open-mmlab · lvhan028 · Jan 4, 2023 · Dec 30, 2022 · Jan 3, 2023 · Jan 3, 2023
diff --git a/docker/CPU/Dockerfile b/docker/CPU/Dockerfile
@@ -1,5 +1,5 @@
 FROM openvino/ubuntu18_dev:2021.4.2
-ARG PYTHON_VERSION=3.7
+ARG PYTHON_VERSION=3.8
 ARG TORCH_VERSION=1.10.0
 ARG TORCHVISION_VERSION=0.11.0
 ARG ONNXRUNTIME_VERSION=1.8.1
@@ -114,4 +114,4 @@ RUN cd mmdeploy && rm -rf build/CM* && mkdir -p build && cd build && cmake .. \
     -DMMDEPLOY_CODEBASES=all &&\
     cmake --build . -- -j$(nproc) && cmake --install . &&\
     export SPDLOG_LEVEL=warn &&\
-    if [ -z ${VERSION} ] ; then echo "Built MMDeploy master for CPU devices successfully!" ; else echo "Built MMDeploy version v${VERSION} for CPU devices successfully!" ; fi
+    if [ -z ${VERSION} ] ; then echo "Built MMDeploy 1.x for CPU devices successfully!" ; else echo "Built MMDeploy version v${VERSION} for CPU devices successfully!" ; fi
diff --git a/docker/GPU/Dockerfile b/docker/GPU/Dockerfile
@@ -101,6 +101,6 @@ RUN cd /root/workspace/mmdeploy &&\
         -DMMDEPLOY_CODEBASES=all &&\
     make -j$(nproc) && make install &&\
     export SPDLOG_LEVEL=warn &&\
-    if [ -z ${VERSION} ] ; then echo "Built MMDeploy master for GPU devices successfully!" ; else echo "Built MMDeploy version v${VERSION} for GPU devices successfully!" ; fi
+    if [ -z ${VERSION} ] ; then echo "Built MMDeploy dev-1.x for GPU devices successfully!" ; else echo "Built MMDeploy version v${VERSION} for GPU devices successfully!" ; fi
 
 ENV LD_LIBRARY_PATH="/root/workspace/mmdeploy/build/lib:${BACKUP_LD_LIBRARY_PATH}"
diff --git a/docs/en/01-how-to-build/linux-x86_64.md b/docs/en/01-how-to-build/linux-x86_64.md
@@ -75,7 +75,8 @@ conda install pytorch==1.8.0 torchvision==0.9.0 cudatoolkit=11.1 -c pytorch -c c
 export cu_version=cu111 # cuda 11.1
 export torch_version=torch1.8
 pip install -U openmim
-mim install "mmcv>=2.0.0rc1"
+mim install mmengine
+mim install "mmcv>=2.0.0rc2"
 </code></pre>
     </td>
   </tr>
@@ -326,7 +327,7 @@ Please check [cmake build option](cmake_option.md).
 
 ```bash
 cd ${MMDEPLOY_DIR}
-pip install -e .
+mim install -e .
 ```
 
 **Note**

diff --git a/docs/en/01-how-to-build/macos-arm64.md b/docs/en/01-how-to-build/macos-arm64.md
@@ -37,7 +37,8 @@ Please refer to [get_started](../get_started.md) to install conda.
 # install pytorch & mmcv
 conda install pytorch==1.9.0 torchvision==0.10.0 -c pytorch
 pip install -U openmim
-mim install "mmcv>=2.0.0rc1"
+mim install mmengine
+mim install "mmcv>=2.0.0rc2"
 ```
 
 ### Install Dependencies for SDK
@@ -146,7 +147,7 @@ conda install grpcio
 
 ```bash
 cd ${MMDEPLOY_DIR}
-pip install -v -e .
+mim install -v -e .
 ```
 
 **Note**

diff --git a/docs/en/get_started.md b/docs/en/get_started.md
@@ -64,6 +64,7 @@ We recommend that users follow our best practices installing MMDeploy.
 
 ```shell
 pip install -U openmim
+mim install mmengine
 mim install "mmcv>=2.0.0rc2"
 ```
 
@@ -172,12 +173,12 @@ Based on the above settings, we provide an example to convert the Faster R-CNN i
 
 ```shell
 # clone mmdeploy to get the deployment config. `--recursive` is not necessary
-git clone https://github.com/open-mmlab/mmdeploy.git
+git clone -b dev-1.x https://github.com/open-mmlab/mmdeploy.git
 
 # clone mmdetection repo. We have to use the config file to build PyTorch nn module
-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 -v -e .
+mim install -v -e .
 cd ..
 
