Data Pipeline V2: Rename all Output implementations to end in Output

CHANGELOG.md

@@ -32,6 +32,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Changed the `SpeechRecognition` task to use `AutoModelForCTC` rather than just `Wav2Vec2ForCTC` ([#874](https://github.com/PyTorchLightning/lightning-flash/pull/874))
 
+- Added `Output` suffix to `Preds`, `FiftyOneDetectionLabels`, `SegmentationLabels`, `FiftyOneDetectionLabels`, `DetectionLabels`, `Classes`, `FiftyOneLabels`, `Labels`, `Logits`, `Probabilities` ([#1011](https://github.com/PyTorchLightning/lightning-flash/pull/1011))
+
 ### Deprecated
 
 - Deprecated `flash.core.data.process.Serializer` in favour of `flash.core.data.io.output.Output` ([#927](https://github.com/PyTorchLightning/lightning-flash/pull/927))

docs/source/api/core.rst

@@ -26,14 +26,14 @@ _________________________
     :nosignatures:
     :template: classtemplate.rst
 
-    ~flash.core.classification.Classes
+    ~flash.core.classification.ClassesOutput
     ~flash.core.classification.ClassificationOutput
     ~flash.core.classification.ClassificationTask
-    ~flash.core.classification.FiftyOneLabels
-    ~flash.core.classification.Labels
-    ~flash.core.classification.Logits
+    ~flash.core.classification.FiftyOneLabelsOutput
+    ~flash.core.classification.LabelsOutput
+    ~flash.core.classification.LogitsOutput
     ~flash.core.classification.PredsClassificationOutput
-    ~flash.core.classification.Probabilities
+    ~flash.core.classification.ProbabilitiesOutput
 
 flash.core.finetuning
 _____________________

docs/source/api/image.rst

@@ -45,7 +45,7 @@ ________________
 
     detection.data.FiftyOneParser
     detection.data.ObjectDetectionFiftyOneInput
-    detection.output.FiftyOneDetectionLabels
+    detection.output.FiftyOneDetectionLabelsOutput
     detection.data.ObjectDetectionInputTransform
 
 Keypoint Detection
@@ -103,8 +103,8 @@ ____________
     segmentation.data.SemanticSegmentationFiftyOneInput
     segmentation.data.SemanticSegmentationDeserializer
     segmentation.model.SemanticSegmentationOutputTransform
-    segmentation.output.FiftyOneSegmentationLabels
-    segmentation.output.SegmentationLabels
+    segmentation.output.FiftyOneSegmentationLabelsOutput
+    segmentation.output.SegmentationLabelsOutput
 
 .. autosummary::
     :toctree: generated/

docs/source/common/finetuning_example.rst

@@ -15,7 +15,7 @@ Here's an example of finetuning.
     from pytorch_lightning import seed_everything
 
     import flash
-    from flash.core.classification import Labels
+    from flash.core.classification import LabelsOutput
     from flash.core.data.utils import download_data
     from flash.image import ImageClassificationData, ImageClassifier
 
@@ -56,7 +56,7 @@ Once you've finetuned, use the model to predict:
 .. testcode:: finetune
 
     # Output predictions as labels, automatically inferred from the training data in part 2.
-    model.output = Labels()
+    model.output = LabelsOutput()
 
     predictions = model.predict(
         [

docs/source/common/training_example.rst

@@ -15,7 +15,7 @@ Here's an example:
     from pytorch_lightning import seed_everything
 
     import flash
-    from flash.core.classification import Labels
+    from flash.core.classification import LabelsOutput
     from flash.core.data.utils import download_data
     from flash.image import ImageClassificationData, ImageClassifier
 

docs/source/general/predictions.rst

@@ -64,7 +64,7 @@ reference below).
 
 .. code-block:: python
 
-    from flash.core.classification import Probabilities
+    from flash.core.classification import ProbabilitiesOutput
     from flash.core.data.utils import download_data
     from flash.image import ImageClassifier
 
@@ -78,7 +78,7 @@ reference below).
     )
 
     # 3. Attach the Output
-    model.output = Probabilities()
+    model.output = ProbabilitiesOutput()
 
     # 4. Predict whether the image contains an ant or a bee
     predictions = model.predict("data/hymenoptera_data/val/bees/65038344_52a45d090d.jpg")

docs/source/integrations/fiftyone.rst

@@ -46,9 +46,9 @@ You can visualize predictions for classification, object detection, and
 semantic segmentation tasks. Doing so is as easy as updating your model to use
 one of the following outputs:
 
