我正在使用一个Pytorch的Unet模型，我向模型输入一张图像作为输入，同时还输入标签作为输入图像的掩码，并在此基础上训练数据集。我从其他地方获取了这个Unet模型，并使用交叉熵损失作为损失函数，但出现了这个维度超出范围的错误，

RuntimeError                              Traceback (most recent call last)<ipython-input-358-fa0ef49a43ae> in <module>()     16 for epoch in range(0, num_epochs):     17     # train for one epoch---> 18     curr_loss = train(train_loader, model, criterion, epoch, num_epochs)     19      20     # store best loss and save a model checkpoint<ipython-input-356-1bd6c6c281fb> in train(train_loader, model, criterion, epoch, num_epochs)     16         # measure loss     17         print (outputs.size(),labels.size())---> 18         loss = criterion(outputs, labels)     19         losses.update(loss.data[0], images.size(0))     20 /usr/local/lib/python3.5/dist-packages/torch/nn/modules/module.py in     _ _call__(self, *input, **kwargs)    323         for hook in self._forward_pre_hooks.values():    324             hook(self, input)--> 325         result = self.forward(*input, **kwargs)    326         for hook in self._forward_hooks.values():    327             hook_result = hook(self, input, result)<ipython-input-355-db66abcdb074> in forward(self, logits, targets)      9         probs_flat = probs.view(-1)     10         targets_flat = targets.view(-1)---> 11         return self.crossEntropy_loss(probs_flat, targets_flat)/usr/local/lib/python3.5/dist-packages/torch/nn/modules/module.py in     __call__(self, *input, **kwargs)    323         for hook in self._forward_pre_hooks.values():    324             hook(self, input)  --> 325         result = self.forward(*input, **kwargs)    326         for hook in self._forward_hooks.values():    327             hook_result = hook(self, input, result)/usr/local/lib/python3.5/dist-packages/torch/nn/modules/loss.py in f orward(self, input, target)    599         _assert_no_grad(target)    600         return F.cross_entropy(input, target, self.weight, self.size_average,--> 601                                self.ignore_index, self.reduce)    602     603 /usr/local/lib/python3.5/dist-packages/torch/nn/functional.py in     cross_entropy(input, target, weight, size_average, ignore_index, reduce)   1138         >>> loss.backward()   1139     """-> 1140     return nll_loss(log_softmax(input, 1), target, weight, size_average, ignore_index, reduce)   1141    1142 /usr/local/lib/python3.5/dist-packages/torch/nn/functional.py in     log_softmax(input, dim, _stacklevel)    784     if dim is None:    785         dim = _get_softmax_dim('log_softmax', input.dim(),      _stacklevel)--> 786     return torch._C._nn.log_softmax(input, dim)    787     788 RuntimeError: dimension out of range (expected to be in range of [-1, 0], but got 1)

我的部分代码如下所示

class crossEntropy(nn.Module):    def __init__(self, weight = None, size_average = True):        super(crossEntropy, self).__init__()        self.crossEntropy_loss = nn.CrossEntropyLoss(weight, size_average)            def forward(self, logits, targets):        probs = F.sigmoid(logits)        probs_flat = probs.view(-1)        targets_flat = targets.view(-1)        return self.crossEntropy_loss(probs_flat, targets_flat)class UNet(nn.Module):    def __init__(self, imsize):        super(UNet, self).__init__()        self.imsize = imsize        self.activation = F.relu                self.pool1 = nn.MaxPool2d(2)        self.pool2 = nn.MaxPool2d(2)        self.pool3 = nn.MaxPool2d(2)        self.pool4 = nn.MaxPool2d(2)        self.conv_block1_64 = UNetConvBlock(4, 64)        self.conv_block64_128 = UNetConvBlock(64, 128)        self.conv_block128_256 = UNetConvBlock(128, 256)        self.conv_block256_512 = UNetConvBlock(256, 512)        self.conv_block512_1024 = UNetConvBlock(512, 1024)        self.up_block1024_512 = UNetUpBlock(1024, 512)        self.up_block512_256 = UNetUpBlock(512, 256)        self.up_block256_128 = UNetUpBlock(256, 128)        self.up_block128_64 = UNetUpBlock(128, 64)        self.last = nn.Conv2d(64, 2, 1)    def forward(self, x):        block1 = self.conv_block1_64(x)        pool1 = self.pool1(block1)        block2 = self.conv_block64_128(pool1)        pool2 = self.pool2(block2)        block3 = self.conv_block128_256(pool2)        pool3 = self.pool3(block3)        block4 = self.conv_block256_512(pool3)        pool4 = self.pool4(block4)        block5 = self.conv_block512_1024(pool4)        up1 = self.up_block1024_512(block5, block4)        up2 = self.up_block512_256(up1, block3)        up3 = self.up_block256_128(up2, block2)        up4 = self.up_block128_64(up3, block1)        return F.log_softmax(self.last(up4))

回答：

根据您的代码：

probs_flat = probs.view(-1)targets_flat = targets.view(-1)return self.crossEntropy_loss(probs_flat, targets_flat)

您向nn.CrossEntropyLoss提供了两个一维张量，但根据文档，它期望的是：

输入：(N,C)，其中C = 类别数目标：(N)，其中每个值为0 <= targets[i] <= C-1输出：标量。如果reduce为False，则为(N)而不是标量。

我认为这是您遇到问题的根本原因。