def evaluate(model: Module, distance_fn: Module, data_loader: DataLoader,
figure_path: Optional[Path] = None) -> EvaluationMetrics:
with torch.no_grad():
distances, labels = [], []
for batch_index, (data_a, data_b, label) in enumerate(tqdm(data_loader)):
data_a, data_b, label = data_a.cuda(), data_b.cuda(), label.cuda()
output_a, output_b = model(data_a), model(data_b)
distance = distance_fn(output_a, output_b) # Euclidean distance
distances.append(distance.cpu().detach().numpy())
labels.append(label.cpu().detach().numpy())
labels = np.array([sublabel for label in labels for sublabel in label])
distances = np.array([subdist for distance in distances for subdist in distance])
(true_positive_rate, false_positive_rate,
precision, recall, accuracy,
roc_auc, best_distances, tar, far) = evaluate_lfw(distances=distances, labels=labels)
# Plot ROC curve
if figure_path is not None:
plot_roc_lfw(false_positive_rate=false_positive_rate,
true_positive_rate=true_positive_rate,
figure_name=str(figure_path))
return EvaluationMetrics(accuracy=float(np.mean(accuracy)),
precision=float(np.mean(precision)),
recall=float(np.mean(recall)),
roc_auc=roc_auc,
tar=float(np.mean(tar)),
far=float(np.mean(far)),
distance=float(np.mean(best_distances)))
def main():
dataroot = args.dataroot
lfw_dataroot = args.lfw
lfw_batch_size = args.lfw_batch_size
lfw_validation_epoch_interval = args.lfw_validation_epoch_interval
model_architecture = args.model
epochs = args.epochs
resume_path = args.resume_path
batch_size = args.batch_size
num_workers = args.num_workers
validation_dataset_split_ratio = args.valid_split
embedding_dimension = args.embedding_dim
pretrained = args.pretrained
optimizer = args.optimizer
learning_rate = args.lr
learning_rate_center_loss = args.center_loss_lr
center_loss_weight = args.center_loss_weight
start_epoch = 0
# Define image data pre-processing transforms
# ToTensor() normalizes pixel values between [0, 1]
# Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) normalizes pixel values between [-1, 1]
# Size 182x182 RGB image -> Center crop size 160x160 RGB image for more model generalization
data_transforms = transforms.Compose([
transforms.RandomCrop(size=160),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
# Size 160x160 RGB image
lfw_transforms = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
# Load the dataset
dataset = torchvision.datasets.ImageFolder(root=dataroot,
transform=data_transforms)
# Subset the dataset into training and validation datasets
num_classes = len(dataset.classes)
print("\nNumber of classes in dataset: {}".format(num_classes))
num_validation = int(num_classes * validation_dataset_split_ratio)
num_train = num_classes - num_validation
indices = list(range(num_classes))
np.random.seed(420)
np.random.shuffle(indices)
train_indices = indices[:num_train]
validation_indices = indices[num_train:]
train_dataset = Subset(dataset=dataset, indices=train_indices)
validation_dataset = Subset(dataset=dataset, indices=validation_indices)
print("Number of classes in training dataset: {}".format(
len(train_dataset)))
print("Number of classes in validation dataset: {}".format(
len(validation_dataset)))
# Define the dataloaders
train_dataloader = DataLoader(dataset=train_dataset,
batch_size=batch_size,
num_workers=num_workers,
shuffle=False)
validation_dataloader = DataLoader(dataset=validation_dataset,
batch_size=batch_size,
num_workers=num_workers,
shuffle=False)
lfw_dataloader = torch.utils.data.DataLoader(dataset=LFWDataset(
dir=lfw_dataroot,
pairs_path='datasets/LFW_pairs.txt',
transform=lfw_transforms),
batch_size=lfw_batch_size,
num_workers=num_workers,
shuffle=False)
# Instantiate model
if model_architecture == "resnet18":
model = Resnet18Center(num_classes=num_classes,
embedding_dimension=embedding_dimension,
pretrained=pretrained)
elif model_architecture == "resnet34":
model = Resnet34Center(num_classes=num_classes,
embedding_dimension=embedding_dimension,
pretrained=pretrained)
elif model_architecture == "resnet50":
model = Resnet50Center(num_classes=num_classes,
embedding_dimension=embedding_dimension,
pretrained=pretrained)
elif model_architecture == "resnet101":
model = Resnet101Center(num_classes=num_classes,
embedding_dimension=embedding_dimension,
pretrained=pretrained)
elif model_architecture == "inceptionresnetv2":
model = InceptionResnetV2Center(
num_classes=num_classes,
embedding_dimension=embedding_dimension,
pretrained=pretrained)
print("\nUsing {} model architecture.".format(model_architecture))
# Load model to GPU or multiple GPUs if available
flag_train_gpu = torch.cuda.is_available()
flag_train_multi_gpu = False
if flag_train_gpu and torch.cuda.device_count() > 1:
model = nn.DataParallel(model)
model.cuda()
flag_train_multi_gpu = True
print('Using multi-gpu training.')
elif flag_train_gpu and torch.cuda.device_count() == 1:
model.cuda()
print('Using single-gpu training.')
# Set loss functions
criterion_crossentropy = nn.CrossEntropyLoss().cuda()
criterion_centerloss = CenterLoss(num_classes=num_classes,
feat_dim=embedding_dimension).cuda()
# Set optimizers
if optimizer == "sgd":
optimizer_model = torch.optim.SGD(model.parameters(), lr=learning_rate)
optimizer_centerloss = torch.optim.SGD(
criterion_centerloss.parameters(), lr=learning_rate_center_loss)
elif optimizer == "adagrad":
optimizer_model = torch.optim.Adagrad(model.parameters(),
lr=learning_rate)
optimizer_centerloss = torch.optim.Adagrad(
criterion_centerloss.parameters(), lr=learning_rate_center_loss)
elif optimizer == "rmsprop":
optimizer_model = torch.optim.RMSprop(model.parameters(),
lr=learning_rate)
optimizer_centerloss = torch.optim.RMSprop(
criterion_centerloss.parameters(), lr=learning_rate_center_loss)
elif optimizer == "adam":
optimizer_model = torch.optim.Adam(model.parameters(),
lr=learning_rate)
optimizer_centerloss = torch.optim.Adam(
criterion_centerloss.parameters(), lr=learning_rate_center_loss)
# Set learning rate decay scheduler
learning_rate_scheduler = optim.lr_scheduler.MultiStepLR(
optimizer=optimizer_model, milestones=[150, 225], gamma=0.1)
# Optionally resume from a checkpoint
if resume_path:
if os.path.isfile(resume_path):
print("\nLoading checkpoint {} ...".format(resume_path))
checkpoint = torch.load(resume_path)
start_epoch = checkpoint['epoch']
# In order to load state dict for optimizers correctly, model has to be loaded to gpu first
if flag_train_multi_gpu:
model.module.load_state_dict(checkpoint['model_state_dict'])
else:
model.load_state_dict(checkpoint['model_state_dict'])
optimizer_model.load_state_dict(
checkpoint['optimizer_model_state_dict'])
optimizer_centerloss.load_state_dict(
checkpoint['optimizer_centerloss_state_dict'])
learning_rate_scheduler.load_state_dict(
checkpoint['learning_rate_scheduler_state_dict'])
print(
"\nCheckpoint loaded: start epoch from checkpoint = {}\nRunning for {} epochs.\n"
.format(start_epoch, epochs - start_epoch))
else:
print(
"WARNING: No checkpoint found at {}!\nTraining from scratch.".
