尝试训练一个pytorch模型（begginner） 我正在使用一个unet模型，我将图像作为输入进给，并且我正在将标签作为输入图像蒙版并在其上进行数据集处理。 我从其他地方拾取的unet模型，我使用交叉熵损失作为损失函数，但我得到这个维度超出范围误差，
`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))`
任何建议，提示都会非常有用
提前致谢。 如果您需要更多代码，请告诉我，
答案 0 :(得分：7)
根据你的代码：
probs_flat = probs.view(-1)
targets_flat = targets.view(-1)
return self.crossEntropy_loss(probs_flat, targets_flat)
你给nn.CrossEntropyLoss两个1d张量，但根据documentation，它预计：
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.
我相信这是你遇到的问题的原因。
答案 1 :(得分：0)
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)
参数 labels 不是 CrossEntropyLoss 所期望的。 labels 应该是一维数组。此数组的长度应该是与代码中的 outputs 匹配的批次大小。每个元素的值应该是从 0 开始的目标类 ID。
这是一个例子。
假设您的批次大小为 B=2，并且每个数据实例都被指定为 K=3 类之一。
此外，假设您的神经网络的最后一层正在为您的批次中的两个实例中的每一个输出以下原始 logits（softmax 之前的值）。这些日志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
那么为了正确调用CrossEntropyLoss，你需要两个变量：
input 的形状 (B, K) 包含对数值target 的形状 B 包含真实类的索引以下是如何正确使用 CrossEntropyLoss 与上述值。我使用的是 torch.__version__ 1.9.0。
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)
我猜你最初收到的错误
RuntimeError: dimension out of range (expected to be in range of [-1, 0], but got 1)
可能 发生是因为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
以下是表示上述数据的代码：
import torch
yhat = torch.Tensor([0.5, 1.5, 0.1])
print(yhat)
# tensor([0.5000, 1.5000, 0.1000])
y = torch.Tensor([0, 1, 0]).to(torch.long)
print(y)
# tensor([0, 1, 0])
loss = torch.nn.CrossEntropyLoss()
cel = loss(input=yhat, target=y)
print(cel)
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，该错误消息令人费解且无法操作。
另见一个类似的问题，但在 TensorFlow 中：
What are logits? What is the difference between softmax and softmax_cross_entropy_with_logits?