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42 lines
5.5 KiB
Markdown
42 lines
5.5 KiB
Markdown
# 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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