[Docs] update 1.x doc links (#1331)

* update 1.x links

* update docs

docs/en/01-how-to-build/android.md

@@ -6,7 +6,6 @@
     - [Install Dependencies](#install-dependencies)
       - [Install Dependencies for SDK](#install-dependencies-for-sdk)
     - [Build MMDeploy](#build-mmdeploy)
-      - [Build Options Spec](#build-options-spec)
       - [Build SDK and Demos](#build-sdk-and-demos)
 
 ______________________________________________________________________
@@ -98,7 +97,7 @@ make -j$(nproc) install
   <tr>
   <td>OpenJDK </td>
   <td>It is necessary for building Java API.</br>
-  See <a href='https://github.com/open-mmlab/mmdeploy/blob/master/csrc/mmdeploy/apis/java/README.md'> Java API build </a> for building tutorials.
+  See <a href='https://github.com/open-mmlab/mmdeploy/blob/1.x/csrc/mmdeploy/apis/java/README.md'> Java API build </a> for building tutorials.
   </td>
   </tr>
 </tbody>

docs/en/01-how-to-build/build_from_source.md

@@ -26,7 +26,7 @@ Note:
 - If it fails when `git clone` via `SSH`, you can try the `HTTPS` protocol like this:
 
   ```shell
-  git clone -b master https://github.com/open-mmlab/mmdeploy.git --recursive
+  git clone -b 1.x https://github.com/open-mmlab/mmdeploy.git --recursive
   ```
 
 ## Build

docs/en/01-how-to-build/jetsons.md

@@ -163,7 +163,7 @@ conda activate mmdeploy
 
 ```shell
 sudo apt-get install -y libssl-dev
-git clone --branch v1.4.0 https://github.com/open-mmlab/mmcv.git
+git clone --branch 2.x https://github.com/open-mmlab/mmcv.git
 cd mmcv
 MMCV_WITH_OPS=1 pip install -e .
 ```
@@ -223,7 +223,7 @@ It takes about 15 minutes to install ppl.cv on a Jetson Nano. So, please be pati
 ## Install MMDeploy
 
 ```shell
-git clone --recursive https://github.com/open-mmlab/mmdeploy.git
+git clone -b 1.x --recursive https://github.com/open-mmlab/mmdeploy.git
 cd mmdeploy
 export MMDEPLOY_DIR=$(pwd)
 ```
@@ -283,15 +283,15 @@ Before running this demo, you need to convert model files to be able to use with
 MMDetection is an open source object detection toolbox based on PyTorch
 
 ```shell
-git clone https://github.com/open-mmlab/mmdetection.git
+git clone -b 3.x https://github.com/open-mmlab/mmdetection.git
 cd mmdetection
 pip install -r requirements/build.txt
 pip install -v -e .  # or "python setup.py develop"
 ```
 
-2. Follow [this document](https://github.com/open-mmlab/mmdeploy/blob/master/docs/en/tutorials/how_to_convert_model.md) on how to convert model files.
+1. Follow [this document](../02-how-to-run/convert_model.md) on how to convert model files.
 
-For this example, we have used [retinanet_r18_fpn_1x_coco.py](https://github.com/open-mmlab/mmdetection/blob/master/configs/retinanet/retinanet_r18_fpn_1x_coco.py) as the model config, and [this file](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r18_fpn_1x_coco/retinanet_r18_fpn_1x_coco_20220407_171055-614fd399.pth) as the corresponding checkpoint file. Also for deploy config, we have used [detection_tensorrt_dynamic-320x320-1344x1344.py](https://github.com/open-mmlab/mmdeploy/blob/master/configs/mmdet/detection/detection_tensorrt_dynamic-320x320-1344x1344.py)
+For this example, we have used [retinanet_r18_fpn_1x_coco.py](https://github.com/open-mmlab/mmdetection/blob/3.x/configs/retinanet/retinanet_r18_fpn_1x_coco.py) as the model config, and [this file](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r18_fpn_1x_coco/retinanet_r18_fpn_1x_coco_20220407_171055-614fd399.pth) as the corresponding checkpoint file. Also for deploy config, we have used [detection_tensorrt_dynamic-320x320-1344x1344.py](https://github.com/open-mmlab/mmdeploy/blob/master/configs/mmdet/detection/detection_tensorrt_dynamic-320x320-1344x1344.py)
 
 ```shell
 python ./tools/deploy.py \

docs/en/01-how-to-build/linux-x86_64.md

@@ -7,9 +7,7 @@
     - [Install Dependencies for SDK](#install-dependencies-for-sdk)
     - [Install Inference Engines for MMDeploy](#install-inference-engines-for-mmdeploy)
   - [Build MMDeploy](#build-mmdeploy)
-    - [Build Options Spec](#build-options-spec)
     - [Build Model Converter](#build-model-converter)
-      - [Build Custom Ops](#build-custom-ops)
     - [Install Model Converter](#install-model-converter)
     - [Build SDK and Demo](#build-sdk-and-demo)
 
@@ -71,12 +69,13 @@ conda install pytorch==1.8.0 torchvision==0.9.0 cudatoolkit=11.1 -c pytorch -c c
     </td>
   </tr>
   <tr>
-    <td>mmcv-full </td>
+    <td>mmcv </td>
-    <td>Install mmcv-full as follows. Refer to the <a href="https://github.com/open-mmlab/mmcv#installation">guide</a> for details.
+    <td>Install mmcv as follows. Refer to the <a href="https://github.com/open-mmlab/mmcv/tree/2.x#installation">guide</a> for details.
 <pre><code>
 export cu_version=cu111 # cuda 11.1
 export torch_version=torch1.8
-pip install mmcv-full==1.4.0 -f https://download.openmmlab.com/mmcv/dist/${cu_version}/${torch_version}/index.html
+pip install -U openmim
+mim install "mmcv>=2.0.0rc1"
 </code></pre>
     </td>
   </tr>

docs/en/01-how-to-build/macos-arm64.md

@@ -34,9 +34,10 @@
 Please refer to [get_started](../get_started.md) to install conda.
 
