cnn_optimizer.step()
xentropy_loss_avg += xentropy_loss.item()
# Calculate running average of accuracy
pred = torch.max(pred.data, 1)[1]
total += labels.size(0)
correct += (pred == labels.data).sum().item()
accuracy = correct / total
progress_bar.set_postfix(xentropy='%.3f' % (xentropy_loss_avg /
(i + 1)),
acc='%.3f' % accuracy)
test_acc = test(test_loader)
flops = cnn.flops()
score = flops / mobilenet_flops + params / mobilenet_params
tqdm.write('test_acc: %.3f, flops: %s, parameters: %s, score %s' %
(test_acc, flops, params, score))
scheduler.step(epoch)
row = {
'epoch': str(epoch),
'train_acc': str(accuracy),
'test_acc': str(test_acc)
}
csv_logger.writerow(row)
torch.save(cnn.state_dict(), 'checkpoints/' + test_id + '.pt')
csv_logger.close()
def training(args):
if not os.path.isdir('logs'):
os.makedirs('logs')
train_loader = torch.utils.data.DataLoader(dataset=MaskDataset(root=args.data_dir,random=True,isTraining=True),
batch_size=int(512),
shuffle=True,
pin_memory=True,
num_workers=16)
val_loader = torch.utils.data.DataLoader(
dataset=MaskDataset(root=args.data_dir+'validation/',random=True,isTraining=False),
batch_size=1,
shuffle=False,
pin_memory=True,
num_workers=2)
cnn_optimizer = torch.optim.SGD(cnn.parameters(), lr=float(0.1), momentum=0.9, nesterov=True,
weight_decay=0.0) # 0.0001
scheduler = StepLR(cnn_optimizer, gamma=0.1, step_size=3)
criterion=TripletLoss(distance=args.loss).cuda()
early_stopping = True
patience = 20
epochs_no_improvement = 0
max_val_fscore = 0.0
best_weights = None
best_epoch = -1
filename = 'logs/' + str(args.loss) + '.csv'
csv_logger = CSVLogger(args=None, fieldnames=['epoch', 'TotalLoss', 'positive_loss','negative_loss','negative_positive', 'val_acc'], filename=filename)
init_val_fscore, val_fscore_imposter = validation_init(val_loader)
# set model to train mode
cnn.train()
tqdm.write('genuine: %.5f' % (init_val_fscore))
tqdm.write('imposter: %.5f' % (val_fscore_imposter))
update_weight_loss=True
val_fscore=0.
for epoch in range(1, 1 + args.epoch):
loss_total = 0.
fscore_total = 0.
positive_loss_totoal=0.
negative_loss_total=0.
negative_positive_total=0.
progress_bar = tqdm(train_loader)
for i, (mask_embedding,face_embedding,negative_embedding,label,_) in enumerate(progress_bar):
progress_bar.set_description('Epoch ' + str(epoch))
mask_embedding = mask_embedding.cuda()
face_embedding =face_embedding.cuda()
negative_embedding=negative_embedding.cuda()
label=label.cuda()
cnn.zero_grad()
pred = cnn(mask_embedding)
loss, positive_loss,negative_loss , negative_positive= criterion(pred, face_embedding, negative_embedding)
loss.backward()
cnn_optimizer.step()
loss_total += loss.item()
positive_loss_totoal+=positive_loss.item()
negative_loss_total+=negative_loss.item()
negative_positive_total+=negative_positive.item()
row = {'epoch': str(epoch)+str("-")+str(i), 'TotalLoss': str(loss_total / (i + 1)), 'positive_loss': str(positive_loss_totoal / (i + 1)), 'negative_loss': str(negative_loss_total / (i + 1)),'negative_positive':str(negative_positive_total / (i + 1)),'val_acc':str(val_fscore)}
csv_logger.writerow(row)
progress_bar.set_postfix(
loss='%.5f' % (loss_total / (i + 1)),negative_loss='%.5f' % (negative_loss_total/(i+1) ),positive_loss='%.5f' % (positive_loss_totoal / (i + 1)),negative_positive='%.5f' % (negative_positive_total / (i + 1)) )
val_fscore ,val_fscore_imposter= validation(val_loader)
tqdm.write('fscore: %.5f' % (val_fscore))
tqdm.write('imposter: %.5f' % (val_fscore_imposter))
# scheduler.step(epoch) # Use this line for PyTorch <1.4
scheduler.step() # Use this line for PyTorch >=1.4
#row = {'epoch': str(epoch), 'train_acc': str(train_fscore), 'val_acc': str(val_fscore)}
#csv_logger.writerow(row)
do_stop=False
if early_stopping:
if val_fscore > max_val_fscore:
max_val_fscore = val_fscore
epochs_no_improvement = 0
best_weights = cnn.state_dict()
best_epoch = epoch
else:
epochs_no_improvement += 1
if epochs_no_improvement >= patience and do_stop:
print(f"EARLY STOPPING at {best_epoch}: {max_val_fscore}")
break
else:
best_weights = cnn.state_dict()
if not os.path.isdir(os.path.join(args.weights,str(args.loss))):
os.makedirs(os.path.join(args.weights,str(args.loss)))
torch.save(best_weights, os.path.join(args.weights,str(args.loss),'weights.pt'))
csv_logger.close()
def run_cutout(dataset="cifar10",
model="resnet18",
epochs=200,
batch_size=128,
learning_rate=0.1,
data_augmentation=False,
cutout=False,
n_holes=1,
length=8,
no_cuda=False,
seed=0):
cuda = not no_cuda and torch.cuda.is_available()
cudnn.benchmark = True # Should make training should go faster for large models
torch.manual_seed(seed)
if cuda:
torch.cuda.manual_seed(seed)
test_id = dataset + '_' + model
# Image Preprocessing
if dataset == 'svhn':
normalize = transforms.Normalize(
mean=[x / 255.0 for x in [109.9, 109.7, 113.8]],
std=[x / 255.0 for x in [50.1, 50.6, 50.8]])
else:
normalize = transforms.Normalize(
mean=[x / 255.0 for x in [125.3, 123.0, 113.9]],
std=[x / 255.0 for x in [63.0, 62.1, 66.7]])
train_transform = transforms.Compose([])
if data_augmentation:
train_transform.transforms.append(transforms.RandomCrop(32, padding=4))
train_transform.transforms.append(transforms.RandomHorizontalFlip())
train_transform.transforms.append(transforms.ToTensor())
train_transform.transforms.append(normalize)
if cutout:
train_transform.transforms.append(
Cutout(n_holes=n_holes, length=length))
test_transform = transforms.Compose([transforms.ToTensor(), normalize])
if dataset == 'cifar10':
num_classes = 10
train_dataset = datasets.CIFAR10(root='data/',
train=True,
transform=train_transform,
download=True)
test_dataset = datasets.CIFAR10(root='data/',
train=False,
transform=test_transform,
download=True)
elif dataset == 'cifar100':
num_classes = 100
train_dataset = datasets.CIFAR100(root='data/',
train=True,
transform=train_transform,
download=True)
test_dataset = datasets.CIFAR100(root='data/',
train=False,
transform=test_transform,
download=True)
elif dataset == 'svhn':
num_classes = 10
train_dataset = datasets.SVHN(root='data/',
split='train',
transform=train_transform,
download=True)
extra_dataset = datasets.SVHN(root='data/',
split='extra',
transform=train_transform,
download=True)
# Combine both training splits (https://arxiv.org/pdf/1605.07146.pdf)
data = np.concatenate([train_dataset.data, extra_dataset.data], axis=0)
labels = np.concatenate([train_dataset.labels, extra_dataset.labels],
axis=0)
train_dataset.data = data
train_dataset.labels = labels
test_dataset = datasets.SVHN(root='data/',
split='test',
transform=test_transform,
download=True)
# Data Loader (Input Pipeline)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=batch_size,
shuffle=True,
pin_memory=True,
num_workers=2)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
batch_size=batch_size,
shuffle=False,
pin_memory=True,
num_workers=2)
if model == 'resnet18':
cnn = ResNet18(num_classes=num_classes)
elif model == 'wideresnet':
if dataset == 'svhn':
cnn = WideResNet(depth=16,
num_classes=num_classes,
widen_factor=8,
dropRate=0.4)
else:
cnn = WideResNet(depth=28,
num_classes=num_classes,
widen_factor=10,
dropRate=0.3)
cnn = cnn.cuda()
criterion = nn.CrossEntropyLoss().cuda()
cnn_optimizer = torch.optim.SGD(cnn.parameters(),
lr=learning_rate,
momentum=0.9,
nesterov=True,
weight_decay=5e-4)
if dataset == 'svhn':
scheduler = MultiStepLR(cnn_optimizer, milestones=[80, 120], gamma=0.1)
else:
scheduler = MultiStepLR(cnn_optimizer,
milestones=[60, 120, 160],
gamma=0.2)
#TODO: change path to relative path
filename = "/beegfs/work/workspace/ws/fr_mn119-augment-0/logs/{}.csv".format(
test_id)
# filename = 'logs/' + test_id + '.csv'
args = argparse.Namespace(
**{
"dataset": dataset,
"model": model,
"epochs": epochs,
"batch_size": batch_size,
"learning_rate": learning_rate,
"data_augmentation": data_augmentation,
"cutout": cutout,
"n_holes": n_holes,
"length": length,
"no_cuda": no_cuda,
"seed": seed
})
csv_logger = CSVLogger(args=args,
fieldnames=['epoch', 'train_acc', 'test_acc'],
filename=filename)
def test(loader):
cnn.eval() # Change model to 'eval' mode (BN uses moving mean/var).
