添加openmmlab测试用例

openmmlab_test/mmdetection-speed_xinpian/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco.py

+_base_ = './cascade_mask_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'))

openmmlab_test/mmdetection-speed_xinpian/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco.py

+_base_ = './cascade_mask_rcnn_r50_fpn_20e_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'))

openmmlab_test/mmdetection-speed_xinpian/configs/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco.py

+_base_ = './cascade_rcnn_r50_caffe_fpn_1x_coco.py'
+model = dict(
+    pretrained='open-mmlab://detectron2/resnet101_caffe',
+    backbone=dict(depth=101))

openmmlab_test/mmdetection-speed_xinpian/configs/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py

+_base_ = './cascade_rcnn_r50_fpn_1x_coco.py'
+model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))

openmmlab_test/mmdetection-speed_xinpian/configs/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco.py

+_base_ = './cascade_rcnn_r50_fpn_20e_coco.py'
+model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))

openmmlab_test/mmdetection-speed_xinpian/configs/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco.py

+_base_ = './cascade_rcnn_r50_fpn_1x_coco.py'
+
+model = dict(
+    pretrained='open-mmlab://detectron2/resnet50_caffe',
+    backbone=dict(norm_cfg=dict(requires_grad=False), 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/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py

+_base_ = [
+    '../_base_/models/cascade_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]

openmmlab_test/mmdetection-speed_xinpian/configs/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco.py

+_base_ = './cascade_rcnn_r50_fpn_1x_coco.py'
+# learning policy
+lr_config = dict(step=[16, 19])
+runner = dict(type='EpochBasedRunner', max_epochs=20)

openmmlab_test/mmdetection-speed_xinpian/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco.py

+_base_ = './cascade_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/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco.py

+_base_ = './cascade_rcnn_r50_fpn_20e_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/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco.py

+_base_ = './cascade_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    type='CascadeRCNN',
+    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'))

openmmlab_test/mmdetection-speed_xinpian/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco.py

+_base_ = './cascade_rcnn_r50_fpn_20e_coco.py'
+model = dict(
+    type='CascadeRCNN',
+    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'))

