[Docs] Add NPU support page (#2307)

docs/en/device/npu.md

@@ -0,0 +1,29 @@
+# NPU (HUAWEI Ascend)
+
+## 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 on NPU devices.
+
+Here we use 4 NPUs on your computer to train the model with the following command:
+
+```shell
+bash tools/dist_train.sh configs/body/2d_kpt_sview_rgb_img/associative_embedding/coco/hrnet_w32_coco_512x512.py 4
+```
+
+Also, you can use only one NPU to train the model with the following command:
+
+```shell
+python tools/train.py configs/body/2d_kpt_sview_rgb_img/associative_embedding/coco/hrnet_w32_coco_512x512.py
+```
+
+## Models Results
+
+|                         Model                         | Input Size |  AP   | AP50  | AP75  |  AR   | AR50  | Config                                                  | Download                                                  |
+| :---------------------------------------------------: | :--------: | :---: | :---: | :---: | :---: | :---: | :------------------------------------------------------ | :-------------------------------------------------------- |
+| [HigherHRNet](https://mmpose.readthedocs.io/zh_CN/0.x/papers/backbones.html#associative-embedding-higherhrnet-on-coco) |  512x512   | 0.670 | 0.859 | 0.732 | 0.724 | 0.893 | [config](https://github.com/open-mmlab/mmpose/blob/dev-0.x/configs/body/2d_kpt_sview_rgb_img/associative_embedding/coco/higherhrnet_w32_coco_512x512.py) | [log](https://download.openmmlab.com/mmpose/device/npu/hrnet_20230413_035450.log.json) |
+
+**Notes:**
+
+- If not specially marked, the results on NPU with amp are the basically same as those on the GPU with FP32.
+
+**All above models are provided by Huawei Ascend group.**

docs/zh_cn/device/npu_zh.md

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+# NPU (华为昇腾)
+
+## 使用方法
+
+首先，请参考[MMCV](https://mmcv.readthedocs.io/zh_CN/latest/get_started/build.html#npu-mmcv-full) 安装带有 NPU 支持的 MMCV。
+使用如下命令，可以利用 4 个 NPU 训练模型（以 HRNet为例）：
+
+```shell
+bash tools/dist_train.sh configs/body/2d_kpt_sview_rgb_img/associative_embedding/coco/hrnet_w32_coco_512x512.py 4
+```
+
+或者，使用如下命令，在一个 NPU 上训练模型（以 HRNet为例）：
+
+```shell
+python tools/train.py configs/body/2d_kpt_sview_rgb_img/associative_embedding/coco/hrnet_w32_coco_512x512.py
+```
+
+## 经过验证的模型
+
+|                         Model                         | Input Size |  AP   | AP50  | AP75  |  AR   | AR50  | Config                                                  | Download                                                  |
+| :---------------------------------------------------: | :--------: | :---: | :---: | :---: | :---: | :---: | :------------------------------------------------------ | :-------------------------------------------------------- |
+| [HigherHRNet](https://mmpose.readthedocs.io/zh_CN/0.x/papers/backbones.html#associative-embedding-higherhrnet-on-coco) |  512x512   | 0.670 | 0.859 | 0.732 | 0.724 | 0.893 | [config](https://github.com/open-mmlab/mmpose/blob/dev-0.x/configs/body/2d_kpt_sview_rgb_img/associative_embedding/coco/higherhrnet_w32_coco_512x512.py) | [log](https://download.openmmlab.com/mmpose/device/npu/hrnet_20230413_035450.log.json) |
+
+**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。
+
+**以上所有模型权重及训练日志均由华为昇腾团队提供**

model-index.yml

@@ -110,8 +110,8 @@ Import:
 - configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/wflw/hrnetv2_dark_wflw.yml
 - configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/wflw/hrnetv2_wflw.yml
 - configs/fashion/2d_kpt_sview_rgb_img/deeppose/deepfashion/resnet_deepfashion.yml
-- configs/fashion/2d_kpt_sview_rgb_img/topdown_heatmap/deepfashion/resnet_deepfashion.yml
 - configs/fashion/2d_kpt_sview_rgb_img/topdown_heatmap/deepfashion2/resnet_deepfashion2.yml
+- configs/fashion/2d_kpt_sview_rgb_img/topdown_heatmap/deepfashion/resnet_deepfashion.yml
 - configs/hand/2d_kpt_sview_rgb_img/deeppose/onehand10k/resnet_onehand10k.yml
 - configs/hand/2d_kpt_sview_rgb_img/deeppose/panoptic2d/resnet_panoptic2d.yml
 - configs/hand/2d_kpt_sview_rgb_img/deeppose/rhd2d/resnet_rhd2d.yml