CODEWITHSUNDEEP
pythondeep-learningpytorchvgg-net
user5006261
user5006261

Reputation:

Size mismatch error during VGG finetuning

I have been following the ants and bees transfer learning tutorial from the official PyTorch Docs (http://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html). I am trying to finetune a VGG19 model by changing the final layer to predict one of two classes. I am able to modify the last fc layer using the following code.

But I get an error when executing the train_model function. The error is “size mismatch at /opt/conda/conda-bld/pytorch_1513368888240/work/torch/lib/THC/generic/THCTensorMathBlas.cu:243”. Any idea what the issue is ?

model_conv = torchvision.models.vgg19(pretrained=True)
for param in model_conv.parameters():
    param.requires_grad = False

model_conv = nn.Sequential(*list(model_conv.classifier.children())[:-1] +
                     [nn.Linear(in_features=4096, out_features=2)])
if use_gpu:
    model_conv = model_conv.cuda()

criterion = nn.CrossEntropyLoss()

optimizer_conv = optim.SGD(model_conv._modules['6'].parameters(), lr=0.001, momentum=0.9)
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)

model_conv = train_model(model_conv, criterion, optimizer_conv, exp_lr_scheduler, num_epochs=25)

Upvotes: 0

Views: 650

Answers (1)

Manuel Lagunas
Manuel Lagunas

Reputation: 2751

When you are defining your model you are just considering the classifier which consists on the fully connected part of the network only. Then, when feeding the 224*224*3 image to the model it tries to "go through" a linear layer with 25K features as the input. To solve it you just need to add the convolutional part before, to do so redefine the model like this:

class newModel(nn.Module):
    def __init__(self, old_model):
        super(newModel, self).__init__()

        self.features = old_model.features
        self.classifier = nn.Sequential(*list(old_model.classifier.children())[:-1] +
                                         [nn.Linear(in_features=4096, out_features=2)])

    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), -1)
        x = self.classifier(x)
        return x

model_conv = newModel(model_conv)

Now you just also tell the parameters to optimize, if you just want to train the last layer (the one that is newly added) do :

optimizer_conv = optim.SGD(model_conv.classifier._modules['6'].parameters(), lr=0.001, momentum=0.9)

The rest of the code remains the same.

Hope it helps!

Upvotes: 0

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