openmmlab_test/mmdetection-speed_xinpian/configs/cascade_rcnn/metafile.yml

+Collections:
+  - Name: Cascade R-CNN
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x NVIDIA V100 GPUs
+      Architecture:
+        - Cascade R-CNN
+        - FPN
+        - RPN
+        - ResNet
+        - RoIAlign
+    Paper: http://dx.doi.org/10.1109/tpami.2019.2956516
+    README: configs/cascade_rcnn/README.md
+
+Models:
+  - Name: cascade_rcnn_r50_caffe_fpn_1x_coco
+    In Collection: Cascade R-CNN
+    Config: configs/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 4.2
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco/cascade_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.404_20200504_174853-b857be87.pth
+
+  - Name: cascade_rcnn_r50_fpn_1x_coco
+    In Collection: Cascade R-CNN
+    Config: configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 4.4
+      inference time (s/im): 0.06211
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.3
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco/cascade_rcnn_r50_fpn_1x_coco_20200316-3dc56deb.pth
+
+  - Name: cascade_rcnn_r50_fpn_20e_coco
+    In Collection: Cascade R-CNN
+    Config: configs/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco.py
+    Metadata:
+      Training Memory (GB): 4.4
+      inference time (s/im): 0.06211
+      Epochs: 20
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco/cascade_rcnn_r50_fpn_20e_coco_bbox_mAP-0.41_20200504_175131-e9872a90.pth
+
+  - Name: cascade_rcnn_r101_caffe_fpn_1x_coco
+    In Collection: Cascade R-CNN
+    Config: configs/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 6.2
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.3
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco/cascade_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.423_20200504_175649-cab8dbd5.pth
+
+  - Name: cascade_rcnn_r101_fpn_1x_coco
+    In Collection: Cascade R-CNN
+    Config: configs/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 6.4
+      inference time (s/im): 0.07407
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco/cascade_rcnn_r101_fpn_1x_coco_20200317-0b6a2fbf.pth
+
+  - Name: cascade_rcnn_r101_fpn_20e_coco
+    In Collection: Cascade R-CNN
+    Config: configs/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco.py
+    Metadata:
+      Training Memory (GB): 6.4
+      inference time (s/im): 0.07407
+      Epochs: 20
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco/cascade_rcnn_r101_fpn_20e_coco_bbox_mAP-0.425_20200504_231812-5057dcc5.pth
+
+  - Name: cascade_rcnn_x101_32x4d_fpn_1x_coco
+    In Collection: Cascade R-CNN
+    Config: configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 7.6
+      inference time (s/im): 0.09174
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 43.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco/cascade_rcnn_x101_32x4d_fpn_1x_coco_20200316-95c2deb6.pth
+
+  - Name: cascade_rcnn_x101_32x4d_fpn_20e_coco
+    In Collection: Cascade R-CNN
+    Config: configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco.py
+    Metadata:
+      Training Memory (GB): 7.6
+      Epochs: 20
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 43.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco/cascade_rcnn_x101_32x4d_fpn_20e_coco_20200906_134608-9ae0a720.pth
+
+  - Name: cascade_rcnn_x101_64x4d_fpn_1x_coco
+    In Collection: Cascade R-CNN
+    Config: configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 10.7
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 44.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco/cascade_rcnn_x101_64x4d_fpn_1x_coco_20200515_075702-43ce6a30.pth
+
+  - Name: cascade_rcnn_x101_64x4d_fpn_20e_coco
+    In Collection: Cascade R-CNN
+    Config: configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco.py
+    Metadata:
+      Training Memory (GB): 10.7
+      Epochs: 20
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 44.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco/cascade_rcnn_x101_64x4d_fpn_20e_coco_20200509_224357-051557b1.pth
+
+  - Name: cascade_mask_rcnn_r50_caffe_fpn_1x_coco
+    In Collection: Cascade R-CNN
+    Config: configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 5.9
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.2
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  36.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco/cascade_mask_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.412__segm_mAP-0.36_20200504_174659-5004b251.pth
+
+  - Name: cascade_mask_rcnn_r50_fpn_1x_coco
+    In Collection: Cascade R-CNN
+    Config: configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 6.0
+      inference time (s/im): 0.08929
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.2
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  35.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco/cascade_mask_rcnn_r50_fpn_1x_coco_20200203-9d4dcb24.pth
+
+  - Name: cascade_mask_rcnn_r50_fpn_20e_coco
+    In Collection: Cascade R-CNN
+    Config: configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco.py
+    Metadata:
+      Training Memory (GB): 6.0
+      inference time (s/im): 0.08929
+      Epochs: 20
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.9
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  36.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco/cascade_mask_rcnn_r50_fpn_20e_coco_bbox_mAP-0.419__segm_mAP-0.365_20200504_174711-4af8e66e.pth
+
+  - Name: cascade_mask_rcnn_r101_caffe_fpn_1x_coco
+    In Collection: Cascade R-CNN
+    Config: configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 7.8
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 43.2
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  37.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco/cascade_mask_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.432__segm_mAP-0.376_20200504_174813-5c1e9599.pth
+
+  - Name: cascade_mask_rcnn_r101_fpn_1x_coco
+    In Collection: Cascade R-CNN
+    Config: configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 7.9
+      inference time (s/im): 0.10204
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.9
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  37.3
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco/cascade_mask_rcnn_r101_fpn_1x_coco_20200203-befdf6ee.pth
+
+  - Name: cascade_mask_rcnn_r101_fpn_20e_coco
+    In Collection: Cascade R-CNN
+    Config: configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco.py
+    Metadata:
+      Training Memory (GB): 7.9
+      inference time (s/im): 0.10204
+      Epochs: 20
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 43.4
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  37.8
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco/cascade_mask_rcnn_r101_fpn_20e_coco_bbox_mAP-0.434__segm_mAP-0.378_20200504_174836-005947da.pth
+
+  - Name: cascade_mask_rcnn_x101_32x4d_fpn_1x_coco
+    In Collection: Cascade R-CNN
+    Config: configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 9.2
+      inference time (s/im): 0.11628
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 44.3
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  38.3
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco_20200201-0f411b1f.pth
+
+  - Name: cascade_mask_rcnn_x101_32x4d_fpn_20e_coco
+    In Collection: Cascade R-CNN
+    Config: configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco.py
+    Metadata:
+      Training Memory (GB): 9.2
+      inference time (s/im): 0.11628
+      Epochs: 20
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 45.0
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  39.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco_20200528_083917-ed1f4751.pth
+
+  - Name: cascade_mask_rcnn_x101_64x4d_fpn_1x_coco
+    In Collection: Cascade R-CNN
+    Config: configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 12.2
+      inference time (s/im): 0.14925
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 45.3
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  39.2
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco_20200203-9a2db89d.pth
+
+  - Name: cascade_mask_rcnn_x101_64x4d_fpn_20e_coco
+    In Collection: Cascade R-CNN
+    Config: configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco.py
+    Metadata:
+      Training Memory (GB): 12.2
+      Epochs: 20
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 45.6
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 39.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco_20200512_161033-bdb5126a.pth

