def validate_classification()

in src/transformers/utils/image_classification.py [0:0]

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

def validate_classification(cfg, data_loader, model, epoch, writer=None):
    batch_time = AverageMeter()
    data_time = AverageMeter()
    losses = AverageMeter()
    top1 = AverageMeter()
    top5 = AverageMeter()

    criterion = torch.nn.CrossEntropyLoss()
    model.eval()

    end = time.time()
    for step, (samples, targets) in enumerate(data_loader):
        samples = samples.cuda(non_blocking=True)
        targets = targets.cuda(non_blocking=True)
        data_time.update(time.time() - end)

        # consider 5 crops and 10 crops evaluation
        if len(samples.shape) == 4:
            samples.unsqueeze_(dim=1)
        b, n, c, w, h = samples.shape
        samples = samples.reshape(-1, c, w, h)
        outputs = model(samples)
        outputs = outputs.reshape(b, n, -1)
        outputs = torch.mean(outputs, dim=1)

        loss = criterion(outputs, targets)
        acc1, acc5 = accuracy(outputs.data, targets, topk=(1, 5))

        acc1 = reduce_tensor(acc1)
        acc5 = reduce_tensor(acc5)
        loss = reduce_tensor(loss)

        losses.update(loss.item(), targets.size(0))
        top1.update(acc1.item(), targets.size(0))
        top5.update(acc5.item(), targets.size(0))

        batch_time.update(time.time() - end)
        end = time.time()

        if cfg.DDP_CONFIG.GPU_WORLD_RANK == 0 and step % cfg.CONFIG.LOG.DISPLAY_FREQ == 0:
            print('----validation----')
            print_string = 'Epoch: [{0}][{1}/{2}]'.format(epoch, step + 1, len(data_loader))
            print(print_string)
            memory_used = torch.cuda.max_memory_allocated() / (1024.0 * 1024.0)
            print_string = 'memory used: {memory:.0f}MB'.format(memory=memory_used)
            print(print_string)
            print_string = 'data_time: {data_time:.3f}, batch time: {batch_time:.3f}'.format(
                data_time=data_time.val,
                batch_time=batch_time.val)
            print(print_string)
            print_string = 'loss: {loss:.5f}'.format(loss=losses.avg)
            print(print_string)
            print_string = 'Top-1 accuracy: {top1_acc:.2f}%, Top-5 accuracy: {top5_acc:.2f}%'.format(
                top1_acc=top1.avg,
                top5_acc=top5.avg)
            print(print_string)

    if cfg.DDP_CONFIG.GPU_WORLD_RANK == 0 and writer is not None:
        writer.add_scalar('val_loss_epoch', losses.avg, epoch)
        writer.add_scalar('val_top1_acc_epoch', top1.avg, epoch)
        writer.add_scalar('val_top5_acc_epoch', top5.avg, epoch)

    return top1.avg, top5.avg, losses.avg