mirror of
https://github.com/immich-app/immich.git
synced 2024-12-29 11:24:37 +02:00
feat(ml): model unloading (#2661)
* model cache * fixed revalidation when using cache namespace * fixed ttl not being set, added lock
This commit is contained in:
Mert
GitHub
parent
6ce35d47f5
commit
d0cc231782
84
machine-learning/app/cache.py
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84
machine-learning/app/cache.py
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@ -0,0 +1,84 @@
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from aiocache.plugins import TimingPlugin, BasePlugin
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from aiocache.backends.memory import SimpleMemoryCache
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from aiocache.lock import OptimisticLock
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from typing import Any
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from models import get_model
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class ModelCache:
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"""Fetches a model from an in-memory cache, instantiating it if it's missing."""
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def __init__(
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self,
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ttl: int | None = None,
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revalidate: bool = False,
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timeout: int | None = None,
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profiling: bool = False,
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):
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"""
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Args:
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ttl: Unloads model after this duration. Disabled if None. Defaults to None.
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revalidate: Resets TTL on cache hit. Useful to keep models in memory while active. Defaults to False.
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timeout: Maximum allowed time for model to load. Disabled if None. Defaults to None.
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profiling: Collects metrics for cache operations, adding slight overhead. Defaults to False.
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"""
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self.ttl = ttl
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plugins = []
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if revalidate:
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plugins.append(RevalidationPlugin())
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if profiling:
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plugins.append(TimingPlugin())
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self.cache = SimpleMemoryCache(
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ttl=ttl, timeout=timeout, plugins=plugins, namespace=None
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)
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async def get_cached_model(
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self, model_name: str, model_type: str, **model_kwargs
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) -> Any:
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"""
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Args:
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model_name: Name of model in the model hub used for the task.
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model_type: Model type or task, which determines which model zoo is used.
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Returns:
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model: The requested model.
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"""
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key = self.cache.build_key(model_name, model_type)
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model = await self.cache.get(key)
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if model is None:
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async with OptimisticLock(self.cache, key) as lock:
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model = get_model(model_name, model_type, **model_kwargs)
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await lock.cas(model, ttl=self.ttl)
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return model
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async def get_profiling(self) -> dict[str, float] | None:
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if not hasattr(self.cache, "profiling"):
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return None
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return self.cache.profiling # type: ignore
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class RevalidationPlugin(BasePlugin):
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"""Revalidates cache item's TTL after cache hit."""
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async def post_get(self, client, key, ret=None, namespace=None, **kwargs):
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if ret is None:
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return
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if namespace is not None:
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key = client.build_key(key, namespace)
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if key in client._handlers:
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await client.expire(key, client.ttl)
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async def post_multi_get(self, client, keys, ret=None, namespace=None, **kwargs):
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if ret is None:
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return
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for key, val in zip(keys, ret):
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if namespace is not None:
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key = client.build_key(key, namespace)
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if val is not None and key in client._handlers:
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await client.expire(key, client.ttl)
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@ -1,5 +1,7 @@
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import os
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from typing import Any
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from cache import ModelCache
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from schemas import (
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EmbeddingResponse,
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FaceResponse,
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@ -9,16 +11,11 @@ from schemas import (
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TextResponse,
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VisionModelRequest,
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)
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import cv2 as cv
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import uvicorn
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from insightface.app import FaceAnalysis
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from transformers import pipeline
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from sentence_transformers import SentenceTransformer
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from transformers import Pipeline
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from PIL import Image
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from fastapi import FastAPI
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from fastapi import FastAPI, HTTPException
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from models import get_model, run_classification, run_facial_recognition
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classification_model = os.getenv(
