问题描述

我想在我自己的项目更换其他的模型，下载的预训练模型出现了FC层不匹配的问题，找了好多人都写了这个点，今天总结一下：
首先我们遇到的问题如下：
他的意思是resnet50的fc层是1000分类，而我的是62分类，所以不匹配，那怎么处理呢？

RuntimeError: Error(s) in loading state_dict for ResNet50:size mismatch for fc.weight: copying a param with shape torch.Size([1000, 2048]) from checkpoint, the shape in current model is torch.Size([62, 2048]).size mismatch for fc.bias: copying a param with shape torch.Size([1000]) from checkpoint, the shape in current model is torch.Size([62]).

网传不可靠方法：

好多人都这么写

应用了pop，p掉全连接的参数，
这样呢，我试了，

 File "/mnt/ImageClassification_test/tools/resnet50.py", line 126, in choose_model50model.pop("fc.bias")File "/root/miniconda3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 778, in __getattr__raise ModuleAttributeError("'{}' object has no attribute '{}'".format(
torch.nn.modules.module.**ModuleAttributeError: 'ResNet50' object has no attribute 'pop'**

根本就不行，别着急，我还有牛逼的方法：

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。：

def choose_model50(name, num_classes, device, weights):if name == 'ResNet50':# 选择模型model = ResNet50(num_classes=num_classes).to(device)# 加载pth预训练模型pretrained_dict = torch.load(weights, map_location=device)model_dict = model.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)model.load_state_dict(model_dict)return model

这样不论你的模型文件和预训练文件放在那，都可以传入
对应的信息改一下，这回保证没问题
跑起来了，非常nice

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size mismatch for fc.weight: copying a param with shape torch.Size([1000, 2048]) from checkpoint, th

问题描述

网传不可靠方法：

解决方案：

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