def validate()

in imagenet_1k_eval.py [0:0]

• 28 lines of code
• 6 McCabe index (conditional complexity)

def validate(val_loader, model, criterion, args):
    batch_time = AverageMeter("Time", ":6.3f", Summary.NONE)
    losses = AverageMeter("Loss", ":.4e", Summary.NONE)
    top1 = AverageMeter("Acc@1", ":6.2f", Summary.AVERAGE)
    top5 = AverageMeter("Acc@5", ":6.2f", Summary.AVERAGE)
    progress = ProgressMeter(
        len(val_loader), [batch_time, losses, top1, top5], prefix="Test: "
    )

    # switch to evaluate mode
    model.eval()

    with torch.no_grad():
        end = time.time()
        for i, (images, target) in enumerate(val_loader):
            if args.gpu is not None:
                images = images.cuda(args.gpu, non_blocking=True)
            if torch.cuda.is_available():
                target = target.cuda(args.gpu, non_blocking=True)

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

            # measure accuracy and record loss
            acc1, acc5 = 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))

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

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

        progress.display_summary()

    return top1.avg