feat(ml) backend takes image over HTTP (#2783)

* using pydantic BaseSetting

* ML API takes image file as input

* keeping image in memory

* reducing duplicate code

* using bytes instead of UploadFile & other small code improvements

* removed form-multipart, using HTTP body

* format code

---------

Co-authored-by: Alex Tran <alex.tran1502@gmail.com>

docker/docker-compose.dev.yml

@@ -36,7 +36,6 @@ services:
       - 3003:3003
     volumes:
       - ../machine-learning/app:/usr/src/app
-      - ${UPLOAD_LOCATION}:/usr/src/app/upload
       - model-cache:/cache
     env_file:
       - .env

docker/docker-compose.yml

@@ -33,7 +33,6 @@ services:
     container_name: immich_machine_learning
     image: ghcr.io/immich-app/immich-machine-learning:${IMMICH_VERSION:-release}
     volumes:
-      - ${UPLOAD_LOCATION}:/usr/src/app/upload
       - model-cache:/cache
     env_file:
       - .env

machine-learning/app/config.py

@@ -0,0 +1,22 @@
+from pydantic import BaseSettings
+
+class Settings(BaseSettings):
+    cache_folder: str = "/cache"
+    classification_model: str = "microsoft/resnet-50"
+    clip_image_model: str = "clip-ViT-B-32"
+    clip_text_model: str = "clip-ViT-B-32"
+    facial_recognition_model: str = "buffalo_l"
+    min_tag_score: float = 0.9
+    eager_startup: bool = True
+    model_ttl: int = 300
+    host: str = "0.0.0.0"
+    port: int = 3003
+    workers: int = 1
+    min_face_score: float = 0.7
+
+    class Config(BaseSettings.Config):
+        env_prefix = 'MACHINE_LEARNING_'
+        case_sensitive = False
+
+
+settings = Settings()

machine-learning/app/main.py

@@ -1,4 +1,5 @@
 import os
+import io
 from typing import Any
 
 from cache import ModelCache
@@ -9,52 +10,44 @@ from schemas import (
     MessageResponse,
     TextModelRequest,
     TextResponse,
-    VisionModelRequest,
 )
 import uvicorn
-
 from PIL import Image
-from fastapi import FastAPI, HTTPException
+from fastapi import FastAPI, HTTPException, Depends, Body
 from models import get_model, run_classification, run_facial_recognition
-
-classification_model = os.getenv(
-    "MACHINE_LEARNING_CLASSIFICATION_MODEL", "microsoft/resnet-50"
-)
-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"
-)
-
-min_tag_score = float(os.getenv("MACHINE_LEARNING_MIN_TAG_SCORE", 0.9))
-eager_startup = (
-    os.getenv("MACHINE_LEARNING_EAGER_STARTUP", "true") == "true"
-)  # loads all models at startup
-model_ttl = int(os.getenv("MACHINE_LEARNING_MODEL_TTL", 300))
+from config import settings
 
 _model_cache = None
+
 app = FastAPI()
 
 
 @app.on_event("startup")
 async def startup_event() -> None:
     global _model_cache
-    _model_cache = ModelCache(ttl=model_ttl, revalidate=True)
+    _model_cache = ModelCache(ttl=settings.model_ttl, revalidate=True)
     models = [
-        (classification_model, "image-classification"),
-        (clip_image_model, "clip"),
-        (clip_text_model, "clip"),
-        (facial_recognition_model, "facial-recognition"),
+        (settings.classification_model, "image-classification"),
+        (settings.clip_image_model, "clip"),
+        (settings.clip_text_model, "clip"),
+        (settings.facial_recognition_model, "facial-recognition"),
     ]
 
     # Get all models
     for model_name, model_type in models:
-        if eager_startup:
+        if settings.eager_startup:
             await _model_cache.get_cached_model(model_name, model_type)
         else:
             get_model(model_name, model_type)
 
 
+def dep_model_cache():
+    if _model_cache is None:
+        raise HTTPException(status_code=500, detail="Unable to load model.")
+
+def dep_input_image(image: bytes = Body(...)) -> Image:
+    return Image.open(io.BytesIO(image))
+
 @app.get("/", response_model=MessageResponse)
 async def root() -> dict[str, str]:
     return {"message": "Immich ML"}
@@ -65,15 +58,23 @@ def ping() -> str:
     return "pong"
 
 
-@app.post("/image-classifier/tag-image", response_model=TagResponse, status_code=200)
-async def image_classification(payload: VisionModelRequest) -> list[str]:
-    if _model_cache is None:
-        raise HTTPException(status_code=500, detail="Unable to load model.")
-
+@app.post(
+    "/image-classifier/tag-image",
+    response_model=TagResponse,
+    status_code=200,
+    dependencies=[Depends(dep_model_cache)],
+)
+async def image_classification(
+    image: Image = Depends(dep_input_image)
+) -> list[str]:
+    try:
         model = await _model_cache.get_cached_model(
-        classification_model, "image-classification"
+            settings.classification_model, "image-classification"
         )
-    labels = run_classification(model, payload.image_path, min_tag_score)
+        labels = run_classification(model, image, settings.min_tag_score)
+    except Exception as ex:
+        raise HTTPException(status_code=500, detail=str(ex))
+    else:
         return labels
 
