Rename Serializer to Output and move to flash.core.data.io.output

docs/source/api/core.rst

@@ -27,12 +27,12 @@ _________________________
     :template: classtemplate.rst
 
     ~flash.core.classification.Classes
-    ~flash.core.classification.ClassificationSerializer
+    ~flash.core.classification.ClassificationOutput
     ~flash.core.classification.ClassificationTask
     ~flash.core.classification.FiftyOneLabels
     ~flash.core.classification.Labels
     ~flash.core.classification.Logits
-    ~flash.core.classification.PredsClassificationSerializer
+    ~flash.core.classification.PredsClassificationOutput
     ~flash.core.classification.Probabilities
 
 flash.core.finetuning

docs/source/api/data.rst

@@ -7,6 +7,17 @@ flash.core.data
     :local:
     :backlinks: top
 
+flash.core.data.io.output
+_________________________
+
+.. autosummary::
+    :toctree: generated/
+    :nosignatures:
+    :template: classtemplate.rst
+
+    ~flash.core.data.io.output.Output
+    ~flash.core.data.io.output.OutputMapping
+
 flash.core.data.auto_dataset
 ____________________________
 
@@ -114,8 +125,6 @@ _______________________
     ~flash.core.data.process.Deserializer
     ~flash.core.data.process.Postprocess
     ~flash.core.data.process.Preprocess
-    ~flash.core.data.process.SerializerMapping
-    ~flash.core.data.process.Serializer
 
 flash.core.data.properties
 __________________________

docs/source/api/flash.rst

@@ -12,6 +12,6 @@ flash
     ~flash.core.data.callback.FlashCallback
     ~flash.core.data.process.Preprocess
     ~flash.core.data.process.Postprocess
-    ~flash.core.data.process.Serializer
+    ~flash.core.data.io.output.Output
     ~flash.core.model.Task
     ~flash.core.trainer.Trainer

docs/source/general/data.rst

@@ -26,7 +26,7 @@ Here are common terms you need to be familiar with:
    * - :class:`~flash.core.data.data_module.DataModule`
      - The :class:`~flash.core.data.data_module.DataModule` contains the datasets, transforms and dataloaders.
    * - :class:`~flash.core.data.data_pipeline.DataPipeline`
-     - The :class:`~flash.core.data.data_pipeline.DataPipeline` is Flash internal object to manage :class:`~flash.core.data.Deserializer`, :class:`~flash.core.data.data_source.DataSource`, :class:`~flash.core.data.process.Preprocess`, :class:`~flash.core.data.process.Postprocess`, and :class:`~flash.core.data.process.Serializer` objects.
+     - The :class:`~flash.core.data.data_pipeline.DataPipeline` is Flash internal object to manage :class:`~flash.core.data.Deserializer`, :class:`~flash.core.data.data_source.DataSource`, :class:`~flash.core.data.process.Preprocess`, :class:`~flash.core.data.process.Postprocess`, and :class:`~flash.core.data.io.output.Output` objects.
    * - :class:`~flash.core.data.data_source.DataSource`
      - The :class:`~flash.core.data.data_source.DataSource` provides :meth:`~flash.core.data.data_source.DataSource.load_data` and :meth:`~flash.core.data.data_source.DataSource.load_sample` hooks for creating data sets from metadata (such as folder names).
    * - :class:`~flash.core.data.process.Preprocess`
@@ -37,8 +37,8 @@ Here are common terms you need to be familiar with:
    * - :class:`~flash.core.data.process.Postprocess`
      - The :class:`~flash.core.data.process.Postprocess` provides a simple hook-based API to encapsulate your post-processing logic.
         The :class:`~flash.core.data.process.Postprocess` hooks cover from model outputs to predictions export.
-   * - :class:`~flash.core.data.process.Serializer`
-     - The :class:`~flash.core.data.process.Serializer` provides a single :meth:`~flash.core.data.process.Serializer.serialize` method that is used to convert model outputs (after the :class:`~flash.core.data.process.Postprocess`) to the desired output format during prediction.
+   * - :class:`~flash.core.data.io.output.Output`
+     - The :class:`~flash.core.data.io.output.Output` provides a single :meth:`~flash.core.data.io.output.Output.serialize` method that is used to convert model outputs (after the :class:`~flash.core.data.process.Postprocess`) to the desired output format during prediction.
 
