460 lines
13 KiB
Python
460 lines
13 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 unittest.mock import MagicMock
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from typing import Optional
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import pytest
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import torch
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from torch.utils.data import DataLoader, random_split
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from pytorch_lightning import LightningDataModule, Trainer
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from pytorch_lightning.trainer.states import TrainerState
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from tests.base import EvalModelTemplate
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from tests.base.datasets import TrialMNIST
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from tests.base.datamodules import TrialMNISTDataModule
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from tests.base.develop_utils import reset_seed
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from pytorch_lightning.utilities.model_helpers import is_overridden
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from pytorch_lightning.accelerators.gpu_accelerator import GPUAccelerator
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from pytorch_lightning.callbacks import ModelCheckpoint
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def test_can_prepare_data(tmpdir):
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dm = TrialMNISTDataModule()
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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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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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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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trainer.node_rank = 0
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trainer.local_rank = 0
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assert trainer.data_connector.can_prepare_data()
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# global rank = 1 (False)
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trainer.node_rank = 1
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trainer.local_rank = 0
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assert not trainer.data_connector.can_prepare_data()
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trainer.node_rank = 0
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trainer.local_rank = 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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trainer.local_rank = 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_base_datamodule(tmpdir):
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dm = TrialMNISTDataModule()
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dm.prepare_data()
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dm.setup()
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def test_base_datamodule_with_verbose_setup(tmpdir):
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dm = TrialMNISTDataModule()
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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 = TrialMNISTDataModule()
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assert dm.has_prepared_data is False
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assert dm.has_setup_fit is False
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assert dm.has_setup_test is False
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dm.prepare_data()
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assert dm.has_prepared_data is True
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assert dm.has_setup_fit is False
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assert dm.has_setup_test is False
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dm.setup()
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assert dm.has_prepared_data is True
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assert dm.has_setup_fit is True
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assert dm.has_setup_test is True
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def test_data_hooks_called_verbose(tmpdir):
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dm = TrialMNISTDataModule()
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assert dm.has_prepared_data is False
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assert dm.has_setup_fit is False
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assert dm.has_setup_test is False
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dm.prepare_data()
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assert dm.has_prepared_data is True
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assert dm.has_setup_fit is False
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assert dm.has_setup_test is False
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dm.setup('fit')
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assert dm.has_prepared_data is True
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assert dm.has_setup_fit is True
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assert dm.has_setup_test is False
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dm.setup('test')
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assert dm.has_prepared_data is True
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assert dm.has_setup_fit is True
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assert dm.has_setup_test is True
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def test_data_hooks_called_with_stage_kwarg(tmpdir):
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dm = TrialMNISTDataModule()
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dm.prepare_data()
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assert dm.has_prepared_data is True
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dm.setup(stage='fit')
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assert dm.has_setup_fit is True
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assert dm.has_setup_test is False
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dm.setup(stage='test')
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assert dm.has_setup_fit is True
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assert dm.has_setup_test is True
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def test_dm_add_argparse_args(tmpdir):
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parser = ArgumentParser()
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parser = TrialMNISTDataModule.add_argparse_args(parser)
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args = parser.parse_args(['--data_dir', './my_data'])
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assert args.data_dir == './my_data'
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def test_dm_init_from_argparse_args(tmpdir):
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parser = ArgumentParser()
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parser = TrialMNISTDataModule.add_argparse_args(parser)
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args = parser.parse_args(['--data_dir', './my_data'])
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dm = TrialMNISTDataModule.from_argparse_args(args)
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dm.prepare_data()
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dm.setup()
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def test_dm_pickle_after_init(tmpdir):
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dm = TrialMNISTDataModule()
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pickle.dumps(dm)
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def test_train_loop_only(tmpdir):
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dm = TrialMNISTDataModule(tmpdir)
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model = EvalModelTemplate()
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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=3,
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weights_summary=None,
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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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assert trainer.logger_connector.callback_metrics['loss'] < 0.6
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def test_train_val_loop_only(tmpdir):
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reset_seed()
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dm = TrialMNISTDataModule(tmpdir)
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model = EvalModelTemplate()
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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=3,
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weights_summary=None,
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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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assert trainer.logger_connector.callback_metrics['loss'] < 0.6
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def test_dm_checkpoint_save(tmpdir):
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reset_seed()
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dm = TrialMNISTDataModule(tmpdir)
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model = EvalModelTemplate()
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trainer = Trainer(
