def test_preprocessing_data_pipeline_no_running_stage(with_dataset):
pipe = DataPipeline(_AutoDatasetTestPreprocess(with_dataset))
dataset = pipe._generate_auto_dataset(range(10), running_stage=None)
with pytest.raises(RuntimeError, match='`__len__` for `load_sample`'):
for idx in range(len(dataset)):
dataset[idx]
# will be triggered when running stage is set
if with_dataset:
assert not hasattr(dataset, 'load_sample_was_called')
assert not hasattr(dataset, 'load_data_was_called')
assert pipe._preprocess_pipeline.load_sample_with_dataset_count == 0
assert pipe._preprocess_pipeline.load_data_with_dataset_count == 0
else:
assert pipe._preprocess_pipeline.load_sample_count == 0
assert pipe._preprocess_pipeline.load_data_count == 0
dataset.running_stage = RunningStage.TRAINING
if with_dataset:
assert pipe._preprocess_pipeline.train_load_data_with_dataset_count == 1
assert dataset.train_load_data_was_called
else:
assert pipe._preprocess_pipeline.train_load_data_count == 1
def autogenerate_dataset(
cls,
data: Any,
running_stage: RunningStage,
whole_data_load_fn: Optional[Callable] = None,
per_sample_load_fn: Optional[Callable] = None,
data_pipeline: Optional[DataPipeline] = None,
) -> AutoDataset:
"""
This function is used to generate an ``AutoDataset`` from a ``DataPipeline`` if provided
or from the provided ``whole_data_load_fn``, ``per_sample_load_fn`` functions directly
"""
if whole_data_load_fn is None:
whole_data_load_fn = getattr(
cls.preprocess_cls,
DataPipeline._resolve_function_hierarchy(
'load_data', cls.preprocess_cls, running_stage,
Preprocess))
if per_sample_load_fn is None:
per_sample_load_fn = getattr(
cls.preprocess_cls,
DataPipeline._resolve_function_hierarchy(
'load_sample', cls.preprocess_cls, running_stage,
Preprocess))
return AutoDataset(data,
whole_data_load_fn,
per_sample_load_fn,
data_pipeline,
running_stage=running_stage)
def test_saving_with_serializers(tmpdir):
checkpoint_file = os.path.join(tmpdir, 'tmp.ckpt')
class CustomModel(Task):
def __init__(self):
super().__init__(model=torch.nn.Linear(1, 1),
loss_fn=torch.nn.MSELoss())
serializer = Labels(["a", "b"])
model = CustomModel()
trainer = Trainer(fast_dev_run=True)
data_pipeline = DataPipeline(DefaultPreprocess(), serializer=serializer)
data_pipeline.initialize()
model.data_pipeline = data_pipeline
assert isinstance(model.preprocess, DefaultPreprocess)
dummy_data = DataLoader(
list(
zip(torch.arange(10, dtype=torch.float),
torch.arange(10, dtype=torch.float))))
trainer.fit(model, train_dataloader=dummy_data)
trainer.save_checkpoint(checkpoint_file)
model = CustomModel.load_from_checkpoint(checkpoint_file)
assert isinstance(model.preprocess._data_pipeline_state, DataPipelineState)
assert model.preprocess._data_pipeline_state._state[
ClassificationState] == ClassificationState(['a', 'b'])
def test_is_overriden_recursive(tmpdir):
class TestPreprocess(DefaultPreprocess):
def collate(self, *_):
pass
def val_collate(self, *_):
pass
preprocess = TestPreprocess()
assert DataPipeline._is_overriden_recursive("collate",
preprocess,
Preprocess,
prefix="val")
assert DataPipeline._is_overriden_recursive("collate",
preprocess,
Preprocess,
prefix="train")
assert not DataPipeline._is_overriden_recursive(
"per_batch_transform_on_device",
preprocess,
Preprocess,
prefix="train")
assert not DataPipeline._is_overriden_recursive(
"per_batch_transform_on_device", preprocess, Preprocess)
with pytest.raises(
MisconfigurationException,
match="This function doesn't belong to the parent class"):
assert not DataPipeline._is_overriden_recursive(
"chocolate", preprocess, Preprocess)
def test_data_pipeline_predict_worker_preprocessor_and_device_preprocessor():
preprocess = CustomPreprocess()
data_pipeline = DataPipeline(preprocess)
data_pipeline.worker_preprocessor(RunningStage.TRAINING)
with pytest.raises(MisconfigurationException, match="are mutual exclusive"):
data_pipeline.worker_preprocessor(RunningStage.VALIDATING)
with pytest.raises(MisconfigurationException, match="are mutual exclusive"):
data_pipeline.worker_preprocessor(RunningStage.TESTING)
data_pipeline.worker_preprocessor(RunningStage.PREDICTING)
def generate_dataset(
self,
data: Optional[DATA_TYPE],
running_stage: RunningStage,
) -> Optional[Union[AutoDataset, IterableAutoDataset]]:
is_none = data is None
if isinstance(data, Sequence):
is_none = data[0] is None
if not is_none:
from flash.data.data_pipeline import DataPipeline
mock_dataset = typing.cast(AutoDataset, MockDataset())
with CurrentRunningStageFuncContext(running_stage, "load_data", self):
load_data: Callable[[DATA_TYPE, Optional[Any]], Any] = getattr(
self, DataPipeline._resolve_function_hierarchy(
"load_data",
self,
running_stage,
DataSource,
)
)
parameters = signature(load_data).parameters
if len(parameters) > 1 and "dataset" in parameters: # TODO: This was DATASET_KEY before
data = load_data(data, mock_dataset)
else:
data = load_data(data)
if has_len(data):
dataset = AutoDataset(data, self, running_stage)
else:
dataset = IterableAutoDataset(data, self, running_stage)
dataset.__dict__.update(mock_dataset.metadata)
return dataset
def test_predict_numpy():
img = np.ones((1, 3, 10, 20))
model = SemanticSegmentation(2)
data_pipe = DataPipeline(preprocess=SemanticSegmentationPreprocess())
out = model.predict(img, data_source="numpy", data_pipeline=data_pipe)
assert isinstance(out[0], torch.Tensor)
assert out[0].shape == (196, 196)
def test_serialization_data_pipeline(tmpdir):
model = CustomModel()
checkpoint_file = os.path.join(tmpdir, 'tmp.ckpt')
checkpoint = ModelCheckpoint(tmpdir, 'test.ckpt')
trainer = Trainer(callbacks=[checkpoint], max_epochs=1)
dummy_data = DataLoader(
list(
zip(torch.arange(10, dtype=torch.float),
torch.arange(10, dtype=torch.float))))
trainer.fit(model, dummy_data)
assert model.data_pipeline is None
trainer.save_checkpoint(checkpoint_file)
loaded_model = CustomModel.load_from_checkpoint(checkpoint_file)
assert loaded_model.data_pipeline is None
model.data_pipeline = DataPipeline(CustomPreprocess())
trainer.fit(model, dummy_data)
assert model.data_pipeline
assert isinstance(model.preprocess, CustomPreprocess)
trainer.save_checkpoint(checkpoint_file)
loaded_model = CustomModel.load_from_checkpoint(checkpoint_file)
assert loaded_model.data_pipeline
assert isinstance(loaded_model.preprocess, CustomPreprocess)
for file in os.listdir(tmpdir):
if file.endswith('.ckpt'):
os.remove(os.path.join(tmpdir, file))
def test_autodataset_warning():
with pytest.warns(
UserWarning,
match=
"``datapipeline`` is specified but load_sample and/or load_data are also specified"
):
AutoDataset(range(10),
load_data=lambda x: x,
load_sample=lambda x: x,
data_pipeline=DataPipeline())
def test_preprocessing_data_pipeline_with_running_stage(with_dataset):
pipe = DataPipeline(_AutoDatasetTestPreprocess(with_dataset))
running_stage = RunningStage.TRAINING
dataset = pipe._generate_auto_dataset(range(10), running_stage=running_stage)
assert len(dataset) == 10
for idx in range(len(dataset)):
dataset[idx]
if with_dataset:
assert dataset.train_load_sample_was_called
assert dataset.train_load_data_was_called
assert pipe._preprocess_pipeline.train_load_sample_with_dataset_count == len(dataset)
assert pipe._preprocess_pipeline.train_load_data_with_dataset_count == 1
else:
assert pipe._preprocess_pipeline.train_load_sample_count == len(dataset)
assert pipe._preprocess_pipeline.train_load_data_count == 1
def from_load_data_inputs(
cls,
train_load_data_input: Optional[Any] = None,
val_load_data_input: Optional[Any] = None,
test_load_data_input: Optional[Any] = None,
predict_load_data_input: Optional[Any] = None,
preprocess: Optional[Preprocess] = None,
postprocess: Optional[Postprocess] = None,
**kwargs,
) -> 'DataModule':
"""
This functions is an helper to generate a ``DataModule`` from a ``DataPipeline``.
