def test_restored_training_returns_best_epoch_metrics_even_if_no_better_epoch_is_found_after_restoring(self):
# Instead of -loss, use +loss to assure 2nd epoch is considered worse.
# Run 1 epoch of original training.
original_trainer = CallbackTrainer(self.model,
training_data=self.instances,
iterator=self.iterator,
optimizer=self.optimizer,
callbacks=self.default_callbacks(validation_metric="+loss"),
num_epochs=1, serialization_dir=self.TEST_DIR)
training_metrics = original_trainer.train()
# Run 1 epoch of restored training.
restored_trainer = CallbackTrainer(
self.model,
training_data=self.instances,
iterator=self.iterator,
optimizer=self.optimizer,
callbacks=self.default_callbacks(validation_metric="+loss"),
num_epochs=2, serialization_dir=self.TEST_DIR)
restored_metrics = restored_trainer.train()
assert "best_validation_loss" in restored_metrics
assert "best_validation_accuracy" in restored_metrics
assert "best_validation_accuracy3" in restored_metrics
assert "best_epoch" in restored_metrics
# Epoch 2 validation loss should be lesser than that of Epoch 1
assert training_metrics["best_validation_loss"] == restored_metrics["best_validation_loss"]
assert training_metrics["best_epoch"] == 0
assert training_metrics["validation_loss"] > restored_metrics["validation_loss"]
def test_trainer_can_run_and_resume_with_momentum_scheduler(self):
scheduler = MomentumScheduler.from_params(
self.optimizer, Params({"type": "inverted_triangular", "cool_down": 2, "warm_up": 2}))
callbacks = self.default_callbacks() + [UpdateMomentum(scheduler)]
trainer = CallbackTrainer(model=self.model,
training_data=self.instances,
iterator=self.iterator,
optimizer=self.optimizer,
num_epochs=4,
callbacks=callbacks,
serialization_dir=self.TEST_DIR)
trainer.train()
new_scheduler = MomentumScheduler.from_params(
self.optimizer, Params({"type": "inverted_triangular", "cool_down": 2, "warm_up": 2}))
new_callbacks = self.default_callbacks() + [UpdateMomentum(new_scheduler)]
new_trainer = CallbackTrainer(model=self.model,
training_data=self.instances,
iterator=self.iterator,
optimizer=self.optimizer,
num_epochs=6,
callbacks=new_callbacks,
serialization_dir=self.TEST_DIR)
new_trainer.handler.fire_event(Events.TRAINING_START)
assert new_trainer.epoch_number == 4
assert new_scheduler.last_epoch == 3
new_trainer.train()
def test_trainer_can_resume_with_lr_scheduler(self):
lr_scheduler = LearningRateScheduler.from_params(
self.optimizer, Params({"type": "exponential", "gamma": 0.5}))
callbacks = self.default_callbacks() + [UpdateLearningRate(lr_scheduler)]
trainer = CallbackTrainer(model=self.model,
training_data=self.instances,
iterator=self.iterator,
optimizer=self.optimizer,
callbacks=callbacks,
num_epochs=2, serialization_dir=self.TEST_DIR)
trainer.train()
new_lr_scheduler = LearningRateScheduler.from_params(
self.optimizer, Params({"type": "exponential", "gamma": 0.5}))
callbacks = self.default_callbacks() + [UpdateLearningRate(new_lr_scheduler)]
new_trainer = CallbackTrainer(model=self.model,
training_data=self.instances,
iterator=self.iterator,
optimizer=self.optimizer,
callbacks=callbacks,
num_epochs=4, serialization_dir=self.TEST_DIR)
new_trainer.handler.fire_event(Events.TRAINING_START)
assert new_trainer.epoch_number == 2
assert new_lr_scheduler.lr_scheduler.last_epoch == 1
new_trainer.train()
def test_trainer_can_resume_training_for_exponential_moving_average(self):
moving_average = ExponentialMovingAverage(self.model.named_parameters())
callbacks = self.default_callbacks() + [UpdateMovingAverage(moving_average)]
trainer = CallbackTrainer(self.model,
