train.py 文件源码

def test_nll(args, epoch, model, testLoader, optimizer, testF, weights):
    model.eval()
    test_loss = 0
    dice_loss = 0
    incorrect = 0
    numel = 0
    for data, target in testLoader:
        if args.cuda:
            data, target = data.cuda(), target.cuda()
        data, target = Variable(data, volatile=True), Variable(target)
        target = target.view(target.numel())
        numel += target.numel()
        output = model(data)
        test_loss += F.nll_loss(output, target, weight=weights).data[0]
        dice_loss += bioloss.dice_error(output, target)
        pred = output.data.max(1)[1]  # get the index of the max log-probability
        incorrect += pred.ne(target.data).cpu().sum()

    test_loss /= len(testLoader)  # loss function already averages over batch size
    dice_loss /= len(testLoader)
    err = 100.*incorrect/numel
    print('\nTest set: Average loss: {:.4f}, Error: {}/{} ({:.3f}%) Dice: {:.6f}\n'.format(
        test_loss, incorrect, numel, err, dice_loss))

    testF.write('{},{},{}\n'.format(epoch, test_loss, err))
    testF.flush()
    return err