format(resume_path))
# Start Training loop
print(
"\nTraining using cross entropy loss with center loss starting for {} epochs:\n"
.format(epochs - start_epoch))
total_time_start = time.time()
start_epoch = start_epoch
end_epoch = start_epoch + epochs
for epoch in range(start_epoch, end_epoch):
epoch_time_start = time.time()
flag_validate_lfw = (epoch +
1) % lfw_validation_epoch_interval == 0 or (
epoch + 1) % epochs == 0
train_loss_sum = 0
validation_loss_sum = 0
# Training the model
model.train()
learning_rate_scheduler.step()
progress_bar = enumerate(tqdm(train_dataloader))
for batch_index, (data, labels) in progress_bar:
data, labels = data.cuda(), labels.cuda()
# Forward pass
if flag_train_multi_gpu:
embedding, logits = model.module.forward_training(data)
else:
embedding, logits = model.forward_training(data)
# Calculate losses
cross_entropy_loss = criterion_crossentropy(
logits.cuda(), labels.cuda())
center_loss = criterion_centerloss(embedding, labels)
loss = (center_loss * center_loss_weight) + cross_entropy_loss
# Backward pass
optimizer_centerloss.zero_grad()
optimizer_model.zero_grad()
loss.backward()
optimizer_centerloss.step()
optimizer_model.step()
# Remove center_loss_weight impact on the learning of center vectors
for param in criterion_centerloss.parameters():
param.grad.data *= (1. / center_loss_weight)
# Update training loss sum
train_loss_sum += loss.item() * data.size(0)
# Validating the model
model.eval()
correct, total = 0, 0
with torch.no_grad():
progress_bar = enumerate(tqdm(validation_dataloader))
for batch_index, (data, labels) in progress_bar:
data, labels = data.cuda(), labels.cuda()
# Forward pass
if flag_train_multi_gpu:
embedding, logits = model.module.forward_training(data)
else:
embedding, logits = model.forward_training(data)
# Calculate losses
cross_entropy_loss = criterion_crossentropy(
logits.cuda(), labels.cuda())
center_loss = criterion_centerloss(embedding, labels)
loss = (center_loss * center_loss_weight) + cross_entropy_loss
# Update average validation loss
validation_loss_sum += loss.item() * data.size(0)
# Calculate training performance metrics
predictions = logits.data.max(1)[1]
total += labels.size(0)
correct += (predictions == labels.data).sum()
# Calculate average losses in epoch
avg_train_loss = train_loss_sum / len(train_dataloader.dataset)
avg_validation_loss = validation_loss_sum / len(
validation_dataloader.dataset)
# Calculate training performance statistics in epoch
classification_accuracy = correct * 100. / total
classification_error = 100. - classification_accuracy
epoch_time_end = time.time()
# Print training and validation statistics and add to log
print(
'Epoch {}:\t Average Training Loss: {:.4f}\tAverage Validation Loss: {:.4f}\tClassification Accuracy: {:.2f}%\tClassification Error: {:.2f}%\tEpoch Time: {:.3f} hours'
.format(epoch + 1, avg_train_loss, avg_validation_loss,
classification_accuracy, classification_error,
(epoch_time_end - epoch_time_start) / 3600))
with open('logs/{}_log_center.txt'.format(model_architecture),
'a') as f:
val_list = [
epoch + 1, avg_train_loss, avg_validation_loss,
classification_accuracy.item(),
classification_error.item()
]
log = '\t'.join(str(value) for value in val_list)
f.writelines(log + '\n')
try:
# Plot plot for Cross Entropy Loss and Center Loss on training and validation sets
plot_training_validation_losses_center(
log_dir="logs/{}_log_center.txt".format(model_architecture),
epochs=epochs,
figure_name="plots/training_validation_losses_{}_center.png".
format(model_architecture))
except Exception as e:
print(e)
# Validating on LFW dataset using KFold based on Euclidean distance metric
if flag_validate_lfw:
model.eval()
with torch.no_grad():
l2_distance = PairwiseDistance(2).cuda()
distances, labels = [], []
print("Validating on LFW! ...")
progress_bar = enumerate(tqdm(lfw_dataloader))
for batch_index, (data_a, data_b, label) in progress_bar:
data_a, data_b, label = data_a.cuda(), data_b.cuda(
), label.cuda()
output_a, output_b = model(data_a), model(data_b)
distance = l2_distance.forward(
output_a, output_b) # Euclidean distance
distances.append(distance.cpu().detach().numpy())
labels.append(label.cpu().detach().numpy())
labels = np.array(
[sublabel for label in labels for sublabel in label])
distances = np.array([
subdist for distance in distances for subdist in distance
])
true_positive_rate, false_positive_rate, precision, recall, accuracy, roc_auc, best_distances, \
tar, far = evaluate_lfw(
distances=distances,
labels=labels
)
# Print statistics and add to log
print(
"Accuracy on LFW: {:.4f}+-{:.4f}\tPrecision {:.4f}+-{:.4f}\tRecall {:.4f}+-{:.4f}\tROC Area Under Curve: {:.4f}\tBest distance threshold: {:.2f}+-{:.2f}\tTAR: {:.4f}+-{:.4f} @ FAR: {:.4f}"
.format(np.mean(accuracy), np.std(accuracy),
np.mean(precision), np.std(precision),
np.mean(recall), np.std(recall), roc_auc,
np.mean(best_distances), np.std(best_distances),
np.mean(tar), np.std(tar), np.mean(far)))
with open(
'logs/lfw_{}_log_center.txt'.format(
model_architecture), 'a') as f:
val_list = [
epoch + 1,
np.mean(accuracy),
np.std(accuracy),
np.mean(precision),
np.std(precision),
np.mean(recall),
np.std(recall), roc_auc,
np.mean(best_distances),
np.std(best_distances),
np.mean(tar)
]
log = '\t'.join(str(value) for value in val_list)
f.writelines(log + '\n')
try:
# Plot ROC curve
plot_roc_lfw(
false_positive_rate=false_positive_rate,
true_positive_rate=true_positive_rate,
figure_name="plots/roc_plots/roc_{}_epoch_{}_center.png".
