Resume Training from

I’m attempting to save and load best model through torch, where I’ve defined my training function as follows:

def train_model(model, train_loader, test_loader, device, learning_rate=1e-1, num_epochs=200):

    # The training configurations were not carefully selected.

    criterion = nn.CrossEntropyLoss()

    # It seems that SGD optimizer is better than Adam optimizer for ResNet18 training on CIFAR10.
    optimizer = optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9, weight_decay=1e-4)
    # scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=500)
    scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[65, 75], gamma=0.75, last_epoch=-1)
    # optimizer = optim.Adam(model.parameters(), lr=learning_rate, betas=(0.9, 0.999), eps=1e-08, weight_decay=0, amsgrad=False)

    # Evaluation
    eval_loss, eval_accuracy = evaluate_model(model=model, test_loader=test_loader, device=device, criterion=criterion)
    print("Epoch: {:02d} Eval Loss: {:.3f} Eval Acc: {:.3f}".format(-1, eval_loss, eval_accuracy))

    load_model = input('Load a model?')
    for epoch in range(num_epochs):

        if  epoch//2 == 0:
          write_checkpoint(model=model, epoch=epoch, scheduler=scheduler, optimizer=optimizer)          
          model, optimizer, epoch, scheduler = load_checkpoint(model=model, scheduler=scheduler, optimizer=optimizer)    
          for state in optimizer.state.values():
            for k, v in state.items():
                if isinstance(v, torch.Tensor):
                    state[k] =
        # Training

        running_loss = 0
        running_corrects = 0

        for inputs, labels in train_loader:
            inputs = torch.FloatTensor(inputs)
            inputs =
            labels =

            # zero the parameter gradients

            # forward + backward + optimize
            outputs = model(inputs)
            _, preds = torch.max(outputs, 1)
            loss = criterion(outputs, labels)

            # statistics
            running_loss += loss.item() * inputs.size(0)
            running_corrects += torch.sum(preds ==

        train_loss = running_loss / len(train_loader.dataset)
        train_accuracy = running_corrects / len(train_loader.dataset)

        # Evaluation
        eval_loss, eval_accuracy = evaluate_model(model=model, test_loader=test_loader, device=device, criterion=criterion)

        # Set learning rate scheduler

        print("Epoch: {:03d} Train Loss: {:.3f} Train Acc: {:.3f} Eval Loss: {:.3f} Eval Acc: {:.3f}".format(epoch, train_loss, train_accuracy, eval_loss, eval_accuracy))

    return model

Where I’d like to be able to load a model, and start training from the epoch where model was saved.

So far I have methods to save model, optimizer,scheduler states and the epoch via some existing open discussions

def write_checkpoint(model, optimizer, epoch, scheduler):
  state = {'epoch': epoch + 1, 'state_dict': model.state_dict(),
             'optimizer': optimizer.state_dict(), 'scheduler': scheduler.state_dict(), }
  filename = '/content/model_', filename + f'CP_epoch{epoch + 1}.pth')

def load_checkpoint(model, optimizer, scheduler, filename='/content/checkpoint.pth'):
    # Note: Input model & optimizer should be pre-defined.  This routine only updates their states.
    start_epoch = 0
    if os.path.isfile(filename):
        print("=> loading checkpoint '{}'".format(filename))
        checkpoint = torch.load(filename)
        start_epoch = checkpoint['epoch']
        scheduler = checkpoint['scheduler']
        print("=> loaded checkpoint '{}' (epoch {})"
                  .format(filename, checkpoint['epoch']))
        print("=> no checkpoint found at '{}'".format(filename))

    return model, optimizer, start_epoch, scheduler

But I can’t seem to come up with the logic of how I’d update the epoch to restart at the correct value rather than what value of epoch is in the for loop.