485 lines
16 KiB
Python
485 lines
16 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import sys
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from typing import Optional, Union
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from unittest import mock
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from unittest.mock import ANY, call, Mock
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import pytest
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import torch
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from pytorch_lightning import Trainer
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from pytorch_lightning.callbacks import ModelCheckpoint, ProgressBar, ProgressBarBase
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from pytorch_lightning.callbacks.progress import tqdm
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from tests.helpers import BoringModel
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@pytest.mark.parametrize(
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'callbacks,refresh_rate', [
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([], None),
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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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)
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def test_progress_bar_on(tmpdir, callbacks: list, refresh_rate: Optional[int]):
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"""Test different ways the progress bar can be turned on."""
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trainer = Trainer(
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default_root_dir=tmpdir,
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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_batches=5,
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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(
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'callbacks,refresh_rate', [
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([], 0),
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([], False),
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([ModelCheckpoint(dirpath='../trainer')], 0),
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]
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)
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def test_progress_bar_off(tmpdir, callbacks: list, refresh_rate: Union[bool, int]):
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"""Test different ways the progress bar can be turned off."""
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trainer = Trainer(
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default_root_dir=tmpdir,
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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(dirpath='../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(tmpdir):
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"""Test that the progress finishes with the correct total steps processed."""
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model = BoringModel()
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trainer = Trainer(
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default_root_dir=tmpdir,
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progress_bar_refresh_rate=1,
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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.validate(model)
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assert bar.val_progress_bar.total == m
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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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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(tmpdir):
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model = BoringModel()
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trainer = Trainer(
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default_root_dir=tmpdir,
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fast_dev_run=True,
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)
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trainer.fit(model)
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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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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.validate(model)
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# the validation progress bar should display 1 batch
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assert 1 == progress_bar.val_batch_idx
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assert 1 == progress_bar.val_progress_bar.total
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assert 1 == progress_bar.val_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(tmpdir, refresh_rate: int):
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"""Test that the three progress bars get correctly updated when using different refresh rates."""
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model = BoringModel()
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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_train_batch_start(self, trainer, pl_module, batch, batch_idx, dataloader_idx):
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super().on_train_batch_start(trainer, pl_module, batch, batch_idx, dataloader_idx)
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assert self.train_batch_idx == trainer.train_loop.batch_idx
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def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
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super().on_train_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx)
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assert self.train_batch_idx == trainer.train_loop.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, outputs, batch, batch_idx, dataloader_idx):
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super().on_validation_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx)
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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, outputs, batch, batch_idx, dataloader_idx):
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super().on_test_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx)
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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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default_root_dir=tmpdir,
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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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limit_train_batches=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
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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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assert progress_bar.test_batches_seen == 0
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trainer.validate(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 == 4 * progress_bar.total_val_batches + trainer.num_sanity_val_steps
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assert progress_bar.test_batches_seen == 0
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trainer.test(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 == 4 * progress_bar.total_val_batches + trainer.num_sanity_val_steps
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assert progress_bar.test_batches_seen == progress_bar.total_test_batches
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@pytest.mark.parametrize('limit_val_batches', (0, 5))
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def test_num_sanity_val_steps_progress_bar(tmpdir, limit_val_batches: int):
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"""
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Test val_progress_bar total with 'num_sanity_val_steps' Trainer argument.
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"""
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class CurrentProgressBar(ProgressBar):
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val_pbar_total = 0
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sanity_pbar_total = 0
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def on_sanity_check_end(self, *args):
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self.sanity_pbar_total = self.val_progress_bar.total
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super().on_sanity_check_end(*args)
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def on_validation_epoch_end(self, *args):
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self.val_pbar_total = self.val_progress_bar.total
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super().on_validation_epoch_end(*args)
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model = BoringModel()
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progress_bar = CurrentProgressBar()
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num_sanity_val_steps = 2
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trainer = Trainer(
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default_root_dir=tmpdir,
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max_epochs=1,
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num_sanity_val_steps=num_sanity_val_steps,
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limit_train_batches=1,
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limit_val_batches=limit_val_batches,
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callbacks=[progress_bar],
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logger=False,
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checkpoint_callback=False,
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)
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trainer.fit(model)
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assert progress_bar.sanity_pbar_total == min(num_sanity_val_steps, limit_val_batches)
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assert progress_bar.val_pbar_total == limit_val_batches
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def test_progress_bar_default_value(tmpdir):
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""" Test that a value of None defaults to refresh rate 1. """
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trainer = Trainer(default_root_dir=tmpdir)
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assert trainer.progress_bar_callback.refresh_rate == 1
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trainer = Trainer(default_root_dir=tmpdir, progress_bar_refresh_rate=None)
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assert trainer.progress_bar_callback.refresh_rate == 1
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@mock.patch.dict(os.environ, {'COLAB_GPU': '1'})
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def test_progress_bar_value_on_colab(tmpdir):
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""" Test that Trainer will override the default in Google COLAB. """
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trainer = Trainer(default_root_dir=tmpdir)
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assert trainer.progress_bar_callback.refresh_rate == 20
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trainer = Trainer(default_root_dir=tmpdir, progress_bar_refresh_rate=None)
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assert trainer.progress_bar_callback.refresh_rate == 20
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trainer = Trainer(default_root_dir=tmpdir, progress_bar_refresh_rate=19)
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assert trainer.progress_bar_callback.refresh_rate == 19
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class MockedUpdateProgressBars(ProgressBar):
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""" Mocks the update method once bars get initializied. """
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def _mock_bar_update(self, bar):
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bar.update = Mock(wraps=bar.update)
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return bar
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def init_train_tqdm(self):
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bar = super().init_train_tqdm()
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return self._mock_bar_update(bar)
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def init_validation_tqdm(self):
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bar = super().init_validation_tqdm()
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return self._mock_bar_update(bar)
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def init_test_tqdm(self):
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bar = super().init_test_tqdm()
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return self._mock_bar_update(bar)
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@pytest.mark.parametrize(
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"train_batches,val_batches,refresh_rate,train_deltas,val_deltas", [
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[2, 3, 1, [1, 1, 1, 1, 1], [1, 1, 1]],
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[0, 0, 3, [], []],
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[1, 0, 3, [1], []],
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[1, 1, 3, [2], [1]],
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[5, 0, 3, [3, 2], []],
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[5, 2, 3, [3, 3, 1], [2]],
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[5, 2, 6, [6, 1], [2]],
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]
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)
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def test_main_progress_bar_update_amount(
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tmpdir, train_batches: int, val_batches: int, refresh_rate: int, train_deltas: list, val_deltas: list
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):
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"""
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Test that the main progress updates with the correct amount together with the val progress. At the end of
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the epoch, the progress must not overshoot if the number of steps is not divisible by the refresh rate.
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"""
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model = BoringModel()
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progress_bar = MockedUpdateProgressBars(refresh_rate=refresh_rate)
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trainer = Trainer(
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default_root_dir=tmpdir,
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max_epochs=1,
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limit_train_batches=train_batches,
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limit_val_batches=val_batches,
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callbacks=[progress_bar],
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logger=False,
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checkpoint_callback=False,
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)
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trainer.fit(model)
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progress_bar.main_progress_bar.update.assert_has_calls([call(delta) for delta in train_deltas])
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if val_batches > 0:
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progress_bar.val_progress_bar.update.assert_has_calls([call(delta) for delta in val_deltas])
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@pytest.mark.parametrize("test_batches,refresh_rate,test_deltas", [
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[1, 3, [1]],
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[3, 1, [1, 1, 1]],
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[5, 3, [3, 2]],
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])
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def test_test_progress_bar_update_amount(tmpdir, test_batches: int, refresh_rate: int, test_deltas: list):
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"""
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Test that test progress updates with the correct amount.
