Yixiao Fang
GitHub
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# NPU (HUAWEI Ascend)
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## Usage
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### General Usage
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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.
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Here we use 8 NPUs on your computer to train the model with the following command:
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```shell
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bash ./tools/dist_train.sh configs/resnet/resnet50_8xb32_in1k.py 8
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```
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Also, you can use only one NPU to train the model with the following command:
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```shell
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python ./tools/train.py configs/resnet/resnet50_8xb32_in1k.py
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```
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## Models Results
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| Model | Top-1 (%) | Top-5 (%) | Config | Download |
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| :---------------------------------------------------------: | :-------: | :-------: | :----------------------------------------------------------: | :-------------------------------------------------------------: |
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| [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) |
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| [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) |
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| [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) |
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| [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) |
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| [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) |
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| [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) |
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| [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) |
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| [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) |
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| [\*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) |
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| [\*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) |
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| [\*\*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) |
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**Notes:**
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- If not specially marked, the results are almost same between results on the NPU and results on the GPU with FP32.
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- (\*) The training results of these models are lower than the results on the readme in the corresponding model, mainly
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because the results on the readme are directly the weight of the timm of the eval, and the results on this side are
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retrained according to the config with mmcls. The results of the config training on the GPU are consistent with the
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results of the NPU.
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- (\*\*) The accuracy of this model is slightly lower because config is a 4-card config, we use 8 cards to run, and users
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can adjust hyperparameters to get the best accuracy results.
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**All above models are provided by Huawei Ascend group.**
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@ -141,6 +141,12 @@ We always welcome *PRs* and *Issues* for the betterment of MMPretrain.
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notes/pretrain_custom_dataset.md
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notes/finetune_custom_dataset.md
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.. toctree::
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:maxdepth: 1
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:caption: Device Support
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device/npu.md
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Indices and tables
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==================
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# NPU (华为昇腾)
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## 使用方法
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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。
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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）：
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```shell
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bash tools/dist_train.sh configs/cspnet/resnet50_8xb32_in1k.py 8
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```
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或者，使用如下命令，在一个 NPU 上训练模型（以 ResNet 为例）：
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```shell
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python tools/train.py configs/cspnet/resnet50_8xb32_in1k.py
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```
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## 经过验证的模型
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| Model | Top-1 (%) | Top-5 (%) | Config | Download |
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| :---------------------------------------------------------: | :-------: | :-------: | :----------------------------------------------------------: | :-------------------------------------------------------------: |
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| [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) |
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| [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) |
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| [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) |
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| [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) |
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| [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) |
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| [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) |
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| [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) |
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| [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) |
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| [\*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) |
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| [\*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) |
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| [\*\*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) |
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**注意:**
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- 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。
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- (\*) 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。
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- (\*\*)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。
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**以上所有模型权重及训练日志均由华为昇腾团队提供**
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@ -127,6 +127,12 @@ MMPretrain 上手路线
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notes/pretrain_custom_dataset.md
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notes/finetune_custom_dataset.md
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.. toctree::
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:maxdepth: 1
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:caption: 设备支持
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device/npu.md
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.. toctree::
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:caption: 切换语言
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