def main(args):
"""Main train and evaluation function.
Parameters
----------
args: argparse.Namespace
Arguments
"""
formatter = logging.Formatter('%(asctime)s %(levelname)s - %(funcName)s: %(message)s',
"%H:%M:%S")
logger = logging.getLogger(__name__)
logger.setLevel(args.log_level.upper())
stream = logging.StreamHandler()
stream.setLevel(args.log_level.upper())
stream.setFormatter(formatter)
logger.addHandler(stream)
set_seed(args.seed)
device = get_device(is_gpu=not args.no_cuda)
exp_dir = os.path.join(RES_DIR, args.name)
feature_dir = os.path.join(exp_dir, 'training_features')
logger.info("Root directory for saving and loading experiments: {}".format(exp_dir))
if not args.is_eval_only:
create_safe_directory(feature_dir, logger=logger)
# Setting number of epochs to 1, as we need to extract features
args.epochs = 1
args.batch_size = 1
# PREPARES DATA
data_loader = get_dataloaders(args.dataset,
batch_size=args.batch_size,
logger=logger, test=False)
logger.info("Train {} with {} samples".format(args.dataset, len(data_loader.dataset)))
# PREPARES MODEL
args.img_size = get_img_size(args.dataset) # stores for metadata
model = load_model(exp_dir, filename='model.pt')
logger.info('Num parameters in model: {}'.format(get_n_param(model)))
# Extract Features
model = model.to(device) # make sure trainer and viz on same device
fe = FeatureExtractor(model,
save_dir=exp_dir,
is_progress_bar=not args.no_progress_bar)
fe(data_loader,
epochs=args.epochs,
checkpoint_every=args.checkpoint_every, feature_dir=feature_dir)
# SAVE MODEL AND EXPERIMENT INFORMATION
# save_model(trainer.model, exp_dir, metadata=vars(args))
print('Done.')
def main(args):
set_seed(args.seed)
device = torch.device(
'cuda:{}'.format(args.gpu) if torch.cuda.is_available() else 'cpu')
exp_dir = os.path.join(RES_DIR, args.name)
print("save and load experiments at : {}".format(exp_dir))
if not args.is_eval_only: #train
create_directory(exp_dir)
# PREPARES TRAINING DATA
train_loader = get_dataloaders(args.dataset,
batch_size=args.batch_size)
##############
# PREPARES MODEL
args.img_size = get_img_size(args.dataset) # stores for metadata
cs = [1, 64, 128, 1024]
model = VLAE(args, args.latent_dim, cs)
#TRAINS
optimizer = optim.Adam(model.parameters(), lr=args.lr)
model = model.to(device) # make sure trainer and viz on same device
gif_visualizer = GifTraversalsTraining(model, args.dataset, exp_dir)
reg_coeff = [args.reg_coeff0, args.reg_coeff1, args.reg_coeff2]
trainer = Trainer(model,
optimizer,
reg_coeff,
device=device,
save_dir=exp_dir,
is_progress_bar=not args.no_progress_bar,
gif_visualizer=gif_visualizer)
trainer(args,
train_loader,
epochs=args.epochs,
checkpoint_every=args.checkpoint_every)
#SAVE MODEL AND EXPERIMENT INFORMATION
save_model(trainer.model, exp_dir, metadata=vars(args))
print("Model has been saved")
def main(args):
"""Main train and evaluation function.
Parameters
----------
args: argparse.Namespace
Arguments
"""
formatter = logging.Formatter(
'%(asctime)s %(levelname)s - %(funcName)s: %(message)s', "%H:%M:%S")
logger = logging.getLogger(__name__)
logger.setLevel(args.log_level.upper())
stream = logging.StreamHandler()
stream.setLevel(args.log_level.upper())
stream.setFormatter(formatter)
logger.addHandler(stream)
set_seed(args.seed)
device = get_device(is_gpu=not args.no_cuda)
exp_dir = os.path.join(RES_DIR, args.name)
logger.info("Root directory for saving and loading experiments: {}".format(
exp_dir))
if not args.is_eval_only:
create_safe_directory(exp_dir, logger=logger)
if args.loss == "factor":
logger.info(
"FactorVae needs 2 batches per iteration. To replicate this behavior while being consistent, we double the batch size and the the number of epochs."
)
args.batch_size *= 2
args.epochs *= 2
# PREPARES DATA
train_loader = get_dataloaders(args.dataset,
batch_size=args.batch_size,
logger=logger)
logger.info("Train {} with {} samples".format(
args.dataset, len(train_loader.dataset)))
# PREPARES MODEL
args.img_size = get_img_size(args.dataset) # stores for metadata
model = init_specific_model(args.model_type, args.img_size,
args.latent_dim)
logger.info('Num parameters in model: {}'.format(get_n_param(model)))
# TRAINS
optimizer = optim.Adam(model.parameters(), lr=args.lr)
model = model.to(device) # make sure trainer and viz on same device
gif_visualizer = GifTraversalsTraining(model, args.dataset, exp_dir)
loss_f = get_loss_f(args.loss,
n_data=len(train_loader.dataset),
device=device,
**vars(args))
trainer = Trainer(model,
optimizer,
loss_f,
device=device,
logger=logger,
save_dir=exp_dir,
is_progress_bar=not args.no_progress_bar,
gif_visualizer=gif_visualizer)
trainer(
train_loader,
epochs=args.epochs,
checkpoint_every=args.checkpoint_every,
)
# SAVE MODEL AND EXPERIMENT INFORMATION
save_model(trainer.model, exp_dir, metadata=vars(args))
if args.is_metrics or not args.no_test:
model = load_model(exp_dir, is_gpu=not args.no_cuda)
metadata = load_metadata(exp_dir)
# TO-DO: currently uses train datatset
test_loader = get_dataloaders(metadata["dataset"],
batch_size=args.eval_batchsize,
shuffle=False,
logger=logger)
loss_f = get_loss_f(args.loss,
n_data=len(test_loader.dataset),
device=device,
**vars(args))
use_wandb = False
if use_wandb:
loss = args.loss
wandb.init(project="atmlbetavae", config={"VAE_loss": args.loss})
if loss == "betaH":
beta = loss_f.beta
wandb.config["Beta"] = beta
evaluator = Evaluator(model,
loss_f,
device=device,
logger=logger,
save_dir=exp_dir,
is_progress_bar=not args.no_progress_bar,
use_wandb=use_wandb)
evaluator(test_loader,
is_metrics=args.is_metrics,
is_losses=not args.no_test)
def main(args: argparse.Namespace):
"""Main train and evaluation function."""
