def demo(data_root,
train_list,
validate_list,
test_list,
save,
n_epochs=1,
batch_size=64,
lr=0.01,
wd=0.0005,
momentum=0.9,
seed=None):
"""
A demo to show off training and testing of :
"Deep facial age estimation using conditional multitask learning with weak label esxpansion."
Trains and evaluates a mean-variance loss on MOPPH Album2 dataset.
Args:
data_root (str) - path to directory where data exist
train_list (str) - path to directory where train_data_list exist
validation_list (str) - path to directory where validation_data_list exist
test_list (str) - path to directory where test_data_list exist
save (str) - path to save the model and results to
n_epochs (int) - number of epochs for training (default 3)
batch_size (int) - size of minibatch (default 64)
lr (float) - base lerning rate (default 0.001)
wd (float) -weight deday (default 0.0001)
momentum (float) momentum (default 0.9)
seed (int) - manually set the random seed (default None)
"""
# Mean and std value from Imagenet
mean = [0.485, 0.456, 0.406]
stdv = [0.229, 0.224, 0.225]
train_transforms = transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.RandomRotation(5),
transforms.ColorJitter(0.05, 0.05, 0.05, 0.05),
transforms.ToTensor(),
transforms.Normalize(mean=mean, std=stdv),
])
test_transforms = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=mean, std=stdv),
])
if os.path.exists(
os.path.join(save, 'DLDL_ResNet18_nesterov_results_1_1.csv')):
os.remove(os.path.join(save, 'DLDL_ResNet18_nesterov_results_1_1.csv'))
with open(os.path.join(save, 'DLDL_ResNet18_nesterov_results_1_1.csv'),
'w') as f:
f.write(
'******************************************************************\n'
)
f.write('records on MORPH2 dataset under ' 'S1-S2-S3' ' protocol\n')
f.write(
'******************************************************************\n'
)
f.write('\n')
f.write('\n')
train_set = data_prepare(data_root=data_root,
data_list=train_list,
transform=train_transforms)
valid_set = data_prepare(data_root=data_root,
data_list=validate_list,
transform=test_transforms)
test_set = data_prepare(data_root=data_root,
data_list=test_list,
transform=test_transforms)
resnet18_model = models.resnet18(pretrained=True)
fc_features = resnet18_model.fc.in_features
resnet18_model.fc = nn.Linear(fc_features, 101)
model = resnet18_model
# Make save directory
if not os.path.exists(save):
os.makedirs(save)
if not os.path.isdir(save):
raise Exception('%s is not a dir' % save)
# Model on cuda
use_cuda = torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
# prepare data
if seed is not None:
torch.manual_seed(seed)
if use_cuda:
torch.cuda.manual_seed_all(seed)
train_loader = torch.utils.data.DataLoader(
train_set,
batch_size=batch_size,
shuffle=True,
pin_memory=(torch.cuda.is_available()),
num_workers=4)
valid_loader = torch.utils.data.DataLoader(
valid_set,
batch_size=batch_size,
shuffle=False,
pin_memory=(torch.cuda.is_available()),
num_workers=4)
test_loader = torch.utils.data.DataLoader(
test_set,
batch_size=batch_size,
shuffle=False,
pin_memory=(torch.cuda.is_available()),
num_workers=4)
# Wrap model for multi-GPUs, if necessary
if torch.cuda.is_available() and torch.cuda.device_count() > 1:
# model.features = torch.nn.DataParallel(model.features)
model = model = torch.nn.DataParallel(model)
model_wrapper = model.to(device)
# Optimizer
optimizer = torch.optim.SGD(model_wrapper.parameters(),
lr=lr,
momentum=momentum,
nesterov=True,
weight_decay=wd)
# scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[0.5 * n_epochs, 0.75 * n_epochs],
# gamma=0.1)
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,
milestones=[30, 50, 60],
gamma=0.1)
# # Train and validate model
best_argmax_MAE = 100
best_exp_MAE = 100
model_state_name_1 = 'DLDL_resnet18_nesterov_model_1_1_1.dat'
model_state_name_2 = 'DLDL_resnet18_nesterov_model_1_1_2.dat'
model_state_dir_1 = os.path.join(save, model_state_name_1)
model_state_dir_2 = os.path.join(save, model_state_name_2)
with open(os.path.join(save, 'DLDL_ResNet18_nesterov_results_1_1.csv'),
'a') as f:
f.write(
'epoch, train_loss, train_argmax_MAE, train_exp_MAE, valid_loss, valid_argmax_MAE, valid_exp_MAE\n'
)
for epoch in range(n_epochs):
scheduler.step()
_, train_loss, train_argmax_MAE, train_exp_MAE = train(
model=model_wrapper,
loader=train_loader,
optimizer=optimizer,
epoch=epoch,
n_epochs=n_epochs,
device=device)
_, valid_loss, valid_argmax_MAE, valid_exp_MAE = validate(
model=model_wrapper,
loader=valid_loader,
epoch=epoch,
n_epochs=n_epochs,
device=device)
