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 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 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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# ResNet50 with 3 outputs.
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model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 66)
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# model = hopenet.Hopenet(torchvision.models.resnet.BasicBlock, [2, 2, 2, 2], 66)
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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),
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transforms.RandomCrop(224), transforms.ToTensor()])
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pose_dataset = datasets.AFLW2000_binned(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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total = 0
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n_margins = 20
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yaw_correct = np.zeros(n_margins)
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pitch_correct = np.zeros(n_margins)
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roll_correct = np.zeros(n_margins)
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idx_tensor = [idx for idx in xrange(66)]
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idx_tensor = torch.FloatTensor(idx_tensor).cuda(gpu)
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yaw_error = .0
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pitch_error = .0
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roll_error = .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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total += labels.size(0)
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label_yaw = labels[:,0]
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label_pitch = labels[:,1]
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label_roll = labels[:,2]
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yaw, pitch, roll = model(images)
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# _, yaw_predicted = torch.max(yaw.data, 1)
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# _, pitch_predicted = torch.max(pitch.data, 1)
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# _, roll_predicted = torch.max(roll.data, 1)
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yaw_predicted = F.softmax(yaw)
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pitch_predicted = F.softmax(pitch)
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roll_predicted = F.softmax(roll)
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yaw_predicted = torch.sum(yaw_predicted.data[0] * idx_tensor)
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pitch_predicted = torch.sum(pitch_predicted.data[0] * idx_tensor)
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roll_predicted = torch.sum(roll_predicted.data[0] * idx_tensor)
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yaw_error += abs(yaw_predicted - label_yaw[0]) * 3
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pitch_error += abs(pitch_predicted - label_pitch[0]) * 3
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roll_error += abs(roll_predicted - label_roll[0]) * 3
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# print yaw_predicted * 3, label_yaw[0] * 3, abs(yaw_predicted - label_yaw[0]) * 3
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# for er in xrange(0,n_margins):
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# yaw_correct[er] += (label_yaw[0] in range(yaw_predicted[0,0] - er, yaw_predicted[0,0] + er + 1))
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# pitch_correct[er] += (label_pitch[0] in range(pitch_predicted[0,0] - er, pitch_predicted[0,0] + er + 1))
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# roll_correct[er] += (label_roll[0] in range(roll_predicted[0,0] - er, roll_predicted[0,0] + er + 1))
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# print label_yaw[0], yaw_predicted[0,0]
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# 4 -> 15
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print('Test error in degrees of the model on the ' + str(total) +
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' test images. Yaw: %.4f, Pitch: %.4f, Roll: %.4f' % (yaw_error / total,
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pitch_error / total, roll_error / total))
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# for idx in xrange(len(yaw_correct)):
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# print yaw_correct[idx] / total, pitch_correct[idx] / total, roll_correct[idx] / total
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