Code accompanying "Multirate Training of Neural Networks" arXiv:2106.10771, ICML 2022

We provide a torch.optimizer corresponding to the following multirate algorithm (with base algorithm SGD with momentum):

To generate the transfer learning results in Section 4 of our paper one simply loads in the optimizer using:

from Optimizer_multirate import Multirate
optimizer = Multirate(net.parameters(),lr=h,momentum=mu)  # where net is the considered neural network, and h and mu are as defined in Algorithm 1

Then one initializes the momenta in epoch 0 for the first batch using: optimizer.initmom() (after calling loss.backward).
If one sets the fast parameters to be the final fully connected (fc) layer, then the training loop becomes:

for batch_idx, (inputs, targets) in enumerate(loader_train): 
    if (batch_idx+1) % k == 0: #where k is as defined in Algorithm 1
        for param in net.parameters():
            param.requires_grad = True

        for param in net.fc.parameters():
            param.requires_grad = False

        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, targets) 
        loss.backward()

        optimizer.stepslow()

        for param in net.parameters():
            param.requires_grad = False

        for param in net.fc.parameters():
            param.requires_grad = True

    else:
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, targets) 
        loss.backward()

        optimizer.stepfast()