[RFC] Fix capitalisation of Pre/PostProcessor (#366)

* Replace Pre/PostProcess with Pre/Postprocess

* Replace Pre/PostProcess with Pre/Postprocess

* Replace Pre/PostProcess with Pre/Postprocess

Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com>

docs/source/general/data.rst

@@ -393,7 +393,7 @@ Flash takes care of calling the right hooks for each stage.
 
 Example::
 
-    # This will be wrapped into a :class:`~flash.core.data.batch._PreProcessor`.
+    # This will be wrapped into a :class:`~flash.core.data.batch._Preprocessor`.
     def collate_fn(samples: Sequence[Any]) -> Any:
 
         # This will be wrapped into a :class:`~flash.core.data.batch._Sequential`
@@ -423,7 +423,7 @@ Flash takes care of calling the right hooks for each stage.
 
 Example::
 
-    # This will be wrapped into a :class:`~flash.core.data.batch._PreProcessor`
+    # This will be wrapped into a :class:`~flash.core.data.batch._Preprocessor`
     def collate_fn(samples: Sequence[Any]) -> Any:
 
         # if ``per_batch_transform`` hook is overridden, those functions below will be no-ops
@@ -459,7 +459,7 @@ Here is the pseudo-code:
 
 Example::
 
-    # This will be wrapped into a :class:`~flash.core.data.batch._PreProcessor`
+    # This will be wrapped into a :class:`~flash.core.data.batch._Preprocessor`
     def uncollate_fn(batch: Any) -> Any:
 
         batch = per_batch_transform(batch)

flash/core/data/batch.py

@@ -98,7 +98,7 @@ def __str__(self) -> str:
         )
 
 
-class _PreProcessor(torch.nn.Module):
+class _Preprocessor(torch.nn.Module):
     """
         This class is used to encapsultate the following functions of a Preprocess Object:
         Inside a worker:
@@ -190,7 +190,7 @@ def forward(self, samples: Sequence[Any]) -> Any:
     def __str__(self) -> str:
         # todo: define repr function which would take object and string attributes to be shown
         return (
-            "_PreProcessor:\n"
+            "_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"
@@ -200,7 +200,7 @@ def __str__(self) -> str:
         )
 
 
-class _PostProcessor(torch.nn.Module):
+class _Postprocessor(torch.nn.Module):
     """
         This class is used to encapsultate the following functions of a Postprocess Object:
         Inside main process:
@@ -245,7 +245,7 @@ def forward(self, batch: Sequence[Any]):
 
     def __str__(self) -> str:
         return (
-            "_PostProcessor:\n"
+            "_Postprocessor:\n"
             f"\t(per_batch_transform): {str(self.per_batch_transform)}\n"
             f"\t(uncollate_fn): {str(self.uncollate_fn)}\n"
             f"\t(per_sample_transform): {str(self.per_sample_transform)}\n"

flash/core/data/data_pipeline.py

@@ -25,7 +25,7 @@
 from torch.utils.data._utils.collate import default_collate
 
 from flash.core.data.auto_dataset import IterableAutoDataset
-from flash.core.data.batch import _PostProcessor, _PreProcessor, _Sequential
+from flash.core.data.batch import _Postprocessor, _Preprocessor, _Sequential
 from flash.core.data.data_source import DataSource
 from flash.core.data.process import DefaultPreprocess, Postprocess, Preprocess, Serializer
 from flash.core.data.properties import ProcessState
@@ -163,13 +163,13 @@ def _is_overriden_recursive(
     def _identity(samples: Sequence[Any]) -> Sequence[Any]:
         return samples
 
-    def worker_preprocessor(self, running_stage: RunningStage) -> _PreProcessor:
+    def worker_preprocessor(self, running_stage: RunningStage) -> _Preprocessor:
         return self._create_collate_preprocessors(running_stage)[0]
 
-    def device_preprocessor(self, running_stage: RunningStage) -> _PreProcessor:
+    def device_preprocessor(self, running_stage: RunningStage) -> _Preprocessor:
         return self._create_collate_preprocessors(running_stage)[1]
 
-    def postprocessor(self, running_stage: RunningStage) -> _PostProcessor:
+    def postprocessor(self, running_stage: RunningStage) -> _Postprocessor:
         return self._create_uncollate_postprocessors(running_stage)
 
     @classmethod
@@ -208,7 +208,7 @@ def _create_collate_preprocessors(
         self,
         stage: RunningStage,
         collate_fn: Optional[Callable] = None,
-    ) -> Tuple[_PreProcessor, _PreProcessor]:
+    ) -> Tuple[_Preprocessor, _Preprocessor]:
 
         original_collate_fn = collate_fn
 
@@ -254,14 +254,14 @@ def _create_collate_preprocessors(
             )
 
         worker_collate_fn = worker_collate_fn.collate_fn if isinstance(
-            worker_collate_fn, _PreProcessor
+            worker_collate_fn, _Preprocessor
         ) else worker_collate_fn
 
         assert_contains_tensor = self._is_overriden_recursive(
             "to_tensor_transform", preprocess, Preprocess, prefix=_STAGES_PREFIX[stage]
         )
 
-        worker_preprocessor = _PreProcessor(
+        worker_preprocessor = _Preprocessor(
             preprocess, worker_collate_fn,
             _Sequential(
                 preprocess,
@@ -273,7 +273,7 @@ def _create_collate_preprocessors(
             ), getattr(preprocess, func_names['per_batch_transform']), stage
         )
         worker_preprocessor._original_collate_fn = original_collate_fn
-        device_preprocessor = _PreProcessor(
+        device_preprocessor = _Preprocessor(
             preprocess,
             device_collate_fn,
             getattr(preprocess, func_names['per_sample_transform_on_device']),
@@ -286,7 +286,7 @@ def _create_collate_preprocessors(
 
