_dicts + + +def custom_load_sem_seg(gt_root, image_root, gt_ext="png", image_ext="jpg"): + """ + Load semantic segmentation datasets. All files under "gt_root" with "gt_ext" extension are + treated as ground truth annotations and all files under "image_root" with "image_ext" extension + as input images. Ground truth and input images are matched using file paths relative to + "gt_root" and "image_root" respectively without taking into account file extensions. + This works for COCO as well as some other datasets. + + Args: + gt_root (str): full path to ground truth semantic segmentation files. Semantic segmentation + annotations are stored as images with integer values in pixels that represent + corresponding semantic labels. + image_root (str): the directory where the input images are. + gt_ext (str): file extension for ground truth annotations. + image_ext (str): file extension for input images. + + Returns: + list[dict]: + a list of dicts in detectron2 standard format without instance-level + annotation. + + Notes: + 1. This function does not read the image and ground truth files. + The results do not have the "image" and "sem_seg" fields. + """ + + # We match input images with ground truth based on their relative filepaths (without file + # extensions) starting from 'image_root' and 'gt_root' respectively. + def file2id(folder_path, file_path): + # extract relative path starting from `folder_path` + image_id = os.path.normpath(os.path.relpath(file_path, start=folder_path)) + # remove file extension + image_id = os.path.splitext(image_id)[0] + return image_id + + input_files = sorted( + (os.path.join(image_root, f) for f in PathManager.ls(image_root) if f.endswith(image_ext)), + key=lambda file_path: file2id(image_root, file_path), + ) + gt_files = sorted( + (os.path.join(gt_root, f) for f in PathManager.ls(gt_root) if f.endswith(gt_ext)), + key=lambda file_path: file2id(gt_root, file_path), + ) + + assert len(gt_files) > 0, "No annotations found in {}.".format(gt_root) + + # Use the intersection, so that val2017_100 annotations can run smoothly with val2017 images + if len(input_files) != len(gt_files): + logger.warn( + "Directory {} and {} has {} and {} files, respectively.".format( + image_root, gt_root, len(input_files), len(gt_files) + ) + ) + input_basenames = [os.path.basename(f)[: -len(image_ext)] for f in input_files] + gt_basenames = [os.path.basename(f)[: -len(gt_ext)] for f in gt_files] + intersect = list(set(input_basenames) & set(gt_basenames)) + # sort, otherwise each worker may obtain a list[dict] in different order + intersect = sorted(intersect) + logger.warn("Will use their intersection of {} files.".format(len(intersect))) + input_files = [os.path.join(image_root, f + image_ext) for f in intersect] + gt_files = [os.path.join(gt_root, f + gt_ext) for f in intersect] + + logger.info( + "Loaded {} images with semantic segmentation from {}".format(len(input_files), image_root) + ) + + dataset_dicts = [] + for (img_path, gt_path) in zip(input_files, gt_files): + record = {} + record["file_name"] = img_path + record["sem_seg_file_name"] = gt_path + dataset_dicts.append(record) + + return dataset_dicts + + +def custom_load_sem_seg_list(gt_root, image_root, gt_ext="png", image_ext="jpg"): + if isinstance(image_root, list): + image_roots = image_root + else: + image_roots = [image_root] + if isinstance(gt_root, list): + gt_roots = gt_root + else: + gt_roots = [gt_root] + + dataset_dicts = [] + for gt_root, image_root in zip(gt_roots, image_roots): + dataset_dicts.extend(custom_load_sem_seg(gt_root, image_root, gt_ext, image_ext)) + + if len(image_roots) > 1: + logger.info( + "Loaded {} images with semantic segmentation from {}".format( + len(dataset_dicts), image_roots + ) + ) + + return dataset_dicts + + +def custom_register_coco_instances(name, metadata, json_file, image_root): + """ + Register a dataset in COCO's json annotation format for + instance detection, instance segmentation and keypoint detection. + (i.e., Type 1 and 2 in http://cocodataset.org/#format-data. + `instances*.json` and `person_keypoints*.json` in the dataset). + + This is an example of how to register a new dataset. + You can do something similar to this function, to register new datasets. + + Args: + name (str): the name that identifies a dataset, e.g. "coco_2014_train". + metadata (dict): extra metadata associated with this dataset. You can + leave it as an empty dict. + json_file (str): path to the json instance annotation file. + image_root (str or path-like): directory which contains all the images. + """ + assert isinstance(name, str), name + assert isinstance(json_file, (str, os.PathLike)), json_file + assert isinstance(image_root, (str, os.PathLike)), image_root + # 1. register a function which returns dicts + DatasetCatalog.register(name, lambda: custom_load_coco_json(json_file, image_root, name)) + + # 2. Optionally, add metadata about this dataset, + # since they might be useful in evaluation, visualization or logging + MetadataCatalog.get(name).set( + json_file=json_file, image_root=image_root, evaluator_type="coco", **metadata + ) + + +def custom_register_coco_semseg(name, metadata, sem_seg_root, image_root): + assert isinstance(name, str), name + assert isinstance(sem_seg_root, (str, os.PathLike, list)), sem_seg_root + assert isinstance(image_root, (str, os.PathLike, list)), image_root + # 1. register a function which returns dicts + DatasetCatalog.register(name, lambda: custom_load_sem_seg_list(sem_seg_root, image_root)) + + # 2. Optionally, add metadata about this dataset, + # since they might be useful in evaluation, visualization or logging + MetadataCatalog.get(name).set( + sem_seg_root=sem_seg_root, + image_root=image_root, + evaluator_type="sem_seg", + ignore_label=255, + **metadata, + ) diff --git a/ape/data/datasets/d_cube.py b/ape/data/datasets/d_cube.py new file mode 100644 index 0000000..10bfad5 --- /dev/null +++ b/ape/data/datasets/d_cube.py @@ -0,0 +1,275 @@ +import logging +import os + +import pycocotools.mask as mask_util + +from detectron2.data import DatasetCatalog, MetadataCatalog +from detectron2.data.datasets.builtin_meta import _get_coco_instances_meta +from detectron2.data.datasets.lvis_v0_5_categories import LVIS_CATEGORIES as LVIS_V0_5_CATEGORIES +from detectron2.data.datasets.lvis_v1_categories import LVIS_CATEGORIES as LVIS_V1_CATEGORIES +from detectron2.structures import BoxMode +from detectron2.utils.file_io import PathManager +from fvcore.common.timer import Timer + +from .lvis_v1_coco_category_image_count import LVIS_V1_COCO_CATEGORY_IMAGE_COUNT + +""" +This file contains functions to parse LVIS-format annotations into dicts in the +"Detectron2 format". +""" + +logger = logging.getLogger(__name__) + +__all__ = ["load_d3_json", "register_d3_instances"] + + +def register_d3_instances(name, metadata, json_file, image_root, anno_root): + """ + Register a dataset in LVIS's json annotation format for instance detection and segmentation. + + Args: + name (str): a name that identifies the dataset, e.g. "lvis_v0.5_train". + metadata (dict): extra metadata associated with this dataset. It can be an empty dict. + json_file (str): path to the json instance annotation file. + image_root (str or path-like): directory which contains all the images. + """ + DatasetCatalog.register(name, lambda: load_d3_json(json_file, image_root, anno_root, name)) + MetadataCatalog.get(name).set( + json_file=json_file, image_root=image_root, evaluator_type="d3", **metadata + ) + + +def load_d3_json(json_file, image_root, anno_root, dataset_name=None, extra_annotation_keys=None): + """ + Load a json file in LVIS's annotation format. + + Args: + json_file (str): full path to the LVIS json annotation file. + image_root (str): the directory where the images in this json file exists. + dataset_name (str): the name of the dataset (e.g., "lvis_v0.5_train"). + If provided, this function will put "thing_classes" into the metadata + associated with this dataset. + extra_annotation_keys (list[str]): list of per-annotation keys that should also be + loaded into the dataset dict (besides "bbox", "bbox_mode", "category_id", + "segmentation"). The values for these keys will be returned as-is. + + Returns: + list[dict]: a list of dicts in Detectron2 standard format. (See + `Using Custom Datasets `_ ) + + Notes: + 1. This function does not read the image files. + The results do not have the "image" field. + """ + from d_cube import D3 + + timer = Timer() + + d3 = D3(image_root, anno_root) + + if timer.seconds() > 1: + logger.info("Loading d3 takes {:.2f} seconds.".format(timer.seconds())) + + id_map = None + if dataset_name is not None: + meta = MetadataCatalog.get(dataset_name) + cat_ids = sorted(d3.get_sent_ids()) + cats = d3.load_sents(cat_ids) + # The categories in a custom json file may not be sorted. + thing_classes = [c["raw_sent"] for c in sorted(cats, key=lambda x: x["id"])] + meta.thing_classes = thing_classes + + # In COCO, certain category ids are artificially removed, + # and by convention they are always ignored. + # We deal with COCO's id issue and translate + # the category ids to contiguous ids in [0, 80). + + # It works by looking at the "categories" field in the json, therefore + # if users' own json also have incontiguous ids, we'll + # apply this mapping as well but print a warning. + if not (min(cat_ids) == 1 and max(cat_ids) == len(cat_ids)): + if "coco" not in dataset_name: + logger.warning( + """ +Category ids in annotations are not in [1, #categories]! We'll apply a mapping for you. +""" + ) + id_map = {v: i for i, v in enumerate(cat_ids)} + meta.thing_dataset_id_to_contiguous_id = id_map + + img_ids = d3.get_img_ids() + imgs = d3.load_imgs(img_ids) + anno_ids = [d3.get_anno_ids(img_ids=img_id) for img_id in img_ids] + anns = [d3.load_annos(anno_ids=anno_id) for anno_id in anno_ids] + total_num_valid_anns = sum([len(x) for x in anns]) + total_num_anns = len(d3.load_annos()) + if total_num_valid_anns < total_num_anns: + logger.warning( + f"{anno_root} contains {total_num_anns} annotations, but only " + f"{total_num_valid_anns} of them match to images in the file." + ) + + imgs_anns = list(zip(imgs, anns)) + logger.info("Loaded {} images in COCO format from {}".format(len(imgs_anns), json_file)) + + dataset_dicts = [] + + ann_keys = ["iscrowd", "bbox", "keypoints", "sent_id"] + (extra_annotation_keys or []) + + num_instances_without_valid_segmentation = 0 + + for (img_dict, anno_dict_list) in imgs_anns: + record = {} + record["file_name"] = os.path.join(image_root, img_dict["file_name"]) + record["height"] = img_dict["height"] + record["width"] = img_dict["width"] + image_id = record["image_id"] = img_dict["id"] + + if meta.group == "intra": + group_ids = d3.get_group_ids(img_ids=[image_id]) + sent_ids = d3.get_sent_ids(group_ids=group_ids) + sent_list = d3.load_sents(sent_ids=sent_ids) + # assert len(anno_dict_list) == len(sent_ids) + elif meta.group == "inter": + sent_ids = d3.get_sent_ids() + sent_list = d3.load_sents(sent_ids=sent_ids) + # sent_list = d3.load_sents() + else: + assert False + ref_list = [sent["raw_sent"] for sent in sent_list] + record["expressions"] = ref_list + if id_map: + record["sent_ids"] = [id_map[x] for x in sent_ids] + + objs = [] + for anno in anno_dict_list: + # Check that the image_id in this annotation is the same as + # the image_id we're looking at. + # This fails only when the data parsing logic or the annotation file is buggy. + + # The original COCO valminusminival2014 & minival2014 annotation files + # actually contains bugs that, together with certain ways of using COCO API, + # can trigger this assertion. + assert anno["image_id"] == image_id + + assert anno.get("ignore", 0) == 0, '"ignore" in COCO json file is not supported.' + + obj = {key: anno[key] for key in ann_keys if key in anno} + if "bbox" in obj and len(obj["bbox"]) == 0: + raise ValueError( + f"One annotation of image {image_id} contains empty 'bbox' value! " + "This json does not have valid COCO format." + ) + + assert len(obj["bbox"]) == 1 + obj["bbox"] = list(obj["bbox"][0]) + # assert len(obj["sent_id"]) == 1 + obj["sent_id"] = obj["sent_id"][0] + + segm = anno.get("segmentation", None) + assert len(segm) == 1 + segm = segm[0] + if segm: # either list[list[float]] or dict(RLE) + if isinstance(segm, dict): + if isinstance(segm["counts"], list): + # convert to compressed RLE + segm = mask_util.frPyObjects(segm, *segm["size"]) + else: + # filter out invalid polygons (< 3 points) + segm = [poly for poly in segm if len(poly) % 2 == 0 and len(poly) >= 6] + if len(segm) == 0: + num_instances_without_valid_segmentation += 1 + continue # ignore this instance + obj["segmentation"] = segm + + keypts = anno.get("keypoints", None) + if keypts: # list[int] + for idx, v in enumerate(keypts): + if idx % 3 != 2: + # COCO's segmentation coordinates are floating points in [0, H or W], + # but keypoint coordinates are integers in [0, H-1 or W-1] + # Therefore we assume the coordinates are "pixel indices" and + # add 0.5 to convert to floating point coordinates. + keypts[idx] = v + 0.5 + obj["keypoints"] = keypts + + obj["bbox_mode"] = BoxMode.XYWH_ABS + if id_map: + annotation_category_id = obj["sent_id"] + try: + obj["sent_id"] = id_map[annotation_category_id] + except KeyError as e: + raise KeyError( + f"Encountered sent_id={annotation_category_id} " + "but this id does not exist in 'categories' of the json file." + ) from e + obj["category_id"] = obj["sent_id"] + obj["iscrowd"] = 0 + objs.append(obj) + record["annotations"] = objs + dataset_dicts.append(record) + + if num_instances_without_valid_segmentation > 0: + logger.warning( + "Filtered out {} instances without valid segmentation. ".format( + num_instances_without_valid_segmentation + ) + + "There might be issues in your dataset generation process. Please " + "check https://detectron2.readthedocs.io/en/latest/tutorials/datasets.html carefully" + ) + return dataset_dicts + + +def get_d3_instances_meta(dataset_name): + if "intra_scenario" in dataset_name: + group = "intra" + elif "inter_scenario" in dataset_name: + group = "inter" + else: + assert False + return {"group": group} + + +_PREDEFINED_SPLITS_D3 = { + "d3_inter_scenario": { + "d3_inter_scenario": ( + "D3/d3_images/", + { + "FULL": "D3/d3_json/d3_full_annotations.json", + "PRES": "D3/d3_json/d3_pres_annotations.json", + "ABS": "D3/d3_json/d3_abs_annotations.json", + }, + "D3/d3_pkl/", + ), + }, + "d3_intra_scenario": { + "d3_intra_scenario": ( + "D3/d3_images/", + { + "FULL": "D3/d3_json/d3_full_annotations.json", + "PRES": "D3/d3_json/d3_pres_annotations.json", + "ABS": "D3/d3_json/d3_abs_annotations.json", + }, + "D3/d3_pkl/", + ), + }, +} + + +def register_all_D3(root): + for dataset_name, splits_per_dataset in _PREDEFINED_SPLITS_D3.items(): + for key, (image_root, json_file, anno_root) in splits_per_dataset.items(): + register_d3_instances( + key, + get_d3_instances_meta(dataset_name), + # os.path.join(root, json_file) if "://" not in json_file else json_file, + {k: os.path.join(root, v) for k, v in json_file.items()}, + os.path.join(root, image_root), + os.path.join(root, anno_root), + ) + + +if __name__.endswith(".d_cube"): + # Assume pre-defined datasets live in `./datasets`. + _root = os.getenv("DETECTRON2_DATASETS", "datasets") + register_all_D3(_root) diff --git a/ape/data/datasets/flickr30k.py b/ape/data/datasets/flickr30k.py new file mode 100644 index 0000000..be9a6fb --- /dev/null +++ b/ape/data/datasets/flickr30k.py @@ -0,0 +1,68 @@ +import logging +import os + +from .coco import custom_register_coco_instances + +logger = logging.getLogger(__name__) + + +def _get_builtin_metadata(dataset_name): + return _get_flickr30k_metadata([]) + + raise KeyError("No built-in metadata for dataset {}".format(dataset_name)) + + +def _get_flickr30k_metadata(categories): + if len(categories) == 0: + return {} + id_to_name = {x["id"]: x["name"] for x in categories} + thing_dataset_id_to_contiguous_id = {i + 1: i for i in range(len(categories))} + thing_classes = [id_to_name[k] for k in sorted(id_to_name)] + return { + "thing_dataset_id_to_contiguous_id": thing_dataset_id_to_contiguous_id, + "thing_classes": thing_classes, + } + + +_PREDEFINED_SPLITS_FLICKR30k = {} +_PREDEFINED_SPLITS_FLICKR30k["flickr30k"] = { + "flickr30k": ( + "flickr30k/flickr30k-images", + "flickr30k/flickr30k.json", + ), + "flickr30k_separateGT_train": ( + "flickr30k/flickr30k-images", + "flickr30k/flickr30k_separateGT_train.json", + ), + "flickr30k_separateGT_val": ( + "flickr30k/flickr30k-images", + "flickr30k/flickr30k_separateGT_val.json", + ), + "flickr30k_mergedGT_train": ( + "flickr30k/flickr30k-images", + "flickr30k/flickr30k_mergedGT_train.json", + ), + "flickr30k_mergedGT_val": ( + "flickr30k/flickr30k-images", + "flickr30k/flickr30k_mergedGT_val.json", + ), +} + + +def register_all_flickr30k(root): + for dataset_name, splits_per_dataset in _PREDEFINED_SPLITS_FLICKR30k.items(): + for key, (image_root, json_file) in splits_per_dataset.items(): + custom_register_coco_instances( + key, + _get_builtin_metadata(dataset_name), + os.path.join(root, json_file) if "://" not in json_file else json_file, + os.path.join(root, image_root), + ) + + +# True for open source; +# Internally at fb, we register them elsewhere +if __name__.endswith(".flickr30k"): + # Assume pre-defined datasets live in `./datasets`. + _root = os.path.expanduser(os.getenv("DETECTRON2_DATASETS", "datasets")) + register_all_flickr30k(_root) diff --git a/ape/data/datasets/gqa.py b/ape/data/datasets/gqa.py new file mode 100644 index 0000000..f58e887 --- /dev/null +++ b/ape/data/datasets/gqa.py @@ -0,0 +1,60 @@ +import logging +import os + +from .coco import custom_register_coco_instances + +logger = logging.getLogger(__name__) + + +def _get_builtin_metadata(dataset_name): + return _get_gqa_metadata([]) + + raise KeyError("No built-in metadata for dataset {}".format(dataset_name)) + + +def _get_gqa_metadata(categories): + if len(categories) == 0: + return {} + id_to_name = {x["id"]: x["name"] for x in categories} + thing_dataset_id_to_contiguous_id = {i + 1: i for i in range(len(categories))} + thing_classes = [id_to_name[k] for k in sorted(id_to_name)] + return { + "thing_dataset_id_to_contiguous_id": thing_dataset_id_to_contiguous_id, + "thing_classes": thing_classes, + } + + +_PREDEFINED_SPLITS_GQA = {} +_PREDEFINED_SPLITS_GQA["gqa_region"] = { + "gqa_region": ( + "gqa/images", + "gqa/gqa_region.json", + ), + "gqa_region_train": ( + "gqa/images", + "gqa/gqa_region_train.json", + ), + "gqa_region_val": ( + "gqa/images", + "gqa/gqa_region_val.json", + ), +} + + +def register_all_gqa(root): + for dataset_name, splits_per_dataset in _PREDEFINED_SPLITS_GQA.items(): + for key, (image_root, json_file) in splits_per_dataset.items(): + custom_register_coco_instances( + key, + _get_builtin_metadata(dataset_name), + os.path.join(root, json_file) if "://" not in json_file else json_file, + os.path.join(root, image_root), + ) + + +# True for open source; +# Internally at fb, we register them elsewhere +if __name__.endswith(".gqa"): + # Assume pre-defined datasets live in `./datasets`. + _root = os.path.expanduser(os.getenv("DETECTRON2_DATASETS", "datasets")) + register_all_gqa(_root) diff --git a/ape/data/datasets/grit.py b/ape/data/datasets/grit.py new file mode 100644 index 0000000..efcb24d --- /dev/null +++ b/ape/data/datasets/grit.py @@ -0,0 +1,60 @@ +import os + +from .coco import custom_register_coco_instances + +GRIT_CATEGORIES = [ + {"id": 0, "name": "object"}, +] + + +def _get_builtin_metadata(dataset_name): + id_to_name = {x["id"]: x["name"] for x in GRIT_CATEGORIES} + thing_dataset_id_to_contiguous_id = {i: i for i in range(len(GRIT_CATEGORIES))} + thing_classes = [id_to_name[k] for k in sorted(id_to_name)] + return { + "thing_dataset_id_to_contiguous_id": thing_dataset_id_to_contiguous_id, + "thing_classes": thing_classes, + } + + +_PREDEFINED_SPLITS_GRIT = { + "grit": ("GRIT/images", "GRIT/grit.json"), + "grit_0_snappy": ("GRIT/images", "GRIT/grit_0_snappy.json"), + "grit_1_snappy": ("GRIT/images", "GRIT/grit_1_snappy.json"), + "grit_2_snappy": ("GRIT/images", "GRIT/grit_2_snappy.json"), + "grit_3_snappy": ("GRIT/images", "GRIT/grit_3_snappy.json"), + "grit_4_snappy": ("GRIT/images", "GRIT/grit_4_snappy.json"), + "grit_5_snappy": ("GRIT/images", "GRIT/grit_5_snappy.json"), + "grit_6_snappy": ("GRIT/images", "GRIT/grit_6_snappy.json"), + "grit_7_snappy": ("GRIT/images", "GRIT/grit_7_snappy.json"), + "grit_8_snappy": ("GRIT/images", "GRIT/grit_8_snappy.json"), + "grit_9_snappy": ("GRIT/images", "GRIT/grit_9_snappy.json"), + "grit_10_snappy": ("GRIT/images", "GRIT/grit_10_snappy.json"), + "grit_11_snappy": ("GRIT/images", "GRIT/grit_11_snappy.json"), + "grit_12_snappy": ("GRIT/images", "GRIT/grit_12_snappy.json"), + "grit_13_snappy": ("GRIT/images", "GRIT/grit_13_snappy.json"), + "grit_14_snappy": ("GRIT/images", "GRIT/grit_14_snappy.json"), + "grit_15_snappy": ("GRIT/images", "GRIT/grit_15_snappy.json"), + "grit_16_snappy": ("GRIT/images", "GRIT/grit_16_snappy.json"), + "grit_17_snappy": ("GRIT/images", "GRIT/grit_17_snappy.json"), + "grit_18_snappy": ("GRIT/images", "GRIT/grit_18_snappy.json"), + "grit_19_snappy": ("GRIT/images", "GRIT/grit_19_snappy.json"), + "grit_20_snappy": ("GRIT/images", "GRIT/grit_20_snappy.json"), + "grit_21_snappy": ("GRIT/images", "GRIT/grit_21_snappy.json"), +} + + +def register_all_GRIT(root): + for key, (image_root, json_file) in _PREDEFINED_SPLITS_GRIT.items(): + custom_register_coco_instances( + key, + _get_builtin_metadata(key), + os.path.join(root, json_file) if "://" not in json_file else json_file, + os.path.join(root, image_root), + ) + + +if __name__.endswith(".grit"): + # Assume pre-defined datasets live in `./datasets`. + _root = os.getenv("DETECTRON2_DATASETS", "datasets") + register_all_GRIT(_root) diff --git a/ape/data/datasets/inst_categories.py b/ape/data/datasets/inst_categories.py new file mode 100644 index 0000000..2c943d9 --- /dev/null +++ b/ape/data/datasets/inst_categories.py @@ -0,0 +1,488 @@ +categories = { + "coco": [ + {"color": [220, 20, 60], "isthing": 1, "id": 1, "name": "person"}, + {"color": [119, 11, 32], "isthing": 1, "id": 2, "name": "bicycle"}, + {"color": [0, 0, 142], "isthing": 1, "id": 3, "name": "car"}, + {"color": [0, 0, 230], "isthing": 1, "id": 4, "name": "motorcycle"}, + {"color": [106, 0, 228], "isthing": 1, "id": 5, "name": "airplane"}, + {"color": [0, 60, 100], "isthing": 1, "id": 6, "name": "bus"}, + {"color": [0, 80, 100], "isthing": 1, "id": 7, "name": "train"}, + {"color": [0, 0, 70], "isthing": 1, "id": 8, "name": "truck"}, + {"color": [0, 0, 192], "isthing": 1, "id": 9, "name": "boat"}, + {"color": [250, 170, 30], "isthing": 1, "id": 10, "name": "traffic light"}, + {"color": [100, 170, 30], "isthing": 1, "id": 11, "name": "fire hydrant"}, + {"color": [220, 220, 0], "isthing": 1, "id": 13, "name": "stop sign"}, + {"color": [175, 116, 175], "isthing": 1, "id": 14, "name": "parking meter"}, + {"color": [250, 0, 30], "isthing": 1, "id": 15, "name": "bench"}, + {"color": [165, 42, 42], "isthing": 1, "id": 16, "name": "bird"}, + {"color": [255, 77, 255], "isthing": 1, "id": 17, "name": "cat"}, + {"color": [0, 226, 252], "isthing": 1, "id": 18, "name": "dog"}, + {"color": [182, 182, 255], "isthing": 1, "id": 19, "name": "horse"}, + {"color": [0, 82, 0], "isthing": 1, "id": 20, "name": "sheep"}, + {"color": [120, 166, 157], "isthing": 1, "id": 21, "name": "cow"}, + {"color": [110, 76, 0], "isthing": 1, "id": 22, "name": "elephant"}, + {"color": [174, 57, 255], "isthing": 1, "id": 23, "name": "bear"}, + {"color": [199, 100, 0], "isthing": 1, "id": 24, "name": "zebra"}, + {"color": [72, 0, 118], "isthing": 1, "id": 25, "name": "giraffe"}, + {"color": [255, 179, 240], "isthing": 1, "id": 27, "name": "backpack"}, + {"color": [0, 125, 92], "isthing": 1, "id": 28, "name": "umbrella"}, + {"color": [209, 0, 151], "isthing": 1, "id": 31, "name": "handbag"}, + {"color": [188, 208, 182], "isthing": 1, "id": 32, "name": "tie"}, + {"color": [0, 220, 176], "isthing": 1, "id": 33, "name": "suitcase"}, + {"color": [255, 99, 164], "isthing": 1, "id": 34, "name": "frisbee"}, + {"color": [92, 0, 73], "isthing": 1, "id": 35, "name": "skis"}, + {"color": [133, 129, 255], "isthing": 1, "id": 36, "name": "snowboard"}, + {"color": [78, 180, 255], "isthing": 1, "id": 37, "name": "sports ball"}, + {"color": [0, 228, 0], "isthing": 1, "id": 38, "name": "kite"}, + {"color": [174, 255, 243], "isthing": 1, "id": 39, "name": "baseball bat"}, + {"color": [45, 89, 255], "isthing": 1, "id": 40, "name": "baseball glove"}, + {"color": [134, 134, 103], "isthing": 1, "id": 41, "name": "skateboard"}, + {"color": [145, 148, 174], "isthing": 1, "id": 42, "name": "surfboard"}, + {"color": [255, 208, 186], "isthing": 1, "id": 43, "name": "tennis racket"}, + {"color": [197, 226, 255], "isthing": 1, "id": 44, "name": "bottle"}, + {"color": [171, 134, 1], "isthing": 1, "id": 46, "name": "wine glass"}, + {"color": [109, 63, 54], "isthing": 1, "id": 47, "name": "cup"}, + {"color": [207, 138, 255], "isthing": 1, "id": 48, "name": "fork"}, + {"color": [151, 0, 95], "isthing": 1, "id": 49, "name": "knife"}, + {"color": [9, 80, 61], "isthing": 1, "id": 50, "name": "spoon"}, + {"color": [84, 105, 51], "isthing": 1, "id": 51, "name": "bowl"}, + {"color": [74, 65, 105], "isthing": 1, "id": 52, "name": "banana"}, + {"color": [166, 196, 102], "isthing": 1, "id": 53, "name": "apple"}, + {"color": [208, 195, 210], "isthing": 1, "id": 54, "name": "sandwich"}, + {"color": [255, 109, 65], "isthing": 1, "id": 55, "name": "orange"}, + {"color": [0, 143, 149], "isthing": 1, "id": 56, "name": "broccoli"}, + {"color": [179, 0, 194], "isthing": 1, "id": 57, "name": "carrot"}, + {"color": [209, 99, 106], "isthing": 1, "id": 58, "name": "hot dog"}, + {"color": [5, 121, 0], "isthing": 1, "id": 59, "name": "pizza"}, + {"color": [227, 255, 205], "isthing": 1, "id": 60, "name": "donut"}, + {"color": [147, 186, 208], "isthing": 1, "id": 61, "name": "cake"}, + {"color": [153, 69, 1], "isthing": 1, "id": 62, "name": "chair"}, + {"color": [3, 95, 161], "isthing": 1, "id": 63, "name": "couch"}, + {"color": [163, 255, 0], "isthing": 1, "id": 64, "name": "potted plant"}, + {"color": [119, 0, 170], "isthing": 1, "id": 65, "name": "bed"}, + {"color": [0, 182, 199], "isthing": 1, "id": 67, "name": "dining table"}, + {"color": [0, 165, 120], "isthing": 1, "id": 70, "name": "toilet"}, + {"color": [183, 130, 88], "isthing": 1, "id": 72, "name": "tv"}, + {"color": [95, 32, 0], "isthing": 1, "id": 73, "name": "laptop"}, + {"color": [130, 114, 135], "isthing": 1, "id": 74, "name": "mouse"}, + {"color": [110, 129, 133], "isthing": 1, "id": 75, "name": "remote"}, + {"color": [166, 74, 118], "isthing": 1, "id": 76, "name": "keyboard"}, + {"color": [219, 142, 185], "isthing": 1, "id": 77, "name": "cell phone"}, + {"color": [79, 210, 114], "isthing": 1, "id": 78, "name": "microwave"}, + {"color": [178, 90, 62], "isthing": 1, "id": 79, "name": "oven"}, + {"color": [65, 70, 15], "isthing": 1, "id": 80, "name": "toaster"}, + {"color": [127, 167, 115], "isthing": 1, "id": 81, "name": "sink"}, + {"color": [59, 105, 106], "isthing": 1, "id": 82, "name": "refrigerator"}, + {"color": [142, 108, 45], "isthing": 1, "id": 84, "name": "book"}, + {"color": [196, 172, 0], "isthing": 1, "id": 85, "name": "clock"}, + {"color": [95, 54, 80], "isthing": 1, "id": 86, "name": "vase"}, + {"color": [128, 76, 255], "isthing": 1, "id": 87, "name": "scissors"}, + {"color": [201, 57, 1], "isthing": 1, "id": 88, "name": "teddy bear"}, + {"color": [246, 0, 122], "isthing": 1, "id": 89, "name": "hair drier"}, + {"color": [191, 162, 208], "isthing": 1, "id": 90, "name": "toothbrush"}, + ], + "cityscapes": [ + {"id": i + 1, "name": x} + for i, x in enumerate( + ["person", "rider", "car", "truck", "bus", "train", "motorcycle", "bicycle"] + ) + ], + "mapillary": [ + {"id": 1, "name": "animal--bird"}, + {"id": 2, "name": "animal--ground-animal"}, + {"id": 9, "name": "construction--flat--crosswalk-plain"}, + {"id": 20, "name": "human--person"}, + {"id": 21, "name": "human--rider--bicyclist"}, + {"id": 22, "name": "human--rider--motorcyclist"}, + {"id": 23, "name": "human--rider--other-rider"}, + {"id": 24, "name": "marking--crosswalk-zebra"}, + {"id": 33, "name": "object--banner"}, + {"id": 34, "name": "object--bench"}, + {"id": 35, "name": "object--bike-rack"}, + {"id": 36, "name": "object--billboard"}, + {"id": 37, "name": "object--catch-basin"}, + {"id": 38, "name": "object--cctv-camera"}, + {"id": 39, "name": "object--fire-hydrant"}, + {"id": 40, "name": "object--junction-box"}, + {"id": 41, "name": "object--mailbox"}, + {"id": 42, "name": "object--manhole"}, + {"id": 43, "name": "object--phone-booth"}, + {"id": 45, "name": "object--street-light"}, + {"id": 46, "name": "object--support--pole"}, + {"id": 47, "name": "object--support--traffic-sign-frame"}, + {"id": 48, "name": "object--support--utility-pole"}, + {"id": 49, "name": "object--traffic-light"}, + {"id": 50, "name": "object--traffic-sign--back"}, + {"id": 51, "name": "object--traffic-sign--front"}, + {"id": 52, "name": "object--trash-can"}, + {"id": 53, "name": "object--vehicle--bicycle"}, + {"id": 54, "name": "object--vehicle--boat"}, + {"id": 55, "name": "object--vehicle--bus"}, + {"id": 56, "name": "object--vehicle--car"}, + {"id": 57, "name": "object--vehicle--caravan"}, + {"id": 58, "name": "object--vehicle--motorcycle"}, + {"id": 60, "name": "object--vehicle--other-vehicle"}, + {"id": 61, "name": "object--vehicle--trailer"}, + {"id": 62, "name": "object--vehicle--truck"}, + {"id": 63, "name": "object--vehicle--wheeled-slow"}, + ], + "viper": [ + {"id": 13, "name": "trafficlight", "supercategory": ""}, + {"id": 16, "name": "firehydrant", "supercategory": ""}, + {"id": 17, "name": "chair", "supercategory": ""}, + {"id": 19, "name": "trashcan", "supercategory": ""}, + {"id": 20, "name": "person", "supercategory": ""}, + {"id": 23, "name": "motorcycle", "supercategory": ""}, + {"id": 24, "name": "car", "supercategory": ""}, + {"id": 25, "name": "van", "supercategory": ""}, + {"id": 26, "name": "bus", "supercategory": ""}, + {"id": 27, "name": "truck", "supercategory": ""}, + ], + "scannet": [ + {"id": 3, "name": "cabinet", "supercategory": "furniture"}, + {"id": 4, "name": "bed", "supercategory": "furniture"}, + {"id": 5, "name": "chair", "supercategory": "furniture"}, + {"id": 6, "name": "sofa", "supercategory": "furniture"}, + {"id": 7, "name": "table", "supercategory": "furniture"}, + {"id": 8, "name": "door", "supercategory": "furniture"}, + {"id": 9, "name": "window", "supercategory": "furniture"}, + {"id": 10, "name": "bookshelf", "supercategory": "furniture"}, + {"id": 11, "name": "picture", "supercategory": "furniture"}, + {"id": 12, "name": "counter", "supercategory": "furniture"}, + {"id": 14, "name": "desk", "supercategory": "furniture"}, + {"id": 16, "name": "curtain", "supercategory": "furniture"}, + {"id": 24, "name": "refrigerator", "supercategory": "appliance"}, + {"id": 28, "name": "shower curtain", "supercategory": "furniture"}, + {"id": 33, "name": "toilet", "supercategory": "furniture"}, + {"id": 34, "name": "sink", "supercategory": "appliance"}, + {"id": 36, "name": "bathtub", "supercategory": "furniture"}, + {"id": 39, "name": "otherfurniture", "supercategory": "furniture"}, + ], + "oid": [ + {"id": 1, "name": "Screwdriver", "freebase_id": "/m/01bms0"}, + {"id": 2, "name": "Light switch", "freebase_id": "/m/03jbxj"}, + {"id": 3, "name": "Doughnut", "freebase_id": "/m/0jy4k"}, + {"id": 4, "name": "Toilet paper", "freebase_id": "/m/09gtd"}, + {"id": 5, "name": "Wrench", "freebase_id": "/m/01j5ks"}, + {"id": 6, "name": "Toaster", "freebase_id": "/m/01k6s3"}, + {"id": 7, "name": "Tennis ball", "freebase_id": "/m/05ctyq"}, + {"id": 8, "name": "Radish", "freebase_id": "/m/015x5n"}, + {"id": 9, "name": "Pomegranate", "freebase_id": "/m/0jwn_"}, + {"id": 10, "name": "Kite", "freebase_id": "/m/02zt3"}, + {"id": 11, "name": "Table tennis racket", "freebase_id": "/m/05_5p_0"}, + {"id": 12, "name": "Hamster", "freebase_id": "/m/03qrc"}, + {"id": 13, "name": "Barge", "freebase_id": "/m/01btn"}, + {"id": 14, "name": "Shower", "freebase_id": "/m/02f9f_"}, + {"id": 15, "name": "Printer", "freebase_id": "/m/01m4t"}, + {"id": 16, "name": "Snowmobile", "freebase_id": "/m/01x3jk"}, + {"id": 17, "name": "Fire hydrant", "freebase_id": "/m/01pns0"}, + {"id": 18, "name": "Limousine", "freebase_id": "/m/01lcw4"}, + {"id": 19, "name": "Whale", "freebase_id": "/m/084zz"}, + {"id": 20, "name": "Microwave oven", "freebase_id": "/m/0fx9l"}, + {"id": 21, "name": "Asparagus", "freebase_id": "/m/0cjs7"}, + {"id": 22, "name": "Lion", "freebase_id": "/m/096mb"}, + {"id": 23, "name": "Spatula", "freebase_id": "/m/02d1br"}, + {"id": 24, "name": "Torch", "freebase_id": "/m/07dd4"}, + {"id": 25, "name": "Volleyball", "freebase_id": "/m/02rgn06"}, + {"id": 26, "name": "Ambulance", "freebase_id": "/m/012n7d"}, + {"id": 27, "name": "Chopsticks", "freebase_id": "/m/01_5g"}, + {"id": 28, "name": "Raccoon", "freebase_id": "/m/0dq75"}, + {"id": 29, "name": "Blue jay", "freebase_id": "/m/01f8m5"}, + {"id": 30, "name": "Lynx", "freebase_id": "/m/04g2r"}, + {"id": 31, "name": "Dice", "freebase_id": "/m/029b3"}, + {"id": 32, "name": "Filing cabinet", "freebase_id": "/m/047j0r"}, + {"id": 33, "name": "Ruler", "freebase_id": "/m/0hdln"}, + {"id": 34, "name": "Power plugs and sockets", "freebase_id": "/m/03bbps"}, + {"id": 35, "name": "Bell pepper", "freebase_id": "/m/0jg57"}, + {"id": 36, "name": "Binoculars", "freebase_id": "/m/0lt4_"}, + {"id": 37, "name": "Pretzel", "freebase_id": "/m/01f91_"}, + {"id": 38, "name": "Hot dog", "freebase_id": "/m/01b9xk"}, + {"id": 39, "name": "Missile", "freebase_id": "/m/04ylt"}, + {"id": 40, "name": "Common fig", "freebase_id": "/m/043nyj"}, + {"id": 41, "name": "Croissant", "freebase_id": "/m/015wgc"}, + {"id": 42, "name": "Adhesive tape", "freebase_id": "/m/03m3vtv"}, + {"id": 43, "name": "Slow cooker", "freebase_id": "/m/02tsc9"}, + {"id": 44, "name": "Dog bed", "freebase_id": "/m/0h8n6f9"}, + {"id": 45, "name": "Harpsichord", "freebase_id": "/m/03q5t"}, + {"id": 46, "name": "Billiard table", "freebase_id": "/m/04p0qw"}, + {"id": 47, "name": "Alpaca", "freebase_id": "/m/0pcr"}, + {"id": 48, "name": "Harbor seal", "freebase_id": "/m/02l8p9"}, + {"id": 49, "name": "Grape", "freebase_id": "/m/0388q"}, + {"id": 50, "name": "Nail", "freebase_id": "/m/05bm6"}, + {"id": 51, "name": "Paper towel", "freebase_id": "/m/02w3r3"}, + {"id": 52, "name": "Alarm clock", "freebase_id": "/m/046dlr"}, + {"id": 53, "name": "Guacamole", "freebase_id": "/m/02g30s"}, + {"id": 54, "name": "Starfish", "freebase_id": "/m/01h8tj"}, + {"id": 55, "name": "Zebra", "freebase_id": "/m/0898b"}, + {"id": 56, "name": "Segway", "freebase_id": "/m/076bq"}, + {"id": 57, "name": "Sea turtle", "freebase_id": "/m/0120dh"}, + {"id": 58, "name": "Scissors", "freebase_id": "/m/01lsmm"}, + {"id": 59, "name": "Rhinoceros", "freebase_id": "/m/03d443"}, + {"id": 60, "name": "Kangaroo", "freebase_id": "/m/04c0y"}, + {"id": 61, "name": "Jaguar", "freebase_id": "/m/0449p"}, + {"id": 62, "name": "Leopard", "freebase_id": "/m/0c29q"}, + {"id": 63, "name": "Dumbbell", "freebase_id": "/m/04h8sr"}, + {"id": 64, "name": "Envelope", "freebase_id": "/m/0frqm"}, + {"id": 65, "name": "Winter melon", "freebase_id": "/m/02cvgx"}, + {"id": 66, "name": "Teapot", "freebase_id": "/m/01fh4r"}, + {"id": 67, "name": "Camel", "freebase_id": "/m/01x_v"}, + {"id": 68, "name": "Beaker", "freebase_id": "/m/0d20w4"}, + {"id": 69, "name": "Brown bear", "freebase_id": "/m/01dxs"}, + {"id": 70, "name": "Toilet", "freebase_id": "/m/09g1w"}, + {"id": 71, "name": "Teddy bear", "freebase_id": "/m/0kmg4"}, + {"id": 72, "name": "Briefcase", "freebase_id": "/m/0584n8"}, + {"id": 73, "name": "Stop sign", "freebase_id": "/m/02pv19"}, + {"id": 74, "name": "Tiger", "freebase_id": "/m/07dm6"}, + {"id": 75, "name": "Cabbage", "freebase_id": "/m/0fbw6"}, + {"id": 76, "name": "Giraffe", "freebase_id": "/m/03bk1"}, + {"id": 77, "name": "Polar bear", "freebase_id": "/m/0633h"}, + {"id": 78, "name": "Shark", "freebase_id": "/m/0by6g"}, + {"id": 79, "name": "Rabbit", "freebase_id": "/m/06mf6"}, + {"id": 80, "name": "Swim cap", "freebase_id": "/m/04tn4x"}, + {"id": 81, "name": "Pressure cooker", "freebase_id": "/m/0h8ntjv"}, + {"id": 82, "name": "Kitchen knife", "freebase_id": "/m/058qzx"}, + {"id": 83, "name": "Submarine sandwich", "freebase_id": "/m/06pcq"}, + {"id": 84, "name": "Flashlight", "freebase_id": "/m/01kb5b"}, + {"id": 85, "name": "Penguin", "freebase_id": "/m/05z6w"}, + {"id": 86, "name": "Snake", "freebase_id": "/m/078jl"}, + {"id": 87, "name": "Zucchini", "freebase_id": "/m/027pcv"}, + {"id": 88, "name": "Bat", "freebase_id": "/m/01h44"}, + {"id": 89, "name": "Food processor", "freebase_id": "/m/03y6mg"}, + {"id": 90, "name": "Ostrich", "freebase_id": "/m/05n4y"}, + {"id": 91, "name": "Sea lion", "freebase_id": "/m/0gd36"}, + {"id": 92, "name": "Goldfish", "freebase_id": "/m/03fj2"}, + {"id": 93, "name": "Elephant", "freebase_id": "/m/0bwd_0j"}, + {"id": 94, "name": "Rocket", "freebase_id": "/m/09rvcxw"}, + {"id": 95, "name": "Mouse", "freebase_id": "/m/04rmv"}, + {"id": 96, "name": "Oyster", "freebase_id": "/m/0_cp5"}, + {"id": 97, "name": "Digital clock", "freebase_id": "/m/06_72j"}, + {"id": 98, "name": "Otter", "freebase_id": "/m/0cn6p"}, + {"id": 99, "name": "Dolphin", "freebase_id": "/m/02hj4"}, + {"id": 100, "name": "Punching bag", "freebase_id": "/m/0420v5"}, + {"id": 101, "name": "Corded phone", "freebase_id": "/m/0h8lkj8"}, + {"id": 102, "name": "Tennis racket", "freebase_id": "/m/0h8my_4"}, + {"id": 103, "name": "Pancake", "freebase_id": "/m/01dwwc"}, + {"id": 104, "name": "Mango", "freebase_id": "/m/0fldg"}, + {"id": 105, "name": "Crocodile", "freebase_id": "/m/09f_2"}, + {"id": 106, "name": "Waffle", "freebase_id": "/m/01dwsz"}, + {"id": 107, "name": "Computer mouse", "freebase_id": "/m/020lf"}, + {"id": 108, "name": "Kettle", "freebase_id": "/m/03s_tn"}, + {"id": 109, "name": "Tart", "freebase_id": "/m/02zvsm"}, + {"id": 110, "name": "Oven", "freebase_id": "/m/029bxz"}, + {"id": 111, "name": "Banana", "freebase_id": "/m/09qck"}, + {"id": 112, "name": "Cheetah", "freebase_id": "/m/0cd4d"}, + {"id": 113, "name": "Raven", "freebase_id": "/m/06j2d"}, + {"id": 114, "name": "Frying pan", "freebase_id": "/m/04v6l4"}, + {"id": 115, "name": "Pear", "freebase_id": "/m/061_f"}, + {"id": 116, "name": "Fox", "freebase_id": "/m/0306r"}, + {"id": 117, "name": "Skateboard", "freebase_id": "/m/06_fw"}, + {"id": 118, "name": "Rugby ball", "freebase_id": "/m/0wdt60w"}, + {"id": 119, "name": "Watermelon", "freebase_id": "/m/0kpqd"}, + {"id": 120, "name": "Flute", "freebase_id": "/m/0l14j_"}, + {"id": 121, "name": "Canary", "freebase_id": "/m/0ccs93"}, + {"id": 122, "name": "Door handle", "freebase_id": "/m/03c7gz"}, + {"id": 123, "name": "Saxophone", "freebase_id": "/m/06ncr"}, + {"id": 124, "name": "Burrito", "freebase_id": "/m/01j3zr"}, + {"id": 125, "name": "Suitcase", "freebase_id": "/m/01s55n"}, + {"id": 126, "name": "Roller skates", "freebase_id": "/m/02p3w7d"}, + {"id": 127, "name": "Dagger", "freebase_id": "/m/02gzp"}, + {"id": 128, "name": "Seat belt", "freebase_id": "/m/0dkzw"}, + {"id": 129, "name": "Washing machine", "freebase_id": "/m/0174k2"}, + {"id": 130, "name": "Jet ski", "freebase_id": "/m/01xs3r"}, + {"id": 131, "name": "Sombrero", "freebase_id": "/m/02jfl0"}, + {"id": 132, "name": "Pig", "freebase_id": "/m/068zj"}, + {"id": 133, "name": "Drinking straw", "freebase_id": "/m/03v5tg"}, + {"id": 134, "name": "Peach", "freebase_id": "/m/0dj6p"}, + {"id": 135, "name": "Tortoise", "freebase_id": "/m/011k07"}, + {"id": 136, "name": "Towel", "freebase_id": "/m/0162_1"}, + {"id": 137, "name": "Tablet computer", "freebase_id": "/m/0bh9flk"}, + {"id": 138, "name": "Cucumber", "freebase_id": "/m/015x4r"}, + {"id": 139, "name": "Mule", "freebase_id": "/m/0dbzx"}, + {"id": 140, "name": "Potato", "freebase_id": "/m/05vtc"}, + {"id": 141, "name": "Frog", "freebase_id": "/m/09ld4"}, + {"id": 142, "name": "Bear", "freebase_id": "/m/01dws"}, + {"id": 143, "name": "Lighthouse", "freebase_id": "/m/04h7h"}, + {"id": 144, "name": "Belt", "freebase_id": "/m/0176mf"}, + {"id": 145, "name": "Baseball bat", "freebase_id": "/m/03g8mr"}, + {"id": 146, "name": "Racket", "freebase_id": "/m/0dv9c"}, + {"id": 147, "name": "Sword", "freebase_id": "/m/06y5r"}, + {"id": 148, "name": "Bagel", "freebase_id": "/m/01fb_0"}, + {"id": 149, "name": "Goat", "freebase_id": 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"freebase_id": "/m/084rd"}, + {"id": 167, "name": "Broccoli", "freebase_id": "/m/0hkxq"}, + {"id": 168, "name": "Pitcher", "freebase_id": "/m/054fyh"}, + {"id": 169, "name": "Whiteboard", "freebase_id": "/m/02d9qx"}, + {"id": 170, "name": "Squirrel", "freebase_id": "/m/071qp"}, + {"id": 171, "name": "Jug", "freebase_id": "/m/08hvt4"}, + {"id": 172, "name": "Woodpecker", "freebase_id": "/m/01dy8n"}, + {"id": 173, "name": "Pizza", "freebase_id": "/m/0663v"}, + {"id": 174, "name": "Surfboard", "freebase_id": "/m/019w40"}, + {"id": 175, "name": "Sofa bed", "freebase_id": "/m/03m3pdh"}, + {"id": 176, "name": "Sheep", "freebase_id": "/m/07bgp"}, + {"id": 177, "name": "Candle", "freebase_id": "/m/0c06p"}, + {"id": 178, "name": "Muffin", "freebase_id": "/m/01tcjp"}, + {"id": 179, "name": "Cookie", "freebase_id": "/m/021mn"}, + {"id": 180, "name": "Apple", "freebase_id": "/m/014j1m"}, + {"id": 181, "name": "Chest of drawers", "freebase_id": "/m/05kyg_"}, + {"id": 182, "name": "Skull", "freebase_id": "/m/016m2d"}, + {"id": 183, "name": "Chicken", "freebase_id": "/m/09b5t"}, + {"id": 184, "name": "Loveseat", "freebase_id": "/m/0703r8"}, + {"id": 185, "name": "Baseball glove", "freebase_id": "/m/03grzl"}, + {"id": 186, "name": "Piano", "freebase_id": "/m/05r5c"}, + {"id": 187, "name": "Waste container", "freebase_id": "/m/0bjyj5"}, + {"id": 188, "name": "Barrel", "freebase_id": "/m/02zn6n"}, + {"id": 189, "name": "Swan", "freebase_id": "/m/0dftk"}, + {"id": 190, "name": "Taxi", "freebase_id": "/m/0pg52"}, + {"id": 191, "name": "Lemon", "freebase_id": "/m/09k_b"}, + {"id": 192, "name": "Pumpkin", "freebase_id": "/m/05zsy"}, + {"id": 193, "name": "Sparrow", "freebase_id": "/m/0h23m"}, + {"id": 194, "name": "Orange", "freebase_id": "/m/0cyhj_"}, + {"id": 195, "name": "Tank", "freebase_id": "/m/07cmd"}, + {"id": 196, "name": "Sandwich", "freebase_id": "/m/0l515"}, + {"id": 197, "name": "Coffee", "freebase_id": "/m/02vqfm"}, + {"id": 198, "name": "Juice", "freebase_id": "/m/01z1kdw"}, + {"id": 199, "name": "Coin", "freebase_id": "/m/0242l"}, + {"id": 200, "name": "Pen", "freebase_id": "/m/0k1tl"}, + {"id": 201, "name": "Watch", "freebase_id": "/m/0gjkl"}, + {"id": 202, "name": "Eagle", "freebase_id": "/m/09csl"}, + {"id": 203, "name": "Goose", "freebase_id": "/m/0dbvp"}, + {"id": 204, "name": "Falcon", "freebase_id": "/m/0f6wt"}, + {"id": 205, "name": "Christmas tree", "freebase_id": "/m/025nd"}, + {"id": 206, "name": "Sunflower", "freebase_id": "/m/0ftb8"}, + {"id": 207, "name": "Vase", "freebase_id": "/m/02s195"}, + {"id": 208, "name": "Football", "freebase_id": "/m/01226z"}, + {"id": 209, "name": "Canoe", "freebase_id": "/m/0ph39"}, + {"id": 210, "name": "High heels", "freebase_id": "/m/06k2mb"}, + {"id": 211, "name": "Spoon", "freebase_id": "/m/0cmx8"}, + {"id": 212, "name": "Mug", "freebase_id": "/m/02jvh9"}, + {"id": 213, "name": "Swimwear", "freebase_id": "/m/01gkx_"}, + {"id": 214, "name": "Duck", "freebase_id": "/m/09ddx"}, + {"id": 215, "name": "Cat", "freebase_id": "/m/01yrx"}, + {"id": 216, "name": "Tomato", "freebase_id": "/m/07j87"}, + {"id": 217, "name": "Cocktail", "freebase_id": "/m/024g6"}, + {"id": 218, "name": "Clock", "freebase_id": "/m/01x3z"}, + {"id": 219, "name": "Cowboy hat", "freebase_id": "/m/025rp__"}, + {"id": 220, "name": "Miniskirt", "freebase_id": "/m/01cmb2"}, + {"id": 221, "name": "Cattle", "freebase_id": "/m/01xq0k1"}, + {"id": 222, "name": "Strawberry", "freebase_id": "/m/07fbm7"}, + {"id": 223, "name": "Bronze sculpture", "freebase_id": "/m/01yx86"}, + {"id": 224, "name": "Pillow", "freebase_id": "/m/034c16"}, + {"id": 225, "name": "Squash", "freebase_id": "/m/0dv77"}, + {"id": 226, "name": "Traffic light", "freebase_id": "/m/015qff"}, + {"id": 227, "name": "Saucer", "freebase_id": "/m/03q5c7"}, + {"id": 228, "name": "Reptile", "freebase_id": "/m/06bt6"}, + {"id": 229, "name": "Cake", "freebase_id": "/m/0fszt"}, + {"id": 230, "name": "Plastic bag", "freebase_id": "/m/05gqfk"}, + {"id": 231, "name": "Studio couch", "freebase_id": "/m/026qbn5"}, + {"id": 232, "name": "Beer", "freebase_id": "/m/01599"}, + {"id": 233, "name": "Scarf", "freebase_id": "/m/02h19r"}, + {"id": 234, "name": "Coffee cup", "freebase_id": "/m/02p5f1q"}, + {"id": 235, "name": "Wine", "freebase_id": "/m/081qc"}, + {"id": 236, "name": "Mushroom", "freebase_id": "/m/052sf"}, + {"id": 237, "name": "Traffic sign", "freebase_id": "/m/01mqdt"}, + {"id": 238, "name": "Camera", "freebase_id": "/m/0dv5r"}, + {"id": 239, "name": "Rose", "freebase_id": "/m/06m11"}, + {"id": 240, "name": "Couch", "freebase_id": "/m/02crq1"}, + {"id": 241, "name": "Handbag", "freebase_id": "/m/080hkjn"}, + {"id": 242, "name": "Fedora", "freebase_id": "/m/02fq_6"}, + {"id": 243, "name": "Sock", "freebase_id": "/m/01nq26"}, + {"id": 244, "name": "Computer keyboard", "freebase_id": "/m/01m2v"}, + {"id": 245, "name": "Mobile phone", "freebase_id": "/m/050k8"}, + {"id": 246, "name": "Ball", "freebase_id": "/m/018xm"}, + {"id": 247, "name": "Balloon", "freebase_id": "/m/01j51"}, + {"id": 248, "name": "Horse", "freebase_id": "/m/03k3r"}, + {"id": 249, "name": "Boot", "freebase_id": "/m/01b638"}, + {"id": 250, "name": "Fish", "freebase_id": "/m/0ch_cf"}, + {"id": 251, "name": "Backpack", "freebase_id": "/m/01940j"}, + {"id": 252, "name": "Skirt", "freebase_id": "/m/02wv6h6"}, + {"id": 253, "name": "Van", "freebase_id": "/m/0h2r6"}, + {"id": 254, "name": "Bread", "freebase_id": "/m/09728"}, + {"id": 255, "name": "Glove", "freebase_id": "/m/0174n1"}, + {"id": 256, "name": "Dog", "freebase_id": "/m/0bt9lr"}, + {"id": 257, "name": "Airplane", "freebase_id": "/m/0cmf2"}, + {"id": 258, "name": "Motorcycle", "freebase_id": "/m/04_sv"}, + {"id": 259, "name": "Drink", "freebase_id": "/m/0271t"}, + {"id": 260, "name": "Book", "freebase_id": "/m/0bt_c3"}, + {"id": 261, "name": "Train", "freebase_id": "/m/07jdr"}, + {"id": 262, "name": "Flower", "freebase_id": "/m/0c9ph5"}, + {"id": 263, "name": "Carnivore", "freebase_id": "/m/01lrl"}, + {"id": 264, "name": "Human ear", "freebase_id": "/m/039xj_"}, + {"id": 265, "name": "Toy", "freebase_id": "/m/0138tl"}, + {"id": 266, "name": "Box", "freebase_id": "/m/025dyy"}, + {"id": 267, "name": "Truck", "freebase_id": "/m/07r04"}, + {"id": 268, "name": "Wheel", "freebase_id": "/m/083wq"}, + {"id": 269, "name": "Aircraft", "freebase_id": "/m/0k5j"}, + {"id": 270, "name": "Bus", "freebase_id": "/m/01bjv"}, + {"id": 271, "name": "Human mouth", "freebase_id": "/m/0283dt1"}, + {"id": 272, "name": "Sculpture", "freebase_id": "/m/06msq"}, + {"id": 273, "name": "Shirt", "freebase_id": "/m/01n4qj"}, + {"id": 274, "name": "Hat", "freebase_id": "/m/02dl1y"}, + {"id": 275, "name": "Vehicle registration plate", "freebase_id": "/m/01jfm_"}, + {"id": 276, "name": "Guitar", "freebase_id": "/m/0342h"}, + {"id": 277, "name": "Sun hat", "freebase_id": "/m/02wbtzl"}, + {"id": 278, "name": "Bottle", "freebase_id": "/m/04dr76w"}, + {"id": 279, "name": "Luggage and bags", "freebase_id": "/m/0hf58v5"}, + {"id": 280, "name": "Trousers", "freebase_id": "/m/07mhn"}, + {"id": 281, "name": "Bicycle wheel", "freebase_id": "/m/01bqk0"}, + {"id": 282, "name": "Suit", "freebase_id": "/m/01xyhv"}, + {"id": 283, "name": "Bowl", "freebase_id": "/m/04kkgm"}, + {"id": 284, "name": "Man", "freebase_id": "/m/04yx4"}, + {"id": 285, "name": "Flowerpot", "freebase_id": "/m/0fm3zh"}, + {"id": 286, "name": "Laptop", "freebase_id": "/m/01c648"}, + {"id": 287, "name": "Boy", "freebase_id": "/m/01bl7v"}, + {"id": 288, "name": "Picture frame", "freebase_id": "/m/06z37_"}, + {"id": 289, "name": "Bird", "freebase_id": "/m/015p6"}, + {"id": 290, "name": "Car", "freebase_id": "/m/0k4j"}, + {"id": 291, "name": "Shorts", "freebase_id": "/m/01bfm9"}, + {"id": 292, "name": "Woman", "freebase_id": "/m/03bt1vf"}, + {"id": 293, "name": "Platter", "freebase_id": "/m/099ssp"}, + {"id": 294, "name": "Tie", "freebase_id": "/m/01rkbr"}, + {"id": 295, "name": "Girl", "freebase_id": "/m/05r655"}, + {"id": 296, "name": "Skyscraper", "freebase_id": "/m/079cl"}, + {"id": 297, "name": "Person", "freebase_id": "/m/01g317"}, + {"id": 298, "name": "Flag", "freebase_id": "/m/03120"}, + {"id": 299, "name": "Jeans", "freebase_id": "/m/0fly7"}, + {"id": 300, "name": "Dress", "freebase_id": "/m/01d40f"}, + ], + "kitti": [ + {"id": 24, "name": "person"}, + {"id": 25, "name": "rider"}, + {"id": 26, "name": "car"}, + {"id": 27, "name": "truck"}, + {"id": 28, "name": "bus"}, + {"id": 31, "name": "train"}, + {"id": 32, "name": "motorcycle"}, + {"id": 33, "name": "bicycle"}, + ], + "wilddash": [ + {"id": 1, "name": "ego vehicle"}, + {"id": 24, "name": "person"}, + {"id": 25, "name": "rider"}, + {"id": 26, "name": "car"}, + {"id": 27, "name": "truck"}, + {"id": 28, "name": "bus"}, + {"id": 29, "name": "caravan"}, + {"id": 30, "name": "trailer"}, + {"id": 31, "name": "train"}, + {"id": 32, "name": "motorcycle"}, + {"id": 33, "name": "bicycle"}, + {"id": 34, "name": "pickup"}, + {"id": 35, "name": "van"}, + ], +} diff --git a/ape/data/datasets/lvis_coco.py b/ape/data/datasets/lvis_coco.py new file mode 100644 index 0000000..df23ebb --- /dev/null +++ b/ape/data/datasets/lvis_coco.py @@ -0,0 +1,284 @@ +import logging +import os + +import pycocotools.mask as mask_util + +from detectron2.data import DatasetCatalog, MetadataCatalog +from detectron2.data.datasets.builtin_meta import _get_coco_instances_meta +from detectron2.data.datasets.lvis_v0_5_categories import LVIS_CATEGORIES as LVIS_V0_5_CATEGORIES +from detectron2.data.datasets.lvis_v1_categories import LVIS_CATEGORIES as LVIS_V1_CATEGORIES +from detectron2.structures import BoxMode +from detectron2.utils.file_io import PathManager +from fvcore.common.timer import Timer + +from .lvis_v1_coco_category_image_count import LVIS_V1_COCO_CATEGORY_IMAGE_COUNT + +""" +This file contains functions to parse LVIS-format annotations into dicts in the +"Detectron2 format". +""" + +logger = logging.getLogger(__name__) + +__all__ = ["custom_load_lvis_json", "custom_register_lvis_instances"] + + +def custom_register_lvis_instances(name, metadata, json_file, image_root): + """ + Register a dataset in LVIS's json annotation format for instance detection and segmentation. + + Args: + name (str): a name that identifies the dataset, e.g. "lvis_v0.5_train". + metadata (dict): extra metadata associated with this dataset. It can be an empty dict. + json_file (str): path to the json instance annotation file. + image_root (str or path-like): directory which contains all the images. + """ + DatasetCatalog.register(name, lambda: custom_load_lvis_json(json_file, image_root, name)) + MetadataCatalog.get(name).set( + json_file=json_file, image_root=image_root, evaluator_type="lvis", **metadata + ) + + +def custom_load_lvis_json(json_file, image_root, dataset_name=None, extra_annotation_keys=None): + """ + Load a json file in LVIS's annotation format. + + Args: + json_file (str): full path to the LVIS json annotation file. + image_root (str): the directory where the images in this json file exists. + dataset_name (str): the name of the dataset (e.g., "lvis_v0.5_train"). + If provided, this function will put "thing_classes" into the metadata + associated with this dataset. + extra_annotation_keys (list[str]): list of per-annotation keys that should also be + loaded into the dataset dict (besides "bbox", "bbox_mode", "category_id", + "segmentation"). The values for these keys will be returned as-is. + + Returns: + list[dict]: a list of dicts in Detectron2 standard format. (See + `Using Custom Datasets `_ ) + + Notes: + 1. This function does not read the image files. + The results do not have the "image" field. + """ + from lvis import LVIS + + json_file = PathManager.get_local_path(json_file) + + timer = Timer() + lvis_api = LVIS(json_file) + if timer.seconds() > 1: + logger.info("Loading {} takes {:.2f} seconds.".format(json_file, timer.seconds())) + + if dataset_name is not None: + meta = get_lvis_instances_meta(dataset_name) + MetadataCatalog.get(dataset_name).set(**meta) + + # sort indices for reproducible results + img_ids = sorted(lvis_api.imgs.keys()) + # imgs is a list of dicts, each looks something like: + # {'license': 4, + # 'url': 'http://farm6.staticflickr.com/5454/9413846304_881d5e5c3b_z.jpg', + # 'file_name': 'COCO_val2014_000000001268.jpg', + # 'height': 427, + # 'width': 640, + # 'date_captured': '2013-11-17 05:57:24', + # 'id': 1268} + imgs = lvis_api.load_imgs(img_ids) + # anns is a list[list[dict]], where each dict is an annotation + # record for an object. The inner list enumerates the objects in an image + # and the outer list enumerates over images. Example of anns[0]: + # [{'segmentation': [[192.81, + # 247.09, + # ... + # 219.03, + # 249.06]], + # 'area': 1035.749, + # 'image_id': 1268, + # 'bbox': [192.81, 224.8, 74.73, 33.43], + # 'category_id': 16, + # 'id': 42986}, + # ...] + anns = [lvis_api.img_ann_map[img_id] for img_id in img_ids] + + # Sanity check that each annotation has a unique id + ann_ids = [ann["id"] for anns_per_image in anns for ann in anns_per_image] + assert len(set(ann_ids)) == len(ann_ids), "Annotation ids in '{}' are not unique".format( + json_file + ) + + imgs_anns = list(zip(imgs, anns)) + + logger.info("Loaded {} images in the LVIS format from {}".format(len(imgs_anns), json_file)) + + if extra_annotation_keys: + logger.info( + "The following extra annotation keys will be loaded: {} ".format(extra_annotation_keys) + ) + else: + extra_annotation_keys = [] + + def get_file_name(img_root, img_dict): + # Determine the path including the split folder ("train2017", "val2017", "test2017") from + # the coco_url field. Example: + # 'coco_url': 'http://images.cocodataset.org/train2017/000000155379.jpg' + if "file_name" in img_dict: + file_name = img_dict["file_name"] + if img_dict["file_name"].startswith("COCO"): + file_name = file_name[-16:] + return os.path.join(image_root, file_name) + split_folder, file_name = img_dict["coco_url"].split("/")[-2:] + return os.path.join(img_root + split_folder, file_name) + + dataset_dicts = [] + + for (img_dict, anno_dict_list) in imgs_anns: + record = {} + record["file_name"] = get_file_name(image_root, img_dict) + record["height"] = img_dict["height"] + record["width"] = img_dict["width"] + record["not_exhaustive_category_ids"] = img_dict.get("not_exhaustive_category_ids", []) + record["neg_category_ids"] = img_dict.get("neg_category_ids", []) + record["pos_category_ids"] = img_dict.get("pos_category_ids", []) + if dataset_name is not None and "thing_dataset_id_to_contiguous_id" in meta: + record["neg_category_ids"] = [ + meta["thing_dataset_id_to_contiguous_id"][x] for x in record["neg_category_ids"] + ] + record["pos_category_ids"] = [ + meta["thing_dataset_id_to_contiguous_id"][x] for x in record["pos_category_ids"] + ] + else: + record["neg_category_ids"] = [x - 1 for x in record["neg_category_ids"]] + record["pos_category_ids"] = [x - 1 for x in record["pos_category_ids"]] + if "captions" in img_dict: + record["captions"] = img_dict["captions"] + if "caption_features" in img_dict: + record["caption_features"] = img_dict["caption_features"] + image_id = record["image_id"] = img_dict["id"] + + objs = [] + for anno in anno_dict_list: + assert anno["image_id"] == image_id + if anno.get("iscrowd", 0) > 0: + continue + # Check that the image_id in this annotation is the same as + # the image_id we're looking at. + # This fails only when the data parsing logic or the annotation file is buggy. + assert anno["image_id"] == image_id + obj = {"bbox": anno["bbox"], "bbox_mode": BoxMode.XYWH_ABS} + # LVIS data loader can be used to load COCO dataset categories. In this case `meta` + # variable will have a field with COCO-specific category mapping. + if dataset_name is not None and "thing_dataset_id_to_contiguous_id" in meta: + obj["category_id"] = meta["thing_dataset_id_to_contiguous_id"][anno["category_id"]] + else: + obj["category_id"] = anno["category_id"] - 1 # Convert 1-indexed to 0-indexed + # segm = anno["segmentation"] # list[list[float]] + # # filter out invalid polygons (< 3 points) + # valid_segm = [poly for poly in segm if len(poly) % 2 == 0 and len(poly) >= 6] + # assert len(segm) == len( + # valid_segm + # ), "Annotation contains an invalid polygon with < 3 points" + # assert len(segm) > 0 + # obj["segmentation"] = segm + segm = anno.get("segmentation", None) + if segm: # either list[list[float]] or dict(RLE) + if isinstance(segm, dict): + if isinstance(segm["counts"], list): + # convert to compressed RLE + segm = mask_util.frPyObjects(segm, *segm["size"]) + else: + # filter out invalid polygons (< 3 points) + segm = [poly for poly in segm if len(poly) % 2 == 0 and len(poly) >= 6] + if len(segm) == 0: + num_instances_without_valid_segmentation += 1 + continue # ignore this instance + obj["segmentation"] = segm + + phrase = anno.get("phrase", None) + if phrase: + obj["phrase"] = phrase + + for extra_ann_key in extra_annotation_keys: + obj[extra_ann_key] = anno[extra_ann_key] + objs.append(obj) + record["annotations"] = objs + dataset_dicts.append(record) + + return dataset_dicts + + +def get_lvis_instances_meta(dataset_name): + """ + Load LVIS metadata. + + Args: + dataset_name (str): LVIS dataset name without the split name (e.g., "lvis_v0.5"). + + Returns: + dict: LVIS metadata with keys: thing_classes + """ + if "cocofied" in dataset_name: + return _get_coco_instances_meta() + if "v0.5" in dataset_name: + return _get_lvis_instances_meta_v0_5() + elif "v1" in dataset_name: + return _get_lvis_instances_meta_v1() + logger.info("No built-in metadata for dataset {}".format(dataset_name)) + return {} + raise ValueError("No built-in metadata for dataset {}".format(dataset_name)) + + +def _get_lvis_instances_meta_v0_5(): + assert len(LVIS_V0_5_CATEGORIES) == 1230 + cat_ids = [k["id"] for k in LVIS_V0_5_CATEGORIES] + assert min(cat_ids) == 1 and max(cat_ids) == len( + cat_ids + ), "Category ids are not in [1, #categories], as expected" + # Ensure that the category list is sorted by id + lvis_categories = sorted(LVIS_V0_5_CATEGORIES, key=lambda x: x["id"]) + thing_classes = [k["synonyms"][0] for k in lvis_categories] + meta = {"thing_classes": thing_classes} + return meta + + +def _get_lvis_instances_meta_v1(): + assert len(LVIS_V1_CATEGORIES) == 1203 + cat_ids = [k["id"] for k in LVIS_V1_CATEGORIES] + assert min(cat_ids) == 1 and max(cat_ids) == len( + cat_ids + ), "Category ids are not in [1, #categories], as expected" + # Ensure that the category list is sorted by id + lvis_categories = sorted(LVIS_V1_CATEGORIES, key=lambda x: x["id"]) + thing_classes = [k["synonyms"][0] for k in lvis_categories] + meta = {"thing_classes": thing_classes, "class_image_count": LVIS_V1_COCO_CATEGORY_IMAGE_COUNT} + return meta + + +_PREDEFINED_SPLITS_LVIS = { + "lvis_v1_train+coco": { + "lvis_v1_train+coco": ("coco/", "lvis/lvis_v1_train+coco_mask.json"), + }, + "lvis_v1_val+coco": { + "lvis_v1_val+coco": ("coco/", "lvis/lvis_v1_val+coco_mask.json"), + }, + "lvis_v1_minival": { + "lvis_v1_minival": ("coco/", "lvis/lvis_v1_minival_inserted_image_name.json"), + }, +} + + +def register_all_lvis_coco(root): + for dataset_name, splits_per_dataset in _PREDEFINED_SPLITS_LVIS.items(): + for key, (image_root, json_file) in splits_per_dataset.items(): + custom_register_lvis_instances( + key, + get_lvis_instances_meta(dataset_name), + os.path.join(root, json_file) if "://" not in json_file else json_file, + os.path.join(root, image_root), + ) + + +if __name__.endswith(".lvis_coco"): + # Assume pre-defined datasets live in `./datasets`. + _root = os.getenv("DETECTRON2_DATASETS", "datasets") + register_all_lvis_coco(_root) diff --git a/ape/data/datasets/lvis_coco_panoptic.py b/ape/data/datasets/lvis_coco_panoptic.py new file mode 100644 index 0000000..e4174f6 --- /dev/null +++ b/ape/data/datasets/lvis_coco_panoptic.py @@ -0,0 +1,192 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import copy +import json +import os + +from detectron2.data import DatasetCatalog, MetadataCatalog +from detectron2.data.datasets.builtin_meta import COCO_CATEGORIES +from detectron2.data.datasets.coco import load_sem_seg +from detectron2.utils.file_io import PathManager + +from .lvis_coco import custom_load_lvis_json, get_lvis_instances_meta + +__all__ = ["register_lvis_panoptic_separated"] + + +def register_lvis_panoptic_separated( + name, metadata, image_root, panoptic_root, panoptic_json, sem_seg_root, instances_json +): + """ + Register a "separated" version of COCO panoptic segmentation dataset named `name`. + The annotations in this registered dataset will contain both instance annotations and + semantic annotations, each with its own contiguous ids. Hence it's called "separated". + + It follows the setting used by the PanopticFPN paper: + + 1. The instance annotations directly come from polygons in the COCO + instances annotation task, rather than from the masks in the COCO panoptic annotations. + + The two format have small differences: + Polygons in the instance annotations may have overlaps. + The mask annotations are produced by labeling the overlapped polygons + with depth ordering. + + 2. The semantic annotations are converted from panoptic annotations, where + all "things" are assigned a semantic id of 0. + All semantic categories will therefore have ids in contiguous + range [1, #stuff_categories]. + + This function will also register a pure semantic segmentation dataset + named ``name + '_stuffonly'``. + + Args: + name (str): the name that identifies a dataset, + e.g. "coco_2017_train_panoptic" + metadata (dict): extra metadata associated with this dataset. + image_root (str): directory which contains all the images + panoptic_root (str): directory which contains panoptic annotation images + panoptic_json (str): path to the json panoptic annotation file + sem_seg_root (str): directory which contains all the ground truth segmentation annotations. + instances_json (str): path to the json instance annotation file + """ + panoptic_name = name + "_separated" + split_folder = sem_seg_root.split("_")[-1] # datasets/coco/panoptic_stuff_train2017 + DatasetCatalog.register( + panoptic_name, + lambda: merge_to_panoptic( + custom_load_lvis_json(instances_json, image_root, panoptic_name), + load_sem_seg(sem_seg_root, os.path.join(image_root, split_folder)), + ), + ) + MetadataCatalog.get(panoptic_name).set( + panoptic_root=panoptic_root, + image_root=image_root, + panoptic_json=panoptic_json, + sem_seg_root=sem_seg_root, + json_file=instances_json, # TODO rename + evaluator_type="coco_panoptic_seg", + ignore_label=255, + **metadata, + ) + + semantic_name = name + "_stuffonly" + DatasetCatalog.register(semantic_name, lambda: load_sem_seg(sem_seg_root, image_root)) + MetadataCatalog.get(semantic_name).set( + sem_seg_root=sem_seg_root, + image_root=image_root, + evaluator_type="sem_seg", + ignore_label=255, + **metadata, + ) + + +def merge_to_panoptic(detection_dicts, sem_seg_dicts): + """ + Create dataset dicts for panoptic segmentation, by + merging two dicts using "file_name" field to match their entries. + + Args: + detection_dicts (list[dict]): lists of dicts for object detection or instance segmentation. + sem_seg_dicts (list[dict]): lists of dicts for semantic segmentation. + + Returns: + list[dict] (one per input image): Each dict contains all (key, value) pairs from dicts in + both detection_dicts and sem_seg_dicts that correspond to the same image. + The function assumes that the same key in different dicts has the same value. + """ + results = [] + sem_seg_file_to_entry = {x["file_name"]: x for x in sem_seg_dicts} + assert len(sem_seg_file_to_entry) > 0 + + for det_dict in detection_dicts: + dic = copy.copy(det_dict) + dic.update(sem_seg_file_to_entry[dic["file_name"]]) + results.append(dic) + return results + + +def _get_builtin_metadata(dataset_name): + if dataset_name == "lvis_panoptic_separated": + return _get_lvis_panoptic_separated_meta() + + raise KeyError("No built-in metadata for dataset {}".format(dataset_name)) + + +def _get_lvis_panoptic_separated_meta(): + """ + Returns metadata for "separated" version of the panoptic segmentation dataset. + """ + stuff_ids = [k["id"] for k in COCO_CATEGORIES if k["isthing"] == 0] + assert len(stuff_ids) == 53, len(stuff_ids) + + # For semantic segmentation, this mapping maps from contiguous stuff id + # (in [0, 53], used in models) to ids in the dataset (used for processing results) + # The id 0 is mapped to an extra category "thing". + stuff_dataset_id_to_contiguous_id = {k: i + 1 for i, k in enumerate(stuff_ids)} + # When converting COCO panoptic annotations to semantic annotations + # We label the "thing" category to 0 + stuff_dataset_id_to_contiguous_id[0] = 0 + + # 54 names for COCO stuff categories (including "things") + stuff_classes = ["things"] + [ + k["name"].replace("-other", "").replace("-merged", "").replace("-stuff", "") + for k in COCO_CATEGORIES + if k["isthing"] == 0 + ] + + # NOTE: I randomly picked a color for things + stuff_colors = [[82, 18, 128]] + [k["color"] for k in COCO_CATEGORIES if k["isthing"] == 0] + ret = { + "stuff_dataset_id_to_contiguous_id": stuff_dataset_id_to_contiguous_id, + "stuff_classes": stuff_classes, + "stuff_colors": stuff_colors, + } + ret.update(get_lvis_instances_meta("v1")) + return ret + + +_PREDEFINED_SPLITS_LVIS_PANOPTIC = { + "lvis_v1_train+coco_panoptic": ( + # This is the original panoptic annotation directory + "coco/panoptic_train2017", + "coco/annotations/panoptic_train2017.json", + # This directory contains semantic annotations that are + # converted from panoptic annotations. + # It is used by PanopticFPN. + # You can use the script at detectron2/datasets/prepare_panoptic_fpn.py + # to create these directories. + "coco/panoptic_stuff_train2017", + ), + "lvis_v1_val+coco_panoptic": ( + "coco/panoptic_val2017", + "coco/annotations/panoptic_val2017.json", + "coco/panoptic_stuff_val2017", + ), +} + + +def register_all_lvis_coco_panoptic(root): + for ( + prefix, + (panoptic_root, panoptic_json, semantic_root), + ) in _PREDEFINED_SPLITS_LVIS_PANOPTIC.items(): + prefix_instances = prefix[: -len("_panoptic")] + instances_meta = MetadataCatalog.get(prefix_instances) + image_root, instances_json = instances_meta.image_root, instances_meta.json_file + # The "separated" version of COCO panoptic segmentation dataset, + # e.g. used by Panoptic FPN + register_lvis_panoptic_separated( + prefix, + _get_builtin_metadata("lvis_panoptic_separated"), + image_root, + os.path.join(root, panoptic_root), + os.path.join(root, panoptic_json), + os.path.join(root, semantic_root), + instances_json, + ) + + +if __name__.endswith(".lvis_coco_panoptic"): + # Assume pre-defined datasets live in `./datasets`. + _root = os.getenv("DETECTRON2_DATASETS", "datasets") + register_all_lvis_coco_panoptic(_root) diff --git a/ape/data/datasets/lvis_v1_coco_category_image_count.py b/ape/data/datasets/lvis_v1_coco_category_image_count.py new file mode 100644 index 0000000..c0d0ea7 --- /dev/null +++ b/ape/data/datasets/lvis_v1_coco_category_image_count.py @@ -0,0 +1,20 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Autogen with +# with open("lvis_v1_train.json", "r") as f: +# a = json.load(f) +# c = a["categories"] +# for x in c: +# del x["name"] +# del x["instance_count"] +# del x["def"] +# del x["synonyms"] +# del x["frequency"] +# del x["synset"] +# LVIS_CATEGORY_IMAGE_COUNT = repr(c) + " # noqa" +# with open("/tmp/lvis_category_image_count.py", "wt") as f: +# f.write(f"LVIS_CATEGORY_IMAGE_COUNT = {LVIS_CATEGORY_IMAGE_COUNT}") +# Then paste the contents of that file below + +# fmt: off +LVIS_V1_COCO_CATEGORY_IMAGE_COUNT = [{'image_count': 64, 'id': 1}, {'image_count': 364, 'id': 2}, {'image_count': 2596, 'id': 3}, {'image_count': 149, 'id': 4}, {'image_count': 29, 'id': 5}, {'image_count': 26, 'id': 6}, {'image_count': 59, 'id': 7}, {'image_count': 22, 'id': 8}, {'image_count': 12, 'id': 9}, {'image_count': 28, 'id': 10}, {'image_count': 505, 'id': 11}, {'image_count': 1626, 'id': 12}, {'image_count': 4, 'id': 13}, {'image_count': 10, 'id': 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'id': 1156}, {'image_count': 1, 'id': 1157}, {'image_count': 1, 'id': 1158}, {'image_count': 10, 'id': 1159}, {'image_count': 54, 'id': 1160}, {'image_count': 1923, 'id': 1161}, {'image_count': 630, 'id': 1162}, {'image_count': 31, 'id': 1163}, {'image_count': 69, 'id': 1164}, {'image_count': 7, 'id': 1165}, {'image_count': 11, 'id': 1166}, {'image_count': 1, 'id': 1167}, {'image_count': 30, 'id': 1168}, {'image_count': 50, 'id': 1169}, {'image_count': 45, 'id': 1170}, {'image_count': 28, 'id': 1171}, {'image_count': 114, 'id': 1172}, {'image_count': 193, 'id': 1173}, {'image_count': 21, 'id': 1174}, {'image_count': 91, 'id': 1175}, {'image_count': 31, 'id': 1176}, {'image_count': 1469, 'id': 1177}, {'image_count': 1924, 'id': 1178}, {'image_count': 87, 'id': 1179}, {'image_count': 77, 'id': 1180}, {'image_count': 11, 'id': 1181}, {'image_count': 47, 'id': 1182}, {'image_count': 21, 'id': 1183}, {'image_count': 47, 'id': 1184}, {'image_count': 70, 'id': 1185}, {'image_count': 1838, 'id': 1186}, {'image_count': 19, 'id': 1187}, {'image_count': 531, 'id': 1188}, {'image_count': 11, 'id': 1189}, {'image_count': 2179, 'id': 1190}, {'image_count': 113, 'id': 1191}, {'image_count': 26, 'id': 1192}, {'image_count': 5, 'id': 1193}, {'image_count': 56, 'id': 1194}, {'image_count': 73, 'id': 1195}, {'image_count': 32, 'id': 1196}, {'image_count': 128, 'id': 1197}, {'image_count': 623, 'id': 1198}, {'image_count': 12, 'id': 1199}, {'image_count': 52, 'id': 1200}, {'image_count': 11, 'id': 1201}, {'image_count': 1687, 'id': 1202}, {'image_count': 81, 'id': 1203}] # noqa +# fmt: on diff --git a/ape/data/datasets/objects365.py b/ape/data/datasets/objects365.py new file mode 100644 index 0000000..867de66 --- /dev/null +++ b/ape/data/datasets/objects365.py @@ -0,0 +1,799 @@ +import os + +from detectron2.data.datasets.register_coco import register_coco_instances + +OBJECTS365_CATEGORIES_FIXNAME = [ + {"id": 1, "name": "Person"}, + {"id": 2, "name": "Sneakers"}, + {"id": 3, "name": "Chair"}, + {"id": 4, "name": "Other Shoes"}, + {"id": 5, "name": "Hat"}, + {"id": 6, "name": "Car"}, + {"id": 7, "name": "Lamp"}, + {"id": 8, "name": "Glasses"}, + {"id": 9, "name": "Bottle"}, + {"id": 10, "name": "Desk"}, + {"id": 11, "name": "Cup"}, + {"id": 12, "name": "Street Lights"}, + {"id": 13, "name": "Cabinet/shelf"}, + {"id": 14, "name": "Handbag/Satchel"}, + {"id": 15, "name": "Bracelet"}, + {"id": 16, "name": "Plate"}, + {"id": 17, "name": "Picture/Frame"}, + {"id": 18, "name": "Helmet"}, + {"id": 19, "name": "Book"}, + {"id": 20, "name": "Gloves"}, + {"id": 21, "name": "Storage box"}, + {"id": 22, "name": "Boat"}, + {"id": 23, "name": "Leather Shoes"}, + {"id": 24, "name": "Flower"}, + {"id": 25, "name": "Bench"}, + {"id": 26, "name": "Potted Plant"}, + {"id": 27, "name": "Bowl/Basin"}, + {"id": 28, "name": "Flag"}, + {"id": 29, "name": "Pillow"}, + {"id": 30, "name": "Boots"}, + {"id": 31, "name": "Vase"}, + {"id": 32, "name": "Microphone"}, + {"id": 33, "name": "Necklace"}, + {"id": 34, "name": "Ring"}, + {"id": 35, "name": "SUV"}, + {"id": 36, "name": "Wine Glass"}, + {"id": 37, "name": "Belt"}, + {"id": 38, "name": "Monitor/TV"}, + {"id": 39, "name": "Backpack"}, + {"id": 40, "name": "Umbrella"}, + {"id": 41, "name": "Traffic Light"}, + {"id": 42, "name": "Speaker"}, + {"id": 43, "name": "Watch"}, + {"id": 44, "name": "Tie"}, + {"id": 45, "name": "Trash bin Can"}, + {"id": 46, "name": "Slippers"}, + {"id": 47, "name": "Bicycle"}, + {"id": 48, "name": "Stool"}, + {"id": 49, "name": "Barrel/bucket"}, + {"id": 50, "name": "Van"}, + {"id": 51, "name": "Couch"}, + {"id": 52, "name": "Sandals"}, + {"id": 53, "name": "Basket"}, + {"id": 54, "name": "Drum"}, + {"id": 55, "name": "Pen/Pencil"}, + {"id": 56, "name": "Bus"}, + {"id": 57, "name": "Wild Bird"}, + {"id": 58, "name": "High Heels"}, + {"id": 59, "name": "Motorcycle"}, + {"id": 60, "name": "Guitar"}, + {"id": 61, "name": "Carpet"}, + {"id": 62, "name": "Cell Phone"}, + {"id": 63, "name": "Bread"}, + {"id": 64, "name": "Camera"}, + {"id": 65, "name": "Canned"}, + {"id": 66, "name": "Truck"}, + {"id": 67, "name": "Traffic cone"}, + {"id": 68, "name": "Cymbal"}, + {"id": 69, "name": "Lifesaver"}, + {"id": 70, "name": "Towel"}, + {"id": 71, "name": "Stuffed Toy"}, + {"id": 72, "name": "Candle"}, + {"id": 73, "name": "Sailboat"}, + {"id": 74, "name": "Laptop"}, + {"id": 75, "name": "Awning"}, + {"id": 76, "name": "Bed"}, + {"id": 77, "name": "Faucet"}, + {"id": 78, "name": "Tent"}, + {"id": 79, "name": "Horse"}, + {"id": 80, "name": "Mirror"}, + {"id": 81, "name": "Power outlet"}, + {"id": 82, "name": "Sink"}, + {"id": 83, "name": "Apple"}, + {"id": 84, "name": "Air Conditioner"}, + {"id": 85, "name": "Knife"}, + {"id": 86, "name": "Hockey Stick"}, + {"id": 87, "name": "Paddle"}, + {"id": 88, "name": "Pickup Truck"}, + {"id": 89, "name": "Fork"}, + {"id": 90, "name": "Traffic Sign"}, + {"id": 91, "name": "Ballon"}, + {"id": 92, "name": "Tripod"}, + {"id": 93, "name": "Dog"}, + {"id": 94, "name": "Spoon"}, + {"id": 95, "name": "Clock"}, + {"id": 96, "name": "Pot"}, + {"id": 97, "name": "Cow"}, + {"id": 98, "name": "Cake"}, + {"id": 99, "name": "Dining Table"}, + {"id": 100, "name": "Sheep"}, + {"id": 101, "name": "Hanger"}, + {"id": 102, "name": "Blackboard/Whiteboard"}, + {"id": 103, "name": "Napkin"}, + {"id": 104, "name": "Other Fish"}, + {"id": 105, "name": "Orange/Tangerine"}, + {"id": 106, "name": "Toiletry"}, + {"id": 107, "name": "Keyboard"}, + {"id": 108, "name": "Tomato"}, + {"id": 109, "name": "Lantern"}, + {"id": 110, "name": "Machinery Vehicle"}, + {"id": 111, "name": "Fan"}, + {"id": 112, "name": "Green Vegetables"}, + {"id": 113, "name": "Banana"}, + {"id": 114, "name": "Baseball Glove"}, + {"id": 115, "name": "Airplane"}, + {"id": 116, "name": "Mouse"}, + {"id": 117, "name": "Train"}, + {"id": 118, "name": "Pumpkin"}, + {"id": 119, "name": "Soccer"}, + {"id": 120, "name": "Skiboard"}, + {"id": 121, "name": "Luggage"}, + {"id": 122, "name": "Nightstand"}, + {"id": 123, "name": "Teapot"}, + {"id": 124, "name": "Telephone"}, + {"id": 125, "name": "Trolley"}, + {"id": 126, "name": "Head Phone"}, + {"id": 127, "name": "Sports Car"}, + {"id": 128, "name": "Stop Sign"}, + {"id": 129, "name": "Dessert"}, + {"id": 130, "name": "Scooter"}, + {"id": 131, "name": "Stroller"}, + {"id": 132, "name": "Crane"}, + {"id": 133, "name": "Remote"}, + {"id": 134, "name": "Refrigerator"}, + {"id": 135, "name": "Oven"}, + {"id": 136, "name": "Lemon"}, + {"id": 137, "name": "Duck"}, + {"id": 138, "name": "Baseball Bat"}, + {"id": 139, "name": "Surveillance Camera"}, + {"id": 140, "name": "Cat"}, + {"id": 141, "name": "Jug"}, + {"id": 142, "name": "Broccoli"}, + {"id": 143, "name": "Piano"}, + {"id": 144, "name": "Pizza"}, + {"id": 145, "name": "Elephant"}, + {"id": 146, "name": "Skateboard"}, + {"id": 147, "name": "Surfboard"}, + {"id": 148, "name": "Gun"}, + {"id": 149, "name": "Skating and Skiing shoes"}, + {"id": 150, "name": "Gas stove"}, + {"id": 151, "name": "Donut"}, + {"id": 152, "name": "Bow Tie"}, + {"id": 153, "name": "Carrot"}, + {"id": 154, "name": "Toilet"}, + {"id": 155, "name": "Kite"}, + {"id": 156, "name": "Strawberry"}, + {"id": 157, "name": "Other Balls"}, + {"id": 158, "name": "Shovel"}, + {"id": 159, "name": "Pepper"}, + {"id": 160, "name": "Computer Box"}, + {"id": 161, "name": "Toilet Paper"}, + {"id": 162, "name": "Cleaning Products"}, + {"id": 163, "name": "Chopsticks"}, + {"id": 164, "name": "Microwave"}, + {"id": 165, "name": "Pigeon"}, + {"id": 166, "name": "Baseball"}, + {"id": 167, "name": "Cutting/chopping Board"}, + {"id": 168, "name": "Coffee Table"}, + {"id": 169, "name": "Side Table"}, + {"id": 170, "name": "Scissors"}, + {"id": 171, "name": "Marker"}, + {"id": 172, "name": "Pie"}, + {"id": 173, "name": "Ladder"}, + {"id": 174, "name": "Snowboard"}, + {"id": 175, "name": "Cookies"}, + {"id": 176, "name": "Radiator"}, + {"id": 177, "name": "Fire Hydrant"}, + {"id": 178, "name": "Basketball"}, + {"id": 179, "name": "Zebra"}, + {"id": 180, "name": "Grape"}, + {"id": 181, "name": "Giraffe"}, + {"id": 182, "name": "Potato"}, + {"id": 183, "name": "Sausage"}, + {"id": 184, "name": "Tricycle"}, + {"id": 185, "name": "Violin"}, + {"id": 186, "name": "Egg"}, + {"id": 187, "name": "Fire Extinguisher"}, + {"id": 188, "name": "Candy"}, + {"id": 189, "name": "Fire Truck"}, + {"id": 190, "name": "Billards"}, + {"id": 191, "name": "Converter"}, + {"id": 192, "name": "Bathtub"}, + {"id": 193, "name": "Wheelchair"}, + {"id": 194, "name": "Golf Club"}, + {"id": 195, "name": "Briefcase"}, + {"id": 196, "name": "Cucumber"}, + {"id": 197, "name": "Cigar/Cigarette "}, + {"id": 198, "name": "Paint Brush"}, + {"id": 199, "name": "Pear"}, + {"id": 200, "name": "Heavy Truck"}, + {"id": 201, "name": "Hamburger"}, + {"id": 202, "name": "Extractor"}, + {"id": 203, "name": "Extension Cord"}, + {"id": 204, "name": "Tong"}, + {"id": 205, "name": "Tennis Racket"}, + {"id": 206, "name": "Folder"}, + {"id": 207, "name": "American Football"}, + {"id": 208, "name": "earphone"}, + {"id": 209, "name": "Mask"}, + {"id": 210, "name": "Kettle"}, + {"id": 211, "name": "Tennis"}, + {"id": 212, "name": "Ship"}, + {"id": 213, "name": "Swing"}, + {"id": 214, "name": "Coffee Machine"}, + {"id": 215, "name": "Slide"}, + {"id": 216, "name": "Carriage"}, + {"id": 217, "name": "Onion"}, + {"id": 218, "name": "Green beans"}, + {"id": 219, "name": "Projector"}, + {"id": 220, "name": "Frisbee"}, + {"id": 221, "name": "Washing Machine/Drying Machine"}, + {"id": 222, "name": "Chicken"}, + {"id": 223, "name": "Printer"}, + {"id": 224, "name": "Watermelon"}, + {"id": 225, "name": "Saxophone"}, + {"id": 226, "name": "Tissue"}, + {"id": 227, "name": "Toothbrush"}, + {"id": 228, "name": "Ice cream"}, + {"id": 229, "name": "Hot air balloon"}, + {"id": 230, "name": "Cello"}, + {"id": 231, "name": "French Fries"}, + {"id": 232, "name": "Scale"}, + {"id": 233, "name": "Trophy"}, + {"id": 234, "name": "Cabbage"}, + {"id": 235, "name": "Hot dog"}, + {"id": 236, "name": "Blender"}, + {"id": 237, "name": "Peach"}, + {"id": 238, "name": "Rice"}, + {"id": 239, "name": "Wallet/Purse"}, + {"id": 240, "name": "Volleyball"}, + {"id": 241, "name": "Deer"}, + {"id": 242, "name": "Goose"}, + {"id": 243, "name": "Tape"}, + {"id": 244, "name": "Tablet"}, + {"id": 245, "name": "Cosmetics"}, + {"id": 246, "name": "Trumpet"}, + {"id": 247, "name": "Pineapple"}, + {"id": 248, "name": "Golf Ball"}, + {"id": 249, "name": "Ambulance"}, + {"id": 250, "name": "Parking meter"}, + {"id": 251, "name": "Mango"}, + {"id": 252, "name": "Key"}, + {"id": 253, "name": "Hurdle"}, + {"id": 254, "name": "Fishing Rod"}, + {"id": 255, "name": "Medal"}, + {"id": 256, "name": "Flute"}, + {"id": 257, "name": "Brush"}, + {"id": 258, "name": "Penguin"}, + {"id": 259, "name": "Megaphone"}, + {"id": 260, "name": "Corn"}, + {"id": 261, "name": "Lettuce"}, + {"id": 262, "name": "Garlic"}, + {"id": 263, "name": "Swan"}, + {"id": 264, "name": "Helicopter"}, + {"id": 265, "name": "Green Onion"}, + {"id": 266, "name": "Sandwich"}, + {"id": 267, "name": "Nuts"}, + {"id": 268, "name": "Speed Limit Sign"}, + {"id": 269, "name": "Induction Cooker"}, + {"id": 270, "name": "Broom"}, + {"id": 271, "name": "Trombone"}, + {"id": 272, "name": "Plum"}, + {"id": 273, "name": "Rickshaw"}, + {"id": 274, "name": "Goldfish"}, + {"id": 275, "name": "Kiwi fruit"}, + {"id": 276, "name": "Router/modem"}, + {"id": 277, "name": "Poker Card"}, + {"id": 278, "name": "Toaster"}, + {"id": 279, "name": "Shrimp"}, + {"id": 280, "name": "Sushi"}, + {"id": 281, "name": "Cheese"}, + {"id": 282, "name": "Notepaper"}, + {"id": 283, "name": "Cherry"}, + {"id": 284, "name": "Pliers"}, + {"id": 285, "name": "CD"}, + {"id": 286, "name": "Pasta"}, + {"id": 287, "name": "Hammer"}, + {"id": 288, "name": "Cue"}, + {"id": 289, "name": "Avocado"}, + {"id": 290, "name": "Hami melon"}, + {"id": 291, "name": "Flask"}, + {"id": 292, "name": "Mushroom"}, + {"id": 293, "name": "Screwdriver"}, + {"id": 294, "name": "Soap"}, + {"id": 295, "name": "Recorder"}, + {"id": 296, "name": "Bear"}, + {"id": 297, "name": "Eggplant"}, + {"id": 298, "name": "Board Eraser"}, + {"id": 299, "name": "Coconut"}, + {"id": 300, "name": "Tape Measure/ Ruler"}, + {"id": 301, "name": "Pig"}, + {"id": 302, "name": "Showerhead"}, + {"id": 303, "name": "Globe"}, + {"id": 304, "name": "Chips"}, + {"id": 305, "name": "Steak"}, + {"id": 306, "name": "Crosswalk Sign"}, + {"id": 307, "name": "Stapler"}, + {"id": 308, "name": "Camel"}, + {"id": 309, "name": "Formula 1 "}, + {"id": 310, "name": "Pomegranate"}, + {"id": 311, "name": "Dishwasher"}, + {"id": 312, "name": "Crab"}, + {"id": 313, "name": "Hoverboard"}, + {"id": 314, "name": "Meatball"}, + {"id": 315, "name": "Rice Cooker"}, + {"id": 316, "name": "Tuba"}, + {"id": 317, "name": "Calculator"}, + {"id": 318, "name": "Papaya"}, + {"id": 319, "name": "Antelope"}, + {"id": 320, "name": "Parrot"}, + {"id": 321, "name": "Seal"}, + {"id": 322, "name": "Butterfly"}, + {"id": 323, "name": "Dumbbell"}, + {"id": 324, "name": "Donkey"}, + {"id": 325, "name": "Lion"}, + {"id": 326, "name": "Urinal"}, + {"id": 327, "name": "Dolphin"}, + {"id": 328, "name": "Electric Drill"}, + {"id": 329, "name": "Hair Dryer"}, + {"id": 330, "name": "Egg tart"}, + {"id": 331, "name": "Jellyfish"}, + {"id": 332, "name": "Treadmill"}, + {"id": 333, "name": "Lighter"}, + {"id": 334, "name": "Grapefruit"}, + {"id": 335, "name": "Game board"}, + {"id": 336, "name": "Mop"}, + {"id": 337, "name": "Radish"}, + {"id": 338, "name": "Baozi"}, + {"id": 339, "name": "Target"}, + {"id": 340, "name": "French"}, + {"id": 341, "name": "Spring Rolls"}, + {"id": 342, "name": "Monkey"}, + {"id": 343, "name": "Rabbit"}, + {"id": 344, "name": "Pencil Case"}, + {"id": 345, "name": "Yak"}, + {"id": 346, "name": "Red Cabbage"}, + {"id": 347, "name": "Binoculars"}, + {"id": 348, "name": "Asparagus"}, + {"id": 349, "name": "Barbell"}, + {"id": 350, "name": "Scallop"}, + {"id": 351, "name": "Noddles"}, + {"id": 352, "name": "Comb"}, + {"id": 353, "name": "Dumpling"}, + {"id": 354, "name": "Oyster"}, + {"id": 355, "name": "Table Tennis paddle"}, + {"id": 356, "name": "Cosmetics Brush/Eyeliner Pencil"}, + {"id": 357, "name": "Chainsaw"}, + {"id": 358, "name": "Eraser"}, + {"id": 359, "name": "Lobster"}, + {"id": 360, "name": "Durian"}, + {"id": 361, "name": "Okra"}, + {"id": 362, "name": "Lipstick"}, + {"id": 363, "name": "Cosmetics Mirror"}, + {"id": 364, "name": "Curling"}, + {"id": 365, "name": "Table Tennis "}, +] + +OBJECTS365_CATEGORIES = [ + {"id": 1, "name": "Person"}, + {"id": 2, "name": "Sneakers"}, + {"id": 3, "name": "Chair"}, + {"id": 4, "name": "Other Shoes"}, + {"id": 5, "name": "Hat"}, + {"id": 6, "name": "Car"}, + {"id": 7, "name": "Lamp"}, + {"id": 8, "name": "Glasses"}, + {"id": 9, "name": "Bottle"}, + {"id": 10, "name": "Desk"}, + {"id": 11, "name": "Cup"}, + {"id": 12, "name": "Street Lights"}, + {"id": 13, "name": "Cabinet/shelf"}, + {"id": 14, "name": "Handbag/Satchel"}, + {"id": 15, "name": "Bracelet"}, + {"id": 16, "name": "Plate"}, + {"id": 17, "name": "Picture/Frame"}, + {"id": 18, "name": "Helmet"}, + {"id": 19, "name": "Book"}, + {"id": 20, "name": "Gloves"}, + {"id": 21, "name": "Storage box"}, + {"id": 22, "name": "Boat"}, + {"id": 23, "name": "Leather Shoes"}, + {"id": 24, "name": "Flower"}, + {"id": 25, "name": "Bench"}, + {"id": 26, "name": "Potted Plant"}, + {"id": 27, "name": "Bowl/Basin"}, + {"id": 28, "name": "Flag"}, + {"id": 29, "name": "Pillow"}, + {"id": 30, "name": "Boots"}, + {"id": 31, "name": "Vase"}, + {"id": 32, "name": "Microphone"}, + {"id": 33, "name": "Necklace"}, + {"id": 34, "name": "Ring"}, + {"id": 35, "name": "SUV"}, + {"id": 36, "name": "Wine Glass"}, + {"id": 37, "name": "Belt"}, + {"id": 38, "name": "Moniter/TV"}, + {"id": 39, "name": "Backpack"}, + {"id": 40, "name": "Umbrella"}, + {"id": 41, "name": "Traffic Light"}, + {"id": 42, "name": "Speaker"}, + {"id": 43, "name": "Watch"}, + {"id": 44, "name": "Tie"}, + {"id": 45, "name": "Trash bin Can"}, + {"id": 46, "name": "Slippers"}, + {"id": 47, "name": "Bicycle"}, + {"id": 48, "name": "Stool"}, + {"id": 49, "name": "Barrel/bucket"}, + {"id": 50, "name": "Van"}, + {"id": 51, "name": "Couch"}, + {"id": 52, "name": "Sandals"}, + {"id": 53, "name": "Bakset"}, + {"id": 54, "name": "Drum"}, + {"id": 55, "name": "Pen/Pencil"}, + {"id": 56, "name": "Bus"}, + {"id": 57, "name": "Wild Bird"}, + {"id": 58, "name": "High Heels"}, + {"id": 59, "name": "Motorcycle"}, + {"id": 60, "name": "Guitar"}, + {"id": 61, "name": "Carpet"}, + {"id": 62, "name": "Cell Phone"}, + {"id": 63, "name": "Bread"}, + {"id": 64, "name": "Camera"}, + {"id": 65, "name": "Canned"}, + {"id": 66, "name": "Truck"}, + {"id": 67, "name": "Traffic cone"}, + {"id": 68, "name": "Cymbal"}, + {"id": 69, "name": "Lifesaver"}, + {"id": 70, "name": "Towel"}, + {"id": 71, "name": "Stuffed Toy"}, + {"id": 72, "name": "Candle"}, + {"id": 73, "name": "Sailboat"}, + {"id": 74, "name": "Laptop"}, + {"id": 75, "name": "Awning"}, + {"id": 76, "name": "Bed"}, + {"id": 77, "name": "Faucet"}, + {"id": 78, "name": "Tent"}, + {"id": 79, "name": "Horse"}, + {"id": 80, "name": "Mirror"}, + {"id": 81, "name": "Power outlet"}, + {"id": 82, "name": "Sink"}, + {"id": 83, "name": "Apple"}, + {"id": 84, "name": "Air Conditioner"}, + {"id": 85, "name": "Knife"}, + {"id": 86, "name": "Hockey Stick"}, + {"id": 87, "name": "Paddle"}, + {"id": 88, "name": "Pickup Truck"}, + {"id": 89, "name": "Fork"}, + {"id": 90, "name": "Traffic Sign"}, + {"id": 91, "name": "Ballon"}, + {"id": 92, "name": "Tripod"}, + {"id": 93, "name": "Dog"}, + {"id": 94, "name": "Spoon"}, + {"id": 95, "name": "Clock"}, + {"id": 96, "name": "Pot"}, + {"id": 97, "name": "Cow"}, + {"id": 98, "name": "Cake"}, + {"id": 99, "name": "Dinning Table"}, + {"id": 100, "name": "Sheep"}, + {"id": 101, "name": "Hanger"}, + {"id": 102, "name": "Blackboard/Whiteboard"}, + {"id": 103, "name": "Napkin"}, + {"id": 104, "name": "Other Fish"}, + {"id": 105, "name": "Orange/Tangerine"}, + {"id": 106, "name": "Toiletry"}, + {"id": 107, "name": "Keyboard"}, + {"id": 108, "name": "Tomato"}, + {"id": 109, "name": "Lantern"}, + {"id": 110, "name": "Machinery Vehicle"}, + {"id": 111, "name": "Fan"}, + {"id": 112, "name": "Green Vegetables"}, + {"id": 113, "name": "Banana"}, + {"id": 114, "name": "Baseball Glove"}, + {"id": 115, "name": "Airplane"}, + {"id": 116, "name": "Mouse"}, + {"id": 117, "name": "Train"}, + {"id": 118, "name": "Pumpkin"}, + {"id": 119, "name": "Soccer"}, + {"id": 120, "name": "Skiboard"}, + {"id": 121, "name": "Luggage"}, + {"id": 122, "name": "Nightstand"}, + {"id": 123, "name": "Tea pot"}, + {"id": 124, "name": "Telephone"}, + {"id": 125, "name": "Trolley"}, + {"id": 126, "name": "Head Phone"}, + {"id": 127, "name": "Sports Car"}, + {"id": 128, "name": "Stop Sign"}, + {"id": 129, "name": "Dessert"}, + {"id": 130, "name": "Scooter"}, + {"id": 131, "name": "Stroller"}, + {"id": 132, "name": "Crane"}, + {"id": 133, "name": "Remote"}, + {"id": 134, "name": "Refrigerator"}, + {"id": 135, "name": "Oven"}, + {"id": 136, "name": "Lemon"}, + {"id": 137, "name": "Duck"}, + {"id": 138, "name": "Baseball Bat"}, + {"id": 139, "name": "Surveillance Camera"}, + {"id": 140, "name": "Cat"}, + {"id": 141, "name": "Jug"}, + {"id": 142, "name": "Broccoli"}, + {"id": 143, "name": "Piano"}, + {"id": 144, "name": "Pizza"}, + {"id": 145, "name": "Elephant"}, + {"id": 146, "name": "Skateboard"}, + {"id": 147, "name": "Surfboard"}, + {"id": 148, "name": "Gun"}, + {"id": 149, "name": "Skating and Skiing shoes"}, + {"id": 150, "name": "Gas stove"}, + {"id": 151, "name": "Donut"}, + {"id": 152, "name": "Bow Tie"}, + {"id": 153, "name": "Carrot"}, + {"id": 154, "name": "Toilet"}, + {"id": 155, "name": "Kite"}, + {"id": 156, "name": "Strawberry"}, + {"id": 157, "name": "Other Balls"}, + {"id": 158, "name": "Shovel"}, + {"id": 159, "name": "Pepper"}, + {"id": 160, "name": "Computer Box"}, + {"id": 161, "name": "Toilet Paper"}, + {"id": 162, "name": "Cleaning Products"}, + {"id": 163, "name": "Chopsticks"}, + {"id": 164, "name": "Microwave"}, + {"id": 165, "name": "Pigeon"}, + {"id": 166, "name": "Baseball"}, + {"id": 167, "name": "Cutting/chopping Board"}, + {"id": 168, "name": "Coffee Table"}, + {"id": 169, "name": "Side Table"}, + {"id": 170, "name": "Scissors"}, + {"id": 171, "name": "Marker"}, + {"id": 172, "name": "Pie"}, + {"id": 173, "name": "Ladder"}, + {"id": 174, "name": "Snowboard"}, + {"id": 175, "name": "Cookies"}, + {"id": 176, "name": "Radiator"}, + {"id": 177, "name": "Fire Hydrant"}, + {"id": 178, "name": "Basketball"}, + {"id": 179, "name": "Zebra"}, + {"id": 180, "name": "Grape"}, + {"id": 181, "name": "Giraffe"}, + {"id": 182, "name": "Potato"}, + {"id": 183, "name": "Sausage"}, + {"id": 184, "name": "Tricycle"}, + {"id": 185, "name": "Violin"}, + {"id": 186, "name": "Egg"}, + {"id": 187, "name": "Fire Extinguisher"}, + {"id": 188, "name": "Candy"}, + {"id": 189, "name": "Fire Truck"}, + {"id": 190, "name": "Billards"}, + {"id": 191, "name": "Converter"}, + {"id": 192, "name": "Bathtub"}, + {"id": 193, "name": "Wheelchair"}, + {"id": 194, "name": "Golf Club"}, + {"id": 195, "name": "Briefcase"}, + {"id": 196, "name": "Cucumber"}, + {"id": 197, "name": "Cigar/Cigarette "}, + {"id": 198, "name": "Paint Brush"}, + {"id": 199, "name": "Pear"}, + {"id": 200, "name": "Heavy Truck"}, + {"id": 201, "name": "Hamburger"}, + {"id": 202, "name": "Extractor"}, + {"id": 203, "name": "Extention Cord"}, + {"id": 204, "name": "Tong"}, + {"id": 205, "name": "Tennis Racket"}, + {"id": 206, "name": "Folder"}, + {"id": 207, "name": "American Football"}, + {"id": 208, "name": "earphone"}, + {"id": 209, "name": "Mask"}, + {"id": 210, "name": "Kettle"}, + {"id": 211, "name": "Tennis"}, + {"id": 212, "name": "Ship"}, + {"id": 213, "name": "Swing"}, + {"id": 214, "name": "Coffee Machine"}, + {"id": 215, "name": "Slide"}, + {"id": 216, "name": "Carriage"}, + {"id": 217, "name": "Onion"}, + {"id": 218, "name": "Green beans"}, + {"id": 219, "name": "Projector"}, + {"id": 220, "name": "Frisbee"}, + {"id": 221, "name": "Washing Machine/Drying Machine"}, + {"id": 222, "name": "Chicken"}, + {"id": 223, "name": "Printer"}, + {"id": 224, "name": "Watermelon"}, + {"id": 225, "name": "Saxophone"}, + {"id": 226, "name": "Tissue"}, + {"id": 227, "name": "Toothbrush"}, + {"id": 228, "name": "Ice cream"}, + {"id": 229, "name": "Hotair ballon"}, + {"id": 230, "name": "Cello"}, + {"id": 231, "name": "French Fries"}, + {"id": 232, "name": "Scale"}, + {"id": 233, "name": "Trophy"}, + {"id": 234, "name": "Cabbage"}, + {"id": 235, "name": "Hot dog"}, + {"id": 236, "name": "Blender"}, + {"id": 237, "name": "Peach"}, + {"id": 238, "name": "Rice"}, + {"id": 239, "name": "Wallet/Purse"}, + {"id": 240, "name": "Volleyball"}, + {"id": 241, "name": "Deer"}, + {"id": 242, "name": "Goose"}, + {"id": 243, "name": "Tape"}, + {"id": 244, "name": "Tablet"}, + {"id": 245, "name": "Cosmetics"}, + {"id": 246, "name": "Trumpet"}, + {"id": 247, "name": "Pineapple"}, + {"id": 248, "name": "Golf Ball"}, + {"id": 249, "name": "Ambulance"}, + {"id": 250, "name": "Parking meter"}, + {"id": 251, "name": "Mango"}, + {"id": 252, "name": "Key"}, + {"id": 253, "name": "Hurdle"}, + {"id": 254, "name": "Fishing Rod"}, + {"id": 255, "name": "Medal"}, + {"id": 256, "name": "Flute"}, + {"id": 257, "name": "Brush"}, + {"id": 258, "name": "Penguin"}, + {"id": 259, "name": "Megaphone"}, + {"id": 260, "name": "Corn"}, + {"id": 261, "name": "Lettuce"}, + {"id": 262, "name": "Garlic"}, + {"id": 263, "name": "Swan"}, + {"id": 264, "name": "Helicopter"}, + {"id": 265, "name": "Green Onion"}, + {"id": 266, "name": "Sandwich"}, + {"id": 267, "name": "Nuts"}, + {"id": 268, "name": "Speed Limit Sign"}, + {"id": 269, "name": "Induction Cooker"}, + {"id": 270, "name": "Broom"}, + {"id": 271, "name": "Trombone"}, + {"id": 272, "name": "Plum"}, + {"id": 273, "name": "Rickshaw"}, + {"id": 274, "name": "Goldfish"}, + {"id": 275, "name": "Kiwi fruit"}, + {"id": 276, "name": "Router/modem"}, + {"id": 277, "name": "Poker Card"}, + {"id": 278, "name": "Toaster"}, + {"id": 279, "name": "Shrimp"}, + {"id": 280, "name": "Sushi"}, + {"id": 281, "name": "Cheese"}, + {"id": 282, "name": "Notepaper"}, + {"id": 283, "name": "Cherry"}, + {"id": 284, "name": "Pliers"}, + {"id": 285, "name": "CD"}, + {"id": 286, "name": "Pasta"}, + {"id": 287, "name": "Hammer"}, + {"id": 288, "name": "Cue"}, + {"id": 289, "name": "Avocado"}, + {"id": 290, "name": "Hamimelon"}, + {"id": 291, "name": "Flask"}, + {"id": 292, "name": "Mushroon"}, + {"id": 293, "name": "Screwdriver"}, + {"id": 294, "name": "Soap"}, + {"id": 295, "name": "Recorder"}, + {"id": 296, "name": "Bear"}, + {"id": 297, "name": "Eggplant"}, + {"id": 298, "name": "Board Eraser"}, + {"id": 299, "name": "Coconut"}, + {"id": 300, "name": "Tape Measur/ Ruler"}, + {"id": 301, "name": "Pig"}, + {"id": 302, "name": "Showerhead"}, + {"id": 303, "name": "Globe"}, + {"id": 304, "name": "Chips"}, + {"id": 305, "name": "Steak"}, + {"id": 306, "name": "Crosswalk Sign"}, + {"id": 307, "name": "Stapler"}, + {"id": 308, "name": "Campel"}, + {"id": 309, "name": "Formula 1 "}, + {"id": 310, "name": "Pomegranate"}, + {"id": 311, "name": "Dishwasher"}, + {"id": 312, "name": "Crab"}, + {"id": 313, "name": "Hoverboard"}, + {"id": 314, "name": "Meat ball"}, + {"id": 315, "name": "Rice Cooker"}, + {"id": 316, "name": "Tuba"}, + {"id": 317, "name": "Calculator"}, + {"id": 318, "name": "Papaya"}, + {"id": 319, "name": "Antelope"}, + {"id": 320, "name": "Parrot"}, + {"id": 321, "name": "Seal"}, + {"id": 322, "name": "Buttefly"}, + {"id": 323, "name": "Dumbbell"}, + {"id": 324, "name": "Donkey"}, + {"id": 325, "name": "Lion"}, + {"id": 326, "name": "Urinal"}, + {"id": 327, "name": "Dolphin"}, + {"id": 328, "name": "Electric Drill"}, + {"id": 329, "name": "Hair Dryer"}, + {"id": 330, "name": "Egg tart"}, + {"id": 331, "name": "Jellyfish"}, + {"id": 332, "name": "Treadmill"}, + {"id": 333, "name": "Lighter"}, + {"id": 334, "name": "Grapefruit"}, + {"id": 335, "name": "Game board"}, + {"id": 336, "name": "Mop"}, + {"id": 337, "name": "Radish"}, + {"id": 338, "name": "Baozi"}, + {"id": 339, "name": "Target"}, + {"id": 340, "name": "French"}, + {"id": 341, "name": "Spring Rolls"}, + {"id": 342, "name": "Monkey"}, + {"id": 343, "name": "Rabbit"}, + {"id": 344, "name": "Pencil Case"}, + {"id": 345, "name": "Yak"}, + {"id": 346, "name": "Red Cabbage"}, + {"id": 347, "name": "Binoculars"}, + {"id": 348, "name": "Asparagus"}, + {"id": 349, "name": "Barbell"}, + {"id": 350, "name": "Scallop"}, + {"id": 351, "name": "Noddles"}, + {"id": 352, "name": "Comb"}, + {"id": 353, "name": "Dumpling"}, + {"id": 354, "name": "Oyster"}, + {"id": 355, "name": "Table Teniis paddle"}, + {"id": 356, "name": "Cosmetics Brush/Eyeliner Pencil"}, + {"id": 357, "name": "Chainsaw"}, + {"id": 358, "name": "Eraser"}, + {"id": 359, "name": "Lobster"}, + {"id": 360, "name": "Durian"}, + {"id": 361, "name": "Okra"}, + {"id": 362, "name": "Lipstick"}, + {"id": 363, "name": "Cosmetics Mirror"}, + {"id": 364, "name": "Curling"}, + {"id": 365, "name": "Table Tennis "}, +] + + +def _get_builtin_metadata(key): + # return {} + if "fixname" in key: + id_to_name = {x["id"]: x["name"] for x in OBJECTS365_CATEGORIES_FIXNAME} + thing_dataset_id_to_contiguous_id = { + i + 1: i for i in range(len(OBJECTS365_CATEGORIES_FIXNAME)) + } + else: + id_to_name = {x["id"]: x["name"] for x in OBJECTS365_CATEGORIES} + thing_dataset_id_to_contiguous_id = {i + 1: i for i in range(len(OBJECTS365_CATEGORIES))} + thing_classes = [id_to_name[k] for k in sorted(id_to_name)] + return { + "thing_dataset_id_to_contiguous_id": thing_dataset_id_to_contiguous_id, + "thing_classes": thing_classes, + } + + +_PREDEFINED_SPLITS_OBJECTS365 = { + "objects365_train": ("objects365/train", "objects365/annotations/objects365_train.json"), + "objects365_val": ("objects365/val", "objects365/annotations/objects365_val.json"), + "objects365_minival": ("objects365/val", "objects365/annotations/objects365_minival.json"), + "objects365_train_fixname": ( + "objects365/train", + "objects365/annotations/objects365_train_fixname.json", + ), + "objects365_val_fixname": ( + "objects365/val", + "objects365/annotations/objects365_val_fixname.json", + ), + "objects365_minival_fixname": ( + "objects365/val", + "objects365/annotations/objects365_minival_fixname.json", + ), + "objects365_train_fixname_fixmiss": ( + "objects365/train", + "objects365/annotations/objects365_train_fixname_fixmiss.json", + ), + "objects365_val_fixname_fixmiss": ( + "objects365/val", + "objects365/annotations/objects365_val_fixname_fixmiss.json", + ), +} + + +def register_all_objects365(root): + for key, (image_root, json_file) in _PREDEFINED_SPLITS_OBJECTS365.items(): + register_coco_instances( + key, + _get_builtin_metadata(key), + os.path.join(root, json_file) if "://" not in json_file else json_file, + os.path.join(root, image_root), + ) + + +if __name__.endswith(".objects365"): + # Assume pre-defined datasets live in `./datasets`. + _root = os.getenv("DETECTRON2_DATASETS", "datasets") + register_all_objects365(_root) diff --git a/ape/data/datasets/odinw_categories.py b/ape/data/datasets/odinw_categories.py new file mode 100644 index 0000000..7caad1d --- /dev/null +++ b/ape/data/datasets/odinw_categories.py @@ -0,0 +1,377 @@ +ODINW_CATEGORIES = { + "AerialMaritimeDrone": [ + {"id": 1, "name": "boat", "supercategory": "movable-objects"}, + {"id": 2, "name": "car", "supercategory": "movable-objects"}, + {"id": 3, "name": "dock", "supercategory": "movable-objects"}, + {"id": 4, "name": "jetski", "supercategory": "movable-objects"}, + {"id": 5, "name": "lift", "supercategory": "movable-objects"}, + ], + "AmericanSignLanguageLetters": [ + {"id": 1, "name": "A", "supercategory": "Letters"}, + {"id": 2, "name": "B", "supercategory": "Letters"}, + {"id": 3, "name": "C", "supercategory": "Letters"}, + {"id": 4, "name": "D", "supercategory": "Letters"}, + {"id": 5, "name": "E", "supercategory": "Letters"}, + {"id": 6, "name": "F", "supercategory": "Letters"}, + {"id": 7, "name": "G", "supercategory": "Letters"}, + {"id": 8, "name": "H", "supercategory": "Letters"}, + {"id": 9, "name": "I", "supercategory": "Letters"}, + {"id": 10, "name": "J", "supercategory": "Letters"}, + {"id": 11, "name": "K", "supercategory": "Letters"}, + {"id": 12, "name": "L", "supercategory": "Letters"}, + {"id": 13, "name": "M", "supercategory": "Letters"}, + {"id": 14, "name": "N", "supercategory": "Letters"}, + {"id": 15, "name": "O", "supercategory": "Letters"}, + {"id": 16, "name": "P", "supercategory": "Letters"}, + {"id": 17, "name": "Q", "supercategory": "Letters"}, + {"id": 18, "name": "R", "supercategory": "Letters"}, + {"id": 19, "name": "S", "supercategory": "Letters"}, + {"id": 20, "name": "T", "supercategory": "Letters"}, + {"id": 21, "name": "U", "supercategory": "Letters"}, + {"id": 22, "name": "V", "supercategory": "Letters"}, + {"id": 23, "name": "W", "supercategory": "Letters"}, + {"id": 24, "name": "X", "supercategory": "Letters"}, + {"id": 25, "name": "Y", "supercategory": "Letters"}, + {"id": 26, "name": "Z", "supercategory": "Letters"}, + ], + "Aquarium": [ + {"id": 1, "name": "fish", "supercategory": "creatures"}, + {"id": 2, "name": "jellyfish", "supercategory": "creatures"}, + {"id": 3, "name": "penguin", "supercategory": "creatures"}, + {"id": 4, "name": "puffin", "supercategory": "creatures"}, + {"id": 5, "name": "shark", "supercategory": "creatures"}, + {"id": 6, "name": "starfish", "supercategory": "creatures"}, + {"id": 7, "name": "stingray", "supercategory": "creatures"}, + ], + "BCCD": [ + {"id": 1, "name": "Platelets", "supercategory": "cells"}, + {"id": 2, "name": "RBC", "supercategory": "cells"}, + {"id": 3, "name": "WBC", "supercategory": "cells"}, + ], + "boggleBoards": [ + {"id": 1, "name": "Q", "supercategory": "letters"}, + {"id": 2, "name": "a", "supercategory": "letters"}, + {"id": 3, "name": "an", "supercategory": "letters"}, + {"id": 4, "name": "b", "supercategory": "letters"}, + {"id": 5, "name": "c", "supercategory": "letters"}, + {"id": 6, "name": "d", "supercategory": "letters"}, + {"id": 7, "name": "e", "supercategory": "letters"}, + {"id": 8, "name": "er", "supercategory": "letters"}, + {"id": 9, "name": "f", "supercategory": "letters"}, + {"id": 10, "name": "g", "supercategory": "letters"}, + {"id": 11, "name": "h", "supercategory": "letters"}, + {"id": 12, "name": "he", "supercategory": "letters"}, + {"id": 13, "name": "i", "supercategory": "letters"}, + {"id": 14, "name": "in", "supercategory": "letters"}, + {"id": 15, "name": "j", "supercategory": "letters"}, + {"id": 16, "name": "k", "supercategory": "letters"}, + {"id": 17, "name": "l", "supercategory": "letters"}, + {"id": 18, "name": "m", "supercategory": "letters"}, + {"id": 19, "name": "n", "supercategory": "letters"}, + {"id": 20, "name": "o", "supercategory": "letters"}, + {"id": 21, "name": "o ", "supercategory": "letters"}, + {"id": 22, "name": "p", "supercategory": "letters"}, + {"id": 23, "name": "q", "supercategory": "letters"}, + {"id": 24, "name": "qu", "supercategory": "letters"}, + {"id": 25, "name": "r", "supercategory": "letters"}, + {"id": 26, "name": "s", "supercategory": "letters"}, + {"id": 27, "name": "t", "supercategory": "letters"}, + {"id": 28, "name": "t\\", "supercategory": "letters"}, + {"id": 29, "name": "th", "supercategory": "letters"}, + {"id": 30, "name": "u", "supercategory": "letters"}, + {"id": 31, "name": "v", "supercategory": "letters"}, + {"id": 32, "name": "w", "supercategory": "letters"}, + {"id": 33, "name": "wild", "supercategory": "letters"}, + {"id": 34, "name": "x", "supercategory": "letters"}, + {"id": 35, "name": "y", "supercategory": "letters"}, + {"id": 36, "name": "z", "supercategory": "letters"}, + ], + "brackishUnderwater": [ + {"id": 1, "name": "crab", "supercategory": "animals"}, + {"id": 2, "name": "fish", "supercategory": "animals"}, + {"id": 3, "name": "jellyfish", "supercategory": "animals"}, + {"id": 4, "name": "shrimp", "supercategory": "animals"}, + {"id": 5, "name": "small_fish", "supercategory": "animals"}, + {"id": 6, "name": "starfish", "supercategory": "animals"}, + ], + "ChessPieces": [ + {"id": 1, "name": "bishop", "supercategory": "pieces"}, + {"id": 2, "name": "black-bishop", "supercategory": "pieces"}, + {"id": 3, "name": "black-king", "supercategory": "pieces"}, + {"id": 4, "name": "black-knight", "supercategory": "pieces"}, + {"id": 5, "name": "black-pawn", "supercategory": "pieces"}, + {"id": 6, "name": "black-queen", "supercategory": "pieces"}, + {"id": 7, "name": "black-rook", "supercategory": "pieces"}, + {"id": 8, "name": "white-bishop", "supercategory": "pieces"}, + {"id": 9, "name": "white-king", "supercategory": "pieces"}, + {"id": 10, "name": "white-knight", "supercategory": "pieces"}, + {"id": 11, "name": "white-pawn", "supercategory": "pieces"}, + {"id": 12, "name": "white-queen", "supercategory": "pieces"}, + {"id": 13, "name": "white-rook", "supercategory": "pieces"}, + ], + "CottontailRabbits": [ + {"id": 1, "name": "Cottontail-Rabbit", "supercategory": "Cottontail-Rabbit"} + ], + "dice": [ + {"id": 1, "name": "1", "supercategory": "dice"}, + {"id": 2, "name": "2", "supercategory": "dice"}, + {"id": 3, "name": "3", "supercategory": "dice"}, + {"id": 4, "name": "4", "supercategory": "dice"}, + {"id": 5, "name": "5", "supercategory": "dice"}, + {"id": 6, "name": "6", "supercategory": "dice"}, + ], + "DroneControl": [ + {"id": 1, "name": "follow", "supercategory": "actions"}, + {"id": 2, "name": "follow_hand", "supercategory": "actions"}, + {"id": 3, "name": "land", "supercategory": "actions"}, + {"id": 4, "name": "land_hand", "supercategory": "actions"}, + {"id": 5, "name": "null", "supercategory": "actions"}, + {"id": 6, "name": "object", "supercategory": "actions"}, + {"id": 7, "name": "takeoff", "supercategory": "actions"}, + {"id": 8, "name": "takeoff-hand", "supercategory": "actions"}, + ], + "EgoHands-generic": [ + {"id": 1, "name": "hand", "supercategory": "hands"}, + ], + "EgoHands-specific": [ + {"id": 1, "name": "myleft", "supercategory": "hands"}, + {"id": 2, "name": "myright", "supercategory": "hands"}, + {"id": 3, "name": "yourleft", "supercategory": "hands"}, + {"id": 4, "name": "yourright", "supercategory": "hands"}, + ], + "HardHatWorkers": [ + {"id": 1, "name": "head", "supercategory": "Workers"}, + {"id": 2, "name": "helmet", "supercategory": "Workers"}, + {"id": 3, "name": "person", "supercategory": "Workers"}, + ], + "MaskWearing": [ + {"id": 1, "name": "mask", "supercategory": "People"}, + {"id": 2, "name": "no-mask", "supercategory": "People"}, + ], + "MountainDewCommercial": [ + {"id": 1, "name": "bottle", "supercategory": "bottles"}, + ], + "NorthAmericaMushrooms": [ + {"id": 1, "name": "CoW", "supercategory": "mushroom"}, + {"id": 2, "name": "chanterelle", "supercategory": "mushroom"}, + ], + "openPoetryVision": [ + {"id": 1, "name": "American Typewriter", "supercategory": "text"}, + {"id": 2, "name": "Andale Mono", "supercategory": "text"}, + {"id": 3, "name": "Apple Chancery", "supercategory": "text"}, + {"id": 4, "name": "Arial", "supercategory": "text"}, + {"id": 5, "name": "Avenir", "supercategory": "text"}, + {"id": 6, "name": "Baskerville", "supercategory": "text"}, + {"id": 7, "name": "Big Caslon", "supercategory": "text"}, + {"id": 8, "name": "Bradley Hand", "supercategory": "text"}, + {"id": 9, "name": "Brush Script MT", "supercategory": "text"}, + {"id": 10, "name": "Chalkboard", "supercategory": "text"}, + {"id": 11, "name": "Comic Sans MS", "supercategory": "text"}, + {"id": 12, "name": "Copperplate", "supercategory": "text"}, + {"id": 13, "name": "Courier", "supercategory": "text"}, + {"id": 14, "name": "Didot", "supercategory": "text"}, + {"id": 15, "name": "Futura", "supercategory": "text"}, + {"id": 16, "name": "Geneva", "supercategory": "text"}, + {"id": 17, "name": "Georgia", "supercategory": "text"}, + {"id": 18, "name": "Gill Sans", "supercategory": "text"}, + {"id": 19, "name": "Helvetica", "supercategory": "text"}, + {"id": 20, "name": "Herculanum", "supercategory": "text"}, + {"id": 21, "name": "Impact", "supercategory": "text"}, + {"id": 22, "name": "Kefa", "supercategory": "text"}, + {"id": 23, "name": "Lucida Grande", "supercategory": "text"}, + {"id": 24, "name": "Luminari", "supercategory": "text"}, + {"id": 25, "name": "Marker Felt", "supercategory": "text"}, + {"id": 26, "name": "Menlo", "supercategory": "text"}, + {"id": 27, "name": "Monaco", "supercategory": "text"}, + {"id": 28, "name": "Noteworthy", "supercategory": "text"}, + {"id": 29, "name": "Optima", "supercategory": "text"}, + {"id": 30, "name": "PT Sans", "supercategory": "text"}, + {"id": 31, "name": "PT Serif", "supercategory": "text"}, + {"id": 32, "name": "Palatino", "supercategory": "text"}, + {"id": 33, "name": "Papyrus", "supercategory": "text"}, + {"id": 34, "name": "Phosphate", "supercategory": "text"}, + {"id": 35, "name": "Rockwell", "supercategory": "text"}, + {"id": 36, "name": "SF Pro", "supercategory": "text"}, + {"id": 37, "name": "SignPainter", "supercategory": "text"}, + {"id": 38, "name": "Skia", "supercategory": "text"}, + {"id": 39, "name": "Snell Roundhand", "supercategory": "text"}, + {"id": 40, "name": "Tahoma", "supercategory": "text"}, + {"id": 41, "name": "Times New Roman", "supercategory": "text"}, + {"id": 42, "name": "Trebuchet MS", "supercategory": "text"}, + {"id": 43, "name": "Verdana", "supercategory": "text"}, + ], + "OxfordPets-by-breed": [ + {"id": 1, "name": "cat-Abyssinian", "supercategory": "pets"}, + {"id": 2, "name": "cat-Bengal", "supercategory": "pets"}, + {"id": 3, "name": "cat-Birman", "supercategory": "pets"}, + {"id": 4, "name": "cat-Bombay", "supercategory": "pets"}, + {"id": 5, "name": "cat-British_Shorthair", "supercategory": "pets"}, + {"id": 6, "name": "cat-Egyptian_Mau", "supercategory": "pets"}, + {"id": 7, "name": "cat-Maine_Coon", "supercategory": "pets"}, + {"id": 8, "name": "cat-Persian", "supercategory": "pets"}, + {"id": 9, "name": "cat-Ragdoll", "supercategory": "pets"}, + {"id": 10, "name": "cat-Russian_Blue", "supercategory": "pets"}, + {"id": 11, "name": "cat-Siamese", "supercategory": "pets"}, + {"id": 12, "name": "cat-Sphynx", "supercategory": "pets"}, + {"id": 13, "name": "dog-american_bulldog", "supercategory": "pets"}, + {"id": 14, "name": "dog-american_pit_bull_terrier", "supercategory": "pets"}, + {"id": 15, "name": "dog-basset_hound", "supercategory": "pets"}, + {"id": 16, "name": "dog-beagle", "supercategory": "pets"}, + {"id": 17, "name": "dog-boxer", "supercategory": "pets"}, + {"id": 18, "name": "dog-chihuahua", "supercategory": "pets"}, + {"id": 19, "name": "dog-english_cocker_spaniel", "supercategory": "pets"}, + {"id": 20, "name": "dog-english_setter", "supercategory": "pets"}, + {"id": 21, "name": "dog-german_shorthaired", "supercategory": "pets"}, + {"id": 22, "name": "dog-great_pyrenees", "supercategory": "pets"}, + {"id": 23, "name": "dog-havanese", "supercategory": "pets"}, + {"id": 24, "name": "dog-japanese_chin", "supercategory": "pets"}, + {"id": 25, "name": "dog-keeshond", "supercategory": "pets"}, + {"id": 26, "name": "dog-leonberger", "supercategory": "pets"}, + {"id": 27, "name": "dog-miniature_pinscher", "supercategory": "pets"}, + {"id": 28, "name": "dog-newfoundland", "supercategory": "pets"}, + {"id": 29, "name": "dog-pomeranian", "supercategory": "pets"}, + {"id": 30, "name": "dog-pug", "supercategory": "pets"}, + {"id": 31, "name": "dog-saint_bernard", "supercategory": "pets"}, + {"id": 32, "name": "dog-samoyed", "supercategory": "pets"}, + {"id": 33, "name": "dog-scottish_terrier", "supercategory": "pets"}, + {"id": 34, "name": "dog-shiba_inu", "supercategory": "pets"}, + {"id": 35, "name": "dog-staffordshire_bull_terrier", "supercategory": "pets"}, + {"id": 36, "name": "dog-wheaten_terrier", "supercategory": "pets"}, + {"id": 37, "name": "dog-yorkshire_terrier", "supercategory": "pets"}, + ], + "OxfordPets-by-species": [ + {"id": 1, "name": "cat", "supercategory": "pets"}, + {"id": 2, "name": "dog", "supercategory": "pets"}, + ], + "Packages": [{"id": 1, "name": "package", "supercategory": "packages"}], + "PascalVOC": [ + {"id": 1, "name": "aeroplane", "supercategory": "VOC"}, + {"id": 2, "name": "bicycle", "supercategory": "VOC"}, + {"id": 3, "name": "bird", "supercategory": "VOC"}, + {"id": 4, "name": "boat", "supercategory": "VOC"}, + {"id": 5, "name": "bottle", "supercategory": "VOC"}, + {"id": 6, "name": "bus", "supercategory": "VOC"}, + {"id": 7, "name": "car", "supercategory": "VOC"}, + {"id": 8, "name": "cat", "supercategory": "VOC"}, + {"id": 9, "name": "chair", "supercategory": "VOC"}, + {"id": 10, "name": "cow", "supercategory": "VOC"}, + {"id": 11, "name": "diningtable", "supercategory": "VOC"}, + {"id": 12, "name": "dog", "supercategory": "VOC"}, + {"id": 13, "name": "horse", "supercategory": "VOC"}, + {"id": 14, "name": "motorbike", "supercategory": "VOC"}, + {"id": 15, "name": "person", "supercategory": "VOC"}, + {"id": 16, "name": "pottedplant", "supercategory": "VOC"}, + {"id": 17, "name": "sheep", "supercategory": "VOC"}, + {"id": 18, "name": "sofa", "supercategory": "VOC"}, + {"id": 19, "name": "train", "supercategory": "VOC"}, + {"id": 20, "name": "tvmonitor", "supercategory": "VOC"}, + ], + "pistols": [ + {"id": 1, "name": "pistol", "supercategory": "Guns"}, + ], + "PKLot": [ + {"id": 1, "name": "space-empty", "supercategory": "spaces"}, + {"id": 2, "name": "space-occupied", "supercategory": "spaces"}, + ], + "plantdoc": [ + {"id": 1, "name": "Apple Scab Leaf", "supercategory": "leaves"}, + {"id": 2, "name": "Apple leaf", "supercategory": "leaves"}, + {"id": 3, "name": "Apple rust leaf", "supercategory": "leaves"}, + {"id": 4, "name": "Bell_pepper leaf", "supercategory": "leaves"}, + {"id": 5, "name": "Bell_pepper leaf spot", "supercategory": "leaves"}, + {"id": 6, "name": "Blueberry leaf", "supercategory": "leaves"}, + {"id": 7, "name": "Cherry leaf", "supercategory": "leaves"}, + {"id": 8, "name": "Corn Gray leaf spot", "supercategory": "leaves"}, + {"id": 9, "name": "Corn leaf blight", "supercategory": "leaves"}, + {"id": 10, "name": "Corn rust leaf", "supercategory": "leaves"}, + {"id": 11, "name": "Peach leaf", "supercategory": "leaves"}, + {"id": 12, "name": "Potato leaf", "supercategory": "leaves"}, + {"id": 13, "name": "Potato leaf early blight", "supercategory": "leaves"}, + {"id": 14, "name": "Potato leaf late blight", "supercategory": "leaves"}, + {"id": 15, "name": "Raspberry leaf", "supercategory": "leaves"}, + {"id": 16, "name": "Soyabean leaf", "supercategory": "leaves"}, + {"id": 17, "name": "Soybean leaf", "supercategory": "leaves"}, + {"id": 18, "name": "Squash Powdery mildew leaf", "supercategory": "leaves"}, + {"id": 19, "name": "Strawberry leaf", "supercategory": "leaves"}, + {"id": 20, "name": "Tomato Early blight leaf", "supercategory": "leaves"}, + {"id": 21, "name": "Tomato Septoria leaf spot", "supercategory": "leaves"}, + {"id": 22, "name": "Tomato leaf", "supercategory": "leaves"}, + {"id": 23, "name": "Tomato leaf bacterial spot", "supercategory": "leaves"}, + {"id": 24, "name": "Tomato leaf late blight", "supercategory": "leaves"}, + {"id": 25, "name": "Tomato leaf mosaic virus", "supercategory": "leaves"}, + {"id": 26, "name": "Tomato leaf yellow virus", "supercategory": "leaves"}, + {"id": 27, "name": "Tomato mold leaf", "supercategory": "leaves"}, + {"id": 28, "name": "Tomato two spotted spider mites leaf", "supercategory": "leaves"}, + {"id": 29, "name": "grape leaf", "supercategory": "leaves"}, + {"id": 30, "name": "grape leaf black rot", "supercategory": "leaves"}, + ], + "pothole": [ + {"id": 1, "name": "pothole", "supercategory": "potholes"}, + ], + "Raccoon": [ + {"id": 1, "name": "raccoon", "supercategory": "raccoons"}, + ], + "selfdrivingCar": [ + {"id": 1, "name": "biker", "supercategory": "obstacles"}, + {"id": 2, "name": "car", "supercategory": "obstacles"}, + {"id": 3, "name": "pedestrian", "supercategory": "obstacles"}, + {"id": 4, "name": "trafficLight", "supercategory": "obstacles"}, + {"id": 5, "name": "trafficLight-Green", "supercategory": "obstacles"}, + {"id": 6, "name": "trafficLight-GreenLeft", "supercategory": "obstacles"}, + {"id": 7, "name": "trafficLight-Red", "supercategory": "obstacles"}, + {"id": 8, "name": "trafficLight-RedLeft", "supercategory": "obstacles"}, + {"id": 9, "name": "trafficLight-Yellow", "supercategory": "obstacles"}, + {"id": 10, "name": "trafficLight-YellowLeft", "supercategory": "obstacles"}, + {"id": 11, "name": "truck", "supercategory": "obstacles"}, + ], + "ShellfishOpenImages": [ + {"id": 1, "name": "Crab", "supercategory": "shellfish"}, + {"id": 2, "name": "Lobster", "supercategory": "shellfish"}, + {"id": 3, "name": "Shrimp", "supercategory": "shellfish"}, + ], + "ThermalCheetah": [ + {"id": 1, "name": "cheetah", "supercategory": "cheetah"}, + {"id": 2, "name": "human", "supercategory": "cheetah"}, + ], + "thermalDogsAndPeople": [ + {"id": 1, "name": "dog", "supercategory": "dogs-person"}, + {"id": 2, "name": "person", "supercategory": "dogs-person"}, + ], + "UnoCards": [ + {"id": 1, "name": "0", "supercategory": "Card-Types"}, + {"id": 2, "name": "1", "supercategory": "Card-Types"}, + {"id": 3, "name": "2", "supercategory": "Card-Types"}, + {"id": 4, "name": "3", "supercategory": "Card-Types"}, + {"id": 5, "name": "4", "supercategory": "Card-Types"}, + {"id": 6, "name": "5", "supercategory": "Card-Types"}, + {"id": 7, "name": "6", "supercategory": "Card-Types"}, + {"id": 8, "name": "7", "supercategory": "Card-Types"}, + {"id": 9, "name": "8", "supercategory": "Card-Types"}, + {"id": 10, "name": "9", "supercategory": "Card-Types"}, + {"id": 11, "name": "10", "supercategory": "Card-Types"}, + {"id": 12, "name": "11", "supercategory": "Card-Types"}, + {"id": 13, "name": "12", "supercategory": "Card-Types"}, + {"id": 14, "name": "13", "supercategory": "Card-Types"}, + {"id": 15, "name": "14", "supercategory": "Card-Types"}, + ], + "VehiclesOpenImages": [ + {"id": 1, "name": "Ambulance", "supercategory": "vehicles"}, + {"id": 2, "name": "Bus", "supercategory": "vehicles"}, + {"id": 3, "name": "Car", "supercategory": "vehicles"}, + {"id": 4, "name": "Motorcycle", "supercategory": "vehicles"}, + {"id": 5, "name": "Truck", "supercategory": "vehicles"}, + ], + "websiteScreenshots": [ + {"id": 1, "name": "button", "supercategory": "elements"}, + {"id": 2, "name": "field", "supercategory": "elements"}, + {"id": 3, "name": "heading", "supercategory": "elements"}, + {"id": 4, "name": "iframe", "supercategory": "elements"}, + {"id": 5, "name": "image", "supercategory": "elements"}, + {"id": 6, "name": "label", "supercategory": "elements"}, + {"id": 7, "name": "link", "supercategory": "elements"}, + {"id": 8, "name": "text", "supercategory": "elements"}, + ], + "WildfireSmoke": [ + {"id": 1, "name": "smoke", "supercategory": "Smoke"}, + ], +} diff --git a/ape/data/datasets/odinw_instance.py b/ape/data/datasets/odinw_instance.py new file mode 100644 index 0000000..1e0ebea --- /dev/null +++ b/ape/data/datasets/odinw_instance.py @@ -0,0 +1,824 @@ +import contextlib +import io +import logging +import os + +import pycocotools.mask as mask_util + +from detectron2.data import DatasetCatalog, MetadataCatalog +from detectron2.structures import BoxMode +from detectron2.utils.file_io import PathManager +from fvcore.common.timer import Timer + +from .odinw_categories import ODINW_CATEGORIES +from .odinw_prompts import ODINW_PROMPTS + +logger = logging.getLogger(__name__) + + +def load_coco_json(json_file, image_root, dataset_name=None, extra_annotation_keys=None): + """ + Load a json file with COCO's instances annotation format. + Currently supports instance detection, instance segmentation, + and person keypoints annotations. + + Args: + json_file (str): full path to the json file in COCO instances annotation format. + image_root (str or path-like): the directory where the images in this json file exists. + dataset_name (str or None): the name of the dataset (e.g., coco_2017_train). + When provided, this function will also do the following: + + * Put "thing_classes" into the metadata associated with this dataset. + * Map the category ids into a contiguous range (needed by standard dataset format), + and add "thing_dataset_id_to_contiguous_id" to the metadata associated + with this dataset. + + This option should usually be provided, unless users need to load + the original json content and apply more processing manually. + extra_annotation_keys (list[str]): list of per-annotation keys that should also be + loaded into the dataset dict (besides "iscrowd", "bbox", "keypoints", + "category_id", "segmentation"). The values for these keys will be returned as-is. + For example, the densepose annotations are loaded in this way. + + Returns: + list[dict]: a list of dicts in Detectron2 standard dataset dicts format (See + `Using Custom Datasets `_ ) when `dataset_name` is not None. + If `dataset_name` is None, the returned `category_ids` may be + incontiguous and may not conform to the Detectron2 standard format. + + Notes: + 1. This function does not read the image files. + The results do not have the "image" field. + """ + from pycocotools.coco import COCO + + timer = Timer() + json_file = PathManager.get_local_path(json_file) + with contextlib.redirect_stdout(io.StringIO()): + coco_api = COCO(json_file) + if timer.seconds() > 1: + logger.info("Loading {} takes {:.2f} seconds.".format(json_file, timer.seconds())) + + id_map = None + if dataset_name is not None: + meta = MetadataCatalog.get(dataset_name) + cat_ids = sorted(coco_api.getCatIds()) + # cats = coco_api.loadCats(cat_ids) + # The categories in a custom json file may not be sorted. + # thing_classes = [c["name"] for c in sorted(cats, key=lambda x: x["id"])] + # meta.thing_classes = thing_classes + + # In COCO, certain category ids are artificially removed, + # and by convention they are always ignored. + # We deal with COCO's id issue and translate + # the category ids to contiguous ids in [0, 80). + + # It works by looking at the "categories" field in the json, therefore + # if users' own json also have incontiguous ids, we'll + # apply this mapping as well but print a warning. + if not (min(cat_ids) == 1 and max(cat_ids) == len(cat_ids)): + if "coco" not in dataset_name: + logger.warning( + """ +Category ids in annotations are not in [1, #categories]! We'll apply a mapping for you. +""" + ) + id_map = {v: i for i, v in enumerate(cat_ids)} + meta.thing_dataset_id_to_contiguous_id = id_map + + # sort indices for reproducible results + img_ids = sorted(coco_api.imgs.keys()) + # imgs is a list of dicts, each looks something like: + # {'license': 4, + # 'url': 'http://farm6.staticflickr.com/5454/9413846304_881d5e5c3b_z.jpg', + # 'file_name': 'COCO_val2014_000000001268.jpg', + # 'height': 427, + # 'width': 640, + # 'date_captured': '2013-11-17 05:57:24', + # 'id': 1268} + imgs = coco_api.loadImgs(img_ids) + # anns is a list[list[dict]], where each dict is an annotation + # record for an object. The inner list enumerates the objects in an image + # and the outer list enumerates over images. Example of anns[0]: + # [{'segmentation': [[192.81, + # 247.09, + # ... + # 219.03, + # 249.06]], + # 'area': 1035.749, + # 'iscrowd': 0, + # 'image_id': 1268, + # 'bbox': [192.81, 224.8, 74.73, 33.43], + # 'category_id': 16, + # 'id': 42986}, + # ...] + anns = [coco_api.imgToAnns[img_id] for img_id in img_ids] + total_num_valid_anns = sum([len(x) for x in anns]) + total_num_anns = len(coco_api.anns) + if total_num_valid_anns < total_num_anns: + logger.warning( + f"{json_file} contains {total_num_anns} annotations, but only " + f"{total_num_valid_anns} of them match to images in the file." + ) + + if "minival" not in json_file: + # The popular valminusminival & minival annotations for COCO2014 contain this bug. + # However the ratio of buggy annotations there is tiny and does not affect accuracy. + # Therefore we explicitly white-list them. + ann_ids = [ann["id"] for anns_per_image in anns for ann in anns_per_image] + assert len(set(ann_ids)) == len(ann_ids), "Annotation ids in '{}' are not unique!".format( + json_file + ) + + imgs_anns = list(zip(imgs, anns)) + logger.info("Loaded {} images in COCO format from {}".format(len(imgs_anns), json_file)) + + dataset_dicts = [] + + ann_keys = ["iscrowd", "bbox", "keypoints", "category_id"] + (extra_annotation_keys or []) + + num_instances_without_valid_segmentation = 0 + + for (img_dict, anno_dict_list) in imgs_anns: + record = {} + record["file_name"] = os.path.join(image_root, img_dict["file_name"]) + # record["height"] = img_dict["height"] + # record["width"] = img_dict["width"] + image_id = record["image_id"] = img_dict["id"] + + objs = [] + for anno in anno_dict_list: + # Check that the image_id in this annotation is the same as + # the image_id we're looking at. + # This fails only when the data parsing logic or the annotation file is buggy. + + # The original COCO valminusminival2014 & minival2014 annotation files + # actually contains bugs that, together with certain ways of using COCO API, + # can trigger this assertion. + assert anno["image_id"] == image_id + + assert anno.get("ignore", 0) == 0, '"ignore" in COCO json file is not supported.' + + obj = {key: anno[key] for key in ann_keys if key in anno} + if "bbox" in obj and len(obj["bbox"]) == 0: + raise ValueError( + f"One annotation of image {image_id} contains empty 'bbox' value! " + "This json does not have valid COCO format." + ) + + segm = anno.get("segmentation", None) + if segm: # either list[list[float]] or dict(RLE) + if isinstance(segm, dict): + if isinstance(segm["counts"], list): + # convert to compressed RLE + segm = mask_util.frPyObjects(segm, *segm["size"]) + else: + # filter out invalid polygons (< 3 points) + segm = [poly for poly in segm if len(poly) % 2 == 0 and len(poly) >= 6] + if len(segm) == 0: + num_instances_without_valid_segmentation += 1 + continue # ignore this instance + obj["segmentation"] = segm + + keypts = anno.get("keypoints", None) + if keypts: # list[int] + for idx, v in enumerate(keypts): + if idx % 3 != 2: + # COCO's segmentation coordinates are floating points in [0, H or W], + # but keypoint coordinates are integers in [0, H-1 or W-1] + # Therefore we assume the coordinates are "pixel indices" and + # add 0.5 to convert to floating point coordinates. + keypts[idx] = v + 0.5 + obj["keypoints"] = keypts + + obj["bbox_mode"] = BoxMode.XYWH_ABS + if id_map: + annotation_category_id = obj["category_id"] + try: + obj["category_id"] = id_map[annotation_category_id] + except KeyError as e: + raise KeyError( + f"Encountered category_id={annotation_category_id} " + "but this id does not exist in 'categories' of the json file." + ) from e + objs.append(obj) + record["annotations"] = objs + dataset_dicts.append(record) + + if num_instances_without_valid_segmentation > 0: + logger.warning( + "Filtered out {} instances without valid segmentation. ".format( + num_instances_without_valid_segmentation + ) + + "There might be issues in your dataset generation process. Please " + "check https://detectron2.readthedocs.io/en/latest/tutorials/datasets.html carefully" + ) + return dataset_dicts + + +def register_coco_instances(name, metadata, json_file, image_root): + """ + Register a dataset in COCO's json annotation format for + instance detection, instance segmentation and keypoint detection. + (i.e., Type 1 and 2 in http://cocodataset.org/#format-data. + `instances*.json` and `person_keypoints*.json` in the dataset). + + This is an example of how to register a new dataset. + You can do something similar to this function, to register new datasets. + + Args: + name (str): the name that identifies a dataset, e.g. "coco_2014_train". + metadata (dict): extra metadata associated with this dataset. You can + leave it as an empty dict. + json_file (str): path to the json instance annotation file. + image_root (str or path-like): directory which contains all the images. + """ + assert isinstance(name, str), name + assert isinstance(json_file, (str, os.PathLike)), json_file + assert isinstance(image_root, (str, os.PathLike)), image_root + # 1. register a function which returns dicts + DatasetCatalog.register(name, lambda: load_coco_json(json_file, image_root, name)) + + # 2. Optionally, add metadata about this dataset, + # since they might be useful in evaluation, visualization or logging + MetadataCatalog.get(name).set( + json_file=json_file, image_root=image_root, evaluator_type="coco", **metadata + ) + + +_PREDEFINED_SPLITS_ODINW = { + "odinw_AerialMaritimeDrone_large": { + "odinw_AerialMaritimeDrone_large_train": ( + "odinw/AerialMaritimeDrone/large/train/", + "odinw/AerialMaritimeDrone/large/train/annotations_without_background_converted.json", + ), + "odinw_AerialMaritimeDrone_large_val": ( + "odinw/AerialMaritimeDrone/large/valid/", + "odinw/AerialMaritimeDrone/large/valid/annotations_without_background_converted.json", + ), + "odinw_AerialMaritimeDrone_large_test": ( + "odinw/AerialMaritimeDrone/large/test/", + "odinw/AerialMaritimeDrone/large/test/annotations_without_background_converted.json", + ), + }, + "odinw_AerialMaritimeDrone_tiled": { + "odinw_AerialMaritimeDrone_tiled_train": ( + "odinw/AerialMaritimeDrone/tiled/train/", + "odinw/AerialMaritimeDrone/tiled/train/annotations_without_background_converted.json", + ), + "odinw_AerialMaritimeDrone_tiled_val": ( + "odinw/AerialMaritimeDrone/tiled/valid/", + "odinw/AerialMaritimeDrone/tiled/valid/annotations_without_background_converted.json", + ), + "odinw_AerialMaritimeDrone_tiled_test": ( + "odinw/AerialMaritimeDrone/tiled/test/", + "odinw/AerialMaritimeDrone/tiled/test/annotations_without_background_converted.json", + ), + }, + "odinw_AmericanSignLanguageLetters_American_Sign_Language_Letters.v1-v1.coco": { + "odinw_AmericanSignLanguageLetters_American_Sign_Language_Letters.v1-v1.coco_train": ( + "odinw/AmericanSignLanguageLetters/American Sign Language Letters.v1-v1.coco/train/", + "odinw/AmericanSignLanguageLetters/American Sign Language Letters.v1-v1.coco/train/annotations_without_background_converted.json", + ), + "odinw_AmericanSignLanguageLetters_American_Sign_Language_Letters.v1-v1.coco_val": ( + "odinw/AmericanSignLanguageLetters/American Sign Language Letters.v1-v1.coco/valid/", + "odinw/AmericanSignLanguageLetters/American Sign Language Letters.v1-v1.coco/valid/annotations_without_background_converted.json", + ), + "odinw_AmericanSignLanguageLetters_American_Sign_Language_Letters.v1-v1.coco_test": ( + "odinw/AmericanSignLanguageLetters/American Sign Language Letters.v1-v1.coco/test/", + "odinw/AmericanSignLanguageLetters/American Sign Language Letters.v1-v1.coco/test/annotations_without_background_converted.json", + ), + }, + "odinw_Aquarium_Aquarium_Combined.v2-raw-1024.coco": { + "odinw_Aquarium_Aquarium_Combined.v2-raw-1024.coco_train": ( + "odinw/Aquarium/Aquarium Combined.v2-raw-1024.coco/train/", + "odinw/Aquarium/Aquarium Combined.v2-raw-1024.coco/train/annotations_without_background_converted.json", + ), + "odinw_Aquarium_Aquarium_Combined.v2-raw-1024.coco_val": ( + "odinw/Aquarium/Aquarium Combined.v2-raw-1024.coco/valid/", + "odinw/Aquarium/Aquarium Combined.v2-raw-1024.coco/valid/annotations_without_background_converted.json", + ), + "odinw_Aquarium_Aquarium_Combined.v2-raw-1024.coco_test": ( + "odinw/Aquarium/Aquarium Combined.v2-raw-1024.coco/test/", + "odinw/Aquarium/Aquarium Combined.v2-raw-1024.coco/test/annotations_without_background_converted.json", + ), + }, + "odinw_BCCD_BCCD.v3-raw.coco": { + "odinw_BCCD_BCCD.v3-raw.coco_train": ( + "odinw/BCCD/BCCD.v3-raw.coco/train/", + "odinw/BCCD/BCCD.v3-raw.coco/train/annotations_without_background_converted.json", + ), + "odinw_BCCD_BCCD.v3-raw.coco_val": ( + "odinw/BCCD/BCCD.v3-raw.coco/valid/", + "odinw/BCCD/BCCD.v3-raw.coco/valid/annotations_without_background_converted.json", + ), + "odinw_BCCD_BCCD.v3-raw.coco_test": ( + "odinw/BCCD/BCCD.v3-raw.coco/test/", + "odinw/BCCD/BCCD.v3-raw.coco/test/annotations_without_background_converted.json", + ), + }, + "odinw_boggleBoards_416x416AutoOrient_export_": { + "odinw_boggleBoards_416x416AutoOrient_export_train": ( + "odinw/boggleBoards/416x416AutoOrient/export/", + "odinw/boggleBoards/416x416AutoOrient/export/train_annotations_without_background_converted.json", + ), + "odinw_boggleBoards_416x416AutoOrient_export_val": ( + "odinw/boggleBoards/416x416AutoOrient/export/", + "odinw/boggleBoards/416x416AutoOrient/export/val_annotations_without_background_converted.json", + ), + "odinw_boggleBoards_416x416AutoOrient_export_test": ( + "odinw/boggleBoards/416x416AutoOrient/export/", + "odinw/boggleBoards/416x416AutoOrient/export/test_annotations_without_background_converted.json", + ), + }, + "odinw_brackishUnderwater_960x540": { + "odinw_brackishUnderwater_960x540_train": ( + "odinw/brackishUnderwater/960x540/train/", + "odinw/brackishUnderwater/960x540/train/annotations_without_background_converted.json", + ), + "odinw_brackishUnderwater_960x540_val": ( + "odinw/brackishUnderwater/960x540/valid/", + "odinw/brackishUnderwater/960x540/valid/annotations_without_background_converted.json", + ), + "odinw_brackishUnderwater_960x540_minival": ( + "odinw/brackishUnderwater/960x540/mini_val/", + "odinw/brackishUnderwater/960x540/mini_val/annotations_without_background_converted.json", + ), + "odinw_brackishUnderwater_960x540_test": ( + "odinw/brackishUnderwater/960x540/test/", + "odinw/brackishUnderwater/960x540/test/annotations_without_background_converted.json", + ), + }, + "odinw_ChessPieces_Chess_Pieces.v23-raw.coco": { + "odinw_ChessPieces_Chess_Pieces.v23-raw.coco_train": ( + "odinw/ChessPieces/Chess Pieces.v23-raw.coco/train/", + "odinw/ChessPieces/Chess Pieces.v23-raw.coco/train/annotations_without_background_converted.json", + ), + "odinw_ChessPieces_Chess_Pieces.v23-raw.coco_val": ( + "odinw/ChessPieces/Chess Pieces.v23-raw.coco/valid/", + "odinw/ChessPieces/Chess Pieces.v23-raw.coco/valid/annotations_without_background_converted.json", + ), + "odinw_ChessPieces_Chess_Pieces.v23-raw.coco_test": ( + "odinw/ChessPieces/Chess Pieces.v23-raw.coco/test/", + "odinw/ChessPieces/Chess Pieces.v23-raw.coco/test/annotations_without_background_converted.json", + ), + }, + "odinw_CottontailRabbits": { + "odinw_CottontailRabbits_train": ( + "odinw/CottontailRabbits/train/", + "odinw/CottontailRabbits/train/annotations_without_background_converted.json", + ), + "odinw_CottontailRabbits_val": ( + "odinw/CottontailRabbits/valid/", + "odinw/CottontailRabbits/valid/annotations_without_background_converted.json", + ), + "odinw_CottontailRabbits_test": ( + "odinw/CottontailRabbits/test/", + "odinw/CottontailRabbits/test/annotations_without_background_converted.json", + ), + }, + "odinw_dice_mediumColor_export": { + "odinw_dice_mediumColor_export_train": ( + "odinw/dice/mediumColor/export/", + "odinw/dice/mediumColor/export/train_annotations_without_background_converted.json", + ), + "odinw_dice_mediumColor_export_val": ( + "odinw/dice/mediumColor/export/", + "odinw/dice/mediumColor/export/val_annotations_without_background_converted.json", + ), + "odinw_dice_mediumColor_export_test": ( + "odinw/dice/mediumColor/export/", + "odinw/dice/mediumColor/export/test_annotations_without_background_converted.json", + ), + }, + "odinw_DroneControl_Drone_Control.v3-raw.coco": { + "odinw_DroneControl_Drone_Control.v3-raw.coco_train": ( + "odinw/DroneControl/Drone Control.v3-raw.coco/train/", + "odinw/DroneControl/Drone Control.v3-raw.coco/train/annotations_without_background_converted.json", + ), + "odinw_DroneControl_Drone_Control.v3-raw.coco_val": ( + "odinw/DroneControl/Drone Control.v3-raw.coco/valid/", + "odinw/DroneControl/Drone Control.v3-raw.coco/valid/annotations_without_background_converted.json", + ), + "odinw_DroneControl_Drone_Control.v3-raw.coco_minival": ( + "odinw/DroneControl/Drone Control.v3-raw.coco/mini_val/", + "odinw/DroneControl/Drone Control.v3-raw.coco/mini_val/annotations_without_background_converted.json", + ), + "odinw_DroneControl_Drone_Control.v3-raw.coco_test": ( + "odinw/DroneControl/Drone Control.v3-raw.coco/test/", + "odinw/DroneControl/Drone Control.v3-raw.coco/test/annotations_without_background_converted.json", + ), + }, + "odinw_EgoHands-generic": { + "odinw_EgoHands_generic_train": ( + "odinw/EgoHands/generic/train/", + "odinw/EgoHands/generic/train/annotations_without_background_converted.json", + ), + "odinw_EgoHands_generic_val": ( + "odinw/EgoHands/generic/valid/", + "odinw/EgoHands/generic/valid/annotations_without_background_converted.json", + ), + "odinw_EgoHands_generic_minival": ( + "odinw/EgoHands/generic/mini_val/", + "odinw/EgoHands/generic/mini_val/annotations_without_background_converted.json", + ), + "odinw_EgoHands_generic_test": ( + "odinw/EgoHands/generic/test/", + "odinw/EgoHands/generic/test/annotations_without_background_converted.json", + ), + }, + "odinw_EgoHands-specific": { + "odinw_EgoHands_specific_train": ( + "odinw/EgoHands/specific/train/", + "odinw/EgoHands/specific/train/annotations_without_background_converted.json", + ), + "odinw_EgoHands_specific_val": ( + "odinw/EgoHands/specific/valid/", + "odinw/EgoHands/specific/valid/annotations_without_background_converted.json", + ), + "odinw_EgoHands_specific_minival": ( + "odinw/EgoHands/specific/mini_val/", + "odinw/EgoHands/specific/mini_val/annotations_without_background_converted.json", + ), + "odinw_EgoHands_specific_test": ( + "odinw/EgoHands/specific/test/", + "odinw/EgoHands/specific/test/annotations_without_background_converted.json", + ), + }, + "odinw_HardHatWorkers_raw": { + "odinw_HardHatWorkers_raw_train": ( + "odinw/HardHatWorkers/raw/train/", + "odinw/HardHatWorkers/raw/train/annotations_without_background_converted.json", + ), + "odinw_HardHatWorkers_raw_val": ( + "odinw/HardHatWorkers/raw/valid/", + "odinw/HardHatWorkers/raw/valid/annotations_without_background_converted.json", + ), + "odinw_HardHatWorkers_raw_test": ( + "odinw/HardHatWorkers/raw/test/", + "odinw/HardHatWorkers/raw/test/annotations_without_background_converted.json", + ), + }, + "odinw_MaskWearing_raw": { + "odinw_MaskWearing_raw_train": ( + "odinw/MaskWearing/raw/train/", + "odinw/MaskWearing/raw/train/annotations_without_background_converted.json", + ), + "odinw_MaskWearing_raw_val": ( + "odinw/MaskWearing/raw/valid/", + "odinw/MaskWearing/raw/valid/annotations_without_background_converted.json", + ), + "odinw_MaskWearing_raw_test": ( + "odinw/MaskWearing/raw/test/", + "odinw/MaskWearing/raw/test/annotations_without_background_converted.json", + ), + }, + "odinw_MountainDewCommercial": { + "odinw_MountainDewCommercial_train": ( + "odinw/MountainDewCommercial/train/", + "odinw/MountainDewCommercial/train/annotations_without_background_converted.json", + ), + "odinw_MountainDewCommercial_val": ( + "odinw/MountainDewCommercial/valid/", + "odinw/MountainDewCommercial/valid/annotations_without_background_converted.json", + ), + "odinw_MountainDewCommercial_test": ( + "odinw/MountainDewCommercial/test/", + "odinw/MountainDewCommercial/test/annotations_without_background_converted.json", + ), + }, + "odinw_NorthAmericaMushrooms_North_American_Mushrooms.v1-416x416.coco": { + "odinw_NorthAmericaMushrooms_North_American_Mushrooms.v1-416x416.coco_train": ( + "odinw/NorthAmericaMushrooms/North American Mushrooms.v1-416x416.coco/train/", + "odinw/NorthAmericaMushrooms/North American Mushrooms.v1-416x416.coco/train/annotations_without_background_converted.json", + ), + "odinw_NorthAmericaMushrooms_North_American_Mushrooms.v1-416x416.coco_val": ( + "odinw/NorthAmericaMushrooms/North American Mushrooms.v1-416x416.coco/valid/", + "odinw/NorthAmericaMushrooms/North American Mushrooms.v1-416x416.coco/valid/annotations_without_background_converted.json", + ), + "odinw_NorthAmericaMushrooms_North_American_Mushrooms.v1-416x416.coco_test": ( + "odinw/NorthAmericaMushrooms/North American Mushrooms.v1-416x416.coco/test/", + "odinw/NorthAmericaMushrooms/North American Mushrooms.v1-416x416.coco/test/annotations_without_background_converted.json", + ), + }, + "odinw_openPoetryVision_512x512": { + "odinw_openPoetryVision_512x512_train": ( + "odinw/openPoetryVision/512x512/train/", + "odinw/openPoetryVision/512x512/train/annotations_without_background_converted.json", + ), + "odinw_openPoetryVision_512x512_val": ( + "odinw/openPoetryVision/512x512/valid/", + "odinw/openPoetryVision/512x512/valid/annotations_without_background_converted.json", + ), + "odinw_openPoetryVision_512x512_minival": ( + "odinw/openPoetryVision/512x512/mini_val/", + "odinw/openPoetryVision/512x512/mini_val/annotations_without_background_converted.json", + ), + "odinw_openPoetryVision_512x512_test": ( + "odinw/openPoetryVision/512x512/test/", + "odinw/openPoetryVision/512x512/test/annotations_without_background_converted.json", + ), + }, + "odinw_OxfordPets-by-breed": { + "odinw_OxfordPets_by-breed_train": ( + "odinw/OxfordPets/by-breed/train/", + "odinw/OxfordPets/by-breed/train/annotations_without_background_converted.json", + ), + "odinw_OxfordPets_by-breed_val": ( + "odinw/OxfordPets/by-breed/valid/", + "odinw/OxfordPets/by-breed/valid/annotations_without_background_converted.json", + ), + "odinw_OxfordPets_by-breed_minival": ( + "odinw/OxfordPets/by-breed/mini_val/", + "odinw/OxfordPets/by-breed/mini_val/annotations_without_background_converted.json", + ), + "odinw_OxfordPets_by-breed_test": ( + "odinw/OxfordPets/by-breed/test/", + "odinw/OxfordPets/by-breed/test/annotations_without_background_converted.json", + ), + }, + "odinw_OxfordPets-by-species": { + "odinw_OxfordPets_by-species_train": ( + "odinw/OxfordPets/by-species/train/", + "odinw/OxfordPets/by-species/train/annotations_without_background_converted.json", + ), + "odinw_OxfordPets_by-species_val": ( + "odinw/OxfordPets/by-species/valid/", + "odinw/OxfordPets/by-species/valid/annotations_without_background_converted.json", + ), + "odinw_OxfordPets_by-species_minival": ( + "odinw/OxfordPets/by-species/mini_val/", + "odinw/OxfordPets/by-species/mini_val/annotations_without_background_converted.json", + ), + "odinw_OxfordPets_by-species_test": ( + "odinw/OxfordPets/by-species/test/", + "odinw/OxfordPets/by-species/test/annotations_without_background_converted.json", + ), + }, + "odinw_Packages_Raw": { + "odinw_Packages_Raw_train": ( + "odinw/Packages/Raw/train/", + "odinw/Packages/Raw/train/annotations_without_background_converted.json", + ), + "odinw_Packages_Raw_val": ( + "odinw/Packages/Raw/valid/", + "odinw/Packages/Raw/valid/annotations_without_background_converted.json", + ), + "odinw_Packages_Raw_test": ( + "odinw/Packages/Raw/test/", + "odinw/Packages/Raw/test/annotations_without_background_converted.json", + # "odinw/Packages/Raw/test/_annotations.coco_converted.json", + ), + }, + "odinw_PascalVOC": { + "odinw_PascalVOC_train": ( + "odinw/PascalVOC/train/", + "odinw/PascalVOC/train/annotations_without_background_converted.json", + ), + "odinw_PascalVOC_val": ( + "odinw/PascalVOC/valid/", + "odinw/PascalVOC/valid/annotations_without_background_converted.json", + ), + }, + "odinw_pistols_export": { + "odinw_pistols_export_train": ( + "odinw/pistols/export/", + "odinw/pistols/export/train_annotations_without_background_converted.json", + ), + "odinw_pistols_export_val": ( + "odinw/pistols/export/", + "odinw/pistols/export/val_annotations_without_background_converted.json", + ), + "odinw_pistols_export_test": ( + "odinw/pistols/export/", + "odinw/pistols/export/test_annotations_without_background_converted.json", + ), + }, + "odinw_PKLot_640": { + "odinw_PKLot_640_train": ( + "odinw/PKLot/640/train/", + "odinw/PKLot/640/train/annotations_without_background_converted.json", + ), + "odinw_PKLot_640_val": ( + "odinw/PKLot/640/valid/", + "odinw/PKLot/640/valid/annotations_without_background_converted.json", + ), + "odinw_PKLot_640_minival": ( + "odinw/PKLot/640/mini_val/", + "odinw/PKLot/640/mini_val/annotations_without_background_converted.json", + ), + "odinw_PKLot_640_test": ( + "odinw/PKLot/640/test/", + "odinw/PKLot/640/test/annotations_without_background_converted.json", + ), + }, + "odinw_plantdoc_100x100": { + "odinw_plantdoc_100x100_train": ( + "odinw/plantdoc/100x100/train/", + "odinw/plantdoc/100x100/train/annotations_without_background_converted.json", + ), + "odinw_plantdoc_100x100_val": ( + "odinw/plantdoc/100x100/valid/", + "odinw/plantdoc/100x100/valid/annotations_without_background_converted.json", + ), + "odinw_plantdoc_100x100_test": ( + "odinw/plantdoc/100x100/test/", + "odinw/plantdoc/100x100/test/annotations_without_background_converted.json", + ), + }, + "odinw_plantdoc_416x416": { + "odinw_plantdoc_416x416_train": ( + "odinw/plantdoc/416x416/train/", + "odinw/plantdoc/416x416/train/annotations_without_background_converted.json", + ), + "odinw_plantdoc_416x416_val": ( + "odinw/plantdoc/416x416/valid/", + "odinw/plantdoc/416x416/valid/annotations_without_background_converted.json", + ), + "odinw_plantdoc_416x416_test": ( + "odinw/plantdoc/416x416/test/", + "odinw/plantdoc/416x416/test/annotations_without_background_converted.json", + ), + }, + "odinw_pothole": { + "odinw_pothole_train": ( + "odinw/pothole/train/", + "odinw/pothole/train/annotations_without_background_converted.json", + ), + "odinw_pothole_val": ( + "odinw/pothole/valid/", + "odinw/pothole/valid/annotations_without_background_converted.json", + ), + "odinw_pothole_test": ( + "odinw/pothole/test/", + "odinw/pothole/test/annotations_without_background_converted.json", + ), + }, + "odinw_Raccoon_Raccoon.v2-raw.coco": { + "odinw_Raccoon_Raccoon.v2-raw.coco_train": ( + "odinw/Raccoon/Raccoon.v2-raw.coco/train/", + "odinw/Raccoon/Raccoon.v2-raw.coco/train/annotations_without_background_converted.json", + ), + "odinw_Raccoon_Raccoon.v2-raw.coco_val": ( + "odinw/Raccoon/Raccoon.v2-raw.coco/valid/", + "odinw/Raccoon/Raccoon.v2-raw.coco/valid/annotations_without_background_converted.json", + ), + "odinw_Raccoon_Raccoon.v2-raw.coco_test": ( + "odinw/Raccoon/Raccoon.v2-raw.coco/test/", + "odinw/Raccoon/Raccoon.v2-raw.coco/test/annotations_without_background_converted.json", + ), + }, + "odinw_selfdrivingCar_fixedLarge_export": { + "odinw_selfdrivingCar_fixedLarge_export_train": ( + "odinw/selfdrivingCar/fixedLarge/export/", + "odinw/selfdrivingCar/fixedLarge/export/train_annotations_without_background_converted.json", + ), + "odinw_selfdrivingCar_fixedLarge_export_val": ( + "odinw/selfdrivingCar/fixedLarge/export/", + "odinw/selfdrivingCar/fixedLarge/export/val_annotations_without_background_converted.json", + ), + "odinw_selfdrivingCar_fixedLarge_export_test": ( + "odinw/selfdrivingCar/fixedLarge/export/", + "odinw/selfdrivingCar/fixedLarge/export/test_annotations_without_background_converted.json", + ), + }, + "odinw_ShellfishOpenImages_raw": { + "odinw_ShellfishOpenImages_raw_train": ( + "odinw/ShellfishOpenImages/raw/train/", + "odinw/ShellfishOpenImages/raw/train/annotations_without_background_converted.json", + ), + "odinw_ShellfishOpenImages_raw_val": ( + "odinw/ShellfishOpenImages/raw/valid/", + "odinw/ShellfishOpenImages/raw/valid/annotations_without_background_converted.json", + ), + "odinw_ShellfishOpenImages_raw_test": ( + "odinw/ShellfishOpenImages/raw/test/", + "odinw/ShellfishOpenImages/raw/test/annotations_without_background_converted.json", + ), + }, + "odinw_ThermalCheetah": { + "odinw_ThermalCheetah_train": ( + "odinw/ThermalCheetah/train/", + "odinw/ThermalCheetah/train/annotations_without_background_converted.json", + ), + "odinw_ThermalCheetah_val": ( + "odinw/ThermalCheetah/valid/", + "odinw/ThermalCheetah/valid/annotations_without_background_converted.json", + ), + "odinw_ThermalCheetah_test": ( + "odinw/ThermalCheetah/test/", + "odinw/ThermalCheetah/test/annotations_without_background_converted.json", + ), + }, + "odinw_thermalDogsAndPeople": { + "odinw_thermalDogsAndPeople_train": ( + "odinw/thermalDogsAndPeople/train/", + "odinw/thermalDogsAndPeople/train/annotations_without_background_converted.json", + ), + "odinw_thermalDogsAndPeople_val": ( + "odinw/thermalDogsAndPeople/valid/", + "odinw/thermalDogsAndPeople/valid/annotations_without_background_converted.json", + ), + "odinw_thermalDogsAndPeople_test": ( + "odinw/thermalDogsAndPeople/test/", + "odinw/thermalDogsAndPeople/test/annotations_without_background_converted.json", + ), + }, + "odinw_UnoCards_raw": { + "odinw_UnoCards_raw_train": ( + "odinw/UnoCards/raw/train/", + "odinw/UnoCards/raw/train/annotations_without_background_converted.json", + ), + "odinw_UnoCards_raw_val": ( + "odinw/UnoCards/raw/valid/", + "odinw/UnoCards/raw/valid/annotations_without_background_converted.json", + ), + "odinw_UnoCards_raw_minival": ( + "odinw/UnoCards/raw/mini_val/", + "odinw/UnoCards/raw/mini_val/annotations_without_background_converted.json", + ), + "odinw_UnoCards_raw_test": ( + "odinw/UnoCards/raw/test/", + "odinw/UnoCards/raw/test/annotations_without_background_converted.json", + ), + }, + "odinw_VehiclesOpenImages_416x416": { + "odinw_VehiclesOpenImages_416x416_train": ( + "odinw/VehiclesOpenImages/416x416/train/", + "odinw/VehiclesOpenImages/416x416/train/annotations_without_background_converted.json", + ), + "odinw_VehiclesOpenImages_416x416_val": ( + "odinw/VehiclesOpenImages/416x416/valid/", + "odinw/VehiclesOpenImages/416x416/valid/annotations_without_background_converted.json", + ), + "odinw_VehiclesOpenImages_416x416_minival": ( + "odinw/VehiclesOpenImages/416x416/mini_val/", + "odinw/VehiclesOpenImages/416x416/mini_val/annotations_without_background_converted.json", + ), + "odinw_VehiclesOpenImages_416x416_test": ( + "odinw/VehiclesOpenImages/416x416/test/", + "odinw/VehiclesOpenImages/416x416/test/annotations_without_background_converted.json", + ), + }, + "odinw_websiteScreenshots": { + "odinw_websiteScreenshots_train": ( + "odinw/websiteScreenshots/train/", + "odinw/websiteScreenshots/train/annotations_without_background_converted.json", + ), + "odinw_websiteScreenshots_val": ( + "odinw/websiteScreenshots/valid/", + "odinw/websiteScreenshots/valid/annotations_without_background_converted.json", + ), + "odinw_websiteScreenshots_minival": ( + "odinw/websiteScreenshots/mini_val/", + "odinw/websiteScreenshots/mini_val/annotations_without_background_converted.json", + ), + "odinw_websiteScreenshots_test": ( + "odinw/websiteScreenshots/test/", + "odinw/websiteScreenshots/test/annotations_without_background_converted.json", + ), + }, + "odinw_WildfireSmoke": { + "odinw_WildfireSmoke_train": ( + "odinw/WildfireSmoke/train/", + "odinw/WildfireSmoke/train/annotations_without_background_converted.json", + ), + "odinw_WildfireSmoke_val": ( + "odinw/WildfireSmoke/valid/", + "odinw/WildfireSmoke/valid/annotations_without_background_converted.json", + ), + "odinw_WildfireSmoke_test": ( + "odinw/WildfireSmoke/test/", + "odinw/WildfireSmoke/test/annotations_without_background_converted.json", + ), + }, +} + + +def _get_builtin_metadata(name): + meta = {} + if name.split("_")[1] in ODINW_PROMPTS: + meta["thing_classes"] = [ + ODINW_PROMPTS[name.split("_")[1]](m["name"]) + for m in ODINW_CATEGORIES[name.split("_")[1]] + ] + else: + meta["thing_classes"] = [m["name"] for m in ODINW_CATEGORIES[name.split("_")[1]]] + return meta + + +def register_all_odinw(root): + for dataset_name, splits_per_dataset in _PREDEFINED_SPLITS_ODINW.items(): + for key, (image_root, json_file) in splits_per_dataset.items(): + register_coco_instances( + key, + _get_builtin_metadata(dataset_name), + os.path.join(root, json_file) if "://" not in json_file else json_file, + os.path.join(root, image_root), + ) + + +if __name__.endswith(".odinw_instance"): + # Assume pre-defined datasets live in `./datasets`. + _root = os.getenv("DATASET", "datasets") + register_all_odinw(_root) diff --git a/ape/data/datasets/odinw_prompts.py b/ape/data/datasets/odinw_prompts.py new file mode 100644 index 0000000..5fe1a38 --- /dev/null +++ b/ape/data/datasets/odinw_prompts.py @@ -0,0 +1,75 @@ +ODINW_PROMPTS = { + "AerialMaritimeDrone": lambda name: "a ship" if name == "boat" else name, + "AmericanSignLanguageLetters": lambda name: "hand gesture '{}'".format(name), + "BCCD": lambda name: "Red-Blood-Cell-(RBC)" + if name == "RBC" + else "White-Blood-Cell-(WBC)" + if name == "WBC" + else "Blood-Platelet-Cell-(BPC)" + if name == "Platelets" + else name, + "boggleBoards": lambda name: "letter '{}'".format(name.upper()), + "brackishUnderwater": lambda name: "big_fish" if name == "fish" else name, + "ChessPieces": lambda name: "chess piece {}".format(name), + "dice": lambda name: "dice {}".format(name), + "DroneControl": lambda name: "body gesture '{}'".format(name), + "EgoHands-specific": lambda name: "{} hand".format(name), + # "EgoHands-specific": lambda name: "my left hand" + # if name == "myleft" + # else "my right hand" + # if name == "myright" + # else "your right hand" + # if name == "yourright" + # else "your left hand" + # if name == "yourleft" + # else name, + "HardHatWorkers": lambda name: "human head wearing a helmet" + if name == "helmet" + else "human head" + if name == "head" + else name, + "MaskWearing": lambda name: "human head wearing a mask" + if name == "mask" + else "human head" + if name == "no-mask" + else name, + "MountainDewCommercial": lambda name: "small {}".format(name), + "NorthAmericaMushrooms": lambda name: "mushroom {}".format(name), + "openPoetryVision": lambda name: "some text with font {}".format(name), + "OxfordPets-by-breed": lambda name: "head of {}".format(name), + "OxfordPets-by-species": lambda name: "head of {}".format(name), + "PKLot": lambda name: "{} parking slot".format(name), + "pothole": lambda name: "broken {}".format(name), + "ThermalCheetah": lambda name: "person" if name == "human" else name, + "UnoCards": lambda name: "Arabic numerals 0" + if name == "0" + else "Arabic numerals 1" + if name == "1" + else "Arabic numerals +4" + if name == "2" + else "Arabic numerals +2" + if name == "3" + else "two arrows" + if name == "4" + else "cross cycle" + if name == "5" + else "colorful cycle" + if name == "6" + else "Arabic numerals 2" + if name == "7" + else "Arabic numerals 3" + if name == "8" + else "Arabic numerals 4" + if name == "9" + else "Arabic numerals 5" + if name == "10" + else "Arabic numerals 6" + if name == "11" + else "Arabic numerals 7" + if name == "12" + else "Arabic numerals 8" + if name == "13" + else "Arabic numerals 9" + if name == "14" + else name, +} diff --git a/ape/data/datasets/oid.py b/ape/data/datasets/oid.py new file mode 100644 index 0000000..8f85a20 --- /dev/null +++ b/ape/data/datasets/oid.py @@ -0,0 +1,1571 @@ +import contextlib +import io +import logging +import os + +from detectron2.data import DatasetCatalog, MetadataCatalog + +from .coco import custom_load_coco_json + + +def register_oid_instances(name, metadata, json_file, image_root): + """ + Register a dataset in COCO's json annotation format for + instance detection, instance segmentation and keypoint detection. + (i.e., Type 1 and 2 in http://cocodataset.org/#format-data. + `instances*.json` and `person_keypoints*.json` in the dataset). + + This is an example of how to register a new dataset. + You can do something similar to this function, to register new datasets. + + Args: + name (str): the name that identifies a dataset, e.g. "coco_2014_train". + metadata (dict): extra metadata associated with this dataset. You can + leave it as an empty dict. + json_file (str): path to the json instance annotation file. + image_root (str or path-like): directory which contains all the images. + """ + assert isinstance(name, str), name + assert isinstance(json_file, (str, os.PathLike)), json_file + assert isinstance(image_root, (str, os.PathLike)), image_root + # 1. register a function which returns dicts + DatasetCatalog.register(name, lambda: custom_load_coco_json(json_file, image_root, name)) + + # 2. Optionally, add metadata about this dataset, + # since they might be useful in evaluation, visualization or logging + MetadataCatalog.get(name).set( + json_file=json_file, image_root=image_root, evaluator_type="oid", **metadata + ) + + +OPENIMAGES_2019_CATEGORIES = [ + {"id": 1, "name": "Infant bed", "freebase_id": "/m/061hd_"}, + {"id": 2, "name": "Rose", "freebase_id": "/m/06m11"}, + {"id": 3, "name": "Flag", "freebase_id": "/m/03120"}, + {"id": 4, "name": "Flashlight", "freebase_id": "/m/01kb5b"}, + {"id": 5, "name": "Sea turtle", "freebase_id": "/m/0120dh"}, + {"id": 6, "name": "Camera", "freebase_id": "/m/0dv5r"}, + {"id": 7, "name": "Animal", "freebase_id": "/m/0jbk"}, + {"id": 8, "name": "Glove", "freebase_id": "/m/0174n1"}, + {"id": 9, "name": "Crocodile", "freebase_id": "/m/09f_2"}, + {"id": 10, "name": "Cattle", "freebase_id": "/m/01xq0k1"}, + {"id": 11, "name": "House", "freebase_id": "/m/03jm5"}, + {"id": 12, "name": "Guacamole", "freebase_id": "/m/02g30s"}, + {"id": 13, "name": "Penguin", "freebase_id": "/m/05z6w"}, + {"id": 14, "name": "Vehicle registration plate", "freebase_id": "/m/01jfm_"}, + {"id": 15, "name": "Bench", "freebase_id": "/m/076lb9"}, + {"id": 16, "name": "Ladybug", "freebase_id": "/m/0gj37"}, + {"id": 17, "name": "Human nose", "freebase_id": "/m/0k0pj"}, + {"id": 18, "name": "Watermelon", "freebase_id": "/m/0kpqd"}, + {"id": 19, "name": "Flute", "freebase_id": "/m/0l14j_"}, + {"id": 20, "name": "Butterfly", "freebase_id": "/m/0cyf8"}, + {"id": 21, "name": "Washing machine", "freebase_id": "/m/0174k2"}, + {"id": 22, "name": "Raccoon", "freebase_id": "/m/0dq75"}, + {"id": 23, "name": "Segway", "freebase_id": "/m/076bq"}, + {"id": 24, "name": "Taco", "freebase_id": "/m/07crc"}, + {"id": 25, "name": "Jellyfish", "freebase_id": "/m/0d8zb"}, + {"id": 26, "name": "Cake", "freebase_id": "/m/0fszt"}, + {"id": 27, "name": "Pen", "freebase_id": "/m/0k1tl"}, + {"id": 28, "name": "Cannon", "freebase_id": "/m/020kz"}, + {"id": 29, "name": "Bread", "freebase_id": "/m/09728"}, + {"id": 30, "name": "Tree", "freebase_id": "/m/07j7r"}, + {"id": 31, "name": "Shellfish", "freebase_id": "/m/0fbdv"}, + {"id": 32, "name": "Bed", "freebase_id": "/m/03ssj5"}, + {"id": 33, "name": "Hamster", "freebase_id": "/m/03qrc"}, + {"id": 34, "name": "Hat", "freebase_id": "/m/02dl1y"}, + {"id": 35, "name": "Toaster", "freebase_id": "/m/01k6s3"}, + {"id": 36, "name": "Sombrero", "freebase_id": "/m/02jfl0"}, + {"id": 37, "name": "Tiara", "freebase_id": "/m/01krhy"}, + {"id": 38, "name": "Bowl", "freebase_id": "/m/04kkgm"}, + {"id": 39, "name": "Dragonfly", "freebase_id": "/m/0ft9s"}, + {"id": 40, "name": "Moths and butterflies", "freebase_id": "/m/0d_2m"}, + {"id": 41, "name": "Antelope", "freebase_id": "/m/0czz2"}, + {"id": 42, "name": "Vegetable", "freebase_id": "/m/0f4s2w"}, + {"id": 43, "name": "Torch", "freebase_id": "/m/07dd4"}, + {"id": 44, "name": "Building", "freebase_id": "/m/0cgh4"}, + {"id": 45, "name": "Power plugs and sockets", "freebase_id": "/m/03bbps"}, + {"id": 46, "name": "Blender", "freebase_id": "/m/02pjr4"}, + {"id": 47, "name": "Billiard table", "freebase_id": "/m/04p0qw"}, + {"id": 48, "name": "Cutting board", "freebase_id": "/m/02pdsw"}, + {"id": 49, "name": "Bronze sculpture", "freebase_id": "/m/01yx86"}, + {"id": 50, "name": "Turtle", "freebase_id": "/m/09dzg"}, + {"id": 51, "name": "Broccoli", "freebase_id": "/m/0hkxq"}, + {"id": 52, "name": "Tiger", "freebase_id": "/m/07dm6"}, + {"id": 53, "name": "Mirror", "freebase_id": "/m/054_l"}, + {"id": 54, "name": "Bear", "freebase_id": "/m/01dws"}, + {"id": 55, "name": "Zucchini", "freebase_id": "/m/027pcv"}, + {"id": 56, "name": "Dress", "freebase_id": "/m/01d40f"}, + {"id": 57, "name": "Volleyball", "freebase_id": "/m/02rgn06"}, + {"id": 58, "name": "Guitar", "freebase_id": "/m/0342h"}, + {"id": 59, "name": "Reptile", "freebase_id": "/m/06bt6"}, + {"id": 60, "name": "Golf cart", "freebase_id": "/m/0323sq"}, + {"id": 61, "name": "Tart", "freebase_id": "/m/02zvsm"}, + {"id": 62, "name": "Fedora", "freebase_id": "/m/02fq_6"}, + {"id": 63, "name": "Carnivore", "freebase_id": "/m/01lrl"}, + {"id": 64, "name": "Car", "freebase_id": "/m/0k4j"}, + {"id": 65, "name": "Lighthouse", "freebase_id": "/m/04h7h"}, + {"id": 66, "name": "Coffeemaker", "freebase_id": "/m/07xyvk"}, + {"id": 67, "name": "Food processor", "freebase_id": "/m/03y6mg"}, + {"id": 68, "name": "Truck", "freebase_id": "/m/07r04"}, + {"id": 69, "name": "Bookcase", "freebase_id": "/m/03__z0"}, + {"id": 70, "name": "Surfboard", "freebase_id": "/m/019w40"}, + {"id": 71, "name": "Footwear", "freebase_id": "/m/09j5n"}, + {"id": 72, "name": "Bench", "freebase_id": "/m/0cvnqh"}, + {"id": 73, "name": "Necklace", "freebase_id": "/m/01llwg"}, + {"id": 74, "name": "Flower", "freebase_id": "/m/0c9ph5"}, + {"id": 75, "name": "Radish", "freebase_id": "/m/015x5n"}, + {"id": 76, "name": "Marine mammal", "freebase_id": "/m/0gd2v"}, + {"id": 77, "name": "Frying pan", "freebase_id": "/m/04v6l4"}, + {"id": 78, "name": "Tap", "freebase_id": "/m/02jz0l"}, + {"id": 79, "name": "Peach", "freebase_id": "/m/0dj6p"}, + {"id": 80, "name": "Knife", "freebase_id": "/m/04ctx"}, + {"id": 81, "name": "Handbag", "freebase_id": "/m/080hkjn"}, + {"id": 82, "name": "Laptop", "freebase_id": "/m/01c648"}, + {"id": 83, "name": "Tent", "freebase_id": "/m/01j61q"}, + {"id": 84, "name": "Ambulance", "freebase_id": "/m/012n7d"}, + {"id": 85, "name": "Christmas tree", "freebase_id": "/m/025nd"}, + {"id": 86, "name": "Eagle", "freebase_id": "/m/09csl"}, + {"id": 87, "name": "Limousine", "freebase_id": "/m/01lcw4"}, + {"id": 88, "name": "Kitchen & dining room table", "freebase_id": "/m/0h8n5zk"}, + {"id": 89, "name": "Polar bear", "freebase_id": "/m/0633h"}, + {"id": 90, "name": "Tower", "freebase_id": "/m/01fdzj"}, + {"id": 91, "name": "Football", "freebase_id": "/m/01226z"}, + {"id": 92, "name": "Willow", "freebase_id": "/m/0mw_6"}, + {"id": 93, "name": "Human head", "freebase_id": 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"name": "Backpack", "freebase_id": "/m/01940j"}, + {"id": 252, "name": "Skirt", "freebase_id": "/m/02wv6h6"}, + {"id": 253, "name": "Van", "freebase_id": "/m/0h2r6"}, + {"id": 254, "name": "Bread", "freebase_id": "/m/09728"}, + {"id": 255, "name": "Glove", "freebase_id": "/m/0174n1"}, + {"id": 256, "name": "Dog", "freebase_id": "/m/0bt9lr"}, + {"id": 257, "name": "Airplane", "freebase_id": "/m/0cmf2"}, + {"id": 258, "name": "Motorcycle", "freebase_id": "/m/04_sv"}, + {"id": 259, "name": "Drink", "freebase_id": "/m/0271t"}, + {"id": 260, "name": "Book", "freebase_id": "/m/0bt_c3"}, + {"id": 261, "name": "Train", "freebase_id": "/m/07jdr"}, + {"id": 262, "name": "Flower", "freebase_id": "/m/0c9ph5"}, + {"id": 263, "name": "Carnivore", "freebase_id": "/m/01lrl"}, + {"id": 264, "name": "Human ear", "freebase_id": "/m/039xj_"}, + {"id": 265, "name": "Toy", "freebase_id": "/m/0138tl"}, + {"id": 266, "name": "Box", "freebase_id": "/m/025dyy"}, + {"id": 267, "name": "Truck", "freebase_id": "/m/07r04"}, + {"id": 268, "name": "Wheel", "freebase_id": "/m/083wq"}, + {"id": 269, "name": "Aircraft", "freebase_id": "/m/0k5j"}, + {"id": 270, "name": "Bus", "freebase_id": "/m/01bjv"}, + {"id": 271, "name": "Human mouth", "freebase_id": "/m/0283dt1"}, + {"id": 272, "name": "Sculpture", "freebase_id": "/m/06msq"}, + {"id": 273, "name": "Shirt", "freebase_id": "/m/01n4qj"}, + {"id": 274, "name": "Hat", "freebase_id": "/m/02dl1y"}, + {"id": 275, "name": "Vehicle registration plate", "freebase_id": "/m/01jfm_"}, + {"id": 276, "name": "Guitar", "freebase_id": "/m/0342h"}, + {"id": 277, "name": "Sun hat", "freebase_id": "/m/02wbtzl"}, + {"id": 278, "name": "Bottle", "freebase_id": "/m/04dr76w"}, + {"id": 279, "name": "Luggage and bags", "freebase_id": "/m/0hf58v5"}, + {"id": 280, "name": "Trousers", "freebase_id": "/m/07mhn"}, + {"id": 281, "name": "Bicycle wheel", "freebase_id": "/m/01bqk0"}, + {"id": 282, "name": "Suit", "freebase_id": "/m/01xyhv"}, + {"id": 283, "name": "Bowl", "freebase_id": "/m/04kkgm"}, + {"id": 284, "name": "Man", "freebase_id": "/m/04yx4"}, + {"id": 285, "name": "Flowerpot", "freebase_id": "/m/0fm3zh"}, + {"id": 286, "name": "Laptop", "freebase_id": "/m/01c648"}, + {"id": 287, "name": "Boy", "freebase_id": "/m/01bl7v"}, + {"id": 288, "name": "Picture frame", "freebase_id": "/m/06z37_"}, + {"id": 289, "name": "Bird", "freebase_id": "/m/015p6"}, + {"id": 290, "name": "Car", "freebase_id": "/m/0k4j"}, + {"id": 291, "name": "Shorts", "freebase_id": "/m/01bfm9"}, + {"id": 292, "name": "Woman", "freebase_id": "/m/03bt1vf"}, + {"id": 293, "name": "Platter", "freebase_id": "/m/099ssp"}, + {"id": 294, "name": "Tie", "freebase_id": "/m/01rkbr"}, + {"id": 295, "name": "Girl", "freebase_id": "/m/05r655"}, + {"id": 296, "name": "Skyscraper", "freebase_id": "/m/079cl"}, + {"id": 297, "name": "Person", "freebase_id": "/m/01g317"}, + {"id": 298, "name": "Flag", "freebase_id": "/m/03120"}, + {"id": 299, "name": "Jeans", "freebase_id": "/m/0fly7"}, + {"id": 300, "name": "Dress", "freebase_id": "/m/01d40f"}, +] + + +def _get_builtin_metadata(cats): + id_to_name = {x["id"]: x["name"] for x in cats} + thing_dataset_id_to_contiguous_id = {i + 1: i for i in range(len(cats))} + thing_classes = [x["name"] for x in sorted(cats, key=lambda x: x["id"])] + return { + "thing_dataset_id_to_contiguous_id": thing_dataset_id_to_contiguous_id, + "thing_classes": thing_classes, + } + + +_PREDEFINED_SPLITS_OID = { + "oid_train": ("oid/images/train/", "oid/annotations/oid_challenge_2019_train_bbox.json"), + "oid_val": ("oid/images/validation/", "oid/annotations/oid_challenge_2019_val.json"), + "oid_val_expanded": ( + "oid/images/validation/", + "oid/annotations/oid_challenge_2019_val_expanded.json", + ), + "oid_kaggle_test": ("oid/images/test/", "oid/annotations/oid_kaggle_test_image_info.json"), +} + + +for key, (image_root, json_file) in _PREDEFINED_SPLITS_OID.items(): + register_oid_instances( + key, + _get_builtin_metadata(OPENIMAGES_2019_CATEGORIES), + os.path.join("datasets", json_file) if "://" not in json_file else json_file, + os.path.join("datasets", image_root), + ) + +_PREDEFINED_SPLITS_OID_SEG = { + "oid_seg_train": ("oid/images/train/", "oid/annotations/openimages_instances_train.json"), + "oid_seg_val": ("oid/images/validation/", "oid/annotations/openimages_instances_val.json"), + "oid_seg_kaggle_test": ( + "oid/images/test/", + "oid/annotations/openimages_instances_kaggle_test_image_info.json", + ), +} + + +for key, (image_root, json_file) in _PREDEFINED_SPLITS_OID_SEG.items(): + register_oid_instances( + key, + _get_builtin_metadata(categories_seg), + os.path.join("datasets", json_file) if "://" not in json_file else json_file, + os.path.join("datasets", image_root), + ) + + +_PREDEFINED_SPLITS_OPENIMAGES_DETECTION = { + "openimages_challenge_2019_train": ( + "openimages/train/", + "openimages/annotations/openimages_challenge_2019_train_bbox.json", + ), + "openimages_challenge_2019_val": ( + "openimages/validation/", + "openimages/annotations/openimages_challenge_2019_val_bbox.json", + ), +} + + +for key, (image_root, json_file) in _PREDEFINED_SPLITS_OPENIMAGES_DETECTION.items(): + register_oid_instances( + key, + _get_builtin_metadata(OPENIMAGES_2019_CATEGORIES), + os.path.join("datasets", json_file) if "://" not in json_file else json_file, + os.path.join("datasets", image_root), + ) + + +_PREDEFINED_SPLITS_OPENIMAGES_V6_DETECTION = { + "openimages_v6_train_bbox": ( + "openimages/train/", + "openimages/annotations/openimages_v6_train_bbox.json", + ), + "openimages_v6_train_bbox_nogroup": ( + "openimages/train/", + "openimages/annotations/openimages_v6_train_bbox_nogroup.json", + ), + "openimages_v6_val_bbox": ( + "openimages/validation/", + "openimages/annotations/openimages_v6_val_bbox.json", + ), + "openimages_v6_val_bbox_nogroup": ( + "openimages/validation/", + "openimages/annotations/openimages_v6_val_bbox_nogroup.json", + ), + "openimages_v6_train_instance": ( + "openimages/train/", + "openimages/annotations/openimages_v6_train_instance.json", + ), + "openimages_v6_val_instance": ( + "openimages/validation/", + "openimages/annotations/openimages_v6_val_instance.json", + ), + "openimages_v6_train_bbox_instance": ( + "openimages/train/", + "openimages/annotations/openimages_v6_train_bbox_instance.json", + ), + "openimages_v6_val_bbox_instance": ( + "openimages/validation/", + "openimages/annotations/openimages_v6_val_bbox_instance.json", + ), +} + + +def register_all_oid(root): + for key, (image_root, json_file) in _PREDEFINED_SPLITS_OPENIMAGES_V6_DETECTION.items(): + register_oid_instances( + key, + _get_builtin_metadata(OPENIMAGES_V6_CATEGORIES), + os.path.join(root, json_file) if "://" not in json_file else json_file, + os.path.join(root, image_root), + ) + + +if __name__.endswith(".oid"): + # Assume pre-defined datasets live in `./datasets`. + _root = os.getenv("DETECTRON2_DATASETS", "datasets") + register_all_oid(_root) diff --git a/ape/data/datasets/pascal_voc_external.py b/ape/data/datasets/pascal_voc_external.py new file mode 100644 index 0000000..8453c65 --- /dev/null +++ b/ape/data/datasets/pascal_voc_external.py @@ -0,0 +1,1217 @@ +import os + +from detectron2.data import DatasetCatalog, MetadataCatalog +from detectron2.data.datasets import load_sem_seg + +from .coco import custom_register_coco_instances + +PASCAL_CTX_59_CATEGORIES = [ + {"color": [180, 120, 120], "id": 0, "isthing": 0, "name": "aeroplane"}, + {"color": [6, 230, 230], "id": 1, "isthing": 0, "name": "bag"}, + {"color": [80, 50, 50], "id": 2, "isthing": 0, "name": "bed"}, + {"color": [4, 200, 3], "id": 3, "isthing": 0, "name": "bedclothes"}, + {"color": [120, 120, 80], "id": 4, "isthing": 0, "name": "bench"}, + {"color": [140, 140, 140], "id": 5, "isthing": 0, "name": "bicycle"}, + {"color": [204, 5, 255], "id": 6, "isthing": 0, "name": "bird"}, + {"color": [230, 230, 230], "id": 7, "isthing": 0, "name": "boat"}, + {"color": [4, 250, 7], "id": 8, "isthing": 0, "name": "book"}, + {"color": [224, 5, 255], "id": 9, "isthing": 0, "name": "bottle"}, + {"color": [235, 255, 7], "id": 10, "isthing": 0, "name": "building"}, + {"color": [150, 5, 61], "id": 11, "isthing": 0, "name": "bus"}, + {"color": [120, 120, 70], "id": 12, "isthing": 0, "name": "cabinet"}, + {"color": [8, 255, 51], "id": 13, "isthing": 0, "name": "car"}, + {"color": [255, 6, 82], "id": 14, "isthing": 0, "name": "cat"}, + {"color": [143, 255, 140], "id": 15, "isthing": 0, "name": "ceiling"}, + {"color": [204, 255, 4], "id": 16, "isthing": 0, "name": "chair"}, + {"color": [255, 51, 7], "id": 17, "isthing": 0, "name": "cloth"}, + {"color": [204, 70, 3], "id": 18, "isthing": 0, "name": "computer"}, + {"color": [0, 102, 200], "id": 19, "isthing": 0, "name": "cow"}, + {"color": [61, 230, 250], "id": 20, "isthing": 0, "name": "cup"}, + {"color": [255, 6, 51], "id": 21, "isthing": 0, "name": "curtain"}, + {"color": [11, 102, 255], "id": 22, "isthing": 0, "name": "dog"}, + {"color": [255, 7, 71], "id": 23, "isthing": 0, "name": "door"}, + {"color": [255, 9, 224], "id": 24, "isthing": 0, "name": "fence"}, + {"color": [9, 7, 230], "id": 25, "isthing": 0, "name": "floor"}, + {"color": [220, 220, 220], "id": 26, "isthing": 0, "name": "flower"}, + {"color": [255, 9, 92], "id": 27, "isthing": 0, "name": "food"}, + {"color": [112, 9, 255], "id": 28, "isthing": 0, "name": "grass"}, + {"color": [8, 255, 214], "id": 29, "isthing": 0, "name": "ground"}, + {"color": [7, 255, 224], "id": 30, "isthing": 0, "name": "horse"}, + {"color": [255, 184, 6], "id": 31, "isthing": 0, "name": "keyboard"}, + {"color": [10, 255, 71], "id": 32, "isthing": 0, "name": "light"}, + {"color": [255, 41, 10], "id": 33, "isthing": 0, "name": "motorbike"}, + {"color": [7, 255, 255], "id": 34, "isthing": 0, "name": "mountain"}, + {"color": [224, 255, 8], "id": 35, "isthing": 0, "name": "mouse"}, + {"color": [102, 8, 255], "id": 36, "isthing": 0, "name": "person"}, + {"color": [255, 61, 6], "id": 37, "isthing": 0, "name": "plate"}, + {"color": [255, 194, 7], "id": 38, "isthing": 0, "name": "platform"}, + {"color": [255, 122, 8], "id": 39, "isthing": 0, "name": "pottedplant"}, + {"color": [0, 255, 20], "id": 40, "isthing": 0, "name": "road"}, + {"color": [255, 8, 41], "id": 41, "isthing": 0, "name": "rock"}, + {"color": [255, 5, 153], "id": 42, "isthing": 0, "name": "sheep"}, + {"color": [6, 51, 255], "id": 43, "isthing": 0, "name": "shelves"}, + {"color": [235, 12, 255], "id": 44, "isthing": 0, "name": "sidewalk"}, + {"color": [160, 150, 20], "id": 45, "isthing": 0, "name": "sign"}, + {"color": [0, 163, 255], "id": 46, "isthing": 0, "name": "sky"}, + {"color": [140, 140, 140], "id": 47, "isthing": 0, "name": "snow"}, + {"color": [250, 10, 15], "id": 48, "isthing": 0, "name": "sofa"}, + {"color": [20, 255, 0], "id": 49, "isthing": 0, "name": "diningtable"}, + {"color": [31, 255, 0], "id": 50, "isthing": 0, "name": "track"}, + {"color": [255, 31, 0], "id": 51, "isthing": 0, "name": "train"}, + {"color": [255, 224, 0], "id": 52, "isthing": 0, "name": "tree"}, + {"color": [153, 255, 0], "id": 53, "isthing": 0, "name": "truck"}, + {"color": [0, 0, 255], "id": 54, "isthing": 0, "name": "tvmonitor"}, + {"color": [255, 71, 0], "id": 55, "isthing": 0, "name": "wall"}, + {"color": [0, 235, 255], "id": 56, "isthing": 0, "name": "water"}, + {"color": [0, 173, 255], "id": 57, "isthing": 0, "name": "window"}, + {"color": [31, 0, 255], "id": 58, "isthing": 0, "name": "wood"}, +] + +PASCAL_CTX_459_CATEGORIES = [ + {"color": [120, 120, 120], "id": 0, "isthing": 0, "name": "accordion"}, + {"color": [180, 120, 120], "id": 1, "isthing": 0, "name": "aeroplane"}, + {"color": [6, 230, 230], "id": 2, "isthing": 0, "name": "air conditioner"}, + {"color": [80, 50, 50], "id": 3, "isthing": 0, "name": "antenna"}, + {"color": [4, 200, 3], "id": 4, "isthing": 0, "name": "artillery"}, + {"color": [120, 120, 80], "id": 5, "isthing": 0, "name": "ashtray"}, + {"color": [140, 140, 140], "id": 6, "isthing": 0, "name": "atrium"}, + {"color": [204, 5, 255], "id": 7, "isthing": 0, "name": "baby carriage"}, + {"color": [230, 230, 230], "id": 8, "isthing": 0, "name": "bag"}, + {"color": [4, 250, 7], "id": 9, "isthing": 0, "name": "ball"}, + {"color": [224, 5, 255], "id": 10, "isthing": 0, "name": "balloon"}, + {"color": [235, 255, 7], "id": 11, "isthing": 0, "name": "bamboo weaving"}, + {"color": [150, 5, 61], "id": 12, "isthing": 0, "name": "barrel"}, + {"color": [120, 120, 70], "id": 13, "isthing": 0, "name": "baseball bat"}, + {"color": [8, 255, 51], "id": 14, "isthing": 0, "name": "basket"}, + {"color": [255, 6, 82], "id": 15, "isthing": 0, "name": "basketball backboard"}, + {"color": [143, 255, 140], "id": 16, "isthing": 0, "name": "bathtub"}, + {"color": [204, 255, 4], "id": 17, "isthing": 0, "name": "bed"}, + {"color": [255, 51, 7], "id": 18, "isthing": 0, "name": "bedclothes"}, + {"color": [204, 70, 3], "id": 19, "isthing": 0, "name": "beer"}, + {"color": [0, 102, 200], "id": 20, "isthing": 0, "name": "bell"}, + {"color": [61, 230, 250], "id": 21, "isthing": 0, "name": "bench"}, + {"color": [255, 6, 51], "id": 22, "isthing": 0, "name": "bicycle"}, + {"color": [11, 102, 255], "id": 23, "isthing": 0, "name": "binoculars"}, + {"color": [255, 7, 71], "id": 24, "isthing": 0, "name": "bird"}, + {"color": [255, 9, 224], "id": 25, "isthing": 0, "name": "bird cage"}, + {"color": [9, 7, 230], "id": 26, "isthing": 0, "name": "bird feeder"}, + {"color": [220, 220, 220], "id": 27, "isthing": 0, "name": "bird nest"}, + {"color": [255, 9, 92], "id": 28, "isthing": 0, "name": "blackboard"}, + {"color": [112, 9, 255], "id": 29, "isthing": 0, "name": "board"}, + {"color": [8, 255, 214], "id": 30, "isthing": 0, "name": "boat"}, + {"color": [7, 255, 224], "id": 31, "isthing": 0, "name": "bone"}, + {"color": [255, 184, 6], "id": 32, "isthing": 0, "name": "book"}, + {"color": [10, 255, 71], "id": 33, "isthing": 0, "name": "bottle"}, + {"color": [255, 41, 10], "id": 34, "isthing": 0, "name": "bottle opener"}, + {"color": [7, 255, 255], "id": 35, "isthing": 0, "name": "bowl"}, + {"color": [224, 255, 8], "id": 36, "isthing": 0, "name": "box"}, + {"color": [102, 8, 255], "id": 37, "isthing": 0, "name": "bracelet"}, + {"color": [255, 61, 6], "id": 38, "isthing": 0, "name": "brick"}, + {"color": [255, 194, 7], "id": 39, "isthing": 0, "name": "bridge"}, + {"color": [255, 122, 8], "id": 40, "isthing": 0, "name": "broom"}, + {"color": [0, 255, 20], "id": 41, "isthing": 0, "name": "brush"}, + {"color": [255, 8, 41], "id": 42, "isthing": 0, "name": "bucket"}, + {"color": [255, 5, 153], "id": 43, "isthing": 0, "name": "building"}, + {"color": [6, 51, 255], "id": 44, "isthing": 0, "name": "bus"}, + {"color": [235, 12, 255], "id": 45, "isthing": 0, "name": "cabinet"}, + {"color": [160, 150, 20], "id": 46, "isthing": 0, "name": "cabinet door"}, + {"color": [0, 163, 255], "id": 47, "isthing": 0, "name": "cage"}, + {"color": [140, 140, 140], "id": 48, "isthing": 0, "name": "cake"}, + {"color": [250, 10, 15], "id": 49, "isthing": 0, "name": "calculator"}, + {"color": [20, 255, 0], "id": 50, "isthing": 0, "name": "calendar"}, + {"color": [31, 255, 0], "id": 51, "isthing": 0, "name": "camel"}, + {"color": [255, 31, 0], "id": 52, "isthing": 0, "name": "camera"}, + {"color": [255, 224, 0], "id": 53, "isthing": 0, "name": "camera lens"}, + {"color": [153, 255, 0], "id": 54, "isthing": 0, "name": "can"}, + {"color": [0, 0, 255], "id": 55, "isthing": 0, "name": "candle"}, + {"color": [255, 71, 0], "id": 56, "isthing": 0, "name": "candle holder"}, + {"color": [0, 235, 255], "id": 57, "isthing": 0, "name": "cap"}, + {"color": [0, 173, 255], "id": 58, "isthing": 0, "name": "car"}, + {"color": [31, 0, 255], "id": 59, "isthing": 0, "name": "card"}, + {"color": [120, 120, 120], "id": 60, "isthing": 0, "name": "cart"}, + {"color": [180, 120, 120], "id": 61, "isthing": 0, "name": "case"}, + {"color": [6, 230, 230], "id": 62, "isthing": 0, "name": "casette recorder"}, + {"color": [80, 50, 50], "id": 63, "isthing": 0, "name": "cash register"}, + {"color": [4, 200, 3], "id": 64, "isthing": 0, "name": "cat"}, + {"color": [120, 120, 80], "id": 65, "isthing": 0, "name": "cd"}, + {"color": [140, 140, 140], "id": 66, "isthing": 0, "name": "cd player"}, + {"color": [204, 5, 255], "id": 67, "isthing": 0, "name": "ceiling"}, + {"color": [230, 230, 230], "id": 68, "isthing": 0, "name": "cell phone"}, + {"color": [4, 250, 7], "id": 69, "isthing": 0, "name": "cello"}, + {"color": [224, 5, 255], "id": 70, "isthing": 0, "name": "chain"}, + {"color": [235, 255, 7], "id": 71, "isthing": 0, "name": "chair"}, + {"color": [150, 5, 61], "id": 72, "isthing": 0, "name": "chessboard"}, + {"color": [120, 120, 70], "id": 73, "isthing": 0, "name": "chicken"}, + {"color": [8, 255, 51], "id": 74, "isthing": 0, "name": "chopstick"}, + {"color": [255, 6, 82], "id": 75, "isthing": 0, "name": "clip"}, + {"color": [143, 255, 140], "id": 76, "isthing": 0, "name": "clippers"}, + {"color": [204, 255, 4], "id": 77, "isthing": 0, "name": "clock"}, + {"color": [255, 51, 7], "id": 78, "isthing": 0, "name": "closet"}, + {"color": [204, 70, 3], "id": 79, "isthing": 0, "name": "cloth"}, + {"color": [0, 102, 200], "id": 80, "isthing": 0, "name": "clothes tree"}, + {"color": [61, 230, 250], "id": 81, "isthing": 0, "name": "coffee"}, + {"color": [255, 6, 51], "id": 82, "isthing": 0, "name": "coffee machine"}, + {"color": [11, 102, 255], "id": 83, "isthing": 0, "name": "comb"}, + {"color": [255, 7, 71], "id": 84, "isthing": 0, "name": "computer"}, + {"color": [255, 9, 224], "id": 85, "isthing": 0, "name": "concrete"}, + {"color": [9, 7, 230], "id": 86, "isthing": 0, "name": "cone"}, + {"color": [220, 220, 220], "id": 87, "isthing": 0, "name": "container"}, + {"color": [255, 9, 92], "id": 88, "isthing": 0, "name": "control booth"}, + {"color": [112, 9, 255], "id": 89, "isthing": 0, "name": "controller"}, + {"color": [8, 255, 214], "id": 90, "isthing": 0, "name": "cooker"}, + {"color": [7, 255, 224], "id": 91, "isthing": 0, "name": "copying machine"}, + {"color": [255, 184, 6], "id": 92, "isthing": 0, "name": "coral"}, + {"color": [10, 255, 71], "id": 93, "isthing": 0, "name": "cork"}, + {"color": [255, 41, 10], "id": 94, "isthing": 0, "name": "corkscrew"}, + {"color": [7, 255, 255], "id": 95, "isthing": 0, "name": "counter"}, + {"color": [224, 255, 8], "id": 96, "isthing": 0, "name": "court"}, + {"color": [102, 8, 255], "id": 97, "isthing": 0, "name": "cow"}, + {"color": [255, 61, 6], "id": 98, "isthing": 0, "name": "crabstick"}, + {"color": [255, 194, 7], "id": 99, "isthing": 0, "name": "crane"}, + {"color": [255, 122, 8], "id": 100, "isthing": 0, "name": "crate"}, + {"color": [0, 255, 20], "id": 101, "isthing": 0, "name": "cross"}, + {"color": [255, 8, 41], "id": 102, "isthing": 0, "name": "crutch"}, + {"color": [255, 5, 153], "id": 103, "isthing": 0, "name": "cup"}, + {"color": [6, 51, 255], "id": 104, "isthing": 0, "name": "curtain"}, + {"color": [235, 12, 255], "id": 105, "isthing": 0, "name": "cushion"}, + {"color": [160, 150, 20], "id": 106, "isthing": 0, "name": "cutting board"}, + {"color": [0, 163, 255], "id": 107, "isthing": 0, "name": "dais"}, + {"color": [140, 140, 140], "id": 108, "isthing": 0, "name": "disc"}, + {"color": [250, 10, 15], "id": 109, "isthing": 0, "name": "disc case"}, + {"color": [20, 255, 0], "id": 110, "isthing": 0, "name": "dishwasher"}, + {"color": [31, 255, 0], "id": 111, "isthing": 0, "name": "dock"}, + {"color": [255, 31, 0], "id": 112, "isthing": 0, "name": "dog"}, + {"color": [255, 224, 0], "id": 113, "isthing": 0, "name": "dolphin"}, + {"color": [153, 255, 0], "id": 114, "isthing": 0, "name": "door"}, + {"color": [0, 0, 255], "id": 115, "isthing": 0, "name": "drainer"}, + {"color": [255, 71, 0], "id": 116, "isthing": 0, "name": "dray"}, + {"color": [0, 235, 255], "id": 117, "isthing": 0, "name": "drink dispenser"}, + {"color": 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339, "isthing": 0, "name": "screen"}, + {"color": [255, 122, 8], "id": 340, "isthing": 0, "name": "screwdriver"}, + {"color": [0, 255, 20], "id": 341, "isthing": 0, "name": "sculpture"}, + {"color": [255, 8, 41], "id": 342, "isthing": 0, "name": "scythe"}, + {"color": [255, 5, 153], "id": 343, "isthing": 0, "name": "sewer"}, + {"color": [6, 51, 255], "id": 344, "isthing": 0, "name": "sewing machine"}, + {"color": [235, 12, 255], "id": 345, "isthing": 0, "name": "shed"}, + {"color": [160, 150, 20], "id": 346, "isthing": 0, "name": "sheep"}, + {"color": [0, 163, 255], "id": 347, "isthing": 0, "name": "shell"}, + {"color": [140, 140, 140], "id": 348, "isthing": 0, "name": "shelves"}, + {"color": [250, 10, 15], "id": 349, "isthing": 0, "name": "shoe"}, + {"color": [20, 255, 0], "id": 350, "isthing": 0, "name": "shopping cart"}, + {"color": [31, 255, 0], "id": 351, "isthing": 0, "name": "shovel"}, + {"color": [255, 31, 0], "id": 352, "isthing": 0, "name": "sidecar"}, + {"color": [255, 224, 0], "id": 353, "isthing": 0, "name": "sidewalk"}, + {"color": [153, 255, 0], "id": 354, "isthing": 0, "name": "sign"}, + {"color": [0, 0, 255], "id": 355, "isthing": 0, "name": "signal light"}, + {"color": [255, 71, 0], "id": 356, "isthing": 0, "name": "sink"}, + {"color": [0, 235, 255], "id": 357, "isthing": 0, "name": "skateboard"}, + {"color": [0, 173, 255], "id": 358, "isthing": 0, "name": "ski"}, + {"color": [31, 0, 255], "id": 359, "isthing": 0, "name": "sky"}, + {"color": [120, 120, 120], "id": 360, "isthing": 0, "name": "sled"}, + {"color": [180, 120, 120], "id": 361, "isthing": 0, "name": "slippers"}, + {"color": [6, 230, 230], "id": 362, "isthing": 0, "name": "smoke"}, + {"color": [80, 50, 50], "id": 363, "isthing": 0, "name": "snail"}, + {"color": [4, 200, 3], "id": 364, "isthing": 0, "name": "snake"}, + {"color": [120, 120, 80], "id": 365, "isthing": 0, "name": "snow"}, + {"color": [140, 140, 140], "id": 366, "isthing": 0, "name": "snowmobiles"}, + {"color": [204, 5, 255], "id": 367, "isthing": 0, "name": "sofa"}, + {"color": [230, 230, 230], "id": 368, "isthing": 0, "name": "spanner"}, + {"color": [4, 250, 7], "id": 369, "isthing": 0, "name": "spatula"}, + {"color": [224, 5, 255], "id": 370, "isthing": 0, "name": "speaker"}, + {"color": [235, 255, 7], "id": 371, "isthing": 0, "name": "speed bump"}, + {"color": [150, 5, 61], "id": 372, "isthing": 0, "name": "spice container"}, + {"color": [120, 120, 70], "id": 373, "isthing": 0, "name": "spoon"}, + {"color": [8, 255, 51], "id": 374, "isthing": 0, "name": "sprayer"}, + {"color": [255, 6, 82], "id": 375, "isthing": 0, "name": "squirrel"}, + {"color": [143, 255, 140], "id": 376, "isthing": 0, "name": "stage"}, + {"color": [204, 255, 4], "id": 377, "isthing": 0, "name": "stair"}, + {"color": [255, 51, 7], "id": 378, "isthing": 0, "name": "stapler"}, + {"color": [204, 70, 3], "id": 379, "isthing": 0, "name": "stick"}, + {"color": [0, 102, 200], "id": 380, "isthing": 0, "name": "sticky note"}, + {"color": [61, 230, 250], "id": 381, "isthing": 0, "name": "stone"}, + {"color": [255, 6, 51], "id": 382, "isthing": 0, "name": "stool"}, + {"color": [11, 102, 255], "id": 383, "isthing": 0, "name": "stove"}, + {"color": [255, 7, 71], "id": 384, "isthing": 0, "name": "straw"}, + {"color": [255, 9, 224], "id": 385, "isthing": 0, "name": "stretcher"}, + {"color": [9, 7, 230], "id": 386, "isthing": 0, "name": "sun"}, + {"color": [220, 220, 220], "id": 387, "isthing": 0, "name": "sunglass"}, + {"color": [255, 9, 92], "id": 388, "isthing": 0, "name": "sunshade"}, + {"color": [112, 9, 255], "id": 389, "isthing": 0, "name": "surveillance camera"}, + {"color": [8, 255, 214], "id": 390, "isthing": 0, "name": "swan"}, + {"color": [7, 255, 224], "id": 391, "isthing": 0, "name": "sweeper"}, + {"color": [255, 184, 6], "id": 392, "isthing": 0, "name": "swim ring"}, + {"color": [10, 255, 71], "id": 393, "isthing": 0, "name": "swimming pool"}, + {"color": [255, 41, 10], "id": 394, "isthing": 0, "name": "swing"}, + {"color": [7, 255, 255], "id": 395, "isthing": 0, "name": "switch"}, + {"color": [224, 255, 8], "id": 396, "isthing": 0, "name": "table"}, + {"color": [102, 8, 255], "id": 397, "isthing": 0, "name": "tableware"}, + {"color": [255, 61, 6], "id": 398, "isthing": 0, "name": "tank"}, + {"color": [255, 194, 7], "id": 399, "isthing": 0, "name": "tap"}, + {"color": [255, 122, 8], "id": 400, "isthing": 0, "name": "tape"}, + {"color": [0, 255, 20], "id": 401, "isthing": 0, "name": "tarp"}, + {"color": [255, 8, 41], "id": 402, "isthing": 0, "name": "telephone"}, + {"color": [255, 5, 153], "id": 403, "isthing": 0, "name": "telephone booth"}, + {"color": [6, 51, 255], "id": 404, "isthing": 0, "name": "tent"}, + {"color": [235, 12, 255], "id": 405, "isthing": 0, "name": "tire"}, + {"color": [160, 150, 20], "id": 406, "isthing": 0, "name": "toaster"}, + {"color": [0, 163, 255], "id": 407, "isthing": 0, "name": "toilet"}, + {"color": [140, 140, 140], "id": 408, "isthing": 0, "name": "tong"}, + {"color": [250, 10, 15], "id": 409, "isthing": 0, "name": "tool"}, + {"color": [20, 255, 0], "id": 410, "isthing": 0, "name": "toothbrush"}, + {"color": [31, 255, 0], "id": 411, "isthing": 0, "name": "towel"}, + {"color": [255, 31, 0], "id": 412, "isthing": 0, "name": "toy"}, + {"color": [255, 224, 0], "id": 413, "isthing": 0, "name": "toy car"}, + {"color": [153, 255, 0], "id": 414, "isthing": 0, "name": "track"}, + {"color": [0, 0, 255], "id": 415, "isthing": 0, "name": "train"}, + {"color": [255, 71, 0], "id": 416, "isthing": 0, "name": "trampoline"}, + {"color": [0, 235, 255], "id": 417, "isthing": 0, "name": "trash bin"}, + {"color": [0, 173, 255], "id": 418, "isthing": 0, "name": "tray"}, + {"color": [31, 0, 255], "id": 419, "isthing": 0, "name": "tree"}, + {"color": [120, 120, 120], "id": 420, "isthing": 0, "name": "tricycle"}, + {"color": [180, 120, 120], "id": 421, "isthing": 0, "name": "tripod"}, + {"color": [6, 230, 230], "id": 422, "isthing": 0, "name": "trophy"}, + {"color": [80, 50, 50], "id": 423, "isthing": 0, "name": "truck"}, + {"color": [4, 200, 3], "id": 424, "isthing": 0, "name": "tube"}, + {"color": [120, 120, 80], "id": 425, "isthing": 0, "name": "turtle"}, + {"color": [140, 140, 140], "id": 426, "isthing": 0, "name": "tvmonitor"}, + {"color": [204, 5, 255], "id": 427, "isthing": 0, "name": "tweezers"}, + {"color": [230, 230, 230], "id": 428, "isthing": 0, "name": "typewriter"}, + {"color": [4, 250, 7], "id": 429, "isthing": 0, "name": "umbrella"}, + {"color": [224, 5, 255], "id": 430, "isthing": 0, "name": "unknown"}, + {"color": [235, 255, 7], "id": 431, "isthing": 0, "name": "vacuum cleaner"}, + {"color": [150, 5, 61], "id": 432, "isthing": 0, "name": "vending machine"}, + {"color": [120, 120, 70], "id": 433, "isthing": 0, "name": "video camera"}, + {"color": [8, 255, 51], "id": 434, "isthing": 0, "name": "video game console"}, + {"color": [255, 6, 82], "id": 435, "isthing": 0, "name": "video player"}, + {"color": [143, 255, 140], "id": 436, "isthing": 0, "name": "video tape"}, + {"color": [204, 255, 4], "id": 437, "isthing": 0, "name": "violin"}, + {"color": [255, 51, 7], "id": 438, "isthing": 0, "name": "wakeboard"}, + {"color": [204, 70, 3], "id": 439, "isthing": 0, "name": "wall"}, + {"color": [0, 102, 200], "id": 440, "isthing": 0, "name": "wallet"}, + {"color": [61, 230, 250], "id": 441, "isthing": 0, "name": "wardrobe"}, + {"color": [255, 6, 51], "id": 442, "isthing": 0, "name": "washing machine"}, + {"color": [11, 102, 255], "id": 443, "isthing": 0, "name": "watch"}, + {"color": [255, 7, 71], "id": 444, "isthing": 0, "name": "water"}, + {"color": [255, 9, 224], "id": 445, "isthing": 0, "name": "water dispenser"}, + {"color": [9, 7, 230], "id": 446, "isthing": 0, "name": "water pipe"}, + {"color": [220, 220, 220], "id": 447, "isthing": 0, "name": "water skate board"}, + {"color": [255, 9, 92], "id": 448, "isthing": 0, "name": "watermelon"}, + {"color": [112, 9, 255], "id": 449, "isthing": 0, "name": "whale"}, + {"color": [8, 255, 214], "id": 450, "isthing": 0, "name": "wharf"}, + {"color": [7, 255, 224], "id": 451, "isthing": 0, "name": "wheel"}, + {"color": [255, 184, 6], "id": 452, "isthing": 0, "name": "wheelchair"}, + {"color": [10, 255, 71], "id": 453, "isthing": 0, "name": "window"}, + {"color": [255, 41, 10], "id": 454, "isthing": 0, "name": "window blinds"}, + {"color": [7, 255, 255], "id": 455, "isthing": 0, "name": "wineglass"}, + {"color": [224, 255, 8], "id": 456, "isthing": 0, "name": "wire"}, + {"color": [102, 8, 255], "id": 457, "isthing": 0, "name": "wood"}, + {"color": [255, 61, 6], "id": 458, "isthing": 0, "name": "wool"}, +] + +PASCAL_VOC_21_CATEGORIES = [ + {"color": [0, 0, 0], "id": 0, "isthing": 1, "name": "background"}, + {"color": [128, 0, 0], "id": 1, "isthing": 1, "name": "aeroplane"}, + {"color": [0, 128, 0], "id": 2, "isthing": 1, "name": "bicycle"}, + {"color": [128, 128, 0], "id": 3, "isthing": 1, "name": "bird"}, + {"color": [0, 0, 128], "id": 4, "isthing": 1, "name": "boat"}, + {"color": [128, 0, 128], "id": 5, "isthing": 1, "name": "bottle"}, + {"color": [0, 128, 128], "id": 6, "isthing": 1, "name": "bus"}, + {"color": [128, 128, 128], "id": 7, "isthing": 1, "name": "car"}, + {"color": [64, 0, 0], "id": 8, "isthing": 1, "name": "cat"}, + {"color": [192, 0, 0], "id": 9, "isthing": 1, "name": "chair"}, + {"color": [64, 128, 0], "id": 10, "isthing": 1, "name": "cow"}, + {"color": [192, 128, 0], "id": 11, "isthing": 1, "name": "diningtable"}, + {"color": [64, 0, 128], "id": 12, "isthing": 1, "name": "dog"}, + {"color": [192, 0, 128], "id": 13, "isthing": 1, "name": "horse"}, + {"color": [64, 128, 128], "id": 14, "isthing": 1, "name": "motorbike"}, + {"color": [192, 128, 128], "id": 15, "isthing": 1, "name": "person"}, + {"color": [0, 64, 0], "id": 16, "isthing": 1, "name": "pottedplant"}, + {"color": [128, 64, 0], "id": 17, "isthing": 1, "name": "sheep"}, + {"color": [0, 192, 0], "id": 18, "isthing": 1, "name": "sofa"}, + {"color": [128, 192, 0], "id": 19, "isthing": 1, "name": "train"}, + {"color": [0, 64, 128], "id": 20, "isthing": 1, "name": "tvmonitor"}, +] + +PASCAL_PARTS_CATEGORIES = [ + {"id": 1, "name": "aeroplane body", "color": [231, 4, 237]}, + {"id": 2, "name": "aeroplane stern", "color": [116, 80, 69]}, + {"id": 3, "name": "aeroplane wing", "color": [214, 86, 123]}, + {"id": 4, "name": "aeroplane tail", "color": [22, 174, 172]}, + {"id": 5, "name": "aeroplane engine", "color": [197, 128, 182]}, + {"id": 6, "name": "aeroplane wheel", "color": [82, 197, 247]}, + {"id": 7, "name": "bicycle body", "color": [125, 34, 155]}, + {"id": 8, "name": "bicycle wheel", "color": [240, 6, 206]}, + {"id": 9, "name": "bicycle saddle", "color": [0, 67, 113]}, + {"id": 10, "name": "bicycle handlebar", "color": [112, 158, 137]}, + {"id": 11, "name": "bicycle headlight", "color": [255, 182, 87]}, + {"id": 12, "name": "bird torso", "color": [189, 249, 133]}, + {"id": 13, "name": "bird head", "color": [104, 202, 100]}, + {"id": 14, "name": "bird neck", "color": [158, 181, 70]}, + {"id": 15, "name": "bird wing", "color": [61, 245, 238]}, + {"id": 16, "name": "bird leg", "color": [55, 126, 0]}, + {"id": 17, "name": "bird foot", "color": [225, 182, 182]}, + {"id": 18, "name": "bird tail", "color": [68, 62, 33]}, + {"id": 19, "name": "boat ", "color": [200, 219, 162]}, + {"id": 20, "name": "bottle body", "color": [172, 155, 96]}, + {"id": 21, "name": "bottle cap", "color": [185, 14, 216]}, + {"id": 22, "name": "bus body", "color": [3, 58, 66]}, + {"id": 23, "name": "bus frontside", "color": [26, 173, 31]}, + {"id": 24, "name": "bus leftside", "color": [205, 197, 47]}, + {"id": 25, "name": "bus rightside", "color": [6, 223, 194]}, + {"id": 26, "name": "bus backside", "color": [10, 232, 224]}, + {"id": 27, "name": "bus roofside", "color": [189, 124, 163]}, + {"id": 28, "name": "bus mirror", "color": [253, 98, 118]}, + {"id": 29, "name": "bus fliplate", "color": [134, 124, 251]}, + {"id": 30, "name": "bus bliplate", "color": [86, 248, 252]}, + {"id": 31, "name": "bus door", "color": [104, 232, 186]}, + {"id": 32, "name": "bus wheel", "color": [73, 10, 81]}, + {"id": 33, "name": "bus headlight", "color": [83, 15, 206]}, + {"id": 34, "name": "bus window", "color": [182, 248, 35]}, + {"id": 35, "name": "car body", "color": [111, 175, 136]}, + {"id": 36, "name": "car mirror", "color": [244, 27, 39]}, + {"id": 37, "name": "car tmirror", "color": [60, 75, 197]}, + {"id": 38, "name": "car fliplate", "color": [32, 124, 177]}, + {"id": 39, "name": "car bliplate", "color": [132, 107, 137]}, + {"id": 40, "name": "car door", "color": [29, 145, 220]}, + {"id": 41, "name": "car wheel", "color": [211, 58, 216]}, + {"id": 42, "name": "car headlight", "color": [253, 195, 114]}, + {"id": 43, "name": "car window", "color": [51, 163, 166]}, + {"id": 44, "name": "cat torso", "color": [68, 44, 17]}, + {"id": 45, "name": "cat head", "color": [148, 109, 203]}, + {"id": 46, "name": "cat eye", "color": [221, 235, 212]}, + {"id": 47, "name": "cat ear", "color": [25, 226, 114]}, + {"id": 48, "name": "cat nose", "color": [99, 126, 184]}, + {"id": 49, "name": "cat neck", "color": [54, 164, 161]}, + {"id": 50, "name": "cat leg", "color": [114, 251, 219]}, + {"id": 51, "name": "cat pawn", "color": [145, 28, 176]}, + {"id": 52, "name": "cat tail", "color": [22, 29, 245]}, + {"id": 53, "name": "chair ", "color": [174, 108, 109]}, + {"id": 54, "name": "cow torso", "color": [153, 207, 125]}, + {"id": 55, "name": "cow head", "color": [243, 197, 251]}, + {"id": 56, "name": "cow eye", "color": [99, 87, 120]}, + {"id": 57, "name": "cow ear", "color": [194, 7, 114]}, + {"id": 58, "name": "cow muzzle", "color": [242, 122, 177]}, + {"id": 59, "name": "cow horn", "color": [202, 242, 232]}, + {"id": 60, "name": "cow neck", "color": [250, 136, 178]}, + {"id": 61, "name": "cow leg", "color": [171, 46, 206]}, + {"id": 62, "name": "cow tail", "color": [186, 133, 2]}, + {"id": 63, "name": "table ", "color": [201, 1, 108]}, + {"id": 64, "name": "dog torso", "color": [245, 11, 186]}, + {"id": 65, "name": "dog head", "color": [33, 191, 131]}, + {"id": 66, "name": "dog eye", "color": [225, 95, 66]}, + {"id": 67, "name": "dog ear", "color": [124, 25, 24]}, + {"id": 68, "name": "dog nose", "color": [214, 234, 112]}, + {"id": 69, "name": "dog neck", "color": [129, 83, 21]}, + {"id": 70, "name": "dog leg", "color": [185, 76, 143]}, + {"id": 71, "name": "dog pawn", "color": [180, 1, 74]}, + {"id": 72, "name": "dog tail", "color": [121, 134, 63]}, + {"id": 73, "name": "dog muzzle", "color": [90, 58, 214]}, + {"id": 74, "name": "horse body", "color": [223, 7, 152]}, + {"id": 75, "name": "horse head", "color": [154, 96, 130]}, + {"id": 76, "name": "horse eye", "color": [221, 98, 183]}, + {"id": 77, "name": "horse ear", "color": [230, 145, 183]}, + {"id": 78, "name": "horse muzzle", "color": [213, 203, 88]}, + {"id": 79, "name": "horse torso", "color": [183, 92, 254]}, + {"id": 80, "name": "horse neck", "color": [206, 114, 11]}, + {"id": 81, "name": "horse leg", "color": [214, 238, 15]}, + {"id": 82, "name": "horse tail", "color": [57, 239, 109]}, + {"id": 83, "name": "motorbike body", "color": [197, 138, 146]}, + {"id": 84, "name": "motorbike wheel", "color": [124, 107, 252]}, + {"id": 85, "name": "motorbike handlebar", "color": [163, 225, 169]}, + {"id": 86, "name": "motorbike saddle", "color": [254, 180, 116]}, + {"id": 87, "name": "motorbike headlight", "color": [119, 52, 22]}, + {"id": 88, "name": "person body", "color": [198, 68, 18]}, + {"id": 89, "name": "person head", "color": [40, 30, 77]}, + {"id": 90, "name": "person eye", "color": [237, 64, 148]}, + {"id": 91, "name": "person ear", "color": [49, 186, 234]}, + {"id": 92, "name": "person ebrow", "color": [242, 204, 127]}, + {"id": 93, "name": "person nose", "color": [101, 145, 176]}, + {"id": 94, "name": "person mouth", "color": [31, 78, 216]}, + {"id": 95, "name": "person hair", "color": [95, 148, 151]}, + {"id": 96, "name": "person torso", "color": [126, 117, 235]}, + {"id": 97, "name": "person neck", "color": [13, 146, 62]}, + {"id": 98, "name": "person lower arm", "color": [9, 41, 5]}, + {"id": 99, "name": "person upper arm", "color": [110, 109, 109]}, + {"id": 100, "name": "person hand", "color": [58, 227, 163]}, + {"id": 101, "name": "person lower leg", "color": [132, 63, 32]}, + {"id": 102, "name": "person upper leg", "color": [212, 118, 174]}, + {"id": 103, "name": "person foot", "color": [45, 66, 254]}, + {"id": 104, "name": "pottedplant plant", "color": [236, 149, 209]}, + {"id": 105, "name": "pottedplant pot", "color": [80, 197, 134]}, + {"id": 106, "name": "sheep torso", "color": [241, 111, 194]}, + {"id": 107, "name": "sheep head", "color": [31, 13, 13]}, + {"id": 108, "name": "sheep eye", "color": [34, 207, 63]}, + {"id": 109, "name": "sheep ear", "color": [249, 117, 121]}, + {"id": 110, "name": "sheep muzzle", "color": [172, 128, 70]}, + {"id": 111, "name": "sheep horn", "color": [97, 144, 104]}, + {"id": 112, "name": "sheep neck", "color": [121, 163, 14]}, + {"id": 113, "name": "sheep leg", "color": [38, 79, 231]}, + {"id": 114, "name": "sheep tail", "color": [218, 195, 52]}, + {"id": 115, "name": "sofa ", "color": [102, 8, 225]}, + {"id": 116, "name": "train body", "color": [150, 44, 180]}, + {"id": 117, "name": "train head", "color": [99, 250, 180]}, + {"id": 118, "name": "train headlight", "color": [24, 148, 249]}, + {"id": 119, "name": "train coach", "color": [143, 232, 181]}, + {"id": 120, "name": "tvmonitor frame", "color": [68, 191, 134]}, + {"id": 121, "name": "tvmonitor screen", "color": [186, 6, 38]}, + {"id": 122, "name": "bag ", "color": [215, 253, 9]}, + {"id": 123, "name": "basket ", "color": [150, 44, 154]}, + {"id": 124, "name": "bed ", "color": [66, 132, 108]}, + {"id": 125, "name": "bedclothes ", "color": [193, 84, 92]}, + {"id": 126, "name": "bench ", "color": [84, 154, 254]}, + {"id": 127, "name": "bird cage ", "color": [2, 93, 169]}, + {"id": 128, "name": "board ", "color": [41, 254, 95]}, + {"id": 129, "name": "book ", "color": [157, 228, 148]}, + {"id": 130, "name": "bowl ", "color": [201, 198, 2]}, + {"id": 131, "name": "box ", "color": [237, 151, 223]}, + {"id": 132, "name": "bridge ", "color": [74, 200, 197]}, + {"id": 133, "name": "brush ", "color": [157, 2, 192]}, + {"id": 134, "name": "bucket ", "color": [62, 8, 145]}, + {"id": 135, "name": "building ", "color": [244, 158, 23]}, + {"id": 136, "name": "cabinet ", "color": [143, 34, 160]}, + {"id": 137, "name": "cage ", "color": [74, 182, 153]}, + {"id": 138, "name": "case ", "color": [44, 161, 32]}, + {"id": 139, "name": "ceiling ", "color": [22, 207, 172]}, + {"id": 140, "name": "cloth ", "color": [62, 233, 51]}, + {"id": 141, "name": "computer ", "color": [203, 221, 8]}, + {"id": 142, "name": "counter ", "color": [155, 154, 208]}, + {"id": 143, "name": "cup ", "color": [136, 170, 161]}, + {"id": 144, "name": "curtain ", "color": [69, 238, 67]}, + {"id": 145, "name": "cushion ", "color": [250, 140, 63]}, + {"id": 146, "name": "door ", "color": [228, 29, 142]}, + {"id": 147, "name": "fence ", "color": [149, 149, 255]}, + {"id": 148, "name": "fire place ", "color": [25, 17, 14]}, + {"id": 149, "name": "floor ", "color": [141, 121, 107]}, + {"id": 150, "name": "flower ", "color": [196, 171, 99]}, + {"id": 151, "name": "food ", "color": [246, 30, 195]}, + {"id": 152, "name": "fridge ", "color": [95, 2, 42]}, + {"id": 153, "name": "grandstand ", "color": [174, 116, 162]}, + {"id": 154, "name": "grass ", "color": [251, 58, 246]}, + {"id": 155, "name": "ground ", "color": [138, 68, 168]}, + {"id": 156, "name": "horse-drawn carriage ", "color": [236, 220, 194]}, + {"id": 157, "name": "keyboard ", "color": [228, 180, 129]}, + {"id": 158, "name": "laptop ", "color": [41, 39, 187]}, + {"id": 159, "name": "light ", "color": [18, 155, 71]}, + {"id": 160, "name": "mat ", "color": [81, 149, 168]}, + {"id": 161, "name": "metal ", "color": [222, 250, 122]}, + {"id": 162, "name": "mirror ", "color": [27, 14, 162]}, + {"id": 163, "name": "mountain ", "color": [96, 67, 42]}, + {"id": 164, "name": "mouse ", "color": [248, 27, 142]}, + {"id": 165, "name": "pack ", "color": [48, 208, 79]}, + {"id": 166, "name": "paper ", "color": [85, 44, 114]}, + {"id": 167, "name": "picture ", "color": [8, 66, 36]}, + {"id": 168, "name": "pillow ", "color": [199, 38, 36]}, + {"id": 169, "name": "plant ", "color": [45, 67, 214]}, + {"id": 170, "name": "plate ", "color": [176, 85, 199]}, + {"id": 171, "name": "platform ", "color": [118, 46, 134]}, + {"id": 172, "name": "pole ", "color": [66, 53, 97]}, + {"id": 173, "name": "poster ", "color": [134, 95, 198]}, + {"id": 174, "name": "pot ", "color": [56, 185, 27]}, + {"id": 175, "name": "road ", "color": [12, 12, 242]}, + {"id": 176, "name": "rock ", "color": [141, 182, 239]}, + {"id": 177, "name": "rope ", "color": [242, 15, 134]}, + {"id": 178, "name": "rug ", "color": [119, 78, 116]}, + {"id": 179, "name": "sand ", "color": [159, 25, 177]}, + {"id": 180, "name": "sculpture ", "color": [155, 71, 2]}, + {"id": 181, "name": "shelves ", "color": [13, 156, 172]}, + {"id": 182, "name": "sidewalk ", "color": [153, 56, 74]}, + {"id": 183, "name": "sign ", "color": [132, 5, 169]}, + {"id": 184, "name": "sink ", "color": [202, 115, 244]}, + {"id": 185, "name": "sky ", "color": [189, 81, 126]}, + {"id": 186, "name": "smoke ", "color": [50, 105, 141]}, + {"id": 187, "name": "snow ", "color": [163, 75, 126]}, + {"id": 188, "name": "speaker ", "color": [25, 28, 9]}, + {"id": 189, "name": "stage ", "color": [57, 175, 211]}, + {"id": 190, "name": "stair ", "color": [36, 182, 123]}, + {"id": 191, "name": "tent ", "color": [210, 184, 159]}, + {"id": 192, "name": "toy ", "color": [139, 14, 196]}, + {"id": 193, "name": "track ", "color": [204, 225, 55]}, + {"id": 194, "name": "tree ", "color": [145, 64, 92]}, + {"id": 195, "name": "truck ", "color": [43, 65, 241]}, + {"id": 196, "name": "wall ", "color": [220, 189, 61]}, + {"id": 197, "name": "water ", "color": [250, 95, 220]}, + {"id": 198, "name": "window ", "color": [176, 117, 245]}, + {"id": 199, "name": "wineglass ", "color": [102, 162, 66]}, + {"id": 200, "name": "wood ", "color": [100, 60, 45]}, +] + +PASCAL_PARTS_PARTS_ONLY = [ + {"id": 1, "name": "aeroplane body", "color": [30, 178, 112]}, + {"id": 2, "name": "aeroplane stern", "color": [0, 80, 42]}, + {"id": 3, "name": "aeroplane wing", "color": [160, 237, 245]}, + {"id": 4, "name": "aeroplane engine", "color": [144, 222, 51]}, + {"id": 5, "name": "aeroplane wheel", "color": [155, 121, 20]}, + {"id": 6, "name": "bicycle body", "color": [24, 50, 96]}, + {"id": 7, "name": "bicycle wheel", "color": [247, 201, 171]}, + {"id": 8, "name": "bird torso", "color": [63, 162, 50]}, + {"id": 9, "name": "bird head", "color": [143, 18, 27]}, + {"id": 10, "name": "bird wing", "color": [204, 34, 128]}, + {"id": 11, "name": "bird leg", "color": [31, 37, 39]}, + {"id": 12, "name": "boat ", "color": [175, 16, 226]}, + {"id": 13, "name": "bottle cap", "color": [31, 13, 221]}, + {"id": 14, "name": "bottle body", "color": [120, 248, 33]}, + {"id": 15, "name": "bus body", "color": [243, 104, 244]}, + {"id": 16, "name": "bus wheel", "color": [247, 196, 104]}, + {"id": 17, "name": "bus window", "color": [63, 138, 111]}, + {"id": 18, "name": "car body", "color": [200, 176, 116]}, + {"id": 19, "name": "car license plate", "color": [79, 146, 205]}, + {"id": 20, "name": "car wheel", "color": [231, 126, 229]}, + {"id": 21, "name": "car light", "color": [120, 219, 85]}, + {"id": 22, "name": "car window", "color": [240, 73, 236]}, + {"id": 23, "name": "cat torso", "color": [24, 254, 246]}, + {"id": 24, "name": "cat head", "color": [38, 29, 151]}, + {"id": 25, "name": "cat leg", "color": [229, 8, 161]}, + {"id": 26, "name": "cat tail", "color": [212, 191, 142]}, + {"id": 27, "name": "chair ", "color": [235, 90, 210]}, + {"id": 28, "name": "cow torso", "color": [72, 26, 132]}, + {"id": 29, "name": "cow head", "color": [28, 249, 68]}, + {"id": 30, "name": "cow leg", "color": [69, 62, 39]}, + {"id": 31, "name": "cow tail", "color": [238, 140, 59]}, + {"id": 32, "name": "table ", "color": [73, 170, 102]}, + {"id": 33, "name": "dog torso", "color": [51, 140, 200]}, + {"id": 34, "name": "dog head", "color": [141, 130, 240]}, + {"id": 35, "name": "dog leg", "color": [223, 199, 36]}, + {"id": 36, "name": "dog tail", "color": [40, 192, 182]}, + {"id": 37, "name": "horse torso", "color": [212, 206, 245]}, + {"id": 38, "name": "horse head", "color": [59, 63, 103]}, + {"id": 39, "name": "horse leg", "color": [50, 72, 178]}, + {"id": 40, "name": "horse tail", "color": [49, 64, 103]}, + {"id": 41, "name": "motorbike body", "color": [226, 39, 217]}, + {"id": 42, "name": "motorbike wheel", "color": [11, 110, 195]}, + {"id": 43, "name": "person torso", "color": [155, 219, 139]}, + {"id": 44, "name": "person head", "color": [168, 137, 15]}, + {"id": 45, "name": "person lower arm", "color": [187, 194, 167]}, + {"id": 46, "name": "person upper arm", "color": [60, 80, 21]}, + {"id": 47, "name": "person lower leg", "color": [180, 219, 17]}, + {"id": 48, "name": "person upper leg", "color": [240, 249, 227]}, + {"id": 49, "name": "pottedplant plant", "color": [191, 176, 151]}, + {"id": 50, "name": "pottedplant pot", "color": [13, 133, 225]}, + {"id": 51, "name": "sheep torso", "color": [178, 101, 246]}, + {"id": 52, "name": "sheep head", "color": [52, 108, 42]}, + {"id": 53, "name": "sheep leg", "color": [92, 169, 47]}, + {"id": 54, "name": "sofa ", "color": [45, 45, 192]}, + {"id": 55, "name": "train body", "color": [168, 7, 178]}, + {"id": 56, "name": "tvmonitor frame", "color": [59, 89, 2]}, + {"id": 57, "name": "tvmonitor screen", "color": [51, 85, 167]}, +] + + +def _get_ctx59_meta(): + # Id 0 is reserved for ignore_label, we change ignore_label for 0 + # to 255 in our pre-processing, so all ids are shifted by 1. + stuff_ids = [k["id"] for k in PASCAL_CTX_59_CATEGORIES] + assert len(stuff_ids) == 59, len(stuff_ids) + + # For semantic segmentation, this mapping maps from contiguous stuff id + # (in [0, 91], used in models) to ids in the dataset (used for processing results) + stuff_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(stuff_ids)} + stuff_classes = [k["name"] for k in PASCAL_CTX_59_CATEGORIES] + + ret = { + "stuff_dataset_id_to_contiguous_id": stuff_dataset_id_to_contiguous_id, + "stuff_classes": stuff_classes, + } + return ret + + +def register_all_ctx59(root): + root = os.path.join(root, "pascal_ctx_d2") + meta = _get_ctx59_meta() + for name, dirname in [("train", "training"), ("val", "validation")]: + image_dir = os.path.join(root, "images", dirname) + gt_dir = os.path.join(root, "annotations_ctx59", dirname) + name = f"ctx59_sem_seg_{name}" + DatasetCatalog.register( + name, + lambda x=image_dir, y=gt_dir: load_sem_seg( + y, x, gt_ext="png", image_ext="jpg", dataset_name="pascal_context_59" + ), + ) + MetadataCatalog.get(name).set( + stuff_classes=meta["stuff_classes"][:], + thing_dataset_id_to_contiguous_id={}, # to make Mask2Former happy + stuff_dataset_id_to_contiguous_id=meta["stuff_dataset_id_to_contiguous_id"], + image_root=image_dir, + sem_seg_root=gt_dir, + evaluator_type="sem_seg", + ignore_label=255, + ) + + +def _get_pascal21_meta(): + # Id 0 is reserved for ignore_label, we change ignore_label for 0 + # to 255 in our pre-processing, so all ids are shifted by 1. + stuff_ids = [k["id"] for k in PASCAL_VOC_21_CATEGORIES] + assert len(stuff_ids) == 21, len(stuff_ids) + + # For semantic segmentation, this mapping maps from contiguous stuff id + # (in [0, 91], used in models) to ids in the dataset (used for processing results) + stuff_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(stuff_ids)} + stuff_classes = [k["name"] for k in PASCAL_VOC_21_CATEGORIES] + + ret = { + "stuff_dataset_id_to_contiguous_id": stuff_dataset_id_to_contiguous_id, + "stuff_classes": stuff_classes, + } + return ret + + +def register_all_pascal21(root): + root = os.path.join(root, "pascal_voc_d2") + meta = _get_pascal21_meta() + for name, dirname in [("train", "training"), ("val", "validation")]: + image_dir = os.path.join(root, "images", dirname) + gt_dir = os.path.join(root, "annotations_pascal21", dirname) + name = f"pascal21_sem_seg_{name}" + DatasetCatalog.register( + name, + lambda x=image_dir, y=gt_dir: load_sem_seg( + y, x, gt_ext="png", image_ext="jpg", dataset_name="pascal_voc_21" + ), + ) + MetadataCatalog.get(name).set( + stuff_classes=meta["stuff_classes"][:], + thing_dataset_id_to_contiguous_id={}, # to make Mask2Former happy + stuff_dataset_id_to_contiguous_id=meta["stuff_dataset_id_to_contiguous_id"], + image_root=image_dir, + sem_seg_root=gt_dir, + evaluator_type="sem_seg", + ignore_label=255, + ) + + +def _get_ctx459_meta(): + # Id 0 is reserved for ignore_label, we change ignore_label for 0 + # to 255 in our pre-processing, so all ids are shifted by 1. + stuff_ids = [k["id"] for k in PASCAL_CTX_459_CATEGORIES] + assert len(stuff_ids) == 459, len(stuff_ids) + + # For semantic segmentation, this mapping maps from contiguous stuff id + # (in [0, 91], used in models) to ids in the dataset (used for processing results) + stuff_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(stuff_ids)} + stuff_classes = [k["name"] for k in PASCAL_CTX_459_CATEGORIES] + + ret = { + "stuff_dataset_id_to_contiguous_id": stuff_dataset_id_to_contiguous_id, + "stuff_classes": stuff_classes, + } + return ret + + +def register_all_ctx459(root): + root = os.path.join(root, "pascal_ctx_d2") + meta = _get_ctx459_meta() + for name, dirname in [("train", "training"), ("val", "validation")]: + image_dir = os.path.join(root, "images", dirname) + gt_dir = os.path.join(root, "annotations_ctx459", dirname) + name = f"ctx459_sem_seg_{name}" + DatasetCatalog.register( + name, + lambda x=image_dir, y=gt_dir: load_sem_seg( + y, x, gt_ext="tif", image_ext="jpg", dataset_name="pascal_context_459" + ), + ) + MetadataCatalog.get(name).set( + stuff_classes=meta["stuff_classes"][:], + thing_dataset_id_to_contiguous_id={}, # to make Mask2Former happy + stuff_dataset_id_to_contiguous_id=meta["stuff_dataset_id_to_contiguous_id"], + image_root=image_dir, + sem_seg_root=gt_dir, + evaluator_type="sem_seg", + ignore_label=65535, # NOTE: gt is saved in 16-bit TIFF images + ) + + +def _get_parts_meta(): + # Id 0 is reserved for ignore_label, we change ignore_label for 0 + # to 255 in our pre-processing, so all ids are shifted by 1. + stuff_ids = [k["id"] for k in PASCAL_PARTS_CATEGORIES] + # assert len(stuff_ids) == 459, len(stuff_ids) + + # For semantic segmentation, this mapping maps from contiguous stuff id + # (in [0, 91], used in models) to ids in the dataset (used for processing results) + stuff_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(stuff_ids)} + stuff_classes = [k["name"] for k in PASCAL_PARTS_CATEGORIES] + + ret = { + "stuff_dataset_id_to_contiguous_id": stuff_dataset_id_to_contiguous_id, + "stuff_classes": stuff_classes, + } + return ret + + +def _get_parts_only_meta(): + # Id 0 is reserved for ignore_label, we change ignore_label for 0 + # to 255 in our pre-processing, so all ids are shifted by 1. + stuff_ids = [k["id"] for k in PASCAL_PARTS_PARTS_ONLY] + # assert len(stuff_ids) == 459, len(stuff_ids) + + # For semantic segmentation, this mapping maps from contiguous stuff id + # (in [0, 91], used in models) to ids in the dataset (used for processing results) + stuff_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(stuff_ids)} + stuff_classes = [k["name"] for k in PASCAL_PARTS_PARTS_ONLY] + + ret = { + "stuff_dataset_id_to_contiguous_id": stuff_dataset_id_to_contiguous_id, + "stuff_classes": stuff_classes, + } + return ret + + +def register_all_pascal_parts_only(root): + data_root = root + root = os.path.join(root, "pascal_parts") + meta = _get_parts_only_meta() + for name, dirname in [ + ("train", "training_merged"), + ("val", "validation_merged"), + ("test", "test_merged"), + ]: + image_dir = os.path.join(data_root, "VOCdevkit/VOC2010/JPEGImages") + gt_dir = os.path.join(root, "labels", dirname) + name = f"pascal_parts_merged_{name}" + DatasetCatalog.register( + name, + lambda x=image_dir, y=gt_dir: load_sem_seg( + y, x, gt_ext="tif", image_ext="jpg", dataset_name="pascal_parts_merged" + ), + ) + MetadataCatalog.get(name).set( + stuff_classes=meta["stuff_classes"][:], + thing_dataset_id_to_contiguous_id={}, # to make Mask2Former happy + stuff_dataset_id_to_contiguous_id=meta["stuff_dataset_id_to_contiguous_id"], + image_root=image_dir, + sem_seg_root=gt_dir, + evaluator_type="sem_seg", + ignore_label=0, # NOTE: gt is saved in 16-bit TIFF images + ) + + +PASCAL_LABEL_PART_GROUP = { + 1: 1, + 2: 2, + 3: 3, + 4: 2, + 5: 4, + 6: 5, + 7: 6, + 8: 7, + 9: 6, + 10: 6, + 11: 6, + 12: 8, + 13: 9, + 14: 9, + 15: 10, + 16: 11, + 17: 11, + 18: 8, + 19: 12, + 20: 14, + 21: 13, + 22: 15, + 23: 15, + 24: 15, + 25: 15, + 26: 15, + 27: 15, + 28: 15, + 29: 15, + 30: 15, + 31: 15, + 32: 16, + 33: 15, + 34: 17, + 35: 18, + 36: 18, + 37: 18, + 38: 19, + 39: 19, + 40: 18, + 41: 20, + 42: 21, + 43: 22, + 44: 23, + 45: 24, + 46: 24, + 47: 24, + 48: 24, + 49: 23, + 50: 25, + 51: 25, + 52: 26, + 53: 27, + 54: 28, + 55: 29, + 56: 29, + 57: 29, + 58: 29, + 59: 29, + 60: 28, + 61: 30, + 62: 31, + 63: 32, + 64: 33, + 65: 34, + 66: 34, + 67: 34, + 68: 34, + 69: 33, + 70: 35, + 71: 35, + 72: 36, + 73: 34, + 74: 37, + 75: 38, + 76: 38, + 77: 38, + 78: 38, + 79: 37, + 80: 37, + 81: 39, + 82: 40, + 83: 41, + 84: 42, + 85: 41, + 86: 41, + 87: 41, + 88: 43, + 89: 44, + 90: 44, + 91: 44, + 92: 44, + 93: 44, + 94: 44, + 95: 44, + 96: 43, + 97: 43, + 98: 45, + 99: 46, + 100: 45, + 101: 47, + 102: 48, + 103: 47, + 104: 49, + 105: 50, + 106: 51, + 107: 52, + 108: 52, + 109: 52, + 110: 52, + 111: 52, + 112: 51, + 113: 53, + 114: 51, + 115: 54, + 116: 55, + 117: 55, + 118: 55, + 119: 55, + 120: 56, + 121: 57, +} + + +def register_all_pascal_parts(root): + data_root = root + register_all_pascal_parts_only(data_root) + root = os.path.join(root, "pascal_parts") + meta = _get_parts_meta() + for name, dirname in [("train", "training"), ("val", "validation"), ("test", "test_pano")]: + image_dir = os.path.join(data_root, "VOCdevkit/VOC2010/JPEGImages") + gt_dir = os.path.join(root, "labels", dirname) + name = f"pascal_parts_{name}" + DatasetCatalog.register( + name, lambda x=image_dir, y=gt_dir: load_sem_seg(y, x, gt_ext="tif", image_ext="jpg") + ) + MetadataCatalog.get(name).set( + stuff_classes=meta["stuff_classes"][:], + thing_dataset_id_to_contiguous_id={}, # to make Mask2Former happy + stuff_dataset_id_to_contiguous_id=meta["stuff_dataset_id_to_contiguous_id"], + image_root=image_dir, + sem_seg_root=gt_dir, + evaluator_type="sem_seg", + label_group=PASCAL_LABEL_PART_GROUP, + # ignore_label=0, # NOTE: gt is saved in 16-bit TIFF images + ignore_label=255, # NOTE: gt is saved in 16-bit TIFF images + ) + + +_PREDEFINED_SPLITS_PASCALVOCPART = {} +_PREDEFINED_SPLITS_PASCALVOCPART["pascalvocpart"] = { + "pascalvocpart": ( + "VOCdevkit/VOC2010/JPEGImages", + "pascal_parts/pascalvocpart_training_instance.json", + ), + "pascalvocpart_train": ( + "VOCdevkit/VOC2010/JPEGImages", + "pascal_parts/pascalvocpart_training_instance.json", + ), + "pascalvocpart_val": ( + "VOCdevkit/VOC2010/JPEGImages", + "pascal_parts/pascalvocpart_validation_instance.json", + ), +} + + +def _get_builtin_metadata(dataset_name): + return _get_pascalvocpart_metadata([]) + + raise KeyError("No built-in metadata for dataset {}".format(dataset_name)) + + +def _get_pascalvocpart_metadata(categories): + if len(categories) == 0: + return {} + id_to_name = {x["id"]: x["name"] for x in categories} + thing_dataset_id_to_contiguous_id = {i: i for i in range(len(categories))} + thing_classes = [id_to_name[k] for k in sorted(id_to_name)] + return { + "thing_dataset_id_to_contiguous_id": thing_dataset_id_to_contiguous_id, + "thing_classes": thing_classes, + } + + +def register_all_pascalvocpart(root): + for dataset_name, splits_per_dataset in _PREDEFINED_SPLITS_PASCALVOCPART.items(): + for key, (image_root, json_file) in splits_per_dataset.items(): + custom_register_coco_instances( + key, + _get_builtin_metadata(dataset_name), + os.path.join(root, json_file) if "://" not in json_file else json_file, + os.path.join(root, image_root), + ) + + +# register_all_ctx59(os.getenv("DETECTRON2_DATASETS", "datasets")) +# register_all_pascal21(os.getenv("DETECTRON2_DATASETS", "datasets")) +# register_all_ctx459(os.getenv("DETECTRON2_DATASETS", "datasets")) + +# True for open source; +# Internally at fb, we register them elsewhere +if __name__.endswith(".pascal_voc_external"): + # Assume pre-defined datasets live in `./datasets`. + _root = os.path.expanduser(os.getenv("DETECTRON2_DATASETS", "datasets")) + # register_all_pascal_parts(_root) + register_all_pascalvocpart(_root) diff --git a/ape/data/datasets/phrasecut.py b/ape/data/datasets/phrasecut.py new file mode 100644 index 0000000..2a14bf6 --- /dev/null +++ b/ape/data/datasets/phrasecut.py @@ -0,0 +1,60 @@ +import logging +import os + +from .coco import custom_register_coco_instances + +logger = logging.getLogger(__name__) + + +def _get_builtin_metadata(dataset_name): + return _get_phrasecut_metadata([]) + + raise KeyError("No built-in metadata for dataset {}".format(dataset_name)) + + +def _get_phrasecut_metadata(categories): + if len(categories) == 0: + return {} + id_to_name = {x["id"]: x["name"] for x in categories} + thing_dataset_id_to_contiguous_id = {i + 1: i for i in range(len(categories))} + thing_classes = [id_to_name[k] for k in sorted(id_to_name)] + return { + "thing_dataset_id_to_contiguous_id": thing_dataset_id_to_contiguous_id, + "thing_classes": thing_classes, + } + + +_PREDEFINED_SPLITS_PHRASECUT = {} +_PREDEFINED_SPLITS_PHRASECUT["phrasecut"] = { + "phrasecut": ( + "phrasecut/images", + "phrasecut/phrasecut.json", + ), + "phrasecut_train": ( + "phrasecut/images", + "phrasecut/phrasecut_train.json", + ), + "phrasecut_val": ( + "phrasecut/images", + "phrasecut/phrasecut_val.json", + ), +} + + +def register_all_phrasecut(root): + for dataset_name, splits_per_dataset in _PREDEFINED_SPLITS_PHRASECUT.items(): + for key, (image_root, json_file) in splits_per_dataset.items(): + custom_register_coco_instances( + key, + _get_builtin_metadata(dataset_name), + os.path.join(root, json_file) if "://" not in json_file else json_file, + os.path.join(root, image_root), + ) + + +# True for open source; +# Internally at fb, we register them elsewhere +if __name__.endswith(".phrasecut"): + # Assume pre-defined datasets live in `./datasets`. + _root = os.path.expanduser(os.getenv("DETECTRON2_DATASETS", "datasets")) + register_all_phrasecut(_root) diff --git a/ape/data/datasets/refcoco.py b/ape/data/datasets/refcoco.py new file mode 100644 index 0000000..aa6150c --- /dev/null +++ b/ape/data/datasets/refcoco.py @@ -0,0 +1,337 @@ +import contextlib +import io +import logging +import os + +import numpy as np +import pycocotools.mask as mask_util +from PIL import Image + +from detectron2.data import DatasetCatalog, MetadataCatalog +from detectron2.structures import Boxes, BoxMode, PolygonMasks, RotatedBoxes +from detectron2.utils.file_io import PathManager +from fvcore.common.timer import Timer +from iopath.common.file_io import file_lock + +""" +This file contains functions to parse COCO-format annotations into dicts in "Detectron2 format". +""" + + +logger = logging.getLogger(__name__) + +__all__ = ["load_refcoco_json", "convert_to_coco_json", "register_refcoco"] + +REFCOCO_CATEGORIES = [ + {"color": [220, 20, 60], "isthing": 1, "id": 1, "name": "object"} +] # only one class for visual grounding + + +def _get_refcoco_meta(): + thing_ids = [k["id"] for k in REFCOCO_CATEGORIES if k["isthing"] == 1] + thing_colors = [k["color"] for k in REFCOCO_CATEGORIES if k["isthing"] == 1] + assert len(thing_ids) == 1, len(thing_ids) + + thing_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(thing_ids)} + thing_classes = [k["name"] for k in REFCOCO_CATEGORIES if k["isthing"] == 1] + ret = { + "thing_dataset_id_to_contiguous_id": thing_dataset_id_to_contiguous_id, + "thing_classes": thing_classes, + "thing_colors": thing_colors, + } + return ret + + +def load_refcoco_json(json_file, image_root, dataset_name=None, extra_annotation_keys=None): + """ + Load a json file with COCO's instances annotation format. + Currently supports instance detection, instance segmentation, + and person keypoints annotations. + + Args: + json_file (str): full path to the json file in COCO instances annotation format. + image_root (str or path-like): the directory where the images in this json file exists. + dataset_name (str or None): the name of the dataset (e.g., coco_2017_train). + When provided, this function will also do the following: + + * Put "thing_classes" into the metadata associated with this dataset. + * Map the category ids into a contiguous range (needed by standard dataset format), + and add "thing_dataset_id_to_contiguous_id" to the metadata associated + with this dataset. + + This option should usually be provided, unless users need to load + the original json content and apply more processing manually. + extra_annotation_keys (list[str]): list of per-annotation keys that should also be + loaded into the dataset dict (besides "iscrowd", "bbox", "keypoints", + "category_id", "segmentation"). The values for these keys will be returned as-is. + For example, the densepose annotations are loaded in this way. + + Returns: + list[dict]: a list of dicts in Detectron2 standard dataset dicts format (See + `Using Custom Datasets `_ ) when `dataset_name` is not None. + If `dataset_name` is None, the returned `category_ids` may be + incontiguous and may not conform to the Detectron2 standard format. + + Notes: + 1. This function does not read the image files. + The results do not have the "image" field. + """ + from pycocotools.coco import COCO + + timer = Timer() + json_file = PathManager.get_local_path(json_file) + with contextlib.redirect_stdout(io.StringIO()): + coco_api = COCO(json_file) + if timer.seconds() > 1: + logger.info("Loading {} takes {:.2f} seconds.".format(json_file, timer.seconds())) + + id_map = None + if dataset_name is not None: + meta = MetadataCatalog.get(dataset_name) + cat_ids = sorted(coco_api.getCatIds()) + cats = coco_api.loadCats(cat_ids) + # The categories in a custom json file may not be sorted. + thing_classes = [c["name"] for c in sorted(cats, key=lambda x: x["id"])] + meta.thing_classes = thing_classes + + # In COCO, certain category ids are artificially removed, + # and by convention they are always ignored. + # We deal with COCO's id issue and translate + # the category ids to contiguous ids in [0, 80). + + # It works by looking at the "categories" field in the json, therefore + # if users' own json also have incontiguous ids, we'll + # apply this mapping as well but print a warning. + if not (min(cat_ids) == 1 and max(cat_ids) == len(cat_ids)): + if "coco" not in dataset_name: + logger.warning( + """ +Category ids in annotations are not in [1, #categories]! We'll apply a mapping for you. +""" + ) + id_map = {v: i for i, v in enumerate(cat_ids)} + meta.thing_dataset_id_to_contiguous_id = id_map + + cat_ids = cat_ids + list(range(max(cat_ids) + 1, 100000)) + id_map = {v: i for i, v in enumerate(cat_ids)} + + # sort indices for reproducible results + img_ids = sorted(coco_api.imgs.keys()) + # imgs is a list of dicts, each looks something like: + # {'license': 4, + # 'url': 'http://farm6.staticflickr.com/5454/9413846304_881d5e5c3b_z.jpg', + # 'file_name': 'COCO_val2014_000000001268.jpg', + # 'height': 427, + # 'width': 640, + # 'date_captured': '2013-11-17 05:57:24', + # 'id': 1268} + imgs = coco_api.loadImgs(img_ids) + # anns is a list[list[dict]], where each dict is an annotation + # record for an object. The inner list enumerates the objects in an image + # and the outer list enumerates over images. Example of anns[0]: + # [{'segmentation': [[192.81, + # 247.09, + # ... + # 219.03, + # 249.06]], + # 'area': 1035.749, + # 'iscrowd': 0, + # 'image_id': 1268, + # 'bbox': [192.81, 224.8, 74.73, 33.43], + # 'category_id': 16, + # 'id': 42986}, + # ...] + anns = [coco_api.imgToAnns[img_id] for img_id in img_ids] + total_num_valid_anns = sum([len(x) for x in anns]) + total_num_anns = len(coco_api.anns) + if total_num_valid_anns < total_num_anns: + logger.warning( + f"{json_file} contains {total_num_anns} annotations, but only " + f"{total_num_valid_anns} of them match to images in the file." + ) + + if "minival" not in json_file: + # The popular valminusminival & minival annotations for COCO2014 contain this bug. + # However the ratio of buggy annotations there is tiny and does not affect accuracy. + # Therefore we explicitly white-list them. + ann_ids = [ann["id"] for anns_per_image in anns for ann in anns_per_image] + assert len(set(ann_ids)) == len(ann_ids), "Annotation ids in '{}' are not unique!".format( + json_file + ) + + imgs_anns = list(zip(imgs, anns)) + logger.info("Loaded {} images in COCO format from {}".format(len(imgs_anns), json_file)) + + dataset_dicts = [] + + ann_keys = ["iscrowd", "bbox", "keypoints", "category_id"] + (extra_annotation_keys or []) + + num_instances_without_valid_segmentation = 0 + + for (img_dict, anno_dict_list) in imgs_anns: + record = {} + record["file_name"] = os.path.join(image_root, img_dict["file_name"]) + record["height"] = img_dict["height"] + record["width"] = img_dict["width"] + if "expressions" in img_dict: + record["expressions"] = img_dict["expressions"] + image_id = record["image_id"] = img_dict["id"] + + objs = [] + for anno in anno_dict_list: + # Check that the image_id in this annotation is the same as + # the image_id we're looking at. + # This fails only when the data parsing logic or the annotation file is buggy. + + # The original COCO valminusminival2014 & minival2014 annotation files + # actually contains bugs that, together with certain ways of using COCO API, + # can trigger this assertion. + assert anno["image_id"] == image_id + + assert anno.get("ignore", 0) == 0, '"ignore" in COCO json file is not supported.' + + obj = {key: anno[key] for key in ann_keys if key in anno} + if "bbox" in obj and len(obj["bbox"]) == 0: + raise ValueError( + f"One annotation of image {image_id} contains empty 'bbox' value! " + "This json does not have valid COCO format." + ) + + segm = anno.get("segmentation", None) + if segm: # either list[list[float]] or dict(RLE) + if isinstance(segm, dict): + if isinstance(segm["counts"], list): + # convert to compressed RLE + segm = mask_util.frPyObjects(segm, *segm["size"]) + else: + # filter out invalid polygons (< 3 points) + segm = [poly for poly in segm if len(poly) % 2 == 0 and len(poly) >= 6] + if len(segm) == 0: + num_instances_without_valid_segmentation += 1 + continue # ignore this instance + obj["segmentation"] = segm + + keypts = anno.get("keypoints", None) + if keypts: # list[int] + for idx, v in enumerate(keypts): + if idx % 3 != 2: + # COCO's segmentation coordinates are floating points in [0, H or W], + # but keypoint coordinates are integers in [0, H-1 or W-1] + # Therefore we assume the coordinates are "pixel indices" and + # add 0.5 to convert to floating point coordinates. + keypts[idx] = v + 0.5 + obj["keypoints"] = keypts + + phrase = anno.get("phrase", None) + if phrase: + obj["phrase"] = phrase + obj["bbox_mode"] = BoxMode.XYWH_ABS + if id_map: + annotation_category_id = obj["category_id"] + try: + obj["category_id"] = id_map[annotation_category_id] + except KeyError as e: + raise KeyError( + f"Encountered category_id={annotation_category_id} " + "but this id does not exist in 'categories' of the json file." + ) from e + objs.append(obj) + record["annotations"] = objs + record["task"] = "grounding" + dataset_dicts.append(record) + + if num_instances_without_valid_segmentation > 0: + logger.warning( + "Filtered out {} instances without valid segmentation. ".format( + num_instances_without_valid_segmentation + ) + + "There might be issues in your dataset generation process. Please " + "check https://detectron2.readthedocs.io/en/latest/tutorials/datasets.html carefully" + ) + return dataset_dicts + + +def register_refcoco(name, metadata, json_file, image_root): + """ + Register a dataset in COCO's json annotation format for + instance detection, instance segmentation and keypoint detection. + (i.e., Type 1 and 2 in http://cocodataset.org/#format-data. + `instances*.json` and `person_keypoints*.json` in the dataset). + + This is an example of how to register a new dataset. + You can do something similar to this function, to register new datasets. + + Args: + name (str): the name that identifies a dataset, e.g. "coco_2014_train". + metadata (dict): extra metadata associated with this dataset. You can + leave it as an empty dict. + json_file (str): path to the json instance annotation file. + image_root (str or path-like): directory which contains all the images. + """ + assert isinstance(name, str), name + assert isinstance(json_file, (str, os.PathLike)), json_file + assert isinstance(image_root, (str, os.PathLike)), image_root + # 1. register a function which returns dicts + DatasetCatalog.register(name, lambda: load_refcoco_json(json_file, image_root, name)) + + # 2. Optionally, add metadata about this dataset, + # since they might be useful in evaluation, visualization or logging + MetadataCatalog.get(name).set( + json_file=json_file, image_root=image_root, evaluator_type="refcoco", **metadata + ) + + +# ==== Predefined splits for REFCOCO datasets =========== +_PREDEFINED_SPLITS_REFCOCO = { + # refcoco + "refcoco-unc-train": ("coco/train2014", "SeqTR/refcoco-unc/instances_cocofied_train.json"), + "refcoco-unc-val": ("coco/train2014", "SeqTR/refcoco-unc/instances_cocofied_val.json"), + "refcoco-unc-testA": ("coco/train2014", "SeqTR/refcoco-unc/instances_cocofied_testA.json"), + "refcoco-unc-testB": ("coco/train2014", "SeqTR/refcoco-unc/instances_cocofied_testB.json"), + # refcocog + "refcocog-umd-train": ("coco/train2014", "SeqTR/refcocog-umd/instances_cocofied_train.json"), + "refcocog-umd-val": ("coco/train2014", "SeqTR/refcocog-umd/instances_cocofied_val.json"), + "refcocog-umd-test": ("coco/train2014", "SeqTR/refcocog-umd/instances_cocofied_test.json"), + "refcocog-google-val": ("coco/train2014", "SeqTR/refcocog-google/instances_cocofied_val.json"), + # refcoco+ + "refcocoplus-unc-train": ( + "coco/train2014", + "SeqTR/refcocoplus-unc/instances_cocofied_train.json", + ), + "refcocoplus-unc-val": ("coco/train2014", "SeqTR/refcocoplus-unc/instances_cocofied_val.json"), + "refcocoplus-unc-testA": ( + "coco/train2014", + "SeqTR/refcocoplus-unc/instances_cocofied_testA.json", + ), + "refcocoplus-unc-testB": ( + "coco/train2014", + "SeqTR/refcocoplus-unc/instances_cocofied_testB.json", + ), + # mixed + "refcoco-mixed": ("coco/train2014", "SeqTR/refcoco-mixed/instances_cocofied_train.json"), + "refcoco-mixed-filter": ( + "coco/train2014", + "SeqTR/refcoco-mixed/instances_cocofied_train_filter.json", + ), + "refcoco-mixed_group-by-image": ( + "coco/train2014", + "SeqTR/refcoco-mixed_group-by-image/instances_cocofied_train.json", + ), +} + + +def register_all_refcoco(root): + for key, (image_root, json_file) in _PREDEFINED_SPLITS_REFCOCO.items(): + # Assume pre-defined datasets live in `./datasets`. + register_refcoco( + key, + _get_refcoco_meta(), + os.path.join(root, json_file) if "://" not in json_file else json_file, + os.path.join(root, image_root), + ) + + +if __name__.endswith(".refcoco"): + # Assume pre-defined datasets live in `./datasets`. + _root = os.getenv("DETECTRON2_DATASETS", "datasets") + register_all_refcoco(_root) diff --git a/ape/data/datasets/register_bdd100k_panoseg.py b/ape/data/datasets/register_bdd100k_panoseg.py new file mode 100644 index 0000000..ebf81a6 --- /dev/null +++ b/ape/data/datasets/register_bdd100k_panoseg.py @@ -0,0 +1,277 @@ +# -------------------------------------------------------- +# X-Decoder -- Generalized Decoding for Pixel, Image, and Language +# Copyright (c) 2022 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Modified by Xueyan Zou (xueyan@cs.wisc.edu) +# -------------------------------------------------------- +# Copyright (c) Facebook, Inc. and its affiliates. +import json +import os +from collections import namedtuple + +from detectron2.data import DatasetCatalog, MetadataCatalog +from detectron2.utils.file_io import PathManager + +Label = namedtuple( + "Label", + [ + "name", # The identifier of this label, e.g. 'car', 'person', ... . + # We use them to uniquely name a class + "id", # An integer ID that is associated with this label. + # The IDs are used to represent the label in ground truth images An ID + # of -1 means that this label does not have an ID and thus is ignored + # when creating ground truth images (e.g. license plate). Do not modify + # these IDs, since exactly these IDs are expected by the evaluation + # server. + "trainId", + # Feel free to modify these IDs as suitable for your method. Then + # create ground truth images with train IDs, using the tools provided + # in the 'preparation' folder. However, make sure to validate or submit + # results to our evaluation server using the regular IDs above! For + # trainIds, multiple labels might have the same ID. Then, these labels + # are mapped to the same class in the ground truth images. For the + # inverse mapping, we use the label that is defined first in the list + # below. For example, mapping all void-type classes to the same ID in + # training, might make sense for some approaches. Max value is 255! + "category", # The name of the category that this label belongs to + "categoryId", + # The ID of this category. Used to create ground truth images + # on category level. + "hasInstances", + # Whether this label distinguishes between single instances or not + "ignoreInEval", + # Whether pixels having this class as ground truth label are ignored + # during evaluations or not + "color", # The color of this label + ], +) + + +# Our extended list of label types. Our train id is compatible with Cityscapes +BDD_CATEGORIES = [ + # name id trainId category catId + # hasInstances ignoreInEval color + # Label("unlabeled", 0, 255, "void", 0, False, True, (0, 0, 0)), + Label("dynamic", 1, 255, "void", 0, False, True, (111, 74, 0)), + Label("ego vehicle", 2, 255, "void", 0, False, True, (0, 0, 0)), + Label("ground", 3, 255, "void", 0, False, True, (81, 0, 81)), + Label("static", 4, 255, "void", 0, False, True, (0, 0, 0)), + Label("parking", 5, 255, "flat", 1, False, True, (250, 170, 160)), + Label("rail track", 6, 255, "flat", 1, False, True, (230, 150, 140)), + Label("road", 7, 0, "flat", 1, False, False, (128, 64, 128)), + Label("sidewalk", 8, 1, "flat", 1, False, False, (244, 35, 232)), + Label("bridge", 9, 255, "construction", 2, False, True, (150, 100, 100)), + Label("building", 10, 2, "construction", 2, False, False, (70, 70, 70)), + Label("fence", 11, 4, "construction", 2, False, False, (190, 153, 153)), + Label("garage", 12, 255, "construction", 2, False, True, (180, 100, 180)), + Label("guard rail", 13, 255, "construction", 2, False, True, (180, 165, 180)), + Label("tunnel", 14, 255, "construction", 2, False, True, (150, 120, 90)), + Label("wall", 15, 3, "construction", 2, False, False, (102, 102, 156)), + Label("banner", 16, 255, "object", 3, False, True, (250, 170, 100)), + Label("billboard", 17, 255, "object", 3, False, True, (220, 220, 250)), + Label("lane divider", 18, 255, "object", 3, False, True, (255, 165, 0)), + Label("parking sign", 19, 255, "object", 3, False, False, (220, 20, 60)), + Label("pole", 20, 5, "object", 3, False, False, (153, 153, 153)), + Label("polegroup", 21, 255, "object", 3, False, True, (153, 153, 153)), + Label("street light", 22, 255, "object", 3, False, True, (220, 220, 100)), + Label("traffic cone", 23, 255, "object", 3, False, True, (255, 70, 0)), + Label("traffic device", 24, 255, "object", 3, False, True, (220, 220, 220)), + Label("traffic light", 25, 6, "object", 3, False, False, (250, 170, 30)), + Label("traffic sign", 26, 7, "object", 3, False, False, (220, 220, 0)), + Label( + "traffic sign frame", + 27, + 255, + "object", + 3, + False, + True, + (250, 170, 250), + ), + Label("terrain", 28, 9, "nature", 4, False, False, (152, 251, 152)), + Label("vegetation", 29, 8, "nature", 4, False, False, (107, 142, 35)), + Label("sky", 30, 10, "sky", 5, False, False, (70, 130, 180)), + Label("person", 31, 11, "human", 6, True, False, (220, 20, 60)), + Label("rider", 32, 12, "human", 6, True, False, (255, 0, 0)), + Label("bicycle", 33, 18, "vehicle", 7, True, False, (119, 11, 32)), + Label("bus", 34, 15, "vehicle", 7, True, False, (0, 60, 100)), + Label("car", 35, 13, "vehicle", 7, True, False, (0, 0, 142)), + Label("caravan", 36, 255, "vehicle", 7, True, True, (0, 0, 90)), + Label("motorcycle", 37, 17, "vehicle", 7, True, False, (0, 0, 230)), + Label("trailer", 38, 255, "vehicle", 7, True, True, (0, 0, 110)), + Label("train", 39, 16, "vehicle", 7, True, False, (0, 80, 100)), + Label("truck", 40, 14, "vehicle", 7, True, False, (0, 0, 70)), +] + +BDD_COLORS = [k.color for k in BDD_CATEGORIES] + +MetadataCatalog.get("bdd100k_pano_val").set( + stuff_colors=BDD_COLORS[:], +) + + +def load_bdd_panoptic_json(json_file, image_dir, gt_dir, meta): + """ + Args: + image_dir (str): path to the raw dataset. e.g., "~/coco/train2017". + gt_dir (str): path to the raw annotations. e.g., "~/coco/panoptic_train2017". + json_file (str): path to the json file. e.g., "~/coco/annotations/panoptic_train2017.json". + Returns: + list[dict]: a list of dicts in Detectron2 standard format. (See + `Using Custom Datasets `_ ) + """ + + def _convert_category_id(segment_info, meta): + if segment_info["category_id"] in meta["thing_dataset_id_to_contiguous_id"]: + segment_info["category_id"] = meta["thing_dataset_id_to_contiguous_id"][ + segment_info["category_id"] + ] + segment_info["isthing"] = True + else: + segment_info["category_id"] = meta["stuff_dataset_id_to_contiguous_id"][ + segment_info["category_id"] + ] + segment_info["isthing"] = False + return segment_info + + with PathManager.open(json_file) as f: + json_info = json.load(f) + + ret = [] + for ann in json_info["annotations"]: + image_id = ann["image_id"] + # TODO: currently we assume image and label has the same filename but + # different extension, and images have extension ".jpg" for COCO. Need + # to make image extension a user-provided argument if we extend this + # function to support other COCO-like datasets. + file_name = ann["file_name"].replace("png", "jpg") + + image_file = os.path.join(image_dir, file_name) + label_file = os.path.join(gt_dir, ann["file_name"]) + + segments_info = [_convert_category_id(x, meta) for x in ann["segments_info"]] + ret.append( + { + "file_name": image_file, + "image_id": image_id, + "pan_seg_file_name": label_file, + "segments_info": segments_info, + } + ) + assert len(ret), f"No images found in {image_dir}!" + assert PathManager.isfile(ret[0]["file_name"]), ret[0]["file_name"] + assert PathManager.isfile(ret[0]["pan_seg_file_name"]), ret[0]["pan_seg_file_name"] + return ret + + +def register_bdd_panoptic( + name, + metadata, + image_root, + panoptic_root, + panoptic_json, +): + """ + Register a "standard" version of ADE20k panoptic segmentation dataset named `name`. + The dictionaries in this registered dataset follows detectron2's standard format. + Hence it's called "standard". + Args: + name (str): the name that identifies a dataset, + e.g. "ade20k_panoptic_train" + metadata (dict): extra metadata associated with this dataset. + image_root (str): directory which contains all the images + panoptic_root (str): directory which contains panoptic annotation images in COCO format + panoptic_json (str): path to the json panoptic annotation file in COCO format + sem_seg_root (none): not used, to be consistent with + `register_coco_panoptic_separated`. + instances_json (str): path to the json instance annotation file + """ + panoptic_name = name + DatasetCatalog.register( + panoptic_name, + lambda: load_bdd_panoptic_json(panoptic_json, image_root, panoptic_root, metadata), + ) + MetadataCatalog.get(panoptic_name).set( + panoptic_root=panoptic_root, + image_root=image_root, + panoptic_json=panoptic_json, + evaluator_type="bdd_panoptic_pano", + ignore_label=0, + label_divisor=1000, + **metadata, + ) + + +_PREDEFINED_SPLITS_SCANNET_PANOPTIC = { + "bdd10k_40_panoptic_val": ( + "bdd100k/images/10k/val", + "bdd100k/labels/pan_seg/coco_pano/val", + "bdd100k/labels/pan_seg/meta/coco_val.json", + ), +} + + +def get_metadata(): + meta = {} + # The following metadata maps contiguous id from [0, #thing categories + + # #stuff categories) to their names and colors. We have to replica of the + # same name and color under "thing_*" and "stuff_*" because the current + # visualization function in D2 handles thing and class classes differently + # due to some heuristic used in Panoptic FPN. We keep the same naming to + # enable reusing existing visualization functions. + thing_classes = [k.name for k in BDD_CATEGORIES if k.hasInstances == True] + thing_colors = [k.color for k in BDD_CATEGORIES if k.hasInstances == True] + stuff_classes = [k.name for k in BDD_CATEGORIES] + stuff_colors = [k.color for k in BDD_CATEGORIES] + + meta["thing_classes"] = thing_classes + meta["thing_colors"] = thing_colors + meta["stuff_classes"] = stuff_classes + meta["stuff_colors"] = stuff_colors + + # Convert category id for training: + # category id: like semantic segmentation, it is the class id for each + # pixel. Since there are some classes not used in evaluation, the category + # id is not always contiguous and thus we have two set of category ids: + # - original category id: category id in the original dataset, mainly + # used for evaluation. + # - contiguous category id: [0, #classes), in order to train the linear + # softmax classifier. + thing_dataset_id_to_contiguous_id = {} + stuff_dataset_id_to_contiguous_id = {} + + for i, cat in enumerate(BDD_CATEGORIES): + if cat.hasInstances: + thing_dataset_id_to_contiguous_id[cat.id] = i + # else: + # stuff_dataset_id_to_contiguous_id[cat["id"]] = i + + # in order to use sem_seg evaluator + stuff_dataset_id_to_contiguous_id[cat.id] = i + + meta["thing_dataset_id_to_contiguous_id"] = thing_dataset_id_to_contiguous_id + meta["stuff_dataset_id_to_contiguous_id"] = stuff_dataset_id_to_contiguous_id + return meta + + +def register_all_bdd_panoptic(root): + metadata = get_metadata() + for ( + prefix, + (image_root, panoptic_root, panoptic_json), + ) in _PREDEFINED_SPLITS_SCANNET_PANOPTIC.items(): + # The "standard" version of COCO panoptic segmentation dataset, + # e.g. used by Panoptic-DeepLab + register_bdd_panoptic( + prefix, + metadata, + os.path.join(root, image_root), + os.path.join(root, panoptic_root), + os.path.join(root, panoptic_json), + ) + + +if __name__.endswith(".register_bdd100k_panoseg"): + # Assume pre-defined datasets live in `./datasets`. + _root = os.getenv("DETECTRON2_DATASETS", "datasets") + register_all_bdd_panoptic(_root) diff --git a/ape/data/datasets/register_bdd100k_semseg.py b/ape/data/datasets/register_bdd100k_semseg.py new file mode 100644 index 0000000..2eee0ed --- /dev/null +++ b/ape/data/datasets/register_bdd100k_semseg.py @@ -0,0 +1,98 @@ +# -------------------------------------------------------- +# X-Decoder -- Generalized Decoding for Pixel, Image, and Language +# Copyright (c) 2022 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Modified by Xueyan Zou (xueyan@cs.wisc.edu) +# -------------------------------------------------------- +# Copyright (c) Facebook, Inc. and its affiliates. +import glob +import os +from typing import List, Tuple, Union + +import numpy as np + +from detectron2.data import DatasetCatalog, MetadataCatalog +from detectron2.utils.file_io import PathManager + +BDD_SEM = [ + "road", + "sidewalk", + "building", + "wall", + "fence", + "pole", + "traffic light", + "traffic sign", + "vegetation", + "terrain", + "sky", + "person", + "rider", + "car", + "truck", + "bus", + "train", + "motorcycle", + "bicycle", +] + +__all__ = ["load_scannet_instances", "register_scannet_context"] + + +def load_bdd_instances( + name: str, dirname: str, split: str, class_names: Union[List[str], Tuple[str, ...]] +): + """ + Load BDD annotations to Detectron2 format. + + Args: + dirname: Contain "Annotations", "ImageSets", "JPEGImages" + split (str): one of "train", "test", "val", "trainval" + class_names: list or tuple of class names + """ + img_folder = os.path.join(dirname, "images", "10k", split) + img_pths = sorted(glob.glob(os.path.join(img_folder, "*.jpg"))) + + sem_folder = os.path.join(dirname, "labels", "sem_seg", "masks", split) + sem_pths = sorted(glob.glob(os.path.join(sem_folder, "*.png"))) + + assert len(img_pths) == len(sem_pths) + + dicts = [] + for img_pth, sem_pth in zip(img_pths, sem_pths): + r = { + "file_name": img_pth, + "sem_seg_file_name": sem_pth, + "image_id": img_pth.split("/")[-1].split(".")