mmdeploy框架轉(zhuǎn)化模型

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1 從源碼編譯

參考鏈接
reference2

git clone -b main https://github.com/open-mmlab/mmdeploy.git --recursive
cd mmdeploy

1.1 安裝onnxruntime后端

python 的onnx后端安裝.

pip install onnxruntime==1.8.1

注：在另外的機(jī)器上python3.11.5版本的環(huán)境下沒(méi)有onnxruntime==1.8.1，安裝了onnxruntime1.15.1,可以正常轉(zhuǎn)換模型。
c++ 的onnx庫(kù)，在你喜歡的目錄下下載解壓，設(shè)置環(huán)境變量即可。


wget https://github.com/microsoft/onnxruntime/releases/download/v1.8.1/onnxruntime-linux-x64-1.8.1.tgz
tar -zxvf onnxruntime-linux-x64-1.8.1.tgz
export ONNXRUNTIME_DIR=$(pwd)/onnxruntime-linux-x64-1.8.1
export LD_LIBRARY_PATH=$ONNXRUNTIME_DIR/lib:$LD_LIBRARY_PATH

可以用下面的方式把環(huán)境變量設(shè)為永久

echo '# set env for onnxruntime' >> ~/.bashrc
echo "export ONNXRUNTIME_DIR=${ONNXRUNTIME_DIR}" >> ~/.bashrc
echo "export LD_LIBRARY_PATH=$ONNXRUNTIME_DIR/lib:$LD_LIBRARY_PATH" >> ~/.bashrc
source ~/.bashrc

1.2 build mmdeploy

cd /the/root/path/of/MMDeploy
export MMDEPLOY_DIR=$(pwd)
mkdir -p build && cd build
cmake -DCMAKE_CXX_COMPILER=g++ -DMMDEPLOY_TARGET_BACKENDS=ort -DONNXRUNTIME_DIR=${ONNXRUNTIME_DIR} ..
make -j$(nproc) && make install

輸出

(py3) xp@hello:/media/xp/data/pydoc/mmlab/mmdeploy/build$ cmake -DCMAKE_CXX_COMPILER=g++ -DMMDEPLOY_TARGET_BACKENDS=ort -DONNXRUNTIME_DIR=${ONNXRUNTIME_DIR} ..
-- CMAKE_INSTALL_PREFIX: /media/xp/data/pydoc/mmlab/mmdeploy/build/install
-- Build ONNXRUNTIME custom ops.
-- Configuring done
-- Generating done
-- Build files have been written to: /media/xp/data/pydoc/mmlab/mmdeploy/build
(py3) xp@hello:/media/xp/data/pydoc/mmlab/mmdeploy/build$ make -j$(nproc) && make install
[ 12%] Building CXX object csrc/mmdeploy/backend_ops/onnxruntime/CMakeFiles/mmdeploy_onnxruntime_ops_obj.dir/common/ort_utils.cpp.o
[ 25%] Building CXX object csrc/mmdeploy/backend_ops/onnxruntime/CMakeFiles/mmdeploy_onnxruntime_ops_obj.dir/grid_sample/grid_sample.cpp.o
[ 37%] Building CXX object csrc/mmdeploy/backend_ops/onnxruntime/CMakeFiles/mmdeploy_onnxruntime_ops_obj.dir/modulated_deform_conv/modulated_deform_conv.cpp.o
[ 50%] Building CXX object csrc/mmdeploy/backend_ops/onnxruntime/CMakeFiles/mmdeploy_onnxruntime_ops_obj.dir/nms_match/nms_match.cpp.o
[ 62%] Building CXX object csrc/mmdeploy/backend_ops/onnxruntime/CMakeFiles/mmdeploy_onnxruntime_ops_obj.dir/nms_rotated/nms_rotated.cpp.o
[ 75%] Building CXX object csrc/mmdeploy/backend_ops/onnxruntime/CMakeFiles/mmdeploy_onnxruntime_ops_obj.dir/onnxruntime_register.cpp.o
[ 87%] Building CXX object csrc/mmdeploy/backend_ops/onnxruntime/CMakeFiles/mmdeploy_onnxruntime_ops_obj.dir/roi_align_rotated/roi_align_rotated.cpp.o
[ 87%] Built target mmdeploy_onnxruntime_ops_obj
[100%] Linking CXX shared library ../../../../lib/libmmdeploy_onnxruntime_ops.so
[100%] Built target mmdeploy_onnxruntime_ops
Consolidate compiler generated dependencies of target mmdeploy_onnxruntime_ops_obj
[ 87%] Built target mmdeploy_onnxruntime_ops_obj
[100%] Built target mmdeploy_onnxruntime_ops
Install the project...
-- Install configuration: "Release"
-- Installing: /media/xp/data/pydoc/mmlab/mmdeploy/mmdeploy/lib/libmmdeploy_onnxruntime_ops.so
-- Set runtime path of "/media/xp/data/pydoc/mmlab/mmdeploy/mmdeploy/lib/libmmdeploy_onnxruntime_ops.so" to "$ORIGIN"

1.3 install model converter

到mmdeploy的根目錄下執(zhí)行

mim install -e .

