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How to use report_test_error method in Slash

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pytorch_logreg_example.py

Source:pytorch_logreg_example.py

...68        ax.set_xlabel('x');69        ax.set_ylabel('y');70        ax.set_zlabel('z')71        plt.show()72def report_test_error():73    correct = 074    total = 075    with torch.no_grad():76        for data in test_loader:77            samples, labels = data78            outputs = net(samples)79            _, predicted = torch.max(outputs, 1)80            total += labels.size(0)81            correct += (predicted == labels).sum().item()82    print('Accuracy of the network on the 10000 test images: %d %%' % (83            100 * correct / total))84if __name__ == '__main__':85    ################################################################################86    # Build or load data87    ################################################################################88    input_dim = 389    output_dim = 290    X_train, Y_train = build_gaussian_data(1000, 200)91    X_test, Y_test = build_gaussian_data(2000, 2000)92    # specify training and testing datasets93    train_dataset = DataLogReg(X_train, Y_train)94    test_dataset = DataLogReg(X_test, Y_test)95    train_dataset.plot()96    ################################################################################97    # Initialize network class98    ################################################################################99    net = LogRegNet(input_dim, output_dim)100    print(net)101    # inspect network class102    params = list(net.parameters())103    print('Num of params matrices to flag_train:', len(params))104    print(params[0].size())105    sample_input = torch.randn(1, input_dim)     # first dimension is the batch dimension when feeding to nn layers106    features_out = net(sample_input)             # expect size batch_size x 2107    print(features_out.size())108    print(features_out)109    ################################################################################110    # Choose loss111    ################################################################################112    criterion = nn.NLLLoss()113    output = net(sample_input)114    target = torch.tensor([1])      # a dummy target class in [0,1], for example115    loss = criterion(output, target)116    print(loss)117    print(loss.grad_fn)  # MSELoss118    print(loss.grad_fn.next_functions[0][0])  # Linear119    print(loss.grad_fn.next_functions[0][0].next_functions[0][0])  # ReLU120    ################################################################################121    # Optimization122    ################################################################################123    # Training loop hyperparameters124    batch_size = 20125    epochs = 10126    learning_rate = 0.001127    # Choose an optimizer or create one128    import torch.optim as optim129    optimizer = optim.SGD(net.parameters(), lr=learning_rate, momentum=0.9)130    # Setup data batching131    nwork = 0132    train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=nwork)133    test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=True, num_workers=nwork)134    # Set device135    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")136    net.to(device)137    ########inputs, labels = data[0].to(device), data[1].to(device)  # what to send to deive? the Dataset object?138    print(device)139    for epoch in range(epochs):       # loop over the dataset multiple times140        print(epoch)141        running_loss = 0.0142        for i, data in enumerate(train_loader, 0):143            inputs, labels = data  # get the inputs; data is a list of [inputs, labels]144            # zero the parameter gradients145            optimizer.zero_grad()146            # forward + backward + optimize147            outputs = net(inputs)148            loss = criterion(outputs, labels)149            loss.backward()150            optimizer.step()151            # print statistics152            running_loss += loss.item()153            if i % 10 == 9:    # print every 9154                print('Epoch: %d, batch: %5d, loss: %.3f' %155                      (epoch, i + 1, running_loss))156                report_test_error()157                running_loss = 0.0158    print('Finished Training')159    ################################################################################160    # Save model161    ################################################################################162    import os163    model_path = DIR_MODELS + os.sep + 'net_logreg.pth'164    torch.save(net.state_dict(), model_path)165    ################################################################################166    # Load model167    ################################################################################168    dataiter = iter(test_loader)169    samples, labels = dataiter.next()170    net = LogRegNet(input_dim, output_dim)171    net.load_state_dict(torch.load(model_path))172    outputs = net(samples)173    _, predicted = torch.max(outputs, 1)174    print('Predicted: ', ' '.join('%5s' % predicted[j] for j in range(4)))175    print('True: ', ' '.join('%5s' % labels[j] for j in range(4)))...

reporter_interface.py

Source:reporter_interface.py

...26            self.report_test_success(test, result)27        elif result.is_skip():28            self.report_test_skip(test, result)29        elif result.is_error():30            self.report_test_error(test, result)31        elif result.is_interrupted():32            self.report_test_interrupted(test, result)33        else:34            assert result.is_failure()35            self.report_test_failure(test, result)36    def report_test_success(self, test, result):37        pass38    def report_test_skip(self, test, result):39        pass40    def report_test_error(self, test, result):41        pass42    def report_test_failure(self, test, result):43        pass44    def report_test_interrupted(self, test, result):45        pass46    def report_test_error_added(self, test, error):47        pass48    def report_test_failure_added(self, test, error):49        pass50    def report_test_skip_added(self, test, reason):51        pass52    def report_fancy_message(self, headline, message):53        pass54    def report_message(self, message):...

test_accuracy.py

Source:test_accuracy.py

2import numpy as np 3import sample 4import sys5import animation6def report_test_error(savefile_name, action_class, test_inds, pre_step, prune_viewpoints):7	summ = 08	for ind in test_inds:9		ind = int(ind)10		print("Checking error for test_ind ", ind)11		action = sample.extract_action('data/joint_positions', action_class, ind, prune_viewpoints)12		initial_input = sample.generate_initial_input(action)13		pred_result = sample.sample('data/', initial_input, savefile_name, pre_step)14		animation.animate_action(pred_result, action_class, ind,'Prediction of %s'%action_class)15		summ += sample.mean_squared_error(pred_result, action)16	return summ / len(test_inds)17if __name__ == '__main__':18	savefile_name = sys.argv[1]19	action_class = sys.argv[2]20	test_inds = sys.argv[3]21	pre_step = 1022	prune_viewpoints = False23	err = report_test_error(savefile_name, action_class, test_inds.split(','), pre_step, prune_viewpoints)...

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