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[Enhancement] Switch pip to mim in Docs and Dockerfile (#1591)
* change pip install to mim install in docs and Dockerfil * merge with latest dev-1.x
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‎docker/CPU/Dockerfile

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@@ -1,5 +1,5 @@
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FROM openvino/ubuntu18_dev:2021.4.2
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ARG PYTHON_VERSION=3.7
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ARG PYTHON_VERSION=3.8
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ARG TORCH_VERSION=1.10.0
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ARG TORCHVISION_VERSION=0.11.0
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ARG ONNXRUNTIME_VERSION=1.8.1
@@ -114,4 +114,4 @@ RUN cd mmdeploy && rm -rf build/CM* && mkdir -p build && cd build && cmake .. \
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-DMMDEPLOY_CODEBASES=all &&\
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cmake --build . -- -j$(nproc) && cmake --install . &&\
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export SPDLOG_LEVEL=warn &&\
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if [ -z ${VERSION} ] ; then echo "Built MMDeploy master for CPU devices successfully!" ; else echo "Built MMDeploy version v${VERSION} for CPU devices successfully!" ; fi
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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

‎docker/GPU/Dockerfile

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@@ -101,6 +101,6 @@ RUN cd /root/workspace/mmdeploy &&\
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-DMMDEPLOY_CODEBASES=all &&\
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make -j$(nproc) && make install &&\
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export SPDLOG_LEVEL=warn &&\
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if [ -z ${VERSION} ] ; then echo "Built MMDeploy master for GPU devices successfully!" ; else echo "Built MMDeploy version v${VERSION} for GPU devices successfully!" ; fi
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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
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ENV LD_LIBRARY_PATH="/root/workspace/mmdeploy/build/lib:${BACKUP_LD_LIBRARY_PATH}"

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

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@@ -75,7 +75,8 @@ conda install pytorch==1.8.0 torchvision==0.9.0 cudatoolkit=11.1 -c pytorch -c c
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export cu_version=cu111 # cuda 11.1
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export torch_version=torch1.8
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pip install -U openmim
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mim install "mmcv>=2.0.0rc1"
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mim install mmengine
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mim install "mmcv>=2.0.0rc2"
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</code></pre>
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</td>
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</tr>
@@ -326,7 +327,7 @@ Please check [cmake build option](cmake_option.md).
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```bash
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cd ${MMDEPLOY_DIR}
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pip install -e .
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mim install -e .
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```
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**Note**

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

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@@ -37,7 +37,8 @@ Please refer to [get_started](../get_started.md) to install conda.
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# install pytorch & mmcv
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conda install pytorch==1.9.0 torchvision==0.10.0 -c pytorch
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pip install -U openmim
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mim install "mmcv>=2.0.0rc1"
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mim install mmengine
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mim install "mmcv>=2.0.0rc2"
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```
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### Install Dependencies for SDK
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```bash
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cd ${MMDEPLOY_DIR}
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pip install -v -e .
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mim install -v -e .
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```
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**Note**

‎docs/en/get_started.md

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@@ -64,6 +64,7 @@ We recommend that users follow our best practices installing MMDeploy.
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```shell
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pip install -U openmim
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mim install mmengine
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mim install "mmcv>=2.0.0rc2"
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```
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@@ -172,12 +173,12 @@ Based on the above settings, we provide an example to convert the Faster R-CNN i
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```shell
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# clone mmdeploy to get the deployment config. `--recursive` is not necessary
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git clone https://github.com/open-mmlab/mmdeploy.git
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git clone -b dev-1.x https://github.com/open-mmlab/mmdeploy.git
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# clone mmdetection repo. We have to use the config file to build PyTorch nn module
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git clone https://github.com/open-mmlab/mmdetection.git
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git clone -b 3.x https://github.com/open-mmlab/mmdetection.git
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cd mmdetection
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pip install -v -e .
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mim install -v -e .
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cd ..
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# download Faster R-CNN checkpoint
@@ -186,7 +187,7 @@ wget -P checkpoints https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/
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# run the command to start model conversion
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python mmdeploy/tools/deploy.py \
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mmdeploy/configs/mmdet/detection/detection_tensorrt_dynamic-320x320-1344x1344.py \
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mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \
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mmdetection/configs/faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py \
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checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \
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mmdetection/demo/demo.jpg \
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--work-dir mmdeploy_model/faster-rcnn \
@@ -201,7 +202,7 @@ For more details about model conversion, you can read [how_to_convert_model](02-
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```{tip}
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If MMDeploy-ONNXRuntime prebuilt package is installed, you can convert the above model to onnx model and perform ONNX Runtime inference
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just by 'changing detection_tensorrt_dynamic-320x320-1344x1344.py' to 'detection_onnxruntime_dynamic.py' and making '--device' as 'cpu'.
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just by changing 'detection_tensorrt_dynamic-320x320-1344x1344.py' to 'detection_onnxruntime_dynamic.py' and making '--device' as 'cpu'.
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```
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## Inference Model

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

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

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

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# install pytoch & mmcv
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conda install pytorch==1.9.0 torchvision==0.10.0 -c pytorch
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pip install -U openmim
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mim install "mmcv>=2.0.0rc1"
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mim install mmengine
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mim install "mmcv>=2.0.0rc2"
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```
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#### 安装 MMDeploy SDK 依赖
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```bash
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cd ${MMDEPLOY_DIR}
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pip install -v -e .
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mim install -v -e .
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```
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**注意**

‎docs/zh_cn/get_started.md

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@@ -167,13 +167,13 @@ export LD_LIBRARY_PATH=$CUDNN_DIR/lib64:$LD_LIBRARY_PATH
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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.
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```shell
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# 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
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git clone --recursive https://github.com/open-mmlab/mmdeploy.git
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# 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
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git clone -b dev-1.x --recursive https://github.com/open-mmlab/mmdeploy.git
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# 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
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git clone https://github.com/open-mmlab/mmdetection.git
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git clone -b 3.x https://github.com/open-mmlab/mmdetection.git
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cd mmdetection
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pip install -v -e .
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mim install -v -e .
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cd ..
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# 下载 Faster R-CNN 模型权重
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# 执行转换命令，实现端到端的转换
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python mmdeploy/tools/deploy.py \
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mmdeploy/configs/mmdet/detection/detection_tensorrt_dynamic-320x320-1344x1344.py \
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mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \
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mmdetection/configs/faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py \
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checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \
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mmdetection/demo/demo.jpg \
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--work-dir mmdeploy_model/faster-rcnn \

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