 ```bash
-# install pytorch & mmcv-full
+# install pytorch & mmcv
 conda install pytorch==1.9.0 torchvision==0.10.0 -c pytorch
-pip install mmcv-full==1.6.0 -f https://download.openmmlab.com/mmcv/dist/cpu/torch1.9.0/index.html
+pip install -U openmim
+mim install "mmcv>=2.0.0rc1"
 ```
 
 ### Install Dependencies for SDK

docs/en/01-how-to-build/rockchip.md

@@ -42,7 +42,7 @@ It is recommended to create a virtual environment for the project.
 conda install pytorch==1.8.0 torchvision==0.9.0 cudatoolkit=11.1 -c pytorch -c conda-forge
 ```
 
-To work with models from [MMClassification](https://mmclassification.readthedocs.io/en/latest/getting_started.html), you may need to install it additionally.
+To work with models from [MMClassification](https://mmclassification.readthedocs.io/en/1.x/get_started.html), you may need to install it additionally.
 
 ## Usage
 
@@ -158,7 +158,7 @@ label: 65, score: 0.95
 
 - MMDet models.
 
-  YOLOV3 & YOLOX: you may paste the following partition configuration into [detection_rknn_static.py](https://github.com/open-mmlab/mmdeploy/blob/master/configs/mmdet/detection/detection_rknn_static.py):
+  YOLOV3 & YOLOX: you may paste the following partition configuration into [detection_rknn_static-320x320.py](https://github.com/open-mmlab/mmdeploy/blob/master/configs/mmdet/detection/detection_rknn_static-320x320.py):
 
   ```python
   # yolov3, yolox
@@ -173,7 +173,7 @@ label: 65, score: 0.95
       ])
   ```
 
-  RetinaNet & SSD & FSAF with rknn-toolkit2, you may paste the following partition configuration into [detection_rknn_static.py](https://github.com/open-mmlab/mmdeploy/blob/master/configs/mmdet/detection/detection_rknn_static.py). Users with rknn-toolkit can directly use default config.
+  RetinaNet & SSD & FSAF with rknn-toolkit2, you may paste the following partition configuration into [detection_rknn_static-320x320.py](https://github.com/open-mmlab/mmdeploy/blob/master/configs/mmdet/detection/detection_rknn_static-320x320.py). Users with rknn-toolkit can directly use default config.
 
   ```python
   # retinanet, ssd

docs/en/01-how-to-build/snpe.md

@@ -71,7 +71,7 @@ $ python3 -m pip install -e .
 
 ## 3. Test the model
 
-Take Resnet-18 as an example. First refer to [documentation to install mmcls](https://github.com/open-mmlab/mmclassification)  and  use `tools/deploy.py` to convert the model.
+Take Resnet-18 as an example. First refer to [documentation to install mmcls](https://github.com/open-mmlab/mmclassification/tree/1.x)  and  use `tools/deploy.py` to convert the model.
 
 ```bash
 $ export MODEL_CONFIG=/path/to/mmclassification/configs/resnet/resnet18_8xb16_cifar10.py

docs/en/01-how-to-build/windows.md

@@ -8,9 +8,7 @@
       - [Install Dependencies for SDK](#install-dependencies-for-sdk)
       - [Install Inference Engines for MMDeploy](#install-inference-engines-for-mmdeploy)
     - [Build MMDeploy](#build-mmdeploy)
-      - [Build Options Spec](#build-options-spec)
       - [Build Model Converter](#build-model-converter)
-        - [Build Custom Ops](#build-custom-ops)
       - [Install Model Converter](#install-model-converter)
       - [Build SDK and Demos](#build-sdk-and-demos)
     - [Note](#note)
@@ -58,12 +56,13 @@ pip install torch==1.8.0+cu111 torchvision==0.9.0+cu111 torchaudio==0.8.0 -f htt
     </td>
   </tr>
   <tr>
-    <td>mmcv-full </td>
+    <td>mmcv </td>
-    <td>Install mmcv-full as follows. Refer to the <a href="https://github.com/open-mmlab/mmcv#installation">guide</a> for details.
+    <td>Install mmcv as follows. Refer to the <a href="https://github.com/open-mmlab/mmcv/tree/2.x#installation">guide</a> for details.
 <pre><code>
 $env:cu_version="cu111"
 $env:torch_version="torch1.8.0"
-pip install mmcv-full==1.4.0 -f https://download.openmmlab.com/mmcv/dist/${cu_version}/${torch_version}/index.html
+pip install -U openmim
+mim install "mmcv>=2.0.0rc1"
 </code></pre>
     </td>
   </tr>

