90fbeed3469b8bb2d5731789624b12e4c84df461,code/deep/MRAN/MRAN.py,,test,#Any#Any#,114

Before Change

    test_loss = 0
    correct = 0

    for data, target in test_loader:
        if args.cuda:
            data, target = data.cuda(), target.cuda()
        data, target = Variable(data, volatile=True), Variable(target)
        s_output, t_output = model(data, data, target)
        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(test_loader.dataset)
    print(args.test_dir, "\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n".format(
        test_loss, correct, len(test_loader.dataset),
        100. * correct / len(test_loader.dataset)))

After Change

    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            if args.cuda:
                data, target = data.cuda(), target.cuda()
            s_output, t_output = model(data, data, target)
            test_loss += F.nll_loss(F.log_softmax(s_output, dim = 1), target, reduction="sum").item()// 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(test_loader.dataset)
        print(args.test_dir, "\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n".format(
            test_loss, correct, len(test_loader.dataset),
            100. * correct / len(test_loader.dataset)))
    return correct


if __name__ == "__main__":

Instances