544 lines
18 KiB
Python
544 lines
18 KiB
Python
# Copyright The PyTorch Lightning team.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
import pickle
|
|
from argparse import ArgumentParser
|
|
from typing import Any, Dict
|
|
from unittest import mock
|
|
from unittest.mock import call, PropertyMock
|
|
|
|
import pytest
|
|
import torch
|
|
|
|
from pytorch_lightning import LightningDataModule, Trainer
|
|
from pytorch_lightning.callbacks import ModelCheckpoint
|
|
from pytorch_lightning.utilities import AttributeDict
|
|
from pytorch_lightning.utilities.model_helpers import is_overridden
|
|
from tests.helpers import BoringDataModule, BoringModel
|
|
from tests.helpers.datamodules import ClassifDataModule
|
|
from tests.helpers.runif import RunIf
|
|
from tests.helpers.simple_models import ClassificationModel
|
|
from tests.helpers.utils import reset_seed
|
|
|
|
|
|
@mock.patch("pytorch_lightning.trainer.trainer.Trainer.node_rank", new_callable=PropertyMock)
|
|
@mock.patch("pytorch_lightning.trainer.trainer.Trainer.local_rank", new_callable=PropertyMock)
|
|
def test_can_prepare_data(local_rank, node_rank):
|
|
|
|
model = BoringModel()
|
|
dm = BoringDataModule()
|
|
trainer = Trainer()
|
|
trainer.datamodule = dm
|
|
|
|
# 1 no DM
|
|
# prepare_data_per_node = True
|
|
# local rank = 0 (True)
|
|
trainer.prepare_data_per_node = True
|
|
|
|
dm.random_full = None
|
|
dm._has_prepared_data = False
|
|
local_rank.return_value = 0
|
|
assert trainer.local_rank == 0
|
|
assert trainer.data_connector.can_prepare_data()
|
|
|
|
trainer.data_connector.prepare_data(model)
|
|
assert dm.random_full is not None
|
|
|
|
# local rank = 1 (False)
|
|
dm.random_full = None
|
|
dm._has_prepared_data = False
|
|
local_rank.return_value = 1
|
|
assert trainer.local_rank == 1
|
|
assert not trainer.data_connector.can_prepare_data()
|
|
|
|
trainer.data_connector.prepare_data(model)
|
|
assert dm.random_full is None
|
|
|
|
# prepare_data_per_node = False (prepare across all nodes)
|
|
# global rank = 0 (True)
|
|
dm.random_full = None
|
|
dm._has_prepared_data = False
|
|
trainer.prepare_data_per_node = False
|
|
node_rank.return_value = 0
|
|
local_rank.return_value = 0
|
|
assert trainer.data_connector.can_prepare_data()
|
|
|
|
trainer.data_connector.prepare_data(model)
|
|
assert dm.random_full is not None
|
|
|
|
# global rank = 1 (False)
|
|
dm.random_full = None
|
|
dm._has_prepared_data = False
|
|
node_rank.return_value = 1
|
|
local_rank.return_value = 0
|
|
assert not trainer.data_connector.can_prepare_data()
|
|
|
|
trainer.data_connector.prepare_data(model)
|
|
assert dm.random_full is None
|
|
|
|
node_rank.return_value = 0
|
|
local_rank.return_value = 1
|
|
assert not trainer.data_connector.can_prepare_data()
|
|
|
|
trainer.data_connector.prepare_data(model)
|
|
assert dm.random_full is None
|
|
|
|
# 2 dm
|
|
# prepar per node = True
|
|
# local rank = 0 (True)
|
|
trainer.prepare_data_per_node = True
|
|
local_rank.return_value = 0
|
|
|
|
# is_overridden prepare data = True
|
|
# has been called
|
|
# False
|
|
dm._has_prepared_data = True
|
|
assert not trainer.data_connector.can_prepare_data()
|
|
|
|
# has not been called
|
|
# True
|
|
dm._has_prepared_data = False
|
|
assert trainer.data_connector.can_prepare_data()
|
|
|
|
# is_overridden prepare data = False
|
|
# True
|
|
dm.prepare_data = None
|
|
assert trainer.data_connector.can_prepare_data()
|
|
|
|
|
|
def test_hooks_no_recursion_error():
