def test_trainer_saves_models_at_specified_interval(self):
iterator = BasicIterator(batch_size=4)
iterator.index_with(self.vocab)
trainer = Trainer(self.model,
self.optimizer,
iterator,
self.instances,
num_epochs=2,
serialization_dir=self.TEST_DIR,
model_save_interval=0.0001)
trainer.train()
# Now check the serialized files for models saved during the epoch.
prefix = 'model_state_epoch_*'
file_names = sorted(glob.glob(os.path.join(self.TEST_DIR, prefix)))
epochs = [
re.search(r"_([0-9\.\-]+)\.th", fname).group(1)
for fname in file_names
]
# We should have checkpoints at the end of each epoch and during each, e.g.
# [0.timestamp, 0, 1.timestamp, 1]
assert len(epochs) == 4
assert epochs[3] == '1'
assert '.' in epochs[0]
# Now make certain we can restore from timestamped checkpoint.
# To do so, remove the checkpoint from the end of epoch 1&2, so
# that we are forced to restore from the timestamped checkpoints.
for k in range(2):
os.remove(
os.path.join(self.TEST_DIR,
'model_state_epoch_{}.th'.format(k)))
os.remove(
os.path.join(self.TEST_DIR,
'training_state_epoch_{}.th'.format(k)))
os.remove(os.path.join(self.TEST_DIR, 'best.th'))
restore_trainer = Trainer(self.model,
self.optimizer,
self.iterator,
self.instances,
num_epochs=2,
serialization_dir=self.TEST_DIR,
model_save_interval=0.0001)
epoch, _ = restore_trainer._restore_checkpoint() # pylint: disable=protected-access
assert epoch == 2
# One batch per epoch.
assert restore_trainer._batch_num_total == 2 # pylint: disable=protected-access
def test_trainer_can_resume_training(self):
trainer = Trainer(self.model,
self.optimizer,
self.iterator,
self.instances,
validation_dataset=self.instances,
num_epochs=1,
serialization_dir=self.TEST_DIR)
trainer.train()
new_trainer = Trainer(self.model,
self.optimizer,
self.iterator,
self.instances,
validation_dataset=self.instances,
num_epochs=3,
serialization_dir=self.TEST_DIR)
epoch, val_metrics_per_epoch = new_trainer._restore_checkpoint() # pylint: disable=protected-access
assert epoch == 1
assert len(val_metrics_per_epoch) == 1
assert isinstance(val_metrics_per_epoch[0], float)
assert val_metrics_per_epoch[0] != 0.
new_trainer.train()