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[Fix] Fix mdformat version to support python3.6 (#8195)

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jbwang1997 3 years ago committed by GitHub
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  1. 4
      .pre-commit-config.yaml
  2. 10
      docs/en/get_started.md
  3. 80
      docs/zh_cn/get_started.md
  4. 46
      docs/zh_cn/model_zoo.md

4
.pre-commit-config.yaml
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@ -29,12 +29,12 @@ repos:
hooks:
- id: codespell
- repo: https://github.com/executablebooks/mdformat
rev: 0.7.14
rev: 0.7.9
hooks:
- id: mdformat
args: ["--number"]
additional_dependencies:
- mdformat-gfm
- mdformat-openmmlab
- mdformat_frontmatter
- linkify-it-py
- repo: https://github.com/myint/docformatter

10
docs/en/get_started.md
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@ -147,12 +147,12 @@ However some functionalities are gone in this mode:
If you try to train/test/inference a model containing above ops, an error will be raised.
The following table lists affected algorithms.
| Operator | Model |
| :-----------------------------------------------------: | :-------------------------------------------------------------------------------: |
| Operator | Model |
| :-----------------------------------------------------: | :--------------------------------------------------------------------------------------: |
| Deformable Convolution/Modulated Deformable Convolution | DCN、Guided Anchoring、RepPoints、CentripetalNet、VFNet、CascadeRPN、NAS-FCOS、DetectoRS |
| MaskedConv2d | Guided Anchoring |
| CARAFE | CARAFE |
| SyncBatchNorm | ResNeSt |
| MaskedConv2d | Guided Anchoring |
| CARAFE | CARAFE |
| SyncBatchNorm | ResNeSt |
### Install on Google Colab

80
docs/zh_cn/get_started.md
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@ -9,41 +9,41 @@
MMDetection 和 MMCV 版本兼容性如下所示，需要安装正确的 MMCV 版本以避免安装出现问题。
| MMDetection 版本 | MMCV 版本 |
| :------------: | :------------------------: |
| master | mmcv-full>=1.3.17, \<1.6.0 |
| 2.25.0 | mmcv-full>=1.3.17, \<1.6.0 |
| 2.24.1 | mmcv-full>=1.3.17, \<1.6.0 |
| 2.24.0 | mmcv-full>=1.3.17, \<1.6.0 |
| 2.23.0 | mmcv-full>=1.3.17, \<1.5.0 |
| 2.22.0 | mmcv-full>=1.3.17, \<1.5.0 |
| 2.21.0 | mmcv-full>=1.3.17, \<1.5.0 |
| 2.20.0 | mmcv-full>=1.3.17, \<1.5.0 |
| 2.19.1 | mmcv-full>=1.3.17, \<1.5.0 |
| 2.19.0 | mmcv-full>=1.3.17, \<1.5.0 |
| 2.18.1 | mmcv-full>=1.3.17, \<1.4.0 |
| 2.18.0 | mmcv-full>=1.3.14, \<1.4.0 |
| 2.17.0 | mmcv-full>=1.3.14, \<1.4.0 |
| 2.16.0 | mmcv-full>=1.3.8, \<1.4.0 |
| 2.15.1 | mmcv-full>=1.3.8, \<1.4.0 |
| 2.15.0 | mmcv-full>=1.3.8, \<1.4.0 |
| 2.14.0 | mmcv-full>=1.3.8, \<1.4.0 |
| 2.13.0 | mmcv-full>=1.3.3, \<1.4.0 |
| 2.12.0 | mmcv-full>=1.3.3, \<1.4.0 |
| 2.11.0 | mmcv-full>=1.2.4, \<1.4.0 |
| 2.10.0 | mmcv-full>=1.2.4, \<1.4.0 |
| 2.9.0 | mmcv-full>=1.2.4, \<1.4.0 |
| 2.8.0 | mmcv-full>=1.2.4, \<1.4.0 |
| 2.7.0 | mmcv-full>=1.1.5, \<1.4.0 |
| 2.6.0 | mmcv-full>=1.1.5, \<1.4.0 |
| 2.5.0 | mmcv-full>=1.1.5, \<1.4.0 |
| 2.4.0 | mmcv-full>=1.1.1, \<1.4.0 |
| 2.3.0 | mmcv-full==1.0.5 |
| 2.3.0rc0 | mmcv-full>=1.0.2 |
| 2.2.1 | mmcv==0.6.2 |
| 2.2.0 | mmcv==0.6.2 |
| 2.1.0 | mmcv>=0.5.9, \<=0.6.1 |
| 2.0.0 | mmcv>=0.5.1, \<=0.5.8 |
| MMDetection 版本 | MMCV 版本 |
| :--------------: | :------------------------: |
| master | mmcv-full>=1.3.17, \<1.6.0 |
| 2.25.0 | mmcv-full>=1.3.17, \<1.6.0 |
| 2.24.1 | mmcv-full>=1.3.17, \<1.6.0 |
| 2.24.0 | mmcv-full>=1.3.17, \<1.6.0 |
| 2.23.0 | mmcv-full>=1.3.17, \<1.5.0 |
| 2.22.0 | mmcv-full>=1.3.17, \<1.5.0 |
| 2.21.0 | mmcv-full>=1.3.17, \<1.5.0 |
| 2.20.0 | mmcv-full>=1.3.17, \<1.5.0 |
| 2.19.1 | mmcv-full>=1.3.17, \<1.5.0 |
| 2.19.0 | mmcv-full>=1.3.17, \<1.5.0 |
| 2.18.1 | mmcv-full>=1.3.17, \<1.4.0 |
| 2.18.0 | mmcv-full>=1.3.14, \<1.4.0 |
| 2.17.0 | mmcv-full>=1.3.14, \<1.4.0 |
| 2.16.0 | mmcv-full>=1.3.8, \<1.4.0 |
| 2.15.1 | mmcv-full>=1.3.8, \<1.4.0 |
| 2.15.0 | mmcv-full>=1.3.8, \<1.4.0 |
| 2.14.0 | mmcv-full>=1.3.8, \<1.4.0 |
| 2.13.0 | mmcv-full>=1.3.3, \<1.4.0 |
| 2.12.0 | mmcv-full>=1.3.3, \<1.4.0 |
| 2.11.0 | mmcv-full>=1.2.4, \<1.4.0 |
| 2.10.0 | mmcv-full>=1.2.4, \<1.4.0 |
| 2.9.0 | mmcv-full>=1.2.4, \<1.4.0 |
| 2.8.0 | mmcv-full>=1.2.4, \<1.4.0 |
| 2.7.0 | mmcv-full>=1.1.5, \<1.4.0 |
| 2.6.0 | mmcv-full>=1.1.5, \<1.4.0 |
| 2.5.0 | mmcv-full>=1.1.5, \<1.4.0 |
| 2.4.0 | mmcv-full>=1.1.1, \<1.4.0 |
| 2.3.0 | mmcv-full==1.0.5 |
| 2.3.0rc0 | mmcv-full>=1.0.2 |
| 2.2.1 | mmcv==0.6.2 |
| 2.2.0 | mmcv==0.6.2 |
| 2.1.0 | mmcv>=0.5.9, \<=0.6.1 |
| 2.0.0 | mmcv>=0.5.1, \<=0.5.8 |
\*\*注意：\*\*如果已经安装了 mmcv，首先需要使用 `pip uninstall mmcv` 卸载已安装的 mmcv，如果同时安装了 mmcv 和 mmcv-full，将会报 `ModuleNotFoundError` 错误。
@ -203,12 +203,12 @@ MIM 能够自动地安装 OpenMMLab 的项目以及对应的依赖包。
因此，如果尝试使用包含上述操作的模型进行训练/测试/推理，将会报错。下表列出了由于依赖上述算子而无法在 CPU 上运行的相关模型：
| 操作 | 模型 |
| :-----------------------------------------------------: | :-------------------------------------------------------------------------------: |
| 操作 | 模型 |
| :-----------------------------------------------------: | :--------------------------------------------------------------------------------------: |
| Deformable Convolution/Modulated Deformable Convolution | DCN、Guided Anchoring、RepPoints、CentripetalNet、VFNet、CascadeRPN、NAS-FCOS、DetectoRS |
| MaskedConv2d | Guided Anchoring |
| CARAFE | CARAFE |
| SyncBatchNorm | ResNeSt |
| MaskedConv2d | Guided Anchoring |
| CARAFE | CARAFE |
| SyncBatchNorm | ResNeSt |
### 另一种选择： Docker 镜像

46
docs/zh_cn/model_zoo.md
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@ -24,14 +24,14 @@
MMdetection 常用到的主干网络细节如下表所示：
| 模型 | 来源 | 链接 | 描述 |
| ---------------- | ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| ResNet50 | TorchVision | [torchvision 中的 ResNet-50](https://download.pytorch.org/models/resnet50-19c8e357.pth) | 来自 [torchvision 中的 ResNet-50](https://download.pytorch.org/models/resnet50-19c8e357.pth)。 |
| ResNet101 | TorchVision | [torchvision 中的 ResNet-101](https://download.pytorch.org/models/resnet101-5d3b4d8f.pth) | 来自 [torchvision 中的 ResNet-101](https://download.pytorch.org/models/resnet101-5d3b4d8f.pth)。 |
| RegNetX | Pycls | [RegNetX_3.2gf](https://download.openmmlab.com/pretrain/third_party/regnetx_3.2gf-c2599b0f.pth)，[RegNetX_800mf](https://download.openmmlab.com/pretrain/third_party/regnetx_800mf-1f4be4c7.pth) 等 | 来自 [pycls](https://github.com/facebookresearch/pycls)。 |
