[Docs] Add NPU support page. (#1149)

* init readme

* [Docs] Finish the HUAWEI Ascend device support docs.

Co-authored-by: mzr1996 <mzr1996@163.com>

docs/en/_static/css/readthedocs.css

@@ -9,12 +9,19 @@ pre {
     white-space: pre;
 }
 
-article.pytorch-article .section :not(dt) > code {
+article.pytorch-article section code {
   padding: .2em .4em;
   background-color: #f3f4f7;
   border-radius: 5px;
 }
 
-table.colwidths-auto td {
+/* Disable the change in tables */
+article.pytorch-article section table code {
+  padding: unset;
+  background-color: unset;
+  border-radius: unset;
+}
+
+table.autosummary td {
   width: 50%
 }

docs/en/conf.py

@@ -48,7 +48,6 @@ extensions = [
     'sphinx.ext.intersphinx',
     'sphinx.ext.napoleon',
     'sphinx.ext.viewcode',
-    'sphinx_markdown_tables',
     'myst_parser',
     'sphinx_copybutton',
 ]

docs/en/device/npu.md

@@ -0,0 +1,34 @@
+# NPU (HUAWEI Ascend)
+
+## Usage
+
+Please install MMCV with NPU device support according to {external+mmcv:doc}`the tutorial <get_started/build>`.
+
+Here we use 8 NPUs on your computer to train the model with the following command:
+
+```shell
+bash tools/dist_train.sh configs/cspnet/resnet50_8xb32_in1k.py 8 --device npu
+```
+
+Also, you can use only one NPU to trian the model with the following command:
+
+```shell
+python tools/train.py configs/cspnet/resnet50_8xb32_in1k.py --device npu
+```
+
+## Verified Models
+
+|                           Model                            | Top-1 (%) | Top-5 (%) |                            Config                             |                            Download                             |
+| :--------------------------------------------------------: | :-------: | :-------: | :-----------------------------------------------------------: | :-------------------------------------------------------------: |
+|            [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) |
+|            [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) |
+| [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) |
+|              [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) |
+|            [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](<>)                     |
+|              [ResNet-50](../papers/resnet.md)              |   76.40   |     -     | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb32_in1k.py) |                    [model](<>) \| [log](<>)                     |
+|         [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) |
+|           [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) |
+|                 [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) |
+|      [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) |
+
+**All above models are provided by Huawei Ascend group.**

docs/en/index.rst

@@ -78,6 +78,13 @@ You can switch between Chinese and English documentation in the lower-left corne
    compatibility.md
    faq.md
 
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Device Support
+
+   device/npu.md
+
 .. toctree::
    :caption: Language Switch
 

docs/zh_CN/_static/css/readthedocs.css

@@ -9,12 +9,19 @@ pre {
     white-space: pre;
 }
 
-article.pytorch-article .section :not(dt) > code {
+article.pytorch-article section code {
   padding: .2em .4em;
   background-color: #f3f4f7;
   border-radius: 5px;
 }
 
-table.colwidths-auto td {
+/* Disable the change in tables */
+article.pytorch-article section table code {
+  padding: unset;
+  background-color: unset;
+  border-radius: unset;
+}
+
+table.autosummary td {
   width: 50%
 }

docs/zh_CN/conf.py

@@ -48,7 +48,6 @@ extensions = [
     'sphinx.ext.intersphinx',
     'sphinx.ext.napoleon',
     'sphinx.ext.viewcode',
-    'sphinx_markdown_tables',
     'myst_parser',
     'sphinx_copybutton',
 ]
@@ -214,7 +213,7 @@ intersphinx_mapping = {
     'python': ('https://docs.python.org/3', None),
     'numpy': ('https://numpy.org/doc/stable', None),
     'torch': ('https://pytorch.org/docs/stable/', None),
-    'mmcv': ('https://mmcv.readthedocs.io/en/master/', None),
+    'mmcv': ('https://mmcv.readthedocs.io/zh_CN/latest/', None),
 }
 
 

docs/zh_CN/device/npu.md

@@ -0,0 +1,34 @@
+# NPU (华为昇腾)
+
+## 使用方法
+
+首先，请参考 {external+mmcv:doc}`教程 <get_started/build>` 安装带有 NPU 支持的 MMCV。
+
+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 --device npu
+```
+
+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 --device npu
+```
+
+## 经过验证的模型
+
+|                            模型                            | Top-1 (%) | Top-5 (%) |                            配置文件                            |                            相关下载                            |
+| :--------------------------------------------------------: | :-------: | :-------: | :------------------------------------------------------------: | :------------------------------------------------------------: |
+|            [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) |
+|            [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) |
+| [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) |
+|              [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) |
+|            [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](<>)                    |
+|              [ResNet-50](../papers/resnet.md)              |   76.40   |     -     | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb32_in1k.py) |                    [model](<>) \| [log](<>)                    |
+|         [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) |
+|           [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) |
+|                 [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) |
+|      [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](<>)                    |
+
+**以上所有模型权重及训练日志均由华为昇腾团队提供**

docs/zh_CN/index.rst

@@ -78,6 +78,13 @@ You can switch between Chinese and English documentation in the lower-left corne
    faq.md
 
 
+.. toctree::
+   :maxdepth: 1
+   :caption: 设备支持
+
+   device/npu.md
+
+
 .. toctree::
    :caption: 语言切换