pytorch转换onnx,测试onnx,pytorch 模型结果是否一致

小灰灰 2023-01-03 04:16 403阅读 0赞

def to_onnx():
        dummy_input = torch.randn(1, 3, 112, 112, dtype=torch.float)
        # model = model_res()
        model = model_osnet()
    
        input_names = ["data"]
        output_names = ["fc"]
        torch.onnx.export(
            model,
            dummy_input,
            "./osnet.onnx",
            verbose=True,
            input_names=input_names,
            output_names=output_names,
        )
        print("转换模型成功^^")
    
    
    def pytorch_out(input):
        model = model_res() #model.eval
        # input = input.cuda()
        # model.cuda()
        torch.no_grad()
        output = model(input)
        # print output[0].flatten()[70:80]
        return output
    
    def pytorch_onnx_test():
        import onnxruntime
        from onnxruntime.datasets import get_example
    
        def to_numpy(tensor):
            return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()
    
        # 测试数据
        torch.manual_seed(66)
        dummy_input = torch.randn(1, 3, 112, 112, device='cpu')
    
        example_model = get_example("/home/shiyy/nas/all_workspace/pytorch_code/reid/InsightFace-v2/res50.onnx")
        # netron.start(example_model) 使用 netron python 包可视化网络
        sess = onnxruntime.InferenceSession(example_model)
    
        # onnx 网络输出
        onnx_out = np.array(sess.run(None, { "data": to_numpy(dummy_input)}))  #fc 输出是三维列表
        print("==============>")
        print(onnx_out)
        print(onnx_out.shape)
        print("==============>")
        torch_out_res = pytorch_out(dummy_input).detach().numpy()   #fc输出是二维 列表
        print(torch_out_res)
        print(torch_out_res.shape)
    
        print("===================================>")
        print("输出结果验证小数点后五位是否正确,都变成一维np")
    
        torch_out_res = torch_out_res.flatten()
        onnx_out = onnx_out.flatten()
    
        pytor = np.array(torch_out_res,dtype="float32") #need to float32
        onn=np.array(onnx_out,dtype="float32")  ##need to float32
        np.testing.assert_almost_equal(pytor,onn, decimal=5)  #精确到小数点后5位，验证是否正确，不正确会自动打印信息
        print("恭喜你 ^^ ，onnx 和 pytorch 结果一致 ， Exported model has been executed decimal=5 and the result looks good!")

[[...
    	  -5.49954772e-02 -7.31383190e-02  2.37192452e-01  1.48571879e-02
    	  -2.97113061e-02 -5.73663861e-02 -6.34638742e-02 -5.24816178e-02]]
    	(1, 512)
    	===================================>
    	输出结果验证小数点后五位是否正确,都变成一维np
    	恭喜你 ^^ ，onnx 和 pytorch 结果一致 ， Exported model has been executed decimal=5 and the result looks good!