輸出

Downloading grpcio-1.62.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.6 MB)
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 5.6/5.6 MB 599.6 kB/s eta 0:00:00
Downloading multiprocess-0.70.16-py38-none-any.whl (132 kB)
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 132.6/132.6 kB 673.0 kB/s eta 0:00:00
Downloading prettytable-3.10.0-py3-none-any.whl (28 kB)
Downloading terminaltables-3.1.10-py2.py3-none-any.whl (15 kB)
Downloading dill-0.3.8-py3-none-any.whl (116 kB)
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 116.3/116.3 kB 799.7 kB/s eta 0:00:00
Downloading wcwidth-0.2.13-py2.py3-none-any.whl (34 kB)
Installing collected packages: wcwidth, aenum, terminaltables, protobuf, prettytable, grpcio, dill, multiprocess, mmdeploy
  Attempting uninstall: protobuf
    Found existing installation: protobuf 3.20.3
    Uninstalling protobuf-3.20.3:
      Successfully uninstalled protobuf-3.20.3
  Running setup.py develop for mmdeploy
Successfully installed aenum-3.1.15 dill-0.3.8 grpcio-1.62.2 mmdeploy-1.3.1 multiprocess-0.70.16 prettytable-3.10.0 protobuf-3.20.2 terminaltables-3.1.10 wcwidth-0.2.13

2 模型轉(zhuǎn)換

reference3

2.1 deploy用法

python ./tools/deploy.py \
    ${DEPLOY_CFG_PATH} \
    ${MODEL_CFG_PATH} \
    ${MODEL_CHECKPOINT_PATH} \
    ${INPUT_IMG} \
    --test-img ${TEST_IMG} \
    --work-dir ${WORK_DIR} \
    --calib-dataset-cfg ${CALIB_DATA_CFG} \
    --device ${DEVICE} \
    --log-level INFO \
    --show \
    --dump-info

參數(shù)描述
deploy_cfg : mmdeploy 針對(duì)此模型的部署配置，包含推理框架類型、是否量化、輸入 shape 是否動(dòng)態(tài)等。配置文件之間可能有引用關(guān)系，configs/mmpretrain/classification_ncnn_static.py 是一個(gè)示例。

model_cfg : mm 算法庫(kù)的模型配置，例如 mmpretrain/configs/vision_transformer/vit-base-p32_ft-64xb64_in1k-384.py，與 mmdeploy 的路徑無(wú)關(guān)。

checkpoint : torch 模型路徑?？梢?http/https 開(kāi)頭，詳見(jiàn) mmcv.FileClient 的實(shí)現(xiàn)。

img : 模型轉(zhuǎn)換時(shí)，用做測(cè)試的圖像或點(diǎn)云文件路徑。

–test-img : 用于測(cè)試模型的圖像文件路徑。默認(rèn)設(shè)置成None。

–work-dir : 工作目錄，用來(lái)保存日志和模型文件。

–calib-dataset-cfg : 此參數(shù)只有int8模式下生效，用于校準(zhǔn)數(shù)據(jù)集配置文件。若在int8模式下未傳入?yún)?shù)，則會(huì)自動(dòng)使用模型配置文件中的’val’數(shù)據(jù)集進(jìn)行校準(zhǔn)。

–device : 用于模型轉(zhuǎn)換的設(shè)備。默認(rèn)是cpu，對(duì)于 trt 可使用 cuda:0 這種形式。

–log-level : 設(shè)置日記的等級(jí)，選項(xiàng)包括’CRITICAL’， ‘FATAL’， ‘ERROR’， ‘WARN’， ‘WARNING’， ‘INFO’， ‘DEBUG’， ‘NOTSET’。默認(rèn)是INFO。

–show : 是否顯示檢測(cè)的結(jié)果。

–dump-info : 是否輸出 SDK 信息。

2.2 示例

python tools/deploy.py \
configs/mmpretrain/classification_onnxruntime_static.py \
../mmpretrain/z_my_config/my_mobilenetv3.py \
../mmpretrain/work_dirs/my_mobilenetv3/epoch_150.pth \
/media/xp/data/image/deep_image/mini_cat_and_dog/val/cat/9835.jpg

