def test_checkpoint_callbacks_are_last(tmpdir):
"""Test that checkpoint callbacks always get moved to the end of the list, with preserved order."""
checkpoint1 = ModelCheckpoint(tmpdir)
checkpoint2 = ModelCheckpoint(tmpdir)
model_summary = ModelSummary()
early_stopping = EarlyStopping()
lr_monitor = LearningRateMonitor()
progress_bar = ProgressBar()
# no model reference
trainer = Trainer(callbacks=[checkpoint1, progress_bar, lr_monitor, model_summary, checkpoint2])
cb_connector = CallbackConnector(trainer)
cb_connector._attach_model_callbacks()
assert trainer.callbacks == [progress_bar, lr_monitor, model_summary, checkpoint1, checkpoint2]
# no model callbacks
model = LightningModule()
model.configure_callbacks = lambda: []
trainer.model = model
cb_connector._attach_model_callbacks()
assert trainer.callbacks == [progress_bar, lr_monitor, model_summary, checkpoint1, checkpoint2]
# with model-specific callbacks that substitute ones in Trainer
model = LightningModule()
model.configure_callbacks = lambda: [checkpoint1, early_stopping, model_summary, checkpoint2]
trainer = Trainer(callbacks=[progress_bar, lr_monitor, ModelCheckpoint(tmpdir)])
trainer.model = model
cb_connector = CallbackConnector(trainer)
cb_connector._attach_model_callbacks()
assert trainer.callbacks == [progress_bar, lr_monitor, early_stopping, model_summary, checkpoint1, checkpoint2]
def assert_composition(trainer_callbacks, model_callbacks, expected):
model = LightningModule()
model.configure_callbacks = lambda: model_callbacks
trainer = Trainer(checkpoint_callback=False, progress_bar_refresh_rate=0, callbacks=trainer_callbacks)
trainer.model = model
cb_connector = CallbackConnector(trainer)
cb_connector._attach_model_callbacks()
assert trainer.callbacks == expected
def test_attach_model_callbacks_override_info(caplog):
"""Test that the logs contain the info about overriding callbacks returned by configure_callbacks."""
model = LightningModule()
model.configure_callbacks = lambda: [LearningRateMonitor(), EarlyStopping()]
trainer = Trainer(checkpoint_callback=False, callbacks=[EarlyStopping(), LearningRateMonitor(), ProgressBar()])
trainer.model = model
cb_connector = CallbackConnector(trainer)
with caplog.at_level(logging.INFO):
cb_connector._attach_model_callbacks()
assert "existing callbacks passed to Trainer: EarlyStopping, LearningRateMonitor" in caplog.text
def _attach_callbacks(trainer_callbacks, model_callbacks):
model = LightningModule()
model.configure_callbacks = lambda: model_callbacks
has_progress_bar = any(isinstance(cb, ProgressBarBase) for cb in trainer_callbacks + model_callbacks)
trainer = Trainer(
enable_checkpointing=False,
enable_progress_bar=has_progress_bar,
enable_model_summary=False,
callbacks=trainer_callbacks,
)
trainer.model = model
cb_connector = CallbackConnector(trainer)
cb_connector._attach_model_callbacks()
return trainer