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我尝试使用项目中的例子执行: python tools/predict.py --config configs/ppmattingv2/ppmattingv2-stdc1-human_512.yml --model_path pretrained_models/ppmattingv2-stdc1-human_512.pdparams --image_path demo/1.jpg --save_dir ./output/results --fg_estimate True 得到的图像alpha值和前景图准确度很高,但当我使用下列两行命令转出onnx模型后,再利用djl框架进行推理时,和之前得到的结果是不同的,需要先对图片进行预处理吗 (1)python tools/export.py --config configs/ppmattingv2/ppmattingv2-stdc1-human_512.yml --model_path pretrained_models/ppmattingv2-stdc1-human_512.pdparams --save_dir output/inference_model --input_shape 1 3 1024 1024 (2)paddle2onnx --model_dir inference_model --model_filename model.pdmodel --params_filename model.pdiparams --opset_version 11 --save_file output1.onnx
想请教一下: 执行导出命令时,能否不指定--input_shape 1 3 1024 1024,怎么能动态根据输入图片的大小进行处理,因为Matting示例下是根据输入照片自动处理的,但是上面导出转化命令行,不指定该值,执行paddle2onnx转换时会报错
The text was updated successfully, but these errors were encountered:
你好,可以看到这里是导出后并进行推理时出现精度异常,由于动态图模型精度正确,说明是paddle2onnx或者推理框架出现错误,请尝试切换不同的推理后端获得正确精度。
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shiyutang
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我尝试使用项目中的例子执行:
python tools/predict.py --config configs/ppmattingv2/ppmattingv2-stdc1-human_512.yml --model_path pretrained_models/ppmattingv2-stdc1-human_512.pdparams --image_path demo/1.jpg --save_dir ./output/results --fg_estimate True
得到的图像alpha值和前景图准确度很高,但当我使用下列两行命令转出onnx模型后,再利用djl框架进行推理时,和之前得到的结果是不同的,需要先对图片进行预处理吗
(1)python tools/export.py --config configs/ppmattingv2/ppmattingv2-stdc1-human_512.yml --model_path pretrained_models/ppmattingv2-stdc1-human_512.pdparams --save_dir output/inference_model --input_shape 1 3 1024 1024
(2)paddle2onnx --model_dir inference_model --model_filename model.pdmodel --params_filename model.pdiparams --opset_version 11 --save_file output1.onnx
想请教一下:
执行导出命令时,能否不指定--input_shape 1 3 1024 1024,怎么能动态根据输入图片的大小进行处理,因为Matting示例下是根据输入照片自动处理的,但是上面导出转化命令行,不指定该值,执行paddle2onnx转换时会报错
The text was updated successfully, but these errors were encountered: