add predict_kwargs in ObjectDetectionModel in order to filter the pre…

CHANGELOG.md

@@ -8,6 +8,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Added
 
+- Added predict_kwargs in `ObjectDetector`, `InstanceSegmentation`, `KeypointDetector` ([#990](https://github.com/PyTorchLightning/lightning-flash/pull/990))
+
 - Added backbones for `GraphClassifier` ([#592](https://github.com/PyTorchLightning/lightning-flash/pull/592))
 
 - Added `GraphEmbedder` task ([#592](https://github.com/PyTorchLightning/lightning-flash/pull/592))

flash/core/integrations/icevision/adapter.py

@@ -46,13 +46,14 @@ class IceVisionAdapter(Adapter):
 
     required_extras: str = "image"
 
-    def __init__(self, model_type, model, icevision_adapter, backbone):
+    def __init__(self, model_type, model, icevision_adapter, backbone, predict_kwargs):
         super().__init__()
 
         self.model_type = model_type
         self.model = model
         self.icevision_adapter = icevision_adapter
         self.backbone = backbone
+        self.predict_kwargs = predict_kwargs
 
     @classmethod
     @catch_url_error
@@ -62,6 +63,7 @@ def from_task(
         num_classes: int,
         backbone: str,
         head: str,
+        predict_kwargs: Dict,
         pretrained: bool = True,
         metrics: Optional["IceVisionMetric"] = None,
         image_size: Optional = None,
@@ -77,7 +79,7 @@ def from_task(
             **kwargs,
         )
         icevision_adapter = icevision_adapter(model=model, metrics=metrics)
-        return cls(model_type, model, icevision_adapter, backbone)
+        return cls(model_type, model, icevision_adapter, backbone, predict_kwargs)
 
     @staticmethod
     def _collate_fn(collate_fn, samples, metadata: Optional[List[Dict[str, Any]]] = None):
@@ -198,7 +200,9 @@ def predict_step(self, batch: Any, batch_idx: int, dataloader_idx: int = 0) -> A
         return batch
 
     def forward(self, batch: Any) -> Any:
-        return from_icevision_predictions(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, **self.predict_kwargs)
+        )
 
     def training_epoch_end(self, outputs) -> None:
         return self.icevision_adapter.training_epoch_end(outputs)

flash/image/detection/model.py

@@ -33,6 +33,7 @@ class ObjectDetector(AdapterTask):
         lr_scheduler: The LR scheduler to use during training.
         learning_rate: The learning rate to use for training.
         output: The :class:`~flash.core.data.io.output.Output` to use when formatting prediction outputs.
+        predict_kwargs: dictionary containing parameters that will be used during the prediction phase.
         kwargs: additional kwargs nessesary for initializing the backbone task
     """
 
@@ -50,17 +51,20 @@ def __init__(
         lr_scheduler: LR_SCHEDULER_TYPE = None,
         learning_rate: float = 5e-3,
         output: OUTPUT_TYPE = None,
+        predict_kwargs: Dict = None,
         **kwargs: Any,
     ):
         self.save_hyperparameters()
 
+        predict_kwargs = predict_kwargs if predict_kwargs else {}
         metadata = self.heads.get(head, with_metadata=True)
         adapter = metadata["metadata"]["adapter"].from_task(
             self,
             num_classes=num_classes,
             backbone=backbone,
             head=head,
             pretrained=pretrained,
+            predict_kwargs=predict_kwargs,
             **kwargs,
         )
 
@@ -75,3 +79,12 @@ def __init__(
     def _ci_benchmark_fn(self, history: List[Dict[str, Any]]) -> None:
         """This function is used only for debugging usage with CI."""
         # todo
+
+    @property
+    def predict_kwargs(self) -> Dict[str, Any]:
+        """The kwargs used for the prediction step."""
+        return self.adapter.predict_kwargs
+
+    @predict_kwargs.setter
+    def predict_kwargs(self, predict_kwargs: Dict[str, Any]):
+        self.adapter.predict_kwargs = predict_kwargs

flash/image/instance_segmentation/model.py

@@ -40,6 +40,7 @@ class InstanceSegmentation(AdapterTask):
         lr_scheduler: The LR scheduler to use during training.
         learning_rate: The learning rate to use for training.
         output: The :class:`~flash.core.data.io.output.Output` to use when formatting prediction outputs.
+        predict_kwargs: dictionary containing parameters that will be used during the prediction phase.
         **kwargs: additional kwargs used for initializing the task
     """
 