# Determine if model is the best
if valid_argmax_MAE < best_argmax_MAE:
best_argmax_MAE = valid_argmax_MAE
if os.path.exists(model_state_dir_1):
os.remove(model_state_dir_1)
torch.save(model_wrapper.state_dict(), model_state_dir_1)
if valid_exp_MAE < best_exp_MAE:
best_exp_MAE = valid_exp_MAE
if os.path.exists(model_state_dir_2):
os.remove(model_state_dir_2)
torch.save(model_wrapper.state_dict(), model_state_dir_2)
with open(os.path.join(save, 'DLDL_ResNet18_nesterov_results_1_1.csv'),
'a') as f:
f.write('%03d, %0.3f, %0.3f, %0.3f, %0.3f, %0.3f, %0.3f\n' %
((epoch + 1), train_loss, train_argmax_MAE, train_exp_MAE,
valid_loss, valid_argmax_MAE, valid_exp_MAE))
if math.isnan(float(train_argmax_MAE)) or math.isnan(
float(train_exp_MAE)):
break
# Test model
if os.path.exists(model_state_dir_1):
_, test_loss, test_argmax_MAE, AE_list, predict_age_list, real_age_list = test(
model=model_wrapper,
loader=test_loader,
device=device,
MAE_mode='argmax',
model_state_dir=model_state_dir_1)
os.remove(model_state_dir_1)
# os.remove(model_state_dir)
with open(os.path.join(save, 'DLDL_ResNet18_nesterov_results_1_1.csv'),
'a') as f:
f.write('test_loss, test_argmax_MAE:\n')
f.write('%0.3f, %0.3f\n' % (test_loss, test_argmax_MAE))
# f.write('\n')
CS_1_numerator = CS_2_numerator = CS_3_numerator = CS_4_numerator = CS_5_numerator = CS_6_numerator = CS_7_numerator = CS_8_numerator = CS_9_numerator = CS_10_numerator = 0
for i in range(len(AE_list)):
if AE_list[i] <= 1:
CS_1_numerator += 1
if AE_list[i] <= 2:
CS_2_numerator += 1
if AE_list[i] <= 3:
CS_3_numerator += 1
if AE_list[i] <= 4:
CS_4_numerator += 1
if AE_list[i] <= 5:
CS_5_numerator += 1
if AE_list[i] <= 6:
CS_6_numerator += 1
if AE_list[i] <= 7:
CS_7_numerator += 1
if AE_list[i] <= 8:
CS_8_numerator += 1
if AE_list[i] <= 9:
CS_9_numerator += 1
if AE_list[i] <= 10:
CS_10_numerator += 1
CS_1 = CS_1_numerator / len(AE_list)
CS_2 = CS_2_numerator / len(AE_list)
CS_3 = CS_3_numerator / len(AE_list)
CS_4 = CS_4_numerator / len(AE_list)
CS_5 = CS_5_numerator / len(AE_list)
CS_6 = CS_6_numerator / len(AE_list)
CS_7 = CS_7_numerator / len(AE_list)
CS_8 = CS_8_numerator / len(AE_list)
CS_9 = CS_9_numerator / len(AE_list)
CS_10 = CS_10_numerator / len(AE_list)
with open(os.path.join(save, 'DLDL_ResNet18_nesterov_results_1_1.csv'),
'a') as f:
f.write(
'CS_1, CS_2, CS_3, CS_4, CS_5, CS_6, CS_7, CS_8, CS_9, CS_10:\n'
)
f.write(
'%0.3f, %0.3f, %0.3f, %0.3f, %0.3f, %0.3f, %0.3f, %0.3f, %0.3f, %0.3f\n'
%
(CS_1, CS_2, CS_3, CS_4, CS_5, CS_6, CS_7, CS_8, CS_9, CS_10))
f.write('\n')
#'***************************'
if os.path.exists(model_state_dir_2):
_, test_loss, test_exp_MAE, AE_list, predict_age_list, real_age_list = test(
model=model_wrapper,
loader=test_loader,
device=device,
MAE_mode='exp',
model_state_dir=model_state_dir_2)
os.remove(model_state_dir_2)
# os.remove(model_state_dir)
with open(os.path.join(save, 'DLDL_ResNet18_nesterov_results_1_1.csv'),
'a') as f:
f.write('test_loss, test_exp_MAE:\n')
f.write('%0.3f, %0.3f\n' % (test_loss, test_exp_MAE))
# f.write('\n')
CS_1_numerator = CS_2_numerator = CS_3_numerator = CS_4_numerator = CS_5_numerator = CS_6_numerator = CS_7_numerator = CS_8_numerator = CS_9_numerator = CS_10_numerator = 0
for i in range(len(AE_list)):
if AE_list[i] <= 1:
CS_1_numerator += 1
if AE_list[i] <= 2:
CS_2_numerator += 1
if AE_list[i] <= 3:
CS_3_numerator += 1
if AE_list[i] <= 4:
CS_4_numerator += 1
if AE_list[i] <= 5:
CS_5_numerator += 1
if AE_list[i] <= 6:
CS_6_numerator += 1
if AE_list[i] <= 7:
CS_7_numerator += 1
if AE_list[i] <= 8:
CS_8_numerator += 1
if AE_list[i] <= 9:
CS_9_numerator += 1
if AE_list[i] <= 10:
CS_10_numerator += 1
CS_1 = CS_1_numerator / len(AE_list)
CS_2 = CS_2_numerator / len(AE_list)
CS_3 = CS_3_numerator / len(AE_list)
CS_4 = CS_4_numerator / len(AE_list)
CS_5 = CS_5_numerator / len(AE_list)
CS_6 = CS_6_numerator / len(AE_list)
CS_7 = CS_7_numerator / len(AE_list)
CS_8 = CS_8_numerator / len(AE_list)
CS_9 = CS_9_numerator / len(AE_list)
CS_10 = CS_10_numerator / len(AE_list)
with open(os.path.join(save, 'DLDL_ResNet18_nesterov_results_1_1.csv'),
'a') as f:
f.write(
'CS_1, CS_2, CS_3, CS_4, CS_5, CS_6, CS_7, CS_8, CS_9, CS_10:\n'
)
f.write(
'%0.3f, %0.3f, %0.3f, %0.3f, %0.3f, %0.3f, %0.3f, %0.3f, %0.3f, %0.3f\n'
%
(CS_1, CS_2, CS_3, CS_4, CS_5, CS_6, CS_7, CS_8, CS_9, CS_10))
f.write('\n')
def demo(data_root, test_list, save, batch_size=64, seed=None):
"""
A demo to show off training and testing of :
"Deep facial age estimation using conditional multitask learning with weak label esxpansion."
