From 0be0ecf0a8fc6df1f9e354f8aea12b7008f658f1 Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期三, 27 九月 2017 06:21:54 +0800
Subject: [PATCH] hopenet experiments

---
 code/hopenet.py            |   90 ++++++++
 code/train_finetune_new.py |  192 +++++++++++++++++
 code/test_new.py           |  136 ++++++++++++
 code/train_hopenet_new.py  |  221 ++++++++++++++++++++
 4 files changed, 639 insertions(+), 0 deletions(-)

diff --git a/code/hopenet.py b/code/hopenet.py
index c6bf0db..de2f4ec 100644
--- a/code/hopenet.py
+++ b/code/hopenet.py
@@ -340,3 +340,93 @@
         angles.append(preangles)
 
         return pre_yaw, pre_pitch, pre_roll, angles, sr_output
+
+class Hopenet_new(nn.Module):
+    # This is just Hopenet with 3 output layers for yaw, pitch and roll.
+    def __init__(self, block, layers, num_bins):
+        self.inplanes = 64
+        super(Hopenet_new, self).__init__()
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
+                               bias=False)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.relu = nn.ReLU(inplace=True)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.layer1 = self._make_layer(block, 64, layers[0])
+        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
+        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
+        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
+        self.avgpool = nn.AvgPool2d(7)
+        self.fc_yaw = nn.Linear(512 * block.expansion, num_bins)
+        self.fc_pitch = nn.Linear(512 * block.expansion, num_bins)
+        self.fc_roll = nn.Linear(512 * block.expansion, num_bins)
+
+        self.softmax = nn.Softmax()
+        self.fc_finetune_new = nn.Linear(512 * block.expansion + 256 * block.expansion + 3, 3)
+        self.conv1x1 = nn.Conv2d(1024, 64, kernel_size = 1, stride = 1, bias=False)
+        self.maxpool_interm = nn.MaxPool2d(kernel_size=5, stride=3, padding=1)
+
+        self.idx_tensor = Variable(torch.FloatTensor(range(66))).cuda()
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+                m.weight.data.normal_(0, math.sqrt(2. / n))
+            elif isinstance(m, nn.BatchNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+
+    def _make_layer(self, block, planes, blocks, stride=1):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(self.inplanes, planes * block.expansion,
+                          kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(planes * block.expansion),
+            )
+
+        layers = []
+        layers.append(block(self.inplanes, planes, stride, downsample))
+        self.inplanes = planes * block.expansion
+        for i in range(1, blocks):
+            layers.append(block(self.inplanes, planes))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x_interm = self.conv1x1(x)
+        x_interm = self.relu(x_interm)
+        x_interm = self.maxpool_interm(x_interm)
+        x_interm = x_interm.view(x_interm.size(0), -1)
+
+        x = self.layer4(x)
+
+        x = self.avgpool(x)
+        x = x.view(x.size(0), -1)
+        pre_yaw = self.fc_yaw(x)
+        pre_pitch = self.fc_pitch(x)
+        pre_roll = self.fc_roll(x)
+
+        yaw = self.softmax(pre_yaw)
+        yaw = Variable(torch.sum(yaw.data * self.idx_tensor.data, 1), requires_grad=True) * 3 - 99
+        pitch = self.softmax(pre_pitch)
+        pitch = Variable(torch.sum(pitch.data * self.idx_tensor.data, 1), requires_grad=True) * 3 - 99
+        roll = self.softmax(pre_roll)
+        roll = Variable(torch.sum(roll.data * self.idx_tensor.data, 1), requires_grad=True) * 3 - 99
+        yaw = yaw.view(yaw.size(0), 1)
+        pitch = pitch.view(pitch.size(0), 1)
+        roll = roll.view(roll.size(0), 1)
+        preangles = torch.cat([yaw, pitch, roll], 1)
+
+        # angles predicts the residual
+        residuals = self.fc_finetune_new(torch.cat((preangles, x_interm, x), 1))
+        final_angles = preangles + residuals
+
+        return pre_yaw, pre_pitch, pre_roll, preangles, final_angles
diff --git a/code/test_new.py b/code/test_new.py
new file mode 100644
index 0000000..3c3f394
--- /dev/null
+++ b/code/test_new.py
@@ -0,0 +1,136 @@
+import numpy as np
+import torch
+import torch.nn as nn
+from torch.autograd import Variable
+from torch.utils.data import DataLoader
+from torchvision import transforms
+import torch.backends.cudnn as cudnn
+import torchvision
+import torch.nn.functional as F
+
+import cv2
+import matplotlib.pyplot as plt
+import sys
+import os
+import argparse
+
+import datasets
+import hopenet
+import utils
+
+def parse_args():
+    """Parse input arguments."""
