Add prediction unwrapping and serialization for icevision tasks

CHANGELOG.md

@@ -54,6 +54,10 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Added a `QuestionAnswering` task for extractive question answering ([#607](https://github.com/PyTorchLightning/lightning-flash/pull/607))
 
+- Added automatic unwrapping of IceVision prediciton objects ([#727](https://github.com/PyTorchLightning/lightning-flash/pull/727))
+
+- Added support for the `ObjectDetector` with FiftyOne ([#727](https://github.com/PyTorchLightning/lightning-flash/pull/727))
+
 ### Changed
 
 - Changed how pretrained flag works for loading weights for ImageClassifier task ([#560](https://github.com/PyTorchLightning/lightning-flash/pull/560))

docs/source/api/image.rst

@@ -45,7 +45,6 @@ ________________
     detection.data.FiftyOneParser
     detection.data.ObjectDetectionFiftyOneDataSource
     detection.data.ObjectDetectionPreprocess
-    detection.serialization.DetectionLabels
     detection.serialization.FiftyOneDetectionLabels
 
 Keypoint Detection

flash/core/data/batch.py

@@ -344,13 +344,13 @@ def default_uncollate(batch: Any):
             return batch
         return list(torch.unbind(batch, 0))
 
-    if isinstance(batch, Mapping):
+    if isinstance(batch, dict):
         return [batch_type(dict(zip(batch, default_uncollate(t)))) for t in zip(*batch.values())]
 
     if isinstance(batch, tuple) and hasattr(batch, "_fields"):  # namedtuple
-        return [batch_type(*default_uncollate(sample)) for sample in zip(*batch)]
+        return [batch_type(*sample) for sample in zip(*batch)]
 
     if isinstance(batch, Sequence) and not isinstance(batch, str):
-        return [default_uncollate(sample) for sample in batch]
+        return [sample for sample in batch]
 
     return batch

flash/core/data/serialization.py

@@ -0,0 +1,24 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import Any, List, Union
+
+from flash.core.data.data_source import DefaultDataKeys
+from flash.core.data.process import Serializer
+
+
+class Preds(Serializer):
+    """A :class:`~flash.core.data.process.Serializer` which returns the "preds" from the model outputs."""
+
+    def serialize(self, sample: Any) -> Union[int, List[int]]:
+        return sample.get(DefaultDataKeys.PREDS, sample) if isinstance(sample, dict) else sample

flash/core/integrations/icevision/adapter.py

@@ -19,7 +19,7 @@
 from flash.core.adapter import Adapter
 from flash.core.data.auto_dataset import BaseAutoDataset
 from flash.core.data.data_source import DefaultDataKeys
-from flash.core.integrations.icevision.transforms import to_icevision_record
+from flash.core.integrations.icevision.transforms import from_icevision_predictions, to_icevision_record
 from flash.core.model import Task
 from flash.core.utilities.imports import _ICEVISION_AVAILABLE
 from flash.core.utilities.url_error import catch_url_error
@@ -81,9 +81,12 @@ def from_task(
     @staticmethod
     def _collate_fn(collate_fn, samples, metadata: Optional[List[Dict[str, Any]]] = None):
         metadata = metadata or [None] * len(samples)
-        return collate_fn(
-            [to_icevision_record({**sample, DefaultDataKeys.METADATA: m}) for sample, m in zip(samples, metadata)]
-        )
+        return {
+            DefaultDataKeys.INPUT: collate_fn(
+                [to_icevision_record({**sample, DefaultDataKeys.METADATA: m}) for sample, m in zip(samples, metadata)]
+            ),
+            DefaultDataKeys.METADATA: metadata,
+        }
 
     def process_train_dataset(
         self,
@@ -178,19 +181,20 @@ def process_predict_dataset(
         return data_loader
 
     def training_step(self, batch, batch_idx) -> Any:
-        return self.icevision_adapter.training_step(batch, batch_idx)
+        return self.icevision_adapter.training_step(batch[DefaultDataKeys.INPUT], batch_idx)
 
     def validation_step(self, batch, batch_idx):
-        return self.icevision_adapter.validation_step(batch, batch_idx)
+        return self.icevision_adapter.validation_step(batch[DefaultDataKeys.INPUT], batch_idx)
 
