[Docs] Add NPU support page. (#1149)
* init readme * [Docs] Finish the HUAWEI Ascend device support docs. Co-authored-by: mzr1996 <mzr1996@163.com>pull/1198/head
wangjiangben-hw
GitHub
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17ed870fd1
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@ -9,12 +9,19 @@ pre {
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white-space: pre;
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}
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article.pytorch-article .section :not(dt) > code {
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article.pytorch-article section code {
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padding: .2em .4em;
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background-color: #f3f4f7;
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border-radius: 5px;
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}
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table.colwidths-auto td {
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/* Disable the change in tables */
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article.pytorch-article section table code {
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padding: unset;
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table.autosummary td {
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width: 50%
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@ -48,7 +48,6 @@ extensions = [
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'sphinx.ext.intersphinx',
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'sphinx.ext.napoleon',
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'sphinx.ext.viewcode',
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'sphinx_markdown_tables',
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'myst_parser',
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'sphinx_copybutton',
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]
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@ -0,0 +1,34 @@
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# NPU (HUAWEI Ascend)
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## Usage
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Please install MMCV with NPU device support according to {external+mmcv:doc}`the tutorial <get_started/build>`.
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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/cspnet/resnet50_8xb32_in1k.py 8 --device npu
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```
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Also, you can use only one NPU to trian the model with the following command:
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```shell
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python tools/train.py configs/cspnet/resnet50_8xb32_in1k.py --device npu
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```
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## Verified Models
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| Model | Top-1 (%) | Top-5 (%) | Config | Download |
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| :--------------------------------------------------------: | :-------: | :-------: | :-----------------------------------------------------------: | :-------------------------------------------------------------: |
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| [CSPResNeXt50](../papers/cspnet.md) | 77.10 | 93.55 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/cspnet/cspresnext50_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/cspresnext50_8xb32_in1k.log.json) |
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| [DenseNet121](../papers/densenet.md) | 72.62 | 91.04 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet121_4xb256_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/densenet121_4xb256_in1k.log.json) |
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| [EfficientNet-B4(AA + AdvProp)](../papers/efficientnet.md) | 75.55 | 92.86 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b4_8xb32-01norm_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/efficientnet-b4_8xb32-01norm_in1k.log.json) |
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| [HRNet-W18](../papers/hrnet.md) | 77.01 | 93.46 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w18_4xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/hrnet-w18_4xb32_in1k.log.json) |
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| [ResNetV1D-152](../papers/resnet.md) | 77.11 | 94.54 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d152_8xb32_in1k.py) | [model](<>) \| [log](<>) |
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| [ResNet-50](../papers/resnet.md) | 76.40 | - | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb32_in1k.py) | [model](<>) \| [log](<>) |
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| [ResNetXt-32x4d-50](../papers/resnext.md) | 77.55 | 93.75 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext50-32x4d_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/resnext50-32x4d_8xb32_in1k.log.json) |
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| [SE-ResNet-50](../papers/seresnet.md) | 77.64 | 93.76 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/seresnet/seresnet50_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/seresnet50_8xb32_in1k.log.json) |
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| [VGG-11](../papers/vgg.md) | 68.92 | 88.83 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg11_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/vgg11_8xb32_in1k.log.json) |
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| [ShuffleNetV2 1.0x](../papers/shufflenet_v2.md) | 69.53 | 88.82 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/shufflenet_v2/shufflenet-v2-1x_16xb64_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/shufflenet-v2-1x_16xb64_in1k.json) |
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**All above models are provided by Huawei Ascend group.**
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@ -78,6 +78,13 @@ You can switch between Chinese and English documentation in the lower-left corne
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compatibility.md
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faq.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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.. toctree::
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:caption: Language Switch
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@ -9,12 +9,19 @@ pre {
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white-space: pre;
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}
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article.pytorch-article .section :not(dt) > code {
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article.pytorch-article section code {
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padding: .2em .4em;
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background-color: #f3f4f7;
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border-radius: 5px;
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}
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table.colwidths-auto td {
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/* Disable the change in tables */
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article.pytorch-article section table code {
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padding: unset;
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background-color: unset;
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border-radius: unset;
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}
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table.autosummary td {
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width: 50%
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}
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@ -48,7 +48,6 @@ extensions = [
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'sphinx.ext.intersphinx',
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'sphinx.ext.napoleon',
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'sphinx.ext.viewcode',
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'sphinx_markdown_tables',
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'myst_parser',
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'sphinx_copybutton',
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]
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@ -214,7 +213,7 @@ intersphinx_mapping = {
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'python': ('https://docs.python.org/3', None),
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'numpy': ('https://numpy.org/doc/stable', None),
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'torch': ('https://pytorch.org/docs/stable/', None),
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'mmcv': ('https://mmcv.readthedocs.io/en/master/', None),
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'mmcv': ('https://mmcv.readthedocs.io/zh_CN/latest/', None),
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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 {external+mmcv:doc}`教程 <get_started/build>` 安装带有 NPU 支持的 MMCV。
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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 --device npu
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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 --device npu
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```
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## 经过验证的模型
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| 模型 | Top-1 (%) | Top-5 (%) | 配置文件 | 相关下载 |
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| :--------------------------------------------------------: | :-------: | :-------: | :------------------------------------------------------------: | :------------------------------------------------------------: |
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| [CSPResNeXt50](../papers/cspnet.md) | 77.10 | 93.55 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/cspnet/cspresnext50_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/cspresnext50_8xb32_in1k.log.json) |
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| [DenseNet121](../papers/densenet.md) | 72.62 | 91.04 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet121_4xb256_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/densenet121_4xb256_in1k.log.json) |
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| [EfficientNet-B4(AA + AdvProp)](../papers/efficientnet.md) | 75.55 | 92.86 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b4_8xb32-01norm_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/efficientnet-b4_8xb32-01norm_in1k.log.json) |
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| [HRNet-W18](../papers/hrnet.md) | 77.01 | 93.46 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w18_4xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/hrnet-w18_4xb32_in1k.log.json) |
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| [ResNetV1D-152](../papers/resnet.md) | 77.11 | 94.54 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d152_8xb32_in1k.py) | [model](<>) \| [log](<>) |
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| [ResNet-50](../papers/resnet.md) | 76.40 | - | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb32_in1k.py) | [model](<>) \| [log](<>) |
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| [ResNetXt-32x4d-50](../papers/resnext.md) | 77.55 | 93.75 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext50-32x4d_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/resnext50-32x4d_8xb32_in1k.log.json) |
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| [SE-ResNet-50](../papers/seresnet.md) | 77.64 | 93.76 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/seresnet/seresnet50_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/seresnet50_8xb32_in1k.log.json) |
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| [VGG-11](../papers/vgg.md) | 68.92 | 88.83 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg11_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/vgg11_8xb32_in1k.log.json) |
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| [ShuffleNetV2 1.0x](../papers/shufflenet_v2.md) | 69.53 | 88.82 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/shufflenet_v2/shufflenet-v2-1x_16xb64_in1k.py) | [model](<>) \| [log](<>) |
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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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@ -78,6 +78,13 @@ You can switch between Chinese and English documentation in the lower-left corne
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faq.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: 语言切换
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