[CodeCamp2023-605] Add new configs of deformable_detr (#10936)

mmdet/configs/deformable_detr/deformable_detr_r50_16xb2_50e_coco.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+
+# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
+# mmcv >= 2.0.1
+# mmengine >= 0.8.0
+
+from mmengine.config import read_base
+
+with read_base():
+    from .._base_.datasets.coco_detection import *
+    from .._base_.default_runtime import *
+
+from mmcv.transforms import LoadImageFromFile, RandomChoice, RandomChoiceResize
+from mmengine.optim.optimizer import OptimWrapper
+from mmengine.optim.scheduler import MultiStepLR
+from mmengine.runner.loops import EpochBasedTrainLoop, TestLoop, ValLoop
+from torch.optim.adamw import AdamW
+
+from mmdet.datasets.transforms import (LoadAnnotations, PackDetInputs,
+                                       RandomCrop, RandomFlip, Resize)
+from mmdet.models.backbones import ResNet
+from mmdet.models.data_preprocessors import DetDataPreprocessor
+from mmdet.models.dense_heads import DeformableDETRHead
+from mmdet.models.detectors import DeformableDETR
+from mmdet.models.losses import FocalLoss, GIoULoss, L1Loss
+from mmdet.models.necks import ChannelMapper
+from mmdet.models.task_modules import (BBoxL1Cost, FocalLossCost,
+                                       HungarianAssigner, IoUCost)
+
+model = dict(
+    type=DeformableDETR,
+    num_queries=300,
+    num_feature_levels=4,
+    with_box_refine=False,
+    as_two_stage=False,
+    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='BN', requires_grad=False),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', 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='GN', num_groups=32),
+        num_outs=4),
+    encoder=dict(  # DeformableDetrTransformerEncoder
+        num_layers=6,
+        layer_cfg=dict(  # DeformableDetrTransformerEncoderLayer
+            self_attn_cfg=dict(  # MultiScaleDeformableAttention
+                embed_dims=256,
+                batch_first=True),
+            ffn_cfg=dict(
+                embed_dims=256, feedforward_channels=1024, ffn_drop=0.1))),
+    decoder=dict(  # DeformableDetrTransformerDecoder
+        num_layers=6,
+        return_intermediate=True,
+        layer_cfg=dict(  # DeformableDetrTransformerDecoderLayer
+            self_attn_cfg=dict(  # MultiheadAttention
+                embed_dims=256,
+                num_heads=8,
+                dropout=0.1,
+                batch_first=True),
+            cross_attn_cfg=dict(  # MultiScaleDeformableAttention
+                embed_dims=256,
+                batch_first=True),
+            ffn_cfg=dict(
+                embed_dims=256, feedforward_channels=1024, ffn_drop=0.1)),
+        post_norm_cfg=None),
+    positional_encoding=dict(num_feats=128, normalize=True, offset=-0.5),
+    bbox_head=dict(
+        type=DeformableDETRHead,
+        num_classes=80,
+        sync_cls_avg_factor=True,
+        loss_cls=dict(
+            type=FocalLoss,
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=2.0),
+        loss_bbox=dict(type=L1Loss, loss_weight=5.0),
+        loss_iou=dict(type=GIoULoss, loss_weight=2.0)),
+    # 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=100))
+
+# 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,
+                    scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
+                            (608, 1333), (640, 1333), (672, 1333), (704, 1333),
+                            (736, 1333), (768, 1333), (800, 1333)],
+                    resize_type=Resize,
+                    keep_ratio=True)
+            ],
+            [
+                dict(
+                    type=RandomChoiceResize,
+                    # The radio of all image in train dataset < 7
+                    # follow the original implement
+                    scales=[(400, 4200), (500, 4200), (600, 4200)],
+                    resize_type=Resize,
+                    keep_ratio=True),
+                dict(
+                    type=RandomCrop,
+                    crop_type='absolute_range',
+                    crop_size=(384, 600),
+                    allow_negative_crop=True),
+                dict(
+                    type=RandomChoiceResize,
+                    scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
+                            (608, 1333), (640, 1333), (672, 1333), (704, 1333),
+                            (736, 1333), (768, 1333), (800, 1333)],
+                    resize_type=Resize,
+                    keep_ratio=True)
+            ]
+        ]),
+    dict(type=PackDetInputs)
+]
+train_dataloader.update(
+    dict(
+        dataset=dict(
+            filter_cfg=dict(filter_empty_gt=False), pipeline=train_pipeline)))
+
+# optimizer
+optim_wrapper = dict(
+    type=OptimWrapper,
+    optimizer=dict(type=AdamW, lr=0.0002, 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),
+            'sampling_offsets': dict(lr_mult=0.1),
+            'reference_points': dict(lr_mult=0.1)
+        }))
+
+# learning policy
+max_epochs = 50
+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=[40],
+        gamma=0.1)
+]
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (16 GPUs) x (2 samples per GPU)
+auto_scale_lr = dict(base_batch_size=32)

mmdet/configs/deformable_detr/deformable_detr_refine_r50_16xb2_50e_coco.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+
+# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
+# mmcv >= 2.0.1
+# mmengine >= 0.8.0
+
+from mmengine.config import read_base
+
+with read_base():
+    from .deformable_detr_r50_16xb2_50e_coco import *
+
+model.update(dict(with_box_refine=True))

mmdet/configs/deformable_detr/deformable_detr_refine_twostage_r50_16xb2_50e_coco.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+
+# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
+# mmcv >= 2.0.1
+# mmengine >= 0.8.0
+
+from mmengine.config import read_base
+
+with read_base():
+    from .deformable_detr_refine_r50_16xb2_50e_coco import *
+
+model.update(dict(as_two_stage=True))