     @staticmethod
     def _model_transfer_to_device_wrapper(
-        func: Callable, preprocessor: _PreProcessor, model: 'Task', stage: RunningStage
+        func: Callable, preprocessor: _Preprocessor, model: 'Task', stage: RunningStage
     ) -> Callable:
 
         if not isinstance(func, _StageOrchestrator):
@@ -296,7 +296,7 @@ def _model_transfer_to_device_wrapper(
         return func
 
     @staticmethod
-    def _model_predict_step_wrapper(func: Callable, postprocessor: _PostProcessor, model: 'Task') -> Callable:
+    def _model_predict_step_wrapper(func: Callable, postprocessor: _Postprocessor, model: 'Task') -> Callable:
 
         if not isinstance(func, _StageOrchestrator):
             _original = func
@@ -400,7 +400,7 @@ def _attach_preprocess_to_model(
                 self._model_transfer_to_device_wrapper(model.transfer_batch_to_device, device_collate_fn, model, stage)
             )
 
-    def _create_uncollate_postprocessors(self, stage: RunningStage) -> _PostProcessor:
+    def _create_uncollate_postprocessors(self, stage: RunningStage) -> _Postprocessor:
         save_per_sample = None
         save_fn = None
 
@@ -422,7 +422,7 @@ def _create_uncollate_postprocessors(self, stage: RunningStage) -> _PostProcesso
             else:
                 save_fn: Callable = getattr(postprocess, func_names["save_data"])
 
-        return _PostProcessor(
+        return _Postprocessor(
             getattr(postprocess, func_names["uncollate"]),
             getattr(postprocess, func_names["per_batch_transform"]),
             getattr(postprocess, func_names["per_sample_transform"]),
@@ -491,7 +491,7 @@ def _detach_preprocessing_from_model(self, model: 'Task', stage: Optional[Runnin
                 if isinstance(loader, DataLoader):
                     dl_args = {k: v for k, v in vars(loader).items() if not k.startswith("_")}
 
-                    if isinstance(dl_args['collate_fn'], _PreProcessor):
+                    if isinstance(dl_args['collate_fn'], _Preprocessor):
                         dl_args["collate_fn"] = dl_args["collate_fn"]._original_collate_fn
 
                         if isinstance(dl_args["dataset"], IterableAutoDataset):

tests/core/data/test_batch.py

@@ -19,7 +19,7 @@
 from torch.testing import assert_allclose
 from torch.utils.data._utils.collate import default_collate
 
-from flash.core.data.batch import _PostProcessor, _PreProcessor, _Sequential, default_uncollate
+from flash.core.data.batch import _Postprocessor, _Preprocessor, _Sequential, default_uncollate
 
 
 def test_sequential_str():
@@ -42,7 +42,7 @@ def test_sequential_str():
 
 
 def test_preprocessor_str():
-    preprocessor = _PreProcessor(
+    preprocessor = _Preprocessor(
         Mock(name="preprocess"),
         default_collate,
         torch.relu,
@@ -52,7 +52,7 @@ def test_preprocessor_str():
         True,
     )
     assert str(preprocessor) == (
-        "_PreProcessor:\n"
+        "_Preprocessor:\n"
         "\t(per_sample_transform): FuncModule(relu)\n"
         "\t(collate_fn): FuncModule(default_collate)\n"
         "\t(per_batch_transform): FuncModule(softmax)\n"
@@ -63,14 +63,14 @@ def test_preprocessor_str():
 
 
 def test_postprocessor_str():
-    postprocessor = _PostProcessor(
+    postprocessor = _Postprocessor(
         default_uncollate,
         torch.relu,
         torch.softmax,
         None,
     )
     assert str(postprocessor) == (
-        "_PostProcessor:\n"
+        "_Postprocessor:\n"
         "\t(per_batch_transform): FuncModule(relu)\n"
         "\t(uncollate_fn): FuncModule(default_uncollate)\n"
         "\t(per_sample_transform): FuncModule(softmax)\n"

tests/core/data/test_data_pipeline.py

@@ -26,7 +26,7 @@
 from torch.utils.data._utils.collate import default_collate
 
 from flash.core.data.auto_dataset import IterableAutoDataset
-from flash.core.data.batch import _PostProcessor, _PreProcessor
+from flash.core.data.batch import _Postprocessor, _Preprocessor
 from flash.core.data.data_module import DataModule
 from flash.core.data.data_pipeline import _StageOrchestrator, DataPipeline, DataPipelineState
 from flash.core.data.data_source import DataSource
@@ -318,7 +318,7 @@ def train_dataloader(self) -> Any:
     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.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
@@ -412,7 +412,7 @@ def _compare_pre_processor(self, p1, p2):
             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
+            self, stage_mapping: Dict, current_running_stage: RunningStage, cls=_Preprocessor
         ):
             assert isinstance(stage_mapping[current_running_stage], cls)
             assert stage_mapping[current_running_stage]
@@ -471,7 +471,7 @@ def on_predict_dataloader(self) -> None:
             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
+                self.predict_step._stage_mapping, current_running_stage, cls=_Postprocessor
             )
 
         def on_fit_end(self) -> None:
@@ -505,7 +505,7 @@ def test_stage_orchestrator_state_attach_detach(tmpdir):
 
     class CustomDataPipeline(DataPipeline):
 
-        def _attach_postprocess_to_model(self, model: 'Task', _postprocesssor: _PostProcessor) -> 'Task':
+        def _attach_postprocess_to_model(self, model: 'Task', _postprocesssor: _Postprocessor) -> 'Task':
             model.predict_step = self._model_predict_step_wrapper(model.predict_step, _postprocesssor, model)
             return model