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test_callbacks.py
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test_callbacks.py
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import pytest
import tests.base.utils as tutils
from pytorch_lightning import Callback
from pytorch_lightning import Trainer, LightningModule
from pytorch_lightning.callbacks import EarlyStopping, ProgressBarBase, ProgressBar
from tests.base import (
LightTrainDataloader,
LightTestMixin,
LightValidationMixin,
TestModelBase
)
def test_trainer_callback_system(tmpdir):
"""Test the callback system."""
class CurrentTestModel(
LightTrainDataloader,
LightTestMixin,
LightValidationMixin,
TestModelBase,
):
pass
hparams = tutils.get_default_hparams()
model = CurrentTestModel(hparams)
def _check_args(trainer, pl_module):
assert isinstance(trainer, Trainer)
assert isinstance(pl_module, LightningModule)
class TestCallback(Callback):
def __init__(self):
super().__init__()
self.on_init_start_called = False
self.on_init_end_called = False
self.on_sanity_check_start_called = False
self.on_sanity_check_end_called = False
self.on_epoch_start_called = False
self.on_epoch_end_called = False
self.on_batch_start_called = False
self.on_batch_end_called = False
self.on_validation_batch_start_called = False
self.on_validation_batch_end_called = False
self.on_test_batch_start_called = False
self.on_test_batch_end_called = False
self.on_train_start_called = False
self.on_train_end_called = False
self.on_validation_start_called = False
self.on_validation_end_called = False
self.on_test_start_called = False
self.on_test_end_called = False
def on_init_start(self, trainer):
assert isinstance(trainer, Trainer)
self.on_init_start_called = True
def on_init_end(self, trainer):
assert isinstance(trainer, Trainer)
self.on_init_end_called = True
def on_sanity_check_start(self, trainer, pl_module):
_check_args(trainer, pl_module)
self.on_sanity_check_start_called = True
def on_sanity_check_end(self, trainer, pl_module):
_check_args(trainer, pl_module)
self.on_sanity_check_end_called = True
def on_epoch_start(self, trainer, pl_module):
_check_args(trainer, pl_module)
self.on_epoch_start_called = True
def on_epoch_end(self, trainer, pl_module):
_check_args(trainer, pl_module)
self.on_epoch_end_called = True
def on_batch_start(self, trainer, pl_module):
_check_args(trainer, pl_module)
self.on_batch_start_called = True
def on_batch_end(self, trainer, pl_module):
_check_args(trainer, pl_module)
self.on_batch_end_called = True
def on_validation_batch_start(self, trainer, pl_module):
_check_args(trainer, pl_module)
self.on_validation_batch_start_called = True
def on_validation_batch_end(self, trainer, pl_module):
_check_args(trainer, pl_module)
self.on_validation_batch_end_called = True
def on_test_batch_start(self, trainer, pl_module):
_check_args(trainer, pl_module)
self.on_test_batch_start_called = True
def on_test_batch_end(self, trainer, pl_module):
_check_args(trainer, pl_module)
self.on_test_batch_end_called = True
def on_train_start(self, trainer, pl_module):
_check_args(trainer, pl_module)
self.on_train_start_called = True
def on_train_end(self, trainer, pl_module):
_check_args(trainer, pl_module)
self.on_train_end_called = True
def on_validation_start(self, trainer, pl_module):
_check_args(trainer, pl_module)
self.on_validation_start_called = True
def on_validation_end(self, trainer, pl_module):
_check_args(trainer, pl_module)
self.on_validation_end_called = True
def on_test_start(self, trainer, pl_module):
_check_args(trainer, pl_module)
self.on_test_start_called = True
def on_test_end(self, trainer, pl_module):
_check_args(trainer, pl_module)
self.on_test_end_called = True
test_callback = TestCallback()
trainer_options = {
'callbacks': [test_callback],
'max_epochs': 1,
'val_percent_check': 0.1,
'train_percent_check': 0.2,
'progress_bar_refresh_rate': 0
}
assert not test_callback.on_init_start_called
assert not test_callback.on_init_end_called
assert not test_callback.on_sanity_check_start_called
assert not test_callback.on_sanity_check_end_called
assert not test_callback.on_epoch_start_called
