Docstring for object detection data from fityone

CHANGELOG.md

@@ -68,6 +68,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Fixed a bug where `from_fiftyone` classmethods did not work correctly with a `predict_dataset` ([#1136](https://github.com/PyTorchLightning/lightning-flash/pull/1136))
 
+- Fixed a bug where the `labels` property would return `None` when using `ObjectDetectionData.from_fiftyone` ([#1136](https://github.com/PyTorchLightning/lightning-flash/pull/1136))
+
 ### Removed
 
 - Removed the `Seq2SeqData` base class (use `TranslationData` or `SummarizationData` directly) ([#1128](https://github.com/PyTorchLightning/lightning-flash/pull/1128))

flash/image/detection/data.py

@@ -598,26 +598,111 @@ def from_fiftyone(
         val_dataset: Optional[SampleCollection] = None,
         test_dataset: Optional[SampleCollection] = None,
         predict_dataset: Optional[SampleCollection] = None,
+        label_field: str = "ground_truth",
+        iscrowd: str = "iscrowd",
         train_transform: INPUT_TRANSFORM_TYPE = IceVisionInputTransform,
         val_transform: INPUT_TRANSFORM_TYPE = IceVisionInputTransform,
         test_transform: INPUT_TRANSFORM_TYPE = IceVisionInputTransform,
         predict_transform: INPUT_TRANSFORM_TYPE = IceVisionInputTransform,
-        label_field: str = "ground_truth",
-        iscrowd: str = "iscrowd",
         input_cls: Type[Input] = ObjectDetectionFiftyOneInput,
         transform_kwargs: Optional[Dict] = None,
         **data_module_kwargs: Any,
     ) -> "ObjectDetectionData":
+        """Load the :class:`~flash.image.detection.data.ObjectDetectionData` from FiftyOne ``SampleCollection``
+        objects.
+
+        Targets will be extracted from the ``label_field`` in the ``SampleCollection`` objects.
+        To learn how to customize the transforms applied for each stage, read our
+        :ref:`customizing transforms guide <customizing_transforms>`.
+
+        Args:
+            train_dataset: The ``SampleCollection`` to use when training.
+            val_dataset: The ``SampleCollection`` to use when validating.
+            test_dataset: The ``SampleCollection`` to use when testing.
+            predict_dataset: The ``SampleCollection`` to use when predicting.
+            label_field: The field in the ``SampleCollection`` objects containing the targets.
+            iscrowd: The field in the ``SampleCollection`` objects containing the ``iscrowd`` annotation (if required).
+            train_transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use when training.
+            val_transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use when validating.
+            test_transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use when testing.
+            predict_transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use when
+              predicting.
+            input_cls: The :class:`~flash.core.data.io.input.Input` type to use for loading the data.
+            transform_kwargs: Dict of keyword arguments to be provided when instantiating the transforms.
+            data_module_kwargs: Additional keyword arguments to provide to the
+              :class:`~flash.core.data.data_module.DataModule` constructor.
+
+        Returns:
+            The constructed :class:`~flash.image.detection.data.ObjectDetectionData`.
+
+        Examples
+        ________
+
+        .. testsetup::
+
+            >>> import numpy as np
+            >>> from PIL import Image
+            >>> rand_image = Image.fromarray(np.random.randint(0, 255, (64, 64, 3), dtype="uint8"))
+            >>> _ = [rand_image.save(f"image_{i}.png") for i in range(1, 4)]
+            >>> _ = [rand_image.save(f"predict_image_{i}.png") for i in range(1, 4)]
+
+        .. doctest::
+
+            >>> import numpy as np
+            >>> import fiftyone as fo
+            >>> from flash import Trainer
+            >>> from flash.image import ObjectDetector, ObjectDetectionData
+            >>> train_dataset = fo.Dataset.from_images(
+            ...     ["image_1.png", "image_2.png", "image_3.png"]
+            ... )  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
+            33%...
+            >>> samples = [train_dataset[filepath] for filepath in train_dataset.values("filepath")]
+            >>> for sample, label, bounding_box in zip(
+            ...     samples,
+            ...     ["cat", "dog", "cat"],
+            ...     [[0.1, 0.2, 0.15, 0.3], [0.2, 0.3, 0.3, 0.4], [0.1, 0.2, 0.15, 0.45]],
+            ... ):
+            ...     sample["ground_truth"] = fo.Detections(
+            ...         detections=[fo.Detection(label=label, bounding_box=bounding_box)],
+            ...     )
+            ...     sample.save()
+            ...
+            >>> predict_dataset = fo.Dataset.from_images(
+            ...     ["predict_image_1.png", "predict_image_2.png", "predict_image_3.png"]
+            ... )  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
+            33%...
+            >>> datamodule = ObjectDetectionData.from_fiftyone(
+            ...     train_dataset=train_dataset,
+            ...     predict_dataset=predict_dataset,
+            ...     transform_kwargs=dict(image_size=(128, 128)),
+            ...     batch_size=2,
+            ... )  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
+            Computing...
+            >>> datamodule.num_classes
+            3
+            >>> datamodule.labels
+            ['background', 'cat', 'dog']
+            >>> model = ObjectDetector(num_classes=datamodule.num_classes)
+            >>> trainer = Trainer(fast_dev_run=True)
+            >>> trainer.fit(model, datamodule=datamodule)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
+            Training...
+            >>> trainer.predict(model, datamodule=datamodule)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
+            Predicting...
+
+        .. testcleanup::
+
+            >>> import os
+            >>> _ = [os.remove(f"image_{i}.png") for i in range(1, 4)]
+            >>> _ = [os.remove(f"predict_image_{i}.png") for i in range(1, 4)]
+        """
 
