import numpy as np
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import torch
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import torch.nn as nn
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from torch.autograd import Variable
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from torch.utils.data import DataLoader
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from torchvision import transforms
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import torchvision
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import torch.backends.cudnn as cudnn
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import cv2
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import matplotlib.pyplot as plt
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import sys
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import os
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import argparse
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from datasets import Pose_300W_LP
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import hopenet
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def parse_args():
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"""Parse input arguments."""
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parser = argparse.ArgumentParser(description='Head pose estimation using the Hopenet network.')
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parser.add_argument('--gpu', dest='gpu_id', help='GPU device id to use [0]',
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default=0, type=int)
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parser.add_argument('--num_epochs', dest='num_epochs', help='Maximum number of training epochs.',
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default=5, type=int)
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parser.add_argument('--batch_size', dest='batch_size', help='Batch size.',
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default=16, type=int)
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parser.add_argument('--lr', dest='lr', help='Base learning rate.',
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default=0.001, type=float)
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parser.add_argument('--data_dir', dest='data_dir', help='Directory path for data.',
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default='', type=str)
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parser.add_argument('--filename_list', dest='filename_list', help='Path to text file containing relative paths for every example.',
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default='', type=str)
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args = parser.parse_args()
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return args
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if __name__ == '__main__':
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args = parse_args()
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cudnn.enabled = True
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num_epochs = args.num_epochs
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batch_size = args.batch_size
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gpu = args.gpu_id
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if not os.path.exists('output/snapshots'):
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os.makedirs('output/snapshots')
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model = torchvision.models.resnet18(pretrained=True)
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# for param in model.parameters():
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# param.requires_grad = False
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# Parameters of newly constructed modules have requires_grad=True by default
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num_ftrs = model.fc.in_features
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model.fc = nn.Linear(num_ftrs, 3)
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print 'Loading data.'
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transformations = transforms.Compose([transforms.Scale(230),transforms.RandomCrop(224),
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transforms.ToTensor()])
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pose_dataset = Pose_300W_LP(args.data_dir, args.filename_list,
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transformations)
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train_loader = torch.utils.data.DataLoader(dataset=pose_dataset,
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batch_size=batch_size,
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shuffle=True,
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num_workers=2)
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model.cuda(gpu)
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criterion = nn.MSELoss(size_average = True)
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optimizer = torch.optim.Adam(model.parameters(), lr = args.lr)
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print 'Ready to train network.'
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for epoch in range(num_epochs):
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for i, (images, labels) in enumerate(train_loader):
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images = Variable(images).cuda(gpu)
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labels = Variable(labels).cuda(gpu)
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optimizer.zero_grad()
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outputs = model(images)
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loss = criterion(outputs, labels)
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loss.backward()
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optimizer.step()
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if (i+1) % 100 == 0:
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print ('Epoch [%d/%d], Iter [%d/%d] Loss: %.4f'
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%(epoch+1, num_epochs, i+1, len(pose_dataset)//batch_size, loss.data[0]))
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# Save models at even numbered epochs.
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if epoch % 1 == 0 and epoch < num_epochs - 1:
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print 'Taking snapshot...'
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torch.save(model.state_dict(),
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'output/snapshots/resnet18_epoch_' + str(epoch+1) + '.pkl')
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# Save the final Trained Model
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torch.save(model.state_dict(), 'output/snapshots/resnet18_epoch_' + str(epoch+1) + '.pkl')
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