 
 *******************************************
@@ -59,7 +59,7 @@ Usually, extra processing logic should be added to bridge the gap between traini
 
 The :class:`~flash.core.data.data_source.DataSource` class can be used to generate data sets from multiple sources (e.g. folders, numpy, etc.), that can then all be transformed in the same way.
 The :class:`~flash.core.data.process.Preprocess` and :class:`~flash.core.data.process.Postprocess` classes can be used to manage the preprocessing and postprocessing transforms.
-The :class:`~flash.core.data.process.Serializer` class provides the logic for converting :class:`~flash.core.data.process.Postprocess` outputs to the desired predict format (e.g. classes, labels, probabilities, etc.).
+The :class:`~flash.core.data.io.output.Output` class provides the logic for converting :class:`~flash.core.data.process.Postprocess` outputs to the desired predict format (e.g. classes, labels, probabilities, etc.).
 
 By providing a series of hooks that can be overridden with custom data processing logic (or just targeted with transforms),
 Flash gives the user much more granular control over their data processing flow.
@@ -383,18 +383,18 @@ Example::
     predictions = lightning_module(data)
 
 
-Postprocess and Serializer
+Postprocess and Output
 __________________________
 
 
 Once the predictions have been generated by the Flash :class:`~flash.core.model.Task`, the Flash
 :class:`~flash.core.data.data_pipeline.DataPipeline` will execute the :class:`~flash.core.data.process.Postprocess` hooks and the
-:class:`~flash.core.data.process.Serializer` behind the scenes.
+:class:`~flash.core.data.io.output.Output` behind the scenes.
 
 First, the :meth:`~flash.core.data.process.Postprocess.per_batch_transform` hooks will be applied on the batch predictions.
 Then, the :meth:`~flash.core.data.process.Postprocess.uncollate` will split the batch into individual predictions.
 Next, the :meth:`~flash.core.data.process.Postprocess.per_sample_transform` will be applied on each prediction.
-Finally, the :meth:`~flash.core.data.process.Serializer.serialize` method will be called to serialize the predictions.
+Finally, the :meth:`~flash.core.data.io.output.Output.serialize` method will be called to serialize the predictions.
 
 .. note:: The transform can be applied either on device or ``CPU``.
 

docs/source/general/predictions.rst

@@ -53,7 +53,7 @@ Predict on a csv file
 Serializing predictions
 =======================
 
-To change how predictions are serialized you can attach a :class:`~flash.core.data.process.Serializer` to your
+To change how predictions are serialized you can attach a :class:`~flash.core.data.io.output.Output` to your
 :class:`~flash.core.model.Task`. For example, you can choose to serialize outputs as probabilities (for more options see the API
 reference below).
 
@@ -71,7 +71,7 @@ reference below).
     # 2. Load the model from a checkpoint
     model = ImageClassifier.load_from_checkpoint("https://flash-weights.s3.amazonaws.com/image_classification_model.pt")
 
-    # 3. Attach the Serializer
+    # 3. Attach the Output
     model.serializer = Probabilities()
 
     # 4. Predict whether the image contains an ant or a bee

docs/source/template/optional.rst

@@ -25,16 +25,16 @@ Add output serializers to your Task
 
 We recommend that you do most of the heavy lifting in the :class:`~flash.core.data.process.Postprocess`.
 Specifically, it should include any formatting and transforms that should always be applied to the predictions.
-If you want to support different use cases that require different prediction formats, you should add some :class:`~flash.core.data.process.Serializer` implementations in a ``serialization.py`` file.
+If you want to support different use cases that require different prediction formats, you should add some :class:`~flash.core.data.io.output.Output` implementations in a ``serialization.py`` file.
 
 Some good examples are in `flash/core/classification.py <https://github.com/PyTorchLightning/lightning-flash/blob/master/flash/core/classification.py>`_.
-Here's the :class:`~flash.core.classification.Classes` :class:`~flash.core.data.process.Serializer`:
+Here's the :class:`~flash.core.classification.Classes` :class:`~flash.core.data.io.output.Output`:
 
 .. literalinclude:: ../../../flash/core/classification.py
     :language: python
     :pyobject: Classes
 
-Alternatively, here's the :class:`~flash.core.classification.Logits` :class:`~flash.core.data.process.Serializer`:
+Alternatively, here's the :class:`~flash.core.classification.Logits` :class:`~flash.core.data.io.output.Output`:
 
 .. literalinclude:: ../../../flash/core/classification.py
     :language: python

flash/__init__.py

@@ -24,7 +24,8 @@
     from flash.core.data.data_source import DataSource
     from flash.core.data.datasets import FlashDataset, FlashIterableDataset
     from flash.core.data.input_transform import InputTransform
-    from flash.core.data.process import Postprocess, Preprocess, Serializer
+    from flash.core.data.io.output import Output
+    from flash.core.data.process import Postprocess, Preprocess
     from flash.core.model import Task  # noqa: E402
     from flash.core.trainer import Trainer  # noqa: E402
 