docs/en/02-how-to-run/convert_model.md

@@ -11,7 +11,7 @@ Notes:
 ### Prerequisite
 
 1. Install and build your target backend. You could refer to [ONNXRuntime-install](../05-supported-backends/onnxruntime.md), [TensorRT-install](../05-supported-backends/tensorrt.md), [ncnn-install](../05-supported-backends/ncnn.md), [PPLNN-install](../05-supported-backends/pplnn.md), [OpenVINO-install](../05-supported-backends/openvino.md) for more information.
-2. Install and build your target codebase. You could refer to [MMClassification-install](https://github.com/open-mmlab/mmclassification/blob/master/docs/en/install.md), [MMDetection-install](https://github.com/open-mmlab/mmdetection/blob/master/docs/en/get_started.md), [MMSegmentation-install](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/get_started.md#installation), [MMOCR-install](https://mmocr.readthedocs.io/en/latest/install.html), [MMEditing-install](https://github.com/open-mmlab/mmediting/blob/master/docs/en/install.md).
+2. Install and build your target codebase. You could refer to [MMClassification-install](https://github.com/open-mmlab/mmclassification/blob/1.x/docs/en/get_started.md#installation), [MMDetection-install](https://github.com/open-mmlab/mmdetection/blob/3.x/docs/en/get_started.md), [MMSegmentation-install](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/get_started.md#installation), [MMOCR-install](https://github.com/open-mmlab/mmocr/blob/1.x/docs/en/get_started/install.md), [MMEditing-install](https://github.com/open-mmlab/mmediting/blob/1.x/docs/en/2_get_started.md#installation).
 
 ### Usage
 
@@ -55,8 +55,8 @@ python ./tools/deploy.py \
 ```bash
 python ./tools/deploy.py \
     configs/mmdet/detection/detection_tensorrt_dynamic-320x320-1344x1344.py \
-    $PATH_TO_MMDET/configs/yolo/yolov3_d53_mstrain-608_273e_coco.py \
+    $PATH_TO_MMDET/configs/yolo/yolov3_d53_8xb8-ms-608-273e_coco.py \
-    $PATH_TO_MMDET/checkpoints/yolo/yolov3_d53_mstrain-608_273e_coco.pth \
+    $PATH_TO_MMDET/checkpoints/yolo/yolov3_d53_mstrain-608_273e_coco_20210518_115020-a2c3acb8.pth \
     $PATH_TO_MMDET/demo/demo.jpg \
     --work-dir work_dir \
     --show \

docs/en/02-how-to-run/prebuilt_package_windows.md

@@ -47,7 +47,7 @@ In order to use the prebuilt package, you need to install some third-party depen
 2. Clone the mmdeploy repository
 
    ```bash
-   git clone https://github.com/open-mmlab/mmdeploy.git
+   git clone -b 1.x https://github.com/open-mmlab/mmdeploy.git
    ```
 
    :point_right: The main purpose here is to use the configs, so there is no need to compile `mmdeploy`.
@@ -55,14 +55,14 @@ In order to use the prebuilt package, you need to install some third-party depen
 3. Install mmclassification
 
    ```bash
-   git clone https://github.com/open-mmlab/mmclassification.git
+   git clone -b 1.x https://github.com/open-mmlab/mmclassification.git
    cd mmclassification
    pip install -e .
    ```
 
 4. Prepare a PyTorch model as our example
 
-   Download the pth [resnet18_8xb32_in1k_20210831-fbbb1da6.pth](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_8xb32_in1k_20210831-fbbb1da6.pth). The corresponding config of the model is [resnet18_8xb32_in1k.py](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet18_8xb32_in1k.py)
+   Download the pth [resnet18_8xb32_in1k_20210831-fbbb1da6.pth](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_8xb32_in1k_20210831-fbbb1da6.pth). The corresponding config of the model is [resnet18_8xb32_in1k.py](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/resnet/resnet18_8xb32_in1k.py)
 
 After the above work is done, the structure of the current working directory should be:
 

docs/en/02-how-to-run/write_config.md

@@ -177,4 +177,4 @@ detection_tensorrt-int8_dynamic-320x320-1344x1344.py
 
 ## 6. How to write model config
 
-According to model's codebase, write the model config file. Model's config file is used to initialize the model, referring to [MMClassification](https://github.com/open-mmlab/mmclassification/blob/master/docs/en/tutorials/config.md), [MMDetection](https://github.com/open-mmlab/mmdetection/blob/master/docs/en/tutorials/config.md), [MMSegmentation](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/tutorials/config.md), [MMOCR](https://github.com/open-mmlab/mmocr/blob/main/docs/en/tutorials/config.md), [MMEditing](https://github.com/open-mmlab/mmediting/blob/master/docs/en/config.md).
+According to model's codebase, write the model config file. Model's config file is used to initialize the model, referring to [MMClassification](https://github.com/open-mmlab/mmclassification/blob/1.x/docs/en/user_guides/config.md), [MMDetection](https://github.com/open-mmlab/mmdetection/blob/3.x/docs/en/user_guides/config.md), [MMSegmentation](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/user_guides/1_config.md), [MMOCR](https://github.com/open-mmlab/mmocr/blob/1.x/docs/en/user_guides/config.md), [MMEditing](https://github.com/open-mmlab/mmediting/blob/1.x/docs/en/user_guides/1_config.md).

docs/en/07-developer-guide/partition_model.md

@@ -6,7 +6,7 @@ MMDeploy supports exporting PyTorch models to partitioned onnx models. With this
 
 To support the model partition, we need to add `Mark` nodes in the ONNX model. This could be done with mmdeploy's `@mark` decorator. Note that to make the `mark` work, the marking operation should be included in a rewriting function.
 
-At first, we would mark the model input, which could be done by marking the input tensor `img` in the `forward` method of `BaseDetector` class, which is the parent class of all detector classes. Thus we name this marking point as `detector_forward` and mark the inputs as `input`. Since there could be three outputs for detectors such as `Mask RCNN`, the outputs are marked as  `dets`, `labels`, and `masks`. The following code shows the idea of adding mark functions and calling the mark functions in the rewrite. For source code, you could refer to [mmdeploy/codebase/mmdet/models/detectors/base.py](https://github.com/open-mmlab/mmdeploy/blob/86a50e343a3a45d7bc2ba3256100accc4973e71d/mmdeploy/codebase/mmdet/models/detectors/base.py)
+At first, we would mark the model input, which could be done by marking the input tensor `img` in the `forward` method of `BaseDetector` class, which is the parent class of all detector classes. Thus we name this marking point as `detector_forward` and mark the inputs as `input`. Since there could be three outputs for detectors such as `Mask RCNN`, the outputs are marked as  `dets`, `labels`, and `masks`. The following code shows the idea of adding mark functions and calling the mark functions in the rewrite. For source code, you could refer to [mmdeploy/codebase/mmdet/models/detectors/single_stage.py](https://github.com/open-mmlab/mmdeploy/blob/4fc8828af84281b62be143012cd9f9dafd1e7cc2/mmdeploy/codebase/mmdet/models/detectors/single_stage.py)
 
