Handle confidence=None in old inference path

Author: leeclemnet

inference/core/models/classification_base.py

@@ -1,6 +1,6 @@
 from io import BytesIO
 from time import perf_counter
-from typing import Any, List, Tuple, Union
+from typing import Any, List, Optional, Tuple, Union
 
 import numpy as np
 
@@ -299,7 +299,7 @@ def make_response(
         self,
         predictions,
         img_dims,
-        confidence: float = 0.5,
+        confidence: Optional[float] = 0.5,
         **kwargs,
     ) -> Union[ClassificationInferenceResponse, List[ClassificationInferenceResponse]]:
         """
@@ -320,6 +320,11 @@ def make_response(
             - Predictions below the confidence threshold are filtered out.
         """
         responses = []
+        # request.dict() unpacking can pass confidence=None explicitly via
+        # kwargs, which overrides the signature default — coalesce so the
+        # existing default behavior is preserved.
+        if confidence is None:
+            confidence = 0.5
         confidence_threshold = float(confidence)
         for ind, prediction in enumerate(predictions):
             if self.multiclass:

inference/core/models/instance_segmentation_base.py

@@ -1,4 +1,4 @@
-from typing import Any, List, Tuple, Union
+from typing import Any, List, Optional, Tuple, Union
 
 import numpy as np
 
@@ -49,7 +49,7 @@ def infer(
         self,
         image: Any,
         class_agnostic_nms: bool = False,
-        confidence: float = DEFAULT_CONFIDENCE,
+        confidence: Optional[float] = DEFAULT_CONFIDENCE,
         disable_preproc_auto_orient: bool = False,
         disable_preproc_contrast: bool = False,
         disable_preproc_grayscale: bool = False,
@@ -94,6 +94,11 @@ def infer(
             - Applies non-maximum suppression to the predictions.
             - Decodes the masks according to the specified mode.
         """
+        # request.dict() unpacking can pass confidence=None explicitly, which
+        # overrides the signature default — coalesce so the existing default
+        # behavior is preserved.
+        if confidence is None:
+            confidence = DEFAULT_CONFIDENCE
         return super().infer(
             image,
             class_agnostic_nms=class_agnostic_nms,

inference/core/models/object_detection_base.py

@@ -43,7 +43,7 @@ def infer(
         self,
         image: Any,
         class_agnostic_nms: bool = DEFAULT_CLASS_AGNOSTIC_NMS,
-        confidence: float = DEFAULT_CONFIDENCE,
+        confidence: Optional[float] = DEFAULT_CONFIDENCE,
         disable_preproc_auto_orient: bool = False,
         disable_preproc_contrast: bool = False,
         disable_preproc_grayscale: bool = False,
@@ -81,6 +81,11 @@ def infer(
         Raises:
             ValueError: If batching is not enabled for the model and more than one image is passed for processing.
         """
+        # request.dict() unpacking can pass confidence=None explicitly, which
+        # overrides the signature default — coalesce so the existing default
+        # behavior is preserved.
+        if confidence is None:
+            confidence = DEFAULT_CONFIDENCE
         return super().infer(
             image,
             class_agnostic_nms=class_agnostic_nms,