modify installation verification

demo/docs/2d_human_pose_demo.md

@@ -1,6 +1,6 @@
 ## 2D Human Pose Demo
 
-<img src="https://raw.githubusercontent.com/open-mmlab/mmpose/master/demo/resources/demo_coco.gif" width="600px" alt><br>
+We provide demo scripts to perform human pose estimation on images or videos.
 
 ### 2D Human Pose Top-Down Image Demo
 
@@ -18,7 +18,7 @@ python demo/image_demo.py \
 
 If you use a heatmap-based model and set argument `--draw-heatmap`, the predicted heatmap will be visualized together with the keypoints.
 
-The pre-trained hand pose estimation model can be downloaded from [model zoo](https://mmpose.readthedocs.io/en/1.x/model_zoo/body_2d_keypoint.html).
+The pre-trained human pose estimation model can be downloaded from [model zoo](https://mmpose.readthedocs.io/en/1.x/model_zoo/body_2d_keypoint.html).
 Take [coco model](https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth) as an example:
 
 ```shell
@@ -62,7 +62,7 @@ python demo/topdown_demo_with_mmdet.py \
     [--bbox-thr ${BBOX_SCORE_THR} --kpt-thr ${KPT_SCORE_THR}]
 ```
 
-Examples:
+Example:
 
 ```shell
 python demo/topdown_demo_with_mmdet.py \
@@ -84,7 +84,7 @@ The above demo script can also take video as input, and run mmdet for human dete
 
 Assume that you have already installed [mmdet](https://github.com/open-mmlab/mmdetection) with version >= 3.0.
 
-Examples:
+Example:
 
 ```shell
 python demo/topdown_demo_with_mmdet.py \
@@ -93,7 +93,7 @@ python demo/topdown_demo_with_mmdet.py \
     configs/body_2d_keypoint/topdown_heatmap/coco/td-hm_hrnet-w32_8xb64-210e_coco-256x192.py \
     https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w32_coco_256x192-c78dce93_20200708.pth \
     --input tests/data/posetrack18/videos/000001_mpiinew_test/000001_mpiinew_test.mp4 \
---output-root=vis_results/demo --show --draw-heatmap
+    --output-root=vis_results/demo --show --draw-heatmap
 ```
 
 ### Speed Up Inference

docs/en/installation.md

@@ -65,7 +65,7 @@ mim install mmengine
 mim install "mmcv>=2.0.0rc1"
 ```
 
-Note that some of the demo scripts in MMPose require [MMDetection](https://github.com/open-mmlab/mmdetection) (mmdet)  for human detection. If you want to run these demo scripts with mmdet, you can install mmdet as a dependency by running:
+Note that some of the demo scripts in MMPose require [MMDetection](https://github.com/open-mmlab/mmdetection) (mmdet)  for human detection. If you want to run these demo scripts with mmdet, you can easily install mmdet as a dependency by running:
 
 ```shell
 mim install "mmdet>=3.0.0rc0"
@@ -94,13 +94,25 @@ mim install "mmpose>=1.0.0b0"
 
 ### Verify the installation
 
-To verify that MMPose is installed correctly, you can run the following inference demo script:
+To verify that MMPose is installed correctly, you can run an inference demo with the following steps.
+
+**Step 1.** We need to download config and checkpoint files.
+
+```shell
+mim download mmpose --config td-hm_hrnet-w48_8xb32-210e_coco-256x192  --dest .
+```
+
+The downloading will take several seconds or more, depending on your network environment. When it is done, you will find two files `td-hm_hrnet-w48_8xb32-210e_coco-256x192.py` and `hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth` in your current folder.
+
+**Step 2.** Run the inference demo.
+
+Option (A). If you install mmpose from source, just run the following command under the folder `$MMPOSE`:
 
