[Docs] update links (#2161)

open-mmlab · Apr 6, 2023 · c08d272 · c08d272
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diff --git a/.circleci/test.yml b/.circleci/test.yml
@@ -135,7 +135,7 @@ workflows:
             branches:
               ignore:
                 - dev-1.x
-                - 1.x
+                - main
   pr_stage_test:
     when:
       not:
@@ -147,7 +147,7 @@ workflows:
             branches:
               ignore:
                 - dev-1.x
-                - 1.x
+                - main
       - build_cpu:
           name: minimum_version_cpu
           torch: 1.7.1
@@ -189,3 +189,4 @@ workflows:
             branches:
               only:
                 - dev-1.x
+                - main
diff --git a/README.md b/README.md
diff --git a/README_CN.md b/README_CN.md
diff --git a/configs/body_2d_keypoint/topdown_heatmap/coco/cspnext_udp_coco.md b/configs/body_2d_keypoint/topdown_heatmap/coco/cspnext_udp_coco.md
@@ -64,6 +64,6 @@ Results on COCO val2017 with detector having human AP of 56.4 on COCO val2017 da
 | [pose_cspnext_m_udp_aic_coco](/configs/body_2d_keypoint/topdown_heatmap/coco/cspnext-m_udp_8xb256-210e_aic-coco-256x192.py) |  256x192   | 0.748 |      0.925      |      0.818      | 0.777 |      0.933      | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmpose/cspnext-m_udp-aic-coco_210e-256x192-f2f7d6f6_20230130.pth) | [log](https://download.openmmlab.com/mmpose/v1/projects/rtmpose/cspnext-m_udp-aic-coco_210e-256x192-f2f7d6f6_20230130.json) |
 | [pose_cspnext_l_udp_aic_coco](/configs/body_2d_keypoint/topdown_heatmap/coco/cspnext-l_udp_8xb256-210e_aic-coco-256x192.py) |  256x192   | 0.772 |      0.936      |      0.839      | 0.799 |      0.943      | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmpose/cspnext-l_udp-aic-coco_210e-256x192-273b7631_20230130.pth) | [log](https://download.openmmlab.com/mmpose/v1/projects/rtmpose/cspnext-l_udp-aic-coco_210e-256x192-273b7631_20230130.json) |
 
-Note that, UDP also adopts the unbiased encoding/decoding algorithm of [DARK](https://mmpose.readthedocs.io/en/1.x/model_zoo_papers/techniques.html#darkpose-cvpr-2020).
+Note that, UDP also adopts the unbiased encoding/decoding algorithm of [DARK](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/techniques.html#darkpose-cvpr-2020).
 
 Flip test and detector is not used in the result of aic-coco training.
diff --git a/configs/body_2d_keypoint/topdown_heatmap/coco/hrnet_udp_coco.md b/configs/body_2d_keypoint/topdown_heatmap/coco/hrnet_udp_coco.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_keypoint/topdown_heatmap/coco/td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288.py) |  384x288   | 0.773 |      0.911      |      0.836      | 0.821 |      0.946      | [ckpt](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/coco/td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288-70d7ab01_20220913.pth) | [log](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/coco/td-hm_hrnet-w48_udp-8xb32-210e_coco-384x288_20220913.log) |
 | [pose_hrnet_w32_udp_regress](/configs/body_2d_keypoint/topdown_heatmap/coco/td-hm_hrnet-w32_udp-regress-8xb64-210e_coco-256x192.py) |  256x192   | 0.759 |      0.907      |      0.827      | 0.813 |      0.943      | [ckpt](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/coco/td-hm_hrnet-w32_udp-regress-8xb64-210e_coco-256x192-9c0b77b4_20220926.pth) | [log](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/coco/td-hm_hrnet-w32_udp-regress-8xb64-210e_coco-256x192_20220226.log) |
 
