Function validate

utils/msdnet_function.py:22–87

(val_loader, model, criterion)

    args.num_classes = 1000
    
def validate(val_loader, model, criterion):
    print(args.nBlocks)
    batch_time = AverageMeter()
    losses = AverageMeter()
    data_time = AverageMeter()
    top1, top5 = [], []
    for i in range(args.nBlocks):
        top1.append(AverageMeter())
        top5.append(AverageMeter())

    # switch to evaluate mode
    model.eval()

    end = time.time()
    with torch.no_grad():
        for i, (input, target) in enumerate(val_loader):
            target = target.cuda(non_blocking=True)
            input = input.cuda()

            input_var = torch.autograd.Variable(input)
            target_var = torch.autograd.Variable(target)

            data_time.update(time.time() - end)

            # compute output
            output, _ = model(input_var)
            if not isinstance(output, list):
                output = [output]

            loss = 0.0
            for j in range(len(output)):
                loss += criterion(output[j], target_var)

            # measure error and record loss
            losses.update(loss.item(), input.size(0))

            for j in range(len(output)):
                err1, err5 = accuracy(output[j].data, target, topk=(1, 5))
                top1[j].update(err1.item(), input.size(0))
                top5[j].update(err5.item(), input.size(0))

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

            if i % args.print_freq == 0:
                print('Epoch: [{0}/{1}]\t'
                      'Time {batch_time.avg:.3f}\t'
                      'Data {data_time.avg:.3f}\t'
                      'Loss {loss.val:.4f}\t'
                      'Err@1 {top1.val:.4f}\t'
                      'Err@5 {top5.val:.4f}'.format(
                        i + 1, len(val_loader),
                        batch_time=batch_time, data_time=data_time,
                        loss=losses, top1=top1[-1], top5=top5[-1]))
                # break
    for j in range(args.nBlocks):
        print(' * Err@1 {top1.avg:.3f} Err@5 {top5.avg:.3f}'.format(top1=top1[j], top5=top5[j]))