| + | name | +Checkpoint | +Config | +
|---|---|---|---|
| 1 | +APE-A | +HF link | +link | +
| 2 | +APE-B | +HF link + | link | +
| 3 | +APE-C | +HF link + | link | +
| 4 | +APE-D | +HF link + | link | +
+
+
+## :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"},
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+ {"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': 14}, {'image_count': 500, 'id': 15}, {'image_count': 33, 'id': 16}, {'image_count': 3, 'id': 17}, {'image_count': 44, 'id': 18}, {'image_count': 561, 'id': 19}, {'image_count': 8, 'id': 20}, {'image_count': 9, 'id': 21}, {'image_count': 33, 'id': 22}, {'image_count': 1883, 'id': 23}, {'image_count': 98, 'id': 24}, {'image_count': 70, 'id': 25}, {'image_count': 46, 'id': 26}, {'image_count': 117, 'id': 27}, {'image_count': 41, 'id': 28}, {'image_count': 1395, 'id': 29}, {'image_count': 7, 'id': 30}, {'image_count': 1, 'id': 31}, {'image_count': 314, 'id': 32}, {'image_count': 31, 'id': 33}, {'image_count': 4965, 'id': 34}, {'image_count': 6090, 'id': 35}, {'image_count': 2183, 'id': 36}, {'image_count': 47, 'id': 37}, {'image_count': 3, 'id': 38}, {'image_count': 3, 'id': 39}, {'image_count': 1, 'id': 40}, {'image_count': 3780, 'id': 41}, {'image_count': 6, 'id': 42}, {'image_count': 210, 'id': 43}, {'image_count': 36, 'id': 44}, {'image_count': 2000, 'id': 45}, {'image_count': 17, 'id': 46}, {'image_count': 51, 'id': 47}, {'image_count': 138, 'id': 48}, {'image_count': 3, 'id': 49}, {'image_count': 1470, 'id': 50}, {'image_count': 3, 'id': 51}, {'image_count': 2, 'id': 52}, {'image_count': 186, 'id': 53}, {'image_count': 76, 'id': 54}, {'image_count': 26, 'id': 55}, {'image_count': 303, 'id': 56}, {'image_count': 738, 'id': 57}, {'image_count': 2216, 'id': 58}, {'image_count': 1934, 'id': 59}, {'image_count': 2287, 'id': 60}, {'image_count': 1622, 'id': 61}, {'image_count': 41, 'id': 62}, {'image_count': 4, 'id': 63}, {'image_count': 11, 'id': 64}, {'image_count': 270, 'id': 65}, {'image_count': 349, 'id': 66}, {'image_count': 42, 'id': 67}, {'image_count': 823, 'id': 68}, {'image_count': 6, 'id': 69}, {'image_count': 48, 'id': 70}, {'image_count': 3, 'id': 71}, {'image_count': 42, 'id': 72}, {'image_count': 24, 'id': 73}, {'image_count': 16, 'id': 74}, {'image_count': 605, 'id': 75}, {'image_count': 898, 'id': 76}, {'image_count': 3194, 'id': 77}, {'image_count': 2, 'id': 78}, {'image_count': 125, 'id': 79}, {'image_count': 1739, 'id': 80}, {'image_count': 140, 'id': 81}, {'image_count': 4, 'id': 82}, {'image_count': 322, 'id': 83}, {'image_count': 60, 'id': 84}, {'image_count': 2, 'id': 85}, {'image_count': 231, 'id': 86}, {'image_count': 333, 'id': 87}, {'image_count': 1941, 'id': 88}, {'image_count': 367, 'id': 89}, {'image_count': 4908, 'id': 90}, {'image_count': 18, 'id': 91}, {'image_count': 81, 'id': 92}, {'image_count': 1, 'id': 93}, {'image_count': 2940, 'id': 94}, {'image_count': 430, 'id': 95}, {'image_count': 247, 'id': 96}, {'image_count': 94, 'id': 97}, {'image_count': 21, 'id': 98}, {'image_count': 2950, 'id': 99}, {'image_count': 16, 'id': 100}, {'image_count': 12, 'id': 101}, {'image_count': 25, 'id': 102}, {'image_count': 41, 'id': 103}, {'image_count': 244, 'id': 104}, {'image_count': 7, 'id': 105}, {'image_count': 1, 'id': 106}, {'image_count': 40, 'id': 107}, {'image_count': 40, 'id': 108}, {'image_count': 104, 'id': 109}, {'image_count': 1671, 'id': 110}, {'image_count': 49, 'id': 111}, {'image_count': 243, 'id': 112}, {'image_count': 2, 'id': 113}, {'image_count': 242, 'id': 114}, {'image_count': 271, 'id': 115}, {'image_count': 104, 'id': 116}, {'image_count': 8, 'id': 117}, {'image_count': 2674, 'id': 118}, {'image_count': 1, 'id': 119}, {'image_count': 48, 'id': 120}, {'image_count': 14, 'id': 121}, {'image_count': 40, 'id': 122}, {'image_count': 1, 'id': 123}, {'image_count': 37, 'id': 124}, {'image_count': 1510, 'id': 125}, {'image_count': 6, 'id': 126}, {'image_count': 4808, 'id': 127}, {'image_count': 70, 'id': 128}, {'image_count': 86, 'id': 129}, {'image_count': 7, 'id': 130}, {'image_count': 5, 'id': 131}, {'image_count': 1406, 'id': 132}, {'image_count': 7655, 'id': 133}, {'image_count': 15, 'id': 134}, {'image_count': 28, 'id': 135}, {'image_count': 6, 'id': 136}, {'image_count': 494, 'id': 137}, {'image_count': 234, 'id': 138}, {'image_count': 6332, 'id': 139}, {'image_count': 1, 'id': 140}, {'image_count': 35, 'id': 141}, {'image_count': 5, 'id': 142}, {'image_count': 1828, 'id': 143}, {'image_count': 8, 'id': 144}, {'image_count': 63, 'id': 145}, {'image_count': 1668, 'id': 146}, {'image_count': 4, 'id': 147}, {'image_count': 95, 'id': 148}, {'image_count': 17, 'id': 149}, {'image_count': 1567, 'id': 150}, {'image_count': 2, 'id': 151}, {'image_count': 103, 'id': 152}, {'image_count': 50, 'id': 153}, {'image_count': 1678, 'id': 154}, {'image_count': 6, 'id': 155}, {'image_count': 92, 'id': 156}, 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{'image_count': 4, 'id': 250}, {'image_count': 1, 'id': 251}, {'image_count': 858, 'id': 252}, {'image_count': 661, 'id': 253}, {'image_count': 168, 'id': 254}, {'image_count': 210, 'id': 255}, {'image_count': 65, 'id': 256}, {'image_count': 4, 'id': 257}, {'image_count': 2, 'id': 258}, {'image_count': 159, 'id': 259}, {'image_count': 31, 'id': 260}, {'image_count': 811, 'id': 261}, {'image_count': 1, 'id': 262}, {'image_count': 42, 'id': 263}, {'image_count': 27, 'id': 264}, {'image_count': 2, 'id': 265}, {'image_count': 5, 'id': 266}, {'image_count': 95, 'id': 267}, {'image_count': 32, 'id': 268}, {'image_count': 1, 'id': 269}, {'image_count': 1, 'id': 270}, {'image_count': 4053, 'id': 271}, {'image_count': 897, 'id': 272}, {'image_count': 31, 'id': 273}, {'image_count': 23, 'id': 274}, {'image_count': 1, 'id': 275}, {'image_count': 202, 'id': 276}, {'image_count': 746, 'id': 277}, {'image_count': 44, 'id': 278}, {'image_count': 14, 'id': 279}, {'image_count': 26, 'id': 280}, {'image_count': 1, 'id': 281}, {'image_count': 2, 'id': 282}, {'image_count': 25, 'id': 283}, {'image_count': 238, 'id': 284}, {'image_count': 592, 'id': 285}, {'image_count': 26, 'id': 286}, {'image_count': 5, 'id': 287}, {'image_count': 42, 'id': 288}, {'image_count': 13, 'id': 289}, {'image_count': 46, 'id': 290}, {'image_count': 1, 'id': 291}, {'image_count': 8, 'id': 292}, {'image_count': 34, 'id': 293}, {'image_count': 5, 'id': 294}, {'image_count': 1, 'id': 295}, {'image_count': 2356, 'id': 296}, {'image_count': 717, 'id': 297}, {'image_count': 1010, 'id': 298}, {'image_count': 679, 'id': 299}, {'image_count': 3, 'id': 300}, {'image_count': 4, 'id': 301}, {'image_count': 1, 'id': 302}, {'image_count': 166, 'id': 303}, {'image_count': 2, 'id': 304}, {'image_count': 266, 'id': 305}, {'image_count': 101, 'id': 306}, {'image_count': 6, 'id': 307}, {'image_count': 14, 'id': 308}, {'image_count': 133, 'id': 309}, {'image_count': 2, 'id': 310}, {'image_count': 38, 'id': 311}, 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1083}, {'image_count': 4, 'id': 1084}, {'image_count': 67, 'id': 1085}, {'image_count': 26, 'id': 1086}, {'image_count': 25, 'id': 1087}, {'image_count': 33, 'id': 1088}, {'image_count': 37, 'id': 1089}, {'image_count': 50, 'id': 1090}, {'image_count': 270, 'id': 1091}, {'image_count': 12, 'id': 1092}, {'image_count': 316, 'id': 1093}, {'image_count': 41, 'id': 1094}, {'image_count': 299, 'id': 1095}, {'image_count': 105, 'id': 1096}, {'image_count': 2879, 'id': 1097}, {'image_count': 1021, 'id': 1098}, {'image_count': 1213, 'id': 1099}, {'image_count': 172, 'id': 1100}, {'image_count': 28, 'id': 1101}, {'image_count': 899, 'id': 1102}, {'image_count': 187, 'id': 1103}, {'image_count': 147, 'id': 1104}, {'image_count': 136, 'id': 1105}, {'image_count': 34, 'id': 1106}, {'image_count': 41, 'id': 1107}, {'image_count': 636, 'id': 1108}, {'image_count': 570, 'id': 1109}, {'image_count': 1149, 'id': 1110}, {'image_count': 61, 'id': 1111}, {'image_count': 3556, 'id': 1112}, {'image_count': 18, 'id': 1113}, {'image_count': 143, 'id': 1114}, {'image_count': 2951, 'id': 1115}, {'image_count': 7, 'id': 1116}, {'image_count': 943, 'id': 1117}, {'image_count': 6, 'id': 1118}, {'image_count': 1, 'id': 1119}, {'image_count': 11, 'id': 1120}, {'image_count': 101, 'id': 1121}, {'image_count': 1909, 'id': 1122}, {'image_count': 5302, 'id': 1123}, {'image_count': 1, 'id': 1124}, {'image_count': 44, 'id': 1125}, {'image_count': 3, 'id': 1126}, {'image_count': 44, 'id': 1127}, {'image_count': 31, 'id': 1128}, {'image_count': 7, 'id': 1129}, {'image_count': 20, 'id': 1130}, {'image_count': 11, 'id': 1131}, {'image_count': 13, 'id': 1132}, {'image_count': 3452, 'id': 1133}, {'image_count': 113, 'id': 1134}, {'image_count': 2, 'id': 1135}, {'image_count': 139, 'id': 1136}, {'image_count': 12, 'id': 1137}, {'image_count': 37, 'id': 1138}, {'image_count': 3213, 'id': 1139}, {'image_count': 47, 'id': 1140}, {'image_count': 1468, 'id': 1141}, {'image_count': 729, 'id': 1142}, {'image_count': 24, 'id': 1143}, {'image_count': 1, 'id': 1144}, {'image_count': 10, 'id': 1145}, {'image_count': 3, 'id': 1146}, {'image_count': 14, 'id': 1147}, {'image_count': 4, 'id': 1148}, {'image_count': 29, 'id': 1149}, {'image_count': 4, 'id': 1150}, {'image_count': 70, 'id': 1151}, {'image_count': 46, 'id': 1152}, {'image_count': 14, 'id': 1153}, {'image_count': 48, 'id': 1154}, {'image_count': 1855, 'id': 1155}, {'image_count': 113, '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": "/m/04hgtk"},
+ {"id": 94, "name": "Stop sign", "freebase_id": "/m/02pv19"},
+ {"id": 95, "name": "Banana", "freebase_id": "/m/09qck"},
+ {"id": 96, "name": "Mixer", "freebase_id": "/m/063rgb"},
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+ {"id": 375, "name": "Tea", "freebase_id": "/m/07clx"},
+ {"id": 376, "name": "Tennis ball", "freebase_id": "/m/05ctyq"},
+ {"id": 377, "name": "Waste container", "freebase_id": "/m/0bjyj5"},
+ {"id": 378, "name": "Mule", "freebase_id": "/m/0dbzx"},
+ {"id": 379, "name": "Cricket ball", "freebase_id": "/m/02ctlc"},
+ {"id": 380, "name": "Pineapple", "freebase_id": "/m/0fp6w"},
+ {"id": 381, "name": "Coconut", "freebase_id": "/m/0djtd"},
+ {"id": 382, "name": "Doll", "freebase_id": "/m/0167gd"},
+ {"id": 383, "name": "Coffee table", "freebase_id": "/m/078n6m"},
+ {"id": 384, "name": "Snowman", "freebase_id": "/m/0152hh"},
+ {"id": 385, "name": "Lavender", "freebase_id": "/m/04gth"},
+ {"id": 386, "name": "Shrimp", "freebase_id": "/m/0ll1f78"},
+ {"id": 387, "name": "Maple", "freebase_id": "/m/0cffdh"},
+ {"id": 388, "name": "Cowboy hat", "freebase_id": "/m/025rp__"},
+ {"id": 389, "name": "Goggles", "freebase_id": "/m/02_n6y"},
+ {"id": 390, "name": "Rugby ball", "freebase_id": "/m/0wdt60w"},
+ {"id": 391, "name": "Caterpillar", "freebase_id": "/m/0cydv"},
+ {"id": 392, "name": "Poster", "freebase_id": "/m/01n5jq"},
+ {"id": 393, "name": "Rocket", "freebase_id": "/m/09rvcxw"},
+ {"id": 394, "name": "Organ", "freebase_id": "/m/013y1f"},
+ {"id": 395, "name": "Saxophone", "freebase_id": "/m/06ncr"},
+ {"id": 396, "name": "Traffic light", "freebase_id": "/m/015qff"},
+ {"id": 397, "name": "Cocktail", "freebase_id": "/m/024g6"},
+ {"id": 398, "name": "Plastic bag", "freebase_id": "/m/05gqfk"},
+ {"id": 399, "name": "Squash", "freebase_id": "/m/0dv77"},
+ {"id": 400, "name": "Mushroom", "freebase_id": "/m/052sf"},
+ {"id": 401, "name": "Hamburger", "freebase_id": "/m/0cdn1"},
+ {"id": 402, "name": "Light switch", "freebase_id": "/m/03jbxj"},
+ {"id": 403, "name": "Parachute", "freebase_id": "/m/0cyfs"},
+ {"id": 404, "name": "Teddy bear", "freebase_id": "/m/0kmg4"},
+ {"id": 405, "name": "Winter melon", "freebase_id": "/m/02cvgx"},
+ {"id": 406, "name": "Deer", "freebase_id": "/m/09kx5"},
+ {"id": 407, "name": "Musical keyboard", "freebase_id": "/m/057cc"},
+ {"id": 408, "name": "Plumbing fixture", "freebase_id": "/m/02pkr5"},
+ {"id": 409, "name": "Scoreboard", "freebase_id": "/m/057p5t"},
+ {"id": 410, "name": "Baseball bat", "freebase_id": "/m/03g8mr"},
+ {"id": 411, "name": "Envelope", "freebase_id": "/m/0frqm"},
+ {"id": 412, "name": "Adhesive tape", "freebase_id": "/m/03m3vtv"},
+ {"id": 413, "name": "Briefcase", "freebase_id": "/m/0584n8"},
+ {"id": 414, "name": "Paddle", "freebase_id": "/m/014y4n"},
+ {"id": 415, "name": "Bow and arrow", "freebase_id": "/m/01g3x7"},
+ {"id": 416, "name": "Telephone", "freebase_id": "/m/07cx4"},
+ {"id": 417, "name": "Sheep", "freebase_id": "/m/07bgp"},
+ {"id": 418, "name": "Jacket", "freebase_id": "/m/032b3c"},
+ {"id": 419, "name": "Boy", "freebase_id": "/m/01bl7v"},
+ {"id": 420, "name": "Pizza", "freebase_id": "/m/0663v"},
+ {"id": 421, "name": "Otter", "freebase_id": "/m/0cn6p"},
+ {"id": 422, "name": "Office supplies", "freebase_id": "/m/02rdsp"},
+ {"id": 423, "name": "Couch", "freebase_id": "/m/02crq1"},
+ {"id": 424, "name": "Cello", "freebase_id": "/m/01xqw"},
+ {"id": 425, "name": "Bull", "freebase_id": "/m/0cnyhnx"},
+ {"id": 426, "name": "Camel", "freebase_id": "/m/01x_v"},
+ {"id": 427, "name": "Ball", "freebase_id": "/m/018xm"},
+ {"id": 428, "name": "Duck", "freebase_id": "/m/09ddx"},
+ {"id": 429, "name": "Whale", "freebase_id": "/m/084zz"},
+ {"id": 430, "name": "Shirt", "freebase_id": "/m/01n4qj"},
+ {"id": 431, "name": "Tank", "freebase_id": "/m/07cmd"},
+ {"id": 432, "name": "Motorcycle", "freebase_id": "/m/04_sv"},
+ {"id": 433, "name": "Accordion", "freebase_id": "/m/0mkg"},
+ {"id": 434, "name": "Owl", "freebase_id": "/m/09d5_"},
+ {"id": 435, "name": "Porcupine", "freebase_id": "/m/0c568"},
+ {"id": 436, "name": "Sun hat", "freebase_id": "/m/02wbtzl"},
+ {"id": 437, "name": "Nail", "freebase_id": "/m/05bm6"},
+ {"id": 438, "name": "Scissors", "freebase_id": "/m/01lsmm"},
+ {"id": 439, "name": "Swan", "freebase_id": "/m/0dftk"},
+ {"id": 440, "name": "Lamp", "freebase_id": "/m/0dtln"},
+ {"id": 441, "name": "Crown", "freebase_id": "/m/0nl46"},
+ {"id": 442, "name": "Piano", "freebase_id": "/m/05r5c"},
+ {"id": 443, "name": "Sculpture", "freebase_id": "/m/06msq"},
+ {"id": 444, "name": "Cheetah", "freebase_id": "/m/0cd4d"},
+ {"id": 445, "name": "Oboe", "freebase_id": "/m/05kms"},
+ {"id": 446, "name": "Tin can", "freebase_id": "/m/02jnhm"},
+ {"id": 447, "name": "Mango", "freebase_id": "/m/0fldg"},
+ {"id": 448, "name": "Tripod", "freebase_id": "/m/073bxn"},
+ {"id": 449, "name": "Oven", "freebase_id": "/m/029bxz"},
+ {"id": 450, "name": "Mouse", "freebase_id": "/m/020lf"},
+ {"id": 451, "name": "Barge", "freebase_id": "/m/01btn"},
+ {"id": 452, "name": "Coffee", "freebase_id": "/m/02vqfm"},
+ {"id": 453, "name": "Snowboard", "freebase_id": "/m/06__v"},
+ {"id": 454, "name": "Common fig", "freebase_id": "/m/043nyj"},
+ {"id": 455, "name": "Salad", "freebase_id": "/m/0grw1"},
+ {"id": 456, "name": "Marine invertebrates", "freebase_id": "/m/03hl4l9"},
+ {"id": 457, "name": "Umbrella", "freebase_id": "/m/0hnnb"},
+ {"id": 458, "name": "Kangaroo", "freebase_id": "/m/04c0y"},
+ {"id": 459, "name": "Human arm", "freebase_id": "/m/0dzf4"},
+ {"id": 460, "name": "Measuring cup", "freebase_id": "/m/07v9_z"},
+ {"id": 461, "name": "Snail", "freebase_id": "/m/0f9_l"},
+ {"id": 462, "name": "Loveseat", "freebase_id": "/m/0703r8"},
+ {"id": 463, "name": "Suit", "freebase_id": "/m/01xyhv"},
+ {"id": 464, "name": "Teapot", "freebase_id": "/m/01fh4r"},
+ {"id": 465, "name": "Bottle", "freebase_id": "/m/04dr76w"},
+ {"id": 466, "name": "Alpaca", "freebase_id": "/m/0pcr"},
+ {"id": 467, "name": "Kettle", "freebase_id": "/m/03s_tn"},
+ {"id": 468, "name": "Trousers", "freebase_id": "/m/07mhn"},
+ {"id": 469, "name": "Popcorn", "freebase_id": "/m/01hrv5"},
+ {"id": 470, "name": "Centipede", "freebase_id": "/m/019h78"},
+ {"id": 471, "name": "Spider", "freebase_id": "/m/09kmb"},
+ {"id": 472, "name": "Sparrow", "freebase_id": "/m/0h23m"},
+ {"id": 473, "name": "Plate", "freebase_id": "/m/050gv4"},
+ {"id": 474, "name": "Bagel", "freebase_id": "/m/01fb_0"},
+ {"id": 475, "name": "Personal care", "freebase_id": "/m/02w3_ws"},
+ {"id": 476, "name": "Apple", "freebase_id": "/m/014j1m"},
+ {"id": 477, "name": "Brassiere", "freebase_id": "/m/01gmv2"},
+ {"id": 478, "name": "Bathroom cabinet", "freebase_id": "/m/04y4h8h"},
+ {"id": 479, "name": "studio couch", "freebase_id": "/m/026qbn5"},
+ {"id": 480, "name": "Computer keyboard", "freebase_id": "/m/01m2v"},
+ {"id": 481, "name": "Table tennis racket", "freebase_id": "/m/05_5p_0"},
+ {"id": 482, "name": "Sushi", "freebase_id": "/m/07030"},
+ {"id": 483, "name": "Cabinetry", "freebase_id": "/m/01s105"},
+ {"id": 484, "name": "Street light", "freebase_id": "/m/033rq4"},
+ {"id": 485, "name": "Towel", "freebase_id": "/m/0162_1"},
+ {"id": 486, "name": "Nightstand", "freebase_id": "/m/02z51p"},
+ {"id": 487, "name": "Rabbit", "freebase_id": "/m/06mf6"},
+ {"id": 488, "name": "Dolphin", "freebase_id": "/m/02hj4"},
+ {"id": 489, "name": "Dog", "freebase_id": "/m/0bt9lr"},
+ {"id": 490, "name": "Jug", "freebase_id": "/m/08hvt4"},
+ {"id": 491, "name": "Wok", "freebase_id": "/m/084rd"},
+ {"id": 492, "name": "Fire hydrant", "freebase_id": "/m/01pns0"},
+ {"id": 493, "name": "Human eye", "freebase_id": "/m/014sv8"},
+ {"id": 494, "name": "Skyscraper", "freebase_id": "/m/079cl"},
+ {"id": 495, "name": "Backpack", "freebase_id": "/m/01940j"},
+ {"id": 496, "name": "Potato", "freebase_id": "/m/05vtc"},
+ {"id": 497, "name": "Paper towel", "freebase_id": "/m/02w3r3"},
+ {"id": 498, "name": "Lifejacket", "freebase_id": "/m/054xkw"},
+ {"id": 499, "name": "Bicycle wheel", "freebase_id": "/m/01bqk0"},
+ {"id": 500, "name": "Toilet", "freebase_id": "/m/09g1w"},
+]
