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

CHANGELOG.md

@@ -30,6 +30,8 @@ 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` ([#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/image.rst

@@ -44,7 +44,7 @@ ________________
 
     detection.data.FiftyOneParser
     detection.data.ObjectDetectionFiftyOneInput
-    detection.output.FiftyOneDetectionLabels
+    detection.output.FiftyOneDetectionLabelsOutput
     detection.data.ObjectDetectionInputTransform
 
 Keypoint Detection
@@ -102,8 +102,8 @@ ____________
     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/integrations/fiftyone.rst

@@ -47,8 +47,8 @@ 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:`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

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/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

@@ -28,7 +28,7 @@
     fo = None
 
 
-class FiftyOneDetectionLabels(Output):
+class FiftyOneDetectionLabelsOutput(Output):
     """A :class:`.Output` which converts model outputs to FiftyOne detection format.
 
     Args:
@@ -57,7 +57,7 @@ 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(),
         )
 

flash/image/segmentation/output.py

@@ -46,7 +46,7 @@
     K = None
 
 
-class SegmentationLabels(Output):
+class SegmentationLabelsOutput(Output):
     """A :class:`.Output` which converts the model outputs to the label of the argmax classification per pixel in
     the image for semantic segmentation tasks.
 
@@ -100,7 +100,7 @@ def transform(self, sample: Dict[str, torch.Tensor]) -> torch.Tensor:
         return labels.tolist()
 
 
-class FiftyOneSegmentationLabels(SegmentationLabels):
+class FiftyOneSegmentationLabelsOutput(SegmentationLabelsOutput):
     """A :class:`.Output` which converts the model outputs to FiftyOne segmentation format.
 
     Args:

flash_examples/integrations/fiftyone/object_detection.py

@@ -17,7 +17,7 @@
 from flash.core.integrations.fiftyone import visualize
 from flash.core.utilities.imports import example_requires
 from flash.image import ObjectDetectionData, ObjectDetector
-from flash.image.detection.output import FiftyOneDetectionLabels
+from flash.image.detection.output import FiftyOneDetectionLabelsOutput
 
 example_requires("image")
 
@@ -42,7 +42,7 @@
 trainer.finetune(model, datamodule=datamodule, strategy="freeze")
 
 # 4. Set the output and get some predictions
-model.output = FiftyOneDetectionLabels(return_filepath=True)  # output FiftyOne format
+model.output = FiftyOneDetectionLabelsOutput(return_filepath=True)  # output FiftyOne format
 predictions = trainer.predict(model, datamodule=datamodule)
 predictions = list(chain.from_iterable(predictions))  # flatten batches
 

flash_examples/serve/semantic_segmentation/inference_server.py

@@ -12,10 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from flash.image import SemanticSegmentation
-from flash.image.segmentation.output import SegmentationLabels
+from flash.image.segmentation.output import SegmentationLabelsOutput
 
 model = SemanticSegmentation.load_from_checkpoint(
     "https://flash-weights.s3.amazonaws.com/0.6.0/semantic_segmentation_model.pt"
 )
-model.output = SegmentationLabels(visualize=False)
+model.output = SegmentationLabelsOutput(visualize=False)
 model.serve()

tests/image/detection/test_output.py

@@ -4,24 +4,24 @@
 
 from flash.core.data.io.input import DataKeys
 from flash.core.utilities.imports import _FIFTYONE_AVAILABLE, _IMAGE_AVAILABLE
-from flash.image.detection.output import FiftyOneDetectionLabels
+from flash.image.detection.output import FiftyOneDetectionLabelsOutput
 
 
 @pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed.")
 @pytest.mark.skipif(not _FIFTYONE_AVAILABLE, reason="fiftyone is not installed for testing")
-class TestFiftyOneDetectionLabels:
+class TestFiftyOneDetectionLabelsOutput:
     @staticmethod
     def test_smoke():
-        serial = FiftyOneDetectionLabels()
+        serial = FiftyOneDetectionLabelsOutput()
         assert serial is not None
 
     @staticmethod
     def test_serialize_fiftyone():
         labels = ["class_1", "class_2", "class_3"]
-        serial = FiftyOneDetectionLabels()
-        filepath_serial = FiftyOneDetectionLabels(return_filepath=True)
-        threshold_serial = FiftyOneDetectionLabels(threshold=0.9)
-        labels_serial = FiftyOneDetectionLabels(labels=labels)
+        serial = FiftyOneDetectionLabelsOutput()
+        filepath_serial = FiftyOneDetectionLabelsOutput(return_filepath=True)
+        threshold_serial = FiftyOneDetectionLabelsOutput(threshold=0.9)
+        labels_serial = FiftyOneDetectionLabelsOutput(labels=labels)
 
         sample = {
             DataKeys.PREDS: {

tests/image/segmentation/test_output.py

@@ -16,23 +16,23 @@
 
 from flash.core.data.io.input import DataKeys
 from flash.core.utilities.imports import _FIFTYONE_AVAILABLE, _IMAGE_AVAILABLE
-from flash.image.segmentation.output import FiftyOneSegmentationLabels, SegmentationLabels
+from flash.image.segmentation.output import FiftyOneSegmentationLabelsOutput, SegmentationLabelsOutput
 from tests.helpers.utils import _IMAGE_TESTING
 
 
-class TestSemanticSegmentationLabels:
+class TestSemanticSegmentationLabelsOutput:
     @pytest.mark.skipif(not _IMAGE_TESTING, "image libraries aren't installed.")
     @staticmethod
     def test_smoke():
-        serial = SegmentationLabels()
+        serial = SegmentationLabelsOutput()
         assert serial is not None
         assert serial.labels_map is None
         assert serial.visualize is False
 
     @pytest.mark.skipif(not _IMAGE_TESTING, "image libraries aren't installed.")
     @staticmethod
     def test_exception():
-        serial = SegmentationLabels()
+        serial = SegmentationLabelsOutput()
 
         with pytest.raises(Exception):
             sample = torch.zeros(1, 5, 2, 3)
@@ -45,7 +45,7 @@ def test_exception():
     @pytest.mark.skipif(not _IMAGE_TESTING, "image libraries aren't installed.")
     @staticmethod
     def test_serialize():
-        serial = SegmentationLabels()
+        serial = SegmentationLabelsOutput()
 
         sample = torch.zeros(5, 2, 3)
         sample[1, 1, 2] = 1  # add peak in class 2
@@ -59,8 +59,8 @@ def test_serialize():
     @pytest.mark.skipif(not _FIFTYONE_AVAILABLE, reason="fiftyone is not installed for testing")
     @staticmethod
     def test_serialize_fiftyone():
-        serial = FiftyOneSegmentationLabels()
-        filepath_serial = FiftyOneSegmentationLabels(return_filepath=True)
+        serial = FiftyOneSegmentationLabelsOutput()
+        filepath_serial = FiftyOneSegmentationLabelsOutput(return_filepath=True)
 
         preds = torch.zeros(5, 2, 3)
         preds[1, 1, 2] = 1  # add peak in class 2