500 lines
16 KiB
Python
500 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 pickle
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from argparse import ArgumentParser
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from typing import Any, Dict
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from unittest import mock
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from unittest.mock import PropertyMock
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import pytest
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import torch
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from pytorch_lightning import LightningDataModule, Trainer
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from pytorch_lightning.callbacks import ModelCheckpoint
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from pytorch_lightning.trainer.states import TrainerState
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from pytorch_lightning.utilities.model_helpers import is_overridden
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from tests.helpers import BoringDataModule, BoringModel
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from tests.helpers.datamodules import ClassifDataModule
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from tests.helpers.runif import RunIf
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from tests.helpers.simple_models import ClassificationModel
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from tests.helpers.utils import reset_seed
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@mock.patch("pytorch_lightning.trainer.trainer.Trainer.node_rank", new_callable=PropertyMock)
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@mock.patch("pytorch_lightning.trainer.trainer.Trainer.local_rank", new_callable=PropertyMock)
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def test_can_prepare_data(local_rank, node_rank):
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dm = BoringDataModule()
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trainer = Trainer()
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trainer.datamodule = dm
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# 1 no DM
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# prepare_data_per_node = True
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# local rank = 0 (True)
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trainer.prepare_data_per_node = True
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local_rank.return_value = 0
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assert trainer.local_rank == 0
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assert trainer.data_connector.can_prepare_data()
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# local rank = 1 (False)
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local_rank.return_value = 1
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assert trainer.local_rank == 1
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assert not trainer.data_connector.can_prepare_data()
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# prepare_data_per_node = False (prepare across all nodes)
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# global rank = 0 (True)
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trainer.prepare_data_per_node = False
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node_rank.return_value = 0
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local_rank.return_value = 0
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assert trainer.data_connector.can_prepare_data()
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# global rank = 1 (False)
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node_rank.return_value = 1
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local_rank.return_value = 0
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assert not trainer.data_connector.can_prepare_data()
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node_rank.return_value = 0
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local_rank.return_value = 1
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assert not trainer.data_connector.can_prepare_data()
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# 2 dm
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# prepar per node = True
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# local rank = 0 (True)
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trainer.prepare_data_per_node = True
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local_rank.return_value = 0
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# is_overridden prepare data = True
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# has been called
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# False
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dm._has_prepared_data = True
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assert not trainer.data_connector.can_prepare_data()
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# has not been called
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# True
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dm._has_prepared_data = False
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assert trainer.data_connector.can_prepare_data()
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# is_overridden prepare data = False
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# True
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dm.prepare_data = None
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assert trainer.data_connector.can_prepare_data()
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def test_hooks_no_recursion_error(tmpdir):
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# hooks were appended in cascade every tine a new data module was instantiated leading to a recursion error.
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# See https://github.com/PyTorchLightning/pytorch-lightning/issues/3652
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class DummyDM(LightningDataModule):
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def setup(self, *args, **kwargs):
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pass
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def prepare_data(self, *args, **kwargs):
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pass
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for i in range(1005):
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dm = DummyDM()
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dm.setup()
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dm.prepare_data()
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def test_helper_boringdatamodule(tmpdir):
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dm = BoringDataModule()
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dm.prepare_data()
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dm.setup()
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def test_helper_boringdatamodule_with_verbose_setup(tmpdir):
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dm = BoringDataModule()
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dm.prepare_data()
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dm.setup('fit')
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dm.setup('test')
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def test_data_hooks_called(tmpdir):
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dm = BoringDataModule()
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assert not dm.has_prepared_data
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assert not dm.has_setup_fit
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assert not dm.has_setup_test
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assert not dm.has_setup_validate
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assert not dm.has_setup_predict
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assert not dm.has_teardown_fit
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assert not dm.has_teardown_test
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assert not dm.has_teardown_validate