+
+
+OPENIMAGES_V6_CATEGORIES = [
+ {"id": 1, "name": "Tortoise", "freebase_id": "/m/011k07"},
+ {"id": 2, "name": "Container", "freebase_id": "/m/011q46kg"},
+ {"id": 3, "name": "Magpie", "freebase_id": "/m/012074"},
+ {"id": 4, "name": "Sea turtle", "freebase_id": "/m/0120dh"},
+ {"id": 5, "name": "Football", "freebase_id": "/m/01226z"},
+ {"id": 6, "name": "Ambulance", "freebase_id": "/m/012n7d"},
+ {"id": 7, "name": "Ladder", "freebase_id": "/m/012w5l"},
+ {"id": 8, "name": "Toothbrush", "freebase_id": "/m/012xff"},
+ {"id": 9, "name": "Syringe", "freebase_id": "/m/012ysf"},
+ {"id": 10, "name": "Sink", "freebase_id": "/m/0130jx"},
+ {"id": 11, "name": "Toy", "freebase_id": "/m/0138tl"},
+ {"id": 12, "name": "Organ (Musical Instrument)", "freebase_id": "/m/013y1f"},
+ {"id": 13, "name": "Cassette deck", "freebase_id": "/m/01432t"},
+ {"id": 14, "name": "Apple", "freebase_id": "/m/014j1m"},
+ {"id": 15, "name": "Human eye", "freebase_id": "/m/014sv8"},
+ {"id": 16, "name": "Cosmetics", "freebase_id": "/m/014trl"},
+ {"id": 17, "name": "Paddle", "freebase_id": "/m/014y4n"},
+ {"id": 18, "name": "Snowman", "freebase_id": "/m/0152hh"},
+ {"id": 19, "name": "Beer", "freebase_id": "/m/01599"},
+ {"id": 20, "name": "Chopsticks", "freebase_id": "/m/01_5g"},
+ {"id": 21, "name": "Human beard", "freebase_id": "/m/015h_t"},
+ {"id": 22, "name": "Bird", "freebase_id": "/m/015p6"},
+ {"id": 23, "name": "Parking meter", "freebase_id": "/m/015qbp"},
+ {"id": 24, "name": "Traffic light", "freebase_id": "/m/015qff"},
+ {"id": 25, "name": "Croissant", "freebase_id": "/m/015wgc"},
+ {"id": 26, "name": "Cucumber", "freebase_id": "/m/015x4r"},
+ {"id": 27, "name": "Radish", "freebase_id": "/m/015x5n"},
+ {"id": 28, "name": "Towel", "freebase_id": "/m/0162_1"},
+ {"id": 29, "name": "Doll", "freebase_id": "/m/0167gd"},
+ {"id": 30, "name": "Skull", "freebase_id": "/m/016m2d"},
+ {"id": 31, "name": "Washing machine", "freebase_id": "/m/0174k2"},
+ {"id": 32, "name": "Glove", "freebase_id": "/m/0174n1"},
+ {"id": 33, "name": "Tick", "freebase_id": "/m/0175cv"},
+ {"id": 34, "name": "Belt", "freebase_id": "/m/0176mf"},
+ {"id": 35, "name": "Sunglasses", "freebase_id": "/m/017ftj"},
+ {"id": 36, "name": "Banjo", "freebase_id": "/m/018j2"},
+ {"id": 37, "name": "Cart", "freebase_id": "/m/018p4k"},
+ {"id": 38, "name": "Ball", "freebase_id": "/m/018xm"},
+ {"id": 39, "name": "Backpack", "freebase_id": "/m/01940j"},
+ {"id": 40, "name": "Bicycle", "freebase_id": "/m/0199g"},
+ {"id": 41, "name": "Home appliance", "freebase_id": "/m/019dx1"},
+ {"id": 42, "name": "Centipede", "freebase_id": "/m/019h78"},
+ {"id": 43, "name": "Boat", "freebase_id": "/m/019jd"},
+ {"id": 44, "name": "Surfboard", "freebase_id": "/m/019w40"},
+ {"id": 45, "name": "Boot", "freebase_id": "/m/01b638"},
+ {"id": 46, "name": "Headphones", "freebase_id": "/m/01b7fy"},
+ {"id": 47, "name": "Hot dog", "freebase_id": "/m/01b9xk"},
+ {"id": 48, "name": "Shorts", "freebase_id": "/m/01bfm9"},
+ {"id": 49, "name": "Fast food", "freebase_id": "/m/01_bhs"},
+ {"id": 50, "name": "Bus", "freebase_id": "/m/01bjv"},
+ {"id": 51, "name": "Boy", "freebase_id": "/m/01bl7v"},
+ {"id": 52, "name": "Screwdriver", "freebase_id": "/m/01bms0"},
+ {"id": 53, "name": "Bicycle wheel", "freebase_id": "/m/01bqk0"},
+ {"id": 54, "name": "Barge", "freebase_id": "/m/01btn"},
+ {"id": 55, "name": "Laptop", "freebase_id": "/m/01c648"},
+ {"id": 56, "name": "Miniskirt", "freebase_id": "/m/01cmb2"},
+ {"id": 57, "name": "Drill (Tool)", "freebase_id": "/m/01d380"},
+ {"id": 58, "name": "Dress", "freebase_id": "/m/01d40f"},
+ {"id": 59, "name": "Bear", "freebase_id": "/m/01dws"},
+ {"id": 60, "name": "Waffle", "freebase_id": "/m/01dwsz"},
+ {"id": 61, "name": "Pancake", "freebase_id": "/m/01dwwc"},
+ {"id": 62, "name": "Brown bear", "freebase_id": "/m/01dxs"},
+ {"id": 63, "name": "Woodpecker", "freebase_id": "/m/01dy8n"},
+ {"id": 64, "name": "Blue jay", "freebase_id": "/m/01f8m5"},
+ {"id": 65, "name": "Pretzel", "freebase_id": "/m/01f91_"},
+ {"id": 66, "name": "Bagel", "freebase_id": "/m/01fb_0"},
+ {"id": 67, "name": "Tower", "freebase_id": "/m/01fdzj"},
+ {"id": 68, "name": "Teapot", "freebase_id": "/m/01fh4r"},
+ {"id": 69, "name": "Person", "freebase_id": "/m/01g317"},
+ {"id": 70, "name": "Bow and arrow", "freebase_id": "/m/01g3x7"},
+ {"id": 71, "name": "Swimwear", "freebase_id": "/m/01gkx_"},
+ {"id": 72, "name": "Beehive", "freebase_id": "/m/01gllr"},
+ {"id": 73, "name": "Brassiere", "freebase_id": "/m/01gmv2"},
+ {"id": 74, "name": "Bee", "freebase_id": "/m/01h3n"},
+ {"id": 75, "name": "Bat (Animal)", "freebase_id": "/m/01h44"},
+ {"id": 76, "name": "Starfish", "freebase_id": "/m/01h8tj"},
+ {"id": 77, "name": "Popcorn", "freebase_id": "/m/01hrv5"},
+ {"id": 78, "name": "Burrito", "freebase_id": "/m/01j3zr"},
+ {"id": 79, "name": "Chainsaw", "freebase_id": "/m/01j4z9"},
+ {"id": 80, "name": "Balloon", "freebase_id": "/m/01j51"},
+ {"id": 81, "name": "Wrench", "freebase_id": "/m/01j5ks"},
+ {"id": 82, "name": "Tent", "freebase_id": "/m/01j61q"},
+ {"id": 83, "name": "Vehicle registration plate", "freebase_id": "/m/01jfm_"},
+ {"id": 84, "name": "Lantern", "freebase_id": "/m/01jfsr"},
+ {"id": 85, "name": "Toaster", "freebase_id": "/m/01k6s3"},
+ {"id": 86, "name": "Flashlight", "freebase_id": "/m/01kb5b"},
+ {"id": 87, "name": "Billboard", "freebase_id": "/m/01knjb"},
+ {"id": 88, "name": "Tiara", "freebase_id": "/m/01krhy"},
+ {"id": 89, "name": "Limousine", "freebase_id": "/m/01lcw4"},
+ {"id": 90, "name": "Necklace", "freebase_id": "/m/01llwg"},
+ {"id": 91, "name": "Carnivore", "freebase_id": "/m/01lrl"},
+ {"id": 92, "name": "Scissors", "freebase_id": "/m/01lsmm"},
+ {"id": 93, "name": "Stairs", "freebase_id": "/m/01lynh"},
+ {"id": 94, "name": "Computer keyboard", "freebase_id": "/m/01m2v"},
+ {"id": 95, "name": "Printer", "freebase_id": "/m/01m4t"},
+ {"id": 96, "name": "Traffic sign", "freebase_id": "/m/01mqdt"},
+ {"id": 97, "name": "Chair", "freebase_id": "/m/01mzpv"},
+ {"id": 98, "name": "Shirt", "freebase_id": "/m/01n4qj"},
+ {"id": 99, "name": "Poster", "freebase_id": "/m/01n5jq"},
+ {"id": 100, "name": "Cheese", "freebase_id": "/m/01nkt"},
+ {"id": 101, "name": "Sock", "freebase_id": "/m/01nq26"},
+ {"id": 102, "name": "Fire hydrant", "freebase_id": "/m/01pns0"},
+ {"id": 103, "name": "Land vehicle", "freebase_id": "/m/01prls"},
+ {"id": 104, "name": "Earrings", "freebase_id": "/m/01r546"},
+ {"id": 105, "name": "Tie", "freebase_id": "/m/01rkbr"},
+ {"id": 106, "name": "Watercraft", "freebase_id": "/m/01rzcn"},
+ {"id": 107, "name": "Cabinetry", "freebase_id": "/m/01s105"},
+ {"id": 108, "name": "Suitcase", "freebase_id": "/m/01s55n"},
+ {"id": 109, "name": "Muffin", "freebase_id": "/m/01tcjp"},
+ {"id": 110, "name": "Bidet", "freebase_id": "/m/01vbnl"},
+ {"id": 111, "name": "Snack", "freebase_id": "/m/01ww8y"},
+ {"id": 112, "name": "Snowmobile", "freebase_id": "/m/01x3jk"},
+ {"id": 113, "name": "Clock", "freebase_id": "/m/01x3z"},
+ {"id": 114, "name": "Medical equipment", "freebase_id": "/m/01xgg_"},
+ {"id": 115, "name": "Cattle", "freebase_id": "/m/01xq0k1"},
+ {"id": 116, "name": "Cello", "freebase_id": "/m/01xqw"},
+ {"id": 117, "name": "Jet ski", "freebase_id": "/m/01xs3r"},
+ {"id": 118, "name": "Camel", "freebase_id": "/m/01x_v"},
+ {"id": 119, "name": "Coat", "freebase_id": "/m/01xygc"},
+ {"id": 120, "name": "Suit", "freebase_id": "/m/01xyhv"},
+ {"id": 121, "name": "Desk", "freebase_id": "/m/01y9k5"},
+ {"id": 122, "name": "Cat", "freebase_id": "/m/01yrx"},
+ {"id": 123, "name": "Bronze sculpture", "freebase_id": "/m/01yx86"},
+ {"id": 124, "name": "Juice", "freebase_id": "/m/01z1kdw"},
+ {"id": 125, "name": "Gondola", "freebase_id": "/m/02068x"},
+ {"id": 126, "name": "Beetle", "freebase_id": "/m/020jm"},
+ {"id": 127, "name": "Cannon", "freebase_id": "/m/020kz"},
+ {"id": 128, "name": "Computer mouse", "freebase_id": "/m/020lf"},
+ {"id": 129, "name": "Cookie", "freebase_id": "/m/021mn"},
+ {"id": 130, "name": "Office building", "freebase_id": "/m/021sj1"},
+ {"id": 131, "name": "Fountain", "freebase_id": "/m/0220r2"},
+ {"id": 132, "name": "Coin", "freebase_id": "/m/0242l"},
+ {"id": 133, "name": "Calculator", "freebase_id": "/m/024d2"},
+ {"id": 134, "name": "Cocktail", "freebase_id": "/m/024g6"},
+ {"id": 135, "name": "Computer monitor", "freebase_id": "/m/02522"},
+ {"id": 136, "name": "Box", "freebase_id": "/m/025dyy"},
+ {"id": 137, "name": "Stapler", "freebase_id": "/m/025fsf"},
+ {"id": 138, "name": "Christmas tree", "freebase_id": "/m/025nd"},
+ {"id": 139, "name": "Cowboy hat", "freebase_id": "/m/025rp__"},
+ {"id": 140, "name": "Hiking equipment", "freebase_id": "/m/0268lbt"},
+ {"id": 141, "name": "Studio couch", "freebase_id": "/m/026qbn5"},
+ {"id": 142, "name": "Drum", "freebase_id": "/m/026t6"},
+ {"id": 143, "name": "Dessert", "freebase_id": "/m/0270h"},
+ {"id": 144, "name": "Wine rack", "freebase_id": "/m/0271qf7"},
+ {"id": 145, "name": "Drink", "freebase_id": "/m/0271t"},
+ {"id": 146, "name": "Zucchini", "freebase_id": "/m/027pcv"},
+ {"id": 147, "name": "Ladle", "freebase_id": "/m/027rl48"},
+ {"id": 148, "name": "Human mouth", "freebase_id": "/m/0283dt1"},
+ {"id": 149, "name": "Dairy Product", "freebase_id": "/m/0284d"},
+ {"id": 150, "name": "Dice", "freebase_id": "/m/029b3"},
+ {"id": 151, "name": "Oven", "freebase_id": "/m/029bxz"},
+ {"id": 152, "name": "Dinosaur", "freebase_id": "/m/029tx"},
+ {"id": 153, "name": "Ratchet (Device)", "freebase_id": "/m/02bm9n"},
+ {"id": 154, "name": "Couch", "freebase_id": "/m/02crq1"},
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+ {"id": 520, "name": "Cake", "freebase_id": "/m/0fszt"},
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+ {"id": 522, "name": "Common sunflower", "freebase_id": "/m/0ftb8"},
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+ {"id": 524, "name": "Honeycomb", "freebase_id": "/m/0fz0h"},
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+ {"id": 542, "name": "Wall clock", "freebase_id": "/m/0h8mzrc"},
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+ {"id": 549, "name": "Facial tissue holder", "freebase_id": "/m/0h8nsvg"},
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+ {"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": "/m/03fwl"},
+ {"id": 150, "name": "Lizard", "freebase_id": "/m/04m9y"},
+ {"id": 151, "name": "Parrot", "freebase_id": "/m/0gv1x"},
+ {"id": 152, "name": "Owl", "freebase_id": "/m/09d5_"},
+ {"id": 153, "name": "Turkey", "freebase_id": "/m/0jly1"},
+ {"id": 154, "name": "Cello", "freebase_id": "/m/01xqw"},
+ {"id": 155, "name": "Knife", "freebase_id": "/m/04ctx"},
+ {"id": 156, "name": "Handgun", "freebase_id": "/m/0gxl3"},
+ {"id": 157, "name": "Carrot", "freebase_id": "/m/0fj52s"},
+ {"id": 158, "name": "Hamburger", "freebase_id": "/m/0cdn1"},
+ {"id": 159, "name": "Grapefruit", "freebase_id": "/m/0hqkz"},
+ {"id": 160, "name": "Tap", "freebase_id": "/m/02jz0l"},
+ {"id": 161, "name": "Tea", "freebase_id": "/m/07clx"},
+ {"id": 162, "name": "Bull", "freebase_id": "/m/0cnyhnx"},
+ {"id": 163, "name": "Turtle", "freebase_id": "/m/09dzg"},
+ {"id": 164, "name": "Bust", "freebase_id": "/m/04yqq2"},
+ {"id": 165, "name": "Monkey", "freebase_id": "/m/08pbxl"},
+ {"id": 166, "name": "Wok", "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"},
+]
+
+
+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": [0, 173, 255], "id": 118, "isthing": 0, "name": "drinking machine"},
+ {"color": [31, 0, 255], "id": 119, "isthing": 0, "name": "drop"},
+ {"color": [120, 120, 120], "id": 120, "isthing": 0, "name": "drug"},
+ {"color": [180, 120, 120], "id": 121, "isthing": 0, "name": "drum"},
+ {"color": [6, 230, 230], "id": 122, "isthing": 0, "name": "drum kit"},
+ {"color": [80, 50, 50], "id": 123, "isthing": 0, "name": "duck"},
+ {"color": [4, 200, 3], "id": 124, "isthing": 0, "name": "dumbbell"},
+ {"color": [120, 120, 80], "id": 125, "isthing": 0, "name": "earphone"},
+ {"color": [140, 140, 140], "id": 126, "isthing": 0, "name": "earrings"},
+ {"color": [204, 5, 255], "id": 127, "isthing": 0, "name": "egg"},
+ {"color": [230, 230, 230], "id": 128, "isthing": 0, "name": "electric fan"},
+ {"color": [4, 250, 7], "id": 129, "isthing": 0, "name": "electric iron"},
+ {"color": [224, 5, 255], "id": 130, "isthing": 0, "name": "electric pot"},
+ {"color": [235, 255, 7], "id": 131, "isthing": 0, "name": "electric saw"},
+ {"color": [150, 5, 61], "id": 132, "isthing": 0, "name": "electronic keyboard"},
+ {"color": [120, 120, 70], "id": 133, "isthing": 0, "name": "engine"},
+ {"color": [8, 255, 51], "id": 134, "isthing": 0, "name": "envelope"},
+ {"color": [255, 6, 82], "id": 135, "isthing": 0, "name": "equipment"},
+ {"color": [143, 255, 140], "id": 136, "isthing": 0, "name": "escalator"},
+ {"color": [204, 255, 4], "id": 137, "isthing": 0, "name": "exhibition booth"},
+ {"color": [255, 51, 7], "id": 138, "isthing": 0, "name": "extinguisher"},
+ {"color": [204, 70, 3], "id": 139, "isthing": 0, "name": "eyeglass"},
+ {"color": [0, 102, 200], "id": 140, "isthing": 0, "name": "fan"},
+ {"color": [61, 230, 250], "id": 141, "isthing": 0, "name": "faucet"},
+ {"color": [255, 6, 51], "id": 142, "isthing": 0, "name": "fax machine"},
+ {"color": [11, 102, 255], "id": 143, "isthing": 0, "name": "fence"},
+ {"color": [255, 7, 71], "id": 144, "isthing": 0, "name": "ferris wheel"},
+ {"color": [255, 9, 224], "id": 145, "isthing": 0, "name": "fire extinguisher"},
+ {"color": [9, 7, 230], "id": 146, "isthing": 0, "name": "fire hydrant"},
+ {"color": [220, 220, 220], "id": 147, "isthing": 0, "name": "fire place"},
+ {"color": [255, 9, 92], "id": 148, "isthing": 0, "name": "fish"},
+ {"color": [112, 9, 255], "id": 149, "isthing": 0, "name": "fish tank"},
+ {"color": [8, 255, 214], "id": 150, "isthing": 0, "name": "fishbowl"},
+ {"color": [7, 255, 224], "id": 151, "isthing": 0, "name": "fishing net"},
+ {"color": [255, 184, 6], "id": 152, "isthing": 0, "name": "fishing pole"},
+ {"color": [10, 255, 71], "id": 153, "isthing": 0, "name": "flag"},
+ {"color": [255, 41, 10], "id": 154, "isthing": 0, "name": "flagstaff"},
+ {"color": [7, 255, 255], "id": 155, "isthing": 0, "name": "flame"},
+ {"color": [224, 255, 8], "id": 156, "isthing": 0, "name": "flashlight"},
+ {"color": [102, 8, 255], "id": 157, "isthing": 0, "name": "floor"},
+ {"color": [255, 61, 6], "id": 158, "isthing": 0, "name": "flower"},
+ {"color": [255, 194, 7], "id": 159, "isthing": 0, "name": "fly"},
+ {"color": [255, 122, 8], "id": 160, "isthing": 0, "name": "foam"},
+ {"color": [0, 255, 20], "id": 161, "isthing": 0, "name": "food"},
+ {"color": [255, 8, 41], "id": 162, "isthing": 0, "name": "footbridge"},
+ {"color": [255, 5, 153], "id": 163, "isthing": 0, "name": "forceps"},
+ {"color": [6, 51, 255], "id": 164, "isthing": 0, "name": "fork"},
+ {"color": [235, 12, 255], "id": 165, "isthing": 0, "name": "forklift"},
+ {"color": [160, 150, 20], "id": 166, "isthing": 0, "name": "fountain"},
+ {"color": [0, 163, 255], "id": 167, "isthing": 0, "name": "fox"},
+ {"color": [140, 140, 140], "id": 168, "isthing": 0, "name": "frame"},
+ {"color": [250, 10, 15], "id": 169, "isthing": 0, "name": "fridge"},
+ {"color": [20, 255, 0], "id": 170, "isthing": 0, "name": "frog"},
+ {"color": [31, 255, 0], "id": 171, "isthing": 0, "name": "fruit"},
+ {"color": [255, 31, 0], "id": 172, "isthing": 0, "name": "funnel"},
+ {"color": [255, 224, 0], "id": 173, "isthing": 0, "name": "furnace"},
+ {"color": [153, 255, 0], "id": 174, "isthing": 0, "name": "game controller"},
+ {"color": [0, 0, 255], "id": 175, "isthing": 0, "name": "game machine"},
+ {"color": [255, 71, 0], "id": 176, "isthing": 0, "name": "gas cylinder"},
+ {"color": [0, 235, 255], "id": 177, "isthing": 0, "name": "gas hood"},
+ {"color": [0, 173, 255], "id": 178, "isthing": 0, "name": "gas stove"},
+ {"color": [31, 0, 255], "id": 179, "isthing": 0, "name": "gift box"},
+ {"color": [120, 120, 120], "id": 180, "isthing": 0, "name": "glass"},
+ {"color": [180, 120, 120], "id": 181, "isthing": 0, "name": "glass marble"},
+ {"color": [6, 230, 230], "id": 182, "isthing": 0, "name": "globe"},
+ {"color": [80, 50, 50], "id": 183, "isthing": 0, "name": "glove"},
+ {"color": [4, 200, 3], "id": 184, "isthing": 0, "name": "goal"},
+ {"color": [120, 120, 80], "id": 185, "isthing": 0, "name": "grandstand"},
+ {"color": [140, 140, 140], "id": 186, "isthing": 0, "name": "grass"},
+ {"color": [204, 5, 255], "id": 187, "isthing": 0, "name": "gravestone"},
+ {"color": [230, 230, 230], "id": 188, "isthing": 0, "name": "ground"},
+ {"color": [4, 250, 7], "id": 189, "isthing": 0, "name": "guardrail"},
+ {"color": [224, 5, 255], "id": 190, "isthing": 0, "name": "guitar"},
+ {"color": [235, 255, 7], "id": 191, "isthing": 0, "name": "gun"},
+ {"color": [150, 5, 61], "id": 192, "isthing": 0, "name": "hammer"},
+ {"color": [120, 120, 70], "id": 193, "isthing": 0, "name": "hand cart"},
+ {"color": [8, 255, 51], "id": 194, "isthing": 0, "name": "handle"},
+ {"color": [255, 6, 82], "id": 195, "isthing": 0, "name": "handrail"},
+ {"color": [143, 255, 140], "id": 196, "isthing": 0, "name": "hanger"},
+ {"color": [204, 255, 4], "id": 197, "isthing": 0, "name": "hard disk drive"},
+ {"color": [255, 51, 7], "id": 198, "isthing": 0, "name": "hat"},
+ {"color": [204, 70, 3], "id": 199, "isthing": 0, "name": "hay"},
+ {"color": [0, 102, 200], "id": 200, "isthing": 0, "name": "headphone"},
+ {"color": [61, 230, 250], "id": 201, "isthing": 0, "name": "heater"},
+ {"color": [255, 6, 51], "id": 202, "isthing": 0, "name": "helicopter"},
