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 torch.backends.cudnn as cudnn
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import torchvision
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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 AFLW2000
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import hopenet
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import utils
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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('--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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parser.add_argument('--snapshot', dest='snapshot', help='Name of model snapshot.',
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default='', type=str)
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parser.add_argument('--batch_size', dest='batch_size', help='Batch size.',
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default=1, type=int)
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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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batch_size = 1
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gpu = args.gpu_id
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snapshot_path = os.path.join('output/snapshots', args.snapshot + '.pkl')
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model = torchvision.models.resnet18()
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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 snapshot.'
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# Load snapshot
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saved_state_dict = torch.load(snapshot_path)
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model.load_state_dict(saved_state_dict)
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print 'Loading data.'
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transformations = transforms.Compose([transforms.Scale(224),transforms.RandomCrop(224), transforms.ToTensor()])
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pose_dataset = AFLW2000(args.data_dir, args.filename_list,
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transformations)
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test_loader = torch.utils.data.DataLoader(dataset=pose_dataset,
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batch_size=batch_size,
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num_workers=2)
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model.cuda(gpu)
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print 'Ready to test network.'
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# Test the Model
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model.eval() # Change model to 'eval' mode (BN uses moving mean/var).
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error = .0
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total = 0
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for i, (images, labels, name) in enumerate(test_loader):
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images = Variable(images).cuda(gpu)
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labels = Variable(labels).cuda(gpu)
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outputs = model(images)
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_, predicted = torch.max(outputs.data, 1)
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total += labels.size(0)
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# TODO: There are more efficient ways.
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for idx in xrange(len(outputs)):
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# if abs(outputs[idx].data[1] - labels[idx].data[1]) * 180 / np.pi > 30:
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print name
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print abs(outputs[idx].data - labels[idx].data) * 180 / np.pi, 180 * outputs[idx].data / np.pi, labels[idx].data * 180 / np.pi
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# error += utils.mse_loss(outputs[idx], labels[idx])
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error += abs(outputs[idx].data - labels[idx].data) * 180 / np.pi
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print('Test MSE error of the model on the ' + str(total) +
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' test images: %.4f' % (error / total))
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