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assert not dm.has_teardown_predict
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dm.prepare_data()
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assert dm.has_prepared_data
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assert not dm.has_setup_fit
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assert not dm.has_setup_test
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assert not dm.has_setup_validate
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assert not dm.has_setup_predict
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assert not dm.has_teardown_fit
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assert not dm.has_teardown_test
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assert not dm.has_teardown_validate
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assert not dm.has_teardown_predict
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dm.setup()
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assert dm.has_prepared_data
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assert dm.has_setup_fit
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assert dm.has_setup_test
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assert dm.has_setup_validate
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assert not dm.has_setup_predict
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assert not dm.has_teardown_fit
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assert not dm.has_teardown_test
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assert not dm.has_teardown_validate
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assert not dm.has_teardown_predict
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dm.teardown()
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assert dm.has_prepared_data
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assert dm.has_setup_fit
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assert dm.has_setup_test
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assert dm.has_setup_validate
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assert not dm.has_setup_predict
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assert dm.has_teardown_fit
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assert dm.has_teardown_test
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assert dm.has_teardown_validate
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assert not dm.has_teardown_predict
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@pytest.mark.parametrize("use_kwarg", (False, True))
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def test_data_hooks_called_verbose(tmpdir, use_kwarg):
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dm = BoringDataModule()
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dm.prepare_data()
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assert not dm.has_setup_fit
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assert not dm.has_setup_test
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assert not dm.has_setup_validate
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assert not dm.has_setup_predict
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assert not dm.has_teardown_fit
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assert not dm.has_teardown_test
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assert not dm.has_teardown_validate
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assert not dm.has_teardown_predict
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dm.setup(stage='fit') if use_kwarg else dm.setup('fit')
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assert dm.has_setup_fit
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assert not dm.has_setup_validate
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assert not dm.has_setup_test
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assert not dm.has_setup_predict
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dm.setup(stage='validate') if use_kwarg else dm.setup('validate')
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assert dm.has_setup_fit
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assert dm.has_setup_validate
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assert not dm.has_setup_test
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assert not dm.has_setup_predict
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dm.setup(stage='test') if use_kwarg else dm.setup('test')
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assert dm.has_setup_fit
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assert dm.has_setup_validate
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assert dm.has_setup_test
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assert not dm.has_setup_predict
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dm.setup(stage='predict') if use_kwarg else dm.setup('predict')
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assert dm.has_setup_fit
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assert dm.has_setup_validate
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assert dm.has_setup_test
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assert dm.has_setup_predict
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dm.teardown(stage='fit') if use_kwarg else dm.teardown('fit')
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assert dm.has_teardown_fit
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assert not dm.has_teardown_validate
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assert not dm.has_teardown_test
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assert not dm.has_teardown_predict
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dm.teardown(stage='validate') if use_kwarg else dm.teardown('validate')
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assert dm.has_teardown_fit
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assert dm.has_teardown_validate
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assert not dm.has_teardown_test
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assert not dm.has_teardown_predict
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dm.teardown(stage='test') if use_kwarg else dm.teardown('test')
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assert dm.has_teardown_fit
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assert dm.has_teardown_validate
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assert dm.has_teardown_test
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assert not dm.has_teardown_predict
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dm.teardown(stage='predict') if use_kwarg else dm.teardown('predict')
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assert dm.has_teardown_fit
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assert dm.has_teardown_validate
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assert dm.has_teardown_test
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assert dm.has_teardown_predict
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def test_dm_add_argparse_args(tmpdir):
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parser = ArgumentParser()
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parser = BoringDataModule.add_argparse_args(parser)
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args = parser.parse_args(['--data_dir', str(tmpdir)])
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assert args.data_dir == str(tmpdir)
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def test_dm_init_from_argparse_args(tmpdir):
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parser = ArgumentParser()
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parser = BoringDataModule.add_argparse_args(parser)
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args = parser.parse_args(['--data_dir', str(tmpdir)])
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dm = BoringDataModule.from_argparse_args(args)
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dm.prepare_data()
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dm.setup()
