fix(deps): update machine-learning (#19803)

* fix(deps): update machine-learning

* typing fixes

---------

Co-authored-by: renovate[bot] <29139614+renovate[bot]@users.noreply.github.com>
Co-authored-by: mertalev <101130780+mertalev@users.noreply.github.com>

machine-learning/Dockerfile

@@ -1,6 +1,6 @@
 ARG DEVICE=cpu
 
-FROM python:3.11-bookworm@sha256:ce3b954c9285a7a145cba620bae03db836ab890b6b9e0d05a3ca522ea00dfbc9 AS builder-cpu
+FROM python:3.11-bookworm@sha256:c1239cb82bf08176c4c90421ab425a1696257b098d9ce21e68de9319c255a47d AS builder-cpu
 
 FROM builder-cpu AS builder-openvino
 
@@ -59,7 +59,7 @@ ENV PYTHONDONTWRITEBYTECODE=1 \
 
 RUN apt-get update && apt-get install -y --no-install-recommends g++
 
-COPY --from=ghcr.io/astral-sh/uv:latest@sha256:9653efd4380d5a0e5511e337dcfc3b8ba5bc4e6ea7fa3be7716598261d5503fa /uv /uvx /bin/
+COPY --from=ghcr.io/astral-sh/uv:latest@sha256:67b2bcccdc103d608727d1b577e58008ef810f751ed324715eb60b3f0c040d30 /uv /uvx /bin/
 RUN --mount=type=cache,target=/root/.cache/uv \
     --mount=type=bind,source=uv.lock,target=uv.lock \
     --mount=type=bind,source=pyproject.toml,target=pyproject.toml \
@@ -68,11 +68,11 @@ RUN if [ "$DEVICE" = "rocm" ]; then \
     uv pip install /opt/onnxruntime_rocm-*.whl; \
     fi
 
-FROM python:3.11-slim-bookworm@sha256:9e1912aab0a30bbd9488eb79063f68f42a68ab0946cbe98fecf197fe5b085506 AS prod-cpu
+FROM python:3.11-slim-bookworm@sha256:0ce77749ac83174a31d5e107ce0cfa6b28a2fd6b0615e029d9d84b39c48976ee AS prod-cpu
 
 ENV LD_PRELOAD=/usr/lib/libmimalloc.so.2
 
-FROM python:3.11-slim-bookworm@sha256:9e1912aab0a30bbd9488eb79063f68f42a68ab0946cbe98fecf197fe5b085506 AS prod-openvino
+FROM python:3.11-slim-bookworm@sha256:0ce77749ac83174a31d5e107ce0cfa6b28a2fd6b0615e029d9d84b39c48976ee AS prod-openvino
 
 RUN apt-get update && \
     apt-get install --no-install-recommends -yqq ocl-icd-libopencl1 wget && \

machine-learning/immich_ml/models/transforms.py

@@ -36,7 +36,7 @@ def to_numpy(img: Image.Image) -> NDArray[np.float32]:
 def normalize(
     img: NDArray[np.float32], mean: float | NDArray[np.float32], std: float | NDArray[np.float32]
 ) -> NDArray[np.float32]:
-    return np.divide(img - mean, std, dtype=np.float32)
+    return (img - mean) / std
 
 
 def get_pil_resampling(resample: str) -> Image.Resampling:
@@ -58,11 +58,13 @@ def decode_pil(image_bytes: bytes | IO[bytes] | Image.Image) -> Image.Image:
 
 
 def decode_cv2(image_bytes: NDArray[np.uint8] | bytes | Image.Image) -> NDArray[np.uint8]:
-    if isinstance(image_bytes, bytes):
-        image_bytes = decode_pil(image_bytes)  # pillow is much faster than cv2
-    if isinstance(image_bytes, Image.Image):
-        return pil_to_cv2(image_bytes)
-    return image_bytes
+    match image_bytes:
+        case bytes() | memoryview() | bytearray():
+            return pil_to_cv2(decode_pil(image_bytes))  # pillow is much faster than cv2
+        case Image.Image():
+            return pil_to_cv2(image_bytes)
+        case _:
+            return image_bytes
 
 
 def clean_text(text: str, canonicalize: bool = False) -> str:

machine-learning/immich_ml/schemas.py

@@ -112,8 +112,4 @@ def has_profiling(obj: Any) -> TypeGuard[HasProfiling]:
     return hasattr(obj, "profiling") and isinstance(obj.profiling, dict)
 
 
-def is_ndarray(obj: Any, dtype: "type[np._DTypeScalar_co]") -> "TypeGuard[npt.NDArray[np._DTypeScalar_co]]":
-    return isinstance(obj, np.ndarray) and obj.dtype == dtype
-
-
 T = TypeVar("T")

machine-learning/pyproject.toml

@@ -12,6 +12,7 @@ dependencies = [
     "gunicorn>=21.1.0",
     "huggingface-hub>=0.20.1,<1.0",
     "insightface>=0.7.3,<1.0",
+    "numpy<2",
     "opencv-python-headless>=4.7.0.72,<5.0",
     "orjson>=3.9.5",
     "pillow>=9.5.0,<11.0",