[0], + } + dicts.append(r) + return dicts + + +def register_bdd_context(name, dirname, split, class_names=BDD_SEM): + DatasetCatalog.register(name, lambda: load_bdd_instances(name, dirname, split, class_names)) + MetadataCatalog.get(name).set( + stuff_classes=class_names, + dirname=dirname, + split=split, + ignore_label=[255], + thing_dataset_id_to_contiguous_id={}, + class_offset=0, + keep_sem_bgd=False, + ) + + +def register_all_bdd_semseg(root): + SPLITS = [ + ("bdd10k_val_sem_seg", "bdd100k", "val"), + ] + + for name, dirname, split in SPLITS: + register_bdd_context(name, os.path.join(root, dirname), split) + MetadataCatalog.get(name).evaluator_type = "sem_seg" + + +if __name__.endswith(".register_bdd100k_semseg"): + # Assume pre-defined datasets live in `./datasets`. + _root = os.getenv("DATASET", "datasets") + register_all_bdd_semseg(_root) diff --git a/ape/data/datasets/register_pascal_context.py b/ape/data/datasets/register_pascal_context.py new file mode 100644 index 0000000..2c67325 --- /dev/null +++ b/ape/data/datasets/register_pascal_context.py @@ -0,0 +1,587 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import os + +from detectron2.data import DatasetCatalog, MetadataCatalog +from detectron2.data.datasets import load_sem_seg + +PASCALCONTEX59_NAMES = ( + "aeroplane", + "bicycle", + "bird", + "boat", + "bottle", + "bus", + "car", + "cat", + "chair", + "cow", + "table", + "dog", + "horse", + "motorbike", + "person", + "pottedplant", + "sheep", + "sofa", + "train", + "tvmonitor", + "bag", + "bed", + "bench", + "book", + "building", + "cabinet", + "ceiling", + "cloth", + "computer", + "cup", + "door", + "fence", + "floor", + "flower", + "food", + "grass", + "ground", + "keyboard", + "light", + "mountain", + "mouse", + "curtain", + "platform", + "sign", + "plate", + "road", + "rock", + "shelves", + "sidewalk", + "sky", + "snow", + "bedclothes", + "track", + "tree", + "truck", + "wall", + "water", + "window", + "wood", +) + +PASCALCONTEX459_NAMES = ( + "accordion", + "aeroplane", + "air conditioner", + "antenna", + "artillery", + "ashtray", + "atrium", + "baby carriage", + "bag", + "ball", + "balloon", + "bamboo weaving", + "barrel", + "baseball bat", + "basket", + "basketball backboard", + "bathtub", + "bed", + "bedclothes", + "beer", + "bell", + "bench", + "bicycle", + "binoculars", + "bird", + "bird cage", + "bird feeder", + "bird nest", + "blackboard", + "board", + "boat", + "bone", + "book", + "bottle", + "bottle opener", + "bowl", + "box", + "bracelet", + "brick", + "bridge", + "broom", + "brush", + "bucket", + "building", + "bus", + "cabinet", + "cabinet door", + "cage", + "cake", + "calculator", + "calendar", + "camel", + "camera", + "camera lens", + "can", + "candle", + "candle holder", + "cap", + "car", + "card", + "cart", + "case", + "casette recorder", + "cash register", + "cat", + "cd", + "cd player", + "ceiling", + "cell phone", + "cello", + "chain", + "chair", + "chessboard", + "chicken", + "chopstick", + "clip", + "clippers", + "clock", + "closet", + "cloth", + "clothes tree", + "coffee", + "coffee machine", + "comb", + "computer", + "concrete", + "cone", + "container", + "control booth", + "controller", + "cooker", + "copying machine", + "coral", + "cork", + "corkscrew", + "counter", + "court", + "cow", + "crabstick", + "crane", + "crate", + "cross", + "crutch", + "cup", + "curtain", + "cushion", + "cutting board", + "dais", + "disc", + "disc case", + "dishwasher", + "dock", + "dog", + "dolphin", + "door", + "drainer", + "dray", + "drink dispenser", + "drinking machine", + "drop", + "drug", + "drum", + "drum kit", + "duck", + "dumbbell", + "earphone", + "earrings", + "egg", + "electric fan", + "electric iron", + "electric pot", + "electric saw", + "electronic keyboard", + "engine", + "envelope", + "equipment", + "escalator", + "exhibition booth", + "extinguisher", + "eyeglass", + "fan", + "faucet", + "fax machine", + "fence", + "ferris wheel", + "fire extinguisher", + "fire hydrant", + "fire place", + "fish", + "fish tank", + "fishbowl", + "fishing net", + "fishing pole", + "flag", + "flagstaff", + "flame", + "flashlight", + "floor", + "flower", + "fly", + "foam", + "food", + "footbridge", + "forceps", + "fork", + "forklift", + "fountain", + "fox", + "frame", + "fridge", + "frog", + "fruit", + "funnel", + "furnace", + "game controller", + "game machine", + "gas cylinder", + "gas hood", + "gas stove", + "gift box", + "glass", + "glass marble", + "globe", + "glove", + "goal", + "grandstand", + "grass", + "gravestone", + "ground", + "guardrail", + "guitar", + "gun", + "hammer", + "hand cart", + "handle", + "handrail", + "hanger", + "hard disk drive", + "hat", + "hay", + "headphone", + "heater", + "helicopter", + "helmet", + "holder", + "hook", + "horse", + "horse-drawn carriage", + "hot-air balloon", + "hydrovalve", + "ice", + "inflator pump", + "ipod", + "iron", + "ironing board", + "jar", + "kart", + "kettle", + "key", + "keyboard", + "kitchen range", + "kite", + "knife", + "knife block", + "ladder", + "ladder truck", + "ladle", + "laptop", + "leaves", + "lid", + "life buoy", + "light", + "light bulb", + "lighter", + "line", + "lion", + "lobster", + "lock", + "machine", + "mailbox", + "mannequin", + "map", + "mask", + "mat", + "match book", + "mattress", + "menu", + "metal", + "meter box", + "microphone", + "microwave", + "mirror", + "missile", + "model", + "money", + "monkey", + "mop", + "motorbike", + "mountain", + "mouse", + "mouse pad", + "musical instrument", + "napkin", + "net", + "newspaper", + "oar", + "ornament", + "outlet", + "oven", + "oxygen bottle", + "pack", + "pan", + "paper", + "paper box", + "paper cutter", + "parachute", + "parasol", + "parterre", + "patio", + "pelage", + "pen", + "pen container", + "pencil", + "person", + "photo", + "piano", + "picture", + "pig", + "pillar", + "pillow", + "pipe", + "pitcher", + "plant", + "plastic", + "plate", + "platform", + "player", + "playground", + "pliers", + "plume", + "poker", + "poker chip", + "pole", + "pool table", + "postcard", + "poster", + "pot", + "pottedplant", + "printer", + "projector", + "pumpkin", + "rabbit", + "racket", + "radiator", + "radio", + "rail", + "rake", + "ramp", + "range hood", + "receiver", + "recorder", + "recreational machines", + "remote control", + "road", + "robot", + "rock", + "rocket", + "rocking horse", + "rope", + "rug", + "ruler", + "runway", + "saddle", + "sand", + "saw", + "scale", + "scanner", + "scissors", + "scoop", + "screen", + "screwdriver", + "sculpture", + "scythe", + "sewer", + "sewing machine", + "shed", + "sheep", + "shell", + "shelves", + "shoe", + "shopping cart", + "shovel", + "sidecar", + "sidewalk", + "sign", + "signal light", + "sink", + "skateboard", + "ski", + "sky", + "sled", + "slippers", + "smoke", + "snail", + "snake", + "snow", + "snowmobiles", + "sofa", + "spanner", + "spatula", + "speaker", + "speed bump", + "spice container", + "spoon", + "sprayer", + "squirrel", + "stage", + "stair", + "stapler", + "stick", + "sticky note", + "stone", + "stool", + "stove", + "straw", + "stretcher", + "sun", + "sunglass", + "sunshade", + "surveillance camera", + "swan", + "sweeper", + "swim ring", + "swimming pool", + "swing", + "switch", + "table", + "tableware", + "tank", + "tap", + "tape", + "tarp", + "telephone", + "telephone booth", + "tent", + "tire", + "toaster", + "toilet", + "tong", + "tool", + "toothbrush", + "towel", + "toy", + "toy car", + "track", + "train", + "trampoline", + "trash bin", + "tray", + "tree", + "tricycle", + "tripod", + "trophy", + "truck", + "tube", + "turtle", + "tvmonitor", + "tweezers", + "typewriter", + "umbrella", + "unknown", + "vacuum cleaner", + "vending machine", + "video camera", + "video game console", + "video player", + "video tape", + "violin", + "wakeboard", + "wall", + "wallet", + "wardrobe", + "washing machine", + "watch", + "water", + "water dispenser", + "water pipe", + "water skate board", + "watermelon", + "whale", + "wharf", + "wheel", + "wheelchair", + "window", + "window blinds", + "wineglass", + "wire", + "wood", + "wool", +) + + +def _get_voc_meta(cat_list): + ret = { + "stuff_classes": cat_list, + } + return ret + + +def register_pascal_context_59(root): + root = os.path.join(root, "VOCdevkit/VOC2010") + meta = _get_voc_meta(PASCALCONTEX59_NAMES) + for name, image_dirname, sem_seg_dirname in [ + ("val", "JPEGImages", "annotations_detectron2/pc59_val"), + ]: + image_dir = os.path.join(root, image_dirname) + gt_dir = os.path.join(root, sem_seg_dirname) + all_name = f"pascal_context_59_sem_seg_{name}" + DatasetCatalog.register( + all_name, + lambda x=image_dir, y=gt_dir: load_sem_seg(y, x, gt_ext="png", image_ext="jpg"), + ) + MetadataCatalog.get(all_name).set( + image_root=image_dir, + sem_seg_root=gt_dir, + evaluator_type="sem_seg", + ignore_label=255, + **meta, + ) + + +def register_pascal_context_459(root): + root = os.path.join(root, "VOCdevkit/VOC2010") + meta = _get_voc_meta(PASCALCONTEX459_NAMES) + for name, image_dirname, sem_seg_dirname in [ + ("val", "JPEGImages", "annotations_detectron2/pc459_val"), + ]: + image_dir = os.path.join(root, image_dirname) + gt_dir = os.path.join(root, sem_seg_dirname) + all_name = f"pascal_context_459_sem_seg_{name}" + DatasetCatalog.register( + all_name, + lambda x=image_dir, y=gt_dir: load_sem_seg(y, x, gt_ext="tif", image_ext="jpg"), + ) + MetadataCatalog.get(all_name).set( + image_root=image_dir, + sem_seg_root=gt_dir, + evaluator_type="sem_seg", + ignore_label=65535, # NOTE: gt is saved in 16-bit TIFF images + **meta, + ) + + +if __name__.endswith(".register_pascal_context"): + # Assume pre-defined datasets live in `./datasets`. + _root = os.getenv("DETECTRON2_DATASETS", "datasets") + register_pascal_context_59(_root) + register_pascal_context_459(_root) diff --git a/ape/data/datasets/register_voc_seg.py b/ape/data/datasets/register_voc_seg.py new file mode 100644 index 0000000..e6f3d7c --- /dev/null +++ b/ape/data/datasets/register_voc_seg.py @@ -0,0 +1,64 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import os + +from detectron2.data import DatasetCatalog, MetadataCatalog +from detectron2.data.datasets import load_sem_seg + +PASCALVOC20_NAMES = ( + "aeroplane", + "bicycle", + "bird", + "boat", + "bottle", + "bus", + "car", + "cat", + "chair", + "cow", + "diningtable", + "dog", + "horse", + "motorbike", + "person", + "pottedplant", + "sheep", + "sofa", + "train", + "tvmonitor", +) + + +def _get_voc_meta(cat_list): + ret = { + "stuff_classes": cat_list, + } + return ret + + +def register_pascalvoc(root): + root = os.path.join(root, "VOCdevkit/VOC2012") + meta = _get_voc_meta(PASCALVOC20_NAMES) + + for name, image_dirname, sem_seg_dirname in [ + ("val", "JPEGImages", "annotations_detectron2/val"), + ]: + image_dir = os.path.join(root, image_dirname) + gt_dir = os.path.join(root, sem_seg_dirname) + all_name = f"pascalvoc20_sem_seg_{name}" + DatasetCatalog.register( + all_name, + lambda x=image_dir, y=gt_dir: load_sem_seg(y, x, gt_ext="png", image_ext="jpg"), + ) + MetadataCatalog.get(all_name).set( + image_root=image_dir, + sem_seg_root=gt_dir, + evaluator_type="sem_seg", + ignore_label=255, + **meta, + ) + + +if __name__.endswith(".register_voc_seg"): + # Assume pre-defined datasets live in `./datasets`. + _root = os.getenv("DETECTRON2_DATASETS", "datasets") + register_pascalvoc(_root) diff --git a/ape/data/datasets/sa1b.py b/ape/data/datasets/sa1b.py new file mode 100644 index 0000000..2a6d6e3 --- /dev/null +++ b/ape/data/datasets/sa1b.py @@ -0,0 +1,44 @@ +import os + +from detectron2.data.datasets.register_coco import register_coco_instances + +SA1B_CATEGORIES = [ + {"id": 1, "name": "object"}, +] + + +def _get_builtin_metadata(key): + id_to_name = {x["id"]: x["name"] for x in SA1B_CATEGORIES} + thing_dataset_id_to_contiguous_id = {i + 1: i for i in range(len(SA1B_CATEGORIES))} + thing_classes = [id_to_name[k] for k in sorted(id_to_name)] + return { + "thing_dataset_id_to_contiguous_id": thing_dataset_id_to_contiguous_id, + "thing_classes": thing_classes, + } + + +_PREDEFINED_SPLITS_SA1B = { + "sa1b": ("SA-1B/images", "SA-1B/sam1b_instance.json"), + "sa1b_1m": ("SA-1B/images", "SA-1B/sam1b_instance_1000000.json"), + "sa1b_2m": ("SA-1B/images", "SA-1B/sam1b_instance_2000000.json"), + "sa1b_4m": ("SA-1B/images", "SA-1B/sam1b_instance_4000000.json"), + "sa1b_6m": ("SA-1B/images", "SA-1B/sam1b_instance_6000000.json"), + "sa1b_8m": ("SA-1B/images", "SA-1B/sam1b_instance_8000000.json"), + "sa1b_10m": ("SA-1B/images", "SA-1B/sam1b_instance_10000000.json"), +} + + +def register_all_sa1b(root): + for key, (image_root, json_file) in _PREDEFINED_SPLITS_SA1B.items(): + register_coco_instances( + key, + _get_builtin_metadata(key), + os.path.join(root, json_file) if "://" not in json_file else json_file, + os.path.join(root, image_root), + ) + + +if __name__.endswith(".sa1b"): + # Assume pre-defined datasets live in `./datasets`. + _root = os.getenv("DETECTRON2_DATASETS", "datasets") + register_all_sa1b(_root) diff --git a/ape/data/datasets/seginw_categories.py b/ape/data/datasets/seginw_categories.py new file mode 100644 index 0000000..72078ea --- /dev/null +++ b/ape/data/datasets/seginw_categories.py @@ -0,0 +1,89 @@ +SEGINW_CATEGORIES = { + "seginw_Helmet-Head": ["Helmet"], + "seginw_Line-Contour": ["line-structure"], + "seginw_Elephants": ["elephant"], + "seginw_Hand-Metal": ["hand", "metal"], + "seginw_Watermelon": ["watermelon"], + "seginw_House-Parts": [ + "aluminium door", + "aluminium window", + "cellar window", + "mint cond roof", + "plaster", + "plastic door", + "plastic window", + "plate fascade", + "wooden door", + "wooden fascade", + "wooden window", + "worn cond roof", + ], + "seginw_HouseHold-Items": ["bottle", "mouse", "perfume", "phone"], + "seginw_Strawberry": ["R_strawberry", "people"], + "seginw_WareHouse-Box": ["box", "dmg box", "label"], + "seginw_Fruits": ["apple", "lemon", "orange", "pear", "strawberry"], + "seginw_Nutterfly-Squireel": ["butterfly", "squirrel"], + "seginw_Hand": ["Hand-Segmentation", "hand"], + "seginw_Garbage": ["bin", "garbage", "pavement", "road"], + "seginw_Chicken": ["chicken"], + "seginw_Rail": ["rail"], + "seginw_Airplane-Parts": ["Airplane", "Body", "Cockpit", "Engine", "Wing"], + "seginw_Face-Mask": ["Mask"], + "seginw_Brain-Tumor": ["tumor"], + "seginw_Poles": ["poles"], + "seginw_Car-Parts": [ + "back_bumper", + "back_door", + "back_glass", + "back_light", + "front_bumper", + "front_door", + "front_glass", + "front_light", + "hood", + ], + "seginw_Electric-Shaver": ["caorau"], + "seginw_Bottles": ["bottle", "can", "label"], + "seginw_Toolkits": [ + "Allen-key", + "block", + "gasket", + "plier", + "prism", + "screw", + "screwdriver", + "wrench", + ], + "seginw_Trash": [ + "Aluminium foil", + "Cigarette", + "Clear plastic bottle", + "Corrugated carton", + "Disposable plastic cup", + "Drink Can", + "Egg Carton", + "Foam cup", + "Food Can", + "Garbage bag", + "Glass bottle", + "Glass cup", + "Metal bottle cap", + "Other carton", + "Other plastic bottle", + "Paper cup", + "Plastic bag - wrapper", + "Plastic bottle cap", + "Plastic lid", + "Plastic straw", + "Pop tab", + "Styrofoam piece", + ], + "seginw_Salmon-Fillet": ["Salmon_fillet"], + "seginw_Puppies": ["puppy"], + "seginw_Tablets": ["tablets"], + "seginw_Cable": ["cable"], + "seginw_Fire": ["fire"], + "seginw_Phones": ["phone"], + "seginw_Cows": ["cow"], + "seginw_Ginger-Garlic": ["garlic", "ginger"], +} diff --git a/ape/data/datasets/seginw_instance.py b/ape/data/datasets/seginw_instance.py new file mode 100644 index 0000000..9c67f30 --- /dev/null +++ b/ape/data/datasets/seginw_instance.py @@ -0,0 +1,142 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import collections +import json +import os + +from detectron2.data import DatasetCatalog, MetadataCatalog +from detectron2.data.datasets import load_sem_seg +from detectron2.data.datasets.builtin_meta import COCO_CATEGORIES +from detectron2.utils.file_io import PathManager + +from .seginw_categories import SEGINW_CATEGORIES + +_CATEGORIES = [ + "Elephants", + "Hand-Metal", + "Watermelon", + "House-Parts", + "HouseHold-Items", + "Strawberry", + "Fruits", + "Nutterfly-Squireel", + "Hand", + "Garbage", + "Chicken", + "Rail", + "Airplane-Parts", + "Brain-Tumor", + "Poles", + "Electric-Shaver", + "Bottles", + "Toolkits", + "Trash", + "Salmon-Fillet", + "Puppies", + "Tablets", + "Phones", + "Cows", + "Ginger-Garlic", +] + +_PREDEFINED_SPLITS_SEGINW = { + "seginw_{}_val".format(cat): ( + "valid", + "seginw/{}".format(cat), # image_root + "_annotations_min1cat.coco.json", # annot_root + ) + for cat in _CATEGORIES +} +_PREDEFINED_SPLITS_SEGINW.update( + { + "seginw_{}_train".format(cat): ( + "train", + "seginw/{}".format(cat), # image_root + "_annotations_min1cat.coco.json", # annot_root + ) + for cat in _CATEGORIES + } +) + + +def get_metadata(name): + # meta = {"thing_dataset_id_to_contiguous_id": {}} + meta = {} + meta["thing_classes"] = SEGINW_CATEGORIES[name.replace("_train", "").replace("_val", "")] + meta["thing_dataset_id_to_contiguous_id"] = {i: i for i in range(len(meta["thing_classes"]))} + return meta + + +def load_seginw_json(name, image_root, annot_json, metadata): + """ + Args: + image_dir (str): path to the raw dataset. e.g., "~/coco/train2017". + gt_dir (str): path to the raw annotations. e.g., "~/coco/panoptic_train2017". + json_file (str): path to the json file. e.g., "~/coco/annotations/panoptic_train2017.json". + Returns: + list[dict]: a list of dicts in Detectron2 standard format. (See + `Using Custom Datasets `_ ) + """ + + with PathManager.open(annot_json) as f: + json_info = json.load(f) + + # build dictionary for grounding + grd_dict = collections.defaultdict(list) + for grd_ann in json_info["annotations"]: + image_id = int(grd_ann["image_id"]) + grd_dict[image_id].append(grd_ann) + + ret = [] + for image in json_info["images"]: + image_id = int(image["id"]) + image_file = os.path.join(image_root, image["file_name"]) + grounding_anno = grd_dict[image_id] + + if "train" in name and len(grounding_anno) == 0: + continue + + ret.append( + { + "file_name": image_file, + "image_id": image_id, + "inst_info": grounding_anno, + } + ) + + assert len(ret), f"No images found in {image_root}!" + assert PathManager.isfile(ret[0]["file_name"]), ret[0]["file_name"] + return ret + + +def register_seginw(name, metadata, image_root, annot_json): + DatasetCatalog.register( + name, + lambda: load_seginw_json(name, image_root, annot_json, metadata), + ) + MetadataCatalog.get(name).set( + image_root=image_root, + json_file=annot_json, + evaluator_type="seginw", + ignore_label=255, + label_divisor=1000, + **metadata, + ) + + +def register_all_seginw(root): + for ( + prefix, + (split, folder_name, annot_name), + ) in _PREDEFINED_SPLITS_SEGINW.items(): + register_seginw( + prefix, + get_metadata(prefix), + os.path.join(root, folder_name, split), + os.path.join(root, folder_name, split, annot_name), + ) + + +if __name__.endswith(".seginw_instance"): + # Assume pre-defined datasets live in `./datasets`. + _root = os.getenv("DATASET", "datasets") + register_all_seginw(_root) diff --git a/ape/data/datasets/visualgenome.py b/ape/data/datasets/visualgenome.py new file mode 100644 index 0000000..1cd329e --- /dev/null +++ b/ape/data/datasets/visualgenome.py @@ -0,0 +1,220 @@ +import logging +import os + +from .coco import custom_register_coco_instances +from .visualgenome_categories import ( + VISUALGENOME_150_CATEGORIES, + VISUALGENOME_1356_CATEGORIES, + VISUALGENOME_1356MINUS150_CATEGORIES, + VISUALGENOME_77962_CATEGORIES, + VISUALGENOME_77962MINUS150_CATEGORIES, +) + +logger = logging.getLogger(__name__) + + +def _get_builtin_metadata(dataset_name): + if dataset_name == "visualgenome_150_box": + return _get_visualgenome_metadata(VISUALGENOME_150_CATEGORIES) + + if dataset_name == "visualgenome_region": + return _get_visualgenome_metadata([]) + + if dataset_name == "visualgenome_150_box_and_region": + return _get_visualgenome_metadata(VISUALGENOME_150_CATEGORIES) + + if dataset_name == "visualgenome_77962_box_and_region": + return _get_visualgenome_metadata(VISUALGENOME_77962_CATEGORIES) + + if dataset_name == "visualgenome_77962_box": + return _get_visualgenome_metadata(VISUALGENOME_77962_CATEGORIES) + + if dataset_name == "visualgenome_77962minus150_box": + return _get_visualgenome_metadata(VISUALGENOME_77962MINUS150_CATEGORIES) + + if dataset_name == "visualgenome_77962minus2319_box": + return _get_visualgenome_metadata(VISUALGENOME_77962MINUS150_CATEGORIES) + + if dataset_name == "visualgenome_1356_box": + return _get_visualgenome_metadata(VISUALGENOME_1356_CATEGORIES) + + if dataset_name == "visualgenome_1356minus150_box": + return _get_visualgenome_metadata(VISUALGENOME_1356MINUS150_CATEGORIES) + + if dataset_name == "visualgenome_1356minus2319_box": + return _get_visualgenome_metadata(VISUALGENOME_1356MINUS150_CATEGORIES) + + raise KeyError("No built-in metadata for dataset {}".format(dataset_name)) + + +def _get_visualgenome_metadata(categories): + if len(categories) == 0: + return {} + id_to_name = {x["id"]: x["name"] for x in categories} + thing_dataset_id_to_contiguous_id = {i + 1: i for i in range(len(categories))} + thing_classes = [id_to_name[k] for k in sorted(id_to_name)] + return { + "thing_dataset_id_to_contiguous_id": thing_dataset_id_to_contiguous_id, + "thing_classes": thing_classes, + } + + +_PREDEFINED_SPLITS_VISUALGENOME = {} +_PREDEFINED_SPLITS_VISUALGENOME["visualgenome_150_box"] = { + "visualgenome_150_box_train": ( + "visualgenome", + "visualgenome/annotations/visualgenome_150_box_train.json", + ), + "visualgenome_150_box_val": ( + "visualgenome", + "visualgenome/annotations/visualgenome_150_box_val.json", + ), +} + +_PREDEFINED_SPLITS_VISUALGENOME["visualgenome_150_box_and_region"] = { + "visualgenome_150_box_and_region_train": ( + "visualgenome", + "visualgenome/annotations/visualgenome_150_box_and_region_train.json", + ), + "visualgenome_150_box_and_region_val": ( + "visualgenome", + "visualgenome/annotations/visualgenome_150_box_and_region_val.json", + ), +} + +_PREDEFINED_SPLITS_VISUALGENOME["visualgenome_77962_box"] = { + "visualgenome_77962_box": ( + "visualgenome", + "visualgenome/annotations/visualgenome_77962_box.json", + ), + "visualgenome_77962_box_train": ( + "visualgenome", + "visualgenome/annotations/visualgenome_77962_box_train.json", + ), + "visualgenome_77962_box_val": ( + "visualgenome", + "visualgenome/annotations/visualgenome_77962_box_val.json", + ), +} + +_PREDEFINED_SPLITS_VISUALGENOME["visualgenome_77962_box_and_region"] = { + "visualgenome_77962_box_and_region": ( + "visualgenome", + "visualgenome/annotations/visualgenome_77962_box_and_region.json", + ), + "visualgenome_77962_box_and_region_train": ( + "visualgenome", + "visualgenome/annotations/visualgenome_77962_box_and_region_train.json", + ), + "visualgenome_77962_box_and_region_val": ( + "visualgenome", + "visualgenome/annotations/visualgenome_77962_box_and_region_val.json", + ), +} + +_PREDEFINED_SPLITS_VISUALGENOME["visualgenome_region"] = { + "visualgenome_region": ( + "visualgenome", + "visualgenome/annotations/visualgenome_region.json", + ), + "visualgenome_region_train": ( + "visualgenome", + "visualgenome/annotations/visualgenome_region_train.json", + ), + "visualgenome_region_val": ( + "visualgenome", + "visualgenome/annotations/visualgenome_region_val.json", + ), +} + +_PREDEFINED_SPLITS_VISUALGENOME["visualgenome_77962minus150_box"] = { + "visualgenome_77962minus150_box": ( + "visualgenome", + "visualgenome/annotations/visualgenome_77962minus150_box.json", + ), + "visualgenome_77962minus150_box_train": ( + "visualgenome", + "visualgenome/annotations/visualgenome_77962minus150_box_train.json", + ), + "visualgenome_77962minus150_box_val": ( + "visualgenome", + "visualgenome/annotations/visualgenome_77962minus150_box_val.json", + ), +} + +_PREDEFINED_SPLITS_VISUALGENOME["visualgenome_77962minus2319_box"] = { + "visualgenome_77962minus2319_box": ( + "visualgenome", + "visualgenome/annotations/visualgenome_77962minus2319_box.json", + ), + "visualgenome_77962minus2319_box_train": ( + "visualgenome", + "visualgenome/annotations/visualgenome_77962minus2319_box_train.json", + ), + "visualgenome_77962minus2319_box_val": ( + "visualgenome", + "visualgenome/annotations/visualgenome_77962minus2319_box_val.json", + ), +} + +_PREDEFINED_SPLITS_VISUALGENOME["visualgenome_1356_box"] = { + "visualgenome_1356_box": ( + "visualgenome", + "visualgenome/annotations/visualgenome_1356_box.json", + ), + "visualgenome_1356_box_train": ( + "visualgenome", + "visualgenome/annotations/visualgenome_1356_box_train.json", + ), + "visualgenome_1356_box_val": ( + "visualgenome", + "visualgenome/annotations/visualgenome_1356_box_val.json", + ), +} + +_PREDEFINED_SPLITS_VISUALGENOME["visualgenome_1356minus150_box"] = { + "visualgenome_1356minus150_box": ( + "visualgenome", + "visualgenome/annotations/visualgenome_1356minus150_box.json", + ), + "visualgenome_1356minus150_box_train": ( + "visualgenome", + "visualgenome/annotations/visualgenome_1356minus150_box_train.json", + ), + "visualgenome_1356minus150_box_val": ( + "visualgenome", + "visualgenome/annotations/visualgenome_1356minus150_box_val.json", + ), +} + +_PREDEFINED_SPLITS_VISUALGENOME["visualgenome_1356minus2319_box"] = { + "visualgenome_1356minus2319_box": ( + "visualgenome", + "visualgenome/annotations/visualgenome_1356minus2319_box.json", + ), + "visualgenome_1356minus2319_box_train": ( + "visualgenome", + "visualgenome/annotations/visualgenome_1356minus2319_box_train.json", + ), + "visualgenome_1356minus2319_box_val": ( + "visualgenome", + "visualgenome/annotations/visualgenome_1356minus2319_box_val.json", + ), +} + + +def register_all_visualgenome(root): + for dataset_name, splits_per_dataset in _PREDEFINED_SPLITS_VISUALGENOME.items(): + for key, (image_root, json_file) in splits_per_dataset.items(): + custom_register_coco_instances( + key, + _get_builtin_metadata(dataset_name), + os.path.join(root, json_file) if "://" not in json_file else json_file, + os.path.join(root, image_root), + ) + + +if __name__.endswith(".visualgenome"): + # Assume pre-defined datasets live in `./datasets`. + _root = os.path.expanduser(os.getenv("DETECTRON2_DATASETS", "datasets")) + register_all_visualgenome(_root) diff --git a/ape/data/datasets/visualgenome_categories.py b/ape/data/datasets/visualgenome_categories.py new file mode 100644 index 0000000..5369657 --- /dev/null +++ b/ape/data/datasets/visualgenome_categories.py @@ -0,0 +1,18 @@ +# fmt: off + +VISUALGENOME_150_CATEGORIES = [{"id": 1, "name": "airplane"}, {"id": 2, "name": "animal"}, {"id": 3, "name": "arm"}, {"id": 4, "name": "bag"}, {"id": 5, "name": "banana"}, {"id": 6, "name": "basket"}, {"id": 7, "name": "beach"}, {"id": 8, "name": "bear"}, {"id": 9, "name": "bed"}, {"id": 10, "name": "bench"}, {"id": 11, "name": "bike"}, {"id": 12, "name": "bird"}, {"id": 13, "name": "board"}, {"id": 14, "name": "boat"}, {"id": 15, "name": "book"}, {"id": 16, "name": "boot"}, {"id": 17, "name": "bottle"}, {"id": 18, "name": "bowl"}, {"id": 19, "name": "box"}, {"id": 20, "name": "boy"}, {"id": 21, "name": "branch"}, {"id": 22, "name": "building"}, {"id": 23, "name": "bus"}, {"id": 24, "name": "cabinet"}, {"id": 25, "name": "cap"}, {"id": 26, "name": "car"}, {"id": 27, "name": "cat"}, {"id": 28, "name": "chair"}, {"id": 29, "name": "child"}, {"id": 30, "name": "clock"}, {"id": 31, "name": "coat"}, {"id": 32, "name": "counter"}, {"id": 33, "name": "cow"}, {"id": 34, "name": "cup"}, {"id": 35, "name": "curtain"}, {"id": 36, "name": "desk"}, {"id": 37, "name": "dog"}, {"id": 38, "name": "door"}, {"id": 39, "name": "drawer"}, {"id": 40, "name": "ear"}, {"id": 41, "name": "elephant"}, {"id": 42, "name": "engine"}, {"id": 43, "name": "eye"}, {"id": 44, "name": "face"}, {"id": 45, "name": "fence"}, {"id": 46, "name": "finger"}, {"id": 47, "name": "flag"}, {"id": 48, "name": "flower"}, {"id": 49, "name": "food"}, {"id": 50, "name": "fork"}, {"id": 51, "name": "fruit"}, {"id": 52, "name": "giraffe"}, {"id": 53, "name": "girl"}, {"id": 54, "name": "glass"}, {"id": 55, "name": "glove"}, {"id": 56, "name": "guy"}, {"id": 57, "name": "hair"}, {"id": 58, "name": "hand"}, {"id": 59, "name": "handle"}, {"id": 60, "name": "hat"}, {"id": 61, "name": "head"}, {"id": 62, "name": "helmet"}, {"id": 63, "name": "hill"}, {"id": 64, "name": "horse"}, {"id": 65, "name": "house"}, {"id": 66, "name": "jacket"}, {"id": 67, "name": "jean"}, {"id": 68, "name": "kid"}, {"id": 69, "name": "kite"}, {"id": 70, "name": "lady"}, {"id": 71, "name": "lamp"}, {"id": 72, "name": "laptop"}, {"id": 73, "name": "leaf"}, {"id": 74, "name": "leg"}, {"id": 75, "name": "letter"}, {"id": 76, "name": "light"}, {"id": 77, "name": "logo"}, {"id": 78, "name": "man"}, {"id": 79, "name": "men"}, {"id": 80, "name": "motorcycle"}, {"id": 81, "name": "mountain"}, {"id": 82, "name": "mouth"}, {"id": 83, "name": "neck"}, {"id": 84, "name": "nose"}, {"id": 85, "name": "number"}, {"id": 86, "name": "orange"}, {"id": 87, "name": "pant"}, {"id": 88, "name": "paper"}, {"id": 89, "name": "paw"}, {"id": 90, "name": "people"}, {"id": 91, "name": "person"}, {"id": 92, "name": "phone"}, {"id": 93, "name": "pillow"}, {"id": 94, "name": "pizza"}, {"id": 95, "name": "plane"}, {"id": 96, "name": "plant"}, {"id": 97, "name": "plate"}, {"id": 98, "name": "player"}, {"id": 99, "name": "pole"}, {"id": 100, "name": "post"}, {"id": 101, "name": "pot"}, {"id": 102, "name": "racquet"}, {"id": 103, "name": "railing"}, {"id": 104, "name": "rock"}, {"id": 105, "name": "roof"}, {"id": 106, "name": "room"}, {"id": 107, "name": "screen"}, {"id": 108, "name": "seat"}, {"id": 109, "name": "sheep"}, {"id": 110, "name": "shelf"}, {"id": 111, "name": "shirt"}, {"id": 112, "name": "shoe"}, {"id": 113, "name": "short"}, {"id": 114, "name": "sidewalk"}, {"id": 115, "name": "sign"}, {"id": 116, "name": "sink"}, {"id": 117, "name": "skateboard"}, {"id": 118, "name": "ski"}, {"id": 119, "name": "skier"}, {"id": 120, "name": "sneaker"}, {"id": 121, "name": "snow"}, {"id": 122, "name": "sock"}, {"id": 123, "name": "stand"}, {"id": 124, "name": "street"}, {"id": 125, "name": "surfboard"}, {"id": 126, "name": "table"}, {"id": 127, "name": "tail"}, {"id": 128, "name": "tie"}, {"id": 129, "name": "tile"}, {"id": 130, "name": "tire"}, {"id": 131, "name": "toilet"}, {"id": 132, "name": "towel"}, {"id": 133, "name": "tower"}, {"id": 134, "name": "track"}, {"id": 135, "name": "train"}, {"id": 136, "name": "tree"}, {"id": 137, "name": "truck"}, {"id": 138, "name": "trunk"}, {"id": 139, "name": "umbrella"}, {"id": 140, "name": "vase"}, {"id": 141, "name": "vegetable"}, {"id": 142, "name": "vehicle"}, {"id": 143, "name": "wave"}, {"id": 144, "name": "wheel"}, {"id": 145, "name": "window"}, {"id": 146, "name": "windshield"}, {"id": 147, "name": "wing"}, {"id": 148, "name": "wire"}, {"id": 149, "name": "woman"}, {"id": 150, "name": "zebra"}] + +VISUALGENOME_1356_CATEGORIES = [{"id": 1, "name": "ad"}, {"id": 2, "name": "adult"}, {"id": 3, "name": "advertisement"}, {"id": 4, "name": "air"}, {"id": 5, "name": "air conditioner"}, {"id": 6, "name": "air vent"}, {"id": 7, "name": "aircraft"}, {"id": 8, "name": "airplane"}, {"id": 9, "name": "airport"}, {"id": 10, "name": "alarm clock"}, {"id": 11, "name": "alley"}, {"id": 12, "name": "american flag"}, {"id": 13, "name": "animal"}, {"id": 14, "name": "ankle"}, {"id": 15, "name": "antelope"}, {"id": 16, "name": "antenna"}, {"id": 17, "name": "apartment"}, {"id": 18, "name": "apple"}, {"id": 19, "name": "apron"}, {"id": 20, "name": "arch"}, {"id": 21, "name": "archway"}, {"id": 22, "name": "area rug"}, {"id": 23, "name": "area"}, {"id": 24, "name": "arm"}, {"id": 25, "name": "armchair"}, {"id": 26, "name": "armrest"}, {"id": 27, "name": "arrow"}, {"id": 28, "name": "art"}, {"id": 29, "name": "artwork"}, {"id": 30, "name": "asparagus"}, {"id": 31, "name": "asphalt"}, {"id": 32, "name": "audience"}, {"id": 33, "name": "avocado"}, {"id": 34, "name": "awning"}, {"id": 35, "name": "baby"}, {"id": 36, "name": "back pack"}, {"id": 37, "name": "back wheel"}, {"id": 38, "name": "back"}, {"id": 39, "name": "background"}, {"id": 40, "name": "backpack"}, {"id": 41, "name": "backsplash"}, {"id": 42, "name": "bacon"}, {"id": 43, "name": "bag"}, {"id": 44, "name": "bagel"}, {"id": 45, "name": "balcony"}, {"id": 46, "name": "ball cap"}, {"id": 47, "name": "ball"}, {"id": 48, "name": "balloon"}, {"id": 49, "name": "bamboo"}, {"id": 50, "name": "banana bunch"}, {"id": 51, "name": "banana peel"}, {"id": 52, "name": "banana slice"}, {"id": 53, "name": "banana"}, {"id": 54, "name": "band"}, {"id": 55, "name": "bandana"}, {"id": 56, "name": "bang"}, {"id": 57, "name": "bank"}, {"id": 58, "name": "banner"}, {"id": 59, "name": "bar"}, {"id": 60, "name": "bark"}, {"id": 61, "name": "barn"}, {"id": 62, "name": "barrel"}, {"id": 63, "name": "barricade"}, {"id": 64, "name": "barrier"}, {"id": 65, "name": "base"}, {"id": 66, "name": "baseball bat"}, {"id": 67, "name": "baseball cap"}, {"id": 68, "name": "baseball field"}, {"id": 69, "name": "baseball game"}, {"id": 70, "name": "baseball glove"}, {"id": 71, "name": "baseball mitt"}, {"id": 72, "name": "baseball player"}, {"id": 73, "name": "baseball players"}, {"id": 74, "name": "baseball uniform"}, {"id": 75, "name": "baseball"}, {"id": 76, "name": "baseboard"}, {"id": 77, "name": "baseline"}, {"id": 78, "name": "basin"}, {"id": 79, "name": "basket"}, {"id": 80, "name": "bat"}, {"id": 81, "name": "bath tub"}, {"id": 82, "name": "bathing suit"}, {"id": 83, "name": "bathroom sink"}, {"id": 84, "name": "bathroom"}, {"id": 85, "name": "bathtub"}, {"id": 86, "name": "batter"}, {"id": 87, "name": "beach chair"}, {"id": 88, "name": "beach"}, {"id": 89, "name": "bead"}, {"id": 90, "name": "beak"}, {"id": 91, "name": "beam"}, {"id": 92, "name": "bean"}, {"id": 93, "name": "beanie"}, {"id": 94, "name": "bear"}, {"id": 95, "name": "beard"}, {"id": 96, "name": "bed frame"}, {"id": 97, "name": "bed"}, {"id": 98, "name": "bedding"}, {"id": 99, "name": "bedroom"}, {"id": 100, "name": "bedspread"}, {"id": 101, "name": "beef"}, {"id": 102, "name": "beer bottle"}, {"id": 103, "name": "beer"}, {"id": 104, "name": "bell"}, {"id": 105, "name": "belt"}, {"id": 106, "name": "bench"}, {"id": 107, "name": "berry"}, {"id": 108, "name": "beverage"}, {"id": 109, "name": "bib"}, {"id": 110, "name": "bicycle"}, {"id": 111, "name": "bike rack"}, {"id": 112, "name": "bike"}, {"id": 113, "name": "biker"}, {"id": 114, "name": "bikini"}, {"id": 115, "name": "billboard"}, {"id": 116, "name": "bin"}, {"id": 117, "name": "bird"}, {"id": 118, "name": "birthday cake"}, {"id": 119, "name": "biscuit"}, {"id": 120, "name": "black"}, {"id": 121, "name": "blade"}, {"id": 122, "name": "blanket"}, {"id": 123, "name": "blazer"}, {"id": 124, "name": "bleacher"}, {"id": 125, "name": "blender"}, {"id": 126, "name": "blind"}, {"id": 127, "name": "block"}, {"id": 128, "name": "blouse"}, {"id": 129, "name": "blueberry"}, {"id": 130, "name": "board"}, {"id": 131, "name": "boardwalk"}, {"id": 132, "name": "boat"}, {"id": 133, "name": "body"}, {"id": 134, "name": "bolt"}, {"id": 135, "name": "book shelf"}, {"id": 136, "name": "book"}, {"id": 137, "name": "bookcase"}, {"id": 138, "name": 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{"id": 1228, "name": "toy"}, {"id": 1229, "name": "track"}, {"id": 1230, "name": "tractor"}, {"id": 1231, "name": "traffic cone"}, {"id": 1232, "name": "traffic light"}, {"id": 1233, "name": "traffic lights"}, {"id": 1234, "name": "traffic sign"}, {"id": 1235, "name": "traffic signal"}, {"id": 1236, "name": "traffic"}, {"id": 1237, "name": "trail"}, {"id": 1238, "name": "trailer"}, {"id": 1239, "name": "train car"}, {"id": 1240, "name": "train cars"}, {"id": 1241, "name": "train engine"}, {"id": 1242, "name": "train front"}, {"id": 1243, "name": "train platform"}, {"id": 1244, "name": "train station"}, {"id": 1245, "name": "train track"}, {"id": 1246, "name": "train tracks"}, {"id": 1247, "name": "train"}, {"id": 1248, "name": "trash"}, {"id": 1249, "name": "trash bag"}, {"id": 1250, "name": "trash bin"}, {"id": 1251, "name": "trash can"}, {"id": 1252, "name": "trashcan"}, {"id": 1253, "name": "tray"}, {"id": 1254, "name": "tree branch"}, {"id": 1255, "name": "tree branches"}, {"id": 1256, "name": "tree line"}, {"id": 1257, "name": "tree trunk"}, {"id": 1258, "name": "tree"}, {"id": 1259, "name": "triangle"}, {"id": 1260, "name": "trick"}, {"id": 1261, "name": "trolley"}, {"id": 1262, "name": "trough"}, {"id": 1263, "name": "trouser"}, {"id": 1264, "name": "truck"}, {"id": 1265, "name": "trunk"}, {"id": 1266, "name": "tshirt"}, {"id": 1267, "name": "tub"}, {"id": 1268, "name": "tube"}, {"id": 1269, "name": "tunnel"}, {"id": 1270, "name": "turbine"}, {"id": 1271, "name": "turf"}, {"id": 1272, "name": "tusk"}, {"id": 1273, "name": "tv stand"}, {"id": 1274, "name": "twig"}, {"id": 1275, "name": "umbrella"}, {"id": 1276, "name": "umpire"}, {"id": 1277, "name": "undershirt"}, {"id": 1278, "name": "uniform"}, {"id": 1279, "name": "urinal"}, {"id": 1280, "name": "urn"}, {"id": 1281, "name": "utensil"}, {"id": 1282, "name": "utility pole"}, {"id": 1283, "name": "va"}, {"id": 1284, "name": "valley"}, {"id": 1285, "name": "van"}, {"id": 1286, "name": "vane"}, {"id": 1287, "name": "vanity"}, {"id": 1288, "name": "vase"}, {"id": 1289, "name": "vegetable"}, {"id": 1290, "name": "veggie"}, {"id": 1291, "name": "vehicle"}, {"id": 1292, "name": "vent"}, {"id": 1293, "name": "vest"}, {"id": 1294, "name": "view"}, {"id": 1295, "name": "vine"}, {"id": 1296, "name": "visor"}, {"id": 1297, "name": "wagon"}, {"id": 1298, "name": "waist"}, {"id": 1299, "name": "wake"}, {"id": 1300, "name": "walkway"}, {"id": 1301, "name": "wall"}, {"id": 1302, "name": "wallet"}, {"id": 1303, "name": "wallpaper"}, {"id": 1304, "name": "washer"}, {"id": 1305, "name": "watch"}, {"id": 1306, "name": "water bottle"}, {"id": 1307, "name": "water tank"}, {"id": 1308, "name": "water"}, {"id": 1309, "name": "watermark"}, {"id": 1310, "name": "watermelon"}, {"id": 1311, "name": "wave"}, {"id": 1312, "name": "weather"}, {"id": 1313, "name": "weather vane"}, {"id": 1314, "name": "website"}, {"id": 1315, "name": "weed"}, {"id": 1316, "name": "wet suit"}, {"id": 1317, "name": "wetsuit"}, {"id": 1318, "name": "wheel"}, {"id": 1319, "name": "wheelchair"}, {"id": 1320, "name": "whisker"}, {"id": 1321, "name": "wii"}, {"id": 1322, "name": "wii remote"}, {"id": 1323, "name": "wild"}, {"id": 1324, "name": "window sill"}, {"id": 1325, "name": "window"}, {"id": 1326, "name": "windowsill"}, {"id": 1327, "name": "windshield wiper"}, {"id": 1328, "name": "windshield wipers"}, {"id": 1329, "name": "windshield"}, {"id": 1330, "name": "wine bottle"}, {"id": 1331, "name": "wine glass"}, {"id": 1332, "name": "wine glasses"}, {"id": 1333, "name": "wine"}, {"id": 1334, "name": "wing"}, {"id": 1335, "name": "wiper"}, {"id": 1336, "name": "wire fence"}, {"id": 1337, "name": "wire"}, {"id": 1338, "name": "woman"}, {"id": 1339, "name": "wood"}, {"id": 1340, "name": "wool"}, {"id": 1341, "name": "word"}, {"id": 1342, "name": "worker"}, {"id": 1343, "name": "wrapper"}, {"id": 1344, "name": "wrist band"}, {"id": 1345, "name": "wrist watch"}, {"id": 1346, "name": "wrist"}, {"id": 1347, "name": "wristband"}, {"id": 1348, "name": "wristwatch"}, {"id": 1349, "name": "writing"}, {"id": 1350, "name": "yacht"}, {"id": 1351, "name": "yard"}, {"id": 1352, "name": "yellow"}, {"id": 1353, "name": "yolk"}, {"id": 1354, "name": "young man"}, {"id": 1355, "name": "zebra"}, {"id": 1356, "name": "zoo"}] + +VISUALGENOME_1356MINUS150_CATEGORIES = VISUALGENOME_1356_CATEGORIES + +VISUALGENOME_1356MINUS2319_CATEGORIES = VISUALGENOME_1356_CATEGORIES + +# VISUALGENOME_77962_CATEGORIES = [] +VISUALGENOME_77962_CATEGORIES = [{"id": 1, "name": "button"}, {"id": 2, "name": "least 11 panes"}, {"id": 3, "name": "least 12 panes"}, {"id": 4, "name": "quilmes"}, {"id": 5, "name": "sign"}, {"id": 6, "name": "sumbol"}, {"id": 7, "name": "their tops"}, {"id": 8, "name": "yellow"}, {"id": 9, "name": "0"}, {"id": 10, "name": "0 10"}, {"id": 11, "name": "0 button"}, {"id": 12, "name": "0 key"}, {"id": 13, "name": "00"}, {"id": 14, "name": "000"}, {"id": 15, "name": "001"}, {"id": 16, "name": "002"}, {"id": 17, "name": "007"}, {"id": 18, "name": "007 logo"}, {"id": 19, "name": "00slm"}, {"id": 20, "name": "01"}, {"id": 21, "name": "012"}, {"id": 22, "name": "015"}, {"id": 23, "name": "02"}, {"id": 24, "name": "02 abc"}, {"id": 25, "name": "02047"}, {"id": 26, "name": "023"}, {"id": 27, "name": "040446 pm"}, {"id": 28, "name": "04232006"}, {"id": 29, "name": "0439 am"}, {"id": 30, "name": "0452"}, {"id": 31, "name": "0578"}, {"id": 32, "name": "06"}, {"id": 33, "name": "06022010"}, {"id": 34, "name": "0629"}, {"id": 35, "name": "07"}, {"id": 36, "name": "0735"}, {"id": 37, "name": "09"}, {"id": 38, "name": "092"}, {"id": 39, "name": "1"}, {"id": 40, "name": "1 12"}, {"id": 41, "name": "1 and 5"}, {"id": 42, "name": "1 av"}, {"id": 43, "name": "1 botton"}, {"id": 44, "name": "1 button"}, {"id": 45, "name": "1 cup of sugar"}, {"id": 46, "name": "1 fence"}, {"id": 47, "name": "1 hour"}, {"id": 48, "name": "1 key"}, {"id": 49, "name": "1 oclock"}, {"id": 50, "name": "1 on a clock"}, {"id": 51, "name": "10"}, {"id": 52, "name": "100"}, {"id": 53, "name": "100 ave"}, {"id": 54, "name": "100 m"}, {"id": 55, "name": "100 natural"}, {"id": 56, "name": "100 street"}, {"id": 57, "name": "100 w"}, {"id": 58, "name": "100 yen"}, {"id": 59, "name": "1000"}, {"id": 60, "name": "1005"}, {"id": 61, "name": "1009"}, {"id": 62, "name": "101"}, {"id": 63, "name": "1010"}, {"id": 64, "name": "101093"}, {"id": 65, "name": "1014"}, {"id": 66, "name": "1015"}, {"id": 67, "name": "10150"}, {"id": 68, "name": "1017"}, {"id": 69, "name": "1018"}, {"id": 70, "name": "102"}, {"id": 71, "name": "1020"}, {"id": 72, "name": "1022"}, {"id": 73, "name": "1023"}, {"id": 74, "name": "1025"}, {"id": 75, "name": "103"}, {"id": 76, "name": "1030"}, {"id": 77, "name": "1032"}, {"id": 78, "name": "1035"}, {"id": 79, "name": "1036"}, {"id": 80, "name": "1037"}, {"id": 81, "name": "103on bike"}, {"id": 82, "name": "104 mph"}, {"id": 83, "name": "1042"}, {"id": 84, "name": "1045"}, {"id": 85, "name": "1049"}, {"id": 86, "name": "104th st"}, {"id": 87, "name": "105"}, {"id": 88, "name": "1054 am"}, {"id": 89, "name": "1056"}, {"id": 90, "name": "106"}, {"id": 91, "name": "1060"}, {"id": 92, "name": "10612"}, {"id": 93, "name": "108"}, {"id": 94, "name": "10key"}, {"id": 95, "name": "11"}, {"id": 96, "name": "11 and 1"}, {"id": 97, "name": "11 ave"}, {"id": 98, "name": "11 ounce"}, {"id": 99, "name": "11 st"}, {"id": 100, "name": "110"}, {"id": 101, "name": "1100"}, {"id": 102, "name": "1100 am"}, {"id": 103, "name": "1105"}, {"id": 104, "name": "111"}, {"id": 105, "name": "1110"}, {"id": 106, "name": "1111"}, {"id": 107, "name": "1113"}, {"id": 108, "name": "1114"}, {"id": 109, "name": "1115"}, {"id": 110, "name": "1119"}, {"id": 111, "name": "112"}, {"id": 112, "name": "1120"}, {"id": 113, "name": "112552"}, {"id": 114, "name": "112717 p"}, {"id": 115, "name": "113"}, {"id": 116, "name": "1130"}, {"id": 117, "name": "1132"}, {"id": 118, "name": "1136"}, {"id": 119, "name": "1137"}, {"id": 120, "name": "1148"}, {"id": 121, "name": "115"}, {"id": 122, "name": "1150"}, {"id": 123, "name": "115012"}, {"id": 124, "name": "115061"}, {"id": 125, "name": "1154 am"}, {"id": 126, "name": "115yo"}, {"id": 127, "name": "116"}, {"id": 128, "name": "117"}, {"id": 129, "name": "118"}, {"id": 130, "name": "118000"}, {"id": 131, "name": "118120"}, {"id": 132, "name": "119"}, {"id": 133, "name": "11e"}, {"id": 134, "name": "11ish"}, {"id": 135, "name": "11th street"}, {"id": 136, "name": "12"}, {"id": 137, "name": "12 ave e"}, {"id": 138, "name": "12 cupcakes"}, {"id": 139, "name": "12 number"}, {"id": 140, "name": "12 numbers"}, {"id": 141, "name": "12 oclock"}, {"id": 142, "name": "12 on a clock"}, {"id": 143, "name": "12 pane window"}, {"id": 144, "name": "120"}, {"id": 145, "name": "1200"}, {"id": 146, "name": "1203"}, {"id": 147, "name": "120572"}, {"id": 148, "name": "1208"}, {"id": 149, "name": "121"}, {"id": 150, "name": "121 avenue"}, {"id": 151, "name": "1212"}, {"id": 152, "name": "1213"}, {"id": 153, "name": "1214"}, {"id": 154, "name": "1215"}, {"id": 155, "name": "1220"}, {"id": 156, "name": "1221"}, {"id": 157, "name": "12253"}, {"id": 158, "name": "1226"}, {"id": 159, "name": "123"}, {"id": 160, "name": "1232"}, {"id": 161, "name": "124"}, {"id": 162, "name": "1240"}, {"id": 163, "name": "1241"}, {"id": 164, "name": "1243"}, {"id": 165, "name": "1249"}, {"id": 166, "name": "125"}, {"id": 167, "name": "1250"}, {"id": 168, "name": "129"}, {"id": 169, "name": "129 kilo \u20ac"}, {"id": 170, "name": "13"}, {"id": 171, "name": "13 donuts"}, {"id": 172, "name": "130"}, {"id": 173, "name": "1300"}, {"id": 174, "name": "13006"}, {"id": 175, "name": "1305"}, {"id": 176, "name": "130yen"}, {"id": 177, "name": "131"}, {"id": 178, "name": "1311"}, {"id": 179, "name": "1313"}, {"id": 180, "name": "132"}, {"id": 181, "name": "133"}, {"id": 182, "name": "1335"}, {"id": 183, "name": "134"}, {"id": 184, "name": "135"}, {"id": 185, "name": "135c"}, {"id": 186, "name": "137"}, {"id": 187, "name": "139"}, {"id": 188, "name": "14"}, {"id": 189, "name": "140"}, {"id": 190, "name": "1402"}, {"id": 191, "name": "141"}, {"id": 192, "name": "1411"}, {"id": 193, "name": "142031"}, {"id": 194, "name": "1423"}, {"id": 195, "name": "143"}, {"id": 196, "name": "14386"}, {"id": 197, "name": "145"}, {"id": 198, "name": "146"}, {"id": 199, "name": "14606"}, {"id": 200, "name": "1466"}, {"id": 201, "name": "147"}, {"id": 202, "name": "148"}, {"id": 203, "name": "148 kph"}, {"id": 204, "name": "149"}, {"id": 205, "name": "14th street"}, {"id": 206, "name": "15"}, {"id": 207, "name": "15 freeway"}, {"id": 208, "name": "15 sheep"}, {"id": 209, "name": "150"}, {"id": 210, "name": "1500"}, {"id": 211, "name": "1503"}, {"id": 212, "name": "1504"}, {"id": 213, "name": "151"}, {"id": 214, "name": "152"}, {"id": 215, "name": "152 0774"}, {"id": 216, "name": "1523"}, {"id": 217, "name": "153"}, {"id": 218, "name": "153354"}, {"id": 219, "name": "1536"}, {"id": 220, "name": "154"}, {"id": 221, "name": "155"}, {"id": 222, "name": "1581"}, {"id": 223, "name": "158711"}, {"id": 224, "name": "158751"}, {"id": 225, "name": "158821 number"}, {"id": 226, "name": "159106"}, {"id": 227, "name": "1595"}, {"id": 228, "name": "1598"}, {"id": 229, "name": "16"}, {"id": 230, "name": "160"}, {"id": 231, "name": "1600"}, {"id": 232, "name": "1616"}, {"id": 233, "name": "162"}, {"id": 234, "name": "163"}, {"id": 235, "name": "1632"}, {"id": 236, "name": "164th av"}, {"id": 237, "name": "165"}, {"id": 238, "name": "165035"}, {"id": 239, "name": "167"}, {"id": 240, "name": "168106"}, {"id": 241, "name": "16957"}, {"id": 242, "name": "16th street"}, {"id": 243, "name": "17"}, {"id": 244, "name": "170"}, {"id": 245, "name": "17000"}, {"id": 246, "name": "1706"}, {"id": 247, "name": "17105"}, {"id": 248, "name": "1715"}, {"id": 249, "name": "172"}, {"id": 250, "name": "172213"}, {"id": 251, "name": "173"}, {"id": 252, "name": "1730"}, {"id": 253, "name": "1737"}, {"id": 254, "name": "175"}, {"id": 255, "name": "1750"}, {"id": 256, "name": "175110"}, {"id": 257, "name": "176"}, {"id": 258, "name": "1770"}, {"id": 259, "name": "17836"}, {"id": 260, "name": "1795"}, {"id": 261, "name": "18"}, {"id": 262, "name": "18 wheeler"}, {"id": 263, "name": "180"}, {"id": 264, "name": "1800"}, {"id": 265, "name": "1800 grant"}, {"id": 266, "name": "1800 zurich hb"}, {"id": 267, "name": "18125"}, {"id": 268, "name": "1813"}, {"id": 269, "name": "1818"}, {"id": 270, "name": "186"}, {"id": 271, "name": "1860"}, {"id": 272, "name": "1863"}, {"id": 273, "name": "1866"}, {"id": 274, "name": "1872"}, {"id": 275, "name": "1873"}, {"id": 276, "name": "1877fogs out"}, {"id": 277, "name": "1879"}, {"id": 278, "name": "188"}, {"id": 279, "name": "189"}, {"id": 280, "name": "1893"}, {"id": 281, "name": "1894"}, {"id": 282, "name": "18t truck"}, {"id": 283, "name": "18th street"}, {"id": 284, "name": "18wheeler"}, {"id": 285, "name": "19"}, {"id": 286, "name": "190 on front of bus"}, {"id": 287, "name": "1900"}, {"id": 288, "name": "1905"}, {"id": 289, "name": "1911"}, {"id": 290, "name": "1918"}, {"id": 291, "name": "1919"}, {"id": 292, "name": "192"}, {"id": 293, "name": "1926"}, {"id": 294, "name": "1932"}, {"id": 295, "name": "1937"}, {"id": 296, "name": "1955"}, {"id": 297, "name": "197"}, {"id": 298, "name": "197 rosemont"}, {"id": 299, "name": "19751982"}, {"id": 300, "name": "1977"}, {"id": 301, "name": "198"}, {"id": 302, "name": "1985"}, {"id": 303, "name": "1987"}, {"id": 304, "name": "1989"}, {"id": 305, "name": "1990"}, {"id": 306, "name": "1995"}, {"id": 307, "name": "1998"}, {"id": 308, "name": "1999"}, {"id": 309, "name": "19th ave"}, {"id": 310, "name": "1b"}, {"id": 311, "name": "1hour"}, {"id": 312, "name": "1ra"}, {"id": 313, "name": "1st"}, {"id": 314, "name": "1st base"}, {"id": 315, "name": "1st class"}, {"id": 316, "name": "1st place"}, {"id": 317, "name": "2 ave"}, {"id": 318, "name": "2 base"}, {"id": 319, "name": "2 bear"}, {"id": 320, "name": "2 blue plates"}, {"id": 321, "name": "2 bottles"}, {"id": 322, "name": "2 bunches"}, {"id": 323, "name": "2 button"}, {"id": 324, "name": "2 calfs"}, {"id": 325, "name": "2 carton"}, {"id": 326, "name": "2 cartoons"}, {"id": 327, "name": "2 chairs"}, {"id": 328, "name": "2 clocks"}, {"id": 329, "name": "2 container"}, {"id": 330, "name": "2 designs"}, {"id": 331, "name": "2 desk"}, {"id": 332, "name": "2 disks"}, {"id": 333, "name": "2 doors"}, {"id": 334, "name": "2 eyes"}, {"id": 335, "name": "2 for 3"}, {"id": 336, "name": "2 game"}, {"id": 337, "name": "2 handle"}, {"id": 338, "name": "2 hands"}, {"id": 339, "name": "2 headphones"}, {"id": 340, "name": "2 hour"}, {"id": 341, "name": "2 hour limit sign"}, {"id": 342, "name": "2 hour parking"}, {"id": 343, "name": "2 is white"}, {"id": 344, "name": "2 key"}, {"id": 345, "name": "2 kite surfers"}, {"id": 346, "name": "2 kites flying"}, {"id": 347, "name": "2 knobs"}, {"id": 348, "name": "2 lambs"}, {"id": 349, "name": "2 levels"}, {"id": 350, "name": "2 lifted wings"}, {"id": 351, "name": "2 liter"}, {"id": 352, "name": "2 lumps"}, {"id": 353, "name": "2 men"}, {"id": 354, "name": "2 milk"}, {"id": 355, "name": "2 on a clock"}, {"id": 356, "name": "2 parked food trucks"}, {"id": 357, "name": "2 people"}, {"id": 358, "name": "2 pineapples"}, {"id": 359, "name": "2 planes"}, {"id": 360, "name": "2 pockets"}, {"id": 361, "name": "2 points"}, {"id": 362, "name": "2 poles"}, {"id": 363, "name": "2 rvp100"}, {"id": 364, "name": "2 screen"}, {"id": 365, "name": "2 sets"}, {"id": 366, "name": "2 skis"}, {"id": 367, "name": "2 store signs"}, {"id": 368, "name": "2 stoves"}, {"id": 369, "name": "2 swan"}, {"id": 370, "name": "2 swords"}, {"id": 371, "name": "2 tables"}, {"id": 372, "name": "2 towels"}, {"id": 373, "name": "2 traffic signals"}, {"id": 374, "name": "2 train doors"}, {"id": 375, "name": "2 utensils"}, {"id": 376, "name": "2 wheels"}, {"id": 377, "name": "2 windows"}, {"id": 378, "name": "2 woman"}, {"id": 379, "name": "2 women"}, {"id": 380, "name": "2 zebras"}, {"id": 381, "name": "2"}, {"id": 382, "name": "20"}, {"id": 383, "name": "20 bill"}, {"id": 384, "name": "20 new messages"}, {"id": 385, "name": "20 panes"}, {"id": 386, "name": "200"}, {"id": 387, "name": "200 yen"}, {"id": 388, "name": "2000"}, {"id": 389, "name": "2002"}, {"id": 390, "name": "2003"}, {"id": 391, "name": "2004"}, {"id": 392, "name": "2005"}, {"id": 393, "name": "2006"}, {"id": 394, "name": "2007"}, {"id": 395, "name": "2008"}, {"id": 396, "name": "2009"}, {"id": 397, "name": "2009jjp"}, {"id": 398, "name": "200feet"}, {"id": 399, "name": "201"}, {"id": 400, "name": "201 station"}, {"id": 401, "name": "2010"}, {"id": 402, "name": "2011"}, {"id": 403, "name": "2012"}, {"id": 404, "name": "2012 date"}, {"id": 405, "name": "2013"}, {"id": 406, "name": "2013 ddindy"}, {"id": 407, "name": "202"}, {"id": 408, "name": "2024"}, {"id": 409, "name": "2026"}, {"id": 410, "name": "20350"}, {"id": 411, "name": "204"}, {"id": 412, "name": "207"}, {"id": 413, "name": "208"}, {"id": 414, "name": "209"}, {"id": 415, "name": "20th"}, {"id": 416, "name": "21"}, {"id": 417, "name": "21 sign"}, {"id": 418, "name": "210"}, {"id": 419, "name": "2103 is written"}, {"id": 420, "name": "211"}, {"id": 421, "name": "212"}, {"id": 422, "name": "21240"}, {"id": 423, "name": "213"}, {"id": 424, "name": "214"}, {"id": 425, "name": "215"}, {"id": 426, "name": "216"}, {"id": 427, "name": "2165"}, {"id": 428, "name": "218"}, {"id": 429, "name": "219x"}, {"id": 430, "name": "21st street"}, {"id": 431, "name": "22"}, {"id": 432, "name": "22 34"}, {"id": 433, "name": "22000"}, {"id": 434, "name": "2212"}, {"id": 435, "name": "222"}, {"id": 436, "name": "222 003"}, {"id": 437, "name": "223"}, {"id": 438, "name": "224"}, {"id": 439, "name": "2248"}, {"id": 440, "name": "2249"}, {"id": 441, "name": "225"}, {"id": 442, "name": "228"}, {"id": 443, "name": "229"}, {"id": 444, "name": "23"}, {"id": 445, "name": "230"}, {"id": 446, "name": "2306"}, {"id": 447, "name": "2307"}, {"id": 448, "name": "232"}, {"id": 449, "name": "234"}, {"id": 450, "name": "235"}, {"id": 451, "name": "235pm"}, {"id": 452, "name": "236"}, {"id": 453, "name": "24"}, {"id": 454, "name": "24 2010"}, {"id": 455, "name": "24 hours"}, {"id": 456, "name": "24 shirt"}, {"id": 457, "name": "240"}, {"id": 458, "name": "24042010"}, {"id": 459, "name": "241"}, {"id": 460, "name": "242"}, {"id": 461, "name": "243"}, {"id": 462, "name": "247"}, {"id": 463, "name": "2477"}, {"id": 464, "name": "2478"}, {"id": 465, "name": "248"}, {"id": 466, "name": "25"}, {"id": 467, "name": "25 cent mark"}, {"id": 468, "name": "25 cents"}, {"id": 469, "name": "25 mph"}, {"id": 470, "name": "250"}, {"id": 471, "name": "2504"}, {"id": 472, "name": "251"}, {"id": 473, "name": "2531"}, {"id": 474, "name": "2540"}, {"id": 475, "name": "2542"}, {"id": 476, "name": "2565"}, {"id": 477, "name": "258"}, {"id": 478, "name": "259"}, {"id": 479, "name": "25c logo"}, {"id": 480, "name": "26"}, {"id": 481, "name": "26 of dec"}, {"id": 482, "name": "26 sign"}, {"id": 483, "name": "261"}, {"id": 484, "name": "261 3"}, {"id": 485, "name": "262"}, {"id": 486, "name": "2632"}, {"id": 487, "name": "2664760"}, {"id": 488, "name": "26th"}, {"id": 489, "name": "27"}, {"id": 490, "name": "2700"}, {"id": 491, "name": "271"}, {"id": 492, "name": "279"}, {"id": 493, "name": "279 number"}, {"id": 494, "name": "28"}, {"id": 495, "name": "2831"}, {"id": 496, "name": "287"}, {"id": 497, "name": "2877"}, {"id": 498, "name": "29"}, {"id": 499, "name": "29 cents"}, {"id": 500, "name": "29 sign"}, {"id": 501, "name": "2900 block"}, {"id": 502, "name": "295"}, {"id": 503, "name": "29710"}, {"id": 504, "name": "299"}, {"id": 505, "name": "2999"}, {"id": 506, "name": "2b"}, {"id": 507, "name": "2h08"}, {"id": 508, "name": "2hour parking"}, {"id": 509, "name": "2kite strings"}, {"id": 510, "name": "2liter"}, {"id": 511, "name": "2nd class"}, {"id": 512, 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{"id": 22002, "name": "door jet"}, {"id": 22003, "name": "door knob"}, {"id": 22004, "name": "door knobs"}, {"id": 22005, "name": "door knocker"}, {"id": 22006, "name": "door kob"}, {"id": 22007, "name": "door latch"}, {"id": 22008, "name": "door latches"}, {"id": 22009, "name": "door lever"}, {"id": 22010, "name": "door lifted"}, {"id": 22011, "name": "door lock"}, {"id": 22012, "name": "door mat"}, {"id": 22013, "name": "door of bathroom"}, {"id": 22014, "name": "door of oven"}, {"id": 22015, "name": "door of pantry"}, {"id": 22016, "name": "door on stove"}, {"id": 22017, "name": "door open"}, {"id": 22018, "name": "door opener"}, {"id": 22019, "name": "door opening"}, {"id": 22020, "name": "door overhang"}, {"id": 22021, "name": "door paint"}, {"id": 22022, "name": "door pane"}, {"id": 22023, "name": "door panel"}, {"id": 22024, "name": "door part"}, {"id": 22025, "name": "door pathway"}, {"id": 22026, "name": "door plate"}, {"id": 22027, "name": "door post"}, {"id": 22028, "name": 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{"id": 32307, "name": "hampton"}, {"id": 32308, "name": "hams slice"}, {"id": 32309, "name": "hamster"}, {"id": 32310, "name": "hamstring muscle"}, {"id": 32311, "name": "han"}, {"id": 32312, "name": "hanbag"}, {"id": 32313, "name": "hancrafted jewellery"}, {"id": 32314, "name": "hand and apple"}, {"id": 32315, "name": "hand and arm"}, {"id": 32316, "name": "hand bag"}, {"id": 32317, "name": "hand bar"}, {"id": 32318, "name": "hand bars"}, {"id": 32319, "name": "hand behind"}, {"id": 32320, "name": "hand brake"}, {"id": 32321, "name": "hand broom"}, {"id": 32322, "name": "hand cart"}, {"id": 32323, "name": "hand chair"}, {"id": 32324, "name": "hand clock"}, {"id": 32325, "name": "hand counter"}, {"id": 32326, "name": "hand covers"}, {"id": 32327, "name": "hand crank"}, {"id": 32328, "name": "hand cream"}, {"id": 32329, "name": "hand dispenser"}, {"id": 32330, "name": "hand drawn"}, {"id": 32331, "name": "hand drier"}, {"id": 32332, "name": "hand drum"}, {"id": 32333, "name": "hand 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37813, "name": "left foot of player"}, {"id": 37814, "name": "left footing"}, {"id": 37815, "name": "left forearm"}, {"id": 37816, "name": "left foreleg"}, {"id": 37817, "name": "left fork"}, {"id": 37818, "name": "left fron wheel"}, {"id": 37819, "name": "left front"}, {"id": 37820, "name": "left front fender"}, {"id": 37821, "name": "left front foot"}, {"id": 37822, "name": "left front leg"}, {"id": 37823, "name": "left front paw"}, {"id": 37824, "name": "left front side"}, {"id": 37825, "name": "left front tire"}, {"id": 37826, "name": "left front wheel"}, {"id": 37827, "name": "left glove"}, {"id": 37828, "name": "left half"}, {"id": 37829, "name": "left hand"}, {"id": 37830, "name": "left hand side"}, {"id": 37831, "name": "left handle"}, {"id": 37832, "name": "left handle bar"}, {"id": 37833, "name": "left handlebar"}, {"id": 37834, "name": "left headlight"}, {"id": 37835, "name": "left headlights"}, {"id": 37836, "name": "left heel"}, {"id": 37837, "name": "left hind leg"}, {"id": 37838, "name": "left hindleg"}, {"id": 37839, "name": "left hinge"}, {"id": 37840, "name": "left hip"}, {"id": 37841, "name": "left hole"}, {"id": 37842, "name": "left hoof"}, {"id": 37843, "name": "left horn"}, {"id": 37844, "name": "left hot dog"}, {"id": 37845, "name": "left human"}, {"id": 37846, "name": "left index"}, {"id": 37847, "name": "left index finger"}, {"id": 37848, "name": "left iris"}, {"id": 37849, "name": "left jean pant"}, {"id": 37850, "name": "left key"}, {"id": 37851, "name": "left knee"}, {"id": 37852, "name": "left knee of goat"}, {"id": 37853, "name": "left knob"}, {"id": 37854, "name": "left lamp"}, {"id": 37855, "name": "left lane"}, {"id": 37856, "name": "left lapel"}, {"id": 37857, "name": "left left"}, {"id": 37858, "name": "left leg"}, {"id": 37859, "name": "left leg is raised"}, {"id": 37860, "name": "left leg of a dog"}, {"id": 37861, "name": "left leg of a man"}, {"id": 37862, "name": "left leg of man"}, {"id": 37863, "name": "left leg of skater"}, {"id": 37864, "name": "left lens"}, {"id": 37865, "name": "left light"}, {"id": 37866, "name": "left lower corner"}, {"id": 37867, "name": "left man"}, {"id": 37868, "name": "left mirror"}, {"id": 37869, "name": "left nipple"}, {"id": 37870, "name": "left nostril"}, {"id": 37871, "name": "left numbers"}, {"id": 37872, "name": "left of pix"}, {"id": 37873, "name": "left of skim milk"}, {"id": 37874, "name": "left of train"}, {"id": 37875, "name": "left open"}, {"id": 37876, "name": "left opening"}, {"id": 37877, "name": "left oven mitt"}, {"id": 37878, "name": "left pane"}, {"id": 37879, "name": "left pant"}, {"id": 37880, "name": "left pant leg"}, {"id": 37881, "name": "left pants cuff"}, {"id": 37882, "name": "left part"}, {"id": 37883, "name": "left paw"}, {"id": 37884, "name": "left pedal"}, {"id": 37885, "name": "left person"}, {"id": 37886, "name": "left picture"}, {"id": 37887, "name": "left pillow"}, {"id": 37888, "name": "left pinky"}, {"id": 37889, "name": "left pizza"}, {"id": 37890, "name": "left plate"}, {"id": 37891, "name": "left pocket"}, {"id": 37892, "name": "left portion"}, {"id": 37893, "name": "left propeller"}, {"id": 37894, "name": "left propellor"}, {"id": 37895, "name": "left pupil"}, {"id": 37896, "name": "left rear leg"}, {"id": 37897, "name": "left rear paw"}, {"id": 37898, "name": "left rear tire"}, {"id": 37899, "name": "left rear wheel"}, {"id": 37900, "name": "left rearview mirror"}, {"id": 37901, "name": "left rock"}, {"id": 37902, "name": "left rolled towel"}, {"id": 37903, "name": "left sandal"}, {"id": 37904, "name": "left sandwich"}, {"id": 37905, "name": "left section"}, {"id": 37906, "name": "left shift key"}, {"id": 37907, "name": "left shin"}, {"id": 37908, "name": "left shoe"}, {"id": 37909, "name": "left shoulder"}, {"id": 37910, "name": "left shutter"}, {"id": 37911, "name": "left side burn"}, {"id": 37912, "name": "left side mirror"}, {"id": 37913, "name": "left side skin"}, {"id": 37914, "name": "left side 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"name": "left tennis shoe"}, {"id": 37941, "name": "left thigh"}, {"id": 37942, "name": "left thumb"}, {"id": 37943, "name": "left tire"}, {"id": 37944, "name": "left toes"}, {"id": 37945, "name": "left tower"}, {"id": 37946, "name": "left traffic light"}, {"id": 37947, "name": "left turn"}, {"id": 37948, "name": "left turn signal"}, {"id": 37949, "name": "left tusk"}, {"id": 37950, "name": "left waiting"}, {"id": 37951, "name": "left wall"}, {"id": 37952, "name": "left water knob"}, {"id": 37953, "name": "left weight"}, {"id": 37954, "name": "left wheel"}, {"id": 37955, "name": "left wheels"}, {"id": 37956, "name": "left whiskers"}, {"id": 37957, "name": "left white tusk"}, {"id": 37958, "name": "left window"}, {"id": 37959, "name": "left windows"}, {"id": 37960, "name": "left windshield"}, {"id": 37961, "name": "left wing"}, {"id": 37962, "name": "left wingtip"}, {"id": 37963, "name": "left wiper"}, {"id": 37964, "name": "left wrist"}, {"id": 37965, "name": "left zebra"}, {"id": 37966, "name": "leftarm"}, {"id": 37967, "name": "leftbottom"}, {"id": 37968, "name": "leftear"}, {"id": 37969, "name": "lefteye"}, {"id": 37970, "name": "leftgiraffes legs"}, {"id": 37971, "name": "leftground"}, {"id": 37972, "name": "lefthand"}, {"id": 37973, "name": "leftheadlights"}, {"id": 37974, "name": "leftleg"}, {"id": 37975, "name": "leftmost column"}, {"id": 37976, "name": "leftmost duck"}, {"id": 37977, "name": "leftover food"}, {"id": 37978, "name": "leftover"}, {"id": 37979, "name": "leftright light"}, {"id": 37980, "name": "leftside"}, {"id": 37981, "name": "leftsleeve"}, {"id": 37982, "name": "leftstreet corner"}, {"id": 37983, "name": "leftwing"}, {"id": 37984, "name": "leg above the field"}, {"id": 37985, "name": "leg back"}, {"id": 37986, "name": "leg band"}, {"id": 37987, "name": "leg bent"}, {"id": 37988, "name": "leg bottom"}, {"id": 37989, "name": "leg bottoms"}, {"id": 37990, "name": "leg brace"}, {"id": 37991, "name": "leg chair"}, {"id": 37992, "name": "leg cover"}, {"id": 37993, "name": "leg covering"}, {"id": 37994, "name": "leg covers"}, {"id": 37995, "name": "leg crossed"}, {"id": 37996, "name": "leg dog"}, {"id": 37997, "name": "leg forward"}, {"id": 37998, "name": "leg fur"}, {"id": 37999, "name": "leg gear"}, {"id": 38000, "name": "leg griaffe"}, {"id": 38001, "name": "leg guard"}, {"id": 38002, "name": "leg guards"}, {"id": 38003, "name": "leg hair"}, {"id": 38004, "name": "leg is crossed"}, {"id": 38005, "name": "leg is extended"}, {"id": 38006, "name": "leg is in front"}, {"id": 38007, "name": "leg is metal"}, {"id": 38008, "name": "leg is steel"}, {"id": 38009, "name": "leg is wooden"}, {"id": 38010, "name": "leg is yellow"}, {"id": 38011, "name": "leg kite"}, {"id": 38012, "name": "leg leash"}, {"id": 38013, "name": "leg lift"}, {"id": 38014, "name": "leg muscle"}, {"id": 38015, "name": "leg of a chair"}, {"id": 38016, "name": "leg of a child"}, {"id": 38017, "name": "leg of a cow"}, {"id": 38018, "name": "leg of a dog"}, {"id": 38019, "name": "leg of a giraffe"}, {"id": 38020, "name": "leg of a lady"}, {"id": 38021, "name": "leg of a person"}, {"id": 38022, "name": "leg of a woman"}, {"id": 38023, "name": "leg of a zebra"}, {"id": 38024, "name": "leg of brown bear"}, {"id": 38025, "name": "leg of chair"}, {"id": 38026, "name": "leg of dog is deep"}, {"id": 38027, "name": "leg of elephant"}, {"id": 38028, "name": "leg of table"}, {"id": 38029, "name": "leg of the chair"}, {"id": 38030, "name": "leg of the giraffe"}, {"id": 38031, "name": "leg on boat seat"}, {"id": 38032, "name": "leg on surfboard"}, {"id": 38033, "name": "leg pad"}, {"id": 38034, "name": "leg padding"}, {"id": 38035, "name": "leg pads"}, {"id": 38036, "name": "leg part"}, {"id": 38037, "name": "leg person"}, {"id": 38038, "name": "leg post"}, {"id": 38039, "name": "leg protection"}, {"id": 38040, "name": "leg protector"}, {"id": 38041, "name": "leg protectors"}, {"id": 38042, "name": "leg raised"}, {"id": 38043, "name": "leg rest"}, {"id": 38044, "name": "leg rope"}, {"id": 38045, "name": "leg sleeve"}, {"id": 38046, "name": "leg spot"}, {"id": 38047, "name": "leg strap"}, {"id": 38048, "name": "leg tracker"}, {"id": 38049, "name": "leg up"}, {"id": 38050, "name": "leg warmer"}, {"id": 38051, "name": "leg warmers"}, {"id": 38052, "name": "leg wrap"}, {"id": 38053, "name": "leg zebra"}, {"id": 38054, "name": "leg"}, {"id": 38055, "name": "legal pad"}, {"id": 38056, "name": "legal paper"}, {"id": 38057, "name": "lege"}, {"id": 38058, "name": "legend"}, {"id": 38059, "name": "legg"}, {"id": 38060, "name": "legged"}, {"id": 38061, "name": "leggigs"}, {"id": 38062, "name": "legging"}, {"id": 38063, "name": "leggins"}, {"id": 38064, "name": "leggs"}, {"id": 38065, "name": "leging"}, {"id": 38066, "name": "legman"}, {"id": 38067, "name": "lego bench"}, {"id": 38068, "name": "lego block"}, {"id": 38069, "name": "lego blocks"}, {"id": 38070, "name": "lego board"}, {"id": 38071, "name": "lego car"}, {"id": 38072, "name": "lego head"}, {"id": 38073, "name": "lego house"}, {"id": 38074, "name": "lego man"}, {"id": 38075, "name": "lego person"}, {"id": 38076, "name": "lego toilet"}, {"id": 38077, "name": "lego toothbrush"}, {"id": 38078, "name": "lego toy"}, {"id": 38079, "name": "lego trooper"}, {"id": 38080, "name": "lego wall"}, {"id": 38081, "name": "lego woman"}, {"id": 38082, "name": "lego"}, {"id": 38083, "name": "legpaw"}, {"id": 38084, "name": "legpost"}, {"id": 38085, "name": "legreen bananas"}, {"id": 38086, "name": "legs and"}, {"id": 38087, "name": "legs apart"}, {"id": 38088, "name": "legs are apart"}, {"id": 38089, "name": "legs are covered"}, {"id": 38090, "name": "legs are long"}, {"id": 38091, "name": "legs are silver"}, {"id": 38092, "name": "legs crossed"}, {"id": 38093, "name": "legs down"}, {"id": 38094, "name": "legs fence"}, {"id": 38095, "name": "legs folded"}, {"id": 38096, "name": "legs have shadow"}, {"id": 38097, "name": "legs of a dog"}, {"id": 38098, "name": "legs of a giraffe"}, {"id": 38099, "name": "legs of a girl"}, {"id": 38100, "name": "legs of adult elepha"}, {"id": 38101, "name": "legs of bear"}, {"id": 38102, "name": "legs of elephant"}, {"id": 38103, "name": "legs of the bear"}, {"id": 38104, "name": "legs of the elephant"}, {"id": 38105, "name": "legs of the giraffe"}, {"id": 38106, "name": "legs of the zebra"}, {"id": 38107, "name": "legs on giraffe"}, {"id": 38108, "name": "legs on zebras"}, {"id": 38109, "name": "legs out"}, {"id": 38110, "name": "legs skateboard"}, {"id": 38111, "name": "legs zebra"}, {"id": 38112, "name": "legscalves"}, {"id": 38113, "name": "legsfeet"}, {"id": 38114, "name": "legszebras"}, {"id": 38115, "name": "legtrees"}, {"id": 38116, "name": "legume"}, {"id": 38117, "name": "lei"}, {"id": 38118, "name": "leith st"}, {"id": 38119, "name": "lejla"}, {"id": 38120, "name": "lemmon"}, {"id": 38121, "name": "lemon bars"}, {"id": 38122, "name": "lemon dip"}, {"id": 38123, "name": "lemon end"}, {"id": 38124, "name": "lemon is on top"}, {"id": 38125, "name": "lemon juice"}, {"id": 38126, "name": "lemon peel"}, {"id": 38127, "name": "lemon piece"}, {"id": 38128, "name": "lemon rind"}, {"id": 38129, "name": "lemon skin"}, {"id": 38130, "name": "lemon slice"}, {"id": 38131, "name": "lemon slices"}, {"id": 38132, "name": "lemon slide"}, {"id": 38133, "name": "lemon tart"}, {"id": 38134, "name": "lemon tree"}, {"id": 38135, "name": "lemon trees"}, {"id": 38136, "name": "lemon wedge"}, {"id": 38137, "name": "lemon wedges"}, {"id": 38138, "name": "lemon"}, {"id": 38139, "name": "lemonade"}, {"id": 38140, "name": "lemons in english"}, {"id": 38141, "name": "lemons in the glass"}, {"id": 38142, "name": "len"}, {"id": 38143, "name": "length"}, {"id": 38144, "name": "length fence"}, {"id": 38145, "name": "length is short"}, {"id": 38146, "name": "lengthy clouds"}, {"id": 38147, "name": "lenox"}, {"id": 38148, "name": "lens camera"}, {"id": 38149, "name": "lens cap"}, {"id": 38150, "name": "lens cleaning paper"}, {"id": 38151, "name": "lens cover"}, {"id": 38152, "name": "lens flare"}, {"id": 38153, "name": "lens reflection"}, {"id": 38154, "name": "lens"}, {"id": 38155, "name": "lenscap"}, {"id": 38156, "name": "lense"}, {"id": 38157, "name": "lentil"}, {"id": 38158, "name": "leo"}, {"id": 38159, "name": "leopard"}, {"id": 38160, "name": "leopard pattern"}, {"id": 38161, "name": "leopard print"}, {"id": 38162, "name": "leoprad skin"}, {"id": 38163, "name": "leotard"}, {"id": 38164, "name": "lephant"}, {"id": 38165, "name": "leprechaun"}, {"id": 38166, "name": "leprechaun head"}, {"id": 38167, "name": "leroy"}, {"id": 38168, "name": "less clouds"}, {"id": 38169, "name": "lesso"}, {"id": 38170, "name": "lesson"}, {"id": 38171, "name": "let"}, {"id": 38172, "name": "leter"}, {"id": 38173, "name": "leter s"}, {"id": 38174, "name": "leters"}, {"id": 38175, "name": "lette"}, {"id": 38176, "name": "lettears"}, {"id": 38177, "name": "letter 2"}, {"id": 38178, "name": "letter and image"}, {"id": 38179, "name": "letter b"}, {"id": 38180, "name": "letter blue"}, {"id": 38181, "name": "letter bottle"}, {"id": 38182, "name": "letter c"}, {"id": 38183, "name": "letter cs"}, {"id": 38184, "name": "letter d"}, {"id": 38185, "name": "letter e"}, {"id": 38186, "name": "letter f"}, {"id": 38187, "name": "letter g"}, {"id": 38188, "name": "letter h"}, {"id": 38189, "name": "letter i"}, {"id": 38190, "name": "letter in her hand"}, {"id": 38191, "name": "letter is black"}, {"id": 38192, "name": "letter is white"}, {"id": 38193, "name": "letter j"}, {"id": 38194, "name": "letter k"}, {"id": 38195, "name": "letter key"}, {"id": 38196, "name": "letter l"}, {"id": 38197, "name": "letter m"}, {"id": 38198, "name": "letter n"}, {"id": 38199, "name": "letter numbers"}, {"id": 38200, "name": "letter o"}, {"id": 38201, "name": "letter on"}, {"id": 38202, "name": "letter on box"}, {"id": 38203, "name": "letter on sign"}, {"id": 38204, "name": "letter onumber2"}, {"id": 38205, "name": "letter opener"}, {"id": 38206, "name": "letter p"}, {"id": 38207, "name": "letter painted"}, {"id": 38208, "name": "letter print"}, {"id": 38209, "name": "letter q"}, {"id": 38210, "name": "letter r"}, {"id": 38211, "name": "letter s"}, {"id": 38212, "name": "letter s printed"}, {"id": 38213, "name": "letter sign"}, {"id": 38214, "name": "letter slot"}, {"id": 38215, "name": "letter sorter"}, {"id": 38216, "name": "letter stop"}, {"id": 38217, "name": "letter t"}, {"id": 38218, "name": "letter u"}, {"id": 38219, "name": "letter v"}, {"id": 38220, "name": "letter w"}, {"id": 38221, "name": "letter x"}, {"id": 38222, "name": "letter y"}, {"id": 38223, "name": "letter z"}, {"id": 38224, "name": "letter"}, {"id": 38225, "name": "letterb"}, {"id": 38226, "name": "lettered sign"}, {"id": 38227, "name": "letterhead"}, {"id": 38228, "name": "letterig"}, {"id": 38229, "name": "letterin"}, {"id": 38230, "name": "lettering box"}, {"id": 38231, "name": "lettering is black"}, {"id": 38232, "name": "lettering is red"}, {"id": 38233, "name": "lettering item"}, {"id": 38234, "name": "lettering label"}, {"id": 38235, "name": "lettering of number"}, {"id": 38236, "name": "lettering on board"}, {"id": 38237, "name": "lettering painted"}, {"id": 38238, "name": "lettering"}, {"id": 38239, "name": "lettero"}, {"id": 38240, "name": "letterosign"}, {"id": 38241, "name": "letters 139002"}, {"id": 38242, "name": "letters 3d"}, {"id": 38243, "name": "letters aaa"}, {"id": 38244, "name": "letters above a door"}, {"id": 38245, "name": "letters am"}, {"id": 38246, "name": "letters and numbers"}, {"id": 38247, "name": "letters are black"}, {"id": 38248, "name": "letters ave"}, {"id": 38249, "name": "letters banner"}, {"id": 38250, "name": "letters bls"}, {"id": 38251, "name": "letters c"}, {"id": 38252, "name": "letters ch"}, {"id": 38253, "name": "letters cp"}, {"id": 38254, "name": "letters cton"}, {"id": 38255, "name": "letters dc"}, {"id": 38256, "name": "letters de"}, {"id": 38257, "name": "letters dfw"}, {"id": 38258, "name": "letters dk"}, {"id": 38259, "name": "letters dr"}, {"id": 38260, "name": "letters e r"}, {"id": 38261, "name": "letters fitness"}, {"id": 38262, "name": "letters green"}, {"id": 38263, "name": "letters in red"}, {"id": 38264, "name": "letters in white"}, {"id": 38265, "name": "letters klm"}, {"id": 38266, "name": "letters mph"}, {"id": 38267, "name": "letters nn"}, {"id": 38268, "name": "letters numbers"}, {"id": 38269, "name": "letters obb"}, {"id": 38270, "name": "letters on"}, {"id": 38271, "name": "letters on box"}, {"id": 38272, "name": "letters on shirt"}, {"id": 38273, "name": "letters on side"}, {"id": 38274, "name": "letters on sign"}, {"id": 38275, "name": "letters on the back"}, {"id": 38276, "name": "letters on the side"}, {"id": 38277, "name": "letters p"}, {"id": 38278, "name": "letters sc"}, {"id": 38279, "name": "letters sign"}, {"id": 38280, "name": "letters st"}, {"id": 38281, "name": "letters terr"}, {"id": 38282, "name": "letters top"}, {"id": 38283, "name": "letters tp"}, {"id": 38284, "name": "letters ty"}, {"id": 38285, "name": "letters ue"}, {"id": 38286, "name": "letters vb"}, {"id": 38287, "name": "letters vgn"}, {"id": 38288, "name": "letters wr"}, {"id": 38289, "name": "letters xt"}, {"id": 38290, "name": "lettersnumbers"}, {"id": 38291, "name": "lettersshirt"}, {"id": 38292, "name": "letteru"}, {"id": 38293, "name": "lettes"}, {"id": 38294, "name": "lettiering"}, {"id": 38295, "name": "letting"}, {"id": 38296, "name": "lettr"}, {"id": 38297, "name": "lettter"}, {"id": 38298, "name": "letttering"}, {"id": 38299, "name": "lettters"}, {"id": 38300, "name": "lettuce"}, {"id": 38301, "name": "lettuce and ham"}, {"id": 38302, "name": "lettuce and tomato"}, {"id": 38303, "name": "lettuce and tomatoes"}, {"id": 38304, "name": "lettuce bed"}, {"id": 38305, "name": "lettuce head"}, {"id": 38306, "name": "lettuce heads"}, {"id": 38307, "name": "lettuce leaf"}, {"id": 38308, "name": "lettuce leaves"}, {"id": 38309, "name": "lettuce package"}, {"id": 38310, "name": "lettuce piece"}, {"id": 38311, "name": "lettuce slice"}, {"id": 38312, "name": "lettuce sliver"}, {"id": 38313, "name": "lettue"}, {"id": 38314, "name": "letture"}, {"id": 38315, "name": "letuce"}, {"id": 38316, "name": "level path"}, {"id": 38317, "name": "level stones"}, {"id": 38318, "name": "level tool"}, {"id": 38319, "name": "level"}, {"id": 38320, "name": "leveled floors"}, {"id": 38321, "name": "leveler"}, {"id": 38322, "name": "levels of windows"}, {"id": 38323, "name": "lever"}, {"id": 38324, "name": "leves"}, {"id": 38325, "name": "levi"}, {"id": 38326, "name": "levy jewelers"}, {"id": 38327, "name": "lewis park"}, {"id": 38328, "name": "lewisham"}, {"id": 38329, "name": "lexington avenue"}, {"id": 38330, "name": "lexus"}, {"id": 38331, "name": "lexus logo"}, {"id": 38332, "name": "lexus symbol"}, {"id": 38333, "name": "lg"}, {"id": 38334, "name": "lg emblem"}, {"id": 38335, "name": "lg logo"}, {"id": 38336, "name": "lg name"}, {"id": 38337, "name": "lg store"}, {"id": 38338, "name": "lg symbol"}, {"id": 38339, "name": "lgiht"}, {"id": 38340, "name": "lianas boutique"}, {"id": 38341, "name": "liberty"}, {"id": 38342, "name": "liberty grill"}, {"id": 38343, "name": "liberty way"}, {"id": 38344, "name": "library"}, {"id": 38345, "name": "library books"}, {"id": 38346, "name": "library card"}, {"id": 38347, "name": "library catalog"}, {"id": 38348, "name": "licence"}, {"id": 38349, "name": "licence plata"}, {"id": 38350, "name": "licence plate"}, {"id": 38351, "name": "licenceplate"}, {"id": 38352, "name": "licences plate"}, {"id": 38353, "name": "licene plate"}, {"id": 38354, "name": "licenese plate"}, {"id": 38355, "name": "license number"}, {"id": 38356, "name": "license plae"}, {"id": 38357, "name": "license plant"}, {"id": 38358, "name": "license plate"}, {"id": 38359, "name": "license plate number"}, {"id": 38360, "name": "license plates"}, {"id": 38361, "name": "license tag"}, {"id": 38362, "name": "license"}, {"id": 38363, "name": "licenseholder"}, {"id": 38364, "name": "licenseplate"}, {"id": 38365, "name": "licese plate"}, {"id": 38366, "name": "lichen"}, {"id": 38367, "name": "licking"}, {"id": 38368, "name": "licorice"}, {"id": 38369, "name": "lid container"}, {"id": 38370, "name": "lid cover"}, {"id": 38371, "name": "lid down"}, {"id": 38372, "name": "lid edge"}, {"id": 38373, "name": "lid guard"}, {"id": 38374, "name": "lid hole"}, {"id": 38375, "name": "lid is brown"}, {"id": 38376, "name": "lid is down"}, {"id": 38377, "name": "lid is wooden"}, {"id": 38378, "name": "lid of bottle"}, {"id": 38379, "name": "lid of container"}, {"id": 38380, "name": "lid of suitcase"}, {"id": 38381, "name": "lid reflection"}, {"id": 38382, "name": "lid stopper"}, {"id": 38383, "name": "lid up"}, {"id": 38384, "name": "lid"}, {"id": 38385, "name": "lidded container"}, {"id": 38386, "name": "lidded eye"}, {"id": 38387, "name": "lide out oven"}, {"id": 38388, "name": "lidge"}, {"id": 38389, "name": "lie"}, {"id": 38390, "name": "lien"}, {"id": 38391, "name": "liene"}, {"id": 38392, "name": "liesaver"}, {"id": 38393, "name": "life"}, {"id": 38394, "name": "life belt"}, {"id": 38395, "name": "life boat"}, {"id": 38396, "name": "life bouy"}, {"id": 38397, "name": "life buoy"}, {"id": 38398, "name": "life circle"}, {"id": 38399, "name": "life guard"}, {"id": 38400, "name": "life guard bench"}, {"id": 38401, "name": "life guard stand"}, {"id": 38402, "name": "life jacket"}, {"id": 38403, "name": "life jacket floaty"}, {"id": 38404, "name": "life jackets"}, {"id": 38405, "name": "life perserver"}, {"id": 38406, "name": "life perservers"}, {"id": 38407, "name": "life preserver"}, {"id": 38408, "name": "life preservers"}, {"id": 38409, "name": "life presever"}, {"id": 38410, "name": "life raft"}, {"id": 38411, "name": "life ring"}, {"id": 38412, "name": "life rings"}, {"id": 38413, "name": "life saver"}, {"id": 38414, "name": "life savers"}, {"id": 38415, "name": "life ticket"}, {"id": 38416, "name": "life tube"}, {"id": 38417, "name": "life vest"}, {"id": 38418, "name": "life vests"}, {"id": 38419, "name": "life wheel"}, {"id": 38420, "name": "lifeboat"}, {"id": 38421, "name": "lifeform"}, {"id": 38422, "name": "lifegaurd on beach"}, {"id": 38423, "name": "lifeguard chair"}, {"id": 38424, "name": "lifeguard cross"}, {"id": 38425, "name": "lifeguard post"}, {"id": 38426, "name": "lifeguard seat"}, {"id": 38427, "name": "lifeguard shack"}, {"id": 38428, "name": "lifeguard stand"}, {"id": 38429, "name": "lifeguard station"}, {"id": 38430, "name": "lifeguard structure"}, {"id": 38431, "name": "lifeguard tower"}, {"id": 38432, "name": "lifeguard"}, {"id": 38433, "name": "lifejacket"}, {"id": 38434, "name": "lifering"}, {"id": 38435, "name": "lifesaver"}, {"id": 38436, "name": "lifevest"}, {"id": 38437, "name": "lift apparatus"}, {"id": 38438, "name": "lift basket"}, {"id": 38439, "name": "lift cab"}, {"id": 38440, "name": "lift cable"}, {"id": 38441, "name": "lift car"}, {"id": 38442, "name": "lift cars"}, {"id": 38443, "name": "lift chair"}, {"id": 38444, "name": "lift chairs"}, {"id": 38445, "name": "lift foot"}, {"id": 38446, "name": "lift gate"}, {"id": 38447, "name": "lift line"}, {"id": 38448, "name": "lift lines"}, {"id": 38449, "name": "lift pass"}, {"id": 38450, "name": "lift pole"}, {"id": 38451, "name": "lift rope"}, {"id": 38452, "name": "lift storage"}, {"id": 38453, "name": "lift ticket"}, {"id": 38454, "name": "lift tower"}, {"id": 38455, "name": "lift trail"}, {"id": 38456, "name": "lift"}, {"id": 38457, "name": "lifted"}, {"id": 38458, "name": "lifted hand"}, {"id": 38459, "name": "lifted handle"}, {"id": 38460, "name": "lifted knee"}, {"id": 38461, "name": "lifted off"}, {"id": 38462, "name": "lifter"}, {"id": 38463, "name": "lifting"}, {"id": 38464, "name": "lifts his foot"}, {"id": 38465, "name": "liggage"}, {"id": 38466, "name": "ligh"}, {"id": 38467, "name": "ligh pole"}, {"id": 38468, "name": "lighbulbs"}, {"id": 38469, "name": "lighhts"}, {"id": 38470, "name": "lighs"}, {"id": 38471, "name": "lighst"}, {"id": 38472, "name": "light fit"}, {"id": 38473, "name": "light 1"}, {"id": 38474, "name": "light area"}, {"id": 38475, "name": "light arm"}, {"id": 38476, "name": "light at night"}, {"id": 38477, "name": "light ball"}, {"id": 38478, "name": "light bar"}, {"id": 38479, "name": "light bars"}, {"id": 38480, "name": "light base"}, {"id": 38481, "name": "light beam"}, {"id": 38482, "name": "light bear"}, {"id": 38483, "name": "light bed"}, {"id": 38484, "name": "light beer"}, {"id": 38485, "name": "light behind"}, {"id": 38486, "name": "light blue"}, {"id": 38487, "name": "light blue cone"}, {"id": 38488, "name": "light blue ipod"}, {"id": 38489, "name": "light blue jacket"}, {"id": 38490, "name": "light blue shirt"}, {"id": 38491, "name": "light blue sky"}, {"id": 38492, "name": "light blue sweater"}, {"id": 38493, "name": "light blue tile"}, {"id": 38494, "name": "light blue tshirt"}, {"id": 38495, "name": "light blue watch"}, {"id": 38496, "name": "light blue wristband"}, {"id": 38497, "name": "light border"}, {"id": 38498, "name": "light bouncing off"}, {"id": 38499, "name": "light box"}, {"id": 38500, "name": "light brown"}, {"id": 38501, "name": "light brown boot"}, {"id": 38502, "name": "light brown cows"}, {"id": 38503, "name": "light brown dirt"}, {"id": 38504, "name": "light brown grass"}, {"id": 38505, "name": "light brown hair"}, {"id": 38506, "name": "light brown pants"}, {"id": 38507, "name": "light brown shoes"}, {"id": 38508, "name": "light brownpart"}, {"id": 38509, "name": "light building"}, {"id": 38510, "name": "light bulb"}, {"id": 38511, "name": "light bulb logo"}, {"id": 38512, "name": "light bulbs"}, {"id": 38513, "name": "light buld"}, {"id": 38514, "name": "light bus"}, {"id": 38515, "name": "light button"}, {"id": 38516, "name": "light capprd top"}, {"id": 38517, "name": "light case"}, {"id": 38518, "name": "light cast"}, {"id": 38519, "name": "light cheese"}, {"id": 38520, "name": "light circle"}, {"id": 38521, "name": "light clothing"}, {"id": 38522, "name": "light cloud"}, {"id": 38523, "name": "light clouds"}, {"id": 38524, "name": "light color"}, {"id": 38525, "name": "light colored"}, {"id": 38526, "name": "light colored hair"}, {"id": 38527, "name": "light colored jacket"}, {"id": 38528, "name": "light colored shorts"}, {"id": 38529, "name": "light colored wall"}, {"id": 38530, "name": "light coming"}, {"id": 38531, "name": "light coming through"}, {"id": 38532, "name": "light cover"}, {"id": 38533, "name": "light covers"}, {"id": 38534, "name": "light dot"}, {"id": 38535, "name": "light ears"}, {"id": 38536, "name": "light face"}, {"id": 38537, "name": "light fixture"}, {"id": 38538, "name": "light fixtures"}, {"id": 38539, "name": "light fixure"}, {"id": 38540, "name": "light flare"}, {"id": 38541, "name": "light from sun"}, {"id": 38542, "name": "light glare"}, {"id": 38543, "name": "light gleaming"}, {"id": 38544, "name": "light glimmer"}, {"id": 38545, "name": "light glistening"}, {"id": 38546, "name": "light globe"}, {"id": 38547, "name": "light globes"}, {"id": 38548, "name": "light grass"}, {"id": 38549, "name": "light gray"}, {"id": 38550, "name": "light green"}, {"id": 38551, "name": "light green blue"}, {"id": 38552, "name": "light green stripe"}, {"id": 38553, "name": "light grey"}, {"id": 38554, "name": "light grey jacket"}, {"id": 38555, "name": "light grey uniform"}, {"id": 38556, "name": "light grey wall"}, {"id": 38557, "name": "light grout"}, {"id": 38558, "name": "light hair"}, {"id": 38559, "name": "light hanging"}, {"id": 38560, "name": "light hanging on pol"}, {"id": 38561, "name": "light hangs"}, {"id": 38562, "name": "light hat"}, {"id": 38563, "name": "light hitting"}, {"id": 38564, "name": "light house"}, {"id": 38565, "name": "light in ceiling"}, {"id": 38566, "name": "light in the distanc"}, {"id": 38567, "name": "light in the sky"}, {"id": 38568, "name": "light in the thames"}, {"id": 38569, "name": "light indicator"}, {"id": 38570, "name": "light is bright"}, {"id": 38571, "name": "light is hanging"}, {"id": 38572, "name": "light is on"}, {"id": 38573, "name": "light is on street"}, {"id": 38574, "name": "light is orange"}, {"id": 38575, "name": "light is red"}, {"id": 38576, "name": "light is shining"}, {"id": 38577, "name": "light jeans"}, {"id": 38578, "name": "light lamp"}, {"id": 38579, "name": "light leaves"}, {"id": 38580, "name": "light light"}, {"id": 38581, "name": "light line"}, {"id": 38582, "name": "light lines"}, {"id": 38583, "name": "light lit"}, {"id": 38584, "name": "light mark"}, {"id": 38585, "name": "light marks"}, {"id": 38586, "name": "light motorcycle"}, {"id": 38587, "name": "light mounted"}, {"id": 38588, "name": "light nuts"}, {"id": 38589, "name": "light of a bus"}, {"id": 38590, "name": "light of a train"}, {"id": 38591, "name": "light of motorcycle"}, {"id": 38592, "name": "light of the bike"}, {"id": 38593, "name": "light of vespa"}, {"id": 38594, "name": "light off"}, {"id": 38595, "name": "light on"}, {"id": 38596, "name": "light on a pole"}, {"id": 38597, "name": "light on a wall"}, {"id": 38598, "name": "light on bike"}, {"id": 38599, "name": "light on bus"}, {"id": 38600, "name": "light on court"}, {"id": 38601, "name": "light on flowers"}, {"id": 38602, "name": "light on front"}, {"id": 38603, "name": "light on ground"}, {"id": 38604, "name": "light on it"}, {"id": 38605, "name": "light on lake"}, {"id": 38606, "name": "light on laptop"}, {"id": 38607, "name": "light on poll"}, {"id": 38608, "name": "light on right"}, {"id": 38609, "name": "light on the ceiling"}, {"id": 38610, "name": "light on the wall"}, {"id": 38611, "name": "light on truck"}, {"id": 38612, "name": "light on wii remote"}, {"id": 38613, "name": "light orange"}, {"id": 38614, "name": "light outside"}, {"id": 38615, "name": "light overhead"}, {"id": 38616, "name": "light panel"}, {"id": 38617, "name": "light panels"}, {"id": 38618, "name": "light pants"}, {"id": 38619, "name": "light pants on"}, {"id": 38620, "name": "light part"}, {"id": 38621, "name": "light patch"}, {"id": 38622, "name": "light paws"}, {"id": 38623, "name": "light pink"}, {"id": 38624, "name": "light plastic"}, {"id": 38625, "name": "light plate"}, {"id": 38626, "name": "light point"}, {"id": 38627, "name": "light pole"}, {"id": 38628, "name": "light pole on left"}, {"id": 38629, "name": "light pole on right"}, {"id": 38630, "name": "light pole on stairs"}, {"id": 38631, "name": "light polecorner"}, {"id": 38632, "name": "light poles"}, {"id": 38633, "name": "light poll"}, {"id": 38634, "name": "light polle"}, {"id": 38635, "name": "light post"}, {"id": 38636, "name": "light post shadow"}, {"id": 38637, "name": "light posts"}, {"id": 38638, "name": "light purple shirt"}, {"id": 38639, "name": "light rail"}, {"id": 38640, "name": "light rails"}, {"id": 38641, "name": "light ray"}, {"id": 38642, "name": "light rays"}, {"id": 38643, "name": "light red"}, {"id": 38644, "name": "light refection"}, {"id": 38645, "name": "light reflected"}, {"id": 38646, "name": "light reflecter"}, {"id": 38647, "name": "light reflecting"}, {"id": 38648, "name": "light reflection"}, {"id": 38649, "name": "light reflections"}, {"id": 38650, "name": "light reflectionsnow"}, {"id": 38651, "name": "light reflecton"}, {"id": 38652, "name": "light reflector"}, {"id": 38653, "name": "light reflects"}, {"id": 38654, "name": "light relecting"}, {"id": 38655, "name": "light ripples"}, {"id": 38656, "name": "light room"}, {"id": 38657, "name": "light row"}, {"id": 38658, "name": "light run"}, {"id": 38659, "name": "light sand"}, {"id": 38660, "name": "light sconce"}, {"id": 38661, "name": "light sconces"}, {"id": 38662, "name": "light scone"}, {"id": 38663, "name": "light scooter"}, {"id": 38664, "name": "light scopes"}, {"id": 38665, "name": "light screens"}, {"id": 38666, "name": "light section"}, {"id": 38667, "name": "light set"}, {"id": 38668, "name": "light shade"}, {"id": 38669, "name": "light shadow"}, {"id": 38670, "name": "light shadows"}, {"id": 38671, "name": "light shine"}, {"id": 38672, "name": "light shining"}, {"id": 38673, "name": "light shining off"}, {"id": 38674, "name": "light shinning"}, {"id": 38675, "name": "light shirt"}, {"id": 38676, "name": "light shorts"}, {"id": 38677, "name": "light show"}, {"id": 38678, "name": "light shown on"}, {"id": 38679, "name": "light shows"}, {"id": 38680, "name": "light shows man"}, {"id": 38681, "name": "light signal"}, {"id": 38682, "name": "light signals"}, {"id": 38683, "name": "light skin"}, {"id": 38684, "name": "light skinned"}, {"id": 38685, "name": "light sky"}, {"id": 38686, "name": "light socket"}, {"id": 38687, "name": "light sockets"}, {"id": 38688, "name": "light source"}, {"id": 38689, "name": "light speck"}, {"id": 38690, "name": "light specks"}, {"id": 38691, "name": "light spot"}, {"id": 38692, "name": "light spots"}, {"id": 38693, "name": "light stand"}, {"id": 38694, "name": "light steam"}, {"id": 38695, "name": "light strand"}, {"id": 38696, "name": "light strands"}, {"id": 38697, "name": "light streak"}, {"id": 38698, "name": "light streaks"}, {"id": 38699, "name": "light street"}, {"id": 38700, "name": "light string"}, {"id": 38701, "name": "light strip"}, {"id": 38702, "name": "light strips"}, {"id": 38703, "name": "light structure"}, {"id": 38704, "name": "light swich plate"}, {"id": 38705, "name": "light switch"}, {"id": 38706, "name": "light switch panel"}, {"id": 38707, "name": "light switch plate"}, {"id": 38708, "name": "light switches"}, {"id": 38709, "name": "light switcheswall"}, {"id": 38710, "name": "light switchplate"}, {"id": 38711, "name": "light table"}, {"id": 38712, "name": "light tail"}, {"id": 38713, "name": "light tan wallpaper"}, {"id": 38714, "name": "light tent"}, {"id": 38715, "name": "light tents"}, {"id": 38716, "name": "light tiles"}, {"id": 38717, "name": "light top"}, {"id": 38718, "name": "light tower"}, {"id": 38719, "name": "light trails"}, {"id": 38720, "name": "light train"}, {"id": 38721, "name": "light tube"}, {"id": 38722, "name": "light umbrella"}, {"id": 38723, "name": "light up"}, {"id": 38724, "name": "light up neon sign"}, {"id": 38725, "name": "light vehickle"}, {"id": 38726, "name": "light wall"}, {"id": 38727, "name": "light water"}, {"id": 38728, "name": "light wave"}, {"id": 38729, "name": "light window"}, {"id": 38730, "name": "light wood"}, {"id": 38731, "name": "light"}, {"id": 38732, "name": "lightbar"}, {"id": 38733, "name": "lightblue"}, {"id": 38734, "name": "lightblue section"}, {"id": 38735, "name": "lightblue shirt"}, {"id": 38736, "name": "lightblue umbrella"}, {"id": 38737, "name": "lightboard"}, {"id": 38738, "name": "lightbrown collar"}, {"id": 38739, "name": "lightbrown grass"}, {"id": 38740, "name": "lightbrown tile"}, {"id": 38741, "name": "lightbrown tiles"}, {"id": 38742, "name": "lightbuilding"}, {"id": 38743, "name": "lightbulb symbol"}, {"id": 38744, "name": "lightbulb"}, {"id": 38745, "name": "lightcoat"}, {"id": 38746, "name": "lightcolored jacket"}, {"id": 38747, "name": "lightdark"}, {"id": 38748, "name": "lighted"}, {"id": 38749, "name": "lighted arrow"}, {"id": 38750, "name": "lighted at night"}, {"id": 38751, "name": "lighted building"}, {"id": 38752, "name": "lighted candle"}, {"id": 38753, "name": "lighted clock"}, {"id": 38754, "name": "lighted corner"}, {"id": 38755, "name": "lighted decorations"}, {"id": 38756, "name": "lighted globe"}, {"id": 38757, "name": "lighted grass"}, {"id": 38758, "name": "lighted object"}, {"id": 38759, "name": "lighted route"}, {"id": 38760, "name": "lighted screen"}, {"id": 38761, "name": "lighted sign"}, {"id": 38762, "name": "lighted tree"}, {"id": 38763, "name": "lightening"}, {"id": 38764, "name": "lightening rod"}, {"id": 38765, "name": "lighter"}, {"id": 38766, "name": "lighter building"}, {"id": 38767, "name": "lighter floor"}, {"id": 38768, "name": "lighter pants"}, {"id": 38769, "name": "lighter stove"}, {"id": 38770, "name": "lighter water"}, {"id": 38771, "name": "lightes"}, {"id": 38772, "name": "lightest part"}, {"id": 38773, "name": "lightfixture"}, {"id": 38774, "name": "lightfixtures"}, {"id": 38775, "name": "lightgray floor"}, {"id": 38776, "name": "lightgreen leaf"}, {"id": 38777, "name": "lighthoues"}, {"id": 38778, "name": "lighthouse"}, {"id": 38779, "name": "lighthouse base"}, {"id": 38780, "name": "lighthouse cottage"}, {"id": 38781, "name": "lighthouse picture"}, {"id": 38782, "name": "lighthouse top"}, {"id": 38783, "name": "lighthouse window"}, {"id": 38784, "name": "lightig"}, {"id": 38785, "name": "lighting bolts"}, {"id": 38786, "name": "lighting controls"}, {"id": 38787, "name": "lighting device"}, {"id": 38788, "name": "lighting fixture"}, {"id": 38789, "name": "lighting fixtures"}, {"id": 38790, "name": "lighting post"}, {"id": 38791, "name": "lighting reflection"}, {"id": 38792, "name": "lighting rig"}, {"id": 38793, "name": "lighting rod"}, {"id": 38794, "name": "lighting system"}, {"id": 38795, "name": "lighting"}, {"id": 38796, "name": "lightinside"}, {"id": 38797, "name": "lightlawn"}, {"id": 38798, "name": "lightning"}, {"id": 38799, "name": "lightning bolt"}, {"id": 38800, "name": "lightning rod"}, {"id": 38801, "name": "lightpole"}, {"id": 38802, "name": "lightpoles"}, {"id": 38803, "name": "lightpost"}, {"id": 38804, "name": "lightposts"}, {"id": 38805, "name": "lights above"}, {"id": 38806, "name": "lights are red"}, {"id": 38807, "name": "lights are used"}, {"id": 38808, "name": "lights background"}, {"id": 38809, "name": "lights bus"}, {"id": 38810, "name": "lights for kitchen"}, {"id": 38811, "name": "lights hanging"}, {"id": 38812, "name": "lights in upstairs"}, {"id": 38813, "name": "lights mounted"}, {"id": 38814, "name": "lights of train"}, {"id": 38815, "name": "lights off"}, {"id": 38816, "name": "lights on"}, {"id": 38817, "name": "lights on back"}, {"id": 38818, "name": "lights on ceiling"}, {"id": 38819, "name": "lights on inside"}, {"id": 38820, "name": "lights on pole"}, {"id": 38821, "name": "lights on street"}, {"id": 38822, "name": "lights on top"}, {"id": 38823, "name": "lights on traffic"}, {"id": 38824, "name": "lights on wall"}, {"id": 38825, "name": "lights pole"}, {"id": 38826, "name": "lights poles"}, {"id": 38827, "name": "lights reflected"}, {"id": 38828, "name": "lights reflection"}, {"id": 38829, "name": "lights row"}, {"id": 38830, "name": "lights set"}, {"id": 38831, "name": "lights shining"}, {"id": 38832, "name": "lights sink"}, {"id": 38833, "name": "lights strung"}, {"id": 38834, "name": "lights that"}, {"id": 38835, "name": "lights to illuminate"}, {"id": 38836, "name": "lights water"}, {"id": 38837, "name": "lightsaber"}, {"id": 38838, "name": "lightsceiling"}, {"id": 38839, "name": "lightshade"}, {"id": 38840, "name": "lightshadow"}, {"id": 38841, "name": "lightshadows"}, {"id": 38842, "name": 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{"id": 58238, "name": "shadowy"}, {"id": 58239, "name": "shadowy area"}, {"id": 58240, "name": "shadowy outlines"}, {"id": 58241, "name": "shadw"}, {"id": 58242, "name": "shadwo"}, {"id": 58243, "name": "shady area"}, {"id": 58244, "name": "shady grass"}, {"id": 58245, "name": "shady spot"}, {"id": 58246, "name": "shady street"}, {"id": 58247, "name": "shady trees"}, {"id": 58248, "name": "shaedow"}, {"id": 58249, "name": "shaes"}, {"id": 58250, "name": "shaft"}, {"id": 58251, "name": "shag carpet"}, {"id": 58252, "name": "shaggy"}, {"id": 58253, "name": "shaggy fur"}, {"id": 58254, "name": "shaggy tail"}, {"id": 58255, "name": "shair"}, {"id": 58256, "name": "shake"}, {"id": 58257, "name": "shaker bottles"}, {"id": 58258, "name": "shaker shaker"}, {"id": 58259, "name": "shaker top"}, {"id": 58260, "name": "shaker tops"}, {"id": 58261, "name": "shaker"}, {"id": 58262, "name": "shakespeare"}, {"id": 58263, "name": "shaking hands"}, {"id": 58264, "name": "shall"}, {"id": 58265, "name": 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59536, "name": "signal light"}, {"id": 59537, "name": "signal lights"}, {"id": 59538, "name": "signal pole"}, {"id": 59539, "name": "signal post"}, {"id": 59540, "name": "signal sign"}, {"id": 59541, "name": "signal structure"}, {"id": 59542, "name": "signal switch"}, {"id": 59543, "name": "signal tower"}, {"id": 59544, "name": "signal"}, {"id": 59545, "name": "signalbox"}, {"id": 59546, "name": "signaling"}, {"id": 59547, "name": "signaling device"}, {"id": 59548, "name": "signalling system"}, {"id": 59549, "name": "signalpole"}, {"id": 59550, "name": "signals pole"}, {"id": 59551, "name": "signature"}, {"id": 59552, "name": "signboard"}, {"id": 59553, "name": "signbuilding"}, {"id": 59554, "name": "signe"}, {"id": 59555, "name": "signed"}, {"id": 59556, "name": "signholder"}, {"id": 59557, "name": "signing"}, {"id": 59558, "name": "signla"}, {"id": 59559, "name": "signpole"}, {"id": 59560, "name": "signpost"}, {"id": 59561, "name": "signs attached"}, {"id": 59562, "name": "signs 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{"id": 59740, "name": "silver hub"}, {"id": 59741, "name": "silver hub caps"}, {"id": 59742, "name": "silver hubcap"}, {"id": 59743, "name": "silver jar"}, {"id": 59744, "name": "silver key"}, {"id": 59745, "name": "silver key hole"}, {"id": 59746, "name": "silver keyboard"}, {"id": 59747, "name": "silver keys"}, {"id": 59748, "name": "silver knife"}, {"id": 59749, "name": "silver knob"}, {"id": 59750, "name": "silver knobs"}, {"id": 59751, "name": "silver ladder"}, {"id": 59752, "name": "silver laddle"}, {"id": 59753, "name": "silver ladle"}, {"id": 59754, "name": "silver lamp"}, {"id": 59755, "name": "silver laptop"}, {"id": 59756, "name": "silver ledge"}, {"id": 59757, "name": "silver leg"}, {"id": 59758, "name": "silver legs"}, {"id": 59759, "name": "silver lettering"}, {"id": 59760, "name": "silver lever"}, {"id": 59761, "name": "silver lid"}, {"id": 59762, "name": "silver light"}, {"id": 59763, "name": "silver lights"}, {"id": 59764, "name": "silver limo"}, {"id": 59765, "name": "silver lines"}, {"id": 59766, "name": "silver lining"}, {"id": 59767, "name": "silver link"}, {"id": 59768, "name": "silver lion foot"}, {"id": 59769, "name": "silver lock"}, {"id": 59770, "name": "silver logo"}, {"id": 59771, "name": "silver machine"}, {"id": 59772, "name": "silver makings"}, {"id": 59773, "name": "silver metal"}, {"id": 59774, "name": "silver metal frame"}, {"id": 59775, "name": "silver metal handle"}, {"id": 59776, "name": "silver microphone"}, {"id": 59777, "name": "silver microwave"}, {"id": 59778, "name": "silver minivan"}, {"id": 59779, "name": "silver mirror"}, {"id": 59780, "name": "silver n"}, {"id": 59781, "name": "silver necklace"}, {"id": 59782, "name": "silver nozzle"}, {"id": 59783, "name": "silver number"}, {"id": 59784, "name": "silver nut"}, {"id": 59785, "name": "silver object"}, {"id": 59786, "name": "silver opening"}, {"id": 59787, "name": "silver oven"}, {"id": 59788, "name": "silver paint"}, {"id": 59789, "name": "silver pan"}, {"id": 59790, 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{"id": 67581, "name": "tile under counter"}, {"id": 67582, "name": "tile walkway"}, {"id": 67583, "name": "tile wall"}, {"id": 67584, "name": "tile white"}, {"id": 67585, "name": "tile work"}, {"id": 67586, "name": "tile"}, {"id": 67587, "name": "tiled"}, {"id": 67588, "name": "tiled area"}, {"id": 67589, "name": "tiled backsplash"}, {"id": 67590, "name": "tiled bathroom"}, {"id": 67591, "name": "tiled border"}, {"id": 67592, "name": "tiled ceiling"}, {"id": 67593, "name": "tiled counter"}, {"id": 67594, "name": "tiled designs"}, {"id": 67595, "name": "tiled doorway"}, {"id": 67596, "name": "tiled floor"}, {"id": 67597, "name": "tiled flooring"}, {"id": 67598, "name": "tiled frame"}, {"id": 67599, "name": "tiled ground"}, {"id": 67600, "name": "tiled ledge"}, {"id": 67601, "name": "tiled mirror"}, {"id": 67602, "name": "tiled roof"}, {"id": 67603, "name": "tiled stone"}, {"id": 67604, "name": "tiled surface"}, {"id": 67605, "name": "tiled walkways"}, {"id": 67606, "name": "tiled 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"name": "tire of a motor"}, {"id": 67792, "name": "tire on"}, {"id": 67793, "name": "tire on a bike"}, {"id": 67794, "name": "tire on a motorcycle"}, {"id": 67795, "name": "tire on bus"}, {"id": 67796, "name": "tire on the car"}, {"id": 67797, "name": "tire panel"}, {"id": 67798, "name": "tire pile"}, {"id": 67799, "name": "tire protector"}, {"id": 67800, "name": "tire rack"}, {"id": 67801, "name": "tire reflection"}, {"id": 67802, "name": "tire rim"}, {"id": 67803, "name": "tire ring"}, {"id": 67804, "name": "tire rutes"}, {"id": 67805, "name": "tire secured"}, {"id": 67806, "name": "tire skateboard"}, {"id": 67807, "name": "tire spoke"}, {"id": 67808, "name": "tire swing"}, {"id": 67809, "name": "tire track"}, {"id": 67810, "name": "tire tracks"}, {"id": 67811, "name": "tire tread"}, {"id": 67812, "name": "tire visible"}, {"id": 67813, "name": "tire wall"}, {"id": 67814, "name": "tire well"}, {"id": 67815, "name": "tire wheel"}, {"id": 67816, "name": "tire"}, {"id": 67817, "name": 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{"id": 67843, "name": "titan logo"}, {"id": 67844, "name": "titans way"}, {"id": 67845, "name": "title page"}, {"id": 67846, "name": "title shaped blocks"}, {"id": 67847, "name": "title"}, {"id": 67848, "name": "tiver"}, {"id": 67849, "name": "tjire"}, {"id": 67850, "name": "tjook"}, {"id": 67851, "name": "tjornin"}, {"id": 67852, "name": "tk"}, {"id": 67853, "name": "tk letters"}, {"id": 67854, "name": "tlayada"}, {"id": 67855, "name": "tleft mirror"}, {"id": 67856, "name": "tlie"}, {"id": 67857, "name": "tlight"}, {"id": 67858, "name": "tm"}, {"id": 67859, "name": "tmobile"}, {"id": 67860, "name": "tmobile graphic"}, {"id": 67861, "name": "to"}, {"id": 67862, "name": "to a belt loop"}, {"id": 67863, "name": "to a computer"}, {"id": 67864, "name": "to a person"}, {"id": 67865, "name": "to avoid train"}, {"id": 67866, "name": "to bed"}, {"id": 67867, "name": "to cart"}, {"id": 67868, "name": "to enter"}, {"id": 67869, "name": "to fence"}, {"id": 67870, "name": "to file folders"}, {"id": 67871, "name": "to go container"}, {"id": 67872, "name": "to hit"}, {"id": 67873, "name": "to hitch"}, {"id": 67874, "name": "to hold pizza"}, {"id": 67875, "name": "to kite"}, {"id": 67876, "name": "to lamp"}, {"id": 67877, "name": "to manhattan"}, {"id": 67878, "name": "to pillar"}, {"id": 67879, "name": "to plug"}, {"id": 67880, "name": "to pole"}, {"id": 67881, "name": "to ride on"}, {"id": 67882, "name": "to rust"}, {"id": 67883, "name": "to serve"}, {"id": 67884, "name": "to shore"}, {"id": 67885, "name": "to side"}, {"id": 67886, "name": "to sit on"}, {"id": 67887, "name": "to stop"}, {"id": 67888, "name": "to street"}, {"id": 67889, "name": "to strike"}, {"id": 67890, "name": "to take a picture"}, {"id": 67891, "name": "to the bench"}, {"id": 67892, "name": "to the left"}, {"id": 67893, "name": "to the side"}, {"id": 67894, "name": "to the sink"}, {"id": 67895, "name": "to the wall"}, {"id": 67896, "name": "to toilet"}, {"id": 67897, "name": "to tracks"}, {"id": 67898, 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"toronto"}, {"id": 68472, "name": "torque"}, {"id": 68473, "name": "torrence"}, {"id": 68474, "name": "torres"}, {"id": 68475, "name": "torse"}, {"id": 68476, "name": "torso neck"}, {"id": 68477, "name": "torso of a person"}, {"id": 68478, "name": "torso part"}, {"id": 68479, "name": "torso"}, {"id": 68480, "name": "torte"}, {"id": 68481, "name": "tortellini"}, {"id": 68482, "name": "tortilla chip"}, {"id": 68483, "name": "tortilla chips"}, {"id": 68484, "name": "tortilla roll"}, {"id": 68485, "name": "tortilla"}, {"id": 68486, "name": "tortoise"}, {"id": 68487, "name": "tortoise head"}, {"id": 68488, "name": "tos"}, {"id": 68489, "name": "tosarajevo"}, {"id": 68490, "name": "tose"}, {"id": 68491, "name": "toshiba"}, {"id": 68492, "name": "tossed"}, {"id": 68493, "name": "tossle"}, {"id": 68494, "name": "toster"}, {"id": 68495, "name": "toster oven"}, {"id": 68496, "name": "tostito"}, {"id": 68497, "name": "tot"}, {"id": 68498, "name": "total area"}, {"id": 68499, "name": "tote"}, {"id": 68500, "name": "tote bag"}, {"id": 68501, "name": "tote bags"}, {"id": 68502, "name": "tote box"}, {"id": 68503, "name": "totem pole"}, {"id": 68504, "name": "totem poles"}, {"id": 68505, "name": "toucan"}, {"id": 68506, "name": "touch"}, {"id": 68507, "name": "touch controls"}, {"id": 68508, "name": "touch o grey"}, {"id": 68509, "name": "touch pad"}, {"id": 68510, "name": "touch screen"}, {"id": 68511, "name": "touch strip"}, {"id": 68512, "name": "touchatag"}, {"id": 68513, "name": "touched"}, {"id": 68514, "name": "touches the elephant"}, {"id": 68515, "name": "touching"}, {"id": 68516, "name": "touching the ground"}, {"id": 68517, "name": "touching thier face"}, {"id": 68518, "name": "touchpad"}, {"id": 68519, "name": "touchscreen"}, {"id": 68520, "name": "touchup"}, {"id": 68521, "name": "tough"}, {"id": 68522, "name": "tough lips"}, {"id": 68523, "name": "tougne"}, {"id": 68524, "name": "tound design"}, {"id": 68525, "name": "tounge"}, {"id": 68526, "name": "toungue"}, {"id": 68527, "name": "toupee"}, {"id": 68528, "name": "tour 2013"}, {"id": 68529, "name": "tour bus"}, {"id": 68530, "name": "tour car"}, {"id": 68531, "name": "tour group"}, {"id": 68532, "name": "tour guide"}, {"id": 68533, "name": "tour vehicle"}, {"id": 68534, "name": "tour word"}, {"id": 68535, "name": "tour"}, {"id": 68536, "name": "touratech"}, {"id": 68537, "name": "tourist attraction"}, {"id": 68538, "name": "tourist boat"}, {"id": 68539, "name": "tourist head"}, {"id": 68540, "name": "tourist organization"}, {"id": 68541, "name": "tourist"}, {"id": 68542, "name": "tourmaline ave"}, {"id": 68543, "name": "tournagrip"}, {"id": 68544, "name": "tournament logo"}, {"id": 68545, "name": "tournament name"}, {"id": 68546, "name": "tournament official"}, {"id": 68547, "name": "tournament sign"}, {"id": 68548, "name": "tourney"}, {"id": 68549, "name": "tourtists"}, {"id": 68550, "name": "tous"}, {"id": 68551, "name": "tovar"}, {"id": 68552, "name": "tow"}, {"id": 68553, "name": "tow arm"}, {"id": 68554, "name": "tow away"}, {"id": 68555, "name": "tow bar"}, {"id": 68556, "name": "tow bars"}, {"id": 68557, "name": "tow bed"}, {"id": 68558, "name": "tow belt"}, {"id": 68559, "name": "tow hitch"}, {"id": 68560, "name": "tow line"}, {"id": 68561, "name": "tow motor"}, {"id": 68562, "name": "tow rope"}, {"id": 68563, "name": "tow sign"}, {"id": 68564, "name": "tow trailer"}, {"id": 68565, "name": "tow truck"}, {"id": 68566, "name": "tow vehicle"}, {"id": 68567, "name": "tow zone"}, {"id": 68568, "name": "tow zone sign"}, {"id": 68569, "name": "toward"}, {"id": 68570, "name": "towards"}, {"id": 68571, "name": "towards the ground"}, {"id": 68572, "name": "towed"}, {"id": 68573, "name": "towed away"}, {"id": 68574, "name": "towel bar"}, {"id": 68575, "name": "towel counter"}, {"id": 68576, "name": "towel dispenser"}, {"id": 68577, "name": "towel drawer"}, {"id": 68578, "name": "towel edge"}, {"id": 68579, "name": "towel handlebar"}, {"id": 68580, "name": "towel hanger"}, {"id": 68581, "name": "towel hanging"}, {"id": 68582, "name": "towel holder"}, {"id": 68583, "name": "towel holder reflect"}, {"id": 68584, "name": "towel hoock"}, {"id": 68585, "name": "towel hook"}, {"id": 68586, "name": "towel is white"}, {"id": 68587, "name": "towel mat"}, {"id": 68588, "name": "towel on rack"}, {"id": 68589, "name": "towel paper"}, {"id": 68590, "name": "towel rack"}, {"id": 68591, "name": "towel racks"}, {"id": 68592, "name": "towel reflection"}, {"id": 68593, "name": "towel ring"}, {"id": 68594, "name": "towel rod"}, {"id": 68595, "name": "towel roll"}, {"id": 68596, "name": "towel rolls"}, {"id": 68597, "name": "towel section"}, {"id": 68598, "name": "towel set"}, {"id": 68599, "name": "towel shelf"}, {"id": 68600, "name": "towel sink"}, {"id": 68601, "name": "towel warmer"}, {"id": 68602, "name": "towel"}, {"id": 68603, "name": "towelette"}, {"id": 68604, "name": "towell"}, {"id": 68605, "name": "towelrack"}, {"id": 68606, "name": "towelrod"}, {"id": 68607, "name": "towelroll"}, {"id": 68608, "name": "towels hanging"}, {"id": 68609, "name": "towels in reflection"}, {"id": 68610, "name": "towels on rack"}, {"id": 68611, "name": "tower base"}, {"id": 68612, "name": "tower body"}, {"id": 68613, "name": "tower bridge"}, {"id": 68614, "name": "tower case"}, {"id": 68615, "name": "tower ceiling"}, {"id": 68616, "name": "tower clock"}, {"id": 68617, "name": "tower edge"}, {"id": 68618, "name": "tower flag"}, {"id": 68619, "name": "tower has a flag"}, {"id": 68620, "name": "tower has floor"}, {"id": 68621, "name": "tower has stripes"}, {"id": 68622, "name": "tower has window"}, {"id": 68623, "name": "tower in distance"}, {"id": 68624, "name": "tower pc"}, {"id": 68625, "name": "tower roof"}, {"id": 68626, "name": "tower side"}, {"id": 68627, "name": "tower structure"}, {"id": 68628, "name": "tower support"}, {"id": 68629, "name": "tower symbol"}, {"id": 68630, "name": "tower tip"}, {"id": 68631, "name": "tower top"}, {"id": 68632, "name": "tower tops"}, {"id": 68633, "name": "tower wall"}, {"id": 68634, "name": "tower window"}, {"id": 68635, "name": "tower windows"}, {"id": 68636, "name": "tower with red sign"}, {"id": 68637, "name": "tower"}, {"id": 68638, "name": "towers edge"}, {"id": 68639, "name": "towertop"}, {"id": 68640, "name": "towhook"}, {"id": 68641, "name": "towing"}, {"id": 68642, "name": "towing airplane"}, {"id": 68643, "name": "towing bed"}, {"id": 68644, "name": "towing equipment"}, {"id": 68645, "name": "towing hitch"}, {"id": 68646, "name": "towing trailer"}, {"id": 68647, "name": "towing truck"}, {"id": 68648, "name": "towing zone sign"}, {"id": 68649, "name": "towl"}, {"id": 68650, "name": "towle"}, {"id": 68651, "name": "towls"}, {"id": 68652, "name": "town"}, {"id": 68653, "name": "town fair"}, {"id": 68654, "name": "town house"}, {"id": 68655, "name": "town meter"}, {"id": 68656, "name": "town scene"}, {"id": 68657, "name": "town square"}, {"id": 68658, "name": "townetown"}, {"id": 68659, "name": "townhome"}, {"id": 68660, "name": "townhomes"}, {"id": 68661, "name": "townhouses"}, {"id": 68662, "name": "towns metropolis"}, {"id": 68663, "name": "townscape"}, {"id": 68664, "name": "township"}, {"id": 68665, "name": "townsperson"}, {"id": 68666, "name": "towsign"}, {"id": 68667, "name": "toy airplane"}, {"id": 68668, "name": "toy baby"}, {"id": 68669, "name": "toy balls"}, {"id": 68670, "name": "toy bat"}, {"id": 68671, "name": "toy bear"}, {"id": 68672, "name": "toy bears"}, {"id": 68673, "name": "toy bicycle"}, {"id": 68674, "name": "toy blocks"}, {"id": 68675, "name": "toy boat"}, {"id": 68676, "name": "toy boats"}, {"id": 68677, "name": "toy bottle"}, {"id": 68678, "name": "toy box"}, {"id": 68679, "name": "toy bridge"}, {"id": 68680, "name": "toy broom"}, {"id": 68681, "name": "toy bus"}, {"id": 68682, "name": "toy car"}, {"id": 68683, "name": "toy cars"}, {"id": 68684, "name": "toy cart"}, {"id": 68685, "name": "toy cast"}, {"id": 68686, "name": "toy castle"}, {"id": 68687, "name": "toy cat"}, {"id": 68688, "name": "toy chair"}, {"id": 68689, "name": "toy chest"}, {"id": 68690, "name": "toy dinosaur"}, {"id": 68691, "name": "toy dog"}, {"id": 68692, "name": "toy doll"}, {"id": 68693, "name": "toy edge"}, {"id": 68694, "name": "toy eye"}, {"id": 68695, "name": "toy farms"}, {"id": 68696, "name": "toy figure"}, {"id": 68697, "name": "toy figures"}, {"id": 68698, "name": "toy frog"}, {"id": 68699, "name": "toy giraffe"}, {"id": 68700, "name": "toy guitar"}, {"id": 68701, "name": "toy gun"}, {"id": 68702, "name": "toy hand"}, {"id": 68703, "name": "toy holders"}, {"id": 68704, "name": "toy horse"}, {"id": 68705, "name": "toy house"}, {"id": 68706, "name": "toy jack"}, {"id": 68707, "name": "toy kitchen"}, {"id": 68708, "name": "toy landscape"}, {"id": 68709, "name": "toy lawnmower"}, {"id": 68710, "name": "toy leg"}, {"id": 68711, "name": "toy machine"}, {"id": 68712, "name": "toy man"}, {"id": 68713, "name": "toy models"}, {"id": 68714, "name": "toy monkey"}, {"id": 68715, "name": "toy monkeys"}, {"id": 68716, "name": "toy nose"}, {"id": 68717, "name": "toy park"}, {"id": 68718, "name": "toy phone"}, {"id": 68719, "name": "toy pile"}, {"id": 68720, "name": "toy play table"}, {"id": 68721, "name": "toy poodle"}, {"id": 68722, "name": "toy pot"}, {"id": 68723, "name": "toy rabbit"}, {"id": 68724, "name": "toy refrigerator"}, {"id": 68725, "name": "toy restaurant"}, {"id": 68726, "name": "toy seated"}, {"id": 68727, "name": "toy set"}, {"id": 68728, "name": "toy shop"}, {"id": 68729, "name": "toy snake"}, {"id": 68730, "name": "toy soldier"}, {"id": 68731, "name": "toy store"}, {"id": 68732, "name": "toy stove"}, {"id": 68733, "name": "toy stroller"}, {"id": 68734, "name": "toy sunglasses"}, {"id": 68735, "name": "toy sword"}, {"id": 68736, "name": "toy teddy bear"}, {"id": 68737, "name": "toy train"}, {"id": 68738, "name": "toy trash can"}, {"id": 68739, "name": "toy tree"}, {"id": 68740, "name": "toy truck"}, {"id": 68741, "name": "toy"}, {"id": 68742, "name": "toybox"}, {"id": 68743, "name": "toyota"}, {"id": 68744, "name": "toyota advertisement"}, {"id": 68745, "name": "toyota banner"}, {"id": 68746, "name": "toyota emblem"}, {"id": 68747, "name": "toyota logo"}, {"id": 68748, "name": "toyota sedan"}, {"id": 68749, "name": "toyota sign"}, {"id": 68750, "name": "toyota vehicle"}, {"id": 68751, "name": "toys eye"}, {"id": 68752, "name": "toys ground"}, {"id": 68753, "name": "toys paw"}, {"id": 68754, "name": "tp"}, {"id": 68755, "name": "tp holder"}, {"id": 68756, "name": "tp roll"}, {"id": 68757, "name": "tpeople"}, {"id": 68758, "name": "tpp"}, {"id": 68759, "name": "tra"}, {"id": 68760, "name": "trace"}, {"id": 68761, "name": "tracjs"}, {"id": 68762, "name": "track advertisement"}, {"id": 68763, "name": "track area"}, {"id": 68764, "name": "track ball"}, {"id": 68765, "name": "track ballast"}, {"id": 68766, "name": "track beam"}, {"id": 68767, "name": "track bed"}, {"id": 68768, "name": "track field"}, {"id": 68769, "name": "track information"}, {"id": 68770, "name": "track is metal"}, {"id": 68771, "name": "track lanes"}, {"id": 68772, "name": "track light"}, {"id": 68773, "name": "track lighting"}, {"id": 68774, "name": "track lights"}, {"id": 68775, "name": "track mark"}, {"id": 68776, "name": "track marks"}, {"id": 68777, "name": "track pad"}, {"id": 68778, "name": "track post"}, {"id": 68779, "name": "track prints"}, {"id": 68780, "name": "track rails"}, {"id": 68781, "name": "track section"}, {"id": 68782, "name": "track side"}, {"id": 68783, "name": "track signal"}, {"id": 68784, "name": "track stop"}, {"id": 68785, "name": "track suit"}, {"id": 68786, "name": "track ties"}, {"id": 68787, "name": "track track"}, {"id": 68788, "name": "track train"}, {"id": 68789, "name": "track"}, {"id": 68790, "name": "trackball"}, {"id": 68791, "name": "tracker"}, {"id": 68792, "name": "tracking clip"}, {"id": 68793, "name": "tracking pad"}, {"id": 68794, "name": "tracking wheel"}, {"id": 68795, "name": "tracking"}, {"id": 68796, "name": "trackpad"}, {"id": 68797, "name": "tracks beside train"}, {"id": 68798, "name": "tracks end"}, {"id": 68799, "name": "tracks ground"}, {"id": 68800, "name": "tracks in snow"}, {"id": 68801, "name": "tracks next"}, {"id": 68802, "name": "tracks side by side"}, {"id": 68803, "name": "tracks sign"}, {"id": 68804, "name": "tracks under train"}, {"id": 68805, "name": "trackside"}, {"id": 68806, "name": "tracksuit"}, {"id": 68807, "name": "tract"}, {"id": 68808, "name": "traction"}, {"id": 68809, "name": "traction nubs"}, {"id": 68810, "name": "traction pad"}, {"id": 68811, "name": "tractor arm"}, {"id": 68812, "name": "tractor tire"}, {"id": 68813, "name": "tractor traileer"}, {"id": 68814, "name": "tractor trailer"}, {"id": 68815, "name": "tractor trailors"}, {"id": 68816, "name": "tractor"}, {"id": 68817, "name": "tractortrailer"}, {"id": 68818, "name": "trade"}, {"id": 68819, "name": "trade show"}, {"id": 68820, "name": "trademark"}, {"id": 68821, "name": "trademark polo"}, {"id": 68822, "name": "trademark symbol"}, {"id": 68823, "name": "tradename"}, {"id": 68824, "name": "trader"}, {"id": 68825, "name": "trader joes"}, {"id": 68826, "name": "trading"}, {"id": 68827, "name": "traditional clothing"}, {"id": 68828, "name": "traditional garb"}, {"id": 68829, "name": "trafalgar"}, {"id": 68830, "name": "traffi sign"}, {"id": 68831, "name": "traffic arrow"}, {"id": 68832, "name": "traffic arrows"}, {"id": 68833, "name": "traffic bar"}, {"id": 68834, "name": "traffic barrel"}, {"id": 68835, "name": "traffic barrier"}, {"id": 68836, "name": "traffic barriers"}, {"id": 68837, "name": "traffic blockage"}, {"id": 68838, "name": "traffic bolalrds"}, {"id": 68839, "name": "traffic bollard"}, {"id": 68840, "name": "traffic bottled"}, {"id": 68841, "name": "traffic box"}, {"id": 68842, "name": "traffic cam"}, {"id": 68843, "name": "traffic camera"}, {"id": 68844, "name": "traffic circle"}, {"id": 68845, "name": "traffic cone"}, {"id": 68846, "name": "traffic cones"}, {"id": 68847, "name": "traffic control"}, {"id": 68848, "name": "traffic control sign"}, {"id": 68849, "name": "traffic controller"}, {"id": 68850, "name": "traffic dash"}, {"id": 68851, "name": "traffic directions"}, {"id": 68852, "name": "traffic equipment"}, {"id": 68853, "name": "traffic fixture"}, {"id": 68854, "name": "traffic guard"}, {"id": 68855, "name": "traffic holder"}, {"id": 68856, "name": "traffic horse"}, {"id": 68857, "name": "traffic in the stree"}, {"id": 68858, "name": "traffic indicator"}, {"id": 68859, "name": "traffic indicators"}, {"id": 68860, "name": "traffic information"}, {"id": 68861, "name": "traffic is stopped"}, {"id": 68862, "name": "traffic island"}, {"id": 68863, "name": "traffic jam"}, {"id": 68864, "name": "traffic lamp"}, {"id": 68865, "name": "traffic licght"}, {"id": 68866, "name": "traffic ligh"}, {"id": 68867, "name": "traffic light"}, {"id": 68868, "name": "traffic light sign"}, {"id": 68869, "name": "traffic light casing"}, {"id": 68870, "name": "traffic light is red"}, {"id": 68871, "name": "traffic light lenses"}, {"id": 68872, "name": "traffic light pole"}, {"id": 68873, "name": "traffic light signal"}, {"id": 68874, "name": "traffic lightfixture"}, {"id": 68875, "name": "traffic lights"}, {"id": 68876, "name": "traffic line"}, {"id": 68877, "name": "traffic lines"}, {"id": 68878, "name": "traffic llight"}, {"id": 68879, "name": "traffic mark"}, {"id": 68880, "name": "traffic marker"}, {"id": 68881, "name": "traffic marking"}, {"id": 68882, "name": "traffic meter"}, {"id": 68883, "name": "traffic monitor"}, {"id": 68884, "name": "traffic notations"}, {"id": 68885, "name": "traffic on a street"}, {"id": 68886, "name": "traffic overpass"}, {"id": 68887, "name": "traffic pillar"}, {"id": 68888, "name": "traffic pole"}, {"id": 68889, "name": "traffic poles"}, {"id": 68890, "name": "traffic post"}, {"id": 68891, "name": "traffic safety sign"}, {"id": 68892, "name": "traffic sawhorse"}, {"id": 68893, "name": "traffic semaphore"}, {"id": 68894, "name": "traffic sigal"}, {"id": 68895, "name": "traffic sign"}, {"id": 68896, "name": "traffic signal"}, {"id": 68897, "name": "traffic signal hangs"}, {"id": 68898, "name": "traffic signal light"}, {"id": 68899, "name": "traffic signals"}, {"id": 68900, "name": "traffic signs"}, {"id": 68901, "name": "traffic stop"}, {"id": 68902, "name": "traffic stop light"}, {"id": 68903, "name": "traffic tape"}, {"id": 68904, "name": "traffic tower"}, {"id": 68905, "name": "traffic vehicles"}, {"id": 68906, "name": "traffic zone"}, {"id": 68907, "name": "traffic"}, {"id": 68908, "name": "trafficbarrier"}, {"id": 68909, "name": "trafficcamera"}, {"id": 68910, "name": "trafficcone"}, {"id": 68911, "name": "trafficcones"}, {"id": 68912, "name": "traffice signal"}, {"id": 68913, "name": "trafficlight"}, {"id": 68914, "name": "trafficlights"}, {"id": 68915, "name": "trafficline"}, {"id": 68916, "name": "trafficsign"}, {"id": 68917, "name": 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{"id": 68994, "name": "train doorway"}, {"id": 68995, "name": "train driver"}, {"id": 68996, "name": "train edge"}, {"id": 68997, "name": "train end"}, {"id": 68998, "name": "train engine"}, {"id": 68999, "name": "train engine car"}, {"id": 69000, "name": "train engineer"}, {"id": 69001, "name": "train entrance"}, {"id": 69002, "name": "train face"}, {"id": 69003, "name": "train flooring"}, {"id": 69004, "name": "train front"}, {"id": 69005, "name": "train front wheels"}, {"id": 69006, "name": "train gate"}, {"id": 69007, "name": "train going across"}, {"id": 69008, "name": "train guides"}, {"id": 69009, "name": "train handle"}, {"id": 69010, "name": "train has a light"}, {"id": 69011, "name": "train has headlights"}, {"id": 69012, "name": "train has number"}, {"id": 69013, "name": "train has stack"}, {"id": 69014, "name": "train has wheel"}, {"id": 69015, "name": "train head"}, {"id": 69016, "name": "train head light"}, {"id": 69017, "name": "train headlights"}, {"id": 69018, "name": 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{"id": 69197, "name": "transport bus"}, {"id": 69198, "name": "transport car"}, {"id": 69199, "name": "transport cars"}, {"id": 69200, "name": "transport ramp"}, {"id": 69201, "name": "transport terminal"}, {"id": 69202, "name": "transport trailer"}, {"id": 69203, "name": "transport truck"}, {"id": 69204, "name": "transport vehicle"}, {"id": 69205, "name": "transportation"}, {"id": 69206, "name": "transportation bus"}, {"id": 69207, "name": "transportation seats"}, {"id": 69208, "name": "transportation station"}, {"id": 69209, "name": "transporter"}, {"id": 69210, "name": "transporteselcome"}, {"id": 69211, "name": "transulcent"}, {"id": 69212, "name": "tranz"}, {"id": 69213, "name": "trap"}, {"id": 69214, "name": "trapeze"}, {"id": 69215, "name": "tras can"}, {"id": 69216, "name": "trash"}, {"id": 69217, "name": "trash background"}, {"id": 69218, "name": "trash bag"}, {"id": 69219, "name": "trash bags"}, {"id": 69220, "name": "trash barrel"}, {"id": 69221, "name": "trash basket"}, {"id": 69222, "name": "trash bin"}, {"id": 69223, "name": "trash bins"}, {"id": 69224, "name": "trash bucket"}, {"id": 69225, "name": "trash ca"}, {"id": 69226, "name": "trash can"}, {"id": 69227, "name": "trash can is blue"}, {"id": 69228, "name": "trash can is grey"}, {"id": 69229, "name": "trash can lid"}, {"id": 69230, "name": "trash can on sand"}, {"id": 69231, "name": "trash cans"}, {"id": 69232, "name": "trash cans top"}, {"id": 69233, "name": "trash cn"}, {"id": 69234, "name": "trash container"}, {"id": 69235, "name": "trash dumpster"}, {"id": 69236, "name": "trash fence"}, {"id": 69237, "name": "trash in the grass"}, {"id": 69238, "name": "trash liner"}, {"id": 69239, "name": "trash littering"}, {"id": 69240, "name": "trash pack"}, {"id": 69241, "name": "trash pail"}, {"id": 69242, "name": "trash piece"}, {"id": 69243, "name": "trash receptable"}, {"id": 69244, "name": "trash receptables"}, {"id": 69245, "name": "trash receptacle"}, {"id": 69246, "name": "trash receptical"}, {"id": 69247, "name": "trash sign"}, {"id": 69248, "name": "trashbag"}, {"id": 69249, "name": "trashbags"}, {"id": 69250, "name": "trashbiln"}, {"id": 69251, "name": "trashbin"}, {"id": 69252, "name": "trashcan"}, {"id": 69253, "name": "trashcan lid"}, {"id": 69254, "name": "trashcan with graffi"}, {"id": 69255, "name": "trashcans"}, {"id": 69256, "name": "trask"}, {"id": 69257, "name": "trasn can"}, {"id": 69258, "name": "trauf"}, {"id": 69259, "name": "travel"}, {"id": 69260, "name": "travel bag"}, {"id": 69261, "name": "travel case"}, {"id": 69262, "name": "travel case handle"}, {"id": 69263, "name": "travel gear"}, {"id": 69264, "name": "travel items"}, {"id": 69265, "name": "travel marquee"}, {"id": 69266, "name": "travel mug"}, {"id": 69267, "name": "travel pack"}, {"id": 69268, "name": "travel schedule"}, {"id": 69269, "name": "travel system"}, {"id": 69270, "name": "travel tags"}, {"id": 69271, "name": "traveler"}, {"id": 69272, "name": "traveling"}, {"id": 69273, "name": "traveling suitcase"}, {"id": 69274, "name": "traveller"}, {"id": 69275, "name": "travelling"}, {"id": 69276, "name": "tray boat"}, {"id": 69277, "name": "tray cart"}, {"id": 69278, "name": "tray contents"}, {"id": 69279, "name": "tray donuts"}, {"id": 69280, "name": "tray edge"}, {"id": 69281, "name": "tray full"}, {"id": 69282, "name": "tray holder"}, {"id": 69283, "name": "tray is white"}, {"id": 69284, "name": "tray molding"}, {"id": 69285, "name": "tray part"}, {"id": 69286, "name": "tray rack"}, {"id": 69287, "name": "tray stand"}, {"id": 69288, "name": "tray table"}, {"id": 69289, "name": "tray"}, {"id": 69290, "name": "trazos"}, {"id": 69291, "name": "trcks"}, {"id": 69292, "name": "tre"}, {"id": 69293, "name": "tread line"}, {"id": 69294, "name": "tread marks"}, {"id": 69295, "name": "tread pattern"}, {"id": 69296, "name": "tread"}, {"id": 69297, "name": "treadmill"}, {"id": 69298, "name": "treas"}, {"id": 69299, "name": "treat"}, {"id": 69300, "name": "treatment"}, {"id": 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"tree lawn"}, {"id": 69377, "name": "tree leaf"}, {"id": 69378, "name": "tree leaves"}, {"id": 69379, "name": "tree leaves hanging"}, {"id": 69380, "name": "tree level"}, {"id": 69381, "name": "tree lights"}, {"id": 69382, "name": "tree like growths"}, {"id": 69383, "name": "tree limb"}, {"id": 69384, "name": "tree limb hanging"}, {"id": 69385, "name": "tree limb shadows"}, {"id": 69386, "name": "tree limbs"}, {"id": 69387, "name": "tree line"}, {"id": 69388, "name": "tree lined"}, {"id": 69389, "name": "tree lines"}, {"id": 69390, "name": "tree lines road"}, {"id": 69391, "name": "tree log"}, {"id": 69392, "name": "tree logging"}, {"id": 69393, "name": "tree logs"}, {"id": 69394, "name": "tree motorcycle"}, {"id": 69395, "name": "tree near"}, {"id": 69396, "name": "tree near building"}, {"id": 69397, "name": "tree needles"}, {"id": 69398, "name": "tree next to"}, {"id": 69399, "name": "tree next to zebra"}, {"id": 69400, "name": "tree on edge"}, {"id": 69401, "name": "tree on right"}, 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77471, "name": "yellow spine"}, {"id": 77472, "name": "yellow spongie"}, {"id": 77473, "name": "yellow spot"}, {"id": 77474, "name": "yellow spots"}, {"id": 77475, "name": "yellow sprinkle"}, {"id": 77476, "name": "yellow square"}, {"id": 77477, "name": "yellow squash"}, {"id": 77478, "name": "yellow stamens"}, {"id": 77479, "name": "yellow star"}, {"id": 77480, "name": "yellow stars"}, {"id": 77481, "name": "yellow steam"}, {"id": 77482, "name": "yellow stems"}, {"id": 77483, "name": "yellow step"}, {"id": 77484, "name": "yellow steps"}, {"id": 77485, "name": "yellow stick"}, {"id": 77486, "name": "yellow sticker"}, {"id": 77487, "name": "yellow stickie"}, {"id": 77488, "name": "yellow sticky"}, {"id": 77489, "name": "yellow stool"}, {"id": 77490, "name": "yellow strand"}, {"id": 77491, "name": "yellow strap"}, {"id": 77492, "name": "yellow straps"}, {"id": 77493, "name": "yellow straw"}, {"id": 77494, "name": "yellow streak"}, {"id": 77495, "name": "yellow street sign"}, {"id": 77496, "name": "yellow string"}, {"id": 77497, "name": "yellow strip"}, {"id": 77498, "name": "yellow stripe"}, {"id": 77499, "name": "yellow stripes"}, {"id": 77500, "name": "yellow stuff"}, {"id": 77501, "name": "yellow submarine"}, {"id": 77502, "name": "yellow sufboard"}, {"id": 77503, "name": "yellow suit"}, {"id": 77504, "name": "yellow suitcase"}, {"id": 77505, "name": "yellow sun"}, {"id": 77506, "name": "yellow surfboard"}, {"id": 77507, "name": "yellow sweater"}, {"id": 77508, "name": "yellow symbol"}, {"id": 77509, "name": "yellow table"}, {"id": 77510, "name": "yellow tablecloth"}, {"id": 77511, "name": "yellow tag"}, {"id": 77512, "name": "yellow tags"}, {"id": 77513, "name": "yellow tail"}, {"id": 77514, "name": "yellow tank"}, {"id": 77515, "name": "yellow tank top"}, {"id": 77516, "name": "yellow tape"}, {"id": 77517, "name": "yellow tape seen"}, {"id": 77518, "name": "yellow tapes"}, {"id": 77519, "name": "yellow tarp"}, {"id": 77520, "name": "yellow tassle"}, {"id": 77521, "name": "yellow tassles"}, {"id": 77522, "name": "yellow taxi"}, {"id": 77523, "name": "yellow taxis"}, {"id": 77524, "name": "yellow teletubby"}, {"id": 77525, "name": "yellow tennis ball"}, {"id": 77526, "name": "yellow tent"}, {"id": 77527, "name": "yellow text"}, {"id": 77528, "name": "yellow tie"}, {"id": 77529, "name": "yellow tile"}, {"id": 77530, "name": "yellow tiled"}, {"id": 77531, "name": "yellow tiles"}, {"id": 77532, "name": "yellow tip"}, {"id": 77533, "name": "yellow tips"}, {"id": 77534, "name": "yellow tomato"}, {"id": 77535, "name": "yellow tooth"}, {"id": 77536, "name": "yellow top"}, {"id": 77537, "name": "yellow top floor"}, {"id": 77538, "name": "yellow toppings"}, {"id": 77539, "name": "yellow towel"}, {"id": 77540, "name": "yellow traffic"}, {"id": 77541, "name": "yellow train"}, {"id": 77542, "name": "yellow train cab"}, {"id": 77543, "name": "yellow trash"}, {"id": 77544, "name": "yellow tray"}, {"id": 77545, "name": "yellow tree"}, {"id": 77546, "name": "yellow trees"}, {"id": 77547, "name": "yellow triangle"}, {"id": 77548, "name": "yellow trim"}, {"id": 77549, "name": "yellow trimming"}, {"id": 77550, "name": "yellow truck"}, {"id": 77551, "name": "yellow trucks"}, {"id": 77552, "name": "yellow tshirt"}, {"id": 77553, "name": "yellow tub"}, {"id": 77554, "name": "yellow tubes"}, {"id": 77555, "name": "yellow umbrella"}, {"id": 77556, "name": "yellow umbrella on"}, {"id": 77557, "name": "yellow umbrellas"}, {"id": 77558, "name": "yellow valance"}, {"id": 77559, "name": "yellow van"}, {"id": 77560, "name": "yellow vane"}, {"id": 77561, "name": "yellow vase"}, {"id": 77562, "name": "yellow vegetables"}, {"id": 77563, "name": "yellow veggies"}, {"id": 77564, "name": "yellow vehicle"}, {"id": 77565, "name": "yellow vein"}, {"id": 77566, "name": "yellow vest"}, {"id": 77567, "name": "yellow visor"}, {"id": 77568, "name": "yellow wall"}, {"id": 77569, "name": "yellow walls"}, {"id": 77570, "name": "yellow wedge"}, {"id": 77571, "name": "yellow weeds"}, {"id": 77572, "name": "yellow wheel"}, {"id": 77573, "name": "yellow wheels"}, {"id": 77574, "name": "yellow white"}, {"id": 77575, "name": "yellow wing"}, {"id": 77576, "name": "yellow wings"}, {"id": 77577, "name": "yellow wire"}, {"id": 77578, "name": "yellow wires"}, {"id": 77579, "name": "yellow word"}, {"id": 77580, "name": "yellow wording"}, {"id": 77581, "name": "yellow words"}, {"id": 77582, "name": "yellow wrapper"}, {"id": 77583, "name": "yellow writing"}, {"id": 77584, "name": "yellow x"}, {"id": 77585, "name": "yellow yolk"}, {"id": 77586, "name": "yellow zucchini"}, {"id": 77587, "name": "yellow"}, {"id": 77588, "name": "yellowballs"}, {"id": 77589, "name": "yellowbears hand"}, {"id": 77590, "name": "yellowblack bike"}, {"id": 77591, "name": "yellowblack lines"}, {"id": 77592, "name": "yellowblack signs"}, {"id": 77593, "name": "yellowblackred sign"}, {"id": 77594, "name": "yellowbrick paving"}, {"id": 77595, "name": "yellowbuilding"}, {"id": 77596, "name": "yellowcar"}, {"id": 77597, "name": "yellowcaution tape"}, {"id": 77598, "name": "yellowclouds part"}, {"id": 77599, "name": "yellowcorn bits"}, {"id": 77600, "name": "yelloweye"}, {"id": 77601, "name": "yellowflorescent vest"}, {"id": 77602, "name": "yellowflowers"}, {"id": 77603, "name": "yellowgray pole"}, {"id": 77604, "name": "yellowgreen grass"}, {"id": 77605, "name": "yellowgreen sign"}, {"id": 77606, "name": "yellowhandle bar"}, {"id": 77607, "name": "yellowish"}, {"id": 77608, "name": "yellowish bloom"}, {"id": 77609, "name": "yellowish eye"}, {"id": 77610, "name": "yellowish green bike"}, {"id": 77611, "name": "yellowish growth"}, {"id": 77612, "name": "yellowline"}, {"id": 77613, "name": "yellowlines"}, {"id": 77614, "name": "yellowmailbox"}, {"id": 77615, "name": "yellowmetal bar"}, {"id": 77616, "name": "yellowobject"}, {"id": 77617, "name": "yelloworange building"}, {"id": 77618, "name": "yellowpaint roller"}, {"id": 77619, "name": "yellowpainted line"}, {"id": 77620, "name": "yellowpaper tablet"}, {"id": 77621, "name": "yellowpoles"}, {"id": 77622, "name": "yellowrain coat"}, {"id": 77623, "name": "yellowred breadcrumb"}, {"id": 77624, "name": "yellowred cup"}, {"id": 77625, "name": "yellowred plane"}, {"id": 77626, "name": "yellowred vehicle"}, {"id": 77627, "name": "yellowredneon tsign"}, {"id": 77628, "name": "yellowripe bananas"}, {"id": 77629, "name": "yellowshirt"}, {"id": 77630, "name": "yellowstrip"}, {"id": 77631, "name": "yellowuniform shirt"}, {"id": 77632, "name": "yellowvests"}, {"id": 77633, "name": "yellowwheel"}, {"id": 77634, "name": "yellowwhite blue"}, {"id": 77635, "name": "yellowwhite lines"}, {"id": 77636, "name": "yellowwood flooring"}, {"id": 77637, "name": "yellowy substance"}, {"id": 77638, "name": "yelow canopy"}, {"id": 77639, "name": "yelow plane"}, {"id": 77640, "name": "yes"}, {"id": 77641, "name": "yes and no words"}, {"id": 77642, "name": "yet"}, {"id": 77643, "name": "yeti"}, {"id": 77644, "name": "yield"}, {"id": 77645, "name": "yield light"}, {"id": 77646, "name": "yield sign"}, {"id": 77647, "name": "yin yang"}, {"id": 77648, "name": "ying yang"}, {"id": 77649, "name": "ying yang symbol"}, {"id": 77650, "name": "yocconvo"}, {"id": 77651, "name": "yoda showcase"}, {"id": 77652, "name": "yoga"}, {"id": 77653, "name": "yoga mat"}, {"id": 77654, "name": "yoga pants"}, {"id": 77655, "name": "yoghurt"}, {"id": 77656, "name": "yogurt"}, {"id": 77657, "name": "yogurt carton"}, {"id": 77658, "name": "yogurt container"}, {"id": 77659, "name": "yogurt cup"}, {"id": 77660, "name": "yoke"}, {"id": 77661, "name": "yolk up"}, {"id": 77662, "name": "yolk"}, {"id": 77663, "name": "yonex"}, {"id": 77664, "name": "yorghut can"}, {"id": 77665, "name": "york"}, {"id": 77666, "name": "yorkie"}, {"id": 77667, "name": "yosemite shuttle"}, {"id": 77668, "name": "you"}, {"id": 77669, "name": "yougurt"}, {"id": 77670, "name": "young"}, {"id": 77671, "name": "young adult"}, {"id": 77672, "name": "young adults"}, {"id": 77673, "name": "young boy and girl"}, {"id": 77674, "name": "young boy"}, {"id": 77675, "name": "young catcher"}, {"id": 77676, "name": "young child"}, {"id": 77677, "name": "young children"}, {"id": 77678, "name": "young couple"}, {"id": 77679, "name": "young elephant"}, {"id": 77680, "name": "young elephants"}, {"id": 77681, "name": "young focused kid"}, {"id": 77682, "name": "young giraffe"}, {"id": 77683, "name": "young girl"}, {"id": 77684, "name": "young girl is skiing"}, {"id": 77685, "name": "young guy"}, {"id": 77686, "name": "young kid"}, {"id": 77687, "name": "young lady"}, {"id": 77688, "name": "young lady playing"}, {"id": 77689, "name": "young man"}, {"id": 77690, "name": "young mans face"}, {"id": 77691, "name": "young men"}, {"id": 77692, "name": "young people"}, {"id": 77693, "name": "young person"}, {"id": 77694, "name": "young player"}, {"id": 77695, "name": "young trees"}, {"id": 77696, "name": "young woman"}, {"id": 77697, "name": "young womans face"}, {"id": 77698, "name": "young women"}, {"id": 77699, "name": "young zebra"}, {"id": 77700, "name": "young zebras"}, {"id": 77701, "name": "youngchild ear"}, {"id": 77702, "name": "youngchild eyes"}, {"id": 77703, "name": "younger elephants"}, {"id": 77704, "name": "younger sheep"}, {"id": 77705, "name": "younger trees"}, {"id": 77706, "name": "younggirl"}, {"id": 77707, "name": "youngman"}, {"id": 77708, "name": "youngmans hat"}, {"id": 77709, "name": "youngster"}, {"id": 77710, "name": "your"}, {"id": 77711, "name": "your luggage"}, {"id": 77712, "name": "youre almost there"}, {"id": 77713, "name": "youth"}, {"id": 77714, "name": "youtube"}, {"id": 77715, "name": "yoyo"}, {"id": 77716, "name": "yshape"}, {"id": 77717, "name": "ytww"}, {"id": 77718, "name": "yuca"}, {"id": 77719, "name": "yuca root"}, {"id": 77720, "name": "yucas"}, {"id": 77721, "name": "yucca"}, {"id": 77722, "name": "yuento"}, {"id": 77723, "name": "yukon"}, {"id": 77724, "name": "yuma"}, {"id": 77725, "name": "yummy"}, {"id": 77726, "name": "yunker"}, {"id": 77727, "name": "yurt"}, {"id": 77728, "name": "yw"}, {"id": 77729, "name": "ywca"}, {"id": 77730, "name": "z"}, {"id": 77731, "name": "z design"}, {"id": 77732, "name": "z key"}, {"id": 77733, "name": "z161 ed"}, {"id": 77734, "name": "zaffiro sign"}, {"id": 77735, "name": "zambrano"}, {"id": 77736, "name": "zander"}, {"id": 77737, "name": "zane"}, {"id": 77738, "name": "zapper"}, {"id": 77739, "name": "zdm3"}, {"id": 77740, "name": "zealand"}, {"id": 77741, "name": "zebra in a pen"}, {"id": 77742, "name": "zebra area"}, {"id": 77743, "name": "zebra back"}, {"id": 77744, "name": "zebra belly"}, {"id": 77745, "name": "zebra body"}, {"id": 77746, "name": "zebra branch"}, {"id": 77747, "name": "zebra butt"}, {"id": 77748, "name": "zebra butts"}, {"id": 77749, "name": "zebra cage"}, {"id": 77750, "name": "zebra camera"}, {"id": 77751, "name": "zebra chin"}, {"id": 77752, "name": "zebra coat"}, {"id": 77753, "name": "zebra coats"}, {"id": 77754, "name": "zebra color"}, {"id": 77755, "name": "zebra cookie"}, {"id": 77756, "name": "zebra crossing"}, {"id": 77757, "name": "zebra ear"}, {"id": 77758, "name": "zebra ears"}, {"id": 77759, "name": "zebra eating"}, {"id": 77760, "name": "zebra eating grass"}, {"id": 77761, "name": "zebra enclosure"}, {"id": 77762, "name": "zebra eye"}, {"id": 77763, "name": "zebra eyes"}, {"id": 77764, "name": "zebra face"}, {"id": 77765, "name": "zebra feeding"}, {"id": 77766, "name": "zebra field"}, {"id": 77767, "name": "zebra food"}, {"id": 77768, "name": "zebra fur"}, {"id": 77769, "name": "zebra grass"}, {"id": 77770, "name": "zebra grazing"}, {"id": 77771, "name": "zebra group"}, {"id": 77772, "name": "zebra habitat"}, {"id": 77773, "name": "zebra hair"}, {"id": 77774, "name": "zebra hay"}, {"id": 77775, "name": "zebra head"}, {"id": 77776, "name": "zebra herd"}, {"id": 77777, "name": "zebra hind leg"}, {"id": 77778, "name": "zebra hoof"}, {"id": 77779, "name": "zebra hooves"}, {"id": 77780, "name": "zebra hoves"}, {"id": 77781, "name": "zebra image"}, {"id": 77782, "name": "zebra inside ear"}, {"id": 77783, "name": "zebra is bent over"}, {"id": 77784, "name": "zebra leg"}, {"id": 77785, "name": "zebra legs"}, {"id": 77786, "name": "zebra looking"}, {"id": 77787, "name": "zebra mane"}, {"id": 77788, "name": "zebra mohawk"}, {"id": 77789, "name": "zebra mouth"}, {"id": 77790, "name": "zebra muzzle"}, {"id": 77791, "name": "zebra neck"}, {"id": 77792, "name": "zebra nose"}, {"id": 77793, "name": "zebra paint"}, {"id": 77794, "name": "zebra painting"}, {"id": 77795, "name": "zebra part"}, {"id": 77796, "name": "zebra pattern"}, {"id": 77797, "name": "zebra pen"}, {"id": 77798, "name": "zebra penn"}, {"id": 77799, "name": "zebra pride"}, {"id": 77800, "name": "zebra print"}, {"id": 77801, "name": "zebra road"}, {"id": 77802, "name": "zebra rump"}, {"id": 77803, "name": "zebra shadow"}, {"id": 77804, "name": "zebra snout"}, {"id": 77805, "name": "zebra spot"}, {"id": 77806, "name": "zebra statue"}, {"id": 77807, "name": "zebra stomach"}, {"id": 77808, "name": "zebra stripe"}, {"id": 77809, "name": "zebra stripes"}, {"id": 77810, "name": "zebra tail"}, {"id": 77811, "name": "zebra taking"}, {"id": 77812, "name": "zebra tooth"}, {"id": 77813, "name": "zebra torso"}, {"id": 77814, "name": "zebra utters"}, {"id": 77815, "name": "zebra vegetation"}, {"id": 77816, "name": "zebra"}, {"id": 77817, "name": "zebraears"}, {"id": 77818, "name": "zebrarear"}, {"id": 77819, "name": "zebras are eating"}, {"id": 77820, "name": "zebras are playing"}, {"id": 77821, "name": "zebras are standing"}, {"id": 77822, "name": "zebras are striped"}, {"id": 77823, "name": "zebras back"}, {"id": 77824, "name": "zebras behind"}, {"id": 77825, "name": "zebras belly"}, {"id": 77826, "name": "zebras body"}, {"id": 77827, "name": "zebras butt"}, {"id": 77828, "name": "zebras chest"}, {"id": 77829, "name": "zebras ear"}, {"id": 77830, "name": "zebras ears"}, {"id": 77831, "name": "zebras eye"}, {"id": 77832, "name": "zebras eyelash"}, {"id": 77833, "name": "zebras eyes"}, {"id": 77834, "name": "zebras face"}, {"id": 77835, "name": "zebras feet"}, {"id": 77836, "name": "zebras field"}, {"id": 77837, "name": "zebras foot"}, {"id": 77838, "name": "zebras fur"}, {"id": 77839, "name": "zebras grass"}, {"id": 77840, "name": "zebras hair"}, {"id": 77841, "name": "zebras head"}, {"id": 77842, "name": "zebras hoof"}, {"id": 77843, "name": "zebras hooves"}, {"id": 77844, "name": "zebras jaw"}, {"id": 77845, "name": "zebras leg"}, {"id": 77846, "name": "zebras legs"}, {"id": 77847, "name": "zebras mane"}, {"id": 77848, "name": "zebras mouth"}, {"id": 77849, "name": "zebras neck"}, {"id": 77850, "name": "zebras nose"}, {"id": 77851, "name": "zebras noses"}, {"id": 77852, "name": "zebras on the sand"}, {"id": 77853, "name": "zebras pattern"}, {"id": 77854, "name": "zebras pen"}, {"id": 77855, "name": "zebras running"}, {"id": 77856, "name": "zebras shadow"}, {"id": 77857, "name": "zebras side"}, {"id": 77858, "name": "zebras standing"}, {"id": 77859, "name": "zebras stomach"}, {"id": 77860, "name": "zebras stripes"}, {"id": 77861, "name": "zebras tail"}, {"id": 77862, "name": "zebras thigh"}, {"id": 77863, "name": "zebras together"}, {"id": 77864, "name": "zebras torso"}, {"id": 77865, "name": "zebras water"}, {"id": 77866, "name": "zebrashead"}, {"id": 77867, "name": "zebraswhite underbelly"}, {"id": 77868, "name": "zeebra"}, {"id": 77869, "name": "zeldas"}, {"id": 77870, "name": "zen"}, {"id": 77871, "name": "zephyr express"}, {"id": 77872, "name": "zephyrhills"}, {"id": 77873, "name": "zeppelin"}, {"id": 77874, "name": "zerbra"}, {"id": 77875, "name": "zero and two"}, {"id": 77876, "name": "zero button"}, {"id": 77877, "name": "zero key"}, {"id": 77878, "name": "zero"}, {"id": 77879, "name": "zeros europe"}, {"id": 77880, "name": "zest"}, {"id": 77881, "name": "zezbra"}, {"id": 77882, "name": "zig zag"}, {"id": 77883, "name": "zig zag line"}, {"id": 77884, "name": "zig zags"}, {"id": 77885, "name": "zig zags on"}, {"id": 77886, "name": "zigg zagg"}, {"id": 77887, "name": "zigzag lines"}, {"id": 77888, "name": "zigzag pattern"}, {"id": 77889, "name": "zigzag string"}, {"id": 77890, "name": "zigzag"}, {"id": 77891, "name": "zimmerman"}, {"id": 77892, "name": "zip bag"}, {"id": 77893, "name": "zip binder"}, {"id": 77894, "name": "zip tabs"}, {"id": 77895, "name": "zip tie"}, {"id": 77896, "name": "zip ties"}, {"id": 77897, "name": "zip up"}, {"id": 77898, "name": "zip up sweater"}, {"id": 77899, "name": "zip"}, {"id": 77900, "name": "ziploc bag"}, {"id": 77901, "name": "ziploc box"}, {"id": 77902, "name": "ziplock bag"}, {"id": 77903, "name": "zipped"}, {"id": 77904, "name": "zipped up"}, {"id": 77905, "name": "zipper closure"}, {"id": 77906, "name": "zipper cover"}, {"id": 77907, "name": "zipper end"}, {"id": 77908, "name": "zipper handle"}, {"id": 77909, "name": "zipper handles"}, {"id": 77910, "name": "zipper on luggage"}, {"id": 77911, "name": "zipper pocket"}, {"id": 77912, "name": "zipper pull"}, {"id": 77913, "name": "zipper pulls"}, {"id": 77914, "name": "zipper string"}, {"id": 77915, "name": "zipper tag"}, {"id": 77916, "name": "zipper teeth"}, {"id": 77917, "name": "zipper"}, {"id": 77918, "name": "zippered bag"}, {"id": 77919, "name": "zippered pocket"}, {"id": 77920, "name": "zit"}, {"id": 77921, "name": "ziti"}, {"id": 77922, "name": "zline"}, {"id": 77923, "name": "zodiac"}, {"id": 77924, "name": "zodiac animals"}, {"id": 77925, "name": "zodiac chart"}, {"id": 77926, "name": "zodiac clock"}, {"id": 77927, "name": "zodiac crab"}, {"id": 77928, "name": "zodiac face"}, {"id": 77929, "name": "zodiac sign"}, {"id": 77930, "name": "zodiac signs"}, {"id": 77931, "name": "zodiac symbols"}, {"id": 77932, "name": "zombie man"}, {"id": 77933, "name": "zombie"}, {"id": 77934, "name": "zone"}, {"id": 77935, "name": "zone b"}, {"id": 77936, "name": "zone ends"}, {"id": 77937, "name": "zoo"}, {"id": 77938, "name": "zoo building"}, {"id": 77939, "name": "zoo compound"}, {"id": 77940, "name": "zoo enclosure"}, {"id": 77941, "name": "zoo keeper"}, {"id": 77942, "name": "zoo park"}, {"id": 77943, "name": "zoo patrons"}, {"id": 77944, "name": "zoo pen"}, {"id": 77945, "name": "zoo setting"}, {"id": 77946, "name": "zoo with animals"}, {"id": 77947, "name": "zoogoers"}, {"id": 77948, "name": "zookeeper"}, {"id": 77949, "name": "zoom"}, {"id": 77950, "name": "zoom lens"}, {"id": 77951, "name": "zoomphotoca"}, {"id": 77952, "name": "zuccchini"}, {"id": 77953, "name": "zucchini and tomato"}, {"id": 77954, "name": "zucchini piece"}, {"id": 77955, "name": "zucchini seeds"}, {"id": 77956, "name": "zucchini slice"}, {"id": 77957, "name": "zucchini spear"}, {"id": 77958, "name": "zucchini sticks"}, {"id": 77959, "name": "zucchini"}, {"id": 77960, "name": "zuccini"}, {"id": 77961, "name": "zuchini"}, {"id": 77962, "name": "zuchinni"}] + +VISUALGENOME_77962MINUS150_CATEGORIES = VISUALGENOME_77962_CATEGORIES + +VISUALGENOME_77962MINUS2319_CATEGORIES = VISUALGENOME_77962_CATEGORIES + +# fmt: on diff --git a/ape/data/detection_utils.py b/ape/data/detection_utils.py new file mode 100644 index 0000000..ad4c5f1 --- /dev/null +++ b/ape/data/detection_utils.py @@ -0,0 +1,233 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + +""" +Common data processing utilities that are used in a +typical object detection data pipeline. +""" +import json +import logging +import os +from typing import List, Union + +import numpy as np +import pycocotools.mask as mask_util +import torch + +from detectron2.data import transforms as T +from detectron2.data.catalog import MetadataCatalog +from detectron2.data.detection_utils import build_augmentation as build_augmentation_d2 +from detectron2.data.detection_utils import check_metadata_consistency + +from .transforms import AutoAugment, LargeScaleJitter + +__all__ = [ + "build_augmentation", +] + + +def load_fed_loss_cls_weights(class_freq_path: str, freq_weight_power=1.0): + logger = logging.getLogger(__name__) + logger.info("Loading " + class_freq_path) + assert os.path.exists(class_freq_path) + + class_info = json.load(open(class_freq_path, "r")) + class_freq = torch.tensor([c["image_count"] for c in sorted(class_info, key=lambda x: x["id"])]) + + class_freq_weight = class_freq.float() ** freq_weight_power + return class_freq_weight + + +def get_fed_loss_cls_weights(dataset_names: Union[str, List[str]], freq_weight_power=1.0): + """ + Get frequency weight for each class sorted by class id. + We now calcualte freqency weight using image_count to the power freq_weight_power. + + Args: + dataset_names: list of dataset names + freq_weight_power: power value + """ + if isinstance(dataset_names, str): + dataset_names = [dataset_names] + + logger = logging.getLogger(__name__) + class_freq_path = MetadataCatalog.get(dataset_names[0]).json_file[:-5] + "_cat_info.json" + if os.path.exists(class_freq_path): + logger.info( + "Search outside metadata 'image_count' for dataset '{}' from '{}'".format( + dataset_names[0], class_freq_path + ) + ) + return load_fed_loss_cls_weights(class_freq_path, freq_weight_power) + logger.info("Using builtin metadata 'image_count' for dataset '{}'".format(dataset_names)) + + check_metadata_consistency("class_image_count", dataset_names) + + meta = MetadataCatalog.get(dataset_names[0]) + class_freq_meta = meta.class_image_count + class_freq = torch.tensor( + [c["image_count"] for c in sorted(class_freq_meta, key=lambda x: x["id"])] + ) + class_freq_weight = class_freq.float() ** freq_weight_power + return class_freq_weight + + +def get_fed_loss_cls_weights_v2(dataset_names: Union[str, List[str]], freq_weight_power=1.0): + """ + Get frequency weight for each class sorted by class id. + We now calcualte freqency weight using image_count to the power freq_weight_power. + + Args: + dataset_names: list of dataset names + freq_weight_power: power value + """ + if isinstance(dataset_names, str): + dataset_names = [dataset_names] + + logger = logging.getLogger(__name__) + + class_freq_weight_list = [] + for dataset_name in dataset_names: + if MetadataCatalog.get(dataset_name).get("json_file") is None: + continue + class_freq_path = MetadataCatalog.get(dataset_name).json_file[:-5] + "_cat_info.json" + if os.path.exists(class_freq_path): + logger.info( + "Search outside metadata 'image_count' for dataset '{}' from '{}'".format( + dataset_name, class_freq_path + ) + ) + # return load_fed_loss_cls_weights(class_freq_path, freq_weight_power) + class_freq_weight_list.append( + load_fed_loss_cls_weights(class_freq_path, freq_weight_power) + ) + continue + else: + logger.info( + "Nofind outside metadata 'image_count' for dataset '{}' from '{}'".format( + dataset_name, class_freq_path + ) + ) + + logger.info("Using builtin metadata 'image_count' for dataset '{}'".format(dataset_name)) + + # check_metadata_consistency("class_image_count", dataset_names) + + meta = MetadataCatalog.get(dataset_name) + class_freq_meta = meta.class_image_count + class_freq = torch.tensor( + [c["image_count"] for c in sorted(class_freq_meta, key=lambda x: x["id"])] + ) + class_freq_weight = class_freq.float() ** freq_weight_power + # return class_freq_weight + class_freq_weight_list.append(class_freq_weight) + + return class_freq_weight_list[0] if len(class_freq_weight_list) == 1 else class_freq_weight_list + + +def build_augmentation(cfg, is_train): + """ + Create a list of default :class:`Augmentation` from config. + Now it includes resizing and flipping. + + Returns: + list[Augmentation] + """ + assert not (cfg.INPUT.AUTOAUGMENT.ENABLED and cfg.INPUT.LSJ.ENABLED) + + augmentation = [] + if is_train and cfg.INPUT.AUTOAUGMENT.ENABLED: + augmentation.append(AutoAugment(cfg)) + + if cfg.INPUT.RANDOM_FLIP != "none": + augmentation.append( + T.RandomFlip( + horizontal=cfg.INPUT.RANDOM_FLIP == "horizontal", + vertical=cfg.INPUT.RANDOM_FLIP == "vertical", + ) + ) + if cfg.INPUT.RANDOM_COLOR.ENABLED: + augmentation.append(T.RandomBrightness(0.5, 1.5)) + augmentation.append(T.RandomContrast(0.5, 1.5)) + augmentation.append(T.RandomSaturation(0.0, 2.0)) + return augmentation + + if is_train and cfg.INPUT.LSJ.ENABLED: + augmentation.append(LargeScaleJitter(cfg)) + + if cfg.INPUT.RANDOM_FLIP != "none": + augmentation.append( + T.RandomFlip( + horizontal=cfg.INPUT.RANDOM_FLIP == "horizontal", + vertical=cfg.INPUT.RANDOM_FLIP == "vertical", + ) + ) + if cfg.INPUT.RANDOM_COLOR.ENABLED: + augmentation.append(T.RandomBrightness(0.5, 1.5)) + augmentation.append(T.RandomContrast(0.5, 1.5)) + augmentation.append(T.RandomSaturation(0.0, 2.0)) + return augmentation + + return build_augmentation_d2(cfg, is_train) + + +def build_augmentation_lsj(cfg, is_train): + """ + Create a list of default :class:`Augmentation` from config. + Now it includes resizing and flipping. + + Returns: + list[Augmentation] + """ + augmentation = [] + if is_train: + augmentation.append(LargeScaleJitter(cfg)) + + if cfg.INPUT.RANDOM_FLIP != "none": + augmentation.append( + T.RandomFlip( + horizontal=cfg.INPUT.RANDOM_FLIP == "horizontal", + vertical=cfg.INPUT.RANDOM_FLIP == "vertical", + ) + ) + if cfg.INPUT.RANDOM_COLOR.ENABLED: + augmentation.append(T.RandomBrightness(0.5, 1.5)) + augmentation.append(T.RandomContrast(0.5, 1.5)) + augmentation.append(T.RandomSaturation(0.0, 2.0)) + return augmentation + + return build_augmentation_d2(cfg, is_train) + + +def build_augmentation_aa(cfg, is_train): + """ + Create a list of default :class:`Augmentation` from config. + Now it includes resizing and flipping. + + Returns: + list[Augmentation] + """ + augmentation = [] + if is_train: + augmentation.append(AutoAugment(cfg)) + + if cfg.INPUT.RANDOM_FLIP != "none": + augmentation.append( + T.RandomFlip( + horizontal=cfg.INPUT.RANDOM_FLIP == "horizontal", + vertical=cfg.INPUT.RANDOM_FLIP == "vertical", + ) + ) + if cfg.INPUT.RANDOM_COLOR.ENABLED: + augmentation.append(T.RandomBrightness(0.5, 1.5)) + augmentation.append(T.RandomContrast(0.5, 1.5)) + augmentation.append(T.RandomSaturation(0.0, 2.0)) + return augmentation + + return build_augmentation_d2(cfg, is_train) + + +build_transform_gen = build_augmentation +""" +Alias for backward-compatibility. +""" diff --git a/ape/data/mapper_utils.py b/ape/data/mapper_utils.py new file mode 100644 index 0000000..b75ed35 --- /dev/null +++ b/ape/data/mapper_utils.py @@ -0,0 +1,488 @@ +# -*- coding: utf-8 -*- +import copy +import json +import logging +import os +import random +import re + +import cv2 +import numpy as np +import pycocotools.mask as mask_util +import torch +from scipy.ndimage import gaussian_filter + +from detectron2.data import detection_utils as utils +from detectron2.structures import ( + BitMasks, + Boxes, + BoxMode, + Instances, + PolygonMasks, + polygons_to_bitmask, +) +from fvcore.transforms.transform import HFlipTransform + +__all__ = [ + "copypaste", + "maybe_load_annotation_from_file", +] + + +def clean_string(phrase): + # return re.sub(r"([.,'!?\"()*#:;])", "", phrase.lower()).replace("-", " ").replace("/", " ") + + phrase = re.sub(r"([.,'!?\"()*#:;])", "", phrase.lower()).replace("-", " ").replace("/", " ") + phrase = phrase.strip("\n").strip("\r").strip().lstrip(" ").rstrip(" ") + phrase = re.sub(" +", " ", phrase) + + replacements = { + "½": "half", + "—": "-", + "™": "", + "¢": "cent", + "ç": "c", + "û": "u", + "é": "e", + "°": " degree", + "è": "e", + "…": "", + } + for k, v in replacements.items(): + phrase = phrase.replace(k, v) + + return phrase + + +def transform_phrases(phrases, transforms): + # clean + phrases = [clean_string(phrase) for phrase in phrases] + # hflip + for x in transforms: + if isinstance(x, HFlipTransform): + phrases = [ + phrase.replace("left", "@").replace("right", "left").replace("@", "right") + for phrase in phrases + ] + return phrases + + +def transform_expressions(expressions, transforms): + # pick one expression if there are multiple expressions + expression = expressions[np.random.choice(len(expressions))] + expression = clean_string(expression) + # deal with hflip for expression + for x in transforms: + if isinstance(x, HFlipTransform): + expression = ( + expression.replace("left", "@").replace("right", "left").replace("@", "right") + ) + return expression + + +def has_ordinal_num(phrases): + # oridinal numbers + ordinal_nums = [ + "first", + "second", + "third", + "fourth", + "fifth", + "sixth", + "seventh", + "eighth", + "ninth", + "tenth", + ] + + flag = False + for phrase in phrases: + phrase_low = phrase.lower() + for word in ordinal_nums: + if word in phrase_low: + flag = True + break + if flag == True: + break + return flag + + +# from detectron2/utils/visualizer.py +def mask_to_polygons_2(mask): + # cv2.RETR_CCOMP flag retrieves all the contours and arranges them to a 2-level + # hierarchy. External contours (boundary) of the object are placed in hierarchy-1. + # Internal contours (holes) are placed in hierarchy-2. + # cv2.CHAIN_APPROX_NONE flag gets vertices of polygons from contours. + mask = np.ascontiguousarray(mask) # some versions of cv2 does not support incontiguous arr + res = cv2.findContours(mask.astype("uint8"), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + hierarchy = res[-1] + if hierarchy is None: # empty mask + return [], False + has_holes = (hierarchy.reshape(-1, 4)[:, 3] >= 0).sum() > 0 + res = res[-2] + res = [x.flatten() for x in res] + # These coordinates from OpenCV are integers in range [0, W-1 or H-1]. + # We add 0.5 to turn them into real-value coordinate space. A better solution + # would be to first +0.5 and then dilate the returned polygon by 0.5. + res = [x + 0.5 for x in res if len(x) >= 6] + return res, has_holes + + +# from detectron2/utils/visualizer.py +def mask_to_polygons(mask): + # cv2.RETR_CCOMP flag retrieves all the contours and arranges them to a 2-level + # hierarchy. External contours (boundary) of the object are placed in hierarchy-1. + # Internal contours (holes) are placed in hierarchy-2. + # cv2.CHAIN_APPROX_NONE flag gets vertices of polygons from contours. + mask = np.ascontiguousarray(mask) # some versions of cv2 does not support incontiguous arr + res = cv2.findContours(mask.astype("uint8"), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE) + hierarchy = res[-1] + if hierarchy is None: # empty mask + return [], False + has_holes = (hierarchy.reshape(-1, 4)[:, 3] >= 0).sum() > 0 + res = res[-2] + res = [x.flatten() for x in res] + # These coordinates from OpenCV are integers in range [0, W-1 or H-1]. + # We add 0.5 to turn them into real-value coordinate space. A better solution + # would be to first +0.5 and then dilate the returned polygon by 0.5. + res = [x + 0.5 for x in res if len(x) >= 6] + return res, has_holes + + +def close_contour(contour): + if not np.array_equal(contour[0], contour[-1]): + contour = np.vstack((contour, contour[0])) + return contour + + +# from pycococreatortools/pycococreatortools.py +def binary_mask_to_polygon(binary_mask, tolerance=0): + """Converts a binary mask to COCO polygon representation + Args: + binary_mask: a 2D binary numpy array where '1's represent the object + tolerance: Maximum distance from original points of polygon to approximated + polygonal chain. If tolerance is 0, the original coordinate array is returned. + """ "" + polygons = [] + # pad mask to close contours of shapes which start and end at an edge + padded_binary_mask = np.pad(binary_mask, pad_width=1, mode="constant", constant_values=0) + contours = measure.find_contours(padded_binary_mask, 0.5) + contours = np.subtract(contours, 1) + for contour in contours: + contour = close_contour(contour) + contour = measure.approximate_polygon(contour, tolerance) + if len(contour) < 3: + continue + contour = np.flip(contour, axis=1) + segmentation = contour.ravel().tolist() + # after padding and subtracting 1 we may get -0.5 points in our segmentation + segmentation = [0 if i < 0 else i for i in segmentation] + polygons.append(segmentation) + + return polygons + + +def instances_to_annotations(instances, img_id, bbox_mode, instance_mask_format): + num_instance = len(instances) + if num_instance == 0: + return [] + + boxes = instances.gt_boxes.tensor.numpy() + boxes = BoxMode.convert(boxes, BoxMode.XYXY_ABS, bbox_mode) + boxes = boxes.tolist() + classes = instances.gt_classes.tolist() + + if instance_mask_format == "polygon": + segms = [[p.reshape(-1) for p in mask] for mask in instances.gt_masks] + + elif instance_mask_format == "bitmask" and False: + masks = [np.array(mask, dtype=np.uint8) for mask in instances.gt_masks] + + else: + rles = [ + mask_util.encode(np.array(mask[:, :, None], order="F", dtype="uint8"))[0] + for mask in instances.gt_masks + ] + for rle in rles: + # "counts" is an array encoded by mask_util as a byte-stream. Python3's + # json writer which always produces strings cannot serialize a bytestream + # unless you decode it. Thankfully, utf-8 works out (which is also what + # the pycocotools/_mask.pyx does). + rle["counts"] = rle["counts"].decode("utf-8") + + annotations = [] + for k in range(num_instance): + anno = { + "image_id": img_id, + "category_id": classes[k], + "bbox": boxes[k], + "bbox_mode": bbox_mode, + } + if instance_mask_format == "polygon": + anno["segmentation"] = segms[k] + elif instance_mask_format == "bitmask" and False: + anno["segmentation"] = masks[k] + else: + anno["segmentation"] = rles[k] + annotations.append(anno) + + return annotations + + +def copypaste(dataset_dict, dataset_dict_bg, image_format, instance_mask_format): + dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below + # USER: Write your own image loading if it's not from a file + image = utils.read_image(dataset_dict["file_name"], format=image_format) + utils.check_image_size(dataset_dict, image) + + dataset_dict_bg = copy.deepcopy(dataset_dict_bg) # it will be modified by code below + # USER: Write your own image loading if it's not from a file + image_bg = utils.read_image(dataset_dict_bg["file_name"], format=image_format) + utils.check_image_size(dataset_dict_bg, image_bg) + + image_bg = image_bg.copy() + + image_size = image_shape = image.shape[:2] # h, w + image_size_bg = image_shape_bg = image_bg.shape[:2] # h, w + + instances = utils.annotations_to_instances( + # dataset_dict["annotations"], + [obj for obj in dataset_dict["annotations"] if obj.get("iscrowd", 0) == 0], + image_shape, + mask_format=instance_mask_format, + ) + if "annotations" in dataset_dict_bg: + instances_bg = utils.annotations_to_instances( + # dataset_dict_bg["annotations"], + [obj for obj in dataset_dict_bg["annotations"] if obj.get("iscrowd", 0) == 0], + image_shape_bg, + mask_format=instance_mask_format, + ) + else: + instances_bg = None + + if instances_bg is None or len(instances_bg) == 0: + bitmasks_bg = torch.zeros((1, image_size_bg[0], image_size_bg[1])).to(torch.bool) + elif instance_mask_format == "polygon": + bitmasks_bg = [ + polygons_to_bitmask(polygon, *image_size_bg) for polygon in instances_bg.gt_masks + ] + bitmasks_bg = torch.tensor(np.array(bitmasks_bg)) + else: + bitmasks_bg = instances_bg.gt_masks.tensor + + if instance_mask_format == "polygon": + bitmasks = [polygons_to_bitmask(polygon, *image_size) for polygon in instances.gt_masks] + bitmasks = torch.tensor(np.array(bitmasks)) + else: + bitmasks = instances.gt_masks.tensor + + assert bitmasks_bg.dtype == torch.bool, bitmasks_bg.dtype + # foreground_mask = torch.sum(bitmasks_bg, dim=0) + foreground_mask = torch.max(bitmasks_bg, dim=0)[0] + copypaste_mask = torch.zeros_like(foreground_mask) + + if instance_mask_format == "polygon": + mask_areas = instances.gt_masks.area().numpy() + else: + mask_areas = instances.gt_masks.tensor.sum(dim=1).sum(dim=1).numpy() + + instance_list = [] + for i in mask_areas.argsort(): + i = int(i) + + box = instances.gt_boxes[i].tensor.numpy()[0] + x1 = int(box[0]) + y1 = int(box[1]) + x2 = int(box[2]) + y2 = int(box[3]) + + if x1 + 1 > x2 or y1 + 1 > y2: + continue + + image_p = image[y1:y2, x1:x2, :] + bitmasks_p = bitmasks[i, y1:y2, x1:x2] + + h, w = bitmasks_p.shape + + trial = 10 + for _ in range(trial): + if w + 10 >= image_size_bg[1] or h + 10 >= image_size_bg[0]: + break + + x1 = random.randint(0, image_size_bg[1] - w) + y1 = random.randint(0, image_size_bg[0] - h) + x2 = x1 + w + y2 = y1 + h + + bitmask = torch.zeros_like(foreground_mask) + bitmask[y1:y2, x1:x2] = bitmasks_p + + # bitmask = bitmask * (1 - foreground_mask) + bitmask = bitmask & (~foreground_mask) + + if bitmask.sum() < 100: + continue + + instance = Instances(image_size_bg) + instance.gt_classes = instances[i].gt_classes + + # if bitmask.sum() < bitmasks_p.sum(): + bitmasks_p = bitmask[y1:y2, x1:x2] + + if instance_mask_format == "polygon": + mask = [mask_to_polygons(bitmask)[0]] + instance.gt_masks = PolygonMasks(mask) + else: + instance.gt_masks = BitMasks(bitmask.unsqueeze(0)) + + bitmasks_p = bitmasks_p.numpy() + if bitmask.sum() > 128 * 64: + bitmasks_p = gaussian_filter(bitmasks_p.astype(float), sigma=5, truncate=1) + + image_bg_p = image_bg[y1:y2, x1:x2, :] + image_fgbg_p = image_p * bitmasks_p[..., np.newaxis] + image_bg_p * ( + 1 - bitmasks_p[..., np.newaxis] + ) + + image_bg[y1:y2, x1:x2, :] = image_fgbg_p + + foreground_mask = foreground_mask | bitmask + copypaste_mask = copypaste_mask | bitmask + + instance_list.append(instance) + break + + if len(instance_list) > 0: + instances = Instances.cat(instance_list) + instances.gt_boxes = instances.gt_masks.get_bounding_boxes() + + image_id = dataset_dict["image_id"] + bbox_mode = dataset_dict["annotations"][0]["bbox_mode"] + annotations = instances_to_annotations(instances, image_id, bbox_mode, instance_mask_format) + + for annotation in annotations: + annotation["copypaste"] = 1 + + if "annotations" in dataset_dict_bg: + dataset_dict_bg["annotations"] += annotations + else: + dataset_dict_bg["annotations"] = annotations + + dataset_dict_bg["image_id"] = ( + str(dataset_dict["image_id"]) + "_" + str(dataset_dict_bg["image_id"]) + ) + + dataset_dict_bg["copypaste_mask"] = copypaste_mask.numpy() + + return image_bg, dataset_dict_bg + else: + return None, None + + +# from SotA-T/ape/data/datasets/coco.py +def maybe_load_annotation_from_file(record, meta=None, extra_annotation_keys=None): + + file_name = record["file_name"] + image_ext = file_name.split(".")[-1] + file_name = file_name[: -len(image_ext)] + "json" + + if not os.path.isfile(file_name): + return record + + try: + with open(file_name, "r") as f: + json_data = json.load(f) + except Exception as e: + logger = logging.getLogger(__name__) + logger.warning(f"json.load fails: {file_name}") + logger.warning(f"json.load fails: {e}") + return record + if "image" not in json_data or "annotations" not in json_data: + return record + + image_id = record["image_id"] + if "image_id" in json_data["image"]: + assert json_data["image"]["image_id"] == image_id + if "id" in json_data["image"]: + assert json_data["image"]["id"] == image_id + + id_map = meta.thing_dataset_id_to_contiguous_id if meta is not None else None + ann_keys = ["iscrowd", "bbox", "keypoints", "category_id"] + (extra_annotation_keys or []) + + ann_keys += ["phrase", "isobject"] + + num_instances_without_valid_segmentation = 0 + + if True: + anno_dict_list = json_data["annotations"] + + objs = [] + for anno in anno_dict_list: + if "image_id" not in anno: + anno["image_id"] = image_id + # Check that the image_id in this annotation is the same as + # the image_id we're looking at. + # This fails only when the data parsing logic or the annotation file is buggy. + + # The original COCO valminusminival2014 & minival2014 annotation files + # actually contains bugs that, together with certain ways of using COCO API, + # can trigger this assertion. + assert anno["image_id"] == image_id + + assert anno.get("ignore", 0) == 0, '"ignore" in COCO json file is not supported.' + + obj = {key: anno[key] for key in ann_keys if key in anno} + if "bbox" in obj and len(obj["bbox"]) == 0: + raise ValueError( + f"One annotation of image {image_id} contains empty 'bbox' value! " + "This json does not have valid COCO format." + ) + + segm = anno.get("segmentation", None) + if segm: # either list[list[float]] or dict(RLE) + if isinstance(segm, dict): + if isinstance(segm["counts"], list): + # convert to compressed RLE + segm = mask_util.frPyObjects(segm, *segm["size"]) + else: + # filter out invalid polygons (< 3 points) + segm = [poly for poly in segm if len(poly) % 2 == 0 and len(poly) >= 6] + if len(segm) == 0: + num_instances_without_valid_segmentation += 1 + continue # ignore this instance + obj["segmentation"] = segm + + keypts = anno.get("keypoints", None) + if keypts: # list[int] + for idx, v in enumerate(keypts): + if idx % 3 != 2: + # COCO's segmentation coordinates are floating points in [0, H or W], + # but keypoint coordinates are integers in [0, H-1 or W-1] + # Therefore we assume the coordinates are "pixel indices" and + # add 0.5 to convert to floating point coordinates. + keypts[idx] = v + 0.5 + obj["keypoints"] = keypts + + # phrase = anno.get("phrase", None) + # if phrase: + # obj["phrase"] = phrase + + # isobject = anno.get("isobject", None) + # if isobject: + # obj["isobject"] = isobject + + obj["bbox_mode"] = BoxMode.XYWH_ABS + if id_map: + annotation_category_id = obj["category_id"] + try: + obj["category_id"] = id_map[annotation_category_id] + except KeyError as e: + raise KeyError( + f"Encountered category_id={annotation_category_id} " + "but this id does not exist in 'categories' of the json file." + ) from e + objs.append(obj) + record["annotations"] = objs + + return record diff --git a/ape/data/samplers/__init__.py b/ape/data/samplers/__init__.py new file mode 100644 index 0000000..08a4a26 --- /dev/null +++ b/ape/data/samplers/__init__.py @@ -0,0 +1,5 @@ +from .distributed_sampler_multi_dataset import MultiDatasetTrainingSampler + +__all__ = [ + "MultiDatasetTrainingSampler", +] diff --git a/ape/data/samplers/distributed_sampler_multi_dataset.py b/ape/data/samplers/distributed_sampler_multi_dataset.py new file mode 100644 index 0000000..e180a3e --- /dev/null +++ b/ape/data/samplers/distributed_sampler_multi_dataset.py @@ -0,0 +1,137 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import itertools +import logging +import math +from collections import defaultdict +from typing import Optional + +import torch +from torch.utils.data.sampler import Sampler + +from detectron2.data.samplers import RepeatFactorTrainingSampler +from detectron2.utils import comm + +logger = logging.getLogger(__name__) + + +class MultiDatasetTrainingSampler(Sampler): + def __init__(self, repeat_factors, *, shuffle=True, seed=None): + self._shuffle = shuffle + if seed is None: + seed = comm.shared_random_seed() + self._seed = int(seed) + + self._rank = comm.get_rank() + self._world_size = comm.get_world_size() + + # Split into whole number (_int_part) and fractional (_frac_part) parts. + self._int_part = torch.trunc(repeat_factors) + self._frac_part = repeat_factors - self._int_part + + @staticmethod + def get_repeat_factors( + dataset_dicts, num_datasets, dataset_ratio, use_rfs, use_cas, repeat_thresh, cas_lambda + ): + sizes = [0 for _ in range(num_datasets)] + for d in dataset_dicts: + sizes[d["dataset_id"]] += 1 + + assert len(dataset_ratio) == len( + sizes + ), "length of dataset ratio {} should be equal to number if dataset {}".format( + len(dataset_ratio), len(sizes) + ) + dataset_weight = [ + torch.ones(s, dtype=torch.float32) * max(sizes) / s * r + for i, (r, s) in enumerate(zip(dataset_ratio, sizes)) + ] + + logger = logging.getLogger(__name__) + logger.info( + "Training sampler dataset weight: {}".format( + str([max(sizes) / s * r for i, (r, s) in enumerate(zip(dataset_ratio, sizes))]) + ) + ) + + st = 0 + repeat_factors = [] + for i, s in enumerate(sizes): + assert use_rfs[i] * use_cas[i] == 0 + if use_rfs[i]: + repeat_factor = RepeatFactorTrainingSampler.repeat_factors_from_category_frequency( + dataset_dicts[st : st + s], repeat_thresh + ) + elif use_cas[i]: + repeat_factor = MultiDatasetTrainingSampler.get_class_balance_factor_per_dataset( + dataset_dicts[st : st + s], l=cas_lambda + ) + repeat_factor = repeat_factor * (s / repeat_factor.sum()) + else: + repeat_factor = torch.ones(s, dtype=torch.float32) + logger.info( + "Training sampler class weight: {} {} {}".format( + repeat_factor.size(), repeat_factor.max(), repeat_factor.min() + ) + ) + repeat_factors.append(repeat_factor) + st = st + s + repeat_factors = torch.cat(repeat_factors) + dataset_weight = torch.cat(dataset_weight) + repeat_factors = dataset_weight * repeat_factors + + return repeat_factors + + @staticmethod + def get_class_balance_factor_per_dataset(dataset_dicts, l=1.0): + rep_factors = [] + category_freq = defaultdict(int) + for dataset_dict in dataset_dicts: # For each image (without repeats) + cat_ids = {ann["category_id"] for ann in dataset_dict["annotations"]} + for cat_id in cat_ids: + category_freq[cat_id] += 1 + for dataset_dict in dataset_dicts: + cat_ids = {ann["category_id"] for ann in dataset_dict["annotations"]} + rep_factor = sum([1.0 / (category_freq[cat_id] ** l) for cat_id in cat_ids]) + rep_factors.append(rep_factor) + + return torch.tensor(rep_factors, dtype=torch.float32) + + def _get_epoch_indices(self, generator): + """ + Create a list of dataset indices (with repeats) to use for one epoch. + + Args: + generator (torch.Generator): pseudo random number generator used for + stochastic rounding. + + Returns: + torch.Tensor: list of dataset indices to use in one epoch. Each index + is repeated based on its calculated repeat factor. + """ + # Since repeat factors are fractional, we use stochastic rounding so + # that the target repeat factor is achieved in expectation over the + # course of training + rands = torch.rand(len(self._frac_part), generator=generator) + rep_factors = self._int_part + (rands < self._frac_part).float() + # Construct a list of indices in which we repeat images as specified + indices = [] + for dataset_index, rep_factor in enumerate(rep_factors): + indices.extend([dataset_index] * int(rep_factor.item())) + return torch.tensor(indices, dtype=torch.int64) + + def __iter__(self): + start = self._rank + yield from itertools.islice(self._infinite_indices(), start, None, self._world_size) + + def _infinite_indices(self): + g = torch.Generator() + g.manual_seed(self._seed) + while True: + # Sample indices with repeats determined by stochastic rounding; each + # "epoch" may have a slightly different size due to the rounding. + indices = self._get_epoch_indices(g) + if self._shuffle: + randperm = torch.randperm(len(indices), generator=g) + yield from indices[randperm].tolist() + else: + yield from indices.tolist() diff --git a/ape/data/transforms/__init__.py b/ape/data/transforms/__init__.py new file mode 100644 index 0000000..61bcc64 --- /dev/null +++ b/ape/data/transforms/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from .augmentation_aa import * +from .augmentation_lsj import * + +__all__ = [k for k in globals().keys() if not k.startswith("_")] diff --git a/ape/data/transforms/augmentation_aa.py b/ape/data/transforms/augmentation_aa.py new file mode 100644 index 0000000..2495799 --- /dev/null +++ b/ape/data/transforms/augmentation_aa.py @@ -0,0 +1,39 @@ +from detectron2.data import transforms as T +from fvcore.transforms.transform import Transform, TransformList + + +class AutoAugment(T.Augmentation): + def __init__(self, cfg): + super().__init__() + self.resize = T.AugmentationList( + [ + T.ResizeShortestEdge( + [480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800], + 1333, + sample_style="choice", + ), + ] + ) + self.resize_crop_resize = T.AugmentationList( + [ + T.ResizeShortestEdge([400, 500, 600], 1333, sample_style="choice"), + T.RandomCrop("absolute_range", (384, 600)), + T.ResizeShortestEdge( + [480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800], + 1333, + sample_style="choice", + ), + ] + ) + + def __call__(self, aug_input) -> Transform: + + do = self._rand_range(low=0.0, high=1.0) + if do > 0.5: + return self.resize(aug_input) + else: + return self.resize_crop_resize(aug_input) + + def __repr__(self): + msgs = [str(self.resize), str(self.resize_crop_resize)] + return "AutoAugment[{}]".format(", ".join(msgs)) diff --git a/ape/data/transforms/augmentation_lsj.py b/ape/data/transforms/augmentation_lsj.py new file mode 100644 index 0000000..5202356 --- /dev/null +++ b/ape/data/transforms/augmentation_lsj.py @@ -0,0 +1,38 @@ +from detectron2.data import transforms as T +from fvcore.transforms.transform import Transform, TransformList + + +class LargeScaleJitter(T.Augmentation): + def __init__(self, cfg): + super().__init__() + + image_size = cfg.INPUT.LSJ.IMAGE_SIZE + min_scale = cfg.INPUT.LSJ.MIN_SCALE + max_scale = cfg.INPUT.LSJ.MAX_SCALE + # pad_value = 128.0 + pad_value = 1.0 * sum(cfg.MODEL.PIXEL_MEAN) / len(cfg.MODEL.PIXEL_MEAN) + seg_pad_value = cfg.INPUT.SEG_PAD_VALUE + + self.resize_crop = T.AugmentationList( + [ + T.ResizeScale( + min_scale=min_scale, + max_scale=max_scale, + target_height=image_size, + target_width=image_size, + ), + T.FixedSizeCrop( + crop_size=(image_size, image_size), + pad_value=pad_value, + seg_pad_value=seg_pad_value, + ), + ] + ) + + def __call__(self, aug_input) -> Transform: + + return self.resize_crop(aug_input) + + def __repr__(self): + msgs = str(self.resize_crop) + return "LargeScaleJitter[{}]".format(msgs) diff --git a/ape/engine/__init__.py b/ape/engine/__init__.py new file mode 100644 index 0000000..ee63d6e --- /dev/null +++ b/ape/engine/__init__.py @@ -0,0 +1,4 @@ +from .defaults import * +from .train_loop import * + +__all__ = [k for k in globals().keys() if not k.startswith("_")] diff --git a/ape/engine/defaults.py b/ape/engine/defaults.py new file mode 100644 index 0000000..de9ac42 --- /dev/null +++ b/ape/engine/defaults.py @@ -0,0 +1,97 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + +""" +This file contains components with some default boilerplate logic user may need +in training / testing. They will not work for everyone, but many users may find them useful. + +The behavior of functions/classes in this file is subject to change, +since they are meant to represent the "common default behavior" people need in their projects. +""" + +import copy +import os +import sys + +import torch + +from ape.checkpoint import DetectionCheckpointer +from detectron2.config import instantiate + +__all__ = [ + "DefaultPredictor", +] + + +class DefaultPredictor: + """ + Create a simple end-to-end predictor with the given config that runs on + single device for a single input image. + + Compared to using the model directly, this class does the following additions: + + 1. Load checkpoint from `cfg.MODEL.WEIGHTS`. + 2. Always take BGR image as the input and apply conversion defined by `cfg.INPUT.FORMAT`. + 3. Apply resizing defined by `cfg.INPUT.{MIN,MAX}_SIZE_TEST`. + 4. Take one input image and produce a single output, instead of a batch. + + This is meant for simple demo purposes, so it does the above steps automatically. + This is not meant for benchmarks or running complicated inference logic. + If you'd like to do anything more complicated, please refer to its source code as + examples to build and use the model manually. + + Attributes: + metadata (Metadata): the metadata of the underlying dataset, obtained from + cfg.DATASETS.TEST. + + Examples: + :: + pred = DefaultPredictor(cfg) + inputs = cv2.imread("input.jpg") + outputs = pred(inputs) + """ + + def __init__(self, cfg): + self.cfg = copy.deepcopy(cfg) # cfg can be modified by model + self.model = instantiate(cfg.model) + self.model.to(cfg.train.device) + self.model.eval() + + checkpointer = DetectionCheckpointer(self.model) + checkpointer.load(cfg.train.init_checkpoint) + + self.aug = instantiate(cfg.dataloader.test.mapper.augmentations[0]) + if "model_vision" in cfg.model: + self.input_format = cfg.model.model_vision.input_format + else: + self.input_format = cfg.model.input_format + assert self.input_format in ["RGB", "BGR"], self.input_format + + def __call__(self, original_image, text_prompt=None, mask_prompt=None): + """ + Args: + original_image (np.ndarray): an image of shape (H, W, C) (in BGR order). + + Returns: + predictions (dict): + the output of the model for one image only. + See :doc:`/tutorials/models` for details about the format. + """ + with torch.no_grad(): # https://github.com/sphinx-doc/sphinx/issues/4258 + # Apply pre-processing to image. + if self.input_format == "RGB": + # whether the model expects BGR inputs or RGB + original_image = original_image[:, :, ::-1] + height, width = original_image.shape[:2] + image = self.aug.get_transform(original_image).apply_image(original_image) + image = torch.as_tensor(image.astype("float32").transpose(2, 0, 1)) + + inputs = {"image": image, "height": height, "width": width} + if text_prompt is not None: + inputs["prompt"] = "text" + inputs["text_prompt"] = text_prompt + if mask_prompt is not None: + mask_prompt = self.aug.get_transform(mask_prompt).apply_image(mask_prompt) + inputs["mask_prompt"] = torch.as_tensor(mask_prompt.astype("float32")) + predictions = self.model([inputs])[0] + return predictions diff --git a/ape/engine/train_loop.py b/ape/engine/train_loop.py new file mode 100644 index 0000000..380b3e7 --- /dev/null +++ b/ape/engine/train_loop.py @@ -0,0 +1,415 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. +import concurrent.futures +import logging +import time +import weakref +from typing import List, Mapping, Optional + +import numpy as np +import torch +from torch.nn.parallel import DataParallel, DistributedDataParallel + +import detectron2.utils.comm as comm +from detectron2.engine.train_loop import HookBase, TrainerBase +from detectron2.utils.events import EventStorage, get_event_storage +from detectron2.utils.logger import _log_api_usage + +__all__ = ["SimpleTrainer", "AMPTrainer"] + + +class SimpleTrainer(TrainerBase): + """ + A simple trainer for the most common type of task: + single-cost single-optimizer single-data-source iterative optimization, + optionally using data-parallelism. + It assumes that every step, you: + + 1. Compute the loss with a data from the data_loader. + 2. Compute the gradients with the above loss. + 3. Update the model with the optimizer. + + All other tasks during training (checkpointing, logging, evaluation, LR schedule) + are maintained by hooks, which can be registered by :meth:`TrainerBase.register_hooks`. + + If you want to do anything fancier than this, + either subclass TrainerBase and implement your own `run_step`, + or write your own training loop. + """ + + def __init__( + self, + model, + data_loader, + optimizer, + gather_metric_period=1, + zero_grad_before_forward=False, + async_write_metrics=False, + ): + """ + Args: + model: a torch Module. Takes a data from data_loader and returns a + dict of losses. + data_loader: an iterable. Contains data to be used to call model. + optimizer: a torch optimizer. + gather_metric_period: an int. Every gather_metric_period iterations + the metrics are gathered from all the ranks to rank 0 and logged. + zero_grad_before_forward: whether to zero the gradients before the forward. + async_write_metrics: bool. If True, then write metrics asynchronously to improve + training speed + """ + super().__init__() + + """ + We set the model to training mode in the trainer. + However it's valid to train a model that's in eval mode. + If you want your model (or a submodule of it) to behave + like evaluation during training, you can overwrite its train() method. + """ + model.train() + + self.model = model + self.data_loader = data_loader + # to access the data loader iterator, call `self._data_loader_iter` + self._data_loader_iter_obj = None + self.optimizer = optimizer + self.gather_metric_period = gather_metric_period + self.zero_grad_before_forward = zero_grad_before_forward + self.async_write_metrics = async_write_metrics + # create a thread pool that can execute non critical logic in run_step asynchronically + # use only 1 worker so tasks will be executred in order of submitting. + self.concurrent_executor = concurrent.futures.ThreadPoolExecutor(max_workers=1) + + def run_step(self): + """ + Implement the standard training logic described above. + """ + assert self.model.training, "[SimpleTrainer] model was changed to eval mode!" + start = time.perf_counter() + """ + If you want to do something with the data, you can wrap the dataloader. + """ + data = next(self._data_loader_iter) + data_time = time.perf_counter() - start + + # ------------------------------------------------------------------ + for d in data: + self.dataset_image_counts[self.dataset_names[d.get("dataset_id", 0)]] += 1 + self.dataset_object_counts[self.dataset_names[d.get("dataset_id", 0)]] += len( + d.get("instances", []) + ) + dataset_image_counts = {f"count_image/{k}": v for k, v in self.dataset_image_counts.items()} + dataset_object_counts = { + f"count_object/{k}": v for k, v in self.dataset_object_counts.items() + } + if self.async_write_metrics: + # write metrics asynchronically + self.concurrent_executor.submit( + self._write_metrics_common, dataset_image_counts, iter=self.iter + ) + self.concurrent_executor.submit( + self._write_metrics_common, dataset_object_counts, iter=self.iter + ) + else: + self._write_metrics_common(dataset_image_counts) + self._write_metrics_common(dataset_object_counts) + # ------------------------------------------------------------------ + + if self.zero_grad_before_forward: + """ + If you need to accumulate gradients or do something similar, you can + wrap the optimizer with your custom `zero_grad()` method. + """ + self.optimizer.zero_grad() + + """ + If you want to do something with the losses, you can wrap the model. + """ + loss_dict = self.model(data) + if isinstance(loss_dict, torch.Tensor): + losses = loss_dict + loss_dict = {"total_loss": loss_dict} + else: + losses = sum(loss_dict.values()) + if not self.zero_grad_before_forward: + """ + If you need to accumulate gradients or do something similar, you can + wrap the optimizer with your custom `zero_grad()` method. + """ + self.optimizer.zero_grad() + losses.backward() + + self.after_backward() + + if self.async_write_metrics: + # write metrics asynchronically + self.concurrent_executor.submit( + self._write_metrics, loss_dict, data_time, iter=self.iter + ) + else: + self._write_metrics(loss_dict, data_time) + + """ + If you need gradient clipping/scaling or other processing, you can + wrap the optimizer with your custom `step()` method. But it is + suboptimal as explained in https://arxiv.org/abs/2006.15704 Sec 3.2.4 + """ + self.optimizer.step() + + @property + def _data_loader_iter(self): + # only create the data loader iterator when it is used + if self._data_loader_iter_obj is None: + self._data_loader_iter_obj = iter(self.data_loader) + return self._data_loader_iter_obj + + def reset_data_loader(self, data_loader_builder): + """ + Delete and replace the current data loader with a new one, which will be created + by calling `data_loader_builder` (without argument). + """ + del self.data_loader + data_loader = data_loader_builder() + self.data_loader = data_loader + self._data_loader_iter_obj = None + + def _write_metrics( + self, + loss_dict: Mapping[str, torch.Tensor], + data_time: float, + prefix: str = "", + iter: Optional[int] = None, + ) -> None: + logger = logging.getLogger(__name__) + + iter = self.iter if iter is None else iter + if (iter + 1) % self.gather_metric_period == 0: + try: + SimpleTrainer.write_metrics(loss_dict, data_time, iter, prefix) + except Exception: + logger.exception("Exception in writing metrics: ") + raise + + @staticmethod + def write_metrics( + loss_dict: Mapping[str, torch.Tensor], + data_time: float, + cur_iter: int, + prefix: str = "", + ) -> None: + """ + Args: + loss_dict (dict): dict of scalar losses + data_time (float): time taken by the dataloader iteration + prefix (str): prefix for logging keys + """ + metrics_dict = {k: v.detach().cpu().item() for k, v in loss_dict.items()} + metrics_dict["data_time"] = data_time + + # Gather metrics among all workers for logging + # This assumes we do DDP-style training, which is currently the only + # supported method in detectron2. + all_metrics_dict = comm.gather(metrics_dict) + + if comm.is_main_process(): + storage = get_event_storage() + + # data_time among workers can have high variance. The actual latency + # caused by data_time is the maximum among workers. + data_time = np.max([x.pop("data_time") for x in all_metrics_dict]) + storage.put_scalar("data_time", data_time, cur_iter=cur_iter) + + # average the rest metrics + all_metrics_key = [] + for metrics_dict in all_metrics_dict: + for key in metrics_dict.keys(): + if key not in all_metrics_key: + all_metrics_key.append(key) + metrics_dict = { + k: np.mean([x[k] for x in all_metrics_dict if k in x]) for k in all_metrics_key + } + total_losses_reduced = sum(metrics_dict.values()) + if not np.isfinite(total_losses_reduced): + raise FloatingPointError( + f"Loss became infinite or NaN at iteration={cur_iter}!\n" + f"loss_dict = {metrics_dict}" + ) + + storage.put_scalar( + "{}total_loss".format(prefix), total_losses_reduced, cur_iter=cur_iter + ) + if len(metrics_dict) > 1: + storage.put_scalars(cur_iter=cur_iter, **metrics_dict) + + def state_dict(self): + ret = super().state_dict() + ret["optimizer"] = self.optimizer.state_dict() + return ret + + def load_state_dict(self, state_dict): + super().load_state_dict(state_dict) + self.optimizer.load_state_dict(state_dict["optimizer"]) + + def after_train(self): + super().after_train() + self.concurrent_executor.shutdown(wait=True) + + def _write_metrics_common( + self, + metrics_dict: Mapping[str, torch.Tensor], + prefix: str = "", + iter: Optional[int] = None, + ) -> None: + logger = logging.getLogger(__name__) + + iter = self.iter if iter is None else iter + if (iter + 1) % self.gather_metric_period == 0: + try: + SimpleTrainer.write_metrics_common(metrics_dict, iter, prefix) + except Exception: + logger.exception("Exception in writing metrics: ") + raise + + @staticmethod + def write_metrics_common( + metrics_dict: Mapping[str, torch.Tensor], + cur_iter: int, + prefix: str = "", + ) -> None: + """ + Args: + metrics_dict (dict): dict of scalar losses + prefix (str): prefix for logging keys + """ + metrics_dict = {k: v.detach().cpu().item() for k, v in metrics_dict.items()} + all_metrics_dict = comm.gather(metrics_dict) + if comm.is_main_process(): + storage = get_event_storage() + + metrics_dict = { + k: np.sum([x[k] for x in all_metrics_dict]) for k in all_metrics_dict[0].keys() + } + + if len(metrics_dict) > 1: + storage.put_scalars(cur_iter=cur_iter, **metrics_dict) + + +class AMPTrainer(SimpleTrainer): + """ + Like :class:`SimpleTrainer`, but uses PyTorch's native automatic mixed precision + in the training loop. + """ + + def __init__( + self, + model, + data_loader, + optimizer, + gather_metric_period=1, + zero_grad_before_forward=False, + grad_scaler=None, + precision: torch.dtype = torch.float16, + log_grad_scaler: bool = False, + async_write_metrics=False, + ): + """ + Args: + model, data_loader, optimizer, gather_metric_period, zero_grad_before_forward, + async_write_metrics: same as in :class:`SimpleTrainer`. + grad_scaler: torch GradScaler to automatically scale gradients. + precision: torch.dtype as the target precision to cast to in computations + """ + unsupported = "AMPTrainer does not support single-process multi-device training!" + if isinstance(model, DistributedDataParallel): + assert not (model.device_ids and len(model.device_ids) > 1), unsupported + assert not isinstance(model, DataParallel), unsupported + + super().__init__( + model, data_loader, optimizer, gather_metric_period, zero_grad_before_forward + ) + + if grad_scaler is None: + from torch.cuda.amp import GradScaler + + grad_scaler = GradScaler() + self.grad_scaler = grad_scaler + self.precision = precision + self.log_grad_scaler = log_grad_scaler + + def run_step(self): + """ + Implement the AMP training logic. + """ + assert self.model.training, "[AMPTrainer] model was changed to eval mode!" + assert torch.cuda.is_available(), "[AMPTrainer] CUDA is required for AMP training!" + from torch.cuda.amp import autocast + + start = time.perf_counter() + data = next(self._data_loader_iter) + data_time = time.perf_counter() - start + + # ------------------------------------------------------------------ + for d in data: + self.dataset_image_counts[self.dataset_names[d.get("dataset_id", 0)]] += 1 + self.dataset_object_counts[self.dataset_names[d.get("dataset_id", 0)]] += len( + d.get("instances", []) + ) + dataset_image_counts = { + f"count_image/{k}": v for k, v in self.dataset_image_counts.items() + } + dataset_object_counts = { + f"count_object/{k}": v for k, v in self.dataset_object_counts.items() + } + if self.async_write_metrics: + # write metrics asynchronically + self.concurrent_executor.submit( + self._write_metrics_common, dataset_image_counts, iter=self.iter + ) + self.concurrent_executor.submit( + self._write_metrics_common, dataset_object_counts, iter=self.iter + ) + else: + self._write_metrics_common(dataset_image_counts) + self._write_metrics_common(dataset_object_counts) + # ------------------------------------------------------------------ + + if self.zero_grad_before_forward: + self.optimizer.zero_grad() + with autocast(dtype=self.precision): + loss_dict = self.model(data) + if isinstance(loss_dict, torch.Tensor): + losses = loss_dict + loss_dict = {"total_loss": loss_dict} + else: + losses = sum(loss_dict.values()) + + if not self.zero_grad_before_forward: + self.optimizer.zero_grad() + + self.grad_scaler.scale(losses).backward() + + if self.log_grad_scaler: + storage = get_event_storage() + storage.put_scalar("[metric] grad_scaler", self.grad_scaler.get_scale()) + + self.after_backward() + + if self.async_write_metrics: + # write metrics asynchronically + self.concurrent_executor.submit( + self._write_metrics, loss_dict, data_time, iter=self.iter + ) + else: + self._write_metrics(loss_dict, data_time) + + self.grad_scaler.step(self.optimizer) + self.grad_scaler.update() + + def state_dict(self): + ret = super().state_dict() + ret["grad_scaler"] = self.grad_scaler.state_dict() + return ret + + def load_state_dict(self, state_dict): + super().load_state_dict(state_dict) + self.grad_scaler.load_state_dict(state_dict["grad_scaler"]) diff --git a/ape/evaluation/__init__.py b/ape/evaluation/__init__.py new file mode 100644 index 0000000..150dcc3 --- /dev/null +++ b/ape/evaluation/__init__.py @@ -0,0 +1,8 @@ +from .d3_evaluation import D3Evaluator +from .evaluator import inference_on_dataset +from .instance_evaluation import InstanceSegEvaluator +from .lvis_evaluation import LVISEvaluator +from .oideval import OIDEvaluator +from .refcoco_evaluation import RefCOCOEvaluator + +__all__ = [k for k in globals().keys() if not k.startswith("_")] diff --git a/ape/evaluation/d3_evaluation.py b/ape/evaluation/d3_evaluation.py new file mode 100644 index 0000000..0f4db04 --- /dev/null +++ b/ape/evaluation/d3_evaluation.py @@ -0,0 +1,771 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import contextlib +import copy +import io +import itertools +import json +import logging +import os +import pickle +from collections import OrderedDict + +import numpy as np +import pycocotools.mask as mask_util +import torch +from pycocotools.coco import COCO +from pycocotools.cocoeval import COCOeval + +import detectron2.utils.comm as comm +from detectron2.config import CfgNode +from detectron2.data import MetadataCatalog +from detectron2.data.datasets.coco import convert_to_coco_json +from detectron2.evaluation import DatasetEvaluator +from detectron2.structures import Boxes, BoxMode, pairwise_iou +from detectron2.utils.file_io import PathManager +from detectron2.utils.logger import create_small_table +from tabulate import tabulate + +try: + from detectron2.evaluation.fast_eval_api import COCOeval_opt +except ImportError: + COCOeval_opt = COCOeval + + +class D3Evaluator(DatasetEvaluator): + """ + Evaluate AR for object proposals, AP for instance detection/segmentation, AP + for keypoint detection outputs using COCO's metrics. + See http://cocodataset.org/#detection-eval and + http://cocodataset.org/#keypoints-eval to understand its metrics. + The metrics range from 0 to 100 (instead of 0 to 1), where a -1 or NaN means + the metric cannot be computed (e.g. due to no predictions made). + + In addition to COCO, this evaluator is able to support any bounding box detection, + instance segmentation, or keypoint detection dataset. + """ + + def __init__( + self, + dataset_name, + tasks=None, + distributed=True, + output_dir=None, + *, + max_dets_per_image=None, + use_fast_impl=True, + kpt_oks_sigmas=(), + allow_cached_coco=True, + mode="FULL", # FULL, PRES, ABS + ): + """ + Args: + dataset_name (str): name of the dataset to be evaluated. + It must have either the following corresponding metadata: + + "json_file": the path to the COCO format annotation + + Or it must be in detectron2's standard dataset format + so it can be converted to COCO format automatically. + tasks (tuple[str]): tasks that can be evaluated under the given + configuration. A task is one of "bbox", "segm", "keypoints". + By default, will infer this automatically from predictions. + distributed (True): if True, will collect results from all ranks and run evaluation + in the main process. + Otherwise, will only evaluate the results in the current process. + output_dir (str): optional, an output directory to dump all + results predicted on the dataset. The dump contains two files: + + 1. "instances_predictions.pth" a file that can be loaded with `torch.load` and + contains all the results in the format they are produced by the model. + 2. "coco_instances_results.json" a json file in COCO's result format. + max_dets_per_image (int): limit on the maximum number of detections per image. + By default in COCO, this limit is to 100, but this can be customized + to be greater, as is needed in evaluation metrics AP fixed and AP pool + (see https://arxiv.org/pdf/2102.01066.pdf) + This doesn't affect keypoint evaluation. + use_fast_impl (bool): use a fast but **unofficial** implementation to compute AP. + Although the results should be very close to the official implementation in COCO + API, it is still recommended to compute results with the official API for use in + papers. The faster implementation also uses more RAM. + kpt_oks_sigmas (list[float]): The sigmas used to calculate keypoint OKS. + See http://cocodataset.org/#keypoints-eval + When empty, it will use the defaults in COCO. + Otherwise it should be the same length as ROI_KEYPOINT_HEAD.NUM_KEYPOINTS. + allow_cached_coco (bool): Whether to use cached coco json from previous validation + runs. You should set this to False if you need to use different validation data. + Defaults to True. + """ + self._logger = logging.getLogger(__name__) + self._distributed = distributed + self._output_dir = output_dir + + if use_fast_impl and (COCOeval_opt is COCOeval): + self._logger.info("Fast COCO eval is not built. Falling back to official COCO eval.") + use_fast_impl = False + self._use_fast_impl = use_fast_impl + + # COCOeval requires the limit on the number of detections per image (maxDets) to be a list + # with at least 3 elements. The default maxDets in COCOeval is [1, 10, 100], in which the + # 3rd element (100) is used as the limit on the number of detections per image when + # evaluating AP. COCOEvaluator expects an integer for max_dets_per_image, so for COCOeval, + # we reformat max_dets_per_image into [1, 10, max_dets_per_image], based on the defaults. + if max_dets_per_image is None: + max_dets_per_image = [1, 10, 100] + else: + max_dets_per_image = [1, 10, max_dets_per_image] + self._max_dets_per_image = max_dets_per_image + + if tasks is not None and isinstance(tasks, CfgNode): + kpt_oks_sigmas = ( + tasks.TEST.KEYPOINT_OKS_SIGMAS if not kpt_oks_sigmas else kpt_oks_sigmas + ) + self._logger.warn( + "COCO Evaluator instantiated using config, this is deprecated behavior." + " Please pass in explicit arguments instead." + ) + self._tasks = None # Infering it from predictions should be better + else: + self._tasks = tasks + + self._cpu_device = torch.device("cpu") + + self._metadata = MetadataCatalog.get(dataset_name) + if not hasattr(self._metadata, "json_file"): + if output_dir is None: + raise ValueError( + "output_dir must be provided to COCOEvaluator " + "for datasets not in COCO format." + ) + self._logger.info(f"Trying to convert '{dataset_name}' to COCO format ...") + + cache_path = os.path.join(output_dir, f"{dataset_name}_coco_format.json") + self._metadata.json_file = {} + self._metadata.json_file[mode] = cache_path + convert_to_coco_json(dataset_name, cache_path, allow_cached=allow_cached_coco) + + json_file = PathManager.get_local_path(self._metadata.json_file[mode]) + with contextlib.redirect_stdout(io.StringIO()): + self._coco_api = COCO(json_file) + + # Test set json files do not contain annotations (evaluation must be + # performed using the COCO evaluation server). + self._do_evaluation = "annotations" in self._coco_api.dataset + if self._do_evaluation: + self._kpt_oks_sigmas = kpt_oks_sigmas + + self.mode = mode + + def reset(self): + self._predictions = [] + + def process(self, inputs, outputs): + """ + Args: + inputs: the inputs to a COCO model (e.g., GeneralizedRCNN). + It is a list of dict. Each dict corresponds to an image and + contains keys like "height", "width", "file_name", "image_id". + outputs: the outputs of a COCO model. It is a list of dicts with key + "instances" that contains :class:`Instances`. + """ + for input, output in zip(inputs, outputs): + prediction = {"image_id": input["image_id"]} + + if "instances" in output: + instances = output["instances"].to(self._cpu_device) + + if self._metadata.group == "intra": + instances = instances[instances.pred_classes < len(input["sent_ids"])] + instances.pred_classes = torch.as_tensor( + [ + input["sent_ids"][pred_class] + for pred_class in instances.pred_classes.tolist() + ] + ) + elif self._metadata.group == "inter": + pass + else: + assert False + + prediction["instances"] = instances_to_coco_json(instances, input["image_id"]) + if "proposals" in output: + prediction["proposals"] = output["proposals"].to(self._cpu_device) + if len(prediction) > 1: + self._predictions.append(prediction) + + def evaluate(self, img_ids=None): + """ + Args: + img_ids: a list of image IDs to evaluate on. Default to None for the whole dataset + """ + if self._distributed: + comm.synchronize() + predictions = comm.gather(self._predictions, dst=0) + predictions = list(itertools.chain(*predictions)) + + if not comm.is_main_process(): + return {} + else: + predictions = self._predictions + + if len(predictions) == 0: + self._logger.warning("[COCOEvaluator] Did not receive valid predictions.") + return {} + + if self._output_dir: + PathManager.mkdirs(self._output_dir) + file_path = os.path.join(self._output_dir, f"instances_predictions_{self.mode}.pth") + with PathManager.open(file_path, "wb") as f: + torch.save(predictions, f) + + self._results = OrderedDict() + if "proposals" in predictions[0]: + self._eval_box_proposals(predictions) + if "instances" in predictions[0]: + self._eval_predictions(predictions, img_ids=img_ids) + # Copy so the caller can do whatever with results + self._results = {f"{k}_{self.mode}": v for k, v in self._results.items()} + return copy.deepcopy(self._results) + + def _tasks_from_predictions(self, predictions): + """ + Get COCO API "tasks" (i.e. iou_type) from COCO-format predictions. + """ + tasks = {"bbox"} + for pred in predictions: + if "segmentation" in pred: + tasks.add("segm") + if "keypoints" in pred: + tasks.add("keypoints") + return sorted(tasks) + + def _eval_predictions(self, predictions, img_ids=None): + """ + Evaluate predictions. Fill self._results with the metrics of the tasks. + """ + self._logger.info("Preparing results for COCO format ...") + coco_results = list(itertools.chain(*[x["instances"] for x in predictions])) + tasks = self._tasks or self._tasks_from_predictions(coco_results) + + # unmap the category ids for COCO + if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"): + dataset_id_to_contiguous_id = self._metadata.thing_dataset_id_to_contiguous_id + all_contiguous_ids = list(dataset_id_to_contiguous_id.values()) + num_classes = len(all_contiguous_ids) + assert min(all_contiguous_ids) == 0 and max(all_contiguous_ids) == num_classes - 1 + + reverse_id_mapping = {v: k for k, v in dataset_id_to_contiguous_id.items()} + for result in coco_results: + category_id = result["category_id"] + assert category_id < num_classes, ( + f"A prediction has class={category_id}, " + f"but the dataset only has {num_classes} classes and " + f"predicted class id should be in [0, {num_classes - 1}]." + ) + result["category_id"] = reverse_id_mapping[category_id] + + if self._output_dir: + file_path = os.path.join(self._output_dir, f"coco_instances_results_{self.mode}.json") + self._logger.info("Saving results to {}".format(file_path)) + with PathManager.open(file_path, "w") as f: + f.write(json.dumps(coco_results)) + f.flush() + + if not self._do_evaluation: + self._logger.info("Annotations are not available for evaluation.") + return + + self._logger.info( + "Evaluating predictions with {} COCO API...".format( + "unofficial" if self._use_fast_impl else "official" + ) + ) + for task in sorted(tasks): + assert task in {"bbox", "segm", "keypoints"}, f"Got unknown task: {task}!" + coco_eval = ( + _evaluate_predictions_on_coco( + self._coco_api, + coco_results, + task, + kpt_oks_sigmas=self._kpt_oks_sigmas, + cocoeval_fn=COCOeval_opt if self._use_fast_impl else COCOeval, + img_ids=img_ids, + max_dets_per_image=self._max_dets_per_image, + ) + if len(coco_results) > 0 + else None # cocoapi does not handle empty results very well + ) + + res = self._derive_coco_results( + coco_eval, task, class_names=self._metadata.get("thing_classes") + ) + self._results[task] = res + + def _eval_box_proposals(self, predictions): + """ + Evaluate the box proposals in predictions. + Fill self._results with the metrics for "box_proposals" task. + """ + if self._output_dir: + # Saving generated box proposals to file. + # Predicted box_proposals are in XYXY_ABS mode. + bbox_mode = BoxMode.XYXY_ABS.value + ids, boxes, objectness_logits = [], [], [] + for prediction in predictions: + ids.append(prediction["image_id"]) + boxes.append(prediction["proposals"].proposal_boxes.tensor.numpy()) + objectness_logits.append(prediction["proposals"].objectness_logits.numpy()) + + proposal_data = { + "boxes": boxes, + "objectness_logits": objectness_logits, + "ids": ids, + "bbox_mode": bbox_mode, + } + with PathManager.open(os.path.join(self._output_dir, "box_proposals.pkl"), "wb") as f: + pickle.dump(proposal_data, f) + + if not self._do_evaluation: + self._logger.info("Annotations are not available for evaluation.") + return + + self._logger.info("Evaluating bbox proposals ...") + res = {} + areas = {"all": "", "small": "s", "medium": "m", "large": "l"} + for limit in [100, 1000]: + for area, suffix in areas.items(): + stats = _evaluate_box_proposals(predictions, self._coco_api, area=area, limit=limit) + key = "AR{}@{:d}".format(suffix, limit) + res[key] = float(stats["ar"].item() * 100) + self._logger.info("Proposal metrics: \n" + create_small_table(res)) + self._results["box_proposals"] = res + + def _derive_coco_results(self, coco_eval, iou_type, class_names=None): + """ + Derive the desired score numbers from summarized COCOeval. + + Args: + coco_eval (None or COCOEval): None represents no predictions from model. + iou_type (str): + class_names (None or list[str]): if provided, will use it to predict + per-category AP. + + Returns: + a dict of {metric name: score} + """ + + metrics = { + "bbox": [ + "AP", + "AP50", + "AP75", + "APs", + "APm", + "APl", + "AR@1", + "AR@10", + "AR@100", + "ARs", + "ARm", + "ARl", + ], + "segm": [ + "AP", + "AP50", + "AP75", + "APs", + "APm", + "APl", + "AR@1", + "AR@10", + "AR@100", + "ARs", + "ARm", + "ARl", + ], + "keypoints": ["AP", "AP50", "AP75", "APm", "APl"], + }[iou_type] + + if coco_eval is None: + self._logger.warn("No predictions from the model!") + return {metric: float("nan") for metric in metrics} + + # the standard metrics + results = { + metric: float(coco_eval.stats[idx] * 100 if coco_eval.stats[idx] >= 0 else "nan") + for idx, metric in enumerate(metrics) + } + self._logger.info( + "Evaluation results for {}: \n".format(iou_type) + create_small_table(results) + ) + if not np.isfinite(sum(results.values())): + self._logger.info("Some metrics cannot be computed and is shown as NaN.") + + if class_names is None or len(class_names) <= 1: + return results + # Compute per-category AP + # from https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L222-L252 # noqa + precisions = coco_eval.eval["precision"] + # precision has dims (iou, recall, cls, area range, max dets) + + if len(class_names) > precisions.shape[2]: + class_names = [category["name"] for category in self._coco_api.dataset["categories"]] + + assert len(class_names) == precisions.shape[2] + + results_per_category = [] + for idx, name in enumerate(class_names): + # area range index 0: all area ranges + # max dets index -1: typically 100 per image + precision = precisions[:, :, idx, 0, -1] + precision = precision[precision > -1] + ap = np.mean(precision) if precision.size else float("nan") + results_per_category.append(("{}".format(name), float(ap * 100))) + + # tabulate it + N_COLS = min(6, len(results_per_category) * 2) + results_flatten = list(itertools.chain(*results_per_category)) + results_2d = itertools.zip_longest(*[results_flatten[i::N_COLS] for i in range(N_COLS)]) + table = tabulate( + results_2d, + tablefmt="pipe", + floatfmt=".3f", + headers=["category", "AP"] * (N_COLS // 2), + numalign="left", + ) + self._logger.info("Per-category {} AP: \n".format(iou_type) + table) + + results.update({"AP-" + name: ap for name, ap in results_per_category}) + return results + + +def instances_to_coco_json(instances, img_id): + """ + Dump an "Instances" object to a COCO-format json that's used for evaluation. + + Args: + instances (Instances): + img_id (int): the image id + + Returns: + list[dict]: list of json annotations in COCO format. + """ + num_instance = len(instances) + if num_instance == 0: + return [] + + boxes = instances.pred_boxes.tensor.numpy() + boxes = BoxMode.convert(boxes, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS) + boxes = boxes.tolist() + scores = instances.scores.tolist() + classes = instances.pred_classes.tolist() + + has_mask = instances.has("pred_masks") + if has_mask: + # use RLE to encode the masks, because they are too large and takes memory + # since this evaluator stores outputs of the entire dataset + rles = [ + mask_util.encode(np.array(mask[:, :, None], order="F", dtype="uint8"))[0] + for mask in instances.pred_masks + ] + for rle in rles: + # "counts" is an array encoded by mask_util as a byte-stream. Python3's + # json writer which always produces strings cannot serialize a bytestream + # unless you decode it. Thankfully, utf-8 works out (which is also what + # the pycocotools/_mask.pyx does). + rle["counts"] = rle["counts"].decode("utf-8") + + has_keypoints = instances.has("pred_keypoints") + if has_keypoints: + keypoints = instances.pred_keypoints + + results = [] + for k in range(num_instance): + result = { + "image_id": img_id, + "category_id": classes[k], + "bbox": boxes[k], + "score": scores[k], + } + if has_mask: + result["segmentation"] = rles[k] + if has_keypoints: + # In COCO annotations, + # keypoints coordinates are pixel indices. + # However our predictions are floating point coordinates. + # Therefore we subtract 0.5 to be consistent with the annotation format. + # This is the inverse of data loading logic in `datasets/coco.py`. + keypoints[k][:, :2] -= 0.5 + result["keypoints"] = keypoints[k].flatten().tolist() + results.append(result) + return results + + +# inspired from Detectron: +# https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L255 # noqa +def _evaluate_box_proposals(dataset_predictions, coco_api, thresholds=None, area="all", limit=None): + """ + Evaluate detection proposal recall metrics. This function is a much + faster alternative to the official COCO API recall evaluation code. However, + it produces slightly different results. + """ + # Record max overlap value for each gt box + # Return vector of overlap values + areas = { + "all": 0, + "small": 1, + "medium": 2, + "large": 3, + "96-128": 4, + "128-256": 5, + "256-512": 6, + "512-inf": 7, + } + area_ranges = [ + [0**2, 1e5**2], # all + [0**2, 32**2], # small + [32**2, 96**2], # medium + [96**2, 1e5**2], # large + [96**2, 128**2], # 96-128 + [128**2, 256**2], # 128-256 + [256**2, 512**2], # 256-512 + [512**2, 1e5**2], + ] # 512-inf + assert area in areas, "Unknown area range: {}".format(area) + area_range = area_ranges[areas[area]] + gt_overlaps = [] + num_pos = 0 + + for prediction_dict in dataset_predictions: + predictions = prediction_dict["proposals"] + + # sort predictions in descending order + # TODO maybe remove this and make it explicit in the documentation + inds = predictions.objectness_logits.sort(descending=True)[1] + predictions = predictions[inds] + + ann_ids = coco_api.getAnnIds(imgIds=prediction_dict["image_id"]) + anno = coco_api.loadAnns(ann_ids) + gt_boxes = [ + BoxMode.convert(obj["bbox"], BoxMode.XYWH_ABS, BoxMode.XYXY_ABS) + for obj in anno + if obj["iscrowd"] == 0 + ] + gt_boxes = torch.as_tensor(gt_boxes).reshape(-1, 4) # guard against no boxes + gt_boxes = Boxes(gt_boxes) + gt_areas = torch.as_tensor([obj["area"] for obj in anno if obj["iscrowd"] == 0]) + + if len(gt_boxes) == 0 or len(predictions) == 0: + continue + + valid_gt_inds = (gt_areas >= area_range[0]) & (gt_areas <= area_range[1]) + gt_boxes = gt_boxes[valid_gt_inds] + + num_pos += len(gt_boxes) + + if len(gt_boxes) == 0: + continue + + if limit is not None and len(predictions) > limit: + predictions = predictions[:limit] + + overlaps = pairwise_iou(predictions.proposal_boxes, gt_boxes) + + _gt_overlaps = torch.zeros(len(gt_boxes)) + for j in range(min(len(predictions), len(gt_boxes))): + # find which proposal box maximally covers each gt box + # and get the iou amount of coverage for each gt box + max_overlaps, argmax_overlaps = overlaps.max(dim=0) + + # find which gt box is 'best' covered (i.e. 'best' = most iou) + gt_ovr, gt_ind = max_overlaps.max(dim=0) + assert gt_ovr >= 0 + # find the proposal box that covers the best covered gt box + box_ind = argmax_overlaps[gt_ind] + # record the iou coverage of this gt box + _gt_overlaps[j] = overlaps[box_ind, gt_ind] + assert _gt_overlaps[j] == gt_ovr + # mark the proposal box and the gt box as used + overlaps[box_ind, :] = -1 + overlaps[:, gt_ind] = -1 + + # append recorded iou coverage level + gt_overlaps.append(_gt_overlaps) + gt_overlaps = ( + torch.cat(gt_overlaps, dim=0) if len(gt_overlaps) else torch.zeros(0, dtype=torch.float32) + ) + gt_overlaps, _ = torch.sort(gt_overlaps) + + if thresholds is None: + step = 0.05 + thresholds = torch.arange(0.5, 0.95 + 1e-5, step, dtype=torch.float32) + recalls = torch.zeros_like(thresholds) + # compute recall for each iou threshold + for i, t in enumerate(thresholds): + recalls[i] = (gt_overlaps >= t).float().sum() / float(num_pos) + # ar = 2 * np.trapz(recalls, thresholds) + ar = recalls.mean() + return { + "ar": ar, + "recalls": recalls, + "thresholds": thresholds, + "gt_overlaps": gt_overlaps, + "num_pos": num_pos, + } + + +def _evaluate_predictions_on_coco( + coco_gt, + coco_results, + iou_type, + kpt_oks_sigmas=None, + cocoeval_fn=COCOeval_opt, + img_ids=None, + max_dets_per_image=None, +): + """ + Evaluate the coco results using COCOEval API. + """ + assert len(coco_results) > 0 + + if iou_type == "segm": + coco_results = copy.deepcopy(coco_results) + # When evaluating mask AP, if the results contain bbox, cocoapi will + # use the box area as the area of the instance, instead of the mask area. + # This leads to a different definition of small/medium/large. + # We remove the bbox field to let mask AP use mask area. + for c in coco_results: + c.pop("bbox", None) + + coco_dt = coco_gt.loadRes(coco_results) + coco_eval = cocoeval_fn(coco_gt, coco_dt, iou_type) + # For COCO, the default max_dets_per_image is [1, 10, 100]. + if max_dets_per_image is None: + max_dets_per_image = [1, 10, 100] # Default from COCOEval + else: + assert ( + len(max_dets_per_image) >= 3 + ), "COCOeval requires maxDets (and max_dets_per_image) to have length at least 3" + # In the case that user supplies a custom input for max_dets_per_image, + # apply COCOevalMaxDets to evaluate AP with the custom input. + if max_dets_per_image[2] != 100: + coco_eval = COCOevalMaxDets(coco_gt, coco_dt, iou_type) + if iou_type != "keypoints": + coco_eval.params.maxDets = max_dets_per_image + + if img_ids is not None: + coco_eval.params.imgIds = img_ids + + if iou_type == "keypoints": + # Use the COCO default keypoint OKS sigmas unless overrides are specified + if kpt_oks_sigmas: + assert hasattr(coco_eval.params, "kpt_oks_sigmas"), "pycocotools is too old!" + coco_eval.params.kpt_oks_sigmas = np.array(kpt_oks_sigmas) + # COCOAPI requires every detection and every gt to have keypoints, so + # we just take the first entry from both + num_keypoints_dt = len(coco_results[0]["keypoints"]) // 3 + num_keypoints_gt = len(next(iter(coco_gt.anns.values()))["keypoints"]) // 3 + num_keypoints_oks = len(coco_eval.params.kpt_oks_sigmas) + assert num_keypoints_oks == num_keypoints_dt == num_keypoints_gt, ( + f"[COCOEvaluator] Prediction contain {num_keypoints_dt} keypoints. " + f"Ground truth contains {num_keypoints_gt} keypoints. " + f"The length of cfg.TEST.KEYPOINT_OKS_SIGMAS is {num_keypoints_oks}. " + "They have to agree with each other. For meaning of OKS, please refer to " + "http://cocodataset.org/#keypoints-eval." + ) + + coco_eval.evaluate() + coco_eval.accumulate() + coco_eval.summarize() + + return coco_eval + + +class COCOevalMaxDets(COCOeval): + """ + Modified version of COCOeval for evaluating AP with a custom + maxDets (by default for COCO, maxDets is 100) + """ + + def summarize(self): + """ + Compute and display summary metrics for evaluation results given + a custom value for max_dets_per_image + """ + + def _summarize(ap=1, iouThr=None, areaRng="all", maxDets=100): + p = self.params + iStr = " {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} ] = {:0.3f}" + titleStr = "Average Precision" if ap == 1 else "Average Recall" + typeStr = "(AP)" if ap == 1 else "(AR)" + iouStr = ( + "{:0.2f}:{:0.2f}".format(p.iouThrs[0], p.iouThrs[-1]) + if iouThr is None + else "{:0.2f}".format(iouThr) + ) + + aind = [i for i, aRng in enumerate(p.areaRngLbl) if aRng == areaRng] + mind = [i for i, mDet in enumerate(p.maxDets) if mDet == maxDets] + if ap == 1: + # dimension of precision: [TxRxKxAxM] + s = self.eval["precision"] + # IoU + if iouThr is not None: + t = np.where(iouThr == p.iouThrs)[0] + s = s[t] + s = s[:, :, :, aind, mind] + else: + # dimension of recall: [TxKxAxM] + s = self.eval["recall"] + if iouThr is not None: + t = np.where(iouThr == p.iouThrs)[0] + s = s[t] + s = s[:, :, aind, mind] + if len(s[s > -1]) == 0: + mean_s = -1 + else: + mean_s = np.mean(s[s > -1]) + print(iStr.format(titleStr, typeStr, iouStr, areaRng, maxDets, mean_s)) + return mean_s + + def _summarizeDets(): + stats = np.zeros((12,)) + # Evaluate AP using the custom limit on maximum detections per image + stats[0] = _summarize(1, maxDets=self.params.maxDets[2]) + stats[1] = _summarize(1, iouThr=0.5, maxDets=self.params.maxDets[2]) + stats[2] = _summarize(1, iouThr=0.75, maxDets=self.params.maxDets[2]) + stats[3] = _summarize(1, areaRng="small", maxDets=self.params.maxDets[2]) + stats[4] = _summarize(1, areaRng="medium", maxDets=self.params.maxDets[2]) + stats[5] = _summarize(1, areaRng="large", maxDets=self.params.maxDets[2]) + stats[6] = _summarize(0, maxDets=self.params.maxDets[0]) + stats[7] = _summarize(0, maxDets=self.params.maxDets[1]) + stats[8] = _summarize(0, maxDets=self.params.maxDets[2]) + stats[9] = _summarize(0, areaRng="small", maxDets=self.params.maxDets[2]) + stats[10] = _summarize(0, areaRng="medium", maxDets=self.params.maxDets[2]) + stats[11] = _summarize(0, areaRng="large", maxDets=self.params.maxDets[2]) + return stats + + def _summarizeKps(): + stats = np.zeros((10,)) + stats[0] = _summarize(1, maxDets=20) + stats[1] = _summarize(1, maxDets=20, iouThr=0.5) + stats[2] = _summarize(1, maxDets=20, iouThr=0.75) + stats[3] = _summarize(1, maxDets=20, areaRng="medium") + stats[4] = _summarize(1, maxDets=20, areaRng="large") + stats[5] = _summarize(0, maxDets=20) + stats[6] = _summarize(0, maxDets=20, iouThr=0.5) + stats[7] = _summarize(0, maxDets=20, iouThr=0.75) + stats[8] = _summarize(0, maxDets=20, areaRng="medium") + stats[9] = _summarize(0, maxDets=20, areaRng="large") + return stats + + if not self.eval: + raise Exception("Please run accumulate() first") + iouType = self.params.iouType + if iouType == "segm" or iouType == "bbox": + summarize = _summarizeDets + elif iouType == "keypoints": + summarize = _summarizeKps + self.stats = summarize() + + def __str__(self): + self.summarize() diff --git a/ape/evaluation/evaluator.py b/ape/evaluation/evaluator.py new file mode 100644 index 0000000..0fb5b89 --- /dev/null +++ b/ape/evaluation/evaluator.py @@ -0,0 +1,177 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import datetime +import logging +import time +from collections import abc +from contextlib import ExitStack +from typing import List, Union + +import torch +from torch import nn + +from detectron2.evaluation import DatasetEvaluator, DatasetEvaluators, inference_context +from detectron2.utils.comm import get_world_size +from detectron2.utils.logger import log_every_n_seconds + + +def inference_on_dataset( + model, data_loader, evaluator: Union[DatasetEvaluator, List[DatasetEvaluator], None] +): + """ + Run model on the data_loader and evaluate the metrics with evaluator. + Also benchmark the inference speed of `model.__call__` accurately. + The model will be used in eval mode. + + Args: + model (callable): a callable which takes an object from + `data_loader` and returns some outputs. + + If it's an nn.Module, it will be temporarily set to `eval` mode. + If you wish to evaluate a model in `training` mode instead, you can + wrap the given model and override its behavior of `.eval()` and `.train()`. + data_loader: an iterable object with a length. + The elements it generates will be the inputs to the model. + evaluator: the evaluator(s) to run. Use `None` if you only want to benchmark, + but don't want to do any evaluation. + + Returns: + The return value of `evaluator.evaluate()` + """ + num_devices = get_world_size() + logger = logging.getLogger(__name__) + logger.info("Start inference on {} batches".format(len(data_loader))) + + total = len(data_loader) # inference data loader must have a fixed length + if evaluator is None: + # create a no-op evaluator + evaluator = DatasetEvaluators([]) + if isinstance(evaluator, abc.MutableSequence): + evaluator = DatasetEvaluators(evaluator) + evaluator.reset() + + num_warmup = min(5, total - 1) + start_time = time.perf_counter() + total_data_time = 0 + total_compute_time = 0 + total_eval_time = 0 + + total_preprocess_time = 0 + total_backbone_time = 0 + total_transformer_time = 0 + total_postprocess_time = 0 + + with ExitStack() as stack: + if isinstance(model, nn.Module): + stack.enter_context(inference_context(model)) + stack.enter_context(torch.no_grad()) + + start_data_time = time.perf_counter() + for idx, inputs in enumerate(data_loader): + total_data_time += time.perf_counter() - start_data_time + if idx == num_warmup: + start_time = time.perf_counter() + total_data_time = 0 + total_compute_time = 0 + total_eval_time = 0 + + total_preprocess_time = 0 + total_backbone_time = 0 + total_transformer_time = 0 + total_postprocess_time = 0 + + start_compute_time = time.perf_counter() + outputs = model(inputs) + if torch.cuda.is_available(): + torch.cuda.synchronize() + total_compute_time += time.perf_counter() - start_compute_time + + start_eval_time = time.perf_counter() + evaluator.process(inputs, outputs) + total_eval_time += time.perf_counter() - start_eval_time + + if hasattr(model.module, "preprocess_time"): + total_preprocess_time += model.module.preprocess_time + if hasattr(model.module, "model_vision") and hasattr( + model.module.model_vision, "preprocess_time" + ): + total_preprocess_time += model.module.model_vision.preprocess_time + if hasattr(model.module, "backbone_time"): + total_backbone_time += model.module.backbone_time + if hasattr(model.module, "model_vision") and hasattr( + model.module.model_vision, "backbone_time" + ): + total_backbone_time += model.module.model_vision.backbone_time + if hasattr(model.module, "transformer_time"): + total_transformer_time += model.module.transformer_time + if hasattr(model.module, "model_vision") and hasattr( + model.module.model_vision, "transformer_time" + ): + total_transformer_time += model.module.model_vision.transformer_time + if hasattr(model.module, "postprocess_time"): + total_postprocess_time += model.module.postprocess_time + if hasattr(model.module, "model_vision") and hasattr( + model.module.model_vision, "postprocess_time" + ): + total_postprocess_time += model.module.model_vision.postprocess_time + + iters_after_start = idx + 1 - num_warmup * int(idx >= num_warmup) + data_seconds_per_iter = total_data_time / iters_after_start + compute_seconds_per_iter = total_compute_time / iters_after_start + eval_seconds_per_iter = total_eval_time / iters_after_start + total_seconds_per_iter = (time.perf_counter() - start_time) / iters_after_start + + preprocess_seconds_per_iter = total_preprocess_time / iters_after_start + backbone_seconds_per_iter = total_backbone_time / iters_after_start + transformer_seconds_per_iter = total_transformer_time / iters_after_start + postprocess_seconds_per_iter = total_postprocess_time / iters_after_start + + if idx >= num_warmup * 2 or compute_seconds_per_iter > 5: + eta = datetime.timedelta(seconds=int(total_seconds_per_iter * (total - idx - 1))) + if torch.cuda.is_available(): + max_mem_mb = torch.cuda.max_memory_allocated() / 1024.0 / 1024.0 + else: + max_mem_mb = 0 + log_every_n_seconds( + logging.INFO, + ( + f"Inference done {idx + 1}/{total}. " + f"Dataloading: {data_seconds_per_iter:.4f} s/iter. " + f"Inference: {compute_seconds_per_iter:.4f} s/iter. " + f"Eval: {eval_seconds_per_iter:.4f} s/iter. " + f"Total: {total_seconds_per_iter:.4f} s/iter. " + f"ETA={eta}" + f". " + f"preprocess: {preprocess_seconds_per_iter:.4f} s/iter. " + f"backbone: {backbone_seconds_per_iter:.4f} s/iter. " + f"transformer: {transformer_seconds_per_iter:.4f} s/iter. " + f"postprocess: {postprocess_seconds_per_iter:.4f} s/iter. " + f"max_mem: {max_mem_mb:.0f}M. " + ), + n=5, + ) + if idx < num_warmup * 2: + torch.cuda.reset_peak_memory_stats() + start_data_time = time.perf_counter() + + # Measure the time only for this worker (before the synchronization barrier) + total_time = time.perf_counter() - start_time + total_time_str = str(datetime.timedelta(seconds=total_time)) + # NOTE this format is parsed by grep + logger.info( + "Total inference time: {} ({:.6f} s / iter per device, on {} devices)".format( + total_time_str, total_time / (total - num_warmup), num_devices + ) + ) + total_compute_time_str = str(datetime.timedelta(seconds=int(total_compute_time))) + logger.info( + "Total inference pure compute time: {} ({:.6f} s / iter per device, on {} devices)".format( + total_compute_time_str, total_compute_time / (total - num_warmup), num_devices + ) + ) + + results = evaluator.evaluate() + # An evaluator may return None when not in main process. + # Replace it by an empty dict instead to make it easier for downstream code to handle + if results is None: + results = {} + return results diff --git a/ape/evaluation/instance_evaluation.py b/ape/evaluation/instance_evaluation.py new file mode 100644 index 0000000..7ed95db --- /dev/null +++ b/ape/evaluation/instance_evaluation.py @@ -0,0 +1,112 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import contextlib +import copy +import io +import itertools +import json +import logging +import os +import pickle +from collections import OrderedDict + +import numpy as np +import pycocotools.mask as mask_util +import torch +from pycocotools.coco import COCO +from pycocotools.cocoeval import COCOeval + +import detectron2.utils.comm as comm +from detectron2.config import CfgNode +from detectron2.data import MetadataCatalog +from detectron2.data.datasets.coco import convert_to_coco_json +from detectron2.evaluation.coco_evaluation import COCOEvaluator, _evaluate_predictions_on_coco +from detectron2.structures import Boxes, BoxMode, pairwise_iou +from detectron2.utils.file_io import PathManager +from detectron2.utils.logger import create_small_table +from tabulate import tabulate + +try: + from detectron2.evaluation.fast_eval_api import COCOeval_opt +except ImportError: + COCOeval_opt = COCOeval + + +# modified from COCOEvaluator for instance segmetnat +class InstanceSegEvaluator(COCOEvaluator): + """ + Evaluate AR for object proposals, AP for instance detection/segmentation, AP + for keypoint detection outputs using COCO's metrics. + See http://cocodataset.org/#detection-eval and + http://cocodataset.org/#keypoints-eval to understand its metrics. + The metrics range from 0 to 100 (instead of 0 to 1), where a -1 or NaN means + the metric cannot be computed (e.g. due to no predictions made). + + In addition to COCO, this evaluator is able to support any bounding box detection, + instance segmentation, or keypoint detection dataset. + """ + + def _eval_predictions(self, predictions, img_ids=None): + """ + Evaluate predictions. Fill self._results with the metrics of the tasks. + """ + self._logger.info("Preparing results for COCO format ...") + coco_results = list(itertools.chain(*[x["instances"] for x in predictions])) + tasks = self._tasks or self._tasks_from_predictions(coco_results) + + # unmap the category ids for COCO + if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"): + dataset_id_to_contiguous_id = self._metadata.thing_dataset_id_to_contiguous_id + # all_contiguous_ids = list(dataset_id_to_contiguous_id.values()) + # num_classes = len(all_contiguous_ids) + # assert min(all_contiguous_ids) == 0 and max(all_contiguous_ids) == num_classes - 1 + + reverse_id_mapping = {v: k for k, v in dataset_id_to_contiguous_id.items()} + for result in coco_results: + category_id = result["category_id"] + # assert category_id < num_classes, ( + # f"A prediction has class={category_id}, " + # f"but the dataset only has {num_classes} classes and " + # f"predicted class id should be in [0, {num_classes - 1}]." + # ) + assert category_id in reverse_id_mapping, ( + f"A prediction has class={category_id}, " + f"but the dataset only has class ids in {dataset_id_to_contiguous_id}." + ) + result["category_id"] = reverse_id_mapping[category_id] + + if self._output_dir: + file_path = os.path.join(self._output_dir, "coco_instances_results.json") + self._logger.info("Saving results to {}".format(file_path)) + with PathManager.open(file_path, "w") as f: + f.write(json.dumps(coco_results)) + f.flush() + + if not self._do_evaluation: + self._logger.info("Annotations are not available for evaluation.") + return + + self._logger.info( + "Evaluating predictions with {} COCO API...".format( + "unofficial" if self._use_fast_impl else "official" + ) + ) + for task in sorted(tasks): + assert task in {"bbox", "segm", "keypoints"}, f"Got unknown task: {task}!" + coco_eval = ( + _evaluate_predictions_on_coco( + self._coco_api, + coco_results, + task, + kpt_oks_sigmas=self._kpt_oks_sigmas, + cocoeval_fn=COCOeval_opt if self._use_fast_impl else COCOeval, + img_ids=img_ids, + max_dets_per_image=self._max_dets_per_image, + ) + if len(coco_results) > 0 + else None # cocoapi does not handle empty results very well + ) + + res = self._derive_coco_results( + coco_eval, task, class_names=self._metadata.get("thing_classes") + ) + self._results[task] = res diff --git a/ape/evaluation/lvis_evaluation.py b/ape/evaluation/lvis_evaluation.py new file mode 100644 index 0000000..89db2a0 --- /dev/null +++ b/ape/evaluation/lvis_evaluation.py @@ -0,0 +1,453 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import copy +import itertools +import json +import logging +import os +import pickle +from collections import OrderedDict + +import numpy as np +import torch + +import detectron2.utils.comm as comm +from detectron2.config import CfgNode +from detectron2.data import MetadataCatalog +from detectron2.evaluation.coco_evaluation import instances_to_coco_json +from detectron2.evaluation.evaluator import DatasetEvaluator +from detectron2.structures import Boxes, BoxMode, pairwise_iou +from detectron2.utils.file_io import PathManager +from detectron2.utils.logger import create_small_table +from tabulate import tabulate + + +class LVISEvaluator(DatasetEvaluator): + """ + Evaluate object proposal and instance detection/segmentation outputs using + LVIS's metrics and evaluation API. + """ + + def __init__( + self, + dataset_name, + tasks=None, + distributed=True, + output_dir=None, + *, + max_dets_per_image=None, + ): + """ + Args: + dataset_name (str): name of the dataset to be evaluated. + It must have the following corresponding metadata: + "json_file": the path to the LVIS format annotation + tasks (tuple[str]): tasks that can be evaluated under the given + configuration. A task is one of "bbox", "segm". + By default, will infer this automatically from predictions. + distributed (True): if True, will collect results from all ranks for evaluation. + Otherwise, will evaluate the results in the current process. + output_dir (str): optional, an output directory to dump results. + max_dets_per_image (None or int): limit on maximum detections per image in evaluating AP + This limit, by default of the LVIS dataset, is 300. + """ + from lvis import LVIS + + self._logger = logging.getLogger(__name__) + + if tasks is not None and isinstance(tasks, CfgNode): + self._logger.warn( + "COCO Evaluator instantiated using config, this is deprecated behavior." + " Please pass in explicit arguments instead." + ) + self._tasks = None # Infering it from predictions should be better + else: + self._tasks = tasks + + self._distributed = distributed + self._output_dir = output_dir + self._max_dets_per_image = max_dets_per_image + + self._cpu_device = torch.device("cpu") + + self._metadata = MetadataCatalog.get(dataset_name) + json_file = PathManager.get_local_path(self._metadata.json_file) + self._lvis_api = LVIS(json_file) + # Test set json files do not contain annotations (evaluation must be + # performed using the LVIS evaluation server). + self._do_evaluation = len(self._lvis_api.get_ann_ids()) > 0 + + def reset(self): + self._predictions = [] + + def process(self, inputs, outputs): + """ + Args: + inputs: the inputs to a LVIS model (e.g., GeneralizedRCNN). + It is a list of dict. Each dict corresponds to an image and + contains keys like "height", "width", "file_name", "image_id". + outputs: the outputs of a LVIS model. It is a list of dicts with key + "instances" that contains :class:`Instances`. + """ + for input, output in zip(inputs, outputs): + prediction = {"image_id": input["image_id"]} + + if "instances" in output: + instances = output["instances"].to(self._cpu_device) + prediction["instances"] = instances_to_coco_json(instances, input["image_id"]) + if "proposals" in output: + prediction["proposals"] = output["proposals"].to(self._cpu_device) + self._predictions.append(prediction) + + def evaluate(self): + if self._distributed: + comm.synchronize() + predictions = comm.gather(self._predictions, dst=0) + predictions = list(itertools.chain(*predictions)) + + if not comm.is_main_process(): + return + else: + predictions = self._predictions + + if len(predictions) == 0: + self._logger.warning("[LVISEvaluator] Did not receive valid predictions.") + return {} + + if self._output_dir: + PathManager.mkdirs(self._output_dir) + file_path = os.path.join(self._output_dir, "instances_predictions.pth") + with PathManager.open(file_path, "wb") as f: + torch.save(predictions, f) + + self._results = OrderedDict() + if "proposals" in predictions[0]: + self._eval_box_proposals(predictions) + if "instances" in predictions[0]: + self._eval_predictions(predictions) + # Copy so the caller can do whatever with results + return copy.deepcopy(self._results) + + def _tasks_from_predictions(self, predictions): + for pred in predictions: + if "segmentation" in pred: + return ("bbox", "segm") + return ("bbox",) + + def _eval_predictions(self, predictions): + """ + Evaluate predictions. Fill self._results with the metrics of the tasks. + + Args: + predictions (list[dict]): list of outputs from the model + """ + self._logger.info("Preparing results in the LVIS format ...") + lvis_results = list(itertools.chain(*[x["instances"] for x in predictions])) + tasks = self._tasks or self._tasks_from_predictions(lvis_results) + + # LVIS evaluator can be used to evaluate results for COCO dataset categories. + # In this case `_metadata` variable will have a field with COCO-specific category mapping. + if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"): + reverse_id_mapping = { + v: k for k, v in self._metadata.thing_dataset_id_to_contiguous_id.items() + } + for result in lvis_results: + result["category_id"] = reverse_id_mapping[result["category_id"]] + else: + # unmap the category ids for LVIS (from 0-indexed to 1-indexed) + for result in lvis_results: + result["category_id"] += 1 + + if self._output_dir: + file_path = os.path.join(self._output_dir, "lvis_instances_results.json") + self._logger.info("Saving results to {}".format(file_path)) + with PathManager.open(file_path, "w") as f: + f.write(json.dumps(lvis_results)) + f.flush() + + if not self._do_evaluation: + self._logger.info("Annotations are not available for evaluation.") + return + + self._logger.info("Evaluating predictions ...") + for task in sorted(tasks): + lvis_eval = _evaluate_predictions_on_lvis( + self._lvis_api, + lvis_results, + task, + max_dets_per_image=self._max_dets_per_image, + class_names=self._metadata.get("thing_classes"), + ) + + res = self._derive_lvis_results( + lvis_eval, task, class_names=self._metadata.get("thing_classes") + ) + self._results[task] = res + + def _eval_box_proposals(self, predictions): + """ + Evaluate the box proposals in predictions. + Fill self._results with the metrics for "box_proposals" task. + """ + if self._output_dir: + # Saving generated box proposals to file. + # Predicted box_proposals are in XYXY_ABS mode. + bbox_mode = BoxMode.XYXY_ABS.value + ids, boxes, objectness_logits = [], [], [] + for prediction in predictions: + ids.append(prediction["image_id"]) + boxes.append(prediction["proposals"].proposal_boxes.tensor.numpy()) + objectness_logits.append(prediction["proposals"].objectness_logits.numpy()) + + proposal_data = { + "boxes": boxes, + "objectness_logits": objectness_logits, + "ids": ids, + "bbox_mode": bbox_mode, + } + with PathManager.open(os.path.join(self._output_dir, "box_proposals.pkl"), "wb") as f: + pickle.dump(proposal_data, f) + + if not self._do_evaluation: + self._logger.info("Annotations are not available for evaluation.") + return + + self._logger.info("Evaluating bbox proposals ...") + res = {} + areas = {"all": "", "small": "s", "medium": "m", "large": "l"} + for limit in [100, 1000]: + for area, suffix in areas.items(): + stats = _evaluate_box_proposals(predictions, self._lvis_api, area=area, limit=limit) + key = "AR{}@{:d}".format(suffix, limit) + res[key] = float(stats["ar"].item() * 100) + self._logger.info("Proposal metrics: \n" + create_small_table(res)) + self._results["box_proposals"] = res + + def _derive_lvis_results(self, lvis_eval, iou_type, class_names=None): + """ + Derive the desired score numbers from summarized COCOeval. + + Args: + lvis_eval (None or LVISEval): None represents no predictions from model. + iou_type (str): + class_names (None or list[str]): if provided, will use it to predict + per-category AP. + + Returns: + a dict of {metric name: score} + """ + + metrics = { + "bbox": ["AP", "AP50", "AP75", "APs", "APm", "APl", "APr", "APc", "APf"], + "segm": ["AP", "AP50", "AP75", "APs", "APm", "APl", "APr", "APc", "APf"], + }[iou_type] + + if lvis_eval is None: + self._logger.warn("No predictions from the model!") + return {metric: float("nan") for metric in metrics} + + # the standard metrics + # Pull the standard metrics from the LVIS results + results = lvis_eval.get_results() + results = {metric: float(results[metric] * 100) for metric in metrics} + self._logger.info( + "Evaluation results for {}: \n".format(iou_type) + create_small_table(results) + ) + if not np.isfinite(sum(results.values())): + self._logger.info("Some metrics cannot be computed and is shown as NaN.") + + if class_names is None or len(class_names) <= 1: + return results + # Compute per-category AP + # from https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L222-L252 # noqa + precisions = lvis_eval.eval["precision"] + # precision has dims (iou, recall, cls, area range, max dets) + assert len(class_names) == precisions.shape[2] + + results_per_category = [] + for idx, name in enumerate(class_names): + # area range index 0: all area ranges + precision = precisions[:, :, idx, 0] + precision = precision[precision > -1] + ap = np.mean(precision) if precision.size else float("nan") + results_per_category.append(("{}".format(name), float(ap * 100))) + + # tabulate it + N_COLS = min(6, len(results_per_category) * 2) + results_flatten = list(itertools.chain(*results_per_category)) + results_2d = itertools.zip_longest(*[results_flatten[i::N_COLS] for i in range(N_COLS)]) + table = tabulate( + results_2d, + tablefmt="pipe", + floatfmt=".3f", + headers=["category", "AP"] * (N_COLS // 2), + numalign="left", + ) + self._logger.info("Per-category {} AP: \n".format(iou_type) + table) + + results.update({"AP-" + name: ap for name, ap in results_per_category}) + return results + + +# inspired from Detectron: +# https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L255 # noqa +def _evaluate_box_proposals(dataset_predictions, lvis_api, thresholds=None, area="all", limit=None): + """ + Evaluate detection proposal recall metrics. This function is a much + faster alternative to the official LVIS API recall evaluation code. However, + it produces slightly different results. + """ + # Record max overlap value for each gt box + # Return vector of overlap values + areas = { + "all": 0, + "small": 1, + "medium": 2, + "large": 3, + "96-128": 4, + "128-256": 5, + "256-512": 6, + "512-inf": 7, + } + area_ranges = [ + [0**2, 1e5**2], # all + [0**2, 32**2], # small + [32**2, 96**2], # medium + [96**2, 1e5**2], # large + [96**2, 128**2], # 96-128 + [128**2, 256**2], # 128-256 + [256**2, 512**2], # 256-512 + [512**2, 1e5**2], + ] # 512-inf + assert area in areas, "Unknown area range: {}".format(area) + area_range = area_ranges[areas[area]] + gt_overlaps = [] + num_pos = 0 + + for prediction_dict in dataset_predictions: + predictions = prediction_dict["proposals"] + + # sort predictions in descending order + # TODO maybe remove this and make it explicit in the documentation + inds = predictions.objectness_logits.sort(descending=True)[1] + predictions = predictions[inds] + + ann_ids = lvis_api.get_ann_ids(img_ids=[prediction_dict["image_id"]]) + anno = lvis_api.load_anns(ann_ids) + gt_boxes = [ + BoxMode.convert(obj["bbox"], BoxMode.XYWH_ABS, BoxMode.XYXY_ABS) for obj in anno + ] + gt_boxes = torch.as_tensor(gt_boxes).reshape(-1, 4) # guard against no boxes + gt_boxes = Boxes(gt_boxes) + gt_areas = torch.as_tensor([obj["area"] for obj in anno]) + + if len(gt_boxes) == 0 or len(predictions) == 0: + continue + + valid_gt_inds = (gt_areas >= area_range[0]) & (gt_areas <= area_range[1]) + gt_boxes = gt_boxes[valid_gt_inds] + + num_pos += len(gt_boxes) + + if len(gt_boxes) == 0: + continue + + if limit is not None and len(predictions) > limit: + predictions = predictions[:limit] + + overlaps = pairwise_iou(predictions.proposal_boxes, gt_boxes) + + _gt_overlaps = torch.zeros(len(gt_boxes)) + for j in range(min(len(predictions), len(gt_boxes))): + # find which proposal box maximally covers each gt box + # and get the iou amount of coverage for each gt box + max_overlaps, argmax_overlaps = overlaps.max(dim=0) + + # find which gt box is 'best' covered (i.e. 'best' = most iou) + gt_ovr, gt_ind = max_overlaps.max(dim=0) + assert gt_ovr >= 0 + # find the proposal box that covers the best covered gt box + box_ind = argmax_overlaps[gt_ind] + # record the iou coverage of this gt box + _gt_overlaps[j] = overlaps[box_ind, gt_ind] + assert _gt_overlaps[j] == gt_ovr + # mark the proposal box and the gt box as used + overlaps[box_ind, :] = -1 + overlaps[:, gt_ind] = -1 + + # append recorded iou coverage level + gt_overlaps.append(_gt_overlaps) + gt_overlaps = ( + torch.cat(gt_overlaps, dim=0) if len(gt_overlaps) else torch.zeros(0, dtype=torch.float32) + ) + gt_overlaps, _ = torch.sort(gt_overlaps) + + if thresholds is None: + step = 0.05 + thresholds = torch.arange(0.5, 0.95 + 1e-5, step, dtype=torch.float32) + recalls = torch.zeros_like(thresholds) + # compute recall for each iou threshold + for i, t in enumerate(thresholds): + recalls[i] = (gt_overlaps >= t).float().sum() / float(num_pos) + # ar = 2 * np.trapz(recalls, thresholds) + ar = recalls.mean() + return { + "ar": ar, + "recalls": recalls, + "thresholds": thresholds, + "gt_overlaps": gt_overlaps, + "num_pos": num_pos, + } + + +def _evaluate_predictions_on_lvis( + lvis_gt, lvis_results, iou_type, max_dets_per_image=None, class_names=None +): + """ + Args: + iou_type (str): + max_dets_per_image (None or int): limit on maximum detections per image in evaluating AP + This limit, by default of the LVIS dataset, is 300. + class_names (None or list[str]): if provided, will use it to predict + per-category AP. + + Returns: + a dict of {metric name: score} + """ + metrics = { + "bbox": ["AP", "AP50", "AP75", "APs", "APm", "APl", "APr", "APc", "APf"], + "segm": ["AP", "AP50", "AP75", "APs", "APm", "APl", "APr", "APc", "APf"], + }[iou_type] + + logger = logging.getLogger(__name__) + + if len(lvis_results) == 0: # TODO: check if needed + logger.warn("No predictions from the model!") + return None + return {metric: float("nan") for metric in metrics} + + if iou_type == "segm": + lvis_results = copy.deepcopy(lvis_results) + # When evaluating mask AP, if the results contain bbox, LVIS API will + # use the box area as the area of the instance, instead of the mask area. + # This leads to a different definition of small/medium/large. + # We remove the bbox field to let mask AP use mask area. + for c in lvis_results: + c.pop("bbox", None) + + if max_dets_per_image is None: + max_dets_per_image = 300 # Default for LVIS dataset + + from lvis import LVISEval, LVISResults + + logger.info(f"Evaluating with max detections per image = {max_dets_per_image}") + lvis_results = LVISResults(lvis_gt, lvis_results, max_dets=max_dets_per_image) + lvis_eval = LVISEval(lvis_gt, lvis_results, iou_type) + lvis_eval.run() + lvis_eval.print_results() + + # Pull the standard metrics from the LVIS results + results = lvis_eval.get_results() + results = {metric: float(results[metric] * 100) for metric in metrics} + logger.info("Evaluation results for {}: \n".format(iou_type) + create_small_table(results)) + return lvis_eval + return results diff --git a/ape/evaluation/multi_dataset_evaluator.py b/ape/evaluation/multi_dataset_evaluator.py new file mode 100644 index 0000000..9948db1 --- /dev/null +++ b/ape/evaluation/multi_dataset_evaluator.py @@ -0,0 +1,382 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +# Modified by Xingyi Zhou +import copy +import glob +import itertools +import json +import os +from collections import OrderedDict + +import pycocotools.mask as mask_util +from PIL import Image + +import detectron2.utils.comm as comm +from detectron2.evaluation.coco_evaluation import ( + COCOEvaluator, + _evaluate_predictions_on_coco, + instances_to_coco_json, +) +from fvcore.common.file_io import PathManager + +from .oideval import OIDEvaluator, _evaluate_predictions_on_oid + + +def get_unified_evaluator(evaluator_type, dataset_name, cfg, distributed, output_dir): + unified_label_file = cfg.MULTI_DATASET.UNIFIED_LABEL_FILE + if evaluator_type == "coco": + evaluator = UnifiedCOCOEvaluator( + unified_label_file, dataset_name, cfg, distributed, output_dir + ) + elif evaluator_type == "oid": + evaluator = UnifiedOIDEvaluator( + unified_label_file, dataset_name, cfg, distributed, output_dir + ) + elif evaluator_type == "cityscapes_instance": + evaluator = UnifiedCityscapesEvaluator( + unified_label_file, dataset_name, cfg, distributed, output_dir + ) + else: + assert 0, evaluator_type + return evaluator + + +def map_back_unified_id(results, map_back, reverse_id_mapping=None): + ret = [] + for result in results: + if result["category_id"] in map_back: + result["category_id"] = map_back[result["category_id"]] + if reverse_id_mapping is not None: + result["category_id"] = reverse_id_mapping[result["category_id"]] + ret.append(result) + return ret + + +def map_back_unified_id_novel_classes(results, map_back, reverse_id_mapping=None): + ret = [] + for result in results: + if result["category_id"] in map_back: + original_id_list = map_back[result["category_id"]] + for original_id in original_id_list: + result_copy = copy.deepcopy(result) + result_copy["category_id"] = original_id + if reverse_id_mapping is not None: + result_copy["category_id"] = reverse_id_mapping[result_copy["category_id"]] + ret.append(result_copy) + return ret + + +class UnifiedCOCOEvaluator(COCOEvaluator): + def _eval_predictions(self, tasks, predictions): + """ + Evaluate predictions. Fill self._results with the metrics of the tasks. + """ + self._logger.info("Preparing results for COCO format ...") + coco_results = list(itertools.chain(*[x["instances"] for x in predictions])) + tasks = self._tasks or self._tasks_from_predictions(coco_results) + + # unmap the category ids for COCO + if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id") and False: + dataset_id_to_contiguous_id = self._metadata.thing_dataset_id_to_contiguous_id + all_contiguous_ids = list(dataset_id_to_contiguous_id.values()) + num_classes = len(all_contiguous_ids) + assert min(all_contiguous_ids) == 0 and max(all_contiguous_ids) == num_classes - 1 + + reverse_id_mapping = {v: k for k, v in dataset_id_to_contiguous_id.items()} + for result in coco_results: + category_id = result["category_id"] + assert category_id < num_classes, ( + f"A prediction has class={category_id}, " + f"but the dataset only has {num_classes} classes and " + f"predicted class id should be in [0, {num_classes - 1}]." + ) + result["category_id"] = reverse_id_mapping[category_id] + + if self._output_dir: + file_path = os.path.join(self._output_dir, "coco_instances_results.json") + self._logger.info("Saving results to {}".format(file_path)) + with PathManager.open(file_path, "w") as f: + f.write(json.dumps(coco_results)) + f.flush() + + if not self._do_evaluation and False: + self._logger.info("Annotations are not available for evaluation.") + return + + self._logger.info( + "Evaluating predictions with {} COCO API...".format( + "unofficial" if self._use_fast_impl else "official" + ) + ) + for task in sorted(tasks): + assert task in {"bbox", "segm", "keypoints"}, f"Got unknown task: {task}!" + coco_eval = ( + _evaluate_predictions_on_coco( + self._coco_api, + coco_results, + task, + kpt_oks_sigmas=self._kpt_oks_sigmas, + use_fast_impl=self._use_fast_impl, + img_ids=img_ids, + max_dets_per_image=self._max_dets_per_image, + ) + if len(coco_results) > 0 + else None # cocoapi does not handle empty results very well + ) + + res = self._derive_coco_results( + coco_eval, task, class_names=self._metadata.get("thing_classes") + ) + self._results[task] = res + + +class UnifiedCityscapesEvaluator(COCOEvaluator): + def __init__(self, unified_label_file, dataset_name, cfg, distributed, output_dir=None): + super().__init__(dataset_name, cfg, distributed, output_dir=output_dir) + meta_dataset_name = dataset_name[: dataset_name.find("_")] + print("meta_dataset_name", meta_dataset_name) + + self.unified_novel_classes_eval = cfg.MULTI_DATASET.UNIFIED_NOVEL_CLASSES_EVAL + if self.unified_novel_classes_eval: + match_novel_classes_file = cfg.MULTI_DATASET.MATCH_NOVEL_CLASSES_FILE + print("Loading map back from", match_novel_classes_file) + novel_classes_map = json.load(open(match_novel_classes_file, "r"))[meta_dataset_name] + self.map_back = {} + for c, match in enumerate(novel_classes_map): + for m in match: + self.map_back[m] = c + else: + unified_label_data = json.load(open(unified_label_file, "r")) + label_map = unified_label_data["label_map"] + label_map = label_map[meta_dataset_name] + self.map_back = {int(v): i for i, v in enumerate(label_map)} + + self._logger.info("saving outputs to {}".format(self._output_dir)) + self._temp_dir = self._output_dir + "/cityscapes_style_eval_tmp/" + self._logger.info( + "Writing cityscapes results to temporary directory {} ...".format(self._temp_dir) + ) + PathManager.mkdirs(self._temp_dir) + + def process(self, inputs, outputs): + """ + Args: + inputs: the inputs to a COCO model (e.g., GeneralizedRCNN). + It is a list of dict. Each dict corresponds to an image and + contains keys like "height", "width", "file_name", "image_id". + outputs: the outputs of a COCO model. It is a list of dicts with key + "instances" that contains :class:`Instances`. + """ + for input, output in zip(inputs, outputs): + prediction = {"image_id": input["image_id"], "file_name": input["file_name"]} + + instances = output["instances"].to(self._cpu_device) + prediction["instances"] = instances_to_coco_json(instances, input["image_id"]) + for x in prediction["instances"]: + x["file_name"] = input["file_name"] + # if len(prediction['instances']) == 0: + # self._logger.info("No prediction for {}".format(x['file_name'])) + # prediction['instances'] = [ + # {'file_name': input['file_name'], + # ''}] + self._predictions.append(prediction) + + def _eval_predictions(self, tasks, predictions): + self._logger.info("Preparing results for COCO format ...") + _unified_results = list(itertools.chain(*[x["instances"] for x in predictions])) + all_file_names = [x["file_name"] for x in predictions] + file_path = os.path.join(self._output_dir, "unified_instances_results.json") + self._logger.info("Saving results to {}".format(file_path)) + with PathManager.open(file_path, "w") as f: + f.write(json.dumps(_unified_results)) + f.flush() + + mapped = False + thing_classes = None + if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"): + self._logger.info( + "Evaluating COCO-stype cityscapes! " + "Using buildin meta to mapback IDs." + ) + reverse_id_mapping = { + v: k for k, v in self._metadata.thing_dataset_id_to_contiguous_id.items() + } + mapped = True + thing_classes = { + k: self._metadata.thing_classes[v] + for k, v in self._metadata.thing_dataset_id_to_contiguous_id.items() + } + else: + self._logger.info("Evaluating cityscapes! " + "Using eval script to map back IDs.") + reverse_id_mapping = None + thing_classes = self._metadata.thing_classes + + if self.unified_novel_classes_eval: + coco_results = map_back_unified_id_novel_classes( + _unified_results, self.map_back, reverse_id_mapping=reverse_id_mapping + ) + else: + coco_results = map_back_unified_id( + _unified_results, self.map_back, reverse_id_mapping=reverse_id_mapping + ) + + self.write_as_cityscapes( + coco_results, + all_file_names, + temp_dir=self._temp_dir, + mapped=mapped, + thing_classes=thing_classes, + ) + + os.environ["CITYSCAPES_DATASET"] = os.path.abspath( + os.path.join(self._metadata.gt_dir, "..", "..") + ) + # Load the Cityscapes eval script *after* setting the required env var, + # since the script reads CITYSCAPES_DATASET into global variables at load time. + import cityscapesscripts.evaluation.evalInstanceLevelSemanticLabeling as cityscapes_eval + + self._logger.info("Evaluating results under {} ...".format(self._temp_dir)) + # set some global states in cityscapes evaluation API, before evaluating + cityscapes_eval.args.predictionPath = os.path.abspath(self._temp_dir) + cityscapes_eval.args.predictionWalk = None + cityscapes_eval.args.JSONOutput = False + cityscapes_eval.args.colorized = False + cityscapes_eval.args.gtInstancesFile = os.path.join(self._temp_dir, "gtInstances.json") + + # These lines are adopted from + # https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalInstanceLevelSemanticLabeling.py # noqa + groundTruthImgList = glob.glob(cityscapes_eval.args.groundTruthSearch) + assert len( + groundTruthImgList + ), "Cannot find any ground truth images to use for evaluation. Searched for: {}".format( + cityscapes_eval.args.groundTruthSearch + ) + predictionImgList = [] + for gt in groundTruthImgList: + predictionImgList.append(cityscapes_eval.getPrediction(gt, cityscapes_eval.args)) + results = cityscapes_eval.evaluateImgLists( + predictionImgList, groundTruthImgList, cityscapes_eval.args + )["averages"] + + ret = OrderedDict() + ret["segm"] = {"AP": results["allAp"] * 100, "AP50": results["allAp50%"] * 100} + return ret + + @staticmethod + def write_as_cityscapes( + coco_results, + all_file_names, + temp_dir, + mapped=False, + thing_classes=None, + ext="_pred.txt", + subfolder="", + ): + from cityscapesscripts.helpers.labels import name2label + + results_per_image = {x: [] for x in all_file_names} + for x in coco_results: + results_per_image[x["file_name"]].append(x) + if subfolder != "": + PathManager.mkdirs(temp_dir + "/" + subfolder) + N = len(results_per_image) + for i, (file_name, coco_list) in enumerate(results_per_image.items()): + if i % (N // 10) == 0: + print("{}%".format(i // (N // 10) * 10), end=",", flush=True) + basename = os.path.splitext(os.path.basename(file_name))[0] + pred_txt = os.path.join(temp_dir, basename + ext) + + num_instances = len(coco_list) + with open(pred_txt, "w") as fout: + for i in range(num_instances): + if not mapped: + pred_class = coco_list[i]["category_id"] + classes = thing_classes[pred_class] + class_id = name2label[classes].id + else: + class_id = coco_list[i]["category_id"] + classes = thing_classes[class_id] + score = coco_list[i]["score"] + mask = mask_util.decode(coco_list[i]["segmentation"])[:, :].astype("uint8") + # mask = output.pred_masks[i].numpy().astype("uint8") + if subfolder != "": + png_filename = os.path.join( + temp_dir, + subfolder, + basename + "_{}_{}.png".format(i, classes.replace(" ", "_")), + ) + Image.fromarray(mask * 255).save(png_filename) + fout.write( + "{} {} {}\n".format( + subfolder + "/" + os.path.basename(png_filename), class_id, score + ) + ) + + else: + png_filename = os.path.join( + temp_dir, basename + "_{}_{}.png".format(i, classes.replace(" ", "_")) + ) + + Image.fromarray(mask * 255).save(png_filename) + fout.write( + "{} {} {}\n".format(os.path.basename(png_filename), class_id, score) + ) + + +class UnifiedOIDEvaluator(OIDEvaluator): + def __init__(self, unified_label_file, dataset_name, cfg, distributed, output_dir=None): + super().