Fixing hopenet

natanielruiz

2017-09-14 dd62d6fa4a85f18a29de009a972f5599b19ec946

 code/train.py

@@ -43,6 +43,11 @@
          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)
    parser.add_argument('--output_string', dest='output_string', help='String appended to output snapshots.', default = '', type=str)
    parser.add_argument('--alpha', dest='alpha', help='Regression loss coefficient.',
          default=0.001, type=float)
    parser.add_argument('--iter_ref', dest='iter_ref', help='Number of iterative refinement passes.',
          default=1, type=int)
    args = parser.parse_args()
    return args

@@ -51,26 +56,37 @@
    b = []
    b.append(model.conv1)
    b.append(model.bn1)
    for i in range(len(b)):
        for module_name, module in b[i].named_modules():
            if 'bn' in module_name:
                module.eval()
            for name, param in module.named_parameters():
                yield param

def get_non_ignored_params(model):
    # Generator function that yields params that will be optimized.
    b = []
    b.append(model.layer1)
    b.append(model.layer2)
    b.append(model.layer3)
    b.append(model.layer4)
    for i in range(len(b)):
        for j in b[i].modules():
            for k in j.parameters():
                yield k
        for module_name, module in b[i].named_modules():
            if 'bn' in module_name:
                module.eval()
            for name, param in module.named_parameters():
                yield param

def get_non_ignored_params(model):
    # Generator function that yields params that will be optimized.
def get_fc_params(model):
    b = []
    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():
                    yield k
        for module_name, module in b[i].named_modules():
            for name, param in module.named_parameters():
                yield param

def load_filtered_state_dict(model, snapshot):
    # By user apaszke from discuss.pytorch.org
@@ -97,18 +113,14 @@
    # 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, args.iter_ref)
    # 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.'

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

    transformations = transforms.Compose([transforms.Scale(250),
    transformations = transforms.Compose([transforms.Scale(240),
    transforms.RandomCrop(224), transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])

@@ -120,17 +132,19 @@
                                               num_workers=2)

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

    idx_tensor = [idx for idx in xrange(66)]
    idx_tensor = torch.FloatTensor(idx_tensor).cuda(gpu)
    idx_tensor = Variable(torch.FloatTensor(idx_tensor)).cuda(gpu)

    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.Adam([{'params': get_ignored_params(model), 'lr': 0},
                                  {'params': get_non_ignored_params(model), 'lr': args.lr},
                                  {'params': get_fc_params(model), 'lr': args.lr * 2}],
                                   lr = args.lr)

    print 'Ready to train network.'

@@ -153,13 +167,13 @@
            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 = softmax(pre_yaw)
            pitch_predicted = softmax(pre_pitch)
            roll_predicted = 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)
            yaw_predicted = torch.sum(yaw_predicted * idx_tensor, 1)
            pitch_predicted = torch.sum(pitch_predicted * idx_tensor, 1)
            roll_predicted = torch.sum(roll_predicted * idx_tensor, 1)

            loss_reg_yaw = reg_criterion(yaw_predicted, label_yaw.float())
            loss_reg_pitch = reg_criterion(pitch_predicted, label_pitch.float())
@@ -180,13 +194,13 @@
                       %(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/hopenet50_epoch_'+ str(i+1) + '.pkl')
                #     'output/snapshots/' + args.output_string + '_iter_'+ str(i+1) + '.pkl')

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

    print 'Second phase of training (finetuning layer).'
    for epoch in range(num_epochs_ft):
@@ -208,13 +222,13 @@
            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 = softmax(pre_yaw)
            pitch_predicted = softmax(pre_pitch)
            roll_predicted = 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)
            yaw_predicted = torch.sum(yaw_predicted * idx_tensor, 1)
            pitch_predicted = torch.sum(pitch_predicted * idx_tensor, 1)
            roll_predicted = torch.sum(roll_predicted * idx_tensor, 1)

            loss_reg_yaw = reg_criterion(yaw_predicted, label_yaw.float())
            loss_reg_pitch = reg_criterion(pitch_predicted, label_pitch.float())
@@ -226,9 +240,11 @@
            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]
            for idx in xrange(args.iter_ref+1):
                loss_angles = reg_criterion(angles[idx], label_angles.float())
                loss_seq.append(loss_angles)

            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()
@@ -238,14 +254,10 @@
                       %(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')
                #     'output/snapshots/' + args.output_string + '_iter_'+ str(i+1) + '.pkl')

        # Save models at numbered epochs.
        if epoch % 1 == 0 and epoch < num_epochs_ft - 1:
        if epoch % 1 == 0 and epoch < num_epochs_ft:
            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/hopenet50_epoch_' + str(num_epochs+epoch+1) + '.pkl')
            'output/snapshots/' + args.output_string + '_epoch_'+ str(num_epochs+epoch+1) + '.pkl')