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| Original file line number | Diff line number | Diff line change |
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| English | [简体中文](README_CN.md) | ||
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| ## Deploy the PaddleSeg model using Paddle Inference C++ | ||
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| ### 1、Install | ||
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| - Paddle Inference C++ | ||
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| - OpenCV | ||
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| - Yaml | ||
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| More install informations，please refer to [Tutorial](../../../../docs/deployment/inference/cpp_inference.md)。 | ||
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| ### 2、Models and Pictures | ||
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| - Downdload model | ||
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| Enter to `LaneSeg/` directory, and execute commands as follows: | ||
| ```shell | ||
| mkdir output # if not exists | ||
| wget -P output https://paddleseg.bj.bcebos.com/lane_seg/bisenet/model.pdparams | ||
| ``` | ||
| - Export Model | ||
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| ```shell | ||
| python export.py \ | ||
| --config configs/bisenetV2_tusimple_640x368_300k.yml \ | ||
| --model_path output/model.pdparams \ | ||
| --save_dir output/export | ||
| ``` | ||
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| - Using the image `data/test_images/3.jpg` | ||
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| ### 3、Compile and execute | ||
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| Enter to the `LaneSeg/deploy/cpp` | ||
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| Execute `sh run_seg_cpu.sh`, it will compile and then perform prediction on X86 CPU. | ||
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| Execute `sh run_seg_gpu.sh`, it will compile and then perform prediction on Nvidia GPU. | ||
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| The result will be saved in the`out_img_seg.jpg` and `out_image_points.jpg` images | ||
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| - Note：For the path of the model and image, you can change the files `run_seg_cpu.sh` and `run_seg_gpu.sh` as needed |
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| Original file line number | Diff line number | Diff line change |
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| 简体中文 | [English](README.md) | ||
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| ## 使用Paddle Inference C++部署PaddleSeg模型 | ||
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| ### 1、安装 | ||
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| - Paddle Inference C++ | ||
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| - OpenCV | ||
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| - Yaml | ||
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| 更多的安装信息，请参考[教程](../../../../docs/deployment/inference/cpp_inference_cn.md)。 | ||
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| ### 2、模型和图片 | ||
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| - 下载模型 | ||
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| 进入`LaneSeg/`目录下，执行如下命令: | ||
| ```shell | ||
| mkdir output # if not exists | ||
| wget -P output https://paddleseg.bj.bcebos.com/lane_seg/bisenet/model.pdparams | ||
| ``` | ||
| - 导出模型 | ||
| ```shell | ||
| python export.py \ | ||
| --config configs/bisenetV2_tusimple_640x368_300k.yml \ | ||
| --model_path output/model.pdparams \ | ||
| --save_dir output/export | ||
| ``` | ||
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| - 图片使用 `data/test_images/3.jpg` | ||
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| ### 3、编译、执行 | ||
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| 进入目录`LaneSeg/deploy/cpp` | ||
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| 执行`sh run_seg_cpu.sh`，会进行编译，然后在X86 CPU上执行预测。 | ||
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| 执行`sh run_seg_gpu.sh`，会进行编译，然后在Nvidia GPU上执行预测。 | ||
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| 结果会保存在当前目录的`out_img_seg.jpg`和`out_image_points.jpg`图片。 | ||
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| - 注意：对于模型和图片的路径，可以按需要对文件`run_seg_cpu.sh`和`run_seg_gpu.sh`进行修改。 |
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| #!/bin/bash | ||
| set +x | ||
| set -e | ||
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| WITH_MKL=ON | ||
| WITH_GPU=OFF | ||
| USE_TENSORRT=OFF | ||
| DEMO_NAME=test_seg | ||
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| work_path=$(dirname $(readlink -f $0)) | ||
| LIB_DIR="${work_path}/paddle_inference" | ||
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| # compile | ||
| mkdir -p build | ||
| cd build | ||
| rm -rf * | ||
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| cmake .. \ | ||
| -DDEMO_NAME=${DEMO_NAME} \ | ||
| -DWITH_MKL=${WITH_MKL} \ | ||
| -DWITH_GPU=${WITH_GPU} \ | ||
| -DUSE_TENSORRT=${USE_TENSORRT} \ | ||
| -DWITH_STATIC_LIB=OFF \ | ||
| -DPADDLE_LIB=${LIB_DIR} | ||
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| make -j | ||
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| # run | ||
| cd .. | ||
| # change model_dir and img_path according to your needs | ||
| ./build/test_seg \ | ||
| --model_dir=../../output/export/ \ | ||
| --img_path=../../data/test_images/3.jpg \ | ||
| --use_cpu=true \ | ||
| --use_mkldnn=true |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,34 @@ | ||
| #!/bin/bash | ||
| set +x | ||
| set -e | ||
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| WITH_MKL=ON | ||
| WITH_GPU=ON | ||
| USE_TENSORRT=OFF | ||
| DEMO_NAME=test_seg | ||
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| work_path=$(dirname $(readlink -f $0)) | ||
| LIB_DIR="${work_path}/paddle_inference" | ||
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| # compile | ||
| mkdir -p build | ||
| cd build | ||
| rm -rf * | ||
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| cmake .. \ | ||
| -DDEMO_NAME=${DEMO_NAME} \ | ||
| -DWITH_MKL=${WITH_MKL} \ | ||
| -DWITH_GPU=${WITH_GPU} \ | ||
| -DUSE_TENSORRT=${USE_TENSORRT} \ | ||
| -DWITH_STATIC_LIB=OFF \ | ||
| -DPADDLE_LIB=${LIB_DIR} | ||
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| make -j | ||
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| # run | ||
| cd .. | ||
| # change model_dir and img_path according to your needs | ||
| ./build/test_seg \ | ||
| --model_dir=../../output/export/ \ | ||
| --img_path=../../data/test_images/3.jpg \ | ||
| --use_cpu=false |
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