def __call__(self, data_loader, epochs=10, checkpoint_every=10):
"""
Trains the model.
Parameters
----------
data_loader: torch.utils.data.DataLoader
epochs: int, optional
Number of epochs to train the model for.
checkpoint_every: int, optional
Save a checkpoint of the trained model every n epoch.
"""
start = default_timer()
self.model.train()
for epoch in range(epochs):
storer = defaultdict(list)
mean_epoch_loss = self._train_epoch(data_loader, storer, epoch)
self.logger.info('Epoch: {} Average loss per image: {:.2f}'.format(
epoch + 1, mean_epoch_loss))
self.losses_logger.log(epoch, storer)
if self.gif_visualizer is not None:
self.gif_visualizer()
if epoch % checkpoint_every == 0:
save_model(self.model,
self.save_dir,
filename="model-{}.pt".format(epoch))
if self.gif_visualizer is not None:
self.gif_visualizer.save_reset()
self.model.eval()
delta_time = (default_timer() - start) / 60
self.logger.info(
'Finished training after {:.1f} min.'.format(delta_time))
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)
device=device,
**vars(args))
wandb.watch(model, optimizer, log="parameters", log_freq=1000)
trainer = Trainer(model, optimizer, loss_f=loss_f,
loss_name=args.loss,
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))
evaluator = Evaluator(model, loss_f,
device=device,