     def test_step(self, batch, batch_idx):
-        return self.icevision_adapter.validation_step(batch, batch_idx)
+        return self.icevision_adapter.validation_step(batch[DefaultDataKeys.INPUT], batch_idx)
 
     def predict_step(self, batch: Any, batch_idx: int, dataloader_idx: int = 0) -> Any:
-        return self(batch)
+        batch[DefaultDataKeys.PREDS] = self(batch[DefaultDataKeys.INPUT])
+        return batch
 
     def forward(self, batch: Any) -> Any:
-        return self.model_type.predict_from_dl(self.model, [batch], show_pbar=False)
+        return from_icevision_predictions(self.model_type.predict_from_dl(self.model, [batch], show_pbar=False))
 
     def training_epoch_end(self, outputs) -> None:
         return self.icevision_adapter.training_epoch_end(outputs)

flash/core/integrations/icevision/data.py

@@ -11,18 +11,19 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import inspect
 from typing import Any, Callable, Dict, Optional, Sequence, Tuple, Type
 
 import numpy as np
 
-from flash.core.data.data_source import DefaultDataKeys
+from flash.core.data.data_source import DefaultDataKeys, LabelsState
 from flash.core.integrations.icevision.transforms import from_icevision_record
 from flash.core.utilities.imports import _ICEVISION_AVAILABLE
 from flash.image.data import ImagePathsDataSource
 
 if _ICEVISION_AVAILABLE:
     from icevision.core.record import BaseRecord
-    from icevision.core.record_components import ClassMapRecordComponent, ImageRecordComponent, tasks
+    from icevision.core.record_components import ClassMapRecordComponent, FilepathRecordComponent, tasks
     from icevision.data.data_splitter import SingleSplitSplitter
     from icevision.parsers.parser import Parser
 
@@ -36,10 +37,14 @@ def load_sample(self, sample: Dict[str, Any]) -> Dict[str, Any]:
         return from_icevision_record(record)
 
     def predict_load_sample(self, sample: Dict[str, Any]) -> Dict[str, Any]:
+        if isinstance(sample[DefaultDataKeys.INPUT], BaseRecord):
+            return self.load_sample(sample)
+        filepath = sample[DefaultDataKeys.INPUT]
         sample = super().load_sample(sample)
         image = np.array(sample[DefaultDataKeys.INPUT])
-        record = BaseRecord([ImageRecordComponent()])
 
+        record = BaseRecord([FilepathRecordComponent()])
+        record.filepath = filepath
         record.set_img(image)
         record.add_component(ClassMapRecordComponent(task=tasks.detection))
         return from_icevision_record(record)
@@ -51,29 +56,23 @@ def __init__(self, parser: Optional[Type["Parser"]] = None):
         self.parser = parser
 
     def load_data(self, data: Tuple[str, str], dataset: Optional[Any] = None) -> Sequence[Dict[str, Any]]:
-        root, ann_file = data
-
         if self.parser is not None:
-            parser = self.parser(ann_file, root)
-            dataset.num_classes = len(parser.class_map)
+            if inspect.isclass(self.parser) and issubclass(self.parser, Parser):
+                root, ann_file = data
+                parser = self.parser(ann_file, root)
+            elif isinstance(self.parser, Callable):
+                parser = self.parser(data)
+            else:
+                raise ValueError("The parser must be a callable or an IceVision Parser type.")
+            dataset.num_classes = parser.class_map.num_classes
+            self.set_state(LabelsState([parser.class_map.get_by_id(i) for i in range(dataset.num_classes)]))
             records = parser.parse(data_splitter=SingleSplitSplitter())
             return [{DefaultDataKeys.INPUT: record} for record in records[0]]
         else:
-            raise ValueError("The parser type must be provided")
-
-
-class IceDataParserDataSource(IceVisionPathsDataSource):
-    def __init__(self, parser: Optional[Callable] = None):
-        super().__init__()
-        self.parser = parser
+            raise ValueError("The parser argument must be provided.")
 
