test_loader = data_utils.DataLoader(test_data,
batch_size=10,
shuffle=False)
model.eval()
for sample_batched in test_loader:
if args.use_gpu and torch.cuda.is_available():
x_1 = torch.autograd.Variable(sample_batched['glove'].cuda())
x_2 = torch.autograd.Variable(sample_batched['res'].cuda())
x_3 = torch.autograd.Variable(sample_batched['bitrate'].cuda())
x_4 = torch.autograd.Variable(sample_batched['gender'].cuda())
x_5 = torch.autograd.Variable(sample_batched['age'].cuda())
target = torch.autograd.Variable(sample_batched['label'].cuda())
else:
x_1 = torch.autograd.Variable(sample_batched['glove'])
x_2 = torch.autograd.Variable(sample_batched['res'])
x_3 = torch.autograd.Variable(sample_batched['bitrate'])
x_4 = torch.autograd.Variable(sample_batched['gender'])
x_5 = torch.autograd.Variable(sample_batched['age'])
target = torch.autograd.Variable(sample_batched['label'])
output, _ = model(x_1, x_2, x_3, x_4, x_5)
pred = output.data.max(1, keepdim=True)[1]
print("True: {}".format(target))
print("Predict: {}".format(pred))
if __name__ == '__main__':
test_confusion_matrix(parse_arguments(sys.argv[1:]))
x_1 = torch.autograd.Variable(sample_batched['glove'])
x_2 = torch.autograd.Variable(sample_batched['res'])
x_3 = torch.autograd.Variable(sample_batched['bitrate'])
x_4 = torch.autograd.Variable(sample_batched['gender'])
x_5 = torch.autograd.Variable(sample_batched['age'])
_, fc2_test = model(x_1, x_2, x_3, x_4, x_5)
features_test.append(fc2_test.data.cpu().numpy())
test_features = np.concatenate(features_test, 0)
total_deep = float(
(datetime.datetime.now() - start_deep).total_seconds()) / float(
len(test_data))
print("DeepQoE total cost {}s".format(total_deep))
print(len(test_data))
prediction = clf.predict(test_features)
acc = accuracy_score(prediction, y_test)
print("{} uses DeepQoE features can get {}%".format(
cfg.CLASSIFIER_NAME[args.classifier], acc * 100.0))
clf_ori = cfg.CLASSIFIER[args.classifier]
clf_ori.fit(x_train.astype(float), y_train)
prediction_ori = clf_ori.predict(x_test.astype(float))
acc_ori = accuracy_score(prediction_ori, y_test)
print("{} uses original features can get {}%".format(
cfg.CLASSIFIER_NAME[args.classifier], acc_ori * 100.0))
if __name__ == '__main__':
train_test_SVM(parse_arguments(sys.argv[1:]))
test_loader = data_utils.DataLoader(test_data,
batch_size=18,
shuffle=False)
model.eval()
test_loss = 0
for sample_batched in test_loader:
if args.use_gpu and torch.cuda.is_available():
x_1 = torch.autograd.Variable(sample_batched['glove'].cuda())
x_2 = torch.autograd.Variable(sample_batched['res'].cuda())
x_3 = torch.autograd.Variable(sample_batched['bitrate'].cuda())
target = torch.autograd.Variable(sample_batched['label'].cuda())
else:
x_1 = torch.autograd.Variable(sample_batched['glove'])
x_2 = torch.autograd.Variable(sample_batched['res'])
x_3 = torch.autograd.Variable(sample_batched['bitrate'])
target = torch.autograd.Variable(sample_batched['label'])
output, _ = model(x_1, x_2, x_3)
test_loss += F.mse_loss(output, target.float(),
size_average=False).data[0]
# print (output)
print(target.float())
test_loss /= len(test_loader.dataset)
print('\nTest set: Average loss: {:.4f}'.format(test_loss))
if __name__ == '__main__':
main(parse_arguments(sys.argv[1:]))