[Refactor] move model.pretrained to model.backbone.init_cfg (open-mml…

…ab#5370)

* [Refactor] move model pretrained to backbone init_cfg

* fix some bug and add a test unit

* add

* fix bug

* fix priority

* fix bug

* delete test backbone init cfg

* fix potential bug

* fix potential bug

* add detectors test unit

* fix logic

* add test unit

* fix

configs/_base_/models/cascade_mask_rcnn_r50_fpn.py

@@ -1,7 +1,6 @@
 # model settings
 model = dict(
     type='CascadeRCNN',
-    pretrained='torchvision://resnet50',
     backbone=dict(
         type='ResNet',
         depth=50,
@@ -10,7 +9,8 @@
         frozen_stages=1,
         norm_cfg=dict(type='BN', requires_grad=True),
         norm_eval=True,
-        style='pytorch'),
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
     neck=dict(
         type='FPN',
         in_channels=[256, 512, 1024, 2048],

configs/_base_/models/cascade_rcnn_r50_fpn.py

@@ -1,7 +1,6 @@
 # model settings
 model = dict(
     type='CascadeRCNN',
-    pretrained='torchvision://resnet50',
     backbone=dict(
         type='ResNet',
         depth=50,
@@ -10,7 +9,8 @@
         frozen_stages=1,
         norm_cfg=dict(type='BN', requires_grad=True),
         norm_eval=True,
-        style='pytorch'),
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
     neck=dict(
         type='FPN',
         in_channels=[256, 512, 1024, 2048],

configs/_base_/models/fast_rcnn_r50_fpn.py

@@ -1,7 +1,6 @@
 # model settings
 model = dict(
     type='FastRCNN',
-    pretrained='torchvision://resnet50',
     backbone=dict(
         type='ResNet',
         depth=50,
@@ -10,7 +9,8 @@
         frozen_stages=1,
         norm_cfg=dict(type='BN', requires_grad=True),
         norm_eval=True,
-        style='pytorch'),
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
     neck=dict(
         type='FPN',
         in_channels=[256, 512, 1024, 2048],

configs/_base_/models/faster_rcnn_r50_caffe_c4.py

@@ -2,7 +2,6 @@
 norm_cfg = dict(type='BN', requires_grad=False)
 model = dict(
     type='FasterRCNN',
-    pretrained='open-mmlab://detectron2/resnet50_caffe',
     backbone=dict(
         type='ResNet',
         depth=50,
@@ -13,7 +12,10 @@
         frozen_stages=1,
         norm_cfg=norm_cfg,
         norm_eval=True,
-        style='caffe'),
+        style='caffe',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet50_caffe')),
     rpn_head=dict(
         type='RPNHead',
         in_channels=1024,

configs/_base_/models/faster_rcnn_r50_caffe_dc5.py

@@ -2,7 +2,6 @@
 norm_cfg = dict(type='BN', requires_grad=False)
 model = dict(
     type='FasterRCNN',
-    pretrained='open-mmlab://detectron2/resnet50_caffe',
     backbone=dict(
         type='ResNet',
         depth=50,
@@ -13,7 +12,10 @@
         frozen_stages=1,
         norm_cfg=norm_cfg,
         norm_eval=True,
-        style='caffe'),
+        style='caffe',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet50_caffe')),
     rpn_head=dict(
         type='RPNHead',
         in_channels=2048,

configs/_base_/models/faster_rcnn_r50_fpn.py

@@ -1,7 +1,6 @@
 # model settings
 model = dict(
     type='FasterRCNN',
-    pretrained='torchvision://resnet50',
     backbone=dict(
         type='ResNet',
         depth=50,
@@ -10,7 +9,8 @@
         frozen_stages=1,
         norm_cfg=dict(type='BN', requires_grad=True),
         norm_eval=True,
-        style='pytorch'),
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
     neck=dict(
         type='FPN',
         in_channels=[256, 512, 1024, 2048],