formatter = logging.Formatter(
'%(asctime)s %(levelname)s - %(funcName)s: %(message)s', "%H:%M:%S")
logger = logging.getLogger(__name__)
logger.setLevel("INFO")
stream = logging.StreamHandler()
stream.setLevel("INFO")
stream.setFormatter(formatter)
logger.addHandler(stream)
set_seed(args.seed)
device = get_device(is_gpu=not args.no_cuda)
exp_dir = os.path.join(RES_DIR, args.name)
logger.info(
f"Root directory for saving and loading experiments: {exp_dir}")
if not args.is_eval_only:
create_safe_directory(exp_dir, logger=logger)
if args.loss == "factor":
logger.info(
"FactorVae needs 2 batches per iteration." +
"To replicate this behavior, double batch size and epochs.")
args.batch_size *= 2
args.epochs *= 2
# PREPARES DATA
train_loader = get_dataloaders(args.dataset,
noise=args.noise,
batch_size=args.batch_size,
logger=logger)
logger.info(
f"Train {args.dataset} with {len(train_loader.dataset)} samples")
# PREPARES MODEL
args.img_size = get_img_size(args.dataset) # stores for metadata
model = VAE(args.img_size, args.latent_dim)
logger.info(f'Num parameters in model: {get_n_param(model)}')
# TRAINS
optimizer = optim.Adam(model.parameters(), lr=args.lr)
model = model.to(device)
gif_visualizer = GifTraversalsTraining(model, args.dataset, exp_dir)
loss_f = get_loss_f(args.loss,
n_data=len(train_loader.dataset),
device=device,
**vars(args))
if args.loss in ['tdGJS', 'tGJS']:
loss_optimizer = optim.Adam(loss_f.parameters(), lr=args.lr)
else:
loss_optimizer = None
print(loss_optimizer)
trainer = Trainer(model,
optimizer,
loss_f,
device=device,
logger=logger,
save_dir=exp_dir,
is_progress_bar=not args.no_progress_bar,
gif_visualizer=gif_visualizer,
loss_optimizer=loss_optimizer,
denoise=args.noise is not None)
trainer(
train_loader,
epochs=args.epochs,
checkpoint_every=args.checkpoint_every,
)
# SAVE MODEL AND EXPERIMENT INFORMATION
save_model(trainer.model, exp_dir, metadata=vars(args))
# Eval
model = load_model(exp_dir, is_gpu=not args.no_cuda)
metadata = load_metadata(exp_dir)
test_loader = get_dataloaders(metadata["dataset"],
noise=args.noise,
train=False,
batch_size=128,
logger=logger)
loss_f = get_loss_f(args.loss,
n_data=len(test_loader.dataset),
device=device,
**vars(args))
evaluator = Evaluator(model,
loss_f,
device=device,
is_metrics=args.is_metrics,
is_train=False,
logger=logger,
save_dir=exp_dir,
is_progress_bar=not args.no_progress_bar,
denoise=args.noise is not None)
evaluator(test_loader)
# Train set also
test_loader = get_dataloaders(metadata["dataset"],
train=True,
batch_size=128,
logger=logger)
loss_f = get_loss_f(args.loss,
n_data=len(test_loader.dataset),
device=device,
**vars(args))
evaluator = Evaluator(model,
loss_f,
device=device,
is_metrics=args.is_metrics,
is_train=True,
logger=logger,
save_dir=exp_dir,
is_progress_bar=not args.no_progress_bar)
evaluator(test_loader)
create_safe_directory(exp_dir, logger=logger)
if args.loss == "factor":
logger.info("FactorVae needs 2 batches per iteration. To replicate this behavior while being consistent, we double the batch size and the the number of epochs.")
args.batch_size *= 2
args.epochs *= 2
# PREPARES DATA
train_loader = get_dataloaders(args.dataset,
batch_size=args.batch_size,
logger=logger)
logger.info("Train {} with {} samples".format(args.dataset, len(train_loader.dataset)))
# PREPARES MODEL
args.img_size = get_img_size(args.dataset) # stores for metadata
model = init_specific_model(args.model_type, args.img_size, args.latent_dim)
logger.info('Num parameters in model: {}'.format(get_n_param(model)))
# TRAINS
optimizer = optim.Adam(model.parameters(), lr=args.lr)
model = model.to(device) # make sure trainer and viz on same device
gif_visualizer = GifTraversalsTraining(model, args.dataset, exp_dir)
loss_f = get_loss_f(args.loss,
n_data=len(train_loader.dataset),
device=device,
**vars(args))
wandb.watch(model, optimizer, log="parameters", log_freq=1000)
