def train()

in main_cls.py [0:0]

• 35 lines of code
• 8 McCabe index (conditional complexity)

def train(train_loader, model, criterion, optimizer, epoch, args):
    batch_time = utils.AverageMeter('Time', ':6.3f')
    data_time = utils.AverageMeter('Data', ':6.3f')
    losses = utils.AverageMeter('Loss', ':.4e')
    top1 = utils.AverageMeter('Acc@1', ':6.2f')
    top5 = utils.AverageMeter('Acc@5', ':6.2f')
    if len(optimizer.param_groups) > 1:
        curr_lr = [param_group['lr'] for param_group in optimizer.param_groups]
    else:
        curr_lr = optimizer.param_groups[0]['lr']
    progress = utils.ProgressMeter(
        len(train_loader),
        [batch_time, data_time, losses, top1, top5],
        prefix="Epoch: [{}/{}]\t"
               "LR: {}\t".format(epoch, args.epochs, curr_lr))

    # switch to train mode
    model.train()

    end = time.time()
    for i, (images, target) in enumerate(train_loader):
        # measure data loading time
        data_time.update(time.time() - end)

        if args.gpu is not None:
            images = images.cuda(args.gpu, non_blocking=True)
        target = target.cuda(args.gpu, non_blocking=True)

        # compute output
        output = model(images)
        loss = criterion(output, target)

        # measure accuracy and record loss
        acc1, acc5 = utils.accuracy(output, target, topk=(1, 5))
        losses.update(loss.item(), images.size(0))
        top1.update(acc1[0], images.size(0))
        top5.update(acc5[0], images.size(0))

        # compute gradient and do SGD step
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        # measure elapsed time
        batch_time.update(time.time() - end)
        end = time.time()

        if i % args.print_freq == 0:
            progress.display(i)