training_data=self.instances,
iterator=self.iterator,
optimizer=self.optimizer,
num_epochs=1, serialization_dir=self.TEST_DIR,
callbacks=callbacks)
trainer.train()
new_moving_average = ExponentialMovingAverage(self.model.named_parameters())
new_callbacks = self.default_callbacks() + [UpdateMovingAverage(new_moving_average)]
new_trainer = CallbackTrainer(self.model,
training_data=self.instances,
iterator=self.iterator,
optimizer=self.optimizer,
num_epochs=3, serialization_dir=self.TEST_DIR,
callbacks=new_callbacks)
new_trainer.handler.fire_event(Events.TRAINING_START) # pylint: disable=protected-access
assert new_trainer.epoch_number == 1
tracker = trainer.metric_tracker # pylint: disable=protected-access
assert tracker.is_best_so_far()
assert tracker._best_so_far is not None # pylint: disable=protected-access
new_trainer.train()
def test_training_metrics_consistent_with_and_without_validation(self):
default_callbacks = self.default_callbacks(serialization_dir=None)
default_callbacks_without_validation = [
callback for callback in default_callbacks
if not isinstance(callback, Validate)
]
trainer1 = CallbackTrainer(
copy.deepcopy(self.model),
copy.deepcopy(self.optimizer),
callbacks=default_callbacks_without_validation,
num_epochs=1,
serialization_dir=None)
trainer1.train()
trainer2 = CallbackTrainer(copy.deepcopy(self.model),
copy.deepcopy(self.optimizer),
callbacks=default_callbacks,
num_epochs=1,
serialization_dir=None)
trainer2.train()
metrics1 = trainer1.train_metrics
metrics2 = trainer2.train_metrics
assert metrics1.keys() == metrics2.keys()
for key in ['accuracy', 'accuracy3', 'loss']:
np.testing.assert_almost_equal(metrics1[key], metrics2[key])
def test_trainer_can_resume_training(self):
trainer = CallbackTrainer(
self.model,
training_data=self.instances,
iterator=self.iterator,
optimizer=self.optimizer,
callbacks=self.default_callbacks(),
num_epochs=1,
serialization_dir=self.TEST_DIR,
)
trainer.train()
new_trainer = CallbackTrainer(
self.model,
training_data=self.instances,
iterator=self.iterator,
optimizer=self.optimizer,
callbacks=self.default_callbacks(),
num_epochs=3,
serialization_dir=self.TEST_DIR,
)
new_trainer.handler.fire_event(Events.TRAINING_START)
assert new_trainer.epoch_number == 1
tracker = new_trainer.metric_tracker
assert tracker is not None
assert tracker.is_best_so_far()
assert tracker._best_so_far is not None
new_trainer.train()
def test_trainer_saves_and_loads_best_validation_metrics_correctly_2(self):
# Use -loss and run 1 epoch of original-training, and one of restored-training
# Run 1 epoch of original training.
trainer = CallbackTrainer(
self.model, self.optimizer,
callbacks=self.default_callbacks(validation_metric="+loss"),
num_epochs=1, serialization_dir=self.TEST_DIR)
trainer.train()
_ = trainer.handler.fire_event(Events.RESTORE_CHECKPOINT)
best_epoch_1 = trainer.metric_tracker.best_epoch
best_validation_metrics_epoch_1 = trainer.metric_tracker.best_epoch_metrics
# best_validation_metrics_epoch_1: {'accuracy': 0.75, 'accuracy3': 1.0, 'loss': 0.6243013441562653}
assert isinstance(best_validation_metrics_epoch_1, dict)
assert "loss" in best_validation_metrics_epoch_1
# Run 1 more epoch of restored training.
restore_trainer = CallbackTrainer(
self.model, self.optimizer,
callbacks=self.default_callbacks(validation_metric="+loss"),
num_epochs=2, serialization_dir=self.TEST_DIR)
restore_trainer.train()
_ = restore_trainer.handler.fire_event(Events.RESTORE_CHECKPOINT)
best_epoch_2 = restore_trainer.metric_tracker.best_epoch
best_validation_metrics_epoch_2 = restore_trainer.metric_tracker.best_epoch_metrics
