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chore(ml): load models on start up (#2487)

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* chore(ml): load models on start up

* Download correct model
This commit is contained in:
Alex
2023-05-19 22:37:01 -05:00
committed by GitHub
parent 89edbcacfa
commit 84cfa38510
1 changed files with 51 additions and 38 deletions
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89
machine-learning/src/main.py
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@@ -5,7 +5,7 @@ import uvicorn
from insightface.app import FaceAnalysis
from transformers import pipeline
from sentence_transformers import SentenceTransformer, util
from sentence_transformers import SentenceTransformer
from PIL import Image
from fastapi import FastAPI
from pydantic import BaseModel
@@ -20,22 +20,32 @@ class ClipRequestBody(BaseModel):
classification_model = os.getenv(
'MACHINE_LEARNING_CLASSIFICATION_MODEL', 'microsoft/resnet-50')
object_model = os.getenv('MACHINE_LEARNING_OBJECT_MODEL', 'hustvl/yolos-tiny')
clip_image_model = os.getenv(
'MACHINE_LEARNING_CLIP_IMAGE_MODEL', 'clip-ViT-B-32')
clip_text_model = os.getenv(
'MACHINE_LEARNING_CLIP_TEXT_MODEL', 'clip-ViT-B-32')
"MACHINE_LEARNING_CLASSIFICATION_MODEL", "microsoft/resnet-50"
)
object_model = os.getenv("MACHINE_LEARNING_OBJECT_MODEL", "hustvl/yolos-tiny")
clip_image_model = os.getenv("MACHINE_LEARNING_CLIP_IMAGE_MODEL", "clip-ViT-B-32")
clip_text_model = os.getenv("MACHINE_LEARNING_CLIP_TEXT_MODEL", "clip-ViT-B-32")
facial_recognition_model = os.getenv(
'MACHINE_LEARNING_FACIAL_RECOGNITION_MODEL', 'buffalo_l')
"MACHINE_LEARNING_FACIAL_RECOGNITION_MODEL", "buffalo_l"
)
cache_folder = os.getenv('MACHINE_LEARNING_CACHE_FOLDER', '/cache')
cache_folder = os.getenv("MACHINE_LEARNING_CACHE_FOLDER", "/cache")
_model_cache = {}
app = FastAPI()
@app.on_event("startup")
async def startup_event():
# Get all models
_get_model(object_model, "object-detection")
_get_model(classification_model, "image-classification")
_get_model(clip_image_model)
_get_model(clip_text_model)
_get_model(facial_recognition_model, "facial-recognition")
@app.get("/")
async def root():
return {"message": "Immich ML"}
@@ -48,14 +58,14 @@ def ping():
@app.post("/object-detection/detect-object", status_code=200)
def object_detection(payload: MlRequestBody):
model = _get_model(object_model, 'object-detection')
model = _get_model(object_model, "object-detection")
assetPath = payload.thumbnailPath
return run_engine(model, assetPath)
@app.post("/image-classifier/tag-image", status_code=200)
def image_classification(payload: MlRequestBody):
model = _get_model(classification_model, 'image-classification')
model = _get_model(classification_model, "image-classification")
assetPath = payload.thumbnailPath
return run_engine(model, assetPath)
@@ -76,31 +86,32 @@ def clip_encode_text(payload: ClipRequestBody):
@app.post("/facial-recognition/detect-faces", status_code=200)
def facial_recognition(payload: MlRequestBody):
model = _get_model(facial_recognition_model, 'facial-recognition')
model = _get_model(facial_recognition_model, "facial-recognition")
assetPath = payload.thumbnailPath
img = cv.imread(assetPath)
height, width, _ = img.shape
results = []
faces = model.get(img)
for face in faces:
if face.det_score < 0.7:
continue
x1, y1, x2, y2 = face.bbox
# min face size as percent of original image
# if (x2 - x1) / width < 0.03 or (y2 - y1) / height < 0.05:
# continue
results.append({
"imageWidth": width,
"imageHeight": height,
"boundingBox": {
"x1": round(x1),
"y1": round(y1),
"x2": round(x2),
"y2": round(y2),
},
"score": face.det_score.item(),
"embedding": face.normed_embedding.tolist()
})
results.append(
{
"imageWidth": width,
"imageHeight": height,
"boundingBox": {
"x1": round(x1),
"y1": round(y1),
"x2": round(x2),
"y2": round(y2),
},
"score": face.det_score.item(),
"embedding": face.normed_embedding.tolist(),
}
)
return results
@@ -109,11 +120,11 @@ def run_engine(engine, path):
predictions = engine(path)
for index, pred in enumerate(predictions):
tags = pred['label'].split(', ')
if (pred['score'] > 0.9):
tags = pred["label"].split(", ")
if pred["score"] > 0.9:
result = [*result, *tags]
if (len(result) > 1):
if len(result) > 1:
result = list(set(result))
return result
@@ -121,25 +132,27 @@ def run_engine(engine, path):
def _get_model(model, task=None):
global _model_cache
key = '|'.join([model, str(task)])
key = "|".join([model, str(task)])
if key not in _model_cache:
if task:
if task == 'facial-recognition':
if task == "facial-recognition":
face_model = FaceAnalysis(
name=model, root=cache_folder, allowed_modules=["detection", "recognition"])
name=model,
root=cache_folder,
allowed_modules=["detection", "recognition"],
)
face_model.prepare(ctx_id=0, det_size=(640, 640))
_model_cache[key] = face_model
else:
_model_cache[key] = pipeline(model=model, task=task)
else:
_model_cache[key] = SentenceTransformer(
model, cache_folder=cache_folder)
_model_cache[key] = SentenceTransformer(model, cache_folder=cache_folder)
return _model_cache[key]
if __name__ == "__main__":
host = os.getenv('MACHINE_LEARNING_HOST', '0.0.0.0')
port = int(os.getenv('MACHINE_LEARNING_PORT', 3003))
is_dev = os.getenv('NODE_ENV') == 'development'
host = os.getenv("MACHINE_LEARNING_HOST", "0.0.0.0")
port = int(os.getenv("MACHINE_LEARNING_PORT", 3003))
is_dev = os.getenv("NODE_ENV") == "development"
uvicorn.run("main:app", host=host, port=port, reload=is_dev, workers=1)