def __init__(
self,
preprocess: "Preprocess",
pre_tensor_transform: Optional[Callable],
to_tensor_transform: Optional[Callable],
post_tensor_transform: Callable,
stage: RunningStage,
assert_contains_tensor: bool = False,
):
super().__init__()
self.preprocess = preprocess
self.callback = ControlFlow(self.preprocess.callbacks)
self.pre_tensor_transform = convert_to_modules(pre_tensor_transform)
self.to_tensor_transform = convert_to_modules(to_tensor_transform)
self.post_tensor_transform = convert_to_modules(post_tensor_transform)
self.stage = stage
self.assert_contains_tensor = assert_contains_tensor
self._current_stage_context = CurrentRunningStageContext(stage,
preprocess,
reset=False)
self._pre_tensor_transform_context = CurrentFuncContext(
"pre_tensor_transform", preprocess)
self._to_tensor_transform_context = CurrentFuncContext(
"to_tensor_transform", preprocess)
self._post_tensor_transform_context = CurrentFuncContext(
"post_tensor_transform", preprocess)
def __init__(
self,
preprocess: "Preprocess",
collate_fn: Callable,
per_sample_transform: Union[Callable, _Sequential],
per_batch_transform: Callable,
stage: RunningStage,
apply_per_sample_transform: bool = True,
on_device: bool = False,
):
super().__init__()
self.preprocess = preprocess
self.callback = ControlFlow(self.preprocess.callbacks)
self.collate_fn = convert_to_modules(collate_fn)
self.per_sample_transform = convert_to_modules(per_sample_transform)
self.per_batch_transform = convert_to_modules(per_batch_transform)
self.apply_per_sample_transform = apply_per_sample_transform
self.stage = stage
self.on_device = on_device
extension = f"{'_on_device' if self.on_device else ''}"
self._current_stage_context = CurrentRunningStageContext(
stage, preprocess)
self._per_sample_transform_context = CurrentFuncContext(
f"per_sample_transform{extension}", preprocess)
self._collate_context = CurrentFuncContext("collate", preprocess)
self._per_batch_transform_context = CurrentFuncContext(
f"per_batch_transform{extension}", preprocess)
def __init__(
self,
input_transform: InputTransform,
collate_fn: Callable,
per_sample_transform: Callable,
per_batch_transform: Callable,
stage: RunningStage,
apply_per_sample_transform: bool = True,
on_device: bool = False,
):
super().__init__()
self.input_transform = input_transform
self.callback = ControlFlow(self.input_transform.callbacks or [])
self.collate_fn = collate_fn
self.per_sample_transform = per_sample_transform
self.per_batch_transform = per_batch_transform
self.apply_per_sample_transform = apply_per_sample_transform
self.stage = stage
self.on_device = on_device
def __init__(
self,
deserializer: "Deserializer",
preprocess: "Preprocess",
pre_tensor_transform: Callable,
to_tensor_transform: Callable,
):
super().__init__()
self.preprocess = preprocess
self.callback = ControlFlow(self.preprocess.callbacks)
self.deserializer = convert_to_modules(deserializer)
self.pre_tensor_transform = convert_to_modules(pre_tensor_transform)
self.to_tensor_transform = convert_to_modules(to_tensor_transform)
self._current_stage_context = CurrentRunningStageContext(
RunningStage.PREDICTING, preprocess, reset=False)
self._pre_tensor_transform_context = CurrentFuncContext(
"pre_tensor_transform", preprocess)
self._to_tensor_transform_context = CurrentFuncContext(
"to_tensor_transform", preprocess)
class _DeserializeProcessor(torch.nn.Module):
def __init__(
self,
deserializer: "Deserializer",
preprocess: "Preprocess",
pre_tensor_transform: Callable,
to_tensor_transform: Callable,
):
super().__init__()
self.preprocess = preprocess
self.callback = ControlFlow(self.preprocess.callbacks)
self.deserializer = convert_to_modules(deserializer)
self.pre_tensor_transform = convert_to_modules(pre_tensor_transform)
self.to_tensor_transform = convert_to_modules(to_tensor_transform)
self._current_stage_context = CurrentRunningStageContext(
RunningStage.PREDICTING, preprocess, reset=False)
self._pre_tensor_transform_context = CurrentFuncContext(
"pre_tensor_transform", preprocess)
self._to_tensor_transform_context = CurrentFuncContext(
"to_tensor_transform", preprocess)
def forward(self, sample: str):
sample = self.deserializer(sample)
with self._current_stage_context:
with self._pre_tensor_transform_context:
sample = self.pre_tensor_transform(sample)
self.callback.on_pre_tensor_transform(sample,
RunningStage.PREDICTING)
with self._to_tensor_transform_context:
sample = self.to_tensor_transform(sample)
self.callback.on_to_tensor_transform(sample,
RunningStage.PREDICTING)
return sample
class _Sequential(torch.nn.Module):
"""This class is used to chain 3 functions together for the _Preprocessor ``per_sample_transform`` function.