 ```python
 from mmdeploy.core import FUNCTION_REWRITER, mark
@@ -25,7 +25,7 @@ def base_detector__forward(ctx, self, img, img_metas=None, **kwargs):
     return __forward_impl(...)
 ```
 
-Then, we have to mark the output feature of `YOLOV3Head`, which is the input argument `pred_maps` in `get_bboxes` method of `YOLOV3Head` class. We could add a internal function to only mark the `pred_maps` inside [`yolov3_head__get_bboxes`](https://github.com/open-mmlab/mmdeploy/blob/86a50e343a3a45d7bc2ba3256100accc4973e71d/mmdeploy/codebase/mmdet/models/dense_heads/yolo_head.py#L14) function as following.
+Then, we have to mark the output feature of `YOLOV3Head`, which is the input argument `pred_maps` in `get_bboxes` method of `YOLOV3Head` class. We could add a internal function to only mark the `pred_maps` inside [`yolov3_head__get_bboxes`](https://github.com/open-mmlab/mmdeploy/blob/4fc8828af84281b62be143012cd9f9dafd1e7cc2/mmdeploy/codebase/mmdet/models/dense_heads/yolo_head.py#L16) function as following.
 
 ```python
 from mmdeploy.core import FUNCTION_REWRITER, mark
@@ -78,7 +78,7 @@ Once we have marks of nodes and the deployment config with `parition_config` bei
 ```shell
 python tools/torch2onnx.py \
 configs/mmdet/detection/yolov3_partition_onnxruntime_static.py \
-../mmdetection/configs/yolo/yolov3_d53_mstrain-608_273e_coco.py \
+../mmdetection/configs/yolo/yolov3_d53_8xb8-ms-608-273e_coco.py \
 https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_mstrain-608_273e_coco/yolov3_d53_mstrain-608_273e_coco_20210518_115020-a2c3acb8.pth \
 ../mmdetection/demo/demo.jpg \
 --work-dir ./work-dirs/mmdet/yolov3/ort/partition

docs/zh_cn/01-how-to-build/android.md

@@ -6,7 +6,6 @@
     - [安装依赖包](#安装依赖包)
       - [安装 MMDeploy SDK 依赖](#安装-mmdeploy-sdk-依赖)
     - [编译 MMDeploy](#编译-mmdeploy)
-      - [编译选项说明](#编译选项说明)
       - [编译 SDK 和 Demos](#编译-sdk-和-demos)
 
 ______________________________________________________________________
@@ -99,7 +98,7 @@ make -j$(nproc) install
     <tr>
   <td>OpenJDK </td>
   <td>编译Java API之前需要先准备OpenJDK开发环境</br>
-  请参考 <a href='https://github.com/open-mmlab/mmdeploy/blob/master/csrc/mmdeploy/apis/java/README.md'> Java API 编译 </a> 进行构建.
+  请参考 <a href='https://github.com/open-mmlab/mmdeploy/blob/1.x/csrc/mmdeploy/apis/java/README.md'> Java API 编译 </a> 进行构建.
   </td>
   </tr>
 </tbody>

docs/zh_cn/01-how-to-build/build_from_source.md

@@ -27,7 +27,7 @@ git clone -b master git@github.com:open-mmlab/mmdeploy.git --recursive
 - 如果以 `SSH` 方式 `git clone` 代码失败，您可以尝试使用 `HTTPS` 协议下载代码：
 
   ```bash
-  git clone -b master https://github.com/open-mmlab/mmdeploy.git MMDeploy
+  git clone -b 1.x https://github.com/open-mmlab/mmdeploy.git MMDeploy
   cd MMDeploy
   git submodule update --init --recursive
   ```

docs/zh_cn/01-how-to-build/jetsons.md

@@ -149,9 +149,8 @@ conda activate mmdeploy
 
   ```shell
   sudo apt-get install -y libssl-dev
-  git clone https://github.com/open-mmlab/mmcv.git
+  git clone --branch 2.x https://github.com/open-mmlab/mmcv.git
   cd mmcv
-  git checkout v1.4.0
   MMCV_WITH_OPS=1 pip install -e .
   ```
 
@@ -200,7 +199,7 @@ conda activate mmdeploy
 ## 安装 MMDeploy
 