-    def load_data(self, data: Tuple[str, str], dataset: Optional[Any] = None) -> Sequence[Dict[str, Any]]:
-        root = data
-
-        if self.parser is not None:
-            parser = self.parser(root)
-            dataset.num_classes = len(parser.class_map)
-            records = parser.parse(data_splitter=SingleSplitSplitter())
-            return [{DefaultDataKeys.INPUT: record} for record in records[0]]
-        else:
-            raise ValueError("The parser must be provided")
+    def predict_load_data(self, data: Any, dataset: Optional[Any] = None) -> Sequence[Dict[str, Any]]:
+        result = super().predict_load_data(data, dataset)
+        if len(result) == 0:
+            result = self.load_data(data, dataset)
+        return result

flash/core/integrations/icevision/transforms.py

@@ -11,7 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from typing import Any, Callable, Dict, Tuple
+from typing import Any, Callable, Dict, List, Tuple
 
 from torch import nn
 
@@ -34,6 +34,7 @@
         MasksRecordComponent,
         RecordIDRecordComponent,
     )
+    from icevision.data.prediction import Prediction
     from icevision.tfms import A
 
 
@@ -101,51 +102,39 @@ def to_icevision_record(sample: Dict[str, Any]):
     return record
 
 
-def from_icevision_record(record: "BaseRecord"):
-    sample = {
-        DefaultDataKeys.METADATA: {
-            "image_id": record.record_id,
-        }
-    }
-
-    if record.img is not None:
-        sample[DefaultDataKeys.INPUT] = record.img
-        filepath = getattr(record, "filepath", None)
-        if filepath is not None:
-            sample[DefaultDataKeys.METADATA]["filepath"] = filepath
-    elif record.filepath is not None:
-        sample[DefaultDataKeys.INPUT] = record.filepath
+def from_icevision_detection(record: "BaseRecord"):
+    detection = record.detection
 
-    sample[DefaultDataKeys.TARGET] = {}
+    result = {}
 
-    if hasattr(record.detection, "bboxes"):
-        sample[DefaultDataKeys.TARGET]["bboxes"] = []
-        for bbox in record.detection.bboxes:
+    if hasattr(detection, "bboxes"):
+        result["bboxes"] = []
+        for bbox in detection.bboxes:
             bbox_list = list(bbox.xywh)
             bbox_dict = {
                 "xmin": bbox_list[0],
                 "ymin": bbox_list[1],
                 "width": bbox_list[2],
                 "height": bbox_list[3],
             }
-            sample[DefaultDataKeys.TARGET]["bboxes"].append(bbox_dict)
+            result["bboxes"].append(bbox_dict)
 
-    if hasattr(record.detection, "masks"):
-        masks = record.detection.masks
+    if hasattr(detection, "masks"):
+        masks = detection.masks
 
         if isinstance(masks, EncodedRLEs):
             masks = masks.to_mask(record.height, record.width)
 
         if isinstance(masks, MaskArray):
-            sample[DefaultDataKeys.TARGET]["masks"] = masks.data
+            result["masks"] = masks.data
         else:
             raise RuntimeError("Masks are expected to be a MaskArray or EncodedRLEs.")
 
-    if hasattr(record.detection, "keypoints"):
-        keypoints = record.detection.keypoints
+    if hasattr(detection, "keypoints"):
+        keypoints = detection.keypoints
 
-        sample[DefaultDataKeys.TARGET]["keypoints"] = []
-        sample[DefaultDataKeys.TARGET]["keypoints_metadata"] = []
+        result["keypoints"] = []
+        result["keypoints_metadata"] = []
 
         for keypoint in keypoints:
             keypoints_list = []
@@ -157,20 +146,56 @@ def from_icevision_record(record: "BaseRecord"):
                         "visible": v,
                     }
                 )
-            sample[DefaultDataKeys.TARGET]["keypoints"].append(keypoints_list)
+            result["keypoints"].append(keypoints_list)
 
             # TODO: Unpack keypoints_metadata
-            sample[DefaultDataKeys.TARGET]["keypoints_metadata"].append(keypoint.metadata)
+            result["keypoints_metadata"].append(keypoint.metadata)
+
+    if getattr(detection, "label_ids", None) is not None:
+        result["labels"] = list(detection.label_ids)
 
