discuss.pytorch.org/t/concatenate-layer-output-with-additional-input-data/20462
Concatenate layer output with additional input data
I want to build a CNN model that takes additional input data besides the image at a certain layer. To do that, I plan to use a standard CNN model, take one of its last FC layers, concatenate it with the additional input data and add FC layers processing bo
discuss.pytorch.org
사실 간단하지만 궁금했던부분이다.
torch 에서는 이런식으로 layer 를 추가해주고 forward 부분에
x = torch.cat((x1, x2), dim=1)
이런식으로 하는게 특징이다.
class MyModel(nn.Module):
def __init__(self):
super(MyModel, self).__init__()
self.cnn = models.inception_v3(pretrained=False, aux_logits=False)
self.cnn.fc = nn.Linear(
self.cnn.fc.in_features, 20)
self.fc1 = nn.Linear(20 + 10, 60)
self.fc2 = nn.Linear(60, 5)
def forward(self, image, data):
x1 = self.cnn(image)
x2 = data
x = torch.cat((x1, x2), dim=1)
x = F.relu(self.fc1(x))
x = self.fc2(x)
return x
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