fix_mmdetection

mmde/mmdet/.mim/configs/dcn/cascade-rcnn_r101-dconv-c3-c5_fpn_1x_coco.py

+_base_ = '../cascade_rcnn/cascade-rcnn_r101_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
+        stage_with_dcn=(False, True, True, True)))

mmde/mmdet/.mim/configs/dcn/cascade-rcnn_r50-dconv-c3-c5_fpn_1x_coco.py

+_base_ = '../cascade_rcnn/cascade-rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
+        stage_with_dcn=(False, True, True, True)))

mmde/mmdet/.mim/configs/dcn/faster-rcnn_r101-dconv-c3-c5_fpn_1x_coco.py

+_base_ = '../faster_rcnn/faster-rcnn_r101_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
+        stage_with_dcn=(False, True, True, True)))

mmde/mmdet/.mim/configs/dcn/faster-rcnn_r50-dconv-c3-c5_fpn_1x_coco.py

+_base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
+        stage_with_dcn=(False, True, True, True)))

mmde/mmdet/.mim/configs/dcn/faster-rcnn_r50_fpn_dpool_1x_coco.py

+_base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    roi_head=dict(
+        bbox_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(
+                _delete_=True,
+                type='DeformRoIPoolPack',
+                output_size=7,
+                output_channels=256),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32])))

mmde/mmdet/.mim/configs/dcn/faster-rcnn_x101-32x4d-dconv-c3-c5_fpn_1x_coco.py

+_base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=32,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
+        stage_with_dcn=(False, True, True, True),
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))

mmde/mmdet/.mim/configs/dcn/mask-rcnn_r101-dconv-c3-c5_fpn_1x_coco.py

+_base_ = '../mask_rcnn/mask-rcnn_r101_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
+        stage_with_dcn=(False, True, True, True)))

mmde/mmdet/.mim/configs/dcn/mask-rcnn_r50-dconv-c3-c5_fpn_1x_coco.py

+_base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
+        stage_with_dcn=(False, True, True, True)))

mmde/mmdet/.mim/configs/dcn/mask-rcnn_r50-dconv-c3-c5_fpn_amp-1x_coco.py

+_base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
+        stage_with_dcn=(False, True, True, True)))
+
+# MMEngine support the following two ways, users can choose
+# according to convenience
+# optim_wrapper = dict(type='AmpOptimWrapper')
+_base_.optim_wrapper.type = 'AmpOptimWrapper'