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assert dm.data_dir == args.data_dir == str(tmpdir)
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def test_dm_pickle_after_init(tmpdir):
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dm = BoringDataModule()
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pickle.dumps(dm)
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def test_train_loop_only(tmpdir):
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reset_seed()
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dm = ClassifDataModule()
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model = ClassificationModel()
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model.validation_step = None
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model.validation_step_end = None
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model.validation_epoch_end = None
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model.test_step = None
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model.test_step_end = None
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model.test_epoch_end = None
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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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weights_summary=None,
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)
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# fit model
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result = trainer.fit(model, datamodule=dm)
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assert trainer.state == TrainerState.FINISHED, f"Training failed with {trainer.state}"
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assert result
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assert trainer.callback_metrics['train_loss'] < 1.0
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def test_train_val_loop_only(tmpdir):
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reset_seed()
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dm = ClassifDataModule()
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model = ClassificationModel()
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model.validation_step = None
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model.validation_step_end = None
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model.validation_epoch_end = None
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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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weights_summary=None,
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)
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# fit model
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result = trainer.fit(model, datamodule=dm)
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assert trainer.state == TrainerState.FINISHED, f"Training failed with {trainer.state}"
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assert result
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assert trainer.callback_metrics['train_loss'] < 1.0
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def test_dm_checkpoint_save(tmpdir):
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class CustomBoringModel(BoringModel):
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def validation_step(self, batch, batch_idx):
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out = super().validation_step(batch, batch_idx)
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self.log('early_stop_on', out['x'])
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return out
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class CustomBoringDataModule(BoringDataModule):
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def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
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checkpoint[self.__class__.__name__] = self.__class__.__name__
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def on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
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self.checkpoint_state = checkpoint.get(self.__class__.__name__)
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reset_seed()
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dm = CustomBoringDataModule()
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model = CustomBoringModel()
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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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limit_val_batches=1,
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weights_summary=None,
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callbacks=[ModelCheckpoint(dirpath=tmpdir, monitor='early_stop_on')],
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)
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# fit model
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trainer.fit(model, dm)
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assert trainer.state == TrainerState.FINISHED, f"Training failed with {trainer.state}"
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checkpoint_path = list(trainer.checkpoint_callback.best_k_models.keys())[0]
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checkpoint = torch.load(checkpoint_path)
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assert dm.__class__.__name__ in checkpoint
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assert checkpoint[dm.__class__.__name__] == dm.__class__.__name__
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def test_full_loop(tmpdir):
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reset_seed()
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dm = ClassifDataModule()
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model = ClassificationModel()
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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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weights_summary=None,
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deterministic=True,
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)
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# fit model
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result = trainer.fit(model, dm)
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assert trainer.state == TrainerState.FINISHED, f"Training failed with {trainer.state}"
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assert dm.trainer is not None
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assert result
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# validate
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result = trainer.validate(datamodule=dm)
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assert dm.trainer is not None
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assert result[0]['val_acc'] > 0.7
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# test
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result = trainer.test(datamodule=dm)
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assert dm.trainer is not None
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assert result[0]['test_acc'] > 0.6
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@RunIf(min_gpus=1)
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@mock.patch("pytorch_lightning.accelerators.accelerator.Accelerator.lightning_module", new_callable=PropertyMock)
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def test_dm_apply_batch_transfer_handler(get_module_mock):
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expected_device = torch.device('cuda', 0)
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class CustomBatch:
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def __init__(self, data):
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self.samples = data[0]
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self.targets = data[1]
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class CurrentTestDM(LightningDataModule):
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rank = 0
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transfer_batch_to_device_hook_rank = None
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on_before_batch_transfer_hook_rank = None
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on_after_batch_transfer_hook_rank = None
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def on_before_batch_transfer(self, batch, dataloader_idx):
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self.on_before_batch_transfer_hook_rank = self.rank
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self.rank += 1
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batch.samples += 1
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return batch
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def on_after_batch_transfer(self, batch, dataloader_idx):
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assert batch.samples.device == batch.targets.device == expected_device
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self.on_after_batch_transfer_hook_rank = self.rank
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self.rank += 1
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batch.targets *= 2
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return batch
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def transfer_batch_to_device(self, batch, device):
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self.transfer_batch_to_device_hook_rank = self.rank
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self.rank += 1
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batch.samples = batch.samples.to(device)
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batch.targets = batch.targets.to(device)
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return batch
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dm = CurrentTestDM()
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model = BoringModel()
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batch = CustomBatch((torch.zeros(5, 32), torch.ones(5, 1, dtype=torch.long)))
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trainer = Trainer(gpus=1)
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# running .fit() would require us to implement custom data loaders, we mock the model reference instead
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get_module_mock.return_value = model
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if is_overridden('transfer_batch_to_device', dm):
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model.transfer_batch_to_device = dm.transfer_batch_to_device
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model.on_before_batch_transfer = dm.on_before_batch_transfer
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model.transfer_batch_to_device = dm.transfer_batch_to_device
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model.on_after_batch_transfer = dm.on_after_batch_transfer
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batch_gpu = trainer.accelerator.batch_to_device(batch, expected_device)
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assert dm.on_before_batch_transfer_hook_rank == 0
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assert dm.transfer_batch_to_device_hook_rank == 1
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assert dm.on_after_batch_transfer_hook_rank == 2
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assert batch_gpu.samples.device == batch_gpu.targets.device == expected_device
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assert torch.allclose(batch_gpu.samples.cpu(), torch.ones(5, 32))
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assert torch.allclose(batch_gpu.targets.cpu(), torch.ones(5, 1, dtype=torch.long) * 2)
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def test_dm_reload_dataloaders_every_epoch(tmpdir):
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"""Test datamodule, where trainer argument
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reload_dataloaders_every_epoch is set to True/False"""
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class CustomBoringDataModule(BoringDataModule):
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def __init__(self):
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super().__init__()
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self._epochs_called_for = []
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def train_dataloader(self):
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assert self.trainer.current_epoch not in self._epochs_called_for
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self._epochs_called_for.append(self.trainer.current_epoch)
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return super().train_dataloader()
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dm = CustomBoringDataModule()
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model = BoringModel()
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model.validation_step = None
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model.validation_step_end = None
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model.validation_epoch_end = None
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model.test_step = None
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model.test_step_end = None
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model.test_epoch_end = None
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trainer = Trainer(
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default_root_dir=tmpdir,
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max_epochs=2,
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limit_train_batches=0.01,
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reload_dataloaders_every_epoch=True,
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)
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trainer.fit(model, dm)
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class DummyDS(torch.utils.data.Dataset):
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def __getitem__(self, index):
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return 1
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def __len__(self):
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return 100
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def test_dm_init_from_datasets(tmpdir):
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train_ds = DummyDS()
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valid_ds = DummyDS()
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test_ds = DummyDS()
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valid_dss = [DummyDS(), DummyDS()]
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test_dss = [DummyDS(), DummyDS()]
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dm = LightningDataModule.from_datasets(train_ds, batch_size=4, num_workers=0)
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assert torch.all(next(iter(dm.train_dataloader())) == torch.ones(4))
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assert dm.val_dataloader() is None
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assert dm.test_dataloader() is None
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dm = LightningDataModule.from_datasets(train_ds, valid_ds, test_ds, batch_size=4, num_workers=0)
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assert torch.all(next(iter(dm.val_dataloader())) == torch.ones(4))
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assert torch.all(next(iter(dm.test_dataloader())) == torch.ones(4))
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dm = LightningDataModule.from_datasets(train_ds, valid_dss, test_dss, batch_size=4, num_workers=0)
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assert torch.all(next(iter(dm.val_dataloader()[0])) == torch.ones(4))
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assert torch.all(next(iter(dm.val_dataloader()[1])) == torch.ones(4))
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assert torch.all(next(iter(dm.test_dataloader()[0])) == torch.ones(4))
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assert torch.all(next(iter(dm.test_dataloader()[1])) == torch.ones(4))
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