Ejemplo n.º 1
0
                                          batch_size=batch_size,
                                          sampler=test_sample)

# GPU setup
device = torch.device('cuda')

net = AlexNet(in_channel=2, classes=10).to(device=device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.001)  #, momentum=0.9)

# Train loop
num_epochs = 25
for epoch in range(num_epochs):

    print("Epoch: {} - Train".format(epoch))
    net.train()
    running_loss = 0.
    # Train:
    for batch_index, (signals, labels) in enumerate(tqdm(train_loader)):

        signals, labels = signals.to(device=device), labels.to(device=device)

        optimizer.zero_grad()

        outputs = net(signals)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        #if batch_index > 50:
Ejemplo n.º 2
0
def main(args):

    logs = []

    transform = transforms.Compose([
                transforms.ToTensor(),
                transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
            ])

    trainloader, testloader = get_dataset(args, transform)
    net = AlexNet()

    if args.no_distributed:
        optimizer = optim.SGD(net.parameters(), lr=args.lr, momentum=0.0)
    else:
        optimizer = DownpourSGD(net.parameters(), lr=args.lr, n_push=args.num_push, n_pull=args.num_pull, model=net)
    scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=1, verbose=True, min_lr=1e-3)

    # train
    net.train()
    if args.cuda:
        net = net.cuda()

    for epoch in range(args.epochs):  # loop over the dataset multiple times
        print("Training for epoch {}".format(epoch))
        for i, data in enumerate(trainloader, 0):
            # get the inputs
            inputs, labels = data

            if args.cuda:
                inputs, labels = inputs.cuda(), labels.cuda()

            # zero the parameter gradients
            optimizer.zero_grad()
            # forward + backward + optimize
            outputs = net(inputs)
            loss = F.cross_entropy(outputs, labels)
            loss.backward()
            optimizer.step()

            _, predicted = torch.max(outputs, 1)
            accuracy = accuracy_score(predicted, labels)

            log_obj = {
                'timestamp': datetime.now(),
                'iteration': i,
                'training_loss': loss.item(),
                'training_accuracy': accuracy,
            }

            if i % args.log_interval == 0 and i > 0:    # print every n mini-batches
                log_obj['test_loss'], log_obj['test_accuracy']= evaluate( net, testloader, args)
                print("Timestamp: {timestamp} | "
                      "Iteration: {iteration:6} | "
                      "Loss: {training_loss:6.4f} | "
                      "Accuracy : {training_accuracy:6.4f} | "
                      "Test Loss: {test_loss:6.4f} | "
                      "Test Accuracy: {test_accuracy:6.4f}".format(**log_obj))

            logs.append(log_obj)
                
        val_loss, val_accuracy = evaluate(net, testloader, args, verbose=True)
        scheduler.step(val_loss)

    df = pd.DataFrame(logs)
    print(df)
    if args.no_distributed:
        if args.cuda:
            df.to_csv('log/gpu.csv', index_label='index')
        else:
            df.to_csv('log/single.csv', index_label='index')
    else:
        df.to_csv('log/node{}.csv'.format(dist.get_rank()), index_label='index')

    print('Finished Training')
Ejemplo n.º 3
0
                else:
                    # GoogLeNet
                    output, _1, _2 = model(image)
                _, pred = torch.max(output.data, 1)
                correct = (pred == label).sum()
                loss = loss_func(output, label.long())
                total_loss += loss.item()
                total_acc += correct.cpu().numpy()
                step += 1
            total_loss /= step
            total_acc /= 10000
            return total_acc, total_loss


    # train model
    model.train()
    steps = len(train_loader)
    bar = ProgressBar(maxStep=steps)   # initialize progress bar
    for epoch in range(epochs):
        epoch_loss = 0.0
        epoch_acc = 0.0
        for i, (image, label) in enumerate(train_loader):
            image, label = image.to(Device), label.to(Device)  # step 1.
            optimizer.zero_grad()  # step 2.
            if modelName != 'GoogLeNet':
                # Others
                output = model(image)  # step 3.
                loss = loss_func(output, label.long())  # step 4.
            else:
                # GoogLeNet
                output, auxOut_1, auxOut_2 = model(image)