From 20be42cec0db99130f7b4830c723683549ccc23f Mon Sep 17 00:00:00 2001 From: "renovate[bot]" <29139614+renovate[bot]@users.noreply.github.com> Date: Sat, 13 Jan 2024 05:00:09 +0000 Subject: [PATCH] chore(deps): update machine-learning (#6302) * chore(deps): update machine-learning * fix typing, use new lifespan syntax * wrap in try / finally * move log --------- 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 | 4 +- machine-learning/ann/ann.py | 2 +- machine-learning/app/conftest.py | 4 +- machine-learning/app/main.py | 43 +-- machine-learning/app/models/ann.py | 10 +- machine-learning/app/models/clip.py | 17 +- .../app/models/facial_recognition.py | 20 +- machine-learning/app/models/transforms.py | 9 +- machine-learning/app/schemas.py | 13 +- machine-learning/export/Dockerfile | 2 +- machine-learning/poetry.lock | 325 +++++++++--------- machine-learning/pyproject.toml | 1 - 12 files changed, 238 insertions(+), 212 deletions(-) diff --git a/machine-learning/Dockerfile b/machine-learning/Dockerfile index b32a564dfb..d0af8f95aa 100644 --- a/machine-learning/Dockerfile +++ b/machine-learning/Dockerfile @@ -1,4 +1,4 @@ -FROM python:3.11-bookworm@sha256:291405e32318285d8913b7b03293777c255fb1e89305c82aa495ac747b0049fe as builder +FROM python:3.11-bookworm@sha256:497c00ec2cff14316a6859c4e30fc88e7ab1f11dd254fb43b8f4b201ca657596 as builder ENV PYTHONDONTWRITEBYTECODE=1 \ PYTHONUNBUFFERED=1 \ @@ -27,7 +27,7 @@ RUN if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \ fi -FROM python:3.11-slim-bookworm@sha256:8f64a67710f3d981cf3008d6f9f1dbe61accd7927f165f4e37ea3f8b883ccc3f +FROM python:3.11-slim-bookworm@sha256:637774748f62b832dc11e7b286e48cd716727ed04b45a0322776c01bc526afc3 ARG TARGETPLATFORM RUN apt-get update && apt-get install -y --no-install-recommends tini libmimalloc2.0 && rm -rf /var/lib/apt/lists/* diff --git a/machine-learning/ann/ann.py b/machine-learning/ann/ann.py index 8f8c5473c7..94f665bfc7 100644 --- a/machine-learning/ann/ann.py +++ b/machine-learning/ann/ann.py @@ -2,7 +2,7 @@ from __future__ import annotations from ctypes import CDLL, Array, c_bool, c_char_p, c_int, c_ulong, c_void_p from os.path import exists -from typing import Any, Generic, Protocol, Type, TypeVar +from typing import Any, Protocol, TypeVar import numpy as np from numpy.typing import NDArray diff --git a/machine-learning/app/conftest.py b/machine-learning/app/conftest.py index 977e110de2..da43732060 100644 --- a/machine-learning/app/conftest.py +++ b/machine-learning/app/conftest.py @@ -5,10 +5,10 @@ from unittest import mock import numpy as np import pytest from fastapi.testclient import TestClient +from numpy.typing import NDArray from PIL import Image from .main import app -from .schemas import ndarray_f32 @pytest.fixture @@ -17,7 +17,7 @@ def pil_image() -> Image.Image: @pytest.fixture -def cv_image(pil_image: Image.Image) -> ndarray_f32: +def cv_image(pil_image: Image.Image) -> NDArray[np.float32]: return np.asarray(pil_image)[:, :, ::-1] # PIL uses RGB while cv2 uses BGR diff --git a/machine-learning/app/main.py b/machine-learning/app/main.py index 7631fe080b..eb3227acc0 100644 --- a/machine-learning/app/main.py +++ b/machine-learning/app/main.py @@ -2,11 +2,11 @@ import asyncio import gc import os import signal -import sys import threading import time from concurrent.futures import ThreadPoolExecutor -from typing import