+ {"color": [11, 102, 255], "id": 203, "isthing": 0, "name": "helmet"},
+ {"color": [255, 7, 71], "id": 204, "isthing": 0, "name": "holder"},
+ {"color": [255, 9, 224], "id": 205, "isthing": 0, "name": "hook"},
+ {"color": [9, 7, 230], "id": 206, "isthing": 0, "name": "horse"},
+ {"color": [220, 220, 220], "id": 207, "isthing": 0, "name": "horse-drawn carriage"},
+ {"color": [255, 9, 92], "id": 208, "isthing": 0, "name": "hot-air balloon"},
+ {"color": [112, 9, 255], "id": 209, "isthing": 0, "name": "hydrovalve"},
+ {"color": [8, 255, 214], "id": 210, "isthing": 0, "name": "ice"},
+ {"color": [7, 255, 224], "id": 211, "isthing": 0, "name": "inflator pump"},
+ {"color": [255, 184, 6], "id": 212, "isthing": 0, "name": "ipod"},
+ {"color": [10, 255, 71], "id": 213, "isthing": 0, "name": "iron"},
+ {"color": [255, 41, 10], "id": 214, "isthing": 0, "name": "ironing board"},
+ {"color": [7, 255, 255], "id": 215, "isthing": 0, "name": "jar"},
+ {"color": [224, 255, 8], "id": 216, "isthing": 0, "name": "kart"},
+ {"color": [102, 8, 255], "id": 217, "isthing": 0, "name": "kettle"},
+ {"color": [255, 61, 6], "id": 218, "isthing": 0, "name": "key"},
+ {"color": [255, 194, 7], "id": 219, "isthing": 0, "name": "keyboard"},
+ {"color": [255, 122, 8], "id": 220, "isthing": 0, "name": "kitchen range"},
+ {"color": [0, 255, 20], "id": 221, "isthing": 0, "name": "kite"},
+ {"color": [255, 8, 41], "id": 222, "isthing": 0, "name": "knife"},
+ {"color": [255, 5, 153], "id": 223, "isthing": 0, "name": "knife block"},
+ {"color": [6, 51, 255], "id": 224, "isthing": 0, "name": "ladder"},
+ {"color": [235, 12, 255], "id": 225, "isthing": 0, "name": "ladder truck"},
+ {"color": [160, 150, 20], "id": 226, "isthing": 0, "name": "ladle"},
+ {"color": [0, 163, 255], "id": 227, "isthing": 0, "name": "laptop"},
+ {"color": [140, 140, 140], "id": 228, "isthing": 0, "name": "leaves"},
+ {"color": [250, 10, 15], "id": 229, "isthing": 0, "name": "lid"},
+ {"color": [20, 255, 0], "id": 230, "isthing": 0, "name": "life buoy"},
+ {"color": [31, 255, 0], "id": 231, "isthing": 0, "name": "light"},
+ {"color": [255, 31, 0], "id": 232, "isthing": 0, "name": "light bulb"},
+ {"color": [255, 224, 0], "id": 233, "isthing": 0, "name": "lighter"},
+ {"color": [153, 255, 0], "id": 234, "isthing": 0, "name": "line"},
+ {"color": [0, 0, 255], "id": 235, "isthing": 0, "name": "lion"},
+ {"color": [255, 71, 0], "id": 236, "isthing": 0, "name": "lobster"},
+ {"color": [0, 235, 255], "id": 237, "isthing": 0, "name": "lock"},
+ {"color": [0, 173, 255], "id": 238, "isthing": 0, "name": "machine"},
+ {"color": [31, 0, 255], "id": 239, "isthing": 0, "name": "mailbox"},
+ {"color": [120, 120, 120], "id": 240, "isthing": 0, "name": "mannequin"},
+ {"color": [180, 120, 120], "id": 241, "isthing": 0, "name": "map"},
+ {"color": [6, 230, 230], "id": 242, "isthing": 0, "name": "mask"},
+ {"color": [80, 50, 50], "id": 243, "isthing": 0, "name": "mat"},
+ {"color": [4, 200, 3], "id": 244, "isthing": 0, "name": "match book"},
+ {"color": [120, 120, 80], "id": 245, "isthing": 0, "name": "mattress"},
+ {"color": [140, 140, 140], "id": 246, "isthing": 0, "name": "menu"},
+ {"color": [204, 5, 255], "id": 247, "isthing": 0, "name": "metal"},
+ {"color": [230, 230, 230], "id": 248, "isthing": 0, "name": "meter box"},
+ {"color": [4, 250, 7], "id": 249, "isthing": 0, "name": "microphone"},
+ {"color": [224, 5, 255], "id": 250, "isthing": 0, "name": "microwave"},
+ {"color": [235, 255, 7], "id": 251, "isthing": 0, "name": "mirror"},
+ {"color": [150, 5, 61], "id": 252, "isthing": 0, "name": "missile"},
+ {"color": [120, 120, 70], "id": 253, "isthing": 0, "name": "model"},
+ {"color": [8, 255, 51], "id": 254, "isthing": 0, "name": "money"},
+ {"color": [255, 6, 82], "id": 255, "isthing": 0, "name": "monkey"},
+ {"color": [143, 255, 140], "id": 256, "isthing": 0, "name": "mop"},
+ {"color": [204, 255, 4], "id": 257, "isthing": 0, "name": "motorbike"},
+ {"color": [255, 51, 7], "id": 258, "isthing": 0, "name": "mountain"},
+ {"color": [204, 70, 3], "id": 259, "isthing": 0, "name": "mouse"},
+ {"color": [0, 102, 200], "id": 260, "isthing": 0, "name": "mouse pad"},
+ {"color": [61, 230, 250], "id": 261, "isthing": 0, "name": "musical instrument"},
+ {"color": [255, 6, 51], "id": 262, "isthing": 0, "name": "napkin"},
+ {"color": [11, 102, 255], "id": 263, "isthing": 0, "name": "net"},
+ {"color": [255, 7, 71], "id": 264, "isthing": 0, "name": "newspaper"},
+ {"color": [255, 9, 224], "id": 265, "isthing": 0, "name": "oar"},
+ {"color": [9, 7, 230], "id": 266, "isthing": 0, "name": "ornament"},
+ {"color": [220, 220, 220], "id": 267, "isthing": 0, "name": "outlet"},
+ {"color": [255, 9, 92], "id": 268, "isthing": 0, "name": "oven"},
+ {"color": [112, 9, 255], "id": 269, "isthing": 0, "name": "oxygen bottle"},
+ {"color": [8, 255, 214], "id": 270, "isthing": 0, "name": "pack"},
+ {"color": [7, 255, 224], "id": 271, "isthing": 0, "name": "pan"},
+ {"color": [255, 184, 6], "id": 272, "isthing": 0, "name": "paper"},
+ {"color": [10, 255, 71], "id": 273, "isthing": 0, "name": "paper box"},
+ {"color": [255, 41, 10], "id": 274, "isthing": 0, "name": "paper cutter"},
+ {"color": [7, 255, 255], "id": 275, "isthing": 0, "name": "parachute"},
+ {"color": [224, 255, 8], "id": 276, "isthing": 0, "name": "parasol"},
+ {"color": [102, 8, 255], "id": 277, "isthing": 0, "name": "parterre"},
+ {"color": [255, 61, 6], "id": 278, "isthing": 0, "name": "patio"},
+ {"color": [255, 194, 7], "id": 279, "isthing": 0, "name": "pelage"},
+ {"color": [255, 122, 8], "id": 280, "isthing": 0, "name": "pen"},
+ {"color": [0, 255, 20], "id": 281, "isthing": 0, "name": "pen container"},
+ {"color": [255, 8, 41], "id": 282, "isthing": 0, "name": "pencil"},
+ {"color": [255, 5, 153], "id": 283, "isthing": 0, "name": "person"},
+ {"color": [6, 51, 255], "id": 284, "isthing": 0, "name": "photo"},
+ {"color": [235, 12, 255], "id": 285, "isthing": 0, "name": "piano"},
+ {"color": [160, 150, 20], "id": 286, "isthing": 0, "name": "picture"},
+ {"color": [0, 163, 255], "id": 287, "isthing": 0, "name": "pig"},
+ {"color": [140, 140, 140], "id": 288, "isthing": 0, "name": "pillar"},
+ {"color": [250, 10, 15], "id": 289, "isthing": 0, "name": "pillow"},
+ {"color": [20, 255, 0], "id": 290, "isthing": 0, "name": "pipe"},
+ {"color": [31, 255, 0], "id": 291, "isthing": 0, "name": "pitcher"},
+ {"color": [255, 31, 0], "id": 292, "isthing": 0, "name": "plant"},
+ {"color": [255, 224, 0], "id": 293, "isthing": 0, "name": "plastic"},
+ {"color": [153, 255, 0], "id": 294, "isthing": 0, "name": "plate"},
+ {"color": [0, 0, 255], "id": 295, "isthing": 0, "name": "platform"},
+ {"color": [255, 71, 0], "id": 296, "isthing": 0, "name": "player"},
+ {"color": [0, 235, 255], "id": 297, "isthing": 0, "name": "playground"},
+ {"color": [0, 173, 255], "id": 298, "isthing": 0, "name": "pliers"},
+ {"color": [31, 0, 255], "id": 299, "isthing": 0, "name": "plume"},
+ {"color": [120, 120, 120], "id": 300, "isthing": 0, "name": "poker"},
+ {"color": [180, 120, 120], "id": 301, "isthing": 0, "name": "poker chip"},
+ {"color": [6, 230, 230], "id": 302, "isthing": 0, "name": "pole"},
+ {"color": [80, 50, 50], "id": 303, "isthing": 0, "name": "pool table"},
+ {"color": [4, 200, 3], "id": 304, "isthing": 0, "name": "postcard"},
+ {"color": [120, 120, 80], "id": 305, "isthing": 0, "name": "poster"},
+ {"color": [140, 140, 140], "id": 306, "isthing": 0, "name": "pot"},
+ {"color": [204, 5, 255], "id": 307, "isthing": 0, "name": "pottedplant"},
+ {"color": [230, 230, 230], "id": 308, "isthing": 0, "name": "printer"},
+ {"color": [4, 250, 7], "id": 309, "isthing": 0, "name": "projector"},
+ {"color": [224, 5, 255], "id": 310, "isthing": 0, "name": "pumpkin"},
+ {"color": [235, 255, 7], "id": 311, "isthing": 0, "name": "rabbit"},
+ {"color": [150, 5, 61], "id": 312, "isthing": 0, "name": "racket"},
+ {"color": [120, 120, 70], "id": 313, "isthing": 0, "name": "radiator"},
+ {"color": [8, 255, 51], "id": 314, "isthing": 0, "name": "radio"},
+ {"color": [255, 6, 82], "id": 315, "isthing": 0, "name": "rail"},
+ {"color": [143, 255, 140], "id": 316, "isthing": 0, "name": "rake"},
+ {"color": [204, 255, 4], "id": 317, "isthing": 0, "name": "ramp"},
+ {"color": [255, 51, 7], "id": 318, "isthing": 0, "name": "range hood"},
+ {"color": [204, 70, 3], "id": 319, "isthing": 0, "name": "receiver"},
+ {"color": [0, 102, 200], "id": 320, "isthing": 0, "name": "recorder"},
+ {"color": [61, 230, 250], "id": 321, "isthing": 0, "name": "recreational machines"},
+ {"color": [255, 6, 51], "id": 322, "isthing": 0, "name": "remote control"},
+ {"color": [11, 102, 255], "id": 323, "isthing": 0, "name": "road"},
+ {"color": [255, 7, 71], "id": 324, "isthing": 0, "name": "robot"},
+ {"color": [255, 9, 224], "id": 325, "isthing": 0, "name": "rock"},
+ {"color": [9, 7, 230], "id": 326, "isthing": 0, "name": "rocket"},
+ {"color": [220, 220, 220], "id": 327, "isthing": 0, "name": "rocking horse"},
+ {"color": [255, 9, 92], "id": 328, "isthing": 0, "name": "rope"},
+ {"color": [112, 9, 255], "id": 329, "isthing": 0, "name": "rug"},
+ {"color": [8, 255, 214], "id": 330, "isthing": 0, "name": "ruler"},
+ {"color": [7, 255, 224], "id": 331, "isthing": 0, "name": "runway"},
+ {"color": [255, 184, 6], "id": 332, "isthing": 0, "name": "saddle"},
+ {"color": [10, 255, 71], "id": 333, "isthing": 0, "name": "sand"},
+ {"color": [255, 41, 10], "id": 334, "isthing": 0, "name": "saw"},
+ {"color": [7, 255, 255], "id": 335, "isthing": 0, "name": "scale"},
+ {"color": [224, 255, 8], "id": 336, "isthing": 0, "name": "scanner"},
+ {"color": [102, 8, 255], "id": 337, "isthing": 0, "name": "scissors"},
+ {"color": [255, 61, 6], "id": 338, "isthing": 0, "name": "scoop"},
+ {"color": [255, 194, 7], "id": 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": "bookshelf"}, {"id": 139, "name": "boot"}, {"id": 140, "name": "booth"}, {"id": 141, "name": "border"}, {"id": 142, "name": "bottle"}, {"id": 143, "name": "bottom"}, {"id": 144, "name": "boulder"}, {"id": 145, "name": "bouquet"}, {"id": 146, "name": "bow tie"}, {"id": 147, "name": "bow"}, {"id": 148, "name": "bowl"}, {"id": 149, "name": "box"}, {"id": 150, "name": "boy"}, {"id": 151, "name": "bracelet"}, {"id": 152, "name": "bracket"}, {"id": 153, "name": "brake light"}, {"id": 154, "name": "branch"}, {"id": 155, "name": "bread"}, {"id": 156, "name": "breakfast"}, {"id": 157, "name": "breast"}, {"id": 158, "name": "brick wall"}, {"id": 159, "name": "brick"}, {"id": 160, "name": "bride"}, {"id": 161, "name": "bridge"}, {"id": 162, "name": "bridle"}, {"id": 163, "name": "brim"}, {"id": 164, "name": "broccoli"}, {"id": 165, "name": "brocolli"}, {"id": 166, "name": "brush"}, {"id": 167, "name": "bucket"}, {"id": 168, "name": "buckle"}, {"id": 169, "name": "bud"}, {"id": 170, "name": "building"}, {"id": 171, "name": "bull"}, {"id": 172, "name": "bumper"}, {"id": 173, "name": "bun"}, {"id": 174, "name": "bunch"}, {"id": 175, "name": "buoy"}, {"id": 176, "name": "burger"}, {"id": 177, "name": "burner"}, {"id": 178, "name": "bus stop"}, {"id": 179, "name": "bus"}, {"id": 180, "name": "bush"}, {"id": 181, "name": "butter"}, {"id": 182, "name": "butter knife"}, {"id": 183, "name": "butterfly"}, {"id": 184, "name": "button"}, {"id": 185, "name": "cab"}, {"id": 186, "name": "cabbage"}, {"id": 187, "name": "cabin"}, {"id": 188, "name": "cabinet door"}, {"id": 189, "name": "cabinet"}, {"id": 190, "name": "cable"}, {"id": 191, "name": "caboose"}, {"id": 192, "name": "cage"}, {"id": 193, "name": "cake"}, {"id": 194, "name": "calculator"}, {"id": 195, "name": "calendar"}, {"id": 196, "name": "calf"}, {"id": 197, "name": "camera"}, {"id": 198, "name": "can"}, {"id": 199, "name": "canal"}, {"id": 200, "name": "candle holder"}, {"id": 201, "name": "candle"}, {"id": 202, "name": "canoe"}, {"id": 203, "name": "canopy"}, {"id": 204, "name": "cap"}, {"id": 205, "name": "car"}, {"id": 206, "name": "card"}, {"id": 207, "name": "cardboard box"}, {"id": 208, "name": "cardboard"}, {"id": 209, "name": "cargo"}, {"id": 210, "name": "carpet"}, {"id": 211, "name": "carriage"}, {"id": 212, "name": "carrot"}, {"id": 213, "name": "cart"}, {"id": 214, "name": "carton"}, {"id": 215, "name": "case"}, {"id": 216, "name": "castle"}, {"id": 217, "name": "cat"}, {"id": 218, "name": "catcher"}, {"id": 219, "name": "cattle"}, {"id": 220, "name": "cauliflower"}, {"id": 221, "name": "cd"}, {"id": 222, "name": "ceiling fan"}, {"id": 223, "name": "ceiling"}, {"id": 224, "name": "celery"}, {"id": 225, "name": "cell phone"}, {"id": 226, "name": "cellphone"}, {"id": 227, "name": "cement"}, {"id": 228, "name": "cereal"}, {"id": 229, "name": "chain"}, {"id": 230, "name": "chair"}, {"id": 231, "name": "chandelier"}, {"id": 232, "name": "cheek"}, {"id": 233, "name": "cheese"}, {"id": 234, "name": "chef"}, {"id": 235, "name": "cherry"}, {"id": 236, "name": "chest"}, {"id": 237, "name": "chicken"}, {"id": 238, "name": "child"}, {"id": 239, "name": "chimney"}, {"id": 240, "name": "chin"}, {"id": 241, "name": "chip"}, {"id": 242, "name": "chocolate"}, {"id": 243, "name": "chopstick"}, {"id": 244, "name": "christmas tree"}, {"id": 245, "name": "church"}, {"id": 246, "name": "circle"}, {"id": 247, "name": "city"}, {"id": 248, "name": "claw"}, {"id": 249, "name": "clay"}, {"id": 250, "name": "cleat"}, {"id": 251, "name": "cliff"}, {"id": 252, "name": "clock face"}, {"id": 253, "name": "clock hand"}, {"id": 254, "name": "clock tower"}, {"id": 255, "name": "clock"}, {"id": 256, "name": "closet"}, {"id": 257, "name": "cloth"}, {"id": 258, "name": "clothes"}, {"id": 259, "name": "clothing"}, {"id": 260, "name": "cloud"}, {"id": 261, "name": "coaster"}, {"id": 262, "name": "coat"}, {"id": 263, "name": "cockpit"}, {"id": 264, "name": "coffee"}, {"id": 265, "name": "coffee cup"}, {"id": 266, "name": "coffee maker"}, {"id": 267, "name": "coffee pot"}, {"id": 268, "name": "coffee table"}, {"id": 269, "name": "coin slot"}, {"id": 270, "name": "collar"}, {"id": 271, "name": "column"}, {"id": 272, "name": "comforter"}, {"id": 273, "name": "computer keyboard"}, {"id": 274, "name": "computer monitor"}, {"id": 275, "name": "computer mouse"}, {"id": 276, "name": "computer screen"}, {"id": 277, "name": "computer"}, {"id": 278, "name": "concrete"}, {"id": 279, "name": "condiment"}, {"id": 280, "name": "conductor"}, {"id": 281, "name": "cone"}, {"id": 282, "name": "container"}, {"id": 283, "name": "control panel"}, {"id": 284, "name": "control"}, {"id": 285, "name": "controller"}, {"id": 286, "name": "cookie"}, {"id": 287, "name": "cooler"}, {"id": 288, "name": "copyright"}, {"id": 289, "name": "cord"}, {"id": 290, "name": "corn"}, {"id": 291, "name": "costume"}, {"id": 292, "name": "couch"}, {"id": 293, "name": "counter top"}, {"id": 294, "name": "counter"}, {"id": 295, "name": "countertop"}, {"id": 296, "name": "couple"}, {"id": 297, "name": "court"}, {"id": 298, "name": "courtyard"}, {"id": 299, "name": "cover"}, {"id": 300, "name": "cow"}, {"id": 301, "name": "cowboy hat"}, {"id": 302, "name": "cpu"}, {"id": 303, "name": "crane"}, {"id": 304, "name": "crate"}, {"id": 305, "name": "cream"}, {"id": 306, "name": "croissant"}, {"id": 307, "name": "cross"}, {"id": 308, "name": "crosswalk"}, {"id": 309, "name": "crowd"}, {"id": 310, "name": "crown"}, {"id": 311, "name": "crumb"}, {"id": 312, "name": "crust"}, {"id": 313, "name": "cub"}, {"id": 314, "name": "cucumber"}, {"id": 315, "name": "cuff"}, {"id": 316, "name": "cup"}, {"id": 317, "name": "cupboard"}, {"id": 318, "name": "cupcake"}, {"id": 319, "name": "curb"}, {"id": 320, "name": "curtain"}, {"id": 321, "name": "cushion"}, {"id": 322, "name": "cutting board"}, {"id": 323, "name": "cycle"}, {"id": 324, "name": "cyclist"}, {"id": 325, "name": "dashboard"}, {"id": 326, "name": "date"}, {"id": 327, "name": "day"}, {"id": 328, "name": "decal"}, {"id": 329, "name": "deck"}, {"id": 330, "name": "decoration"}, {"id": 331, "name": "deer"}, {"id": 332, "name": "desert"}, {"id": 333, "name": "design"}, {"id": 334, "name": "desk"}, {"id": 335, "name": "dessert"}, {"id": 336, "name": "device"}, {"id": 337, "name": "dial"}, {"id": 338, "name": "diamond"}, {"id": 339, "name": "dinner"}, {"id": 340, "name": "dirt bike"}, {"id": 341, "name": "dirt road"}, {"id": 342, "name": "dirt"}, {"id": 343, "name": "disc"}, {"id": 344, "name": "dish"}, {"id": 345, "name": "dishwasher"}, {"id": 346, "name": "dispenser"}, {"id": 347, "name": "display case"}, {"id": 348, 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852, "name": "plug"}, {"id": 853, "name": "plumbing"}, {"id": 854, "name": "pocket"}, {"id": 855, "name": "polar bear"}, {"id": 856, "name": "pole"}, {"id": 857, "name": "police"}, {"id": 858, "name": "police car"}, {"id": 859, "name": "police officer"}, {"id": 860, "name": "policeman"}, {"id": 861, "name": "pond"}, {"id": 862, "name": "pony tail"}, {"id": 863, "name": "pony"}, {"id": 864, "name": "ponytail"}, {"id": 865, "name": "pool"}, {"id": 866, "name": "portrait"}, {"id": 867, "name": "post"}, {"id": 868, "name": "poster"}, {"id": 869, "name": "pot"}, {"id": 870, "name": "potato"}, {"id": 871, "name": "power line"}, {"id": 872, "name": "power lines"}, {"id": 873, "name": "power pole"}, {"id": 874, "name": "print"}, {"id": 875, "name": "printer"}, {"id": 876, "name": "produce"}, {"id": 877, "name": "product"}, {"id": 878, "name": "prong"}, {"id": 879, "name": "propeller"}, {"id": 880, "name": "pumpkin"}, {"id": 881, "name": "puppy"}, {"id": 882, "name": "purse"}, {"id": 883, "name": "quilt"}, {"id": 884, "name": "racer"}, {"id": 885, "name": "rack"}, {"id": 