[人工智能]pytorch 网络循环

import torch.nn as nn
class A (nn.Module):
    def __init__(self):
        super(A, self).__init__()
        self.c1 = nn.Conv2d(256,256,1)
        self.c2 = nn.Conv2d(256,256,1)

    def forward(self,x):
        x1=x
        for i in range(3):
            print('i',i)
            output = self.c1(x)
            output = self.c2(output)
            if i ==0:
                x = x1+output
            else:
                x = output
            # x = output
            # print('output',output)
        return output

# if __name__ == '__main__':
#     # # from time import time
#     import torch
#     from tensorboardX import SummaryWriter
#
#     pose = A()  # .cuda()
#     for param in pose.parameters():
#         if param.requires_grad:
#             print('param autograd')
#             break
#
#     # t0 = time()
#     input = torch.randn(1, 256, 256, 256)  # .cuda()
#     # print(pose)
#     output = pose(input)  # type: torch.Tensor
#     output[0][0].sum().backward()
#
#     with SummaryWriter(comment='testnetwork') as w:
#         w.add_graph(pose, (input,))

if __name__ == '__main__':
    import torch.onnx
    import netron

    #####hourglass是自己的网络代码，自己定义的网络结构类名
    pose = A()  # .cuda()
    dummy_input = torch.randn(1, 256, 256, 256)
    # 输出的文件名称，一般是在当前定义网络路径下
    onnx_path = "pose.onnx"
    torch.onnx.export(pose, dummy_input, "pose.onnx")  # netron --host=localhost
    # 自动跳转到netron的网址下
    netron.start(onnx_path)

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