assert not test_callback.on_epoch_start_called
assert not test_callback.on_batch_start_called
assert not test_callback.on_batch_end_called
assert not test_callback.on_validation_batch_start_called
assert not test_callback.on_validation_batch_end_called
assert not test_callback.on_test_batch_start_called
assert not test_callback.on_test_batch_end_called
assert not test_callback.on_train_start_called
assert not test_callback.on_train_end_called
assert not test_callback.on_validation_start_called
assert not test_callback.on_validation_end_called
assert not test_callback.on_test_start_called
assert not test_callback.on_test_end_called
# fit model
trainer = Trainer(**trainer_options)
assert trainer.callbacks[0] == test_callback
assert test_callback.on_init_start_called
assert test_callback.on_init_end_called
assert not test_callback.on_sanity_check_start_called
assert not test_callback.on_sanity_check_end_called
assert not test_callback.on_epoch_start_called
assert not test_callback.on_epoch_start_called
assert not test_callback.on_batch_start_called
assert not test_callback.on_batch_end_called
assert not test_callback.on_validation_batch_start_called
assert not test_callback.on_validation_batch_end_called
assert not test_callback.on_test_batch_start_called
assert not test_callback.on_test_batch_end_called
assert not test_callback.on_train_start_called
assert not test_callback.on_train_end_called
assert not test_callback.on_validation_start_called
assert not test_callback.on_validation_end_called
assert not test_callback.on_test_start_called
assert not test_callback.on_test_end_called
trainer.fit(model)
assert test_callback.on_init_start_called
assert test_callback.on_init_end_called
assert test_callback.on_sanity_check_start_called
assert test_callback.on_sanity_check_end_called
assert test_callback.on_epoch_start_called
assert test_callback.on_epoch_start_called
assert test_callback.on_batch_start_called
assert test_callback.on_batch_end_called
assert test_callback.on_validation_batch_start_called
assert test_callback.on_validation_batch_end_called
assert test_callback.on_train_start_called
assert test_callback.on_train_end_called
assert test_callback.on_validation_start_called
assert test_callback.on_validation_end_called
assert not test_callback.on_test_batch_start_called
assert not test_callback.on_test_batch_end_called
assert not test_callback.on_test_start_called
assert not test_callback.on_test_end_called
test_callback = TestCallback()
trainer_options['callbacks'] = [test_callback]
trainer = Trainer(**trainer_options)
trainer.test(model)
assert test_callback.on_test_batch_start_called
assert test_callback.on_test_batch_end_called
assert test_callback.on_test_start_called
assert test_callback.on_test_end_called
assert not test_callback.on_validation_start_called
assert not test_callback.on_validation_end_called
assert not test_callback.on_validation_batch_end_called
assert not test_callback.on_validation_batch_start_called
def test_early_stopping_without_val_step(tmpdir):
"""Test that early stopping callback falls back to training metrics when no validation defined."""
tutils.reset_seed()
class ModelWithoutValStep(LightTrainDataloader, TestModelBase):
def training_step(self, *args, **kwargs):
output = super().training_step(*args, **kwargs)
loss = output['loss'] # could be anything else
output.update({'my_train_metric': loss})
return output
hparams = tutils.get_default_hparams()
model = ModelWithoutValStep(hparams)
stopping = EarlyStopping(monitor='my_train_metric', min_delta=0.1)
trainer_options = dict(
default_root_dir=tmpdir,
early_stop_callback=stopping,
overfit_pct=0.20,
max_epochs=5,
)
trainer = Trainer(**trainer_options)
result = trainer.fit(model)
assert result == 1, 'training failed to complete'
assert trainer.current_epoch < trainer.max_epochs
@pytest.mark.parametrize('callback', [ProgressBar(), True, False, None])
@pytest.mark.parametrize('refresh_rate', [0, 1, 50])
def test_progress_bar_on_off(tmpdir, callback, refresh_rate):
"""Test different ways the progress bar can be turned on or off."""