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"MACHINE_LEARNING_CLASSIFICATION_MODEL", "microsoft/resnet-50"
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@ -29,21 +26,20 @@ facial_recognition_model = os.getenv(
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"MACHINE_LEARNING_FACIAL_RECOGNITION_MODEL", "buffalo_l"
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)
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min_face_score = float(os.getenv("MACHINE_LEARNING_MIN_FACE_SCORE", 0.7))
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min_tag_score = float(os.getenv("MACHINE_LEARNING_MIN_TAG_SCORE", 0.9))
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eager_startup = (
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os.getenv("MACHINE_LEARNING_EAGER_STARTUP", "true") == "true"
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) # loads all models at startup
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model_ttl = int(os.getenv("MACHINE_LEARNING_MODEL_TTL", 300))
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cache_folder = os.getenv("MACHINE_LEARNING_CACHE_FOLDER", "/cache")
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_model_cache = {}
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_model_cache = None
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app = FastAPI()
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@app.on_event("startup")
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async def startup_event() -> None:
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global _model_cache
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_model_cache = ModelCache(ttl=model_ttl, revalidate=True)
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models = [
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(classification_model, "image-classification"),
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(clip_image_model, "clip"),
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@ -54,9 +50,9 @@ async def startup_event() -> None:
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# Get all models
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for model_name, model_type in models:
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if eager_startup:
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get_cached_model(model_name, model_type)
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await _model_cache.get_cached_model(model_name, model_type)
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else:
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_get_model(model_name, model_type)
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get_model(model_name, model_type)
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@app.get("/", response_model=MessageResponse)
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@ -70,10 +66,14 @@ def ping() -> str:
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@app.post("/image-classifier/tag-image", response_model=TagResponse, status_code=200)
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def image_classification(payload: VisionModelRequest) -> list[str]:
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model = get_cached_model(classification_model, "image-classification")
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assetPath = payload.image_path
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labels = run_engine(model, assetPath)
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async def image_classification(payload: VisionModelRequest) -> list[str]:
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if _model_cache is None:
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raise HTTPException(status_code=500, detail="Unable to load model.")
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model = await _model_cache.get_cached_model(
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classification_model, "image-classification"
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)
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labels = run_classification(model, payload.image_path, min_tag_score)
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return labels
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@ -82,10 +82,14 @@ def image_classification(payload: VisionModelRequest) -> list[str]:
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response_model=EmbeddingResponse,
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status_code=200,
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)
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def clip_encode_image(payload: VisionModelRequest) -> list[float]:
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model = get_cached_model(clip_image_model, "clip")
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async def clip_encode_image(payload: VisionModelRequest) -> list[float]:
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if _model_cache is None:
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raise HTTPException(status_code=500, detail="Unable to load model.")
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model = await _model_cache.get_cached_model(clip_image_model, "clip")
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image = Image.open(payload.image_path)
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return model.encode(image).tolist()
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embedding = model.encode(image).tolist()
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return embedding
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@app.post(
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@ -93,82 +97,27 @@ def clip_encode_image(payload: VisionModelRequest) -> list[float]:
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response_model=EmbeddingResponse,
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status_code=200,
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)
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def clip_encode_text(payload: TextModelRequest) -> list[float]:
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model = get_cached_model(clip_text_model, "clip")
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return model.encode(payload.text).tolist()
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async def clip_encode_text(payload: TextModelRequest) -> list[float]:
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if _model_cache is None:
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raise HTTPException(status_code=500, detail="Unable to load model.")
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model = await _model_cache.get_cached_model(clip_text_model, "clip")
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embedding = model.encode(payload.text).tolist()
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return embedding
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@app.post(
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"/facial-recognition/detect-faces", response_model=FaceResponse, status_code=200
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)
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def facial_recognition(payload: VisionModelRequest) -> list[dict[str, Any]]:
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model = get_cached_model(facial_recognition_model, "facial-recognition")
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img = cv.imread(payload.image_path)
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height, width, _ = img.shape
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results = []
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faces = model.get(img)
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async def facial_recognition(payload: VisionModelRequest) -> list[dict[str, Any]]:
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if _model_cache is None:
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raise HTTPException(status_code=500, detail="Unable to load model.")