-Note that, UDP also adopts the unbiased encoding/decoding algorithm of [DARK](https://mmpose.readthedocs.io/en/1.x/model_zoo_papers/techniques.html#darkpose-cvpr-2020).
+Note that, UDP also adopts the unbiased encoding/decoding algorithm of [DARK](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/techniques.html#darkpose-cvpr-2020).
diff --git a/demo/docs/2d_animal_demo.md b/demo/docs/2d_animal_demo.md
@@ -15,7 +15,7 @@ python demo/topdown_demo_with_mmdet.py \
     [--device ${GPU_ID or CPU}]
 ```
 
-The pre-trained animal pose estimation model can be found from [model zoo](https://mmpose.readthedocs.io/en/1.x/model_zoo/animal_2d_keypoint.html).
+The pre-trained animal pose estimation model can be found from [model zoo](https://mmpose.readthedocs.io/en/latest/model_zoo/animal_2d_keypoint.html).
 Take [animalpose model](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_animalpose_256x256-1aa7f075_20210426.pth) as an example:
 
 ```shell

diff --git a/demo/docs/2d_face_demo.md b/demo/docs/2d_face_demo.md
@@ -16,7 +16,7 @@ python demo/topdown_demo_with_mmdet.py \
     [--kpt-thr ${KPT_SCORE_THR}] [--bbox-thr ${BBOX_SCORE_THR}]
 ```
 
-The pre-trained face keypoint estimation models can be found from [model zoo](https://mmpose.readthedocs.io/en/1.x/model_zoo/face_2d_keypoint.html).
+The pre-trained face keypoint estimation models can be found from [model zoo](https://mmpose.readthedocs.io/en/latest/model_zoo/face_2d_keypoint.html).
 Take [aflw model](https://download.openmmlab.com/mmpose/face/hrnetv2/hrnetv2_w18_aflw_256x256-f2bbc62b_20210125.pth) as an example:
 
 ```shell

diff --git a/demo/docs/2d_hand_demo.md b/demo/docs/2d_hand_demo.md
@@ -17,7 +17,7 @@ python demo/topdown_demo_with_mmdet.py \
 
 ```
 
-The pre-trained hand pose estimation model can be downloaded from [model zoo](https://mmpose.readthedocs.io/en/1.x/model_zoo/hand_2d_keypoint.html).
+The pre-trained hand pose estimation model can be downloaded from [model zoo](https://mmpose.readthedocs.io/en/latest/model_zoo/hand_2d_keypoint.html).
 Take [onehand10k model](https://download.openmmlab.com/mmpose/hand/hrnetv2/hrnetv2_w18_onehand10k_256x256-30bc9c6b_20210330.pth) as an example:
 
 ```shell

diff --git a/demo/docs/2d_human_pose_demo.md b/demo/docs/2d_human_pose_demo.md
@@ -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 human pose estimation models 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 models can be downloaded from [model zoo](https://mmpose.readthedocs.io/en/latest/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

diff --git a/demo/docs/2d_wholebody_pose_demo.md b/demo/docs/2d_wholebody_pose_demo.md
@@ -14,7 +14,7 @@ python demo/image_demo.py \
     [--draw_heatmap]
 ```
 
-The pre-trained hand pose estimation models can be downloaded from [model zoo](https://mmpose.readthedocs.io/en/1.x/model_zoo/2d_wholebody_keypoint.html).
+The pre-trained hand pose estimation models can be downloaded from [model zoo](https://mmpose.readthedocs.io/en/latest/model_zoo/2d_wholebody_keypoint.html).
 Take [coco-wholebody_vipnas_res50_dark](https://download.openmmlab.com/mmpose/top_down/vipnas/vipnas_res50_wholebody_256x192_dark-67c0ce35_20211112.pth) model as an example:
 
 ```shell

diff --git a/demo/docs/webcam_api_demo.md b/demo/docs/webcam_api_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/1.x/configs/body_2d_keypoint/topdown_heatmap) pose estimation models for humans and animals respectively. Users can choose models from the [MMPose Model Zoo](https://mmpose.readthedocs.io/en/1.x/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/latest/configs/body_2d_keypoint/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.
 