Trains and evaluates a mean-variance loss on MOPPH Album2 dataset.
Args:
data_root (str) - path to directory where data exist
train_list (str) - path to directory where train_data_list exist
validation_list (str) - path to directory where validation_data_list exist
test_list (str) - path to directory where test_data_list exist
save (str) - path to save the model and results to
n_epochs (int) - number of epochs for training (default 3)
batch_size (int) - size of minibatch (default 64)
lr (float) - base lerning rate (default 0.001)
wd (float) -weight deday (default 0.0001)
momentum (float) momentum (default 0.9)
seed (int) - manually set the random seed (default None)
"""
# Mean and std value from Imagenet
mean = [0.485, 0.456, 0.406]
stdv = [0.229, 0.224, 0.225]
test_transforms = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=mean, std=stdv),
])
if os.path.exists(
os.path.join(
save,
'ordinal_regression_resnet18_nesterov_results_consistency_broken_counting_1_5.csv'
)):
os.remove(
os.path.join(
save,
'ordinal_regression_resnet18_nesterov_results_consistency_broken_counting_1_5.csv'
))
with open(
os.path.join(
save,
'ordinal_regression_resnet18_nesterov_results_consistency_broken_counting_1_5.csv'
), 'w') as f:
f.write(
'******************************************************************\n'
)
f.write('records on AgeDB dataset under '
'five-fold cross-validation'
' protocol\n')
f.write(
'******************************************************************\n'
)
f.write('\n')
f.write('\n')
test_set = data_prepare(data_root=data_root,
data_list=test_list,
transform=test_transforms)
ensemble_learning_model = el_resnet18(num_classes=2)
model = ensemble_learning_model
# Make save directory
if not os.path.exists(save):
os.makedirs(save)
if not os.path.isdir(save):
raise Exception('%s is not a dir' % save)
# Model on cuda
use_cuda = torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
# prepare data
if seed is not None:
torch.manual_seed(seed)
if use_cuda:
torch.cuda.manual_seed_all(seed)
test_loader = torch.utils.data.DataLoader(
test_set,
batch_size=batch_size,
shuffle=False,
pin_memory=(torch.cuda.is_available()),
num_workers=4)
# Wrap model for multi-GPUs, if necessary
if torch.cuda.is_available() and torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model)
model_wrapper = model.to(device)
######################fold one#######################################
model_state_name_1 = 'el_resnet18_nesterov_two_group_model_1_5_1.dat'
model_state_dir_1 = os.path.join(save, model_state_name_1)
# Test model
if os.path.exists(model_state_dir_1):
_, test_total_loss, test_argmax_MAE, test_accuracy, AE_list, predict_age_list, real_age_list, total_consistency_broken_count, total_test_images_count = test(
model=model_wrapper,
loader=test_loader,
device=device,
model_state_dir=model_state_dir_1,
mode='ordinal_regression')
with open(
os.path.join(
save,
'ordinal_regression_resnet18_nesterov_results_consistency_broken_counting_1_5.csv'
), 'a') as f:
f.write(
'####test with original ordinal regression method################'
)
f.write('test_total_loss, test_argmax_MAE, test_accuracy:\n')
f.write('%0.4f, %0.4f, %0.4f\n' %
(test_total_loss, test_argmax_MAE, test_accuracy))
CS_1_numerator = CS_2_numerator = CS_3_numerator = CS_4_numerator = CS_5_numerator = CS_6_numerator = CS_7_numerator = CS_8_numerator = CS_9_numerator = CS_10_numerator = 0
for i in range(len(AE_list)):
if AE_list[i] <= 1:
CS_1_numerator += 1
if AE_list[i] <= 2:
CS_2_numerator += 1