# Because of using +loss, 2nd epoch won't be better than 1st. So best val metrics should be same.
assert best_epoch_1 == best_epoch_2 == 0
assert best_validation_metrics_epoch_2 == best_validation_metrics_epoch_1
def test_trainer_can_resume_with_lr_scheduler(self):
lr_scheduler = LearningRateScheduler.from_params(
self.optimizer, Params({
"type": "exponential",
"gamma": 0.5
}))
callbacks = self.default_callbacks() + [LrsCallback(lr_scheduler)]
trainer = CallbackTrainer(model=self.model,
optimizer=self.optimizer,
callbacks=callbacks,
num_epochs=2,
serialization_dir=self.TEST_DIR)
trainer.train()
new_lr_scheduler = LearningRateScheduler.from_params(
self.optimizer, Params({
"type": "exponential",
"gamma": 0.5
}))
callbacks = self.default_callbacks() + [LrsCallback(new_lr_scheduler)]
new_trainer = CallbackTrainer(model=self.model,
optimizer=self.optimizer,
callbacks=callbacks,
num_epochs=4,
serialization_dir=self.TEST_DIR)
new_trainer.handler.fire_event(Events.RESTORE_CHECKPOINT)
assert new_trainer.epoch_number == 2
assert new_lr_scheduler.lr_scheduler.last_epoch == 1
new_trainer.train()
def test_validation_metrics_consistent_with_and_without_tracking(self):
default_callbacks = self.default_callbacks(serialization_dir=None)
default_callbacks_without_tracking = [
callback for callback in default_callbacks
if not isinstance(callback, TrackMetrics)
]
trainer1 = CallbackTrainer(
copy.deepcopy(self.model),
training_data=self.instances,
iterator=self.iterator,
optimizer=copy.deepcopy(self.optimizer),
callbacks=default_callbacks_without_tracking,
num_epochs=1,
serialization_dir=None,
)
trainer1.train()
trainer2 = CallbackTrainer(
copy.deepcopy(self.model),
training_data=self.instances,
iterator=self.iterator,
optimizer=copy.deepcopy(self.optimizer),
callbacks=default_callbacks,
num_epochs=1,
serialization_dir=None,
)
trainer2.train()
metrics1 = trainer1.val_metrics
metrics2 = trainer2.val_metrics
assert metrics1.keys() == metrics2.keys()
for key in ["accuracy", "accuracy3", "loss"]:
np.testing.assert_almost_equal(metrics1[key], metrics2[key])
def test_trainer_can_resume_training(self):
trainer = CallbackTrainer(self.model,
self.optimizer,
callbacks=self.default_callbacks(),
num_epochs=1,
serialization_dir=self.TEST_DIR)
trainer.train()
new_trainer = CallbackTrainer(self.model,
self.optimizer,
callbacks=self.default_callbacks(),
num_epochs=3,
serialization_dir=self.TEST_DIR)
new_trainer.handler.fire_event(Events.RESTORE_CHECKPOINT)
assert new_trainer.epoch_number == 1
tracker = new_trainer.metric_tracker
assert tracker is not None
assert tracker.is_best_so_far()
assert tracker._best_so_far is not None
new_trainer.train()
def test_trainer_saves_and_loads_best_validation_metrics_correctly_1(self):
# Use -loss and run 1 epoch of original-training, and one of restored-training
# Run 1 epoch of original training.
trainer = CallbackTrainer(
self.model,
training_data=self.instances,
iterator=self.iterator,
optimizer=self.optimizer,
callbacks=self.default_callbacks(),
num_epochs=1, serialization_dir=self.TEST_DIR)
trainer.train()
trainer.handler.fire_event(Events.TRAINING_START)
best_epoch_1 = trainer.metric_tracker.best_epoch
best_validation_metrics_epoch_1 = trainer.metric_tracker.best_epoch_metrics
# best_validation_metrics_epoch_1: {'accuracy': 0.75, 'accuracy3': 1.0, 'loss': 0.6243013441562653}
assert isinstance(best_validation_metrics_epoch_1, dict)
assert "loss" in best_validation_metrics_epoch_1
# Run 1 epoch of restored training.
restore_trainer = CallbackTrainer(
self.model,
training_data=self.instances,
iterator=self.iterator,
optimizer=self.optimizer,
callbacks=self.default_callbacks(),
num_epochs=2, serialization_dir=self.TEST_DIR)
restore_trainer.train()
restore_trainer.handler.fire_event(Events.TRAINING_START)
best_epoch_2 = restore_trainer.metric_tracker.best_epoch
best_validation_metrics_epoch_2 = restore_trainer.metric_tracker.best_epoch_metrics
# Because of using -loss, 2nd epoch would be better than 1st. So best val metrics should not be same.
assert best_epoch_1 == 0 and best_epoch_2 == 1
assert best_validation_metrics_epoch_2 != best_validation_metrics_epoch_1
def test_should_stop_early_with_early_stopping_disabled(self):
# Increasing metric
trainer = CallbackTrainer(
self.model,
training_data=self.instances,
iterator=self.iterator,
optimizer=self.optimizer,
num_epochs=100,
callbacks=self.default_callbacks(validation_metric="+test"),
)
tracker = trainer.metric_tracker
tracker.add_metrics([float(i) for i in reversed(range(20))])
assert not tracker.should_stop_early()
# Decreasing metric
trainer = CallbackTrainer(
self.model,
training_data=self.instances,
iterator=self.iterator,
optimizer=self.optimizer,
num_epochs=100,
callbacks=self.default_callbacks(validation_metric="-test"),
)
tracker = trainer.metric_tracker
tracker.add_metrics([float(i) for i in range(20)])
assert not tracker.should_stop_early()
def test_trainer_can_run(self):
trainer = CallbackTrainer(model=self.model,
optimizer=self.optimizer,
callbacks=self.default_callbacks(serialization_dir=None),
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)
assert 'peak_cpu_memory_MB' in metrics
# Making sure that both increasing and decreasing validation metrics work.
trainer = CallbackTrainer(model=self.model,
optimizer=self.optimizer,
callbacks=self.default_callbacks(validation_metric="+loss",
serialization_dir=None),
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)
assert 'peak_cpu_memory_MB' in metrics
assert isinstance(metrics['peak_cpu_memory_MB'], float)
assert metrics['peak_cpu_memory_MB'] > 0
def test_trainer_saves_models_at_specified_interval(self):
iterator = BasicIterator(batch_size=4)
iterator.index_with(self.vocab)
trainer = CallbackTrainer(
self.model,
training_data=self.instances,
iterator=iterator,
optimizer=self.optimizer,
num_epochs=2,
serialization_dir=self.TEST_DIR,
callbacks=self.default_callbacks(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 = CallbackTrainer(
self.model,
training_data=self.instances,
iterator=iterator,
optimizer=self.optimizer,
num_epochs=2,
serialization_dir=self.TEST_DIR,
callbacks=self.default_callbacks(model_save_interval=0.0001),
)
restore_trainer.handler.fire_event(Events.TRAINING_START)
assert restore_trainer.epoch_number == 2
# One batch per epoch.
assert restore_trainer.batch_num_total == 2
def test_production_rule_field_with_multiple_gpus(self):
wikitables_dir = 'allennlp/tests/fixtures/data/wikitables/'
offline_lf_directory = wikitables_dir + 'action_space_walker_output/'
wikitables_reader = WikiTablesDatasetReader(
tables_directory=wikitables_dir,
offline_logical_forms_directory=offline_lf_directory)
instances = wikitables_reader.read(wikitables_dir +
'sample_data.examples')
archive_path = self.FIXTURES_ROOT / 'semantic_parsing' / 'wikitables' / 'serialization' / 'model.tar.gz'
model = load_archive(archive_path).model
model.cuda()
multigpu_iterator = BasicIterator(batch_size=4)
multigpu_iterator.index_with(model.vocab)
trainer = CallbackTrainer(model,
self.optimizer,
num_epochs=2,
cuda_device=[0, 1],
callbacks=[
GenerateTrainingBatches(
instances, multigpu_iterator),
TrainSupervised()
])
trainer.train()
def test_production_rule_field_with_multiple_gpus(self):
wikitables_dir = "allennlp/tests/fixtures/data/wikitables/"
offline_lf_directory = wikitables_dir + "action_space_walker_output/"
wikitables_reader = WikiTablesDatasetReader(
tables_directory=wikitables_dir,
offline_logical_forms_directory=offline_lf_directory)
instances = wikitables_reader.read(wikitables_dir +
"sample_data.examples")
archive_path = (self.FIXTURES_ROOT / "semantic_parsing" /
"wikitables" / "serialization" / "model.tar.gz")
model = load_archive(archive_path).model