 # download Faster R-CNN checkpoint
@@ -186,7 +187,7 @@ wget -P checkpoints https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/
 # run the command to start model conversion
 python mmdeploy/tools/deploy.py \
     mmdeploy/configs/mmdet/detection/detection_tensorrt_dynamic-320x320-1344x1344.py \
-    mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \
+    mmdetection/configs/faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py \
     checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \
     mmdetection/demo/demo.jpg \
     --work-dir mmdeploy_model/faster-rcnn \
@@ -201,7 +202,7 @@ For more details about model conversion, you can read [how_to_convert_model](02-
 
 ```{tip}
 If MMDeploy-ONNXRuntime prebuilt package is installed, you can convert the above model to onnx model and perform ONNX Runtime inference
-just by 'changing detection_tensorrt_dynamic-320x320-1344x1344.py' to 'detection_onnxruntime_dynamic.py' and making '--device' as 'cpu'.
+just by changing 'detection_tensorrt_dynamic-320x320-1344x1344.py' to 'detection_onnxruntime_dynamic.py' and making '--device' as 'cpu'.
 ```
 
 ## Inference Model

diff --git a/docs/zh_cn/01-how-to-build/linux-x86_64.md b/docs/zh_cn/01-how-to-build/linux-x86_64.md
@@ -76,7 +76,8 @@ conda install pytorch==1.8.0 torchvision==0.9.0 cudatoolkit=11.1 -c pytorch -c c
 export cu_version=cu111 # cuda 11.1
 export torch_version=torch1.8
 pip install -U openmim
-mim install "mmcv>=2.0.0rc1"
+mim install mmengine
+mim install "mmcv>=2.0.0rc2"
 </code></pre>
     </td>
   </tr>
@@ -323,7 +324,7 @@ export MMDEPLOY_DIR=$(pwd)
 
 ```bash
 cd ${MMDEPLOY_DIR}
-pip install -e .
+mim install -e .
 ```
 
 **注意**

diff --git a/docs/zh_cn/01-how-to-build/macos-arm64.md b/docs/zh_cn/01-how-to-build/macos-arm64.md
@@ -40,7 +40,8 @@
 # install pytoch & mmcv
 conda install pytorch==1.9.0 torchvision==0.10.0 -c pytorch
 pip install -U openmim
-mim install "mmcv>=2.0.0rc1"
+mim install mmengine
+mim install "mmcv>=2.0.0rc2"
 ```
 
 #### 安装 MMDeploy SDK 依赖
@@ -147,7 +148,7 @@ conda install grpcio
 
 ```bash
 cd ${MMDEPLOY_DIR}
-pip install -v -e .
+mim install -v -e .
 ```
 
 **注意**

diff --git a/docs/zh_cn/get_started.md b/docs/zh_cn/get_started.md
@@ -167,13 +167,13 @@ export LD_LIBRARY_PATH=$CUDNN_DIR/lib64:$LD_LIBRARY_PATH
 以 [MMDetection](https://github.com/open-mmlab/mmdetection) 中的 `Faster R-CNN` 为例，我们可以使用如下命令，将 PyTorch 模型转换为 TenorRT 模型，从而部署到 NVIDIA GPU 上.
 
 ```shell
-# 克隆 mmdeploy 仓库。转换时，需要使用 mmdeploy 仓库中的配置文件，建立转换流水线
-git clone --recursive https://github.com/open-mmlab/mmdeploy.git
+# 克隆 mmdeploy 仓库。转换时，需要使用 mmdeploy 仓库中的配置文件，建立转换流水线, `--recursive` 不是必须的
+git clone -b dev-1.x --recursive https://github.com/open-mmlab/mmdeploy.git
 
 # 安装 mmdetection。转换时，需要使用 mmdetection 仓库中的模型配置文件，构建 PyTorch nn module
-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 -v -e .
+mim install -v -e .
 cd ..
 
 # 下载 Faster R-CNN 模型权重
@@ -182,7 +182,7 @@ wget -P checkpoints https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/
 # 执行转换命令，实现端到端的转换
 python mmdeploy/tools/deploy.py \
     mmdeploy/configs/mmdet/detection/detection_tensorrt_dynamic-320x320-1344x1344.py \
-    mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \
+    mmdetection/configs/faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py \
     checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \
     mmdetection/demo/demo.jpg \
     --work-dir mmdeploy_model/faster-rcnn \