correct = 0.
total = 0.
for images, labels in loader:
if dataset == 'svhn':
# SVHN labels are from 1 to 10, not 0 to 9, so subtract 1
labels = labels.type_as(torch.LongTensor()).view(-1) - 1
images = Variable(images, volatile=True).cuda()
labels = Variable(labels, volatile=True).cuda()
pred = cnn(images)
pred = torch.max(pred.data, 1)[1]
total += labels.size(0)
correct += (pred == labels.data).sum()
val_acc = correct / total
cnn.train()
return val_acc
for epoch in range(epochs):
xentropy_loss_avg = 0.
correct = 0.
total = 0.
progress_bar = tqdm(train_loader)
for i, (images, labels) in enumerate(progress_bar):
progress_bar.set_description('Epoch ' + str(epoch))
if dataset == 'svhn':
# SVHN labels are from 1 to 10, not 0 to 9, so subtract 1
labels = labels.type_as(torch.LongTensor()).view(-1) - 1
images = Variable(images).cuda(async=True)
labels = Variable(labels).cuda(async=True)
cnn.zero_grad()
pred = cnn(images)
xentropy_loss = criterion(pred, labels)
xentropy_loss.backward()
cnn_optimizer.step()
xentropy_loss_avg += xentropy_loss.data[0]
# Calculate running average of accuracy
_, pred = torch.max(pred.data, 1)
total += labels.size(0)
correct += (pred == labels.data).sum()
accuracy = correct / total
progress_bar.set_postfix(xentropy='%.3f' % (xentropy_loss_avg /
(i + 1)),
acc='%.3f' % accuracy)
test_acc = test(test_loader)
tqdm.write('test_acc: %.3f' % (test_acc))
scheduler.step(epoch)
row = {
'epoch': str(epoch),
'train_acc': str(accuracy),
'test_acc': str(test_acc)
}
csv_logger.writerow(row)
# torch.save(cnn.state_dict(), 'checkpoints/' + test_id + '.pt')
csv_logger.close()
results = {
'epoch': epoch,
'train_error': 1 - accuracy,
'test_error': 1 - test_acc
}
# validation error for hyperband
return results
scheduler.step(epoch)
row = {
'epoch': str(epoch),
'train_acc': str(accuracy),
'val_acc': str(val_acc)
}
csv_logger.writerow(row)
acc_train[epoch] = accuracy
acc_val[epoch] = val_acc
loss_train[epoch] = xentropy_loss_avg
torch.save(cnn.state_dict(), model_weights_file)
csv_logger.close()
print("Training took {}".format(datetime.now() - start_time))
# Plot Accuracies/losses
fig, ax_arr = plt.subplots(1, 2)
ax_arr[0].plot(np.arange(args.epochs),
loss_train / i,
label='train',
color='b')
ax_arr[0].set_xlabel("Epoch")
ax_arr[0].set_ylabel("Loss")
ax_arr[1].plot(np.arange(args.epochs), acc_train, label='train', color='b')
ax_arr[1].plot(np.arange(args.epochs), acc_val, label='validation', color='r')
ax_arr[1].set_xlabel("Epoch")
ax_arr[1].set_ylabel("Accuracy")