__init__(dataset_name, cfg, distributed, output_dir=output_dir) + meta_dataset_name = dataset_name[: dataset_name.find("_")] + print("meta_dataset_name", meta_dataset_name) + unified_label_data = json.load(open(unified_label_file, "r")) + label_map = unified_label_data["label_map"] + label_map = label_map[meta_dataset_name] + self.map_back = {int(v): i for i, v in enumerate(label_map)} + self._logger.info("saving outputs to {}".format(self._output_dir)) + + def evaluate(self): + if self._distributed: + comm.synchronize() + self._predictions = comm.gather(self._predictions, dst=0) + self._predictions = list(itertools.chain(*self._predictions)) + + if not comm.is_main_process(): + return + + if len(self._predictions) == 0: + self._logger.warning("[LVISEvaluator] Did not receive valid predictions.") + return {} + + self._logger.info("Preparing results in the OID format ...") + _unified_results = list(itertools.chain(*[x["instances"] for x in self._predictions])) + + if self._output_dir: + PathManager.mkdirs(self._output_dir) + + file_path = os.path.join(self._output_dir, "unified_instances_results.json") + self._logger.info("Saving results to {}".format(file_path)) + with PathManager.open(file_path, "w") as f: + f.write(json.dumps(_unified_results)) + f.flush() + + self._oid_results = map_back_unified_id(_unified_results, self.map_back) + + # unmap the category ids for LVIS (from 0-indexed to 1-indexed) + for result in self._oid_results: + result["category_id"] += 1 + + PathManager.mkdirs(self._output_dir) + file_path = os.path.join(self._output_dir, "oid_instances_results.json") + self._logger.info("Saving results to {}".format(file_path)) + with PathManager.open(file_path, "w") as f: + f.write(json.dumps(self._oid_results)) + f.flush() + + if not self._do_evaluation: + self._logger.info("Annotations are not available for evaluation.") + return + + self._logger.info("Evaluating predictions ...") + self._results = OrderedDict() + res = _evaluate_predictions_on_oid(self._oid_api, file_path, eval_seg=self._mask_on) + self._results["bbox"] = res + + return copy.deepcopy(self._results) diff --git a/ape/evaluation/oideval.py b/ape/evaluation/oideval.py new file mode 100644 index 0000000..d88afbc --- /dev/null +++ b/ape/evaluation/oideval.py @@ -0,0 +1,905 @@ +# Part of the code is from https://github.com/tensorflow/models/blob/master/research/object_detection/metrics/oid_challenge_evaluation.py +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# The original code is under Apache License, Version 2.0 (the "License"); +# Part of the code is from https://github.com/lvis-dataset/lvis-api/blob/master/lvis/eval.py +# Copyright (c) 2019, Agrim Gupta and Ross Girshick +# Modified by Xingyi Zhou +# This script re-implement OpenImages evaluation in detectron2 +import copy +import datetime +import itertools +import json +import logging +import os +from collections import OrderedDict, defaultdict + +import numpy as np +import pycocotools.mask as mask_utils +import torch +from lvis.lvis import LVIS +from lvis.results import LVISResults + +import detectron2.utils.comm as comm +from detectron2.data import MetadataCatalog +from detectron2.evaluation import DatasetEvaluator +from detectron2.evaluation.coco_evaluation import instances_to_coco_json +from detectron2.utils.logger import create_small_table +from fvcore.common.file_io import PathManager +from tabulate import tabulate + + +def compute_average_precision(precision, recall): + """Compute Average Precision according to the definition in VOCdevkit. + + Precision is modified to ensure that it does not decrease as recall + decrease. + + Args: + precision: A float [N, 1] numpy array of precisions + recall: A float [N, 1] numpy array of recalls + + Raises: + ValueError: if the input is not of the correct format + + Returns: + average_precison: The area under the precision recall curve. NaN if + precision and recall are None. + + """ + if precision is None: + if recall is not None: + raise ValueError("If precision is None, recall must also be None") + return np.NAN + + if not isinstance(precision, np.ndarray) or not isinstance(recall, np.ndarray): + raise ValueError("precision and recall must be numpy array") + if precision.dtype != np.float or recall.dtype != np.float: + raise ValueError("input must be float numpy array.") + if len(precision) != len(recall): + raise ValueError("precision and recall must be of the same size.") + if not precision.size: + return 0.0 + if np.amin(precision) < 0 or np.amax(precision) > 1: + raise ValueError("Precision must be in the range of [0, 1].") + if np.amin(recall) < 0 or np.amax(recall) > 1: + raise ValueError("recall must be in the range of [0, 1].") + if not all(recall[i] <= recall[i + 1] for i in range(len(recall) - 1)): + raise ValueError("recall must be a non-decreasing array") + + recall = np.concatenate([[0], recall, [1]]) + precision = np.concatenate([[0], precision, [0]]) + + for i in range(len(precision) - 2, -1, -1): + precision[i] = np.maximum(precision[i], precision[i + 1]) + indices = np.where(recall[1:] != recall[:-1])[0] + 1 + average_precision = np.sum((recall[indices] - recall[indices - 1]) * precision[indices]) + return average_precision + + +class OIDEval: + def __init__( + self, + lvis_gt, + lvis_dt, + iou_type="bbox", + expand_pred_label=False, + oid_hierarchy_path="./datasets/openimages/annotations/challenge-2019-label500-hierarchy.json", + ): + """Constructor for OIDEval. + Args: + lvis_gt (LVIS class instance, or str containing path of annotation file) + lvis_dt (LVISResult class instance, or str containing path of result file, + or list of dict) + iou_type (str): segm or bbox evaluation + """ + self.logger = logging.getLogger(__name__) + + if iou_type not in ["bbox", "segm"]: + raise ValueError("iou_type: {} is not supported.".format(iou_type)) + + if isinstance(lvis_gt, LVIS): + self.lvis_gt = lvis_gt + elif isinstance(lvis_gt, str): + self.lvis_gt = LVIS(lvis_gt) + else: + raise TypeError("Unsupported type {} of lvis_gt.".format(lvis_gt)) + + if isinstance(lvis_dt, LVISResults): + self.lvis_dt = lvis_dt + elif isinstance(lvis_dt, (str, list)): + self.lvis_dt = LVISResults(self.lvis_gt, lvis_dt, max_dets=-1) + else: + raise TypeError("Unsupported type {} of lvis_dt.".format(lvis_dt)) + + if expand_pred_label: + oid_hierarchy = json.load(open(oid_hierarchy_path, "r")) + cat_info = self.lvis_gt.dataset["categories"] + freebase2id = {x["freebase_id"]: x["id"] for x in cat_info} + id2freebase = {x["id"]: x["freebase_id"] for x in cat_info} + id2name = {x["id"]: x["name"] for x in cat_info} + + fas = defaultdict(set) + + def dfs(hierarchy, cur_id): + all_childs = set() + all_keyed_child = {} + if "Subcategory" in hierarchy: + for x in hierarchy["Subcategory"]: + childs = dfs(x, freebase2id[x["LabelName"]]) + all_childs.update(childs) + if cur_id != -1: + for c in all_childs: + fas[c].add(cur_id) + all_childs.add(cur_id) + return all_childs + + dfs(oid_hierarchy, -1) + + expanded_pred = [] + id_count = 0 + for d in self.lvis_dt.dataset["annotations"]: + cur_id = d["category_id"] + ids = [cur_id] + [x for x in fas[cur_id]] + for cat_id in ids: + new_box = copy.deepcopy(d) + id_count = id_count + 1 + new_box["id"] = id_count + new_box["category_id"] = cat_id + expanded_pred.append(new_box) + + self.logger.info( + "Expanding original {} preds to {} preds".format( + len(self.lvis_dt.dataset["annotations"]), len(expanded_pred) + ) + ) + self.lvis_dt.dataset["annotations"] = expanded_pred + self.lvis_dt._create_index() + + # per-image per-category evaluation results + self.eval_imgs = defaultdict(list) + self.eval = {} # accumulated evaluation results + self._gts = defaultdict(list) # gt for evaluation + self._dts = defaultdict(list) # dt for evaluation + self.params = Params(iou_type=iou_type) # parameters + self.results = OrderedDict() + self.ious = {} # ious between all gts and dts + + self.params.img_ids = sorted(self.lvis_gt.get_img_ids()) + self.params.cat_ids = sorted(self.lvis_gt.get_cat_ids()) + + def _to_mask(self, anns, lvis): + for ann in anns: + rle = lvis.ann_to_rle(ann) + ann["segmentation"] = rle + + def _prepare(self): + """Prepare self._gts and self._dts for evaluation based on params.""" + + cat_ids = self.params.cat_ids if self.params.cat_ids else None + + gts = self.lvis_gt.load_anns( + self.lvis_gt.get_ann_ids(img_ids=self.params.img_ids, cat_ids=cat_ids) + ) + dts = self.lvis_dt.load_anns( + self.lvis_dt.get_ann_ids(img_ids=self.params.img_ids, cat_ids=cat_ids) + ) + # convert ground truth to mask if iou_type == 'segm' + if self.params.iou_type == "segm": + self._to_mask(gts, self.lvis_gt) + self._to_mask(dts, self.lvis_dt) + + for gt in gts: + self._gts[gt["image_id"], gt["category_id"]].append(gt) + + # For federated dataset evaluation we will filter out all dt for an + # image which belong to categories not present in gt and not present in + # the negative list for an image. In other words detector is not penalized + # for categories about which we don't have gt information about their + # presence or absence in an image. + img_data = self.lvis_gt.load_imgs(ids=self.params.img_ids) + # per image map of categories not present in image + img_nl = {d["id"]: d["neg_category_ids"] for d in img_data} + # per image list of categories present in image + img_pl = {d["id"]: d["pos_category_ids"] for d in img_data} + # img_pl = defaultdict(set) + for ann in gts: + # img_pl[ann["image_id"]].add(ann["category_id"]) + assert ann["category_id"] in img_pl[ann["image_id"]] + + for dt in dts: + img_id, cat_id = dt["image_id"], dt["category_id"] + if cat_id not in img_nl[img_id] and cat_id not in img_pl[img_id]: + continue + self._dts[img_id, cat_id].append(dt) + + self.freq_groups = self._prepare_freq_group() + + def _prepare_freq_group(self): + freq_groups = [[] for _ in self.params.img_count_lbl] + cat_data = self.lvis_gt.load_cats(self.params.cat_ids) + for idx, _cat_data in enumerate(cat_data): + if "frequency" in _cat_data: + frequency = _cat_data["frequency"] + else: + frequency = "f" + freq_groups[self.params.img_count_lbl.index(frequency)].append(idx) + return freq_groups + + def evaluate(self): + """ + Run per image evaluation on given images and store results + (a list of dict) in self.eval_imgs. + """ + self.logger.info("Running per image evaluation.") + self.logger.info("Evaluate annotation type *{}*".format(self.params.iou_type)) + + self.params.img_ids = list(np.unique(self.params.img_ids)) + + if self.params.use_cats: + cat_ids = self.params.cat_ids + else: + cat_ids = [-1] + + self._prepare() + + self.ious = { + (img_id, cat_id): self.compute_iou(img_id, cat_id) + for img_id in self.params.img_ids + for cat_id in cat_ids + } + + # loop through images, area range, max detection number + self.eval_imgs = [ + self.evaluate_img_google(img_id, cat_id, area_rng) + for cat_id in cat_ids + for area_rng in self.params.area_rng + for img_id in self.params.img_ids + ] + + def _get_gt_dt(self, img_id, cat_id): + """Create gt, dt which are list of anns/dets. If use_cats is true + only anns/dets corresponding to tuple (img_id, cat_id) will be + used. Else, all anns/dets in image are used and cat_id is not used. + """ + if self.params.use_cats: + gt = self._gts[img_id, cat_id] + dt = self._dts[img_id, cat_id] + else: + gt = [_ann for _cat_id in self.params.cat_ids for _ann in self._gts[img_id, cat_id]] + dt = [_ann for _cat_id in self.params.cat_ids for _ann in self._dts[img_id, cat_id]] + return gt, dt + + def compute_iou(self, img_id, cat_id): + gt, dt = self._get_gt_dt(img_id, cat_id) + + if len(gt) == 0 and len(dt) == 0: + return [] + + # Sort detections in decreasing order of score. + idx = np.argsort([-d["score"] for d in dt], kind="mergesort") + dt = [dt[i] for i in idx] + + # iscrowd = [int(False)] * len(gt) + iscrowd = [int("iscrowd" in g and g["iscrowd"] > 0) for g in gt] + + if self.params.iou_type == "segm": + ann_type = "segmentation" + elif self.params.iou_type == "bbox": + ann_type = "bbox" + else: + raise ValueError("Unknown iou_type for iou computation.") + gt = [g[ann_type] for g in gt] + dt = [d[ann_type] for d in dt] + + # compute iou between each dt and gt region + # will return array of shape len(dt), len(gt) + ious = mask_utils.iou(dt, gt, iscrowd) + return ious + + def evaluate_img_google(self, img_id, cat_id, area_rng): + """Perform evaluation for single category and image.""" + gt, dt = self._get_gt_dt(img_id, cat_id) + + if len(gt) == 0 and len(dt) == 0: + return None + + if len(dt) == 0: + return { + "image_id": img_id, + "category_id": cat_id, + "area_rng": area_rng, + "dt_ids": [], + "dt_matches": np.array([], dtype=np.int32).reshape(1, -1), + "dt_scores": [], + "dt_ignore": np.array([], dtype=np.int32).reshape(1, -1), + "num_gt": len(gt), + } + + no_crowd_inds = [i for i, g in enumerate(gt) if ("iscrowd" not in g) or g["iscrowd"] == 0] + crowd_inds = [i for i, g in enumerate(gt) if "iscrowd" in g and g["iscrowd"] == 1] + dt_idx = np.argsort([-d["score"] for d in dt], kind="mergesort") + + if len(self.ious[img_id, cat_id]) > 0: + ious = self.ious[img_id, cat_id] + iou = ious[:, no_crowd_inds] + iou = iou[dt_idx] + ioa = ious[:, crowd_inds] + ioa = ioa[dt_idx] + else: + iou = np.zeros((len(dt_idx), 0)) + ioa = np.zeros((len(dt_idx), 0)) + scores = np.array([dt[i]["score"] for i in dt_idx]) + + num_detected_boxes = len(dt) + tp_fp_labels = np.zeros(num_detected_boxes, dtype=bool) + is_matched_to_group_of = np.zeros(num_detected_boxes, dtype=bool) + + def compute_match_iou(iou): + max_overlap_gt_ids = np.argmax(iou, axis=1) + is_gt_detected = np.zeros(iou.shape[1], dtype=bool) + for i in range(num_detected_boxes): + gt_id = max_overlap_gt_ids[i] + is_evaluatable = ( + not tp_fp_labels[i] and iou[i, gt_id] >= 0.5 and not is_matched_to_group_of[i] + ) + if is_evaluatable: + if not is_gt_detected[gt_id]: + tp_fp_labels[i] = True + is_gt_detected[gt_id] = True + + def compute_match_ioa(ioa): + scores_group_of = np.zeros(ioa.shape[1], dtype=float) + tp_fp_labels_group_of = np.ones(ioa.shape[1], dtype=float) + max_overlap_group_of_gt_ids = np.argmax(ioa, axis=1) + for i in range(num_detected_boxes): + gt_id = max_overlap_group_of_gt_ids[i] + is_evaluatable = ( + not tp_fp_labels[i] and ioa[i, gt_id] >= 0.5 and not is_matched_to_group_of[i] + ) + if is_evaluatable: + is_matched_to_group_of[i] = True + scores_group_of[gt_id] = max(scores_group_of[gt_id], scores[i]) + selector = np.where((scores_group_of > 0) & (tp_fp_labels_group_of > 0)) + scores_group_of = scores_group_of[selector] + tp_fp_labels_group_of = tp_fp_labels_group_of[selector] + + return scores_group_of, tp_fp_labels_group_of + + if iou.shape[1] > 0: + compute_match_iou(iou) + + scores_box_group_of = np.ndarray([0], dtype=float) + tp_fp_labels_box_group_of = np.ndarray([0], dtype=float) + + if ioa.shape[1] > 0: + scores_box_group_of, tp_fp_labels_box_group_of = compute_match_ioa(ioa) + + valid_entries = ~is_matched_to_group_of + + scores = np.concatenate((scores[valid_entries], scores_box_group_of)) + tp_fps = np.concatenate( + (tp_fp_labels[valid_entries].astype(float), tp_fp_labels_box_group_of) + ) + + return { + "image_id": img_id, + "category_id": cat_id, + "area_rng": area_rng, + "dt_matches": np.array([1 if x > 0 else 0 for x in tp_fps], dtype=np.int32).reshape( + 1, -1 + ), + "dt_scores": [x for x in scores], + "dt_ignore": np.array([0 for x in scores], dtype=np.int32).reshape(1, -1), + "num_gt": len(gt), + } + + def accumulate(self): + """Accumulate per image evaluation results and store the result in + self.eval. + """ + self.logger.info("Accumulating evaluation results.") + + if not self.eval_imgs: + self.logger.warn("Please run evaluate first.") + + if self.params.use_cats: + cat_ids = self.params.cat_ids + else: + cat_ids = [-1] + + num_thrs = len(self.params.iou_thrs) + num_recalls = len(self.params.rec_thrs) + num_cats = len(cat_ids) + num_area_rngs = len(self.params.area_rng) + num_imgs = len(self.params.img_ids) + + # -1 for absent categories + precision = -np.ones((num_thrs, num_recalls, num_cats, num_area_rngs)) + recall = -np.ones((num_thrs, num_cats, num_area_rngs)) + + # Initialize dt_pointers + dt_pointers = {} + for cat_idx in range(num_cats): + dt_pointers[cat_idx] = {} + for area_idx in range(num_area_rngs): + dt_pointers[cat_idx][area_idx] = {} + + # Per category evaluation + for cat_idx in range(num_cats): + Nk = cat_idx * num_area_rngs * num_imgs + for area_idx in range(num_area_rngs): + Na = area_idx * num_imgs + E = [self.eval_imgs[Nk + Na + img_idx] for img_idx in range(num_imgs)] + # Remove elements which are None + E = [e for e in E if not e is None] + if len(E) == 0: + continue + + dt_scores = np.concatenate([e["dt_scores"] for e in E], axis=0) + dt_idx = np.argsort(-dt_scores, kind="mergesort") + dt_scores = dt_scores[dt_idx] + dt_m = np.concatenate([e["dt_matches"] for e in E], axis=1)[:, dt_idx] + dt_ig = np.concatenate([e["dt_ignore"] for e in E], axis=1)[:, dt_idx] + + num_gt = sum([e["num_gt"] for e in E]) + if num_gt == 0: + continue + + tps = np.logical_and(dt_m, np.logical_not(dt_ig)) + fps = np.logical_and(np.logical_not(dt_m), np.logical_not(dt_ig)) + + tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float) + fp_sum = np.cumsum(fps, axis=1).astype(dtype=np.float) + + dt_pointers[cat_idx][area_idx] = { + "tps": tps, + "fps": fps, + } + + for iou_thr_idx, (tp, fp) in enumerate(zip(tp_sum, fp_sum)): + tp = np.array(tp) + fp = np.array(fp) + num_tp = len(tp) + rc = tp / num_gt + if num_tp: + recall[iou_thr_idx, cat_idx, area_idx] = rc[-1] + else: + recall[iou_thr_idx, cat_idx, area_idx] = 0 + + # np.spacing(1) ~= eps + pr = tp / (fp + tp + np.spacing(1)) + pr = pr.tolist() + + # Replace each precision value with the maximum precision + # value to the right of that recall level. This ensures + # that the calculated AP value will be less suspectable + # to small variations in the ranking. + for i in range(num_tp - 1, 0, -1): + if pr[i] > pr[i - 1]: + pr[i - 1] = pr[i] + + mAP = compute_average_precision( + np.array(pr, np.float).reshape(-1), np.array(rc, np.float).reshape(-1) + ) + precision[iou_thr_idx, :, cat_idx, area_idx] = mAP + + self.eval = { + "params": self.params, + "counts": [num_thrs, num_recalls, num_cats, num_area_rngs], + "date": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), + "precision": precision, + "recall": recall, + "dt_pointers": dt_pointers, + } + + def _summarize(self, summary_type, iou_thr=None, area_rng="all", freq_group_idx=None): + aidx = [ + idx for idx, _area_rng in enumerate(self.params.area_rng_lbl) if _area_rng == area_rng + ] + + if summary_type == "ap": + s = self.eval["precision"] + if iou_thr is not None: + tidx = np.where(iou_thr == self.params.iou_thrs)[0] + s = s[tidx] + if freq_group_idx is not None: + s = s[:, :, self.freq_groups[freq_group_idx], aidx] + else: + s = s[:, :, :, aidx] + else: + s = self.eval["recall"] + if iou_thr is not None: + tidx = np.where(iou_thr == self.params.iou_thrs)[0] + s = s[tidx] + s = s[:, :, aidx] + + if len(s[s > -1]) == 0: + mean_s = -1 + else: + mean_s = np.mean(s[s > -1]) + return mean_s + + def summarize(self): + """Compute and display summary metrics for evaluation results.""" + if not self.eval: + raise RuntimeError("Please run accumulate() first.") + + max_dets = self.params.max_dets + + self.results["AP"] = self._summarize("ap") + self.results["AP50"] = self._summarize("ap", iou_thr=0.50) + self.results["AP75"] = self._summarize("ap", iou_thr=0.75) + self.results["APs"] = self._summarize("ap", area_rng="small") + self.results["APm"] = self._summarize("ap", area_rng="medium") + self.results["APl"] = self._summarize("ap", area_rng="large") + self.results["APr"] = self._summarize("ap", freq_group_idx=0) + self.results["APc"] = self._summarize("ap", freq_group_idx=1) + self.results["APf"] = self._summarize("ap", freq_group_idx=2) + + key = "AR@{}".format(max_dets) + self.results[key] = self._summarize("ar") + + for area_rng in ["small", "medium", "large"]: + key = "AR{}@{}".format(area_rng[0], max_dets) + self.results[key] = self._summarize("ar", area_rng=area_rng) + + def run(self): + """Wrapper function which calculates the results.""" + self.evaluate() + self.accumulate() + self.summarize() + + def print_results(self): + template = " {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} catIds={:>3s}] = {:0.3f}" + + for key, value in self.results.items(): + max_dets = self.params.max_dets + if "AP" in key: + title = "Average Precision" + _type = "(AP)" + else: + title = "Average Recall" + _type = "(AR)" + + if len(key) > 2 and key[2].isdigit(): + iou_thr = float(key[2:]) / 100 + iou = "{:0.2f}".format(iou_thr) + else: + iou = "{:0.2f}:{:0.2f}".format(self.params.iou_thrs[0], self.params.iou_thrs[-1]) + + if len(key) > 2 and key[2] in ["r", "c", "f"]: + cat_group_name = key[2] + else: + cat_group_name = "all" + + if len(key) > 2 and key[2] in ["s", "m", "l"]: + area_rng = key[2] + else: + area_rng = "all" + + self.logger.info( + template.format(title, _type, iou, area_rng, max_dets, cat_group_name, value) + ) + + def get_results(self): + if not self.results: + self.logger.warn("results is empty. Call run().") + return self.results + + +class Params: + def __init__(self, iou_type): + """Params for LVIS evaluation API.""" + self.img_ids = [] + self.cat_ids = [] + # np.arange causes trouble. the data point on arange is slightly + # larger than the true value + self.iou_thrs = np.linspace( + 0.5, 0.95, int(np.round((0.95 - 0.5) / 0.05)) + 1, endpoint=True + ) + self.rec_thrs = np.linspace( + 0.0, 1.00, int(np.round((1.00 - 0.0) / 0.01)) + 1, endpoint=True + ) + self.max_dets = 1000 + self.google_style = True + + self.area_rng = [ + [0**2, 1e5**2], + [0**2, 32**2], + [32**2, 96**2], + [96**2, 1e5**2], + ] + self.area_rng_lbl = ["all", "small", "medium", "large"] + self.use_cats = 1 + # We bin categories in three bins based how many images of the training + # set the category is present in. + # r: Rare : < 10 + # c: Common : >= 10 and < 100 + # f: Frequent: >= 100 + self.img_count_lbl = ["r", "c", "f"] + self.iou_type = iou_type + + +class OIDEvaluator(DatasetEvaluator): + def __init__( + self, + dataset_name, + tasks=None, + distributed=True, + output_dir=None, + *, + max_dets_per_image=None, + ): + """ + Args: + dataset_name (str): name of the dataset to be evaluated. + It must have the following corresponding metadata: + "json_file": the path to the LVIS format annotation + tasks (tuple[str]): tasks that can be evaluated under the given + configuration. A task is one of "bbox", "segm". + By default, will infer this automatically from predictions. + distributed (True): if True, will collect results from all ranks for evaluation. + Otherwise, will evaluate the results in the current process. + output_dir (str): optional, an output directory to dump results. + max_dets_per_image (None or int): limit on maximum detections per image in evaluating AP + This limit, by default of the LVIS dataset, is 300. + """ + from lvis import LVIS + + self._logger = logging.getLogger(__name__) + + if tasks is not None and isinstance(tasks, CfgNode): + self._logger.warn( + "COCO Evaluator instantiated using config, this is deprecated behavior." + " Please pass in explicit arguments instead." + ) + self._tasks = None # Infering it from predictions should be better + else: + self._tasks = tasks + + self._distributed = distributed + self._output_dir = output_dir + self._max_dets_per_image = max_dets_per_image + + self._cpu_device = torch.device("cpu") + + self._metadata = MetadataCatalog.get(dataset_name) + json_file = PathManager.get_local_path(self._metadata.json_file) + self._oid_api = LVIS(json_file) + # Test set json files do not contain annotations (evaluation must be + # performed using the LVIS evaluation server). + self._do_evaluation = len(self._oid_api.get_ann_ids()) > 0 + + def reset(self): + self._predictions = [] + + def process(self, inputs, outputs): + """ + Args: + inputs: the inputs to a LVIS model (e.g., GeneralizedRCNN). + It is a list of dict. Each dict corresponds to an image and + contains keys like "height", "width", "file_name", "image_id". + outputs: the outputs of a LVIS model. It is a list of dicts with key + "instances" that contains :class:`Instances`. + """ + for input, output in zip(inputs, outputs): + prediction = {"image_id": input["image_id"]} + + if "instances" in output: + instances = output["instances"].to(self._cpu_device) + prediction["instances"] = instances_to_coco_json(instances, input["image_id"]) + if "proposals" in output: + prediction["proposals"] = output["proposals"].to(self._cpu_device) + self._predictions.append(prediction) + + def evaluate(self): + if self._distributed: + comm.synchronize() + predictions = comm.gather(self._predictions, dst=0) + predictions = list(itertools.chain(*predictions)) + + if not comm.is_main_process(): + return + else: + predictions = self._predictions + + if len(predictions) == 0: + self._logger.warning("[LVISEvaluator] Did not receive valid predictions.") + return {} + + if self._output_dir: + PathManager.mkdirs(self._output_dir) + file_path = os.path.join(self._output_dir, "instances_predictions.pth") + with PathManager.open(file_path, "wb") as f: + torch.save(predictions, f) + + self._results = OrderedDict() + if "proposals" in predictions[0]: + self._eval_box_proposals(predictions) + if "instances" in predictions[0]: + self._eval_predictions(predictions) + # Copy so the caller can do whatever with results + return copy.deepcopy(self._results) + + def _tasks_from_predictions(self, predictions): + return ("bbox", "bbox_expand") + for pred in predictions: + if "segmentation" in pred: + return ("bbox", "bbox_expand", "segm") + return ("bbox", "bbox_expand") + + def _eval_predictions(self, predictions): + """ + Evaluate predictions. Fill self._results with the metrics of the tasks. + + Args: + predictions (list[dict]): list of outputs from the model + """ + self._logger.info("Preparing results in the OID format ...") + oid_results = list(itertools.chain(*[x["instances"] for x in predictions])) + tasks = self._tasks or self._tasks_from_predictions(oid_results) + + # LVIS evaluator can be used to evaluate results for COCO dataset categories. + # In this case `_metadata` variable will have a field with COCO-specific category mapping. + if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"): + reverse_id_mapping = { + v: k for k, v in self._metadata.thing_dataset_id_to_contiguous_id.items() + } + for result in oid_results: + result["category_id"] = reverse_id_mapping[result["category_id"]] + else: + # unmap the category ids for LVIS (from 0-indexed to 1-indexed) + for result in oid_results: + result["category_id"] += 1 + + if self._output_dir: + file_path = os.path.join(self._output_dir, "oid_instances_results.json") + self._logger.info("Saving results to {}".format(file_path)) + with PathManager.open(file_path, "w") as f: + f.write(json.dumps(oid_results)) + f.flush() + + if not self._do_evaluation: + self._logger.info("Annotations are not available for evaluation.") + return + + self._logger.info("Evaluating predictions ...") + for task in sorted(tasks): + oid_eval = _evaluate_predictions_on_oid( + self._oid_api, + oid_results, + task, + max_dets_per_image=self._max_dets_per_image, + ) + + res = self._derive_oid_results( + oid_eval, task, class_names=self._metadata.get("thing_classes") + ) + self._results[task] = res + + def _derive_oid_results(self, oid_eval, iou_type, class_names=None): + """ + Derive the desired score numbers from summarized COCOeval. + + Args: + lvis_eval (None or LVISEval): None represents no predictions from model. + iou_type (str): + class_names (None or list[str]): if provided, will use it to predict + per-category AP. + + Returns: + a dict of {metric name: score} + """ + + metrics = { + "bbox": ["AP", "AP50", "AP75", "APs", "APm", "APl", "APr", "APc", "APf"], + "bbox_expand": ["AP", "AP50", "AP75", "APs", "APm", "APl", "APr", "APc", "APf"], + "segm": ["AP", "AP50", "AP75", "APs", "APm", "APl", "APr", "APc", "APf"], + }[iou_type] + + if oid_eval is None: + self._logger.warn("No predictions from the model!") + return {metric: float("nan") for metric in metrics} + + # the standard metrics + # Pull the standard metrics from the LVIS results + results = oid_eval.get_results() + results = {metric: float(results[metric] * 100) for metric in metrics} + self._logger.info( + "Evaluation results for {}: \n".format(iou_type) + create_small_table(results) + ) + if not np.isfinite(sum(results.values())): + self._logger.info("Some metrics cannot be computed and is shown as NaN.") + + if class_names is None or len(class_names) <= 1: + return results + # Compute per-category AP + # from https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L222-L252 # noqa + precisions = oid_eval.eval["precision"] + # precision has dims (iou, recall, cls, area range, max dets) + assert len(class_names) == precisions.shape[2] + + results_per_category = [] + for idx, name in enumerate(class_names): + # area range index 0: all area ranges + precision = precisions[:, :, idx, 0] + precision = precision[precision > -1] + ap = np.mean(precision) if precision.size else float("nan") + results_per_category.append(("{}".format(name), float(ap * 100))) + + # tabulate it + N_COLS = min(6, len(results_per_category) * 2) + results_flatten = list(itertools.chain(*results_per_category)) + results_2d = itertools.zip_longest(*[results_flatten[i::N_COLS] for i in range(N_COLS)]) + table = tabulate( + results_2d, + tablefmt="pipe", + floatfmt=".3f", + headers=["category", "AP"] * (N_COLS // 2), + numalign="left", + ) + self._logger.info("Per-category {} AP: \n".format(iou_type) + table) + + results.update({"AP-" + name: ap for name, ap in results_per_category}) + return results + + +def _evaluate_predictions_on_oid( + oid_gt, + oid_results, + iou_type, + max_dets_per_image=None, +): + """ + Args: + iou_type (str): + max_dets_per_image (None or int): limit on maximum detections per image in evaluating AP + This limit, by default of the LVIS dataset, is 300. + class_names (None or list[str]): if provided, will use it to predict + per-category AP. + + Returns: + a dict of {metric name: score} + """ + metrics = { + "bbox": ["AP", "AP50", "AP75", "APs", "APm", "APl", "APr", "APc", "APf"], + "bbox_expand": ["AP", "AP50", "AP75", "APs", "APm", "APl", "APr", "APc", "APf"], + "segm": ["AP", "AP50", "AP75", "APs", "APm", "APl", "APr", "APc", "APf"], + }[iou_type] + + logger = logging.getLogger(__name__) + + if len(oid_results) == 0: # TODO: check if needed + logger.warn("No predictions from the model!") + return {metric: float("nan") for metric in metrics} + + if max_dets_per_image is None: + max_dets_per_image = 1000 # Default for OID dataset + + from lvis import LVISEval, LVISResults + + logger.info(f"Evaluating with max detections per image = {max_dets_per_image}") + oid_results = LVISResults(oid_gt, oid_results, max_dets=max_dets_per_image) + + if "segm" in iou_type: + oid_eval = OIDEval(oid_gt, oid_results, "segm", expand_pred_label=False) + oid_eval.run() + oid_eval.print_results() + elif "bbox_expand" in iou_type: + oid_eval = OIDEval(oid_gt, oid_results, "bbox", expand_pred_label=True) + oid_eval.run() + oid_eval.print_results() + elif "bbox" in iou_type: + oid_eval = OIDEval(oid_gt, oid_results, "bbox", expand_pred_label=False) + oid_eval.run() + oid_eval.print_results() + else: + return None + return {metric: float("nan") for metric in metrics} + + # Pull the standard metrics from the LVIS results + results = oid_eval.get_results() + results = {metric: float(results[metric] * 100) for metric in metrics} + logger.info("Evaluation results for {}: \n".format(iou_type) + create_small_table(results)) + return oid_eval + return results diff --git a/ape/evaluation/refcoco_evaluation.py b/ape/evaluation/refcoco_evaluation.py new file mode 100644 index 0000000..532c637 --- /dev/null +++ b/ape/evaluation/refcoco_evaluation.py @@ -0,0 +1,753 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import contextlib +import copy +import io +import itertools +import json +import logging +import os +import pickle +from collections import OrderedDict + +import numpy as np +import pycocotools.mask as mask_util +import torch +from pycocotools.coco import COCO +from pycocotools.cocoeval import COCOeval + +import detectron2.utils.comm as comm +from detectron2.config import CfgNode +from detectron2.data import MetadataCatalog +from detectron2.data.datasets.coco import convert_to_coco_json +from detectron2.structures import Boxes, BoxMode, pairwise_iou +from detectron2.utils.file_io import PathManager +from detectron2.utils.logger import create_small_table +from tabulate import tabulate + +from .evaluator import DatasetEvaluator +from .refcocoeval import RefCOCOeval + + +class RefCOCOEvaluator(DatasetEvaluator): + """ + Evaluate AR for object proposals, AP for instance detection/segmentation, AP + for keypoint detection outputs using COCO's metrics. + See http://cocodataset.org/#detection-eval and + http://cocodataset.org/#keypoints-eval to understand its metrics. + The metrics range from 0 to 100 (instead of 0 to 1), where a -1 or NaN means + the metric cannot be computed (e.g. due to no predictions made). + + In addition to COCO, this evaluator is able to support any bounding box detection, + instance segmentation, or keypoint detection dataset. + """ + + def __init__( + self, + dataset_name, + tasks=None, + distributed=True, + output_dir=None, + *, + max_dets_per_image=None, + kpt_oks_sigmas=(), + allow_cached_coco=True, + force_tasks=None, + ): + """ + Args: + dataset_name (str): name of the dataset to be evaluated. + It must have either the following corresponding metadata: + + "json_file": the path to the COCO format annotation + + Or it must be in detectron2's standard dataset format + so it can be converted to COCO format automatically. + tasks (tuple[str]): tasks that can be evaluated under the given + configuration. A task is one of "bbox", "segm", "keypoints". + By default, will infer this automatically from predictions. + distributed (True): if True, will collect results from all ranks and run evaluation + in the main process. + Otherwise, will only evaluate the results in the current process. + output_dir (str): optional, an output directory to dump all + results predicted on the dataset. The dump contains two files: + + 1. "instances_predictions.pth" a file that can be loaded with `torch.load` and + contains all the results in the format they are produced by the model. + 2. "coco_instances_results.json" a json file in COCO's result format. + max_dets_per_image (int): limit on the maximum number of detections per image. + By default in COCO, this limit is to 100, but this can be customized + to be greater, as is needed in evaluation metrics AP fixed and AP pool + (see https://arxiv.org/pdf/2102.01066.pdf) + This doesn't affect keypoint evaluation. + kpt_oks_sigmas (list[float]): The sigmas used to calculate keypoint OKS. + See http://cocodataset.org/#keypoints-eval + When empty, it will use the defaults in COCO. + Otherwise it should be the same length as ROI_KEYPOINT_HEAD.NUM_KEYPOINTS. + allow_cached_coco (bool): Whether to use cached coco json from previous validation + runs. You should set this to False if you need to use different validation data. + Defaults to True. + """ + self.dataset_name = dataset_name + self._logger = logging.getLogger(__name__) + self._distributed = distributed + self._output_dir = output_dir + self.force_tasks = force_tasks + + # COCOeval requires the limit on the number of detections per image (maxDets) to be a list + # with at least 3 elements. The default maxDets in COCOeval is [1, 10, 100], in which the + # 3rd element (100) is used as the limit on the number of detections per image when + # evaluating AP. COCOEvaluator expects an integer for max_dets_per_image, so for COCOeval, + # we reformat max_dets_per_image into [1, 10, max_dets_per_image], based on the defaults. + if max_dets_per_image is None: + max_dets_per_image = [1, 10, 100] + else: + max_dets_per_image = [1, 10, max_dets_per_image] + self._max_dets_per_image = max_dets_per_image + + if tasks is not None and isinstance(tasks, CfgNode): + kpt_oks_sigmas = ( + tasks.TEST.KEYPOINT_OKS_SIGMAS if not kpt_oks_sigmas else kpt_oks_sigmas + ) + self._logger.warn( + "COCO Evaluator instantiated using config, this is deprecated behavior." + " Please pass in explicit arguments instead." + ) + self._tasks = None # Infering it from predictions should be better + else: + self._tasks = tasks + + self._cpu_device = torch.device("cpu") + + self._metadata = MetadataCatalog.get(dataset_name) + if not hasattr(self._metadata, "json_file"): + if output_dir is None: + raise ValueError( + "output_dir must be provided to COCOEvaluator " + "for datasets not in COCO format." + ) + self._logger.info(f"Trying to convert '{dataset_name}' to COCO format ...") + + cache_path = os.path.join(output_dir, f"{dataset_name}_coco_format.json") + self._metadata.json_file = cache_path + convert_to_coco_json(dataset_name, cache_path, allow_cached=allow_cached_coco) + + json_file = PathManager.get_local_path(self._metadata.json_file) + with contextlib.redirect_stdout(io.StringIO()): + self._coco_api = COCO(json_file) + + # Test set json files do not contain annotations (evaluation must be + # performed using the COCO evaluation server). + self._do_evaluation = "annotations" in self._coco_api.dataset + if self._do_evaluation: + self._kpt_oks_sigmas = kpt_oks_sigmas + + def reset(self): + self._predictions = [] + + def process(self, inputs, outputs): + """ + Args: + inputs: the inputs to a COCO model (e.g., GeneralizedRCNN). + It is a list of dict. Each dict corresponds to an image and + contains keys like "height", "width", "file_name", "image_id". + outputs: the outputs of a COCO model. It is a list of dicts with key + "instances" that contains :class:`Instances`. + """ + for input, output in zip(inputs, outputs): + prediction = {"image_id": input["image_id"]} + + if "instances" in output: + instances = output["instances"].to(self._cpu_device) + prediction["instances"] = instances_to_coco_json(instances, input["image_id"]) + if "proposals" in output: + prediction["proposals"] = output["proposals"].to(self._cpu_device) + if len(prediction) > 1: + self._predictions.append(prediction) + + def evaluate(self, img_ids=None): + """ + Args: + img_ids: a list of image IDs to evaluate on. Default to None for the whole dataset + """ + if self._distributed: + comm.synchronize() + predictions = comm.gather(self._predictions, dst=0) + predictions = list(itertools.chain(*predictions)) + + if not comm.is_main_process(): + return {} + else: + predictions = self._predictions + + if len(predictions) == 0: + self._logger.warning("[COCOEvaluator] Did not receive valid predictions.") + return {} + + if self._output_dir: + PathManager.mkdirs(self._output_dir) + file_path = os.path.join(self._output_dir, "instances_predictions.pth") + with PathManager.open(file_path, "wb") as f: + torch.save(predictions, f) + + self._results = OrderedDict() + if "proposals" in predictions[0]: + self._eval_box_proposals(predictions) + if "instances" in predictions[0]: + self._eval_predictions(predictions, img_ids=img_ids) + # Copy so the caller can do whatever with results + return copy.deepcopy(self._results) + + def _tasks_from_predictions(self, predictions): + """ + Get COCO API "tasks" (i.e. iou_type) from COCO-format predictions. + """ + tasks = {"bbox"} + for pred in predictions: + if "segmentation" in pred: + tasks.add("segm") + if "keypoints" in pred: + tasks.add("keypoints") + return sorted(tasks) + + def _eval_predictions(self, predictions, img_ids=None): + """ + Evaluate predictions. Fill self._results with the metrics of the tasks. + """ + self._logger.info("Preparing results for COCO format ...") + coco_results = list(itertools.chain(*[x["instances"] for x in predictions])) + tasks = self._tasks or self._tasks_from_predictions(coco_results) + if self.force_tasks is not None: + tasks = self.force_tasks + # unmap the category ids for COCO + if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"): + dataset_id_to_contiguous_id = self._metadata.thing_dataset_id_to_contiguous_id + all_contiguous_ids = list(dataset_id_to_contiguous_id.values()) + num_classes = len(all_contiguous_ids) + assert min(all_contiguous_ids) == 0 and max(all_contiguous_ids) == num_classes - 1 + + reverse_id_mapping = {v: k for k, v in dataset_id_to_contiguous_id.items()} + for result in coco_results: + category_id = result["category_id"] + assert category_id < num_classes, ( + f"A prediction has class={category_id}, " + f"but the dataset only has {num_classes} classes and " + f"predicted class id should be in [0, {num_classes - 1}]." + ) + result["category_id"] = reverse_id_mapping[category_id] + + if self._output_dir: + file_path = os.path.join( + self._output_dir, "{}_instances_results.json".format(self.dataset_name) + ) + self._logger.info("Saving results to {}".format(file_path)) + with PathManager.open(file_path, "w") as f: + f.write(json.dumps(coco_results)) + f.flush() + + if not self._do_evaluation: + self._logger.info("Annotations are not available for evaluation.") + return + + self._logger.info("Evaluating predictions with RefCOCO API...") + for task in sorted(tasks): + assert task in {"bbox", "segm", "keypoints"}, f"Got unknown task: {task}!" + coco_eval = ( + _evaluate_predictions_on_coco( + self._coco_api, + coco_results, + task, + kpt_oks_sigmas=self._kpt_oks_sigmas, + cocoeval_fn=RefCOCOeval, + img_ids=img_ids, + max_dets_per_image=self._max_dets_per_image, + ) + if len(coco_results) > 0 + else None # cocoapi does not handle empty results very well + ) + res = self._derive_refcoco_results(coco_eval, task) + self._results[task] = res + + def _eval_box_proposals(self, predictions): + """ + Evaluate the box proposals in predictions. + Fill self._results with the metrics for "box_proposals" task. + """ + if self._output_dir: + # Saving generated box proposals to file. + # Predicted box_proposals are in XYXY_ABS mode. + bbox_mode = BoxMode.XYXY_ABS.value + ids, boxes, objectness_logits = [], [], [] + for prediction in predictions: + ids.append(prediction["image_id"]) + boxes.append(prediction["proposals"].proposal_boxes.tensor.numpy()) + objectness_logits.append(prediction["proposals"].objectness_logits.numpy()) + + proposal_data = { + "boxes": boxes, + "objectness_logits": objectness_logits, + "ids": ids, + "bbox_mode": bbox_mode, + } + with PathManager.open(os.path.join(self._output_dir, "box_proposals.pkl"), "wb") as f: + pickle.dump(proposal_data, f) + + if not self._do_evaluation: + self._logger.info("Annotations are not available for evaluation.") + return + + self._logger.info("Evaluating bbox proposals ...") + res = {} + areas = {"all": "", "small": "s", "medium": "m", "large": "l"} + for limit in [100, 1000]: + for area, suffix in areas.items(): + stats = _evaluate_box_proposals(predictions, self._coco_api, area=area, limit=limit) + key = "AR{}@{:d}".format(suffix, limit) + res[key] = float(stats["ar"].item() * 100) + self._logger.info("Proposal metrics: \n" + create_small_table(res)) + self._results["box_proposals"] = res + + def _derive_coco_results(self, coco_eval, iou_type, class_names=None): + """ + Derive the desired score numbers from summarized COCOeval. + + Args: + coco_eval (None or COCOEval): None represents no predictions from model. + iou_type (str): + class_names (None or list[str]): if provided, will use it to predict + per-category AP. + + Returns: + a dict of {metric name: score} + """ + + metrics = { + "bbox": ["AP", "AP50", "AP75", "APs", "APm", "APl"], + "segm": ["AP", "AP50", "AP75", "APs", "APm", "APl"], + "keypoints": ["AP", "AP50", "AP75", "APm", "APl"], + }[iou_type] + + if coco_eval is None: + self._logger.warn("No predictions from the model!") + return {metric: float("nan") for metric in metrics} + + # the standard metrics + results = { + metric: float(coco_eval.stats[idx] * 100 if coco_eval.stats[idx] >= 0 else "nan") + for idx, metric in enumerate(metrics) + } + self._logger.info( + "Evaluation results for {}: \n".format(iou_type) + create_small_table(results) + ) + if not np.isfinite(sum(results.values())): + self._logger.info("Some metrics cannot be computed and is shown as NaN.") + + if class_names is None or len(class_names) <= 1: + return results + # Compute per-category AP + # from https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L222-L252 # noqa + precisions = coco_eval.eval["precision"] + # precision has dims (iou, recall, cls, area range, max dets) + assert len(class_names) == precisions.shape[2] + + results_per_category = [] + for idx, name in enumerate(class_names): + # area range index 0: all area ranges + # max dets index -1: typically 100 per image + precision = precisions[:, :, idx, 0, -1] + precision = precision[precision > -1] + ap = np.mean(precision) if precision.size else float("nan") + results_per_category.append(("{}".format(name), float(ap * 100))) + + # tabulate it + N_COLS = min(6, len(results_per_category) * 2) + results_flatten = list(itertools.chain(*results_per_category)) + results_2d = itertools.zip_longest(*[results_flatten[i::N_COLS] for i in range(N_COLS)]) + table = tabulate( + results_2d, + tablefmt="pipe", + floatfmt=".3f", + headers=["category", "AP"] * (N_COLS // 2), + numalign="left", + ) + self._logger.info("Per-category {} AP: \n".format(iou_type) + table) + + results.update({"AP-" + name: ap for name, ap in results_per_category}) + return results + + def _derive_refcoco_results(self, coco_eval, iou_type): + """ + Derive the desired score numbers from summarized COCOeval. + + Args: + coco_eval (None or COCOEval): None represents no predictions from model. + iou_type (str): + class_names (None or list[str]): if provided, will use it to predict + per-category AP. + + Returns: + a dict of {metric name: score} + """ + + metrics = {"bbox": ["P@0.5", "P@0.6", "P@0.7", "P@0.8", "P@0.9"], "segm": ["oIoU", "mIoU"]}[ + iou_type + ] + + if coco_eval is None: + self._logger.warn("No predictions from the model!") + return {metric: float("nan") for metric in metrics} + + # the standard metrics + results = {metric: float("nan") for idx, metric in enumerate(metrics)} + ious = np.array([v for (k, v) in coco_eval.ious.items()]) + total_intersection_area = coco_eval.total_intersection_area + total_union_area = coco_eval.total_union_area + iou_list = coco_eval.iou_list + # compute metrics + if iou_type == "bbox": + results["P@0.5"] = np.sum(ious > 0.5) / len(ious) * 100 + results["P@0.6"] = np.sum(ious > 0.6) / len(ious) * 100 + results["P@0.7"] = np.sum(ious > 0.7) / len(ious) * 100 + results["P@0.8"] = np.sum(ious > 0.8) / len(ious) * 100 + results["P@0.9"] = np.sum(ious > 0.9) / len(ious) * 100 + elif iou_type == "segm": + results["oIoU"] = total_intersection_area / total_union_area * 100 + results["mIoU"] = np.mean(ious) * 100 + else: + raise ValueError("Unsupported iou_type!") + self._logger.info( + "Evaluation results for {}: \n".format(iou_type) + create_small_table(results) + ) + + # results.update({"AP-" + name: ap for name, ap in results_per_category}) + return results + + +def instances_to_coco_json(instances, img_id): + """ + Dump an "Instances" object to a COCO-format json that's used for evaluation. + + Args: + instances (Instances): + img_id (int): the image id + + Returns: + list[dict]: list of json annotations in COCO format. + """ + num_instance = len(instances) + if num_instance == 0: + return [] + + boxes = instances.pred_boxes.tensor.numpy() + boxes = BoxMode.convert(boxes, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS) + boxes = boxes.tolist() + scores = instances.scores.tolist() + classes = instances.pred_classes.tolist() + + has_mask = instances.has("pred_masks") + if has_mask: + # use RLE to encode the masks, because they are too large and takes memory + # since this evaluator stores outputs of the entire dataset + rles = [ + mask_util.encode(np.array(mask[:, :, None], order="F", dtype="uint8"))[0] + for mask in instances.pred_masks + ] + for rle in rles: + # "counts" is an array encoded by mask_util as a byte-stream. Python3's + # json writer which always produces strings cannot serialize a bytestream + # unless you decode it. Thankfully, utf-8 works out (which is also what + # the pycocotools/_mask.pyx does). + rle["counts"] = rle["counts"].decode("utf-8") + + has_keypoints = instances.has("pred_keypoints") + if has_keypoints: + keypoints = instances.pred_keypoints + + results = [] + for k in range(num_instance): + result = { + "image_id": img_id, + "category_id": classes[k], + "bbox": boxes[k], + "score": scores[k], + } + if has_mask: + result["segmentation"] = rles[k] + if has_keypoints: + # In COCO annotations, + # keypoints coordinates are pixel indices. + # However our predictions are floating point coordinates. + # Therefore we subtract 0.5 to be consistent with the annotation format. + # This is the inverse of data loading logic in `datasets/coco.py`. + keypoints[k][:, :2] -= 0.5 + result["keypoints"] = keypoints[k].flatten().tolist() + results.append(result) + return results + + +# inspired from Detectron: +# https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L255 # noqa +def _evaluate_box_proposals(dataset_predictions, coco_api, thresholds=None, area="all", limit=None): + """ + Evaluate detection proposal recall metrics. This function is a much + faster alternative to the official COCO API recall evaluation code. However, + it produces slightly different results. + """ + # Record max overlap value for each gt box + # Return vector of overlap values + areas = { + "all": 0, + "small": 1, + "medium": 2, + "large": 3, + "96-128": 4, + "128-256": 5, + "256-512": 6, + "512-inf": 7, + } + area_ranges = [ + [0**2, 1e5**2], # all + [0**2, 32**2], # small + [32**2, 96**2], # medium + [96**2, 1e5**2], # large + [96**2, 128**2], # 96-128 + [128**2, 256**2], # 128-256 + [256**2, 512**2], # 256-512 + [512**2, 1e5**2], + ] # 512-inf + assert area in areas, "Unknown area range: {}".format(area) + area_range = area_ranges[areas[area]] + gt_overlaps = [] + num_pos = 0 + + for prediction_dict in dataset_predictions: + predictions = prediction_dict["proposals"] + + # sort predictions in descending order + # TODO maybe remove this and make it explicit in the documentation + inds = predictions.objectness_logits.sort(descending=True)[1] + predictions = predictions[inds] + + ann_ids = coco_api.getAnnIds(imgIds=prediction_dict["image_id"]) + anno = coco_api.loadAnns(ann_ids) + gt_boxes = [ + BoxMode.convert(obj["bbox"], BoxMode.XYWH_ABS, BoxMode.XYXY_ABS) + for obj in anno + if obj["iscrowd"] == 0 + ] + gt_boxes = torch.as_tensor(gt_boxes).reshape(-1, 4) # guard against no boxes + gt_boxes = Boxes(gt_boxes) + gt_areas = torch.as_tensor([obj["area"] for obj in anno if obj["iscrowd"] == 0]) + + if len(gt_boxes) == 0 or len(predictions) == 0: + continue + + valid_gt_inds = (gt_areas >= area_range[0]) & (gt_areas <= area_range[1]) + gt_boxes = gt_boxes[valid_gt_inds] + + num_pos += len(gt_boxes) + + if len(gt_boxes) == 0: + continue + + if limit is not None and len(predictions) > limit: + predictions = predictions[:limit] + + overlaps = pairwise_iou(predictions.proposal_boxes, gt_boxes) + + _gt_overlaps = torch.zeros(len(gt_boxes)) + for j in range(min(len(predictions), len(gt_boxes))): + # find which proposal box maximally covers each gt box + # and get the iou amount of coverage for each gt box + max_overlaps, argmax_overlaps = overlaps.max(dim=0) + + # find which gt box is 'best' covered (i.e. 'best' = most iou) + gt_ovr, gt_ind = max_overlaps.max(dim=0) + assert gt_ovr >= 0 + # find the proposal box that covers the best covered gt box + box_ind = argmax_overlaps[gt_ind] + # record the iou coverage of this gt box + _gt_overlaps[j] = overlaps[box_ind, gt_ind] + assert _gt_overlaps[j] == gt_ovr + # mark the proposal box and the gt box as used + overlaps[box_ind, :] = -1 + overlaps[:, gt_ind] = -1 + + # append recorded iou coverage level + gt_overlaps.append(_gt_overlaps) + gt_overlaps = ( + torch.cat(gt_overlaps, dim=0) if len(gt_overlaps) else torch.zeros(0, dtype=torch.float32) + ) + gt_overlaps, _ = torch.sort(gt_overlaps) + + if thresholds is None: + step = 0.05 + thresholds = torch.arange(0.5, 0.95 + 1e-5, step, dtype=torch.float32) + recalls = torch.zeros_like(thresholds) + # compute recall for each iou threshold + for i, t in enumerate(thresholds): + recalls[i] = (gt_overlaps >= t).float().sum() / float(num_pos) + # ar = 2 * np.trapz(recalls, thresholds) + ar = recalls.mean() + return { + "ar": ar, + "recalls": recalls, + "thresholds": thresholds, + "gt_overlaps": gt_overlaps, + "num_pos": num_pos, + } + + +def _evaluate_predictions_on_coco( + coco_gt, + coco_results, + iou_type, + kpt_oks_sigmas=None, + cocoeval_fn=RefCOCOeval, + img_ids=None, + max_dets_per_image=None, +): + """ + Evaluate the coco results using COCOEval API. + """ + assert len(coco_results) > 0 + + if iou_type == "segm": + coco_results = copy.deepcopy(coco_results) + # When evaluating mask AP, if the results contain bbox, cocoapi will + # use the box area as the area of the instance, instead of the mask area. + # This leads to a different definition of small/medium/large. + # We remove the bbox field to let mask AP use mask area. + for c in coco_results: + c.pop("bbox", None) + + coco_dt = coco_gt.loadRes(coco_results) + coco_eval = cocoeval_fn(coco_gt, coco_dt, iou_type) + # For COCO, the default max_dets_per_image is [1, 10, 100]. + if max_dets_per_image is None: + max_dets_per_image = [1, 10, 100] # Default from COCOEval + else: + assert ( + len(max_dets_per_image) >= 3 + ), "COCOeval requires maxDets (and max_dets_per_image) to have length at least 3" + # In the case that user supplies a custom input for max_dets_per_image, + # apply COCOevalMaxDets to evaluate AP with the custom input. + if max_dets_per_image[2] != 100: + coco_eval = COCOevalMaxDets(coco_gt, coco_dt, iou_type) + if iou_type != "keypoints": + coco_eval.params.maxDets = max_dets_per_image + + if img_ids is not None: + coco_eval.params.imgIds = img_ids + + if iou_type == "keypoints": + # Use the COCO default keypoint OKS sigmas unless overrides are specified + if kpt_oks_sigmas: + assert hasattr(coco_eval.params, "kpt_oks_sigmas"), "pycocotools is too old!" + coco_eval.params.kpt_oks_sigmas = np.array(kpt_oks_sigmas) + # COCOAPI requires every detection and every gt to have keypoints, so + # we just take the first entry from both + num_keypoints_dt = len(coco_results[0]["keypoints"]) // 3 + num_keypoints_gt = len(next(iter(coco_gt.anns.values()))["keypoints"]) // 3 + num_keypoints_oks = len(coco_eval.params.kpt_oks_sigmas) + assert num_keypoints_oks == num_keypoints_dt == num_keypoints_gt, ( + f"[COCOEvaluator] Prediction contain {num_keypoints_dt} keypoints. " + f"Ground truth contains {num_keypoints_gt} keypoints. " + f"The length of cfg.TEST.KEYPOINT_OKS_SIGMAS is {num_keypoints_oks}. " + "They have to agree with each other. For meaning of OKS, please refer to " + "http://cocodataset.org/#keypoints-eval." + ) + + coco_eval.evaluate() + + return coco_eval + + +class COCOevalMaxDets(COCOeval): + """ + Modified version of COCOeval for evaluating AP with a custom + maxDets (by default for COCO, maxDets is 100) + """ + + def summarize(self): + """ + Compute and display summary metrics for evaluation results given + a custom value for max_dets_per_image + """ + + def _summarize(ap=1, iouThr=None, areaRng="all", maxDets=100): + p = self.params + iStr = " {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} ] = {:0.3f}" + titleStr = "Average Precision" if ap == 1 else "Average Recall" + typeStr = "(AP)" if ap == 1 else "(AR)" + iouStr = ( + "{:0.2f}:{:0.2f}".format(p.iouThrs[0], p.iouThrs[-1]) + if iouThr is None + else "{:0.2f}".format(iouThr) + ) + + aind = [i for i, aRng in enumerate(p.areaRngLbl) if aRng == areaRng] + mind = [i for i, mDet in enumerate(p.maxDets) if mDet == maxDets] + if ap == 1: + # dimension of precision: [TxRxKxAxM] + s = self.eval["precision"] + # IoU + if iouThr is not None: + t = np.where(iouThr == p.iouThrs)[0] + s = s[t] + s = s[:, :, :, aind, mind] + else: + # dimension of recall: [TxKxAxM] + s = self.eval["recall"] + if iouThr is not None: + t = np.where(iouThr == p.iouThrs)[0] + s = s[t] + s = s[:, :, aind, mind] + if len(s[s > -1]) == 0: + mean_s = -1 + else: + mean_s = np.mean(s[s > -1]) + print(iStr.format(titleStr, typeStr, iouStr, areaRng, maxDets, mean_s)) + return mean_s + + def _summarizeDets(): + stats = np.zeros((12,)) + # Evaluate AP using the custom limit on maximum detections per image + stats[0] = _summarize(1, maxDets=self.params.maxDets[2]) + stats[1] = _summarize(1, iouThr=0.5, maxDets=self.params.maxDets[2]) + stats[2] = _summarize(1, iouThr=0.75, maxDets=self.params.maxDets[2]) + stats[3] = _summarize(1, areaRng="small", maxDets=self.params.maxDets[2]) + stats[4] = _summarize(1, areaRng="medium", maxDets=self.params.maxDets[2]) + stats[5] = _summarize(1, areaRng="large", maxDets=self.params.maxDets[2]) + stats[6] = _summarize(0, maxDets=self.params.maxDets[0]) + stats[7] = _summarize(0, maxDets=self.params.maxDets[1]) + stats[8] = _summarize(0, maxDets=self.params.maxDets[2]) + stats[9] = _summarize(0, areaRng="small", maxDets=self.params.maxDets[2]) + stats[10] = _summarize(0, areaRng="medium", maxDets=self.params.maxDets[2]) + stats[11] = _summarize(0, areaRng="large", maxDets=self.params.maxDets[2]) + return stats + + def _summarizeKps(): + stats = np.zeros((10,)) + stats[0] = _summarize(1, maxDets=20) + stats[1] = _summarize(1, maxDets=20, iouThr=0.5) + stats[2] = _summarize(1, maxDets=20, iouThr=0.75) + stats[3] = _summarize(1, maxDets=20, areaRng="medium") + stats[4] = _summarize(1, maxDets=20, areaRng="large") + stats[5] = _summarize(0, maxDets=20) + stats[6] = _summarize(0, maxDets=20, iouThr=0.5) + stats[7] = _summarize(0, maxDets=20, iouThr=0.75) + stats[8] = _summarize(0, maxDets=20, areaRng="medium") + stats[9] = _summarize(0, maxDets=20, areaRng="large") + return stats + + if not self.eval: + raise Exception("Please run accumulate() first") + iouType = self.params.iouType + if iouType == "segm" or iouType == "bbox": + summarize = _summarizeDets + elif iouType == "keypoints": + summarize = _summarizeKps + self.stats = summarize() + + def __str__(self): + self.summarize() diff --git a/ape/evaluation/refcocoeval.py b/ape/evaluation/refcocoeval.py new file mode 100644 index 0000000..b2396df --- /dev/null +++ b/ape/evaluation/refcocoeval.py @@ -0,0 +1,593 @@ +__author__ = "tsungyi" + +import copy +import datetime +import time +from collections import defaultdict + +import numpy as np +import torch +from pycocotools import mask as maskUtils +from pycocotools.mask import decode +from torch._C import InterfaceType + +from torchvision.ops.boxes import box_area + + +def compute_bbox_iou(boxes1: torch.Tensor, boxes2: torch.Tensor): + # both boxes: xyxy + area1 = box_area(boxes1) + area2 = box_area(boxes2) + + lt = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2] + rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2] + + wh = (rb - lt).clamp(min=0) # [N,M,2] + inter = wh[:, :, 0] * wh[:, :, 1] # [N,M] + + union = area1[:, None] + area2 - inter + + iou = (inter + 1e-6) / (union + 1e-6) + return iou, inter, union + + +def compute_mask_iou(outputs: torch.Tensor, labels: torch.Tensor, EPS=1e-6): + outputs = outputs.int() + intersection = (outputs & labels).float().sum((1, 2)) # Will be zero if Truth=0 or Prediction=0 + union = (outputs | labels).float().sum((1, 2)) # Will be zero if both are 0 + iou = (intersection + EPS) / (union + EPS) # EPS is used to avoid division by zero + return iou, intersection, union + + +class RefCOCOeval: + # Interface for evaluating detection on the Microsoft COCO dataset. + # + # The usage for CocoEval is as follows: + # cocoGt=..., cocoDt=... # load dataset and results + # E = CocoEval(cocoGt,cocoDt); # initialize CocoEval object + # E.params.recThrs = ...; # set parameters as desired + # E.evaluate(); # run per image evaluation + # E.accumulate(); # accumulate per image results + # E.summarize(); # display summary metrics of results + # For example usage see evalDemo.m and http://mscoco.org/. + # + # The evaluation parameters are as follows (defaults in brackets): + # imgIds - [all] N img ids to use for evaluation + # catIds - [all] K cat ids to use for evaluation + # iouThrs - [.5:.05:.95] T=10 IoU thresholds for evaluation + # recThrs - [0:.01:1] R=101 recall thresholds for evaluation + # areaRng - [...] A=4 object area ranges for evaluation + # maxDets - [1 10 100] M=3 thresholds on max detections per image + # iouType - ['segm'] set iouType to 'segm', 'bbox' or 'keypoints' + # iouType replaced the now DEPRECATED useSegm parameter. + # useCats - [1] if true use category labels for evaluation + # Note: if useCats=0 category labels are ignored as in proposal scoring. + # Note: multiple areaRngs [Ax2] and maxDets [Mx1] can be specified. + # + # evaluate(): evaluates detections on every image and every category and + # concats the results into the "evalImgs" with fields: + # dtIds - [1xD] id for each of the D detections (dt) + # gtIds - [1xG] id for each of the G ground truths (gt) + # dtMatches - [TxD] matching gt id at each IoU or 0 + # gtMatches - [TxG] matching dt id at each IoU or 0 + # dtScores - [1xD] confidence of each dt + # gtIgnore - [1xG] ignore flag for each gt + # dtIgnore - [TxD] ignore flag for each dt at each IoU + # + # accumulate(): accumulates the per-image, per-category evaluation + # results in "evalImgs" into the dictionary "eval" with fields: + # params - parameters used for evaluation + # date - date evaluation was performed + # counts - [T,R,K,A,M] parameter dimensions (see above) + # precision - [TxRxKxAxM] precision for every evaluation setting + # recall - [TxKxAxM] max recall for every evaluation setting + # Note: precision and recall==-1 for settings with no gt objects. + # + # See also coco, mask, pycocoDemo, pycocoEvalDemo + # + # Microsoft COCO Toolbox. version 2.0 + # Data, paper, and tutorials available at: http://mscoco.org/ + # Code written by Piotr Dollar and Tsung-Yi Lin, 2015. + # Licensed under the Simplified BSD License [see coco/license.txt] + def __init__(self, cocoGt=None, cocoDt=None, iouType="segm"): + """ + Initialize CocoEval using coco APIs for gt and dt + :param cocoGt: coco object with ground truth annotations + :param cocoDt: coco object with detection results + :return: None + """ + if not iouType: + print("iouType not specified. use default iouType segm") + self.cocoGt = cocoGt # ground truth COCO API + self.cocoDt = cocoDt # detections COCO API + self.evalImgs = defaultdict( + list + ) # per-image per-category evaluation results [KxAxI] elements + self.eval = {} # accumulated evaluation results + self._gts = defaultdict(list) # gt for evaluation + self._dts = defaultdict(list) # dt for evaluation + self.params = Params(iouType=iouType) # parameters + self._paramsEval = {} # parameters for evaluation + self.stats = [] # result summarization + self.ious = {} # ious between all gts and dts + # for computing overall iou + self.total_intersection_area = 0 + self.total_union_area = 0 + self.iou_list = [] + if not cocoGt is None: + self.params.imgIds = sorted(cocoGt.getImgIds()) + self.params.catIds = sorted(cocoGt.getCatIds()) + + def _prepare(self): + """ + Prepare ._gts and ._dts for evaluation based on params + :return: None + """ + + def _toMask(anns, coco): + # modify ann['segmentation'] by reference + for ann in anns: + rle = coco.annToRLE(ann) + ann["segmentation"] = rle + + p = self.params + if p.useCats: + gts = self.cocoGt.loadAnns(self.cocoGt.getAnnIds(imgIds=p.imgIds, catIds=p.catIds)) + dts = self.cocoDt.loadAnns(self.cocoDt.getAnnIds(imgIds=p.imgIds, catIds=p.catIds)) + else: + gts = self.cocoGt.loadAnns(self.cocoGt.getAnnIds(imgIds=p.imgIds)) + dts = self.cocoDt.loadAnns(self.cocoDt.getAnnIds(imgIds=p.imgIds)) + + # convert ground truth to mask if iouType == 'segm' + if p.iouType == "segm": + _toMask(gts, self.cocoGt) + _toMask(dts, self.cocoDt) + # set ignore flag + for gt in gts: + gt["ignore"] = gt["ignore"] if "ignore" in gt else 0 + gt["ignore"] = "iscrowd" in gt and gt["iscrowd"] + if p.iouType == "keypoints": + gt["ignore"] = (gt["num_keypoints"] == 0) or gt["ignore"] + self._gts = defaultdict(list) # gt for evaluation + self._dts = defaultdict(list) # dt for evaluation + for gt in gts: + self._gts[gt["image_id"], gt["category_id"]].append(gt) + for dt in dts: + self._dts[dt["image_id"], dt["category_id"]].append(dt) + self.evalImgs = defaultdict(list) # per-image per-category evaluation results + self.eval = {} # accumulated evaluation results + + def evaluate(self): + """ + Run per image evaluation on given images and store results (a list of dict) in self.evalImgs + :return: None + """ + tic = time.time() + print("Running per image evaluation...") + p = self.params + # add backward compatibility if useSegm is specified in params + if not p.useSegm is None: + p.iouType = "segm" if p.useSegm == 1 else "bbox" + print("useSegm (deprecated) is not None. Running {} evaluation".format(p.iouType)) + print("Evaluate annotation type *{}*".format(p.iouType)) + p.imgIds = list(np.unique(p.imgIds)) + if p.useCats: + p.catIds = list(np.unique(p.catIds)) + p.maxDets = sorted(p.maxDets) + self.params = p + + self._prepare() + # loop through images, area range, max detection number + catIds = p.catIds if p.useCats else [-1] + + if p.iouType == "segm" or p.iouType == "bbox": + computeIoU = self.computeIoU + elif p.iouType == "keypoints": + computeIoU = self.computeOks + self.ious = { + (imgId, catId): computeIoU(imgId, catId) for imgId in p.imgIds for catId in catIds + } + # evaluateImg = self.evaluateImg + # maxDet = p.maxDets[-1] + # self.evalImgs = [evaluateImg(imgId, catId, areaRng, maxDet) + # for catId in catIds + # for areaRng in p.areaRng + # for imgId in p.imgIds + # ] + # self._paramsEval = copy.deepcopy(self.params) + toc = time.time() + print("DONE (t={:0.2f}s).".format(toc - tic)) + + def computeIoU(self, imgId, catId): + p = self.params + if p.useCats: + gt = self._gts[imgId, catId] + dt = self._dts[imgId, catId] + else: + gt = [_ for cId in p.catIds for _ in self._gts[imgId, cId]] + dt = [_ for cId in p.catIds for _ in self._dts[imgId, cId]] + if len(gt) == 0 and len(dt) == 0: + return [] + inds = np.argsort([-d["score"] for d in dt], kind="mergesort") + dt = [dt[i] for i in inds] + if len(dt) > p.maxDets[-1]: + dt = dt[0 : p.maxDets[-1]] + + if p.iouType == "segm": + g = [g["segmentation"] for g in gt] + d = [d["segmentation"] for d in dt] + elif p.iouType == "bbox": + g = [g["bbox"] for g in gt] + d = [d["bbox"] for d in dt] + else: + raise Exception("unknown iouType for iou computation") + + # compute iou between each dt and gt region + iscrowd = [int(o["iscrowd"]) for o in gt] + ious = maskUtils.iou(d, g, iscrowd) + + # for computing overall iou + # there is only one bbox and segm + if p.iouType == "bbox": + g, d = g[0], d[0] + g_bbox = [g[0], g[1], g[2] + g[0], g[3] + g[1]] # x1y1wh -> x1y1x2y2 + d_bbox = [d[0], d[1], d[2] + d[0], d[3] + d[1]] # x1y1wh -> x1y1x2y2 + g_bbox = torch.tensor(g_bbox).unsqueeze(0) + d_bbox = torch.tensor(d_bbox).unsqueeze(0) + iou, intersection, union = compute_bbox_iou(d_bbox, g_bbox) + elif p.iouType == "segm": + g_segm = decode(g[0]) + d_segm = decode(d[0]) + g_segm = torch.tensor(g_segm).unsqueeze(0) + d_segm = torch.tensor(d_segm).unsqueeze(0) + iou, intersection, union = compute_mask_iou(d_segm, g_segm) + else: + raise Exception("unknown iouType for iou computation") + iou, intersection, union = iou.item(), intersection.item(), union.item() + self.total_intersection_area += intersection + self.total_union_area += union + self.iou_list.append(iou) + return ious + + def evaluateImg(self, imgId, catId, aRng, maxDet): + """ + perform evaluation for single category and image + :return: dict (single image results) + """ + p = self.params + if p.useCats: + gt = self._gts[imgId, catId] + dt = self._dts[imgId, catId] + else: + gt = [_ for cId in p.catIds for _ in self._gts[imgId, cId]] + dt = [_ for cId in p.catIds for _ in self._dts[imgId, cId]] + if len(gt) == 0 and len(dt) == 0: + return None + + for g in gt: + if g["ignore"] or (g["area"] < aRng[0] or g["area"] > aRng[1]): + g["_ignore"] = 1 + else: + g["_ignore"] = 0 + + # sort dt highest score first, sort gt ignore last + gtind = np.argsort([g["_ignore"] for g in gt], kind="mergesort") + gt = [gt[i] for i in gtind] + dtind = np.argsort([-d["score"] for d in dt], kind="mergesort") + dt = [dt[i] for i in dtind[0:maxDet]] + iscrowd = [int(o["iscrowd"]) for o in gt] + # load computed ious + ious = ( + self.ious[imgId, catId][:, gtind] + if len(self.ious[imgId, catId]) > 0 + else self.ious[imgId, catId] + ) + + T = len(p.iouThrs) + G = len(gt) + D = len(dt) + gtm = np.zeros((T, G)) + dtm = np.zeros((T, D)) + gtIg = np.array([g["_ignore"] for g in gt]) + dtIg = np.zeros((T, D)) + if not len(ious) == 0: + for tind, t in enumerate(p.iouThrs): + for dind, d in enumerate(dt): + # information about best match so far (m=-1 -> unmatched) + iou = min([t, 1 - 1e-10]) + m = -1 + for gind, g in enumerate(gt): + # if this gt already matched, and not a crowd, continue + if gtm[tind, gind] > 0 and not iscrowd[gind]: + continue + # if dt matched to reg gt, and on ignore gt, stop + if m > -1 and gtIg[m] == 0 and gtIg[gind] == 1: + break + # continue to next gt unless better match made + if ious[dind, gind] < iou: + continue + # if match successful and best so far, store appropriately + iou = ious[dind, gind] + m = gind + # if match made store id of match for both dt and gt + if m == -1: + continue + dtIg[tind, dind] = gtIg[m] + dtm[tind, dind] = gt[m]["id"] + gtm[tind, m] = d["id"] + # set unmatched detections outside of area range to ignore + a = np.array([d["area"] < aRng[0] or d["area"] > aRng[1] for d in dt]).reshape((1, len(dt))) + dtIg = np.logical_or(dtIg, np.logical_and(dtm == 0, np.repeat(a, T, 0))) + # store results for given image and category + return { + "image_id": imgId, + "category_id": catId, + "aRng": aRng, + "maxDet": maxDet, + "dtIds": [d["id"] for d in dt], + "gtIds": [g["id"] for g in gt], + "dtMatches": dtm, + "gtMatches": gtm, + "dtScores": [d["score"] for d in dt], + "gtIgnore": gtIg, + "dtIgnore": dtIg, + } + + def accumulate(self, p=None): + """ + Accumulate per image evaluation results and store the result in self.eval + :param p: input params for evaluation + :return: None + """ + print("Accumulating evaluation results...") + tic = time.time() + if not self.evalImgs: + print("Please run evaluate() first") + # allows input customized parameters + if p is None: + p = self.params + p.catIds = p.catIds if p.useCats == 1 else [-1] + T = len(p.iouThrs) + R = len(p.recThrs) + K = len(p.catIds) if p.useCats else 1 + A = len(p.areaRng) + M = len(p.maxDets) + precision = -np.ones((T, R, K, A, M)) # -1 for the precision of absent categories + recall = -np.ones((T, K, A, M)) + scores = -np.ones((T, R, K, A, M)) + + # create dictionary for future indexing + _pe = self._paramsEval + catIds = _pe.catIds if _pe.useCats else [-1] + setK = set(catIds) + setA = set(map(tuple, _pe.areaRng)) + setM = set(_pe.maxDets) + setI = set(_pe.imgIds) + # get inds to evaluate + k_list = [n for n, k in enumerate(p.catIds) if k in setK] + m_list = [m for n, m in enumerate(p.maxDets) if m in setM] + a_list = [n for n, a in enumerate(map(lambda x: tuple(x), p.areaRng)) if a in setA] + i_list = [n for n, i in enumerate(p.imgIds) if i in setI] + I0 = len(_pe.imgIds) + A0 = len(_pe.areaRng) + # retrieve E at each category, area range, and max number of detections + for k, k0 in enumerate(k_list): + Nk = k0 * A0 * I0 + for a, a0 in enumerate(a_list): + Na = a0 * I0 + for m, maxDet in enumerate(m_list): + E = [self.evalImgs[Nk + Na + i] for i in i_list] + E = [e for e in E if not e is None] + if len(E) == 0: + continue + dtScores = np.concatenate([e["dtScores"][0:maxDet] for e in E]) + + # different sorting method generates slightly different results. + # mergesort is used to be consistent as Matlab implementation. + inds = np.argsort(-dtScores, kind="mergesort") + dtScoresSorted = dtScores[inds] + + dtm = np.concatenate([e["dtMatches"][:, 0:maxDet] for e in E], axis=1)[:, inds] + dtIg = np.concatenate([e["dtIgnore"][:, 0:maxDet] for e in E], axis=1)[:, inds] + gtIg = np.concatenate([e["gtIgnore"] for e in E]) + npig = np.count_nonzero(gtIg == 0) + if npig == 0: + continue + tps = np.logical_and(dtm, np.logical_not(dtIg)) + fps = np.logical_and(np.logical_not(dtm), np.logical_not(dtIg)) + + tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float) + fp_sum = np.cumsum(fps, axis=1).astype(dtype=np.float) + for t, (tp, fp) in enumerate(zip(tp_sum, fp_sum)): + tp = np.array(tp) + fp = np.array(fp) + nd = len(tp) + rc = tp / npig + pr = tp / (fp + tp + np.spacing(1)) + q = np.zeros((R,)) + ss = np.zeros((R,)) + + if nd: + recall[t, k, a, m] = rc[-1] + else: + recall[t, k, a, m] = 0 + + # numpy is slow without cython optimization for accessing elements + # use python array gets significant speed improvement + pr = pr.tolist() + q = q.tolist() + + for i in range(nd - 1, 0, -1): + if pr[i] > pr[i - 1]: + pr[i - 1] = pr[i] + + inds = np.searchsorted(rc, p.recThrs, side="left") + try: + for ri, pi in enumerate(inds): + q[ri] = pr[pi] + ss[ri] = dtScoresSorted[pi] + except: + pass + precision[t, :, k, a, m] = np.array(q) + scores[t, :, k, a, m] = np.array(ss) + self.eval = { + "params": p, + "counts": [T, R, K, A, M], + "date": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), + "precision": precision, + "recall": recall, + "scores": scores, + } + toc = time.time() + print("DONE (t={:0.2f}s).".format(toc - tic)) + + def summarize(self): + """ + Compute and display summary metrics for evaluation results. + Note this functin can *only* be applied on the default parameter setting + """ + + def _summarize(ap=1, iouThr=None, areaRng="all", maxDets=100): + p = self.params + iStr = " {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} ] = {:0.3f}" + titleStr = "Average Precision" if ap == 1 else "Average Recall" + typeStr = "(AP)" if ap == 1 else "(AR)" + iouStr = ( + "{:0.2f}:{:0.2f}".format(p.iouThrs[0], p.iouThrs[-1]) + if iouThr is None + else "{:0.2f}".format(iouThr) + ) + + aind = [i for i, aRng in enumerate(p.areaRngLbl) if aRng == areaRng] + mind = [i for i, mDet in enumerate(p.maxDets) if mDet == maxDets] + if ap == 1: + # dimension of precision: [TxRxKxAxM] + s = self.eval["precision"] + # IoU + if iouThr is not None: + t = np.where(iouThr == p.iouThrs)[0] + s = s[t] + s = s[:, :, :, aind, mind] + else: + # dimension of recall: [TxKxAxM] + s = self.eval["recall"] + if iouThr is not None: + t = np.where(iouThr == p.iouThrs)[0] + s = s[t] + s = s[:, :, aind, mind] + if len(s[s > -1]) == 0: + mean_s = -1 + else: + mean_s = np.mean(s[s > -1]) + print(iStr.format(titleStr, typeStr, iouStr, areaRng, maxDets, mean_s)) + return mean_s + + def _summarizeDets(): + stats = np.zeros((12,)) + stats[0] = _summarize(1) + stats[1] = _summarize(1, iouThr=0.5, maxDets=self.params.maxDets[2]) + stats[2] = _summarize(1, iouThr=0.75, maxDets=self.params.maxDets[2]) + stats[3] = _summarize(1, areaRng="small", maxDets=self.params.maxDets[2]) + stats[4] = _summarize(1, areaRng="medium", maxDets=self.params.maxDets[2]) + stats[5] = _summarize(1, areaRng="large", maxDets=self.params.maxDets[2]) + stats[6] = _summarize(0, maxDets=self.params.maxDets[0]) + stats[7] = _summarize(0, maxDets=self.params.maxDets[1]) + stats[8] = _summarize(0, maxDets=self.params.maxDets[2]) + stats[9] = _summarize(0, areaRng="small", maxDets=self.params.maxDets[2]) + stats[10] = _summarize(0, areaRng="medium", maxDets=self.params.maxDets[2]) + stats[11] = _summarize(0, areaRng="large", maxDets=self.params.maxDets[2]) + return stats + + def _summarizeKps(): + stats = np.zeros((10,)) + stats[0] = _summarize(1, maxDets=20) + stats[1] = _summarize(1, maxDets=20, iouThr=0.5) + stats[2] = _summarize(1, maxDets=20, iouThr=0.75) + stats[3] = _summarize(1, maxDets=20, areaRng="medium") + stats[4] = _summarize(1, maxDets=20, areaRng="large") + stats[5] = _summarize(0, maxDets=20) + stats[6] = _summarize(0, maxDets=20, iouThr=0.5) + stats[7] = _summarize(0, maxDets=20, iouThr=0.75) + stats[8] = _summarize(0, maxDets=20, areaRng="medium") + stats[9] = _summarize(0, maxDets=20, areaRng="large") + return stats + + if not self.eval: + raise Exception("Please run accumulate() first") + iouType = self.params.iouType + if iouType == "segm" or iouType == "bbox": + summarize = _summarizeDets + elif iouType == "keypoints": + summarize = _summarizeKps + self.stats = summarize() + + def __str__(self): + self.summarize() + + +class Params: + """ + Params for coco evaluation api + """ + + def setDetParams(self): + self.imgIds = [] + self.catIds = [] + # np.arange causes trouble. the data point on arange is slightly larger than the true value + self.iouThrs = np.linspace(0.5, 0.95, int(np.round((0.95 - 0.5) / 0.05)) + 1, endpoint=True) + self.recThrs = np.linspace(0.0, 1.00, int(np.round((1.00 - 0.0) / 0.01)) + 1, endpoint=True) + self.maxDets = [1, 10, 100] + self.areaRng = [ + [0**2, 1e5**2], + [0**2, 32**2], + [32**2, 96**2], + [96**2, 1e5**2], + ] + self.areaRngLbl = ["all", "small", "medium", "large"] + self.useCats = 1 + + def setKpParams(self): + self.imgIds = [] + self.catIds = [] + # np.arange causes trouble. the data point on arange is slightly larger than the true value + self.iouThrs = np.linspace(0.5, 0.95, int(np.round((0.95 - 0.5) / 0.05)) + 1, endpoint=True) + self.recThrs = np.linspace(0.0, 1.00, int(np.round((1.00 - 0.0) / 0.01)) + 1, endpoint=True) + self.maxDets = [20] + self.areaRng = [[0**2, 1e5**2], [32**2, 96**2], [96**2, 1e5**2]] + self.areaRngLbl = ["all", "medium", "large"] + self.useCats = 1 + self.kpt_oks_sigmas = ( + np.array( + [ + 0.26, + 0.25, + 0.25, + 0.35, + 0.35, + 0.79, + 0.79, + 0.72, + 0.72, + 0.62, + 0.62, + 1.07, + 1.07, + 0.87, + 0.87, + 0.89, + 0.89, + ] + ) + / 10.0 + ) + + def __init__(self, iouType="segm"): + if iouType == "segm" or iouType == "bbox": + self.setDetParams() + elif iouType == "keypoints": + self.setKpParams() + else: + raise Exception("iouType not supported") + self.iouType = iouType + # useSegm is deprecated + self.useSegm = None diff --git a/ape/layers/__init__.py b/ape/layers/__init__.py new file mode 100644 index 0000000..c6ac219 --- /dev/null +++ b/ape/layers/__init__.py @@ -0,0 +1,8 @@ +from .fuse_helper import BiAttentionBlock, BiMultiHeadAttention +from .multi_scale_deform_attn import ( + MultiScaleDeformableAttention, + multi_scale_deformable_attn_pytorch, +) +from .vision_language_align import StillClassifier, VisionLanguageAlign +from .vision_language_fusion import VisionLanguageFusion +from .zero_shot_fc import ZeroShotFC diff --git a/ape/layers/csrc/MsDeformAttn/ms_deform_attn.h b/ape/layers/csrc/MsDeformAttn/ms_deform_attn.h new file mode 100644 index 0000000..7e5913b --- /dev/null +++ b/ape/layers/csrc/MsDeformAttn/ms_deform_attn.h @@ -0,0 +1,64 @@ +/*! +************************************************************************************************** +* Deformable DETR +* Copyright (c) 2020 SenseTime. All Rights Reserved. +* Licensed under the Apache License, Version 2.0 [see LICENSE for details] +************************************************************************************************** +* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +************************************************************************************************** +*/ + +#pragma once + +#include "ms_deform_attn_cpu.h" + +#ifdef WITH_CUDA +#include "ms_deform_attn_cuda.h" +#endif + +namespace ape { + +at::Tensor +ms_deform_attn_forward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const int64_t im2col_step) +{ + if (value.type().is_cuda()) + { +#ifdef WITH_CUDA + return ms_deform_attn_cuda_forward( + value, spatial_shapes, level_start_index, sampling_loc, attn_weight, im2col_step); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + AT_ERROR("Not implemented on the CPU"); +} + +std::vector +ms_deform_attn_backward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const at::Tensor &grad_output, + const int64_t im2col_step) +{ + if (value.type().is_cuda()) + { +#ifdef WITH_CUDA + return ms_deform_attn_cuda_backward( + value, spatial_shapes, level_start_index, sampling_loc, attn_weight, grad_output, im2col_step); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + AT_ERROR("Not implemented on the CPU"); +} + +} // namespace ape diff --git a/ape/layers/csrc/MsDeformAttn/ms_deform_attn_cpu.cpp b/ape/layers/csrc/MsDeformAttn/ms_deform_attn_cpu.cpp new file mode 100644 index 0000000..0b9db0b --- /dev/null +++ b/ape/layers/csrc/MsDeformAttn/ms_deform_attn_cpu.cpp @@ -0,0 +1,42 @@ +/*! +************************************************************************************************** +* Deformable DETR +* Copyright (c) 2020 SenseTime. All Rights Reserved. +* Licensed under the Apache License, Version 2.0 [see LICENSE for details] +************************************************************************************************** +* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +************************************************************************************************** +*/ + +#include + +#include + +namespace ape { + +at::Tensor +ms_deform_attn_cpu_forward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const int im2col_step) +{ + AT_ERROR("Not implement on cpu"); +} + +std::vector +ms_deform_attn_cpu_backward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const at::Tensor &grad_output, + const int im2col_step) +{ + AT_ERROR("Not implement on cpu"); +} + +} // namespace ape diff --git a/ape/layers/csrc/MsDeformAttn/ms_deform_attn_cpu.h b/ape/layers/csrc/MsDeformAttn/ms_deform_attn_cpu.h new file mode 100644 index 0000000..47709dd --- /dev/null +++ b/ape/layers/csrc/MsDeformAttn/ms_deform_attn_cpu.h @@ -0,0 +1,35 @@ +/*! +************************************************************************************************** +* Deformable DETR +* Copyright (c) 2020 SenseTime. All Rights Reserved. +* Licensed under the Apache License, Version 2.0 [see LICENSE for details] +************************************************************************************************** +* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +************************************************************************************************** +*/ + +#pragma once +#include + +namespace ape { + +at::Tensor +ms_deform_attn_cpu_forward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const int im2col_step); + +std::vector +ms_deform_attn_cpu_backward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const at::Tensor &grad_output, + const int im2col_step); + +} // namespace ape diff --git a/ape/layers/csrc/MsDeformAttn/ms_deform_attn_cuda.cu b/ape/layers/csrc/MsDeformAttn/ms_deform_attn_cuda.cu new file mode 100644 index 0000000..51bb222 --- /dev/null +++ b/ape/layers/csrc/MsDeformAttn/ms_deform_attn_cuda.cu @@ -0,0 +1,156 @@ +/*! +************************************************************************************************** +* Deformable DETR +* Copyright (c) 2020 SenseTime. All Rights Reserved. +* Licensed under the Apache License, Version 2.0 [see LICENSE for details] +************************************************************************************************** +* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +************************************************************************************************** +*/ + +#include +#include "ms_deform_im2col_cuda.cuh" + +#include +#include +#include +#include + +namespace ape { + +at::Tensor ms_deform_attn_cuda_forward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const int im2col_step) +{ + AT_ASSERTM(value.is_contiguous(), "value tensor has to be contiguous"); + AT_ASSERTM(spatial_shapes.is_contiguous(), "spatial_shapes tensor has to be contiguous"); + AT_ASSERTM(level_start_index.is_contiguous(), "level_start_index tensor has to be contiguous"); + AT_ASSERTM(sampling_loc.is_contiguous(), "sampling_loc tensor has to be contiguous"); + AT_ASSERTM(attn_weight.is_contiguous(), "attn_weight tensor has to be contiguous"); + + AT_ASSERTM(value.type().is_cuda(), "value must be a CUDA tensor"); + AT_ASSERTM(spatial_shapes.type().is_cuda(), "spatial_shapes must be a CUDA tensor"); + AT_ASSERTM(level_start_index.type().is_cuda(), "level_start_index must be a CUDA tensor"); + AT_ASSERTM(sampling_loc.type().is_cuda(), "sampling_loc must be a CUDA tensor"); + AT_ASSERTM(attn_weight.type().is_cuda(), "attn_weight must be a CUDA tensor"); + + const int batch = value.size(0); + const int spatial_size = value.size(1); + const int num_heads = value.size(2); + const int channels = value.size(3); + + const int num_levels = spatial_shapes.size(0); + + const int num_query = sampling_loc.size(1); + const int num_point = sampling_loc.size(4); + + const int im2col_step_ = std::min(batch, im2col_step); + + AT_ASSERTM(batch % im2col_step_ == 0, "batch(%d) must divide im2col_step(%d)", batch, im2col_step_); + + auto output = at::zeros({batch, num_query, num_heads, channels}, value.options()); + + const int batch_n = im2col_step_; + auto output_n = output.view({batch/im2col_step_, batch_n, num_query, num_heads, channels}); + auto per_value_size = spatial_size * num_heads * channels; + auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2; + auto per_attn_weight_size = num_query * num_heads * num_levels * num_point; + for (int n = 0; n < batch/im2col_step_; ++n) + { + auto columns = output_n.select(0, n); + AT_DISPATCH_FLOATING_TYPES_AND_HALF(value.scalar_type(), "ms_deform_attn_forward_cuda", ([&] { + ms_deformable_im2col_cuda(at::cuda::getCurrentCUDAStream(), + value.data() + n * im2col_step_ * per_value_size, + spatial_shapes.data(), + level_start_index.data(), + sampling_loc.data() + n * im2col_step_ * per_sample_loc_size, + attn_weight.data() + n * im2col_step_ * per_attn_weight_size, + batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point, + columns.data()); + + })); + } + + output = output.view({batch, num_query, num_heads*channels}); + + return output; +} + + +std::vector ms_deform_attn_cuda_backward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const at::Tensor &grad_output, + const int im2col_step) +{ + + AT_ASSERTM(value.is_contiguous(), "value tensor has to be contiguous"); + AT_ASSERTM(spatial_shapes.is_contiguous(), "spatial_shapes tensor has to be contiguous"); + AT_ASSERTM(level_start_index.is_contiguous(), "level_start_index tensor has to be contiguous"); + AT_ASSERTM(sampling_loc.is_contiguous(), "sampling_loc tensor has to be contiguous"); + AT_ASSERTM(attn_weight.is_contiguous(), "attn_weight tensor has to be contiguous"); + AT_ASSERTM(grad_output.is_contiguous(), "grad_output tensor has to be contiguous"); + + AT_ASSERTM(value.type().is_cuda(), "value must be a CUDA tensor"); + AT_ASSERTM(spatial_shapes.type().is_cuda(), "spatial_shapes must be a CUDA tensor"); + AT_ASSERTM(level_start_index.type().is_cuda(), "level_start_index must be a CUDA tensor"); + AT_ASSERTM(sampling_loc.type().is_cuda(), "sampling_loc must be a CUDA tensor"); + AT_ASSERTM(attn_weight.type().is_cuda(), "attn_weight must be a CUDA tensor"); + AT_ASSERTM(grad_output.type().is_cuda(), "grad_output must be a CUDA tensor"); + + const int batch = value.size(0); + const int spatial_size = value.size(1); + const int num_heads = value.size(2); + const int channels = value.size(3); + + const int num_levels = spatial_shapes.size(0); + + const int num_query = sampling_loc.size(1); + const int num_point = sampling_loc.size(4); + + const int im2col_step_ = std::min(batch, im2col_step); + + AT_ASSERTM(batch % im2col_step_ == 0, "batch(%d) must divide im2col_step(%d)", batch, im2col_step_); + + auto grad_value = at::zeros_like(value); + auto grad_sampling_loc = at::zeros_like(sampling_loc); + auto grad_attn_weight = at::zeros_like(attn_weight); + + const int batch_n = im2col_step_; + auto per_value_size = spatial_size * num_heads * channels; + auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2; + auto per_attn_weight_size = num_query * num_heads * num_levels * num_point; + auto grad_output_n = grad_output.view({batch/im2col_step_, batch_n, num_query, num_heads, channels}); + + for (int n = 0; n < batch/im2col_step_; ++n) + { + auto grad_output_g = grad_output_n.select(0, n); + AT_DISPATCH_FLOATING_TYPES_AND_HALF(value.type(), "ms_deform_attn_backward_cuda", ([&] { + ms_deformable_col2im_cuda(at::cuda::getCurrentCUDAStream(), + grad_output_g.data(), + value.data() + n * im2col_step_ * per_value_size, + spatial_shapes.data(), + level_start_index.data(), + sampling_loc.data() + n * im2col_step_ * per_sample_loc_size, + attn_weight.data() + n * im2col_step_ * per_attn_weight_size, + batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point, + grad_value.data() + n * im2col_step_ * per_value_size, + grad_sampling_loc.data() + n * im2col_step_ * per_sample_loc_size, + grad_attn_weight.data() + n * im2col_step_ * per_attn_weight_size); + + })); + } + + return { + grad_value, grad_sampling_loc, grad_attn_weight + }; +} + +} // namespace ape diff --git a/ape/layers/csrc/MsDeformAttn/ms_deform_attn_cuda.h b/ape/layers/csrc/MsDeformAttn/ms_deform_attn_cuda.h new file mode 100644 index 0000000..05a1eae --- /dev/null +++ b/ape/layers/csrc/MsDeformAttn/ms_deform_attn_cuda.h @@ -0,0 +1,33 @@ +/*! +************************************************************************************************** +* Deformable DETR +* Copyright (c) 2020 SenseTime. All Rights Reserved. +* Licensed under the Apache License, Version 2.0 [see LICENSE for details] +************************************************************************************************** +* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +************************************************************************************************** +*/ + +#pragma once +#include + +namespace ape { + +at::Tensor ms_deform_attn_cuda_forward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const int im2col_step); + +std::vector ms_deform_attn_cuda_backward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const at::Tensor &grad_output, + const int im2col_step); + +} // namespace ape \ No newline at end of file diff --git a/ape/layers/csrc/MsDeformAttn/ms_deform_im2col_cuda.cuh b/ape/layers/csrc/MsDeformAttn/ms_deform_im2col_cuda.cuh new file mode 100644 index 0000000..6bc2acb --- /dev/null +++ b/ape/layers/csrc/MsDeformAttn/ms_deform_im2col_cuda.cuh @@ -0,0 +1,1327 @@ +/*! +************************************************************************** +* Deformable DETR +* Copyright (c) 2020 SenseTime. All Rights Reserved. +* Licensed under the Apache License, Version 2.0 [see LICENSE for details] +************************************************************************** +* Modified from DCN (https://github.com/msracver/Deformable-ConvNets) +* Copyright (c) 2018 Microsoft +************************************************************************** +*/ + +#include +#include +#include + +#include +#include + +#include + +#define CUDA_KERNEL_LOOP(i, n) \ + for (int i = blockIdx.x * blockDim.x + threadIdx.x; \ + i < (n); \ + i += blockDim.x * gridDim.x) + +const int CUDA_NUM_THREADS = 1024; +inline int GET_BLOCKS(const int N, const int num_threads) +{ + return (N + num_threads - 1) / num_threads; +} + + +template +__device__ scalar_t ms_deform_attn_im2col_bilinear(const scalar_t* &bottom_data, + const int &height, const int &width, const int &nheads, const int &channels, + const scalar_t &h, const scalar_t &w, const int &m, const int &c) +{ + const int h_low = floor(h); + const int w_low = floor(w); + const int h_high = h_low + 1; + const int w_high = w_low + 1; + + const scalar_t lh = h - h_low; + const scalar_t lw = w - w_low; + const scalar_t hh = 1 - lh, hw = 1 - lw; + + const int w_stride = nheads * channels; + const int h_stride = width * w_stride; + const int h_low_ptr_offset = h_low * h_stride; + const int h_high_ptr_offset = h_low_ptr_offset + h_stride; + const int w_low_ptr_offset = w_low * w_stride; + const int w_high_ptr_offset = w_low_ptr_offset + w_stride; + const int base_ptr = m * channels + c; + + scalar_t v1 = 0; + if (h_low >= 0 && w_low >= 0) + { + const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr; + v1 = bottom_data[ptr1]; + } + scalar_t v2 = 0; + if (h_low >= 0 && w_high <= width - 1) + { + const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr; + v2 = bottom_data[ptr2]; + } + scalar_t v3 = 0; + if (h_high <= height - 1 && w_low >= 0) + { + const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr; + v3 = bottom_data[ptr3]; + } + scalar_t v4 = 0; + if (h_high <= height - 1 && w_high <= width - 1) + { + const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr; + v4 = bottom_data[ptr4]; + } + + const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; + + const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + return val; +} + + +template +__device__ void ms_deform_attn_col2im_bilinear(const scalar_t* &bottom_data, + const int &height, const int &width, const int &nheads, const int &channels, + const scalar_t &h, const scalar_t &w, const int &m, const int &c, + const scalar_t &top_grad, + const scalar_t &attn_weight, + scalar_t* &grad_value, + scalar_t* grad_sampling_loc, + scalar_t* grad_attn_weight) +{ + const int h_low = floor(h); + const int w_low = floor(w); + const int h_high = h_low + 1; + const int w_high = w_low + 1; + + const scalar_t lh = h - h_low; + const scalar_t lw = w - w_low; + const scalar_t hh = 1 - lh, hw = 1 - lw; + + const int w_stride = nheads * channels; + const int h_stride = width * w_stride; + const int h_low_ptr_offset = h_low * h_stride; + const int h_high_ptr_offset = h_low_ptr_offset + h_stride; + const int w_low_ptr_offset = w_low * w_stride; + const int w_high_ptr_offset = w_low_ptr_offset + w_stride; + const int base_ptr = m * channels + c; + + const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; + const scalar_t top_grad_value = top_grad * attn_weight; + scalar_t grad_h_weight = 0, grad_w_weight = 0; + + scalar_t v1 = 0; + if (h_low >= 0 && w_low >= 0) + { + const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr; + v1 = bottom_data[ptr1]; + grad_h_weight -= hw * v1; + grad_w_weight -= hh * v1; + atomicAdd(grad_value+ptr1, w1*top_grad_value); + } + scalar_t v2 = 0; + if (h_low >= 0 && w_high <= width - 1) + { + const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr; + v2 = bottom_data[ptr2]; + grad_h_weight -= lw * v2; + grad_w_weight += hh * v2; + atomicAdd(grad_value+ptr2, w2*top_grad_value); + } + scalar_t v3 = 0; + if (h_high <= height - 1 && w_low >= 0) + { + const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr; + v3 = bottom_data[ptr3]; + grad_h_weight += hw * v3; + grad_w_weight -= lh * v3; + atomicAdd(grad_value+ptr3, w3*top_grad_value); + } + scalar_t v4 = 0; + if (h_high <= height - 1 && w_high <= width - 1) + { + const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr; + v4 = bottom_data[ptr4]; + grad_h_weight += lw * v4; + grad_w_weight += lh * v4; + atomicAdd(grad_value+ptr4, w4*top_grad_value); + } + + const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + *grad_attn_weight = top_grad * val; + *grad_sampling_loc = width * grad_w_weight * top_grad_value; + *(grad_sampling_loc + 1) = height * grad_h_weight * top_grad_value; +} + + +template +__device__ void ms_deform_attn_col2im_bilinear_gm(const scalar_t* &bottom_data, + const int &height, const int &width, const int &nheads, const int &channels, + const scalar_t &h, const scalar_t &w, const int &m, const int &c, + const scalar_t &top_grad, + const scalar_t &attn_weight, + scalar_t* &grad_value, + scalar_t* grad_sampling_loc, + scalar_t* grad_attn_weight) +{ + const int h_low = floor(h); + const int w_low = floor(w); + const int h_high = h_low + 1; + const int w_high = w_low + 1; + + const scalar_t lh = h - h_low; + const scalar_t lw = w - w_low; + const scalar_t hh = 1 - lh, hw = 1 - lw; + + const int w_stride = nheads * channels; + const int h_stride = width * w_stride; + const int h_low_ptr_offset = h_low * h_stride; + const int h_high_ptr_offset = h_low_ptr_offset + h_stride; + const int w_low_ptr_offset = w_low * w_stride; + const int w_high_ptr_offset = w_low_ptr_offset + w_stride; + const int base_ptr = m * channels + c; + + const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; + const scalar_t top_grad_value = top_grad * attn_weight; + scalar_t grad_h_weight = 0, grad_w_weight = 0; + + scalar_t v1 = 0; + if (h_low >= 0 && w_low >= 0) + { + const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr; + v1 = bottom_data[ptr1]; + grad_h_weight -= hw * v1; + grad_w_weight -= hh * v1; + atomicAdd(grad_value+ptr1, w1*top_grad_value); + } + scalar_t v2 = 0; + if (h_low >= 0 && w_high <= width - 1) + { + const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr; + v2 = bottom_data[ptr2]; + grad_h_weight -= lw * v2; + grad_w_weight += hh * v2; + atomicAdd(grad_value+ptr2, w2*top_grad_value); + } + scalar_t v3 = 0; + if (h_high <= height - 1 && w_low >= 0) + { + const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr; + v3 = bottom_data[ptr3]; + grad_h_weight += hw * v3; + grad_w_weight -= lh * v3; + atomicAdd(grad_value+ptr3, w3*top_grad_value); + } + scalar_t v4 = 0; + if (h_high <= height - 1 && w_high <= width - 1) + { + const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr; + v4 = bottom_data[ptr4]; + grad_h_weight += lw * v4; + grad_w_weight += lh * v4; + atomicAdd(grad_value+ptr4, w4*top_grad_value); + } + + const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + atomicAdd(grad_attn_weight, top_grad * val); + atomicAdd(grad_sampling_loc, width * grad_w_weight * top_grad_value); + atomicAdd(grad_sampling_loc + 1, height * grad_h_weight * top_grad_value); +} + + +template +__global__ void ms_deformable_im2col_gpu_kernel(const int n, + const scalar_t *data_value, + const int64_t *data_spatial_shapes, + const int64_t *data_level_start_index, + const scalar_t *data_sampling_loc, + const scalar_t *data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t *data_col) +{ + CUDA_KERNEL_LOOP(index, n) + { + int _temp = index; + const int c_col = _temp % channels; + _temp /= channels; + const int sampling_index = _temp; + const int m_col = _temp % num_heads; + _temp /= num_heads; + const int q_col = _temp % num_query; + _temp /= num_query; + const int b_col = _temp; + + scalar_t *data_col_ptr = data_col + index; + int data_weight_ptr = sampling_index * num_levels * num_point; + int data_loc_w_ptr = data_weight_ptr << 1; + const int qid_stride = num_heads * channels; + const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; + scalar_t col = 0; + + for (int l_col=0; l_col < num_levels; ++l_col) + { + const int level_start_id = data_level_start_index[l_col]; + const int spatial_h_ptr = l_col << 1; + const int spatial_h = data_spatial_shapes[spatial_h_ptr]; + const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; + const scalar_t *data_value_ptr = data_value + (data_value_ptr_init_offset + level_start_id * qid_stride); + for (int p_col=0; p_col < num_point; ++p_col) + { + const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; + const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; + const scalar_t weight = data_attn_weight[data_weight_ptr]; + + const scalar_t h_im = loc_h * spatial_h - 0.5; + const scalar_t w_im = loc_w * spatial_w - 0.5; + + if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) + { + col += ms_deform_attn_im2col_bilinear(data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col) * weight; + } + + data_weight_ptr += 1; + data_loc_w_ptr += 2; + } + } + *data_col_ptr = col; + } +} + +template +__global__ void ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1(const int n, + const scalar_t *grad_col, + const scalar_t *data_value, + const int64_t *data_spatial_shapes, + const int64_t *data_level_start_index, + const scalar_t *data_sampling_loc, + const scalar_t *data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t *grad_value, + scalar_t *grad_sampling_loc, + scalar_t *grad_attn_weight) +{ + CUDA_KERNEL_LOOP(index, n) + { + __shared__ scalar_t cache_grad_sampling_loc[blockSize * 2]; + __shared__ scalar_t cache_grad_attn_weight[blockSize]; + unsigned int tid = threadIdx.x; + int _temp = index; + const int c_col = _temp % channels; + _temp /= channels; + const int sampling_index = _temp; + const int m_col = _temp % num_heads; + _temp /= num_heads; + const int q_col = _temp % num_query; + _temp /= num_query; + const int b_col = _temp; + + const scalar_t top_grad = grad_col[index]; + + int data_weight_ptr = sampling_index * num_levels * num_point; + int data_loc_w_ptr = data_weight_ptr << 1; + const int grad_sampling_ptr = data_weight_ptr; + grad_sampling_loc += grad_sampling_ptr << 1; + grad_attn_weight += grad_sampling_ptr; + const int grad_weight_stride = 1; + const int grad_loc_stride = 2; + const int qid_stride = num_heads * channels; + const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; + + for (int l_col=0; l_col < num_levels; ++l_col) + { + const int level_start_id = data_level_start_index[l_col]; + const int spatial_h_ptr = l_col << 1; + const int spatial_h = data_spatial_shapes[spatial_h_ptr]; + const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; + const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; + const scalar_t *data_value_ptr = data_value + value_ptr_offset; + scalar_t *grad_value_ptr = grad_value + value_ptr_offset; + + for (int p_col=0; p_col < num_point; ++p_col) + { + const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; + const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; + const scalar_t weight = data_attn_weight[data_weight_ptr]; + + const scalar_t h_im = loc_h * spatial_h - 0.5; + const scalar_t w_im = loc_w * spatial_w - 0.5; + *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0; + *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0; + *(cache_grad_attn_weight+threadIdx.x)=0; + if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) + { + ms_deform_attn_col2im_bilinear( + data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, + top_grad, weight, grad_value_ptr, + cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x); + } + + __syncthreads(); + if (tid == 0) + { + scalar_t _grad_w=cache_grad_sampling_loc[0], _grad_h=cache_grad_sampling_loc[1], _grad_a=cache_grad_attn_weight[0]; + int sid=2; + for (unsigned int tid = 1; tid < blockSize; ++tid) + { + _grad_w += cache_grad_sampling_loc[sid]; + _grad_h += cache_grad_sampling_loc[sid + 1]; + _grad_a += cache_grad_attn_weight[tid]; + sid += 2; + } + + + *grad_sampling_loc = _grad_w; + *(grad_sampling_loc + 1) = _grad_h; + *grad_attn_weight = _grad_a; + } + __syncthreads(); + + data_weight_ptr += 1; + data_loc_w_ptr += 2; + grad_attn_weight += grad_weight_stride; + grad_sampling_loc += grad_loc_stride; + } + } + } +} + + +template +__global__ void ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2(const int n, + const scalar_t *grad_col, + const scalar_t *data_value, + const int64_t *data_spatial_shapes, + const int64_t *data_level_start_index, + const scalar_t *data_sampling_loc, + const scalar_t *data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t *grad_value, + scalar_t *grad_sampling_loc, + scalar_t *grad_attn_weight) +{ + CUDA_KERNEL_LOOP(index, n) + { + __shared__ scalar_t cache_grad_sampling_loc[blockSize * 2]; + __shared__ scalar_t cache_grad_attn_weight[blockSize]; + unsigned int tid = threadIdx.x; + int _temp = index; + const int c_col = _temp % channels; + _temp /= channels; + const int sampling_index = _temp; + const int m_col = _temp % num_heads; + _temp /= num_heads; + const int q_col = _temp % num_query; + _temp /= num_query; + const int b_col = _temp; + + const scalar_t top_grad = grad_col[index]; + + int data_weight_ptr = sampling_index * num_levels * num_point; + int data_loc_w_ptr = data_weight_ptr << 1; + const int grad_sampling_ptr = data_weight_ptr; + grad_sampling_loc += grad_sampling_ptr << 1; + grad_attn_weight += grad_sampling_ptr; + const int grad_weight_stride = 1; + const int grad_loc_stride = 2; + const int qid_stride = num_heads * channels; + const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; + + for (int l_col=0; l_col < num_levels; ++l_col) + { + const int level_start_id = data_level_start_index[l_col]; + const int spatial_h_ptr = l_col << 1; + const int spatial_h = data_spatial_shapes[spatial_h_ptr]; + const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; + const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; + const scalar_t *data_value_ptr = data_value + value_ptr_offset; + scalar_t *grad_value_ptr = grad_value + value_ptr_offset; + + for (int p_col=0; p_col < num_point; ++p_col) + { + const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; + const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; + const scalar_t weight = data_attn_weight[data_weight_ptr]; + + const scalar_t h_im = loc_h * spatial_h - 0.5; + const scalar_t w_im = loc_w * spatial_w - 0.5; + *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0; + *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0; + *(cache_grad_attn_weight+threadIdx.x)=0; + if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) + { + ms_deform_attn_col2im_bilinear( + data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, + top_grad, weight, grad_value_ptr, + cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x); + } + + __syncthreads(); + + for (unsigned int s=blockSize/2; s>0; s>>=1) + { + if (tid < s) { + const unsigned int xid1 = tid << 1; + const unsigned int xid2 = (tid + s) << 1; + cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s]; + cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2]; + cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1]; + } + __syncthreads(); + } + + if (tid == 0) + { + *grad_sampling_loc = cache_grad_sampling_loc[0]; + *(grad_sampling_loc + 1) = cache_grad_sampling_loc[1]; + *grad_attn_weight = cache_grad_attn_weight[0]; + } + __syncthreads(); + + data_weight_ptr += 1; + data_loc_w_ptr += 2; + grad_attn_weight += grad_weight_stride; + grad_sampling_loc += grad_loc_stride; + } + } + } +} + + +template +__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v1(const int n, + const scalar_t *grad_col, + const scalar_t *data_value, + const int64_t *data_spatial_shapes, + const int64_t *data_level_start_index, + const scalar_t *data_sampling_loc, + const scalar_t *data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t *grad_value, + scalar_t *grad_sampling_loc, + scalar_t *grad_attn_weight) +{ + CUDA_KERNEL_LOOP(index, n) + { + extern __shared__ int _s[]; + scalar_t* cache_grad_sampling_loc = (scalar_t*)_s; + scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x; + unsigned int tid = threadIdx.x; + int _temp = index; + const int c_col = _temp % channels; + _temp /= channels; + const int sampling_index = _temp; + const int m_col = _temp % num_heads; + _temp /= num_heads; + const int q_col = _temp % num_query; + _temp /= num_query; + const int b_col = _temp; + + const scalar_t top_grad = grad_col[index]; + + int data_weight_ptr = sampling_index * num_levels * num_point; + int data_loc_w_ptr = data_weight_ptr << 1; + const int grad_sampling_ptr = data_weight_ptr; + grad_sampling_loc += grad_sampling_ptr << 1; + grad_attn_weight += grad_sampling_ptr; + const int grad_weight_stride = 1; + const int grad_loc_stride = 2; + const int qid_stride = num_heads * channels; + const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; + + for (int l_col=0; l_col < num_levels; ++l_col) + { + const int level_start_id = data_level_start_index[l_col]; + const int spatial_h_ptr = l_col << 1; + const int spatial_h = data_spatial_shapes[spatial_h_ptr]; + const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; + const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; + const scalar_t *data_value_ptr = data_value + value_ptr_offset; + scalar_t *grad_value_ptr = grad_value + value_ptr_offset; + + for (int p_col=0; p_col < num_point; ++p_col) + { + const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; + const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; + const scalar_t weight = data_attn_weight[data_weight_ptr]; + + const scalar_t h_im = loc_h * spatial_h - 0.5; + const scalar_t w_im = loc_w * spatial_w - 0.5; + *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0; + *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0; + *(cache_grad_attn_weight+threadIdx.x)=0; + if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) + { + ms_deform_attn_col2im_bilinear( + data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, + top_grad, weight, grad_value_ptr, + cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x); + } + + __syncthreads(); + if (tid == 0) + { + scalar_t _grad_w=cache_grad_sampling_loc[0], _grad_h=cache_grad_sampling_loc[1], _grad_a=cache_grad_attn_weight[0]; + int sid=2; + for (unsigned int tid = 1; tid < blockDim.x; ++tid) + { + _grad_w += cache_grad_sampling_loc[sid]; + _grad_h += cache_grad_sampling_loc[sid + 1]; + _grad_a += cache_grad_attn_weight[tid]; + sid += 2; + } + + + *grad_sampling_loc = _grad_w; + *(grad_sampling_loc + 1) = _grad_h; + *grad_attn_weight = _grad_a; + } + __syncthreads(); + + data_weight_ptr += 1; + data_loc_w_ptr += 2; + grad_attn_weight += grad_weight_stride; + grad_sampling_loc += grad_loc_stride; + } + } + } +} + +template +__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v2(const int n, + const scalar_t *grad_col, + const scalar_t *data_value, + const int64_t *data_spatial_shapes, + const int64_t *data_level_start_index, + const scalar_t *data_sampling_loc, + const scalar_t *data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t *grad_value, + scalar_t *grad_sampling_loc, + scalar_t *grad_attn_weight) +{ + CUDA_KERNEL_LOOP(index, n) + { + extern __shared__ int _s[]; + scalar_t* cache_grad_sampling_loc = (scalar_t*)_s; + scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x; + unsigned int tid = threadIdx.x; + int _temp = index; + const int c_col = _temp % channels; + _temp /= channels; + const int sampling_index = _temp; + const int m_col = _temp % num_heads; + _temp /= num_heads; + const int q_col = _temp % num_query; + _temp /= num_query; + const int b_col = _temp; + + const scalar_t top_grad = grad_col[index]; + + int data_weight_ptr = sampling_index * num_levels * num_point; + int data_loc_w_ptr = data_weight_ptr << 1; + const int grad_sampling_ptr = data_weight_ptr; + grad_sampling_loc += grad_sampling_ptr << 1; + grad_attn_weight += grad_sampling_ptr; + const int grad_weight_stride = 1; + const int grad_loc_stride = 2; + const int qid_stride = num_heads * channels; + const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; + + for (int l_col=0; l_col < num_levels; ++l_col) + { + const int level_start_id = data_level_start_index[l_col]; + const int spatial_h_ptr = l_col << 1; + const int spatial_h = data_spatial_shapes[spatial_h_ptr]; + const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; + const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; + const scalar_t *data_value_ptr = data_value + value_ptr_offset; + scalar_t *grad_value_ptr = grad_value + value_ptr_offset; + + for (int p_col=0; p_col < num_point; ++p_col) + { + const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; + const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; + const scalar_t weight = data_attn_weight[data_weight_ptr]; + + const scalar_t h_im = loc_h * spatial_h - 0.5; + const scalar_t w_im = loc_w * spatial_w - 0.5; + *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0; + *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0; + *(cache_grad_attn_weight+threadIdx.x)=0; + if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) + { + ms_deform_attn_col2im_bilinear( + data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, + top_grad, weight, grad_value_ptr, + cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x); + } + + __syncthreads(); + + for (unsigned int s=blockDim.x/2, spre=blockDim.x; s>0; s>>=1, spre>>=1) + { + if (tid < s) { + const unsigned int xid1 = tid << 1; + const unsigned int xid2 = (tid + s) << 1; + cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s]; + cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2]; + cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1]; + if (tid + (s << 1) < spre) + { + cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + (s << 1)]; + cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2 + (s << 1)]; + cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1 + (s << 1)]; + } + } + __syncthreads(); + } + + if (tid == 0) + { + *grad_sampling_loc = cache_grad_sampling_loc[0]; + *(grad_sampling_loc + 1) = cache_grad_sampling_loc[1]; + *grad_attn_weight = cache_grad_attn_weight[0]; + } + __syncthreads(); + + data_weight_ptr += 1; + data_loc_w_ptr += 2; + grad_attn_weight += grad_weight_stride; + grad_sampling_loc += grad_loc_stride; + } + } + } +} + +template +__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v2_multi_blocks(const int n, + const scalar_t *grad_col, + const scalar_t *data_value, + const int64_t *data_spatial_shapes, + const int64_t *data_level_start_index, + const scalar_t *data_sampling_loc, + const scalar_t *data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t *grad_value, + scalar_t *grad_sampling_loc, + scalar_t *grad_attn_weight) +{ + CUDA_KERNEL_LOOP(index, n) + { + extern __shared__ int _s[]; + scalar_t* cache_grad_sampling_loc = (scalar_t*)_s; + scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x; + unsigned int tid = threadIdx.x; + int _temp = index; + const int c_col = _temp % channels; + _temp /= channels; + const int sampling_index = _temp; + const int m_col = _temp % num_heads; + _temp /= num_heads; + const int q_col = _temp % num_query; + _temp /= num_query; + const int b_col = _temp; + + const scalar_t top_grad = grad_col[index]; + + int data_weight_ptr = sampling_index * num_levels * num_point; + int data_loc_w_ptr = data_weight_ptr << 1; + const int grad_sampling_ptr = data_weight_ptr; + grad_sampling_loc += grad_sampling_ptr << 1; + grad_attn_weight += grad_sampling_ptr; + const int grad_weight_stride = 1; + const int grad_loc_stride = 2; + const int qid_stride = num_heads * channels; + const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; + + for (int l_col=0; l_col < num_levels; ++l_col) + { + const int level_start_id = data_level_start_index[l_col]; + const int spatial_h_ptr = l_col << 1; + const int spatial_h = data_spatial_shapes[spatial_h_ptr]; + const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; + const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; + const scalar_t *data_value_ptr = data_value + value_ptr_offset; + scalar_t *grad_value_ptr = grad_value + value_ptr_offset; + + for (int p_col=0; p_col < num_point; ++p_col) + { + const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; + const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; + const scalar_t weight = data_attn_weight[data_weight_ptr]; + + const scalar_t h_im = loc_h * spatial_h - 0.5; + const scalar_t w_im = loc_w * spatial_w - 0.5; + *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0; + *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0; + *(cache_grad_attn_weight+threadIdx.x)=0; + if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) + { + ms_deform_attn_col2im_bilinear( + data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, + top_grad, weight, grad_value_ptr, + cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x); + } + + __syncthreads(); + + for (unsigned int s=blockDim.x/2, spre=blockDim.x; s>0; s>>=1, spre>>=1) + { + if (tid < s) { + const unsigned int xid1 = tid << 1; + const unsigned int xid2 = (tid + s) << 1; + cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s]; + cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2]; + cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1]; + if (tid + (s << 1) < spre) + { + cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + (s << 1)]; + cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2 + (s << 1)]; + cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1 + (s << 1)]; + } + } + __syncthreads(); + } + + if (tid == 0) + { + atomicAdd(grad_sampling_loc, cache_grad_sampling_loc[0]); + atomicAdd(grad_sampling_loc + 1, cache_grad_sampling_loc[1]); + atomicAdd(grad_attn_weight, cache_grad_attn_weight[0]); + } + __syncthreads(); + + data_weight_ptr += 1; + data_loc_w_ptr += 2; + grad_attn_weight += grad_weight_stride; + grad_sampling_loc += grad_loc_stride; + } + } + } +} + + +template +__global__ void ms_deformable_col2im_gpu_kernel_gm(const int n, + const scalar_t *grad_col, + const scalar_t *data_value, + const int64_t *data_spatial_shapes, + const int64_t *data_level_start_index, + const scalar_t *data_sampling_loc, + const scalar_t *data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t *grad_value, + scalar_t *grad_sampling_loc, + scalar_t *grad_attn_weight) +{ + CUDA_KERNEL_LOOP(index, n) + { + int _temp = index; + const int c_col = _temp % channels; + _temp /= channels; + const int sampling_index = _temp; + const int m_col = _temp % num_heads; + _temp /= num_heads; + const int q_col = _temp % num_query; + _temp /= num_query; + const int b_col = _temp; + + const scalar_t top_grad = grad_col[index]; + + int data_weight_ptr = sampling_index * num_levels * num_point; + int data_loc_w_ptr = data_weight_ptr << 1; + const int grad_sampling_ptr = data_weight_ptr; + grad_sampling_loc += grad_sampling_ptr << 1; + grad_attn_weight += grad_sampling_ptr; + const int grad_weight_stride = 1; + const int grad_loc_stride = 2; + const int qid_stride = num_heads * channels; + const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; + + for (int l_col=0; l_col < num_levels; ++l_col) + { + const int level_start_id = data_level_start_index[l_col]; + const int spatial_h_ptr = l_col << 1; + const int spatial_h = data_spatial_shapes[spatial_h_ptr]; + const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; + const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; + const scalar_t *data_value_ptr = data_value + value_ptr_offset; + scalar_t *grad_value_ptr = grad_value + value_ptr_offset; + + for (int p_col=0; p_col < num_point; ++p_col) + { + const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; + const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; + const scalar_t weight = data_attn_weight[data_weight_ptr]; + + const scalar_t h_im = loc_h * spatial_h - 0.5; + const scalar_t w_im = loc_w * spatial_w - 0.5; + if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) + { + ms_deform_attn_col2im_bilinear_gm( + data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, + top_grad, weight, grad_value_ptr, + grad_sampling_loc, grad_attn_weight); + } + data_weight_ptr += 1; + data_loc_w_ptr += 2; + grad_attn_weight += grad_weight_stride; + grad_sampling_loc += grad_loc_stride; + } + } + } +} + + +template +void ms_deformable_im2col_cuda(cudaStream_t stream, + const scalar_t* data_value, + const int64_t* data_spatial_shapes, + const int64_t* data_level_start_index, + const scalar_t* data_sampling_loc, + const scalar_t* data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t* data_col) +{ + const int num_kernels = batch_size * num_query * num_heads * channels; + const int num_actual_kernels = batch_size * num_query * num_heads * channels; + const int num_threads = CUDA_NUM_THREADS; + ms_deformable_im2col_gpu_kernel + <<>>( + num_kernels, data_value, data_spatial_shapes, data_level_start_index, data_sampling_loc, data_attn_weight, + batch_size, spatial_size, num_heads, channels, num_levels, num_query, num_point, data_col); + + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) + { + printf("error in ms_deformable_im2col_cuda: %s\n", cudaGetErrorString(err)); + } + +} + +template +void ms_deformable_col2im_cuda(cudaStream_t stream, + const scalar_t* grad_col, + const scalar_t* data_value, + const int64_t * data_spatial_shapes, + const int64_t * data_level_start_index, + const scalar_t * data_sampling_loc, + const scalar_t * data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t* grad_value, + scalar_t* grad_sampling_loc, + scalar_t* grad_attn_weight) +{ + const int num_threads = (channels > CUDA_NUM_THREADS)?CUDA_NUM_THREADS:channels; + const int num_kernels = batch_size * num_query * num_heads * channels; + const int num_actual_kernels = batch_size * num_query * num_heads * channels; + if (channels > 1024) + { + if ((channels & 1023) == 0) + { + ms_deformable_col2im_gpu_kernel_shm_reduce_v2_multi_blocks + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + } + else + { + ms_deformable_col2im_gpu_kernel_gm + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + } + } + else{ + switch(channels) + { + case 1: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 2: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 4: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 8: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 16: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 32: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 64: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 128: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 256: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 512: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 1024: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + default: + if (channels < 64) + { + ms_deformable_col2im_gpu_kernel_shm_reduce_v1 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + } + else + { + ms_deformable_col2im_gpu_kernel_shm_reduce_v2 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + } + } + } + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) + { + printf("error in ms_deformable_col2im_cuda: %s\n", cudaGetErrorString(err)); + } + +} \ No newline at end of file diff --git a/ape/layers/csrc/cuda_version.cu b/ape/layers/csrc/cuda_version.cu new file mode 100644 index 0000000..5bc7946 --- /dev/null +++ b/ape/layers/csrc/cuda_version.cu @@ -0,0 +1,7 @@ +#include + +namespace ape { +int get_cudart_version() { + return CUDART_VERSION; +} +} // namespace ape diff --git a/ape/layers/csrc/vision.cpp b/ape/layers/csrc/vision.cpp new file mode 100644 index 0000000..30f4ad5 --- /dev/null +++ b/ape/layers/csrc/vision.cpp @@ -0,0 +1,80 @@ +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +#include +#include "MsDeformAttn/ms_deform_attn.h" + +namespace ape { + +#if defined(WITH_CUDA) || defined(WITH_HIP) +extern int get_cudart_version(); +#endif + +std::string get_cuda_version() { +#if defined(WITH_CUDA) || defined(WITH_HIP) + std::ostringstream oss; + +#if defined(WITH_CUDA) + oss << "CUDA "; +#else + oss << "HIP "; +#endif + + // copied from + // https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/cuda/detail/CUDAHooks.cpp#L231 + auto printCudaStyleVersion = [&](int v) { + oss << (v / 1000) << "." << (v / 10 % 100); + if (v % 10 != 0) { + oss << "." << (v % 10); + } + }; + printCudaStyleVersion(get_cudart_version()); + return oss.str(); +#else // neither CUDA nor HIP + return std::string("not available"); +#endif +} + +bool has_cuda() { +#if defined(WITH_CUDA) + return true; +#else + return false; +#endif +} + +// similar to +// https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/Version.cpp +std::string get_compiler_version() { + std::ostringstream ss; +#if defined(__GNUC__) +#ifndef __clang__ + +#if ((__GNUC__ <= 4) && (__GNUC_MINOR__ <= 8)) +#error "GCC >= 4.9 is required!" +#endif + + { ss << "GCC " << __GNUC__ << "." << __GNUC_MINOR__; } +#endif +#endif + +#if defined(__clang_major__) + { + ss << "clang " << __clang_major__ << "." << __clang_minor__ << "." + << __clang_patchlevel__; + } +#endif + +#if defined(_MSC_VER) + { ss << "MSVC " << _MSC_FULL_VER; } +#endif + return ss.str(); +} + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { +} + +TORCH_LIBRARY(ape, m) { + m.def("ms_deform_attn_forward", &ms_deform_attn_forward); + m.def("ms_deform_attn_backward", &ms_deform_attn_backward); +} +} // namespace ape diff --git a/ape/layers/fuse_helper.py b/ape/layers/fuse_helper.py new file mode 100644 index 0000000..3fa8008 --- /dev/null +++ b/ape/layers/fuse_helper.py @@ -0,0 +1,230 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +from timm.models.layers import DropPath + + +class BiMultiHeadAttention(nn.Module): + def __init__( + self, + v_dim, + l_dim, + embed_dim, + num_heads, + dropout=0.1, + stable_softmax_2d=False, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_attention_mask_v=False, + ): + super(BiMultiHeadAttention, self).__init__() + + self.embed_dim = embed_dim + self.num_heads = num_heads + self.head_dim = embed_dim // num_heads + self.v_dim = v_dim + self.l_dim = l_dim + + assert ( + self.head_dim * self.num_heads == self.embed_dim + ), f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {self.num_heads})." + self.scale = self.head_dim ** (-0.5) + self.dropout = dropout + + self.v_proj = nn.Linear(self.v_dim, self.embed_dim) + self.l_proj = nn.Linear(self.l_dim, self.embed_dim) + self.values_v_proj = nn.Linear(self.v_dim, self.embed_dim) + self.values_l_proj = nn.Linear(self.l_dim, self.embed_dim) + + self.out_v_proj = nn.Linear(self.embed_dim, self.v_dim) + self.out_l_proj = nn.Linear(self.embed_dim, self.l_dim) + + self.stable_softmax_2d = stable_softmax_2d + self.clamp_min_for_underflow = clamp_min_for_underflow + self.clamp_max_for_overflow = clamp_max_for_overflow + self.use_attention_mask_v = use_attention_mask_v + + self._reset_parameters() + + def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): + return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() + + def _reset_parameters(self): + nn.init.xavier_uniform_(self.v_proj.weight) + self.v_proj.bias.data.fill_(0) + nn.init.xavier_uniform_(self.l_proj.weight) + self.l_proj.bias.data.fill_(0) + nn.init.xavier_uniform_(self.values_v_proj.weight) + self.values_v_proj.bias.data.fill_(0) + nn.init.xavier_uniform_(self.values_l_proj.weight) + self.values_l_proj.bias.data.fill_(0) + nn.init.xavier_uniform_(self.out_v_proj.weight) + self.out_v_proj.bias.data.fill_(0) + nn.init.xavier_uniform_(self.out_l_proj.weight) + self.out_l_proj.bias.data.fill_(0) + + def forward(self, v, l, attention_mask_v=None, attention_mask_l=None): + bsz, tgt_len, _ = v.size() + + query_states = self.v_proj(v) * self.scale + key_states = self._shape(self.l_proj(l), -1, bsz) + value_v_states = self._shape(self.values_v_proj(v), -1, bsz) + value_l_states = self._shape(self.values_l_proj(l), -1, bsz) + + proj_shape = (bsz * self.num_heads, -1, self.head_dim) + query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape) + key_states = key_states.view(*proj_shape) + value_v_states = value_v_states.view(*proj_shape) + value_l_states = value_l_states.view(*proj_shape) + + src_len = key_states.size(1) + attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) + + if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): + raise ValueError( + f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}" + ) + + if self.stable_softmax_2d: + attn_weights = attn_weights - attn_weights.max() + + if self.clamp_min_for_underflow: + attn_weights = torch.clamp( + attn_weights, min=-50000 + ) # Do not increase -50000, data type half has quite limited range + if self.clamp_max_for_overflow: + attn_weights = torch.clamp( + attn_weights, max=50000 + ) # Do not increase 50000, data type half has quite limited range + + attn_weights_T = attn_weights.transpose(1, 2) + attn_weights_l = attn_weights_T - torch.max(attn_weights_T, dim=-1, keepdim=True)[0] + if self.clamp_min_for_underflow: + attn_weights_l = torch.clamp( + attn_weights_l, min=-50000 + ) # Do not increase -50000, data type half has quite limited range + if self.clamp_max_for_overflow: + attn_weights_l = torch.clamp( + attn_weights_l, max=50000 + ) # Do not increase 50000, data type half has quite limited range + + # mask vison for language + if attention_mask_v is not None and self.use_attention_mask_v: + attention_mask_v = ( + attention_mask_v[:, None, None, :].repeat(1, self.num_heads, 1, 1).flatten(0, 1) + ) + attn_weights_l.masked_fill_(attention_mask_v, float("-inf")) + + attn_weights_l = attn_weights_l.softmax(dim=-1) + + # mask language for vision + if attention_mask_l is not None: + # assert attention_mask_l.dim() == 2 # (bs, seq_len) + # attention_mask = attention_mask_l.unsqueeze(1).unsqueeze(1) # (bs, 1, 1, seq_len) + # attention_mask = attention_mask.expand(bsz, 1, tgt_len, src_len) + # attention_mask = attention_mask.masked_fill(attention_mask == 0, -9e15) + + # if attention_mask.size() != (bsz, 1, tgt_len, src_len): + # raise ValueError(f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}") + # attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask + # attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) + attention_mask_l = ( + attention_mask_l[:, None, None, :].repeat(1, self.num_heads, 1, 1).flatten(0, 1) + ) + attn_weights.masked_fill_(attention_mask_l, float("-inf")) + + attn_weights_v = attn_weights.softmax(dim=-1) + + attn_probs_v = F.dropout(attn_weights_v, p=self.dropout, training=self.training) + attn_probs_l = F.dropout(attn_weights_l, p=self.dropout, training=self.training) + + attn_output_v = torch.bmm(attn_probs_v, value_l_states) + attn_output_l = torch.bmm(attn_probs_l, value_v_states) + + if attn_output_v.size() != (bsz * self.num_heads, tgt_len, self.head_dim): + raise ValueError( + f"`attn_output_v` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output_v.size()}" + ) + + if attn_output_l.size() != (bsz * self.num_heads, src_len, self.head_dim): + raise ValueError( + f"`attn_output_l` should be of size {(bsz, self.num_heads, src_len, self.head_dim)}, but is {attn_output_l.size()}" + ) + + attn_output_v = attn_output_v.view(bsz, self.num_heads, tgt_len, self.head_dim) + attn_output_v = attn_output_v.transpose(1, 2) + attn_output_v = attn_output_v.reshape(bsz, tgt_len, self.embed_dim) + + attn_output_l = attn_output_l.view(bsz, self.num_heads, src_len, self.head_dim) + attn_output_l = attn_output_l.transpose(1, 2) + attn_output_l = attn_output_l.reshape(bsz, src_len, self.embed_dim) + + attn_output_v = self.out_v_proj(attn_output_v) + attn_output_l = self.out_l_proj(attn_output_l) + + return attn_output_v, attn_output_l + + def extra_repr(self): + lines = [ + f"stable_softmax_2d={self.stable_softmax_2d}", + f"clamp_min_for_underflow={self.clamp_min_for_underflow}", + f"clamp_max_for_overflow={self.clamp_max_for_overflow}", + f"use_attention_mask_v={self.use_attention_mask_v}", + ] + return "\n".join(lines) + + +class BiAttentionBlock(nn.Module): + def __init__( + self, + v_dim, + l_dim, + embed_dim, + num_heads, + dropout=0.1, + drop_path=0.0, + init_values=1e-4, + stable_softmax_2d=False, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_attention_mask_v=False, + ): + """ + Inputs: + embed_dim - Dimensionality of input and attention feature vectors + num_heads - Number of heads to use in the Multi-Head Attention block + dropout - Amount of dropout to apply in the feed-forward network + """ + super(BiAttentionBlock, self).__init__() + + # pre layer norm + self.layer_norm_v = nn.LayerNorm(v_dim) + self.layer_norm_l = nn.LayerNorm(l_dim) + self.attn = BiMultiHeadAttention( + v_dim=v_dim, + l_dim=l_dim, + embed_dim=embed_dim, + num_heads=num_heads, + dropout=dropout, + stable_softmax_2d=stable_softmax_2d, + clamp_min_for_underflow=clamp_min_for_underflow, + clamp_max_for_overflow=clamp_max_for_overflow, + use_attention_mask_v=use_attention_mask_v, + ) + + # add layer scale for training stability + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.gamma_v = nn.Parameter(init_values * torch.ones((v_dim)), requires_grad=True) + self.gamma_l = nn.Parameter(init_values * torch.ones((l_dim)), requires_grad=True) + + def forward(self, v, l, attention_mask_v=None, attention_mask_l=None): + v = self.layer_norm_v(v.float()) + l = self.layer_norm_l(l.float()) + delta_v, delta_l = self.attn( + v, l, attention_mask_v=attention_mask_v, attention_mask_l=attention_mask_l + ) + # v, l = v + delta_v, l + delta_l + v = v + self.drop_path(self.gamma_v * delta_v) + l = l + self.drop_path(self.gamma_l * delta_l) + return v, l diff --git a/ape/layers/multi_scale_deform_attn.py b/ape/layers/multi_scale_deform_attn.py new file mode 100644 index 0000000..4e10027 --- /dev/null +++ b/ape/layers/multi_scale_deform_attn.py @@ -0,0 +1,420 @@ +# coding=utf-8 +# ------------------------------------------------------------------------------------------------ +# Deformable DETR +# Copyright (c) 2020 SenseTime. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------------------------------ +# Modified from: +# https://github.com/fundamentalvision/Deformable-DETR/blob/main/models/ops/functions/ms_deform_attn_func.py +# https://github.com/fundamentalvision/Deformable-DETR/blob/main/models/ops/modules/ms_deform_attn.py +# https://github.com/open-mmlab/mmcv/blob/master/mmcv/ops/multi_scale_deform_attn.py +# ------------------------------------------------------------------------------------------------ + +import math +import warnings +from typing import Optional + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.autograd import Function +from torch.autograd.function import once_differentiable +from torch.nn.init import constant_, xavier_uniform_ + + +# helpers +def _is_power_of_2(n): + if (not isinstance(n, int)) or (n < 0): + raise ValueError("invalid input for _is_power_of_2: {} (type: {})".format(n, type(n))) + return (n & (n - 1) == 0) and n != 0 + + +class MultiScaleDeformableAttnFunction(Function): + @staticmethod + def forward( + ctx, + value, + value_spatial_shapes, + value_level_start_index, + sampling_locations, + attention_weights, + im2col_step, + ): + ctx.im2col_step = im2col_step + output = torch.ops.ape.ms_deform_attn_forward( + value, + value_spatial_shapes, + value_level_start_index, + sampling_locations, + attention_weights, + ctx.im2col_step, + ) + ctx.save_for_backward( + value, + value_spatial_shapes, + value_level_start_index, + sampling_locations, + attention_weights, + ) + return output + + @staticmethod + @once_differentiable + def backward(ctx, grad_output): + ( + value, + value_spatial_shapes, + value_level_start_index, + sampling_locations, + attention_weights, + ) = ctx.saved_tensors + grad_value, grad_sampling_loc, grad_attn_weight = torch.ops.ape.ms_deform_attn_backward( + value, + value_spatial_shapes, + value_level_start_index, + sampling_locations, + attention_weights, + grad_output, + ctx.im2col_step, + ) + + return grad_value, None, None, grad_sampling_loc, grad_attn_weight, None + + +def multi_scale_deformable_attn_pytorch( + value: torch.Tensor, + value_spatial_shapes: torch.Tensor, + sampling_locations: torch.Tensor, + attention_weights: torch.Tensor, +) -> torch.Tensor: + + bs, _, num_heads, embed_dims = value.shape + _, num_queries, num_heads, num_levels, num_points, _ = sampling_locations.shape + value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes], dim=1) + sampling_grids = 2 * sampling_locations - 1 + sampling_value_list = [] + for level, (H_, W_) in enumerate(value_spatial_shapes): + # bs, H_*W_, num_heads, embed_dims -> + # bs, H_*W_, num_heads*embed_dims -> + # bs, num_heads*embed_dims, H_*W_ -> + # bs*num_heads, embed_dims, H_, W_ + value_l_ = ( + value_list[level].flatten(2).transpose(1, 2).reshape(bs * num_heads, embed_dims, H_, W_) + ) + # bs, num_queries, num_heads, num_points, 2 -> + # bs, num_heads, num_queries, num_points, 2 -> + # bs*num_heads, num_queries, num_points, 2 + sampling_grid_l_ = sampling_grids[:, :, :, level].transpose(1, 2).flatten(0, 1) + # bs*num_heads, embed_dims, num_queries, num_points + sampling_value_l_ = F.grid_sample( + value_l_, sampling_grid_l_, mode="bilinear", padding_mode="zeros", align_corners=False + ) + sampling_value_list.append(sampling_value_l_) + # (bs, num_queries, num_heads, num_levels, num_points) -> + # (bs, num_heads, num_queries, num_levels, num_points) -> + # (bs, num_heads, 1, num_queries, num_levels*num_points) + attention_weights = attention_weights.transpose(1, 2).reshape( + bs * num_heads, 1, num_queries, num_levels * num_points + ) + output = ( + (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights) + .sum(-1) + .view(bs, num_heads * embed_dims, num_queries) + ) + return output.transpose(1, 2).contiguous() + + +class MultiScaleDeformableAttention(nn.Module): + """Multi-Scale Deformable Attention Module used in Deformable-DETR + + `Deformable DETR: Deformable Transformers for End-to-End Object Detection. + `_. + + Args: + embed_dim (int): The embedding dimension of Attention. Default: 256. + num_heads (int): The number of attention heads. Default: 8. + num_levels (int): The number of feature map used in Attention. Default: 4. + num_points (int): The number of sampling points for each query + in each head. Default: 4. + img2col_steps (int): The step used in image_to_column. Defualt: 64. + dropout (float): Dropout layer used in output. Default: 0.1. + batch_first (bool): if ``True``, then the input and output tensor will be + provided as `(bs, n, embed_dim)`. Default: False. `(n, bs, embed_dim)` + """ + + def __init__( + self, + embed_dim: int = 256, + num_heads: int = 8, + num_levels: int = 4, + num_points: int = 4, + img2col_step: int = 64, + dropout: float = 0.1, + batch_first: bool = False, + ): + super().__init__() + if embed_dim % num_heads != 0: + raise ValueError( + "embed_dim must be divisible by num_heads, but got {} and {}".format( + embed_dim, num_heads + ) + ) + head_dim = embed_dim // num_heads + + self.dropout = nn.Dropout(dropout) + self.batch_first = batch_first + + if not _is_power_of_2(head_dim): + warnings.warn( + """ + You'd better set d_model in MSDeformAttn to make sure that + each dim of the attention head a power of 2, which is more efficient. + """ + ) + + self.im2col_step = img2col_step + self.embed_dim = embed_dim + self.num_heads = num_heads + self.num_levels = num_levels + self.num_points = num_points + self.sampling_offsets = nn.Linear(embed_dim, num_heads * num_levels * num_points * 2) + self.attention_weights = nn.Linear(embed_dim, num_heads * num_levels * num_points) + self.value_proj = nn.Linear(embed_dim, embed_dim) + self.output_proj = nn.Linear(embed_dim, embed_dim) + + self.init_weights() + + def init_weights(self): + """ + Default initialization for Parameters of Module. + """ + constant_(self.sampling_offsets.weight.data, 0.0) + thetas = torch.arange(self.num_heads, dtype=torch.float32) * ( + 2.0 * math.pi / self.num_heads + ) + grid_init = torch.stack([thetas.cos(), thetas.sin()], -1) + grid_init = ( + (grid_init / grid_init.abs().max(-1, keepdim=True)[0]) + .view(self.num_heads, 1, 1, 2) + .repeat(1, self.num_levels, self.num_points, 1) + ) + for i in range(self.num_points): + grid_init[:, :, i, :] *= i + 1 + with torch.no_grad(): + self.sampling_offsets.bias = nn.Parameter(grid_init.view(-1)) + constant_(self.attention_weights.weight.data, 0.0) + constant_(self.attention_weights.bias.data, 0.0) + xavier_uniform_(self.value_proj.weight.data) + constant_(self.value_proj.bias.data, 0.0) + xavier_uniform_(self.output_proj.weight.data) + constant_(self.output_proj.bias.data, 0.0) + + def forward( + self, + query: torch.Tensor, + key: Optional[torch.Tensor] = None, + value: Optional[torch.Tensor] = None, + identity: Optional[torch.Tensor] = None, + query_pos: Optional[torch.Tensor] = None, + key_padding_mask: Optional[torch.Tensor] = None, + reference_points: Optional[torch.Tensor] = None, + spatial_shapes: Optional[torch.Tensor] = None, + level_start_index: Optional[torch.Tensor] = None, + **kwargs + ) -> torch.Tensor: + + """Forward Function of MultiScaleDeformableAttention + + Args: + query (torch.Tensor): Query embeddings with shape + `(num_query, bs, embed_dim)` + key (torch.Tensor): Key embeddings with shape + `(num_key, bs, embed_dim)` + value (torch.Tensor): Value embeddings with shape + `(num_key, bs, embed_dim)` + identity (torch.Tensor): The tensor used for addition, with the + same shape as `query`. Default: None. If None, `query` will be + used. + query_pos (torch.Tensor): The position embedding for `query`. Default: None. + key_padding_mask (torch.Tensor): ByteTensor for `query`, with shape `(bs, num_key)`, + indicating which elements within `key` to be ignored in attention. + reference_points (torch.Tensor): The normalized reference points + with shape `(bs, num_query, num_levels, 2)`, + all elements is range in [0, 1], top-left (0, 0), + bottom-right (1, 1), including padding are. + or `(N, Length_{query}, num_levels, 4)`, add additional + two dimensions `(h, w)` to form reference boxes. + spatial_shapes (torch.Tensor): Spatial shape of features in different levels. + With shape `(num_levels, 2)`, last dimension represents `(h, w)`. + level_start_index (torch.Tensor): The start index of each level. A tensor with + shape `(num_levels, )` which can be represented as + `[0, h_0 * w_0, h_0 * w_0 + h_1 * w_1, ...]`. + + Returns: + torch.Tensor: forward results with shape `(num_query, bs, embed_dim)` + """ + + if value is None: + value = query + + if identity is None: + identity = query + if query_pos is not None: + query = query + query_pos + + if not self.batch_first: + # change to (bs, num_query ,embed_dims) + query = query.permute(1, 0, 2) + value = value.permute(1, 0, 2) + + bs, num_query, _ = query.shape + bs, num_value, _ = value.shape + + assert (spatial_shapes[:, 0] * spatial_shapes[:, 1]).sum() == num_value + + value = self.value_proj(value) + if key_padding_mask is not None: + value = value.masked_fill(key_padding_mask[..., None], float(0)) + value = value.view(bs, num_value, self.num_heads, -1) + sampling_offsets = self.sampling_offsets(query).view( + bs, num_query, self.num_heads, self.num_levels, self.num_points, 2 + ) + attention_weights = self.attention_weights(query).view( + bs, num_query, self.num_heads, self.num_levels * self.num_points + ) + attention_weights = attention_weights.softmax(-1) + attention_weights = attention_weights.view( + bs, + num_query, + self.num_heads, + self.num_levels, + self.num_points, + ) + + # bs, num_query, num_heads, num_levels, num_points, 2 + if reference_points.shape[-1] == 2: + offset_normalizer = torch.stack([spatial_shapes[..., 1], spatial_shapes[..., 0]], -1) + sampling_locations = ( + reference_points[:, :, None, :, None, :] + + sampling_offsets / offset_normalizer[None, None, None, :, None, :] + ) + elif reference_points.shape[-1] == 4: + sampling_locations = ( + reference_points[:, :, None, :, None, :2] + + sampling_offsets + / self.num_points + * reference_points[:, :, None, :, None, 2:] + * 0.5 + ) + else: + raise ValueError( + "Last dim of reference_points must be 2 or 4, but get {} instead.".format( + reference_points.shape[-1] + ) + ) + + # the original impl for fp32 training + if torch.cuda.is_available() and value.is_cuda: + if torch.jit.is_scripting() or torch.jit.is_tracing(): + output = torch.ops.ape.ms_deform_attn_forward( + # value.to(torch.float32), + value, + spatial_shapes, + level_start_index, + # sampling_locations.to(torch.float32), + sampling_locations.to(value.dtype), + # attention_weights.to(torch.float32), + attention_weights.to(value.dtype), + self.im2col_step, + ) + else: + output = MultiScaleDeformableAttnFunction.apply( + # value.to(torch.float32), + value, + spatial_shapes, + level_start_index, + # sampling_locations.to(torch.float32), + sampling_locations.to(value.dtype), + # attention_weights.to(torch.float32), + attention_weights.to(value.dtype), + self.im2col_step, + ) + else: + output = multi_scale_deformable_attn_pytorch( + value, spatial_shapes, sampling_locations, attention_weights + ) + + if value.dtype == torch.float16: + output = output.to(torch.float16) + + output = self.output_proj(output) + + if not self.batch_first: + output = output.permute(1, 0, 2) + + return self.dropout(output) + identity + + +def create_dummy_class(klass, dependency, message=""): + """ + When a dependency of a class is not available, create a dummy class which throws ImportError + when used. + + Args: + klass (str): name of the class. + dependency (str): name of the dependency. + message: extra message to print + Returns: + class: a class object + """ + err = "Cannot import '{}', therefore '{}' is not available.".format(dependency, klass) + if message: + err = err + " " + message + + class _DummyMetaClass(type): + # throw error on class attribute access + def __getattr__(_, __): # noqa: B902 + raise ImportError(err) + + class _Dummy(object, metaclass=_DummyMetaClass): + # throw error on constructor + def __init__(self, *args, **kwargs): + raise ImportError(err) + + return _Dummy + + +def create_dummy_func(func, dependency, message=""): + """ + When a dependency of a function is not available, create a dummy function which throws + ImportError when used. + + Args: + func (str): name of the function. + dependency (str or list[str]): name(s) of the dependency. + message: extra message to print + Returns: + function: a function object + """ + err = "Cannot import '{}', therefore '{}' is not available.".format(dependency, func) + if message: + err = err + " " + message + + if isinstance(dependency, (list, tuple)): + dependency = ",".join(dependency) + + def _dummy(*args, **kwargs): + raise ImportError(err) + + return _dummy + + +try: + from ape import _C +except ImportError: + # TODO: register ops natively so there is no need to import _C. + _msg = "ape is not compiled successfully, please build following the instructions!" + _args = ("ape._C", _msg) + MultiScaleDeformableAttention = create_dummy_class( # noqa + "MultiScaleDeformableAttention", *_args + ) diff --git a/ape/layers/vision_language_align.py b/ape/layers/vision_language_align.py new file mode 100644 index 0000000..f0511c3 --- /dev/null +++ b/ape/layers/vision_language_align.py @@ -0,0 +1,61 @@ +import math + +import torch +import torch.nn.functional as F +from torch import nn + + +class VisionLanguageAlign(nn.Module): + def __init__( + self, embed_dim, embed_dim_language, prior_prob=0.01, log_scale=0.0, clamp_dot_product=True + ): + super().__init__() + # initialize the bias for focal loss + bias_value = -math.log((1 - prior_prob) / prior_prob) + + # dot product soft token head + self.dot_product_projection_image = nn.Identity() + self.dot_product_projection_text = nn.Linear( + embed_dim_language, embed_dim, bias=True + ) # 768 -> 256 + self.log_scale = nn.Parameter(torch.Tensor([log_scale]), requires_grad=True) + self.bias_lang = nn.Parameter(torch.zeros(embed_dim_language), requires_grad=True) # (768，) + self.bias0 = nn.Parameter(torch.Tensor([bias_value]), requires_grad=True) # size (1,) + + self.clamp_dot_product = clamp_dot_product + + def forward(self, x, embedding): + """ + x: visual features (bs, num_query, 256) + embedding: language features (bs, L, 768) + """ + embedding = embedding.to(x.dtype) + + # norm + embedding = F.normalize(embedding, p=2, dim=-1) # (bs, L, 768) L is maximum sentence length + dot_product_proj_tokens = self.dot_product_projection_text(embedding / 2.0) # 768 -> 256 + dot_product_proj_tokens_bias = ( + torch.matmul(embedding, self.bias_lang) + self.bias0 + ) # (bs, L, 768) x (768, ) + (1, ) -> (bs, L) + + dot_product_proj_queries = self.dot_product_projection_image(x) # (bs, num_query, 256) + A = dot_product_proj_queries.shape[1] # num_query + bias = dot_product_proj_tokens_bias.unsqueeze(1).repeat(1, A, 1) # (bs, num_query, L) + + dot_product_logit = ( + torch.matmul(dot_product_proj_queries, dot_product_proj_tokens.transpose(-1, -2)) + / self.log_scale.exp() + ) + bias # (bs, num_query, 256) x (bs, 256, L) -> (bs, num_query, L) + if self.clamp_dot_product: + dot_product_logit = torch.clamp(dot_product_logit, max=50000) + dot_product_logit = torch.clamp(dot_product_logit, min=-50000) + return dot_product_logit + + +class StillClassifier(nn.Module): + def __init__(self, hidden_dim): + super().__init__() + self.body = nn.Linear(hidden_dim, 1) + + def forward(self, x, lang_feat=None): + return self.body(x) diff --git a/ape/layers/vision_language_fusion.py b/ape/layers/vision_language_fusion.py new file mode 100644 index 0000000..bbb46aa --- /dev/null +++ b/ape/layers/vision_language_fusion.py @@ -0,0 +1,53 @@ +import torch +import torch.utils.checkpoint as checkpoint + +from .fuse_helper import BiAttentionBlock + + +class VisionLanguageFusion(torch.nn.Module): + """ + Early Fusion Module + """ + + def __init__( + self, + v_dim, + l_dim, + embed_dim, + num_heads, + dropout=0.1, + drop_path=0.0, + init_values=1e-4, + stable_softmax_2d=False, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=False, + use_attention_mask_v=False, + ): + super(VisionLanguageFusion, self).__init__() + self.use_checkpoint = use_checkpoint + + # early fusion module + # bi-direction (text->image, image->text) + self.b_attn = BiAttentionBlock( + v_dim=v_dim, + l_dim=l_dim, + embed_dim=embed_dim, + num_heads=num_heads, + dropout=dropout, + drop_path=drop_path, + init_values=init_values, + stable_softmax_2d=stable_softmax_2d, + clamp_min_for_underflow=clamp_min_for_underflow, + clamp_max_for_overflow=clamp_max_for_overflow, + use_attention_mask_v=use_attention_mask_v, + ) + + def forward(self, v, l, attention_mask_v=None, attention_mask_l=None): + if self.use_checkpoint and self.training: + return checkpoint.checkpoint(self.b_attn, v, l, attention_mask_v, attention_mask_l) + else: + return self.b_attn(v, l, attention_mask_v, attention_mask_l) + + def extra_repr(self): + return f"use_checkpoint={self.use_checkpoint}" diff --git a/ape/layers/zero_shot_fc.py b/ape/layers/zero_shot_fc.py new file mode 100644 index 0000000..e19622a --- /dev/null +++ b/ape/layers/zero_shot_fc.py @@ -0,0 +1,162 @@ +import logging +import math + +import numpy as np +import torch +from torch import nn +from torch.nn import functional as F + +# from sota.modeling.text import build_clip_text_encoder, get_clip_embeddings +# from ..modeling.text import build_clip_text_encoder, get_clip_embeddings + +logger = logging.getLogger(__name__) + + +class ZeroShotFC(nn.Module): + def __init__( + self, + input_size, + *, + num_classes: int, + zs_weight_path: str, + zs_weight_dim: int = 512, + use_bias: float = 0.0, + norm_weight: bool = True, + norm_temperature: float = 50.0, + use_project: bool = True, + use_sigmoid_ce: bool, + prior_prob: float = 0.01, + zs_vocabulary: str = "", + text_model: str = "", + ): + super().__init__() + + # assert use_sigmoid_ce + # assert cls_agnostic_bbox_reg + + self.norm_weight = norm_weight + self.norm_temperature = norm_temperature + self.use_project = use_project + self.zs_weight_dim = zs_weight_dim + + self.use_bias = use_bias < 0 + if self.use_bias: + self.cls_bias = nn.Parameter(torch.ones(1) * use_bias, requires_grad=True) + + if self.use_project: + self.linear = nn.Linear(input_size, zs_weight_dim) + + if use_sigmoid_ce: + bias_value = -math.log((1 - prior_prob) / prior_prob) + else: + bias_value = 0 + torch.nn.init.constant_(self.linear.bias, bias_value) + torch.nn.init.normal_(self.linear.weight, std=0.01) + + if len(zs_vocabulary) > 0: + from sota.modeling.text import get_clip_embeddings + + logger.info("Generating weight for " + zs_vocabulary) + zs_vocabulary = zs_vocabulary.split(",") + num_classes = len(zs_vocabulary) + zs_weight = get_clip_embeddings(text_model, zs_vocabulary) + zs_weight = zs_weight.permute(1, 0).contiguous() + elif zs_weight_path == "rand": + zs_weight = torch.randn((zs_weight_dim, num_classes)) + nn.init.normal_(zs_weight, std=0.01) + elif zs_weight_path == "zeros": + zs_weight = torch.zeros((zs_weight_dim, num_classes)) + elif zs_weight_path == "online": + from sota.modeling.text import build_clip_text_encoder + + zs_weight = torch.zeros((zs_weight_dim, num_classes)) + self.text_encoder = build_clip_text_encoder(text_model, pretrain=True) + self.text_encoder.eval() + else: + logger.info("Loading " + zs_weight_path) + zs_weight = ( + torch.tensor(np.load(zs_weight_path), dtype=torch.float32) + .permute(1, 0) + .contiguous() + ) + logger.info(f"Loaded zs_weight {zs_weight.size()}") + + zs_weight = torch.cat([zs_weight, zs_weight.new_zeros((self.zs_weight_dim, 1))], dim=1) + logger.info(f"Cated zs_weight {zs_weight.size()}") + + if self.norm_weight: + zs_weight = F.normalize(zs_weight, p=2, dim=0) + + if zs_weight_path == "rand": + self.zs_weight = nn.Parameter(zs_weight, requires_grad=True) + else: + self.register_buffer("zs_weight", zs_weight) + + assert ( + self.zs_weight.shape[1] == num_classes + 1 + ), f"zs_weight={self.zs_weight.shape} v.s. num_classes={num_classes}" + + def forward(self, x, classifier=None): + """ + Inputs: + x: B x D or B x N x D + classifier: C x D + """ + x_shape = x.shape + if len(x_shape) == 3: + x = x.reshape(x_shape[0] * x_shape[1], x_shape[2]) + assert x.dim() == 2 + + if self.use_project: + x = self.linear(x) + if classifier is not None: + if isinstance(classifier, str): + from sota.modeling.text import get_clip_embeddings + + zs_weight = get_clip_embeddings( + self.text_encoder, classifier, prompt="", device=x.device + ) + else: + zs_weight = classifier + zs_weight = zs_weight.permute(1, 0).contiguous() + zs_weight = torch.cat([zs_weight, zs_weight.new_zeros((self.zs_weight_dim, 1))], dim=1) + if self.norm_weight: + zs_weight = F.normalize(zs_weight, p=2, dim=0) + else: + zs_weight = self.zs_weight + if self.norm_weight: + x = self.norm_temperature * F.normalize(x, p=2, dim=1) + x = torch.mm(x, zs_weight) + if self.use_bias: + x = x + self.cls_bias + + if len(x_shape) == 3: + x = x.reshape(x_shape[:2] + zs_weight.shape[1:]) + return x + + def set_predictor(self, param_or_path): + if type(param_or_path) == str: + logger.info("Loading " + param_or_path) + zs_weight = ( + torch.tensor(np.load(param_or_path), dtype=torch.float32).permute(1, 0).contiguous() + ) + else: + zs_weight = param_or_path.permute(1, 0).contiguous() + logger.info(f"Loaded zs_weight {zs_weight.size()}") + + zs_weight = torch.cat([zs_weight, zs_weight.new_zeros((self.zs_weight_dim, 1))], dim=1) + logger.info(f"Cated zs_weight {zs_weight.size()}") + + if self.norm_weight: + zs_weight = F.normalize(zs_weight, p=2, dim=0) + + zs_weight = zs_weight.to(self.zs_weight.device) + self.zs_weight = zs_weight + + def extra_repr(self): + extra_repr = "" + valtype = (int, float, bool, str, dict, list) + for attribute, value in self.__dict__.items(): + if type(value) in valtype: + extra_repr += "{}={}, ".format(attribute, value) + return extra_repr[:-2] diff --git a/ape/model_zoo/__init__.py b/ape/model_zoo/__init__.py new file mode 100644 index 0000000..1144331 --- /dev/null +++ b/ape/model_zoo/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +""" +Model Zoo API for Detectron2: a collection of functions to create common model architectures +listed in `MODEL_ZOO.md `_, +and optionally load their pre-trained weights. +""" + +from .model_zoo import get, get_checkpoint_url, get_config, get_config_file + +__all__ = ["get_checkpoint_url", "get", "get_config_file", "get_config"] diff --git a/ape/model_zoo/model_zoo.py b/ape/model_zoo/model_zoo.py new file mode 100644 index 0000000..ca88803 --- /dev/null +++ b/ape/model_zoo/model_zoo.py @@ -0,0 +1,214 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import os +from typing import Optional + +import pkg_resources +import torch + +from detectron2.checkpoint import DetectionCheckpointer +from detectron2.config import CfgNode, LazyConfig, get_cfg, instantiate +from detectron2.modeling import build_model + + +class _ModelZooUrls(object): + """ + Mapping from names to officially released Detectron2 pre-trained models. + """ + + S3_PREFIX = "https://dl.fbaipublicfiles.com/detectron2/" + + # format: {config_path.yaml} -> model_id/model_final_{commit}.pkl + CONFIG_PATH_TO_URL_SUFFIX = { + # COCO Detection with Faster R-CNN + "COCO-Detection/faster_rcnn_R_50_C4_1x": "137257644/model_final_721ade.pkl", + "COCO-Detection/faster_rcnn_R_50_DC5_1x": "137847829/model_final_51d356.pkl", + "COCO-Detection/faster_rcnn_R_50_FPN_1x": "137257794/model_final_b275ba.pkl", + "COCO-Detection/faster_rcnn_R_50_C4_3x": "137849393/model_final_f97cb7.pkl", + "COCO-Detection/faster_rcnn_R_50_DC5_3x": "137849425/model_final_68d202.pkl", + "COCO-Detection/faster_rcnn_R_50_FPN_3x": "137849458/model_final_280758.pkl", + "COCO-Detection/faster_rcnn_R_101_C4_3x": "138204752/model_final_298dad.pkl", + "COCO-Detection/faster_rcnn_R_101_DC5_3x": "138204841/model_final_3e0943.pkl", + "COCO-Detection/faster_rcnn_R_101_FPN_3x": "137851257/model_final_f6e8b1.pkl", + "COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x": "139173657/model_final_68b088.pkl", + # COCO Detection with RetinaNet + "COCO-Detection/retinanet_R_50_FPN_1x": "190397773/model_final_bfca0b.pkl", + "COCO-Detection/retinanet_R_50_FPN_3x": "190397829/model_final_5bd44e.pkl", + "COCO-Detection/retinanet_R_101_FPN_3x": "190397697/model_final_971ab9.pkl", + # COCO Detection with RPN and Fast R-CNN + "COCO-Detection/rpn_R_50_C4_1x": "137258005/model_final_450694.pkl", + "COCO-Detection/rpn_R_50_FPN_1x": "137258492/model_final_02ce48.pkl", + "COCO-Detection/fast_rcnn_R_50_FPN_1x": "137635226/model_final_e5f7ce.pkl", + # COCO Instance Segmentation Baselines with Mask R-CNN + "COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x": "137259246/model_final_9243eb.pkl", + "COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_1x": "137260150/model_final_4f86c3.pkl", + "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x": "137260431/model_final_a54504.pkl", + "COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x": "137849525/model_final_4ce675.pkl", + "COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_3x": "137849551/model_final_84107b.pkl", + "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x": "137849600/model_final_f10217.pkl", + "COCO-InstanceSegmentation/mask_rcnn_R_101_C4_3x": "138363239/model_final_a2914c.pkl", + "COCO-InstanceSegmentation/mask_rcnn_R_101_DC5_3x": "138363294/model_final_0464b7.pkl", + "COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x": "138205316/model_final_a3ec72.pkl", + "COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x": "139653917/model_final_2d9806.pkl", # noqa + # New baselines using Large-Scale Jitter and Longer Training Schedule + "new_baselines/mask_rcnn_R_50_FPN_100ep_LSJ": "42047764/model_final_bb69de.pkl", + "new_baselines/mask_rcnn_R_50_FPN_200ep_LSJ": "42047638/model_final_89a8d3.pkl", + "new_baselines/mask_rcnn_R_50_FPN_400ep_LSJ": "42019571/model_final_14d201.pkl", + "new_baselines/mask_rcnn_R_101_FPN_100ep_LSJ": "42025812/model_final_4f7b58.pkl", + "new_baselines/mask_rcnn_R_101_FPN_200ep_LSJ": "42131867/model_final_0bb7ae.pkl", + "new_baselines/mask_rcnn_R_101_FPN_400ep_LSJ": "42073830/model_final_f96b26.pkl", + "new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_100ep_LSJ": "42047771/model_final_b7fbab.pkl", # noqa + "new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_200ep_LSJ": "42132721/model_final_5d87c1.pkl", # noqa + "new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_400ep_LSJ": "42025447/model_final_f1362d.pkl", # noqa + "new_baselines/mask_rcnn_regnety_4gf_dds_FPN_100ep_LSJ": "42047784/model_final_6ba57e.pkl", # noqa + "new_baselines/mask_rcnn_regnety_4gf_dds_FPN_200ep_LSJ": "42047642/model_final_27b9c1.pkl", # noqa + "new_baselines/mask_rcnn_regnety_4gf_dds_FPN_400ep_LSJ": "42045954/model_final_ef3a80.pkl", # noqa + # COCO Person Keypoint Detection Baselines with Keypoint R-CNN + "COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x": "137261548/model_final_04e291.pkl", + "COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x": "137849621/model_final_a6e10b.pkl", + "COCO-Keypoints/keypoint_rcnn_R_101_FPN_3x": "138363331/model_final_997cc7.pkl", + "COCO-Keypoints/keypoint_rcnn_X_101_32x8d_FPN_3x": "139686956/model_final_5ad38f.pkl", + # COCO Panoptic Segmentation Baselines with Panoptic FPN + "COCO-PanopticSegmentation/panoptic_fpn_R_50_1x": "139514544/model_final_dbfeb4.pkl", + "COCO-PanopticSegmentation/panoptic_fpn_R_50_3x": "139514569/model_final_c10459.pkl", + "COCO-PanopticSegmentation/panoptic_fpn_R_101_3x": "139514519/model_final_cafdb1.pkl", + # LVIS Instance Segmentation Baselines with Mask R-CNN + "LVISv0.5-InstanceSegmentation/mask_rcnn_R_50_FPN_1x": "144219072/model_final_571f7c.pkl", # noqa + "LVISv0.5-InstanceSegmentation/mask_rcnn_R_101_FPN_1x": "144219035/model_final_824ab5.pkl", # noqa + "LVISv0.5-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_1x": "144219108/model_final_5e3439.pkl", # noqa + # Cityscapes & Pascal VOC Baselines + "Cityscapes/mask_rcnn_R_50_FPN": "142423278/model_final_af9cf5.pkl", + "PascalVOC-Detection/faster_rcnn_R_50_C4": "142202221/model_final_b1acc2.pkl", + # Other Settings + "Misc/mask_rcnn_R_50_FPN_1x_dconv_c3-c5": "138602867/model_final_65c703.pkl", + "Misc/mask_rcnn_R_50_FPN_3x_dconv_c3-c5": "144998336/model_final_821d0b.pkl", + "Misc/cascade_mask_rcnn_R_50_FPN_1x": "138602847/model_final_e9d89b.pkl", + "Misc/cascade_mask_rcnn_R_50_FPN_3x": "144998488/model_final_480dd8.pkl", + "Misc/mask_rcnn_R_50_FPN_3x_syncbn": "169527823/model_final_3b3c51.pkl", + "Misc/mask_rcnn_R_50_FPN_3x_gn": "138602888/model_final_dc5d9e.pkl", + "Misc/scratch_mask_rcnn_R_50_FPN_3x_gn": "138602908/model_final_01ca85.pkl", + "Misc/scratch_mask_rcnn_R_50_FPN_9x_gn": "183808979/model_final_da7b4c.pkl", + "Misc/scratch_mask_rcnn_R_50_FPN_9x_syncbn": "184226666/model_final_5ce33e.pkl", + "Misc/panoptic_fpn_R_101_dconv_cascade_gn_3x": "139797668/model_final_be35db.pkl", + "Misc/cascade_mask_rcnn_X_152_32x8d_FPN_IN5k_gn_dconv": "18131413/model_0039999_e76410.pkl", # noqa + # D1 Comparisons + "Detectron1-Comparisons/faster_rcnn_R_50_FPN_noaug_1x": "137781054/model_final_7ab50c.pkl", # noqa + "Detectron1-Comparisons/mask_rcnn_R_50_FPN_noaug_1x": "137781281/model_final_62ca52.pkl", # noqa + "Detectron1-Comparisons/keypoint_rcnn_R_50_FPN_1x": "137781195/model_final_cce136.pkl", + } + + @staticmethod + def query(config_path: str) -> Optional[str]: + """ + Args: + config_path: relative config filename + """ + name = config_path.replace(".yaml", "").replace(".py", "") + if name in _ModelZooUrls.CONFIG_PATH_TO_URL_SUFFIX: + suffix = _ModelZooUrls.CONFIG_PATH_TO_URL_SUFFIX[name] + return _ModelZooUrls.S3_PREFIX + name + "/" + suffix + return None + + +def get_checkpoint_url(config_path): + """ + Returns the URL to the model trained using the given config + + Args: + config_path (str): config file name relative to detectron2's "configs/" + directory, e.g., "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml" + + Returns: + str: a URL to the model + """ + url = _ModelZooUrls.query(config_path) + if url is None: + raise RuntimeError("Pretrained model for {} is not available!".format(config_path)) + return url + + +def get_config_file(config_path): + """ + Returns path to a builtin config file. + + Args: + config_path (str): config file name relative to detectron2's "configs/" + directory, e.g., "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml" + + Returns: + str: the real path to the config file. + """ + cfg_file = pkg_resources.resource_filename( + "ape.model_zoo", os.path.join("configs", config_path) + ) + if not os.path.exists(cfg_file): + raise RuntimeError("{} not available in Model Zoo!".format(config_path)) + return cfg_file + + +def get_config(config_path, trained: bool = False): + """ + Returns a config object for a model in model zoo. + + Args: + config_path (str): config file name relative to detectron2's "configs/" + directory, e.g., "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml" + trained (bool): If True, will set ``MODEL.WEIGHTS`` to trained model zoo weights. + If False, the checkpoint specified in the config file's ``MODEL.WEIGHTS`` is used + instead; this will typically (though not always) initialize a subset of weights using + an ImageNet pre-trained model, while randomly initializing the other weights. + + Returns: + CfgNode or omegaconf.DictConfig: a config object + """ + cfg_file = get_config_file(config_path) + if cfg_file.endswith(".yaml"): + cfg = get_cfg() + cfg.merge_from_file(cfg_file) + if trained: + cfg.MODEL.WEIGHTS = get_checkpoint_url(config_path) + return cfg + elif cfg_file.endswith(".py"): + cfg = LazyConfig.load(cfg_file) + if trained: + url = get_checkpoint_url(config_path) + if "train" in cfg and "init_checkpoint" in cfg.train: + cfg.train.init_checkpoint = url + else: + raise NotImplementedError + return cfg + + +def get(config_path, trained: bool = False, device: Optional[str] = None): + """ + Get a model specified by relative path under Detectron2's official ``configs/`` directory. + + Args: + config_path (str): config file name relative to detectron2's "configs/" + directory, e.g., "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml" + trained (bool): see :func:`get_config`. + device (str or None): overwrite the device in config, if given. + + Returns: + nn.Module: a detectron2 model. Will be in training mode. + + Example: + :: + from detectron2 import model_zoo + model = model_zoo.get("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml", trained=True) + """ + cfg = get_config(config_path, trained) + if device is None and not torch.cuda.is_available(): + device = "cpu" + if device is not None and isinstance(cfg, CfgNode): + cfg.MODEL.DEVICE = device + + if isinstance(cfg, CfgNode): + model = build_model(cfg) + DetectionCheckpointer(model).load(cfg.MODEL.WEIGHTS) + else: + model = instantiate(cfg.model) + if device is not None: + model = model.to(device) + if "train" in cfg and "init_checkpoint" in cfg.train: + DetectionCheckpointer(model).load(cfg.train.init_checkpoint) + return model diff --git a/ape/modeling/__init__.py b/ape/modeling/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/ape/modeling/ape_deta/__init__.py b/ape/modeling/ape_deta/__init__.py new file mode 100644 index 0000000..451fc31 --- /dev/null +++ b/ape/modeling/ape_deta/__init__.py @@ -0,0 +1,16 @@ +from .ape_deta import SomeThing +from .assigner import Stage1Assigner, Stage2Assigner +from .deformable_criterion import DeformableCriterion +from .deformable_detr import DeformableDETR +from .deformable_detr_segm import DeformableDETRSegm +from .deformable_detr_segm_vl import DeformableDETRSegmVL +from .deformable_transformer import ( + DeformableDetrTransformer, + DeformableDetrTransformerDecoder, + DeformableDetrTransformerEncoder, +) +from .deformable_transformer_vl import ( + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) diff --git a/ape/modeling/ape_deta/ape_deta.py b/ape/modeling/ape_deta/ape_deta.py new file mode 100644 index 0000000..f3e30ac --- /dev/null +++ b/ape/modeling/ape_deta/ape_deta.py @@ -0,0 +1,40 @@ +import copy +import math +from typing import Dict, List, Optional, Tuple + +import torch +import torch.nn as nn +import torch.nn.functional as F + +import fvcore.nn.weight_init as weight_init +from detectron2.layers import Conv2d, ShapeSpec, get_norm, move_device_like +from detectron2.modeling import GeneralizedRCNN +from detectron2.modeling.postprocessing import detector_postprocess, sem_seg_postprocess +from detectron2.modeling.roi_heads.fast_rcnn import fast_rcnn_inference +from detectron2.structures import BitMasks, Boxes, ImageList, Instances +from detrex.layers import MLP, box_cxcywh_to_xyxy, box_xyxy_to_cxcywh +from detrex.utils import inverse_sigmoid +from torchvision.ops.boxes import batched_nms + + +class SomeThing(nn.Module): + def __init__( + self, + model_vision, + model_language, + **kwargs, + ): + super().__init__(**kwargs) + + self.model_vision = model_vision + self.model_language = model_language + + self.model_vision.set_model_language(self.model_language) + del self.model_language + + def forward(self, batched_inputs, do_postprocess=True): + losses = self.model_vision(batched_inputs, do_postprocess=do_postprocess) + return losses + + def set_eval_dataset(self, dataset_name): + self.model_vision.set_eval_dataset(dataset_name) diff --git a/ape/modeling/ape_deta/assigner.py b/ape/modeling/ape_deta/assigner.py new file mode 100644 index 0000000..926006d --- /dev/null +++ b/ape/modeling/ape_deta/assigner.py @@ -0,0 +1,364 @@ +from typing import List + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from detrex.layers import box_cxcywh_to_xyxy, box_iou, box_xyxy_to_cxcywh, generalized_box_iou + + +def nonzero_tuple(x): + """ + A 'as_tuple=True' version of torch.nonzero to support torchscript. + because of https://github.com/pytorch/pytorch/issues/38718 + """ + if torch.jit.is_scripting(): + if x.dim() == 0: + return x.unsqueeze(0).nonzero().unbind(1) + return x.nonzero().unbind(1) + else: + return x.nonzero(as_tuple=True) + + +class Matcher(object): + """ + This class assigns to each predicted "element" (e.g., a box) a ground-truth + element. Each predicted element will have exactly zero or one matches; each + ground-truth element may be matched to zero or more predicted elements. + + The matching is determined by the MxN match_quality_matrix, that characterizes + how well each (ground-truth, prediction)-pair match each other. For example, + if the elements are boxes, this matrix may contain box intersection-over-union + overlap values. + + The matcher returns (a) a vector of length N containing the index of the + ground-truth element m in [0, M) that matches to prediction n in [0, N). + (b) a vector of length N containing the labels for each prediction. + """ + + def __init__( + self, thresholds: List[float], labels: List[int], allow_low_quality_matches: bool = False + ): + """ + Args: + thresholds (list): a list of thresholds used to stratify predictions + into levels. + labels (list): a list of values to label predictions belonging at + each level. A label can be one of {-1, 0, 1} signifying + {ignore, negative class, positive class}, respectively. + allow_low_quality_matches (bool): if True, produce additional matches + for predictions with maximum match quality lower than high_threshold. + See set_low_quality_matches_ for more details. + + For example, + thresholds = [0.3, 0.5] + labels = [0, -1, 1] + All predictions with iou < 0.3 will be marked with 0 and + thus will be considered as false positives while training. + All predictions with 0.3 <= iou < 0.5 will be marked with -1 and + thus will be ignored. + All predictions with 0.5 <= iou will be marked with 1 and + thus will be considered as true positives. + """ + thresholds = thresholds[:] + assert thresholds[0] > 0 + thresholds.insert(0, -float("inf")) + thresholds.append(float("inf")) + assert all( + [low <= high for (low, high) in zip(thresholds[:-1], thresholds[1:])] + ), thresholds + assert all([l in [-1, 0, 1] for l in labels]) + assert len(labels) == len(thresholds) - 1 + self.thresholds = thresholds + self.labels = labels + self.allow_low_quality_matches = allow_low_quality_matches + + def __call__(self, match_quality_matrix): + """ + Args: + match_quality_matrix (Tensor[float]): an MxN tensor, containing the + pairwise quality between M ground-truth elements and N predicted + elements. All elements must be >= 0 (due to the us of `torch.nonzero` + for selecting indices in :meth:`set_low_quality_matches_`). + + Returns: + matches (Tensor[int64]): a vector of length N, where matches[i] is a matched + ground-truth index in [0, M) + match_labels (Tensor[int8]): a vector of length N, where pred_labels[i] indicates + whether a prediction is a true or false positive or ignored + """ + assert match_quality_matrix.dim() == 2 + if match_quality_matrix.numel() == 0: + default_matches = match_quality_matrix.new_full( + (match_quality_matrix.size(1),), 0, dtype=torch.int64 + ) + default_match_labels = match_quality_matrix.new_full( + (match_quality_matrix.size(1),), self.labels[0], dtype=torch.int8 + ) + return default_matches, default_match_labels + + assert torch.all(match_quality_matrix >= 0) + + matched_vals, matches = match_quality_matrix.max(dim=0) + + match_labels = matches.new_full(matches.size(), 1, dtype=torch.int8) + + for (l, low, high) in zip(self.labels, self.thresholds[:-1], self.thresholds[1:]): + low_high = (matched_vals >= low) & (matched_vals < high) + match_labels[low_high] = l + + if self.allow_low_quality_matches: + self.set_low_quality_matches_(match_labels, match_quality_matrix) + + return matches, match_labels + + def set_low_quality_matches_(self, match_labels, match_quality_matrix): + """ + Produce additional matches for predictions that have only low-quality matches. + Specifically, for each ground-truth G find the set of predictions that have + maximum overlap with it (including ties); for each prediction in that set, if + it is unmatched, then match it to the ground-truth G. + + This function implements the RPN assignment case (i) in Sec. 3.1.2 of + :paper:`Faster R-CNN`. + """ + highest_quality_foreach_gt, _ = match_quality_matrix.max(dim=1) + _, pred_inds_with_highest_quality = nonzero_tuple( + match_quality_matrix == highest_quality_foreach_gt[:, None] + ) + match_labels[pred_inds_with_highest_quality] = 1 + + +def subsample_labels( + labels: torch.Tensor, num_samples: int, positive_fraction: float, bg_label: int +): + """ + Return `num_samples` (or fewer, if not enough found) + random samples from `labels` which is a mixture of positives & negatives. + It will try to return as many positives as possible without + exceeding `positive_fraction * num_samples`, and then try to + fill the remaining slots with negatives. + + Args: + labels (Tensor): (N, ) label vector with values: + * -1: ignore + * bg_label: background ("negative") class + * otherwise: one or more foreground ("positive") classes + num_samples (int): The total number of labels with value >= 0 to return. + Values that are not sampled will be filled with -1 (ignore). + positive_fraction (float): The number of subsampled labels with values > 0 + is `min(num_positives, int(positive_fraction * num_samples))`. The number + of negatives sampled is `min(num_negatives, num_samples - num_positives_sampled)`. + In order words, if there are not enough positives, the sample is filled with + negatives. If there are also not enough negatives, then as many elements are + sampled as is possible. + bg_label (int): label index of background ("negative") class. + + Returns: + pos_idx, neg_idx (Tensor): + 1D vector of indices. The total length of both is `num_samples` or fewer. + """ + positive = nonzero_tuple((labels != -1) & (labels != bg_label))[0] + negative = nonzero_tuple(labels == bg_label)[0] + + num_pos = int(num_samples * positive_fraction) + num_pos = min(positive.numel(), num_pos) + num_neg = num_samples - num_pos + num_neg = min(negative.numel(), num_neg) + + perm1 = torch.randperm(positive.numel(), device=positive.device)[:num_pos] + perm2 = torch.randperm(negative.numel(), device=negative.device)[:num_neg] + + pos_idx = positive[perm1] + neg_idx = negative[perm2] + return pos_idx, neg_idx + + +def sample_topk_per_gt(pr_inds, gt_inds, iou, k): + if len(gt_inds) == 0: + return pr_inds, gt_inds + gt_inds2, counts = gt_inds.unique(return_counts=True) + scores, pr_inds2 = iou[gt_inds2].topk(k, dim=1) + gt_inds2 = gt_inds2[:, None].repeat(1, k) + + pr_inds3 = torch.cat([pr[:c] for c, pr in zip(counts, pr_inds2)]) + gt_inds3 = torch.cat([gt[:c] for c, gt in zip(counts, gt_inds2)]) + return pr_inds3, gt_inds3 + + +class Stage2Assigner(nn.Module): + def __init__(self, num_queries, num_classes, max_k=4): + super().__init__() + self.positive_fraction = 0.25 + self.num_classes = num_classes + self.batch_size_per_image = num_queries + self.proposal_matcher = Matcher( + thresholds=[0.6], labels=[0, 1], allow_low_quality_matches=True + ) + self.k = max_k + + def _sample_proposals( + self, matched_idxs: torch.Tensor, matched_labels: torch.Tensor, gt_classes: torch.Tensor + ): + """ + Based on the matching between N proposals and M groundtruth, + sample the proposals and set their classification labels. + + Args: + matched_idxs (Tensor): a vector of length N, each is the best-matched + gt index in [0, M) for each proposal. + matched_labels (Tensor): a vector of length N, the matcher's label + (one of cfg.MODEL.ROI_HEADS.IOU_LABELS) for each proposal. + gt_classes (Tensor): a vector of length M. + + Returns: + Tensor: a vector of indices of sampled proposals. Each is in [0, N). + Tensor: a vector of the same length, the classification label for + each sampled proposal. Each sample is labeled as either a category in + [0, num_classes) or the background (num_classes). + """ + has_gt = gt_classes.numel() > 0 + if has_gt: + gt_classes = gt_classes[matched_idxs] + gt_classes[matched_labels == 0] = self.num_classes + gt_classes[matched_labels == -1] = -1 + else: + gt_classes = torch.zeros_like(matched_idxs) + self.num_classes + + sampled_fg_idxs, sampled_bg_idxs = subsample_labels( + gt_classes, self.batch_size_per_image, self.positive_fraction, self.num_classes + ) + + sampled_idxs = torch.cat([sampled_fg_idxs, sampled_bg_idxs], dim=0) + return sampled_idxs, gt_classes[sampled_idxs] + + def forward(self, outputs, targets, return_cost_matrix=False): + + bs = len(targets) + indices = [] + ious = [] + for b in range(bs): + iou, _ = box_iou( + box_cxcywh_to_xyxy(targets[b]["boxes"]), + box_cxcywh_to_xyxy(outputs["init_reference"][b].detach()), + ) + if not torch.all(iou >= 0): + print("iou", iou, iou.max(), iou.min()) + print("targets[b][boxes]", targets[b]["boxes"]) + print( + "outputs[init_reference][b]", + outputs["init_reference"][b], + outputs["init_reference"][b].max(), + outputs["init_reference"][b].min(), + ) + print("outputs", outputs) + matched_idxs, matched_labels = self.proposal_matcher( + iou + ) # proposal_id -> highest_iou_gt_id, proposal_id -> [1 if iou > 0.6, 0 ow] + ( + sampled_idxs, + sampled_gt_classes, + ) = self._sample_proposals( # list of sampled proposal_ids, sampled_id -> [0, num_classes)+[bg_label] + matched_idxs, matched_labels, targets[b]["labels"] + ) + pos_pr_inds = sampled_idxs[sampled_gt_classes != self.num_classes] + pos_gt_inds = matched_idxs[pos_pr_inds] + pos_pr_inds, pos_gt_inds = self.postprocess_indices(pos_pr_inds, pos_gt_inds, iou) + indices.append((pos_pr_inds, pos_gt_inds)) + ious.append(iou) + if return_cost_matrix: + return indices, ious + return indices + + def postprocess_indices(self, pr_inds, gt_inds, iou): + return sample_topk_per_gt(pr_inds, gt_inds, iou, self.k) + + def __repr__(self, _repr_indent=8): + head = "Matcher " + self.__class__.__name__ + body = [] + for attribute, value in self.__dict__.items(): + if attribute.startswith("_"): + continue + body.append("{}: {}".format(attribute, value)) + lines = [head] + [" " * _repr_indent + line for line in body] + return "\n".join(lines) + + +class Stage1Assigner(nn.Module): + def __init__(self, t_low=0.3, t_high=0.7, max_k=4): + super().__init__() + self.positive_fraction = 0.5 + self.batch_size_per_image = 256 + self.k = max_k + self.t_low = t_low + self.t_high = t_high + self.anchor_matcher = Matcher( + thresholds=[t_low, t_high], labels=[0, -1, 1], allow_low_quality_matches=True + ) + + def _subsample_labels(self, label): + """ + Randomly sample a subset of positive and negative examples, and overwrite + the label vector to the ignore value (-1) for all elements that are not + included in the sample. + + Args: + labels (Tensor): a vector of -1, 0, 1. Will be modified in-place and returned. + """ + pos_idx, neg_idx = subsample_labels( + label, self.batch_size_per_image, self.positive_fraction, 0 + ) + label.fill_(-1) + label.scatter_(0, pos_idx, 1) + label.scatter_(0, neg_idx, 0) + return label + + def forward(self, outputs, targets, return_cost_matrix=False): + bs = len(targets) + indices = [] + ious = [] + for b in range(bs): + anchors = outputs["anchors"][b] + if len(targets[b]["boxes"]) == 0: + indices.append( + ( + torch.tensor([], dtype=torch.long, device=anchors.device), + torch.tensor([], dtype=torch.long, device=anchors.device), + ) + ) + continue + iou, _ = box_iou( + box_cxcywh_to_xyxy(targets[b]["boxes"]), + box_cxcywh_to_xyxy(anchors), + ) + matched_idxs, matched_labels = self.anchor_matcher( + iou + ) # proposal_id -> highest_iou_gt_id, proposal_id -> [1 if iou > 0.7, 0 if iou < 0.3, -1 ow] + matched_labels = self._subsample_labels(matched_labels) + + all_pr_inds = torch.arange(len(anchors)).to(matched_labels.device) + pos_pr_inds = all_pr_inds[matched_labels == 1] + pos_gt_inds = matched_idxs[pos_pr_inds] + pos_ious = iou[pos_gt_inds, pos_pr_inds] + pos_pr_inds, pos_gt_inds = self.postprocess_indices(pos_pr_inds, pos_gt_inds, iou) + pos_pr_inds, pos_gt_inds = pos_pr_inds.to(anchors.device), pos_gt_inds.to( + anchors.device + ) + indices.append((pos_pr_inds, pos_gt_inds)) + ious.append(iou) + if return_cost_matrix: + return indices, ious + return indices + + def postprocess_indices(self, pr_inds, gt_inds, iou): + return sample_topk_per_gt(pr_inds, gt_inds, iou, self.k) + + def __repr__(self, _repr_indent=8): + head = "Matcher " + self.__class__.__name__ + body = [] + for attribute, value in self.__dict__.items(): + if attribute.startswith("_"): + continue + body.append("{}: {}".format(attribute, value)) + lines = [head] + [" " * _repr_indent + line for line in body] + return "\n".join(lines) diff --git a/ape/modeling/ape_deta/deformable_criterion.py b/ape/modeling/ape_deta/deformable_criterion.py new file mode 100644 index 0000000..6ac62ec --- /dev/null +++ b/ape/modeling/ape_deta/deformable_criterion.py @@ -0,0 +1,609 @@ +import copy +import logging +from typing import Callable, List, Optional + +import torch +import torch.nn.functional as F + +from detectron2.projects.point_rend.point_features import ( + get_uncertain_point_coords_with_randomness, + point_sample, +) +from detrex.layers import box_cxcywh_to_xyxy, box_iou, generalized_box_iou +from detrex.modeling import SetCriterion +from detrex.modeling.criterion.criterion import sigmoid_focal_loss +from detrex.modeling.losses import dice_loss +from detrex.utils import get_world_size, is_dist_avail_and_initialized + +from .misc import nested_tensor_from_tensor_list + +logger = logging.getLogger(__name__) + + +def sigmoid_ce_loss( + inputs: torch.Tensor, + targets: torch.Tensor, + num_masks: float, +): + """ + Args: + inputs: A float tensor of arbitrary shape. + The predictions for each example. + targets: A float tensor with the same shape as inputs. Stores the binary + classification label for each element in inputs + (0 for the negative class and 1 for the positive class). + Returns: + Loss tensor + """ + loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none") + + return loss.mean(1).sum() / num_masks + + +def calculate_uncertainty(logits): + """ + We estimate uncerainty as L1 distance between 0.0 and the logit prediction in 'logits' for the + foreground class in `classes`. + Args: + logits (Tensor): A tensor of shape (R, 1, ...) for class-specific or + class-agnostic, where R is the total number of predicted masks in all images and C is + the number of foreground classes. The values are logits. + Returns: + scores (Tensor): A tensor of shape (R, 1, ...) that contains uncertainty scores with + the most uncertain locations having the highest uncertainty score. + """ + assert logits.shape[1] == 1 + gt_class_logits = logits.clone() + return -(torch.abs(gt_class_logits)) + + +class DeformableCriterion(SetCriterion): + """This class computes the loss for Deformable-DETR + and two-stage Deformable-DETR + """ + + def __init__( + self, + num_classes, + matcher, + matcher_stage1, + matcher_stage2, + weight_dict, + losses: List[str] = ["class", "boxes"], + eos_coef: float = 0.1, + loss_class_type: str = "focal_loss", + alpha: float = 0.25, + gamma: float = 2.0, + use_fed_loss: bool = False, + get_fed_loss_cls_weights: Optional[Callable] = None, + fed_loss_num_classes: int = 50, + fed_loss_pad_type: str = None, + num_points: int = 12544, + oversample_ratio: float = 3.0, + importance_sample_ratio: float = 0.75, + train_positive_proposal_only: bool = False, + ): + super(DeformableCriterion, self).__init__( + num_classes=num_classes, + matcher=matcher, + weight_dict=weight_dict, + losses=losses, + eos_coef=eos_coef, + loss_class_type=loss_class_type, + alpha=alpha, + gamma=gamma, + ) + + self.matcher_stage1 = matcher_stage1 + self.matcher_stage2 = matcher_stage2 + + self.use_fed_loss = use_fed_loss + if self.use_fed_loss: + fed_loss_cls_weights = get_fed_loss_cls_weights() + logger.info( + f"fed_loss_cls_weights: {fed_loss_cls_weights.size()} num_classes: {num_classes}" + ) + + if len(fed_loss_cls_weights) < num_classes: + if fed_loss_pad_type == "max": + fed_loss_pad_value = fed_loss_cls_weights.max().item() + elif fed_loss_pad_type == "max1000": + fed_loss_pad_value = fed_loss_cls_weights.max().item() * 1000 + elif fed_loss_pad_type == "mean": + fed_loss_pad_value = fed_loss_cls_weights.mean().item() + elif fed_loss_pad_type == "median": + fed_loss_pad_value = fed_loss_cls_weights.median().item() + elif fed_loss_pad_type == "cat": + fed_loss_pad_classes = torch.arange(len(fed_loss_cls_weights), num_classes) + self.register_buffer("fed_loss_pad_classes", fed_loss_pad_classes) + fed_loss_pad_value = 0 + else: + fed_loss_pad_value = torch.kthvalue( + fed_loss_cls_weights, int(num_classes * 7.0 / 10) + )[0].item() + + logger.info( + f"pad fed_loss_cls_weights with type {fed_loss_pad_type} and value {fed_loss_pad_value}" + ) + if getattr(self, "fed_loss_pad_classes", None) is not None: + logger.info(f"pad fed_loss_classes with {self.fed_loss_pad_classes}") + fed_loss_cls_weights = torch.cat( + ( + fed_loss_cls_weights, + fed_loss_cls_weights.new_full( + (num_classes - len(fed_loss_cls_weights),), + fed_loss_pad_value, + ), + ), + dim=0, + ) + + logger.info(f"fed_loss_cls_weights: {fed_loss_cls_weights[-100:]}") + logger.info( + f"fed_loss_cls_weights: {fed_loss_cls_weights.size()} num_classes: {num_classes}" + ) + + assert ( + len(fed_loss_cls_weights) == self.num_classes + ), "Please check the provided fed_loss_cls_weights. Their size should match num_classes" + self.register_buffer("fed_loss_cls_weights", fed_loss_cls_weights) + self.fed_loss_num_classes = fed_loss_num_classes + + self.num_points = num_points + self.oversample_ratio = oversample_ratio + self.importance_sample_ratio = importance_sample_ratio + + self.train_positive_proposal_only = train_positive_proposal_only + self.alpha_old = self.alpha + + def get_fed_loss_classes(self, gt_classes, num_fed_loss_classes, num_classes, weight): + """ + Args: + gt_classes: a long tensor of shape R that contains the gt class label of each proposal. + num_fed_loss_classes: minimum number of classes to keep when calculating federated loss. + Will sample negative classes if number of unique gt_classes is smaller than this value. + num_classes: number of foreground classes + weight: probabilities used to sample negative classes + + Returns: + Tensor: + classes to keep when calculating the federated loss, including both unique gt + classes and sampled negative classes. + """ + unique_gt_classes = torch.unique(gt_classes) + prob = unique_gt_classes.new_ones(num_classes + 1).float() + prob[-1] = 0 + if len(unique_gt_classes) < num_fed_loss_classes: + prob[:num_classes] = weight.float().clone() + prob[unique_gt_classes] = 0 + sampled_negative_classes = torch.multinomial( + prob, num_fed_loss_classes - len(unique_gt_classes), replacement=False + ) + fed_loss_classes = torch.cat([unique_gt_classes, sampled_negative_classes]) + else: + fed_loss_classes = unique_gt_classes + return fed_loss_classes + + def loss_labels(self, outputs, targets, indices, num_boxes): + """Classification loss (Binary focal loss) + targets dicts must contain the key "labels" containing a tensor of dim [nb_target_boxes] + """ + assert "pred_logits" in outputs + src_logits = outputs["pred_logits"] + + if self.loss_class_type == "ce_loss": + num_classes = src_logits.shape[2] - 1 + elif self.loss_class_type == "focal_loss": + num_classes = src_logits.shape[2] + + idx = self._get_src_permutation_idx(indices) + target_classes_o = torch.cat([t["labels"][J] for t, (_, J) in zip(targets, indices)]) + target_classes = torch.full( + src_logits.shape[:2], + num_classes, + dtype=torch.int64, + device=src_logits.device, + ) + target_classes[idx] = target_classes_o + + if self.loss_class_type == "ce_loss": + loss_class = F.cross_entropy( + src_logits.transpose(1, 2), target_classes, self.empty_weight + ) + elif ( + self.loss_class_type == "focal_loss" + and self.use_fed_loss + and num_classes == len(self.fed_loss_cls_weights) + ): + target_classes_onehot = torch.zeros( + [src_logits.shape[0], src_logits.shape[1], src_logits.shape[2] + 1], + dtype=src_logits.dtype, + layout=src_logits.layout, + device=src_logits.device, + ) + target_classes_onehot.scatter_(2, target_classes.unsqueeze(-1), 1) + target_classes_onehot = target_classes_onehot[:, :, :-1] + fed_loss_classes = self.get_fed_loss_classes( + target_classes_o, + num_fed_loss_classes=self.fed_loss_num_classes, + num_classes=target_classes_onehot.shape[2], + weight=self.fed_loss_cls_weights, + ) + + if getattr(self, "fed_loss_pad_classes", None) is not None: + fed_loss_classes = torch.cat([fed_loss_classes, self.fed_loss_pad_classes]) + fed_loss_classes = torch.unique(fed_loss_classes) + + loss_class = ( + sigmoid_focal_loss( + src_logits[:, :, fed_loss_classes], + target_classes_onehot[:, :, fed_loss_classes], + num_boxes=num_boxes, + alpha=self.alpha, + gamma=self.gamma, + ) + * src_logits.shape[1] + ) + elif self.loss_class_type == "focal_loss": + target_classes_onehot = torch.zeros( + [src_logits.shape[0], src_logits.shape[1], src_logits.shape[2] + 1], + dtype=src_logits.dtype, + layout=src_logits.layout, + device=src_logits.device, + ) + target_classes_onehot.scatter_(2, target_classes.unsqueeze(-1), 1) + target_classes_onehot = target_classes_onehot[:, :, :-1] + loss_class = ( + sigmoid_focal_loss( + src_logits, + target_classes_onehot, + num_boxes=num_boxes, + alpha=self.alpha, + gamma=self.gamma, + ) + * src_logits.shape[1] + ) + + if not torch.isfinite(loss_class): + print("loss_class", loss_class) + print("outputs", outputs) + print("targets", targets) + print("indices", indices) + print("num_boxes", num_boxes) + + losses = {"loss_class": loss_class} + + return losses + + def loss_anchor_ious(self, outputs, targets, indices, num_boxes): + assert "pred_logits" in outputs + src_logits = outputs["pred_logits"] + + ious = torch.cat([t["ious"][J, I] for t, (I, J) in zip(targets, indices)]) + predictions = torch.cat([p[I] for p, (I, _) in zip(src_logits, indices)]) + + predictions = predictions.squeeze(1) + + loss_iou = F.mse_loss(predictions, ious, size_average=None, reduce=None, reduction="mean") + + losses = {"loss_iou": loss_iou} + + return losses + + def loss_pred_ious(self, outputs, targets, indices, num_boxes): + assert "pred_boxes" in outputs + idx = self._get_src_permutation_idx(indices) + src_boxes = outputs["pred_boxes"][idx] + target_boxes = torch.cat([t["boxes"][i] for t, (_, i) in zip(targets, indices)], dim=0) + + iou, _ = box_iou( + box_cxcywh_to_xyxy(target_boxes), + box_cxcywh_to_xyxy(src_boxes), + ) + ious = iou[range(len(iou)), range(len(iou))] + + assert "pred_logits" in outputs + src_logits = outputs["pred_logits"][idx] + src_logits = src_logits.squeeze(1) + + loss_iou = F.mse_loss(src_logits, ious, size_average=None, reduce=None, reduction="mean") + + losses = {"loss_iou": loss_iou} + + return losses + + def loss_boxes(self, outputs, targets, indices, num_boxes): + """Compute the losses related to the bounding boxes, the L1 regression loss and the GIoU loss + targets dicts must contain the key "boxes" containing a tensor of dim [nb_target_boxes, 4] + The target boxes are expected in format (center_x, center_y, w, h), normalized by the image size. + """ + assert "pred_boxes" in outputs + idx = self._get_src_permutation_idx(indices) + src_boxes = outputs["pred_boxes"][idx] + target_boxes = torch.cat([t["boxes"][i] for t, (_, i) in zip(targets, indices)], dim=0) + + loss_bbox = F.l1_loss(src_boxes, target_boxes, reduction="none") + + losses = {} + losses["loss_bbox"] = loss_bbox.sum() / num_boxes + + loss_giou = 1 - torch.diag( + generalized_box_iou( + box_cxcywh_to_xyxy(src_boxes), + box_cxcywh_to_xyxy(target_boxes), + ) + ) + losses["loss_giou"] = loss_giou.sum() / num_boxes + + return losses + + def loss_boxes_panoptic(self, outputs, targets, indices, num_boxes): + """Compute the losses related to the bounding boxes, the L1 regression loss and the GIoU loss + targets dicts must contain the key "boxes" containing a tensor of dim [nb_target_boxes, 4] + The target boxes are expected in format (center_x, center_y, w, h), normalized by the image size. + """ + assert "pred_boxes" in outputs + idx = self._get_src_permutation_idx(indices) + src_boxes = outputs["pred_boxes"][idx] + target_boxes = torch.cat([t["boxes"][i] for t, (_, i) in zip(targets, indices)], dim=0) + + if "is_thing" in targets[0]: + is_thing = torch.cat([t["is_thing"][i] for t, (_, i) in zip(targets, indices)], dim=0) + if is_thing.sum() == 0: # no gt + losses = {} + losses["loss_bbox"] = src_boxes.sum() * 0.0 + losses["loss_giou"] = src_boxes.sum() * 0.0 + return losses + target_boxes = target_boxes[is_thing] + src_boxes = src_boxes[is_thing] + + loss_bbox = F.l1_loss(src_boxes, target_boxes, reduction="none") + + losses = {} + losses["loss_bbox"] = loss_bbox.sum() / num_boxes + + loss_giou = 1 - torch.diag( + generalized_box_iou( + box_cxcywh_to_xyxy(src_boxes), + box_cxcywh_to_xyxy(target_boxes), + ) + ) + losses["loss_giou"] = loss_giou.sum() / num_boxes + + return losses + + def loss_masks(self, outputs, targets, indices, num_boxes): + """Compute the losses related to the masks: the focal loss and the dice loss. + targets dicts must contain the key "masks" containing a tensor of dim [nb_target_boxes, h, w] + """ + assert "pred_masks" in outputs + if outputs["pred_masks"] is None: + return {} + src_idx = self._get_src_permutation_idx(indices) + tgt_idx = self._get_tgt_permutation_idx(indices) + + max_mask_num = 128 * len(indices) + if src_idx[0].size(0) > max_mask_num: + perm = torch.sort(torch.randperm(src_idx[0].size(0))[:max_mask_num])[0] + + src_idx = (src_idx[0][perm], src_idx[1][perm]) + tgt_idx = (tgt_idx[0][perm], tgt_idx[1][perm]) + + src_masks = outputs["pred_masks"] + src_masks = src_masks[src_idx] + masks = [t["masks"] for t in targets] + target_masks, valid = nested_tensor_from_tensor_list(masks).decompose() + + if target_masks.size(1) == 0: # no gt + losses = {} + losses["loss_mask"] = src_masks.sum() * 0.0 + losses["loss_dice"] = src_masks.sum() * 0.0 + return losses + + target_masks = target_masks.to(src_masks) + target_masks = target_masks[tgt_idx] + + src_masks = F.interpolate( + src_masks[:, None], size=target_masks.shape[-2:], mode="bilinear", align_corners=False + ) + src_masks = src_masks[:, 0].flatten(1) + + target_masks = target_masks.flatten(1) + target_masks = target_masks.view(src_masks.shape) + + losses = { + "loss_mask": sigmoid_focal_loss(src_masks, target_masks, num_boxes), + "loss_dice": dice_loss( + src_masks.sigmoid(), target_masks, reduction="mean", avg_factor=num_boxes + ), + } + del src_masks + del target_masks + return losses + + def loss_masks_maskdino(self, outputs, targets, indices, num_boxes): + """Compute the losses related to the masks: the focal loss and the dice loss. + targets dicts must contain the key "masks" containing a tensor of dim [nb_target_boxes, h, w] + """ + assert "pred_masks" in outputs + if outputs["pred_masks"] is None: + return {} + src_idx = self._get_src_permutation_idx(indices) + tgt_idx = self._get_tgt_permutation_idx(indices) + src_masks = outputs["pred_masks"] + if not isinstance(src_masks, torch.Tensor): + mask_embeds = src_masks["mask_embeds"] + mask_features = src_masks["mask_features"] + src_masks = torch.cat( + [ + torch.einsum("qc,chw->qhw", mask_embeds[i][src], mask_features[i]) + for i, (src, _) in enumerate(indices) + ], + dim=0, + ) + else: + src_masks = src_masks[src_idx] + masks = [t["masks"] for t in targets] + target_masks, valid = nested_tensor_from_tensor_list(masks).decompose() + + if target_masks.size(1) == 0: # no gt + losses = {} + losses["loss_mask_maskdino"] = src_masks.sum() * 0.0 + losses["loss_dice_maskdino"] = src_masks.sum() * 0.0 + return losses + + target_masks = target_masks.to(src_masks) + target_masks = target_masks[tgt_idx] + + src_masks = src_masks[:, None] + target_masks = target_masks[:, None] + + with torch.no_grad(): + point_coords = get_uncertain_point_coords_with_randomness( + src_masks, + lambda logits: calculate_uncertainty(logits), + self.num_points, + self.oversample_ratio, + self.importance_sample_ratio, + ) + point_labels = point_sample( + target_masks, + point_coords, + align_corners=False, + ).squeeze(1) + + point_logits = point_sample( + src_masks, + point_coords, + align_corners=False, + ).squeeze(1) + + losses = { + "loss_mask_maskdino": sigmoid_ce_loss(point_logits, point_labels, num_boxes), + "loss_dice_maskdino": dice_loss( + point_logits.sigmoid(), point_labels, reduction="mean", avg_factor=num_boxes + ), + } + + del src_masks + del target_masks + return losses + + def get_loss(self, loss, outputs, targets, indices, num_boxes, **kwargs): + loss_map = { + "class": self.loss_labels, + "boxes": self.loss_boxes, + "boxes_panoptic": self.loss_boxes_panoptic, + "masks": self.loss_masks, + "masks_maskdino": self.loss_masks_maskdino, + "anchor_iou": self.loss_anchor_ious, + "pred_iou": self.loss_pred_ious, + } + assert loss in loss_map, f"do you really want to compute {loss} loss?" + return loss_map[loss](outputs, targets, indices, num_boxes, **kwargs) + + def forward(self, outputs, targets): + outputs_without_aux = { + k: v for k, v in outputs.items() if k != "aux_outputs" and k != "enc_outputs" + } + + if self.matcher_stage2 is not None: + indices = self.matcher_stage2(outputs_without_aux, targets) + else: + indices = self.matcher(outputs_without_aux, targets) + + num_boxes = sum(len(t["labels"]) for t in targets) + num_boxes = torch.as_tensor( + [num_boxes], dtype=torch.float, device=next(iter(outputs.values())).device + ) + if is_dist_avail_and_initialized(): + torch.distributed.all_reduce(num_boxes) + num_boxes = torch.clamp(num_boxes / get_world_size(), min=1).item() + + if "is_thing" in targets[0] and False: + unique_classes = torch.cat([t["labels"] for t in targets], dim=0) + is_thing = torch.cat([t["is_thing"][i] for t, (_, i) in zip(targets, indices)], dim=0) + all_classes = torch.cat([t["labels"][i] for t, (_, i) in zip(targets, indices)], dim=0) + thing_classes = all_classes[is_thing] + stuff_classes = all_classes[~is_thing] + + print( + "thing_classes", + 1.0 * len(thing_classes) / max(len(torch.unique(thing_classes)), 1), + "stuff_classes", + 1.0 * len(stuff_classes) / max(len(torch.unique(stuff_classes)), 1), + ) + + losses = {} + for loss in self.losses: + if loss == "pred_iou" or loss == "anchor_iou": + continue + kwargs = {} + losses.update(self.get_loss(loss, outputs, targets, indices, num_boxes, **kwargs)) + + if "aux_outputs" in outputs: + for i, aux_outputs in enumerate(outputs["aux_outputs"]): + if self.matcher_stage2 is not None: + pass + else: + indices = self.matcher(aux_outputs, targets) + for loss in self.losses: + if loss == "masks": + continue + if loss == "pred_iou" or loss == "anchor_iou": + continue + l_dict = self.get_loss(loss, aux_outputs, targets, indices, num_boxes, **kwargs) + l_dict = {k + f"_{i}": v for k, v in l_dict.items()} + losses.update(l_dict) + + if "enc_outputs" in outputs: + if self.train_positive_proposal_only: + self.alpha = 1.0 + enc_outputs = outputs["enc_outputs"] + bin_targets = copy.deepcopy(targets) + for bt in bin_targets: + bt["labels"] = torch.zeros_like(bt["labels"]) + if "is_thing" in bt: + del bt["is_thing"] + if self.matcher_stage1 is not None: + indices, ious = self.matcher_stage1( + enc_outputs, bin_targets, return_cost_matrix=True + ) + for bt, iou in zip(bin_targets, ious): + bt["ious"] = iou + else: + indices = self.matcher(enc_outputs, bin_targets) + for loss in self.losses: + if loss == "masks": + continue + if loss == "masks_maskdino": + continue + if loss == "class" and ("pred_iou" in losses or "anchor_iou" in losses): + continue + l_dict = self.get_loss(loss, enc_outputs, bin_targets, indices, num_boxes, **kwargs) + l_dict = {k + "_enc": v for k, v in l_dict.items()} + losses.update(l_dict) + if self.train_positive_proposal_only: + self.alpha = self.alpha_old + + return losses + + def __repr__(self): + head = "Criterion " + self.__class__.__name__ + body = [ + "matcher: {}".format(self.matcher.__repr__(_repr_indent=8)), + "matcher_stage1: {}".format(self.matcher_stage1), + "matcher_stage2: {}".format(self.matcher_stage2), + "losses: {}".format(self.losses), + "loss_class_type: {}".format(self.loss_class_type), + "weight_dict: {}".format(self.weight_dict), + "num_classes: {}".format(self.num_classes), + "eos_coef: {}".format(self.eos_coef), + "focal loss alpha: {}".format(self.alpha), + "focal loss gamma: {}".format(self.gamma), + "use_fed_loss: {}".format(self.use_fed_loss), + "fed_loss_num_classes: {}".format(self.fed_loss_num_classes), + ] + _repr_indent = 4 + lines = [head] + [" " * _repr_indent + line for line in body] + return "\n".join(lines) diff --git a/ape/modeling/ape_deta/deformable_detr.py b/ape/modeling/ape_deta/deformable_detr.py new file mode 100644 index 0000000..ced5680 --- /dev/null +++ b/ape/modeling/ape_deta/deformable_detr.py @@ -0,0 +1,604 @@ +import copy +import logging +import math +from typing import Dict, List, Optional, Tuple + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ape.layers import VisionLanguageAlign, ZeroShotFC +from detectron2.layers import move_device_like +from detectron2.modeling import GeneralizedRCNN, detector_postprocess +from detectron2.modeling.roi_heads.fast_rcnn import fast_rcnn_inference +from detectron2.structures import Boxes, ImageList, Instances +from detrex.layers import MLP, box_cxcywh_to_xyxy, box_xyxy_to_cxcywh +from detrex.utils import inverse_sigmoid +from torchvision.ops.boxes import batched_nms + +logger = logging.getLogger(__name__) + + +class DeformableDETR(nn.Module): + """Implements the Deformable DETR model. + + Code is modified from the `official github repo + `_. + + More details can be found in the `paper + `_ . + + Args: + backbone (nn.Module): the backbone module. + position_embedding (nn.Module): the position embedding module. + neck (nn.Module): the neck module. + transformer (nn.Module): the transformer module. + embed_dim (int): the dimension of the embedding. + num_classes (int): Number of total categories. + num_queries (int): Number of proposal dynamic anchor boxes in Transformer + criterion (nn.Module): Criterion for calculating the total losses. + pixel_mean (List[float]): Pixel mean value for image normalization. + Default: [123.675, 116.280, 103.530]. + pixel_std (List[float]): Pixel std value for image normalization. + Default: [58.395, 57.120, 57.375]. + aux_loss (bool): whether to use auxiliary loss. Default: True. + with_box_refine (bool): whether to use box refinement. Default: False. + as_two_stage (bool): whether to use two-stage. Default: False. + select_box_nums_for_evaluation (int): the number of topk candidates + slected at postprocess for evaluation. Default: 100. + + """ + + def __init__( + self, + backbone, + position_embedding, + neck, + transformer, + embed_dim, + num_classes, + num_queries, + criterion, + pixel_mean: Tuple[float], + pixel_std: Tuple[float], + aux_loss=True, + with_box_refine=False, + as_two_stage=False, + select_box_nums_for_evaluation=100, + select_box_nums_for_evaluation_list: list = None, + input_format: Optional[str] = None, + vis_period: int = 0, + output_dir: Optional[str] = None, + dataset_names: List[str] = [], + dataset_metas: List[str] = [], + dataset_prompts: List[str] = None, + embed_dim_language: int = 512, + text_feature_batch_repeat: bool = True, + text_feature_bank: bool = False, + text_feature_bank_reset: bool = False, + text_feature_reduce_type: str = "last", + text_feature_reduce_before_fusion: bool = True, + expression_cumulative_gt_class: bool = True, + test_nms_thresh: float = 0.7, + test_score_thresh: float = 0.0, + last_class_embed_use_mlp: bool = False, + openset_classifier: str = "VisionLanguageAlign", + ): + super().__init__() + self.backbone = backbone + self.position_embedding = position_embedding + + self.neck = neck + + self.num_queries = num_queries + if not as_two_stage: + self.query_embedding = nn.Embedding(num_queries, embed_dim * 2) + + self.transformer = transformer + + self.num_classes = num_classes + if criterion[0].loss_class_type == "ce_loss": + self.class_embed = nn.Linear(embed_dim, num_classes + 1) + else: + self.class_embed = nn.Linear(embed_dim, num_classes) + self.bbox_embed = MLP(embed_dim, embed_dim, 4, 3) + + self.aux_loss = aux_loss + self.criterion = nn.ModuleList(criterion) + + self.with_box_refine = with_box_refine + self.as_two_stage = as_two_stage + + prior_prob = 0.01 + bias_value = -math.log((1 - prior_prob) / prior_prob) + if criterion[0].loss_class_type == "ce_loss": + self.class_embed.bias.data = torch.ones(num_classes + 1) * bias_value + else: + self.class_embed.bias.data = torch.ones(num_classes) * bias_value + nn.init.constant_(self.bbox_embed.layers[-1].weight.data, 0) + nn.init.constant_(self.bbox_embed.layers[-1].bias.data, 0) + if self.neck is not None: + for _, neck_layer in self.neck.named_modules(): + if isinstance(neck_layer, nn.Conv2d): + nn.init.xavier_uniform_(neck_layer.weight, gain=1) + nn.init.constant_(neck_layer.bias, 0) + + self.text_feature_batch_repeat = text_feature_batch_repeat + if openset_classifier == "ZeroShotFC": + del self.class_embed + self.class_embed = ZeroShotFC( + input_size=embed_dim, + num_classes=num_classes, + zs_weight_path="zeros", + zs_weight_dim=embed_dim_language, + use_bias=0.0, + norm_weight=True, + norm_temperature=50.0, + use_project=True, + use_sigmoid_ce=True, + prior_prob=0.01, + zs_vocabulary="", + text_model="", + ) + + if openset_classifier == "VisionLanguageAlign": + del self.class_embed + self.class_embed = VisionLanguageAlign(embed_dim, embed_dim_language) + + num_pred = ( + (transformer.decoder.num_layers + 1) if as_two_stage else transformer.decoder.num_layers + ) + if with_box_refine: + self.class_embed = nn.ModuleList( + [copy.deepcopy(self.class_embed) for i in range(num_pred)] + ) + self.bbox_embed = nn.ModuleList( + [copy.deepcopy(self.bbox_embed) for i in range(num_pred)] + ) + nn.init.constant_(self.bbox_embed[0].layers[-1].bias.data[2:], -2.0) + self.transformer.decoder.bbox_embed = self.bbox_embed + else: + nn.init.constant_(self.bbox_embed.layers[-1].bias.data[2:], -2.0) + self.class_embed = nn.ModuleList([self.class_embed for _ in range(num_pred)]) + self.bbox_embed = nn.ModuleList([self.bbox_embed for _ in range(num_pred)]) + self.transformer.decoder.bbox_embed = None + + if as_two_stage: + self.transformer.decoder.class_embed = self.class_embed + if True: + prior_prob = 0.01 + bias_value = -math.log((1 - prior_prob) / prior_prob) + if criterion[0].loss_class_type == "ce_loss": + self.transformer.decoder.class_embed[-1] = nn.Linear(embed_dim, num_classes + 1) + self.transformer.decoder.class_embed[-1].bias.data = ( + torch.ones(num_classes + 1) * bias_value + ) + else: + self.transformer.decoder.class_embed[-1] = nn.Linear(embed_dim, 1) + self.transformer.decoder.class_embed[-1].bias.data = torch.ones(1) * bias_value + if last_class_embed_use_mlp: + self.transformer.decoder.class_embed[-1] = MLP(embed_dim, embed_dim, 1, 3) + self.transformer.decoder.class_embed[-1].layers[-1].bias.data = ( + torch.ones(1) * bias_value + ) + for box_embed in self.bbox_embed: + nn.init.constant_(box_embed.layers[-1].bias.data[2:], 0.0) + + if self.transformer.proposal_ambiguous: + self.transformer.decoder.bbox_embed_ambiguous = nn.ModuleList( + [ + copy.deepcopy(self.transformer.decoder.bbox_embed[-1]) + for _ in range(self.transformer.proposal_ambiguous) + ] + ) + self.transformer.decoder.class_embed_ambiguous = nn.ModuleList( + [ + copy.deepcopy(self.transformer.decoder.class_embed[-1]) + for _ in range(self.transformer.proposal_ambiguous) + ] + ) + + if False: + self.transformer.decoder.bbox_embed_2 = copy.deepcopy( + self.transformer.decoder.bbox_embed[-1] + ) + self.transformer.decoder.class_embed_2 = copy.deepcopy( + self.transformer.decoder.class_embed[-1] + ) + + self.transformer.decoder.bbox_embed_3 = copy.deepcopy( + self.transformer.decoder.bbox_embed[-1] + ) + self.transformer.decoder.class_embed_3 = copy.deepcopy( + self.transformer.decoder.class_embed[-1] + ) + + self.select_box_nums_for_evaluation = select_box_nums_for_evaluation + self.select_box_nums_for_evaluation_list = select_box_nums_for_evaluation_list + + self.test_topk_per_image = self.select_box_nums_for_evaluation + self.test_nms_thresh = test_nms_thresh + self.test_score_thresh = test_score_thresh + + self.input_format = input_format + self.vis_period = vis_period + if vis_period > 0: + assert input_format is not None, "input_format is required for visualization!" + + self.register_buffer("pixel_mean", torch.tensor(pixel_mean).view(-1, 1, 1), False) + self.register_buffer("pixel_std", torch.tensor(pixel_std).view(-1, 1, 1), False) + assert ( + self.pixel_mean.shape == self.pixel_std.shape + ), f"{self.pixel_mean} and {self.pixel_std} have different shapes!" + + self.output_dir = output_dir + + self.dataset_names = dataset_names + from detectron2.data.catalog import MetadataCatalog + + if isinstance(dataset_metas, str): + dataset_metas = [ + dataset_metas, + ] + self.metadata_list = [copy.deepcopy(MetadataCatalog.get(d)) for d in dataset_metas] + + self.dataset_prompts = dataset_prompts + self.dataset_entities = [] + for i, metadata in enumerate(self.metadata_list): + if "stuffonly" in metadata.name: + del metadata.thing_classes + + if ( + metadata.get("thing_classes", None) is not None + and metadata.get("stuff_classes", None) is not None + ): + self.dataset_entities.append("thing+stuff") + elif metadata.get("thing_classes", None) is not None: + self.dataset_entities.append("thing") + elif metadata.get("stuff_classes", None) is not None: + self.dataset_entities.append("stuff") + else: + self.dataset_entities.append("thing") + + logger.info("dataset_id: " + str(i)) + logger.info("dataset_name: " + metadata.name) + logger.info("thing_classes: " + str(metadata.get("thing_classes", None))) + logger.info("stuff_classes: " + str(metadata.get("stuff_classes", None))) + logger.info("dataset_entity: " + self.dataset_entities[i]) + + self.dataset_name_to_idx = {k: i for i, k in enumerate(self.dataset_names)} + self.dataset_name_to_entity = { + k: i for i, k in zip(self.dataset_entities, self.dataset_names) + } + + self.eval_dataset_id = -1 + self.eval_dataset_entity = "" + + self.text_feature_bank = text_feature_bank + self.text_feature_bank_reset = text_feature_bank_reset + if self.text_feature_bank: + features_phrase_bank = torch.zeros( + ( + len(self.criterion), + max([ctr.num_classes for ctr in self.criterion]), + embed_dim_language, + ), + dtype=torch.float, + device=self.device, + ) + self.register_buffer("features_phrase_bank", features_phrase_bank, False) + + self.text_feature_reduce_type = text_feature_reduce_type + self.text_feature_reduce_before_fusion = text_feature_reduce_before_fusion + self.expression_cumulative_gt_class = expression_cumulative_gt_class + self.embed_dim_language = embed_dim_language + + @property + def device(self): + return self.pixel_mean.device + + def _move_to_current_device(self, x): + return move_device_like(x, self.pixel_mean) + + def forward(self, batched_inputs, do_postprocess=True): + images = self.preprocess_image(batched_inputs) + + batch_size, _, H, W = images.tensor.shape + img_masks = images.tensor.new_ones(batch_size, H, W) + for image_id, image_size in enumerate(images.image_sizes): + img_masks[image_id, : image_size[0], : image_size[1]] = 0 + + features = self.backbone(images.tensor) # output feature dict + + if self.neck is not None: + multi_level_feats = self.neck({f: features[f] for f in self.neck.in_features}) + else: + multi_level_feats = [feat for feat_name, feat in features.items()] + multi_level_masks = [] + multi_level_position_embeddings = [] + for feat in multi_level_feats: + multi_level_masks.append( + F.interpolate(img_masks[None], size=feat.shape[-2:]).to(torch.bool).squeeze(0) + ) + multi_level_position_embeddings.append( + self.position_embedding(multi_level_masks[-1]).to(images.tensor.dtype) + ) + + query_embeds = None + if not self.as_two_stage: + query_embeds = self.query_embedding.weight + + ( + inter_states, + init_reference, + inter_references, + enc_outputs_class, + enc_outputs_coord_unact, + anchors, + memory, + ) = self.transformer( + multi_level_feats, multi_level_masks, multi_level_position_embeddings, query_embeds + ) + + outputs_classes = [] + outputs_coords = [] + for lvl in range(inter_states.shape[0]): + if lvl == 0: + reference = init_reference + else: + reference = inter_references[lvl - 1] + reference = inverse_sigmoid(reference) + outputs_class = self.class_embed[lvl](inter_states[lvl]) + tmp = self.bbox_embed[lvl](inter_states[lvl]) + if reference.shape[-1] == 4: + tmp += reference + else: + assert reference.shape[-1] == 2 + tmp[..., :2] += reference + outputs_coord = tmp.sigmoid() + outputs_classes.append(outputs_class) + outputs_coords.append(outputs_coord) + outputs_class = torch.stack(outputs_classes) + outputs_coord = torch.stack(outputs_coords) + + output = { + "pred_logits": outputs_class[-1], + "pred_boxes": outputs_coord[-1], + "init_reference": init_reference, + } + if self.aux_loss: + output["aux_outputs"] = self._set_aux_loss(outputs_class, outputs_coord) + + if self.as_two_stage: + enc_outputs_coord = enc_outputs_coord_unact.sigmoid() + output["enc_outputs"] = { + "pred_logits": enc_outputs_class, + "pred_boxes": enc_outputs_coord, + "anchors": anchors, + } + + if self.training: + gt_instances = [x["instances"].to(self.device) for x in batched_inputs] + targets = self.prepare_targets(gt_instances) + loss_dict = self.criterion(output, targets) + weight_dict = self.criterion.weight_dict + for k in loss_dict.keys(): + if k in weight_dict: + loss_dict[k] *= weight_dict[k] + return loss_dict + else: + del features + del multi_level_feats + + box_cls = output["pred_logits"] + box_pred = output["pred_boxes"] + results, filter_inds = self.inference(box_cls, box_pred, images.image_sizes) + + if do_postprocess: + assert not torch.jit.is_scripting(), "Scripting is not supported for postprocess." + return GeneralizedRCNN._postprocess(results, batched_inputs, images.image_sizes) + return results + + @torch.jit.unused + def _set_aux_loss(self, outputs_class, outputs_coord): + return [ + {"pred_logits": a, "pred_boxes": b} + for a, b in zip(outputs_class[:-1], outputs_coord[:-1]) + ] + + def inference(self, box_cls, box_pred, image_sizes): + """ + Arguments: + box_cls (Tensor): tensor of shape (batch_size, num_queries, K). + The tensor predicts the classification probability for each query. + box_pred (Tensor): tensors of shape (batch_size, num_queries, 4). + The tensor predicts 4-vector (x,y,w,h) box + regression values for every queryx + image_sizes (List[torch.Size]): the input image sizes + + Returns: + results (List[Instances]): a list of #images elements. + """ + + if True: + return NMSPostProcess()( + {"pred_logits": box_cls, "pred_boxes": box_pred}, + torch.tensor([list(x) for x in image_sizes], device=self.device), + self.select_box_nums_for_evaluation, + ) + + scores = torch.cat( + ( + box_cls.sigmoid(), + torch.zeros((box_cls.size(0), box_cls.size(1), 1), device=self.device), + ), + dim=2, + ) + + boxes = box_cxcywh_to_xyxy(box_pred) + + img_h, img_w = torch.tensor(image_sizes, device=self.device).unbind(1) + scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1) + boxes = boxes * scale_fct[:, None, :] + + boxes = boxes.unbind(0) + scores = scores.unbind(0) + image_shapes = image_sizes + + self.test_topk_per_image = self.select_box_nums_for_evaluation + self.test_nms_thresh = 0.7 + self.test_score_thresh = 0.05 + + return fast_rcnn_inference( + boxes, + scores, + image_shapes, + self.test_score_thresh, + self.test_nms_thresh, + self.test_topk_per_image, + ) + + assert len(box_cls) == len(image_sizes) + results = [] + + prob = box_cls.sigmoid() + topk_values, topk_indexes = torch.topk( + prob.view(box_cls.shape[0], -1), self.select_box_nums_for_evaluation, dim=1 + ) + scores = topk_values + topk_boxes = torch.div(topk_indexes, box_cls.shape[2], rounding_mode="floor") + labels = topk_indexes % box_cls.shape[2] + + boxes = torch.gather(box_pred, 1, topk_boxes.unsqueeze(-1).repeat(1, 1, 4)) + + for i, (scores_per_image, labels_per_image, box_pred_per_image, image_size) in enumerate( + zip(scores, labels, boxes, image_sizes) + ): + result = Instances(image_size) + result.pred_boxes = Boxes(box_cxcywh_to_xyxy(box_pred_per_image)) + result.pred_boxes.scale(scale_x=image_size[1], scale_y=image_size[0]) + result.scores = scores_per_image + result.pred_classes = labels_per_image + results.append(result) + return results, topk_indexes + + def prepare_targets(self, targets): + new_targets = [] + for targets_per_image in targets: + h, w = targets_per_image.image_size + image_size_xyxy = torch.as_tensor([w, h, w, h], dtype=torch.float, device=self.device) + gt_classes = targets_per_image.gt_classes + gt_boxes = targets_per_image.gt_boxes.tensor / image_size_xyxy + gt_boxes = box_xyxy_to_cxcywh(gt_boxes) + new_targets.append({"labels": gt_classes, "boxes": gt_boxes}) + return new_targets + + def preprocess_image(self, batched_inputs): + images = [self._move_to_current_device(x["image"]) for x in batched_inputs] + images = [x.to(self.pixel_mean.dtype) for x in images] + images = [(x - self.pixel_mean) / self.pixel_std for x in images] + images = ImageList.from_tensors( + images, + self.backbone.size_divisibility, + padding_constraints=self.backbone.padding_constraints, + ) + return images + + @staticmethod + def _postprocess(instances, batched_inputs: List[Dict[str, torch.Tensor]], image_sizes): + """ + Rescale the output instances to the target size. + """ + processed_results = [] + for results_per_image, input_per_image, image_size in zip( + instances, batched_inputs, image_sizes + ): + height = input_per_image.get("height", image_size[0]) + width = input_per_image.get("width", image_size[1]) + r = detector_postprocess(results_per_image, height, width) + processed_results.append({"instances": r}) + return processed_results + + def set_eval_dataset(self, dataset_name): + for d in self.dataset_names: + if sum([dd in dataset_name for dd in d.split("+")]): + self.eval_dataset_id = self.dataset_name_to_idx[d] + self.eval_dataset_entity = self.dataset_name_to_entity[d] + break + else: + self.eval_dataset_id = -1 + self.eval_dataset_entity = "" + + logger.info( + "Setting eval dataset to: " + + str(d) + + " " + + str(dataset_name) + + " " + + str(self.eval_dataset_id) + ) + logger.info( + "Setting eval entity to: " + + str(d) + + " " + + str(dataset_name) + + " " + + str(self.eval_dataset_entity) + ) + + +class NMSPostProcess(nn.Module): + """This module converts the model's output into the format expected by the coco api""" + + @torch.no_grad() + def forward(self, outputs, target_sizes, select_box_nums_for_evaluation): + """Perform the computation + Parameters: + outputs: raw outputs of the model + target_sizes: tensor of dimension [batch_size x 2] containing the size of each images of the batch + For evaluation, this must be the original image size (before any data augmentation) + For visualization, this should be the image size after data augment, but before padding + """ + out_logits, out_bbox = outputs["pred_logits"], outputs["pred_boxes"] + bs, n_queries, n_cls = out_logits.shape + + assert len(out_logits) == len(target_sizes) + assert target_sizes.shape[1] == 2 + + prob = out_logits.sigmoid() + + all_scores = prob.view(bs, n_queries * n_cls).to(out_logits.device) + all_indexes = torch.arange(n_queries * n_cls)[None].repeat(bs, 1).to(out_logits.device) + all_boxes = torch.div(all_indexes, out_logits.shape[2], rounding_mode="trunc") + all_labels = all_indexes % out_logits.shape[2] + + boxes = box_cxcywh_to_xyxy(out_bbox) + boxes = torch.gather(boxes, 1, all_boxes.unsqueeze(-1).repeat(1, 1, 4)) + + img_h, img_w = target_sizes.unbind(1) + scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1) + boxes = boxes * scale_fct[:, None, :] + + results = [] + keep_inds_all = [] + for b in range(bs): + box = boxes[b] + score = all_scores[b] + lbls = all_labels[b] + + pre_topk = score.topk(10000).indices + box = box[pre_topk] + score = score[pre_topk] + lbls = lbls[pre_topk] + + keep_inds = batched_nms(box, score, lbls, 0.7)[:select_box_nums_for_evaluation] + + result = Instances(target_sizes[b]) + result.pred_boxes = Boxes(box[keep_inds]) + result.scores = score[keep_inds] + result.pred_classes = lbls[keep_inds] + results.append(result) + + keep_inds_all.append(keep_inds) + + return results, keep_inds_all diff --git a/ape/modeling/ape_deta/deformable_detr_segm.py b/ape/modeling/ape_deta/deformable_detr_segm.py new file mode 100644 index 0000000..e81f34c --- /dev/null +++ b/ape/modeling/ape_deta/deformable_detr_segm.py @@ -0,0 +1,1624 @@ +import copy +import math +import os +import time +from typing import Dict, List, Optional, Tuple + +import cv2 +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +import fvcore.nn.weight_init as weight_init +from ape.modeling.text import utils as text_utils +from detectron2.data.detection_utils import convert_image_to_rgb +from detectron2.layers import Conv2d, ShapeSpec, get_norm, move_device_like +from detectron2.modeling import GeneralizedRCNN +from detectron2.modeling.meta_arch.panoptic_fpn import combine_semantic_and_instance_outputs +from detectron2.modeling.postprocessing import detector_postprocess, sem_seg_postprocess +from detectron2.structures import BitMasks, Boxes, ImageList, Instances +from detectron2.utils.events import get_event_storage +from detectron2.utils.memory import retry_if_cuda_oom +from detrex.layers import MLP, box_cxcywh_to_xyxy, box_xyxy_to_cxcywh +from detrex.utils import inverse_sigmoid +from torchvision.ops.boxes import batched_nms + +from .deformable_detr import DeformableDETR +from .fast_rcnn import fast_rcnn_inference +from .segmentation import MaskHeadSmallConv, MHAttentionMap + + +class DeformableDETRSegm(DeformableDETR): + """Implements the Deformable DETR model. + + Code is modified from the `official github repo + `_. + + More details can be found in the `paper + `_ . + + Args: + backbone (nn.Module): the backbone module. + position_embedding (nn.Module): the position embedding module. + neck (nn.Module): the neck module. + transformer (nn.Module): the transformer module. + embed_dim (int): the dimension of the embedding. + num_classes (int): Number of total categories. + num_queries (int): Number of proposal dynamic anchor boxes in Transformer + criterion (nn.Module): Criterion for calculating the total losses. + pixel_mean (List[float]): Pixel mean value for image normalization. + Default: [123.675, 116.280, 103.530]. + pixel_std (List[float]): Pixel std value for image normalization. + Default: [58.395, 57.120, 57.375]. + aux_loss (bool): whether to use auxiliary loss. Default: True. + with_box_refine (bool): whether to use box refinement. Default: False. + as_two_stage (bool): whether to use two-stage. Default: False. + select_box_nums_for_evaluation (int): the number of topk candidates + slected at postprocess for evaluation. Default: 100. + + """ + + def __init__( + self, + instance_on: bool = True, + semantic_on: bool = False, + panoptic_on: bool = False, + freeze_detr=False, + input_shapes=[], + mask_in_features=[], + mask_encode_level=0, + stuff_dataset_learn_thing: bool = True, + stuff_prob_thing: float = -1.0, + name_prompt_fusion_type: str = "none", + name_prompt_fusion_text: bool = None, + test_mask_on: bool = True, + semantic_post_nms: bool = True, + panoptic_post_nms: bool = True, + aux_mask: bool = False, + panoptic_configs: dict = { + "prob": 0.1, + "pano_temp": 0.06, + "transform_eval": True, + "object_mask_threshold": 0.01, + "overlap_threshold": 0.4, + }, + **kwargs, + ): + super().