if AE_list[i] <= 3:
CS_3_numerator += 1
if AE_list[i] <= 4:
CS_4_numerator += 1
if AE_list[i] <= 5:
CS_5_numerator += 1
if AE_list[i] <= 6:
CS_6_numerator += 1
if AE_list[i] <= 7:
CS_7_numerator += 1
if AE_list[i] <= 8:
CS_8_numerator += 1
if AE_list[i] <= 9:
CS_9_numerator += 1
if AE_list[i] <= 10:
CS_10_numerator += 1
CS_1 = CS_1_numerator / len(AE_list)
CS_2 = CS_2_numerator / len(AE_list)
CS_3 = CS_3_numerator / len(AE_list)
CS_4 = CS_4_numerator / len(AE_list)
CS_5 = CS_5_numerator / len(AE_list)
CS_6 = CS_6_numerator / len(AE_list)
CS_7 = CS_7_numerator / len(AE_list)
CS_8 = CS_8_numerator / len(AE_list)
CS_9 = CS_9_numerator / len(AE_list)
CS_10 = CS_10_numerator / len(AE_list)
with open(
os.path.join(
save,
'ordinal_regression_resnet18_nesterov_results_consistency_broken_counting_1_5.csv'
), 'a') as f:
f.write(
'CS_1, CS_2, CS_3, CS_4, CS_5, CS_6, CS_7, CS_8, CS_9, CS_10:\n'
)
f.write(
'%0.3f, %0.3f, %0.3f, %0.3f, %0.3f, %0.3f, %0.3f, %0.3f, %0.3f, %0.3f\n'
%
(CS_1, CS_2, CS_3, CS_4, CS_5, CS_6, CS_7, CS_8, CS_9, CS_10))
f.write('totoal_consistency_broken_count: %d\n' %
total_consistency_broken_count)
f.write('total_test_images_count: %d\n' % total_test_images_count)
f.write('')
f.write('\n')
f.write('\n')
#######################################################################################
_, test_total_loss, test_argmax_MAE, test_accuracy, AE_list, predict_age_list, real_age_list = test(
model=model_wrapper,
loader=test_loader,
device=device,
model_state_dir=model_state_dir_1,
mode='EL_2group')
# os.remove(model_state_dir_1)
with open(
os.path.join(
save,
'ordinal_regression_resnet18_nesterov_results_consistency_broken_counting_1_5.csv'
), 'a') as f:
f.write(
'####test with the proposed EL-2group method################')
f.write('test_total_loss, test_argmax_MAE, test_accuracy:\n')
f.write('%0.4f, %0.4f, %0.4f\n' %
(test_total_loss, test_argmax_MAE, test_accuracy))
CS_1_numerator = CS_2_numerator = CS_3_numerator = CS_4_numerator = CS_5_numerator = CS_6_numerator = CS_7_numerator = CS_8_numerator = CS_9_numerator = CS_10_numerator = 0
for i in range(len(AE_list)):
if AE_list[i] <= 1:
CS_1_numerator += 1
if AE_list[i] <= 2:
CS_2_numerator += 1
if AE_list[i] <= 3:
CS_3_numerator += 1
if AE_list[i] <= 4:
CS_4_numerator += 1
if AE_list[i] <= 5:
CS_5_numerator += 1
if AE_list[i] <= 6:
CS_6_numerator += 1
if AE_list[i] <= 7:
CS_7_numerator += 1
if AE_list[i] <= 8:
CS_8_numerator += 1
if AE_list[i] <= 9:
CS_9_numerator += 1
if AE_list[i] <= 10:
CS_10_numerator += 1
CS_1 = CS_1_numerator / len(AE_list)
CS_2 = CS_2_numerator / len(AE_list)
CS_3 = CS_3_numerator / len(AE_list)
CS_4 = CS_4_numerator / len(AE_list)
CS_5 = CS_5_numerator / len(AE_list)
CS_6 = CS_6_numerator / len(AE_list)
CS_7 = CS_7_numerator / len(AE_list)
CS_8 = CS_8_numerator / len(AE_list)
CS_9 = CS_9_numerator / len(AE_list)
CS_10 = CS_10_numerator / len(AE_list)
with open(
os.path.join(
save,
'ordinal_regression_resnet18_nesterov_results_consistency_broken_counting_1_5.csv'
), 'a') as f:
f.write(
'CS_1, CS_2, CS_3, CS_4, CS_5, CS_6, CS_7, CS_8, CS_9, CS_10:\n'
)
f.write(
'%0.3f, %0.3f, %0.3f, %0.3f, %0.3f, %0.3f, %0.3f, %0.3f, %0.3f, %0.3f\n'
%
(CS_1, CS_2, CS_3, CS_4, CS_5, CS_6, CS_7, CS_8, CS_9, CS_10))
def demo(data_root, test_list, save, batch_size=64, seed=None):
"""
A demo to show off training and testing of :
"Deep facial age estimation using conditional multitask learning with weak label esxpansion."
Trains and evaluates a mean-variance loss on MOPPH Album2 dataset.
