尝试使用unet模型训练pytorch模型（初学者）Im，我将图像输入作为输入，并随同将标签输入作为输入图像蒙版并在其上转换数据集。我从其他地方获得了unet模型，我将交叉熵损失用作损失函数，但是我得到的尺寸超出了范围误差，
`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)`
我的部分代码看起来像这样
`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))`
任何建议，小贴士真的很有帮助
提前致谢。如果您需要更多代码，请告诉我，
sta*_*010 14
问题是您在分类问题中向torch.nn.CrossEntropyLoss传递了错误的参数。
具体来说，在这一行
---> 18 loss = criterion(outputs, labels)
论证labels并不是我们CrossEntropyLoss所期望的。labels应该是一维数组。该数组的长度应该是outputs与代码中匹配的批量大小。每个元素的值应该是从 0 开始的目标类 ID。
这是一个例子。
假设您有批量大小B=2，并且每个数据实例都被赋予一个K=3类。
此外，假设神经网络的最后一层为批次中的两个实例输出以下原始 logits（softmax 之前的值）。这些 logits 和每个数据实例的真实标签如下所示。
Logits (before softmax)
Class 0 Class 1 Class 2 True class
------- ------- ------- ----------
Instance 0: 0.5 1.5 0.1 1
Instance 1: 2.2 1.3 1.7 2
那么为了CrossEntropyLoss正确调用，需要两个变量：
input(B, K)包含 logit 值的形状targetB包含真实类索引的形状以下是如何正确使用CrossEntropyLoss上述值的方法。我用的是torch.__version__1.9.0。
Logits (before softmax)
Class 0 Class 1 Class 2 True class
------- ------- ------- ----------
Instance 0: 0.5 1.5 0.1 1
Instance 1: 2.2 1.3 1.7 2
我猜您最初收到的错误是
RuntimeError: dimension out of range (expected to be in range of [-1, 0], but got 1)
可能发生的原因是您正在尝试计算一个数据实例的交叉熵损失，其中目标被编码为 one-hot。您的数据可能是这样的：
Logits (before softmax)
Class 0 Class 1 Class 2 True class 0 True class 1 True class 2
------- ------- ------- ------------ ------------ ------------
Instance 0: 0.5 1.5 0.1 0 1 0
这是表示上述数据的代码：
import torch
yhat = torch.Tensor([[0.5, 1.5, 0.1], [2.2, 1.3, 1.7]])
print(yhat)
# tensor([[0.5000, 1.5000, 0.1000],
# [2.2000, 1.3000, 1.7000]])
y = torch.Tensor([1, 2]).to(torch.long)
print(y)
# tensor([1, 2])
loss = torch.nn.CrossEntropyLoss()
cel = loss(input=yhat, target=y)
print(cel)
# tensor(0.8393)
此时，我收到以下错误：
---> 10 cel = loss(input=yhat, target=y)
IndexError: Dimension out of range (expected to be in range of [-1, 0], but got 1)
在我看来，该错误消息是难以理解且无法采取行动的。
另请参阅类似的问题，但在 TensorFlow 中：
什么是逻辑？softmax 和 softmax_cross_entropy_with_logits 有什么区别？
根据您的代码：
probs_flat = probs.view(-1)
targets_flat = targets.view(-1)
return self.crossEntropy_loss(probs_flat, targets_flat)
您正在给两个1d张量，nn.CrossEntropyLoss但根据文档，它期望：
Input: (N,C) where C = number of classes
Target: (N) where each value is 0 <= targets[i] <= C-1
Output: scalar. If reduce is False, then (N) instead.
我相信这是您遇到问题的原因。
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