(py3) xp@hello:/media/xp/data/pydoc/mmlab/mmdeploy$ python tools/deploy.py configs/mmpretrain/classification_onnxruntime_static.py ../mmpretrain/z_my_config/my_mobilenetv3.py ../mmpretrain/work_dirs/my_mobilenetv3/epoch_150.pth /media/xp/data/image/deep_image/mini_cat_and_dog/val/cat/9835.jpg 
04/23 15:08:13 - mmengine - INFO - Start pipeline mmdeploy.apis.pytorch2onnx.torch2onnx in subprocess
04/23 15:08:13 - mmengine - WARNING - Failed to search registry with scope "mmpretrain" in the "Codebases" registry tree. As a workaround, the current "Codebases" registry in "mmdeploy" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpretrain" is a correct scope, or whether the registry is initialized.
04/23 15:08:13 - mmengine - WARNING - Failed to search registry with scope "mmpretrain" in the "mmpretrain_tasks" registry tree. As a workaround, the current "mmpretrain_tasks" registry in "mmdeploy" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpretrain" is a correct scope, or whether the registry is initialized.
/home/xp/anaconda3/envs/py3/lib/python3.8/site-packages/mmcv/cnn/bricks/hsigmoid.py:35: UserWarning: In MMCV v1.4.4, we modified the default value of args to align with PyTorch official. Previous Implementation: Hsigmoid(x) = min(max((x + 1) / 2, 0), 1). Current Implementation: Hsigmoid(x) = min(max((x + 3) / 6, 0), 1).
  warnings.warn(
Loads checkpoint by local backend from path: ../mmpretrain/work_dirs/my_mobilenetv3/epoch_150.pth
04/23 15:08:13 - mmengine - WARNING - DeprecationWarning: get_onnx_config will be deprecated in the future. 
04/23 15:08:13 - mmengine - INFO - Export PyTorch model to ONNX: /media/xp/data/pydoc/mmlab/mmdeploy/end2end.onnx.
04/23 15:08:13 - mmengine - INFO - Execute onnx optimize passes.
04/23 15:08:13 - mmengine - INFO - Finish pipeline mmdeploy.apis.pytorch2onnx.torch2onnx
04/23 15:08:13 - mmengine - INFO - Start pipeline mmdeploy.apis.utils.utils.to_backend in main process
04/23 15:08:13 - mmengine - INFO - Finish pipeline mmdeploy.apis.utils.utils.to_backend
04/23 15:08:13 - mmengine - INFO - visualize onnxruntime model start.
04/23 15:08:15 - mmengine - WARNING - Failed to search registry with scope "mmpretrain" in the "Codebases" registry tree. As a workaround, the current "Codebases" registry in "mmdeploy" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpretrain" is a correct scope, or whether the registry is initialized.
04/23 15:08:15 - mmengine - WARNING - Failed to search registry with scope "mmpretrain" in the "mmpretrain_tasks" registry tree. As a workaround, the current "mmpretrain_tasks" registry in "mmdeploy" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpretrain" is a correct scope, or whether the registry is initialized.
04/23 15:08:15 - mmengine - WARNING - Failed to search registry with scope "mmpretrain" in the "backend_classifiers" registry tree. As a workaround, the current "backend_classifiers" registry in "mmdeploy" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpretrain" is a correct scope, or whether the registry is initialized.
04/23 15:08:15 - mmengine - INFO - Successfully loaded onnxruntime custom ops from /media/xp/data/pydoc/mmlab/mmdeploy/mmdeploy/lib/libmmdeploy_onnxruntime_ops.so
04/23 15:08:15 - mmengine - INFO - visualize onnxruntime model success.
04/23 15:08:15 - mmengine - INFO - visualize pytorch model start.
04/23 15:08:16 - mmengine - WARNING - Failed to search registry with scope "mmpretrain" in the "Codebases" registry tree. As a workaround, the current "Codebases" registry in "mmdeploy" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpretrain" is a correct scope, or whether the registry is initialized.
04/23 15:08:16 - mmengine - WARNING - Failed to search registry with scope "mmpretrain" in the "mmpretrain_tasks" registry tree. As a workaround, the current "mmpretrain_tasks" registry in "mmdeploy" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpretrain" is a correct scope, or whether the registry is initialized.
/home/xp/anaconda3/envs/py3/lib/python3.8/site-packages/mmcv/cnn/bricks/hsigmoid.py:35: UserWarning: In MMCV v1.4.4, we modified the default value of args to align with PyTorch official. Previous Implementation: Hsigmoid(x) = min(max((x + 1) / 2, 0), 1). Current Implementation: Hsigmoid(x) = min(max((x + 3) / 6, 0), 1).
  warnings.warn(
Loads checkpoint by local backend from path: ../mmpretrain/work_dirs/my_mobilenetv3/epoch_150.pth
04/23 15:08:17 - mmengine - INFO - visualize pytorch model success.
04/23 15:08:17 - mmengine - INFO - All process success.

輸出一個(gè)end2end.onnx
用netron打開(kāi)來(lái)看一切正常
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