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)