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chore(deps): update machine-learning (#6302)

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* 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>
This commit is contained in:
renovate[bot] 2024-01-13 05:00:09 +00:00 committed by GitHub
parent bd5ae9f31e
commit 20be42cec0
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GPG key ID: 4AEE18F83AFDEB23
12 changed files with 238 additions and 212 deletions
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4
machine-learning/Dockerfile
View file

@ -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 \ ENV PYTHONDONTWRITEBYTECODE=1 \
PYTHONUNBUFFERED=1 \ PYTHONUNBUFFERED=1 \
@ -27,7 +27,7 @@ RUN if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
fi fi
FROM python:3.11-slim-bookworm@sha256:8f64a67710f3d981cf3008d6f9f1dbe61accd7927f165f4e37ea3f8b883ccc3f FROM python:3.11-slim-bookworm@sha256:637774748f62b832dc11e7b286e48cd716727ed04b45a0322776c01bc526afc3
ARG TARGETPLATFORM ARG TARGETPLATFORM
RUN apt-get update && apt-get install -y --no-install-recommends tini libmimalloc2.0 && rm -rf /var/lib/apt/lists/* RUN apt-get update && apt-get install -y --no-install-recommends tini libmimalloc2.0 && rm -rf /var/lib/apt/lists/*

2
machine-learning/ann/ann.py
View file

@ -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 ctypes import CDLL, Array, c_bool, c_char_p, c_int, c_ulong, c_void_p
from os.path import exists from os.path import exists
from typing import Any, Generic, Protocol, Type, TypeVar from typing import Any, Protocol, TypeVar
import numpy as np import numpy as np
from numpy.typing import NDArray from numpy.typing import NDArray

4
machine-learning/app/conftest.py
View file

@ -5,10 +5,10 @@ from unittest import mock
import numpy as np import numpy as np
import pytest import pytest
from fastapi.testclient import TestClient from fastapi.testclient import TestClient
from numpy.typing import NDArray
from PIL import Image from PIL import Image
from .main import app from .main import app
from .schemas import ndarray_f32
@pytest.fixture @pytest.fixture
@ -17,7 +17,7 @@ def pil_image() -> Image.Image:
@pytest.fixture @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 return np.asarray(pil_image)[:, :, ::-1] # PIL uses RGB while cv2 uses BGR

43
machine-learning/app/main.py
View file

@ -2,11 +2,11 @@ import asyncio
import gc import gc
import os import os
import signal import signal
import sys
import threading import threading
import time import time
from concurrent.futures import ThreadPoolExecutor from concurrent.futures import ThreadPoolExecutor
from typing import Any, Iterator from contextlib import asynccontextmanager
from typing import Any, AsyncGenerator, Iterator
from zipfile import BadZipFile from zipfile import BadZipFile
import orjson 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 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) model_cache = ModelCache(ttl=settings.model_ttl, revalidate=settings.model_ttl > 0)
thread_pool: ThreadPoolExecutor | None = None thread_pool: ThreadPoolExecutor | None = None
@ -35,8 +34,8 @@ active_requests = 0
last_called: float | None = None last_called: float | None = None
@app.on_event("startup") @asynccontextmanager
def startup() -> None: async def lifespan(_: FastAPI) -> AsyncGenerator[None, None]:
global thread_pool global thread_pool
log.info( 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'}." 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 try:
if settings.model_ttl > 0 and settings.model_ttl_poll_s > 0: if settings.request_threads > 0:
asyncio.ensure_future(idle_shutdown_task()) # asyncio is a huge bottleneck for performance, so we use a thread pool to run blocking code
log.info(f"Initialized request thread pool with {settings.request_threads} threads.") 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:
@app.on_event("shutdown") asyncio.ensure_future(idle_shutdown_task())
def shutdown() -> None: yield
log.handlers.clear() finally:
for model in model_cache.cache._cache.values(): log.handlers.clear()
del model for model in model_cache.cache._cache.values():
if thread_pool is not None: del model
thread_pool.shutdown() if thread_pool is not None:
gc.collect() thread_pool.shutdown()
gc.collect()
def update_state() -> Iterator[None]: def update_state() -> Iterator[None]:
@ -71,6 +71,9 @@ def update_state() -> Iterator[None]:
active_requests -= 1 active_requests -= 1
app = FastAPI(lifespan=lifespan)
@app.get("/", response_model=MessageResponse) @app.get("/", response_model=MessageResponse)
async def root() -> dict[str, str]: async def root() -> dict[str, str]:
return {"message": "Immich ML"} return {"message": "Immich ML"}