__init__(**kwargs) + + self.instance_on = instance_on + self.semantic_on = semantic_on + self.panoptic_on = panoptic_on + + if freeze_detr: + for p in self.parameters(): + p.requires_grad_(False) + + self.input_shapes = input_shapes + self.mask_in_features = mask_in_features + self.mask_encode_level = mask_encode_level + + hidden_dim = self.transformer.embed_dim + norm = "GN" + use_bias = False + + assert len(self.mask_in_features) == 1 + in_channels = [self.input_shapes[feat_name].channels for feat_name in self.mask_in_features] + in_channel = in_channels[0] + + self.lateral_conv = Conv2d( + in_channel, + hidden_dim, + kernel_size=1, + stride=1, + bias=use_bias, + padding=0, + norm=get_norm(norm, hidden_dim), + ) + self.output_conv = Conv2d( + hidden_dim, + hidden_dim, + kernel_size=3, + stride=1, + bias=use_bias, + padding=1, + norm=get_norm(norm, hidden_dim), + activation=F.relu, + ) + self.mask_conv = Conv2d( + hidden_dim, hidden_dim, kernel_size=1, stride=1, bias=use_bias, padding=0 + ) + + self.mask_embed = MLP(hidden_dim, hidden_dim, hidden_dim, 3) + self.aux_mask = aux_mask + if self.aux_mask: + self.mask_embed = nn.ModuleList( + [copy.deepcopy(self.mask_embed) for i in range(len(self.class_embed) - 1)] + ) + + weight_init.c2_xavier_fill(self.lateral_conv) + weight_init.c2_xavier_fill(self.output_conv) + weight_init.c2_xavier_fill(self.mask_conv) + + self.stuff_dataset_learn_thing = stuff_dataset_learn_thing + self.stuff_prob_thing = stuff_prob_thing + self.test_mask_on = test_mask_on + self.semantic_post_nms = semantic_post_nms + self.panoptic_post_nms = panoptic_post_nms + self.panoptic_configs = panoptic_configs + + self.name_prompt_fusion_type = name_prompt_fusion_type + self.name_prompt_fusion_text = name_prompt_fusion_text + if name_prompt_fusion_type == "learnable": + self.name_prompt_fusion_feature = nn.Parameter( + torch.Tensor(1, 1, self.embed_dim_language) + ) + nn.init.normal_(self.name_prompt_fusion_feature) + elif name_prompt_fusion_type == "zero": + self.name_prompt_fusion_feature = nn.Parameter( + torch.zeros(1, 1, self.embed_dim_language), requires_grad=False + ) + else: + self.name_prompt_fusion_feature = None + + def forward(self, batched_inputs, do_postprocess=True): + if self.training: + if "dataset_id" in batched_inputs[0]: + dataset_ids = [x["dataset_id"] for x in batched_inputs] + assert len(set(dataset_ids)) == 1, dataset_ids + dataset_id = dataset_ids[0] + else: + dataset_id = 0 + else: + dataset_id = self.eval_dataset_id + + if dataset_id >= 0: + prompt = self.dataset_prompts[dataset_id] + elif "prompt" in batched_inputs[0]: + prompt = batched_inputs[0]["prompt"] + else: + prompt = "name" + + if prompt == "expression": + for x in batched_inputs: + if isinstance(x["expressions"], List): + pass + else: + x["expressions"] = [x["expressions"]] + assert all([len(xx) > 0 for xx in x["expressions"]]) + assert all([isinstance(xx, str) for xx in x["expressions"]]) + self.test_topk_per_image = 1 + else: + self.test_topk_per_image = self.select_box_nums_for_evaluation + if self.select_box_nums_for_evaluation_list is not None: + self.test_topk_per_image = self.select_box_nums_for_evaluation_list[dataset_id] + + if self.training and prompt == "phrase": + gt_num = torch.tensor([len(input["instances"]) for input in batched_inputs]).to( + self.device + ) + gt_classes = torch.arange(gt_num.sum()).to(self.device) + gt_cumsum = torch.cumsum(gt_num, dim=0).to(self.device) + for i, input in enumerate(batched_inputs): + if i == 0: + input["instances"].gt_classes = gt_classes[: gt_cumsum[i]] + else: + input["instances"].gt_classes = gt_classes[gt_cumsum[i - 1] : gt_cumsum[i]] + if self.training and prompt == "expression": + gt_num = torch.tensor([len(input["instances"]) for input in batched_inputs]).to( + self.device + ) + gt_classes = torch.arange(gt_num.sum()).to(self.device) + gt_cumsum = torch.cumsum(gt_num, dim=0).to(self.device) + for i, input in enumerate(batched_inputs): + if i == 0: + input["instances"].gt_classes = gt_classes[: gt_cumsum[i]] + else: + input["instances"].gt_classes = gt_classes[gt_cumsum[i - 1] : gt_cumsum[i]] + + if not self.expression_cumulative_gt_class: + input["instances"].gt_classes *= 0 + + if prompt == "text": + texts = [x["text_prompt"] for x in batched_inputs] + text_promp_text_list = [x.strip() for x in ",".join(texts).split(",")] + text_promp_text_list = [x for x in text_promp_text_list if len(x) > 0] + + if any([True if x.count(" ") >= 1 else False for x in text_promp_text_list]): + prompt = "phrase" + else: + prompt = "name" + else: + text_promp_text_list = None + + if prompt == "name": + if text_promp_text_list: + text_list = text_promp_text_list + cache = False + elif dataset_id >= 0: + text_list = get_text_list( + self.metadata_list[dataset_id], self.dataset_entities[dataset_id] + ) + cache = True + else: + text_list = [] + for metadata, dataset_entity in zip(self.metadata_list, self.dataset_entities): + text_list += get_text_list(metadata, dataset_entity) + text_list = text_list[:2500] + cache = True + + from detectron2.data.catalog import MetadataCatalog + + metadata = MetadataCatalog.get("coco_2017_val") + text_list = get_text_list(metadata, "thing") + metadata = MetadataCatalog.get("coco_2017_train_panoptic_stuffonly") + text_list += get_text_list(metadata, "stuff") + + outputs_l = self.model_language.forward_text(text_list, cache=cache) + if "last_hidden_state_eot" in outputs_l: + features_l = outputs_l["last_hidden_state_eot"] + else: + features_l = text_utils.reduce_language_feature( + outputs_l["last_hidden_state"], + outputs_l["attention_mask"], + reduce_type=self.text_feature_reduce_type, + ) + attention_mask_l = None + + if ( + dataset_id >= 0 + and self.dataset_entities[dataset_id] == "stuff" + and self.metadata_list[dataset_id].get("stuff_classes")[0] == "things" + and not self.stuff_dataset_learn_thing + ): + features_l[0, :] *= 0 + if self.training: + for i, input in enumerate(batched_inputs): + input["instances"] = input["instances"][input["instances"].gt_classes > 0] + + if self.text_feature_batch_repeat or True: + features_l = features_l.unsqueeze(0).repeat(len(batched_inputs), 1, 1) + else: + features_l = features_l.unsqueeze(1) + + elif prompt == "phrase" or prompt == "expression": + if text_promp_text_list: + text_list = text_promp_text_list + elif prompt == "phrase": + text_list = [phrase for x in batched_inputs for phrase in x["instances"].phrases] + elif prompt == "expression": + text_list = [xx for x in batched_inputs for xx in x["expressions"]] + + outputs_l = self.model_language.forward_text(text_list) + + if self.text_feature_reduce_before_fusion: + if "last_hidden_state_eot" in outputs_l: + features_l = outputs_l["last_hidden_state_eot"] + else: + features_l = text_utils.reduce_language_feature( + outputs_l["last_hidden_state"], + outputs_l["attention_mask"], + reduce_type=self.text_feature_reduce_type, + ) + attention_mask_l = None + + if ( + self.text_feature_bank + and not self.text_feature_bank_reset + and dataset_id >= 0 + and dataset_id < len(self.metadata_list) + ): + features_l = torch.cat( + [features_l, self.features_phrase_bank[dataset_id]], dim=0 + ) + features_l = features_l[ + : max(len(text_list), self.criterion[dataset_id].num_classes) + ] + self.features_phrase_bank[ + dataset_id, : self.criterion[dataset_id].num_classes + ] = features_l[: self.criterion[dataset_id].num_classes] + elif self.text_feature_bank and self.text_feature_bank_reset: + features_l = torch.cat( + [features_l, self.features_phrase_bank[dataset_id] * 0], dim=0 + ) + features_l = features_l[ + : max(len(text_list), self.criterion[dataset_id].num_classes) + ] + + if self.text_feature_batch_repeat: + features_l = features_l.unsqueeze(0).repeat(len(batched_inputs), 1, 1) + else: + features_l = features_l.unsqueeze(1) + else: + features_l = outputs_l["last_hidden_state"] + attention_mask_l = outputs_l["attention_mask"] + + start_time = time.perf_counter() + images = self.preprocess_image(batched_inputs) + + batch_size, _, H, W = images.tensor.shape + img_masks = images.tensor.new_ones(batch_size, H, W) + for image_id, image_size in enumerate(images.image_sizes): + img_masks[image_id, : image_size[0], : image_size[1]] = 0 + self.preprocess_time = time.perf_counter() - start_time + + start_time = time.perf_counter() + features = self.backbone(images.tensor) # output feature dict + self.backbone_time = time.perf_counter() - start_time + + if self.neck is not None: + multi_level_feats = self.neck({f: features[f] for f in self.neck.in_features}) + else: + multi_level_feats = [feat for feat_name, feat in features.items()] + multi_level_masks = [] + multi_level_position_embeddings = [] + spatial_shapes = [] + for feat in multi_level_feats: + multi_level_masks.append( + F.interpolate(img_masks[None], size=feat.shape[-2:]).to(torch.bool).squeeze(0) + ) + multi_level_position_embeddings.append( + self.position_embedding(multi_level_masks[-1]).to(images.tensor.dtype) + ) + + bs, c, h, w = feat.shape + spatial_shape = (h, w) + spatial_shapes.append(spatial_shape) + + query_embeds = None + if not self.as_two_stage: + query_embeds = self.query_embedding.weight + + start_time = time.perf_counter() + ( + inter_states, + init_reference, + inter_references, + enc_outputs_class, + enc_outputs_coord_unact, + anchors, + memory, + ) = self.transformer( + multi_level_feats, multi_level_masks, multi_level_position_embeddings, query_embeds + ) + self.transformer_time = time.perf_counter() - start_time + + mask_features = self.maskdino_mask_features(memory, features, multi_level_masks) + + outputs_classes = [] + outputs_coords = [] + outputs_masks = [] + for lvl in range(inter_states.shape[0]): + if lvl == 0: + reference = init_reference + else: + reference = inter_references[lvl - 1] + reference = inverse_sigmoid(reference) + if prompt == "name": + outputs_class = self.class_embed[lvl](inter_states[lvl], features_l) + elif prompt == "phrase" or prompt == "expression": + outputs_class = self.class_embed[lvl](inter_states[lvl], features_l) + else: + outputs_class = self.class_embed[lvl](inter_states[lvl]) + tmp = self.bbox_embed[lvl](inter_states[lvl]) + if reference.shape[-1] == 4: + tmp += reference + else: + assert reference.shape[-1] == 2 + tmp[..., :2] += reference + outputs_coord = tmp.sigmoid() + outputs_classes.append(outputs_class) + outputs_coords.append(outputs_coord) + + if self.aux_mask: + mask_embeds = self.mask_embed[lvl](inter_states[lvl]) + else: + mask_embeds = self.mask_embed(inter_states[lvl]) + outputs_mask = torch.einsum("bqc,bchw->bqhw", mask_embeds, mask_features) + outputs_masks.append(outputs_mask) + outputs_class = torch.stack(outputs_classes) + outputs_coord = torch.stack(outputs_coords) + outputs_mask = outputs_masks + outputs_mask[-1] += 0.0 * sum(outputs_mask) + + output = { + "pred_logits": outputs_class[-1], + "pred_boxes": outputs_coord[-1], + "pred_masks": outputs_mask[-1], + "init_reference": init_reference, + } + if self.aux_loss: + output["aux_outputs"] = self._set_aux_loss( + outputs_class, + outputs_coord, + outputs_mask, + ) + + if self.as_two_stage: + enc_outputs_coord = enc_outputs_coord_unact.sigmoid() + output["enc_outputs"] = { + "pred_logits": enc_outputs_class, + "pred_boxes": enc_outputs_coord, + "anchors": anchors, + "spatial_shapes": spatial_shapes, + "image_tensor_size": images.tensor.size()[2:], + } + + if ( + self.vis_period > 0 + and self.training + and get_event_storage().iter % self.vis_period == self.vis_period - 1 + ): + self.visualize_training(batched_inputs, output, images, dataset_id) + self.visualize_training_enc_output(batched_inputs, output, images, dataset_id) + self.visualize_training_enc_output_nonms(batched_inputs, output, images, dataset_id) + self.visualize_training_init_reference(batched_inputs, output, images, dataset_id) + + if self.training: + gt_instances = [x["instances"].to(self.device) for x in batched_inputs] + targets = self.prepare_targets(gt_instances) + + if ( + self.vis_period > 0 + and self.training + and get_event_storage().iter % self.vis_period == self.vis_period - 1 + ): + enc_outputs = output["enc_outputs"] + bin_targets = copy.deepcopy(targets) + for bt in bin_targets: + bt["labels"] = torch.zeros_like(bt["labels"]) + if self.criterion[dataset_id].matcher_stage1 is not None: + tmp1 = self.criterion[dataset_id].matcher_stage1.positive_fraction + tmp2 = self.criterion[dataset_id].matcher_stage1.batch_size_per_image + self.criterion[dataset_id].matcher_stage1.positive_fraction = 1.0 + self.criterion[dataset_id].matcher_stage1.batch_size_per_image = 5120000 + indices, ious = self.criterion[dataset_id].matcher_stage1( + enc_outputs, bin_targets, return_cost_matrix=True + ) + self.criterion[dataset_id].matcher_stage1.positive_fraction = tmp1 + self.criterion[dataset_id].matcher_stage1.batch_size_per_image = tmp2 + + self.visualize_training_enc_output_pos( + batched_inputs, output, images, dataset_id, indices + ) + + if self.criterion[dataset_id].matcher_stage2 is not None: + indices = self.criterion[dataset_id].matcher_stage2(output, targets) + + self.visualize_training_init_reference_pos( + batched_inputs, output, images, dataset_id, indices + ) + + loss_dict = self.criterion[dataset_id](output, targets) + + weight_dict = self.criterion[dataset_id].weight_dict + for k in loss_dict.keys(): + if k in weight_dict: + loss_dict[k] *= weight_dict[k] + return loss_dict + else: + + box_cls = output["pred_logits"] + box_pred = output["pred_boxes"] + mask_pred = output["pred_masks"] + + start_time = time.perf_counter() + + iter_func = retry_if_cuda_oom(F.interpolate) + mask_pred = iter_func( + mask_pred, size=images.tensor.size()[2:], mode="bilinear", align_corners=False + ) + + merged_results = [{} for _ in range(box_cls.size(0))] + if self.instance_on and not ( + self.eval_dataset_entity and "thing" not in self.eval_dataset_entity + ): + if dataset_id >= 0 and dataset_id < len(self.metadata_list): + if is_thing_stuff_overlap(self.metadata_list[dataset_id]): + thing_id = self.metadata_list[ + dataset_id + ].thing_dataset_id_to_contiguous_id.values() + thing_id = torch.Tensor(list(thing_id)).to(torch.long).to(self.device) + + detector_box_cls = torch.zeros_like(box_cls) + detector_box_cls += float("-inf") + detector_box_cls[..., thing_id] = box_cls[..., thing_id] + else: + num_thing_classes = len(self.metadata_list[dataset_id].thing_classes) + detector_box_cls = box_cls[..., :num_thing_classes] + else: + detector_box_cls = box_cls + + use_sigmoid = True + detector_results, filter_inds = self.inference( + detector_box_cls, box_pred, images.image_sizes, use_sigmoid=use_sigmoid + ) + + if self.test_mask_on: + detector_mask_preds = [ + x[filter_ind] for x, filter_ind in zip(mask_pred, filter_inds) + ] + + for result, box_mask in zip(detector_results, detector_mask_preds): + box_mask = box_mask.sigmoid() > 0.5 + box_mask = BitMasks(box_mask).crop_and_resize( + result.pred_boxes.tensor.to(box_mask.device), 128 + ) + result.pred_masks = ( + box_mask.to(result.pred_boxes.tensor.device) + .unsqueeze(1) + .to(dtype=torch.float32) + ) + + if do_postprocess: + assert ( + not torch.jit.is_scripting() + ), "Scripting is not supported for postprocess." + detector_results = DeformableDETRSegm._postprocess_instance( + detector_results, batched_inputs, images.image_sizes + ) + for merged_result, detector_result in zip(merged_results, detector_results): + merged_result.update(detector_result) + + else: + detector_results = None + + if self.semantic_on and not ( + self.eval_dataset_entity and "stuff" not in self.eval_dataset_entity + ): + + semantic_mask_pred = mask_pred.clone() + semantic_box_cls = get_stuff_score( + box_cls, self.metadata_list[dataset_id], self.dataset_entities[dataset_id] + ) + + if self.semantic_post_nms: + _, filter_inds = self.inference(semantic_box_cls, box_pred, images.image_sizes) + semantic_box_cls = torch.stack( + [x[filter_ind] for x, filter_ind in zip(semantic_box_cls, filter_inds)], + dim=0, + ) + semantic_mask_pred = torch.stack( + [x[filter_ind] for x, filter_ind in zip(semantic_mask_pred, filter_inds)], + dim=0, + ) + + if do_postprocess: + assert ( + not torch.jit.is_scripting() + ), "Scripting is not supported for postprocess." + iter_func = retry_if_cuda_oom(DeformableDETRSegm._postprocess_semantic) + semantic_results = iter_func( + semantic_box_cls, semantic_mask_pred, batched_inputs, images + ) + if ( + dataset_id >= 0 + and self.dataset_entities[dataset_id] == "stuff" + and self.metadata_list[dataset_id].get("stuff_classes")[0] == "things" + and self.stuff_prob_thing > 0 + ): + for semantic_result in semantic_results: + semantic_result["sem_seg"][0, ...] = math.log( + self.stuff_prob_thing / (1 - self.stuff_prob_thing) + ) + for merged_result, semantic_result in zip(merged_results, semantic_results): + merged_result.update(semantic_result) + + else: + semantic_results = None + + if self.panoptic_on and not ( + self.eval_dataset_entity and "thing+stuff" not in self.eval_dataset_entity + ): + assert dataset_id >= 0 and dataset_id < len(self.metadata_list) + if do_postprocess: + assert ( + not torch.jit.is_scripting() + ), "Scripting is not supported for postprocess." + if True: + if self.panoptic_post_nms: + _, filter_inds = self.inference(box_cls, box_pred, images.image_sizes) + panoptic_mask_pred = [ + x[filter_ind] for x, filter_ind in zip(mask_pred, filter_inds) + ] + panoptic_box_cls = [ + x[filter_ind] for x, filter_ind in zip(box_cls, filter_inds) + ] + + panoptic_results = DeformableDETRSegm._postprocess_panoptic( + panoptic_box_cls, + panoptic_mask_pred, + batched_inputs, + images, + self.metadata_list[dataset_id], + self.panoptic_configs, + ) + else: + panoptic_results = [] + self.combine_overlap_thresh = 0.5 + self.combine_stuff_area_thresh = 4096 + self.combine_instances_score_thresh = 0.5 + for detector_result, semantic_result in zip( + detector_results, semantic_results + ): + detector_r = detector_result["instances"] + sem_seg_r = semantic_result["sem_seg"] + panoptic_r = combine_semantic_and_instance_outputs( + detector_r, + sem_seg_r.argmax(dim=0), + self.combine_overlap_thresh, + self.combine_stuff_area_thresh, + self.combine_instances_score_thresh, + ) + panoptic_results.append({"panoptic_seg": panoptic_r}) + for merged_result, panoptic_result in zip(merged_results, panoptic_results): + merged_result.update(panoptic_result) + + else: + panoptic_results = None + + self.postprocess_time = time.perf_counter() - start_time + + if do_postprocess: + return merged_results + + return detector_results, semantic_results, panoptic_results + + def maskdino_mask_features(self, encode_feats, multi_level_feats, multi_level_masks): + start_idx = sum( + [mask.shape[1] * mask.shape[2] for mask in multi_level_masks[: self.mask_encode_level]] + ) + end_idx = sum( + [ + mask.shape[1] * mask.shape[2] + for mask in multi_level_masks[: self.mask_encode_level + 1] + ] + ) + b, h, w = multi_level_masks[self.mask_encode_level].size() + + encode_feats = encode_feats[:, start_idx:end_idx, :] + encode_feats = encode_feats.permute(0, 2, 1).reshape(b, -1, h, w) + + x = [multi_level_feats[f] for f in self.mask_in_features] + x = x[0] + x = self.lateral_conv(x) + x = x + F.interpolate(encode_feats, size=x.shape[-2:], mode="bilinear", align_corners=False) + x = self.output_conv(x) + mask_features = self.mask_conv(x) + + return mask_features + + @torch.jit.unused + def _set_aux_loss(self, outputs_class, outputs_coord, outputs_mask): + return [ + {"pred_logits": a, "pred_boxes": b, "pred_masks": c} + for a, b, c in zip(outputs_class[:-1], outputs_coord[:-1], outputs_mask[:-1]) + ] + + def inference(self, box_cls, box_pred, image_sizes, use_sigmoid=True): + """ + Arguments: + box_cls (Tensor): tensor of shape (batch_size, num_queries, K). + The tensor predicts the classification probability for each query. + box_pred (Tensor): tensors of shape (batch_size, num_queries, 4). + The tensor predicts 4-vector (x,y,w,h) box + regression values for every queryx + image_sizes (List[torch.Size]): the input image sizes + + Returns: + results (List[Instances]): a list of #images elements. + """ + + if True: + + if use_sigmoid: + scores = torch.cat( + ( + box_cls.sigmoid(), + torch.zeros((box_cls.size(0), box_cls.size(1), 1), device=self.device), + ), + dim=2, + ) + else: + scores = torch.cat( + ( + box_cls, + torch.zeros((box_cls.size(0), box_cls.size(1), 1), device=self.device), + ), + dim=2, + ) + + boxes = box_cxcywh_to_xyxy(box_pred) + + img_h = torch.tensor([image_size[0] for image_size in image_sizes], device=self.device) + img_w = torch.tensor([image_size[1] for image_size in image_sizes], device=self.device) + scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1) + boxes = boxes * scale_fct[:, None, :] + + boxes = boxes.unbind(0) + scores = scores.unbind(0) + image_shapes = image_sizes + + results, filter_inds = fast_rcnn_inference( + boxes, + scores, + image_shapes, + self.test_score_thresh, + self.test_nms_thresh, + self.test_topk_per_image, + ) + + return results, filter_inds + + assert len(box_cls) == len(image_sizes) + results = [] + + prob = box_cls.sigmoid() + topk_values, topk_indexes = torch.topk( + prob.view(box_cls.shape[0], -1), self.select_box_nums_for_evaluation, dim=1 + ) + scores = topk_values + topk_boxes = torch.div(topk_indexes, box_cls.shape[2], rounding_mode="floor") + labels = topk_indexes % box_cls.shape[2] + + boxes = torch.gather(box_pred, 1, topk_boxes.unsqueeze(-1).repeat(1, 1, 4)) + + for i, (scores_per_image, labels_per_image, box_pred_per_image, image_size) in enumerate( + zip(scores, labels, boxes, image_sizes) + ): + result = Instances(image_size) + result.pred_boxes = Boxes(box_cxcywh_to_xyxy(box_pred_per_image)) + result.pred_boxes.scale(scale_x=image_size[1], scale_y=image_size[0]) + result.scores = scores_per_image + result.pred_classes = labels_per_image + results.append(result) + return results, topk_indexes + + def prepare_targets(self, targets): + new_targets = [] + for targets_per_image in targets: + h, w = targets_per_image.image_size + image_size_xyxy = torch.as_tensor([w, h, w, h], dtype=torch.float, device=self.device) + gt_classes = targets_per_image.gt_classes + gt_boxes = targets_per_image.gt_boxes.tensor / image_size_xyxy + gt_boxes = box_xyxy_to_cxcywh(gt_boxes) + + if not targets_per_image.has("gt_masks"): + gt_masks = torch.zeros((0, h, w), dtype=torch.bool) + else: + gt_masks = targets_per_image.gt_masks + + if not isinstance(gt_masks, torch.Tensor): + if isinstance(gt_masks, BitMasks): + gt_masks = gt_masks.tensor + else: + gt_masks = BitMasks.from_polygon_masks(gt_masks, h, w).tensor + + gt_masks = self._move_to_current_device(gt_masks) + gt_masks = ImageList.from_tensors( + [gt_masks], + self.backbone.size_divisibility, + padding_constraints=self.backbone.padding_constraints, + ).tensor.squeeze(0) + + new_targets.append({"labels": gt_classes, "boxes": gt_boxes, "masks": gt_masks}) + + if targets_per_image.has("is_thing"): + new_targets[-1]["is_thing"] = targets_per_image.is_thing + + return new_targets + + def preprocess_image(self, batched_inputs): + images = [self._move_to_current_device(x["image"]) for x in batched_inputs] + images = [x.to(self.pixel_mean.dtype) for x in images] + images = [(x - self.pixel_mean) / self.pixel_std for x in images] + images = ImageList.from_tensors( + images, + self.backbone.size_divisibility, + padding_constraints=self.backbone.padding_constraints, + ) + return images + + @staticmethod + def _postprocess_instance( + instances, batched_inputs: List[Dict[str, torch.Tensor]], image_sizes + ): + """ + Rescale the output instances to the target size. + """ + processed_results = [] + for results_per_image, input_per_image, image_size in zip( + instances, batched_inputs, image_sizes + ): + height = input_per_image.get("height", image_size[0]) + width = input_per_image.get("width", image_size[1]) + r = detector_postprocess(results_per_image, height, width) + processed_results.append({"instances": r.to("cpu")}) + return processed_results + + @staticmethod + def _postprocess_semantic( + mask_clses, + mask_preds, + batched_inputs: List[Dict[str, torch.Tensor]], + images, + pano_temp=0.06, + transform_eval=True, + ): + processed_results = [] + for mask_cls, mask_pred, input_per_image, image_size in zip( + mask_clses, mask_preds, batched_inputs, images.image_sizes + ): + height = input_per_image.get("height", image_size[0]) + width = input_per_image.get("width", image_size[1]) + + T = pano_temp + mask_cls = mask_cls.sigmoid() + + if transform_eval: + mask_cls = F.softmax(mask_cls / T, dim=-1) # already sigmoid + mask_pred = mask_pred.sigmoid() + result = torch.einsum("qc,qhw->chw", mask_cls, mask_pred) + + r = sem_seg_postprocess(result, image_size, height, width) + processed_results.append({"sem_seg": r}) + return processed_results + + @staticmethod + def _postprocess_panoptic( + mask_clses, + mask_preds, + batched_inputs: List[Dict[str, torch.Tensor]], + images, + metadata, + panoptic_configs, + ): + prob = panoptic_configs["prob"] + pano_temp = panoptic_configs["pano_temp"] + transform_eval = panoptic_configs["transform_eval"] + object_mask_threshold = panoptic_configs["object_mask_threshold"] + overlap_threshold = panoptic_configs["overlap_threshold"] + + processed_results = [] + for mask_cls, mask_pred, input_per_image, image_size in zip( + mask_clses, mask_preds, batched_inputs, images.image_sizes + ): + height = input_per_image.get("height", image_size[0]) + width = input_per_image.get("width", image_size[1]) + + mask_pred = sem_seg_postprocess(mask_pred, image_size, height, width) + + T = pano_temp + scores, labels = mask_cls.sigmoid().max(-1) + mask_pred = mask_pred.sigmoid() + keep = scores > object_mask_threshold + if transform_eval: + scores, labels = F.softmax(mask_cls.sigmoid() / T, dim=-1).max(-1) + cur_scores = scores[keep] + cur_classes = labels[keep] + cur_masks = mask_pred[keep] + cur_prob_masks = cur_scores.view(-1, 1, 1) * cur_masks + + panoptic_seg = torch.zeros((height, width), dtype=torch.int32, device=cur_masks.device) + segments_info = [] + + current_segment_id = 0 + + if cur_masks.size(0) > 0: + + cur_mask_ids = cur_prob_masks.argmax(0) + + stuff_memory_list = {} + for k in range(cur_classes.shape[0]): + pred_class = cur_classes[k].item() + isthing = pred_class in metadata.thing_dataset_id_to_contiguous_id.values() + mask_area = (cur_mask_ids == k).sum().item() + original_area = (cur_masks[k] >= prob).sum().item() + mask = (cur_mask_ids == k) & (cur_masks[k] >= prob) + + if mask_area > 0 and original_area > 0 and mask.sum().item() > 0: + if mask_area / original_area < overlap_threshold: + continue + + if not isthing: + if int(pred_class) in stuff_memory_list.keys(): + panoptic_seg[mask] = stuff_memory_list[int(pred_class)] + continue + else: + stuff_memory_list[int(pred_class)] = current_segment_id + 1 + + current_segment_id += 1 + panoptic_seg[mask] = current_segment_id + + if not isthing and metadata.get("stuff_classes")[0] == "things": + pred_class = int(pred_class) - len(metadata.thing_classes) + 1 + + segments_info.append( + { + "id": current_segment_id, + "isthing": bool(isthing), + "category_id": int(pred_class), + } + ) + + processed_results.append({"panoptic_seg": (panoptic_seg, segments_info)}) + return processed_results + + @torch.no_grad() + def visualize_training( + self, batched_inputs, output, images, dataset_id, suffix="", do_nms=True + ): + if self.output_dir is None: + return + if self.training: + storage = get_event_storage() + os.makedirs(self.output_dir + "/training", exist_ok=True) + else: + os.makedirs(self.output_dir + "/inference", exist_ok=True) + + pred_logits = output["pred_logits"] + pred_boxes = output["pred_boxes"] + pred_masks = output["pred_masks"] + + thing_classes = self.metadata_list[dataset_id].get("thing_classes", []) + stuff_classes = self.metadata_list[dataset_id].get("stuff_classes", []) + if len(thing_classes) > 0 and len(stuff_classes) > 0 and stuff_classes[0] == "things": + stuff_classes = stuff_classes[1:] + if is_thing_stuff_overlap(self.metadata_list[dataset_id]): + class_names = ( + thing_classes if len(thing_classes) > len(stuff_classes) else stuff_classes + ) + else: + class_names = thing_classes + stuff_classes + + if "instances" in batched_inputs[0] and batched_inputs[0]["instances"].has("phrases"): + class_names = [phrase for x in batched_inputs for phrase in x["instances"].phrases] + [ + "unknown" + ] * 1000 + if "expressions" in batched_inputs[0] and self.expression_cumulative_gt_class: + class_names = [x["expressions"] for x in batched_inputs] + ["unknown"] * 1000 + + num_thing_classes = len(class_names) + pred_logits = pred_logits[..., :num_thing_classes] + + if pred_masks is not None: + pred_masks = [ + F.interpolate( + pred_mask.float().cpu().unsqueeze(0), + size=images.tensor.size()[2:], + mode="bilinear", + align_corners=False, + ).squeeze(0) + if pred_mask.size(0) > 0 + else pred_mask + for pred_mask in pred_masks + ] + else: + pred_masks = [ + torch.zeros(pred_box.size(0), image_size[0], image_size[1]) + for pred_box, image_size in zip(pred_boxes, images.image_sizes) + ] + + if do_nms: + results, filter_inds = self.inference(pred_logits, pred_boxes, images.image_sizes) + pred_masks = [ + pred_mask[filter_ind.cpu()] + for pred_mask, filter_ind in zip(pred_masks, filter_inds) + ] + for result, pred_mask in zip(results, pred_masks): + result.pred_masks = pred_mask.sigmoid() > 0.5 + else: + results = [] + for pred_logit, pred_box, pred_mask, image_size in zip( + pred_logits, pred_boxes, pred_masks, images.image_sizes + ): + result = Instances(image_size) + result.pred_boxes = Boxes(pred_box) + result.scores = pred_logit[:, 0] + result.pred_classes = torch.zeros( + len(pred_box), dtype=torch.int64, device=pred_logit.device + ) + result.pred_masks = pred_mask.sigmoid() > 0.5 + + results.append(result) + + from detectron2.utils.visualizer import Visualizer + + for input, result in zip(batched_inputs, results): + + if "expressions" in batched_inputs[0] and not self.expression_cumulative_gt_class: + class_names = [input["expressions"]] + ["unknown"] * 1000 + + img = input["image"] + img = convert_image_to_rgb(img.permute(1, 2, 0), self.input_format) + v_gt = Visualizer(img, None) + + if "instances" in input: + labels = [ + "{}".format(class_names[gt_class]) for gt_class in input["instances"].gt_classes + ] + v_gt = v_gt.overlay_instances( + boxes=input["instances"].gt_boxes, + masks=input["instances"].gt_masks + if input["instances"].has("gt_masks") + else None, + labels=labels, + ) + else: + v_gt = v_gt.output + anno_img = v_gt.get_image() + + labels = [ + "{}_{:.0f}%".format(class_names[pred_class], score * 100) + for pred_class, score in zip(result.pred_classes.cpu(), result.scores.cpu()) + ] + v_pred = Visualizer(img, None) + v_pred = v_pred.overlay_instances( + boxes=result.pred_boxes.tensor.clone().detach().cpu().numpy(), + labels=labels, + masks=result.pred_masks[:, : img.shape[0], : img.shape[1]] + .clone() + .detach() + .cpu() + .numpy() + if result.has("pred_masks") + else None, + ) + pred_img = v_pred.get_image() + + vis_img = np.concatenate((anno_img, pred_img), axis=1) + + if result.has("pred_texts"): + labels = [ + "{}".format(text) for text, score in zip(result.pred_texts, result.scores.cpu()) + ] + v_pred = Visualizer(img, None) + v_pred = v_pred.overlay_instances( + boxes=result.pred_boxes.tensor.clone().detach().cpu().numpy(), + labels=labels, + masks=result.pred_masks.clone().detach().cpu().numpy(), + ) + pred_img = v_pred.get_image() + vis_img = np.concatenate((vis_img, pred_img), axis=1) + + basename = os.path.basename(input["file_name"]) + if self.training: + cv2.imwrite( + os.path.join( + self.output_dir, "training", str(storage.iter) + suffix + "_" + basename + ), + vis_img[:, :, ::-1], + ) + else: + cv2.imwrite( + os.path.join(self.output_dir, "inference", suffix + basename), + vis_img[:, :, ::-1], + ) + + @torch.no_grad() + def visualize_inference_panoptic(self, batched_inputs, results, dataset_id): + if self.output_dir is None: + return + if self.training: + storage = get_event_storage() + os.makedirs(self.output_dir + "/training", exist_ok=True) + else: + os.makedirs(self.output_dir + "/inference", exist_ok=True) + + from detectron2.utils.visualizer import Visualizer + + for input, result in zip(batched_inputs, results): + + img = input["image"] + img = convert_image_to_rgb(img.permute(1, 2, 0), self.input_format) + + height = input["height"] + width = input["width"] + img = cv2.resize(img, (width, height)) + + v_gt = Visualizer(img, self.metadata_list[dataset_id]) + + if "instances" in input: + labels = [ + "{}".format(class_names[gt_class]) for gt_class in input["instances"].gt_classes + ] + v_gt = v_gt.overlay_instances( + boxes=input["instances"].gt_boxes, + masks=input["instances"].gt_masks + if input["instances"].has("gt_masks") + else None, + labels=labels, + ) + else: + v_gt = v_gt.output + anno_img = v_gt.get_image() + + v_pred = Visualizer(img, self.metadata_list[dataset_id]) + + panoptic_seg, segments_info = result["panoptic_seg"] + v_pred = v_pred.draw_panoptic_seg_predictions(panoptic_seg.cpu(), segments_info) + pred_img = v_pred.get_image() + + vis_img = np.concatenate((anno_img, pred_img), axis=1) + + basename = os.path.basename(input["file_name"]) + if self.training: + cv2.imwrite( + os.path.join( + self.output_dir, "training", str(storage.iter) + "_pan_" + basename + ), + vis_img[:, :, ::-1], + ) + else: + cv2.imwrite( + os.path.join(self.output_dir, "inference", "pan_" + basename), + vis_img[:, :, ::-1], + ) + + @torch.no_grad() + def visualize_training_enc_output(self, batched_inputs, output, images, dataset_id, suffix=""): + if self.output_dir is None: + return + if self.training: + storage = get_event_storage() + os.makedirs(self.output_dir + "/training", exist_ok=True) + else: + os.makedirs(self.output_dir + "/inference", exist_ok=True) + + pred_logits = output["enc_outputs"]["pred_logits"] + pred_boxes = output["enc_outputs"]["pred_boxes"] + + results, filter_inds = self.inference(pred_logits, pred_boxes, images.image_sizes) + + from detectron2.utils.visualizer import Visualizer + + for input, result in zip(batched_inputs, results): + img = input["image"] + img = convert_image_to_rgb(img.permute(1, 2, 0), self.input_format) + v_gt = Visualizer(img, None) + if "instances" in input: + v_gt = v_gt.overlay_instances( + boxes=input["instances"].gt_boxes, + ) + else: + v_gt = v_gt.output + anno_img = v_gt.get_image() + + labels = [ + "{}_{:.0f}%".format(pred_class, score * 100) + for pred_class, score in zip(result.pred_classes.cpu(), result.scores.cpu()) + ] + v_pred = Visualizer(img, None) + v_pred = v_pred.overlay_instances( + boxes=result.pred_boxes.tensor.clone().detach().cpu().numpy(), + labels=labels, + ) + pred_img = v_pred.get_image() + + vis_img = np.concatenate((anno_img, pred_img), axis=1) + + basename = os.path.basename(input["file_name"]) + if self.training: + cv2.imwrite( + os.path.join( + self.output_dir, + "training", + str(storage.iter) + suffix + "_enc_output_" + basename, + ), + vis_img[:, :, ::-1], + ) + else: + cv2.imwrite( + os.path.join(self.output_dir, "inference", suffix + "enc_output_" + basename), + vis_img[:, :, ::-1], + ) + + def visualize_training_enc_output_nonms( + self, batched_inputs, output, images, dataset_id, suffix="" + ): + if self.output_dir is None: + return + if self.training: + storage = get_event_storage() + os.makedirs(self.output_dir + "/training", exist_ok=True) + else: + os.makedirs(self.output_dir + "/inference", exist_ok=True) + + pred_logits = output["enc_outputs"]["pred_logits"] + pred_boxes = output["enc_outputs"]["pred_boxes"] + + image_sizes = images.image_sizes + pred_boxes = box_cxcywh_to_xyxy(pred_boxes) + + img_h = torch.tensor([image_size[0] for image_size in image_sizes], device=self.device) + img_w = torch.tensor([image_size[1] for image_size in image_sizes], device=self.device) + scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1) + pred_boxes = pred_boxes * scale_fct[:, None, :] + + pred_boxes = pred_boxes.unbind(0) + pred_logits = pred_logits.unbind(0) + + from detectron2.utils.visualizer import Visualizer + + for input, pred_box, pred_logit in zip(batched_inputs, pred_boxes, pred_logits): + img = input["image"] + img = convert_image_to_rgb(img.permute(1, 2, 0), self.input_format) + v_gt = Visualizer(img, None) + if "instances" in input: + v_gt = v_gt.overlay_instances( + boxes=input["instances"].gt_boxes, + ) + else: + v_gt = v_gt.output + anno_img = v_gt.get_image() + + keep = pred_logit.sigmoid() > 0.1 + if keep.sum() == 0: + continue + pred_box = pred_box[keep.squeeze()] + pred_logit = pred_logit[keep.squeeze()] + + labels = [ + "{:.0f}%".format(score * 100) for score in pred_logit.squeeze().cpu().tolist() + ] + v_pred = Visualizer(img, None) + v_pred = v_pred.overlay_instances( + boxes=pred_box.clone().detach().cpu().numpy(), + labels=labels, + ) + pred_img = v_pred.get_image() + + vis_img = np.concatenate((anno_img, pred_img), axis=1) + + basename = os.path.basename(input["file_name"]) + if self.training: + cv2.imwrite( + os.path.join( + self.output_dir, + "training", + str(storage.iter) + suffix + "_enc_output_nonms_" + basename, + ), + vis_img[:, :, ::-1], + ) + else: + cv2.imwrite( + os.path.join( + self.output_dir, "inference", suffix + "enc_output_nonms_" + basename + ), + vis_img[:, :, ::-1], + ) + + @torch.no_grad() + def visualize_training_init_reference( + self, batched_inputs, output, images, dataset_id, suffix="" + ): + if self.output_dir is None: + return + if self.training: + storage = get_event_storage() + os.makedirs(self.output_dir + "/training", exist_ok=True) + else: + os.makedirs(self.output_dir + "/inference", exist_ok=True) + + pred_boxes = output["init_reference"] + + image_sizes = images.image_sizes + pred_boxes = box_cxcywh_to_xyxy(pred_boxes) + + img_h = torch.tensor([image_size[0] for image_size in image_sizes], device=self.device) + img_w = torch.tensor([image_size[1] for image_size in image_sizes], device=self.device) + scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1) + pred_boxes = pred_boxes * scale_fct[:, None, :] + + pred_boxes = pred_boxes.unbind(0) + + from detectron2.utils.visualizer import Visualizer + + for input, pred_box in zip(batched_inputs, pred_boxes): + img = input["image"] + img = convert_image_to_rgb(img.permute(1, 2, 0), self.input_format) + v_gt = Visualizer(img, None) + if "instances" in input: + v_gt = v_gt.overlay_instances( + boxes=input["instances"].gt_boxes, + ) + else: + v_gt = v_gt.output + anno_img = v_gt.get_image() + + v_pred = Visualizer(img, None) + v_pred = v_pred.overlay_instances( + boxes=pred_box.clone().detach().cpu().numpy(), + ) + pred_img = v_pred.get_image() + + vis_img = np.concatenate((anno_img, pred_img), axis=1) + + basename = os.path.basename(input["file_name"]) + if self.training: + cv2.imwrite( + os.path.join( + self.output_dir, + "training", + str(storage.iter) + suffix + "_init_reference_" + basename, + ), + vis_img[:, :, ::-1], + ) + else: + cv2.imwrite( + os.path.join( + self.output_dir, "inference", suffix + "init_reference_" + basename + ), + vis_img[:, :, ::-1], + ) + + @torch.no_grad() + def visualize_training_enc_output_pos( + self, batched_inputs, output, images, dataset_id, indices, suffix="" + ): + if self.output_dir is None: + return + if self.training: + storage = get_event_storage() + os.makedirs(self.output_dir + "/training", exist_ok=True) + else: + os.makedirs(self.output_dir + "/inference", exist_ok=True) + + anchors = output["enc_outputs"]["anchors"] + + image_sizes = images.image_sizes + anchors = box_cxcywh_to_xyxy(anchors) + + img_h, img_w = torch.tensor(image_sizes, device=self.device).unbind(1) + scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1) + anchors = anchors * scale_fct[:, None, :] + + anchors = anchors.unbind(0) + + from detectron2.utils.visualizer import Visualizer + + for input, anchor, indice in zip(batched_inputs, anchors, indices): + img = input["image"] + img = convert_image_to_rgb(img.permute(1, 2, 0), self.input_format) + v_gt = Visualizer(img, None) + if "instances" in input: + labels = ["{}".format(idx) for idx in range(len(input["instances"]))] + v_gt = v_gt.overlay_instances( + boxes=input["instances"].gt_boxes, + masks=input["instances"].gt_masks + if input["instances"].has("gt_masks") + else None, + labels=labels, + ) + else: + v_gt = v_gt.output + anno_img = v_gt.get_image() + + v_pred = Visualizer(img, None) + v_pred = v_pred.overlay_instances( + boxes=anchor.clone().detach().cpu().numpy(), + ) + pred_img = v_pred.get_image() + + anchor = anchor[indice[0], :] + labels = ["{}".format(idx) for idx in indice[1]] + v_pred = Visualizer(img, None) + v_pred = v_pred.overlay_instances( + boxes=anchor.clone().detach().cpu().numpy(), + labels=labels, + ) + pred_img2 = v_pred.get_image() + + vis_img = np.concatenate((anno_img, pred_img, pred_img2), axis=1) + + basename = os.path.basename(input["file_name"]) + if self.training: + cv2.imwrite( + os.path.join( + self.output_dir, + "training", + str(storage.iter) + suffix + "_enc_output_pos_" + basename, + ), + vis_img[:, :, ::-1], + ) + else: + cv2.imwrite( + os.path.join( + self.output_dir, "inference", suffix + "enc_output_pos_" + basename + ), + vis_img[:, :, ::-1], + ) + + @torch.no_grad() + def visualize_training_init_reference_pos( + self, batched_inputs, output, images, dataset_id, indices, suffix="" + ): + if self.output_dir is None: + return + if self.training: + storage = get_event_storage() + os.makedirs(self.output_dir + "/training", exist_ok=True) + else: + os.makedirs(self.output_dir + "/inference", exist_ok=True) + + pred_boxes = output["init_reference"] + + image_sizes = images.image_sizes + pred_boxes = box_cxcywh_to_xyxy(pred_boxes) + + img_h = torch.tensor([image_size[0] for image_size in image_sizes], device=self.device) + img_w = torch.tensor([image_size[1] for image_size in image_sizes], device=self.device) + scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1) + pred_boxes = pred_boxes * scale_fct[:, None, :] + + pred_boxes = pred_boxes.unbind(0) + + from detectron2.utils.visualizer import Visualizer + + for input, pred_box, indice in zip(batched_inputs, pred_boxes, indices): + img = input["image"] + img = convert_image_to_rgb(img.permute(1, 2, 0), self.input_format) + v_gt = Visualizer(img, None) + if "instances" in input: + labels = ["{}".format(idx) for idx in range(len(input["instances"]))] + v_gt = v_gt.overlay_instances( + boxes=input["instances"].gt_boxes, + masks=input["instances"].gt_masks + if input["instances"].has("gt_masks") + else None, + labels=labels, + ) + else: + v_gt = v_gt.output + anno_img = v_gt.get_image() + + pred_box = pred_box[indice[0]] + labels = ["{}".format(idx) for idx in indice[1]] + v_pred = Visualizer(img, None) + v_pred = v_pred.overlay_instances( + boxes=pred_box.clone().detach().cpu().numpy(), + labels=labels, + ) + pred_img = v_pred.get_image() + + vis_img = np.concatenate((anno_img, pred_img), axis=1) + + basename = os.path.basename(input["file_name"]) + if self.training: + cv2.imwrite( + os.path.join( + self.output_dir, + "training", + str(storage.iter) + suffix + "_init_reference_pos_" + basename, + ), + vis_img[:, :, ::-1], + ) + else: + cv2.imwrite( + os.path.join( + self.output_dir, "inference", suffix + "init_reference_pos_" + basename + ), + vis_img[:, :, ::-1], + ) + + def set_model_language(self, model_language): + self.model_language = model_language + + +class NMSPostProcess(nn.Module): + """This module converts the model's output into the format expected by the coco api""" + + @torch.no_grad() + def forward(self, outputs, target_sizes, select_box_nums_for_evaluation): + """Perform the computation + Parameters: + outputs: raw outputs of the model + target_sizes: tensor of dimension [batch_size x 2] containing the size of each images of the batch + For evaluation, this must be the original image size (before any data augmentation) + For visualization, this should be the image size after data augment, but before padding + """ + out_logits, out_bbox = outputs["pred_logits"], outputs["pred_boxes"] + out_mask = outputs["pred_masks"] + bs, n_queries, n_cls = out_logits.shape + + assert len(out_logits) == len(target_sizes) + assert target_sizes.shape[1] == 2 + + prob = out_logits.sigmoid() + + all_scores = prob.view(bs, n_queries * n_cls).to(out_logits.device) + all_indexes = torch.arange(n_queries * n_cls)[None].repeat(bs, 1).to(out_logits.device) + all_boxes = torch.div(all_indexes, out_logits.shape[2], rounding_mode="trunc") + all_labels = all_indexes % out_logits.shape[2] + + boxes = box_cxcywh_to_xyxy(out_bbox) + boxes = torch.gather(boxes, 1, all_boxes.unsqueeze(-1).repeat(1, 1, 4)) + + img_h, img_w = target_sizes.unbind(1) + scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1) + boxes = boxes * scale_fct[:, None, :] + + results = [] + keep_inds_all = [] + for b in range(bs): + box = boxes[b] + score = all_scores[b] + lbls = all_labels[b] + mask = out_mask[b] + + pre_topk = score.topk(10000).indices + box = box[pre_topk] + score = score[pre_topk] + lbls = lbls[pre_topk] + + keep_inds = batched_nms(box, score, lbls, 0.7)[:select_box_nums_for_evaluation] + + result = Instances(target_sizes[b]) + result.pred_boxes = Boxes(box[keep_inds]) + result.scores = score[keep_inds] + result.pred_classes = lbls[keep_inds] + results.append(result) + + keep_inds_all.append(keep_inds) + + return results, keep_inds_all + + +def is_thing_stuff_overlap(metadata): + thing_classes = metadata.get("thing_classes", []) + stuff_classes = metadata.get("stuff_classes", []) + if len(thing_classes) == 0 or len(stuff_classes) == 0: + return False + + if set(thing_classes).issubset(set(stuff_classes)) or set(stuff_classes).issubset( + set(thing_classes) + ): + return True + else: + return False + + +def get_text_list(metadata, dataset_entity): + thing_classes = metadata.get("thing_classes", []) + stuff_classes = metadata.get("stuff_classes", []) + + if dataset_entity == "thing+stuff" and stuff_classes[0] == "things": + text_list = list(thing_classes) + list(stuff_classes[1:]) + + elif dataset_entity == "thing+stuff" and is_thing_stuff_overlap(metadata): + text_list = thing_classes if len(thing_classes) > len(stuff_classes) else stuff_classes + + elif dataset_entity == "thing+stuff": + text_list = list(thing_classes) + list(stuff_classes) + + elif dataset_entity == "stuff": + text_list = list(stuff_classes) + + elif dataset_entity == "thing": + text_list = list(thing_classes) + + return text_list + + +def get_stuff_score(box_cls, metadata, dataset_entity): + thing_classes = metadata.get("thing_classes", []) + stuff_classes = metadata.get("stuff_classes", []) + + semantic_box_cls = box_cls.clone() + + if dataset_entity == "thing+stuff" and stuff_classes[0] == "things": + num_thing_classes = len(thing_classes) + + semantic_box_cls_0 = box_cls[..., :num_thing_classes] + semantic_box_cls_1 = box_cls[..., num_thing_classes:] + semantic_box_cls_0, _ = semantic_box_cls_0.min(dim=2, keepdim=True) + semantic_box_cls = torch.cat([semantic_box_cls_0, semantic_box_cls_1], dim=2) + + if dataset_entity == "thing+stuff" and is_thing_stuff_overlap(metadata): + semantic_box_cls = box_cls.clone() + + if dataset_entity == "stuff": + semantic_box_cls = box_cls.clone() + + return semantic_box_cls diff --git a/ape/modeling/ape_deta/deformable_detr_segm_vl.py b/ape/modeling/ape_deta/deformable_detr_segm_vl.py new file mode 100644 index 0000000..ea65f88 --- /dev/null +++ b/ape/modeling/ape_deta/deformable_detr_segm_vl.py @@ -0,0 +1,1262 @@ +import copy +import math +import os +import time +from typing import Dict, List, Optional, Tuple + +import cv2 +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +import fvcore.nn.weight_init as weight_init +from ape.modeling.text import utils as text_utils +from detectron2.data.detection_utils import convert_image_to_rgb +from detectron2.layers import Conv2d, ShapeSpec, get_norm, move_device_like +from detectron2.modeling import GeneralizedRCNN +from detectron2.modeling.meta_arch.panoptic_fpn import combine_semantic_and_instance_outputs +from detectron2.modeling.postprocessing import detector_postprocess, sem_seg_postprocess +from detectron2.structures import BitMasks, Boxes, ImageList, Instances +from detectron2.utils.events import get_event_storage +from detectron2.utils.memory import retry_if_cuda_oom +from detrex.layers import MLP, box_cxcywh_to_xyxy, box_xyxy_to_cxcywh +from detrex.utils import inverse_sigmoid +from torchvision.ops.boxes import batched_nms + +from .deformable_detr import DeformableDETR +from .fast_rcnn import fast_rcnn_inference +from .segmentation import MaskHeadSmallConv, MHAttentionMap + + +class DeformableDETRSegmVL(DeformableDETR): + """Implements the Deformable DETR model. + + Code is modified from the `official github repo + `_. + + More details can be found in the `paper + `_ . + + Args: + backbone (nn.Module): the backbone module. + position_embedding (nn.Module): the position embedding module. + neck (nn.Module): the neck module. + transformer (nn.Module): the transformer module. + embed_dim (int): the dimension of the embedding. + num_classes (int): Number of total categories. + num_queries (int): Number of proposal dynamic anchor boxes in Transformer + criterion (nn.Module): Criterion for calculating the total losses. + pixel_mean (List[float]): Pixel mean value for image normalization. + Default: [123.675, 116.280, 103.530]. + pixel_std (List[float]): Pixel std value for image normalization. + Default: [58.395, 57.120, 57.375]. + aux_loss (bool): whether to use auxiliary loss. Default: True. + with_box_refine (bool): whether to use box refinement. Default: False. + as_two_stage (bool): whether to use two-stage. Default: False. + select_box_nums_for_evaluation (int): the number of topk candidates + slected at postprocess for evaluation. Default: 100. + + """ + + def __init__( + self, + instance_on: bool = True, + semantic_on: bool = False, + panoptic_on: bool = False, + freeze_detr=False, + input_shapes=[], + mask_in_features=[], + mask_encode_level=0, + stuff_dataset_learn_thing: bool = True, + stuff_prob_thing: float = -1.0, + name_prompt_fusion_type: str = "none", + name_prompt_fusion_text: bool = None, + test_mask_on: bool = True, + semantic_post_nms: bool = True, + panoptic_post_nms: bool = True, + aux_mask: bool = False, + panoptic_configs: dict = { + "prob": 0.1, + "pano_temp": 0.06, + "transform_eval": True, + "object_mask_threshold": 0.01, + "overlap_threshold": 0.4, + }, + **kwargs, + ): + super().__init__(**kwargs) + + self.instance_on = instance_on + self.semantic_on = semantic_on + self.panoptic_on = panoptic_on + + if freeze_detr: + for p in self.parameters(): + p.requires_grad_(False) + + self.input_shapes = input_shapes + self.mask_in_features = mask_in_features + self.mask_encode_level = mask_encode_level + + hidden_dim = self.transformer.embed_dim + norm = "GN" + use_bias = False + + assert len(self.mask_in_features) == 1 + in_channels = [self.input_shapes[feat_name].channels for feat_name in self.mask_in_features] + in_channel = in_channels[0] + + self.lateral_conv = Conv2d( + in_channel, + hidden_dim, + kernel_size=1, + stride=1, + bias=use_bias, + padding=0, + norm=get_norm(norm, hidden_dim), + ) + self.output_conv = Conv2d( + hidden_dim, + hidden_dim, + kernel_size=3, + stride=1, + bias=use_bias, + padding=1, + norm=get_norm(norm, hidden_dim), + activation=F.relu, + ) + self.mask_conv = Conv2d( + hidden_dim, hidden_dim, kernel_size=1, stride=1, bias=use_bias, padding=0 + ) + + self.mask_embed = MLP(hidden_dim, hidden_dim, hidden_dim, 3) + self.aux_mask = aux_mask + if self.aux_mask: + self.mask_embed = nn.ModuleList( + [copy.deepcopy(self.mask_embed) for i in range(len(self.class_embed) - 1)] + ) + + weight_init.c2_xavier_fill(self.lateral_conv) + weight_init.c2_xavier_fill(self.output_conv) + weight_init.c2_xavier_fill(self.mask_conv) + + self.stuff_dataset_learn_thing = stuff_dataset_learn_thing + self.stuff_prob_thing = stuff_prob_thing + self.test_mask_on = test_mask_on + self.semantic_post_nms = semantic_post_nms + self.panoptic_post_nms = panoptic_post_nms + self.panoptic_configs = panoptic_configs + + self.name_prompt_fusion_type = name_prompt_fusion_type + self.name_prompt_fusion_text = name_prompt_fusion_text + if name_prompt_fusion_type == "learnable": + self.name_prompt_fusion_feature = nn.Parameter( + torch.Tensor(1, 1, self.embed_dim_language) + ) + nn.init.normal_(self.name_prompt_fusion_feature) + elif name_prompt_fusion_type == "zero": + self.name_prompt_fusion_feature = nn.Parameter( + torch.zeros(1, 1, self.embed_dim_language), requires_grad=False + ) + else: + self.name_prompt_fusion_feature = None + + def forward(self, batched_inputs, do_postprocess=True): + if self.training: + if "dataset_id" in batched_inputs[0]: + dataset_ids = [x["dataset_id"] for x in batched_inputs] + assert len(set(dataset_ids)) == 1, dataset_ids + dataset_id = dataset_ids[0] + else: + dataset_id = 0 + else: + dataset_id = self.eval_dataset_id + + if dataset_id >= 0: + prompt = self.dataset_prompts[dataset_id] + elif "prompt" in batched_inputs[0]: + prompt = batched_inputs[0]["prompt"] + else: + prompt = "name" + + if prompt == "expression": + for x in batched_inputs: + if isinstance(x["expressions"], List): + pass + else: + x["expressions"] = [x["expressions"]] + assert all([len(xx) > 0 for xx in x["expressions"]]) + assert all([isinstance(xx, str) for xx in x["expressions"]]) + self.test_topk_per_image = 1 + else: + self.test_topk_per_image = self.select_box_nums_for_evaluation + if self.select_box_nums_for_evaluation_list is not None: + self.test_topk_per_image = self.select_box_nums_for_evaluation_list[dataset_id] + + if self.training and prompt == "phrase": + gt_num = torch.tensor([len(input["instances"]) for input in batched_inputs]).to( + self.device + ) + gt_classes = torch.arange(gt_num.sum()).to(self.device) + gt_cumsum = torch.cumsum(gt_num, dim=0).to(self.device) + for i, input in enumerate(batched_inputs): + if i == 0: + input["instances"].gt_classes = gt_classes[: gt_cumsum[i]] + else: + input["instances"].gt_classes = gt_classes[gt_cumsum[i - 1] : gt_cumsum[i]] + if self.training and prompt == "expression": + gt_num = torch.tensor([len(input["instances"]) for input in batched_inputs]).to( + self.device + ) + gt_classes = torch.arange(gt_num.sum()).to(self.device) + gt_cumsum = torch.cumsum(gt_num, dim=0).to(self.device) + for i, input in enumerate(batched_inputs): + if i == 0: + input["instances"].gt_classes = gt_classes[: gt_cumsum[i]] + else: + input["instances"].gt_classes = gt_classes[gt_cumsum[i - 1] : gt_cumsum[i]] + + if not self.expression_cumulative_gt_class: + input["instances"].gt_classes *= 0 + + if prompt == "text": + texts = [x["text_prompt"] for x in batched_inputs] + text_promp_text_list = [x.strip() for x in ",".join(texts).split(",")] + text_promp_text_list = [x for x in text_promp_text_list if len(x) > 0] + + if any([True if x.count(" ") >= 1 else False for x in text_promp_text_list]): + prompt = "phrase" + else: + prompt = "name" + else: + text_promp_text_list = None + + if prompt == "name": + if text_promp_text_list: + text_list = text_promp_text_list + cache = False + elif dataset_id >= 0: + text_list = get_text_list( + self.metadata_list[dataset_id], self.dataset_entities[dataset_id] + ) + cache = True + else: + text_list = [] + for metadata, dataset_entity in zip(self.metadata_list, self.dataset_entities): + text_list += get_text_list(metadata, dataset_entity) + text_list = text_list[:2500] + cache = True + + from detectron2.data.catalog import MetadataCatalog + + metadata = MetadataCatalog.get("coco_2017_train_panoptic_separated") + text_list = get_text_list(metadata, "thing+stuff") + + outputs_l = self.model_language.forward_text(text_list, cache=cache) + if "last_hidden_state_eot" in outputs_l: + features_l = outputs_l["last_hidden_state_eot"] + else: + features_l = text_utils.reduce_language_feature( + outputs_l["last_hidden_state"], + outputs_l["attention_mask"], + reduce_type=self.text_feature_reduce_type, + ) + attention_mask_l = None + + if ( + dataset_id >= 0 + and self.dataset_entities[dataset_id] == "stuff" + and self.metadata_list[dataset_id].get("stuff_classes")[0] == "things" + and not self.stuff_dataset_learn_thing + ): + features_l[0, :] *= 0 + if self.training: + for i, input in enumerate(batched_inputs): + input["instances"] = input["instances"][input["instances"].gt_classes > 0] + + if self.text_feature_batch_repeat or True: + features_l = features_l.unsqueeze(0).repeat(len(batched_inputs), 1, 1) + else: + features_l = features_l.unsqueeze(1) + + elif prompt == "phrase" or prompt == "expression": + if text_promp_text_list: + text_list = text_promp_text_list + elif prompt == "phrase": + text_list = [phrase for x in batched_inputs for phrase in x["instances"].phrases] + elif prompt == "expression": + text_list = [xx for x in batched_inputs for xx in x["expressions"]] + + outputs_l = self.model_language.forward_text(text_list) + + if self.text_feature_reduce_before_fusion: + if "last_hidden_state_eot" in outputs_l: + features_l = outputs_l["last_hidden_state_eot"] + else: + features_l = text_utils.reduce_language_feature( + outputs_l["last_hidden_state"], + outputs_l["attention_mask"], + reduce_type=self.text_feature_reduce_type, + ) + attention_mask_l = None + + if ( + self.text_feature_bank + and not self.text_feature_bank_reset + and dataset_id >= 0 + and dataset_id < len(self.metadata_list) + ): + features_l = torch.cat( + [features_l, self.features_phrase_bank[dataset_id]], dim=0 + ) + features_l = features_l[ + : max(len(text_list), self.criterion[dataset_id].num_classes) + ] + self.features_phrase_bank[ + dataset_id, : self.criterion[dataset_id].num_classes + ] = features_l[: self.criterion[dataset_id].num_classes] + elif self.text_feature_bank and self.text_feature_bank_reset: + features_l = torch.cat( + [features_l, self.features_phrase_bank[dataset_id] * 0], dim=0 + ) + features_l = features_l[ + : max(len(text_list), self.criterion[dataset_id].num_classes) + ] + + if self.text_feature_batch_repeat: + features_l = features_l.unsqueeze(0).repeat(len(batched_inputs), 1, 1) + else: + features_l = features_l.unsqueeze(1) + else: + features_l = outputs_l["last_hidden_state"] + attention_mask_l = outputs_l["attention_mask"] + + if prompt == "name": + if ( + self.name_prompt_fusion_text is not None + and self.name_prompt_fusion_text[dataset_id] + ): + features_l_fusion = features_l + else: + if self.name_prompt_fusion_feature is not None: + features_l_fusion = self.name_prompt_fusion_feature.repeat( + len(batched_inputs), 1, 1 + ) + else: + features_l_fusion = None + attention_mask_l_fusion = None + elif prompt == "phrase" or prompt == "expression": + features_l_fusion = features_l + attention_mask_l_fusion = attention_mask_l + if self.name_prompt_fusion_feature is not None: + features_l_fusion += 0.0 * self.name_prompt_fusion_feature + + start_time = time.perf_counter() + images = self.preprocess_image(batched_inputs) + + batch_size, _, H, W = images.tensor.shape + img_masks = images.tensor.new_ones(batch_size, H, W) + for image_id, image_size in enumerate(images.image_sizes): + img_masks[image_id, : image_size[0], : image_size[1]] = 0 + self.preprocess_time = time.perf_counter() - start_time + + start_time = time.perf_counter() + features = self.backbone(images.tensor) # output feature dict + self.backbone_time = time.perf_counter() - start_time + + if self.neck is not None: + multi_level_feats = self.neck({f: features[f] for f in self.neck.in_features}) + else: + multi_level_feats = [feat for feat_name, feat in features.items()] + multi_level_masks = [] + multi_level_position_embeddings = [] + spatial_shapes = [] + for feat in multi_level_feats: + multi_level_masks.append( + F.interpolate(img_masks[None], size=feat.shape[-2:]).to(torch.bool).squeeze(0) + ) + multi_level_position_embeddings.append( + self.position_embedding(multi_level_masks[-1]).to(images.tensor.dtype) + ) + + bs, c, h, w = feat.shape + spatial_shape = (h, w) + spatial_shapes.append(spatial_shape) + + if not self.training and "mask_prompt" in batched_inputs[0]: + masks_prompt = [self._move_to_current_device(x["mask_prompt"]) for x in batched_inputs] + masks_prompt = [x.to(self.pixel_mean.dtype) for x in masks_prompt] + masks_prompt = ImageList.from_tensors( + masks_prompt, + self.backbone.size_divisibility, + padding_constraints=self.backbone.padding_constraints, + ) + masks_prompt = masks_prompt.tensor + if masks_prompt.sum() == 0: + masks_prompt[...] = 255 + + multi_level_masks_prompt = [] + for feat in multi_level_feats: + multi_level_masks_prompt.append( + F.interpolate(masks_prompt[None], size=feat.shape[-2:], mode="bilinear") + .to(torch.bool) + .squeeze(0) + ) + for mask_prompt in multi_level_masks_prompt: + print("mask_prompt", mask_prompt.sum(), mask_prompt.size()) + else: + multi_level_masks_prompt = None + + query_embeds = None + if not self.as_two_stage: + query_embeds = self.query_embedding.weight + + start_time = time.perf_counter() + ( + inter_states, + init_reference, + inter_references, + enc_outputs_class, + enc_outputs_coord_unact, + anchors, + memory, + features_l_fusion, + ) = self.transformer( + multi_level_feats, + multi_level_masks, + multi_level_position_embeddings, + query_embeds, + features_l_fusion, + attention_mask_l_fusion, + multi_level_masks_prompt, + ) + self.transformer_time = time.perf_counter() - start_time + + mask_features = self.maskdino_mask_features(memory, features, multi_level_masks) + + if prompt == "name": + features_l = 1.0 * features_l + 0.0 * features_l_fusion + elif prompt == "phrase" or prompt == "expression": + features_l = 0.0 * features_l + 1.0 * features_l_fusion + + if not self.text_feature_reduce_before_fusion: + features_l = text_utils.reduce_language_feature( + features_l, attention_mask_l, reduce_type=self.text_feature_reduce_type + ) + attention_mask_l = None + + if self.text_feature_bank: + features_l = torch.cat( + [features_l, self.features_phrase_bank[dataset_id]], dim=0 + ) + features_l = features_l[: self.criterion[dataset_id].num_classes] + self.features_phrase_bank[ + dataset_id, : self.criterion[dataset_id].num_classes + ] = features_l + elif self.text_feature_bank and not self.training: + features_l = torch.cat( + ( + features_l, + torch.zeros( + (self.criterion[dataset_id].num_classes - 1, features_l.size(1)), + dtype=features_l.dtype, + device=self.device, + ), + ), + dim=0, + ) + + if self.text_feature_batch_repeat: + features_l = features_l.unsqueeze(0).repeat(len(batched_inputs), 1, 1) + else: + features_l = features_l.unsqueeze(1) + + outputs_classes = [] + outputs_coords = [] + outputs_masks = [] + for lvl in range(inter_states.shape[0]): + if lvl == 0: + reference = init_reference + else: + reference = inter_references[lvl - 1] + reference = inverse_sigmoid(reference) + if prompt == "name": + outputs_class = self.class_embed[lvl](inter_states[lvl], features_l) + elif prompt == "phrase" or prompt == "expression": + outputs_class = self.class_embed[lvl](inter_states[lvl], features_l) + else: + outputs_class = self.class_embed[lvl](inter_states[lvl]) + tmp = self.bbox_embed[lvl](inter_states[lvl]) + if reference.shape[-1] == 4: + tmp += reference + else: + assert reference.shape[-1] == 2 + tmp[..., :2] += reference + outputs_coord = tmp.sigmoid() + outputs_classes.append(outputs_class) + outputs_coords.append(outputs_coord) + + if self.aux_mask: + mask_embeds = self.mask_embed[lvl](inter_states[lvl]) + else: + mask_embeds = self.mask_embed(inter_states[lvl]) + outputs_mask = torch.einsum("bqc,bchw->bqhw", mask_embeds, mask_features) + outputs_masks.append(outputs_mask) + outputs_class = torch.stack(outputs_classes) + outputs_coord = torch.stack(outputs_coords) + + outputs_mask = outputs_masks + outputs_mask[-1] += 0.0 * sum(outputs_mask) + + output = { + "pred_logits": outputs_class[-1], + "pred_boxes": outputs_coord[-1], + "pred_masks": outputs_mask[-1], + "init_reference": init_reference, + } + if self.aux_loss: + output["aux_outputs"] = self._set_aux_loss( + outputs_class, + outputs_coord, + outputs_mask, + ) + + if self.as_two_stage: + enc_outputs_coord = enc_outputs_coord_unact.sigmoid() + output["enc_outputs"] = { + "pred_logits": enc_outputs_class, + "pred_boxes": enc_outputs_coord, + "anchors": anchors, + "spatial_shapes": spatial_shapes, + "image_tensor_size": images.tensor.size()[2:], + } + + if ( + self.vis_period > 0 + and self.training + and get_event_storage().iter % self.vis_period == self.vis_period - 1 + ): + self.visualize_training(batched_inputs, output, images, dataset_id) + self.visualize_training_enc_output(batched_inputs, output, images, dataset_id) + + if self.training: + gt_instances = [x["instances"].to(self.device) for x in batched_inputs] + targets = self.prepare_targets(gt_instances) + + loss_dict = self.criterion[dataset_id](output, targets) + + weight_dict = self.criterion[dataset_id].weight_dict + for k in loss_dict.keys(): + if k in weight_dict: + loss_dict[k] *= weight_dict[k] + return loss_dict + else: + + box_cls = output["pred_logits"] + box_pred = output["pred_boxes"] + mask_pred = output["pred_masks"] + + start_time = time.perf_counter() + + iter_func = retry_if_cuda_oom(F.interpolate) + mask_pred = iter_func( + mask_pred, size=images.tensor.size()[2:], mode="bilinear", align_corners=False + ) + + merged_results = [{} for _ in range(box_cls.size(0))] + if self.instance_on and not ( + self.eval_dataset_entity and "thing" not in self.eval_dataset_entity + ): + if dataset_id >= 0 and dataset_id < len(self.metadata_list): + if is_thing_stuff_overlap(self.metadata_list[dataset_id]): + thing_id = self.metadata_list[ + dataset_id + ].thing_dataset_id_to_contiguous_id.values() + thing_id = torch.Tensor(list(thing_id)).to(torch.long).to(self.device) + + detector_box_cls = torch.zeros_like(box_cls) + detector_box_cls += float("-inf") + detector_box_cls[..., thing_id] = box_cls[..., thing_id] + else: + num_thing_classes = len(self.metadata_list[dataset_id].thing_classes) + detector_box_cls = box_cls[..., :num_thing_classes] + else: + detector_box_cls = box_cls + + use_sigmoid = True + detector_results, filter_inds = self.inference( + detector_box_cls, box_pred, images.image_sizes, use_sigmoid=use_sigmoid + ) + + if self.test_mask_on: + detector_mask_preds = [ + x[filter_ind] for x, filter_ind in zip(mask_pred, filter_inds) + ] + + for result, box_mask in zip(detector_results, detector_mask_preds): + box_mask = box_mask.sigmoid() > 0.5 + box_mask = BitMasks(box_mask).crop_and_resize( + result.pred_boxes.tensor.to(box_mask.device), 128 + ) + result.pred_masks = ( + box_mask.to(result.pred_boxes.tensor.device) + .unsqueeze(1) + .to(dtype=torch.float32) + ) + + if do_postprocess: + assert ( + not torch.jit.is_scripting() + ), "Scripting is not supported for postprocess." + detector_results = DeformableDETRSegmVL._postprocess_instance( + detector_results, batched_inputs, images.image_sizes + ) + for merged_result, detector_result in zip(merged_results, detector_results): + merged_result.update(detector_result) + + else: + detector_results = None + + if self.semantic_on and not ( + self.eval_dataset_entity and "stuff" not in self.eval_dataset_entity + ): + + semantic_mask_pred = mask_pred.clone() + semantic_box_cls = get_stuff_score( + box_cls, self.metadata_list[dataset_id], self.dataset_entities[dataset_id] + ) + + if self.semantic_post_nms: + _, filter_inds = self.inference(semantic_box_cls, box_pred, images.image_sizes) + semantic_box_cls = torch.stack( + [x[filter_ind] for x, filter_ind in zip(semantic_box_cls, filter_inds)], + dim=0, + ) + semantic_mask_pred = torch.stack( + [x[filter_ind] for x, filter_ind in zip(semantic_mask_pred, filter_inds)], + dim=0, + ) + + if do_postprocess: + assert ( + not torch.jit.is_scripting() + ), "Scripting is not supported for postprocess." + iter_func = retry_if_cuda_oom(DeformableDETRSegmVL._postprocess_semantic) + semantic_results = iter_func( + semantic_box_cls, semantic_mask_pred, batched_inputs, images + ) + if ( + dataset_id >= 0 + and self.dataset_entities[dataset_id] == "stuff" + and self.metadata_list[dataset_id].get("stuff_classes")[0] == "things" + and self.stuff_prob_thing > 0 + ): + for semantic_result in semantic_results: + semantic_result["sem_seg"][0, ...] = math.log( + self.stuff_prob_thing / (1 - self.stuff_prob_thing) + ) + for merged_result, semantic_result in zip(merged_results, semantic_results): + merged_result.update(semantic_result) + + else: + semantic_results = None + + if self.panoptic_on and not ( + self.eval_dataset_entity and "thing+stuff" not in self.eval_dataset_entity + ): + assert dataset_id >= 0 and dataset_id < len(self.metadata_list) + if do_postprocess: + assert ( + not torch.jit.is_scripting() + ), "Scripting is not supported for postprocess." + if True: + if self.panoptic_post_nms: + _, filter_inds = self.inference(box_cls, box_pred, images.image_sizes) + panoptic_mask_pred = [ + x[filter_ind] for x, filter_ind in zip(mask_pred, filter_inds) + ] + panoptic_box_cls = [ + x[filter_ind] for x, filter_ind in zip(box_cls, filter_inds) + ] + + panoptic_results = DeformableDETRSegmVL._postprocess_panoptic( + panoptic_box_cls, + panoptic_mask_pred, + batched_inputs, + images, + self.metadata_list[dataset_id], + self.panoptic_configs, + ) + else: + panoptic_results = [] + self.combine_overlap_thresh = 0.5 + self.combine_stuff_area_thresh = 4096 + self.combine_instances_score_thresh = 0.5 + for detector_result, semantic_result in zip( + detector_results, semantic_results + ): + detector_r = detector_result["instances"] + sem_seg_r = semantic_result["sem_seg"] + panoptic_r = combine_semantic_and_instance_outputs( + detector_r, + sem_seg_r.argmax(dim=0), + self.combine_overlap_thresh, + self.combine_stuff_area_thresh, + self.combine_instances_score_thresh, + ) + panoptic_results.append({"panoptic_seg": panoptic_r}) + for merged_result, panoptic_result in zip(merged_results, panoptic_results): + merged_result.update(panoptic_result) + + else: + panoptic_results = None + + self.postprocess_time = time.perf_counter() - start_time + + if do_postprocess: + return merged_results + + return detector_results, semantic_results, panoptic_results + + def maskdino_mask_features(self, encode_feats, multi_level_feats, multi_level_masks): + start_idx = sum( + [mask.shape[1] * mask.shape[2] for mask in multi_level_masks[: self.mask_encode_level]] + ) + end_idx = sum( + [ + mask.shape[1] * mask.shape[2] + for mask in multi_level_masks[: self.mask_encode_level + 1] + ] + ) + b, h, w = multi_level_masks[self.mask_encode_level].size() + + encode_feats = encode_feats[:, start_idx:end_idx, :] + encode_feats = encode_feats.permute(0, 2, 1).reshape(b, -1, h, w) + + x = [multi_level_feats[f] for f in self.mask_in_features] + x = x[0] + x = self.lateral_conv(x) + x = x + F.interpolate(encode_feats, size=x.shape[-2:], mode="bilinear", align_corners=False) + x = self.output_conv(x) + mask_features = self.mask_conv(x) + + return mask_features + + @torch.jit.unused + def _set_aux_loss(self, outputs_class, outputs_coord, outputs_mask): + return [ + {"pred_logits": a, "pred_boxes": b, "pred_masks": c} + for a, b, c in zip(outputs_class[:-1], outputs_coord[:-1], outputs_mask[:-1]) + ] + + def inference(self, box_cls, box_pred, image_sizes, use_sigmoid=True): + """ + Arguments: + box_cls (Tensor): tensor of shape (batch_size, num_queries, K). + The tensor predicts the classification probability for each query. + box_pred (Tensor): tensors of shape (batch_size, num_queries, 4). + The tensor predicts 4-vector (x,y,w,h) box + regression values for every queryx + image_sizes (List[torch.Size]): the input image sizes + + Returns: + results (List[Instances]): a list of #images elements. + """ + + if use_sigmoid: + scores = torch.cat( + ( + box_cls.sigmoid(), + torch.zeros((box_cls.size(0), box_cls.size(1), 1), device=self.device), + ), + dim=2, + ) + else: + scores = torch.cat( + ( + box_cls, + torch.zeros((box_cls.size(0), box_cls.size(1), 1), device=self.device), + ), + dim=2, + ) + + boxes = box_cxcywh_to_xyxy(box_pred) + + img_h = torch.tensor([image_size[0] for image_size in image_sizes], device=self.device) + img_w = torch.tensor([image_size[1] for image_size in image_sizes], device=self.device) + scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1) + boxes = boxes * scale_fct[:, None, :] + + boxes = boxes.unbind(0) + scores = scores.unbind(0) + image_shapes = image_sizes + + results, filter_inds = fast_rcnn_inference( + boxes, + scores, + image_shapes, + self.test_score_thresh, + self.test_nms_thresh, + self.test_topk_per_image, + ) + + return results, filter_inds + + def prepare_targets(self, targets): + new_targets = [] + for targets_per_image in targets: + h, w = targets_per_image.image_size + image_size_xyxy = torch.as_tensor([w, h, w, h], dtype=torch.float, device=self.device) + gt_classes = targets_per_image.gt_classes + gt_boxes = targets_per_image.gt_boxes.tensor / image_size_xyxy + gt_boxes = box_xyxy_to_cxcywh(gt_boxes) + + if not targets_per_image.has("gt_masks"): + gt_masks = torch.zeros((0, h, w), dtype=torch.bool) + else: + gt_masks = targets_per_image.gt_masks + + if not isinstance(gt_masks, torch.Tensor): + if isinstance(gt_masks, BitMasks): + gt_masks = gt_masks.tensor + else: + gt_masks = BitMasks.from_polygon_masks(gt_masks, h, w).tensor + + gt_masks = self._move_to_current_device(gt_masks) + gt_masks = ImageList.from_tensors( + [gt_masks], + self.backbone.size_divisibility, + padding_constraints=self.backbone.padding_constraints, + ).tensor.squeeze(0) + + new_targets.append({"labels": gt_classes, "boxes": gt_boxes, "masks": gt_masks}) + + if targets_per_image.has("is_thing"): + new_targets[-1]["is_thing"] = targets_per_image.is_thing + + return new_targets + + def preprocess_image(self, batched_inputs): + images = [self._move_to_current_device(x["image"]) for x in batched_inputs] + images = [x.to(self.pixel_mean.dtype) for x in images] + images = [(x - self.pixel_mean) / self.pixel_std for x in images] + images = ImageList.from_tensors( + images, + self.backbone.size_divisibility, + padding_constraints=self.backbone.padding_constraints, + ) + return images + + @staticmethod + def _postprocess_instance( + instances, batched_inputs: List[Dict[str, torch.Tensor]], image_sizes + ): + """ + Rescale the output instances to the target size. + """ + processed_results = [] + for results_per_image, input_per_image, image_size in zip( + instances, batched_inputs, image_sizes + ): + height = input_per_image.get("height", image_size[0]) + width = input_per_image.get("width", image_size[1]) + r = detector_postprocess(results_per_image, height, width) + processed_results.append({"instances": r.to("cpu")}) + return processed_results + + @staticmethod + def _postprocess_semantic( + mask_clses, + mask_preds, + batched_inputs: List[Dict[str, torch.Tensor]], + images, + pano_temp=0.06, + transform_eval=True, + ): + processed_results = [] + for mask_cls, mask_pred, input_per_image, image_size in zip( + mask_clses, mask_preds, batched_inputs, images.image_sizes + ): + height = input_per_image.get("height", image_size[0]) + width = input_per_image.get("width", image_size[1]) + + T = pano_temp + mask_cls = mask_cls.sigmoid() + + if transform_eval: + mask_cls = F.softmax(mask_cls / T, dim=-1) # already sigmoid + mask_pred = mask_pred.sigmoid() + result = torch.einsum("qc,qhw->chw", mask_cls, mask_pred) + + if True and False: + num_thing_classes = len( + metadata.get( + "thing_classes", + [ + "things", + ], + ) + ) + + result_0 = result[:num_thing_classes, ...] + result_1 = result[num_thing_classes:, ...] + result_0 = result_0.mean(dim=0, keepdim=True) + result = torch.cat([result_0, result_1], dim=0) + + r = sem_seg_postprocess(result, image_size, height, width) + processed_results.append({"sem_seg": r}) + return processed_results + + @staticmethod + def _postprocess_panoptic( + mask_clses, + mask_preds, + batched_inputs: List[Dict[str, torch.Tensor]], + images, + metadata, + panoptic_configs, + ): + prob = panoptic_configs["prob"] + pano_temp = panoptic_configs["pano_temp"] + transform_eval = panoptic_configs["transform_eval"] + object_mask_threshold = panoptic_configs["object_mask_threshold"] + overlap_threshold = panoptic_configs["overlap_threshold"] + + processed_results = [] + for mask_cls, mask_pred, input_per_image, image_size in zip( + mask_clses, mask_preds, batched_inputs, images.image_sizes + ): + height = input_per_image.get("height", image_size[0]) + width = input_per_image.get("width", image_size[1]) + + mask_pred = sem_seg_postprocess(mask_pred, image_size, height, width) + + T = pano_temp + scores, labels = mask_cls.sigmoid().max(-1) + mask_pred = mask_pred.sigmoid() + keep = scores > object_mask_threshold + if transform_eval: + scores, labels = F.softmax(mask_cls.sigmoid() / T, dim=-1).max(-1) + cur_scores = scores[keep] + cur_classes = labels[keep] + cur_masks = mask_pred[keep] + cur_prob_masks = cur_scores.view(-1, 1, 1) * cur_masks + + panoptic_seg = torch.zeros((height, width), dtype=torch.int32, device=cur_masks.device) + segments_info = [] + + current_segment_id = 0 + + if cur_masks.size(0) > 0: + + cur_mask_ids = cur_prob_masks.argmax(0) + + stuff_memory_list = {} + for k in range(cur_classes.shape[0]): + pred_class = cur_classes[k].item() + isthing = pred_class in metadata.thing_dataset_id_to_contiguous_id.values() + mask_area = (cur_mask_ids == k).sum().item() + original_area = (cur_masks[k] >= prob).sum().item() + mask = (cur_mask_ids == k) & (cur_masks[k] >= prob) + + if mask_area > 0 and original_area > 0 and mask.sum().item() > 0: + if mask_area / original_area < overlap_threshold: + continue + + if not isthing: + if int(pred_class) in stuff_memory_list.keys(): + panoptic_seg[mask] = stuff_memory_list[int(pred_class)] + continue + else: + stuff_memory_list[int(pred_class)] = current_segment_id + 1 + + current_segment_id += 1 + panoptic_seg[mask] = current_segment_id + + if not isthing and metadata.get("stuff_classes")[0] == "things": + pred_class = int(pred_class) - len(metadata.thing_classes) + 1 + + segments_info.append( + { + "id": current_segment_id, + "isthing": bool(isthing), + "category_id": int(pred_class), + } + ) + + processed_results.append({"panoptic_seg": (panoptic_seg, segments_info)}) + return processed_results + + @torch.no_grad() + def visualize_training( + self, batched_inputs, output, images, dataset_id, suffix="", do_nms=True + ): + if self.output_dir is None: + return + if self.training: + storage = get_event_storage() + os.makedirs(self.output_dir + "/training", exist_ok=True) + else: + os.makedirs(self.output_dir + "/inference", exist_ok=True) + + pred_logits = output["pred_logits"] + pred_boxes = output["pred_boxes"] + pred_masks = output["pred_masks"] + + thing_classes = self.metadata_list[dataset_id].get("thing_classes", []) + stuff_classes = self.metadata_list[dataset_id].get("stuff_classes", []) + if len(thing_classes) > 0 and len(stuff_classes) > 0 and stuff_classes[0] == "things": + stuff_classes = stuff_classes[1:] + if is_thing_stuff_overlap(self.metadata_list[dataset_id]): + class_names = ( + thing_classes if len(thing_classes) > len(stuff_classes) else stuff_classes + ) + else: + class_names = thing_classes + stuff_classes + + if "instances" in batched_inputs[0] and batched_inputs[0]["instances"].has("phrases"): + class_names = [phrase for x in batched_inputs for phrase in x["instances"].phrases] + [ + "unknown" + ] * 1000 + if "expressions" in batched_inputs[0] and self.expression_cumulative_gt_class: + class_names = [xx for x in batched_inputs for xx in x["expressions"]] + [ + "unknown" + ] * 1000 + + num_thing_classes = len(class_names) + pred_logits = pred_logits[..., :num_thing_classes] + + if pred_masks is not None: + pred_masks = [ + F.interpolate( + pred_mask.float().cpu().unsqueeze(0), + size=images.tensor.size()[2:], + mode="bilinear", + align_corners=False, + ).squeeze(0) + if pred_mask.size(0) > 0 + else pred_mask + for pred_mask in pred_masks + ] + else: + pred_masks = [ + torch.zeros(pred_box.size(0), image_size[0], image_size[1]) + for pred_box, image_size in zip(pred_boxes, images.image_sizes) + ] + + if do_nms: + results, filter_inds = self.inference(pred_logits, pred_boxes, images.image_sizes) + pred_masks = [ + pred_mask[filter_ind.cpu()] + for pred_mask, filter_ind in zip(pred_masks, filter_inds) + ] + for result, pred_mask in zip(results, pred_masks): + result.pred_masks = pred_mask.sigmoid() > 0.5 + else: + results = [] + for pred_logit, pred_box, pred_mask, image_size in zip( + pred_logits, pred_boxes, pred_masks, images.image_sizes + ): + result = Instances(image_size) + result.pred_boxes = Boxes(pred_box) + result.scores = pred_logit[:, 0] + result.pred_classes = torch.zeros( + len(pred_box), dtype=torch.int64, device=pred_logit.device + ) + result.pred_masks = pred_mask.sigmoid() > 0.5 + + results.append(result) + + from detectron2.utils.visualizer import Visualizer + + for input, result in zip(batched_inputs, results): + + if "expressions" in batched_inputs[0] and not self.expression_cumulative_gt_class: + class_names = [xx for xx in input["expressions"]] + ["unknown"] * 1000 + + img = input["image"] + img = convert_image_to_rgb(img.permute(1, 2, 0), self.input_format) + v_gt = Visualizer(img, None) + + if "instances" in input: + labels = [ + "{}".format(class_names[gt_class]) for gt_class in input["instances"].gt_classes + ] + v_gt = v_gt.overlay_instances( + boxes=input["instances"].gt_boxes, + masks=input["instances"].gt_masks + if input["instances"].has("gt_masks") + else None, + labels=labels, + ) + else: + v_gt = v_gt.output + anno_img = v_gt.get_image() + + labels = [ + "{}_{:.0f}%".format(class_names[pred_class], score * 100) + for pred_class, score in zip(result.pred_classes.cpu(), result.scores.cpu()) + ] + v_pred = Visualizer(img, None) + v_pred = v_pred.overlay_instances( + boxes=result.pred_boxes.tensor.clone().detach().cpu().numpy(), + labels=labels, + masks=result.pred_masks[:, : img.shape[0], : img.shape[1]] + .clone() + .detach() + .cpu() + .numpy() + if result.has("pred_masks") + else None, + ) + pred_img = v_pred.get_image() + + vis_img = np.concatenate((anno_img, pred_img), axis=1) + + if result.has("pred_texts"): + labels = [ + "{}".format(text) for text, score in zip(result.pred_texts, result.scores.cpu()) + ] + v_pred = Visualizer(img, None) + v_pred = v_pred.overlay_instances( + boxes=result.pred_boxes.tensor.clone().detach().cpu().numpy(), + labels=labels, + masks=result.pred_masks.clone().detach().cpu().numpy(), + ) + pred_img = v_pred.get_image() + vis_img = np.concatenate((vis_img, pred_img), axis=1) + + basename = os.path.basename(input["file_name"]) + if self.training: + cv2.imwrite( + os.path.join( + self.output_dir, "training", str(storage.iter) + suffix + "_" + basename + ), + vis_img[:, :, ::-1], + ) + else: + cv2.imwrite( + os.path.join(self.output_dir, "inference", suffix + basename), + vis_img[:, :, ::-1], + ) + + @torch.no_grad() + def visualize_training_enc_output(self, batched_inputs, output, images, dataset_id, suffix=""): + if self.output_dir is None: + return + if self.training: + storage = get_event_storage() + os.makedirs(self.output_dir + "/training", exist_ok=True) + else: + os.makedirs(self.output_dir + "/inference", exist_ok=True) + + pred_logits = output["enc_outputs"]["pred_logits"] + pred_boxes = output["enc_outputs"]["pred_boxes"] + + results, filter_inds = self.inference(pred_logits, pred_boxes, images.image_sizes) + + from detectron2.utils.visualizer import Visualizer + + for input, result in zip(batched_inputs, results): + img = input["image"] + img = convert_image_to_rgb(img.permute(1, 2, 0), self.input_format) + v_gt = Visualizer(img, None) + if "instances" in input: + v_gt = v_gt.overlay_instances( + boxes=input["instances"].gt_boxes, + ) + else: + v_gt = v_gt.output + anno_img = v_gt.get_image() + + labels = [ + "{}_{:.0f}%".format(pred_class, score * 100) + for pred_class, score in zip(result.pred_classes.cpu(), result.scores.cpu()) + ] + v_pred = Visualizer(img, None) + v_pred = v_pred.overlay_instances( + boxes=result.pred_boxes.tensor.clone().detach().cpu().numpy(), + labels=labels, + ) + pred_img = v_pred.get_image() + + vis_img = np.concatenate((anno_img, pred_img), axis=1) + + basename = os.path.basename(input["file_name"]) + if self.training: + cv2.imwrite( + os.path.join( + self.output_dir, + "training", + str(storage.iter) + suffix + "_enc_output_" + basename, + ), + vis_img[:, :, ::-1], + ) + else: + cv2.imwrite( + os.path.join(self.output_dir, "inference", suffix + "enc_output_" + basename), + vis_img[:, :, ::-1], + ) + + def set_model_language(self, model_language): + self.model_language = model_language + + +def is_thing_stuff_overlap(metadata): + thing_classes = metadata.get("thing_classes", []) + stuff_classes = metadata.get("stuff_classes", []) + if len(thing_classes) == 0 or len(stuff_classes) == 0: + return False + + if set(thing_classes).issubset(set(stuff_classes)) or set(stuff_classes).issubset( + set(thing_classes) + ): + return True + else: + return False + + +def get_text_list(metadata, dataset_entity): + thing_classes = metadata.get("thing_classes", []) + stuff_classes = metadata.get("stuff_classes", []) + + if dataset_entity == "thing+stuff" and stuff_classes[0] == "things": + text_list = list(thing_classes) + list(stuff_classes[1:]) + + elif dataset_entity == "thing+stuff" and is_thing_stuff_overlap(metadata): + text_list = thing_classes if len(thing_classes) > len(stuff_classes) else stuff_classes + + elif dataset_entity == "thing+stuff": + text_list = list(thing_classes) + list(stuff_classes) + + elif dataset_entity == "stuff": + text_list = list(stuff_classes) + + elif dataset_entity == "thing": + text_list = list(thing_classes) + + return text_list + + +def get_stuff_score(box_cls, metadata, dataset_entity): + thing_classes = metadata.get("thing_classes", []) + stuff_classes = metadata.get("stuff_classes", []) + + semantic_box_cls = box_cls.clone() + + if dataset_entity == "thing+stuff" and stuff_classes[0] == "things": + num_thing_classes = len(thing_classes) + + semantic_box_cls_0 = box_cls[..., :num_thing_classes] + semantic_box_cls_1 = box_cls[..., num_thing_classes:] + semantic_box_cls_0, _ = semantic_box_cls_0.min(dim=2, keepdim=True) + semantic_box_cls = torch.cat([semantic_box_cls_0, semantic_box_cls_1], dim=2) + + if dataset_entity == "thing+stuff" and is_thing_stuff_overlap(metadata): + semantic_box_cls = box_cls.clone() + + if dataset_entity == "stuff": + semantic_box_cls = box_cls.clone() + + return semantic_box_cls diff --git a/ape/modeling/ape_deta/deformable_transformer.py b/ape/modeling/ape_deta/deformable_transformer.py new file mode 100644 index 0000000..29d41a1 --- /dev/null +++ b/ape/modeling/ape_deta/deformable_transformer.py @@ -0,0 +1,619 @@ +import math + +import torch +import torch.nn as nn + +from ape.layers import MultiScaleDeformableAttention +from detrex.layers import ( + FFN, + BaseTransformerLayer, + MultiheadAttention, + TransformerLayerSequence, + box_cxcywh_to_xyxy, +) +from detrex.utils import inverse_sigmoid +from torchvision.ops.boxes import batched_nms + + +class DeformableDetrTransformerEncoder(TransformerLayerSequence): + def __init__( + self, + embed_dim: int = 256, + num_heads: int = 8, + feedforward_dim: int = 1024, + attn_dropout: float = 0.1, + ffn_dropout: float = 0.1, + num_layers: int = 6, + post_norm: bool = False, + num_feature_levels: int = 4, + use_act_checkpoint: bool = False, + ): + super(DeformableDetrTransformerEncoder, self).__init__( + transformer_layers=BaseTransformerLayer( + attn=MultiScaleDeformableAttention( + embed_dim=embed_dim, + num_heads=num_heads, + dropout=attn_dropout, + batch_first=True, + num_levels=num_feature_levels, + ), + ffn=FFN( + embed_dim=embed_dim, + feedforward_dim=feedforward_dim, + output_dim=embed_dim, + num_fcs=2, + ffn_drop=ffn_dropout, + ), + norm=nn.LayerNorm(embed_dim), + operation_order=("self_attn", "norm", "ffn", "norm"), + ), + num_layers=num_layers, + ) + self.embed_dim = self.layers[0].embed_dim + self.pre_norm = self.layers[0].pre_norm + + if post_norm: + self.post_norm_layer = nn.LayerNorm(self.embed_dim) + else: + self.post_norm_layer = None + + if use_act_checkpoint: + from fairscale.nn.checkpoint import checkpoint_wrapper + + for i, layer in enumerate(self.layers): + layer = checkpoint_wrapper(layer) + self.layers[i] = layer + + def forward( + self, + query, + key, + value, + query_pos=None, + key_pos=None, + attn_masks=None, + query_key_padding_mask=None, + key_padding_mask=None, + **kwargs, + ): + + for layer in self.layers: + query = layer( + query, + key, + value, + query_pos=query_pos, + attn_masks=attn_masks, + query_key_padding_mask=query_key_padding_mask, + key_padding_mask=key_padding_mask, + **kwargs, + ) + + if self.post_norm_layer is not None: + query = self.post_norm_layer(query) + return query + + +class DeformableDetrTransformerDecoder(TransformerLayerSequence): + def __init__( + self, + embed_dim: int = 256, + num_heads: int = 8, + feedforward_dim: int = 1024, + attn_dropout: float = 0.1, + ffn_dropout: float = 0.1, + num_layers: int = 6, + return_intermediate: bool = True, + num_feature_levels: int = 4, + use_act_checkpoint: bool = False, + ): + super(DeformableDetrTransformerDecoder, self).__init__( + transformer_layers=BaseTransformerLayer( + attn=[ + MultiheadAttention( + embed_dim=embed_dim, + num_heads=num_heads, + attn_drop=attn_dropout, + batch_first=True, + ), + MultiScaleDeformableAttention( + embed_dim=embed_dim, + num_heads=num_heads, + dropout=attn_dropout, + batch_first=True, + num_levels=num_feature_levels, + ), + ], + ffn=FFN( + embed_dim=embed_dim, + feedforward_dim=feedforward_dim, + output_dim=embed_dim, + ffn_drop=ffn_dropout, + ), + norm=nn.LayerNorm(embed_dim), + operation_order=("self_attn", "norm", "cross_attn", "norm", "ffn", "norm"), + ), + num_layers=num_layers, + ) + self.return_intermediate = return_intermediate + + self.bbox_embed = None + self.class_embed = None + + if use_act_checkpoint: + from fairscale.nn.checkpoint import checkpoint_wrapper + + for i, layer in enumerate(self.layers): + layer = checkpoint_wrapper(layer) + self.layers[i] = layer + + def forward( + self, + query, + key, + value, + query_pos=None, + key_pos=None, + attn_masks=None, + query_key_padding_mask=None, + key_padding_mask=None, + reference_points=None, + valid_ratios=None, + **kwargs, + ): + output = query + + intermediate = [] + intermediate_reference_points = [] + for layer_idx, layer in enumerate(self.layers): + if reference_points.shape[-1] == 4: + reference_points_input = ( + reference_points[:, :, None] + * torch.cat([valid_ratios, valid_ratios], -1)[:, None] + ) + else: + assert reference_points.shape[-1] == 2 + reference_points_input = reference_points[:, :, None] * valid_ratios[:, None] + + output = layer( + output, + key, + value, + query_pos=query_pos, + key_pos=key_pos, + attn_masks=attn_masks, + query_key_padding_mask=query_key_padding_mask, + key_padding_mask=key_padding_mask, + reference_points=reference_points_input, + **kwargs, + ) + + if self.bbox_embed is not None: + tmp = self.bbox_embed[layer_idx](output) + if reference_points.shape[-1] == 4: + new_reference_points = tmp + inverse_sigmoid(reference_points) + new_reference_points = new_reference_points.sigmoid() + else: + assert reference_points.shape[-1] == 2 + new_reference_points = tmp + new_reference_points[..., :2] = tmp[..., :2] + inverse_sigmoid(reference_points) + new_reference_points = new_reference_points.sigmoid() + reference_points = new_reference_points.detach() + + if self.return_intermediate: + intermediate.append(output) + intermediate_reference_points.append(reference_points) + + if self.return_intermediate: + return torch.stack(intermediate), torch.stack(intermediate_reference_points) + + return output, reference_points + + +class DeformableDetrTransformer(nn.Module): + """Transformer module for Deformable DETR + + Args: + encoder (nn.Module): encoder module. + decoder (nn.Module): decoder module. + as_two_stage (bool): whether to use two-stage transformer. Default False. + num_feature_levels (int): number of feature levels. Default 4. + two_stage_num_proposals (int): number of proposals in two-stage transformer. Default 300. + Only used when as_two_stage is True. + """ + + def __init__( + self, + encoder=None, + decoder=None, + num_feature_levels=4, + as_two_stage=False, + two_stage_num_proposals=300, + assign_first_stage=False, + pre_nms_topk=1000, + nms_thresh_enc=0.9, + proposal_ambiguous=0, + ): + super(DeformableDetrTransformer, self).__init__() + self.encoder = encoder + self.decoder = decoder + self.num_feature_levels = num_feature_levels + self.as_two_stage = as_two_stage + self.two_stage_num_proposals = two_stage_num_proposals + self.assign_first_stage = assign_first_stage + self.pre_nms_topk = pre_nms_topk + self.nms_thresh_enc = nms_thresh_enc + self.proposal_ambiguous = proposal_ambiguous + + self.embed_dim = self.encoder.embed_dim + + self.level_embeds = nn.Parameter(torch.Tensor(self.num_feature_levels, self.embed_dim)) + + if self.as_two_stage: + self.enc_output = nn.Linear(self.embed_dim, self.embed_dim) + self.enc_output_norm = nn.LayerNorm(self.embed_dim) + self.pos_trans = nn.Linear(self.embed_dim * 2, self.embed_dim * 2) + self.pos_trans_norm = nn.LayerNorm(self.embed_dim * 2) + self.pix_trans = nn.Linear(self.embed_dim, self.embed_dim) + self.pix_trans_norm = nn.LayerNorm(self.embed_dim) + else: + self.reference_points = nn.Linear(self.embed_dim, 2) + + self.init_weights() + + def init_weights(self): + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + for m in self.modules(): + if isinstance(m, MultiScaleDeformableAttention): + m.init_weights() + if not self.as_two_stage: + nn.init.xavier_normal_(self.reference_points.weight.data, gain=1.0) + nn.init.constant_(self.reference_points.bias.data, 0.0) + nn.init.normal_(self.level_embeds) + + def gen_encoder_output_proposals(self, memory, memory_padding_mask, spatial_shapes): + N, S, C = memory.shape + proposals = [] + _cur = 0 + level_ids = [] + for lvl, (H, W) in enumerate(spatial_shapes): + mask_flatten_ = memory_padding_mask[:, _cur : (_cur + H * W)].view(N, H, W, 1) + valid_H = torch.sum(~mask_flatten_[:, :, 0, 0], 1) + valid_W = torch.sum(~mask_flatten_[:, 0, :, 0], 1) + + grid_y, grid_x = torch.meshgrid( + torch.linspace(0, H - 1, H, dtype=torch.float32, device=memory.device), + torch.linspace(0, W - 1, W, dtype=torch.float32, device=memory.device), + ) + grid = torch.cat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1) + + scale = torch.cat([valid_W.unsqueeze(-1), valid_H.unsqueeze(-1)], 1).view(N, 1, 1, 2) + grid = (grid.unsqueeze(0).expand(N, -1, -1, -1) + 0.5) / scale + wh = torch.ones_like(grid) * 0.05 * (2.0**lvl) + proposal = torch.cat((grid, wh), -1).view(N, -1, 4) + proposals.append(proposal) + _cur += H * W + level_ids.append(grid.new_ones(H * W, dtype=torch.long) * lvl) + + output_proposals = torch.cat(proposals, 1) + output_proposals_valid = ((output_proposals > 0.01) & (output_proposals < 0.99)).all( + -1, keepdim=True + ) + output_proposals = torch.log(output_proposals / (1 - output_proposals)) + output_proposals = output_proposals.masked_fill( + memory_padding_mask.unsqueeze(-1), float("inf") + ) + output_proposals = output_proposals.masked_fill(~output_proposals_valid, float("inf")) + + output_memory = memory + output_memory = output_memory.masked_fill(memory_padding_mask.unsqueeze(-1), float(0)) + output_memory = output_memory.masked_fill(~output_proposals_valid, float(0)) + output_memory = self.enc_output_norm(self.enc_output(output_memory)) + level_ids = torch.cat(level_ids) + output_proposals = output_proposals.to(output_memory.dtype) + return output_memory, output_proposals, level_ids + + @staticmethod + def get_reference_points(spatial_shapes, valid_ratios, device): + """Get the reference points used in decoder. + + Args: + spatial_shapes (Tensor): The shape of all + feature maps, has shape (num_level, 2). + valid_ratios (Tensor): The ratios of valid + points on the feature map, has shape + (bs, num_levels, 2) + device (obj:`device`): The device where + reference_points should be. + + Returns: + Tensor: reference points used in decoder, has \ + shape (bs, num_keys, num_levels, 2). + """ + reference_points_list = [] + for lvl, (H, W) in enumerate(spatial_shapes): + ref_y, ref_x = torch.meshgrid( + torch.linspace(0.5, H - 0.5, H, dtype=torch.float32, device=device), + torch.linspace(0.5, W - 0.5, W, dtype=torch.float32, device=device), + ) + ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * H) + ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * W) + ref = torch.stack((ref_x, ref_y), -1) + reference_points_list.append(ref) + reference_points = torch.cat(reference_points_list, 1) + reference_points = reference_points[:, :, None] * valid_ratios[:, None] + return reference_points + + def get_valid_ratio(self, mask): + """Get the valid ratios of feature maps of all levels.""" + _, H, W = mask.shape + valid_H = torch.sum(~mask[:, :, 0], 1) + valid_W = torch.sum(~mask[:, 0, :], 1) + valid_ratio_h = valid_H.float() / H + valid_ratio_w = valid_W.float() / W + valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1) + return valid_ratio + + def get_proposal_pos_embed(self, proposals, num_pos_feats=128, temperature=10000): + """Get the position embedding of proposal.""" + scale = 2 * math.pi + dim_t = torch.arange(num_pos_feats, dtype=torch.float32, device=proposals.device) + dim_t = temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / num_pos_feats) + proposals = proposals.sigmoid() * scale + pos = proposals[:, :, :, None] / dim_t + pos = torch.stack((pos[:, :, :, 0::2].sin(), pos[:, :, :, 1::2].cos()), dim=4).flatten(2) + return pos + + def forward( + self, + multi_level_feats, + multi_level_masks, + multi_level_pos_embeds, + query_embed, + **kwargs, + ): + assert self.as_two_stage or query_embed is not None + + feat_flatten = [] + mask_flatten = [] + lvl_pos_embed_flatten = [] + spatial_shapes = [] + for lvl, (feat, mask, pos_embed) in enumerate( + zip(multi_level_feats, multi_level_masks, multi_level_pos_embeds) + ): + bs, c, h, w = feat.shape + spatial_shape = (h, w) + spatial_shapes.append(spatial_shape) + + feat = feat.flatten(2).transpose(1, 2) # bs, hw, c + mask = mask.flatten(1) + pos_embed = pos_embed.flatten(2).transpose(1, 2) # bs, hw, c + lvl_pos_embed = pos_embed + self.level_embeds[lvl].view(1, 1, -1) + lvl_pos_embed_flatten.append(lvl_pos_embed) + feat_flatten.append(feat) + mask_flatten.append(mask) + feat_flatten = torch.cat(feat_flatten, 1) + mask_flatten = torch.cat(mask_flatten, 1) + lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1) + spatial_shapes = torch.as_tensor( + spatial_shapes, dtype=torch.long, device=feat_flatten.device + ) + level_start_index = torch.cat( + (spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1]) + ) + valid_ratios = torch.stack([self.get_valid_ratio(m) for m in multi_level_masks], 1) + valid_ratios = valid_ratios.to(feat_flatten.dtype) + + reference_points = self.get_reference_points( + spatial_shapes, valid_ratios, device=feat.device + ) + reference_points = reference_points.to(feat_flatten.dtype) + + memory = self.encoder( + query=feat_flatten, + key=None, + value=None, + query_pos=lvl_pos_embed_flatten, + query_key_padding_mask=mask_flatten, + spatial_shapes=spatial_shapes, + reference_points=reference_points, + level_start_index=level_start_index, + valid_ratios=valid_ratios, + **kwargs, + ) + + bs, _, c = memory.shape + if self.as_two_stage: + output_memory, output_proposals, level_ids = self.gen_encoder_output_proposals( + memory, mask_flatten, spatial_shapes + ) + + enc_outputs_class = self.decoder.class_embed[self.decoder.num_layers](output_memory) + enc_outputs_coord_unact = ( + self.decoder.bbox_embed[self.decoder.num_layers](output_memory) + output_proposals + ) + + if self.proposal_ambiguous: + enc_outputs_class_ambiguous = torch.stack( + [ + enc_outputs_class, + ] + + [x(output_memory) for x in self.decoder.class_embed_ambiguous], + dim=1, + ) + enc_outputs_coord_unact_ambiguous = torch.stack( + [ + enc_outputs_coord_unact, + ] + + [ + x(output_memory) + output_proposals + for x in self.decoder.bbox_embed_ambiguous + ], + dim=1, + ) + + indices = torch.argmax(enc_outputs_class_ambiguous, dim=1, keepdim=True) + enc_outputs_class = torch.gather( + enc_outputs_class_ambiguous, dim=1, index=indices + ).squeeze(dim=1) + enc_outputs_coord_unact = torch.gather( + enc_outputs_coord_unact_ambiguous, dim=1, index=indices.repeat(1, 1, 1, 4) + ).squeeze(dim=1) + + if False: + + enc_outputs_class_3 = self.decoder.class_embed_3(output_memory) + enc_outputs_coord_unact_3 = ( + self.decoder.bbox_embed_3(output_memory) + output_proposals + ) + + enc_outputs_class_2 = self.decoder.class_embed_2(output_memory) + enc_outputs_coord_unact_2 = ( + self.decoder.bbox_embed_2(output_memory) + output_proposals + ) + + enc_outputs_class_1 = enc_outputs_class + enc_outputs_coord_unact_1 = enc_outputs_coord_unact + + enc_outputs_class = torch.stack( + [enc_outputs_class_1, enc_outputs_class_2, enc_outputs_class_3], dim=1 + ) + enc_outputs_coord_unact = torch.stack( + [ + enc_outputs_coord_unact_1, + enc_outputs_coord_unact_2, + enc_outputs_coord_unact_3, + ], + dim=1, + ) + indices = torch.argmax(enc_outputs_class, dim=1, keepdim=True) + enc_outputs_class = torch.gather(enc_outputs_class, dim=1, index=indices).squeeze( + dim=1 + ) + enc_outputs_coord_unact = torch.gather( + enc_outputs_coord_unact, dim=1, index=indices.repeat(1, 1, 1, 4) + ).squeeze(dim=1) + + topk = self.two_stage_num_proposals + + proposal_logit = enc_outputs_class[..., 0] + + if self.assign_first_stage: + proposal_boxes = box_cxcywh_to_xyxy(enc_outputs_coord_unact.sigmoid()).clamp(0, 1) + topk_proposals = [] + for b in range(bs): + prop_boxes_b = proposal_boxes[b] + prop_logits_b = proposal_logit[b] + + pre_nms_topk = self.pre_nms_topk + pre_nms_inds = [] + for lvl in range(len(spatial_shapes)): + lvl_mask = level_ids == lvl + pre_nms_inds.append( + torch.topk( + prop_logits_b.sigmoid() * lvl_mask, + min(pre_nms_topk, prop_logits_b.size(0)), + )[1] + ) + pre_nms_inds = torch.cat(pre_nms_inds) + + post_nms_inds = batched_nms( + prop_boxes_b[pre_nms_inds], + prop_logits_b[pre_nms_inds], + level_ids[pre_nms_inds], + self.nms_thresh_enc, + ) + keep_inds = pre_nms_inds[post_nms_inds] + + if len(keep_inds) < self.two_stage_num_proposals: + print( + f"[WARNING] nms proposals ({len(keep_inds)}) < {self.two_stage_num_proposals}, running naive topk" + ) + keep_inds = torch.topk( + proposal_logit[b], min(topk, proposal_logit[b].size(0)) + )[1] + + q_per_l = topk // len(spatial_shapes) + is_level_ordered = ( + level_ids[keep_inds][None] + == torch.arange(len(spatial_shapes), device=level_ids.device)[:, None] + ) # LS + keep_inds_mask = is_level_ordered & ( + is_level_ordered.cumsum(1) <= q_per_l + ) # LS + keep_inds_mask = keep_inds_mask.any(0) # S + + if keep_inds_mask.sum() < topk: + num_to_add = topk - keep_inds_mask.sum() + pad_inds = (~keep_inds_mask).nonzero()[:num_to_add] + keep_inds_mask[pad_inds] = True + + keep_inds_topk = keep_inds[keep_inds_mask] + topk_proposals.append(keep_inds_topk) + topk_proposals = torch.stack(topk_proposals) + else: + topk_proposals = torch.topk(proposal_logit, topk, dim=1)[1] + + topk_coords_unact = torch.gather( + enc_outputs_coord_unact, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4) + ) + topk_coords_unact = topk_coords_unact.detach() + reference_points = topk_coords_unact.sigmoid() + init_reference_out = reference_points + pos_trans_out = self.pos_trans_norm( + self.pos_trans( + self.get_proposal_pos_embed(topk_coords_unact).to(topk_coords_unact.dtype) + ) + ) + query_pos, query = torch.split(pos_trans_out, c, dim=2) + + topk_feats = torch.stack( + [output_memory[b][topk_proposals[b]] for b in range(bs)] + ).detach() + query = query + self.pix_trans_norm(self.pix_trans(topk_feats)) + else: + query_pos, query = torch.split(query_embed, c, dim=1) + query_pos = query_pos.unsqueeze(0).expand(bs, -1, -1) + query = query.unsqueeze(0).expand(bs, -1, -1) + reference_points = self.reference_points(query_pos).sigmoid() + init_reference_out = reference_points + + if self.proposal_ambiguous and False: + enc_outputs_class = torch.stack([enc_outputs_class_1, enc_outputs_class_2], dim=1) + enc_outputs_coord_unact = torch.stack( + [enc_outputs_coord_unact_1, enc_outputs_coord_unact_2], dim=1 + ) + + indices = torch.argmax(enc_outputs_class, dim=1, keepdim=True) + enc_outputs_class = torch.gather(enc_outputs_class, dim=1, index=indices).squeeze(dim=1) + enc_outputs_coord_unact = torch.gather( + enc_outputs_coord_unact, dim=1, index=indices.repeat(1, 1, 1, 4) + ).squeeze(dim=1) + + inter_states, inter_references = self.decoder( + query=query, # bs, num_queries, embed_dims + key=None, # bs, num_tokens, embed_dims + value=memory, # bs, num_tokens, embed_dims + query_pos=query_pos, + key_padding_mask=mask_flatten, # bs, num_tokens + reference_points=reference_points, # num_queries, 4 + spatial_shapes=spatial_shapes, # nlvl, 2 + level_start_index=level_start_index, # nlvl + valid_ratios=valid_ratios, # bs, nlvl, 2 + **kwargs, + ) + + inter_references_out = inter_references + if self.as_two_stage: + return ( + inter_states, + init_reference_out, + inter_references_out, + enc_outputs_class, + enc_outputs_coord_unact, + output_proposals.sigmoid(), + memory, + ) + return inter_states, init_reference_out, inter_references_out, None, None, None, memory diff --git a/ape/modeling/ape_deta/deformable_transformer_vl.py b/ape/modeling/ape_deta/deformable_transformer_vl.py new file mode 100644 index 0000000..e642746 --- /dev/null +++ b/ape/modeling/ape_deta/deformable_transformer_vl.py @@ -0,0 +1,674 @@ +import copy +import math + +import torch +import torch.nn as nn + +from ape.layers import MultiScaleDeformableAttention +from detrex.layers import ( + FFN, + BaseTransformerLayer, + MultiheadAttention, + TransformerLayerSequence, + box_cxcywh_to_xyxy, +) +from detrex.utils import inverse_sigmoid +from torchvision.ops.boxes import batched_nms + + +class DeformableDetrTransformerEncoderVL(TransformerLayerSequence): + def __init__( + self, + embed_dim: int = 256, + num_heads: int = 8, + feedforward_dim: int = 1024, + attn_dropout: float = 0.1, + ffn_dropout: float = 0.1, + num_layers: int = 6, + post_norm: bool = False, + num_feature_levels: int = 4, + vl_layer=None, + use_act_checkpoint=False, + ): + super(DeformableDetrTransformerEncoderVL, self).__init__( + transformer_layers=BaseTransformerLayer( + attn=MultiScaleDeformableAttention( + embed_dim=embed_dim, + num_heads=num_heads, + dropout=attn_dropout, + batch_first=True, + num_levels=num_feature_levels, + ), + ffn=FFN( + embed_dim=embed_dim, + feedforward_dim=feedforward_dim, + output_dim=embed_dim, + num_fcs=2, + ffn_drop=ffn_dropout, + ), + norm=nn.LayerNorm(embed_dim), + operation_order=("self_attn", "norm", "ffn", "norm"), + ), + num_layers=num_layers, + ) + self.embed_dim = self.layers[0].embed_dim + self.pre_norm = self.layers[0].pre_norm + + if post_norm: + self.post_norm_layer = nn.LayerNorm(self.embed_dim) + else: + self.post_norm_layer = None + + self.vl_layers = nn.ModuleList([copy.deepcopy(vl_layer) for _ in range(num_layers)]) + + if use_act_checkpoint: + from fairscale.nn.checkpoint import checkpoint_wrapper + + for i, layer in enumerate(self.layers): + layer = checkpoint_wrapper(layer) + self.layers[i] = layer + + def forward( + self, + query, + key, + value, + query_l, + attention_mask_l, + query_pos=None, + key_pos=None, + attn_masks=None, + query_key_padding_mask=None, + key_padding_mask=None, + **kwargs, + ): + + for vl_layer, layer in zip(self.vl_layers, self.layers): + if vl_layer is not None and query_l is not None: + query, query_l = vl_layer( + query, + query_l, + attention_mask_v=query_key_padding_mask, + attention_mask_l=attention_mask_l, + ) + query = layer( + query, + key, + value, + query_pos=query_pos, + attn_masks=attn_masks, + query_key_padding_mask=query_key_padding_mask, + key_padding_mask=key_padding_mask, + **kwargs, + ) + + if self.post_norm_layer is not None: + query = self.post_norm_layer(query) + if query_l is None: + query_l = sum([_.sum() for _ in self.vl_layers.parameters()]) * 0.0 + return query, query_l + + +class DeformableDetrTransformerDecoderVL(TransformerLayerSequence): + def __init__( + self, + embed_dim: int = 256, + num_heads: int = 8, + feedforward_dim: int = 1024, + attn_dropout: float = 0.1, + ffn_dropout: float = 0.1, + num_layers: int = 6, + return_intermediate: bool = True, + num_feature_levels: int = 4, + use_act_checkpoint: bool = False, + look_forward_twice: bool = False, + ): + super(DeformableDetrTransformerDecoderVL, self).__init__( + transformer_layers=BaseTransformerLayer( + attn=[ + MultiheadAttention( + embed_dim=embed_dim, + num_heads=num_heads, + attn_drop=attn_dropout, + batch_first=True, + ), + MultiScaleDeformableAttention( + embed_dim=embed_dim, + num_heads=num_heads, + dropout=attn_dropout, + batch_first=True, + num_levels=num_feature_levels, + ), + ], + ffn=FFN( + embed_dim=embed_dim, + feedforward_dim=feedforward_dim, + output_dim=embed_dim, + ffn_drop=ffn_dropout, + ), + norm=nn.LayerNorm(embed_dim), + operation_order=("self_attn", "norm", "cross_attn", "norm", "ffn", "norm"), + ), + num_layers=num_layers, + ) + self.return_intermediate = return_intermediate + + self.bbox_embed = None + self.class_embed = None + + if use_act_checkpoint: + from fairscale.nn.checkpoint import checkpoint_wrapper + + for i, layer in enumerate(self.layers): + layer = checkpoint_wrapper(layer) + self.layers[i] = layer + + self.look_forward_twice = look_forward_twice + + def forward( + self, + query, + key, + value, + query_pos=None, + key_pos=None, + attn_masks=None, + query_key_padding_mask=None, + key_padding_mask=None, + reference_points=None, + valid_ratios=None, + **kwargs, + ): + output = query + + intermediate = [] + intermediate_reference_points = [] + for layer_idx, layer in enumerate(self.layers): + if reference_points.shape[-1] == 4: + reference_points_input = ( + reference_points[:, :, None] + * torch.cat([valid_ratios, valid_ratios], -1)[:, None] + ) + else: + assert reference_points.shape[-1] == 2 + reference_points_input = reference_points[:, :, None] * valid_ratios[:, None] + + output = layer( + output, + key, + value, + query_pos=query_pos, + key_pos=key_pos, + attn_masks=attn_masks, + query_key_padding_mask=query_key_padding_mask, + key_padding_mask=key_padding_mask, + reference_points=reference_points_input, + **kwargs, + ) + + if self.bbox_embed is not None: + tmp = self.bbox_embed[layer_idx](output) + if reference_points.shape[-1] == 4: + new_reference_points = tmp + inverse_sigmoid(reference_points) + new_reference_points = new_reference_points.sigmoid() + else: + assert reference_points.shape[-1] == 2 + new_reference_points = tmp + new_reference_points[..., :2] = tmp[..., :2] + inverse_sigmoid(reference_points) + new_reference_points = new_reference_points.sigmoid() + reference_points = new_reference_points.detach() + + if self.return_intermediate: + intermediate.append(output) + intermediate_reference_points.append( + new_reference_points if self.look_forward_twice else reference_points + ) + + if self.return_intermediate: + return torch.stack(intermediate), torch.stack(intermediate_reference_points) + + return output, reference_points + + +class DeformableDetrTransformerVL(nn.Module): + """Transformer module for Deformable DETR + + Args: + encoder (nn.Module): encoder module. + decoder (nn.Module): decoder module. + as_two_stage (bool): whether to use two-stage transformer. Default False. + num_feature_levels (int): number of feature levels. Default 4. + two_stage_num_proposals (int): number of proposals in two-stage transformer. Default 300. + Only used when as_two_stage is True. + """ + + def __init__( + self, + encoder=None, + decoder=None, + num_feature_levels=4, + as_two_stage=False, + two_stage_num_proposals=300, + assign_first_stage=False, + pre_nms_topk=1000, + nms_thresh_enc=0.9, + proposal_ambiguous=0, + ): + super(DeformableDetrTransformerVL, self).__init__() + self.encoder = encoder + self.decoder = decoder + self.num_feature_levels = num_feature_levels + self.as_two_stage = as_two_stage + self.two_stage_num_proposals = two_stage_num_proposals + self.assign_first_stage = assign_first_stage + self.pre_nms_topk = pre_nms_topk + self.nms_thresh_enc = nms_thresh_enc + self.proposal_ambiguous = proposal_ambiguous + + self.embed_dim = self.encoder.embed_dim + + self.level_embeds = nn.Parameter(torch.Tensor(self.num_feature_levels, self.embed_dim)) + + if self.as_two_stage: + self.enc_output = nn.Linear(self.embed_dim, self.embed_dim) + self.enc_output_norm = nn.LayerNorm(self.embed_dim) + self.pos_trans = nn.Linear(self.embed_dim * 2, self.embed_dim * 2) + self.pos_trans_norm = nn.LayerNorm(self.embed_dim * 2) + self.pix_trans = nn.Linear(self.embed_dim, self.embed_dim) + self.pix_trans_norm = nn.LayerNorm(self.embed_dim) + else: + self.reference_points = nn.Linear(self.embed_dim, 2) + + self.init_weights() + + def init_weights(self): + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + for m in self.modules(): + if isinstance(m, MultiScaleDeformableAttention): + m.init_weights() + if not self.as_two_stage: + nn.init.xavier_normal_(self.reference_points.weight.data, gain=1.0) + nn.init.constant_(self.reference_points.bias.data, 0.0) + nn.init.normal_(self.level_embeds) + + def gen_encoder_output_proposals( + self, memory, memory_padding_mask, spatial_shapes, mask_prompt_flatten + ): + N, S, C = memory.shape + proposals = [] + _cur = 0 + level_ids = [] + for lvl, (H, W) in enumerate(spatial_shapes): + mask_flatten_ = memory_padding_mask[:, _cur : (_cur + H * W)].view(N, H, W, 1) + valid_H = torch.sum(~mask_flatten_[:, :, 0, 0], 1) + valid_W = torch.sum(~mask_flatten_[:, 0, :, 0], 1) + + grid_y, grid_x = torch.meshgrid( + torch.linspace(0, H - 1, H, dtype=torch.float32, device=memory.device), + torch.linspace(0, W - 1, W, dtype=torch.float32, device=memory.device), + ) + grid = torch.cat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1) + + scale = torch.cat([valid_W.unsqueeze(-1), valid_H.unsqueeze(-1)], 1).view(N, 1, 1, 2) + grid = (grid.unsqueeze(0).expand(N, -1, -1, -1) + 0.5) / scale + wh = torch.ones_like(grid) * 0.05 * (2.0**lvl) + proposal = torch.cat((grid, wh), -1).view(N, -1, 4) + proposals.append(proposal) + _cur += H * W + level_ids.append(grid.new_ones(H * W, dtype=torch.long) * lvl) + + output_proposals = torch.cat(proposals, 1) + output_proposals_valid = ((output_proposals > 0.01) & (output_proposals < 0.99)).all( + -1, keepdim=True + ) + output_proposals = torch.log(output_proposals / (1 - output_proposals)) + output_proposals = output_proposals.masked_fill( + memory_padding_mask.unsqueeze(-1), float("inf") + ) + output_proposals = output_proposals.masked_fill(~output_proposals_valid, float("inf")) + if mask_prompt_flatten is not None: + output_proposals = output_proposals.masked_fill( + ~mask_prompt_flatten.unsqueeze(-1), float("inf") + ) + + output_memory = memory + output_memory = output_memory.masked_fill(memory_padding_mask.unsqueeze(-1), float(0)) + output_memory = output_memory.masked_fill(~output_proposals_valid, float(0)) + if mask_prompt_flatten is not None: + output_memory = output_memory.masked_fill(~mask_prompt_flatten.unsqueeze(-1), float(0)) + output_memory = self.enc_output_norm(self.enc_output(output_memory)) + level_ids = torch.cat(level_ids) + output_proposals = output_proposals.to(output_memory.dtype) + return output_memory, output_proposals, level_ids + + @staticmethod + def get_reference_points(spatial_shapes, valid_ratios, device): + """Get the reference points used in decoder. + + Args: + spatial_shapes (Tensor): The shape of all + feature maps, has shape (num_level, 2). + valid_ratios (Tensor): The ratios of valid + points on the feature map, has shape + (bs, num_levels, 2) + device (obj:`device`): The device where + reference_points should be. + + Returns: + Tensor: reference points used in decoder, has \ + shape (bs, num_keys, num_levels, 2). + """ + reference_points_list = [] + for lvl, (H, W) in enumerate(spatial_shapes): + ref_y, ref_x = torch.meshgrid( + torch.linspace(0.5, H - 0.5, H, dtype=torch.float32, device=device), + torch.linspace(0.5, W - 0.5, W, dtype=torch.float32, device=device), + ) + ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * H) + ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * W) + ref = torch.stack((ref_x, ref_y), -1) + reference_points_list.append(ref) + reference_points = torch.cat(reference_points_list, 1) + reference_points = reference_points[:, :, None] * valid_ratios[:, None] + return reference_points + + def get_valid_ratio(self, mask): + """Get the valid ratios of feature maps of all levels.""" + _, H, W = mask.shape + valid_H = torch.sum(~mask[:, :, 0], 1) + valid_W = torch.sum(~mask[:, 0, :], 1) + valid_ratio_h = valid_H.float() / H + valid_ratio_w = valid_W.float() / W + valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1) + return valid_ratio + + def get_proposal_pos_embed(self, proposals, num_pos_feats=128, temperature=10000): + """Get the position embedding of proposal.""" + scale = 2 * math.pi + dim_t = torch.arange(num_pos_feats, dtype=torch.float32, device=proposals.device) + dim_t = temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / num_pos_feats) + proposals = proposals.sigmoid() * scale + pos = proposals[:, :, :, None] / dim_t + pos = torch.stack((pos[:, :, :, 0::2].sin(), pos[:, :, :, 1::2].cos()), dim=4).flatten(2) + return pos + + def forward( + self, + multi_level_feats, + multi_level_masks, + multi_level_pos_embeds, + query_embed, + query_l, + attention_mask_l, + multi_level_masks_prompt, + **kwargs, + ): + assert self.as_two_stage or query_embed is not None + + feat_flatten = [] + mask_flatten = [] + lvl_pos_embed_flatten = [] + spatial_shapes = [] + for lvl, (feat, mask, pos_embed) in enumerate( + zip(multi_level_feats, multi_level_masks, multi_level_pos_embeds) + ): + bs, c, h, w = feat.shape + spatial_shape = (h, w) + spatial_shapes.append(spatial_shape) + + feat = feat.flatten(2).transpose(1, 2) # bs, hw, c + mask = mask.flatten(1) + pos_embed = pos_embed.flatten(2).transpose(1, 2) # bs, hw, c + lvl_pos_embed = pos_embed + self.level_embeds[lvl].view(1, 1, -1) + lvl_pos_embed_flatten.append(lvl_pos_embed) + feat_flatten.append(feat) + mask_flatten.append(mask) + feat_flatten = torch.cat(feat_flatten, 1) + mask_flatten = torch.cat(mask_flatten, 1) + lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1) + spatial_shapes = torch.as_tensor( + spatial_shapes, dtype=torch.long, device=feat_flatten.device + ) + level_start_index = torch.cat( + (spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1]) + ) + valid_ratios = torch.stack([self.get_valid_ratio(m) for m in multi_level_masks], 1) + valid_ratios = valid_ratios.to(feat_flatten.dtype) + + if multi_level_masks_prompt is not None: + mask_prompt_flatten = [] + for mask_prompt in multi_level_masks_prompt: + mask_prompt = mask_prompt.flatten(1) + mask_prompt_flatten.append(mask_prompt) + mask_prompt_flatten = torch.cat(mask_prompt_flatten, 1) + else: + mask_prompt_flatten = None + + reference_points = self.get_reference_points( + spatial_shapes, valid_ratios, device=feat.device + ) + reference_points = reference_points.to(feat_flatten.dtype) + + memory, query_l = self.encoder( + query=feat_flatten, + key=None, + value=None, + query_l=query_l, + attention_mask_l=attention_mask_l, + query_pos=lvl_pos_embed_flatten, + query_key_padding_mask=mask_flatten, + spatial_shapes=spatial_shapes, + reference_points=reference_points, + level_start_index=level_start_index, + valid_ratios=valid_ratios, + **kwargs, + ) + + bs, _, c = memory.shape + if self.as_two_stage: + output_memory, output_proposals, level_ids = self.gen_encoder_output_proposals( + memory, + mask_flatten, + spatial_shapes, + mask_prompt_flatten, + ) + + enc_outputs_class = self.decoder.class_embed[self.decoder.num_layers](output_memory) + enc_outputs_coord_unact = ( + self.decoder.bbox_embed[self.decoder.num_layers](output_memory) + output_proposals + ) + + if self.proposal_ambiguous: + enc_outputs_class_ambiguous = torch.stack( + [ + enc_outputs_class, + ] + + [x(output_memory) for x in self.decoder.class_embed_ambiguous], + dim=1, + ) + enc_outputs_coord_unact_ambiguous = torch.stack( + [ + enc_outputs_coord_unact, + ] + + [ + x(output_memory) + output_proposals + for x in self.decoder.bbox_embed_ambiguous + ], + dim=1, + ) + + indices = torch.argmax(enc_outputs_class_ambiguous, dim=1, keepdim=True) + enc_outputs_class = torch.gather( + enc_outputs_class_ambiguous, dim=1, index=indices + ).squeeze(dim=1) + enc_outputs_coord_unact = torch.gather( + enc_outputs_coord_unact_ambiguous, dim=1, index=indices.repeat(1, 1, 1, 4) + ).squeeze(dim=1) + + if False: + + enc_outputs_class_3 = self.decoder.class_embed_3(output_memory) + enc_outputs_coord_unact_3 = ( + self.decoder.bbox_embed_3(output_memory) + output_proposals + ) + + enc_outputs_class_2 = self.decoder.class_embed_2(output_memory) + enc_outputs_coord_unact_2 = ( + self.decoder.bbox_embed_2(output_memory) + output_proposals + ) + + enc_outputs_class_1 = enc_outputs_class + enc_outputs_coord_unact_1 = enc_outputs_coord_unact + + enc_outputs_class = torch.stack( + [enc_outputs_class_1, enc_outputs_class_2, enc_outputs_class_3], dim=1 + ) + enc_outputs_coord_unact = torch.stack( + [ + enc_outputs_coord_unact_1, + enc_outputs_coord_unact_2, + enc_outputs_coord_unact_3, + ], + dim=1, + ) + indices = torch.argmax(enc_outputs_class, dim=1, keepdim=True) + enc_outputs_class = torch.gather(enc_outputs_class, dim=1, index=indices).squeeze( + dim=1 + ) + enc_outputs_coord_unact = torch.gather( + enc_outputs_coord_unact, dim=1, index=indices.repeat(1, 1, 1, 4) + ).squeeze(dim=1) + + topk = self.two_stage_num_proposals + + proposal_logit = enc_outputs_class[..., 0] + + if self.assign_first_stage: + proposal_boxes = box_cxcywh_to_xyxy(enc_outputs_coord_unact.sigmoid()).clamp(0, 1) + topk_proposals = [] + for b in range(bs): + prop_boxes_b = proposal_boxes[b] + prop_logits_b = proposal_logit[b] + + pre_nms_topk = self.pre_nms_topk + pre_nms_inds = [] + for lvl in range(len(spatial_shapes)): + lvl_mask = level_ids == lvl + pre_nms_inds.append( + torch.topk( + prop_logits_b.sigmoid() * lvl_mask, + min(pre_nms_topk, prop_logits_b.size(0)), + )[1] + ) + pre_nms_inds = torch.cat(pre_nms_inds) + + post_nms_inds = batched_nms( + prop_boxes_b[pre_nms_inds], + prop_logits_b[pre_nms_inds], + level_ids[pre_nms_inds], + self.nms_thresh_enc, + ) + keep_inds = pre_nms_inds[post_nms_inds] + + if len(keep_inds) < self.two_stage_num_proposals: + print( + f"[WARNING] nms proposals ({len(keep_inds)}) < {self.two_stage_num_proposals}, running naive topk" + ) + keep_inds = torch.topk( + proposal_logit[b], min(topk, proposal_logit[b].size(0)) + )[1] + + q_per_l = topk // len(spatial_shapes) + is_level_ordered = ( + level_ids[keep_inds][None] + == torch.arange(len(spatial_shapes), device=level_ids.device)[:, None] + ) # LS + keep_inds_mask = is_level_ordered & ( + is_level_ordered.cumsum(1) <= q_per_l + ) # LS + keep_inds_mask = keep_inds_mask.any(0) # S + + if keep_inds_mask.sum() < topk: + num_to_add = topk - keep_inds_mask.sum() + pad_inds = (~keep_inds_mask).nonzero()[:num_to_add] + keep_inds_mask[pad_inds] = True + + keep_inds_topk = keep_inds[keep_inds_mask] + topk_proposals.append(keep_inds_topk) + topk_proposals = torch.stack(topk_proposals) + else: + topk_proposals = torch.topk(proposal_logit, topk, dim=1)[1] + + topk_coords_unact = torch.gather( + enc_outputs_coord_unact, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4) + ) + topk_coords_unact = topk_coords_unact.detach() + reference_points = topk_coords_unact.sigmoid() + init_reference_out = reference_points + pos_trans_out = self.pos_trans_norm( + self.pos_trans( + self.get_proposal_pos_embed(topk_coords_unact).to(topk_coords_unact.dtype) + ) + ) + query_pos, query = torch.split(pos_trans_out, c, dim=2) + + topk_feats = torch.stack( + [output_memory[b][topk_proposals[b]] for b in range(bs)] + ).detach() + query = query + self.pix_trans_norm(self.pix_trans(topk_feats)) + else: + query_pos, query = torch.split(query_embed, c, dim=1) + query_pos = query_pos.unsqueeze(0).expand(bs, -1, -1) + query = query.unsqueeze(0).expand(bs, -1, -1) + reference_points = self.reference_points(query_pos).sigmoid() + init_reference_out = reference_points + + if self.proposal_ambiguous and False: + enc_outputs_class = torch.stack([enc_outputs_class_1, enc_outputs_class_2], dim=1) + enc_outputs_coord_unact = torch.stack( + [enc_outputs_coord_unact_1, enc_outputs_coord_unact_2], dim=1 + ) + + indices = torch.argmax(enc_outputs_class, dim=1, keepdim=True) + enc_outputs_class = torch.gather(enc_outputs_class, dim=1, index=indices).squeeze(dim=1) + enc_outputs_coord_unact = torch.gather( + enc_outputs_coord_unact, dim=1, index=indices.repeat(1, 1, 1, 4) + ).squeeze(dim=1) + + inter_states, inter_references = self.decoder( + query=query, # bs, num_queries, embed_dims + key=None, # bs, num_tokens, embed_dims + value=memory, # bs, num_tokens, embed_dims + query_pos=query_pos, + key_padding_mask=mask_flatten, # bs, num_tokens + reference_points=reference_points, # num_queries, 4 + spatial_shapes=spatial_shapes, # nlvl, 2 + level_start_index=level_start_index, # nlvl + valid_ratios=valid_ratios, # bs, nlvl, 2 + **kwargs, + ) + + inter_references_out = inter_references + if self.as_two_stage: + return ( + inter_states, + init_reference_out, + inter_references_out, + enc_outputs_class, + enc_outputs_coord_unact, + output_proposals.sigmoid(), + memory, + query_l, + ) + return ( + inter_states, + init_reference_out, + inter_references_out, + None, + None, + None, + memory, + query_l, + ) diff --git a/ape/modeling/ape_deta/fast_rcnn.py b/ape/modeling/ape_deta/fast_rcnn.py new file mode 100644 index 0000000..239e396 --- /dev/null +++ b/ape/modeling/ape_deta/fast_rcnn.py @@ -0,0 +1,201 @@ +import warnings +from typing import List, Tuple + +import torch + +from detectron2.layers import batched_nms +from detectron2.structures import Boxes, Instances + +__all__ = [ + "fast_rcnn_inference", +] + + +""" +Shape shorthand in this module: + + N: number of images in the minibatch + R: number of ROIs, combined over all images, in the minibatch + Ri: number of ROIs in image i + K: number of foreground classes. E.g.,there are 80 foreground classes in COCO. + +Naming convention: + + deltas: refers to the 4-d (dx, dy, dw, dh) deltas that parameterize the box2box + transform (see :class:`box_regression.Box2BoxTransform`). + + pred_class_logits: predicted class scores in [-inf, +inf]; use + softmax(pred_class_logits) to estimate P(class). + + gt_classes: ground-truth classification labels in [0, K], where [0, K) represent + foreground object classes and K represents the background class. + + pred_proposal_deltas: predicted box2box transform deltas for transforming proposals + to detection box predictions. + + gt_proposal_deltas: ground-truth box2box transform deltas +""" + + +def fast_rcnn_inference( + boxes: List[torch.Tensor], + scores: List[torch.Tensor], + image_shapes: List[Tuple[int, int]], + score_thresh: float, + nms_thresh: float, + topk_per_image: int, + use_soft_nms: bool = False, + soft_nms_method: str = "linear", + soft_nms_iou_threshold: float = 0.3, + soft_nms_sigma: float = 0.5, + soft_nms_class_wise: bool = False, +): + """ + Call `fast_rcnn_inference_single_image` for all images. + + Args: + boxes (list[Tensor]): A list of Tensors of predicted class-specific or class-agnostic + boxes for each image. Element i has shape (Ri, K * 4) if doing + class-specific regression, or (Ri, 4) if doing class-agnostic + regression, where Ri is the number of predicted objects for image i. + This is compatible with the output of :meth:`FastRCNNOutputLayers.predict_boxes`. + scores (list[Tensor]): A list of Tensors of predicted class scores for each image. + Element i has shape (Ri, K + 1), where Ri is the number of predicted objects + for image i. Compatible with the output of :meth:`FastRCNNOutputLayers.predict_probs`. + image_shapes (list[tuple]): A list of (width, height) tuples for each image in the batch. + score_thresh (float): Only return detections with a confidence score exceeding this + threshold. + nms_thresh (float): The threshold to use for box non-maximum suppression. Value in [0, 1]. + topk_per_image (int): The number of top scoring detections to return. Set < 0 to return + all detections. + + Returns: + instances: (list[Instances]): A list of N instances, one for each image in the batch, + that stores the topk most confidence detections. + kept_indices: (list[Tensor]): A list of 1D tensor of length of N, each element indicates + the corresponding boxes/scores index in [0, Ri) from the input, for image i. + """ + result_per_image = [ + fast_rcnn_inference_single_image( + boxes_per_image, + scores_per_image, + image_shape, + score_thresh, + nms_thresh, + topk_per_image, + use_soft_nms=use_soft_nms, + soft_nms_method=soft_nms_method, + soft_nms_iou_threshold=soft_nms_iou_threshold, + soft_nms_sigma=soft_nms_sigma, + soft_nms_class_wise=soft_nms_class_wise, + ) + for scores_per_image, boxes_per_image, image_shape in zip(scores, boxes, image_shapes) + ] + return [x[0] for x in result_per_image], [x[1] for x in result_per_image] + + +def fast_rcnn_inference_single_image( + boxes, + scores, + image_shape: Tuple[int, int], + score_thresh: float, + nms_thresh: float, + topk_per_image: int, + use_soft_nms: bool = False, + soft_nms_method: str = "linear", + soft_nms_iou_threshold: float = 0.3, + soft_nms_sigma: float = 0.5, + soft_nms_class_wise: bool = False, +): + """ + Single-image inference. Return bounding-box detection results by thresholding + on scores and applying non-maximum suppression (NMS). + + Args: + Same as `fast_rcnn_inference`, but with boxes, scores, and image shapes + per image. + + Returns: + Same as `fast_rcnn_inference`, but for only one image. + """ + valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) + if not valid_mask.all(): + boxes = boxes[valid_mask] + scores = scores[valid_mask] + + scores = scores[:, :-1] + num_bbox_reg_classes = boxes.shape[1] // 4 + boxes = Boxes(boxes.reshape(-1, 4)) + boxes.clip(image_shape) + boxes = boxes.tensor.view(-1, num_bbox_reg_classes, 4) # R x C x 4 + + filter_mask = scores > score_thresh # R x K + filter_inds = filter_mask.nonzero() + if num_bbox_reg_classes == 1: + boxes = boxes[filter_inds[:, 0], 0] + else: + boxes = boxes[filter_mask] + scores = scores[filter_mask] + + if use_soft_nms: + from mmcv.ops import soft_nms + + if not soft_nms_class_wise: + dets, keep = soft_nms( + boxes=boxes, + scores=scores, + iou_threshold=soft_nms_iou_threshold, + sigma=soft_nms_sigma, + min_score=1e-3, + method=soft_nms_method, + ) + boxes, scores = dets[:, :4], dets[:, -1] + else: + try: + max_coordinate = boxes.max() + except: + print(boxes.shape) # empty + warnings.warn("setting max_coordinate to 0") + max_coordinate = 0 + idxs = filter_inds[:, 1] + offsets = idxs.to(boxes) * (max_coordinate + torch.tensor(1).to(boxes)) + boxes_for_nms = boxes + offsets[:, None] + dets, keep = soft_nms( + boxes=boxes_for_nms, + scores=scores, + iou_threshold=soft_nms_iou_threshold, + sigma=soft_nms_sigma, + min_score=1e-3, + method=soft_nms_method, + ) + + if topk_per_image >= 0: + keep = keep[:topk_per_image] + boxes, scores, filter_inds = boxes[keep], scores[keep], filter_inds[keep] + + result = Instances(image_shape) + result.pred_boxes = Boxes(boxes) + result.scores = scores + result.pred_classes = filter_inds[:, 1] + return result, filter_inds[:, 0] + + boxes = boxes[keep] + scores = dets[:, -1] # scores are updated in soft-nms + + result = Instances(image_shape) + result.pred_boxes = Boxes(boxes) + result.scores = scores + filter_inds = filter_inds[keep] + result.pred_classes = filter_inds[:, 1] + return result, filter_inds[:, 0] + + keep = batched_nms(boxes, scores, filter_inds[:, 1], nms_thresh) + if topk_per_image >= 0: + keep = keep[:topk_per_image] + boxes, scores, filter_inds = boxes[keep], scores[keep], filter_inds[keep] + + result = Instances(image_shape) + result.pred_boxes = Boxes(boxes) + result.scores = scores + result.pred_classes = filter_inds[:, 1] + return result, filter_inds[:, 0] diff --git a/ape/modeling/ape_deta/misc.py b/ape/modeling/ape_deta/misc.py new file mode 100644 index 0000000..d697fe8 --- /dev/null +++ b/ape/modeling/ape_deta/misc.py @@ -0,0 +1,469 @@ +""" +Misc functions, including distributed helpers. + +Mostly copy-paste from torchvision references. +""" +import datetime +import os +import pickle +import subprocess +import time +from collections import defaultdict, deque +from typing import List, Optional + +import torch +import torch.distributed as dist +from packaging import version +from torch import Tensor + +import torchvision + +if version.parse(torchvision.__version__) < version.parse("0.7"): + from torchvision.ops import _new_empty_tensor + from torchvision.ops.misc import _output_size + + +class SmoothedValue(object): + """Track a series of values and provide access to smoothed values over a + window or the global series average. + """ + + def __init__(self, window_size=20, fmt=None): + if fmt is None: + fmt = "{median:.4f} ({global_avg:.4f})" + self.deque = deque(maxlen=window_size) + self.total = 0.0 + self.count = 0 + self.fmt = fmt + + def update(self, value, n=1): + self.deque.append(value) + self.count += n + self.total += value * n + + def synchronize_between_processes(self): + """ + Warning: does not synchronize the deque! + """ + if not is_dist_avail_and_initialized(): + return + t = torch.tensor([self.count, self.total], dtype=torch.float64, device="cuda") + dist.barrier() + dist.all_reduce(t) + t = t.tolist() + self.count = int(t[0]) + self.total = t[1] + + @property + def median(self): + d = torch.tensor(list(self.deque)) + return d.median().item() + + @property + def avg(self): + d = torch.tensor(list(self.deque), dtype=torch.float32) + return d.mean().item() + + @property + def global_avg(self): + return self.total / self.count + + @property + def max(self): + return max(self.deque) + + @property + def value(self): + return self.deque[-1] + + def __str__(self): + return self.fmt.format( + median=self.median, + avg=self.avg, + global_avg=self.global_avg, + max=self.max, + value=self.value, + ) + + +def all_gather(data): + """ + Run all_gather on arbitrary picklable data (not necessarily tensors) + Args: + data: any picklable object + Returns: + list[data]: list of data gathered from each rank + """ + world_size = get_world_size() + if world_size == 1: + return [data] + + buffer = pickle.dumps(data) + storage = torch.ByteStorage.from_buffer(buffer) + tensor = torch.ByteTensor(storage).to("cuda") + + local_size = torch.tensor([tensor.numel()], device="cuda") + size_list = [torch.tensor([0], device="cuda") for _ in range(world_size)] + dist.all_gather(size_list, local_size) + size_list = [int(size.item()) for size in size_list] + max_size = max(size_list) + + tensor_list = [] + for _ in size_list: + tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device="cuda")) + if local_size != max_size: + padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device="cuda") + tensor = torch.cat((tensor, padding), dim=0) + dist.all_gather(tensor_list, tensor) + + data_list = [] + for size, tensor in zip(size_list, tensor_list): + buffer = tensor.cpu().numpy().tobytes()[:size] + data_list.append(pickle.loads(buffer)) + + return data_list + + +def reduce_dict(input_dict, average=True): + """ + Args: + input_dict (dict): all the values will be reduced + average (bool): whether to do average or sum + Reduce the values in the dictionary from all processes so that all processes + have the averaged results. Returns a dict with the same fields as + input_dict, after reduction. + """ + world_size = get_world_size() + if world_size < 2: + return input_dict + with torch.no_grad(): + names = [] + values = [] + for k in sorted(input_dict.keys()): + names.append(k) + values.append(input_dict[k]) + values = torch.stack(values, dim=0) + dist.all_reduce(values) + if average: + values /= world_size + reduced_dict = {k: v for k, v in zip(names, values)} + return reduced_dict + + +class MetricLogger(object): + def __init__(self, delimiter="\t"): + self.meters = defaultdict(SmoothedValue) + self.delimiter = delimiter + + def update(self, **kwargs): + for k, v in kwargs.items(): + if isinstance(v, torch.Tensor): + v = v.item() + assert isinstance(v, (float, int)) + self.meters[k].update(v) + + def __getattr__(self, attr): + if attr in self.meters: + return self.meters[attr] + if attr in self.__dict__: + return self.__dict__[attr] + raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, attr)) + + def __str__(self): + loss_str = [] + for name, meter in self.meters.items(): + loss_str.append("{}: {}".format(name, str(meter))) + return self.delimiter.join(loss_str) + + def synchronize_between_processes(self): + for meter in self.meters.values(): + meter.synchronize_between_processes() + + def add_meter(self, name, meter): + self.meters[name] = meter + + def log_every(self, iterable, print_freq, header=None): + i = 0 + if not header: + header = "" + start_time = time.time() + end = time.time() + iter_time = SmoothedValue(fmt="{avg:.4f}") + data_time = SmoothedValue(fmt="{avg:.4f}") + space_fmt = ":" + str(len(str(len(iterable)))) + "d" + if torch.cuda.is_available(): + log_msg = self.delimiter.join( + [ + header, + "[{0" + space_fmt + "}/{1}]", + "eta: {eta}", + "{meters}", + "time: {time}", + "data: {data}", + "max mem: {memory:.0f}", + ] + ) + else: + log_msg = self.delimiter.join( + [ + header, + "[{0" + space_fmt + "}/{1}]", + "eta: {eta}", + "{meters}", + "time: {time}", + "data: {data}", + ] + ) + MB = 1024.0 * 1024.0 + for obj in iterable: + data_time.update(time.time() - end) + yield obj + iter_time.update(time.time() - end) + if i % print_freq == 0 or i == len(iterable) - 1: + eta_seconds = iter_time.global_avg * (len(iterable) - i) + eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) + if torch.cuda.is_available(): + print( + log_msg.format( + i, + len(iterable), + eta=eta_string, + meters=str(self), + time=str(iter_time), + data=str(data_time), + memory=torch.cuda.max_memory_allocated() / MB, + ) + ) + else: + print( + log_msg.format( + i, + len(iterable), + eta=eta_string, + meters=str(self), + time=str(iter_time), + data=str(data_time), + ) + ) + i += 1 + end = time.time() + total_time = time.time() - start_time + total_time_str = str(datetime.timedelta(seconds=int(total_time))) + print( + "{} Total time: {} ({:.4f} s / it)".format( + header, total_time_str, total_time / len(iterable) + ) + ) + + +def get_sha(): + cwd = os.path.dirname(os.path.abspath(__file__)) + + def _run(command): + return subprocess.check_output(command, cwd=cwd).decode("ascii").strip() + + sha = "N/A" + diff = "clean" + branch = "N/A" + try: + sha = _run(["git", "rev-parse", "HEAD"]) + subprocess.check_output(["git", "diff"], cwd=cwd) + diff = _run(["git", "diff-index", "HEAD"]) + diff = "has uncommited changes" if diff else "clean" + branch = _run(["git", "rev-parse", "--abbrev-ref", "HEAD"]) + except Exception: + pass + message = f"sha: {sha}, status: {diff}, branch: {branch}" + return message + + +def collate_fn(batch): + batch = list(zip(*batch)) + batch[0] = nested_tensor_from_tensor_list(batch[0]) + return tuple(batch) + + +def _max_by_axis(the_list): + maxes = the_list[0] + for sublist in the_list[1:]: + for index, item in enumerate(sublist): + maxes[index] = max(maxes[index], item) + return maxes + + +class NestedTensor(object): + def __init__(self, tensors, mask: Optional[Tensor]): + self.tensors = tensors + self.mask = mask + + def to(self, device): + cast_tensor = self.tensors.to(device) + mask = self.mask + if mask is not None: + assert mask is not None + cast_mask = mask.to(device) + else: + cast_mask = None + return NestedTensor(cast_tensor, cast_mask) + + def decompose(self): + return self.tensors, self.mask + + def __repr__(self): + return str(self.tensors) + + +def nested_tensor_from_tensor_list(tensor_list: List[Tensor]): + if tensor_list[0].ndim == 3: + if torchvision._is_tracing(): + return _onnx_nested_tensor_from_tensor_list(tensor_list) + + max_size = _max_by_axis([list(img.shape) for img in tensor_list]) + batch_shape = [len(tensor_list)] + max_size + b, c, h, w = batch_shape + dtype = tensor_list[0].dtype + device = tensor_list[0].device + tensor = torch.zeros(batch_shape, dtype=dtype, device=device) + mask = torch.ones((b, h, w), dtype=torch.bool, device=device) + for img, pad_img, m in zip(tensor_list, tensor, mask): + pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img) + m[: img.shape[1], : img.shape[2]] = False + else: + raise ValueError("not supported") + return NestedTensor(tensor, mask) + + +@torch.jit.unused +def _onnx_nested_tensor_from_tensor_list(tensor_list: List[Tensor]) -> NestedTensor: + max_size = [] + for i in range(tensor_list[0].dim()): + max_size_i = torch.max( + torch.stack([img.shape[i] for img in tensor_list]).to(torch.float32) + ).to(torch.int64) + max_size.append(max_size_i) + max_size = tuple(max_size) + + padded_imgs = [] + padded_masks = [] + for img in tensor_list: + padding = [(s1 - s2) for s1, s2 in zip(max_size, tuple(img.shape))] + padded_img = torch.nn.functional.pad(img, (0, padding[2], 0, padding[1], 0, padding[0])) + padded_imgs.append(padded_img) + + m = torch.zeros_like(img[0], dtype=torch.int, device=img.device) + padded_mask = torch.nn.functional.pad(m, (0, padding[2], 0, padding[1]), "constant", 1) + padded_masks.append(padded_mask.to(torch.bool)) + + tensor = torch.stack(padded_imgs) + mask = torch.stack(padded_masks) + + return NestedTensor(tensor, mask=mask) + + +def setup_for_distributed(is_master): + """ + This function disables printing when not in master process + """ + import builtins as __builtin__ + + builtin_print = __builtin__.print + + def print(*args, **kwargs): + force = kwargs.pop("force", False) + if is_master or force: + builtin_print(*args, **kwargs) + + __builtin__.print = print + + +def is_dist_avail_and_initialized(): + if not dist.is_available(): + return False + if not dist.is_initialized(): + return False + return True + + +def get_world_size(): + if not is_dist_avail_and_initialized(): + return 1 + return dist.get_world_size() + + +def get_rank(): + if not is_dist_avail_and_initialized(): + return 0 + return dist.get_rank() + + +def is_main_process(): + return get_rank() == 0 + + +def save_on_master(*args, **kwargs): + if is_main_process(): + torch.save(*args, **kwargs) + + +def init_distributed_mode(args): + if "RANK" in os.environ and "WORLD_SIZE" in os.environ: + args.rank = int(os.environ["RANK"]) + args.world_size = int(os.environ["WORLD_SIZE"]) + args.gpu = int(os.environ["LOCAL_RANK"]) + elif "SLURM_PROCID" in os.environ: + args.rank = int(os.environ["SLURM_PROCID"]) + args.gpu = args.rank % torch.cuda.device_count() + else: + print("Not using distributed mode") + args.distributed = False + return + + args.distributed = True + + torch.cuda.set_device(args.gpu) + args.dist_backend = "nccl" + print("| distributed init (rank {}): {}".format(args.rank, args.dist_url), flush=True) + torch.distributed.init_process_group( + backend=args.dist_backend, + init_method=args.dist_url, + world_size=args.world_size, + rank=args.rank, + ) + torch.distributed.barrier() + setup_for_distributed(args.rank == 0) + + +@torch.no_grad() +def accuracy(output, target, topk=(1,)): + """Computes the precision@k for the specified values of k""" + if target.numel() == 0: + return [torch.zeros([], device=output.device)] + maxk = max(topk) + batch_size = target.size(0) + + _, pred = output.topk(maxk, 1, True, True) + pred = pred.t() + correct = pred.eq(target.view(1, -1).expand_as(pred)) + + res = [] + for k in topk: + correct_k = correct[:k].view(-1).float().sum(0) + res.append(correct_k.mul_(100.0 / batch_size)) + return res + + +def interpolate(input, size=None, scale_factor=None, mode="nearest", align_corners=None): + """ + Equivalent to nn.functional.interpolate, but with support for empty batch sizes. + This will eventually be supported natively by PyTorch, and this + class can go away. + """ + if version.parse(torchvision.__version__) < version.parse("0.7"): + if input.numel() > 0: + return torch.nn.functional.interpolate(input, size, scale_factor, mode, align_corners) + + output_shape = _output_size(2, input, size, scale_factor) + output_shape = list(input.shape[:-2]) + list(output_shape) + return _new_empty_tensor(input, output_shape) + else: + return torchvision.ops.misc.interpolate(input, size, scale_factor, mode, align_corners) diff --git a/ape/modeling/ape_deta/segmentation.py b/ape/modeling/ape_deta/segmentation.py new file mode 100644 index 0000000..95decb8 --- /dev/null +++ b/ape/modeling/ape_deta/segmentation.py @@ -0,0 +1,378 @@ +""" +This file provides the definition of the convolutional heads used to predict masks, as well as the losses +""" +import io +from collections import defaultdict + +import torch +import torch.nn as nn +import torch.nn.functional as F +from PIL import Image + +from detrex.layers import box_cxcywh_to_xyxy + +try: + from panopticapi.utils import id2rgb, rgb2id +except ImportError: + pass + + +class DETRsegm(nn.Module): + def __init__(self, detr, freeze_detr=False): + super().__init__() + self.detr = detr + + if freeze_detr: + for p in self.parameters(): + p.requires_grad_(False) + + hidden_dim, nheads = detr.transformer.d_model, detr.transformer.nhead + self.bbox_attention = MHAttentionMap(hidden_dim, hidden_dim, nheads, dropout=0) + self.mask_head = MaskHeadSmallConv(hidden_dim + nheads, [1024, 512, 256], hidden_dim) + + def forward(self, samples): + if not isinstance(samples, NestedTensor): + samples = nested_tensor_from_tensor_list(samples) + features, pos = self.detr.backbone(samples) + + bs = features[-1].tensors.shape[0] + + src, mask = features[-1].decompose() + src_proj = self.detr.input_proj(src) + hs, memory = self.detr.transformer(src_proj, mask, self.detr.query_embed.weight, pos[-1]) + + outputs_class = self.detr.class_embed(hs) + outputs_coord = self.detr.bbox_embed(hs).sigmoid() + out = {"pred_logits": outputs_class[-1], "pred_boxes": outputs_coord[-1]} + if self.detr.aux_loss: + out["aux_outputs"] = [ + {"pred_logits": a, "pred_boxes": b} + for a, b in zip(outputs_class[:-1], outputs_coord[:-1]) + ] + + bbox_mask = self.bbox_attention(hs[-1], memory, mask=mask) + + seg_masks = self.mask_head( + src_proj, bbox_mask, [features[2].tensors, features[1].tensors, features[0].tensors] + ) + outputs_seg_masks = seg_masks.view( + bs, self.detr.num_queries, seg_masks.shape[-2], seg_masks.shape[-1] + ) + + out["pred_masks"] = outputs_seg_masks + return out + + +class MaskHeadSmallConv(nn.Module): + """ + Simple convolutional head, using group norm. + Upsampling is done using a FPN approach + """ + + def __init__(self, dim, fpn_dims, context_dim): + super().__init__() + + inter_dims = [ + dim, + context_dim // 2, + context_dim // 4, + context_dim // 8, + context_dim // 16, + context_dim // 64, + ] + self.lay1 = torch.nn.Conv2d(dim, dim, 3, padding=1) + self.gn1 = torch.nn.GroupNorm(8, dim) + self.lay2 = torch.nn.Conv2d(dim, inter_dims[1], 3, padding=1) + self.gn2 = torch.nn.GroupNorm(8, inter_dims[1]) + self.lay3 = torch.nn.Conv2d(inter_dims[1], inter_dims[2], 3, padding=1) + self.gn3 = torch.nn.GroupNorm(8, inter_dims[2]) + self.lay4 = torch.nn.Conv2d(inter_dims[2], inter_dims[3], 3, padding=1) + self.gn4 = torch.nn.GroupNorm(8, inter_dims[3]) + self.lay5 = torch.nn.Conv2d(inter_dims[3], inter_dims[4], 3, padding=1) + self.gn5 = torch.nn.GroupNorm(8, inter_dims[4]) + self.out_lay = torch.nn.Conv2d(inter_dims[4], 1, 3, padding=1) + + self.dim = dim + + self.adapter1 = torch.nn.Conv2d(fpn_dims[0], inter_dims[1], 1) + self.adapter2 = torch.nn.Conv2d(fpn_dims[1], inter_dims[2], 1) + self.adapter3 = torch.nn.Conv2d(fpn_dims[2], inter_dims[3], 1) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_uniform_(m.weight, a=1) + nn.init.constant_(m.bias, 0) + + def forward(self, x, bbox_mask, fpns): + def expand(tensor, length): + return tensor.unsqueeze(1).repeat(1, int(length), 1, 1, 1).flatten(0, 1) + + x = torch.cat([expand(x, bbox_mask.shape[1]), bbox_mask.flatten(0, 1)], 1) + + x = self.lay1(x) + x = self.gn1(x) + x = F.relu(x) + x = self.lay2(x) + x = self.gn2(x) + x = F.relu(x) + + cur_fpn = self.adapter1(fpns[0]) + if cur_fpn.size(0) != x.size(0): + cur_fpn = expand(cur_fpn, x.size(0) / cur_fpn.size(0)) + x = cur_fpn + F.interpolate(x, size=cur_fpn.shape[-2:], mode="nearest") + x = self.lay3(x) + x = self.gn3(x) + x = F.relu(x) + + cur_fpn = self.adapter2(fpns[1]) + if cur_fpn.size(0) != x.size(0): + cur_fpn = expand(cur_fpn, x.size(0) / cur_fpn.size(0)) + x = cur_fpn + F.interpolate(x, size=cur_fpn.shape[-2:], mode="nearest") + x = self.lay4(x) + x = self.gn4(x) + x = F.relu(x) + + cur_fpn = self.adapter3(fpns[2]) + if cur_fpn.size(0) != x.size(0): + cur_fpn = expand(cur_fpn, x.size(0) / cur_fpn.size(0)) + x = cur_fpn + F.interpolate(x, size=cur_fpn.shape[-2:], mode="nearest") + x = self.lay5(x) + x = self.gn5(x) + x = F.relu(x) + + x = self.out_lay(x) + return x + + +class MHAttentionMap(nn.Module): + """This is a 2D attention module, which only returns the attention softmax (no multiplication by value)""" + + def __init__(self, query_dim, hidden_dim, num_heads, dropout=0, bias=True): + super().__init__() + self.num_heads = num_heads + self.hidden_dim = hidden_dim + self.dropout = nn.Dropout(dropout) + + self.q_linear = nn.Linear(query_dim, hidden_dim, bias=bias) + self.k_linear = nn.Linear(query_dim, hidden_dim, bias=bias) + + nn.init.zeros_(self.k_linear.bias) + nn.init.zeros_(self.q_linear.bias) + nn.init.xavier_uniform_(self.k_linear.weight) + nn.init.xavier_uniform_(self.q_linear.weight) + self.normalize_fact = float(hidden_dim / self.num_heads) ** -0.5 + + def forward(self, q, k, mask=None): + q = self.q_linear(q) + k = F.conv2d(k, self.k_linear.weight.unsqueeze(-1).unsqueeze(-1), self.k_linear.bias) + qh = q.view(q.shape[0], q.shape[1], self.num_heads, self.hidden_dim // self.num_heads) + kh = k.view( + k.shape[0], self.num_heads, self.hidden_dim // self.num_heads, k.shape[-2], k.shape[-1] + ) + weights = torch.einsum("bqnc,bnchw->bqnhw", qh * self.normalize_fact, kh) + + if mask is not None: + weights.masked_fill_(mask.unsqueeze(1).unsqueeze(1), float("-inf")) + weights = F.softmax(weights.flatten(2), dim=-1).view_as(weights) + weights = self.dropout(weights) + return weights + + +def dice_loss(inputs, targets, num_boxes): + """ + Compute the DICE loss, similar to generalized IOU for masks + Args: + inputs: A float tensor of arbitrary shape. + The predictions for each example. + targets: A float tensor with the same shape as inputs. Stores the binary + classification label for each element in inputs + (0 for the negative class and 1 for the positive class). + """ + inputs = inputs.sigmoid() + inputs = inputs.flatten(1) + numerator = 2 * (inputs * targets).sum(1) + denominator = inputs.sum(-1) + targets.sum(-1) + loss = 1 - (numerator + 1) / (denominator + 1) + return loss.sum() / num_boxes + + +def sigmoid_focal_loss(inputs, targets, num_boxes, alpha: float = 0.25, gamma: float = 2): + """ + Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002. + Args: + inputs: A float tensor of arbitrary shape. + The predictions for each example. + targets: A float tensor with the same shape as inputs. Stores the binary + classification label for each element in inputs + (0 for the negative class and 1 for the positive class). + alpha: (optional) Weighting factor in range (0,1) to balance + positive vs negative examples. Default = -1 (no weighting). + gamma: Exponent of the modulating factor (1 - p_t) to + balance easy vs hard examples. + Returns: + Loss tensor + """ + prob = inputs.sigmoid() + ce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none") + p_t = prob * targets + (1 - prob) * (1 - targets) + loss = ce_loss * ((1 - p_t) ** gamma) + + if alpha >= 0: + alpha_t = alpha * targets + (1 - alpha) * (1 - targets) + loss = alpha_t * loss + + return loss.mean(1).sum() / num_boxes + + +class PostProcessSegm(nn.Module): + def __init__(self, threshold=0.5): + super().__init__() + self.threshold = threshold + + @torch.no_grad() + def forward(self, results, outputs, orig_target_sizes, max_target_sizes): + assert len(orig_target_sizes) == len(max_target_sizes) + max_h, max_w = max_target_sizes.max(0)[0].tolist() + outputs_masks = outputs["pred_masks"].squeeze(2) + outputs_masks = F.interpolate( + outputs_masks, size=(max_h, max_w), mode="bilinear", align_corners=False + ) + outputs_masks = (outputs_masks.sigmoid() > self.threshold).cpu() + + for i, (cur_mask, t, tt) in enumerate( + zip(outputs_masks, max_target_sizes, orig_target_sizes) + ): + img_h, img_w = t[0], t[1] + results[i]["masks"] = cur_mask[:, :img_h, :img_w].unsqueeze(1) + results[i]["masks"] = F.interpolate( + results[i]["masks"].float(), size=tuple(tt.tolist()), mode="nearest" + ).byte() + + return results + + +class PostProcessPanoptic(nn.Module): + """This class converts the output of the model to the final panoptic result, in the format expected by the + coco panoptic API""" + + def __init__(self, is_thing_map, threshold=0.85): + """ + Parameters: + is_thing_map: This is a whose keys are the class ids, and the values a boolean indicating whether + the class is a thing (True) or a stuff (False) class + threshold: confidence threshold: segments with confidence lower than this will be deleted + """ + super().__init__() + self.threshold = threshold + self.is_thing_map = is_thing_map + + def forward(self, outputs, processed_sizes, target_sizes=None): + """This function computes the panoptic prediction from the model's predictions. + Parameters: + outputs: This is a dict coming directly from the model. See the model doc for the content. + processed_sizes: This is a list of tuples (or torch tensors) of sizes of the images that were passed to the + model, ie the size after data augmentation but before batching. + target_sizes: This is a list of tuples (or torch tensors) corresponding to the requested final size + of each prediction. If left to None, it will default to the processed_sizes + """ + if target_sizes is None: + target_sizes = processed_sizes + assert len(processed_sizes) == len(target_sizes) + out_logits, raw_masks, raw_boxes = ( + outputs["pred_logits"], + outputs["pred_masks"], + outputs["pred_boxes"], + ) + assert len(out_logits) == len(raw_masks) == len(target_sizes) + preds = [] + + def to_tuple(tup): + if isinstance(tup, tuple): + return tup + return tuple(tup.cpu().tolist()) + + for cur_logits, cur_masks, cur_boxes, size, target_size in zip( + out_logits, raw_masks, raw_boxes, processed_sizes, target_sizes + ): + scores, labels = cur_logits.softmax(-1).max(-1) + keep = labels.ne(outputs["pred_logits"].shape[-1] - 1) & (scores > self.threshold) + cur_scores, cur_classes = cur_logits.softmax(-1).max(-1) + cur_scores = cur_scores[keep] + cur_classes = cur_classes[keep] + cur_masks = cur_masks[keep] + cur_masks = F.interpolate(cur_masks[None], to_tuple(size), mode="bilinear").squeeze(0) + cur_boxes = box_cxcywh_to_xyxy(cur_boxes[keep]) + + h, w = cur_masks.shape[-2:] + assert len(cur_boxes) == len(cur_classes) + + cur_masks = cur_masks.flatten(1) + stuff_equiv_classes = defaultdict(lambda: []) + for k, label in enumerate(cur_classes): + if not self.is_thing_map[label.item()]: + stuff_equiv_classes[label.item()].append(k) + + def get_ids_area(masks, scores, dedup=False): + + m_id = masks.transpose(0, 1).softmax(-1) + + if m_id.shape[-1] == 0: + m_id = torch.zeros((h, w), dtype=torch.long, device=m_id.device) + else: + m_id = m_id.argmax(-1).view(h, w) + + if dedup: + for equiv in stuff_equiv_classes.values(): + if len(equiv) > 1: + for eq_id in equiv: + m_id.masked_fill_(m_id.eq(eq_id), equiv[0]) + + final_h, final_w = to_tuple(target_size) + + seg_img = Image.fromarray(id2rgb(m_id.view(h, w).cpu().numpy())) + seg_img = seg_img.resize(size=(final_w, final_h), resample=Image.NEAREST) + + np_seg_img = ( + torch.ByteTensor(torch.ByteStorage.from_buffer(seg_img.tobytes())) + .view(final_h, final_w, 3) + .numpy() + ) + m_id = torch.from_numpy(rgb2id(np_seg_img)) + + area = [] + for i in range(len(scores)): + area.append(m_id.eq(i).sum().item()) + return area, seg_img + + area, seg_img = get_ids_area(cur_masks, cur_scores, dedup=True) + if cur_classes.numel() > 0: + while True: + filtered_small = torch.as_tensor( + [area[i] <= 4 for i, c in enumerate(cur_classes)], + dtype=torch.bool, + device=keep.device, + ) + if filtered_small.any().item(): + cur_scores = cur_scores[~filtered_small] + cur_classes = cur_classes[~filtered_small] + cur_masks = cur_masks[~filtered_small] + area, seg_img = get_ids_area(cur_masks, cur_scores) + else: + break + + else: + cur_classes = torch.ones(1, dtype=torch.long, device=cur_classes.device) + + segments_info = [] + for i, a in enumerate(area): + cat = cur_classes[i].item() + segments_info.append( + {"id": i, "isthing": self.is_thing_map[cat], "category_id": cat, "area": a} + ) + del cur_classes + + with io.BytesIO() as out: + seg_img.save(out, format="PNG") + predictions = {"png_string": out.getvalue(), "segments_info": segments_info} + preds.append(predictions) + return preds diff --git a/ape/modeling/backbone/__init__.py b/ape/modeling/backbone/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/ape/modeling/backbone/utils_eva.py b/ape/modeling/backbone/utils_eva.py new file mode 100644 index 0000000..6475342 --- /dev/null +++ b/ape/modeling/backbone/utils_eva.py @@ -0,0 +1,222 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import math +import numpy as np +from scipy import interpolate +import torch +import torch.nn as nn +import torch.nn.functional as F + +__all__ = [ + "window_partition", + "window_unpartition", + "add_decomposed_rel_pos", + "get_abs_pos", + "PatchEmbed", +] + + +def window_partition(x, window_size): + """ + Partition into non-overlapping windows with padding if needed. + Args: + x (tensor): input tokens with [B, H, W, C]. + window_size (int): window size. + + Returns: + windows: windows after partition with [B * num_windows, window_size, window_size, C]. + (Hp, Wp): padded height and width before partition + """ + B, H, W, C = x.shape + + pad_h = (window_size - H % window_size) % window_size + pad_w = (window_size - W % window_size) % window_size + if pad_h > 0 or pad_w > 0: + x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h)) + Hp, Wp = H + pad_h, W + pad_w + + x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C) + windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + return windows, (Hp, Wp) + + +def window_unpartition(windows, window_size, pad_hw, hw): + """ + Window unpartition into original sequences and removing padding. + Args: + x (tensor): input tokens with [B * num_windows, window_size, window_size, C]. + window_size (int): window size. + pad_hw (Tuple): padded height and width (Hp, Wp). + hw (Tuple): original height and width (H, W) before padding. + + Returns: + x: unpartitioned sequences with [B, H, W, C]. + """ + Hp, Wp = pad_hw + H, W = hw + B = windows.shape[0] // (Hp * Wp // window_size // window_size) + x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1) + + if Hp > H or Wp > W: + x = x[:, :H, :W, :].contiguous() + return x + + +def get_rel_pos(q_size, k_size, rel_pos, interp_type): + """ + Get relative positional embeddings according to the relative positions of + query and key sizes. + Args: + q_size (int): size of query q. + k_size (int): size of key k. + rel_pos (Tensor): relative position embeddings (L, C). + + Returns: + Extracted positional embeddings according to relative positions. + """ + max_rel_dist = int(2 * max(q_size, k_size) - 1) + # Interpolate rel pos if needed. + if rel_pos.shape[0] != max_rel_dist: + if interp_type == "vitdet": + # the vitdet impl: + # https://github.com/facebookresearch/detectron2/blob/96c752ce821a3340e27edd51c28a00665dd32a30/detectron2/modeling/backbone/utils.py#L77. + + rel_pos_resized = F.interpolate( + rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1), + size=max_rel_dist, + mode="linear", + ) + rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0) + elif interp_type == "beit": + # steal from beit https://github.com/microsoft/unilm/tree/master/beit + # modified by Yuxin Fang + + src_size = rel_pos.shape[0] + dst_size = max_rel_dist + + q = 1.0903078 + dis = [] + + cur = 1 + for i in range(src_size // 2): + dis.append(cur) + cur += q ** (i + 1) + + r_ids = [-_ for _ in reversed(dis)] + x = r_ids + [0] + dis + t = dst_size // 2.0 + dx = np.arange(-t, t + 0.1, 1.0) + + all_rel_pos_bias = [] + for i in range(rel_pos.shape[1]): + # a hack from https://github.com/baaivision/EVA/issues/8, + # could also be used in fine-tuning but the performance haven't been tested. + z = rel_pos[:, i].view(src_size).cpu().float().detach().numpy() + f = interpolate.interp1d(x, z, kind='cubic', fill_value="extrapolate") + all_rel_pos_bias.append( + torch.Tensor(f(dx)).contiguous().view(-1, 1).to(rel_pos.device)) + rel_pos_resized = torch.cat(all_rel_pos_bias, dim=-1) + else: + raise NotImplementedError() + else: + rel_pos_resized = rel_pos + + # Scale the coords with short length if shapes for q and k are different. + q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0) + k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0) + relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0) + + return rel_pos_resized[relative_coords.long()] + + +def add_decomposed_rel_pos(attn, q, rel_pos_h, rel_pos_w, q_size, k_size, interp_type): + """ + Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`. + https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py # noqa B950 + Args: + attn (Tensor): attention map. + q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C). + rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis. + rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis. + q_size (Tuple): spatial sequence size of query q with (q_h, q_w). + k_size (Tuple): spatial sequence size of key k with (k_h, k_w). + + Returns: + attn (Tensor): attention map with added relative positional embeddings. + """ + q_h, q_w = q_size + k_h, k_w = k_size + Rh = get_rel_pos(q_h, k_h, rel_pos_h, interp_type) + Rw = get_rel_pos(q_w, k_w, rel_pos_w, interp_type) + + B, _, dim = q.shape + r_q = q.reshape(B, q_h, q_w, dim) + rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh) + rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw) + + attn = ( + attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :] + ).view(B, q_h * q_w, k_h * k_w) + + return attn + + +def get_abs_pos(abs_pos, has_cls_token, hw): + """ + Calculate absolute positional embeddings. If needed, resize embeddings and remove cls_token + dimension for the original embeddings. + Args: + abs_pos (Tensor): absolute positional embeddings with (1, num_position, C). + has_cls_token (bool): If true, has 1 embedding in abs_pos for cls token. + hw (Tuple): size of input image tokens. + + Returns: + Absolute positional embeddings after processing with shape (1, H, W, C) + """ + h, w = hw + if has_cls_token: + abs_pos = abs_pos[:, 1:] + xy_num = abs_pos.shape[1] + size = int(math.sqrt(xy_num)) + assert size * size == xy_num + + if size != h or size != w: + new_abs_pos = F.interpolate( + abs_pos.reshape(1, size, size, -1).permute(0, 3, 1, 2), + size=(h, w), + mode="bicubic", + align_corners=False, + ) + + return new_abs_pos.permute(0, 2, 3, 1) + else: + return abs_pos.reshape(1, h, w, -1) + + +class PatchEmbed(nn.Module): + """ + Image to Patch Embedding. + """ + + def __init__( + self, kernel_size=(16, 16), stride=(16, 16), padding=(0, 0), in_chans=3, embed_dim=768 + ): + """ + Args: + kernel_size (Tuple): kernel size of the projection layer. + stride (Tuple): stride of the projection layer. + padding (Tuple): padding size of the projection layer. + in_chans (int): Number of input image channels. + embed_dim (int): embed_dim (int): Patch embedding dimension. + """ + super().__init__() + + self.proj = nn.Conv2d( + in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding + ) + + def forward(self, x): + x = self.proj(x) + # B C H W -> B H W C + x = x.permute(0, 2, 3, 1) + return x diff --git a/ape/modeling/backbone/utils_eva02.py b/ape/modeling/backbone/utils_eva02.py new file mode 100644 index 0000000..d7412d0 --- /dev/null +++ b/ape/modeling/backbone/utils_eva02.py @@ -0,0 +1,347 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import math +import numpy as np +from scipy import interpolate +import torch +import torch.nn as nn +import torch.nn.functional as F + +__all__ = [ + "window_partition", + "window_unpartition", + "add_decomposed_rel_pos", + "get_abs_pos", + "PatchEmbed", + "VisionRotaryEmbeddingFast", +] + + +def window_partition(x, window_size): + """ + Partition into non-overlapping windows with padding if needed. + Args: + x (tensor): input tokens with [B, H, W, C]. + window_size (int): window size. + + Returns: + windows: windows after partition with [B * num_windows, window_size, window_size, C]. + (Hp, Wp): padded height and width before partition + """ + B, H, W, C = x.shape + + pad_h = (window_size - H % window_size) % window_size + pad_w = (window_size - W % window_size) % window_size + if pad_h > 0 or pad_w > 0: + x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h)) + Hp, Wp = H + pad_h, W + pad_w + + x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C) + windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + return windows, (Hp, Wp) + + +def window_unpartition(windows, window_size, pad_hw, hw): + """ + Window unpartition into original sequences and removing padding. + Args: + x (tensor): input tokens with [B * num_windows, window_size, window_size, C]. + window_size (int): window size. + pad_hw (Tuple): padded height and width (Hp, Wp). + hw (Tuple): original height and width (H, W) before padding. + + Returns: + x: unpartitioned sequences with [B, H, W, C]. + """ + Hp, Wp = pad_hw + H, W = hw + B = windows.shape[0] // (Hp * Wp // window_size // window_size) + x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1) + + if Hp > H or Wp > W: + x = x[:, :H, :W, :].contiguous() + return x + + +def get_rel_pos(q_size, k_size, rel_pos): + """ + Get relative positional embeddings according to the relative positions of + query and key sizes. + Args: + q_size (int): size of query q. + k_size (int): size of key k. + rel_pos (Tensor): relative position embeddings (L, C). + + Returns: + Extracted positional embeddings according to relative positions. + """ + max_rel_dist = int(2 * max(q_size, k_size) - 1) + use_log_interpolation = True + + # Interpolate rel pos if needed. + if rel_pos.shape[0] != max_rel_dist: + if not use_log_interpolation: + # Interpolate rel pos. + rel_pos_resized = F.interpolate( + rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1), + size=max_rel_dist, + mode="linear", + ) + rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0) + else: + src_size = rel_pos.shape[0] + dst_size = max_rel_dist + + # q = 1.13492 + q = 1.0903078 + dis = [] + + cur = 1 + for i in range(src_size // 2): + dis.append(cur) + cur += q ** (i + 1) + + r_ids = [-_ for _ in reversed(dis)] + x = r_ids + [0] + dis + t = dst_size // 2.0 + dx = np.arange(-t, t + 0.1, 1.0) + all_rel_pos_bias = [] + for i in range(rel_pos.shape[1]): + z = rel_pos[:, i].view(src_size).cpu().float().numpy() + f = interpolate.interp1d(x, z, kind='cubic', fill_value="extrapolate") + all_rel_pos_bias.append( + torch.Tensor(f(dx)).contiguous().view(-1, 1).to(rel_pos.device)) + rel_pos_resized = torch.cat(all_rel_pos_bias, dim=-1) + else: + rel_pos_resized = rel_pos + + # Scale the coords with short length if shapes for q and k are different. + q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0) + k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0) + relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0) + + return rel_pos_resized[relative_coords.long()] + + +def add_decomposed_rel_pos(attn, q, rel_pos_h, rel_pos_w, q_size, k_size): + """ + Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`. + https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py # noqa B950 + Args: + attn (Tensor): attention map. + q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C). + rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis. + rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis. + q_size (Tuple): spatial sequence size of query q with (q_h, q_w). + k_size (Tuple): spatial sequence size of key k with (k_h, k_w). + + Returns: + attn (Tensor): attention map with added relative positional embeddings. + """ + q_h, q_w = q_size + k_h, k_w = k_size + Rh = get_rel_pos(q_h, k_h, rel_pos_h) + Rw = get_rel_pos(q_w, k_w, rel_pos_w) + + B, _, dim = q.shape + r_q = q.reshape(B, q_h, q_w, dim) + rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh) + rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw) + + attn = ( + attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :] + ).view(B, q_h * q_w, k_h * k_w) + + return attn + + +def get_abs_pos(abs_pos, has_cls_token, hw): + """ + Calculate absolute positional embeddings. If needed, resize embeddings and remove cls_token + dimension for the original embeddings. + Args: + abs_pos (Tensor): absolute positional embeddings with (1, num_position, C). + has_cls_token (bool): If true, has 1 embedding in abs_pos for cls token. + hw (Tuple): size of input image tokens. + + Returns: + Absolute positional embeddings after processing with shape (1, H, W, C) + """ + h, w = hw + if has_cls_token: + abs_pos = abs_pos[:, 1:] + xy_num = abs_pos.shape[1] + size = int(math.sqrt(xy_num)) + assert size * size == xy_num + + if size != h or size != w: + new_abs_pos = F.interpolate( + abs_pos.reshape(1, size, size, -1).permute(0, 3, 1, 2), + size=(h, w), + mode="bicubic", + align_corners=False, + ) + + return new_abs_pos.permute(0, 2, 3, 1) + else: + return abs_pos.reshape(1, h, w, -1) + + +class PatchEmbed(nn.Module): + """ + Image to Patch Embedding. + """ + + def __init__( + self, kernel_size=(16, 16), stride=(16, 16), padding=(0, 0), in_chans=3, embed_dim=768 + ): + """ + Args: + kernel_size (Tuple): kernel size of the projection layer. + stride (Tuple): stride of the projection layer. + padding (Tuple): padding size of the projection layer. + in_chans (int): Number of input image channels. + embed_dim (int): embed_dim (int): Patch embedding dimension. + """ + super().__init__() + + self.proj = nn.Conv2d( + in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding + ) + + def forward(self, x): + x = self.proj(x) + # B C H W -> B H W C + x = x.permute(0, 2, 3, 1) + return x + + + + +from math import pi + +import torch +from torch import nn + +from einops import rearrange, repeat + + + +def broadcat(tensors, dim = -1): + num_tensors = len(tensors) + shape_lens = set(list(map(lambda t: len(t.shape), tensors))) + assert len(shape_lens) == 1, 'tensors must all have the same number of dimensions' + shape_len = list(shape_lens)[0] + dim = (dim + shape_len) if dim < 0 else dim + dims = list(zip(*map(lambda t: list(t.shape), tensors))) + expandable_dims = [(i, val) for i, val in enumerate(dims) if i != dim] + assert all([*map(lambda t: len(set(t[1])) <= 2, expandable_dims)]), 'invalid dimensions for broadcastable concatentation' + max_dims = list(map(lambda t: (t[0], max(t[1])), expandable_dims)) + expanded_dims = list(map(lambda t: (t[0], (t[1],) * num_tensors), max_dims)) + expanded_dims.insert(dim, (dim, dims[dim])) + expandable_shapes = list(zip(*map(lambda t: t[1], expanded_dims))) + tensors = list(map(lambda t: t[0].expand(*t[1]), zip(tensors, expandable_shapes))) + return torch.cat(tensors, dim = dim) + + + +def rotate_half(x): + x = rearrange(x, '... (d r) -> ... d r', r = 2) + x1, x2 = x.unbind(dim = -1) + x = torch.stack((-x2, x1), dim = -1) + return rearrange(x, '... d r -> ... (d r)') + + + +class VisionRotaryEmbedding(nn.Module): + def __init__( + self, + dim, + pt_seq_len, + ft_seq_len=None, + custom_freqs = None, + freqs_for = 'lang', + theta = 10000, + max_freq = 10, + num_freqs = 1, + ): + super().__init__() + if custom_freqs: + freqs = custom_freqs + elif freqs_for == 'lang': + freqs = 1. / (theta ** (torch.arange(0, dim, 2)[:(dim // 2)].float() / dim)) + elif freqs_for == 'pixel': + freqs = torch.linspace(1., max_freq / 2, dim // 2) * pi + elif freqs_for == 'constant': + freqs = torch.ones(num_freqs).float() + else: + raise ValueError(f'unknown modality {freqs_for}') + + if ft_seq_len is None: ft_seq_len = pt_seq_len + t = torch.arange(ft_seq_len) / ft_seq_len * pt_seq_len + + freqs_h = torch.einsum('..., f -> ... f', t, freqs) + freqs_h = repeat(freqs_h, '... n -> ... (n r)', r = 2) + + freqs_w = torch.einsum('..., f -> ... f', t, freqs) + freqs_w = repeat(freqs_w, '... n -> ... (n r)', r = 2) + + freqs = broadcat((freqs_h[:, None, :], freqs_w[None, :, :]), dim = -1) + + self.register_buffer("freqs_cos", freqs.cos()) + self.register_buffer("freqs_sin", freqs.sin()) + + print('======== shape of rope freq', self.freqs_cos.shape, '========') + + def forward(self, t, start_index = 0): + rot_dim = self.freqs_cos.shape[-1] + end_index = start_index + rot_dim + assert rot_dim <= t.shape[-1], f'feature dimension {t.shape[-1]} is not of sufficient size to rotate in all the positions {rot_dim}' + t_left, t, t_right = t[..., :start_index], t[..., start_index:end_index], t[..., end_index:] + t = (t * self.freqs_cos) + (rotate_half(t) * self.freqs_sin) + return torch.cat((t_left, t, t_right), dim = -1) + + + + +class VisionRotaryEmbeddingFast(nn.Module): + def __init__( + self, + dim, + pt_seq_len=16, + ft_seq_len=None, + custom_freqs = None, + freqs_for = 'lang', + theta = 10000, + max_freq = 10, + num_freqs = 1, + ): + super().__init__() + if custom_freqs: + freqs = custom_freqs + elif freqs_for == 'lang': + freqs = 1. / (theta ** (torch.arange(0, dim, 2)[:(dim // 2)].float() / dim)) + elif freqs_for == 'pixel': + freqs = torch.linspace(1., max_freq / 2, dim // 2) * pi + elif freqs_for == 'constant': + freqs = torch.ones(num_freqs).float() + else: + raise ValueError(f'unknown modality {freqs_for}') + + if ft_seq_len is None: ft_seq_len = pt_seq_len + t = torch.arange(ft_seq_len) / ft_seq_len * pt_seq_len + + freqs = torch.einsum('..., f -> ... f', t, freqs) + freqs = repeat(freqs, '... n -> ... (n r)', r = 2) + freqs = broadcat((freqs[:, None, :], freqs[None, :, :]), dim = -1) + + freqs_cos = freqs.cos().view(-1, freqs.shape[-1]) + freqs_sin = freqs.sin().view(-1, freqs.shape[-1]) + + self.register_buffer("freqs_cos", freqs_cos) + self.register_buffer("freqs_sin", freqs_sin) + + print('======== shape of rope freq', self.freqs_cos.shape, '========') + + def forward(self, t): return t * self.freqs_cos + rotate_half(t) * self.freqs_sin + diff --git a/ape/modeling/backbone/vit.py b/ape/modeling/backbone/vit.py new file mode 100644 index 0000000..ded59f6 --- /dev/null +++ b/ape/modeling/backbone/vit.py @@ -0,0 +1,30 @@ +import logging + +logger = logging.getLogger(__name__) + + +__all__ = ["get_vit_lr_decay_rate"] + +def get_vit_lr_decay_rate(name, lr_decay_rate=1.0, num_layers=12): + """ + Calculate lr decay rate for different ViT blocks. + Args: + name (string): parameter name. + lr_decay_rate (float): base lr decay rate. + num_layers (int): number of ViT blocks. + + Returns: + lr decay rate for the given parameter. + """ + if name.startswith("model_vision."): + name = name[len("model_vision."):] + + layer_id = num_layers + 1 + if name.startswith("backbone"): + if ".pos_embed" in name or ".patch_embed" in name: + layer_id = 0 + elif ".blocks." in name and ".residual." not in name: + layer_id = int(name[name.find(".blocks.") :].split(".")[2]) + 1 + + logger.info("get_vit_lr_decay_rate: name={} num_layers={} layer_id={} lr_decay_rate={}".format(name, num_layers, layer_id, lr_decay_rate ** (num_layers + 1 - layer_id))) + return lr_decay_rate ** (num_layers + 1 - layer_id) diff --git a/ape/modeling/backbone/vit_eva.py b/ape/modeling/backbone/vit_eva.py new file mode 100644 index 0000000..1effd59 --- /dev/null +++ b/ape/modeling/backbone/vit_eva.py @@ -0,0 +1,644 @@ +import logging +import math +import fvcore.nn.weight_init as weight_init +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch import Tensor, Size +from typing import Union, List +from torch.nn.parameter import Parameter +import numbers + +from detectron2.layers import CNNBlockBase, Conv2d, get_norm +from detectron2.modeling.backbone.fpn import _assert_strides_are_log2_contiguous + +from fairscale.nn.checkpoint import checkpoint_wrapper +from timm.models.layers import DropPath, Mlp, trunc_normal_ + +from detectron2.modeling.backbone import Backbone +from .utils_eva import ( + PatchEmbed, + add_decomposed_rel_pos, + get_abs_pos, + window_partition, + window_unpartition, +) + +logger = logging.getLogger(__name__) + + +__all__ = ["ViT", "SimpleFeaturePyramid", "get_vit_lr_decay_rate"] + + +_shape_t = Union[int, List[int], Size] + + +# steal from beit https://github.com/microsoft/unilm/tree/master/beit +class LayerNormWithForceFP32(nn.Module): + __constants__ = ['normalized_shape', 'eps', 'elementwise_affine'] + normalized_shape: _shape_t + eps: float + elementwise_affine: bool + + def __init__(self, normalized_shape: _shape_t, eps: float = 1e-5, elementwise_affine: bool = True) -> None: + super(LayerNormWithForceFP32, self).__init__() + if isinstance(normalized_shape, numbers.Integral): + normalized_shape = (normalized_shape,) + self.normalized_shape = tuple(normalized_shape) + self.eps = eps + self.elementwise_affine = elementwise_affine + if self.elementwise_affine: + self.weight = Parameter(torch.Tensor(*normalized_shape)) + self.bias = Parameter(torch.Tensor(*normalized_shape)) + else: + self.register_parameter('weight', None) + self.register_parameter('bias', None) + self.reset_parameters() + + def reset_parameters(self) -> None: + if self.elementwise_affine: + nn.init.ones_(self.weight) + nn.init.zeros_(self.bias) + + def forward(self, input: Tensor) -> Tensor: + return F.layer_norm( + input.float(), self.normalized_shape, self.weight.float(), self.bias.float(), self.eps).type_as(input) + + def extra_repr(self) -> Tensor: + return '{normalized_shape}, eps={eps}, ' \ + 'elementwise_affine={elementwise_affine}'.format(**self.__dict__) + + +class Attention(nn.Module): + """Multi-head Attention block with relative position embeddings.""" + + def __init__( + self, + dim, + num_heads=8, + qkv_bias=True, + beit_like_qkv_bias=False, + use_rel_pos=False, + rel_pos_zero_init=True, + input_size=None, + interp_type="vitdet", + ): + """ + Args: + dim (int): Number of input channels. + num_heads (int): Number of attention heads. + qkv_bias (bool: If True, add a learnable bias to query, key, value. + rel_pos (bool): If True, add relative positional embeddings to the attention map. + rel_pos_zero_init (bool): If True, zero initialize relative positional parameters. + input_size (int or None): Input resolution for calculating the relative positional + parameter size. + """ + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = head_dim**-0.5 + + self.beit_like_qkv_bias = beit_like_qkv_bias + if beit_like_qkv_bias: + self.q_bias = nn.Parameter(torch.zeros(dim)) + self.v_bias = nn.Parameter(torch.zeros(dim)) + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.proj = nn.Linear(dim, dim) + + self.use_rel_pos = use_rel_pos + self.interp_type = interp_type + if self.use_rel_pos: + # initialize relative positional embeddings + self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim)) + self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim)) + + if not rel_pos_zero_init: + trunc_normal_(self.rel_pos_h, std=0.02) + trunc_normal_(self.rel_pos_w, std=0.02) + self.qk_float = False + + def forward(self, x): + B, H, W, _ = x.shape + # qkv with shape (3, B, nHead, H * W, C) + if self.beit_like_qkv_bias: + qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias)) + qkv = torch.nn.functional.linear(input=x, weight=self.qkv.weight, bias=qkv_bias) + qkv = qkv.reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + else: + qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + # q, k, v with shape (B * nHead, H * W, C) + q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0) + + if self.qk_float: + attn = (q.float() * self.scale) @ k.float().transpose(-2, -1) + if self.use_rel_pos: + attn = add_decomposed_rel_pos(attn, q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W), self.interp_type) + attn = attn.softmax(dim=-1).type_as(x) + else: + attn = (q * self.scale) @ k.transpose(-2, -1) + if self.use_rel_pos: + attn = add_decomposed_rel_pos(attn, q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W), self.interp_type) + attn = attn.softmax(dim=-1) + x = (attn @ v).view(B, self.num_heads, H, W, -1).permute(0, 2, 3, 1, 4).reshape(B, H, W, -1) + x = self.proj(x) + + return x + + +class ResBottleneckBlock(CNNBlockBase): + """ + The standard bottleneck residual block without the last activation layer. + It contains 3 conv layers with kernels 1x1, 3x3, 1x1. + """ + + def __init__( + self, + in_channels, + out_channels, + bottleneck_channels, + norm="LN", + act_layer=nn.GELU, + ): + """ + Args: + in_channels (int): Number of input channels. + out_channels (int): Number of output channels. + bottleneck_channels (int): number of output channels for the 3x3 + "bottleneck" conv layers. + norm (str or callable): normalization for all conv layers. + See :func:`layers.get_norm` for supported format. + act_layer (callable): activation for all conv layers. + """ + super().__init__(in_channels, out_channels, 1) + + self.conv1 = Conv2d(in_channels, bottleneck_channels, 1, bias=False) + self.norm1 = get_norm(norm, bottleneck_channels) + self.act1 = act_layer() + + self.conv2 = Conv2d( + bottleneck_channels, + bottleneck_channels, + 3, + padding=1, + bias=False, + ) + self.norm2 = get_norm(norm, bottleneck_channels) + self.act2 = act_layer() + + self.conv3 = Conv2d(bottleneck_channels, out_channels, 1, bias=False) + self.norm3 = get_norm(norm, out_channels) + + for layer in [self.conv1, self.conv2, self.conv3]: + weight_init.c2_msra_fill(layer) + for layer in [self.norm1, self.norm2]: + layer.weight.data.fill_(1.0) + layer.bias.data.zero_() + # zero init last norm layer. + self.norm3.weight.data.zero_() + self.norm3.bias.data.zero_() + + def forward(self, x): + out = x + for layer in self.children(): + out = layer(out) + + out = x + out + return out + + +class Block(nn.Module): + """Transformer blocks with support of window attention and residual propagation blocks""" + + def __init__( + self, + dim, + num_heads, + mlp_ratio=4.0, + qkv_bias=True, + drop_path=0.0, + norm_layer=LayerNormWithForceFP32, + act_layer=nn.GELU, + use_rel_pos=False, + rel_pos_zero_init=True, + window_size=0, + use_residual_block=False, + input_size=None, + beit_like_qkv_bias=False, + beit_like_gamma=False, + interp_type="vitdet", + ): + """ + Args: + dim (int): Number of input channels. + num_heads (int): Number of attention heads in each ViT block. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool): If True, add a learnable bias to query, key, value. + drop_path (float): Stochastic depth rate. + norm_layer (nn.Module): Normalization layer. + act_layer (nn.Module): Activation layer. + use_rel_pos (bool): If True, add relative positional embeddings to the attention map. + rel_pos_zero_init (bool): If True, zero initialize relative positional parameters. + window_size (int): Window size for window attention blocks. If it equals 0, then not + use window attention. + use_residual_block (bool): If True, use a residual block after the MLP block. + input_size (int or None): Input resolution for calculating the relative positional + parameter size. + beit_like_qkv_bias (bool) + beit_like_gamma (bool) + """ + super().__init__() + self.norm1 = norm_layer(dim) + self.attn = Attention( + dim, + num_heads=num_heads, + qkv_bias=qkv_bias, + use_rel_pos=use_rel_pos, + rel_pos_zero_init=rel_pos_zero_init, + input_size=input_size if window_size == 0 else (window_size, window_size), + beit_like_qkv_bias=beit_like_qkv_bias, + interp_type=interp_type, + ) + + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.norm2 = norm_layer(dim) + self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer) + + self.window_size = window_size + + self.use_residual_block = use_residual_block + if use_residual_block: + # Use a residual block with bottleneck channel as dim // 2 + self.residual = ResBottleneckBlock( + in_channels=dim, + out_channels=dim, + bottleneck_channels=dim // 2, + norm="LN", + act_layer=act_layer, + ) + + self.beit_like_gamma = beit_like_gamma + if beit_like_gamma: + self.gamma_1 = nn.Parameter(torch.ones((dim)), requires_grad=True) + self.gamma_2 = nn.Parameter(torch.ones((dim)), requires_grad=True) + + def forward(self, x): + shortcut = x + x = self.norm1(x) + # Window partition + if self.window_size > 0: + H, W = x.shape[1], x.shape[2] + x, pad_hw = window_partition(x, self.window_size) + + x = self.attn(x) + # Reverse window partition + if self.window_size > 0: + x = window_unpartition(x, self.window_size, pad_hw, (H, W)) + + if self.beit_like_gamma: + x = shortcut + self.drop_path(self.gamma_1 * x) + x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x))) + else: + x = shortcut + self.drop_path(x) + x = x + self.drop_path(self.mlp(self.norm2(x))) + + if self.use_residual_block: + x = self.residual(x.permute(0, 3, 1, 2)).permute(0, 2, 3, 1) + + return x + + +class ViT(Backbone): + """ + This module implements Vision Transformer (ViT) backbone in :paper:`vitdet`. + "Exploring Plain Vision Transformer Backbones for Object Detection", + https://arxiv.org/abs/2203.16527 + """ + + def __init__( + self, + img_size=1024, + patch_size=16, + in_chans=3, + embed_dim=768, + depth=12, + num_heads=12, + mlp_ratio=4.0, + qkv_bias=True, + drop_path_rate=0.0, + norm_layer=LayerNormWithForceFP32, + act_layer=nn.GELU, + use_abs_pos=True, + use_rel_pos=False, + rel_pos_zero_init=True, + window_size=0, + window_block_indexes=(), + residual_block_indexes=(), + use_act_checkpoint=False, + pretrain_img_size=224, + pretrain_use_cls_token=True, + out_feature="last_feat", + beit_like_qkv_bias=True, + beit_like_gamma=False, + freeze_patch_embed=False, + interp_type="vitdet", + frozen_stages=-1, + ): + """ + Args: + img_size (int): Input image size. + patch_size (int): Patch size. + in_chans (int): Number of input image channels. + embed_dim (int): Patch embedding dimension. + depth (int): Depth of ViT. + num_heads (int): Number of attention heads in each ViT block. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool): If True, add a learnable bias to query, key, value. + drop_path_rate (float): Stochastic depth rate. + norm_layer (nn.Module): Normalization layer. + act_layer (nn.Module): Activation layer. + use_abs_pos (bool): If True, use absolute positional embeddings. + use_rel_pos (bool): If True, add relative positional embeddings to the attention map. + rel_pos_zero_init (bool): If True, zero initialize relative positional parameters. + window_size (int): Window size for window attention blocks. + window_block_indexes (list): Indexes for blocks using window attention. + residual_block_indexes (list): Indexes for blocks using conv propagation. + use_act_checkpoint (bool): If True, use activation checkpointing. + pretrain_img_size (int): input image size for pretraining models. + pretrain_use_cls_token (bool): If True, pretrainig models use class token. + out_feature (str): name of the feature from the last block. + beit_like_qkv_bias (bool): beit_like_model that has gamma_1 and gamma_2 in blocks and qkv_bias=False + beit_like_gamma (bool) + freeze_patch_embed (bool) + interp_type: "vitdet" for training / fine-ting, "beit" for eval (slightly improvement at a higher res) + """ + super().__init__() + self.pretrain_use_cls_token = pretrain_use_cls_token + + self.patch_embed = PatchEmbed( + kernel_size=(patch_size, patch_size), + stride=(patch_size, patch_size), + in_chans=in_chans, + embed_dim=embed_dim, + ) + + if use_abs_pos: + # Initialize absolute positional embedding with pretrain image size. + num_patches = (pretrain_img_size // patch_size) * (pretrain_img_size // patch_size) + num_positions = (num_patches + 1) if pretrain_use_cls_token else num_patches + self.pos_embed = nn.Parameter(torch.zeros(1, num_positions, embed_dim)) + else: + self.pos_embed = None + + # stochastic depth decay rule + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] + + self.blocks = nn.ModuleList() + if beit_like_qkv_bias: + qkv_bias = False + for i in range(depth): + block = Block( + dim=embed_dim, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + drop_path=dpr[i], + norm_layer=norm_layer, + act_layer=act_layer, + use_rel_pos=use_rel_pos, + rel_pos_zero_init=rel_pos_zero_init, + window_size=window_size if i in window_block_indexes else 0, + use_residual_block=i in residual_block_indexes, + input_size=(img_size // patch_size, img_size // patch_size), + beit_like_qkv_bias=beit_like_qkv_bias, + beit_like_gamma=beit_like_gamma, + interp_type=interp_type, + ) + if use_act_checkpoint and i > frozen_stages - 1: + block = checkpoint_wrapper(block) + self.blocks.append(block) + + self._out_feature_channels = {out_feature: embed_dim} + self._out_feature_strides = {out_feature: patch_size} + self._out_features = [out_feature] + + if self.pos_embed is not None: + nn.init.trunc_normal_(self.pos_embed, std=0.02) + + self.freeze_patch_embed = freeze_patch_embed + self.apply(self._init_weights) + + self.frozen_stages = frozen_stages + self._freeze_stages() + + def _freeze_stages(self): + if self.frozen_stages >= 0: + self.patch_embed.eval() + for param in self.patch_embed.parameters(): + param.requires_grad = False + + if self.frozen_stages >= 1 and self.pos_embed is not None: + self.pos_embed.requires_grad = False + + if self.frozen_stages >= 2: + for i in range(0, self.frozen_stages - 1): + m = self.blocks[i] + m.eval() + for name, param in m.named_parameters(): + vit_lr_decay_rate = get_vit_lr_decay_rate(f"backbone.net.blocks.{i}.{name}", lr_decay_rate=0.9, num_layers=len(self.blocks)) + logger.info(f"freeze blocks.{i}.{name} {param.size()} {vit_lr_decay_rate}") + param.requires_grad = False + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + nn.init.trunc_normal_(m.weight, std=0.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, LayerNormWithForceFP32): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + if self.freeze_patch_embed: + for n, p in self.patch_embed.named_parameters(): + p.requires_grad = False + + def forward(self, x): + x = self.patch_embed(x) + if self.pos_embed is not None: + x = x + get_abs_pos( + self.pos_embed, self.pretrain_use_cls_token, (x.shape[1], x.shape[2]) + ) + + for blk in self.blocks: + x = blk(x) + + outputs = {self._out_features[0]: x.permute(0, 3, 1, 2)} + return outputs + + +class SimpleFeaturePyramid(Backbone): + """ + This module implements SimpleFeaturePyramid in :paper:`vitdet`. + It creates pyramid features built on top of the input feature map. + """ + + def __init__( + self, + net, + in_feature, + out_channels, + scale_factors, + top_block=None, + norm="LN", + square_pad=0, + ): + """ + Args: + net (Backbone): module representing the subnetwork backbone. + Must be a subclass of :class:`Backbone`. + in_feature (str): names of the input feature maps coming + from the net. + out_channels (int): number of channels in the output feature maps. + scale_factors (list[float]): list of scaling factors to upsample or downsample + the input features for creating pyramid features. + top_block (nn.Module or None): if provided, an extra operation will + be performed on the output of the last (smallest resolution) + pyramid output, and the result will extend the result list. The top_block + further downsamples the feature map. It must have an attribute + "num_levels", meaning the number of extra pyramid levels added by + this block, and "in_feature", which is a string representing + its input feature (e.g., p5). + norm (str): the normalization to use. + square_pad (int): If > 0, require input images to be padded to specific square size. + """ + super(SimpleFeaturePyramid, self).__init__() + assert isinstance(net, Backbone) + + self.scale_factors = scale_factors + + input_shapes = net.output_shape() + strides = [int(input_shapes[in_feature].stride / scale) for scale in scale_factors] + _assert_strides_are_log2_contiguous(strides) + + dim = input_shapes[in_feature].channels + self.stages = [] + use_bias = norm == "" + for idx, scale in enumerate(scale_factors): + out_dim = dim + if scale == 4.0: + layers = [ + nn.ConvTranspose2d(dim, dim // 2, kernel_size=2, stride=2), + get_norm(norm, dim // 2), + nn.GELU(), + nn.ConvTranspose2d(dim // 2, dim // 4, kernel_size=2, stride=2), + ] + out_dim = dim // 4 + elif scale == 2.0: + layers = [nn.ConvTranspose2d(dim, dim // 2, kernel_size=2, stride=2)] + out_dim = dim // 2 + elif scale == 1.0: + layers = [] + elif scale == 0.5: + layers = [nn.MaxPool2d(kernel_size=2, stride=2)] + else: + raise NotImplementedError(f"scale_factor={scale} is not supported yet.") + + layers.extend( + [ + Conv2d( + out_dim, + out_channels, + kernel_size=1, + bias=use_bias, + norm=get_norm(norm, out_channels), + ), + Conv2d( + out_channels, + out_channels, + kernel_size=3, + padding=1, + bias=use_bias, + norm=get_norm(norm, out_channels), + ), + ] + ) + layers = nn.Sequential(*layers) + + stage = int(math.log2(strides[idx])) + self.add_module(f"simfp_{stage}", layers) + self.stages.append(layers) + + self.net = net + self.in_feature = in_feature + self.top_block = top_block + # Return feature names are "p", like ["p2", "p3", ..., "p6"] + self._out_feature_strides = {"p{}".format(int(math.log2(s))): s for s in strides} + # top block output feature maps. + if self.top_block is not None: + for s in range(stage, stage + self.top_block.num_levels): + self._out_feature_strides["p{}".format(s + 1)] = 2 ** (s + 1) + + self._out_features = list(self._out_feature_strides.keys()) + self._out_feature_channels = {k: out_channels for k in self._out_features} + self._size_divisibility = strides[-1] + self._square_pad = square_pad + + @property + def padding_constraints(self): + return { + "size_divisiblity": self._size_divisibility, + "square_size": self._square_pad, + } + + def forward(self, x): + """ + Args: + x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``. + + Returns: + dict[str->Tensor]: + mapping from feature map name to pyramid feature map tensor + in high to low resolution order. Returned feature names follow the FPN + convention: "p", where stage has stride = 2 ** stage e.g., + ["p2", "p3", ..., "p6"]. + """ + bottom_up_features = self.net(x) + features = bottom_up_features[self.in_feature] + results = [] + + for stage in self.stages: + results.append(stage(features)) + + if self.top_block is not None: + if self.top_block.in_feature in bottom_up_features: + top_block_in_feature = bottom_up_features[self.top_block.in_feature] + else: + top_block_in_feature = results[self._out_features.index(self.top_block.in_feature)] + results.extend(self.top_block(top_block_in_feature)) + assert len(self._out_features) == len(results) + return {f: res for f, res in zip(self._out_features, results)} + + +def get_vit_lr_decay_rate(name, lr_decay_rate=1.0, num_layers=12): + """ + Calculate lr decay rate for different ViT blocks. + Args: + name (string): parameter name. + lr_decay_rate (float): base lr decay rate. + num_layers (int): number of ViT blocks. + + Returns: + lr decay rate for the given parameter. + """ + if name.startswith("model_vision."): + name = name[len("model_vision."):] + + layer_id = num_layers + 1 + if name.startswith("backbone"): + if ".pos_embed" in name or ".patch_embed" in name: + layer_id = 0 + elif ".blocks." in name and ".residual." not in name: + layer_id = int(name[name.find(".blocks.") :].split(".")[2]) + 1 + + logger.info("get_vit_lr_decay_rate: name={} num_layers={} layer_id={} lr_decay_rate={}".format(name, num_layers, layer_id, lr_decay_rate ** (num_layers + 1 - layer_id))) + return lr_decay_rate ** (num_layers + 1 - layer_id) diff --git a/ape/modeling/backbone/vit_eva02.py b/ape/modeling/backbone/vit_eva02.py new file mode 100644 index 0000000..2e6f814 --- /dev/null +++ b/ape/modeling/backbone/vit_eva02.py @@ -0,0 +1,625 @@ +import logging +import math +from functools import partial + +import fvcore.nn.weight_init as weight_init +import torch +import torch.nn as nn +import torch.nn.functional as F + +from detectron2.layers import CNNBlockBase, Conv2d, get_norm +from detectron2.modeling.backbone.fpn import _assert_strides_are_log2_contiguous + +from detectron2.modeling.backbone import Backbone +from .utils_eva02 import ( + PatchEmbed, + add_decomposed_rel_pos, + get_abs_pos, + window_partition, + window_unpartition, + VisionRotaryEmbeddingFast, +) + +try: + import xformers.ops as xops +except: + pass + +try: + from apex.normalization import FusedLayerNorm +except: + pass + + +logger = logging.getLogger(__name__) + + + +__all__ = ["ViT", "SimpleFeaturePyramid", "get_vit_lr_decay_rate"] + + + +class SwiGLU(nn.Module): + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.SiLU, drop=0., + norm_layer=nn.LayerNorm, subln=False + ): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + + self.w1 = nn.Linear(in_features, hidden_features) + self.w2 = nn.Linear(in_features, hidden_features) + + self.act = act_layer() + self.ffn_ln = norm_layer(hidden_features) if subln else nn.Identity() + self.w3 = nn.Linear(hidden_features, out_features) + + self.drop = nn.Dropout(drop) + + def forward(self, x): + x1 = self.w1(x) + x2 = self.w2(x) + hidden = self.act(x1) * x2 + x = self.ffn_ln(hidden) + x = self.w3(x) + x = self.drop(x) + return x + + +class Attention(nn.Module): + def __init__( + self, + dim, + num_heads=8, + qkv_bias=True, + qk_scale=None, + attn_head_dim=None, + rope=None, + xattn=True, + ): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + if attn_head_dim is not None: + head_dim = attn_head_dim + all_head_dim = head_dim * self.num_heads + self.scale = qk_scale or head_dim ** -0.5 + + self.q_proj = nn.Linear(dim, all_head_dim, bias=False) + self.k_proj = nn.Linear(dim, all_head_dim, bias=False) + self.v_proj = nn.Linear(dim, all_head_dim, bias=False) + + if qkv_bias: + self.q_bias = nn.Parameter(torch.zeros(all_head_dim)) + self.v_bias = nn.Parameter(torch.zeros(all_head_dim)) + else: + self.q_bias = None + self.v_bias = None + + self.rope = rope + self.xattn = xattn + self.proj = nn.Linear(all_head_dim, dim) + + def forward(self, x): + B, H, W, C = x.shape + x = x.view(B, -1, C) + N = H * W + + q = F.linear(input=x, weight=self.q_proj.weight, bias=self.q_bias) + k = F.linear(input=x, weight=self.k_proj.weight, bias=None) + v = F.linear(input=x, weight=self.v_proj.weight, bias=self.v_bias) + + q = q.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3) # B, num_heads, N, C + k = k.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3) + v = v.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3) + + ## rope + q = self.rope(q).type_as(v) + k = self.rope(k).type_as(v) + + if self.xattn and not (torch.jit.is_scripting() or torch.jit.is_tracing()): + q = q.permute(0, 2, 1, 3) # B, num_heads, N, C -> B, N, num_heads, C + k = k.permute(0, 2, 1, 3) + v = v.permute(0, 2, 1, 3) + + x = xops.memory_efficient_attention(q, k, v) + x = x.reshape(B, N, -1) + else: + q = q * self.scale + attn = (q @ k.transpose(-2, -1)) + attn = attn.softmax(dim=-1).type_as(x) + x = (attn @ v).transpose(1, 2).reshape(B, N, -1) + + x = self.proj(x) + x = x.view(B, H, W, C) + + return x + + +class ResBottleneckBlock(CNNBlockBase): + """ + The standard bottleneck residual block without the last activation layer. + It contains 3 conv layers with kernels 1x1, 3x3, 1x1. + """ + + def __init__( + self, + in_channels, + out_channels, + bottleneck_channels, + norm="LN", + act_layer=nn.GELU, + ): + """ + Args: + in_channels (int): Number of input channels. + out_channels (int): Number of output channels. + bottleneck_channels (int): number of output channels for the 3x3 + "bottleneck" conv layers. + norm (str or callable): normalization for all conv layers. + See :func:`layers.get_norm` for supported format. + act_layer (callable): activation for all conv layers. + """ + super().__init__(in_channels, out_channels, 1) + + self.conv1 = Conv2d(in_channels, bottleneck_channels, 1, bias=False) + self.norm1 = get_norm(norm, bottleneck_channels) + self.act1 = act_layer() + + self.conv2 = Conv2d( + bottleneck_channels, + bottleneck_channels, + 3, + padding=1, + bias=False, + ) + self.norm2 = get_norm(norm, bottleneck_channels) + self.act2 = act_layer() + + self.conv3 = Conv2d(bottleneck_channels, out_channels, 1, bias=False) + self.norm3 = get_norm(norm, out_channels) + + for layer in [self.conv1, self.conv2, self.conv3]: + weight_init.c2_msra_fill(layer) + for layer in [self.norm1, self.norm2]: + layer.weight.data.fill_(1.0) + layer.bias.data.zero_() + # zero init last norm layer. + self.norm3.weight.data.zero_() + self.norm3.bias.data.zero_() + + def forward(self, x): + out = x + for layer in self.children(): + out = layer(out) + + out = x + out + return out + + +class Block(nn.Module): + """Transformer blocks with support of window attention and residual propagation blocks""" + + def __init__( + self, + dim, + num_heads, + mlp_ratio=4*2/3, + qkv_bias=True, + drop_path=0.0, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + window_size=0, + use_residual_block=False, + rope=None, + xattn=True, + ): + """ + Args: + dim (int): Number of input channels. + num_heads (int): Number of attention heads in each ViT block. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool): If True, add a learnable bias to query, key, value. + drop_path (float): Stochastic depth rate. + norm_layer (nn.Module): Normalization layer. + act_layer (nn.Module): Activation layer. + use_rel_pos (bool): If True, add relative positional embeddings to the attention map. + rel_pos_zero_init (bool): If True, zero initialize relative positional parameters. + window_size (int): Window size for window attention blocks. If it equals 0, then not + use window attention. + use_residual_block (bool): If True, use a residual block after the MLP block. + input_size (int or None): Input resolution for calculating the relative positional + parameter size. + """ + super().__init__() + self.norm1 = norm_layer(dim) + self.attn = Attention( + dim, + num_heads=num_heads, + qkv_bias=qkv_bias, + rope=rope, + xattn=xattn, + ) + + from timm.models.layers import DropPath + + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.norm2 = norm_layer(dim) + self.mlp = SwiGLU( + in_features=dim, + hidden_features=int(dim * mlp_ratio), + subln=True, + norm_layer=norm_layer, + ) + + self.window_size = window_size + + self.use_residual_block = use_residual_block + if use_residual_block: + # Use a residual block with bottleneck channel as dim // 2 + self.residual = ResBottleneckBlock( + in_channels=dim, + out_channels=dim, + bottleneck_channels=dim // 2, + norm="LN", + ) + + def forward(self, x): + shortcut = x + x = self.norm1(x) + + # Window partition + if self.window_size > 0: + H, W = x.shape[1], x.shape[2] + x, pad_hw = window_partition(x, self.window_size) + + x = self.attn(x) + + # Reverse window partition + if self.window_size > 0: + x = window_unpartition(x, self.window_size, pad_hw, (H, W)) + + x = shortcut + self.drop_path(x) + x = x + self.drop_path(self.mlp(self.norm2(x))) + + if self.use_residual_block: + x = self.residual(x.permute(0, 3, 1, 2)).permute(0, 2, 3, 1) + + return x + + +class ViT(Backbone): + """ + This module implements Vision Transformer (ViT) backbone in :paper:`vitdet`. + "Exploring Plain Vision Transformer Backbones for Object Detection", + https://arxiv.org/abs/2203.16527 + """ + + def __init__( + self, + img_size=1024, + patch_size=16, + in_chans=3, + embed_dim=768, + depth=12, + num_heads=12, + mlp_ratio=4*2/3, + qkv_bias=True, + drop_path_rate=0.0, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + act_layer=nn.GELU, + use_abs_pos=True, + use_rel_pos=False, + rope=True, + pt_hw_seq_len=16, + intp_freq=True, + window_size=0, + window_block_indexes=(), + residual_block_indexes=(), + use_act_checkpoint=False, + pretrain_img_size=224, + pretrain_use_cls_token=True, + out_feature="last_feat", + xattn=True, + frozen_stages=-1, + ): + """ + Args: + img_size (int): Input image size. + patch_size (int): Patch size. + in_chans (int): Number of input image channels. + embed_dim (int): Patch embedding dimension. + depth (int): Depth of ViT. + num_heads (int): Number of attention heads in each ViT block. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool): If True, add a learnable bias to query, key, value. + drop_path_rate (float): Stochastic depth rate. + norm_layer (nn.Module): Normalization layer. + act_layer (nn.Module): Activation layer. + use_abs_pos (bool): If True, use absolute positional embeddings. + use_rel_pos (bool): If True, add relative positional embeddings to the attention map. + rel_pos_zero_init (bool): If True, zero initialize relative positional parameters. + window_size (int): Window size for window attention blocks. + window_block_indexes (list): Indexes for blocks using window attention. + residual_block_indexes (list): Indexes for blocks using conv propagation. + use_act_checkpoint (bool): If True, use activation checkpointing. + pretrain_img_size (int): input image size for pretraining models. + pretrain_use_cls_token (bool): If True, pretrainig models use class token. + out_feature (str): name of the feature from the last block. + """ + super().__init__() + self.pretrain_use_cls_token = pretrain_use_cls_token + + self.patch_embed = PatchEmbed( + kernel_size=(patch_size, patch_size), + stride=(patch_size, patch_size), + in_chans=in_chans, + embed_dim=embed_dim, + ) + + if use_abs_pos: + # Initialize absolute positional embedding with pretrain image size. + num_patches = (pretrain_img_size // patch_size) * (pretrain_img_size // patch_size) + num_positions = (num_patches + 1) if pretrain_use_cls_token else num_patches + self.pos_embed = nn.Parameter(torch.zeros(1, num_positions, embed_dim)) + else: + self.pos_embed = None + + + half_head_dim = embed_dim // num_heads // 2 + hw_seq_len = img_size // patch_size + + self.rope_win = VisionRotaryEmbeddingFast( + dim=half_head_dim, + pt_seq_len=pt_hw_seq_len, + ft_seq_len=window_size if intp_freq else None, + ) + self.rope_glb = VisionRotaryEmbeddingFast( + dim=half_head_dim, + pt_seq_len=pt_hw_seq_len, + ft_seq_len=hw_seq_len if intp_freq else None, + ) + + # stochastic depth decay rule + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] + + self.blocks = nn.ModuleList() + for i in range(depth): + block = Block( + dim=embed_dim, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + drop_path=dpr[i], + norm_layer=norm_layer, + window_size=window_size if i in window_block_indexes else 0, + use_residual_block=i in residual_block_indexes, + rope=self.rope_win if i in window_block_indexes else self.rope_glb, + xattn=xattn + ) + if use_act_checkpoint and i > frozen_stages - 1: + # TODO: use torch.utils.checkpoint + from fairscale.nn.checkpoint import checkpoint_wrapper + + block = checkpoint_wrapper(block) + self.blocks.append(block) + + self._out_feature_channels = {out_feature: embed_dim} + self._out_feature_strides = {out_feature: patch_size} + self._out_features = [out_feature] + + if self.pos_embed is not None: + nn.init.trunc_normal_(self.pos_embed, std=0.02) + + self.apply(self._init_weights) + + self.frozen_stages = frozen_stages + self._freeze_stages() + + def _freeze_stages(self): + if self.frozen_stages >= 0: + self.patch_embed.eval() + for param in self.patch_embed.parameters(): + param.requires_grad = False + + if self.frozen_stages >= 1 and self.pos_embed is not None: + self.pos_embed.requires_grad = False + + if self.frozen_stages >= 2: + for i in range(0, self.frozen_stages - 1): + m = self.blocks[i] + m.eval() + for name, param in m.named_parameters(): + vit_lr_decay_rate = get_vit_lr_decay_rate(f"backbone.net.blocks.{i}.{name}", lr_decay_rate=0.9, num_layers=len(self.blocks)) + logger.info(f"freeze blocks.{i}.{name} {param.size()} {vit_lr_decay_rate}") + param.requires_grad = False + + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + nn.init.trunc_normal_(m.weight, std=0.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def forward(self, x): + x = self.patch_embed(x) + if self.pos_embed is not None: + x = x + get_abs_pos( + self.pos_embed, self.pretrain_use_cls_token, (x.shape[1], x.shape[2]) + ) + + for blk in self.blocks: + x = blk(x) + + outputs = {self._out_features[0]: x.permute(0, 3, 1, 2)} + return outputs + + +class SimpleFeaturePyramid(Backbone): + """ + This module implements SimpleFeaturePyramid in :paper:`vitdet`. + It creates pyramid features built on top of the input feature map. + """ + + def __init__( + self, + net, + in_feature, + out_channels, + scale_factors, + top_block=None, + norm="LN", + square_pad=0, + ): + """ + Args: + net (Backbone): module representing the subnetwork backbone. + Must be a subclass of :class:`Backbone`. + in_feature (str): names of the input feature maps coming + from the net. + out_channels (int): number of channels in the output feature maps. + scale_factors (list[float]): list of scaling factors to upsample or downsample + the input features for creating pyramid features. + top_block (nn.Module or None): if provided, an extra operation will + be performed on the output of the last (smallest resolution) + pyramid output, and the result will extend the result list. The top_block + further downsamples the feature map. It must have an attribute + "num_levels", meaning the number of extra pyramid levels added by + this block, and "in_feature", which is a string representing + its input feature (e.g., p5). + norm (str): the normalization to use. + square_pad (int): If > 0, require input images to be padded to specific square size. + """ + super(SimpleFeaturePyramid, self).__init__() + assert isinstance(net, Backbone) + + self.scale_factors = scale_factors + + input_shapes = net.output_shape() + strides = [int(input_shapes[in_feature].stride / scale) for scale in scale_factors] + _assert_strides_are_log2_contiguous(strides) + + dim = input_shapes[in_feature].channels + self.stages = [] + use_bias = norm == "" + for idx, scale in enumerate(scale_factors): + out_dim = dim + if scale == 4.0: + layers = [ + nn.ConvTranspose2d(dim, dim // 2, kernel_size=2, stride=2), + get_norm(norm, dim // 2), + nn.GELU(), + nn.ConvTranspose2d(dim // 2, dim // 4, kernel_size=2, stride=2), + ] + out_dim = dim // 4 + elif scale == 2.0: + layers = [nn.ConvTranspose2d(dim, dim // 2, kernel_size=2, stride=2)] + out_dim = dim // 2 + elif scale == 1.0: + layers = [] + elif scale == 0.5: + layers = [nn.MaxPool2d(kernel_size=2, stride=2)] + else: + raise NotImplementedError(f"scale_factor={scale} is not supported yet.") + + layers.extend( + [ + Conv2d( + out_dim, + out_channels, + kernel_size=1, + bias=use_bias, + norm=get_norm(norm, out_channels), + ), + Conv2d( + out_channels, + out_channels, + kernel_size=3, + padding=1, + bias=use_bias, + norm=get_norm(norm, out_channels), + ), + ] + ) + layers = nn.Sequential(*layers) + + stage = int(math.log2(strides[idx])) + self.add_module(f"simfp_{stage}", layers) + self.stages.append(layers) + + self.net = net + self.in_feature = in_feature + self.top_block = top_block + # Return feature names are "p", like ["p2", "p3", ..., "p6"] + self._out_feature_strides = {"p{}".format(int(math.log2(s))): s for s in strides} + # top block output feature maps. + if self.top_block is not None: + for s in range(stage, stage + self.top_block.num_levels): + self._out_feature_strides["p{}".format(s + 1)] = 2 ** (s + 1) + + self._out_features = list(self._out_feature_strides.keys()) + self._out_feature_channels = {k: out_channels for k in self._out_features} + self._size_divisibility = strides[-1] + self._square_pad = square_pad + + @property + def padding_constraints(self): + return { + "size_divisiblity": self._size_divisibility, + "square_size": self._square_pad, + } + + def forward(self, x): + """ + Args: + x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``. + + Returns: + dict[str->Tensor]: + mapping from feature map name to pyramid feature map tensor + in high to low resolution order. Returned feature names follow the FPN + convention: "p", where stage has stride = 2 ** stage e.g., + ["p2", "p3", ..., "p6"]. + """ + bottom_up_features = self.net(x) + features = bottom_up_features[self.in_feature] + results = [] + + for stage in self.stages: + results.append(stage(features)) + + if self.top_block is not None: + if self.top_block.in_feature in bottom_up_features: + top_block_in_feature = bottom_up_features[self.top_block.in_feature] + else: + top_block_in_feature = results[self._out_features.index(self.top_block.in_feature)] + results.extend(self.top_block(top_block_in_feature)) + assert len(self._out_features) == len(results) + return {f: res for f, res in zip(self._out_features, results)} + + +def get_vit_lr_decay_rate(name, lr_decay_rate=1.0, num_layers=12): + """ + Calculate lr decay rate for different ViT blocks. + Args: + name (string): parameter name. + lr_decay_rate (float): base lr decay rate. + num_layers (int): number of ViT blocks. + + Returns: + lr decay rate for the given parameter. + """ + if name.startswith("model_vision."): + name = name[len("model_vision."):] + + layer_id = num_layers + 1 + if name.startswith("backbone"): + if ".pos_embed" in name or ".patch_embed" in name: + layer_id = 0 + elif ".blocks." in name and ".residual." not in name: + layer_id = int(name[name.find(".blocks.") :].split(".")[2]) + 1 + + logger.info("get_vit_lr_decay_rate: name={} num_layers={} layer_id={} lr_decay_rate={}".format(name, num_layers, layer_id, lr_decay_rate ** (num_layers + 1 - layer_id))) + return lr_decay_rate ** (num_layers + 1 - layer_id) diff --git a/ape/modeling/backbone/vit_eva_clip.py b/ape/modeling/backbone/vit_eva_clip.py new file mode 100644 index 0000000..e0d66d5 --- /dev/null +++ b/ape/modeling/backbone/vit_eva_clip.py @@ -0,0 +1,931 @@ +import logging +import math +from functools import partial + +import fvcore.nn.weight_init as weight_init +import torch +import torch.nn as nn +import torch.nn.functional as F + +from detectron2.layers import CNNBlockBase, Conv2d, get_norm +from detectron2.modeling.backbone.fpn import _assert_strides_are_log2_contiguous + +from detectron2.modeling.backbone import Backbone +from .utils_eva02 import ( + PatchEmbed, + add_decomposed_rel_pos, + get_abs_pos, + window_partition, + window_unpartition, + VisionRotaryEmbeddingFast, +) + +try: + import xformers.ops as xops +except: + pass + +try: + from apex.normalization import FusedLayerNorm +except: + pass + + +logger = logging.getLogger(__name__) + + + +__all__ = ["ViT", "SimpleFeaturePyramid", "get_vit_lr_decay_rate"] + + +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" + + def __init__(self, drop_prob=None): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + + def forward(self, x): + return drop_path(x, self.drop_prob, self.training) + + def extra_repr(self) -> str: + return "p={}".format(self.drop_prob) + + +class Mlp(nn.Module): + def __init__( + self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + drop=0.0, + subln=False, + ): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + + self.ffn_ln = norm_layer(hidden_features) if subln else nn.Identity() + + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + # x = self.drop(x) + # commit this for the orignal BERT implement + x = self.ffn_ln(x) + + x = self.fc2(x) + x = self.drop(x) + return x + + +class SwiGLU(nn.Module): + def __init__( + self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.SiLU, + drop=0.0, + norm_layer=nn.LayerNorm, + subln=False, + ): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + + self.w1 = nn.Linear(in_features, hidden_features) + self.w2 = nn.Linear(in_features, hidden_features) + + self.act = act_layer() + self.ffn_ln = norm_layer(hidden_features) if subln else nn.Identity() + self.w3 = nn.Linear(hidden_features, out_features) + + self.drop = nn.Dropout(drop) + + def forward(self, x): + x1 = self.w1(x) + x2 = self.w2(x) + hidden = self.act(x1) * x2 + x = self.ffn_ln(hidden) + x = self.w3(x) + x = self.drop(x) + return x + + +class Attention(nn.Module): + def __init__( + self, + dim, + num_heads=8, + qkv_bias=False, + qk_scale=None, + attn_drop=0.0, + proj_drop=0.0, + window_size=0, + attn_head_dim=None, + xattn=False, + rope=None, + subln=False, + norm_layer=nn.LayerNorm, + ): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + if attn_head_dim is not None: + head_dim = attn_head_dim + all_head_dim = head_dim * self.num_heads + self.scale = qk_scale or head_dim**-0.5 + + self.subln = subln + if self.subln: + self.q_proj = nn.Linear(dim, all_head_dim, bias=False) + self.k_proj = nn.Linear(dim, all_head_dim, bias=False) + self.v_proj = nn.Linear(dim, all_head_dim, bias=False) + else: + self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False) + + if qkv_bias: + self.q_bias = nn.Parameter(torch.zeros(all_head_dim)) + self.v_bias = nn.Parameter(torch.zeros(all_head_dim)) + else: + self.q_bias = None + self.v_bias = None + + if window_size and False: + self.window_size = window_size + self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3 + self.relative_position_bias_table = nn.Parameter( + torch.zeros(self.num_relative_distance, num_heads) + ) # 2*Wh-1 * 2*Ww-1, nH + # cls to token & token 2 cls & cls to cls + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(window_size[0]) + coords_w = torch.arange(window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = ( + coords_flatten[:, :, None] - coords_flatten[:, None, :] + ) # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * window_size[1] - 1 + relative_position_index = torch.zeros( + size=(window_size[0] * window_size[1] + 1,) * 2, dtype=relative_coords.dtype + ) + relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + relative_position_index[0, 0:] = self.num_relative_distance - 3 + relative_position_index[0:, 0] = self.num_relative_distance - 2 + relative_position_index[0, 0] = self.num_relative_distance - 1 + + self.register_buffer("relative_position_index", relative_position_index) + else: + self.window_size = None + self.relative_position_bias_table = None + self.relative_position_index = None + + self.attn_drop = nn.Dropout(attn_drop) + self.inner_attn_ln = norm_layer(all_head_dim) if subln else nn.Identity() + # self.proj = nn.Linear(all_head_dim, all_head_dim) + self.proj = nn.Linear(all_head_dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + self.xattn = xattn + self.xattn_drop = attn_drop + + self.rope = rope + + def forward(self, x, rel_pos_bias=None, attn_mask=None): + # B, N, C = x.shape + + B, H, W, C = x.shape + x = x.view(B, -1, C) + N = H * W + + if self.subln: + q = F.linear(input=x, weight=self.q_proj.weight, bias=self.q_bias) + k = F.linear(input=x, weight=self.k_proj.weight, bias=None) + v = F.linear(input=x, weight=self.v_proj.weight, bias=self.v_bias) + + q = q.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3) # B, num_heads, N, C + k = k.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3) + v = v.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3) + else: + + qkv_bias = None + if self.q_bias is not None: + qkv_bias = torch.cat( + (self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias) + ) + + qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias) + qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute( + 2, 0, 3, 1, 4 + ) # 3, B, num_heads, N, C + q, k, v = qkv[0], qkv[1], qkv[2] + + if self.rope: + # slightly fast impl + # q_t = q[:, :, 1:, :] + # ro_q_t = self.rope(q_t) + # q = torch.cat((q[:, :, :1, :], ro_q_t), -2).type_as(v) + + # k_t = k[:, :, 1:, :] + # ro_k_t = self.rope(k_t) + # k = torch.cat((k[:, :, :1, :], ro_k_t), -2).type_as(v) + + ## rope + q = self.rope(q).type_as(v) + k = self.rope(k).type_as(v) + + if self.xattn and not (torch.jit.is_scripting() or torch.jit.is_tracing()): + q = q.permute(0, 2, 1, 3) # B, num_heads, N, C -> B, N, num_heads, C + k = k.permute(0, 2, 1, 3) + v = v.permute(0, 2, 1, 3) + + x = xops.memory_efficient_attention( + q, + k, + v, + p=self.xattn_drop, + scale=self.scale, + ) + x = x.reshape(B, N, -1) + x = self.inner_attn_ln(x) + x = self.proj(x) + x = self.proj_drop(x) + else: + q = q * self.scale + attn = q @ k.transpose(-2, -1) + + if self.relative_position_bias_table is not None: + relative_position_bias = self.relative_position_bias_table[ + self.relative_position_index.view(-1) + ].view( + self.window_size[0] * self.window_size[1] + 1, + self.window_size[0] * self.window_size[1] + 1, + -1, + ) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute( + 2, 0, 1 + ).contiguous() # nH, Wh*Ww, Wh*Ww + attn = attn + relative_position_bias.unsqueeze(0).type_as(attn) + + if rel_pos_bias is not None: + attn = attn + rel_pos_bias.type_as(attn) + + if attn_mask is not None: + attn_mask = attn_mask.bool() + attn = attn.masked_fill(~attn_mask[:, None, None, :], float("-inf")) + + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, -1) + x = self.inner_attn_ln(x) + x = self.proj(x) + x = self.proj_drop(x) + + x = x.view(B, H, W, C) + + return x + + +class ResBottleneckBlock(CNNBlockBase): + """ + The standard bottleneck residual block without the last activation layer. + It contains 3 conv layers with kernels 1x1, 3x3, 1x1. + """ + + def __init__( + self, + in_channels, + out_channels, + bottleneck_channels, + norm="LN", + act_layer=nn.GELU, + ): + """ + Args: + in_channels (int): Number of input channels. + out_channels (int): Number of output channels. + bottleneck_channels (int): number of output channels for the 3x3 + "bottleneck" conv layers. + norm (str or callable): normalization for all conv layers. + See :func:`layers.get_norm` for supported format. + act_layer (callable): activation for all conv layers. + """ + super().__init__(in_channels, out_channels, 1) + + self.conv1 = Conv2d(in_channels, bottleneck_channels, 1, bias=False) + self.norm1 = get_norm(norm, bottleneck_channels) + self.act1 = act_layer() + + self.conv2 = Conv2d( + bottleneck_channels, + bottleneck_channels, + 3, + padding=1, + bias=False, + ) + self.norm2 = get_norm(norm, bottleneck_channels) + self.act2 = act_layer() + + self.conv3 = Conv2d(bottleneck_channels, out_channels, 1, bias=False) + self.norm3 = get_norm(norm, out_channels) + + for layer in [self.conv1, self.conv2, self.conv3]: + weight_init.c2_msra_fill(layer) + for layer in [self.norm1, self.norm2]: + layer.weight.data.fill_(1.0) + layer.bias.data.zero_() + # zero init last norm layer. + self.norm3.weight.data.zero_() + self.norm3.bias.data.zero_() + + def forward(self, x): + out = x + for layer in self.children(): + out = layer(out) + + out = x + out + return out + + +class Block(nn.Module): + """Transformer blocks with support of window attention and residual propagation blocks""" + + def __init__( + self, + dim, + num_heads, + mlp_ratio=4.0, + qkv_bias=False, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + init_values=None, + act_layer=nn.GELU, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + window_size=0, + use_residual_block=False, + attn_head_dim=None, + rope=None, + xattn=False, + postnorm=False, + subln=False, + naiveswiglu=False, + ): + """ + Args: + dim (int): Number of input channels. + num_heads (int): Number of attention heads in each ViT block. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool): If True, add a learnable bias to query, key, value. + drop_path (float): Stochastic depth rate. + norm_layer (nn.Module): Normalization layer. + act_layer (nn.Module): Activation layer. + use_rel_pos (bool): If True, add relative positional embeddings to the attention map. + rel_pos_zero_init (bool): If True, zero initialize relative positional parameters. + window_size (int): Window size for window attention blocks. If it equals 0, then not + use window attention. + use_residual_block (bool): If True, use a residual block after the MLP block. + input_size (int or None): Input resolution for calculating the relative positional + parameter size. + """ + super().__init__() + self.norm1 = norm_layer(dim) + self.attn = Attention( + dim, + num_heads=num_heads, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop, + window_size=window_size, + attn_head_dim=attn_head_dim, + rope=rope, + xattn=xattn, + subln=subln, + norm_layer=norm_layer, + ) + + from timm.models.layers import DropPath + + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + + if naiveswiglu: + self.mlp = SwiGLU( + in_features=dim, + hidden_features=mlp_hidden_dim, + subln=subln, + norm_layer=norm_layer, + ) + else: + self.mlp = Mlp( + in_features=dim, + hidden_features=mlp_hidden_dim, + act_layer=act_layer, + subln=subln, + drop=drop, + ) + + if init_values is not None and init_values > 0: + self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True) + self.gamma_2 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True) + else: + self.gamma_1, self.gamma_2 = None, None + + self.postnorm = postnorm + + self.window_size = window_size + + self.use_residual_block = use_residual_block + if use_residual_block: + # Use a residual block with bottleneck channel as dim // 2 + self.residual = ResBottleneckBlock( + in_channels=dim, + out_channels=dim, + bottleneck_channels=dim // 2, + norm="LN", + ) + + def forward(self, x, rel_pos_bias=None, attn_mask=None): + if self.gamma_1 is None: + if self.postnorm: + shortcut = x + + # Window partition + if self.window_size > 0: + H, W = x.shape[1], x.shape[2] + x, pad_hw = window_partition(x, self.window_size) + + x = self.attn(x, rel_pos_bias=rel_pos_bias, attn_mask=attn_mask) + + # Reverse window partition + if self.window_size > 0: + x = window_unpartition(x, self.window_size, pad_hw, (H, W)) + + x = self.norm1(x) + x = shortcut + self.drop_path(x) + + # x = x + self.drop_path(self.norm1(self.attn(x, rel_pos_bias=rel_pos_bias, attn_mask=attn_mask))) + x = x + self.drop_path(self.norm2(self.mlp(x))) + else: + shortcut = x + x = self.norm1(x) + + # Window partition + if self.window_size > 0: + H, W = x.shape[1], x.shape[2] + x, pad_hw = window_partition(x, self.window_size) + + x = self.attn(x, rel_pos_bias=rel_pos_bias, attn_mask=attn_mask) + + # Reverse window partition + if self.window_size > 0: + x = window_unpartition(x, self.window_size, pad_hw, (H, W)) + + x = shortcut + self.drop_path(x) + + # x = x + self.drop_path(self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias, attn_mask=attn_mask)) + x = x + self.drop_path(self.mlp(self.norm2(x))) + else: + if self.postnorm: + shortcut = x + + # Window partition + if self.window_size > 0: + H, W = x.shape[1], x.shape[2] + x, pad_hw = window_partition(x, self.window_size) + + x = self.attn(x, rel_pos_bias=rel_pos_bias, attn_mask=attn_mask) + + # Reverse window partition + if self.window_size > 0: + x = window_unpartition(x, self.window_size, pad_hw, (H, W)) + + x = self.norm1(x) + x = shortcut + self.drop_path(self.gamma_1 * x) + + # x = x + self.drop_path(self.gamma_1 * self.norm1(self.attn(x, rel_pos_bias=rel_pos_bias, attn_mask=attn_mask))) + x = x + self.drop_path(self.gamma_2 * self.norm2(self.mlp(x))) + else: + shortcut = x + x = self.norm1(x) + + # Window partition + if self.window_size > 0: + H, W = x.shape[1], x.shape[2] + x, pad_hw = window_partition(x, self.window_size) + + x = self.attn(x, rel_pos_bias=rel_pos_bias, attn_mask=attn_mask) + + # Reverse window partition + if self.window_size > 0: + x = window_unpartition(x, self.window_size, pad_hw, (H, W)) + + x = shortcut + self.drop_path(self.gamma_1 * x) + + # x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias, attn_mask=attn_mask)) + x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x))) + + if self.use_residual_block: + x = self.residual(x.permute(0, 3, 1, 2)).permute(0, 2, 3, 1) + + return x + + +class ViT(Backbone): + """ + This module implements Vision Transformer (ViT) backbone in :paper:`vitdet`. + "Exploring Plain Vision Transformer Backbones for Object Detection", + https://arxiv.org/abs/2203.16527 + """ + + def __init__( + self, + img_size=1024, + patch_size=16, + in_chans=3, + embed_dim=768, + depth=12, + num_heads=12, + mlp_ratio=4.0, + qkv_bias=False, + qk_scale=None, + drop_rate=0.0, + attn_drop_rate=0.0, + drop_path_rate=0.0, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + init_values=None, + use_abs_pos=True, + use_rel_pos=False, + rope=False, + postnorm=False, + pt_hw_seq_len=16, + intp_freq=False, + naiveswiglu=False, + subln=False, + window_size=0, + window_block_indexes=(), + residual_block_indexes=(), + use_act_checkpoint=False, + pretrain_img_size=224, + pretrain_use_cls_token=True, + out_feature="last_feat", + xattn=False, + frozen_stages=-1, + ): + """ + Args: + img_size (int): Input image size. + patch_size (int): Patch size. + in_chans (int): Number of input image channels. + embed_dim (int): Patch embedding dimension. + depth (int): Depth of ViT. + num_heads (int): Number of attention heads in each ViT block. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool): If True, add a learnable bias to query, key, value. + drop_path_rate (float): Stochastic depth rate. + norm_layer (nn.Module): Normalization layer. + act_layer (nn.Module): Activation layer. + use_abs_pos (bool): If True, use absolute positional embeddings. + use_rel_pos (bool): If True, add relative positional embeddings to the attention map. + rel_pos_zero_init (bool): If True, zero initialize relative positional parameters. + window_size (int): Window size for window attention blocks. + window_block_indexes (list): Indexes for blocks using window attention. + residual_block_indexes (list): Indexes for blocks using conv propagation. + use_act_checkpoint (bool): If True, use activation checkpointing. + pretrain_img_size (int): input image size for pretraining models. + pretrain_use_cls_token (bool): If True, pretrainig models use class token. + out_feature (str): name of the feature from the last block. + """ + super().__init__() + self.pretrain_use_cls_token = pretrain_use_cls_token + + self.patch_embed = PatchEmbed( + kernel_size=(patch_size, patch_size), + stride=(patch_size, patch_size), + in_chans=in_chans, + embed_dim=embed_dim, + ) + + if use_abs_pos: + # Initialize absolute positional embedding with pretrain image size. + num_patches = (pretrain_img_size // patch_size) * (pretrain_img_size // patch_size) + num_positions = (num_patches + 1) if pretrain_use_cls_token else num_patches + self.pos_embed = nn.Parameter(torch.zeros(1, num_positions, embed_dim)) + else: + self.pos_embed = None + + if rope: + half_head_dim = embed_dim // num_heads // 2 + hw_seq_len = img_size // patch_size + self.rope_win = VisionRotaryEmbeddingFast( + dim=half_head_dim, + pt_seq_len=pt_hw_seq_len, + ft_seq_len=window_size if intp_freq else None, + ) + self.rope_glb = VisionRotaryEmbeddingFast( + dim=half_head_dim, + pt_seq_len=pt_hw_seq_len, + ft_seq_len=hw_seq_len if intp_freq else None, + ) + else: + self.rope_win = None + self.rope_glb = None + + self.naiveswiglu = naiveswiglu + + # stochastic depth decay rule + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] + + self.blocks = nn.ModuleList() + for i in range(depth): + block = Block( + dim=embed_dim, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[i], + norm_layer=norm_layer, + init_values=init_values, + window_size=window_size if i in window_block_indexes else 0, + postnorm=postnorm, + subln=subln, + naiveswiglu=naiveswiglu, + use_residual_block=i in residual_block_indexes, + rope=self.rope_win if i in window_block_indexes else self.rope_glb, + xattn=xattn, + ) + if use_act_checkpoint and i > frozen_stages - 1: + # TODO: use torch.utils.checkpoint + from fairscale.nn.checkpoint import checkpoint_wrapper + + block = checkpoint_wrapper(block) + self.blocks.append(block) + + self._out_feature_channels = {out_feature: embed_dim} + self._out_feature_strides = {out_feature: patch_size} + self._out_features = [out_feature] + + if self.pos_embed is not None: + nn.init.trunc_normal_(self.pos_embed, std=0.02) + + self.apply(self._init_weights) + + self.frozen_stages = frozen_stages + self._freeze_stages() + + def _freeze_stages(self): + if self.frozen_stages >= 0: + self.patch_embed.eval() + for param in self.patch_embed.parameters(): + param.requires_grad = False + + if self.frozen_stages >= 1 and self.pos_embed is not None: + self.pos_embed.requires_grad = False + + if self.frozen_stages >= 2: + for i in range(0, self.frozen_stages - 1): + m = self.blocks[i] + m.eval() + for name, param in m.named_parameters(): + vit_lr_decay_rate = get_vit_lr_decay_rate(f"backbone.net.blocks.{i}.{name}", lr_decay_rate=0.9, num_layers=len(self.blocks)) + logger.info(f"freeze blocks.{i}.{name} {param.size()} {vit_lr_decay_rate}") + param.requires_grad = False + + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + nn.init.trunc_normal_(m.weight, std=0.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def forward(self, x): + x = self.patch_embed(x) + if self.pos_embed is not None: + x = x + get_abs_pos( + self.pos_embed, self.pretrain_use_cls_token, (x.shape[1], x.shape[2]) + ) + + for blk in self.blocks: + x = blk(x) + + outputs = {self._out_features[0]: x.permute(0, 3, 1, 2)} + return outputs + + +class SimpleFeaturePyramid(Backbone): + """ + This module implements SimpleFeaturePyramid in :paper:`vitdet`. + It creates pyramid features built on top of the input feature map. + """ + + def __init__( + self, + net, + in_feature, + out_channels, + scale_factors, + top_block=None, + norm="LN", + square_pad=0, + ): + """ + Args: + net (Backbone): module representing the subnetwork backbone. + Must be a subclass of :class:`Backbone`. + in_feature (str): names of the input feature maps coming + from the net. + out_channels (int): number of channels in the output feature maps. + scale_factors (list[float]): list of scaling factors to upsample or downsample + the input features for creating pyramid features. + top_block (nn.Module or None): if provided, an extra operation will + be performed on the output of the last (smallest resolution) + pyramid output, and the result will extend the result list. The top_block + further downsamples the feature map. It must have an attribute + "num_levels", meaning the number of extra pyramid levels added by + this block, and "in_feature", which is a string representing + its input feature (e.g., p5). + norm (str): the normalization to use. + square_pad (int): If > 0, require input images to be padded to specific square size. + """ + super(SimpleFeaturePyramid, self).__init__() + assert isinstance(net, Backbone) + + self.scale_factors = scale_factors + + input_shapes = net.output_shape() + strides = [int(input_shapes[in_feature].stride / scale) for scale in scale_factors] + _assert_strides_are_log2_contiguous(strides) + + dim = input_shapes[in_feature].channels + self.stages = [] + use_bias = norm == "" + for idx, scale in enumerate(scale_factors): + out_dim = dim + if scale == 4.0: + layers = [ + nn.ConvTranspose2d(dim, dim // 2, kernel_size=2, stride=2), + get_norm(norm, dim // 2), + nn.GELU(), + nn.ConvTranspose2d(dim // 2, dim // 4, kernel_size=2, stride=2), + ] + out_dim = dim // 4 + elif scale == 2.0: + layers = [nn.ConvTranspose2d(dim, dim // 2, kernel_size=2, stride=2)] + out_dim = dim // 2 + elif scale == 1.0: + layers = [] + elif scale == 0.5: + layers = [nn.MaxPool2d(kernel_size=2, stride=2)] + else: + raise NotImplementedError(f"scale_factor={scale} is not supported yet.") + + layers.extend( + [ + Conv2d( + out_dim, + out_channels, + kernel_size=1, + bias=use_bias, + norm=get_norm(norm, out_channels), + ), + Conv2d( + out_channels, + out_channels, + kernel_size=3, + padding=1, + bias=use_bias, + norm=get_norm(norm, out_channels), + ), + ] + ) + layers = nn.Sequential(*layers) + + stage = int(math.log2(strides[idx])) + self.add_module(f"simfp_{stage}", layers) + self.stages.append(layers) + + self.net = net + self.in_feature = in_feature + self.top_block = top_block + # Return feature names are "p", like ["p2", "p3", ..., "p6"] + self._out_feature_strides = {"p{}".format(int(math.log2(s))): s for s in strides} + # top block output feature maps. + if self.top_block is not None: + for s in range(stage, stage + self.top_block.num_levels): + self._out_feature_strides["p{}".format(s + 1)] = 2 ** (s + 1) + + self._out_features = list(self._out_feature_strides.keys()) + self._out_feature_channels = {k: out_channels for k in self._out_features} + self._size_divisibility = strides[-1] + self._square_pad = square_pad + + @property + def padding_constraints(self): + return { + "size_divisiblity": self._size_divisibility, + "square_size": self._square_pad, + } + + def forward(self, x): + """ + Args: + x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``. + + Returns: + dict[str->Tensor]: + mapping from feature map name to pyramid feature map tensor + in high to low resolution order. Returned feature names follow the FPN + convention: "p", where stage has stride = 2 ** stage e.g., + ["p2", "p3", ..., "p6"]. + """ + bottom_up_features = self.net(x) + features = bottom_up_features[self.in_feature] + results = [] + + for stage in self.stages: + results.append(stage(features)) + # with torch.cuda.amp.autocast(enabled=False): + # results.append(stage(features.float())) + if torch.any(torch.isnan(results[-1])): + v = results[-1] + print("stage", len(results), v, v.size(), v.max(), v.min(), "all finite:", torch.all(torch.isfinite(v)), "any nan:", torch.any(torch.isnan(v))) + v = features + print(self.in_feature, v, v.size(), v.max(), v.min(), "all finite:", torch.all(torch.isfinite(v)), "any nan:", torch.any(torch.isnan(v))) + print("stage parameters", stage, sum([_.sum() for _ in stage.parameters()])) + for name, param in stage.named_parameters(): + print(name, param.size(), param.sum(), param.max(), param.min(), "all finite:", torch.all(torch.isfinite(param)), "any nan:", torch.any(torch.isnan(param))) + for name, buf in stage.named_buffers(): + print(name, buf.size(), buf.sum(), buf.max(), buf.min(), "all finite:", torch.all(torch.isfinite(buf)), "any nan:", torch.any(torch.isnan(buf))) + + with torch.cuda.amp.autocast(enabled=False): + results.pop() + results.append(stage(features.float())) + # results[-1] = stage(features.float()) + + if self.top_block is not None: + if self.top_block.in_feature in bottom_up_features: + top_block_in_feature = bottom_up_features[self.top_block.in_feature] + else: + top_block_in_feature = results[self._out_features.index(self.top_block.in_feature)] + results.extend(self.top_block(top_block_in_feature)) + # with torch.cuda.amp.autocast(enabled=False): + # results.extend(self.top_block(top_block_in_feature.float())) + if torch.any(torch.isnan(results[-1])): + v = results[-1] + print(len(results), v, v.size(), v.max(), v.min(), "all finite:", torch.all(torch.isfinite(v)), "any nan:", torch.any(torch.isnan(v))) + v = top_block_in_feature + print(self.top_block.in_feature, v, v.size(), v.max(), v.min(), "all finite:", torch.all(torch.isfinite(v)), "any nan:", torch.any(torch.isnan(v))) + print("top_block parameters", self.top_block, sum([_.sum() for _ in self.top_block.parameters()])) + assert len(self._out_features) == len(results) + return {f: res for f, res in zip(self._out_features, results)} + + +def get_vit_lr_decay_rate(name, lr_decay_rate=1.0, num_layers=12): + """ + Calculate lr decay rate for different ViT blocks. + Args: + name (string): parameter name. + lr_decay_rate (float): base lr decay rate. + num_layers (int): number of ViT blocks. + + Returns: + lr decay rate for the given parameter. + """ + if name.startswith("model_vision."): + name = name[len("model_vision.") :] + + layer_id = num_layers + 1 + if name.startswith("backbone"): + if ".pos_embed" in name or ".patch_embed" in name: + layer_id = 0 + elif ".blocks." in name and ".residual." not in name: + layer_id = int(name[name.find(".blocks.") :].split(".")[2]) + 1 + + logger.info( + "get_vit_lr_decay_rate: name={} num_layers={} layer_id={} lr_decay_rate={}".format( + name, num_layers, layer_id, lr_decay_rate ** (num_layers + 1 - layer_id) + ) + ) + return lr_decay_rate ** (num_layers + 1 - layer_id) diff --git a/ape/modeling/deta/__init__.py b/ape/modeling/deta/__init__.py new file mode 100644 index 0000000..184b9e7 --- /dev/null +++ b/ape/modeling/deta/__init__.py @@ -0,0 +1,9 @@ +from .assigner import Stage1Assigner, Stage2Assigner +from .deformable_criterion import DeformableCriterion +from .deformable_detr import DeformableDETR +from .deformable_detr_segm import DeformableDETRSegm +from .deformable_transformer import ( + DeformableDetrTransformer, + DeformableDetrTransformerDecoder, + DeformableDetrTransformerEncoder, +) diff --git a/ape/modeling/deta/assigner.py b/ape/modeling/deta/assigner.py new file mode 100644 index 0000000..a6ee56c --- /dev/null +++ b/ape/modeling/deta/assigner.py @@ -0,0 +1,358 @@ +from typing import List + +import torch +import torch.nn as nn + +from ape.utils.box_ops import box_cxcywh_to_xyxy, box_iou, box_xyxy_to_cxcywh, generalized_box_iou + + +def nonzero_tuple(x): + """ + A 'as_tuple=True' version of torch.nonzero to support torchscript. + because of https://github.com/pytorch/pytorch/issues/38718 + """ + if torch.jit.is_scripting(): + if x.dim() == 0: + return x.unsqueeze(0).nonzero().unbind(1) + return x.nonzero().unbind(1) + else: + return x.nonzero(as_tuple=True) + + +class Matcher(object): + """ + This class assigns to each predicted "element" (e.g., a box) a ground-truth + element. Each predicted element will have exactly zero or one matches; each + ground-truth element may be matched to zero or more predicted elements. + + The matching is determined by the MxN match_quality_matrix, that characterizes + how well each (ground-truth, prediction)-pair match each other. For example, + if the elements are boxes, this matrix may contain box intersection-over-union + overlap values. + + The matcher returns (a) a vector of length N containing the index of the + ground-truth element m in [0, M) that matches to prediction n in [0, N). + (b) a vector of length N containing the labels for each prediction. + """ + + def __init__( + self, thresholds: List[float], labels: List[int], allow_low_quality_matches: bool = False + ): + """ + Args: + thresholds (list): a list of thresholds used to stratify predictions + into levels. + labels (list): a list of values to label predictions belonging at + each level. A label can be one of {-1, 0, 1} signifying + {ignore, negative class, positive class}, respectively. + allow_low_quality_matches (bool): if True, produce additional matches + for predictions with maximum match quality lower than high_threshold. + See set_low_quality_matches_ for more details. + + For example, + thresholds = [0.3, 0.5] + labels = [0, -1, 1] + All predictions with iou < 0.3 will be marked with 0 and + thus will be considered as false positives while training. + All predictions with 0.3 <= iou < 0.5 will be marked with -1 and + thus will be ignored. + All predictions with 0.5 <= iou will be marked with 1 and + thus will be considered as true positives. + """ + thresholds = thresholds[:] + assert thresholds[0] > 0 + thresholds.insert(0, -float("inf")) + thresholds.append(float("inf")) + assert all( + [low <= high for (low, high) in zip(thresholds[:-1], thresholds[1:])] + ), thresholds + assert all([l in [-1, 0, 1] for l in labels]) + assert len(labels) == len(thresholds) - 1 + self.thresholds = thresholds + self.labels = labels + self.allow_low_quality_matches = allow_low_quality_matches + + def __call__(self, match_quality_matrix): + """ + Args: + match_quality_matrix (Tensor[float]): an MxN tensor, containing the + pairwise quality between M ground-truth elements and N predicted + elements. All elements must be >= 0 (due to the us of `torch.nonzero` + for selecting indices in :meth:`set_low_quality_matches_`). + + Returns: + matches (Tensor[int64]): a vector of length N, where matches[i] is a matched + ground-truth index in [0, M) + match_labels (Tensor[int8]): a vector of length N, where pred_labels[i] indicates + whether a prediction is a true or false positive or ignored + """ + assert match_quality_matrix.dim() == 2 + if match_quality_matrix.numel() == 0: + default_matches = match_quality_matrix.new_full( + (match_quality_matrix.size(1),), 0, dtype=torch.int64 + ) + default_match_labels = match_quality_matrix.new_full( + (match_quality_matrix.size(1),), self.labels[0], dtype=torch.int8 + ) + return default_matches, default_match_labels + + assert torch.all(match_quality_matrix >= 0) + + matched_vals, matches = match_quality_matrix.max(dim=0) + + match_labels = matches.new_full(matches.size(), 1, dtype=torch.int8) + + for (l, low, high) in zip(self.labels, self.thresholds[:-1], self.thresholds[1:]): + low_high = (matched_vals >= low) & (matched_vals < high) + match_labels[low_high] = l + + if self.allow_low_quality_matches: + self.set_low_quality_matches_(match_labels, match_quality_matrix) + + return matches, match_labels + + def set_low_quality_matches_(self, match_labels, match_quality_matrix): + """ + Produce additional matches for predictions that have only low-quality matches. + Specifically, for each ground-truth G find the set of predictions that have + maximum overlap with it (including ties); for each prediction in that set, if + it is unmatched, then match it to the ground-truth G. + + This function implements the RPN assignment case (i) in Sec. 3.1.2 of + :paper:`Faster R-CNN`. + """ + highest_quality_foreach_gt, _ = match_quality_matrix.max(dim=1) + _, pred_inds_with_highest_quality = nonzero_tuple( + match_quality_matrix == highest_quality_foreach_gt[:, None] + ) + match_labels[pred_inds_with_highest_quality] = 1 + + +def subsample_labels( + labels: torch.Tensor, num_samples: int, positive_fraction: float, bg_label: int +): + """ + Return `num_samples` (or fewer, if not enough found) + random samples from `labels` which is a mixture of positives & negatives. + It will try to return as many positives as possible without + exceeding `positive_fraction * num_samples`, and then try to + fill the remaining slots with negatives. + + Args: + labels (Tensor): (N, ) label vector with values: + * -1: ignore + * bg_label: background ("negative") class + * otherwise: one or more foreground ("positive") classes + num_samples (int): The total number of labels with value >= 0 to return. + Values that are not sampled will be filled with -1 (ignore). + positive_fraction (float): The number of subsampled labels with values > 0 + is `min(num_positives, int(positive_fraction * num_samples))`. The number + of negatives sampled is `min(num_negatives, num_samples - num_positives_sampled)`. + In order words, if there are not enough positives, the sample is filled with + negatives. If there are also not enough negatives, then as many elements are + sampled as is possible. + bg_label (int): label index of background ("negative") class. + + Returns: + pos_idx, neg_idx (Tensor): + 1D vector of indices. The total length of both is `num_samples` or fewer. + """ + positive = nonzero_tuple((labels != -1) & (labels != bg_label))[0] + negative = nonzero_tuple(labels == bg_label)[0] + + num_pos = int(num_samples * positive_fraction) + num_pos = min(positive.numel(), num_pos) + num_neg = num_samples - num_pos + num_neg = min(negative.numel(), num_neg) + + perm1 = torch.randperm(positive.numel(), device=positive.device)[:num_pos] + perm2 = torch.randperm(negative.numel(), device=negative.device)[:num_neg] + + pos_idx = positive[perm1] + neg_idx = negative[perm2] + return pos_idx, neg_idx + + +def sample_topk_per_gt(pr_inds, gt_inds, iou, k): + if len(gt_inds) == 0: + return pr_inds, gt_inds + gt_inds2, counts = gt_inds.unique(return_counts=True) + scores, pr_inds2 = iou[gt_inds2].topk(k, dim=1) + gt_inds2 = gt_inds2[:, None].repeat(1, k) + + pr_inds3 = torch.cat([pr[:c] for c, pr in zip(counts, pr_inds2)]) + gt_inds3 = torch.cat([gt[:c] for c, gt in zip(counts, gt_inds2)]) + return pr_inds3, gt_inds3 + + +class Stage2Assigner(nn.Module): + def __init__(self, num_queries, num_classes, max_k=4): + super().__init__() + self.positive_fraction = 0.25 + self.num_classes = num_classes + self.batch_size_per_image = num_queries + self.proposal_matcher = Matcher( + thresholds=[0.6], labels=[0, 1], allow_low_quality_matches=True + ) + self.k = max_k + + def _sample_proposals( + self, matched_idxs: torch.Tensor, matched_labels: torch.Tensor, gt_classes: torch.Tensor + ): + """ + Based on the matching between N proposals and M groundtruth, + sample the proposals and set their classification labels. + + Args: + matched_idxs (Tensor): a vector of length N, each is the best-matched + gt index in [0, M) for each proposal. + matched_labels (Tensor): a vector of length N, the matcher's label + (one of cfg.MODEL.ROI_HEADS.IOU_LABELS) for each proposal. + gt_classes (Tensor): a vector of length M. + + Returns: + Tensor: a vector of indices of sampled proposals. Each is in [0, N). + Tensor: a vector of the same length, the classification label for + each sampled proposal. Each sample is labeled as either a category in + [0, num_classes) or the background (num_classes). + """ + has_gt = gt_classes.numel() > 0 + if has_gt: + gt_classes = gt_classes[matched_idxs] + gt_classes[matched_labels == 0] = self.num_classes + gt_classes[matched_labels == -1] = -1 + else: + gt_classes = torch.zeros_like(matched_idxs) + self.num_classes + + sampled_fg_idxs, sampled_bg_idxs = subsample_labels( + gt_classes, self.batch_size_per_image, self.positive_fraction, self.num_classes + ) + + sampled_idxs = torch.cat([sampled_fg_idxs, sampled_bg_idxs], dim=0) + return sampled_idxs, gt_classes[sampled_idxs] + + def forward(self, outputs, targets, return_cost_matrix=False): + + bs = len(targets) + indices = [] + ious = [] + for b in range(bs): + iou, _ = box_iou( + box_cxcywh_to_xyxy(targets[b]["boxes"]), + box_cxcywh_to_xyxy(outputs["init_reference"][b].detach()), + ) + if not torch.all(iou >= 0): + print("iou", iou, iou.max(), iou.min()) + print("targets[b][boxes]", targets[b]["boxes"]) + print( + "outputs[init_reference][b]", + outputs["init_reference"][b], + outputs["init_reference"][b].max(), + outputs["init_reference"][b].min(), + ) + matched_idxs, matched_labels = self.proposal_matcher( + iou + ) # proposal_id -> highest_iou_gt_id, proposal_id -> [1 if iou > 0.6, 0 ow] + ( + sampled_idxs, + sampled_gt_classes, + ) = self._sample_proposals( # list of sampled proposal_ids, sampled_id -> [0, num_classes)+[bg_label] + matched_idxs, matched_labels, targets[b]["labels"] + ) + pos_pr_inds = sampled_idxs[sampled_gt_classes != self.num_classes] + pos_gt_inds = matched_idxs[pos_pr_inds] + pos_pr_inds, pos_gt_inds = self.postprocess_indices(pos_pr_inds, pos_gt_inds, iou) + indices.append((pos_pr_inds, pos_gt_inds)) + ious.append(iou) + if return_cost_matrix: + return indices, ious + return indices + + def postprocess_indices(self, pr_inds, gt_inds, iou): + return sample_topk_per_gt(pr_inds, gt_inds, iou, self.k) + + def __repr__(self, _repr_indent=8): + head = "Matcher " + self.__class__.__name__ + body = [] + for attribute, value in self.__dict__.items(): + if attribute.startswith("_"): + continue + body.append("{}: {}".format(attribute, value)) + lines = [head] + [" " * _repr_indent + line for line in body] + return "\n".join(lines) + + +class Stage1Assigner(nn.Module): + def __init__(self, t_low=0.3, t_high=0.7, max_k=4): + super().__init__() + self.positive_fraction = 0.5 + self.batch_size_per_image = 256 + self.k = max_k + self.t_low = t_low + self.t_high = t_high + self.anchor_matcher = Matcher( + thresholds=[t_low, t_high], labels=[0, -1, 1], allow_low_quality_matches=True + ) + + def _subsample_labels(self, label): + """ + Randomly sample a subset of positive and negative examples, and overwrite + the label vector to the ignore value (-1) for all elements that are not + included in the sample. + + Args: + labels (Tensor): a vector of -1, 0, 1. Will be modified in-place and returned. + """ + pos_idx, neg_idx = subsample_labels( + label, self.batch_size_per_image, self.positive_fraction, 0 + ) + label.fill_(-1) + label.scatter_(0, pos_idx, 1) + label.scatter_(0, neg_idx, 0) + return label + + def forward(self, outputs, targets): + bs = len(targets) + indices = [] + for b in range(bs): + anchors = outputs["anchors"][b] + if len(targets[b]["boxes"]) == 0: + indices.append( + ( + torch.tensor([], dtype=torch.long, device=anchors.device), + torch.tensor([], dtype=torch.long, device=anchors.device), + ) + ) + continue + iou, _ = box_iou( + box_cxcywh_to_xyxy(targets[b]["boxes"]), + box_cxcywh_to_xyxy(anchors), + ) + matched_idxs, matched_labels = self.anchor_matcher( + iou + ) # proposal_id -> highest_iou_gt_id, proposal_id -> [1 if iou > 0.7, 0 if iou < 0.3, -1 ow] + matched_labels = self._subsample_labels(matched_labels) + + all_pr_inds = torch.arange(len(anchors)) + pos_pr_inds = all_pr_inds[matched_labels == 1] + pos_gt_inds = matched_idxs[pos_pr_inds] + pos_ious = iou[pos_gt_inds, pos_pr_inds] + pos_pr_inds, pos_gt_inds = self.postprocess_indices(pos_pr_inds, pos_gt_inds, iou) + pos_pr_inds, pos_gt_inds = pos_pr_inds.to(anchors.device), pos_gt_inds.to( + anchors.device + ) + indices.append((pos_pr_inds, pos_gt_inds)) + return indices + + def postprocess_indices(self, pr_inds, gt_inds, iou): + return sample_topk_per_gt(pr_inds, gt_inds, iou, self.k) + + def __repr__(self, _repr_indent=8): + head = "Matcher " + self.__class__.__name__ + body = [] + for attribute, value in self.__dict__.items(): + if attribute.startswith("_"): + continue + body.append("{}: {}".format(attribute, value)) + lines = [head] + [" " * _repr_indent + line for line in body] + return "\n".join(lines) diff --git a/ape/modeling/deta/deformable_criterion.py b/ape/modeling/deta/deformable_criterion.py new file mode 100644 index 0000000..d0c5a80 --- /dev/null +++ b/ape/modeling/deta/deformable_criterion.py @@ -0,0 +1,533 @@ +import copy +import logging +from typing import Callable, List, Optional + +import torch +import torch.nn.functional as F + +from detectron2.projects.point_rend.point_features import ( + get_uncertain_point_coords_with_randomness, + point_sample, +) +from detrex.layers import box_cxcywh_to_xyxy, generalized_box_iou +from detrex.modeling import SetCriterion +from detrex.modeling.criterion.criterion import sigmoid_focal_loss +from detrex.modeling.losses import dice_loss +from detrex.utils import get_world_size, is_dist_avail_and_initialized + +from .misc import nested_tensor_from_tensor_list + +logger = logging.getLogger(__name__) + + +def sigmoid_ce_loss( + inputs: torch.Tensor, + targets: torch.Tensor, + num_masks: float, +): + """ + Args: + inputs: A float tensor of arbitrary shape. + The predictions for each example. + targets: A float tensor with the same shape as inputs. Stores the binary + classification label for each element in inputs + (0 for the negative class and 1 for the positive class). + Returns: + Loss tensor + """ + loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none") + + return loss.mean(1).sum() / num_masks + + +def calculate_uncertainty(logits): + """ + We estimate uncerainty as L1 distance between 0.0 and the logit prediction in 'logits' for the + foreground class in `classes`. + Args: + logits (Tensor): A tensor of shape (R, 1, ...) for class-specific or + class-agnostic, where R is the total number of predicted masks in all images and C is + the number of foreground classes. The values are logits. + Returns: + scores (Tensor): A tensor of shape (R, 1, ...) that contains uncertainty scores with + the most uncertain locations having the highest uncertainty score. + """ + assert logits.shape[1] == 1 + gt_class_logits = logits.clone() + return -(torch.abs(gt_class_logits)) + + +class DeformableCriterion(SetCriterion): + """This class computes the loss for Deformable-DETR + and two-stage Deformable-DETR + """ + + def __init__( + self, + num_classes, + matcher, + matcher_stage1, + matcher_stage2, + weight_dict, + losses: List[str] = ["class", "boxes"], + eos_coef: float = 0.1, + loss_class_type: str = "focal_loss", + alpha: float = 0.25, + gamma: float = 2.0, + use_fed_loss: bool = False, + get_fed_loss_cls_weights: Optional[Callable] = None, + fed_loss_num_classes: int = 50, + fed_loss_pad_type: str = None, + num_points: int = 12544, + oversample_ratio: float = 3.0, + importance_sample_ratio: float = 0.75, + ): + super(DeformableCriterion, self).__init__( + num_classes=num_classes, + matcher=matcher, + weight_dict=weight_dict, + losses=losses, + eos_coef=eos_coef, + loss_class_type=loss_class_type, + alpha=alpha, + gamma=gamma, + ) + + self.matcher_stage1 = matcher_stage1 + self.matcher_stage2 = matcher_stage2 + + self.use_fed_loss = use_fed_loss + if self.use_fed_loss: + fed_loss_cls_weights = get_fed_loss_cls_weights() + logger.info( + f"fed_loss_cls_weights: {fed_loss_cls_weights.size()} num_classes: {num_classes}" + ) + + if len(fed_loss_cls_weights) < num_classes: + if fed_loss_pad_type == "max": + fed_loss_pad_value = fed_loss_cls_weights.max().item() + elif fed_loss_pad_type == "max1000": + fed_loss_pad_value = fed_loss_cls_weights.max().item() * 1000 + elif fed_loss_pad_type == "mean": + fed_loss_pad_value = fed_loss_cls_weights.mean().item() + elif fed_loss_pad_type == "median": + fed_loss_pad_value = fed_loss_cls_weights.median().item() + elif fed_loss_pad_type == "cat": + fed_loss_pad_classes = torch.arange(len(fed_loss_cls_weights), num_classes) + self.register_buffer("fed_loss_pad_classes", fed_loss_pad_classes) + fed_loss_pad_value = 0 + else: + fed_loss_pad_value = torch.kthvalue( + fed_loss_cls_weights, int(num_classes * 7.0 / 10) + )[0].item() + + logger.info( + f"pad fed_loss_cls_weights with type {fed_loss_pad_type} and value {fed_loss_pad_value}" + ) + if getattr(self, "fed_loss_pad_classes", None) is not None: + logger.info(f"pad fed_loss_classes with {self.fed_loss_pad_classes}") + fed_loss_cls_weights = torch.cat( + ( + fed_loss_cls_weights, + fed_loss_cls_weights.new_full( + (num_classes - len(fed_loss_cls_weights),), + fed_loss_pad_value, + ), + ), + dim=0, + ) + + logger.info(f"fed_loss_cls_weights: {fed_loss_cls_weights[-100:]}") + logger.info( + f"fed_loss_cls_weights: {fed_loss_cls_weights.size()} num_classes: {num_classes}" + ) + + assert ( + len(fed_loss_cls_weights) == self.num_classes + ), "Please check the provided fed_loss_cls_weights. Their size should match num_classes" + self.register_buffer("fed_loss_cls_weights", fed_loss_cls_weights) + self.fed_loss_num_classes = fed_loss_num_classes + + self.num_points = num_points + self.oversample_ratio = oversample_ratio + self.importance_sample_ratio = importance_sample_ratio + + def get_fed_loss_classes(self, gt_classes, num_fed_loss_classes, num_classes, weight): + """ + Args: + gt_classes: a long tensor of shape R that contains the gt class label of each proposal. + num_fed_loss_classes: minimum number of classes to keep when calculating federated loss. + Will sample negative classes if number of unique gt_classes is smaller than this value. + num_classes: number of foreground classes + weight: probabilities used to sample negative classes + + Returns: + Tensor: + classes to keep when calculating the federated loss, including both unique gt + classes and sampled negative classes. + """ + unique_gt_classes = torch.unique(gt_classes) + prob = unique_gt_classes.new_ones(num_classes + 1).float() + prob[-1] = 0 + if len(unique_gt_classes) < num_fed_loss_classes: + prob[:num_classes] = weight.float().clone() + prob[unique_gt_classes] = 0 + sampled_negative_classes = torch.multinomial( + prob, num_fed_loss_classes - len(unique_gt_classes), replacement=False + ) + fed_loss_classes = torch.cat([unique_gt_classes, sampled_negative_classes]) + else: + fed_loss_classes = unique_gt_classes + return fed_loss_classes + + def loss_labels(self, outputs, targets, indices, num_boxes): + """Classification loss (Binary focal loss) + targets dicts must contain the key "labels" containing a tensor of dim [nb_target_boxes] + """ + assert "pred_logits" in outputs + src_logits = outputs["pred_logits"] + + if self.loss_class_type == "ce_loss": + num_classes = src_logits.shape[2] - 1 + elif self.loss_class_type == "focal_loss": + num_classes = src_logits.shape[2] + + idx = self._get_src_permutation_idx(indices) + target_classes_o = torch.cat([t["labels"][J] for t, (_, J) in zip(targets, indices)]) + target_classes = torch.full( + src_logits.shape[:2], + num_classes, + dtype=torch.int64, + device=src_logits.device, + ) + target_classes[idx] = target_classes_o + + if self.loss_class_type == "ce_loss": + loss_class = F.cross_entropy( + src_logits.transpose(1, 2), target_classes, self.empty_weight + ) + elif ( + self.loss_class_type == "focal_loss" + and self.use_fed_loss + and num_classes == len(self.fed_loss_cls_weights) + ): + target_classes_onehot = torch.zeros( + [src_logits.shape[0], src_logits.shape[1], src_logits.shape[2] + 1], + dtype=src_logits.dtype, + layout=src_logits.layout, + device=src_logits.device, + ) + target_classes_onehot.scatter_(2, target_classes.unsqueeze(-1), 1) + target_classes_onehot = target_classes_onehot[:, :, :-1] + fed_loss_classes = self.get_fed_loss_classes( + target_classes_o, + num_fed_loss_classes=self.fed_loss_num_classes, + num_classes=target_classes_onehot.shape[2], + weight=self.fed_loss_cls_weights, + ) + + if getattr(self, "fed_loss_pad_classes", None) is not None: + fed_loss_classes = torch.cat([fed_loss_classes, self.fed_loss_pad_classes]) + fed_loss_classes = torch.unique(fed_loss_classes) + + loss_class = ( + sigmoid_focal_loss( + src_logits[:, :, fed_loss_classes], + target_classes_onehot[:, :, fed_loss_classes], + num_boxes=num_boxes, + alpha=self.alpha, + gamma=self.gamma, + ) + * src_logits.shape[1] + ) + elif self.loss_class_type == "focal_loss": + target_classes_onehot = torch.zeros( + [src_logits.shape[0], src_logits.shape[1], src_logits.shape[2] + 1], + dtype=src_logits.dtype, + layout=src_logits.layout, + device=src_logits.device, + ) + target_classes_onehot.scatter_(2, target_classes.unsqueeze(-1), 1) + target_classes_onehot = target_classes_onehot[:, :, :-1] + loss_class = ( + sigmoid_focal_loss( + src_logits, + target_classes_onehot, + num_boxes=num_boxes, + alpha=self.alpha, + gamma=self.gamma, + ) + * src_logits.shape[1] + ) + + losses = {"loss_class": loss_class} + + return losses + + def loss_boxes(self, outputs, targets, indices, num_boxes): + """Compute the losses related to the bounding boxes, the L1 regression loss and the GIoU loss + targets dicts must contain the key "boxes" containing a tensor of dim [nb_target_boxes, 4] + The target boxes are expected in format (center_x, center_y, w, h), normalized by the image size. + """ + assert "pred_boxes" in outputs + idx = self._get_src_permutation_idx(indices) + src_boxes = outputs["pred_boxes"][idx] + target_boxes = torch.cat([t["boxes"][i] for t, (_, i) in zip(targets, indices)], dim=0) + + loss_bbox = F.l1_loss(src_boxes, target_boxes, reduction="none") + + losses = {} + losses["loss_bbox"] = loss_bbox.sum() / num_boxes + + loss_giou = 1 - torch.diag( + generalized_box_iou( + box_cxcywh_to_xyxy(src_boxes), + box_cxcywh_to_xyxy(target_boxes), + ) + ) + losses["loss_giou"] = loss_giou.sum() / num_boxes + + return losses + + def loss_boxes_panoptic(self, outputs, targets, indices, num_boxes): + """Compute the losses related to the bounding boxes, the L1 regression loss and the GIoU loss + targets dicts must contain the key "boxes" containing a tensor of dim [nb_target_boxes, 4] + The target boxes are expected in format (center_x, center_y, w, h), normalized by the image size. + """ + assert "pred_boxes" in outputs + idx = self._get_src_permutation_idx(indices) + src_boxes = outputs["pred_boxes"][idx] + target_boxes = torch.cat([t["boxes"][i] for t, (_, i) in zip(targets, indices)], dim=0) + + if "is_thing" in targets[0]: + is_thing = torch.cat([t["is_thing"][i] for t, (_, i) in zip(targets, indices)], dim=0) + if is_thing.sum() == 0: # no gt + losses = {} + losses["loss_bbox"] = src_boxes.sum() * 0.0 + losses["loss_giou"] = src_boxes.sum() * 0.0 + return losses + target_boxes = target_boxes[is_thing] + src_boxes = src_boxes[is_thing] + + loss_bbox = F.l1_loss(src_boxes, target_boxes, reduction="none") + + losses = {} + losses["loss_bbox"] = loss_bbox.sum() / num_boxes + + loss_giou = 1 - torch.diag( + generalized_box_iou( + box_cxcywh_to_xyxy(src_boxes), + box_cxcywh_to_xyxy(target_boxes), + ) + ) + losses["loss_giou"] = loss_giou.sum() / num_boxes + + return losses + + def loss_masks(self, outputs, targets, indices, num_boxes): + """Compute the losses related to the masks: the focal loss and the dice loss. + targets dicts must contain the key "masks" containing a tensor of dim [nb_target_boxes, h, w] + """ + assert "pred_masks" in outputs + src_idx = self._get_src_permutation_idx(indices) + tgt_idx = self._get_tgt_permutation_idx(indices) + src_masks = outputs["pred_masks"] + src_masks = src_masks[src_idx] + masks = [t["masks"] for t in targets] + target_masks, valid = nested_tensor_from_tensor_list(masks).decompose() + + if target_masks.size(1) == 0: # no gt + losses = {} + losses["loss_mask"] = src_masks.sum() * 0.0 + losses["loss_dice"] = src_masks.sum() * 0.0 + return losses + + target_masks = target_masks.to(src_masks) + target_masks = target_masks[tgt_idx] + + src_masks = F.interpolate( + src_masks[:, None], size=target_masks.shape[-2:], mode="bilinear", align_corners=False + ) + src_masks = src_masks[:, 0].flatten(1) + + target_masks = target_masks.flatten(1) + target_masks = target_masks.view(src_masks.shape) + + losses = { + "loss_mask": sigmoid_focal_loss(src_masks, target_masks, num_boxes), + "loss_dice": dice_loss( + src_masks.sigmoid(), target_masks, reduction="mean", avg_factor=num_boxes + ), + } + del src_masks + del target_masks + return losses + + def loss_masks_maskdino(self, outputs, targets, indices, num_boxes): + """Compute the losses related to the masks: the focal loss and the dice loss. + targets dicts must contain the key "masks" containing a tensor of dim [nb_target_boxes, h, w] + """ + assert "pred_masks" in outputs + src_idx = self._get_src_permutation_idx(indices) + tgt_idx = self._get_tgt_permutation_idx(indices) + src_masks = outputs["pred_masks"] + if not isinstance(src_masks, torch.Tensor): + mask_embeds = src_masks["mask_embeds"] + mask_features = src_masks["mask_features"] + src_masks = torch.cat( + [ + torch.einsum("qc,chw->qhw", mask_embeds[i][src], mask_features[i]) + for i, (src, _) in enumerate(indices) + ], + dim=0, + ) + else: + src_masks = src_masks[src_idx] + masks = [t["masks"] for t in targets] + target_masks, valid = nested_tensor_from_tensor_list(masks).decompose() + + if target_masks.size(1) == 0: # no gt + losses = {} + losses["loss_mask_maskdino"] = src_masks.sum() * 0.0 + losses["loss_dice_maskdino"] = src_masks.sum() * 0.0 + return losses + + target_masks = target_masks.to(src_masks) + target_masks = target_masks[tgt_idx] + + src_masks = src_masks[:, None] + target_masks = target_masks[:, None] + + with torch.no_grad(): + point_coords = get_uncertain_point_coords_with_randomness( + src_masks, + lambda logits: calculate_uncertainty(logits), + self.num_points, + self.oversample_ratio, + self.importance_sample_ratio, + ) + point_labels = point_sample( + target_masks, + point_coords, + align_corners=False, + ).squeeze(1) + + point_logits = point_sample( + src_masks, + point_coords, + align_corners=False, + ).squeeze(1) + + losses = { + "loss_mask_maskdino": sigmoid_ce_loss(point_logits, point_labels, num_boxes), + "loss_dice_maskdino": dice_loss( + point_logits.sigmoid(), point_labels, reduction="mean", avg_factor=num_boxes + ), + } + + del src_masks + del target_masks + return losses + + def get_loss(self, loss, outputs, targets, indices, num_boxes, **kwargs): + loss_map = { + "class": self.loss_labels, + "boxes": self.loss_boxes, + "boxes_panoptic": self.loss_boxes_panoptic, + "masks": self.loss_masks, + "masks_maskdino": self.loss_masks_maskdino, + } + assert loss in loss_map, f"do you really want to compute {loss} loss?" + return loss_map[loss](outputs, targets, indices, num_boxes, **kwargs) + + def forward(self, outputs, targets): + outputs_without_aux = { + k: v for k, v in outputs.items() if k != "aux_outputs" and k != "enc_outputs" + } + + if self.matcher_stage2 is not None: + indices = self.matcher_stage2(outputs_without_aux, targets) + else: + indices = self.matcher(outputs_without_aux, targets) + + num_boxes = sum(len(t["labels"]) for t in targets) + num_boxes = torch.as_tensor( + [num_boxes], dtype=torch.float, device=next(iter(outputs.values())).device + ) + if is_dist_avail_and_initialized(): + torch.distributed.all_reduce(num_boxes) + num_boxes = torch.clamp(num_boxes / get_world_size(), min=1).item() + + if "is_thing" in targets[0] and False: + unique_classes = torch.cat([t["labels"] for t in targets], dim=0) + is_thing = torch.cat([t["is_thing"][i] for t, (_, i) in zip(targets, indices)], dim=0) + all_classes = torch.cat([t["labels"][i] for t, (_, i) in zip(targets, indices)], dim=0) + thing_classes = all_classes[is_thing] + stuff_classes = all_classes[~is_thing] + + print( + "thing_classes", + 1.0 * len(thing_classes) / max(len(torch.unique(thing_classes)), 1), + "stuff_classes", + 1.0 * len(stuff_classes) / max(len(torch.unique(stuff_classes)), 1), + ) + + losses = {} + for loss in self.losses: + kwargs = {} + losses.update(self.get_loss(loss, outputs, targets, indices, num_boxes, **kwargs)) + + if "aux_outputs" in outputs: + for i, aux_outputs in enumerate(outputs["aux_outputs"]): + if self.matcher_stage2 is not None: + pass + else: + indices = self.matcher(aux_outputs, targets) + for loss in self.losses: + if loss == "masks": + continue + l_dict = self.get_loss(loss, aux_outputs, targets, indices, num_boxes, **kwargs) + l_dict = {k + f"_{i}": v for k, v in l_dict.items()} + losses.update(l_dict) + + if "enc_outputs" in outputs: + enc_outputs = outputs["enc_outputs"] + bin_targets = copy.deepcopy(targets) + for bt in bin_targets: + bt["labels"] = torch.zeros_like(bt["labels"]) + if "is_thing" in bt: + del bt["is_thing"] + if self.matcher_stage1 is not None: + indices = self.matcher_stage1(enc_outputs, bin_targets) + else: + indices = self.matcher(enc_outputs, bin_targets) + for loss in self.losses: + if loss == "masks": + continue + if loss == "masks_maskdino": + continue + l_dict = self.get_loss(loss, enc_outputs, bin_targets, indices, num_boxes, **kwargs) + l_dict = {k + "_enc": v for k, v in l_dict.items()} + losses.update(l_dict) + + return losses + + def __repr__(self): + head = "Criterion " + self.__class__.__name__ + body = [ + "matcher: {}".format(self.matcher.__repr__(_repr_indent=8)), + "matcher_stage1: {}".format(self.matcher_stage1), + "matcher_stage2: {}".format(self.matcher_stage2), + "losses: {}".format(self.losses), + "loss_class_type: {}".format(self.loss_class_type), + "weight_dict: {}".format(self.weight_dict), + "num_classes: {}".format(self.num_classes), + "eos_coef: {}".format(self.eos_coef), + "focal loss alpha: {}".format(self.alpha), + "focal loss gamma: {}".format(self.gamma), + "use_fed_loss: {}".format(self.use_fed_loss), + "fed_loss_num_classes: {}".format(self.fed_loss_num_classes), + ] + _repr_indent = 4 + lines = [head] + [" " * _repr_indent + line for line in body] + return "\n".join(lines) diff --git a/ape/modeling/deta/deformable_detr.py b/ape/modeling/deta/deformable_detr.py new file mode 100644 index 0000000..c00deff --- /dev/null +++ b/ape/modeling/deta/deformable_detr.py @@ -0,0 +1,453 @@ +import copy +import math +from typing import Dict, List, Optional, Tuple + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from detectron2.layers import move_device_like +from detectron2.modeling import GeneralizedRCNN, detector_postprocess +from detectron2.modeling.roi_heads.fast_rcnn import fast_rcnn_inference +from detectron2.structures import Boxes, ImageList, Instances +from detrex.layers import MLP, box_cxcywh_to_xyxy, box_xyxy_to_cxcywh +from detrex.utils import inverse_sigmoid +from torchvision.ops.boxes import batched_nms + + +class DeformableDETR(nn.Module): + """Implements the Deformable DETR model. + + Code is modified from the `official github repo + `_. + + More details can be found in the `paper + `_ . + + Args: + backbone (nn.Module): the backbone module. + position_embedding (nn.Module): the position embedding module. + neck (nn.Module): the neck module. + transformer (nn.Module): the transformer module. + embed_dim (int): the dimension of the embedding. + num_classes (int): Number of total categories. + num_queries (int): Number of proposal dynamic anchor boxes in Transformer + criterion (nn.Module): Criterion for calculating the total losses. + pixel_mean (List[float]): Pixel mean value for image normalization. + Default: [123.675, 116.280, 103.530]. + pixel_std (List[float]): Pixel std value for image normalization. + Default: [58.395, 57.120, 57.375]. + aux_loss (bool): whether to use auxiliary loss. Default: True. + with_box_refine (bool): whether to use box refinement. Default: False. + as_two_stage (bool): whether to use two-stage. Default: False. + select_box_nums_for_evaluation (int): the number of topk candidates + slected at postprocess for evaluation. Default: 100. + + """ + + def __init__( + self, + backbone, + position_embedding, + neck, + transformer, + embed_dim, + num_classes, + num_queries, + criterion, + pixel_mean: Tuple[float], + pixel_std: Tuple[float], + aux_loss=True, + with_box_refine=False, + as_two_stage=False, + select_box_nums_for_evaluation=100, + select_box_nums_for_evaluation_list: list = None, + input_format: Optional[str] = None, + vis_period: int = 0, + output_dir: Optional[str] = None, + dataset_names: List[str] = [], + dataset_metas: List[str] = [], + test_nms_thresh: float = 0.7, + test_score_thresh: float = 0.0, + ): + super().