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for face in faces:
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if face.det_score < min_face_score:
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continue
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x1, y1, x2, y2 = face.bbox
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results.append(
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{
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"imageWidth": width,
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"imageHeight": height,
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"boundingBox": {
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"x1": round(x1),
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"y1": round(y1),
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"x2": round(x2),
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"y2": round(y2),
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},
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"score": face.det_score.item(),
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"embedding": face.normed_embedding.tolist(),
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}
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)
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return results
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def run_engine(engine: Pipeline, path: str) -> list[str]:
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result: list[str] = []
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predictions: list[dict[str, Any]] = engine(path) # type: ignore
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for pred in predictions:
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tags = pred["label"].split(", ")
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if pred["score"] > min_tag_score:
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result = [*result, *tags]
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if len(result) > 1:
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result = list(set(result))
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return result
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def get_cached_model(model, task) -> Any:
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global _model_cache
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key = "|".join([model, str(task)])
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if key not in _model_cache:
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model = _get_model(model, task)
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_model_cache[key] = model
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return _model_cache[key]
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def _get_model(model, task) -> Any:
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match task:
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case "facial-recognition":
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model = FaceAnalysis(
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name=model,
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root=cache_folder,
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allowed_modules=["detection", "recognition"],
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)
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model.prepare(ctx_id=0, det_size=(640, 640))
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case "clip":
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model = SentenceTransformer(model, cache_folder=cache_folder)
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case _:
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model = pipeline(model=model, task=task)
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return model
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model = await _model_cache.get_cached_model(
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facial_recognition_model, "facial-recognition"
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)
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faces = run_facial_recognition(model, payload.image_path)
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return faces
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if __name__ == "__main__":
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117
machine-learning/app/models.py
Normal file
117
machine-learning/app/models.py
Normal file
@ -0,0 +1,117 @@
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import torch
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from insightface.app import FaceAnalysis
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from pathlib import Path
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import os
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from transformers import pipeline, Pipeline
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from sentence_transformers import SentenceTransformer
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from typing import Any
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import cv2 as cv
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cache_folder = os.getenv("MACHINE_LEARNING_CACHE_FOLDER", "/cache")
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device = "cuda" if torch.cuda.is_available() else "cpu"
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def get_model(model_name: str, model_type: str, **model_kwargs):
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"""
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Instantiates the specified model.
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Args:
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model_name: Name of model in the model hub used for the task.
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model_type: Model type or task, which determines which model zoo is used.
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`facial-recognition` uses Insightface, while all other models use the HF Model Hub.
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Options:
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`image-classification`, `clip`,`facial-recognition`, `tokenizer`, `processor`
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Returns:
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model: The requested model.