886, "name": "racquet"}, {"id": 887, "name": "radiator"}, {"id": 888, "name": "radio"}, {"id": 889, "name": "radish"}, {"id": 890, "name": "raft"}, {"id": 891, "name": "rail"}, {"id": 892, "name": "railing"}, {"id": 893, "name": "railroad tracks"}, {"id": 894, "name": "railroad"}, {"id": 895, "name": "railway"}, {"id": 896, "name": "rain"}, {"id": 897, "name": "rainbow"}, {"id": 898, "name": "ram"}, {"id": 899, "name": "ramp"}, {"id": 900, "name": "range"}, {"id": 901, "name": "receipt"}, {"id": 902, "name": "recliner"}, {"id": 903, "name": "referee"}, {"id": 904, "name": "reflection"}, {"id": 905, "name": "reflector"}, {"id": 906, "name": "reign"}, {"id": 907, "name": "remote control"}, {"id": 908, "name": "remote"}, {"id": 909, "name": "restaurant"}, {"id": 910, "name": "restroom"}, {"id": 911, "name": "ribbon"}, {"id": 912, "name": "rice"}, {"id": 913, "name": "rider"}, {"id": 914, "name": "rim"}, {"id": 915, "name": "ring"}, {"id": 916, "name": "ripple"}, {"id": 917, "name": "river"}, {"id": 918, "name": "road"}, {"id": 919, "name": "roadway"}, {"id": 920, "name": "robe"}, {"id": 921, "name": "rock wall"}, {"id": 922, "name": "rock"}, {"id": 923, "name": "rod"}, {"id": 924, "name": "roll"}, {"id": 925, "name": "roman numeral"}, {"id": 926, "name": "roman numerals"}, {"id": 927, "name": "roof"}, {"id": 928, "name": "room"}, {"id": 929, "name": "rope"}, {"id": 930, "name": "rose"}, {"id": 931, "name": "rug"}, {"id": 932, "name": "runway"}, {"id": 933, "name": "sack"}, {"id": 934, "name": "saddle"}, {"id": 935, "name": "safety cone"}, {"id": 936, "name": "sail"}, {"id": 937, "name": "sailboat"}, {"id": 938, "name": "salad"}, {"id": 939, "name": "salt"}, {"id": 940, "name": "salt shaker"}, {"id": 941, "name": "sand"}, {"id": 942, "name": "sandal"}, {"id": 943, "name": "sandwhich"}, {"id": 944, "name": "sandwich"}, {"id": 945, "name": "sauce"}, {"id": 946, "name": "saucer"}, {"id": 947, "name": "sausage"}, {"id": 948, "name": "scaffolding"}, {"id": 949, "name": "scale"}, {"id": 950, "name": "scarf"}, {"id": 951, "name": "scene"}, {"id": 952, "name": "scissor"}, {"id": 953, "name": "scissors"}, {"id": 954, "name": "scooter"}, {"id": 955, "name": "scoreboard"}, {"id": 956, "name": "screen"}, {"id": 957, "name": "screw"}, {"id": 958, "name": "sculpture"}, {"id": 959, "name": "sea"}, {"id": 960, "name": "seagull"}, {"id": 961, "name": "seasoning"}, {"id": 962, "name": "seat"}, {"id": 963, "name": "seaweed"}, {"id": 964, "name": "second floor"}, {"id": 965, "name": "sedan"}, {"id": 966, "name": "seed"}, {"id": 967, "name": "shack"}, {"id": 968, "name": "shade"}, {"id": 969, "name": "shadow"}, {"id": 970, "name": "shaker"}, {"id": 971, "name": "she"}, {"id": 972, "name": "shed"}, {"id": 973, "name": "sheep"}, {"id": 974, "name": "sheet"}, {"id": 975, "name": "shelf"}, {"id": 976, "name": "shin guard"}, {"id": 977, "name": "shin guards"}, {"id": 978, "name": "shingle"}, {"id": 979, "name": "ship"}, {"id": 980, "name": "shirt"}, {"id": 981, "name": "shoe"}, {"id": 982, "name": "shop"}, {"id": 983, "name": "shore"}, {"id": 984, "name": "shoreline"}, {"id": 985, "name": "short"}, {"id": 986, "name": "shower curtain"}, {"id": 987, "name": "shower door"}, {"id": 988, "name": "shower head"}, {"id": 989, "name": "shower"}, {"id": 990, "name": "shrimp"}, {"id": 991, "name": "shrub"}, {"id": 992, "name": "shutter"}, {"id": 993, "name": "side mirror"}, {"id": 994, "name": "side window"}, {"id": 995, "name": "sidewalk"}, {"id": 996, "name": "sign post"}, {"id": 997, "name": "sign"}, {"id": 998, "name": "signal light"}, {"id": 999, "name": "signal"}, {"id": 1000, "name": "silverware"}, {"id": 1001, "name": "sink"}, {"id": 1002, "name": "skate park"}, {"id": 1003, "name": "skateboard ramp"}, {"id": 1004, "name": "skateboard"}, {"id": 1005, "name": "skateboarder"}, {"id": 1006, "name": "skatepark"}, {"id": 1007, "name": "skater"}, {"id": 1008, "name": "ski boot"}, {"id": 1009, "name": "ski boots"}, {"id": 1010, "name": "ski goggles"}, {"id": 1011, "name": "ski jacket"}, {"id": 1012, "name": "ski lift"}, {"id": 1013, "name": "ski pants"}, {"id": 1014, "name": "ski pole"}, {"id": 1015, "name": "ski poles"}, {"id": 1016, "name": "ski slope"}, {"id": 1017, "name": "ski suit"}, {"id": 1018, "name": "ski tracks"}, {"id": 1019, "name": "ski"}, {"id": 1020, "name": "skier"}, {"id": 1021, "name": "skiier"}, {"id": 1022, "name": "skiis"}, {"id": 1023, "name": "skillet"}, {"id": 1024, "name": "skin"}, {"id": 1025, "name": "skirt"}, {"id": 1026, "name": "skull"}, {"id": 1027, "name": "sky"}, {"id": 1028, "name": "skylight"}, {"id": 1029, "name": "skyscraper"}, {"id": 1030, "name": "slab"}, {"id": 1031, "name": "slack"}, {"id": 1032, "name": "slat"}, {"id": 1033, "name": "sled"}, {"id": 1034, "name": "sleeve"}, {"id": 1035, "name": "slice"}, {"id": 1036, "name": "slope"}, {"id": 1037, "name": "slot"}, {"id": 1038, "name": "smartphone"}, {"id": 1039, "name": "smile"}, {"id": 1040, "name": "smoke"}, {"id": 1041, "name": "sneaker"}, {"id": 1042, "name": "snout"}, {"id": 1043, "name": "snow board"}, {"id": 1044, "name": "snow pants"}, {"id": 1045, "name": "snow suit"}, {"id": 1046, "name": "snow"}, {"id": 1047, "name": "snowboard"}, {"id": 1048, "name": "snowboarder"}, {"id": 1049, "name": "snowsuit"}, {"id": 1050, "name": "soap dispenser"}, {"id": 1051, "name": "soap"}, {"id": 1052, "name": "soccer ball"}, {"id": 1053, "name": "soccer field"}, {"id": 1054, "name": "soccer player"}, {"id": 1055, "name": "sock"}, {"id": 1056, "name": "socket"}, {"id": 1057, "name": "soda can"}, {"id": 1058, "name": "soda"}, {"id": 1059, "name": "sofa"}, {"id": 1060, "name": "soil"}, {"id": 1061, "name": "soup"}, {"id": 1062, "name": "spatula"}, {"id": 1063, "name": "speaker"}, {"id": 1064, "name": "spectator"}, {"id": 1065, "name": "spinach"}, {"id": 1066, "name": "spire"}, {"id": 1067, "name": "splash"}, {"id": 1068, "name": "spoke"}, {"id": 1069, "name": "spoon"}, {"id": 1070, "name": "spot"}, {"id": 1071, "name": "spray"}, {"id": 1072, "name": "sprinkle"}, {"id": 1073, "name": "squash"}, {"id": 1074, "name": "stabilizer"}, {"id": 1075, "name": "stack"}, {"id": 1076, "name": "stadium"}, {"id": 1077, "name": "stair"}, {"id": 1078, "name": "staircase"}, {"id": 1079, "name": "stairway"}, {"id": 1080, "name": "stall"}, {"id": 1081, "name": "stand"}, {"id": 1082, "name": "star"}, {"id": 1083, "name": "station wagon"}, {"id": 1084, "name": "station"}, {"id": 1085, "name": "statue"}, {"id": 1086, "name": "steam"}, {"id": 1087, "name": "steeple"}, {"id": 1088, "name": "steering wheel"}, {"id": 1089, "name": "stem"}, {"id": 1090, "name": "step"}, {"id": 1091, "name": "stereo"}, {"id": 1092, "name": "stick"}, {"id": 1093, "name": "sticker"}, {"id": 1094, "name": "stone wall"}, {"id": 1095, "name": "stone"}, {"id": 1096, "name": "stool"}, {"id": 1097, "name": "stop light"}, {"id": 1098, "name": "stop sign"}, {"id": 1099, "name": "stop"}, {"id": 1100, "name": "stoplight"}, {"id": 1101, "name": "store"}, {"id": 1102, "name": "storefront"}, {"id": 1103, "name": "stove top"}, {"id": 1104, "name": "stove"}, {"id": 1105, "name": "strap"}, {"id": 1106, "name": "straw"}, {"id": 1107, "name": "strawberry"}, {"id": 1108, "name": "stream"}, {"id": 1109, "name": "streamer"}, {"id": 1110, "name": "street lamp"}, {"id": 1111, "name": "street sign"}, {"id": 1112, "name": "street"}, {"id": 1113, "name": "streetlight"}, {"id": 1114, "name": "string"}, {"id": 1115, "name": "strip"}, {"id": 1116, "name": "stripes"}, {"id": 1117, "name": "stroller"}, {"id": 1118, "name": "structure"}, {"id": 1119, "name": "stuffed animal"}, {"id": 1120, "name": "stuffed animals"}, {"id": 1121, "name": "stuffed bear"}, {"id": 1122, "name": "stump"}, {"id": 1123, "name": "suit jacket"}, {"id": 1124, "name": "suit"}, {"id": 1125, "name": "suitcase"}, {"id": 1126, "name": "sun"}, {"id": 1127, "name": "sunglasses"}, {"id": 1128, "name": "sunset"}, {"id": 1129, "name": "surf"}, {"id": 1130, "name": "surface"}, {"id": 1131, "name": "surfboard"}, {"id": 1132, "name": "surfer"}, {"id": 1133, "name": "surfing"}, {"id": 1134, "name": "suspender"}, {"id": 1135, "name": "suv"}, {"id": 1136, "name": "swan"}, {"id": 1137, "name": "sweat band"}, {"id": 1138, "name": "sweatband"}, {"id": 1139, "name": "sweater"}, {"id": 1140, "name": "sweatpants"}, {"id": 1141, "name": "sweatshirt"}, {"id": 1142, "name": "swim trunks"}, {"id": 1143, "name": "swimsuit"}, {"id": 1144, "name": "switch"}, {"id": 1145, "name": "symbol"}, {"id": 1146, "name": "syrup"}, {"id": 1147, "name": "table cloth"}, {"id": 1148, "name": "table lamp"}, {"id": 1149, "name": "table"}, {"id": 1150, "name": "tablecloth"}, {"id": 1151, "name": "tablet"}, {"id": 1152, "name": "tabletop"}, {"id": 1153, "name": "tag"}, {"id": 1154, "name": "tail feathers"}, {"id": 1155, "name": "tail fin"}, {"id": 1156, "name": "tail lights"}, {"id": 1157, "name": "tail wing"}, {"id": 1158, "name": "tail"}, {"id": 1159, "name": "taillight"}, {"id": 1160, "name": "tangerine"}, {"id": 1161, "name": "tank top"}, {"id": 1162, "name": "tank"}, {"id": 1163, "name": "tap"}, {"id": 1164, "name": "tarmac"}, {"id": 1165, "name": "tarp"}, {"id": 1166, "name": "tattoo"}, {"id": 1167, "name": "taxi cab"}, {"id": 1168, "name": "taxi"}, {"id": 1169, "name": "tea kettle"}, {"id": 1170, "name": "tea pot"}, {"id": 1171, "name": "tea"}, {"id": 1172, "name": "teapot"}, {"id": 1173, "name": "teddy bear"}, {"id": 1174, "name": "teddy bears"}, {"id": 1175, "name": "teddy"}, {"id": 1176, "name": "tee shirt"}, {"id": 1177, "name": "teeth"}, {"id": 1178, "name": "telephone pole"}, {"id": 1179, "name": "telephone"}, {"id": 1180, "name": "television"}, {"id": 1181, "name": "tennis ball"}, {"id": 1182, "name": "tennis court"}, {"id": 1183, "name": "tennis match"}, {"id": 1184, "name": "tennis net"}, {"id": 1185, "name": "tennis player"}, {"id": 1186, "name": "tennis racket"}, {"id": 1187, "name": "tennis shoe"}, {"id": 1188, "name": "tennis shoes"}, {"id": 1189, "name": "tennis"}, {"id": 1190, "name": "tent"}, {"id": 1191, "name": "terminal"}, {"id": 1192, "name": "text"}, {"id": 1193, "name": "throw pillow"}, {"id": 1194, "name": "thumb"}, {"id": 1195, "name": "tie"}, {"id": 1196, "name": "tights"}, {"id": 1197, "name": "tile floor"}, {"id": 1198, "name": "tile"}, {"id": 1199, "name": "tine"}, {"id": 1200, "name": "tire"}, {"id": 1201, "name": "tissue box"}, {"id": 1202, "name": "tissue"}, {"id": 1203, "name": "toast"}, {"id": 1204, "name": "toaster"}, {"id": 1205, "name": "toddler"}, {"id": 1206, "name": "toilet bowl"}, {"id": 1207, "name": "toilet brush"}, {"id": 1208, "name": "toilet lid"}, {"id": 1209, "name": "toilet paper"}, {"id": 1210, "name": "toilet seat"}, {"id": 1211, "name": "toilet tank"}, {"id": 1212, "name": "toilet"}, {"id": 1213, "name": "toiletry"}, {"id": 1214, "name": "tomato slice"}, {"id": 1215, "name": "tomato"}, {"id": 1216, "name": "tongs"}, {"id": 1217, "name": "tongue"}, {"id": 1218, "name": "tool"}, {"id": 1219, "name": "toothbrush"}, {"id": 1220, "name": "toothpaste"}, {"id": 1221, "name": "toothpick"}, {"id": 1222, "name": "top"}, {"id": 1223, "name": "topping"}, {"id": 1224, "name": "towel rack"}, {"id": 1225, "name": "towel"}, {"id": 1226, "name": "tower"}, {"id": 1227, "name": "town"}, {"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, 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1812, "name": "alert ears"}, {"id": 1813, "name": "alert zebra ear"}, {"id": 1814, "name": "aletter"}, {"id": 1815, "name": "alex rodriguez"}, {"id": 1816, "name": "alexandre dellolivo"}, {"id": 1817, "name": "alexis"}, {"id": 1818, "name": "alfalfa"}, {"id": 1819, "name": "alfalfa sprouts"}, {"id": 1820, "name": "alfredo sauce"}, {"id": 1821, "name": "algae"}, {"id": 1822, "name": "alge"}, {"id": 1823, "name": "algea"}, {"id": 1824, "name": "alice"}, {"id": 1825, "name": "alice springs"}, {"id": 1826, "name": "alices restaurant"}, {"id": 1827, "name": "alien face"}, {"id": 1828, "name": "alien"}, {"id": 1829, "name": "alienation"}, {"id": 1830, "name": "aligator"}, {"id": 1831, "name": "alight"}, {"id": 1832, "name": "alitalia"}, {"id": 1833, "name": "all"}, {"id": 1834, "name": "all bananas"}, {"id": 1835, "name": "all black"}, {"id": 1836, "name": "all black clothing"}, {"id": 1837, "name": "all canoe"}, {"id": 1838, "name": "all change words"}, {"id": 1839, "name": "all colors"}, {"id": 1840, "name": "all day"}, {"id": 1841, "name": "all destinations"}, {"id": 1842, "name": "all fours"}, {"id": 1843, "name": "all fully grown"}, {"id": 1844, "name": "all green trees"}, {"id": 1845, "name": "all hands"}, {"id": 1846, "name": "all interior frames"}, {"id": 1847, "name": "all items"}, {"id": 1848, "name": "all leg stripes"}, {"id": 1849, "name": "all natural"}, {"id": 1850, "name": "all players"}, {"id": 1851, "name": "all purpose flour"}, {"id": 1852, "name": "all sheep feeding"}, {"id": 1853, "name": "all signs"}, {"id": 1854, "name": "all speakers"}, {"id": 1855, "name": "all street lamps"}, {"id": 1856, "name": "all the buttons"}, {"id": 1857, "name": "all the leaves"}, {"id": 1858, "name": "all the way"}, {"id": 1859, "name": "all toliet"}, {"id": 1860, "name": "all war"}, {"id": 1861, "name": "all way"}, {"id": 1862, "name": "all way sign"}, {"id": 1863, "name": "all way stop"}, {"id": 1864, "name": "all white"}, {"id": 1865, "name": "all white sheep"}, {"id": 1866, "name": "allen st"}, {"id": 1867, "name": "allen wrench"}, {"id": 1868, "name": "allerton"}, {"id": 1869, "name": "alleway"}, {"id": 1870, "name": "alley"}, {"id": 1871, "name": "alley way"}, {"id": 1872, "name": "alleyway"}, {"id": 1873, "name": "allfours"}, {"id": 1874, "name": "alliance"}, {"id": 1875, "name": "alligator"}, {"id": 1876, "name": "alligator kite"}, {"id": 1877, "name": "alligator logo"}, {"id": 1878, "name": "alligator magnet"}, {"id": 1879, "name": "allowance"}, {"id": 1880, "name": "allwar"}, {"id": 1881, "name": "allway"}, {"id": 1882, "name": "allway sign"}, {"id": 1883, "name": "ally"}, {"id": 1884, "name": "ally way"}, {"id": 1885, "name": "almond breeze"}, {"id": 1886, "name": "almond butter"}, {"id": 1887, "name": "almond milk"}, {"id": 1888, "name": "almond slice"}, {"id": 1889, "name": "almond slices"}, {"id": 1890, "name": "almond"}, {"id": 1891, "name": "almonds and cucumber"}, {"id": 1892, "name": "almondspistachiosbowl"}, {"id": 1893, "name": "almost"}, {"id": 1894, "name": "almost 400"}, {"id": 1895, "name": "almost empty"}, {"id": 1896, "name": "almost folded arms"}, {"id": 1897, "name": "almost whole pizza"}, {"id": 1898, "name": "aloe"}, {"id": 1899, "name": "aloe plant"}, {"id": 1900, "name": "alone"}, {"id": 1901, "name": "alone on tray"}, {"id": 1902, "name": "along"}, {"id": 1903, "name": "along coast"}, {"id": 1904, "name": "along counter top"}, {"id": 1905, "name": "along curb"}, {"id": 1906, "name": "along slope"}, {"id": 1907, "name": "along the beach"}, {"id": 1908, "name": "along the side"}, {"id": 1909, "name": "alongside train"}, {"id": 1910, "name": "alot"}, {"id": 1911, "name": "alpaca"}, {"id": 1912, "name": "alpha mu omega"}, {"id": 1913, "name": "alphabet e"}, {"id": 1914, "name": "alphabet k"}, {"id": 1915, "name": "alphabet keys"}, {"id": 1916, "name": "alphabet letter"}, {"id": 1917, "name": "alphabet magnet"}, {"id": 1918, "name": "alphabet mat"}, {"id": 1919, "name": "alphabet"}, {"id": 1920, "name": "alphanumeric"}, {"id": 1921, "name": "alpine"}, {"id": 1922, "name": "also grass on beach"}, {"id": 1923, "name": "alt"}, {"id": 1924, "name": "alt key"}, {"id": 1925, "name": "alta vista"}, {"id": 1926, "name": "altar"}, {"id": 1927, "name": "alter"}, {"id": 1928, "name": "alter candles"}, {"id": 1929, "name": "alter schmuck"}, {"id": 1930, "name": "altered"}, {"id": 1931, "name": "alternate"}, {"id": 1932, "name": "althletic suit"}, {"id": 1933, "name": "altoids"}, {"id": 1934, "name": "altonia"}, {"id": 1935, "name": "aluminium foil"}, {"id": 1936, "name": "aluminium pipe"}, {"id": 1937, "name": "aluminum bat"}, {"id": 1938, "name": "aluminum bowl"}, {"id": 1939, "name": "aluminum box"}, {"id": 1940, "name": "aluminum can"}, {"id": 1941, "name": "aluminum canoe"}, {"id": 1942, "name": "aluminum cola can"}, {"id": 1943, "name": "aluminum container"}, {"id": 1944, "name": "aluminum foil"}, {"id": 1945, "name": "aluminum frame"}, {"id": 1946, "name": "aluminum legs"}, {"id": 1947, "name": "aluminum pan"}, {"id": 1948, "name": "aluminum paper"}, {"id": 1949, "name": "aluminum plate"}, {"id": 1950, "name": "aluminum plates"}, {"id": 1951, "name": "aluminum rail"}, {"id": 1952, "name": "aluminum rivets"}, {"id": 1953, "name": "aluminum siding"}, {"id": 1954, "name": "aluminum slates"}, {"id": 1955, "name": "aluminum splash"}, {"id": 1956, "name": "aluminum tray"}, {"id": 1957, "name": "aluminum wrap"}, {"id": 1958, "name": "aluminum wrapper"}, {"id": 1959, "name": "aluminum"}, {"id": 1960, "name": "alumium foil"}, {"id": 1961, "name": "alumnum cans"}, {"id": 1962, "name": "alutrailer"}, {"id": 1963, "name": "always"}, {"id": 1964, "name": "am hof"}, {"id": 1965, "name": "am"}, {"id": 1966, "name": "amalia"}, {"id": 1967, "name": "aman"}, {"id": 1968, "name": "amana"}, {"id": 1969, "name": "amanda"}, {"id": 1970, "name": "amber"}, {"id": 1971, "name": "amber beer"}, {"id": 1972, "name": "amber grisler"}, {"id": 1973, "name": "amber light"}, {"id": 1974, "name": "amber liquid"}, {"id": 1975, "name": "ambiance"}, {"id": 1976, "name": "amblane"}, {"id": 1977, "name": "ambulance"}, {"id": 1978, "name": "ambulances front"}, {"id": 1979, "name": "ambulans"}, {"id": 1980, "name": "amc logo"}, {"id": 1981, "name": "amco"}, {"id": 