@@ -44,10 +45,10 @@
         "FlashCallback",
         "FlashDataset",
         "FlashIterableDataset",
-        "Preprocess",
         "InputTransform",
+        "Output",
         "Postprocess",
-        "Serializer",
+        "Preprocess",
         "Task",
         "Trainer",
     ]

flash/audio/speech_recognition/model.py

@@ -25,7 +25,7 @@
 from flash.core.model import Task
 from flash.core.registry import FlashRegistry
 from flash.core.utilities.imports import _AUDIO_AVAILABLE
-from flash.core.utilities.types import LR_SCHEDULER_TYPE, OPTIMIZER_TYPE, SERIALIZER_TYPE
+from flash.core.utilities.types import LR_SCHEDULER_TYPE, OPTIMIZER_TYPE, OUTPUT_TYPE
 
 if _AUDIO_AVAILABLE:
     from transformers import Wav2Vec2Processor
@@ -41,7 +41,7 @@ class SpeechRecognition(Task):
         learning_rate: Learning rate to use for training, defaults to ``1e-5``.
         optimizer: Optimizer to use for training.
         lr_scheduler: The LR scheduler to use during training.
-        serializer: The :class:`~flash.core.data.process.Serializer` to use when serializing prediction outputs.
+        output: The :class:`~flash.core.data.io.output.Output` to use when serializing prediction outputs.
     """
 
     backbones: FlashRegistry = SPEECH_RECOGNITION_BACKBONES
@@ -54,7 +54,7 @@ def __init__(
         optimizer: OPTIMIZER_TYPE = "Adam",
         lr_scheduler: LR_SCHEDULER_TYPE = None,
         learning_rate: float = 1e-5,
-        serializer: SERIALIZER_TYPE = None,
+        output: OUTPUT_TYPE = None,
     ):
         os.environ["TOKENIZERS_PARALLELISM"] = "TRUE"
         # disable HF thousand warnings
@@ -68,7 +68,7 @@ def __init__(
             optimizer=optimizer,
             lr_scheduler=lr_scheduler,
             learning_rate=learning_rate,
-            serializer=serializer,
+            output=output,
         )
 
         self.save_hyperparameters()

flash/core/classification.py

@@ -20,7 +20,7 @@
 
 from flash.core.adapter import AdapterTask
 from flash.core.data.data_source import DefaultDataKeys, LabelsState
-from flash.core.data.process import Serializer
+from flash.core.data.io.output import Output
 from flash.core.model import Task
 from flash.core.utilities.imports import _FIFTYONE_AVAILABLE, lazy_import, requires
 
@@ -68,7 +68,7 @@ def __init__(
         loss_fn: Optional[Callable] = None,
         metrics: Union[torchmetrics.Metric, Mapping, Sequence, None] = None,
         multi_label: bool = False,
-        serializer: Optional[Union[Serializer, Mapping[str, Serializer]]] = None,
+        output: Optional[Union[Output, Mapping[str, Output]]] = None,
         **kwargs,
     ) -> None:
 
@@ -78,7 +78,7 @@ def __init__(
             *args,
             loss_fn=loss_fn,
             metrics=metrics,
-            serializer=serializer or Classes(multi_label=multi_label),
+            output=output or Classes(multi_label=multi_label),
             **kwargs,
         )
 
@@ -91,7 +91,7 @@ def __init__(
         loss_fn: Optional[Callable] = None,
         metrics: Union[torchmetrics.Metric, Mapping, Sequence, None] = None,
         multi_label: bool = False,
-        serializer: Optional[Union[Serializer, Mapping[str, Serializer]]] = None,
+        output: Optional[Union[Output, Mapping[str, Output]]] = None,
         **kwargs,
     ) -> None:
 
@@ -101,13 +101,13 @@ def __init__(
             *args,
             loss_fn=loss_fn,
             metrics=metrics,
-            serializer=serializer or Classes(multi_label=multi_label),
+            output=output or Classes(multi_label=multi_label),
             **kwargs,
         )
 
 
-class ClassificationSerializer(Serializer):
-    """A base class for classification serializers.
+class ClassificationOutput(Output):
+    """A base class for classification outputs.
 