| 模型 | 来源 | 链接 | 描述 |
| ---------------- | ----------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| ResNet50 | TorchVision | [torchvision 中的 ResNet-50](https://download.pytorch.org/models/resnet50-19c8e357.pth) | 来自 [torchvision 中的 ResNet-50](https://download.pytorch.org/models/resnet50-19c8e357.pth)。 |
| ResNet101 | TorchVision | [torchvision 中的 ResNet-101](https://download.pytorch.org/models/resnet101-5d3b4d8f.pth) | 来自 [torchvision 中的 ResNet-101](https://download.pytorch.org/models/resnet101-5d3b4d8f.pth)。 |
| RegNetX | Pycls | [RegNetX_3.2gf](https://download.openmmlab.com/pretrain/third_party/regnetx_3.2gf-c2599b0f.pth)，[RegNetX_800mf](https://download.openmmlab.com/pretrain/third_party/regnetx_800mf-1f4be4c7.pth) 等 | 来自 [pycls](https://github.com/facebookresearch/pycls)。 |
| ResNet50_Caffe | MSRA | [MSRA 中的 ResNet-50](https://download.openmmlab.com/pretrain/third_party/resnet50_caffe-788b5fa3.pth) | 由 [Detectron2 中的 R-50.pkl](https://dl.fbaipublicfiles.com/detectron2/ImageNetPretrained/MSRA/R-50.pkl) 转化的副本。原始权重文件来自 [MSRA 中的原始 ResNet-50](https://github.com/KaimingHe/deep-residual-networks)。 |
| ResNet101_Caffe | MSRA | [MSRA 中的 ResNet-101](https://download.openmmlab.com/pretrain/third_party/resnet101_caffe-3ad79236.pth) | 由 [Detectron2 中的 R-101.pkl](https://dl.fbaipublicfiles.com/detectron2/ImageNetPretrained/MSRA/R-101.pkl) 转化的副本。原始权重文件来自 [MSRA 中的原始 ResNet-101](https://github.com/KaimingHe/deep-residual-networks)。 |
| ResNext101_32x8d | Caffe2 | [Caffe2 ResNext101_32x8d](https://download.openmmlab.com/pretrain/third_party/resnext101_32x8d-1516f1aa.pth) | 由 [Detectron2 中的 X-101-32x8d.pkl](https://dl.fbaipublicfiles.com/detectron2/ImageNetPretrained/FAIR/X-101-32x8d.pkl) 转化的副本。原始 ResNeXt-101-32x8d 由 FB 使用 Caffe2 训练。 |
| ResNext101_32x8d | Caffe2 | [Caffe2 ResNext101_32x8d](https://download.openmmlab.com/pretrain/third_party/resnext101_32x8d-1516f1aa.pth) | 由 [Detectron2 中的 X-101-32x8d.pkl](https://dl.fbaipublicfiles.com/detectron2/ImageNetPretrained/FAIR/X-101-32x8d.pkl) 转化的副本。原始 ResNeXt-101-32x8d 由 FB 使用 Caffe2 训练。 |
## Baselines
@ -255,15 +255,15 @@ MMdetection 常用到的主干网络细节如下表所示：
我们与其他流行框架的 Mask R-CNN 训练速度进行比较（数据是从 [detectron2](https://github.com/facebookresearch/detectron2/blob/master/docs/notes/benchmarks.md/) 复制而来）。在 mmdetection 中，我们使用 [mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1.py](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1.py) 进行基准测试。它与 detectron2 的 [mask_rcnn_R_50_FPN_noaug_1x.yaml](https://github.com/facebookresearch/detectron2/blob/master/configs/Detectron1-Comparisons/mask_rcnn_R_50_FPN_noaug_1x.yaml) 设置完全一样。同时，我们还提供了[模型权重](https://download.openmmlab.com/mmdetection/v2.0/benchmark/mask_rcnn_r50_caffe_fpn_poly_1x_coco_no_aug/mask_rcnn_r50_caffe_fpn_poly_1x_coco_no_aug_compare_20200518-10127928.pth)和[训练 log](https://download.openmmlab.com/mmdetection/v2.0/benchmark/mask_rcnn_r50_caffe_fpn_poly_1x_coco_no_aug/mask_rcnn_r50_caffe_fpn_poly_1x_coco_no_aug_20200518_105755.log.json) 作为参考。为了跳过 GPU 预热时间，吞吐量按照100-500次迭代之间的平均吞吐量来计算。