 ```shell
-git clone --recursive https://github.com/open-mmlab/mmdeploy.git
+git clone -b 1.x --recursive https://github.com/open-mmlab/mmdeploy.git
 cd mmdeploy
 export MMDEPLOY_DIR=$(pwd)
 ```

docs/zh_cn/01-how-to-build/linux-x86_64.md

@@ -8,9 +8,7 @@
       - [安装 MMDeploy SDK 依赖](#安装-mmdeploy-sdk-依赖)
       - [安装推理引擎](#安装推理引擎)
     - [编译 MMDeploy](#编译-mmdeploy)
-      - [编译选项说明](#编译选项说明)
+      - [编译 Model Converter](#编译-model-converter)
-      - [编译安装 Model Converter](#编译安装-model-converter)
-        - [编译自定义算子](#编译自定义算子)
       - [安装 Model Converter](#安装-model-converter)
       - [编译 SDK 和 Demos](#编译-sdk-和-demos)
 
@@ -72,12 +70,13 @@ conda install pytorch==1.8.0 torchvision==0.9.0 cudatoolkit=11.1 -c pytorch -c c
     </td>
   </tr>
   <tr>
-    <td>mmcv-full </td>
+    <td>mmcv </td>
-    <td>参考如下命令安装 mmcv-full。更多安装方式，可查看 <a href="https://github.com/open-mmlab/mmcv">mmcv 官网</a><br>
+    <td>参考如下命令安装 mmcv。更多安装方式，可查看 <a href="https://github.com/open-mmlab/mmcv">mmcv 官网</a><br>
 <pre><code>
 export cu_version=cu111 # cuda 11.1
 export torch_version=torch1.8
-pip install mmcv-full==1.4.0 -f https://download.openmmlab.com/mmcv/dist/${cu_version}/${torch_version}/index.html
+pip install -U openmim
+mim install "mmcv>=2.0.0rc1"
 </code></pre>
     </td>
   </tr>

docs/zh_cn/01-how-to-build/macos-arm64.md

@@ -37,9 +37,10 @@
 参考[get_started](../get_started.md)文档，安装conda。
 
 ```bash
-# install pytoch & mmcv-full
+# install pytoch & mmcv
 conda install pytorch==1.9.0 torchvision==0.10.0 -c pytorch
-pip install mmcv-full==1.6.0 -f https://download.openmmlab.com/mmcv/dist/cpu/torch1.9.0/index.html
+pip install -U openmim
+mim install "mmcv>=2.0.0rc1"
 ```
 
 #### 安装 MMDeploy SDK 依赖

docs/zh_cn/01-how-to-build/rockchip.md

@@ -42,7 +42,7 @@
 conda install pytorch==1.8.0 torchvision==0.9.0 cudatoolkit=11.1 -c pytorch -c conda-forge
 ```
 
-如要使用 [MMClassification](https://mmclassification.readthedocs.io/en/latest/getting_started.html)， 需要用户自己安装使用。
+如要使用 [MMClassification](https://mmclassification.readthedocs.io/en/1.x/get_started.html)， 需要用户自己安装使用。
 
 ## 使用
 
@@ -158,7 +158,7 @@ label: 65, score: 0.95
 
 - MMDet 模型.
 
-  YOLOV3 & YOLOX: 将下面的模型拆分配置写入到 [detection_rknn_static.py](https://github.com/open-mmlab/mmdeploy/blob/master/configs/mmdet/detection/detection_rknn_static.py):
+  YOLOV3 & YOLOX: 将下面的模型拆分配置写入到 [detection_rknn_static-320x320.py](https://github.com/open-mmlab/mmdeploy/blob/1.x/configs/mmdet/detection/detection_rknn_static-320x320.py):
 
   ```python
   # yolov3, yolox
@@ -173,7 +173,7 @@ label: 65, score: 0.95
       ])
   ```
 
-  RetinaNet & SSD & FSAF with rknn-toolkit2, 将下面的模型拆分配置写入到 [detection_rknn_static.py](https://github.com/open-mmlab/mmdeploy/blob/master/configs/mmdet/detection/detection_rknn_static.py)。使用 rknn-toolkit 的用户则不用。
+  RetinaNet & SSD & FSAF with rknn-toolkit2, 将下面的模型拆分配置写入到 [detection_rknn_static-320x320.py](https://github.com/open-mmlab/mmdeploy/blob/1.x/configs/mmdet/detection/detection_rknn_static-320x320.py)。使用 rknn-toolkit 的用户则不用。
 
   ```python
   # retinanet, ssd

docs/zh_cn/01-how-to-build/snpe.md

@@ -75,7 +75,7 @@ tips:
 
 ## 三、测试模型
 
-以 Resnet-18 为例。先参照[文档安装 mmcls](https://github.com/open-mmlab/mmclassification)，然后使用 `tools/deploy.py` 转换模型。
+以 Resnet-18 为例。先参照[文档安装 mmcls](https://github.com/open-mmlab/mmclassification/tree/1.x)，然后使用 `tools/deploy.py` 转换模型。
 
