Python 3.x 从训练有素的UNet获取编码器

我已经在一些图像上训练了一个UNet模型，但是现在，我想提取模型的编码器部分。我的UNet具有以下体系结构：

UNet(
  (conv_final): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))
  (down_convs): ModuleList(
    (0): DownConv(
      (conv1): Conv2d(1, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    )
    (1): DownConv(
      (conv1): Conv2d(8, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    )
    (2): DownConv(
      (conv1): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    )
    (3): DownConv(
      (conv1): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    )
    (4): DownConv(
      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    )
  )
  (up_convs): ModuleList(
    (0): UpConv(
      (upconv): ConvTranspose2d(128, 64, kernel_size=(2, 2), stride=(2, 2))
      (conv1): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    )
    (1): UpConv(
      (upconv): ConvTranspose2d(64, 32, kernel_size=(2, 2), stride=(2, 2))
      (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    )
    (2): UpConv(
      (upconv): ConvTranspose2d(32, 16, kernel_size=(2, 2), stride=(2, 2))
      (conv1): Conv2d(32, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    )
    (3): UpConv(
      (upconv): ConvTranspose2d(16, 8, kernel_size=(2, 2), stride=(2, 2))
      (conv1): Conv2d(16, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    )
  )
)

我试图通过model.down_convs加载编码器层，但出现以下错误：

中的TypeError回溯（最近一次调用） ---->1 res=编码器（列车img）

~/anaconda3/envs/work/lib/python3.8/site-packages/torch/nn/modules/module.py 在调用（self，*输入，**kwargs）548结果中= self.\u slow\u forward（*输入，**kwargs）549其他：–>550结果= 自动前进（*输入，**kwargs）551用于钩入 self.\u forward\u hooks.values（）：552 hook\u result=hook（self，input， 结果）

TypeError:forward（）接受1个位置参数，但给出了2个

我已经附上了，所以你可以尝试一下。以及来自

请让我知道。

这应该行得通

net = UNet(8) # network object having 8 classes
net.load_state_dict(torch.load('PATH'))
print(net) #see the names of the layers of encoder. 
net1 = net.down_convs #as you have named the encoder as down_convs

#net1 is your encoder. 

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我试过了。原来的帖子是这样的：我也有同样的错误