def evaluate(valloader, args, params, mode):
print(f'Teacher Accuracy')
unet = models.unet.Unet('resnet34',
classes=params['num_classes'],
encoder_weights=None).to(args.gpu)
unet.load_state_dict(
torch.load('../saved_models/' + args.dataset +
'/resnet34/pretrained_0.pt',
map_location=args.gpu))
current_val_iou = mean_iou(unet, valloader, args)
print(round(current_val_iou, 5))
print(f'Fractional data results for {mode}')
for perc in [10, 20, 30, 40]:
print('perc : ', perc)
for model_name in [
'resnet10', 'resnet14', 'resnet18', 'resnet20', 'resnet26'
]:
params['model'] = model_name
print('model : ', model_name)
unet = models.unet.Unet(model_name,
classes=params['num_classes'],
encoder_weights=None).to(args.gpu)
unet.load_state_dict(
torch.load(
get_savename(params,
dataset=args.dataset,
mode=mode,
p=perc)))
current_val_iou = mean_iou(unet, valloader, args)
print(round(current_val_iou, 5))
print(f'Full data results for {mode}')
for model_name in [
'resnet10', 'resnet14', 'resnet18', 'resnet20', 'resnet26'
]:
print('model : ', model_name)
unet = models.unet.Unet(model_name,
classes=params['num_classes'],
encoder_weights=None).to(args.gpu)
unet.load_state_dict(
torch.load(get_savename(params, dataset=args.dataset, mode=mode)))
current_val_iou = mean_iou(unet, valloader, args)
print(round(current_val_iou, 5))
def train_simultaneous(hyper_params, teacher, student, sf_teacher, sf_student,
trainloader, valloader, args):
if args.api_key:
project_name = 'simultaneous-' + hyper_params[
'dataset'] + '-' + hyper_params['model']
experiment = Experiment(api_key=args.api_key,
project_name=project_name,
workspace=args.workspace)
experiment.log_parameters(hyper_params)
optimizer = torch.optim.Adam(student.parameters(),
lr=hyper_params['learning_rate'])
scheduler = torch.optim.lr_scheduler.OneCycleLR(
optimizer,
max_lr=1e-2,
steps_per_epoch=len(trainloader),
epochs=hyper_params['num_epochs'])
if hyper_params['dataset'] == 'camvid':
criterion = nn.CrossEntropyLoss(ignore_index=11)
else:
criterion = nn.CrossEntropyLoss(ignore_index=250)
hyper_params['num_classes'] = 19
criterion2 = nn.MSELoss()
savename = get_savename(hyper_params,
dataset=args.dataset,
mode='simultaneous',
p=args.percentage)
highest_iou = 0
for epoch in range(hyper_params['num_epochs']):
_, _, train_loss, val_loss, avg_iou, avg_px_acc, avg_dice_coeff = train_simult(
model=student,
teacher=teacher,
sf_teacher=sf_teacher,
sf_student=sf_student,
train_loader=trainloader,
val_loader=valloader,
num_classes=hyper_params['num_classes'],
loss_function=criterion,
loss_function2=criterion2,
optimiser=optimizer,
scheduler=scheduler,
epoch=epoch,
num_epochs=hyper_params['num_epochs'],
savename=savename,
highest_iou=highest_iou,
args=args)
if args.api_key:
experiment.log_metric('train_loss', train_loss)
experiment.log_metric('val_loss', val_loss)
experiment.log_metric('avg_iou', avg_iou)
experiment.log_metric('avg_pixel_acc', avg_px_acc)
experiment.log_metric('avg_dice_coeff', avg_dice_coeff)
def pretrain(hyper_params, unet, trainloader, valloader, args):
if args.api_key:
project_name = 'pretrain-' + hyper_params[
'dataset'] + '-' + hyper_params['model']
experiment = Experiment(api_key=args.api_key,
project_name=project_name,
workspace=args.workspace)
experiment.log_parameters(hyper_params)
optimizer = torch.optim.Adam(unet.parameters(),
lr=hyper_params['learning_rate'])
scheduler = torch.optim.lr_scheduler.OneCycleLR(
optimizer,
max_lr=1e-2,
steps_per_epoch=len(trainloader),
epochs=hyper_params['num_epochs'])
if args.dataset == 'camvid':
criterion = nn.CrossEntropyLoss(ignore_index=11)
num_classes = 12
elif args.dataset == 'cityscapes':
criterion = nn.CrossEntropyLoss(ignore_index=250)