 ```bash
 $ export MODEL_CONFIG=/path/to/mmclassification/configs/resnet/resnet18_8xb16_cifar10.py

docs/zh_cn/01-how-to-build/windows.md

@@ -8,9 +8,7 @@
       - [安装 MMDeploy SDK 依赖](#安装-mmdeploy-sdk-依赖)
       - [安装推理引擎](#安装推理引擎)
     - [编译 MMDeploy](#编译-mmdeploy)
-      - [编译选项说明](#编译选项说明)
+      - [编译 Model Converter](#编译-model-converter)
-      - [编译安装 Model Converter](#编译安装-model-converter)
-        - [编译自定义算子](#编译自定义算子)
       - [安装 Model Converter](#安装-model-converter)
       - [编译 SDK 和 Demos](#编译-sdk-和-demos)
     - [注意事项](#注意事项)
@@ -56,12 +54,13 @@ pip install torch==1.8.0+cu111 torchvision==0.9.0+cu111 torchaudio==0.8.0 -f htt
     </td>
   </tr>
   <tr>
-    <td>mmcv-full </td>
+    <td>mmcv </td>
-    <td>参考如下命令安装 mmcv-full。更多安装方式，可查看 <a href="https://github.com/open-mmlab/mmcv">mmcv 官网</a><br>
+    <td>参考如下命令安装 mmcv。更多安装方式，可查看 <a href="https://github.com/open-mmlab/mmcv/tree/2.x#installation">mmcv 官网</a><br>
 <pre><code>
 $env:cu_version="cu111"
 $env:torch_version="torch1.8"
-pip install mmcv-full==1.4.0 -f https://download.openmmlab.com/mmcv/dist/$env:cu_version/$env:torch_version/index.html
+pip install -U openmim
+mim install "mmcv>=2.0.0rc1"
 </code></pre>
     </td>
   </tr>

docs/zh_cn/02-how-to-run/convert_model.md

@@ -26,7 +26,7 @@
 ### 准备工作
 
 1. 安装您的目标后端。 您可以参考 [ONNXRuntime-install](../05-supported-backends/onnxruntime.md) ，[TensorRT-install](../05-supported-backends/tensorrt.md) ，[ncnn-install](../05-supported-backends/ncnn.md) ，[PPLNN-install](../05-supported-backends/pplnn.md), [OpenVINO-install](../05-supported-backends/openvino.md)。
-2. 安装您的目标代码库。 您可以参考 [MMClassification-install](https://github.com/open-mmlab/mmclassification/blob/master/docs/zh_CN/install.md)， [MMDetection-install](https://github.com/open-mmlab/mmdetection/blob/master/docs/zh_cn/get_started.md)， [MMSegmentation-install](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/get_started.md#installation)， [MMOCR-install](https://mmocr.readthedocs.io/zh_CN/latest/install.html)， [MMEditing-install](https://github.com/open-mmlab/mmediting/blob/master/docs/zh_cn/install.md)。
+2. 安装您的目标代码库。 您可以参考 [MMClassification-install](https://github.com/open-mmlab/mmclassification/blob/1.x/docs/zh_CN/get_started.md#%E5%AE%89%E8%A3%85)， [MMDetection-install](https://github.com/open-mmlab/mmdetection/blob/3.x/docs/zh_cn/get_started.md)， [MMSegmentation-install](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/zh_cn/get_started.md#installation)， [MMOCR-install](https://github.com/open-mmlab/mmocr/blob/1.x/docs/zh_cn/get_started/install.md)， [MMEditing-install](https://github.com/open-mmlab/mmediting/blob/1.x/docs/en/2_get_started.md#installation)。
 