+    parser = argparse.ArgumentParser(description='Head pose estimation using the Hopenet network.')
+    parser.add_argument('--gpu', dest='gpu_id', help='GPU device id to use [0]',
+            default=0, type=int)
+    parser.add_argument('--data_dir', dest='data_dir', help='Directory path for data.',
+          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('--snapshot', dest='snapshot', help='Path of model snapshot.',
+          default='', type=str)
+    parser.add_argument('--batch_size', dest='batch_size', help='Batch size.',
+          default=1, type=int)
+    parser.add_argument('--save_viz', dest='save_viz', help='Save images with pose cube.',
+          default=False, type=bool)
+    parser.add_argument('--dataset', dest='dataset', help='Dataset type.', default='AFLW2000', type=str)
+
+    args = parser.parse_args()
+
+    return args
+
+if __name__ == '__main__':
+    args = parse_args()
+
+    cudnn.enabled = True
+    gpu = args.gpu_id
+    snapshot_path = args.snapshot
+
+    # ResNet101 with 3 outputs.
+    # model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 23, 3], 66)
+    # ResNet50
+    model = hopenet.Hopenet_new(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 66)
+    # ResNet18
+    # model = hopenet.Hopenet(torchvision.models.resnet.BasicBlock, [2, 2, 2, 2], 66)
+
+    print 'Loading snapshot.'
+    # Load snapshot
+    saved_state_dict = torch.load(snapshot_path)
+    model.load_state_dict(saved_state_dict)
+
+    print 'Loading data.'
+
+    transformations = transforms.Compose([transforms.Scale(224),
+    transforms.CenterCrop(224), transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
+
+    if 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 == 'Pose_300W_LP':
+        pose_dataset = datasets.Pose_300W_LP(args.data_dir, args.filename_list, transformations)
+    elif args.dataset == 'AFW':
+        pose_dataset = datasets.AFW(args.data_dir, args.filename_list, transformations)
+    else:
+        print 'Error: not a valid dataset name'
+        sys.exit()
+    test_loader = torch.utils.data.DataLoader(dataset=pose_dataset,
+                                               batch_size=args.batch_size,
+                                               num_workers=2)
+
+    model.cuda(gpu)
+
+    print 'Ready to test network.'
+
+    # Test the Model
+    model.eval()  # Change model to 'eval' mode (BN uses moving mean/var).
+    total = 0
+    yaw_error = .0
+    pitch_error = .0
+    roll_error = .0
+
+    l1loss = torch.nn.L1Loss(size_average=False)
+
+    for i, (images, labels, cont_labels, name) in enumerate(test_loader):
+        images = Variable(images).cuda(gpu)
+        total += cont_labels.size(0)
+        label_yaw = cont_labels[:,0].float()
+        label_pitch = cont_labels[:,1].float()
+        label_roll = cont_labels[:,2].float()
+
+        pre_yaw, pre_pitch, pre_roll, preangles, final_angles = model(images)
+        yaw = final_angles[:,0].cpu().data
+        pitch = final_angles[:,1].cpu().data
+        roll = final_angles[:,2].cpu().data
+
+        # Mean absolute error
+        yaw_error += torch.sum(torch.abs(yaw - label_yaw))
+        pitch_error += torch.sum(torch.abs(pitch - label_pitch))
+        roll_error += torch.sum(torch.abs(roll - label_roll))
+
+        # Save images with pose cube.
+        # TODO: fix for larger batch size
+        if args.save_viz:
+            name = name[0]
+            if args.dataset == 'BIWI':
+                cv2_img = cv2.imread(os.path.join(args.data_dir, name + '_rgb.png'))
+            else:
+                cv2_img = cv2.imread(os.path.join(args.data_dir, name + '.jpg'))
+
+            if args.batch_size == 1:
+                error_string = 'y %.4f, p %.4f, r %.4f' % (torch.sum(torch.abs(yaw - label_yaw)), torch.sum(torch.abs(pitch - label_pitch)), torch.sum(torch.abs(roll - label_roll)))
+                cv2_img = cv2.putText(cv2_img, error_string, (30, cv2_img.shape[0]- 30), fontFace=1, fontScale=2, color=(0,255,0), thickness=2)
+            utils.plot_pose_cube(cv2_img, yaw[0], pitch[0], roll[0])
+            cv2.imwrite(os.path.join('output/images', name + '.jpg'), cv2_img)
+
+    print('Test error in degrees of the model on the ' + str(total) +
+    ' test images. Yaw: %.4f, Pitch: %.4f, Roll: %.4f' % (yaw_error / total,
+    pitch_error / total, roll_error / total))
+
+    # Binned accuracy
+    # for idx in xrange(len(yaw_correct)):
+    #     print yaw_correct[idx] / total, pitch_correct[idx] / total, roll_correct[idx] / total
diff --git a/code/train_finetune_new.py b/code/train_finetune_new.py
new file mode 100644
index 0000000..1f77d54
--- /dev/null
+++ b/code/train_finetune_new.py
@@ -0,0 +1,192 @@
+import numpy as np
+import torch
+import torch.nn as nn
+from torch.autograd import Variable
+from torch.utils.data import DataLoader
+from torchvision import transforms
+import torchvision
+import torch.backends.cudnn as cudnn
+import torch.nn.functional as F
+
+import cv2
+import matplotlib.pyplot as plt
+import sys
+import os
+import argparse
+
+import datasets
+import hopenet
+import torch.utils.model_zoo as model_zoo
+
+model_urls = {
+    'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
+    'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
+    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
+    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
+    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
+}
+
+def parse_args():
+    """Parse input arguments."""
+    parser = argparse.ArgumentParser(description='Head pose estimation using the Hopenet network.')
+    parser.add_argument('--gpu', dest='gpu_id', help='GPU device id to use [0]',
+            default=0, 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.',
+          default=0.001, type=float)
+    parser.add_argument('--data_dir', dest='data_dir', help='Directory path for data.',
+          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)
+    parser.add_argument('--dataset', dest='dataset', help='Dataset type.', default='Pose_300W_LP', type=str)
+    parser.add_argument('--snapshot', dest='snapshot', help='Snapshot to start finetuning', default='', type=str)
+    args = parser.parse_args()
+    return args
+
+def get_ignored_params(model):
+    # Generator function that yields ignored params.
+    b = []
+    b.append(model.conv1)
+    b.append(model.bn1)
+    b.append(model.layer1)
+    b.append(model.layer2)
+    b.append(model.layer3)
+    b.append(model.layer4)
+    b.append(model.fc_yaw)
+    b.append(model.fc_pitch)
+    b.append(model.fc_roll)
+    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.conv1x1)
+    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_fc_params(model):
+    b = []
+    b.append(model.fc_finetune_new)
+    for i in range(len(b)):
+        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
+    model_dict = model.state_dict()
+    # 1. filter out unnecessary keys
+    snapshot = {k: v for k, v in snapshot.items() if k in model_dict}
+    # 2. overwrite entries in the existing state dict
+    model_dict.update(snapshot)
+    # 3. load the new state dict
+    model.load_state_dict(model_dict)
+
+if __name__ == '__main__':
+    args = parse_args()
+
+    cudnn.enabled = True
+    num_epochs_ft = args.num_epochs_ft
+    batch_size = args.batch_size
+    gpu = args.gpu_id
+
+    if not os.path.exists('output/snapshots'):
+        os.makedirs('output/snapshots')
+
+
+    model = hopenet.Hopenet_new(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 66)
+
+    if args.snapshot != '':
+        load_filtered_state_dict(model, torch.load(args.snapshot))
+    else:
+        load_filtered_state_dict(model, model_zoo.load_url(model_urls['resnet50']))
+
+    print 'Loading data.'
+
+    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)
+    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:
+        print 'Error: not a valid dataset name'
+        sys.exit()
+    train_loader = torch.utils.data.DataLoader(dataset=pose_dataset,
+                                               batch_size=batch_size,
+                                               shuffle=True,
+                                               num_workers=2)
+
+    model.cuda(gpu)
+    softmax = nn.Softmax()
+    criterion = nn.CrossEntropyLoss().cuda()
+    reg_criterion = nn.MSELoss().cuda()
+    smooth_l1_loss = nn.SmoothL1Loss().cuda()
+    # Regression loss coefficient
+    alpha = args.alpha
+
+    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}],
+                                   lr = args.lr)
+
+    print 'Ready to train network.'
+
+    print 'Second phase of training (finetuning layer).'
+    for epoch in range(num_epochs_ft):
+        for i, (images, labels, cont_labels, name) in enumerate(train_loader):
+            images = Variable(images.cuda(gpu))
+
+            label_angles = Variable(cont_labels[:,:3].cuda(gpu))
+
+            optimizer.zero_grad()
+            model.zero_grad()
+
+            pre_yaw, pre_pitch, pre_roll, preangles, final_angles = model(images)
+
+            # Finetuning loss
+            loss_seq = []
+
+            loss_angles = smooth_l1_loss(final_angles, label_angles)
+            loss_seq.append(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: finetuning %.4f'
+                       %(epoch+1, num_epochs_ft, i+1, len(pose_dataset)//batch_size, loss_angles.data[0]))
+
+        # Save models at numbered epochs.
