import sys, os, argparse
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import numpy as np
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import cv2
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import matplotlib.pyplot as plt
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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 datasets, hopenet, 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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parser.add_argument('--save_viz', dest='save_viz', help='Save images with pose cube.',
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default=False, type=bool)
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parser.add_argument('--dataset', dest='dataset', help='Dataset type.', default='AFLW2000', type=str)
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args = parser.parse_args()
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return args
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def load_filtered_state_dict(model, snapshot):
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# By user apaszke from discuss.pytorch.org
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model_dict = model.state_dict()
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snapshot = {k: v for k, v in snapshot.items() if k in model_dict}
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model_dict.update(snapshot)
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model.load_state_dict(model_dict)
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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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gpu = args.gpu_id
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snapshot_path = args.snapshot
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# ResNet50 structure
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model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 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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# load_filtered_state_dict(model, 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.CenterCrop(224), transforms.ToTensor(),
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transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
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if args.dataset == 'Pose_300W_LP':
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pose_dataset = datasets.Pose_300W_LP(args.data_dir, args.filename_list, transformations)
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elif args.dataset == 'Pose_300W_LP_random_ds':
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pose_dataset = datasets.Pose_300W_LP_random_ds(args.data_dir, args.filename_list, transformations)
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elif args.dataset == 'AFLW2000':
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pose_dataset = datasets.AFLW2000(args.data_dir, args.filename_list, transformations)
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elif args.dataset == 'AFLW2000_ds':
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pose_dataset = datasets.AFLW2000_ds(args.data_dir, args.filename_list, transformations)
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elif args.dataset == 'BIWI':
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pose_dataset = datasets.BIWI(args.data_dir, args.filename_list, transformations)
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elif args.dataset == 'AFLW':
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pose_dataset = datasets.AFLW(args.data_dir, args.filename_list, transformations)
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elif args.dataset == 'AFLW_aug':
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pose_dataset = datasets.AFLW_aug(args.data_dir, args.filename_list, transformations)
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elif args.dataset == 'AFW':
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pose_dataset = datasets.AFW(args.data_dir, args.filename_list, transformations)
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else:
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print 'Error: not a valid dataset name'
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sys.exit()
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test_loader = torch.utils.data.DataLoader(dataset=pose_dataset,
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batch_size=args.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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yaw_error = .0
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pitch_error = .0
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roll_error = .0
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l1loss = torch.nn.L1Loss(size_average=False)
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for i, (images, labels, cont_labels, name) in enumerate(test_loader):
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images = Variable(images).cuda(gpu)
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total += cont_labels.size(0)
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label_yaw = cont_labels[:,0].float()
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label_pitch = cont_labels[:,1].float()
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label_roll = cont_labels[:,2].float()
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yaw, pitch, roll, angles = model(images)
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# Binned predictions
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_, yaw_bpred = torch.max(yaw.data, 1)
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_, pitch_bpred = torch.max(pitch.data, 1)
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_, roll_bpred = torch.max(roll.data, 1)
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# Continuous predictions
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yaw_predicted = utils.softmax_temperature(yaw.data, 1)
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pitch_predicted = utils.softmax_temperature(pitch.data, 1)
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roll_predicted = utils.softmax_temperature(roll.data, 1)
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yaw_predicted = torch.sum(yaw_predicted * idx_tensor, 1).cpu() * 3 - 99
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pitch_predicted = torch.sum(pitch_predicted * idx_tensor, 1).cpu() * 3 - 99
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roll_predicted = torch.sum(roll_predicted * idx_tensor, 1).cpu() * 3 - 99
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# Mean absolute error
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yaw_error += torch.sum(torch.abs(yaw_predicted - label_yaw))
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pitch_error += torch.sum(torch.abs(pitch_predicted - label_pitch))
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roll_error += torch.sum(torch.abs(roll_predicted - label_roll))
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# Save first image in batch with pose cube or axis.
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if args.save_viz:
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name = name[0]
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if args.dataset == 'BIWI':
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cv2_img = cv2.imread(os.path.join(args.data_dir, name + '_rgb.png'))
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else:
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cv2_img = cv2.imread(os.path.join(args.data_dir, name + '.jpg'))
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if args.batch_size == 1:
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error_string = 'y %.2f, p %.2f, r %.2f' % (torch.sum(torch.abs(yaw_predicted - label_yaw)), torch.sum(torch.abs(pitch_predicted - label_pitch)), torch.sum(torch.abs(roll_predicted - label_roll)))
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cv2.putText(cv2_img, error_string, (30, cv2_img.shape[0]- 30), fontFace=1, fontScale=1, color=(0,0,255), thickness=2)
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# utils.plot_pose_cube(cv2_img, yaw_predicted[0], pitch_predicted[0], roll_predicted[0], size=100)
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utils.draw_axis(cv2_img, yaw_predicted[0], pitch_predicted[0], roll_predicted[0], tdx = 200, tdy= 200, size=100)
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cv2.imwrite(os.path.join('output/images', name + '.jpg'), cv2_img)
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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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