 
@@ -81,13 +82,12 @@ async def image_classification(payload: VisionModelRequest) -> list[str]:
     "/sentence-transformer/encode-image",
     response_model=EmbeddingResponse,
     status_code=200,
+    dependencies=[Depends(dep_model_cache)],
 )
-async def clip_encode_image(payload: VisionModelRequest) -> list[float]:
-    if _model_cache is None:
-        raise HTTPException(status_code=500, detail="Unable to load model.")
-
-    model = await _model_cache.get_cached_model(clip_image_model, "clip")
-    image = Image.open(payload.image_path)
+async def clip_encode_image(
+    image: Image = Depends(dep_input_image)
+) -> list[float]:
+    model = await _model_cache.get_cached_model(settings.clip_image_model, "clip")
     embedding = model.encode(image).tolist()
     return embedding
 
@@ -96,33 +96,38 @@ async def clip_encode_image(payload: VisionModelRequest) -> list[float]:
     "/sentence-transformer/encode-text",
     response_model=EmbeddingResponse,
     status_code=200,
+    dependencies=[Depends(dep_model_cache)],
 )
-async def clip_encode_text(payload: TextModelRequest) -> list[float]:
-    if _model_cache is None:
-        raise HTTPException(status_code=500, detail="Unable to load model.")
-
-    model = await _model_cache.get_cached_model(clip_text_model, "clip")
+async def clip_encode_text(
+    payload: TextModelRequest
+) -> list[float]:
+    model = await _model_cache.get_cached_model(settings.clip_text_model, "clip")
     embedding = model.encode(payload.text).tolist()
     return embedding
 
 
 @app.post(
-    "/facial-recognition/detect-faces", response_model=FaceResponse, status_code=200
+    "/facial-recognition/detect-faces",
+    response_model=FaceResponse,
+    status_code=200,
+    dependencies=[Depends(dep_model_cache)],
 )
-async def facial_recognition(payload: VisionModelRequest) -> list[dict[str, Any]]:
-    if _model_cache is None:
-        raise HTTPException(status_code=500, detail="Unable to load model.")
-
+async def facial_recognition(
+    image: bytes = Body(...),
+) -> list[dict[str, Any]]:
     model = await _model_cache.get_cached_model(
-        facial_recognition_model, "facial-recognition"
+        settings.facial_recognition_model, "facial-recognition"
     )
-    faces = run_facial_recognition(model, payload.image_path)
+    faces = run_facial_recognition(model, image)
     return faces
 
 
 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"
-
-    uvicorn.run("main:app", host=host, port=port, reload=is_dev, workers=1)
+    uvicorn.run(
+        "main:app",
+        host=settings.host,
+        port=settings.port,
+        reload=is_dev,
+        workers=settings.workers,
+    )

machine-learning/app/models.py

@@ -1,14 +1,15 @@
 import torch
 from insightface.app import FaceAnalysis
 from pathlib import Path
-import os
 
 from transformers import pipeline, Pipeline
 from sentence_transformers import SentenceTransformer
-from typing import Any
+from typing import Any, BinaryIO
 import cv2 as cv
+import numpy as np
+from PIL import Image
+from config import settings
 
-cache_folder = os.getenv("MACHINE_LEARNING_CACHE_FOLDER", "/cache")
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
 
@@ -49,9 +50,9 @@ def get_model(model_name: str, model_type: str, **model_kwargs):
 
 
 def run_classification(
-    model: Pipeline, image_path: str, min_score: float | None = None
+    model: Pipeline, image: Image, min_score: float | None = None
 ):
-    predictions: list[dict[str, Any]] = model(image_path)  # type: ignore
+    predictions: list[dict[str, Any]] = model(image)  # type: ignore
     result = {
         tag
         for pred in predictions
@@ -63,9 +64,10 @@ def run_classification(
 
 
 def run_facial_recognition(
-    model: FaceAnalysis, image_path: str
+    model: FaceAnalysis, image: bytes
 ) -> list[dict[str, Any]]:
-    img = cv.imread(image_path)
+    file_bytes = np.frombuffer(image, dtype=np.uint8)
+    img = cv.imdecode(file_bytes, cv.IMREAD_COLOR)
     height, width, _ = img.shape
     results = []
     faces = model.get(img)
@@ -101,7 +103,7 @@ def _load_facial_recognition(
     if isinstance(cache_dir, Path):
         cache_dir = cache_dir.as_posix()
     if min_face_score is None:
-        min_face_score = float(os.getenv("MACHINE_LEARNING_MIN_FACE_SCORE", 0.7))
+        min_face_score = settings.min_face_score
 
     model = FaceAnalysis(
         name=model_name,
@@ -114,4 +116,4 @@ def _load_facial_recognition(
 
 
 def _get_cache_dir(model_name: str, model_type: str) -> Path:
-    return Path(cache_folder, device, model_type, model_name)
+    return Path(settings.cache_folder, device, model_type, model_name)

machine-learning/app/schemas.py

@@ -9,14 +9,6 @@ def to_lower_camel(string: str) -> str:
     return "".join(tokens)
 