num_classes = 19
savename = get_savename(hyper_params,
dataset=args.dataset,
mode='pretrain',
p=args.percentage)
highest_iou = 0
losses = []
for epoch in range(hyper_params['num_epochs']):
unet, highest_iou, train_loss, val_loss, avg_iou, avg_pixel_acc, avg_dice_coeff = train(
model=unet,
train_loader=trainloader,
val_loader=valloader,
num_classes=num_classes,
loss_function=criterion,
optimiser=optimizer,
scheduler=scheduler,
epoch=epoch,
num_epochs=hyper_params['num_epochs'],
savename=savename,
highest_iou=highest_iou,
args=args)
if args.api_key:
experiment.log_metric('train_loss', train_loss)
experiment.log_metric('val_loss', val_loss)
experiment.log_metric('avg_iou', avg_iou)
experiment.log_metric('avg_pixel_acc', avg_pixel_acc)
experiment.log_metric('avg_dice_coeff', avg_dice_coeff)
def train_traditional(hyper_params, teacher, student, sf_teacher, sf_student,
trainloader, valloader, args):
for stage in range(2):
# Load previous stage model (except zeroth stage)
if stage != 0:
hyper_params['stage'] = stage - 1
student.load_state_dict(
torch.load(
get_savename(hyper_params,
args.dataset,
mode='traditional-stage',
p=args.percentage)))
# update hyperparams dictionary
hyper_params['stage'] = stage
# Freeze all stages except current stage
student = unfreeze_trad(student, hyper_params['stage'])
project_name = 'trad-kd-' + hyper_params[
'dataset'] + '-' + hyper_params['model']
experiment = Experiment(api_key="1jNZ1sunRoAoI2TyremCNnYLO",
project_name=project_name,
workspace="semseg_kd")
experiment.log_parameters(hyper_params)
optimizer = torch.optim.Adam(student.parameters(),
lr=hyper_params['learning_rate'])
scheduler = torch.optim.lr_scheduler.OneCycleLR(
optimizer,
max_lr=1e-2,
steps_per_epoch=len(trainloader),
epochs=hyper_params['num_epochs'])
criterion = nn.MSELoss()
savename = get_savename(hyper_params,
args.dataset,
mode='traditional-stage',
p=args.percentage)
lowest_val_loss = 100
for epoch in range(hyper_params['num_epochs']):
student, lowest_val_loss, train_loss, val_loss = train_stage(
model=student,
teacher=teacher,
stage=hyper_params['stage'],
sf_student=sf_student,
sf_teacher=sf_teacher,
train_loader=trainloader,
val_loader=valloader,
loss_function=criterion,
optimiser=optimizer,
scheduler=scheduler,
epoch=epoch,
num_epochs=hyper_params['num_epochs'],
savename=savename,
lowest_val=lowest_val_loss,
args=args)
experiment.log_metric('train_loss', train_loss)
experiment.log_metric('val_loss', val_loss)
print(round(val_loss, 6))
# Classifier training
hyper_params['stage'] = 1
student.load_state_dict(
torch.load(
get_savename(hyper_params,
args.dataset,
mode='traditional-stage',
p=args.percentage)))
hyper_params['stage'] = 2
# Freeze all stages except current stage
student = unfreeze_trad(student, hyper_params['stage'])
project_name = 'trad-kd-' + hyper_params['dataset'] + '-' + hyper_params[
'model']
experiment = Experiment(api_key="1jNZ1sunRoAoI2TyremCNnYLO",
project_name=project_name,
workspace="semseg_kd")
experiment.log_parameters(hyper_params)
optimizer = torch.optim.Adam(student.parameters(),
lr=hyper_params['learning_rate'])
scheduler = torch.optim.lr_scheduler.OneCycleLR(
optimizer,
max_lr=1e-2,
steps_per_epoch=len(trainloader),
epochs=hyper_params['num_epochs'])
if hyper_params['dataset'] == 'camvid':
criterion = nn.CrossEntropyLoss(ignore_index=11)
else:
criterion = nn.CrossEntropyLoss(ignore_index=250)
hyper_params['num_classes'] = 19
savename = get_savename(hyper_params,
args.dataset,
mode='traditional-kd',
p=args.percentage)
highest_iou = 0
for epoch in range(hyper_params['num_epochs']):
student, highest_iou, train_loss, val_loss, avg_iou, avg_pixel_acc, avg_dice_coeff = train(
model=student,
train_loader=trainloader,