10
machine-learning/app/models/ann.py
View file

@ -3,10 +3,10 @@ from __future__ import annotations
from pathlib import Path from pathlib import Path
from typing import Any, NamedTuple from typing import Any, NamedTuple
from numpy import ascontiguousarray import numpy as np
from numpy.typing import NDArray
from ann.ann import Ann from ann.ann import Ann
from app.schemas import ndarray_f32, ndarray_i32
from ..config import log, settings from ..config import log, settings
@ -56,10 +56,10 @@ class AnnSession:
def run( def run(
self, self,
output_names: list[str] | None, 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, run_options: Any = None,
) -> list[ndarray_f32]: ) -> list[NDArray[np.float32]]:
inputs: list[ndarray_f32] = [ascontiguousarray(v) for v in input_feed.values()] inputs: list[NDArray[np.float32]] = [np.ascontiguousarray(v) for v in input_feed.values()]
return self.ann.execute(self.model, inputs) return self.ann.execute(self.model, inputs)

17
machine-learning/app/models/clip.py
View file

@ -6,12 +6,13 @@ from pathlib import Path
from typing import Any, Literal from typing import Any, Literal
import numpy as np import numpy as np
from numpy.typing import NDArray
from PIL import Image from PIL import Image
from tokenizers import Encoding, Tokenizer from tokenizers import Encoding, Tokenizer
from app.config import clean_name, log from app.config import clean_name, log
from app.models.transforms import crop, get_pil_resampling, normalize, resize, to_numpy 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 from .base import InferenceModel
@ -40,7 +41,7 @@ class BaseCLIPEncoder(InferenceModel):
self.vision_model = self._make_session(self.visual_path) self.vision_model = self._make_session(self.visual_path)
log.debug(f"Loaded clip vision model '{self.model_name}'") 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): if isinstance(image_or_text, bytes):
image_or_text = Image.open(BytesIO(image_or_text)) image_or_text = Image.open(BytesIO(image_or_text))
@ -48,7 +49,7 @@ class BaseCLIPEncoder(InferenceModel):
case Image.Image(): case Image.Image():
if self.mode == "text": if self.mode == "text":
raise TypeError("Cannot encode image as text-only model") 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(): case str():
if self.mode == "vision": if self.mode == "vision":
raise TypeError("Cannot encode text as vision-only model") raise TypeError("Cannot encode text as vision-only model")
@ -59,11 +60,11 @@ class BaseCLIPEncoder(InferenceModel):
return outputs return outputs
@abstractmethod @abstractmethod
def tokenize(self, text: str) -> dict[str, ndarray_i32]: def tokenize(self, text: str) -> dict[str, NDArray[np.int32]]:
pass pass
@abstractmethod @abstractmethod
def transform(self, image: Image.Image) -> dict[str, ndarray_f32]: def transform(self, image: Image.Image) -> dict[str, NDArray[np.float32]]:
pass pass
@property @property
@ -161,11 +162,11 @@ class OpenCLIPEncoder(BaseCLIPEncoder):
self.tokenizer.enable_truncation(max_length=context_length) self.tokenizer.enable_truncation(max_length=context_length)
log.debug(f"Loaded tokenizer for CLIP model '{self.model_name}'") 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) tokens: Encoding = self.tokenizer.encode(text)
return {"text": np.array([tokens.ids], dtype=np.int32)} 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 = resize(image, self.size)
image = crop(image, self.size) image = crop(image, self.size)
image_np = to_numpy(image) image_np = to_numpy(image)
@ -174,7 +175,7 @@ class OpenCLIPEncoder(BaseCLIPEncoder):
class MCLIPEncoder(OpenCLIPEncoder): 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) tokens: Encoding = self.tokenizer.encode(text)
return { return {
"input_ids": np.array([tokens.ids], dtype=np.int32), "input_ids": np.array([tokens.ids], dtype=np.int32),

