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train.py
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train.py
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import os
import argparse
import time
import torch.utils.model_zoo as model_zoo
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.utils.data import DataLoader
from torchvision import transforms
import torch.backends.cudnn as cudnn
import torchvision
from l2cs import L2CS, select_device, Gaze360, Mpiigaze
def parse_args():
"""Parse input arguments."""
parser = argparse.ArgumentParser(description='Gaze estimation using L2CSNet.')
# Gaze360
parser.add_argument(
'--gaze360image_dir', dest='gaze360image_dir', help='Directory path for gaze images.',
default='datasets/Gaze360/Image', type=str)
parser.add_argument(
'--gaze360label_dir', dest='gaze360label_dir', help='Directory path for gaze labels.',
default='datasets/Gaze360/Label/train.label', type=str)
# mpiigaze
parser.add_argument(
'--gazeMpiimage_dir', dest='gazeMpiimage_dir', help='Directory path for gaze images.',
default='datasets/MPIIFaceGaze/Image', type=str)
parser.add_argument(
'--gazeMpiilabel_dir', dest='gazeMpiilabel_dir', help='Directory path for gaze labels.',
default='datasets/MPIIFaceGaze/Label', type=str)
# Important args -------------------------------------------------------------------------------------------------------
# ----------------------------------------------------------------------------------------------------------------------
parser.add_argument(
'--dataset', dest='dataset', help='mpiigaze, rtgene, gaze360, ethgaze',
default= "gaze360", type=str)
parser.add_argument(
'--output', dest='output', help='Path of output models.',
default='output/snapshots/', type=str)
parser.add_argument(
'--snapshot', dest='snapshot', help='Path of model snapshot.',
default='', type=str)
parser.add_argument(
'--gpu', dest='gpu_id', help='GPU device id to use [0] or multiple 0,1,2,3',
default='0', type=str)
parser.add_argument(
'--num_epochs', dest='num_epochs', help='Maximum number of training epochs.',
default=60, type=int)
parser.add_argument(
'--batch_size', dest='batch_size', help='Batch size.',
default=1, type=int)
parser.add_argument(
'--arch', dest='arch', help='Network architecture, can be: ResNet18, ResNet34, [ResNet50], ''ResNet101, ResNet152, Squeezenet_1_0, Squeezenet_1_1, MobileNetV2',
default='ResNet50', type=str)
parser.add_argument(
'--alpha', dest='alpha', help='Regression loss coefficient.',
default=1, type=float)
parser.add_argument(
'--lr', dest='lr', help='Base learning rate.',
default=0.00001, type=float)
# ---------------------------------------------------------------------------------------------------------------------
# Important args ------------------------------------------------------------------------------------------------------
args = parser.parse_args()
return args
def get_ignored_params(model):
# Generator function that yields ignored params.
b = [model.conv1, model.bn1, model.fc_finetune]
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 = [model.layer1, model.layer2, model.layer3, model.layer4]
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):
# Generator function that yields fc layer params.
b = [model.fc_yaw_gaze, model.fc_pitch_gaze]
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)
def getArch_weights(arch, bins):
if arch == 'ResNet18':
model = L2CS(torchvision.models.resnet.BasicBlock, [2, 2, 2, 2], bins)
pre_url = 'https://download.pytorch.org/models/resnet18-5c106cde.pth'
elif arch == 'ResNet34':
model = L2CS(torchvision.models.resnet.BasicBlock, [3, 4, 6, 3], bins)
pre_url = 'https://download.pytorch.org/models/resnet34-333f7ec4.pth'
elif arch == 'ResNet101':
model = L2CS(torchvision.models.resnet.Bottleneck, [3, 4, 23, 3], bins)
pre_url = 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth'
elif arch == 'ResNet152':
model = L2CS(torchvision.models.resnet.Bottleneck,[3, 8, 36, 3], bins)
pre_url = 'https://download.pytorch.org/models/resnet152-b121ed2d.pth'
else:
if arch != 'ResNet50':
print('Invalid value for architecture is passed! '
'The default value of ResNet50 will be used instead!')
