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 torch.nn.functional as F
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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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import datasets
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import hopenet
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import torch.utils.model_zoo as model_zoo
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if __name__ == '__main__':
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batch_size = 1
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print 'Loading data.'
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transformations = transforms.Compose([transforms.Scale(224),
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transforms.ToTensor()])
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pose_dataset = datasets.Pose_300W_LP('data/300W_LP', 'data/300W_LP/filename_list_filtered.txt',
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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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print 'Ready to get mean.'
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for i, (images, labels, name) in enumerate(train_loader):
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print images
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