-* :class:`FiftyOneLabels(return_filepath=True)<flash.core.classification.FiftyOneLabels>`
-* :class:`FiftyOneSegmentationLabels(return_filepath=True)<flash.image.segmentation.output.FiftyOneSegmentationLabels>`
-* :class:`FiftyOneDetectionLabels(return_filepath=True)<flash.image.detection.output.FiftyOneDetectionLabels>`
+* :class:`FiftyOneLabelsOutput(return_filepath=True)<flash.core.classification.FiftyOneLabelsOutput>`
+* :class:`FiftyOneSegmentationLabelsOutput(return_filepath=True)<flash.image.segmentation.output.FiftyOneSegmentationLabelsOutput>`
+* :class:`FiftyOneDetectionLabelsOutput(return_filepath=True)<flash.image.detection.output.FiftyOneDetectionLabelsOutput>`
 
 The :func:`~flash.core.integrations.fiftyone.visualize` function then lets you visualize
 your predictions in the

docs/source/template/optional.rst

@@ -28,17 +28,17 @@ Specifically, it should include any formatting and transforms that should always
 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 an ``output.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.io.output.Output`:
+Here's the :class:`~flash.core.classification.ClassesOutput` :class:`~flash.core.data.io.output.Output`:
 
 .. literalinclude:: ../../../flash/core/classification.py
     :language: python
-    :pyobject: Classes
+    :pyobject: ClassesOutput
 
-Alternatively, here's the :class:`~flash.core.classification.Logits` :class:`~flash.core.data.io.output.Output`:
+Alternatively, here's the :class:`~flash.core.classification.LogitsOutput` :class:`~flash.core.data.io.output.Output`:
 
 .. literalinclude:: ../../../flash/core/classification.py
     :language: python
-    :pyobject: Logits
+    :pyobject: LogitsOutput
 
 Take a look at :ref:`predictions` to learn more.
 

flash/core/classification.py

@@ -16,7 +16,7 @@
 import torch
 import torch.nn.functional as F
 import torchmetrics
-from pytorch_lightning.utilities import rank_zero_warn
+from pytorch_lightning.utilities import rank_zero_deprecation, rank_zero_warn
 
 from flash.core.adapter import AdapterTask
 from flash.core.data.io.classification_input import ClassificationState
@@ -79,7 +79,7 @@ def __init__(
             *args,
             loss_fn=loss_fn,
             metrics=metrics,
-            output=output or Classes(multi_label=multi_label),
+            output=output or ClassesOutput(multi_label=multi_label),
             **kwargs,
         )
 
@@ -102,7 +102,7 @@ def __init__(
             *args,
             loss_fn=loss_fn,
             metrics=metrics,
-            output=output or Classes(multi_label=multi_label),
+            output=output or ClassesOutput(multi_label=multi_label),
             **kwargs,
         )
 
@@ -137,14 +137,14 @@ def transform(self, sample: Any) -> Any:
         return sample
 
 
-class Logits(PredsClassificationOutput):
+class LogitsOutput(PredsClassificationOutput):
     """A :class:`.Output` which simply converts the model outputs (assumed to be logits) to a list."""
 
     def transform(self, sample: Any) -> Any:
         return super().transform(sample).tolist()
 
 
-class Probabilities(PredsClassificationOutput):
+class ProbabilitiesOutput(PredsClassificationOutput):
     """A :class:`.Output` which applies a softmax to the model outputs (assumed to be logits) and converts to a
     list."""
 
@@ -155,7 +155,7 @@ def transform(self, sample: Any) -> Any:
         return torch.softmax(sample, -1).tolist()
 
 
-class Classes(PredsClassificationOutput):
+class ClassesOutput(PredsClassificationOutput):
     """A :class:`.Output` which applies an argmax to the model outputs (either logits or probabilities) and
     converts to a list.
 
@@ -181,7 +181,7 @@ def transform(self, sample: Any) -> Union[int, List[int]]:
         return torch.argmax(sample, -1).tolist()
 
 
-class Labels(Classes):
+class LabelsOutput(ClassesOutput):
     """A :class:`.Output` which converts the model outputs (either logits or probabilities) to the label of the
     argmax classification.
 
@@ -219,7 +219,7 @@ def transform(self, sample: Any) -> Union[int, List[int], str, List[str]]:
         return classes
 
 
-class FiftyOneLabels(ClassificationOutput):
+class FiftyOneLabelsOutput(ClassificationOutput):
     """A :class:`.Output` which converts the model outputs to FiftyOne classification format.
 
