如何为Resnet创建新层

Question

我通过调整和微调用于CIFAR10的ResNet18来练习迁移学习。我想用新的fc层替换最后一个fc层。所以，我想创建一个新的图层，但我没有。如何创建新图层？

下载Resnet18

OrigResNet18 = None
OrigResNet18 = torch.hub.load('pytorch/vision:v0.9.0', 'resnet18', pretrained=True)

快速层

(fc(：线性(in_features=512，out_features=1000，bias=True(

我试过了，但我不确定这个代码：

num_in_features=OrigResNet18.fc.in_features
num_out_features=OrigResNet18.fc.out_features
NewResNet18.conv1=nn.Conv2d(in_channels=1,out_channels=16, kernel_size= 
(3,3)) 
NewResNet18.fc=nn.Linear(in_features=num_in_features,out_features=num_out_features)

我有错误

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-58-f8fe748d1e75> in <module>()
32 NewResNet18 = NewResNet18.to(device)
33 epochs = 1
---> 34 loss_history = train(NewResNet18, criterion, optimizer, epochs, trainloader)
6 frames
<ipython-input-57-a35bfc25b940> in train(model, criterion, optimizer, epochs, dataloader, verbose)
19 
20       # Obtain the scores
---> 21       outputs = model(inputs)
22 
23       # Calculate loss
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
887             result = self._slow_forward(*input, **kwargs)
888         else:
--> 889             result = self.forward(*input, **kwargs)
890         for hook in itertools.chain(
891                 _global_forward_hooks.values(),
/usr/local/lib/python3.7/dist-packages/torchvision/models/resnet.py in forward(self, x)
247 
248     def forward(self, x: Tensor) -> Tensor:
--> 249         return self._forward_impl(x)
250 
251 
/usr/local/lib/python3.7/dist-packages/torchvision/models/resnet.py in _forward_impl(self, x)
230     def _forward_impl(self, x: Tensor) -> Tensor:
231         # See note [TorchScript super()]
--> 232         x = self.conv1(x)
233         x = self.bn1(x)
234         x = self.relu(x)
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
887             result = self._slow_forward(*input, **kwargs)
888         else:
--> 889             result = self.forward(*input, **kwargs)
890         for hook in itertools.chain(
891                 _global_forward_hooks.values(),
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py in forward(self, input)
397 
398     def forward(self, input: Tensor) -> Tensor:
--> 399         return self._conv_forward(input, self.weight, self.bias)
400 
401 class Conv3d(_ConvNd):
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py in _conv_forward(self, input, weight, bias)
394                             _pair(0), self.dilation, self.groups)
395         return F.conv2d(input, weight, bias, self.stride,
--> 396                         self.padding, self.dilation, self.groups)
397 
398     def forward(self, input: Tensor) -> Tensor:
RuntimeError: Given groups=1, weight of size [16, 1, 3, 3], expected input[8, 3, 224, 224] to have 1 channels, but got 3 channels instead

Answer 1

错误来自以下行：

NewResNet18.conv1=nn.Conv2d(in_channels=1,out_channels=16, kernel_size= 
(3,3)) 

您正在将第一个卷积更改为具有1个输入通道(即灰度图像(，但您正在为其提供3通道图像(即RGB图像(。

如果你只想改变分类器的大小，你可以使用：

num_in_features=OrigResNet18.fc.in_features
num_out_features=OrigResNet18.fc.out_features
NewResNet18.fc=nn.Linear(in_features=num_in_features,out_features=num_out_features)