Args:
cls: ``DataModule`` subclass
train_load_data_input: Data to be received by the ``train_load_data`` function from this ``Preprocess``
val_load_data_input: Data to be received by the ``val_load_data`` function from this ``Preprocess``
test_load_data_input: Data to be received by the ``test_load_data`` function from this ``Preprocess``
predict_load_data_input: Data to be received by the ``predict_load_data`` function from this ``Preprocess``
kwargs: Any extra arguments to instantiate the provided ``DataModule``
"""
# trick to get data_pipeline from empty DataModule
if preprocess or postprocess:
data_pipeline = DataPipeline(
preprocess or cls(**kwargs).preprocess,
postprocess or cls(**kwargs).postprocess,
)
else:
data_pipeline = cls(**kwargs).data_pipeline
train_dataset = cls._generate_dataset_if_possible(
train_load_data_input,
running_stage=RunningStage.TRAINING,
data_pipeline=data_pipeline)
val_dataset = cls._generate_dataset_if_possible(
val_load_data_input,
running_stage=RunningStage.VALIDATING,
data_pipeline=data_pipeline)
test_dataset = cls._generate_dataset_if_possible(
test_load_data_input,
running_stage=RunningStage.TESTING,
data_pipeline=data_pipeline)
predict_dataset = cls._generate_dataset_if_possible(
predict_load_data_input,
running_stage=RunningStage.PREDICTING,
data_pipeline=data_pipeline)
datamodule = cls(train_dataset=train_dataset,
val_dataset=val_dataset,
test_dataset=test_dataset,
predict_dataset=predict_dataset,
**kwargs)
datamodule._preprocess = data_pipeline._preprocess_pipeline
datamodule._postprocess = data_pipeline._postprocess_pipeline
return datamodule
def autogenerate_dataset(
cls,
data: Any,
running_stage: RunningStage,
whole_data_load_fn: Optional[Callable] = None,
per_sample_load_fn: Optional[Callable] = None,
data_pipeline: Optional[DataPipeline] = None,
use_iterable_auto_dataset: bool = False,
) -> BaseAutoDataset:
"""
This function is used to generate an ``BaseAutoDataset`` from a ``DataPipeline`` if provided
or from the provided ``whole_data_load_fn``, ``per_sample_load_fn`` functions directly
"""
preprocess = getattr(data_pipeline, '_preprocess_pipeline', None)
if whole_data_load_fn is None:
whole_data_load_fn = getattr(
preprocess,
DataPipeline._resolve_function_hierarchy(
'load_data', preprocess, running_stage, Preprocess))
if per_sample_load_fn is None:
per_sample_load_fn = getattr(
preprocess,
DataPipeline._resolve_function_hierarchy(
'load_sample', preprocess, running_stage, Preprocess))
if use_iterable_auto_dataset:
return IterableAutoDataset(data,
whole_data_load_fn,
per_sample_load_fn,
data_pipeline,
running_stage=running_stage)
return BaseAutoDataset(data,
whole_data_load_fn,
per_sample_load_fn,
data_pipeline,
running_stage=running_stage)
def test_detach_preprocessing_from_model(tmpdir):
preprocess = CustomPreprocess()
data_pipeline = DataPipeline(preprocess)
model = CustomModel()
model.data_pipeline = data_pipeline
assert model.train_dataloader().collate_fn == default_collate
assert model.transfer_batch_to_device.__self__ == model
model.on_train_dataloader()
assert isinstance(model.train_dataloader().collate_fn, _PreProcessor)
assert isinstance(model.transfer_batch_to_device, _StageOrchestrator)
model.on_fit_end()
assert model.transfer_batch_to_device.__self__ == model
assert model.train_dataloader().collate_fn == default_collate
def generate_dataset(
self,
data: Optional[DATA_TYPE],
running_stage: RunningStage,
) -> Optional[Union[AutoDataset, IterableAutoDataset]]:
"""Generate a single dataset with the given input to :meth:`~flash.data.data_source.DataSource.load_data` for
the given ``running_stage``.
Args:
data: The input to :meth:`~flash.data.data_source.DataSource.load_data` to use to create the dataset.
running_stage: The running_stage for this dataset.
Returns:
The constructed :class:`~flash.data.auto_dataset.BaseAutoDataset`.