     Args:
@@ -339,3 +339,20 @@ def transform(
             filepath = sample[DataKeys.METADATA]["filepath"]
             return {"filepath": filepath, "predictions": fo_predictions}
         return fo_predictions
+
+
+class Labels(LabelsOutput):
+    def __init__(self, labels: Optional[List[str]] = None, multi_label: bool = False, threshold: float = 0.5):
+        rank_zero_deprecation(
+            "`Labels` was deprecated in v0.6.0 and will be removed in v0.7.0." "Please use `LabelsOutput` instead."
+        )
+        super().__init__(labels=labels, multi_label=multi_label, threshold=threshold)
+
+
+class Probabilities(ProbabilitiesOutput):
+    def __init__(self, multi_label: bool = False):
+        rank_zero_deprecation(
+            "`Probabilities` was deprecated in v0.6.0 and will be removed in v0.7.0."
+            "Please use `ProbabilitiesOutput` instead."
+        )
+        super().__init__(multi_label=multi_label)

flash/core/data/output.py

@@ -17,7 +17,7 @@
 from flash.core.data.io.output import Output
 
 
-class Preds(Output):
+class PredsOutput(Output):
     """A :class:`~flash.core.data.io.output.Output` which returns the "preds" from the model outputs."""
 
     def transform(self, sample: Any) -> Union[int, List[int]]:

flash/image/classification/model.py

@@ -17,7 +17,7 @@
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from torch import nn
 
-from flash.core.classification import ClassificationAdapterTask, Labels
+from flash.core.classification import ClassificationAdapterTask, LabelsOutput
 from flash.core.registry import FlashRegistry
 from flash.core.utilities.types import LOSS_FN_TYPE, LR_SCHEDULER_TYPE, METRICS_TYPE, OPTIMIZER_TYPE, OUTPUT_TYPE
 from flash.image.classification.adapters import TRAINING_STRATEGIES
@@ -136,7 +136,7 @@ def __init__(
             optimizer=optimizer,
             lr_scheduler=lr_scheduler,
             multi_label=multi_label,
-            output=output or Labels(multi_label=multi_label),
+            output=output or LabelsOutput(multi_label=multi_label),
         )
 
     @classmethod

flash/image/detection/model.py

@@ -14,7 +14,7 @@
 from typing import Any, Dict, List, Optional
 
 from flash.core.adapter import AdapterTask
-from flash.core.data.output import Preds
+from flash.core.data.output import PredsOutput
 from flash.core.registry import FlashRegistry
 from flash.core.utilities.types import LR_SCHEDULER_TYPE, OPTIMIZER_TYPE, OUTPUT_TYPE
 from flash.image.detection.backbones import OBJECT_DETECTION_HEADS
@@ -73,7 +73,7 @@ def __init__(
             learning_rate=learning_rate,
             optimizer=optimizer,
             lr_scheduler=lr_scheduler,
-            output=output or Preds(),
+            output=output or PredsOutput(),
         )
 
     def _ci_benchmark_fn(self, history: List[Dict[str, Any]]) -> None:

flash/image/detection/output.py

@@ -29,7 +29,7 @@
     fo = None
 
 
-class FiftyOneDetectionLabels(Output):
+class FiftyOneDetectionLabelsOutput(Output):
     """A :class:`.Output` which converts model outputs to FiftyOne detection format.
 
     Args:
@@ -58,7 +58,7 @@ def __init__(
 
     def transform(self, sample: Dict[str, Any]) -> Union[Detections, Dict[str, Any]]:
         if DataKeys.METADATA not in sample:
-            raise ValueError("sample requires DefaultDataKeys.METADATA to use a FiftyOneDetectionLabels output.")
+            raise ValueError("sample requires DefaultDataKeys.METADATA to use a FiftyOneDetectionLabelsOutput output.")
 
         labels = None
         if self._labels is not None:

flash/image/face_detection/model.py

@@ -35,7 +35,7 @@
     import fastface as ff
 
 
-class DetectionLabels(Output):
+class DetectionLabelsOutput(Output):
     """A :class:`.Output` which extracts predictions from sample dict."""
 