@@ -57,17 +58,20 @@ def __init__(
         lr_scheduler: LR_SCHEDULER_TYPE = None,
         learning_rate: float = 5e-4,
         output: OUTPUT_TYPE = None,
+        predict_kwargs: Dict = None,
         **kwargs: Any,
     ):
         self.save_hyperparameters()
 
+        predict_kwargs = predict_kwargs if predict_kwargs else {}
         metadata = self.heads.get(head, with_metadata=True)
         adapter = metadata["metadata"]["adapter"].from_task(
             self,
             num_classes=num_classes,
             backbone=backbone,
             head=head,
             pretrained=pretrained,
+            predict_kwargs=predict_kwargs,
             **kwargs,
         )
 
@@ -96,3 +100,12 @@ def on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
                 input_transform=InstanceSegmentationInputTransform(),
                 output_transform=InstanceSegmentationOutputTransform(),
             )
+
+    @property
+    def predict_kwargs(self) -> Dict[str, Any]:
+        """The kwargs used for the prediction step."""
+        return self.adapter.predict_kwargs
+
+    @predict_kwargs.setter
+    def predict_kwargs(self, predict_kwargs: Dict[str, Any]):
+        self.adapter.predict_kwargs = predict_kwargs

flash/image/keypoint_detection/model.py

@@ -34,6 +34,7 @@ class KeypointDetector(AdapterTask):
         lr_scheduler: The LR scheduler to use during training.
         learning_rate: The learning rate to use for training.
         output: The :class:`~flash.core.data.io.output.Output` to use when formatting prediction outputs.
+        predict_kwargs: dictionary containing parameters that will be used during the prediction phase.
         **kwargs: additional kwargs used for initializing the task
     """
 
@@ -52,10 +53,12 @@ def __init__(
         lr_scheduler: LR_SCHEDULER_TYPE = None,
         learning_rate: float = 5e-4,
         output: OUTPUT_TYPE = None,
+        predict_kwargs: Dict = None,
         **kwargs: Any,
     ):
         self.save_hyperparameters()
 
+        predict_kwargs = predict_kwargs if predict_kwargs else {}
         metadata = self.heads.get(head, with_metadata=True)
         adapter = metadata["metadata"]["adapter"].from_task(
             self,
@@ -64,6 +67,7 @@ def __init__(
             backbone=backbone,
             head=head,
             pretrained=pretrained,
+            predict_kwargs=predict_kwargs,
             **kwargs,
         )
 
@@ -78,3 +82,12 @@ def __init__(
     def _ci_benchmark_fn(self, history: List[Dict[str, Any]]) -> None:
         """This function is used only for debugging usage with CI."""
         # todo
+
+    @property
+    def predict_kwargs(self) -> Dict[str, Any]:
+        """The kwargs used for the prediction step."""
+        return self.adapter.predict_kwargs
+
+    @predict_kwargs.setter
+    def predict_kwargs(self, predict_kwargs: Dict[str, Any]):
+        self.adapter.predict_kwargs = predict_kwargs

tests/image/detection/test_model.py

@@ -134,3 +134,20 @@ def test_cli():
             main()
         except SystemExit:
             pass
+
+
+@pytest.mark.parametrize("head", ["retinanet"])
+@pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed.")
+@pytest.mark.skipif(not _ICEVISION_AVAILABLE, reason="IceVision is not installed for testing")
+def test_predict(tmpdir, head):
+    model = ObjectDetector(num_classes=2, head=head, pretrained=False)
+    ds = DummyDetectionDataset((128, 128, 3), 1, 2, 10)
+    trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True)
+    dl = model.process_train_dataset(ds, trainer, 2, 0, False, None)
+    trainer.fit(model, dl)
+    dl = model.process_predict_dataset(ds, batch_size=2)
+    predictions = trainer.predict(model, dl)
+    assert len(predictions[0][0]["bboxes"]) > 0
+    model.predict_kwargs = {"detection_threshold": 2}
+    predictions = trainer.predict(model, dl)
+    assert len(predictions[0][0]["bboxes"]) == 0