def test()

in train.py [0:0]

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

def test(epoch):
    global best_acc
    net.eval()
    test_loss = 0
    correct = 0
    total = 0
    for batch_idx, (inputs, targets) in enumerate(testloader):
        if use_cuda:
            inputs, targets = inputs.cuda(), targets.cuda()
        inputs, targets = Variable(inputs, volatile=True), Variable(targets)
        outputs = net(inputs)
        loss = criterion(outputs, targets)

        test_loss += loss.data[0]
        _, predicted = torch.max(outputs.data, 1)
        total += targets.size(0)
        correct += predicted.eq(targets.data).cpu().sum()

        progress_bar(batch_idx, len(testloader),
                     'Loss: %.3f | Acc: %.3f%% (%d/%d)'
                     % (test_loss/(batch_idx+1), 100.*correct/total,
                        correct, total))
    acc = 100.*correct/total
    if epoch == start_epoch + args.epoch - 1 or acc > best_acc:
        checkpoint(acc, epoch)
    if acc > best_acc:
        best_acc = acc
    return (test_loss/batch_idx, 100.*correct/total)