yanyang
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 > > > >...
> 具体的例子是哪个? falcon和gpt都会这样
# 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...
Have you updated the latest version of SDWebUI?
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