import sys, os, argparse, time
|
|
import numpy as np
|
import cv2
|
import matplotlib.pyplot as plt
|
|
import torch
|
import torch.nn as nn
|
from torch.autograd import Variable
|
from torch.utils.data import DataLoader
|
from torchvision import transforms
|
import torchvision
|
import torch.backends.cudnn as cudnn
|
import torch.nn.functional as F
|
|
import datasets, hopenet
|
import torch.utils.model_zoo as model_zoo
|
|
model_urls = {
|
'alexnet': 'https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth',
|
}
|
|
def parse_args():
|
"""Parse input arguments."""
|
parser = argparse.ArgumentParser(description='Head pose estimation using the Hopenet network.')
|
parser.add_argument('--gpu', dest='gpu_id', help='GPU device id to use [0]',
|
default=0, type=int)
|
parser.add_argument('--num_epochs', dest='num_epochs', help='Maximum number of training epochs.',
|
default=5, type=int)
|
parser.add_argument('--batch_size', dest='batch_size', help='Batch size.',
|
default=16, type=int)
|
parser.add_argument('--lr', dest='lr', help='Base learning rate.',
|
default=0.001, type=float)
|
parser.add_argument('--data_dir', dest='data_dir', help='Directory path for data.',
|
default='', type=str)
|
parser.add_argument('--filename_list', dest='filename_list', help='Path to text file containing relative paths for every example.',
|
default='', type=str)
|
parser.add_argument('--output_string', dest='output_string', help='String appended to output snapshots.', default = '', type=str)
|
parser.add_argument('--alpha', dest='alpha', help='Regression loss coefficient.',
|
default=0.001, type=float)
|
parser.add_argument('--dataset', dest='dataset', help='Dataset type.', default='Pose_300W_LP', type=str)
|
args = parser.parse_args()
|
return args
|
|
def get_ignored_params(model):
|
# Generator function that yields ignored params.
|
b = [model.features[0], model.features[1], model.features[2]]
|
for i in range(len(b)):
|
for module_name, module in b[i].named_modules():
|
if 'bn' in module_name:
|
module.eval()
|
for name, param in module.named_parameters():
|
yield param
|
|
def get_non_ignored_params(model):
|
# Generator function that yields params that will be optimized.
|
b = []
|
for idx in xrange(3, len(model.features)):
|
b.append(model.features[idx])
|
for layer in model.classifier:
|
b.append(layer)
|
for i in range(len(b)):
|
for module_name, module in b[i].named_modules():
|
if 'bn' in module_name:
|
module.eval()
|
for name, param in module.named_parameters():
|
yield param
|
|
def get_fc_params(model):
|
b = [model.fc_yaw, model.fc_pitch, model.fc_roll]
|
for i in range(len(b)):
|
for module_name, module in b[i].named_modules():
|
for name, param in module.named_parameters():
|
yield param
|
|
def load_filtered_state_dict(model, snapshot):
|
# By user apaszke from discuss.pytorch.org
|
model_dict = model.state_dict()
|
snapshot = {k: v for k, v in snapshot.items() if k in model_dict}
|
model_dict.update(snapshot)
|
model.load_state_dict(model_dict)
|
|
if __name__ == '__main__':
|
args = parse_args()
|
|
cudnn.enabled = True
|
num_epochs = args.num_epochs
|
batch_size = args.batch_size
|
gpu = args.gpu_id
|
|
if not os.path.exists('output/snapshots'):
|
os.makedirs('output/snapshots')
|
|
model = hopenet.AlexNet(66)
|
load_filtered_state_dict(model, model_zoo.load_url(model_urls['alexnet']))
|
|
print 'Loading data.'
