[MMSIG] Support mmdeploy Docker for Jetson

README.md

@@ -284,6 +284,7 @@ Please read [getting_started](docs/en/get_started.md) for the basic usage of MMD
   - [Build for Win10](docs/en/01-how-to-build/windows.md)
   - [Build for Android](docs/en/01-how-to-build/android.md)
   - [Build for Jetson](docs/en/01-how-to-build/jetsons.md)
+  - [BUild for Jetson Docker](docs/en/01-how-to-build/Jetson_docker.md)
   - [Build for SNPE](docs/en/01-how-to-build/snpe.md)
   - [Cross Build for aarch64](docs/en/01-how-to-build/cross_build_ncnn_aarch64.md)
 - User Guide

README_zh-CN.md

@@ -268,6 +268,7 @@ MMDeploy 是 [OpenMMLab](https://openmmlab.com/) 模型部署工具箱，**为
   - [Build for Win10](docs/zh_cn/01-how-to-build/windows.md)
   - [Build for Android](docs/zh_cn/01-how-to-build/android.md)
   - [Build for Jetson](docs/zh_cn/01-how-to-build/jetsons.md)
+  - [BUild for Jetson Docker](docs/zh_cn/01-how-to-build/Jetson_docker.md)
   - [Build for SNPE](docs/zh_cn/01-how-to-build/snpe.md)
   - [Cross Build for aarch64](docs/zh_cn/01-how-to-build/cross_build_ncnn_aarch64.md)
 - 使用

docker/Jetson/Jetpack4.6/Dockerfile

@@ -0,0 +1,72 @@
+FROM nvcr.io/nvidia/l4t-pytorch:r32.7.1-pth1.10-py3
+
+ARG MMDEPLOY_VERSION=main
+ENV NVIDIA_VISIBLE_DEVICE all
+ENV NVIDIA_DRIVER_CAPABILITIES all
+ENV CUDA_HOME="/usr/local/cuda"
+ENV PATH="/usr/local/cuda/bin:${PATH}"
+ENV LD_LIBRARY_PATH="/usr/local/cuda/lib64:/usr/local/lib/python3.6/site-packages/opencv-python.libs/${LD_LIBRARY_PATH}"
+ENV TENSORRT_DIR="/usr/include/aarch64-linux-gnu"
+
+ENV DEBIAN_FRONTEND=nointeractive
+ENV FORCE_CUDA="1"
+
+USER root
+WORKDIR /root/workspace
+
+# install dependencies
+RUN  apt-key adv --keyserver keyserver.ubuntu.com --recv-keys 42D5A192B819C5DA &&\
+     apt-get update &&\ 
+     apt-get install -y vim wget libspdlog-dev libssl-dev libpng-dev pkg-config libhdf5-100 libhdf5-dev --no-install-recommends
+
+RUN python3 -m pip install --upgrade pip &&\    
+    python3 -m pip install onnx==1.10 versioned-hdf5
+
+# install onnxruntime
+RUN wget https://nvidia.box.com/shared/static/jy7nqva7l88mq9i8bw3g3sklzf4kcnn2.whl -O onnxruntime_gpu-1.10.0-cp36-cp36m-linux_aarch64.whl &&\
+    python3 -m pip install --no-cache-dir onnxruntime_gpu-1.10.0-cp36-cp36m-linux_aarch64.whl
+
+# install mmcv
+RUN git clone --branch 2.x https://github.com/open-mmlab/mmcv.git
+RUN cd mmcv &&\ 
+    python3 -m pip install --no-cache-dir opencv-python==4.5.4.60 &&\
+    MMCV_WITH_OPS=1 python3 -m pip install -e .
+
+# build ppl.cv
+RUN git clone https://github.com/openppl-public/ppl.cv.git &&\
+    echo "export PPLCV_DIR=/root/workspace/ppl.cv" >> ~/.bashrc &&\
+    cd ppl.cv &&\
+    ./build.sh cuda
+
+# download mmdeploy
+RUN git clone --recursive -b $MMDEPLOY_VERSION --depth 1 https://github.com/open-mmlab/mmdeploy
+
+# build TRT custom op
+RUN cd mmdeploy &&\
+    mkdir -p build && cd build &&\
+    cmake .. \
+    	-DMMDEPLOY_TARGET_BACKENDS="trt" \
+    	-DTENSORRT_DIR=TENSORRT_DIR    &&\
+    make -j$(nproc) && make install
+RUN cd mmdeploy &&\ 
+    python3 -m pip install --upgrade setuptools &&\ 
+    python3 -m pip install -e .
+
+# build mmdeploy
+RUN cd mmdeploy &&\
+    mkdir -p build && cd build &&\
+    cmake .. \
+    	-DMMDEPLOY_BUILD_SDK=ON \
+    	-DMMDEPLOY_BUILD_SDK_PYTHON_API=ON \
+    	-DMMDEPLOY_BUILD_EXAMPLES=ON \
+    	-DMMDEPLOY_TARGET_DEVICES="cuda;cpu" \
+    	-DMMDEPLOY_TARGET_BACKENDS="trt" \
+    	-DTENSORRT_DIR=TENSORRT_DIR \ 
+    	-Dpplcv_DIR=/root/workspace/ppl.cv/cuda-build/install/lib/cmake/ppl \
+    	-DMMDEPLOY_CODEBASES=all && \
+    make -j$(nproc) && make install
+
+ENV MMDeploy_DIR="/root/workspace/mmdeploy/build/install/lib/cmake/MMDeploy"
+ENV LD_LIBRARY_PATH="/root/workspace/mmdeploy/build/lib:${BACKUP_LD_LIBRARY_PATH}"
+ENV PATH="/root/workspace/mmdeploy/build/bin:${PATH}"
+ENV PYTHONPATH="/root/workspace/mmdeploy:${PYTHONPATH}"