 ### 使用方法
 
@@ -70,8 +70,8 @@ python ./tools/deploy.py \
 ```bash
 python ./tools/deploy.py \
     configs/mmdet/detection/detection_tensorrt_dynamic-320x320-1344x1344.py \
-    $PATH_TO_MMDET/configs/yolo/yolov3_d53_mstrain-608_273e_coco.py \
+    $PATH_TO_MMDET/configs/yolo/yolov3_d53_8xb8-ms-608-273e_coco.py \
-    $PATH_TO_MMDET/checkpoints/yolo/yolov3_d53_mstrain-608_273e_coco.pth \
+    $PATH_TO_MMDET/checkpoints/yolo/yolov3_d53_mstrain-608_273e_coco_20210518_115020-a2c3acb8.pth \
     $PATH_TO_MMDET/demo/demo.jpg \
     --work-dir work_dir \
     --show \

docs/zh_cn/02-how-to-run/prebuilt_package_windows.md

@@ -55,7 +55,7 @@ ______________________________________________________________________
 2. 克隆mmdeploy仓库
 
    ```bash
-   git clone https://github.com/open-mmlab/mmdeploy.git
+   git clone -b 1.x https://github.com/open-mmlab/mmdeploy.git
    ```
 
    :point_right: 这里主要为了使用configs文件，所以没有加`--recursive`来下载submodule，也不需要编译`mmdeploy`
@@ -63,14 +63,14 @@ ______________________________________________________________________
 3. 安装mmclassification
 
    ```bash
-   git clone https://github.com/open-mmlab/mmclassification.git
+   git clone -b 1.x https://github.com/open-mmlab/mmclassification.git
    cd mmclassification
    pip install -e .
    ```
 
 4. 准备一个PyTorch的模型文件当作我们的示例
 
-   这里选择了[resnet18_8xb32_in1k_20210831-fbbb1da6.pth](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_8xb32_in1k_20210831-fbbb1da6.pth)，对应的训练config为[resnet18_8xb32_in1k.py](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet18_8xb32_in1k.py)
+   这里选择了[resnet18_8xb32_in1k_20210831-fbbb1da6.pth](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_8xb32_in1k_20210831-fbbb1da6.pth)，对应的训练config为[resnet18_8xb32_in1k.py](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/resnet/resnet18_8xb32_in1k.py)
 
 做好以上工作后，当前工作目录的结构应为：
 

docs/zh_cn/02-how-to-run/write_config.md

@@ -6,7 +6,7 @@
 
 <!-- TOC -->
 
-- [如何编写配置文件](#如何编写配置文件)
+- [如何写模型转换配置](#如何写模型转换配置)
   - [1. 如何编写ONNX配置](#1-如何编写onnx配置)
     - [ONNX配置参数说明](#onnx配置参数说明)
       - [示例](#示例)
@@ -21,8 +21,6 @@
   - [5. 部署配置文件命名规则](#5-部署配置文件命名规则)
     - [示例](#示例-4)
   - [6. 如何编写模型配置文件](#6-如何编写模型配置文件)
-  - [7. 注意事项](#7-注意事项)
-  - [8. 常见问题](#8-常见问题)
 
 <!-- TOC -->
 
@@ -106,7 +104,7 @@ codebase_config = dict(type='mmcls', task='Classification')
 
 ## 3. 如何编写推理框架配置
 
-推理框架配置主要用于指定模型运行在哪个推理框架，并提供模型在推理框架运行时所需的信息，具体参考 [ONNX Runtime](https://github.com/open-mmlab/mmdeploy/blob/master/docs/en/backends/onnxruntime.md)， [TensorRT](https://github.com/open-mmlab/mmdeploy/blob/master/docs/en/backends/tensorrt.md)， [ncnn](https://github.com/open-mmlab/mmdeploy/blob/master/docs/en/backends/ncnn.md)， [PPLNN](https://github.com/open-mmlab/mmdeploy/blob/master/docs/en/backends/pplnn.md)。
+推理框架配置主要用于指定模型运行在哪个推理框架，并提供模型在推理框架运行时所需的信息，具体参考 [ONNX Runtime](../05-supported-backends/onnxruntime.md)， [TensorRT](../05-supported-backends/tensorrt.md)， [ncnn](../05-supported-backends/ncnn.md)， [PPLNN](../05-supported-backends/pplnn.md)。
 
 - `type`: 模型推理框架, 包括 `onnxruntime`， `ncnn`， `pplnn`， `tensorrt`， `openvino`。
 
@@ -189,4 +187,4 @@ detection_tensorrt-int8_dynamic-320x320-1344x1344.py
 
 ## 6. 如何编写模型配置文件
 
-请根据模型具体任务的代码库，编写模型配置文件。 模型配置文件用于初始化模型，详情请参考[MMClassification](https://github.com/open-mmlab/mmclassification/blob/master/docs/zh_CN/tutorials/config.md)，[MMDetection](https://github.com/open-mmlab/mmdetection/blob/master/docs/zh_cn/tutorials/config.md)， [MMSegmentation](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/tutorials/config.md)， [MMOCR](https://github.com/open-mmlab/mmocr/blob/main/docs/en/tutorials/config.md)，[MMEditing](https://github.com/open-mmlab/mmediting/blob/master/docs/zh_cn/config.md)。
+请根据模型具体任务的代码库，编写模型配置文件。 模型配置文件用于初始化模型，详情请参考[MMClassification](https://github.com/open-mmlab/mmclassification/blob/1.x/docs/zh_CN/user_guides/config.md)，[MMDetection](https://github.com/open-mmlab/mmdetection/blob/3.x/docs/zh_cn/user_guides/config.md)， [MMSegmentation](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/zh_cn/user_guides/1_config.md)， [MMOCR](https://github.com/open-mmlab/mmocr/blob/1.x/docs/en/user_guides/config.md)，[MMEditing](https://github.com/open-mmlab/mmediting/blob/1.x/docs/en/user_guides/1_config.md)。

docs/zh_cn/07-developer-guide/partition_model.md

@@ -6,7 +6,7 @@ MMDeploy 支持将PyTorch模型导出到onnx模型并进行拆分得到多个onn
 
 为了进行图拆分，我们定义了`Mark`类型op，标记模型导出的边界。在实现方法上，采用`mark`装饰器对函数的输入、输出`Tensor`打标记。需要注意的是，我们的标记函数需要在某个重写函数中执行才能生效。
 
-为了对YOLOV3进行拆分，首先我们需要标记模型的输入。这里为了通用性，我们标记检测器父类`BaseDetector`的`forward`方法中的`img` `Tensor`，同时为了支持其他拆分方案，也对`forward`函数的输出进行了标记，分别是`dets`, `labels`和`masks`。下面的代码是截图[mmdeploy/codebase/mmdet/models/detectors/base.py](https://github.com/open-mmlab/mmdeploy/blob/86a50e343a3a45d7bc2ba3256100accc4973e71d/mmdeploy/codebase/mmdet/models/detectors/base.py)中的一部分，可以看出我们使用`mark`装饰器标记了`__forward_impl`函数的输入输出，并在重写函数`base_detector__forward`进行了调用，从而完成了对检测器输入的标记。
+为了对YOLOV3进行拆分，首先我们需要标记模型的输入。这里为了通用性，我们标记检测器父类`BaseDetector`的`forward`方法中的`img` `Tensor`，同时为了支持其他拆分方案，也对`forward`函数的输出进行了标记，分别是`dets`, `labels`和`masks`。下面的代码是截图[mmdeploy/codebase/mmdet/models/detectors/single_stage.py](https://github.com/open-mmlab/mmdeploy/blob/4fc8828af84281b62be143012cd9f9dafd1e7cc2/mmdeploy/codebase/mmdet/models/detectors/single_stage.py)中的一部分，可以看出我们使用`mark`装饰器标记了`__forward_impl`函数的输入输出，并在重写函数`base_detector__forward`进行了调用，从而完成了对检测器输入的标记。
 
