From 02571fe4b849f23e64f57c6bc04b318a1c245820 Mon Sep 17 00:00:00 2001 From: Yixiao Fang <36138628+fangyixiao18@users.noreply.github.com> Date: Fri, 14 Apr 2023 13:58:10 +0800 Subject: [PATCH] [Docs] Add NPU support page (#1481) * add npu docs * fix lint --- docs/en/device/npu.md | 47 ++++++++++++++++++++++++++++++++++++++++ docs/en/index.rst | 6 +++++ docs/zh_CN/device/npu.md | 41 +++++++++++++++++++++++++++++++++++ docs/zh_CN/index.rst | 6 +++++ 4 files changed, 100 insertions(+) create mode 100644 docs/en/device/npu.md create mode 100644 docs/zh_CN/device/npu.md diff --git a/docs/en/device/npu.md b/docs/en/device/npu.md new file mode 100644 index 00000000..5503b7e5 --- /dev/null +++ b/docs/en/device/npu.md @@ -0,0 +1,47 @@ +# NPU (HUAWEI Ascend) + +## Usage + +### General Usage + +Please refer to the [building documentation of MMCV](https://mmcv.readthedocs.io/en/latest/get_started/build.html#build-mmcv-full-on-ascend-npu-machine) to install MMCV and [MMEngine](https://mmengine.readthedocs.io/en/latest/get_started/installation.html#build-from-source) on NPU devices. + +Here we use 8 NPUs on your computer to train the model with the following command: + +```shell +bash ./tools/dist_train.sh configs/resnet/resnet50_8xb32_in1k.py 8 +``` + +Also, you can use only one NPU to train the model with the following command: + +```shell +python ./tools/train.py configs/resnet/resnet50_8xb32_in1k.py +``` + +## Models Results + +| Model | Top-1 (%) | Top-5 (%) | Config | Download | +| :---------------------------------------------------------: | :-------: | :-------: | :----------------------------------------------------------: | :-------------------------------------------------------------: | +| [ResNet-50](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/resnet/README.md) | 76.40 | 93.21 | [config](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/resnet/resnet50_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v1/device/npu/resnet50_8xb32_in1k.log) | +| [ResNetXt-32x4d-50](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/resnext/README.md) | 77.48 | 93.75 | [config](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/resnext/resnext50-32x4d_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v1/device/npu/resnext50-32x4d_8xb32_in1k.log) | +| [HRNet-W18](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/README.md) | 77.06 | 93.57 | [config](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/hrnet/hrnet-w18_4xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v1/device/npu/hrnet-w18_4xb32_in1k.log) | +| [ResNetV1D-152](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/resnet/README.md) | 79.41 | 94.48 | [config](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/resnet/resnetv1d152_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v1/device/npu/resnetv1d152_8xb32_in1k.log) | +| [SE-ResNet-50](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/seresnet/README.md) | 77.65 | 93.74 | [config](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/seresnet/seresnet50_8xb32_in1k.py) | [model](<>) \|[log](https://download.openmmlab.com/mmclassification/v1/device/npu/seresnet50_8xb32_in1k.log) | +| [ShuffleNetV2 1.0x](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/shufflenet_v2/README.md) | 69.52 | 88.79 | [config](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/shufflenet_v2/shufflenet-v2-1x_16xb64_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v1/device/npu/shufflenet-v2-1x_16xb64_in1k.log) | +| [MobileNetV2](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/mobilenet_v2) | 71.74 | 90.28 | [config](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v1/device/npu/mobilenet-v2_8xb32_in1k.log) | +| [MobileNetV3-Small](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/mobilenet_v3/README.md) | 67.09 | 87.17 | [config](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/mobilenet_v3/mobilenet-v3-small_8xb128_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v1/device/npu/mobilenet-v3-small.log) | +| [\*CSPResNeXt50](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/cspnet/README.md) | 77.25 | 93.46 | [config](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/cspnet/cspresnext50_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v1/device/npu/cspresnext50_8xb32_in1k.log) | +| [\*EfficientNet-B4](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/efficientnet/README.md) | 75.73 | 92.91 | [config](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/efficientnet/efficientnet-b4_8xb32_in1k.py) | [model](<>) \|[log](https://download.openmmlab.com/mmclassification/v1/device/npu/efficientnet-b4_8xb32_in1k.log) | +| [\*\*DenseNet121](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/densenet/README.md) | 72.53 | 90.85 | [config](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/densenet/densenet121_4xb256_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v1/device/npu/densenet121_4xb256_in1k.log) | + +**Notes:** + +- If not specially marked, the results are almost same between results on the NPU and results on the GPU with FP32. +- (\*) The training results of these models are lower than the results on the readme in the corresponding model, mainly + because the results on the readme are directly the weight of the timm of the eval, and the results on this side are + retrained according to the config with mmcls. The results of the config training on the GPU are consistent with the + results of the NPU. +- (\*\*) The accuracy of this model is slightly lower because config is a 4-card config, we use 8 cards to run, and users + can adjust hyperparameters to get the best accuracy results. + +**All above models are provided by Huawei Ascend group.