docker/Jetson/Jetpack5/Dockerfile

@@ -0,0 +1,69 @@
+FROM nvcr.io/nvidia/l4t-pytorch:r35.2.1-pth2.0-py3
+
+ARG MMDEPLOY_VERSION=main
+ENV NVIDIA_VISIBLE_DEVICE all
+ENV NVIDIA_DRIVER_CAPABILITIES all
+ENV CUDA_HOME="/usr/local/cuda"
+ENV PATH="/usr/local/cuda/bin:${PATH}"
+ENV LD_LIBRARY_PATH="/usr/local/cuda/lib64:/usr/local/lib/python3.8/site-packages/opencv-python.libs${LD_LIBRARY_PATH}"
+ENV TENSORRT_DIR="/usr/include/aarch64-linux-gnu"
+
+ENV DEBIAN_FRONTEND=nointeractive
+ENV FORCE_CUDA="1"
+
+USER root
+WORKDIR /root/workspace
+
+# install dependencies
+RUN  apt-get update &&\ 
+     apt-get install -y vim wget libspdlog-dev libssl-dev libpng-dev pkg-config libhdf5-103 libhdf5-dev --no-install-recommends
+
+# install onnx
+RUN python3 -m pip install onnx versioned-hdf5
+
+# install onnxruntime
+RUN wget https://nvidia.box.com/shared/static/mvdcltm9ewdy2d5nurkiqorofz1s53ww.whl -O onnxruntime_gpu-1.15.1.whl &&\
+    python3 -m pip install --no-cache-dir onnxruntime_gpu-1.15.1-cp38-cp38-linux_aarch64.whl
+
+# install mmcv
+RUN git clone --branch 2.x https://github.com/open-mmlab/mmcv.git &&\
+    python3 -m pip install --no-cache-dir opencv-python==4.5.4.60 opencv-contrib-python==4.5.4.60 opencv-python-headless==4.5.4.60 &&\
+    MMCV_WITH_OPS=1 python3 -m pip install -e .
+
+# build ppl.cv
+RUN git clone https://github.com/openppl-public/ppl.cv.git &&\
+    echo "export PPLCV_DIR=/root/workspace/ppl.cv" >> ~/.bashrc &&\
+    ./build.sh cuda
+
+# download mmdeploy
+RUN git clone --recursive -b $MMDEPLOY_VERSION --depth 1 https://github.com/open-mmlab/mmdeploy
+
+# build TRT custom op
+RUN cd mmdeploy &&\
+    mkdir -p build && cd build &&\
+    cmake .. \
+    	-DMMDEPLOY_TARGET_BACKENDS="trt" \
+    	-DTENSORRT_DIR=TENSORRT_DIR    &&\
+    make -j$(nproc) && make install
+RUN cd mmdeploy &&\
+    python3 -m pip install --upgrade setuptools &&\
+    python3 -m pip install -e .
+
+# build mmdeploy
+RUN cd mmdeploy &&\
+    mkdir -p build && cd build &&\
+    cmake .. \
+    	-DMMDEPLOY_BUILD_SDK=ON \
+    	-DMMDEPLOY_BUILD_SDK_PYTHON_API=ON \
+    	-DMMDEPLOY_BUILD_EXAMPLES=ON \
+    	-DMMDEPLOY_TARGET_DEVICES="cuda;cpu" \
+    	-DMMDEPLOY_TARGET_BACKENDS="trt" \
+    	-DTENSORRT_DIR=TENSORRT_DIR \ 
+    	-Dpplcv_DIR=/root/workspace/ppl.cv/cuda-build/install/lib/cmake/ppl \
+    	-DMMDEPLOY_CODEBASES=all && \
+    make -j$(nproc) && make install
+
+ENV MMDeploy_DIR="/root/workspace/mmdeploy/build/install/lib/cmake/MMDeploy"
+ENV LD_LIBRARY_PATH="/root/workspace/mmdeploy/build/lib:${BACKUP_LD_LIBRARY_PATH}"
+ENV PATH="/root/workspace/mmdeploy/build/bin:${PATH}"
+ENV PYTHONPATH="/root/workspace/mmdeploy:${PYTHONPATH}"

