import sys, os, argparse, time
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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 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 datasets, hopenet
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import torch.utils.model_zoo as model_zoo
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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('--dataset', dest='dataset', help='Dataset type.', default='Pose_300W_LP', type=str)
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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('--output_string', dest='output_string', help='String appended to output snapshots.', default = '', type=str)
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parser.add_argument('--alpha', dest='alpha', help='Regression loss coefficient.',
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default=0.001, type=float)
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parser.add_argument('--snapshot', dest='snapshot', help='Path of model snapshot.',
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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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def get_ignored_params(model):
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# Generator function that yields ignored params.
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b = [model.conv1, model.bn1, model.fc_finetune]
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for i in range(len(b)):
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for module_name, module in b[i].named_modules():
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if 'bn' in module_name:
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module.eval()
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for name, param in module.named_parameters():
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yield param
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def get_non_ignored_params(model):
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# Generator function that yields params that will be optimized.
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b = [model.layer1, model.layer2, model.layer3, model.layer4]
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for i in range(len(b)):
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for module_name, module in b[i].named_modules():
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if 'bn' in module_name:
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module.eval()
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for name, param in module.named_parameters():
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yield param
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def get_fc_params(model):
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# Generator function that yields fc layer params.
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b = [model.fc_yaw, model.fc_pitch, model.fc_roll]
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for i in range(len(b)):
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for module_name, module in b[i].named_modules():
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for name, param in module.named_parameters():
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yield param
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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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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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# ResNet50 structure
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model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 66)
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if args.snapshot == '':
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load_filtered_state_dict(model, model_zoo.load_url('https://download.pytorch.org/models/resnet50-19c8e357.pth'))
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else:
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saved_state_dict = torch.load(args.snapshot)
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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(240),
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transforms.RandomCrop(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 == 'Synhead':
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pose_dataset = datasets.Synhead(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 == '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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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.CrossEntropyLoss().cuda(gpu)
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reg_criterion = nn.MSELoss().cuda(gpu)
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# Regression loss coefficient
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alpha = args.alpha
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softmax = nn.Softmax().cuda(gpu)
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idx_tensor = [idx for idx in xrange(66)]
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idx_tensor = Variable(torch.FloatTensor(idx_tensor)).cuda(gpu)
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optimizer = torch.optim.Adam([{'params': get_ignored_params(model), 'lr': 0},
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{'params': get_non_ignored_params(model), 'lr': args.lr},
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{'params': get_fc_params(model), 'lr': args.lr * 5}],
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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, cont_labels, name) in enumerate(train_loader):
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images = Variable(images).cuda(gpu)
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# Binned labels
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label_yaw = Variable(labels[:,0]).cuda(gpu)
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label_pitch = Variable(labels[:,1]).cuda(gpu)
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label_roll = Variable(labels[:,2]).cuda(gpu)
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# Continuous labels
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label_yaw_cont = Variable(cont_labels[:,0]).cuda(gpu)
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label_pitch_cont = Variable(cont_labels[:,1]).cuda(gpu)
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label_roll_cont = Variable(cont_labels[:,2]).cuda(gpu)
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# Forward pass
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yaw, pitch, roll = model(images)
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# Cross entropy loss
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loss_yaw = criterion(yaw, label_yaw)
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loss_pitch = criterion(pitch, label_pitch)
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loss_roll = criterion(roll, label_roll)
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# MSE loss
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yaw_predicted = softmax(yaw)
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pitch_predicted = softmax(pitch)
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roll_predicted = softmax(roll)
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yaw_predicted = torch.sum(yaw_predicted * idx_tensor, 1) * 3 - 99
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pitch_predicted = torch.sum(pitch_predicted * idx_tensor, 1) * 3 - 99
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roll_predicted = torch.sum(roll_predicted * idx_tensor, 1) * 3 - 99
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loss_reg_yaw = reg_criterion(yaw_predicted, label_yaw_cont)
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loss_reg_pitch = reg_criterion(pitch_predicted, label_pitch_cont)
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loss_reg_roll = reg_criterion(roll_predicted, label_roll_cont)
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# Total loss
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loss_yaw += alpha * loss_reg_yaw
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loss_pitch += alpha * loss_reg_pitch
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loss_roll += alpha * loss_reg_roll
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loss_seq = [loss_yaw, loss_pitch, loss_roll]
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grad_seq = [torch.ones(1).cuda(gpu) for _ in range(len(loss_seq))]
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optimizer.zero_grad()
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torch.autograd.backward(loss_seq, grad_seq)
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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] Losses: Yaw %.4f, Pitch %.4f, Roll %.4f'
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%(epoch+1, num_epochs, i+1, len(pose_dataset)//batch_size, loss_yaw.data[0], loss_pitch.data[0], loss_roll.data[0]))
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# Save models at numbered epochs.
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if epoch % 1 == 0 and epoch < num_epochs:
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print 'Taking snapshot...'
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torch.save(model.state_dict(),
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'output/snapshots/' + args.output_string + '_epoch_'+ str(epoch+1) + '.pkl')
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