|
|
# hooks were appended in cascade every tine a new data module was instantiated leading to a recursion error.
|
|
# See https://github.com/PyTorchLightning/pytorch-lightning/issues/3652
|
|
class DummyDM(LightningDataModule):
|
|
def setup(self, *args, **kwargs):
|
|
pass
|
|
|
|
def prepare_data(self, *args, **kwargs):
|
|
pass
|
|
|
|
for i in range(1005):
|
|
dm = DummyDM()
|
|
dm.setup()
|
|
dm.prepare_data()
|
|
|
|
|
|
def test_helper_boringdatamodule():
|
|
dm = BoringDataModule()
|
|
dm.prepare_data()
|
|
dm.setup()
|
|
|
|
|
|
def test_helper_boringdatamodule_with_verbose_setup():
|
|
dm = BoringDataModule()
|
|
dm.prepare_data()
|
|
dm.setup("fit")
|
|
dm.setup("test")
|
|
|
|
|
|
def test_data_hooks_called():
|
|
dm = BoringDataModule()
|
|
assert not dm.has_prepared_data
|
|
assert not dm.has_setup_fit
|
|
assert not dm.has_setup_test
|
|
assert not dm.has_setup_validate
|
|
assert not dm.has_setup_predict
|
|
assert not dm.has_teardown_fit
|
|
assert not dm.has_teardown_test
|
|
assert not dm.has_teardown_validate
|
|
assert not dm.has_teardown_predict
|
|
|
|
dm.prepare_data()
|
|
assert dm.has_prepared_data
|
|
assert not dm.has_setup_fit
|
|
assert not dm.has_setup_test
|
|
assert not dm.has_setup_validate
|
|
assert not dm.has_setup_predict
|
|
assert not dm.has_teardown_fit
|
|
assert not dm.has_teardown_test
|
|
assert not dm.has_teardown_validate
|
|
assert not dm.has_teardown_predict
|
|
|
|
dm.setup()
|
|
assert dm.has_prepared_data
|
|
assert dm.has_setup_fit
|
|
assert dm.has_setup_test
|
|
assert dm.has_setup_validate
|
|
assert not dm.has_setup_predict
|
|
assert not dm.has_teardown_fit
|
|
assert not dm.has_teardown_test
|
|
assert not dm.has_teardown_validate
|
|
assert not dm.has_teardown_predict
|
|
|
|
dm.teardown()
|
|
assert dm.has_prepared_data
|
|
assert dm.has_setup_fit
|
|
assert dm.has_setup_test
|
|
assert dm.has_setup_validate
|
|
assert not dm.has_setup_predict
|
|
assert dm.has_teardown_fit
|
|
assert dm.has_teardown_test
|
|
assert dm.has_teardown_validate
|
|
assert not dm.has_teardown_predict
|
|
|
|
|
|
@pytest.mark.parametrize("use_kwarg", (False, True))
|
|
def test_data_hooks_called_verbose(use_kwarg):
|
|
dm = BoringDataModule()
|
|
dm.prepare_data()
|
|
assert not dm.has_setup_fit
|
|
assert not dm.has_setup_test
|
|
assert not dm.has_setup_validate
|
|
assert not dm.has_setup_predict
|
|
assert not dm.has_teardown_fit
|
|
assert not dm.has_teardown_test
|
|
assert not dm.has_teardown_validate
|
|
assert not dm.has_teardown_predict
|
|
|
|
dm.setup(stage="fit") if use_kwarg else dm.setup("fit")
|
|
assert dm.has_setup_fit
|
|
assert not dm.has_setup_validate
|
|
assert not dm.has_setup_test
|
|
assert not dm.has_setup_predict
|
|
|
|
dm.setup(stage="validate") if use_kwarg else dm.setup("validate")
|
|
assert dm.has_setup_fit
|
|
assert dm.has_setup_validate
|
|
assert not dm.has_setup_test
|
|
assert not dm.has_setup_predict
|
|
|
|
dm.setup(stage="test") if use_kwarg else dm.setup("test")
|
|
assert dm.has_setup_fit
|
|
assert dm.has_setup_validate
|
|
assert dm.has_setup_test
|
|
assert not dm.has_setup_predict
|
|
|
|
dm.setup(stage="predict") if use_kwarg else dm.setup("predict")
|
|
assert dm.has_setup_fit
|
|
assert dm.has_setup_validate
|
|
assert dm.has_setup_test
|
|
assert dm.has_setup_predict
|
|
|
|
dm.teardown(stage="fit") if use_kwarg else dm.teardown("fit")
|
|
assert dm.has_teardown_fit
|
|
assert not dm.has_teardown_validate
|
|
assert not dm.has_teardown_test
|
|
assert not dm.has_teardown_predict
|
|
|
|
dm.teardown(stage="validate") if use_kwarg else dm.teardown("validate")
|
|
assert dm.has_teardown_fit
|
|
assert dm.has_teardown_validate
|
|
assert not dm.has_teardown_test
|
|
assert not dm.has_teardown_predict
|
|
|
|
dm.teardown(stage="test") if use_kwarg else dm.teardown("test")
|
|
assert dm.has_teardown_fit
|
|
assert dm.has_teardown_validate
|
|
assert dm.has_teardown_test
|
|
assert not dm.has_teardown_predict
|
|
|
|
dm.teardown(stage="predict") if use_kwarg else dm.teardown("predict")
|
|
assert dm.has_teardown_fit
|
|
assert dm.has_teardown_validate
|
|
assert dm.has_teardown_test
|
|
assert dm.has_teardown_predict
|
|
|
|
|
|
def test_dm_add_argparse_args(tmpdir):
|
|
parser = ArgumentParser()
|
|
parser = BoringDataModule.add_argparse_args(parser)
|
|
args = parser.parse_args(["--data_dir", str(tmpdir)])
|
|
assert args.data_dir == str(tmpdir)
|
|
|
|
|
|
def test_dm_init_from_argparse_args(tmpdir):
|
|
parser = ArgumentParser()
|
|
parser = BoringDataModule.add_argparse_args(parser)
|
|
args = parser.parse_args(["--data_dir", str(tmpdir)])
|
|
dm = BoringDataModule.from_argparse_args(args)
|
|
dm.prepare_data()
|
|
dm.setup()
|
|
assert dm.data_dir == args.data_dir == str(tmpdir)
|
|
|
|
|
|
def test_dm_pickle_after_init():
|
|
dm = BoringDataModule()
|
|
pickle.dumps(dm)
|
|
|
|
|
|
def test_train_loop_only(tmpdir):
|
|
reset_seed()
|
|
|
|
dm = ClassifDataModule()
|
|
model = ClassificationModel()
|
|
|
|
model.validation_step = None
|
|
model.validation_step_end = None
|
|
model.validation_epoch_end = None
|
|
model.test_step = None
|
|
model.test_step_end = None
|
|
model.test_epoch_end = None
|
|
|
|
trainer = Trainer(default_root_dir=tmpdir, max_epochs=1, weights_summary=None)
|
|
|
|
# fit model
|
|
trainer.fit(model, datamodule=dm)
|
|
assert trainer.state.finished, f"Training failed with {trainer.state}"
|
|
assert trainer.callback_metrics["train_loss"] < 1.0
|
|
|
|
|
|
def test_train_val_loop_only(tmpdir):
|
|
reset_seed()
|
|
|
|
dm = ClassifDataModule()
|
|
model = ClassificationModel()
|
|
|
|
model.validation_step = None
|
|
model.validation_step_end = None
|
|
model.validation_epoch_end = None
|
|
|
|
trainer = Trainer(default_root_dir=tmpdir, max_epochs=1, weights_summary=None)
|
|
|
|
# fit model
|
|
trainer.fit(model, datamodule=dm)
|
|
assert trainer.state.finished, f"Training failed with {trainer.state}"
|
|
assert trainer.callback_metrics["train_loss"] < 1.0
|
|
|
|
|
|
def test_dm_checkpoint_save(tmpdir):
|
|
class CustomBoringModel(BoringModel):
|
|
def validation_step(self, batch, batch_idx):
|
|
out = super().validation_step(batch, batch_idx)
|
|
self.log("early_stop_on", out["x"])
|
|
return out
|
|
|
|
class CustomBoringDataModule(BoringDataModule):
|
|
def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
|
|
checkpoint[self.__class__.__name__] = self.__class__.__name__
|
|
|
|
def on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
|
|
self.checkpoint_state = checkpoint.get(self.__class__.__name__)
|
|
|
|
reset_seed()
|
|
dm = CustomBoringDataModule()
|
|
model = CustomBoringModel()
|
|
|
|
trainer = Trainer(
|
|
default_root_dir=tmpdir,
|
|
max_epochs=1,
|
|
limit_train_batches=2,
|
|
limit_val_batches=1,
|
|
weights_summary=None,
|
|
callbacks=[ModelCheckpoint(dirpath=tmpdir, monitor="early_stop_on")],
|
|
)
|
|
|
|
# fit model
|
|
trainer.fit(model, dm)
|
|
assert trainer.state.finished, f"Training failed with {trainer.state}"
|
|
checkpoint_path = list(trainer.checkpoint_callback.best_k_models.keys())[0]
|
|
checkpoint = torch.load(checkpoint_path)
|
|
assert dm.__class__.__name__ in checkpoint
|
|
assert checkpoint[dm.__class__.__name__] == dm.__class__.__name__
|
|
|
|
|
|
def test_full_loop(tmpdir):
|
|
reset_seed()
|
|
|
|
dm = ClassifDataModule()
|
|
model = ClassificationModel()
|
|
|
|
trainer = Trainer(default_root_dir=tmpdir, max_epochs=1, weights_summary=None, deterministic=True)
|
|
|
|
# fit model
|
|
trainer.fit(model, dm)
|
|
assert trainer.state.finished, f"Training failed with {trainer.state}"
|
|
assert dm.trainer is not None
|
|
|
|
# validate
|
|
result = trainer.validate(model, dm)
|
|
assert dm.trainer is not None
|
|
assert result[0]["val_acc"] > 0.7
|
|
|
|
# test
|
|
result = trainer.test(model, dm)
|
|
assert dm.trainer is not None
|
|
assert result[0]["test_acc"] > 0.6
|
|
|
|
|
|
@RunIf(min_gpus=1)
|
|
@mock.patch("pytorch_lightning.accelerators.accelerator.Accelerator.lightning_module", new_callable=PropertyMock)
|
|
def test_dm_apply_batch_transfer_handler(get_module_mock):
|
|
expected_device = torch.device("cuda", 0)
|
|
|
|
class CustomBatch:
|
|
def __init__(self, data):
|
|
self.samples = data[0]
|
|
self.targets = data[1]
|
|
|
|
class CurrentTestDM(LightningDataModule):
|
|
rank = 0
|
|
transfer_batch_to_device_hook_rank = None
|
|
on_before_batch_transfer_hook_rank = None
|
|
on_after_batch_transfer_hook_rank = None
|
|
|
|
def on_before_batch_transfer(self, batch, dataloader_idx):
|
|
assert dataloader_idx is None
|
|
self.on_before_batch_transfer_hook_rank = self.rank
|
|
self.rank += 1
|
|
batch.samples += 1
|
|
return batch
|
|
|
|
def on_after_batch_transfer(self, batch, dataloader_idx):
|
|
assert dataloader_idx is None
|
|
assert batch.samples.device == batch.targets.device == expected_device
|
|
self.on_after_batch_transfer_hook_rank = self.rank
|
|
self.rank += 1
|
|
batch.targets *= 2
|
|
return batch
|
|
|
|
def transfer_batch_to_device(self, batch, device, dataloader_idx):
|
|
assert dataloader_idx is None
|
|
self.transfer_batch_to_device_hook_rank = self.rank
|
|
self.rank += 1
|
|
batch.samples = batch.samples.to(device)
|
|
batch.targets = batch.targets.to(device)
|
|
return batch
|
|
|
|
dm = CurrentTestDM()
|
|
model = BoringModel()
|
|
|
|
batch = CustomBatch((torch.zeros(5, 32), torch.ones(5, 1, dtype=torch.long)))
|
|
|
|
trainer = Trainer(gpus=1)
|
|
# running .fit() would require us to implement custom data loaders, we mock the model reference instead
|
|
get_module_mock.return_value = model
|
|
if is_overridden("transfer_batch_to_device", dm):
|
|
model.transfer_batch_to_device = dm.transfer_batch_to_device
|
|
|
|
model.on_before_batch_transfer = dm.on_before_batch_transfer
|
|