configs/_base_/models/mask_rcnn_r50_caffe_c4.py

@@ -2,7 +2,6 @@
 norm_cfg = dict(type='BN', requires_grad=False)
 model = dict(
     type='MaskRCNN',
-    pretrained='open-mmlab://detectron2/resnet50_caffe',
     backbone=dict(
         type='ResNet',
         depth=50,
@@ -13,7 +12,10 @@
         frozen_stages=1,
         norm_cfg=norm_cfg,
         norm_eval=True,
-        style='caffe'),
+        style='caffe',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet50_caffe')),
     rpn_head=dict(
         type='RPNHead',
         in_channels=1024,

configs/_base_/models/mask_rcnn_r50_fpn.py

@@ -1,7 +1,6 @@
 # model settings
 model = dict(
     type='MaskRCNN',
-    pretrained='torchvision://resnet50',
     backbone=dict(
         type='ResNet',
         depth=50,
@@ -10,7 +9,8 @@
         frozen_stages=1,
         norm_cfg=dict(type='BN', requires_grad=True),
         norm_eval=True,
-        style='pytorch'),
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
     neck=dict(
         type='FPN',
         in_channels=[256, 512, 1024, 2048],

configs/_base_/models/retinanet_r50_fpn.py

@@ -1,7 +1,6 @@
 # model settings
 model = dict(
     type='RetinaNet',
-    pretrained='torchvision://resnet50',
     backbone=dict(
         type='ResNet',
         depth=50,
@@ -10,7 +9,8 @@
         frozen_stages=1,
         norm_cfg=dict(type='BN', requires_grad=True),
         norm_eval=True,
-        style='pytorch'),
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
     neck=dict(
         type='FPN',
         in_channels=[256, 512, 1024, 2048],

configs/_base_/models/rpn_r50_caffe_c4.py

@@ -1,7 +1,6 @@
 # model settings
 model = dict(
     type='RPN',
-    pretrained='open-mmlab://detectron2/resnet50_caffe',
     backbone=dict(
         type='ResNet',
         depth=50,
@@ -12,7 +11,10 @@
         frozen_stages=1,
         norm_cfg=dict(type='BN', requires_grad=False),
         norm_eval=True,
-        style='caffe'),
+        style='caffe',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet50_caffe')),
     neck=None,
     rpn_head=dict(
         type='RPNHead',

configs/_base_/models/rpn_r50_fpn.py

@@ -1,7 +1,6 @@
 # model settings
 model = dict(
     type='RPN',
-    pretrained='torchvision://resnet50',
     backbone=dict(
         type='ResNet',
         depth=50,
@@ -10,7 +9,8 @@
         frozen_stages=1,
         norm_cfg=dict(type='BN', requires_grad=True),
         norm_eval=True,
-        style='pytorch'),
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
     neck=dict(
         type='FPN',
         in_channels=[256, 512, 1024, 2048],

configs/_base_/models/ssd300.py

@@ -2,14 +2,15 @@
 input_size = 300
 model = dict(
     type='SingleStageDetector',
-    pretrained='open-mmlab://vgg16_caffe',
     backbone=dict(
         type='SSDVGG',
         depth=16,
         with_last_pool=False,
         ceil_mode=True,
         out_indices=(3, 4),
-        out_feature_indices=(22, 34)),
+        out_feature_indices=(22, 34),
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://vgg16_caffe')),
     neck=dict(
         type='SSDNeck',
         in_channels=(512, 1024),

configs/atss/atss_r101_fpn_1x_coco.py

@@ -1,5 +1,6 @@
 _base_ = './atss_r50_fpn_1x_coco.py'
 model = dict(
-    pretrained='torchvision://resnet101',
-    backbone=dict(depth=101),
-)
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))

configs/atss/atss_r50_fpn_1x_coco.py

@@ -4,7 +4,6 @@
 ]
 model = dict(
     type='ATSS',
-    pretrained='torchvision://resnet50',
     backbone=dict(
         type='ResNet',
         depth=50,
@@ -13,7 +12,8 @@
         frozen_stages=1,
         norm_cfg=dict(type='BN', requires_grad=True),
         norm_eval=True,
-        style='pytorch'),
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
     neck=dict(
         type='FPN',
         in_channels=[256, 512, 1024, 2048],

configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py

@@ -6,7 +6,6 @@
 ]
 model = dict(
     type='AutoAssign',
-    pretrained='open-mmlab://detectron2/resnet50_caffe',
     backbone=dict(
         type='ResNet',
         depth=50,
@@ -15,7 +14,10 @@
         frozen_stages=1,
         norm_cfg=dict(type='BN', requires_grad=False),
         norm_eval=True,
-        style='caffe'),
+        style='caffe',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet50_caffe')),
     neck=dict(
         type='FPN',
         in_channels=[256, 512, 1024, 2048],

configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco.py

@@ -1,4 +1,7 @@
 _base_ = './cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py'
 model = dict(
-    pretrained='open-mmlab://detectron2/resnet101_caffe',
-    backbone=dict(depth=101))
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet101_caffe')))

configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco.py

@@ -1,2 +1,6 @@
 _base_ = './cascade_mask_rcnn_r50_fpn_1x_coco.py'
-model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))

configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco.py

@@ -1,2 +1,6 @@
 _base_ = './cascade_mask_rcnn_r50_fpn_20e_coco.py'
-model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))

configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py

@@ -1,10 +1,11 @@
 _base_ = ['./cascade_mask_rcnn_r50_fpn_1x_coco.py']
 
 model = dict(
-    pretrained='open-mmlab://detectron2/resnet50_caffe',
     backbone=dict(
-        norm_cfg=dict(requires_grad=False), norm_eval=True, style='caffe'))
-
+        norm_cfg=dict(requires_grad=False), norm_eval=True, style='caffe'),
+    init_cfg=dict(
+        type='Pretrained',
+        checkpoint='open-mmlab://detectron2/resnet50_caffe'))
 img_norm_cfg = dict(
     mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
 train_pipeline = [

configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py

@@ -1,6 +1,5 @@
 _base_ = './cascade_mask_rcnn_r50_fpn_1x_coco.py'
 model = dict(
-    pretrained='open-mmlab://resnext101_32x4d',
     backbone=dict(
         type='ResNeXt',
         depth=101,
@@ -10,4 +9,6 @@
         out_indices=(0, 1, 2, 3),
         frozen_stages=1,
         norm_cfg=dict(type='BN', requires_grad=True),
-        style='pytorch'))
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))

configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco.py

@@ -1,6 +1,5 @@
 _base_ = './cascade_mask_rcnn_r50_fpn_20e_coco.py'
 model = dict(
-    pretrained='open-mmlab://resnext101_32x4d',
     backbone=dict(
         type='ResNeXt',
         depth=101,
@@ -10,4 +9,6 @@
         out_indices=(0, 1, 2, 3),
         frozen_stages=1,
         norm_cfg=dict(type='BN', requires_grad=True),
-        style='pytorch'))
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))

configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco.py

@@ -1,6 +1,5 @@
 _base_ = './cascade_mask_rcnn_r50_fpn_1x_coco.py'
 model = dict(
-    pretrained='open-mmlab://resnext101_64x4d',
     backbone=dict(
         type='ResNeXt',
         depth=101,
@@ -10,4 +9,6 @@
         out_indices=(0, 1, 2, 3),
         frozen_stages=1,
         norm_cfg=dict(type='BN', requires_grad=True),
-        style='pytorch'))
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))

configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco.py

@@ -1,6 +1,5 @@
 _base_ = './cascade_mask_rcnn_r50_fpn_20e_coco.py'
 model = dict(
-    pretrained='open-mmlab://resnext101_64x4d',
     backbone=dict(
         type='ResNeXt',
         depth=101,
@@ -10,4 +9,6 @@
         out_indices=(0, 1, 2, 3),
         frozen_stages=1,
         norm_cfg=dict(type='BN', requires_grad=True),
-        style='pytorch'))
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))

configs/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco.py

@@ -1,4 +1,7 @@
 _base_ = './cascade_rcnn_r50_caffe_fpn_1x_coco.py'
 model = dict(
-    pretrained='open-mmlab://detectron2/resnet101_caffe',
-    backbone=dict(depth=101))
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet101_caffe')))

configs/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py

@@ -1,2 +1,6 @@
 _base_ = './cascade_rcnn_r50_fpn_1x_coco.py'
-model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))

configs/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco.py

@@ -1,2 +1,6 @@
 _base_ = './cascade_rcnn_r50_fpn_20e_coco.py'
-model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))