Args:
data_root (str) - path to directory where data exist
train_list (str) - path to directory where train_data_list exist
validation_list (str) - path to directory where validation_data_list exist
test_list (str) - path to directory where test_data_list exist
save (str) - path to save the model and results to
n_epochs (int) - number of epochs for training (default 3)
batch_size (int) - size of minibatch (default 64)
lr (float) - base lerning rate (default 0.001)
wd (float) -weight deday (default 0.0001)
momentum (float) momentum (default 0.9)
seed (int) - manually set the random seed (default None)
"""
# Mean and std value from Imagenet
mean=[0.485, 0.456, 0.406]
stdv=[0.229, 0.224, 0.225]
test_transforms = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=mean, std=stdv),
])
if os.path.exists(os.path.join(save, 'el_resnet101_nesterov_three_group_test_record_1_1.csv')):
os.remove(os.path.join(save, 'el_resnet101_nesterov_three_group_test_record_1_1.csv'))
with open(os.path.join(save, 'el_resnet101_nesterov_three_group_test_record_1_1.csv'), 'w') as f:
f.write('******************************************************************\n')
f.write('records on AgeDB dataset under ''80%-20%'' protocol\n')
f.write('******************************************************************\n')
f.write('\n')
f.write('\n')
test_set = data_prepare(data_root=data_root, data_list=test_list, transform=test_transforms)
ensemble_learning_model = el_resnet101(num_classes=3)
pretrained_dict=model_zoo.load_url(model_urls['resnet101'])
model_dict=ensemble_learning_model.state_dict()
pretrained_dict={k:v for k,v in pretrained_dict.items() if k in model_dict}
model_dict.update(pretrained_dict)
ensemble_learning_model.load_state_dict(model_dict)
model=ensemble_learning_model
# Make save directory
if not os.path.exists(save):
os.makedirs(save)
if not os.path.isdir(save):
raise Exception('%s is not a dir' % save)
# Model on cuda
use_cuda=torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
# prepare data
if seed is not None:
torch.manual_seed(seed)
if use_cuda:
torch.cuda.manual_seed_all(seed)
test_loader = torch.utils.data.DataLoader(test_set, batch_size=batch_size, shuffle=False,
pin_memory=(torch.cuda.is_available()), num_workers=4)
# Wrap model for multi-GPUs, if necessary
if torch.cuda.is_available() and torch.cuda.device_count() > 1:
# model.features = torch.nn.DataParallel(model.features)
model = model = torch.nn.DataParallel(model)
model_wrapper = model.to(device)
# Train and validate model
# best_argmax_MAE = 100
# best_accuracy = 0
model_state_name_1='el_resnet101_nesterov_three_group_model_1_1_1.dat'
model_state_dir_1=os.path.join(save, model_state_name_1)
# Test model
if os.path.exists(model_state_dir_1):
_, test_total_loss, test_cross_entropy_loss, test_kl_loss, test_argmax_MAE, test_accuracy, AE_list, predict_age_list, real_age_list= test(
model=model_wrapper,
loader=test_loader,
device=device,
model_state_dir=model_state_dir_1,
)
os.remove(model_state_dir_1)
with open(os.path.join(save, 'el_resnet101_nesterov_three_group_test_record_1_1.csv'), 'a') as f:
f.write('test_total_loss, test_cross_entropy_loss, test_kl_loss, test_argmax_MAE, test_accuracy:\n')
f.write('%0.4f, %0.4f, %0.4f, %0.4f, %0.4f\n' % (test_total_loss, test_cross_entropy_loss, test_kl_loss, test_argmax_MAE, test_accuracy))
# f.write('\n')
CS_1_numerator=CS_2_numerator=CS_3_numerator=CS_4_numerator=CS_5_numerator=CS_6_numerator=CS_7_numerator=CS_8_numerator=CS_9_numerator=CS_10_numerator=0
for i in range(len(AE_list)):
if AE_list[i]<=1:
CS_1_numerator+=1
if AE_list[i]<=2:
CS_2_numerator+=1
if AE_list[i]<=3:
CS_3_numerator+=1
if AE_list[i]<=4:
CS_4_numerator+=1
if AE_list[i]<=5:
CS_5_numerator+=1
if AE_list[i]<=6:
CS_6_numerator+=1
if AE_list[i]<=7:
CS_7_numerator+=1
if AE_list[i]<=8:
CS_8_numerator+=1
if AE_list[i]<=9:
CS_9_numerator+=1
if AE_list[i]<=10:
CS_10_numerator+=1
CS_1=CS_1_numerator/len(AE_list)
CS_2=CS_2_numerator/len(AE_list)
CS_3=CS_3_numerator/len(AE_list)
CS_4=CS_4_numerator/len(AE_list)
CS_5=CS_5_numerator/len(AE_list)
CS_6=CS_6_numerator/len(AE_list)
CS_7=CS_7_numerator/len(AE_list)
CS_8=CS_8_numerator/len(AE_list)
CS_9=CS_9_numerator/len(AE_list)
CS_10=CS_10_numerator/len(AE_list)
with open(os.path.join(save, 'el_resnet101_nesterov_three_group_test_record_1_1.csv'), 'a') as f:
f.write('CS_1, CS_2, CS_3, CS_4, CS_5, CS_6, CS_7, CS_8, CS_9, CS_10:\n')
f.write('%0.3f, %0.3f, %0.3f, %0.3f, %0.3f, %0.3f, %0.3f, %0.3f, %0.3f, %0.3f\n'
% (CS_1, CS_2, CS_3, CS_4, CS_5, CS_6, CS_7, CS_8, CS_9, CS_10))
f.write('\n')
with open(test_list) as f:
test_lists_record=f.readlines()
with open(os.path.join(save, 'el_resnet101_nesterov_three_group_test_record_1_1.csv'), 'a') as f:
f.write('image_name, true_age, predict_age, AE:\n')
for i in range(len(AE_list)):
img_name = test_lists_record[i].split()[0]
true_age = real_age_list[i]
predict_age = predict_age_list[i]
AE = AE_list[i]
with open(os.path.join(save, 'el_resnet101_nesterov_three_group_test_record_1_1.csv'), 'a') as f:
f.write('%s, %d, %d, %d\n' % (img_name, true_age, predict_age, AE))
def demo(data_root,
train_list,
test_list,
save,
n_epochs=1,
batch_size=64,
lr=0.01,
wd=0.0005,
momentum=0.9,
lemta1=0.1,
lemta2=0.004,
seed=None):
#def demo(data_root, train_list, validation_list, test_list, save, n_epochs=1,
# batch_size=64, lr=0.001, wd=0.0005, seed=None):
"""
A demo to show off training and testing of :
"Deep facial age estimation using conditional multitask learning with weak label esxpansion."
