原因：这个原因是因为在使用Crossentropyloss作为损失函数时，output=net(input)的output应该是[batchsize, n_class, height, weight]，而label则是[batchsize, height, weight]，label是单通道灰度图;BCELoss与CrossEntropyLoss都是用于分类问题。BCELoss是CrossEntropyLoss的一个特例，只用于二分类问题，而CrossEntropyLoss可以用于二分类，也可以用于多分类。
（1）If logit.shape is torch.Size ([4, 31, 256, 256]) and target.shape is [4, 256, 256, 1], where 4 is the batchSize and 31 is the number of categories.
Solution:维度压缩
loss = criterion(logit, torch.squeeze(target).long())
to change the target to [4, 256, 256].
（2）1only batches of spatial targets supported (non-empty 3D tensors) but got targets of size: : [2, 321, 321, 4]
Solution:使用BCELoss
# criterion = nn.CrossEntropyLoss(weight=self.weight, ignore_index=self.ignore_index, reduction='mean')
criterion = nn.BCELoss(weight=self.weight,reduction='mean')
在BCELoss中，logit.size和target.size()都是[batchsize, n_class, height, weight].