"""
is_none = data is None
if isinstance(data, Sequence):
is_none = data[0] is None
if not is_none:
from flash.data.data_pipeline import DataPipeline
mock_dataset = typing.cast(AutoDataset, MockDataset())
with CurrentRunningStageFuncContext(running_stage, "load_data",
self):
load_data: Callable[[DATA_TYPE, Optional[Any]], Any] = getattr(
self,
DataPipeline._resolve_function_hierarchy(
"load_data",
self,
running_stage,
DataSource,
))
parameters = signature(load_data).parameters
if len(
parameters
) > 1 and "dataset" in parameters: # TODO: This was DATASET_KEY before
data = load_data(data, mock_dataset)
else:
data = load_data(data)
if has_len(data):
dataset = AutoDataset(data, self, running_stage)
else:
dataset = IterableAutoDataset(data, self, running_stage)
dataset.__dict__.update(mock_dataset.metadata)
return dataset
def running_stage(self, running_stage: RunningStage) -> None:
from flash.data.data_pipeline import DataPipeline # noqa F811
from flash.data.data_source import DataSource # noqa F811 # TODO: something better than this
self._running_stage = running_stage
self._load_sample_context = CurrentRunningStageFuncContext(
self.running_stage, "load_sample", self.data_source)
self.load_sample: Callable[[DATA_TYPE, Optional[Any]], Any] = getattr(
self.data_source,
DataPipeline._resolve_function_hierarchy(
'load_sample',
self.data_source,
self.running_stage,
DataSource,
))
def test_data_pipeline_init_and_assignement(use_preprocess, use_postprocess, tmpdir):
class SubPreprocess(Preprocess):
pass
class SubPostprocess(Postprocess):
pass
data_pipeline = DataPipeline(
SubPreprocess() if use_preprocess else None,
SubPostprocess() if use_postprocess else None,
)
assert isinstance(data_pipeline._preprocess_pipeline, SubPreprocess if use_preprocess else Preprocess)
assert isinstance(data_pipeline._postprocess_pipeline, SubPostprocess if use_postprocess else Postprocess)
model = CustomModel(Postprocess())
model.data_pipeline = data_pipeline
assert isinstance(model._preprocess, SubPreprocess if use_preprocess else Preprocess)
assert isinstance(model._postprocess, SubPostprocess if use_postprocess else Postprocess)
def data_pipeline(self) -> Optional[DataPipeline]:
if self._data_pipeline is not None:
return self._data_pipeline
elif self.preprocess is not None or self.postprocess is not None:
# use direct attributes here to avoid recursion with properties that also check the data_pipeline property
return DataPipeline(self.preprocess, self.postprocess)
elif self.datamodule is not None and getattr(
self.datamodule, 'data_pipeline', None) is not None:
return self.datamodule.data_pipeline
elif self.trainer is not None and hasattr(
self.trainer, 'datamodule') and getattr(
self.trainer.datamodule, 'data_pipeline',
None) is not None:
return self.trainer.datamodule.data_pipeline
return self._data_pipeline
def test_data_pipeline_init_and_assignement(use_preprocess, use_postprocess,
tmpdir):
class CustomModel(Task):
def __init__(self, postprocess: Optional[Postprocess] = None):
super().__init__(model=torch.nn.Linear(1, 1),
loss_fn=torch.nn.MSELoss())
self._postprocess = postprocess
def train_dataloader(self) -> Any:
return DataLoader(DummyDataset())
class SubPreprocess(DefaultPreprocess):
pass
class SubPostprocess(Postprocess):
pass
data_pipeline = DataPipeline(
preprocess=SubPreprocess() if use_preprocess else None,
postprocess=SubPostprocess() if use_postprocess else None,
)
assert isinstance(data_pipeline._preprocess_pipeline,
SubPreprocess if use_preprocess else DefaultPreprocess)
assert isinstance(data_pipeline._postprocess_pipeline,
SubPostprocess if use_postprocess else Postprocess)
model = CustomModel(postprocess=Postprocess())
model.data_pipeline = data_pipeline
# TODO: the line below should make the same effect but it's not
# data_pipeline._attach_to_model(model)
if use_preprocess:
assert isinstance(model._preprocess, SubPreprocess)
else:
assert model._preprocess is None or isinstance(model._preprocess,
Preprocess)
if use_postprocess:
assert isinstance(model._postprocess, SubPostprocess)
else:
assert model._postprocess is None or isinstance(
model._postprocess, Postprocess)
def test_detach_preprocessing_from_model(tmpdir):
class CustomModel(Task):
def __init__(self, postprocess: Optional[Postprocess] = None):
super().__init__(model=torch.nn.Linear(1, 1),
loss_fn=torch.nn.MSELoss())
self._postprocess = postprocess
def train_dataloader(self) -> Any:
return DataLoader(DummyDataset())
preprocess = CustomPreprocess()
data_pipeline = DataPipeline(preprocess=preprocess)
model = CustomModel()
model.data_pipeline = data_pipeline
assert model.train_dataloader().collate_fn == default_collate
assert model.transfer_batch_to_device.__self__ == model
model.on_train_dataloader()
assert isinstance(model.train_dataloader().collate_fn, _PreProcessor)
assert isinstance(model.transfer_batch_to_device, _StageOrchestrator)
model.on_fit_end()
assert model.transfer_batch_to_device.__self__ == model
assert model.train_dataloader().collate_fn == default_collate
def test_attaching_datapipeline_to_model(tmpdir):
class SubPreprocess(DefaultPreprocess):
pass
preprocess = SubPreprocess()
data_pipeline = DataPipeline(preprocess=preprocess)
class CustomModel(Task):
def __init__(self):
super().__init__(model=torch.nn.Linear(1, 1),
loss_fn=torch.nn.MSELoss())
self._postprocess = Postprocess()
def training_step(self, batch: Any, batch_idx: int) -> Any:
pass
def validation_step(self, batch: Any, batch_idx: int) -> Any:
pass
def test_step(self, batch: Any, batch_idx: int) -> Any:
pass
def train_dataloader(self) -> Any:
return DataLoader(DummyDataset())
def val_dataloader(self) -> Any:
return DataLoader(DummyDataset())
def test_dataloader(self) -> Any:
return DataLoader(DummyDataset())
def predict_dataloader(self) -> Any:
return DataLoader(DummyDataset())
class TestModel(CustomModel):
stages = [
RunningStage.TRAINING, RunningStage.VALIDATING,
RunningStage.TESTING, RunningStage.PREDICTING
]
on_train_start_called = False
on_val_start_called = False
on_test_start_called = False
on_predict_start_called = False
def on_fit_start(self):
assert self.predict_step.__self__ == self
self._saved_predict_step = self.predict_step
def _compare_pre_processor(self, p1, p2):
p1_seq = p1.per_sample_transform
p2_seq = p2.per_sample_transform
assert p1_seq.pre_tensor_transform.func == p2_seq.pre_tensor_transform.func
assert p1_seq.to_tensor_transform.func == p2_seq.to_tensor_transform.func
assert p1_seq.post_tensor_transform.func == p2_seq.post_tensor_transform.func
assert p1.collate_fn.func == p2.collate_fn.func
assert p1.per_batch_transform.func == p2.per_batch_transform.func
def _assert_stage_orchestrator_state(
self,
stage_mapping: Dict,
current_running_stage: RunningStage,
cls=_PreProcessor):
assert isinstance(stage_mapping[current_running_stage], cls)
assert stage_mapping[current_running_stage]
def on_train_dataloader(self) -> None:
current_running_stage = RunningStage.TRAINING
self.on_train_dataloader_called = True
collate_fn = self.train_dataloader().collate_fn # noqa F811
assert collate_fn == default_collate
assert not isinstance(self.transfer_batch_to_device,
_StageOrchestrator)
super().on_train_dataloader()
collate_fn = self.train_dataloader().collate_fn # noqa F811
assert collate_fn.stage == current_running_stage
self._compare_pre_processor(
collate_fn,