1982, "name": "amd"}, {"id": 1983, "name": "amenity"}, {"id": 1984, "name": "amerian flag"}, {"id": 1985, "name": "america"}, {"id": 1986, "name": "america flag"}, {"id": 1987, "name": "american"}, {"id": 1988, "name": "american airline"}, {"id": 1989, "name": "american airlines"}, {"id": 1990, "name": "american apparel"}, {"id": 1991, "name": "american art"}, {"id": 1992, "name": "american cheese"}, {"id": 1993, "name": "american colors"}, {"id": 1994, "name": "american darling"}, {"id": 1995, "name": "american eagle"}, {"id": 1996, "name": "american express"}, {"id": 1997, "name": "american flag decal"}, {"id": 1998, "name": "american flag flat"}, {"id": 1999, "name": "american flag patch"}, {"id": 2000, "name": "american flag"}, {"id": 2001, "name": "american flagbus"}, {"id": 2002, "name": "american flags"}, {"id": 2003, "name": "americana flag"}, {"id": 2004, "name": "americanflag"}, {"id": 2005, "name": "amherst"}, {"id": 2006, "name": "amiercan flag"}, {"id": 2007, "name": "amle goat"}, {"id": 2008, "name": "ammo pouch"}, {"id": 2009, "name": "ammobox"}, {"id": 2010, "name": "ammunition box"}, {"id": 2011, "name": "amoire"}, {"id": 2012, "name": "among tree"}, {"id": 2013, "name": "amount"}, {"id": 2014, "name": "amount of spice"}, {"id": 2015, "name": "amp"}, {"id": 2016, "name": "ampersand"}, {"id": 2017, "name": "amphitheater"}, {"id": 2018, "name": "amphora"}, {"id": 2019, "name": "amplifer"}, {"id": 2020, "name": "amplifier"}, {"id": 2021, "name": "ampliphier"}, {"id": 2022, "name": "amputated arm"}, {"id": 2023, "name": "amsterdam"}, {"id": 2024, "name": "amtrack"}, {"id": 2025, "name": "amtrack logo"}, {"id": 2026, "name": "amtrak"}, {"id": 2027, "name": "amtrak emblem"}, {"id": 2028, "name": "amtrak logo"}, {"id": 2029, "name": "amtrak title"}, {"id": 2030, "name": "amtrak train"}, {"id": 2031, "name": "amulet"}, {"id": 2032, "name": "amused"}, {"id": 2033, "name": "amusement park"}, {"id": 2034, "name": "amusement ride"}, {"id": 2035, "name": "amusement"}, {"id": 2036, "name": "amy"}, {"id": 2037, "name": "an"}, {"id": 2038, "name": "ana"}, {"id": 2039, "name": "ana logo"}, {"id": 2040, "name": "anal view dr"}, {"id": 2041, "name": "analog"}, {"id": 2042, "name": "analog clock"}, {"id": 2043, "name": "analog light"}, {"id": 2044, "name": "analogue clock"}, {"id": 2045, "name": "analope"}, {"id": 2046, "name": "anchor chain"}, {"id": 2047, "name": "anchor decal"}, {"id": 2048, "name": "anchor decor"}, {"id": 2049, "name": "anchor line"}, {"id": 2050, "name": "anchor rope"}, {"id": 2051, "name": "anchor"}, {"id": 2052, "name": "anchored boat"}, {"id": 2053, "name": "anchoring"}, {"id": 2054, "name": "anchovie"}, {"id": 2055, "name": "anchovy"}, {"id": 2056, "name": "ancient"}, {"id": 2057, "name": "ancient man"}, {"id": 2058, "name": "ancient part"}, {"id": 2059, "name": "ancient ruin"}, {"id": 2060, "name": "ancient ruins"}, {"id": 2061, "name": "ancient scissors"}, {"id": 2062, "name": "and"}, {"id": 2063, "name": "and a brown belt"}, {"id": 2064, "name": "and a computer"}, {"id": 2065, "name": "and a white jacket"}, {"id": 2066, "name": "and black"}, {"id": 2067, "name": "and black stripes"}, {"id": 2068, "name": "and blue"}, {"id": 2069, "name": "and blue lei"}, {"id": 2070, "name": "and blue sunset"}, {"id": 2071, "name": "and blurry"}, {"id": 2072, "name": "and brown"}, {"id": 2073, "name": "and clear"}, {"id": 2074, "name": "and cloudless"}, {"id": 2075, "name": "and glasses"}, {"id": 2076, "name": "and green"}, {"id": 2077, "name": "and necktie"}, {"id": 2078, "name": "and no shoes"}, {"id": 2079, "name": "and numbers"}, {"id": 2080, "name": "and patchy"}, {"id": 2081, "name": "and rocks"}, {"id": 2082, "name": "and sign"}, {"id": 2083, "name": "and socks"}, {"id": 2084, "name": "and white"}, {"id": 2085, "name": "and white hand"}, {"id": 2086, "name": "andle"}, {"id": 2087, "name": "andratx"}, {"id": 2088, "name": "andre agassi"}, {"id": 2089, "name": "andrew"}, {"id": 2090, "name": "android"}, {"id": 2091, "name": "andy"}, {"id": 2092, "name": "andy murray"}, {"id": 2093, "name": "anetennas"}, {"id": 2094, "name": "anf e19"}, {"id": 2095, "name": "angel face"}, {"id": 2096, "name": "angel fish"}, {"id": 2097, "name": "angel sculpture"}, {"id": 2098, "name": "angel statue"}, {"id": 2099, "name": "angel wing"}, {"id": 2100, "name": "angel wings"}, {"id": 2101, "name": "angel"}, {"id": 2102, "name": "angeles"}, {"id": 2103, "name": "angkor"}, {"id": 2104, "name": "angle bolt"}, {"id": 2105, "name": "angle statue"}, {"id": 2106, "name": "angle"}, {"id": 2107, "name": "angled arms"}, {"id": 2108, "name": "angled building"}, {"id": 2109, "name": "angled clock"}, {"id": 2110, "name": "angled corner"}, {"id": 2111, "name": "angled cut"}, {"id": 2112, "name": "angled door"}, {"id": 2113, "name": "angled edges"}, {"id": 2114, "name": "angled floors"}, {"id": 2115, "name": "angled hoof"}, {"id": 2116, "name": "angled lines"}, {"id": 2117, "name": "angled palm trees"}, {"id": 2118, "name": "angled roof"}, {"id": 2119, "name": "angled stand"}, {"id": 2120, "name": "angled wall"}, {"id": 2121, "name": "angler"}, {"id": 2122, "name": "angling club"}, {"id": 2123, "name": "angry bird"}, {"id": 2124, "name": "angry bird graphic"}, {"id": 2125, "name": "angry cat"}, {"id": 2126, "name": "aniaml"}, {"id": 2127, "name": "anima"}, {"id": 2128, "name": "animal area"}, {"id": 2129, "name": "animal back"}, {"id": 2130, "name": "animal backs"}, {"id": 2131, "name": "animal body"}, {"id": 2132, "name": "animal bones"}, {"id": 2133, "name": "animal box"}, {"id": 2134, "name": "animal cage"}, {"id": 2135, "name": "animal carcass"}, {"id": 2136, "name": "animal cutouts"}, {"id": 2137, "name": "animal decoration"}, {"id": 2138, "name": "animal display"}, {"id": 2139, "name": "animal dog"}, {"id": 2140, "name": "animal dogs"}, {"id": 2141, "name": "animal drawing"}, {"id": 2142, "name": "animal droppins"}, {"id": 2143, "name": "animal ear"}, {"id": 2144, "name": "animal eating"}, {"id": 2145, "name": "animal enclosure"}, {"id": 2146, "name": "animal exhibit"}, {"id": 2147, "name": "animal eye"}, {"id": 2148, "name": "animal face"}, {"id": 2149, "name": "animal feet"}, {"id": 2150, "name": "animal field"}, {"id": 2151, "name": "animal figurine"}, {"id": 2152, "name": "animal fur"}, {"id": 2153, "name": "animal group"}, {"id": 2154, "name": "animal hair"}, {"id": 2155, "name": "animal has horns"}, {"id": 2156, "name": "animal hat"}, {"id": 2157, "name": "animal head"}, {"id": 2158, "name": "animal heads"}, {"id": 2159, "name": "animal herd"}, {"id": 2160, "name": "animal impression"}, {"id": 2161, "name": "animal kites"}, {"id": 2162, "name": "animal leg"}, {"id": 2163, "name": "animal legs"}, {"id": 2164, "name": "animal lying"}, {"id": 2165, "name": "animal magazine"}, {"id": 2166, "name": "animal nose"}, {"id": 2167, "name": "animal on star"}, {"id": 2168, "name": "animal ornaments"}, {"id": 2169, "name": "animal park"}, {"id": 2170, "name": "animal part"}, {"id": 2171, "name": "animal pasture"}, {"id": 2172, "name": "animal paws"}, {"id": 2173, "name": "animal pen"}, {"id": 2174, "name": "animal pens"}, {"id": 2175, "name": "animal print"}, {"id": 2176, "name": "animal prints"}, {"id": 2177, "name": "animal reflections"}, {"id": 2178, "name": "animal sanctuary"}, {"id": 2179, "name": "animal shape"}, {"id": 2180, "name": "animal shelter"}, {"id": 2181, "name": "animal sitting"}, {"id": 2182, "name": "animal skeleton"}, {"id": 2183, "name": "animal skin"}, {"id": 2184, "name": "animal slipper"}, {"id": 2185, "name": "animal statue"}, {"id": 2186, "name": "animal tounge"}, {"id": 2187, "name": "animal toy"}, {"id": 2188, "name": "animal track"}, {"id": 2189, "name": "animal tracks"}, {"id": 2190, "name": "animal trainer"}, {"id": 2191, "name": "animal with tail"}, {"id": 2192, "name": "animal"}, {"id": 2193, "name": "animalcorner"}, {"id": 2194, "name": "animals chest"}, {"id": 2195, "name": "animals ear"}, {"id": 2196, "name": "animals extension"}, {"id": 2197, "name": "animals eye"}, {"id": 2198, "name": "animals eyes"}, {"id": 2199, "name": "animals face"}, {"id": 2200, "name": "animals grazing"}, {"id": 2201, "name": "animals head"}, {"id": 2202, "name": "animals herd"}, {"id": 2203, "name": "animals horn"}, {"id": 2204, "name": "animals legs"}, {"id": 2205, "name": "animals neck"}, {"id": 2206, "name": "animalskeleton"}, {"id": 2207, "name": "animap"}, {"id": 2208, "name": "animated bird"}, {"id": 2209, "name": "animated character"}, {"id": 2210, "name": "animated kid"}, {"id": 2211, "name": "animated person"}, {"id": 2212, "name": "animation figure"}, {"id": 2213, "name": "anime face"}, {"id": 2214, "name": "aniseed"}, {"id": 2215, "name": "ankara shirt"}, {"id": 2216, "name": "ankle attachment"}, {"id": 2217, "name": "ankle band"}, {"id": 2218, "name": "ankle bands"}, {"id": 2219, "name": "ankle brace"}, {"id": 2220, "name": "ankle bracelet"}, {"id": 2221, "name": "ankle braces"}, {"id": 2222, "name": "ankle cable"}, {"id": 2223, "name": "ankle chained"}, {"id": 2224, "name": "ankle cord"}, {"id": 2225, "name": "ankle guard"}, {"id": 2226, "name": "ankle harness"}, {"id": 2227, "name": "ankle leash"}, {"id": 2228, "name": "ankle pad"}, {"id": 2229, "name": "ankle rope"}, {"id": 2230, "name": "ankle sock"}, {"id": 2231, "name": "ankle socks"}, {"id": 2232, "name": "ankle strap"}, {"id": 2233, "name": "ankle support"}, {"id": 2234, "name": "ankle tie"}, {"id": 2235, "name": "ankle weight"}, {"id": 2236, "name": "ankle weights"}, {"id": 2237, "name": "ankle wrap"}, {"id": 2238, "name": "ankle wraps"}, {"id": 2239, "name": "ankle"}, {"id": 2240, "name": "anklet"}, {"id": 2241, "name": "anklet sock"}, {"id": 2242, "name": 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{"id": 4270, "name": "banana peeling"}, {"id": 4271, "name": "banana peels"}, {"id": 4272, "name": "banana pepper"}, {"id": 4273, "name": "banana peppers"}, {"id": 4274, "name": "banana piece"}, {"id": 4275, "name": "banana pile"}, {"id": 4276, "name": "banana plant"}, {"id": 4277, "name": "banana plantation"}, {"id": 4278, "name": "banana plate"}, {"id": 4279, "name": "banana row"}, {"id": 4280, "name": "banana sandwich"}, {"id": 4281, "name": "banana sap"}, {"id": 4282, "name": "banana seller"}, {"id": 4283, "name": "banana shadow"}, {"id": 4284, "name": "banana shop"}, {"id": 4285, "name": "banana skin"}, {"id": 4286, "name": "banana slice"}, {"id": 4287, "name": "banana slices"}, {"id": 4288, "name": "banana split"}, {"id": 4289, "name": "banana spots"}, {"id": 4290, "name": "banana stack"}, {"id": 4291, "name": "banana stalk"}, {"id": 4292, "name": "banana stall"}, {"id": 4293, "name": "banana stand"}, {"id": 4294, "name": "banana stem"}, {"id": 4295, "name": "banana sticker"}, {"id": 4296, "name": "banana stock"}, {"id": 4297, "name": "banana suit"}, {"id": 4298, "name": "banana table"}, {"id": 4299, "name": "banana tip"}, {"id": 4300, "name": "banana tips"}, {"id": 4301, "name": "banana top"}, {"id": 4302, "name": "banana tops"}, {"id": 4303, "name": "banana toy"}, {"id": 4304, "name": "banana tree"}, {"id": 4305, "name": "banana trees"}, {"id": 4306, "name": "banana trunk"}, {"id": 4307, "name": "banana"}, {"id": 4308, "name": "bananabanana"}, {"id": 4309, "name": "bananabunch"}, {"id": 4310, "name": "bananameat"}, {"id": 4311, "name": "bananans"}, {"id": 4312, "name": "bananas cherries"}, {"id": 4313, "name": "bananas are green"}, {"id": 4314, "name": "bananas bulb"}, {"id": 4315, "name": "bananas bunch"}, {"id": 4316, "name": "bananas ground"}, {"id": 4317, "name": "bananas hanging"}, {"id": 4318, "name": "bananas in a crate"}, {"id": 4319, "name": "bananas left eye"}, {"id": 4320, "name": "bananas not ripe yet"}, {"id": 4321, "name": "bananas paper"}, 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{"id": 14166, "name": "cat statue"}, {"id": 14167, "name": "cat sticker"}, {"id": 14168, "name": "cat suitcase"}, {"id": 14169, "name": "cat sun"}, {"id": 14170, "name": "cat table"}, {"id": 14171, "name": "cat tail"}, {"id": 14172, "name": "cat tails"}, {"id": 14173, "name": "cat tie"}, {"id": 14174, "name": "cat tongue"}, {"id": 14175, "name": "cat tower"}, {"id": 14176, "name": "cat toy"}, {"id": 14177, "name": "cat toys"}, {"id": 14178, "name": "cat tree"}, {"id": 14179, "name": "cat utensils"}, {"id": 14180, "name": "cat walk"}, {"id": 14181, "name": "cat whisker"}, {"id": 14182, "name": "cat whiskers"}, {"id": 14183, "name": "cat window"}, {"id": 14184, "name": "cat with gray fur"}, {"id": 14185, "name": "cat with green eyes"}, {"id": 14186, "name": "cat"}, {"id": 14187, "name": "catalog"}, {"id": 14188, "name": "catalogue"}, {"id": 14189, "name": "catalunya sign"}, {"id": 14190, "name": "catamaran"}, {"id": 14191, "name": "catbaby"}, {"id": 14192, "name": "catbox"}, {"id": 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{"id": 22066, "name": "doorstep"}, {"id": 22067, "name": "doorstop"}, {"id": 22068, "name": "doorstopper"}, {"id": 22069, "name": "doort"}, {"id": 22070, "name": "doorwa"}, {"id": 22071, "name": "doorwall"}, {"id": 22072, "name": "doorway cut"}, {"id": 22073, "name": "doorway frame"}, {"id": 22074, "name": "doorway has door"}, {"id": 22075, "name": "doorway is open"}, {"id": 22076, "name": "doorway opening"}, {"id": 22077, "name": "doorway side"}, {"id": 22078, "name": "doorway top"}, {"id": 22079, "name": "doorway"}, {"id": 22080, "name": "doorways church"}, {"id": 22081, "name": "dooughnut"}, {"id": 22082, "name": "dooway"}, {"id": 22083, "name": "dor"}, {"id": 22084, "name": "dorag"}, {"id": 22085, "name": "doric column"}, {"id": 22086, "name": "doric sort of column"}, {"id": 22087, "name": "doris"}, {"id": 22088, "name": "dorm room"}, {"id": 22089, "name": "dormant shrubs"}, {"id": 22090, "name": "dormant tree"}, {"id": 22091, "name": "dormat"}, {"id": 22092, "name": "dormer 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puddle"}, {"id": 37228, "name": "large purse"}, {"id": 37229, "name": "large pyramid"}, {"id": 37230, "name": "large red"}, {"id": 37231, "name": "large red flower"}, {"id": 37232, "name": "large red ketchup"}, {"id": 37233, "name": "large red kite"}, {"id": 37234, "name": "large refrigerator"}, {"id": 37235, "name": "large rock"}, {"id": 37236, "name": "large rock formation"}, {"id": 37237, "name": "large rocks"}, {"id": 37238, "name": "large roof"}, {"id": 37239, "name": "large room"}, {"id": 37240, "name": "large round"}, {"id": 37241, "name": "large row"}, {"id": 37242, "name": "large rug"}, {"id": 37243, "name": "large runway"}, {"id": 37244, "name": "large rv"}, {"id": 37245, "name": "large salad"}, {"id": 37246, "name": "large sandwich"}, {"id": 37247, "name": "large scissors"}, {"id": 37248, "name": "large screen"}, {"id": 37249, "name": "large screw"}, {"id": 37250, "name": "large seawall"}, {"id": 37251, "name": "large shadow"}, {"id": 37252, "name": "large sheep"}, {"id": 37253, "name": "large shelving"}, {"id": 37254, "name": "large ship"}, {"id": 37255, "name": "large sign"}, {"id": 37256, "name": "large ski chalet"}, {"id": 37257, "name": "large skyscraper"}, {"id": 37258, "name": "large slice"}, {"id": 37259, "name": "large soup pan"}, {"id": 37260, "name": "large spoked"}, {"id": 37261, "name": "large spoons"}, {"id": 37262, "name": "large spot"}, {"id": 37263, "name": "large spot light"}, {"id": 37264, "name": "large spots"}, {"id": 37265, "name": "large stack"}, {"id": 37266, "name": "large stalk"}, {"id": 37267, "name": "large stem"}, {"id": 37268, "name": "large stick"}, {"id": 37269, "name": "large stone"}, {"id": 37270, "name": "large stones"}, {"id": 37271, "name": "large suitcase"}, {"id": 37272, "name": "large t"}, {"id": 37273, "name": "large table"}, {"id": 37274, "name": "large tables"}, {"id": 37275, "name": "large tan"}, {"id": 37276, "name": "large tan line"}, {"id": 37277, "name": "large teddybear"}, {"id": 37278, "name": "large 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water"}, {"id": 37305, "name": "large waterfall"}, {"id": 37306, "name": "large wave"}, {"id": 37307, "name": "large wheel"}, {"id": 37308, "name": "large wheels"}, {"id": 37309, "name": "large white"}, {"id": 37310, "name": "large white boat"}, {"id": 37311, "name": "large white dish"}, {"id": 37312, "name": "large white orb"}, {"id": 37313, "name": "large white slabs"}, {"id": 37314, "name": "large white spot"}, {"id": 37315, "name": "large white stone"}, {"id": 37316, "name": "large white tusks"}, {"id": 37317, "name": "large white van"}, {"id": 37318, "name": "large window"}, {"id": 37319, "name": "large windows"}, {"id": 37320, "name": "large windshield"}, {"id": 37321, "name": "large wing"}, {"id": 37322, "name": "large woman"}, {"id": 37323, "name": "large wooden branch"}, {"id": 37324, "name": "large yellow talons"}, {"id": 37325, "name": "large"}, {"id": 37326, "name": "largeblack bag"}, {"id": 37327, "name": "largebody water"}, {"id": 37328, "name": "largebrown rock"}, {"id": 37329, "name": "largebrown window"}, {"id": 37330, "name": "largeclock"}, {"id": 37331, "name": "largecrane"}, {"id": 37332, "name": "largefish aquarium"}, {"id": 37333, "name": "largegray pole"}, {"id": 37334, "name": "largegreen leaf"}, {"id": 37335, "name": "largegreen tree"}, {"id": 37336, "name": "largemetal hoop"}, {"id": 37337, "name": "largemodel plane"}, {"id": 37338, "name": "largenumber"}, {"id": 37339, "name": "largeoutdoor trashcan"}, {"id": 37340, "name": "largepatchofgrass"}, {"id": 37341, "name": "largepavement stairs"}, {"id": 37342, "name": "larger"}, {"id": 37343, "name": "larger bell"}, {"id": 37344, "name": "larger boulder"}, {"id": 