   ```python
   # 'TopdownPoseEstimatorNode':

diff --git a/docker/Dockerfile b/docker/Dockerfile
@@ -28,7 +28,7 @@ RUN mim install mmengine "mmcv>=2.0.0rc1"
 RUN conda clean --all
 RUN git clone https://github.com/open-mmlab/mmpose.git /mmpose
 WORKDIR /mmpose
-RUN git checkout 1.x
+RUN git checkout main
 ENV FORCE_CUDA="1"
 RUN pip install -r requirements/build.txt
 RUN pip install --no-cache-dir -e .
diff --git a/docs/en/advanced_guides/codecs.md b/docs/en/advanced_guides/codecs.md
@@ -1,4 +1,4 @@
-# Codecs
+# Learn about Codecs
 
 In the keypoint detection task, depending on the algorithm, it is often necessary to generate targets in different formats, such as normalized coordinates, vectors and heatmaps, etc. Similarly, for the model outputs, a decoding process is required to transform them into coordinates.
 

diff --git a/docs/en/conf.py b/docs/en/conf.py
@@ -77,7 +77,7 @@ def get_version():
     'menu': [
         {
             'name': 'GitHub',
-            'url': 'https://github.com/open-mmlab/mmpose/tree/1.x'
+            'url': 'https://github.com/open-mmlab/mmpose/tree/main'
         },
     ],
     # Specify the language of the shared menu

diff --git a/docs/en/index.rst b/docs/en/index.rst
@@ -22,8 +22,8 @@ You can change the documentation language at the lower-left corner of the page.
 
    user_guides/inference.md
    user_guides/configs.md
-   user_guides/train_and_test.md
    user_guides/prepare_datasets.md
+   user_guides/train_and_test.md
 
 .. toctree::
    :maxdepth: 1

diff --git a/docs/en/installation.md b/docs/en/installation.md
@@ -75,8 +75,7 @@ mim install "mmdet>=3.0.0rc6"
 To develop and run mmpose directly, install it from source:
 
 ```shell
-git clone https://github.com/open-mmlab/mmpose.git -b 1.x
-# "-b 1.x" means checkout to the `1.x` branch.
+git clone https://github.com/open-mmlab/mmpose.git
 cd mmpose
 pip install -r requirements.txt
 pip install -v -e .
@@ -138,7 +137,7 @@ model = init_model(config_file, checkpoint_file, device='cpu')  # or device='cud
 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 `demo.jpg` can be downloaded from [Github](https://raw.githubusercontent.com/open-mmlab/mmpose/main/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.
 
@@ -199,7 +198,7 @@ thus we only need to install MMEngine, MMCV and MMPose with the following comman
 **Step 2.** Install MMPose from the source.
 
 ```shell
-!git clone https://github.com/open-mmlab/mmpose.git -b 1.x
+!git clone https://github.com/open-mmlab/mmpose.git
 %cd mmpose
 !pip install -e .
 ```

diff --git a/docs/en/merge_docs.sh b/docs/en/merge_docs.sh
@@ -2,7 +2,7 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 
 sed -i '$a\\n' ../../demo/docs/*_demo.md
-cat ../../demo/docs/*_demo.md | sed "s/^## 2D\(.*\)Demo/##\1Estimation/" | sed "s/md###t/html#t/g" | sed '1i\# Demos\n' | sed 's=](/docs/en/=](/=g' | sed 's=](/=](https://github.com/open-mmlab/mmpose/tree/1.x/=g' >demos.md
+cat ../../demo/docs/*_demo.md | sed "s/^## 2D\(.*\)Demo/##\1Estimation/" | sed "s/md###t/html#t/g" | sed '1i\# Demos\n' | sed 's=](/docs/en/=](/=g' | sed 's=](/=](https://github.com/open-mmlab/mmpose/tree/main/=g' >demos.md
 
  # remove /docs/ for link used in doc site
 sed -i 's=](/docs/en/=](=g' overview.md
@@ -18,14 +18,14 @@ sed -i 's=](/docs/en/=](=g' ./notes/*.md
 sed -i 's=](/docs/en/=](=g' ./projects/*.md
 
