fix(ml): widen empty half-precision arrays (#31962)

fix(ml): widen empty half-precision arrays (#31950)

(cherry picked from commit b11e400a36a40fd588364ebf31fd671f97cdf446)

Co-authored-by: Mert <101130780+mertalev@users.noreply.github.com>

machine-learning/immich_ml/models/transforms.py

@@ -74,8 +74,8 @@ def serialize_np_array(arr: NDArray[np.float32]) -> str:
 
 def widen(array: NDArray[Any]) -> NDArray[Any]:
     """Single precision from half through OpenCV, as numpy converts one value at a time without native half support."""
-    if array.dtype != np.float16:
-        return array
+    if array.dtype != np.float16 or array.size == 0:
+        return array.astype(np.float32, copy=False)
     return cv2.convertFp16(array.reshape(-1, array.shape[-1]).view(np.int16)).reshape(array.shape)  # int16 carries half
 
 

machine-learning/test_main.py

@@ -1801,6 +1801,15 @@ class TestOcr:
         assert indices.tolist() == [[2, 1, 1, 0]]
         assert np.allclose(confidence, [[1 / (1 + 2 * np.exp(-10)), 1 / (1 + np.exp(-1) + np.exp(-2)), 0, 0]])
 
+    def test_rec_decodes_half_precision_logits_that_are_all_blank(self) -> None:
+        raw = np.full((2, 3, 1, 3), -5.0, dtype=np.float16)
+        raw[..., 0] = 5.0  # every step reads as the blank, so no character is kept
+
+        indices, confidence = logits(raw)
+
+        assert indices.tolist() == [[0, 0, 0], [0, 0, 0]]
+        assert confidence.dtype == np.float32 and not confidence.any()
+
     def test_set_rec_set_default_max_batch_size(
         self, ort_session: mock.Mock, path: mock.Mock, mocker: MockerFixture
     ) -> None: