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natanielruiz

2017-09-21 9a02f63f4d5692399a95cb889e8f7629a165c28e

 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

@@ -123,7 +122,6 @@
    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])])


    if args.dataset == 'Pose_300W_LP':
        pose_dataset = datasets.Pose_300W_LP(args.data_dir, args.filename_list, transformations)
@@ -146,9 +144,9 @@
                                               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

@@ -161,25 +159,26 @@
                                   lr = args.lr)

    print 'Ready to train network.'

    print 'First phase of training.'
    for epoch in range(num_epochs):
        start = time.time()
        for i, (images, labels, cont_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))
            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))
            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)
@@ -198,7 +197,6 @@
            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
@@ -209,6 +207,8 @@
            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'
                       %(epoch+1, num_epochs, i+1, len(pose_dataset)//batch_size, loss_yaw.data[0], loss_pitch.data[0], loss_roll.data[0]))