     Args:
         multi_label: If true, treats outputs as multi label logits.
@@ -123,39 +123,39 @@ def multi_label(self) -> bool:
         return self._mutli_label
 
 
-class PredsClassificationSerializer(ClassificationSerializer):
-    """A :class:`~flash.core.classification.ClassificationSerializer` which gets the
+class PredsClassificationOutput(ClassificationOutput):
+    """A :class:`~flash.core.classification.ClassificationOutput` which gets the
     :attr:`~flash.core.data.data_source.DefaultDataKeys.PREDS` from the sample.
     """
 
-    def serialize(self, sample: Any) -> Any:
+    def transform(self, sample: Any) -> Any:
         if isinstance(sample, Mapping) and DefaultDataKeys.PREDS in sample:
             sample = sample[DefaultDataKeys.PREDS]
         if not isinstance(sample, torch.Tensor):
             sample = torch.tensor(sample)
         return sample
 
 
-class Logits(PredsClassificationSerializer):
-    """A :class:`.Serializer` which simply converts the model outputs (assumed to be logits) to a list."""
+class Logits(PredsClassificationOutput):
+    """A :class:`.Output` which simply converts the model outputs (assumed to be logits) to a list."""
 
-    def serialize(self, sample: Any) -> Any:
-        return super().serialize(sample).tolist()
+    def transform(self, sample: Any) -> Any:
+        return super().transform(sample).tolist()
 
 
-class Probabilities(PredsClassificationSerializer):
-    """A :class:`.Serializer` which applies a softmax to the model outputs (assumed to be logits) and converts to a
+class Probabilities(PredsClassificationOutput):
+    """A :class:`.Output` which applies a softmax to the model outputs (assumed to be logits) and converts to a
     list."""
 
-    def serialize(self, sample: Any) -> Any:
-        sample = super().serialize(sample)
+    def transform(self, sample: Any) -> Any:
+        sample = super().transform(sample)
         if self.multi_label:
             return torch.sigmoid(sample).tolist()
         return torch.softmax(sample, -1).tolist()
 
 
-class Classes(PredsClassificationSerializer):
-    """A :class:`.Serializer` which applies an argmax to the model outputs (either logits or probabilities) and
+class Classes(PredsClassificationOutput):
+    """A :class:`.Output` which applies an argmax to the model outputs (either logits or probabilities) and
     converts to a list.
 
     Args:
@@ -168,8 +168,8 @@ def __init__(self, multi_label: bool = False, threshold: float = 0.5):
 
         self.threshold = threshold
 
-    def serialize(self, sample: Any) -> Union[int, List[int]]:
-        sample = super().serialize(sample)
+    def transform(self, sample: Any) -> Union[int, List[int]]:
+        sample = super().transform(sample)
         if self.multi_label:
             one_hot = (sample.sigmoid() > self.threshold).int().tolist()
             result = []
@@ -181,7 +181,7 @@ def serialize(self, sample: Any) -> Union[int, List[int]]:
 
 
 class Labels(Classes):
-    """A :class:`.Serializer` which converts the model outputs (either logits or probabilities) to the label of the
+    """A :class:`.Output` which converts the model outputs (either logits or probabilities) to the label of the
     argmax classification.
 
     Args:
@@ -198,7 +198,7 @@ def __init__(self, labels: Optional[List[str]] = None, multi_label: bool = False
         if labels is not None:
             self.set_state(LabelsState(labels))
 
-    def serialize(self, sample: Any) -> Union[int, List[int], str, List[str]]:
+    def transform(self, sample: Any) -> Union[int, List[int], str, List[str]]:
         labels = None
 
         if self._labels is not None:
@@ -208,18 +208,18 @@ def serialize(self, sample: Any) -> Union[int, List[int], str, List[str]]:
             if state is not None:
                 labels = state.labels
 
-        classes = super().serialize(sample)
+        classes = super().transform(sample)
 
         if labels is not None:
             if self.multi_label:
                 return [labels[cls] for cls in classes]
             return labels[classes]
-        rank_zero_warn("No LabelsState was found, this serializer will act as a Classes serializer.", UserWarning)
+        rank_zero_warn("No LabelsState was found, this output will act as a Classes output.", UserWarning)
         return classes
 
 
-class FiftyOneLabels(ClassificationSerializer):
-    """A :class:`.Serializer` which converts the model outputs to FiftyOne classification format.
+class FiftyOneLabels(ClassificationOutput):
+    """A :class:`.Output` which converts the model outputs to FiftyOne classification format.
 
     Args:
         labels: A list of labels, assumed to map the class index to the label for that class. If ``labels`` is not
@@ -254,7 +254,7 @@ def __init__(
         if labels is not None:
             self.set_state(LabelsState(labels))
 
-    def serialize(
+    def transform(
         self,
         sample: Any,
     ) -> Union[Classification, Classifications, Dict[str, Any], Dict[str, Any]]: