yanyang

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...

> 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。

> > > 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 > > > >...

使用cpu版本，解决全部问题！！

> 具体的例子是哪个？ falcon和gpt都会这样