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"""
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cache_dir = _get_cache_dir(model_name, model_type)
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match model_type:
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case "facial-recognition":
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model = _load_facial_recognition(
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model_name, cache_dir=cache_dir, **model_kwargs
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)
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case "clip":
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model = SentenceTransformer(
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model_name, cache_folder=cache_dir, **model_kwargs
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)
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case _:
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model = pipeline(
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model_type,
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model_name,
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model_kwargs={"cache_dir": cache_dir, **model_kwargs},
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)
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return model
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def run_classification(
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model: Pipeline, image_path: str, min_score: float | None = None
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):
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predictions: list[dict[str, Any]] = model(image_path) # type: ignore
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result = {
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tag
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for pred in predictions
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for tag in pred["label"].split(", ")
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if min_score is None or pred["score"] >= min_score
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}
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return list(result)
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def run_facial_recognition(
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model: FaceAnalysis, image_path: str
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) -> list[dict[str, Any]]:
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img = cv.imread(image_path)
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height, width, _ = img.shape
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results = []
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faces = model.get(img)
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for face in faces:
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x1, y1, x2, y2 = face.bbox
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results.append(
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{
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"imageWidth": width,
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"imageHeight": height,
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"boundingBox": {
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"x1": round(x1),
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"y1": round(y1),
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"x2": round(x2),
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"y2": round(y2),
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},
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"score": face.det_score.item(),
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"embedding": face.normed_embedding.tolist(),
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}
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)
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return results
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def _load_facial_recognition(
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model_name: str,
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min_face_score: float | None = None,
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cache_dir: Path | str | None = None,
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**model_kwargs,
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):
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if cache_dir is None:
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cache_dir = _get_cache_dir(model_name, "facial-recognition")
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if isinstance(cache_dir, Path):
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cache_dir = cache_dir.as_posix()
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if min_face_score is None:
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min_face_score = float(os.getenv("MACHINE_LEARNING_MIN_FACE_SCORE", 0.7))
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model = FaceAnalysis(
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name=model_name,
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root=cache_dir,
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allowed_modules=["detection", "recognition"],
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**model_kwargs,
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)
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model.prepare(ctx_id=0, det_thresh=min_face_score, det_size=(640, 640))
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return model
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def _get_cache_dir(model_name: str, model_type: str) -> Path:
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return Path(cache_folder, device, model_type, model_name)
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298
machine-learning/poetry.lock
generated
298
machine-learning/poetry.lock
generated
@ -1,5 +1,21 @@
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# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand.
|
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|
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[[package]]
|
||||
name = "aiocache"
|
||||
version = "0.12.1"
|
||||
description = "multi backend asyncio cache"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "aiocache-0.12.1-py2.py3-none-any.whl", hash = "sha256:15eb17188e12d7226678c22a890824ff9c406cf7e5b418f86944395db63a3c1b"},
|
||||
{file = "aiocache-0.12.1.tar.gz", hash = "sha256:7d9ffbd4ca2b1c51e5b213fd7d42dc43b80af01a436b14be5ebcbde199709fb3"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
memcached = ["aiomcache (>=0.5.2)"]
|
||||
msgpack = ["msgpack (>=0.5.5)"]
|
||||
redis = ["redis (>=4.2.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "albumentations"
|
||||
version = "1.3.0"
|
||||
@ -789,61 +805,61 @@ test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "markupsafe"
|
||||
version = "2.1.2"
|
||||
version = "2.1.3"
|
||||
description = "Safely add untrusted strings to HTML/XML markup."
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "MarkupSafe-2.1.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:665a36ae6f8f20a4676b53224e33d456a6f5a72657d9c83c2aa00765072f31f7"},
|
||||
{file = "MarkupSafe-2.1.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:340bea174e9761308703ae988e982005aedf427de816d1afe98147668cc03036"},
|
||||
{file = "MarkupSafe-2.1.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:22152d00bf4a9c7c83960521fc558f55a1adbc0631fbb00a9471e097b19d72e1"},
|
||||
{file = "MarkupSafe-2.1.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:28057e985dace2f478e042eaa15606c7efccb700797660629da387eb289b9323"},
|
||||
{file = "MarkupSafe-2.1.2-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ca244fa73f50a800cf8c3ebf7fd93149ec37f5cb9596aa8873ae2c1d23498601"},
|
||||
{file = "MarkupSafe-2.1.2-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:d9d971ec1e79906046aa3ca266de79eac42f1dbf3612a05dc9368125952bd1a1"},
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{file = "MarkupSafe-2.1.3-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:7ef3cb2ebbf91e330e3bb937efada0edd9003683db6b57bb108c4001f37a02ea"},
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||||
{file = "MarkupSafe-2.1.3-cp39-cp39-win_amd64.whl", hash = "sha256:3fd4abcb888d15a94f32b75d8fd18ee162ca0c064f35b11134be77050296d6ba"},
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||||
{file = "MarkupSafe-2.1.3.tar.gz", hash = "sha256:af598ed32d6ae86f1b747b82783958b1a4ab8f617b06fe68795c7f026abbdcad"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@ -1575,99 +1591,99 @@ typing-extensions = "*"
|
||||
|
||||
[[package]]
|
||||
name = "regex"
|
||||
version = "2023.5.5"
|
||||
version = "2023.6.3"
|
||||
description = "Alternative regular expression module, to replace re."