model.cuda()
multigpu_iterator = BasicIterator(batch_size=4)
multigpu_iterator.index_with(model.vocab)
trainer = CallbackTrainer(
model,
instances,
multigpu_iterator,
self.optimizer,
num_epochs=2,
cuda_device=[0, 1],
callbacks=[GradientNormAndClip()],
)
trainer.train()
def test_metric_only_considered_best_so_far_when_strictly_better_than_those_before_it_decreasing_metric(
self):
new_trainer = CallbackTrainer(
self.model,
training_data=self.instances,
iterator=self.iterator,
optimizer=self.optimizer,
num_epochs=3,
serialization_dir=self.TEST_DIR,
callbacks=self.default_callbacks(patience=5),
)
tracker = new_trainer.metric_tracker
# when it is the only metric it should be considered the best
new_tracker = copy.deepcopy(tracker)
new_tracker.add_metric(1)
assert new_tracker.is_best_so_far()
# when it is the same as one before it it is not considered the best
new_tracker = copy.deepcopy(tracker)
new_tracker.add_metrics([0.3, 0.3, 0.3, 0.2, 0.5, 0.1, 0.3])
assert not new_tracker.is_best_so_far()
# when it is the best it is considered the best
new_tracker = copy.deepcopy(tracker)
new_tracker.add_metrics([0.3, 0.3, 0.3, 0.2, 0.5, 0.1, 0.0013])
assert new_tracker.is_best_so_far()
# when it is not the the best it is not considered the best
new_tracker = copy.deepcopy(tracker)
new_tracker.add_metrics([0.3, 0.3, 0.3, 0.2, 0.5, 0.1, 13])
def test_trainer_can_run_multiple_gpu(self):
self.model.cuda()
class MetaDataCheckWrapper(Model):
"""
Checks that the metadata field has been correctly split across the batch dimension
when running on multiple gpus.
"""
def __init__(self, model):
super().__init__(model.vocab)
self.model = model
def forward(self, **kwargs) -> Dict[str, torch.Tensor]: # type: ignore # pylint: disable=arguments-differ
assert 'metadata' in kwargs and 'tags' in kwargs, \
f'tokens and metadata must be provided. Got {kwargs.keys()} instead.'
batch_size = kwargs['tokens']['tokens'].size()[0]
assert len(kwargs['metadata']) == batch_size, \
f'metadata must be split appropriately. Expected {batch_size} elements, ' \
f"got {len(kwargs['metadata'])} elements."
return self.model.forward(**kwargs)
multigpu_iterator = BasicIterator(batch_size=4)
multigpu_iterator.index_with(self.vocab)
trainer = CallbackTrainer(MetaDataCheckWrapper(self.model), self.optimizer,
num_epochs=2,
callbacks=self.default_callbacks(iterator=multigpu_iterator),
cuda_device=[0, 1])
metrics = trainer.train()
assert 'peak_cpu_memory_MB' in metrics
assert isinstance(metrics['peak_cpu_memory_MB'], float)
assert metrics['peak_cpu_memory_MB'] > 0
assert 'peak_gpu_0_memory_MB' in metrics
assert isinstance(metrics['peak_gpu_0_memory_MB'], int)
assert 'peak_gpu_1_memory_MB' in metrics
assert isinstance(metrics['peak_gpu_1_memory_MB'], int)
def test_trainer_can_run_cuda(self):
self.model.cuda()
trainer = CallbackTrainer(self.model, self.optimizer,
num_epochs=2,
callbacks=self.default_callbacks(),
cuda_device=0)
trainer.train()
def test_trainer_can_run_exponential_moving_average(self):
moving_average = ExponentialMovingAverage(self.model.named_parameters(), decay=0.9999)
callbacks = self.default_callbacks() + [ComputeMovingAverage(moving_average)]
trainer = CallbackTrainer(model=self.model,
optimizer=self.optimizer,
num_epochs=2,
callbacks=callbacks)
trainer.train()
def test_trainer_saves_models_at_specified_interval(self):
trainer = CallbackTrainer(
self.model,
self.optimizer,
num_epochs=2,
serialization_dir=self.TEST_DIR,
callbacks=self.default_callbacks(batch_size=4),
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 = CallbackTrainer(self.model,
self.optimizer,
num_epochs=2,
serialization_dir=self.TEST_DIR,
callbacks=self.default_callbacks(),
model_save_interval=0.0001)
restore_trainer.handler.fire_event(Events.RESTORE_CHECKPOINT)
