Cleanup

natanielruiz

2017-08-12 fdf1fedb0d3b4beb672464a438c22b94b9cb7d0f

 code/train.py

@@ -95,10 +95,10 @@
    # ResNet101 with 3 outputs
    # model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 23, 3], 66)
    # ResNet50
    # model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 66)
    model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 66)
    # ResNet18
    model = hopenet.Hopenet(torchvision.models.resnet.BasicBlock, [2, 2, 2, 2], 66)
    load_filtered_state_dict(model, model_zoo.load_url(model_urls['resnet18']))
    # model = hopenet.Hopenet(torchvision.models.resnet.BasicBlock, [2, 2, 2, 2], 66)
    load_filtered_state_dict(model, model_zoo.load_url(model_urls['resnet50']))

    print 'Loading data.'

@@ -113,11 +113,10 @@
                                               num_workers=2)

    model.cuda(gpu)
    criterion = nn.CrossEntropyLoss()
    reg_criterion = nn.MSELoss()
    criterion = nn.CrossEntropyLoss().cuda()
    reg_criterion = nn.MSELoss().cuda()
    # Regression loss coefficient
    alpha = 0.01
    lsm = nn.Softmax()

    idx_tensor = [idx for idx in xrange(66)]
    idx_tensor = torch.FloatTensor(idx_tensor).cuda(gpu)
@@ -125,33 +124,25 @@
    optimizer = torch.optim.Adam([{'params': get_ignored_params(model), 'lr': args.lr},
                                  {'params': get_non_ignored_params(model), 'lr': args.lr * 10}],
                                  lr = args.lr)
    # optimizer = torch.optim.SGD([{'params': get_ignored_params(model), 'lr': args.lr},
    #                              {'params': get_non_ignored_params(model), 'lr': args.lr}],
    #                               lr = args.lr, momentum=0.9)
    # optimizer = torch.optim.RMSprop([{'params': get_ignored_params(model), 'lr': args.lr},
    #                               {'params': get_non_ignored_params(model), 'lr': args.lr * 10}],
    #                               lr = args.lr)

    print 'Ready to train network.'

    for epoch in range(num_epochs):
        for i, (images, labels, name) in enumerate(train_loader):
            images = Variable(images).cuda(gpu)
            label_yaw = Variable(labels[:,0]).cuda(gpu)
            label_pitch = Variable(labels[:,1]).cuda(gpu)
            label_roll = Variable(labels[:,2]).cuda(gpu)
            images = Variable(images.cuda(gpu))
            label_yaw = Variable(labels[:,0].cuda(gpu))
            label_pitch = Variable(labels[:,1].cuda(gpu))
            label_roll = Variable(labels[:,2].cuda(gpu))

            optimizer.zero_grad()
            model.zero_grad()

            yaw, pitch, roll = model(images)

            # Cross entropy loss
            loss_yaw = criterion(yaw, label_yaw)
            loss_pitch = criterion(pitch, label_pitch)
            loss_roll = criterion(roll, label_roll)

            # loss_seq = [loss_yaw, loss_pitch, loss_roll]
            # grad_seq = [torch.Tensor(1).cuda(gpu) for _ in range(len(loss_seq))]
            # torch.autograd.backward(loss_seq, grad_seq)
            # optimizer.step()

            # MSE loss
            yaw_predicted = F.softmax(yaw)
@@ -166,15 +157,13 @@
            loss_reg_pitch = reg_criterion(pitch_predicted, label_pitch.float())
            loss_reg_roll = reg_criterion(roll_predicted, label_roll.float())

            # print yaw_predicted[0], label_yaw.data[0]

            # Total loss
            loss_yaw += alpha * loss_reg_yaw
            loss_pitch += alpha * loss_reg_pitch
            loss_roll += alpha * loss_reg_roll

            loss_seq = [loss_yaw, loss_pitch, loss_roll]
            grad_seq = [torch.Tensor(1).cuda(gpu) for _ in range(len(loss_seq))]
            model.zero_grad()
            torch.autograd.backward(loss_seq, grad_seq)
            optimizer.step()

@@ -184,15 +173,15 @@
            if (i+1) % 100 == 0:
                print ('Epoch [%d/%d], Iter [%d/%d] Losses: Yaw %.4f, Pitch %.4f, Roll %.4f'
                       %(epoch+1, num_epochs, i+1, len(pose_dataset)//batch_size, loss_yaw.data[0], loss_pitch.data[0], loss_roll.data[0]))
                # if epoch == 0:
                #     torch.save(model.state_dict(),
                #     'output/snapshots/resnet18_sgd_iter_'+ str(i+1) + '.pkl')
                if epoch == 0:
                    torch.save(model.state_dict(),
                    'output/snapshots/resnet50_iter_'+ str(i+1) + '.pkl')

        # Save models at numbered epochs.
        if epoch % 1 == 0 and epoch < num_epochs - 1:
            print 'Taking snapshot...'
            torch.save(model.state_dict(),
            'output/snapshots/resnet18_sgd_epoch_'+ str(epoch+1) + '.pkl')
            'output/snapshots/resnet50_epoch_'+ str(epoch+1) + '.pkl')

    # Save the final Trained Model
    torch.save(model.state_dict(), 'output/snapshots/resnet18_sgd_epoch_' + str(epoch+1) + '.pkl')
    torch.save(model.state_dict(), 'output/snapshots/resnet50_epoch' + str(epoch+1) + '.pkl')