self.data_pipeline.worker_preprocessor(current_running_stage))
assert isinstance(self.transfer_batch_to_device,
_StageOrchestrator)
self._assert_stage_orchestrator_state(
self.transfer_batch_to_device._stage_mapping,
current_running_stage)
def on_val_dataloader(self) -> None:
current_running_stage = RunningStage.VALIDATING
self.on_val_dataloader_called = True
collate_fn = self.val_dataloader().collate_fn # noqa F811
assert collate_fn == default_collate
assert isinstance(self.transfer_batch_to_device,
_StageOrchestrator)
super().on_val_dataloader()
collate_fn = self.val_dataloader().collate_fn # noqa F811
assert collate_fn.stage == current_running_stage
self._compare_pre_processor(
collate_fn,
self.data_pipeline.worker_preprocessor(current_running_stage))
assert isinstance(self.transfer_batch_to_device,
_StageOrchestrator)
self._assert_stage_orchestrator_state(
self.transfer_batch_to_device._stage_mapping,
current_running_stage)
def on_test_dataloader(self) -> None:
current_running_stage = RunningStage.TESTING
self.on_test_dataloader_called = True
collate_fn = self.test_dataloader().collate_fn # noqa F811
assert collate_fn == default_collate
assert not isinstance(self.transfer_batch_to_device,
_StageOrchestrator)
super().on_test_dataloader()
collate_fn = self.test_dataloader().collate_fn # noqa F811
assert collate_fn.stage == current_running_stage
self._compare_pre_processor(
collate_fn,
self.data_pipeline.worker_preprocessor(current_running_stage))
assert isinstance(self.transfer_batch_to_device,
_StageOrchestrator)
self._assert_stage_orchestrator_state(
self.transfer_batch_to_device._stage_mapping,
current_running_stage)
def on_predict_dataloader(self) -> None:
current_running_stage = RunningStage.PREDICTING
self.on_predict_dataloader_called = True
collate_fn = self.predict_dataloader().collate_fn # noqa F811
assert collate_fn == default_collate
assert isinstance(self.transfer_batch_to_device,
_StageOrchestrator)
assert self.predict_step == self._saved_predict_step
super().on_predict_dataloader()
collate_fn = self.predict_dataloader().collate_fn # noqa F811
assert collate_fn.stage == current_running_stage
self._compare_pre_processor(
collate_fn,
self.data_pipeline.worker_preprocessor(current_running_stage))
assert isinstance(self.transfer_batch_to_device,
_StageOrchestrator)
assert isinstance(self.predict_step, _StageOrchestrator)
self._assert_stage_orchestrator_state(
self.transfer_batch_to_device._stage_mapping,
current_running_stage)
self._assert_stage_orchestrator_state(
self.predict_step._stage_mapping,
current_running_stage,
cls=_PostProcessor)
def on_fit_end(self) -> None:
super().on_fit_end()
assert self.train_dataloader().collate_fn == default_collate
assert self.val_dataloader().collate_fn == default_collate
assert self.test_dataloader().collate_fn == default_collate
assert self.predict_dataloader().collate_fn == default_collate
assert not isinstance(self.transfer_batch_to_device,
_StageOrchestrator)
assert self.predict_step == self._saved_predict_step
model = TestModel()
model.data_pipeline = data_pipeline
trainer = Trainer(fast_dev_run=True)
trainer.fit(model)
trainer.test(model)
trainer.predict(model)
assert model.on_train_dataloader_called
assert model.on_val_dataloader_called
assert model.on_test_dataloader_called
assert model.on_predict_dataloader_called
def from_load_data_inputs(
cls,
train_load_data_input: Optional[Any] = None,
val_load_data_input: Optional[Any] = None,
test_load_data_input: Optional[Any] = None,
predict_load_data_input: Optional[Any] = None,
data_fetcher: BaseDataFetcher = None,
preprocess: Optional[Preprocess] = None,
postprocess: Optional[Postprocess] = None,
use_iterable_auto_dataset: bool = False,
seed: int = 42,
val_split: Optional[float] = None,
**kwargs,
) -> 'DataModule':
"""
This functions is an helper to generate a ``DataModule`` from a ``DataPipeline``.
Args:
cls: ``DataModule`` subclass
train_load_data_input: Data to be received by the ``train_load_data`` function
from this :class:`~flash.data.process.Preprocess`
val_load_data_input: Data to be received by the ``val_load_data`` function
from this :class:`~flash.data.process.Preprocess`
test_load_data_input: Data to be received by the ``test_load_data`` function
from this :class:`~flash.data.process.Preprocess`
predict_load_data_input: Data to be received by the ``predict_load_data`` function
from this :class:`~flash.data.process.Preprocess`
kwargs: Any extra arguments to instantiate the provided ``DataModule``
"""
# trick to get data_pipeline from empty DataModule
if preprocess or postprocess:
data_pipeline = DataPipeline(
preprocess or cls(**kwargs).preprocess,
postprocess or cls(**kwargs).postprocess,
)
else:
data_pipeline = cls(**kwargs).data_pipeline
data_fetcher: BaseDataFetcher = data_fetcher or cls.configure_data_fetcher(
)
data_fetcher.attach_to_preprocess(data_pipeline._preprocess_pipeline)
train_dataset = cls._generate_dataset_if_possible(
train_load_data_input,
running_stage=RunningStage.TRAINING,
data_pipeline=data_pipeline,
use_iterable_auto_dataset=use_iterable_auto_dataset,
)
val_dataset = cls._generate_dataset_if_possible(
val_load_data_input,
running_stage=RunningStage.VALIDATING,
data_pipeline=data_pipeline,
use_iterable_auto_dataset=use_iterable_auto_dataset,
)
test_dataset = cls._generate_dataset_if_possible(
test_load_data_input,
running_stage=RunningStage.TESTING,
data_pipeline=data_pipeline,
use_iterable_auto_dataset=use_iterable_auto_dataset,
)
predict_dataset = cls._generate_dataset_if_possible(
predict_load_data_input,
running_stage=RunningStage.PREDICTING,
data_pipeline=data_pipeline,
use_iterable_auto_dataset=use_iterable_auto_dataset,
)
if train_dataset is not None and (val_split is not None
and val_dataset is None):
train_dataset, val_dataset = cls._split_train_val(
train_dataset, val_split)
datamodule = cls(train_dataset=train_dataset,
val_dataset=val_dataset,
test_dataset=test_dataset,
predict_dataset=predict_dataset,
**kwargs)
datamodule._preprocess = data_pipeline._preprocess_pipeline
datamodule._postprocess = data_pipeline._postprocess_pipeline
data_fetcher.attach_to_datamodule(datamodule)
return datamodule
class Task(LightningModule):
"""A general Task.
Args:
model: Model to use for the task.
loss_fn: Loss function for training
optimizer: Optimizer to use for training, defaults to `torch.optim.Adam`.
metrics: Metrics to compute for training and evaluation.
learning_rate: Learning rate to use for training, defaults to `5e-5`
"""
def __init__(
self,
model: Optional[nn.Module] = None,
loss_fn: Optional[Union[Callable, Mapping, Sequence]] = None,
optimizer: Type[torch.optim.Optimizer] = torch.optim.Adam,
metrics: Union[torchmetrics.Metric, Mapping, Sequence, None] = None,
learning_rate: float = 5e-5,
):
super().__init__()
if model is not None:
self.model = model
self.loss_fn = {} if loss_fn is None else get_callable_dict(loss_fn)
self.optimizer_cls = optimizer
self.metrics = nn.ModuleDict(
{} if metrics is None else get_callable_dict(metrics))
self.learning_rate = learning_rate
# TODO: should we save more? Bug on some regarding yaml if we save metrics
self.save_hyperparameters("learning_rate", "optimizer")
self._data_pipeline = None
self._preprocess = None
self._postprocess = None
def step(self, batch: Any, batch_idx: int) -> Any:
"""
The training/validation/test step. Override for custom behavior.