 
-class VisionModelRequest(BaseModel):
-    image_path: str
-
-    class Config:
-        alias_generator = to_lower_camel
-        allow_population_by_field_name = True
-
-
 class TextModelRequest(BaseModel):
     text: str
 

machine-learning/poetry.lock

@@ -1733,6 +1733,8 @@ files = [
     {file = "scikit_image-0.21.0-cp39-cp39-macosx_12_0_arm64.whl", hash = "sha256:c01e3ab0a1fabfd8ce30686d4401b7ed36e6126c9d4d05cb94abf6bdc46f7ac9"},
     {file = "scikit_image-0.21.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8ef5d8d1099317b7b315b530348cbfa68ab8ce32459de3c074d204166951025c"},
     {file = "scikit_image-0.21.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:78b1e96c59cab640ca5c5b22c501524cfaf34cbe0cb51ba73bd9a9ede3fb6e1d"},
+    {file = "scikit_image-0.21.0-cp39-cp39-win_amd64.whl", hash = "sha256:9cffcddd2a5594c0a06de2ae3e1e25d662745a26f94fda31520593669677c010"},
+    {file = "scikit_image-0.21.0.tar.gz", hash = "sha256:b33e823c54e6f11873ea390ee49ef832b82b9f70752c8759efd09d5a4e3d87f0"},
 ]
 
 [package.dependencies]
@@ -2088,9 +2090,9 @@ opt-einsum = ["opt-einsum (>=3.3)"]
 [[package]]
 name = "torch"
 version = "2.0.1+cpu"
-description = ""
+description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
 optional = false
-python-versions = "*"
+python-versions = ">=3.8.0"
 files = [
     {file = "torch-2.0.1+cpu-cp310-cp310-linux_x86_64.whl", hash = "sha256:fec257249ba014c68629a1994b0c6e7356e20e1afc77a87b9941a40e5095285d"},
     {file = "torch-2.0.1+cpu-cp310-cp310-win_amd64.whl", hash = "sha256:ca88b499973c4c027e32c4960bf20911d7e984bd0c55cda181dc643559f3d93f"},
@@ -2102,6 +2104,16 @@ files = [
     {file = "torch-2.0.1+cpu-cp39-cp39-win_amd64.whl", hash = "sha256:f263f8e908288427ae81441fef540377f61e339a27632b1bbe33cf78292fdaea"},
 ]
 
+[package.dependencies]
+filelock = "*"
+jinja2 = "*"
+networkx = "*"
+sympy = "*"
+typing-extensions = "*"
+
+[package.extras]
+opt-einsum = ["opt-einsum (>=3.3)"]
+
 [package.source]
 type = "legacy"
 url = "https://download.pytorch.org/whl/cpu"

server/src/infra/repositories/machine-learning.repository.ts

@@ -1,21 +1,26 @@
 import { DetectFaceResult, IMachineLearningRepository, MachineLearningInput, MACHINE_LEARNING_URL } from '@app/domain';
 import { Injectable } from '@nestjs/common';
 import axios from 'axios';
+import { createReadStream } from 'fs';
 
 const client = axios.create({ baseURL: MACHINE_LEARNING_URL });
 
 @Injectable()
 export class MachineLearningRepository implements IMachineLearningRepository {
+  private post<T>(input: MachineLearningInput, endpoint: string): Promise<T> {
+    return client.post<T>(endpoint, createReadStream(input.imagePath)).then((res) => res.data);
+  }
+
   classifyImage(input: MachineLearningInput): Promise<string[]> {
-    return client.post<string[]>('/image-classifier/tag-image', input).then((res) => res.data);
+    return this.post<string[]>(input, '/image-classifier/tag-image');
   }
 
   detectFaces(input: MachineLearningInput): Promise<DetectFaceResult[]> {
-    return client.post<DetectFaceResult[]>('/facial-recognition/detect-faces', input).then((res) => res.data);
+    return this.post<DetectFaceResult[]>(input, '/facial-recognition/detect-faces');
   }
 
   encodeImage(input: MachineLearningInput): Promise<number[]> {
-    return client.post<number[]>('/sentence-transformer/encode-image', input).then((res) => res.data);
+    return this.post<number[]>(input, '/sentence-transformer/encode-image');
   }
 
   encodeText(input: string): Promise<number[]> {