[CodeCamp2023-474] Add new configuration files for DINO algorithm in …

…mmdetection. (open-mmlab#10901)

configs/dino/dino-5scale_swin-l_8xb2-12e_coco.py

@@ -1,6 +1,5 @@
 _base_ = './dino-4scale_r50_8xb2-12e_coco.py'
 
-fp16 = dict(loss_scale=512.)
 pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth'  # noqa
 num_levels = 5
 model = dict(

mmdet/configs/dino/dino_4scale_r50_8xb2_12e_coco.py

@@ -0,0 +1,190 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmcv.transforms import RandomChoice, RandomChoiceResize
+from mmcv.transforms.loading import LoadImageFromFile
+from mmengine.config import read_base
+from mmengine.model.weight_init import PretrainedInit
+from mmengine.optim.optimizer.optimizer_wrapper import OptimWrapper
+from mmengine.optim.scheduler.lr_scheduler import MultiStepLR
+from mmengine.runner.loops import EpochBasedTrainLoop, TestLoop, ValLoop
+from torch.nn.modules.batchnorm import BatchNorm2d
+from torch.nn.modules.normalization import GroupNorm
+from torch.optim.adamw import AdamW
+
+from mmdet.datasets.transforms import (LoadAnnotations, PackDetInputs,
+                                       RandomCrop, RandomFlip, Resize)
+from mmdet.models import (DINO, ChannelMapper, DetDataPreprocessor, DINOHead,
+                          ResNet)
+from mmdet.models.losses.focal_loss import FocalLoss
+from mmdet.models.losses.iou_loss import GIoULoss
+from mmdet.models.losses.smooth_l1_loss import L1Loss
+from mmdet.models.task_modules import (BBoxL1Cost, FocalLossCost,
+                                       HungarianAssigner, IoUCost)
+
+with read_base():
+    from .._base_.datasets.coco_detection import *
+    from .._base_.default_runtime import *
+
+model = dict(
+    type=DINO,
+    num_queries=900,  # num_matching_queries
+    with_box_refine=True,
+    as_two_stage=True,
+    data_preprocessor=dict(
+        type=DetDataPreprocessor,
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        bgr_to_rgb=True,
+        pad_size_divisor=1),
+    backbone=dict(
+        type=ResNet,
+        depth=50,
+        num_stages=4,
+        out_indices=(1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type=BatchNorm2d, requires_grad=False),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(
+            type=PretrainedInit, checkpoint='torchvision://resnet50')),
+    neck=dict(
+        type=ChannelMapper,
+        in_channels=[512, 1024, 2048],
+        kernel_size=1,
+        out_channels=256,
+        act_cfg=None,
+        norm_cfg=dict(type=GroupNorm, num_groups=32),
+        num_outs=4),
+    encoder=dict(
+        num_layers=6,
+        layer_cfg=dict(
+            self_attn_cfg=dict(embed_dims=256, num_levels=4,
+                               dropout=0.0),  # 0.1 for DeformDETR
+            ffn_cfg=dict(
+                embed_dims=256,
+                feedforward_channels=2048,  # 1024 for DeformDETR
+                ffn_drop=0.0))),  # 0.1 for DeformDETR
+    decoder=dict(
+        num_layers=6,
+        return_intermediate=True,
+        layer_cfg=dict(
+            self_attn_cfg=dict(embed_dims=256, num_heads=8,
+                               dropout=0.0),  # 0.1 for DeformDETR
+            cross_attn_cfg=dict(embed_dims=256, num_levels=4,
+                                dropout=0.0),  # 0.1 for DeformDETR
+            ffn_cfg=dict(
+                embed_dims=256,
+                feedforward_channels=2048,  # 1024 for DeformDETR
+                ffn_drop=0.0)),  # 0.1 for DeformDETR
+        post_norm_cfg=None),
+    positional_encoding=dict(
+        num_feats=128,
+        normalize=True,
+        offset=0.0,  # -0.5 for DeformDETR
+        temperature=20),  # 10000 for DeformDETR
+    bbox_head=dict(
+        type=DINOHead,