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"""
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model = BoringModel()
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progress_bar = MockedUpdateProgressBars(refresh_rate=refresh_rate)
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trainer = Trainer(
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default_root_dir=tmpdir,
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max_epochs=1,
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limit_test_batches=test_batches,
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callbacks=[progress_bar],
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logger=False,
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checkpoint_callback=False,
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)
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trainer.test(model)
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progress_bar.test_progress_bar.update.assert_has_calls([call(delta) for delta in test_deltas])
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def test_tensor_to_float_conversion(tmpdir):
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"""Check tensor gets converted to float"""
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class TestModel(BoringModel):
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def training_step(self, batch, batch_idx):
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self.log('foo', torch.tensor(0.123), prog_bar=True)
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self.log('bar', {"baz": torch.tensor([1])}, prog_bar=True)
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return super().training_step(batch, batch_idx)
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trainer = Trainer(
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default_root_dir=tmpdir,
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max_epochs=1,
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limit_train_batches=2,
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logger=False,
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checkpoint_callback=False,
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)
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trainer.fit(TestModel())
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pbar = trainer.progress_bar_callback.main_progress_bar
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actual = str(pbar.postfix)
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assert actual.endswith("foo=0.123, bar={'baz': tensor([1])}")
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@pytest.mark.parametrize(
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"input_num, expected", [
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[1, '1'],
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[1.0, '1.000'],
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[0.1, '0.100'],
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[1e-3, '0.001'],
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[1e-5, '1e-5'],
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['1.0', '1.000'],
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['10000', '10000'],
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['abc', 'abc'],
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]
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)
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def test_tqdm_format_num(input_num: Union[str, int, float], expected: str):
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""" Check that the specialized tqdm.format_num appends 0 to floats and strings """
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assert tqdm.format_num(input_num) == expected
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class PrintModel(BoringModel):
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def training_step(self, *args, **kwargs):
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self.print("training_step", end="")
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return super().training_step(*args, **kwargs)
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def validation_step(self, *args, **kwargs):
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self.print("validation_step", file=sys.stderr)
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return super().validation_step(*args, **kwargs)
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def test_step(self, *args, **kwargs):
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self.print("test_step")
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return super().test_step(*args, **kwargs)
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@mock.patch("pytorch_lightning.callbacks.progress.tqdm.write")
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def test_progress_bar_print(tqdm_write, tmpdir):
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""" Test that printing in the LightningModule redirects arguments to the progress bar. """
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model = PrintModel()
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bar = ProgressBar()
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trainer = Trainer(
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default_root_dir=tmpdir,
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num_sanity_val_steps=0,
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limit_train_batches=1,
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limit_val_batches=1,
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limit_test_batches=1,
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max_steps=1,
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callbacks=[bar],
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)
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trainer.fit(model)
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trainer.test(model)
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assert tqdm_write.call_count == 3
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assert tqdm_write.call_args_list == [
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call("training_step", end="", file=None, nolock=False),
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call("validation_step", end=os.linesep, file=sys.stderr, nolock=False),
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call("test_step", end=os.linesep, file=None, nolock=False),
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]
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@mock.patch('builtins.print')
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@mock.patch("pytorch_lightning.callbacks.progress.tqdm.write")
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def test_progress_bar_print_disabled(tqdm_write, mock_print, tmpdir):
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""" Test that printing in LightningModule goes through built-in print function when progress bar is disabled. """
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model = PrintModel()
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bar = ProgressBar()
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trainer = Trainer(
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default_root_dir=tmpdir,
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num_sanity_val_steps=0,
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limit_train_batches=1,
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limit_val_batches=1,
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limit_test_batches=1,
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max_steps=1,
|
|
callbacks=[bar],
|
|
)
|
|
bar.disable()
|
|
trainer.fit(model)
|
|
trainer.test(model)
|
|
|
|
mock_print.assert_has_calls([
|
|
call("training_step", end=""),
|
|
call("validation_step", file=ANY),
|
|
call("test_step"),
|
|
])
|
|
tqdm_write.assert_not_called()
|