Use detection threshold use to API and CLI

src/batdetect2/api_v2.py

@@ -303,6 +303,7 @@ class BatDetect2API:
         self,
         audio_file: data.PathLike,
         batch_size: int | None = None,
+        detection_threshold: float | None = None,
     ) -> ClipDetections:
         recording = data.Recording.from_file(audio_file, compute_hash=False)
 
@@ -313,6 +314,7 @@ class BatDetect2API:
                 if batch_size is not None
                 else self.inference_config.loader.batch_size
             ),
+            detection_threshold=detection_threshold,
         )
         detections = [
             detection
@@ -333,14 +335,19 @@ class BatDetect2API:
     def process_audio(
         self,
         audio: np.ndarray,
+        detection_threshold: float | None = None,
     ) -> list[Detection]:
         spec = self.generate_spectrogram(audio)
-        return self.process_spectrogram(spec)
+        return self.process_spectrogram(
+            spec,
+            detection_threshold=detection_threshold,
+        )
 
     def process_spectrogram(
         self,
         spec: torch.Tensor,
         start_time: float = 0,
+        detection_threshold: float | None = None,
     ) -> list[Detection]:
         if spec.ndim == 4 and spec.shape[0] > 1:
             raise ValueError("Batched spectrograms not supported.")
@@ -352,6 +359,7 @@ class BatDetect2API:
 
         detections = self.postprocessor(
             outputs,
+            detection_threshold=detection_threshold,
         )[0]
         return self.output_transform.to_detections(
             detections=detections,
@@ -361,9 +369,13 @@ class BatDetect2API:
     def process_directory(
         self,
         audio_dir: data.PathLike,
+        detection_threshold: float | None = None,
     ) -> list[ClipDetections]:
         files = list(get_audio_files(audio_dir))
-        return self.process_files(files)
+        return self.process_files(
+            files,
+            detection_threshold=detection_threshold,
+        )
 
     def process_files(
         self,
@@ -373,6 +385,7 @@ class BatDetect2API:
         audio_config: AudioConfig | None = None,
         inference_config: InferenceConfig | None = None,
         output_config: OutputsConfig | None = None,
+        detection_threshold: float | None = None,
     ) -> list[ClipDetections]:
         return process_file_list(
             self.model,
@@ -386,6 +399,7 @@ class BatDetect2API:
             audio_config=audio_config or self.audio_config,
             inference_config=inference_config or self.inference_config,
             output_config=output_config or self.outputs_config,
+            detection_threshold=detection_threshold,
         )
 
     def process_clips(
@@ -396,6 +410,7 @@ class BatDetect2API:
         audio_config: AudioConfig | None = None,
         inference_config: InferenceConfig | None = None,
         output_config: OutputsConfig | None = None,
+        detection_threshold: float | None = None,
     ) -> list[ClipDetections]:
         return run_batch_inference(
             self.model,
@@ -409,6 +424,7 @@ class BatDetect2API:
             audio_config=audio_config or self.audio_config,
             inference_config=inference_config or self.inference_config,
             output_config=output_config or self.outputs_config,
+            detection_threshold=detection_threshold,
         )
 
     def save_predictions(

src/batdetect2/cli/inference.py

@@ -87,6 +87,15 @@ def common_predict_options(func):
             "the default output format is used."
         ),
     )
+    @click.option(
+        "--detection-threshold",
+        type=click.FloatRange(min=0.0, max=1.0),
+        default=None,
+        help=(
+            "Optional detection score threshold override. If omitted, "
+            "the model default threshold is used."
+        ),
+    )
     @wraps(func)
     def wrapped(*args, **kwargs):
         return func(*args, **kwargs)
@@ -147,6 +156,7 @@ def _run_prediction(
     batch_size: int | None,
     num_workers: int,
     format_name: str | None,
+    detection_threshold: float | None,
 ) -> None:
     logger.info("Initiating prediction process...")
 
@@ -167,6 +177,7 @@ def _run_prediction(
         audio_config=audio_conf,
         inference_config=inference_conf,
         output_config=outputs_conf,
+        detection_threshold=detection_threshold,
     )
 
     common_path = audio_files[0].parent if audio_files else None
@@ -201,6 +212,7 @@ def predict_directory_command(
     batch_size: int | None,
     num_workers: int,
     format_name: str | None,
+    detection_threshold: float | None,
 ) -> None:
     """Predict on all audio files in a directory.
 
@@ -219,6 +231,7 @@ def predict_directory_command(
         batch_size=batch_size,
         num_workers=num_workers,
         format_name=format_name,
+        detection_threshold=detection_threshold,
     )
 
 
@@ -241,6 +254,7 @@ def predict_file_list_command(
     batch_size: int | None,
     num_workers: int,
     format_name: str | None,
+    detection_threshold: float | None,
 ) -> None:
     """Predict on audio files listed in a text file.
 
@@ -265,6 +279,7 @@ def predict_file_list_command(
         batch_size=batch_size,
         num_workers=num_workers,
         format_name=format_name,
+        detection_threshold=detection_threshold,
     )
 
 
@@ -287,6 +302,7 @@ def predict_dataset_command(
     batch_size: int | None,
     num_workers: int,
     format_name: str | None,
+    detection_threshold: float | None,
 ) -> None:
     """Predict on recordings referenced in an annotation dataset.
 
@@ -313,4 +329,5 @@ def predict_dataset_command(
         batch_size=batch_size,
         num_workers=num_workers,
         format_name=format_name,
+        detection_threshold=detection_threshold,
     )

src/batdetect2/inference/batch.py

@@ -31,6 +31,7 @@ def run_batch_inference(
     output_transform: OutputTransformProtocol | None = None,
     output_config: OutputsConfig | None = None,
     inference_config: InferenceConfig | None = None,
+    detection_threshold: float | None = None,
     num_workers: int = 1,
     batch_size: int | None = None,
 ) -> list[ClipDetections]:
@@ -62,6 +63,7 @@ def run_batch_inference(
     module = InferenceModule(
         model,
         output_transform=output_transform,
+        detection_threshold=detection_threshold,
     )
     trainer = Trainer(enable_checkpointing=False, logger=False)
     outputs = trainer.predict(module, loader)
@@ -82,6 +84,7 @@ def process_file_list(
     inference_config: InferenceConfig | None = None,
     output_config: OutputsConfig | None = None,
     output_transform: OutputTransformProtocol | None = None,
+    detection_threshold: float | None = None,
     batch_size: int | None = None,
     num_workers: int = 0,
 ) -> list[ClipDetections]:
@@ -106,4 +109,5 @@ def process_file_list(
         audio_config=audio_config,
         output_transform=output_transform,
         inference_config=inference_config,
+        detection_threshold=detection_threshold,
     )

src/batdetect2/inference/lightning.py

@@ -14,9 +14,11 @@ class InferenceModule(LightningModule):
         self,
         model: Model,
         output_transform: OutputTransformProtocol | None = None,
+        detection_threshold: float | None = None,
     ):
         super().__init__()
         self.model = model
+        self.detection_threshold = detection_threshold
         self.output_transform = output_transform or build_output_transform(
             targets=model.targets
         )
@@ -33,7 +35,10 @@ class InferenceModule(LightningModule):
 
         outputs = self.model.detector(batch.spec)
 
-        clip_detections = self.model.postprocessor(outputs)
+        clip_detections = self.model.postprocessor(
+            outputs,
+            detection_threshold=self.detection_threshold,
+        )
 
         return [
             self.output_transform.to_clip_detections(