format(model_architecture, epoch + 1))
# Plot LFW accuracies plot
plot_accuracy_lfw(
log_dir="logs/lfw_{}_log_center.txt".format(
model_architecture),
epochs=epochs,
figure_name="plots/lfw_accuracies_{}_center.png".format(
model_architecture))
except Exception as e:
print(e)
# Save model checkpoint
state = {
'epoch':
epoch + 1,
'num_classes':
num_classes,
'embedding_dimension':
embedding_dimension,
'batch_size_training':
batch_size,
'model_state_dict':
model.state_dict(),
'model_architecture':
model_architecture,
'optimizer_model_state_dict':
optimizer_model.state_dict(),
'optimizer_centerloss_state_dict':
optimizer_centerloss.state_dict(),
'learning_rate_scheduler_state_dict':
learning_rate_scheduler.state_dict()
}
# For storing data parallel model's state dictionary without 'module' parameter
if flag_train_multi_gpu:
state['model_state_dict'] = model.module.state_dict()
# For storing best euclidean distance threshold during LFW validation
if flag_validate_lfw:
state['best_distance_threshold'] = np.mean(best_distances)
# Save model checkpoint
torch.save(
state,
'Model_training_checkpoints/model_{}_center_epoch_{}.pt'.format(
model_architecture, epoch + 1))
# Training loop end
total_time_end = time.time()
total_time_elapsed = total_time_end - total_time_start
print("\nTraining finished: total time elapsed: {:.2f} hours.".format(
total_time_elapsed / 3600))
def main():
dataroot = args.dataroot
lfw_dataroot = args.lfw
dataset_csv = args.dataset_csv
lfw_batch_size = args.lfw_batch_size
lfw_validation_epoch_interval = args.lfw_validation_epoch_interval
model_architecture = args.model
epochs = args.epochs
training_triplets_path = args.training_triplets_path
num_triplets_train = args.num_triplets_train
resume_path = args.resume_path
batch_size = args.batch_size
num_workers = args.num_workers
embedding_dimension = args.embedding_dim
pretrained = args.pretrained
optimizer = args.optimizer
learning_rate = args.lr
margin = args.margin
start_epoch = 0
# Define image data pre-processing transforms
# ToTensor() normalizes pixel values between [0, 1]
# Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) normalizes pixel values between [-1, 1]
# Size 182x182 RGB image -> Center crop size 160x160 RGB image for more model generalization
data_transforms = transforms.Compose([
transforms.RandomCrop(size=160),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
# Size 160x160 RGB image
lfw_transforms = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
# Set dataloaders
train_dataloader = torch.utils.data.DataLoader(dataset=TripletFaceDataset(
root_dir=dataroot,
csv_name=dataset_csv,
num_triplets=num_triplets_train,
training_triplets_path=training_triplets_path,
transform=data_transforms),
batch_size=batch_size,
num_workers=num_workers,
shuffle=False)
lfw_dataloader = torch.utils.data.DataLoader(dataset=LFWDataset(
dir=lfw_dataroot,
pairs_path='datasets/LFW_pairs.txt',
transform=lfw_transforms),
batch_size=lfw_batch_size,
num_workers=num_workers,
shuffle=False)
# Instantiate model
if model_architecture == "resnet18":
model = Resnet18Triplet(embedding_dimension=embedding_dimension,
pretrained=pretrained)
elif model_architecture == "resnet34":
model = Resnet34Triplet(embedding_dimension=embedding_dimension,
pretrained=pretrained)
elif model_architecture == "resnet50":
model = Resnet50Triplet(embedding_dimension=embedding_dimension,
pretrained=pretrained)
elif model_architecture == "resnet101":
model = Resnet101Triplet(embedding_dimension=embedding_dimension,
pretrained=pretrained)
elif model_architecture == "inceptionresnetv2":
model = InceptionResnetV2Triplet(
embedding_dimension=embedding_dimension, pretrained=pretrained)
print("Using {} model architecture.".format(model_architecture))
# Load model to GPU or multiple GPUs if available
flag_train_gpu = torch.cuda.is_available()
flag_train_multi_gpu = False
if flag_train_gpu and torch.cuda.device_count() > 1:
model = nn.DataParallel(model)
model.cuda()
flag_train_multi_gpu = True
print('Using multi-gpu training.')
elif flag_train_gpu and torch.cuda.device_count() == 1:
model.cuda()
print('Using single-gpu training.')
# Set optimizers
if optimizer == "sgd":
optimizer_model = torch.optim.SGD(model.parameters(), lr=learning_rate)
elif optimizer == "adagrad":
optimizer_model = torch.optim.Adagrad(model.parameters(),
lr=learning_rate)
elif optimizer == "rmsprop":
optimizer_model = torch.optim.RMSprop(model.parameters(),
lr=learning_rate)
elif optimizer == "adam":
optimizer_model = torch.optim.Adam(model.parameters(),
lr=learning_rate)
# Optionally resume from a checkpoint
if resume_path:
if os.path.isfile(resume_path):
print("\nLoading checkpoint {} ...".format(resume_path))
checkpoint = torch.load(resume_path)
start_epoch = checkpoint['epoch']
# In order to load state dict for optimizers correctly, model has to be loaded to gpu first
if flag_train_multi_gpu:
model.module.load_state_dict(checkpoint['model_state_dict'])
else:
model.load_state_dict(checkpoint['model_state_dict'])
optimizer_model.load_state_dict(
checkpoint['optimizer_model_state_dict'])
print(
"\nCheckpoint loaded: start epoch from checkpoint = {}\nRunning for {} epochs.\n"
.format(start_epoch, epochs - start_epoch))
else:
print(
"WARNING: No checkpoint found at {}!\nTraining from scratch.".
format(resume_path))
# Start Training loop
print(
"\nTraining using triplet loss on {} triplets starting for {} epochs:\n"
.format(num_triplets_train, epochs - start_epoch))
total_time_start = time.time()
start_epoch = start_epoch
end_epoch = start_epoch + epochs
l2_distance = PairwiseDistance(2).cuda()
for epoch in range(start_epoch, end_epoch):
epoch_time_start = time.time()
flag_validate_lfw = (epoch +
1) % lfw_validation_epoch_interval == 0 or (
epoch + 1) % epochs == 0
triplet_loss_sum = 0
num_valid_training_triplets = 0
# Training pass
model.train()
progress_bar = enumerate(tqdm(train_dataloader))
for batch_idx, (batch_sample) in progress_bar:
anc_img = batch_sample['anc_img'].cuda()
pos_img = batch_sample['pos_img'].cuda()
neg_img = batch_sample['neg_img'].cuda()
# Forward pass - compute embeddings
anc_embedding, pos_embedding, neg_embedding = model(
anc_img), model(pos_img), model(neg_img)
# Forward pass - choose hard negatives only for training
pos_dist = l2_distance.forward(anc_embedding, pos_embedding)
neg_dist = l2_distance.forward(anc_embedding, neg_embedding)
all = (neg_dist - pos_dist < margin).cpu().numpy().flatten()
hard_triplets = np.where(all == 1)
if len(hard_triplets[0]) == 0:
continue
anc_hard_embedding = anc_embedding[hard_triplets].cuda()
pos_hard_embedding = pos_embedding[hard_triplets].cuda()
neg_hard_embedding = neg_embedding[hard_triplets].cuda()
# Calculate triplet loss
triplet_loss = TripletLoss(margin=margin).forward(
anchor=anc_hard_embedding,
positive=pos_hard_embedding,
negative=neg_hard_embedding).cuda()
# Calculating loss
triplet_loss_sum += triplet_loss.item()
num_valid_training_triplets += len(anc_hard_embedding)
# Backward pass
optimizer_model.zero_grad()
triplet_loss.backward()
optimizer_model.step()
# Model only trains on hard negative triplets
avg_triplet_loss = 0 if (
num_valid_training_triplets
== 0) else triplet_loss_sum / num_valid_training_triplets
epoch_time_end = time.time()
# Print training statistics and add to log
print(
'Epoch {}:\tAverage Triplet Loss: {:.4f}\tEpoch Time: {:.3f} hours\tNumber of valid training triplets in epoch: {}'
.format(epoch + 1, avg_triplet_loss,
(epoch_time_end - epoch_time_start) / 3600,
num_valid_training_triplets))
with open('logs/{}_log_triplet.txt'.format(model_architecture),
'a') as f:
val_list = [
epoch + 1, avg_triplet_loss, num_valid_training_triplets
]
log = '\t'.join(str(value) for value in val_list)
f.writelines(log + '\n')
try:
# Plot Triplet losses plot
plot_triplet_losses(
log_dir="logs/{}_log_triplet.txt".format(model_architecture),
epochs=epochs,
figure_name="plots/triplet_losses_{}.png".format(
model_architecture))
except Exception as e:
print(e)
# Evaluation pass on LFW dataset
if flag_validate_lfw:
model.eval()
with torch.no_grad():
distances, labels = [], []
print("Validating on LFW! ...")
progress_bar = enumerate(tqdm(lfw_dataloader))
for batch_index, (data_a, data_b, label) in progress_bar:
data_a, data_b, label = data_a.cuda(), data_b.cuda(
), label.cuda()
output_a, output_b = model(data_a), model(data_b)
distance = l2_distance.forward(
output_a, output_b) # Euclidean distance
distances.append(distance.cpu().detach().numpy())
labels.append(label.cpu().detach().numpy())
labels = np.array(
[sublabel for label in labels for sublabel in label])
distances = np.array([
subdist for distance in distances for subdist in distance
])
true_positive_rate, false_positive_rate, precision, recall, accuracy, roc_auc, best_distances, \
tar, far = evaluate_lfw(
distances=distances,
labels=labels
)
# Print statistics and add to log
print(
"Accuracy on LFW: {:.4f}+-{:.4f}\tPrecision {:.4f}+-{:.4f}\tRecall {:.4f}+-{:.4f}\tROC Area Under Curve: {:.4f}\tBest distance threshold: {:.2f}+-{:.2f}\tTAR: {:.4f}+-{:.4f} @ FAR: {:.4f}"
.format(np.mean(accuracy), np.std(accuracy),
np.mean(precision), np.std(precision),
np.mean(recall), np.std(recall), roc_auc,
np.mean(best_distances), np.std(best_distances),
np.mean(tar), np.std(tar), np.mean(far)))
with open(
'logs/lfw_{}_log_triplet.txt'.format(
model_architecture), 'a') as f:
val_list = [
epoch + 1,
np.mean(accuracy),
np.std(accuracy),
np.mean(precision),
np.std(precision),
np.mean(recall),
np.std(recall), roc_auc,
np.mean(best_distances),
np.std(best_distances),
np.mean(tar)
]
log = '\t'.join(str(value) for value in val_list)
f.writelines(log + '\n')
try:
# Plot ROC curve
plot_roc_lfw(
false_positive_rate=false_positive_rate,
true_positive_rate=true_positive_rate,
figure_name="plots/roc_plots/roc_{}_epoch_{}_triplet.png".
format(model_architecture, epoch + 1))
# Plot LFW accuracies plot
plot_accuracy_lfw(
log_dir="logs/lfw_{}_log_triplet.txt".format(
model_architecture),
epochs=epochs,
figure_name="plots/lfw_accuracies_{}_triplet.png".format(
model_architecture))
except Exception as e:
print(e)
# Save model checkpoint
state = {
'epoch': epoch + 1,
'embedding_dimension': embedding_dimension,
'batch_size_training': batch_size,
'model_state_dict': model.state_dict(),
'model_architecture': model_architecture,
'optimizer_model_state_dict': optimizer_model.state_dict()
}
# For storing data parallel model's state dictionary without 'module' parameter
if flag_train_multi_gpu:
state['model_state_dict'] = model.module.state_dict()
# For storing best euclidean distance threshold during LFW validation
if flag_validate_lfw:
state['best_distance_threshold'] = np.mean(best_distances)
# Save model checkpoint
torch.save(
state,
'Model_training_checkpoints/model_{}_triplet_epoch_{}.pt'.format(
model_architecture, epoch + 1))
# Training loop end
total_time_end = time.time()
total_time_elapsed = total_time_end - total_time_start
print("\nTraining finished: total time elapsed: {:.2f} hours.".format(
total_time_elapsed / 3600))