         ds_kw = dict(data_pipeline_state=DataPipelineState(), transform_kwargs=transform_kwargs)
 
         return cls(
             input_cls(RunningStage.TRAINING, train_dataset, label_field, iscrowd, transform=train_transform, **ds_kw),
             input_cls(RunningStage.VALIDATING, val_dataset, label_field, iscrowd, transform=val_transform, **ds_kw),
             input_cls(RunningStage.TESTING, test_dataset, label_field, iscrowd, transform=test_transform, **ds_kw),
-            input_cls(
-                RunningStage.PREDICTING, predict_dataset, label_field, iscrowd, transform=predict_transform, **ds_kw
-            ),
+            input_cls(RunningStage.PREDICTING, predict_dataset, transform=predict_transform, **ds_kw),
             **data_module_kwargs,
         )
 

flash/image/detection/input.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 from typing import Any, Dict, Hashable, Sequence
 
+from flash.core.data.io.classification_input import ClassificationState
 from flash.core.data.io.input import DataKeys
 from flash.core.integrations.fiftyone.utils import FiftyOneLabelUtilities
 from flash.core.integrations.icevision.data import IceVisionInput
@@ -110,6 +111,8 @@ def load_data(
         classes = label_utilities.get_classes(sample_collection)
         class_map = ClassMap(classes)
         self.num_classes = len(class_map)
+        self.labels = [class_map.get_by_id(i) for i in range(self.num_classes)]
+        self.set_state(ClassificationState(self.labels))
 
         parser = FiftyOneParser(sample_collection, class_map, label_field, iscrowd)
         records = parser.parse(data_splitter=SingleSplitSplitter())

flash/image/segmentation/data.py

@@ -555,6 +555,9 @@ def from_fiftyone(
             predict_transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use when
               predicting.
             input_cls: The :class:`~flash.core.data.io.input.Input` type to use for loading the data.
+            num_classes: The number of segmentation classes.
+            labels_map: An optional mapping from class to RGB tuple indicating the colour to use when visualizing masks.
+                If not provided, a random mapping will be used.
             transform_kwargs: Dict of keyword arguments to be provided when instantiating the transforms.
             data_module_kwargs: Additional keyword arguments to provide to the
               :class:`~flash.core.data.data_module.DataModule` constructor.