+        if epoch % 1 == 0 and epoch < num_epochs_ft:
+            print 'Taking snapshot...'
+            torch.save(model.state_dict(),
+            'output/snapshots/' + args.output_string + '_epoch_'+ str(epoch+1) + '.pkl')
diff --git a/code/train_hopenet_new.py b/code/train_hopenet_new.py
new file mode 100644
index 0000000..988f58f
--- /dev/null
+++ b/code/train_hopenet_new.py
@@ -0,0 +1,221 @@
+import numpy as np
+import torch
+import torch.nn as nn
+from torch.autograd import Variable
+from torch.utils.data import DataLoader
+from torchvision import transforms
+import torchvision
+import torch.backends.cudnn as cudnn
+import torch.nn.functional as F
+
+import cv2
+import matplotlib.pyplot as plt
+import sys
+import os
+import argparse
+
+import datasets
+import hopenet
+import torch.utils.model_zoo as model_zoo
+
+model_urls = {
+    'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
+    'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
+    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
+    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
+    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
+}
+
+def parse_args():
+    """Parse input arguments."""
+    parser = argparse.ArgumentParser(description='Head pose estimation using the Hopenet network.')
+    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)
+    parser.add_argument('--lr', dest='lr', help='Base learning rate.',
+          default=0.001, type=float)
+    parser.add_argument('--data_dir', dest='data_dir', help='Directory path for data.',
+          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)
+    parser.add_argument('--dataset', dest='dataset', help='Dataset type.', default='Pose_300W_LP', type=str)
+    args = parser.parse_args()
+    return args
+
+def get_ignored_params(model):
+    # Generator function that yields ignored params.
+    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 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_fc_params(model):
+    b = []
+    b.append(model.fc_yaw)
+    b.append(model.fc_pitch)
+    b.append(model.fc_roll)
+    b.append(model.lstm)
+    b.append(model.fc_lstm)
+    for i in range(len(b)):
+        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
+    model_dict = model.state_dict()
+    # 1. filter out unnecessary keys
+    snapshot = {k: v for k, v in snapshot.items() if k in model_dict}
+    # 2. overwrite entries in the existing state dict
+    model_dict.update(snapshot)
+    # 3. load the new state dict
+    model.load_state_dict(model_dict)
+
+if __name__ == '__main__':
+    args = parse_args()
+
+    cudnn.enabled = True
+    num_epochs = args.num_epochs
+    num_epochs_ft = args.num_epochs_ft
+    batch_size = args.batch_size
+    gpu = args.gpu_id
+
+    if not os.path.exists('output/snapshots'):
+        os.makedirs('output/snapshots')
+
+    model = hopenet.Hopenet_new(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 66)
+
+    load_filtered_state_dict(model, model_zoo.load_url(model_urls['resnet50']))
+
+    print 'Loading data.'
+
+    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)
+    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:
+        print 'Error: not a valid dataset name'
+        sys.exit()
+    train_loader = torch.utils.data.DataLoader(dataset=pose_dataset,
+                                               batch_size=batch_size,
+                                               shuffle=True,
+                                               num_workers=2)
+
+    model.cuda(gpu)
+    softmax = nn.Softmax()
+    criterion = nn.CrossEntropyLoss().cuda()
+    reg_criterion = nn.MSELoss().cuda()
+    smooth_l1_loss = nn.SmoothL1Loss().cuda()
+    # Regression loss coefficient
+    alpha = args.alpha
+
+    idx_tensor = [idx for idx in xrange(66)]
+    idx_tensor = Variable(torch.FloatTensor(idx_tensor)).cuda(gpu)
+
+    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 * 5}],
+                                   lr = args.lr)
+
+    print 'Ready to train network.'
+
+    print 'Second phase of training (finetuning layer).'
+    for epoch in range(num_epochs_ft):
+        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))
+
+            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, preangles, final_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 = preangles[0]
+            pitch_predicted = preangles[1]
+            roll_predicted = preangles[2]
+
+            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)
+
+            # Total loss
+            loss_yaw += alpha * loss_reg_yaw
+            loss_pitch += alpha * loss_reg_pitch
+            loss_roll += alpha * loss_reg_roll
+
+            # LSTM loss
+            loss_seq = [loss_yaw, loss_pitch, loss_rol]
+            loss_lstm = reg_criterion(final_angles, label_angles)
+            loss_seq.append(loss_lstm)
+
+            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:
+            print 'Taking snapshot...'
+            torch.save(model.state_dict(),
+            'output/snapshots/' + args.output_string + '_epoch_'+ str(num_epochs+epoch+1) + '.pkl')

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