37345, "name": "larger building"}, {"id": 37346, "name": "larger fork"}, {"id": 37347, "name": "larger one"}, {"id": 37348, "name": "larger sheep"}, {"id": 37349, "name": "larger tub"}, {"id": 37350, "name": "larger wooden"}, {"id": 37351, "name": "largerbears head"}, {"id": 37352, "name": "largertower"}, {"id": 37353, "name": "largesnow mound"}, {"id": 37354, "name": "largest"}, {"id": 37355, "name": "largest base"}, {"id": 37356, "name": "largest tree"}, {"id": 37357, "name": "largest umbrella"}, {"id": 37358, "name": "largestore window"}, {"id": 37359, "name": "largetelephoto lens"}, {"id": 37360, "name": "largetennis racket"}, {"id": 37361, "name": "largetrash bin"}, {"id": 37362, "name": "largetree trunk"}, {"id": 37363, "name": "largetruck"}, {"id": 37364, "name": "largewhite buiding"}, {"id": 37365, "name": "largewidetall beam"}, {"id": 37366, "name": "largeyellow bin"}, {"id": 37367, "name": "lark"}, {"id": 37368, "name": "las vegas"}, {"id": 37369, "name": "las vegas blvd"}, {"id": 37370, "name": "lasagna"}, {"id": 37371, "name": "lasagne"}, {"id": 37372, "name": "laser light"}, {"id": 37373, "name": "laser printer"}, {"id": 37374, "name": "lash"}, {"id": 37375, "name": "lasso"}, {"id": 37376, "name": "last"}, {"id": 37377, "name": "last ball"}, {"id": 37378, "name": "last car"}, {"id": 37379, 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"name": "laying down"}, {"id": 37486, "name": "laying face up"}, {"id": 37487, "name": "laying on"}, {"id": 37488, "name": "laying on the dirt"}, {"id": 37489, "name": "laying person"}, {"id": 37490, "name": "laying woman"}, {"id": 37491, "name": "laynard"}, {"id": 37492, "name": "laynyard"}, {"id": 37493, "name": "layout"}, {"id": 37494, "name": "lays logo"}, {"id": 37495, "name": "lazy susan"}, {"id": 37496, "name": "lazysusan"}, {"id": 37497, "name": "lcd"}, {"id": 37498, "name": "lcd hd tvs"}, {"id": 37499, "name": "lcd screen"}, {"id": 37500, "name": "lcd tv"}, {"id": 37501, "name": "lcock face"}, {"id": 37502, "name": "lcs"}, {"id": 37503, "name": "le petit"}, {"id": 37504, "name": "le"}, {"id": 37505, "name": "lea"}, {"id": 37506, "name": "leach"}, {"id": 37507, "name": "lead car"}, {"id": 37508, "name": "lead plane"}, {"id": 37509, "name": "lead"}, {"id": 37510, "name": "leader"}, {"id": 37511, "name": "leading"}, {"id": 37512, "name": "leading down"}, {"id": 37513, "name": 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{"id": 37618, "name": "leather back"}, {"id": 37619, "name": "leather bag"}, {"id": 37620, "name": "leather band"}, {"id": 37621, "name": "leather belt"}, {"id": 37622, "name": "leather boot"}, {"id": 37623, "name": "leather boots"}, {"id": 37624, "name": "leather chair"}, {"id": 37625, "name": "leather chaps"}, {"id": 37626, "name": "leather coat"}, {"id": 37627, "name": "leather couch"}, {"id": 37628, "name": "leather cushion"}, {"id": 37629, "name": "leather glove"}, {"id": 37630, "name": "leather gloves"}, {"id": 37631, "name": "leather harness"}, {"id": 37632, "name": "leather jacket"}, {"id": 37633, "name": "leather lace"}, {"id": 37634, "name": "leather leash"}, {"id": 37635, "name": "leather like"}, {"id": 37636, "name": "leather ottoman"}, {"id": 37637, "name": "leather outfit"}, {"id": 37638, "name": "leather pants"}, {"id": 37639, "name": "leather patch"}, {"id": 37640, "name": "leather pouch"}, {"id": 37641, "name": "leather reigns"}, {"id": 37642, "name": "leather 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{"id": 37668, "name": "leaveless trees"}, {"id": 37669, "name": "leaves and branches"}, {"id": 37670, "name": "leaves are brown"}, {"id": 37671, "name": "leaves are gree"}, {"id": 37672, "name": "leaves are green"}, {"id": 37673, "name": "leaves are lit"}, {"id": 37674, "name": "leaves are yellow"}, {"id": 37675, "name": "leaves canopy"}, {"id": 37676, "name": "leaves collected"}, {"id": 37677, "name": "leaves from the tree"}, {"id": 37678, "name": "leaves green"}, {"id": 37679, "name": "leaves hanging"}, {"id": 37680, "name": "leaves in trees"}, {"id": 37681, "name": "leaves litter"}, {"id": 37682, "name": "leaves of a palm"}, {"id": 37683, "name": "leaves of a plant"}, {"id": 37684, "name": "leaves of a tree"}, {"id": 37685, "name": "leaves of tree"}, {"id": 37686, "name": "leaves on a stalk"}, {"id": 37687, "name": "leaves on a tree"}, {"id": 37688, "name": "leaves on branches"}, {"id": 37689, "name": "leaves on pavement"}, {"id": 37690, "name": "leaves on the grass"}, {"id": 37691, "name": "leaves on the tree"}, {"id": 37692, "name": "leaves on the trees"}, {"id": 37693, "name": "leaves on tree"}, {"id": 37694, "name": "leaves on"}, {"id": 37695, "name": "leaves shadow"}, {"id": 37696, "name": "leaves tree"}, {"id": 37697, "name": "leaves trees"}, {"id": 37698, "name": "leaves tulips"}, {"id": 37699, "name": "leaves underside"}, {"id": 37700, "name": "leaves wall"}, {"id": 37701, "name": "leavesa"}, {"id": 37702, "name": "leavesbranches"}, {"id": 37703, "name": "leaveselephant"}, {"id": 37704, "name": "leavesgrass"}, {"id": 37705, "name": "leavesground"}, {"id": 37706, "name": "leavesstems"}, {"id": 37707, "name": "leavesvase"}, {"id": 37708, "name": "leaving"}, {"id": 37709, "name": "leavs"}, {"id": 37710, "name": "leck"}, {"id": 37711, "name": "lectern"}, {"id": 37712, "name": "lecttuce"}, {"id": 37713, "name": "lecture"}, {"id": 37714, "name": "led bulbs"}, {"id": 37715, "name": "led display"}, {"id": 37716, "name": "led letters"}, {"id": 37717, "name": "led 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37745, "name": "left armrest"}, {"id": 37746, "name": "left arrow"}, {"id": 37747, "name": "left back"}, {"id": 37748, "name": "left back leg"}, {"id": 37749, "name": "left back paw"}, {"id": 37750, "name": "left back wheel"}, {"id": 37751, "name": "left bank"}, {"id": 37752, "name": "left beam"}, {"id": 37753, "name": "left bear"}, {"id": 37754, "name": "left bench"}, {"id": 37755, "name": "left bicep"}, {"id": 37756, "name": "left black eye"}, {"id": 37757, "name": "left blade"}, {"id": 37758, "name": "left boat"}, {"id": 37759, "name": "left boot"}, {"id": 37760, "name": "left bottom corner"}, {"id": 37761, "name": "left bow"}, {"id": 37762, "name": "left bowl"}, {"id": 37763, "name": "left brake"}, {"id": 37764, "name": "left brake light"}, {"id": 37765, "name": "left brakelight"}, {"id": 37766, "name": "left breast"}, {"id": 37767, "name": "left breast place"}, {"id": 37768, "name": "left buckle"}, {"id": 37769, "name": "left buildings"}, {"id": 37770, "name": "left bus"}, {"id": 37771, "name": "left button"}, {"id": 37772, "name": "left calve"}, {"id": 37773, "name": "left candle"}, {"id": 37774, "name": "left cap"}, {"id": 37775, "name": "left chain"}, {"id": 37776, "name": "left chair"}, {"id": 37777, "name": "left cheek"}, {"id": 37778, "name": "left chest area"}, {"id": 37779, "name": "left claw"}, {"id": 37780, "name": "left click button"}, {"id": 37781, "name": "left clock face"}, {"id": 37782, "name": "left collar"}, {"id": 37783, "name": "left container"}, {"id": 37784, "name": "left corner"}, {"id": 37785, "name": "left curve"}, {"id": 37786, "name": "left donut"}, {"id": 37787, "name": "left door"}, {"id": 37788, "name": "left drawers"}, {"id": 37789, "name": "left ear"}, {"id": 37790, "name": "left ear is pink"}, {"id": 37791, "name": "left earphon"}, {"id": 37792, "name": "left earring"}, {"id": 37793, "name": "left ee"}, {"id": 37794, "name": "left elbow"}, {"id": 37795, "name": "left end"}, {"id": 37796, "name": "left engine"}, {"id": 37797, "name": "left exhaust"}, {"id": 37798, "name": "left eye"}, {"id": 37799, "name": "left eye of a dog"}, {"id": 37800, "name": "left eye of a person"}, {"id": 37801, "name": "left eye of goat"}, {"id": 37802, "name": "left eyebrow"}, {"id": 37803, "name": "left faucet"}, {"id": 37804, "name": "left faucet handle"}, {"id": 37805, "name": "left field"}, {"id": 37806, "name": "left fielder"}, {"id": 37807, "name": "left finger"}, {"id": 37808, "name": "left fingers"}, {"id": 37809, "name": "left fist"}, {"id": 37810, "name": "left flip flop"}, {"id": 37811, "name": "left foot"}, {"id": 37812, "name": "left foot heel"}, {"id": 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": 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"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": 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{"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": 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"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": 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{"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": 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"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": 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"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 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38853, "name": "liines"}, {"id": 38854, "name": "like"}, {"id": 38855, "name": "like clown"}, {"id": 38856, "name": "like horses"}, {"id": 38857, "name": "lilac bush"}, {"id": 38858, "name": "lilac hint"}, {"id": 38859, "name": "lilac tree"}, {"id": 38860, "name": "lilac"}, {"id": 38861, "name": "lillie"}, {"id": 38862, "name": "lilly"}, {"id": 38863, "name": "lilly pad"}, {"id": 38864, "name": "lillypad"}, {"id": 38865, "name": "lily flower"}, {"id": 38866, "name": "lily pad"}, {"id": 38867, "name": "lily pads"}, {"id": 38868, "name": "lily"}, {"id": 38869, "name": "lilypad"}, {"id": 38870, "name": "lilypads"}, {"id": 38871, "name": "lima bean"}, {"id": 38872, "name": "lima beans"}, {"id": 38873, "name": "limb"}, {"id": 38874, "name": "lime bag"}, {"id": 38875, "name": "lime bean"}, {"id": 38876, "name": "lime beverage"}, {"id": 38877, "name": "lime green"}, {"id": 38878, "name": "lime green section"}, {"id": 38879, "name": "lime green tshirt"}, {"id": 38880, "name": "lime half"}, {"id": 38881, "name": "lime jacket"}, {"id": 38882, "name": "lime juice"}, {"id": 38883, "name": "lime piece"}, {"id": 38884, "name": "lime pile"}, {"id": 38885, "name": "lime shirt"}, {"id": 38886, "name": "lime slice"}, {"id": 38887, "name": "lime sliceglass"}, {"id": 38888, "name": "lime squeezer"}, {"id": 38889, "name": "lime tree"}, {"id": 38890, "name": "lime twist"}, {"id": 38891, "name": "lime"}, {"id": 38892, "name": "limepo"}, {"id": 38893, "name": "limes 4 for 100"}, {"id": 38894, "name": "limewhite food"}, {"id": 38895, "name": "limit sign"}, {"id": 38896, "name": "limit"}, {"id": 38897, "name": "limited no 2"}, {"id": 38898, "name": "limo"}, {"id": 38899, "name": "limosine"}, {"id": 38900, "name": "limousine"}, {"id": 38901, "name": "lincense"}, {"id": 38902, "name": "lincol st sign"}, {"id": 38903, "name": "lincoln"}, {"id": 38904, "name": "linda and carl"}, {"id": 38905, "name": "lindquist"}, {"id": 38906, "name": "lindsay davenport"}, {"id": 38907, "name": "line"}, {"id": 38908, "name": "line background"}, {"id": 38909, "name": "line border"}, {"id": 38910, "name": "line break"}, {"id": 38911, "name": "line change"}, {"id": 38912, "name": "line design"}, {"id": 38913, "name": "line dirt"}, {"id": 38914, "name": "line edge"}, {"id": 38915, "name": "line for crosswalk"}, {"id": 38916, "name": "line ground"}, {"id": 38917, "name": "line in ceiling"}, {"id": 38918, "name": "line in the road"}, {"id": 38919, "name": "line is painted"}, {"id": 38920, "name": "line is white"}, {"id": 38921, "name": "line judge"}, {"id": 38922, "name": "line judge on court"}, {"id": 38923, "name": "line junction"}, {"id": 38924, "name": "line machine"}, {"id": 38925, "name": "line man"}, {"id": 38926, "name": "line mark"}, {"id": 38927, "name": "line markers"}, {"id": 38928, "name": "line markings"}, {"id": 38929, "name": "line of cars"}, {"id": 38930, "name": "line of people"}, {"id": 38931, "name": "line of street light"}, {"id": 38932, "name": "line of thin"}, {"id": 38933, "name": "line of trees"}, {"id": 38934, "name": "line on face"}, {"id": 38935, "name": "line on road"}, {"id": 38936, "name": "line on silver"}, {"id": 38937, "name": "line on the grass"}, {"id": 38938, "name": "line on the ground"}, {"id": 38939, "name": "line on the pavement"}, {"id": 38940, "name": "line on top"}, {"id": 38941, "name": "line painted"}, {"id": 38942, "name": "line part"}, {"id": 38943, "name": "line poll"}, {"id": 38944, "name": "line post"}, {"id": 38945, "name": "line racks"}, {"id": 38946, "name": "line ripples"}, {"id": 38947, "name": "line road"}, {"id": 38948, "name": "line seperators"}, {"id": 38949, "name": "line shorts"}, {"id": 38950, "name": "line sky"}, {"id": 38951, "name": "line street"}, {"id": 38952, "name": "line through"}, {"id": 38953, "name": "line tie"}, {"id": 38954, "name": "line tracks"}, {"id": 38955, "name": "line truck"}, {"id": 38956, "name": "line"}, {"id": 38957, "name": "linebacker"}, {"id": 38958, "name": "linecourt"}, {"id": 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{"id": 41684, "name": "metal casing"}, {"id": 41685, "name": "metal catch"}, {"id": 41686, "name": "metal chain"}, {"id": 41687, "name": "metal chain link"}, {"id": 41688, "name": "metal chains"}, {"id": 41689, "name": "metal chair"}, {"id": 41690, "name": "metal chairs"}, {"id": 41691, "name": "metal circle"}, {"id": 41692, "name": "metal clamp"}, {"id": 41693, "name": "metal clasp"}, {"id": 41694, "name": "metal clasps"}, {"id": 41695, "name": "metal claw"}, {"id": 41696, "name": "metal cleats"}, {"id": 41697, "name": "metal clip"}, {"id": 41698, "name": "metal clock"}, {"id": 41699, "name": "metal column"}, {"id": 41700, "name": "metal connector"}, {"id": 41701, "name": "metal connectors"}, {"id": 41702, "name": "metal container"}, {"id": 41703, "name": "metal containers"}, {"id": 41704, "name": "metal contraption"}, {"id": 41705, "name": "metal corner"}, {"id": 41706, "name": "metal corners"}, {"id": 41707, "name": "metal cover"}, {"id": 41708, "name": "metal crate"}, {"id": 41709, 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{"id": 41760, "name": "metal grating"}, {"id": 41761, "name": "metal grid"}, {"id": 41762, "name": "metal grill"}, {"id": 41763, "name": "metal guard rail"}, {"id": 41764, "name": "metal guardrail"}, {"id": 41765, "name": "metal gutter"}, {"id": 41766, "name": "metal handle"}, {"id": 41767, "name": "metal handlebars"}, {"id": 41768, "name": "metal handles"}, {"id": 41769, "name": "metal handrail"}, {"id": 41770, "name": "metal hanger"}, {"id": 41771, "name": "metal hardware"}, {"id": 41772, "name": "metal head"}, {"id": 41773, "name": "metal hinge"}, {"id": 41774, "name": "metal hinges"}, {"id": 41775, "name": "metal holder"}, {"id": 41776, "name": "metal hook"}, {"id": 41777, "name": "metal hoop"}, {"id": 41778, "name": "metal hoops"}, {"id": 41779, "name": "metal horse"}, {"id": 41780, "name": "metal hose"}, {"id": 41781, "name": "metal instrument"}, {"id": 41782, "name": "metal is brown"}, {"id": 41783, "name": "metal is rusted"}, {"id": 41784, "name": "metal is white"}, {"id": 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{"id": 41811, "name": "metal mat"}, {"id": 41812, "name": "metal mesh"}, {"id": 41813, "name": "metal microwave"}, {"id": 41814, "name": "metal mount"}, {"id": 41815, "name": "metal nozzle"}, {"id": 41816, "name": "metal nut"}, {"id": 41817, "name": "metal object"}, {"id": 41818, "name": "metal objects"}, {"id": 41819, "name": "metal on shower head"}, {"id": 41820, "name": "metal on the walls"}, {"id": 41821, "name": "metal oven"}, {"id": 41822, "name": "metal overhang"}, {"id": 41823, "name": "metal pail"}, {"id": 41824, "name": "metal pan"}, {"id": 41825, "name": "metal pane"}, {"id": 41826, "name": "metal panel"}, {"id": 41827, "name": "metal panels"}, {"id": 41828, "name": "metal pans"}, {"id": 41829, "name": "metal parking meter"}, {"id": 41830, "name": "metal part"}, {"id": 41831, "name": "metal partition"}, {"id": 41832, "name": "metal parts"}, {"id": 41833, "name": "metal patch"}, {"id": 41834, "name": "metal path"}, {"id": 41835, "name": "metal patio"}, {"id": 41836, "name": 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"name": "metal steps"}, {"id": 41939, "name": "metal stool"}, {"id": 41940, "name": "metal stove"}, {"id": 41941, "name": "metal strap"}, {"id": 41942, "name": "metal streetlight"}, {"id": 41943, "name": "metal strip"}, {"id": 41944, "name": "metal strips"}, {"id": 41945, "name": "metal structure"}, {"id": 41946, "name": "metal structures"}, {"id": 41947, "name": "metal struts"}, {"id": 41948, "name": "metal stub"}, {"id": 41949, "name": "metal stud"}, {"id": 41950, "name": "metal studs"}, {"id": 41951, "name": "metal sunglasses"}, {"id": 41952, "name": "metal support"}, {"id": 41953, "name": "metal supports"}, {"id": 41954, "name": "metal supprts"}, {"id": 41955, "name": "metal surface"}, {"id": 41956, "name": "metal switch"}, {"id": 41957, "name": "metal tab"}, {"id": 41958, "name": "metal table"}, {"id": 41959, "name": "metal tag"}, {"id": 41960, "name": "metal tags"}, {"id": 41961, "name": "metal tank"}, {"id": 41962, "name": "metal tanks"}, {"id": 41963, "name": "metal thing"}, {"id": 41964, "name": "metal tin"}, {"id": 41965, "name": "metal tip"}, {"id": 41966, "name": "metal toaster"}, {"id": 41967, "name": "metal tongs"}, {"id": 41968, "name": "metal top"}, {"id": 41969, "name": "metal top of bucket"}, {"id": 41970, "name": "metal top part"}, {"id": 41971, "name": "metal tops"}, {"id": 41972, "name": "metal tower"}, {"id": 41973, "name": "metal towers"}, {"id": 41974, "name": "metal track"}, {"id": 41975, "name": "metal tracks"}, {"id": 41976, "name": "metal