Trains and evaluates a mean-variance loss on MOPPH Album2 dataset.
Args:
data_root (str) - path to directory where data exist
train_list (str) - path to directory where train_data_list exist
validation_list (str) - path to directory where validation_data_list exist
test_list (str) - path to directory where test_data_list exist
save (str) - path to save the model and results to
n_epochs (int) - number of epochs for training (default 3)
batch_size (int) - size of minibatch (default 64)
lr (float) - base lerning rate (default 0.001)
wd (float) -weight deday (default 0.0001)
momentum (float) momentum (default 0.9)
seed (int) - manually set the random seed (default None)
"""
# Mean and std value from Imagenet
mean = [0.485, 0.456, 0.406]
stdv = [0.229, 0.224, 0.225]
# mean=[0.5, 0.5, 0.5]
# stdv=[0.5, 0.5, 0.5]
train_transforms = transforms.Compose([
# transforms.Resize(146),
# transforms.RandomCrop(128),
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.RandomRotation(5),
transforms.ColorJitter(0.05, 0.05, 0.05, 0.05),
transforms.ToTensor(),
transforms.Normalize(mean=mean, std=stdv),
])
test_transforms = transforms.Compose([
# transforms.Resize(146),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=mean, std=stdv),
])
# Datasets
train_set = data_prepare(data_root=data_root,
data_list=train_list,
transform=train_transforms)
test_set = data_prepare(data_root=data_root,
data_list=test_list,
transform=test_transforms)
pretrained_dict = model_zoo.load_url(model_urls['vgg16'])
# print(pretrained_dict.keys())
model = conditional_VGG16()
# print(model)
model_dict = model.state_dict()
# print(model_dict.keys())
# os._exit(0)
# del model_dict[]
pretrained_dict = {
k: v
for k, v in pretrained_dict.items() if k in model_dict
}
# print(pretrained_dict.keys())
# os._exit(0)
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
# Make save directory
if not os.path.exists(save):
os.makedirs(save)
if not os.path.isdir(save):
raise Exception('%s is not a dir' % save)
# Model on cuda
use_cuda = torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
# prepare data
if seed is not None:
torch.manual_seed(seed)
if use_cuda:
torch.cuda.manual_seed_all(seed)
train_loader = torch.utils.data.DataLoader(
train_set,
batch_size=batch_size,
shuffle=True,
pin_memory=(torch.cuda.is_available()),
num_workers=4)
test_loader = torch.utils.data.DataLoader(
test_set,
batch_size=batch_size,
shuffle=False,
pin_memory=(torch.cuda.is_available()),
num_workers=4)
# Wrap model for multi-GPUs, if necessary
if torch.cuda.is_available() and torch.cuda.device_count() > 1:
model.features = torch.nn.DataParallel(model.features)
model_wrapper = model.to(device)
# Optimizer
optimizer = torch.optim.SGD(model_wrapper.parameters(),
lr=lr,
momentum=momentum,
nesterov=True,
weight_decay=wd)
# scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[0.5 * n_epochs, 0.75 * n_epochs],
# gamma=0.1)
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,
milestones=[50, 70, 90],
gamma=0.1)
# optimizer = torch.optim.Adam(model_wrapper.parameters(), lr=lr, weight_decay=wd, amsgrad=True)
# Start log
if os.path.exists(
os.path.join(save, 'conditional_VGG16_nesterov_results_3.csv')):
os.remove(
os.path.join(save, 'conditional_VGG16_nesterov_results_3.csv'))
with open(os.path.join(save, 'conditional_VGG16_nesterov_results_3.csv'),
'w') as f:
f.write(
'epoch, '
'train_softmax_loss_male_black, train_mean_loss_male_black, train_deviation_loss_male_black, '
'train_softmax_loss_male_white, train_mean_loss_male_white, train_deviation_loss_male_white, '
'train_softmax_loss_female_black, train_mean_loss_female_black, train_deviation_loss_female_black, '
'train_softmax_loss_female_white, train_mean_loss_female_white, train_deviation_loss_female_white, '
'train_age_loss, train_race_loss, train_gender_loss, train_total_loss, train_race_accuracy, train_gender_accuracy, train_MAE\n'
)
# Train and validate model
best_MAE = 100
