0ad33d606682537466f3430fc6d6ac7d47460f1a,intermediate_source/spatial_transformer_tutorial.py,,test,#,183

Before Change

    model.eval()
    test_loss = 0
    correct = 0
    for data, target in test_loader:
        if use_cuda:
            data, target = data.cuda(), target.cuda()
        data, target = Variable(data, volatile=True), Variable(target)
        output = model(data)

        // sum up batch loss
        test_loss += F.nll_loss(output, target, size_average=False).data[0]
        // get the index of the max log-probability
        pred = output.data.max(1, keepdim=True)[1]
        correct += pred.eq(target.data.view_as(pred)).cpu().sum()

    test_loss /= len(test_loader.dataset)
    print("\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n"
          .format(test_loss, correct, len(test_loader.dataset),
                  100. * correct / len(test_loader.dataset)))

After Change

def test():
    with torch.no_grad():
        model.eval()
        test_loss = 0
        correct = 0
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)

            // sum up batch loss
            test_loss += F.nll_loss(output, target, size_average=False).item()
            // get the index of the max log-probability
            pred = output.max(1, keepdim=True)[1]
            correct += pred.eq(target.view_as(pred)).sum().item()

        test_loss /= len(test_loader.dataset)
        print("\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n"
              .format(test_loss, correct, len(test_loader.dataset),
                      100. * correct / len(test_loader.dataset)))

////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// Visualizing the STN results
// ---------------------------
//
// Now, we will inspect the results of our learned visual attention
// mechanism.
//
// We define a small helper function in order to visualize the
// transformations while training.


def convert_image_np(inp):
    Convert a Tensor to numpy image.
    inp = inp.numpy().transpose((1, 2, 0))
    mean = np.array([0.485, 0.456, 0.406])

Instances