Function validate

main_linear.py:188–226

validation

(val_loader, model, classifier, criterion, opt)

def validate(val_loader, model, classifier, criterion, opt):
    """validation"""
    model.eval()
    classifier.eval()

    batch_time = AverageMeter()
    losses = AverageMeter()
    top1 = AverageMeter()

    with torch.no_grad():
        end = time.time()
        for idx, (images, labels) in enumerate(val_loader):
            images = images.float().cuda()
            labels = labels.cuda()
            bsz = labels.shape[0]

            # forward
            output = classifier(model.encoder(images))
            loss = criterion(output, labels)

            # update metric
            losses.update(loss.item(), bsz)
            acc1, acc5 = accuracy(output, labels, topk=(1, 5))
            top1.update(acc1[0], bsz)

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

            if idx % opt.print_freq == 0:
                print('Test: [{0}/{1}]\t'
                      'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                      'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
                      'Acc@1 {top1.val:.3f} ({top1.avg:.3f})'.format(
                       idx, len(val_loader), batch_time=batch_time,
                       loss=losses, top1=top1))

    print(' * Acc@1 {top1.avg:.3f}'.format(top1=top1))
    return losses.avg, top1.avg


def main():