__init__() + self.backbone = backbone + self.position_embedding = position_embedding + + self.neck = neck + + self.num_queries = num_queries + if not as_two_stage: + self.query_embedding = nn.Embedding(num_queries, embed_dim * 2) + + self.transformer = transformer + + self.num_classes = num_classes + if criterion.loss_class_type == "ce_loss": + self.class_embed = nn.Linear(embed_dim, num_classes + 1) + else: + self.class_embed = nn.Linear(embed_dim, num_classes) + self.bbox_embed = MLP(embed_dim, embed_dim, 4, 3) + + self.aux_loss = aux_loss + self.criterion = criterion + + self.with_box_refine = with_box_refine + self.as_two_stage = as_two_stage + + prior_prob = 0.01 + bias_value = -math.log((1 - prior_prob) / prior_prob) + if criterion.loss_class_type == "ce_loss": + self.class_embed.bias.data = torch.ones(num_classes + 1) * bias_value + else: + self.class_embed.bias.data = torch.ones(num_classes) * bias_value + nn.init.constant_(self.bbox_embed.layers[-1].weight.data, 0) + nn.init.constant_(self.bbox_embed.layers[-1].bias.data, 0) + if self.neck is not None: + for _, neck_layer in self.neck.named_modules(): + if isinstance(neck_layer, nn.Conv2d): + nn.init.xavier_uniform_(neck_layer.weight, gain=1) + nn.init.constant_(neck_layer.bias, 0) + + num_pred = ( + (transformer.decoder.num_layers + 1) if as_two_stage else transformer.decoder.num_layers + ) + if with_box_refine: + self.class_embed = nn.ModuleList( + [copy.deepcopy(self.class_embed) for i in range(num_pred)] + ) + self.bbox_embed = nn.ModuleList( + [copy.deepcopy(self.bbox_embed) for i in range(num_pred)] + ) + nn.init.constant_(self.bbox_embed[0].layers[-1].bias.data[2:], -2.0) + self.transformer.decoder.bbox_embed = self.bbox_embed + else: + nn.init.constant_(self.bbox_embed.layers[-1].bias.data[2:], -2.0) + self.class_embed = nn.ModuleList([self.class_embed for _ in range(num_pred)]) + self.bbox_embed = nn.ModuleList([self.bbox_embed for _ in range(num_pred)]) + self.transformer.decoder.bbox_embed = None + + if as_two_stage: + self.transformer.decoder.class_embed = self.class_embed + if True: + prior_prob = 0.01 + bias_value = -math.log((1 - prior_prob) / prior_prob) + if criterion.loss_class_type == "ce_loss": + self.transformer.decoder.class_embed[-1] = nn.Linear(embed_dim, num_classes + 1) + self.transformer.decoder.class_embed[-1].bias.data = ( + torch.ones(num_classes + 1) * bias_value + ) + else: + self.transformer.decoder.class_embed[-1] = nn.Linear(embed_dim, 1) + self.transformer.decoder.class_embed[-1].bias.data = torch.ones(1) * bias_value + for box_embed in self.bbox_embed: + nn.init.constant_(box_embed.layers[-1].bias.data[2:], 0.0) + + self.select_box_nums_for_evaluation = select_box_nums_for_evaluation + self.select_box_nums_for_evaluation_list = select_box_nums_for_evaluation_list + + self.test_topk_per_image = self.select_box_nums_for_evaluation + self.test_nms_thresh = test_nms_thresh + self.test_score_thresh = test_score_thresh + + self.input_format = input_format + self.vis_period = vis_period + if vis_period > 0: + assert input_format is not None, "input_format is required for visualization!" + + self.register_buffer("pixel_mean", torch.tensor(pixel_mean).view(-1, 1, 1), False) + self.register_buffer("pixel_std", torch.tensor(pixel_std).view(-1, 1, 1), False) + assert ( + self.pixel_mean.shape == self.pixel_std.shape + ), f"{self.pixel_mean} and {self.pixel_std} have different shapes!" + + self.output_dir = output_dir + + self.dataset_names = dataset_names + from detectron2.data.catalog import MetadataCatalog + + if isinstance(dataset_metas, str): + dataset_metas = [dataset_metas] + self.metadata_list = [copy.deepcopy(MetadataCatalog.get(d)) for d in dataset_metas] + assert all(x == self.metadata_list[0] for x in self.metadata_list) + self.metadata = self.metadata_list[0] + + @property + def device(self): + return self.pixel_mean.device + + def _move_to_current_device(self, x): + return move_device_like(x, self.pixel_mean) + + def forward(self, batched_inputs, do_postprocess=True): + images = self.preprocess_image(batched_inputs) + + batch_size, _, H, W = images.tensor.shape + img_masks = images.tensor.new_ones(batch_size, H, W) + for image_id, image_size in enumerate(images.image_sizes): + img_masks[image_id, : image_size[0], : image_size[1]] = 0 + + features = self.backbone(images.tensor) # output feature dict + + if self.neck is not None: + multi_level_feats = self.neck(features) + else: + multi_level_feats = [feat for feat_name, feat in features.items()] + multi_level_masks = [] + multi_level_position_embeddings = [] + for feat in multi_level_feats: + multi_level_masks.append( + F.interpolate(img_masks[None], size=feat.shape[-2:]).to(torch.bool).squeeze(0) + ) + multi_level_position_embeddings.append( + self.position_embedding(multi_level_masks[-1]).to(images.tensor.dtype) + ) + + query_embeds = None + if not self.as_two_stage: + query_embeds = self.query_embedding.weight + + ( + inter_states, + init_reference, + inter_references, + enc_outputs_class, + enc_outputs_coord_unact, + anchors, + memory, + ) = self.transformer( + multi_level_feats, multi_level_masks, multi_level_position_embeddings, query_embeds + ) + + outputs_classes = [] + outputs_coords = [] + for lvl in range(inter_states.shape[0]): + if lvl == 0: + reference = init_reference + else: + reference = inter_references[lvl - 1] + reference = inverse_sigmoid(reference) + outputs_class = self.class_embed[lvl](inter_states[lvl]) + tmp = self.bbox_embed[lvl](inter_states[lvl]) + if reference.shape[-1] == 4: + tmp += reference + else: + assert reference.shape[-1] == 2 + tmp[..., :2] += reference + outputs_coord = tmp.sigmoid() + outputs_classes.append(outputs_class) + outputs_coords.append(outputs_coord) + outputs_class = torch.stack(outputs_classes) + outputs_coord = torch.stack(outputs_coords) + + output = { + "pred_logits": outputs_class[-1], + "pred_boxes": outputs_coord[-1], + "init_reference": init_reference, + } + if self.aux_loss: + output["aux_outputs"] = self._set_aux_loss(outputs_class, outputs_coord) + + if self.as_two_stage: + enc_outputs_coord = enc_outputs_coord_unact.sigmoid() + output["enc_outputs"] = { + "pred_logits": enc_outputs_class, + "pred_boxes": enc_outputs_coord, + "anchors": anchors, + } + + if self.training: + gt_instances = [x["instances"].to(self.device) for x in batched_inputs] + targets = self.prepare_targets(gt_instances) + loss_dict = self.criterion(output, targets) + weight_dict = self.criterion.weight_dict + for k in loss_dict.keys(): + if k in weight_dict: + loss_dict[k] *= weight_dict[k] + return loss_dict + else: + box_cls = output["pred_logits"] + box_pred = output["pred_boxes"] + results, filter_inds = self.inference(box_cls, box_pred, images.image_sizes) + + if do_postprocess: + assert not torch.jit.is_scripting(), "Scripting is not supported for postprocess." + return GeneralizedRCNN._postprocess(results, batched_inputs, images.image_sizes) + return results + + @torch.jit.unused + def _set_aux_loss(self, outputs_class, outputs_coord): + return [ + {"pred_logits": a, "pred_boxes": b} + for a, b in zip(outputs_class[:-1], outputs_coord[:-1]) + ] + + def inference(self, box_cls, box_pred, image_sizes): + """ + Arguments: + box_cls (Tensor): tensor of shape (batch_size, num_queries, K). + The tensor predicts the classification probability for each query. + box_pred (Tensor): tensors of shape (batch_size, num_queries, 4). + The tensor predicts 4-vector (x,y,w,h) box + regression values for every queryx + image_sizes (List[torch.Size]): the input image sizes + + Returns: + results (List[Instances]): a list of #images elements. + """ + + if True: + return NMSPostProcess()( + {"pred_logits": box_cls, "pred_boxes": box_pred}, + torch.tensor([list(x) for x in image_sizes], device=self.device), + self.select_box_nums_for_evaluation, + ) + + scores = torch.cat( + ( + box_cls.sigmoid(), + torch.zeros((box_cls.size(0), box_cls.size(1), 1), device=self.device), + ), + dim=2, + ) + + boxes = box_cxcywh_to_xyxy(box_pred) + + img_h, img_w = torch.tensor(image_sizes, device=self.device).unbind(1) + scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1) + boxes = boxes * scale_fct[:, None, :] + + boxes = boxes.unbind(0) + scores = scores.unbind(0) + image_shapes = image_sizes + + self.test_topk_per_image = self.select_box_nums_for_evaluation + self.test_nms_thresh = 0.7 + self.test_score_thresh = 0.05 + + return fast_rcnn_inference( + boxes, + scores, + image_shapes, + self.test_score_thresh, + self.test_nms_thresh, + self.test_topk_per_image, + ) + + assert len(box_cls) == len(image_sizes) + results = [] + + prob = box_cls.sigmoid() + topk_values, topk_indexes = torch.topk( + prob.view(box_cls.shape[0], -1), self.select_box_nums_for_evaluation, dim=1 + ) + scores = topk_values + topk_boxes = torch.div(topk_indexes, box_cls.shape[2], rounding_mode="floor") + labels = topk_indexes % box_cls.shape[2] + + boxes = torch.gather(box_pred, 1, topk_boxes.unsqueeze(-1).repeat(1, 1, 4)) + + for i, (scores_per_image, labels_per_image, box_pred_per_image, image_size) in enumerate( + zip(scores, labels, boxes, image_sizes) + ): + result = Instances(image_size) + result.pred_boxes = Boxes(box_cxcywh_to_xyxy(box_pred_per_image)) + result.pred_boxes.scale(scale_x=image_size[1], scale_y=image_size[0]) + result.scores = scores_per_image + result.pred_classes = labels_per_image + results.append(result) + return results, topk_indexes + + def prepare_targets(self, targets): + new_targets = [] + for targets_per_image in targets: + h, w = targets_per_image.image_size + image_size_xyxy = torch.as_tensor([w, h, w, h], dtype=torch.float, device=self.device) + gt_classes = targets_per_image.gt_classes + gt_boxes = targets_per_image.gt_boxes.tensor / image_size_xyxy + gt_boxes = box_xyxy_to_cxcywh(gt_boxes) + new_targets.append({"labels": gt_classes, "boxes": gt_boxes}) + return new_targets + + def preprocess_image(self, batched_inputs): + images = [self._move_to_current_device(x["image"]) for x in batched_inputs] + images = [x.to(self.pixel_mean.dtype) for x in images] + images = [(x - self.pixel_mean) / self.pixel_std for x in images] + images = ImageList.from_tensors( + images, + self.backbone.size_divisibility, + padding_constraints=self.backbone.padding_constraints, + ) + return images + + @staticmethod + def _postprocess(instances, batched_inputs: List[Dict[str, torch.Tensor]], image_sizes): + """ + Rescale the output instances to the target size. + """ + processed_results = [] + for results_per_image, input_per_image, image_size in zip( + instances, batched_inputs, image_sizes + ): + height = input_per_image.get("height", image_size[0]) + width = input_per_image.get("width", image_size[1]) + r = detector_postprocess(results_per_image, height, width) + processed_results.append({"instances": r}) + return processed_results + + +class NMSPostProcess(nn.Module): + """This module converts the model's output into the format expected by the coco api""" + + @torch.no_grad() + def forward(self, outputs, target_sizes, select_box_nums_for_evaluation): + """Perform the computation + Parameters: + outputs: raw outputs of the model + target_sizes: tensor of dimension [batch_size x 2] containing the size of each images of the batch + For evaluation, this must be the original image size (before any data augmentation) + For visualization, this should be the image size after data augment, but before padding + """ + out_logits, out_bbox = outputs["pred_logits"], outputs["pred_boxes"] + bs, n_queries, n_cls = out_logits.shape + + assert len(out_logits) == len(target_sizes) + assert target_sizes.shape[1] == 2 + + prob = out_logits.sigmoid() + + all_scores = prob.view(bs, n_queries * n_cls).to(out_logits.device) + all_indexes = torch.arange(n_queries * n_cls)[None].repeat(bs, 1).to(out_logits.device) + all_boxes = torch.div(all_indexes, out_logits.shape[2], rounding_mode="trunc") + all_labels = all_indexes % out_logits.shape[2] + + boxes = box_cxcywh_to_xyxy(out_bbox) + boxes = torch.gather(boxes, 1, all_boxes.unsqueeze(-1).repeat(1, 1, 4)) + + img_h, img_w = target_sizes.unbind(1) + scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1) + boxes = boxes * scale_fct[:, None, :] + + results = [] + keep_inds_all = [] + for b in range(bs): + box = boxes[b] + score = all_scores[b] + lbls = all_labels[b] + + pre_topk = score.topk(10000).indices + box = box[pre_topk] + score = score[pre_topk] + lbls = lbls[pre_topk] + + keep_inds = batched_nms(box, score, lbls, 0.7)[:select_box_nums_for_evaluation] + + result = Instances(target_sizes[b]) + result.pred_boxes = Boxes(box[keep_inds]) + result.scores = score[keep_inds] + result.pred_classes = lbls[keep_inds] + results.append(result) + + keep_inds_all.append(keep_inds) + + return results, keep_inds_all diff --git a/ape/modeling/deta/deformable_detr_segm.py b/ape/modeling/deta/deformable_detr_segm.py new file mode 100644 index 0000000..74c8ab3 --- /dev/null +++ b/ape/modeling/deta/deformable_detr_segm.py @@ -0,0 +1,940 @@ +import copy +import math +import os +from typing import Dict, List, Optional, Tuple + +import cv2 +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +import fvcore.nn.weight_init as weight_init +from detectron2.data.detection_utils import convert_image_to_rgb +from detectron2.layers import Conv2d, ShapeSpec, get_norm, move_device_like +from detectron2.modeling import GeneralizedRCNN +from detectron2.modeling.meta_arch.panoptic_fpn import combine_semantic_and_instance_outputs +from detectron2.modeling.postprocessing import detector_postprocess, sem_seg_postprocess +from detectron2.modeling.roi_heads.fast_rcnn import fast_rcnn_inference +from detectron2.structures import BitMasks, Boxes, ImageList, Instances +from detectron2.utils.events import get_event_storage +from detectron2.utils.memory import retry_if_cuda_oom +from detrex.layers import MLP, box_cxcywh_to_xyxy, box_xyxy_to_cxcywh +from detrex.utils import inverse_sigmoid +from torchvision.ops.boxes import batched_nms + +from .deformable_detr import DeformableDETR +from .segmentation import MaskHeadSmallConv, MHAttentionMap + + +class DeformableDETRSegm(DeformableDETR): + """Implements the Deformable DETR model. + + Code is modified from the `official github repo + `_. + + More details can be found in the `paper + `_ . + + Args: + backbone (nn.Module): the backbone module. + position_embedding (nn.Module): the position embedding module. + neck (nn.Module): the neck module. + transformer (nn.Module): the transformer module. + embed_dim (int): the dimension of the embedding. + num_classes (int): Number of total categories. + num_queries (int): Number of proposal dynamic anchor boxes in Transformer + criterion (nn.Module): Criterion for calculating the total losses. + pixel_mean (List[float]): Pixel mean value for image normalization. + Default: [123.675, 116.280, 103.530]. + pixel_std (List[float]): Pixel std value for image normalization. + Default: [58.395, 57.120, 57.375]. + aux_loss (bool): whether to use auxiliary loss. Default: True. + with_box_refine (bool): whether to use box refinement. Default: False. + as_two_stage (bool): whether to use two-stage. Default: False. + select_box_nums_for_evaluation (int): the number of topk candidates + slected at postprocess for evaluation. Default: 100. + + """ + + def __init__( + self, + instance_on: bool = True, + semantic_on: bool = False, + panoptic_on: bool = False, + freeze_detr=False, + input_shapes=[], + mask_in_features=[], + mask_encode_level=0, + stuff_dataset_learn_thing: bool = True, + stuff_prob_thing: float = -1.0, + test_mask_on: bool = True, + semantic_post_nms: bool = True, + panoptic_post_nms: bool = True, + aux_mask: bool = True, + **kwargs, + ): + super().__init__(**kwargs) + + self.instance_on = instance_on + self.semantic_on = semantic_on + self.panoptic_on = panoptic_on + + if freeze_detr: + for p in self.parameters(): + p.requires_grad_(False) + + self.input_shapes = input_shapes + self.mask_in_features = mask_in_features + self.mask_encode_level = mask_encode_level + + hidden_dim = self.transformer.embed_dim + norm = "GN" + use_bias = False + + assert len(self.mask_in_features) == 1 + in_channels = [self.input_shapes[feat_name].channels for feat_name in self.mask_in_features] + in_channel = in_channels[0] + + self.lateral_conv = Conv2d( + in_channel, + hidden_dim, + kernel_size=1, + stride=1, + bias=use_bias, + padding=0, + norm=get_norm(norm, hidden_dim), + ) + self.output_conv = Conv2d( + hidden_dim, + hidden_dim, + kernel_size=3, + stride=1, + bias=use_bias, + padding=1, + norm=get_norm(norm, hidden_dim), + activation=F.relu, + ) + self.mask_conv = Conv2d( + hidden_dim, hidden_dim, kernel_size=1, stride=1, bias=use_bias, padding=0 + ) + + self.mask_embed = MLP(hidden_dim, hidden_dim, hidden_dim, 3) + self.aux_mask = aux_mask + if self.aux_mask: + self.mask_embed = nn.ModuleList( + [copy.deepcopy(self.mask_embed) for i in range(len(self.class_embed) - 1)] + ) + + weight_init.c2_xavier_fill(self.lateral_conv) + weight_init.c2_xavier_fill(self.output_conv) + weight_init.c2_xavier_fill(self.mask_conv) + + self.stuff_dataset_learn_thing = stuff_dataset_learn_thing + self.stuff_prob_thing = stuff_prob_thing + self.test_mask_on = test_mask_on + self.semantic_post_nms = semantic_post_nms + self.panoptic_post_nms = panoptic_post_nms + + def forward(self, batched_inputs, do_postprocess=True): + images = self.preprocess_image(batched_inputs) + + batch_size, _, H, W = images.tensor.shape + img_masks = images.tensor.new_ones(batch_size, H, W) + for image_id, image_size in enumerate(images.image_sizes): + img_masks[image_id, : image_size[0], : image_size[1]] = 0 + + features = self.backbone(images.tensor) # output feature dict + + if self.neck is not None: + multi_level_feats = self.neck({f: features[f] for f in self.neck.in_features}) + else: + multi_level_feats = [feat for feat_name, feat in features.items()] + multi_level_masks = [] + multi_level_position_embeddings = [] + for feat in multi_level_feats: + multi_level_masks.append( + F.interpolate(img_masks[None], size=feat.shape[-2:]).to(torch.bool).squeeze(0) + ) + multi_level_position_embeddings.append( + self.position_embedding(multi_level_masks[-1]).to(images.tensor.dtype) + ) + + query_embeds = None + if not self.as_two_stage: + query_embeds = self.query_embedding.weight + + ( + inter_states, + init_reference, + inter_references, + enc_outputs_class, + enc_outputs_coord_unact, + anchors, + memory, + ) = self.transformer( + multi_level_feats, multi_level_masks, multi_level_position_embeddings, query_embeds + ) + + mask_features = self.maskdino_mask_features(memory, features, multi_level_masks) + + outputs_classes = [] + outputs_coords = [] + outputs_masks = [] + for lvl in range(inter_states.shape[0]): + if lvl == 0: + reference = init_reference + else: + reference = inter_references[lvl - 1] + reference = inverse_sigmoid(reference) + outputs_class = self.class_embed[lvl](inter_states[lvl]) + tmp = self.bbox_embed[lvl](inter_states[lvl]) + if reference.shape[-1] == 4: + tmp += reference + else: + assert reference.shape[-1] == 2 + tmp[..., :2] += reference + outputs_coord = tmp.sigmoid() + outputs_classes.append(outputs_class) + outputs_coords.append(outputs_coord) + + if self.aux_mask: + mask_embeds = self.mask_embed[lvl](inter_states[lvl]) + else: + mask_embeds = self.mask_embed(inter_states[lvl]) + outputs_mask = torch.einsum("bqc,bchw->bqhw", mask_embeds, mask_features) + outputs_masks.append(outputs_mask) + outputs_class = torch.stack(outputs_classes) + outputs_coord = torch.stack(outputs_coords) + outputs_mask = outputs_masks + if self.aux_mask: + outputs_mask[-1] += 0.0 * sum(outputs_mask) + + output = { + "pred_logits": outputs_class[-1], + "pred_boxes": outputs_coord[-1], + "pred_masks": outputs_mask[-1], + "init_reference": init_reference, + } + if self.aux_loss: + output["aux_outputs"] = self._set_aux_loss( + outputs_class, + outputs_coord, + outputs_mask, + ) + + if self.as_two_stage: + enc_outputs_coord = enc_outputs_coord_unact.sigmoid() + output["enc_outputs"] = { + "pred_logits": enc_outputs_class, + "pred_boxes": enc_outputs_coord, + "anchors": anchors, + } + + if ( + self.vis_period > 0 + and self.training + and get_event_storage().iter % self.vis_period == self.vis_period - 1 + ): + self.visualize_training(batched_inputs, output, images) + + if self.training: + gt_instances = [x["instances"].to(self.device) for x in batched_inputs] + targets = self.prepare_targets(gt_instances) + + loss_dict = self.criterion(output, targets) + weight_dict = self.criterion.weight_dict + for k in loss_dict.keys(): + if k in weight_dict: + loss_dict[k] *= weight_dict[k] + return loss_dict + else: + + box_cls = output["pred_logits"] + box_pred = output["pred_boxes"] + mask_pred = output["pred_masks"] + + iter_func = retry_if_cuda_oom(F.interpolate) + mask_pred = iter_func( + mask_pred, size=images.tensor.size()[2:], mode="bilinear", align_corners=False + ) + + merged_results = [{} for _ in range(box_cls.size(0))] + if self.instance_on: + if self.metadata is not None: + if is_thing_stuff_overlap(self.metadata): + thing_id = self.metadata.thing_dataset_id_to_contiguous_id.values() + thing_id = torch.Tensor(list(thing_id)).to(torch.long).to(self.device) + + detector_box_cls = torch.zeros_like(box_cls) + detector_box_cls += float("-inf") + detector_box_cls[..., thing_id] = box_cls[..., thing_id] + else: + num_thing_classes = len(self.metadata.thing_classes) + detector_box_cls = box_cls[..., :num_thing_classes] + else: + detector_box_cls = box_cls + + detector_results, filter_inds = self.inference( + detector_box_cls, box_pred, images.image_sizes + ) + + if self.test_mask_on: + detector_mask_preds = [ + x[filter_ind] for x, filter_ind in zip(mask_pred, filter_inds) + ] + + for result, box_mask in zip(detector_results, detector_mask_preds): + box_mask = box_mask.sigmoid() > 0.5 + box_mask = BitMasks(box_mask).crop_and_resize( + result.pred_boxes.tensor.to(box_mask.device), 128 + ) + result.pred_masks = ( + box_mask.to(result.pred_boxes.tensor.device) + .unsqueeze(1) + .to(dtype=torch.float32) + ) + + if do_postprocess: + assert ( + not torch.jit.is_scripting() + ), "Scripting is not supported for postprocess." + detector_results = DeformableDETRSegm._postprocess_instance( + detector_results, batched_inputs, images.image_sizes + ) + for merged_result, detector_result in zip(merged_results, detector_results): + merged_result.update(detector_result) + + else: + detector_results = None + + if self.semantic_on: + + semantic_mask_pred = mask_pred.clone() + + if self.metadata is not None: + if is_thing_stuff_overlap(self.metadata): + semantic_box_cls = box_cls.clone() + + else: + num_thing_classes = len(self.metadata.get("thing_classes", ["things"])) + + semantic_box_cls_0 = box_cls[..., :num_thing_classes] + semantic_box_cls_1 = box_cls[..., num_thing_classes:] + semantic_box_cls_0, _ = semantic_box_cls_0.min(dim=2, keepdim=True) + semantic_box_cls = torch.cat( + [semantic_box_cls_0, semantic_box_cls_1], dim=2 + ) + else: + semantic_box_cls = box_cls.clone() + + if self.semantic_post_nms: + _, filter_inds = self.inference(semantic_box_cls, box_pred, images.image_sizes) + semantic_box_cls = torch.stack( + [x[filter_ind] for x, filter_ind in zip(semantic_box_cls, filter_inds)], + dim=0, + ) + semantic_mask_pred = torch.stack( + [x[filter_ind] for x, filter_ind in zip(semantic_mask_pred, filter_inds)], + dim=0, + ) + + if do_postprocess: + assert ( + not torch.jit.is_scripting() + ), "Scripting is not supported for postprocess." + semantic_results = DeformableDETRSegm._postprocess_semantic( + semantic_box_cls, semantic_mask_pred, batched_inputs, images + ) + for merged_result, semantic_result in zip(merged_results, semantic_results): + if self.stuff_prob_thing > 0: + semantic_result["sem_seg"][0, ...] = math.log( + self.stuff_prob_thing / (1 - self.stuff_prob_thing) + ) + merged_result.update(semantic_result) + + else: + semantic_results = None + + if self.panoptic_on: + assert self.metadata is not None + if do_postprocess: + assert ( + not torch.jit.is_scripting() + ), "Scripting is not supported for postprocess." + if True: + if self.panoptic_post_nms: + _, filter_inds = self.inference(box_cls, box_pred, images.image_sizes) + panoptic_mask_pred = [ + x[filter_ind] for x, filter_ind in zip(mask_pred, filter_inds) + ] + panoptic_box_cls = [ + x[filter_ind] for x, filter_ind in zip(box_cls, filter_inds) + ] + + panoptic_results = DeformableDETRSegm._postprocess_panoptic( + panoptic_box_cls, + panoptic_mask_pred, + batched_inputs, + images, + self.metadata, + ) + else: + panoptic_results = [] + self.combine_overlap_thresh = 0.5 + self.combine_stuff_area_thresh = 4096 + self.combine_instances_score_thresh = 0.5 + for detector_result, semantic_result in zip( + detector_results, semantic_results + ): + detector_r = detector_result["instances"] + sem_seg_r = semantic_result["sem_seg"] + panoptic_r = combine_semantic_and_instance_outputs( + detector_r, + sem_seg_r.argmax(dim=0), + self.combine_overlap_thresh, + self.combine_stuff_area_thresh, + self.combine_instances_score_thresh, + ) + panoptic_results.append({"panoptic_seg": panoptic_r}) + for merged_result, panoptic_result in zip(merged_results, panoptic_results): + merged_result.update(panoptic_result) + + else: + panoptic_results = None + + if do_postprocess: + return merged_results + + return detector_results, semantic_results, panoptic_results + + def maskdino_mask_features(self, encode_feats, multi_level_feats, multi_level_masks): + start_idx = sum( + [mask.shape[1] * mask.shape[2] for mask in multi_level_masks[: self.mask_encode_level]] + ) + end_idx = sum( + [ + mask.shape[1] * mask.shape[2] + for mask in multi_level_masks[: self.mask_encode_level + 1] + ] + ) + b, h, w = multi_level_masks[self.mask_encode_level].size() + + encode_feats = encode_feats[:, start_idx:end_idx, :] + encode_feats = encode_feats.permute(0, 2, 1).reshape(b, -1, h, w) + + x = [multi_level_feats[f] for f in self.mask_in_features] + x = x[0] + x = self.lateral_conv(x) + x = x + F.interpolate(encode_feats, size=x.shape[-2:], mode="bilinear", align_corners=False) + x = self.output_conv(x) + mask_features = self.mask_conv(x) + + return mask_features + + @torch.jit.unused + def _set_aux_loss(self, outputs_class, outputs_coord, outputs_mask): + return [ + {"pred_logits": a, "pred_boxes": b, "pred_masks": c} + for a, b, c in zip(outputs_class[:-1], outputs_coord[:-1], outputs_mask[:-1]) + ] + + def inference(self, box_cls, box_pred, image_sizes): + """ + Arguments: + box_cls (Tensor): tensor of shape (batch_size, num_queries, K). + The tensor predicts the classification probability for each query. + box_pred (Tensor): tensors of shape (batch_size, num_queries, 4). + The tensor predicts 4-vector (x,y,w,h) box + regression values for every queryx + image_sizes (List[torch.Size]): the input image sizes + + Returns: + results (List[Instances]): a list of #images elements. + """ + + if True: + + scores = torch.cat( + ( + box_cls.sigmoid(), + torch.zeros((box_cls.size(0), box_cls.size(1), 1), device=self.device), + ), + dim=2, + ) + + boxes = box_cxcywh_to_xyxy(box_pred) + + img_h, img_w = torch.tensor(image_sizes, device=self.device).unbind(1) + scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1) + boxes = boxes * scale_fct[:, None, :] + + boxes = boxes.unbind(0) + scores = scores.unbind(0) + image_shapes = image_sizes + + results, filter_inds = fast_rcnn_inference( + boxes, + scores, + image_shapes, + self.test_score_thresh, + self.test_nms_thresh, + self.test_topk_per_image, + ) + + return results, filter_inds + + assert len(box_cls) == len(image_sizes) + results = [] + + prob = box_cls.sigmoid() + topk_values, topk_indexes = torch.topk( + prob.view(box_cls.shape[0], -1), self.select_box_nums_for_evaluation, dim=1 + ) + scores = topk_values + topk_boxes = torch.div(topk_indexes, box_cls.shape[2], rounding_mode="floor") + labels = topk_indexes % box_cls.shape[2] + + boxes = torch.gather(box_pred, 1, topk_boxes.unsqueeze(-1).repeat(1, 1, 4)) + + for i, (scores_per_image, labels_per_image, box_pred_per_image, image_size) in enumerate( + zip(scores, labels, boxes, image_sizes) + ): + result = Instances(image_size) + result.pred_boxes = Boxes(box_cxcywh_to_xyxy(box_pred_per_image)) + result.pred_boxes.scale(scale_x=image_size[1], scale_y=image_size[0]) + result.scores = scores_per_image + result.pred_classes = labels_per_image + results.append(result) + return results, topk_indexes + + def prepare_targets(self, targets): + new_targets = [] + for targets_per_image in targets: + h, w = targets_per_image.image_size + image_size_xyxy = torch.as_tensor([w, h, w, h], dtype=torch.float, device=self.device) + gt_classes = targets_per_image.gt_classes + gt_boxes = targets_per_image.gt_boxes.tensor / image_size_xyxy + gt_boxes = box_xyxy_to_cxcywh(gt_boxes) + + if not targets_per_image.has("gt_masks"): + gt_masks = torch.zeros((0, h, w), dtype=torch.bool) + else: + gt_masks = targets_per_image.gt_masks + + if not isinstance(gt_masks, torch.Tensor): + if isinstance(gt_masks, BitMasks): + gt_masks = gt_masks.tensor + else: + gt_masks = BitMasks.from_polygon_masks(gt_masks, h, w).tensor + + gt_masks = self._move_to_current_device(gt_masks) + gt_masks = ImageList.from_tensors( + [gt_masks], + self.backbone.size_divisibility, + padding_constraints=self.backbone.padding_constraints, + ).tensor.squeeze(0) + + new_targets.append({"labels": gt_classes, "boxes": gt_boxes, "masks": gt_masks}) + + if targets_per_image.has("is_thing"): + new_targets[-1]["is_thing"] = targets_per_image.is_thing + + return new_targets + + def preprocess_image(self, batched_inputs): + images = [self._move_to_current_device(x["image"]) for x in batched_inputs] + images = [x.to(self.pixel_mean.dtype) for x in images] + images = [(x - self.pixel_mean) / self.pixel_std for x in images] + images = ImageList.from_tensors( + images, + self.backbone.size_divisibility, + padding_constraints=self.backbone.padding_constraints, + ) + return images + + @staticmethod + def _postprocess_instance( + instances, batched_inputs: List[Dict[str, torch.Tensor]], image_sizes + ): + """ + Rescale the output instances to the target size. + """ + processed_results = [] + for results_per_image, input_per_image, image_size in zip( + instances, batched_inputs, image_sizes + ): + height = input_per_image.get("height", image_size[0]) + width = input_per_image.get("width", image_size[1]) + r = detector_postprocess(results_per_image, height, width) + processed_results.append({"instances": r.to("cpu")}) + return processed_results + + @staticmethod + def _postprocess_semantic( + mask_clses, + mask_preds, + batched_inputs: List[Dict[str, torch.Tensor]], + images, + pano_temp=0.06, + transform_eval=True, + ): + processed_results = [] + for mask_cls, mask_pred, input_per_image, image_size in zip( + mask_clses, mask_preds, batched_inputs, images.image_sizes + ): + height = input_per_image.get("height", image_size[0]) + width = input_per_image.get("width", image_size[1]) + + T = pano_temp + mask_cls = mask_cls.sigmoid() + + if transform_eval: + mask_cls = F.softmax(mask_cls / T, dim=-1) # already sigmoid + mask_pred = mask_pred.sigmoid() + result = torch.einsum("qc,qhw->chw", mask_cls, mask_pred) + + r = sem_seg_postprocess(result, image_size, height, width) + processed_results.append({"sem_seg": r}) + return processed_results + + @staticmethod + def _postprocess_panoptic( + mask_clses, + mask_preds, + batched_inputs: List[Dict[str, torch.Tensor]], + images, + metadata, + prob=0.5, + pano_temp=0.06, + transform_eval=True, + object_mask_threshold=0.25, + overlap_threshold=0.8, + ): + num_classes = len(metadata.thing_classes) + len(metadata.stuff_classes) - 1 + + object_mask_threshold = 0.01 + overlap_threshold = 0.4 + prob = 0.1 + + processed_results = [] + for mask_cls, mask_pred, input_per_image, image_size in zip( + mask_clses, mask_preds, batched_inputs, images.image_sizes + ): + height = input_per_image.get("height", image_size[0]) + width = input_per_image.get("width", image_size[1]) + + mask_pred = sem_seg_postprocess(mask_pred, image_size, height, width) + + T = pano_temp + scores, labels = mask_cls.sigmoid().max(-1) + mask_pred = mask_pred.sigmoid() + keep = labels.ne(num_classes) & (scores > object_mask_threshold) + if transform_eval: + scores, labels = F.softmax(mask_cls.sigmoid() / T, dim=-1).max(-1) + cur_scores = scores[keep] + cur_classes = labels[keep] + cur_masks = mask_pred[keep] + cur_prob_masks = cur_scores.view(-1, 1, 1) * cur_masks + + panoptic_seg = torch.zeros((height, width), dtype=torch.int32, device=cur_masks.device) + segments_info = [] + + current_segment_id = 0 + + if cur_masks.size(0) > 0: + + cur_mask_ids = cur_prob_masks.argmax(0) + + stuff_memory_list = {} + for k in range(cur_classes.shape[0]): + pred_class = cur_classes[k].item() + isthing = pred_class in metadata.thing_dataset_id_to_contiguous_id.values() + mask_area = (cur_mask_ids == k).sum().item() + original_area = (cur_masks[k] >= prob).sum().item() + mask = (cur_mask_ids == k) & (cur_masks[k] >= prob) + + if mask_area > 0 and original_area > 0 and mask.sum().item() > 0: + if mask_area / original_area < overlap_threshold: + continue + + if not isthing: + if int(pred_class) in stuff_memory_list.keys(): + panoptic_seg[mask] = stuff_memory_list[int(pred_class)] + continue + else: + stuff_memory_list[int(pred_class)] = current_segment_id + 1 + + current_segment_id += 1 + panoptic_seg[mask] = current_segment_id + + if not isthing and not is_thing_stuff_overlap(metadata): + pred_class = int(pred_class) - len(metadata.thing_classes) + 1 + + segments_info.append( + { + "id": current_segment_id, + "isthing": bool(isthing), + "category_id": int(pred_class), + } + ) + + processed_results.append({"panoptic_seg": (panoptic_seg, segments_info)}) + return processed_results + + @torch.no_grad() + def visualize_training(self, batched_inputs, output, images): + if self.output_dir is None: + return + if self.training: + storage = get_event_storage() + os.makedirs(self.output_dir + "/training", exist_ok=True) + else: + os.makedirs(self.output_dir + "/inference", exist_ok=True) + + pred_logits = output["pred_logits"] + pred_boxes = output["pred_boxes"] + pred_masks = output["pred_masks"] + + thing_classes = self.metadata.get("thing_classes", []) + stuff_classes = self.metadata.get("stuff_classes", []) + if len(thing_classes) > 0 and len(stuff_classes) > 0 and stuff_classes[0] == "things": + stuff_classes = stuff_classes[1:] + if is_thing_stuff_overlap(self.metadata): + class_names = ( + thing_classes if len(thing_classes) > len(stuff_classes) else stuff_classes + ) + else: + class_names = thing_classes + stuff_classes + + num_thing_classes = len(class_names) + pred_logits = pred_logits[..., :num_thing_classes] + + if pred_masks is not None: + pred_masks = [ + F.interpolate( + pred_mask.float().cpu().unsqueeze(0), + size=images.tensor.size()[2:], + mode="bilinear", + align_corners=False, + ).squeeze(0) + if pred_mask.size(0) > 0 + else pred_mask + for pred_mask in pred_masks + ] + else: + pred_masks = [ + torch.zeros(pred_box.size(0), image_size[0], image_size[1]) + for pred_box, image_size in zip(pred_boxes, images.image_sizes) + ] + + if True: + results, filter_inds = self.inference(pred_logits, pred_boxes, images.image_sizes) + pred_masks = [ + pred_mask[filter_ind.cpu()] + for pred_mask, filter_ind in zip(pred_masks, filter_inds) + ] + for result, pred_mask in zip(results, pred_masks): + result.pred_masks = pred_mask.sigmoid() > 0.5 + else: + results = [] + for pred_logit, pred_box, pred_mask, image_size in zip( + pred_logits, pred_boxes, pred_masks, images.image_sizes + ): + result = Instances(image_size) + result.pred_boxes = Boxes(pred_box) + result.scores = pred_logit[:, 0] + result.pred_classes = torch.zeros( + len(pred_box), dtype=torch.int64, device=pred_logit.device + ) + result.pred_masks = pred_mask.sigmoid() > 0.5 + + results.append(result) + + from detectron2.utils.visualizer import Visualizer + + for input, result in zip(batched_inputs, results): + + img = input["image"] + img = convert_image_to_rgb(img.permute(1, 2, 0), self.input_format) + v_gt = Visualizer(img, None) + + if "instances" in input: + labels = [ + "{}".format(class_names[gt_class]) for gt_class in input["instances"].gt_classes + ] + v_gt = v_gt.overlay_instances( + boxes=input["instances"].gt_boxes, + masks=input["instances"].gt_masks + if input["instances"].has("gt_masks") + else None, + labels=labels, + ) + else: + v_gt = v_gt.output + anno_img = v_gt.get_image() + + labels = [ + "{}_{:.0f}%".format(class_names[pred_class], score * 100) + for pred_class, score in zip(result.pred_classes.cpu(), result.scores.cpu()) + ] + v_pred = Visualizer(img, None) + v_pred = v_pred.overlay_instances( + boxes=result.pred_boxes.tensor.clone().detach().cpu().numpy(), + labels=labels, + masks=result.pred_masks[:, : img.shape[0], : img.shape[1]] + .clone() + .detach() + .cpu() + .numpy() + if result.has("pred_masks") + else None, + ) + pred_img = v_pred.get_image() + + vis_img = np.concatenate((anno_img, pred_img), axis=1) + + basename = os.path.basename(input["file_name"]) + if self.training: + cv2.imwrite( + os.path.join(self.output_dir, "training", str(storage.iter) + "_" + basename), + vis_img[:, :, ::-1], + ) + else: + cv2.imwrite( + os.path.join(self.output_dir, "inference", basename), vis_img[:, :, ::-1] + ) + + @torch.no_grad() + def visualize_inference_panoptic(self, batched_inputs, results): + if self.output_dir is None: + return + if self.training: + storage = get_event_storage() + os.makedirs(self.output_dir + "/training", exist_ok=True) + else: + os.makedirs(self.output_dir + "/inference", exist_ok=True) + + from detectron2.utils.visualizer import Visualizer + + for input, result in zip(batched_inputs, results): + + img = input["image"] + img = convert_image_to_rgb(img.permute(1, 2, 0), self.input_format) + + height = input["height"] + width = input["width"] + img = cv2.resize(img, (width, height)) + + v_gt = Visualizer(img, self.metadata) + + if "instances" in input: + labels = [ + "{}".format(class_names[gt_class]) for gt_class in input["instances"].gt_classes + ] + v_gt = v_gt.overlay_instances( + boxes=input["instances"].gt_boxes, + masks=input["instances"].gt_masks + if input["instances"].has("gt_masks") + else None, + labels=labels, + ) + else: + v_gt = v_gt.output + anno_img = v_gt.get_image() + + v_pred = Visualizer(img, self.metadata) + + panoptic_seg, segments_info = result["panoptic_seg"] + v_pred = v_pred.draw_panoptic_seg_predictions(panoptic_seg.cpu(), segments_info) + pred_img = v_pred.get_image() + + vis_img = np.concatenate((anno_img, pred_img), axis=1) + + basename = os.path.basename(input["file_name"]) + if self.training: + cv2.imwrite( + os.path.join( + self.output_dir, "training", str(storage.iter) + "_pan_" + basename + ), + vis_img[:, :, ::-1], + ) + else: + cv2.imwrite( + os.path.join(self.output_dir, "inference", "pan_" + basename), + vis_img[:, :, ::-1], + ) + + +class NMSPostProcess(nn.Module): + """This module converts the model's output into the format expected by the coco api""" + + @torch.no_grad() + def forward(self, outputs, target_sizes, select_box_nums_for_evaluation): + """Perform the computation + Parameters: + outputs: raw outputs of the model + target_sizes: tensor of dimension [batch_size x 2] containing the size of each images of the batch + For evaluation, this must be the original image size (before any data augmentation) + For visualization, this should be the image size after data augment, but before padding + """ + out_logits, out_bbox = outputs["pred_logits"], outputs["pred_boxes"] + out_mask = outputs["pred_masks"] + bs, n_queries, n_cls = out_logits.shape + print("PostProcessSegm", out_logits.size(), out_bbox.size(), out_mask.size()) + + assert len(out_logits) == len(target_sizes) + assert target_sizes.shape[1] == 2 + + prob = out_logits.sigmoid() + + all_scores = prob.view(bs, n_queries * n_cls).to(out_logits.device) + all_indexes = torch.arange(n_queries * n_cls)[None].repeat(bs, 1).to(out_logits.device) + all_boxes = torch.div(all_indexes, out_logits.shape[2], rounding_mode="trunc") + all_labels = all_indexes % out_logits.shape[2] + + boxes = box_cxcywh_to_xyxy(out_bbox) + boxes = torch.gather(boxes, 1, all_boxes.unsqueeze(-1).repeat(1, 1, 4)) + + img_h, img_w = target_sizes.unbind(1) + scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1) + boxes = boxes * scale_fct[:, None, :] + + results = [] + keep_inds_all = [] + for b in range(bs): + box = boxes[b] + score = all_scores[b] + lbls = all_labels[b] + mask = out_mask[b] + + pre_topk = score.topk(10000).indices + box = box[pre_topk] + score = score[pre_topk] + lbls = lbls[pre_topk] + + keep_inds = batched_nms(box, score, lbls, 0.7)[:select_box_nums_for_evaluation] + + result = Instances(target_sizes[b]) + result.pred_boxes = Boxes(box[keep_inds]) + result.scores = score[keep_inds] + result.pred_classes = lbls[keep_inds] + results.append(result) + + keep_inds_all.append(keep_inds) + + return results, keep_inds_all + + +def is_thing_stuff_overlap(metadata): + thing_classes = metadata.get("thing_classes", []) + stuff_classes = metadata.get("stuff_classes", []) + if len(thing_classes) == 0 or len(stuff_classes) == 0: + return False + + if set(thing_classes).issubset(set(stuff_classes)) or set(stuff_classes).issubset( + set(thing_classes) + ): + return True + else: + return False diff --git a/ape/modeling/deta/deformable_transformer.py b/ape/modeling/deta/deformable_transformer.py new file mode 100644 index 0000000..d191142 --- /dev/null +++ b/ape/modeling/deta/deformable_transformer.py @@ -0,0 +1,524 @@ +import math + +import torch +import torch.nn as nn + +from ape.layers import MultiScaleDeformableAttention +from detrex.layers import FFN # MultiScaleDeformableAttention, +from detrex.layers import ( + BaseTransformerLayer, + MultiheadAttention, + TransformerLayerSequence, + box_cxcywh_to_xyxy, +) +from detrex.utils import inverse_sigmoid +from torchvision.ops.boxes import batched_nms + + +class DeformableDetrTransformerEncoder(TransformerLayerSequence): + def __init__( + self, + embed_dim: int = 256, + num_heads: int = 8, + feedforward_dim: int = 1024, + attn_dropout: float = 0.1, + ffn_dropout: float = 0.1, + num_layers: int = 6, + post_norm: bool = False, + num_feature_levels: int = 4, + ): + super(DeformableDetrTransformerEncoder, self).__init__( + transformer_layers=BaseTransformerLayer( + attn=MultiScaleDeformableAttention( + embed_dim=embed_dim, + num_heads=num_heads, + dropout=attn_dropout, + batch_first=True, + num_levels=num_feature_levels, + ), + ffn=FFN( + embed_dim=embed_dim, + feedforward_dim=feedforward_dim, + output_dim=embed_dim, + num_fcs=2, + ffn_drop=ffn_dropout, + ), + norm=nn.LayerNorm(embed_dim), + operation_order=("self_attn", "norm", "ffn", "norm"), + ), + num_layers=num_layers, + ) + self.embed_dim = self.layers[0].embed_dim + self.pre_norm = self.layers[0].pre_norm + + if post_norm: + self.post_norm_layer = nn.LayerNorm(self.embed_dim) + else: + self.post_norm_layer = None + + def forward( + self, + query, + key, + value, + query_pos=None, + key_pos=None, + attn_masks=None, + query_key_padding_mask=None, + key_padding_mask=None, + **kwargs, + ): + + for layer in self.layers: + query = layer( + query, + key, + value, + query_pos=query_pos, + attn_masks=attn_masks, + query_key_padding_mask=query_key_padding_mask, + key_padding_mask=key_padding_mask, + **kwargs, + ) + + if self.post_norm_layer is not None: + query = self.post_norm_layer(query) + return query + + +class DeformableDetrTransformerDecoder(TransformerLayerSequence): + def __init__( + self, + embed_dim: int = 256, + num_heads: int = 8, + feedforward_dim: int = 1024, + attn_dropout: float = 0.1, + ffn_dropout: float = 0.1, + num_layers: int = 6, + return_intermediate: bool = True, + num_feature_levels: int = 4, + ): + super(DeformableDetrTransformerDecoder, self).__init__( + transformer_layers=BaseTransformerLayer( + attn=[ + MultiheadAttention( + embed_dim=embed_dim, + num_heads=num_heads, + attn_drop=attn_dropout, + batch_first=True, + ), + MultiScaleDeformableAttention( + embed_dim=embed_dim, + num_heads=num_heads, + dropout=attn_dropout, + batch_first=True, + num_levels=num_feature_levels, + ), + ], + ffn=FFN( + embed_dim=embed_dim, + feedforward_dim=feedforward_dim, + output_dim=embed_dim, + ffn_drop=ffn_dropout, + ), + norm=nn.LayerNorm(embed_dim), + operation_order=("self_attn", "norm", "cross_attn", "norm", "ffn", "norm"), + ), + num_layers=num_layers, + ) + self.return_intermediate = return_intermediate + + self.bbox_embed = None + self.class_embed = None + + def forward( + self, + query, + key, + value, + query_pos=None, + key_pos=None, + attn_masks=None, + query_key_padding_mask=None, + key_padding_mask=None, + reference_points=None, + valid_ratios=None, + **kwargs, + ): + output = query + + intermediate = [] + intermediate_reference_points = [] + for layer_idx, layer in enumerate(self.layers): + if reference_points.shape[-1] == 4: + reference_points_input = ( + reference_points[:, :, None] + * torch.cat([valid_ratios, valid_ratios], -1)[:, None] + ) + else: + assert reference_points.shape[-1] == 2 + reference_points_input = reference_points[:, :, None] * valid_ratios[:, None] + + output = layer( + output, + key, + value, + query_pos=query_pos, + key_pos=key_pos, + attn_masks=attn_masks, + query_key_padding_mask=query_key_padding_mask, + key_padding_mask=key_padding_mask, + reference_points=reference_points_input, + **kwargs, + ) + + if self.bbox_embed is not None: + tmp = self.bbox_embed[layer_idx](output) + if reference_points.shape[-1] == 4: + new_reference_points = tmp + inverse_sigmoid(reference_points) + new_reference_points = new_reference_points.sigmoid() + else: + assert reference_points.shape[-1] == 2 + new_reference_points = tmp + new_reference_points[..., :2] = tmp[..., :2] + inverse_sigmoid(reference_points) + new_reference_points = new_reference_points.sigmoid() + reference_points = new_reference_points.detach() + + if self.return_intermediate: + intermediate.append(output) + intermediate_reference_points.append(reference_points) + + if self.return_intermediate: + return torch.stack(intermediate), torch.stack(intermediate_reference_points) + + return output, reference_points + + +class DeformableDetrTransformer(nn.Module): + """Transformer module for Deformable DETR + + Args: + encoder (nn.Module): encoder module. + decoder (nn.Module): decoder module. + as_two_stage (bool): whether to use two-stage transformer. Default False. + num_feature_levels (int): number of feature levels. Default 4. + two_stage_num_proposals (int): number of proposals in two-stage transformer. Default 300. + Only used when as_two_stage is True. + """ + + def __init__( + self, + encoder=None, + decoder=None, + num_feature_levels=4, + as_two_stage=False, + two_stage_num_proposals=300, + assign_first_stage=False, + ): + super(DeformableDetrTransformer, self).__init__() + self.encoder = encoder + self.decoder = decoder + self.num_feature_levels = num_feature_levels + self.as_two_stage = as_two_stage + self.two_stage_num_proposals = two_stage_num_proposals + self.assign_first_stage = assign_first_stage + + self.embed_dim = self.encoder.embed_dim + + self.level_embeds = nn.Parameter(torch.Tensor(self.num_feature_levels, self.embed_dim)) + + if self.as_two_stage: + self.enc_output = nn.Linear(self.embed_dim, self.embed_dim) + self.enc_output_norm = nn.LayerNorm(self.embed_dim) + self.pos_trans = nn.Linear(self.embed_dim * 2, self.embed_dim * 2) + self.pos_trans_norm = nn.LayerNorm(self.embed_dim * 2) + self.pix_trans = nn.Linear(self.embed_dim, self.embed_dim) + self.pix_trans_norm = nn.LayerNorm(self.embed_dim) + else: + self.reference_points = nn.Linear(self.embed_dim, 2) + + self.init_weights() + + def init_weights(self): + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + for m in self.modules(): + if isinstance(m, MultiScaleDeformableAttention): + m.init_weights() + if not self.as_two_stage: + nn.init.xavier_normal_(self.reference_points.weight.data, gain=1.0) + nn.init.constant_(self.reference_points.bias.data, 0.0) + nn.init.normal_(self.level_embeds) + + def gen_encoder_output_proposals(self, memory, memory_padding_mask, spatial_shapes): + N, S, C = memory.shape + proposals = [] + _cur = 0 + level_ids = [] + for lvl, (H, W) in enumerate(spatial_shapes): + mask_flatten_ = memory_padding_mask[:, _cur : (_cur + H * W)].view(N, H, W, 1) + valid_H = torch.sum(~mask_flatten_[:, :, 0, 0], 1) + valid_W = torch.sum(~mask_flatten_[:, 0, :, 0], 1) + + grid_y, grid_x = torch.meshgrid( + torch.linspace(0, H - 1, H, dtype=torch.float32, device=memory.device), + torch.linspace(0, W - 1, W, dtype=torch.float32, device=memory.device), + ) + grid = torch.cat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1) + + scale = torch.cat([valid_W.unsqueeze(-1), valid_H.unsqueeze(-1)], 1).view(N, 1, 1, 2) + grid = (grid.unsqueeze(0).expand(N, -1, -1, -1) + 0.5) / scale + wh = torch.ones_like(grid) * 0.05 * (2.0**lvl) + proposal = torch.cat((grid, wh), -1).view(N, -1, 4) + proposals.append(proposal) + _cur += H * W + level_ids.append(grid.new_ones(H * W, dtype=torch.long) * lvl) + + output_proposals = torch.cat(proposals, 1) + output_proposals_valid = ((output_proposals > 0.01) & (output_proposals < 0.99)).all( + -1, keepdim=True + ) + output_proposals = torch.log(output_proposals / (1 - output_proposals)) + output_proposals = output_proposals.masked_fill( + memory_padding_mask.unsqueeze(-1), float("inf") + ) + output_proposals = output_proposals.masked_fill(~output_proposals_valid, float("inf")) + + output_memory = memory + output_memory = output_memory.masked_fill(memory_padding_mask.unsqueeze(-1), float(0)) + output_memory = output_memory.masked_fill(~output_proposals_valid, float(0)) + output_memory = self.enc_output_norm(self.enc_output(output_memory)) + level_ids = torch.cat(level_ids) + output_proposals = output_proposals.to(output_memory.dtype) + return output_memory, output_proposals, level_ids + + @staticmethod + def get_reference_points(spatial_shapes, valid_ratios, device): + """Get the reference points used in decoder. + + Args: + spatial_shapes (Tensor): The shape of all + feature maps, has shape (num_level, 2). + valid_ratios (Tensor): The ratios of valid + points on the feature map, has shape + (bs, num_levels, 2) + device (obj:`device`): The device where + reference_points should be. + + Returns: + Tensor: reference points used in decoder, has \ + shape (bs, num_keys, num_levels, 2). + """ + reference_points_list = [] + for lvl, (H, W) in enumerate(spatial_shapes): + ref_y, ref_x = torch.meshgrid( + torch.linspace(0.5, H - 0.5, H, dtype=torch.float32, device=device), + torch.linspace(0.5, W - 0.5, W, dtype=torch.float32, device=device), + ) + ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * H) + ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * W) + ref = torch.stack((ref_x, ref_y), -1) + reference_points_list.append(ref) + reference_points = torch.cat(reference_points_list, 1) + reference_points = reference_points[:, :, None] * valid_ratios[:, None] + return reference_points + + def get_valid_ratio(self, mask): + """Get the valid ratios of feature maps of all levels.""" + _, H, W = mask.shape + valid_H = torch.sum(~mask[:, :, 0], 1) + valid_W = torch.sum(~mask[:, 0, :], 1) + valid_ratio_h = valid_H.float() / H + valid_ratio_w = valid_W.float() / W + valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1) + return valid_ratio + + def get_proposal_pos_embed(self, proposals, num_pos_feats=128, temperature=10000): + """Get the position embedding of proposal.""" + scale = 2 * math.pi + dim_t = torch.arange(num_pos_feats, dtype=torch.float32, device=proposals.device) + dim_t = temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / num_pos_feats) + proposals = proposals.sigmoid() * scale + pos = proposals[:, :, :, None] / dim_t + pos = torch.stack((pos[:, :, :, 0::2].sin(), pos[:, :, :, 1::2].cos()), dim=4).flatten(2) + return pos + + def forward( + self, + multi_level_feats, + multi_level_masks, + multi_level_pos_embeds, + query_embed, + **kwargs, + ): + assert self.as_two_stage or query_embed is not None + + feat_flatten = [] + mask_flatten = [] + lvl_pos_embed_flatten = [] + spatial_shapes = [] + for lvl, (feat, mask, pos_embed) in enumerate( + zip(multi_level_feats, multi_level_masks, multi_level_pos_embeds) + ): + bs, c, h, w = feat.shape + spatial_shape = (h, w) + spatial_shapes.append(spatial_shape) + + feat = feat.flatten(2).transpose(1, 2) # bs, hw, c + mask = mask.flatten(1) + pos_embed = pos_embed.flatten(2).transpose(1, 2) # bs, hw, c + lvl_pos_embed = pos_embed + self.level_embeds[lvl].view(1, 1, -1) + lvl_pos_embed_flatten.append(lvl_pos_embed) + feat_flatten.append(feat) + mask_flatten.append(mask) + feat_flatten = torch.cat(feat_flatten, 1) + mask_flatten = torch.cat(mask_flatten, 1) + lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1) + spatial_shapes = torch.as_tensor( + spatial_shapes, dtype=torch.long, device=feat_flatten.device + ) + level_start_index = torch.cat( + (spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1]) + ) + valid_ratios = torch.stack([self.get_valid_ratio(m) for m in multi_level_masks], 1) + valid_ratios = valid_ratios.to(feat_flatten.dtype) + + reference_points = self.get_reference_points( + spatial_shapes, valid_ratios, device=feat.device + ) + reference_points = reference_points.to(feat_flatten.dtype) + + memory = self.encoder( + query=feat_flatten, + key=None, + value=None, + query_pos=lvl_pos_embed_flatten, + query_key_padding_mask=mask_flatten, + spatial_shapes=spatial_shapes, + reference_points=reference_points, + level_start_index=level_start_index, + valid_ratios=valid_ratios, + **kwargs, + ) + + bs, _, c = memory.shape + if self.as_two_stage: + output_memory, output_proposals, level_ids = self.gen_encoder_output_proposals( + memory, mask_flatten, spatial_shapes + ) + + enc_outputs_class = self.decoder.class_embed[self.decoder.num_layers](output_memory) + enc_outputs_coord_unact = ( + self.decoder.bbox_embed[self.decoder.num_layers](output_memory) + output_proposals + ) + + topk = self.two_stage_num_proposals + + proposal_logit = enc_outputs_class[..., 0] + + if self.assign_first_stage: + proposal_boxes = box_cxcywh_to_xyxy(enc_outputs_coord_unact.sigmoid()).clamp(0, 1) + topk_proposals = [] + for b in range(bs): + prop_boxes_b = proposal_boxes[b] + prop_logits_b = proposal_logit[b] + + pre_nms_topk = 1000 + pre_nms_inds = [] + for lvl in range(len(spatial_shapes)): + lvl_mask = level_ids == lvl + pre_nms_inds.append( + torch.topk( + prop_logits_b.sigmoid() * lvl_mask, + min(pre_nms_topk, prop_logits_b.size(0)), + )[1] + ) + pre_nms_inds = torch.cat(pre_nms_inds) + + post_nms_inds = batched_nms( + prop_boxes_b[pre_nms_inds], + prop_logits_b[pre_nms_inds], + level_ids[pre_nms_inds], + 0.9, + ) + keep_inds = pre_nms_inds[post_nms_inds] + + if len(keep_inds) < self.two_stage_num_proposals: + print( + f"[WARNING] nms proposals ({len(keep_inds)}) < {self.two_stage_num_proposals}, running naive topk" + ) + keep_inds = torch.topk( + proposal_logit[b], min(topk, proposal_logit[b].size(0)) + )[1] + + q_per_l = topk // len(spatial_shapes) + is_level_ordered = ( + level_ids[keep_inds][None] + == torch.arange(len(spatial_shapes), device=level_ids.device)[:, None] + ) # LS + keep_inds_mask = is_level_ordered & ( + is_level_ordered.cumsum(1) <= q_per_l + ) # LS + keep_inds_mask = keep_inds_mask.any(0) # S + + if keep_inds_mask.sum() < topk: + num_to_add = topk - keep_inds_mask.sum() + pad_inds = (~keep_inds_mask).nonzero()[:num_to_add] + keep_inds_mask[pad_inds] = True + + keep_inds_topk = keep_inds[keep_inds_mask] + topk_proposals.append(keep_inds_topk) + topk_proposals = torch.stack(topk_proposals) + else: + topk_proposals = torch.topk(proposal_logit, topk, dim=1)[1] + + topk_coords_unact = torch.gather( + enc_outputs_coord_unact, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4) + ) + topk_coords_unact = topk_coords_unact.detach() + reference_points = topk_coords_unact.sigmoid() + init_reference_out = reference_points + pos_trans_out = self.pos_trans_norm( + self.pos_trans( + self.get_proposal_pos_embed(topk_coords_unact).to(topk_coords_unact.dtype) + ) + ) + query_pos, query = torch.split(pos_trans_out, c, dim=2) + + topk_feats = torch.stack( + [output_memory[b][topk_proposals[b]] for b in range(bs)] + ).detach() + query = query + self.pix_trans_norm(self.pix_trans(topk_feats)) + else: + query_pos, query = torch.split(query_embed, c, dim=1) + query_pos = query_pos.unsqueeze(0).expand(bs, -1, -1) + query = query.unsqueeze(0).expand(bs, -1, -1) + reference_points = self.reference_points(query_pos).sigmoid() + init_reference_out = reference_points + + inter_states, inter_references = self.decoder( + query=query, # bs, num_queries, embed_dims + key=None, # bs, num_tokens, embed_dims + value=memory, # bs, num_tokens, embed_dims + query_pos=query_pos, + key_padding_mask=mask_flatten, # bs, num_tokens + reference_points=reference_points, # num_queries, 4 + spatial_shapes=spatial_shapes, # nlvl, 2 + level_start_index=level_start_index, # nlvl + valid_ratios=valid_ratios, # bs, nlvl, 2 + **kwargs, + ) + + inter_references_out = inter_references + if self.as_two_stage: + return ( + inter_states, + init_reference_out, + inter_references_out, + enc_outputs_class, + enc_outputs_coord_unact, + output_proposals.sigmoid(), + memory, + ) + return inter_states, init_reference_out, inter_references_out, None, None, None, memory diff --git a/ape/modeling/deta/misc.py b/ape/modeling/deta/misc.py new file mode 100644 index 0000000..d697fe8 --- /dev/null +++ b/ape/modeling/deta/misc.py @@ -0,0 +1,469 @@ +""" +Misc functions, including distributed helpers. + +Mostly copy-paste from torchvision references. +""" +import datetime +import os +import pickle +import subprocess +import time +from collections import defaultdict, deque +from typing import List, Optional + +import torch +import torch.distributed as dist +from packaging import version +from torch import Tensor + +import torchvision + +if version.parse(torchvision.__version__) < version.parse("0.7"): + from torchvision.ops import _new_empty_tensor + from torchvision.ops.misc import _output_size + + +class SmoothedValue(object): + """Track a series of values and provide access to smoothed values over a + window or the global series average. + """ + + def __init__(self, window_size=20, fmt=None): + if fmt is None: + fmt = "{median:.4f} ({global_avg:.4f})" + self.deque = deque(maxlen=window_size) + self.total = 0.0 + self.count = 0 + self.fmt = fmt + + def update(self, value, n=1): + self.deque.append(value) + self.count += n + self.total += value * n + + def synchronize_between_processes(self): + """ + Warning: does not synchronize the deque! + """ + if not is_dist_avail_and_initialized(): + return + t = torch.tensor([self.count, self.total], dtype=torch.float64, device="cuda") + dist.barrier() + dist.all_reduce(t) + t = t.tolist() + self.count = int(t[0]) + self.total = t[1] + + @property + def median(self): + d = torch.tensor(list(self.deque)) + return d.median().item() + + @property + def avg(self): + d = torch.tensor(list(self.deque), dtype=torch.float32) + return d.mean().item() + + @property + def global_avg(self): + return self.total / self.count + + @property + def max(self): + return max(self.deque) + + @property + def value(self): + return self.deque[-1] + + def __str__(self): + return self.fmt.format( + median=self.median, + avg=self.avg, + global_avg=self.global_avg, + max=self.max, + value=self.value, + ) + + +def all_gather(data): + """ + Run all_gather on arbitrary picklable data (not necessarily tensors) + Args: + data: any picklable object + Returns: + list[data]: list of data gathered from each rank + """ + world_size = get_world_size() + if world_size == 1: + return [data] + + buffer = pickle.dumps(data) + storage = torch.ByteStorage.from_buffer(buffer) + tensor = torch.ByteTensor(storage).to("cuda") + + local_size = torch.tensor([tensor.numel()], device="cuda") + size_list = [torch.tensor([0], device="cuda") for _ in range(world_size)] + dist.all_gather(size_list, local_size) + size_list = [int(size.item()) for size in size_list] + max_size = max(size_list) + + tensor_list = [] + for _ in size_list: + tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device="cuda")) + if local_size != max_size: + padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device="cuda") + tensor = torch.cat((tensor, padding), dim=0) + dist.all_gather(tensor_list, tensor) + + data_list = [] + for size, tensor in zip(size_list, tensor_list): + buffer = tensor.cpu().numpy().tobytes()[:size] + data_list.append(pickle.loads(buffer)) + + return data_list + + +def reduce_dict(input_dict, average=True): + """ + Args: + input_dict (dict): all the values will be reduced + average (bool): whether to do average or sum + Reduce the values in the dictionary from all processes so that all processes + have the averaged results. Returns a dict with the same fields as + input_dict, after reduction. + """ + world_size = get_world_size() + if world_size < 2: + return input_dict + with torch.no_grad(): + names = [] + values = [] + for k in sorted(input_dict.keys()): + names.append(k) + values.append(input_dict[k]) + values = torch.stack(values, dim=0) + dist.all_reduce(values) + if average: + values /= world_size + reduced_dict = {k: v for k, v in zip(names, values)} + return reduced_dict + + +class MetricLogger(object): + def __init__(self, delimiter="\t"): + self.meters = defaultdict(SmoothedValue) + self.delimiter = delimiter + + def update(self, **kwargs): + for k, v in kwargs.items(): + if isinstance(v, torch.Tensor): + v = v.item() + assert isinstance(v, (float, int)) + self.meters[k].update(v) + + def __getattr__(self, attr): + if attr in self.meters: + return self.meters[attr] + if attr in self.__dict__: + return self.__dict__[attr] + raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, attr)) + + def __str__(self): + loss_str = [] + for name, meter in self.meters.items(): + loss_str.append("{}: {}".format(name, str(meter))) + return self.delimiter.join(loss_str) + + def synchronize_between_processes(self): + for meter in self.meters.values(): + meter.synchronize_between_processes() + + def add_meter(self, name, meter): + self.meters[name] = meter + + def log_every(self, iterable, print_freq, header=None): + i = 0 + if not header: + header = "" + start_time = time.time() + end = time.time() + iter_time = SmoothedValue(fmt="{avg:.4f}") + data_time = SmoothedValue(fmt="{avg:.4f}") + space_fmt = ":" + str(len(str(len(iterable)))) + "d" + if torch.cuda.is_available(): + log_msg = self.delimiter.join( + [ + header, + "[{0" + space_fmt + "}/{1}]", + "eta: {eta}", + "{meters}", + "time: {time}", + "data: {data}", + "max mem: {memory:.0f}", + ] + ) + else: + log_msg = self.delimiter.join( + [ + header, + "[{0" + space_fmt + "}/{1}]", + "eta: {eta}", + "{meters}", + "time: {time}", + "data: {data}", + ] + ) + MB = 1024.0 * 1024.0 + for obj in iterable: + data_time.update(time.time() - end) + yield obj + iter_time.update(time.time() - end) + if i % print_freq == 0 or i == len(iterable) - 1: + eta_seconds = iter_time.global_avg * (len(iterable) - i) + eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) + if torch.cuda.is_available(): + print( + log_msg.format( + i, + len(iterable), + eta=eta_string, + meters=str(self), + time=str(iter_time), + data=str(data_time), + memory=torch.cuda.max_memory_allocated() / MB, + ) + ) + else: + print( + log_msg.format( + i, + len(iterable), + eta=eta_string, + meters=str(self), + time=str(iter_time), + data=str(data_time), + ) + ) + i += 1 + end = time.time() + total_time = time.time() - start_time + total_time_str = str(datetime.timedelta(seconds=int(total_time))) + print( + "{} Total time: {} ({:.4f} s / it)".format( + header, total_time_str, total_time / len(iterable) + ) + ) + + +def get_sha(): + cwd = os.path.dirname(os.path.abspath(__file__)) + + def _run(command): + return subprocess.check_output(command, cwd=cwd).decode("ascii").strip() + + sha = "N/A" + diff = "clean" + branch = "N/A" + try: + sha = _run(["git", "rev-parse", "HEAD"]) + subprocess.check_output(["git", "diff"], cwd=cwd) + diff = _run(["git", "diff-index", "HEAD"]) + diff = "has uncommited changes" if diff else "clean" + branch = _run(["git", "rev-parse", "--abbrev-ref", "HEAD"]) + except Exception: + pass + message = f"sha: {sha}, status: {diff}, branch: {branch}" + return message + + +def collate_fn(batch): + batch = list(zip(*batch)) + batch[0] = nested_tensor_from_tensor_list(batch[0]) + return tuple(batch) + + +def _max_by_axis(the_list): + maxes = the_list[0] + for sublist in the_list[1:]: + for index, item in enumerate(sublist): + maxes[index] = max(maxes[index], item) + return maxes + + +class NestedTensor(object): + def __init__(self, tensors, mask: Optional[Tensor]): + self.tensors = tensors + self.mask = mask + + def to(self, device): + cast_tensor = self.tensors.to(device) + mask = self.mask + if mask is not None: + assert mask is not None + cast_mask = mask.to(device) + else: + cast_mask = None + return NestedTensor(cast_tensor, cast_mask) + + def decompose(self): + return self.tensors, self.mask + + def __repr__(self): + return str(self.tensors) + + +def nested_tensor_from_tensor_list(tensor_list: List[Tensor]): + if tensor_list[0].ndim == 3: + if torchvision._is_tracing(): + return _onnx_nested_tensor_from_tensor_list(tensor_list) + + max_size = _max_by_axis([list(img.shape) for img in tensor_list]) + batch_shape = [len(tensor_list)] + max_size + b, c, h, w = batch_shape + dtype = tensor_list[0].dtype + device = tensor_list[0].device + tensor = torch.zeros(batch_shape, dtype=dtype, device=device) + mask = torch.ones((b, h, w), dtype=torch.bool, device=device) + for img, pad_img, m in zip(tensor_list, tensor, mask): + pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img) + m[: img.shape[1], : img.shape[2]] = False + else: + raise ValueError("not supported") + return NestedTensor(tensor, mask) + + +@torch.jit.unused +def _onnx_nested_tensor_from_tensor_list(tensor_list: List[Tensor]) -> NestedTensor: + max_size = [] + for i in range(tensor_list[0].dim()): + max_size_i = torch.max( + torch.stack([img.shape[i] for img in tensor_list]).to(torch.float32) + ).to(torch.int64) + max_size.append(max_size_i) + max_size = tuple(max_size) + + padded_imgs = [] + padded_masks = [] + for img in tensor_list: + padding = [(s1 - s2) for s1, s2 in zip(max_size, tuple(img.shape))] + padded_img = torch.nn.functional.pad(img, (0, padding[2], 0, padding[1], 0, padding[0])) + padded_imgs.append(padded_img) + + m = torch.zeros_like(img[0], dtype=torch.int, device=img.device) + padded_mask = torch.nn.functional.pad(m, (0, padding[2], 0, padding[1]), "constant", 1) + padded_masks.append(padded_mask.to(torch.bool)) + + tensor = torch.stack(padded_imgs) + mask = torch.stack(padded_masks) + + return NestedTensor(tensor, mask=mask) + + +def setup_for_distributed(is_master): + """ + This function disables printing when not in master process + """ + import builtins as __builtin__ + + builtin_print = __builtin__.print + + def print(*args, **kwargs): + force = kwargs.pop("force", False) + if is_master or force: + builtin_print(*args, **kwargs) + + __builtin__.print = print + + +def is_dist_avail_and_initialized(): + if not dist.is_available(): + return False + if not dist.is_initialized(): + return False + return True + + +def get_world_size(): + if not is_dist_avail_and_initialized(): + return 1 + return dist.get_world_size() + + +def get_rank(): + if not is_dist_avail_and_initialized(): + return 0 + return dist.get_rank() + + +def is_main_process(): + return get_rank() == 0 + + +def save_on_master(*args, **kwargs): + if is_main_process(): + torch.save(*args, **kwargs) + + +def init_distributed_mode(args): + if "RANK" in os.environ and "WORLD_SIZE" in os.environ: + args.rank = int(os.environ["RANK"]) + args.world_size = int(os.environ["WORLD_SIZE"]) + args.gpu = int(os.environ["LOCAL_RANK"]) + elif "SLURM_PROCID" in os.environ: + args.rank = int(os.environ["SLURM_PROCID"]) + args.gpu = args.rank % torch.cuda.device_count() + else: + print("Not using distributed mode") + args.distributed = False + return + + args.distributed = True + + torch.cuda.set_device(args.gpu) + args.dist_backend = "nccl" + print("| distributed init (rank {}): {}".format(args.rank, args.dist_url), flush=True) + torch.distributed.init_process_group( + backend=args.dist_backend, + init_method=args.dist_url, + world_size=args.world_size, + rank=args.rank, + ) + torch.distributed.barrier() + setup_for_distributed(args.rank == 0) + + +@torch.no_grad() +def accuracy(output, target, topk=(1,)): + """Computes the precision@k for the specified values of k""" + if target.numel() == 0: + return [torch.zeros([], device=output.device)] + maxk = max(topk) + batch_size = target.size(0) + + _, pred = output.topk(maxk, 1, True, True) + pred = pred.t() + correct = pred.eq(target.view(1, -1).expand_as(pred)) + + res = [] + for k in topk: + correct_k = correct[:k].view(-1).float().sum(0) + res.append(correct_k.mul_(100.0 / batch_size)) + return res + + +def interpolate(input, size=None, scale_factor=None, mode="nearest", align_corners=None): + """ + Equivalent to nn.functional.interpolate, but with support for empty batch sizes. + This will eventually be supported natively by PyTorch, and this + class can go away. + """ + if version.parse(torchvision.__version__) < version.parse("0.7"): + if input.numel() > 0: + return torch.nn.functional.interpolate(input, size, scale_factor, mode, align_corners) + + output_shape = _output_size(2, input, size, scale_factor) + output_shape = list(input.shape[:-2]) + list(output_shape) + return _new_empty_tensor(input, output_shape) + else: + return torchvision.ops.misc.interpolate(input, size, scale_factor, mode, align_corners) diff --git a/ape/modeling/deta/segmentation.py b/ape/modeling/deta/segmentation.py new file mode 100644 index 0000000..95decb8 --- /dev/null +++ b/ape/modeling/deta/segmentation.py @@ -0,0 +1,378 @@ +""" +This file provides the definition of the convolutional heads used to predict masks, as well as the losses +""" +import io +from collections import defaultdict + +import torch +import torch.nn as nn +import torch.nn.functional as F +from PIL import Image + +from detrex.layers import box_cxcywh_to_xyxy + +try: + from panopticapi.utils import id2rgb, rgb2id +except ImportError: + pass + + +class DETRsegm(nn.Module): + def __init__(self, detr, freeze_detr=False): + super().__init__() + self.detr = detr + + if freeze_detr: + for p in self.parameters(): + p.requires_grad_(False) + + hidden_dim, nheads = detr.transformer.d_model, detr.transformer.nhead + self.bbox_attention = MHAttentionMap(hidden_dim, hidden_dim, nheads, dropout=0) + self.mask_head = MaskHeadSmallConv(hidden_dim + nheads, [1024, 512, 256], hidden_dim) + + def forward(self, samples): + if not isinstance(samples, NestedTensor): + samples = nested_tensor_from_tensor_list(samples) + features, pos = self.detr.backbone(samples) + + bs = features[-1].tensors.shape[0] + + src, mask = features[-1].decompose() + src_proj = self.detr.input_proj(src) + hs, memory = self.detr.transformer(src_proj, mask, self.detr.query_embed.weight, pos[-1]) + + outputs_class = self.detr.class_embed(hs) + outputs_coord = self.detr.bbox_embed(hs).sigmoid() + out = {"pred_logits": outputs_class[-1], "pred_boxes": outputs_coord[-1]} + if self.detr.aux_loss: + out["aux_outputs"] = [ + {"pred_logits": a, "pred_boxes": b} + for a, b in zip(outputs_class[:-1], outputs_coord[:-1]) + ] + + bbox_mask = self.bbox_attention(hs[-1], memory, mask=mask) + + seg_masks = self.mask_head( + src_proj, bbox_mask, [features[2].tensors, features[1].tensors, features[0].tensors] + ) + outputs_seg_masks = seg_masks.view( + bs, self.detr.num_queries, seg_masks.shape[-2], seg_masks.shape[-1] + ) + + out["pred_masks"] = outputs_seg_masks + return out + + +class MaskHeadSmallConv(nn.Module): + """ + Simple convolutional head, using group norm. + Upsampling is done using a FPN approach + """ + + def __init__(self, dim, fpn_dims, context_dim): + super().__init__() + + inter_dims = [ + dim, + context_dim // 2, + context_dim // 4, + context_dim // 8, + context_dim // 16, + context_dim // 64, + ] + self.lay1 = torch.nn.Conv2d(dim, dim, 3, padding=1) + self.gn1 = torch.nn.GroupNorm(8, dim) + self.lay2 = torch.nn.Conv2d(dim, inter_dims[1], 3, padding=1) + self.gn2 = torch.nn.GroupNorm(8, inter_dims[1]) + self.lay3 = torch.nn.Conv2d(inter_dims[1], inter_dims[2], 3, padding=1) + self.gn3 = torch.nn.GroupNorm(8, inter_dims[2]) + self.lay4 = torch.nn.Conv2d(inter_dims[2], inter_dims[3], 3, padding=1) + self.gn4 = torch.nn.GroupNorm(8, inter_dims[3]) + self.lay5 = torch.nn.Conv2d(inter_dims[3], inter_dims[4], 3, padding=1) + self.gn5 = torch.nn.GroupNorm(8, inter_dims[4]) + self.out_lay = torch.nn.Conv2d(inter_dims[4], 1, 3, padding=1) + + self.dim = dim + + self.adapter1 = torch.nn.Conv2d(fpn_dims[0], inter_dims[1], 1) + self.adapter2 = torch.nn.Conv2d(fpn_dims[1], inter_dims[2], 1) + self.adapter3 = torch.nn.Conv2d(fpn_dims[2], inter_dims[3], 1) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_uniform_(m.weight, a=1) + nn.init.constant_(m.bias, 0) + + def forward(self, x, bbox_mask, fpns): + def expand(tensor, length): + return tensor.unsqueeze(1).repeat(1, int(length), 1, 1, 1).flatten(0, 1) + + x = torch.cat([expand(x, bbox_mask.shape[1]), bbox_mask.flatten(0, 1)], 1) + + x = self.lay1(x) + x = self.gn1(x) + x = F.relu(x) + x = self.lay2(x) + x = self.gn2(x) + x = F.relu(x) + + cur_fpn = self.adapter1(fpns[0]) + if cur_fpn.size(0) != x.size(0): + cur_fpn = expand(cur_fpn, x.size(0) / cur_fpn.size(0)) + x = cur_fpn + F.interpolate(x, size=cur_fpn.shape[-2:], mode="nearest") + x = self.lay3(x) + x = self.gn3(x) + x = F.relu(x) + + cur_fpn = self.adapter2(fpns[1]) + if cur_fpn.size(0) != x.size(0): + cur_fpn = expand(cur_fpn, x.size(0) / cur_fpn.size(0)) + x = cur_fpn + F.interpolate(x, size=cur_fpn.shape[-2:], mode="nearest") + x = self.lay4(x) + x = self.gn4(x) + x = F.relu(x) + + cur_fpn = self.adapter3(fpns[2]) + if cur_fpn.size(0) != x.size(0): + cur_fpn = expand(cur_fpn, x.size(0) / cur_fpn.size(0)) + x = cur_fpn + F.interpolate(x, size=cur_fpn.shape[-2:], mode="nearest") + x = self.lay5(x) + x = self.gn5(x) + x = F.relu(x) + + x = self.out_lay(x) + return x + + +class MHAttentionMap(nn.Module): + """This is a 2D attention module, which only returns the attention softmax (no multiplication by value)""" + + def __init__(self, query_dim, hidden_dim, num_heads, dropout=0, bias=True): + super().__init__() + self.num_heads = num_heads + self.hidden_dim = hidden_dim + self.dropout = nn.Dropout(dropout) + + self.q_linear = nn.Linear(query_dim, hidden_dim, bias=bias) + self.k_linear = nn.Linear(query_dim, hidden_dim, bias=bias) + + nn.init.zeros_(self.k_linear.bias) + nn.init.zeros_(self.q_linear.bias) + nn.init.xavier_uniform_(self.k_linear.weight) + nn.init.xavier_uniform_(self.q_linear.weight) + self.normalize_fact = float(hidden_dim / self.num_heads) ** -0.5 + + def forward(self, q, k, mask=None): + q = self.q_linear(q) + k = F.conv2d(k, self.k_linear.weight.unsqueeze(-1).unsqueeze(-1), self.k_linear.bias) + qh = q.view(q.shape[0], q.shape[1], self.num_heads, self.hidden_dim // self.num_heads) + kh = k.view( + k.shape[0], self.num_heads, self.hidden_dim // self.num_heads, k.shape[-2], k.shape[-1] + ) + weights = torch.einsum("bqnc,bnchw->bqnhw", qh * self.normalize_fact, kh) + + if mask is not None: + weights.masked_fill_(mask.unsqueeze(1).unsqueeze(1), float("-inf")) + weights = F.softmax(weights.flatten(2), dim=-1).view_as(weights) + weights = self.dropout(weights) + return weights + + +def dice_loss(inputs, targets, num_boxes): + """ + Compute the DICE loss, similar to generalized IOU for masks + Args: + inputs: A float tensor of arbitrary shape. + The predictions for each example. + targets: A float tensor with the same shape as inputs. Stores the binary + classification label for each element in inputs + (0 for the negative class and 1 for the positive class). + """ + inputs = inputs.sigmoid() + inputs = inputs.flatten(1) + numerator = 2 * (inputs * targets).sum(1) + denominator = inputs.sum(-1) + targets.sum(-1) + loss = 1 - (numerator + 1) / (denominator + 1) + return loss.sum() / num_boxes + + +def sigmoid_focal_loss(inputs, targets, num_boxes, alpha: float = 0.25, gamma: float = 2): + """ + Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002. + Args: + inputs: A float tensor of arbitrary shape. + The predictions for each example. + targets: A float tensor with the same shape as inputs. Stores the binary + classification label for each element in inputs + (0 for the negative class and 1 for the positive class). + alpha: (optional) Weighting factor in range (0,1) to balance + positive vs negative examples. Default = -1 (no weighting). + gamma: Exponent of the modulating factor (1 - p_t) to + balance easy vs hard examples. + Returns: + Loss tensor + """ + prob = inputs.sigmoid() + ce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none") + p_t = prob * targets + (1 - prob) * (1 - targets) + loss = ce_loss * ((1 - p_t) ** gamma) + + if alpha >= 0: + alpha_t = alpha * targets + (1 - alpha) * (1 - targets) + loss = alpha_t * loss + + return loss.mean(1).sum() / num_boxes + + +class PostProcessSegm(nn.Module): + def __init__(self, threshold=0.5): + super().__init__() + self.threshold = threshold + + @torch.no_grad() + def forward(self, results, outputs, orig_target_sizes, max_target_sizes): + assert len(orig_target_sizes) == len(max_target_sizes) + max_h, max_w = max_target_sizes.max(0)[0].tolist() + outputs_masks = outputs["pred_masks"].squeeze(2) + outputs_masks = F.interpolate( + outputs_masks, size=(max_h, max_w), mode="bilinear", align_corners=False + ) + outputs_masks = (outputs_masks.sigmoid() > self.threshold).cpu() + + for i, (cur_mask, t, tt) in enumerate( + zip(outputs_masks, max_target_sizes, orig_target_sizes) + ): + img_h, img_w = t[0], t[1] + results[i]["masks"] = cur_mask[:, :img_h, :img_w].unsqueeze(1) + results[i]["masks"] = F.interpolate( + results[i]["masks"].float(), size=tuple(tt.tolist()), mode="nearest" + ).byte() + + return results + + +class PostProcessPanoptic(nn.Module): + """This class converts the output of the model to the final panoptic result, in the format expected by the + coco panoptic API""" + + def __init__(self, is_thing_map, threshold=0.85): + """ + Parameters: + is_thing_map: This is a whose keys are the class ids, and the values a boolean indicating whether + the class is a thing (True) or a stuff (False) class + threshold: confidence threshold: segments with confidence lower than this will be deleted + """ + super().__init__() + self.threshold = threshold + self.is_thing_map = is_thing_map + + def forward(self, outputs, processed_sizes, target_sizes=None): + """This function computes the panoptic prediction from the model's predictions. + Parameters: + outputs: This is a dict coming directly from the model. See the model doc for the content. + processed_sizes: This is a list of tuples (or torch tensors) of sizes of the images that were passed to the + model, ie the size after data augmentation but before batching. + target_sizes: This is a list of tuples (or torch tensors) corresponding to the requested final size + of each prediction. If left to None, it will default to the processed_sizes + """ + if target_sizes is None: + target_sizes = processed_sizes + assert len(processed_sizes) == len(target_sizes) + out_logits, raw_masks, raw_boxes = ( + outputs["pred_logits"], + outputs["pred_masks"], + outputs["pred_boxes"], + ) + assert len(out_logits) == len(raw_masks) == len(target_sizes) + preds = [] + + def to_tuple(tup): + if isinstance(tup, tuple): + return tup + return tuple(tup.cpu().tolist()) + + for cur_logits, cur_masks, cur_boxes, size, target_size in zip( + out_logits, raw_masks, raw_boxes, processed_sizes, target_sizes + ): + scores, labels = cur_logits.softmax(-1).max(-1) + keep = labels.ne(outputs["pred_logits"].shape[-1] - 1) & (scores > self.threshold) + cur_scores, cur_classes = cur_logits.softmax(-1).max(-1) + cur_scores = cur_scores[keep] + cur_classes = cur_classes[keep] + cur_masks = cur_masks[keep] + cur_masks = F.interpolate(cur_masks[None], to_tuple(size), mode="bilinear").squeeze(0) + cur_boxes = box_cxcywh_to_xyxy(cur_boxes[keep]) + + h, w = cur_masks.shape[-2:] + assert len(cur_boxes) == len(cur_classes) + + cur_masks = cur_masks.flatten(1) + stuff_equiv_classes = defaultdict(lambda: []) + for k, label in enumerate(cur_classes): + if not self.is_thing_map[label.item()]: + stuff_equiv_classes[label.item()].append(k) + + def get_ids_area(masks, scores, dedup=False): + + m_id = masks.transpose(0, 1).softmax(-1) + + if m_id.shape[-1] == 0: + m_id = torch.zeros((h, w), dtype=torch.long, device=m_id.device) + else: + m_id = m_id.argmax(-1).view(h, w) + + if dedup: + for equiv in stuff_equiv_classes.values(): + if len(equiv) > 1: + for eq_id in equiv: + m_id.masked_fill_(m_id.eq(eq_id), equiv[0]) + + final_h, final_w = to_tuple(target_size) + + seg_img = Image.fromarray(id2rgb(m_id.view(h, w).cpu().numpy())) + seg_img = seg_img.resize(size=(final_w, final_h), resample=Image.NEAREST) + + np_seg_img = ( + torch.ByteTensor(torch.ByteStorage.from_buffer(seg_img.tobytes())) + .view(final_h, final_w, 3) + .numpy() + ) + m_id = torch.from_numpy(rgb2id(np_seg_img)) + + area = [] + for i in range(len(scores)): + area.append(m_id.eq(i).sum().item()) + return area, seg_img + + area, seg_img = get_ids_area(cur_masks, cur_scores, dedup=True) + if cur_classes.numel() > 0: + while True: + filtered_small = torch.as_tensor( + [area[i] <= 4 for i, c in enumerate(cur_classes)], + dtype=torch.bool, + device=keep.device, + ) + if filtered_small.any().item(): + cur_scores = cur_scores[~filtered_small] + cur_classes = cur_classes[~filtered_small] + cur_masks = cur_masks[~filtered_small] + area, seg_img = get_ids_area(cur_masks, cur_scores) + else: + break + + else: + cur_classes = torch.ones(1, dtype=torch.long, device=cur_classes.device) + + segments_info = [] + for i, a in enumerate(area): + cat = cur_classes[i].item() + segments_info.append( + {"id": i, "isthing": self.is_thing_map[cat], "category_id": cat, "area": a} + ) + del cur_classes + + with io.BytesIO() as out: + seg_img.save(out, format="PNG") + predictions = {"png_string": out.getvalue(), "segments_info": segments_info} + preds.append(predictions) + return preds diff --git a/ape/modeling/text/__init__.py b/ape/modeling/text/__init__.py new file mode 100644 index 0000000..088d1d2 --- /dev/null +++ b/ape/modeling/text/__init__.py @@ -0,0 +1,8 @@ +from .bert_wrapper import Bert +from .clip_wrapper import build_clip_text_encoder, get_clip_embeddings +from .clip_wrapper_eva01 import EVA01CLIP +from .clip_wrapper_eva02 import EVA02CLIP +from .clip_wrapper_open import build_openclip_text_encoder, get_openclip_embeddings +from .llama2_wrapper import Llama2 +from .t5_wrapper import T5_warpper +from .text_encoder import TextModel diff --git a/ape/modeling/text/bert_wrapper.py b/ape/modeling/text/bert_wrapper.py new file mode 100644 index 0000000..06adb43 --- /dev/null +++ b/ape/modeling/text/bert_wrapper.py @@ -0,0 +1,107 @@ +import torch +from torch import nn +from torch.cuda.amp import autocast + +from transformers import ( + AutoConfig, + AutoModelForSeq2SeqLM, + AutoTokenizer, + BertConfig, + BertModel, + RobertaConfig, + RobertaModel, +) + + +class Bert(nn.Module): + def __init__( + self, + pretrained_model_name_or_path, + dtype="float32", + **kwargs, + ): + super().__init__(**kwargs) + + self.dtype = getattr(torch, dtype) + + self.config = BertConfig.from_pretrained( + pretrained_model_name_or_path=pretrained_model_name_or_path + ) + self.bert_model = BertModel.from_pretrained( + pretrained_model_name_or_path=pretrained_model_name_or_path, + add_pooling_layer=False, + ) + self.tokenizer = AutoTokenizer.from_pretrained( + pretrained_model_name_or_path=pretrained_model_name_or_path + ) + + self.bert_model.eval() + for name, param in self.bert_model.named_parameters(): + param.requires_grad = False + param.data = param.data.to(self.dtype) + + self.register_buffer("unused_tensor", torch.zeros(1), False) + + self.text_list_to_feature = {} + + @property + def device(self): + return self.unused_tensor.device + + @autocast(enabled=False) + @torch.no_grad() + def forward_text(self, text_list, cache=False): + + if cache and tuple(text_list) in self.text_list_to_feature: + return self.text_list_to_feature[tuple(text_list)] + + tokenized = self.tokenizer.batch_encode_plus( + text_list, + max_length=256, + padding="max_length" if True else "longest", + return_special_tokens_mask=True, + return_tensors="pt", + truncation=True, + ).to(self.device) + + input_ids = tokenized.input_ids # (bs, seq_len) + attention_mask = tokenized.attention_mask # (bs, seq_len) + + max_batch_size = 500 + if len(input_ids) > max_batch_size: + chunck_num = len(input_ids) // max_batch_size + 1 + outputss = [ + self.bert_model( + input_ids=input_ids[ + chunck_id * max_batch_size : (chunck_id + 1) * max_batch_size + ], + attention_mask=attention_mask[ + chunck_id * max_batch_size : (chunck_id + 1) * max_batch_size + ], + ) + for chunck_id in range(chunck_num) + ] + + last_hidden_state = torch.cat( + [outputs.last_hidden_state for outputs in outputss], dim=0 + ) + else: + outputs = self.bert_model( + input_ids=input_ids, + attention_mask=attention_mask, + ) + + last_hidden_state = outputs.last_hidden_state + + end_token_idx = input_ids.argmin(dim=-1) - 1 + + ret = { + "end_token_idx": end_token_idx, + "attention_mask": attention_mask, + "last_hidden_state": last_hidden_state, + } + + if cache: + self.text_list_to_feature[tuple(text_list)] = ret + + return ret diff --git a/ape/modeling/text/clip_wrapper.py b/ape/modeling/text/clip_wrapper.py new file mode 100644 index 0000000..3d693f1 --- /dev/null +++ b/ape/modeling/text/clip_wrapper.py @@ -0,0 +1,224 @@ +import logging +from collections import OrderedDict +from typing import List, Union + +import torch +from torch import nn + +from clip.simple_tokenizer import SimpleTokenizer as _Tokenizer + +__all__ = ["tokenize"] + +count = 0 + + +class LayerNorm(nn.LayerNorm): + """Subclass torch's LayerNorm to handle fp16.""" + + def forward(self, x: torch.Tensor): + orig_type = x.dtype + ret = super().forward(x.type(torch.float32)) + return ret.type(orig_type) + + +class QuickGELU(nn.Module): + def forward(self, x: torch.Tensor): + return x * torch.sigmoid(1.702 * x) + + +class ResidualAttentionBlock(nn.Module): + def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None): + super().__init__() + + self.attn = nn.MultiheadAttention(d_model, n_head) + self.ln_1 = LayerNorm(d_model) + self.mlp = nn.Sequential( + OrderedDict( + [ + ("c_fc", nn.Linear(d_model, d_model * 4)), + ("gelu", QuickGELU()), + ("c_proj", nn.Linear(d_model * 4, d_model)), + ] + ) + ) + self.ln_2 = LayerNorm(d_model) + self.attn_mask = attn_mask + + def attention(self, x: torch.Tensor): + self.attn_mask = ( + self.attn_mask.to(dtype=x.dtype, device=x.device) + if self.attn_mask is not None + else None + ) + return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0] + + def forward(self, x: torch.Tensor): + x = x + self.attention(self.ln_1(x)) + x = x + self.mlp(self.ln_2(x)) + return x + + +class Transformer(nn.Module): + def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None): + super().__init__() + self.width = width + self.layers = layers + self.resblocks = nn.Sequential( + *[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers)] + ) + + def forward(self, x: torch.Tensor): + return self.resblocks(x) + + +class CLIPTEXT(nn.Module): + def __init__( + self, + embed_dim=512, + context_length=77, + vocab_size=49408, + transformer_width=512, + transformer_heads=8, + transformer_layers=12, + ): + super().__init__() + + self._tokenizer = _Tokenizer() + self.context_length = context_length + + self.transformer = Transformer( + width=transformer_width, + layers=transformer_layers, + heads=transformer_heads, + attn_mask=self.build_attention_mask(), + ) + + self.vocab_size = vocab_size + self.token_embedding = nn.Embedding(vocab_size, transformer_width) + self.positional_embedding = nn.Parameter( + torch.empty(self.context_length, transformer_width) + ) + self.ln_final = LayerNorm(transformer_width) + + self.text_projection = nn.Parameter(torch.empty(transformer_width, embed_dim)) + + self.initialize_parameters() + + def initialize_parameters(self): + nn.init.normal_(self.token_embedding.weight, std=0.02) + nn.init.normal_(self.positional_embedding, std=0.01) + + proj_std = (self.transformer.width**-0.5) * ((2 * self.transformer.layers) ** -0.5) + attn_std = self.transformer.width**-0.5 + fc_std = (2 * self.transformer.width) ** -0.5 + for block in self.transformer.resblocks: + nn.init.normal_(block.attn.in_proj_weight, std=attn_std) + nn.init.normal_(block.attn.out_proj.weight, std=proj_std) + nn.init.normal_(block.mlp.c_fc.weight, std=fc_std) + nn.init.normal_(block.mlp.c_proj.weight, std=proj_std) + + if self.text_projection is not None: + nn.init.normal_(self.text_projection, std=self.transformer.width**-0.5) + + def build_attention_mask(self): + mask = torch.empty(self.context_length, self.context_length) + mask.fill_(float("-inf")) + mask.triu_(1) # zero out the lower diagonal + return mask + + @property + def device(self): + return self.text_projection.device + + @property + def dtype(self): + return self.text_projection.dtype + + def tokenize(self, texts: Union[str, List[str]], context_length: int = 77) -> torch.LongTensor: + """ """ + if isinstance(texts, str): + texts = [texts] + + sot_token = self._tokenizer.encoder["<|startoftext|>"] + eot_token = self._tokenizer.encoder["<|endoftext|>"] + all_tokens = [[sot_token] + self._tokenizer.encode(text) + [eot_token] for text in texts] + result = torch.zeros(len(all_tokens), context_length, dtype=torch.long) + + for i, tokens in enumerate(all_tokens): + if len(tokens) > context_length: + st = torch.randint(len(tokens) - context_length + 1, (1,))[0].item() + tokens = tokens[st : st + context_length] + result[i, : len(tokens)] = torch.tensor(tokens) + + return result + + def encode_text(self, text): + x = self.token_embedding(text).type(self.dtype) # [batch_size, n_ctx, d_model] + x = x + self.positional_embedding.type(self.dtype) + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer(x) + x = x.permute(1, 0, 2) # LND -> NLD + x = self.ln_final(x).type(self.dtype) + x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection + return x + + def forward(self, captions): + """ + captions: list of strings + """ + text = self.tokenize(captions).to(self.device) # B x L x D + features = self.encode_text(text) # B x D + return features + + +def build_clip_text_encoder(model_path, pretrain=True): + logger = logging.getLogger(__name__) + if pretrain: + logger.info("Loading pretrained CLIP " + model_path) + import clip + + print(model_path) + pretrained_model, _ = clip.load(model_path, device="cpu") + state_dict = pretrained_model.state_dict() + to_delete_keys = ["logit_scale", "input_resolution", "context_length", "vocab_size"] + [ + k for k in state_dict.keys() if k.startswith("visual.") + ] + for k in to_delete_keys: + if k in state_dict: + del state_dict[k] + + embed_dim = state_dict["text_projection"].shape[1] + context_length = state_dict["positional_embedding"].shape[0] + vocab_size = state_dict["token_embedding.weight"].shape[0] + transformer_width = state_dict["ln_final.weight"].shape[0] + transformer_heads = transformer_width // 64 + transformer_layers = len( + set(k.split(".")[2] for k in state_dict if k.startswith(f"transformer.resblocks")) + ) + + text_encoder = CLIPTEXT( + embed_dim, + context_length, + vocab_size, + transformer_width, + transformer_heads, + transformer_layers, + ) + text_encoder.load_state_dict(state_dict) + + else: + logger.info("Building CLIPTEXT") + text_encoder = CLIPTEXT(embed_dim=embed_dim) + return text_encoder + + +def get_clip_embeddings(text_model, vocabulary, prompt="a "): + if isinstance(text_model, str): + text_encoder = build_clip_text_encoder(text_model, pretrain=True) + text_encoder.eval() + else: + text_encoder = text_model + text_encoder.eval() + texts = [prompt + x for x in vocabulary] + emb = text_encoder(texts).detach().contiguous() + return emb diff --git a/ape/modeling/text/clip_wrapper_eva01.py b/ape/modeling/text/clip_wrapper_eva01.py new file mode 100644 index 0000000..e9de9a2 --- /dev/null +++ b/ape/modeling/text/clip_wrapper_eva01.py @@ -0,0 +1,146 @@ +import torch +import torch.nn as nn +from torch.cuda.amp import autocast + +from clip import tokenize + +from .eva01_clip import build_eva_model_and_transforms + + +class EVA01CLIP(nn.Module): + def __init__( + self, + clip_model="EVA_CLIP_g_14", + cache_dir="eva_clip_psz14.pt", + dtype="float32", + max_batch_size=2560, + ): + super().__init__() + self.net, _ = build_eva_model_and_transforms(clip_model, pretrained=cache_dir) + + if dtype == "bfloat16": + self.dtype = torch.bfloat16 + elif dtype == "float16": + self.dtype = torch.float16 + else: + self.dtype = torch.float32 + + del self.net.visual + self.net.eval() + for name, param in self.net.named_parameters(): + param.requires_grad = False + param.data = param.data.to(self.dtype) + + self.register_buffer("unused_tensor", torch.zeros(1), False) + + self.text_list_to_feature = {} + + self.max_batch_size = max_batch_size + + @property + def device(self): + return self.unused_tensor.device + + def infer_image(self, features): + x = features["image"][0] + x = self.net.encode_image(x) + return x + + @autocast(enabled=False) + @torch.no_grad() + def encode_text(self, text_list, cache=False): + if cache and tuple(text_list) in self.text_list_to_feature: + return self.text_list_to_feature[tuple(text_list)] + + text_token = tokenize(text_list, context_length=77, truncate=True).to(self.device) + + max_batch_size = self.max_batch_size + if self.device.type == "cpu" or torch.cuda.mem_get_info(self.device)[0] / 1024**3 < 5: + max_batch_size = min(256, max_batch_size) + if len(text_token) > max_batch_size: + chunck_num = len(text_token) // max_batch_size + 1 + encoder_outputs = torch.cat( + [ + self.net.encode_text( + text_token[chunck_id * max_batch_size : (chunck_id + 1) * max_batch_size] + ) + for chunck_id in range(chunck_num) + ], + dim=0, + ) + else: + encoder_outputs = self.net.encode_text(text_token) + + ret = { + "last_hidden_state_eot": encoder_outputs, + } + + if cache: + self.text_list_to_feature[tuple(text_list)] = ret + + return ret + + @autocast(enabled=False) + @torch.no_grad() + def forward_text(self, text_list, cache=False): + if cache and tuple(text_list) in self.text_list_to_feature: + return self.text_list_to_feature[tuple(text_list)] + + text_token = tokenize(text_list, context_length=77, truncate=True).to(self.device) + + max_batch_size = self.max_batch_size + if self.device.type == "cpu" or torch.cuda.mem_get_info(self.device)[0] / 1024**3 < 5: + max_batch_size = min(256, max_batch_size) + if len(text_token) > max_batch_size: + chunck_num = len(text_token) // max_batch_size + 1 + encoder_outputs = [ + self.custom_encode_text( + text_token[chunck_id * max_batch_size : (chunck_id + 1) * max_batch_size], + self.net.text, + ) + for chunck_id in range(chunck_num) + ] + encoder_outputs_x = torch.cat([x for (x, _) in encoder_outputs], dim=0) + encoder_outputs_xx = torch.cat([xx for (_, xx) in encoder_outputs], dim=0) + else: + encoder_outputs_x, encoder_outputs_xx = self.custom_encode_text( + text_token, self.net.text + ) + + end_token_idx = text_token.argmax(dim=-1) + attention_mask = end_token_idx.new_zeros(encoder_outputs_xx.size()[:2]) + for i in range(attention_mask.size(0)): + attention_mask[i, : end_token_idx[i] + 1] = 1 + + ret = { + "end_token_idx": end_token_idx, + "attention_mask": attention_mask, + "last_hidden_state": encoder_outputs_xx, + "last_hidden_state_eot": encoder_outputs_x, + } + + if cache: + self.text_list_to_feature[tuple(text_list)] = ret + + return ret + + @autocast(enabled=False) + @torch.no_grad() + def custom_encode_text(self, text, m): + x = m.token_embedding(text) # [batch_size, n_ctx, d_model] + + x = x + m.positional_embedding + x = x.permute(1, 0, 2) # NLD -> LND + x = m.transformer(x, attn_mask=m.attn_mask) + x = x.permute(1, 0, 2) # LND -> NLD + x = m.ln_final(x) + + if m.text_projection is not None: + xx = x @ m.text_projection + + x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] + + if m.text_projection is not None: + x = x @ m.text_projection + + return x, xx diff --git a/ape/modeling/text/clip_wrapper_eva02.py b/ape/modeling/text/clip_wrapper_eva02.py new file mode 100644 index 0000000..bed4492 --- /dev/null +++ b/ape/modeling/text/clip_wrapper_eva02.py @@ -0,0 +1,148 @@ +import torch +import torch.nn as nn +from torch.cuda.amp import autocast + +from .eva02_clip import create_model_and_transforms, get_tokenizer + + +class EVA02CLIP(nn.Module): + def __init__( + self, + clip_model="EVA02-CLIP-B-16", + cache_dir="EVA02_CLIP_B_psz16_s8B.pt", + dtype="float32", + max_batch_size=2560, + ): + super().__init__() + self.net, _, _ = create_model_and_transforms( + clip_model, pretrained=cache_dir, force_custom_clip=True + ) + self.tokenizer = get_tokenizer(clip_model) + + if dtype == "bfloat16": + self.dtype = torch.bfloat16 + elif dtype == "float16": + self.dtype = torch.float16 + else: + self.dtype = torch.float32 + + del self.net.visual + self.net.eval() + for name, param in self.net.named_parameters(): + param.requires_grad = False + param.data = param.data.to(self.dtype) + + self.register_buffer("unused_tensor", torch.zeros(1), False) + + self.text_list_to_feature = {} + + self.max_batch_size = max_batch_size + + @property + def device(self): + return self.unused_tensor.device + + def infer_image(self, features): + x = features["image"][0] + x = self.net.encode_image(x) + return x + + @autocast(enabled=False) + @torch.no_grad() + def encode_text(self, text_list, cache=False): + if cache and tuple(text_list) in self.text_list_to_feature: + return self.text_list_to_feature[tuple(text_list)] + + text_token = self.tokenizer(text_list, context_length=77).to(self.device) + + max_batch_size = self.max_batch_size + if self.device.type == "cpu" or torch.cuda.mem_get_info(self.device)[0] / 1024**3 < 5: + max_batch_size = min(256, max_batch_size) + if len(text_token) > max_batch_size: + chunck_num = len(text_token) // max_batch_size + 1 + encoder_outputs = torch.cat( + [ + self.net.encode_text( + text_token[chunck_id * max_batch_size : (chunck_id + 1) * max_batch_size] + ) + for chunck_id in range(chunck_num) + ], + dim=0, + ) + else: + encoder_outputs = self.net.encode_text(text_token) + + ret = { + "last_hidden_state_eot": encoder_outputs, + } + + if cache: + self.text_list_to_feature[tuple(text_list)] = ret + + return ret + + @autocast(enabled=False) + @torch.no_grad() + def forward_text(self, text_list, cache=False): + if cache and tuple(text_list) in self.text_list_to_feature: + return self.text_list_to_feature[tuple(text_list)] + + text_token = self.tokenizer(text_list, context_length=77).to(self.device) + + max_batch_size = self.max_batch_size + if self.device.type == "cpu" or torch.cuda.mem_get_info(self.device)[0] / 1024**3 < 5: + max_batch_size = min(256, max_batch_size) + if len(text_token) > max_batch_size: + chunck_num = len(text_token) // max_batch_size + 1 + encoder_outputs = [ + self.custom_encode_text( + text_token[chunck_id * max_batch_size : (chunck_id + 1) * max_batch_size], + self.net.text, + ) + for chunck_id in range(chunck_num) + ] + encoder_outputs_x = torch.cat([x for (x, _) in encoder_outputs], dim=0) + encoder_outputs_xx = torch.cat([xx for (_, xx) in encoder_outputs], dim=0) + else: + encoder_outputs_x, encoder_outputs_xx = self.custom_encode_text( + text_token, self.net.text + ) + + end_token_idx = text_token.argmax(dim=-1) + attention_mask = end_token_idx.new_zeros(encoder_outputs_xx.size()[:2]) + for i in range(attention_mask.size(0)): + attention_mask[i, : end_token_idx[i] + 1] = 1 + + ret = { + "end_token_idx": end_token_idx, + "attention_mask": attention_mask, + "last_hidden_state": encoder_outputs_xx, + "last_hidden_state_eot": encoder_outputs_x, + } + + if cache: + self.text_list_to_feature[tuple(text_list)] = ret + + return ret + + @autocast(enabled=False) + @torch.no_grad() + def custom_encode_text(self, text, m, normalize: bool = False): + cast_dtype = m.transformer.get_cast_dtype() + + x = m.token_embedding(text).to(cast_dtype) # [batch_size, n_ctx, d_model] + + x = x + m.positional_embedding.to(cast_dtype) + x = x.permute(1, 0, 2) # NLD -> LND + x = m.transformer(x, attn_mask=m.attn_mask) + x = x.permute(1, 0, 2) # LND -> NLD + x = m.ln_final(x) # [batch_size, n_ctx, transformer.width] + + xx = x @ m.text_projection + + x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ m.text_projection + + return ( + F.normalize(x, dim=-1) if normalize else x, + F.normalize(xx, dim=-1) if normalize else xx, + ) diff --git a/ape/modeling/text/clip_wrapper_open.py b/ape/modeling/text/clip_wrapper_open.py new file mode 100644 index 0000000..768076f --- /dev/null +++ b/ape/modeling/text/clip_wrapper_open.py @@ -0,0 +1,51 @@ +import logging +from collections import OrderedDict +from typing import List, Union + +import torch +from torch import nn + +from clip.simple_tokenizer import SimpleTokenizer as _Tokenizer + + +def build_openclip_text_encoder(open_clip_name, open_clip_model): + import open_clip + + logger = logging.getLogger(__name__) + + print(open_clip.list_pretrained()) + logger.info("Loading pretrained CLIP " + open_clip_name + " " + open_clip_model) + + model, _, preprocess = open_clip.create_model_and_transforms( + open_clip_name, pretrained=open_clip_model + ) + tokenizer = open_clip.get_tokenizer(open_clip_name) + + del model.visual + + model.eval() + + return model, tokenizer + + +def get_openclip_embeddings(model, tokenizer, vocabulary, prompt="a "): + model.eval() + + sentences = [prompt + x for x in vocabulary] + text = tokenizer(sentences).to(model.token_embedding.weight.device) + + with torch.no_grad(): + if len(text) > 10000: + text_features = torch.cat( + [ + model.encode_text(text[: len(text) // 2]), + model.encode_text(text[len(text) // 2 :]), + ], + dim=0, + ) + else: + text_features = model.encode_text(text) + + text_features = text_features.detach().contiguous() + + return text_features diff --git a/ape/modeling/text/eva01_clip/README.md b/ape/modeling/text/eva01_clip/README.md new file mode 100644 index 0000000..e950798 --- /dev/null +++ b/ape/modeling/text/eva01_clip/README.md @@ -0,0 +1,79 @@ +# Contrastive Language-Image Pre-Training with EVA (EVA-CLIP) + +**Table of Contents** + +- [Contrastive Language-Image Pre-Training with EVA (EVA-CLIP)](#contrastive-language-image-pre-training-with-eva-eva-clip) + - [Model Card](#model-card) + - [Usage](#usage) + - [Acknowledgement](#acknowledgement) + + +## Model Card + +

      + +> The ImageNet-1K zero-shot classification performance is higher than our paper (`78.5` *v.s.* `78.2`) because of longer training. + +We choose to train a 1.3B CLIP model, not because it is easy, but because it is hard. Please refer to [this note](https://docs.google.com/document/d/1FXosAZ3wMrzThgnWR6KSkXIz4IMItq3umDGos38pJps/edit) for a glance of the challenges in training very large CLIP. + +To our knowledge, EVA-CLIP is **the largest performant open-sourced CLIP model** evaluated via zero-shot classification performance. +We will updates the results in our paper soon. +For more details of EVA-CLIP, please refer to Section 2.3.5 of [our paper](https://arxiv.org/pdf/2211.07636.pdf). + +We hope open-sourcing EVA-CLIP can facilitate future research in multi-modal learning, representation leaning, AIGC, *etc*. + + +## Usage + +The usege of EVA-CLIP is similar to [OpenAI CLIP](https://github.com/openai/CLIP) and [Open CLIP](https://github.com/mlfoundations/open_clip). +Here we provide a showcase in zero-shot image classification. + +First, [install PyTorch 1.7.1](https://pytorch.org/get-started/locally/) (or later) and torchvision, as well as small additional dependencies, and then install this repo as a Python package. On a CUDA GPU machine, the following will do the trick: + +```bash +$ conda install --yes -c pytorch pytorch=1.7.1 torchvision cudatoolkit=11.0 +$ pip install ftfy regex tqdm +``` + +The training code of our 1.3B EVA-CLIP will be available at [FlagAI](https://github.com/FlagAI-Open/FlagAI). Please stay tuned. + + +An example: +```python +import torch +from eva_clip import build_eva_model_and_transforms +from clip import tokenize +from PIL import Image + +eva_clip_path = "/path/to/eva_clip_psz14.pt" # https://huggingface.co/BAAI/EVA/blob/main/eva_clip_psz14.pt +model_name = "EVA_CLIP_g_14" +image_path = "CLIP.png" +caption = ["a diagram", "a dog", "a cat"] + +device = "cuda" if torch.cuda.is_available() else "cpu" +model, preprocess = build_eva_model_and_transforms(model_name, pretrained=eva_clip_path) +model = model.to(device) + +image = preprocess(Image.open(image_path)).unsqueeze(0).to(device) +text = tokenize(caption).to(device) + +with torch.no_grad(): + image_features = model.encode_image(image) + text_features = model.encode_text(text) + image_features /= image_features.norm(dim=-1, keepdim=True) + text_features /= text_features.norm(dim=-1, keepdim=True) + + text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1) + +print("Label probs:", text_probs) # prints: [1.0000e+00, 2.0857e-10, 4.8534e-12] +``` + + +## Acknowledgement +EVA-CLIP is bulit with [OpenAI CLIP](https://github.com/openai/CLIP), [Open CLIP](https://github.com/mlfoundations/open_clip) and [CLIP Benchmark](https://github.com/LAION-AI/CLIP_benchmark). Thanks for their awesome work! diff --git a/ape/modeling/text/eva01_clip/__init__.py b/ape/modeling/text/eva01_clip/__init__.py new file mode 100644 index 0000000..90694ef --- /dev/null +++ b/ape/modeling/text/eva01_clip/__init__.py @@ -0,0 +1,7 @@ +# from .clip import * +# from .eva_clip import * +# from .model import * +# from .simple_tokenizer import * +# from .vit_model import * + +from .eva_clip import build_eva_model_and_transforms diff --git a/ape/modeling/text/eva01_clip/bpe_simple_vocab_16e6.txt.gz b/ape/modeling/text/eva01_clip/bpe_simple_vocab_16e6.txt.gz new file mode 100644 index 0000000000000000000000000000000000000000..7b5088a527f720063f044eb928eee315f63b2fc0 GIT binary patch literal 1356917 zcmV(nK=QvIiwFQl6I)*Z19ZK~vMkwgB)E^SaG(|_PzuTJUep5J0`N~DK81(h@F{(W zxN)TxRpd|fvY8l2bh9uNNe~1;Qsm*`zuHufsU3f;?gfyU@7){Weg+%V)YQIRE$xrC zeq4t3M~}HKs~`QZ|GE9oU+wSve|WU(*3Z-Ti~r@T|LxKj(`7Gim(u>Z7Onkry|nhf z{Z_R9$DcocaOtO_C?efA9V8_G0Eg*J8Fm z+xYK;eN$i5_?`6oQ_=8WTL0%e=^%gKm6r4`|-ox)!xtl 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ztxONk0vixS8S>(hzAbw(#0>;C#ks|_ha+((8DqrW_Ad=hHlbPG7JCqoQYc=?dg}X> z=@SQQ4dhuRpq4|p(g#`?$z1JCMe_-s9ZCq=j%b8zcK3ZHG1D=BRYyW{d_f?@sE3m= z&|HmgkeI;Mhm!J^35$8OxjG^dfASN<0q3^NpF2JSh2Ov5RXTanenuCT&t8oT^u^mh zg}2{*uTwPnR^NXf-ae+l{Il@(HKu7_1qjU1RQlcj|G(kSeL{bx0Jc0m-n()VY;oHw zIzVB?O@_32+^NgTKrF&CdZi=_#;fQd&@66bKGZ$rBa(EUG?_*5Y5sg0q!@<>3)C3x zDHjQ08Yct5byJU)RZx}jpJ%P1PloKt@FZl9Bj8yPOMy-m){oxJYiEr>nJ)qwWT{Qp zj_mW)R04a=pMh1%Dv(aj1Q1nfowhih;WqMv}`9*1Gi{T$c5f9??B^HHhv{z(}TVD%q4)no;HK6^>A)q~X hSpdDrox+^4cgglrz7rRzDPZO0{{oPT@#kp3FaTBF64n3! literal 0 HcmV?d00001 diff --git a/ape/modeling/text/eva01_clip/clip.py b/ape/modeling/text/eva01_clip/clip.py new file mode 100644 index 0000000..4815fdd --- /dev/null +++ b/ape/modeling/text/eva01_clip/clip.py @@ -0,0 +1,232 @@ +import hashlib +import os +import urllib +import warnings +from typing import Any, Union, List +from pkg_resources import packaging + +import torch +from PIL import Image +from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize +from tqdm import tqdm + +from .model import build_model +from .simple_tokenizer import SimpleTokenizer as _Tokenizer + +try: + from torchvision.transforms import InterpolationMode + BICUBIC = InterpolationMode.BICUBIC +except ImportError: + BICUBIC = Image.BICUBIC + + +if packaging.version.parse(torch.__version__) < packaging.version.parse("1.7.1"): + warnings.warn("PyTorch version 1.7.1 or higher is recommended") + + +__all__ = ["available_models", "load", "tokenize"] +_tokenizer = _Tokenizer() + +_MODELS = { + "RN50": "https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt", + "RN101": "https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt", + "RN50x4": "https://openaipublic.azureedge.net/clip/models/7e526bd135e493cef0776de27d5f42653e6b4c8bf9e0f653bb11773263205fdd/RN50x4.pt", + "RN50x16": "https://openaipublic.azureedge.net/clip/models/52378b407f34354e150460fe41077663dd5b39c54cd0bfd2b27167a4a06ec9aa/RN50x16.pt", + "RN50x64": "https://openaipublic.azureedge.net/clip/models/be1cfb55d75a9666199fb2206c106743da0f6468c9d327f3e0d0a543a9919d9c/RN50x64.pt", + "ViT-B/32": "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt", + "ViT-B/16": "https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt", + "ViT-L/14": "https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt", + "ViT-L/14@336px": "https://openaipublic.azureedge.net/clip/models/3035c92b350959924f9f00213499208652fc7ea050643e8b385c2dac08641f02/ViT-L-14-336px.pt", +} + + +def _download(url: str, root: str): + os.makedirs(root, exist_ok=True) + filename = os.path.basename(url) + + expected_sha256 = url.split("/")[-2] + download_target = os.path.join(root, filename) + + if os.path.exists(download_target) and not os.path.isfile(download_target): + raise RuntimeError(f"{download_target} exists and is not a regular file") + + if os.path.isfile(download_target): + if hashlib.sha256(open(download_target, "rb").read()).hexdigest() == expected_sha256: + return download_target + else: + warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file") + + with urllib.request.urlopen(url) as source, open(download_target, "wb") as output: + with tqdm(total=int(source.info().get("Content-Length")), ncols=80, unit='iB', unit_scale=True, unit_divisor=1024) as loop: + while True: + buffer = source.read(8192) + if not buffer: + break + + output.write(buffer) + loop.update(len(buffer)) + + if hashlib.sha256(open(download_target, "rb").read()).hexdigest() != expected_sha256: + raise RuntimeError(f"Model has been downloaded but the SHA256 checksum does not not match") + + return download_target + + +def _convert_image_to_rgb(image): + return image.convert("RGB") + + +def _transform(n_px): + return Compose([ + Resize(n_px, interpolation=BICUBIC), + CenterCrop(n_px), + _convert_image_to_rgb, + ToTensor(), + Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)), + ]) + + +def available_models() -> List[str]: + """Returns the names of available CLIP models""" + return list(_MODELS.keys()) + + +def load(name: str, device: Union[str, torch.device] = "cuda" if torch.cuda.is_available() else "cpu", jit: bool = False, download_root: str = None): + """Load a CLIP model + + Parameters + ---------- + name : str + A model name listed by `clip.available_models()`, or the path to a model checkpoint containing the state_dict + + device : Union[str, torch.device] + The device to put the loaded model + + jit : bool + Whether to load the optimized JIT model or more hackable non-JIT model (default). + + download_root: str + path to download the model files; by default, it uses "~/.cache/clip" + + Returns + ------- + model : torch.nn.Module + The CLIP model + + preprocess : Callable[[PIL.Image], torch.Tensor] + A torchvision transform that converts a PIL image into a tensor that the returned model can take as its input + """ + if name in _MODELS: + model_path = _download(_MODELS[name], download_root or os.path.expanduser("~/.cache/clip")) + elif os.path.isfile(name): + model_path = name + else: + raise RuntimeError(f"Model {name} not found; available models = {available_models()}") + + try: + # loading JIT archive + model = torch.jit.load(model_path, map_location=device if jit else "cpu").eval() + state_dict = None + except RuntimeError: + # loading saved state dict + if jit: + warnings.warn(f"File {model_path} is not a JIT archive. Loading as a state dict instead") + jit = False + state_dict = torch.load(model_path, map_location="cpu") + + if not jit: + model = build_model(state_dict or model.state_dict()).to(device) + if str(device) == "cpu": + model.float() + return model, _transform(model.visual.input_resolution) + + # patch the device names + device_holder = torch.jit.trace(lambda: torch.ones([]).to(torch.device(device)), example_inputs=[]) + device_node = [n for n in device_holder.graph.findAllNodes("prim::Constant") if "Device" in repr(n)][-1] + + def patch_device(module): + try: + graphs = [module.graph] if hasattr(module, "graph") else [] + except RuntimeError: + graphs = [] + + if hasattr(module, "forward1"): + graphs.append(module.forward1.graph) + + for graph in graphs: + for node in graph.findAllNodes("prim::Constant"): + if "value" in node.attributeNames() and str(node["value"]).startswith("cuda"): + node.copyAttributes(device_node) + + model.apply(patch_device) + patch_device(model.encode_image) + patch_device(model.encode_text) + + # patch dtype to float32 on CPU + if str(device) == "cpu": + float_holder = torch.jit.trace(lambda: torch.ones([]).float(), example_inputs=[]) + float_input = list(float_holder.graph.findNode("aten::to").inputs())[1] + float_node = float_input.node() + + def patch_float(module): + try: + graphs = [module.graph] if hasattr(module, "graph") else [] + except RuntimeError: + graphs = [] + + if hasattr(module, "forward1"): + graphs.append(module.forward1.graph) + + for graph in graphs: + for node in graph.findAllNodes("aten::to"): + inputs = list(node.inputs()) + for i in [1, 2]: # dtype can be the second or third argument to aten::to() + if inputs[i].node()["value"] == 5: + inputs[i].node().copyAttributes(float_node) + + model.apply(patch_float) + patch_float(model.encode_image) + patch_float(model.encode_text) + + model.float() + + return model, _transform(model.input_resolution.item()) + + +def tokenize(texts: Union[str, List[str]], context_length: int = 77, truncate: bool = False) -> torch.LongTensor: + """ + Returns the tokenized representation of given input string(s) + + Parameters + ---------- + texts : Union[str, List[str]] + An input string or a list of input strings to tokenize + + context_length : int + The context length to use; all CLIP models use 77 as the context length + + truncate: bool + Whether to truncate the text in case its encoding is longer than the context length + + Returns + ------- + A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length] + """ + if isinstance(texts, str): + texts = [texts] + + sot_token = _tokenizer.encoder["<|startoftext|>"] + eot_token = _tokenizer.encoder["<|endoftext|>"] + all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token] for text in texts] + result = torch.zeros(len(all_tokens), context_length, dtype=torch.long) + + for i, tokens in enumerate(all_tokens): + if len(tokens) > context_length: + if truncate: + tokens = tokens[:context_length] + tokens[-1] = eot_token + else: + raise RuntimeError(f"Input {texts[i]} is too long for context length {context_length}") + result[i, :len(tokens)] = torch.tensor(tokens) + + return result diff --git a/ape/modeling/text/eva01_clip/eva_clip.py b/ape/modeling/text/eva01_clip/eva_clip.py new file mode 100644 index 0000000..42489d4 --- /dev/null +++ b/ape/modeling/text/eva01_clip/eva_clip.py @@ -0,0 +1,173 @@ +import json +import logging +import os +import pathlib +import re +from copy import deepcopy +from pathlib import Path +from tkinter import E +from typing import Optional, Tuple, Any, Union, List + +import torch +from torchvision.transforms import Normalize, Compose, InterpolationMode, ToTensor, Resize, CenterCrop + +from .eva_model import EVA_CLIP, convert_weights_to_fp16 + +OPENAI_DATASET_MEAN = (0.48145466, 0.4578275, 0.40821073) +OPENAI_DATASET_STD = (0.26862954, 0.26130258, 0.27577711) + +_MODEL_CONFIG_PATHS = [Path(__file__).parent / f"model_configs/"] +_MODEL_CONFIGS = {} # directory (model_name: config) of model architecture configs + + +def _natural_key(string_): + return [int(s) if s.isdigit() else s for s in re.split(r'(\d+)', string_.lower())] + + +def _rescan_model_configs(): + global _MODEL_CONFIGS + + config_ext = ('.json',) + config_files = [] + for config_path in _MODEL_CONFIG_PATHS: + if config_path.is_file() and config_path.suffix in config_ext: + config_files.append(config_path) + elif config_path.is_dir(): + for ext in config_ext: + config_files.extend(config_path.glob(f'*{ext}')) + + for cf in config_files: + with open(cf, 'r') as f: + model_cfg = json.load(f) + if all(a in model_cfg for a in ('embed_dim', 'vision_cfg', 'text_cfg')): + _MODEL_CONFIGS[cf.stem] = model_cfg + + _MODEL_CONFIGS = {k: v for k, v in sorted(_MODEL_CONFIGS.items(), key=lambda x: _natural_key(x[0]))} + + +_rescan_model_configs() # initial populate of model config registry + +def list_models(): + """ enumerate available model architectures based on config files """ + return list(_MODEL_CONFIGS.keys()) + + +def add_model_config(path): + """ add model config path or file and update registry """ + if not isinstance(path, Path): + path = Path(path) + _MODEL_CONFIG_PATHS.append(path) + _rescan_model_configs() + +def get_model_config(model_name): + if model_name in _MODEL_CONFIGS: + return deepcopy(_MODEL_CONFIGS[model_name]) + else: + return None + +def load_state_dict(checkpoint_path: str, map_location: str='cpu', model_key='model|module|state_dict'): + checkpoint = torch.load(checkpoint_path, map_location=map_location) + + for mk in model_key.split('|'): + if isinstance(checkpoint, dict) and mk in checkpoint: + state_dict = checkpoint[mk] + break + else: + state_dict = checkpoint + if next(iter(state_dict.items()))[0].startswith('module'): + state_dict = {k[7:]: v for k, v in state_dict.items()} + return state_dict + +def load_checkpoint(model, checkpoint_path, model_key="model|module|state_dict", strict=True): + state_dict = load_state_dict(checkpoint_path, model_key=model_key) + incompatible_keys = model.load_state_dict(state_dict, strict=strict) + print(incompatible_keys) + return incompatible_keys + +def create_model( + model_name: str, + pretrained: str = '', + precision: str = 'fp32', + device: torch.device = torch.device('cpu'), + force_quick_gelu: bool = False, +): + model_name = model_name.replace('/', '-') # for callers using old naming with / in ViT names + + if model_name in _MODEL_CONFIGS: + logging.info(f'Loading {model_name} model config.') + model_cfg = deepcopy(_MODEL_CONFIGS[model_name]) + else: + logging.error(f'Model config for {model_name} not found; available models {list_models()}.') + raise RuntimeError(f'Model config for {model_name} not found.') + + if force_quick_gelu: + # override for use of QuickGELU on non-OpenAI transformer models + model_cfg["quick_gelu"] = True + + model = EVA_CLIP(**model_cfg) + + if pretrained: + load_checkpoint(model, pretrained) + + model.to(device=device) + if precision == "fp16": + assert device.type != 'cpu' + convert_weights_to_fp16(model) + + # set image / mean metadata from pretrained_cfg if available, or use default + model.visual.image_mean = OPENAI_DATASET_MEAN + model.visual.image_std = OPENAI_DATASET_STD + + return model + +def _convert_to_rgb(image): + return image.convert('RGB') + +def image_transform( + image_size: int, + mean: Optional[Tuple[float, ...]] = None, + std: Optional[Tuple[float, ...]] = None, +): + mean = mean or OPENAI_DATASET_MEAN + if not isinstance(mean, (list, tuple)): + mean = (mean,) * 3 + + std = std or OPENAI_DATASET_STD + if not isinstance(std, (list, tuple)): + std = (std,) * 3 + + if isinstance(image_size, (list, tuple)) and image_size[0] == image_size[1]: + # for square size, pass size as int so that Resize() uses aspect preserving shortest edge + image_size = image_size[0] + + normalize = Normalize(mean=mean, std=std) + + transforms = [ + Resize(image_size, interpolation=InterpolationMode.BICUBIC), + CenterCrop(image_size), + ] + transforms.extend([ + _convert_to_rgb, + ToTensor(), + normalize, + ]) + return Compose(transforms) + +def build_eva_model_and_transforms( + model_name: str, + pretrained: str = '', + precision: str = 'fp32', + device: torch.device = torch.device('cpu'), + force_quick_gelu: bool = False, + image_mean: Optional[Tuple[float, ...]] = None, + image_std: Optional[Tuple[float, ...]] = None, +): + model = create_model( + model_name, pretrained, precision, device, + force_quick_gelu=force_quick_gelu) + + image_mean = image_mean or getattr(model.visual, 'image_mean', None) + image_std = image_std or getattr(model.visual, 'image_std', None) + preprocess_val = image_transform(model.visual.image_size, mean=image_mean, std=image_std) + + return model, preprocess_val diff --git a/ape/modeling/text/eva01_clip/eva_model.py b/ape/modeling/text/eva01_clip/eva_model.py new file mode 100644 index 0000000..04002e5 --- /dev/null +++ b/ape/modeling/text/eva01_clip/eva_model.py @@ -0,0 +1,368 @@ +""" CLIP Model + +Adapted from https://github.com/mlfoundations/open_clip + +""" +import math +from dataclasses import dataclass +from typing import Tuple, Union, Callable, Optional +from functools import partial +import numpy as np +from collections import OrderedDict + +import torch +import torch.nn.functional as F +from torch import nn + +from .vit_model import VisionTransformer + +try: + from apex.normalization import FusedLayerNorm +except: + pass + + +class LayerNorm(nn.LayerNorm): + """Subclass torch's LayerNorm to handle fp16.""" + + def forward(self, x: torch.Tensor): + orig_type = x.dtype + x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps) + return x.to(orig_type) + + +class QuickGELU(nn.Module): + # NOTE This is slower than nn.GELU or nn.SiLU and uses more GPU memory + def forward(self, x: torch.Tensor): + return x * torch.sigmoid(1.702 * x) + + +class Attention(nn.Module): + def __init__( + self, + dim, + num_heads=8, + qkv_bias=True, + scaled_cosine=False, + scale_heads=False, + logit_scale_max=math.log(1. / 0.01), + attn_drop=0., + proj_drop=0. + ): + super().__init__() + self.scaled_cosine = scaled_cosine + self.scale_heads = scale_heads + assert dim % num_heads == 0, 'dim should be divisible by num_heads' + self.num_heads = num_heads + self.head_dim = dim // num_heads + self.scale = self.head_dim ** -0.5 + self.logit_scale_max = logit_scale_max + + # keeping in_proj in this form (instead of nn.Linear) to match weight scheme of original + self.in_proj_weight = nn.Parameter(torch.randn((dim * 3, dim)) * self.scale) + if qkv_bias: + self.in_proj_bias = nn.Parameter(torch.zeros(dim * 3)) + else: + self.in_proj_bias = None + + if self.scaled_cosine: + self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1)))) + else: + self.logit_scale = None + self.attn_drop = nn.Dropout(attn_drop) + if self.scale_heads: + self.head_scale = nn.Parameter(torch.ones((num_heads, 1, 1))) + else: + self.head_scale = None + self.out_proj = nn.Linear(dim, dim) + self.out_drop = nn.Dropout(proj_drop) + + def forward(self, x, attn_mask: Optional[torch.Tensor] = None): + L, N, C = x.shape + q, k, v = F.linear(x, self.in_proj_weight, self.in_proj_bias).chunk(3, dim=-1) + q = q.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1) + k = k.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1) + v = v.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1) + + if self.logit_scale is not None: + attn = torch.bmm(F.normalize(q, dim=-1), F.normalize(k, dim=-1).transpose(-1, -2)) + logit_scale = torch.clamp(self.logit_scale, max=self.logit_scale_max).exp() + attn = attn.view(N, self.num_heads, L, L) * logit_scale + attn = attn.view(-1, L, L) + else: + q = q * self.scale + attn = torch.bmm(q, k.transpose(-1, -2)) + + if attn_mask is not None: + if attn_mask.dtype == torch.bool: + new_attn_mask = torch.zeros_like(attn_mask, dtype=q.dtype) + new_attn_mask.masked_fill_(attn_mask, float("-inf")) + attn_mask = new_attn_mask + attn += attn_mask + + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = torch.bmm(attn, v) + if self.head_scale is not None: + x = x.view(N, self.num_heads, L, C) * self.head_scale + x = x.view(-1, L, C) + x = x.transpose(0, 1).reshape(L, N, C) + x = self.out_proj(x) + x = self.out_drop(x) + return x + + +class ResidualAttentionBlock(nn.Module): + def __init__( + self, + d_model: int, + n_head: int, + mlp_ratio: float = 4.0, + act_layer: Callable = nn.GELU, + scale_cosine_attn: bool = False, + scale_heads: bool = False, + scale_attn: bool = False, + scale_fc: bool = False, + ): + super().__init__() + + self.ln_1 = LayerNorm(d_model) + # FIXME torchscript issues need to be resolved for custom attention + # if scale_cosine_attn or scale_heads: + # self.attn = Attention( + # d_model, n_head, + # scaled_cosine=scale_cosine_attn, + # scale_heads=scale_heads, + # ) + self.attn = nn.MultiheadAttention(d_model, n_head) + self.ln_attn = LayerNorm(d_model) if scale_attn else nn.Identity() + + self.ln_2 = LayerNorm(d_model) + mlp_width = int(d_model * mlp_ratio) + self.mlp = nn.Sequential(OrderedDict([ + ("c_fc", nn.Linear(d_model, mlp_width)), + ('ln', LayerNorm(mlp_width) if scale_fc else nn.Identity()), + ("gelu", act_layer()), + ("c_proj", nn.Linear(mlp_width, d_model)) + ])) + + def attention(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None): + return self.attn(x, x, x, need_weights=False, attn_mask=attn_mask)[0] + # FIXME torchscript issues need resolving for custom attention option to work + # if self.use_torch_attn: + # return self.attn(x, x, x, need_weights=False, attn_mask=attn_mask)[0] + # else: + # return self.attn(x, attn_mask=attn_mask) + + def cross_attention(self, x: torch.Tensor, context: torch.Tensor, attn_mask: Optional[torch.Tensor] = None): + return self.attn(x, context, context, need_weights=False, attn_mask=attn_mask)[0] + + + def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None): + x = x + self.ln_attn(self.attention(self.ln_1(x), attn_mask=attn_mask)) + x = x + self.mlp(self.ln_2(x)) + return x + +class Transformer(nn.Module): + def __init__(self, width: int, layers: int, heads: int, mlp_ratio: float = 4.0, act_layer: Callable = nn.GELU): + super().__init__() + self.width = width + self.layers = layers + + self.resblocks = nn.ModuleList([ + ResidualAttentionBlock(width, heads, mlp_ratio, act_layer=act_layer) + for _ in range(layers) + ]) + + def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None): + for r in self.resblocks: + x = r(x, attn_mask=attn_mask) + return x + +class TextTransformer(nn.Module): + def __init__( + self, + vocab_size: int, + width: int, + layers: int, + heads: int, + context_length: int, + embed_dim: int, + act_layer: Callable = nn.GELU, + ): + super().__init__() + self.transformer = Transformer( + width=width, + layers=layers, + heads=heads, + act_layer=act_layer, + ) + self.context_length = context_length + self.vocab_size = vocab_size + self.token_embedding = nn.Embedding(vocab_size, width) + self.positional_embedding = nn.Parameter(torch.empty(context_length, width)) + self.ln_final = LayerNorm(width) + + self.text_projection = nn.Parameter(torch.empty(width, embed_dim)) + self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07)) + self.register_buffer('attn_mask', self.build_attention_mask(), persistent=False) + + self.init_parameters() + + def init_parameters(self): + nn.init.normal_(self.token_embedding.weight, std=0.02) + nn.init.normal_(self.positional_embedding, std=0.01) + nn.init.constant_(self.logit_scale, np.log(1 / 0.07)) + + proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5) + attn_std = self.transformer.width ** -0.5 + fc_std = (2 * self.transformer.width) ** -0.5 + for block in self.transformer.resblocks: + nn.init.normal_(block.attn.in_proj_weight, std=attn_std) + nn.init.normal_(block.attn.out_proj.weight, std=proj_std) + nn.init.normal_(block.mlp.c_fc.weight, std=fc_std) + nn.init.normal_(block.mlp.c_proj.weight, std=proj_std) + + if self.text_projection is not None: + nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5) + + def build_attention_mask(self): + # lazily create causal attention mask, with full attention between the vision tokens + # pytorch uses additive attention mask; fill with -inf + mask = torch.empty(self.context_length, self.context_length) + mask.fill_(float("-inf")) + mask.triu_(1) # zero out the lower diagonal + return mask + + def forward_features(self, text: torch.Tensor): + x = self.token_embedding(text) # [batch_size, n_ctx, d_model] + + x = x + self.positional_embedding + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer(x, attn_mask=self.attn_mask) + x = x.permute(1, 0, 2) # LND -> NLD + x = self.ln_final(x) + + # x.shape = [batch_size, n_ctx, transformer.width] + # take features from the eot embedding (eot_token is the highest number in each sequence) + x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] + return x + + def forward(self, x: torch.Tensor): + x = self.forward_features(x) + if self.text_projection is not None: + x = x @ self.text_projection + return x + +@dataclass +class CLIPVisionCfg: + layers: Union[Tuple[int, int, int, int], int] = 12 + width: int = 768 + head_width: int = 64 + mlp_ratio: float = 4.0 + patch_size: int = 16 + image_size: Union[Tuple[int, int], int] = 224 + layer_scale_init_value: float = None + drop_path_rate: float = 0. + fusedLN: bool = False + xattn: bool = False + + +@dataclass +class CLIPTextCfg: + context_length: int = 77 + vocab_size: int = 49408 + width: int = 512 + heads: int = 8 + layers: int = 12 + + + +class EVA_CLIP(nn.Module): + def __init__( + self, + embed_dim: int, + vision_cfg: CLIPVisionCfg, + text_cfg: CLIPTextCfg, + quick_gelu: bool = False, + ): + super().__init__() + if isinstance(vision_cfg, dict): + vision_cfg = CLIPVisionCfg(**vision_cfg) + if isinstance(text_cfg, dict): + text_cfg = CLIPTextCfg(**text_cfg) + + # OpenAI models are pretrained w/ QuickGELU but native nn.GELU is both faster and more + # memory efficient in recent PyTorch releases (>= 1.10). + act_layer = QuickGELU if quick_gelu else nn.GELU + + vision_heads = vision_cfg.width // vision_cfg.head_width + self.visual = VisionTransformer( + img_size=vision_cfg.image_size, + patch_size=vision_cfg.patch_size, + num_classes=embed_dim, + use_mean_pooling=False, + init_values=vision_cfg.layer_scale_init_value, + embed_dim=vision_cfg.width, + depth=vision_cfg.layers, + num_heads=vision_heads, + mlp_ratio=vision_cfg.mlp_ratio, + qkv_bias=True, + drop_path_rate=vision_cfg.drop_path_rate, + norm_layer= partial(FusedLayerNorm, eps=1e-6) if vision_cfg.fusedLN else partial(nn.LayerNorm, eps=1e-6), + xattn=vision_cfg.xattn + ) + + self.text = TextTransformer( + vocab_size=text_cfg.vocab_size, + width=text_cfg.width, + layers=text_cfg.layers, + heads=text_cfg.heads, + context_length=text_cfg.context_length, + embed_dim=embed_dim, + act_layer=act_layer + ) + + def encode_image(self, image): + return self.visual(image) + + def encode_text(self, text): + return self.text(text) + + def forward(self, image, text): + if image is None: + return self.encode_text(text) + elif text is None: + return self.encode_image(image) + image_features = self.encode_image(image) + image_features = F.normalize(image_features, dim=-1) + + text_features = self.encode_text(text) + text_features = F.normalize(text_features, dim=-1) + + return image_features, text_features, self.text.logit_scale.exp() + + +def convert_weights_to_fp16(model: nn.Module): + """Convert applicable model parameters to fp16""" + + def _convert_weights_to_fp16(l): + if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)): + l.weight.data = l.weight.data.half() + if l.bias is not None: + l.bias.data = l.bias.data.half() + + if isinstance(l, (nn.MultiheadAttention, Attention)): + for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]: + tensor = getattr(l, attr) + if tensor is not None: + tensor.data = tensor.data.half() + + for name in ["text_projection", "proj"]: + if hasattr(l, name): + attr = getattr(l, name) + if attr is not None: + attr.data = attr.data.half() + + model.apply(_convert_weights_to_fp16) diff --git a/ape/modeling/text/eva01_clip/model.py b/ape/modeling/text/eva01_clip/model.py new file mode 100644 index 0000000..e452989 --- /dev/null +++ b/ape/modeling/text/eva01_clip/model.py @@ -0,0 +1,471 @@ +from collections import OrderedDict +from typing import Tuple, Union + +import numpy as np +import torch +import math +import torch.nn.functional as F +from torch import nn + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1): + super().__init__() + + # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1 + self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + + self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + + self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity() + + self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False) + self.bn3 = nn.BatchNorm2d(planes * self.expansion) + + self.relu = nn.ReLU(inplace=True) + self.downsample = None + self.stride = stride + + if stride > 1 or inplanes != planes * Bottleneck.expansion: + # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1 + self.downsample = nn.Sequential(OrderedDict([ + ("-1", nn.AvgPool2d(stride)), + ("0", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)), + ("1", nn.BatchNorm2d(planes * self.expansion)) + ])) + + def forward(self, x: torch.Tensor): + identity = x + + out = self.relu(self.bn1(self.conv1(x))) + out = self.relu(self.bn2(self.conv2(out))) + out = self.avgpool(out) + out = self.bn3(self.conv3(out)) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.relu(out) + return out + + +class AttentionPool2d(nn.Module): + def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None): + super().__init__() + self.positional_embedding = nn.Parameter(torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5) + self.k_proj = nn.Linear(embed_dim, embed_dim) + self.q_proj = nn.Linear(embed_dim, embed_dim) + self.v_proj = nn.Linear(embed_dim, embed_dim) + self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim) + self.num_heads = num_heads + + def forward(self, x, return_all_tokens=False): + x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3]).permute(2, 0, 1) # NCHW -> (HW)NC + x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (HW+1)NC + x = x + self.positional_embedding[:, None, :].to(x.dtype) # (HW+1)NC + x, _ = F.multi_head_attention_forward( + query=x, key=x, value=x, + embed_dim_to_check=x.shape[-1], + num_heads=self.num_heads, + q_proj_weight=self.q_proj.weight, + k_proj_weight=self.k_proj.weight, + v_proj_weight=self.v_proj.weight, + in_proj_weight=None, + in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]), + bias_k=None, + bias_v=None, + add_zero_attn=False, + dropout_p=0, + out_proj_weight=self.c_proj.weight, + out_proj_bias=self.c_proj.bias, + use_separate_proj_weight=True, + training=self.training, + need_weights=False + ) + if return_all_tokens: + return x + else: + return x[0] + + +class ModifiedResNet(nn.Module): + """ + A ResNet class that is similar to torchvision's but contains the following changes: + - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool. + - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1 + - The final pooling layer is a QKV attention instead of an average pool + """ + + def __init__(self, layers, output_dim, heads, input_resolution=224, width=64): + super().__init__() + self.output_dim = output_dim + self.input_resolution = input_resolution + + # the 3-layer stem + self.conv1 = nn.Conv2d(3, width // 2, kernel_size=3, stride=2, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(width // 2) + self.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(width // 2) + self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False) + self.bn3 = nn.BatchNorm2d(width) + self.avgpool = nn.AvgPool2d(2) + self.relu = nn.ReLU(inplace=True) + + # residual layers + self._inplanes = width # this is a *mutable* variable used during construction + self.layer1 = self._make_layer(width, layers[0]) + self.layer2 = self._make_layer(width * 2, layers[1], stride=2) + self.layer3 = self._make_layer(width * 4, layers[2], stride=2) + self.layer4 = self._make_layer(width * 8, layers[3], stride=2) + + embed_dim = width * 32 # the ResNet feature dimension + self.attnpool = AttentionPool2d(input_resolution // 32, embed_dim, heads, output_dim) + + def _make_layer(self, planes, blocks, stride=1): + layers = [Bottleneck(self._inplanes, planes, stride)] + + self._inplanes = planes * Bottleneck.expansion + for _ in range(1, blocks): + layers.append(Bottleneck(self._inplanes, planes)) + + return nn.Sequential(*layers) + + def forward(self, x, return_side_out=False, return_all_tokens=False): + def stem(x): + for conv, bn in [(self.conv1, self.bn1), (self.conv2, self.bn2), (self.conv3, self.bn3)]: + x = self.relu(bn(conv(x))) + x = self.avgpool(x) + return x + out = [] + x = x.type(self.conv1.weight.dtype) + x = stem(x) + x = self.layer1(x) + if return_side_out: + out.append(x) + x = self.layer2(x) + if return_side_out: + out.append(x) + x = self.layer3(x) + if return_side_out: + out.append(x) + x = self.layer4(x) + if return_side_out: + out.append(x) + x = self.attnpool(x, return_all_tokens) + out.append(x) + if len(out) == 1: + return x + else: + return out + + +class LayerNorm(nn.LayerNorm): + """Subclass torch's LayerNorm to handle fp16.""" + + def forward(self, x: torch.Tensor): + orig_type = x.dtype + ret = super().forward(x.type(torch.float32)) + return ret.type(orig_type) + + +class QuickGELU(nn.Module): + def forward(self, x: torch.Tensor): + return x * torch.sigmoid(1.702 * x) + + +class ResidualAttentionBlock(nn.Module): + def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None): + super().__init__() + + self.attn = nn.MultiheadAttention(d_model, n_head) + self.ln_1 = LayerNorm(d_model) + self.mlp = nn.Sequential(OrderedDict([ + ("c_fc", nn.Linear(d_model, d_model * 4)), + ("gelu", QuickGELU()), + ("c_proj", nn.Linear(d_model * 4, d_model)) + ])) + self.ln_2 = LayerNorm(d_model) + self.attn_mask = attn_mask + + def attention(self, x: torch.Tensor): + self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None + return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0] + + + def forward(self, x: torch.Tensor): + x = x + self.attention(self.ln_1(x)) + x = x + self.mlp(self.ln_2(x)) + return x + + +class Transformer(nn.Module): + def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None): + super().__init__() + self.width = width + self.layers = layers + self.resblocks = nn.Sequential(*[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers)]) + + def forward(self, x: torch.Tensor): + return self.resblocks(x) + + +class VisionTransformer(nn.Module): + def __init__(self, input_resolution: int, patch_size: int, width: int, layers: int, heads: int, output_dim: int): + super().__init__() + self.input_resolution = input_resolution + self.output_dim = output_dim + self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False) + + scale = width ** -0.5 + self.class_embedding = nn.Parameter(scale * torch.randn(width)) + self.positional_embedding = nn.Parameter(scale * torch.randn((input_resolution // patch_size) ** 2 + 1, width)) + self.ln_pre = LayerNorm(width) + self.patch_shape = (input_resolution // patch_size, ) * 2 + + self.patch_size = patch_size + + self.transformer = Transformer(width, layers, heads) + + self.ln_post = LayerNorm(width) + self.proj = nn.Parameter(scale * torch.randn(width, output_dim)) + + def interpolate_pos_encoding(self, x, w, h): + class_pos_embed = self.positional_embedding[0] + patch_pos_embed = self.positional_embedding[1:] + dim = x.shape[-1] + # we add a small number to avoid floating point error in the interpolation + # see discussion at https://github.com/facebookresearch/dino/issues/8 + # w0, h0 = w + 0.1, h + 0.1 + n, m = self.patch_shape + patch_pos_embed = nn.functional.interpolate( + patch_pos_embed.reshape(1, n, m, dim).permute(0, 3, 1, 2), + scale_factor=(w / n, h / m), + mode='bicubic', + ) + assert int(w) == patch_pos_embed.shape[-2] and int(h) == patch_pos_embed.shape[-1] + patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(-1, dim) + return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=0) + + def forward(self, x: torch.Tensor): + x = self.conv1(x) # shape = [*, width, grid, grid] + bsz, _, w, h = x.size() + x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2] + x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width] + x = torch.cat([self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1) # shape = [*, grid ** 2 + 1, width] + if w != self.patch_shape[0] or h != self.patch_shape[1]: + x = x + self.interpolate_pos_encoding(x, w, h) + else: + x = x + self.positional_embedding.to(x.dtype) + x = self.ln_pre(x) + + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer(x) + x = x.permute(1, 0, 2) # LND -> NLD + + x = x[:, 1:, :] ### w/o cls token + x = self.ln_post(x) ### norm + if self.proj is not None: + x = x @ self.proj ### proj to low dim + return x + + + +class CLIP(nn.Module): + def __init__(self, + embed_dim: int, + # vision + image_resolution: int, + vision_layers: Union[Tuple[int, int, int, int], int], + vision_width: int, + vision_patch_size: int, + # text + context_length: int, + vocab_size: int, + transformer_width: int, + transformer_heads: int, + transformer_layers: int + ): + super().__init__() + + self.context_length = context_length + + if isinstance(vision_layers, (tuple, list)): + vision_heads = vision_width * 32 // 64 + self.visual = ModifiedResNet( + layers=vision_layers, + output_dim=embed_dim, + heads=vision_heads, + input_resolution=image_resolution, + width=vision_width + ) + else: + vision_heads = vision_width // 64 + self.visual = VisionTransformer( + input_resolution=image_resolution, + patch_size=vision_patch_size, + width=vision_width, + layers=vision_layers, + heads=vision_heads, + output_dim=embed_dim + ) + + self.transformer = Transformer( + width=transformer_width, + layers=transformer_layers, + heads=transformer_heads, + attn_mask=self.build_attention_mask() + ) + + self.vocab_size = vocab_size + self.token_embedding = nn.Embedding(vocab_size, transformer_width) + self.positional_embedding = nn.Parameter(torch.empty(self.context_length, transformer_width)) + self.ln_final = LayerNorm(transformer_width) + + self.text_projection = nn.Parameter(torch.empty(transformer_width, embed_dim)) + self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07)) + + self.initialize_parameters() + + def initialize_parameters(self): + nn.init.normal_(self.token_embedding.weight, std=0.02) + nn.init.normal_(self.positional_embedding, std=0.01) + + if isinstance(self.visual, ModifiedResNet): + if self.visual.attnpool is not None: + std = self.visual.attnpool.c_proj.in_features ** -0.5 + nn.init.normal_(self.visual.attnpool.q_proj.weight, std=std) + nn.init.normal_(self.visual.attnpool.k_proj.weight, std=std) + nn.init.normal_(self.visual.attnpool.v_proj.weight, std=std) + nn.init.normal_(self.visual.attnpool.c_proj.weight, std=std) + + for resnet_block in [self.visual.layer1, self.visual.layer2, self.visual.layer3, self.visual.layer4]: + for name, param in resnet_block.named_parameters(): + if name.endswith("bn3.weight"): + nn.init.zeros_(param) + + proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5) + attn_std = self.transformer.width ** -0.5 + fc_std = (2 * self.transformer.width) ** -0.5 + for block in self.transformer.resblocks: + nn.init.normal_(block.attn.in_proj_weight, std=attn_std) + nn.init.normal_(block.attn.out_proj.weight, std=proj_std) + nn.init.normal_(block.mlp.c_fc.weight, std=fc_std) + nn.init.normal_(block.mlp.c_proj.weight, std=proj_std) + + if self.text_projection is not None: + nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5) + + def build_attention_mask(self): + # lazily create causal attention mask, with full attention between the vision tokens + # pytorch uses additive attention mask; fill with -inf + mask = torch.empty(self.context_length, self.context_length) + mask.fill_(float("-inf")) + mask.triu_(1) # zero out the lower diagonal + return mask + + @property + def dtype(self): + return self.visual.conv1.weight.dtype + + def encode_image(self, image): + return self.visual(image.type(self.dtype)) + + def encode_text(self, text): + x = self.token_embedding(text).type(self.dtype) # [batch_size, n_ctx, d_model] + + x = x + self.positional_embedding.type(self.dtype) + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer(x) + x = x.permute(1, 0, 2) # LND -> NLD + x = self.ln_final(x).type(self.dtype) + + # x.shape = [batch_size, n_ctx, transformer.width] + # take features from the eot embedding (eot_token is the highest number in each sequence) + x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection + + return x + + def forward(self, image, text): + image_features = self.encode_image(image) + text_features = self.encode_text(text) + + # normalized features + image_features = image_features / image_features.norm(dim=-1, keepdim=True) + text_features = text_features / text_features.norm(dim=-1, keepdim=True) + + # cosine similarity as logits + logit_scale = self.logit_scale.exp() + logits_per_image = logit_scale * image_features @ text_features.t() + logits_per_text = logits_per_image.t() + + # shape = [global_batch_size, global_batch_size] + return logits_per_image, logits_per_text + + +def convert_weights(model: nn.Module): + """Convert applicable model parameters to fp16""" + + def _convert_weights_to_fp16(l): + if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)): + l.weight.data = l.weight.data.half() + if l.bias is not None: + l.bias.data = l.bias.data.half() + + if isinstance(l, nn.MultiheadAttention): + for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]: + tensor = getattr(l, attr) + if tensor is not None: + tensor.data = tensor.data.half() + + for name in ["text_projection", "proj"]: + if hasattr(l, name): + attr = getattr(l, name) + if attr is not None: + attr.data = attr.data.half() + + model.apply(_convert_weights_to_fp16) + + + +def build_model(state_dict: dict): + vit = "visual.proj" in state_dict + + if vit: + vision_width = state_dict["visual.conv1.weight"].shape[0] + vision_layers = len([k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")]) + vision_patch_size = state_dict["visual.conv1.weight"].shape[-1] + grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5) + image_resolution = vision_patch_size * grid_size + else: + counts: list = [len(set(k.split(".")[2] for k in state_dict if k.startswith(f"visual.layer{b}"))) for b in [1, 2, 3, 4]] + vision_layers = tuple(counts) + vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0] + output_width = round((state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5) + vision_patch_size = None + assert output_width ** 2 + 1 == state_dict["visual.attnpool.positional_embedding"].shape[0] + image_resolution = output_width * 32 + + embed_dim = state_dict["text_projection"].shape[1] + context_length = state_dict["positional_embedding"].shape[0] + vocab_size = state_dict["token_embedding.weight"].shape[0] + transformer_width = state_dict["ln_final.weight"].shape[0] + transformer_heads = transformer_width // 64 + transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith(f"transformer.resblocks"))) + + model = CLIP( + embed_dim, + image_resolution, vision_layers, vision_width, vision_patch_size, + context_length, vocab_size, transformer_width, transformer_heads, transformer_layers + ) + + for key in ["input_resolution", "context_length", "vocab_size"]: + if key in state_dict: + del state_dict[key] + + convert_weights(model) + model.load_state_dict(state_dict) + return model.eval() diff --git a/ape/modeling/text/eva01_clip/model_configs/EVA_CLIP_g_14.json b/ape/modeling/text/eva01_clip/model_configs/EVA_CLIP_g_14.json new file mode 100644 index 0000000..e737f71 --- /dev/null +++ b/ape/modeling/text/eva01_clip/model_configs/EVA_CLIP_g_14.json @@ -0,0 +1,19 @@ +{ + "embed_dim": 1024, + "vision_cfg": { + "image_size": 224, + "layers": 40, + "width": 1408, + "head_width": 88, + "mlp_ratio": 4.3637, + "patch_size": 14, + "drop_path_rate": 0.0 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 768, + "heads": 12, + "layers": 12 + } +} \ No newline at end of file diff --git a/ape/modeling/text/eva01_clip/model_configs/EVA_CLIP_g_14_X.json b/ape/modeling/text/eva01_clip/model_configs/EVA_CLIP_g_14_X.json new file mode 100644 index 0000000..f827f69 --- /dev/null +++ b/ape/modeling/text/eva01_clip/model_configs/EVA_CLIP_g_14_X.json @@ -0,0 +1,21 @@ +{ + "embed_dim": 1024, + "vision_cfg": { + "image_size": 224, + "layers": 40, + "width": 1408, + "head_width": 88, + "mlp_ratio": 4.3637, + "patch_size": 14, + "drop_path_rate": 0.0, + "xattn": true, + "fusedLN": true + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 768, + "heads": 12, + "layers": 12 + } +} \ No newline at end of file diff --git a/ape/modeling/text/eva01_clip/simple_tokenizer.py b/ape/modeling/text/eva01_clip/simple_tokenizer.py new file mode 100644 index 0000000..0a66286 --- /dev/null +++ b/ape/modeling/text/eva01_clip/simple_tokenizer.py @@ -0,0 +1,132 @@ +import gzip +import html +import os +from functools import lru_cache + +import ftfy +import regex as re + + +@lru_cache() +def default_bpe(): + return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz") + + +@lru_cache() +def bytes_to_unicode(): + """ + Returns list of utf-8 byte and a corresponding list of unicode strings. + The reversible bpe codes work on unicode strings. + This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. + When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. + This is a signficant percentage of your normal, say, 32K bpe vocab. + To avoid that, we want lookup tables between utf-8 bytes and unicode strings. + And avoids mapping to whitespace/control characters the bpe code barfs on. + """ + bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1)) + cs = bs[:] + n = 0 + for b in range(2**8): + if b not in bs: + bs.append(b) + cs.append(2**8+n) + n += 1 + cs = [chr(n) for n in cs] + return dict(zip(bs, cs)) + + +def get_pairs(word): + """Return set of symbol pairs in a word. + Word is represented as tuple of symbols (symbols being variable-length strings). + """ + pairs = set() + prev_char = word[0] + for char in word[1:]: + pairs.add((prev_char, char)) + prev_char = char + return pairs + + +def basic_clean(text): + text = ftfy.fix_text(text) + text = html.unescape(html.unescape(text)) + return text.strip() + + +def whitespace_clean(text): + text = re.sub(r'\s+', ' ', text) + text = text.strip() + return text + + +class SimpleTokenizer(object): + def __init__(self, bpe_path: str = default_bpe()): + self.byte_encoder = bytes_to_unicode() + self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} + merges = gzip.open(bpe_path).read().decode("utf-8").split('\n') + merges = merges[1:49152-256-2+1] + merges = [tuple(merge.split()) for merge in merges] + vocab = list(bytes_to_unicode().values()) + vocab = vocab + [v+'' for v in vocab] + for merge in merges: + vocab.append(''.join(merge)) + vocab.extend(['<|startoftext|>', '<|endoftext|>']) + self.encoder = dict(zip(vocab, range(len(vocab)))) + self.decoder = {v: k for k, v in self.encoder.items()} + self.bpe_ranks = dict(zip(merges, range(len(merges)))) + self.cache = {'<|startoftext|>': '<|startoftext|>', '<|endoftext|>': '<|endoftext|>'} + self.pat = re.compile(r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", re.IGNORECASE) + + def bpe(self, token): + if token in self.cache: + return self.cache[token] + word = tuple(token[:-1]) + ( token[-1] + '',) + pairs = get_pairs(word) + + if not pairs: + return token+'' + + while True: + bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf'))) + if bigram not in self.bpe_ranks: + break + first, second = bigram + new_word = [] + i = 0 + while i < len(word): + try: + j = word.index(first, i) + new_word.extend(word[i:j]) + i = j + except: + new_word.extend(word[i:]) + break + + if word[i] == first and i < len(word)-1 and word[i+1] == second: + new_word.append(first+second) + i += 2 + else: + new_word.append(word[i]) + i += 1 + new_word = tuple(new_word) + word = new_word + if len(word) == 1: + break + else: + pairs = get_pairs(word) + word = ' '.join(word) + self.cache[token] = word + return word + + def encode(self, text): + bpe_tokens = [] + text = whitespace_clean(basic_clean(text)).lower() + for token in re.findall(self.pat, text): + token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8')) + bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' ')) + return bpe_tokens + + def decode(self, tokens): + text = ''.join([self.decoder[token] for token in tokens]) + text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors="replace").replace('', ' ') + return text diff --git a/ape/modeling/text/eva01_clip/vit_model.py b/ape/modeling/text/eva01_clip/vit_model.py new file mode 100644 index 0000000..7b4753d --- /dev/null +++ b/ape/modeling/text/eva01_clip/vit_model.py @@ -0,0 +1,389 @@ +# -------------------------------------------------------- +# BEIT: BERT Pre-Training of Image Transformers (https://arxiv.org/abs/2106.08254) +# Github source: https://github.com/microsoft/unilm/tree/master/beit +# Copyright (c) 2021 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# By Hangbo Bao +# Based on timm and DeiT code bases +# https://github.com/rwightman/pytorch-image-models/tree/master/timm +# https://github.com/facebookresearch/deit/ +# https://github.com/facebookresearch/dino +# --------------------------------------------------------' +import math +from functools import partial + +import torch +import torch.nn as nn +import torch.nn.functional as F +from timm.models.layers import drop_path, to_2tuple, trunc_normal_ +# from timm.models.registry import register_model + +try: + import xformers.ops as xops +except: + pass + +def _cfg(url='', **kwargs): + return { + 'url': url, + 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None, + 'crop_pct': .9, 'interpolation': 'bicubic', + 'mean': (0.5, 0.5, 0.5), 'std': (0.5, 0.5, 0.5), + **kwargs + } + + +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + """ + def __init__(self, drop_prob=None): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + + def forward(self, x): + return drop_path(x, self.drop_prob, self.training) + + def extra_repr(self) -> str: + return 'p={}'.format(self.drop_prob) + + +class Mlp(nn.Module): + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + # x = self.drop(x) + # commit this for the orignal BERT implement + x = self.fc2(x) + x = self.drop(x) + return x + + +class Attention(nn.Module): + def __init__( + self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., + proj_drop=0., window_size=None, attn_head_dim=None, + xattn=False + ): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + if attn_head_dim is not None: + head_dim = attn_head_dim + all_head_dim = head_dim * self.num_heads + self.scale = qk_scale or head_dim ** -0.5 + + self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False) + if qkv_bias: + self.q_bias = nn.Parameter(torch.zeros(all_head_dim)) + self.v_bias = nn.Parameter(torch.zeros(all_head_dim)) + else: + self.q_bias = None + self.v_bias = None + + if window_size: + self.window_size = window_size + self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3 + self.relative_position_bias_table = nn.Parameter( + torch.zeros(self.num_relative_distance, num_heads)) # 2*Wh-1 * 2*Ww-1, nH + # cls to token & token 2 cls & cls to cls + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(window_size[0]) + coords_w = torch.arange(window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * window_size[1] - 1 + relative_position_index = \ + torch.zeros(size=(window_size[0] * window_size[1] + 1, ) * 2, dtype=relative_coords.dtype) + relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + relative_position_index[0, 0:] = self.num_relative_distance - 3 + relative_position_index[0:, 0] = self.num_relative_distance - 2 + relative_position_index[0, 0] = self.num_relative_distance - 1 + + self.register_buffer("relative_position_index", relative_position_index) + else: + self.window_size = None + self.relative_position_bias_table = None + self.relative_position_index = None + + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(all_head_dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + self.xattn = xattn + + def forward(self, x, rel_pos_bias=None): + B, N, C = x.shape + qkv_bias = None + if self.q_bias is not None: + qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias)) + + qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias) + + if self.xattn: + qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 1, 3, 4) # 3, B, N, num_heads, C + else: + qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) # 3, B, num_heads, N, C + + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + if self.xattn: + x = xops.memory_efficient_attention(q, k, v) + x = x.reshape(B, N, -1) + x = self.proj(x) + x = self.proj_drop(x) + else: + q = q * self.scale + attn = (q @ k.transpose(-2, -1)) + + if self.relative_position_bias_table is not None: + relative_position_bias = \ + self.relative_position_bias_table[self.relative_position_index.view(-1)].view( + self.window_size[0] * self.window_size[1] + 1, + self.window_size[0] * self.window_size[1] + 1, -1) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww + attn = attn + relative_position_bias.unsqueeze(0) + + if rel_pos_bias is not None: + attn = attn + rel_pos_bias + + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, -1) + x = self.proj(x) + x = self.proj_drop(x) + + return x + + +class Block(nn.Module): + + def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., + drop_path=0., init_values=None, act_layer=nn.GELU, norm_layer=nn.LayerNorm, + window_size=None, attn_head_dim=None, + xattn=False): + super().__init__() + self.norm1 = norm_layer(dim) + self.attn = Attention( + dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, + attn_drop=attn_drop, proj_drop=drop, window_size=window_size, attn_head_dim=attn_head_dim, + xattn=xattn) + # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + if init_values is not None and init_values > 0: + self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True) + self.gamma_2 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True) + else: + self.gamma_1, self.gamma_2 = None, None + + def forward(self, x, rel_pos_bias=None): + if self.gamma_1 is None: + x = x + self.drop_path(self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias)) + x = x + self.drop_path(self.mlp(self.norm2(x))) + else: + x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias)) + x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x))) + return x + + +class PatchEmbed(nn.Module): + """ Image to Patch Embedding + """ + def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0]) + self.patch_shape = (img_size[0] // patch_size[0], img_size[1] // patch_size[1]) + self.img_size = img_size + self.patch_size = patch_size + self.num_patches = num_patches + + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) + + def forward(self, x, **kwargs): + B, C, H, W = x.shape + # FIXME look at relaxing size constraints + assert H == self.img_size[0] and W == self.img_size[1], \ + f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})." + x = self.proj(x).flatten(2).transpose(1, 2) + return x + + +class RelativePositionBias(nn.Module): + + def __init__(self, window_size, num_heads): + super().__init__() + self.window_size = window_size + self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3 + self.relative_position_bias_table = nn.Parameter( + torch.zeros(self.num_relative_distance, num_heads)) # 2*Wh-1 * 2*Ww-1, nH + # cls to token & token 2 cls & cls to cls + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(window_size[0]) + coords_w = torch.arange(window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * window_size[1] - 1 + relative_position_index = \ + torch.zeros(size=(window_size[0] * window_size[1] + 1,) * 2, dtype=relative_coords.dtype) + relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + relative_position_index[0, 0:] = self.num_relative_distance - 3 + relative_position_index[0:, 0] = self.num_relative_distance - 2 + relative_position_index[0, 0] = self.num_relative_distance - 1 + + self.register_buffer("relative_position_index", relative_position_index) + + # trunc_normal_(self.relative_position_bias_table, std=.02) + + def forward(self): + relative_position_bias = \ + self.relative_position_bias_table[self.relative_position_index.view(-1)].view( + self.window_size[0] * self.window_size[1] + 1, + self.window_size[0] * self.window_size[1] + 1, -1) # Wh*Ww,Wh*Ww,nH + return relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww + + +class VisionTransformer(nn.Module): + """ Vision Transformer with support for patch or hybrid CNN input stage + """ + def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12, + num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0., + drop_path_rate=0., norm_layer=nn.LayerNorm, init_values=None, + use_abs_pos_emb=True, use_rel_pos_bias=False, use_shared_rel_pos_bias=False, + use_mean_pooling=False, init_scale=0.001, + xattn=False): + super().__init__() + self.image_size = img_size + self.output_dim = self.num_classes = num_classes + self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models + + self.patch_embed = PatchEmbed( + img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) + num_patches = self.patch_embed.num_patches + + self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + if use_abs_pos_emb: + self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim)) + else: + self.pos_embed = None + self.pos_drop = nn.Dropout(p=drop_rate) + + if use_shared_rel_pos_bias: + self.rel_pos_bias = RelativePositionBias(window_size=self.patch_embed.patch_shape, num_heads=num_heads) + else: + self.rel_pos_bias = None + + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule + self.use_rel_pos_bias = use_rel_pos_bias + self.blocks = nn.ModuleList([ + Block( + dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, + init_values=init_values, window_size=self.patch_embed.patch_shape if use_rel_pos_bias else None, + xattn=xattn) + for i in range(depth)]) + self.norm = nn.Identity() if use_mean_pooling else norm_layer(embed_dim) + self.fc_norm = norm_layer(embed_dim) if use_mean_pooling else None + self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + if self.pos_embed is not None: + trunc_normal_(self.pos_embed, std=.02) + trunc_normal_(self.cls_token, std=.02) + # trunc_normal_(self.mask_token, std=.02) + if isinstance(self.head, nn.Linear): + trunc_normal_(self.head.weight, std=.02) + self.apply(self._init_weights) + self.fix_init_weight() + + if isinstance(self.head, nn.Linear): + self.head.weight.data.mul_(init_scale) + self.head.bias.data.mul_(init_scale) + + def fix_init_weight(self): + def rescale(param, layer_id): + param.div_(math.sqrt(2.0 * layer_id)) + + for layer_id, layer in enumerate(self.blocks): + rescale(layer.attn.proj.weight.data, layer_id + 1) + rescale(layer.mlp.fc2.weight.data, layer_id + 1) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def get_classifier(self): + return self.head + + def reset_classifier(self, num_classes, global_pool=''): + self.num_classes = num_classes + self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + def forward_features(self, x): + x = self.patch_embed(x) + batch_size, seq_len, _ = x.size() + + cls_tokens = self.cls_token.expand(batch_size, -1, -1) # stole cls_tokens impl from Phil Wang, thanks + x = torch.cat((cls_tokens, x), dim=1) + if self.pos_embed is not None: + x = x + self.pos_embed + x = self.pos_drop(x) + + rel_pos_bias = self.rel_pos_bias() if self.rel_pos_bias is not None else None + for blk in self.blocks: + x = blk(x, rel_pos_bias=rel_pos_bias) + + x = self.norm(x) + x = x[:, 1:, :] + return x + + def forward(self, x): + x = self.forward_features(x) + x = self.head(x) + return x + + def get_intermediate_layers(self, x): + x = self.patch_embed(x) + batch_size, seq_len, _ = x.size() + + cls_tokens = self.cls_token.expand(batch_size, -1, -1) # stole cls_tokens impl from Phil Wang, thanks + x = torch.cat((cls_tokens, x), dim=1) + if self.pos_embed is not None: + x = x + self.pos_embed + x = self.pos_drop(x) + + features = [] + rel_pos_bias = self.rel_pos_bias() if self.rel_pos_bias is not None else None + for blk in self.blocks: + x = blk(x, rel_pos_bias) + features.append(x) + + return features diff --git a/ape/modeling/text/eva02_clip/__init__.py b/ape/modeling/text/eva02_clip/__init__.py new file mode 100644 index 0000000..0da1b24 --- /dev/null +++ b/ape/modeling/text/eva02_clip/__init__.py @@ -0,0 +1,13 @@ +# from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD +# from .factory import create_model, create_model_and_transforms, create_model_from_pretrained, get_tokenizer +# from .factory import list_models, add_model_config, get_model_config, load_checkpoint +# from .loss import ClipLoss +# from .model import CLIP, CustomCLIP, CLIPTextCfg, CLIPVisionCfg,\ +# convert_weights_to_lp, convert_weights_to_fp16, trace_model, get_cast_dtype +# from .openai import load_openai_model, list_openai_models +# from .pretrained import list_pretrained, list_pretrained_models_by_tag, list_pretrained_tags_by_model,\ +# get_pretrained_url, download_pretrained_from_url, is_pretrained_cfg, get_pretrained_cfg, download_pretrained +# from .tokenizer import SimpleTokenizer, tokenize +# from .transform import image_transform + +from .factory import create_model_and_transforms, get_tokenizer diff --git a/ape/modeling/text/eva02_clip/bpe_simple_vocab_16e6.txt.gz b/ape/modeling/text/eva02_clip/bpe_simple_vocab_16e6.txt.gz new file mode 100644 index 0000000000000000000000000000000000000000..7b5088a527f720063f044eb928eee315f63b2fc0 GIT binary patch literal 1356917 zcmV(nK=QvIiwFQl6I)*Z19ZK~vMkwgB)E^SaG(|_PzuTJUep5J0`N~DK81(h@F{(W 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new file mode 100644 index 0000000..9343903 --- /dev/null +++ b/ape/modeling/text/eva02_clip/eva_vit_model.py @@ -0,0 +1,531 @@ +# -------------------------------------------------------- +# Adapted from https://github.com/microsoft/unilm/tree/master/beit +# -------------------------------------------------------- +import math +import os +from functools import partial +import torch +import torch.nn as nn +import torch.nn.functional as F +try: + from timm.models.layers import drop_path, to_2tuple, trunc_normal_ +except: + from timm.layers import drop_path, to_2tuple, trunc_normal_ + +from .transformer import PatchDropout +from .rope import VisionRotaryEmbedding, VisionRotaryEmbeddingFast + +if os.getenv('ENV_TYPE') == 'deepspeed': + try: + from deepspeed.runtime.activation_checkpointing.checkpointing import checkpoint + except: + from torch.utils.checkpoint import checkpoint +else: + from torch.utils.checkpoint import checkpoint + +try: + import xformers.ops as xops +except ImportError: + xops = None + print("Please 'pip install xformers'") + + +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + """ + def __init__(self, drop_prob=None): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + + def forward(self, x): + return drop_path(x, self.drop_prob, self.training) + + def extra_repr(self) -> str: + return 'p={}'.format(self.drop_prob) + + +class Mlp(nn.Module): + def __init__( + self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + drop=0., + subln=False, + + ): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + + self.ffn_ln = norm_layer(hidden_features) if subln else nn.Identity() + + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + # x = self.drop(x) + # commit this for the orignal BERT implement + x = self.ffn_ln(x) + + x = self.fc2(x) + x = self.drop(x) + return x + +class SwiGLU(nn.Module): + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.SiLU, drop=0., + norm_layer=nn.LayerNorm, subln=False): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + + self.w1 = nn.Linear(in_features, hidden_features) + self.w2 = nn.Linear(in_features, hidden_features) + + self.act = act_layer() + self.ffn_ln = norm_layer(hidden_features) if subln else nn.Identity() + self.w3 = nn.Linear(hidden_features, out_features) + + self.drop = nn.Dropout(drop) + + def forward(self, x): + x1 = self.w1(x) + x2 = self.w2(x) + hidden = self.act(x1) * x2 + x = self.ffn_ln(hidden) + x = self.w3(x) + x = self.drop(x) + return x + +class Attention(nn.Module): + def __init__( + self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., + proj_drop=0., window_size=None, attn_head_dim=None, xattn=False, rope=None, subln=False, norm_layer=nn.LayerNorm): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + if attn_head_dim is not None: + head_dim = attn_head_dim + all_head_dim = head_dim * self.num_heads + self.scale = qk_scale or head_dim ** -0.5 + + self.subln = subln + if self.subln: + self.q_proj = nn.Linear(dim, all_head_dim, bias=False) + self.k_proj = nn.Linear(dim, all_head_dim, bias=False) + self.v_proj = nn.Linear(dim, all_head_dim, bias=False) + else: + self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False) + + if qkv_bias: + self.q_bias = nn.Parameter(torch.zeros(all_head_dim)) + self.v_bias = nn.Parameter(torch.zeros(all_head_dim)) + else: + self.q_bias = None + self.v_bias = None + + if window_size: + self.window_size = window_size + self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3 + self.relative_position_bias_table = nn.Parameter( + torch.zeros(self.num_relative_distance, num_heads)) # 2*Wh-1 * 2*Ww-1, nH + # cls to token & token 2 cls & cls to cls + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(window_size[0]) + coords_w = torch.arange(window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * window_size[1] - 1 + relative_position_index = \ + torch.zeros(size=(window_size[0] * window_size[1] + 1, ) * 2, dtype=relative_coords.dtype) + relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + relative_position_index[0, 0:] = self.num_relative_distance - 3 + relative_position_index[0:, 0] = self.num_relative_distance - 2 + relative_position_index[0, 0] = self.num_relative_distance - 1 + + self.register_buffer("relative_position_index", relative_position_index) + else: + self.window_size = None + self.relative_position_bias_table = None + self.relative_position_index = None + + self.attn_drop = nn.Dropout(attn_drop) + self.inner_attn_ln = norm_layer(all_head_dim) if subln else nn.Identity() + # self.proj = nn.Linear(all_head_dim, all_head_dim) + self.proj = nn.Linear(all_head_dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + self.xattn = xattn + self.xattn_drop = attn_drop + + self.rope = rope + + def forward(self, x, rel_pos_bias=None, attn_mask=None): + B, N, C = x.shape + if self.subln: + q = F.linear(input=x, weight=self.q_proj.weight, bias=self.q_bias) + k = F.linear(input=x, weight=self.k_proj.weight, bias=None) + v = F.linear(input=x, weight=self.v_proj.weight, bias=self.v_bias) + + q = q.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3) # B, num_heads, N, C + k = k.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3) + v = v.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3) + else: + + qkv_bias = None + if self.q_bias is not None: + qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias)) + + qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias) + qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) # 3, B, num_heads, N, C + q, k, v = qkv[0], qkv[1], qkv[2] + + if self.rope: + # slightly fast impl + q_t = q[:, :, 1:, :] + ro_q_t = self.rope(q_t) + q = torch.cat((q[:, :, :1, :], ro_q_t), -2).type_as(v) + + k_t = k[:, :, 1:, :] + ro_k_t = self.rope(k_t) + k = torch.cat((k[:, :, :1, :], ro_k_t), -2).type_as(v) + + if self.xattn: + q = q.permute(0, 2, 1, 3) # B, num_heads, N, C -> B, N, num_heads, C + k = k.permute(0, 2, 1, 3) + v = v.permute(0, 2, 1, 3) + + x = xops.memory_efficient_attention( + q, k, v, + p=self.xattn_drop, + scale=self.scale, + ) + x = x.reshape(B, N, -1) + x = self.inner_attn_ln(x) + x = self.proj(x) + x = self.proj_drop(x) + else: + q = q * self.scale + attn = (q @ k.transpose(-2, -1)) + + if self.relative_position_bias_table is not None: + relative_position_bias = \ + self.relative_position_bias_table[self.relative_position_index.view(-1)].view( + self.window_size[0] * self.window_size[1] + 1, + self.window_size[0] * self.window_size[1] + 1, -1) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww + attn = attn + relative_position_bias.unsqueeze(0).type_as(attn) + + if rel_pos_bias is not None: + attn = attn + rel_pos_bias.type_as(attn) + + if attn_mask is not None: + attn_mask = attn_mask.bool() + attn = attn.masked_fill(~attn_mask[:, None, None, :], float("-inf")) + + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, -1) + x = self.inner_attn_ln(x) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class Block(nn.Module): + + def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., + drop_path=0., init_values=None, act_layer=nn.GELU, norm_layer=nn.LayerNorm, + window_size=None, attn_head_dim=None, xattn=False, rope=None, postnorm=False, + subln=False, naiveswiglu=False): + super().__init__() + self.norm1 = norm_layer(dim) + self.attn = Attention( + dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, + attn_drop=attn_drop, proj_drop=drop, window_size=window_size, attn_head_dim=attn_head_dim, + xattn=xattn, rope=rope, subln=subln, norm_layer=norm_layer) + # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + + if naiveswiglu: + self.mlp = SwiGLU( + in_features=dim, + hidden_features=mlp_hidden_dim, + subln=subln, + norm_layer=norm_layer, + ) + else: + self.mlp = Mlp( + in_features=dim, + hidden_features=mlp_hidden_dim, + act_layer=act_layer, + subln=subln, + drop=drop + ) + + if init_values is not None and init_values > 0: + self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True) + self.gamma_2 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True) + else: + self.gamma_1, self.gamma_2 = None, None + + self.postnorm = postnorm + + def forward(self, x, rel_pos_bias=None, attn_mask=None): + if self.gamma_1 is None: + if self.postnorm: + x = x + self.drop_path(self.norm1(self.attn(x, rel_pos_bias=rel_pos_bias, attn_mask=attn_mask))) + x = x + self.drop_path(self.norm2(self.mlp(x))) + else: + x = x + self.drop_path(self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias, attn_mask=attn_mask)) + x = x + self.drop_path(self.mlp(self.norm2(x))) + else: + if self.postnorm: + x = x + self.drop_path(self.gamma_1 * self.norm1(self.attn(x, rel_pos_bias=rel_pos_bias, attn_mask=attn_mask))) + x = x + self.drop_path(self.gamma_2 * self.norm2(self.mlp(x))) + else: + x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias, attn_mask=attn_mask)) + x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x))) + return x + + +class PatchEmbed(nn.Module): + """ Image to Patch Embedding + """ + def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0]) + self.patch_shape = (img_size[0] // patch_size[0], img_size[1] // patch_size[1]) + self.img_size = img_size + self.patch_size = patch_size + self.num_patches = num_patches + + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) + + def forward(self, x, **kwargs): + B, C, H, W = x.shape + # FIXME look at relaxing size constraints + assert H == self.img_size[0] and W == self.img_size[1], \ + f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})." + x = self.proj(x).flatten(2).transpose(1, 2) + return x + + +class RelativePositionBias(nn.Module): + + def __init__(self, window_size, num_heads): + super().__init__() + self.window_size = window_size + self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3 + self.relative_position_bias_table = nn.Parameter( + torch.zeros(self.num_relative_distance, num_heads)) # 2*Wh-1 * 2*Ww-1, nH + # cls to token & token 2 cls & cls to cls + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(window_size[0]) + coords_w = torch.arange(window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * window_size[1] - 1 + relative_position_index = \ + torch.zeros(size=(window_size[0] * window_size[1] + 1,) * 2, dtype=relative_coords.dtype) + relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + relative_position_index[0, 0:] = self.num_relative_distance - 3 + relative_position_index[0:, 0] = self.num_relative_distance - 2 + relative_position_index[0, 0] = self.num_relative_distance - 1 + + self.register_buffer("relative_position_index", relative_position_index) + + def forward(self): + relative_position_bias = \ + self.relative_position_bias_table[self.relative_position_index.view(-1)].view( + self.window_size[0] * self.window_size[1] + 1, + self.window_size[0] * self.window_size[1] + 1, -1) # Wh*Ww,Wh*Ww,nH + return relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww + + +class EVAVisionTransformer(nn.Module): + """ Vision Transformer with support for patch or hybrid CNN input stage + """ + def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12, + num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0., + drop_path_rate=0., norm_layer=nn.LayerNorm, init_values=None, patch_dropout=0., + use_abs_pos_emb=True, use_rel_pos_bias=False, use_shared_rel_pos_bias=False, rope=False, + use_mean_pooling=True, init_scale=0.001, grad_checkpointing=False, xattn=False, postnorm=False, + pt_hw_seq_len=16, intp_freq=False, naiveswiglu=False, subln=False): + super().__init__() + self.image_size = img_size + self.num_classes = num_classes + self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models + + self.patch_embed = PatchEmbed( + img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) + num_patches = self.patch_embed.num_patches + + self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + # self.mask_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + if use_abs_pos_emb: + self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim)) + else: + self.pos_embed = None + self.pos_drop = nn.Dropout(p=drop_rate) + + if use_shared_rel_pos_bias: + self.rel_pos_bias = RelativePositionBias(window_size=self.patch_embed.patch_shape, num_heads=num_heads) + else: + self.rel_pos_bias = None + + if rope: + half_head_dim = embed_dim // num_heads // 2 + hw_seq_len = img_size // patch_size + self.rope = VisionRotaryEmbeddingFast( + dim=half_head_dim, + pt_seq_len=pt_hw_seq_len, + ft_seq_len=hw_seq_len if intp_freq else None, + # patch_dropout=patch_dropout + ) + else: + self.rope = None + + self.naiveswiglu = naiveswiglu + + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule + self.use_rel_pos_bias = use_rel_pos_bias + self.blocks = nn.ModuleList([ + Block( + dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, + init_values=init_values, window_size=self.patch_embed.patch_shape if use_rel_pos_bias else None, + xattn=xattn, rope=self.rope, postnorm=postnorm, subln=subln, naiveswiglu=naiveswiglu) + for i in range(depth)]) + self.norm = nn.Identity() if use_mean_pooling else norm_layer(embed_dim) + self.fc_norm = norm_layer(embed_dim) if use_mean_pooling else None + self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + if self.pos_embed is not None: + trunc_normal_(self.pos_embed, std=.02) + + trunc_normal_(self.cls_token, std=.02) + # trunc_normal_(self.mask_token, std=.02) + + self.apply(self._init_weights) + self.fix_init_weight() + + if isinstance(self.head, nn.Linear): + trunc_normal_(self.head.weight, std=.02) + self.head.weight.data.mul_(init_scale) + self.head.bias.data.mul_(init_scale) + + # setting a patch_dropout of 0. would mean it is disabled and this function would be the identity fn + self.patch_dropout = PatchDropout(patch_dropout) if patch_dropout > 0. else nn.Identity() + + self.grad_checkpointing = grad_checkpointing + + def fix_init_weight(self): + def rescale(param, layer_id): + param.div_(math.sqrt(2.0 * layer_id)) + + for layer_id, layer in enumerate(self.blocks): + rescale(layer.attn.proj.weight.data, layer_id + 1) + if self.naiveswiglu: + rescale(layer.mlp.w3.weight.data, layer_id + 1) + else: + rescale(layer.mlp.fc2.weight.data, layer_id + 1) + + def get_cast_dtype(self) -> torch.dtype: + return self.blocks[0].mlp.fc2.weight.dtype + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def get_num_layers(self): + return len(self.blocks) + + def lock(self, unlocked_groups=0, freeze_bn_stats=False): + assert unlocked_groups == 0, 'partial locking not currently supported for this model' + for param in self.parameters(): + param.requires_grad = False + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.grad_checkpointing = enable + + @torch.jit.ignore + def no_weight_decay(self): + return {'pos_embed', 'cls_token'} + + def get_classifier(self): + return self.head + + def reset_classifier(self, num_classes, global_pool=''): + self.num_classes = num_classes + self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + def forward_features(self, x, return_all_features=False): + + x = self.patch_embed(x) + batch_size, seq_len, _ = x.size() + + cls_tokens = self.cls_token.expand(batch_size, -1, -1) # stole cls_tokens impl from Phil Wang, thanks + x = torch.cat((cls_tokens, x), dim=1) + if self.pos_embed is not None: + x = x + self.pos_embed + x = self.pos_drop(x) + + # a patch_dropout of 0. would mean it is disabled and this function would do nothing but return what was passed in + if os.getenv('RoPE') == '1': + if self.training and not isinstance(self.patch_dropout, nn.Identity): + x, patch_indices_keep = self.patch_dropout(x) + self.rope.forward = partial(self.rope.forward, patch_indices_keep=patch_indices_keep) + else: + self.rope.forward = partial(self.rope.forward, patch_indices_keep=None) + x = self.patch_dropout(x) + else: + x = self.patch_dropout(x) + + rel_pos_bias = self.rel_pos_bias() if self.rel_pos_bias is not None else None + for blk in self.blocks: + if self.grad_checkpointing: + x = checkpoint(blk, x, (rel_pos_bias,)) + else: + x = blk(x, rel_pos_bias=rel_pos_bias) + + if not return_all_features: + x = self.norm(x) + if self.fc_norm is not None: + return self.fc_norm(x.mean(1)) + else: + return x[:, 0] + return x + + def forward(self, x, return_all_features=False): + if return_all_features: + return self.forward_features(x, return_all_features) + x = self.forward_features(x) + x = self.head(x) + return x diff --git a/ape/modeling/text/eva02_clip/factory.py b/ape/modeling/text/eva02_clip/factory.py new file mode 100644 index 0000000..dbd3979 --- /dev/null +++ b/ape/modeling/text/eva02_clip/factory.py @@ -0,0 +1,460 @@ +import json +import logging +import os +import pathlib +import re +from copy import deepcopy +from pathlib import Path +from typing import Optional, Tuple, Union, Dict, Any +import torch + +from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD +from .model import CLIP, CustomCLIP, convert_weights_to_lp, convert_to_custom_text_state_dict,\ + get_cast_dtype +from .openai import load_openai_model +from .pretrained import is_pretrained_cfg, get_pretrained_cfg, download_pretrained, list_pretrained_tags_by_model +from .transform import image_transform +from .tokenizer import HFTokenizer, tokenize +from .utils import resize_clip_pos_embed, resize_evaclip_pos_embed, resize_visual_pos_embed, resize_eva_pos_embed + + +_MODEL_CONFIG_PATHS = [Path(__file__).parent / f"model_configs/"] +_MODEL_CONFIGS = {} # directory (model_name: config) of model architecture configs + + +def _natural_key(string_): + return [int(s) if s.isdigit() else s for s in re.split(r'(\d+)', string_.lower())] + + +def _rescan_model_configs(): + global _MODEL_CONFIGS + + config_ext = ('.json',) + config_files = [] + for config_path in _MODEL_CONFIG_PATHS: + if config_path.is_file() and config_path.suffix in config_ext: + config_files.append(config_path) + elif config_path.is_dir(): + for ext in config_ext: + config_files.extend(config_path.glob(f'*{ext}')) + + for cf in config_files: + with open(cf, "r", encoding="utf8") as f: + model_cfg = json.load(f) + if all(a in model_cfg for a in ('embed_dim', 'vision_cfg', 'text_cfg')): + _MODEL_CONFIGS[cf.stem] = model_cfg + + _MODEL_CONFIGS = dict(sorted(_MODEL_CONFIGS.items(), key=lambda x: _natural_key(x[0]))) + + +_rescan_model_configs() # initial populate of model config registry + + +def list_models(): + """ enumerate available model architectures based on config files """ + return list(_MODEL_CONFIGS.keys()) + + +def add_model_config(path): + """ add model config path or file and update registry """ + if not isinstance(path, Path): + path = Path(path) + _MODEL_CONFIG_PATHS.append(path) + _rescan_model_configs() + + +def get_model_config(model_name): + if model_name in _MODEL_CONFIGS: + return deepcopy(_MODEL_CONFIGS[model_name]) + else: + return None + + +def get_tokenizer(model_name): + config = get_model_config(model_name) + tokenizer = HFTokenizer(config['text_cfg']['hf_tokenizer_name']) if 'hf_tokenizer_name' in config['text_cfg'] else tokenize + return tokenizer + + +# loading openai CLIP weights when is_openai=True for training +def load_state_dict(checkpoint_path: str, map_location: str='cpu', model_key: str='model|module|state_dict', is_openai: bool=False, skip_list: list=[]): + if is_openai: + model = torch.jit.load(checkpoint_path, map_location="cpu").eval() + state_dict = model.state_dict() + for key in ["input_resolution", "context_length", "vocab_size"]: + state_dict.pop(key, None) + else: + checkpoint = torch.load(checkpoint_path, map_location=map_location) + for mk in model_key.split('|'): + if isinstance(checkpoint, dict) and mk in checkpoint: + state_dict = checkpoint[mk] + break + else: + state_dict = checkpoint + if next(iter(state_dict.items()))[0].startswith('module'): + state_dict = {k[7:]: v for k, v in state_dict.items()} + + for k in skip_list: + if k in list(state_dict.keys()): + logging.info(f"Removing key {k} from pretrained checkpoint") + del state_dict[k] + + if os.getenv('RoPE') == '1': + for k in list(state_dict.keys()): + if 'freqs_cos' in k or 'freqs_sin' in k: + del state_dict[k] + return state_dict + + + +def load_checkpoint(model, checkpoint_path, model_key="model|module|state_dict", strict=True): + state_dict = load_state_dict(checkpoint_path, model_key=model_key, is_openai=False) + # detect old format and make compatible with new format + if 'positional_embedding' in state_dict and not hasattr(model, 'positional_embedding'): + state_dict = convert_to_custom_text_state_dict(state_dict) + if 'text.logit_scale' in state_dict and hasattr(model, 'logit_scale'): + state_dict['logit_scale'] = state_dict['text.logit_scale'] + del state_dict['text.logit_scale'] + + # resize_clip_pos_embed for CLIP and open CLIP + if 'visual.positional_embedding' in state_dict: + resize_clip_pos_embed(state_dict, model) + # specified to eva_vit_model + elif 'visual.pos_embed' in state_dict: + resize_evaclip_pos_embed(state_dict, model) + + # resize_clip_pos_embed(state_dict, model) + incompatible_keys = model.load_state_dict(state_dict, strict=strict) + logging.info(f"incompatible_keys.missing_keys: {incompatible_keys.missing_keys}") + return incompatible_keys + +def load_clip_visual_state_dict(checkpoint_path: str, map_location: str='cpu', is_openai: bool=False, skip_list:list=[]): + state_dict = load_state_dict(checkpoint_path, map_location=map_location, is_openai=is_openai, skip_list=skip_list) + + for k in list(state_dict.keys()): + if not k.startswith('visual.'): + del state_dict[k] + for k in list(state_dict.keys()): + if k.startswith('visual.'): + new_k = k[7:] + state_dict[new_k] = state_dict[k] + del state_dict[k] + return state_dict + +def load_clip_text_state_dict(checkpoint_path: str, map_location: str='cpu', is_openai: bool=False, skip_list:list=[]): + state_dict = load_state_dict(checkpoint_path, map_location=map_location, is_openai=is_openai, skip_list=skip_list) + + for k in list(state_dict.keys()): + if k.startswith('visual.'): + del state_dict[k] + return state_dict + +def get_pretrained_tag(pretrained_model): + pretrained_model = pretrained_model.lower() + if "laion" in pretrained_model or "open_clip" in pretrained_model: + return "open_clip" + elif "openai" in pretrained_model: + return "clip" + elif "eva" in pretrained_model and "clip" in pretrained_model: + return "eva_clip" + else: + return "other" + +def load_pretrained_checkpoint( + model, + visual_checkpoint_path, + text_checkpoint_path, + strict=True, + visual_model=None, + text_model=None, + model_key="model|module|state_dict", + skip_list=[]): + visual_tag = get_pretrained_tag(visual_model) + text_tag = get_pretrained_tag(text_model) + + logging.info(f"num of model state_dict keys: {len(model.state_dict().keys())}") + visual_incompatible_keys, text_incompatible_keys = None, None + if visual_checkpoint_path: + if visual_tag == "eva_clip" or visual_tag == "open_clip": + visual_state_dict = load_clip_visual_state_dict(visual_checkpoint_path, is_openai=False, skip_list=skip_list) + elif visual_tag == "clip": + visual_state_dict = load_clip_visual_state_dict(visual_checkpoint_path, is_openai=True, skip_list=skip_list) + else: + visual_state_dict = load_state_dict(visual_checkpoint_path, model_key=model_key, is_openai=False, skip_list=skip_list) + + # resize_clip_pos_embed for CLIP and open CLIP + if 'positional_embedding' in visual_state_dict: + resize_visual_pos_embed(visual_state_dict, model) + # specified to EVA model + elif 'pos_embed' in visual_state_dict: + resize_eva_pos_embed(visual_state_dict, model) + + visual_incompatible_keys = model.visual.load_state_dict(visual_state_dict, strict=strict) + logging.info(f"num of loaded visual_state_dict keys: {len(visual_state_dict.keys())}") + logging.info(f"visual_incompatible_keys.missing_keys: {visual_incompatible_keys.missing_keys}") + + if text_checkpoint_path: + if text_tag == "eva_clip" or text_tag == "open_clip": + text_state_dict = load_clip_text_state_dict(text_checkpoint_path, is_openai=False, skip_list=skip_list) + elif text_tag == "clip": + text_state_dict = load_clip_text_state_dict(text_checkpoint_path, is_openai=True, skip_list=skip_list) + else: + text_state_dict = load_state_dict(visual_checkpoint_path, model_key=model_key, is_openai=False, skip_list=skip_list) + + text_incompatible_keys = model.text.load_state_dict(text_state_dict, strict=strict) + + logging.info(f"num of loaded text_state_dict keys: {len(text_state_dict.keys())}") + logging.info(f"text_incompatible_keys.missing_keys: {text_incompatible_keys.missing_keys}") + + return visual_incompatible_keys, text_incompatible_keys + +def create_model( + model_name: str, + pretrained: Optional[str] = None, + precision: str = 'fp32', + device: Union[str, torch.device] = 'cpu', + jit: bool = False, + force_quick_gelu: bool = False, + force_custom_clip: bool = False, + force_patch_dropout: Optional[float] = None, + pretrained_image: str = '', + pretrained_text: str = '', + pretrained_hf: bool = True, + pretrained_visual_model: str = None, + pretrained_text_model: str = None, + cache_dir: Optional[str] = None, + skip_list: list = [], +): + model_name = model_name.replace('/', '-') # for callers using old naming with / in ViT names + if isinstance(device, str): + device = torch.device(device) + + if pretrained and pretrained.lower() == 'openai': + logging.info(f'Loading pretrained {model_name} from OpenAI.') + model = load_openai_model( + model_name, + precision=precision, + device=device, + jit=jit, + cache_dir=cache_dir, + ) + else: + model_cfg = get_model_config(model_name) + if model_cfg is not None: + logging.info(f'Loaded {model_name} model config.') + else: + logging.error(f'Model config for {model_name} not found; available models {list_models()}.') + raise RuntimeError(f'Model config for {model_name} not found.') + + if 'rope' in model_cfg.get('vision_cfg', {}): + if model_cfg['vision_cfg']['rope']: + os.environ['RoPE'] = "1" + else: + os.environ['RoPE'] = "0" + + if force_quick_gelu: + # override for use of QuickGELU on non-OpenAI transformer models + model_cfg["quick_gelu"] = True + + if force_patch_dropout is not None: + # override the default patch dropout value + model_cfg['vision_cfg']["patch_dropout"] = force_patch_dropout + + cast_dtype = get_cast_dtype(precision) + custom_clip = model_cfg.pop('custom_text', False) or force_custom_clip or ('hf_model_name' in model_cfg['text_cfg']) + + + if custom_clip: + if 'hf_model_name' in model_cfg.get('text_cfg', {}): + model_cfg['text_cfg']['hf_model_pretrained'] = pretrained_hf + model = CustomCLIP(**model_cfg, cast_dtype=cast_dtype) + else: + model = CLIP(**model_cfg, cast_dtype=cast_dtype) + + pretrained_cfg = {} + if pretrained: + checkpoint_path = '' + pretrained_cfg = get_pretrained_cfg(model_name, pretrained) + if pretrained_cfg: + checkpoint_path = download_pretrained(pretrained_cfg, cache_dir=cache_dir) + elif os.path.exists(pretrained): + checkpoint_path = pretrained + + if checkpoint_path: + logging.info(f'Loading pretrained {model_name} weights ({pretrained}).') + load_checkpoint(model, + checkpoint_path, + model_key="model|module|state_dict", + strict=False + ) + else: + error_str = ( + f'Pretrained weights ({pretrained}) not found for model {model_name}.' + f'Available pretrained tags ({list_pretrained_tags_by_model(model_name)}.') + logging.warning(error_str) + raise RuntimeError(error_str) + else: + visual_checkpoint_path = '' + text_checkpoint_path = '' + + if pretrained_image: + pretrained_visual_model = pretrained_visual_model.replace('/', '-') # for callers using old naming with / in ViT names + pretrained_image_cfg = get_pretrained_cfg(pretrained_visual_model, pretrained_image) + if 'timm_model_name' in model_cfg.get('vision_cfg', {}): + # pretrained weight loading for timm models set via vision_cfg + model_cfg['vision_cfg']['timm_model_pretrained'] = True + elif pretrained_image_cfg: + visual_checkpoint_path = download_pretrained(pretrained_image_cfg, cache_dir=cache_dir) + elif os.path.exists(pretrained_image): + visual_checkpoint_path = pretrained_image + else: + logging.warning(f'Pretrained weights ({visual_checkpoint_path}) not found for model {model_name}.visual.') + raise RuntimeError(f'Pretrained weights ({visual_checkpoint_path}) not found for model {model_name}.visual.') + + if pretrained_text: + pretrained_text_model = pretrained_text_model.replace('/', '-') # for callers using old naming with / in ViT names + pretrained_text_cfg = get_pretrained_cfg(pretrained_text_model, pretrained_text) + if pretrained_image_cfg: + text_checkpoint_path = download_pretrained(pretrained_text_cfg, cache_dir=cache_dir) + elif os.path.exists(pretrained_text): + text_checkpoint_path = pretrained_text + else: + logging.warning(f'Pretrained weights ({text_checkpoint_path}) not found for model {model_name}.text.') + raise RuntimeError(f'Pretrained weights ({text_checkpoint_path}) not found for model {model_name}.text.') + + if visual_checkpoint_path: + logging.info(f'Loading pretrained {model_name}.visual weights ({visual_checkpoint_path}).') + if text_checkpoint_path: + logging.info(f'Loading pretrained {model_name}.text weights ({text_checkpoint_path}).') + + if visual_checkpoint_path or text_checkpoint_path: + load_pretrained_checkpoint( + model, + visual_checkpoint_path, + text_checkpoint_path, + strict=False, + visual_model=pretrained_visual_model, + text_model=pretrained_text_model, + model_key="model|module|state_dict", + skip_list=skip_list + ) + + if "fp16" in precision or "bf16" in precision: + logging.info(f'convert precision to {precision}') + model = model.to(torch.bfloat16) if 'bf16' in precision else model.to(torch.float16) + + model.to(device=device) + + # set image / mean metadata from pretrained_cfg if available, or use default + model.visual.image_mean = pretrained_cfg.get('mean', None) or OPENAI_DATASET_MEAN + model.visual.image_std = pretrained_cfg.get('std', None) or OPENAI_DATASET_STD + + if jit: + model = torch.jit.script(model) + + return model + + +def create_model_and_transforms( + model_name: str, + pretrained: Optional[str] = None, + precision: str = 'fp32', + device: Union[str, torch.device] = 'cpu', + jit: bool = False, + force_quick_gelu: bool = False, + force_custom_clip: bool = False, + force_patch_dropout: Optional[float] = None, + pretrained_image: str = '', + pretrained_text: str = '', + pretrained_hf: bool = True, + pretrained_visual_model: str = None, + pretrained_text_model: str = None, + image_mean: Optional[Tuple[float, ...]] = None, + image_std: Optional[Tuple[float, ...]] = None, + cache_dir: Optional[str] = None, + skip_list: list = [], +): + model = create_model( + model_name, + pretrained, + precision=precision, + device=device, + jit=jit, + force_quick_gelu=force_quick_gelu, + force_custom_clip=force_custom_clip, + force_patch_dropout=force_patch_dropout, + pretrained_image=pretrained_image, + pretrained_text=pretrained_text, + pretrained_hf=pretrained_hf, + pretrained_visual_model=pretrained_visual_model, + pretrained_text_model=pretrained_text_model, + cache_dir=cache_dir, + skip_list=skip_list, + ) + + image_mean = image_mean or getattr(model.visual, 'image_mean', None) + image_std = image_std or getattr(model.visual, 'image_std', None) + preprocess_train = image_transform( + model.visual.image_size, + is_train=True, + mean=image_mean, + std=image_std + ) + preprocess_val = image_transform( + model.visual.image_size, + is_train=False, + mean=image_mean, + std=image_std + ) + + return model, preprocess_train, preprocess_val + +def create_model_from_pretrained( + model_name: str, + pretrained: str, + precision: str = 'fp32', + device: Union[str, torch.device] = 'cpu', + jit: bool = False, + force_quick_gelu: bool = False, + force_custom_clip: bool = False, + force_patch_dropout: Optional[float] = None, + return_transform: bool = True, + image_mean: Optional[Tuple[float, ...]] = None, + image_std: Optional[Tuple[float, ...]] = None, + cache_dir: Optional[str] = None, + is_frozen: bool = False, +): + if not is_pretrained_cfg(model_name, pretrained) and not os.path.exists(pretrained): + raise RuntimeError( + f'{pretrained} is not a valid pretrained cfg or checkpoint for {model_name}.' + f' Use open_clip.list_pretrained() to find one.') + + model = create_model( + model_name, + pretrained, + precision=precision, + device=device, + jit=jit, + force_quick_gelu=force_quick_gelu, + force_custom_clip=force_custom_clip, + force_patch_dropout=force_patch_dropout, + cache_dir=cache_dir, + ) + + if is_frozen: + for param in model.parameters(): + param.requires_grad = False + + if not return_transform: + return model + + image_mean = image_mean or getattr(model.visual, 'image_mean', None) + image_std = image_std or getattr(model.visual, 'image_std', None) + preprocess = image_transform( + model.visual.image_size, + is_train=False, + mean=image_mean, + std=image_std + ) + + return model, preprocess diff --git a/ape/modeling/text/eva02_clip/hf_configs.py b/ape/modeling/text/eva02_clip/hf_configs.py new file mode 100644 index 0000000..a8c9b70 --- /dev/null +++ b/ape/modeling/text/eva02_clip/hf_configs.py @@ -0,0 +1,57 @@ +# HF architecture dict: +arch_dict = { + # https://huggingface.co/docs/transformers/model_doc/roberta#roberta + "roberta": { + "config_names": { + "context_length": "max_position_embeddings", + "vocab_size": "vocab_size", + "width": "hidden_size", + "heads": "num_attention_heads", + "layers": "num_hidden_layers", + "layer_attr": "layer", + "token_embeddings_attr": "embeddings" + }, + "pooler": "mean_pooler", + }, + # https://huggingface.co/docs/transformers/model_doc/xlm-roberta#transformers.XLMRobertaConfig + "xlm-roberta": { + "config_names": { + "context_length": "max_position_embeddings", + "vocab_size": "vocab_size", + "width": "hidden_size", + "heads": "num_attention_heads", + "layers": "num_hidden_layers", + "layer_attr": "layer", + "token_embeddings_attr": "embeddings" + }, + "pooler": "mean_pooler", + }, + # https://huggingface.co/docs/transformers/model_doc/mt5#mt5 + "mt5": { + "config_names": { + # unlimited seqlen + # https://github.com/google-research/text-to-text-transfer-transformer/issues/273 + # https://github.com/huggingface/transformers/blob/v4.24.0/src/transformers/models/t5/modeling_t5.py#L374 + "context_length": "", + "vocab_size": "vocab_size", + "width": "d_model", + "heads": "num_heads", + "layers": "num_layers", + "layer_attr": "block", + "token_embeddings_attr": "embed_tokens" + }, + "pooler": "mean_pooler", + }, + "bert": { + "config_names": { + "context_length": "max_position_embeddings", + "vocab_size": "vocab_size", + "width": "hidden_size", + "heads": "num_attention_heads", + "layers": "num_hidden_layers", + "layer_attr": "layer", + "token_embeddings_attr": "embeddings" + }, + "pooler": "mean_pooler", + } +} diff --git a/ape/modeling/text/eva02_clip/hf_model.py b/ape/modeling/text/eva02_clip/hf_model.py new file mode 100644 index 0000000..c4b9fd8 --- /dev/null +++ b/ape/modeling/text/eva02_clip/hf_model.py @@ -0,0 +1,248 @@ +""" huggingface model adapter + +Wraps HuggingFace transformers (https://github.com/huggingface/transformers) models for use as a text tower in CLIP model. +""" + +import re + +import torch +import torch.nn as nn +from torch.nn import functional as F +from torch import TensorType +try: + import transformers + from transformers import AutoModel, AutoModelForMaskedLM, AutoTokenizer, AutoConfig, PretrainedConfig + from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, \ + BaseModelOutputWithPoolingAndCrossAttentions +except ImportError as e: + transformers = None + + + class BaseModelOutput: + pass + + + class PretrainedConfig: + pass + +from .hf_configs import arch_dict + +# utils +def _camel2snake(s): + return re.sub(r'(? TensorType: + # image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(x.device) + # attn_mask = (x != self.config.pad_token_id).long() + # out = self.transformer( + # input_ids=x, + # attention_mask=attn_mask, + # encoder_hidden_states = image_embeds, + # encoder_attention_mask = image_atts, + # ) + # pooled_out = self.pooler(out, attn_mask) + + # return self.itm_proj(pooled_out) + + def mask(self, input_ids, vocab_size, device, targets=None, masked_indices=None, probability_matrix=None): + if masked_indices is None: + masked_indices = torch.bernoulli(probability_matrix).bool() + + masked_indices[input_ids == self.tokenizer.pad_token_id] = False + masked_indices[input_ids == self.tokenizer.cls_token_id] = False + + if targets is not None: + targets[~masked_indices] = -100 # We only compute loss on masked tokens + + # 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK]) + indices_replaced = torch.bernoulli(torch.full(input_ids.shape, 0.8)).bool() & masked_indices + input_ids[indices_replaced] = self.tokenizer.mask_token_id + + # 10% of the time, we replace masked input tokens with random word + indices_random = torch.bernoulli(torch.full(input_ids.shape, 0.5)).bool() & masked_indices & ~indices_replaced + random_words = torch.randint(vocab_size, input_ids.shape, dtype=torch.long).to(device) + input_ids[indices_random] = random_words[indices_random] + # The rest of the time (10% of the time) we keep the masked input tokens unchanged + + if targets is not None: + return input_ids, targets + else: + return input_ids + + def forward_mlm(self, input_ids, image_embeds, mlm_probability=0.25): + labels = input_ids.clone() + attn_mask = (input_ids != self.config.pad_token_id).long() + image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(input_ids.device) + vocab_size = getattr(self.config, arch_dict[self.config.model_type]["config_names"]["vocab_size"]) + probability_matrix = torch.full(labels.shape, mlm_probability) + input_ids, labels = self.mask(input_ids, vocab_size, input_ids.device, targets=labels, + probability_matrix = probability_matrix) + mlm_output = self.transformer(input_ids, + attention_mask = attn_mask, + encoder_hidden_states = image_embeds, + encoder_attention_mask = image_atts, + return_dict = True, + labels = labels, + ) + return mlm_output.loss + # mlm_output = self.transformer(input_ids, + # attention_mask = attn_mask, + # encoder_hidden_states = image_embeds, + # encoder_attention_mask = image_atts, + # return_dict = True, + # ).last_hidden_state + # logits = self.mlm_proj(mlm_output) + + # # logits = logits[:, :-1, :].contiguous().view(-1, vocab_size) + # logits = logits[:, 1:, :].contiguous().view(-1, vocab_size) + # labels = labels[:, 1:].contiguous().view(-1) + + # mlm_loss = F.cross_entropy( + # logits, + # labels, + # # label_smoothing=0.1, + # ) + # return mlm_loss + + + def forward(self, x:TensorType) -> TensorType: + attn_mask = (x != self.config.pad_token_id).long() + out = self.transformer(input_ids=x, attention_mask=attn_mask) + pooled_out = self.pooler(out, attn_mask) + + return self.proj(pooled_out) + + def lock(self, unlocked_layers:int=0, freeze_layer_norm:bool=True): + if not unlocked_layers: # full freezing + for n, p in self.transformer.named_parameters(): + p.requires_grad = (not freeze_layer_norm) if "LayerNorm" in n.split(".") else False + return + + encoder = self.transformer.encoder if hasattr(self.transformer, 'encoder') else self.transformer + layer_list = getattr(encoder, arch_dict[self.config.model_type]["config_names"]["layer_attr"]) + print(f"Unlocking {unlocked_layers}/{len(layer_list) + 1} layers of hf model") + embeddings = getattr( + self.transformer, arch_dict[self.config.model_type]["config_names"]["token_embeddings_attr"]) + modules = [embeddings, *layer_list][:-unlocked_layers] + # freeze layers + for module in modules: + for n, p in module.named_parameters(): + p.requires_grad = (not freeze_layer_norm) if "LayerNorm" in n.split(".") else False + + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.transformer.gradient_checkpointing_enable() + + def get_num_layers(self): + encoder = self.transformer.encoder if hasattr(self.transformer, 'encoder') else self.transformer + layer_list = getattr(encoder, arch_dict[self.config.model_type]["config_names"]["layer_attr"]) + return len(layer_list) + + def init_parameters(self): + pass diff --git a/ape/modeling/text/eva02_clip/loss.py b/ape/modeling/text/eva02_clip/loss.py new file mode 100644 index 0000000..473f60d --- /dev/null +++ b/ape/modeling/text/eva02_clip/loss.py @@ -0,0 +1,138 @@ +import math +import torch +import torch.nn as nn +from torch.nn import functional as F + +try: + import torch.distributed.nn + from torch import distributed as dist + has_distributed = True +except ImportError: + has_distributed = False + +try: + import horovod.torch as hvd +except ImportError: + hvd = None + +from timm.loss import LabelSmoothingCrossEntropy + + +def gather_features( + image_features, + text_features, + local_loss=False, + gather_with_grad=False, + rank=0, + world_size=1, + use_horovod=False +): + assert has_distributed, 'torch.distributed did not import correctly, please use a PyTorch version with support.' + if use_horovod: + assert hvd is not None, 'Please install horovod' + if gather_with_grad: + all_image_features = hvd.allgather(image_features) + all_text_features = hvd.allgather(text_features) + else: + with torch.no_grad(): + all_image_features = hvd.allgather(image_features) + all_text_features = hvd.allgather(text_features) + if not local_loss: + # ensure grads for local rank when all_* features don't have a gradient + gathered_image_features = list(all_image_features.chunk(world_size, dim=0)) + gathered_text_features = list(all_text_features.chunk(world_size, dim=0)) + gathered_image_features[rank] = image_features + gathered_text_features[rank] = text_features + all_image_features = torch.cat(gathered_image_features, dim=0) + all_text_features = torch.cat(gathered_text_features, dim=0) + else: + # We gather tensors from all gpus + if gather_with_grad: + all_image_features = torch.cat(torch.distributed.nn.all_gather(image_features), dim=0) + all_text_features = torch.cat(torch.distributed.nn.all_gather(text_features), dim=0) + # all_image_features = torch.cat(torch.distributed.nn.all_gather(image_features, async_op=True), dim=0) + # all_text_features = torch.cat(torch.distributed.nn.all_gather(text_features, async_op=True), dim=0) + else: + gathered_image_features = [torch.zeros_like(image_features) for _ in range(world_size)] + gathered_text_features = [torch.zeros_like(text_features) for _ in range(world_size)] + dist.all_gather(gathered_image_features, image_features) + dist.all_gather(gathered_text_features, text_features) + if not local_loss: + # ensure grads for local rank when all_* features don't have a gradient + gathered_image_features[rank] = image_features + gathered_text_features[rank] = text_features + all_image_features = torch.cat(gathered_image_features, dim=0) + all_text_features = torch.cat(gathered_text_features, dim=0) + + return all_image_features, all_text_features + + +class ClipLoss(nn.Module): + + def __init__( + self, + local_loss=False, + gather_with_grad=False, + cache_labels=False, + rank=0, + world_size=1, + use_horovod=False, + smoothing=0., + ): + super().__init__() + self.local_loss = local_loss + self.gather_with_grad = gather_with_grad + self.cache_labels = cache_labels + self.rank = rank + self.world_size = world_size + self.use_horovod = use_horovod + self.label_smoothing_cross_entropy = LabelSmoothingCrossEntropy(smoothing=smoothing) if smoothing > 0 else None + + # cache state + self.prev_num_logits = 0 + self.labels = {} + + def forward(self, image_features, text_features, logit_scale=1.): + device = image_features.device + if self.world_size > 1: + all_image_features, all_text_features = gather_features( + image_features, text_features, + self.local_loss, self.gather_with_grad, self.rank, self.world_size, self.use_horovod) + + if self.local_loss: + logits_per_image = logit_scale * image_features @ all_text_features.T + logits_per_text = logit_scale * text_features @ all_image_features.T + else: + logits_per_image = logit_scale * all_image_features @ all_text_features.T + logits_per_text = logits_per_image.T + else: + logits_per_image = logit_scale * image_features @ text_features.T + logits_per_text = logit_scale * text_features @ image_features.T + # calculated ground-truth and cache if enabled + num_logits = logits_per_image.shape[0] + if self.prev_num_logits != num_logits or device not in self.labels: + labels = torch.arange(num_logits, device=device, dtype=torch.long) + if self.world_size > 1 and self.local_loss: + labels = labels + num_logits * self.rank + if self.cache_labels: + self.labels[device] = labels + self.prev_num_logits = num_logits + else: + labels = self.labels[device] + + if self.label_smoothing_cross_entropy: + total_loss = ( + self.label_smoothing_cross_entropy(logits_per_image, labels) + + self.label_smoothing_cross_entropy(logits_per_text, labels) + ) / 2 + else: + total_loss = ( + F.cross_entropy(logits_per_image, labels) + + F.cross_entropy(logits_per_text, labels) + ) / 2 + + acc = None + i2t_acc = (logits_per_image.argmax(-1) == labels).sum() / len(logits_per_image) + t2i_acc = (logits_per_text.argmax(-1) == labels).sum() / len(logits_per_text) + acc = {"i2t": i2t_acc, "t2i": t2i_acc} + return total_loss, acc \ No newline at end of file diff --git a/ape/modeling/text/eva02_clip/model.py b/ape/modeling/text/eva02_clip/model.py new file mode 100644 index 0000000..da3bbd7 --- /dev/null +++ b/ape/modeling/text/eva02_clip/model.py @@ -0,0 +1,439 @@ +""" CLIP Model + +Adapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI. +""" +import os +from dataclasses import dataclass +from typing import Optional, Tuple, Union +from functools import partial + +import numpy as np +import torch +import torch.nn.functional as F +from torch import nn + +try: + from .hf_model import HFTextEncoder +except: + HFTextEncoder = None +from .modified_resnet import ModifiedResNet +from .timm_model import TimmModel +from .eva_vit_model import EVAVisionTransformer +from .transformer import LayerNorm, QuickGELU, Attention, VisionTransformer, TextTransformer + +try: + from apex.normalization import FusedLayerNorm +except: + FusedLayerNorm = LayerNorm + print("Please 'pip install apex'") + +try: + import xformers.ops as xops +except ImportError: + xops = None + print("Please 'pip install xformers'") + +@dataclass +class CLIPVisionCfg: + layers: Union[Tuple[int, int, int, int], int] = 12 + width: int = 768 + head_width: int = 64 + mlp_ratio: float = 4.0 + patch_size: int = 16 + image_size: Union[Tuple[int, int], int] = 224 + ls_init_value: Optional[float] = None # layer scale initial value + patch_dropout: float = 0. # what fraction of patches to dropout during training (0 would mean disabled and no patches dropped) - 0.5 to 0.75 recommended in the paper for optimal results + global_average_pool: bool = False # whether to global average pool the last embedding layer, instead of using CLS token (https://arxiv.org/abs/2205.01580) + drop_path_rate: Optional[float] = None # drop path rate + timm_model_name: str = None # a valid model name overrides layers, width, patch_size + timm_model_pretrained: bool = False # use (imagenet) pretrained weights for named model + timm_pool: str = 'avg' # feature pooling for timm model ('abs_attn', 'rot_attn', 'avg', '') + timm_proj: str = 'linear' # linear projection for timm model output ('linear', 'mlp', '') + timm_proj_bias: bool = False # enable bias final projection + eva_model_name: str = None # a valid eva model name overrides layers, width, patch_size + qkv_bias: bool = True + fusedLN: bool = False + xattn: bool = False + postnorm: bool = False + rope: bool = False + pt_hw_seq_len: int = 16 # 224/14 + intp_freq: bool = False + naiveswiglu: bool = False + subln: bool = False + + +@dataclass +class CLIPTextCfg: + context_length: int = 77 + vocab_size: int = 49408 + width: int = 512 + heads: int = 8 + layers: int = 12 + ls_init_value: Optional[float] = None # layer scale initial value + hf_model_name: str = None + hf_tokenizer_name: str = None + hf_model_pretrained: bool = True + proj: str = 'mlp' + pooler_type: str = 'mean_pooler' + masked_language_modeling: bool = False + fusedLN: bool = False + xattn: bool = False + attn_mask: bool = True + +def get_cast_dtype(precision: str): + cast_dtype = None + if precision == 'bf16': + cast_dtype = torch.bfloat16 + elif precision == 'fp16': + cast_dtype = torch.float16 + return cast_dtype + + +def _build_vision_tower( + embed_dim: int, + vision_cfg: CLIPVisionCfg, + quick_gelu: bool = False, + cast_dtype: Optional[torch.dtype] = None +): + if isinstance(vision_cfg, dict): + vision_cfg = CLIPVisionCfg(**vision_cfg) + + # OpenAI models are pretrained w/ QuickGELU but native nn.GELU is both faster and more + # memory efficient in recent PyTorch releases (>= 1.10). + # NOTE: timm models always use native GELU regardless of quick_gelu flag. + act_layer = QuickGELU if quick_gelu else nn.GELU + + if vision_cfg.eva_model_name: + vision_heads = vision_cfg.width // vision_cfg.head_width + norm_layer = LayerNorm + + visual = EVAVisionTransformer( + img_size=vision_cfg.image_size, + patch_size=vision_cfg.patch_size, + num_classes=embed_dim, + use_mean_pooling=vision_cfg.global_average_pool, #False + init_values=vision_cfg.ls_init_value, + patch_dropout=vision_cfg.patch_dropout, + embed_dim=vision_cfg.width, + depth=vision_cfg.layers, + num_heads=vision_heads, + mlp_ratio=vision_cfg.mlp_ratio, + qkv_bias=vision_cfg.qkv_bias, + drop_path_rate=vision_cfg.drop_path_rate, + norm_layer= partial(FusedLayerNorm, eps=1e-6) if vision_cfg.fusedLN else partial(norm_layer, eps=1e-6), + xattn=vision_cfg.xattn, + rope=vision_cfg.rope, + postnorm=vision_cfg.postnorm, + pt_hw_seq_len= vision_cfg.pt_hw_seq_len, # 224/14 + intp_freq= vision_cfg.intp_freq, + naiveswiglu= vision_cfg.naiveswiglu, + subln= vision_cfg.subln + ) + elif vision_cfg.timm_model_name: + visual = TimmModel( + vision_cfg.timm_model_name, + pretrained=vision_cfg.timm_model_pretrained, + pool=vision_cfg.timm_pool, + proj=vision_cfg.timm_proj, + proj_bias=vision_cfg.timm_proj_bias, + embed_dim=embed_dim, + image_size=vision_cfg.image_size + ) + act_layer = nn.GELU # so that text transformer doesn't use QuickGELU w/ timm models + elif isinstance(vision_cfg.layers, (tuple, list)): + vision_heads = vision_cfg.width * 32 // vision_cfg.head_width + visual = ModifiedResNet( + layers=vision_cfg.layers, + output_dim=embed_dim, + heads=vision_heads, + image_size=vision_cfg.image_size, + width=vision_cfg.width + ) + else: + vision_heads = vision_cfg.width // vision_cfg.head_width + norm_layer = LayerNormFp32 if cast_dtype in (torch.float16, torch.bfloat16) else LayerNorm + visual = VisionTransformer( + image_size=vision_cfg.image_size, + patch_size=vision_cfg.patch_size, + width=vision_cfg.width, + layers=vision_cfg.layers, + heads=vision_heads, + mlp_ratio=vision_cfg.mlp_ratio, + ls_init_value=vision_cfg.ls_init_value, + patch_dropout=vision_cfg.patch_dropout, + global_average_pool=vision_cfg.global_average_pool, + output_dim=embed_dim, + act_layer=act_layer, + norm_layer=norm_layer, + ) + + return visual + + +def _build_text_tower( + embed_dim: int, + text_cfg: CLIPTextCfg, + quick_gelu: bool = False, + cast_dtype: Optional[torch.dtype] = None, +): + if isinstance(text_cfg, dict): + text_cfg = CLIPTextCfg(**text_cfg) + + if text_cfg.hf_model_name: + text = HFTextEncoder( + text_cfg.hf_model_name, + output_dim=embed_dim, + tokenizer_name=text_cfg.hf_tokenizer_name, + proj=text_cfg.proj, + pooler_type=text_cfg.pooler_type, + masked_language_modeling=text_cfg.masked_language_modeling + ) + else: + act_layer = QuickGELU if quick_gelu else nn.GELU + norm_layer = LayerNorm + + text = TextTransformer( + context_length=text_cfg.context_length, + vocab_size=text_cfg.vocab_size, + width=text_cfg.width, + heads=text_cfg.heads, + layers=text_cfg.layers, + ls_init_value=text_cfg.ls_init_value, + output_dim=embed_dim, + act_layer=act_layer, + norm_layer= FusedLayerNorm if text_cfg.fusedLN else norm_layer, + xattn=text_cfg.xattn, + attn_mask=text_cfg.attn_mask, + ) + return text + +class CLIP(nn.Module): + def __init__( + self, + embed_dim: int, + vision_cfg: CLIPVisionCfg, + text_cfg: CLIPTextCfg, + quick_gelu: bool = False, + cast_dtype: Optional[torch.dtype] = None, + ): + super().__init__() + self.visual = _build_vision_tower(embed_dim, vision_cfg, quick_gelu, cast_dtype) + + text = _build_text_tower(embed_dim, text_cfg, quick_gelu, cast_dtype) + self.transformer = text.transformer + self.vocab_size = text.vocab_size + self.token_embedding = text.token_embedding + self.positional_embedding = text.positional_embedding + self.ln_final = text.ln_final + self.text_projection = text.text_projection + self.register_buffer('attn_mask', text.attn_mask, persistent=False) + + self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07)) + + def lock_image_tower(self, unlocked_groups=0, freeze_bn_stats=False): + # lock image tower as per LiT - https://arxiv.org/abs/2111.07991 + self.visual.lock(unlocked_groups=unlocked_groups, freeze_bn_stats=freeze_bn_stats) + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.visual.set_grad_checkpointing(enable) + self.transformer.grad_checkpointing = enable + + @torch.jit.ignore + def no_weight_decay(self): + return {'logit_scale'} + + def encode_image(self, image, normalize: bool = False): + features = self.visual(image) + return F.normalize(features, dim=-1) if normalize else features + + def encode_text(self, text, normalize: bool = False): + cast_dtype = self.transformer.get_cast_dtype() + + x = self.token_embedding(text).to(cast_dtype) # [batch_size, n_ctx, d_model] + + x = x + self.positional_embedding.to(cast_dtype) + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer(x, attn_mask=self.attn_mask) + x = x.permute(1, 0, 2) # LND -> NLD + x = self.ln_final(x) # [batch_size, n_ctx, transformer.width] + # take features from the eot embedding (eot_token is the highest number in each sequence) + x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection + return F.normalize(x, dim=-1) if normalize else x + + def forward(self, image, text): + image_features = self.encode_image(image, normalize=True) + text_features = self.encode_text(text, normalize=True) + return image_features, text_features, self.logit_scale.exp() + + +class CustomCLIP(nn.Module): + def __init__( + self, + embed_dim: int, + vision_cfg: CLIPVisionCfg, + text_cfg: CLIPTextCfg, + quick_gelu: bool = False, + cast_dtype: Optional[torch.dtype] = None, + itm_task: bool = False, + ): + super().__init__() + self.visual = _build_vision_tower(embed_dim, vision_cfg, quick_gelu, cast_dtype) + self.text = _build_text_tower(embed_dim, text_cfg, quick_gelu, cast_dtype) + self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07)) + + def lock_image_tower(self, unlocked_groups=0, freeze_bn_stats=False): + # lock image tower as per LiT - https://arxiv.org/abs/2111.07991 + self.visual.lock(unlocked_groups=unlocked_groups, freeze_bn_stats=freeze_bn_stats) + + def lock_text_tower(self, unlocked_layers:int=0, freeze_layer_norm:bool=True): + self.text.lock(unlocked_layers, freeze_layer_norm) + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.visual.set_grad_checkpointing(enable) + self.text.set_grad_checkpointing(enable) + + @torch.jit.ignore + def no_weight_decay(self): + return {'logit_scale'} + + def encode_image(self, image, normalize: bool = False): + features = self.visual(image) + return F.normalize(features, dim=-1) if normalize else features + + def encode_text(self, text, normalize: bool = False): + features = self.text(text) + return F.normalize(features, dim=-1) if normalize else features + + def forward(self, image, text): + image_features = self.encode_image(image, normalize=True) + text_features = self.encode_text(text, normalize=True) + return image_features, text_features, self.logit_scale.exp() + + +def convert_weights_to_lp(model: nn.Module, dtype=torch.float16): + """Convert applicable model parameters to low-precision (bf16 or fp16)""" + + def _convert_weights(l): + + if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)): + l.weight.data = l.weight.data.to(dtype) + if l.bias is not None: + l.bias.data = l.bias.data.to(dtype) + + if isinstance(l, (nn.MultiheadAttention, Attention)): + for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]: + tensor = getattr(l, attr, None) + if tensor is not None: + tensor.data = tensor.data.to(dtype) + + if isinstance(l, nn.Parameter): + l.data = l.data.to(dtype) + + for name in ["text_projection", "proj"]: + if hasattr(l, name) and isinstance(l, nn.Parameter): + attr = getattr(l, name, None) + if attr is not None: + attr.data = attr.data.to(dtype) + + model.apply(_convert_weights) + + +convert_weights_to_fp16 = convert_weights_to_lp # backwards compat + + +# used to maintain checkpoint compatibility +def convert_to_custom_text_state_dict(state_dict: dict): + if 'text_projection' in state_dict: + # old format state_dict, move text tower -> .text + new_state_dict = {} + for k, v in state_dict.items(): + if any(k.startswith(p) for p in ( + 'text_projection', + 'positional_embedding', + 'token_embedding', + 'transformer', + 'ln_final', + 'logit_scale' + )): + k = 'text.' + k + new_state_dict[k] = v + return new_state_dict + return state_dict + + +def build_model_from_openai_state_dict( + state_dict: dict, + quick_gelu=True, + cast_dtype=torch.float16, +): + vit = "visual.proj" in state_dict + + if vit: + vision_width = state_dict["visual.conv1.weight"].shape[0] + vision_layers = len( + [k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")]) + vision_patch_size = state_dict["visual.conv1.weight"].shape[-1] + grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5) + image_size = vision_patch_size * grid_size + else: + counts: list = [ + len(set(k.split(".")[2] for k in state_dict if k.startswith(f"visual.layer{b}"))) for b in [1, 2, 3, 4]] + vision_layers = tuple(counts) + vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0] + output_width = round((state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5) + vision_patch_size = None + assert output_width ** 2 + 1 == state_dict["visual.attnpool.positional_embedding"].shape[0] + image_size = output_width * 32 + + embed_dim = state_dict["text_projection"].shape[1] + context_length = state_dict["positional_embedding"].shape[0] + vocab_size = state_dict["token_embedding.weight"].shape[0] + transformer_width = state_dict["ln_final.weight"].shape[0] + transformer_heads = transformer_width // 64 + transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith(f"transformer.resblocks"))) + + vision_cfg = CLIPVisionCfg( + layers=vision_layers, + width=vision_width, + patch_size=vision_patch_size, + image_size=image_size, + ) + text_cfg = CLIPTextCfg( + context_length=context_length, + vocab_size=vocab_size, + width=transformer_width, + heads=transformer_heads, + layers=transformer_layers + ) + model = CLIP( + embed_dim, + vision_cfg=vision_cfg, + text_cfg=text_cfg, + quick_gelu=quick_gelu, # OpenAI models were trained with QuickGELU + cast_dtype=cast_dtype, + ) + + for key in ["input_resolution", "context_length", "vocab_size"]: + state_dict.pop(key, None) + + convert_weights_to_fp16(model) # OpenAI state dicts are partially converted to float16 + model.load_state_dict(state_dict) + return model.eval() + + +def trace_model(model, batch_size=256, device=torch.device('cpu')): + model.eval() + image_size = model.visual.image_size + example_images = torch.ones((batch_size, 3, image_size, image_size), device=device) + example_text = torch.zeros((batch_size, model.context_length), dtype=torch.int, device=device) + model = torch.jit.trace_module( + model, + inputs=dict( + forward=(example_images, example_text), + encode_text=(example_text,), + encode_image=(example_images,) + )) + model.visual.image_size = image_size + return model diff --git a/ape/modeling/text/eva02_clip/model_configs/EVA01-CLIP-B-16.json b/ape/modeling/text/eva02_clip/model_configs/EVA01-CLIP-B-16.json new file mode 100644 index 0000000..aad2058 --- /dev/null +++ b/ape/modeling/text/eva02_clip/model_configs/EVA01-CLIP-B-16.json @@ -0,0 +1,19 @@ +{ + "embed_dim": 512, + "vision_cfg": { + "image_size": 224, + "layers": 12, + "width": 768, + "patch_size": 16, + "eva_model_name": "eva-clip-b-16", + "ls_init_value": 0.1, + "drop_path_rate": 0.0 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 12 + } +} \ No newline at end of file diff --git a/ape/modeling/text/eva02_clip/model_configs/EVA01-CLIP-g-14-plus.json b/ape/modeling/text/eva02_clip/model_configs/EVA01-CLIP-g-14-plus.json new file mode 100644 index 0000000..1002795 --- /dev/null +++ b/ape/modeling/text/eva02_clip/model_configs/EVA01-CLIP-g-14-plus.json @@ -0,0 +1,24 @@ +{ + "embed_dim": 1024, + "vision_cfg": { + "image_size": 224, + "layers": 40, + "width": 1408, + "head_width": 88, + "mlp_ratio": 4.3637, + "patch_size": 14, + "eva_model_name": "eva-clip-g-14-x", + "drop_path_rate": 0, + "xattn": true, + "fusedLN": true + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 1024, + "heads": 16, + "layers": 24, + "xattn": false, + "fusedLN": true + } +} \ No newline at end of file diff --git a/ape/modeling/text/eva02_clip/model_configs/EVA01-CLIP-g-14.json b/ape/modeling/text/eva02_clip/model_configs/EVA01-CLIP-g-14.json new file mode 100644 index 0000000..5d338b4 --- /dev/null +++ b/ape/modeling/text/eva02_clip/model_configs/EVA01-CLIP-g-14.json @@ -0,0 +1,24 @@ +{ + "embed_dim": 1024, + "vision_cfg": { + "image_size": 224, + "layers": 40, + "width": 1408, + "head_width": 88, + "mlp_ratio": 4.3637, + "patch_size": 14, + "eva_model_name": "eva-clip-g-14-x", + "drop_path_rate": 0.4, + "xattn": true, + "fusedLN": true + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 768, + "heads": 12, + "layers": 12, + "xattn": false, + "fusedLN": true + } +} \ No newline at end of file diff --git a/ape/modeling/text/eva02_clip/model_configs/EVA02-CLIP-B-16.json b/ape/modeling/text/eva02_clip/model_configs/EVA02-CLIP-B-16.json new file mode 100644 index 0000000..e4a6e72 --- /dev/null +++ b/ape/modeling/text/eva02_clip/model_configs/EVA02-CLIP-B-16.json @@ -0,0 +1,29 @@ +{ + "embed_dim": 512, + "vision_cfg": { + "image_size": 224, + "layers": 12, + "width": 768, + "head_width": 64, + "patch_size": 16, + "mlp_ratio": 2.6667, + "eva_model_name": "eva-clip-b-16-X", + "drop_path_rate": 0.0, + "xattn": true, + "fusedLN": true, + "rope": true, + "pt_hw_seq_len": 16, + "intp_freq": true, + "naiveswiglu": true, + "subln": true + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 12, + "xattn": true, + "fusedLN": true + } +} \ No newline at end of file diff --git a/ape/modeling/text/eva02_clip/model_configs/EVA02-CLIP-L-14-336.json b/ape/modeling/text/eva02_clip/model_configs/EVA02-CLIP-L-14-336.json new file mode 100644 index 0000000..3e1d124 --- /dev/null +++ b/ape/modeling/text/eva02_clip/model_configs/EVA02-CLIP-L-14-336.json @@ -0,0 +1,29 @@ +{ + "embed_dim": 768, + "vision_cfg": { + "image_size": 336, + "layers": 24, + "width": 1024, + "drop_path_rate": 0, + "head_width": 64, + "mlp_ratio": 2.6667, + "patch_size": 14, + "eva_model_name": "eva-clip-l-14-336", + "xattn": true, + "fusedLN": true, + "rope": true, + "pt_hw_seq_len": 16, + "intp_freq": true, + "naiveswiglu": true, + "subln": true + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 768, + "heads": 12, + "layers": 12, + "xattn": false, + "fusedLN": true + } +} \ No newline at end of file diff --git a/ape/modeling/text/eva02_clip/model_configs/EVA02-CLIP-L-14.json b/ape/modeling/text/eva02_clip/model_configs/EVA02-CLIP-L-14.json new file mode 100644 index 0000000..03b22ad --- /dev/null +++ b/ape/modeling/text/eva02_clip/model_configs/EVA02-CLIP-L-14.json @@ -0,0 +1,29 @@ +{ + "embed_dim": 768, + "vision_cfg": { + "image_size": 224, + "layers": 24, + "width": 1024, + "drop_path_rate": 0, + "head_width": 64, + "mlp_ratio": 2.6667, + "patch_size": 14, + "eva_model_name": "eva-clip-l-14", + "xattn": true, + "fusedLN": true, + "rope": true, + "pt_hw_seq_len": 16, + "intp_freq": true, + "naiveswiglu": true, + "subln": true + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 768, + "heads": 12, + "layers": 12, + "xattn": false, + "fusedLN": true + } +} \ No newline at end of file diff --git a/ape/modeling/text/eva02_clip/model_configs/EVA02-CLIP-bigE-14-plus.json b/ape/modeling/text/eva02_clip/model_configs/EVA02-CLIP-bigE-14-plus.json new file mode 100644 index 0000000..aa04e25 --- /dev/null +++ b/ape/modeling/text/eva02_clip/model_configs/EVA02-CLIP-bigE-14-plus.json @@ -0,0 +1,25 @@ +{ + "embed_dim": 1024, + "vision_cfg": { + "image_size": 224, + "layers": 64, + "width": 1792, + "head_width": 112, + "mlp_ratio": 8.571428571428571, + "patch_size": 14, + "eva_model_name": "eva-clip-4b-14-x", + "drop_path_rate": 0, + "xattn": true, + "postnorm": true, + "fusedLN": true + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 1280, + "heads": 20, + "layers": 32, + "xattn": false, + "fusedLN": true + } +} diff --git a/ape/modeling/text/eva02_clip/model_configs/EVA02-CLIP-bigE-14.json b/ape/modeling/text/eva02_clip/model_configs/EVA02-CLIP-bigE-14.json new file mode 100644 index 0000000..747ffcc --- /dev/null +++ b/ape/modeling/text/eva02_clip/model_configs/EVA02-CLIP-bigE-14.json @@ -0,0 +1,25 @@ +{ + "embed_dim": 1024, + "vision_cfg": { + "image_size": 224, + "layers": 64, + "width": 1792, + "head_width": 112, + "mlp_ratio": 8.571428571428571, + "patch_size": 14, + "eva_model_name": "eva-clip-4b-14-x", + "drop_path_rate": 0, + "xattn": true, + "postnorm": true, + "fusedLN": true + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 1024, + "heads": 16, + "layers": 24, + "xattn": false, + "fusedLN": true + } +} \ No newline at end of file diff --git a/ape/modeling/text/eva02_clip/modified_resnet.py b/ape/modeling/text/eva02_clip/modified_resnet.py new file mode 100644 index 0000000..6a8d3ae --- /dev/null +++ b/ape/modeling/text/eva02_clip/modified_resnet.py @@ -0,0 +1,181 @@ +from collections import OrderedDict + +import torch +from torch import nn +from torch.nn import functional as F + +from .utils import freeze_batch_norm_2d + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1): + super().__init__() + + # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1 + self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.act1 = nn.ReLU(inplace=True) + + self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.act2 = nn.ReLU(inplace=True) + + self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity() + + self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False) + self.bn3 = nn.BatchNorm2d(planes * self.expansion) + self.act3 = nn.ReLU(inplace=True) + + self.downsample = None + self.stride = stride + + if stride > 1 or inplanes != planes * Bottleneck.expansion: + # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1 + self.downsample = nn.Sequential(OrderedDict([ + ("-1", nn.AvgPool2d(stride)), + ("0", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)), + ("1", nn.BatchNorm2d(planes * self.expansion)) + ])) + + def forward(self, x: torch.Tensor): + identity = x + + out = self.act1(self.bn1(self.conv1(x))) + out = self.act2(self.bn2(self.conv2(out))) + out = self.avgpool(out) + out = self.bn3(self.conv3(out)) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.act3(out) + return out + + +class AttentionPool2d(nn.Module): + def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None): + super().__init__() + self.positional_embedding = nn.Parameter(torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5) + self.k_proj = nn.Linear(embed_dim, embed_dim) + self.q_proj = nn.Linear(embed_dim, embed_dim) + self.v_proj = nn.Linear(embed_dim, embed_dim) + self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim) + self.num_heads = num_heads + + def forward(self, x): + x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3]).permute(2, 0, 1) # NCHW -> (HW)NC + x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (HW+1)NC + x = x + self.positional_embedding[:, None, :].to(x.dtype) # (HW+1)NC + x, _ = F.multi_head_attention_forward( + query=x, key=x, value=x, + embed_dim_to_check=x.shape[-1], + num_heads=self.num_heads, + q_proj_weight=self.q_proj.weight, + k_proj_weight=self.k_proj.weight, + v_proj_weight=self.v_proj.weight, + in_proj_weight=None, + in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]), + bias_k=None, + bias_v=None, + add_zero_attn=False, + dropout_p=0., + out_proj_weight=self.c_proj.weight, + out_proj_bias=self.c_proj.bias, + use_separate_proj_weight=True, + training=self.training, + need_weights=False + ) + + return x[0] + + +class ModifiedResNet(nn.Module): + """ + A ResNet class that is similar to torchvision's but contains the following changes: + - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool. + - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1 + - The final pooling layer is a QKV attention instead of an average pool + """ + + def __init__(self, layers, output_dim, heads, image_size=224, width=64): + super().__init__() + self.output_dim = output_dim + self.image_size = image_size + + # the 3-layer stem + self.conv1 = nn.Conv2d(3, width // 2, kernel_size=3, stride=2, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(width // 2) + self.act1 = nn.ReLU(inplace=True) + self.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(width // 2) + self.act2 = nn.ReLU(inplace=True) + self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False) + self.bn3 = nn.BatchNorm2d(width) + self.act3 = nn.ReLU(inplace=True) + self.avgpool = nn.AvgPool2d(2) + + # residual layers + self._inplanes = width # this is a *mutable* variable used during construction + self.layer1 = self._make_layer(width, layers[0]) + self.layer2 = self._make_layer(width * 2, layers[1], stride=2) + self.layer3 = self._make_layer(width * 4, layers[2], stride=2) + self.layer4 = self._make_layer(width * 8, layers[3], stride=2) + + embed_dim = width * 32 # the ResNet feature dimension + self.attnpool = AttentionPool2d(image_size // 32, embed_dim, heads, output_dim) + + self.init_parameters() + + def _make_layer(self, planes, blocks, stride=1): + layers = [Bottleneck(self._inplanes, planes, stride)] + + self._inplanes = planes * Bottleneck.expansion + for _ in range(1, blocks): + layers.append(Bottleneck(self._inplanes, planes)) + + return nn.Sequential(*layers) + + def init_parameters(self): + if self.attnpool is not None: + std = self.attnpool.c_proj.in_features ** -0.5 + nn.init.normal_(self.attnpool.q_proj.weight, std=std) + nn.init.normal_(self.attnpool.k_proj.weight, std=std) + nn.init.normal_(self.attnpool.v_proj.weight, std=std) + nn.init.normal_(self.attnpool.c_proj.weight, std=std) + + for resnet_block in [self.layer1, self.layer2, self.layer3, self.layer4]: + for name, param in resnet_block.named_parameters(): + if name.endswith("bn3.weight"): + nn.init.zeros_(param) + + def lock(self, unlocked_groups=0, freeze_bn_stats=False): + assert unlocked_groups == 0, 'partial locking not currently supported for this model' + for param in self.parameters(): + param.requires_grad = False + if freeze_bn_stats: + freeze_batch_norm_2d(self) + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + # FIXME support for non-transformer + pass + + def stem(self, x): + x = self.act1(self.bn1(self.conv1(x))) + x = self.act2(self.bn2(self.conv2(x))) + x = self.act3(self.bn3(self.conv3(x))) + x = self.avgpool(x) + return x + + def forward(self, x): + x = self.stem(x) + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + x = self.attnpool(x) + + return x diff --git a/ape/modeling/text/eva02_clip/openai.py b/ape/modeling/text/eva02_clip/openai.py new file mode 100644 index 0000000..cc4e13e --- /dev/null +++ b/ape/modeling/text/eva02_clip/openai.py @@ -0,0 +1,144 @@ +""" OpenAI pretrained model functions + +Adapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI. +""" + +import os +import warnings +from typing import List, Optional, Union + +import torch + +from .model import build_model_from_openai_state_dict, convert_weights_to_lp, get_cast_dtype +from .pretrained import get_pretrained_url, list_pretrained_models_by_tag, download_pretrained_from_url + +__all__ = ["list_openai_models", "load_openai_model"] + + +def list_openai_models() -> List[str]: + """Returns the names of available CLIP models""" + return list_pretrained_models_by_tag('openai') + + +def load_openai_model( + name: str, + precision: Optional[str] = None, + device: Optional[Union[str, torch.device]] = None, + jit: bool = True, + cache_dir: Optional[str] = None, +): + """Load a CLIP model + + Parameters + ---------- + name : str + A model name listed by `clip.available_models()`, or the path to a model checkpoint containing the state_dict + precision: str + Model precision, if None defaults to 'fp32' if device == 'cpu' else 'fp16'. + device : Union[str, torch.device] + The device to put the loaded model + jit : bool + Whether to load the optimized JIT model (default) or more hackable non-JIT model. + cache_dir : Optional[str] + The directory to cache the downloaded model weights + + Returns + ------- + model : torch.nn.Module + The CLIP model + preprocess : Callable[[PIL.Image], torch.Tensor] + A torchvision transform that converts a PIL image into a tensor that the returned model can take as its input + """ + if device is None: + device = "cuda" if torch.cuda.is_available() else "cpu" + if precision is None: + precision = 'fp32' if device == 'cpu' else 'fp16' + + if get_pretrained_url(name, 'openai'): + model_path = download_pretrained_from_url(get_pretrained_url(name, 'openai'), cache_dir=cache_dir) + elif os.path.isfile(name): + model_path = name + else: + raise RuntimeError(f"Model {name} not found; available models = {list_openai_models()}") + + try: + # loading JIT archive + model = torch.jit.load(model_path, map_location=device if jit else "cpu").eval() + state_dict = None + except RuntimeError: + # loading saved state dict + if jit: + warnings.warn(f"File {model_path} is not a JIT archive. Loading as a state dict instead") + jit = False + state_dict = torch.load(model_path, map_location="cpu") + + if not jit: + # Build a non-jit model from the OpenAI jitted model state dict + cast_dtype = get_cast_dtype(precision) + try: + model = build_model_from_openai_state_dict(state_dict or model.state_dict(), cast_dtype=cast_dtype) + except KeyError: + sd = {k[7:]: v for k, v in state_dict["state_dict"].items()} + model = build_model_from_openai_state_dict(sd, cast_dtype=cast_dtype) + + # model from OpenAI state dict is in manually cast fp16 mode, must be converted for AMP/fp32/bf16 use + model = model.to(device) + if precision.startswith('amp') or precision == 'fp32': + model.float() + elif precision == 'bf16': + convert_weights_to_lp(model, dtype=torch.bfloat16) + + return model + + # patch the device names + device_holder = torch.jit.trace(lambda: torch.ones([]).to(torch.device(device)), example_inputs=[]) + device_node = [n for n in device_holder.graph.findAllNodes("prim::Constant") if "Device" in repr(n)][-1] + + def patch_device(module): + try: + graphs = [module.graph] if hasattr(module, "graph") else [] + except RuntimeError: + graphs = [] + + if hasattr(module, "forward1"): + graphs.append(module.forward1.graph) + + for graph in graphs: + for node in graph.findAllNodes("prim::Constant"): + if "value" in node.attributeNames() and str(node["value"]).startswith("cuda"): + node.copyAttributes(device_node) + + model.apply(patch_device) + patch_device(model.encode_image) + patch_device(model.encode_text) + + # patch dtype to float32 (typically for CPU) + if precision == 'fp32': + float_holder = torch.jit.trace(lambda: torch.ones([]).float(), example_inputs=[]) + float_input = list(float_holder.graph.findNode("aten::to").inputs())[1] + float_node = float_input.node() + + def patch_float(module): + try: + graphs = [module.graph] if hasattr(module, "graph") else [] + except RuntimeError: + graphs = [] + + if hasattr(module, "forward1"): + graphs.append(module.forward1.graph) + + for graph in graphs: + for node in graph.findAllNodes("aten::to"): + inputs = list(node.inputs()) + for i in [1, 2]: # dtype can be the second or third argument to aten::to() + if inputs[i].node()["value"] == 5: + inputs[i].node().copyAttributes(float_node) + + model.apply(patch_float) + patch_float(model.encode_image) + patch_float(model.encode_text) + model.float() + + # ensure image_size attr available at consistent location for both jit and non-jit + model.visual.image_size = model.input_resolution.item() + return model diff --git a/ape/modeling/text/eva02_clip/pretrained.py b/ape/modeling/text/eva02_clip/pretrained.py new file mode 100644 index 0000000..a1e55dc --- /dev/null +++ b/ape/modeling/text/eva02_clip/pretrained.py @@ -0,0 +1,332 @@ +import hashlib +import os +import urllib +import warnings +from functools import partial +from typing import Dict, Union + +from tqdm import tqdm + +try: + from huggingface_hub import hf_hub_download + _has_hf_hub = True +except ImportError: + hf_hub_download = None + _has_hf_hub = False + + +def _pcfg(url='', hf_hub='', filename='', mean=None, std=None): + return dict( + url=url, + hf_hub=hf_hub, + mean=mean, + std=std, + ) + +_VITB32 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt"), + laion400m_e31=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt"), + laion400m_e32=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt"), + laion2b_e16=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-laion2b_e16-af8dbd0c.pth"), + laion2b_s34b_b79k=_pcfg(hf_hub='laion/CLIP-ViT-B-32-laion2B-s34B-b79K/') +) + +_VITB32_quickgelu = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt"), + laion400m_e31=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt"), + laion400m_e32=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt"), +) + +_VITB16 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt"), + laion400m_e31=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16-laion400m_e31-00efa78f.pt"), + laion400m_e32=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16-laion400m_e32-55e67d44.pt"), + laion2b_s34b_b88k=_pcfg(hf_hub='laion/CLIP-ViT-B-16-laion2B-s34B-b88K/'), +) + +_EVAB16 = dict( + eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_B_psz14to16.pt'), + eva02=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_B_psz14to16.pt'), + eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_B_psz16_s8B.pt'), + eva02_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_B_psz16_s8B.pt'), +) + +_VITB16_PLUS_240 = dict( + laion400m_e31=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16_plus_240-laion400m_e31-8fb26589.pt"), + laion400m_e32=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16_plus_240-laion400m_e32-699c4b84.pt"), +) + +_VITL14 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt"), + laion400m_e31=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_l_14-laion400m_e31-69988bb6.pt"), + laion400m_e32=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_l_14-laion400m_e32-3d133497.pt"), + laion2b_s32b_b82k=_pcfg( + hf_hub='laion/CLIP-ViT-L-14-laion2B-s32B-b82K/', + mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)), +) + +_EVAL14 = dict( + eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_L_psz14.pt'), + eva02=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_L_psz14.pt'), + eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_psz14_s4B.pt'), + eva02_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_psz14_s4B.pt'), +) + +_VITL14_336 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/3035c92b350959924f9f00213499208652fc7ea050643e8b385c2dac08641f02/ViT-L-14-336px.pt"), +) + +_EVAL14_336 = dict( + eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14_s6B.pt'), + eva02_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14_s6B.pt'), + eva_clip_224to336=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_psz14_224to336.pt'), + eva02_clip_224to336=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_psz14_224to336.pt'), +) + +_VITH14 = dict( + laion2b_s32b_b79k=_pcfg(hf_hub='laion/CLIP-ViT-H-14-laion2B-s32B-b79K/'), +) + +_VITg14 = dict( + laion2b_s12b_b42k=_pcfg(hf_hub='laion/CLIP-ViT-g-14-laion2B-s12B-b42K/'), + laion2b_s34b_b88k=_pcfg(hf_hub='laion/CLIP-ViT-g-14-laion2B-s34B-b88K/'), +) + +_EVAg14 = dict( + eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/'), + eva01=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_g_psz14.pt'), + eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_CLIP_g_14_psz14_s11B.pt'), + eva01_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_CLIP_g_14_psz14_s11B.pt'), +) + +_EVAg14_PLUS = dict( + eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/'), + eva01=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_g_psz14.pt'), + eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_CLIP_g_14_plus_psz14_s11B.pt'), + eva01_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_CLIP_g_14_plus_psz14_s11B.pt'), +) + +_VITbigG14 = dict( + laion2b_s39b_b160k=_pcfg(hf_hub='laion/CLIP-ViT-bigG-14-laion2B-39B-b160k/'), +) + +_EVAbigE14 = dict( + eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_E_psz14.pt'), + eva02=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_E_psz14.pt'), + eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_s4B.pt'), + eva02_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_s4B.pt'), +) + +_EVAbigE14_PLUS = dict( + eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_E_psz14.pt'), + eva02=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_E_psz14.pt'), + eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt'), + eva02_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt'), +) + + +_PRETRAINED = { + # "ViT-B-32": _VITB32, + "OpenaiCLIP-B-32": _VITB32, + "OpenCLIP-B-32": _VITB32, + + # "ViT-B-32-quickgelu": _VITB32_quickgelu, + "OpenaiCLIP-B-32-quickgelu": _VITB32_quickgelu, + "OpenCLIP-B-32-quickgelu": _VITB32_quickgelu, + + # "ViT-B-16": _VITB16, + "OpenaiCLIP-B-16": _VITB16, + "OpenCLIP-B-16": _VITB16, + + "EVA02-B-16": _EVAB16, + "EVA02-CLIP-B-16": _EVAB16, + + # "ViT-B-16-plus-240": _VITB16_PLUS_240, + "OpenCLIP-B-16-plus-240": _VITB16_PLUS_240, + + # "ViT-L-14": _VITL14, + "OpenaiCLIP-L-14": _VITL14, + "OpenCLIP-L-14": _VITL14, + + "EVA02-L-14": _EVAL14, + "EVA02-CLIP-L-14": _EVAL14, + + # "ViT-L-14-336": _VITL14_336, + "OpenaiCLIP-L-14-336": _VITL14_336, + + "EVA02-CLIP-L-14-336": _EVAL14_336, + + # "ViT-H-14": _VITH14, + # "ViT-g-14": _VITg14, + "OpenCLIP-H-14": _VITH14, + "OpenCLIP-g-14": _VITg14, + + "EVA01-CLIP-g-14": _EVAg14, + "EVA01-CLIP-g-14-plus": _EVAg14_PLUS, + + # "ViT-bigG-14": _VITbigG14, + "OpenCLIP-bigG-14": _VITbigG14, + + "EVA02-CLIP-bigE-14": _EVAbigE14, + "EVA02-CLIP-bigE-14-plus": _EVAbigE14_PLUS, +} + + +def _clean_tag(tag: str): + # normalize pretrained tags + return tag.lower().replace('-', '_') + + +def list_pretrained(as_str: bool = False): + """ returns list of pretrained models + Returns a tuple (model_name, pretrain_tag) by default or 'name:tag' if as_str == True + """ + return [':'.join([k, t]) if as_str else (k, t) for k in _PRETRAINED.keys() for t in _PRETRAINED[k].keys()] + + +def list_pretrained_models_by_tag(tag: str): + """ return all models having the specified pretrain tag """ + models = [] + tag = _clean_tag(tag) + for k in _PRETRAINED.keys(): + if tag in _PRETRAINED[k]: + models.append(k) + return models + + +def list_pretrained_tags_by_model(model: str): + """ return all pretrain tags for the specified model architecture """ + tags = [] + if model in _PRETRAINED: + tags.extend(_PRETRAINED[model].keys()) + return tags + + +def is_pretrained_cfg(model: str, tag: str): + if model not in _PRETRAINED: + return False + return _clean_tag(tag) in _PRETRAINED[model] + + +def get_pretrained_cfg(model: str, tag: str): + if model not in _PRETRAINED: + return {} + model_pretrained = _PRETRAINED[model] + return model_pretrained.get(_clean_tag(tag), {}) + + +def get_pretrained_url(model: str, tag: str): + cfg = get_pretrained_cfg(model, _clean_tag(tag)) + return cfg.get('url', '') + + +def download_pretrained_from_url( + url: str, + cache_dir: Union[str, None] = None, +): + if not cache_dir: + cache_dir = os.path.expanduser("~/.cache/clip") + os.makedirs(cache_dir, exist_ok=True) + filename = os.path.basename(url) + + if 'openaipublic' in url: + expected_sha256 = url.split("/")[-2] + elif 'mlfoundations' in url: + expected_sha256 = os.path.splitext(filename)[0].split("-")[-1] + else: + expected_sha256 = '' + + download_target = os.path.join(cache_dir, filename) + + if os.path.exists(download_target) and not os.path.isfile(download_target): + raise RuntimeError(f"{download_target} exists and is not a regular file") + + if os.path.isfile(download_target): + if expected_sha256: + if hashlib.sha256(open(download_target, "rb").read()).hexdigest().startswith(expected_sha256): + return download_target + else: + warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file") + else: + return download_target + + with urllib.request.urlopen(url) as source, open(download_target, "wb") as output: + with tqdm(total=int(source.headers.get("Content-Length")), ncols=80, unit='iB', unit_scale=True) as loop: + while True: + buffer = source.read(8192) + if not buffer: + break + + output.write(buffer) + loop.update(len(buffer)) + + if expected_sha256 and not hashlib.sha256(open(download_target, "rb").read()).hexdigest().startswith(expected_sha256): + raise RuntimeError(f"Model has been downloaded but the SHA256 checksum does not not match") + + return download_target + + +def has_hf_hub(necessary=False): + if not _has_hf_hub and necessary: + # if no HF Hub module installed, and it is necessary to continue, raise error + raise RuntimeError( + 'Hugging Face hub model specified but package not installed. Run `pip install huggingface_hub`.') + return _has_hf_hub + + +def download_pretrained_from_hf( + model_id: str, + filename: str = 'open_clip_pytorch_model.bin', + revision=None, + cache_dir: Union[str, None] = None, +): + has_hf_hub(True) + cached_file = hf_hub_download(model_id, filename, revision=revision, cache_dir=cache_dir) + return cached_file + + +def download_pretrained( + cfg: Dict, + force_hf_hub: bool = False, + cache_dir: Union[str, None] = None, +): + target = '' + if not cfg: + return target + + download_url = cfg.get('url', '') + download_hf_hub = cfg.get('hf_hub', '') + if download_hf_hub and force_hf_hub: + # use HF hub even if url exists + download_url = '' + + if download_url: + target = download_pretrained_from_url(download_url, cache_dir=cache_dir) + elif download_hf_hub: + has_hf_hub(True) + # we assume the hf_hub entries in pretrained config combine model_id + filename in + # 'org/model_name/filename.pt' form. To specify just the model id w/o filename and + # use 'open_clip_pytorch_model.bin' default, there must be a trailing slash 'org/model_name/'. + model_id, filename = os.path.split(download_hf_hub) + if filename: + target = download_pretrained_from_hf(model_id, filename=filename, cache_dir=cache_dir) + else: + target = download_pretrained_from_hf(model_id, cache_dir=cache_dir) + + return target diff --git a/ape/modeling/text/eva02_clip/rope.py b/ape/modeling/text/eva02_clip/rope.py new file mode 100644 index 0000000..69030c3 --- /dev/null +++ b/ape/modeling/text/eva02_clip/rope.py @@ -0,0 +1,137 @@ +from math import pi +import torch +from torch import nn +from einops import rearrange, repeat +import logging + +def broadcat(tensors, dim = -1): + num_tensors = len(tensors) + shape_lens = set(list(map(lambda t: len(t.shape), tensors))) + assert len(shape_lens) == 1, 'tensors must all have the same number of dimensions' + shape_len = list(shape_lens)[0] + dim = (dim + shape_len) if dim < 0 else dim + dims = list(zip(*map(lambda t: list(t.shape), tensors))) + expandable_dims = [(i, val) for i, val in enumerate(dims) if i != dim] + assert all([*map(lambda t: len(set(t[1])) <= 2, expandable_dims)]), 'invalid dimensions for broadcastable concatentation' + max_dims = list(map(lambda t: (t[0], max(t[1])), expandable_dims)) + expanded_dims = list(map(lambda t: (t[0], (t[1],) * num_tensors), max_dims)) + expanded_dims.insert(dim, (dim, dims[dim])) + expandable_shapes = list(zip(*map(lambda t: t[1], expanded_dims))) + tensors = list(map(lambda t: t[0].expand(*t[1]), zip(tensors, expandable_shapes))) + return torch.cat(tensors, dim = dim) + +def rotate_half(x): + x = rearrange(x, '... (d r) -> ... d r', r = 2) + x1, x2 = x.unbind(dim = -1) + x = torch.stack((-x2, x1), dim = -1) + return rearrange(x, '... d r -> ... (d r)') + + +class VisionRotaryEmbedding(nn.Module): + def __init__( + self, + dim, + pt_seq_len, + ft_seq_len=None, + custom_freqs = None, + freqs_for = 'lang', + theta = 10000, + max_freq = 10, + num_freqs = 1, + ): + super().