"""
x, y = batch
y_hat = self(x)
output = {"y_hat": y_hat}
losses = {name: l_fn(y_hat, y) for name, l_fn in self.loss_fn.items()}
logs = {}
for name, metric in self.metrics.items():
if isinstance(metric, torchmetrics.metric.Metric):
metric(y_hat, y)
logs[
name] = metric # log the metric itself if it is of type Metric
else:
logs[name] = metric(y_hat, y)
logs.update(losses)
if len(losses.values()) > 1:
logs["total_loss"] = sum(losses.values())
return logs["total_loss"], logs
output["loss"] = list(losses.values())[0]
output["logs"] = logs
output["y"] = y
return output
def forward(self, x: Any) -> Any:
return self.model(x)
def training_step(self, batch: Any, batch_idx: int) -> Any:
output = self.step(batch, batch_idx)
self.log_dict({f"train_{k}": v
for k, v in output["logs"].items()},
on_step=True,
on_epoch=True,
prog_bar=True)
return output["loss"]
def validation_step(self, batch: Any, batch_idx: int) -> None:
output = self.step(batch, batch_idx)
self.log_dict({f"val_{k}": v
for k, v in output["logs"].items()},
on_step=False,
on_epoch=True,
prog_bar=True)
def test_step(self, batch: Any, batch_idx: int) -> None:
output = self.step(batch, batch_idx)
self.log_dict({f"test_{k}": v
for k, v in output["logs"].items()},
on_step=False,
on_epoch=True,
prog_bar=True)
@predict_context
def predict(
self,
x: Any,
data_pipeline: Optional[DataPipeline] = None,
) -> Any:
"""
Predict function for raw data or processed data
Args:
x: Input to predict. Can be raw data or processed data. If str, assumed to be a folder of data.
data_pipeline: Use this to override the current data pipeline
Returns:
The post-processed model predictions
"""
running_stage = RunningStage.PREDICTING
data_pipeline = data_pipeline or self.data_pipeline
x = [x for x in data_pipeline._generate_auto_dataset(x, running_stage)]
x = data_pipeline.worker_preprocessor(running_stage)(x)
x = self.transfer_batch_to_device(x, self.device)
x = data_pipeline.device_preprocessor(running_stage)(x)
predictions = self.predict_step(
x, 0) # batch_idx is always 0 when running with `model.predict`
predictions = data_pipeline.postprocessor(predictions)
return predictions
def predict_step(self,
batch: Any,
batch_idx: int,
dataloader_idx: int = 0) -> Any:
if isinstance(batch, tuple):
batch = batch[0]
elif isinstance(batch, list):
# Todo: Understand why stack is needed
batch = torch.stack(batch)
return self(batch)
def configure_optimizers(self) -> torch.optim.Optimizer:
return self.optimizer_cls(filter(lambda p: p.requires_grad,
self.parameters()),
lr=self.learning_rate)
def configure_finetune_callback(self) -> List[Callback]:
return []
@property
def preprocess(self) -> Optional[Preprocess]:
return getattr(self._data_pipeline, '_preprocess_pipeline',
None) or self._preprocess
@preprocess.setter
def preprocess(self, preprocess: Preprocess) -> None:
self._preprocess = preprocess
self.data_pipeline = DataPipeline(preprocess, self.postprocess)
@property
def postprocess(self) -> Postprocess:
return getattr(self._data_pipeline, '_postprocess_pipeline',
None) or self._postprocess
@postprocess.setter
def postprocess(self, postprocess: Postprocess) -> None:
self.data_pipeline = DataPipeline(self.preprocess, postprocess)
self._postprocess = postprocess
@property
def data_pipeline(self) -> Optional[DataPipeline]:
if self._data_pipeline is not None:
return self._data_pipeline
elif self.preprocess is not None or self.postprocess is not None:
# use direct attributes here to avoid recursion with properties that also check the data_pipeline property
return DataPipeline(self.preprocess, self.postprocess)
elif self.datamodule is not None and getattr(
self.datamodule, 'data_pipeline', None) is not None:
return self.datamodule.data_pipeline
elif self.trainer is not None and hasattr(
self.trainer, 'datamodule') and getattr(
self.trainer.datamodule, 'data_pipeline',
None) is not None:
return self.trainer.datamodule.data_pipeline
return self._data_pipeline
@data_pipeline.setter
def data_pipeline(self, data_pipeline: Optional[DataPipeline]) -> None:
self._data_pipeline = data_pipeline
if data_pipeline is not None and getattr(
data_pipeline, '_preprocess_pipeline', None) is not None:
self._preprocess = data_pipeline._preprocess_pipeline
if data_pipeline is not None and getattr(
data_pipeline, '_postprocess_pipeline', None) is not None:
if type(data_pipeline._postprocess_pipeline) != Postprocess:
self._postprocess = data_pipeline._postprocess_pipeline
def on_train_dataloader(self) -> None:
if self.data_pipeline is not None:
self.data_pipeline._detach_from_model(self, RunningStage.TRAINING)
self.data_pipeline._attach_to_model(self, RunningStage.TRAINING)
super().on_train_dataloader()
def on_val_dataloader(self) -> None:
if self.data_pipeline is not None:
self.data_pipeline._detach_from_model(self,
RunningStage.VALIDATING)
self.data_pipeline._attach_to_model(self, RunningStage.VALIDATING)
super().on_val_dataloader()
def on_test_dataloader(self, *_) -> None:
if self.data_pipeline is not None:
self.data_pipeline._detach_from_model(self, RunningStage.TESTING)
self.data_pipeline._attach_to_model(self, RunningStage.TESTING)
super().on_test_dataloader()
def on_predict_dataloader(self) -> None:
if self.data_pipeline is not None:
self.data_pipeline._detach_from_model(self,
RunningStage.PREDICTING)
self.data_pipeline._attach_to_model(self, RunningStage.PREDICTING)
super().on_predict_dataloader()
def on_predict_end(self) -> None:
if self.data_pipeline is not None:
self.data_pipeline._detach_from_model(self)
super().on_predict_end()
def on_fit_end(self) -> None:
if self.data_pipeline is not None:
self.data_pipeline._detach_from_model(self)
super().on_fit_end()
@staticmethod
def _sanetize_funcs(obj: Any) -> Any:
if hasattr(obj, "__dict__"):
for k, v in obj.__dict__.items():
if isinstance(v, Callable):
obj.__dict__[k] = inspect.unwrap(v)