 
-sed -i 's=](/=](https://github.com/open-mmlab/mmpose/tree/1.x/=g' overview.md
-sed -i 's=](/=](https://github.com/open-mmlab/mmpose/tree/1.x/=g' installation.md
-sed -i 's=](/=](https://github.com/open-mmlab/mmpose/tree/1.x/=g' quick_run.md
-sed -i 's=](/=](https://github.com/open-mmlab/mmpose/tree/1.x/=g' migration.md
-sed -i 's=](/=](https://github.com/open-mmlab/mmpose/tree/1.x/=g' ./advanced_guides/*.md
-sed -i 's=](/=](https://github.com/open-mmlab/mmpose/tree/1.x/=g' ./model_zoo/*.md
-sed -i 's=](/=](https://github.com/open-mmlab/mmpose/tree/1.x/=g' ./model_zoo_papers/*.md
-sed -i 's=](/=](https://github.com/open-mmlab/mmpose/tree/1.x/=g' ./user_guides/*.md
-sed -i 's=](/=](https://github.com/open-mmlab/mmpose/tree/1.x/=g' ./dataset_zoo/*.md
-sed -i 's=](/=](https://github.com/open-mmlab/mmpose/tree/1.x/=g' ./notes/*.md
-sed -i 's=](/=](https://github.com/open-mmlab/mmpose/tree/1.x/=g' ./projects/*.md
+sed -i 's=](/=](https://github.com/open-mmlab/mmpose/tree/main/=g' overview.md
+sed -i 's=](/=](https://github.com/open-mmlab/mmpose/tree/main/=g' installation.md
+sed -i 's=](/=](https://github.com/open-mmlab/mmpose/tree/main/=g' quick_run.md
+sed -i 's=](/=](https://github.com/open-mmlab/mmpose/tree/main/=g' migration.md
+sed -i 's=](/=](https://github.com/open-mmlab/mmpose/tree/main/=g' ./advanced_guides/*.md
+sed -i 's=](/=](https://github.com/open-mmlab/mmpose/tree/main/=g' ./model_zoo/*.md
+sed -i 's=](/=](https://github.com/open-mmlab/mmpose/tree/main/=g' ./model_zoo_papers/*.md
+sed -i 's=](/=](https://github.com/open-mmlab/mmpose/tree/main/=g' ./user_guides/*.md
+sed -i 's=](/=](https://github.com/open-mmlab/mmpose/tree/main/=g' ./dataset_zoo/*.md
+sed -i 's=](/=](https://github.com/open-mmlab/mmpose/tree/main/=g' ./notes/*.md
+sed -i 's=](/=](https://github.com/open-mmlab/mmpose/tree/main/=g' ./projects/*.md
diff --git a/docs/en/notes/changelog.md b/docs/en/notes/changelog.md
@@ -99,7 +99,7 @@ Built upon the new [training engine](https://github.com/open-mmlab/mmengine).
 
 - **Unified interfaces**. As a part of the OpenMMLab 2.0 projects, MMPose 1.x unifies and refactors the interfaces and internal logics of train, testing, datasets, models, evaluation, and visualization. All the OpenMMLab 2.0 projects share the same design in those interfaces and logics to allow the emergence of multi-task/modality algorithms.
 
-- **More documentation and tutorials**. We add a bunch of documentation and tutorials to help users get started more smoothly. Read it [here](https://mmpose.readthedocs.io/en/1.x/).
+- **More documentation and tutorials**. We add a bunch of documentation and tutorials to help users get started more smoothly. Read it [here](https://mmpose.readthedocs.io/en/latest/).
 
 **Breaking Changes**
 

diff --git a/docs/en/overview.md b/docs/en/overview.md
@@ -32,20 +32,35 @@ MMPose consists of **8** main components:
 We have prepared detailed guidelines for all types of users:
 
