def train(
self,
model,
crit, optimizer,
train_loader, valid_loader,
src_vocab, tgt_vocab,
n_epochs,
lr_scheduler=None
):
if src_vocab is not None and tgt_vocab is not None:
raise NotImplementedError('You should assign None one of vocab to designate target language.')
if src_vocab is None:
is_src_target = False
elif tgt_vocab is None:
is_src_target = True
else:
raise NotImplementedError('You cannot assign None both vocab.')
trainer = Engine(self.step)
trainer.config = self.config
trainer.model, trainer.crit = model, crit
trainer.optimizer, trainer.lr_scheduler = optimizer, lr_scheduler
trainer.epoch_idx = 0
trainer.is_src_target = is_src_target
evaluator = Engine(self.validate)
evaluator.config = self.config
evaluator.model, evaluator.crit = model, crit
evaluator.best_loss = np.inf
evaluator.is_src_target = is_src_target
self.attach(trainer, evaluator, verbose=self.config.verbose)
def run_validation(engine, evaluator, valid_loader):
evaluator.run(valid_loader, max_epochs=1)
if engine.lr_scheduler is not None:
engine.lr_scheduler.step()
trainer.add_event_handler(
Events.EPOCH_COMPLETED, run_validation, evaluator, valid_loader
)
evaluator.add_event_handler(
Events.EPOCH_COMPLETED, self.check_best
)
evaluator.add_event_handler(
Events.EPOCH_COMPLETED,
self.save_model,
trainer,
self.config,
src_vocab,
tgt_vocab,
)
trainer.run(train_loader, max_epochs=n_epochs)
if n_epochs > 0:
model.load_state_dict(evaluator.best_model)
return model
def train(self,
models,
language_models,
crits,
optimizers,
train_loader,
valid_loader,
vocabs,
n_epochs,
lr_schedulers=None):
trainer = Engine(self.step)
trainer.config = self.config
trainer.models, trainer.crits = models, crits
trainer.optimizers, trainer.lr_schedulers = optimizers, lr_schedulers
trainer.language_models = language_models
trainer.epoch_idx = 0
evaluator = Engine(self.validate)
evaluator.config = self.config
evaluator.models, evaluator.crits = models, crits
evaluator.best_x2y, evaluator.best_y2x = np.inf, np.inf
self.attach(trainer, evaluator, verbose=self.config.verbose)
def run_validation(engine, evaluator, valid_loader):
evaluator.run(valid_loader, max_epochs=1)
if engine.lr_schedulers is not None:
for s in engine.lr_schedulers:
s.step()
trainer.add_event_handler(Events.EPOCH_COMPLETED, run_validation,
evaluator, valid_loader)
evaluator.add_event_handler(Events.EPOCH_COMPLETED, self.check_best)
evaluator.add_event_handler(
Events.EPOCH_COMPLETED,
self.save_model,
trainer,
self.config,
vocabs,
)
trainer.run(train_loader, max_epochs=n_epochs)
return models
def train(
self,
model, crit, optimizer,
train_loader, valid_loader,
src_vocab, tgt_vocab,
n_epochs,
lr_scheduler=None
):
trainer = Engine(self.step)
trainer.config = self.config
trainer.model, trainer.crit = model, crit
trainer.optimizer, trainer.lr_scheduler = optimizer, lr_scheduler
trainer.epoch_idx = 0
evaluator = Engine(self.validate)
evaluator.config = self.config
evaluator.model, evaluator.crit = model, crit
evaluator.best_loss = np.inf
self.attach(trainer, evaluator, verbose=self.config.verbose)
def run_validation(engine, evaluator, valid_loader):
evaluator.run(valid_loader, max_epochs=1)
if engine.lr_scheduler is not None:
engine.lr_scheduler.step()
trainer.add_event_handler(
Events.EPOCH_COMPLETED, run_validation, evaluator, valid_loader
)
evaluator.add_event_handler(
Events.EPOCH_COMPLETED, self.check_best
)
evaluator.add_event_handler(
Events.EPOCH_COMPLETED,
self.save_model,
trainer,
self.config,
src_vocab,
tgt_vocab,
)
trainer.run(train_loader, max_epochs=n_epochs)
return model
