一、pytorch 损失函数中输入输出不匹配问题

File "C:\Users\Rain\AppData\Local\Programs\Python\Anaconda.3.5.1\envs\python35\python35\lib\site-packages\torch\nn\modules\module.py", line 491, in __call__  result = self.forward(*input, **kwargs)

File "C:\Users\Rain\AppData\Local\Programs\Python\Anaconda.3.5.1\envs\python35\python35\lib\site-packages\torch\nn\modules\loss.py", line 500, in forward reduce=self.reduce)
 
File "C:\Users\Rain\AppData\Local\Programs\Python\Anaconda.3.5.1\envs\python35\python35\lib\site-packages\torch\nn\functional.py", line 1514, in binary_cross_entropy_with_logits
 
raise ValueError("Target size ({}) must be the same as input size ({})".format(target.size(), input.size()))
 
ValueError: Target size (torch.Size([32])) must be the same as input size (torch.Size([32,2]))

原因

input 和 target 尺寸不匹配

解决方案：

将target转为onehot

例如：

one_hot = torch.nn.functional.one_hot(masks, num_classes=args.num_classes)

二、Pytorch遇到权重不匹配的问题

最近，楼主在pytorch微调模型时遇到

size mismatch for fc.weight: copying a param with shape torch.Size([1000, 2048]) from checkpoint, the shape in current model is torch.Size([2, 2048]).

size mismatch for fc.bias: copying a param with shape torch.Size([1000]) from checkpoint, the shape in current model is torch.Size([2]).

这个是因为楼主下载的预训练模型中的全连接层是1000类别的，而楼主本人的类别只有2类，所以会报不匹配的错误

解决方案：

从报错信息可以看出，是fc层的权重参数不匹配，那我们只要不load 这一层的参数就可以了。

net = se_resnet50(num_classes=2)
pretrained_dict = torch.load("./senet/seresnet50-60a8950a85b2b.pkl")
model_dict = net.state_dict()
# 重新制作预训练的权重，主要是减去参数不匹配的层，楼主这边层名为“fc”
pretrained_dict = {k: v for k, v in pretrained_dict.items() if (k in model_dict and 'fc' not in k)}
# 更新权重
model_dict.update(pretrained_dict)
net.load_state_dict(model_dict)

以上为个人经验，希望能给大家一个参考，也希望大家多多支持站长博客。