model.transfer_batch_to_device = dm.transfer_batch_to_device
|
|
model.on_after_batch_transfer = dm.on_after_batch_transfer
|
|
|
|
batch_gpu = trainer.accelerator.batch_to_device(batch, expected_device)
|
|
|
|
assert dm.on_before_batch_transfer_hook_rank == 0
|
|
assert dm.transfer_batch_to_device_hook_rank == 1
|
|
assert dm.on_after_batch_transfer_hook_rank == 2
|
|
assert batch_gpu.samples.device == batch_gpu.targets.device == expected_device
|
|
assert torch.allclose(batch_gpu.samples.cpu(), torch.ones(5, 32))
|
|
assert torch.allclose(batch_gpu.targets.cpu(), torch.ones(5, 1, dtype=torch.long) * 2)
|
|
|
|
|
|
def test_dm_reload_dataloaders_every_n_epochs(tmpdir):
|
|
"""
|
|
Test datamodule, where trainer argument
|
|
reload_dataloaders_every_n_epochs is set to a non negative integer
|
|
"""
|
|
|
|
class CustomBoringDataModule(BoringDataModule):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self._epochs_called_for = []
|
|
|
|
def train_dataloader(self):
|
|
assert self.trainer.current_epoch not in self._epochs_called_for
|
|
self._epochs_called_for.append(self.trainer.current_epoch)
|
|
return super().train_dataloader()
|
|
|
|
dm = CustomBoringDataModule()
|
|
model = BoringModel()
|
|
|
|
model.validation_step = None
|
|
model.validation_step_end = None
|
|
model.validation_epoch_end = None
|
|
model.test_step = None
|
|
model.test_step_end = None
|
|
model.test_epoch_end = None
|
|
|
|
trainer = Trainer(default_root_dir=tmpdir, max_epochs=3, limit_train_batches=2, reload_dataloaders_every_n_epochs=2)
|
|
trainer.fit(model, dm)
|
|
|
|
|
|
class DummyDS(torch.utils.data.Dataset):
|
|
def __getitem__(self, index):
|
|
return 1
|
|
|
|
def __len__(self):
|
|
return 100
|
|
|
|
|
|
class DummyIDS(torch.utils.data.IterableDataset):
|
|
def __iter__(self):
|
|
yield 1
|
|
|
|
|
|
@pytest.mark.parametrize("iterable", (False, True))
|
|
def test_dm_init_from_datasets_dataloaders(iterable):
|
|
ds = DummyIDS if iterable else DummyDS
|
|
|
|
train_ds = ds()
|
|
dm = LightningDataModule.from_datasets(train_ds, batch_size=4, num_workers=0)
|
|
with mock.patch("pytorch_lightning.core.datamodule.DataLoader") as dl_mock:
|
|
dm.train_dataloader()
|
|
dl_mock.assert_called_once_with(train_ds, batch_size=4, shuffle=not iterable, num_workers=0, pin_memory=True)
|
|
assert dm.val_dataloader() is None
|
|
assert dm.test_dataloader() is None
|
|
|
|
train_ds_sequence = [ds(), ds()]
|
|
dm = LightningDataModule.from_datasets(train_ds_sequence, batch_size=4, num_workers=0)
|
|
with mock.patch("pytorch_lightning.core.datamodule.DataLoader") as dl_mock:
|
|
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),
|
|
]
|
|
)
|
|
assert dm.val_dataloader() is None
|
|
assert dm.test_dataloader() is None
|
|
|
|
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)
|
|
assert dm.train_dataloader() is None
|
|
|
|
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),
|
|
]
|
|
)
|
|
|
|
|
|
class DataModuleWithHparams(LightningDataModule):
|
|
def __init__(self, arg0, arg1, kwarg0=None):
|
|
super().__init__()
|
|
self.save_hyperparameters()
|
|
|
|
|
|
def test_simple_hyperparameters_saving():
|
|
data = DataModuleWithHparams(10, "foo", kwarg0="bar")
|
|
assert data.hparams == AttributeDict({"arg0": 10, "arg1": "foo", "kwarg0": "bar"})
|