|
||||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
files = [
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||||
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||||
{file = "regex-2023.6.3-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a4c3b7fa4cdaa69268748665a1a6ff70c014d39bb69c50fda64b396c9116cf77"},
|
||||
{file = "regex-2023.6.3-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:457b6cce21bee41ac292d6753d5e94dcbc5c9e3e3a834da285b0bde7aa4a11e9"},
|
||||
{file = "regex-2023.6.3-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:aad51907d74fc183033ad796dd4c2e080d1adcc4fd3c0fd4fd499f30c03011cd"},
|
||||
{file = "regex-2023.6.3-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:0385e73da22363778ef2324950e08b689abdf0b108a7d8decb403ad7f5191938"},
|
||||
{file = "regex-2023.6.3-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:c6a57b742133830eec44d9b2290daf5cbe0a2f1d6acee1b3c7b1c7b2f3606df7"},
|
||||
{file = "regex-2023.6.3-cp39-cp39-musllinux_1_1_s390x.whl", hash = "sha256:3e5219bf9e75993d73ab3d25985c857c77e614525fac9ae02b1bebd92f7cecac"},
|
||||
{file = "regex-2023.6.3-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:e5087a3c59eef624a4591ef9eaa6e9a8d8a94c779dade95d27c0bc24650261cd"},
|
||||
{file = "regex-2023.6.3-cp39-cp39-win32.whl", hash = "sha256:20326216cc2afe69b6e98528160b225d72f85ab080cbdf0b11528cbbaba2248f"},
|
||||
{file = "regex-2023.6.3-cp39-cp39-win_amd64.whl", hash = "sha256:bdff5eab10e59cf26bc479f565e25ed71a7d041d1ded04ccf9aee1d9f208487a"},
|
||||
{file = "regex-2023.6.3.tar.gz", hash = "sha256:72d1a25bf36d2050ceb35b517afe13864865268dfb45910e2e17a84be6cbfeb0"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@ -2441,4 +2457,4 @@ files = [
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = "^3.11"
|
||||
content-hash = "5c63ad195f75d0d66874c22860de716df81aceb5a3c6bf65081bedd871f9733b"
|
||||
content-hash = "0c347e972e004807bb2cd1f50f60ccf8bb32496575f813f48745f24389ab1a2c"
|
||||
|
||||
@ -10,7 +10,7 @@ packages = [{include = "app"}]
|
||||
python = "^3.11"
|
||||
torch = [
|
||||
{markers = "platform_machine == 'arm64' or platform_machine == 'aarch64'", version = "=2.0.1", source = "pypi"},
|
||||
{markers = "platform_machine == 'amd64' or platform_machine == 'x86_64'", version = "=2.0.1+cpu", source = "pytorch-cpu"}
|
||||
{markers = "platform_machine == 'amd64' or platform_machine == 'x86_64'", version = "=2.0.1", source = "pytorch-cpu"}
|
||||
]
|
||||
transformers = "^4.29.2"
|
||||
sentence-transformers = "^2.2.2"
|
||||
@ -21,6 +21,7 @@ pillow = "^9.5.0"
|
||||
fastapi = "^0.95.2"
|
||||
uvicorn = {extras = ["standard"], version = "^0.22.0"}
|
||||
pydantic = "^1.10.8"
|
||||
aiocache = "^0.12.1"
|
||||
|
||||
[tool.poetry.group.dev.dependencies]
|
||||
mypy = "^1.3.0"
|
||||
|
||||
Loading…
Reference in New Issue
Block a user