     def transform(self, sample: Any) -> Dict[str, Any]:
@@ -89,7 +89,7 @@ def __init__(
             learning_rate=learning_rate,
             optimizer=optimizer,
             lr_scheduler=lr_scheduler,
-            output=output or DetectionLabels(),
+            output=output or DetectionLabelsOutput(),
             input_transform=input_transform or FaceDetectionInputTransform(),
         )
 

flash/image/instance_segmentation/model.py

@@ -17,7 +17,7 @@
 
 from flash.core.adapter import AdapterTask
 from flash.core.data.data_pipeline import DataPipeline
-from flash.core.data.output import Preds
+from flash.core.data.output import PredsOutput
 from flash.core.registry import FlashRegistry
 from flash.core.utilities.types import LR_SCHEDULER_TYPE, OPTIMIZER_TYPE, OUTPUT_TYPE
 from flash.image.instance_segmentation.backbones import INSTANCE_SEGMENTATION_HEADS
@@ -80,7 +80,7 @@ def __init__(
             learning_rate=learning_rate,
             optimizer=optimizer,
             lr_scheduler=lr_scheduler,
-            output=output or Preds(),
+            output=output or PredsOutput(),
         )
 
     def _ci_benchmark_fn(self, history: List[Dict[str, Any]]) -> None:

flash/image/keypoint_detection/model.py

@@ -14,7 +14,7 @@
 from typing import Any, Dict, List, Optional
 
 from flash.core.adapter import AdapterTask
-from flash.core.data.output import Preds
+from flash.core.data.output import PredsOutput
 from flash.core.registry import FlashRegistry
 from flash.core.utilities.types import LR_SCHEDULER_TYPE, OPTIMIZER_TYPE, OUTPUT_TYPE
 from flash.image.keypoint_detection.backbones import KEYPOINT_DETECTION_HEADS
@@ -76,7 +76,7 @@ def __init__(
             learning_rate=learning_rate,
             optimizer=optimizer,
             lr_scheduler=lr_scheduler,
-            output=output or Preds(),
+            output=output or PredsOutput(),
         )
 
     def _ci_benchmark_fn(self, history: List[Dict[str, Any]]) -> None:

flash/image/segmentation/data.py

@@ -46,7 +46,7 @@
 )
 from flash.core.utilities.stages import RunningStage
 from flash.image.data import ImageDeserializer, IMG_EXTENSIONS
-from flash.image.segmentation.output import SegmentationLabels
+from flash.image.segmentation.output import SegmentationLabelsOutput
 from flash.image.segmentation.transforms import default_transforms, predict_default_transforms, train_default_transforms
 
 SampleCollection = None
@@ -244,7 +244,7 @@ def __init__(
         self.image_size = image_size
         self.num_classes = num_classes
         if num_classes:
-            labels_map = labels_map or SegmentationLabels.create_random_labels_map(num_classes)
+            labels_map = labels_map or SegmentationLabelsOutput.create_random_labels_map(num_classes)
 
         super().__init__(
             train_transform=train_transform,
@@ -329,9 +329,9 @@ def from_input(
 
         num_classes = input_transform_kwargs["num_classes"]
 
-        labels_map = getattr(input_transform_kwargs, "labels_map", None) or SegmentationLabels.create_random_labels_map(
-            num_classes
-        )
+        labels_map = getattr(
+            input_transform_kwargs, "labels_map", None
+        ) or SegmentationLabelsOutput.create_random_labels_map(num_classes)
 
         data_fetcher = data_fetcher or cls.configure_data_fetcher(labels_map)
 
@@ -494,7 +494,7 @@ def _show_images_and_labels(self, data: List[Any], num_samples: int, title: str)
                 raise TypeError(f"Unknown data type. Got: {type(data)}.")
             # convert images and labels to numpy and stack horizontally
             image_vis: np.ndarray = self._to_numpy(image.byte())
-            label_tmp: torch.Tensor = SegmentationLabels.labels_to_image(label.squeeze().byte(), self.labels_map)
+            label_tmp: torch.Tensor = SegmentationLabelsOutput.labels_to_image(label.squeeze().byte(), self.labels_map)
             label_vis: np.ndarray = self._to_numpy(label_tmp)
             img_vis = np.hstack((image_vis, label_vis))
             # send to visualiser

flash/image/segmentation/model.py

@@ -34,7 +34,7 @@
 )
 from flash.image.segmentation.backbones import SEMANTIC_SEGMENTATION_BACKBONES
 from flash.image.segmentation.heads import SEMANTIC_SEGMENTATION_HEADS
-from flash.image.segmentation.output import SegmentationLabels
+from flash.image.segmentation.output import SegmentationLabelsOutput
 
 if _KORNIA_AVAILABLE:
     import kornia as K
@@ -114,7 +114,7 @@ def __init__(
             lr_scheduler=lr_scheduler,
             metrics=metrics,
             learning_rate=learning_rate,
-            output=output or SegmentationLabels(),
+            output=output or SegmentationLabelsOutput(),
             output_transform=output_transform or self.output_transform_cls(),
         )