Merge branch 'dev-0.x' into ViTPose

Author: LareinaM

.pre-commit-config.yaml

@@ -50,7 +50,7 @@ repos:
         name: update-model-index
         description: Collect model information and update model-index.yml
         entry: .dev_scripts/github/update_model_index.py
-        additional_dependencies: [mmcv]
+        additional_dependencies: ['mmcv==1.7.1']
         language: python
         files: ^configs/.*\.md$
         require_serial: true

README.md

@@ -60,4 +60,4 @@ Results on COCO val2017 with detector having human AP of 56.4 on COCO val2017 da
 | [pose_hrnet_w48_udp](/configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/hrnet_w48_coco_384x288_udp.py) |  384x288   | 0.772 |      0.910      |      0.835      | 0.820 |      0.945      | [ckpt](https://download.openmmlab.com/mmpose/top_down/udp/hrnet_w48_coco_384x288_udp-0f89c63e_20210223.pth) | [log](https://download.openmmlab.com/mmpose/top_down/udp/hrnet_w48_coco_384x288_udp_20210223.log.json) |
 | [pose_hrnet_w32_udp_regress](/configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/hrnet_w32_coco_256x192_udp_regress.py) |  256x192   | 0.758 |      0.908      |      0.823      | 0.812 |      0.943      | [ckpt](https://download.openmmlab.com/mmpose/top_down/udp/hrnet_w32_coco_256x192_udp_regress-be2dbba4_20210222.pth) | [log](https://download.openmmlab.com/mmpose/top_down/udp/hrnet_w32_coco_256x192_udp_regress_20210222.log.json) |
 
-Note that, UDP also adopts the unbiased encoding/decoding algorithm of [DARK](https://mmpose.readthedocs.io/en/latest/papers/techniques.html#div-align-center-darkpose-cvpr-2020-div).
+Note that, UDP also adopts the unbiased encoding/decoding algorithm of [DARK](https://mmpose.readthedocs.io/en/0.x/papers/techniques.html#div-align-center-darkpose-cvpr-2020-div).

README_CN.md

@@ -463,7 +463,7 @@
     "\n",
     "### Add a new dataset\n",
     "\n",
-    "There are two methods to support a customized dataset in MMPose. The first one is to convert the data to a supported format (e.g. COCO) and use the corresponding dataset class (e.g. TopdownCOCODataset), as described in the [document](https://mmpose.readthedocs.io/en/latest/tutorials/2_new_dataset.html#reorganize-dataset-to-existing-format). The second one is to add a new dataset class. In this tutorial, we give an example of the second method.\n",
+    "There are two methods to support a customized dataset in MMPose. The first one is to convert the data to a supported format (e.g. COCO) and use the corresponding dataset class (e.g. TopdownCOCODataset), as described in the [document](https://mmpose.readthedocs.io/en/0.x/tutorials/2_new_dataset.html#reorganize-dataset-to-existing-format). The second one is to add a new dataset class. In this tutorial, we give an example of the second method.\n",
     "\n",
     "We first download the demo dataset, which contains 100 samples (75 for training and 25 for validation) selected from COCO train2017 dataset. The annotations are stored in a different format from the original COCO format.\n",
     "\n"
@@ -925,7 +925,7 @@
    "source": [
     "### Create a config file\n",
     "\n",
-    "In the next step, we create a config file which configures the model, dataset and runtime settings. More information can be found at [Learn about Configs](https://mmpose.readthedocs.io/en/latest/tutorials/0_config.html). A common practice to create a config file is deriving from a existing one. In this tutorial, we load a config file that trains a HRNet on COCO dataset, and modify it to adapt to the COCOTiny dataset."
+    "In the next step, we create a config file which configures the model, dataset and runtime settings. More information can be found at [Learn about Configs](https://mmpose.readthedocs.io/en/0.x/tutorials/0_config.html). A common practice to create a config file is deriving from a existing one. In this tutorial, we load a config file that trains a HRNet on COCO dataset, and modify it to adapt to the COCOTiny dataset."
    ]
   },
   {

configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/hrnet_udp_coco.md

@@ -7,7 +7,7 @@
 We provide a demo script to test a single image, given gt json file.
 
 *Pose Model Preparation:*
-The pre-trained pose estimation model can be downloaded from [model zoo](https://mmpose.readthedocs.io/en/latest/topics/animal.html).
+The pre-trained pose estimation model can be downloaded from [model zoo](https://mmpose.readthedocs.io/en/0.x/topics/animal.html).
 Take [macaque model](https://download.openmmlab.com/mmpose/animal/resnet/res50_macaque_256x192-98f1dd3a_20210407.pth) as an example:
 
 ```shell
@@ -113,7 +113,7 @@ python demo/top_down_video_demo_with_mmdet.py \
 **Other Animals**
 
 For other animals, we have also provided some pre-trained animal detection models (1-class models). Supported models can be found in [det model zoo](/demo/docs/mmdet_modelzoo.md).
-The pre-trained animal pose estimation model can be found in [pose model zoo](https://mmpose.readthedocs.io/en/latest/topics/animal.html).
+The pre-trained animal pose estimation model can be found in [pose model zoo](https://mmpose.readthedocs.io/en/0.x/topics/animal.html).
 