+        num_classes=80,
+        sync_cls_avg_factor=True,
+        loss_cls=dict(
+            type=FocalLoss,
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),  # 2.0 in DeformDETR
+        loss_bbox=dict(type=L1Loss, loss_weight=5.0),
+        loss_iou=dict(type=GIoULoss, loss_weight=2.0)),
+    dn_cfg=dict(  # TODO: Move to model.train_cfg ?
+        label_noise_scale=0.5,
+        box_noise_scale=1.0,  # 0.4 for DN-DETR
+        group_cfg=dict(dynamic=True, num_groups=None,
+                       num_dn_queries=100)),  # TODO: half num_dn_queries
+    # training and testing settings
+    train_cfg=dict(
+        assigner=dict(
+            type=HungarianAssigner,
+            match_costs=[
+                dict(type=FocalLossCost, weight=2.0),
+                dict(type=BBoxL1Cost, weight=5.0, box_format='xywh'),
+                dict(type=IoUCost, iou_mode='giou', weight=2.0)
+            ])),
+    test_cfg=dict(max_per_img=300))  # 100 for DeformDETR
+
+# train_pipeline, NOTE the img_scale and the Pad's size_divisor is different
+# from the default setting in mmdet.
+train_pipeline = [
+    dict(type=LoadImageFromFile, backend_args=backend_args),
+    dict(type=LoadAnnotations, with_bbox=True),
+    dict(type=RandomFlip, prob=0.5),
+    dict(
+        type=RandomChoice,
+        transforms=[
+            [
+                dict(
+                    type=RandomChoiceResize,
+                    resize_type=Resize,
+                    scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
+                            (608, 1333), (640, 1333), (672, 1333), (704, 1333),
+                            (736, 1333), (768, 1333), (800, 1333)],
+                    keep_ratio=True)
+            ],
+            [
+                dict(
+                    type=RandomChoiceResize,
+                    resize_type=Resize,
+                    # The radio of all image in train dataset < 7
+                    # follow the original implement
+                    scales=[(400, 4200), (500, 4200), (600, 4200)],
+                    keep_ratio=True),
+                dict(
+                    type=RandomCrop,
+                    crop_type='absolute_range',
+                    crop_size=(384, 600),
+                    allow_negative_crop=True),
+                dict(
+                    type=RandomChoiceResize,
+                    resize_type=Resize,
+                    scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
+                            (608, 1333), (640, 1333), (672, 1333), (704, 1333),
+                            (736, 1333), (768, 1333), (800, 1333)],
+                    keep_ratio=True)
+            ]
+        ]),
+    dict(type=PackDetInputs)
+]
+train_dataloader.update(
+    dataset=dict(
+        filter_cfg=dict(filter_empty_gt=False), pipeline=train_pipeline))
+
+# optimizer
+optim_wrapper = dict(
+    type=OptimWrapper,
+    optimizer=dict(
+        type=AdamW,
+        lr=0.0001,  # 0.0002 for DeformDETR
+        weight_decay=0.0001),
+    clip_grad=dict(max_norm=0.1, norm_type=2),
+    paramwise_cfg=dict(custom_keys={'backbone': dict(lr_mult=0.1)})
+)  # custom_keys contains sampling_offsets and reference_points in DeformDETR  # noqa
+
+# learning policy
+max_epochs = 12
+train_cfg = dict(
+    type=EpochBasedTrainLoop, max_epochs=max_epochs, val_interval=1)
+
+val_cfg = dict(type=ValLoop)
+test_cfg = dict(type=TestLoop)
+
+param_scheduler = [
+    dict(
+        type=MultiStepLR,
+        begin=0,
+        end=max_epochs,
+        by_epoch=True,
+        milestones=[11],
+        gamma=0.1)
+]
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (2 samples per GPU)
+auto_scale_lr = dict(base_batch_size=16)