** diff --git a/docs/en/index.rst b/docs/en/index.rst index b8a00932..a3d2a851 100644 --- a/docs/en/index.rst +++ b/docs/en/index.rst @@ -141,6 +141,12 @@ We always welcome *PRs* and *Issues* for the betterment of MMPretrain. notes/pretrain_custom_dataset.md notes/finetune_custom_dataset.md +.. toctree:: + :maxdepth: 1 + :caption: Device Support + + device/npu.md + Indices and tables ================== diff --git a/docs/zh_CN/device/npu.md b/docs/zh_CN/device/npu.md new file mode 100644 index 00000000..b81c1751 --- /dev/null +++ b/docs/zh_CN/device/npu.md @@ -0,0 +1,41 @@ +# NPU (华为昇腾) + +## 使用方法 + +首先，请参考[链接](https://mmcv.readthedocs.io/zh_CN/latest/get_started/build.html#npu-mmcv-full)安装带有 NPU 支持的 MMCV 和[链接](https://mmengine.readthedocs.io/en/latest/get_started/installation.html#build-from-source)安装 MMEngine。 + +使用如下命令，可以利用 8 个 NPU 在机器上训练模型（以 ResNet 为例）： + +```shell +bash tools/dist_train.sh configs/cspnet/resnet50_8xb32_in1k.py 8 +``` + +或者，使用如下命令，在一个 NPU 上训练模型（以 ResNet 为例）： + +```shell +python tools/train.py configs/cspnet/resnet50_8xb32_in1k.py +``` + +## 经过验证的模型 + +| Model | Top-1 (%) | Top-5 (%) | Config | Download | +| :---------------------------------------------------------: | :-------: | :-------: | :----------------------------------------------------------: | :-------------------------------------------------------------: | +| [ResNet-50](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/resnet/README.md) | 76.40 | 93.21 | [config](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/resnet/resnet50_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v1/device/npu/resnet50_8xb32_in1k.log) | +| [ResNetXt-32x4d-50](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/resnext/README.md) | 77.48 | 93.75 | [config](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/resnext/resnext50-32x4d_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v1/device/npu/resnext50-32x4d_8xb32_in1k.log) | +| [HRNet-W18](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/README.md) | 77.06 | 93.57 | [config](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/hrnet/hrnet-w18_4xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v1/device/npu/hrnet-w18_4xb32_in1k.log) | +| [ResNetV1D-152](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/resnet/README.md) | 79.41 | 94.48 | [config](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/resnet/resnetv1d152_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v1/device/npu/resnetv1d152_8xb32_in1k.log) | +| [SE-ResNet-50](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/seresnet/README.md) | 77.65 | 93.74 | [config](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/seresnet/seresnet50_8xb32_in1k.py) | [model](<>) \|[log](https://download.openmmlab.com/mmclassification/v1/device/npu/seresnet50_8xb32_in1k.log) | +| [ShuffleNetV2 1.0x](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/shufflenet_v2/README.md) | 69.52 | 88.79 | [config](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/shufflenet_v2/shufflenet-v2-1x_16xb64_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v1/device/npu/shufflenet-v2-1x_16xb64_in1k.log) | +| [MobileNetV2](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/mobilenet_v2) | 71.74 | 90.28 | [config](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v1/device/npu/mobilenet-v2_8xb32_in1k.log) | +| [MobileNetV3-Small](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/mobilenet_v3/README.md) | 67.09 | 87.17 | [config](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/mobilenet_v3/mobilenet-v3-small_8xb128_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v1/device/npu/mobilenet-v3-small.log) | +| [\*CSPResNeXt50](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/cspnet/README.md) | 77.25 | 93.46 | [config](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/cspnet/cspresnext50_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v1/device/npu/cspresnext50_8xb32_in1k.log) | +| [\*EfficientNet-B4](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/efficientnet/README.md) | 75.73 | 92.9100 | [config](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/efficientnet/efficientnet-b4_8xb32_in1k.py) | [model](<>) \|[log](https://download.openmmlab.com/mmclassification/v1/device/npu/efficientnet-b4_8xb32_in1k.log) | +| [\*\*DenseNet121](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/densenet/README.md) | 72.53 | 90.85 | [config](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/densenet/densenet121_4xb256_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v1/device/npu/densenet121_4xb256_in1k.log) | + +**注意:** + +- 如果没有特别标记，NPU 上的结果与使用 FP32 的 GPU 上的结果结果相同。 +- (\*) 这些模型的训练结果低于相应模型中自述文件上的结果，主要是因为自述文件上的结果直接是 timm 训练得出的权重，而这边的结果是根据 mmcls 的配置重新训练得到的结果。GPU 上的配置训练结果与 NPU 的结果相同。 +- (\*\*)这个模型的精度略低，因为 config 是 4 张卡的配置，我们使用 8 张卡来运行，用户可以调整超参数以获得最佳精度结果。 + +**以上所有模型权重及训练日志均由华为昇腾团队提供** diff --git a/docs/zh_CN/index.rst b/docs/zh_CN/index.rst index cceff12b..7865da8e 100644 --- a/docs/zh_CN/index.rst +++ b/docs/zh_CN/index.rst @@ -127,6 +127,12 @@ MMPretrain 上手路线 notes/pretrain_custom_dataset.md notes/finetune_custom_dataset.md +.. toctree:: + :maxdepth: 1 + :caption: 设备支持 + + device/npu.md + .. toctree:: :caption: 切换语言