assert restore_trainer.epoch_number == 2
# One batch per epoch.
assert restore_trainer.batch_num_total == 2
def test_trainer_raises_on_model_with_no_loss_key(self):
class FakeModel(Model):
def forward(self, **kwargs): # pylint: disable=arguments-differ,unused-argument
return {}
with pytest.raises(RuntimeError):
trainer = CallbackTrainer(FakeModel(None), self.optimizer,
callbacks=self.default_callbacks(),
num_epochs=2, serialization_dir=self.TEST_DIR)
trainer.train()
def test_should_stop_early_with_invalid_patience(self):
for patience in [0, -1, -2, 1.5, 'None']:
with pytest.raises(ConfigurationError):
CallbackTrainer(self.model,
training_data=self.instances,
iterator=self.iterator,
optimizer=self.optimizer,
num_epochs=100,
callbacks=self.default_callbacks(patience=patience, validation_metric="+test"))
def test_trainer_can_run_ema_from_params(self):
uma_params = Params({"moving_average": {"decay": 0.9999}})
callbacks = self.default_callbacks() + [UpdateMovingAverage.from_params(uma_params, self.model)]
trainer = CallbackTrainer(model=self.model,
training_data=self.instances,
iterator=self.iterator,
optimizer=self.optimizer,
num_epochs=2,
callbacks=callbacks)
trainer.train()
def test_trainer_can_run_cuda(self):
self.model.cuda()
trainer = CallbackTrainer(self.model,
training_data=self.instances,
iterator=self.iterator,
optimizer=self.optimizer,
num_epochs=2,
callbacks=self.default_callbacks(),
cuda_device=0)
trainer.train()
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)
callbacks = self.default_callbacks() + [UpdateLearningRate(lr_scheduler)]
trainer = CallbackTrainer(model=self.model,
optimizer=self.optimizer,
callbacks=callbacks,
num_epochs=2)
trainer.train()
def test_trainer_can_run(self):
trainer = CallbackTrainer(
model=self.model,
training_data=self.instances,
iterator=self.iterator,
optimizer=self.optimizer,
callbacks=self.default_callbacks(serialization_dir=None),
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)
assert "peak_cpu_memory_MB" in metrics
# Making sure that both increasing and decreasing validation metrics work.
trainer = CallbackTrainer(
model=self.model,
training_data=self.instances,
iterator=self.iterator,
optimizer=self.optimizer,
callbacks=self.default_callbacks(validation_metric="+loss",
serialization_dir=None),
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)
assert "peak_cpu_memory_MB" in metrics
assert isinstance(metrics["peak_cpu_memory_MB"], float)
assert metrics["peak_cpu_memory_MB"] > 0
def test_trainer_can_run_exponential_moving_average(self):
moving_average = ExponentialMovingAverage(self.model.named_parameters(), decay=0.9999)
callbacks = self.default_callbacks() + [UpdateMovingAverage(moving_average)]
trainer = CallbackTrainer(
model=self.model,
training_data=self.instances,
iterator=self.iterator,
optimizer=self.optimizer,
num_epochs=2,
callbacks=callbacks,
)
trainer.train()
def test_trainer_can_log_learning_rates_tensorboard(self):
callbacks = [cb for cb in self.default_callbacks() if not isinstance(cb, LogToTensorboard)]
# The lambda: None is unfortunate, but it will get replaced by the callback.
tensorboard = TensorboardWriter(lambda: None, should_log_learning_rate=True, summary_interval=2)
callbacks.append(LogToTensorboard(tensorboard))
trainer = CallbackTrainer(self.model, self.optimizer,
num_epochs=2,
serialization_dir=self.TEST_DIR,
callbacks=callbacks)
trainer.train()
def test_trainer_respects_num_serialized_models_to_keep(self):
trainer = CallbackTrainer(self.model, self.optimizer,
num_epochs=5,
serialization_dir=self.TEST_DIR,
callbacks=self.default_callbacks(max_checkpoints=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]