mmdet/configs/dino/dino_4scale_r50_8xb2_24e_coco.py

@@ -0,0 +1,12 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmengine.config import read_base
+from mmengine.runner.loops import EpochBasedTrainLoop
+
+with read_base():
+    from .dino_4scale_r50_8xb2_12e_coco import *
+
+max_epochs = 24
+train_cfg.update(
+    dict(type=EpochBasedTrainLoop, max_epochs=max_epochs, val_interval=1))
+
+param_scheduler[0].update(dict(milestones=[20]))

mmdet/configs/dino/dino_4scale_r50_8xb2_36e_coco.py

@@ -0,0 +1,12 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmengine.config import read_base
+from mmengine.runner.loops import EpochBasedTrainLoop
+
+with read_base():
+    from .dino_4scale_r50_8xb2_12e_coco import *
+
+max_epochs = 36
+train_cfg.update(
+    dict(type=EpochBasedTrainLoop, max_epochs=max_epochs, val_interval=1))
+
+param_scheduler[0].update(dict(milestones=[30]))

mmdet/configs/dino/dino_4scale_r50_improved_8xb2_12e_coco.py

@@ -0,0 +1,24 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmengine.config import read_base
+
+with read_base():
+    from .dino_4scale_r50_8xb2_12e_coco import *
+
+# from deformable detr hyper
+model.update(
+    dict(
+        backbone=dict(frozen_stages=-1),
+        bbox_head=dict(loss_cls=dict(loss_weight=2.0)),
+        positional_encoding=dict(offset=-0.5, temperature=10000),
+        dn_cfg=dict(group_cfg=dict(num_dn_queries=300))))
+
+# optimizer
+optim_wrapper.update(
+    dict(
+        optimizer=dict(lr=0.0002),
+        paramwise_cfg=dict(
+            custom_keys={
+                'backbone': dict(lr_mult=0.1),
+                'sampling_offsets': dict(lr_mult=0.1),
+                'reference_points': dict(lr_mult=0.1)
+            })))

mmdet/configs/dino/dino_5scale_swin_l_8xb2_12e_coco.py

@@ -0,0 +1,40 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmengine.config import read_base
+from mmengine.model.weight_init import PretrainedInit
+
+from mmdet.models import SwinTransformer
+
+with read_base():
+    from .dino_4scale_r50_8xb2_12e_coco import *
+
+pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth'  # noqa
+num_levels = 5
+model.merge(
+    dict(
+        num_feature_levels=num_levels,
+        backbone=dict(
+            _delete_=True,
+            type=SwinTransformer,
+            pretrain_img_size=384,
+            embed_dims=192,
+            depths=[2, 2, 18, 2],
+            num_heads=[6, 12, 24, 48],
+            window_size=12,
+            mlp_ratio=4,
+            qkv_bias=True,
+            qk_scale=None,
+            drop_rate=0.,
+            attn_drop_rate=0.,
+            drop_path_rate=0.2,
+            patch_norm=True,
+            out_indices=(0, 1, 2, 3),
+            # Please only add indices that would be used
+            # in FPN, otherwise some parameter will not be used
+            with_cp=True,
+            convert_weights=True,
+            init_cfg=dict(type=PretrainedInit, checkpoint=pretrained)),
+        neck=dict(in_channels=[192, 384, 768, 1536], num_outs=num_levels),
+        encoder=dict(
+            layer_cfg=dict(self_attn_cfg=dict(num_levels=num_levels))),
+        decoder=dict(
+            layer_cfg=dict(cross_attn_cfg=dict(num_levels=num_levels)))))

mmdet/configs/dino/dino_5scale_swin_l_8xb2_36e_coco.py

@@ -0,0 +1,12 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmengine.config import read_base
+from mmengine.runner.loops import EpochBasedTrainLoop
+
+with read_base():
+    from .dino_5scale_swin_l_8xb2_12e_coco import *
+
+max_epochs = 36
+train_cfg.update(
+    dict(type=EpochBasedTrainLoop, max_epochs=max_epochs, val_interval=1))
+
+param_scheduler[0].update(dict(milestones=[27, 33]))