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量化的飞浆模型部署正常,但是转化为onnx模型出现问题(非量化的模型转为onnx可以正常部署) #12139
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为了对比我提供飞浆模型的运行结果飞浆模型非onnx模型的微调量化部署python tools/infer/predict_system.py --det_model_dir=output/CCPD/det_quant/infer/ --rec_model_dir=output/CCPD/rec_quant/infer/ --image_dir="F:/DataSet/CCPD2020/ccpd_green/test/04131106321839081-92_258-159&509_530&611-527&611_172&599_159&509_530&525-0_0_3_32_30_31_30_30-109-106.jpg" --rec_image_shape=3,48,320 运行结果[2024/05/18 02:34:52] ppocr INFO: In PP-OCRv3, rec_image_shape parameter defaults to '3, 48, 320', if you are using recognition model with PP-OCRv2 or an older version, please set --rec_image_shape='3,32,320 |
这个应该是paddle2onnx转换问题。之前试过,paddle2onnx不支持转换量化后的模型的。 |
请提供下述完整信息以便快速定位问题/Please provide the following information to quickly locate the problem
paddle2onnx 1.0.6
paddlepaddle 2.5.2
paddleslim 2.6.0
微调量化onnx部署
python tools/infer/predict_system.py --use_onnx=True --use_gpu=False --det_model_dir=output/CCPD/det_quant/onnx/model.onnx --rec_model_dir=output/CCPD/rec_quant/onnx/model.onnx --image_dir="F:/DataSet/CCPD2020/ccpd_green/test/04131106321839081-92_258-159&509_530&611-527&611_172&599_159&509_530&525-0_0_3_32_30_31_30_30-109-106.jpg" --rec_image_shape=3,48,320
[2024/05/18 02:21:46] ppocr INFO: In PP-OCRv3, rec_image_shape parameter defaults to '3, 48, 320', if you are using recognition model with PP-OCRv2 or an older version, please set --rec_image_shape='3,32,320
[2024/05/18 02:21:46] ppocr DEBUG: dt_boxes num : 0, elapsed : 0.13833069801330566
[2024/05/18 02:21:46] ppocr DEBUG: rec_res num : 0, elapsed : 0.0
[2024/05/18 02:21:46] ppocr DEBUG: 0 Predict time of F:/DataSet/CCPD2020/ccpd_green/test/04131106321839081-92_258-159&509_530&611-527&611_172&599_159&509_530&525-0_0_3_32_30_31_30_30-109-106.jpg: 0.141s
[2024/05/18 02:21:46] ppocr DEBUG: The visualized image saved in ./inference_results\04131106321839081-92_258-159&509_530&611-527&611_172&599_159&509_530&525-0_0_3_32_30_31_30_30-109-106.jpg
[2024/05/18 02:21:46] ppocr INFO: The predict total time is 0.1968538761138916
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