用导出的微调量化模型（车牌检测和识别都一样）对测试集进行预测出现报错input_data_type:5 != filter_data_type:21.

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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

• 系统环境/System Environment：3.10.4

• 版本号/Version：Paddle：2.6.1 PaddleOCR： 问题相关组件/Related components：tools/infer/predict_system.py

• 运行指令/Command Code：python tools/infer/predict_system.py --det_model_dir=output/CCPD/det_quant/infer --rec_model_dir=output/CCPD/rec_quant/infer --det_limit_side_len=736 --det_limit_type=min --image_dir=F:/DataSet/CCPD2020/ccpd_green/test/ --draw_img_save_dir=infer/quant --use_dilation=true

• 完整报错/Complete Error Message：

• (base) (venv) PS F:\GithubProject\PaddleOCR> python tools/infer/predict_system.py --det_model_dir=output/CCPD/det_quant/infer --rec_model_dir=output/CCPD/rec_quant/infer --det_limit_side_len=736 --det_limit_type=min --image_dir=F:/DataSet/CCPD2020/ccpd_green/test/ --draw_img_save_dir=infer/quant --use_dilation=true
  [2024/05/08 10:27:31] 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
  Traceback (most recent call last):
  File "F:\GithubProject\PaddleOCR\tools\infer\predict_system.py", line 295, in
  main(args)
  File "F:\GithubProject\PaddleOCR\tools\infer\predict_system.py", line 196, in main
  dt_boxes, rec_res, time_dict = text_sys(img)
  File "F:\GithubProject\PaddleOCR\tools\infer\predict_system.py", line 83, in call
  dt_boxes, elapse = self.text_detector(img)
  File "F:\GithubProject\PaddleOCR\tools\infer\predict_det.py", line 379, in call
  dt_boxes, elapse = self.predict(img)
  File "F:\GithubProject\PaddleOCR\tools\infer\predict_det.py", line 240, in predict
  self.predictor.run()
  ValueError: (InvalidArgument) input and filter data type should be consistent, but received input data type is float and filter type is int8_t
  [Hint: Expected input_data_type == filter_data_type, but received input_data_type:5 != filter_data_type:21.] (at ..\paddle\fluid\operators\generator\get_expected_kernel_func.cc:431)
  [operator < conv2d > error]

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CCPD车牌检测数据集fine-tune+量化训练：
python deploy/slim/quantization/quant.py -c configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_student.yml -o Global.pretrained_model=output/CCPD/det/best_accuracy.pdparams Global.save_model_dir=output/CCPD/det_quant Global.eval_batch_step="[0, 772]" Optimizer.lr.name=Const Optimizer.lr.learning_rate=0.0005 Optimizer.lr.warmup_epoch=0 Train.dataset.data_dir=F:/DataSet/CCPD2020/ccpd_green Train.dataset.label_file_list=[F:/DataSet/CCPD2020/PPOCR/train/det.txt] Eval.dataset.data_dir=F:/DataSet/CCPD2020/ccpd_green Eval.dataset.label_file_list=[F:/DataSet/CCPD2020/PPOCR/test/det.txt]

检测量化模型导出：
python deploy/slim/quantization/export_model.py -c configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_student.yml -o Global.pretrained_model=output/CCPD/det_quant/best_accuracy.pdparams Global.save_inference_dir=output/CCPD/det_quant/infer Eval.dataset.data_dir=F:/DataSet/CCPD2020/ccpd_green Eval.dataset.label_file_list=[F:/DataSet/CCPD2020/PPOCR/test/det.txt] Eval.loader.num_workers=0

CCPD车牌识别数据集fine-tune+量化训练：
python deploy/slim/quantization/quant.py -c configs/rec/PP-OCRv3/ch_PP-OCRv3_rec.yml -o Global.pretrained_model=output/CCPD/rec/best_accuracy.pdparams Global.save_model_dir=output/CCPD/rec_quant/ Global.eval_batch_step="[0, 90]" Optimizer.lr.name=Const Optimizer.lr.learning_rate=0.0005 Optimizer.lr.warmup_epoch=0 Train.dataset.data_dir=F:/DataSet/CCPD2020/PPOCR Train.dataset.label_file_list=[F:/DataSet/CCPD2020/PPOCR/train/rec.txt] Eval.dataset.data_dir=F:/DataSet/CCPD2020/PPOCR Eval.dataset.label_file_list=[F:/DataSet/CCPD2020/PPOCR/test/rec.txt] Train.loader.batch_size_per_card=64

识别量化模型导出：
python deploy/slim/quantization/export_model.py -c configs/rec/PP-OCRv3/ch_PP-OCRv3_rec.yml -o Global.pretrained_model=output/CCPD/rec_quant/best_accuracy.pdparams Global.save_inference_dir=output/CCPD/rec_quant/infer Eval.dataset.data_dir=F:/DataSet/CCPD2020/PPOCR Eval.dataset.label_file_list=[F:/DataSet/CCPD2020/PPOCR/test/rec.txt]

用导出的量化模型对测试集进行预测：
python tools/infer/predict_system.py --det_model_dir=output/CCPD/det_quant/infer --rec_model_dir=output/CCPD/rec_quant/infer --det_limit_side_len=736 --det_limit_type=min --image_dir=F:/DataSet/CCPD2020/ccpd_green/test/ --draw_img_save_dir=infer/quant --use_dilation=true

[Sunting78]

看起来是ValueError: (InvalidArgument) input and filter data type should be consistent, but received input data type is float and filter type is int8_t 显示的转下数据类型看看

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看起来是ValueError: (InvalidArgument) input and filter data type should be consistent, but received input data type is float and filter type is int8_t 显示的转下数据类型看看

请问：deploy/slim/quantization/README.md 里面说：“备注：量化训练后的模型参数是float32类型，转inference model预测时相对不量化无加速效果，原因是量化后模型结构之间存在量化和反量化算子，如果要使用量化模型部署，建议使用TensorRT并设置precision为INT8加速量化模型的预测时间。”那我应该在命令后面加入参数--use_tensorrt=USE_TENSORRT --precision=INT8，我加入后依然一样的报错。

[hellohahaw]

看起来是ValueError: (InvalidArgument) input and filter data type should be consistent, but received input data type is float and filter type is int8_t 显示的转下数据类型看看

请问：deploy/slim/quantization/README.md 里面说：“备注：量化训练后的模型参数是float32类型，转inference model预测时相对不量化无加速效果，原因是量化后模型结构之间存在量化和反量化算子，如果要使用量化模型部署，建议使用TensorRT并设置precision为INT8加速量化模型的预测时间。”那我应该在命令后面加入参数--use_tensorrt=USE_TENSORRT --precision=INT8，我加入后依然一样的报错。

请问您是怎么处理双行车牌的识别的呢？

[stringency]

看起来是ValueError: (InvalidArgument) input and filter data type should be consistent, but received input data type is float and filter type is int8_t 显示的转下数据类型看看

请问：deploy/slim/quantization/README.md 里面说：“备注：量化训练后的模型参数是float32类型，转inference model预测时相对不量化无加速效果，原因是量化后模型结构之间存在量化和反量化算子，如果要使用量化模型部署，建议使用TensorRT并设置precision为INT8加速量化模型的预测时间。”那我应该在命令后面加入参数--use_tensorrt=USE_TENSORRT --precision=INT8，我加入后依然一样的报错。

请问您是怎么处理双行车牌的识别的呢？

我的是单行的识别

[Sunting78]

显示的转下数据类型尝试一下 把输入的图像转int8

[stringency]

使用cpu版本，解决全部问题！！