model_state_dir = os.path.join(save,
'conditional_VGG16_nesterov_model_3.dat')
for epoch in range(n_epochs):
scheduler.step()
_, tra_slmb, tra_mlmb, tra_dlmb, tra_slmw, tra_mlmw, tra_dlmw, tra_slfb, tra_mlfb, tra_dlvb, tra_slfw, tra_mlfw, tra_dlfw, tra_al, tra_rl, tra_gl, tra_tl, tra_ra, tra_ga, train_MAE = train(
model=model_wrapper,
loader=train_loader,
optimizer=optimizer,
epoch=epoch,
n_epochs=n_epochs,
device=device,
lemta1=lemta1,
lemta2=lemta2)
print('*********************************')
# Determine if model is the best
if train_MAE < best_MAE:
best_MAE = train_MAE
print('New best MAE: %.4f' % best_MAE)
if os.path.exists(model_state_dir):
os.remove(model_state_dir)
torch.save(model_wrapper.state_dict(), model_state_dir)
with open(
os.path.join(save, 'conditional_VGG16_nesterov_results_3.csv'),
'a') as f:
f.write('%03d, '
'%0.3f, %0.3f, %0.3f, '
'%0.3f, %0.3f, %0.3f, '
'%0.3f, %0.3f, %0.3f, '
'%0.3f, %0.3f, %0.3f, '
'%0.3f, %0.3f, %0.3f, %0.3f, %0.3f, %0.3f, %0.3f\n' %
((epoch + 1), tra_slmb, tra_mlmb, tra_dlmb, tra_slmw,
tra_mlmw, tra_dlmw, tra_slfb, tra_mlfb, tra_dlvb,
tra_slfw, tra_mlfw, tra_dlfw, tra_al, tra_rl, tra_gl,
tra_tl, tra_ra, tra_ga, train_MAE))
# Test model
_, tes_ra, tes_ga, test_MAE, AE_list, predict_age_list, real_age_list = test(
model=model_wrapper,
loader=test_loader,
device=device,
model_state_dir=model_state_dir,
lemta1=lemta1,
lemta2=lemta2)
CS_1_numerator = CS_2_numerator = CS_3_numerator = CS_4_numerator = CS_5_numerator = CS_6_numerator = CS_7_numerator = CS_8_numerator = CS_9_numerator = CS_10_numerator = 0
for i in range(len(AE_list)):
if AE_list[i] <= 1:
CS_1_numerator += 1
if AE_list[i] <= 2:
CS_2_numerator += 1
if AE_list[i] <= 3:
CS_3_numerator += 1
if AE_list[i] <= 4:
CS_4_numerator += 1
if AE_list[i] <= 5:
CS_5_numerator += 1
if AE_list[i] <= 6:
CS_6_numerator += 1
if AE_list[i] <= 7:
CS_7_numerator += 1
if AE_list[i] <= 8:
CS_8_numerator += 1
if AE_list[i] <= 9:
CS_9_numerator += 1
if AE_list[i] <= 10:
CS_10_numerator += 1
CS_1 = CS_1_numerator / len(AE_list)
CS_2 = CS_2_numerator / len(AE_list)
CS_3 = CS_3_numerator / len(AE_list)
CS_4 = CS_4_numerator / len(AE_list)
CS_5 = CS_5_numerator / len(AE_list)
CS_6 = CS_6_numerator / len(AE_list)
CS_7 = CS_7_numerator / len(AE_list)
CS_8 = CS_8_numerator / len(AE_list)
CS_9 = CS_9_numerator / len(AE_list)
CS_10 = CS_10_numerator / len(AE_list)
with open(test_list) as f:
test_lines = f.readlines()
index = 0
with open(os.path.join(save, 'conditional_VGG16_nesterov_results_3.csv'),
'a') as f:
f.write('******************************\n')
f.write('\n')
f.write('******************************\n')
f.write('test_img_name, real_age, predict_age, absolute_error(AE):\n')
for test_line in test_lines:
img_name = test_line.split()[0]
img_predict_age = predict_age_list[index].item()
img_real_age = real_age_list[index].item()
img_AE = AE_list[index].item()
record_line = img_name + ' ' + str(round(
img_real_age, 2)) + ' ' + str(round(
img_predict_age, 2)) + ' ' + str(round(img_AE, 2)) + '\n'
index += 1
with open(
os.path.join(save, 'conditional_VGG16_nesterov_results_3.csv'),
'a') as f:
f.write(record_line)
with open(os.path.join(save, 'conditional_VGG16_nesterov_results_3.csv'),
'a') as f:
f.write('******************************\n')
f.write('test_race_accuracy, test_gender_accuracy, test_MAE\n')
f.write('%0.3f, %0.3f, %0.3f\n' % (tes_ra, tes_ga, test_MAE))
f.write(
'CS_1, CS_2, CS_3, CS_4, CS_5, CS_6, CS_7, CS_8, CS_9, CS_10:\n')
f.write(
'%0.3f, %0.3f, %0.3f, %0.3f, %0.3f, %0.3f, %0.3f, %0.3f, %0.3f, %0.3f\n'
% (CS_1, CS_2, CS_3, CS_4, CS_5, CS_6, CS_7, CS_8, CS_9, CS_10))
def demo(data_root,
train_list,
validate_list,
save,
n_epochs=1,
batch_size=64,
lr=0.01,
wd=0.0005,
momentum=0.9,
seed=None):
"""
A demo to show off training and testing of :
"Deep facial age estimation using conditional multitask learning with weak label esxpansion."
Trains and evaluates a mean-variance loss on MOPPH Album2 dataset.