 ```shell
 python demo/image_demo.py \
     tests/data/coco/000000000785.jpg \
-    configs/body_2d_keypoint/topdown_heatmap/coco/td-hm_hrnet-w48_8xb32-210e_coco-256x192.py \
-    https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth \
+    td-hm_hrnet-w48_8xb32-210e_coco-256x192.py \
+    hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth \
     --out-file vis_results.jpg \
     --draw-heatmap
 ```
@@ -109,7 +121,27 @@ If everything goes fine, you will get this visualization result:
 
 ![image](https://user-images.githubusercontent.com/87690686/187824033-2cce0f55-034a-4127-82e2-52744178bc32.jpg)
 
-And the output will be saved as `$MMPOSE/vis_results.jpg`.
+And the visualization result will be saved as `vis_results.jpg` on your current folder, where the predicted keypoints and heatmaps are plotted on the person in the image.
+
+Option (B). If you install mmpose with pip, open you python interpreter and copy & paste the following codes.
+
+```python
+from mmpose.apis import inference_topdown, init_model
+from mmpose.utils import register_all_modules
+
+register_all_modules()
+
+config_file = 'td-hm_hrnet-w48_8xb32-210e_coco-256x192.py'
+checkpoint_file = 'hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth'
+model = init_model(config_file, checkpoint_file, device='cpu')  # or device='cuda:0'
+
+# please prepare an image with person
+results = inference_topdown(model, 'demo.jpg')
+```
+
+The `demo.jpg` can be downloaded from [Github](https://raw.githubusercontent.com/open-mmlab/mmpose/1.x/tests/data/coco/000000000785.jpg).
+
+The inference results will be a list of `PoseDataSample`, and the predictions are in the `pred_instances`, indicating the detected keypoint locations and scores.
 
 ### Customize Installation
 

docs/zh_cn/installation.md

@@ -98,13 +98,25 @@ mim install "mmpose>=1.0.0b0"
 
 ### 验证安装
 
-为了验证 MMPose 是否安装正确，您可以运行以下推理示例脚本：
+为了验证 MMPose 是否安装正确，您可以通过以下步骤运行模型推理。
+
+**第 1 步** 我们需要下载配置文件和模型权重文件
+
+```shell
+mim download mmpose --config td-hm_hrnet-w48_8xb32-210e_coco-256x192  --dest .
+```
+
+下载过程往往需要几秒或更多的时间，这取决于您的网络环境。完成之后，您会在当前目录下找到这两个文件：`td-hm_hrnet-w48_8xb32-210e_coco-256x192.py` 和 `hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth`, 分别是配置文件和对应的模型权重文件。
+
+**第 2 步** 验证推理示例
+
+如果您是**从源码安装**的 mmpose，可以直接运行以下命令进行验证：
 
 ```shell
 python demo/image_demo.py \
     tests/data/coco/000000000785.jpg \
-    configs/body_2d_keypoint/topdown_heatmap/coco/td-hm_hrnet-w48_8xb32-210e_coco-256x192.py \
-    https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth \
+    td-hm_hrnet-w48_8xb32-210e_coco-256x192.py \
+    hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth \
     --out-file vis_results.jpg \
     --draw-heatmap
 ```
@@ -113,7 +125,26 @@ python demo/image_demo.py \
 
 ![image](https://user-images.githubusercontent.com/87690686/187824033-2cce0f55-034a-4127-82e2-52744178bc32.jpg)
 
-输出图片将会保存到 `$MMPOSE/vis_results.jpg`.
+代码会将预测的关键点和热图绘制在图像中的人体上，并保存到当前文件夹下的 `vis_results.jpg`。
+
+如果您是**作为 Python 包安装**，可以打开您的 Python 解释器，复制并粘贴如下代码：
+
+```python
+from mmpose.apis import inference_topdown, init_model
+from mmpose.utils import register_all_modules
+
+register_all_modules()
+
+config_file = 'td-hm_hrnet-w48_8xb32-210e_coco-256x192.py'
+checkpoint_file = 'hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth'
+model = init_model(config_file, checkpoint_file, device='cpu')  # or device='cuda:0'
+
+# 请准备好一张带有人体的图片
+results = inference_topdown(model, 'demo.jpg')
+```
+
+示例图片 `demo.jpg` 可以从 [Github](https://raw.githubusercontent.com/open-mmlab/mmpose/1.x/tests/data/coco/000000000785.jpg) 下载。
+推理结果是一个 `PoseDataSample` 列表，预测结果将会保存在 `pred_instances` 中，包括检测到的关键点位置和置信度。
 
 ### 自定义安装