def main(conf):
exp_dir = conf['main_args']['exp_dir']
# Define Dataloader
train_loader, val_loader = make_dataloaders(**conf['data'],
**conf['training'])
conf['masknet'].update({'n_src': conf['data']['n_src']})
# Define model, optimizer + scheduler
model, optimizer = make_model_and_optimizer(conf)
scheduler = None
if conf['training']['half_lr']:
scheduler = ReduceLROnPlateau(optimizer=optimizer,
factor=0.5,
patience=5)
# Save config
os.makedirs(exp_dir, exist_ok=True)
conf_path = os.path.join(exp_dir, 'conf.yml')
with open(conf_path, 'w') as outfile:
yaml.safe_dump(conf, outfile)
# Define loss function
loss_func = ChimeraLoss(alpha=conf['training']['loss_alpha'])
# Put together in System
system = ChimeraSystem(model=model,
loss_func=loss_func,
optimizer=optimizer,
train_loader=train_loader,
val_loader=val_loader,
scheduler=scheduler,
config=conf)
# Callbacks
checkpoint_dir = os.path.join(exp_dir, 'checkpoints/')
checkpoint = ModelCheckpoint(checkpoint_dir,
monitor='val_loss',
mode='min',
save_top_k=5,
verbose=1)
early_stopping = False
if conf['training']['early_stop']:
early_stopping = EarlyStopping(monitor='val_loss',
patience=30,
verbose=1)
gpus = -1
# Don't ask GPU if they are not available.
if not torch.cuda.is_available():
print('No available GPU were found, set gpus to None')
gpus = None
# Train model
trainer = pl.Trainer(
max_nb_epochs=conf['training']['epochs'],
checkpoint_callback=checkpoint,
early_stop_callback=early_stopping,
default_save_path=exp_dir,
gpus=gpus,
distributed_backend='dp',
train_percent_check=1.0, # Useful for fast experiment
gradient_clip_val=200,
)
trainer.fit(system)
with open(os.path.join(exp_dir, "best_k_models.json"), "w") as f:
json.dump(checkpoint.best_k_models, f, indent=0)
# Save last model for convenience
torch.save(system.model.state_dict(),
os.path.join(exp_dir, 'checkpoints/final.pth'))
def main(conf):
exp_dir = conf["main_args"]["exp_dir"]
# Define Dataloader
train_loader, val_loader = make_dataloaders(**conf["data"],
**conf["training"])
conf["masknet"].update({"n_src": conf["data"]["n_src"]})
# Define model, optimizer + scheduler
model, optimizer = make_model_and_optimizer(conf)
scheduler = None
if conf["training"]["half_lr"]:
scheduler = ReduceLROnPlateau(optimizer=optimizer,
factor=0.5,
patience=5)
# Save config
os.makedirs(exp_dir, exist_ok=True)
conf_path = os.path.join(exp_dir, "conf.yml")
with open(conf_path, "w") as outfile:
yaml.safe_dump(conf, outfile)
# Define loss function
loss_func = ChimeraLoss(alpha=conf["training"]["loss_alpha"])
# Put together in System
system = ChimeraSystem(
model=model,
loss_func=loss_func,
optimizer=optimizer,
train_loader=train_loader,
val_loader=val_loader,
scheduler=scheduler,
config=conf,
)
# Callbacks
checkpoint_dir = os.path.join(exp_dir, "checkpoints/")
checkpoint = ModelCheckpoint(checkpoint_dir,
monitor="val_loss",
mode="min",
save_top_k=5,
verbose=True)
early_stopping = False
if conf["training"]["early_stop"]:
early_stopping = EarlyStopping(monitor="val_loss",
patience=30,
verbose=True)
gpus = -1
# Don't ask GPU if they are not available.
if not torch.cuda.is_available():
print("No available GPU were found, set gpus to None")
gpus = None
# Train model
trainer = pl.Trainer(
max_epochs=conf["training"]["epochs"],
checkpoint_callback=checkpoint,
early_stop_callback=early_stopping,
default_root_dir=exp_dir,
gpus=gpus,
distributed_backend="dp",
train_percent_check=1.0, # Useful for fast experiment
gradient_clip_val=200,
)
trainer.fit(system)
with open(os.path.join(exp_dir, "best_k_models.json"), "w") as f:
json.dump(checkpoint.best_k_models, f, indent=0)
# Save last model for convenience
torch.save(system.model.state_dict(),
os.path.join(exp_dir, "checkpoints/final.pth"))