openmmlab_test/mmdetection-speed_xinpian/configs/cascade_rpn/README.md

+# Cascade RPN
+
+<!-- [ALGORITHM] -->
+
+We provide the code for reproducing experiment results of [Cascade RPN](https://arxiv.org/abs/1909.06720).
+
+```
+@inproceedings{vu2019cascade,
+  title={Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution},
+  author={Vu, Thang and Jang, Hyunjun and Pham, Trung X and Yoo, Chang D},
+  booktitle={Conference on Neural Information Processing Systems (NeurIPS)},
+  year={2019}
+}
+```
+
+## Benchmark
+
+### Region proposal performance
+
+| Method | Backbone | Style | Mem (GB) | Train time (s/iter) | Inf time (fps) | AR 1000 |                Download                |
+|:------:|:--------:|:-----:|:--------:|:-------------------:|:--------------:|:-------:|:--------------------------------------:|
+|  CRPN  | R-50-FPN | caffe |     -    |          -          |        -       |   72.0  | [model](https://drive.google.com/file/d/1qxVdOnCgK-ee7_z0x6mvAir_glMu2Ihi/view?usp=sharing) |
+
+### Detection performance
+
+|     Method    |   Proposal  | Backbone |  Style  | Schedule | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP |                   Download                   |
+|:-------------:|:-----------:|:--------:|:-------:|:--------:|:--------:|:-------------------:|:--------------:|:------:|:--------------------------------------------:|
+|   Fast R-CNN  | Cascade RPN | R-50-FPN |  caffe  |    1x    |    -     |          -          |        -       |  39.9  | [model](https://drive.google.com/file/d/1NmbnuY5VHi8I9FE8xnp5uNvh2i-t-6_L/view?usp=sharing) |
+|  Faster R-CNN | Cascade RPN | R-50-FPN |  caffe  |    1x    |    -     |          -          |        -       |  40.4  | [model](https://drive.google.com/file/d/1dS3Q66qXMJpcuuQgDNkLp669E5w1UMuZ/view?usp=sharing) |

openmmlab_test/mmdetection-speed_xinpian/configs/cascade_rpn/crpn_fast_rcnn_r50_caffe_fpn_1x_coco.py