20
machine-learning/app/models/facial_recognition.py
View file

@ -5,9 +5,10 @@ import cv2
import numpy as np import numpy as np
from insightface.model_zoo import ArcFaceONNX, RetinaFace from insightface.model_zoo import ArcFaceONNX, RetinaFace
from insightface.utils.face_align import norm_crop from insightface.utils.face_align import norm_crop
from numpy.typing import NDArray
from app.config import clean_name 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 from .base import InferenceModel
@ -36,22 +37,25 @@ class FaceRecognizer(InferenceModel):
) )
self.rec_model.prepare(ctx_id=0) 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): if isinstance(image, bytes):
image = cv2.imdecode(np.frombuffer(image, np.uint8), cv2.IMREAD_COLOR) decoded_image = cv2.imdecode(np.frombuffer(image, np.uint8), cv2.IMREAD_COLOR)
bboxes, kpss = self.det_model.detect(image) else:
decoded_image = image
assert is_ndarray(decoded_image, np.uint8)
bboxes, kpss = self.det_model.detect(decoded_image)
if bboxes.size == 0: if bboxes.size == 0:
return [] return []
assert isinstance(image, np.ndarray) and isinstance(kpss, np.ndarray) assert is_ndarray(kpss, np.float32)
scores = bboxes[:, 4].tolist() scores = bboxes[:, 4].tolist()
bboxes = bboxes[:, :4].round().tolist() bboxes = bboxes[:, :4].round().tolist()
results = [] results = []
height, width, _ = image.shape height, width, _ = decoded_image.shape
for (x1, y1, x2, y2), score, kps in zip(bboxes, scores, kpss): for (x1, y1, x2, y2), score, kps in zip(bboxes, scores, kpss):
cropped_img = norm_crop(image, kps) cropped_img = norm_crop(decoded_image, kps)
embedding: ndarray_f32 = self.rec_model.get_feat(cropped_img)[0] embedding: NDArray[np.float32] = self.rec_model.get_feat(cropped_img)[0]
face: Face = { face: Face = {
"imageWidth": width, "imageWidth": width,
"imageHeight": height, "imageHeight": height,

9
machine-learning/app/models/transforms.py
View file

@ -1,8 +1,7 @@
import numpy as np import numpy as np
from numpy.typing import NDArray
from PIL import Image from PIL import Image
from app.schemas import ndarray_f32
_PIL_RESAMPLING_METHODS = {resampling.name.lower(): resampling for resampling in Image.Resampling} _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)) 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 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 return (img - mean) / std

13
machine-learning/app/schemas.py
View file

@ -1,13 +1,10 @@
from enum import StrEnum 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 as np
import numpy.typing as npt
from pydantic import BaseModel 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): class TextResponse(BaseModel):
__root__: str __root__: str
@ -35,7 +32,7 @@ class HasProfiling(Protocol):
class Face(TypedDict): class Face(TypedDict):
boundingBox: BoundingBox boundingBox: BoundingBox
embedding: ndarray_f32 embedding: npt.NDArray[np.float32]
imageWidth: int imageWidth: int
imageHeight: int imageHeight: int
score: float score: float
@ -43,3 +40,7 @@ class Face(TypedDict):
def has_profiling(obj: Any) -> TypeGuard[HasProfiling]: def has_profiling(obj: Any) -> TypeGuard[HasProfiling]:
return hasattr(obj, "profiling") and isinstance(obj.profiling, dict) 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

2
machine-learning/export/Dockerfile
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@ -1,4 +1,4 @@
FROM mambaorg/micromamba:bookworm-slim@sha256:a7033851ca71454ed8cfbc958fdd62a72e6fa90f71ccb3aa9b15e67a98f66a1e as builder FROM mambaorg/micromamba:bookworm-slim@sha256:e2586bd4097636856cbf725304ce240c968bea4123bece888d9e02ca2572ea2d as builder
ENV NODE_ENV=production \ ENV NODE_ENV=production \
TRANSFORMERS_CACHE=/cache \ TRANSFORMERS_CACHE=/cache \

325
machine-learning/poetry.lock generated
View file

@ -63,33 +63,33 @@ trio = ["trio (<0.22)"]
[[package]] [[package]]
name = "black" name = "black"
version = "23.12.0" version = "23.12.1"
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] ]
[package.dependencies] [package.dependencies]

1
machine-learning/pyproject.toml
View file

@ -60,7 +60,6 @@ warn_untyped_fields = true
line-length = 120 line-length = 120
target-version = "py311" target-version = "py311"
select = ["E", "F", "I"] select = ["E", "F", "I"]
ignore = ["F401"]
[tool.ruff.per-file-ignores] [tool.ruff.per-file-ignores]
"test_main.py" = ["F403"] "test_main.py" = ["F403"]