fbec92e2363493126b4927a167039fbd037f17bc,code/deep/DAN/DAN.py,,test,#,90

Before Change

    test_loss = 0
    correct = 0

    for data, target in target_test_loader:
        if cuda:
            data, target = data.cuda(), target.cuda()
        data, target = Variable(data, volatile=True), Variable(target)
        s_output, t_output = model(data, data)
        test_loss += F.nll_loss(F.log_softmax(s_output, dim = 1), target, size_average=False).data[0] // sum up batch loss
        pred = s_output.data.max(1)[1] // get the index of the max log-probability
        correct += pred.eq(target.data.view_as(pred)).cpu().sum()

    test_loss /= len_target_dataset
    print("\n{} set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)\n".format(
        target_name, test_loss, correct, len_target_dataset,
        100. * correct / len_target_dataset))

After Change

    test_loss = 0
    correct = 0

    with torch.no_grad():
        for tgt_test_data, tgt_test_label in tgt_test_loader:
            if cuda:
                tgt_test_data, tgt_test_label = tgt_test_data.cuda(), tgt_test_label.cuda()
            tgt_test_data, tgt_test_label = Variable(tgt_test_data), Variable(tgt_test_label)
            tgt_pred, mmd_loss = model(tgt_test_data, tgt_test_data)
            test_loss += F.nll_loss(F.log_softmax(tgt_pred, dim = 1), tgt_test_label, reduction="sum").item() // sum up batch loss
            pred = tgt_pred.data.max(1)[1] // get the index of the max log-probability
            correct += pred.eq(tgt_test_label.data.view_as(pred)).cpu().sum()

    test_loss /= tgt_dataset_len
    print("\n{} set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)\n".format(
        tgt_name, test_loss, correct, tgt_dataset_len,
        100. * correct / tgt_dataset_len))

Instances