+_base_ = '../fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    pretrained='open-mmlab://detectron2/resnet50_caffe',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=False),
+        norm_eval=True,
+        style='caffe'),
+    roi_head=dict(
+        bbox_head=dict(
+            bbox_coder=dict(target_stds=[0.04, 0.04, 0.08, 0.08]),
+            loss_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.5),
+            loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
+    # model training and testing settings
+    train_cfg=dict(
+        rcnn=dict(
+            assigner=dict(
+                pos_iou_thr=0.65, neg_iou_thr=0.65, min_pos_iou=0.65),
+            sampler=dict(num=256))),
+    test_cfg=dict(rcnn=dict(score_thr=1e-3)))
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+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='LoadProposals', num_max_proposals=300),
+    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', 'proposals', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadProposals', num_max_proposals=300),
+    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='ToTensor', keys=['proposals']),
+            dict(
+                type='ToDataContainer',
+                fields=[dict(key='proposals', stack=False)]),
+            dict(type='Collect', keys=['img', 'proposals']),
+        ])
+]
+data = dict(
+    train=dict(
+        proposal_file=data_root +
+        'proposals/crpn_r50_caffe_fpn_1x_train2017.pkl',
+        pipeline=train_pipeline),
+    val=dict(
+        proposal_file=data_root +
+        'proposals/crpn_r50_caffe_fpn_1x_val2017.pkl',
+        pipeline=test_pipeline),
+    test=dict(
+        proposal_file=data_root +
+        'proposals/crpn_r50_caffe_fpn_1x_val2017.pkl',
+        pipeline=test_pipeline))
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))

openmmlab_test/mmdetection-speed_xinpian/configs/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py

+_base_ = '../faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py'
+rpn_weight = 0.7
+model = dict(
+    rpn_head=dict(
+        _delete_=True,
+        type='CascadeRPNHead',
+        num_stages=2,
+        stages=[
+            dict(
+                type='StageCascadeRPNHead',
+                in_channels=256,
+                feat_channels=256,
+                anchor_generator=dict(
+                    type='AnchorGenerator',
+                    scales=[8],
+                    ratios=[1.0],
+                    strides=[4, 8, 16, 32, 64]),
+                adapt_cfg=dict(type='dilation', dilation=3),
+                bridged_feature=True,
+                sampling=False,
+                with_cls=False,
+                reg_decoded_bbox=True,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=(.0, .0, .0, .0),
+                    target_stds=(0.1, 0.1, 0.5, 0.5)),
+                loss_bbox=dict(
+                    type='IoULoss', linear=True,
+                    loss_weight=10.0 * rpn_weight)),
+            dict(
+                type='StageCascadeRPNHead',
+                in_channels=256,
+                feat_channels=256,
+                adapt_cfg=dict(type='offset'),
+                bridged_feature=False,
+                sampling=True,
+                with_cls=True,
+                reg_decoded_bbox=True,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=(.0, .0, .0, .0),
+                    target_stds=(0.05, 0.05, 0.1, 0.1)),
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=True,
+                    loss_weight=1.0 * rpn_weight),
+                loss_bbox=dict(
+                    type='IoULoss', linear=True,
+                    loss_weight=10.0 * rpn_weight))
+        ]),
+    roi_head=dict(
+        bbox_head=dict(
+            bbox_coder=dict(target_stds=[0.04, 0.04, 0.08, 0.08]),
+            loss_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.5),
+            loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
+    # model training and testing settings
+    train_cfg=dict(
+        rpn=[
+            dict(
+                assigner=dict(
+                    type='RegionAssigner', center_ratio=0.2, ignore_ratio=0.5),
+                allowed_border=-1,
+                pos_weight=-1,
+                debug=False),
+            dict(
+                assigner=dict(
+                    type='MaxIoUAssigner',
+                    pos_iou_thr=0.7,
+                    neg_iou_thr=0.7,
+                    min_pos_iou=0.3,
+                    ignore_iof_thr=-1),
+                sampler=dict(
+                    type='RandomSampler',
+                    num=256,
+                    pos_fraction=0.5,
+                    neg_pos_ub=-1,
+                    add_gt_as_proposals=False),
+                allowed_border=-1,
+                pos_weight=-1,
+                debug=False)
+        ],
+        rpn_proposal=dict(max_per_img=300, nms=dict(iou_threshold=0.8)),
+        rcnn=dict(
+            assigner=dict(
+                pos_iou_thr=0.65, neg_iou_thr=0.65, min_pos_iou=0.65),
+            sampler=dict(type='RandomSampler', num=256))),
+    test_cfg=dict(
+        rpn=dict(max_per_img=300, nms=dict(iou_threshold=0.8)),
+        rcnn=dict(score_thr=1e-3)))
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))

openmmlab_test/mmdetection-speed_xinpian/configs/cascade_rpn/crpn_r50_caffe_fpn_1x_coco.py

