添加openmmlab测试用例

openmmlab_test/mmdetection-speed_xinpian/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py

+_base_ = './faster_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'))
+# use caffe img_norm
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], 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/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person-bicycle-car.py

+_base_ = './faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py'
+model = dict(roi_head=dict(bbox_head=dict(num_classes=3)))
+classes = ('person', 'bicycle', 'car')
+data = dict(
+    train=dict(classes=classes),
+    val=dict(classes=classes),
+    test=dict(classes=classes))
+
+load_from = 'https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_bbox_mAP-0.398_20200504_163323-30042637.pth'  # noqa

openmmlab_test/mmdetection-speed_xinpian/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person.py

+_base_ = './faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py'
+model = dict(roi_head=dict(bbox_head=dict(num_classes=1)))
+classes = ('person', )
+data = dict(
+    train=dict(classes=classes),
+    val=dict(classes=classes),
+    test=dict(classes=classes))
+
+load_from = 'https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_bbox_mAP-0.398_20200504_163323-30042637.pth'  # noqa

openmmlab_test/mmdetection-speed_xinpian/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py

+_base_ = './faster_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'))
+# use caffe img_norm
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
+                   (1333, 768), (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))

openmmlab_test/mmdetection-speed_xinpian/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco.py

+_base_ = './faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py'
+# learning policy
+lr_config = dict(step=[16, 23])
+runner = dict(type='EpochBasedRunner', max_epochs=24)

openmmlab_test/mmdetection-speed_xinpian/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py

+_base_ = 'faster_rcnn_r50_fpn_mstrain_3x_coco.py'
+model = dict(
+    pretrained='open-mmlab://detectron2/resnet50_caffe',
+    backbone=dict(
+        norm_cfg=dict(requires_grad=False), norm_eval=True, style='caffe'))
+
+# use caffe img_norm
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], 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='range',
+        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(dataset=dict(pipeline=train_pipeline)),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))

openmmlab_test/mmdetection-speed_xinpian/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_90k_coco.py

+_base_ = 'faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py'
+
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.001,
+    step=[60000, 80000])
+
+# Runner type
+runner = dict(_delete_=True, type='IterBasedRunner', max_iters=90000)
+
+checkpoint_config = dict(interval=10000)
+evaluation = dict(interval=10000, metric='bbox')

openmmlab_test/mmdetection-speed_xinpian/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py

+_base_ = [
+    '../_base_/models/faster_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]

openmmlab_test/mmdetection-speed_xinpian/configs/faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py

+_base_ = [
+    '../_base_/models/faster_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
+]

openmmlab_test/mmdetection-speed_xinpian/configs/faster_rcnn/faster_rcnn_r50_fpn_bounded_iou_1x_coco.py

+_base_ = './faster_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    roi_head=dict(
+        bbox_head=dict(
+            reg_decoded_bbox=True,
+            loss_bbox=dict(type='BoundedIoULoss', loss_weight=10.0))))

openmmlab_test/mmdetection-speed_xinpian/configs/faster_rcnn/faster_rcnn_r50_fpn_giou_1x_coco.py

+_base_ = './faster_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    roi_head=dict(
+        bbox_head=dict(
+            reg_decoded_bbox=True,
+            loss_bbox=dict(type='GIoULoss', loss_weight=10.0))))

openmmlab_test/mmdetection-speed_xinpian/configs/faster_rcnn/faster_rcnn_r50_fpn_iou_1x_coco.py

+_base_ = './faster_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    roi_head=dict(
+        bbox_head=dict(
+            reg_decoded_bbox=True,
+            loss_bbox=dict(type='IoULoss', loss_weight=10.0))))

openmmlab_test/mmdetection-speed_xinpian/configs/faster_rcnn/faster_rcnn_r50_fpn_mstrain_3x_coco.py

+_base_ = [
+    '../common/mstrain_3x_coco.py', '../_base_/models/faster_rcnn_r50_fpn.py'
+]

openmmlab_test/mmdetection-speed_xinpian/configs/faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py

+_base_ = './faster_rcnn_r50_fpn_1x_coco.py'
+model = dict(train_cfg=dict(rcnn=dict(sampler=dict(type='OHEMSampler'))))

openmmlab_test/mmdetection-speed_xinpian/configs/faster_rcnn/faster_rcnn_r50_fpn_soft_nms_1x_coco.py

+_base_ = [
+    '../_base_/models/faster_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+model = dict(
+    test_cfg=dict(
+        rcnn=dict(
+            score_thr=0.05,
+            nms=dict(type='soft_nms', iou_threshold=0.5),
+            max_per_img=100)))

openmmlab_test/mmdetection-speed_xinpian/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco.py

+_base_ = './faster_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    pretrained='open-mmlab://resnext101_32x4d',
+    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'))

openmmlab_test/mmdetection-speed_xinpian/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco.py

+_base_ = './faster_rcnn_r50_fpn_2x_coco.py'
+model = dict(
+    pretrained='open-mmlab://resnext101_32x4d',
+    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'))

openmmlab_test/mmdetection-speed_xinpian/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py

+_base_ = [
+    '../common/mstrain_3x_coco.py', '../_base_/models/faster_rcnn_r50_fpn.py'
+]
+model = dict(
+    pretrained='open-mmlab://resnext101_32x4d',
+    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'))

openmmlab_test/mmdetection-speed_xinpian/configs/faster_rcnn/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py

+_base_ = [
+    '../common/mstrain_3x_coco.py', '../_base_/models/faster_rcnn_r50_fpn.py'
+]
+model = dict(
+    pretrained='open-mmlab://detectron2/resnext101_32x8d',
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=32,
+        base_width=8,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=False),
+        style='pytorch'))
+
+# ResNeXt-101-32x8d model trained with Caffe2 at FB,
+# so the mean and std need to be changed.
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675],
+    std=[57.375, 57.120, 58.395],
+    to_rgb=False)
+
+# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)],
+# multiscale_mode='range'
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 800)],
+        multiscale_mode='range',
+        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']),
+        ])
+]
+
+# Use RepeatDataset to speed up training
+data = dict(
+    train=dict(dataset=dict(pipeline=train_pipeline)),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))

openmmlab_test/mmdetection-speed_xinpian/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco.py

+_base_ = './faster_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    pretrained='open-mmlab://resnext101_64x4d',
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=64,
+        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'))