| 框架 | 吞吐量 (img/s) |
| -------------------------------------------------------------------------------------- | ----------- |
| [Detectron2](https://github.com/facebookresearch/detectron2) | 62 |
| [MMDetection](https://github.com/open-mmlab/mmdetection) | 61 |
| [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark/) | 53 |
| [tensorpack](https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN) | 50 |
| [simpledet](https://github.com/TuSimple/simpledet/) | 39 |
| [Detectron](https://github.com/facebookresearch/Detectron) | 19 |
| [matterport/Mask_RCNN](https://github.com/matterport/Mask_RCNN/) | 14 |
| 框架 | 吞吐量 (img/s) |
| -------------------------------------------------------------------------------------- | -------------- |
| [Detectron2](https://github.com/facebookresearch/detectron2) | 62 |
| [MMDetection](https://github.com/open-mmlab/mmdetection) | 61 |
| [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark/) | 53 |
| [tensorpack](https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN) | 50 |
| [simpledet](https://github.com/TuSimple/simpledet/) | 39 |
| [Detectron](https://github.com/facebookresearch/Detectron) | 19 |
| [matterport/Mask_RCNN](https://github.com/matterport/Mask_RCNN/) | 14 |
### 推理时间基准
@ -295,17 +295,17 @@ python tools/benchmark.py ${CONFIG} ${CHECKPOINT} [--log-interval $[LOG-INTERVAL
### 精度
| 模型 | 训练策略 | Detectron2 | mmdetection | 下载 |
| -------------------------------------------------------------------------------------------------------------------------------------- | ---- | -------------------------------------------------------------------------------------------------------------------------------------- | ----------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| [Faster R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py) | 1x | [37.9](https://github.com/facebookresearch/detectron2/blob/master/configs/COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml) | 38.0 | [model](https://download.openmmlab.com/mmdetection/v2.0/benchmark/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-5324cff8.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/benchmark/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco_20200429_234554.log.json) |
| [Mask R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py) | 1x | [38.6 & 35.2](https://github.com/facebookresearch/detectron2/blob/master/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml) | 38.8 & 35.4 | [model](https://download.openmmlab.com/mmdetection/v2.0/benchmark/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco-dbecf295.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/benchmark/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco_20200430_054239.log.json) |
| [Retinanet](https://github.com/open-mmlab/mmdetection/blob/master/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_1x_coco.py) | 1x | [36.5](https://github.com/facebookresearch/detectron2/blob/master/configs/COCO-Detection/retinanet_R_50_FPN_1x.yaml) | 37.0 | [model](https://download.openmmlab.com/mmdetection/v2.0/benchmark/retinanet_r50_caffe_fpn_mstrain_1x_coco/retinanet_r50_caffe_fpn_mstrain_1x_coco-586977a0.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/benchmark/retinanet_r50_caffe_fpn_mstrain_1x_coco/retinanet_r50_caffe_fpn_mstrain_1x_coco_20200430_014748.log.json) |