+_base_ = '../rpn/rpn_r50_caffe_fpn_1x_coco.py'
+model = dict(
+    rpn_head=dict(
+        _delete_=True,
+        type='CascadeRPNHead',
+        num_stages=2,
+        stages=[
+            dict(
+                type='StageCascadeRPNHead',
+                in_channels=256,
+                feat_channels=256,
+                anchor_generator=dict(
+                    type='AnchorGenerator',
+                    scales=[8],
+                    ratios=[1.0],
+                    strides=[4, 8, 16, 32, 64]),
+                adapt_cfg=dict(type='dilation', dilation=3),
+                bridged_feature=True,
+                sampling=False,
+                with_cls=False,
+                reg_decoded_bbox=True,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=(.0, .0, .0, .0),
+                    target_stds=(0.1, 0.1, 0.5, 0.5)),
+                loss_bbox=dict(type='IoULoss', linear=True, loss_weight=10.0)),
+            dict(
+                type='StageCascadeRPNHead',
+                in_channels=256,
+                feat_channels=256,
+                adapt_cfg=dict(type='offset'),
+                bridged_feature=False,
+                sampling=True,
+                with_cls=True,
+                reg_decoded_bbox=True,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=(.0, .0, .0, .0),
+                    target_stds=(0.05, 0.05, 0.1, 0.1)),
+                loss_cls=dict(
+                    type='CrossEntropyLoss', use_sigmoid=True,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='IoULoss', linear=True, loss_weight=10.0))
+        ]),
+    train_cfg=dict(rpn=[
+        dict(
+            assigner=dict(
+                type='RegionAssigner', center_ratio=0.2, ignore_ratio=0.5),
+            allowed_border=-1,
+            pos_weight=-1,
+            debug=False),
+        dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.7,
+                neg_iou_thr=0.7,
+                min_pos_iou=0.3,
+                ignore_iof_thr=-1,
+                iou_calculator=dict(type='BboxOverlaps2D')),
+            sampler=dict(
+                type='RandomSampler',
+                num=256,
+                pos_fraction=0.5,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=False),
+            allowed_border=-1,
+            pos_weight=-1,
+            debug=False)
+    ]),
+    test_cfg=dict(
+        rpn=dict(
+            nms_pre=2000,
+            max_per_img=2000,
+            nms=dict(type='nms', iou_threshold=0.8),
+            min_bbox_size=0)))
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))

openmmlab_test/mmdetection-speed_xinpian/configs/centernet/README.md

+# CenterNet
+
+## Introduction
+
+<!-- [ALGORITHM] -->
+
+```latex
+@article{zhou2019objects,
+  title={Objects as Points},
+  author={Zhou, Xingyi and Wang, Dequan and Kr{\"a}henb{\"u}hl, Philipp},
+  booktitle={arXiv preprint arXiv:1904.07850},
+  year={2019}
+}
+```
+
+## Results and models
+
+| Backbone        | DCN |  Mem (GB) | Box AP | Flip box AP| Config | Download |
+| :-------------: | :--------: |:----------------: | :------: | :------------: | :----: | :----: |
+| ResNet-18 | N | 3.45 | 26.0 | 27.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/centernet/centernet_resnet18_140e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/centernet/centernet_resnet18_140e_coco/centernet_resnet18_140e_coco_20210519_092334-eafe8ccd.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/centernet/centernet_resnet18_140e_coco/centernet_resnet18_140e_coco_20210519_092334.log.json) |
+| ResNet-18 | Y | 3.47 | 29.5 | 31.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/centernet/centernet_resnet18_dcnv2_140e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/centernet/centernet_resnet18_dcnv2_140e_coco/centernet_resnet18_dcnv2_140e_coco_20210520_101209-da388ba2.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/centernet/centernet_resnet18_dcnv2_140e_coco/centernet_resnet18_dcnv2_140e_coco_20210520_101209.log.json) |
+
+Note:
+
+- Flip box AP setting is single-scale and `flip=True`.
+- Due to complex data enhancement, we find that the performance is unstable and may fluctuate by about 0.4 mAP. mAP 29.4 ~ 29.8 is acceptable in ResNet-18-DCNv2.
+- Compared to the source code, we refer to [CenterNet-Better](https://github.com/FateScript/CenterNet-better), and make the following changes
+  - fix wrong image mean and variance in image normalization to be compatible with the pre-trained backbone.
+  - Use SGD rather than ADAM optimizer and add warmup and grad clip.
+  - Use DistributedDataParallel as other models in MMDetection rather than using DataParallel.

