Hello,
is it possible to register forward hooks to CNN layers inside a network, calculate their L1 loss and backpropagate on this?
The aim is to train two feature maps to look the same.
Example (slightly pseudo code…)
class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = torch.flatten(x, 1) # flatten all dimensions except batch
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

activation = {}
def get_activation(name):
    def hook(model, input, output):
        activation[name] = output.detach()
    return hook

model = Net()

model.conv1.register_forward_hook(get_activation("conv1"))
model.conv2.register_forward_hook(get_activation("conv2"))

model(x)
l1_loss = torch.nn.L1Loss()
loss = l1_loss(activation["conv1"], activation["conv2"])
loss.mean().backward()