def test_early_stopping_no_val_step(tmpdir):
"""Test that early stopping callback falls back to training metrics when no validation defined."""
model = ClassificationModel()
dm = ClassifDataModule()
model.validation_step = None
model.val_dataloader = None
stopping = EarlyStopping(monitor="train_loss", min_delta=0.1, patience=0, check_on_train_epoch_end=True)
trainer = Trainer(default_root_dir=tmpdir, callbacks=[stopping], overfit_batches=0.20, max_epochs=10)
trainer.fit(model, datamodule=dm)
assert trainer.state.finished, f"Training failed with {trainer.state}"
assert trainer.current_epoch < trainer.max_epochs - 1
def test_train_val_loop_only(tmpdir):
reset_seed()
dm = ClassifDataModule()
model = ClassificationModel()
model.validation_step = None
model.validation_step_end = None
model.validation_epoch_end = None
trainer = Trainer(default_root_dir=tmpdir,
max_epochs=1,
enable_model_summary=False)
# fit model
trainer.fit(model, datamodule=dm)
assert trainer.state.finished, f"Training failed with {trainer.state}"
assert trainer.callback_metrics["train_loss"] < 1.0
def test_train_val_loop_only(tmpdir):
reset_seed()
dm = ClassifDataModule()
model = ClassificationModel()
model.validation_step = None
model.validation_step_end = None
model.validation_epoch_end = None
trainer = Trainer(
default_root_dir=tmpdir,
max_epochs=1,
weights_summary=None,
)
# fit model
trainer.fit(model, datamodule=dm)
assert trainer.state == TrainerState.FINISHED, f"Training failed with {trainer.state}"
assert trainer.callback_metrics['train_loss'] < 1.0