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

@@ -0,0 +1,130 @@
+# Use Jetson Docker Image
+
+This document guides how to install mmdeploy with [Docker](https://docs.docker.com/get-docker/) on Jetson.
+
+## Get prebuilt docker images
+
+MMDeploy provides prebuilt docker images for the convenience of its users on [Docker Hub](https://hub.docker.com/r/openmmlab/mmdeploy). The docker images are built on
+the latest and released versions. We release two docker version, for Jetpack=5.1 and Jetpack=4.6.1
+For instance, the image with tag `openmmlab/mmdeploy_jetpack5:v1` is built for Jetpack5.1 and the image with tag `openmmlab/mmdeploy_jetpack4.6.1:v1` is build for Jetpack 4.6.1.
+The specifications of the Docker Images are shown below.
+
+- jetpack5.1
+
+|    Item     |   Version   |
+| :---------: | :---------: |
+| Jetpack     |     5.1     |
+|   Python    |   3.8.10    |
+|    Torch    |    2.0.0    |
+| TorchVision |   0.15.0    |
+
+- jetpack4.6.1
+
+|    Item     |   Version   |
+| :---------: | :---------: |
+|   Jetpack   |   4.6.1     |
+|   Python    |   3.8.10    |
+|    Torch    |   1.10.0    |
+| TorchVision |   0.11.0    |
+
+- jetpack 5.1
+```shell
+export TAG=openmmlab/mmdeploy_jetpack5:v1
+docker pull $TAG
+```
+- jetpack 4.6.1
+```shell
+export TAG=openmmlab/mmdeploy_jetpack4.6:v1
+docker pull $TAG
+```
+## Build docker images (optional)
+
+If the prebuilt docker images do not meet your requirements,
+then you can build your own image by running the following script.
+The docker file is `docker/jetson/jetpack5/Dockerfile` and `docker/jetson/jetpack4.6/Dockerfile`,
+
+```shell
+sudo docker build docker/jetson/jetpack5 -t openmmlab/mmdeploy_jetpack5:v1 .
+//
+sudo docker build docker/jetson/jetpack4.6 -t openmmlab/mmdeploy_jetpack4.6:v1 .
+```
+
+## Run docker container
+
+After pulling or building the docker image, you can use `docker run` to launch the docker service:
+
+```shell
+sudo docker run -it --rm --runtime nvidia --network host openmmlab/mmdeploy_jetpack5:v1
+//
+sudo docker run -it --rm --runtime nvidia --network host openmmlab/mmdeploy_jetpack4.6:v1
+```
+
+## TroubleShooting
+If you using the jetpack5, it has some question need to solve.
+1. OpenCV problem
+  if you find import cv2 wrong, can't find the libpng15.so
+```shell
+  ln -s /usr/local/lib/python3.x/dist-packages/opencv-python.libs/* /usr/lib
+```
+
+2. mmdetection problem
+  if you find installed the mmdetection, but import the mmdet failed. you should add the mmdet path to PYTHONPATH
+```shell
+  export PYTHONPATH=$PYTHONPATH/your/mmdetection/you/git/clone
+```
+
+3. Jetson No distributed problem
+  if you convert the model like [Jetson.md](https://github.com/open-mmlab/mmdeploy/blob/main/docs/en/01-how-to-build/jetsons.md)
+  you may find torch.distributed has no attribute ReduceOp.
+  I just issue and make a simple patch, add file jetson_patch.py on ./mmdeploy/tools/
+```python
+import torch.distributed
+if not torch.distributed.is_available():
+  torch.distributed.ReduceOp = lambda: None
+```
+  and import jetson_patch at the beginning which file you want.
+  I know is not quietly ellegant, but it works well...(for Jetson AGX Orin)
+4. Jetpack with PyTorch 2.0 has some issue
+  we need to modify torch.onnx._run_symbolic_method 
+  **from**
+```python
+def _run_symbolic_method(g, op_name, symbolic_fn, args):
+    r"""
+    This trampoline function gets invoked for every symbolic method
+    call from C++.
+    """
+    try:
+        return symbolic_fn(g, *args)
+    except TypeError as e:
+        # Handle the specific case where we didn't successfully dispatch
+        # to symbolic_fn.  Otherwise, the backtrace will have the clues
+        # you need.
+        e.args = ("{} (occurred when translating {})".format(e.args[0], op_name),)
+        raise
+```
+  **to**
+```python
+@_beartype.beartype
+def _run_symbolic_method(g, op_name, symbolic_fn, args):
+    r"""
+    This trampoline function gets invoked for every symbolic method
+    call from C++.
+    """
+    try:
+        graph_context = jit_utils.GraphContext(
+            graph=g,
+            block=g.block(),
+            opset=GLOBALS.export_onnx_opset_version,
+            original_node=None,  # type: ignore[arg-type]
+            params_dict=_params_dict,
+            env={},
+        )
+        return symbolic_fn(graph_context, *args)
+    except TypeError as e:
+        # Handle the specific case where we didn't successfully dispatch
+        # to symbolic_fn.  Otherwise, the backtrace will have the clues
+        # you need.
+        e.args = (f"{e.args[0]} (occurred when translating {op_name})",)
+        raise
+  ```
+Finally we can use Jetpack5.1 && MMDeploy happily:)