Finetune layer working

natanielruiz

2017-09-08 0b8e19c1cc8ad03805d4ca68f32df6e4806a36e8

 code/train.py

@@ -33,6 +33,8 @@
            default=0, type=int)
    parser.add_argument('--num_epochs', dest='num_epochs', help='Maximum number of training epochs.',
          default=5, type=int)
    parser.add_argument('--num_epochs_ft', dest='num_epochs_ft', help='Maximum number of finetuning epochs.',
          default=5, type=int)
    parser.add_argument('--batch_size', dest='batch_size', help='Batch size.',
          default=16, type=int)
    parser.add_argument('--lr', dest='lr', help='Base learning rate.',
@@ -41,9 +43,7 @@
          default='', type=str)
    parser.add_argument('--filename_list', dest='filename_list', help='Path to text file containing relative paths for every example.',
          default='', type=str)

    args = parser.parse_args()

    return args

def get_ignored_params(model):
@@ -66,6 +66,7 @@
    b.append(model.fc_yaw)
    b.append(model.fc_pitch)
    b.append(model.fc_roll)
    b.append(model.fc_finetune)
    for i in range(len(b)):
        for j in b[i].modules():
            for k in j.parameters():
@@ -86,6 +87,7 @@

    cudnn.enabled = True
    num_epochs = args.num_epochs
    num_epochs_ft = args.num_epochs_ft
    batch_size = args.batch_size
    gpu = args.gpu_id

@@ -102,10 +104,15 @@

    print 'Loading data.'

    transformations = transforms.Compose([transforms.Scale(224),transforms.RandomCrop(224),
                                          transforms.ToTensor()])
    # transformations = transforms.Compose([transforms.Scale(224),
    #                                       transforms.RandomCrop(224),
    #                                       transforms.ToTensor()])

    pose_dataset = datasets.Pose_300W_LP_binned(args.data_dir, args.filename_list,
    transformations = transforms.Compose([transforms.Scale(250),
    transforms.RandomCrop(224), transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])

    pose_dataset = datasets.Pose_300W_LP(args.data_dir, args.filename_list,
                                transformations)
    train_loader = torch.utils.data.DataLoader(dataset=pose_dataset,
                                               batch_size=batch_size,
@@ -127,6 +134,7 @@

    print 'Ready to train network.'

    print 'First phase of training.'
    for epoch in range(num_epochs):
        for i, (images, labels, name) in enumerate(train_loader):
            images = Variable(images.cuda(gpu))
@@ -137,17 +145,17 @@
            optimizer.zero_grad()
            model.zero_grad()

            yaw, pitch, roll = model(images)
            pre_yaw, pre_pitch, pre_roll, angles = model(images)

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

            # MSE loss
            yaw_predicted = F.softmax(yaw)
            pitch_predicted = F.softmax(pitch)
            roll_predicted = F.softmax(roll)
            yaw_predicted = F.softmax(pre_yaw)
            pitch_predicted = F.softmax(pre_pitch)
            roll_predicted = F.softmax(pre_roll)

            yaw_predicted = torch.sum(yaw_predicted.data * idx_tensor, 1)
            pitch_predicted = torch.sum(pitch_predicted.data * idx_tensor, 1)
@@ -167,21 +175,77 @@
            torch.autograd.backward(loss_seq, grad_seq)
            optimizer.step()

            # 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 (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/resnet50_iter_'+ str(i+1) + '.pkl')
                # if epoch == 0:
                #     torch.save(model.state_dict(),
                #     'output/snapshots/hopenet50_epoch_'+ str(i+1) + '.pkl')

        # Save models at numbered epochs.
        if epoch % 1 == 0 and epoch < num_epochs - 1:
        if epoch % 1 == 0 and epoch < num_epochs:
            print 'Taking snapshot...'
            torch.save(model.state_dict(),
            'output/snapshots/resnet50_epoch_'+ str(epoch+1) + '.pkl')
            'output/snapshots/hopenet50_epoch_'+ str(epoch+1) + '.pkl')

    print 'Second phase of training (finetuning layer).'
    for epoch in range(num_epochs_ft):
        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))
            label_angles = Variable(labels[:,:3].cuda(gpu))

            optimizer.zero_grad()
            model.zero_grad()

            pre_yaw, pre_pitch, pre_roll, angles = model(images)

            # Cross entropy loss
            loss_yaw = criterion(pre_yaw, label_yaw)
            loss_pitch = criterion(pre_pitch, label_pitch)
            loss_roll = criterion(pre_roll, label_roll)

            # MSE loss
            yaw_predicted = F.softmax(pre_yaw)
            pitch_predicted = F.softmax(pre_pitch)
            roll_predicted = F.softmax(pre_roll)

            yaw_predicted = torch.sum(yaw_predicted.data * idx_tensor, 1)
            pitch_predicted = torch.sum(pitch_predicted.data * idx_tensor, 1)
            roll_predicted = torch.sum(roll_predicted.data * idx_tensor, 1)

            loss_reg_yaw = reg_criterion(yaw_predicted, label_yaw.float())
            loss_reg_pitch = reg_criterion(pitch_predicted, label_pitch.float())
            loss_reg_roll = reg_criterion(roll_predicted, label_roll.float())

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

            # Finetuning loss
            loss_angles = reg_criterion(angles[0], label_angles.float())

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

            if (i+1) % 100 == 0:
                print ('Epoch [%d/%d], Iter [%d/%d] Losses: pre-yaw %.4f, pre-pitch %.4f, pre-roll %.4f, finetuning %.4f'
                       %(epoch+1, num_epochs_ft, i+1, len(pose_dataset)//batch_size, loss_yaw.data[0], loss_pitch.data[0], loss_roll.data[0], loss_angles.data[0]))
                # if epoch == 0:
                #     torch.save(model.state_dict(),
                #     'output/snapshots/hopenet50_iter_'+ str(i+1) + '.pkl')

        # Save models at numbered epochs.
        if epoch % 1 == 0 and epoch < num_epochs_ft - 1:
            print 'Taking snapshot...'
            torch.save(model.state_dict(),
            'output/snapshots/hopenet50_epoch_'+ str(num_epochs+epoch+1) + '.pkl')


    # Save the final Trained Model
    torch.save(model.state_dict(), 'output/snapshots/resnet50_epoch' + str(epoch+1) + '.pkl')
    torch.save(model.state_dict(), 'output/snapshots/hopenet50_epoch_' + str(num_epochs+epoch+1) + '.pkl')