openmmlab_test/mmdetection-speed_xinpian/configs/centernet/centernet_resnet18_140e_coco.py

+_base_ = './centernet_resnet18_dcnv2_140e_coco.py'
+
+model = dict(neck=dict(use_dcn=False))

openmmlab_test/mmdetection-speed_xinpian/configs/centernet/centernet_resnet18_dcnv2_140e_coco.py

+_base_ = [
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+model = dict(
+    type='CenterNet',
+    pretrained='torchvision://resnet18',
+    backbone=dict(
+        type='ResNet', depth=18, norm_eval=False, norm_cfg=dict(type='BN')),
+    neck=dict(
+        type='CTResNetNeck',
+        in_channel=512,
+        num_deconv_filters=(256, 128, 64),
+        num_deconv_kernels=(4, 4, 4),
+        use_dcn=True),
+    bbox_head=dict(
+        type='CenterNetHead',
+        num_classes=80,
+        in_channel=64,
+        feat_channel=64,
+        loss_center_heatmap=dict(type='GaussianFocalLoss', loss_weight=1.0),
+        loss_wh=dict(type='L1Loss', loss_weight=0.1),
+        loss_offset=dict(type='L1Loss', loss_weight=1.0)),
+    train_cfg=None,
+    test_cfg=dict(topk=100, local_maximum_kernel=3, max_per_img=100))
+
+# We fixed the incorrect img_norm_cfg problem in the source code.
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+
+train_pipeline = [
+    dict(type='LoadImageFromFile', to_float32=True, color_type='color'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='PhotoMetricDistortion',
+        brightness_delta=32,
+        contrast_range=(0.5, 1.5),
+        saturation_range=(0.5, 1.5),
+        hue_delta=18),
+    dict(
+        type='RandomCenterCropPad',
+        crop_size=(512, 512),
+        ratios=(0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3),
+        mean=[0, 0, 0],
+        std=[1, 1, 1],
+        to_rgb=True,
+        test_pad_mode=None),
+    dict(type='Resize', img_scale=(512, 512), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile', to_float32=True),
+    dict(
+        type='MultiScaleFlipAug',
+        scale_factor=1.0,
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(
+                type='RandomCenterCropPad',
+                ratios=None,
+                border=None,
+                mean=[0, 0, 0],
+                std=[1, 1, 1],
+                to_rgb=True,
+                test_mode=True,
+                test_pad_mode=['logical_or', 31],
+                test_pad_add_pix=1),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='DefaultFormatBundle'),
+            dict(
+                type='Collect',
+                meta_keys=('filename', 'ori_shape', 'img_shape', 'pad_shape',
+                           'scale_factor', 'flip', 'flip_direction',
+                           'img_norm_cfg', 'border'),
+                keys=['img'])
+        ])
+]
+data = dict(
+    samples_per_gpu=16,
+    workers_per_gpu=4,
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+
+# optimizer
+# Based on the default settings of modern detectors, the SGD effect is better
+# than the Adam in the source code, so we use SGD default settings and
+# if you use adam+lr5e-4, the map is 29.1.
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
+
+# learning policy
+# Based on the default settings of modern detectors, we added warmup settings.
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=1000,
+    warmup_ratio=1.0 / 1000,
+    step=[90, 120])
+runner = dict(max_epochs=140)
+
+# Avoid evaluation and saving weights too frequently
+evaluation = dict(interval=5, metric='bbox')
+checkpoint_config = dict(interval=5)