Any, Iterator +from contextlib import asynccontextmanager +from typing import Any, AsyncGenerator, Iterator from zipfile import BadZipFile import orjson @@ -26,7 +26,6 @@ from .schemas import ( ) MultiPartParser.max_file_size = 2**26 # spools to disk if payload is 64 MiB or larger -app = FastAPI() model_cache = ModelCache(ttl=settings.model_ttl, revalidate=settings.model_ttl > 0) thread_pool: ThreadPoolExecutor | None = None @@ -35,8 +34,8 @@ active_requests = 0 last_called: float | None = None -@app.on_event("startup") -def startup() -> None: +@asynccontextmanager +async def lifespan(_: FastAPI) -> AsyncGenerator[None, None]: global thread_pool log.info( ( @@ -44,21 +43,22 @@ def startup() -> None: f"{f'after {settings.model_ttl}s of inactivity' if settings.model_ttl > 0 else 'disabled'}." ) ) - # asyncio is a huge bottleneck for performance, so we use a thread pool to run blocking code - thread_pool = ThreadPoolExecutor(settings.request_threads) if settings.request_threads > 0 else None - if settings.model_ttl > 0 and settings.model_ttl_poll_s > 0: - asyncio.ensure_future(idle_shutdown_task()) - log.info(f"Initialized request thread pool with {settings.request_threads} threads.") - - -@app.on_event("shutdown") -def shutdown() -> None: - log.handlers.clear() - for model in model_cache.cache._cache.values(): - del model - if thread_pool is not None: - thread_pool.shutdown() - gc.collect() + + try: + if settings.request_threads > 0: + # asyncio is a huge bottleneck for performance, so we use a thread pool to run blocking code + thread_pool = ThreadPoolExecutor(settings.request_threads) if settings.request_threads > 0 else None + log.info(f"Initialized request thread pool with {settings.request_threads} threads.") + if settings.model_ttl > 0 and settings.model_ttl_poll_s > 0: + asyncio.ensure_future(idle_shutdown_task()) + yield + finally: + log.handlers.clear() + for model in model_cache.cache._cache.values(): + del model + if thread_pool is not None: + thread_pool.shutdown() + gc.collect() def update_state() -> Iterator[None]: @@ -71,6 +71,9 @@ def update_state() -> Iterator[None]: active_requests -= 1 +app = FastAPI(lifespan=lifespan) + + @app.get("/", response_model=MessageResponse) async def root() -> dict[str, str]: return {"message": "Immich ML"} diff --git a/machine-learning/app/models/ann.py b/machine-learning/app/models/ann.py index 55781729fa..6bd3065ab9 100644 --- a/machine-learning/app/models/ann.py +++ b/machine-learning/app/models/ann.py @@ -3,10 +3,10 @@ from __future__ import annotations from pathlib import Path from typing import Any, NamedTuple -from numpy import ascontiguousarray +import numpy as np +from numpy.typing import NDArray from ann.ann import Ann -from app.schemas import ndarray_f32, ndarray_i32 from ..config import log, settings @@ -56,10 +56,10 @@ class AnnSession: def run( self, output_names: list[str] | None, - input_feed: dict[str, ndarray_f32] | dict[str, ndarray_i32], + input_feed: dict[str, NDArray[np.float32]] | dict[str, NDArray[np.int32]], run_options: Any = None, - ) -> list[ndarray_f32]: - inputs: list[ndarray_f32] = [ascontiguousarray(v) for v in input_feed.values()] + ) -> list[NDArray[np.float32]]: + inputs: list[NDArray[np.float32]] = [np.ascontiguousarray(v) for v in input_feed.values()] return self.ann.execute(self.model, inputs) diff --git a/machine-learning/app/models/clip.py