train"}, {"id": 41977, "name": "metal train track"}, {"id": 41978, "name": "metal train tracks"}, {"id": 41979, "name": "metal trash"}, {"id": 41980, "name": "metal trashcan"}, {"id": 41981, "name": "metal tray"}, {"id": 41982, "name": "metal trim"}, {"id": 41983, "name": "metal trough"}, {"id": 41984, "name": "metal tub"}, {"id": 41985, "name": "metal tube"}, {"id": 41986, "name": "metal tubing"}, {"id": 41987, "name": "metal utensil"}, {"id": 41988, "name": "metal valve"}, {"id": 41989, "name": "metal vase"}, {"id": 41990, "name": "metal vent"}, {"id": 41991, "name": "metal wagon"}, {"id": 41992, "name": "metal walkway"}, {"id": 41993, "name": "metal wall"}, {"id": 41994, "name": "metal waterbottle"}, {"id": 41995, "name": "metal wheel"}, {"id": 41996, "name": "metal whisk"}, {"id": 41997, "name": "metal window"}, {"id": 41998, "name": "metal wing"}, {"id": 41999, "name": "metal wire"}, {"id": 42000, "name": "metal wires"}, {"id": 42001, "name": "metal with bumps"}, {"id": 42002, "name": "metal work"}, {"id": 42003, "name": "metal wrist watch"}, {"id": 42004, "name": "metal zipper"}, {"id": 42005, "name": "metal"}, {"id": 42006, "name": "metalbars"}, {"id": 42007, "name": "metalbike"}, {"id": 42008, "name": "metalcake"}, {"id": 42009, "name": "metalfence"}, {"id": 42010, "name": "metalgray pole"}, {"id": 42011, "name": "metalhandle"}, {"id": 42012, "name": "metalic"}, {"id": 42013, "name": "metalic bag"}, {"id": 42014, "name": "metallic"}, {"id": 42015, "name": "metallic ball"}, {"id": 42016, "name": "metallic blue"}, {"id": 42017, "name": "metallic braces"}, {"id": 42018, "name": "metallic clock"}, {"id": 42019, "name": "metallic container"}, {"id": 42020, "name": "metallic door"}, {"id": 42021, "name": "metallic fence"}, {"id": 42022, "name": "metallic knife"}, {"id": 42023, "name": "metallic object"}, {"id": 42024, "name": "metallic pole"}, {"id": 42025, "name": "metallic pump"}, {"id": 42026, "name": "metallic rod"}, {"id": 42027, "name": "metallic surface"}, {"id": 42028, "name": "metallic tap"}, {"id": 42029, "name": "metallic vase"}, {"id": 42030, "name": "metallic wall"}, {"id": 42031, "name": "metalmade"}, {"id": 42032, "name": "metalmanhole cover"}, {"id": 42033, "name": "metalpole"}, {"id": 42034, "name": "metalpost"}, {"id": 42035, "name": "metalpush handles"}, {"id": 42036, "name": "metalrail"}, {"id": 42037, "name": "metalridge"}, {"id": 42038, "name": "metalroof"}, {"id": 42039, "name": "metalsign"}, {"id": 42040, "name": "metalsink 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42066, "name": "meter unit"}, {"id": 42067, "name": "meter window"}, {"id": 42068, "name": "meter word"}, {"id": 42069, "name": "meter"}, {"id": 42070, "name": "metered"}, {"id": 42071, "name": "metered parking"}, {"id": 42072, "name": "meters pole"}, {"id": 42073, "name": "methow valley"}, {"id": 42074, "name": "metla tower"}, {"id": 42075, "name": "metors"}, {"id": 42076, "name": "metre"}, {"id": 42077, "name": "metro"}, {"id": 42078, "name": "metro bus"}, {"id": 42079, "name": "metro is a word"}, {"id": 42080, "name": "metro line"}, {"id": 42081, "name": "metro liner"}, {"id": 42082, "name": "metro map"}, {"id": 42083, "name": "metro sign"}, {"id": 42084, "name": "metro station"}, {"id": 42085, "name": "metro train"}, {"id": 42086, "name": "metrobus"}, {"id": 42087, "name": "metroline"}, {"id": 42088, "name": "metronome"}, {"id": 42089, "name": "metropcs banner"}, {"id": 42090, "name": "metropolis"}, {"id": 42091, "name": "metropolitan"}, {"id": 42092, "name": "metropolitan area"}, {"id": 42093, "name": "metrosign"}, {"id": 42094, "name": "mets"}, {"id": 42095, "name": "mets logo"}, {"id": 42096, "name": "mets player"}, {"id": 42097, "name": "mettle"}, {"id": 42098, "name": "mexicali"}, {"id": 42099, "name": "mexican"}, {"id": 42100, "name": "mexican dress"}, {"id": 42101, "name": "mexican flag"}, {"id": 42102, "name": "mexican food"}, {"id": 42103, "name": "mexico"}, {"id": 42104, "name": "mezzanine"}, {"id": 42105, "name": "mhp"}, {"id": 42106, "name": "miami"}, {"id": 42107, "name": "mic"}, {"id": 42108, "name": "mic boom"}, {"id": 42109, "name": "mic jack"}, {"id": 42110, "name": "mic pack"}, {"id": 42111, "name": "mic sheets"}, {"id": 42112, "name": "mic stand"}, {"id": 42113, "name": "mic vents"}, {"id": 42114, "name": "mica table"}, {"id": 42115, "name": "michael"}, {"id": 42116, "name": "michael bard"}, {"id": 42117, "name": "michael jackson"}, {"id": 42118, "name": "michigan"}, {"id": 42119, "name": "michigan state logo"}, {"id": 42120, "name": 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47078, "name": "palm tree"}, {"id": 47079, "name": "palm treeleaves"}, {"id": 47080, "name": "palm trees"}, {"id": 47081, "name": "palm trees in front"}, {"id": 47082, "name": "palm trees on beach"}, {"id": 47083, "name": "palm"}, {"id": 47084, "name": "palmagranettes"}, {"id": 47085, "name": "palmer"}, {"id": 47086, "name": "palmtree"}, {"id": 47087, "name": "palmtrees"}, {"id": 47088, "name": "palne"}, {"id": 47089, "name": "palomino"}, {"id": 47090, "name": "palstic bottle"}, {"id": 47091, "name": "palte"}, {"id": 47092, "name": "paltform"}, {"id": 47093, "name": "pam"}, {"id": 47094, "name": "pampanga sign"}, {"id": 47095, "name": "pampas grass"}, {"id": 47096, "name": "pampers"}, {"id": 47097, "name": "pamphlet"}, {"id": 47098, "name": "pamplets"}, {"id": 47099, "name": "pamplona"}, {"id": 47100, "name": "pamt"}, {"id": 47101, "name": "pamts"}, {"id": 47102, "name": "pan corner"}, {"id": 47103, "name": "pan cover"}, {"id": 47104, "name": "pan cupboard"}, {"id": 47105, "name": "pan 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47132, "name": "panda enclosure"}, {"id": 47133, "name": "panda express"}, {"id": 47134, "name": "panda plushie"}, {"id": 47135, "name": "panda the word"}, {"id": 47136, "name": "panda tooth"}, {"id": 47137, "name": "panda"}, {"id": 47138, "name": "pandas body"}, {"id": 47139, "name": "pandas feet"}, {"id": 47140, "name": "pandas fur"}, {"id": 47141, "name": "pandas paw"}, {"id": 47142, "name": "pandora app"}, {"id": 47143, "name": "pane 2"}, {"id": 47144, "name": "pane 3"}, {"id": 47145, "name": "pane 4"}, {"id": 47146, "name": "pane 5"}, {"id": 47147, "name": "pane is large"}, {"id": 47148, "name": "pane of glass"}, {"id": 47149, "name": "pane"}, {"id": 47150, "name": "paned window"}, {"id": 47151, "name": "paned windows"}, {"id": 47152, "name": "panel floor"}, {"id": 47153, "name": "panel framing"}, {"id": 47154, "name": "panel lights"}, {"id": 47155, "name": "panel of buttons"}, {"id": 47156, "name": "panel of knobs"}, {"id": 47157, "name": "panel of lights"}, {"id": 47158, "name": "panel on door"}, {"id": 47159, "name": "panel on wetsuit"}, {"id": 47160, "name": "panel open"}, {"id": 47161, "name": "panel suspension"}, {"id": 47162, "name": "panel truck"}, {"id": 47163, "name": "panel tub"}, {"id": 47164, "name": "panel wall"}, {"id": 47165, "name": "panel"}, {"id": 47166, "name": "paneled"}, {"id": 47167, "name": "paneled door"}, {"id": 47168, "name": "paneled floor"}, {"id": 47169, "name": "paneled shutters"}, {"id": 47170, "name": "paneled wall"}, {"id": 47171, "name": "paneled window"}, {"id": 47172, "name": "paneling"}, {"id": 47173, "name": "paneling strip"}, {"id": 47174, "name": "paneling wall"}, {"id": 47175, "name": "panelled wall"}, {"id": 47176, "name": "panelling"}, {"id": 47177, "name": "panera bread"}, {"id": 47178, "name": "panini grill"}, {"id": 47179, "name": "panini maker"}, {"id": 47180, "name": "panini"}, {"id": 47181, "name": "pannel"}, {"id": 47182, "name": "panneling"}, {"id": 47183, "name": "pannier"}, {"id": 47184, "name": "pans 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{"id": 47314, "name": "paper placemat"}, {"id": 47315, "name": "paper plane"}, {"id": 47316, "name": "paper plate"}, {"id": 47317, "name": "paper plates"}, {"id": 47318, "name": "paper ramikin"}, {"id": 47319, "name": "paper ring"}, {"id": 47320, "name": "paper roll"}, {"id": 47321, "name": "paper roll holder"}, {"id": 47322, "name": "paper rolls"}, {"id": 47323, "name": "paper sack"}, {"id": 47324, "name": "paper scrap"}, {"id": 47325, "name": "paper sheet"}, {"id": 47326, "name": "paper shredder"}, {"id": 47327, "name": "paper sign"}, {"id": 47328, "name": "paper signs"}, {"id": 47329, "name": "paper sleeve"}, {"id": 47330, "name": "paper slip"}, {"id": 47331, "name": "paper slips"}, {"id": 47332, "name": "paper stack"}, {"id": 47333, "name": "paper stacks"}, {"id": 47334, "name": "paper stand"}, {"id": 47335, "name": "paper stick"}, {"id": 47336, "name": "paper strawberry"}, {"id": 47337, "name": "paper streamers"}, {"id": 47338, "name": "paper strip"}, {"id": 47339, "name": "paper 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"name": "sign for a musical"}, {"id": 59445, "name": "sign for parking"}, {"id": 59446, "name": "sign for the station"}, {"id": 59447, "name": "sign frames"}, {"id": 59448, "name": "sign hanger"}, {"id": 59449, "name": "sign hanging"}, {"id": 59450, "name": "sign has ipad"}, {"id": 59451, "name": "sign holder"}, {"id": 59452, "name": "sign in background"}, {"id": 59453, "name": "sign in front"}, {"id": 59454, "name": "sign in front window"}, {"id": 59455, "name": "sign is black"}, {"id": 59456, "name": "sign is blue"}, {"id": 59457, "name": "sign is colorful"}, {"id": 59458, "name": "sign is for buses"}, {"id": 59459, "name": "sign is green"}, {"id": 59460, "name": "sign is octagon"}, {"id": 59461, "name": "sign is on building"}, {"id": 59462, "name": "sign is on pole"}, {"id": 59463, "name": "sign is on post"}, {"id": 59464, "name": "sign is on sidewalk"}, {"id": 59465, "name": "sign is on street"}, {"id": 59466, "name": "sign is portable"}, {"id": 59467, "name": "sign is pretty 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"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 back"}, {"id": 59563, "name": "signs by road"}, {"id": 59564, "name": "signs grass"}, {"id": 59565, "name": "signs group"}, {"id": 59566, "name": "signs indicating sto"}, {"id": 59567, "name": "signs on partition"}, {"id": 59568, "name": "signs pair"}, {"id": 59569, "name": "signs 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{"id": 59597, "name": "siloh"}, {"id": 59598, "name": "silvder dot"}, {"id": 59599, "name": "silver black handl"}, {"id": 59600, "name": "silver faucet"}, {"id": 59601, "name": "silver 2"}, {"id": 59602, "name": "silver 2002"}, {"id": 59603, "name": "silver aluminum foil"}, {"id": 59604, "name": "silver and"}, {"id": 59605, "name": "silver and black"}, {"id": 59606, "name": "silver and blue"}, {"id": 59607, "name": "silver antenna"}, {"id": 59608, "name": "silver appliances"}, {"id": 59609, "name": "silver area"}, {"id": 59610, "name": "silver armor"}, {"id": 59611, "name": "silver back splash"}, {"id": 59612, "name": "silver backpack"}, {"id": 59613, "name": "silver bag"}, {"id": 59614, "name": "silver ball"}, {"id": 59615, "name": "silver balls"}, {"id": 59616, "name": "silver band"}, {"id": 59617, "name": "silver bands"}, {"id": 59618, "name": "silver bar"}, {"id": 59619, "name": "silver barrier"}, {"id": 59620, "name": "silver bars"}, {"id": 59621, "name": "silver base"}, {"id": 59622, "name": "silver bat"}, {"id": 59623, "name": "silver bathroom"}, {"id": 59624, "name": "silver bathtub"}, {"id": 59625, "name": "silver bats"}, {"id": 59626, "name": "silver bead"}, {"id": 59627, "name": "silver bear"}, {"id": 59628, "name": "silver belt"}, {"id": 59629, "name": "silver bench"}, {"id": 59630, "name": "silver bender"}, {"id": 59631, "name": "silver black"}, {"id": 59632, "name": "silver blade"}, {"id": 59633, "name": "silver boarder"}, {"id": 59634, "name": "silver body"}, {"id": 59635, "name": "silver bolt"}, {"id": 59636, "name": "silver bolts"}, {"id": 59637, "name": "silver border"}, {"id": 59638, "name": "silver bottle"}, {"id": 59639, "name": "silver bottom"}, {"id": 59640, "name": "silver bowl"}, {"id": 59641, "name": "silver bowls"}, {"id": 59642, "name": "silver box"}, {"id": 59643, "name": "silver bracelet"}, {"id": 59644, "name": "silver bracket"}, {"id": 59645, "name": "silver brake"}, {"id": 59646, "name": "silver brakes"}, {"id": 59647, "name": 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59697, "name": "silver facet"}, {"id": 59698, "name": "silver fan"}, {"id": 59699, "name": "silver faucet"}, {"id": 59700, "name": "silver feet"}, {"id": 59701, "name": "silver fence"}, {"id": 59702, "name": "silver fence in back"}, {"id": 59703, "name": "silver fencing"}, {"id": 59704, "name": "silver finding"}, {"id": 59705, "name": "silver fixture"}, {"id": 59706, "name": "silver foot"}, {"id": 59707, "name": "silver ford"}, {"id": 59708, "name": "silver foreman"}, {"id": 59709, "name": "silver fork"}, {"id": 59710, "name": "silver forks"}, {"id": 59711, "name": "silver frame"}, {"id": 59712, "name": "silver fridge"}, {"id": 59713, "name": "silver funnel"}, {"id": 59714, "name": "silver gate"}, {"id": 59715, "name": "silver glasses"}, {"id": 59716, "name": "silver goggles"}, {"id": 59717, "name": "silver grate"}, {"id": 59718, "name": "silver grates"}, {"id": 59719, "name": "silver greater"}, {"id": 59720, "name": "silver grill"}, {"id": 59721, "name": "silver grille"}, {"id": 59722, "name": "silver hair"}, {"id": 59723, "name": "silver hand brake"}, {"id": 59724, "name": "silver hand rail"}, {"id": 59725, "name": "silver handle"}, {"id": 59726, "name": "silver handlebar"}, {"id": 59727, "name": "silver handles"}, {"id": 59728, "name": "silver hands"}, {"id": 59729, "name": "silver hardware"}, {"id": 59730, "name": "silver hatch"}, {"id": 59731, "name": "silver headlight"}, {"id": 59732, "name": "silver helmet"}, {"id": 59733, "name": "silver hinge"}, {"id": 59734, "name": "silver hinges"}, {"id": 59735, "name": "silver holder"}, {"id": 59736, "name": "silver hook"}, {"id": 59737, "name": "silver hoop"}, {"id": 59738, "name": "silver hoop earrings"}, {"id": 59739, "name": "silver hose"}, {"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 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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, "name": "silver panel"}, {"id": 59791, "name": "silver pants"}, {"id": 59792, "name": "silver parked car"}, {"id": 59793, "name": "silver part"}, {"id": 59794, "name": "silver parts"}, {"id": 59795, "name": "silver patch"}, {"id": 59796, "name": "silver pedal"}, {"id": 59797, "name": "silver pedals"}, {"id": 59798, "name": "silver pen"}, {"id": 59799, "name": "silver phone"}, {"id": 59800, "name": "silver piece"}, {"id": 59801, "name": "silver pile"}, {"id": 59802, "name": "silver pipe"}, {"id": 59803, "name": "silver pipes"}, {"id": 59804, "name": "silver piping"}, {"id": 59805, "name": "silver pitcher"}, {"id": 59806, "name": "silver plane"}, {"id": 59807, "name": "silver planter"}, {"id": 59808, "name": "silver plate"}, {"id": 59809, "name": "silver platter"}, {"id": 59810, "name": "silver plumbing"}, {"id": 59811, "name": "silver pole"}, {"id": 59812, "name": "silver poles"}, {"id": 59813, "name": "silver portion"}, {"id": 59814, "name": "silver post"}, {"id": 59815, "name": "silver posts"}, {"id": 59816, "name": "silver pot"}, {"id": 59817, "name": "silver pots"}, {"id": 59818, "name": "silver prong"}, {"id": 59819, "name": "silver pull"}, {"id": 59820, "name": "silver rack"}, {"id": 59821, "name": "silver racket"}, {"id": 59822, "name": "silver rail"}, {"id": 59823, "name": "silver railing"}, {"id": 59824, "name": "silver railings"}, {"id": 59825, "name": "silver rails"}, {"id": 59826, "name": "silver refrigerator"}, {"id": 59827, "name": "silver ridge"}, {"id": 59828, "name": "silver ridges"}, {"id": 59829, "name": "silver rim"}, {"id": 59830, "name": "silver rims"}, {"id": 59831, "name": "silver ring"}, {"id": 59832, "name": "silver rings"}, {"id": 59833, "name": "silver rivets"}, {"id": 59834, "name": "silver roof"}, {"id": 59835, "name": "silver scissors"}, {"id": 59836, "name": "silver screw"}, {"id": 59837, "name": "silver screws"}, {"id": 59838, "name": "silver sedan"}, {"id": 59839, "name": "silver server"}, {"id": 59840, "name": "silver shirt"}, {"id": 59841, "name": "silver shoes"}, {"id": 59842, "name": "silver side"}, {"id": 59843, "name": "silver side mirror"}, {"id": 59844, "name": "silver sign"}, {"id": 59845, "name": "silver sink"}, {"id": 59846, "name": "silver sink facet"}, {"id": 59847, "name": "silver ski jacket"}, {"id": 59848, "name": "silver skis"}, {"id": 59849, "name": "silver slot"}, {"id": 59850, "name": "silver snap"}, {"id": 59851, "name": "silver spatuala"}, {"id": 59852, "name": "silver spatula"}, {"id": 59853, "name": "silver speaker"}, {"id": 59854, "name": "silver spindle"}, {"id": 59855, "name": "silver spiral"}, {"id": 59856, "name": "silver spokes"}, {"id": 59857, "name": "silver spoon"}, {"id": 59858, "name": "silver spoons"}, {"id": 59859, "name": "silver spot"}, {"id": 59860, "name": "silver spout"}, {"id": 59861, "name": "silver stand"}, {"id": 59862, "name": "silver star"}, {"id": 59863, "name": "silver steps"}, {"id": 59864, "name": "silver stereo"}, {"id": 59865, "name": "silver strip"}, {"id": 59866, "name": "silver stripes"}, {"id": 59867, "name": "silver studs"}, {"id": 59868, "name": "silver suitcase"}, {"id": 59869, "name": "silver support"}, {"id": 59870, "name": "silver surface"}, {"id": 59871, "name": "silver suv"}, {"id": 59872, "name": 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{"id": 65786, "name": "tall column"}, {"id": 65787, "name": "tall cone"}, {"id": 65788, "name": "tall cranes"}, {"id": 65789, "name": "tall distant"}, {"id": 65790, "name": "tall domed building"}, {"id": 