190 lines
6.5 KiB
Python
190 lines
6.5 KiB
Python
import pytest
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import tests.base.utils as tutils
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from pytorch_lightning import Trainer
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from pytorch_lightning.callbacks import ProgressBarBase, ProgressBar, ModelCheckpoint
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from tests.base import EvalModelTemplate
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@pytest.mark.parametrize('callbacks,refresh_rate', [
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([], 1),
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([], 2),
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([ProgressBar(refresh_rate=1)], 0),
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([ProgressBar(refresh_rate=2)], 0),
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([ProgressBar(refresh_rate=2)], 1),
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])
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def test_progress_bar_on(callbacks, refresh_rate):
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"""Test different ways the progress bar can be turned on."""
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trainer = Trainer(
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callbacks=callbacks,
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progress_bar_refresh_rate=refresh_rate,
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max_epochs=1,
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overfit_pct=0.2,
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)
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progress_bars = [c for c in trainer.callbacks if isinstance(c, ProgressBarBase)]
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# Trainer supports only a single progress bar callback at the moment
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assert len(progress_bars) == 1
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assert progress_bars[0] is trainer.progress_bar_callback
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@pytest.mark.parametrize('callbacks,refresh_rate', [
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([], 0),
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([], False),
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([ModelCheckpoint('../trainer')], 0),
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])
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def test_progress_bar_off(callbacks, refresh_rate):
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"""Test different ways the progress bar can be turned off."""
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trainer = Trainer(
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callbacks=callbacks,
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progress_bar_refresh_rate=refresh_rate,
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)
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progress_bars = [c for c in trainer.callbacks if isinstance(c, ProgressBar)]
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assert 0 == len(progress_bars)
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assert not trainer.progress_bar_callback
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def test_progress_bar_misconfiguration():
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"""Test that Trainer doesn't accept multiple progress bars."""
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callbacks = [ProgressBar(), ProgressBar(), ModelCheckpoint('../trainer')]
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with pytest.raises(MisconfigurationException, match=r'^You added multiple progress bar callbacks'):
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Trainer(callbacks=callbacks)
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def test_progress_bar_totals():
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"""Test that the progress finishes with the correct total steps processed."""
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model = EvalModelTemplate(tutils.get_default_hparams())
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trainer = Trainer(
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progress_bar_refresh_rate=1,
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val_percent_check=1.0,
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max_epochs=1,
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)
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bar = trainer.progress_bar_callback
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assert 0 == bar.total_train_batches
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assert 0 == bar.total_val_batches
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assert 0 == bar.total_test_batches
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trainer.fit(model)
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# check main progress bar total
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n = bar.total_train_batches
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m = bar.total_val_batches
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assert len(trainer.train_dataloader) == n
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assert bar.main_progress_bar.total == n + m
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# check val progress bar total
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assert sum(len(loader) for loader in trainer.val_dataloaders) == m
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assert bar.val_progress_bar.total == m
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# main progress bar should have reached the end (train batches + val batches)
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assert bar.main_progress_bar.n == n + m
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assert bar.train_batch_idx == n
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# val progress bar should have reached the end
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assert bar.val_progress_bar.n == m
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assert bar.val_batch_idx == m
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# check that the test progress bar is off
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assert 0 == bar.total_test_batches
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assert bar.test_progress_bar is None
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trainer.test(model)
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# check test progress bar total
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k = bar.total_test_batches
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assert sum(len(loader) for loader in trainer.test_dataloaders) == k
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assert bar.test_progress_bar.total == k
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# test progress bar should have reached the end
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assert bar.test_progress_bar.n == k
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assert bar.test_batch_idx == k
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def test_progress_bar_fast_dev_run():
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model = EvalModelTemplate(tutils.get_default_hparams())
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trainer = Trainer(
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fast_dev_run=True,
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)
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progress_bar = trainer.progress_bar_callback
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assert 1 == progress_bar.total_train_batches
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# total val batches are known only after val dataloaders have reloaded
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trainer.fit(model)
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assert 1 == progress_bar.total_val_batches
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assert 1 == progress_bar.train_batch_idx
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assert 1 == progress_bar.val_batch_idx
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assert 0 == progress_bar.test_batch_idx
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# the main progress bar should display 2 batches (1 train, 1 val)
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assert 2 == progress_bar.main_progress_bar.total
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assert 2 == progress_bar.main_progress_bar.n
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trainer.test(model)
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# the test progress bar should display 1 batch
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assert 1 == progress_bar.test_batch_idx
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assert 1 == progress_bar.test_progress_bar.total
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assert 1 == progress_bar.test_progress_bar.n
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@pytest.mark.parametrize('refresh_rate', [0, 1, 50])
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def test_progress_bar_progress_refresh(refresh_rate):
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"""Test that the three progress bars get correctly updated when using different refresh rates."""
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model = EvalModelTemplate(tutils.get_default_hparams())
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class CurrentProgressBar(ProgressBar):
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train_batches_seen = 0
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val_batches_seen = 0
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test_batches_seen = 0
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def on_batch_start(self, trainer, pl_module):
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super().on_batch_start(trainer, pl_module)
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assert self.train_batch_idx == trainer.batch_idx
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def on_batch_end(self, trainer, pl_module):
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super().on_batch_end(trainer, pl_module)
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assert self.train_batch_idx == trainer.batch_idx + 1
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if not self.is_disabled and self.train_batch_idx % self.refresh_rate == 0:
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assert self.main_progress_bar.n == self.train_batch_idx
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self.train_batches_seen += 1
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def on_validation_batch_end(self, trainer, pl_module):
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super().on_validation_batch_end(trainer, pl_module)
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if not self.is_disabled and self.val_batch_idx % self.refresh_rate == 0:
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assert self.val_progress_bar.n == self.val_batch_idx
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self.val_batches_seen += 1
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def on_test_batch_end(self, trainer, pl_module):
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super().on_test_batch_end(trainer, pl_module)
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if not self.is_disabled and self.test_batch_idx % self.refresh_rate == 0:
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assert self.test_progress_bar.n == self.test_batch_idx
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self.test_batches_seen += 1
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progress_bar = CurrentProgressBar(refresh_rate=refresh_rate)
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trainer = Trainer(
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callbacks=[progress_bar],
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progress_bar_refresh_rate=101, # should not matter if custom callback provided
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train_percent_check=1.0,
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num_sanity_val_steps=2,
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max_epochs=3,
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)
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assert trainer.progress_bar_callback.refresh_rate == refresh_rate != trainer.progress_bar_refresh_rate
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trainer.fit(model)
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assert progress_bar.train_batches_seen == 3 * progress_bar.total_train_batches
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assert progress_bar.val_batches_seen == 3 * progress_bar.total_val_batches + trainer.num_sanity_val_steps
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trainer.test(model)
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assert progress_bar.test_batches_seen == progress_bar.total_test_batches
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