val_loader=valloader,
num_classes=12,
loss_function=criterion,
optimiser=optimizer,
scheduler=scheduler,
epoch=epoch,
num_epochs=hyper_params['num_epochs'],
savename=savename,
highest_iou=highest_iou,
args=args)
experiment.log_metric('train_loss', train_loss)
experiment.log_metric('val_loss', val_loss)
experiment.log_metric('avg_iou', avg_iou)
experiment.log_metric('avg_pixel_acc', avg_pixel_acc)
experiment.log_metric('avg_dice_coeff', avg_dice_coeff)
def train_stagewise(hyper_params, teacher, student, sf_teacher, sf_student,
trainloader, valloader, args):
for stage in range(10):
# Load previous stage model (except zeroth stage)
if stage != 0:
# hyperparams dict for loading previous stage weights
hyper_params['stage'] = stage - 1
student.load_state_dict(
torch.load(
get_savename(hyper_params,
mode='stagewise',
p=args.percentage)))
# update hyperparams dictionary for current stage
hyper_params['stage'] = stage
# Freeze all stages except current stage
student = unfreeze(student, hyper_params['stage'])
if args.api_key:
project_name = 'stagewise-' + hyper_params['model']
experiment = Experiment(api_key=args.api_key,
project_name=project_name,
workspace=args.workspace)
experiment.log_parameters(hyper_params)
optimizer = torch.optim.Adam(student.parameters(),
lr=hyper_params['learning_rate'])
scheduler = torch.optim.lr_scheduler.OneCycleLR(
optimizer,
max_lr=1e-2,
steps_per_epoch=len(trainloader),
epochs=hyper_params['num_epochs'])
criterion = nn.MSELoss()
savename = get_savename(hyper_params,
dataset=args.dataset,
mode='stagewise',
p=args.percentage)
lowest_val_loss = 100
losses = []
for epoch in range(hyper_params['num_epochs']):
student, lowest_val_loss, train_loss, val_loss = train_stage(
model=student,
teacher=teacher,
stage=hyper_params['stage'],
sf_student=sf_student,
sf_teacher=sf_teacher,
train_loader=trainloader,
val_loader=valloader,
loss_function=criterion,
optimiser=optimizer,
scheduler=scheduler,
epoch=epoch,
num_epochs=hyper_params['num_epochs'],
savename=savename,
lowest_val=lowest_val_loss,
args=args)
if args.api_key:
experiment.log_metric('train_loss', train_loss)
experiment.log_metric('val_loss', val_loss)
hyper_params['stage'] = 9
student.load_state_dict(
torch.load(
get_savename(hyper_params, mode='stagewise', p=args.percentage)))
hyper_params['stage'] = 10
# Freeze all stages except current stage
student = unfreeze(student, hyper_params['stage'])
if args.api_key:
project_name = 'stagewise-' + hyper_params['model']
experiment = Experiment(api_key=args.api_key,
project_name=project_name,
workspace=args.workspace)
experiment.log_parameters(hyper_params)
optimizer = torch.optim.Adam(student.parameters(),
lr=hyper_params['learning_rate'])
scheduler = torch.optim.lr_scheduler.OneCycleLR(
optimizer,
max_lr=1e-2,
steps_per_epoch=len(trainloader),
epochs=hyper_params['num_epochs'])
criterion = nn.CrossEntropyLoss(ignore_index=11)
savename = get_savename(hyper_params,
dataset=args.dataset,
mode='classifier',
p=args.percentage)
highest_iou = 0
losses = []
for epoch in range(hyper_params['num_epochs']):
student, highest_iou, train_loss, val_loss, avg_iou, avg_pixel_acc, avg_dice_coeff = train(
model=student,
train_loader=trainloader,
val_loader=valloader,
num_classes=hyper_params['num_classes'],
loss_function=criterion,
optimiser=optimizer,
scheduler=scheduler,
epoch=epoch,
num_epochs=hyper_params['num_epochs'],
savename=savename,
highest_iou=highest_iou,
args=args)
if args.api_key:
experiment.log_metric('train_loss', train_loss)
experiment.log_metric('val_loss', val_loss)
experiment.log_metric('avg_iou', avg_iou)
experiment.log_metric('avg_pixel_acc', avg_pixel_acc)
experiment.log_metric('avg_dice_coeff', avg_dice_coeff)