-    if getattr(record.detection, "label_ids", None) is not None:
-        sample[DefaultDataKeys.TARGET]["labels"] = list(record.detection.label_ids)
+    if getattr(detection, "scores", None) is not None:
+        result["scores"] = list(detection.scores)
+
+    return result
+
+
+def from_icevision_record(record: "BaseRecord"):
+    sample = {
+        DefaultDataKeys.METADATA: {
+            "size": (record.height, record.width),
+        }
+    }
+
+    if getattr(record, "record_id", None) is not None:
+        sample[DefaultDataKeys.METADATA]["image_id"] = record.image_id
+
+    if getattr(record, "filepath", None) is not None:
+        sample[DefaultDataKeys.METADATA]["filepath"] = record.filepath
+
+    if record.img is not None:
+        sample[DefaultDataKeys.INPUT] = record.img
+        filepath = getattr(record, "filepath", None)
+        if filepath is not None:
+            sample[DefaultDataKeys.METADATA]["filepath"] = filepath
+    elif record.filepath is not None:
+        sample[DefaultDataKeys.INPUT] = record.filepath
+
+    sample[DefaultDataKeys.TARGET] = from_icevision_detection(record)
 
     if getattr(record.detection, "class_map", None) is not None:
         sample[DefaultDataKeys.METADATA]["class_map"] = record.detection.class_map
 
     return sample
 
 
+def from_icevision_predictions(predictions: List["Prediction"]):
+    result = []
+    for prediction in predictions:
+        result.append(from_icevision_detection(prediction.pred))
+    return result
+
+
 class IceVisionTransformAdapter(nn.Module):
     def __init__(self, transform):
         super().__init__()

flash/image/detection/data.py

@@ -17,11 +17,7 @@
 from flash.core.data.data_module import DataModule
 from flash.core.data.data_source import DefaultDataKeys, DefaultDataSources, FiftyOneDataSource
 from flash.core.data.process import Preprocess
-from flash.core.integrations.icevision.data import (
-    IceDataParserDataSource,
-    IceVisionParserDataSource,
-    IceVisionPathsDataSource,
-)
+from flash.core.integrations.icevision.data import IceVisionParserDataSource, IceVisionPathsDataSource
 from flash.core.integrations.icevision.transforms import default_transforms
 from flash.core.utilities.imports import _FIFTYONE_AVAILABLE, _ICEVISION_AVAILABLE, lazy_import, requires
 
@@ -160,7 +156,7 @@ def __init__(
                 "via": IceVisionParserDataSource(parser=VIABBoxParser),
                 "voc": IceVisionParserDataSource(parser=VOCBBoxParser),
                 DefaultDataSources.FILES: IceVisionPathsDataSource(),
-                DefaultDataSources.FOLDERS: IceDataParserDataSource(parser=parser),
+                DefaultDataSources.FOLDERS: IceVisionParserDataSource(parser=parser),
                 DefaultDataSources.FIFTYONE: ObjectDetectionFiftyOneDataSource(**data_source_kwargs),
             },
             default_data_source=DefaultDataSources.FILES,

flash/image/detection/model.py

@@ -18,6 +18,7 @@
 
 from flash.core.adapter import AdapterTask
 from flash.core.data.process import Serializer
+from flash.core.data.serialization import Preds
 from flash.core.registry import FlashRegistry
 from flash.image.detection.backbones import OBJECT_DETECTION_HEADS
 
@@ -57,7 +58,7 @@ def __init__(
         head: Optional[str] = "retinanet",
         pretrained: bool = True,
         optimizer: Type[Optimizer] = torch.optim.Adam,
-        learning_rate: float = 5e-4,
+        learning_rate: float = 5e-3,
         serializer: Optional[Union[Serializer, Mapping[str, Serializer]]] = None,
         **kwargs: Any,
     ):
@@ -77,7 +78,7 @@ def __init__(
             adapter,
             learning_rate=learning_rate,
             optimizer=optimizer,
-            serializer=serializer,
+            serializer=serializer or Preds(),
         )
 
     def _ci_benchmark_fn(self, history: List[Dict[str, Any]]) -> None: