699 lines
24 KiB
Python
699 lines
24 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, Namespace
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from dataclasses import dataclass
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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 call, PropertyMock
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import pytest
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import torch
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from omegaconf import OmegaConf
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from torch.utils.data import DataLoader
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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.supporters import CombinedLoader
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from pytorch_lightning.utilities import AttributeDict
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from pytorch_lightning.utilities.model_helpers import is_overridden
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from tests.helpers import BoringDataModule, BoringModel, RandomDataset
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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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dm.random_full = None
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dm._has_prepared_data = False
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local_rank.return_value = 0
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assert trainer.local_rank == 0
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trainer.data_connector.prepare_data()
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assert dm.random_full is not None
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# local rank = 1 (False)
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dm.random_full = None
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dm._has_prepared_data = False
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local_rank.return_value = 1
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assert trainer.local_rank == 1
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trainer.data_connector.prepare_data()
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assert dm.random_full is None
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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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dm.random_full = None
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dm._has_prepared_data = False
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dm.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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trainer.data_connector.prepare_data()
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assert dm.random_full is not None
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# global rank = 1 (False)
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dm.random_full = None
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dm._has_prepared_data = False
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node_rank.return_value = 1
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local_rank.return_value = 0
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trainer.data_connector.prepare_data()
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assert dm.random_full is None
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node_rank.return_value = 0
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local_rank.return_value = 1
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trainer.data_connector.prepare_data()
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assert dm.random_full is None
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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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dm.prepare_data_per_node = True
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local_rank.return_value = 0
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with mock.patch.object(trainer.datamodule, "prepare_data") as dm_mock:
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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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trainer.data_connector.prepare_data()
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dm_mock.assert_not_called()
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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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trainer.data_connector.prepare_data()
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dm_mock.assert_called_once()
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def test_hooks_no_recursion_error():
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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():
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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():
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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():
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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(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():
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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(default_root_dir=tmpdir, max_epochs=1, enable_model_summary=False)
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# fit model
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trainer.fit(model, datamodule=dm)
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assert trainer.state.finished, f"Training failed with {trainer.state}"
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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(default_root_dir=tmpdir, max_epochs=1, enable_model_summary=False)
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# fit model
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trainer.fit(model, datamodule=dm)
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assert trainer.state.finished, f"Training failed with {trainer.state}"
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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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enable_model_summary=False,
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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.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(default_root_dir=tmpdir, max_epochs=1, enable_model_summary=False, deterministic=True)
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# fit model
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trainer.fit(model, dm)
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assert trainer.state.finished, f"Training failed with {trainer.state}"
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assert dm.trainer is not None
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# validate
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result = trainer.validate(model, 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(model, 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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assert dataloader_idx == 0
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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 dataloader_idx == 0
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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, dataloader_idx):
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assert dataloader_idx == 0
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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_n_epochs(tmpdir):
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"""Test datamodule, where trainer argument reload_dataloaders_every_n_epochs is set to a non negative
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integer."""
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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(default_root_dir=tmpdir, max_epochs=3, limit_train_batches=2, reload_dataloaders_every_n_epochs=2)
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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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class DummyIDS(torch.utils.data.IterableDataset):
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def __iter__(self):
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yield 1
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@pytest.mark.parametrize("iterable", (False, True))
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def test_dm_init_from_datasets_dataloaders(iterable):
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ds = DummyIDS if iterable else DummyDS
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train_ds = ds()
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dm = LightningDataModule.from_datasets(train_ds, batch_size=4, num_workers=0)
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with mock.patch("pytorch_lightning.core.datamodule.DataLoader") as dl_mock:
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dm.train_dataloader()
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dl_mock.assert_called_once_with(train_ds, batch_size=4, shuffle=not iterable, num_workers=0, pin_memory=True)
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with pytest.raises(NotImplementedError):
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_ = dm.val_dataloader()
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with pytest.raises(NotImplementedError):
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_ = dm.test_dataloader()
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train_ds_sequence = [ds(), ds()]
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dm = LightningDataModule.from_datasets(train_ds_sequence, batch_size=4, num_workers=0)
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with mock.patch("pytorch_lightning.core.datamodule.DataLoader") as dl_mock:
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dm.train_dataloader()
|
|
dl_mock.assert_has_calls(
|
|
[
|
|
call(train_ds_sequence[0], batch_size=4, shuffle=not iterable, num_workers=0, pin_memory=True),
|
|
call(train_ds_sequence[1], batch_size=4, shuffle=not iterable, num_workers=0, pin_memory=True),
|
|
]
|
|
)
|
|
with pytest.raises(NotImplementedError):
|
|
_ = dm.val_dataloader()
|
|
with pytest.raises(NotImplementedError):
|
|
_ = dm.test_dataloader()
|
|
|
|
valid_ds = ds()
|
|
test_ds = ds()
|
|
dm = LightningDataModule.from_datasets(val_dataset=valid_ds, test_dataset=test_ds, batch_size=2, num_workers=0)
|
|
with mock.patch("pytorch_lightning.core.datamodule.DataLoader") as dl_mock:
|
|
dm.val_dataloader()
|
|
dl_mock.assert_called_with(valid_ds, batch_size=2, shuffle=False, num_workers=0, pin_memory=True)
|
|
dm.test_dataloader()
|
|
dl_mock.assert_called_with(test_ds, batch_size=2, shuffle=False, num_workers=0, pin_memory=True)
|
|
with pytest.raises(NotImplementedError):
|
|
_ = dm.train_dataloader()
|
|
|
|
valid_dss = [ds(), ds()]
|
|
test_dss = [ds(), ds()]
|
|
dm = LightningDataModule.from_datasets(train_ds, valid_dss, test_dss, batch_size=4, num_workers=0)
|
|
with mock.patch("pytorch_lightning.core.datamodule.DataLoader") as dl_mock:
|
|
dm.val_dataloader()
|
|
dm.test_dataloader()
|
|
dl_mock.assert_has_calls(
|
|
[
|
|
call(valid_dss[0], batch_size=4, shuffle=False, num_workers=0, pin_memory=True),
|
|
call(valid_dss[1], batch_size=4, shuffle=False, num_workers=0, pin_memory=True),
|
|
call(test_dss[0], batch_size=4, shuffle=False, num_workers=0, pin_memory=True),
|
|
call(test_dss[1], batch_size=4, shuffle=False, num_workers=0, pin_memory=True),
|
|
]
|
|
)
|
|
|
|
|
|
# all args
|
|
class DataModuleWithHparams_0(LightningDataModule):
|
|
def __init__(self, arg0, arg1, kwarg0=None):
|
|
super().__init__()
|
|
self.save_hyperparameters()
|
|
|
|
|
|
# single arg
|
|
class DataModuleWithHparams_1(LightningDataModule):
|
|
def __init__(self, arg0, *args, **kwargs):
|
|
super().__init__()
|
|
self.save_hyperparameters(arg0)
|
|
|
|
|
|
def test_hyperparameters_saving():
|
|
data = DataModuleWithHparams_0(10, "foo", kwarg0="bar")
|
|
assert data.hparams == AttributeDict({"arg0": 10, "arg1": "foo", "kwarg0": "bar"})
|
|
|
|
data = DataModuleWithHparams_1(Namespace(**{"hello": "world"}), "foo", kwarg0="bar")
|
|
assert data.hparams == AttributeDict({"hello": "world"})
|
|
|
|
data = DataModuleWithHparams_1({"hello": "world"}, "foo", kwarg0="bar")
|
|
assert data.hparams == AttributeDict({"hello": "world"})
|
|
|
|
data = DataModuleWithHparams_1(OmegaConf.create({"hello": "world"}), "foo", kwarg0="bar")
|
|
assert data.hparams == OmegaConf.create({"hello": "world"})
|
|
|
|
|
|
def test_define_as_dataclass():
|
|
# makes sure that no functionality is broken and the user can still manually make
|
|
# super().__init__ call with parameters
|
|
# also tests all the dataclass features that can be enabled without breaking anything
|
|
@dataclass(init=True, repr=True, eq=True, order=True, unsafe_hash=True, frozen=False)
|
|
class BoringDataModule1(LightningDataModule):
|
|
batch_size: int
|
|
dims: int = 2
|
|
|
|
def train_dataloader(self):
|
|
return DataLoader(torch.randn(self.batch_size * 2, 10), batch_size=self.batch_size)
|
|
|
|
# asserts for the different dunder methods added by dataclass, when __init__ is implemented, i.e.
|
|
# __repr__, __eq__, __lt__, __le__, etc.
|
|
assert BoringDataModule1(batch_size=64).dims == 2
|
|
assert BoringDataModule1(batch_size=32)
|
|
assert len(BoringDataModule1(batch_size=32)) == 2
|
|
assert hasattr(BoringDataModule1, "__repr__")
|
|
assert BoringDataModule1(batch_size=32) == BoringDataModule1(batch_size=32)
|
|
|
|
# asserts inherent calling of super().__init__ in case user doesn't make the call
|
|
@dataclass
|
|
class BoringDataModule2(LightningDataModule):
|
|
batch_size: int
|
|
|
|
# asserts for the different dunder methods added by dataclass, when super class is inherently initialized, i.e.
|
|
# __init__, __repr__, __eq__, __lt__, __le__, etc.
|
|
assert BoringDataModule2(batch_size=32) is not None
|
|
assert BoringDataModule2(batch_size=32).batch_size == 32
|
|
assert len(BoringDataModule2(batch_size=32)) == 0
|
|
assert hasattr(BoringDataModule2, "__repr__")
|
|
assert BoringDataModule2(batch_size=32).prepare_data() is None
|
|
assert BoringDataModule2(batch_size=32) == BoringDataModule2(batch_size=32)
|
|
|
|
# checking for all the different multilevel inhertiance scenarios, for init call on LightningDataModule
|
|
@dataclass
|
|
class BoringModuleBase1(LightningDataModule):
|
|
num_features: int
|
|
|
|
class BoringModuleBase2(LightningDataModule):
|
|
def __init__(self, num_features: int):
|
|
self.num_features = num_features
|
|
|
|
@dataclass
|
|
class BoringModuleDerived1(BoringModuleBase1):
|
|
...