 ```python
 from mmdeploy.core import FUNCTION_REWRITER, mark
@@ -25,7 +25,7 @@ def base_detector__forward(ctx, self, img, img_metas=None, **kwargs):
     return __forward_impl(...)
 ```
 
-接下来，我们只需要对`YOLOV3Head`中最后一层输出特征`Tensor`进行标记就可以将整个`YOLOV3`模型拆分成两部分。通过查看`mmdet`源码我们可以知道`YOLOV3Head`的`get_bboxes`方法中输入参数`pred_maps`就是我们想要的拆分点，因此可以在重写函数[`yolov3_head__get_bboxes`](https://github.com/open-mmlab/mmdeploy/blob/86a50e343a3a45d7bc2ba3256100accc4973e71d/mmdeploy/codebase/mmdet/models/dense_heads/yolo_head.py#L14)中添加内部函数对`pred_mapes`进行标记，具体参考如下示例代码。值得注意的是，输入参数`pred_maps`是由三个`Tensor`组成的列表，所以我们在onnx模型中添加了三个`Mark`标记节点。
+接下来，我们只需要对`YOLOV3Head`中最后一层输出特征`Tensor`进行标记就可以将整个`YOLOV3`模型拆分成两部分。通过查看`mmdet`源码我们可以知道`YOLOV3Head`的`get_bboxes`方法中输入参数`pred_maps`就是我们想要的拆分点，因此可以在重写函数[`yolov3_head__get_bboxes`](https://github.com/open-mmlab/mmdeploy/blob/4fc8828af84281b62be143012cd9f9dafd1e7cc2/mmdeploy/codebase/mmdet/models/dense_heads/yolo_head.py#L16)中添加内部函数对`pred_mapes`进行标记，具体参考如下示例代码。值得注意的是，输入参数`pred_maps`是由三个`Tensor`组成的列表，所以我们在onnx模型中添加了三个`Mark`标记节点。
 
 ```python
 from mmdeploy.core import FUNCTION_REWRITER, mark
@@ -76,7 +76,7 @@ partition_config = dict(
 ```shell
 python tools/torch2onnx.py \
 configs/mmdet/detection/yolov3_partition_onnxruntime_static.py \
-../mmdetection/configs/yolo/yolov3_d53_mstrain-608_273e_coco.py \
+../mmdetection/configs/yolo/yolov3_d53_8xb8-ms-608-273e_coco.py \
 https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_mstrain-608_273e_coco/yolov3_d53_mstrain-608_273e_coco_20210518_115020-a2c3acb8.pth \
 ../mmdetection/demo/demo.jpg \
 --work-dir ./work-dirs/mmdet/yolov3/ort/partition

docs/zh_cn/07-developer-guide/support_new_model.md

@@ -24,7 +24,7 @@ def repeat_static(ctx, input, *size):
 - `backend`是推理引擎。当模型被导入到引擎的时候，函数会被重载。如果没有给出，重载默认的参数就是重载的参数。如果后端的重载的参数不存在，将会按照预设的默认模式进行重载。
   当参数与原始的参数相同时，除了把上下文信息`ctx` 作为第一的参数外，上下文也提供了一些有用的信息，例如:部署的配置`ctx.cfg` 和原始的函数（已经被重载）`ctx.origin_func`。
 
-可参照[这些样例代码](https://github.com/open-mmlab/mmdeploy/blob/master/mmdeploy/codebase/mmcls/models/backbones/shufflenet_v2.py)。
+可参照[这些样例代码](https://github.com/open-mmlab/mmdeploy/blob/4d8ea40f55ae9a7ab39d208870fb791de9d46bf9/mmdeploy/codebase/mmcls/models/backbones/shufflenet_v2.py)。
 
 ## 模型重载器
 
@@ -87,4 +87,4 @@ def squeeze_default(ctx, g, self, dim=None):
 
 就像函数重载器的`ctx`一样，第一个参数会提供上下文信息。上下文中了一些有用的信息，例如部署配置ctx.cfg和原始功能（已被重载）`ctx.origin_func`。请注意， `ctx.origin_func`只能在`is_pytorch==False`时使用。
 
-[这里](https://github.com/open-mmlab/mmdeploy/tree/master/mmdeploy/pytorch/ops)有很多实现可参考。
+[这里](https://github.com/open-mmlab/mmdeploy/tree/6420e2044515ff2052960c0f8bb9e351e6a7f2c2/mmdeploy/pytorch/symbolics)有很多实现可参考。