285 lines
10 KiB
Python
285 lines
10 KiB
Python
from pathlib import Path
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import pytest
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import tests.base.utils as tutils
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from pytorch_lightning import Callback
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from pytorch_lightning import Trainer, LightningModule
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from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
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from pytorch_lightning.loggers import TensorBoardLogger
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from tests.base import EvalModelTemplate
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def test_trainer_callback_system(tmpdir):
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"""Test the callback system."""
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hparams = EvalModelTemplate.get_default_hparams()
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model = EvalModelTemplate(**hparams)
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def _check_args(trainer, pl_module):
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assert isinstance(trainer, Trainer)
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assert isinstance(pl_module, LightningModule)
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class TestCallback(Callback):
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def __init__(self):
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super().__init__()
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self.on_init_start_called = False
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self.on_init_end_called = False
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self.on_sanity_check_start_called = False
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self.on_sanity_check_end_called = False
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self.on_epoch_start_called = False
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self.on_epoch_end_called = False
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self.on_batch_start_called = False
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self.on_batch_end_called = False
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self.on_validation_batch_start_called = False
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self.on_validation_batch_end_called = False
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self.on_test_batch_start_called = False
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self.on_test_batch_end_called = False
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self.on_train_start_called = False
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self.on_train_end_called = False
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self.on_validation_start_called = False
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self.on_validation_end_called = False
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self.on_test_start_called = False
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self.on_test_end_called = False
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def on_init_start(self, trainer):
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assert isinstance(trainer, Trainer)
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self.on_init_start_called = True
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def on_init_end(self, trainer):
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assert isinstance(trainer, Trainer)
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self.on_init_end_called = True
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def on_sanity_check_start(self, trainer, pl_module):
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_check_args(trainer, pl_module)
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self.on_sanity_check_start_called = True
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def on_sanity_check_end(self, trainer, pl_module):
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_check_args(trainer, pl_module)
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self.on_sanity_check_end_called = True
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def on_epoch_start(self, trainer, pl_module):
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_check_args(trainer, pl_module)
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self.on_epoch_start_called = True
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def on_epoch_end(self, trainer, pl_module):
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_check_args(trainer, pl_module)
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self.on_epoch_end_called = True
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def on_batch_start(self, trainer, pl_module):
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_check_args(trainer, pl_module)
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self.on_batch_start_called = True
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def on_batch_end(self, trainer, pl_module):
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_check_args(trainer, pl_module)
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self.on_batch_end_called = True
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def on_validation_batch_start(self, trainer, pl_module):
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_check_args(trainer, pl_module)
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self.on_validation_batch_start_called = True
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def on_validation_batch_end(self, trainer, pl_module):
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_check_args(trainer, pl_module)
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self.on_validation_batch_end_called = True
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def on_test_batch_start(self, trainer, pl_module):
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_check_args(trainer, pl_module)
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self.on_test_batch_start_called = True
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def on_test_batch_end(self, trainer, pl_module):
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_check_args(trainer, pl_module)
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self.on_test_batch_end_called = True
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def on_train_start(self, trainer, pl_module):
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_check_args(trainer, pl_module)
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self.on_train_start_called = True
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def on_train_end(self, trainer, pl_module):
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_check_args(trainer, pl_module)
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self.on_train_end_called = True
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def on_validation_start(self, trainer, pl_module):
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_check_args(trainer, pl_module)
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self.on_validation_start_called = True
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def on_validation_end(self, trainer, pl_module):
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_check_args(trainer, pl_module)
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self.on_validation_end_called = True
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def on_test_start(self, trainer, pl_module):
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_check_args(trainer, pl_module)
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self.on_test_start_called = True
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def on_test_end(self, trainer, pl_module):
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_check_args(trainer, pl_module)
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self.on_test_end_called = True
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test_callback = TestCallback()
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trainer_options = dict(
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callbacks=[test_callback],
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max_epochs=1,
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val_percent_check=0.1,
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train_percent_check=0.2,
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progress_bar_refresh_rate=0,
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)
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assert not test_callback.on_init_start_called
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assert not test_callback.on_init_end_called
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assert not test_callback.on_sanity_check_start_called
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assert not test_callback.on_sanity_check_end_called
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assert not test_callback.on_epoch_start_called
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assert not test_callback.on_epoch_start_called
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assert not test_callback.on_batch_start_called
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assert not test_callback.on_batch_end_called
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assert not test_callback.on_validation_batch_start_called
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assert not test_callback.on_validation_batch_end_called
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assert not test_callback.on_test_batch_start_called
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assert not test_callback.on_test_batch_end_called
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assert not test_callback.on_train_start_called
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assert not test_callback.on_train_end_called
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assert not test_callback.on_validation_start_called
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assert not test_callback.on_validation_end_called
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assert not test_callback.on_test_start_called
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assert not test_callback.on_test_end_called
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# fit model
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trainer = Trainer(**trainer_options)
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assert trainer.callbacks[0] == test_callback
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assert test_callback.on_init_start_called
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assert test_callback.on_init_end_called
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assert not test_callback.on_sanity_check_start_called
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assert not test_callback.on_sanity_check_end_called
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assert not test_callback.on_epoch_start_called
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assert not test_callback.on_epoch_start_called
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assert not test_callback.on_batch_start_called
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assert not test_callback.on_batch_end_called
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assert not test_callback.on_validation_batch_start_called
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assert not test_callback.on_validation_batch_end_called
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assert not test_callback.on_test_batch_start_called
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assert not test_callback.on_test_batch_end_called
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assert not test_callback.on_train_start_called
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assert not test_callback.on_train_end_called
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assert not test_callback.on_validation_start_called
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assert not test_callback.on_validation_end_called
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assert not test_callback.on_test_start_called
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assert not test_callback.on_test_end_called
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trainer.fit(model)
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assert test_callback.on_init_start_called
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assert test_callback.on_init_end_called
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assert test_callback.on_sanity_check_start_called
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assert test_callback.on_sanity_check_end_called
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assert test_callback.on_epoch_start_called
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assert test_callback.on_epoch_start_called
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assert test_callback.on_batch_start_called
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assert test_callback.on_batch_end_called
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assert test_callback.on_validation_batch_start_called
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assert test_callback.on_validation_batch_end_called
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assert test_callback.on_train_start_called
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assert test_callback.on_train_end_called
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assert test_callback.on_validation_start_called
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assert test_callback.on_validation_end_called
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assert not test_callback.on_test_batch_start_called
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assert not test_callback.on_test_batch_end_called
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assert not test_callback.on_test_start_called
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assert not test_callback.on_test_end_called
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test_callback = TestCallback()
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trainer_options.update(callbacks=[test_callback])
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trainer = Trainer(**trainer_options)
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trainer.test(model)
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assert test_callback.on_test_batch_start_called
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assert test_callback.on_test_batch_end_called
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assert test_callback.on_test_start_called
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assert test_callback.on_test_end_called
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assert not test_callback.on_validation_start_called
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assert not test_callback.on_validation_end_called
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assert not test_callback.on_validation_batch_end_called
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assert not test_callback.on_validation_batch_start_called
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def test_early_stopping_no_val_step(tmpdir):
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"""Test that early stopping callback falls back to training metrics when no validation defined."""
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class CurrentModel(EvalModelTemplate):
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def training_step(self, *args, **kwargs):
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output = super().training_step(*args, **kwargs)
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output.update({'my_train_metric': output['loss']}) # could be anything else
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return output
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model = CurrentModel()
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model.validation_step = None
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model.val_dataloader = None
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stopping = EarlyStopping(monitor='my_train_metric', min_delta=0.1)
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trainer = Trainer(
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default_root_dir=tmpdir,
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early_stop_callback=stopping,
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overfit_pct=0.20,
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max_epochs=2,
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)
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result = trainer.fit(model)
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assert result == 1, 'training failed to complete'
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assert trainer.current_epoch < trainer.max_epochs
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def test_pickling(tmpdir):
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import pickle
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early_stopping = EarlyStopping()
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ckpt = ModelCheckpoint(tmpdir)
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early_stopping_pickled = pickle.dumps(early_stopping)
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ckpt_pickled = pickle.dumps(ckpt)
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early_stopping_loaded = pickle.loads(early_stopping_pickled)
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ckpt_loaded = pickle.loads(ckpt_pickled)
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assert vars(early_stopping) == vars(early_stopping_loaded)
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assert vars(ckpt) == vars(ckpt_loaded)
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@pytest.mark.parametrize('save_top_k', [-1, 0, 1, 2])
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def test_model_checkpoint_with_non_string_input(tmpdir, save_top_k):
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""" Test that None in checkpoint callback is valid and that chkp_path is set correctly """
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tutils.reset_seed()
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model = EvalModelTemplate()
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checkpoint = ModelCheckpoint(filepath=None, save_top_k=save_top_k)
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trainer = Trainer(default_root_dir=tmpdir,
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checkpoint_callback=checkpoint,
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overfit_pct=0.20,
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max_epochs=2
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)
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trainer.fit(model)
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# These should be different if the dirpath has be overridden
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assert trainer.ckpt_path != trainer.default_root_dir
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@pytest.mark.parametrize(
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'logger_version,expected',
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[(None, 'version_0'), (1, 'version_1'), ('awesome', 'awesome')],
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)
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def test_model_checkpoint_path(tmpdir, logger_version, expected):
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"""Test that "version_" prefix is only added when logger's version is an integer"""
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tutils.reset_seed()
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model = EvalModelTemplate()
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logger = TensorBoardLogger(str(tmpdir), version=logger_version)
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trainer = Trainer(
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default_root_dir=tmpdir,
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overfit_pct=0.2,
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max_epochs=2,
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logger=logger
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)
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trainer.fit(model)
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ckpt_version = Path(trainer.ckpt_path).parent.name
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assert ckpt_version == expected
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