 ```shell
 python demo/top_down_video_demo_with_mmdet.py \

demo/MMPose_Tutorial.ipynb

@@ -9,7 +9,7 @@
 We provide a demo script to test a single image, given gt json file.
 
 *Face Keypoint Model Preparation:*
-The pre-trained face keypoint estimation model can be found from [model zoo](https://mmpose.readthedocs.io/en/latest/topics/face.html).
+The pre-trained face keypoint estimation model can be found from [model zoo](https://mmpose.readthedocs.io/en/0.x/topics/face.html).
 Take [aflw model](https://download.openmmlab.com/mmpose/face/hrnetv2/hrnetv2_w18_aflw_256x256-f2bbc62b_20210125.pth) as an example:
 
 ```shell

demo/docs/2d_animal_demo.md

@@ -9,7 +9,7 @@
 We provide a demo script to test a single image, given gt json file.
 
 *Hand Pose Model Preparation:*
-The pre-trained hand pose estimation model can be downloaded from [model zoo](https://mmpose.readthedocs.io/en/latest/topics/hand%282d%2Ckpt%2Crgb%2Cimg%29.html).
+The pre-trained hand pose estimation model can be downloaded from [model zoo](https://mmpose.readthedocs.io/en/0.x/topics/hand%282d%2Ckpt%2Crgb%2Cimg%29.html).
 Take [onehand10k model](https://download.openmmlab.com/mmpose/top_down/resnet/res50_onehand10k_256x256-e67998f6_20200813.pth) as an example:
 
 ```shell
@@ -50,7 +50,7 @@ Assume that you have already installed [mmdet](https://github.com/open-mmlab/mmd
 
 *Hand Box Model Preparation:* The pre-trained hand box estimation model can be found in [det model zoo](/demo/docs/mmdet_modelzoo.md).
 
-*Hand Pose Model Preparation:* The pre-trained hand pose estimation model can be downloaded from [pose model zoo](https://mmpose.readthedocs.io/en/latest/topics/hand%282d%2Ckpt%2Crgb%2Cimg%29.html).
+*Hand Pose Model Preparation:* The pre-trained hand pose estimation model can be downloaded from [pose model zoo](https://mmpose.readthedocs.io/en/0.x/topics/hand%282d%2Ckpt%2Crgb%2Cimg%29.html).
 
 ```shell
 python demo/top_down_img_demo_with_mmdet.py \
@@ -80,7 +80,7 @@ Assume that you have already installed [mmdet](https://github.com/open-mmlab/mmd
 
 *Hand Box Model Preparation:* The pre-trained hand box estimation model can be found in [det model zoo](/demo/docs/mmdet_modelzoo.md).
 
-*Hand Pose Model Preparation:* The pre-trained hand pose estimation model can be found in [pose model zoo](https://mmpose.readthedocs.io/en/latest/topics/hand%282d%2Ckpt%2Crgb%2Cimg%29.html).
+*Hand Pose Model Preparation:* The pre-trained hand pose estimation model can be found in [pose model zoo](https://mmpose.readthedocs.io/en/0.x/topics/hand%282d%2Ckpt%2Crgb%2Cimg%29.html).
 
 ```shell
 python demo/top_down_video_demo_with_mmdet.py \

demo/docs/2d_face_demo.md

@@ -63,7 +63,7 @@ Detailed configurations can be found in the config file.
   ```
 
 - **Configure pose estimation models**
-  In this demo we use two [top-down](https://github.com/open-mmlab/mmpose/tree/master/configs/body/2d_kpt_sview_rgb_img/topdown_heatmap) pose estimation models for humans and animals respectively. Users can choose models from the [MMPose Model Zoo](https://mmpose.readthedocs.io/en/latest/modelzoo.html). To apply different pose models on different instance types, you can add multiple pose estimator nodes with `cls_names` set accordingly.
+  In this demo we use two [top-down](https://github.com/open-mmlab/mmpose/tree/master/configs/body/2d_kpt_sview_rgb_img/topdown_heatmap) pose estimation models for humans and animals respectively. Users can choose models from the [MMPose Model Zoo](https://mmpose.readthedocs.io/en/0.x/modelzoo.html). To apply different pose models on different instance types, you can add multiple pose estimator nodes with `cls_names` set accordingly.
 
   ```python
   # 'TopDownPoseEstimatorNode':

demo/docs/2d_hand_demo.md

@@ -132,7 +132,7 @@ python demo/top_down_img_demo.py \
     --out-img-root vis_results
 ```
 
-More examples and details can be found in the [demo folder](/demo) and the [demo docs](https://mmpose.readthedocs.io/en/latest/demo.html).
+More examples and details can be found in the [demo folder](/demo) and the [demo docs](https://mmpose.readthedocs.io/en/0.x/demo.html).
 
 ## Train a model