分别使用OpenCV、ONNXRuntime部署PP-HumanSeg肖像分割模型，包含C++和Python两个版本的程序
分别使用OpenCV、ONNXRuntime部署PP-HumanSeg肖像分割模型，包含C++和Python两个版本的程序
百度PaddlePaddle团队发布的PP-HumanSeg人像分割模型，功能挺强的。 原始程序需要依赖PaddlePaddle框架才能运行，于是我编写了一套分别使用OpenCV、ONNXRuntime部署PP-HumanSeg肖像分割 的程序，彻底摆脱对任何深度学习框架的依赖，.onnx模型文件很小，只有5.9M，可以直接上传到github仓库里。
1、仓库提供的model_float32.onnx模型的OUTPUTS格式为name: tf.identity、type: float32[1,192,192,2]
2、自己按照PaddleSeg
官方指导导出的pp_liteseg_stdc2.onnx模型的OUTPUTS格式为name: argmax_0.tmp_0、type: int64[-1,-1,-1]
3、使用大佬提供的模型可以跑成功，但换成我自己导出的这个就没有结果，可则个pp_liteseg_stdc2.onnx模型使用PaddleSeg提供的Python版本的deploy/python/infer_onnx.py程序又可以出正确的结果。
4、所以请问大佬的onnx模型导出是自己写的导出程序还是PaddleSeg官方提供的程序导出的，如何得到与提供的model_float32.onnx模型相同的的OUTPUTS格式，我试过导出多个PaddleSeg模型，感觉OUTPUTS格式都一样
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