Pytorch-目标和输入必须具有相同数量的元素

如何解决Pytorch-目标和输入必须具有相同数量的元素

我在pytorch中遇到此错误： ValueError：目标和输入必须具有相同数量的元素。目标要素（16）！=输入要素（8388608）

我的模特：

class DoubleConv(nn.Module):

    def __init__(self,in_channels,out_channels,mid_channels=None):
        super().__init__()
        if not mid_channels:
            mid_channels = out_channels
        self.double_conv = nn.Sequential(
            nn.Conv2d(in_channels,mid_channels,kernel_size=3,padding=1),nn.Batchnorm2d(mid_channels),nn.ReLU(inplace=True),nn.Conv2d(mid_channels,nn.Batchnorm2d(out_channels),nn.ReLU(inplace=True)
        )

    def forward(self,x):
        return self.double_conv(x)

class Down(nn.Module):
    def __init__(self,out_channels):
        super().__init__()
        self.maxpool_conv = nn.Sequential(
            nn.MaxPool2d(2),DoubleConv(in_channels,out_channels)
        )

    def forward(self,x):
        return self.maxpool_conv(x)

class Up(nn.Module):
    def __init__(self,bilinear=True):
        super().__init__()

        if bilinear:
            self.up = nn.Upsample(scale_factor=2,mode='bilinear',align_corners=True)
            self.conv = DoubleConv(in_channels,in_channels / 2)
        else:
            self.up = nn.ConvTranspose2d(in_channels,in_channels / 2,kernel_size=2,stride=2)
            self.conv = DoubleConv(in_channels,out_channels)


    def forward(self,x1,x2):
        x1 = self.up(x1)
        
        diffY = x2.size()[2] - x1.size()[2]
        diffX = x2.size()[3] - x1.size()[3]

        x1 = F.pad(x1,[diffX / 2,diffX - diffX / 2,diffY / 2,diffY - diffY / 2])
        x = torch.cat([x2,x1],dim=1)
        return self.conv(x)

class OutConv(nn.Module):
    def __init__(self,out_channels):
        super(OutConv,self).__init__()
        self.conv = nn.Conv2d(in_channels,kernel_size=1)

    def forward(self,x):
        return self.conv(x)



class UNet(nn.Module):
    def __init__(self,n_channels,n_classes,bilinear=True):
        super(UNet,self).__init__()
        self.n_channels = n_channels
        self.n_classes = n_classes
        self.bilinear = bilinear

        self.inc = DoubleConv(n_channels,64)
        self.down1 = Down(64,128)
        self.down2 = Down(128,256)
        self.down3 = Down(256,512)
        factor = 2 if bilinear else 1
        self.down4 = Down(512,1024 / factor)
        self.up1 = Up(1024,512 / factor,bilinear)
        self.up2 = Up(512,256 / factor,bilinear)
        self.up3 = Up(256,128 / factor,bilinear)
        self.up4 = Up(128,64,bilinear)
        self.outc = OutConv(64,n_classes)

    def forward(self,x):
        x1 = self.inc(x)
        x2 = self.down1(x1)
        x3 = self.down2(x2)
        x4 = self.down3(x3)
        x5 = self.down4(x4)
        x = self.up1(x5,x4)
        x = self.up2(x,x3)
        x = self.up3(x,x2)
        x = self.up4(x,x1)
        logits = self.outc(x)
        return logits

model_instance = UNet(n_channels=3,n_classes=8,bilinear=True)

完整日志：

/home/user/.local/lib/python3.6/site-packages/torch/nn/modules/loss.py:529: UserWarning: Using a target size (torch.Size([16])) that is different to the input size (torch.Size([16,8,256,256])) is deprecated. Please ensure they have the same size.
  return F.binary_cross_entropy(input,target,weight=self.weight,reduction=self.reduction)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-13-aad65c925500> in <module>()
      1 for _epoch in range(1,epoch):
----> 2     train(model_instance,_epoch)
      3     test(model_instance)

<ipython-input-11-3b706bbe2772> in train(model,epoch)
      6         optimizer.zero_grad()
      7         output = model.forward(sat.float())
----> 8         loss = criterion(output.float(),mask.float())
      9         optimizer.zero_grad()
     10         loss.backward()

/home/user/.local/lib/python3.6/site-packages/torch/nn/modules/module.py in _call_impl(self,*input,**kwargs)
    720             result = self._slow_forward(*input,**kwargs)
    721         else:
--> 722             result = self.forward(*input,**kwargs)
    723         for hook in itertools.chain(
    724                 _global_forward_hooks.values(),/home/user/.local/lib/python3.6/site-packages/torch/nn/modules/loss.py in forward(self,input,target)
    527 
    528     def forward(self,input: Tensor,target: Tensor) -> Tensor:
--> 529         return F.binary_cross_entropy(input,reduction=self.reduction)
    530 
    531 

/home/user/.local/lib/python3.6/site-packages/torch/nn/functional.py in binary_cross_entropy(input,weight,size_average,reduce,reduction)
   2475     if input.numel() != target.numel():
   2476         raise ValueError("Target and input must have the same number of elements. target nelement ({}) "
-> 2477                          "!= input nelement ({})".format(target.numel(),input.numel()))
   2478 
   2479     if weight is not None:

ValueError: Target and input must have the same number of elements. target nelement (16) != input nelement (8388608)

我的模特有问题吗？我可以增强它吗？各种各样的建议都值得赞赏。

我正在输入 256x256 3通道图像作为输入，并输入 256x256 3通道作为遮罩。