blame | 历史 | 补丁 | 提交 | 提交对比 | ignore whitespace
natanielruiz
2017-09-27 43416c4717d2430c3e11f042294d12b781fee2e1 
 code/train_preangles.py


@@ -18,6 +18,8 @@


import hopenet


import torch.utils.model_zoo as model_zoo




import time




model_urls = {


    'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',


    'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',


@@ -32,8 +34,6 @@


    parser.add_argument('--gpu', dest='gpu_id', help='GPU device id to use [0]',


            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)


@@ -103,7 +103,6 @@




    cudnn.enabled = True


    num_epochs = args.num_epochs


    num_epochs_ft = args.num_epochs_ft


    batch_size = args.batch_size


    gpu = args.gpu_id




@@ -124,15 +123,18 @@


    transforms.RandomCrop(224), transforms.ToTensor(),


    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])






    if args.dataset == 'Pose_300W_LP':


        pose_dataset = datasets.Pose_300W_LP(args.data_dir, args.filename_list, transformations)


    if args.dataset == 'Pose_300W_LP_random_ds':


        pose_dataset = datasets.Pose_300W_LP_random_ds(args.data_dir, args.filename_list, transformations)


    elif args.dataset == 'AFLW2000':


        pose_dataset = datasets.AFLW2000(args.data_dir, args.filename_list, transformations)


    elif args.dataset == 'BIWI':


        pose_dataset = datasets.BIWI(args.data_dir, args.filename_list, transformations)


    elif args.dataset == 'AFLW':


        pose_dataset = datasets.AFLW(args.data_dir, args.filename_list, transformations)


    elif args.dataset == 'AFLW_aug':


        pose_dataset = datasets.AFLW_aug(args.data_dir, args.filename_list, transformations)


    elif args.dataset == 'AFW':


        pose_dataset = datasets.AFW(args.data_dir, args.filename_list, transformations)


    else:


@@ -144,9 +146,9 @@


                                               num_workers=2)




    model.cuda(gpu)


    softmax = nn.Softmax()


    criterion = nn.CrossEntropyLoss().cuda()


    reg_criterion = nn.MSELoss().cuda()


    softmax = nn.Softmax().cuda(gpu)


    criterion = nn.CrossEntropyLoss().cuda(gpu)


    reg_criterion = nn.MSELoss().cuda(gpu)


    # Regression loss coefficient


    alpha = args.alpha




@@ -155,24 +157,30 @@




    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}],


                                  {'params': get_fc_params(model), 'lr': args.lr * 5}],


                                   lr = args.lr)




    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))


            label_yaw = Variable(labels[:,0].cuda(gpu))


            label_pitch = Variable(labels[:,1].cuda(gpu))


            label_roll = Variable(labels[:,2].cuda(gpu))


        # start = time.time()


        for i, (images, labels, cont_labels, name) in enumerate(train_loader):


            # print i


            # print 'start: ', time.time() - start


            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(cont_labels[:,:3]).cuda(gpu)


            label_yaw_cont = Variable(cont_labels[:,0]).cuda(gpu)


            label_pitch_cont = Variable(cont_labels[:,1]).cuda(gpu)


            label_roll_cont = Variable(cont_labels[:,2]).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)


@@ -183,27 +191,25 @@


            pitch_predicted = softmax(pre_pitch)


            roll_predicted = softmax(pre_roll)




            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)


            yaw_predicted = torch.sum(yaw_predicted * idx_tensor, 1) * 3 - 99


            pitch_predicted = torch.sum(pitch_predicted * idx_tensor, 1) * 3 - 99


            roll_predicted = torch.sum(roll_predicted * idx_tensor, 1) * 3 - 99




            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())


            loss_reg_yaw = reg_criterion(yaw_predicted, label_yaw_cont)


            loss_reg_pitch = reg_criterion(pitch_predicted, label_pitch_cont)


            loss_reg_roll = reg_criterion(roll_predicted, label_roll_cont)




            # print yaw_predicted, label_yaw.float(), loss_reg_yaw


            # Total loss


            loss_yaw += alpha * loss_reg_yaw


            loss_pitch += alpha * loss_reg_pitch


            loss_roll += alpha * loss_reg_roll




            loss_yaw *= 1




            loss_seq = [loss_yaw, loss_pitch, loss_roll]


            # loss_seq = [loss_reg_yaw, loss_reg_pitch, loss_reg_roll]


            grad_seq = [torch.Tensor(1).cuda(gpu) for _ in range(len(loss_seq))]


            torch.autograd.backward(loss_seq, grad_seq)


            optimizer.step()




            # print 'end: ', time.time() - start




            if (i+1) % 100 == 0:


                print ('Epoch [%d/%d], Iter [%d/%d] Losses: Yaw %.4f, Pitch %.4f, Roll %.4f'


@@ -217,65 +223,3 @@


            print 'Taking snapshot...'


            torch.save(model.state_dict(),


            'output/snapshots/' + args.output_string + '_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 = 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)




            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/' + args.output_string + '_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/' + args.output_string + '_epoch_'+ str(num_epochs+epoch+1) + '.pkl')






    # Save the final Trained Model


    torch.save(model.state_dict(), 'output/snapshots/' + args.output_string + '_epoch_' + str(num_epochs+epoch+1) + '.pkl')