1. ``pre_tensor_transform``
2. ``to_tensor_transform``
3. ``post_tensor_transform``
"""
def __init__(
self,
preprocess: "Preprocess",
pre_tensor_transform: Optional[Callable],
to_tensor_transform: Optional[Callable],
post_tensor_transform: Callable,
stage: RunningStage,
assert_contains_tensor: bool = False,
):
super().__init__()
self.preprocess = preprocess
self.callback = ControlFlow(self.preprocess.callbacks)
self.pre_tensor_transform = convert_to_modules(pre_tensor_transform)
self.to_tensor_transform = convert_to_modules(to_tensor_transform)
self.post_tensor_transform = convert_to_modules(post_tensor_transform)
self.stage = stage
self.assert_contains_tensor = assert_contains_tensor
self._current_stage_context = CurrentRunningStageContext(stage,
preprocess,
reset=False)
self._pre_tensor_transform_context = CurrentFuncContext(
"pre_tensor_transform", preprocess)
self._to_tensor_transform_context = CurrentFuncContext(
"to_tensor_transform", preprocess)
self._post_tensor_transform_context = CurrentFuncContext(
"post_tensor_transform", preprocess)
def forward(self, sample: Any) -> Any:
self.callback.on_load_sample(sample, self.stage)
with self._current_stage_context:
if self.pre_tensor_transform is not None:
with self._pre_tensor_transform_context:
sample = self.pre_tensor_transform(sample)
self.callback.on_pre_tensor_transform(sample, self.stage)
if self.to_tensor_transform is not None:
with self._to_tensor_transform_context:
sample = self.to_tensor_transform(sample)
self.callback.on_to_tensor_transform(sample, self.stage)
if self.assert_contains_tensor:
if not _contains_any_tensor(sample):
raise MisconfigurationException(
"When ``to_tensor_transform`` is overriden, "
"``DataPipeline`` expects the outputs to be ``tensors``"
)
with self._post_tensor_transform_context:
sample = self.post_tensor_transform(sample)
self.callback.on_post_tensor_transform(sample, self.stage)
return sample
def __str__(self) -> str:
return (
f"{self.__class__.__name__}:\n"
f"\t(pre_tensor_transform): {str(self.pre_tensor_transform)}\n"
f"\t(to_tensor_transform): {str(self.to_tensor_transform)}\n"
f"\t(post_tensor_transform): {str(self.post_tensor_transform)}\n"
f"\t(assert_contains_tensor): {str(self.assert_contains_tensor)}\n"
f"\t(stage): {str(self.stage)}")
class _Preprocessor(torch.nn.Module):
"""
This class is used to encapsultate the following functions of a Preprocess Object:
Inside a worker:
per_sample_transform: Function to transform an individual sample
Inside a worker, it is actually make of 3 functions:
* pre_tensor_transform
* to_tensor_transform
* post_tensor_transform
collate: Function to merge sample into a batch
per_batch_transform: Function to transform an individual batch
* per_batch_transform
Inside main process:
per_sample_transform: Function to transform an individual sample
* per_sample_transform_on_device
collate: Function to merge sample into a batch
per_batch_transform: Function to transform an individual batch
* per_batch_transform_on_device
"""
def __init__(
self,
preprocess: "Preprocess",
collate_fn: Callable,
per_sample_transform: Union[Callable, _Sequential],
per_batch_transform: Callable,
stage: RunningStage,
apply_per_sample_transform: bool = True,
on_device: bool = False,
):
super().__init__()
self.preprocess = preprocess
self.callback = ControlFlow(self.preprocess.callbacks)
self.collate_fn = convert_to_modules(collate_fn)
self.per_sample_transform = convert_to_modules(per_sample_transform)
self.per_batch_transform = convert_to_modules(per_batch_transform)
self.apply_per_sample_transform = apply_per_sample_transform
self.stage = stage
self.on_device = on_device
extension = f"{'_on_device' if self.on_device else ''}"
self._current_stage_context = CurrentRunningStageContext(
stage, preprocess)
self._per_sample_transform_context = CurrentFuncContext(
f"per_sample_transform{extension}", preprocess)
self._collate_context = CurrentFuncContext("collate", preprocess)
self._per_batch_transform_context = CurrentFuncContext(
f"per_batch_transform{extension}", preprocess)
@staticmethod
def _extract_metadata(
samples: List[Dict[str, Any]],
) -> Tuple[List[Dict[str, Any]], Optional[List[Dict[str, Any]]]]:
metadata = [
s.pop(DefaultDataKeys.METADATA, None)
if isinstance(s, Mapping) else None for s in samples
]
return samples, metadata if any(m is not None
for m in metadata) else None
def forward(self, samples: Sequence[Any]) -> Any:
# we create a new dict to prevent from potential memory leaks
# assuming that the dictionary samples are stored in between and
# potentially modified before the transforms are applied.
if isinstance(samples, dict):
samples = dict(samples.items())
with self._current_stage_context:
if self.apply_per_sample_transform:
with self._per_sample_transform_context:
_samples = []
if isinstance(samples, Mapping):
samples = [samples]
for sample in samples:
sample = self.per_sample_transform(sample)
if self.on_device:
self.callback.on_per_sample_transform_on_device(
sample, self.stage)
_samples.append(sample)
samples = type(_samples)(_samples)
with self._collate_context:
samples, metadata = self._extract_metadata(samples)
try:
samples = self.collate_fn(samples, metadata)
except TypeError:
samples = self.collate_fn(samples)
if metadata and isinstance(samples, dict):
samples[DefaultDataKeys.METADATA] = metadata
self.callback.on_collate(samples, self.stage)
with self._per_batch_transform_context:
samples = self.per_batch_transform(samples)
if self.on_device:
self.callback.on_per_batch_transform_on_device(
samples, self.stage)
else:
self.callback.on_per_batch_transform(samples, self.stage)
return samples
def __str__(self) -> str:
# todo: define repr function which would take object and string attributes to be shown
return (
"_Preprocessor:\n"
f"\t(per_sample_transform): {str(self.per_sample_transform)}\n"
f"\t(collate_fn): {str(self.collate_fn)}\n"
f"\t(per_batch_transform): {str(self.per_batch_transform)}\n"
f"\t(apply_per_sample_transform): {str(self.apply_per_sample_transform)}\n"
f"\t(on_device): {str(self.on_device)}\n"
f"\t(stage): {str(self.stage)}")
class _InputTransformProcessor:
"""
This class is used to encapsulate the following functions of an `InputTransform` Object:
Inside a worker:
per_sample_transform: Function to transform an individual sample
collate: Function to merge sample into a batch
per_batch_transform: Function to transform an individual batch
Inside main process:
per_sample_transform_on_device: Function to transform an individual sample
collate: Function to merge sample into a batch
per_batch_transform_on_device: Function to transform an individual batch
"""
def __init__(
self,
input_transform: InputTransform,
collate_fn: Callable,
per_sample_transform: Callable,
per_batch_transform: Callable,
stage: RunningStage,
apply_per_sample_transform: bool = True,
on_device: bool = False,
):
super().__init__()
self.input_transform = input_transform
self.callback = ControlFlow(self.input_transform.callbacks or [])
self.collate_fn = collate_fn
self.per_sample_transform = per_sample_transform
self.per_batch_transform = per_batch_transform
self.apply_per_sample_transform = apply_per_sample_transform
self.stage = stage
self.on_device = on_device
def __call__(self, samples: Sequence[Any]) -> Any:
if not self.on_device:
for sample in samples:
self.callback.on_load_sample(sample, self.stage)
if self.apply_per_sample_transform:
if not isinstance(samples, list):
list_samples = [samples]
else:
list_samples = samples
transformed_samples = [
self.per_sample_transform(sample, self.stage)
for sample in list_samples
]
for sample in transformed_samples:
if self.on_device:
self.callback.on_per_sample_transform_on_device(
sample, self.stage)
else:
self.callback.on_per_sample_transform(sample, self.stage)
collated_samples = self.collate_fn(transformed_samples, self.stage)
self.callback.on_collate(collated_samples, self.stage)
else:
collated_samples = samples
transformed_collated_samples = self.per_batch_transform(
collated_samples, self.stage)
if self.on_device:
self.callback.on_per_batch_transform_on_device(
transformed_collated_samples, self.stage)
else:
self.callback.on_per_batch_transform(transformed_collated_samples,
self.stage)
return transformed_collated_samples
def __str__(self) -> str:
# todo: define repr function which would take object and string attributes to be shown
return (
"_InputTransformProcessor:\n"
f"\t(per_sample_transform): {str(self.per_sample_transform)}\n"
f"\t(collate_fn): {str(self.collate_fn)}\n"
f"\t(per_batch_transform): {str(self.per_batch_transform)}\n"
f"\t(apply_per_sample_transform): {str(self.apply_per_sample_transform)}\n"
f"\t(on_device): {str(self.on_device)}\n"
f"\t(stage): {str(self.stage)}")