trainer = Trainer(
progress_bar_callback=callback,
progress_bar_refresh_rate=refresh_rate,
)
for callback in trainer.callbacks:
if refresh_rate > 0 or callback:
assert isinstance(callback, ProgressBarBase)
else:
assert not isinstance(callback, ProgressBarBase)
def test_progress_bar_totals(tmpdir):
"""Test that the progress finishes with the correct total steps processed."""
class CurrentTestModel(
LightTrainDataloader,
LightTestMixin,
LightValidationMixin,
TestModelBase,
):
pass
hparams = tutils.get_default_hparams()
model = CurrentTestModel(hparams)
trainer = Trainer(
progress_bar_callback=True,
progress_bar_refresh_rate=1,
val_percent_check=1.0,
max_epochs=1,
)
progress_bar = trainer.progress_bar_callback
assert 0 == progress_bar.total_train_batches
assert 0 == progress_bar.total_val_batches
assert 0 == progress_bar.total_test_batches
trainer.fit(model)
assert progress_bar.total_train_batches == len(trainer.train_dataloader)
assert progress_bar.total_val_batches == progress_bar.val_progress_bar.total
assert progress_bar.total_val_batches == sum(len(loader) for loader in trainer.val_dataloaders)
assert 0 == progress_bar.total_test_batches
trainer.test(model)
assert progress_bar.total_test_batches == progress_bar.test_progress_bar.total
assert progress_bar.total_test_batches == sum(len(loader) for loader in trainer.test_dataloaders)
# @pytest.mark.parametrize('refresh_rate', [0, 1, 50])
def test_progress_bar_progress_refresh():
"""Test that the three progress bars get correctly updated when using different refresh rates."""
refresh_rate = 0
class CurrentTestModel(
LightTrainDataloader,
LightTestMixin,
LightValidationMixin,
TestModelBase,
):
pass
hparams = tutils.get_default_hparams()
model = CurrentTestModel(hparams)
class CurrentProgressBar(ProgressBar):
train_batches_seen = 0
val_batches_seen = 0
test_batches_seen = 0
def on_batch_start(self, trainer, pl_module):
super().on_batch_start(trainer, pl_module)
assert self.train_batch_idx == trainer.batch_idx
def on_batch_end(self, trainer, pl_module):
super().on_batch_end(trainer, pl_module)
assert self.train_batch_idx == trainer.batch_idx + 1
if not self.is_disabled and self.train_batch_idx % self.refresh_rate == 0:
assert self.main_progress_bar.n == self.train_batch_idx
self.train_batches_seen += 1
def on_validation_batch_end(self, trainer, pl_module):
super().on_validation_batch_end(trainer, pl_module)
if not self.is_disabled and self.val_batch_idx % self.refresh_rate == 0:
assert self.val_progress_bar.n == self.val_batch_idx
self.val_batches_seen += 1
def on_test_batch_end(self, trainer, pl_module):
super().on_test_batch_end(trainer, pl_module)
if not self.is_disabled and self.test_batch_idx % self.refresh_rate == 0:
assert self.test_progress_bar.n == self.test_batch_idx
self.test_batches_seen += 1
progress_bar = CurrentProgressBar(refresh_rate=refresh_rate)
trainer = Trainer(
progress_bar_callback=progress_bar,
progress_bar_refresh_rate=101, # should not matter if custom callback provided
train_percent_check=1.0,
num_sanity_val_steps=1,
max_epochs=3,
)
assert trainer.progress_bar_callback.refresh_rate == refresh_rate
trainer.fit(model)
assert progress_bar.train_batches_seen == 3 * progress_bar.total_train_batches
assert progress_bar.val_batches_seen == 3 * progress_bar.total_val_batches + trainer.num_sanity_val_steps
trainer.test(model)
assert progress_bar.test_batches_seen == progress_bar.total_test_batches