| 模型 | 训练策略 | Detectron2 | mmdetection | 下载 |
| -------------------------------------------------------------------------------------------------------------------------------------- | -------- | -------------------------------------------------------------------------------------------------------------------------------------- | ----------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| [Faster R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py) | 1x | [37.9](https://github.com/facebookresearch/detectron2/blob/master/configs/COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml) | 38.0 | [model](https://download.openmmlab.com/mmdetection/v2.0/benchmark/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-5324cff8.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/benchmark/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco_20200429_234554.log.json) |
| [Mask R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py) | 1x | [38.6 & 35.2](https://github.com/facebookresearch/detectron2/blob/master/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml) | 38.8 & 35.4 | [model](https://download.openmmlab.com/mmdetection/v2.0/benchmark/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco-dbecf295.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/benchmark/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco_20200430_054239.log.json) |
| [Retinanet](https://github.com/open-mmlab/mmdetection/blob/master/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_1x_coco.py) | 1x | [36.5](https://github.com/facebookresearch/detectron2/blob/master/configs/COCO-Detection/retinanet_R_50_FPN_1x.yaml) | 37.0 | [model](https://download.openmmlab.com/mmdetection/v2.0/benchmark/retinanet_r50_caffe_fpn_mstrain_1x_coco/retinanet_r50_caffe_fpn_mstrain_1x_coco-586977a0.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/benchmark/retinanet_r50_caffe_fpn_mstrain_1x_coco/retinanet_r50_caffe_fpn_mstrain_1x_coco_20200430_014748.log.json) |
### 训练速度
训练速度使用 s/iter 来度量。结果越低越好。
| 模型 | Detectron2 | mmdetection |
| 模型 | Detectron2 | mmdetection |
| ------------ | ---------- | ----------- |
| Faster R-CNN | 0.210 | 0.216 |
| Mask R-CNN | 0.261 | 0.265 |
@ -318,7 +318,7 @@ python tools/benchmark.py ${CONFIG} ${CHECKPOINT} [--log-interval $[LOG-INTERVAL
对于 Mask RCNN，我们去除了后处理中 RLE 编码的时间。
我们在括号中给出了官方给出的速度。由于硬件差异，官方给出的速度会比我们所测试得到的速度快一些。
| 模型 | Detectron2 | mmdetection |
| 模型 | Detectron2 | mmdetection |
| ------------ | ----------- | ----------- |
| Faster R-CNN | 25.6 (26.3) | 22.2 |
| Mask R-CNN | 22.5 (23.3) | 19.6 |
@ -326,7 +326,7 @@ python tools/benchmark.py ${CONFIG} ${CHECKPOINT} [--log-interval $[LOG-INTERVAL
### 训练内存
| 模型 | Detectron2 | mmdetection |
| 模型 | Detectron2 | mmdetection |
| ------------ | ---------- | ----------- |
| Faster R-CNN | 3.0 | 3.8 |
| Mask R-CNN | 3.4 | 3.9 |

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