Set dtype for numpy binding on protobuf repeated float field to prevent breaks by protobuf numpy change.

PiperOrigin-RevId: 832129663

Author: anandolee

tensorflow_lite_support/python/task/processor/proto/segmentations_pb2.py

@@ -57,7 +57,9 @@ def to_pb2(self) -> _ConfidenceMaskProto:
   def create_from_pb2(cls, pb2_obj: _ConfidenceMaskProto, height: int,
                       width: int) -> "ConfidenceMask":
     """Creates a `ConfidenceMask` object from the given protobuf and size."""
-    return ConfidenceMask(value=np.array(pb2_obj.value).reshape(height, width))
+    return ConfidenceMask(
+        value=np.array(pb2_obj.value, np.float32).reshape(height, width)
+    )
 
   def __eq__(self, other: Any) -> bool:
     """Checks if this object is equal to the given object.

tensorflow_lite_support/python/test/task/vision/image_segmenter_test.py

@@ -224,7 +224,7 @@ def test_segmentation_confidence_mask_matches_category_mask(self):
         [confidence_mask.value for confidence_mask in confidence_masks])
 
     # Check if data type of `confidence_masks` are correct.
-    self.assertEqual(confidence_mask_array.dtype, float)
+    self.assertEqual(confidence_mask_array.dtype, np.float32)
 
     # Compute the category mask from the created confidence mask.
     calculated_category_mask = np.argmax(confidence_mask_array, axis=0)