return obj
def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
# TODO: Is this the best way to do this? or should we also use some kind of hparams here?
# This may be an issue since here we create the same problems with pickle as in
# https://pytorch.org/docs/stable/notes/serialization.html
if self.data_pipeline is not None and 'data_pipeline' not in checkpoint:
self._preprocess = self._sanetize_funcs(self._preprocess)
checkpoint['data_pipeline'] = self.data_pipeline
# todo (tchaton) re-wrap visualization
super().on_save_checkpoint(checkpoint)
def on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
super().on_load_checkpoint(checkpoint)
if 'data_pipeline' in checkpoint:
self.data_pipeline = checkpoint['data_pipeline']
def preprocess(self, preprocess: Preprocess) -> None:
self._preprocess = preprocess
self.data_pipeline = DataPipeline(preprocess, self.postprocess)
def data_pipeline(self) -> DataPipeline:
return DataPipeline(self.preprocess, self.postprocess)
def test_data_pipeline_is_overriden_and_resolve_function_hierarchy(tmpdir):
class CustomPreprocess(Preprocess):
def load_data(self, *_, **__):
pass
def test_load_data(self, *_, **__):
pass
def predict_load_data(self, *_, **__):
pass
def predict_load_sample(self, *_, **__):
pass
def val_load_sample(self, *_, **__):
pass
def val_pre_tensor_transform(self, *_, **__):
pass
def predict_to_tensor_transform(self, *_, **__):
pass
def train_post_tensor_transform(self, *_, **__):
pass
def test_collate(self, *_, **__):
pass
def val_per_sample_transform_on_device(self, *_, **__):
pass
def train_per_batch_transform_on_device(self, *_, **__):
pass
def test_per_batch_transform_on_device(self, *_, **__):
pass
preprocess = CustomPreprocess()
data_pipeline = DataPipeline(preprocess)
train_func_names = {
k: data_pipeline._resolve_function_hierarchy(
k, data_pipeline._preprocess_pipeline, RunningStage.TRAINING, Preprocess
)
for k in data_pipeline.PREPROCESS_FUNCS
}
val_func_names = {
k: data_pipeline._resolve_function_hierarchy(
k, data_pipeline._preprocess_pipeline, RunningStage.VALIDATING, Preprocess
)
for k in data_pipeline.PREPROCESS_FUNCS
}
test_func_names = {
k: data_pipeline._resolve_function_hierarchy(
k, data_pipeline._preprocess_pipeline, RunningStage.TESTING, Preprocess
)
for k in data_pipeline.PREPROCESS_FUNCS
}
predict_func_names = {
k: data_pipeline._resolve_function_hierarchy(
k, data_pipeline._preprocess_pipeline, RunningStage.PREDICTING, Preprocess
)
for k in data_pipeline.PREPROCESS_FUNCS
}
# load_data
assert train_func_names["load_data"] == "load_data"
assert val_func_names["load_data"] == "load_data"
assert test_func_names["load_data"] == "test_load_data"
assert predict_func_names["load_data"] == "predict_load_data"
# load_sample
assert train_func_names["load_sample"] == "load_sample"
assert val_func_names["load_sample"] == "val_load_sample"
assert test_func_names["load_sample"] == "load_sample"
assert predict_func_names["load_sample"] == "predict_load_sample"
# pre_tensor_transform
assert train_func_names["pre_tensor_transform"] == "pre_tensor_transform"
assert val_func_names["pre_tensor_transform"] == "val_pre_tensor_transform"
assert test_func_names["pre_tensor_transform"] == "pre_tensor_transform"
assert predict_func_names["pre_tensor_transform"] == "pre_tensor_transform"
# to_tensor_transform
assert train_func_names["to_tensor_transform"] == "to_tensor_transform"
assert val_func_names["to_tensor_transform"] == "to_tensor_transform"
assert test_func_names["to_tensor_transform"] == "to_tensor_transform"
assert predict_func_names["to_tensor_transform"] == "predict_to_tensor_transform"
# post_tensor_transform
assert train_func_names["post_tensor_transform"] == "train_post_tensor_transform"
assert val_func_names["post_tensor_transform"] == "post_tensor_transform"
assert test_func_names["post_tensor_transform"] == "post_tensor_transform"
assert predict_func_names["post_tensor_transform"] == "post_tensor_transform"
# collate
assert train_func_names["collate"] == "collate"
assert val_func_names["collate"] == "collate"
assert test_func_names["collate"] == "test_collate"
assert predict_func_names["collate"] == "collate"
# per_sample_transform_on_device
assert train_func_names["per_sample_transform_on_device"] == "per_sample_transform_on_device"
assert val_func_names["per_sample_transform_on_device"] == "val_per_sample_transform_on_device"
assert test_func_names["per_sample_transform_on_device"] == "per_sample_transform_on_device"
assert predict_func_names["per_sample_transform_on_device"] == "per_sample_transform_on_device"
# per_batch_transform_on_device
assert train_func_names["per_batch_transform_on_device"] == "train_per_batch_transform_on_device"
assert val_func_names["per_batch_transform_on_device"] == "per_batch_transform_on_device"
assert test_func_names["per_batch_transform_on_device"] == "test_per_batch_transform_on_device"
assert predict_func_names["per_batch_transform_on_device"] == "per_batch_transform_on_device"
train_worker_preprocessor = data_pipeline.worker_preprocessor(RunningStage.TRAINING)
val_worker_preprocessor = data_pipeline.worker_preprocessor(RunningStage.VALIDATING)
test_worker_preprocessor = data_pipeline.worker_preprocessor(RunningStage.TESTING)
predict_worker_preprocessor = data_pipeline.worker_preprocessor(RunningStage.PREDICTING)
_seq = train_worker_preprocessor.per_sample_transform
assert _seq.pre_tensor_transform.func == preprocess.pre_tensor_transform
assert _seq.to_tensor_transform.func == preprocess.to_tensor_transform
assert _seq.post_tensor_transform.func == preprocess.train_post_tensor_transform
assert train_worker_preprocessor.collate_fn.func == default_collate
assert train_worker_preprocessor.per_batch_transform.func == preprocess.per_batch_transform
_seq = val_worker_preprocessor.per_sample_transform
assert _seq.pre_tensor_transform.func == preprocess.val_pre_tensor_transform
assert _seq.to_tensor_transform.func == preprocess.to_tensor_transform
assert _seq.post_tensor_transform.func == preprocess.post_tensor_transform
assert val_worker_preprocessor.collate_fn.func == data_pipeline._identity
assert val_worker_preprocessor.per_batch_transform.func == preprocess.per_batch_transform
_seq = test_worker_preprocessor.per_sample_transform
assert _seq.pre_tensor_transform.func == preprocess.pre_tensor_transform
assert _seq.to_tensor_transform.func == preprocess.to_tensor_transform
assert _seq.post_tensor_transform.func == preprocess.post_tensor_transform
assert test_worker_preprocessor.collate_fn.func == preprocess.test_collate
assert test_worker_preprocessor.per_batch_transform.func == preprocess.per_batch_transform
_seq = predict_worker_preprocessor.per_sample_transform
assert _seq.pre_tensor_transform.func == preprocess.pre_tensor_transform
assert _seq.to_tensor_transform.func == preprocess.predict_to_tensor_transform
assert _seq.post_tensor_transform.func == preprocess.post_tensor_transform
assert predict_worker_preprocessor.collate_fn.func == default_collate
assert predict_worker_preprocessor.per_batch_transform.func == preprocess.per_batch_transform