model = L2CS(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], bins)
pre_url = 'https://download.pytorch.org/models/resnet50-19c8e357.pth'
return model, pre_url
if __name__ == '__main__':
args = parse_args()
cudnn.enabled = True
num_epochs = args.num_epochs
batch_size = args.batch_size
gpu = select_device(args.gpu_id, batch_size=args.batch_size)
data_set=args.dataset
alpha = args.alpha
output=args.output
transformations = transforms.Compose([
transforms.Resize(448),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]
)
])
if data_set=="gaze360":
model, pre_url = getArch_weights(args.arch, 90)
if args.snapshot == '':
load_filtered_state_dict(model, model_zoo.load_url(pre_url))
else:
saved_state_dict = torch.load(args.snapshot)
model.load_state_dict(saved_state_dict)
model.cuda(gpu)
dataset=Gaze360(args.gaze360label_dir, args.gaze360image_dir, transformations, 180, 4)
print('Loading data.')
train_loader_gaze = DataLoader(
dataset=dataset,
batch_size=int(batch_size),
shuffle=True,
num_workers=0,
pin_memory=True)
torch.backends.cudnn.benchmark = True
summary_name = '{}_{}'.format('L2CS-gaze360-', int(time.time()))
output=os.path.join(output, summary_name)
if not os.path.exists(output):
os.makedirs(output)
criterion = nn.CrossEntropyLoss().cuda(gpu)
reg_criterion = nn.MSELoss().cuda(gpu)
softmax = nn.Softmax(dim=1).cuda(gpu)
idx_tensor = [idx for idx in range(90)]
idx_tensor = Variable(torch.FloatTensor(idx_tensor)).cuda(gpu)
# Optimizer gaze
optimizer_gaze = 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}
], args.lr)
configuration = f"\ntrain configuration, gpu_id={args.gpu_id}, batch_size={batch_size}, model_arch={args.arch}\nStart testing dataset={data_set}, loader={len(train_loader_gaze)}------------------------- \n"
print(configuration)
for epoch in range(num_epochs):
sum_loss_pitch_gaze = sum_loss_yaw_gaze = iter_gaze = 0
for i, (images_gaze, labels_gaze, cont_labels_gaze,name) in enumerate(train_loader_gaze):
images_gaze = Variable(images_gaze).cuda(gpu)
# Binned labels
label_pitch_gaze = Variable(labels_gaze[:, 0]).cuda(gpu)
label_yaw_gaze = Variable(labels_gaze[:, 1]).cuda(gpu)
# Continuous labels
label_pitch_cont_gaze = Variable(cont_labels_gaze[:, 0]).cuda(gpu)
label_yaw_cont_gaze = Variable(cont_labels_gaze[:, 1]).cuda(gpu)
pitch, yaw = model(images_gaze)
# Cross entropy loss
loss_pitch_gaze = criterion(pitch, label_pitch_gaze)
loss_yaw_gaze = criterion(yaw, label_yaw_gaze)
# MSE loss
pitch_predicted = softmax(pitch)
yaw_predicted = softmax(yaw)
pitch_predicted = \
torch.sum(pitch_predicted * idx_tensor, 1) * 4 - 180
yaw_predicted = \
torch.sum(yaw_predicted * idx_tensor, 1) * 4 - 180
loss_reg_pitch = reg_criterion(
pitch_predicted, label_pitch_cont_gaze)
loss_reg_yaw = reg_criterion(
yaw_predicted, label_yaw_cont_gaze)
# Total loss
loss_pitch_gaze += alpha * loss_reg_pitch
loss_yaw_gaze += alpha * loss_reg_yaw
sum_loss_pitch_gaze += loss_pitch_gaze
sum_loss_yaw_gaze += loss_yaw_gaze
loss_seq = [loss_pitch_gaze, loss_yaw_gaze]
grad_seq = [torch.tensor(1.0).cuda(gpu) for _ in range(len(loss_seq))]
optimizer_gaze.zero_grad(set_to_none=True)
torch.autograd.backward(loss_seq, grad_seq)
optimizer_gaze.step()
# scheduler.step()
iter_gaze += 1
if (i+1) % 100 == 0:
print('Epoch [%d/%d], Iter [%d/%d] Losses: '
'Gaze Yaw %.4f,Gaze Pitch %.4f' % (
epoch+1,
num_epochs,
i+1,
len(dataset)//batch_size,
sum_loss_pitch_gaze/iter_gaze,
sum_loss_yaw_gaze/iter_gaze
)
)
if epoch % 1 == 0 and epoch < num_epochs:
print('Taking snapshot...',
torch.save(model.state_dict(),
output +'/'+
'_epoch_' + str(epoch+1) + '.pkl')
)
elif data_set=="mpiigaze":
folder = os.listdir(args.gazeMpiilabel_dir)
folder.sort()
testlabelpathombined = [os.path.join(args.gazeMpiilabel_dir, j) for j in folder]
for fold in range(15):
model, pre_url = getArch_weights(args.arch, 28)
load_filtered_state_dict(model, model_zoo.load_url(pre_url))
model = nn.DataParallel(model)
model.to(gpu)
print('Loading data.')
dataset=Mpiigaze(testlabelpathombined,args.gazeMpiimage_dir, transformations, True, fold)
train_loader_gaze = DataLoader(
dataset=dataset,
batch_size=int(batch_size),
shuffle=True,
num_workers=4,
pin_memory=True)
torch.backends.cudnn.benchmark = True
summary_name = '{}_{}'.format('L2CS-mpiigaze', int(time.time()))
if not os.path.exists(os.path.join(output+'/{}'.format(summary_name),'fold' + str(fold))):
os.makedirs(os.path.join(output+'/{}'.format(summary_name),'fold' + str(fold)))
criterion = nn.CrossEntropyLoss().cuda(gpu)
reg_criterion = nn.MSELoss().cuda(gpu)
softmax = nn.Softmax(dim=1).cuda(gpu)
idx_tensor = [idx for idx in range(28)]
idx_tensor = Variable(torch.FloatTensor(idx_tensor)).cuda(gpu)
# Optimizer gaze
optimizer_gaze = torch.optim.Adam([
{'params': get_ignored_params(model, args.arch), 'lr': 0},
{'params': get_non_ignored_params(model, args.arch), 'lr': args.lr},
{'params': get_fc_params(model, args.arch), 'lr': args.lr}
], args.lr)
configuration = f"\ntrain configuration, gpu_id={args.gpu_id}, batch_size={batch_size}, model_arch={args.arch}\n Start training dataset={data_set}, loader={len(train_loader_gaze)}, fold={fold}--------------\n"
print(configuration)
for epoch in range(num_epochs):
sum_loss_pitch_gaze = sum_loss_yaw_gaze = iter_gaze = 0
for i, (images_gaze, labels_gaze, cont_labels_gaze,name) in enumerate(train_loader_gaze):
images_gaze = Variable(images_gaze).cuda(gpu)
# Binned labels
label_pitch_gaze = Variable(labels_gaze[:, 0]).cuda(gpu)
label_yaw_gaze = Variable(labels_gaze[:, 1]).cuda(gpu)
# Continuous labels
label_pitch_cont_gaze = Variable(cont_labels_gaze[:, 0]).cuda(gpu)
label_yaw_cont_gaze = Variable(cont_labels_gaze[:, 1]).cuda(gpu)
pitch, yaw = model(images_gaze)
# Cross entropy loss
loss_pitch_gaze = criterion(pitch, label_pitch_gaze)
loss_yaw_gaze = criterion(yaw, label_yaw_gaze)
# MSE loss
pitch_predicted = softmax(pitch)
yaw_predicted = softmax(yaw)
pitch_predicted = \
torch.sum(pitch_predicted * idx_tensor, 1) * 3 - 42
yaw_predicted = \
torch.sum(yaw_predicted * idx_tensor, 1) * 3 - 42
loss_reg_pitch = reg_criterion(
pitch_predicted, label_pitch_cont_gaze)
loss_reg_yaw = reg_criterion(
yaw_predicted, label_yaw_cont_gaze)
# Total loss
loss_pitch_gaze += alpha * loss_reg_pitch
loss_yaw_gaze += alpha * loss_reg_yaw
sum_loss_pitch_gaze += loss_pitch_gaze
sum_loss_yaw_gaze += loss_yaw_gaze
loss_seq = [loss_pitch_gaze, loss_yaw_gaze]
grad_seq = \
[torch.tensor(1.0).cuda(gpu) for _ in range(len(loss_seq))]
optimizer_gaze.zero_grad(set_to_none=True)
torch.autograd.backward(loss_seq, grad_seq)
optimizer_gaze.step()
iter_gaze += 1
if (i+1) % 100 == 0:
print('Epoch [%d/%d], Iter [%d/%d] Losses: '
'Gaze Yaw %.4f,Gaze Pitch %.4f' % (
epoch+1,
num_epochs,
i+1,
len(dataset)//batch_size,
sum_loss_pitch_gaze/iter_gaze,
sum_loss_yaw_gaze/iter_gaze
)
)
# Save models at numbered epochs.
if epoch % 1 == 0 and epoch < num_epochs:
print('Taking snapshot...',
torch.save(model.state_dict(),
output+'/fold' + str(fold) +'/'+
'_epoch_' + str(epoch+1) + '.pkl')
)