def test_trainer_can_run(self):
trainer = Trainer(model=self.model,
optimizer=self.optimizer,
iterator=self.iterator,
train_dataset=self.instances,
validation_dataset=self.instances,
num_epochs=2)
metrics = trainer.train()
assert 'best_validation_loss' in metrics
assert isinstance(metrics['best_validation_loss'], float)
assert 'best_validation_accuracy' in metrics
assert isinstance(metrics['best_validation_accuracy'], float)
assert 'best_validation_accuracy3' in metrics
assert isinstance(metrics['best_validation_accuracy3'], float)
assert 'best_epoch' in metrics
assert isinstance(metrics['best_epoch'], int)
# Making sure that both increasing and decreasing validation metrics work.
trainer = Trainer(model=self.model,
optimizer=self.optimizer,
iterator=self.iterator,
train_dataset=self.instances,
validation_dataset=self.instances,
validation_metric='+loss',
num_epochs=2)
metrics = trainer.train()
assert 'best_validation_loss' in metrics
assert isinstance(metrics['best_validation_loss'], float)
assert 'best_validation_accuracy' in metrics
assert isinstance(metrics['best_validation_accuracy'], float)
assert 'best_validation_accuracy3' in metrics
assert isinstance(metrics['best_validation_accuracy3'], float)
assert 'best_epoch' in metrics
assert isinstance(metrics['best_epoch'], int)
def test_trainer_can_run_cuda(self):
trainer = Trainer(self.model,
self.optimizer,
self.iterator,
self.instances,
num_epochs=2,
cuda_device=0)
trainer.train()
def test_trainer_can_run_multiple_gpu(self):
multigpu_iterator = BasicIterator(batch_size=4)
multigpu_iterator.index_with(self.vocab)
trainer = Trainer(self.model,
self.optimizer,
multigpu_iterator,
self.instances,
num_epochs=2,
cuda_device=[0, 1])
trainer.train()
def test_trainer_can_log_histograms(self):
# enable activation logging
for module in self.model.modules():
module.should_log_activations = True
trainer = Trainer(self.model,
self.optimizer,
self.iterator,
self.instances,
num_epochs=3,
serialization_dir=self.TEST_DIR,
histogram_interval=2)
trainer.train()
def test_trainer_raises_on_model_with_no_loss_key(self):
class FakeModel(torch.nn.Module):
def forward(self, **kwargs): # pylint: disable=arguments-differ,unused-argument
return {}
with pytest.raises(RuntimeError):
trainer = Trainer(FakeModel(),
self.optimizer,
self.iterator,
self.instances,
num_epochs=2,
serialization_dir=self.TEST_DIR)
trainer.train()
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_run_with_lr_scheduler(self):
lr_params = Params({"type": "reduce_on_plateau"})
lr_scheduler = LearningRateScheduler.from_params(
self.optimizer, lr_params)
trainer = Trainer(model=self.model,
optimizer=self.optimizer,
iterator=self.iterator,
learning_rate_scheduler=lr_scheduler,
validation_metric="-loss",
train_dataset=self.instances,
validation_dataset=self.instances,
num_epochs=2)
trainer.train()
def test_trainer_respects_num_serialized_models_to_keep(self):
trainer = Trainer(self.model,
self.optimizer,
self.iterator,
self.instances,
num_epochs=5,
serialization_dir=self.TEST_DIR,
num_serialized_models_to_keep=3)
trainer.train()
# Now check the serialized files
for prefix in ['model_state_epoch_*', 'training_state_epoch_*']:
file_names = glob.glob(os.path.join(self.TEST_DIR, prefix))
epochs = [
int(re.search(r"_([0-9])\.th", fname).group(1))
for fname in file_names
]
assert sorted(epochs) == [2, 3, 4]
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()
def test_trainer_respects_keep_serialized_model_every_num_seconds(self):
# To test:
# Create an iterator that sleeps for 2.5 second per epoch, so the total training
# time for one epoch is slightly greater then 2.5 seconds.
# Run for 6 epochs, keeping the last 2 models, models also kept every 5 seconds.
# Check the resulting checkpoints. Should then have models at epochs
# 2, 4, plus the last two at 5 and 6.
class WaitingIterator(BasicIterator):
# pylint: disable=arguments-differ
def _create_batches(self, *args, **kwargs):
time.sleep(2.5)
return super(WaitingIterator,
self)._create_batches(*args, **kwargs)
iterator = WaitingIterator(batch_size=2)
iterator.index_with(self.vocab)
trainer = Trainer(self.model,
self.optimizer,
iterator,
self.instances,
num_epochs=6,
serialization_dir=self.TEST_DIR,
num_serialized_models_to_keep=2,
keep_serialized_model_every_num_seconds=5)
trainer.train()
# Now check the serialized files
for prefix in ['model_state_epoch_*', 'training_state_epoch_*']:
file_names = glob.glob(os.path.join(self.TEST_DIR, prefix))
epochs = [
int(re.search(r"_([0-9])\.th", fname).group(1))
for fname in file_names
]
# epoch N has N-1 in file name
assert sorted(epochs) == [1, 3, 4, 5]