diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 82dd58c69c0..fb0883d5e69 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -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 diff --git a/docs/en/get_started.md b/docs/en/get_started.md index 79e0df5a0fd..a28972c1fce 100644 --- a/docs/en/get_started.md +++ b/docs/en/get_started.md @@ -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 diff --git a/docs/zh_cn/get_started.md b/docs/zh_cn/get_started.md index cab1b3b0db8..c9d7e52b159 100644 --- a/docs/zh_cn/get_started.md +++ b/docs/zh_cn/get_started.md @@ -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 diff --git a/docs/zh_cn/model_zoo.md b/docs/zh_cn/model_zoo.md index c34637a3540..afa74505861 100644 --- a/docs/zh_cn/model_zoo.md +++ b/docs/zh_cn/model_zoo.md @@ -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 |