65791, "name": "tall dry grass"}, {"id": 65792, "name": "tall fence"}, {"id": 65793, "name": "tall flag pole"}, {"id": 65794, "name": "tall furniture"}, {"id": 65795, "name": "tall giraffe"}, {"id": 65796, "name": "tall glass"}, {"id": 65797, "name": "tall grass"}, {"id": 65798, "name": "tall grass plains"}, {"id": 65799, "name": "tall grasses"}, {"id": 65800, "name": "tall gray building"}, {"id": 65801, "name": "tall green grass"}, {"id": 65802, "name": "tall green section"}, {"id": 65803, "name": "tall green tree"}, {"id": 65804, "name": "tall green trees"}, {"id": 65805, "name": "tall green weeds"}, {"id": 65806, "name": "tall grey mountain"}, {"id": 65807, "name": "tall guy"}, {"id": 65808, "name": "tall hat"}, {"id": 65809, "name": "tall hedges"}, {"id": 65810, "name": "tall 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"tight grip"}, {"id": 67536, "name": "tight pants"}, {"id": 67537, "name": "tightening pulley"}, {"id": 67538, "name": "tightly"}, {"id": 67539, "name": "tights"}, {"id": 67540, "name": "tighty whiteys"}, {"id": 67541, "name": "tigo"}, {"id": 67542, "name": "tiki"}, {"id": 67543, "name": "tiki hut"}, {"id": 67544, "name": "tiki lounge"}, {"id": 67545, "name": "tikithemed covering"}, {"id": 67546, "name": "til indelukket"}, {"id": 67547, "name": "tile backsplash"}, {"id": 67548, "name": "tile block"}, {"id": 67549, "name": "tile border"}, {"id": 67550, "name": "tile ceiling"}, {"id": 67551, "name": "tile counter"}, {"id": 67552, "name": "tile deck"}, {"id": 67553, "name": "tile decoration"}, {"id": 67554, "name": "tile design"}, {"id": 67555, "name": "tile edge"}, {"id": 67556, "name": "tile edges"}, {"id": 67557, "name": "tile edging"}, {"id": 67558, "name": "tile floor"}, {"id": 67559, "name": "tile flooring"}, {"id": 67560, "name": "tile floors"}, {"id": 67561, "name": "tile formation"}, {"id": 67562, "name": "tile ground"}, {"id": 67563, "name": "tile grout"}, {"id": 67564, "name": "tile hearth"}, {"id": 67565, "name": "tile inlay"}, {"id": 67566, "name": "tile lining"}, {"id": 67567, "name": "tile next to mirror"}, {"id": 67568, "name": "tile on floor"}, {"id": 67569, "name": "tile on grass"}, {"id": 67570, "name": "tile on side of tub"}, {"id": 67571, "name": "tile on the wall"}, {"id": 67572, "name": "tile pattern"}, {"id": 67573, "name": "tile road"}, {"id": 67574, "name": "tile roof"}, {"id": 67575, "name": "tile shapes"}, {"id": 67576, "name": "tile sidewalk"}, {"id": 67577, "name": "tile square"}, {"id": 67578, "name": "tile strip"}, {"id": 67579, "name": "tile tile"}, {"id": 67580, "name": "tile trim"}, {"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 wall"}, {"id": 67607, "name": "tiled walls"}, {"id": 67608, "name": "tiledwall"}, {"id": 67609, "name": "tilefloor"}, {"id": 67610, "name": "tiles are brick"}, {"id": 67611, "name": "tiles floor"}, {"id": 67612, "name": "tiles lines"}, {"id": 67613, "name": "tiles on side of tub"}, {"id": 67614, "name": "tiles on the roof"}, {"id": 67615, "name": "tiles on wall"}, {"id": 67616, "name": "tiles roof"}, {"id": 67617, "name": "tiles that are brown"}, {"id": 67618, "name": "tiling"}, {"id": 67619, "name": "till"}, {"id": 67620, "name": "tillamook"}, {"id": 67621, "name": "tillary st"}, {"id": 67622, "name": "tilled soil"}, {"id": 67623, "name": "tiller"}, {"id": 67624, "name": "tilling equipment"}, {"id": 67625, "name": "tilted"}, {"id": 67626, "name": "tilted floor"}, {"id": 67627, "name": "tilted pole"}, {"id": 67628, "name": "tilted stones"}, {"id": 67629, "name": "tilted waterway"}, {"id": 67630, "name": "tilting"}, {"id": 67631, "name": "tim tam"}, {"id": 67632, "name": "timber beams"}, {"id": 67633, "name": "timber cross beams"}, {"id": 67634, "name": "timber"}, {"id": 67635, "name": "time 449"}, {"id": 67636, "name": "time area"}, {"id": 67637, "name": "time clock"}, {"id": 67638, "name": "time display"}, {"id": 67639, "name": "time indicator"}, {"id": 67640, "name": "time label"}, {"id": 67641, "name": "time lapse"}, {"id": 67642, "name": "time lapse photo"}, {"id": 67643, "name": "time magazine"}, {"id": 67644, "name": "time mark"}, {"id": 67645, "name": "time notification"}, {"id": 67646, "name": "time of photo"}, {"id": 67647, "name": "time of picture"}, {"id": 67648, "name": "time on clock"}, {"id": 67649, "name": "time panel"}, {"id": 67650, "name": "time portion"}, {"id": 67651, "name": "time reads 1035"}, {"id": 67652, "name": "time screen"}, {"id": 67653, "name": "time shown"}, {"id": 67654, "name": "time stamp"}, {"id": 67655, "name": "time sticker"}, {"id": 67656, "name": "time zone"}, {"id": 67657, "name": "time"}, {"id": 67658, "name": "timeable"}, {"id": 67659, "name": "timepiece"}, {"id": 67660, "name": "timer"}, {"id": 67661, "name": "timer knob"}, {"id": 67662, "name": "times of use"}, {"id": 67663, "name": "times square"}, {"id": 67664, "name": "timeshare"}, {"id": 67665, "name": "timestamp"}, {"id": 67666, "name": "timetable"}, {"id": 67667, "name": "timing clock"}, {"id": 67668, "name": "tin box"}, {"id": 67669, "name": "tin can"}, {"id": 67670, "name": "tin can of tea"}, {"id": 67671, "name": "tin container"}, {"id": 67672, "name": "tin foil"}, {"id": 67673, "name": "tin holder"}, {"id": 67674, "name": "tin pipe"}, {"id": 67675, "name": "tin roof"}, {"id": 67676, "name": "tin shed"}, {"id": 67677, "name": "tin siding"}, {"id": 67678, "name": "tin stand"}, {"id": 67679, "name": "tin"}, {"id": 67680, "name": "tina"}, {"id": 67681, "name": "tine"}, {"id": 67682, "name": "tinfoil"}, {"id": 67683, "name": "ting"}, {"id": 67684, "name": "tinges of yellow"}, {"id": 67685, "name": "tiniest palm"}, {"id": 67686, "name": "tink"}, {"id": 67687, "name": "tinkerbell"}, {"id": 67688, "name": "tinnis shoe"}, {"id": 67689, "name": "tinsel"}, {"id": 67690, "name": "tinsil"}, {"id": 67691, "name": "tint"}, {"id": 67692, "name": "tinted"}, {"id": 67693, "name": "tinted glass"}, {"id": 67694, "name": "tinted plastic"}, {"id": 67695, "name": "tinted sunglasses"}, {"id": 67696, "name": "tinted window"}, {"id": 67697, "name": "tinted windows"}, {"id": 67698, "name": "tintin"}, {"id": 67699, "name": "tiny"}, {"id": 67700, "name": "tiny baseball"}, {"id": 67701, "name": "tiny blue luggage"}, {"id": 67702, "name": "tiny brake lights"}, {"id": 67703, "name": "tiny branch"}, {"id": 67704, "name": "tiny bubbles"}, {"id": 67705, "name": "tiny bumps"}, {"id": 67706, "name": "tiny curls"}, {"id": 67707, "name": "tiny ears"}, {"id": 67708, "name": "tiny figurine"}, {"id": 67709, "name": "tiny filaments"}, {"id": 67710, "name": "tiny fin"}, {"id": 67711, "name": "tiny flowers"}, {"id": 67712, "name": "tiny forehead dent"}, {"id": 67713, "name": "tiny green tree"}, {"id": 67714, "name": "tiny hole"}, {"id": 67715, "name": "tiny holes"}, {"id": 67716, "name": "tiny leaves"}, {"id": 67717, "name": "tiny legs"}, {"id": 67718, "name": "tiny light"}, {"id": 67719, "name": "tiny lights"}, {"id": 67720, "name": "tiny little fingers"}, {"id": 67721, "name": "tiny nubs"}, {"id": 67722, "name": "tiny opening"}, {"id": 67723, "name": "tiny piece"}, {"id": 67724, "name": "tiny present"}, {"id": 67725, "name": "tiny ripples"}, {"id": 67726, "name": "tiny rock"}, {"id": 67727, "name": "tiny rocks"}, {"id": 67728, "name": "tiny section"}, {"id": 67729, "name": "tiny square"}, {"id": 67730, "name": "tiny stones"}, {"id": 67731, "name": "tiny wave"}, {"id": 67732, "name": "tiny wheel"}, {"id": 67733, "name": "tiny window"}, {"id": 67734, "name": "tiny woman"}, {"id": 67735, "name": "tiolet"}, {"id": 67736, "name": "tip cup"}, {"id": 67737, "name": "tip finger"}, {"id": 67738, "name": "tip jar"}, {"id": 67739, "name": "tip of a brown boat"}, {"id": 67740, "name": "tip of banana"}, {"id": 67741, "name": "tip of board"}, {"id": 67742, "name": "tip of boat"}, {"id": 67743, "name": "tip of ear"}, {"id": 67744, "name": "tip of hat"}, {"id": 67745, "name": "tip of horn"}, {"id": 67746, "name": "tip of hot dog"}, {"id": 67747, "name": "tip of index finger"}, {"id": 67748, "name": "tip of mans nose"}, {"id": 67749, "name": "tip of nose"}, {"id": 67750, "name": "tip of ship"}, {"id": 67751, "name": "tip of ski"}, {"id": 67752, "name": "tip of surfboard"}, {"id": 67753, "name": "tip of tail"}, {"id": 67754, "name": "tip of the grass"}, {"id": 67755, "name": "tip of the trunk"}, {"id": 67756, "name": "tip of trunk"}, {"id": 67757, "name": "tip of white"}, {"id": 67758, "name": "tip of wing"}, {"id": 67759, "name": "tip on umbrella"}, {"id": 67760, "name": "tip table"}, {"id": 67761, "name": "tip tail"}, {"id": 67762, "name": "tip toes"}, {"id": 67763, "name": "tip top"}, {"id": 67764, "name": "tip tray"}, {"id": 67765, "name": "tip"}, {"id": 67766, "name": "tipped lampshade"}, {"id": 67767, "name": "tipper"}, {"id": 67768, "name": "tipping"}, {"id": 67769, "name": "tips of skis"}, {"id": 67770, "name": "tiptail feathers"}, {"id": 67771, "name": "tiptoe"}, {"id": 67772, "name": "tiptop"}, {"id": 67773, "name": "tiramisu"}, {"id": 67774, "name": "tire and wheel"}, {"id": 67775, "name": "tire bike"}, {"id": 67776, "name": "tire bumper"}, {"id": 67777, "name": "tire cover"}, {"id": 67778, "name": "tire edge"}, {"id": 67779, "name": "tire flap"}, {"id": 67780, "name": "tire guard"}, {"id": 67781, "name": "tire has wall"}, {"id": 67782, "name": "tire in photo"}, {"id": 67783, "name": "tire is on bus"}, {"id": 67784, "name": "tire is there"}, {"id": 67785, "name": "tire is white"}, {"id": 67786, "name": "tire maks"}, {"id": 67787, "name": "tire mark"}, {"id": 67788, "name": "tire marks"}, {"id": 67789, "name": "tire mount"}, {"id": 67790, "name": "tire of a bike"}, {"id": 67791, "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": "tirecycle"}, {"id": 67818, "name": "tired"}, {"id": 67819, "name": "tirerim"}, {"id": 67820, "name": "tires are attached"}, {"id": 67821, "name": "tires markings"}, {"id": 67822, "name": "tires on the shelf"}, {"id": 67823, "name": "tires on the toy"}, {"id": 67824, "name": "tires on the truck"}, {"id": 67825, "name": "tirewell"}, {"id": 67826, "name": "tissue roll"}, {"id": 67827, "name": "tissue box"}, {"id": 67828, "name": "tissue container"}, {"id": 67829, "name": "tissue dispenser"}, {"id": 67830, "name": "tissue dispesor"}, {"id": 67831, "name": "tissue holder"}, {"id": 67832, "name": "tissue holders"}, {"id": 67833, "name": "tissue is coming"}, {"id": 67834, "name": "tissue paper"}, {"id": 67835, "name": "tissue piece"}, {"id": 67836, "name": "tissue roll"}, {"id": 67837, "name": "tissue"}, {"id": 67838, "name": "tissuebox"}, {"id": 67839, "name": "tissueholder"}, {"id": 67840, "name": "tissuepaper"}, {"id": 67841, "name": "tissus dispenser"}, {"id": 67842, "name": "tit"}, {"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, "name": "to urnial"}, {"id": 67899, "name": "toad"}, {"id": 67900, "name": "toad truck"}, {"id": 67901, "name": "toamto"}, {"id": 67902, "name": "toast"}, {"id": 67903, "name": "toasted"}, {"id": 67904, "name": "toasted bread"}, {"id": 67905, "name": "toasted bun"}, {"id": 67906, "name": "toasted edges"}, {"id": 67907, "name": "toaster lever"}, {"id": 67908, "name": "toaster oven"}, {"id": 67909, "name": "toaster over"}, {"id": 67910, "name": "toaster slots"}, {"id": 67911, "name": "toaster"}, {"id": 67912, "name": "toasty"}, {"id": 67913, "name": "tobacco can"}, {"id": 67914, "name": "tobacco products"}, {"id": 67915, "name": "tobacco sauce"}, {"id": 67916, "name": "tobacco store"}, {"id": 67917, "name": "tobbogan"}, {"id": 67918, "name": "tobbogans"}, {"id": 67919, "name": "tobogan"}, {"id": 67920, "name": "toboggan"}, {"id": 67921, "name": "toboggan cap"}, {"id": 67922, "name": "toboggan hat"}, {"id": 67923, "name": "today"}, {"id": 67924, "name": "todd"}, {"id": 67925, "name": "toddler chair"}, {"id": 67926, "name": "toddler hand"}, {"id": 67927, "name": "toddler outfit"}, {"id": 67928, "name": "toddler pants"}, {"id": 67929, "name": "toddler playing"}, {"id": 67930, "name": "toddler playset"}, {"id": 67931, "name": "toddler seat"}, {"id": 67932, "name": "toddler"}, {"id": 67933, "name": "toddlers cup"}, {"id": 67934, "name": "toddlers hair"}, {"id": 67935, "name": "toddlers hand"}, {"id": 67936, "name": "toddlers wrist"}, {"id": 67937, "name": "toe nail"}, {"id": 67938, "name": "toe nails"}, {"id": 67939, "name": "toe pad"}, {"id": 67940, "name": "toe pads"}, {"id": 67941, "name": "toe ring"}, {"id": 67942, "name": "toe"}, {"id": 67943, "name": "toed"}, {"id": 67944, "name": "toenail is painted"}, {"id": 67945, "name": "toenail"}, {"id": 67946, "name": "toes on feet"}, {"id": 67947, "name": "toffee"}, {"id": 67948, "name": "tofu"}, {"id": 67949, "name": "tofu cubes"}, {"id": 67950, "name": "tofu pieces"}, {"id": 67951, "name": "toga"}, {"id": 67952, "name": "together"}, {"id": 67953, "name": "toggle"}, {"id": 67954, "name": "toggle pull"}, {"id": 67955, "name": "togo box"}, {"id": 67956, "name": "toielt"}, {"id": 67957, "name": "toiilet"}, {"id": 67958, "name": "toil bowl"}, {"id": 67959, "name": "toile paper"}, {"id": 67960, "name": "toiled lid"}, {"id": 67961, "name": "toiler"}, {"id": 67962, "name": "toiler paper"}, {"id": 67963, "name": "toiler seat"}, {"id": 67964, "name": "toilet tank"}, {"id": 67965, "name": "toilet area"}, {"id": 67966, "name": "toilet arm"}, {"id": 67967, "name": "toilet back"}, {"id": 67968, "name": "toilet backing"}, {"id": 67969, "name": "toilet ball"}, {"id": 67970, "name": "toilet base"}, {"id": 67971, "name": "toilet basin"}, {"id": 67972, "name": "toilet bottom"}, {"id": 67973, "name": "toilet bowels"}, {"id": 67974, "name": "toilet bowl"}, {"id": 67975, "name": "toilet bowl brush"}, {"id": 67976, "name": "toilet bowl cleaner"}, {"id": 67977, "name": "toilet bowl handle"}, {"id": 67978, "name": "toilet bowl is white"}, {"id": 67979, "name": "toilet bowl water"}, {"id": 67980, "name": "toilet bowls"}, {"id": 67981, "name": "toilet box"}, {"id": 67982, "name": "toilet brush"}, {"id": 67983, "name": "toilet brush handle"}, {"id": 67984, "name": "toilet brush 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{"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": 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"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": "trafficsignal"}, {"id": 68918, "name": "trafficsignssignals"}, {"id": 68919, "name": "trafic light"}, {"id": 68920, "name": "trafic lights"}, {"id": 68921, "name": "tragardh"}, {"id": 68922, "name": "trai"}, {"id": 68923, "name": "traiangle"}, {"id": 68924, "name": "trail line"}, {"id": 68925, "name": "trail mark"}, {"id": 68926, "name": "trail marker"}, {"id": 68927, "name": "trail marker by tree"}, {"id": 68928, "name": "trail markers"}, {"id": 68929, "name": "trail marks"}, {"id": 68930, "name": "trail mix"}, {"id": 68931, "name": "trail name"}, {"id": 68932, "name": "trail of smoke"}, {"id": 68933, "name": "trail of water"}, {"id": 68934, "name": "trail side"}, {"id": 68935, "name": "trail"}, {"id": 68936, "name": "trailer background"}, {"id": 68937, "name": "trailer bed"}, {"id": 68938, "name": "trailer door"}, {"id": 68939, "name": "trailer hitch"}, {"id": 68940, "name": "trailer home"}, {"id": 68941, "name": "trailer house"}, {"id": 68942, "name": "trailer in a field"}, {"id": 68943, "name": "trailer mount"}, {"id": 68944, "name": "trailer parked"}, {"id": 68945, "name": "trailer ramp"}, {"id": 68946, "name": "trailer tires"}, {"id": 68947, "name": "trailer"}, {"id": 68948, "name": "trailhead meter"}, {"id": 68949, "name": "traillight"}, {"id": 68950, "name": "trailor"}, {"id": 68951, "name": "trailor hitch"}, {"id": 68952, "name": "trailside"}, {"id": 68953, "name": "trailways bus depot"}, {"id": 68954, "name": "train 55"}, {"id": 68955, "name": "train area"}, {"id": 68956, "name": "train back"}, {"id": 68957, "name": "train barriers"}, {"id": 68958, "name": "train bed"}, {"id": 68959, "name": "train boarding"}, {"id": 68960, "name": "train body"}, {"id": 68961, "name": "train bottom"}, {"id": 68962, "name": "train box"}, {"id": 68963, "name": "train boxcar"}, {"id": 68964, "name": "train brand"}, {"id": 68965, "name": "train bridge"}, {"id": 68966, "name": "train buffer"}, {"id": 68967, "name": "train bumper"}, {"id": 68968, "name": "train cab"}, {"id": 68969, "name": "train cables"}, {"id": 68970, "name": "train caboose"}, {"id": 68971, "name": "train cakes"}, {"id": 68972, "name": "train car"}, {"id": 68973, "name": "train car is red"}, {"id": 68974, "name": "train carriage"}, {"id": 68975, "name": 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70864, "name": "under skiers feet"}, {"id": 70865, "name": "under table"}, {"id": 70866, "name": "under the vases"}, {"id": 70867, "name": "under top"}, {"id": 70868, "name": "under tree"}, {"id": 70869, "name": "under vents"}, {"id": 70870, "name": "under water"}, {"id": 70871, "name": "under wear"}, {"id": 70872, "name": "under window"}, {"id": 70873, "name": "under wing"}, {"id": 70874, "name": "under"}, {"id": 70875, "name": "underarch"}, {"id": 70876, "name": "underarm"}, {"id": 70877, "name": "underbell"}, {"id": 70878, "name": "underbelly of bear"}, {"id": 70879, "name": "underbelly of deer"}, {"id": 70880, "name": "underbelly wings"}, {"id": 70881, "name": "underbelly"}, {"id": 70882, "name": "underbite"}, {"id": 70883, "name": "underbody"}, {"id": 70884, "name": "underbrim"}, {"id": 70885, "name": "underbrush"}, {"id": 70886, "name": "undercarriage"}, {"id": 70887, "name": "undercoat"}, {"id": 70888, "name": "underfeathers"}, {"id": 70889, "name": "underfeet"}, {"id": 70890, 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"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