__init__() + if custom_freqs: + freqs = custom_freqs + elif freqs_for == 'lang': + freqs = 1. / (theta ** (torch.arange(0, dim, 2)[:(dim // 2)].float() / dim)) + elif freqs_for == 'pixel': + freqs = torch.linspace(1., max_freq / 2, dim // 2) * pi + elif freqs_for == 'constant': + freqs = torch.ones(num_freqs).float() + else: + raise ValueError(f'unknown modality {freqs_for}') + + if ft_seq_len is None: ft_seq_len = pt_seq_len + t = torch.arange(ft_seq_len) / ft_seq_len * pt_seq_len + + freqs_h = torch.einsum('..., f -> ... f', t, freqs) + freqs_h = repeat(freqs_h, '... n -> ... (n r)', r = 2) + + freqs_w = torch.einsum('..., f -> ... f', t, freqs) + freqs_w = repeat(freqs_w, '... n -> ... (n r)', r = 2) + + freqs = broadcat((freqs_h[:, None, :], freqs_w[None, :, :]), dim = -1) + + self.register_buffer("freqs_cos", freqs.cos()) + self.register_buffer("freqs_sin", freqs.sin()) + + logging.info(f'Shape of rope freq: {self.freqs_cos.shape}') + + def forward(self, t, start_index = 0): + rot_dim = self.freqs_cos.shape[-1] + end_index = start_index + rot_dim + assert rot_dim <= t.shape[-1], f'feature dimension {t.shape[-1]} is not of sufficient size to rotate in all the positions {rot_dim}' + t_left, t, t_right = t[..., :start_index], t[..., start_index:end_index], t[..., end_index:] + t = (t * self.freqs_cos) + (rotate_half(t) * self.freqs_sin) + + return torch.cat((t_left, t, t_right), dim = -1) + +class VisionRotaryEmbeddingFast(nn.Module): + def __init__( + self, + dim, + pt_seq_len, + ft_seq_len=None, + custom_freqs = None, + freqs_for = 'lang', + theta = 10000, + max_freq = 10, + num_freqs = 1, + patch_dropout = 0. + ): + super().__init__() + if custom_freqs: + freqs = custom_freqs + elif freqs_for == 'lang': + freqs = 1. / (theta ** (torch.arange(0, dim, 2)[:(dim // 2)].float() / dim)) + elif freqs_for == 'pixel': + freqs = torch.linspace(1., max_freq / 2, dim // 2) * pi + elif freqs_for == 'constant': + freqs = torch.ones(num_freqs).float() + else: + raise ValueError(f'unknown modality {freqs_for}') + + if ft_seq_len is None: ft_seq_len = pt_seq_len + t = torch.arange(ft_seq_len) / ft_seq_len * pt_seq_len + + freqs = torch.einsum('..., f -> ... f', t, freqs) + freqs = repeat(freqs, '... n -> ... (n r)', r = 2) + freqs = broadcat((freqs[:, None, :], freqs[None, :, :]), dim = -1) + + freqs_cos = freqs.cos().view(-1, freqs.shape[-1]) + freqs_sin = freqs.sin().view(-1, freqs.shape[-1]) + + self.patch_dropout = patch_dropout + + self.register_buffer("freqs_cos", freqs_cos) + self.register_buffer("freqs_sin", freqs_sin) + + logging.info(f'Shape of rope freq: {self.freqs_cos.shape}') + + def forward(self, t, patch_indices_keep=None): + if patch_indices_keep is not None: + batch = t.size()[0] + batch_indices = torch.arange(batch) + batch_indices = batch_indices[..., None] + + freqs_cos = repeat(self.freqs_cos, 'i j -> n i m j', n=t.shape[0], m=t.shape[1]) + freqs_sin = repeat(self.freqs_sin, 'i j -> n i m j', n=t.shape[0], m=t.shape[1]) + + freqs_cos = freqs_cos[batch_indices, patch_indices_keep] + freqs_cos = rearrange(freqs_cos, 'n i m j -> n m i j') + freqs_sin = freqs_sin[batch_indices, patch_indices_keep] + freqs_sin = rearrange(freqs_sin, 'n i m j -> n m i j') + + return t * freqs_cos + rotate_half(t) * freqs_sin + + return t * self.freqs_cos + rotate_half(t) * self.freqs_sin \ No newline at end of file diff --git a/ape/modeling/text/eva02_clip/timm_model.py b/ape/modeling/text/eva02_clip/timm_model.py new file mode 100644 index 0000000..b58122c --- /dev/null +++ b/ape/modeling/text/eva02_clip/timm_model.py @@ -0,0 +1,122 @@ +""" timm model adapter + +Wraps timm (https://github.com/rwightman/pytorch-image-models) models for use as a vision tower in CLIP model. +""" +import logging +from collections import OrderedDict + +import torch +import torch.nn as nn + +try: + import timm + from timm.models.layers import Mlp, to_2tuple + try: + # old timm imports < 0.8.1 + from timm.models.layers.attention_pool2d import RotAttentionPool2d + from timm.models.layers.attention_pool2d import AttentionPool2d as AbsAttentionPool2d + except ImportError: + # new timm imports >= 0.8.1 + from timm.layers import RotAttentionPool2d + from timm.layers import AttentionPool2d as AbsAttentionPool2d +except ImportError: + timm = None + +from .utils import freeze_batch_norm_2d + + +class TimmModel(nn.Module): + """ timm model adapter + # FIXME this adapter is a work in progress, may change in ways that break weight compat + """ + + def __init__( + self, + model_name, + embed_dim, + image_size=224, + pool='avg', + proj='linear', + proj_bias=False, + drop=0., + pretrained=False): + super().__init__() + if timm is None: + raise RuntimeError("Please `pip install timm` to use timm models.") + + self.image_size = to_2tuple(image_size) + self.trunk = timm.create_model(model_name, pretrained=pretrained) + feat_size = self.trunk.default_cfg.get('pool_size', None) + feature_ndim = 1 if not feat_size else 2 + if pool in ('abs_attn', 'rot_attn'): + assert feature_ndim == 2 + # if attn pooling used, remove both classifier and default pool + self.trunk.reset_classifier(0, global_pool='') + else: + # reset global pool if pool config set, otherwise leave as network default + reset_kwargs = dict(global_pool=pool) if pool else {} + self.trunk.reset_classifier(0, **reset_kwargs) + prev_chs = self.trunk.num_features + + head_layers = OrderedDict() + if pool == 'abs_attn': + head_layers['pool'] = AbsAttentionPool2d(prev_chs, feat_size=feat_size, out_features=embed_dim) + prev_chs = embed_dim + elif pool == 'rot_attn': + head_layers['pool'] = RotAttentionPool2d(prev_chs, out_features=embed_dim) + prev_chs = embed_dim + else: + assert proj, 'projection layer needed if non-attention pooling is used.' + + # NOTE attention pool ends with a projection layer, so proj should usually be set to '' if such pooling is used + if proj == 'linear': + head_layers['drop'] = nn.Dropout(drop) + head_layers['proj'] = nn.Linear(prev_chs, embed_dim, bias=proj_bias) + elif proj == 'mlp': + head_layers['mlp'] = Mlp(prev_chs, 2 * embed_dim, embed_dim, drop=drop, bias=(True, proj_bias)) + + self.head = nn.Sequential(head_layers) + + def lock(self, unlocked_groups=0, freeze_bn_stats=False): + """ lock modules + Args: + unlocked_groups (int): leave last n layer groups unlocked (default: 0) + """ + if not unlocked_groups: + # lock full model + for param in self.trunk.parameters(): + param.requires_grad = False + if freeze_bn_stats: + freeze_batch_norm_2d(self.trunk) + else: + # NOTE: partial freeze requires latest timm (master) branch and is subject to change + try: + # FIXME import here until API stable and in an official release + from timm.models.helpers import group_parameters, group_modules + except ImportError: + raise RuntimeError( + 'Please install latest timm `pip install git+https://github.com/rwightman/pytorch-image-models`') + matcher = self.trunk.group_matcher() + gparams = group_parameters(self.trunk, matcher) + max_layer_id = max(gparams.keys()) + max_layer_id = max_layer_id - unlocked_groups + for group_idx in range(max_layer_id + 1): + group = gparams[group_idx] + for param in group: + self.trunk.get_parameter(param).requires_grad = False + if freeze_bn_stats: + gmodules = group_modules(self.trunk, matcher, reverse=True) + gmodules = {k for k, v in gmodules.items() if v <= max_layer_id} + freeze_batch_norm_2d(self.trunk, gmodules) + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + try: + self.trunk.set_grad_checkpointing(enable) + except Exception as e: + logging.warning('grad checkpointing not supported for this timm image tower, continuing without...') + + def forward(self, x): + x = self.trunk(x) + x = self.head(x) + return x diff --git a/ape/modeling/text/eva02_clip/tokenizer.py b/ape/modeling/text/eva02_clip/tokenizer.py new file mode 100644 index 0000000..41482f8 --- /dev/null +++ b/ape/modeling/text/eva02_clip/tokenizer.py @@ -0,0 +1,201 @@ +""" CLIP tokenizer + +Copied from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI. +""" +import gzip +import html +import os +from functools import lru_cache +from typing import Union, List + +import ftfy +import regex as re +import torch + +# https://stackoverflow.com/q/62691279 +import os +os.environ["TOKENIZERS_PARALLELISM"] = "false" + + +@lru_cache() +def default_bpe(): + return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz") + + +@lru_cache() +def bytes_to_unicode(): + """ + Returns list of utf-8 byte and a corresponding list of unicode strings. + The reversible bpe codes work on unicode strings. + This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. + When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. + This is a signficant percentage of your normal, say, 32K bpe vocab. + To avoid that, we want lookup tables between utf-8 bytes and unicode strings. + And avoids mapping to whitespace/control characters the bpe code barfs on. + """ + bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1)) + cs = bs[:] + n = 0 + for b in range(2**8): + if b not in bs: + bs.append(b) + cs.append(2**8+n) + n += 1 + cs = [chr(n) for n in cs] + return dict(zip(bs, cs)) + + +def get_pairs(word): + """Return set of symbol pairs in a word. + Word is represented as tuple of symbols (symbols being variable-length strings). + """ + pairs = set() + prev_char = word[0] + for char in word[1:]: + pairs.add((prev_char, char)) + prev_char = char + return pairs + + +def basic_clean(text): + text = ftfy.fix_text(text) + text = html.unescape(html.unescape(text)) + return text.strip() + + +def whitespace_clean(text): + text = re.sub(r'\s+', ' ', text) + text = text.strip() + return text + + +class SimpleTokenizer(object): + def __init__(self, bpe_path: str = default_bpe(), special_tokens=None): + self.byte_encoder = bytes_to_unicode() + self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} + merges = gzip.open(bpe_path).read().decode("utf-8").split('\n') + merges = merges[1:49152-256-2+1] + merges = [tuple(merge.split()) for merge in merges] + vocab = list(bytes_to_unicode().values()) + vocab = vocab + [v+'' for v in vocab] + for merge in merges: + vocab.append(''.join(merge)) + if not special_tokens: + special_tokens = ['', ''] + else: + special_tokens = ['', ''] + special_tokens + vocab.extend(special_tokens) + self.encoder = dict(zip(vocab, range(len(vocab)))) + self.decoder = {v: k for k, v in self.encoder.items()} + self.bpe_ranks = dict(zip(merges, range(len(merges)))) + self.cache = {t:t for t in special_tokens} + special = "|".join(special_tokens) + self.pat = re.compile(special + r"""|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", re.IGNORECASE) + + self.vocab_size = len(self.encoder) + self.all_special_ids = [self.encoder[t] for t in special_tokens] + + def bpe(self, token): + if token in self.cache: + return self.cache[token] + word = tuple(token[:-1]) + ( token[-1] + '',) + pairs = get_pairs(word) + + if not pairs: + return token+'' + + while True: + bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf'))) + if bigram not in self.bpe_ranks: + break + first, second = bigram + new_word = [] + i = 0 + while i < len(word): + try: + j = word.index(first, i) + new_word.extend(word[i:j]) + i = j + except: + new_word.extend(word[i:]) + break + + if word[i] == first and i < len(word)-1 and word[i+1] == second: + new_word.append(first+second) + i += 2 + else: + new_word.append(word[i]) + i += 1 + new_word = tuple(new_word) + word = new_word + if len(word) == 1: + break + else: + pairs = get_pairs(word) + word = ' '.join(word) + self.cache[token] = word + return word + + def encode(self, text): + bpe_tokens = [] + text = whitespace_clean(basic_clean(text)).lower() + for token in re.findall(self.pat, text): + token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8')) + bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' ')) + return bpe_tokens + + def decode(self, tokens): + text = ''.join([self.decoder[token] for token in tokens]) + text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors="replace").replace('', ' ') + return text + + +_tokenizer = SimpleTokenizer() + + +def tokenize(texts: Union[str, List[str]], context_length: int = 77) -> torch.LongTensor: + """ + Returns the tokenized representation of given input string(s) + + Parameters + ---------- + texts : Union[str, List[str]] + An input string or a list of input strings to tokenize + context_length : int + The context length to use; all CLIP models use 77 as the context length + + Returns + ------- + A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length] + """ + if isinstance(texts, str): + texts = [texts] + + sot_token = _tokenizer.encoder[""] + eot_token = _tokenizer.encoder[""] + all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token] for text in texts] + result = torch.zeros(len(all_tokens), context_length, dtype=torch.long) + + for i, tokens in enumerate(all_tokens): + if len(tokens) > context_length: + tokens = tokens[:context_length] # Truncate + tokens[-1] = eot_token + result[i, :len(tokens)] = torch.tensor(tokens) + + return result + + +class HFTokenizer: + "HuggingFace tokenizer wrapper" + def __init__(self, tokenizer_name:str): + from transformers import AutoTokenizer + self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name) + + def __call__(self, texts:Union[str, List[str]], context_length:int=77) -> torch.Tensor: + # same cleaning as for default tokenizer, except lowercasing + # adding lower (for case-sensitive tokenizers) will make it more robust but less sensitive to nuance + if isinstance(texts, str): + texts = [texts] + texts = [whitespace_clean(basic_clean(text)) for text in texts] + input_ids = self.tokenizer(texts, return_tensors='pt', max_length=context_length, padding='max_length', truncation=True).input_ids + return input_ids diff --git a/ape/modeling/text/eva02_clip/transform.py b/ape/modeling/text/eva02_clip/transform.py new file mode 100644 index 0000000..39f3e4c --- /dev/null +++ b/ape/modeling/text/eva02_clip/transform.py @@ -0,0 +1,103 @@ +from typing import Optional, Sequence, Tuple + +import torch +import torch.nn as nn +import torchvision.transforms.functional as F + +from torchvision.transforms import Normalize, Compose, RandomResizedCrop, InterpolationMode, ToTensor, Resize, \ + CenterCrop + +from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD + + +class ResizeMaxSize(nn.Module): + + def __init__(self, max_size, interpolation=InterpolationMode.BICUBIC, fn='max', fill=0): + super().__init__() + if not isinstance(max_size, int): + raise TypeError(f"Size should be int. Got {type(max_size)}") + self.max_size = max_size + self.interpolation = interpolation + self.fn = min if fn == 'min' else min + self.fill = fill + + def forward(self, img): + if isinstance(img, torch.Tensor): + height, width = img.shape[:2] + else: + width, height = img.size + scale = self.max_size / float(max(height, width)) + if scale != 1.0: + new_size = tuple(round(dim * scale) for dim in (height, width)) + img = F.resize(img, new_size, self.interpolation) + pad_h = self.max_size - new_size[0] + pad_w = self.max_size - new_size[1] + img = F.pad(img, padding=[pad_w//2, pad_h//2, pad_w - pad_w//2, pad_h - pad_h//2], fill=self.fill) + return img + + +def _convert_to_rgb(image): + return image.convert('RGB') + + +# class CatGen(nn.Module): +# def __init__(self, num=4): +# self.num = num +# def mixgen_batch(image, text): +# batch_size = image.shape[0] +# index = np.random.permutation(batch_size) + +# cat_images = [] +# for i in range(batch_size): +# # image mixup +# image[i,:] = lam * image[i,:] + (1 - lam) * image[index[i],:] +# # text concat +# text[i] = tokenizer((str(text[i]) + " " + str(text[index[i]])))[0] +# text = torch.stack(text) +# return image, text + + +def image_transform( + image_size: int, + is_train: bool, + mean: Optional[Tuple[float, ...]] = None, + std: Optional[Tuple[float, ...]] = None, + resize_longest_max: bool = False, + fill_color: int = 0, +): + mean = mean or OPENAI_DATASET_MEAN + if not isinstance(mean, (list, tuple)): + mean = (mean,) * 3 + + std = std or OPENAI_DATASET_STD + if not isinstance(std, (list, tuple)): + std = (std,) * 3 + + if isinstance(image_size, (list, tuple)) and image_size[0] == image_size[1]: + # for square size, pass size as int so that Resize() uses aspect preserving shortest edge + image_size = image_size[0] + + normalize = Normalize(mean=mean, std=std) + if is_train: + return Compose([ + RandomResizedCrop(image_size, scale=(0.9, 1.0), interpolation=InterpolationMode.BICUBIC), + _convert_to_rgb, + ToTensor(), + normalize, + ]) + else: + if resize_longest_max: + transforms = [ + ResizeMaxSize(image_size, fill=fill_color) + ] + else: + transforms = [ + Resize(image_size, interpolation=InterpolationMode.BICUBIC), + CenterCrop(image_size), + ] + transforms.extend([ + _convert_to_rgb, + ToTensor(), + normalize, + ]) + return Compose(transforms) diff --git a/ape/modeling/text/eva02_clip/transformer.py b/ape/modeling/text/eva02_clip/transformer.py new file mode 100644 index 0000000..33e89ff --- /dev/null +++ b/ape/modeling/text/eva02_clip/transformer.py @@ -0,0 +1,737 @@ +import os +import logging +from collections import OrderedDict +import math +from typing import Callable, Optional, Sequence +import numpy as np +import torch +from torch import nn +from torch.nn import functional as F + +try: + from timm.models.layers import trunc_normal_ +except: + from timm.layers import trunc_normal_ + +from .rope import VisionRotaryEmbedding, VisionRotaryEmbeddingFast +from .utils import to_2tuple + +if os.getenv('ENV_TYPE') == 'deepspeed': + try: + import deepspeed + from deepspeed.runtime.activation_checkpointing.checkpointing import checkpoint + except: + print("Please 'pip install deepspeed'") + deepspeed = None + from torch.utils.checkpoint import checkpoint +else: + from torch.utils.checkpoint import checkpoint + +try: + import xformers.ops as xops +except ImportError: + xops = None + print("Please 'pip install xformers'") + +class LayerNormFp32(nn.LayerNorm): + """Subclass torch's LayerNorm to handle fp16 (by casting to float32 and back).""" + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def forward(self, x: torch.Tensor): + output = F.layer_norm( + x.float(), + self.normalized_shape, + self.weight.float() if self.weight is not None else None, + self.bias.float() if self.bias is not None else None, + self.eps, + ) + return output.type_as(x) + + +class LayerNorm(nn.LayerNorm): + """Subclass torch's LayerNorm (with cast back to input dtype).""" + + def forward(self, x: torch.Tensor): + orig_type = x.dtype + x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps) + return x.to(orig_type) + +class QuickGELU(nn.Module): + # NOTE This is slower than nn.GELU or nn.SiLU and uses more GPU memory + def forward(self, x: torch.Tensor): + return x * torch.sigmoid(1.702 * x) + + +class LayerScale(nn.Module): + def __init__(self, dim, init_values=1e-5, inplace=False): + super().__init__() + self.inplace = inplace + self.gamma = nn.Parameter(init_values * torch.ones(dim)) + + def forward(self, x): + return x.mul_(self.gamma) if self.inplace else x * self.gamma + +class PatchDropout(nn.Module): + """ + https://arxiv.org/abs/2212.00794 + """ + + def __init__(self, prob, exclude_first_token=True): + super().__init__() + assert 0 <= prob < 1. + self.prob = prob + self.exclude_first_token = exclude_first_token # exclude CLS token + logging.info(f"os.getenv('RoPE')={os.getenv('RoPE')}") + + def forward(self, x): + if not self.training or self.prob == 0.: + return x + + if self.exclude_first_token: + cls_tokens, x = x[:, :1], x[:, 1:] + else: + cls_tokens = torch.jit.annotate(torch.Tensor, x[:, :1]) + + batch = x.size()[0] + num_tokens = x.size()[1] + + batch_indices = torch.arange(batch) + batch_indices = batch_indices[..., None] + + keep_prob = 1 - self.prob + num_patches_keep = max(1, int(num_tokens * keep_prob)) + + rand = torch.randn(batch, num_tokens) + patch_indices_keep = rand.topk(num_patches_keep, dim=-1).indices + + x = x[batch_indices, patch_indices_keep] + + if self.exclude_first_token: + x = torch.cat((cls_tokens, x), dim=1) + + if self.training and os.getenv('RoPE') == '1': + return x, patch_indices_keep + + return x + + +def _in_projection_packed( + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + w: torch.Tensor, + b: Optional[torch.Tensor] = None, + ): + """ + https://github.com/pytorch/pytorch/blob/db2a237763eb8693a20788be94f8c192e762baa8/torch/nn/functional.py#L4726 + """ + E = q.size(-1) + if k is v: + if q is k: + # self-attention + return F.linear(q, w, b).chunk(3, dim=-1) + else: + # encoder-decoder attention + w_q, w_kv = w.split([E, E * 2]) + if b is None: + b_q = b_kv = None + else: + b_q, b_kv = b.split([E, E * 2]) + return (F.linear(q, w_q, b_q),) + F.linear(k, w_kv, b_kv).chunk(2, dim=-1) + else: + w_q, w_k, w_v = w.chunk(3) + if b is None: + b_q = b_k = b_v = None + else: + b_q, b_k, b_v = b.chunk(3) + return F.linear(q, w_q, b_q), F.linear(k, w_k, b_k), F.linear(v, w_v, b_v) + +class Attention(nn.Module): + def __init__( + self, + dim, + num_heads=8, + qkv_bias=True, + scaled_cosine=False, + scale_heads=False, + logit_scale_max=math.log(1. / 0.01), + attn_drop=0., + proj_drop=0., + xattn=False, + rope=False + ): + super().__init__() + self.scaled_cosine = scaled_cosine + self.scale_heads = scale_heads + assert dim % num_heads == 0, 'dim should be divisible by num_heads' + self.num_heads = num_heads + self.head_dim = dim // num_heads + self.scale = self.head_dim ** -0.5 + self.logit_scale_max = logit_scale_max + + # keeping in_proj in this form (instead of nn.Linear) to match weight scheme of original + self.in_proj_weight = nn.Parameter(torch.randn((dim * 3, dim)) * self.scale) + if qkv_bias: + self.in_proj_bias = nn.Parameter(torch.zeros(dim * 3)) + else: + self.in_proj_bias = None + + if self.scaled_cosine: + self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1)))) + else: + self.logit_scale = None + self.attn_drop = nn.Dropout(attn_drop) + if self.scale_heads: + self.head_scale = nn.Parameter(torch.ones((num_heads, 1, 1))) + else: + self.head_scale = None + self.out_proj = nn.Linear(dim, dim) + self.out_drop = nn.Dropout(proj_drop) + self.xattn = xattn + self.xattn_drop = attn_drop + self.rope = rope + + def forward(self, x, attn_mask: Optional[torch.Tensor] = None): + L, N, C = x.shape + q, k, v = F.linear(x, self.in_proj_weight, self.in_proj_bias).chunk(3, dim=-1) + if self.xattn: + q = q.contiguous().view(L, N, self.num_heads, -1).transpose(0, 1) + k = k.contiguous().view(L, N, self.num_heads, -1).transpose(0, 1) + v = v.contiguous().view(L, N, self.num_heads, -1).transpose(0, 1) + + x = xops.memory_efficient_attention( + q, k, v, + p=self.xattn_drop, + scale=self.scale if self.logit_scale is None else None, + attn_bias=xops.LowerTriangularMask() if attn_mask is not None else None, + ) + else: + q = q.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1) + k = k.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1) + v = v.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1) + + if self.logit_scale is not None: + attn = torch.bmm(F.normalize(q, dim=-1), F.normalize(k, dim=-1).transpose(-1, -2)) + logit_scale = torch.clamp(self.logit_scale, max=self.logit_scale_max).exp() + attn = attn.view(N, self.num_heads, L, L) * logit_scale + attn = attn.view(-1, L, L) + else: + q = q * self.scale + attn = torch.bmm(q, k.transpose(-1, -2)) + + if attn_mask is not None: + if attn_mask.dtype == torch.bool: + new_attn_mask = torch.zeros_like(attn_mask, dtype=q.dtype) + new_attn_mask.masked_fill_(attn_mask, float("-inf")) + attn_mask = new_attn_mask + attn += attn_mask + + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = torch.bmm(attn, v) + + if self.head_scale is not None: + x = x.view(N, self.num_heads, L, C) * self.head_scale + x = x.view(-1, L, C) + x = x.transpose(0, 1).reshape(L, N, C) + x = self.out_proj(x) + x = self.out_drop(x) + return x + +class CustomAttention(nn.Module): + def __init__( + self, + dim, + num_heads=8, + qkv_bias=True, + scaled_cosine=True, + scale_heads=False, + logit_scale_max=math.log(1. / 0.01), + attn_drop=0., + proj_drop=0., + xattn=False + ): + super().__init__() + self.scaled_cosine = scaled_cosine + self.scale_heads = scale_heads + assert dim % num_heads == 0, 'dim should be divisible by num_heads' + self.num_heads = num_heads + self.head_dim = dim // num_heads + self.scale = self.head_dim ** -0.5 + self.logit_scale_max = logit_scale_max + + # keeping in_proj in this form (instead of nn.Linear) to match weight scheme of original + self.in_proj_weight = nn.Parameter(torch.randn((dim * 3, dim)) * self.scale) + if qkv_bias: + self.in_proj_bias = nn.Parameter(torch.zeros(dim * 3)) + else: + self.in_proj_bias = None + + if self.scaled_cosine: + self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1)))) + else: + self.logit_scale = None + self.attn_drop = nn.Dropout(attn_drop) + if self.scale_heads: + self.head_scale = nn.Parameter(torch.ones((num_heads, 1, 1))) + else: + self.head_scale = None + self.out_proj = nn.Linear(dim, dim) + self.out_drop = nn.Dropout(proj_drop) + self.xattn = xattn + self.xattn_drop = attn_drop + + def forward(self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, attn_mask: Optional[torch.Tensor] = None): + q, k, v = _in_projection_packed(query, key, value, self.in_proj_weight, self.in_proj_bias) + N_q, B_q, C_q = q.shape + N_k, B_k, C_k = k.shape + N_v, B_v, C_v = v.shape + if self.xattn: + # B, N, C -> B, N, num_heads, C + q = q.permute(1, 0, 2).reshape(B_q, N_q, self.num_heads, -1) + k = k.permute(1, 0, 2).reshape(B_k, N_k, self.num_heads, -1) + v = v.permute(1, 0, 2).reshape(B_v, N_v, self.num_heads, -1) + + x = xops.memory_efficient_attention( + q, k, v, + p=self.xattn_drop, + scale=self.scale if self.logit_scale is None else None, + attn_bias=xops.LowerTriangularMask() if attn_mask is not None else None + ) + else: + # B*H, L, C + q = q.contiguous().view(N_q, B_q * self.num_heads, -1).transpose(0, 1) + k = k.contiguous().view(N_k, B_k * self.num_heads, -1).transpose(0, 1) + v = v.contiguous().view(N_v, B_v * self.num_heads, -1).transpose(0, 1) + + if self.logit_scale is not None: + # B*H, N_q, N_k + attn = torch.bmm(F.normalize(q, dim=-1), F.normalize(k, dim=-1).transpose(-1, -2)) + logit_scale = torch.clamp(self.logit_scale, max=self.logit_scale_max).exp() + attn = attn.view(B_q, self.num_heads, N_q, N_k) * logit_scale + attn = attn.view(-1, N_q, N_k) + else: + q = q * self.scale + attn = torch.bmm(q, k.transpose(-1, -2)) + + if attn_mask is not None: + if attn_mask.dtype == torch.bool: + new_attn_mask = torch.zeros_like(attn_mask, dtype=q.dtype) + new_attn_mask.masked_fill_(attn_mask, float("-inf")) + attn_mask = new_attn_mask + attn += attn_mask + + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = torch.bmm(attn, v) + + if self.head_scale is not None: + x = x.view(B_q, self.num_heads, N_q, C_q) * self.head_scale + x = x.view(-1, N_q, C_q) + x = x.transpose(0, 1).reshape(N_q, B_q, C_q) + x = self.out_proj(x) + x = self.out_drop(x) + return x + +class CustomResidualAttentionBlock(nn.Module): + def __init__( + self, + d_model: int, + n_head: int, + mlp_ratio: float = 4.0, + ls_init_value: float = None, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + scale_cosine_attn: bool = False, + scale_heads: bool = False, + scale_attn: bool = False, + scale_fc: bool = False, + cross_attn: bool = False, + xattn: bool = False, + ): + super().__init__() + + self.ln_1 = norm_layer(d_model) + self.ln_1_k = norm_layer(d_model) if cross_attn else self.ln_1 + self.ln_1_v = norm_layer(d_model) if cross_attn else self.ln_1 + self.attn = CustomAttention( + d_model, n_head, + qkv_bias=True, + attn_drop=0., + proj_drop=0., + scaled_cosine=scale_cosine_attn, + scale_heads=scale_heads, + xattn=xattn + ) + + self.ln_attn = norm_layer(d_model) if scale_attn else nn.Identity() + self.ls_1 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity() + + self.ln_2 = norm_layer(d_model) + mlp_width = int(d_model * mlp_ratio) + self.mlp = nn.Sequential(OrderedDict([ + ("c_fc", nn.Linear(d_model, mlp_width)), + ('ln', norm_layer(mlp_width) if scale_fc else nn.Identity()), + ("gelu", act_layer()), + ("c_proj", nn.Linear(mlp_width, d_model)) + ])) + + self.ls_2 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity() + + def forward(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, attn_mask: Optional[torch.Tensor] = None): + q = q + self.ls_1(self.ln_attn(self.attn(self.ln_1(q), self.ln_1_k(k), self.ln_1_v(v), attn_mask=attn_mask))) + q = q + self.ls_2(self.mlp(self.ln_2(q))) + return q + +class CustomTransformer(nn.Module): + def __init__( + self, + width: int, + layers: int, + heads: int, + mlp_ratio: float = 4.0, + ls_init_value: float = None, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + scale_cosine_attn: bool = True, + scale_heads: bool = False, + scale_attn: bool = False, + scale_fc: bool = False, + cross_attn: bool = False, + xattn: bool = False, + ): + super().__init__() + self.width = width + self.layers = layers + self.grad_checkpointing = False + self.xattn = xattn + + self.resblocks = nn.ModuleList([ + CustomResidualAttentionBlock( + width, + heads, + mlp_ratio, + ls_init_value=ls_init_value, + act_layer=act_layer, + norm_layer=norm_layer, + scale_cosine_attn=scale_cosine_attn, + scale_heads=scale_heads, + scale_attn=scale_attn, + scale_fc=scale_fc, + cross_attn=cross_attn, + xattn=xattn) + for _ in range(layers) + ]) + + def get_cast_dtype(self) -> torch.dtype: + return self.resblocks[0].mlp.c_fc.weight.dtype + + def forward(self, q: torch.Tensor, k: torch.Tensor = None, v: torch.Tensor = None, attn_mask: Optional[torch.Tensor] = None): + if k is None and v is None: + k = v = q + for r in self.resblocks: + if self.grad_checkpointing and not torch.jit.is_scripting(): + q = checkpoint(r, q, k, v, attn_mask) + else: + q = r(q, k, v, attn_mask=attn_mask) + return q + + +class ResidualAttentionBlock(nn.Module): + def __init__( + self, + d_model: int, + n_head: int, + mlp_ratio: float = 4.0, + ls_init_value: float = None, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + xattn: bool = False, + ): + super().__init__() + + self.ln_1 = norm_layer(d_model) + if xattn: + self.attn = Attention(d_model, n_head, xattn=True) + else: + self.attn = nn.MultiheadAttention(d_model, n_head) + self.ls_1 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity() + + self.ln_2 = norm_layer(d_model) + mlp_width = int(d_model * mlp_ratio) + self.mlp = nn.Sequential(OrderedDict([ + ("c_fc", nn.Linear(d_model, mlp_width)), + ("gelu", act_layer()), + ("c_proj", nn.Linear(mlp_width, d_model)) + ])) + + self.ls_2 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity() + self.xattn = xattn + + def attention(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None): + attn_mask = attn_mask.to(x.dtype) if attn_mask is not None else None + if self.xattn: + return self.attn(x, attn_mask=attn_mask) + return self.attn(x, x, x, need_weights=False, attn_mask=attn_mask)[0] + + def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None): + x = x + self.ls_1(self.attention(self.ln_1(x), attn_mask=attn_mask)) + x = x + self.ls_2(self.mlp(self.ln_2(x))) + return x + +class Transformer(nn.Module): + def __init__( + self, + width: int, + layers: int, + heads: int, + mlp_ratio: float = 4.0, + ls_init_value: float = None, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + xattn: bool = False, + ): + super().__init__() + self.width = width + self.layers = layers + self.grad_checkpointing = False + + self.resblocks = nn.ModuleList([ + ResidualAttentionBlock( + width, heads, mlp_ratio, ls_init_value=ls_init_value, act_layer=act_layer, norm_layer=norm_layer, xattn=xattn) + for _ in range(layers) + ]) + + def get_cast_dtype(self) -> torch.dtype: + return self.resblocks[0].mlp.c_fc.weight.dtype + + def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None): + for r in self.resblocks: + if self.grad_checkpointing and not torch.jit.is_scripting(): + x = checkpoint(r, x, attn_mask) + else: + x = r(x, attn_mask=attn_mask) + return x + + +class VisionTransformer(nn.Module): + def __init__( + self, + image_size: int, + patch_size: int, + width: int, + layers: int, + heads: int, + mlp_ratio: float, + ls_init_value: float = None, + patch_dropout: float = 0., + global_average_pool: bool = False, + output_dim: int = 512, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + xattn: bool = False, + ): + super().__init__() + self.image_size = to_2tuple(image_size) + self.patch_size = to_2tuple(patch_size) + self.grid_size = (self.image_size[0] // self.patch_size[0], self.image_size[1] // self.patch_size[1]) + self.output_dim = output_dim + self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False) + + scale = width ** -0.5 + self.class_embedding = nn.Parameter(scale * torch.randn(width)) + self.positional_embedding = nn.Parameter(scale * torch.randn(self.grid_size[0] * self.grid_size[1] + 1, width)) + + # setting a patch_dropout of 0. would mean it is disabled and this function would be the identity fn + self.patch_dropout = PatchDropout(patch_dropout) if patch_dropout > 0. else nn.Identity() + self.ln_pre = norm_layer(width) + + self.transformer = Transformer( + width, + layers, + heads, + mlp_ratio, + ls_init_value=ls_init_value, + act_layer=act_layer, + norm_layer=norm_layer, + xattn=xattn + ) + + self.global_average_pool = global_average_pool + self.ln_post = norm_layer(width) + self.proj = nn.Parameter(scale * torch.randn(width, output_dim)) + + def lock(self, unlocked_groups=0, freeze_bn_stats=False): + for param in self.parameters(): + param.requires_grad = False + + if unlocked_groups != 0: + groups = [ + [ + self.conv1, + self.class_embedding, + self.positional_embedding, + self.ln_pre, + ], + *self.transformer.resblocks[:-1], + [ + self.transformer.resblocks[-1], + self.ln_post, + ], + self.proj, + ] + + def _unlock(x): + if isinstance(x, Sequence): + for g in x: + _unlock(g) + else: + if isinstance(x, torch.nn.Parameter): + x.requires_grad = True + else: + for p in x.parameters(): + p.requires_grad = True + + _unlock(groups[-unlocked_groups:]) + + def get_num_layers(self): + return self.transformer.layers + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.transformer.grad_checkpointing = enable + + @torch.jit.ignore + def no_weight_decay(self): + return {'positional_embedding', 'class_embedding'} + + def forward(self, x: torch.Tensor, return_all_features: bool=False): + x = self.conv1(x) # shape = [*, width, grid, grid] + x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2] + x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width] + x = torch.cat( + [self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), + x], dim=1) # shape = [*, grid ** 2 + 1, width] + x = x + self.positional_embedding.to(x.dtype) + + # a patch_dropout of 0. would mean it is disabled and this function would do nothing but return what was passed in + x = self.patch_dropout(x) + x = self.ln_pre(x) + + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer(x) + x = x.permute(1, 0, 2) # LND -> NLD + + if not return_all_features: + if self.global_average_pool: + x = x.mean(dim=1) #x = x[:,1:,:].mean(dim=1) + else: + x = x[:, 0] + + x = self.ln_post(x) + + if self.proj is not None: + x = x @ self.proj + + return x + + +class TextTransformer(nn.Module): + def __init__( + self, + context_length: int = 77, + vocab_size: int = 49408, + width: int = 512, + heads: int = 8, + layers: int = 12, + ls_init_value: float = None, + output_dim: int = 512, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + xattn: bool= False, + attn_mask: bool = True + ): + super().__init__() + self.context_length = context_length + self.vocab_size = vocab_size + self.width = width + self.output_dim = output_dim + + self.token_embedding = nn.Embedding(vocab_size, width) + self.positional_embedding = nn.Parameter(torch.empty(self.context_length, width)) + self.transformer = Transformer( + width=width, + layers=layers, + heads=heads, + ls_init_value=ls_init_value, + act_layer=act_layer, + norm_layer=norm_layer, + xattn=xattn + ) + + self.xattn = xattn + self.ln_final = norm_layer(width) + self.text_projection = nn.Parameter(torch.empty(width, output_dim)) + + if attn_mask: + self.register_buffer('attn_mask', self.build_attention_mask(), persistent=False) + else: + self.attn_mask = None + + self.init_parameters() + + def init_parameters(self): + nn.init.normal_(self.token_embedding.weight, std=0.02) + nn.init.normal_(self.positional_embedding, std=0.01) + + proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5) + attn_std = self.transformer.width ** -0.5 + fc_std = (2 * self.transformer.width) ** -0.5 + for block in self.transformer.resblocks: + nn.init.normal_(block.attn.in_proj_weight, std=attn_std) + nn.init.normal_(block.attn.out_proj.weight, std=proj_std) + nn.init.normal_(block.mlp.c_fc.weight, std=fc_std) + nn.init.normal_(block.mlp.c_proj.weight, std=proj_std) + + if self.text_projection is not None: + nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5) + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.transformer.grad_checkpointing = enable + + @torch.jit.ignore + def no_weight_decay(self): + # return {'positional_embedding', 'token_embedding'} + return {'positional_embedding'} + + def get_num_layers(self): + return self.transformer.layers + + def build_attention_mask(self): + # lazily create causal attention mask, with full attention between the vision tokens + # pytorch uses additive attention mask; fill with -inf + mask = torch.empty(self.context_length, self.context_length) + mask.fill_(float("-inf")) + mask.triu_(1) # zero out the lower diagonal + return mask + + def forward(self, text, return_all_features: bool=False): + cast_dtype = self.transformer.get_cast_dtype() + x = self.token_embedding(text).to(cast_dtype) # [batch_size, n_ctx, d_model] + + x = x + self.positional_embedding.to(cast_dtype) + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer(x, attn_mask=self.attn_mask) + # x = self.transformer(x) # no attention mask is applied + x = x.permute(1, 0, 2) # LND -> NLD + x = self.ln_final(x) + + if not return_all_features: + # x.shape = [batch_size, n_ctx, transformer.width] + # take features from the eot embedding (eot_token is the highest number in each sequence) + x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection + return x diff --git a/ape/modeling/text/eva02_clip/utils.py b/ape/modeling/text/eva02_clip/utils.py new file mode 100644 index 0000000..bdc5a7a --- /dev/null +++ b/ape/modeling/text/eva02_clip/utils.py @@ -0,0 +1,326 @@ +from itertools import repeat +import collections.abc +import logging +import math +import numpy as np + +import torch +from torch import nn as nn +from torchvision.ops.misc import FrozenBatchNorm2d +import torch.nn.functional as F + +# open CLIP +def resize_clip_pos_embed(state_dict, model, interpolation: str = 'bicubic', seq_dim=1): + # Rescale the grid of position embeddings when loading from state_dict + old_pos_embed = state_dict.get('visual.positional_embedding', None) + if old_pos_embed is None or not hasattr(model.visual, 'grid_size'): + return + grid_size = to_2tuple(model.visual.grid_size) + extra_tokens = 1 # FIXME detect different token configs (ie no class token, or more) + new_seq_len = grid_size[0] * grid_size[1] + extra_tokens + if new_seq_len == old_pos_embed.shape[0]: + return + + if extra_tokens: + pos_emb_tok, pos_emb_img = old_pos_embed[:extra_tokens], old_pos_embed[extra_tokens:] + else: + pos_emb_tok, pos_emb_img = None, old_pos_embed + old_grid_size = to_2tuple(int(math.sqrt(len(pos_emb_img)))) + + logging.info('Resizing position embedding grid-size from %s to %s', old_grid_size, grid_size) + pos_emb_img = pos_emb_img.reshape(1, old_grid_size[0], old_grid_size[1], -1).permute(0, 3, 1, 2) + pos_emb_img = F.interpolate( + pos_emb_img, + size=grid_size, + mode=interpolation, + align_corners=True, + ) + pos_emb_img = pos_emb_img.permute(0, 2, 3, 1).reshape(1, grid_size[0] * grid_size[1], -1)[0] + if pos_emb_tok is not None: + new_pos_embed = torch.cat([pos_emb_tok, pos_emb_img], dim=0) + else: + new_pos_embed = pos_emb_img + state_dict['visual.positional_embedding'] = new_pos_embed + + +def resize_visual_pos_embed(state_dict, model, interpolation: str = 'bicubic', seq_dim=1): + # Rescale the grid of position embeddings when loading from state_dict + old_pos_embed = state_dict.get('positional_embedding', None) + if old_pos_embed is None or not hasattr(model.visual, 'grid_size'): + return + grid_size = to_2tuple(model.visual.grid_size) + extra_tokens = 1 # FIXME detect different token configs (ie no class token, or more) + new_seq_len = grid_size[0] * grid_size[1] + extra_tokens + if new_seq_len == old_pos_embed.shape[0]: + return + + if extra_tokens: + pos_emb_tok, pos_emb_img = old_pos_embed[:extra_tokens], old_pos_embed[extra_tokens:] + else: + pos_emb_tok, pos_emb_img = None, old_pos_embed + old_grid_size = to_2tuple(int(math.sqrt(len(pos_emb_img)))) + + logging.info('Resizing position embedding grid-size from %s to %s', old_grid_size, grid_size) + pos_emb_img = pos_emb_img.reshape(1, old_grid_size[0], old_grid_size[1], -1).permute(0, 3, 1, 2) + pos_emb_img = F.interpolate( + pos_emb_img, + size=grid_size, + mode=interpolation, + align_corners=True, + ) + pos_emb_img = pos_emb_img.permute(0, 2, 3, 1).reshape(1, grid_size[0] * grid_size[1], -1)[0] + if pos_emb_tok is not None: + new_pos_embed = torch.cat([pos_emb_tok, pos_emb_img], dim=0) + else: + new_pos_embed = pos_emb_img + state_dict['positional_embedding'] = new_pos_embed + +def resize_evaclip_pos_embed(state_dict, model, interpolation: str = 'bicubic', seq_dim=1): + all_keys = list(state_dict.keys()) + # interpolate position embedding + if 'visual.pos_embed' in state_dict: + pos_embed_checkpoint = state_dict['visual.pos_embed'] + embedding_size = pos_embed_checkpoint.shape[-1] + num_patches = model.visual.patch_embed.num_patches + num_extra_tokens = model.visual.pos_embed.shape[-2] - num_patches + # height (== width) for the checkpoint position embedding + orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5) + # height (== width) for the new position embedding + new_size = int(num_patches ** 0.5) + # class_token and dist_token are kept unchanged + if orig_size != new_size: + print("Position interpolate from %dx%d to %dx%d" % (orig_size, orig_size, new_size, new_size)) + extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens] + # only the position tokens are interpolated + pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:] + pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2) + pos_tokens = torch.nn.functional.interpolate( + pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False) + pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2) + new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) + state_dict['visual.pos_embed'] = new_pos_embed + + patch_embed_proj = state_dict['visual.patch_embed.proj.weight'] + patch_size = model.visual.patch_embed.patch_size + state_dict['visual.patch_embed.proj.weight'] = torch.nn.functional.interpolate( + patch_embed_proj.float(), size=patch_size, mode='bicubic', align_corners=False) + + +def resize_eva_pos_embed(state_dict, model, interpolation: str = 'bicubic', seq_dim=1): + all_keys = list(state_dict.keys()) + # interpolate position embedding + if 'pos_embed' in state_dict: + pos_embed_checkpoint = state_dict['pos_embed'] + embedding_size = pos_embed_checkpoint.shape[-1] + num_patches = model.visual.patch_embed.num_patches + num_extra_tokens = model.visual.pos_embed.shape[-2] - num_patches + # height (== width) for the checkpoint position embedding + orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5) + # height (== width) for the new position embedding + new_size = int(num_patches ** 0.5) + # class_token and dist_token are kept unchanged + if orig_size != new_size: + print("Position interpolate from %dx%d to %dx%d" % (orig_size, orig_size, new_size, new_size)) + extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens] + # only the position tokens are interpolated + pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:] + pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2) + pos_tokens = torch.nn.functional.interpolate( + pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False) + pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2) + new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) + state_dict['pos_embed'] = new_pos_embed + + patch_embed_proj = state_dict['patch_embed.proj.weight'] + patch_size = model.visual.patch_embed.patch_size + state_dict['patch_embed.proj.weight'] = torch.nn.functional.interpolate( + patch_embed_proj.float(), size=patch_size, mode='bicubic', align_corners=False) + + +def resize_rel_pos_embed(state_dict, model, interpolation: str = 'bicubic', seq_dim=1): + all_keys = list(state_dict.keys()) + for key in all_keys: + if "relative_position_index" in key: + state_dict.pop(key) + + if "relative_position_bias_table" in key: + rel_pos_bias = state_dict[key] + src_num_pos, num_attn_heads = rel_pos_bias.size() + dst_num_pos, _ = model.visual.state_dict()[key].size() + dst_patch_shape = model.visual.patch_embed.patch_shape + if dst_patch_shape[0] != dst_patch_shape[1]: + raise NotImplementedError() + num_extra_tokens = dst_num_pos - (dst_patch_shape[0] * 2 - 1) * (dst_patch_shape[1] * 2 - 1) + src_size = int((src_num_pos - num_extra_tokens) ** 0.5) + dst_size = int((dst_num_pos - num_extra_tokens) ** 0.5) + if src_size != dst_size: + print("Position interpolate for %s from %dx%d to %dx%d" % ( + key, src_size, src_size, dst_size, dst_size)) + extra_tokens = rel_pos_bias[-num_extra_tokens:, :] + rel_pos_bias = rel_pos_bias[:-num_extra_tokens, :] + + def geometric_progression(a, r, n): + return a * (1.0 - r ** n) / (1.0 - r) + + left, right = 1.01, 1.5 + while right - left > 1e-6: + q = (left + right) / 2.0 + gp = geometric_progression(1, q, src_size // 2) + if gp > dst_size // 2: + right = q + else: + left = q + + # if q > 1.090307: + # q = 1.090307 + + dis = [] + cur = 1 + for i in range(src_size // 2): + dis.append(cur) + cur += q ** (i + 1) + + r_ids = [-_ for _ in reversed(dis)] + + x = r_ids + [0] + dis + y = r_ids + [0] + dis + + t = dst_size // 2.0 + dx = np.arange(-t, t + 0.1, 1.0) + dy = np.arange(-t, t + 0.1, 1.0) + + print("Original positions = %s" % str(x)) + print("Target positions = %s" % str(dx)) + + all_rel_pos_bias = [] + + for i in range(num_attn_heads): + z = rel_pos_bias[:, i].view(src_size, src_size).float().numpy() + f = F.interpolate.interp2d(x, y, z, kind='cubic') + all_rel_pos_bias.append( + torch.Tensor(f(dx, dy)).contiguous().view(-1, 1).to(rel_pos_bias.device)) + + rel_pos_bias = torch.cat(all_rel_pos_bias, dim=-1) + + new_rel_pos_bias = torch.cat((rel_pos_bias, extra_tokens), dim=0) + state_dict[key] = new_rel_pos_bias + + # interpolate position embedding + if 'pos_embed' in state_dict: + pos_embed_checkpoint = state_dict['pos_embed'] + embedding_size = pos_embed_checkpoint.shape[-1] + num_patches = model.visual.patch_embed.num_patches + num_extra_tokens = model.visual.pos_embed.shape[-2] - num_patches + # height (== width) for the checkpoint position embedding + orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5) + # height (== width) for the new position embedding + new_size = int(num_patches ** 0.5) + # class_token and dist_token are kept unchanged + if orig_size != new_size: + print("Position interpolate from %dx%d to %dx%d" % (orig_size, orig_size, new_size, new_size)) + extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens] + # only the position tokens are interpolated + pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:] + pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2) + pos_tokens = torch.nn.functional.interpolate( + pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False) + pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2) + new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) + state_dict['pos_embed'] = new_pos_embed + + patch_embed_proj = state_dict['patch_embed.proj.weight'] + patch_size = model.visual.patch_embed.patch_size + state_dict['patch_embed.proj.weight'] = torch.nn.functional.interpolate( + patch_embed_proj.float(), size=patch_size, mode='bicubic', align_corners=False) + + +def freeze_batch_norm_2d(module, module_match={}, name=''): + """ + Converts all `BatchNorm2d` and `SyncBatchNorm` layers of provided module into `FrozenBatchNorm2d`. If `module` is + itself an instance of either `BatchNorm2d` or `SyncBatchNorm`, it is converted into `FrozenBatchNorm2d` and + returned. Otherwise, the module is walked recursively and submodules are converted in place. + + Args: + module (torch.nn.Module): Any PyTorch module. + module_match (dict): Dictionary of full module names to freeze (all if empty) + name (str): Full module name (prefix) + + Returns: + torch.nn.Module: Resulting module + + Inspired by https://github.com/pytorch/pytorch/blob/a5895f85be0f10212791145bfedc0261d364f103/torch/nn/modules/batchnorm.py#L762 + """ + res = module + is_match = True + if module_match: + is_match = name in module_match + if is_match and isinstance(module, (nn.modules.batchnorm.BatchNorm2d, nn.modules.batchnorm.SyncBatchNorm)): + res = FrozenBatchNorm2d(module.num_features) + res.num_features = module.num_features + res.affine = module.affine + if module.affine: + res.weight.data = module.weight.data.clone().detach() + res.bias.data = module.bias.data.clone().detach() + res.running_mean.data = module.running_mean.data + res.running_var.data = module.running_var.data + res.eps = module.eps + else: + for child_name, child in module.named_children(): + full_child_name = '.'.join([name, child_name]) if name else child_name + new_child = freeze_batch_norm_2d(child, module_match, full_child_name) + if new_child is not child: + res.add_module(child_name, new_child) + return res + + +# From PyTorch internals +def _ntuple(n): + def parse(x): + if isinstance(x, collections.abc.Iterable): + return x + return tuple(repeat(x, n)) + return parse + + +to_1tuple = _ntuple(1) +to_2tuple = _ntuple(2) +to_3tuple = _ntuple(3) +to_4tuple = _ntuple(4) +to_ntuple = lambda n, x: _ntuple(n)(x) + + +def is_logging(args): + def is_global_master(args): + return args.rank == 0 + + def is_local_master(args): + return args.local_rank == 0 + + def is_master(args, local=False): + return is_local_master(args) if local else is_global_master(args) + return is_master + + +class AllGather(torch.autograd.Function): + """An autograd function that performs allgather on a tensor. + Performs all_gather operation on the provided tensors. + *** Warning ***: torch.distributed.all_gather has no gradient. + """ + + @staticmethod + def forward(ctx, tensor, rank, world_size): + tensors_gather = [torch.empty_like(tensor) for _ in range(world_size)] + torch.distributed.all_gather(tensors_gather, tensor) + ctx.rank = rank + ctx.batch_size = tensor.shape[0] + return torch.cat(tensors_gather, 0) + + @staticmethod + def backward(ctx, grad_output): + return ( + grad_output[ctx.batch_size * ctx.rank: ctx.batch_size * (ctx.rank + 1)], + None, + None + ) + +allgather = AllGather.apply \ No newline at end of file diff --git a/ape/modeling/text/llama2_wrapper.py b/ape/modeling/text/llama2_wrapper.py new file mode 100644 index 0000000..ec1e0ae --- /dev/null +++ b/ape/modeling/text/llama2_wrapper.py @@ -0,0 +1,154 @@ +import copy +import logging +import math +import time +from typing import Dict, List, Optional, Tuple + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.cuda.amp import autocast +from torch.nn import CrossEntropyLoss + +import fvcore.nn.weight_init as weight_init +from detectron2.data.catalog import DatasetCatalog, MetadataCatalog +from detectron2.layers import Conv2d, ShapeSpec, get_norm, move_device_like +from detectron2.modeling import GeneralizedRCNN +from detectron2.modeling.postprocessing import detector_postprocess, sem_seg_postprocess +from detectron2.modeling.roi_heads.fast_rcnn import fast_rcnn_inference +from detectron2.structures import BitMasks, Boxes, ImageList, Instances +from detectron2.utils import comm +from detrex.layers import MLP, box_cxcywh_to_xyxy, box_xyxy_to_cxcywh +from detrex.utils import inverse_sigmoid +from torchvision.ops.boxes import batched_nms +from transformers import BitsAndBytesConfig, LlamaConfig, LlamaForCausalLM, LlamaTokenizer +from transformers.modeling_outputs import BaseModelOutput + + +class Llama2(nn.Module): + def __init__( + self, + pretrained_model_name_or_path, + bg_word="", + dtype="bfloat16", + loss_type="CE", + use_fed_loss=False, + fed_loss_num_classes=1000, + inference_text=False, + inference_prob=False, + inference_prob_fast=False, + train_positive_only=False, + test_constraint=False, + vision_port="encoder", + eval_only=False, + load_in_4bit=False, + load_in_8bit=False, + **kwargs, + ): + super().__init__(**kwargs) + + self.dtype = getattr(torch, dtype) + + self.config = LlamaConfig.from_pretrained( + pretrained_model_name_or_path=pretrained_model_name_or_path + ) + + if load_in_4bit: + quantization_config = BitsAndBytesConfig( + load_in_4bit=True, + bnb_4bit_quant_type="nf4", + bnb_4bit_compute_dtype=self.dtype, + bnb_4bit_use_double_quant=True, + ) + device_map = {"": comm.get_local_rank()} + elif load_in_8bit: + quantization_config = BitsAndBytesConfig( + load_in_8bit=True, + bnb_8bit_quant_type="nf4", + bnb_8bit_compute_dtype=self.dtype, + bnb_8bit_use_double_quant=True, + ) + device_map = {"": comm.get_local_rank()} + else: + quantization_config = None + device_map = None + self.model = LlamaForCausalLM.from_pretrained( + pretrained_model_name_or_path=pretrained_model_name_or_path, + quantization_config=quantization_config, + device_map=device_map, + ) + + if quantization_config is None: + for name, param in self.model.named_parameters(): + param.data = param.data.to(self.dtype) + + self.tokenizer = LlamaTokenizer.from_pretrained( + pretrained_model_name_or_path=pretrained_model_name_or_path + ) + + self.tokenizer.add_special_tokens({"pad_token": ""}) + self.tokenizer.padding_side = "left" + + self.model.resize_token_embeddings(len(self.tokenizer)) + self.model.config.pad_token_id = self.tokenizer.pad_token_id + + if eval_only: + self.model.eval() + for name, param in self.model.named_parameters(): + param.requires_grad = False + + logger = logging.getLogger(__name__) + logger.info("memory footprint: {}G".format(self.model.get_memory_footprint() / 1024**3)) + + self.text_list_to_feature = {} + + @autocast(enabled=False) + @torch.no_grad() + def forward_text(self, text_list, cache=False): + if cache and tuple(text_list) in self.text_list_to_feature: + return self.text_list_to_feature[tuple(text_list)] + + text_token = self.tokenizer( + text_list, + return_tensors="pt", + padding="longest", + ).to(self.device) + input_ids = text_token.input_ids + attention_mask = text_token.attention_mask + + max_batch_size = 128 + if torch.cuda.mem_get_info(self.device)[0] / 1024**3 < 5: + max_batch_size = 128 + + chunck_num = input_ids.size(0) // max_batch_size + 1 + last_hidden_state = [] + for chunck_id in range(chunck_num): + outputs = self.model( + input_ids=input_ids[chunck_id * max_batch_size : (chunck_id + 1) * max_batch_size], + attention_mask=attention_mask[ + chunck_id * max_batch_size : (chunck_id + 1) * max_batch_size + ], + inputs_embeds=None, + output_attentions=True, + output_hidden_states=True, + return_dict=True, + ) + last_hidden_state.append(outputs.hidden_states[-1].clone().detach()) + + last_hidden_state = torch.cat(last_hidden_state, dim=0) + + last_hidden_state = torch.nan_to_num(last_hidden_state, nan=0.0, posinf=0.0, neginf=0.0) + + ret = { + "attention_mask": attention_mask, + "last_hidden_state": last_hidden_state, + } + + if cache: + self.text_list_to_feature[tuple(text_list)] = ret + + return ret + + @property + def device(self): + return self.model.device diff --git a/ape/modeling/text/t5_wrapper.py b/ape/modeling/text/t5_wrapper.py new file mode 100644 index 0000000..2567e19 --- /dev/null +++ b/ape/modeling/text/t5_wrapper.py @@ -0,0 +1,103 @@ +import copy +import math +import time +from typing import Dict, List, Optional, Tuple + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.cuda.amp import autocast +from torch.nn import CrossEntropyLoss + +import fvcore.nn.weight_init as weight_init +from detectron2.data.catalog import DatasetCatalog, MetadataCatalog +from detectron2.layers import Conv2d, ShapeSpec, get_norm, move_device_like +from detectron2.modeling import GeneralizedRCNN +from detectron2.modeling.postprocessing import detector_postprocess, sem_seg_postprocess +from detectron2.modeling.roi_heads.fast_rcnn import fast_rcnn_inference +from detectron2.structures import BitMasks, Boxes, ImageList, Instances +from detrex.layers import MLP, box_cxcywh_to_xyxy, box_xyxy_to_cxcywh +from detrex.utils import inverse_sigmoid +from torchvision.ops.boxes import batched_nms +from transformers import AutoConfig, AutoModelForSeq2SeqLM, AutoTokenizer +from transformers.modeling_outputs import BaseModelOutput + + +class T5_warpper(nn.Module): + def __init__( + self, + pretrained_model_name_or_path, + bg_word="", + dtype="bfloat16", + loss_type="CE", + use_fed_loss=False, + fed_loss_num_classes=1000, + inference_text=False, + inference_prob=False, + inference_prob_fast=False, + train_positive_only=False, + test_constraint=False, + vision_port="encoder", + eval_only=False, + **kwargs, + ): + super().__init__(**kwargs) + + self.dtype = getattr(torch, dtype) + + self.config = AutoConfig.from_pretrained( + pretrained_model_name_or_path=pretrained_model_name_or_path + ) + self.t5_model = AutoModelForSeq2SeqLM.from_pretrained( + pretrained_model_name_or_path=pretrained_model_name_or_path + ) + self.tokenizer = AutoTokenizer.from_pretrained( + pretrained_model_name_or_path=pretrained_model_name_or_path + ) + + if eval_only: + self.t5_model.eval() + for name, param in self.t5_model.named_parameters(): + param.requires_grad = False + param.data = param.data.to(self.dtype) + + self.eos_token_id = self.tokenizer("\n", add_special_tokens=False).input_ids[0] + + self.text_list_to_feature = {} + + @autocast(enabled=False) + @torch.no_grad() + def forward_text(self, text_list, cache=False): + if cache and tuple(text_list) in self.text_list_to_feature: + return self.text_list_to_feature[tuple(text_list)] + + text_token = self.tokenizer( + text_list, + return_tensors="pt", + padding="longest", + ).to(self.device) + input_ids = text_token.input_ids + attention_mask = text_token.attention_mask + + encoder_outputs = self.t5_model.encoder( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=None, + head_mask=None, + output_attentions=True, + output_hidden_states=True, + return_dict=True, + ) + + last_hidden_state = encoder_outputs.last_hidden_state + + feature = agg_lang_feat(last_hidden_state, attention_mask).clone().detach() + + if cache: + self.text_list_to_feature[tuple(text_list)] = feature + + return feature + + @property + def device(self): + return self.t5_model.device diff --git a/ape/modeling/text/text_encoder.py b/ape/modeling/text/text_encoder.py new file mode 100644 index 0000000..9c9dd62 --- /dev/null +++ b/ape/modeling/text/text_encoder.py @@ -0,0 +1,38 @@ +import logging +from collections import OrderedDict +from typing import List, Union + +import torch +from torch import nn + +from .clip_wrapper import build_clip_text_encoder, get_clip_embeddings +from .clip_wrapper_open import build_openclip_text_encoder, get_openclip_embeddings + + +class TextModel(nn.Module): + def __init__( + self, + model_type, + model_name, + model_path, + ): + super().__init__() + + self.model_type = model_type + self.model_name = model_name + self.model_path = model_path + + if self.model_type == "CLIP": + self.model = build_clip_text_encoder(model_path, pretrain=True) + + if self.model_type == "OPENCLIP": + self.model, self.tokenizer = build_openclip_text_encoder(model_name, model_path) + + self.model.eval() + + def forward_text(self, text, prompt="a "): + if self.model_type == "CLIP": + return get_clip_embeddings(self.model, text, prompt) + + if self.model_type == "OPENCLIP": + return get_openclip_embeddings(self.model, self.tokenizer, text, prompt) diff --git a/ape/modeling/text/utils.py b/ape/modeling/text/utils.py new file mode 100644 index 0000000..932fe05 --- /dev/null +++ b/ape/modeling/text/utils.py @@ -0,0 +1,32 @@ +import torch + + +def clean_name(name): + name = re.sub(r"\(.*\)", "", name) + name = re.sub(r"_", " ", name) + name = re.sub(r" ", " ", name) + return name + + +def reduce_language_feature(features, mask, reduce_type="average"): + """average pooling of language features""" "" + if reduce_type == "average": + embedded = ( + features * mask.unsqueeze(-1).float() + ) # use mask to zero out invalid token features + aggregate = embedded.sum(1) / (mask.sum(-1).unsqueeze(-1).float() + 1e-6) + elif reduce_type == "max": + out = [] + for i in range(len(features)): + pool_feat, _ = torch.max(features[i][mask[i]], 0) # (L, C) -> (C, ) + out.append(pool_feat) + aggregate = torch.stack(out, dim=0) # (bs, C) + elif reduce_type == "last": + out = [] + for i in range(len(features)): + pool_feat = features[i][torch.argmin(mask[i]) - 1] # (L, C) -> (C, ) + out.append(pool_feat) + aggregate = torch.stack(out, dim=0) # (bs, C) + else: + raise ValueError("reduce_type should be average or max or last.") + return aggregate diff --git a/ape/utils/__init__.py b/ape/utils/__init__.py new file mode 100644 index 0000000..4ebdc90 --- /dev/null +++ b/ape/utils/__init__.py @@ -0,0 +1,8 @@ +# ------------------------------------------------------------------------ +# Deformable DETR +# Copyright (c) 2020 SenseTime. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ +# Modified from DETR (https://github.com/facebookresearch/detr) +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +# ------------------------------------------------------------------------ diff --git a/ape/utils/box_ops.py b/ape/utils/box_ops.py new file mode 100644 index 0000000..bf23fb4 --- /dev/null +++ b/ape/utils/box_ops.py @@ -0,0 +1,95 @@ +# ------------------------------------------------------------------------ +# Deformable DETR +# Copyright (c) 2020 SenseTime. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ +# Modified from DETR (https://github.com/facebookresearch/detr) +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +# ------------------------------------------------------------------------ + +""" +Utilities for bounding box manipulation and GIoU. +""" +import torch + +from torchvision.ops.boxes import box_area + + +def box_cxcywh_to_xyxy(x): + x_c, y_c, w, h = x.unbind(-1) + b = [(x_c - 0.5 * w), (y_c - 0.5 * h), (x_c + 0.5 * w), (y_c + 0.5 * h)] + return torch.stack(b, dim=-1) + + +def box_xyxy_to_cxcywh(x): + x0, y0, x1, y1 = x.unbind(-1) + b = [(x0 + x1) / 2, (y0 + y1) / 2, (x1 - x0), (y1 - y0)] + return torch.stack(b, dim=-1) + + +# modified from torchvision to also return the union +def box_iou(boxes1, boxes2): + area1 = box_area(boxes1) + area2 = box_area(boxes2) + + lt = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2] + rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2] + + wh = (rb - lt).clamp(min=0) # [N,M,2] + inter = wh[:, :, 0] * wh[:, :, 1] # [N,M] + + union = area1[:, None] + area2 - inter + + iou = inter / union + return iou, union + + +def generalized_box_iou(boxes1, boxes2): + """ + Generalized IoU from https://giou.stanford.edu/ + + The boxes should be in [x0, y0, x1, y1] format + + Returns a [N, M] pairwise matrix, where N = len(boxes1) + and M = len(boxes2) + """ + # degenerate boxes gives inf / nan results + # so do an early check + assert (boxes1[:, 2:] >= boxes1[:, :2]).all() + assert (boxes2[:, 2:] >= boxes2[:, :2]).all() + iou, union = box_iou(boxes1, boxes2) + + lt = torch.min(boxes1[:, None, :2], boxes2[:, :2]) + rb = torch.max(boxes1[:, None, 2:], boxes2[:, 2:]) + + wh = (rb - lt).clamp(min=0) # [N,M,2] + area = wh[:, :, 0] * wh[:, :, 1] + + return iou - (area - union) / area + + +def masks_to_boxes(masks): + """Compute the bounding boxes around the provided masks + + The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions. + + Returns a [N, 4] tensors, with the boxes in xyxy format + """ + if masks.numel() == 0: + return torch.zeros((0, 4), device=masks.device) + + h, w = masks.shape[-2:] + + y = torch.arange(0, h, dtype=torch.float) + x = torch.arange(0, w, dtype=torch.float) + y, x = torch.meshgrid(y, x) + + x_mask = masks * x.unsqueeze(0) + x_max = x_mask.flatten(1).max(-1)[0] + x_min = x_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0] + + y_mask = masks * y.unsqueeze(0) + y_max = y_mask.flatten(1).max(-1)[0] + y_min = y_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0] + + return torch.stack([x_min, y_min, x_max, y_max], 1) diff --git a/ape/utils/misc.py b/ape/utils/misc.py new file mode 100644 index 0000000..2d24f88 --- /dev/null +++ b/ape/utils/misc.py @@ -0,0 +1,547 @@ +# ------------------------------------------------------------------------ +# Deformable DETR +# Copyright (c) 2020 SenseTime. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ +# Modified from DETR (https://github.com/facebookresearch/detr) +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +# ------------------------------------------------------------------------ + +""" +Misc functions, including distributed helpers. + +Mostly copy-paste from torchvision references. +""" +import datetime +import os +import pickle +import subprocess +import time +from collections import defaultdict, deque +from typing import List, Optional + +import torch +import torch.distributed as dist +import torch.nn as nn +from torch import Tensor + +# needed due to empty tensor bug in pytorch and torchvision 0.5 +import torchvision + +if ( + float(torchvision.__version__.split(".")[0]) == 0 + and float(torchvision.__version__.split(".")[1]) < 5 +): + import math + from torchvision.ops.misc import _NewEmptyTensorOp + + def _check_size_scale_factor(dim, size, scale_factor): + # type: (int, Optional[List[int]], Optional[float]) -> None + if size is None and scale_factor is None: + raise ValueError("either size or scale_factor should be defined") + if size is not None and scale_factor is not None: + raise ValueError("only one of size or scale_factor should be defined") + if not (scale_factor is not None and len(scale_factor) != dim): + raise ValueError( + "scale_factor shape must match input shape. " + "Input is {}D, scale_factor size is {}".format(dim, len(scale_factor)) + ) + + def _output_size(dim, input, size, scale_factor): + # type: (int, Tensor, Optional[List[int]], Optional[float]) -> List[int] + assert dim == 2 + _check_size_scale_factor(dim, size, scale_factor) + if size is not None: + return size + # if dim is not 2 or scale_factor is iterable use _ntuple instead of concat + assert scale_factor is not None and isinstance(scale_factor, (int, float)) + scale_factors = [scale_factor, scale_factor] + # math.floor might return float in py2.7 + return [int(math.floor(input.size(i + 2) * scale_factors[i])) for i in range(dim)] + +elif ( + float(torchvision.__version__.split(".")[0]) == 0 + and float(torchvision.__version__.split(".")[1]) < 7 +): + from torchvision.ops import _new_empty_tensor + from torchvision.ops.misc import _output_size + + +class SmoothedValue(object): + """Track a series of values and provide access to smoothed values over a + window or the global series average. + """ + + def __init__(self, window_size=20, fmt=None): + if fmt is None: + fmt = "{median:.4f} ({global_avg:.4f})" + self.deque = deque(maxlen=window_size) + self.total = 0.0 + self.count = 0 + self.fmt = fmt + + def update(self, value, n=1): + self.deque.append(value) + self.count += n + self.total += value * n + + def synchronize_between_processes(self): + """ + Warning: does not synchronize the deque! + """ + if not is_dist_avail_and_initialized(): + return + t = torch.tensor([self.count, self.total], dtype=torch.float64, device="cuda") + dist.barrier() + dist.all_reduce(t) + t = t.tolist() + self.count = int(t[0]) + self.total = t[1] + + @property + def median(self): + d = torch.tensor(list(self.deque)) + return d.median().item() + + @property + def avg(self): + d = torch.tensor(list(self.deque), dtype=torch.float32) + return d.mean().item() + + @property + def global_avg(self): + return self.total / self.count + + @property + def max(self): + return max(self.deque) + + @property + def value(self): + return self.deque[-1] + + def __str__(self): + return self.fmt.format( + median=self.median, + avg=self.avg, + global_avg=self.global_avg, + max=self.max, + value=self.value, + ) + + +def all_gather(data): + """ + Run all_gather on arbitrary picklable data (not necessarily tensors) + Args: + data: any picklable object + Returns: + list[data]: list of data gathered from each rank + """ + world_size = get_world_size() + if world_size == 1: + return [data] + + # serialized to a Tensor + buffer = pickle.dumps(data) + storage = torch.ByteStorage.from_buffer(buffer) + tensor = torch.ByteTensor(storage).to("cuda") + + # obtain Tensor size of each rank + local_size = torch.tensor([tensor.numel()], device="cuda") + size_list = [torch.tensor([0], device="cuda") for _ in range(world_size)] + dist.all_gather(size_list, local_size) + size_list = [int(size.item()) for size in size_list] + max_size = max(size_list) + + # receiving Tensor from all ranks + # we pad the tensor because torch all_gather does not support + # gathering tensors of different shapes + tensor_list = [] + for _ in size_list: + tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device="cuda")) + if local_size != max_size: + padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device="cuda") + tensor = torch.cat((tensor, padding), dim=0) + dist.all_gather(tensor_list, tensor) + + data_list = [] + for size, tensor in zip(size_list, tensor_list): + buffer = tensor.cpu().numpy().tobytes()[:size] + data_list.append(pickle.loads(buffer)) + + return data_list + + +def reduce_dict(input_dict, average=True): + """ + Args: + input_dict (dict): all the values will be reduced + average (bool): whether to do average or sum + Reduce the values in the dictionary from all processes so that all processes + have the averaged results. Returns a dict with the same fields as + input_dict, after reduction. + """ + world_size = get_world_size() + if world_size < 2: + return input_dict + with torch.no_grad(): + names = [] + values = [] + # sort the keys so that they are consistent across processes + for k in sorted(input_dict.keys()): + names.append(k) + values.append(input_dict[k]) + values = torch.stack(values, dim=0) + dist.all_reduce(values) + if average: + values /= world_size + reduced_dict = {k: v for k, v in zip(names, values)} + return reduced_dict + + +class MetricLogger(object): + def __init__(self, delimiter="\t"): + self.meters = defaultdict(SmoothedValue) + self.delimiter = delimiter + + def update(self, **kwargs): + for k, v in kwargs.items(): + if isinstance(v, torch.Tensor): + v = v.item() + assert isinstance(v, (float, int)) + self.meters[k].update(v) + + def __getattr__(self, attr): + if attr in self.meters: + return self.meters[attr] + if attr in self.__dict__: + return self.__dict__[attr] + raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, attr)) + + def __str__(self): + loss_str = [] + for name, meter in self.meters.items(): + loss_str.append("{}: {}".format(name, str(meter))) + return self.delimiter.join(loss_str) + + def synchronize_between_processes(self): + for meter in self.meters.values(): + meter.synchronize_between_processes() + + def add_meter(self, name, meter): + self.meters[name] = meter + + def log_every(self, iterable, print_freq, header=None): + i = 0 + if not header: + header = "" + start_time = time.time() + end = time.time() + iter_time = SmoothedValue(fmt="{avg:.4f}") + data_time = SmoothedValue(fmt="{avg:.4f}") + space_fmt = ":" + str(len(str(len(iterable)))) + "d" + if torch.cuda.is_available(): + log_msg = self.delimiter.join( + [ + header, + "[{0" + space_fmt + "}/{1}]", + "eta: {eta}", + "{meters}", + "time: {time}", + "data: {data}", + "max mem: {memory:.0f}", + ] + ) + else: + log_msg = self.delimiter.join( + [ + header, + "[{0" + space_fmt + "}/{1}]", + "eta: {eta}", + "{meters}", + "time: {time}", + "data: {data}", + ] + ) + MB = 1024.0 * 1024.0 + for obj in iterable: + data_time.update(time.time() - end) + yield obj + iter_time.update(time.time() - end) + if i % print_freq == 0 or i == len(iterable) - 1: + eta_seconds = iter_time.global_avg * (len(iterable) - i) + eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) + if torch.cuda.is_available(): + print( + log_msg.format( + i, + len(iterable), + eta=eta_string, + meters=str(self), + time=str(iter_time), + data=str(data_time), + memory=torch.cuda.max_memory_allocated() / MB, + ) + ) + else: + print( + log_msg.format( + i, + len(iterable), + eta=eta_string, + meters=str(self), + time=str(iter_time), + data=str(data_time), + ) + ) + i += 1 + end = time.time() + total_time = time.time() - start_time + total_time_str = str(datetime.timedelta(seconds=int(total_time))) + print( + "{} Total time: {} ({:.4f} s / it)".format( + header, total_time_str, total_time / len(iterable) + ) + ) + + +def get_sha(): + cwd = os.path.dirname(os.path.abspath(__file__)) + + def _run(command): + return subprocess.check_output(command, cwd=cwd).decode("ascii").strip() + + sha = "N/A" + diff = "clean" + branch = "N/A" + try: + sha = _run(["git", "rev-parse", "HEAD"]) + subprocess.check_output(["git", "diff"], cwd=cwd) + diff = _run(["git", "diff-index", "HEAD"]) + diff = "has uncommited changes" if diff else "clean" + branch = _run(["git", "rev-parse", "--abbrev-ref", "HEAD"]) + except Exception: + pass + message = f"sha: {sha}, status: {diff}, branch: {branch}" + return message + + +def collate_fn(batch): + batch = list(zip(*batch)) + batch[0] = nested_tensor_from_tensor_list(batch[0]) + return tuple(batch) + + +def _max_by_axis(the_list): + # type: (List[List[int]]) -> List[int] + maxes = the_list[0] + for sublist in the_list[1:]: + for index, item in enumerate(sublist): + maxes[index] = max(maxes[index], item) + return maxes + + +def nested_tensor_from_tensor_list(tensor_list: List[Tensor]): + # TODO make this more general + if tensor_list[0].ndim == 3: + # TODO make it support different-sized images + max_size = _max_by_axis([list(img.shape) for img in tensor_list]) + # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list])) + batch_shape = [len(tensor_list)] + max_size + b, c, h, w = batch_shape + dtype = tensor_list[0].dtype + device = tensor_list[0].device + tensor = torch.zeros(batch_shape, dtype=dtype, device=device) + mask = torch.ones((b, h, w), dtype=torch.bool, device=device) + for img, pad_img, m in zip(tensor_list, tensor, mask): + pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img) + m[: img.shape[1], : img.shape[2]] = False + else: + raise ValueError("not supported") + return NestedTensor(tensor, mask) + + +class NestedTensor(object): + def __init__(self, tensors, mask: Optional[Tensor]): + self.tensors = tensors + self.mask = mask + + def to(self, device, non_blocking=False): + # type: (Device) -> NestedTensor # noqa + cast_tensor = self.tensors.to(device, non_blocking=non_blocking) + mask = self.mask + if mask is not None: + assert mask is not None + cast_mask = mask.to(device, non_blocking=non_blocking) + else: + cast_mask = None + return NestedTensor(cast_tensor, cast_mask) + + def record_stream(self, *args, **kwargs): + self.tensors.record_stream(*args, **kwargs) + if self.mask is not None: + self.mask.record_stream(*args, **kwargs) + + def decompose(self): + return self.tensors, self.mask + + def __repr__(self): + return str(self.tensors) + + +def setup_for_distributed(is_master): + """ + This function disables printing when not in master process + """ + import builtins as __builtin__ + + builtin_print = __builtin__.print + + def print(*args, **kwargs): + force = kwargs.pop("force", False) + if is_master or force: + builtin_print(*args, **kwargs) + + __builtin__.print = print + + +def is_dist_avail_and_initialized(): + if not dist.is_available(): + return False + if not dist.is_initialized(): + return False + return True + + +def get_world_size(): + if not is_dist_avail_and_initialized(): + return 1 + return dist.get_world_size() + + +def get_rank(): + if not is_dist_avail_and_initialized(): + return 0 + return dist.get_rank() + + +def get_local_size(): + if not is_dist_avail_and_initialized(): + return 1 + return int(os.environ["LOCAL_SIZE"]) + + +def get_local_rank(): + if not is_dist_avail_and_initialized(): + return 0 + return int(os.environ["LOCAL_RANK"]) + + +def is_main_process(): + return get_rank() == 0 + + +def save_on_master(*args, **kwargs): + if is_main_process(): + torch.save(*args, **kwargs) + + +def init_distributed_mode(args): + if "RANK" in os.environ and "WORLD_SIZE" in os.environ: + args.rank = int(os.environ["RANK"]) + args.world_size = int(os.environ["WORLD_SIZE"]) + args.gpu = int(os.environ["LOCAL_RANK"]) + args.dist_url = "env://" + os.environ["LOCAL_SIZE"] = str(torch.cuda.device_count()) + elif "SLURM_PROCID" in os.environ: + proc_id = int(os.environ["SLURM_PROCID"]) + ntasks = int(os.environ["SLURM_NTASKS"]) + node_list = os.environ["SLURM_NODELIST"] + num_gpus = torch.cuda.device_count() + addr = subprocess.getoutput("scontrol show hostname {} | head -n1".format(node_list)) + os.environ["MASTER_PORT"] = os.environ.get("MASTER_PORT", "29500") + os.environ["MASTER_ADDR"] = addr + os.environ["WORLD_SIZE"] = str(ntasks) + os.environ["RANK"] = str(proc_id) + os.environ["LOCAL_RANK"] = str(proc_id % num_gpus) + os.environ["LOCAL_SIZE"] = str(num_gpus) + args.dist_url = "env://" + args.world_size = ntasks + args.rank = proc_id + args.gpu = proc_id % num_gpus + else: + print("Not using distributed mode") + args.distributed = False + return + + args.distributed = True + + torch.cuda.set_device(args.gpu) + args.dist_backend = "nccl" + print("| distributed init (rank {}): {}".format(args.rank, args.dist_url), flush=True) + torch.distributed.init_process_group( + backend=args.dist_backend, + init_method=args.dist_url, + world_size=args.world_size, + rank=args.rank, + ) + torch.distributed.barrier() + setup_for_distributed(args.rank == 0) + + +@torch.no_grad() +def accuracy(output, target, topk=(1,)): + """Computes the precision@k for the specified values of k""" + if target.numel() == 0: + return [torch.zeros([], device=output.device)] + maxk = max(topk) + batch_size = target.size(0) + + _, pred = output.topk(maxk, 1, True, True) + pred = pred.t() + correct = pred.eq(target.view(1, -1).expand_as(pred)) + + res = [] + for k in topk: + correct_k = correct[:k].view(-1).float().sum(0) + res.append(correct_k.mul_(100.0 / batch_size)) + return res + + +def interpolate(input, size=None, scale_factor=None, mode="nearest", align_corners=None): + # type: (Tensor, Optional[List[int]], Optional[float], str, Optional[bool]) -> Tensor + """ + Equivalent to nn.functional.interpolate, but with support for empty batch sizes. + This will eventually be supported natively by PyTorch, and this + class can go away. + """ + if float(torchvision.__version__[:3]) < 0.7: + if input.numel() > 0: + return torch.nn.functional.interpolate(input, size, scale_factor, mode, align_corners) + + output_shape = _output_size(2, input, size, scale_factor) + output_shape = list(input.shape[:-2]) + list(output_shape) + if float(torchvision.__version__[:3]) < 0.5: + return _NewEmptyTensorOp.apply(input, output_shape) + return _new_empty_tensor(input, output_shape) + else: + return torchvision.ops.misc.interpolate(input, size, scale_factor, mode, align_corners) + + +def get_total_grad_norm(parameters, norm_type=2): + parameters = list(filter(lambda p: p.grad is not None, parameters)) + norm_type = float(norm_type) + device = parameters[0].grad.device + total_norm = torch.norm( + torch.stack([torch.norm(p.grad.detach(), norm_type).to(device) for p in parameters]), + norm_type, + ) + return total_norm + + +def inverse_sigmoid(x, eps=1e-5): + x = x.clamp(min=0, max=1) + x1 = x.clamp(min=eps) + x2 = (1 - x).clamp(min=eps) + return torch.log(x1 / x2) diff --git a/ape/utils/plot_utils.py b/ape/utils/plot_utils.py new file mode 100644 index 0000000..0a89b4e --- /dev/null +++ b/ape/utils/plot_utils.py @@ -0,0 +1,120 @@ +# ------------------------------------------------------------------------ +# Deformable DETR +# Copyright (c) 2020 SenseTime. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ +# Modified from DETR (https://github.com/facebookresearch/detr) +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +# ------------------------------------------------------------------------ + +""" +Plotting utilities to visualize training logs. +""" +from pathlib import Path, PurePath + +import matplotlib.pyplot as plt +import pandas as pd +import torch + +import seaborn as sns + + +def plot_logs( + logs, fields=("class_error", "loss_bbox_unscaled", "mAP"), ewm_col=0, log_name="log.txt" +): + """ + Function to plot specific fields from training log(s). Plots both training and test results. + + :: Inputs - logs = list containing Path objects, each pointing to individual dir with a log file + - fields = which results to plot from each log file - plots both training and test for each field. + - ewm_col = optional, which column to use as the exponential weighted smoothing of the plots + - log_name = optional, name of log file if different than default 'log.txt'. + + :: Outputs - matplotlib plots of results in fields, color coded for each log file. + - solid lines are training results, dashed lines are test results. + + """ + func_name = "plot_utils.py::plot_logs" + + # verify logs is a list of Paths (list[Paths]) or single Pathlib object Path, + # convert single Path to list to avoid 'not iterable' error + + if not isinstance(logs, list): + if isinstance(logs, PurePath): + logs = [logs] + print(f"{func_name} info: logs param expects a list argument, converted to list[Path].") + else: + raise ValueError( + f"{func_name} - invalid argument for logs parameter.\n \ + Expect list[Path] or single Path obj, received {type(logs)}" + ) + + # verify valid dir(s) and that every item in list is Path object + for i, dir in enumerate(logs): + if not isinstance(dir, PurePath): + raise ValueError( + f"{func_name} - non-Path object in logs argument of {type(dir)}: \n{dir}" + ) + if dir.exists(): + continue + raise ValueError(f"{func_name} - invalid directory in logs argument:\n{dir}") + + # load log file(s) and plot + dfs = [pd.read_json(Path(p) / log_name, lines=True) for p in logs] + + fig, axs = plt.subplots(ncols=len(fields), figsize=(16, 5)) + + for df, color in zip(dfs, sns.color_palette(n_colors=len(logs))): + for j, field in enumerate(fields): + if field == "mAP": + coco_eval = ( + pd.DataFrame(pd.np.stack(df.test_coco_eval.dropna().values)[:, 1]) + .ewm(com=ewm_col) + .mean() + ) + axs[j].plot(coco_eval, c=color) + else: + df.interpolate().ewm(com=ewm_col).mean().plot( + y=[f"train_{field}", f"test_{field}"], + ax=axs[j], + color=[color] * 2, + style=["-", "--"], + ) + for ax, field in zip(axs, fields): + ax.legend([Path(p).name for p in logs]) + ax.set_title(field) + + +def plot_precision_recall(files, naming_scheme="iter"): + if naming_scheme == "exp_id": + # name becomes exp_id + names = [f.parts[-3] for f in files] + elif naming_scheme == "iter": + names = [f.stem for f in files] + else: + raise ValueError(f"not supported {naming_scheme}") + fig, axs = plt.subplots(ncols=2, figsize=(16, 5)) + for f, color, name in zip(files, sns.color_palette("Blues", n_colors=len(files)), names): + data = torch.load(f) + # precision is n_iou, n_points, n_cat, n_area, max_det + precision = data["precision"] + recall = data["params"].recThrs + scores = data["scores"] + # take precision for all classes, all areas and 100 detections + precision = precision[0, :, :, 0, -1].mean(1) + scores = scores[0, :, :, 0, -1].mean(1) + prec = precision.mean() + rec = data["recall"][0, :, 0, -1].mean() + print( + f"{naming_scheme} {name}: mAP@50={prec * 100: 05.1f}, " + + f"score={scores.mean():0.3f}, " + + f"f1={2 * prec * rec / (prec + rec + 1e-8):0.3f}" + ) + axs[0].plot(recall, precision, c=color) + axs[1].plot(recall, scores, c=color) + + axs[0].set_title("Precision / Recall") + axs[0].legend(names) + axs[1].set_title("Scores / Recall") + axs[1].legend(names) + return fig, axs diff --git a/configs/ADE20kFull_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024.py b/configs/ADE20kFull_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024.py new file mode 100644 index 0000000..90390fb --- /dev/null +++ b/configs/ADE20kFull_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024.py @@ -0,0 +1,101 @@ +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detrex.modeling.neck import ChannelMapper +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) +from ape.modeling.text import EVA02CLIP + +from ...common.backbone.vitl_eva02_clip import backbone +from .ape_deta_vitl_eva02_lsj1024 import dataloader, lr_multiplier, model, optimizer, train + +model.model_vision.backbone = backbone + +train.init_checkpoint = ( + "models/QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14to16_s6B.pt?matching_heuristics=True" +) + +model.model_language = L(EVA02CLIP)( + clip_model="EVA02-CLIP-bigE-14-plus", + cache_dir="models/QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt", + dtype="float16", +) +model.model_vision.embed_dim_language = 1024 + +model.model_vision.neck = L(ChannelMapper)( + input_shapes={ + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), + }, + in_features=["p2", "p3", "p4", "p5", "p6"], + out_channels=256, + num_outs=5, + kernel_size=1, + norm_layer=L(nn.GroupNorm)(num_groups=32, num_channels=256), +) + +model.model_vision.mask_in_features = ["p2"] +model.model_vision.input_shapes = { + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), +} + +model.model_vision.transformer.encoder.num_layers = 6 +model.model_vision.transformer.decoder.num_layers = 6 +model.model_vision.transformer.encoder.embed_dim = 256 +model.model_vision.transformer.decoder.embed_dim = 256 +model.model_vision.embed_dim = 256 +model.model_vision.backbone.out_channels = 256 + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +model.model_vision.stuff_dataset_learn_thing = False +model.model_vision.stuff_prob_thing = -1.0 +model.model_vision.transformer.proposal_ambiguous = 1 + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 12800 diff --git a/configs/ADE20kFull_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024.py b/configs/ADE20kFull_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024.py new file mode 100644 index 0000000..5cdad80 --- /dev/null +++ b/configs/ADE20kFull_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024.py @@ -0,0 +1,32 @@ +from detectron2.data import MetadataCatalog + +from ...COCO_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_lsj1024_cp_12ep import ( + lr_multiplier, + model, + optimizer, + train, +) +from ...common.data.ade20kfull_semantic_lsj1024 import dataloader + +stuff_classes = MetadataCatalog.get("ade20k_full_sem_seg_train").stuff_classes +del MetadataCatalog.get("ade20k_full_sem_seg_train").stuff_classes +MetadataCatalog.get("ade20k_full_sem_seg_train").set( + stuff_classes=[x.split(",")[0] for x in stuff_classes] +) + +model.model_vision.dataset_prompts = ["name"] +model.model_vision.name_prompt_fusion_text = [False] +model.model_vision.dataset_names = ["ade20k_full_sem_seg"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +model.model_vision.num_classes = 847 +model.model_vision.criterion[0].num_classes = 847 +model.model_vision.select_box_nums_for_evaluation = 300 + +model.model_vision.instance_on = False +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = False + +model.model_vision.stuff_prob_thing = -1.0 + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/ADE20kFull_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024.py b/configs/ADE20kFull_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024.py new file mode 100644 index 0000000..1a3ad59 --- /dev/null +++ b/configs/ADE20kFull_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024.py @@ -0,0 +1,47 @@ +from detectron2.config import LazyCall as L +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) + +from .ape_deta_vitl_eva02_lsj1024 import dataloader, lr_multiplier, model, optimizer, train + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 12800 diff --git a/configs/ADE20k_PanopticSegmentation/ape_deta/ape_deta_r50_160k.py b/configs/ADE20k_PanopticSegmentation/ape_deta/ape_deta_r50_160k.py new file mode 100644 index 0000000..1f2ed64 --- /dev/null +++ b/configs/ADE20k_PanopticSegmentation/ape_deta/ape_deta_r50_160k.py @@ -0,0 +1,46 @@ +from detectron2.config import LazyCall as L +from detectron2.solver import WarmupParamScheduler +from fvcore.common.param_scheduler import MultiStepParamScheduler + +from ...COCO_InstanceSegmentation.ape_deta.ape_deta_r50_12ep import ( + lr_multiplier, + model, + optimizer, + train, +) +from ...common.data.ade20k_panoptic import dataloader + +num_classes = 150 +model.num_classes = num_classes +model.criterion.num_classes = num_classes +model.criterion.matcher_stage2.num_classes = num_classes + +model.model_vision.dataset_prompts = ["name"] +model.model_vision.dataset_names = ["ade20k_panoptic"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +model.model_vision.instance_on = True +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = True + +model.model_vision.stuff_prob_thing = -1.0 + +train.max_iter = 160000 +train.eval_period = 5000 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1, 0.01], + milestones=[135000, 150000], + num_updates=160000, + ), + warmup_length=1000 / 160000, + warmup_method="linear", + warmup_factor=0.001, +) + +model.model_vision.semantic_post_nms = False +model.model_vision.panoptic_post_nms = True +model.model_vision.aux_mask = True + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/ADE20k_PanopticSegmentation/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024.py b/configs/ADE20k_PanopticSegmentation/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024.py new file mode 100644 index 0000000..90390fb --- /dev/null +++ b/configs/ADE20k_PanopticSegmentation/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024.py @@ -0,0 +1,101 @@ +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detrex.modeling.neck import ChannelMapper +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) +from ape.modeling.text import EVA02CLIP + +from ...common.backbone.vitl_eva02_clip import backbone +from .ape_deta_vitl_eva02_lsj1024 import dataloader, lr_multiplier, model, optimizer, train + +model.model_vision.backbone = backbone + +train.init_checkpoint = ( + "models/QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14to16_s6B.pt?matching_heuristics=True" +) + +model.model_language = L(EVA02CLIP)( + clip_model="EVA02-CLIP-bigE-14-plus", + cache_dir="models/QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt", + dtype="float16", +) +model.model_vision.embed_dim_language = 1024 + +model.model_vision.neck = L(ChannelMapper)( + input_shapes={ + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), + }, + in_features=["p2", "p3", "p4", "p5", "p6"], + out_channels=256, + num_outs=5, + kernel_size=1, + norm_layer=L(nn.GroupNorm)(num_groups=32, num_channels=256), +) + +model.model_vision.mask_in_features = ["p2"] +model.model_vision.input_shapes = { + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), +} + +model.model_vision.transformer.encoder.num_layers = 6 +model.model_vision.transformer.decoder.num_layers = 6 +model.model_vision.transformer.encoder.embed_dim = 256 +model.model_vision.transformer.decoder.embed_dim = 256 +model.model_vision.embed_dim = 256 +model.model_vision.backbone.out_channels = 256 + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +model.model_vision.stuff_dataset_learn_thing = False +model.model_vision.stuff_prob_thing = -1.0 +model.model_vision.transformer.proposal_ambiguous = 1 + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 12800 diff --git a/configs/ADE20k_PanopticSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024.py b/configs/ADE20k_PanopticSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024.py new file mode 100644 index 0000000..d4b04c2 --- /dev/null +++ b/configs/ADE20k_PanopticSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024.py @@ -0,0 +1,22 @@ +from ...COCO_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_lsj1024_cp_12ep import ( + lr_multiplier, + model, + optimizer, + train, +) +from ...common.data.ade20k_panoptic_lsj1024 import dataloader + +model.model_vision.dataset_prompts = ["name"] +model.model_vision.name_prompt_fusion_text = [False] +model.model_vision.dataset_names = ["ade20k"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +model.model_vision.select_box_nums_for_evaluation = 300 + +model.model_vision.instance_on = True +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = True + +model.model_vision.stuff_prob_thing = -1.0 + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/ADE20k_PanopticSegmentation/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024.py b/configs/ADE20k_PanopticSegmentation/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024.py new file mode 100644 index 0000000..1a3ad59 --- /dev/null +++ b/configs/ADE20k_PanopticSegmentation/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024.py @@ -0,0 +1,47 @@ +from detectron2.config import LazyCall as L +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) + +from .ape_deta_vitl_eva02_lsj1024 import dataloader, lr_multiplier, model, optimizer, train + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 12800 diff --git a/configs/ADE20k_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024.py b/configs/ADE20k_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024.py new file mode 100644 index 0000000..90390fb --- /dev/null +++ b/configs/ADE20k_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024.py @@ -0,0 +1,101 @@ +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detrex.modeling.neck import ChannelMapper +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) +from ape.modeling.text import EVA02CLIP + +from ...common.backbone.vitl_eva02_clip import backbone +from .ape_deta_vitl_eva02_lsj1024 import dataloader, lr_multiplier, model, optimizer, train + +model.model_vision.backbone = backbone + +train.init_checkpoint = ( + "models/QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14to16_s6B.pt?matching_heuristics=True" +) + +model.model_language = L(EVA02CLIP)( + clip_model="EVA02-CLIP-bigE-14-plus", + cache_dir="models/QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt", + dtype="float16", +) +model.model_vision.embed_dim_language = 1024 + +model.model_vision.neck = L(ChannelMapper)( + input_shapes={ + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), + }, + in_features=["p2", "p3", "p4", "p5", "p6"], + out_channels=256, + num_outs=5, + kernel_size=1, + norm_layer=L(nn.GroupNorm)(num_groups=32, num_channels=256), +) + +model.model_vision.mask_in_features = ["p2"] +model.model_vision.input_shapes = { + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), +} + +model.model_vision.transformer.encoder.num_layers = 6 +model.model_vision.transformer.decoder.num_layers = 6 +model.model_vision.transformer.encoder.embed_dim = 256 +model.model_vision.transformer.decoder.embed_dim = 256 +model.model_vision.embed_dim = 256 +model.model_vision.backbone.out_channels = 256 + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +model.model_vision.stuff_dataset_learn_thing = False +model.model_vision.stuff_prob_thing = -1.0 +model.model_vision.transformer.proposal_ambiguous = 1 + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 12800 diff --git a/configs/ADE20k_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024.py b/configs/ADE20k_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024.py new file mode 100644 index 0000000..07f43a9 --- /dev/null +++ b/configs/ADE20k_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024.py @@ -0,0 +1,22 @@ +from ...COCO_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_lsj1024_cp_12ep import ( + lr_multiplier, + model, + optimizer, + train, +) +from ...common.data.ade20k_semantic_lsj1024 import dataloader + +model.model_vision.dataset_prompts = ["name"] +model.model_vision.name_prompt_fusion_text = [False] +model.model_vision.dataset_names = ["ade20k"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +model.model_vision.select_box_nums_for_evaluation = 300 + +model.model_vision.instance_on = False +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = False + +model.model_vision.stuff_prob_thing = -1.0 + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/ADE20k_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024.py b/configs/ADE20k_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024.py new file mode 100644 index 0000000..1a3ad59 --- /dev/null +++ b/configs/ADE20k_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024.py @@ -0,0 +1,47 @@ +from detectron2.config import LazyCall as L +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) + +from .ape_deta_vitl_eva02_lsj1024 import dataloader, lr_multiplier, model, optimizer, train + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 12800 diff --git a/configs/ADE20k_SemanticSegmentation/deformable_deta/deformable_deta_segm_r50_160k.py b/configs/ADE20k_SemanticSegmentation/deformable_deta/deformable_deta_segm_r50_160k.py new file mode 100644 index 0000000..2d5d82f --- /dev/null +++ b/configs/ADE20k_SemanticSegmentation/deformable_deta/deformable_deta_segm_r50_160k.py @@ -0,0 +1,45 @@ +from detectron2.config import LazyCall as L +from detectron2.solver import WarmupParamScheduler +from fvcore.common.param_scheduler import MultiStepParamScheduler + +from ...COCO_InstanceSegmentation.deformable_deta.deformable_deta_segm_r50_12ep import ( + lr_multiplier, + model, + optimizer, + train, +) +from ...common.data.ade20k_semantic import dataloader + +num_classes = 150 +model.num_classes = num_classes +model.criterion.num_classes = num_classes +model.criterion.matcher_stage2.num_classes = num_classes +model.criterion.eos_coef = 1.0 + +model.instance_on = False +model.semantic_on = True +model.panoptic_on = False + +train.max_iter = 160000 +train.eval_period = 5000 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1, 0.01], + milestones=[135000, 150000], + num_updates=160000, + ), + warmup_length=1000 / 160000, + warmup_method="linear", + warmup_factor=0.001, +) + +train.init_checkpoint = "detectron2://ImageNetPretrained/torchvision/R-50.pkl" +train.init_checkpoint = "models/torchvision/R-50.pkl" +train.output_dir = "output/" + __file__[:-3] + +train.amp.enabled = True +train.ddp.fp16_compression = True +train.ddp.find_unused_parameters = True + +model.dataset_metas = dataloader.train.dataset.names diff --git a/configs/BDD10k_PanopticSegmentation/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024.py b/configs/BDD10k_PanopticSegmentation/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024.py new file mode 100644 index 0000000..427cdfa --- /dev/null +++ b/configs/BDD10k_PanopticSegmentation/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024.py @@ -0,0 +1,109 @@ +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detrex.modeling.neck import ChannelMapper +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) +from ape.modeling.text import EVA02CLIP + +from ...common.backbone.vitl_eva02_clip import backbone +from .ape_deta_vitl_eva02_lsj1024 import dataloader, lr_multiplier, model, optimizer, train + +model.model_vision.backbone = backbone + +train.init_checkpoint = ( + "models/QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14to16_s6B.pt?matching_heuristics=True" +) + +model.model_language = L(EVA02CLIP)( + clip_model="EVA02-CLIP-bigE-14-plus", + cache_dir="models/QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt", + dtype="float16", +) +model.model_vision.embed_dim_language = 1024 + +model.model_vision.neck = L(ChannelMapper)( + input_shapes={ + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), + }, + in_features=["p2", "p3", "p4", "p5", "p6"], + out_channels=256, + num_outs=5, + kernel_size=1, + norm_layer=L(nn.GroupNorm)(num_groups=32, num_channels=256), +) + +model.model_vision.mask_in_features = ["p2"] +model.model_vision.input_shapes = { + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), +} + +model.model_vision.transformer.encoder.num_layers = 6 +model.model_vision.transformer.decoder.num_layers = 6 +model.model_vision.transformer.encoder.embed_dim = 256 +model.model_vision.transformer.decoder.embed_dim = 256 +model.model_vision.embed_dim = 256 +model.model_vision.backbone.out_channels = 256 + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +model.model_vision.stuff_dataset_learn_thing = False +model.model_vision.stuff_prob_thing = -1.0 +model.model_vision.transformer.proposal_ambiguous = 1 + +model.model_vision.panoptic_configs = { + "prob": 0.01, + "pano_temp": 0.06, + "transform_eval": True, + "object_mask_threshold": 0.0001, + "overlap_threshold": 0.4, +} + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 12800 diff --git a/configs/BDD10k_PanopticSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024.py b/configs/BDD10k_PanopticSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024.py new file mode 100644 index 0000000..2c96079 --- /dev/null +++ b/configs/BDD10k_PanopticSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024.py @@ -0,0 +1,23 @@ +from ...COCO_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_lsj1024_cp_12ep import ( + lr_multiplier, + model, + optimizer, + train, +) +from ...common.data.bdd10k_panoptic_lsj1024 import dataloader + +model.model_vision.dataset_prompts = ["name"] +model.model_vision.name_prompt_fusion_text = [False] +model.model_vision.dataset_names = ["bdd10k"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +model.model_vision.select_box_nums_for_evaluation = 300 + + +model.model_vision.instance_on = True +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = True + +model.model_vision.stuff_prob_thing = -1.0 + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/BDD10k_PanopticSegmentation/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024.py b/configs/BDD10k_PanopticSegmentation/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024.py new file mode 100644 index 0000000..1a3ad59 --- /dev/null +++ b/configs/BDD10k_PanopticSegmentation/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024.py @@ -0,0 +1,47 @@ +from detectron2.config import LazyCall as L +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) + +from .ape_deta_vitl_eva02_lsj1024 import dataloader, lr_multiplier, model, optimizer, train + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 12800 diff --git a/configs/BDD10k_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024.py b/configs/BDD10k_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024.py new file mode 100644 index 0000000..90390fb --- /dev/null +++ b/configs/BDD10k_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024.py @@ -0,0 +1,101 @@ +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detrex.modeling.neck import ChannelMapper +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) +from ape.modeling.text import EVA02CLIP + +from ...common.backbone.vitl_eva02_clip import backbone +from .ape_deta_vitl_eva02_lsj1024 import dataloader, lr_multiplier, model, optimizer, train + +model.model_vision.backbone = backbone + +train.init_checkpoint = ( + "models/QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14to16_s6B.pt?matching_heuristics=True" +) + +model.model_language = L(EVA02CLIP)( + clip_model="EVA02-CLIP-bigE-14-plus", + cache_dir="models/QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt", + dtype="float16", +) +model.model_vision.embed_dim_language = 1024 + +model.model_vision.neck = L(ChannelMapper)( + input_shapes={ + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), + }, + in_features=["p2", "p3", "p4", "p5", "p6"], + out_channels=256, + num_outs=5, + kernel_size=1, + norm_layer=L(nn.GroupNorm)(num_groups=32, num_channels=256), +) + +model.model_vision.mask_in_features = ["p2"] +model.model_vision.input_shapes = { + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), +} + +model.model_vision.transformer.encoder.num_layers = 6 +model.model_vision.transformer.decoder.num_layers = 6 +model.model_vision.transformer.encoder.embed_dim = 256 +model.model_vision.transformer.decoder.embed_dim = 256 +model.model_vision.embed_dim = 256 +model.model_vision.backbone.out_channels = 256 + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +model.model_vision.stuff_dataset_learn_thing = False +model.model_vision.stuff_prob_thing = -1.0 +model.model_vision.transformer.proposal_ambiguous = 1 + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 12800 diff --git a/configs/BDD10k_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024.py b/configs/BDD10k_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024.py new file mode 100644 index 0000000..5865c38 --- /dev/null +++ b/configs/BDD10k_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024.py @@ -0,0 +1,22 @@ +from ...COCO_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_lsj1024_cp_12ep import ( + lr_multiplier, + model, + optimizer, + train, +) +from ...common.data.bdd10k_semantic_lsj1024 import dataloader + +model.model_vision.dataset_prompts = ["name"] +model.model_vision.name_prompt_fusion_text = [False] +model.model_vision.dataset_names = ["bdd10k"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +model.model_vision.select_box_nums_for_evaluation = 300 + +model.model_vision.instance_on = False +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = False + +model.model_vision.stuff_prob_thing = -1.0 + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/BDD10k_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024.py b/configs/BDD10k_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024.py new file mode 100644 index 0000000..1a3ad59 --- /dev/null +++ b/configs/BDD10k_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024.py @@ -0,0 +1,47 @@ +from detectron2.config import LazyCall as L +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) + +from .ape_deta_vitl_eva02_lsj1024 import dataloader, lr_multiplier, model, optimizer, train + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 12800 diff --git a/configs/COCO_Detection/deformable_deta/deformable_deta_r50_12ep.py b/configs/COCO_Detection/deformable_deta/deformable_deta_r50_12ep.py new file mode 100644 index 0000000..f8c7048 --- /dev/null +++ b/configs/COCO_Detection/deformable_deta/deformable_deta_r50_12ep.py @@ -0,0 +1,48 @@ +from detrex.config import get_config + +from .models.deformable_deta_r50 import model + +dataloader = get_config("common/data/coco_detr.py").dataloader +lr_multiplier = get_config("common/coco_schedule.py").lr_multiplier_12ep +lr_multiplier.scheduler.milestones = [75000, 90000] +optimizer = get_config("common/optim.py").AdamW +train = get_config("common/train.py").train + +train.init_checkpoint = "detectron2://ImageNetPretrained/torchvision/R-50.pkl" +train.init_checkpoint = "models/torchvision/R-50.pkl" +train.output_dir = "output/" + __file__[:-3] + +train.max_iter = 90000 + +train.eval_period = 5000 + +train.log_period = 20 + +train.checkpointer.period = 5000 +train.checkpointer.max_to_keep = 2 + +train.clip_grad.enabled = True +train.clip_grad.params.max_norm = 0.1 +train.clip_grad.params.norm_type = 2 + +train.device = "cuda" + +optimizer.lr = 2e-4 +optimizer.betas = (0.9, 0.999) +optimizer.weight_decay = 1e-4 +optimizer.params.lr_factor_func = ( + lambda module_name: 0.1 + if "backbone" in module_name + or "reference_points" in module_name + or "sampling_offsets" in module_name + else 1 +) +optimizer.params.weight_decay_norm = None + +dataloader.train.num_workers = 16 + +dataloader.train.total_batch_size = 16 + + +train.amp.enabled = False +train.ddp.fp16_compression = False diff --git a/configs/COCO_Detection/deformable_deta/deformable_deta_r50_24ep.py b/configs/COCO_Detection/deformable_deta/deformable_deta_r50_24ep.py new file mode 100644 index 0000000..b1f43c6 --- /dev/null +++ b/configs/COCO_Detection/deformable_deta/deformable_deta_r50_24ep.py @@ -0,0 +1,47 @@ +from detrex.config import get_config + +from .models.deformable_deta_r50 import model + +dataloader = get_config("common/data/coco_detr.py").dataloader +lr_multiplier = get_config("common/coco_schedule.py").lr_multiplier_24ep +optimizer = get_config("common/optim.py").AdamW +train = get_config("common/train.py").train + +train.init_checkpoint = "detectron2://ImageNetPretrained/torchvision/R-50.pkl" +train.init_checkpoint = "models/torchvision/R-50.pkl" +train.output_dir = "output/" + __file__[:-3] + +train.max_iter = 180000 + +train.eval_period = 5000 + +train.log_period = 20 + +train.checkpointer.period = 5000 +train.checkpointer.max_to_keep = 2 + +train.clip_grad.enabled = True +train.clip_grad.params.max_norm = 0.1 +train.clip_grad.params.norm_type = 2 + +train.device = "cuda" + +optimizer.lr = 2e-4 +optimizer.betas = (0.9, 0.999) +optimizer.weight_decay = 1e-4 +optimizer.params.lr_factor_func = ( + lambda module_name: 0.1 + if "backbone" in module_name + or "reference_points" in module_name + or "sampling_offsets" in module_name + else 1 +) +optimizer.params.weight_decay_norm = None + +dataloader.train.num_workers = 16 + +dataloader.train.total_batch_size = 16 + + +train.amp.enabled = False +train.ddp.fp16_compression = False diff --git a/configs/COCO_Detection/deformable_deta/deformable_deta_vitb_clip_openai_lsj1024_cp_12ep.py b/configs/COCO_Detection/deformable_deta/deformable_deta_vitb_clip_openai_lsj1024_cp_12ep.py new file mode 100644 index 0000000..4682cf6 --- /dev/null +++ b/configs/COCO_Detection/deformable_deta/deformable_deta_vitb_clip_openai_lsj1024_cp_12ep.py @@ -0,0 +1,20 @@ + + +from ...common.data.coco_lsj1024_cp import dataloader +from ...common.data.constants import constants +from .deformable_deta_vitb_lsj1024_12ep import lr_multiplier, model, optimizer, train + +model.pixel_mean = constants.openai_imagenet_rgb256_mean +model.pixel_std = constants.openai_imagenet_rgb256_std +model.input_format = "RGB" +dataloader.train.mapper.image_format = "RGB" + + + + +train.init_checkpoint = "models/CLIP/ViT-B-16.pt" + +train.output_dir = "output/" + __file__[:-3] +dataloader.evaluator.output_dir = train.output_dir +dataloader.train.mapper.output_dir = train.output_dir +dataloader.train.mapper.vis_period = 1 diff --git a/configs/COCO_Detection/deformable_deta/deformable_deta_vitb_lsj1024_12ep.py b/configs/COCO_Detection/deformable_deta/deformable_deta_vitb_lsj1024_12ep.py new file mode 100644 index 0000000..9a0d18c --- /dev/null +++ b/configs/COCO_Detection/deformable_deta/deformable_deta_vitb_lsj1024_12ep.py @@ -0,0 +1,79 @@ +from functools import partial + +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.modeling import SimpleFeaturePyramid, ViT +from detectron2.modeling.backbone.fpn import LastLevelMaxPool +from detectron2.modeling.backbone.vit import get_vit_lr_decay_rate + +from .....detectron2.configs.common.data.constants import constants +from .....detectron2.projects.ViTDet.configs.common.coco_loader_lsj import dataloader +from .deformable_deta_r50_12ep import lr_multiplier, model, optimizer, train + +model.pixel_mean = constants.imagenet_rgb256_mean +model.pixel_std = constants.imagenet_rgb256_std +model.input_format = "RGB" +dataloader.train.mapper.image_format = "RGB" +dataloader.train.total_batch_size = 16 + + +embed_dim, depth, num_heads, dp = 768, 12, 12, 0.1 +model.backbone = L(SimpleFeaturePyramid)( + net=L(ViT)( # Single-scale ViT backbone + img_size=1024, + patch_size=16, + embed_dim=embed_dim, + depth=depth, + num_heads=num_heads, + drop_path_rate=dp, + window_size=14, + mlp_ratio=4, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + window_block_indexes=[ + 0, + 1, + 3, + 4, + 6, + 7, + 9, + 10, + ], + residual_block_indexes=[], + use_rel_pos=True, + out_feature="last_feat", + ), + in_feature="${.net.out_feature}", + out_channels=256, + scale_factors=(4.0, 2.0, 1.0, 0.5), + top_block=L(LastLevelMaxPool)(), + norm="LN", + square_pad=1024, +) + +model.neck = None + +optimizer.params.lr_factor_func = ( + lambda module_name: 0.1 + if "reference_points" in module_name or "sampling_offsets" in module_name + else get_vit_lr_decay_rate(module_name, lr_decay_rate=0.7, num_layers=12) + if "backbone" in module_name + else 1 +) +optimizer.params.overrides = {"pos_embed": {"weight_decay": 0.0}} + + +lr_multiplier.warmup_length = 1000 / train.max_iter + +train.amp.enabled = False +train.ddp.fp16_compression = False + +train.init_checkpoint = ( + "detectron2://ImageNetPretrained/MAE/mae_pretrain_vit_base.pth?matching_heuristics=True" +) +train.init_checkpoint = "models/MAE/mae_pretrain_vit_base.pth?matching_heuristics=True" + +train.output_dir = "output/" + __file__[:-3] +dataloader.evaluator.output_dir = train.output_dir diff --git a/configs/COCO_Detection/deformable_deta/deformable_deta_vitg_eva_lsj1024_12ep.py b/configs/COCO_Detection/deformable_deta/deformable_deta_vitg_eva_lsj1024_12ep.py new file mode 100644 index 0000000..83334d0 --- /dev/null +++ b/configs/COCO_Detection/deformable_deta/deformable_deta_vitg_eva_lsj1024_12ep.py @@ -0,0 +1,62 @@ +from functools import partial + +from ape.modeling.backbone.vit_eva import SimpleFeaturePyramid, ViT, get_vit_lr_decay_rate + +from ..common.coco_loader_lsj1280 import dataloader +from .deformable_deta_vitb_lsj1024_12ep import lr_multiplier, model, optimizer, train + +model.backbone.update( + _target_=SimpleFeaturePyramid, +) +model.backbone.net.update( + _target_=ViT, +) + +dataloader.train.total_batch_size = 16 + +model.backbone.net.beit_like_qkv_bias = True +model.backbone.net.beit_like_gamma = False +model.backbone.net.freeze_patch_embed = True +model.backbone.square_pad = 1280 +model.backbone.net.img_size = 1280 +model.backbone.net.patch_size = 16 +model.backbone.net.window_size = 16 +model.backbone.net.embed_dim = 1408 +model.backbone.net.depth = 40 +model.backbone.net.num_heads = 16 +model.backbone.net.mlp_ratio = 6144 / 1408 +model.backbone.net.use_act_checkpoint = True +model.backbone.net.drop_path_rate = 0.6 # 0.5 --> 0.6 +model.backbone.net.window_block_indexes = ( + list(range(0, 3)) + + list(range(4, 7)) + + list(range(8, 11)) + + list(range(12, 15)) + + list(range(16, 19)) + + list(range(20, 23)) + + list(range(24, 27)) + + list(range(28, 31)) + + list(range(32, 35)) + + list(range(36, 39)) +) + +optimizer.lr = 2e-4 +optimizer.params.lr_factor_func = ( + lambda module_name: 0.1 + if "reference_points" in module_name or "sampling_offsets" in module_name + else get_vit_lr_decay_rate(module_name, lr_decay_rate=0.9, num_layers=40) + if "backbone" in module_name + else 1 +) +optimizer.params.overrides = {"pos_embed": {"weight_decay": 0.0}} +optimizer.params.weight_decay_norm = None + +train.amp.enabled = False +train.ddp.fp16_compression = False + +model.backbone.net.use_act_checkpoint = False +model.backbone.net.frozen_stages = 41 + +train.init_checkpoint = "models/BAAI/EVA/eva_o365.pth?matching_heuristics=True" +train.output_dir = "output/" + __file__[:-3] +dataloader.evaluator.output_dir = train.output_dir diff --git a/configs/COCO_Detection/deformable_deta/deformable_deta_vitg_eva_lsj1024_cp_12ep.py b/configs/COCO_Detection/deformable_deta/deformable_deta_vitg_eva_lsj1024_cp_12ep.py new file mode 100644 index 0000000..5e38089 --- /dev/null +++ b/configs/COCO_Detection/deformable_deta/deformable_deta_vitg_eva_lsj1024_cp_12ep.py @@ -0,0 +1,12 @@ +from ....configs.common.data.coco_lsj1024_cp import dataloader +from .deformable_deta_vitg_eva_lsj1024_12ep import lr_multiplier, model, optimizer, train + +train.amp.enabled = True +train.ddp.fp16_compression = True + +model.backbone.net.use_act_checkpoint = True +model.backbone.net.frozen_stages = 20 + +train.output_dir = "output/" + __file__[:-3] +dataloader.evaluator.output_dir = train.output_dir +dataloader.train.mapper.output_dir = train.output_dir diff --git a/configs/COCO_Detection/deformable_deta/deformable_deta_vitl_eva02_lsj1024_cp_12ep.py b/configs/COCO_Detection/deformable_deta/deformable_deta_vitl_eva02_lsj1024_cp_12ep.py new file mode 100644 index 0000000..fde4dca --- /dev/null +++ b/configs/COCO_Detection/deformable_deta/deformable_deta_vitl_eva02_lsj1024_cp_12ep.py @@ -0,0 +1,101 @@ +from functools import partial + +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.data.catalog import MetadataCatalog +from detectron2.layers import ShapeSpec +from detectron2.modeling.backbone.fpn import LastLevelMaxPool +from detrex.config import get_config +from ape.modeling.backbone.vit_eva02 import SimpleFeaturePyramid, ViT, get_vit_lr_decay_rate + +from .....detectron2.configs.common.data.constants import constants +from ...common.data.coco_instance_lsj1024_cp import dataloader +from .models.deformable_deta_r50 import model + +model.pixel_mean = constants.imagenet_rgb256_mean +model.pixel_std = constants.imagenet_rgb256_std +model.input_format = "RGB" + +model.backbone = L(SimpleFeaturePyramid)( + net=L(ViT)( # Single-scale ViT backbone + img_size=1024, + patch_size=16, + embed_dim=1024, + depth=24, + num_heads=16, + drop_path_rate=0.4, + window_size=16, + mlp_ratio=4 * 2 / 3, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + window_block_indexes=list(range(0, 5)) + + list(range(6, 11)) + + list(range(12, 17)) + + list(range(18, 23)), + residual_block_indexes=[], + use_rel_pos=True, + out_feature="last_feat", + use_act_checkpoint=False, + xattn=True, + ), + in_feature="${.net.out_feature}", + out_channels=256, + scale_factors=(4.0, 2.0, 1.0, 0.5), + top_block=L(LastLevelMaxPool)(), + norm="LN", + square_pad=1024, +) + +model.neck = None + +optimizer = get_config("common/optim.py").AdamW +optimizer.params.lr_factor_func = ( + lambda module_name: 0.1 + if "reference_points" in module_name or "sampling_offsets" in module_name + else get_vit_lr_decay_rate(module_name, lr_decay_rate=0.8, num_layers=24) + if "backbone" in module_name + else 1 +) +optimizer.params.overrides = {"pos_embed": {"weight_decay": 0.0}} +optimizer.params.weight_decay_norm = None + +optimizer.lr = 2e-4 +optimizer.weight_decay = 1e-4 + +train = get_config("common/train.py").train +train.max_iter = 90000 +train.eval_period = 5000 +train.log_period = 20 + +train.checkpointer.period = 5000 +train.checkpointer.max_to_keep = 2 + +train.clip_grad.enabled = True +train.clip_grad.params.max_norm = 0.1 +train.clip_grad.params.norm_type = 2 + +train.device = "cuda" + +train.init_checkpoint = ( + "models/Yunxin-CV/EVA-02/eva02/pt/eva02_L_pt_in21k_p14to16.pt?matching_heuristics=True" +) + +train.amp.enabled = True +train.ddp.fp16_compression = True + +lr_multiplier = get_config("common/coco_schedule.py").lr_multiplier_12ep +lr_multiplier.scheduler.milestones = [75000, 90000] +lr_multiplier.warmup_length = 1000 / train.max_iter + +dataloader.train.num_workers = 16 +dataloader.train.total_batch_size = 16 +dataloader.train.mapper.image_format = "RGB" + +if isinstance(dataloader.train.dataset.names, str): + model.metadata = MetadataCatalog.get(dataloader.train.dataset.names) +else: + model.metadata = MetadataCatalog.get(dataloader.train.dataset.names[0]) + +train.output_dir = "output/" + __file__[:-3] +dataloader.train.mapper.output_dir = train.output_dir diff --git a/configs/COCO_Detection/deformable_deta/deformable_deta_vitl_eva_lsj1024_cp_12ep.py b/configs/COCO_Detection/deformable_deta/deformable_deta_vitl_eva_lsj1024_cp_12ep.py new file mode 100644 index 0000000..244c3d9 --- /dev/null +++ b/configs/COCO_Detection/deformable_deta/deformable_deta_vitl_eva_lsj1024_cp_12ep.py @@ -0,0 +1,25 @@ +from detectron2.modeling.backbone.vit import get_vit_lr_decay_rate + +from ...common.data.coco_lsj1024_cp import dataloader +from .deformable_deta_vitl_lsj1024_12ep import lr_multiplier, model, optimizer, train + +train.init_checkpoint = "models/BAAI/EVA/eva_l_psz14to16.pt?matching_heuristics=True" + +optimizer.params.lr_factor_func = ( + lambda module_name: 0.1 + if "reference_points" in module_name or "sampling_offsets" in module_name + else get_vit_lr_decay_rate(module_name, lr_decay_rate=0.8, num_layers=24) + if "backbone" in module_name + else 1 +) + +optimizer.lr = 2e-4 +optimizer.weight_decay = 1e-4 + +train.amp.enabled = True +train.ddp.fp16_compression = True +model.backbone.net.use_act_checkpoint = False + +train.output_dir = "output/" + __file__[:-3] +dataloader.evaluator.output_dir = train.output_dir +dataloader.train.mapper.output_dir = train.output_dir diff --git a/configs/COCO_Detection/deformable_deta/deformable_deta_vitl_lsj1024_12ep.py b/configs/COCO_Detection/deformable_deta/deformable_deta_vitl_lsj1024_12ep.py new file mode 100644 index 0000000..67b1fa3 --- /dev/null +++ b/configs/COCO_Detection/deformable_deta/deformable_deta_vitl_lsj1024_12ep.py @@ -0,0 +1,35 @@ +from detectron2.modeling.backbone.vit import get_vit_lr_decay_rate + +from .deformable_deta_vitb_lsj1024_12ep import dataloader, lr_multiplier, model, optimizer, train + +model.backbone.net.embed_dim = 1024 +model.backbone.net.depth = 24 +model.backbone.net.num_heads = 16 +model.backbone.net.drop_path_rate = 0.4 +model.backbone.net.window_block_indexes = ( + list(range(0, 5)) + list(range(6, 11)) + list(range(12, 17)) + list(range(18, 23)) +) + +optimizer.params.lr_factor_func = ( + lambda module_name: 0.1 + if "reference_points" in module_name or "sampling_offsets" in module_name + else get_vit_lr_decay_rate(module_name, lr_decay_rate=0.8, num_layers=24) + if "backbone" in module_name + else 1 +) +optimizer.params.overrides = {"pos_embed": {"weight_decay": 0.0}} + +optimizer.lr = 2e-4 +optimizer.weight_decay = 0.05 + +train.init_checkpoint = ( + "detectron2://ImageNetPretrained/MAE/mae_pretrain_vit_large.pth?matching_heuristics=True" +) +train.init_checkpoint = "models/MAE/mae_pretrain_vit_large.pth?matching_heuristics=True" + +train.amp.enabled = True +train.ddp.fp16_compression = True +model.backbone.net.use_act_checkpoint = False + +train.output_dir = "output/" + __file__[:-3] +dataloader.evaluator.output_dir = train.output_dir diff --git a/configs/COCO_Detection/deformable_deta/models/deformable_deta_r50.py b/configs/COCO_Detection/deformable_deta/models/deformable_deta_r50.py new file mode 100644 index 0000000..35ba17f --- /dev/null +++ b/configs/COCO_Detection/deformable_deta/models/deformable_deta_r50.py @@ -0,0 +1,124 @@ +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detectron2.modeling.backbone import BasicStem, ResNet +from detrex.layers import PositionEmbeddingSine +from detrex.modeling.matcher import HungarianMatcher +from detrex.modeling.neck import ChannelMapper +from ape.modeling.deta import ( + DeformableCriterion, + DeformableDETR, + DeformableDetrTransformer, + DeformableDetrTransformerDecoder, + DeformableDetrTransformerEncoder, + Stage1Assigner, + Stage2Assigner, +) + + + +model = L(DeformableDETR)( + backbone=L(ResNet)( + stem=L(BasicStem)(in_channels=3, out_channels=64, norm="FrozenBN"), + stages=L(ResNet.make_default_stages)( + depth=50, + stride_in_1x1=False, + norm="FrozenBN", + ), + out_features=["res3", "res4", "res5"], + freeze_at=2, + ), + position_embedding=L(PositionEmbeddingSine)( + num_pos_feats=128, + temperature=10000, + normalize=True, + offset=-0.5, + ), + neck=L(ChannelMapper)( + input_shapes={ + "res3": ShapeSpec(channels=512), + "res4": ShapeSpec(channels=1024), + "res5": ShapeSpec(channels=2048), + }, + in_features=["res3", "res4", "res5"], + out_channels=256, + num_outs=5, + kernel_size=1, + norm_layer=L(nn.GroupNorm)(num_groups=32, num_channels=256), + ), + transformer=L(DeformableDetrTransformer)( + encoder=L(DeformableDetrTransformerEncoder)( + embed_dim=256, + num_heads=8, + feedforward_dim=2048, + attn_dropout=0.0, + ffn_dropout=0.0, + num_layers=6, + post_norm=False, + num_feature_levels="${..num_feature_levels}", + ), + decoder=L(DeformableDetrTransformerDecoder)( + embed_dim=256, + num_heads=8, + feedforward_dim=2048, + attn_dropout=0.0, + ffn_dropout=0.0, + num_layers=6, + return_intermediate=True, + num_feature_levels="${..num_feature_levels}", + ), + as_two_stage="${..as_two_stage}", + num_feature_levels=5, + two_stage_num_proposals="${..num_queries}", + assign_first_stage=True, + ), + embed_dim=256, + num_classes=80, + num_queries=900, + aux_loss=True, + with_box_refine=True, + as_two_stage=True, + criterion=L(DeformableCriterion)( + num_classes=80, + matcher=L(HungarianMatcher)( + cost_class=2.0, + cost_bbox=5.0, + cost_giou=2.0, + cost_class_type="focal_loss_cost", + alpha=0.25, + gamma=2.0, + ), + matcher_stage1=L(Stage1Assigner)( + t_low=0.3, + t_high=0.7, + max_k=4, + ), + matcher_stage2=L(Stage2Assigner)( + num_queries="${...num_queries}", + num_classes="${...num_classes}", + max_k=4, + ), + weight_dict={ + "loss_class": 1.0, + "loss_bbox": 5.0, + "loss_giou": 2.0, + }, + loss_class_type="focal_loss", + alpha=0.25, + gamma=2.0, + ), + pixel_mean=[123.675, 116.280, 103.530], + pixel_std=[58.395, 57.120, 57.375], + select_box_nums_for_evaluation=100, + input_format="RGB", +) + +if model.aux_loss: + weight_dict = model.criterion.weight_dict + aux_weight_dict = {} + for i in range(model.transformer.decoder.num_layers - 1): + aux_weight_dict.update({k + f"_{i}": v for k, v in weight_dict.items()}) + aux_weight_dict.update({k + "_enc": v for k, v in weight_dict.items()}) + weight_dict.update(aux_weight_dict) + model.criterion.weight_dict = weight_dict diff --git a/configs/COCO_Detection/deformable_detr/deformable_detr_r50_50ep.py b/configs/COCO_Detection/deformable_detr/deformable_detr_r50_50ep.py new file mode 100644 index 0000000..62b0e8f --- /dev/null +++ b/configs/COCO_Detection/deformable_detr/deformable_detr_r50_50ep.py @@ -0,0 +1,38 @@ +from detrex.config import get_config + +from .models.deformable_detr_r50 import model + +dataloader = get_config("common/data/coco_detr.py").dataloader +lr_multiplier = get_config("common/coco_schedule.py").lr_multiplier_50ep +optimizer = get_config("common/optim.py").AdamW +train = get_config("common/train.py").train + +train.init_checkpoint = "detectron2://ImageNetPretrained/torchvision/R-50.pkl" +train.output_dir = "./output/deformable_detr_r50_50ep" + +train.max_iter = 375000 + +train.eval_period = 5000 + +train.log_period = 20 + +train.checkpointer.period = 5000 +train.checkpointer.max_to_keep = 2 + +train.clip_grad.enabled = True +train.clip_grad.params.max_norm = 0.1 +train.clip_grad.params.norm_type = 2 + +train.device = "cuda" +model.device = train.device + +optimizer.lr = 1e-4 +optimizer.betas = (0.9, 0.999) +optimizer.weight_decay = 1e-4 +optimizer.params.lr_factor_func = lambda module_name: 0.1 if "backbone" in module_name else 1 + +dataloader.train.num_workers = 16 + +dataloader.train.total_batch_size = 16 + +dataloader.evaluator.output_dir = train.output_dir diff --git a/configs/COCO_Detection/deformable_detr/deformable_detr_r50_two_stage_50ep.py b/configs/COCO_Detection/deformable_detr/deformable_detr_r50_two_stage_50ep.py new file mode 100644 index 0000000..f4a7e11 --- /dev/null +++ b/configs/COCO_Detection/deformable_detr/deformable_detr_r50_two_stage_50ep.py @@ -0,0 +1,7 @@ +from .deformable_detr_r50_50ep import dataloader, lr_multiplier, model, optimizer, train + +model.with_box_refine = True +model.as_two_stage = True + +train.init_checkpoint = "detectron2://ImageNetPretrained/torchvision/R-50.pkl" +train.output_dir = "./output/deformable_detr_r50_two_stage_50ep" diff --git a/configs/COCO_Detection/deformable_detr/deformable_detr_r50_with_box_refinement_50ep.py b/configs/COCO_Detection/deformable_detr/deformable_detr_r50_with_box_refinement_50ep.py new file mode 100644 index 0000000..74c7d60 --- /dev/null +++ b/configs/COCO_Detection/deformable_detr/deformable_detr_r50_with_box_refinement_50ep.py @@ -0,0 +1,6 @@ +from .deformable_detr_r50_50ep import dataloader, lr_multiplier, model, optimizer, train + +model.with_box_refine = True + +train.init_checkpoint = "detectron2://ImageNetPretrained/torchvision/R-50.pkl" +train.output_dir = "./output/deformable_detr_with_box_refinement_50ep" diff --git a/configs/COCO_Detection/deformable_detr/improved_deformable_detr_r50_12ep.py b/configs/COCO_Detection/deformable_detr/improved_deformable_detr_r50_12ep.py new file mode 100644 index 0000000..820ae77 --- /dev/null +++ b/configs/COCO_Detection/deformable_detr/improved_deformable_detr_r50_12ep.py @@ -0,0 +1,46 @@ +from detrex.config import get_config + +from .models.improved_deformable_detr_r50 import model + +dataloader = get_config("common/data/coco_detr.py").dataloader +lr_multiplier = get_config("common/coco_schedule.py").lr_multiplier_12ep +lr_multiplier.scheduler.milestones = [75000, 90000] +optimizer = get_config("common/optim.py").AdamW +train = get_config("common/train.py").train + +train.init_checkpoint = "detectron2://ImageNetPretrained/torchvision/R-50.pkl" +train.output_dir = "./output/improved_deformable_detr_r50_12ep" + +train.max_iter = 90000 + +train.eval_period = 5000 + +train.log_period = 20 + +train.checkpointer.period = 5000 +train.checkpointer.max_to_keep = 2 + +train.clip_grad.enabled = True +train.clip_grad.params.max_norm = 0.1 +train.clip_grad.params.norm_type = 2 + +train.device = "cuda" +model.device = train.device + +optimizer.lr = 2e-4 +optimizer.betas = (0.9, 0.999) +optimizer.weight_decay = 1e-4 +optimizer.params.lr_factor_func = ( + lambda module_name: 0.1 + if "backbone" in module_name + or "reference_points" in module_name + or "sampling_offsets" in module_name + else 1 +) +optimizer.params.weight_decay_norm = None + +dataloader.train.num_workers = 16 + +dataloader.train.total_batch_size = 16 + +dataloader.evaluator.output_dir = train.output_dir diff --git a/configs/COCO_Detection/deformable_detr/improved_deformable_detr_r50_50ep.py b/configs/COCO_Detection/deformable_detr/improved_deformable_detr_r50_50ep.py new file mode 100644 index 0000000..e740801 --- /dev/null +++ b/configs/COCO_Detection/deformable_detr/improved_deformable_detr_r50_50ep.py @@ -0,0 +1,45 @@ +from detrex.config import get_config + +from .models.improved_deformable_detr_r50 import model + +dataloader = get_config("common/data/coco_detr.py").dataloader +lr_multiplier = get_config("common/coco_schedule.py").lr_multiplier_50ep +optimizer = get_config("common/optim.py").AdamW +train = get_config("common/train.py").train + +train.init_checkpoint = "detectron2://ImageNetPretrained/torchvision/R-50.pkl" +train.output_dir = "./output/improved_deformable_detr_r50_50ep" + +train.max_iter = 375000 + +train.eval_period = 5000 + +train.log_period = 20 + +train.checkpointer.period = 5000 +train.checkpointer.max_to_keep = 2 + +train.clip_grad.enabled = True +train.clip_grad.params.max_norm = 0.1 +train.clip_grad.params.norm_type = 2 + +train.device = "cuda" +model.device = train.device + +optimizer.lr = 2e-4 +optimizer.betas = (0.9, 0.999) +optimizer.weight_decay = 1e-4 +optimizer.params.lr_factor_func = ( + lambda module_name: 0.1 + if "backbone" in module_name + or "reference_points" in module_name + or "sampling_offsets" in module_name + else 1 +) +optimizer.params.weight_decay_norm = None + +dataloader.train.num_workers = 16 + +dataloader.train.total_batch_size = 16 + +dataloader.evaluator.output_dir = train.output_dir diff --git a/configs/COCO_Detection/deformable_detr/improved_deformable_detr_r50_two_stage_12ep.py b/configs/COCO_Detection/deformable_detr/improved_deformable_detr_r50_two_stage_12ep.py new file mode 100644 index 0000000..8d1692e --- /dev/null +++ b/configs/COCO_Detection/deformable_detr/improved_deformable_detr_r50_two_stage_12ep.py @@ -0,0 +1,7 @@ +from .improved_deformable_detr_r50_12ep import dataloader, lr_multiplier, model, optimizer, train + +model.with_box_refine = True +model.as_two_stage = True + +train.init_checkpoint = "detectron2://ImageNetPretrained/torchvision/R-50.pkl" +train.output_dir = "./output/improved_deformable_detr_r50_two_stage_12ep" diff --git a/configs/COCO_Detection/deformable_detr/improved_deformable_detr_r50_two_stage_50ep.py b/configs/COCO_Detection/deformable_detr/improved_deformable_detr_r50_two_stage_50ep.py new file mode 100644 index 0000000..a6b528c --- /dev/null +++ b/configs/COCO_Detection/deformable_detr/improved_deformable_detr_r50_two_stage_50ep.py @@ -0,0 +1,7 @@ +from .improved_deformable_detr_r50_50ep import dataloader, lr_multiplier, model, optimizer, train + +model.with_box_refine = True +model.as_two_stage = True + +train.init_checkpoint = "detectron2://ImageNetPretrained/torchvision/R-50.pkl" +train.output_dir = "./output/improved_deformable_detr_r50_two_stage_50ep" diff --git a/configs/COCO_Detection/deformable_detr/models/deformable_detr_r50.py b/configs/COCO_Detection/deformable_detr/models/deformable_detr_r50.py new file mode 100644 index 0000000..d421cc3 --- /dev/null +++ b/configs/COCO_Detection/deformable_detr/models/deformable_detr_r50.py @@ -0,0 +1,109 @@ +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detectron2.modeling.backbone import BasicStem, ResNet +from detrex.layers import PositionEmbeddingSine +from detrex.modeling.matcher import HungarianMatcher +from detrex.modeling.neck import ChannelMapper +from projects.deformable_detr.modeling import ( + DeformableCriterion, + DeformableDETR, + DeformableDetrTransformer, + DeformableDetrTransformerDecoder, + DeformableDetrTransformerEncoder, +) + +model = L(DeformableDETR)( + backbone=L(ResNet)( + stem=L(BasicStem)(in_channels=3, out_channels=64, norm="FrozenBN"), + stages=L(ResNet.make_default_stages)( + depth=50, + stride_in_1x1=False, + norm="FrozenBN", + ), + out_features=["res3", "res4", "res5"], + freeze_at=1, + ), + position_embedding=L(PositionEmbeddingSine)( + num_pos_feats=128, + temperature=10000, + normalize=True, + offset=-0.5, + ), + neck=L(ChannelMapper)( + input_shapes={ + "res3": ShapeSpec(channels=512), + "res4": ShapeSpec(channels=1024), + "res5": ShapeSpec(channels=2048), + }, + in_features=["res3", "res4", "res5"], + out_channels=256, + num_outs=4, + kernel_size=1, + norm_layer=L(nn.GroupNorm)(num_groups=32, num_channels=256), + ), + transformer=L(DeformableDetrTransformer)( + encoder=L(DeformableDetrTransformerEncoder)( + embed_dim=256, + num_heads=8, + feedforward_dim=1024, + attn_dropout=0.1, + ffn_dropout=0.1, + num_layers=6, + post_norm=False, + num_feature_levels="${..num_feature_levels}", + ), + decoder=L(DeformableDetrTransformerDecoder)( + embed_dim=256, + num_heads=8, + feedforward_dim=1024, + attn_dropout=0.1, + ffn_dropout=0.1, + num_layers=6, + return_intermediate=True, + num_feature_levels="${..num_feature_levels}", + ), + as_two_stage="${..as_two_stage}", + num_feature_levels=4, + two_stage_num_proposals="${..num_queries}", + ), + embed_dim=256, + num_classes=80, + num_queries=300, + aux_loss=True, + with_box_refine=False, + as_two_stage=False, + criterion=L(DeformableCriterion)( + num_classes=80, + matcher=L(HungarianMatcher)( + cost_class=2.0, + cost_bbox=5.0, + cost_giou=2.0, + cost_class_type="focal_loss_cost", + alpha=0.25, + gamma=2.0, + ), + weight_dict={ + "loss_class": 1.0, + "loss_bbox": 5.0, + "loss_giou": 2.0, + }, + loss_class_type="focal_loss", + alpha=0.25, + gamma=2.0, + ), + pixel_mean=[123.675, 116.280, 103.530], + pixel_std=[58.395, 57.120, 57.375], + select_box_nums_for_evaluation=300, + device="cuda", +) + +if model.aux_loss: + weight_dict = model.criterion.weight_dict + aux_weight_dict = {} + for i in range(model.transformer.decoder.num_layers - 1): + aux_weight_dict.update({k + f"_{i}": v for k, v in weight_dict.items()}) + aux_weight_dict.update({k + "_enc": v for k, v in weight_dict.items()}) + weight_dict.update(aux_weight_dict) + model.criterion.weight_dict = weight_dict diff --git a/configs/COCO_Detection/deformable_detr/models/improved_deformable_detr_r50.py b/configs/COCO_Detection/deformable_detr/models/improved_deformable_detr_r50.py new file mode 100644 index 0000000..40f9066 --- /dev/null +++ b/configs/COCO_Detection/deformable_detr/models/improved_deformable_detr_r50.py @@ -0,0 +1,109 @@ +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detectron2.modeling.backbone import BasicStem, ResNet +from detrex.layers import PositionEmbeddingSine +from detrex.modeling.matcher import HungarianMatcher +from detrex.modeling.neck import ChannelMapper +from projects.deformable_detr.modeling import ( + DeformableCriterion, + DeformableDETR, + DeformableDetrTransformer, + DeformableDetrTransformerDecoder, + DeformableDetrTransformerEncoder, +) + +model = L(DeformableDETR)( + backbone=L(ResNet)( + stem=L(BasicStem)(in_channels=3, out_channels=64, norm="FrozenBN"), + stages=L(ResNet.make_default_stages)( + depth=50, + stride_in_1x1=False, + norm="FrozenBN", + ), + out_features=["res3", "res4", "res5"], + freeze_at=2, + ), + position_embedding=L(PositionEmbeddingSine)( + num_pos_feats=128, + temperature=10000, + normalize=True, + offset=-0.5, + ), + neck=L(ChannelMapper)( + input_shapes={ + "res3": ShapeSpec(channels=512), + "res4": ShapeSpec(channels=1024), + "res5": ShapeSpec(channels=2048), + }, + in_features=["res3", "res4", "res5"], + out_channels=256, + num_outs=5, + kernel_size=1, + norm_layer=L(nn.GroupNorm)(num_groups=32, num_channels=256), + ), + transformer=L(DeformableDetrTransformer)( + encoder=L(DeformableDetrTransformerEncoder)( + embed_dim=256, + num_heads=8, + feedforward_dim=2048, + attn_dropout=0.0, + ffn_dropout=0.0, + num_layers=6, + post_norm=False, + num_feature_levels="${..num_feature_levels}", + ), + decoder=L(DeformableDetrTransformerDecoder)( + embed_dim=256, + num_heads=8, + feedforward_dim=2048, + attn_dropout=0.0, + ffn_dropout=0.0, + num_layers=6, + return_intermediate=True, + num_feature_levels="${..num_feature_levels}", + ), + as_two_stage="${..as_two_stage}", + num_feature_levels=5, + two_stage_num_proposals="${..num_queries}", + ), + embed_dim=256, + num_classes=80, + num_queries=900, + aux_loss=True, + with_box_refine=False, + as_two_stage=False, + criterion=L(DeformableCriterion)( + num_classes=80, + matcher=L(HungarianMatcher)( + cost_class=2.0, + cost_bbox=5.0, + cost_giou=2.0, + cost_class_type="focal_loss_cost", + alpha=0.25, + gamma=2.0, + ), + weight_dict={ + "loss_class": 1.0, + "loss_bbox": 5.0, + "loss_giou": 2.0, + }, + loss_class_type="focal_loss", + alpha=0.25, + gamma=2.0, + ), + pixel_mean=[123.675, 116.280, 103.530], + pixel_std=[58.395, 57.120, 57.375], + select_box_nums_for_evaluation=300, + device="cuda", +) + +if model.aux_loss: + weight_dict = model.criterion.weight_dict + aux_weight_dict = {} + for i in range(model.transformer.decoder.num_layers - 1): + aux_weight_dict.update({k + f"_{i}": v for k, v in weight_dict.items()}) + aux_weight_dict.update({k + "_enc": v for k, v in weight_dict.items()}) + weight_dict.update(aux_weight_dict) + model.criterion.weight_dict = weight_dict diff --git a/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_r50_12ep.py b/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_r50_12ep.py new file mode 100644 index 0000000..e044aab --- /dev/null +++ b/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_r50_12ep.py @@ -0,0 +1,63 @@ +from detectron2.config import LazyCall as L +from detrex.config import get_config +from ape.modeling.text import EVA01CLIP + +from ...common.data.coco_instance import dataloader +from .models.ape_deta_r50 import model + +lr_multiplier = get_config("common/coco_schedule.py").lr_multiplier_12ep +lr_multiplier.scheduler.milestones = [75000, 90000] +optimizer = get_config("common/optim.py").AdamW +train = get_config("common/train.py").train + +train.init_checkpoint = "detectron2://ImageNetPretrained/torchvision/R-50.pkl" +train.init_checkpoint = "models/torchvision/R-50.pkl" + +train.max_iter = 90000 + +train.eval_period = 5000 + +train.log_period = 20 + +train.checkpointer.period = 5000 +train.checkpointer.max_to_keep = 2 + +train.clip_grad.enabled = True +train.clip_grad.params.max_norm = 0.1 +train.clip_grad.params.norm_type = 2 + +train.device = "cuda" + +optimizer.lr = 2e-4 +optimizer.betas = (0.9, 0.999) +optimizer.weight_decay = 1e-4 +optimizer.params.lr_factor_func = ( + lambda module_name: 0.1 + if "backbone" in module_name + or "reference_points" in module_name + or "sampling_offsets" in module_name + else 1 +) +optimizer.params.weight_decay_norm = None + +dataloader.train.num_workers = 16 + +dataloader.train.total_batch_size = 16 + +dataloader.train.mapper.use_instance_mask = True + +train.amp.enabled = True +train.ddp.fp16_compression = True + +model.model_vision.dataset_prompts = ["name"] +model.model_vision.dataset_names = ["coco_2017"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +train.output_dir = "output/" + __file__[:-3] + +model.model_language = L(EVA01CLIP)( + clip_model="EVA_CLIP_g_14_X", cache_dir="models/BAAI/EVA/eva_clip_psz14.pt" +) +model.model_vision.embed_dim_language = 1024 +model.model_vision.text_feature_reduce_type = "last" +model.model_vision.text_feature_reduce_before_fusion = True diff --git a/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_r50_vlf_12ep.py b/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_r50_vlf_12ep.py new file mode 100644 index 0000000..10ab968 --- /dev/null +++ b/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_r50_vlf_12ep.py @@ -0,0 +1,46 @@ +from detectron2.config import LazyCall as L +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) + +from .ape_deta_r50_12ep import dataloader, lr_multiplier, model, optimizer, train + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 12800 diff --git a/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_vite_eva02_clip_lsj1536_cp_64x90k.py b/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_vite_eva02_clip_lsj1536_cp_64x90k.py new file mode 100644 index 0000000..e75ac75 --- /dev/null +++ b/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_vite_eva02_clip_lsj1536_cp_64x90k.py @@ -0,0 +1,85 @@ +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detrex.config import get_config +from ape.modeling.backbone.vit import get_vit_lr_decay_rate +from ape.modeling.text import EVA02CLIP + +from .....detectron2.configs.common.data.constants import constants +from ...common.backbone.vite_eva02_clip_1536 import backbone +from ...common.data.coco_instance_lsj1536_cp import dataloader +from .models.ape_deta_r50 import model + +model.model_vision.pixel_mean = constants.imagenet_rgb256_mean +model.model_vision.pixel_std = constants.imagenet_rgb256_std +model.model_vision.input_format = "RGB" + +model.model_vision.backbone = backbone + +model.model_vision.neck = None + +model.model_vision.mask_in_features = ["p2"] +model.model_vision.input_shapes = { + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), +} + +optimizer = get_config("common/optim.py").AdamW +optimizer.params.lr_factor_func = ( + lambda module_name: 0.1 + if "reference_points" in module_name or "sampling_offsets" in module_name + else get_vit_lr_decay_rate(module_name, lr_decay_rate=0.8, num_layers=64) + if "backbone.net" in module_name + else 1 +) +optimizer.params.overrides = {"pos_embed": {"weight_decay": 0.0}} +optimizer.params.weight_decay_norm = None + +optimizer.lr = 2e-4 +optimizer.betas = (0.9, 0.999) +optimizer.weight_decay = 1e-4 + +train = get_config("common/train.py").train +train.max_iter = 90000 +train.eval_period = 5000 +train.log_period = 20 + +train.checkpointer.period = 5000 +train.checkpointer.max_to_keep = 2 + +train.clip_grad.enabled = True +train.clip_grad.params.max_norm = 0.1 +train.clip_grad.params.norm_type = 2 + +train.device = "cuda" + +train.init_checkpoint = ( + "models/QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14to16_plus_s9B.pt?matching_heuristics=True" +) + +train.amp.enabled = True +train.ddp.fp16_compression = True + +lr_multiplier = get_config("common/coco_schedule.py").lr_multiplier_12ep +lr_multiplier.scheduler.milestones = [75000, 90000] +lr_multiplier.warmup_length = 1000 / train.max_iter + +dataloader.train.num_workers = 8 +dataloader.train.total_batch_size = 64 +dataloader.train.mapper.image_format = "RGB" +dataloader.train.mapper.use_instance_mask = True + +model.model_vision.dataset_prompts = ["name"] +model.model_vision.dataset_names = ["coco_2017"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +train.output_dir = "output/" + __file__[:-3] + +model.model_language = L(EVA02CLIP)( + clip_model="EVA02-CLIP-bigE-14-plus", + cache_dir="models/QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt", + dtype="float16", +) +model.model_vision.embed_dim_language = 1024 diff --git a/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_vitg_eva01_clip_lsj1536_cp_128x45k.py b/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_vitg_eva01_clip_lsj1536_cp_128x45k.py new file mode 100644 index 0000000..8ecc808 --- /dev/null +++ b/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_vitg_eva01_clip_lsj1536_cp_128x45k.py @@ -0,0 +1,84 @@ +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detrex.config import get_config +from ape.modeling.backbone.vit import get_vit_lr_decay_rate +from ape.modeling.text import EVA02CLIP + +from .....detectron2.configs.common.data.constants import constants +from ...common.backbone.vitg_eva01_clip_1536 import backbone +from ...common.data.coco_instance_lsj1536_cp import dataloader +from .models.ape_deta_r50 import model + +model.model_vision.pixel_mean = constants.imagenet_rgb256_mean +model.model_vision.pixel_std = constants.imagenet_rgb256_std +model.model_vision.input_format = "RGB" + +model.model_vision.backbone = backbone + +model.model_vision.neck = None + +model.model_vision.mask_in_features = ["p2"] +model.model_vision.input_shapes = { + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), +} + +optimizer = get_config("common/optim.py").AdamW +optimizer.params.lr_factor_func = ( + lambda module_name: 0.1 + if "reference_points" in module_name or "sampling_offsets" in module_name + else get_vit_lr_decay_rate(module_name, lr_decay_rate=0.9, num_layers=40) + if "backbone.net" in module_name + else 1 +) +optimizer.params.overrides = {"pos_embed": {"weight_decay": 0.0}} +optimizer.params.weight_decay_norm = None + +optimizer.lr = 2e-4 +optimizer.betas = (0.9, 0.999) +optimizer.weight_decay = 1e-4 + +train = get_config("common/train.py").train +train.max_iter = 45000 +train.eval_period = 5000 +train.log_period = 20 + +train.checkpointer.period = 2500 +train.checkpointer.max_to_keep = 2 + +train.clip_grad.enabled = True +train.clip_grad.params.max_norm = 0.1 +train.clip_grad.params.norm_type = 2 + +train.device = "cuda" + +train.init_checkpoint = ( + "models/QuanSun/EVA-CLIP/EVA01_CLIP_g_14_plus_psz14to16_s11B.pt?matching_heuristics=True" +) + +train.amp.enabled = True +train.ddp.fp16_compression = True + +lr_multiplier = get_config("common/coco_schedule.py").lr_multiplier_12ep +lr_multiplier.scheduler.milestones = [37500, 45000] +lr_multiplier.warmup_length = 1000 / train.max_iter + +dataloader.train.num_workers = 16 +dataloader.train.total_batch_size = 128 +dataloader.train.mapper.image_format = "RGB" +dataloader.train.mapper.use_instance_mask = True + +model.model_vision.dataset_prompts = ["name"] +model.model_vision.dataset_names = ["coco_2017"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +train.output_dir = "output/" + __file__[:-3] + +model.model_language = L(EVA02CLIP)( + clip_model="EVA01-CLIP-g-14-plus", + cache_dir="models/QuanSun/EVA-CLIP/EVA01_CLIP_g_14_plus_psz14_s11B.pt", +) +model.model_vision.embed_dim_language = 1024 diff --git a/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_vitg_eva01_clip_lsj1536_cp_64x90k.py b/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_vitg_eva01_clip_lsj1536_cp_64x90k.py new file mode 100644 index 0000000..1ade902 --- /dev/null +++ b/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_vitg_eva01_clip_lsj1536_cp_64x90k.py @@ -0,0 +1,84 @@ +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detrex.config import get_config +from ape.modeling.backbone.vit import get_vit_lr_decay_rate +from ape.modeling.text import EVA02CLIP + +from .....detectron2.configs.common.data.constants import constants +from ...common.backbone.vitg_eva01_clip_1536 import backbone +from ...common.data.coco_instance_lsj1536_cp import dataloader +from .models.ape_deta_r50 import model + +model.model_vision.pixel_mean = constants.imagenet_rgb256_mean +model.model_vision.pixel_std = constants.imagenet_rgb256_std +model.model_vision.input_format = "RGB" + +model.model_vision.backbone = backbone + +model.model_vision.neck = None + +model.model_vision.mask_in_features = ["p2"] +model.model_vision.input_shapes = { + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), +} + +optimizer = get_config("common/optim.py").AdamW +optimizer.params.lr_factor_func = ( + lambda module_name: 0.1 + if "reference_points" in module_name or "sampling_offsets" in module_name + else get_vit_lr_decay_rate(module_name, lr_decay_rate=0.9, num_layers=40) + if "backbone.net" in module_name + else 1 +) +optimizer.params.overrides = {"pos_embed": {"weight_decay": 0.0}} +optimizer.params.weight_decay_norm = None + +optimizer.lr = 2e-4 +optimizer.betas = (0.9, 0.999) +optimizer.weight_decay = 1e-4 + +train = get_config("common/train.py").train +train.max_iter = 90000 +train.eval_period = 5000 +train.log_period = 20 + +train.checkpointer.period = 5000 +train.checkpointer.max_to_keep = 2 + +train.clip_grad.enabled = True +train.clip_grad.params.max_norm = 0.1 +train.clip_grad.params.norm_type = 2 + +train.device = "cuda" + +train.init_checkpoint = ( + "models/QuanSun/EVA-CLIP/EVA01_CLIP_g_14_plus_psz14to16_s11B.pt?matching_heuristics=True" +) + +train.amp.enabled = True +train.ddp.fp16_compression = True + +lr_multiplier = get_config("common/coco_schedule.py").lr_multiplier_12ep +lr_multiplier.scheduler.milestones = [75000, 90000] +lr_multiplier.warmup_length = 1000 / train.max_iter + +dataloader.train.num_workers = 16 +dataloader.train.total_batch_size = 64 +dataloader.train.mapper.image_format = "RGB" +dataloader.train.mapper.use_instance_mask = True + +model.model_vision.dataset_prompts = ["name"] +model.model_vision.dataset_names = ["coco_2017"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +train.output_dir = "output/" + __file__[:-3] + +model.model_language = L(EVA02CLIP)( + clip_model="EVA01-CLIP-g-14-plus", + cache_dir="models/QuanSun/EVA-CLIP/EVA01_CLIP_g_14_plus_psz14_s11B.pt", +) +model.model_vision.embed_dim_language = 1024 diff --git a/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_vitg_eva01_lsj1536_cp_64x90k.py b/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_vitg_eva01_lsj1536_cp_64x90k.py new file mode 100644 index 0000000..ee31877 --- /dev/null +++ b/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_vitg_eva01_lsj1536_cp_64x90k.py @@ -0,0 +1,81 @@ +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detrex.config import get_config +from ape.modeling.backbone.vit import get_vit_lr_decay_rate +from ape.modeling.text import EVA01CLIP + +from .....detectron2.configs.common.data.constants import constants +from ...common.backbone.vitg_eva01_1536 import backbone +from ...common.data.coco_instance_lsj1536_cp import dataloader +from .models.ape_deta_r50 import model + +model.model_vision.pixel_mean = constants.imagenet_rgb256_mean +model.model_vision.pixel_std = constants.imagenet_rgb256_std +model.model_vision.input_format = "RGB" + +model.model_vision.backbone = backbone + +model.model_vision.neck = None + +model.model_vision.mask_in_features = ["p2"] +model.model_vision.input_shapes = { + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), +} + +optimizer = get_config("common/optim.py").AdamW +optimizer.params.lr_factor_func = ( + lambda module_name: 0.1 + if "reference_points" in module_name or "sampling_offsets" in module_name + else get_vit_lr_decay_rate(module_name, lr_decay_rate=0.9, num_layers=40) + if "backbone.net" in module_name + else 1 +) +optimizer.params.overrides = {"pos_embed": {"weight_decay": 0.0}} +optimizer.params.weight_decay_norm = None + +optimizer.lr = 2e-4 +optimizer.betas = (0.9, 0.999) +optimizer.weight_decay = 1e-4 + +train = get_config("common/train.py").train +train.max_iter = 90000 +train.eval_period = 5000 +train.log_period = 20 + +train.checkpointer.period = 5000 +train.checkpointer.max_to_keep = 2 + +train.clip_grad.enabled = True +train.clip_grad.params.max_norm = 0.1 +train.clip_grad.params.norm_type = 2 + +train.device = "cuda" + +train.init_checkpoint = "models/BAAI/EVA/eva_o365.pth?matching_heuristics=True" + +train.amp.enabled = True +train.ddp.fp16_compression = True + +lr_multiplier = get_config("common/coco_schedule.py").lr_multiplier_12ep +lr_multiplier.scheduler.milestones = [75000, 90000] +lr_multiplier.warmup_length = 1000 / train.max_iter + +dataloader.train.num_workers = 16 +dataloader.train.total_batch_size = 64 +dataloader.train.mapper.image_format = "RGB" +dataloader.train.mapper.use_instance_mask = True + +model.model_vision.dataset_prompts = ["name"] +model.model_vision.dataset_names = ["coco_2017"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +train.output_dir = "output/" + __file__[:-3] + +model.model_language = L(EVA01CLIP)( + clip_model="EVA_CLIP_g_14_X", cache_dir="models/BAAI/EVA/eva_clip_psz14.pt" +) +model.model_vision.embed_dim_language = 1024 diff --git a/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_clip_lsj1024_cp_12ep.py b/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_clip_lsj1024_cp_12ep.py new file mode 100644 index 0000000..32adc0b --- /dev/null +++ b/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_clip_lsj1024_cp_12ep.py @@ -0,0 +1,95 @@ +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detectron2.model_zoo import get_config as get_config_d2 +from detrex.config import get_config as get_config_detrex +from ape.modeling.backbone.vit import get_vit_lr_decay_rate + +from ape.modeling.text import EVA02CLIP + +from ...common.backbone.vitl_eva02_clip import backbone +from ...common.data.coco_instance_lsj1024_cp import dataloader +from .models.ape_deta_r50 import model + +constants = get_config_d2("common/data/constants.py").constants + +model.model_vision.pixel_mean = constants.imagenet_rgb256_mean +model.model_vision.pixel_std = constants.imagenet_rgb256_std +model.model_vision.input_format = "RGB" + +model.model_vision.backbone = backbone + +model.model_vision.neck.input_shapes = { + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), +} +model.model_vision.neck.in_features = ["p2", "p3", "p4", "p5", "p6"] + +model.model_vision.mask_in_features = ["p2"] +model.model_vision.input_shapes = { + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), +} + +optimizer = get_config_detrex("common/optim.py").AdamW +optimizer.params.lr_factor_func = ( + lambda module_name: 0.1 + if "reference_points" in module_name or "sampling_offsets" in module_name + else get_vit_lr_decay_rate(module_name, lr_decay_rate=0.8, num_layers=24) + if "backbone.net" in module_name + else 1 +) +optimizer.params.overrides = {"pos_embed": {"weight_decay": 0.0}} +optimizer.params.weight_decay_norm = None + +optimizer.lr = 2e-4 +optimizer.betas = (0.9, 0.999) +optimizer.weight_decay = 1e-4 + +train = get_config_detrex("common/train.py").train +train.max_iter = 90000 +train.eval_period = 5000 +train.log_period = 20 + +train.checkpointer.period = 5000 +train.checkpointer.max_to_keep = 2 + +train.clip_grad.enabled = True +train.clip_grad.params.max_norm = 0.1 +train.clip_grad.params.norm_type = 2 + +train.device = "cuda" + +train.init_checkpoint = ( + "models/QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14to16_s6B.pt?matching_heuristics=True" +) + +train.amp.enabled = True +train.ddp.fp16_compression = True + +lr_multiplier = get_config_detrex("common/coco_schedule.py").lr_multiplier_12ep +lr_multiplier.scheduler.milestones = [75000, 90000] +lr_multiplier.warmup_length = 1000 / train.max_iter + +dataloader.train.num_workers = 16 +dataloader.train.total_batch_size = 16 +dataloader.train.mapper.image_format = "RGB" +dataloader.train.mapper.use_instance_mask = True + +model.model_vision.dataset_prompts = ["name"] +model.model_vision.dataset_names = ["coco_2017"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +train.output_dir = "output/" + __file__[:-3] + +model.model_language = L(EVA02CLIP)( + clip_model="EVA02-CLIP-bigE-14-plus", + cache_dir="models/QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt", + dtype="float16", +) +model.model_vision.embed_dim_language = 1024 diff --git a/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_clip_lsj1536_cp_128x45k.py b/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_clip_lsj1536_cp_128x45k.py new file mode 100644 index 0000000..a6faed9 --- /dev/null +++ b/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_clip_lsj1536_cp_128x45k.py @@ -0,0 +1,19 @@ +from .ape_deta_vitl_eva02_clip_lsj1536_cp_64x90k import ( + dataloader, + lr_multiplier, + model, + optimizer, + train, +) + +train.max_iter = 45000 + +train.eval_period = 2500 + +train.checkpointer.period = 2500 + +lr_multiplier.scheduler.milestones = [37500, 45000] + +dataloader.train.total_batch_size = 128 + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_clip_lsj1536_cp_64x90k.py b/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_clip_lsj1536_cp_64x90k.py new file mode 100644 index 0000000..063ed92 --- /dev/null +++ b/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_clip_lsj1536_cp_64x90k.py @@ -0,0 +1,92 @@ +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detrex.config import get_config +from ape.modeling.backbone.vit import get_vit_lr_decay_rate + +from ape.modeling.text import EVA02CLIP + +from .....detectron2.configs.common.data.constants import constants +from ...common.backbone.vitl_eva02_clip_1536 import backbone +from ...common.data.coco_instance_lsj1536_cp import dataloader +from .models.ape_deta_r50 import model + +model.model_vision.pixel_mean = constants.imagenet_rgb256_mean +model.model_vision.pixel_std = constants.imagenet_rgb256_std +model.model_vision.input_format = "RGB" + +model.model_vision.backbone = backbone + +model.model_vision.neck.input_shapes = { + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), +} +model.model_vision.neck.in_features = ["p2", "p3", "p4", "p5", "p6"] + +model.model_vision.mask_in_features = ["p2"] +model.model_vision.input_shapes = { + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), +} + +optimizer = get_config("common/optim.py").AdamW +optimizer.params.lr_factor_func = ( + lambda module_name: 0.1 + if "reference_points" in module_name or "sampling_offsets" in module_name + else get_vit_lr_decay_rate(module_name, lr_decay_rate=0.8, num_layers=24) + if "backbone.net" in module_name + else 1 +) +optimizer.params.overrides = {"pos_embed": {"weight_decay": 0.0}} +optimizer.params.weight_decay_norm = None + +optimizer.lr = 2e-4 +optimizer.betas = (0.9, 0.999) +optimizer.weight_decay = 1e-4 + +train = get_config("common/train.py").train +train.max_iter = 90000 +train.eval_period = 5000 +train.log_period = 20 + +train.checkpointer.period = 5000 +train.checkpointer.max_to_keep = 2 + +train.clip_grad.enabled = True +train.clip_grad.params.max_norm = 0.1 +train.clip_grad.params.norm_type = 2 + +train.device = "cuda" + +train.init_checkpoint = ( + "models/QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14to16_s6B.pt?matching_heuristics=True" +) + +train.amp.enabled = True +train.ddp.fp16_compression = True + +lr_multiplier = get_config("common/coco_schedule.py").lr_multiplier_12ep +lr_multiplier.scheduler.milestones = [75000, 90000] +lr_multiplier.warmup_length = 1000 / train.max_iter + +dataloader.train.num_workers = 16 +dataloader.train.total_batch_size = 64 +dataloader.train.mapper.image_format = "RGB" +dataloader.train.mapper.use_instance_mask = True + +model.model_vision.dataset_prompts = ["name"] +model.model_vision.dataset_names = ["coco_2017"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +train.output_dir = "output/" + __file__[:-3] + +model.model_language = L(EVA02CLIP)( + clip_model="EVA02-CLIP-bigE-14-plus", + cache_dir="models/QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt", +) +model.model_vision.embed_dim_language = 1024 diff --git a/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_12ep.py b/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_12ep.py new file mode 100644 index 0000000..ceaf8c1 --- /dev/null +++ b/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_12ep.py @@ -0,0 +1,52 @@ +from detectron2.config import LazyCall as L +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) + +from .ape_deta_vitl_eva02_clip_lsj1024_cp_12ep import ( + dataloader, + lr_multiplier, + model, + optimizer, + train, +) + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 12800 diff --git a/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024_cp_12ep.py b/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024_cp_12ep.py new file mode 100644 index 0000000..045e0cd --- /dev/null +++ b/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024_cp_12ep.py @@ -0,0 +1,86 @@ +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec + +from detectron2.model_zoo import get_config as get_config_d2 +from detrex.config import get_config +from ape.modeling.backbone.vit import get_vit_lr_decay_rate +from ape.modeling.text import EVA01CLIP + +from ...common.backbone.vitl_eva02 import backbone +from ...common.data.coco_instance_lsj1024_cp import dataloader +from .models.ape_deta_r50 import model + +constants = get_config_d2("common/data/constants.py").constants + +model.model_vision.pixel_mean = constants.imagenet_rgb256_mean +model.model_vision.pixel_std = constants.imagenet_rgb256_std +model.model_vision.input_format = "RGB" + +model.model_vision.backbone = backbone + +model.model_vision.neck = None + +model.model_vision.mask_in_features = ["p2"] +model.model_vision.input_shapes = { + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), +} + +optimizer = get_config("common/optim.py").AdamW +optimizer.params.lr_factor_func = ( + lambda module_name: 0.1 + if "reference_points" in module_name or "sampling_offsets" in module_name + else get_vit_lr_decay_rate(module_name, lr_decay_rate=0.8, num_layers=24) + if "backbone.net" in module_name + else 1 +) +optimizer.params.overrides = {"pos_embed": {"weight_decay": 0.0}} +optimizer.params.weight_decay_norm = None + +optimizer.lr = 2e-4 +optimizer.betas = (0.9, 0.999) +optimizer.weight_decay = 1e-4 + +train = get_config("common/train.py").train +train.max_iter = 90000 +train.eval_period = 5000 +train.log_period = 20 + +train.checkpointer.period = 5000 +train.checkpointer.max_to_keep = 2 + +train.clip_grad.enabled = True +train.clip_grad.params.max_norm = 0.1 +train.clip_grad.params.norm_type = 2 + +train.device = "cuda" + +train.init_checkpoint = ( + "models/Yuxin-CV/EVA-02/eva02/pt/eva02_L_pt_in21k_p14to16.pt?matching_heuristics=True" +) + +train.amp.enabled = True +train.ddp.fp16_compression = True + +lr_multiplier = get_config("common/coco_schedule.py").lr_multiplier_12ep +lr_multiplier.scheduler.milestones = [75000, 90000] +lr_multiplier.warmup_length = 1000 / train.max_iter + +dataloader.train.num_workers = 16 +dataloader.train.total_batch_size = 16 +dataloader.train.mapper.image_format = "RGB" +dataloader.train.mapper.use_instance_mask = True + +model.model_vision.dataset_prompts = ["name"] +model.model_vision.dataset_names = ["coco_2017"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +train.output_dir = "output/" + __file__[:-3] + +model.model_language = L(EVA01CLIP)( + clip_model="EVA_CLIP_g_14_X", cache_dir="models/BAAI/EVA/eva_clip_psz14.pt" +) +model.model_vision.embed_dim_language = 1024 diff --git a/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1536_cp_128x90k.py b/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1536_cp_128x90k.py new file mode 100644 index 0000000..80315b2 --- /dev/null +++ b/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1536_cp_128x90k.py @@ -0,0 +1,11 @@ +from .ape_deta_vitl_eva02_lsj1536_cp_64x90k import ( + dataloader, + lr_multiplier, + model, + optimizer, + train, +) + +dataloader.train.total_batch_size = 128 + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1536_cp_12ep.py b/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1536_cp_12ep.py new file mode 100644 index 0000000..b225e91 --- /dev/null +++ b/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1536_cp_12ep.py @@ -0,0 +1,83 @@ +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detrex.config import get_config +from ape.modeling.backbone.vit import get_vit_lr_decay_rate +from ape.modeling.text import EVA01CLIP + +from .....detectron2.configs.common.data.constants import constants +from ...common.backbone.vitl_eva02_1536 import backbone +from ...common.data.coco_instance_lsj1536_cp import dataloader +from .models.ape_deta_r50 import model + +model.model_vision.pixel_mean = constants.imagenet_rgb256_mean +model.model_vision.pixel_std = constants.imagenet_rgb256_std +model.model_vision.input_format = "RGB" + +model.model_vision.backbone = backbone + +model.model_vision.neck = None + +model.model_vision.mask_in_features = ["p2"] +model.model_vision.input_shapes = { + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), +} + +optimizer = get_config("common/optim.py").AdamW +optimizer.params.lr_factor_func = ( + lambda module_name: 0.1 + if "reference_points" in module_name or "sampling_offsets" in module_name + else get_vit_lr_decay_rate(module_name, lr_decay_rate=0.8, num_layers=24) + if "backbone.net" in module_name + else 1 +) +optimizer.params.overrides = {"pos_embed": {"weight_decay": 0.0}} +optimizer.params.weight_decay_norm = None + +optimizer.lr = 2e-4 +optimizer.betas = (0.9, 0.999) +optimizer.weight_decay = 1e-4 + +train = get_config("common/train.py").train +train.max_iter = 90000 +train.eval_period = 5000 +train.log_period = 20 + +train.checkpointer.period = 5000 +train.checkpointer.max_to_keep = 2 + +train.clip_grad.enabled = True +train.clip_grad.params.max_norm = 0.1 +train.clip_grad.params.norm_type = 2 + +train.device = "cuda" + +train.init_checkpoint = ( + "models/Yuxin-CV/EVA-02/eva02/pt/eva02_L_pt_in21k_p14to16.pt?matching_heuristics=True" +) + +train.amp.enabled = True +train.ddp.fp16_compression = True + +lr_multiplier = get_config("common/coco_schedule.py").lr_multiplier_12ep +lr_multiplier.scheduler.milestones = [75000, 90000] +lr_multiplier.warmup_length = 1000 / train.max_iter + +dataloader.train.num_workers = 16 +dataloader.train.total_batch_size = 16 +dataloader.train.mapper.image_format = "RGB" +dataloader.train.mapper.use_instance_mask = True + +model.model_vision.dataset_prompts = ["name"] +model.model_vision.dataset_names = ["coco_2017"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +train.output_dir = "output/" + __file__[:-3] + +model.model_language = L(EVA01CLIP)( + clip_model="EVA_CLIP_g_14_X", cache_dir="models/BAAI/EVA/eva_clip_psz14.pt" +) +model.model_vision.embed_dim_language = 1024 diff --git a/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1536_cp_64x90k.py b/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1536_cp_64x90k.py new file mode 100644 index 0000000..2374345 --- /dev/null +++ b/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1536_cp_64x90k.py @@ -0,0 +1,83 @@ +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detrex.config import get_config +from ape.modeling.backbone.vit import get_vit_lr_decay_rate +from ape.modeling.text import EVA01CLIP + +from .....detectron2.configs.common.data.constants import constants +from ...common.backbone.vitl_eva02_1536 import backbone +from ...common.data.coco_instance_lsj1536_cp import dataloader +from .models.ape_deta_r50 import model + +model.model_vision.pixel_mean = constants.imagenet_rgb256_mean +model.model_vision.pixel_std = constants.imagenet_rgb256_std +model.model_vision.input_format = "RGB" + +model.model_vision.backbone = backbone + +model.model_vision.neck = None + +model.model_vision.mask_in_features = ["p2"] +model.model_vision.input_shapes = { + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), +} + +optimizer = get_config("common/optim.py").AdamW +optimizer.params.lr_factor_func = ( + lambda module_name: 0.1 + if "reference_points" in module_name or "sampling_offsets" in module_name + else get_vit_lr_decay_rate(module_name, lr_decay_rate=0.8, num_layers=24) + if "backbone.net" in module_name + else 1 +) +optimizer.params.overrides = {"pos_embed": {"weight_decay": 0.0}} +optimizer.params.weight_decay_norm = None + +optimizer.lr = 2e-4 +optimizer.betas = (0.9, 0.999) +optimizer.weight_decay = 1e-4 + +train = get_config("common/train.py").train +train.max_iter = 90000 +train.eval_period = 5000 +train.log_period = 20 + +train.checkpointer.period = 5000 +train.checkpointer.max_to_keep = 2 + +train.clip_grad.enabled = True +train.clip_grad.params.max_norm = 0.1 +train.clip_grad.params.norm_type = 2 + +train.device = "cuda" + +train.init_checkpoint = ( + "models/Yuxin-CV/EVA-02/eva02/pt/eva02_L_pt_in21k_p14to16.pt?matching_heuristics=True" +) + +train.amp.enabled = True +train.ddp.fp16_compression = True + +lr_multiplier = get_config("common/coco_schedule.py").lr_multiplier_12ep +lr_multiplier.scheduler.milestones = [75000, 90000] +lr_multiplier.warmup_length = 1000 / train.max_iter + +dataloader.train.num_workers = 16 +dataloader.train.total_batch_size = 64 +dataloader.train.mapper.image_format = "RGB" +dataloader.train.mapper.use_instance_mask = True + +model.model_vision.dataset_prompts = ["name"] +model.model_vision.dataset_names = ["coco_2017"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +train.output_dir = "output/" + __file__[:-3] + +model.model_language = L(EVA01CLIP)( + clip_model="EVA_CLIP_g_14_X", cache_dir="models/BAAI/EVA/eva_clip_psz14.pt" +) +model.model_vision.embed_dim_language = 1024 diff --git a/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_12ep.py b/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_12ep.py new file mode 100644 index 0000000..cfb7a2e --- /dev/null +++ b/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_12ep.py @@ -0,0 +1,46 @@ +from detectron2.config import LazyCall as L +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) + +from .ape_deta_vitl_eva02_lsj1024_cp_12ep import dataloader, lr_multiplier, model, optimizer, train + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 12800 diff --git a/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_vitl_lsj1024_cp_12ep.py b/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_vitl_lsj1024_cp_12ep.py new file mode 100644 index 0000000..dcdd383 --- /dev/null +++ b/configs/COCO_InstanceSegmentation/ape_deta/ape_deta_vitl_lsj1024_cp_12ep.py @@ -0,0 +1,118 @@ +from functools import partial + +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec + +from detectron2.model_zoo import get_config as get_config_d2 +from detectron2.modeling.backbone.fpn import LastLevelMaxPool +from detectron2.modeling.backbone.vit import SimpleFeaturePyramid, ViT, get_vit_lr_decay_rate +from detrex.config import get_config +from ape.modeling.text import EVA01CLIP + +from ...common.data.coco_instance_lsj1024_cp import dataloader +from .models.ape_deta_r50 import model + +constants = get_config_d2("common/data/constants.py").constants + +model.model_vision.pixel_mean = constants.imagenet_rgb256_mean +model.model_vision.pixel_std = constants.imagenet_rgb256_std +model.model_vision.input_format = "RGB" + +model.model_vision.backbone = L(SimpleFeaturePyramid)( + net=L(ViT)( # Single-scale ViT backbone + img_size=1024, + patch_size=16, + embed_dim=1024, + depth=24, + num_heads=16, + drop_path_rate=0.4, + window_size=14, + mlp_ratio=4, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + window_block_indexes=list(range(0, 5)) + + list(range(6, 11)) + + list(range(12, 17)) + + list(range(18, 23)), + residual_block_indexes=[], + use_rel_pos=True, + out_feature="last_feat", + use_act_checkpoint=True, + ), + in_feature="${.net.out_feature}", + out_channels=256, + scale_factors=(4.0, 2.0, 1.0, 0.5), + top_block=L(LastLevelMaxPool)(), + norm="LN", + square_pad=1024, +) + +model.model_vision.neck = None + +model.model_vision.mask_in_features = ["p2"] +model.model_vision.input_shapes = { + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), +} + +optimizer = get_config("common/optim.py").AdamW +optimizer.params.lr_factor_func = ( + lambda module_name: 0.1 + if "reference_points" in module_name or "sampling_offsets" in module_name + else get_vit_lr_decay_rate(module_name, lr_decay_rate=0.8, num_layers=24) + if "backbone" in module_name + else 1 +) +optimizer.params.overrides = {"pos_embed": {"weight_decay": 0.0}} + +optimizer.lr = 2e-4 +optimizer.weight_decay = 0.05 + +train = get_config("common/train.py").train +train.max_iter = 90000 +train.eval_period = 5000 +train.log_period = 20 + +train.checkpointer.period = 5000 +train.checkpointer.max_to_keep = 2 + +train.clip_grad.enabled = True +train.clip_grad.params.max_norm = 0.1 +train.clip_grad.params.norm_type = 2 + +train.device = "cuda" + +train.init_checkpoint = ( + "detectron2://ImageNetPretrained/MAE/mae_pretrain_vit_large.pth?matching_heuristics=True" +) +train.init_checkpoint = "models/MAE/mae_pretrain_vit_large.pth?matching_heuristics=True" + +train.amp.enabled = True +train.ddp.fp16_compression = True + +lr_multiplier = get_config("common/coco_schedule.py").lr_multiplier_12ep +lr_multiplier.scheduler.milestones = [75000, 90000] +lr_multiplier.warmup_length = 1000 / train.max_iter + +dataloader.train.num_workers = 16 +dataloader.train.total_batch_size = 16 +dataloader.train.mapper.image_format = "RGB" +dataloader.train.mapper.use_instance_mask = True + +model.model_vision.dataset_prompts = ["name"] +model.model_vision.dataset_names = ["coco_2017"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.output_dir = train.output_dir +dataloader.train.mapper.output_dir = train.output_dir +dataloader.train.mapper.vis_period = 12800 + +model.model_language = L(EVA01CLIP)( + clip_model="EVA_CLIP_g_14_X", cache_dir="models/BAAI/EVA/eva_clip_psz14.pt" +) +model.model_vision.embed_dim_language = 1024 diff --git a/configs/COCO_InstanceSegmentation/ape_deta/models/ape_deta_r50.py b/configs/COCO_InstanceSegmentation/ape_deta/models/ape_deta_r50.py new file mode 100644 index 0000000..713aef9 --- /dev/null +++ b/configs/COCO_InstanceSegmentation/ape_deta/models/ape_deta_r50.py @@ -0,0 +1,155 @@ +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detectron2.modeling.backbone import BasicStem, ResNet +from detrex.layers import PositionEmbeddingSine +from detrex.modeling.matcher import HungarianMatcher +from detrex.modeling.neck import ChannelMapper +from ape.modeling.ape_deta import ( + DeformableCriterion, + DeformableDETR, + DeformableDETRSegm, + DeformableDetrTransformer, + DeformableDetrTransformerDecoder, + DeformableDetrTransformerEncoder, + SomeThing, + Stage1Assigner, + Stage2Assigner, +) +from ape.modeling.text import T5_warpper + + + +model_vision = L(DeformableDETRSegm)( + backbone=L(ResNet)( + stem=L(BasicStem)(in_channels=3, out_channels=64, norm="FrozenBN"), + stages=L(ResNet.make_default_stages)( + depth=50, + stride_in_1x1=False, + norm="FrozenBN", + ), + out_features=["res2", "res3", "res4", "res5"], + freeze_at=1, + ), + position_embedding=L(PositionEmbeddingSine)( + num_pos_feats=128, + temperature=10000, + normalize=True, + offset=-0.5, + ), + neck=L(ChannelMapper)( + input_shapes={ + "res3": ShapeSpec(channels=512), + "res4": ShapeSpec(channels=1024), + "res5": ShapeSpec(channels=2048), + }, + in_features=["res3", "res4", "res5"], + out_channels=256, + num_outs=5, + kernel_size=1, + norm_layer=L(nn.GroupNorm)(num_groups=32, num_channels=256), + ), + transformer=L(DeformableDetrTransformer)( + encoder=L(DeformableDetrTransformerEncoder)( + embed_dim=256, + num_heads=8, + feedforward_dim=2048, + attn_dropout=0.0, + ffn_dropout=0.0, + num_layers=6, + post_norm=False, + num_feature_levels="${..num_feature_levels}", + ), + decoder=L(DeformableDetrTransformerDecoder)( + embed_dim=256, + num_heads=8, + feedforward_dim=2048, + attn_dropout=0.0, + ffn_dropout=0.0, + num_layers=6, + return_intermediate=True, + num_feature_levels="${..num_feature_levels}", + ), + as_two_stage="${..as_two_stage}", + num_feature_levels=5, + two_stage_num_proposals="${..num_queries}", + assign_first_stage=True, + ), + embed_dim=256, + num_classes=80, + num_queries=900, + aux_loss=True, + with_box_refine=True, + as_two_stage=True, + criterion=[ + L(DeformableCriterion)( + num_classes="${...num_classes}", + matcher=L(HungarianMatcher)( + cost_class=2.0, + cost_bbox=5.0, + cost_giou=2.0, + cost_class_type="focal_loss_cost", + alpha=0.25, + gamma=2.0, + ), + matcher_stage1=L(Stage1Assigner)( + t_low=0.3, + t_high=0.7, + max_k=4, + ), + matcher_stage2=L(Stage2Assigner)( + num_queries="${model.model_vision.num_queries}", + num_classes="${..num_classes}", + max_k=4, + ), + weight_dict={ + "loss_class": 1.0, + "loss_bbox": 5.0, + "loss_giou": 2.0, + "loss_mask": 5, + "loss_dice": 5, + }, + loss_class_type="focal_loss", + alpha=0.25, + gamma=2.0, + losses=["class", "boxes", "masks"], + ), + ], + pixel_mean=[123.675, 116.280, 103.530], + pixel_std=[58.395, 57.120, 57.375], + select_box_nums_for_evaluation=100, + input_format="RGB", + mask_encode_level=0, + mask_in_features=["res2"], + input_shapes={ + "res2": ShapeSpec(channels=256), + "res3": ShapeSpec(channels=512), + "res4": ShapeSpec(channels=1024), + "res5": ShapeSpec(channels=2048), + }, + output_dir=None, + vis_period=0, + embed_dim_language=1024, + instance_on=True, + semantic_on=False, + panoptic_on=False, +) + +if model_vision.aux_loss: + for j in range(len(model_vision.criterion)): + weight_dict = model_vision.criterion[j].weight_dict + aux_weight_dict = {} + for i in range(model_vision.transformer.decoder.num_layers - 1): + aux_weight_dict.update({k + f"_{i}": v for k, v in weight_dict.items()}) + aux_weight_dict.update({k + "_enc": v for k, v in weight_dict.items()}) + weight_dict.update(aux_weight_dict) + model_vision.criterion[j].weight_dict = weight_dict + +model = L(SomeThing)( + model_vision=model_vision, + model_language=L(T5_warpper)( + pretrained_model_name_or_path="models/google/flan-t5-large/", + eval_only=True, + ), +) diff --git a/configs/COCO_InstanceSegmentation/deformable_deta/deformable_deta_segm_r50_12ep.py b/configs/COCO_InstanceSegmentation/deformable_deta/deformable_deta_segm_r50_12ep.py new file mode 100644 index 0000000..2976503 --- /dev/null +++ b/configs/COCO_InstanceSegmentation/deformable_deta/deformable_deta_segm_r50_12ep.py @@ -0,0 +1,53 @@ +from detrex.config import get_config + +from ...common.data.coco_instance import dataloader +from .models.deformable_deta_segm_r50 import model + +lr_multiplier = get_config("common/coco_schedule.py").lr_multiplier_12ep +lr_multiplier.scheduler.milestones = [75000, 90000] +optimizer = get_config("common/optim.py").AdamW +train = get_config("common/train.py").train + +train.init_checkpoint = "detectron2://ImageNetPretrained/torchvision/R-50.pkl" +train.init_checkpoint = "models/torchvision/R-50.pkl" +train.output_dir = "output/" + __file__[:-3] + +train.max_iter = 90000 + +train.eval_period = 5000 + +train.log_period = 20 + +train.checkpointer.period = 5000 +train.checkpointer.max_to_keep = 2 + +train.clip_grad.enabled = True +train.clip_grad.params.max_norm = 0.1 +train.clip_grad.params.norm_type = 2 + +train.device = "cuda" + +optimizer.lr = 2e-4 +optimizer.betas = (0.9, 0.999) +optimizer.weight_decay = 1e-4 +optimizer.params.lr_factor_func = ( + lambda module_name: 0.1 + if "backbone" in module_name + or "reference_points" in module_name + or "sampling_offsets" in module_name + else 1 +) +optimizer.params.weight_decay_norm = None + +dataloader.train.num_workers = 16 + +dataloader.train.total_batch_size = 16 + + +dataloader.train.mapper.use_instance_mask = True + +train.amp.enabled = True +train.ddp.fp16_compression = True +train.ddp.find_unused_parameters = False + +model.dataset_metas = dataloader.train.dataset.names diff --git a/configs/COCO_InstanceSegmentation/deformable_deta/deformable_deta_segm_r50_24ep.py b/configs/COCO_InstanceSegmentation/deformable_deta/deformable_deta_segm_r50_24ep.py new file mode 100644 index 0000000..951b192 --- /dev/null +++ b/configs/COCO_InstanceSegmentation/deformable_deta/deformable_deta_segm_r50_24ep.py @@ -0,0 +1,9 @@ +from detrex.config import get_config + +from .deformable_deta_segm_r50_12ep import dataloader, model, optimizer, train + +lr_multiplier = get_config("common/coco_schedule.py").lr_multiplier_24ep + +train.max_iter = 180000 + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/COCO_InstanceSegmentation/deformable_deta/deformable_deta_segm_vitl_eva02_lsj1024_cp_12ep.py b/configs/COCO_InstanceSegmentation/deformable_deta/deformable_deta_segm_vitl_eva02_lsj1024_cp_12ep.py new file mode 100644 index 0000000..da25c6a --- /dev/null +++ b/configs/COCO_InstanceSegmentation/deformable_deta/deformable_deta_segm_vitl_eva02_lsj1024_cp_12ep.py @@ -0,0 +1,85 @@ +from functools import partial + +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.data.catalog import MetadataCatalog +from detectron2.layers import ShapeSpec +from detectron2.modeling.backbone.fpn import LastLevelMaxPool +from detrex.config import get_config +from ape.modeling.backbone.vit import get_vit_lr_decay_rate +from ape.modeling.backbone.vit_eva02 import SimpleFeaturePyramid, ViT + +from .....detectron2.configs.common.data.constants import constants +from ...common.backbone.vitl_eva02 import backbone +from ...common.data.coco_instance_lsj1024_cp import dataloader +from .models.deformable_deta_segm_r50 import model + +model.pixel_mean = constants.imagenet_rgb256_mean +model.pixel_std = constants.imagenet_rgb256_std +model.input_format = "RGB" + +model.backbone = backbone + +model.neck = None + +model.mask_in_features = ["p2"] +model.input_shapes = { + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), +} + +optimizer = get_config("common/optim.py").AdamW +optimizer.params.lr_factor_func = ( + lambda module_name: 0.1 + if "reference_points" in module_name or "sampling_offsets" in module_name + else get_vit_lr_decay_rate(module_name, lr_decay_rate=0.8, num_layers=24) + if "backbone.net" in module_name + else 1 +) +optimizer.params.overrides = {"pos_embed": {"weight_decay": 0.0}} +optimizer.params.weight_decay_norm = None + +optimizer.lr = 2e-4 +optimizer.weight_decay = 1e-4 + +train = get_config("common/train.py").train +train.max_iter = 90000 +train.eval_period = 5000 +train.log_period = 20 + +train.checkpointer.period = 5000 +train.checkpointer.max_to_keep = 2 + +train.clip_grad.enabled = True +train.clip_grad.params.max_norm = 0.1 +train.clip_grad.params.norm_type = 2 + +train.device = "cuda" + +train.init_checkpoint = ( + "models/Yunxin-CV/EVA-02/eva02/pt/eva02_L_pt_in21k_p14to16.pt?matching_heuristics=True" +) + +train.amp.enabled = True +train.ddp.fp16_compression = True + +lr_multiplier = get_config("common/coco_schedule.py").lr_multiplier_12ep +lr_multiplier.scheduler.milestones = [75000, 90000] +lr_multiplier.warmup_length = 1000 / train.max_iter + +dataloader.train.num_workers = 16 +dataloader.train.total_batch_size = 16 +dataloader.train.mapper.image_format = "RGB" +dataloader.train.mapper.use_instance_mask = True + +if isinstance(dataloader.train.dataset.names, str): + model.metadata = MetadataCatalog.get(dataloader.train.dataset.names) +else: + model.metadata = MetadataCatalog.get(dataloader.train.dataset.names[0]) + +train.output_dir = "output/" + __file__[:-3] +dataloader.train.mapper.output_dir = train.output_dir diff --git a/configs/COCO_InstanceSegmentation/deformable_deta/models/deformable_deta_segm_r50.py b/configs/COCO_InstanceSegmentation/deformable_deta/models/deformable_deta_segm_r50.py new file mode 100644 index 0000000..e96cdfe --- /dev/null +++ b/configs/COCO_InstanceSegmentation/deformable_deta/models/deformable_deta_segm_r50.py @@ -0,0 +1,197 @@ +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detectron2.modeling.backbone import BasicStem, ResNet +from detrex.layers import PositionEmbeddingSine +from detrex.modeling.matcher import HungarianMatcher +from detrex.modeling.neck import ChannelMapper +from ape.modeling.deta import ( + DeformableCriterion, + DeformableDETR, + DeformableDETRSegm, + DeformableDetrTransformer, + DeformableDetrTransformerDecoder, + DeformableDetrTransformerEncoder, + Stage1Assigner, + Stage2Assigner, +) + +model = L(DeformableDETRSegm)( + backbone=L(ResNet)( + stem=L(BasicStem)(in_channels=3, out_channels=64, norm="FrozenBN"), + stages=L(ResNet.make_default_stages)( + depth=50, + stride_in_1x1=False, + norm="FrozenBN", + ), + out_features=["res2", "res3", "res4", "res5"], + freeze_at=1, + ), + position_embedding=L(PositionEmbeddingSine)( + num_pos_feats=128, + temperature=10000, + normalize=True, + offset=-0.5, + ), + neck=L(ChannelMapper)( + input_shapes={ + "res3": ShapeSpec(channels=512), + "res4": ShapeSpec(channels=1024), + "res5": ShapeSpec(channels=2048), + }, + in_features=["res3", "res4", "res5"], + out_channels=256, + num_outs=5, + kernel_size=1, + norm_layer=L(nn.GroupNorm)(num_groups=32, num_channels=256), + ), + transformer=L(DeformableDetrTransformer)( + encoder=L(DeformableDetrTransformerEncoder)( + embed_dim=256, + num_heads=8, + feedforward_dim=2048, + attn_dropout=0.0, + ffn_dropout=0.0, + num_layers=6, + post_norm=False, + num_feature_levels="${..num_feature_levels}", + ), + decoder=L(DeformableDetrTransformerDecoder)( + embed_dim=256, + num_heads=8, + feedforward_dim=2048, + attn_dropout=0.0, + ffn_dropout=0.0, + num_layers=6, + return_intermediate=True, + num_feature_levels="${..num_feature_levels}", + ), + as_two_stage="${..as_two_stage}", + num_feature_levels=5, + two_stage_num_proposals="${..num_queries}", + assign_first_stage=True, + ), + embed_dim=256, + num_classes=80, + num_queries=900, + aux_loss=True, + with_box_refine=True, + as_two_stage=True, + criterion=L(DeformableCriterion)( + num_classes=80, + matcher=L(HungarianMatcher)( + cost_class=2.0, + cost_bbox=5.0, + cost_giou=2.0, + cost_class_type="focal_loss_cost", + alpha=0.25, + gamma=2.0, + ), + matcher_stage1=L(Stage1Assigner)( + t_low=0.3, + t_high=0.7, + max_k=4, + ), + matcher_stage2=L(Stage2Assigner)( + num_queries="${...num_queries}", + num_classes="${...num_classes}", + max_k=4, + ), + weight_dict={ + "loss_class": 1.0, + "loss_bbox": 5.0, + "loss_giou": 2.0, + "loss_mask": 5, + "loss_dice": 5, + }, + loss_class_type="focal_loss", + alpha=0.25, + gamma=2.0, + losses=["class", "boxes", "masks"], + ), + pixel_mean=[123.675, 116.280, 103.530], + pixel_std=[58.395, 57.120, 57.375], + select_box_nums_for_evaluation=100, + input_format="RGB", + mask_encode_level=0, + segm_type="maskdino", + mask_in_features=["res2"], + input_shapes={ + "res2": ShapeSpec(channels=256), + "res3": ShapeSpec(channels=512), + "res4": ShapeSpec(channels=1024), + "res5": ShapeSpec(channels=2048), + }, + output_dir=None, + vis_period=12800, +) + +mask_dino_loss = False + +mask_dino_single_loss = False + +mask_combine_loss = False + +if mask_dino_loss: + model.criterion.weight_dict = { + "loss_class": 4.0, + "loss_bbox": 5.0, + "loss_giou": 2.0, + "loss_mask_maskdino": 5, + "loss_dice_maskdino": 5, + } + model.criterion.losses = ["class", "boxes", "masks_maskdino"] + +if mask_dino_single_loss: + model.criterion.weight_dict = { + "loss_class": 1.0, + "loss_bbox": 5.0, + "loss_giou": 2.0, + "loss_mask_maskdino": 5, + "loss_dice_maskdino": 5, + } + model.criterion.losses = ["class", "boxes", "masks_maskdino"] + +if mask_combine_loss: + model.criterion.weight_dict = { + "loss_class": 1.0, + "loss_bbox": 5.0, + "loss_giou": 2.0, + "loss_mask": 5.0, + "loss_dice": 5.0, + "loss_mask_maskdino": 1, + "loss_dice_maskdino": 1, + } + model.criterion.losses = ["class", "boxes", "masks", "masks_maskdino"] + +if model.aux_loss: + weight_dict = model.criterion.weight_dict + aux_weight_dict = {} + for i in range(model.transformer.decoder.num_layers - 1): + aux_weight_dict.update({k + f"_{i}": v for k, v in weight_dict.items()}) + aux_weight_dict.update({k + "_enc": v for k, v in weight_dict.items()}) + weight_dict.update(aux_weight_dict) + model.criterion.weight_dict = weight_dict + +if mask_dino_single_loss: + weight_dict = model.criterion.weight_dict + + for i in range(model.transformer.decoder.num_layers - 1): + weight_dict.update({f"loss_mask_maskdino_{i}": 0}) + weight_dict.update({f"loss_dice_maskdino_{i}": 0}) + + model.criterion.weight_dict = weight_dict + + +if mask_combine_loss: + weight_dict = model.criterion.weight_dict + + weight_dict.update({"loss_mask_maskdino": 0}) + weight_dict.update({"loss_dice_maskdino": 0}) + + model.criterion.weight_dict = weight_dict + +loss_boxes_panoptic = False +if loss_boxes_panoptic: + model.criterion.losses = ["class", "boxes_panoptic", "masks"] diff --git a/configs/COCO_PanopticSegmentation/ape_deta/ape_deta_r50_12ep.py b/configs/COCO_PanopticSegmentation/ape_deta/ape_deta_r50_12ep.py new file mode 100644 index 0000000..d973b04 --- /dev/null +++ b/configs/COCO_PanopticSegmentation/ape_deta/ape_deta_r50_12ep.py @@ -0,0 +1,24 @@ +from ...COCO_InstanceSegmentation.ape_deta.ape_deta_r50_12ep import ( + lr_multiplier, + model, + optimizer, + train, +) + +from ...common.data.coco_panoptic import dataloader + +model.model_vision.dataset_prompts = ["name"] +model.model_vision.dataset_names = ["coco_2017"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +model.model_vision.instance_on = True +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = True + +model.model_vision.stuff_prob_thing = -1.0 + +model.model_vision.semantic_post_nms = False +model.model_vision.panoptic_post_nms = True +model.model_vision.aux_mask = True + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/COCO_PanopticSegmentation/ape_deta/ape_deta_r50_12ep_separated.py b/configs/COCO_PanopticSegmentation/ape_deta/ape_deta_r50_12ep_separated.py new file mode 100644 index 0000000..d7cad91 --- /dev/null +++ b/configs/COCO_PanopticSegmentation/ape_deta/ape_deta_r50_12ep_separated.py @@ -0,0 +1,24 @@ +from ...COCO_InstanceSegmentation.ape_deta.ape_deta_r50_12ep import ( + lr_multiplier, + model, + optimizer, + train, +) + +from ...common.data.coco_panoptic_separated import dataloader + +model.model_vision.dataset_prompts = ["name"] +model.model_vision.dataset_names = ["coco_2017"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +model.model_vision.instance_on = True +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = True + +model.model_vision.stuff_prob_thing = -1.0 + +model.model_vision.semantic_post_nms = False +model.model_vision.panoptic_post_nms = True +model.model_vision.aux_mask = True + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/COCO_PanopticSegmentation/ape_deta/ape_deta_r50_24ep.py b/configs/COCO_PanopticSegmentation/ape_deta/ape_deta_r50_24ep.py new file mode 100644 index 0000000..951b192 --- /dev/null +++ b/configs/COCO_PanopticSegmentation/ape_deta/ape_deta_r50_24ep.py @@ -0,0 +1,9 @@ +from detrex.config import get_config + +from .deformable_deta_segm_r50_12ep import dataloader, model, optimizer, train + +lr_multiplier = get_config("common/coco_schedule.py").lr_multiplier_24ep + +train.max_iter = 180000 + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/COCO_PanopticSegmentation/ape_deta/ape_deta_r50_lsj1024.py b/configs/COCO_PanopticSegmentation/ape_deta/ape_deta_r50_lsj1024.py new file mode 100644 index 0000000..b0c3194 --- /dev/null +++ b/configs/COCO_PanopticSegmentation/ape_deta/ape_deta_r50_lsj1024.py @@ -0,0 +1,24 @@ +from ...COCO_InstanceSegmentation.ape_deta.ape_deta_r50_12ep import ( + lr_multiplier, + model, + optimizer, + train, +) + +from ...common.data.coco_panoptic_lsj1024 import dataloader + +model.model_vision.dataset_prompts = ["name"] +model.model_vision.dataset_names = ["coco_2017"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +model.model_vision.instance_on = True +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = True + +model.model_vision.stuff_prob_thing = -1.0 + +model.model_vision.semantic_post_nms = False +model.model_vision.panoptic_post_nms = True +model.model_vision.aux_mask = True + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/COCO_PanopticSegmentation/ape_deta/ape_deta_r50_vlf_lsj1024.py b/configs/COCO_PanopticSegmentation/ape_deta/ape_deta_r50_vlf_lsj1024.py new file mode 100644 index 0000000..4b7886e --- /dev/null +++ b/configs/COCO_PanopticSegmentation/ape_deta/ape_deta_r50_vlf_lsj1024.py @@ -0,0 +1,24 @@ +from ...COCO_InstanceSegmentation.ape_deta.ape_deta_r50_vlf_12ep import ( + lr_multiplier, + model, + optimizer, + train, +) + +from ...common.data.coco_panoptic_lsj1024 import dataloader + +model.model_vision.dataset_prompts = ["name"] +model.model_vision.dataset_names = ["coco_2017"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +model.model_vision.instance_on = True +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = True + +model.model_vision.stuff_prob_thing = -1.0 + +model.model_vision.semantic_post_nms = False +model.model_vision.panoptic_post_nms = True +model.model_vision.aux_mask = True + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/COCO_PanopticSegmentation/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024.py b/configs/COCO_PanopticSegmentation/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024.py new file mode 100644 index 0000000..4f12d70 --- /dev/null +++ b/configs/COCO_PanopticSegmentation/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024.py @@ -0,0 +1,20 @@ +from ...COCO_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_12ep import ( + lr_multiplier, + model, + optimizer, + train, +) + +from ...common.data.coco_panoptic_lsj1024 import dataloader + +model.model_vision.dataset_prompts = ["name"] +model.model_vision.dataset_names = ["coco_2017"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +model.model_vision.instance_on = True +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = True + +model.model_vision.stuff_prob_thing = -1.0 + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/COCO_PanopticSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024.py b/configs/COCO_PanopticSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024.py new file mode 100644 index 0000000..7ba49aa --- /dev/null +++ b/configs/COCO_PanopticSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024.py @@ -0,0 +1,20 @@ +from ...COCO_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_lsj1024_cp_12ep import ( + lr_multiplier, + model, + optimizer, + train, +) + +from ...common.data.coco_panoptic_lsj1024 import dataloader + +model.model_vision.dataset_prompts = ["name"] +model.model_vision.dataset_names = ["coco_2017"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +model.model_vision.instance_on = True +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = True + +model.model_vision.stuff_prob_thing = -1.0 + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/COCO_PanopticSegmentation/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024.py b/configs/COCO_PanopticSegmentation/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024.py new file mode 100644 index 0000000..0cb2c73 --- /dev/null +++ b/configs/COCO_PanopticSegmentation/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024.py @@ -0,0 +1,20 @@ +from ...COCO_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_vlf_lsj1024_cp_12ep import ( + lr_multiplier, + model, + optimizer, + train, +) + +from ...common.data.coco_panoptic_lsj1024 import dataloader + +model.model_vision.dataset_prompts = ["name"] +model.model_vision.dataset_names = ["coco_2017"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +model.model_vision.instance_on = True +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = True + +model.model_vision.stuff_prob_thing = -1.0 + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/COCO_PanopticSegmentation/deformable_deta/deformable_deta_segm_r50_12ep.py b/configs/COCO_PanopticSegmentation/deformable_deta/deformable_deta_segm_r50_12ep.py new file mode 100644 index 0000000..98c1c16 --- /dev/null +++ b/configs/COCO_PanopticSegmentation/deformable_deta/deformable_deta_segm_r50_12ep.py @@ -0,0 +1,27 @@ +from ...COCO_InstanceSegmentation.deformable_deta.deformable_deta_segm_r50_12ep import ( + lr_multiplier, + model, + optimizer, + train, +) + +from ...common.data.coco_panoptic import dataloader + +model.num_classes = 133 +model.criterion.num_classes = 133 +model.dataset_metas = dataloader.train.dataset.names + +model.stuff_dataset_learn_thing = False + +model.instance_on = True +model.semantic_on = True +model.panoptic_on = True + +model.stuff_prob_thing = -1.0 + + +model.semantic_post_nms = False +model.panoptic_post_nms = True +model.aux_mask = True + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/COCO_PanopticSegmentation/deformable_deta/deformable_deta_segm_r50_24ep.py b/configs/COCO_PanopticSegmentation/deformable_deta/deformable_deta_segm_r50_24ep.py new file mode 100644 index 0000000..951b192 --- /dev/null +++ b/configs/COCO_PanopticSegmentation/deformable_deta/deformable_deta_segm_r50_24ep.py @@ -0,0 +1,9 @@ +from detrex.config import get_config + +from .deformable_deta_segm_r50_12ep import dataloader, model, optimizer, train + +lr_multiplier = get_config("common/coco_schedule.py").lr_multiplier_24ep + +train.max_iter = 180000 + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/COCO_PanopticSegmentation/deformable_deta/deformable_deta_segm_r50_36ep.py b/configs/COCO_PanopticSegmentation/deformable_deta/deformable_deta_segm_r50_36ep.py new file mode 100644 index 0000000..82f6c13 --- /dev/null +++ b/configs/COCO_PanopticSegmentation/deformable_deta/deformable_deta_segm_r50_36ep.py @@ -0,0 +1,9 @@ +from detrex.config import get_config + +from .deformable_deta_segm_r50_12ep import dataloader, model, optimizer, train + +lr_multiplier = get_config("common/coco_schedule.py").lr_multiplier_36ep + +train.max_iter = 270000 + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/COCO_PanopticSegmentation/deformable_deta/deformable_deta_segm_r50_50ep.py b/configs/COCO_PanopticSegmentation/deformable_deta/deformable_deta_segm_r50_50ep.py new file mode 100644 index 0000000..71bcfea --- /dev/null +++ b/configs/COCO_PanopticSegmentation/deformable_deta/deformable_deta_segm_r50_50ep.py @@ -0,0 +1,9 @@ +from detrex.config import get_config + +from .deformable_deta_segm_r50_12ep import dataloader, model, optimizer, train + +lr_multiplier = get_config("common/coco_schedule.py").lr_multiplier_50ep + +train.max_iter = 375000 + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/COCO_REFCOCO/ape_deta/ape_deta_r50_12ep.py b/configs/COCO_REFCOCO/ape_deta/ape_deta_r50_12ep.py new file mode 100644 index 0000000..7935414 --- /dev/null +++ b/configs/COCO_REFCOCO/ape_deta/ape_deta_r50_12ep.py @@ -0,0 +1,29 @@ +from detectron2.config import LazyCall as L +from detrex.config import get_config + +from ...COCO_InstanceSegmentation.ape_deta.ape_deta_r50_12ep import ( + lr_multiplier, + model, + optimizer, + train, +) +from ...common.data.coco_refcoco_instance import dataloader + +model.model_vision.num_classes = 80 +model.model_vision.select_box_nums_for_evaluation = 300 + +criterion = model.model_vision.criterion[0] +model.model_vision.criterion = [criterion for _ in range(2)] +for criterion, num_classes in zip(model.model_vision.criterion, [80, 1]): + criterion.num_classes = num_classes + +model.model_vision.criterion[1].weight_dict["loss_class_enc"] = 0.0 + +dataloader.train.total_batch_size = 16 +dataloader.train.total_batch_size_list = [16, 16] + +model.model_vision.dataset_prompts = ["name", "expression"] +model.model_vision.dataset_names = ["coco_2017", "refcoco"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/COCO_REFCOCO/ape_deta/ape_deta_r50_24ep.py b/configs/COCO_REFCOCO/ape_deta/ape_deta_r50_24ep.py new file mode 100644 index 0000000..fc8b561 --- /dev/null +++ b/configs/COCO_REFCOCO/ape_deta/ape_deta_r50_24ep.py @@ -0,0 +1,12 @@ +from detrex.config import get_config + +from .ape_deta_r50_12ep import dataloader, model, optimizer, train + +lr_multiplier = get_config("common/coco_schedule.py").lr_multiplier_24ep + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.output_dir = train.output_dir + +train.max_iter = 180000 + +train.eval_period = 10000 diff --git a/configs/COCO_REFCOCO/ape_deta/ape_deta_r50_36ep.py b/configs/COCO_REFCOCO/ape_deta/ape_deta_r50_36ep.py new file mode 100644 index 0000000..1ff0df9 --- /dev/null +++ b/configs/COCO_REFCOCO/ape_deta/ape_deta_r50_36ep.py @@ -0,0 +1,12 @@ +from detrex.config import get_config + +from .ape_deta_r50_12ep import dataloader, model, optimizer, train + +lr_multiplier = get_config("common/coco_schedule.py").lr_multiplier_36ep + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.output_dir = train.output_dir + +train.max_iter = 270000 + +train.eval_period = 15000 diff --git a/configs/COCO_REFCOCO/ape_deta/ape_deta_r50_vlf_12ep.py b/configs/COCO_REFCOCO/ape_deta/ape_deta_r50_vlf_12ep.py new file mode 100644 index 0000000..964fb02 --- /dev/null +++ b/configs/COCO_REFCOCO/ape_deta/ape_deta_r50_vlf_12ep.py @@ -0,0 +1,52 @@ +from detectron2.config import LazyCall as L +from omegaconf import OmegaConf +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) + +from .ape_deta_r50_12ep import dataloader, lr_multiplier, model, optimizer, train + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + cfg=OmegaConf.from_dotlist( + [ + "MODEL.DYHEAD.FUSE_CONFIG.STABLE_SOFTMAX_2D=False", + "MODEL.DYHEAD.FUSE_CONFIG.CLAMP_MIN_FOR_UNDERFLOW=True", + "MODEL.DYHEAD.FUSE_CONFIG.CLAMP_MAX_FOR_OVERFLOW=True", + "MODEL.VL_FUSION_USE_CHECKPOINT=True", + ], + ), +) + + +model.model_vision.text_feature_bank = False +model.model_vision.text_feature_reduce_before_fusion = False +model.model_vision.text_feature_batch_repeat = False +model.model_vision.expression_cumulative_gt_class = False + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 5120 diff --git a/configs/COCO_REFCOCO/ape_deta/ape_deta_r50_vlf_36ep.py b/configs/COCO_REFCOCO/ape_deta/ape_deta_r50_vlf_36ep.py new file mode 100644 index 0000000..af3ecab --- /dev/null +++ b/configs/COCO_REFCOCO/ape_deta/ape_deta_r50_vlf_36ep.py @@ -0,0 +1,12 @@ +from detrex.config import get_config + +from .ape_deta_r50_vlf_12ep import dataloader, model, optimizer, train + +lr_multiplier = get_config("common/coco_schedule.py").lr_multiplier_36ep + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.output_dir = train.output_dir + +train.max_iter = 270000 + +train.eval_period = 15000 diff --git a/configs/COCO_REFCOCO/ape_deta/ape_deta_r50_vlf_bert_36ep.py b/configs/COCO_REFCOCO/ape_deta/ape_deta_r50_vlf_bert_36ep.py new file mode 100644 index 0000000..13be9e1 --- /dev/null +++ b/configs/COCO_REFCOCO/ape_deta/ape_deta_r50_vlf_bert_36ep.py @@ -0,0 +1,21 @@ +from detectron2.config import LazyCall as L +from ape.modeling.text import Bert + +from .ape_deta_r50_vlf_36ep import dataloader, lr_multiplier, model, optimizer, train + +model.model_vision.criterion[1].num_classes = 1 + +model.model_language = L(Bert)( + pretrained_model_name_or_path="models/huggingface/bert-base-uncased/" +) +model.model_vision.embed_dim_language = 768 +model.model_vision.text_feature_reduce_type = "average" + +model.model_vision.text_feature_bank = False +model.model_vision.text_feature_reduce_before_fusion = False +model.model_vision.text_feature_batch_repeat = False +model.model_vision.expression_cumulative_gt_class = False + + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 5120 diff --git a/configs/COCO_REFCOCO/ape_deta/ape_deta_vitl_eva02_lsj1024_12ep.py b/configs/COCO_REFCOCO/ape_deta/ape_deta_vitl_eva02_lsj1024_12ep.py new file mode 100644 index 0000000..98758b3 --- /dev/null +++ b/configs/COCO_REFCOCO/ape_deta/ape_deta_vitl_eva02_lsj1024_12ep.py @@ -0,0 +1,118 @@ +from functools import partial + +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detectron2.modeling.backbone.fpn import LastLevelMaxPool +from detrex.config import get_config +from ape.modeling.backbone.vit import get_vit_lr_decay_rate +from ape.modeling.backbone.vit_eva02 import SimpleFeaturePyramid, ViT +from ape.modeling.text import EVA01CLIP + +from .....detectron2.configs.common.data.constants import constants +from ...common.data.coco_refcoco_instance_lsj1024 import dataloader +from .models.ape_deta_r50 import model + +model.model_vision.pixel_mean = constants.imagenet_rgb256_mean +model.model_vision.pixel_std = constants.imagenet_rgb256_std +model.model_vision.input_format = "RGB" + +model.model_vision.backbone = L(SimpleFeaturePyramid)( + net=L(ViT)( # Single-scale ViT backbone + img_size=1024, + patch_size=16, + embed_dim=1024, + depth=24, + num_heads=16, + drop_path_rate=0.4, + window_size=16, + mlp_ratio=4 * 2 / 3, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + window_block_indexes=list(range(0, 5)) + + list(range(6, 11)) + + list(range(12, 17)) + + list(range(18, 23)), + residual_block_indexes=[], + use_rel_pos=True, + out_feature="last_feat", + use_act_checkpoint=True, + xattn=True, + ), + in_feature="${.net.out_feature}", + out_channels=256, + scale_factors=(4.0, 2.0, 1.0, 0.5), + top_block=L(LastLevelMaxPool)(), + norm="LN", + square_pad=1024, +) + +model.model_vision.neck = None + +model.model_vision.mask_in_features = ["p2"] +model.model_vision.input_shapes = { + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), +} + +optimizer = get_config("common/optim.py").AdamW +optimizer.params.lr_factor_func = ( + lambda module_name: 0.1 + if "reference_points" in module_name or "sampling_offsets" in module_name + else get_vit_lr_decay_rate(module_name, lr_decay_rate=0.8, num_layers=24) + if "backbone.net" in module_name + else 1 +) +optimizer.params.overrides = {"pos_embed": {"weight_decay": 0.0}} +optimizer.params.weight_decay_norm = None + +optimizer.lr = 2e-4 +optimizer.betas = (0.9, 0.999) +optimizer.weight_decay = 1e-4 + +train = get_config("common/train.py").train +train.max_iter = 90000 +train.eval_period = 5000 +train.log_period = 20 + +train.checkpointer.period = 5000 +train.checkpointer.max_to_keep = 2 + +train.clip_grad.enabled = True +train.clip_grad.params.max_norm = 0.1 +train.clip_grad.params.norm_type = 2 + +train.device = "cuda" + +train.init_checkpoint = ( + "models/Yunxin-CV/EVA-02/eva02/pt/eva02_L_pt_in21k_p14to16.pt?matching_heuristics=True" +) + +train.amp.enabled = True +train.ddp.fp16_compression = True + +lr_multiplier = get_config("common/coco_schedule.py").lr_multiplier_12ep +lr_multiplier.scheduler.milestones = [75000, 90000] +lr_multiplier.warmup_length = 1000 / train.max_iter + +dataloader.train.num_workers = 16 +dataloader.train.total_batch_size = 16 +dataloader.train.total_batch_size_list = [16, 16] +dataloader.train.mapper.image_format = "RGB" +dataloader.train.mapper.use_instance_mask = True + +model.model_vision.dataset_prompts = ["name", "expression"] +model.model_vision.dataset_names = ["coco_2017", "refcoco"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.output_dir = train.output_dir + +model.model_language = L(EVA01CLIP)( + clip_model="EVA_CLIP_g_14_X", cache_dir="models/BAAI/EVA/eva_clip_psz14.pt" +) +model.model_vision.embed_dim_language = 1024 diff --git a/configs/COCO_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_36ep.py b/configs/COCO_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_36ep.py new file mode 100644 index 0000000..462c7d3 --- /dev/null +++ b/configs/COCO_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_36ep.py @@ -0,0 +1,118 @@ +from functools import partial + +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detectron2.modeling.backbone.fpn import LastLevelMaxPool +from detrex.config import get_config +from ape.modeling.backbone.vit import get_vit_lr_decay_rate +from ape.modeling.backbone.vit_eva02 import SimpleFeaturePyramid, ViT +from ape.modeling.text import EVA01CLIP + +from .....detectron2.configs.common.data.constants import constants +from ...common.data.coco_refcoco_instance_lsj1024 import dataloader +from .models.ape_deta_r50_vlf import model + +model.model_vision.pixel_mean = constants.imagenet_rgb256_mean +model.model_vision.pixel_std = constants.imagenet_rgb256_std +model.model_vision.input_format = "RGB" + +model.model_vision.backbone = L(SimpleFeaturePyramid)( + net=L(ViT)( # Single-scale ViT backbone + img_size=1024, + patch_size=16, + embed_dim=1024, + depth=24, + num_heads=16, + drop_path_rate=0.4, + window_size=16, + mlp_ratio=4 * 2 / 3, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + window_block_indexes=list(range(0, 5)) + + list(range(6, 11)) + + list(range(12, 17)) + + list(range(18, 23)), + residual_block_indexes=[], + use_rel_pos=True, + out_feature="last_feat", + use_act_checkpoint=False, + xattn=True, + ), + in_feature="${.net.out_feature}", + out_channels=256, + scale_factors=(4.0, 2.0, 1.0, 0.5), + top_block=L(LastLevelMaxPool)(), + norm="LN", + square_pad=1024, +) + +model.model_vision.neck = None + +model.model_vision.mask_in_features = ["p2"] +model.model_vision.input_shapes = { + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), +} + +optimizer = get_config("common/optim.py").AdamW +optimizer.params.lr_factor_func = ( + lambda module_name: 0.1 + if "reference_points" in module_name or "sampling_offsets" in module_name + else get_vit_lr_decay_rate(module_name, lr_decay_rate=0.8, num_layers=24) + if "backbone.net" in module_name + else 1 +) +optimizer.params.overrides = {"pos_embed": {"weight_decay": 0.0}} +optimizer.params.weight_decay_norm = None + +optimizer.lr = 2e-4 +optimizer.betas = (0.9, 0.999) +optimizer.weight_decay = 1e-4 + +train = get_config("common/train.py").train +train.max_iter = 270000 +train.eval_period = 27000 +train.log_period = 20 + +train.checkpointer.period = 5000 +train.checkpointer.max_to_keep = 2 + +train.clip_grad.enabled = True +train.clip_grad.params.max_norm = 0.1 +train.clip_grad.params.norm_type = 2 + +train.device = "cuda" + +train.init_checkpoint = ( + "models/Yunxin-CV/EVA-02/eva02/pt/eva02_L_pt_in21k_p14to16.pt?matching_heuristics=True" +) + +train.amp.enabled = True +train.ddp.fp16_compression = True + +lr_multiplier = get_config("common/coco_schedule.py").lr_multiplier_36ep +lr_multiplier.warmup_length = 1000 / train.max_iter + +dataloader.train.num_workers = 16 +dataloader.train.total_batch_size = 16 +dataloader.train.total_batch_size_list = [16, 16] +dataloader.train.mapper.image_format = "RGB" +dataloader.train.mapper.use_instance_mask = True + +model.model_vision.dataset_prompts = ["name", "expression"] +model.model_vision.dataset_names = ["coco_2017", "refcoco"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.output_dir = train.output_dir + +model.model_language = L(EVA01CLIP)( + clip_model="EVA_CLIP_g_14_X", cache_dir="models/BAAI/EVA/eva_clip_psz14.pt" +) +model.model_vision.embed_dim_language = 1024 + diff --git a/configs/COCO_REFCOCO/ape_deta/ape_deta_vitl_lsj1024_12ep.py b/configs/COCO_REFCOCO/ape_deta/ape_deta_vitl_lsj1024_12ep.py new file mode 100644 index 0000000..61103df --- /dev/null +++ b/configs/COCO_REFCOCO/ape_deta/ape_deta_vitl_lsj1024_12ep.py @@ -0,0 +1,114 @@ +from functools import partial + +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detectron2.modeling.backbone.fpn import LastLevelMaxPool +from detectron2.modeling.backbone.vit import SimpleFeaturePyramid, ViT, get_vit_lr_decay_rate +from detrex.config import get_config +from ape.modeling.text import EVA01CLIP + +from .....detectron2.configs.common.data.constants import constants +from ...common.data.coco_refcoco_instance_lsj1024 import dataloader +from .models.ape_deta_r50 import model + +model.model_vision.pixel_mean = constants.imagenet_rgb256_mean +model.model_vision.pixel_std = constants.imagenet_rgb256_std +model.model_vision.input_format = "RGB" + +model.model_vision.backbone = L(SimpleFeaturePyramid)( + net=L(ViT)( # Single-scale ViT backbone + img_size=1024, + patch_size=16, + embed_dim=1024, + depth=24, + num_heads=16, + drop_path_rate=0.4, + window_size=14, + mlp_ratio=4, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + window_block_indexes=list(range(0, 5)) + + list(range(6, 11)) + + list(range(12, 17)) + + list(range(18, 23)), + residual_block_indexes=[], + use_rel_pos=True, + out_feature="last_feat", + use_act_checkpoint=True, + ), + in_feature="${.net.out_feature}", + out_channels=256, + scale_factors=(4.0, 2.0, 1.0, 0.5), + top_block=L(LastLevelMaxPool)(), + norm="LN", + square_pad=1024, +) + +model.model_vision.neck = None + +model.model_vision.mask_in_features = ["p2"] +model.model_vision.input_shapes = { + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), +} + +optimizer = get_config("common/optim.py").AdamW +optimizer.params.lr_factor_func = ( + lambda module_name: 0.1 + if "reference_points" in module_name or "sampling_offsets" in module_name + else get_vit_lr_decay_rate(module_name, lr_decay_rate=0.8, num_layers=24) + if "backbone" in module_name + else 1 +) +optimizer.params.overrides = {"pos_embed": {"weight_decay": 0.0}} + +optimizer.lr = 2e-4 +optimizer.weight_decay = 0.05 + +train = get_config("common/train.py").train +train.max_iter = 90000 +train.eval_period = 5000 +train.log_period = 20 + +train.checkpointer.period = 5000 +train.checkpointer.max_to_keep = 2 + +train.clip_grad.enabled = True +train.clip_grad.params.max_norm = 0.1 +train.clip_grad.params.norm_type = 2 + +train.device = "cuda" + +train.init_checkpoint = ( + "detectron2://ImageNetPretrained/MAE/mae_pretrain_vit_large.pth?matching_heuristics=True" +) +train.init_checkpoint = "models/MAE/mae_pretrain_vit_large.pth?matching_heuristics=True" + +train.amp.enabled = True +train.ddp.fp16_compression = True + +lr_multiplier = get_config("common/coco_schedule.py").lr_multiplier_12ep +lr_multiplier.scheduler.milestones = [75000, 90000] +lr_multiplier.warmup_length = 1000 / train.max_iter + +dataloader.train.num_workers = 16 +dataloader.train.total_batch_size = 16 +dataloader.train.total_batch_size_list = [16, 16] +dataloader.train.mapper.image_format = "RGB" +dataloader.train.mapper.use_instance_mask = True + +model.model_vision.dataset_tasks = ["name", "expression"] +model.model_vision.dataset_names = ["coco_2017", "refcoco"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.output_dir = train.output_dir + +model.model_language = L(EVA01CLIP)( + clip_model="EVA_CLIP_g_14_X", cache_dir="models/BAAI/EVA/eva_clip_psz14.pt" +) +model.model_vision.embed_dim_language = 1024 diff --git a/configs/COCO_SA1B_InstanceSegmentation/ape_deta/ape_deta_r50_24ep.py b/configs/COCO_SA1B_InstanceSegmentation/ape_deta/ape_deta_r50_24ep.py new file mode 100644 index 0000000..7619c00 --- /dev/null +++ b/configs/COCO_SA1B_InstanceSegmentation/ape_deta/ape_deta_r50_24ep.py @@ -0,0 +1,36 @@ +from detrex.config import get_config + +from ...COCO_InstanceSegmentation.ape_deta.ape_deta_r50_12ep import model, optimizer, train +from ...common.data.coco_sa1b_instance import dataloader + +model.model_vision.num_classes = 80 +criterion = model.model_vision.criterion[0] +model.model_vision.criterion = [criterion for _ in range(2)] +for criterion, num_classes in zip(model.model_vision.criterion, [80, 1]): + criterion.num_classes = num_classes + +for k, v in model.model_vision.criterion[1].weight_dict.items(): + if "_class" in k and "_enc" not in k: + model.model_vision.criterion[1].weight_dict.update({k: 0.0}) + +model.model_vision.stuff_dataset_learn_thing = False +model.model_vision.stuff_prob_thing = 0.9 + +model.model_vision.instance_on = True +model.model_vision.semantic_on = False +model.model_vision.panoptic_on = False + +train.max_iter = 180000 +train.eval_period = 5000 + +lr_multiplier = get_config("common/coco_schedule.py").lr_multiplier_24ep + +dataloader.train.total_batch_size = 16 +dataloader.train.total_batch_size_list = [16, 16] + +model.model_vision.dataset_prompts = ["name", "name"] +model.model_vision.dataset_names = ["coco", "sa1b"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 1280 diff --git a/configs/COCO_SA1B_InstanceSegmentation/ape_deta/ape_deta_r50_24ep_mp.py b/configs/COCO_SA1B_InstanceSegmentation/ape_deta/ape_deta_r50_24ep_mp.py new file mode 100644 index 0000000..d8b4ee9 --- /dev/null +++ b/configs/COCO_SA1B_InstanceSegmentation/ape_deta/ape_deta_r50_24ep_mp.py @@ -0,0 +1,6 @@ +from .ape_deta_r50_24ep import dataloader, lr_multiplier, model, optimizer, train + +model.model_vision.transformer.proposal_ambiguous = 1 + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 128 diff --git a/configs/COCO_SA1B_PanopticSegmentation/ape_deta/ape_deta_r50_24ep.py b/configs/COCO_SA1B_PanopticSegmentation/ape_deta/ape_deta_r50_24ep.py new file mode 100644 index 0000000..91481dc --- /dev/null +++ b/configs/COCO_SA1B_PanopticSegmentation/ape_deta/ape_deta_r50_24ep.py @@ -0,0 +1,36 @@ +from detrex.config import get_config + +from ...COCO_InstanceSegmentation.ape_deta.ape_deta_r50_12ep import model, optimizer, train +from ...common.data.coco_sa1b_panoptic import dataloader + +model.model_vision.num_classes = 133 +criterion = model.model_vision.criterion[0] +model.model_vision.criterion = [criterion for _ in range(2)] +for criterion, num_classes in zip(model.model_vision.criterion, [133, 1]): + criterion.num_classes = num_classes + +for k, v in model.model_vision.criterion[1].weight_dict.items(): + if "_class" in k and "_enc" not in k: + model.model_vision.criterion[1].weight_dict.update({k: 0.0}) + +model.model_vision.stuff_dataset_learn_thing = False +model.model_vision.stuff_prob_thing = 0.9 + +model.model_vision.instance_on = True +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = True + +train.max_iter = 180000 +train.eval_period = 5000 + +lr_multiplier = get_config("common/coco_schedule.py").lr_multiplier_24ep + +dataloader.train.total_batch_size = 16 +dataloader.train.total_batch_size_list = [16, 16] + +model.model_vision.dataset_prompts = ["name", "name"] +model.model_vision.dataset_names = ["coco", "sa1b"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 1280 diff --git a/configs/COCO_SA1B_PanopticSegmentation/ape_deta/ape_deta_r50_24ep_lp.py b/configs/COCO_SA1B_PanopticSegmentation/ape_deta/ape_deta_r50_24ep_lp.py new file mode 100644 index 0000000..a6181fd --- /dev/null +++ b/configs/COCO_SA1B_PanopticSegmentation/ape_deta/ape_deta_r50_24ep_lp.py @@ -0,0 +1,9 @@ +from .ape_deta_r50_24ep import dataloader, lr_multiplier, model, optimizer, train + +model.model_vision.criterion[0].losses += ["iou"] +model.model_vision.criterion[0].weight_dict["loss_ious"] = 1.0 + +model.model_vision.last_class_embed_use_mlp = True + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 128 diff --git a/configs/COCO_SA1B_PanopticSegmentation/ape_deta/ape_deta_r50_24ep_vlf_lp.py b/configs/COCO_SA1B_PanopticSegmentation/ape_deta/ape_deta_r50_24ep_vlf_lp.py new file mode 100644 index 0000000..543575f --- /dev/null +++ b/configs/COCO_SA1B_PanopticSegmentation/ape_deta/ape_deta_r50_24ep_vlf_lp.py @@ -0,0 +1,41 @@ +from detectron2.config import LazyCall as L +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) + +from .ape_deta_r50_24ep_lp import dataloader, lr_multiplier, model, optimizer, train + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=False, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=False, +) + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 128 diff --git a/configs/COCO_SemanticSegmentation/ape_deta/ape_deta_r50_12ep.py b/configs/COCO_SemanticSegmentation/ape_deta/ape_deta_r50_12ep.py new file mode 100644 index 0000000..566bca7 --- /dev/null +++ b/configs/COCO_SemanticSegmentation/ape_deta/ape_deta_r50_12ep.py @@ -0,0 +1,27 @@ +from ...COCO_InstanceSegmentation.ape_deta.ape_deta_r50_12ep import ( + lr_multiplier, + model, + optimizer, + train, +) +from ...common.data.coco_semantic import dataloader + +model.model_vision.dataset_prompts = ["name"] +model.model_vision.dataset_names = ["stuffonly"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +num_classes = 54 +model.model_vision.num_classes = num_classes +model.model_vision.criterion[0].num_classes = num_classes +model.model_vision.criterion[0].matcher_stage2.num_classes = num_classes + +model.model_vision.instance_on = False +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = False + +model.model_vision.stuff_prob_thing = 0.9 + +model.model_vision.semantic_post_nms = False +model.model_vision.aux_mask = True + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/COCO_SemanticSegmentation/ape_deta/ape_deta_r50_vlf_lsj1024_12ep.py b/configs/COCO_SemanticSegmentation/ape_deta/ape_deta_r50_vlf_lsj1024_12ep.py new file mode 100644 index 0000000..7be08c0 --- /dev/null +++ b/configs/COCO_SemanticSegmentation/ape_deta/ape_deta_r50_vlf_lsj1024_12ep.py @@ -0,0 +1,27 @@ +from ...COCO_InstanceSegmentation.ape_deta.ape_deta_r50_vlf_12ep import ( + lr_multiplier, + model, + optimizer, + train, +) +from ...common.data.coco_semantic_lsj1024 import dataloader + +model.model_vision.dataset_prompts = ["name"] +model.model_vision.dataset_names = ["stuffonly"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +num_classes = 54 +model.model_vision.num_classes = num_classes +model.model_vision.criterion[0].num_classes = num_classes +model.model_vision.criterion[0].matcher_stage2.num_classes = num_classes + +model.model_vision.instance_on = False +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = False + +model.model_vision.stuff_prob_thing = 0.9 + +model.model_vision.semantic_post_nms = False +model.model_vision.aux_mask = True + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/COCO_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024_12ep.py b/configs/COCO_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024_12ep.py new file mode 100644 index 0000000..d689ef3 --- /dev/null +++ b/configs/COCO_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024_12ep.py @@ -0,0 +1,33 @@ +from detectron2.config import LazyCall as L +from ape.modeling.text import EVA01CLIP + +from ...COCO_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_lsj1024_cp_12ep import ( + lr_multiplier, + model, + optimizer, + train, +) +from ...common.data.coco_semantic_lsj1024 import dataloader + +num_classes = 54 +model.model_vision.num_classes = num_classes +model.model_vision.criterion[0].num_classes = num_classes +model.model_vision.criterion[0].matcher_stage2.num_classes = num_classes + +model.model_vision.instance_on = False +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = False + +train.output_dir = "output/" + __file__[:-3] + +model.model_vision.dataset_prompts = ["name"] +model.model_vision.dataset_names = ["stuffonly"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.output_dir = train.output_dir + +model.model_language = L(EVA01CLIP)( + clip_model="EVA_CLIP_g_14_X", cache_dir="models/BAAI/EVA/eva_clip_psz14.pt" +) +model.model_vision.embed_dim_language = 1024 diff --git a/configs/COCO_SemanticSegmentation/deformable_deta/deformable_deta_segm_r50_12ep.py b/configs/COCO_SemanticSegmentation/deformable_deta/deformable_deta_segm_r50_12ep.py new file mode 100644 index 0000000..fb3adcc --- /dev/null +++ b/configs/COCO_SemanticSegmentation/deformable_deta/deformable_deta_segm_r50_12ep.py @@ -0,0 +1,22 @@ +from ...COCO_InstanceSegmentation.deformable_deta.deformable_deta_segm_r50_12ep import ( + lr_multiplier, + model, + optimizer, + train, +) +from ...common.data.coco_semantic import dataloader + +num_classes = 54 +model.num_classes = num_classes +model.criterion.num_classes = num_classes +model.criterion.matcher_stage2.num_classes = num_classes + +model.instance_on = False +model.semantic_on = True +model.panoptic_on = False + +train.init_checkpoint = "detectron2://ImageNetPretrained/torchvision/R-50.pkl" +train.init_checkpoint = "models/torchvision/R-50.pkl" +train.output_dir = "output/" + __file__[:-3] + +model.dataset_metas = dataloader.train.dataset.names diff --git a/configs/Cityscapes_PanopticSegmentation/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024.py b/configs/Cityscapes_PanopticSegmentation/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024.py new file mode 100644 index 0000000..90390fb --- /dev/null +++ b/configs/Cityscapes_PanopticSegmentation/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024.py @@ -0,0 +1,101 @@ +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detrex.modeling.neck import ChannelMapper +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) +from ape.modeling.text import EVA02CLIP + +from ...common.backbone.vitl_eva02_clip import backbone +from .ape_deta_vitl_eva02_lsj1024 import dataloader, lr_multiplier, model, optimizer, train + +model.model_vision.backbone = backbone + +train.init_checkpoint = ( + "models/QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14to16_s6B.pt?matching_heuristics=True" +) + +model.model_language = L(EVA02CLIP)( + clip_model="EVA02-CLIP-bigE-14-plus", + cache_dir="models/QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt", + dtype="float16", +) +model.model_vision.embed_dim_language = 1024 + +model.model_vision.neck = L(ChannelMapper)( + input_shapes={ + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), + }, + in_features=["p2", "p3", "p4", "p5", "p6"], + out_channels=256, + num_outs=5, + kernel_size=1, + norm_layer=L(nn.GroupNorm)(num_groups=32, num_channels=256), +) + +model.model_vision.mask_in_features = ["p2"] +model.model_vision.input_shapes = { + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), +} + +model.model_vision.transformer.encoder.num_layers = 6 +model.model_vision.transformer.decoder.num_layers = 6 +model.model_vision.transformer.encoder.embed_dim = 256 +model.model_vision.transformer.decoder.embed_dim = 256 +model.model_vision.embed_dim = 256 +model.model_vision.backbone.out_channels = 256 + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +model.model_vision.stuff_dataset_learn_thing = False +model.model_vision.stuff_prob_thing = -1.0 +model.model_vision.transformer.proposal_ambiguous = 1 + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 12800 diff --git a/configs/Cityscapes_PanopticSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024.py b/configs/Cityscapes_PanopticSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024.py new file mode 100644 index 0000000..a9477ff --- /dev/null +++ b/configs/Cityscapes_PanopticSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024.py @@ -0,0 +1,23 @@ +from ...COCO_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_lsj1024_cp_12ep import ( + lr_multiplier, + model, + optimizer, + train, +) +from ...common.data.cityscapes_panoptic_lsj1024 import dataloader + +model.model_vision.dataset_prompts = ["name"] +model.model_vision.name_prompt_fusion_text = [False] +model.model_vision.dataset_names = ["cityscapes"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +model.model_vision.select_box_nums_for_evaluation = 300 + + +model.model_vision.instance_on = True +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = True + +model.model_vision.stuff_prob_thing = -1.0 + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/Cityscapes_PanopticSegmentation/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024.py b/configs/Cityscapes_PanopticSegmentation/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024.py new file mode 100644 index 0000000..1a3ad59 --- /dev/null +++ b/configs/Cityscapes_PanopticSegmentation/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024.py @@ -0,0 +1,47 @@ +from detectron2.config import LazyCall as L +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) + +from .ape_deta_vitl_eva02_lsj1024 import dataloader, lr_multiplier, model, optimizer, train + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 12800 diff --git a/configs/D3_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024.py b/configs/D3_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024.py new file mode 100644 index 0000000..5d9c300 --- /dev/null +++ b/configs/D3_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024.py @@ -0,0 +1,101 @@ +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detrex.modeling.neck import ChannelMapper +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) +from ape.modeling.text import EVA02CLIP + +from ...common.backbone.vitl_eva02_clip import backbone +from .ape_deta_vitl_eva02_lsj1024 import dataloader, lr_multiplier, model, optimizer, train + +model.model_vision.backbone = backbone + +train.init_checkpoint = ( + "models/QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14to16_s6B.pt?matching_heuristics=True" +) + +model.model_language = L(EVA02CLIP)( + clip_model="EVA02-CLIP-bigE-14-plus", + cache_dir="models/QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt", + dtype="float16", +) +model.model_vision.embed_dim_language = 1024 + +model.model_vision.neck = L(ChannelMapper)( + input_shapes={ + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), + }, + in_features=["p2", "p3", "p4", "p5", "p6"], + out_channels=256, + num_outs=5, + kernel_size=1, + norm_layer=L(nn.GroupNorm)(num_groups=32, num_channels=256), +) + +model.model_vision.mask_in_features = ["p2"] +model.model_vision.input_shapes = { + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), +} + +model.model_vision.transformer.encoder.num_layers = 6 +model.model_vision.transformer.decoder.num_layers = 6 +model.model_vision.transformer.encoder.embed_dim = 256 +model.model_vision.transformer.decoder.embed_dim = 256 +model.model_vision.embed_dim = 256 +model.model_vision.backbone.out_channels = 256 + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_bank_reset = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +model.model_vision.stuff_dataset_learn_thing = False +model.model_vision.stuff_prob_thing = -1.0 +model.model_vision.transformer.proposal_ambiguous = 2 + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 12800 diff --git a/configs/D3_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024.py b/configs/D3_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024.py new file mode 100644 index 0000000..aa03775 --- /dev/null +++ b/configs/D3_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024.py @@ -0,0 +1,24 @@ +from ...COCO_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_lsj1024_cp_12ep import ( + lr_multiplier, + model, + optimizer, + train, +) +from ...common.data.d3_instance_lsj1024 import dataloader + +model.model_vision.dataset_prompts = ["expression"] +model.model_vision.dataset_names = ["d3"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +model.model_vision.num_classes = 256 +model.model_vision.criterion[0].num_classes = 256 +model.model_vision.select_box_nums_for_evaluation = 300 +model.model_vision.select_box_nums_for_evaluation_list = [300, 300] + +model.model_vision.instance_on = True +model.model_vision.semantic_on = False +model.model_vision.panoptic_on = False + +model.model_vision.stuff_prob_thing = -1.0 + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/D3_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024.py b/configs/D3_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024.py new file mode 100644 index 0000000..a322cc1 --- /dev/null +++ b/configs/D3_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024.py @@ -0,0 +1,47 @@ +from detectron2.config import LazyCall as L +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) + +from .ape_deta_vitl_eva02_lsj1024 import dataloader, lr_multiplier, model, optimizer, train + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_bank_reset = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 12800 diff --git a/configs/Flickr30k_VisualGrounding/ape_deta/ape_deta_r50_12ep.py b/configs/Flickr30k_VisualGrounding/ape_deta/ape_deta_r50_12ep.py new file mode 100644 index 0000000..9c5c282 --- /dev/null +++ b/configs/Flickr30k_VisualGrounding/ape_deta/ape_deta_r50_12ep.py @@ -0,0 +1,20 @@ +from ...COCO_InstanceSegmentation.ape_deta.ape_deta_r50_12ep import ( + lr_multiplier, + model, + optimizer, + train, +) +from ...common.data.flickr30k_instance import dataloader + +model.model_vision.num_classes = 200 + +model.model_vision.criterion[0].num_classes = 200 + +dataloader.train.mapper.max_num_phrase = 100 + +model.model_vision.dataset_prompts = ["phrase", "expression"] +model.model_vision.dataset_names = ["flickr30k", "refcoco"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + ["refcoco-mixed"] + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 1280 diff --git a/configs/Flickr30k_VisualGrounding/ape_deta/ape_deta_r50_vlf_12ep.py b/configs/Flickr30k_VisualGrounding/ape_deta/ape_deta_r50_vlf_12ep.py new file mode 100644 index 0000000..cf35b87 --- /dev/null +++ b/configs/Flickr30k_VisualGrounding/ape_deta/ape_deta_r50_vlf_12ep.py @@ -0,0 +1,42 @@ +from detectron2.config import LazyCall as L +from omegaconf import OmegaConf +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) + +from .ape_deta_r50_12ep import dataloader, lr_multiplier, model, optimizer, train + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, + use_attention_mask_v=True, +) + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 1280 diff --git a/configs/Flickr30k_VisualGrounding/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024.py b/configs/Flickr30k_VisualGrounding/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024.py new file mode 100644 index 0000000..a796249 --- /dev/null +++ b/configs/Flickr30k_VisualGrounding/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024.py @@ -0,0 +1,100 @@ +from detectron2.config import LazyCall as L +from detectron2.solver import WarmupParamScheduler +from fvcore.common.param_scheduler import MultiStepParamScheduler +from ape.data.detection_utils import get_fed_loss_cls_weights +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) + +from ...common.data.flickr30k_instance_lsj1024 import dataloader +from ...LVIS_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_clip_lsj1024_cp_24ep import ( + model, + optimizer, + train, +) + +model.model_vision.num_classes = 256 +model.model_vision.select_box_nums_for_evaluation = 100 + +criterion = model.model_vision.criterion[0] +del criterion.use_fed_loss +del criterion.get_fed_loss_cls_weights +model.model_vision.criterion = [criterion for _ in range(1)] +for criterion, num_classes in zip( + model.model_vision.criterion, + [ + 1000, + ] + * 1, +): + criterion.num_classes = num_classes + + +model.model_vision.stuff_dataset_learn_thing = False +model.model_vision.stuff_prob_thing = -1.0 + +model.model_vision.instance_on = True +model.model_vision.semantic_on = False +model.model_vision.panoptic_on = False + +model.model_vision.neck = None + +train.max_iter = 720000 +train.eval_period = 720000 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1], + milestones=[640000], + num_updates=720000, + ), + warmup_length=1000 / 720000, + warmup_method="linear", + warmup_factor=0.001, +) + +model.model_vision.dataset_prompts = ["phrase", "expression"] +model.model_vision.dataset_names = ["flickr30k", "refcoco"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + ["refcoco-mixed"] + + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, + use_attention_mask_v=True, +) + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 12800 diff --git a/configs/Flickr30k_VisualGrounding/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024.py b/configs/Flickr30k_VisualGrounding/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024.py new file mode 100644 index 0000000..4b6da52 --- /dev/null +++ b/configs/Flickr30k_VisualGrounding/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024.py @@ -0,0 +1,100 @@ +from detectron2.config import LazyCall as L +from detectron2.solver import WarmupParamScheduler +from fvcore.common.param_scheduler import MultiStepParamScheduler +from ape.data.detection_utils import get_fed_loss_cls_weights +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) + +from ...common.data.flickr30k_instance_lsj1024 import dataloader +from ...LVIS_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_lsj1024_cp_24ep import ( + model, + optimizer, + train, +) + +model.model_vision.num_classes = 256 +model.model_vision.select_box_nums_for_evaluation = 300 + +criterion = model.model_vision.criterion[0] +del criterion.use_fed_loss +del criterion.get_fed_loss_cls_weights +model.model_vision.criterion = [criterion for _ in range(1)] +for criterion, num_classes in zip( + model.model_vision.criterion, + [ + 1000, + ] + * 1, +): + criterion.num_classes = num_classes + + +model.model_vision.stuff_dataset_learn_thing = False +model.model_vision.stuff_prob_thing = -1.0 + +model.model_vision.instance_on = True +model.model_vision.semantic_on = False +model.model_vision.panoptic_on = False + +model.model_vision.neck = None + +train.max_iter = 720000 +train.eval_period = 720000 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1], + milestones=[640000], + num_updates=720000, + ), + warmup_length=1000 / 720000, + warmup_method="linear", + warmup_factor=0.001, +) + + +model.model_vision.dataset_prompts = ["phrase", "expression"] +model.model_vision.dataset_names = ["phrasecut_train", "phrasecut_val"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + [dataloader.test.dataset.names] + + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 12800 diff --git a/configs/GQA_VisualGrounding/ape_deta/ape_deta_r50_12ep.py b/configs/GQA_VisualGrounding/ape_deta/ape_deta_r50_12ep.py new file mode 100644 index 0000000..74abeab --- /dev/null +++ b/configs/GQA_VisualGrounding/ape_deta/ape_deta_r50_12ep.py @@ -0,0 +1,20 @@ +from ...COCO_InstanceSegmentation.ape_deta.ape_deta_r50_12ep import ( + lr_multiplier, + model, + optimizer, + train, +) +from ...common.data.gqa_region_instance import dataloader + +model.model_vision.num_classes = 200 + +model.model_vision.criterion[0].num_classes = 200 + +dataloader.train.mapper.max_num_phrase = 100 + +model.model_vision.dataset_prompts = ["phrase", "expression"] +model.model_vision.dataset_names = ["gqa", "refcoco"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + ["refcoco-mixed"] + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 1280 diff --git a/configs/GQA_VisualGrounding/ape_deta/ape_deta_r50_12ep_eval_odinw13.py b/configs/GQA_VisualGrounding/ape_deta/ape_deta_r50_12ep_eval_odinw13.py new file mode 100644 index 0000000..2bc5148 --- /dev/null +++ b/configs/GQA_VisualGrounding/ape_deta/ape_deta_r50_12ep_eval_odinw13.py @@ -0,0 +1,10 @@ +from ...common.data.odinw13_instance import dataloader +from .ape_deta_r50_12ep import lr_multiplier, model, optimizer, train + +model.model_vision.dataset_prompts = ["name" for _ in dataloader.tests] +model.model_vision.dataset_names = [ + test.dataset.names.replace("_val", "") for test in dataloader.tests +] +model.model_vision.dataset_metas = [test.dataset.names for test in dataloader.tests] + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/GQA_VisualGrounding/ape_deta/ape_deta_r50_12ep_eval_odinw35.py b/configs/GQA_VisualGrounding/ape_deta/ape_deta_r50_12ep_eval_odinw35.py new file mode 100644 index 0000000..fb0a681 --- /dev/null +++ b/configs/GQA_VisualGrounding/ape_deta/ape_deta_r50_12ep_eval_odinw35.py @@ -0,0 +1,10 @@ +from ...common.data.odinw35_instance import dataloader +from .ape_deta_r50_12ep import lr_multiplier, model, optimizer, train + +model.model_vision.dataset_prompts = ["name" for _ in dataloader.tests] +model.model_vision.dataset_names = [ + test.dataset.names.replace("_val", "") for test in dataloader.tests +] +model.model_vision.dataset_metas = [test.dataset.names for test in dataloader.tests] + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/GQA_VisualGrounding/ape_deta/ape_deta_r50_vlf_12ep.py b/configs/GQA_VisualGrounding/ape_deta/ape_deta_r50_vlf_12ep.py new file mode 100644 index 0000000..d0f6e6d --- /dev/null +++ b/configs/GQA_VisualGrounding/ape_deta/ape_deta_r50_vlf_12ep.py @@ -0,0 +1,46 @@ +from detectron2.config import LazyCall as L +from omegaconf import OmegaConf +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) + +from .ape_deta_r50_12ep import dataloader, lr_multiplier, model, optimizer, train + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + cfg=OmegaConf.from_dotlist( + [ + "MODEL.DYHEAD.FUSE_CONFIG.STABLE_SOFTMAX_2D=False", + "MODEL.DYHEAD.FUSE_CONFIG.CLAMP_MIN_FOR_UNDERFLOW=True", + "MODEL.DYHEAD.FUSE_CONFIG.CLAMP_MAX_FOR_OVERFLOW=True", + "MODEL.VL_FUSION_USE_CHECKPOINT=True", + ], + ), +) + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 1280 diff --git a/configs/GQA_VisualGrounding/ape_deta/ape_deta_r50_vlf_12ep_eval_odinw13.py b/configs/GQA_VisualGrounding/ape_deta/ape_deta_r50_vlf_12ep_eval_odinw13.py new file mode 100644 index 0000000..e017fae --- /dev/null +++ b/configs/GQA_VisualGrounding/ape_deta/ape_deta_r50_vlf_12ep_eval_odinw13.py @@ -0,0 +1,10 @@ +from ...common.data.odinw13_instance import dataloader +from .ape_deta_r50_vlf_12ep import lr_multiplier, model, optimizer, train + +model.model_vision.dataset_prompts = ["name" for _ in dataloader.tests] +model.model_vision.dataset_names = [ + test.dataset.names.replace("_val", "") for test in dataloader.tests +] +model.model_vision.dataset_metas = [test.dataset.names for test in dataloader.tests] + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/GQA_VisualGrounding/ape_deta/ape_deta_r50_vlf_12ep_eval_odinw35.py b/configs/GQA_VisualGrounding/ape_deta/ape_deta_r50_vlf_12ep_eval_odinw35.py new file mode 100644 index 0000000..e09309f --- /dev/null +++ b/configs/GQA_VisualGrounding/ape_deta/ape_deta_r50_vlf_12ep_eval_odinw35.py @@ -0,0 +1,10 @@ +from ...common.data.odinw35_instance import dataloader +from .ape_deta_r50_vlf_12ep import lr_multiplier, model, optimizer, train + +model.model_vision.dataset_prompts = ["name" for _ in dataloader.tests] +model.model_vision.dataset_names = [ + test.dataset.names.replace("_val", "") for test in dataloader.tests +] +model.model_vision.dataset_metas = [test.dataset.names for test in dataloader.tests] + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/GRIT_SA1B_VisualGrounding/ape_deta/ape_deta_r50_24ep.py b/configs/GRIT_SA1B_VisualGrounding/ape_deta/ape_deta_r50_24ep.py new file mode 100644 index 0000000..7f81c55 --- /dev/null +++ b/configs/GRIT_SA1B_VisualGrounding/ape_deta/ape_deta_r50_24ep.py @@ -0,0 +1,42 @@ +from detrex.config import get_config + +from ...COCO_InstanceSegmentation.ape_deta.ape_deta_r50_12ep import ( + lr_multiplier, + model, + optimizer, + train, +) +from ...common.data.grit_sa1b_instance import dataloader + +model.model_vision.num_classes = 200 + +criterion = model.model_vision.criterion[0] +model.model_vision.criterion = [criterion for _ in range(2)] +for criterion, num_classes in zip(model.model_vision.criterion, [200, 1]): + criterion.num_classes = num_classes + +for k, v in model.model_vision.criterion[0].weight_dict.items(): + if "_class" in k and "_enc" in k: + model.model_vision.criterion[1].weight_dict.update({k: 0.0}) + +for k, v in model.model_vision.criterion[1].weight_dict.items(): + if "_class" in k and "_enc" not in k: + model.model_vision.criterion[1].weight_dict.update({k: 0.0}) + +dataloader.train.mapper.max_num_phrase = 100 +dataloader.train.mapper.nms_thresh_phrase = 0.6 + +train.max_iter = 180000 +train.eval_period = 5000 + +lr_multiplier = get_config("common/coco_schedule.py").lr_multiplier_24ep + +dataloader.train.total_batch_size = 16 +dataloader.train.total_batch_size_list = [16, 16] + +model.model_vision.dataset_prompts = ["phrase", "name", "expression"] +model.model_vision.dataset_names = ["grit", "sa1b", "refcoco"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + ["refcoco-mixed"] + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 1280 diff --git a/configs/GRIT_SA1B_VisualGrounding/ape_deta/ape_deta_r50_vlf_24ep.py b/configs/GRIT_SA1B_VisualGrounding/ape_deta/ape_deta_r50_vlf_24ep.py new file mode 100644 index 0000000..aef9059 --- /dev/null +++ b/configs/GRIT_SA1B_VisualGrounding/ape_deta/ape_deta_r50_vlf_24ep.py @@ -0,0 +1,42 @@ +from detectron2.config import LazyCall as L +from omegaconf import OmegaConf +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) + +from .ape_deta_r50_24ep import dataloader, lr_multiplier, model, optimizer, train + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 1280 diff --git a/configs/GRIT_VisualGrounding/ape_deta/ape_deta_r50_400k.py b/configs/GRIT_VisualGrounding/ape_deta/ape_deta_r50_400k.py new file mode 100644 index 0000000..cdff244 --- /dev/null +++ b/configs/GRIT_VisualGrounding/ape_deta/ape_deta_r50_400k.py @@ -0,0 +1,42 @@ +from detectron2.config import LazyCall as L +from detectron2.solver import WarmupParamScheduler +from fvcore.common.param_scheduler import MultiStepParamScheduler + +from ...COCO_InstanceSegmentation.ape_deta.ape_deta_r50_12ep import ( + lr_multiplier, + model, + optimizer, + train, +) +from ...common.data.grit_instance import dataloader + +model.model_vision.num_classes = 200 + +criterion = model.model_vision.criterion[0] +model.model_vision.criterion = [criterion for _ in range(2)] +for criterion, num_classes in zip(model.model_vision.criterion, [200, 200]): + criterion.num_classes = num_classes + +dataloader.train.mapper.max_num_phrase = 200 +dataloader.train.mapper.nms_thresh_phrase = 0.6 + +model.model_vision.dataset_prompts = ["phrase", "expression"] +model.model_vision.dataset_names = ["grit", "refcoco"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + ["refcoco-mixed"] + +train.max_iter = 400000 +train.eval_period = 10000 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1], + milestones=[333000], + num_updates=400000, + ), + warmup_length=2000 / 400000, + warmup_method="linear", + warmup_factor=0.001, +) + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 1280 diff --git a/configs/GRIT_VisualGrounding/ape_deta/ape_deta_r50_vlf_400k.py b/configs/GRIT_VisualGrounding/ape_deta/ape_deta_r50_vlf_400k.py new file mode 100644 index 0000000..13bd411 --- /dev/null +++ b/configs/GRIT_VisualGrounding/ape_deta/ape_deta_r50_vlf_400k.py @@ -0,0 +1,48 @@ +from detectron2.config import LazyCall as L +from omegaconf import OmegaConf +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) + +from .ape_deta_r50_400k import dataloader, lr_multiplier, model, optimizer, train + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 1280 diff --git a/configs/GRIT_VisualGrounding/ape_deta/ape_deta_r50_vlf_lsj224_256x50k.py b/configs/GRIT_VisualGrounding/ape_deta/ape_deta_r50_vlf_lsj224_256x50k.py new file mode 100644 index 0000000..e82b9b3 --- /dev/null +++ b/configs/GRIT_VisualGrounding/ape_deta/ape_deta_r50_vlf_lsj224_256x50k.py @@ -0,0 +1,68 @@ +from detectron2.config import LazyCall as L +from detectron2.solver import WarmupParamScheduler +from fvcore.common.param_scheduler import MultiStepParamScheduler +from omegaconf import OmegaConf +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) + +from ...common.data.grit_instance_lsj224 import dataloader +from .ape_deta_r50_400k import lr_multiplier, model, optimizer, train + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +dataloader.train.total_batch_size = 256 +dataloader.train.total_batch_size_list = [256, 256] + +train.max_iter = 10000 * 5 +train.eval_period = 1000 * 5 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1], + milestones=[8300 * 5], + num_updates=10000 * 5, + ), + warmup_length=2000 / 10000 * 5, + warmup_method="linear", + warmup_factor=0.001, +) + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 1280 diff --git a/configs/LVISCOCOCOCOSTUFF_O365_OID_VG/ape_deta/ape_deta_r50_lsj1024_cp_50ep.py b/configs/LVISCOCOCOCOSTUFF_O365_OID_VG/ape_deta/ape_deta_r50_lsj1024_cp_50ep.py new file mode 100644 index 0000000..17b6333 --- /dev/null +++ b/configs/LVISCOCOCOCOSTUFF_O365_OID_VG/ape_deta/ape_deta_r50_lsj1024_cp_50ep.py @@ -0,0 +1,76 @@ +from detectron2.config import LazyCall as L +from detectron2.data.detection_utils import get_fed_loss_cls_weights +from detrex.config import get_config +from ape.modeling.text import EVA01CLIP + +from ...common.data.lviscocococostuff_o365_oid_vg_panoptic_lsj1024_cp import dataloader +from .models.ape_deta_r50 import model + +model.model_vision.num_classes = 1256 +model.model_vision.criterion[0].num_classes = 1256 +model.model_vision.select_box_nums_for_evaluation = 300 +model.model_vision.criterion[0].use_fed_loss = True +model.model_vision.criterion[0].