def test_attaching_datapipeline_to_model(tmpdir):
preprocess = TestPreprocess()
data_pipeline = DataPipeline(preprocess)
class TestModel(CustomModel):
stages = [RunningStage.TRAINING, RunningStage.VALIDATING, RunningStage.TESTING, RunningStage.PREDICTING]
on_train_start_called = False
on_val_start_called = False
on_test_start_called = False
on_predict_start_called = False
def on_fit_start(self):
assert self.predict_step.__self__ == self
self._saved_predict_step = self.predict_step
def _compare_pre_processor(self, p1, p2):
p1_seq = p1.per_sample_transform
p2_seq = p2.per_sample_transform
assert p1_seq.pre_tensor_transform.func == p2_seq.pre_tensor_transform.func
assert p1_seq.to_tensor_transform.func == p2_seq.to_tensor_transform.func
assert p1_seq.post_tensor_transform.func == p2_seq.post_tensor_transform.func
assert p1.collate_fn.func == p2.collate_fn.func
assert p1.per_batch_transform.func == p2.per_batch_transform.func
def _assert_stage_orchestrator_state(
self, stage_mapping: Dict, current_running_stage: RunningStage, cls=_PreProcessor
):
assert isinstance(stage_mapping[current_running_stage], cls)
assert stage_mapping[current_running_stage]
def on_train_dataloader(self) -> None:
current_running_stage = RunningStage.TRAINING
self.on_train_dataloader_called = True
collate_fn = self.train_dataloader().collate_fn # noqa F811
assert collate_fn == default_collate
assert not isinstance(self.transfer_batch_to_device, _StageOrchestrator)
super().on_train_dataloader()
collate_fn = self.train_dataloader().collate_fn # noqa F811
assert collate_fn.stage == current_running_stage
self._compare_pre_processor(collate_fn, self.data_pipeline.worker_preprocessor(current_running_stage))
assert isinstance(self.transfer_batch_to_device, _StageOrchestrator)
self._assert_stage_orchestrator_state(self.transfer_batch_to_device._stage_mapping, current_running_stage)
def on_val_dataloader(self) -> None:
current_running_stage = RunningStage.VALIDATING
self.on_val_dataloader_called = True
collate_fn = self.val_dataloader().collate_fn # noqa F811
assert collate_fn == default_collate
assert isinstance(self.transfer_batch_to_device, _StageOrchestrator)
super().on_val_dataloader()
collate_fn = self.val_dataloader().collate_fn # noqa F811
assert collate_fn.stage == current_running_stage
self._compare_pre_processor(collate_fn, self.data_pipeline.worker_preprocessor(current_running_stage))
assert isinstance(self.transfer_batch_to_device, _StageOrchestrator)
self._assert_stage_orchestrator_state(self.transfer_batch_to_device._stage_mapping, current_running_stage)
def on_test_dataloader(self) -> None:
current_running_stage = RunningStage.TESTING
self.on_test_dataloader_called = True
collate_fn = self.test_dataloader().collate_fn # noqa F811
assert collate_fn == default_collate
assert not isinstance(self.transfer_batch_to_device, _StageOrchestrator)
super().on_test_dataloader()
collate_fn = self.test_dataloader().collate_fn # noqa F811
assert collate_fn.stage == current_running_stage
self._compare_pre_processor(collate_fn, self.data_pipeline.worker_preprocessor(current_running_stage))
assert isinstance(self.transfer_batch_to_device, _StageOrchestrator)
self._assert_stage_orchestrator_state(self.transfer_batch_to_device._stage_mapping, current_running_stage)
def on_predict_dataloader(self) -> None:
current_running_stage = RunningStage.PREDICTING
self.on_predict_dataloader_called = True
collate_fn = self.predict_dataloader().collate_fn # noqa F811
assert collate_fn == default_collate
assert isinstance(self.transfer_batch_to_device, _StageOrchestrator)
assert self.predict_step == self._saved_predict_step
super().on_predict_dataloader()
collate_fn = self.predict_dataloader().collate_fn # noqa F811
assert collate_fn.stage == current_running_stage
self._compare_pre_processor(collate_fn, self.data_pipeline.worker_preprocessor(current_running_stage))
assert isinstance(self.transfer_batch_to_device, _StageOrchestrator)
assert isinstance(self.predict_step, _StageOrchestrator)
self._assert_stage_orchestrator_state(self.transfer_batch_to_device._stage_mapping, current_running_stage)
self._assert_stage_orchestrator_state(
self.predict_step._stage_mapping, current_running_stage, cls=_PostProcessor
)
def on_fit_end(self) -> None:
super().on_fit_end()
assert self.train_dataloader().collate_fn == default_collate
assert self.val_dataloader().collate_fn == default_collate
assert self.test_dataloader().collate_fn == default_collate
assert self.predict_dataloader().collate_fn == default_collate
assert not isinstance(self.transfer_batch_to_device, _StageOrchestrator)
assert self.predict_step == self._saved_predict_step
datamodule = CustomDataModule()
datamodule._data_pipeline = data_pipeline
model = TestModel()
trainer = Trainer(fast_dev_run=True)
trainer.fit(model, datamodule=datamodule)
trainer.test(model)
trainer.predict(model)
assert model.on_train_dataloader_called
assert model.on_val_dataloader_called
assert model.on_test_dataloader_called
assert model.on_predict_dataloader_called
def postprocess(self, postprocess: Postprocess) -> None:
self.data_pipeline = DataPipeline(self.preprocess, postprocess)
self._postprocess = postprocess
def build_data_pipeline(
self,
data_source: Optional[str] = None,
data_pipeline: Optional[DataPipeline] = None,
) -> Optional[DataPipeline]:
"""Build a :class:`.DataPipeline` incorporating available
:class:`~flash.data.process.Preprocess` and :class:`~flash.data.process.Postprocess`
objects. These will be overridden in the following resolution order (lowest priority first):
- Lightning ``Datamodule``, either attached to the :class:`.Trainer` or to the :class:`.Task`.
- :class:`.Task` defaults given to ``.Task.__init__``.
- :class:`.Task` manual overrides by setting :py:attr:`~data_pipeline`.
- :class:`.DataPipeline` passed to this method.
Args:
data_pipeline: Optional highest priority source of
:class:`~flash.data.process.Preprocess` and :class:`~flash.data.process.Postprocess`.
Returns:
The fully resolved :class:`.DataPipeline`.
"""
old_data_source, preprocess, postprocess, serializer = None, None, None, None
# Datamodule
if self.datamodule is not None and getattr(
self.datamodule, 'data_pipeline', None) is not None:
old_data_source = getattr(self.datamodule.data_pipeline,
'data_source', None)
preprocess = getattr(self.datamodule.data_pipeline,
'_preprocess_pipeline', None)
postprocess = getattr(self.datamodule.data_pipeline,
'_postprocess_pipeline', None)
serializer = getattr(self.datamodule.data_pipeline, '_serializer',
None)
elif self.trainer is not None and hasattr(
self.trainer, 'datamodule') and getattr(
self.trainer.datamodule, 'data_pipeline',
None) is not None:
old_data_source = getattr(self.trainer.datamodule.data_pipeline,
'data_source', None)
preprocess = getattr(self.trainer.datamodule.data_pipeline,
'_preprocess_pipeline', None)
postprocess = getattr(self.trainer.datamodule.data_pipeline,
'_postprocess_pipeline', None)
serializer = getattr(self.trainer.datamodule.data_pipeline,
'_serializer', None)
else:
# TODO: we should log with low severity level that we use defaults to create
# `preprocess`, `postprocess` and `serializer`.
pass
# Defaults / task attributes
preprocess, postprocess, serializer = Task._resolve(
preprocess,
postprocess,
serializer,
self._preprocess,
self._postprocess,
self.serializer,
)
# Datapipeline
if data_pipeline is not None:
preprocess, postprocess, serializer = Task._resolve(
preprocess,
postprocess,
serializer,
getattr(data_pipeline, '_preprocess_pipeline', None),
getattr(data_pipeline, '_postprocess_pipeline', None),
getattr(data_pipeline, '_serializer', None),
)
data_source = data_source or old_data_source
if isinstance(data_source, str):
if preprocess is None:
data_source = DataSource(
) # TODO: warn the user that we are not using the specified data source
else:
data_source = preprocess.data_source_of_name(data_source)
data_pipeline = DataPipeline(data_source, preprocess, postprocess,
serializer)
self._data_pipeline_state = data_pipeline.initialize(
self._data_pipeline_state)
return data_pipeline
def test_preprocess_transforms(tmpdir):
"""
This test makes sure that when a preprocess is being provided transforms as dictionaries,
checking is done properly, and collate_in_worker_from_transform is properly extracted.
"""
with pytest.raises(MisconfigurationException,
match="Transform should be a dict."):
DefaultPreprocess(train_transform="choco")
with pytest.raises(MisconfigurationException,
match="train_transform contains {'choco'}. Only"):
DefaultPreprocess(train_transform={"choco": None})
preprocess = DefaultPreprocess(
train_transform={"to_tensor_transform": torch.nn.Linear(1, 1)})
# keep is None
assert preprocess._train_collate_in_worker_from_transform is True
assert preprocess._val_collate_in_worker_from_transform is None
assert preprocess._test_collate_in_worker_from_transform is None
assert preprocess._predict_collate_in_worker_from_transform is None
with pytest.raises(
MisconfigurationException,
match="`per_batch_transform` and `per_sample_transform_on_device`"
):
preprocess = DefaultPreprocess(
train_transform={
"per_batch_transform": torch.nn.Linear(1, 1),
"per_sample_transform_on_device": torch.nn.Linear(1, 1)
})
preprocess = DefaultPreprocess(
train_transform={"per_batch_transform": torch.nn.Linear(1, 1)},
predict_transform={
"per_sample_transform_on_device": torch.nn.Linear(1, 1)
})
# keep is None
assert preprocess._train_collate_in_worker_from_transform is True
assert preprocess._val_collate_in_worker_from_transform is None
assert preprocess._test_collate_in_worker_from_transform is None
assert preprocess._predict_collate_in_worker_from_transform is False
train_preprocessor = DataPipeline(
preprocess=preprocess).worker_preprocessor(RunningStage.TRAINING)
val_preprocessor = DataPipeline(preprocess=preprocess).worker_preprocessor(
RunningStage.VALIDATING)
test_preprocessor = DataPipeline(
preprocess=preprocess).worker_preprocessor(RunningStage.TESTING)
predict_preprocessor = DataPipeline(
preprocess=preprocess).worker_preprocessor(RunningStage.PREDICTING)
assert train_preprocessor.collate_fn.func == default_collate
assert val_preprocessor.collate_fn.func == default_collate
assert test_preprocessor.collate_fn.func == default_collate
assert predict_preprocessor.collate_fn.func == DataPipeline._identity
class CustomPreprocess(DefaultPreprocess):
def per_sample_transform_on_device(self, sample: Any) -> Any:
return super().per_sample_transform_on_device(sample)
def per_batch_transform(self, batch: Any) -> Any:
return super().per_batch_transform(batch)
preprocess = CustomPreprocess(
train_transform={"per_batch_transform": torch.nn.Linear(1, 1)},
predict_transform={
"per_sample_transform_on_device": torch.nn.Linear(1, 1)
})
# keep is None
assert preprocess._train_collate_in_worker_from_transform is True
assert preprocess._val_collate_in_worker_from_transform is None
assert preprocess._test_collate_in_worker_from_transform is None
assert preprocess._predict_collate_in_worker_from_transform is False
data_pipeline = DataPipeline(preprocess=preprocess)
train_preprocessor = data_pipeline.worker_preprocessor(
RunningStage.TRAINING)
with pytest.raises(
MisconfigurationException,
match="`per_batch_transform` and `per_sample_transform_on_device`"
):
val_preprocessor = data_pipeline.worker_preprocessor(
RunningStage.VALIDATING)
with pytest.raises(
MisconfigurationException,
match="`per_batch_transform` and `per_sample_transform_on_device`"
):
test_preprocessor = data_pipeline.worker_preprocessor(
RunningStage.TESTING)
predict_preprocessor = data_pipeline.worker_preprocessor(
RunningStage.PREDICTING)
assert train_preprocessor.collate_fn.func == default_collate
assert predict_preprocessor.collate_fn.func == DataPipeline._identity
def build_data_pipeline(
self,
data_pipeline: Optional[DataPipeline] = None
) -> Optional[DataPipeline]:
"""Build a :class:`.DataPipeline` incorporating available
:class:`~flash.data.process.Preprocess` and :class:`~flash.data.process.Postprocess`
objects. These will be overridden in the following resolution order (lowest priority first):
- Lightning ``Datamodule``, either attached to the :class:`.Trainer` or to the :class:`.Task`.
- :class:`.Task` defaults given to ``.Task.__init__``.
- :class:`.Task` manual overrides by setting :py:attr:`~data_pipeline`.
- :class:`.DataPipeline` passed to this method.
Args:
data_pipeline: Optional highest priority source of
:class:`~flash.data.process.Preprocess` and :class:`~flash.data.process.Postprocess`.
Returns:
The fully resolved :class:`.DataPipeline`.
"""
preprocess, postprocess, serializer = None, None, None
# Datamodule
if self.datamodule is not None and getattr(
self.datamodule, 'data_pipeline', None) is not None:
preprocess = getattr(self.datamodule.data_pipeline,
'_preprocess_pipeline', None)
postprocess = getattr(self.datamodule.data_pipeline,
'_postprocess_pipeline', None)
serializer = getattr(self.datamodule.data_pipeline, '_serializer',
None)
elif self.trainer is not None and hasattr(
self.trainer, 'datamodule') and getattr(
self.trainer.datamodule, 'data_pipeline',
None) is not None:
preprocess = getattr(self.trainer.datamodule.data_pipeline,
'_preprocess_pipeline', None)
postprocess = getattr(self.trainer.datamodule.data_pipeline,
'_postprocess_pipeline', None)
serializer = getattr(self.trainer.datamodule.data_pipeline,
'_serializer', None)
# Defaults / task attributes
preprocess, postprocess, serializer = Task._resolve(
preprocess,
postprocess,
serializer,
self._preprocess,
self._postprocess,
self.serializer,
)
# Datapipeline
if data_pipeline is not None:
preprocess, postprocess, serializer = Task._resolve(
preprocess,
postprocess,
serializer,
getattr(data_pipeline, '_preprocess_pipeline', None),
getattr(data_pipeline, '_postprocess_pipeline', None),
getattr(data_pipeline, '_serializer', None),
)
data_pipeline = DataPipeline(preprocess, postprocess, serializer)
data_pipeline.initialize()
return data_pipeline