|
|
|
|
class BoringModuleDerived2(BoringModuleBase1):
|
|
def __init__(self):
|
|
...
|
|
|
|
@dataclass
|
|
class BoringModuleDerived3(BoringModuleBase2):
|
|
...
|
|
|
|
class BoringModuleDerived4(BoringModuleBase2):
|
|
def __init__(self):
|
|
...
|
|
|
|
assert hasattr(BoringModuleDerived1(num_features=2), "_has_prepared_data")
|
|
assert hasattr(BoringModuleDerived2(), "_has_prepared_data")
|
|
assert hasattr(BoringModuleDerived3(), "_has_prepared_data")
|
|
assert hasattr(BoringModuleDerived4(), "_has_prepared_data")
|
|
|
|
|
|
def test_inconsistent_prepare_data_per_node(tmpdir):
|
|
with pytest.raises(MisconfigurationException, match="Inconsistent settings found for `prepare_data_per_node`."):
|
|
model = BoringModel()
|
|
dm = BoringDataModule()
|
|
trainer = Trainer(prepare_data_per_node=False)
|
|
trainer.model = model
|
|
trainer.datamodule = dm
|
|
trainer.data_connector.prepare_data()
|
|
|
|
|
|
DATALOADER = DataLoader(RandomDataset(1, 32))
|
|
|
|
|
|
@pytest.mark.parametrize("method_name", ["train_dataloader", "val_dataloader", "test_dataloader", "predict_dataloader"])
|
|
@pytest.mark.parametrize(
|
|
["dataloader", "expected"],
|
|
[
|
|
[DATALOADER, 32],
|
|
[[DATALOADER, DATALOADER], 64],
|
|
[[[DATALOADER], [DATALOADER, DATALOADER]], 96],
|
|
[[{"foo": DATALOADER}, {"foo": DATALOADER, "bar": DATALOADER}], 96],
|
|
[{"foo": DATALOADER, "bar": DATALOADER}, 64],
|
|
[{"foo": {"foo": DATALOADER}, "bar": {"foo": DATALOADER, "bar": DATALOADER}}, 96],
|
|
[{"foo": [DATALOADER], "bar": [DATALOADER, DATALOADER]}, 96],
|
|
[CombinedLoader({"foo": DATALOADER, "bar": DATALOADER}), 64],
|
|
],
|
|
)
|
|
def test_len_different_types(method_name, dataloader, expected):
|
|
dm = LightningDataModule()
|
|
setattr(dm, method_name, lambda: dataloader)
|
|
assert len(dm) == expected
|
|
|
|
|
|
@pytest.mark.parametrize("method_name", ["train_dataloader", "val_dataloader", "test_dataloader", "predict_dataloader"])
|
|
def test_len_dataloader_no_len(method_name):
|
|
class CustomNotImplementedErrorDataloader(DataLoader):
|
|
def __len__(self):
|
|
raise NotImplementedError
|
|
|
|
dataloader = CustomNotImplementedErrorDataloader(RandomDataset(1, 32))
|
|
dm = LightningDataModule()
|
|
setattr(dm, method_name, lambda: dataloader)
|
|
with pytest.warns(UserWarning, match=f"The number of batches for a dataloader in `{method_name}` is counted as 0"):
|
|
assert len(dm) == 0
|
|
|
|
|
|
def test_len_all_dataloader_methods_implemented():
|
|
class BoringDataModule(LightningDataModule):
|
|
def __init__(self, dataloader):
|
|
super().__init__()
|
|
self.dataloader = dataloader
|
|
|
|
def train_dataloader(self):
|
|
return {"foo": self.dataloader, "bar": self.dataloader}
|
|
|
|
def val_dataloader(self):
|
|
return self.dataloader
|
|
|
|
def test_dataloader(self):
|
|
return [self.dataloader]
|
|
|
|
def predict_dataloader(self):
|
|
return [self.dataloader, self.dataloader]
|
|
|
|
dm = BoringDataModule(DATALOADER)
|
|
|
|
# 6 dataloaders each producing 32 batches: 6 * 32 = 192
|
|
assert len(dm) == 192
|
|
|
|
|
|
def test_len_no_dataloader_methods_implemented():
|
|
dm = LightningDataModule()
|
|
with pytest.warns(UserWarning, match="You datamodule does not have any valid dataloader"):
|
|
assert len(dm) == 0
|