b/machine-learning/app/models/clip.py index 61ddf9040a..f70d27f9f2 100644 --- a/machine-learning/app/models/clip.py +++ b/machine-learning/app/models/clip.py @@ -6,12 +6,13 @@ from pathlib import Path from typing import Any, Literal import numpy as np +from numpy.typing import NDArray from PIL import Image from tokenizers import Encoding, Tokenizer from app.config import clean_name, log from app.models.transforms import crop, get_pil_resampling, normalize, resize, to_numpy -from app.schemas import ModelType, ndarray_f32, ndarray_i32 +from app.schemas import ModelType from .base import InferenceModel @@ -40,7 +41,7 @@ class BaseCLIPEncoder(InferenceModel): self.vision_model = self._make_session(self.visual_path) log.debug(f"Loaded clip vision model '{self.model_name}'") - def _predict(self, image_or_text: Image.Image | str) -> ndarray_f32: + def _predict(self, image_or_text: Image.Image | str) -> NDArray[np.float32]: if isinstance(image_or_text, bytes): image_or_text = Image.open(BytesIO(image_or_text)) @@ -48,7 +49,7 @@ class BaseCLIPEncoder(InferenceModel): case Image.Image(): if self.mode == "text": raise TypeError("Cannot encode image as text-only model") - outputs: ndarray_f32 = self.vision_model.run(None, self.transform(image_or_text))[0][0] + outputs: NDArray[np.float32] = self.vision_model.run(None, self.transform(image_or_text))[0][0] case str(): if self.mode == "vision": raise TypeError("Cannot encode text as vision-only model") @@ -59,11 +60,11 @@ class BaseCLIPEncoder(InferenceModel): return outputs @abstractmethod - def tokenize(self, text: str) -> dict[str, ndarray_i32]: + def tokenize(self, text: str) -> dict[str, NDArray[np.int32]]: pass @abstractmethod - def transform(self, image: Image.Image) -> dict[str, ndarray_f32]: + def transform(self, image: Image.Image) -> dict[str, NDArray[np.float32]]: pass @property @@ -161,11 +162,11 @@ class OpenCLIPEncoder(BaseCLIPEncoder): self.tokenizer.enable_truncation(max_length=context_length) log.debug(f"Loaded tokenizer for CLIP model '{self.model_name}'") - def tokenize(self, text: str) -> dict[str, ndarray_i32]: + def tokenize(self, text: str) -> dict[str, NDArray[np.int32]]: tokens: Encoding = self.tokenizer.encode(text) return {"text": np.array([tokens.ids], dtype=np.int32)} - def transform(self, image: Image.Image) -> dict[str, ndarray_f32]: + def transform(self, image: Image.Image) -> dict[str, NDArray[np.float32]]: image = resize(image, self.size) image = crop(image, self.size) image_np = to_numpy(image) @@ -174,7 +175,7 @@ class OpenCLIPEncoder(BaseCLIPEncoder): class MCLIPEncoder(OpenCLIPEncoder): - def tokenize(self, text: str) -> dict[str, ndarray_i32]: + def tokenize(self, text: str) -> dict[str, NDArray[np.int32]]: tokens: Encoding = self.tokenizer.encode(text) return { "input_ids": np.array([tokens.ids], dtype=np.int32), diff --git a/machine-learning/app/models/facial_recognition.py b/machine-learning/app/models/facial_recognition.py index e64de9f65c..6b66e57c47 100644 --- a/machine-learning/app/models/facial_recognition.py +++ b/machine-learning/app/models/facial_recognition.py @@ -5,9 +5,10 @@ import cv2 import numpy as np from insightface.model_zoo import ArcFaceONNX, RetinaFace from insightface.utils.face_align import norm_crop +from numpy.typing import NDArray from app.config import clean_name -from app.schemas import BoundingBox, Face, ModelType, ndarray_f32 +from app.schemas import Face, ModelType, is_ndarray from .base import InferenceModel @@ -36,22 +37,25 @@ class FaceRecognizer(InferenceModel): ) self.rec_model.prepare(ctx_id=0) - def _predict(self, image: ndarray_f32 | bytes) -> list[Face]: + def _predict(self, image: NDArray[np.uint8] | bytes) -> list[Face]: if isinstance(image, bytes): - image = cv2.imdecode(np.frombuffer(image, np.uint8), cv2.IMREAD_COLOR) - bboxes, kpss = self.det_model.detect(image) + decoded_image = cv2.imdecode(np.frombuffer(image, np.uint8), cv2.IMREAD_COLOR) + else: + decoded_image = image + assert is_ndarray(decoded_image, np.uint8) + bboxes, kpss = self.det_model.detect(decoded_image) if bboxes.size == 0: return [] - assert isinstance(image, np.ndarray) and isinstance(kpss, np.ndarray) + assert is_ndarray(kpss, np.float32) scores = bboxes[:, 4].tolist() bboxes = bboxes[:, :4].round().tolist() results = [] - height, width, _ = image.shape + height, width, _ = decoded_image.shape for (x1, y1, x2, y2), score, kps in zip(bboxes, scores, kpss): - cropped_img = norm_crop(image, kps) - embedding: ndarray_f32 = self.rec_model.get_feat(cropped_img)[0] + cropped_img = norm_crop(decoded_image, kps) + embedding: NDArray[np.float32] = self.rec_model.get_feat(cropped_img)[0] face: Face = { "imageWidth": width, "imageHeight": height, diff --git a/machine-learning/app/models/transforms.py b/machine-learning/app/models/transforms.py index 93b98d0eff..a41b7cd3d8 100644 --- a/machine-learning/app/models/transforms.py +++ b/machine-learning/app/models/transforms.py @@ -1,8 +1,7 @@ import numpy as np +from numpy.typing import NDArray from PIL import Image -from app.schemas import ndarray_f32 - _PIL_RESAMPLING_METHODS = {resampling.name.lower(): resampling for resampling in Image.Resampling} @@ -23,11 +22,13 @@ def crop(img: Image.Image, size: int) -> Image.Image: return img.crop((left, upper, right, lower)) -def to_numpy(img: Image.Image) -> ndarray_f32: +def to_numpy(img: Image.Image) -> NDArray[np.float32]: return np.asarray(img.convert("RGB")).astype(np.float32) / 255.0 -def normalize(img: ndarray_f32, mean: float | ndarray_f32, std: float | ndarray_f32) -> ndarray_f32: +def normalize( + img: NDArray[np.float32], mean: float | NDArray[np.float32], std: float | NDArray[np.float32] +) -> NDArray[np.float32]: return (img - mean) / std diff --git a/machine-learning/app/schemas.py b/machine-learning/app/schemas.py index 839287924b..53f66446b8 100644 --- a/machine-learning/app/schemas.py +++ b/machine-learning/app/schemas.py @@ -1,13 +1,10 @@ from enum import StrEnum -from typing import Any, Protocol, TypeAlias, TypedDict, TypeGuard +from typing import Any, Protocol, TypedDict, TypeGuard import numpy as np +import numpy.typing as npt from pydantic import BaseModel -ndarray_f32: TypeAlias = np.ndarray[int, np.dtype[np.float32]] -ndarray_i64: TypeAlias = np.ndarray[int, np.dtype[np.int64]] -ndarray_i32: TypeAlias = np.ndarray[int, np.dtype[np.int32]] - class TextResponse(BaseModel): __root__: str @@ -35,7 +32,7 @@ class HasProfiling(Protocol): class Face(TypedDict): boundingBox: BoundingBox - embedding: ndarray_f32 + embedding: npt.NDArray[np.float32] imageWidth: int imageHeight: int score: float @@ -43,3 +40,7 @@ class Face(TypedDict): 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 diff --git 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