添加openmmlab测试用例

openmmlab_test/mmdetection-speed_xinpian/configs/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco.py

+_base_ = './faster_rcnn_hrnetv2p_w32_1x_coco.py'
+# learning policy
+lr_config = dict(step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)

openmmlab_test/mmdetection-speed_xinpian/configs/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco.py

+_base_ = './faster_rcnn_hrnetv2p_w32_1x_coco.py'
+model = dict(
+    pretrained='open-mmlab://msra/hrnetv2_w40',
+    backbone=dict(
+        type='HRNet',
+        extra=dict(
+            stage2=dict(num_channels=(40, 80)),
+            stage3=dict(num_channels=(40, 80, 160)),
+            stage4=dict(num_channels=(40, 80, 160, 320)))),
+    neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256))

openmmlab_test/mmdetection-speed_xinpian/configs/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco.py

+_base_ = './faster_rcnn_hrnetv2p_w40_1x_coco.py'
+# learning policy
+lr_config = dict(step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)

openmmlab_test/mmdetection-speed_xinpian/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco.py

+_base_ = './fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py'
+model = dict(
+    pretrained='open-mmlab://msra/hrnetv2_w18',
+    backbone=dict(
+        extra=dict(
+            stage2=dict(num_channels=(18, 36)),
+            stage3=dict(num_channels=(18, 36, 72)),
+            stage4=dict(num_channels=(18, 36, 72, 144)))),
+    neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256))

openmmlab_test/mmdetection-speed_xinpian/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco.py

+_base_ = './fcos_hrnetv2p_w18_gn-head_4x4_1x_coco.py'
+# learning policy
+lr_config = dict(step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)

openmmlab_test/mmdetection-speed_xinpian/configs/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco.py

+_base_ = './fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py'
+model = dict(
+    pretrained='open-mmlab://msra/hrnetv2_w18',
+    backbone=dict(
+        extra=dict(
+            stage2=dict(num_channels=(18, 36)),
+            stage3=dict(num_channels=(18, 36, 72)),
+            stage4=dict(num_channels=(18, 36, 72, 144)))),
+    neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256))

openmmlab_test/mmdetection-speed_xinpian/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py

+_base_ = '../fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py'
+model = dict(
+    pretrained='open-mmlab://msra/hrnetv2_w32',
+    backbone=dict(
+        _delete_=True,
+        type='HRNet',
+        extra=dict(
+            stage1=dict(
+                num_modules=1,
+                num_branches=1,
+                block='BOTTLENECK',
+                num_blocks=(4, ),
+                num_channels=(64, )),
+            stage2=dict(
+                num_modules=1,
+                num_branches=2,
+                block='BASIC',
+                num_blocks=(4, 4),
+                num_channels=(32, 64)),
+            stage3=dict(
+                num_modules=4,
+                num_branches=3,
+                block='BASIC',
+                num_blocks=(4, 4, 4),
+                num_channels=(32, 64, 128)),
+            stage4=dict(
+                num_modules=3,
+                num_branches=4,
+                block='BASIC',
+                num_blocks=(4, 4, 4, 4),
+                num_channels=(32, 64, 128, 256)))),
+    neck=dict(
+        _delete_=True,
+        type='HRFPN',
+        in_channels=[32, 64, 128, 256],
+        out_channels=256,
+        stride=2,
+        num_outs=5))
+img_norm_cfg = dict(
+    mean=[103.53, 116.28, 123.675], std=[57.375, 57.12, 58.395], to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))

openmmlab_test/mmdetection-speed_xinpian/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco.py

+_base_ = './fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py'
+# learning policy
+lr_config = dict(step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)

openmmlab_test/mmdetection-speed_xinpian/configs/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py

+_base_ = './fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py'
+img_norm_cfg = dict(
+    mean=[103.53, 116.28, 123.675], std=[57.375, 57.12, 58.395], to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 800)],
+        multiscale_mode='value',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+# learning policy
+lr_config = dict(step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)

openmmlab_test/mmdetection-speed_xinpian/configs/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco.py

+_base_ = './fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py'
+model = dict(
+    pretrained='open-mmlab://msra/hrnetv2_w40',
+    backbone=dict(
+        type='HRNet',
+        extra=dict(
+            stage2=dict(num_channels=(40, 80)),
+            stage3=dict(num_channels=(40, 80, 160)),
+            stage4=dict(num_channels=(40, 80, 160, 320)))),
+    neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256))

openmmlab_test/mmdetection-speed_xinpian/configs/hrnet/htc_hrnetv2p_w18_20e_coco.py

+_base_ = './htc_hrnetv2p_w32_20e_coco.py'
+model = dict(
+    pretrained='open-mmlab://msra/hrnetv2_w18',
+    backbone=dict(
+        extra=dict(
+            stage2=dict(num_channels=(18, 36)),
+            stage3=dict(num_channels=(18, 36, 72)),
+            stage4=dict(num_channels=(18, 36, 72, 144)))),
+    neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256))

openmmlab_test/mmdetection-speed_xinpian/configs/hrnet/htc_hrnetv2p_w32_20e_coco.py

+_base_ = '../htc/htc_r50_fpn_20e_coco.py'
+model = dict(
+    pretrained='open-mmlab://msra/hrnetv2_w32',
+    backbone=dict(
+        _delete_=True,
+        type='HRNet',
+        extra=dict(
+            stage1=dict(
+                num_modules=1,
+                num_branches=1,
+                block='BOTTLENECK',
+                num_blocks=(4, ),
+                num_channels=(64, )),
+            stage2=dict(
+                num_modules=1,
+                num_branches=2,
+                block='BASIC',
+                num_blocks=(4, 4),
+                num_channels=(32, 64)),
+            stage3=dict(
+                num_modules=4,
+                num_branches=3,
+                block='BASIC',
+                num_blocks=(4, 4, 4),
+                num_channels=(32, 64, 128)),
+            stage4=dict(
+                num_modules=3,
+                num_branches=4,
+                block='BASIC',
+                num_blocks=(4, 4, 4, 4),
+                num_channels=(32, 64, 128, 256)))),
+    neck=dict(
+        _delete_=True,
+        type='HRFPN',
+        in_channels=[32, 64, 128, 256],
+        out_channels=256))

openmmlab_test/mmdetection-speed_xinpian/configs/hrnet/htc_hrnetv2p_w40_20e_coco.py

+_base_ = './htc_hrnetv2p_w32_20e_coco.py'
+model = dict(
+    pretrained='open-mmlab://msra/hrnetv2_w40',
+    backbone=dict(
+        type='HRNet',
+        extra=dict(
+            stage2=dict(num_channels=(40, 80)),
+            stage3=dict(num_channels=(40, 80, 160)),
+            stage4=dict(num_channels=(40, 80, 160, 320)))),
+    neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256))

openmmlab_test/mmdetection-speed_xinpian/configs/hrnet/htc_hrnetv2p_w40_28e_coco.py

+_base_ = './htc_hrnetv2p_w40_20e_coco.py'
+# learning policy
+lr_config = dict(step=[24, 27])
+runner = dict(type='EpochBasedRunner', max_epochs=28)

openmmlab_test/mmdetection-speed_xinpian/configs/hrnet/htc_x101_64x4d_fpn_16x1_28e_coco.py

+_base_ = '../htc/htc_x101_64x4d_fpn_16x1_20e_coco.py'
+# learning policy
+lr_config = dict(step=[24, 27])
+runner = dict(type='EpochBasedRunner', max_epochs=28)

openmmlab_test/mmdetection-speed_xinpian/configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py

+_base_ = './mask_rcnn_hrnetv2p_w32_1x_coco.py'
+model = dict(
+    pretrained='open-mmlab://msra/hrnetv2_w18',
+    backbone=dict(
+        extra=dict(
+            stage2=dict(num_channels=(18, 36)),
+            stage3=dict(num_channels=(18, 36, 72)),
+            stage4=dict(num_channels=(18, 36, 72, 144)))),
+    neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256))

openmmlab_test/mmdetection-speed_xinpian/configs/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco.py

+_base_ = './mask_rcnn_hrnetv2p_w18_1x_coco.py'
+# learning policy
+lr_config = dict(step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)

openmmlab_test/mmdetection-speed_xinpian/configs/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco.py

+_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    pretrained='open-mmlab://msra/hrnetv2_w32',
+    backbone=dict(
+        _delete_=True,
+        type='HRNet',
+        extra=dict(
+            stage1=dict(
+                num_modules=1,
+                num_branches=1,
+                block='BOTTLENECK',
+                num_blocks=(4, ),
+                num_channels=(64, )),
+            stage2=dict(
+                num_modules=1,
+                num_branches=2,
+                block='BASIC',
+                num_blocks=(4, 4),
+                num_channels=(32, 64)),
+            stage3=dict(
+                num_modules=4,
+                num_branches=3,
+                block='BASIC',
+                num_blocks=(4, 4, 4),
+                num_channels=(32, 64, 128)),
+            stage4=dict(
+                num_modules=3,
+                num_branches=4,
+                block='BASIC',
+                num_blocks=(4, 4, 4, 4),
+                num_channels=(32, 64, 128, 256)))),
+    neck=dict(
+        _delete_=True,
+        type='HRFPN',
+        in_channels=[32, 64, 128, 256],
+        out_channels=256))

openmmlab_test/mmdetection-speed_xinpian/configs/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco.py

+_base_ = './mask_rcnn_hrnetv2p_w32_1x_coco.py'
+# learning policy
+lr_config = dict(step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)

openmmlab_test/mmdetection-speed_xinpian/configs/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco.py

+_base_ = './mask_rcnn_hrnetv2p_w18_1x_coco.py'
+model = dict(
+    pretrained='open-mmlab://msra/hrnetv2_w40',
+    backbone=dict(
+        type='HRNet',
+        extra=dict(
+            stage2=dict(num_channels=(40, 80)),
+            stage3=dict(num_channels=(40, 80, 160)),
+            stage4=dict(num_channels=(40, 80, 160, 320)))),
+    neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256))