 1. For installation instrunctions:
+
    - [Installation](./installation.md)
+
 2. For the basic usage of MMPose:
-   - [Quick Run](./quick_run.md)
+
+   - [A 20-minute Tour to MMPose](./guide_to_framework.md)
+   - [Demos](./demos.md)
    - [Inference](./user_guides/inference.md)
-3. For users who want to learn more about components of MMPose:
    - [Configs](./user_guides/configs.md)
    - [Prepare Datasets](./user_guides/prepare_datasets.md)
-   - [Codecs](./user_guides/codecs.md)
-   - [Train & Test](./user_guides/train_and_test.md)
-   - [Visualization](./user_guides/visualization.md)
-   - [How to](./user_guides/how_to.md)
-4. For developers who wish to develop based on MMPose:
+   - [Train and Test](./user_guides/train_and_test.md)
+
+3. For developers who wish to develop based on MMPose:
+
+   - [Learn about Codecs](./advanced_guides/codecs.md)
+   - [Dataflow in MMPose](./advanced_guides/dataflow.md)
+   - [Implement New Models](./advanced_guides/implement_new_models.md)
+   - [Customize Datasets](./advanced_guides/customize_datasets.md)
+   - [Customize Data Transforms](./advanced_guides/customize_transforms.md)
+   - [Customize Optimizer](./advanced_guides/customize_optimizer.md)
+   - [Customize Logging](./advanced_guides/customize_logging.md)
+   - [How to Deploy](./advanced_guides/how_to_deploy.md)
+   - [Model Analysis](./advanced_guides/model_analysis.md)
    - [Migration Guide](./migration.md)
-5. For researchers and developers who are willing to contribute to MMPose:
+
+4. For researchers and developers who are willing to contribute to MMPose:
+
    - [Contribution Guide](./contribution_guide.md)
-6. For some common issues, we provide a FAQ list:
+
+5. For some common issues, we provide a FAQ list:
+
    - [FAQ](./faq.md)
diff --git a/docs/en/switch_language.md b/docs/en/switch_language.md
@@ -1,3 +1,3 @@
-## <a href='https://mmpose.readthedocs.io/en/1.x/'>English</a>
+## <a href='https://mmpose.readthedocs.io/en/latest/'>English</a>
 
-## <a href='https://mmpose.readthedocs.io/zh_CN/1.x/'>简体中文</a>
+## <a href='https://mmpose.readthedocs.io/zh_CN/latest/'>简体中文</a>
diff --git a/docs/en/user_guides/inference.md b/docs/en/user_guides/inference.md
@@ -1,11 +1,11 @@
 # Inference with existing models
 
-MMPose provides a wide variety of pre-trained models for pose estimation, which can be found in the [Model Zoo](https://mmpose.readthedocs.io/en/1.x/modelzoo.html).
+MMPose provides a wide variety of pre-trained models for pose estimation, which can be found in the [Model Zoo](https://mmpose.readthedocs.io/en/latest/model_zoo.html).
 This guide will demonstrate **how to perform inference**, or running pose estimation on provided images or videos using trained models.
 
 For instructions on testing existing models on standard datasets, refer to this [guide](./train_and_test.md#test).
 
-In MMPose, a model is defined by a configuration file, while its pre-existing parameters are stored in a checkpoint file. You can find the model configuration files and corresponding checkpoint URLs in the [Model Zoo](https://mmpose.readthedocs.io/en/1.x/modelzoo.html). We recommend starting with the HRNet model, using [this configuration file](/configs/body_2d_keypoint/topdown_heatmap/coco/td-hm_hrnet-w32_8xb64-210e_coco-256x192.py) and [this checkpoint file](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/coco/td-hm_hrnet-w32_8xb64-210e_coco-256x192-81c58e40_20220909.pth).
+In MMPose, a model is defined by a configuration file, while its pre-existing parameters are stored in a checkpoint file. You can find the model configuration files and corresponding checkpoint URLs in the [Model Zoo](https://mmpose.readthedocs.io/en/latest/modelzoo.html). We recommend starting with the HRNet model, using [this configuration file](/configs/body_2d_keypoint/topdown_heatmap/coco/td-hm_hrnet-w32_8xb64-210e_coco-256x192.py) and [this checkpoint file](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/coco/td-hm_hrnet-w32_8xb64-210e_coco-256x192-81c58e40_20220909.pth).
 
 ## Inferencer: a Unified Inference Interface
 

diff --git a/docs/en/user_guides/prepare_datasets.md b/docs/en/user_guides/prepare_datasets.md
@@ -1,6 +1,6 @@
 # Prepare Datasets
 
-MMPose supports multiple tasks and corresponding datasets. You can find them in [dataset zoo](https://mmpose.readthedocs.io/en/1.x/dataset_zoo.html). Please follow the corresponding guidelines for data preparation.
+MMPose supports multiple tasks and corresponding datasets. You can find them in [dataset zoo](https://mmpose.readthedocs.io/en/latest/dataset_zoo.html). Please follow the corresponding guidelines for data preparation.
 
 <!-- TOC -->