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train.dataset.names[0], 0.5 +) +model.model_vision.criterion[0].fed_loss_num_classes = 50 + +optimizer = get_config("common/optim.py").AdamW +optimizer.params.lr_factor_func = ( + lambda module_name: 0.1 + if "backbone" in module_name + or "reference_points" in module_name + or "sampling_offsets" in module_name + else 1 +) +optimizer.params.weight_decay_norm = None + +optimizer.lr = 2e-4 +optimizer.betas = (0.9, 0.999) +optimizer.weight_decay = 1e-4 + +train = get_config("common/train.py").train +train.max_iter = 375000 +train.eval_period = 2000000 +train.log_period = 20 + +train.checkpointer.period = 5000 +train.checkpointer.max_to_keep = 2 + +train.clip_grad.enabled = True +train.clip_grad.params.max_norm = 0.1 +train.clip_grad.params.norm_type = 2 + +train.device = "cuda" + +train.init_checkpoint = "detectron2://ImageNetPretrained/torchvision/R-50.pkl" +train.init_checkpoint = "models/torchvision/R-50.pkl" + +train.amp.enabled = True +train.ddp.fp16_compression = True + +lr_multiplier = get_config("common/coco_schedule.py").lr_multiplier_50ep + +dataloader.train.total_batch_size = 16 +dataloader.train.total_batch_size_list = [16, 16, 16, 16] +dataloader.train.mapper.image_format = "RGB" +dataloader.train.mapper.use_instance_mask = True + +model.model_vision.dataset_prompts = ["name", "name", "name", "name"] +model.model_vision.dataset_names = [ + "lvis+stuffonly", + "objects365", + "openimages", + "visualgenome", +] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.output_dir = train.output_dir +dataloader.train.mapper.output_dir = train.output_dir +dataloader.train.mapper.vis_period = 12800 + +model.model_language = L(EVA01CLIP)( + clip_model="EVA_CLIP_g_14_X", cache_dir="models/BAAI/EVA/eva_clip_psz14.pt" +) +model.model_vision.embed_dim_language = 1024 diff --git a/configs/LVISCOCOCOCOSTUFF_O365_OID_VG/ape_deta/ape_deta_vitl_eva02_lsj1024_cp_180k.py b/configs/LVISCOCOCOCOSTUFF_O365_OID_VG/ape_deta/ape_deta_vitl_eva02_lsj1024_cp_180k.py new file mode 100644 index 0000000..47857f2 --- /dev/null +++ b/configs/LVISCOCOCOCOSTUFF_O365_OID_VG/ape_deta/ape_deta_vitl_eva02_lsj1024_cp_180k.py @@ -0,0 +1,37 @@ +import random + +from detectron2.config import LazyCall as L +from detectron2.solver import WarmupParamScheduler +from fvcore.common.param_scheduler import MultiStepParamScheduler +from ape.data.samplers import MultiDatasetTrainingSampler + +from .ape_deta_vitl_eva02_lsj1024_cp_720k import dataloader, lr_multiplier, model, optimizer, train + +train.max_iter = 180000 +train.eval_period = 180000 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1], + milestones=[150000], + num_updates=180000, + ), + warmup_length=1000 / 180000, + warmup_method="linear", + warmup_factor=0.001, +) + +train.output_dir = "output/" + __file__[:-3] + +dataloader.train.sampler = lambda dataset_dicts: MultiDatasetTrainingSampler( + repeat_factors=MultiDatasetTrainingSampler.get_repeat_factors( + dataset_dicts=dataset_dicts, + num_datasets=5, + dataset_ratio=[1, 1, 1, 1], + use_rfs=[True, True, True, False], + use_cas=[False, False, False, False], + repeat_thresh=0.001, + cas_lambda=1.0, + ), + seed=random.randint(0, 2**31), +) diff --git a/configs/LVISCOCOCOCOSTUFF_O365_OID_VG/ape_deta/ape_deta_vitl_eva02_lsj1024_cp_720k.py b/configs/LVISCOCOCOCOSTUFF_O365_OID_VG/ape_deta/ape_deta_vitl_eva02_lsj1024_cp_720k.py new file mode 100644 index 0000000..95800d9 --- /dev/null +++ b/configs/LVISCOCOCOCOSTUFF_O365_OID_VG/ape_deta/ape_deta_vitl_eva02_lsj1024_cp_720k.py @@ -0,0 +1,85 @@ +import random + +from detectron2.config import LazyCall as L +from detectron2.solver import WarmupParamScheduler +from fvcore.common.param_scheduler import MultiStepParamScheduler + +from ape.data.detection_utils import get_fed_loss_cls_weights +from ape.data.samplers import MultiDatasetTrainingSampler + +from ...common.data.lviscocococostuff_o365_oid_vg_panoptic_lsj1024_cp import dataloader +from ...LVIS_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_lsj1024_cp_24ep import ( + lr_multiplier, + model, + optimizer, + train, +) + +model.model_vision.num_classes = 1256 +model.model_vision.select_box_nums_for_evaluation = 300 + +criterion = model.model_vision.criterion[0] +del criterion.use_fed_loss +del criterion.get_fed_loss_cls_weights +model.model_vision.criterion = [criterion for _ in range(4)] +for criterion, num_classes in zip(model.model_vision.criterion, [1256, 365, 601, 150]): + criterion.num_classes = num_classes + +dataloader.train.mapper.max_num_phrase = 100 +dataloader.train.mapper.nms_thresh_phrase = 0.8 + +model.model_vision.criterion[0].use_fed_loss = True +model.model_vision.criterion[0].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train.dataset.names[0], 0.5 +) +model.model_vision.criterion[0].fed_loss_num_classes = 50 + +model.model_vision.stuff_dataset_learn_thing = False +model.model_vision.stuff_prob_thing = 0.9 + +model.model_vision.instance_on = True +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = False + +model.model_vision.neck = None + +train.max_iter = 720000 +train.eval_period = 720000 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1], + milestones=[640000], + num_updates=720000, + ), + warmup_length=1000 / 720000, + warmup_method="linear", + warmup_factor=0.001, +) + +dataloader.train.total_batch_size = 16 +dataloader.train.total_batch_size_list = [16, 16, 16, 16] + +model.model_vision.dataset_prompts = ["name", "name", "name", "name"] +model.model_vision.dataset_names = [ + "lvis+stuffonly", + "objects365", + "openimages", + "visualgenome", +] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +train.output_dir = "output/" + __file__[:-3] + +dataloader.train.sampler = lambda dataset_dicts: MultiDatasetTrainingSampler( + repeat_factors=MultiDatasetTrainingSampler.get_repeat_factors( + dataset_dicts=dataset_dicts, + num_datasets=4, + dataset_ratio=[1, 1, 1, 1], + use_rfs=[True, True, True, False], + use_cas=[False, False, False, False], + repeat_thresh=0.001, + cas_lambda=1.0, + ), + seed=random.randint(0, 2**31), +) diff --git a/configs/LVISCOCOCOCOSTUFF_O365_OID_VG/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_180k.py b/configs/LVISCOCOCOCOSTUFF_O365_OID_VG/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_180k.py new file mode 100644 index 0000000..2d18f43 --- /dev/null +++ b/configs/LVISCOCOCOCOSTUFF_O365_OID_VG/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_180k.py @@ -0,0 +1,43 @@ +import random + +from detectron2.config import LazyCall as L +from detectron2.solver import WarmupParamScheduler +from fvcore.common.param_scheduler import MultiStepParamScheduler +from ape.data.samplers import MultiDatasetTrainingSampler + +from .ape_deta_vitl_eva02_vlf_lsj1024_cp_720k import ( + dataloader, + lr_multiplier, + model, + optimizer, + train, +) + +train.max_iter = 180000 +train.eval_period = 180000 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1], + milestones=[150000], + num_updates=180000, + ), + warmup_length=1000 / 180000, + warmup_method="linear", + warmup_factor=0.001, +) + +train.output_dir = "output/" + __file__[:-3] + +dataloader.train.sampler = lambda dataset_dicts: MultiDatasetTrainingSampler( + repeat_factors=MultiDatasetTrainingSampler.get_repeat_factors( + dataset_dicts=dataset_dicts, + num_datasets=5, + dataset_ratio=[1, 1, 1, 1], + use_rfs=[True, True, True, False], + use_cas=[False, False, False, False], + repeat_thresh=0.001, + cas_lambda=1.0, + ), + seed=random.randint(0, 2**31), +) diff --git a/configs/LVISCOCOCOCOSTUFF_O365_OID_VG/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_720k.py b/configs/LVISCOCOCOCOSTUFF_O365_OID_VG/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_720k.py new file mode 100644 index 0000000..7069d7a --- /dev/null +++ b/configs/LVISCOCOCOCOSTUFF_O365_OID_VG/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_720k.py @@ -0,0 +1,43 @@ +from detectron2.config import LazyCall as L +from omegaconf import OmegaConf +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) + +from .ape_deta_vitl_eva02_lsj1024_cp_720k import dataloader, lr_multiplier, model, optimizer, train + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=False, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) + +model.model_vision.text_feature_bank = True + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 12800 diff --git a/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_REFCOCO/ape_deta/ape_deta_vitl_eva02_lsj1024_cp_180k.py b/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_REFCOCO/ape_deta/ape_deta_vitl_eva02_lsj1024_cp_180k.py new file mode 100644 index 0000000..5af8388 --- /dev/null +++ b/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_REFCOCO/ape_deta/ape_deta_vitl_eva02_lsj1024_cp_180k.py @@ -0,0 +1,21 @@ +from detectron2.config import LazyCall as L +from detectron2.solver import WarmupParamScheduler +from fvcore.common.param_scheduler import MultiStepParamScheduler + +from .ape_deta_vitl_eva02_lsj1024_cp_720k import dataloader, lr_multiplier, model, optimizer, train + +train.max_iter = 180000 +train.eval_period = 180000 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1], + milestones=[150000], + num_updates=180000, + ), + warmup_length=1000 / 180000, + warmup_method="linear", + warmup_factor=0.001, +) + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_REFCOCO/ape_deta/ape_deta_vitl_eva02_lsj1024_cp_720k.py b/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_REFCOCO/ape_deta/ape_deta_vitl_eva02_lsj1024_cp_720k.py new file mode 100644 index 0000000..769e70a --- /dev/null +++ b/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_REFCOCO/ape_deta/ape_deta_vitl_eva02_lsj1024_cp_720k.py @@ -0,0 +1,83 @@ +from detectron2.config import LazyCall as L +from detectron2.solver import WarmupParamScheduler +from fvcore.common.param_scheduler import MultiStepParamScheduler + +from ape.data.detection_utils import get_fed_loss_cls_weights + +from ...common.data.lviscocococostuff_o365_oid_vgr_refcoco_group_by_image_panoptic_lsj1024_cp import ( + dataloader, +) +from ...LVIS_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_lsj1024_cp_24ep import ( + model, + optimizer, + train, +) + +model.model_vision.num_classes = 1256 +model.model_vision.select_box_nums_for_evaluation = 300 + +criterion = model.model_vision.criterion[0] +del criterion.use_fed_loss +del criterion.get_fed_loss_cls_weights +del criterion.fed_loss_num_classes +model.model_vision.criterion = [criterion for _ in range(6)] +for criterion, num_classes in zip(model.model_vision.criterion, [1256, 365, 601, 200, 200, 200]): + criterion.num_classes = num_classes + +dataloader.train.mapper.max_num_phrase = 100 +dataloader.train.mapper.nms_thresh_phrase = 0.6 + +model.model_vision.criterion[0].use_fed_loss = True +model.model_vision.criterion[0].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train.dataset.names[0], 0.5 +) +model.model_vision.criterion[0].fed_loss_num_classes = 50 +model.model_vision.criterion[0].fed_loss_pad_type = "cat" + +model.model_vision.criterion[3].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[3].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[3].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k: + model.model_vision.criterion[3].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[4].weight_dict["loss_class_enc"] = 0.0 + +model.model_vision.stuff_dataset_learn_thing = False +model.model_vision.stuff_prob_thing = 0.9 + +model.model_vision.instance_on = True +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = False + +model.model_vision.neck = None + +train.max_iter = 720000 +train.eval_period = 720000 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1], + milestones=[640000], + num_updates=720000, + ), + warmup_length=1000 / 720000, + warmup_method="linear", + warmup_factor=0.001, +) + +dataloader.train.total_batch_size = 16 +dataloader.train.total_batch_size_list = [16, 16, 16, 16, 16] + +model.model_vision.dataset_prompts = ["name", "name", "name", "phrase", "phrase", "expression"] +model.model_vision.dataset_names = [ + "lvis+stuffonly", + "objects365", + "openimages", + "vgregion", + "refcoco-mixed_group-by-image", + "refcoco", +] +model.model_vision.dataset_metas = dataloader.train.dataset.names + ["refcoco-mixed"] + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_1080k.py b/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_1080k.py new file mode 100644 index 0000000..a2202c0 --- /dev/null +++ b/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_1080k.py @@ -0,0 +1,27 @@ +from detectron2.config import LazyCall as L +from detectron2.solver import WarmupParamScheduler +from fvcore.common.param_scheduler import MultiStepParamScheduler + +from .ape_deta_vitl_eva02_vlf_lsj1024_cp_720k import ( + dataloader, + lr_multiplier, + model, + optimizer, + train, +) + +train.max_iter = 1080000 +train.eval_period = 1080000 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1], + milestones=[900000], + num_updates=1080000, + ), + warmup_length=2000 / 1080000, + warmup_method="linear", + warmup_factor=0.001, +) + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_180k.py b/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_180k.py new file mode 100644 index 0000000..af32118 --- /dev/null +++ b/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_180k.py @@ -0,0 +1,27 @@ +from detectron2.config import LazyCall as L +from detectron2.solver import WarmupParamScheduler +from fvcore.common.param_scheduler import MultiStepParamScheduler + +from .ape_deta_vitl_eva02_vlf_lsj1024_cp_720k import ( + dataloader, + lr_multiplier, + model, + optimizer, + train, +) + +train.max_iter = 180000 +train.eval_period = 180000 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1], + milestones=[150000], + num_updates=180000, + ), + warmup_length=1000 / 180000, + warmup_method="linear", + warmup_factor=0.001, +) + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_720k.py b/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_720k.py new file mode 100644 index 0000000..06ecd91 --- /dev/null +++ b/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_720k.py @@ -0,0 +1,47 @@ +from detectron2.config import LazyCall as L +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) + +from .ape_deta_vitl_eva02_lsj1024_cp_720k import dataloader, lr_multiplier, model, optimizer, train + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 12800 diff --git a/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_1080k.py b/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_1080k.py new file mode 100644 index 0000000..6917575 --- /dev/null +++ b/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_1080k.py @@ -0,0 +1,75 @@ +from detectron2.config import LazyCall as L + +from ape.data.detection_utils import get_fed_loss_cls_weights + +from ...common.data.lviscocococostuff_o365_oid_vgr_sa1b_refcoco_group_by_image_panoptic_lsj1024_cp import ( + dataloader, +) +from ...LVISCOCOCOCOSTUFF_O365_OID_VGR_REFCOCO.ape_deta.ape_deta_vitl_eva02_vlf_lsj1024_cp_1080k import ( + lr_multiplier, + model, + optimizer, + train, +) + +model.model_vision.num_classes = 1256 +model.model_vision.select_box_nums_for_evaluation = 300 + +criterion = model.model_vision.criterion[0] +del criterion.use_fed_loss +del criterion.get_fed_loss_cls_weights +del criterion.fed_loss_num_classes +model.model_vision.criterion = [criterion for _ in range(7)] +for criterion, num_classes in zip(model.model_vision.criterion, [1256, 365, 601, 200, 1, 200, 200]): + criterion.num_classes = num_classes + +dataloader.train.mapper.max_num_phrase = 100 +dataloader.train.mapper.nms_thresh_phrase = 0.6 + +model.model_vision.criterion[0].use_fed_loss = True +model.model_vision.criterion[0].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train.dataset.names[0], 0.5 +) +model.model_vision.criterion[0].fed_loss_num_classes = 50 +model.model_vision.criterion[0].fed_loss_pad_type = "cat" + +model.model_vision.criterion[3].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[3].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[3].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k: + model.model_vision.criterion[3].weight_dict.update({k: 0.0}) + +for k, v in model.model_vision.criterion[4].weight_dict.items(): + if "_class" in k and "_enc" not in k: + model.model_vision.criterion[4].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[5].weight_dict["loss_class_enc"] = 0.0 + +model.model_vision.stuff_dataset_learn_thing = False +model.model_vision.stuff_prob_thing = 0.9 + +dataloader.train.total_batch_size = 16 +dataloader.train.total_batch_size_list = [16, 16, 16, 16, 16, 16] + +model.model_vision.dataset_prompts = [ + "name", + "name", + "name", + "phrase", + "name", + "phrase", + "expression", +] +model.model_vision.dataset_names = [ + "lvis+stuffonly", + "objects365", + "openimages", + "vgregion", + "sa1b", + "refcoco-mixed_group-by-image", + "refcoco", +] +model.model_vision.dataset_metas = dataloader.train.dataset.names + ["refcoco-mixed"] + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_2160k.py b/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_2160k.py new file mode 100644 index 0000000..1c22ff3 --- /dev/null +++ b/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_2160k.py @@ -0,0 +1,27 @@ +from detectron2.config import LazyCall as L +from detectron2.solver import WarmupParamScheduler +from fvcore.common.param_scheduler import MultiStepParamScheduler + +from .ape_deta_vitl_eva02_vlf_lsj1024_cp_1080k import ( + dataloader, + lr_multiplier, + model, + optimizer, + train, +) + +train.max_iter = 2160000 +train.eval_period = 2160000 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1], + milestones=[1800000], + num_updates=2160000, + ), + warmup_length=4000 / 2160000, + warmup_method="linear", + warmup_factor=0.001, +) + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_08x8x270k.py b/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_08x8x270k.py new file mode 100644 index 0000000..fb64dc7 --- /dev/null +++ b/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_08x8x270k.py @@ -0,0 +1,228 @@ +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detectron2.solver import WarmupParamScheduler +from detrex.modeling.neck import ChannelMapper +from fvcore.common.param_scheduler import MultiStepParamScheduler + +from ape.data.detection_utils import get_fed_loss_cls_weights +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) +from ape.modeling.text import EVA02CLIP + +from ...common.backbone.vitl_eva02_clip import backbone +from ...common.data.lviscocococostuff_o365_oid_vgr_sa1b_refcoco_group_by_image_gqa_phrasecut_flickr30k_panoptic_lsj1024_cp import ( + dataloader, +) +from ...LVIS_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_lsj1024_cp_24ep import ( + model, + optimizer, + train, +) + +model.model_vision.backbone = backbone + +train.init_checkpoint = ( + "models/QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14to16_s6B.pt?matching_heuristics=True" +) + +model.model_language = L(EVA02CLIP)( + clip_model="EVA02-CLIP-bigE-14-plus", + cache_dir="models/QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt", + dtype="float16", +) +model.model_vision.embed_dim_language = 1024 + +model.model_vision.neck = L(ChannelMapper)( + input_shapes={ + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), + }, + in_features=["p2", "p3", "p4", "p5", "p6"], + out_channels=256, + num_outs=5, + kernel_size=1, + norm_layer=L(nn.GroupNorm)(num_groups=32, num_channels=256), +) + +model.model_vision.mask_in_features = ["p2"] +model.model_vision.input_shapes = { + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), +} + +model.model_vision.transformer.encoder.num_layers = 6 +model.model_vision.transformer.decoder.num_layers = 6 +model.model_vision.transformer.encoder.embed_dim = 256 +model.model_vision.transformer.decoder.embed_dim = 256 +model.model_vision.embed_dim = 256 +model.model_vision.backbone.out_channels = 256 + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) +model.model_vision.transformer.encoder.use_act_checkpoint = True + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +model.model_vision.num_classes = 1256 +model.model_vision.select_box_nums_for_evaluation = 300 + +criterion = model.model_vision.criterion[0] +del criterion.use_fed_loss +del criterion.get_fed_loss_cls_weights +del criterion.fed_loss_num_classes +model.model_vision.criterion = [criterion for _ in range(10)] +for criterion, num_classes in zip( + model.model_vision.criterion, [1256, 365, 601, 256, 1, 256, 256, 256, 256, 256] +): + criterion.num_classes = num_classes + +dataloader.train.mapper.max_num_phrase = 128 +dataloader.train.mapper.nms_thresh_phrase = 0.6 + +model.model_vision.criterion[0].use_fed_loss = True +model.model_vision.criterion[0].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train.dataset.names[0], 0.5 +) +model.model_vision.criterion[0].fed_loss_num_classes = 50 +model.model_vision.criterion[0].fed_loss_pad_type = "cat" + +model.model_vision.criterion[2].use_fed_loss = True +model.model_vision.criterion[2].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train.dataset.names[2], 0.5 +) +model.model_vision.criterion[2].fed_loss_num_classes = 50 +model.model_vision.criterion[2].fed_loss_pad_type = "cat" + +model.model_vision.criterion[3].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[3].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[3].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[3].weight_dict.update({k: 0.0}) + +for k, v in model.model_vision.criterion[4].weight_dict.items(): + if "_class" in k and "_enc" not in k: + model.model_vision.criterion[4].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[5].weight_dict["loss_class_enc"] = 0.0 + +model.model_vision.criterion[6].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[6].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[6].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[6].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[7].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[7].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[7].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[7].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[8].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[8].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[8].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[8].weight_dict.update({k: 0.0}) + +model.model_vision.stuff_dataset_learn_thing = False +model.model_vision.stuff_prob_thing = 0.9 +model.model_vision.transformer.proposal_ambiguous = 1 + +model.model_vision.instance_on = True +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = False + +train.max_iter = 270000 +train.eval_period = 270000 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1], + milestones=[225000], + num_updates=270000, + ), + warmup_length=2000 / 270000, + warmup_method="linear", + warmup_factor=0.001, +) + +dataloader.train.total_batch_size = 8 +dataloader.train.total_batch_size_list = [8, 8, 8, 8, 8, 8, 8, 8, 8] +dataloader.train.num_workers = 2 +train.iter_size = 8 + +dataloader.wait_group = 2 +dataloader.wait_time = 30 * 60 + +model.model_vision.dataset_prompts = [ + "name", + "name", + "name", + "phrase", + "name", + "phrase", + "phrase", + "phrase", + "phrase", + "expression", +] +model.model_vision.dataset_names = [ + "lvis+stuffonly", + "objects365", + "openimages", + "vgregion", + "sa1b", + "refcoco-mixed_group-by-image", + "gqa", + "phrasecut", + "flickr30k", + "refcoco", +] +model.model_vision.dataset_metas = dataloader.train.dataset.names + ["refcoco-mixed"] + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_1080k.py b/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_1080k.py new file mode 100644 index 0000000..1ee51a4 --- /dev/null +++ b/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_1080k.py @@ -0,0 +1,225 @@ +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detectron2.solver import WarmupParamScheduler +from detrex.modeling.neck import ChannelMapper +from fvcore.common.param_scheduler import MultiStepParamScheduler + +from ape.data.detection_utils import get_fed_loss_cls_weights +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) +from ape.modeling.text import EVA02CLIP + +from ...common.backbone.vitl_eva02_clip import backbone +from ...common.data.lviscocococostuff_o365_oid_vgr_sa1b_refcoco_group_by_image_gqa_phrasecut_flickr30k_panoptic_lsj1024_cp import ( + dataloader, +) +from ...LVIS_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_lsj1024_cp_24ep import ( + model, + optimizer, + train, +) + +model.model_vision.backbone = backbone + +train.init_checkpoint = ( + "models/QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14to16_s6B.pt?matching_heuristics=True" +) + +model.model_language = L(EVA02CLIP)( + clip_model="EVA02-CLIP-bigE-14-plus", + cache_dir="models/QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt", + dtype="float16", +) +model.model_vision.embed_dim_language = 1024 + +model.model_vision.neck = L(ChannelMapper)( + input_shapes={ + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), + }, + in_features=["p2", "p3", "p4", "p5", "p6"], + out_channels=256, + num_outs=5, + kernel_size=1, + norm_layer=L(nn.GroupNorm)(num_groups=32, num_channels=256), +) + +model.model_vision.mask_in_features = ["p2"] +model.model_vision.input_shapes = { + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), +} + +model.model_vision.transformer.encoder.num_layers = 6 +model.model_vision.transformer.decoder.num_layers = 6 +model.model_vision.transformer.encoder.embed_dim = 256 +model.model_vision.transformer.decoder.embed_dim = 256 +model.model_vision.embed_dim = 256 +model.model_vision.backbone.out_channels = 256 + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) +model.model_vision.transformer.encoder.use_act_checkpoint = True + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +model.model_vision.num_classes = 1256 +model.model_vision.select_box_nums_for_evaluation = 300 + +criterion = model.model_vision.criterion[0] +del criterion.use_fed_loss +del criterion.get_fed_loss_cls_weights +del criterion.fed_loss_num_classes +model.model_vision.criterion = [criterion for _ in range(10)] +for criterion, num_classes in zip( + model.model_vision.criterion, [1256, 365, 601, 200, 1, 200, 200, 200, 200, 200] +): + criterion.num_classes = num_classes + +dataloader.train.mapper.max_num_phrase = 100 +dataloader.train.mapper.nms_thresh_phrase = 0.6 + +model.model_vision.criterion[0].use_fed_loss = True +model.model_vision.criterion[0].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train.dataset.names[0], 0.5 +) +model.model_vision.criterion[0].fed_loss_num_classes = 50 +model.model_vision.criterion[0].fed_loss_pad_type = "cat" + +model.model_vision.criterion[2].use_fed_loss = True +model.model_vision.criterion[2].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train.dataset.names[2], 0.5 +) +model.model_vision.criterion[2].fed_loss_num_classes = 50 +model.model_vision.criterion[2].fed_loss_pad_type = "cat" + +model.model_vision.criterion[3].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[3].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[3].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[3].weight_dict.update({k: 0.0}) + +for k, v in model.model_vision.criterion[4].weight_dict.items(): + if "_class" in k and "_enc" not in k: + model.model_vision.criterion[4].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[5].weight_dict["loss_class_enc"] = 0.0 + +model.model_vision.criterion[6].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[6].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[6].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[6].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[7].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[7].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[7].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[7].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[8].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[8].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[8].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[8].weight_dict.update({k: 0.0}) + +model.model_vision.stuff_dataset_learn_thing = False +model.model_vision.stuff_prob_thing = 0.9 +model.model_vision.transformer.proposal_ambiguous = 1 + +model.model_vision.instance_on = True +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = False + +train.max_iter = 1080000 +train.eval_period = 1080000 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1], + milestones=[900000], + num_updates=1080000, + ), + warmup_length=2000 / 270000, + warmup_method="linear", + warmup_factor=0.001, +) + +dataloader.train.total_batch_size = 16 +dataloader.train.total_batch_size_list = [16, 16, 16, 16, 16, 16, 16, 16, 16] +dataloader.train.num_workers = 4 + + +model.model_vision.dataset_prompts = [ + "name", + "name", + "name", + "phrase", + "name", + "phrase", + "phrase", + "phrase", + "phrase", + "expression", +] +model.model_vision.dataset_names = [ + "lvis+stuffonly", + "objects365", + "openimages", + "vgregion", + "sa1b", + "refcoco-mixed_group-by-image", + "gqa", + "phrasecut", + "flickr30k", + "refcoco", +] +model.model_vision.dataset_metas = dataloader.train.dataset.names + ["refcoco-mixed"] + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 5120 diff --git a/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_16x4_1080k.py b/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_16x4_1080k.py new file mode 100644 index 0000000..ab66e17 --- /dev/null +++ b/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_16x4_1080k.py @@ -0,0 +1,227 @@ +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detectron2.solver import WarmupParamScheduler +from detrex.modeling.neck import ChannelMapper +from fvcore.common.param_scheduler import MultiStepParamScheduler + +from ape.data.detection_utils import get_fed_loss_cls_weights +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) +from ape.modeling.text import EVA02CLIP + +from ...common.backbone.vitl_eva02_clip import backbone +from ...common.data.lviscocococostuff_o365_oid_vgr_sa1b_refcoco_group_by_image_gqa_phrasecut_flickr30k_panoptic_lsj1024_cp import ( + dataloader, +) +from ...LVIS_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_lsj1024_cp_24ep import ( + model, + optimizer, + train, +) + +model.model_vision.backbone = backbone + +train.init_checkpoint = ( + "models/QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14to16_s6B.pt?matching_heuristics=True" +) + +model.model_language = L(EVA02CLIP)( + clip_model="EVA02-CLIP-bigE-14-plus", + cache_dir="models/QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt", + dtype="float16", +) +model.model_vision.embed_dim_language = 1024 + +model.model_vision.neck = L(ChannelMapper)( + input_shapes={ + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), + }, + in_features=["p2", "p3", "p4", "p5", "p6"], + out_channels=256, + num_outs=5, + kernel_size=1, + norm_layer=L(nn.GroupNorm)(num_groups=32, num_channels=256), +) + +model.model_vision.mask_in_features = ["p2"] +model.model_vision.input_shapes = { + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), +} + +model.model_vision.transformer.encoder.num_layers = 6 +model.model_vision.transformer.decoder.num_layers = 6 +model.model_vision.transformer.encoder.embed_dim = 256 +model.model_vision.transformer.decoder.embed_dim = 256 +model.model_vision.embed_dim = 256 +model.model_vision.backbone.out_channels = 256 + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) +model.model_vision.transformer.encoder.use_act_checkpoint = True + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +model.model_vision.num_classes = 1256 +model.model_vision.select_box_nums_for_evaluation = 300 + +criterion = model.model_vision.criterion[0] +del criterion.use_fed_loss +del criterion.get_fed_loss_cls_weights +del criterion.fed_loss_num_classes +model.model_vision.criterion = [criterion for _ in range(10)] +for criterion, num_classes in zip( + model.model_vision.criterion, [1256, 365, 601, 256, 1, 256, 256, 256, 256, 256] +): + criterion.num_classes = num_classes + +dataloader.train.mapper.max_num_phrase = 128 +dataloader.train.mapper.nms_thresh_phrase = 0.6 + +model.model_vision.criterion[0].use_fed_loss = True +model.model_vision.criterion[0].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train.dataset.names[0], 0.5 +) +model.model_vision.criterion[0].fed_loss_num_classes = 50 +model.model_vision.criterion[0].fed_loss_pad_type = "cat" + +model.model_vision.criterion[2].use_fed_loss = True +model.model_vision.criterion[2].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train.dataset.names[2], 0.5 +) +model.model_vision.criterion[2].fed_loss_num_classes = 50 +model.model_vision.criterion[2].fed_loss_pad_type = "cat" + +model.model_vision.criterion[3].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[3].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[3].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[3].weight_dict.update({k: 0.0}) + +for k, v in model.model_vision.criterion[4].weight_dict.items(): + if "_class" in k and "_enc" not in k: + model.model_vision.criterion[4].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[5].weight_dict["loss_class_enc"] = 0.0 + +model.model_vision.criterion[6].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[6].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[6].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[6].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[7].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[7].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[7].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[7].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[8].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[8].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[8].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[8].weight_dict.update({k: 0.0}) + +model.model_vision.stuff_dataset_learn_thing = False +model.model_vision.stuff_prob_thing = 0.9 +model.model_vision.transformer.proposal_ambiguous = 1 + +model.model_vision.instance_on = True +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = False + +train.max_iter = 1080000 +train.eval_period = 1080000 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1], + milestones=[900000], + num_updates=1080000, + ), + warmup_length=2000 / 270000, + warmup_method="linear", + warmup_factor=0.001, +) + +dataloader.train.total_batch_size = 16 +dataloader.train.total_batch_size_list = [16, 16, 16, 16, 16, 16, 16, 16, 16] +dataloader.train.num_workers = 0 +train.iter_size = 4 +train.iter_loop = False + + +model.model_vision.dataset_prompts = [ + "name", + "name", + "name", + "phrase", + "name", + "phrase", + "phrase", + "phrase", + "phrase", + "expression", +] +model.model_vision.dataset_names = [ + "lvis+stuffonly", + "objects365", + "openimages", + "vgregion", + "sa1b", + "refcoco-mixed_group-by-image", + "gqa", + "phrasecut", + "flickr30k", + "refcoco", +] +model.model_vision.dataset_metas = dataloader.train.dataset.names + ["refcoco-mixed"] + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 5120 diff --git a/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_16x4_1080k_mdl.py b/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_16x4_1080k_mdl.py new file mode 100644 index 0000000..e4a3774 --- /dev/null +++ b/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_16x4_1080k_mdl.py @@ -0,0 +1,230 @@ +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detectron2.solver import WarmupParamScheduler +from detrex.modeling.neck import ChannelMapper +from fvcore.common.param_scheduler import MultiStepParamScheduler + +from ape.data.detection_utils import get_fed_loss_cls_weights +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) +from ape.modeling.text import EVA02CLIP + +from ...common.backbone.vitl_eva02_clip import backbone +from ...common.data.lviscocococostuff_o365_oid_vgr_sa1b_refcoco_group_by_image_gqa_phrasecut_flickr30k_panoptic_lsj1024_cp_mdl import ( + dataloader, +) +from ...LVIS_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_lsj1024_cp_24ep import ( + model, + optimizer, + train, +) + +model.model_vision.backbone = backbone + +train.init_checkpoint = ( + "models/QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14to16_s6B.pt?matching_heuristics=True" +) + +model.model_language = L(EVA02CLIP)( + clip_model="EVA02-CLIP-bigE-14-plus", + cache_dir="models/QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt", + dtype="float16", +) +model.model_vision.embed_dim_language = 1024 + +model.model_vision.neck = L(ChannelMapper)( + input_shapes={ + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), + }, + in_features=["p2", "p3", "p4", "p5", "p6"], + out_channels=256, + num_outs=5, + kernel_size=1, + norm_layer=L(nn.GroupNorm)(num_groups=32, num_channels=256), +) + +model.model_vision.mask_in_features = ["p2"] +model.model_vision.input_shapes = { + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), +} + +model.model_vision.transformer.encoder.num_layers = 6 +model.model_vision.transformer.decoder.num_layers = 6 +model.model_vision.transformer.encoder.embed_dim = 256 +model.model_vision.transformer.decoder.embed_dim = 256 +model.model_vision.embed_dim = 256 +model.model_vision.backbone.out_channels = 256 + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) +model.model_vision.transformer.encoder.use_act_checkpoint = True + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +model.model_vision.num_classes = 1256 +model.model_vision.select_box_nums_for_evaluation = 300 + +criterion = model.model_vision.criterion[0] +del criterion.use_fed_loss +del criterion.get_fed_loss_cls_weights +del criterion.fed_loss_num_classes +model.model_vision.criterion = [criterion for _ in range(10)] +for criterion, num_classes in zip( + model.model_vision.criterion, [1256, 365, 601, 256, 1, 256, 256, 256, 256, 256] +): + criterion.num_classes = num_classes + +model.model_vision.criterion[0].use_fed_loss = True +model.model_vision.criterion[0].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train[0].dataset.names, 0.5 +) +model.model_vision.criterion[0].fed_loss_num_classes = 50 +model.model_vision.criterion[0].fed_loss_pad_type = "cat" + +model.model_vision.criterion[2].use_fed_loss = True +model.model_vision.criterion[2].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train[2].dataset.names, 0.5 +) +model.model_vision.criterion[2].fed_loss_num_classes = 50 +model.model_vision.criterion[2].fed_loss_pad_type = "cat" + +model.model_vision.criterion[3].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[3].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[3].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[3].weight_dict.update({k: 0.0}) + +for k, v in model.model_vision.criterion[4].weight_dict.items(): + if "_class" in k and "_enc" not in k: + model.model_vision.criterion[4].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[5].weight_dict["loss_class_enc"] = 0.0 + +model.model_vision.criterion[6].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[6].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[6].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[6].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[7].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[7].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[7].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[7].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[8].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[8].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[8].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[8].weight_dict.update({k: 0.0}) + +model.model_vision.stuff_dataset_learn_thing = False +model.model_vision.stuff_prob_thing = 0.9 +model.model_vision.transformer.proposal_ambiguous = 1 + +model.model_vision.instance_on = True +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = False + +train.max_iter = 1080000 +train.eval_period = 1080000 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1], + milestones=[900000], + num_updates=1080000, + ), + warmup_length=2000 / 270000, + warmup_method="linear", + warmup_factor=0.001, +) + +for i in range(len(dataloader.train)): + dataloader.train[i].mapper.max_num_phrase = 128 + dataloader.train[i].mapper.nms_thresh_phrase = 0.6 + dataloader.train[i].total_batch_size = 16 + dataloader.train[i].total_batch_size_list = [16] + dataloader.train[i].num_workers = 2 + +train.iter_size = 4 +train.iter_loop = False +train.dataset_ratio = [1, 1, 1, 1, 1, 0.1, 0.1, 0.1, 0.1] + +model.model_vision.dataset_prompts = [ + "name", + "name", + "name", + "phrase", + "name", + "phrase", + "phrase", + "phrase", + "phrase", + "expression", +] +model.model_vision.dataset_names = [ + "lvis+stuffonly", + "objects365", + "openimages", + "vgregion", + "sa1b", + "refcoco-mixed_group-by-image", + "gqa", + "phrasecut", + "flickr30k", + "refcoco", +] +model.model_vision.dataset_metas = [xx for x in dataloader.train for xx in x.dataset.names] + [ + "refcoco-mixed" +] + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 5120 diff --git a/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_16x4_1080k_mdl_llama2.py b/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_16x4_1080k_mdl_llama2.py new file mode 100644 index 0000000..d312bc3 --- /dev/null +++ b/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_16x4_1080k_mdl_llama2.py @@ -0,0 +1,235 @@ +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detectron2.solver import WarmupParamScheduler +from detrex.modeling.neck import ChannelMapper +from fvcore.common.param_scheduler import MultiStepParamScheduler + +from ape.data.detection_utils import get_fed_loss_cls_weights +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) +from ape.modeling.text import Llama2 + +from ...common.backbone.vitl_eva02_clip import backbone +from ...common.data.lviscocococostuff_o365_oid_vgr_sa1b_refcoco_group_by_image_gqa_phrasecut_flickr30k_panoptic_lsj1024_cp_mdl import ( + dataloader, +) +from ...LVIS_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_lsj1024_cp_24ep import ( + model, + optimizer, + train, +) + +model.model_vision.backbone = backbone + +train.init_checkpoint = ( + "models/QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14to16_s6B.pt?matching_heuristics=True" +) + +model.model_language = L(Llama2)( + pretrained_model_name_or_path="models/meta-llama/Llama-2-7b-hf/", + dtype="float32", + vision_port="decoder", + eval_only=True, + load_in_4bit=True, + load_in_8bit=False, +) +model.model_vision.embed_dim_language = 4096 +model.model_vision.text_feature_reduce_type = "average" + +model.model_vision.neck = L(ChannelMapper)( + input_shapes={ + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), + }, + in_features=["p2", "p3", "p4", "p5", "p6"], + out_channels=256, + num_outs=5, + kernel_size=1, + norm_layer=L(nn.GroupNorm)(num_groups=32, num_channels=256), +) + +model.model_vision.mask_in_features = ["p2"] +model.model_vision.input_shapes = { + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), +} + +model.model_vision.transformer.encoder.num_layers = 6 +model.model_vision.transformer.decoder.num_layers = 6 +model.model_vision.transformer.encoder.embed_dim = 256 +model.model_vision.transformer.decoder.embed_dim = 256 +model.model_vision.embed_dim = 256 +model.model_vision.backbone.out_channels = 256 + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) +model.model_vision.transformer.encoder.use_act_checkpoint = True +model.model_vision.transformer.decoder.use_act_checkpoint = True + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +model.model_vision.num_classes = 1256 +model.model_vision.select_box_nums_for_evaluation = 300 + +criterion = model.model_vision.criterion[0] +del criterion.use_fed_loss +del criterion.get_fed_loss_cls_weights +del criterion.fed_loss_num_classes +model.model_vision.criterion = [criterion for _ in range(10)] +for criterion, num_classes in zip( + model.model_vision.criterion, [1256, 365, 601, 256, 1, 256, 256, 256, 256, 256] +): + criterion.num_classes = num_classes + +model.model_vision.criterion[0].use_fed_loss = True +model.model_vision.criterion[0].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train[0].dataset.names, 0.5 +) +model.model_vision.criterion[0].fed_loss_num_classes = 50 +model.model_vision.criterion[0].fed_loss_pad_type = "cat" + +model.model_vision.criterion[2].use_fed_loss = True +model.model_vision.criterion[2].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train[2].dataset.names, 0.5 +) +model.model_vision.criterion[2].fed_loss_num_classes = 50 +model.model_vision.criterion[2].fed_loss_pad_type = "cat" + +model.model_vision.criterion[3].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[3].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[3].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[3].weight_dict.update({k: 0.0}) + +for k, v in model.model_vision.criterion[4].weight_dict.items(): + if "_class" in k and "_enc" not in k: + model.model_vision.criterion[4].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[5].weight_dict["loss_class_enc"] = 0.0 + +model.model_vision.criterion[6].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[6].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[6].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[6].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[7].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[7].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[7].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[7].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[8].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[8].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[8].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[8].weight_dict.update({k: 0.0}) + +model.model_vision.stuff_dataset_learn_thing = False +model.model_vision.stuff_prob_thing = 0.9 +model.model_vision.transformer.proposal_ambiguous = 1 + +model.model_vision.instance_on = True +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = False + +train.max_iter = 1080000 +train.eval_period = 1080000 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1], + milestones=[900000], + num_updates=1080000, + ), + warmup_length=2000 / 270000, + warmup_method="linear", + warmup_factor=0.001, +) + +for i in range(len(dataloader.train)): + dataloader.train[i].mapper.max_num_phrase = 128 + dataloader.train[i].mapper.nms_thresh_phrase = 0.6 + dataloader.train[i].total_batch_size = 16 + dataloader.train[i].total_batch_size_list = [16] + dataloader.train[i].num_workers = 2 + +train.iter_size = 4 +train.iter_loop = False +train.dataset_ratio = [1, 1, 1, 1, 1, 0.1, 0.1, 0.1, 0.1] + +model.model_vision.dataset_prompts = [ + "name", + "name", + "name", + "phrase", + "name", + "phrase", + "phrase", + "phrase", + "phrase", + "expression", +] +model.model_vision.dataset_names = [ + "lvis+stuffonly", + "objects365", + "openimages", + "vgregion", + "sa1b", + "refcoco-mixed_group-by-image", + "gqa", + "phrasecut", + "flickr30k", + "refcoco", +] +model.model_vision.dataset_metas = [xx for x in dataloader.train for xx in x.dataset.names] + [ + "refcoco-mixed" +] + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 5120 diff --git a/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_16x4x270k.py b/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_16x4x270k.py new file mode 100644 index 0000000..29857f7 --- /dev/null +++ b/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_16x4x270k.py @@ -0,0 +1,227 @@ +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detectron2.solver import WarmupParamScheduler +from detrex.modeling.neck import ChannelMapper +from fvcore.common.param_scheduler import MultiStepParamScheduler + +from ape.data.detection_utils import get_fed_loss_cls_weights +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) +from ape.modeling.text import EVA02CLIP + +from ...common.backbone.vitl_eva02_clip import backbone +from ...common.data.lviscocococostuff_o365_oid_vgr_sa1b_refcoco_group_by_image_gqa_phrasecut_flickr30k_panoptic_lsj1024_cp import ( + dataloader, +) +from ...LVIS_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_lsj1024_cp_24ep import ( + model, + optimizer, + train, +) + +model.model_vision.backbone = backbone + +train.init_checkpoint = ( + "models/QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14to16_s6B.pt?matching_heuristics=True" +) + +model.model_language = L(EVA02CLIP)( + clip_model="EVA02-CLIP-bigE-14-plus", + cache_dir="models/QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt", + dtype="float16", +) +model.model_vision.embed_dim_language = 1024 + +model.model_vision.neck = L(ChannelMapper)( + input_shapes={ + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), + }, + in_features=["p2", "p3", "p4", "p5", "p6"], + out_channels=256, + num_outs=5, + kernel_size=1, + norm_layer=L(nn.GroupNorm)(num_groups=32, num_channels=256), +) + +model.model_vision.mask_in_features = ["p2"] +model.model_vision.input_shapes = { + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), +} + +model.model_vision.transformer.encoder.num_layers = 6 +model.model_vision.transformer.decoder.num_layers = 6 +model.model_vision.transformer.encoder.embed_dim = 256 +model.model_vision.transformer.decoder.embed_dim = 256 +model.model_vision.embed_dim = 256 +model.model_vision.backbone.out_channels = 256 + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, + use_attention_mask_v=True, +) +model.model_vision.transformer.encoder.use_act_checkpoint = True + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +model.model_vision.num_classes = 1256 +model.model_vision.select_box_nums_for_evaluation = 300 + +criterion = model.model_vision.criterion[0] +del criterion.use_fed_loss +del criterion.get_fed_loss_cls_weights +del criterion.fed_loss_num_classes +model.model_vision.criterion = [criterion for _ in range(10)] +for criterion, num_classes in zip( + model.model_vision.criterion, [1256, 365, 601, 256, 1, 256, 256, 256, 256, 256] +): + criterion.num_classes = num_classes + +dataloader.train.mapper.max_num_phrase = 128 +dataloader.train.mapper.nms_thresh_phrase = 0.6 + +model.model_vision.criterion[0].use_fed_loss = True +model.model_vision.criterion[0].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train.dataset.names[0], 0.5 +) +model.model_vision.criterion[0].fed_loss_num_classes = 50 +model.model_vision.criterion[0].fed_loss_pad_type = "cat" + +model.model_vision.criterion[2].use_fed_loss = True +model.model_vision.criterion[2].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train.dataset.names[2], 0.5 +) +model.model_vision.criterion[2].fed_loss_num_classes = 50 +model.model_vision.criterion[2].fed_loss_pad_type = "cat" + +model.model_vision.criterion[3].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[3].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[3].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[3].weight_dict.update({k: 0.0}) + +for k, v in model.model_vision.criterion[4].weight_dict.items(): + if "_class" in k and "_enc" not in k: + model.model_vision.criterion[4].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[5].weight_dict["loss_class_enc"] = 0.0 + +model.model_vision.criterion[6].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[6].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[6].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[6].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[7].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[7].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[7].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[7].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[8].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[8].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[8].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[8].weight_dict.update({k: 0.0}) + +model.model_vision.stuff_dataset_learn_thing = False +model.model_vision.stuff_prob_thing = 0.9 +model.model_vision.transformer.proposal_ambiguous = 1 + +model.model_vision.instance_on = True +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = False + +train.max_iter = 270000 +train.eval_period = 270000 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1], + milestones=[225000], + num_updates=270000, + ), + warmup_length=2000 / 270000, + warmup_method="linear", + warmup_factor=0.001, +) + +dataloader.train.total_batch_size = 16 +dataloader.train.total_batch_size_list = [16, 16, 16, 16, 16, 16, 16, 16, 16] +dataloader.train.num_workers = 0 +train.iter_size = 4 + + +model.model_vision.dataset_prompts = [ + "name", + "name", + "name", + "phrase", + "name", + "phrase", + "phrase", + "phrase", + "phrase", + "expression", +] +model.model_vision.dataset_names = [ + "lvis+stuffonly", + "objects365", + "openimages", + "vgregion", + "sa1b", + "refcoco-mixed_group-by-image", + "gqa", + "phrasecut", + "flickr30k", + "refcoco", +] +model.model_vision.dataset_metas = dataloader.train.dataset.names + ["refcoco-mixed"] + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 5120 diff --git a/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_16x4x270k_mdl.py b/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_16x4x270k_mdl.py new file mode 100644 index 0000000..39b14d3 --- /dev/null +++ b/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_16x4x270k_mdl.py @@ -0,0 +1,230 @@ +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detectron2.solver import WarmupParamScheduler +from detrex.modeling.neck import ChannelMapper +from fvcore.common.param_scheduler import MultiStepParamScheduler + +from ape.data.detection_utils import get_fed_loss_cls_weights +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) +from ape.modeling.text import EVA02CLIP + +from ...common.backbone.vitl_eva02_clip import backbone +from ...common.data.lviscocococostuff_o365_oid_vgr_sa1b_refcoco_group_by_image_gqa_phrasecut_flickr30k_panoptic_lsj1024_cp_mdl import ( + dataloader, +) +from ...LVIS_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_lsj1024_cp_24ep import ( + model, + optimizer, + train, +) + +model.model_vision.backbone = backbone + +train.init_checkpoint = ( + "models/QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14to16_s6B.pt?matching_heuristics=True" +) + +model.model_language = L(EVA02CLIP)( + clip_model="EVA02-CLIP-bigE-14-plus", + cache_dir="models/QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt", + dtype="float16", +) +model.model_vision.embed_dim_language = 1024 + +model.model_vision.neck = L(ChannelMapper)( + input_shapes={ + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), + }, + in_features=["p2", "p3", "p4", "p5", "p6"], + out_channels=256, + num_outs=5, + kernel_size=1, + norm_layer=L(nn.GroupNorm)(num_groups=32, num_channels=256), +) + +model.model_vision.mask_in_features = ["p2"] +model.model_vision.input_shapes = { + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), +} + +model.model_vision.transformer.encoder.num_layers = 6 +model.model_vision.transformer.decoder.num_layers = 6 +model.model_vision.transformer.encoder.embed_dim = 256 +model.model_vision.transformer.decoder.embed_dim = 256 +model.model_vision.embed_dim = 256 +model.model_vision.backbone.out_channels = 256 + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, + use_attention_mask_v=True, +) +model.model_vision.transformer.encoder.use_act_checkpoint = True + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +model.model_vision.num_classes = 1256 +model.model_vision.select_box_nums_for_evaluation = 300 + +criterion = model.model_vision.criterion[0] +del criterion.use_fed_loss +del criterion.get_fed_loss_cls_weights +del criterion.fed_loss_num_classes +model.model_vision.criterion = [criterion for _ in range(10)] +for criterion, num_classes in zip( + model.model_vision.criterion, [1256, 365, 601, 256, 1, 256, 256, 256, 256, 256] +): + criterion.num_classes = num_classes + +model.model_vision.criterion[0].use_fed_loss = True +model.model_vision.criterion[0].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train[0].dataset.names, 0.5 +) +model.model_vision.criterion[0].fed_loss_num_classes = 50 +model.model_vision.criterion[0].fed_loss_pad_type = "cat" + +model.model_vision.criterion[2].use_fed_loss = True +model.model_vision.criterion[2].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train[2].dataset.names, 0.5 +) +model.model_vision.criterion[2].fed_loss_num_classes = 50 +model.model_vision.criterion[2].fed_loss_pad_type = "cat" + +model.model_vision.criterion[3].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[3].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[3].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[3].weight_dict.update({k: 0.0}) + +for k, v in model.model_vision.criterion[4].weight_dict.items(): + if "_class" in k and "_enc" not in k: + model.model_vision.criterion[4].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[5].weight_dict["loss_class_enc"] = 0.0 + +model.model_vision.criterion[6].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[6].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[6].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[6].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[7].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[7].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[7].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[7].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[8].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[8].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[8].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[8].weight_dict.update({k: 0.0}) + +model.model_vision.stuff_dataset_learn_thing = False +model.model_vision.stuff_prob_thing = 0.9 +model.model_vision.transformer.proposal_ambiguous = 1 + +model.model_vision.instance_on = True +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = False + +train.max_iter = 270000 +train.eval_period = 270000 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1], + milestones=[225000], + num_updates=270000, + ), + warmup_length=2000 / 270000, + warmup_method="linear", + warmup_factor=0.001, +) + +for i in range(len(dataloader.train)): + dataloader.train[i].mapper.max_num_phrase = 128 + dataloader.train[i].mapper.nms_thresh_phrase = 0.6 + dataloader.train[i].total_batch_size = 16 + dataloader.train[i].total_batch_size_list = [16] + dataloader.train[i].num_workers = 2 + +train.iter_size = 4 +train.dataset_ratio = [1, 1, 1, 1, 1, 0.1, 0.1, 0.1, 0.1] + +model.model_vision.dataset_prompts = [ + "name", + "name", + "name", + "phrase", + "name", + "phrase", + "phrase", + "phrase", + "phrase", + "expression", +] +model.model_vision.dataset_names = [ + "lvis+stuffonly", + "objects365", + "openimages", + "vgregion", + "sa1b", + "refcoco-mixed_group-by-image", + "gqa", + "phrasecut", + "flickr30k", + "refcoco", +] +model.model_vision.dataset_metas = [xx for x in dataloader.train for xx in x.dataset.names] + [ + "refcoco-mixed" +] + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 5120 diff --git a/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_16x4x270k_mdl_llama2.py b/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_16x4x270k_mdl_llama2.py new file mode 100644 index 0000000..8f40027 --- /dev/null +++ b/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_16x4x270k_mdl_llama2.py @@ -0,0 +1,235 @@ +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detectron2.solver import WarmupParamScheduler +from detrex.modeling.neck import ChannelMapper +from fvcore.common.param_scheduler import MultiStepParamScheduler + +from ape.data.detection_utils import get_fed_loss_cls_weights +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) +from ape.modeling.text import Llama2 + +from ...common.backbone.vitl_eva02_clip import backbone +from ...common.data.lviscocococostuff_o365_oid_vgr_sa1b_refcoco_group_by_image_gqa_phrasecut_flickr30k_panoptic_lsj1024_cp_mdl import ( + dataloader, +) +from ...LVIS_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_lsj1024_cp_24ep import ( + model, + optimizer, + train, +) + +model.model_vision.backbone = backbone + +train.init_checkpoint = ( + "models/QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14to16_s6B.pt?matching_heuristics=True" +) + +model.model_language = L(Llama2)( + pretrained_model_name_or_path="models/meta-llama/Llama-2-7b-hf/", + dtype="float32", + vision_port="decoder", + eval_only=True, + load_in_4bit=True, + load_in_8bit=False, +) +model.model_vision.embed_dim_language = 4096 +model.model_vision.text_feature_reduce_type = "average" + +model.model_vision.neck = L(ChannelMapper)( + input_shapes={ + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), + }, + in_features=["p2", "p3", "p4", "p5", "p6"], + out_channels=256, + num_outs=5, + kernel_size=1, + norm_layer=L(nn.GroupNorm)(num_groups=32, num_channels=256), +) + +model.model_vision.mask_in_features = ["p2"] +model.model_vision.input_shapes = { + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), +} + +model.model_vision.transformer.encoder.num_layers = 6 +model.model_vision.transformer.decoder.num_layers = 6 +model.model_vision.transformer.encoder.embed_dim = 256 +model.model_vision.transformer.decoder.embed_dim = 256 +model.model_vision.embed_dim = 256 +model.model_vision.backbone.out_channels = 256 + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, + use_attention_mask_v=True, +) +model.model_vision.transformer.encoder.use_act_checkpoint = True +model.model_vision.transformer.decoder.use_act_checkpoint = True + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +model.model_vision.num_classes = 1256 +model.model_vision.select_box_nums_for_evaluation = 300 + +criterion = model.model_vision.criterion[0] +del criterion.use_fed_loss +del criterion.get_fed_loss_cls_weights +del criterion.fed_loss_num_classes +model.model_vision.criterion = [criterion for _ in range(10)] +for criterion, num_classes in zip( + model.model_vision.criterion, [1256, 365, 601, 256, 1, 256, 256, 256, 256, 256] +): + criterion.num_classes = num_classes + +model.model_vision.criterion[0].use_fed_loss = True +model.model_vision.criterion[0].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train[0].dataset.names, 0.5 +) +model.model_vision.criterion[0].fed_loss_num_classes = 50 +model.model_vision.criterion[0].fed_loss_pad_type = "cat" + +model.model_vision.criterion[2].use_fed_loss = True +model.model_vision.criterion[2].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train[2].dataset.names, 0.5 +) +model.model_vision.criterion[2].fed_loss_num_classes = 50 +model.model_vision.criterion[2].fed_loss_pad_type = "cat" + +model.model_vision.criterion[3].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[3].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[3].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[3].weight_dict.update({k: 0.0}) + +for k, v in model.model_vision.criterion[4].weight_dict.items(): + if "_class" in k and "_enc" not in k: + model.model_vision.criterion[4].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[5].weight_dict["loss_class_enc"] = 0.0 + +model.model_vision.criterion[6].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[6].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[6].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[6].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[7].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[7].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[7].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[7].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[8].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[8].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[8].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[8].weight_dict.update({k: 0.0}) + +model.model_vision.stuff_dataset_learn_thing = False +model.model_vision.stuff_prob_thing = 0.9 +model.model_vision.transformer.proposal_ambiguous = 1 + +model.model_vision.instance_on = True +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = False + +train.max_iter = 270000 +train.eval_period = 270000 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1], + milestones=[225000], + num_updates=270000, + ), + warmup_length=2000 / 270000, + warmup_method="linear", + warmup_factor=0.001, +) + +for i in range(len(dataloader.train)): + dataloader.train[i].mapper.max_num_phrase = 128 + dataloader.train[i].mapper.nms_thresh_phrase = 0.6 + dataloader.train[i].total_batch_size = 16 + dataloader.train[i].total_batch_size_list = [16] + dataloader.train[i].num_workers = 2 + +train.iter_size = 4 +train.dataset_ratio = [1, 1, 1, 1, 1, 0.1, 0.1, 0.1, 0.1] + +model.model_vision.dataset_prompts = [ + "name", + "name", + "name", + "phrase", + "name", + "phrase", + "phrase", + "phrase", + "phrase", + "expression", +] +model.model_vision.dataset_names = [ + "lvis+stuffonly", + "objects365", + "openimages", + "vgregion", + "sa1b", + "refcoco-mixed_group-by-image", + "gqa", + "phrasecut", + "flickr30k", + "refcoco", +] +model.model_vision.dataset_metas = [xx for x in dataloader.train for xx in x.dataset.names] + [ + "refcoco-mixed" +] + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 5120 diff --git a/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_16x4x337k_mdl.py b/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_16x4x337k_mdl.py new file mode 100644 index 0000000..b746718 --- /dev/null +++ b/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_16x4x337k_mdl.py @@ -0,0 +1,230 @@ +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detectron2.solver import WarmupParamScheduler +from detrex.modeling.neck import ChannelMapper +from fvcore.common.param_scheduler import MultiStepParamScheduler + +from ape.data.detection_utils import get_fed_loss_cls_weights +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) +from ape.modeling.text import EVA02CLIP + +from ...common.backbone.vitl_eva02_clip import backbone +from ...common.data.lviscocococostuff_o365_oid_vgr_sa1b_refcoco_group_by_image_gqa_phrasecut_flickr30k_panoptic_lsj1024_cp_mdl import ( + dataloader, +) +from ...LVIS_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_lsj1024_cp_24ep import ( + model, + optimizer, + train, +) + +model.model_vision.backbone = backbone + +train.init_checkpoint = ( + "models/QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14to16_s6B.pt?matching_heuristics=True" +) + +model.model_language = L(EVA02CLIP)( + clip_model="EVA02-CLIP-bigE-14-plus", + cache_dir="models/QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt", + dtype="float16", +) +model.model_vision.embed_dim_language = 1024 + +model.model_vision.neck = L(ChannelMapper)( + input_shapes={ + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), + }, + in_features=["p2", "p3", "p4", "p5", "p6"], + out_channels=256, + num_outs=5, + kernel_size=1, + norm_layer=L(nn.GroupNorm)(num_groups=32, num_channels=256), +) + +model.model_vision.mask_in_features = ["p2"] +model.model_vision.input_shapes = { + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), +} + +model.model_vision.transformer.encoder.num_layers = 6 +model.model_vision.transformer.decoder.num_layers = 6 +model.model_vision.transformer.encoder.embed_dim = 256 +model.model_vision.transformer.decoder.embed_dim = 256 +model.model_vision.embed_dim = 256 +model.model_vision.backbone.out_channels = 256 + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, + use_attention_mask_v=True, +) +model.model_vision.transformer.encoder.use_act_checkpoint = True + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +model.model_vision.num_classes = 1256 +model.model_vision.select_box_nums_for_evaluation = 300 + +criterion = model.model_vision.criterion[0] +del criterion.use_fed_loss +del criterion.get_fed_loss_cls_weights +del criterion.fed_loss_num_classes +model.model_vision.criterion = [criterion for _ in range(10)] +for criterion, num_classes in zip( + model.model_vision.criterion, [1256, 365, 601, 256, 1, 256, 256, 256, 256, 256] +): + criterion.num_classes = num_classes + +model.model_vision.criterion[0].use_fed_loss = True +model.model_vision.criterion[0].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train[0].dataset.names, 0.5 +) +model.model_vision.criterion[0].fed_loss_num_classes = 50 +model.model_vision.criterion[0].fed_loss_pad_type = "cat" + +model.model_vision.criterion[2].use_fed_loss = True +model.model_vision.criterion[2].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train[2].dataset.names, 0.5 +) +model.model_vision.criterion[2].fed_loss_num_classes = 50 +model.model_vision.criterion[2].fed_loss_pad_type = "cat" + +model.model_vision.criterion[3].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[3].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[3].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[3].weight_dict.update({k: 0.0}) + +for k, v in model.model_vision.criterion[4].weight_dict.items(): + if "_class" in k and "_enc" not in k: + model.model_vision.criterion[4].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[5].weight_dict["loss_class_enc"] = 0.0 + +model.model_vision.criterion[6].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[6].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[6].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[6].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[7].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[7].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[7].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[7].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[8].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[8].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[8].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[8].weight_dict.update({k: 0.0}) + +model.model_vision.stuff_dataset_learn_thing = False +model.model_vision.stuff_prob_thing = 0.9 +model.model_vision.transformer.proposal_ambiguous = 1 + +model.model_vision.instance_on = True +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = False + +train.max_iter = 337500 +train.eval_period = 337500 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1, 0.01], + milestones=[225000, 300000], + num_updates=337500, + ), + warmup_length=2000 / 270000, + warmup_method="linear", + warmup_factor=0.001, +) + +for i in range(len(dataloader.train)): + dataloader.train[i].mapper.max_num_phrase = 128 + dataloader.train[i].mapper.nms_thresh_phrase = 0.6 + dataloader.train[i].total_batch_size = 16 + dataloader.train[i].total_batch_size_list = [16] + dataloader.train[i].num_workers = 2 + +train.iter_size = 4 +train.dataset_ratio = [1, 1, 1, 1, 1, 0.1, 0.1, 0.1, 0.1] + +model.model_vision.dataset_prompts = [ + "name", + "name", + "name", + "phrase", + "name", + "phrase", + "phrase", + "phrase", + "phrase", + "expression", +] +model.model_vision.dataset_names = [ + "lvis+stuffonly", + "objects365", + "openimages", + "vgregion", + "sa1b", + "refcoco-mixed_group-by-image", + "gqa", + "phrasecut", + "flickr30k", + "refcoco", +] +model.model_vision.dataset_metas = [xx for x in dataloader.train for xx in x.dataset.names] + [ + "refcoco-mixed" +] + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 5120 diff --git a/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_32x2x270k.py b/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_32x2x270k.py new file mode 100644 index 0000000..a4db1c3 --- /dev/null +++ b/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_32x2x270k.py @@ -0,0 +1,228 @@ +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detectron2.solver import WarmupParamScheduler +from detrex.modeling.neck import ChannelMapper +from fvcore.common.param_scheduler import MultiStepParamScheduler + +from ape.data.detection_utils import get_fed_loss_cls_weights +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) +from ape.modeling.text import EVA02CLIP + +from ...common.backbone.vitl_eva02_clip import backbone +from ...common.data.lviscocococostuff_o365_oid_vgr_sa1b_refcoco_group_by_image_gqa_phrasecut_flickr30k_panoptic_lsj1024_cp import ( + dataloader, +) +from ...LVIS_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_lsj1024_cp_24ep import ( + model, + optimizer, + train, +) + +model.model_vision.backbone = backbone + +train.init_checkpoint = ( + "models/QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14to16_s6B.pt?matching_heuristics=True" +) + +model.model_language = L(EVA02CLIP)( + clip_model="EVA02-CLIP-bigE-14-plus", + cache_dir="models/QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt", + dtype="float16", +) +model.model_vision.embed_dim_language = 1024 + +model.model_vision.neck = L(ChannelMapper)( + input_shapes={ + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), + }, + in_features=["p2", "p3", "p4", "p5", "p6"], + out_channels=256, + num_outs=5, + kernel_size=1, + norm_layer=L(nn.GroupNorm)(num_groups=32, num_channels=256), +) + +model.model_vision.mask_in_features = ["p2"] +model.model_vision.input_shapes = { + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), +} + +model.model_vision.transformer.encoder.num_layers = 6 +model.model_vision.transformer.decoder.num_layers = 6 +model.model_vision.transformer.encoder.embed_dim = 256 +model.model_vision.transformer.decoder.embed_dim = 256 +model.model_vision.embed_dim = 256 +model.model_vision.backbone.out_channels = 256 + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) +model.model_vision.transformer.encoder.use_act_checkpoint = True + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +model.model_vision.num_classes = 1256 +model.model_vision.select_box_nums_for_evaluation = 300 + +criterion = model.model_vision.criterion[0] +del criterion.use_fed_loss +del criterion.get_fed_loss_cls_weights +del criterion.fed_loss_num_classes +model.model_vision.criterion = [criterion for _ in range(10)] +for criterion, num_classes in zip( + model.model_vision.criterion, [1256, 365, 601, 256, 1, 256, 256, 256, 256, 256] +): + criterion.num_classes = num_classes + +dataloader.train.mapper.max_num_phrase = 128 +dataloader.train.mapper.nms_thresh_phrase = 0.6 + +model.model_vision.criterion[0].use_fed_loss = True +model.model_vision.criterion[0].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train.dataset.names[0], 0.5 +) +model.model_vision.criterion[0].fed_loss_num_classes = 50 +model.model_vision.criterion[0].fed_loss_pad_type = "cat" + +model.model_vision.criterion[2].use_fed_loss = True +model.model_vision.criterion[2].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train.dataset.names[2], 0.5 +) +model.model_vision.criterion[2].fed_loss_num_classes = 50 +model.model_vision.criterion[2].fed_loss_pad_type = "cat" + +model.model_vision.criterion[3].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[3].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[3].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[3].weight_dict.update({k: 0.0}) + +for k, v in model.model_vision.criterion[4].weight_dict.items(): + if "_class" in k and "_enc" not in k: + model.model_vision.criterion[4].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[5].weight_dict["loss_class_enc"] = 0.0 + +model.model_vision.criterion[6].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[6].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[6].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[6].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[7].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[7].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[7].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[7].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[8].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[8].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[8].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[8].weight_dict.update({k: 0.0}) + +model.model_vision.stuff_dataset_learn_thing = False +model.model_vision.stuff_prob_thing = 0.9 +model.model_vision.transformer.proposal_ambiguous = 1 + +model.model_vision.instance_on = True +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = False + +train.max_iter = 270000 +train.eval_period = 270000 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1], + milestones=[225000], + num_updates=270000, + ), + warmup_length=2000 / 270000, + warmup_method="linear", + warmup_factor=0.001, +) + +dataloader.train.total_batch_size = 32 +dataloader.train.total_batch_size_list = [32, 32, 32, 32, 32, 32, 32, 32, 32] +dataloader.train.num_workers = 2 +train.iter_size = 2 + +dataloader.wait_group = 2 +dataloader.wait_time = 30 * 60 + +model.model_vision.dataset_prompts = [ + "name", + "name", + "name", + "phrase", + "name", + "phrase", + "phrase", + "phrase", + "phrase", + "expression", +] +model.model_vision.dataset_names = [ + "lvis+stuffonly", + "objects365", + "openimages", + "vgregion", + "sa1b", + "refcoco-mixed_group-by-image", + "gqa", + "phrasecut", + "flickr30k", + "refcoco", +] +model.model_vision.dataset_metas = dataloader.train.dataset.names + ["refcoco-mixed"] + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_48x2x270k.py b/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_48x2x270k.py new file mode 100644 index 0000000..00f04e1 --- /dev/null +++ b/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_48x2x270k.py @@ -0,0 +1,225 @@ +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detectron2.solver import WarmupParamScheduler +from detrex.modeling.neck import ChannelMapper +from fvcore.common.param_scheduler import MultiStepParamScheduler + +from ape.data.detection_utils import get_fed_loss_cls_weights +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) +from ape.modeling.text import EVA02CLIP + +from ...common.backbone.vitl_eva02_clip import backbone +from ...common.data.lviscocococostuff_o365_oid_vgr_sa1b_refcoco_group_by_image_gqa_phrasecut_flickr30k_panoptic_lsj1024_cp import ( + dataloader, +) +from ...LVIS_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_lsj1024_cp_24ep import ( + model, + optimizer, + train, +) + +model.model_vision.backbone = backbone + +train.init_checkpoint = ( + "models/QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14to16_s6B.pt?matching_heuristics=True" +) + +model.model_language = L(EVA02CLIP)( + clip_model="EVA02-CLIP-bigE-14-plus", + cache_dir="models/QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt", +) +model.model_vision.embed_dim_language = 1024 + +model.model_vision.neck = L(ChannelMapper)( + input_shapes={ + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), + }, + in_features=["p2", "p3", "p4", "p5", "p6"], + out_channels=256, + num_outs=5, + kernel_size=1, + norm_layer=L(nn.GroupNorm)(num_groups=32, num_channels=256), +) + +model.model_vision.mask_in_features = ["p2"] +model.model_vision.input_shapes = { + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), +} + +model.model_vision.transformer.encoder.num_layers = 6 +model.model_vision.transformer.decoder.num_layers = 6 +model.model_vision.transformer.encoder.embed_dim = 256 +model.model_vision.transformer.decoder.embed_dim = 256 +model.model_vision.embed_dim = 256 +model.model_vision.backbone.out_channels = 256 + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) +model.model_vision.transformer.encoder.use_act_checkpoint = True + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +model.model_vision.num_classes = 1256 +model.model_vision.select_box_nums_for_evaluation = 300 + +criterion = model.model_vision.criterion[0] +del criterion.use_fed_loss +del criterion.get_fed_loss_cls_weights +del criterion.fed_loss_num_classes +model.model_vision.criterion = [criterion for _ in range(10)] +for criterion, num_classes in zip( + model.model_vision.criterion, [1256, 365, 601, 200, 1, 200, 200, 200, 200, 200] +): + criterion.num_classes = num_classes + +dataloader.train.mapper.max_num_phrase = 100 +dataloader.train.mapper.nms_thresh_phrase = 0.6 + +model.model_vision.criterion[0].use_fed_loss = True +model.model_vision.criterion[0].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train.dataset.names[0], 0.5 +) +model.model_vision.criterion[0].fed_loss_num_classes = 50 +model.model_vision.criterion[0].fed_loss_pad_type = "cat" + +model.model_vision.criterion[2].use_fed_loss = True +model.model_vision.criterion[2].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train.dataset.names[2], 0.5 +) +model.model_vision.criterion[2].fed_loss_num_classes = 50 +model.model_vision.criterion[2].fed_loss_pad_type = "cat" + +model.model_vision.criterion[3].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[3].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[3].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[3].weight_dict.update({k: 0.0}) + +for k, v in model.model_vision.criterion[4].weight_dict.items(): + if "_class" in k and "_enc" not in k: + model.model_vision.criterion[4].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[5].weight_dict["loss_class_enc"] = 0.0 + +model.model_vision.criterion[6].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[6].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[6].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[6].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[7].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[7].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[7].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[7].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[8].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[8].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[8].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[8].weight_dict.update({k: 0.0}) + +model.model_vision.stuff_dataset_learn_thing = False +model.model_vision.stuff_prob_thing = 0.9 +model.model_vision.transformer.proposal_ambiguous = 1 + +model.model_vision.instance_on = True +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = False + +train.max_iter = 270000 * 2 +train.eval_period = 270000 * 2 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1], + milestones=[225000 * 2], + num_updates=270000 * 2, + ), + warmup_length=2000 / 270000, + warmup_method="linear", + warmup_factor=0.001, +) + +dataloader.train.total_batch_size = 48 +dataloader.train.total_batch_size_list = [48, 48, 48, 48, 48, 48, 48, 48, 48] +dataloader.train.num_workers = 2 +train.iter_size = 2 + + +model.model_vision.dataset_prompts = [ + "name", + "name", + "name", + "phrase", + "name", + "phrase", + "phrase", + "phrase", + "phrase", + "expression", +] +model.model_vision.dataset_names = [ + "lvis+stuffonly", + "objects365", + "openimages", + "vgregion", + "sa1b", + "refcoco-mixed_group-by-image", + "gqa", + "phrasecut", + "flickr30k", + "refcoco", +] +model.model_vision.dataset_metas = dataloader.train.dataset.names + ["refcoco-mixed"] + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_64x1x270k.py b/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_64x1x270k.py new file mode 100644 index 0000000..76bd256 --- /dev/null +++ b/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_64x1x270k.py @@ -0,0 +1,228 @@ +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detectron2.solver import WarmupParamScheduler +from detrex.modeling.neck import ChannelMapper +from fvcore.common.param_scheduler import MultiStepParamScheduler + +from ape.data.detection_utils import get_fed_loss_cls_weights +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) +from ape.modeling.text import EVA02CLIP + +from ...common.backbone.vitl_eva02_clip import backbone +from ...common.data.lviscocococostuff_o365_oid_vgr_sa1b_refcoco_group_by_image_gqa_phrasecut_flickr30k_panoptic_lsj1024_cp import ( + dataloader, +) +from ...LVIS_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_lsj1024_cp_24ep import ( + model, + optimizer, + train, +) + +model.model_vision.backbone = backbone + +train.init_checkpoint = ( + "models/QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14to16_s6B.pt?matching_heuristics=True" +) + +model.model_language = L(EVA02CLIP)( + clip_model="EVA02-CLIP-bigE-14-plus", + cache_dir="models/QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt", + dtype="float16", +) +model.model_vision.embed_dim_language = 1024 + +model.model_vision.neck = L(ChannelMapper)( + input_shapes={ + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), + }, + in_features=["p2", "p3", "p4", "p5", "p6"], + out_channels=256, + num_outs=5, + kernel_size=1, + norm_layer=L(nn.GroupNorm)(num_groups=32, num_channels=256), +) + +model.model_vision.mask_in_features = ["p2"] +model.model_vision.input_shapes = { + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), +} + +model.model_vision.transformer.encoder.num_layers = 6 +model.model_vision.transformer.decoder.num_layers = 6 +model.model_vision.transformer.encoder.embed_dim = 256 +model.model_vision.transformer.decoder.embed_dim = 256 +model.model_vision.embed_dim = 256 +model.model_vision.backbone.out_channels = 256 + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) +model.model_vision.transformer.encoder.use_act_checkpoint = True + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +model.model_vision.num_classes = 1256 +model.model_vision.select_box_nums_for_evaluation = 300 + +criterion = model.model_vision.criterion[0] +del criterion.use_fed_loss +del criterion.get_fed_loss_cls_weights +del criterion.fed_loss_num_classes +model.model_vision.criterion = [criterion for _ in range(10)] +for criterion, num_classes in zip( + model.model_vision.criterion, [1256, 365, 601, 256, 1, 256, 256, 256, 256, 256] +): + criterion.num_classes = num_classes + +dataloader.train.mapper.max_num_phrase = 128 +dataloader.train.mapper.nms_thresh_phrase = 0.6 + +model.model_vision.criterion[0].use_fed_loss = True +model.model_vision.criterion[0].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train.dataset.names[0], 0.5 +) +model.model_vision.criterion[0].fed_loss_num_classes = 50 +model.model_vision.criterion[0].fed_loss_pad_type = "cat" + +model.model_vision.criterion[2].use_fed_loss = True +model.model_vision.criterion[2].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train.dataset.names[2], 0.5 +) +model.model_vision.criterion[2].fed_loss_num_classes = 50 +model.model_vision.criterion[2].fed_loss_pad_type = "cat" + +model.model_vision.criterion[3].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[3].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[3].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[3].weight_dict.update({k: 0.0}) + +for k, v in model.model_vision.criterion[4].weight_dict.items(): + if "_class" in k and "_enc" not in k: + model.model_vision.criterion[4].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[5].weight_dict["loss_class_enc"] = 0.0 + +model.model_vision.criterion[6].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[6].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[6].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[6].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[7].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[7].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[7].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[7].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[8].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[8].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[8].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[8].weight_dict.update({k: 0.0}) + +model.model_vision.stuff_dataset_learn_thing = False +model.model_vision.stuff_prob_thing = 0.9 +model.model_vision.transformer.proposal_ambiguous = 1 + +model.model_vision.instance_on = True +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = False + +train.max_iter = 270000 +train.eval_period = 270000 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1], + milestones=[225000], + num_updates=270000, + ), + warmup_length=2000 / 270000, + warmup_method="linear", + warmup_factor=0.001, +) + +dataloader.train.total_batch_size = 64 +dataloader.train.total_batch_size_list = [64, 64, 64, 64, 64, 64, 64, 64, 64] +dataloader.train.num_workers = 2 +train.iter_size = 1 + +dataloader.wait_group = 2 +dataloader.wait_time = 30 * 60 + +model.model_vision.dataset_prompts = [ + "name", + "name", + "name", + "phrase", + "name", + "phrase", + "phrase", + "phrase", + "phrase", + "expression", +] +model.model_vision.dataset_names = [ + "lvis+stuffonly", + "objects365", + "openimages", + "vgregion", + "sa1b", + "refcoco-mixed_group-by-image", + "gqa", + "phrasecut", + "flickr30k", + "refcoco", +] +model.model_vision.dataset_metas = dataloader.train.dataset.names + ["refcoco-mixed"] + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1536_cp_08x8x270k.py b/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1536_cp_08x8x270k.py new file mode 100644 index 0000000..fdd05d6 --- /dev/null +++ b/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1536_cp_08x8x270k.py @@ -0,0 +1,225 @@ +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detectron2.solver import WarmupParamScheduler +from detrex.modeling.neck import ChannelMapper +from fvcore.common.param_scheduler import MultiStepParamScheduler + +from ape.data.detection_utils import get_fed_loss_cls_weights +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) +from ape.modeling.text import EVA02CLIP + +from ...common.backbone.vitl_eva02_clip_1536 import backbone +from ...common.data.lviscocococostuff_o365_oid_vgr_sa1b_refcoco_group_by_image_gqa_phrasecut_flickr30k_panoptic_lsj1536_cp import ( + dataloader, +) +from ...LVIS_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_lsj1024_cp_24ep import ( + model, + optimizer, + train, +) + +model.model_vision.backbone = backbone + +train.init_checkpoint = ( + "models/QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14to16_s6B.pt?matching_heuristics=True" +) + +model.model_language = L(EVA02CLIP)( + clip_model="EVA02-CLIP-bigE-14-plus", + cache_dir="models/QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt", + dtype="float16", +) +model.model_vision.embed_dim_language = 1024 + +model.model_vision.neck = L(ChannelMapper)( + input_shapes={ + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), + }, + in_features=["p2", "p3", "p4", "p5", "p6"], + out_channels=256, + num_outs=5, + kernel_size=1, + norm_layer=L(nn.GroupNorm)(num_groups=32, num_channels=256), +) + +model.model_vision.mask_in_features = ["p2"] +model.model_vision.input_shapes = { + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), +} + +model.model_vision.transformer.encoder.num_layers = 6 +model.model_vision.transformer.decoder.num_layers = 6 +model.model_vision.transformer.encoder.embed_dim = 256 +model.model_vision.transformer.decoder.embed_dim = 256 +model.model_vision.embed_dim = 256 +model.model_vision.backbone.out_channels = 256 + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) +model.model_vision.transformer.encoder.use_act_checkpoint = True +model.model_vision.transformer.decoder.use_act_checkpoint = True + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +model.model_vision.num_classes = 1256 +model.model_vision.select_box_nums_for_evaluation = 300 + +criterion = model.model_vision.criterion[0] +del criterion.use_fed_loss +del criterion.get_fed_loss_cls_weights +del criterion.fed_loss_num_classes +model.model_vision.criterion = [criterion for _ in range(10)] +for criterion, num_classes in zip( + model.model_vision.criterion, [1256, 365, 601, 200, 1, 200, 200, 200, 200, 200] +): + criterion.num_classes = num_classes + +dataloader.train.mapper.max_num_phrase = 100 +dataloader.train.mapper.nms_thresh_phrase = 0.6 + +model.model_vision.criterion[0].use_fed_loss = True +model.model_vision.criterion[0].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train.dataset.names[0], 0.5 +) +model.model_vision.criterion[0].fed_loss_num_classes = 50 +model.model_vision.criterion[0].fed_loss_pad_type = "cat" + +model.model_vision.criterion[2].use_fed_loss = True +model.model_vision.criterion[2].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train.dataset.names[2], 0.5 +) +model.model_vision.criterion[2].fed_loss_num_classes = 50 +model.model_vision.criterion[2].fed_loss_pad_type = "cat" + +model.model_vision.criterion[3].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[3].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[3].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[3].weight_dict.update({k: 0.0}) + +for k, v in model.model_vision.criterion[4].weight_dict.items(): + if "_class" in k and "_enc" not in k: + model.model_vision.criterion[4].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[5].weight_dict["loss_class_enc"] = 0.0 + +model.model_vision.criterion[6].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[6].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[6].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[6].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[7].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[7].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[7].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[7].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[8].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[8].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[8].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[8].weight_dict.update({k: 0.0}) + +model.model_vision.stuff_dataset_learn_thing = False +model.model_vision.stuff_prob_thing = 0.9 +model.model_vision.transformer.proposal_ambiguous = 1 + +model.model_vision.instance_on = True +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = False + +train.max_iter = 270000 * 8 +train.eval_period = 270000 * 8 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1], + milestones=[225000 * 8], + num_updates=270000 * 8, + ), + warmup_length=2000 / 270000, + warmup_method="linear", + warmup_factor=0.001, +) + +dataloader.train.total_batch_size = 8 +dataloader.train.total_batch_size_list = [8, 8, 8, 8, 8, 8, 8, 8, 8] +train.iter_size = 8 + +model.model_vision.dataset_prompts = [ + "name", + "name", + "name", + "phrase", + "name", + "phrase", + "phrase", + "phrase", + "phrase", + "expression", +] +model.model_vision.dataset_names = [ + "lvis+stuffonly", + "objects365", + "openimages", + "vgregion", + "sa1b", + "refcoco-mixed_group-by-image", + "gqa", + "phrasecut", + "flickr30k", + "refcoco", +] +model.model_vision.dataset_metas = dataloader.train.dataset.names + ["refcoco-mixed"] + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1536_cp_32x2x270k.py b/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1536_cp_32x2x270k.py new file mode 100644 index 0000000..7c5265d --- /dev/null +++ b/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1536_cp_32x2x270k.py @@ -0,0 +1,222 @@ +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detectron2.solver import WarmupParamScheduler +from detrex.modeling.neck import ChannelMapper +from fvcore.common.param_scheduler import MultiStepParamScheduler + +from ape.data.detection_utils import get_fed_loss_cls_weights +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) +from ape.modeling.text import EVA02CLIP + +from ...common.backbone.vitl_eva02_clip_1536 import backbone +from ...common.data.lviscocococostuff_o365_oid_vgr_sa1b_refcoco_group_by_image_gqa_phrasecut_flickr30k_panoptic_lsj1536_cp import ( + dataloader, +) +from ...LVIS_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_lsj1024_cp_24ep import ( + model, + optimizer, + train, +) + +model.model_vision.backbone = backbone + +train.init_checkpoint = ( + "models/QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14to16_s6B.pt?matching_heuristics=True" +) + +model.model_language = L(EVA02CLIP)( + clip_model="EVA02-CLIP-bigE-14-plus", + cache_dir="models/QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt", +) +model.model_vision.embed_dim_language = 1024 + +model.model_vision.neck = L(ChannelMapper)( + input_shapes={ + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), + }, + in_features=["p2", "p3", "p4", "p5", "p6"], + out_channels=256, + num_outs=5, + kernel_size=1, + norm_layer=L(nn.GroupNorm)(num_groups=32, num_channels=256), +) + +model.model_vision.mask_in_features = ["p2"] +model.model_vision.input_shapes = { + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), +} + +model.model_vision.transformer.encoder.num_layers = 9 +model.model_vision.transformer.decoder.num_layers = 9 +model.model_vision.transformer.encoder.embed_dim = 256 +model.model_vision.transformer.decoder.embed_dim = 256 +model.model_vision.embed_dim = 256 +model.model_vision.backbone.out_channels = 256 + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +model.model_vision.num_classes = 1256 +model.model_vision.select_box_nums_for_evaluation = 300 + +criterion = model.model_vision.criterion[0] +del criterion.use_fed_loss +del criterion.get_fed_loss_cls_weights +del criterion.fed_loss_num_classes +model.model_vision.criterion = [criterion for _ in range(10)] +for criterion, num_classes in zip( + model.model_vision.criterion, [1256, 365, 601, 200, 1, 200, 200, 200, 200, 200] +): + criterion.num_classes = num_classes + +dataloader.train.mapper.max_num_phrase = 100 +dataloader.train.mapper.nms_thresh_phrase = 0.6 + +model.model_vision.criterion[0].use_fed_loss = True +model.model_vision.criterion[0].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train.dataset.names[0], 0.5 +) +model.model_vision.criterion[0].fed_loss_num_classes = 50 +model.model_vision.criterion[0].fed_loss_pad_type = "cat" + +model.model_vision.criterion[2].use_fed_loss = True +model.model_vision.criterion[2].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train.dataset.names[2], 0.5 +) +model.model_vision.criterion[2].fed_loss_num_classes = 50 +model.model_vision.criterion[2].fed_loss_pad_type = "cat" + +model.model_vision.criterion[3].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[3].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[3].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[3].weight_dict.update({k: 0.0}) + +for k, v in model.model_vision.criterion[4].weight_dict.items(): + if "_class" in k and "_enc" not in k: + model.model_vision.criterion[4].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[5].weight_dict["loss_class_enc"] = 0.0 + +model.model_vision.criterion[6].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[6].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[6].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[6].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[7].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[7].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[7].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[7].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[8].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[8].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[8].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[8].weight_dict.update({k: 0.0}) + +model.model_vision.stuff_dataset_learn_thing = False +model.model_vision.stuff_prob_thing = 0.9 +model.model_vision.transformer.proposal_ambiguous = 1 + +model.model_vision.instance_on = True +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = False + +train.max_iter = 270000 * 2 +train.eval_period = 270000 * 2 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1], + milestones=[225000 * 2], + num_updates=270000 * 2, + ), + warmup_length=2000 / 270000, + warmup_method="linear", + warmup_factor=0.001, +) + +dataloader.train.total_batch_size = 32 +dataloader.train.total_batch_size_list = [32, 32, 32, 32, 32, 32, 32, 32, 32] +train.iter_size = 2 + +model.model_vision.dataset_prompts = [ + "name", + "name", + "name", + "phrase", + "name", + "phrase", + "phrase", + "phrase", + "phrase", + "expression", +] +model.model_vision.dataset_names = [ + "lvis+stuffonly", + "objects365", + "openimages", + "vgregion", + "sa1b", + "refcoco-mixed_group-by-image", + "gqa", + "phrasecut", + "flickr30k", + "refcoco", +] +model.model_vision.dataset_metas = dataloader.train.dataset.names + ["refcoco-mixed"] + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1536_cp_64x270k.py b/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1536_cp_64x270k.py new file mode 100644 index 0000000..49a00e1 --- /dev/null +++ b/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1536_cp_64x270k.py @@ -0,0 +1,222 @@ +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detectron2.solver import WarmupParamScheduler +from detrex.modeling.neck import ChannelMapper +from fvcore.common.param_scheduler import MultiStepParamScheduler + +from ape.data.detection_utils import get_fed_loss_cls_weights +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) +from ape.modeling.text import EVA02CLIP + +from ...common.backbone.vitl_eva02_clip_1536 import backbone +from ...common.data.lviscocococostuff_o365_oid_vgr_sa1b_refcoco_group_by_image_gqa_phrasecut_flickr30k_panoptic_lsj1536_cp import ( + dataloader, +) +from ...LVIS_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_lsj1024_cp_24ep import ( + model, + optimizer, + train, +) + +model.model_vision.backbone = backbone + +train.init_checkpoint = ( + "models/QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14to16_s6B.pt?matching_heuristics=True" +) + +model.model_language = L(EVA02CLIP)( + clip_model="EVA02-CLIP-bigE-14-plus", + cache_dir="models/QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt", +) +model.model_vision.embed_dim_language = 1024 + +model.model_vision.neck = L(ChannelMapper)( + input_shapes={ + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), + }, + in_features=["p2", "p3", "p4", "p5", "p6"], + out_channels=256, + num_outs=5, + kernel_size=1, + norm_layer=L(nn.GroupNorm)(num_groups=32, num_channels=256), +) + +model.model_vision.mask_in_features = ["p2"] +model.model_vision.input_shapes = { + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), +} + +model.model_vision.transformer.encoder.num_layers = 9 +model.model_vision.transformer.decoder.num_layers = 9 +model.model_vision.transformer.encoder.embed_dim = 256 +model.model_vision.transformer.decoder.embed_dim = 256 +model.model_vision.embed_dim = 256 +model.model_vision.backbone.out_channels = 256 + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +model.model_vision.num_classes = 1256 +model.model_vision.select_box_nums_for_evaluation = 300 + +criterion = model.model_vision.criterion[0] +del criterion.use_fed_loss +del criterion.get_fed_loss_cls_weights +del criterion.fed_loss_num_classes +model.model_vision.criterion = [criterion for _ in range(10)] +for criterion, num_classes in zip( + model.model_vision.criterion, [1256, 365, 601, 200, 1, 200, 200, 200, 200, 200] +): + criterion.num_classes = num_classes + +dataloader.train.mapper.max_num_phrase = 100 +dataloader.train.mapper.nms_thresh_phrase = 0.6 + +model.model_vision.criterion[0].use_fed_loss = True +model.model_vision.criterion[0].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train.dataset.names[0], 0.5 +) +model.model_vision.criterion[0].fed_loss_num_classes = 50 +model.model_vision.criterion[0].fed_loss_pad_type = "cat" + +model.model_vision.criterion[2].use_fed_loss = True +model.model_vision.criterion[2].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train.dataset.names[2], 0.5 +) +model.model_vision.criterion[2].fed_loss_num_classes = 50 +model.model_vision.criterion[2].fed_loss_pad_type = "cat" + +model.model_vision.criterion[3].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[3].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[3].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[3].weight_dict.update({k: 0.0}) + +for k, v in model.model_vision.criterion[4].weight_dict.items(): + if "_class" in k and "_enc" not in k: + model.model_vision.criterion[4].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[5].weight_dict["loss_class_enc"] = 0.0 + +model.model_vision.criterion[6].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[6].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[6].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[6].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[7].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[7].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[7].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[7].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[8].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[8].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[8].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[8].weight_dict.update({k: 0.0}) + +model.model_vision.stuff_dataset_learn_thing = False +model.model_vision.stuff_prob_thing = 0.9 +model.model_vision.transformer.proposal_ambiguous = 1 + +model.model_vision.instance_on = True +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = False + +train.max_iter = 270000 +train.eval_period = 270000 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1], + milestones=[225000], + num_updates=270000, + ), + warmup_length=2000 / 270000, + warmup_method="linear", + warmup_factor=0.001, +) + +dataloader.train.total_batch_size = 64 +dataloader.train.total_batch_size_list = [64, 64, 64, 64, 64, 64, 64, 64, 64] + + +model.model_vision.dataset_prompts = [ + "name", + "name", + "name", + "phrase", + "name", + "phrase", + "phrase", + "phrase", + "phrase", + "expression", +] +model.model_vision.dataset_names = [ + "lvis+stuffonly", + "objects365", + "openimages", + "vgregion", + "sa1b", + "refcoco-mixed_group-by-image", + "gqa", + "phrasecut", + "flickr30k", + "refcoco", +] +model.model_vision.dataset_metas = dataloader.train.dataset.names + ["refcoco-mixed"] + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_1080k.py b/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_1080k.py new file mode 100644 index 0000000..ff47d95 --- /dev/null +++ b/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_1080k.py @@ -0,0 +1,173 @@ +from detectron2.config import LazyCall as L +from detectron2.solver import WarmupParamScheduler +from fvcore.common.param_scheduler import MultiStepParamScheduler + +from ape.data.detection_utils import get_fed_loss_cls_weights +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) + +from ...common.data.lviscocococostuff_o365_oid_vgr_sa1b_refcoco_group_by_image_gqa_phrasecut_flickr30k_panoptic_lsj1024_cp import ( + dataloader, +) +from ...LVIS_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_lsj1024_cp_24ep import ( + model, + optimizer, + train, +) + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +model.model_vision.num_classes = 1256 +model.model_vision.select_box_nums_for_evaluation = 300 + +criterion = model.model_vision.criterion[0] +del criterion.use_fed_loss +del criterion.get_fed_loss_cls_weights +del criterion.fed_loss_num_classes +model.model_vision.criterion = [criterion for _ in range(10)] +for criterion, num_classes in zip( + model.model_vision.criterion, [1256, 365, 601, 200, 1, 200, 200, 200, 200, 200] +): + criterion.num_classes = num_classes + +dataloader.train.mapper.max_num_phrase = 100 +dataloader.train.mapper.nms_thresh_phrase = 0.6 + +model.model_vision.criterion[0].use_fed_loss = True +model.model_vision.criterion[0].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train.dataset.names[0], 0.5 +) +model.model_vision.criterion[0].fed_loss_num_classes = 50 +model.model_vision.criterion[0].fed_loss_pad_type = "cat" + +model.model_vision.criterion[2].use_fed_loss = True +model.model_vision.criterion[2].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train.dataset.names[2], 0.5 +) +model.model_vision.criterion[2].fed_loss_num_classes = 50 +model.model_vision.criterion[2].fed_loss_pad_type = "cat" + +model.model_vision.criterion[3].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[3].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[3].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[3].weight_dict.update({k: 0.0}) + +for k, v in model.model_vision.criterion[4].weight_dict.items(): + if "_class" in k and "_enc" not in k: + model.model_vision.criterion[4].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[5].weight_dict["loss_class_enc"] = 0.0 + +model.model_vision.criterion[6].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[6].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[6].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[6].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[7].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[7].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[7].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[7].weight_dict.update({k: 0.0}) + +model.model_vision.criterion[8].weight_dict["loss_class_enc"] = 0.0 +for k, v in model.model_vision.criterion[8].weight_dict.items(): + if "_enc" in k: + model.model_vision.criterion[8].weight_dict.update({k: 0.0}) + if "_bbox" in k or "_giou" in k or "_dice" in k or "_mask" in k: + model.model_vision.criterion[8].weight_dict.update({k: 0.0}) + +model.model_vision.stuff_dataset_learn_thing = False +model.model_vision.stuff_prob_thing = 0.9 +model.model_vision.transformer.proposal_ambiguous = 1 + +model.model_vision.instance_on = True +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = False + +train.max_iter = 1080000 +train.eval_period = 1080000 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1], + milestones=[900000], + num_updates=1080000, + ), + warmup_length=2000 / 1080000, + warmup_method="linear", + warmup_factor=0.001, +) + +dataloader.train.total_batch_size = 16 +dataloader.train.total_batch_size_list = [16, 16, 16, 16, 16, 16, 16, 16, 16] +dataloader.train.num_workers = 4 + +model.model_vision.dataset_prompts = [ + "name", + "name", + "name", + "phrase", + "name", + "phrase", + "phrase", + "phrase", + "phrase", + "expression", +] +model.model_vision.dataset_names = [ + "lvis+stuffonly", + "objects365", + "openimages", + "vgregion", + "sa1b", + "refcoco-mixed_group-by-image", + "gqa", + "phrasecut", + "flickr30k", + "refcoco", +] +model.model_vision.dataset_metas = dataloader.train.dataset.names + ["refcoco-mixed"] + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/LVISCOCOCOCOSTUFF_PanopticSegmentation/ape_deta/ape_deta_r50_lsj1024_cp_50ep.py b/configs/LVISCOCOCOCOSTUFF_PanopticSegmentation/ape_deta/ape_deta_r50_lsj1024_cp_50ep.py new file mode 100644 index 0000000..d28db18 --- /dev/null +++ b/configs/LVISCOCOCOCOSTUFF_PanopticSegmentation/ape_deta/ape_deta_r50_lsj1024_cp_50ep.py @@ -0,0 +1,36 @@ +from detectron2.data.detection_utils import get_fed_loss_cls_weights +from detrex.config import get_config + +from ...common.data.lviscocococostuff_panoptic_lsj1024_cp import dataloader +from ...LVIS_InstanceSegmentation.ape_deta.ape_deta_r50_24ep import ( + lr_multiplier, + model, + optimizer, + train, +) + +model.model_vision.num_classes = 1256 +model.model_vision.criterion[0].num_classes = 1256 +model.model_vision.criterion[0].use_fed_loss = True +model.model_vision.criterion[0].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train.dataset.names, 0.5 +) +model.model_vision.criterion[0].fed_loss_num_classes = 50 +model.model_vision.criterion[0].fed_loss_pad_type = "cat" + +model.model_vision.instance_on = True +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = False + +train.max_iter = 375000 +train.eval_period = 20000 + +lr_multiplier = get_config("common/coco_schedule.py").lr_multiplier_50ep + +dataloader.train.total_batch_size = 16 + +model.model_vision.dataset_prompts = ["name"] +model.model_vision.dataset_names = ["lvis+stuffonly"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/LVISCOCOCOCOSTUFF_PanopticSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024_cp_24ep.py b/configs/LVISCOCOCOCOSTUFF_PanopticSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024_cp_24ep.py new file mode 100644 index 0000000..c0b4e6e --- /dev/null +++ b/configs/LVISCOCOCOCOSTUFF_PanopticSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024_cp_24ep.py @@ -0,0 +1,20 @@ +from ...common.data.lviscocococostuff_panoptic_lsj1024_cp import dataloader +from ...LVIS_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_lsj1024_cp_24ep import ( + lr_multiplier, + model, + optimizer, + train, +) + +model.model_vision.num_classes = 1256 +model.model_vision.criterion[0].num_classes = 1256 + +model.model_vision.instance_on = True +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = False + +model.model_vision.dataset_prompts = ["name"] +model.model_vision.dataset_names = ["lvis+stuffonly"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/LVISCOCOCOCOSTUFF_REFCOCO/ape_deta/ape_deta_r50_lsj1024_50ep.py b/configs/LVISCOCOCOCOSTUFF_REFCOCO/ape_deta/ape_deta_r50_lsj1024_50ep.py new file mode 100644 index 0000000..a6367bb --- /dev/null +++ b/configs/LVISCOCOCOCOSTUFF_REFCOCO/ape_deta/ape_deta_r50_lsj1024_50ep.py @@ -0,0 +1,55 @@ +from detectron2.data.detection_utils import get_fed_loss_cls_weights +from detrex.config import get_config + +from ...common.data.lviscocococostuff_refcoco_panoptic_lsj1024 import dataloader +from ...LVIS_InstanceSegmentation.ape_deta.ape_deta_r50_24ep import ( + lr_multiplier, + model, + optimizer, + train, +) + +model.model_vision.num_classes = 1256 +model.model_vision.select_box_nums_for_evaluation = 300 + +criterion = model.model_vision.criterion[0] +del criterion.use_fed_loss +del criterion.get_fed_loss_cls_weights +model.model_vision.criterion = [criterion for _ in range(2)] +for criterion, num_classes in zip(model.model_vision.criterion, [1256, 200]): + criterion.num_classes = num_classes + +model.model_vision.criterion[1].weight_dict["loss_class_enc"] = 0.0 + +model.model_vision.instance_on = True +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = False + +model.model_vision.criterion[0].use_fed_loss = True +model.model_vision.criterion[0].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train.dataset.names[0], 0.5 +) +model.model_vision.criterion[0].fed_loss_num_classes = 50 +model.model_vision.criterion[0].fed_loss_pad_type = "cat" + +model.model_vision.stuff_dataset_learn_thing = False +model.model_vision.stuff_prob_thing = 0.9 + +train.max_iter = 375000 +train.eval_period = 20000 + +lr_multiplier = get_config("common/coco_schedule.py").lr_multiplier_50ep + +dataloader.train.total_batch_size = 16 +dataloader.train.total_batch_size_list = [16, 16] + +model.model_vision.dataset_prompts = ["name", "expression"] +model.model_vision.dataset_names = ["lvis+stuff", "refcoco"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/LVISCOCOCOCOSTUFF_REFCOCO/ape_deta/ape_deta_r50_lsj1024_cp_50ep.py b/configs/LVISCOCOCOCOSTUFF_REFCOCO/ape_deta/ape_deta_r50_lsj1024_cp_50ep.py new file mode 100644 index 0000000..8534d5c --- /dev/null +++ b/configs/LVISCOCOCOCOSTUFF_REFCOCO/ape_deta/ape_deta_r50_lsj1024_cp_50ep.py @@ -0,0 +1,4 @@ +from ...common.data.lviscocococostuff_refcoco_panoptic_lsj1024_cp import dataloader +from .ape_deta_r50_lsj1024_50ep import dataloader, lr_multiplier, model, optimizer, train + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/LVISCOCOCOCOSTUFF_REFCOCO/ape_deta/ape_deta_r50_vlf_lsj1024_cp_50ep.py b/configs/LVISCOCOCOCOSTUFF_REFCOCO/ape_deta/ape_deta_r50_vlf_lsj1024_cp_50ep.py new file mode 100644 index 0000000..2fdec50 --- /dev/null +++ b/configs/LVISCOCOCOCOSTUFF_REFCOCO/ape_deta/ape_deta_r50_vlf_lsj1024_cp_50ep.py @@ -0,0 +1,48 @@ +from detectron2.config import LazyCall as L +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) + +from .ape_deta_r50_lsj1024_cp_50ep import dataloader, lr_multiplier, model, optimizer, train + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=False, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=False, +) + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 5120 diff --git a/configs/LVISCOCOCOCOSTUFF_REFCOCO/ape_deta/ape_deta_r50_vlf_lsj1024_cp_bert_50ep.py b/configs/LVISCOCOCOCOSTUFF_REFCOCO/ape_deta/ape_deta_r50_vlf_lsj1024_cp_bert_50ep.py new file mode 100644 index 0000000..b500c92 --- /dev/null +++ b/configs/LVISCOCOCOCOSTUFF_REFCOCO/ape_deta/ape_deta_r50_vlf_lsj1024_cp_bert_50ep.py @@ -0,0 +1,21 @@ +from detectron2.config import LazyCall as L +from ape.modeling.text import Bert + +from .ape_deta_r50_vlf_lsj1024_cp_50ep import dataloader, lr_multiplier, model, optimizer, train + +model.model_vision.criterion[1].num_classes = 1 + +model.model_language = L(Bert)( + pretrained_model_name_or_path="models/huggingface/bert-base-uncased/" +) +model.model_vision.embed_dim_language = 768 +model.model_vision.text_feature_reduce_type = "average" + +model.model_vision.text_feature_bank = False +model.model_vision.text_feature_reduce_before_fusion = False +model.model_vision.text_feature_batch_repeat = False +model.model_vision.expression_cumulative_gt_class = False +model.model_vision.name_prompt_fusion_type = "none" + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 5120 diff --git a/configs/LVISCOCOCOCOSTUFF_REFCOCO/ape_deta/ape_deta_vitl_eva02_lsj1024_24ep.py b/configs/LVISCOCOCOCOSTUFF_REFCOCO/ape_deta/ape_deta_vitl_eva02_lsj1024_24ep.py new file mode 100644 index 0000000..a268009 --- /dev/null +++ b/configs/LVISCOCOCOCOSTUFF_REFCOCO/ape_deta/ape_deta_vitl_eva02_lsj1024_24ep.py @@ -0,0 +1,43 @@ +from detectron2.data.detection_utils import get_fed_loss_cls_weights + +from ...common.data.lviscocococostuff_refcoco_panoptic_lsj1024 import dataloader +from ...LVIS_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_lsj1024_cp_24ep import ( + lr_multiplier, + model, + optimizer, + train, +) + +model.model_vision.num_classes = 1256 +model.model_vision.select_box_nums_for_evaluation = 300 + +criterion = model.model_vision.criterion[0] +del criterion.use_fed_loss +del criterion.get_fed_loss_cls_weights +model.model_vision.criterion = [criterion for _ in range(2)] +for criterion, num_classes in zip(model.model_vision.criterion, [1256, 200]): + criterion.num_classes = num_classes + +model.model_vision.criterion[1].weight_dict["loss_class_enc"] = 0.0 + +model.model_vision.instance_on = True +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = False + +model.model_vision.criterion[0].use_fed_loss = True +model.model_vision.criterion[0].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train.dataset.names[0], 0.5 +) +model.model_vision.criterion[0].fed_loss_num_classes = 50 +model.model_vision.criterion[0].fed_loss_pad_type = "cat" + +model.model_vision.neck = None + +dataloader.train.total_batch_size = 16 +dataloader.train.total_batch_size_list = [16, 16] + +model.model_vision.dataset_prompts = ["name", "expression"] +model.model_vision.dataset_names = ["lvis+stuff", "refcoco"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/LVISCOCOCOCOSTUFF_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_50ep.py b/configs/LVISCOCOCOCOSTUFF_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_50ep.py new file mode 100644 index 0000000..445b047 --- /dev/null +++ b/configs/LVISCOCOCOCOSTUFF_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_50ep.py @@ -0,0 +1,53 @@ +from detectron2.config import LazyCall as L +from detrex.config import get_config +from omegaconf import OmegaConf +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) + +from .ape_deta_vitl_eva02_lsj1024_24ep import dataloader, lr_multiplier, model, optimizer, train + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) + +model.model_vision.criterion[1].num_classes = 200 +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True + +train.max_iter = 375000 +train.eval_period = 20000 + +lr_multiplier = get_config("common/coco_schedule.py").lr_multiplier_50ep + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/LVISCOCO_COCOSTUFF_O365_OID_VG_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_180k.py b/configs/LVISCOCO_COCOSTUFF_O365_OID_VG_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_180k.py new file mode 100644 index 0000000..af32118 --- /dev/null +++ b/configs/LVISCOCO_COCOSTUFF_O365_OID_VG_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_180k.py @@ -0,0 +1,27 @@ +from detectron2.config import LazyCall as L +from detectron2.solver import WarmupParamScheduler +from fvcore.common.param_scheduler import MultiStepParamScheduler + +from .ape_deta_vitl_eva02_vlf_lsj1024_cp_720k import ( + dataloader, + lr_multiplier, + model, + optimizer, + train, +) + +train.max_iter = 180000 +train.eval_period = 180000 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1], + milestones=[150000], + num_updates=180000, + ), + warmup_length=1000 / 180000, + warmup_method="linear", + warmup_factor=0.001, +) + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/LVISCOCO_COCOSTUFF_O365_OID_VG_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_720k.py b/configs/LVISCOCO_COCOSTUFF_O365_OID_VG_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_720k.py new file mode 100644 index 0000000..1405c87 --- /dev/null +++ b/configs/LVISCOCO_COCOSTUFF_O365_OID_VG_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_720k.py @@ -0,0 +1,120 @@ +import random + +from detectron2.config import LazyCall as L +from detectron2.solver import WarmupParamScheduler +from fvcore.common.param_scheduler import MultiStepParamScheduler + +from ape.data.detection_utils import get_fed_loss_cls_weights +from ape.data.samplers import MultiDatasetTrainingSampler +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) + +from ...common.data.lviscoco_cocostuff_o365_oid_vg_refcoco_panoptic_lsj1024_cp import dataloader +from ...LVIS_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_lsj1024_cp_24ep import ( + lr_multiplier, + model, + optimizer, + train, +) + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=False, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) + +model.model_vision.text_feature_bank = True + +model.model_vision.num_classes = 1203 +model.model_vision.select_box_nums_for_evaluation = 300 + +criterion = model.model_vision.criterion[0] +del criterion.use_fed_loss +del criterion.get_fed_loss_cls_weights +model.model_vision.criterion = [criterion for _ in range(6)] +for criterion, num_classes in zip(model.model_vision.criterion, [1203, 54, 365, 601, 150, 200]): + criterion.num_classes = num_classes + +model.model_vision.criterion[0].use_fed_loss = True +model.model_vision.criterion[0].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train.dataset.names[0], 0.5 +) +model.model_vision.criterion[0].fed_loss_num_classes = 50 + +model.model_vision.criterion[5].weight_dict["loss_class_enc"] = 0.0 + + +model.model_vision.instance_on = True +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = False + +model.model_vision.neck = None + +train.max_iter = 720000 +train.eval_period = 720000 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1], + milestones=[640000], + num_updates=720000, + ), + warmup_length=1000 / 720000, + warmup_method="linear", + warmup_factor=0.001, +) + +dataloader.train.total_batch_size = 16 +dataloader.train.total_batch_size_list = [16, 16, 16, 16, 16] + +model.model_vision.dataset_prompts = ["name", "name", "name", "name", "name", "expression"] +model.model_vision.dataset_names = [ + "lvis", + "stuffonly", + "objects365", + "openimages", + "visualgenome", + "refcoco", +] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +train.output_dir = "output/" + __file__[:-3] + +dataloader.train.sampler = lambda dataset_dicts: MultiDatasetTrainingSampler( + repeat_factors=MultiDatasetTrainingSampler.get_repeat_factors( + dataset_dicts=dataset_dicts, + num_datasets=6, + dataset_ratio=[1, 1, 1, 1, 1, 0], + use_rfs=[True, False, True, True, True, True], + use_cas=[False, False, False, False, False, False], + repeat_thresh=0.001, + cas_lambda=1.0, + ), + seed=random.randint(0, 2**31), +) diff --git a/configs/LVISCOCO_COCOSTUFF_PanopticSegmentation/ape_deta/ape_deta_r50_lsj1024_cp_50ep.py b/configs/LVISCOCO_COCOSTUFF_PanopticSegmentation/ape_deta/ape_deta_r50_lsj1024_cp_50ep.py new file mode 100644 index 0000000..27a53cf --- /dev/null +++ b/configs/LVISCOCO_COCOSTUFF_PanopticSegmentation/ape_deta/ape_deta_r50_lsj1024_cp_50ep.py @@ -0,0 +1,47 @@ +from detectron2.data.detection_utils import get_fed_loss_cls_weights +from detrex.config import get_config + +from ...common.data.lviscoco_cocostuff_panoptic_lsj1024_cp import dataloader +from ...LVIS_InstanceSegmentation.ape_deta.ape_deta_r50_24ep import ( + lr_multiplier, + model, + optimizer, + train, +) + +model.model_vision.num_classes = 1203 +model.model_vision.select_box_nums_for_evaluation = 300 + +criterion = model.model_vision.criterion[0] +criterion.use_fed_loss = False +criterion.get_fed_loss_cls_weights = None +model.model_vision.criterion = [criterion for _ in range(2)] +for criterion, num_classes in zip(model.model_vision.criterion, [1203, 54]): + criterion.num_classes = num_classes + + +model.model_vision.stuff_dataset_learn_thing = False + +model.model_vision.instance_on = True +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = False + +model.model_vision.criterion[0].use_fed_loss = True +model.model_vision.criterion[0].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train.dataset.names[0], 0.5 +) +model.model_vision.criterion[0].fed_loss_num_classes = 50 + +train.max_iter = 375000 +train.eval_period = 20000 + +lr_multiplier = get_config("common/coco_schedule.py").lr_multiplier_50ep + +dataloader.train.total_batch_size = 16 +dataloader.train.total_batch_size_list = [16, 16] + +model.model_vision.dataset_prompts = ["name", "name"] +model.model_vision.dataset_names = ["lvis", "stuff"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/LVIS_Detection/deformable_deta/deformable_deta_r50_lsj1024_24ep.py b/configs/LVIS_Detection/deformable_deta/deformable_deta_r50_lsj1024_24ep.py new file mode 100644 index 0000000..389213c --- /dev/null +++ b/configs/LVIS_Detection/deformable_deta/deformable_deta_r50_lsj1024_24ep.py @@ -0,0 +1,47 @@ +from detectron2.config import LazyCall as L +from detectron2.data.detection_utils import get_fed_loss_cls_weights +from detectron2.data.samplers import RepeatFactorTrainingSampler +from detectron2.evaluation.lvis_evaluation import LVISEvaluator +from detrex.config import get_config + +from .....detectron2.projects.ViTDet.configs.common.coco_loader_lsj import dataloader +from ...COCO_Detection.deformable_deta.deformable_deta_r50_two_stage_12ep import ( + lr_multiplier, + model, + optimizer, + train, +) + +dataloader.train.mapper.image_format = "BGR" + +dataloader.train.total_batch_size = 16 + +dataloader.train.dataset.names = "lvis_v1_train" +dataloader.train.sampler = L(RepeatFactorTrainingSampler)( + repeat_factors=L(RepeatFactorTrainingSampler.repeat_factors_from_category_frequency)( + dataset_dicts="${dataloader.train.dataset}", repeat_thresh=0.001 + ) +) +dataloader.test.dataset.names = "lvis_v1_val" +dataloader.evaluator = L(LVISEvaluator)( + dataset_name="${..test.dataset.names}", + max_dets_per_image=300, +) + +model.num_classes = 1203 +model.criterion.num_classes = 1203 +model.select_box_nums_for_evaluation = 300 +model.criterion.use_fed_loss = True +model.criterion.get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train.dataset.names, 0.5 +) +model.criterion.fed_loss_num_classes = 50 + +train.max_iter = 180000 +train.eval_period = 20000 + +lr_multiplier.scheduler.milestones = [150000, 180000] +lr_multiplier.warmup_length = 250 / train.max_iter + +train.output_dir = "output/" + __file__[:-3] +dataloader.evaluator.output_dir = train.output_dir diff --git a/configs/LVIS_Detection/deformable_deta/deformable_deta_vitb_lsj1024_24ep.py b/configs/LVIS_Detection/deformable_deta/deformable_deta_vitb_lsj1024_24ep.py new file mode 100644 index 0000000..70626c7 --- /dev/null +++ b/configs/LVIS_Detection/deformable_deta/deformable_deta_vitb_lsj1024_24ep.py @@ -0,0 +1,83 @@ +from functools import partial + +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.modeling import SimpleFeaturePyramid, ViT +from detectron2.modeling.backbone.fpn import LastLevelMaxPool +from detectron2.modeling.backbone.vit import get_vit_lr_decay_rate + +from .....detectron2.configs.common.data.constants import constants +from .deformable_deta_r50_two_stage_lsj1024_24ep import ( + dataloader, + lr_multiplier, + model, + optimizer, + train, +) + +model.pixel_mean = constants.imagenet_rgb256_mean +model.pixel_std = constants.imagenet_rgb256_std +model.input_format = "RGB" +dataloader.train.mapper.image_format = "RGB" + + +embed_dim, depth, num_heads, dp = 768, 12, 12, 0.1 +model.backbone = L(SimpleFeaturePyramid)( + net=L(ViT)( # Single-scale ViT backbone + img_size=1024, + patch_size=16, + embed_dim=embed_dim, + depth=depth, + num_heads=num_heads, + drop_path_rate=dp, + window_size=14, + mlp_ratio=4, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + window_block_indexes=[ + 0, + 1, + 3, + 4, + 6, + 7, + 9, + 10, + ], + residual_block_indexes=[], + use_rel_pos=True, + out_feature="last_feat", + ), + in_feature="${.net.out_feature}", + out_channels=256, + scale_factors=(4.0, 2.0, 1.0, 0.5), + top_block=L(LastLevelMaxPool)(), + norm="LN", + square_pad=1024, +) + +model.neck = None + +optimizer.params.lr_factor_func = ( + lambda module_name: 0.1 + if "reference_points" in module_name or "sampling_offsets" in module_name + else get_vit_lr_decay_rate(module_name, lr_decay_rate=0.7, num_layers=12) + if "backbone" in module_name + else 1 +) +optimizer.params.overrides = {"pos_embed": {"weight_decay": 0.0}} + + +lr_multiplier.warmup_length = 1000 / train.max_iter + +train.amp.enabled = False +train.ddp.fp16_compression = False + +train.init_checkpoint = ( + "detectron2://ImageNetPretrained/MAE/mae_pretrain_vit_base.pth?matching_heuristics=True" +) +train.init_checkpoint = "models/MAE/mae_pretrain_vit_base.pth?matching_heuristics=True" + +train.output_dir = "output/" + __file__[:-3] +dataloader.evaluator.output_dir = train.output_dir diff --git a/configs/LVIS_Detection/deformable_deta/deformable_deta_vitg_eva_lsj1024_24ep.py b/configs/LVIS_Detection/deformable_deta/deformable_deta_vitg_eva_lsj1024_24ep.py new file mode 100644 index 0000000..7b4d853 --- /dev/null +++ b/configs/LVIS_Detection/deformable_deta/deformable_deta_vitg_eva_lsj1024_24ep.py @@ -0,0 +1,65 @@ +from ape.modeling.backbone.vit_eva import SimpleFeaturePyramid, ViT, get_vit_lr_decay_rate + +from .deformable_deta_vitb_two_stage_lsj1024_24ep import ( + dataloader, + lr_multiplier, + model, + optimizer, + train, +) + +model.backbone.update( + _target_=SimpleFeaturePyramid, +) +model.backbone.net.update( + _target_=ViT, +) + +dataloader.train.total_batch_size = 16 + +model.backbone.net.beit_like_qkv_bias = True +model.backbone.net.beit_like_gamma = False +model.backbone.net.freeze_patch_embed = True +model.backbone.square_pad = 1280 +model.backbone.net.img_size = 1280 +model.backbone.net.patch_size = 16 +model.backbone.net.window_size = 16 +model.backbone.net.embed_dim = 1408 +model.backbone.net.depth = 40 +model.backbone.net.num_heads = 16 +model.backbone.net.mlp_ratio = 6144 / 1408 +model.backbone.net.use_act_checkpoint = True +model.backbone.net.drop_path_rate = 0.6 # 0.5 --> 0.6 +model.backbone.net.window_block_indexes = ( + list(range(0, 3)) + + list(range(4, 7)) + + list(range(8, 11)) + + list(range(12, 15)) + + list(range(16, 19)) + + list(range(20, 23)) + + list(range(24, 27)) + + list(range(28, 31)) + + list(range(32, 35)) + + list(range(36, 39)) +) + +optimizer.lr = 2e-4 +optimizer.params.lr_factor_func = ( + lambda module_name: 0.1 + if "reference_points" in module_name or "sampling_offsets" in module_name + else get_vit_lr_decay_rate(module_name, lr_decay_rate=0.9, num_layers=40) + if "backbone" in module_name + else 1 +) +optimizer.params.overrides = {"pos_embed": {"weight_decay": 0.0}} +optimizer.params.weight_decay_norm = None + +train.amp.enabled = False +train.ddp.fp16_compression = False + +model.backbone.net.use_act_checkpoint = False +model.backbone.net.frozen_stages = 41 + +train.init_checkpoint = "models/BAAI/EVA/eva_o365.pth" +train.output_dir = "output/" + __file__[:-3] +dataloader.evaluator.output_dir = train.output_dir diff --git a/configs/LVIS_Detection/deformable_deta/deformable_deta_vitg_eva_lsj1024_cp_24ep.py b/configs/LVIS_Detection/deformable_deta/deformable_deta_vitg_eva_lsj1024_cp_24ep.py new file mode 100644 index 0000000..cb98a38 --- /dev/null +++ b/configs/LVIS_Detection/deformable_deta/deformable_deta_vitg_eva_lsj1024_cp_24ep.py @@ -0,0 +1,14 @@ +from ....configs.common.data.lvis_lsj1024_cp import dataloader +from .deformable_deta_vitg_eva_two_stage_lsj1024_24ep import lr_multiplier, model, optimizer, train + +train.amp.enabled = True +train.ddp.fp16_compression = True + +model.backbone.net.use_act_checkpoint = True +model.backbone.net.frozen_stages = 25 + +train.output_dir = "output/" + __file__[:-3] +dataloader.evaluator.output_dir = train.output_dir +dataloader.train.mapper.output_dir = train.output_dir + +optimizer.lr = 1e-4 diff --git a/configs/LVIS_Detection/deformable_deta/deformable_deta_vitl_eva02_lsj1024_cp_24ep.py b/configs/LVIS_Detection/deformable_deta/deformable_deta_vitl_eva02_lsj1024_cp_24ep.py new file mode 100644 index 0000000..28ae87a --- /dev/null +++ b/configs/LVIS_Detection/deformable_deta/deformable_deta_vitl_eva02_lsj1024_cp_24ep.py @@ -0,0 +1,110 @@ +from functools import partial + +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.data.catalog import MetadataCatalog +from detectron2.data.detection_utils import get_fed_loss_cls_weights +from detectron2.layers import ShapeSpec +from detectron2.modeling.backbone.fpn import LastLevelMaxPool +from detrex.config import get_config +from ape.modeling.backbone.vit_eva02 import SimpleFeaturePyramid, ViT, get_vit_lr_decay_rate + +from .....detectron2.configs.common.data.constants import constants +from ...COCO_Detection.deformable_deta.models.deformable_deta_r50 import model +from ...common.data.lvis_instance_lsj1024_cp import dataloader + +model.pixel_mean = constants.imagenet_rgb256_mean +model.pixel_std = constants.imagenet_rgb256_std +model.input_format = "RGB" + +model.num_classes = 1203 +model.criterion.num_classes = 1203 +model.select_box_nums_for_evaluation = 300 +model.criterion.use_fed_loss = True +model.criterion.get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train.dataset.names, 0.5 +) +model.criterion.fed_loss_num_classes = 50 + +model.backbone = L(SimpleFeaturePyramid)( + net=L(ViT)( # Single-scale ViT backbone + img_size=1024, + patch_size=16, + embed_dim=1024, + depth=24, + num_heads=16, + drop_path_rate=0.4, + window_size=16, + mlp_ratio=4 * 2 / 3, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + window_block_indexes=list(range(0, 5)) + + list(range(6, 11)) + + list(range(12, 17)) + + list(range(18, 23)), + residual_block_indexes=[], + use_rel_pos=True, + out_feature="last_feat", + use_act_checkpoint=False, + xattn=True, + ), + in_feature="${.net.out_feature}", + out_channels=256, + scale_factors=(4.0, 2.0, 1.0, 0.5), + top_block=L(LastLevelMaxPool)(), + norm="LN", + square_pad=1024, +) + +model.neck = None + +optimizer = get_config("common/optim.py").AdamW +optimizer.params.lr_factor_func = ( + lambda module_name: 0.1 + if "reference_points" in module_name or "sampling_offsets" in module_name + else get_vit_lr_decay_rate(module_name, lr_decay_rate=0.8, num_layers=24) + if "backbone" in module_name + else 1 +) +optimizer.params.overrides = {"pos_embed": {"weight_decay": 0.0}} + +optimizer.lr = 2e-4 +optimizer.weight_decay = 0.05 + +train = get_config("common/train.py").train +train.max_iter = 180000 +train.eval_period = 20000 +train.log_period = 20 + +train.checkpointer.period = 5000 +train.checkpointer.max_to_keep = 2 + +train.clip_grad.enabled = True +train.clip_grad.params.max_norm = 0.1 +train.clip_grad.params.norm_type = 2 + +train.device = "cuda" + +train.init_checkpoint = ( + "models/Yunxin-CV/EVA-02/eva02/pt/eva02_L_pt_in21k_p14to16.pt?matching_heuristics=True" +) + +train.amp.enabled = True +train.ddp.fp16_compression = True + +lr_multiplier = get_config("common/coco_schedule.py").lr_multiplier_12ep +lr_multiplier.scheduler.milestones = [150000, 180000] +lr_multiplier.warmup_length = 1000 / train.max_iter + +dataloader.train.num_workers = 16 +dataloader.train.total_batch_size = 16 +dataloader.train.mapper.image_format = "RGB" + +if isinstance(dataloader.train.dataset.names, str): + model.metadata = MetadataCatalog.get(dataloader.train.dataset.names) +else: + model.metadata = MetadataCatalog.get(dataloader.train.dataset.names[0]) + +train.output_dir = "output/" + __file__[:-3] +dataloader.train.mapper.output_dir = train.output_dir diff --git a/configs/LVIS_Detection/deformable_deta/deformable_deta_vitl_eva_lsj1024_cp_24ep.py b/configs/LVIS_Detection/deformable_deta/deformable_deta_vitl_eva_lsj1024_cp_24ep.py new file mode 100644 index 0000000..0483ead --- /dev/null +++ b/configs/LVIS_Detection/deformable_deta/deformable_deta_vitl_eva_lsj1024_cp_24ep.py @@ -0,0 +1,13 @@ +from ...common.data.lvis_lsj1024_cp import dataloader +from .deformable_deta_vitl_two_stage_lsj1024_24ep import lr_multiplier, model, optimizer, train + +train.init_checkpoint = "models/BAAI/EVA/eva_l_psz14to16.pt?matching_heuristics=True" + +train.amp.enabled = True +train.ddp.fp16_compression = True + +model.backbone.net.use_act_checkpoint = False + +train.output_dir = "output/" + __file__[:-3] +dataloader.evaluator.output_dir = train.output_dir +dataloader.train.mapper.output_dir = train.output_dir diff --git a/configs/LVIS_Detection/deformable_deta/deformable_deta_vitl_lsj1024_24ep.py b/configs/LVIS_Detection/deformable_deta/deformable_deta_vitl_lsj1024_24ep.py new file mode 100644 index 0000000..886ea45 --- /dev/null +++ b/configs/LVIS_Detection/deformable_deta/deformable_deta_vitl_lsj1024_24ep.py @@ -0,0 +1,41 @@ +from detectron2.modeling.backbone.vit import get_vit_lr_decay_rate + +from .deformable_deta_vitb_two_stage_lsj1024_24ep import ( + dataloader, + lr_multiplier, + model, + optimizer, + train, +) + +model.backbone.net.embed_dim = 1024 +model.backbone.net.depth = 24 +model.backbone.net.num_heads = 16 +model.backbone.net.use_act_checkpoint = False +model.backbone.net.drop_path_rate = 0.4 +model.backbone.net.window_block_indexes = ( + list(range(0, 5)) + list(range(6, 11)) + list(range(12, 17)) + list(range(18, 23)) +) + +optimizer.params.lr_factor_func = ( + lambda module_name: 0.1 + if "reference_points" in module_name or "sampling_offsets" in module_name + else get_vit_lr_decay_rate(module_name, lr_decay_rate=0.8, num_layers=24) + if "backbone" in module_name + else 1 +) +optimizer.params.overrides = {"pos_embed": {"weight_decay": 0.0}} + +optimizer.lr = 2e-4 +optimizer.weight_decay = 0.05 + +train.init_checkpoint = ( + "detectron2://ImageNetPretrained/MAE/mae_pretrain_vit_large.pth?matching_heuristics=True" +) +train.init_checkpoint = "models/MAE/mae_pretrain_vit_large.pth?matching_heuristics=True" + +train.amp.enabled = True +train.ddp.fp16_compression = True + +train.output_dir = "output/" + __file__[:-3] +dataloader.evaluator.output_dir = train.output_dir diff --git a/configs/LVIS_InstanceSegmentation/ape_deta/ape_deta_r50_24ep.py b/configs/LVIS_InstanceSegmentation/ape_deta/ape_deta_r50_24ep.py new file mode 100644 index 0000000..7d77bb4 --- /dev/null +++ b/configs/LVIS_InstanceSegmentation/ape_deta/ape_deta_r50_24ep.py @@ -0,0 +1,45 @@ +from detectron2.config import LazyCall as L +from detectron2.data.detection_utils import get_fed_loss_cls_weights +from detectron2.data.samplers import RepeatFactorTrainingSampler +from detectron2.evaluation.lvis_evaluation import LVISEvaluator + +from ...COCO_InstanceSegmentation.ape_deta.ape_deta_r50_12ep import ( + dataloader, + lr_multiplier, + model, + optimizer, + train, +) + +dataloader.train.dataset.names = "lvis_v1_train" +dataloader.train.sampler = L(RepeatFactorTrainingSampler)( + repeat_factors=L(RepeatFactorTrainingSampler.repeat_factors_from_category_frequency)( + dataset_dicts="${dataloader.train.dataset}", repeat_thresh=0.001 + ) +) +dataloader.test.dataset.names = "lvis_v1_val" +dataloader.evaluator = L(LVISEvaluator)( + dataset_name="${..test.dataset.names}", + max_dets_per_image=300, +) + +model.model_vision.num_classes = 1203 +model.model_vision.select_box_nums_for_evaluation = 300 +model.model_vision.criterion[0].num_classes = 1203 +model.model_vision.criterion[0].use_fed_loss = True +model.model_vision.criterion[0].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train.dataset.names, 0.5 +) +model.model_vision.criterion[0].fed_loss_num_classes = 50 + +train.max_iter = 180000 +train.eval_period = 20000 + +lr_multiplier.scheduler.milestones = [150000, 180000] +lr_multiplier.warmup_length = 250 / train.max_iter + +model.model_vision.dataset_prompts = ["name"] +model.model_vision.dataset_names = ["lvis"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/LVIS_InstanceSegmentation/ape_deta/ape_deta_r50_vlf_24ep.py b/configs/LVIS_InstanceSegmentation/ape_deta/ape_deta_r50_vlf_24ep.py new file mode 100644 index 0000000..08d76e0 --- /dev/null +++ b/configs/LVIS_InstanceSegmentation/ape_deta/ape_deta_r50_vlf_24ep.py @@ -0,0 +1,45 @@ +from detectron2.config import LazyCall as L +from detectron2.data.detection_utils import get_fed_loss_cls_weights +from detectron2.data.samplers import RepeatFactorTrainingSampler +from detectron2.evaluation.lvis_evaluation import LVISEvaluator + +from ...COCO_InstanceSegmentation.ape_deta.ape_deta_r50_vlf_12ep import ( + dataloader, + lr_multiplier, + model, + optimizer, + train, +) + +dataloader.train.dataset.names = "lvis_v1_train" +dataloader.train.sampler = L(RepeatFactorTrainingSampler)( + repeat_factors=L(RepeatFactorTrainingSampler.repeat_factors_from_category_frequency)( + dataset_dicts="${dataloader.train.dataset}", repeat_thresh=0.001 + ) +) +dataloader.test.dataset.names = "lvis_v1_val" +dataloader.evaluator = L(LVISEvaluator)( + dataset_name="${..test.dataset.names}", + max_dets_per_image=300, +) + +model.model_vision.num_classes = 1203 +model.model_vision.select_box_nums_for_evaluation = 300 +model.model_vision.criterion[0].num_classes = 1203 +model.model_vision.criterion[0].use_fed_loss = True +model.model_vision.criterion[0].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train.dataset.names, 0.5 +) +model.model_vision.criterion[0].fed_loss_num_classes = 50 + +train.max_iter = 180000 +train.eval_period = 20000 + +lr_multiplier.scheduler.milestones = [150000, 180000] +lr_multiplier.warmup_length = 250 / train.max_iter + +model.model_vision.dataset_prompts = ["name"] +model.model_vision.dataset_names = ["lvis"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/LVIS_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_clip_lsj1024_cp_24ep.py b/configs/LVIS_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_clip_lsj1024_cp_24ep.py new file mode 100644 index 0000000..406eb78 --- /dev/null +++ b/configs/LVIS_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_clip_lsj1024_cp_24ep.py @@ -0,0 +1,35 @@ +from detectron2.data.detection_utils import get_fed_loss_cls_weights + +from ...COCO_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_clip_lsj1024_cp_12ep import ( + lr_multiplier, + model, + optimizer, + train, +) +from ...common.data.lvis_instance_lsj1024_cp import dataloader + +model.model_vision.num_classes = 1203 +model.model_vision.select_box_nums_for_evaluation = 300 +model.model_vision.test_score_thresh = 0.0 +model.model_vision.criterion[0].num_classes = 1203 +model.model_vision.criterion[0].use_fed_loss = True +model.model_vision.criterion[0].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train.dataset.names, 0.5 +) +model.model_vision.criterion[0].fed_loss_num_classes = 50 + +del optimizer.params.weight_decay_norm + +optimizer.weight_decay = 0.05 + +train.max_iter = 180000 +train.eval_period = 20000 + +lr_multiplier.scheduler.milestones = [150000, 180000] +lr_multiplier.warmup_length = 1000 / train.max_iter + +model.model_vision.dataset_prompts = ["name"] +model.model_vision.dataset_names = ["lvis"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/LVIS_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_clip_lsj1536_cp_64x90k.py b/configs/LVIS_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_clip_lsj1536_cp_64x90k.py new file mode 100644 index 0000000..33b1e41 --- /dev/null +++ b/configs/LVIS_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_clip_lsj1536_cp_64x90k.py @@ -0,0 +1,36 @@ +from detectron2.data.detection_utils import get_fed_loss_cls_weights + +from ...COCO_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_clip_lsj1536_cp_64x90k import ( + lr_multiplier, + model, + optimizer, + train, +) +from ...common.data.lvis_instance_lsj1536_cp import dataloader + +model.model_vision.num_classes = 1203 +model.model_vision.select_box_nums_for_evaluation = 300 +model.model_vision.criterion[0].num_classes = 1203 +model.model_vision.criterion[0].use_fed_loss = True +model.model_vision.criterion[0].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train.dataset.names, 0.5 +) +model.model_vision.criterion[0].fed_loss_num_classes = 50 + +del optimizer.params.weight_decay_norm + +optimizer.weight_decay = 0.05 + +train.max_iter = 90000 +train.eval_period = 10000 + +lr_multiplier.scheduler.milestones = [75000, 90000] +lr_multiplier.warmup_length = 1000 / train.max_iter + +model.model_vision.dataset_prompts = ["name"] +model.model_vision.dataset_names = ["lvis"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +dataloader.train.total_batch_size = 64 + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/LVIS_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_24ep.py b/configs/LVIS_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_24ep.py new file mode 100644 index 0000000..e015f65 --- /dev/null +++ b/configs/LVIS_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_24ep.py @@ -0,0 +1,34 @@ +from detectron2.data.detection_utils import get_fed_loss_cls_weights + +from ...COCO_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_12ep import ( + lr_multiplier, + model, + optimizer, + train, +) +from ...common.data.lvis_instance_lsj1024_cp import dataloader + +model.model_vision.num_classes = 1203 +model.model_vision.select_box_nums_for_evaluation = 300 +model.model_vision.criterion[0].num_classes = 1203 +model.model_vision.criterion[0].use_fed_loss = True +model.model_vision.criterion[0].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train.dataset.names, 0.5 +) +model.model_vision.criterion[0].fed_loss_num_classes = 50 + +del optimizer.params.weight_decay_norm + +optimizer.weight_decay = 0.05 + +train.max_iter = 180000 +train.eval_period = 20000 + +lr_multiplier.scheduler.milestones = [150000, 180000] +lr_multiplier.warmup_length = 1000 / train.max_iter + +model.model_vision.dataset_prompts = ["name"] +model.model_vision.dataset_names = ["lvis"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/LVIS_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024_cp_24ep.py b/configs/LVIS_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024_cp_24ep.py new file mode 100644 index 0000000..1a8630a --- /dev/null +++ b/configs/LVIS_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024_cp_24ep.py @@ -0,0 +1,34 @@ +from detectron2.data.detection_utils import get_fed_loss_cls_weights + +from ...COCO_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_lsj1024_cp_12ep import ( + lr_multiplier, + model, + optimizer, + train, +) +from ...common.data.lvis_instance_lsj1024_cp import dataloader + +model.model_vision.num_classes = 1203 +model.model_vision.select_box_nums_for_evaluation = 300 +model.model_vision.criterion[0].num_classes = 1203 +model.model_vision.criterion[0].use_fed_loss = True +model.model_vision.criterion[0].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train.dataset.names, 0.5 +) +model.model_vision.criterion[0].fed_loss_num_classes = 50 + +del optimizer.params.weight_decay_norm + +optimizer.weight_decay = 0.05 + +train.max_iter = 180000 +train.eval_period = 20000 + +lr_multiplier.scheduler.milestones = [150000, 180000] +lr_multiplier.warmup_length = 1000 / train.max_iter + +model.model_vision.dataset_prompts = ["name"] +model.model_vision.dataset_names = ["lvis"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/LVIS_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1536_cp_64x90k.py b/configs/LVIS_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1536_cp_64x90k.py new file mode 100644 index 0000000..d574e8d --- /dev/null +++ b/configs/LVIS_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1536_cp_64x90k.py @@ -0,0 +1,36 @@ +from detectron2.data.detection_utils import get_fed_loss_cls_weights + +from ...COCO_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_lsj1536_cp_64x90k import ( + lr_multiplier, + model, + optimizer, + train, +) +from ...common.data.lvis_instance_lsj1536_cp import dataloader + +model.model_vision.num_classes = 1203 +model.model_vision.select_box_nums_for_evaluation = 300 +model.model_vision.criterion[0].num_classes = 1203 +model.model_vision.criterion[0].use_fed_loss = True +model.model_vision.criterion[0].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train.dataset.names, 0.5 +) +model.model_vision.criterion[0].fed_loss_num_classes = 50 + +del optimizer.params.weight_decay_norm + +optimizer.weight_decay = 0.05 + +train.max_iter = 90000 +train.eval_period = 10000 + +lr_multiplier.scheduler.milestones = [75000, 90000] +lr_multiplier.warmup_length = 1000 / train.max_iter + +model.model_vision.dataset_prompts = ["name"] +model.model_vision.dataset_names = ["lvis"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +dataloader.train.total_batch_size = 64 + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/LVIS_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_24ep.py b/configs/LVIS_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_24ep.py new file mode 100644 index 0000000..4db1303 --- /dev/null +++ b/configs/LVIS_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_24ep.py @@ -0,0 +1,34 @@ +from detectron2.data.detection_utils import get_fed_loss_cls_weights + +from ...COCO_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_vlf_lsj1024_cp_12ep import ( + lr_multiplier, + model, + optimizer, + train, +) +from ...common.data.lvis_instance_lsj1024_cp import dataloader + +model.model_vision.num_classes = 1203 +model.model_vision.select_box_nums_for_evaluation = 300 +model.model_vision.criterion[0].num_classes = 1203 +model.model_vision.criterion[0].use_fed_loss = True +model.model_vision.criterion[0].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train.dataset.names, 0.5 +) +model.model_vision.criterion[0].fed_loss_num_classes = 50 + +del optimizer.params.weight_decay_norm + +optimizer.weight_decay = 0.05 + +train.max_iter = 180000 +train.eval_period = 20000 + +lr_multiplier.scheduler.milestones = [150000, 180000] +lr_multiplier.warmup_length = 1000 / train.max_iter + +model.model_vision.dataset_prompts = ["name"] +model.model_vision.dataset_names = ["lvis"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/LVIS_InstanceSegmentation/deformable_deta/deformable_deta_segm_vitl_eva02_4scale_lsj1024_cp_24ep.py b/configs/LVIS_InstanceSegmentation/deformable_deta/deformable_deta_segm_vitl_eva02_4scale_lsj1024_cp_24ep.py new file mode 100644 index 0000000..200b663 --- /dev/null +++ b/configs/LVIS_InstanceSegmentation/deformable_deta/deformable_deta_segm_vitl_eva02_4scale_lsj1024_cp_24ep.py @@ -0,0 +1,90 @@ +from detectron2.config import LazyCall as L +from detectron2.data.detection_utils import get_fed_loss_cls_weights +from detectron2.layers import ShapeSpec +from detectron2.modeling.backbone.fpn import LastLevelMaxPool +from detrex.config import get_config +from ape.modeling.backbone.vit import get_vit_lr_decay_rate + +from .....detectron2.configs.common.data.constants import constants +from ...COCO_InstanceSegmentation.deformable_deta.models.deformable_deta_segm_r50 import model +from ...common.backbone.vitl_eva02 import backbone +from ...common.data.lvis_instance_lsj1024_cp import dataloader + +model.pixel_mean = constants.imagenet_rgb256_mean +model.pixel_std = constants.imagenet_rgb256_std +model.input_format = "RGB" + +model.num_classes = 1203 +model.criterion.num_classes = 1203 +model.select_box_nums_for_evaluation = 300 +model.criterion.use_fed_loss = True +model.criterion.get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train.dataset.names, 0.5 +) +model.criterion.fed_loss_num_classes = 50 + +model.backbone = backbone +model.backbone.scale_factors = (2.0, 1.0, 0.5) + +model.transformer.num_feature_levels = 4 +model.transformer.encoder.num_feature_levels = 4 +model.transformer.decoder.num_feature_levels = 4 + +model.neck = None + +model.mask_in_features = ["p3"] +model.input_shapes = { + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), +} + +optimizer = get_config("common/optim.py").AdamW +optimizer.params.lr_factor_func = ( + lambda module_name: 0.1 + if "reference_points" in module_name or "sampling_offsets" in module_name + else get_vit_lr_decay_rate(module_name, lr_decay_rate=0.8, num_layers=24) + if "backbone.net" in module_name + else 1 +) +optimizer.params.overrides = {"pos_embed": {"weight_decay": 0.0}} + +optimizer.lr = 2e-4 +optimizer.weight_decay = 0.05 + +train = get_config("common/train.py").train +train.max_iter = 180000 +train.eval_period = 20000 +train.log_period = 20 + +train.checkpointer.period = 5000 +train.checkpointer.max_to_keep = 2 + +train.clip_grad.enabled = True +train.clip_grad.params.max_norm = 0.1 +train.clip_grad.params.norm_type = 2 + +train.device = "cuda" + +train.init_checkpoint = ( + "models/Yunxin-CV/EVA-02/eva02/pt/eva02_L_pt_in21k_p14to16.pt?matching_heuristics=True" +) + +train.amp.enabled = True +train.ddp.fp16_compression = True + +lr_multiplier = get_config("common/coco_schedule.py").lr_multiplier_12ep +lr_multiplier.scheduler.milestones = [150000, 180000] +lr_multiplier.warmup_length = 1000 / train.max_iter + +dataloader.train.num_workers = 16 +dataloader.train.total_batch_size = 16 +dataloader.train.mapper.image_format = "RGB" +dataloader.train.mapper.use_instance_mask = True + +model.dataset_metas = dataloader.train.dataset.names + +train.output_dir = "output/" + __file__[:-3] +dataloader.train.mapper.vis_period = 12800 diff --git a/configs/LVIS_InstanceSegmentation/deformable_deta/deformable_deta_segm_vitl_eva02_lsj1024_cp_24ep.py b/configs/LVIS_InstanceSegmentation/deformable_deta/deformable_deta_segm_vitl_eva02_lsj1024_cp_24ep.py new file mode 100644 index 0000000..8d90d16 --- /dev/null +++ b/configs/LVIS_InstanceSegmentation/deformable_deta/deformable_deta_segm_vitl_eva02_lsj1024_cp_24ep.py @@ -0,0 +1,33 @@ +from detectron2.data.detection_utils import get_fed_loss_cls_weights + +from ...COCO_InstanceSegmentation.deformable_deta.deformable_deta_segm_vitl_eva02_lsj1024_cp_12ep import ( + lr_multiplier, + model, + optimizer, + train, +) +from ...common.data.lvis_instance_lsj1024_cp import dataloader + +model.num_classes = 1203 +model.select_box_nums_for_evaluation = 300 +model.criterion.num_classes = 1203 +model.criterion.use_fed_loss = True +model.criterion.get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train.dataset.names, 0.5 +) +model.criterion.fed_loss_num_classes = 50 + +del optimizer.params.weight_decay_norm + +optimizer.weight_decay = 0.05 + +train.max_iter = 180000 +train.eval_period = 20000 + +lr_multiplier.scheduler.milestones = [150000, 180000] +lr_multiplier.warmup_length = 1000 / train.max_iter + +model.dataset_metas = dataloader.train.dataset.names + +train.output_dir = "output/" + __file__[:-3] +dataloader.train.mapper.vis_period = 12800 diff --git a/configs/LVIS_SA1B_InstanceSegmentation/ape_deta/ape_deta_r50_50ep.py b/configs/LVIS_SA1B_InstanceSegmentation/ape_deta/ape_deta_r50_50ep.py new file mode 100644 index 0000000..df956ae --- /dev/null +++ b/configs/LVIS_SA1B_InstanceSegmentation/ape_deta/ape_deta_r50_50ep.py @@ -0,0 +1,29 @@ +from detectron2.data.detection_utils import get_fed_loss_cls_weights +from detrex.config import get_config + +from ...COCO_SA1B_InstanceSegmentation.ape_deta.ape_deta_r50_24ep import model, optimizer, train + +from ...common.data.lvis_sa1b_instance import dataloader + +model.model_vision.num_classes = 1203 +model.model_vision.criterion[0].num_classes = 1203 +model.model_vision.select_box_nums_for_evaluation = 300 +model.model_vision.criterion[0].use_fed_loss = True +model.model_vision.criterion[0].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights( + dataloader.train.dataset.names[0], 0.5 +) +model.model_vision.criterion[0].fed_loss_num_classes = 50 + +model.model_vision.semantic_on = False +model.model_vision.panoptic_on = False + +train.max_iter = 375000 +train.eval_period = 20000 + +lr_multiplier = get_config("common/coco_schedule.py").lr_multiplier_50ep + +model.model_vision.dataset_prompts = ["name", "name"] +model.model_vision.dataset_names = ["lvis", "sa1b"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/LVIS_SA1B_InstanceSegmentation/ape_deta/ape_deta_r50_50ep_eval_odinw13.py b/configs/LVIS_SA1B_InstanceSegmentation/ape_deta/ape_deta_r50_50ep_eval_odinw13.py new file mode 100644 index 0000000..e8850dc --- /dev/null +++ b/configs/LVIS_SA1B_InstanceSegmentation/ape_deta/ape_deta_r50_50ep_eval_odinw13.py @@ -0,0 +1,10 @@ +from ...common.data.odinw13_instance import dataloader +from .ape_deta_r50_50ep import lr_multiplier, model, optimizer, train + +model.model_vision.dataset_prompts = ["name" for _ in dataloader.tests] +model.model_vision.dataset_names = [ + test.dataset.names.replace("_val", "") for test in dataloader.tests +] +model.model_vision.dataset_metas = [test.dataset.names for test in dataloader.tests] + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/LVIS_SA1B_InstanceSegmentation/ape_deta/ape_deta_r50_50ep_eval_odinw35.py b/configs/LVIS_SA1B_InstanceSegmentation/ape_deta/ape_deta_r50_50ep_eval_odinw35.py new file mode 100644 index 0000000..47bc758 --- /dev/null +++ b/configs/LVIS_SA1B_InstanceSegmentation/ape_deta/ape_deta_r50_50ep_eval_odinw35.py @@ -0,0 +1,10 @@ +from ...common.data.odinw35_instance import dataloader +from .ape_deta_r50_50ep import lr_multiplier, model, optimizer, train + +model.model_vision.dataset_prompts = ["name" for _ in dataloader.tests] +model.model_vision.dataset_names = [ + test.dataset.names.replace("_val", "") for test in dataloader.tests +] +model.model_vision.dataset_metas = [test.dataset.names for test in dataloader.tests] + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/LVIS_SA1B_InstanceSegmentation/ape_deta/ape_deta_r50_50ep_eval_seginw.py b/configs/LVIS_SA1B_InstanceSegmentation/ape_deta/ape_deta_r50_50ep_eval_seginw.py new file mode 100644 index 0000000..d51d5d6 --- /dev/null +++ b/configs/LVIS_SA1B_InstanceSegmentation/ape_deta/ape_deta_r50_50ep_eval_seginw.py @@ -0,0 +1,11 @@ +from ...common.data.seginw_instance import dataloader +from .ape_deta_r50_50ep import lr_multiplier, model, optimizer, train + + +model.model_vision.dataset_prompts = ["name" for _ in dataloader.tests] +model.model_vision.dataset_names = [ + test.dataset.names.replace("_val", "") for test in dataloader.tests +] +model.model_vision.dataset_metas = [test.dataset.names for test in dataloader.tests] + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/LVIS_SA1B_InstanceSegmentation/ape_deta/ape_deta_r50_50ep_iouloss_lp.py b/configs/LVIS_SA1B_InstanceSegmentation/ape_deta/ape_deta_r50_50ep_iouloss_lp.py new file mode 100644 index 0000000..7727820 --- /dev/null +++ b/configs/LVIS_SA1B_InstanceSegmentation/ape_deta/ape_deta_r50_50ep_iouloss_lp.py @@ -0,0 +1,21 @@ +from ape.modeling.ape_deta import Stage1Assigner_loc, Stage2Assigner_loc + +from .ape_deta_r50_50ep import dataloader, lr_multiplier, model, optimizer, train + +model.model_vision.criterion[0].losses += ["pred_iou"] +model.model_vision.criterion[0].weight_dict["loss_iou"] = 1.0 + +model.model_vision.last_class_embed_use_mlp = True +model.model_vision.transformer.pre_nms_topk = 1000 +model.model_vision.transformer.nms_thresh_enc = 0.9 + +model.model_vision.criterion[0].matcher_stage1.update( + _target_=Stage1Assigner_loc, +) +model.model_vision.criterion[1].matcher_stage1.update( + _target_=Stage1Assigner_loc, +) + + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 1280 diff --git a/configs/LVIS_SA1B_InstanceSegmentation/ape_deta/ape_deta_r50_50ep_mp.py b/configs/LVIS_SA1B_InstanceSegmentation/ape_deta/ape_deta_r50_50ep_mp.py new file mode 100644 index 0000000..ba8c845 --- /dev/null +++ b/configs/LVIS_SA1B_InstanceSegmentation/ape_deta/ape_deta_r50_50ep_mp.py @@ -0,0 +1,7 @@ +from .ape_deta_r50_50ep import dataloader, lr_multiplier, model, optimizer, train + +model.model_vision.transformer.proposal_ambiguous = 1 +model.model_vision.transformer.encoder.use_act_checkpoint = True + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 12800 diff --git a/configs/ODinW_Detection/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_13.py b/configs/ODinW_Detection/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_13.py new file mode 100644 index 0000000..8d10e4a --- /dev/null +++ b/configs/ODinW_Detection/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_13.py @@ -0,0 +1,103 @@ +from detectron2.config import LazyCall as L +from detectron2.solver import WarmupParamScheduler +from fvcore.common.param_scheduler import MultiStepParamScheduler + +from ape.data.detection_utils import get_fed_loss_cls_weights +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) + +from ...common.data.odinw13_instance_lsj1024 import dataloader +from ...LVIS_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_24ep import ( + model, + optimizer, + train, +) + +model.model_vision.num_classes = 1256 +model.model_vision.select_box_nums_for_evaluation = 300 + +criterion = model.model_vision.criterion[0] +del criterion.use_fed_loss +del criterion.get_fed_loss_cls_weights +model.model_vision.criterion = [criterion for _ in range(35)] +for criterion, num_classes in zip( + model.model_vision.criterion, + [ + 1000, + ] + * 35, +): + criterion.num_classes = num_classes + +model.model_vision.instance_on = True +model.model_vision.semantic_on = False +model.model_vision.panoptic_on = False + +train.max_iter = 720000 +train.eval_period = 720000 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1], + milestones=[640000], + num_updates=720000, + ), + warmup_length=1000 / 720000, + warmup_method="linear", + warmup_factor=0.001, +) + +for i in range(len(dataloader.train)): + dataloader.train[i].total_batch_size = 16 + dataloader.train[i].total_batch_size_list = [16] + +model.model_vision.dataset_prompts = ["name" for _ in dataloader.train] +model.model_vision.dataset_names = [ + x.dataset.names[0].replace("_train", "") for x in dataloader.train +] +model.model_vision.dataset_metas = [x.dataset.names[0] for x in dataloader.train] +model.model_vision.name_prompt_fusion_text = dataloader.name_prompt_fusion_text +model.model_vision.select_box_nums_for_evaluation_list = ( + dataloader.select_box_nums_for_evaluation_list +) + + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/ODinW_Detection/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_35.py b/configs/ODinW_Detection/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_35.py new file mode 100644 index 0000000..803f6be --- /dev/null +++ b/configs/ODinW_Detection/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_35.py @@ -0,0 +1,104 @@ +from detectron2.config import LazyCall as L +from detectron2.solver import WarmupParamScheduler +from fvcore.common.param_scheduler import MultiStepParamScheduler + +from ape.data.detection_utils import get_fed_loss_cls_weights +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) + +from ...common.data.odinw35_instance_lsj1024 import dataloader +from ...LVIS_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_24ep import ( + model, + optimizer, + train, +) + +model.model_vision.num_classes = 1256 +model.model_vision.select_box_nums_for_evaluation = 300 + +criterion = model.model_vision.criterion[0] +del criterion.use_fed_loss +del criterion.get_fed_loss_cls_weights +model.model_vision.criterion = [criterion for _ in range(35)] +for criterion, num_classes in zip( + model.model_vision.criterion, + [ + 1000, + ] + * 35, +): + criterion.num_classes = num_classes + +model.model_vision.instance_on = True +model.model_vision.semantic_on = False +model.model_vision.panoptic_on = False + + +train.max_iter = 720000 +train.eval_period = 720000 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1], + milestones=[640000], + num_updates=720000, + ), + warmup_length=1000 / 720000, + warmup_method="linear", + warmup_factor=0.001, +) + +for i in range(len(dataloader.train)): + dataloader.train[i].total_batch_size = 16 + dataloader.train[i].total_batch_size_list = [16] + +model.model_vision.dataset_prompts = ["name" for _ in dataloader.train] +model.model_vision.dataset_names = [ + x.dataset.names[0].replace("_train", "") for x in dataloader.train +] +model.model_vision.dataset_metas = [x.dataset.names[0] for x in dataloader.train] +model.model_vision.name_prompt_fusion_text = dataloader.name_prompt_fusion_text +model.model_vision.select_box_nums_for_evaluation_list = ( + dataloader.select_box_nums_for_evaluation_list +) + + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/ODinW_Detection/ape_deta/ape_deta_vitl_eva02_lsj1024_13.py b/configs/ODinW_Detection/ape_deta/ape_deta_vitl_eva02_lsj1024_13.py new file mode 100644 index 0000000..1e1da9b --- /dev/null +++ b/configs/ODinW_Detection/ape_deta/ape_deta_vitl_eva02_lsj1024_13.py @@ -0,0 +1,71 @@ +from detectron2.config import LazyCall as L +from detectron2.solver import WarmupParamScheduler +from fvcore.common.param_scheduler import MultiStepParamScheduler + +from ape.data.detection_utils import get_fed_loss_cls_weights + +from ...common.data.odinw13_instance_lsj1024 import dataloader +from ...LVIS_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_lsj1024_cp_24ep import ( + model, + optimizer, + train, +) + +model.model_vision.num_classes = 1256 +model.model_vision.select_box_nums_for_evaluation = 300 + +criterion = model.model_vision.criterion[0] +del criterion.use_fed_loss +del criterion.get_fed_loss_cls_weights +model.model_vision.criterion = [criterion for _ in range(35)] +for criterion, num_classes in zip( + model.model_vision.criterion, + [ + 1000, + ] + * 35, +): + criterion.num_classes = num_classes + +model.model_vision.instance_on = True +model.model_vision.semantic_on = False +model.model_vision.panoptic_on = False + +model.model_vision.neck = None + +train.max_iter = 720000 +train.eval_period = 720000 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1], + milestones=[640000], + num_updates=720000, + ), + warmup_length=1000 / 720000, + warmup_method="linear", + warmup_factor=0.001, +) + +for i in range(len(dataloader.train)): + dataloader.train[i].total_batch_size = 16 + dataloader.train[i].total_batch_size_list = [16] + +model.model_vision.dataset_prompts = ["name" for _ in dataloader.train] +model.model_vision.dataset_names = [ + x.dataset.names[0].replace("_train", "") for x in dataloader.train +] +model.model_vision.dataset_metas = [x.dataset.names[0] for x in dataloader.train] +model.model_vision.name_prompt_fusion_text = dataloader.name_prompt_fusion_text +model.model_vision.select_box_nums_for_evaluation_list = ( + dataloader.select_box_nums_for_evaluation_list +) + + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/ODinW_Detection/ape_deta/ape_deta_vitl_eva02_lsj1024_35.py b/configs/ODinW_Detection/ape_deta/ape_deta_vitl_eva02_lsj1024_35.py new file mode 100644 index 0000000..6edfa6c --- /dev/null +++ b/configs/ODinW_Detection/ape_deta/ape_deta_vitl_eva02_lsj1024_35.py @@ -0,0 +1,71 @@ +from detectron2.config import LazyCall as L +from detectron2.solver import WarmupParamScheduler +from fvcore.common.param_scheduler import MultiStepParamScheduler + +from ape.data.detection_utils import get_fed_loss_cls_weights + +from ...common.data.odinw35_instance_lsj1024 import dataloader +from ...LVIS_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_lsj1024_cp_24ep import ( + model, + optimizer, + train, +) + +model.model_vision.num_classes = 1256 +model.model_vision.select_box_nums_for_evaluation = 300 + +criterion = model.model_vision.criterion[0] +del criterion.use_fed_loss +del criterion.get_fed_loss_cls_weights +model.model_vision.criterion = [criterion for _ in range(35)] +for criterion, num_classes in zip( + model.model_vision.criterion, + [ + 1000, + ] + * 35, +): + criterion.num_classes = num_classes + +model.model_vision.instance_on = True +model.model_vision.semantic_on = False +model.model_vision.panoptic_on = False + +model.model_vision.neck = None + +train.max_iter = 720000 +train.eval_period = 720000 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1], + milestones=[640000], + num_updates=720000, + ), + warmup_length=1000 / 720000, + warmup_method="linear", + warmup_factor=0.001, +) + +for i in range(len(dataloader.train)): + dataloader.train[i].total_batch_size = 16 + dataloader.train[i].total_batch_size_list = [16] + +model.model_vision.dataset_prompts = ["name" for _ in dataloader.train] +model.model_vision.dataset_names = [ + x.dataset.names[0].replace("_train", "") for x in dataloader.train +] +model.model_vision.dataset_metas = [x.dataset.names[0] for x in dataloader.train] +model.model_vision.name_prompt_fusion_text = dataloader.name_prompt_fusion_text +model.model_vision.select_box_nums_for_evaluation_list = ( + dataloader.select_box_nums_for_evaluation_list +) + + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/ODinW_Detection/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_13.py b/configs/ODinW_Detection/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_13.py new file mode 100644 index 0000000..96e7428 --- /dev/null +++ b/configs/ODinW_Detection/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_13.py @@ -0,0 +1,105 @@ +from detectron2.config import LazyCall as L +from detectron2.solver import WarmupParamScheduler +from fvcore.common.param_scheduler import MultiStepParamScheduler + +from ape.data.detection_utils import get_fed_loss_cls_weights +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) + +from ...common.data.odinw13_instance_lsj1024 import dataloader +from ...LVIS_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_vlf_lsj1024_cp_24ep import ( + model, + optimizer, + train, +) + +model.model_vision.num_classes = 1256 +model.model_vision.select_box_nums_for_evaluation = 300 + +criterion = model.model_vision.criterion[0] +del criterion.use_fed_loss +del criterion.get_fed_loss_cls_weights +model.model_vision.criterion = [criterion for _ in range(35)] +for criterion, num_classes in zip( + model.model_vision.criterion, + [ + 1000, + ] + * 35, +): + criterion.num_classes = num_classes + +model.model_vision.instance_on = True +model.model_vision.semantic_on = False +model.model_vision.panoptic_on = False + +model.model_vision.neck = None + +train.max_iter = 720000 +train.eval_period = 720000 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1], + milestones=[640000], + num_updates=720000, + ), + warmup_length=1000 / 720000, + warmup_method="linear", + warmup_factor=0.001, +) + +for i in range(len(dataloader.train)): + dataloader.train[i].total_batch_size = 16 + dataloader.train[i].total_batch_size_list = [16] + +model.model_vision.dataset_prompts = ["name" for _ in dataloader.train] +model.model_vision.dataset_names = [ + x.dataset.names[0].replace("_train", "") for x in dataloader.train +] +model.model_vision.dataset_metas = [x.dataset.names[0] for x in dataloader.train] +model.model_vision.name_prompt_fusion_text = dataloader.name_prompt_fusion_text +model.model_vision.select_box_nums_for_evaluation_list = ( + dataloader.select_box_nums_for_evaluation_list +) + + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/ODinW_Detection/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_35.py b/configs/ODinW_Detection/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_35.py new file mode 100644 index 0000000..1040a15 --- /dev/null +++ b/configs/ODinW_Detection/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_35.py @@ -0,0 +1,105 @@ +from detectron2.config import LazyCall as L +from detectron2.solver import WarmupParamScheduler +from fvcore.common.param_scheduler import MultiStepParamScheduler + +from ape.data.detection_utils import get_fed_loss_cls_weights +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) + +from ...common.data.odinw35_instance_lsj1024 import dataloader +from ...LVIS_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_vlf_lsj1024_cp_24ep import ( + model, + optimizer, + train, +) + +model.model_vision.num_classes = 1256 +model.model_vision.select_box_nums_for_evaluation = 300 + +criterion = model.model_vision.criterion[0] +del criterion.use_fed_loss +del criterion.get_fed_loss_cls_weights +model.model_vision.criterion = [criterion for _ in range(35)] +for criterion, num_classes in zip( + model.model_vision.criterion, + [ + 1000, + ] + * 35, +): + criterion.num_classes = num_classes + +model.model_vision.instance_on = True +model.model_vision.semantic_on = False +model.model_vision.panoptic_on = False + +model.model_vision.neck = None + +train.max_iter = 720000 +train.eval_period = 720000 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1], + milestones=[640000], + num_updates=720000, + ), + warmup_length=1000 / 720000, + warmup_method="linear", + warmup_factor=0.001, +) + +for i in range(len(dataloader.train)): + dataloader.train[i].total_batch_size = 16 + dataloader.train[i].total_batch_size_list = [16] + +model.model_vision.dataset_prompts = ["name" for _ in dataloader.train] +model.model_vision.dataset_names = [ + x.dataset.names[0].replace("_train", "") for x in dataloader.train +] +model.model_vision.dataset_metas = [x.dataset.names[0] for x in dataloader.train] +model.model_vision.name_prompt_fusion_text = dataloader.name_prompt_fusion_text +model.model_vision.select_box_nums_for_evaluation_list = ( + dataloader.select_box_nums_for_evaluation_list +) + + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/PascalContext459_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024.py b/configs/PascalContext459_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024.py new file mode 100644 index 0000000..90390fb --- /dev/null +++ b/configs/PascalContext459_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024.py @@ -0,0 +1,101 @@ +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detrex.modeling.neck import ChannelMapper +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) +from ape.modeling.text import EVA02CLIP + +from ...common.backbone.vitl_eva02_clip import backbone +from .ape_deta_vitl_eva02_lsj1024 import dataloader, lr_multiplier, model, optimizer, train + +model.model_vision.backbone = backbone + +train.init_checkpoint = ( + "models/QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14to16_s6B.pt?matching_heuristics=True" +) + +model.model_language = L(EVA02CLIP)( + clip_model="EVA02-CLIP-bigE-14-plus", + cache_dir="models/QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt", + dtype="float16", +) +model.model_vision.embed_dim_language = 1024 + +model.model_vision.neck = L(ChannelMapper)( + input_shapes={ + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), + }, + in_features=["p2", "p3", "p4", "p5", "p6"], + out_channels=256, + num_outs=5, + kernel_size=1, + norm_layer=L(nn.GroupNorm)(num_groups=32, num_channels=256), +) + +model.model_vision.mask_in_features = ["p2"] +model.model_vision.input_shapes = { + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), +} + +model.model_vision.transformer.encoder.num_layers = 6 +model.model_vision.transformer.decoder.num_layers = 6 +model.model_vision.transformer.encoder.embed_dim = 256 +model.model_vision.transformer.decoder.embed_dim = 256 +model.model_vision.embed_dim = 256 +model.model_vision.backbone.out_channels = 256 + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +model.model_vision.stuff_dataset_learn_thing = False +model.model_vision.stuff_prob_thing = -1.0 +model.model_vision.transformer.proposal_ambiguous = 1 + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 12800 diff --git a/configs/PascalContext459_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024.py b/configs/PascalContext459_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024.py new file mode 100644 index 0000000..c9e4ceb --- /dev/null +++ b/configs/PascalContext459_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024.py @@ -0,0 +1,28 @@ +from ...COCO_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_lsj1024_cp_12ep import ( + lr_multiplier, + model, + optimizer, + train, +) +from ...common.data.pascalcontext459_semantic_lsj1024 import dataloader + + + +model.model_vision.dataset_prompts = ["name"] +model.model_vision.name_prompt_fusion_text = [ + False, +] +model.model_vision.dataset_names = ["pascal_context_459_sem_seg"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +model.model_vision.num_classes = 459 +model.model_vision.criterion[0].num_classes = 459 +model.model_vision.select_box_nums_for_evaluation = 300 + +model.model_vision.instance_on = False +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = False + +model.model_vision.stuff_prob_thing = -1.0 + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/PascalContext459_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024.py b/configs/PascalContext459_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024.py new file mode 100644 index 0000000..1a3ad59 --- /dev/null +++ b/configs/PascalContext459_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024.py @@ -0,0 +1,47 @@ +from detectron2.config import LazyCall as L +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) + +from .ape_deta_vitl_eva02_lsj1024 import dataloader, lr_multiplier, model, optimizer, train + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 12800 diff --git a/configs/PascalContext59_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024.py b/configs/PascalContext59_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024.py new file mode 100644 index 0000000..90390fb --- /dev/null +++ b/configs/PascalContext59_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024.py @@ -0,0 +1,101 @@ +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detrex.modeling.neck import ChannelMapper +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) +from ape.modeling.text import EVA02CLIP + +from ...common.backbone.vitl_eva02_clip import backbone +from .ape_deta_vitl_eva02_lsj1024 import dataloader, lr_multiplier, model, optimizer, train + +model.model_vision.backbone = backbone + +train.init_checkpoint = ( + "models/QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14to16_s6B.pt?matching_heuristics=True" +) + +model.model_language = L(EVA02CLIP)( + clip_model="EVA02-CLIP-bigE-14-plus", + cache_dir="models/QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt", + dtype="float16", +) +model.model_vision.embed_dim_language = 1024 + +model.model_vision.neck = L(ChannelMapper)( + input_shapes={ + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), + }, + in_features=["p2", "p3", "p4", "p5", "p6"], + out_channels=256, + num_outs=5, + kernel_size=1, + norm_layer=L(nn.GroupNorm)(num_groups=32, num_channels=256), +) + +model.model_vision.mask_in_features = ["p2"] +model.model_vision.input_shapes = { + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), +} + +model.model_vision.transformer.encoder.num_layers = 6 +model.model_vision.transformer.decoder.num_layers = 6 +model.model_vision.transformer.encoder.embed_dim = 256 +model.model_vision.transformer.decoder.embed_dim = 256 +model.model_vision.embed_dim = 256 +model.model_vision.backbone.out_channels = 256 + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +model.model_vision.stuff_dataset_learn_thing = False +model.model_vision.stuff_prob_thing = -1.0 +model.model_vision.transformer.proposal_ambiguous = 1 + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 12800 diff --git a/configs/PascalContext59_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024.py b/configs/PascalContext59_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024.py new file mode 100644 index 0000000..12ccadf --- /dev/null +++ b/configs/PascalContext59_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024.py @@ -0,0 +1,24 @@ +from ...COCO_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_lsj1024_cp_12ep import ( + lr_multiplier, + model, + optimizer, + train, +) +from ...common.data.pascalcontext59_semantic_lsj1024 import dataloader + +model.model_vision.dataset_prompts = ["name"] +model.model_vision.name_prompt_fusion_text = [False] +model.model_vision.dataset_names = ["pascal_context_59_sem_seg"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +model.model_vision.num_classes = 59 +model.model_vision.criterion[0].num_classes = 59 +model.model_vision.select_box_nums_for_evaluation = 300 + +model.model_vision.instance_on = False +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = False + +model.model_vision.stuff_prob_thing = -1.0 + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/PascalContext59_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024.py b/configs/PascalContext59_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024.py new file mode 100644 index 0000000..1a3ad59 --- /dev/null +++ b/configs/PascalContext59_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024.py @@ -0,0 +1,47 @@ +from detectron2.config import LazyCall as L +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) + +from .ape_deta_vitl_eva02_lsj1024 import dataloader, lr_multiplier, model, optimizer, train + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 12800 diff --git a/configs/PascalVOC20_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024.py b/configs/PascalVOC20_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024.py new file mode 100644 index 0000000..56dbd7b --- /dev/null +++ b/configs/PascalVOC20_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024.py @@ -0,0 +1,100 @@ +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detrex.modeling.neck import ChannelMapper +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) +from ape.modeling.text import EVA02CLIP + +from ...common.backbone.vitl_eva02_clip import backbone +from .ape_deta_vitl_eva02_lsj1024 import dataloader, lr_multiplier, model, optimizer, train + +model.model_vision.backbone = backbone + +train.init_checkpoint = ( + "models/QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14to16_s6B.pt?matching_heuristics=True" +) + +model.model_language = L(EVA02CLIP)( + clip_model="EVA02-CLIP-bigE-14-plus", + cache_dir="models/QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt", + dtype="float16", +) +model.model_vision.embed_dim_language = 1024 + +model.model_vision.neck = L(ChannelMapper)( + input_shapes={ + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), + }, + in_features=["p2", "p3", "p4", "p5", "p6"], + out_channels=256, + num_outs=5, + kernel_size=1, + norm_layer=L(nn.GroupNorm)(num_groups=32, num_channels=256), +) + +model.model_vision.mask_in_features = ["p2"] +model.model_vision.input_shapes = { + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), +} + +model.model_vision.transformer.encoder.num_layers = 6 +model.model_vision.transformer.decoder.num_layers = 6 +model.model_vision.transformer.encoder.embed_dim = 256 +model.model_vision.transformer.decoder.embed_dim = 256 +model.model_vision.embed_dim = 256 +model.model_vision.backbone.out_channels = 256 + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +model.model_vision.stuff_dataset_learn_thing = False +model.model_vision.stuff_prob_thing = -1.0 +model.model_vision.transformer.proposal_ambiguous = 1 + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 12800 diff --git a/configs/PascalVOC20_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024.py b/configs/PascalVOC20_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024.py new file mode 100644 index 0000000..a02c1e4 --- /dev/null +++ b/configs/PascalVOC20_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024.py @@ -0,0 +1,24 @@ +from ...COCO_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_lsj1024_cp_12ep import ( + lr_multiplier, + model, + optimizer, + train, +) +from ...common.data.pascalvoc20_semantic_lsj1024 import dataloader + +model.model_vision.dataset_prompts = ["name"] +model.model_vision.name_prompt_fusion_text = [False] +model.model_vision.dataset_names = ["pascalvoc20_sem_seg"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +model.model_vision.num_classes = 20 +model.model_vision.criterion[0].num_classes = 20 +model.model_vision.select_box_nums_for_evaluation = 300 + +model.model_vision.instance_on = False +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = False + +model.model_vision.stuff_prob_thing = -1.0 + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/PascalVOC20_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024.py b/configs/PascalVOC20_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024.py new file mode 100644 index 0000000..1a01959 --- /dev/null +++ b/configs/PascalVOC20_SemanticSegmentation/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024.py @@ -0,0 +1,46 @@ +from detectron2.config import LazyCall as L +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) + +from .ape_deta_vitl_eva02_lsj1024 import dataloader, lr_multiplier, model, optimizer, train + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 12800 diff --git a/configs/PascalVOCParts_PanopticSegmentation/ape_deta/ape_deta_r50_12ep.py b/configs/PascalVOCParts_PanopticSegmentation/ape_deta/ape_deta_r50_12ep.py new file mode 100644 index 0000000..e08bf2b --- /dev/null +++ b/configs/PascalVOCParts_PanopticSegmentation/ape_deta/ape_deta_r50_12ep.py @@ -0,0 +1,18 @@ +from ...COCO_InstanceSegmentation.ape_deta.ape_deta_r50_12ep import ( + lr_multiplier, + model, + optimizer, + train, +) +from ...common.data.pascalvocpart_panoptic import dataloader + +model.model_vision.num_classes = 200 + +model.model_vision.criterion[0].num_classes = 200 + +model.model_vision.dataset_prompts = ["name"] +model.model_vision.dataset_names = ["pascal_parts"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 1280 diff --git a/configs/PascalVOCParts_PanopticSegmentation/ape_deta/ape_deta_r50_vlf_12ep.py b/configs/PascalVOCParts_PanopticSegmentation/ape_deta/ape_deta_r50_vlf_12ep.py new file mode 100644 index 0000000..d0f6e6d --- /dev/null +++ b/configs/PascalVOCParts_PanopticSegmentation/ape_deta/ape_deta_r50_vlf_12ep.py @@ -0,0 +1,46 @@ +from detectron2.config import LazyCall as L +from omegaconf import OmegaConf +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) + +from .ape_deta_r50_12ep import dataloader, lr_multiplier, model, optimizer, train + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + cfg=OmegaConf.from_dotlist( + [ + "MODEL.DYHEAD.FUSE_CONFIG.STABLE_SOFTMAX_2D=False", + "MODEL.DYHEAD.FUSE_CONFIG.CLAMP_MIN_FOR_UNDERFLOW=True", + "MODEL.DYHEAD.FUSE_CONFIG.CLAMP_MAX_FOR_OVERFLOW=True", + "MODEL.VL_FUSION_USE_CHECKPOINT=True", + ], + ), +) + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 1280 diff --git a/configs/PhraseCut_VisualGrounding/ape_deta/ape_deta_r50_12ep.py b/configs/PhraseCut_VisualGrounding/ape_deta/ape_deta_r50_12ep.py new file mode 100644 index 0000000..d4ce081 --- /dev/null +++ b/configs/PhraseCut_VisualGrounding/ape_deta/ape_deta_r50_12ep.py @@ -0,0 +1,20 @@ +from ...COCO_InstanceSegmentation.ape_deta.ape_deta_r50_12ep import ( + lr_multiplier, + model, + optimizer, + train, +) +from ...common.data.phrasecut_instance import dataloader + +model.model_vision.num_classes = 200 + +model.model_vision.criterion[0].num_classes = 200 + +dataloader.train.mapper.max_num_phrase = 100 + +model.model_vision.dataset_prompts = ["phrase", "expression"] +model.model_vision.dataset_names = ["phrasecut", "refcoco"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + ["refcoco-mixed"] + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 1280 diff --git a/configs/PhraseCut_VisualGrounding/ape_deta/ape_deta_r50_vlf_12ep.py b/configs/PhraseCut_VisualGrounding/ape_deta/ape_deta_r50_vlf_12ep.py new file mode 100644 index 0000000..cf35b87 --- /dev/null +++ b/configs/PhraseCut_VisualGrounding/ape_deta/ape_deta_r50_vlf_12ep.py @@ -0,0 +1,42 @@ +from detectron2.config import LazyCall as L +from omegaconf import OmegaConf +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) + +from .ape_deta_r50_12ep import dataloader, lr_multiplier, model, optimizer, train + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, + use_attention_mask_v=True, +) + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 1280 diff --git a/configs/PhraseCut_VisualGrounding/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024.py b/configs/PhraseCut_VisualGrounding/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024.py new file mode 100644 index 0000000..5fbafaf --- /dev/null +++ b/configs/PhraseCut_VisualGrounding/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024.py @@ -0,0 +1,100 @@ +from detectron2.config import LazyCall as L +from detectron2.solver import WarmupParamScheduler +from fvcore.common.param_scheduler import MultiStepParamScheduler +from ape.data.detection_utils import get_fed_loss_cls_weights +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) + +from ...common.data.phrasecut_instance_lsj1024 import dataloader +from ...LVIS_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_clip_lsj1024_cp_24ep import ( + model, + optimizer, + train, +) + +model.model_vision.num_classes = 256 +model.model_vision.select_box_nums_for_evaluation = 100 + +criterion = model.model_vision.criterion[0] +del criterion.use_fed_loss +del criterion.get_fed_loss_cls_weights +model.model_vision.criterion = [criterion for _ in range(1)] +for criterion, num_classes in zip( + model.model_vision.criterion, + [ + 1000, + ] + * 1, +): + criterion.num_classes = num_classes + + +model.model_vision.stuff_dataset_learn_thing = False +model.model_vision.stuff_prob_thing = -1.0 + +model.model_vision.instance_on = True +model.model_vision.semantic_on = False +model.model_vision.panoptic_on = False + +model.model_vision.neck = None + +train.max_iter = 720000 +train.eval_period = 720000 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1], + milestones=[640000], + num_updates=720000, + ), + warmup_length=1000 / 720000, + warmup_method="linear", + warmup_factor=0.001, +) + +model.model_vision.dataset_prompts = ["phrase", "expression"] +model.model_vision.dataset_names = ["phrasecut", "refcoco"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + ["refcoco-mixed"] + + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, + use_attention_mask_v=True, +) + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 12800 diff --git a/configs/PhraseCut_VisualGrounding/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024.py b/configs/PhraseCut_VisualGrounding/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024.py new file mode 100644 index 0000000..e5dc1eb --- /dev/null +++ b/configs/PhraseCut_VisualGrounding/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024.py @@ -0,0 +1,100 @@ +from detectron2.config import LazyCall as L +from detectron2.solver import WarmupParamScheduler +from fvcore.common.param_scheduler import MultiStepParamScheduler +from ape.data.detection_utils import get_fed_loss_cls_weights +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) + +from ...common.data.phrasecut_instance_lsj1024 import dataloader +from ...LVIS_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_lsj1024_cp_24ep import ( + model, + optimizer, + train, +) + +model.model_vision.num_classes = 256 +model.model_vision.select_box_nums_for_evaluation = 300 + +criterion = model.model_vision.criterion[0] +del criterion.use_fed_loss +del criterion.get_fed_loss_cls_weights +model.model_vision.criterion = [criterion for _ in range(1)] +for criterion, num_classes in zip( + model.model_vision.criterion, + [ + 1000, + ] + * 1, +): + criterion.num_classes = num_classes + + +model.model_vision.stuff_dataset_learn_thing = False +model.model_vision.stuff_prob_thing = -1.0 + +model.model_vision.instance_on = True +model.model_vision.semantic_on = False +model.model_vision.panoptic_on = False + +model.model_vision.neck = None + +train.max_iter = 720000 +train.eval_period = 720000 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1], + milestones=[640000], + num_updates=720000, + ), + warmup_length=1000 / 720000, + warmup_method="linear", + warmup_factor=0.001, +) + + +model.model_vision.dataset_prompts = ["phrase", "expression"] +model.model_vision.dataset_names = ["phrasecut_train", "phrasecut_val"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + [dataloader.test.dataset.names] + + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 12800 diff --git a/configs/REFCOCO_VisualGrounding/ape_deta/ape_deta_r50_12ep.py b/configs/REFCOCO_VisualGrounding/ape_deta/ape_deta_r50_12ep.py new file mode 100644 index 0000000..6c0e817 --- /dev/null +++ b/configs/REFCOCO_VisualGrounding/ape_deta/ape_deta_r50_12ep.py @@ -0,0 +1,27 @@ +from ...COCO_InstanceSegmentation.ape_deta.ape_deta_r50_12ep import ( + lr_multiplier, + model, + optimizer, + train, +) + +from ...common.data.refcoco_group_by_image_instance import dataloader + +model.model_vision.num_classes = 256 + +model.model_vision.select_box_nums_for_evaluation = 1 + +criterion = model.model_vision.criterion[0] +model.model_vision.criterion = [criterion for _ in range(2)] + +model.model_vision.dataset_prompts = ["phrase", "expression"] +model.model_vision.dataset_names = ["refcoco-mixed_group-by-image", "refcoco"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + ["refcoco-mixed"] + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 5120 diff --git a/configs/REFCOCO_VisualGrounding/ape_deta/ape_deta_r50_bert_vlf_12ep.py b/configs/REFCOCO_VisualGrounding/ape_deta/ape_deta_r50_bert_vlf_12ep.py new file mode 100644 index 0000000..9040f84 --- /dev/null +++ b/configs/REFCOCO_VisualGrounding/ape_deta/ape_deta_r50_bert_vlf_12ep.py @@ -0,0 +1,32 @@ +from detectron2.config import LazyCall as L +from ape.modeling.text import Bert + +from ...common.data.refcoco_instance import dataloader +from .ape_deta_r50_vlf_12ep import lr_multiplier, model, optimizer, train + +model.model_vision.num_classes = 1 +model.model_vision.select_box_nums_for_evaluation = 1 + +model.model_vision.criterion[0].num_classes = 1 +criterion = model.model_vision.criterion[0] +model.model_vision.criterion = [criterion for _ in range(2)] + +model.model_vision.dataset_prompts = ["expression"] +model.model_vision.dataset_names = ["refcoco"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + + +model.model_language = L(Bert)( + pretrained_model_name_or_path="models/huggingface/bert-base-uncased/" +) +model.model_vision.embed_dim_language = 768 +model.model_vision.text_feature_reduce_type = "average" + +model.model_vision.text_feature_bank = False +model.model_vision.text_feature_reduce_before_fusion = False +model.model_vision.text_feature_batch_repeat = False +model.model_vision.expression_cumulative_gt_class = False + + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 5120 diff --git a/configs/REFCOCO_VisualGrounding/ape_deta/ape_deta_r50_vlf_12ep.py b/configs/REFCOCO_VisualGrounding/ape_deta/ape_deta_r50_vlf_12ep.py new file mode 100644 index 0000000..5a456e6 --- /dev/null +++ b/configs/REFCOCO_VisualGrounding/ape_deta/ape_deta_r50_vlf_12ep.py @@ -0,0 +1,48 @@ +from detectron2.config import LazyCall as L +from omegaconf import OmegaConf +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) + +from .ape_deta_r50_12ep import dataloader, lr_multiplier, model, optimizer, train + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, + use_attention_mask_v=True, +) + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True + + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 5120 diff --git a/configs/REFCOCO_VisualGrounding/ape_deta/ape_deta_vitl_eva02_clip_lsj1024_12ep.py b/configs/REFCOCO_VisualGrounding/ape_deta/ape_deta_vitl_eva02_clip_lsj1024_12ep.py new file mode 100644 index 0000000..2367609 --- /dev/null +++ b/configs/REFCOCO_VisualGrounding/ape_deta/ape_deta_vitl_eva02_clip_lsj1024_12ep.py @@ -0,0 +1,104 @@ +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detectron2.model_zoo import get_config as get_config_d2 +from detectron2.modeling.backbone.fpn import LastLevelMaxPool +from detrex.config import get_config as get_config_detrex +from ape.modeling.backbone.vit import get_vit_lr_decay_rate +from ape.modeling.backbone.vit_eva02 import SimpleFeaturePyramid, ViT +from ape.modeling.text import EVA02CLIP + +from ...common.backbone.vitl_eva02_clip import backbone +from ...common.data.refcoco_instance_lsj1024 import dataloader +from .ape_deta_r50_12ep import model + +constants = get_config_d2("common/data/constants.py").constants + +model.model_vision.pixel_mean = constants.imagenet_rgb256_mean +model.model_vision.pixel_std = constants.imagenet_rgb256_std +model.model_vision.input_format = "RGB" + +model.model_vision.backbone = backbone + +model.model_vision.neck.input_shapes = { + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), +} +model.model_vision.neck.in_features = ["p2", "p3", "p4", "p5", "p6"] +model.model_vision.neck.num_outs = 5 + +model.model_vision.mask_in_features = ["p2"] +model.model_vision.input_shapes = { + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), +} + +optimizer = get_config_detrex("common/optim.py").AdamW +optimizer.params.lr_factor_func = ( + lambda module_name: 0.1 + if "reference_points" in module_name or "sampling_offsets" in module_name + else get_vit_lr_decay_rate(module_name, lr_decay_rate=0.8, num_layers=24) + if "backbone.net" in module_name + else 1 +) +optimizer.params.overrides = {"pos_embed": {"weight_decay": 0.0}} +optimizer.params.weight_decay_norm = None + +optimizer.lr = 2e-4 +optimizer.weight_decay = 1e-4 + +train = get_config_detrex("common/train.py").train +train.max_iter = 90000 +train.eval_period = 5000 +train.log_period = 20 + +train.checkpointer.period = 5000 +train.checkpointer.max_to_keep = 2 + +train.clip_grad.enabled = True +train.clip_grad.params.max_norm = 0.1 +train.clip_grad.params.norm_type = 2 + +train.device = "cuda" + +train.init_checkpoint = ( + "models/QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14to16_s6B.pt?matching_heuristics=True" +) + +train.amp.enabled = True +train.ddp.fp16_compression = True + +lr_multiplier = get_config_detrex("common/coco_schedule.py").lr_multiplier_12ep +lr_multiplier.scheduler.milestones = [75000, 90000] +lr_multiplier.warmup_length = 1000 / train.max_iter + +dataloader.train.num_workers = 16 +dataloader.train.total_batch_size = 16 +dataloader.train.total_batch_size_list = ["${..total_batch_size}", "${..total_batch_size}"] +dataloader.train.mapper.image_format = "RGB" +dataloader.train.mapper.use_instance_mask = True + +model.model_vision.dataset_prompts = ["expression"] +model.model_vision.dataset_names = ["refcoco"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.output_dir = train.output_dir + +model.model_language = L(EVA02CLIP)( + clip_model="EVA02-CLIP-bigE-14-plus", + cache_dir="models/QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt", + dtype="float16", +) +model.model_vision.embed_dim_language = 1024 + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True + diff --git a/configs/REFCOCO_VisualGrounding/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_12ep.py b/configs/REFCOCO_VisualGrounding/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_12ep.py new file mode 100644 index 0000000..01ed3f7 --- /dev/null +++ b/configs/REFCOCO_VisualGrounding/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_12ep.py @@ -0,0 +1,54 @@ +from detectron2.config import LazyCall as L +from omegaconf import OmegaConf +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) + +from .ape_deta_vitl_eva02_clip_lsj1024_12ep import ( + dataloader, + lr_multiplier, + model, + optimizer, + train, +) + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, + use_attention_mask_v=True, +) + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True + + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 5120 diff --git a/configs/REFCOCO_VisualGrounding/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_12ep.py b/configs/REFCOCO_VisualGrounding/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_12ep.py new file mode 100644 index 0000000..ddbeaec --- /dev/null +++ b/configs/REFCOCO_VisualGrounding/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_12ep.py @@ -0,0 +1,105 @@ +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detectron2.model_zoo import get_config as get_config_d2 +from detectron2.modeling.backbone.fpn import LastLevelMaxPool +from detrex.config import get_config as get_config_detrex +from ape.modeling.backbone.vit import get_vit_lr_decay_rate +from ape.modeling.backbone.vit_eva02 import SimpleFeaturePyramid, ViT +from ape.modeling.text import EVA01CLIP + +from ...common.backbone.vitl_eva02 import backbone + +from ...common.data.refcoco_group_by_image_instance_lsj1024 import dataloader +from .ape_deta_r50_vlf_12ep import model + +model.model_vision.num_classes = 256 +model.model_vision.select_box_nums_for_evaluation = 1 +model.model_vision.criterion[0].num_classes = 256 +model.model_vision.criterion[1].num_classes = 256 + +constants = get_config_d2("common/data/constants.py").constants + +model.model_vision.pixel_mean = constants.imagenet_rgb256_mean +model.model_vision.pixel_std = constants.imagenet_rgb256_std +model.model_vision.input_format = "RGB" + +model.model_vision.backbone = backbone + +model.model_vision.neck = None + +model.model_vision.mask_in_features = ["p2"] +model.model_vision.input_shapes = { + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), +} + +optimizer = get_config_detrex("common/optim.py").AdamW +optimizer.params.lr_factor_func = ( + lambda module_name: 0.1 + if "reference_points" in module_name or "sampling_offsets" in module_name + else get_vit_lr_decay_rate(module_name, lr_decay_rate=0.8, num_layers=24) + if "backbone.net" in module_name + else 1 +) +optimizer.params.overrides = {"pos_embed": {"weight_decay": 0.0}} +optimizer.params.weight_decay_norm = None + +optimizer.lr = 2e-4 +optimizer.weight_decay = 1e-4 + +train = get_config_detrex("common/train.py").train +train.max_iter = 90000 +train.eval_period = 5000 +train.log_period = 20 + +train.checkpointer.period = 5000 +train.checkpointer.max_to_keep = 2 + +train.clip_grad.enabled = True +train.clip_grad.params.max_norm = 0.1 +train.clip_grad.params.norm_type = 2 + +train.device = "cuda" + +train.init_checkpoint = ( + "models/Yunxin-CV/EVA-02/eva02/pt/eva02_L_pt_in21k_p14to16.pt?matching_heuristics=True" +) + +train.amp.enabled = True +train.ddp.fp16_compression = True + +lr_multiplier = get_config_detrex("common/coco_schedule.py").lr_multiplier_12ep +lr_multiplier.scheduler.milestones = [75000, 90000] +lr_multiplier.warmup_length = 1000 / train.max_iter + +dataloader.train.num_workers = 16 +dataloader.train.total_batch_size = 16 +dataloader.train.total_batch_size_list = ["${..total_batch_size}", "${..total_batch_size}"] +dataloader.train.mapper.image_format = "RGB" +dataloader.train.mapper.use_instance_mask = True + +dataloader.train.mapper.max_num_phrase = 128 +dataloader.train.mapper.nms_thresh_phrase = 0.6 + +model.model_vision.dataset_prompts = ["phrase", "expression"] +model.model_vision.dataset_names = ["refcoco-mixed_group-by-image", "refcoco"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + ["refcoco-mixed"] + +model.model_language = L(EVA01CLIP)( + clip_model="EVA_CLIP_g_14_X", + cache_dir="models/BAAI/EVA/eva_clip_psz14.pt", +) +model.model_vision.embed_dim_language = 1024 + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True + +model.model_vision.transformer.encoder.use_act_checkpoint = True +model.model_vision.transformer.decoder.use_act_checkpoint = True + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/REFCOCO_VisualGrounding/ape_deta/ape_deta_vitl_lsj1024_12ep.py b/configs/REFCOCO_VisualGrounding/ape_deta/ape_deta_vitl_lsj1024_12ep.py new file mode 100644 index 0000000..18683f6 --- /dev/null +++ b/configs/REFCOCO_VisualGrounding/ape_deta/ape_deta_vitl_lsj1024_12ep.py @@ -0,0 +1,115 @@ +from functools import partial + +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detectron2.model_zoo import get_config as get_config_d2 +from detectron2.modeling.backbone.fpn import LastLevelMaxPool +from detectron2.modeling.backbone.vit import SimpleFeaturePyramid, ViT +from detrex.config import get_config as get_config_detrex +from ape.modeling.backbone.vit import get_vit_lr_decay_rate +from ape.modeling.text import EVA01CLIP + +from ...common.data.refcoco_instance_lsj1024 import dataloader +from .ape_deta_r50_vlf_12ep import model + +constants = get_config_d2("common/data/constants.py").constants +model.model_vision.pixel_mean = constants.imagenet_rgb256_mean +model.model_vision.pixel_std = constants.imagenet_rgb256_std +model.model_vision.input_format = "RGB" + +model.model_vision.backbone = L(SimpleFeaturePyramid)( + net=L(ViT)( # Single-scale ViT backbone + img_size=1024, + patch_size=16, + embed_dim=1024, + depth=24, + num_heads=16, + drop_path_rate=0.4, + window_size=14, + mlp_ratio=4, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + window_block_indexes=list(range(0, 5)) + + list(range(6, 11)) + + list(range(12, 17)) + + list(range(18, 23)), + residual_block_indexes=[], + use_rel_pos=True, + out_feature="last_feat", + use_act_checkpoint=True, + ), + in_feature="${.net.out_feature}", + out_channels=256, + scale_factors=(4.0, 2.0, 1.0, 0.5), + top_block=L(LastLevelMaxPool)(), + norm="LN", + square_pad=1024, +) + +model.model_vision.neck = None + +model.model_vision.mask_in_features = ["p2"] +model.model_vision.input_shapes = { + "p2": ShapeSpec(channels=256), + "p3": ShapeSpec(channels=256), + "p4": ShapeSpec(channels=256), + "p5": ShapeSpec(channels=256), + "p6": ShapeSpec(channels=256), +} + +optimizer = get_config_detrex("common/optim.py").AdamW +optimizer.params.lr_factor_func = ( + lambda module_name: 0.1 + if "reference_points" in module_name or "sampling_offsets" in module_name + else get_vit_lr_decay_rate(module_name, lr_decay_rate=0.8, num_layers=24) + if "backbone.net" in module_name + else 1 +) +optimizer.params.overrides = {"pos_embed": {"weight_decay": 0.0}} + +optimizer.lr = 2e-4 +optimizer.weight_decay = 0.05 + +train = get_config_detrex("common/train.py").train +train.max_iter = 90000 +train.eval_period = 5000 +train.log_period = 20 + +train.checkpointer.period = 5000 +train.checkpointer.max_to_keep = 2 + +train.clip_grad.enabled = True +train.clip_grad.params.max_norm = 0.1 +train.clip_grad.params.norm_type = 2 + +train.device = "cuda" + +train.init_checkpoint = ( + "detectron2://ImageNetPretrained/MAE/mae_pretrain_vit_large.pth?matching_heuristics=True" +) +train.init_checkpoint = "models/MAE/mae_pretrain_vit_large.pth?matching_heuristics=True" + +train.amp.enabled = True +train.ddp.fp16_compression = True + +lr_multiplier = get_config_detrex("common/coco_schedule.py").lr_multiplier_12ep +lr_multiplier.scheduler.milestones = [75000, 90000] +lr_multiplier.warmup_length = 1000 / train.max_iter + +dataloader.train.num_workers = 16 +dataloader.train.total_batch_size = 16 +dataloader.train.mapper.image_format = "RGB" +dataloader.train.mapper.use_instance_mask = True + +model.model_vision.dataset_prompts = ["expression"] +model.model_vision.dataset_names = ["refcoco"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.output_dir = train.output_dir + +model.model_language = L(EVA01CLIP)( + clip_model="EVA_CLIP_g_14_X", cache_dir="models/BAAI/EVA/eva_clip_psz14.pt" +) +model.model_vision.embed_dim_language = 1024 diff --git a/configs/Roboflow_Detection/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024.py b/configs/Roboflow_Detection/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024.py new file mode 100644 index 0000000..c5e6634 --- /dev/null +++ b/configs/Roboflow_Detection/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024.py @@ -0,0 +1,100 @@ +from detectron2.config import LazyCall as L +from detectron2.solver import WarmupParamScheduler +from fvcore.common.param_scheduler import MultiStepParamScheduler + +from ape.data.detection_utils import get_fed_loss_cls_weights +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) + +from ...common.data.roboflow100_instance_lsj1024 import dataloader +from ...LVIS_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_24ep import ( + model, + optimizer, + train, +) + +model.model_vision.num_classes = 1256 +model.model_vision.select_box_nums_for_evaluation = 300 + +criterion = model.model_vision.criterion[0] +del criterion.use_fed_loss +del criterion.get_fed_loss_cls_weights +model.model_vision.criterion = [criterion for _ in range(100)] +for criterion, num_classes in zip( + model.model_vision.criterion, + [ + 1000, + ] + * 100, +): + criterion.num_classes = num_classes + +model.model_vision.instance_on = True +model.model_vision.semantic_on = False +model.model_vision.panoptic_on = False + + +train.max_iter = 720000 +train.eval_period = 720000 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1], + milestones=[640000], + num_updates=720000, + ), + warmup_length=1000 / 720000, + warmup_method="linear", + warmup_factor=0.001, +) + + +model.model_vision.dataset_prompts = ["name" for _ in dataloader.tests] +model.model_vision.dataset_names = [x.dataset.names for x in dataloader.tests] +model.model_vision.dataset_metas = [x.dataset.names for x in dataloader.tests] +model.model_vision.name_prompt_fusion_text = dataloader.name_prompt_fusion_text +model.model_vision.select_box_nums_for_evaluation_list = ( + dataloader.select_box_nums_for_evaluation_list +) + + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/Roboflow_Detection/ape_deta/ape_deta_vitl_eva02_lsj1024.py b/configs/Roboflow_Detection/ape_deta/ape_deta_vitl_eva02_lsj1024.py new file mode 100644 index 0000000..4603280 --- /dev/null +++ b/configs/Roboflow_Detection/ape_deta/ape_deta_vitl_eva02_lsj1024.py @@ -0,0 +1,66 @@ +from detectron2.config import LazyCall as L +from detectron2.solver import WarmupParamScheduler +from fvcore.common.param_scheduler import MultiStepParamScheduler + +from ape.data.detection_utils import get_fed_loss_cls_weights + +from ...common.data.roboflow100_instance_lsj1024 import dataloader +from ...LVIS_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_vlf_lsj1024_cp_24ep import ( + model, + optimizer, + train, +) + +model.model_vision.num_classes = 1256 +model.model_vision.select_box_nums_for_evaluation = 300 + +criterion = model.model_vision.criterion[0] +del criterion.use_fed_loss +del criterion.get_fed_loss_cls_weights +model.model_vision.criterion = [criterion for _ in range(100)] +for criterion, num_classes in zip( + model.model_vision.criterion, + [ + 1000, + ] + * 100, +): + criterion.num_classes = num_classes + +model.model_vision.instance_on = True +model.model_vision.semantic_on = False +model.model_vision.panoptic_on = False + +model.model_vision.neck = None + +train.max_iter = 720000 +train.eval_period = 720000 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1], + milestones=[640000], + num_updates=720000, + ), + warmup_length=1000 / 720000, + warmup_method="linear", + warmup_factor=0.001, +) + + +model.model_vision.dataset_prompts = ["name" for _ in dataloader.tests] +model.model_vision.dataset_names = [x.dataset.names for x in dataloader.tests] +model.model_vision.dataset_metas = [x.dataset.names for x in dataloader.tests] +model.model_vision.name_prompt_fusion_text = dataloader.name_prompt_fusion_text +model.model_vision.select_box_nums_for_evaluation_list = ( + dataloader.select_box_nums_for_evaluation_list +) + + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/Roboflow_Detection/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024.py b/configs/Roboflow_Detection/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024.py new file mode 100644 index 0000000..27f511f --- /dev/null +++ b/configs/Roboflow_Detection/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024.py @@ -0,0 +1,101 @@ +from detectron2.config import LazyCall as L +from detectron2.solver import WarmupParamScheduler +from fvcore.common.param_scheduler import MultiStepParamScheduler + +from ape.data.detection_utils import get_fed_loss_cls_weights +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) + +from ...common.data.roboflow100_instance_lsj1024 import dataloader +from ...LVIS_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_vlf_lsj1024_cp_24ep import ( + model, + optimizer, + train, +) + +model.model_vision.num_classes = 1256 +model.model_vision.select_box_nums_for_evaluation = 300 + +criterion = model.model_vision.criterion[0] +del criterion.use_fed_loss +del criterion.get_fed_loss_cls_weights +model.model_vision.criterion = [criterion for _ in range(100)] +for criterion, num_classes in zip( + model.model_vision.criterion, + [ + 1000, + ] + * 100, +): + criterion.num_classes = num_classes + +model.model_vision.instance_on = True +model.model_vision.semantic_on = False +model.model_vision.panoptic_on = False + +model.model_vision.neck = None + +train.max_iter = 720000 +train.eval_period = 720000 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1], + milestones=[640000], + num_updates=720000, + ), + warmup_length=1000 / 720000, + warmup_method="linear", + warmup_factor=0.001, +) + + +model.model_vision.dataset_prompts = ["name" for _ in dataloader.tests] +model.model_vision.dataset_names = [x.dataset.names for x in dataloader.tests] +model.model_vision.dataset_metas = [x.dataset.names for x in dataloader.tests] +model.model_vision.name_prompt_fusion_text = dataloader.name_prompt_fusion_text +model.model_vision.select_box_nums_for_evaluation_list = ( + dataloader.select_box_nums_for_evaluation_list +) + + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/SegInW_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024.py b/configs/SegInW_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024.py new file mode 100644 index 0000000..9372147 --- /dev/null +++ b/configs/SegInW_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024.py @@ -0,0 +1,108 @@ +from detectron2.config import LazyCall as L +from detectron2.solver import WarmupParamScheduler +from fvcore.common.param_scheduler import MultiStepParamScheduler +from ape.data.detection_utils import get_fed_loss_cls_weights +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) + +from ...common.data.seginw_instance_lsj1024 import dataloader +from ...LVIS_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_clip_lsj1024_cp_24ep import ( + model, + optimizer, + train, +) + +model.model_vision.num_classes = 1256 +model.model_vision.select_box_nums_for_evaluation = 300 + +criterion = model.model_vision.criterion[0] +del criterion.use_fed_loss +del criterion.get_fed_loss_cls_weights +model.model_vision.criterion = [criterion for _ in range(25)] +for criterion, num_classes in zip( + model.model_vision.criterion, + [ + 1000, + ] + * 25, +): + criterion.num_classes = num_classes + + +model.model_vision.stuff_dataset_learn_thing = False +model.model_vision.stuff_prob_thing = -1.0 + +model.model_vision.instance_on = True +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = False + + +train.max_iter = 720000 +train.eval_period = 720000 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1], + milestones=[640000], + num_updates=720000, + ), + warmup_length=1000 / 720000, + warmup_method="linear", + warmup_factor=0.001, +) + +for i in range(len(dataloader.train)): + dataloader.train[i].total_batch_size = 16 + dataloader.train[i].total_batch_size_list = [16] + +model.model_vision.dataset_prompts = ["name" for _ in dataloader.tests] +model.model_vision.dataset_names = [x.dataset.names.replace("_val", "") for x in dataloader.tests] +model.model_vision.dataset_metas = [x.dataset.names for x in dataloader.tests] + + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 12800 + +train.output_dir = "output/" + __file__[:-3] + + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/SegInW_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024.py b/configs/SegInW_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024.py new file mode 100644 index 0000000..212471b --- /dev/null +++ b/configs/SegInW_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_lsj1024.py @@ -0,0 +1,74 @@ +from detectron2.config import LazyCall as L +from detectron2.solver import WarmupParamScheduler +from fvcore.common.param_scheduler import MultiStepParamScheduler +from ape.data.detection_utils import get_fed_loss_cls_weights + +from ...common.data.seginw_instance_lsj1024 import dataloader +from ...LVIS_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_lsj1024_cp_24ep import ( + model, + optimizer, + train, +) + +model.model_vision.num_classes = 1256 +model.model_vision.select_box_nums_for_evaluation = 300 + +criterion = model.model_vision.criterion[0] +del criterion.use_fed_loss +del criterion.get_fed_loss_cls_weights +model.model_vision.criterion = [criterion for _ in range(25)] +for criterion, num_classes in zip( + model.model_vision.criterion, + [ + 1000, + ] + * 25, +): + criterion.num_classes = num_classes + + +model.model_vision.stuff_dataset_learn_thing = False +model.model_vision.stuff_prob_thing = -1.0 + +model.model_vision.instance_on = True +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = False + +model.model_vision.neck = None + +train.max_iter = 720000 +train.eval_period = 720000 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1], + milestones=[640000], + num_updates=720000, + ), + warmup_length=1000 / 720000, + warmup_method="linear", + warmup_factor=0.001, +) + +for i in range(len(dataloader.train)): + dataloader.train[i].total_batch_size = 16 + dataloader.train[i].total_batch_size_list = [16] + +model.model_vision.dataset_prompts = ["name" for _ in dataloader.tests] +model.model_vision.dataset_names = [x.dataset.names.replace("_val", "") for x in dataloader.tests] +model.model_vision.dataset_metas = [x.dataset.names for x in dataloader.tests] + + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 12800 + +train.output_dir = "output/" + __file__[:-3] + + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/SegInW_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024.py b/configs/SegInW_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024.py new file mode 100644 index 0000000..fb9ebce --- /dev/null +++ b/configs/SegInW_InstanceSegmentation/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024.py @@ -0,0 +1,109 @@ +from detectron2.config import LazyCall as L +from detectron2.solver import WarmupParamScheduler +from fvcore.common.param_scheduler import MultiStepParamScheduler +from ape.data.detection_utils import get_fed_loss_cls_weights +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) + +from ...common.data.seginw_instance_lsj1024 import dataloader +from ...LVIS_InstanceSegmentation.ape_deta.ape_deta_vitl_eva02_lsj1024_cp_24ep import ( + model, + optimizer, + train, +) + +model.model_vision.num_classes = 1256 +model.model_vision.select_box_nums_for_evaluation = 300 + +criterion = model.model_vision.criterion[0] +del criterion.use_fed_loss +del criterion.get_fed_loss_cls_weights +model.model_vision.criterion = [criterion for _ in range(25)] +for criterion, num_classes in zip( + model.model_vision.criterion, + [ + 1000, + ] + * 25, +): + criterion.num_classes = num_classes + + +model.model_vision.stuff_dataset_learn_thing = False +model.model_vision.stuff_prob_thing = -1.0 + +model.model_vision.instance_on = True +model.model_vision.semantic_on = True +model.model_vision.panoptic_on = False + +model.model_vision.neck = None + +train.max_iter = 720000 +train.eval_period = 720000 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1], + milestones=[640000], + num_updates=720000, + ), + warmup_length=1000 / 720000, + warmup_method="linear", + warmup_factor=0.001, +) + +for i in range(len(dataloader.train)): + dataloader.train[i].total_batch_size = 16 + dataloader.train[i].total_batch_size_list = [16] + +model.model_vision.dataset_prompts = ["name" for _ in dataloader.tests] +model.model_vision.dataset_names = [x.dataset.names.replace("_val", "") for x in dataloader.tests] +model.model_vision.dataset_metas = [x.dataset.names for x in dataloader.tests] + + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + stable_softmax_2d=True, + clamp_min_for_underflow=True, + clamp_max_for_overflow=True, + use_checkpoint=True, +) + +model.model_vision.text_feature_bank = True +model.model_vision.text_feature_reduce_before_fusion = True +model.model_vision.text_feature_batch_repeat = True +model.model_vision.expression_cumulative_gt_class = True +model.model_vision.name_prompt_fusion_type = "zero" + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 12800 + +train.output_dir = "output/" + __file__[:-3] + + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/VisualGenome_VisualGrounding/ape_deta/ape_deta_r50_12ep.py b/configs/VisualGenome_VisualGrounding/ape_deta/ape_deta_r50_12ep.py new file mode 100644 index 0000000..cce2bfe --- /dev/null +++ b/configs/VisualGenome_VisualGrounding/ape_deta/ape_deta_r50_12ep.py @@ -0,0 +1,20 @@ +from ...COCO_InstanceSegmentation.ape_deta.ape_deta_r50_12ep import ( + lr_multiplier, + model, + optimizer, + train, +) +from ...common.data.vgregion_instance import dataloader + +model.model_vision.num_classes = 200 + +model.model_vision.criterion[0].num_classes = 200 + +dataloader.train.mapper.max_num_phrase = 100 + +model.model_vision.dataset_prompts = ["phrase", "expression"] +model.model_vision.dataset_names = ["vgregion", "refcoco"] +model.model_vision.dataset_metas = dataloader.train.dataset.names + ["refcoco-mixed"] + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 1280 diff --git a/configs/VisualGenome_VisualGrounding/ape_deta/ape_deta_r50_12ep_eval_odinw13.py b/configs/VisualGenome_VisualGrounding/ape_deta/ape_deta_r50_12ep_eval_odinw13.py new file mode 100644 index 0000000..2bc5148 --- /dev/null +++ b/configs/VisualGenome_VisualGrounding/ape_deta/ape_deta_r50_12ep_eval_odinw13.py @@ -0,0 +1,10 @@ +from ...common.data.odinw13_instance import dataloader +from .ape_deta_r50_12ep import lr_multiplier, model, optimizer, train + +model.model_vision.dataset_prompts = ["name" for _ in dataloader.tests] +model.model_vision.dataset_names = [ + test.dataset.names.replace("_val", "") for test in dataloader.tests +] +model.model_vision.dataset_metas = [test.dataset.names for test in dataloader.tests] + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/VisualGenome_VisualGrounding/ape_deta/ape_deta_r50_12ep_eval_odinw35.py b/configs/VisualGenome_VisualGrounding/ape_deta/ape_deta_r50_12ep_eval_odinw35.py new file mode 100644 index 0000000..fb0a681 --- /dev/null +++ b/configs/VisualGenome_VisualGrounding/ape_deta/ape_deta_r50_12ep_eval_odinw35.py @@ -0,0 +1,10 @@ +from ...common.data.odinw35_instance import dataloader +from .ape_deta_r50_12ep import lr_multiplier, model, optimizer, train + +model.model_vision.dataset_prompts = ["name" for _ in dataloader.tests] +model.model_vision.dataset_names = [ + test.dataset.names.replace("_val", "") for test in dataloader.tests +] +model.model_vision.dataset_metas = [test.dataset.names for test in dataloader.tests] + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/VisualGenome_VisualGrounding/ape_deta/ape_deta_r50_vlf_12ep.py b/configs/VisualGenome_VisualGrounding/ape_deta/ape_deta_r50_vlf_12ep.py new file mode 100644 index 0000000..d0f6e6d --- /dev/null +++ b/configs/VisualGenome_VisualGrounding/ape_deta/ape_deta_r50_vlf_12ep.py @@ -0,0 +1,46 @@ +from detectron2.config import LazyCall as L +from omegaconf import OmegaConf +from ape.layers import VisionLanguageFusion +from ape.modeling.ape_deta import ( + DeformableDETRSegmVL, + DeformableDetrTransformerDecoderVL, + DeformableDetrTransformerEncoderVL, + DeformableDetrTransformerVL, +) + +from .ape_deta_r50_12ep import dataloader, lr_multiplier, model, optimizer, train + +model.model_vision.update( + _target_=DeformableDETRSegmVL, +) +model.model_vision.transformer.update( + _target_=DeformableDetrTransformerVL, +) +model.model_vision.transformer.encoder.update( + _target_=DeformableDetrTransformerEncoderVL, +) +model.model_vision.transformer.decoder.update( + _target_=DeformableDetrTransformerDecoderVL, +) + + +model.model_vision.transformer.encoder.vl_layer = L(VisionLanguageFusion)( + v_dim="${....embed_dim}", + l_dim="${....embed_dim_language}", + embed_dim=2048, + num_heads=8, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / 6, + cfg=OmegaConf.from_dotlist( + [ + "MODEL.DYHEAD.FUSE_CONFIG.STABLE_SOFTMAX_2D=False", + "MODEL.DYHEAD.FUSE_CONFIG.CLAMP_MIN_FOR_UNDERFLOW=True", + "MODEL.DYHEAD.FUSE_CONFIG.CLAMP_MAX_FOR_OVERFLOW=True", + "MODEL.VL_FUSION_USE_CHECKPOINT=True", + ], + ), +) + +train.output_dir = "output/" + __file__[:-3] +model.model_vision.vis_period = 1280 diff --git a/configs/VisualGenome_VisualGrounding/ape_deta/ape_deta_r50_vlf_12ep_eval_odinw13.py b/configs/VisualGenome_VisualGrounding/ape_deta/ape_deta_r50_vlf_12ep_eval_odinw13.py new file mode 100644 index 0000000..e017fae --- /dev/null +++ b/configs/VisualGenome_VisualGrounding/ape_deta/ape_deta_r50_vlf_12ep_eval_odinw13.py @@ -0,0 +1,10 @@ +from ...common.data.odinw13_instance import dataloader +from .ape_deta_r50_vlf_12ep import lr_multiplier, model, optimizer, train + +model.model_vision.dataset_prompts = ["name" for _ in dataloader.tests] +model.model_vision.dataset_names = [ + test.dataset.names.replace("_val", "") for test in dataloader.tests +] +model.model_vision.dataset_metas = [test.dataset.names for test in dataloader.tests] + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/VisualGenome_VisualGrounding/ape_deta/ape_deta_r50_vlf_12ep_eval_odinw35.py b/configs/VisualGenome_VisualGrounding/ape_deta/ape_deta_r50_vlf_12ep_eval_odinw35.py new file mode 100644 index 0000000..e09309f --- /dev/null +++ b/configs/VisualGenome_VisualGrounding/ape_deta/ape_deta_r50_vlf_12ep_eval_odinw35.py @@ -0,0 +1,10 @@ +from ...common.data.odinw35_instance import dataloader +from .ape_deta_r50_vlf_12ep import lr_multiplier, model, optimizer, train + +model.model_vision.dataset_prompts = ["name" for _ in dataloader.tests] +model.model_vision.dataset_names = [ + test.dataset.names.replace("_val", "") for test in dataloader.tests +] +model.model_vision.dataset_metas = [test.dataset.names for test in dataloader.tests] + +train.output_dir = "output/" + __file__[:-3] diff --git a/configs/common/backbone/vite_eva02_clip_1024.py b/configs/common/backbone/vite_eva02_clip_1024.py new file mode 100644 index 0000000..95a7a4d --- /dev/null +++ b/configs/common/backbone/vite_eva02_clip_1024.py @@ -0,0 +1,52 @@ +from functools import partial + +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.modeling.backbone.fpn import LastLevelMaxPool +from ape.modeling.backbone.vit_eva_clip import SimpleFeaturePyramid, ViT + +backbone = L(SimpleFeaturePyramid)( + net=L(ViT)( # Single-scale ViT backbone + img_size=1024, + patch_size=16, + embed_dim=1792, + depth=64, + num_heads=16, + drop_path_rate=0.4, + window_size=32, + mlp_ratio=8.571428571428571, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + window_block_indexes=list(range(0, 3)) + + list(range(4, 7)) + + list(range(8, 11)) + + list(range(12, 15)) + + list(range(16, 19)) + + list(range(20, 23)) + + list(range(24, 27)) + + list(range(28, 31)) + + list(range(32, 35)) + + list(range(36, 39)) + + list(range(40, 43)) + + list(range(44, 47)) + + list(range(48, 51)) + + list(range(52, 55)) + + list(range(56, 59)) + + list(range(60, 63)), + residual_block_indexes=[], + use_rel_pos=True, + out_feature="last_feat", + use_act_checkpoint=True, + xattn=True, + pretrain_img_size=224, + pretrain_use_cls_token=True, + postnorm=True, + ), + in_feature="${.net.out_feature}", + out_channels=256, + scale_factors=(4.0, 2.0, 1.0, 0.5), + top_block=L(LastLevelMaxPool)(), + norm="LN", + square_pad=1024, +) diff --git a/configs/common/backbone/vite_eva02_clip_1536.py b/configs/common/backbone/vite_eva02_clip_1536.py new file mode 100644 index 0000000..6389d5e --- /dev/null +++ b/configs/common/backbone/vite_eva02_clip_1536.py @@ -0,0 +1,52 @@ +from functools import partial + +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.modeling.backbone.fpn import LastLevelMaxPool +from ape.modeling.backbone.vit_eva_clip import SimpleFeaturePyramid, ViT + +backbone = L(SimpleFeaturePyramid)( + net=L(ViT)( # Single-scale ViT backbone + img_size=1536, + patch_size=16, + embed_dim=1792, + depth=64, + num_heads=16, + drop_path_rate=0.4, + window_size=32, + mlp_ratio=8.571428571428571, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + window_block_indexes=list(range(0, 3)) + + list(range(4, 7)) + + list(range(8, 11)) + + list(range(12, 15)) + + list(range(16, 19)) + + list(range(20, 23)) + + list(range(24, 27)) + + list(range(28, 31)) + + list(range(32, 35)) + + list(range(36, 39)) + + list(range(40, 43)) + + list(range(44, 47)) + + list(range(48, 51)) + + list(range(52, 55)) + + list(range(56, 59)) + + list(range(60, 63)), + residual_block_indexes=[], + use_rel_pos=True, + out_feature="last_feat", + use_act_checkpoint=True, + xattn=True, + pretrain_img_size=224, + pretrain_use_cls_token=True, + postnorm=True, + ), + in_feature="${.net.out_feature}", + out_channels=256, + scale_factors=(4.0, 2.0, 1.0, 0.5), + top_block=L(LastLevelMaxPool)(), + norm="LN", + square_pad=1536, +) diff --git a/configs/common/backbone/vitg_eva01.py b/configs/common/backbone/vitg_eva01.py new file mode 100644 index 0000000..df8b5b1 --- /dev/null +++ b/configs/common/backbone/vitg_eva01.py @@ -0,0 +1,45 @@ +from functools import partial + +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.modeling.backbone.fpn import LastLevelMaxPool +from ape.modeling.backbone.vit_eva import SimpleFeaturePyramid, ViT + +backbone = L(SimpleFeaturePyramid)( + net=L(ViT)( # Single-scale ViT backbone + img_size=1024, + patch_size=16, + embed_dim=1408, + depth=40, + num_heads=16, + drop_path_rate=0.6, + window_size=16, + mlp_ratio=6144 / 1408, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + window_block_indexes=list(range(0, 3)) + + list(range(4, 7)) + + list(range(8, 11)) + + list(range(12, 15)) + + list(range(16, 19)) + + list(range(20, 23)) + + list(range(24, 27)) + + list(range(28, 31)) + + list(range(32, 35)) + + list(range(36, 39)), + residual_block_indexes=[], + use_rel_pos=True, + out_feature="last_feat", + use_act_checkpoint=True, + beit_like_qkv_bias=True, + beit_like_gamma=False, + freeze_patch_embed=True, + ), + in_feature="${.net.out_feature}", + out_channels=256, + scale_factors=(4.0, 2.0, 1.0, 0.5), + top_block=L(LastLevelMaxPool)(), + norm="LN", + square_pad=1024, +) diff --git a/configs/common/backbone/vitg_eva01_1536.py b/configs/common/backbone/vitg_eva01_1536.py new file mode 100644 index 0000000..a810d52 --- /dev/null +++ b/configs/common/backbone/vitg_eva01_1536.py @@ -0,0 +1,45 @@ +from functools import partial + +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.modeling.backbone.fpn import LastLevelMaxPool +from ape.modeling.backbone.vit_eva import SimpleFeaturePyramid, ViT + +backbone = L(SimpleFeaturePyramid)( + net=L(ViT)( # Single-scale ViT backbone + img_size=1536, + patch_size=16, + embed_dim=1408, + depth=40, + num_heads=16, + drop_path_rate=0.6, + window_size=32, + mlp_ratio=6144 / 1408, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + window_block_indexes=list(range(0, 3)) + + list(range(4, 7)) + + list(range(8, 11)) + + list(range(12, 15)) + + list(range(16, 19)) + + list(range(20, 23)) + + list(range(24, 27)) + + list(range(28, 31)) + + list(range(32, 35)) + + list(range(36, 39)), + residual_block_indexes=[], + use_rel_pos=True, + out_feature="last_feat", + use_act_checkpoint=True, + beit_like_qkv_bias=True, + beit_like_gamma=False, + freeze_patch_embed=True, + ), + in_feature="${.net.out_feature}", + out_channels=256, + scale_factors=(4.0, 2.0, 1.0, 0.5), + top_block=L(LastLevelMaxPool)(), + norm="LN", + square_pad=1536, +) diff --git a/configs/common/backbone/vitg_eva01_clip_1024.py b/configs/common/backbone/vitg_eva01_clip_1024.py new file mode 100644 index 0000000..808de9d --- /dev/null +++ b/configs/common/backbone/vitg_eva01_clip_1024.py @@ -0,0 +1,45 @@ +from functools import partial + +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.modeling.backbone.fpn import LastLevelMaxPool +from ape.modeling.backbone.vit_eva_clip import SimpleFeaturePyramid, ViT + +backbone = L(SimpleFeaturePyramid)( + net=L(ViT)( # Single-scale ViT backbone + img_size=1024, + patch_size=16, + embed_dim=1408, + depth=40, + num_heads=16, + drop_path_rate=0.6, + window_size=32, + mlp_ratio=6144 / 1408, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + window_block_indexes=list(range(0, 3)) + + list(range(4, 7)) + + list(range(8, 11)) + + list(range(12, 15)) + + list(range(16, 19)) + + list(range(20, 23)) + + list(range(24, 27)) + + list(range(28, 31)) + + list(range(32, 35)) + + list(range(36, 39)), + residual_block_indexes=[], + use_rel_pos=True, + out_feature="last_feat", + use_act_checkpoint=True, + xattn=True, + pretrain_img_size=224, + pretrain_use_cls_token=True, + ), + in_feature="${.net.out_feature}", + out_channels=256, + scale_factors=(4.0, 2.0, 1.0, 0.5), + top_block=L(LastLevelMaxPool)(), + norm="LN", + square_pad=1024, +) diff --git a/configs/common/backbone/vitg_eva01_clip_1536.py b/configs/common/backbone/vitg_eva01_clip_1536.py new file mode 100644 index 0000000..c734ce1 --- /dev/null +++ b/configs/common/backbone/vitg_eva01_clip_1536.py @@ -0,0 +1,45 @@ +from functools import partial + +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.modeling.backbone.fpn import LastLevelMaxPool +from ape.modeling.backbone.vit_eva_clip import SimpleFeaturePyramid, ViT + +backbone = L(SimpleFeaturePyramid)( + net=L(ViT)( # Single-scale ViT backbone + img_size=1536, + patch_size=16, + embed_dim=1408, + depth=40, + num_heads=16, + drop_path_rate=0.6, + window_size=32, + mlp_ratio=6144 / 1408, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + window_block_indexes=list(range(0, 3)) + + list(range(4, 7)) + + list(range(8, 11)) + + list(range(12, 15)) + + list(range(16, 19)) + + list(range(20, 23)) + + list(range(24, 27)) + + list(range(28, 31)) + + list(range(32, 35)) + + list(range(36, 39)), + residual_block_indexes=[], + use_rel_pos=True, + out_feature="last_feat", + use_act_checkpoint=True, + xattn=True, + pretrain_img_size=224, + pretrain_use_cls_token=True, + ), + in_feature="${.net.out_feature}", + out_channels=256, + scale_factors=(4.0, 2.0, 1.0, 0.5), + top_block=L(LastLevelMaxPool)(), + norm="LN", + square_pad=1536, +) diff --git a/configs/common/backbone/vitl_eva02.py b/configs/common/backbone/vitl_eva02.py new file mode 100644 index 0000000..baca81e --- /dev/null +++ b/configs/common/backbone/vitl_eva02.py @@ -0,0 +1,37 @@ +from functools import partial + +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.modeling.backbone.fpn import LastLevelMaxPool +from ape.modeling.backbone.vit_eva02 import SimpleFeaturePyramid, ViT + +backbone = L(SimpleFeaturePyramid)( + net=L(ViT)( # Single-scale ViT backbone + img_size=1024, + patch_size=16, + embed_dim=1024, + depth=24, + num_heads=16, + drop_path_rate=0.4, + window_size=16, + mlp_ratio=4 * 2 / 3, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + window_block_indexes=list(range(0, 5)) + + list(range(6, 11)) + + list(range(12, 17)) + + list(range(18, 23)), + residual_block_indexes=[], + use_rel_pos=True, + out_feature="last_feat", + use_act_checkpoint=True, + xattn=True, + ), + in_feature="${.net.out_feature}", + out_channels=256, + scale_factors=(4.0, 2.0, 1.0, 0.5), + top_block=L(LastLevelMaxPool)(), + norm="LN", + square_pad=1024, +) diff --git a/configs/common/backbone/vitl_eva02_1536.py b/configs/common/backbone/vitl_eva02_1536.py new file mode 100644 index 0000000..e488a3b --- /dev/null +++ b/configs/common/backbone/vitl_eva02_1536.py @@ -0,0 +1,41 @@ +from functools import partial + +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.modeling.backbone.fpn import LastLevelMaxPool +from ape.modeling.backbone.vit_eva02 import SimpleFeaturePyramid, ViT + +backbone = L(SimpleFeaturePyramid)( + net=L(ViT)( # Single-scale ViT backbone + img_size=1536, + patch_size=16, + embed_dim=1024, + depth=24, + num_heads=16, + drop_path_rate=0.4, + window_size=32, + mlp_ratio=4 * 2 / 3, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + window_block_indexes=list(range(0, 2)) + + list(range(3, 5)) + + list(range(6, 8)) + + list(range(9, 11)) + + list(range(12, 14)) + + list(range(15, 17)) + + list(range(18, 20)) + + list(range(21, 23)), + residual_block_indexes=[], + use_rel_pos=True, + out_feature="last_feat", + use_act_checkpoint=True, + xattn=True, + ), + in_feature="${.net.out_feature}", + out_channels=256, + scale_factors=(4.0, 2.0, 1.0, 0.5), + top_block=L(LastLevelMaxPool)(), + norm="LN", + square_pad=1536, +) diff --git a/configs/common/backbone/vitl_eva02_clip.py b/configs/common/backbone/vitl_eva02_clip.py new file mode 100644 index 0000000..bdf3537 --- /dev/null +++ b/configs/common/backbone/vitl_eva02_clip.py @@ -0,0 +1,48 @@ +from functools import partial + +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.modeling.backbone.fpn import LastLevelMaxPool +from ape.modeling.backbone.vit_eva_clip import SimpleFeaturePyramid, ViT + +backbone = L(SimpleFeaturePyramid)( + net=L(ViT)( # Single-scale ViT backbone + img_size=1024, + patch_size=16, + embed_dim=1024, + depth=24, + num_heads=16, + drop_path_rate=0.4, + window_size=32, + mlp_ratio=4 * 2 / 3, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + window_block_indexes=list(range(0, 2)) + + list(range(3, 5)) + + list(range(6, 8)) + + list(range(9, 11)) + + list(range(12, 14)) + + list(range(15, 17)) + + list(range(18, 20)) + + list(range(21, 23)), + residual_block_indexes=[], + use_rel_pos=True, + out_feature="last_feat", + use_act_checkpoint=True, + xattn=True, + rope=True, + pt_hw_seq_len=16, + intp_freq=True, + naiveswiglu=True, + subln=True, + pretrain_img_size=336, + pretrain_use_cls_token=True, + ), + in_feature="${.net.out_feature}", + out_channels=256, + scale_factors=(4.0, 2.0, 1.0, 0.5), + top_block=L(LastLevelMaxPool)(), + norm="LN", + square_pad=1024, +) diff --git a/configs/common/backbone/vitl_eva02_clip_1536.py b/configs/common/backbone/vitl_eva02_clip_1536.py new file mode 100644 index 0000000..557b186 --- /dev/null +++ b/configs/common/backbone/vitl_eva02_clip_1536.py @@ -0,0 +1,48 @@ +from functools import partial + +import torch.nn as nn + +from detectron2.config import LazyCall as L +from detectron2.modeling.backbone.fpn import LastLevelMaxPool +from ape.modeling.backbone.vit_eva_clip import SimpleFeaturePyramid, ViT + +backbone = L(SimpleFeaturePyramid)( + net=L(ViT)( # Single-scale ViT backbone + img_size=1536, + patch_size=16, + embed_dim=1024, + depth=24, + num_heads=16, + drop_path_rate=0.4, + window_size=32, + mlp_ratio=4 * 2 / 3, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + window_block_indexes=list(range(0, 2)) + + list(range(3, 5)) + + list(range(6, 8)) + + list(range(9, 11)) + + list(range(12, 14)) + + list(range(15, 17)) + + list(range(18, 20)) + + list(range(21, 23)), + residual_block_indexes=[], + use_rel_pos=True, + out_feature="last_feat", + use_act_checkpoint=True, + xattn=True, + rope=True, + pt_hw_seq_len=16, + intp_freq=True, + naiveswiglu=True, + subln=True, + pretrain_img_size=336, + pretrain_use_cls_token=True, + ), + in_feature="${.net.out_feature}", + out_channels=256, + scale_factors=(4.0, 2.0, 1.0, 0.5), + top_block=L(LastLevelMaxPool)(), + norm="LN", + square_pad=1536, +) diff --git a/configs/common/data/ade20k_panoptic.py b/configs/common/data/ade20k_panoptic.py new file mode 100644 index 0000000..f821159 --- /dev/null +++ b/configs/common/data/ade20k_panoptic.py @@ -0,0 +1,81 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + build_detection_train_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import COCOPanopticEvaluator, SemSegEvaluator +from omegaconf import OmegaConf +from ape.data import DatasetMapper_detr_panoptic +from ape.evaluation import InstanceSegEvaluator + +dataloader = OmegaConf.create() + +dataloader.train = L(build_detection_train_loader)( + dataset=L(get_detection_dataset_dicts)(names="ade20k_panoptic_train"), + mapper=L(DatasetMapper_detr_panoptic)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(), + L(T.ResizeShortestEdge)( + short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), + max_size=1333, + sample_style="choice", + ), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(), + L(T.ResizeShortestEdge)( + short_edge_length=(400, 500, 600), + sample_style="choice", + ), + L(T.RandomCrop)( + crop_type="absolute_range", + crop_size=(384, 600), + ), + L(T.ResizeShortestEdge)( + short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), + max_size=1333, + sample_style="choice", + ), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + instance_mask_format="bitmask", + ignore_label=MetadataCatalog.get("ade20k_panoptic_train").ignore_label, + stuff_classes_offset=0, + stuff_classes_decomposition=True, + dataset_names="${..dataset.names}", + ), + total_batch_size=16, + aspect_ratio_grouping=True, + num_workers=16, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="ade20k_panoptic_val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=800, max_size=1333), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = [ + L(InstanceSegEvaluator)( + dataset_name="${...test.dataset.names}", + ), + L(SemSegEvaluator)( + dataset_name="${...test.dataset.names}", + ), + L(COCOPanopticEvaluator)( + dataset_name="${...test.dataset.names}", + ), +] diff --git a/configs/common/data/ade20k_panoptic_lsj1024.py b/configs/common/data/ade20k_panoptic_lsj1024.py new file mode 100644 index 0000000..5f619a5 --- /dev/null +++ b/configs/common/data/ade20k_panoptic_lsj1024.py @@ -0,0 +1,72 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + build_detection_train_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import COCOPanopticEvaluator, SemSegEvaluator +from omegaconf import OmegaConf +from ape.data import DatasetMapper_detr_panoptic +from ape.evaluation import InstanceSegEvaluator + +image_size = 1024 + +dataloader = OmegaConf.create() + +dataloader.train = L(build_detection_train_loader)( + dataset=L(get_detection_dataset_dicts)(names="ade20k_panoptic_train"), + mapper=L(DatasetMapper_detr_panoptic)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + instance_mask_format="bitmask", + ignore_label=MetadataCatalog.get("ade20k_panoptic_train").ignore_label, + stuff_classes_offset=0, + stuff_classes_decomposition=True, + dataset_names="${..dataset.names}", + ), + total_batch_size=16, + num_workers=4, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="ade20k_panoptic_val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = [ + L(InstanceSegEvaluator)( + dataset_name="${...test.dataset.names}", + ), + L(SemSegEvaluator)( + dataset_name="${...test.dataset.names}", + ), + L(COCOPanopticEvaluator)( + dataset_name="${...test.dataset.names}", + ), +] diff --git a/configs/common/data/ade20k_semantic.py b/configs/common/data/ade20k_semantic.py new file mode 100644 index 0000000..8a9f713 --- /dev/null +++ b/configs/common/data/ade20k_semantic.py @@ -0,0 +1,68 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + build_detection_train_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import SemSegEvaluator +from omegaconf import OmegaConf +from ape.data import DatasetMapper_detr_semantic + +dataloader = OmegaConf.create() + +dataloader.train = L(build_detection_train_loader)( + dataset=L(get_detection_dataset_dicts)(names="ade20k_sem_seg_train"), + mapper=L(DatasetMapper_detr_semantic)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(), + L(T.ResizeShortestEdge)( + short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), + max_size=1333, + sample_style="choice", + ), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(), + L(T.ResizeShortestEdge)( + short_edge_length=(400, 500, 600), + sample_style="choice", + ), + L(T.RandomCrop)( + crop_type="absolute_range", + crop_size=(384, 600), + ), + L(T.ResizeShortestEdge)( + short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), + max_size=1333, + sample_style="choice", + ), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + ignore_label=MetadataCatalog.get("ade20k_sem_seg_train").ignore_label, + stuff_classes_decomposition=True, + ), + total_batch_size=16, + num_workers=4, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="ade20k_sem_seg_val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=800, max_size=1333), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(SemSegEvaluator)( + dataset_name="${..test.dataset.names}", +) diff --git a/configs/common/data/ade20k_semantic_lsj1024.py b/configs/common/data/ade20k_semantic_lsj1024.py new file mode 100644 index 0000000..2f7c45f --- /dev/null +++ b/configs/common/data/ade20k_semantic_lsj1024.py @@ -0,0 +1,60 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + build_detection_train_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import SemSegEvaluator +from omegaconf import OmegaConf +from ape.data import DatasetMapper_detr_semantic + +image_size = 1024 + +dataloader = OmegaConf.create() + +dataloader.train = L(build_detection_train_loader)( + dataset=L(get_detection_dataset_dicts)(names="ade20k_sem_seg_train"), + mapper=L(DatasetMapper_detr_semantic)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + ignore_label=MetadataCatalog.get("ade20k_sem_seg_train").ignore_label, + stuff_classes_decomposition=True, + ), + total_batch_size=16, + num_workers=4, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="ade20k_sem_seg_val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(SemSegEvaluator)( + dataset_name="${..test.dataset.names}", +) diff --git a/configs/common/data/ade20kfull_semantic_lsj1024.py b/configs/common/data/ade20kfull_semantic_lsj1024.py new file mode 100644 index 0000000..e698a02 --- /dev/null +++ b/configs/common/data/ade20kfull_semantic_lsj1024.py @@ -0,0 +1,60 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + build_detection_train_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import SemSegEvaluator +from omegaconf import OmegaConf +from ape.data import DatasetMapper_detr_semantic + +image_size = 1024 + +dataloader = OmegaConf.create() + +dataloader.train = L(build_detection_train_loader)( + dataset=L(get_detection_dataset_dicts)(names="ade20k_full_sem_seg_train"), + mapper=L(DatasetMapper_detr_semantic)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + ignore_label=MetadataCatalog.get("ade20k_full_sem_seg_train").ignore_label, + stuff_classes_decomposition=True, + ), + total_batch_size=16, + num_workers=4, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="ade20k_full_sem_seg_val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(SemSegEvaluator)( + dataset_name="${..test.dataset.names}", +) diff --git a/configs/common/data/bdd10k_panoptic_lsj1024.py b/configs/common/data/bdd10k_panoptic_lsj1024.py new file mode 100644 index 0000000..7e63304 --- /dev/null +++ b/configs/common/data/bdd10k_panoptic_lsj1024.py @@ -0,0 +1,65 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + build_detection_train_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import COCOPanopticEvaluator +from omegaconf import OmegaConf +from ape.data import DatasetMapper_detr_panoptic + +image_size = 1024 + +dataloader = OmegaConf.create() + +dataloader.train = L(build_detection_train_loader)( + dataset=L(get_detection_dataset_dicts)(names="bdd10k_40_panoptic_val"), + mapper=L(DatasetMapper_detr_panoptic)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + instance_mask_format="bitmask", + ignore_label=MetadataCatalog.get("bdd10k_40_panoptic_val").ignore_label, + stuff_classes_offset=0, + stuff_classes_decomposition=True, + dataset_names=["bdd10k_40_panoptic_val"], + ), + total_batch_size=16, + num_workers=4, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="bdd10k_40_panoptic_val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = [ + L(COCOPanopticEvaluator)( + dataset_name="${...test.dataset.names}", + ), +] diff --git a/configs/common/data/bdd10k_semantic_lsj1024.py b/configs/common/data/bdd10k_semantic_lsj1024.py new file mode 100644 index 0000000..ab31a6c --- /dev/null +++ b/configs/common/data/bdd10k_semantic_lsj1024.py @@ -0,0 +1,65 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + build_detection_train_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import SemSegEvaluator +from omegaconf import OmegaConf +from ape.data import DatasetMapper_detr_panoptic + +image_size = 1024 + +dataloader = OmegaConf.create() + +dataloader.train = L(build_detection_train_loader)( + dataset=L(get_detection_dataset_dicts)(names="bdd10k_val_sem_seg"), + mapper=L(DatasetMapper_detr_panoptic)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + instance_mask_format="bitmask", + ignore_label=MetadataCatalog.get("bdd10k_val_sem_seg").ignore_label, + stuff_classes_offset=0, + stuff_classes_decomposition=True, + dataset_names=["bdd10k_val_sem_seg"], + ), + total_batch_size=16, + num_workers=4, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="bdd10k_val_sem_seg", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = [ + L(SemSegEvaluator)( + dataset_name="${...test.dataset.names}", + ), +] diff --git a/configs/common/data/cityscapes_panoptic_lsj1024.py b/configs/common/data/cityscapes_panoptic_lsj1024.py new file mode 100644 index 0000000..5b23aa8 --- /dev/null +++ b/configs/common/data/cityscapes_panoptic_lsj1024.py @@ -0,0 +1,77 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + build_detection_train_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import ( + CityscapesInstanceEvaluator, + CityscapesSemSegEvaluator, + COCOPanopticEvaluator, +) +from omegaconf import OmegaConf +from ape.data import DatasetMapper_detr_panoptic + +image_size = 1024 + +dataloader = OmegaConf.create() + +dataloader.train = L(build_detection_train_loader)( + dataset=L(get_detection_dataset_dicts)(names="cityscapes_fine_panoptic_train"), + mapper=L(DatasetMapper_detr_panoptic)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + instance_mask_format="bitmask", + ignore_label=MetadataCatalog.get("cityscapes_fine_panoptic_train").ignore_label, + stuff_classes_offset=0, + stuff_classes_decomposition=True, + dataset_names=["cityscapes_fine_panoptic_train"], + ), + total_batch_size=16, + num_workers=4, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)( + names="cityscapes_fine_panoptic_val", filter_empty=False + ), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = [ + L(CityscapesInstanceEvaluator)( + dataset_name="${...test.dataset.names}", + ), + L(CityscapesSemSegEvaluator)( + dataset_name="${...test.dataset.names}", + ), + L(COCOPanopticEvaluator)( + dataset_name="${...test.dataset.names}", + ), +] diff --git a/configs/common/data/cityscapes_semantic_lsj1024.py b/configs/common/data/cityscapes_semantic_lsj1024.py new file mode 100644 index 0000000..f4b61a3 --- /dev/null +++ b/configs/common/data/cityscapes_semantic_lsj1024.py @@ -0,0 +1,65 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + build_detection_train_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import CityscapesSemSegEvaluator +from omegaconf import OmegaConf +from ape.data import DatasetMapper_detr_panoptic + +image_size = 1024 + +dataloader = OmegaConf.create() + +dataloader.train = L(build_detection_train_loader)( + dataset=L(get_detection_dataset_dicts)(names="cityscapes_fine_sem_seg_train"), + mapper=L(DatasetMapper_detr_panoptic)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + instance_mask_format="bitmask", + ignore_label=MetadataCatalog.get("cityscapes_fine_sem_seg_train").ignore_label, + stuff_classes_offset=0, + stuff_classes_decomposition=True, + dataset_names=["cityscapes_fine_sem_train"], + ), + total_batch_size=16, + num_workers=4, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="cityscapes_fine_sem_seg_val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = [ + L(CityscapesSemSegEvaluator)( + dataset_name="${...test.dataset.names}", + ), +] diff --git a/configs/common/data/coco_instance.py b/configs/common/data/coco_instance.py new file mode 100644 index 0000000..753b064 --- /dev/null +++ b/configs/common/data/coco_instance.py @@ -0,0 +1,66 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + build_detection_test_loader, + build_detection_train_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import COCOEvaluator +from omegaconf import OmegaConf +from ape.data import DatasetMapper_detr_instance + +dataloader = OmegaConf.create() + +dataloader.train = L(build_detection_train_loader)( + dataset=L(get_detection_dataset_dicts)(names="coco_2017_train"), + mapper=L(DatasetMapper_detr_instance)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(), + L(T.ResizeShortestEdge)( + short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), + max_size=1333, + sample_style="choice", + ), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(), + L(T.ResizeShortestEdge)( + short_edge_length=(400, 500, 600), + sample_style="choice", + ), + L(T.RandomCrop)( + crop_type="absolute_range", + crop_size=(384, 600), + ), + L(T.ResizeShortestEdge)( + short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), + max_size=1333, + sample_style="choice", + ), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + dataset_names=["coco_2017_train"], + ), + total_batch_size=16, + num_workers=4, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="coco_2017_val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=800, max_size=1333), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(COCOEvaluator)( + dataset_name="${..test.dataset.names}", +) diff --git a/configs/common/data/coco_instance_lsj1024.py b/configs/common/data/coco_instance_lsj1024.py new file mode 100644 index 0000000..15c257c --- /dev/null +++ b/configs/common/data/coco_instance_lsj1024.py @@ -0,0 +1,67 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + build_detection_test_loader, + build_detection_train_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import COCOEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_instance, + build_detection_train_loader_multi_dataset, + get_detection_dataset_dicts_multi_dataset, +) +from ape.evaluation import RefCOCOEvaluator + +image_size = 1024 + +dataloader = OmegaConf.create() + +dataloader.train = L(build_detection_train_loader_multi_dataset)( + dataset=L(get_detection_dataset_dicts_multi_dataset)( + names=("coco_2017_train",), filter_emptys=[True] + ), + mapper=L(DatasetMapper_detr_instance)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + ), + total_batch_size=16, + total_batch_size_list=[16], + num_workers=4, + num_datasets=1, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="coco_2017_val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(COCOEvaluator)( + dataset_name="${..test.dataset.names}", + max_dets_per_image=100, +) diff --git a/configs/common/data/coco_instance_lsj1024_cp.py b/configs/common/data/coco_instance_lsj1024_cp.py new file mode 100644 index 0000000..7da0f66 --- /dev/null +++ b/configs/common/data/coco_instance_lsj1024_cp.py @@ -0,0 +1,58 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import DatasetMapper, build_detection_test_loader, get_detection_dataset_dicts +from detectron2.evaluation import COCOEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_copypaste, + build_detection_train_loader_copypaste, + get_detection_dataset_dicts_copypaste, +) + +image_size = 1024 + +dataloader = OmegaConf.create() + +dataloader.train = L(build_detection_train_loader_copypaste)( + dataset=L(get_detection_dataset_dicts_copypaste)(names=["coco_2017_train"], copypastes=[True]), + dataset_bg=L(get_detection_dataset_dicts)(names=["coco_2017_train"]), + mapper=L(DatasetMapper_copypaste)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_d2=[], + augmentations_aa=[], + augmentations_lsj=[], + augmentations_type=[], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + output_dir=None, + vis_period=12800, + dataset_names=["coco_2017_train"], + ), + total_batch_size=16, + num_workers=4, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="coco_2017_val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(COCOEvaluator)( + dataset_name="${..test.dataset.names}", + max_dets_per_image=100, +) diff --git a/configs/common/data/coco_instance_lsj1536_cp.py b/configs/common/data/coco_instance_lsj1536_cp.py new file mode 100644 index 0000000..b021709 --- /dev/null +++ b/configs/common/data/coco_instance_lsj1536_cp.py @@ -0,0 +1,58 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import DatasetMapper, build_detection_test_loader, get_detection_dataset_dicts +from detectron2.evaluation import COCOEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_copypaste, + build_detection_train_loader_copypaste, + get_detection_dataset_dicts_copypaste, +) + +image_size = 1536 + +dataloader = OmegaConf.create() + +dataloader.train = L(build_detection_train_loader_copypaste)( + dataset=L(get_detection_dataset_dicts_copypaste)(names=["coco_2017_train"], copypastes=[True]), + dataset_bg=L(get_detection_dataset_dicts)(names=["coco_2017_train"]), + mapper=L(DatasetMapper_copypaste)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_d2=[], + augmentations_aa=[], + augmentations_lsj=[], + augmentations_type=[], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + output_dir=None, + vis_period=12800, + dataset_names=["coco_2017_train"], + ), + total_batch_size=16, + num_workers=4, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="coco_2017_val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(COCOEvaluator)( + dataset_name="${..test.dataset.names}", + max_dets_per_image=100, +) diff --git a/configs/common/data/coco_panoptic.py b/configs/common/data/coco_panoptic.py new file mode 100644 index 0000000..99838e9 --- /dev/null +++ b/configs/common/data/coco_panoptic.py @@ -0,0 +1,82 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + build_detection_train_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import COCOEvaluator, COCOPanopticEvaluator, SemSegEvaluator +from omegaconf import OmegaConf +from ape.data import DatasetMapper_detr_panoptic + +dataloader = OmegaConf.create() + +dataloader.train = L(build_detection_train_loader)( + dataset=L(get_detection_dataset_dicts)(names=("coco_2017_train_panoptic",), filter_empty=True), + mapper=L(DatasetMapper_detr_panoptic)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(), + L(T.ResizeShortestEdge)( + short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), + max_size=1333, + sample_style="choice", + ), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(), + L(T.ResizeShortestEdge)( + short_edge_length=(400, 500, 600), + sample_style="choice", + ), + L(T.RandomCrop)( + crop_type="absolute_range", + crop_size=(384, 600), + ), + L(T.ResizeShortestEdge)( + short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), + max_size=1333, + sample_style="choice", + ), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + instance_mask_format="bitmask", + ignore_label=MetadataCatalog.get("coco_2017_train_panoptic").ignore_label, + stuff_classes_offset=0, + stuff_classes_decomposition=True, + dataset_names="${..dataset.names}", + ), + total_batch_size=16, + aspect_ratio_grouping=True, + num_workers=16, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)( + names="coco_2017_val_panoptic_with_sem_seg", filter_empty=False + ), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=800, max_size=1333), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = [ + L(COCOEvaluator)( + dataset_name="${...test.dataset.names}", + ), + L(SemSegEvaluator)( + dataset_name="${...test.dataset.names}", + ), + L(COCOPanopticEvaluator)( + dataset_name="${...test.dataset.names}", + ), +] diff --git a/configs/common/data/coco_panoptic_lsj1024.py b/configs/common/data/coco_panoptic_lsj1024.py new file mode 100644 index 0000000..a2842b7 --- /dev/null +++ b/configs/common/data/coco_panoptic_lsj1024.py @@ -0,0 +1,76 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + build_detection_train_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import COCOEvaluator, COCOPanopticEvaluator, SemSegEvaluator +from omegaconf import OmegaConf +from ape.data import DatasetMapper_detr_panoptic + +image_size = 1024 + +dataloader = OmegaConf.create() + +dataloader.train = L(build_detection_train_loader)( + dataset=L(get_detection_dataset_dicts)( + names="coco_2017_train_panoptic_with_sem_seg", filter_empty=True + ), + mapper=L(DatasetMapper_detr_panoptic)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + instance_mask_format="bitmask", + ignore_label=MetadataCatalog.get("coco_2017_train_panoptic_with_sem_seg").ignore_label, + stuff_classes_offset=0, + stuff_classes_decomposition=True, + dataset_names=["coco_2017_train_panoptic_with_sem_seg"], + ), + total_batch_size=16, + aspect_ratio_grouping=True, + num_workers=16, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)( + names="coco_2017_val_panoptic_with_sem_seg", filter_empty=False + ), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = [ + L(COCOEvaluator)( + dataset_name="${...test.dataset.names}", + ), + L(SemSegEvaluator)( + dataset_name="${...test.dataset.names}", + ), + L(COCOPanopticEvaluator)( + dataset_name="${...test.dataset.names}", + ), +] diff --git a/configs/common/data/coco_panoptic_separated.py b/configs/common/data/coco_panoptic_separated.py new file mode 100644 index 0000000..90e0548 --- /dev/null +++ b/configs/common/data/coco_panoptic_separated.py @@ -0,0 +1,84 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + build_detection_train_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import COCOEvaluator, COCOPanopticEvaluator, SemSegEvaluator +from omegaconf import OmegaConf +from ape.data import DatasetMapper_detr_panoptic + +dataloader = OmegaConf.create() + +dataloader.train = L(build_detection_train_loader)( + dataset=L(get_detection_dataset_dicts)( + names="coco_2017_train_panoptic_separated", filter_empty=True + ), + mapper=L(DatasetMapper_detr_panoptic)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(), + L(T.ResizeShortestEdge)( + short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), + max_size=1333, + sample_style="choice", + ), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(), + L(T.ResizeShortestEdge)( + short_edge_length=(400, 500, 600), + sample_style="choice", + ), + L(T.RandomCrop)( + crop_type="absolute_range", + crop_size=(384, 600), + ), + L(T.ResizeShortestEdge)( + short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), + max_size=1333, + sample_style="choice", + ), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + instance_mask_format="bitmask", + ignore_label=MetadataCatalog.get("coco_2017_train_panoptic_stuffonly").ignore_label, + stuff_classes_offset=80, + stuff_classes_decomposition=True, + dataset_names="${..dataset.names}", + ), + total_batch_size=16, + aspect_ratio_grouping=True, + num_workers=16, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)( + names="coco_2017_val_panoptic_with_sem_seg", filter_empty=False + ), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=800, max_size=1333), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = [ + L(COCOEvaluator)( + dataset_name="${...test.dataset.names}", + ), + L(SemSegEvaluator)( + dataset_name="${...test.dataset.names}", + ), + L(COCOPanopticEvaluator)( + dataset_name="${...test.dataset.names}", + ), +] diff --git a/configs/common/data/coco_refcoco_instance.py b/configs/common/data/coco_refcoco_instance.py new file mode 100644 index 0000000..c2850a5 --- /dev/null +++ b/configs/common/data/coco_refcoco_instance.py @@ -0,0 +1,116 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + build_detection_test_loader, + build_detection_train_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import COCOEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_instance_exp, + build_detection_train_loader_multi_dataset, + get_detection_dataset_dicts_multi_dataset, +) +from ape.evaluation import RefCOCOEvaluator + +dataloader = OmegaConf.create() + +dataloader.train = L(build_detection_train_loader_multi_dataset)( + dataset=L(get_detection_dataset_dicts_multi_dataset)( + names=( + "coco_2017_train", + "refcoco-mixed", + ), + filter_emptys=[True, True], + ), + mapper=L(DatasetMapper_detr_instance_exp)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(), + L(T.ResizeShortestEdge)( + short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), + max_size=1333, + sample_style="choice", + ), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(), + L(T.ResizeShortestEdge)( + short_edge_length=(400, 500, 600), + sample_style="choice", + ), + L(T.RandomCrop)( + crop_type="absolute_range", + crop_size=(384, 600), + ), + L(T.ResizeShortestEdge)( + short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), + max_size=1333, + sample_style="choice", + ), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + dataset_names=( + "coco_2017_train", + "refcoco-mixed", + ), + ), + total_batch_size=16, + total_batch_size_list=[16, 16], + num_workers=4, + num_datasets=2, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="coco_2017_val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=800, max_size=1333), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(COCOEvaluator)( + dataset_name="${..test.dataset.names}", + max_dets_per_image=100, +) + +refcoco_test_dataset_names = [ + "refcoco-unc-val", + "refcoco-unc-testA", + "refcoco-unc-testB", + "refcocoplus-unc-val", + "refcocoplus-unc-testA", + "refcocoplus-unc-testB", + "refcocog-google-val", + "refcocog-umd-val", + "refcocog-umd-test", +] +dataloader.tests = [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names=name, filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=800, max_size=1333), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ) + for name in refcoco_test_dataset_names +] + +dataloader.evaluators = [ + L(RefCOCOEvaluator)( + dataset_name=name, + ) + for name in refcoco_test_dataset_names +] diff --git a/configs/common/data/coco_refcoco_instance_lsj1024.py b/configs/common/data/coco_refcoco_instance_lsj1024.py new file mode 100644 index 0000000..b49f246 --- /dev/null +++ b/configs/common/data/coco_refcoco_instance_lsj1024.py @@ -0,0 +1,108 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + build_detection_test_loader, + build_detection_train_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import COCOEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_instance, + build_detection_train_loader_multi_dataset, + get_detection_dataset_dicts_multi_dataset, +) +from ape.evaluation import RefCOCOEvaluator + +image_size = 1024 + +dataloader = OmegaConf.create() + +dataloader.train = L(build_detection_train_loader_multi_dataset)( + dataset=L(get_detection_dataset_dicts_multi_dataset)( + names=( + "coco_2017_train", + "refcoco-mixed", + ), + filter_emptys=[True, True], + ), + mapper=L(DatasetMapper_detr_instance)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + dataset_names=( + "coco_2017_train", + "refcoco-mixed", + ), + ), + total_batch_size=16, + total_batch_size_list=[16, 16], + num_workers=4, + num_datasets=2, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="coco_2017_val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(COCOEvaluator)( + dataset_name="${..test.dataset.names}", + max_dets_per_image=100, +) + +refcoco_test_dataset_names = [ + "refcoco-unc-val", + "refcoco-unc-testA", + "refcoco-unc-testB", + "refcocoplus-unc-val", + "refcocoplus-unc-testA", + "refcocoplus-unc-testB", + "refcocog-google-val", + "refcocog-umd-val", + "refcocog-umd-test", +] +dataloader.tests = [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names=name, filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ) + for name in refcoco_test_dataset_names +] + +dataloader.evaluators = [ + L(RefCOCOEvaluator)( + dataset_name=name, + ) + for name in refcoco_test_dataset_names +] diff --git a/configs/common/data/coco_sa1b_instance.py b/configs/common/data/coco_sa1b_instance.py new file mode 100644 index 0000000..46b60ef --- /dev/null +++ b/configs/common/data/coco_sa1b_instance.py @@ -0,0 +1,96 @@ +import random + +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import COCOEvaluator, COCOPanopticEvaluator, SemSegEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_panoptic, + build_detection_train_loader_multi_dataset, + get_detection_dataset_dicts_multi_dataset, +) +from ape.data.samplers import MultiDatasetTrainingSampler + +dataloader = OmegaConf.create() + +dataloader.train = L(build_detection_train_loader_multi_dataset)( + dataset=L(get_detection_dataset_dicts_multi_dataset)( + names=( + "coco_2017_train", + "sa1b_4m", + ), + filter_emptys=[True, False], + ), + mapper=L(DatasetMapper_detr_panoptic)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(), + L(T.ResizeShortestEdge)( + short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), + max_size=1333, + sample_style="choice", + ), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(), + L(T.ResizeShortestEdge)( + short_edge_length=(400, 500, 600), + sample_style="choice", + ), + L(T.RandomCrop)( + crop_type="absolute_range", + crop_size=(384, 600), + ), + L(T.ResizeShortestEdge)( + short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), + max_size=1333, + sample_style="choice", + ), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + instance_mask_format="bitmask", + ignore_label=MetadataCatalog.get("coco_2017_train_panoptic_stuffonly").ignore_label, + stuff_classes_offset=80, + dataset_names=["coco_2017_train", "sa1b"], + ), + sampler=lambda dataset_dicts: MultiDatasetTrainingSampler( + repeat_factors=MultiDatasetTrainingSampler.get_repeat_factors( + dataset_dicts=dataset_dicts, + num_datasets=2, + dataset_ratio=[1, 1], + use_rfs=[False, False], + use_cas=[False, False], + repeat_thresh=0.001, + cas_lambda=1.0, + ), + seed=random.randint(0, 2**31), + ), + total_batch_size=16, + total_batch_size_list=[16, 16], + num_workers=4, + num_datasets=2, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="coco_2017_val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=800, max_size=1333), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(COCOEvaluator)( + dataset_name="${..test.dataset.names}", +) diff --git a/configs/common/data/coco_sa1b_panoptic.py b/configs/common/data/coco_sa1b_panoptic.py new file mode 100644 index 0000000..8a65ab4 --- /dev/null +++ b/configs/common/data/coco_sa1b_panoptic.py @@ -0,0 +1,106 @@ +import random + +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import COCOEvaluator, COCOPanopticEvaluator, SemSegEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_panoptic, + build_detection_train_loader_multi_dataset, + get_detection_dataset_dicts_multi_dataset, +) +from ape.data.samplers import MultiDatasetTrainingSampler + +dataloader = OmegaConf.create() + +dataloader.train = L(build_detection_train_loader_multi_dataset)( + dataset=L(get_detection_dataset_dicts_multi_dataset)( + names=( + "coco_2017_train_panoptic_separated", + "sa1b_4m", + ), + filter_emptys=[True, False], + ), + mapper=L(DatasetMapper_detr_panoptic)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(), + L(T.ResizeShortestEdge)( + short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), + max_size=1333, + sample_style="choice", + ), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(), + L(T.ResizeShortestEdge)( + short_edge_length=(400, 500, 600), + sample_style="choice", + ), + L(T.RandomCrop)( + crop_type="absolute_range", + crop_size=(384, 600), + ), + L(T.ResizeShortestEdge)( + short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), + max_size=1333, + sample_style="choice", + ), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + instance_mask_format="bitmask", + ignore_label=MetadataCatalog.get("coco_2017_train_panoptic_stuffonly").ignore_label, + stuff_classes_offset=80, + dataset_names=["coco_2017_train_panoptic_separated", "sa1b"], + ), + sampler=lambda dataset_dicts: MultiDatasetTrainingSampler( + repeat_factors=MultiDatasetTrainingSampler.get_repeat_factors( + dataset_dicts=dataset_dicts, + num_datasets=2, + dataset_ratio=[1, 1], + use_rfs=[False, False], + use_cas=[False, False], + repeat_thresh=0.001, + cas_lambda=1.0, + ), + seed=random.randint(0, 2**31), + ), + total_batch_size=16, + total_batch_size_list=[16, 16], + num_workers=4, + num_datasets=2, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)( + names="coco_2017_val_panoptic_separated", filter_empty=False + ), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=800, max_size=1333), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = [ + L(COCOEvaluator)( + dataset_name="${...test.dataset.names}", + ), + L(SemSegEvaluator)( + dataset_name="${...test.dataset.names}", + ), + L(COCOPanopticEvaluator)( + dataset_name="${...test.dataset.names}", + ), +] diff --git a/configs/common/data/coco_semantic.py b/configs/common/data/coco_semantic.py new file mode 100644 index 0000000..9160ff5 --- /dev/null +++ b/configs/common/data/coco_semantic.py @@ -0,0 +1,69 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + build_detection_train_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import SemSegEvaluator +from omegaconf import OmegaConf +from ape.data import DatasetMapper_detr_semantic + +dataloader = OmegaConf.create() + +dataloader.train = L(build_detection_train_loader)( + dataset=L(get_detection_dataset_dicts)(names="coco_2017_train_panoptic_stuffonly"), + mapper=L(DatasetMapper_detr_semantic)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(), + L(T.ResizeShortestEdge)( + short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), + max_size=1333, + sample_style="choice", + ), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(), + L(T.ResizeShortestEdge)( + short_edge_length=(400, 500, 600), + sample_style="choice", + ), + L(T.RandomCrop)( + crop_type="absolute_range", + crop_size=(384, 600), + ), + L(T.ResizeShortestEdge)( + short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), + max_size=1333, + sample_style="choice", + ), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + ignore_label=MetadataCatalog.get("coco_2017_train_panoptic_stuffonly").ignore_label, + ), + total_batch_size=16, + num_workers=4, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)( + names="coco_2017_val_panoptic_stuffonly", filter_empty=False + ), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=800, max_size=1333), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(SemSegEvaluator)( + dataset_name="${..test.dataset.names}", +) diff --git a/configs/common/data/coco_semantic_lsj1024.py b/configs/common/data/coco_semantic_lsj1024.py new file mode 100644 index 0000000..501bf2b --- /dev/null +++ b/configs/common/data/coco_semantic_lsj1024.py @@ -0,0 +1,62 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + build_detection_train_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import SemSegEvaluator +from omegaconf import OmegaConf +from ape.data import DatasetMapper_detr_semantic + +image_size = 1024 + +dataloader = OmegaConf.create() + +dataloader.train = L(build_detection_train_loader)( + dataset=L(get_detection_dataset_dicts)(names="coco_2017_train_panoptic_stuffonly"), + mapper=L(DatasetMapper_detr_semantic)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + ignore_label=MetadataCatalog.get("coco_2017_train_panoptic_stuffonly").ignore_label, + stuff_classes_decomposition=True, + ), + total_batch_size=16, + num_workers=4, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)( + names="coco_2017_val_panoptic_stuffonly", filter_empty=False + ), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(SemSegEvaluator)( + dataset_name="${..test.dataset.names}", +) diff --git a/configs/common/data/constants.py b/configs/common/data/constants.py new file mode 100644 index 0000000..3aaa07c --- /dev/null +++ b/configs/common/data/constants.py @@ -0,0 +1,4 @@ +constants = dict( + openai_imagenet_rgb256_mean=[122.7709383, 116.7460125, 104.09373615000001], + openai_imagenet_rgb256_std=[68.5005327, 66.6321579, 70.32316304999999], +) diff --git a/configs/common/data/d3_instance_lsj1024.py b/configs/common/data/d3_instance_lsj1024.py new file mode 100644 index 0000000..239d3f4 --- /dev/null +++ b/configs/common/data/d3_instance_lsj1024.py @@ -0,0 +1,100 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + build_detection_test_loader, + build_detection_train_loader, + get_detection_dataset_dicts, +) +from detectron2.data.catalog import DatasetCatalog +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_instance, + build_detection_train_loader_multi_dataset, + get_detection_dataset_dicts_multi_dataset, +) +from ape.evaluation import D3Evaluator + +image_size = 1024 + +dataloader = OmegaConf.create() + +dataloader.train = L(build_detection_train_loader_multi_dataset)( + dataset=L(get_detection_dataset_dicts_multi_dataset)( + names=("d3_inter_scenario",), filter_emptys=[True] + ), + mapper=L(DatasetMapper_detr_instance)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + ), + total_batch_size=16, + total_batch_size_list=[16], + num_workers=4, + num_datasets=1, +) + +dataloader.tests = [] +dataloader.evaluators = [] + +dataloader.tests.append( + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="d3_intra_scenario", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="RGB", + ), + num_workers=4, + ) +) + +dataloader.evaluators.append( + [ + L(D3Evaluator)(dataset_name="d3_intra_scenario", max_dets_per_image=100, mode="FULL"), + L(D3Evaluator)(dataset_name="d3_intra_scenario", max_dets_per_image=100, mode="PRES"), + L(D3Evaluator)(dataset_name="d3_intra_scenario", max_dets_per_image=100, mode="ABS"), + ] +) + +dataloader.tests.append( + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="d3_inter_scenario", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="RGB", + ), + num_workers=4, + ) +) + +dataloader.evaluators.append( + [ + L(D3Evaluator)(dataset_name="d3_inter_scenario", max_dets_per_image=100, mode="FULL"), + L(D3Evaluator)(dataset_name="d3_inter_scenario", max_dets_per_image=100, mode="PRES"), + L(D3Evaluator)(dataset_name="d3_inter_scenario", max_dets_per_image=100, mode="ABS"), + ] +) + +DatasetCatalog.get("d3_intra_scenario") +DatasetCatalog.get("d3_inter_scenario") diff --git a/configs/common/data/flickr30k_instance.py b/configs/common/data/flickr30k_instance.py new file mode 100644 index 0000000..158370f --- /dev/null +++ b/configs/common/data/flickr30k_instance.py @@ -0,0 +1,76 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + build_detection_test_loader, + build_detection_train_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import COCOEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_instance, + build_detection_train_loader_multi_dataset, + get_detection_dataset_dicts_multi_dataset, +) +from ape.evaluation import RefCOCOEvaluator + +dataloader = OmegaConf.create() + +dataloader.train = L(build_detection_train_loader_multi_dataset)( + dataset=L(get_detection_dataset_dicts_multi_dataset)( + names=("flickr30k_separateGT_train",), + filter_emptys=[True], + ), + mapper=L(DatasetMapper_detr_instance)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(), + L(T.ResizeShortestEdge)( + short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), + max_size=1333, + sample_style="choice", + ), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(), + L(T.ResizeShortestEdge)( + short_edge_length=(400, 500, 600), + sample_style="choice", + ), + L(T.RandomCrop)( + crop_type="absolute_range", + crop_size=(384, 600), + ), + L(T.ResizeShortestEdge)( + short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), + max_size=1333, + sample_style="choice", + ), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + dataset_names=("flickr30k_separateGT_train",), + ), + total_batch_size=16, + total_batch_size_list=[16], + num_workers=4, + num_datasets=1, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="flickr30k_separateGT_val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=800, max_size=1333), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(RefCOCOEvaluator)( + dataset_name="${..test.dataset.names}", +) diff --git a/configs/common/data/flickr30k_instance_lsj1024.py b/configs/common/data/flickr30k_instance_lsj1024.py new file mode 100644 index 0000000..027b276 --- /dev/null +++ b/configs/common/data/flickr30k_instance_lsj1024.py @@ -0,0 +1,70 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + build_detection_test_loader, + build_detection_train_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import COCOEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_instance, + build_detection_train_loader_multi_dataset, + get_detection_dataset_dicts_multi_dataset, +) +from ape.evaluation import RefCOCOEvaluator + +dataloader = OmegaConf.create() + +image_size = 1024 + +dataloader.train = L(build_detection_train_loader_multi_dataset)( + dataset=L(get_detection_dataset_dicts_multi_dataset)( + names=("flickr30k_separateGT_train",), + filter_emptys=[True], + ), + mapper=L(DatasetMapper_detr_instance)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + dataset_names=("flickr30k_separateGT_train",), + max_num_phrase=256, + nms_thresh_phrase=0.6, + ), + total_batch_size=16, + total_batch_size_list=[16], + num_workers=4, + num_datasets=1, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="flickr30k_separateGT_val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(RefCOCOEvaluator)( + dataset_name="${..test.dataset.names}", +) diff --git a/configs/common/data/gqa_region_instance.py b/configs/common/data/gqa_region_instance.py new file mode 100644 index 0000000..317446c --- /dev/null +++ b/configs/common/data/gqa_region_instance.py @@ -0,0 +1,109 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + build_detection_test_loader, + build_detection_train_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import COCOEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_instance, + build_detection_train_loader_multi_dataset, + get_detection_dataset_dicts_multi_dataset, +) +from ape.evaluation import RefCOCOEvaluator + +dataloader = OmegaConf.create() + +dataloader.train = L(build_detection_train_loader_multi_dataset)( + dataset=L(get_detection_dataset_dicts_multi_dataset)( + names=("gqa_region_train",), + filter_emptys=[True], + ), + mapper=L(DatasetMapper_detr_instance)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(), + L(T.ResizeShortestEdge)( + short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), + max_size=1333, + sample_style="choice", + ), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(), + L(T.ResizeShortestEdge)( + short_edge_length=(400, 500, 600), + sample_style="choice", + ), + L(T.RandomCrop)( + crop_type="absolute_range", + crop_size=(384, 600), + ), + L(T.ResizeShortestEdge)( + short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), + max_size=1333, + sample_style="choice", + ), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + dataset_names=("visualgenome_77962_box_and_region",), + ), + total_batch_size=16, + total_batch_size_list=[16], + num_workers=4, + num_datasets=1, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="refcoco-unc-val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=800, max_size=1333), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(RefCOCOEvaluator)( + dataset_name="${..test.dataset.names}", +) + +refcoco_test_dataset_names = [ + "refcoco-unc-val", + "refcoco-unc-testA", + "refcoco-unc-testB", + "refcocoplus-unc-val", + "refcocoplus-unc-testA", + "refcocoplus-unc-testB", + "refcocog-google-val", + "refcocog-umd-val", + "refcocog-umd-test", +] +dataloader.tests = [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names=name, filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=800, max_size=1333), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ) + for name in refcoco_test_dataset_names[1:] +] + +dataloader.evaluators = [ + L(RefCOCOEvaluator)( + dataset_name=name, + ) + for name in refcoco_test_dataset_names[1:] +] diff --git a/configs/common/data/grit_instance.py b/configs/common/data/grit_instance.py new file mode 100644 index 0000000..616ef35 --- /dev/null +++ b/configs/common/data/grit_instance.py @@ -0,0 +1,111 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + build_detection_test_loader, + build_detection_train_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import COCOEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_instance, + build_detection_train_loader_multi_dataset, + get_detection_dataset_dicts_multi_dataset, +) +from ape.evaluation import RefCOCOEvaluator + +dataloader = OmegaConf.create() + +dataloader.train = L(build_detection_train_loader_multi_dataset)( + dataset=L(get_detection_dataset_dicts_multi_dataset)( + names=("grit",), + filter_emptys=[True], + ), + mapper=L(DatasetMapper_detr_instance)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(), + L(T.ResizeShortestEdge)( + short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), + max_size=1333, + sample_style="choice", + ), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(), + L(T.ResizeShortestEdge)( + short_edge_length=(400, 500, 600), + sample_style="choice", + ), + L(T.RandomCrop)( + crop_type="absolute_range", + crop_size=(384, 600), + ), + L(T.ResizeShortestEdge)( + short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), + max_size=1333, + sample_style="choice", + ), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + dataset_names=("grit",), + max_num_phrase=100, + nms_thresh_phrase=0.6, + ), + total_batch_size=16, + total_batch_size_list=[16], + num_workers=2, + num_datasets=1, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="refcoco-unc-val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=800, max_size=1333), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(RefCOCOEvaluator)( + dataset_name="${..test.dataset.names}", +) + +refcoco_test_dataset_names = [ + "refcoco-unc-val", + "refcoco-unc-testA", + "refcoco-unc-testB", + "refcocoplus-unc-val", + "refcocoplus-unc-testA", + "refcocoplus-unc-testB", + "refcocog-google-val", + "refcocog-umd-val", + "refcocog-umd-test", +] +dataloader.tests = [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names=name, filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=800, max_size=1333), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ) + for name in refcoco_test_dataset_names[1:] +] + +dataloader.evaluators = [ + L(RefCOCOEvaluator)( + dataset_name=name, + ) + for name in refcoco_test_dataset_names[1:] +] diff --git a/configs/common/data/grit_instance_lsj224.py b/configs/common/data/grit_instance_lsj224.py new file mode 100644 index 0000000..7b24826 --- /dev/null +++ b/configs/common/data/grit_instance_lsj224.py @@ -0,0 +1,103 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + build_detection_test_loader, + build_detection_train_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import COCOEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_instance, + build_detection_train_loader_multi_dataset, + get_detection_dataset_dicts_multi_dataset, +) +from ape.evaluation import RefCOCOEvaluator + +image_size = 224 + +dataloader = OmegaConf.create() + +dataloader.train = L(build_detection_train_loader_multi_dataset)( + dataset=L(get_detection_dataset_dicts_multi_dataset)( + names=("grit",), + filter_emptys=[True], + ), + mapper=L(DatasetMapper_detr_instance)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + dataset_names=("grit",), + max_num_phrase=100, + nms_thresh_phrase=0.6, + ), + total_batch_size=16, + total_batch_size_list=[16], + num_workers=2, + num_datasets=1, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="refcoco-unc-val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(RefCOCOEvaluator)( + dataset_name="${..test.dataset.names}", +) + +refcoco_test_dataset_names = [ + "refcoco-unc-val", + "refcoco-unc-testA", + "refcoco-unc-testB", + "refcocoplus-unc-val", + "refcocoplus-unc-testA", + "refcocoplus-unc-testB", + "refcocog-google-val", + "refcocog-umd-val", + "refcocog-umd-test", +] +dataloader.tests = [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names=name, filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ) + for name in refcoco_test_dataset_names[1:] +] + +dataloader.evaluators = [ + L(RefCOCOEvaluator)( + dataset_name=name, + ) + for name in refcoco_test_dataset_names[1:] +] diff --git a/configs/common/data/grit_sa1b_instance.py b/configs/common/data/grit_sa1b_instance.py new file mode 100644 index 0000000..2fdddb1 --- /dev/null +++ b/configs/common/data/grit_sa1b_instance.py @@ -0,0 +1,130 @@ +import random + +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + build_detection_test_loader, + build_detection_train_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import COCOEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_instance, + build_detection_train_loader_multi_dataset, + get_detection_dataset_dicts_multi_dataset, +) +from ape.data.samplers import MultiDatasetTrainingSampler +from ape.evaluation import RefCOCOEvaluator + +dataloader = OmegaConf.create() + +dataloader.train = L(build_detection_train_loader_multi_dataset)( + dataset=L(get_detection_dataset_dicts_multi_dataset)( + names=("grit_16_snappy", "sa1b_4m"), + filter_emptys=[True, False], + ), + mapper=L(DatasetMapper_detr_instance)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(), + L(T.ResizeShortestEdge)( + short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), + max_size=1333, + sample_style="choice", + ), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(), + L(T.ResizeShortestEdge)( + short_edge_length=(400, 500, 600), + sample_style="choice", + ), + L(T.RandomCrop)( + crop_type="absolute_range", + crop_size=(384, 600), + ), + L(T.ResizeShortestEdge)( + short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), + max_size=1333, + sample_style="choice", + ), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + instance_mask_format="bitmask", + dataset_names=( + "grit", + "sa1b", + ), + max_num_phrase=100, + nms_thresh_phrase=0.6, + ), + sampler=lambda dataset_dicts: MultiDatasetTrainingSampler( + repeat_factors=MultiDatasetTrainingSampler.get_repeat_factors( + dataset_dicts=dataset_dicts, + num_datasets=2, + dataset_ratio=[1, 1], + use_rfs=[True, True], + use_cas=[False, False], + repeat_thresh=0.001, + cas_lambda=1.0, + ), + seed=random.randint(0, 2**31), + ), + total_batch_size=16, + total_batch_size_list=[16, 16], + num_workers=4, + num_datasets=2, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="refcoco-unc-val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=800, max_size=1333), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(RefCOCOEvaluator)( + dataset_name="${..test.dataset.names}", +) + +refcoco_test_dataset_names = [ + "refcoco-unc-val", + "refcoco-unc-testA", + "refcoco-unc-testB", + "refcocoplus-unc-val", + "refcocoplus-unc-testA", + "refcocoplus-unc-testB", + "refcocog-google-val", + "refcocog-umd-val", + "refcocog-umd-test", +] +dataloader.tests = [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names=name, filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=800, max_size=1333), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ) + for name in refcoco_test_dataset_names[1:] +] + +dataloader.evaluators = [ + L(RefCOCOEvaluator)( + dataset_name=name, + ) + for name in refcoco_test_dataset_names[1:] +] diff --git a/configs/common/data/lvis_instance_lsj1024_cp.py b/configs/common/data/lvis_instance_lsj1024_cp.py new file mode 100644 index 0000000..fa30d61 --- /dev/null +++ b/configs/common/data/lvis_instance_lsj1024_cp.py @@ -0,0 +1,69 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import DatasetMapper, build_detection_test_loader, get_detection_dataset_dicts +from detectron2.data.samplers import RepeatFactorTrainingSampler +from detectron2.evaluation import LVISEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_copypaste, + build_detection_train_loader_copypaste, + get_detection_dataset_dicts_copypaste, +) + +image_size = 1024 + +dataloader = OmegaConf.create() + +dataloader.train = L(build_detection_train_loader_copypaste)( + dataset=L(get_detection_dataset_dicts_copypaste)(names=["lvis_v1_train"], copypastes=[True]), + dataset_bg=L(get_detection_dataset_dicts)(names=["lvis_v1_train"]), + mapper=L(DatasetMapper_copypaste)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_d2=[], + augmentations_aa=[], + augmentations_lsj=[], + augmentations_type=[], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + output_dir=None, + vis_period=12800, + dataset_names=["lvis_v1_train"], + ), + sampler=L(RepeatFactorTrainingSampler)( + repeat_factors=L(RepeatFactorTrainingSampler.repeat_factors_from_category_frequency)( + dataset_dicts="${dataloader.train.dataset}", repeat_thresh=0.001 + ) + ), + sampler_bg=L(RepeatFactorTrainingSampler)( + repeat_factors=L(RepeatFactorTrainingSampler.repeat_factors_from_category_frequency)( + dataset_dicts="${dataloader.train.dataset_bg}", repeat_thresh=0.001 + ) + ), + total_batch_size=16, + num_workers=4, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="lvis_v1_val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(LVISEvaluator)( + dataset_name="${..test.dataset.names}", + max_dets_per_image=300, +) diff --git a/configs/common/data/lvis_instance_lsj1536_cp.py b/configs/common/data/lvis_instance_lsj1536_cp.py new file mode 100644 index 0000000..23da027 --- /dev/null +++ b/configs/common/data/lvis_instance_lsj1536_cp.py @@ -0,0 +1,69 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import DatasetMapper, build_detection_test_loader, get_detection_dataset_dicts +from detectron2.data.samplers import RepeatFactorTrainingSampler +from detectron2.evaluation import LVISEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_copypaste, + build_detection_train_loader_copypaste, + get_detection_dataset_dicts_copypaste, +) + +image_size = 1536 + +dataloader = OmegaConf.create() + +dataloader.train = L(build_detection_train_loader_copypaste)( + dataset=L(get_detection_dataset_dicts_copypaste)(names=["lvis_v1_train"], copypastes=[True]), + dataset_bg=L(get_detection_dataset_dicts)(names=["lvis_v1_train"]), + mapper=L(DatasetMapper_copypaste)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_d2=[], + augmentations_aa=[], + augmentations_lsj=[], + augmentations_type=[], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + output_dir=None, + vis_period=12800, + dataset_names=["lvis_v1_train"], + ), + sampler=L(RepeatFactorTrainingSampler)( + repeat_factors=L(RepeatFactorTrainingSampler.repeat_factors_from_category_frequency)( + dataset_dicts="${dataloader.train.dataset}", repeat_thresh=0.001 + ) + ), + sampler_bg=L(RepeatFactorTrainingSampler)( + repeat_factors=L(RepeatFactorTrainingSampler.repeat_factors_from_category_frequency)( + dataset_dicts="${dataloader.train.dataset_bg}", repeat_thresh=0.001 + ) + ), + total_batch_size=16, + num_workers=4, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="lvis_v1_val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(LVISEvaluator)( + dataset_name="${..test.dataset.names}", + max_dets_per_image=300, +) diff --git a/configs/common/data/lvis_sa1b_instance.py b/configs/common/data/lvis_sa1b_instance.py new file mode 100644 index 0000000..9226d8d --- /dev/null +++ b/configs/common/data/lvis_sa1b_instance.py @@ -0,0 +1,95 @@ +import random + +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import LVISEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_instance, + build_detection_train_loader_multi_dataset, + get_detection_dataset_dicts_multi_dataset, +) +from ape.data.samplers import MultiDatasetTrainingSampler + +dataloader = OmegaConf.create() + +dataloader.train = L(build_detection_train_loader_multi_dataset)( + dataset=L(get_detection_dataset_dicts_multi_dataset)( + names=("lvis_v1_train", "sa1b_4m"), + filter_emptys=[True, False], + ), + mapper=L(DatasetMapper_detr_instance)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(), + L(T.ResizeShortestEdge)( + short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), + max_size=1333, + sample_style="choice", + ), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(), + L(T.ResizeShortestEdge)( + short_edge_length=(400, 500, 600), + sample_style="choice", + ), + L(T.RandomCrop)( + crop_type="absolute_range", + crop_size=(384, 600), + ), + L(T.ResizeShortestEdge)( + short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), + max_size=1333, + sample_style="choice", + ), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + instance_mask_format="bitmask", + dataset_names=( + "lvis_v1_train", + "sa1b", + ), + ), + sampler=lambda dataset_dicts: MultiDatasetTrainingSampler( + repeat_factors=MultiDatasetTrainingSampler.get_repeat_factors( + dataset_dicts=dataset_dicts, + num_datasets=2, + dataset_ratio=[1, 1], + use_rfs=[True, True], + use_cas=[False, False], + repeat_thresh=0.001, + cas_lambda=1.0, + ), + seed=random.randint(0, 2**31), + ), + total_batch_size=16, + total_batch_size_list=[16, 16], + num_workers=8, + num_datasets=2, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="lvis_v1_val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=800, max_size=1333), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(LVISEvaluator)( + dataset_name="${..test.dataset.names}", + max_dets_per_image=300, +) diff --git a/configs/common/data/lviscoco_cocostuff_o365_oid_vg_refcoco_panoptic_lsj1024_cp.py b/configs/common/data/lviscoco_cocostuff_o365_oid_vg_refcoco_panoptic_lsj1024_cp.py new file mode 100644 index 0000000..9280b9f --- /dev/null +++ b/configs/common/data/lviscoco_cocostuff_o365_oid_vg_refcoco_panoptic_lsj1024_cp.py @@ -0,0 +1,234 @@ +import random + +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + get_detection_dataset_dicts, +) +from detectron2.data.samplers import RepeatFactorTrainingSampler +from detectron2.evaluation import COCOEvaluator, LVISEvaluator, SemSegEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_panoptic_copypaste, + build_detection_train_loader_multi_dataset_copypaste, + get_detection_dataset_dicts_multi_dataset_copypaste, +) +from ape.data.samplers import MultiDatasetTrainingSampler +from ape.evaluation import RefCOCOEvaluator +from ape.evaluation.oideval import OIDEvaluator + +dataloader = OmegaConf.create() + +image_size = 1024 + +dataloader.train = L(build_detection_train_loader_multi_dataset_copypaste)( + dataset=L(get_detection_dataset_dicts_multi_dataset_copypaste)( + names=( + "lvis_v1_train+coco", + "coco_2017_train_panoptic_stuffonly", + "objects365_train_fixname", + "openimages_v6_train_bbox_nogroup", + "visualgenome_150_box_train", + "refcoco-mixed", + ), + filter_emptys=[True, False, True, True, True, True], + copypastes=[True, False, False, False, False, False], + ), + dataset_bg=L(get_detection_dataset_dicts)(names=["lvis_v1_train+coco"]), + mapper=L(DatasetMapper_detr_panoptic_copypaste)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + instance_mask_format="bitmask", + ignore_label=MetadataCatalog.get("coco_2017_train_panoptic_stuffonly").ignore_label, + stuff_classes_offset=0, + stuff_classes_decomposition=True, + output_dir=None, + vis_period=12800, + dataset_names=[ + "lvis_v1_train+coco", + "coco_2017_train_panoptic_stuffonly", + "objects365_train_fixname", + "openimages_v6_train_bbox_nogroup", + "visualgenome_150_box_train", + "refcoco-mixed", + ], + ), + sampler=lambda dataset_dicts: MultiDatasetTrainingSampler( + repeat_factors=MultiDatasetTrainingSampler.get_repeat_factors( + dataset_dicts=dataset_dicts, + num_datasets=6, + dataset_ratio=[1, 1, 1, 1, 1, 1], + use_rfs=[True, False, True, True, True, True], + use_cas=[False, False, False, False, False, False], + repeat_thresh=0.001, + cas_lambda=1.0, + ), + seed=random.randint(0, 2**31), + ), + sampler_bg=lambda dataset_dicts: RepeatFactorTrainingSampler( + repeat_factors=RepeatFactorTrainingSampler.repeat_factors_from_category_frequency( + dataset_dicts=dataset_dicts, repeat_thresh=0.001 + ), + seed=random.randint(0, 2**31), + ), + total_batch_size=16, + total_batch_size_list=[16, 16, 16, 16, 16, 16], + aspect_ratio_grouping=True, + num_workers=4, + num_datasets=6, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="lvis_v1_val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(LVISEvaluator)( + dataset_name="${..test.dataset.names}", + max_dets_per_image=300, +) + +dataloader.tests = [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)( + names="coco_2017_val_panoptic_stuffonly", filter_empty=False + ), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators = [ + L(SemSegEvaluator)( + dataset_name="coco_2017_val_panoptic_stuffonly", + ), +] + +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="objects365_val_fixname", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators += [ + L(COCOEvaluator)( + dataset_name="objects365_val_fixname", + tasks=("bbox",), + ), +] + +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="openimages_v6_val_bbox", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators += [ + L(OIDEvaluator)( + dataset_name="openimages_v6_val_bbox", + ), +] + +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)( + names="visualgenome_150_box_val", filter_empty=False + ), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators += [ + L(COCOEvaluator)( + dataset_name="visualgenome_150_box_val", + tasks=("bbox",), + ), +] + +refcoco_test_dataset_names = [ + "refcoco-unc-val", + "refcoco-unc-testA", + "refcoco-unc-testB", + "refcocoplus-unc-val", + "refcocoplus-unc-testA", + "refcocoplus-unc-testB", + "refcocog-google-val", + "refcocog-umd-val", + "refcocog-umd-test", +] +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names=name, filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ) + for name in refcoco_test_dataset_names +] + +dataloader.evaluators += [ + L(RefCOCOEvaluator)( + dataset_name=name, + ) + for name in refcoco_test_dataset_names +] diff --git a/configs/common/data/lviscoco_cocostuff_panoptic_lsj1024_cp.py b/configs/common/data/lviscoco_cocostuff_panoptic_lsj1024_cp.py new file mode 100644 index 0000000..8c4953f --- /dev/null +++ b/configs/common/data/lviscoco_cocostuff_panoptic_lsj1024_cp.py @@ -0,0 +1,121 @@ +import random + +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + get_detection_dataset_dicts, +) +from detectron2.data.samplers import RepeatFactorTrainingSampler +from detectron2.evaluation import LVISEvaluator, SemSegEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_panoptic_copypaste, + build_detection_train_loader_multi_dataset_copypaste, + get_detection_dataset_dicts_multi_dataset_copypaste, +) +from ape.data.samplers import MultiDatasetTrainingSampler + +dataloader = OmegaConf.create() + +image_size = 1024 + +dataloader.train = L(build_detection_train_loader_multi_dataset_copypaste)( + dataset=L(get_detection_dataset_dicts_multi_dataset_copypaste)( + names=["lvis_v1_train+coco", "coco_2017_train_panoptic_stuffonly"], + filter_emptys=[True, False], + copypastes=[True, False], + ), + dataset_bg=L(get_detection_dataset_dicts)(names=["lvis_v1_train+coco"]), + mapper=L(DatasetMapper_detr_panoptic_copypaste)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + instance_mask_format="bitmask", + ignore_label=MetadataCatalog.get("coco_2017_train_panoptic_stuffonly").ignore_label, + stuff_classes_offset=0, + stuff_classes_decomposition=True, + output_dir=None, + vis_period=12800, + dataset_names=["lvis_v1_train+coco", "coco_2017_train_panoptic_stuffonly"], + ), + sampler=lambda dataset_dicts: MultiDatasetTrainingSampler( + repeat_factors=MultiDatasetTrainingSampler.get_repeat_factors( + dataset_dicts=dataset_dicts, + num_datasets=2, + dataset_ratio=[1, 1], + use_rfs=[True, False], + use_cas=[False, False], + repeat_thresh=0.001, + cas_lambda=1.0, + ), + seed=random.randint(0, 2**31), + ), + sampler_bg=lambda dataset_dicts: RepeatFactorTrainingSampler( + repeat_factors=RepeatFactorTrainingSampler.repeat_factors_from_category_frequency( + dataset_dicts=dataset_dicts, repeat_thresh=0.001 + ), + seed=random.randint(0, 2**31), + ), + total_batch_size=16, + total_batch_size_list=[16, 16], + aspect_ratio_grouping=True, + num_workers=4, + num_datasets=2, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="lvis_v1_val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(LVISEvaluator)( + dataset_name="${..test.dataset.names}", + max_dets_per_image=300, +) + +dataloader.tests = [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)( + names="coco_2017_val_panoptic_stuffonly", filter_empty=False + ), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators = [ + L(SemSegEvaluator)( + dataset_name="coco_2017_val_panoptic_stuffonly", + ), +] diff --git a/configs/common/data/lviscocococostuff_o365_oid_refcoco_panoptic_lsj1024.py b/configs/common/data/lviscocococostuff_o365_oid_refcoco_panoptic_lsj1024.py new file mode 100644 index 0000000..64863e0 --- /dev/null +++ b/configs/common/data/lviscocococostuff_o365_oid_refcoco_panoptic_lsj1024.py @@ -0,0 +1,198 @@ +import random + +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + get_detection_dataset_dicts, +) +from detectron2.data.samplers import RepeatFactorTrainingSampler +from detectron2.evaluation import COCOEvaluator, LVISEvaluator, SemSegEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_panoptic, + build_detection_train_loader_multi_dataset, + get_detection_dataset_dicts_multi_dataset, +) +from ape.data.samplers import MultiDatasetTrainingSampler +from ape.evaluation import RefCOCOEvaluator +from ape.evaluation.oideval import OIDEvaluator + +dataloader = OmegaConf.create() + +image_size = 1024 + + +dataloader.train = L(build_detection_train_loader_multi_dataset)( + dataset=L(get_detection_dataset_dicts_multi_dataset)( + names=( + "lvis_v1_train+coco_panoptic_separated", + "objects365_train_fixname", + "openimages_v6_train_bbox_nogroup", + "refcoco-mixed", + ), + filter_emptys=[True, True, True, True], + ), + mapper=L(DatasetMapper_detr_panoptic)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + instance_mask_format="bitmask", + ignore_label=MetadataCatalog.get("coco_2017_train_panoptic_stuffonly").ignore_label, + stuff_classes_offset=1203, + stuff_classes_decomposition=True, + dataset_names=[ + "lvis_v1_train+coco_panoptic_separated", + "objects365_train_fixname", + "openimages_v6_train_bbox_nogroup", + "refcoco-mixed", + ], + ), + sampler=lambda dataset_dicts: MultiDatasetTrainingSampler( + repeat_factors=MultiDatasetTrainingSampler.get_repeat_factors( + dataset_dicts=dataset_dicts, + num_datasets=4, + dataset_ratio=[1, 1, 1, 1], + use_rfs=[True, True, True, True], + use_cas=[False, False, False, False], + repeat_thresh=0.001, + cas_lambda=1.0, + ), + seed=random.randint(0, 2**31), + ), + total_batch_size=16, + total_batch_size_list=[16, 16, 16, 16], + aspect_ratio_grouping=True, + num_workers=4, + num_datasets=4, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="lvis_v1_val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(LVISEvaluator)( + dataset_name="${..test.dataset.names}", + max_dets_per_image=300, +) + +dataloader.tests = [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)( + names="coco_2017_val_panoptic_stuffonly", filter_empty=False + ), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators = [ + L(SemSegEvaluator)( + dataset_name="coco_2017_val_panoptic_stuffonly", + ), +] + +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="objects365_val_fixname", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators += [ + L(COCOEvaluator)( + dataset_name="objects365_val_fixname", + tasks=("bbox",), + ), +] + +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="openimages_v6_val_bbox", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators += [ + L(OIDEvaluator)( + dataset_name="openimages_v6_val_bbox", + ), +] + +refcoco_test_dataset_names = [ + "refcoco-unc-val", + "refcoco-unc-testA", + "refcoco-unc-testB", + "refcocoplus-unc-val", + "refcocoplus-unc-testA", + "refcocoplus-unc-testB", + "refcocog-google-val", + "refcocog-umd-val", + "refcocog-umd-test", +] +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names=name, filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ) + for name in refcoco_test_dataset_names +] + +dataloader.evaluators += [ + L(RefCOCOEvaluator)( + dataset_name=name, + ) + for name in refcoco_test_dataset_names +] diff --git a/configs/common/data/lviscocococostuff_o365_oid_refcoco_panoptic_lsj1024_cp.py b/configs/common/data/lviscocococostuff_o365_oid_refcoco_panoptic_lsj1024_cp.py new file mode 100644 index 0000000..54554d0 --- /dev/null +++ b/configs/common/data/lviscocococostuff_o365_oid_refcoco_panoptic_lsj1024_cp.py @@ -0,0 +1,207 @@ +import random + +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + get_detection_dataset_dicts, +) +from detectron2.data.samplers import RepeatFactorTrainingSampler +from detectron2.evaluation import COCOEvaluator, LVISEvaluator, SemSegEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_panoptic_copypaste, + build_detection_train_loader_multi_dataset_copypaste, + get_detection_dataset_dicts_multi_dataset_copypaste, +) +from ape.data.samplers import MultiDatasetTrainingSampler +from ape.evaluation import RefCOCOEvaluator +from ape.evaluation.oideval import OIDEvaluator + +dataloader = OmegaConf.create() + +image_size = 1024 + + +dataloader.train = L(build_detection_train_loader_multi_dataset_copypaste)( + dataset=L(get_detection_dataset_dicts_multi_dataset_copypaste)( + names=( + "lvis_v1_train+coco_panoptic_separated", + "objects365_train_fixname", + "openimages_v6_train_bbox_nogroup", + "refcoco-mixed", + ), + filter_emptys=[True, True, True, True], + copypastes=[True, False, False, False], + ), + dataset_bg=L(get_detection_dataset_dicts)(names=["lvis_v1_train+coco_panoptic_separated"]), + mapper=L(DatasetMapper_detr_panoptic_copypaste)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + instance_mask_format="bitmask", + ignore_label=MetadataCatalog.get("coco_2017_train_panoptic_stuffonly").ignore_label, + stuff_classes_offset=1203, + stuff_classes_decomposition=True, + output_dir=None, + vis_period=12800, + dataset_names=[ + "lvis_v1_train+coco_panoptic_separated", + "objects365_train_fixname", + "openimages_v6_train_bbox_nogroup", + "refcoco-mixed", + ], + ), + sampler=lambda dataset_dicts: MultiDatasetTrainingSampler( + repeat_factors=MultiDatasetTrainingSampler.get_repeat_factors( + dataset_dicts=dataset_dicts, + num_datasets=4, + dataset_ratio=[1, 1, 1, 1], + use_rfs=[True, True, True, True], + use_cas=[False, False, False, False], + repeat_thresh=0.001, + cas_lambda=1.0, + ), + seed=random.randint(0, 2**31), + ), + sampler_bg=lambda dataset_dicts: RepeatFactorTrainingSampler( + repeat_factors=RepeatFactorTrainingSampler.repeat_factors_from_category_frequency( + dataset_dicts=dataset_dicts, repeat_thresh=0.001 + ), + seed=random.randint(0, 2**31), + ), + total_batch_size=16, + total_batch_size_list=[16, 16, 16, 16], + num_workers=4, + num_datasets=4, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="lvis_v1_val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(LVISEvaluator)( + dataset_name="${..test.dataset.names}", + max_dets_per_image=300, +) + +dataloader.tests = [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)( + names="coco_2017_val_panoptic_stuffonly", filter_empty=False + ), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators = [ + L(SemSegEvaluator)( + dataset_name="coco_2017_val_panoptic_stuffonly", + ), +] + +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="objects365_val_fixname", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators += [ + L(COCOEvaluator)( + dataset_name="objects365_val_fixname", + tasks=("bbox",), + ), +] + +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="openimages_v6_val_bbox", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators += [ + L(OIDEvaluator)( + dataset_name="openimages_v6_val_bbox", + ), +] + +refcoco_test_dataset_names = [ + "refcoco-unc-val", + "refcoco-unc-testA", + "refcoco-unc-testB", + "refcocoplus-unc-val", + "refcocoplus-unc-testA", + "refcocoplus-unc-testB", + "refcocog-google-val", + "refcocog-umd-val", + "refcocog-umd-test", +] +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names=name, filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ) + for name in refcoco_test_dataset_names +] + +dataloader.evaluators += [ + L(RefCOCOEvaluator)( + dataset_name=name, + ) + for name in refcoco_test_dataset_names +] diff --git a/configs/common/data/lviscocococostuff_o365_oid_vg_panoptic_lsj1024_cp.py b/configs/common/data/lviscocococostuff_o365_oid_vg_panoptic_lsj1024_cp.py new file mode 100644 index 0000000..1662d68 --- /dev/null +++ b/configs/common/data/lviscocococostuff_o365_oid_vg_panoptic_lsj1024_cp.py @@ -0,0 +1,235 @@ +import random + +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + get_detection_dataset_dicts, +) +from detectron2.data.samplers import RepeatFactorTrainingSampler +from detectron2.evaluation import COCOEvaluator, LVISEvaluator, SemSegEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_panoptic_copypaste, + build_detection_train_loader_multi_dataset_copypaste, + get_detection_dataset_dicts_multi_dataset_copypaste, +) +from ape.data.samplers import MultiDatasetTrainingSampler +from ape.evaluation.oideval import OIDEvaluator + +dataloader = OmegaConf.create() + +image_size = 1024 + +dataloader.train = L(build_detection_train_loader_multi_dataset_copypaste)( + dataset=L(get_detection_dataset_dicts_multi_dataset_copypaste)( + names=( + "lvis_v1_train+coco_panoptic_separated", + "objects365_train_fixname", + "openimages_v6_train_bbox_nogroup", + "visualgenome_150_box_train", + ), + filter_emptys=[True, True, True, True], + copypastes=[True, False, False, False], + ), + dataset_bg=L(get_detection_dataset_dicts)(names=["lvis_v1_train+coco_panoptic_separated"]), + mapper=L(DatasetMapper_detr_panoptic_copypaste)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + instance_mask_format="bitmask", + ignore_label=MetadataCatalog.get("coco_2017_train_panoptic_stuffonly").ignore_label, + stuff_classes_offset=1203, + stuff_classes_decomposition=True, + output_dir=None, + vis_period=12800, + dataset_names="${..dataset.names}", + ), + sampler=lambda dataset_dicts: MultiDatasetTrainingSampler( + repeat_factors=MultiDatasetTrainingSampler.get_repeat_factors( + dataset_dicts=dataset_dicts, + num_datasets=4, + dataset_ratio=[1, 1, 1, 1], + use_rfs=[True, True, True, True], + use_cas=[False, False, False, False], + repeat_thresh=0.001, + cas_lambda=1.0, + ), + seed=random.randint(0, 2**31), + ), + sampler_bg=lambda dataset_dicts: RepeatFactorTrainingSampler( + repeat_factors=RepeatFactorTrainingSampler.repeat_factors_from_category_frequency( + dataset_dicts=dataset_dicts, repeat_thresh=0.001 + ), + seed=random.randint(0, 2**31), + ), + total_batch_size=16, + total_batch_size_list=[16, 16, 16, 16], + aspect_ratio_grouping=True, + num_workers=4, + num_datasets=4, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="lvis_v1_val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(LVISEvaluator)( + dataset_name="${..test.dataset.names}", + max_dets_per_image=300, +) + +dataloader.tests = [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)( + names="coco_2017_val_panoptic_stuffonly", filter_empty=False + ), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators = [ + L(SemSegEvaluator)( + dataset_name="coco_2017_val_panoptic_stuffonly", + ), +] + +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="lvis_v1_minival", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ) +] + +dataloader.evaluators += [ + L(LVISEvaluator)( + dataset_name="lvis_v1_minival", + max_dets_per_image=300, + ) +] + +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)( + names="objects365_minival_fixname", filter_empty=False + ), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators += [ + L(COCOEvaluator)( + dataset_name="objects365_minival_fixname", + tasks=("bbox",), + ), +] + +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="objects365_val_fixname", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators += [ + L(COCOEvaluator)( + dataset_name="objects365_val_fixname", + tasks=("bbox",), + ), +] + +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="openimages_v6_val_bbox", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators += [ + L(OIDEvaluator)( + dataset_name="openimages_v6_val_bbox", + ), +] + +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)( + names="visualgenome_150_box_val", filter_empty=False + ), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators += [ + L(COCOEvaluator)( + dataset_name="visualgenome_150_box_val", + tasks=("bbox",), + ), +] diff --git a/configs/common/data/lviscocococostuff_o365_oid_vg_refcoco_panoptic_lsj1024_cp.py b/configs/common/data/lviscocococostuff_o365_oid_vg_refcoco_panoptic_lsj1024_cp.py new file mode 100644 index 0000000..ea742cf --- /dev/null +++ b/configs/common/data/lviscocococostuff_o365_oid_vg_refcoco_panoptic_lsj1024_cp.py @@ -0,0 +1,270 @@ +import random + +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + get_detection_dataset_dicts, +) +from detectron2.data.samplers import RepeatFactorTrainingSampler +from detectron2.evaluation import COCOEvaluator, LVISEvaluator, SemSegEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_panoptic_copypaste, + build_detection_train_loader_multi_dataset_copypaste, + get_detection_dataset_dicts_multi_dataset_copypaste, +) +from ape.data.samplers import MultiDatasetTrainingSampler +from ape.evaluation import RefCOCOEvaluator +from ape.evaluation.oideval import OIDEvaluator + +dataloader = OmegaConf.create() + +image_size = 1024 + +dataloader.train = L(build_detection_train_loader_multi_dataset_copypaste)( + dataset=L(get_detection_dataset_dicts_multi_dataset_copypaste)( + names=( + "lvis_v1_train+coco_panoptic_separated", + "objects365_train_fixname", + "openimages_v6_train_bbox_nogroup", + "visualgenome_150_box_train", + "refcoco-mixed", + ), + filter_emptys=[True, True, True, True, True], + copypastes=[True, False, False, False, False], + ), + dataset_bg=L(get_detection_dataset_dicts)(names=["lvis_v1_train+coco_panoptic_separated"]), + mapper=L(DatasetMapper_detr_panoptic_copypaste)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + instance_mask_format="bitmask", + ignore_label=MetadataCatalog.get("coco_2017_train_panoptic_stuffonly").ignore_label, + stuff_classes_offset=1203, + stuff_classes_decomposition=True, + output_dir=None, + vis_period=12800, + dataset_names="${..dataset.names}", + ), + sampler=lambda dataset_dicts: MultiDatasetTrainingSampler( + repeat_factors=MultiDatasetTrainingSampler.get_repeat_factors( + dataset_dicts=dataset_dicts, + num_datasets=5, + dataset_ratio=[1, 1, 1, 1, 1], + use_rfs=[True, True, True, True, False], + use_cas=[False, False, False, False, False], + repeat_thresh=0.001, + cas_lambda=1.0, + ), + seed=random.randint(0, 2**31), + ), + sampler_bg=lambda dataset_dicts: RepeatFactorTrainingSampler( + repeat_factors=RepeatFactorTrainingSampler.repeat_factors_from_category_frequency( + dataset_dicts=dataset_dicts, repeat_thresh=0.001 + ), + seed=random.randint(0, 2**31), + ), + total_batch_size=16, + total_batch_size_list=[16, 16, 16, 16, 16], + aspect_ratio_grouping=True, + num_workers=4, + num_datasets=5, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="lvis_v1_val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(LVISEvaluator)( + dataset_name="${..test.dataset.names}", + max_dets_per_image=300, +) + +dataloader.tests = [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)( + names="coco_2017_val_panoptic_stuffonly", filter_empty=False + ), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators = [ + L(SemSegEvaluator)( + dataset_name="coco_2017_val_panoptic_stuffonly", + ), +] + +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="lvis_v1_minival", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ) +] + +dataloader.evaluators += [ + L(LVISEvaluator)( + dataset_name="lvis_v1_minival", + max_dets_per_image=300, + ) +] + +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)( + names="objects365_minival_fixname", filter_empty=False + ), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators += [ + L(COCOEvaluator)( + dataset_name="objects365_minival_fixname", + tasks=("bbox",), + ), +] + +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="objects365_val_fixname", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators += [ + L(COCOEvaluator)( + dataset_name="objects365_val_fixname", + tasks=("bbox",), + ), +] + +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="openimages_v6_val_bbox", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators += [ + L(OIDEvaluator)( + dataset_name="openimages_v6_val_bbox", + ), +] + +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)( + names="visualgenome_150_box_val", filter_empty=False + ), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators += [ + L(COCOEvaluator)( + dataset_name="visualgenome_150_box_val", + tasks=("bbox",), + ), +] + +refcoco_test_dataset_names = [ + "refcoco-unc-val", + "refcoco-unc-testA", + "refcoco-unc-testB", + "refcocoplus-unc-val", + "refcocoplus-unc-testA", + "refcocoplus-unc-testB", + "refcocog-google-val", + "refcocog-umd-val", + "refcocog-umd-test", +] +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names=name, filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ) + for name in refcoco_test_dataset_names +] + +dataloader.evaluators += [ + L(RefCOCOEvaluator)( + dataset_name=name, + ) + for name in refcoco_test_dataset_names +] diff --git a/configs/common/data/lviscocococostuff_o365_oid_vgr_refcoco_group_by_image_panoptic_lsj1024_cp.py b/configs/common/data/lviscocococostuff_o365_oid_vgr_refcoco_group_by_image_panoptic_lsj1024_cp.py new file mode 100644 index 0000000..3fb7675 --- /dev/null +++ b/configs/common/data/lviscocococostuff_o365_oid_vgr_refcoco_group_by_image_panoptic_lsj1024_cp.py @@ -0,0 +1,207 @@ +import random + +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + get_detection_dataset_dicts, +) +from detectron2.data.samplers import RepeatFactorTrainingSampler +from detectron2.evaluation import COCOEvaluator, LVISEvaluator, SemSegEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_panoptic_copypaste, + build_detection_train_loader_multi_dataset_copypaste, + get_detection_dataset_dicts_multi_dataset_copypaste, +) +from ape.data.samplers import MultiDatasetTrainingSampler +from ape.evaluation import RefCOCOEvaluator +from ape.evaluation.oideval import OIDEvaluator + +dataloader = OmegaConf.create() + +image_size = 1024 + +dataloader.train = L(build_detection_train_loader_multi_dataset_copypaste)( + dataset=L(get_detection_dataset_dicts_multi_dataset_copypaste)( + names=( + "lvis_v1_train+coco_panoptic_separated", + "objects365_train_fixname", + "openimages_v6_train_bbox_nogroup", + "visualgenome_77962_box_and_region", + "refcoco-mixed_group-by-image", + ), + filter_emptys=[True, True, True, True, True], + copypastes=[True, False, False, False, False], + ), + dataset_bg=L(get_detection_dataset_dicts)(names=["lvis_v1_train+coco_panoptic_separated"]), + mapper=L(DatasetMapper_detr_panoptic_copypaste)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + instance_mask_format="bitmask", + ignore_label=MetadataCatalog.get("coco_2017_train_panoptic_stuffonly").ignore_label, + stuff_classes_offset=1203, + stuff_classes_decomposition=True, + output_dir=None, + vis_period=12800, + dataset_names="${..dataset.names}", + max_num_phrase=100, + nms_thresh_phrase=0.6, + ), + sampler=lambda dataset_dicts: MultiDatasetTrainingSampler( + repeat_factors=MultiDatasetTrainingSampler.get_repeat_factors( + dataset_dicts=dataset_dicts, + num_datasets=5, + dataset_ratio=[1, 1, 1, 1, 0.1], + use_rfs=[True, True, True, False, False], + use_cas=[False, False, False, False, False], + repeat_thresh=0.001, + cas_lambda=1.0, + ), + seed=random.randint(0, 2**31), + ), + sampler_bg=lambda dataset_dicts: RepeatFactorTrainingSampler( + repeat_factors=RepeatFactorTrainingSampler.repeat_factors_from_category_frequency( + dataset_dicts=dataset_dicts, repeat_thresh=0.001 + ), + seed=random.randint(0, 2**31), + ), + total_batch_size=16, + total_batch_size_list=[16, 16, 16, 16, 16], + aspect_ratio_grouping=True, + num_workers=4, + num_datasets=5, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="lvis_v1_val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(LVISEvaluator)( + dataset_name="${..test.dataset.names}", + max_dets_per_image=300, +) + +dataloader.tests = [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)( + names="coco_2017_val_panoptic_stuffonly", filter_empty=False + ), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators = [ + L(SemSegEvaluator)( + dataset_name="coco_2017_val_panoptic_stuffonly", + ), +] + +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="objects365_val_fixname", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators += [ + L(COCOEvaluator)( + dataset_name="objects365_val_fixname", + tasks=("bbox",), + ), +] + +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="openimages_v6_val_bbox", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators += [ + L(OIDEvaluator)( + dataset_name="openimages_v6_val_bbox", + ), +] + + + +refcoco_test_dataset_names = [ + "refcoco-unc-val", + "refcoco-unc-testA", + "refcoco-unc-testB", + "refcocoplus-unc-val", + "refcocoplus-unc-testA", + "refcocoplus-unc-testB", + "refcocog-google-val", + "refcocog-umd-val", + "refcocog-umd-test", +] +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names=name, filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ) + for name in refcoco_test_dataset_names +] + +dataloader.evaluators += [ + L(RefCOCOEvaluator)( + dataset_name=name, + ) + for name in refcoco_test_dataset_names +] diff --git a/configs/common/data/lviscocococostuff_o365_oid_vgr_refcoco_panoptic_lsj1024_cp.py b/configs/common/data/lviscocococostuff_o365_oid_vgr_refcoco_panoptic_lsj1024_cp.py new file mode 100644 index 0000000..57e9524 --- /dev/null +++ b/configs/common/data/lviscocococostuff_o365_oid_vgr_refcoco_panoptic_lsj1024_cp.py @@ -0,0 +1,211 @@ +import random + +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + get_detection_dataset_dicts, +) +from detectron2.data.samplers import RepeatFactorTrainingSampler +from detectron2.evaluation import COCOEvaluator, LVISEvaluator, SemSegEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_panoptic_copypaste, + build_detection_train_loader_multi_dataset_copypaste, + get_detection_dataset_dicts_multi_dataset_copypaste, +) +from ape.data.samplers import MultiDatasetTrainingSampler +from ape.evaluation import RefCOCOEvaluator +from ape.evaluation.oideval import OIDEvaluator + +dataloader = OmegaConf.create() + +image_size = 1024 + +dataloader.train = L(build_detection_train_loader_multi_dataset_copypaste)( + dataset=L(get_detection_dataset_dicts_multi_dataset_copypaste)( + names=( + "lvis_v1_train+coco_panoptic_separated", + "objects365_train_fixname", + "openimages_v6_train_bbox_nogroup", + "visualgenome_77962_box_and_region", + "refcoco-mixed", + ), + filter_emptys=[True, True, True, True, True], + copypastes=[True, False, False, False, False], + ), + dataset_bg=L(get_detection_dataset_dicts)(names=["lvis_v1_train+coco_panoptic_separated"]), + mapper=L(DatasetMapper_detr_panoptic_copypaste)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + instance_mask_format="bitmask", + ignore_label=MetadataCatalog.get("coco_2017_train_panoptic_stuffonly").ignore_label, + stuff_classes_offset=1203, + stuff_classes_decomposition=True, + output_dir=None, + vis_period=12800, + dataset_names=[ + "lvis_v1_train+coco_panoptic_separated", + "objects365_train_fixname", + "openimages_v6_train_bbox_nogroup", + "visualgenome_77962_box_and_region", + "refcoco-mixed", + ], + ), + sampler=lambda dataset_dicts: MultiDatasetTrainingSampler( + repeat_factors=MultiDatasetTrainingSampler.get_repeat_factors( + dataset_dicts=dataset_dicts, + num_datasets=5, + dataset_ratio=[1, 1, 1, 1, 1], + use_rfs=[True, True, True, False, False], + use_cas=[False, False, False, False, False], + repeat_thresh=0.001, + cas_lambda=1.0, + ), + seed=random.randint(0, 2**31), + ), + sampler_bg=lambda dataset_dicts: RepeatFactorTrainingSampler( + repeat_factors=RepeatFactorTrainingSampler.repeat_factors_from_category_frequency( + dataset_dicts=dataset_dicts, repeat_thresh=0.001 + ), + seed=random.randint(0, 2**31), + ), + total_batch_size=16, + total_batch_size_list=[16, 16, 16, 16, 16], + aspect_ratio_grouping=True, + num_workers=4, + num_datasets=5, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="lvis_v1_val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(LVISEvaluator)( + dataset_name="${..test.dataset.names}", + max_dets_per_image=300, +) + +dataloader.tests = [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)( + names="coco_2017_val_panoptic_stuffonly", filter_empty=False + ), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators = [ + L(SemSegEvaluator)( + dataset_name="coco_2017_val_panoptic_stuffonly", + ), +] + +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="objects365_val_fixname", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators += [ + L(COCOEvaluator)( + dataset_name="objects365_val_fixname", + tasks=("bbox",), + ), +] + +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="openimages_v6_val_bbox", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators += [ + L(OIDEvaluator)( + dataset_name="openimages_v6_val_bbox", + ), +] + + + +refcoco_test_dataset_names = [ + "refcoco-unc-val", + "refcoco-unc-testA", + "refcoco-unc-testB", + "refcocoplus-unc-val", + "refcocoplus-unc-testA", + "refcocoplus-unc-testB", + "refcocog-google-val", + "refcocog-umd-val", + "refcocog-umd-test", +] +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names=name, filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ) + for name in refcoco_test_dataset_names +] + +dataloader.evaluators += [ + L(RefCOCOEvaluator)( + dataset_name=name, + ) + for name in refcoco_test_dataset_names +] diff --git a/configs/common/data/lviscocococostuff_o365_oid_vgr_sa1b_refcoco_group_by_image_gqa_panoptic_lsj1024_cp.py b/configs/common/data/lviscocococostuff_o365_oid_vgr_sa1b_refcoco_group_by_image_gqa_panoptic_lsj1024_cp.py new file mode 100644 index 0000000..9246c05 --- /dev/null +++ b/configs/common/data/lviscocococostuff_o365_oid_vgr_sa1b_refcoco_group_by_image_gqa_panoptic_lsj1024_cp.py @@ -0,0 +1,217 @@ +import random + +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + get_detection_dataset_dicts, +) +from detectron2.data.samplers import RepeatFactorTrainingSampler +from detectron2.evaluation import COCOEvaluator, LVISEvaluator, SemSegEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_panoptic_copypaste, + build_detection_train_loader_multi_dataset_copypaste, + get_detection_dataset_dicts_multi_dataset_copypaste, +) +from ape.data.samplers import MultiDatasetTrainingSampler +from ape.evaluation import RefCOCOEvaluator +from ape.evaluation.oideval import OIDEvaluator + +dataloader = OmegaConf.create() + +image_size = 1024 + +dataloader.train = L(build_detection_train_loader_multi_dataset_copypaste)( + dataset=L(get_detection_dataset_dicts_multi_dataset_copypaste)( + names=( + "lvis_v1_train+coco_panoptic_separated", + "objects365_train_fixname", + "openimages_v6_train_bbox_nogroup", + "visualgenome_77962_box_and_region", + "sa1b", + "refcoco-mixed_group-by-image", + "gqa_region", + ), + filter_emptys=[True, True, True, True, False, True, True], + copypastes=[True, False, False, False, False, False, False], + ), + dataset_bg=L(get_detection_dataset_dicts)(names=["lvis_v1_train+coco_panoptic_separated"]), + mapper=L(DatasetMapper_detr_panoptic_copypaste)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + instance_mask_format="bitmask", + ignore_label=MetadataCatalog.get("coco_2017_train_panoptic_stuffonly").ignore_label, + stuff_classes_offset=1203, + stuff_classes_decomposition=True, + output_dir=None, + vis_period=12800, + dataset_names=[ + "lvis_v1_train+coco_panoptic_separated", + "objects365_train_fixname", + "openimages_v6_train_bbox_nogroup", + "visualgenome_77962_box_and_region", + "sa1b", + "refcoco-mixed_group-by-image", + "gqa_region", + ], + max_num_phrase=100, + nms_thresh_phrase=0.6, + ), + sampler=lambda dataset_dicts: MultiDatasetTrainingSampler( + repeat_factors=MultiDatasetTrainingSampler.get_repeat_factors( + dataset_dicts=dataset_dicts, + num_datasets=7, + dataset_ratio=[1, 1, 1, 1, 1, 0.2, 0.1], + use_rfs=[True, True, True, False, False, False, False], + use_cas=[False, False, False, False, False, False, False], + repeat_thresh=0.001, + cas_lambda=1.0, + ), + seed=random.randint(0, 2**31), + ), + sampler_bg=lambda dataset_dicts: RepeatFactorTrainingSampler( + repeat_factors=RepeatFactorTrainingSampler.repeat_factors_from_category_frequency( + dataset_dicts=dataset_dicts, repeat_thresh=0.001 + ), + seed=random.randint(0, 2**31), + ), + total_batch_size=16, + total_batch_size_list=[16, 16, 16, 16, 16, 16, 16], + aspect_ratio_grouping=True, + num_workers=2, + num_datasets=7, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="lvis_v1_val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(LVISEvaluator)( + dataset_name="${..test.dataset.names}", + max_dets_per_image=300, +) + +dataloader.tests = [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)( + names="coco_2017_val_panoptic_stuffonly", filter_empty=False + ), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators = [ + L(SemSegEvaluator)( + dataset_name="coco_2017_val_panoptic_stuffonly", + ), +] + +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="objects365_val_fixname", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators += [ + L(COCOEvaluator)( + dataset_name="objects365_val_fixname", + tasks=("bbox",), + ), +] + +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="openimages_v6_val_bbox", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators += [ + L(OIDEvaluator)( + dataset_name="openimages_v6_val_bbox", + ), +] + + + +refcoco_test_dataset_names = [ + "refcoco-unc-val", + "refcoco-unc-testA", + "refcoco-unc-testB", + "refcocoplus-unc-val", + "refcocoplus-unc-testA", + "refcocoplus-unc-testB", + "refcocog-google-val", + "refcocog-umd-val", + "refcocog-umd-test", +] +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names=name, filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ) + for name in refcoco_test_dataset_names +] + +dataloader.evaluators += [ + L(RefCOCOEvaluator)( + dataset_name=name, + ) + for name in refcoco_test_dataset_names +] diff --git a/configs/common/data/lviscocococostuff_o365_oid_vgr_sa1b_refcoco_group_by_image_gqa_panoptic_lsj1536_cp.py b/configs/common/data/lviscocococostuff_o365_oid_vgr_sa1b_refcoco_group_by_image_gqa_panoptic_lsj1536_cp.py new file mode 100644 index 0000000..a2f5b66 --- /dev/null +++ b/configs/common/data/lviscocococostuff_o365_oid_vgr_sa1b_refcoco_group_by_image_gqa_panoptic_lsj1536_cp.py @@ -0,0 +1,217 @@ +import random + +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + get_detection_dataset_dicts, +) +from detectron2.data.samplers import RepeatFactorTrainingSampler +from detectron2.evaluation import COCOEvaluator, LVISEvaluator, SemSegEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_panoptic_copypaste, + build_detection_train_loader_multi_dataset_copypaste, + get_detection_dataset_dicts_multi_dataset_copypaste, +) +from ape.data.samplers import MultiDatasetTrainingSampler +from ape.evaluation import RefCOCOEvaluator +from ape.evaluation.oideval import OIDEvaluator + +dataloader = OmegaConf.create() + +image_size = 1536 + +dataloader.train = L(build_detection_train_loader_multi_dataset_copypaste)( + dataset=L(get_detection_dataset_dicts_multi_dataset_copypaste)( + names=( + "lvis_v1_train+coco_panoptic_separated", + "objects365_train_fixname", + "openimages_v6_train_bbox_nogroup", + "visualgenome_77962_box_and_region", + "sa1b", + "refcoco-mixed_group-by-image", + "gqa_region", + ), + filter_emptys=[True, True, True, True, False, True, True], + copypastes=[True, False, False, False, False, False, False], + ), + dataset_bg=L(get_detection_dataset_dicts)(names=["lvis_v1_train+coco_panoptic_separated"]), + mapper=L(DatasetMapper_detr_panoptic_copypaste)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + instance_mask_format="bitmask", + ignore_label=MetadataCatalog.get("coco_2017_train_panoptic_stuffonly").ignore_label, + stuff_classes_offset=1203, + stuff_classes_decomposition=True, + output_dir=None, + vis_period=12800, + dataset_names=[ + "lvis_v1_train+coco_panoptic_separated", + "objects365_train_fixname", + "openimages_v6_train_bbox_nogroup", + "visualgenome_77962_box_and_region", + "sa1b", + "refcoco-mixed_group-by-image", + "gqa_region", + ], + max_num_phrase=100, + nms_thresh_phrase=0.6, + ), + sampler=lambda dataset_dicts: MultiDatasetTrainingSampler( + repeat_factors=MultiDatasetTrainingSampler.get_repeat_factors( + dataset_dicts=dataset_dicts, + num_datasets=7, + dataset_ratio=[1, 1, 1, 1, 1, 0.2, 0.1], + use_rfs=[True, True, True, False, False, False, False], + use_cas=[False, False, False, False, False, False, False], + repeat_thresh=0.001, + cas_lambda=1.0, + ), + seed=random.randint(0, 2**31), + ), + sampler_bg=lambda dataset_dicts: RepeatFactorTrainingSampler( + repeat_factors=RepeatFactorTrainingSampler.repeat_factors_from_category_frequency( + dataset_dicts=dataset_dicts, repeat_thresh=0.001 + ), + seed=random.randint(0, 2**31), + ), + total_batch_size=16, + total_batch_size_list=[16, 16, 16, 16, 16, 16, 16], + aspect_ratio_grouping=True, + num_workers=2, + num_datasets=7, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="lvis_v1_val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(LVISEvaluator)( + dataset_name="${..test.dataset.names}", + max_dets_per_image=300, +) + +dataloader.tests = [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)( + names="coco_2017_val_panoptic_stuffonly", filter_empty=False + ), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators = [ + L(SemSegEvaluator)( + dataset_name="coco_2017_val_panoptic_stuffonly", + ), +] + +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="objects365_val_fixname", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators += [ + L(COCOEvaluator)( + dataset_name="objects365_val_fixname", + tasks=("bbox",), + ), +] + +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="openimages_v6_val_bbox", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators += [ + L(OIDEvaluator)( + dataset_name="openimages_v6_val_bbox", + ), +] + + + +refcoco_test_dataset_names = [ + "refcoco-unc-val", + "refcoco-unc-testA", + "refcoco-unc-testB", + "refcocoplus-unc-val", + "refcocoplus-unc-testA", + "refcocoplus-unc-testB", + "refcocog-google-val", + "refcocog-umd-val", + "refcocog-umd-test", +] +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names=name, filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ) + for name in refcoco_test_dataset_names +] + +dataloader.evaluators += [ + L(RefCOCOEvaluator)( + dataset_name=name, + ) + for name in refcoco_test_dataset_names +] diff --git a/configs/common/data/lviscocococostuff_o365_oid_vgr_sa1b_refcoco_group_by_image_gqa_phrasecut_flickr30k_panoptic_lsj1024_cp.py b/configs/common/data/lviscocococostuff_o365_oid_vgr_sa1b_refcoco_group_by_image_gqa_phrasecut_flickr30k_panoptic_lsj1024_cp.py new file mode 100644 index 0000000..4703f0a --- /dev/null +++ b/configs/common/data/lviscocococostuff_o365_oid_vgr_sa1b_refcoco_group_by_image_gqa_phrasecut_flickr30k_panoptic_lsj1024_cp.py @@ -0,0 +1,257 @@ +import random + +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + get_detection_dataset_dicts, +) +from detectron2.data.samplers import RepeatFactorTrainingSampler +from detectron2.evaluation import COCOEvaluator, LVISEvaluator, SemSegEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_panoptic_copypaste, + build_detection_train_loader_multi_dataset_copypaste, + get_detection_dataset_dicts_multi_dataset_copypaste, +) +from ape.data.samplers import MultiDatasetTrainingSampler +from ape.evaluation import RefCOCOEvaluator +from ape.evaluation.oideval import OIDEvaluator + +dataloader = OmegaConf.create() + +image_size = 1024 + +dataloader.train = L(build_detection_train_loader_multi_dataset_copypaste)( + dataset=L(get_detection_dataset_dicts_multi_dataset_copypaste)( + names=( + "lvis_v1_train+coco_panoptic_separated", + "objects365_train_fixname", + "openimages_v6_train_bbox_nogroup", + "visualgenome_77962_box_and_region", + "sa1b_6m", + "refcoco-mixed_group-by-image", + "gqa_region_train", + "phrasecut_train", + "flickr30k_separateGT_train", + ), + filter_emptys=[True, True, True, True, False, True, True, True, True], + copypastes=[True, False, False, False, False, False, False, False, False], + reduce_memory=True, + reduce_memory_size=1e6, + ), + dataset_bg=L(get_detection_dataset_dicts)(names=["lvis_v1_train+coco_panoptic_separated"]), + mapper=L(DatasetMapper_detr_panoptic_copypaste)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + instance_mask_format="bitmask", + ignore_label=MetadataCatalog.get("coco_2017_train_panoptic_stuffonly").ignore_label, + stuff_classes_offset=1203, + stuff_classes_decomposition=True, + output_dir=None, + vis_period=12800, + dataset_names="${..dataset.names}", + max_num_phrase=128, + nms_thresh_phrase=0.6, + ), + sampler=lambda dataset_dicts: MultiDatasetTrainingSampler( + repeat_factors=MultiDatasetTrainingSampler.get_repeat_factors( + dataset_dicts=dataset_dicts, + num_datasets=9, + dataset_ratio=[1, 1, 1, 1, 1, 0.1, 0.1, 0.1, 0.1], + use_rfs=[True, True, True, False, False, False, False, False, False], + use_cas=[False, False, False, False, False, False, False, False, False], + repeat_thresh=0.001, + cas_lambda=1.0, + ), + seed=random.randint(0, 2**31), + ), + sampler_bg=lambda dataset_dicts: RepeatFactorTrainingSampler( + repeat_factors=RepeatFactorTrainingSampler.repeat_factors_from_category_frequency( + dataset_dicts=dataset_dicts, repeat_thresh=0.001 + ), + seed=random.randint(0, 2**31), + ), + total_batch_size=16, + total_batch_size_list=[16, 16, 16, 16, 16, 16, 16, 16, 16], + aspect_ratio_grouping=True, + num_workers=2, + num_datasets=9, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="lvis_v1_val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(LVISEvaluator)( + dataset_name="${..test.dataset.names}", + max_dets_per_image=300, +) + +dataloader.tests = [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)( + names="coco_2017_val_panoptic_stuffonly", filter_empty=False + ), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators = [ + L(SemSegEvaluator)( + dataset_name="coco_2017_val_panoptic_stuffonly", + ), +] + +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="lvis_v1_minival", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ) +] + +dataloader.evaluators += [ + L(LVISEvaluator)( + dataset_name="lvis_v1_minival", + max_dets_per_image=300, + ) +] + +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)( + names="objects365_minival_fixname", filter_empty=False + ), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators += [ + L(COCOEvaluator)( + dataset_name="objects365_minival_fixname", + tasks=("bbox",), + ), +] + +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="objects365_val_fixname", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators += [ + L(COCOEvaluator)( + dataset_name="objects365_val_fixname", + tasks=("bbox",), + ), +] + +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="openimages_v6_val_bbox", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators += [ + L(OIDEvaluator)( + dataset_name="openimages_v6_val_bbox", + ), +] + + + +refcoco_test_dataset_names = [ + "refcoco-unc-val", + "refcoco-unc-testA", + "refcoco-unc-testB", + "refcocoplus-unc-val", + "refcocoplus-unc-testA", + "refcocoplus-unc-testB", + "refcocog-google-val", + "refcocog-umd-val", + "refcocog-umd-test", +] +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names=name, filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ) + for name in refcoco_test_dataset_names +] + +dataloader.evaluators += [ + L(RefCOCOEvaluator)( + dataset_name=name, + ) + for name in refcoco_test_dataset_names +] diff --git a/configs/common/data/lviscocococostuff_o365_oid_vgr_sa1b_refcoco_group_by_image_gqa_phrasecut_flickr30k_panoptic_lsj1024_cp_mdl.py b/configs/common/data/lviscocococostuff_o365_oid_vgr_sa1b_refcoco_group_by_image_gqa_phrasecut_flickr30k_panoptic_lsj1024_cp_mdl.py new file mode 100644 index 0000000..450c2ef --- /dev/null +++ b/configs/common/data/lviscocococostuff_o365_oid_vgr_sa1b_refcoco_group_by_image_gqa_phrasecut_flickr30k_panoptic_lsj1024_cp_mdl.py @@ -0,0 +1,266 @@ +import random + +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + build_detection_train_loader, + get_detection_dataset_dicts, +) +from detectron2.data.samplers import RepeatFactorTrainingSampler +from detectron2.evaluation import COCOEvaluator, LVISEvaluator, SemSegEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_panoptic, + DatasetMapper_detr_panoptic_copypaste, + build_detection_train_loader_multi_dataset, + build_detection_train_loader_multi_dataset_copypaste, + get_detection_dataset_dicts_multi_dataset, + get_detection_dataset_dicts_multi_dataset_copypaste, +) +from ape.evaluation import RefCOCOEvaluator +from ape.evaluation.oideval import OIDEvaluator + +dataloader = OmegaConf.create() + +image_size = 1024 + +dataloader.train = [ + L(build_detection_train_loader_multi_dataset_copypaste)( + dataset=L(get_detection_dataset_dicts_multi_dataset_copypaste)( + names=(dataset_name,), + filter_emptys=[use_filter], + copypastes=[use_cp], + dataloader_id=dataloader_id, + reduce_memory=True, + reduce_memory_size=1e6, + ), + dataset_bg=L(get_detection_dataset_dicts)( + names=(dataset_name,), + filter_empty=use_filter, + ) + if use_cp + else [[]], + mapper=L(DatasetMapper_detr_panoptic_copypaste)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + instance_mask_format="bitmask", + ignore_label=MetadataCatalog.get(dataset_name).get("ignore_label", None), + stuff_classes_offset=len(MetadataCatalog.get(dataset_name).get("thing_classes", [])), + stuff_classes_decomposition=True, + output_dir=None, + vis_period=12800, + dataset_names=(dataset_name,), + max_num_phrase=128, + nms_thresh_phrase=0.6, + ), + sampler=L(RepeatFactorTrainingSampler)( + repeat_factors=L(RepeatFactorTrainingSampler.repeat_factors_from_category_frequency)( + dataset_dicts="${...dataset}", repeat_thresh=0.001 + ) + ) + if use_rfs + else None, + sampler_bg=L(RepeatFactorTrainingSampler)( + repeat_factors=L(RepeatFactorTrainingSampler.repeat_factors_from_category_frequency)( + dataset_dicts="${...dataset}", repeat_thresh=0.001 + ) + ) + if use_rfs and use_cp + else None, + total_batch_size=16, + total_batch_size_list=[16], + aspect_ratio_grouping=True, + num_workers=2, + num_datasets=1, + ) + for dataloader_id, use_rfs, use_cp, use_filter, dataset_name in [ + [0, True, True, True, "lvis_v1_train+coco_panoptic_separated"], + [1, True, False, True, "objects365_train_fixname"], + [2, True, False, True, "openimages_v6_train_bbox_nogroup"], + [3, False, False, True, "visualgenome_77962_box_and_region"], + [4, False, False, False, "sa1b"], + [5, False, False, True, "refcoco-mixed_group-by-image"], + [6, False, False, True, "gqa_region_train"], + [7, False, False, True, "phrasecut_train"], + [8, False, False, True, "flickr30k_separateGT_train"], + ] +] + + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="lvis_v1_val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="RGB", + ), + num_workers=4, +) + +dataloader.evaluator = L(LVISEvaluator)( + dataset_name="${..test.dataset.names}", + max_dets_per_image=300, +) + +dataloader.tests = [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)( + names="coco_2017_val_panoptic_stuffonly", filter_empty=False + ), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="RGB", + ), + num_workers=4, + ), +] + +dataloader.evaluators = [ + L(SemSegEvaluator)( + dataset_name="coco_2017_val_panoptic_stuffonly", + ), +] + +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="lvis_v1_minival", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="RGB", + ), + num_workers=4, + ) +] + +dataloader.evaluators += [ + L(LVISEvaluator)( + dataset_name="lvis_v1_minival", + max_dets_per_image=300, + ) +] + +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)( + names="objects365_minival_fixname", filter_empty=False + ), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="RGB", + ), + num_workers=4, + ), +] + +dataloader.evaluators += [ + L(COCOEvaluator)( + dataset_name="objects365_minival_fixname", + tasks=("bbox",), + ), +] + +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="objects365_val_fixname", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="RGB", + ), + num_workers=4, + ), +] + +dataloader.evaluators += [ + L(COCOEvaluator)( + dataset_name="objects365_val_fixname", + tasks=("bbox",), + ), +] + +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="openimages_v6_val_bbox", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="RGB", + ), + num_workers=4, + ), +] + +dataloader.evaluators += [ + L(OIDEvaluator)( + dataset_name="openimages_v6_val_bbox", + ), +] + + + +refcoco_test_dataset_names = [ + "refcoco-unc-val", + "refcoco-unc-testA", + "refcoco-unc-testB", + "refcocoplus-unc-val", + "refcocoplus-unc-testA", + "refcocoplus-unc-testB", + "refcocog-google-val", + "refcocog-umd-val", + "refcocog-umd-test", +] +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names=name, filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="RGB", + ), + num_workers=4, + ) + for name in refcoco_test_dataset_names +] + +dataloader.evaluators += [ + L(RefCOCOEvaluator)( + dataset_name=name, + ) + for name in refcoco_test_dataset_names +] diff --git a/configs/common/data/lviscocococostuff_o365_oid_vgr_sa1b_refcoco_group_by_image_gqa_phrasecut_flickr30k_panoptic_lsj1536_cp.py b/configs/common/data/lviscocococostuff_o365_oid_vgr_sa1b_refcoco_group_by_image_gqa_phrasecut_flickr30k_panoptic_lsj1536_cp.py new file mode 100644 index 0000000..68f5b77 --- /dev/null +++ b/configs/common/data/lviscocococostuff_o365_oid_vgr_sa1b_refcoco_group_by_image_gqa_phrasecut_flickr30k_panoptic_lsj1536_cp.py @@ -0,0 +1,231 @@ +import random + +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + get_detection_dataset_dicts, +) +from detectron2.data.samplers import RepeatFactorTrainingSampler +from detectron2.evaluation import COCOEvaluator, LVISEvaluator, SemSegEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_panoptic_copypaste, + build_detection_train_loader_multi_dataset_copypaste, + get_detection_dataset_dicts_multi_dataset_copypaste, +) +from ape.data.samplers import MultiDatasetTrainingSampler +from ape.evaluation import RefCOCOEvaluator +from ape.evaluation.oideval import OIDEvaluator + +dataloader = OmegaConf.create() + +image_size = 1536 + +dataloader.train = L(build_detection_train_loader_multi_dataset_copypaste)( + dataset=L(get_detection_dataset_dicts_multi_dataset_copypaste)( + names=( + "lvis_v1_train+coco_panoptic_separated", + "objects365_train_fixname", + "openimages_v6_train_bbox_nogroup", + "visualgenome_77962_box_and_region", + "sa1b", + "refcoco-mixed_group-by-image", + "gqa_region_train", + "phrasecut_train", + "flickr30k_separateGT_train", + ), + filter_emptys=[True, True, True, True, False, True, True, True, True], + copypastes=[True, False, False, False, False, False, False, False, False], + ), + dataset_bg=L(get_detection_dataset_dicts)(names=["lvis_v1_train+coco_panoptic_separated"]), + mapper=L(DatasetMapper_detr_panoptic_copypaste)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + instance_mask_format="bitmask", + ignore_label=MetadataCatalog.get("coco_2017_train_panoptic_stuffonly").ignore_label, + stuff_classes_offset=1203, + stuff_classes_decomposition=True, + output_dir=None, + vis_period=12800, + dataset_names=[ + "lvis_v1_train+coco_panoptic_separated", + "objects365_train_fixname", + "openimages_v6_train_bbox_nogroup", + "visualgenome_77962_box_and_region", + "sa1b", + "refcoco-mixed_group-by-image", + "gqa_region_train", + "phrasecut_train", + "flickr30k_separateGT_train", + ], + max_num_phrase=100, + nms_thresh_phrase=0.6, + ), + sampler=lambda dataset_dicts: MultiDatasetTrainingSampler( + repeat_factors=MultiDatasetTrainingSampler.get_repeat_factors( + dataset_dicts=dataset_dicts, + num_datasets=9, + dataset_ratio=[1, 1, 1, 1, 1, 0.1, 0.1, 0.1, 0.1], + use_rfs=[True, True, True, False, False, False, False, False, False], + use_cas=[ + False, + False, + False, + False, + False, + False, + False, + False, + False, + ], + repeat_thresh=0.001, + cas_lambda=1.0, + ), + seed=random.randint(0, 2**31), + ), + sampler_bg=lambda dataset_dicts: RepeatFactorTrainingSampler( + repeat_factors=RepeatFactorTrainingSampler.repeat_factors_from_category_frequency( + dataset_dicts=dataset_dicts, repeat_thresh=0.001 + ), + seed=random.randint(0, 2**31), + ), + total_batch_size=16, + total_batch_size_list=[16, 16, 16, 16, 16, 16, 16, 16, 16], + aspect_ratio_grouping=True, + num_workers=2, + num_datasets=9, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="lvis_v1_val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(LVISEvaluator)( + dataset_name="${..test.dataset.names}", + max_dets_per_image=300, +) + +dataloader.tests = [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)( + names="coco_2017_val_panoptic_stuffonly", filter_empty=False + ), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators = [ + L(SemSegEvaluator)( + dataset_name="coco_2017_val_panoptic_stuffonly", + ), +] + +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="objects365_val_fixname", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators += [ + L(COCOEvaluator)( + dataset_name="objects365_val_fixname", + tasks=("bbox",), + ), +] + +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="openimages_v6_val_bbox", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators += [ + L(OIDEvaluator)( + dataset_name="openimages_v6_val_bbox", + ), +] + + + +refcoco_test_dataset_names = [ + "refcoco-unc-val", + "refcoco-unc-testA", + "refcoco-unc-testB", + "refcocoplus-unc-val", + "refcocoplus-unc-testA", + "refcocoplus-unc-testB", + "refcocog-google-val", + "refcocog-umd-val", + "refcocog-umd-test", +] +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names=name, filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ) + for name in refcoco_test_dataset_names +] + +dataloader.evaluators += [ + L(RefCOCOEvaluator)( + dataset_name=name, + ) + for name in refcoco_test_dataset_names +] diff --git a/configs/common/data/lviscocococostuff_o365_oid_vgr_sa1b_refcoco_group_by_image_gqa_phrasecut_panoptic_lsj1024_cp.py b/configs/common/data/lviscocococostuff_o365_oid_vgr_sa1b_refcoco_group_by_image_gqa_phrasecut_panoptic_lsj1024_cp.py new file mode 100644 index 0000000..a24df7d --- /dev/null +++ b/configs/common/data/lviscocococostuff_o365_oid_vgr_sa1b_refcoco_group_by_image_gqa_phrasecut_panoptic_lsj1024_cp.py @@ -0,0 +1,219 @@ +import random + +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + get_detection_dataset_dicts, +) +from detectron2.data.samplers import RepeatFactorTrainingSampler +from detectron2.evaluation import COCOEvaluator, LVISEvaluator, SemSegEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_panoptic_copypaste, + build_detection_train_loader_multi_dataset_copypaste, + get_detection_dataset_dicts_multi_dataset_copypaste, +) +from ape.data.samplers import MultiDatasetTrainingSampler +from ape.evaluation import RefCOCOEvaluator +from ape.evaluation.oideval import OIDEvaluator + +dataloader = OmegaConf.create() + +image_size = 1024 + +dataloader.train = L(build_detection_train_loader_multi_dataset_copypaste)( + dataset=L(get_detection_dataset_dicts_multi_dataset_copypaste)( + names=( + "lvis_v1_train+coco_panoptic_separated", + "objects365_train_fixname", + "openimages_v6_train_bbox_nogroup", + "visualgenome_77962_box_and_region", + "sa1b", + "refcoco-mixed_group-by-image", + "gqa_region_train", + "phrasecut_train", + ), + filter_emptys=[True, True, True, True, False, True, True, True], + copypastes=[True, False, False, False, False, False, False, False], + ), + dataset_bg=L(get_detection_dataset_dicts)(names=["lvis_v1_train+coco_panoptic_separated"]), + mapper=L(DatasetMapper_detr_panoptic_copypaste)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + instance_mask_format="bitmask", + ignore_label=MetadataCatalog.get("coco_2017_train_panoptic_stuffonly").ignore_label, + stuff_classes_offset=1203, + stuff_classes_decomposition=True, + output_dir=None, + vis_period=12800, + dataset_names=[ + "lvis_v1_train+coco_panoptic_separated", + "objects365_train_fixname", + "openimages_v6_train_bbox_nogroup", + "visualgenome_77962_box_and_region", + "sa1b", + "refcoco-mixed_group-by-image", + "gqa_region_train", + "phrasecut_train", + ], + max_num_phrase=100, + nms_thresh_phrase=0.6, + ), + sampler=lambda dataset_dicts: MultiDatasetTrainingSampler( + repeat_factors=MultiDatasetTrainingSampler.get_repeat_factors( + dataset_dicts=dataset_dicts, + num_datasets=8, + dataset_ratio=[1, 1, 1, 1, 1, 0.2, 0.1, 0.2], + use_rfs=[True, True, True, False, False, False, False, False], + use_cas=[False, False, False, False, False, False, False, False], + repeat_thresh=0.001, + cas_lambda=1.0, + ), + seed=random.randint(0, 2**31), + ), + sampler_bg=lambda dataset_dicts: RepeatFactorTrainingSampler( + repeat_factors=RepeatFactorTrainingSampler.repeat_factors_from_category_frequency( + dataset_dicts=dataset_dicts, repeat_thresh=0.001 + ), + seed=random.randint(0, 2**31), + ), + total_batch_size=16, + total_batch_size_list=[16, 16, 16, 16, 16, 16, 16, 16], + aspect_ratio_grouping=True, + num_workers=2, + num_datasets=8, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="lvis_v1_val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(LVISEvaluator)( + dataset_name="${..test.dataset.names}", + max_dets_per_image=300, +) + +dataloader.tests = [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)( + names="coco_2017_val_panoptic_stuffonly", filter_empty=False + ), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators = [ + L(SemSegEvaluator)( + dataset_name="coco_2017_val_panoptic_stuffonly", + ), +] + +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="objects365_val_fixname", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators += [ + L(COCOEvaluator)( + dataset_name="objects365_val_fixname", + tasks=("bbox",), + ), +] + +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="openimages_v6_val_bbox", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators += [ + L(OIDEvaluator)( + dataset_name="openimages_v6_val_bbox", + ), +] + + + +refcoco_test_dataset_names = [ + "refcoco-unc-val", + "refcoco-unc-testA", + "refcoco-unc-testB", + "refcocoplus-unc-val", + "refcocoplus-unc-testA", + "refcocoplus-unc-testB", + "refcocog-google-val", + "refcocog-umd-val", + "refcocog-umd-test", +] +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names=name, filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ) + for name in refcoco_test_dataset_names +] + +dataloader.evaluators += [ + L(RefCOCOEvaluator)( + dataset_name=name, + ) + for name in refcoco_test_dataset_names +] diff --git a/configs/common/data/lviscocococostuff_o365_oid_vgr_sa1b_refcoco_group_by_image_gqa_phrasecut_panoptic_lsj1536_cp.py b/configs/common/data/lviscocococostuff_o365_oid_vgr_sa1b_refcoco_group_by_image_gqa_phrasecut_panoptic_lsj1536_cp.py new file mode 100644 index 0000000..81a6f56 --- /dev/null +++ b/configs/common/data/lviscocococostuff_o365_oid_vgr_sa1b_refcoco_group_by_image_gqa_phrasecut_panoptic_lsj1536_cp.py @@ -0,0 +1,219 @@ +import random + +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + get_detection_dataset_dicts, +) +from detectron2.data.samplers import RepeatFactorTrainingSampler +from detectron2.evaluation import COCOEvaluator, LVISEvaluator, SemSegEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_panoptic_copypaste, + build_detection_train_loader_multi_dataset_copypaste, + get_detection_dataset_dicts_multi_dataset_copypaste, +) +from ape.data.samplers import MultiDatasetTrainingSampler +from ape.evaluation import RefCOCOEvaluator +from ape.evaluation.oideval import OIDEvaluator + +dataloader = OmegaConf.create() + +image_size = 1536 + +dataloader.train = L(build_detection_train_loader_multi_dataset_copypaste)( + dataset=L(get_detection_dataset_dicts_multi_dataset_copypaste)( + names=( + "lvis_v1_train+coco_panoptic_separated", + "objects365_train_fixname", + "openimages_v6_train_bbox_nogroup", + "visualgenome_77962_box_and_region", + "sa1b", + "refcoco-mixed_group-by-image", + "gqa_region_train", + "phrasecut_train", + ), + filter_emptys=[True, True, True, True, False, True, True, True], + copypastes=[True, False, False, False, False, False, False, False], + ), + dataset_bg=L(get_detection_dataset_dicts)(names=["lvis_v1_train+coco_panoptic_separated"]), + mapper=L(DatasetMapper_detr_panoptic_copypaste)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + instance_mask_format="bitmask", + ignore_label=MetadataCatalog.get("coco_2017_train_panoptic_stuffonly").ignore_label, + stuff_classes_offset=1203, + stuff_classes_decomposition=True, + output_dir=None, + vis_period=12800, + dataset_names=[ + "lvis_v1_train+coco_panoptic_separated", + "objects365_train_fixname", + "openimages_v6_train_bbox_nogroup", + "visualgenome_77962_box_and_region", + "sa1b", + "refcoco-mixed_group-by-image", + "gqa_region_train", + "phrasecut_train", + ], + max_num_phrase=100, + nms_thresh_phrase=0.6, + ), + sampler=lambda dataset_dicts: MultiDatasetTrainingSampler( + repeat_factors=MultiDatasetTrainingSampler.get_repeat_factors( + dataset_dicts=dataset_dicts, + num_datasets=8, + dataset_ratio=[1, 1, 1, 1, 1, 0.2, 0.1, 0.2], + use_rfs=[True, True, True, False, False, False, False, False], + use_cas=[False, False, False, False, False, False, False, False], + repeat_thresh=0.001, + cas_lambda=1.0, + ), + seed=random.randint(0, 2**31), + ), + sampler_bg=lambda dataset_dicts: RepeatFactorTrainingSampler( + repeat_factors=RepeatFactorTrainingSampler.repeat_factors_from_category_frequency( + dataset_dicts=dataset_dicts, repeat_thresh=0.001 + ), + seed=random.randint(0, 2**31), + ), + total_batch_size=16, + total_batch_size_list=[16, 16, 16, 16, 16, 16, 16, 16], + aspect_ratio_grouping=True, + num_workers=2, + num_datasets=8, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="lvis_v1_val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(LVISEvaluator)( + dataset_name="${..test.dataset.names}", + max_dets_per_image=300, +) + +dataloader.tests = [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)( + names="coco_2017_val_panoptic_stuffonly", filter_empty=False + ), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators = [ + L(SemSegEvaluator)( + dataset_name="coco_2017_val_panoptic_stuffonly", + ), +] + +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="objects365_val_fixname", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators += [ + L(COCOEvaluator)( + dataset_name="objects365_val_fixname", + tasks=("bbox",), + ), +] + +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="openimages_v6_val_bbox", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators += [ + L(OIDEvaluator)( + dataset_name="openimages_v6_val_bbox", + ), +] + + + +refcoco_test_dataset_names = [ + "refcoco-unc-val", + "refcoco-unc-testA", + "refcoco-unc-testB", + "refcocoplus-unc-val", + "refcocoplus-unc-testA", + "refcocoplus-unc-testB", + "refcocog-google-val", + "refcocog-umd-val", + "refcocog-umd-test", +] +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names=name, filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ) + for name in refcoco_test_dataset_names +] + +dataloader.evaluators += [ + L(RefCOCOEvaluator)( + dataset_name=name, + ) + for name in refcoco_test_dataset_names +] diff --git a/configs/common/data/lviscocococostuff_o365_oid_vgr_sa1b_refcoco_group_by_image_panoptic_lsj1024_cp.py b/configs/common/data/lviscocococostuff_o365_oid_vgr_sa1b_refcoco_group_by_image_panoptic_lsj1024_cp.py new file mode 100644 index 0000000..790d07b --- /dev/null +++ b/configs/common/data/lviscocococostuff_o365_oid_vgr_sa1b_refcoco_group_by_image_panoptic_lsj1024_cp.py @@ -0,0 +1,252 @@ +import random + +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + get_detection_dataset_dicts, +) +from detectron2.data.samplers import RepeatFactorTrainingSampler +from detectron2.evaluation import COCOEvaluator, LVISEvaluator, SemSegEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_panoptic_copypaste, + build_detection_train_loader_multi_dataset_copypaste, + get_detection_dataset_dicts_multi_dataset_copypaste, +) +from ape.data.samplers import MultiDatasetTrainingSampler +from ape.evaluation import RefCOCOEvaluator +from ape.evaluation.oideval import OIDEvaluator + +dataloader = OmegaConf.create() + +image_size = 1024 + +dataloader.train = L(build_detection_train_loader_multi_dataset_copypaste)( + dataset=L(get_detection_dataset_dicts_multi_dataset_copypaste)( + names=( + "lvis_v1_train+coco_panoptic_separated", + "objects365_train_fixname", + "openimages_v6_train_bbox_nogroup", + "visualgenome_77962_box_and_region", + "sa1b_4m", + "refcoco-mixed_group-by-image", + ), + filter_emptys=[True, True, True, True, False, True], + copypastes=[True, False, False, False, False, False], + ), + dataset_bg=L(get_detection_dataset_dicts)(names=["lvis_v1_train+coco_panoptic_separated"]), + mapper=L(DatasetMapper_detr_panoptic_copypaste)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + instance_mask_format="bitmask", + ignore_label=MetadataCatalog.get("coco_2017_train_panoptic_stuffonly").ignore_label, + stuff_classes_offset=1203, + stuff_classes_decomposition=True, + output_dir=None, + vis_period=12800, + dataset_names="${..dataset.names}", + max_num_phrase=128, + nms_thresh_phrase=0.6, + ), + sampler=lambda dataset_dicts: MultiDatasetTrainingSampler( + repeat_factors=MultiDatasetTrainingSampler.get_repeat_factors( + dataset_dicts=dataset_dicts, + num_datasets=6, + dataset_ratio=[1, 1, 1, 1, 1, 0.1], + use_rfs=[True, True, True, False, False, False], + use_cas=[False, False, False, False, False, False], + repeat_thresh=0.001, + cas_lambda=1.0, + ), + seed=random.randint(0, 2**31), + ), + sampler_bg=lambda dataset_dicts: RepeatFactorTrainingSampler( + repeat_factors=RepeatFactorTrainingSampler.repeat_factors_from_category_frequency( + dataset_dicts=dataset_dicts, repeat_thresh=0.001 + ), + seed=random.randint(0, 2**31), + ), + total_batch_size=16, + total_batch_size_list=[16, 16, 16, 16, 16, 16], + aspect_ratio_grouping=True, + num_workers=4, + num_datasets=6, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="lvis_v1_val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(LVISEvaluator)( + dataset_name="${..test.dataset.names}", + max_dets_per_image=300, +) + +dataloader.tests = [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)( + names="coco_2017_val_panoptic_stuffonly", filter_empty=False + ), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators = [ + L(SemSegEvaluator)( + dataset_name="coco_2017_val_panoptic_stuffonly", + ), +] + +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="lvis_v1_minival", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ) +] + +dataloader.evaluators += [ + L(LVISEvaluator)( + dataset_name="lvis_v1_minival", + max_dets_per_image=300, + ) +] + +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)( + names="objects365_minival_fixname", filter_empty=False + ), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators += [ + L(COCOEvaluator)( + dataset_name="objects365_minival_fixname", + tasks=("bbox",), + ), +] + +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="objects365_val_fixname", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators += [ + L(COCOEvaluator)( + dataset_name="objects365_val_fixname", + tasks=("bbox",), + ), +] + +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="openimages_v6_val_bbox", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators += [ + L(OIDEvaluator)( + dataset_name="openimages_v6_val_bbox", + ), +] + + + +refcoco_test_dataset_names = [ + "refcoco-unc-val", + "refcoco-unc-testA", + "refcoco-unc-testB", + "refcocoplus-unc-val", + "refcocoplus-unc-testA", + "refcocoplus-unc-testB", + "refcocog-google-val", + "refcocog-umd-val", + "refcocog-umd-test", +] +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names=name, filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ) + for name in refcoco_test_dataset_names +] + +dataloader.evaluators += [ + L(RefCOCOEvaluator)( + dataset_name=name, + ) + for name in refcoco_test_dataset_names +] diff --git a/configs/common/data/lviscocococostuff_panoptic_lsj1024_cp.py b/configs/common/data/lviscocococostuff_panoptic_lsj1024_cp.py new file mode 100644 index 0000000..0f978de --- /dev/null +++ b/configs/common/data/lviscocococostuff_panoptic_lsj1024_cp.py @@ -0,0 +1,106 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + get_detection_dataset_dicts, +) +from detectron2.data.samplers import RepeatFactorTrainingSampler +from detectron2.evaluation import LVISEvaluator, SemSegEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_panoptic_copypaste, + build_detection_train_loader_copypaste, + get_detection_dataset_dicts_copypaste, +) + +dataloader = OmegaConf.create() + +image_size = 1024 + +dataloader.train = L(build_detection_train_loader_copypaste)( + dataset=L(get_detection_dataset_dicts_copypaste)( + names="lvis_v1_train+coco_panoptic_separated", copypastes=[True] + ), + dataset_bg=L(get_detection_dataset_dicts)(names=["lvis_v1_train+coco_panoptic_separated"]), + mapper=L(DatasetMapper_detr_panoptic_copypaste)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + instance_mask_format="bitmask", + ignore_label=MetadataCatalog.get("coco_2017_train_panoptic_stuffonly").ignore_label, + stuff_classes_offset=1203, + stuff_classes_decomposition=True, + output_dir=None, + vis_period=12800, + dataset_names=["lvis_v1_train+coco_panoptic_separated"], + ), + sampler=L(RepeatFactorTrainingSampler)( + repeat_factors=L(RepeatFactorTrainingSampler.repeat_factors_from_category_frequency)( + dataset_dicts="${dataloader.train.dataset}", repeat_thresh=0.001 + ) + ), + sampler_bg=L(RepeatFactorTrainingSampler)( + repeat_factors=L(RepeatFactorTrainingSampler.repeat_factors_from_category_frequency)( + dataset_dicts="${dataloader.train.dataset_bg}", repeat_thresh=0.001 + ), + ), + total_batch_size=16, + aspect_ratio_grouping=True, + num_workers=16, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="lvis_v1_val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(LVISEvaluator)( + dataset_name="${..test.dataset.names}", + max_dets_per_image=300, +) + +dataloader.tests = [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)( + names="coco_2017_val_panoptic_stuffonly", filter_empty=False + ), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators = [ + L(SemSegEvaluator)( + dataset_name="coco_2017_val_panoptic_stuffonly", + ), +] diff --git a/configs/common/data/lviscocococostuff_refcoco_group_by_image_panoptic_lsj1024_cp.py b/configs/common/data/lviscocococostuff_refcoco_group_by_image_panoptic_lsj1024_cp.py new file mode 100644 index 0000000..502b648 --- /dev/null +++ b/configs/common/data/lviscocococostuff_refcoco_group_by_image_panoptic_lsj1024_cp.py @@ -0,0 +1,157 @@ +import random + +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + get_detection_dataset_dicts, +) +from detectron2.data.samplers import RepeatFactorTrainingSampler +from detectron2.evaluation import LVISEvaluator, SemSegEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_panoptic_copypaste, + build_detection_train_loader_multi_dataset_copypaste, + get_detection_dataset_dicts_multi_dataset_copypaste, +) +from ape.data.samplers import MultiDatasetTrainingSampler +from ape.evaluation import RefCOCOEvaluator + +dataloader = OmegaConf.create() + +image_size = 1024 + +dataloader.train = L(build_detection_train_loader_multi_dataset_copypaste)( + dataset=L(get_detection_dataset_dicts_multi_dataset_copypaste)( + names=("lvis_v1_train+coco_panoptic_separated", "refcoco-mixed_group-by-image"), + filter_emptys=[True, True], + copypastes=[True, False], + ), + dataset_bg=L(get_detection_dataset_dicts)(names=["lvis_v1_train+coco_panoptic_separated"]), + mapper=L(DatasetMapper_detr_panoptic_copypaste)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + instance_mask_format="bitmask", + ignore_label=MetadataCatalog.get("coco_2017_train_panoptic_stuffonly").ignore_label, + stuff_classes_offset=1203, + stuff_classes_decomposition=True, + output_dir=None, + vis_period=12800, + dataset_names=["lvis_v1_train+coco_panoptic_separated", "refcoco-mixed_group-by-image"], + nms_thresh_phrase=0.9, + max_num_phrase=1, + ), + sampler=lambda dataset_dicts: MultiDatasetTrainingSampler( + repeat_factors=MultiDatasetTrainingSampler.get_repeat_factors( + dataset_dicts=dataset_dicts, + num_datasets=2, + dataset_ratio=[1, 1], + use_rfs=[True, True], + use_cas=[False, False], + repeat_thresh=0.001, + cas_lambda=1.0, + ), + seed=random.randint(0, 2**31), + ), + sampler_bg=lambda dataset_dicts: RepeatFactorTrainingSampler( + repeat_factors=RepeatFactorTrainingSampler.repeat_factors_from_category_frequency( + dataset_dicts=dataset_dicts, repeat_thresh=0.001 + ), + seed=random.randint(0, 2**31), + ), + total_batch_size=16, + total_batch_size_list=[16, 16], + aspect_ratio_grouping=True, + num_workers=4, + num_datasets=2, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="lvis_v1_val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(LVISEvaluator)( + dataset_name="${..test.dataset.names}", + max_dets_per_image=300, +) + +dataloader.tests = [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)( + names="coco_2017_val_panoptic_stuffonly", filter_empty=False + ), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators = [ + L(SemSegEvaluator)( + dataset_name="coco_2017_val_panoptic_stuffonly", + ), +] + +refcoco_test_dataset_names = [ + "refcoco-unc-val", + "refcoco-unc-testA", + "refcoco-unc-testB", + "refcocoplus-unc-val", + "refcocoplus-unc-testA", + "refcocoplus-unc-testB", + "refcocog-google-val", + "refcocog-umd-val", + "refcocog-umd-test", +] +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names=name, filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ) + for name in refcoco_test_dataset_names +] + +dataloader.evaluators += [ + L(RefCOCOEvaluator)( + dataset_name=name, + ) + for name in refcoco_test_dataset_names +] diff --git a/configs/common/data/lviscocococostuff_refcoco_panoptic_lsj1024.py b/configs/common/data/lviscocococostuff_refcoco_panoptic_lsj1024.py new file mode 100644 index 0000000..2483396 --- /dev/null +++ b/configs/common/data/lviscocococostuff_refcoco_panoptic_lsj1024.py @@ -0,0 +1,145 @@ +import random + +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + get_detection_dataset_dicts, +) +from detectron2.data.samplers import RepeatFactorTrainingSampler +from detectron2.evaluation import LVISEvaluator, SemSegEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_panoptic, + build_detection_train_loader_multi_dataset, + get_detection_dataset_dicts_multi_dataset, +) +from ape.data.samplers import MultiDatasetTrainingSampler +from ape.evaluation import RefCOCOEvaluator + +dataloader = OmegaConf.create() + +image_size = 1024 + +dataloader.train = L(build_detection_train_loader_multi_dataset)( + dataset=L(get_detection_dataset_dicts_multi_dataset)( + names=("lvis_v1_train+coco_panoptic_separated", "refcoco-mixed"), + filter_emptys=[True, True], + ), + mapper=L(DatasetMapper_detr_panoptic)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + instance_mask_format="bitmask", + ignore_label=MetadataCatalog.get("coco_2017_train_panoptic_stuffonly").ignore_label, + stuff_classes_offset=1203, + stuff_classes_decomposition=True, + dataset_names=["lvis_v1_train+coco_panoptic_separated", "refcoco-mixed"], + ), + sampler=lambda dataset_dicts: MultiDatasetTrainingSampler( + repeat_factors=MultiDatasetTrainingSampler.get_repeat_factors( + dataset_dicts=dataset_dicts, + num_datasets=2, + dataset_ratio=[1, 1], + use_rfs=[True, True], + use_cas=[False, False], + repeat_thresh=0.001, + cas_lambda=1.0, + ), + seed=random.randint(0, 2**31), + ), + total_batch_size=16, + total_batch_size_list=[16, 16], + aspect_ratio_grouping=True, + num_workers=4, + num_datasets=2, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="lvis_v1_val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(LVISEvaluator)( + dataset_name="${..test.dataset.names}", + max_dets_per_image=300, +) + +dataloader.tests = [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)( + names="coco_2017_val_panoptic_stuffonly", filter_empty=False + ), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators = [ + L(SemSegEvaluator)( + dataset_name="coco_2017_val_panoptic_stuffonly", + ), +] + +refcoco_test_dataset_names = [ + "refcoco-unc-val", + "refcoco-unc-testA", + "refcoco-unc-testB", + "refcocoplus-unc-val", + "refcocoplus-unc-testA", + "refcocoplus-unc-testB", + "refcocog-google-val", + "refcocog-umd-val", + "refcocog-umd-test", +] +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names=name, filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ) + for name in refcoco_test_dataset_names +] + +dataloader.evaluators += [ + L(RefCOCOEvaluator)( + dataset_name=name, + ) + for name in refcoco_test_dataset_names +] diff --git a/configs/common/data/lviscocococostuff_refcoco_panoptic_lsj1024_cp.py b/configs/common/data/lviscocococostuff_refcoco_panoptic_lsj1024_cp.py new file mode 100644 index 0000000..28c9cf5 --- /dev/null +++ b/configs/common/data/lviscocococostuff_refcoco_panoptic_lsj1024_cp.py @@ -0,0 +1,155 @@ +import random + +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + get_detection_dataset_dicts, +) +from detectron2.data.samplers import RepeatFactorTrainingSampler +from detectron2.evaluation import LVISEvaluator, SemSegEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_panoptic_copypaste, + build_detection_train_loader_multi_dataset_copypaste, + get_detection_dataset_dicts_multi_dataset_copypaste, +) +from ape.data.samplers import MultiDatasetTrainingSampler +from ape.evaluation import RefCOCOEvaluator + +dataloader = OmegaConf.create() + +image_size = 1024 + +dataloader.train = L(build_detection_train_loader_multi_dataset_copypaste)( + dataset=L(get_detection_dataset_dicts_multi_dataset_copypaste)( + names=("lvis_v1_train+coco_panoptic_separated", "refcoco-mixed"), + filter_emptys=[True, True], + copypastes=[True, False], + ), + dataset_bg=L(get_detection_dataset_dicts)(names=["lvis_v1_train+coco_panoptic_separated"]), + mapper=L(DatasetMapper_detr_panoptic_copypaste)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + instance_mask_format="bitmask", + ignore_label=MetadataCatalog.get("coco_2017_train_panoptic_stuffonly").ignore_label, + stuff_classes_offset=1203, + stuff_classes_decomposition=True, + output_dir=None, + vis_period=12800, + dataset_names=["lvis_v1_train+coco_panoptic_separated", "refcoco-mixed"], + ), + sampler=lambda dataset_dicts: MultiDatasetTrainingSampler( + repeat_factors=MultiDatasetTrainingSampler.get_repeat_factors( + dataset_dicts=dataset_dicts, + num_datasets=2, + dataset_ratio=[1, 1], + use_rfs=[True, False], + use_cas=[False, False], + repeat_thresh=0.001, + cas_lambda=1.0, + ), + seed=random.randint(0, 2**31), + ), + sampler_bg=lambda dataset_dicts: RepeatFactorTrainingSampler( + repeat_factors=RepeatFactorTrainingSampler.repeat_factors_from_category_frequency( + dataset_dicts=dataset_dicts, repeat_thresh=0.001 + ), + seed=random.randint(0, 2**31), + ), + total_batch_size=16, + total_batch_size_list=[16, 16], + aspect_ratio_grouping=True, + num_workers=4, + num_datasets=2, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="lvis_v1_val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(LVISEvaluator)( + dataset_name="${..test.dataset.names}", + max_dets_per_image=300, +) + +dataloader.tests = [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)( + names="coco_2017_val_panoptic_stuffonly", filter_empty=False + ), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators = [ + L(SemSegEvaluator)( + dataset_name="coco_2017_val_panoptic_stuffonly", + ), +] + +refcoco_test_dataset_names = [ + "refcoco-unc-val", + "refcoco-unc-testA", + "refcoco-unc-testB", + "refcocoplus-unc-val", + "refcocoplus-unc-testA", + "refcocoplus-unc-testB", + "refcocog-google-val", + "refcocog-umd-val", + "refcocog-umd-test", +] +dataloader.tests += [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names=name, filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ) + for name in refcoco_test_dataset_names +] + +dataloader.evaluators += [ + L(RefCOCOEvaluator)( + dataset_name=name, + ) + for name in refcoco_test_dataset_names +] diff --git a/configs/common/data/lviscocococostuff_sa1b_panoptic.py b/configs/common/data/lviscocococostuff_sa1b_panoptic.py new file mode 100644 index 0000000..74e254e --- /dev/null +++ b/configs/common/data/lviscocococostuff_sa1b_panoptic.py @@ -0,0 +1,120 @@ +import random + +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import LVISEvaluator, SemSegEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_panoptic, + build_detection_train_loader_multi_dataset, + get_detection_dataset_dicts_multi_dataset, +) +from ape.data.samplers import MultiDatasetTrainingSampler + +dataloader = OmegaConf.create() + +dataloader.train = L(build_detection_train_loader_multi_dataset)( + dataset=L(get_detection_dataset_dicts_multi_dataset)( + names=( + "lvis_v1_train+coco_panoptic_separated", + "sa1b_4m", + ), + filter_emptys=[True, False], + ), + mapper=L(DatasetMapper_detr_panoptic)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(), + L(T.ResizeShortestEdge)( + short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), + max_size=1333, + sample_style="choice", + ), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(), + L(T.ResizeShortestEdge)( + short_edge_length=(400, 500, 600), + sample_style="choice", + ), + L(T.RandomCrop)( + crop_type="absolute_range", + crop_size=(384, 600), + ), + L(T.ResizeShortestEdge)( + short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), + max_size=1333, + sample_style="choice", + ), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + instance_mask_format="bitmask", + ignore_label=MetadataCatalog.get("coco_2017_train_panoptic_stuffonly").ignore_label, + stuff_classes_offset=1203, + stuff_classes_decomposition=True, + dataset_names=["lvis_v1_train+coco_panoptic_separated", "sa1b"], + ), + sampler=lambda dataset_dicts: MultiDatasetTrainingSampler( + repeat_factors=MultiDatasetTrainingSampler.get_repeat_factors( + dataset_dicts=dataset_dicts, + num_datasets=2, + dataset_ratio=[1, 1], + use_rfs=[True, True], + use_cas=[False, False], + repeat_thresh=0.001, + cas_lambda=1.0, + ), + seed=random.randint(0, 2**31), + ), + total_batch_size=16, + total_batch_size_list=[16, 16], + num_workers=8, + num_datasets=2, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="lvis_v1_val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=800, max_size=1333), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(LVISEvaluator)( + dataset_name="${..test.dataset.names}", + max_dets_per_image=300, +) + +dataloader.tests = [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)( + names="coco_2017_val_panoptic_stuffonly", filter_empty=False + ), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=800, max_size=1333), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ), +] + +dataloader.evaluators = [ + L(SemSegEvaluator)( + dataset_name="coco_2017_val_panoptic_stuffonly", + ), +] diff --git a/configs/common/data/o365_instance_lsj1024.py b/configs/common/data/o365_instance_lsj1024.py new file mode 100644 index 0000000..e792ea6 --- /dev/null +++ b/configs/common/data/o365_instance_lsj1024.py @@ -0,0 +1,66 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + build_detection_test_loader, + build_detection_train_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import COCOEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_instance, + build_detection_train_loader_multi_dataset, + get_detection_dataset_dicts_multi_dataset, +) + +image_size = 1024 + +dataloader = OmegaConf.create() + +dataloader.train = L(build_detection_train_loader_multi_dataset)( + dataset=L(get_detection_dataset_dicts_multi_dataset)( + names=("objects365_train_fixname",), filter_emptys=[True] + ), + mapper=L(DatasetMapper_detr_instance)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + ), + total_batch_size=16, + total_batch_size_list=[16], + num_workers=4, + num_datasets=1, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="objects365_val_fixname", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(COCOEvaluator)( + dataset_name="${..test.dataset.names}", + tasks=("bbox",), +) diff --git a/configs/common/data/odinw13_instance.py b/configs/common/data/odinw13_instance.py new file mode 100644 index 0000000..36881a2 --- /dev/null +++ b/configs/common/data/odinw13_instance.py @@ -0,0 +1,165 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import COCOEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_panoptic, + DatasetMapper_detr_panoptic_copypaste, + build_detection_train_loader_multi_dataset, + build_detection_train_loader_multi_dataset_copypaste, + get_detection_dataset_dicts_multi_dataset, + get_detection_dataset_dicts_multi_dataset_copypaste, +) + +dataloader = OmegaConf.create() + +odinw_dataset_metas = [ + "odinw_AerialMaritimeDrone_large_train", + "odinw_Aquarium_Aquarium_Combined.v2-raw-1024.coco_train", + "odinw_CottontailRabbits_train", + "odinw_EgoHands_generic_train", + "odinw_NorthAmericaMushrooms_North_American_Mushrooms.v1-416x416.coco_train", + "odinw_Packages_Raw_train", + "odinw_PascalVOC_train", + "odinw_pistols_export_train", + "odinw_pothole_train", + "odinw_Raccoon_Raccoon.v2-raw.coco_train", + "odinw_ShellfishOpenImages_raw_train", + "odinw_thermalDogsAndPeople_train", + "odinw_VehiclesOpenImages_416x416_train", +] + +dataloader.train = [ + L(build_detection_train_loader_multi_dataset)( + dataset=L(get_detection_dataset_dicts_multi_dataset)( + names=(dataset_name,), + filter_emptys=[True], + dataloader_id=dataloader_id, + reduce_memory=True, + reduce_memory_size=1e6, + ), + mapper=L(DatasetMapper_detr_panoptic)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(), + L(T.ResizeShortestEdge)( + short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), + max_size=1333, + sample_style="choice", + ), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(), + L(T.ResizeShortestEdge)( + short_edge_length=(400, 500, 600), + sample_style="choice", + ), + L(T.RandomCrop)( + crop_type="absolute_range", + crop_size=(384, 600), + ), + L(T.ResizeShortestEdge)( + short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), + max_size=1333, + sample_style="choice", + ), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + instance_mask_format="bitmask", + ignore_label=MetadataCatalog.get(dataset_name).get("ignore_label", None), + stuff_classes_offset=len(MetadataCatalog.get(dataset_name).get("thing_classes", [])), + stuff_classes_decomposition=True, + output_dir=None, + vis_period=12800, + dataset_names=(dataset_name,), + max_num_phrase=128, + nms_thresh_phrase=0.6, + ), + sampler=None, + total_batch_size=16, + total_batch_size_list=[16], + aspect_ratio_grouping=True, + num_workers=16, + num_datasets=1, + ) + for dataloader_id, dataset_name in enumerate(odinw_dataset_metas) +] + +odinw_test_dataset_names = [ + "odinw_AerialMaritimeDrone_large_test", + "odinw_Aquarium_Aquarium_Combined.v2-raw-1024.coco_test", + "odinw_CottontailRabbits_test", + "odinw_EgoHands_generic_test", + "odinw_NorthAmericaMushrooms_North_American_Mushrooms.v1-416x416.coco_test", + "odinw_Packages_Raw_test", + "odinw_PascalVOC_val", + "odinw_pistols_export_test", + "odinw_pothole_test", + "odinw_Raccoon_Raccoon.v2-raw.coco_test", + "odinw_ShellfishOpenImages_raw_test", + "odinw_thermalDogsAndPeople_test", + "odinw_VehiclesOpenImages_416x416_test", +] + +dataloader.tests = [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names=name, filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=800, max_size=1333), + ], + image_format="RGB", + ), + num_workers=4, + ) + for name in odinw_test_dataset_names +] + +dataloader.name_prompt_fusion_text = [ + True, + True, + False, + False, + True, + True, + False, + False, + True, + True, + False, + True, + False, +] + +dataloader.select_box_nums_for_evaluation_list = [ + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, +] + +dataloader.evaluators = [ + L(COCOEvaluator)( + dataset_name=name, + tasks=("bbox",), + ) + for name in odinw_test_dataset_names +] diff --git a/configs/common/data/odinw13_instance_lsj1024.py b/configs/common/data/odinw13_instance_lsj1024.py new file mode 100644 index 0000000..3343dd4 --- /dev/null +++ b/configs/common/data/odinw13_instance_lsj1024.py @@ -0,0 +1,157 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import COCOEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_panoptic, + DatasetMapper_detr_panoptic_copypaste, + build_detection_train_loader_multi_dataset, + build_detection_train_loader_multi_dataset_copypaste, + get_detection_dataset_dicts_multi_dataset, + get_detection_dataset_dicts_multi_dataset_copypaste, +) + +dataloader = OmegaConf.create() + +image_size = 1024 + +odinw_dataset_metas = [ + "odinw_AerialMaritimeDrone_large_train", + "odinw_Aquarium_Aquarium_Combined.v2-raw-1024.coco_train", + "odinw_CottontailRabbits_train", + "odinw_EgoHands_generic_train", + "odinw_NorthAmericaMushrooms_North_American_Mushrooms.v1-416x416.coco_train", + "odinw_Packages_Raw_train", + "odinw_PascalVOC_train", + "odinw_pistols_export_train", + "odinw_pothole_train", + "odinw_Raccoon_Raccoon.v2-raw.coco_train", + "odinw_ShellfishOpenImages_raw_train", + "odinw_thermalDogsAndPeople_train", + "odinw_VehiclesOpenImages_416x416_train", +] + +dataloader.train = [ + L(build_detection_train_loader_multi_dataset)( + dataset=L(get_detection_dataset_dicts_multi_dataset)( + names=(dataset_name,), + filter_emptys=[True], + dataloader_id=dataloader_id, + reduce_memory=True, + reduce_memory_size=1e6, + ), + mapper=L(DatasetMapper_detr_panoptic)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + instance_mask_format="bitmask", + ignore_label=MetadataCatalog.get(dataset_name).get("ignore_label", None), + stuff_classes_offset=len(MetadataCatalog.get(dataset_name).get("thing_classes", [])), + stuff_classes_decomposition=True, + output_dir=None, + vis_period=12800, + dataset_names=(dataset_name,), + max_num_phrase=128, + nms_thresh_phrase=0.6, + ), + sampler=None, + total_batch_size=16, + total_batch_size_list=[16], + aspect_ratio_grouping=True, + num_workers=16, + num_datasets=1, + ) + for dataloader_id, dataset_name in enumerate(odinw_dataset_metas) +] + +odinw_test_dataset_names = [ + "odinw_AerialMaritimeDrone_large_test", + "odinw_Aquarium_Aquarium_Combined.v2-raw-1024.coco_test", + "odinw_CottontailRabbits_test", + "odinw_EgoHands_generic_test", + "odinw_NorthAmericaMushrooms_North_American_Mushrooms.v1-416x416.coco_test", + "odinw_Packages_Raw_test", + "odinw_PascalVOC_val", + "odinw_pistols_export_test", + "odinw_pothole_test", + "odinw_Raccoon_Raccoon.v2-raw.coco_test", + "odinw_ShellfishOpenImages_raw_test", + "odinw_thermalDogsAndPeople_test", + "odinw_VehiclesOpenImages_416x416_test", +] + +dataloader.tests = [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names=name, filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="RGB", + ), + num_workers=4, + ) + for name in odinw_test_dataset_names +] + +dataloader.name_prompt_fusion_text = [ + True, + True, + False, + False, + True, + True, + False, + False, + True, + True, + False, + True, + False, +] + +dataloader.select_box_nums_for_evaluation_list = [ + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, +] + +dataloader.evaluators = [ + L(COCOEvaluator)( + dataset_name=name, + tasks=("bbox",), + ) + for name in odinw_test_dataset_names +] diff --git a/configs/common/data/odinw13_instance_lsj1536.py b/configs/common/data/odinw13_instance_lsj1536.py new file mode 100644 index 0000000..54a5657 --- /dev/null +++ b/configs/common/data/odinw13_instance_lsj1536.py @@ -0,0 +1,157 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import COCOEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_panoptic, + DatasetMapper_detr_panoptic_copypaste, + build_detection_train_loader_multi_dataset, + build_detection_train_loader_multi_dataset_copypaste, + get_detection_dataset_dicts_multi_dataset, + get_detection_dataset_dicts_multi_dataset_copypaste, +) + +dataloader = OmegaConf.create() + +image_size = 1536 + +odinw_dataset_metas = [ + "odinw_AerialMaritimeDrone_large_train", + "odinw_Aquarium_Aquarium_Combined.v2-raw-1024.coco_train", + "odinw_CottontailRabbits_train", + "odinw_EgoHands_generic_train", + "odinw_NorthAmericaMushrooms_North_American_Mushrooms.v1-416x416.coco_train", + "odinw_Packages_Raw_train", + "odinw_PascalVOC_train", + "odinw_pistols_export_train", + "odinw_pothole_train", + "odinw_Raccoon_Raccoon.v2-raw.coco_train", + "odinw_ShellfishOpenImages_raw_train", + "odinw_thermalDogsAndPeople_train", + "odinw_VehiclesOpenImages_416x416_train", +] + +dataloader.train = [ + L(build_detection_train_loader_multi_dataset)( + dataset=L(get_detection_dataset_dicts_multi_dataset)( + names=(dataset_name,), + filter_emptys=[True], + dataloader_id=dataloader_id, + reduce_memory=True, + reduce_memory_size=1e6, + ), + mapper=L(DatasetMapper_detr_panoptic)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + instance_mask_format="bitmask", + ignore_label=MetadataCatalog.get(dataset_name).get("ignore_label", None), + stuff_classes_offset=len(MetadataCatalog.get(dataset_name).get("thing_classes", [])), + stuff_classes_decomposition=True, + output_dir=None, + vis_period=12800, + dataset_names=(dataset_name,), + max_num_phrase=128, + nms_thresh_phrase=0.6, + ), + sampler=None, + total_batch_size=16, + total_batch_size_list=[16], + aspect_ratio_grouping=True, + num_workers=16, + num_datasets=1, + ) + for dataloader_id, dataset_name in enumerate(odinw_dataset_metas) +] + +odinw_test_dataset_names = [ + "odinw_AerialMaritimeDrone_large_test", + "odinw_Aquarium_Aquarium_Combined.v2-raw-1024.coco_test", + "odinw_CottontailRabbits_test", + "odinw_EgoHands_generic_test", + "odinw_NorthAmericaMushrooms_North_American_Mushrooms.v1-416x416.coco_test", + "odinw_Packages_Raw_test", + "odinw_PascalVOC_val", + "odinw_pistols_export_test", + "odinw_pothole_test", + "odinw_Raccoon_Raccoon.v2-raw.coco_test", + "odinw_ShellfishOpenImages_raw_test", + "odinw_thermalDogsAndPeople_test", + "odinw_VehiclesOpenImages_416x416_test", +] + +dataloader.tests = [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names=name, filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="RGB", + ), + num_workers=4, + ) + for name in odinw_test_dataset_names +] + +dataloader.name_prompt_fusion_text = [ + True, + True, + False, + False, + True, + True, + False, + False, + True, + True, + False, + True, + False, +] + +dataloader.select_box_nums_for_evaluation_list = [ + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, +] + +dataloader.evaluators = [ + L(COCOEvaluator)( + dataset_name=name, + tasks=("bbox",), + ) + for name in odinw_test_dataset_names +] diff --git a/configs/common/data/odinw35_instance.py b/configs/common/data/odinw35_instance.py new file mode 100644 index 0000000..4b2db3a --- /dev/null +++ b/configs/common/data/odinw35_instance.py @@ -0,0 +1,253 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import COCOEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_panoptic, + DatasetMapper_detr_panoptic_copypaste, + build_detection_train_loader_multi_dataset, + build_detection_train_loader_multi_dataset_copypaste, + get_detection_dataset_dicts_multi_dataset, + get_detection_dataset_dicts_multi_dataset_copypaste, +) + +dataloader = OmegaConf.create() + +odinw_dataset_metas = [ + "odinw_AerialMaritimeDrone_large_train", + "odinw_AerialMaritimeDrone_tiled_train", + "odinw_AmericanSignLanguageLetters_American_Sign_Language_Letters.v1-v1.coco_train", + "odinw_Aquarium_Aquarium_Combined.v2-raw-1024.coco_train", + "odinw_BCCD_BCCD.v3-raw.coco_train", + "odinw_boggleBoards_416x416AutoOrient_export_train", + "odinw_brackishUnderwater_960x540_train", + "odinw_ChessPieces_Chess_Pieces.v23-raw.coco_train", + "odinw_CottontailRabbits_train", + "odinw_dice_mediumColor_export_train", + "odinw_DroneControl_Drone_Control.v3-raw.coco_train", + "odinw_EgoHands_generic_train", + "odinw_EgoHands_specific_train", + "odinw_HardHatWorkers_raw_train", + "odinw_MaskWearing_raw_train", + "odinw_MountainDewCommercial_train", + "odinw_NorthAmericaMushrooms_North_American_Mushrooms.v1-416x416.coco_train", + "odinw_openPoetryVision_512x512_train", + "odinw_OxfordPets_by-breed_train", + "odinw_OxfordPets_by-species_train", + "odinw_Packages_Raw_train", + "odinw_PascalVOC_train", + "odinw_pistols_export_train", + "odinw_PKLot_640_train", + "odinw_plantdoc_416x416_train", + "odinw_pothole_train", + "odinw_Raccoon_Raccoon.v2-raw.coco_train", + "odinw_selfdrivingCar_fixedLarge_export_train", + "odinw_ShellfishOpenImages_raw_train", + "odinw_ThermalCheetah_train", + "odinw_thermalDogsAndPeople_train", + "odinw_UnoCards_raw_train", + "odinw_VehiclesOpenImages_416x416_train", + "odinw_websiteScreenshots_train", + "odinw_WildfireSmoke_train", +] + +dataloader.train = [ + L(build_detection_train_loader_multi_dataset)( + dataset=L(get_detection_dataset_dicts_multi_dataset)( + names=(dataset_name,), + filter_emptys=[True], + dataloader_id=dataloader_id, + reduce_memory=True, + reduce_memory_size=1e6, + ), + mapper=L(DatasetMapper_detr_panoptic)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(), + L(T.ResizeShortestEdge)( + short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), + max_size=1333, + sample_style="choice", + ), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(), + L(T.ResizeShortestEdge)( + short_edge_length=(400, 500, 600), + sample_style="choice", + ), + L(T.RandomCrop)( + crop_type="absolute_range", + crop_size=(384, 600), + ), + L(T.ResizeShortestEdge)( + short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), + max_size=1333, + sample_style="choice", + ), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + instance_mask_format="bitmask", + ignore_label=MetadataCatalog.get(dataset_name).get("ignore_label", None), + stuff_classes_offset=len(MetadataCatalog.get(dataset_name).get("thing_classes", [])), + stuff_classes_decomposition=True, + output_dir=None, + vis_period=12800, + dataset_names=(dataset_name,), + max_num_phrase=128, + nms_thresh_phrase=0.6, + ), + sampler=None, + total_batch_size=16, + total_batch_size_list=[16], + aspect_ratio_grouping=True, + num_workers=16, + num_datasets=1, + ) + for dataloader_id, dataset_name in enumerate(odinw_dataset_metas) +] + +odinw_test_dataset_names = [ + "odinw_AerialMaritimeDrone_large_test", + "odinw_AerialMaritimeDrone_tiled_test", + "odinw_AmericanSignLanguageLetters_American_Sign_Language_Letters.v1-v1.coco_test", + "odinw_Aquarium_Aquarium_Combined.v2-raw-1024.coco_test", + "odinw_BCCD_BCCD.v3-raw.coco_test", + "odinw_boggleBoards_416x416AutoOrient_export_test", + "odinw_brackishUnderwater_960x540_test", + "odinw_ChessPieces_Chess_Pieces.v23-raw.coco_test", + "odinw_CottontailRabbits_test", + "odinw_dice_mediumColor_export_test", + "odinw_DroneControl_Drone_Control.v3-raw.coco_test", + "odinw_EgoHands_generic_test", + "odinw_EgoHands_specific_test", + "odinw_HardHatWorkers_raw_test", + "odinw_MaskWearing_raw_test", + "odinw_MountainDewCommercial_test", + "odinw_NorthAmericaMushrooms_North_American_Mushrooms.v1-416x416.coco_test", + "odinw_openPoetryVision_512x512_test", + "odinw_OxfordPets_by-breed_test", + "odinw_OxfordPets_by-species_test", + "odinw_Packages_Raw_test", + "odinw_PascalVOC_val", + "odinw_pistols_export_test", + "odinw_PKLot_640_test", + "odinw_plantdoc_416x416_test", + "odinw_pothole_test", + "odinw_Raccoon_Raccoon.v2-raw.coco_test", + "odinw_selfdrivingCar_fixedLarge_export_test", + "odinw_ShellfishOpenImages_raw_test", + "odinw_ThermalCheetah_test", + "odinw_thermalDogsAndPeople_test", + "odinw_UnoCards_raw_test", + "odinw_VehiclesOpenImages_416x416_test", + "odinw_websiteScreenshots_test", + "odinw_WildfireSmoke_test", +] + +dataloader.tests = [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names=name, filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=800, max_size=1333), + ], + image_format="RGB", + ), + num_workers=4, + ) + for name in odinw_test_dataset_names +] + +dataloader.name_prompt_fusion_text = [ + True, + False, + True, + True, + True, + True, + True, + True, + False, + False, + True, + False, + False, + True, + True, + True, + True, + True, + True, + True, + True, + False, + False, + False, + True, + True, + True, + True, + False, + False, + True, + True, + False, + True, + True, +] + +dataloader.select_box_nums_for_evaluation_list = [ + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 1, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, +] + +dataloader.evaluators = [ + L(COCOEvaluator)( + dataset_name=name, + tasks=("bbox",), + ) + for name in odinw_test_dataset_names +] diff --git a/configs/common/data/odinw35_instance_lsj1024.py b/configs/common/data/odinw35_instance_lsj1024.py new file mode 100644 index 0000000..390d7c7 --- /dev/null +++ b/configs/common/data/odinw35_instance_lsj1024.py @@ -0,0 +1,245 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import COCOEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_panoptic, + DatasetMapper_detr_panoptic_copypaste, + build_detection_train_loader_multi_dataset, + build_detection_train_loader_multi_dataset_copypaste, + get_detection_dataset_dicts_multi_dataset, + get_detection_dataset_dicts_multi_dataset_copypaste, +) + +dataloader = OmegaConf.create() + +image_size = 1024 + +odinw_dataset_metas = [ + "odinw_AerialMaritimeDrone_large_train", + "odinw_AerialMaritimeDrone_tiled_train", + "odinw_AmericanSignLanguageLetters_American_Sign_Language_Letters.v1-v1.coco_train", + "odinw_Aquarium_Aquarium_Combined.v2-raw-1024.coco_train", + "odinw_BCCD_BCCD.v3-raw.coco_train", + "odinw_boggleBoards_416x416AutoOrient_export_train", + "odinw_brackishUnderwater_960x540_train", + "odinw_ChessPieces_Chess_Pieces.v23-raw.coco_train", + "odinw_CottontailRabbits_train", + "odinw_dice_mediumColor_export_train", + "odinw_DroneControl_Drone_Control.v3-raw.coco_train", + "odinw_EgoHands_generic_train", + "odinw_EgoHands_specific_train", + "odinw_HardHatWorkers_raw_train", + "odinw_MaskWearing_raw_train", + "odinw_MountainDewCommercial_train", + "odinw_NorthAmericaMushrooms_North_American_Mushrooms.v1-416x416.coco_train", + "odinw_openPoetryVision_512x512_train", + "odinw_OxfordPets_by-breed_train", + "odinw_OxfordPets_by-species_train", + "odinw_Packages_Raw_train", + "odinw_PascalVOC_train", + "odinw_pistols_export_train", + "odinw_PKLot_640_train", + "odinw_plantdoc_416x416_train", + "odinw_pothole_train", + "odinw_Raccoon_Raccoon.v2-raw.coco_train", + "odinw_selfdrivingCar_fixedLarge_export_train", + "odinw_ShellfishOpenImages_raw_train", + "odinw_ThermalCheetah_train", + "odinw_thermalDogsAndPeople_train", + "odinw_UnoCards_raw_train", + "odinw_VehiclesOpenImages_416x416_train", + "odinw_websiteScreenshots_train", + "odinw_WildfireSmoke_train", +] + +dataloader.train = [ + L(build_detection_train_loader_multi_dataset)( + dataset=L(get_detection_dataset_dicts_multi_dataset)( + names=(dataset_name,), + filter_emptys=[True], + dataloader_id=dataloader_id, + reduce_memory=True, + reduce_memory_size=1e6, + ), + mapper=L(DatasetMapper_detr_panoptic)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + instance_mask_format="bitmask", + ignore_label=MetadataCatalog.get(dataset_name).get("ignore_label", None), + stuff_classes_offset=len(MetadataCatalog.get(dataset_name).get("thing_classes", [])), + stuff_classes_decomposition=True, + output_dir=None, + vis_period=12800, + dataset_names=(dataset_name,), + max_num_phrase=128, + nms_thresh_phrase=0.6, + ), + sampler=None, + total_batch_size=16, + total_batch_size_list=[16], + aspect_ratio_grouping=True, + num_workers=16, + num_datasets=1, + ) + for dataloader_id, dataset_name in enumerate(odinw_dataset_metas) +] + +odinw_test_dataset_names = [ + "odinw_AerialMaritimeDrone_large_test", + "odinw_AerialMaritimeDrone_tiled_test", + "odinw_AmericanSignLanguageLetters_American_Sign_Language_Letters.v1-v1.coco_test", + "odinw_Aquarium_Aquarium_Combined.v2-raw-1024.coco_test", + "odinw_BCCD_BCCD.v3-raw.coco_test", + "odinw_boggleBoards_416x416AutoOrient_export_test", + "odinw_brackishUnderwater_960x540_test", + "odinw_ChessPieces_Chess_Pieces.v23-raw.coco_test", + "odinw_CottontailRabbits_test", + "odinw_dice_mediumColor_export_test", + "odinw_DroneControl_Drone_Control.v3-raw.coco_test", + "odinw_EgoHands_generic_test", + "odinw_EgoHands_specific_test", + "odinw_HardHatWorkers_raw_test", + "odinw_MaskWearing_raw_test", + "odinw_MountainDewCommercial_test", + "odinw_NorthAmericaMushrooms_North_American_Mushrooms.v1-416x416.coco_test", + "odinw_openPoetryVision_512x512_test", + "odinw_OxfordPets_by-breed_test", + "odinw_OxfordPets_by-species_test", + "odinw_Packages_Raw_test", + "odinw_PascalVOC_val", + "odinw_pistols_export_test", + "odinw_PKLot_640_test", + "odinw_plantdoc_416x416_test", + "odinw_pothole_test", + "odinw_Raccoon_Raccoon.v2-raw.coco_test", + "odinw_selfdrivingCar_fixedLarge_export_test", + "odinw_ShellfishOpenImages_raw_test", + "odinw_ThermalCheetah_test", + "odinw_thermalDogsAndPeople_test", + "odinw_UnoCards_raw_test", + "odinw_VehiclesOpenImages_416x416_test", + "odinw_websiteScreenshots_test", + "odinw_WildfireSmoke_test", +] + +dataloader.tests = [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names=name, filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="RGB", + ), + num_workers=4, + ) + for name in odinw_test_dataset_names +] + +dataloader.name_prompt_fusion_text = [ + True, + False, + True, + True, + True, + True, + True, + True, + False, + False, + True, + False, + False, + True, + True, + True, + True, + True, + True, + True, + True, + False, + False, + False, + True, + True, + True, + True, + False, + False, + True, + True, + False, + True, + True, +] + +dataloader.select_box_nums_for_evaluation_list = [ + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 1, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, +] + +dataloader.evaluators = [ + L(COCOEvaluator)( + dataset_name=name, + tasks=("bbox",), + ) + for name in odinw_test_dataset_names +] diff --git a/configs/common/data/odinw35_instance_lsj1536.py b/configs/common/data/odinw35_instance_lsj1536.py new file mode 100644 index 0000000..9a604ab --- /dev/null +++ b/configs/common/data/odinw35_instance_lsj1536.py @@ -0,0 +1,244 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import COCOEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_panoptic, + DatasetMapper_detr_panoptic_copypaste, + build_detection_train_loader_multi_dataset, + build_detection_train_loader_multi_dataset_copypaste, + get_detection_dataset_dicts_multi_dataset, + get_detection_dataset_dicts_multi_dataset_copypaste, +) + +dataloader = OmegaConf.create() + +image_size = 1536 + +odinw_dataset_metas = [ + "odinw_AerialMaritimeDrone_large_train", + "odinw_AerialMaritimeDrone_tiled_train", + "odinw_AmericanSignLanguageLetters_American_Sign_Language_Letters.v1-v1.coco_train", + "odinw_Aquarium_Aquarium_Combined.v2-raw-1024.coco_train", + "odinw_BCCD_BCCD.v3-raw.coco_train", + "odinw_boggleBoards_416x416AutoOrient_export_train", + "odinw_brackishUnderwater_960x540_train", + "odinw_ChessPieces_Chess_Pieces.v23-raw.coco_train", + "odinw_CottontailRabbits_train", + "odinw_dice_mediumColor_export_train", + "odinw_DroneControl_Drone_Control.v3-raw.coco_train", + "odinw_EgoHands_generic_train", + "odinw_EgoHands_specific_train", + "odinw_HardHatWorkers_raw_train", + "odinw_MaskWearing_raw_train", + "odinw_MountainDewCommercial_train", + "odinw_NorthAmericaMushrooms_North_American_Mushrooms.v1-416x416.coco_train", + "odinw_openPoetryVision_512x512_train", + "odinw_OxfordPets_by-breed_train", + "odinw_OxfordPets_by-species_train", + "odinw_Packages_Raw_train", + "odinw_PascalVOC_train", + "odinw_pistols_export_train", + "odinw_PKLot_640_train", + "odinw_plantdoc_416x416_train", + "odinw_pothole_train", + "odinw_Raccoon_Raccoon.v2-raw.coco_train", + "odinw_selfdrivingCar_fixedLarge_export_train", + "odinw_ShellfishOpenImages_raw_train", + "odinw_ThermalCheetah_train", + "odinw_thermalDogsAndPeople_train", + "odinw_UnoCards_raw_train", + "odinw_VehiclesOpenImages_416x416_train", + "odinw_websiteScreenshots_train", + "odinw_WildfireSmoke_train", +] + +dataloader.train = [ + L(build_detection_train_loader_multi_dataset)( + dataset=L(get_detection_dataset_dicts_multi_dataset)( + names=(dataset_name,), + filter_emptys=[True], + dataloader_id=dataloader_id, + reduce_memory=True, + reduce_memory_size=1e6, + ), + mapper=L(DatasetMapper_detr_panoptic)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + instance_mask_format="bitmask", + ignore_label=MetadataCatalog.get(dataset_name).get("ignore_label", None), + stuff_classes_offset=len(MetadataCatalog.get(dataset_name).get("thing_classes", [])), + stuff_classes_decomposition=True, + output_dir=None, + vis_period=12800, + dataset_names=(dataset_name,), + max_num_phrase=128, + nms_thresh_phrase=0.6, + ), + sampler=None, + total_batch_size=16, + total_batch_size_list=[16], + aspect_ratio_grouping=True, + num_workers=16, + num_datasets=1, + ) + for dataloader_id, dataset_name in enumerate(odinw_dataset_metas) +] + +odinw_test_dataset_names = [ + "odinw_AerialMaritimeDrone_large_test", + "odinw_AerialMaritimeDrone_tiled_test", + "odinw_AmericanSignLanguageLetters_American_Sign_Language_Letters.v1-v1.coco_test", + "odinw_Aquarium_Aquarium_Combined.v2-raw-1024.coco_test", + "odinw_BCCD_BCCD.v3-raw.coco_test", + "odinw_boggleBoards_416x416AutoOrient_export_test", + "odinw_brackishUnderwater_960x540_test", + "odinw_ChessPieces_Chess_Pieces.v23-raw.coco_test", + "odinw_CottontailRabbits_test", + "odinw_dice_mediumColor_export_test", + "odinw_DroneControl_Drone_Control.v3-raw.coco_test", + "odinw_EgoHands_generic_test", + "odinw_EgoHands_specific_test", + "odinw_HardHatWorkers_raw_test", + "odinw_MaskWearing_raw_test", + "odinw_MountainDewCommercial_test", + "odinw_NorthAmericaMushrooms_North_American_Mushrooms.v1-416x416.coco_test", + "odinw_openPoetryVision_512x512_test", + "odinw_OxfordPets_by-breed_test", + "odinw_OxfordPets_by-species_test", + "odinw_Packages_Raw_test", + "odinw_PascalVOC_val", + "odinw_pistols_export_test", + "odinw_PKLot_640_test", + "odinw_plantdoc_416x416_test", + "odinw_pothole_test", + "odinw_Raccoon_Raccoon.v2-raw.coco_test", + "odinw_selfdrivingCar_fixedLarge_export_test", + "odinw_ShellfishOpenImages_raw_test", + "odinw_ThermalCheetah_test", + "odinw_thermalDogsAndPeople_test", + "odinw_UnoCards_raw_test", + "odinw_VehiclesOpenImages_416x416_test", + "odinw_websiteScreenshots_test", + "odinw_WildfireSmoke_test", +] +dataloader.tests = [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names=name, filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="RGB", + ), + num_workers=4, + ) + for name in odinw_test_dataset_names +] + +dataloader.name_prompt_fusion_text = [ + True, + False, + True, + True, + True, + True, + True, + True, + False, + False, + True, + False, + False, + True, + True, + True, + True, + True, + True, + True, + True, + False, + False, + False, + True, + True, + True, + True, + False, + False, + True, + True, + False, + True, + True, +] + +dataloader.select_box_nums_for_evaluation_list = [ + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 1, + 300, + 300, + 300, + 300, + 300, + 300, + 300, + 300, +] + +dataloader.evaluators = [ + L(COCOEvaluator)( + dataset_name=name, + tasks=("bbox",), + ) + for name in odinw_test_dataset_names +] diff --git a/configs/common/data/odinwvoc_instance_lsj1024.py b/configs/common/data/odinwvoc_instance_lsj1024.py new file mode 100644 index 0000000..da5111a --- /dev/null +++ b/configs/common/data/odinwvoc_instance_lsj1024.py @@ -0,0 +1,43 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import DatasetMapper, build_detection_test_loader, get_detection_dataset_dicts +from detectron2.evaluation import COCOEvaluator +from omegaconf import OmegaConf + +dataloader = OmegaConf.create() + +image_size = 1024 + +odinw_test_dataset_names = [ + "odinw_PascalVOC_val", +] +dataloader.tests = [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names=name, filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="RGB", + ), + num_workers=4, + ) + for name in odinw_test_dataset_names +] + +dataloader.name_prompt_fusion_text = [ + False, +] + +dataloader.select_box_nums_for_evaluation_list = [ + 300, +] + +dataloader.evaluators = [ + L(COCOEvaluator)( + dataset_name=name, + tasks=("bbox",), + ) + for name in odinw_test_dataset_names +] diff --git a/configs/common/data/pascalcontext459_semantic_lsj1024.py b/configs/common/data/pascalcontext459_semantic_lsj1024.py new file mode 100644 index 0000000..7e8daa6 --- /dev/null +++ b/configs/common/data/pascalcontext459_semantic_lsj1024.py @@ -0,0 +1,62 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + build_detection_train_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import SemSegEvaluator +from omegaconf import OmegaConf +from ape.data import DatasetMapper_detr_semantic + +image_size = 1024 + +dataloader = OmegaConf.create() + +dataloader.train = L(build_detection_train_loader)( + dataset=L(get_detection_dataset_dicts)(names="pascal_context_459_sem_seg_val"), + mapper=L(DatasetMapper_detr_semantic)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + ignore_label=MetadataCatalog.get("pascal_context_459_sem_seg_val").ignore_label, + stuff_classes_decomposition=True, + ), + total_batch_size=16, + num_workers=4, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)( + names="pascal_context_459_sem_seg_val", filter_empty=False + ), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(SemSegEvaluator)( + dataset_name="${..test.dataset.names}", +) diff --git a/configs/common/data/pascalcontext59_semantic_lsj1024.py b/configs/common/data/pascalcontext59_semantic_lsj1024.py new file mode 100644 index 0000000..66bc643 --- /dev/null +++ b/configs/common/data/pascalcontext59_semantic_lsj1024.py @@ -0,0 +1,62 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + build_detection_train_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import SemSegEvaluator +from omegaconf import OmegaConf +from ape.data import DatasetMapper_detr_semantic + +image_size = 1024 + +dataloader = OmegaConf.create() + +dataloader.train = L(build_detection_train_loader)( + dataset=L(get_detection_dataset_dicts)(names="pascal_context_59_sem_seg_val"), + mapper=L(DatasetMapper_detr_semantic)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + ignore_label=MetadataCatalog.get("pascal_context_59_sem_seg_val").ignore_label, + stuff_classes_decomposition=True, + ), + total_batch_size=16, + num_workers=4, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)( + names="pascal_context_59_sem_seg_val", filter_empty=False + ), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(SemSegEvaluator)( + dataset_name="${..test.dataset.names}", +) diff --git a/configs/common/data/pascalvoc20_semantic_lsj1024.py b/configs/common/data/pascalvoc20_semantic_lsj1024.py new file mode 100644 index 0000000..f8119ae --- /dev/null +++ b/configs/common/data/pascalvoc20_semantic_lsj1024.py @@ -0,0 +1,60 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + build_detection_train_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import SemSegEvaluator +from omegaconf import OmegaConf +from ape.data import DatasetMapper_detr_semantic + +image_size = 1024 + +dataloader = OmegaConf.create() + +dataloader.train = L(build_detection_train_loader)( + dataset=L(get_detection_dataset_dicts)(names="pascalvoc20_sem_seg_val"), + mapper=L(DatasetMapper_detr_semantic)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + ignore_label=MetadataCatalog.get("pascalvoc20_sem_seg_val").ignore_label, + stuff_classes_decomposition=True, + ), + total_batch_size=16, + num_workers=4, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="pascalvoc20_sem_seg_val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(SemSegEvaluator)( + dataset_name="${..test.dataset.names}", +) diff --git a/configs/common/data/pascalvocpart_panoptic.py b/configs/common/data/pascalvocpart_panoptic.py new file mode 100644 index 0000000..a9ec71b --- /dev/null +++ b/configs/common/data/pascalvocpart_panoptic.py @@ -0,0 +1,109 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + build_detection_train_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import COCOEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_panoptic, + build_detection_train_loader_multi_dataset, + get_detection_dataset_dicts_multi_dataset, +) + +dataloader = OmegaConf.create() + +dataloader.train = L(build_detection_train_loader_multi_dataset)( + dataset=L(get_detection_dataset_dicts_multi_dataset)( + names=("pascalvocpart_train",), filter_emptys=[False] + ), + mapper=L(DatasetMapper_detr_panoptic)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(), + L(T.ResizeShortestEdge)( + short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), + max_size=1333, + sample_style="choice", + ), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(), + L(T.ResizeShortestEdge)( + short_edge_length=(400, 500, 600), + sample_style="choice", + ), + L(T.RandomCrop)( + crop_type="absolute_range", + crop_size=(384, 600), + ), + L(T.ResizeShortestEdge)( + short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), + max_size=1333, + sample_style="choice", + ), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + instance_mask_format="bitmask", + dataset_names=("pascal_parts_train",), + ), + total_batch_size=16, + total_batch_size_list=[16], + num_workers=4, + num_datasets=1, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="refcoco-unc-val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=800, max_size=1333), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(COCOEvaluator)( + dataset_name="${..test.dataset.names}", +) + +refcoco_test_dataset_names = [ + "refcoco-unc-val", + "refcoco-unc-testA", + "refcoco-unc-testB", + "refcocoplus-unc-val", + "refcocoplus-unc-testA", + "refcocoplus-unc-testB", + "refcocog-google-val", + "refcocog-umd-val", + "refcocog-umd-test", +] +dataloader.tests = [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names=name, filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=800, max_size=1333), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ) + for name in refcoco_test_dataset_names[1:] +] + +dataloader.evaluators = [ + L(COCOEvaluator)( + dataset_name=name, + ) + for name in refcoco_test_dataset_names[1:] +] diff --git a/configs/common/data/phrasecut_instance.py b/configs/common/data/phrasecut_instance.py new file mode 100644 index 0000000..67a9ab4 --- /dev/null +++ b/configs/common/data/phrasecut_instance.py @@ -0,0 +1,108 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + build_detection_test_loader, + build_detection_train_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import COCOEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_instance, + build_detection_train_loader_multi_dataset, + get_detection_dataset_dicts_multi_dataset, +) +from ape.evaluation import RefCOCOEvaluator + +dataloader = OmegaConf.create() + +dataloader.train = L(build_detection_train_loader_multi_dataset)( + dataset=L(get_detection_dataset_dicts_multi_dataset)( + names=("phrasecut_train",), filter_emptys=[True] + ), + mapper=L(DatasetMapper_detr_instance)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(), + L(T.ResizeShortestEdge)( + short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), + max_size=1333, + sample_style="choice", + ), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(), + L(T.ResizeShortestEdge)( + short_edge_length=(400, 500, 600), + sample_style="choice", + ), + L(T.RandomCrop)( + crop_type="absolute_range", + crop_size=(384, 600), + ), + L(T.ResizeShortestEdge)( + short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), + max_size=1333, + sample_style="choice", + ), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + dataset_names=("phrasecut_train",), + ), + total_batch_size=16, + total_batch_size_list=[16], + num_workers=4, + num_datasets=1, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="refcoco-unc-val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=800, max_size=1333), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(RefCOCOEvaluator)( + dataset_name="${..test.dataset.names}", +) + +refcoco_test_dataset_names = [ + "refcoco-unc-val", + "refcoco-unc-testA", + "refcoco-unc-testB", + "refcocoplus-unc-val", + "refcocoplus-unc-testA", + "refcocoplus-unc-testB", + "refcocog-google-val", + "refcocog-umd-val", + "refcocog-umd-test", +] +dataloader.tests = [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names=name, filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=800, max_size=1333), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ) + for name in refcoco_test_dataset_names[1:] +] + +dataloader.evaluators = [ + L(RefCOCOEvaluator)( + dataset_name=name, + ) + for name in refcoco_test_dataset_names[1:] +] diff --git a/configs/common/data/phrasecut_instance_lsj1024.py b/configs/common/data/phrasecut_instance_lsj1024.py new file mode 100644 index 0000000..6e07dcb --- /dev/null +++ b/configs/common/data/phrasecut_instance_lsj1024.py @@ -0,0 +1,70 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + build_detection_test_loader, + build_detection_train_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import COCOEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_instance, + DatasetMapper_detr_instance_exp, + build_detection_train_loader_multi_dataset, + get_detection_dataset_dicts_multi_dataset, +) +from ape.evaluation import RefCOCOEvaluator + +dataloader = OmegaConf.create() + +image_size = 1024 + +dataloader.train = L(build_detection_train_loader_multi_dataset)( + dataset=L(get_detection_dataset_dicts_multi_dataset)( + names=("phrasecut_train",), filter_emptys=[True] + ), + mapper=L(DatasetMapper_detr_instance_exp)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + dataset_names=("phrasecut_train",), + max_num_phrase=256, + nms_thresh_phrase=0.6, + ), + total_batch_size=16, + total_batch_size_list=[16], + num_workers=4, + num_datasets=1, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="phrasecut_val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(RefCOCOEvaluator)( + dataset_name="${..test.dataset.names}", +) diff --git a/configs/common/data/refcoco_group_by_image_instance.py b/configs/common/data/refcoco_group_by_image_instance.py new file mode 100644 index 0000000..3820a99 --- /dev/null +++ b/configs/common/data/refcoco_group_by_image_instance.py @@ -0,0 +1,109 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + build_detection_test_loader, + build_detection_train_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import COCOEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_instance, + build_detection_train_loader_multi_dataset, + get_detection_dataset_dicts_multi_dataset, +) +from ape.evaluation import RefCOCOEvaluator + +dataloader = OmegaConf.create() + +dataloader.train = L(build_detection_train_loader_multi_dataset)( + dataset=L(get_detection_dataset_dicts_multi_dataset)( + names=("refcoco-mixed_group-by-image",), filter_emptys=[True] + ), + mapper=L(DatasetMapper_detr_instance)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(), + L(T.ResizeShortestEdge)( + short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), + max_size=1333, + sample_style="choice", + ), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(), + L(T.ResizeShortestEdge)( + short_edge_length=(400, 500, 600), + sample_style="choice", + ), + L(T.RandomCrop)( + crop_type="absolute_range", + crop_size=(384, 600), + ), + L(T.ResizeShortestEdge)( + short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), + max_size=1333, + sample_style="choice", + ), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + dataset_names=("refcoco-mixed_group-by-image",), + nms_thresh_phrase=0.9, + ), + total_batch_size=16, + total_batch_size_list=[16], + num_workers=4, + num_datasets=1, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="refcoco-unc-val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=800, max_size=1333), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(RefCOCOEvaluator)( + dataset_name="${..test.dataset.names}", +) + +refcoco_test_dataset_names = [ + "refcoco-unc-val", + "refcoco-unc-testA", + "refcoco-unc-testB", + "refcocoplus-unc-val", + "refcocoplus-unc-testA", + "refcocoplus-unc-testB", + "refcocog-google-val", + "refcocog-umd-val", + "refcocog-umd-test", +] +dataloader.tests = [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names=name, filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=800, max_size=1333), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ) + for name in refcoco_test_dataset_names[1:] +] + +dataloader.evaluators = [ + L(RefCOCOEvaluator)( + dataset_name=name, + ) + for name in refcoco_test_dataset_names[1:] +] diff --git a/configs/common/data/refcoco_group_by_image_instance_lsj1024.py b/configs/common/data/refcoco_group_by_image_instance_lsj1024.py new file mode 100644 index 0000000..94b532b --- /dev/null +++ b/configs/common/data/refcoco_group_by_image_instance_lsj1024.py @@ -0,0 +1,102 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + build_detection_test_loader, + build_detection_train_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import COCOEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_instance, + build_detection_train_loader_multi_dataset, + get_detection_dataset_dicts_multi_dataset, +) +from ape.evaluation import RefCOCOEvaluator + +image_size = 1024 + +dataloader = OmegaConf.create() + +dataloader.train = L(build_detection_train_loader_multi_dataset)( + dataset=L(get_detection_dataset_dicts_multi_dataset)( + names=("refcoco-mixed_group-by-image",), filter_emptys=[True] + ), + mapper=L(DatasetMapper_detr_instance)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + dataset_names=("refcoco-mixed_group-by-image",), + max_num_phrase=128, + nms_thresh_phrase=0.6, + ), + total_batch_size=16, + total_batch_size_list=[16], + num_workers=4, + num_datasets=1, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="refcoco-unc-val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(RefCOCOEvaluator)( + dataset_name="${..test.dataset.names}", +) + +refcoco_test_dataset_names = [ + "refcoco-unc-val", + "refcoco-unc-testA", + "refcoco-unc-testB", + "refcocoplus-unc-val", + "refcocoplus-unc-testA", + "refcocoplus-unc-testB", + "refcocog-google-val", + "refcocog-umd-val", + "refcocog-umd-test", +] +dataloader.tests = [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names=name, filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ) + for name in refcoco_test_dataset_names[1:] +] + +dataloader.evaluators = [ + L(RefCOCOEvaluator)( + dataset_name=name, + ) + for name in refcoco_test_dataset_names[1:] +] diff --git a/configs/common/data/refcoco_instance.py b/configs/common/data/refcoco_instance.py new file mode 100644 index 0000000..924940f --- /dev/null +++ b/configs/common/data/refcoco_instance.py @@ -0,0 +1,107 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + build_detection_test_loader, + build_detection_train_loader, + get_detection_dataset_dicts, +) +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_instance_exp, + build_detection_train_loader_multi_dataset, + get_detection_dataset_dicts_multi_dataset, +) +from ape.evaluation import RefCOCOEvaluator + +dataloader = OmegaConf.create() + +dataloader.train = L(build_detection_train_loader_multi_dataset)( + dataset=L(get_detection_dataset_dicts_multi_dataset)( + names=("refcoco-mixed",), filter_emptys=[True] + ), + mapper=L(DatasetMapper_detr_instance_exp)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(), + L(T.ResizeShortestEdge)( + short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), + max_size=1333, + sample_style="choice", + ), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(), + L(T.ResizeShortestEdge)( + short_edge_length=(400, 500, 600), + sample_style="choice", + ), + L(T.RandomCrop)( + crop_type="absolute_range", + crop_size=(384, 600), + ), + L(T.ResizeShortestEdge)( + short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), + max_size=1333, + sample_style="choice", + ), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + dataset_names=("refcoco-mixed",), + ), + total_batch_size=16, + total_batch_size_list=[16], + num_workers=16, + num_datasets=1, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="refcoco-unc-val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=800, max_size=1333), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(RefCOCOEvaluator)( + dataset_name="${..test.dataset.names}", +) + +refcoco_test_dataset_names = [ + "refcoco-unc-val", + "refcoco-unc-testA", + "refcoco-unc-testB", + "refcocoplus-unc-val", + "refcocoplus-unc-testA", + "refcocoplus-unc-testB", + "refcocog-google-val", + "refcocog-umd-val", + "refcocog-umd-test", +] +dataloader.tests = [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names=name, filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=800, max_size=1333), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ) + for name in refcoco_test_dataset_names[1:] +] + +dataloader.evaluators = [ + L(RefCOCOEvaluator)( + dataset_name=name, + ) + for name in refcoco_test_dataset_names[1:] +] diff --git a/configs/common/data/refcoco_instance_lsj1024.py b/configs/common/data/refcoco_instance_lsj1024.py new file mode 100644 index 0000000..0052d2c --- /dev/null +++ b/configs/common/data/refcoco_instance_lsj1024.py @@ -0,0 +1,99 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + build_detection_test_loader, + build_detection_train_loader, + get_detection_dataset_dicts, +) +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_instance_exp, + build_detection_train_loader_multi_dataset, + get_detection_dataset_dicts_multi_dataset, +) +from ape.evaluation import RefCOCOEvaluator + +image_size = 1024 + +dataloader = OmegaConf.create() + +dataloader.train = L(build_detection_train_loader_multi_dataset)( + dataset=L(get_detection_dataset_dicts_multi_dataset)( + names=("refcoco-mixed",), filter_emptys=[True] + ), + mapper=L(DatasetMapper_detr_instance_exp)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + dataset_names=("refcoco-mixed",), + ), + total_batch_size=16, + total_batch_size_list=[16], + num_workers=4, + num_datasets=1, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="refcoco-unc-val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(RefCOCOEvaluator)( + dataset_name="${..test.dataset.names}", +) + +refcoco_test_dataset_names = [ + "refcoco-unc-val", + "refcoco-unc-testA", + "refcoco-unc-testB", + "refcocoplus-unc-val", + "refcocoplus-unc-testA", + "refcocoplus-unc-testB", + "refcocog-google-val", + "refcocog-umd-val", + "refcocog-umd-test", +] +dataloader.tests = [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names=name, filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ) + for name in refcoco_test_dataset_names[1:] +] + +dataloader.evaluators = [ + L(RefCOCOEvaluator)( + dataset_name=name, + ) + for name in refcoco_test_dataset_names[1:] +] diff --git a/configs/common/data/roboflow100_instance_lsj1024.py b/configs/common/data/roboflow100_instance_lsj1024.py new file mode 100644 index 0000000..b736708 --- /dev/null +++ b/configs/common/data/roboflow100_instance_lsj1024.py @@ -0,0 +1,76 @@ +import json +import os + +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import DatasetMapper, build_detection_test_loader, get_detection_dataset_dicts +from detectron2.data.datasets.register_coco import register_coco_instances +from detectron2.evaluation import COCOEvaluator +from omegaconf import OmegaConf + +dataloader = OmegaConf.create() + +data_root = "datasets/rf100" + +rf100_dataset_names = [] +for root, dirs, files in os.walk(data_root): + for d in dirs: + if root == data_root: + pass + else: + continue + + rf100_dataset_names.append(d) + + d = os.path.join(root, d) + print(len(rf100_dataset_names), d) +print(rf100_dataset_names, len(rf100_dataset_names)) +assert len(rf100_dataset_names) == 100 + + +def _get_builtin_metadata(name): + meta = {} + json_file = os.path.join(data_root, name, "valid", "_annotations.coco.json") + with open(json_file, "r") as fr: + json_data = json.load(fr) + meta["thing_classes"] = [category["name"] for category in json_data["categories"]] + + return meta + + +for key in rf100_dataset_names: + print("register_coco_instances", key) + register_coco_instances( + "rf100_" + key, + _get_builtin_metadata(key), + os.path.join(data_root, key, "valid", "_annotations.coco.json"), + os.path.join(data_root, key, "valid"), + ) + +dataloader.tests = [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="rf100_" + name, filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=1024, max_size=1024), + ], + image_format="RGB", + ), + num_workers=4, + ) + for name in rf100_dataset_names +] + +dataloader.name_prompt_fusion_text = [True] * len(rf100_dataset_names) + + +dataloader.select_box_nums_for_evaluation_list = [300] * len(rf100_dataset_names) + +dataloader.evaluators = [ + L(COCOEvaluator)( + dataset_name="rf100_" + name, + tasks=("bbox",), + ) + for name in rf100_dataset_names +] diff --git a/configs/common/data/seginw_instance.py b/configs/common/data/seginw_instance.py new file mode 100644 index 0000000..741efe7 --- /dev/null +++ b/configs/common/data/seginw_instance.py @@ -0,0 +1,162 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import COCOEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_panoptic, + DatasetMapper_detr_panoptic_copypaste, + build_detection_train_loader_multi_dataset, + build_detection_train_loader_multi_dataset_copypaste, + get_detection_dataset_dicts_multi_dataset, + get_detection_dataset_dicts_multi_dataset_copypaste, +) + +dataloader = OmegaConf.create() + +seginw_dataset_metas = [ + "seginw_Elephants_train", + "seginw_Hand-Metal_train", + "seginw_Watermelon_train", + "seginw_House-Parts_train", + "seginw_HouseHold-Items_train", + "seginw_Strawberry_train", + "seginw_Fruits_train", + "seginw_Nutterfly-Squireel_train", + "seginw_Hand_train", + "seginw_Garbage_train", + "seginw_Chicken_train", + "seginw_Rail_train", + "seginw_Airplane-Parts_train", + "seginw_Brain-Tumor_train", + "seginw_Poles_train", + "seginw_Electric-Shaver_train", + "seginw_Bottles_train", + "seginw_Toolkits_train", + "seginw_Trash_train", + "seginw_Salmon-Fillet_train", + "seginw_Puppies_train", + "seginw_Tablets_train", + "seginw_Phones_train", + "seginw_Cows_train", + "seginw_Ginger-Garlic_train", +] + +dataloader.train = [ + L(build_detection_train_loader_multi_dataset_copypaste)( + dataset=L(get_detection_dataset_dicts_multi_dataset_copypaste)( + names=(dataset_name,), + filter_emptys=[True], + copypastes=[True], + dataloader_id=dataloader_id, + reduce_memory=True, + reduce_memory_size=1e6, + ), + dataset_bg=L(get_detection_dataset_dicts)( + names=(dataset_name,), + filter_empty=True, + ), + mapper=L(DatasetMapper_detr_panoptic_copypaste)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(), + L(T.ResizeShortestEdge)( + short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), + max_size=1333, + sample_style="choice", + ), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(), + L(T.ResizeShortestEdge)( + short_edge_length=(400, 500, 600), + sample_style="choice", + ), + L(T.RandomCrop)( + crop_type="absolute_range", + crop_size=(384, 600), + ), + L(T.ResizeShortestEdge)( + short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), + max_size=1333, + sample_style="choice", + ), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + instance_mask_format="bitmask", + ignore_label=MetadataCatalog.get(dataset_name).get("ignore_label", None), + stuff_classes_offset=len(MetadataCatalog.get(dataset_name).get("thing_classes", [])), + stuff_classes_decomposition=True, + output_dir=None, + vis_period=12800, + dataset_names=(dataset_name,), + max_num_phrase=128, + nms_thresh_phrase=0.6, + ), + sampler=None, + sampler_bg=None, + total_batch_size=16, + total_batch_size_list=[16], + aspect_ratio_grouping=True, + num_workers=16, + num_datasets=1, + ) + for dataloader_id, dataset_name in enumerate(seginw_dataset_metas) +] + +seginw_test_dataset_names = [ + "seginw_Elephants_val", + "seginw_Hand-Metal_val", + "seginw_Watermelon_val", + "seginw_House-Parts_val", + "seginw_HouseHold-Items_val", + "seginw_Strawberry_val", + "seginw_Fruits_val", + "seginw_Nutterfly-Squireel_val", + "seginw_Hand_val", + "seginw_Garbage_val", + "seginw_Chicken_val", + "seginw_Rail_val", + "seginw_Airplane-Parts_val", + "seginw_Brain-Tumor_val", + "seginw_Poles_val", + "seginw_Electric-Shaver_val", + "seginw_Bottles_val", + "seginw_Toolkits_val", + "seginw_Trash_val", + "seginw_Salmon-Fillet_val", + "seginw_Puppies_val", + "seginw_Tablets_val", + "seginw_Phones_val", + "seginw_Cows_val", + "seginw_Ginger-Garlic_val", +] + +dataloader.tests = [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names=name, filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=800, max_size=1333), + ], + image_format="RGB", + ), + num_workers=4, + ) + for name in seginw_test_dataset_names +] + +dataloader.evaluators = [ + L(COCOEvaluator)( + dataset_name=name, + ) + for name in seginw_test_dataset_names +] diff --git a/configs/common/data/seginw_instance_lsj1024.py b/configs/common/data/seginw_instance_lsj1024.py new file mode 100644 index 0000000..3e995d6 --- /dev/null +++ b/configs/common/data/seginw_instance_lsj1024.py @@ -0,0 +1,154 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import COCOEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_panoptic, + DatasetMapper_detr_panoptic_copypaste, + build_detection_train_loader_multi_dataset, + build_detection_train_loader_multi_dataset_copypaste, + get_detection_dataset_dicts_multi_dataset, + get_detection_dataset_dicts_multi_dataset_copypaste, +) + +dataloader = OmegaConf.create() + +image_size = 1024 + +seginw_dataset_metas = [ + "seginw_Elephants_train", + "seginw_Hand-Metal_train", + "seginw_Watermelon_train", + "seginw_House-Parts_train", + "seginw_HouseHold-Items_train", + "seginw_Strawberry_train", + "seginw_Fruits_train", + "seginw_Nutterfly-Squireel_train", + "seginw_Hand_train", + "seginw_Garbage_train", + "seginw_Chicken_train", + "seginw_Rail_train", + "seginw_Airplane-Parts_train", + "seginw_Brain-Tumor_train", + "seginw_Poles_train", + "seginw_Electric-Shaver_train", + "seginw_Bottles_train", + "seginw_Toolkits_train", + "seginw_Trash_train", + "seginw_Salmon-Fillet_train", + "seginw_Puppies_train", + "seginw_Tablets_train", + "seginw_Phones_train", + "seginw_Cows_train", + "seginw_Ginger-Garlic_train", +] + +dataloader.train = [ + L(build_detection_train_loader_multi_dataset_copypaste)( + dataset=L(get_detection_dataset_dicts_multi_dataset_copypaste)( + names=(dataset_name,), + filter_emptys=[True], + copypastes=[True], + dataloader_id=dataloader_id, + reduce_memory=True, + reduce_memory_size=1e6, + ), + dataset_bg=L(get_detection_dataset_dicts)( + names=(dataset_name,), + filter_empty=True, + ), + mapper=L(DatasetMapper_detr_panoptic_copypaste)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + instance_mask_format="bitmask", + ignore_label=MetadataCatalog.get(dataset_name).get("ignore_label", None), + stuff_classes_offset=len(MetadataCatalog.get(dataset_name).get("thing_classes", [])), + stuff_classes_decomposition=True, + output_dir=None, + vis_period=12800, + dataset_names=(dataset_name,), + max_num_phrase=128, + nms_thresh_phrase=0.6, + ), + sampler=None, + sampler_bg=None, + total_batch_size=16, + total_batch_size_list=[16], + aspect_ratio_grouping=True, + num_workers=16, + num_datasets=1, + ) + for dataloader_id, dataset_name in enumerate(seginw_dataset_metas) +] + +seginw_test_dataset_names = [ + "seginw_Elephants_val", + "seginw_Hand-Metal_val", + "seginw_Watermelon_val", + "seginw_House-Parts_val", + "seginw_HouseHold-Items_val", + "seginw_Strawberry_val", + "seginw_Fruits_val", + "seginw_Nutterfly-Squireel_val", + "seginw_Hand_val", + "seginw_Garbage_val", + "seginw_Chicken_val", + "seginw_Rail_val", + "seginw_Airplane-Parts_val", + "seginw_Brain-Tumor_val", + "seginw_Poles_val", + "seginw_Electric-Shaver_val", + "seginw_Bottles_val", + "seginw_Toolkits_val", + "seginw_Trash_val", + "seginw_Salmon-Fillet_val", + "seginw_Puppies_val", + "seginw_Tablets_val", + "seginw_Phones_val", + "seginw_Cows_val", + "seginw_Ginger-Garlic_val", +] + +dataloader.tests = [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names=name, filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="RGB", + ), + num_workers=4, + ) + for name in seginw_test_dataset_names +] + +dataloader.evaluators = [ + L(COCOEvaluator)( + dataset_name=name, + ) + for name in seginw_test_dataset_names +] diff --git a/configs/common/data/seginw_instance_lsj1536.py b/configs/common/data/seginw_instance_lsj1536.py new file mode 100644 index 0000000..6c46e02 --- /dev/null +++ b/configs/common/data/seginw_instance_lsj1536.py @@ -0,0 +1,154 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + MetadataCatalog, + build_detection_test_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import COCOEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_panoptic, + DatasetMapper_detr_panoptic_copypaste, + build_detection_train_loader_multi_dataset, + build_detection_train_loader_multi_dataset_copypaste, + get_detection_dataset_dicts_multi_dataset, + get_detection_dataset_dicts_multi_dataset_copypaste, +) + +dataloader = OmegaConf.create() + +image_size = 1536 + +seginw_dataset_metas = [ + "seginw_Elephants_train", + "seginw_Hand-Metal_train", + "seginw_Watermelon_train", + "seginw_House-Parts_train", + "seginw_HouseHold-Items_train", + "seginw_Strawberry_train", + "seginw_Fruits_train", + "seginw_Nutterfly-Squireel_train", + "seginw_Hand_train", + "seginw_Garbage_train", + "seginw_Chicken_train", + "seginw_Rail_train", + "seginw_Airplane-Parts_train", + "seginw_Brain-Tumor_train", + "seginw_Poles_train", + "seginw_Electric-Shaver_train", + "seginw_Bottles_train", + "seginw_Toolkits_train", + "seginw_Trash_train", + "seginw_Salmon-Fillet_train", + "seginw_Puppies_train", + "seginw_Tablets_train", + "seginw_Phones_train", + "seginw_Cows_train", + "seginw_Ginger-Garlic_train", +] + +dataloader.train = [ + L(build_detection_train_loader_multi_dataset_copypaste)( + dataset=L(get_detection_dataset_dicts_multi_dataset_copypaste)( + names=(dataset_name,), + filter_emptys=[True], + copypastes=[True], + dataloader_id=dataloader_id, + reduce_memory=True, + reduce_memory_size=1e6, + ), + dataset_bg=L(get_detection_dataset_dicts)( + names=(dataset_name,), + filter_empty=True, + ), + mapper=L(DatasetMapper_detr_panoptic_copypaste)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(horizontal=True), # flip first + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + instance_mask_format="bitmask", + ignore_label=MetadataCatalog.get(dataset_name).get("ignore_label", None), + stuff_classes_offset=len(MetadataCatalog.get(dataset_name).get("thing_classes", [])), + stuff_classes_decomposition=True, + output_dir=None, + vis_period=12800, + dataset_names=(dataset_name,), + max_num_phrase=128, + nms_thresh_phrase=0.6, + ), + sampler=None, + sampler_bg=None, + total_batch_size=16, + total_batch_size_list=[16], + aspect_ratio_grouping=True, + num_workers=16, + num_datasets=1, + ) + for dataloader_id, dataset_name in enumerate(seginw_dataset_metas) +] + +seginw_test_dataset_names = [ + "seginw_Elephants_val", + "seginw_Hand-Metal_val", + "seginw_Watermelon_val", + "seginw_House-Parts_val", + "seginw_HouseHold-Items_val", + "seginw_Strawberry_val", + "seginw_Fruits_val", + "seginw_Nutterfly-Squireel_val", + "seginw_Hand_val", + "seginw_Garbage_val", + "seginw_Chicken_val", + "seginw_Rail_val", + "seginw_Airplane-Parts_val", + "seginw_Brain-Tumor_val", + "seginw_Poles_val", + "seginw_Electric-Shaver_val", + "seginw_Bottles_val", + "seginw_Toolkits_val", + "seginw_Trash_val", + "seginw_Salmon-Fillet_val", + "seginw_Puppies_val", + "seginw_Tablets_val", + "seginw_Phones_val", + "seginw_Cows_val", + "seginw_Ginger-Garlic_val", +] + +dataloader.tests = [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names=name, filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size), + ], + image_format="RGB", + ), + num_workers=4, + ) + for name in seginw_test_dataset_names +] + +dataloader.evaluators = [ + L(COCOEvaluator)( + dataset_name=name, + ) + for name in seginw_test_dataset_names +] diff --git a/configs/common/data/vgregion_instance.py b/configs/common/data/vgregion_instance.py new file mode 100644 index 0000000..d428dc5 --- /dev/null +++ b/configs/common/data/vgregion_instance.py @@ -0,0 +1,109 @@ +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + build_detection_test_loader, + build_detection_train_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import COCOEvaluator +from omegaconf import OmegaConf +from ape.data import ( + DatasetMapper_detr_instance, + build_detection_train_loader_multi_dataset, + get_detection_dataset_dicts_multi_dataset, +) +from ape.evaluation import RefCOCOEvaluator + +dataloader = OmegaConf.create() + +dataloader.train = L(build_detection_train_loader_multi_dataset)( + dataset=L(get_detection_dataset_dicts_multi_dataset)( + names=("visualgenome_77962_box_and_region",), + filter_emptys=[True], + ), + mapper=L(DatasetMapper_detr_instance)( + is_train=True, + augmentations=[ + L(T.RandomFlip)(), + L(T.ResizeShortestEdge)( + short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), + max_size=1333, + sample_style="choice", + ), + ], + augmentations_with_crop=[ + L(T.RandomFlip)(), + L(T.ResizeShortestEdge)( + short_edge_length=(400, 500, 600), + sample_style="choice", + ), + L(T.RandomCrop)( + crop_type="absolute_range", + crop_size=(384, 600), + ), + L(T.ResizeShortestEdge)( + short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), + max_size=1333, + sample_style="choice", + ), + ], + image_format="RGB", + use_instance_mask=True, + recompute_boxes=True, + dataset_names=("visualgenome_77962_box_and_region",), + ), + total_batch_size=16, + total_batch_size_list=[16], + num_workers=4, + num_datasets=1, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="refcoco-unc-val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=800, max_size=1333), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(RefCOCOEvaluator)( + dataset_name="${..test.dataset.names}", +) + +refcoco_test_dataset_names = [ + "refcoco-unc-val", + "refcoco-unc-testA", + "refcoco-unc-testB", + "refcocoplus-unc-val", + "refcocoplus-unc-testA", + "refcocoplus-unc-testB", + "refcocog-google-val", + "refcocog-umd-val", + "refcocog-umd-test", +] +dataloader.tests = [ + L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names=name, filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=800, max_size=1333), + ], + image_format="${....train.mapper.image_format}", + ), + num_workers=4, + ) + for name in refcoco_test_dataset_names[1:] +] + +dataloader.evaluators = [ + L(RefCOCOEvaluator)( + dataset_name=name, + ) + for name in refcoco_test_dataset_names[1:] +] diff --git a/datasets/README.md b/datasets/README.md new file mode 100644 index 0000000..2b9399a --- /dev/null +++ b/datasets/README.md @@ -0,0 +1,166 @@ + +# Detectron2 Builtin Datasets + +Detectron2 has builtin support for a few datasets. + +The datasets are assumed to exist in a directory specified by the environment variable +`DETECTRON2_DATASETS`. + +Under this directory, following [here](https://github.com/facebookresearch/detectron2/blob/main/datasets/README.md) to prepare COCO, LVIS, cityscapes, Pascal VOC and ADE20k. + +The expected structure is described below. +``` +$DETECTRON2_DATASETS/ + coco/ + lvis/ + cityscapes/ + VOC20{07,10,12}/ + ADEChallengeData2016/ +``` + +You can set the location for builtin datasets by `export DETECTRON2_DATASETS=/path/to/datasets`. +If left unset, the default is `./datasets` relative to your current working directory. + + +# APE Builtin Datasets + +## Expected dataset structure for [Objects365](https://data.baai.ac.cn/details/Objects365_2020): +``` +$DETECTRON2_DATASETS/ + objects365/ + annotations/ + objects365_train_fixname.json + objects365_val_fixname.json + train/ + images/ + val/ + images/ +``` + +After downloading and extracting Objects365, create a symbolic link to `./datasets/`. +Then, run +```bash +python3 tools/objects3652coco/get_image_info.py --image_dir datasets/objects365/train/ --json_path datasets/objects365/annotations/zhiyuan_objv2_train.json --output_path datasets/objects365/annotations/image_info_train.txt +python3 tools/objects3652coco/get_image_info.py --image_dir datasets/objects365/val/ --json_path datasets/objects365/annotations/zhiyuan_objv2_val.json --output_path datasets/objects365/annotations/image_info_val.txt + +python3 tools/objects3652coco/convert_annotations.py --root_dir datasets/objects365/ --image_info_path datasets/objects365/annotations/image_info_train.txt --subsets train --apply_exif +python3 tools/objects3652coco/convert_annotations.py --root_dir datasets/objects365/ --image_info_path datasets/objects365/annotations/image_info_val.txt --subsets val --apply_exif +python3 tools/objects3652coco/convert_annotations.py --root_dir datasets/objects365/ --image_info_path datasets/objects365/annotations/image_info_val.txt --subsets minival --apply_exif + +python3 tools/objects3652coco/fix_o365_names.py --ann datasets/objects365/annotations/objects365_train.json +python3 tools/objects3652coco/fix_o365_names.py --ann datasets/objects365/annotations/objects365_val.json +python3 tools/objects3652coco/fix_o365_names.py --ann datasets/objects365/annotations/objects365_minival.json + +python3 tools/generate_img_ann_pair.py --json_path datasets/objects365/annotations/objects365_train_fixname.json --image_root datasets/objects365/train/ +``` + +## Expected dataset structure for [OpenImages](https://storage.googleapis.com/openimages/web/download.html#download_manually): +``` +$DETECTRON2_DATASETS/ + openimages/ + annotations/ + train/ + validation/ +``` + +## Expected dataset structure for [VisualGenome](https://homes.cs.washington.edu/~ranjay/visualgenome/api.html): +``` +$DETECTRON2_DATASETS/ + visualgenome/ + VG_100K/ + VG_100K_2/ +``` + +## Expected dataset structure for [SA-1B](https://ai.meta.com/datasets/segment-anything-downloads/): +``` +$DETECTRON2_DATASETS/ + SA-1B/ + images/ + sam1b_instance.json +``` + +## Expected dataset structure for [RefCOCO](): +``` +$DETECTRON2_DATASETS/ + xxx/ +``` + +## Expected dataset structure for [GQA](): +``` +$DETECTRON2_DATASETS/ + xxx/ +``` + +## Expected dataset structure for [PhraseCut](): +``` +$DETECTRON2_DATASETS/ + xxx/ +``` + +## Expected dataset structure for [Flickr30k](): +``` +$DETECTRON2_DATASETS/ + xxx/ +``` + +## Expected dataset structure for [ODinW](): +``` +$DETECTRON2_DATASETS/ + xxx/ +``` + +## Expected dataset structure for [SegInW](): +``` +$DETECTRON2_DATASETS/ + xxx/ +``` + +## Expected dataset structure for [Roboflow100](): +``` +$DETECTRON2_DATASETS/ + xxx/ +``` + +## Expected dataset structure for [ADE20k](): +``` +$DETECTRON2_DATASETS/ + xxx/ +``` + +## Expected dataset structure for [ADE-full](): +``` +$DETECTRON2_DATASETS/ + xxx/ +``` + +## Expected dataset structure for [BDD10k](): +``` +$DETECTRON2_DATASETS/ + xxx/ +``` + +## Expected dataset structure for [PC459](): +``` +$DETECTRON2_DATASETS/ + xxx/ +``` + +## Expected dataset structure for [PC59](): +``` +$DETECTRON2_DATASETS/ + xxx/ +``` + +## Expected dataset structure for [VOC](): +``` +$DETECTRON2_DATASETS/ + xxx/ +``` + +## Expected dataset structure for [D3](): +``` +$DETECTRON2_DATASETS/ + xxx/ +``` + + diff --git a/datasets/prepare_ade20k_full_sem_seg.py b/datasets/prepare_ade20k_full_sem_seg.py new file mode 100644 index 0000000..e9ec336 --- /dev/null +++ b/datasets/prepare_ade20k_full_sem_seg.py @@ -0,0 +1,1007 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. +import os +import pickle as pkl +from pathlib import Path + +import cv2 +import numpy as np +import tqdm +from PIL import Image + +ADE20K_SEM_SEG_FULL_CATEGORIES = [ + {"name": "wall", "id": 2978, "trainId": 0}, + {"name": "building, edifice", "id": 312, "trainId": 1}, + {"name": "sky", "id": 2420, "trainId": 2}, + {"name": "tree", "id": 2855, "trainId": 3}, + {"name": "road, route", "id": 2131, "trainId": 4}, + {"name": "floor, flooring", "id": 976, "trainId": 5}, + {"name": "ceiling", "id": 447, "trainId": 6}, + {"name": "bed", "id": 165, "trainId": 7}, + {"name": "sidewalk, pavement", "id": 2377, "trainId": 8}, + {"name": "earth, ground", "id": 838, "trainId": 9}, + {"name": "cabinet", "id": 350, "trainId": 10}, + {"name": "person, individual, someone, somebody, mortal, soul", "id": 1831, "trainId": 11}, + {"name": "grass", "id": 1125, "trainId": 12}, + {"name": "windowpane, window", "id": 3055, "trainId": 13}, + {"name": "car, auto, automobile, machine, motorcar", "id": 401, "trainId": 14}, + {"name": "mountain, mount", "id": 1610, "trainId": 15}, + {"name": "plant, flora, plant life", "id": 1910, "trainId": 16}, + {"name": "table", "id": 2684, "trainId": 17}, + {"name": "chair", "id": 471, "trainId": 18}, + {"name": "curtain, drape, drapery, mantle, pall", "id": 687, "trainId": 19}, + {"name": "door", "id": 774, "trainId": 20}, + {"name": "sofa, couch, lounge", "id": 2473, "trainId": 21}, + {"name": "sea", "id": 2264, "trainId": 22}, + {"name": "painting, picture", "id": 1735, "trainId": 23}, + {"name": "water", "id": 2994, "trainId": 24}, + {"name": "mirror", "id": 1564, "trainId": 25}, + {"name": "house", "id": 1276, "trainId": 26}, + {"name": "rug, carpet, carpeting", "id": 2178, "trainId": 27}, + {"name": "shelf", "id": 2329, "trainId": 28}, + {"name": "armchair", "id": 57, "trainId": 29}, + {"name": "fence, fencing", "id": 907, "trainId": 30}, + {"name": "field", "id": 913, "trainId": 31}, + {"name": "lamp", "id": 1395, "trainId": 32}, + {"name": "rock, stone", "id": 2138, "trainId": 33}, + {"name": "seat", "id": 2272, "trainId": 34}, + {"name": "river", "id": 2128, "trainId": 35}, + {"name": "desk", "id": 724, "trainId": 36}, + {"name": "bathtub, bathing tub, bath, tub", "id": 155, "trainId": 37}, + {"name": "railing, rail", "id": 2053, "trainId": 38}, + {"name": "signboard, sign", "id": 2380, "trainId": 39}, + {"name": "cushion", "id": 689, "trainId": 40}, + {"name": "path", "id": 1788, "trainId": 41}, + {"name": "work surface", "id": 3087, "trainId": 42}, + {"name": "stairs, steps", 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"towel paper", "id": 2823, "trainId": 718}, + {"name": "chaise lounge", "id": 476, "trainId": 719}, + {"name": "sun", "id": 2641, "trainId": 720}, + {"name": "toilet paper holder", "id": 2788, "trainId": 721}, + {"name": "rake", "id": 2070, "trainId": 722}, + {"name": "key", "id": 1368, "trainId": 723}, + {"name": "umbrella stand", "id": 2903, "trainId": 724}, + {"name": "dartboard", "id": 699, "trainId": 725}, + {"name": "transformer", "id": 2844, "trainId": 726}, + {"name": "fireplace utensils", "id": 942, "trainId": 727}, + {"name": "sweatshirts", "id": 2663, "trainId": 728}, + { + "name": "cellular telephone, cellular phone, cellphone, cell, mobile phone", + "id": 457, + "trainId": 729, + }, + {"name": "tallboy", "id": 2701, "trainId": 730}, + {"name": "stapler", "id": 2540, "trainId": 731}, + {"name": "sauna", "id": 2231, "trainId": 732}, + {"name": "test tube", "id": 2746, "trainId": 733}, + {"name": "palette", "id": 1738, "trainId": 734}, + {"name": "shopping carts", "id": 2350, "trainId": 735}, + {"name": "tools", "id": 2808, "trainId": 736}, + {"name": "push button, push, button", "id": 2025, "trainId": 737}, + {"name": "star", "id": 2541, "trainId": 738}, + {"name": "roof rack", "id": 2156, "trainId": 739}, + {"name": "barbed wire", "id": 126, "trainId": 740}, + {"name": "spray", "id": 2512, "trainId": 741}, + {"name": "ear", "id": 831, "trainId": 742}, + {"name": "sponge", "id": 2503, "trainId": 743}, + {"name": "racket", "id": 2039, "trainId": 744}, + {"name": "tins", "id": 2774, "trainId": 745}, + {"name": "eyeglasses", "id": 886, "trainId": 746}, + {"name": "file", "id": 919, "trainId": 747}, + {"name": "scarfs", "id": 2240, "trainId": 748}, + {"name": "sugar bowl", "id": 2636, "trainId": 749}, + {"name": "flip flop", "id": 963, "trainId": 750}, + {"name": "headstones", "id": 1218, "trainId": 751}, + {"name": "laptop bag", "id": 1406, "trainId": 752}, + {"name": "leash", "id": 1420, "trainId": 753}, + {"name": "climbing frame", "id": 526, "trainId": 754}, + {"name": "suit hanger", "id": 2639, "trainId": 755}, + {"name": "floor spotlight", "id": 975, "trainId": 756}, + {"name": "plate rack", "id": 1921, "trainId": 757}, + {"name": "sewer", "id": 2305, "trainId": 758}, + {"name": "hard drive", "id": 1193, "trainId": 759}, + {"name": "sprinkler", "id": 2517, "trainId": 760}, + {"name": "tools box", "id": 2809, "trainId": 761}, + {"name": "necklace", "id": 1647, "trainId": 762}, + {"name": "bulbs", "id": 314, "trainId": 763}, + {"name": "steel industry", "id": 2560, "trainId": 764}, + {"name": "club", "id": 545, "trainId": 765}, + {"name": "jack", "id": 1345, "trainId": 766}, + {"name": "door bars", "id": 775, "trainId": 767}, + { + "name": "control panel, instrument panel, control board, board, panel", + "id": 603, + "trainId": 768, + }, + {"name": "hairbrush", "id": 1163, "trainId": 769}, + {"name": "napkin holder", "id": 1641, "trainId": 770}, + {"name": "office", "id": 1678, "trainId": 771}, + {"name": "smoke detector", "id": 2450, "trainId": 772}, + {"name": "utensils", "id": 2915, "trainId": 773}, + {"name": "apron", "id": 42, "trainId": 774}, + {"name": "scissors", "id": 2242, "trainId": 775}, + {"name": "terminal", "id": 2741, "trainId": 776}, + {"name": "grinder", "id": 1143, "trainId": 777}, + {"name": "entry phone", "id": 862, "trainId": 778}, + {"name": "newspaper stand", "id": 1654, "trainId": 779}, + {"name": "pepper shaker", "id": 1826, "trainId": 780}, + {"name": "onions", "id": 1689, "trainId": 781}, + { + "name": "central processing unit, cpu, c p u , central processor, processor, mainframe", + "id": 3124, + "trainId": 782, + }, + {"name": "tape", "id": 2710, "trainId": 783}, + {"name": "bat", "id": 152, "trainId": 784}, + {"name": "coaster", "id": 549, "trainId": 785}, + {"name": "calculator", "id": 360, "trainId": 786}, + {"name": "potatoes", "id": 1982, "trainId": 787}, + {"name": "luggage rack", "id": 1478, "trainId": 788}, + {"name": "salt", "id": 2203, "trainId": 789}, + {"name": "street number", "id": 2612, "trainId": 790}, + {"name": "viewpoint", "id": 2956, "trainId": 791}, + {"name": "sword", "id": 2681, "trainId": 792}, + {"name": "cd", "id": 437, "trainId": 793}, + {"name": "rowing machine", "id": 2171, "trainId": 794}, + {"name": "plug", "id": 1933, "trainId": 795}, + {"name": "andiron, firedog, dog, dog-iron", "id": 3110, "trainId": 796}, + {"name": "pepper", "id": 1824, "trainId": 797}, + {"name": "tongs", "id": 2803, "trainId": 798}, + {"name": "bonfire", "id": 234, "trainId": 799}, + {"name": "dog dish", "id": 764, "trainId": 800}, + {"name": "belt", "id": 177, "trainId": 801}, + {"name": "dumbbells", "id": 817, "trainId": 802}, + {"name": "videocassette recorder, vcr", "id": 3145, "trainId": 803}, + {"name": "hook", "id": 1262, "trainId": 804}, + {"name": "envelopes", "id": 864, "trainId": 805}, + {"name": "shower faucet", "id": 2359, "trainId": 806}, + {"name": "watch", "id": 2992, "trainId": 807}, + {"name": "padlock", "id": 1725, "trainId": 808}, + {"name": "swimming pool ladder", "id": 2667, "trainId": 809}, + {"name": "spanners", "id": 2484, "trainId": 810}, + {"name": "gravy boat", "id": 1133, "trainId": 811}, + {"name": "notice board", "id": 1667, "trainId": 812}, + {"name": "trash bags", "id": 2847, "trainId": 813}, + {"name": "fire alarm", "id": 932, "trainId": 814}, + {"name": "ladle", "id": 1392, "trainId": 815}, + {"name": "stethoscope", "id": 2573, "trainId": 816}, + {"name": "rocket", "id": 2140, "trainId": 817}, + {"name": "funnel", "id": 1046, "trainId": 818}, + {"name": "bowling pins", "id": 264, "trainId": 819}, + {"name": "valve", "id": 2927, "trainId": 820}, + {"name": "thermometer", "id": 2752, "trainId": 821}, + {"name": "cups", "id": 679, "trainId": 822}, + {"name": "spice jar", "id": 2493, "trainId": 823}, + {"name": "night light", "id": 1658, "trainId": 824}, + {"name": "soaps", "id": 2466, "trainId": 825}, + {"name": "games table", "id": 1057, "trainId": 826}, + {"name": "slotted spoon", "id": 2444, "trainId": 827}, + {"name": "reel", "id": 2093, "trainId": 828}, + {"name": "scourer", "id": 2248, "trainId": 829}, + {"name": "sleeping robe", "id": 2432, "trainId": 830}, + {"name": "desk mat", "id": 726, "trainId": 831}, + {"name": "dumbbell", "id": 816, "trainId": 832}, + {"name": "hammer", "id": 1171, "trainId": 833}, + {"name": "tie", "id": 2766, "trainId": 834}, + {"name": "typewriter", "id": 2900, "trainId": 835}, + {"name": "shaker", "id": 2313, "trainId": 836}, + {"name": "cheese dish", "id": 488, "trainId": 837}, + {"name": "sea star", "id": 2265, "trainId": 838}, + {"name": "racquet", "id": 2043, "trainId": 839}, + {"name": "butane gas cylinder", "id": 332, "trainId": 840}, + {"name": "paper weight", "id": 1771, "trainId": 841}, + {"name": "shaving brush", "id": 2320, "trainId": 842}, + {"name": "sunglasses", "id": 2646, "trainId": 843}, + {"name": "gear shift", "id": 1089, "trainId": 844}, + {"name": "towel rail", "id": 2826, "trainId": 845}, + {"name": "adding machine, totalizer, totaliser", "id": 3148, "trainId": 846}, +] + + +def loadAde20K(file): + fileseg = file.replace(".jpg", "_seg.png") + with Image.open(fileseg) as io: + seg = np.array(io) + + R = seg[:, :, 0] + G = seg[:, :, 1] + ObjectClassMasks = (R / 10).astype(np.int32) * 256 + (G.astype(np.int32)) + + return {"img_name": file, "segm_name": fileseg, "class_mask": ObjectClassMasks} + + +if __name__ == "__main__": + dataset_dir = Path(os.getenv("DETECTRON2_DATASETS", "datasets")) + index_file = dataset_dir / "ADE20K_2021_17_01" / "index_ade20k.pkl" + with open(index_file, "rb") as f: + index_ade20k = pkl.load(f) + + id_map = {} + for cat in ADE20K_SEM_SEG_FULL_CATEGORIES: + id_map[cat["id"]] = cat["trainId"] + + # make output dir + for name in ["training", "validation"]: + image_dir = dataset_dir / "ADE20K_2021_17_01" / "images_detectron2" / name + image_dir.mkdir(parents=True, exist_ok=True) + annotation_dir = dataset_dir / "ADE20K_2021_17_01" / "annotations_detectron2" / name + annotation_dir.mkdir(parents=True, exist_ok=True) + + # process image and gt + for i, (folder_name, file_name) in tqdm.tqdm( + enumerate(zip(index_ade20k["folder"], index_ade20k["filename"])), + total=len(index_ade20k["filename"]), + ): + split = "validation" if file_name.split("_")[1] == "val" else "training" + info = loadAde20K(str(dataset_dir / folder_name / file_name)) + + # resize image and label + img = np.asarray(Image.open(info["img_name"])) + lab = np.asarray(info["class_mask"]) + + h, w = img.shape[0], img.shape[1] + max_size = 512 + resize = True + if w >= h > max_size: + h_new, w_new = max_size, round(w / float(h) * max_size) + elif h >= w > max_size: + h_new, w_new = round(h / float(w) * max_size), max_size + else: + resize = False + + if resize: + img = cv2.resize(img, (w_new, h_new), interpolation=cv2.INTER_LINEAR) + lab = cv2.resize(lab, (w_new, h_new), interpolation=cv2.INTER_NEAREST) + + assert img.dtype == np.uint8 + assert lab.dtype == np.int32 + + # apply label conversion and save into uint16 images + output = np.zeros_like(lab, dtype=np.uint16) + 65535 + for obj_id in np.unique(lab): + if obj_id in id_map: + output[lab == obj_id] = id_map[obj_id] + + output_img = dataset_dir / "ADE20K_2021_17_01" / "images_detectron2" / split / file_name + output_lab = ( + dataset_dir + / "ADE20K_2021_17_01" + / "annotations_detectron2" + / split + / file_name.replace(".jpg", ".tif") + ) + Image.fromarray(img).save(output_img) + + assert output.dtype == np.uint16 + Image.fromarray(output).save(output_lab) diff --git a/datasets/prepare_pascal_context.py b/datasets/prepare_pascal_context.py new file mode 100644 index 0000000..25d3846 --- /dev/null +++ b/datasets/prepare_pascal_context.py @@ -0,0 +1,69 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Copyright (c) Meta Platforms, Inc. All Rights Reserved + +import tqdm +import os +import os.path as osp +from pathlib import Path + +import numpy as np +from PIL import Image +import scipy.io + +def convert_pc59(mask_path, new_mask_path, pc59_dict): + mat = scipy.io.loadmat(mask_path) + mask = mat['LabelMap'] + + mask_copy = np.ones_like(mask, dtype=np.uint8) * 255 + for trID, clsID in pc59_dict.items(): + mask_copy[mask == clsID] = trID + + min_value = np.amin(mask_copy) + assert min_value >= 0, print(min_value) + Image.fromarray(mask_copy).save(new_mask_path, "PNG") + +def convert_pc459(mask_path, new_mask_path): + mat = scipy.io.loadmat(mask_path) + mask = mat['LabelMap'] + mask = mask - 1 + min_value = np.amin(mask) + assert min_value >= 0, print(min_value) + Image.fromarray(mask).save(new_mask_path, "TIFF") + + +if __name__ == "__main__": + dataset_dir = Path(os.getenv("DETECTRON2_DATASETS", "datasets")) + print('Caution: we only generate the validation set!') + pc_path = dataset_dir / "VOCdevkit/VOC2010" + + val_list = open(pc_path / "pascalcontext_val.txt", "r") + pc459_labels = open(pc_path / "labels.txt", "r") + pc59_labels = open(pc_path / "59_labels.txt", "r") + + pc459_dict = {} + for line in pc459_labels.readlines(): + if ':' in line: + idx, name = line.split(':') + idx = int(idx.strip()) + name = name.strip() + pc459_dict[name] = idx + + pc59_dict = {} + for i, line in enumerate(pc59_labels.readlines()): + name = line.split(':')[-1].strip() + if name is not '': + pc59_dict[i] = pc459_dict[name] + + pc459_dir = pc_path / "annotations_detectron2" / "pc459_val" + pc459_dir.mkdir(parents=True, exist_ok=True) + pc59_dir = pc_path / "annotations_detectron2" / "pc59_val" + pc59_dir.mkdir(parents=True, exist_ok=True) + + for line in tqdm.tqdm(val_list.readlines()): + fileid = line.strip() + ori_mask = f'{pc_path}/trainval/{fileid}.mat' + pc459_dst = f'{pc459_dir}/{fileid}.tif' + pc59_dst = f'{pc59_dir}/{fileid}.png' + if osp.exists(ori_mask): + convert_pc459(ori_mask, pc459_dst) + convert_pc59(ori_mask, pc59_dst, pc59_dict) diff --git a/datasets/prepare_voc_sem_seg.py b/datasets/prepare_voc_sem_seg.py new file mode 100644 index 0000000..1dbe80a --- /dev/null +++ b/datasets/prepare_voc_sem_seg.py @@ -0,0 +1,71 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Copyright (c) Meta Platforms, Inc. All Rights Reserved +# Modified by Feng Liang from https://github.com/MendelXu/zsseg.baseline/blob/master/datasets/prepare_voc_sem_seg.py + +import os +import os.path as osp +from pathlib import Path +import tqdm + +import numpy as np +from PIL import Image + + +clsID_to_trID = { + 0: 255, + 1: 0, + 2: 1, + 3: 2, + 4: 3, + 5: 4, + 6: 5, + 7: 6, + 8: 7, + 9: 8, + 10: 9, + 11: 10, + 12: 11, + 13: 12, + 14: 13, + 15: 14, + 16: 15, + 17: 16, + 18: 17, + 19: 18, + 20: 19, + 255: 255, +} + +def convert_to_trainID( + maskpath, out_mask_dir, is_train, clsID_to_trID=clsID_to_trID, suffix="" +): + mask = np.array(Image.open(maskpath)) + mask_copy = np.ones_like(mask, dtype=np.uint8) * 255 + for clsID, trID in clsID_to_trID.items(): + mask_copy[mask == clsID] = trID + seg_filename = ( + osp.join(out_mask_dir, "train" + suffix, osp.basename(maskpath)) + if is_train + else osp.join(out_mask_dir, "val" + suffix, osp.basename(maskpath)) + ) + if len(np.unique(mask_copy)) == 1 and np.unique(mask_copy)[0] == 255: + return + Image.fromarray(mask_copy).save(seg_filename, "PNG") + + + +if __name__ == "__main__": + dataset_dir = Path(os.getenv("DETECTRON2_DATASETS", "datasets")) + print('Caution: we only generate the validation set!') + voc_path = dataset_dir / "VOCdevkit" / "VOC2012" + out_mask_dir = voc_path / "annotations_detectron2" + out_image_dir = voc_path / "images_detectron2" + for name in ["val"]: + os.makedirs((out_mask_dir / name), exist_ok=True) + os.makedirs((out_image_dir / name), exist_ok=True) + val_list = [ + osp.join(voc_path, "SegmentationClassAug", f + ".png") + for f in np.loadtxt(osp.join(voc_path, "ImageSets/Segmentation/val.txt"), dtype=np.str).tolist() + ] + for file in tqdm.tqdm(val_list): + convert_to_trainID(file, out_mask_dir, is_train=False) diff --git a/demo/README.md b/demo/README.md new file mode 100644 index 0000000..967853a --- /dev/null +++ b/demo/README.md @@ -0,0 +1,13 @@ +--- +title: APE +emoji: 🌍 +colorFrom: blue +colorTo: indigo +sdk: gradio +sdk_version: 4.7.1 +app_file: app.py +pinned: false +license: apache-2.0 +--- + +Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/demo/app.py b/demo/app.py new file mode 100644 index 0000000..2aa1ebc --- /dev/null +++ b/demo/app.py @@ -0,0 +1,773 @@ +import gc +import multiprocessing as mp +import os +import shutil +import sys +import time +from os import path + +import cv2 +import torch +from huggingface_hub import hf_hub_download +from PIL import Image + +import ape +import detectron2.data.transforms as T +import gradio as gr +from ape.model_zoo import get_config_file +from demo_lazy import get_parser, setup_cfg +from detectron2.config import CfgNode +from detectron2.data.detection_utils import read_image +from detectron2.evaluation.coco_evaluation import instances_to_coco_json +from detectron2.utils.logger import setup_logger +from predictor_lazy import VisualizationDemo + +this_dir = path.dirname(path.abspath(__file__)) + +example_list = [ + [ + this_dir + "/examples/Totoro01.png", + # "Sky, Water, Tree, The biggest Chinchilla, The older girl wearing skirt on branch, Grass", + "Girl with hat", + # 0.05, + 0.25, + ["object detection", "instance segmentation"], + ], + [ + this_dir + "/examples/Totoro01.png", + "Sky, Water, Tree, Chinchilla, Grass, Girl", + 0.15, + ["semantic segmentation"], + ], + [ + this_dir + "/examples/094_56726435.jpg", + "donut with colored granules on the surface", + 0.4, + ["object detection", "instance segmentation"], + ], + [ + this_dir + "/examples/199_3946193540.jpg", + "chess piece of horse head", + 0.30, + ["object detection", "instance segmentation"], + ], + [ + this_dir + "/examples/013_438973263.jpg", + "a male lion with a mane", + 0.25, + ["object detection", "instance segmentation"], + ], + [ + this_dir + "/examples/TheGreatWall.jpg", + "The Great Wall", + 0.1, + ["semantic segmentation"], + ], + [ + this_dir + "/examples/Pisa.jpg", + "Pisa", + 0.01, + ["object detection", "instance segmentation"], + ], + [ + this_dir + "/examples/SolvayConference1927.jpg", + # "Albert Einstein, Madame Curie", + "Madame Curie", + # 0.01, + 0.03, + ["object detection", "instance segmentation"], + ], + [ + this_dir + "/examples/Transformers.webp", + "Optimus Prime", + 0.08, + ["object detection", "instance segmentation"], + ], +] + +ckpt_repo_id = "shenyunhang/APE" + + +def setup_model(name): + gc.collect() + torch.cuda.empty_cache() + + if save_memory: + pass + else: + return + + for key, demo in all_demo.items(): + if key == name: + demo.predictor.model.to(running_device) + else: + demo.predictor.model.to("cpu") + + gc.collect() + torch.cuda.empty_cache() + + +def run_on_image_A(input_image_path, input_text, score_threshold, output_type): + logger.info("run_on_image") + + setup_model("APE_A") + demo = all_demo["APE_A"] + cfg = all_cfg["APE_A"] + demo.predictor.model.model_vision.test_score_thresh = score_threshold + + return run_on_image( + input_image_path, + input_text, + output_type, + demo, + cfg, + ) + + +def run_on_image_C(input_image_path, input_text, score_threshold, output_type): + logger.info("run_on_image_C") + + setup_model("APE_C") + demo = all_demo["APE_C"] + cfg = all_cfg["APE_C"] + demo.predictor.model.model_vision.test_score_thresh = score_threshold + + return run_on_image( + input_image_path, + input_text, + output_type, + demo, + cfg, + ) + + +def run_on_image_D(input_image_path, input_text, score_threshold, output_type): + logger.info("run_on_image_D") + + setup_model("APE_D") + demo = all_demo["APE_D"] + cfg = all_cfg["APE_D"] + demo.predictor.model.model_vision.test_score_thresh = score_threshold + + return run_on_image( + input_image_path, + input_text, + output_type, + demo, + cfg, + ) + + +def run_on_image_comparison(input_image_path, input_text, score_threshold, output_type): + logger.info("run_on_image_comparison") + + r = [] + for key in all_demo.keys(): + logger.info("run_on_image_comparison {}".format(key)) + setup_model(key) + demo = all_demo[key] + cfg = all_cfg[key] + demo.predictor.model.model_vision.test_score_thresh = score_threshold + + img, _ = run_on_image( + input_image_path, + input_text, + output_type, + demo, + cfg, + ) + r.append(img) + + return r + + +def run_on_image( + input_image_path, + input_text, + output_type, + demo, + cfg, +): + with_box = False + with_mask = False + with_sseg = False + if "object detection" in output_type: + with_box = True + if "instance segmentation" in output_type: + with_mask = True + if "semantic segmentation" in output_type: + with_sseg = True + + if isinstance(input_image_path, dict): + input_mask_path = input_image_path["mask"] + input_image_path = input_image_path["image"] + print("input_image_path", input_image_path) + print("input_mask_path", input_mask_path) + else: + input_mask_path = None + + print("input_text", input_text) + + if isinstance(cfg, CfgNode): + input_format = cfg.INPUT.FORMAT + else: + if "model_vision" in cfg.model: + input_format = cfg.model.model_vision.input_format + else: + input_format = cfg.model.input_format + + input_image = read_image(input_image_path, format="BGR") + # img = cv2.imread(input_image_path) + # cv2.imwrite("tmp.jpg", img) + # # input_image = read_image("tmp.jpg", format=input_format) + # input_image = read_image("tmp.jpg", format="BGR") + + if input_mask_path is not None: + input_mask = read_image(input_mask_path, "L").squeeze(2) + print("input_mask", input_mask) + print("input_mask", input_mask.shape) + else: + input_mask = None + + if not with_box and not with_mask and not with_sseg: + return input_image[:, :, ::-1] + + if input_image.shape[0] > 1024 or input_image.shape[1] > 1024: + transform = aug.get_transform(input_image) + input_image = transform.apply_image(input_image) + else: + transform = None + + start_time = time.time() + predictions, visualized_output, _, metadata = demo.run_on_image( + input_image, + text_prompt=input_text, + mask_prompt=input_mask, + with_box=with_box, + with_mask=with_mask, + with_sseg=with_sseg, + ) + + logger.info( + "{} in {:.2f}s".format( + "detected {} instances".format(len(predictions["instances"])) + if "instances" in predictions + else "finished", + time.time() - start_time, + ) + ) + + output_image = visualized_output.get_image() + print("output_image", output_image.shape) + # if input_format == "RGB": + # output_image = output_image[:, :, ::-1] + if transform: + output_image = transform.inverse().apply_image(output_image) + print("output_image", output_image.shape) + + output_image = Image.fromarray(output_image) + + gc.collect() + torch.cuda.empty_cache() + + json_results = instances_to_coco_json(predictions["instances"].to(demo.cpu_device), 0) + for json_result in json_results: + json_result["category_name"] = metadata.thing_classes[json_result["category_id"]] + del json_result["image_id"] + + return output_image, json_results + + +def load_APE_A(): + # init_checkpoint= "output2/APE/configs/LVISCOCOCOCOSTUFF_O365_OID_VG/ape_deta/ape_deta_vitl_eva02_lsj_cp_720k_20230504_002019/model_final.pth" + init_checkpoint = "configs/LVISCOCOCOCOSTUFF_O365_OID_VG/ape_deta/ape_deta_vitl_eva02_lsj_cp_720k_20230504_002019/model_final.pth" + init_checkpoint = hf_hub_download(repo_id=ckpt_repo_id, filename=init_checkpoint) + + args = get_parser().parse_args() + args.config_file = get_config_file( + "LVISCOCOCOCOSTUFF_O365_OID_VG/ape_deta/ape_deta_vitl_eva02_lsj1024_cp_720k.py" + ) + args.confidence_threshold = 0.01 + args.opts = [ + "train.init_checkpoint='{}'".format(init_checkpoint), + "model.model_language.cache_dir=''", + "model.model_vision.select_box_nums_for_evaluation=500", + ] + if running_device == "cpu": + args.opts += [ + "model.model_language.dtype='float32'", + "model.model_vision.backbone.net.xattn=False", + ] + logger.info("Arguments: " + str(args)) + cfg = setup_cfg(args) + + cfg.model.model_vision.criterion[0].use_fed_loss = False + cfg.model.model_vision.criterion[2].use_fed_loss = False + cfg.train.device = running_device + + ape.modeling.text.eva01_clip.eva_clip._MODEL_CONFIGS[cfg.model.model_language.clip_model][ + "vision_cfg" + ]["layers"] = 1 + ape.modeling.text.eva01_clip.eva_clip._MODEL_CONFIGS[cfg.model.model_language.clip_model][ + "vision_cfg" + ]["fusedLN"] = False + + demo = VisualizationDemo(cfg, args=args) + if save_memory: + demo.predictor.model.to("cpu") + # demo.predictor.model.half() + else: + demo.predictor.model.to(running_device) + + all_demo["APE_A"] = demo + all_cfg["APE_A"] = cfg + + +def load_APE_B(): + # init_checkpoint= "output2/APE/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj_cp_1080k_20230702_225418/model_final.pth" + init_checkpoint = "configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj_cp_1080k_20230702_225418/model_final.pth" + init_checkpoint = hf_hub_download(repo_id=ckpt_repo_id, filename=init_checkpoint) + + args = get_parser().parse_args() + args.config_file = get_config_file( + "LVISCOCOCOCOSTUFF_O365_OID_VGR_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_1080k.py" + ) + args.confidence_threshold = 0.01 + args.opts = [ + "train.init_checkpoint='{}'".format(init_checkpoint), + "model.model_language.cache_dir=''", + "model.model_vision.select_box_nums_for_evaluation=500", + "model.model_vision.text_feature_bank_reset=True", + ] + if running_device == "cpu": + args.opts += [ + "model.model_language.dtype='float32'", + "model.model_vision.backbone.net.xattn=False", + ] + logger.info("Arguments: " + str(args)) + cfg = setup_cfg(args) + + cfg.model.model_vision.criterion[0].use_fed_loss = False + cfg.model.model_vision.criterion[2].use_fed_loss = False + cfg.train.device = running_device + + ape.modeling.text.eva01_clip.eva_clip._MODEL_CONFIGS[cfg.model.model_language.clip_model][ + "vision_cfg" + ]["layers"] = 1 + ape.modeling.text.eva01_clip.eva_clip._MODEL_CONFIGS[cfg.model.model_language.clip_model][ + "vision_cfg" + ]["fusedLN"] = False + + demo = VisualizationDemo(cfg, args=args) + if save_memory: + demo.predictor.model.to("cpu") + # demo.predictor.model.half() + else: + demo.predictor.model.to(running_device) + + all_demo["APE_B"] = demo + all_cfg["APE_B"] = cfg + + +def load_APE_C(): + # init_checkpoint= "output2/APE/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj_cp_1080k_20230702_210950/model_final.pth" + init_checkpoint = "configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj_cp_1080k_20230702_210950/model_final.pth" + init_checkpoint = hf_hub_download(repo_id=ckpt_repo_id, filename=init_checkpoint) + + args = get_parser().parse_args() + args.config_file = get_config_file( + "LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_1080k.py" + ) + args.confidence_threshold = 0.01 + args.opts = [ + "train.init_checkpoint='{}'".format(init_checkpoint), + "model.model_language.cache_dir=''", + "model.model_vision.select_box_nums_for_evaluation=500", + "model.model_vision.text_feature_bank_reset=True", + ] + if running_device == "cpu": + args.opts += [ + "model.model_language.dtype='float32'", + "model.model_vision.backbone.net.xattn=False", + ] + logger.info("Arguments: " + str(args)) + cfg = setup_cfg(args) + + cfg.model.model_vision.criterion[0].use_fed_loss = False + cfg.model.model_vision.criterion[2].use_fed_loss = False + cfg.train.device = running_device + + ape.modeling.text.eva01_clip.eva_clip._MODEL_CONFIGS[cfg.model.model_language.clip_model][ + "vision_cfg" + ]["layers"] = 1 + ape.modeling.text.eva01_clip.eva_clip._MODEL_CONFIGS[cfg.model.model_language.clip_model][ + "vision_cfg" + ]["fusedLN"] = False + + demo = VisualizationDemo(cfg, args=args) + if save_memory: + demo.predictor.model.to("cpu") + # demo.predictor.model.half() + else: + demo.predictor.model.to(running_device) + + all_demo["APE_C"] = demo + all_cfg["APE_C"] = cfg + + +def load_APE_D(): + # init_checkpoint= "output2/APE/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_16x4_1080k_mdl_20230829_162438/model_final.pth" + init_checkpoint = "configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_16x4_1080k_mdl_20230829_162438/model_final.pth" + init_checkpoint = hf_hub_download(repo_id=ckpt_repo_id, filename=init_checkpoint) + + args = get_parser().parse_args() + args.config_file = get_config_file( + "LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_16x4_1080k.py" + ) + args.confidence_threshold = 0.01 + args.opts = [ + "train.init_checkpoint='{}'".format(init_checkpoint), + "model.model_language.cache_dir=''", + "model.model_vision.select_box_nums_for_evaluation=500", + "model.model_vision.text_feature_bank_reset=True", + ] + if running_device == "cpu": + args.opts += [ + "model.model_language.dtype='float32'", + "model.model_vision.backbone.net.xattn=False", + ] + logger.info("Arguments: " + str(args)) + cfg = setup_cfg(args) + + cfg.model.model_vision.criterion[0].use_fed_loss = False + cfg.model.model_vision.criterion[2].use_fed_loss = False + cfg.train.device = running_device + + ape.modeling.text.eva02_clip.factory._MODEL_CONFIGS[cfg.model.model_language.clip_model][ + "vision_cfg" + ]["layers"] = 1 + + demo = VisualizationDemo(cfg, args=args) + if save_memory: + demo.predictor.model.to("cpu") + # demo.predictor.model.half() + else: + demo.predictor.model.to(running_device) + + all_demo["APE_D"] = demo + all_cfg["APE_D"] = cfg + + +def APE_A_tab(): + with gr.Tab("APE A"): + with gr.Row(equal_height=False): + with gr.Column(scale=1): + input_image = gr.Image( + sources=["upload"], + type="filepath", + # tool="sketch", + # brush_radius=50, + ) + input_text = gr.Textbox( + label="Object Prompt (optional, if not provided, will only find COCO object.)", + info="格式: word1,word2,word3,...", + ) + + score_threshold = gr.Slider( + label="Score Threshold", minimum=0.01, maximum=1.0, value=0.3, step=0.01 + ) + + output_type = gr.CheckboxGroup( + ["object detection", "instance segmentation"], + value=["object detection", "instance segmentation"], + label="Output Type", + info="Which kind of output is displayed?", + ).style(item_container=True, container=True) + + run_button = gr.Button("Run") + + with gr.Column(scale=2): + gallery = gr.Image( + type="pil", + ) + + example_data = gr.Dataset( + components=[input_image, input_text, score_threshold], + samples=examples, + samples_per_page=5, + ) + example_data.click(fn=set_example, inputs=example_data, outputs=example_data.components) + + # add_tail_info() + output_json = gr.JSON(label="json results") + + run_button.click( + fn=run_on_image, + inputs=[input_image, input_text, score_threshold, output_type], + outputs=[gallery, output_json], + ) + + +def APE_C_tab(): + with gr.Tab("APE C"): + with gr.Row(equal_height=False): + with gr.Column(scale=1): + input_image = gr.Image( + sources=["upload"], + type="filepath", + # tool="sketch", + # brush_radius=50, + ) + input_text = gr.Textbox( + label="Object Prompt (optional, if not provided, will only find COCO object.)", + info="格式: word1,word2,sentence1,sentence2,...", + ) + + score_threshold = gr.Slider( + label="Score Threshold", minimum=0.01, maximum=1.0, value=0.3, step=0.01 + ) + + output_type = gr.CheckboxGroup( + ["object detection", "instance segmentation", "semantic segmentation"], + value=["object detection", "instance segmentation"], + label="Output Type", + info="Which kind of output is displayed?", + ).style(item_container=True, container=True) + + run_button = gr.Button("Run") + + with gr.Column(scale=2): + gallery = gr.Image( + type="pil", + ) + + example_data = gr.Dataset( + components=[input_image, input_text, score_threshold], + samples=example_list, + samples_per_page=5, + ) + example_data.click(fn=set_example, inputs=example_data, outputs=example_data.components) + + # add_tail_info() + output_json = gr.JSON(label="json results") + + run_button.click( + fn=run_on_image_C, + inputs=[input_image, input_text, score_threshold, output_type], + outputs=[gallery, output_json], + ) + + +def APE_D_tab(): + with gr.Tab("APE D"): + with gr.Row(equal_height=False): + with gr.Column(scale=1): + input_image = gr.Image( + sources=["upload"], + type="filepath", + # tool="sketch", + # brush_radius=50, + ) + input_text = gr.Textbox( + label="Object Prompt (optional, if not provided, will only find COCO object.)", + info="格式: word1,word2,sentence1,sentence2,...", + ) + + score_threshold = gr.Slider( + label="Score Threshold", minimum=0.01, maximum=1.0, value=0.3, step=0.01 + ) + + output_type = gr.CheckboxGroup( + ["object detection", "instance segmentation", "semantic segmentation"], + value=["object detection", "instance segmentation"], + label="Output Type", + info="Which kind of output is displayed?", + ) + + run_button = gr.Button("Run") + + with gr.Column(scale=2): + gallery = gr.Image( + type="pil", + ) + + gr.Examples( + examples=example_list, + inputs=[input_image, input_text, score_threshold, output_type], + ) + + # add_tail_info() + output_json = gr.JSON(label="json results") + + run_button.click( + fn=run_on_image_D, + inputs=[input_image, input_text, score_threshold, output_type], + outputs=[gallery, output_json], + ) + + +def comparison_tab(): + with gr.Tab("APE all"): + with gr.Row(equal_height=False): + with gr.Column(scale=1): + input_image = gr.Image( + sources=["upload"], + type="filepath", + # tool="sketch", + # brush_radius=50, + ) + input_text = gr.Textbox( + label="Object Prompt (optional, if not provided, will only find COCO object.)", + info="格式: word1,word2,sentence1,sentence2,...", + ) + + score_threshold = gr.Slider( + label="Score Threshold", minimum=0.01, maximum=1.0, value=0.2, step=0.01 + ) + + output_type = gr.CheckboxGroup( + ["object detection", "instance segmentation", "semantic segmentation"], + value=["object detection", "instance segmentation"], + label="Output Type", + info="Which kind of output is displayed?", + ) + + run_button = gr.Button("Run") + + gallery_all = [] + with gr.Column(scale=2): + for key in all_demo.keys(): + gallery = gr.Image( + label=key, + type="pil", + ) + gallery_all.append(gallery) + + gr.Examples( + examples=example_list, + inputs=[input_image, input_text, score_threshold, output_type], + ) + + # add_tail_info() + + run_button.click( + fn=run_on_image_comparison, + inputs=[input_image, input_text, score_threshold, output_type], + outputs=gallery_all, + ) + + +def is_port_in_use(port: int) -> bool: + import socket + + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: + return s.connect_ex(("localhost", port)) == 0 + + +def add_head_info(max_available_memory): + gr.Markdown( + "# APE: Aligning and Prompting Everything All at Once for Universal Visual Perception" + ) + if max_available_memory: + gr.Markdown( + "Note multiple models are deployed on single GPU, so it may take several minutes to run the models and visualize the results." + ) + else: + gr.Markdown( + "Note multiple models are deployed on CPU, so it may take a while to run the models and visualize the results." + ) + gr.Markdown( + "Noted results computed by CPU are slightly different to results computed by GPU, and some libraries are disabled on CPU." + ) + + gr.Markdown("---") + + +def add_tail_info(): + gr.Markdown("---") + gr.Markdown("### We also support Prompt") + gr.Markdown( + """ + | Location prompt | result | Location prompt | result | + | ---- | ---- | ---- | ---- | + | ![Location prompt](/file=examples/prompt/20230627-131346_11.176.20.67_mask.PNG) | ![结果](/file=examples/prompt/20230627-131346_11.176.20.67_pred.png) | ![Location prompt](/file=examples/prompt/20230627-131530_11.176.20.67_mask.PNG) | ![结果](/file=examples/prompt/20230627-131530_11.176.20.67_pred.png) | + | ![Location prompt](/file=examples/prompt/20230627-131520_11.176.20.67_mask.PNG) | ![结果](/file=examples/prompt/20230627-131520_11.176.20.67_pred.png) | ![Location prompt](/file=examples/prompt/20230627-114219_11.176.20.67_mask.PNG) | ![结果](/file=examples/prompt/20230627-114219_11.176.20.67_pred.png) | + """ + ) + gr.Markdown("---") + + +if __name__ == "__main__": + available_port = [80, 8080] + for port in available_port: + if is_port_in_use(port): + continue + else: + server_port = port + break + print("server_port", server_port) + + available_memory = [ + torch.cuda.mem_get_info(i)[0] / 1024**3 for i in range(torch.cuda.device_count()) + ] + + global running_device + if len(available_memory) > 0: + max_available_memory = max(available_memory) + device_id = available_memory.index(max_available_memory) + + running_device = "cuda:" + str(device_id) + else: + max_available_memory = 0 + running_device = "cpu" + + global save_memory + save_memory = False + if max_available_memory > 0 and max_available_memory < 40: + save_memory = True + + print("available_memory", available_memory) + print("max_available_memory", max_available_memory) + print("running_device", running_device) + print("save_memory", save_memory) + + # ========================================================================================== + + mp.set_start_method("spawn", force=True) + setup_logger(name="fvcore") + setup_logger(name="ape") + global logger + logger = setup_logger() + + global aug + aug = T.ResizeShortestEdge([1024, 1024], 1024) + + global all_demo + all_demo = {} + all_cfg = {} + + load_APE_A() + load_APE_B() + load_APE_C() + load_APE_D() + + title = "APE: Aligning and Prompting Everything All at Once for Universal Visual Perception" + block = gr.Blocks(title=title).queue() + with block: + add_head_info(max_available_memory) + + # APE_A_tab() + # APE_C_tab() + APE_D_tab() + + comparison_tab() + + # add_tail_info() + + block.launch( + share=False, + # server_name="0.0.0.0", + # server_port=server_port, + show_api=False, + show_error=True, + ) diff --git a/demo/demo_lazy.py b/demo/demo_lazy.py new file mode 100755 index 0000000..b858b87 --- /dev/null +++ b/demo/demo_lazy.py @@ -0,0 +1,263 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import argparse +import glob +import json +import multiprocessing as mp +import os +import tempfile +import time +import warnings +from collections import abc + +import cv2 +import numpy as np +import tqdm + +from detectron2.config import LazyConfig, get_cfg +from detectron2.data.detection_utils import read_image +from detectron2.evaluation.coco_evaluation import instances_to_coco_json + +# from detectron2.projects.deeplab import add_deeplab_config +# from detectron2.projects.panoptic_deeplab import add_panoptic_deeplab_config +from detectron2.utils.logger import setup_logger +from predictor_lazy import VisualizationDemo + +# constants +WINDOW_NAME = "APE" + + +def setup_cfg(args): + # load config from file and command-line arguments + cfg = LazyConfig.load(args.config_file) + cfg = LazyConfig.apply_overrides(cfg, args.opts) + + if "output_dir" in cfg.model: + cfg.model.output_dir = cfg.train.output_dir + if "model_vision" in cfg.model and "output_dir" in cfg.model.model_vision: + cfg.model.model_vision.output_dir = cfg.train.output_dir + if "train" in cfg.dataloader: + if isinstance(cfg.dataloader.train, abc.MutableSequence): + for i in range(len(cfg.dataloader.train)): + if "output_dir" in cfg.dataloader.train[i].mapper: + cfg.dataloader.train[i].mapper.output_dir = cfg.train.output_dir + else: + if "output_dir" in cfg.dataloader.train.mapper: + cfg.dataloader.train.mapper.output_dir = cfg.train.output_dir + + if "model_vision" in cfg.model: + cfg.model.model_vision.test_score_thresh = args.confidence_threshold + else: + cfg.model.test_score_thresh = args.confidence_threshold + + # default_setup(cfg, args) + + setup_logger(name="ape") + setup_logger(name="timm") + + return cfg + + +def get_parser(): + parser = argparse.ArgumentParser(description="Detectron2 demo for builtin configs") + parser.add_argument( + "--config-file", + default="configs/quick_schedules/mask_rcnn_R_50_FPN_inference_acc_test.yaml", + metavar="FILE", + help="path to config file", + ) + parser.add_argument("--webcam", action="store_true", help="Take inputs from webcam.") + parser.add_argument("--video-input", help="Path to video file.") + parser.add_argument( + "--input", + nargs="+", + help="A list of space separated input images; " + "or a single glob pattern such as 'directory/*.jpg'", + ) + parser.add_argument( + "--output", + help="A file or directory to save output visualizations. " + "If not given, will show output in an OpenCV window.", + ) + + parser.add_argument( + "--confidence-threshold", + type=float, + default=0.5, + help="Minimum score for instance predictions to be shown", + ) + parser.add_argument( + "--opts", + help="Modify config options using the command-line 'KEY VALUE' pairs", + default=[], + nargs=argparse.REMAINDER, + ) + + parser.add_argument("--text-prompt", default=None) + + parser.add_argument("--with-box", action="store_true", help="show box of instance") + parser.add_argument("--with-mask", action="store_true", help="show mask of instance") + parser.add_argument("--with-sseg", action="store_true", help="show mask of class") + + return parser + + +def test_opencv_video_format(codec, file_ext): + with tempfile.TemporaryDirectory(prefix="video_format_test") as dir: + filename = os.path.join(dir, "test_file" + file_ext) + writer = cv2.VideoWriter( + filename=filename, + fourcc=cv2.VideoWriter_fourcc(*codec), + fps=float(30), + frameSize=(10, 10), + isColor=True, + ) + [writer.write(np.zeros((10, 10, 3), np.uint8)) for _ in range(30)] + writer.release() + if os.path.isfile(filename): + return True + return False + + +if __name__ == "__main__": + mp.set_start_method("spawn", force=True) + args = get_parser().parse_args() + setup_logger(name="fvcore") + setup_logger(name="ape") + logger = setup_logger() + logger.info("Arguments: " + str(args)) + + cfg = setup_cfg(args) + + if args.video_input: + demo = VisualizationDemo(cfg, parallel=True, args=args) + else: + demo = VisualizationDemo(cfg, args=args) + + if args.input: + if len(args.input) == 1: + args.input = glob.glob(os.path.expanduser(args.input[0]), recursive=True) + assert args.input, "The input path(s) was not found" + for path in tqdm.tqdm(args.input, disable=not args.output): + # use PIL, to be consistent with evaluation + try: + img = read_image(path, format="BGR") + except Exception as e: + print("*" * 60) + print("fail to open image: ", e) + print("*" * 60) + continue + start_time = time.time() + predictions, visualized_output, visualized_outputs, metadata = demo.run_on_image( + img, + text_prompt=args.text_prompt, + with_box=args.with_box, + with_mask=args.with_mask, + with_sseg=args.with_sseg, + ) + logger.info( + "{}: {} in {:.2f}s".format( + path, + "detected {} instances".format(len(predictions["instances"])) + if "instances" in predictions + else "finished", + time.time() - start_time, + ) + ) + + if args.output: + if os.path.isdir(args.output): + assert os.path.isdir(args.output), args.output + out_filename = os.path.join(args.output, os.path.basename(path)) + else: + assert len(args.input) == 1, "Please specify a directory with args.output" + out_filename = args.output + out_filename = out_filename.replace(".webp", ".png") + out_filename = out_filename.replace(".crdownload", ".png") + out_filename = out_filename.replace(".jfif", ".png") + visualized_output.save(out_filename) + + for i in range(len(visualized_outputs)): + out_filename = ( + os.path.join(args.output, os.path.basename(path)) + "." + str(i) + ".png" + ) + visualized_outputs[i].save(out_filename) + + # import pickle + # with open(out_filename + ".pkl", "wb") as outp: + # pickle.dump(predictions, outp, pickle.HIGHEST_PROTOCOL) + + if "instances" in predictions: + results = instances_to_coco_json( + predictions["instances"].to(demo.cpu_device), path + ) + for result in results: + result["category_name"] = metadata.thing_classes[result["category_id"]] + result["image_name"] = result["image_id"] + + with open(out_filename + ".json", "w") as outp: + json.dump(results, outp) + else: + cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_NORMAL) + cv2.imshow(WINDOW_NAME, visualized_output.get_image()[:, :, ::-1]) + if cv2.waitKey(0) == 27: + break # esc to quit + elif args.webcam: + assert args.input is None, "Cannot have both --input and --webcam!" + assert args.output is None, "output not yet supported with --webcam!" + cam = cv2.VideoCapture(0) + for vis in tqdm.tqdm(demo.run_on_video(cam)): + cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_NORMAL) + cv2.imshow(WINDOW_NAME, vis) + if cv2.waitKey(1) == 27: + break # esc to quit + cam.release() + cv2.destroyAllWindows() + elif args.video_input: + video = cv2.VideoCapture(args.video_input) + width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH)) + height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT)) + frames_per_second = video.get(cv2.CAP_PROP_FPS) + num_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT)) + basename = os.path.basename(args.video_input) + codec, file_ext = ( + ("x264", ".mkv") if test_opencv_video_format("x264", ".mkv") else ("mp4v", ".mp4") + ) + codec, file_ext = "mp4v", ".mp4" + if codec == ".mp4v": + warnings.warn("x264 codec not available, switching to mp4v") + if args.output: + if os.path.isdir(args.output): + output_fname = os.path.join(args.output, basename) + output_fname = os.path.splitext(output_fname)[0] + file_ext + else: + output_fname = args.output + assert not os.path.isfile(output_fname), output_fname + output_file = cv2.VideoWriter( + filename=output_fname, + # some installation of opencv may not support x264 (due to its license), + # you can try other format (e.g. MPEG) + fourcc=cv2.VideoWriter_fourcc(*codec), + fps=float(frames_per_second), + frameSize=(width, height), + isColor=True, + ) + # i = 0 + assert os.path.isfile(args.video_input) + for vis_frame, predictions in tqdm.tqdm(demo.run_on_video(video), total=num_frames): + if args.output: + output_file.write(vis_frame) + + # import pickle + # with open(output_fname + "." + str(i) + ".pkl", "wb") as outp: + # pickle.dump(predictions, outp, pickle.HIGHEST_PROTOCOL) + # i += 1 + else: + cv2.namedWindow(basename, cv2.WINDOW_NORMAL) + cv2.imshow(basename, vis_frame) + if cv2.waitKey(1) == 27: + break # esc to quit + video.release() + if args.output: + output_file.release() + else: + cv2.destroyAllWindows() diff --git a/demo/examples/Pisa.jpg b/demo/examples/Pisa.jpg new file mode 100644 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