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我们依旧采用FastDeploy来部署应用深度学习模型到OK3588板卡上
进入主机Ubuntu的虚拟环境 conda activate ok3588
安装rknn-toolkit2#xff08;该工具不能在OK3588板卡上完成模型转换#xff09;
git clone https://github.com/rockchip-linux/rknn-to…一、主机模型转换
我们依旧采用FastDeploy来部署应用深度学习模型到OK3588板卡上
进入主机Ubuntu的虚拟环境 conda activate ok3588
安装rknn-toolkit2该工具不能在OK3588板卡上完成模型转换
git clone https://github.com/rockchip-linux/rknn-toolkit2 cd rknn-toolkit2 注意这里需要1.4的版本 git checkout v1.4.0 -f cd packages pip install rknn_toolkit2-1.4.0_22dcfef4-cp36-cp36m-linux_x86_64.whl
下载FastDeploy
git clone https://github.com/PaddlePaddle/FastDeploy cd FastDeploy/examples/vision/ocr/PP-OCR
下载PP-OCRv3文字检测模型
wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar tar -xvf ch_PP-OCRv3_det_infer.tar
下载文字方向分类器模型
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar tar -xvf ch_ppocr_mobile_v2.0_cls_infer.tar
下载PP-OCRv3文字识别模型
wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar tar -xvf ch_PP-OCRv3_rec_infer.tar
安装模型转换工具
pip install paddle2onnx pip install pyyaml
paddle2onnx --model_dir ch_PP-OCRv3_det_infer \--model_filename inference.pdmodel \--params_filename inference.pdiparams \--save_file ch_PP-OCRv3_det_infer/ch_PP-OCRv3_det_infer.onnx \--enable_dev_version True
paddle2onnx --model_dir ch_ppocr_mobile_v2.0_cls_infer \--model_filename inference.pdmodel \--params_filename inference.pdiparams \--save_file ch_ppocr_mobile_v2.0_cls_infer/ch_ppocr_mobile_v2.0_cls_infer.onnx \--enable_dev_version True
paddle2onnx --model_dir ch_PP-OCRv3_rec_infer \--model_filename inference.pdmodel \--params_filename inference.pdiparams \--save_file ch_PP-OCRv3_rec_infer/ch_PP-OCRv3_rec_infer.onnx \--enable_dev_version True固定模型的输入shape
python -m paddle2onnx.optimize --input_model ch_PP-OCRv3_det_infer/ch_PP-OCRv3_det_infer.onnx \--output_model ch_PP-OCRv3_det_infer/ch_PP-OCRv3_det_infer.onnx \--input_shape_dict {x:[1,3,960,960]}
python -m paddle2onnx.optimize --input_model ch_ppocr_mobile_v2.0_cls_infer/ch_ppocr_mobile_v2.0_cls_infer.onnx \--output_model ch_ppocr_mobile_v2.0_cls_infer/ch_ppocr_mobile_v2.0_cls_infer.onnx \--input_shape_dict {x:[1,3,48,192]}
python -m paddle2onnx.optimize --input_model ch_PP-OCRv3_rec_infer/ch_PP-OCRv3_rec_infer.onnx \--output_model ch_PP-OCRv3_rec_infer/ch_PP-OCRv3_rec_infer.onnx \--input_shape_dict {x:[1,3,48,320]}转换成RKNN模型
python rockchip/rknpu2_tools/export.py --config_path tools/rknpu2/config/ppocrv3_det.yaml \--target_platform rk3588
python rockchip/rknpu2_tools/export.py --config_path tools/rknpu2/config/ppocrv3_rec.yaml \--target_platform rk3588
python rockchip/rknpu2_tools/export.py --config_path tools/rknpu2/config/ppocrv3_cls.yaml \--target_platform rk3588这时生成了三个可以部署在OK3588上的模型文件 ch_ppocr_mobile_v20_cls_infer_rk3588_unquantized.rknn ch_PP-OCRv3_rec_infer_rk3588_unquantized.rknn ch_PP-OCRv3_det_infer_rk3588_unquantized.rknn 把这三个文件传输到OK3588板卡上
二、板卡模型部署
进入虚拟环境 conda activate ok3588 cd FastDeploy/examples/vision/ocr/PP-OCR/rockchip/cpp mkdir build cd build cmake … -DFASTDEPLOY_INSTALL_DIR/home/forlinx/FastDeploy/build/fastdeploy-0.0.0/ make -j 得到了编译后的文件 infer_demo
三、执行推理
下载图片和字典文件
wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/doc/imgs/12.jpg wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/ppocr/utils/ppocr_keys_v1.txt
拷贝RKNN模型到build目录
三个模型文件 ch_ppocr_mobile_v20_cls_infer_rk3588_unquantized.rknn ch_PP-OCRv3_rec_infer_rk3588_unquantized.rknn ch_PP-OCRv3_det_infer_rk3588_unquantized.rknn 放在build文件夹里面
RKNPU推理
./infer_demo ./ch_PP-OCRv3_det_infer/ch_PP-OCRv3_det_infer_rk3588_unquantized.rknn \./ch_ppocr_mobile_v2.0_cls_infer/ch_ppocr_mobile_v20_cls_infer_rk3588_unquantized.rknn \./ch_PP-OCRv3_rec_infer/ch_PP-OCRv3_rec_infer_rk3588_unquantized.rknn \./ppocr_keys_v1.txt \./12.jpg \1推理结果展示