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default_root_dir=tmpdir,
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max_epochs=3,
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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_test_loop_only(tmpdir):
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reset_seed()
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dm = TrialMNISTDataModule(tmpdir)
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model = EvalModelTemplate()
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trainer = Trainer(
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default_root_dir=tmpdir,
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max_epochs=3,
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weights_summary=None,
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)
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trainer.test(model, datamodule=dm)
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def test_full_loop(tmpdir):
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reset_seed()
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dm = TrialMNISTDataModule(tmpdir)
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model = EvalModelTemplate()
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trainer = Trainer(
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default_root_dir=tmpdir,
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max_epochs=3,
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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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trainer.fit(model, dm)
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assert trainer.state == TrainerState.FINISHED, f"Training failed with {trainer.state}"
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# test
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result = trainer.test(datamodule=dm)
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result = result[0]
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assert result['test_acc'] > 0.8
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def test_trainer_attached_to_dm(tmpdir):
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reset_seed()
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dm = TrialMNISTDataModule(tmpdir)
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model = EvalModelTemplate()
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trainer = Trainer(
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default_root_dir=tmpdir,
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max_epochs=3,
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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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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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# test
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result = trainer.test(datamodule=dm)
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result = result[0]
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assert dm.trainer is not None
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@pytest.mark.skipif(torch.cuda.device_count() < 1, reason="test requires multi-GPU machine")
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def test_full_loop_single_gpu(tmpdir):
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reset_seed()
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dm = TrialMNISTDataModule(tmpdir)
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model = EvalModelTemplate()
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trainer = Trainer(
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default_root_dir=tmpdir,
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max_epochs=3,
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weights_summary=None,
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gpus=1,
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deterministic=True,
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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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# test
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result = trainer.test(datamodule=dm)
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result = result[0]
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assert result['test_acc'] > 0.8
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@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
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def test_full_loop_dp(tmpdir):
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reset_seed()
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dm = TrialMNISTDataModule(tmpdir)
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model = EvalModelTemplate()
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trainer = Trainer(
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default_root_dir=tmpdir,
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max_epochs=3,
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weights_summary=None,
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accelerator='dp',
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gpus=2,
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deterministic=True,
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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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# test
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result = trainer.test(datamodule=dm)
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result = result[0]
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assert result['test_acc'] > 0.8
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@pytest.mark.skipif(torch.cuda.device_count() < 1, reason="test requires multi-GPU machine")
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def test_dm_transfer_batch_to_device(tmpdir):
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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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hook_called = False
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def transfer_batch_to_device(self, data, device):
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self.hook_called = True
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if isinstance(data, CustomBatch):
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data.samples = data.samples.to(device)
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data.targets = data.targets.to(device)
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else:
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data = super().transfer_batch_to_device(data, device)
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return data
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model = EvalModelTemplate()
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dm = CurrentTestDM()
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batch = CustomBatch((torch.zeros(5, 28), 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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trainer.get_model = MagicMock(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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trainer.accelerator_backend = GPUAccelerator(trainer)
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batch_gpu = trainer.accelerator_backend.batch_to_device(batch, torch.device('cuda:0'))
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expected = torch.device('cuda', 0)
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assert dm.hook_called
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assert batch_gpu.samples.device == batch_gpu.targets.device == expected
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class CustomMNISTDataModule(LightningDataModule):
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def __init__(self, data_dir: str = "./"):
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super().__init__()
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self.data_dir = data_dir
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self._epochs_called_for = []
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def prepare_data(self):
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TrialMNIST(self.data_dir, train=True, download=True)
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def setup(self, stage: Optional[str] = None):
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mnist_full = TrialMNIST(
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root=self.data_dir, train=True, num_samples=64, download=True
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)
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self.mnist_train, self.mnist_val = random_split(mnist_full, [128, 64])
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self.dims = self.mnist_train[0][0].shape
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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 DataLoader(self.mnist_train, batch_size=4)
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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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dm = CustomMNISTDataModule(tmpdir)
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model = EvalModelTemplate()
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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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