Args:
data_root (str) - path to directory where data exist
train_list (str) - path to directory where train_data_list exist
validation_list (str) - path to directory where validation_data_list exist
test_list (str) - path to directory where test_data_list exist
save (str) - path to save the model and results to
n_epochs (int) - number of epochs for training (default 3)
batch_size (int) - size of minibatch (default 64)
lr (float) - base lerning rate (default 0.001)
wd (float) -weight deday (default 0.0001)
momentum (float) momentum (default 0.9)
seed (int) - manually set the random seed (default None)
"""
# Mean and std value from Imagenet
mean = [0.485, 0.456, 0.406]
stdv = [0.229, 0.224, 0.225]
train_transforms = transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.RandomRotation(5),
transforms.ColorJitter(0.05, 0.05, 0.05, 0.05),
transforms.ToTensor(),
transforms.Normalize(mean=mean, std=stdv),
])
test_transforms = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=mean, std=stdv),
])
if os.path.exists(
os.path.join(save, 'el_resnet101_2groups_nesterov_results_1.csv')):
os.remove(
os.path.join(save, 'el_resnet101_2groups_nesterov_results_1.csv'))
with open(
os.path.join(save, 'el_resnet101_2groups_nesterov_results_1.csv'),
'w') as f:
f.write(
'******************************************************************\n'
)
f.write('records on IMDB-WIKI dataset\n')
f.write(
'******************************************************************\n'
)
f.write('\n')
f.write('\n')
train_set = data_prepare(data_root=data_root,
data_list=train_list,
transform=train_transforms)
valid_set = data_prepare(data_root=data_root,
data_list=validate_list,
transform=test_transforms)
ensemble_learning_model = el_resnet101(num_classes=2)
pretrained_dict = model_zoo.load_url(model_urls['resnet101'])
model_dict = ensemble_learning_model.state_dict()
pretrained_dict = {
k: v
for k, v in pretrained_dict.items() if k in model_dict
}
model_dict.update(pretrained_dict)
ensemble_learning_model.load_state_dict(model_dict)
model = ensemble_learning_model
# Make save directory
if not os.path.exists(save):
os.makedirs(save)
if not os.path.isdir(save):
raise Exception('%s is not a dir' % save)
# Model on cuda
use_cuda = torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
# prepare data
if seed is not None:
torch.manual_seed(seed)
if use_cuda:
torch.cuda.manual_seed_all(seed)
train_loader = torch.utils.data.DataLoader(
train_set,
batch_size=batch_size,
shuffle=True,
pin_memory=(torch.cuda.is_available()),
num_workers=4)
valid_loader = torch.utils.data.DataLoader(
valid_set,
batch_size=batch_size,
shuffle=False,
pin_memory=(torch.cuda.is_available()),
num_workers=4)
# Wrap model for multi-GPUs, if necessary
if torch.cuda.is_available() and torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model)
model_wrapper = model.to(device)
# Optimizer
optimizer = torch.optim.SGD(model_wrapper.parameters(),
lr=lr,
momentum=momentum,
nesterov=True,
weight_decay=wd)
# scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[0.5 * n_epochs, 0.75 * n_epochs],
# gamma=0.1)
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,
milestones=[30, 50, 60],
gamma=0.1)
# Train and validate model
# best_argmax_MAE = 100
# model_state_name_1='el_resnet101_nesterov_2groups_model_1.dat'
# model_state_dir_1=os.path.join(save, model_state_name_1)
best_argmax_MAE = 100
best_accuracy = 0
model_state_name_1 = 'el_resnet101_nesterov_2groups_imdb_wiki_model_1_1.dat'
model_state_dir_1 = os.path.join(save, model_state_name_1)
model_state_name_2 = 'el_resnet101_nesterov_2groups_imdb_wiki_model_1_2.dat'
model_state_dir_2 = os.path.join(save, model_state_name_2)
with open(
os.path.join(save, 'el_resnet101_2groups_nesterov_results_1.csv'),
'a') as f:
f.write('epoch, train_total_loss, train_argmax_MAE, train_accuracy, '
'valid_total_loss, valid_argmax_MAE, valid_accuracy\n')
start = time.time()
for epoch in range(n_epochs):
scheduler.step()
_, train_total_loss, train_argmax_MAE, train_accuracy = train(
model=model_wrapper,
loader=train_loader,
optimizer=optimizer,
epoch=epoch,
n_epochs=n_epochs,
device=device)
_, valid_total_loss, valid_argmax_MAE, valid_accuracy = validate(
model=model_wrapper,
loader=valid_loader,
epoch=epoch,
n_epochs=n_epochs,
device=device)
# Determine if model is the best
if valid_argmax_MAE < best_argmax_MAE:
best_argmax_MAE = valid_argmax_MAE
if os.path.exists(model_state_dir_1):
os.remove(model_state_dir_1)
torch.save(model_wrapper.state_dict(), model_state_dir_1)
if valid_accuracy > best_accuracy:
best_accuracy = valid_accuracy
if os.path.exists(model_state_dir_2):
os.remove(model_state_dir_2)
torch.save(model_wrapper.state_dict(), model_state_dir_2)
with open(
os.path.join(save,
'el_resnet101_2groups_nesterov_results_1.csv'),
'a') as f:
f.write('%03d, %0.4f, %0.4f, %0.4f, %0.4f, %0.4f, %0.4f\n' %
((epoch + 1), train_total_loss, train_argmax_MAE,
train_accuracy, valid_total_loss, valid_argmax_MAE,
valid_accuracy))
if math.isnan(float(train_argmax_MAE)):
break
elapsed = (time.time() - start)
print('time cost:', elapsed)