|
|
transformations = transforms.Compose([transforms.Scale(240),
|
transforms.RandomCrop(224), transforms.ToTensor(),
|
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
|
|
if args.dataset == 'Pose_300W_LP':
|
pose_dataset = datasets.Pose_300W_LP(args.data_dir, args.filename_list, transformations)
|
elif args.dataset == 'Pose_300W_LP_random_ds':
|
pose_dataset = datasets.Pose_300W_LP_random_ds(args.data_dir, args.filename_list, transformations)
|
elif args.dataset == 'AFLW2000':
|
pose_dataset = datasets.AFLW2000(args.data_dir, args.filename_list, transformations)
|
elif args.dataset == 'BIWI':
|
pose_dataset = datasets.BIWI(args.data_dir, args.filename_list, transformations)
|
elif args.dataset == 'AFLW':
|
pose_dataset = datasets.AFLW(args.data_dir, args.filename_list, transformations)
|
elif args.dataset == 'AFLW_aug':
|
pose_dataset = datasets.AFLW_aug(args.data_dir, args.filename_list, transformations)
|
elif args.dataset == 'AFW':
|
pose_dataset = datasets.AFW(args.data_dir, args.filename_list, transformations)
|
else:
|
print 'Error: not a valid dataset name'
|
sys.exit()
|
train_loader = torch.utils.data.DataLoader(dataset=pose_dataset,
|
batch_size=batch_size,
|
shuffle=True,
|
num_workers=2)
|
|
model.cuda(gpu)
|
softmax = nn.Softmax().cuda(gpu)
|
criterion = nn.CrossEntropyLoss().cuda(gpu)
|
reg_criterion = nn.MSELoss().cuda(gpu)
|
# Regression loss coefficient
|
alpha = args.alpha
|
|
idx_tensor = [idx for idx in xrange(66)]
|
idx_tensor = Variable(torch.FloatTensor(idx_tensor)).cuda(gpu)
|
|
optimizer = torch.optim.Adam([{'params': get_ignored_params(model), 'lr': 0},
|
{'params': get_non_ignored_params(model), 'lr': args.lr},
|
{'params': get_fc_params(model), 'lr': args.lr * 5}],
|
lr = args.lr)
|
|
print 'Ready to train network.'
|
for epoch in range(num_epochs):
|
for i, (images, labels, cont_labels, name) in enumerate(train_loader):
|
images = Variable(images).cuda(gpu)
|
|
# Binned labels
|
label_yaw = Variable(labels[:,0]).cuda(gpu)
|
label_pitch = Variable(labels[:,1]).cuda(gpu)
|
label_roll = Variable(labels[:,2]).cuda(gpu)
|
|
# Continuous labels
|
label_yaw_cont = Variable(cont_labels[:,0]).cuda(gpu)
|
label_pitch_cont = Variable(cont_labels[:,1]).cuda(gpu)
|
label_roll_cont = Variable(cont_labels[:,2]).cuda(gpu)
|
|
# Forward pass
|
pre_yaw, pre_pitch, pre_roll = model(images)
|
|
# Cross entropy loss
|
loss_yaw = criterion(pre_yaw, label_yaw)
|
loss_pitch = criterion(pre_pitch, label_pitch)
|
loss_roll = criterion(pre_roll, label_roll)
|
|
# MSE loss
|
yaw_predicted = softmax(pre_yaw)
|
pitch_predicted = softmax(pre_pitch)
|
roll_predicted = softmax(pre_roll)
|
|
yaw_predicted = torch.sum(yaw_predicted * idx_tensor, 1) * 3 - 99
|
pitch_predicted = torch.sum(pitch_predicted * idx_tensor, 1) * 3 - 99
|
roll_predicted = torch.sum(roll_predicted * idx_tensor, 1) * 3 - 99
|
|
loss_reg_yaw = reg_criterion(yaw_predicted, label_yaw_cont)
|
loss_reg_pitch = reg_criterion(pitch_predicted, label_pitch_cont)
|
loss_reg_roll = reg_criterion(roll_predicted, label_roll_cont)
|
|
# Total loss
|
loss_yaw += alpha * loss_reg_yaw
|
loss_pitch += alpha * loss_reg_pitch
|
loss_roll += alpha * loss_reg_roll
|
|
loss_seq = [loss_yaw, loss_pitch, loss_roll]
|
grad_seq = [torch.ones(1).cuda(gpu) for _ in range(len(loss_seq))]
|
torch.autograd.backward(loss_seq, grad_seq)
|
optimizer.step()
|
|
if (i+1) % 100 == 0:
|
print ('Epoch [%d/%d], Iter [%d/%d] Losses: Yaw %.4f, Pitch %.4f, Roll %.4f'
|
%(epoch+1, num_epochs, i+1, len(pose_dataset)//batch_size, loss_yaw.data[0], loss_pitch.data[0], loss_roll.data[0]))
|
|
# Save models at numbered epochs.
|
if epoch % 1 == 0 and epoch < num_epochs:
|
print 'Taking snapshot...'
|
torch.save(model.state_dict(),
|
'output/snapshots/' + args.output_string + '_epoch_'+ str(epoch+1) + '.pkl')
|
