diff --git a/docker/docker-compose.dev.yml b/docker/docker-compose.dev.yml index 68bf031dd..a6f7967ac 100644 --- a/docker/docker-compose.dev.yml +++ b/docker/docker-compose.dev.yml @@ -35,7 +35,7 @@ services: ports: - 3003:3003 volumes: - - ../machine-learning/src:/usr/src/app + - ../machine-learning/app:/usr/src/app - ${UPLOAD_LOCATION}:/usr/src/app/upload - model-cache:/cache env_file: diff --git a/machine-learning/Dockerfile b/machine-learning/Dockerfile index e9896807b..d98ed2d65 100644 --- a/machine-learning/Dockerfile +++ b/machine-learning/Dockerfile @@ -1,29 +1,26 @@ -FROM python:3.10 as builder - +FROM python:3.11 as builder ENV PYTHONDONTWRITEBYTECODE=1 \ PYTHONUNBUFFERED=1 \ PIP_NO_CACHE_DIR=true +RUN pip install --upgrade pip && pip install poetry +RUN poetry config installer.max-workers 10 && \ + poetry config virtualenvs.create false RUN python -m venv /opt/venv -RUN /opt/venv/bin/pip install torch --index-url https://download.pytorch.org/whl/cpu -RUN /opt/venv/bin/pip install transformers tqdm numpy scikit-learn scipy nltk sentencepiece fastapi Pillow uvicorn[standard] -RUN /opt/venv/bin/pip install --no-deps sentence-transformers -# Facial Recognition Stuff -RUN /opt/venv/bin/pip install insightface onnxruntime +ENV VIRTUAL_ENV="/opt/venv" PATH="/opt/venv/bin:${PATH}" -FROM python:3.10-slim +COPY poetry.lock pyproject.toml ./ +RUN poetry install --sync --no-interaction --no-ansi --no-root --only main -ENV NODE_ENV=production - -COPY --from=builder /opt/venv /opt/venv - -ENV TRANSFORMERS_CACHE=/cache \ +FROM python:3.11-slim +WORKDIR /usr/src/app +ENV NODE_ENV=production \ + TRANSFORMERS_CACHE=/cache \ PYTHONDONTWRITEBYTECODE=1 \ PYTHONUNBUFFERED=1 \ - PATH="/opt/venv/bin:$PATH" + PATH="/opt/venv/bin:$PATH" \ + PYTHONPATH=`pwd` -WORKDIR /usr/src/app - -COPY . . -ENV PYTHONPATH=`pwd` -CMD ["python", "src/main.py"] +COPY --from=builder /opt/venv /opt/venv +COPY app . +ENTRYPOINT ["python", "main.py"] diff --git a/machine-learning/README.md b/machine-learning/README.md index 5dc8d24ff..cbfa3a28f 100644 --- a/machine-learning/README.md +++ b/machine-learning/README.md @@ -1,5 +1,13 @@ - # Immich Machine Learning -- Object Detection -- Image Classification \ No newline at end of file +- Image classification +- CLIP embeddings +- Facial recognition + +# Setup + +This project uses [Poetry](https://python-poetry.org/docs/#installation), so be sure to install it first. +Running `poetry install --no-root --with dev` will install everything you need in an isolated virtual environment. + +To add or remove dependencies, you can use the commands `poetry add $PACKAGE_NAME` and `poetry remove $PACKAGE_NAME`, respectively. +Be sure to commit the `poetry.lock` and `pyproject.toml` files to reflect any changes in dependencies. diff --git a/machine-learning/src/main.py b/machine-learning/app/main.py similarity index 68% rename from machine-learning/src/main.py rename to machine-learning/app/main.py index 414590940..5ef4da3a8 100644 --- a/machine-learning/src/main.py +++ b/machine-learning/app/main.py @@ -1,22 +1,23 @@ import os -import numpy as np +from typing import Any +from schemas import ( + EmbeddingResponse, + FaceResponse, + TagResponse, + MessageResponse, + TextModelRequest, + TextResponse, + VisionModelRequest, +) import cv2 as cv import uvicorn from insightface.app import FaceAnalysis from transformers import pipeline from sentence_transformers import SentenceTransformer +from transformers import Pipeline from PIL import Image from fastapi import FastAPI -from pydantic import BaseModel - - -class MlRequestBody(BaseModel): - thumbnailPath: str - - -class ClipRequestBody(BaseModel): - text: str classification_model = os.getenv( @@ -42,7 +43,7 @@ app = FastAPI() @app.on_event("startup") -async def startup_event(): +async def startup_event() -> None: models = [ (classification_model, "image-classification"), (clip_image_model, "clip"), @@ -58,42 +59,51 @@ async def startup_event(): _get_model(model_name, model_type) -@app.get("/") -async def root(): +@app.get("/", response_model=MessageResponse) +async def root() -> dict[str, str]: return {"message": "Immich ML"} -@app.get("/ping") -def ping(): +@app.get("/ping", response_model=TextResponse) +def ping() -> str: return "pong" -@app.post("/image-classifier/tag-image", status_code=200) -def image_classification(payload: MlRequestBody): +@app.post("/image-classifier/tag-image", response_model=TagResponse, status_code=200) +def image_classification(payload: VisionModelRequest) -> list[str]: model = get_cached_model(classification_model, "image-classification") - assetPath = payload.thumbnailPath - return run_engine(model, assetPath) + assetPath = payload.image_path + labels = run_engine(model, assetPath) + return labels -@app.post("/sentence-transformer/encode-image", status_code=200) -def clip_encode_image(payload: MlRequestBody): +@app.post( + "/sentence-transformer/encode-image", + response_model=EmbeddingResponse, + status_code=200, +) +def clip_encode_image(payload: VisionModelRequest) -> list[float]: model = get_cached_model(clip_image_model, "clip") - assetPath = payload.thumbnailPath - return model.encode(Image.open(assetPath)).tolist() + image = Image.open(payload.image_path) + return model.encode(image).tolist() -@app.post("/sentence-transformer/encode-text", status_code=200) -def clip_encode_text(payload: ClipRequestBody): +@app.post( + "/sentence-transformer/encode-text", + response_model=EmbeddingResponse, + status_code=200, +) +def clip_encode_text(payload: TextModelRequest) -> list[float]: model = get_cached_model(clip_text_model, "clip") - text = payload.text - return model.encode(text).tolist() + return model.encode(payload.text).tolist() -@app.post("/facial-recognition/detect-faces", status_code=200) -def facial_recognition(payload: MlRequestBody): +@app.post( + "/facial-recognition/detect-faces", response_model=FaceResponse, status_code=200 +) +def facial_recognition(payload: VisionModelRequest) -> list[dict[str, Any]]: model = get_cached_model(facial_recognition_model, "facial-recognition") - assetPath = payload.thumbnailPath - img = cv.imread(assetPath) + img = cv.imread(payload.image_path) height, width, _ = img.shape results = [] faces = model.get(img) @@ -120,11 +130,11 @@ def facial_recognition(payload: MlRequestBody): return results -def run_engine(engine, path): - result = [] - predictions = engine(path) +def run_engine(engine: Pipeline, path: str) -> list[str]: + result: list[str] = [] + predictions: list[dict[str, Any]] = engine(path) # type: ignore - for index, pred in enumerate(predictions): + for pred in predictions: tags = pred["label"].split(", ") if pred["score"] > min_tag_score: result = [*result, *tags] @@ -135,7 +145,7 @@ def run_engine(engine, path): return result -def get_cached_model(model, task): +def get_cached_model(model, task) -> Any: global _model_cache key = "|".join([model, str(task)]) if key not in _model_cache: @@ -145,7 +155,7 @@ def get_cached_model(model, task): return _model_cache[key] -def _get_model(model, task): +def _get_model(model, task) -> Any: match task: case "facial-recognition": model = FaceAnalysis( diff --git a/machine-learning/app/schemas.py b/machine-learning/app/schemas.py new file mode 100644 index 000000000..5a27f5b98 --- /dev/null +++ b/machine-learning/app/schemas.py @@ -0,0 +1,64 @@ +from pydantic import BaseModel + + +def to_lower_camel(string: str) -> str: + tokens = [ + token.capitalize() if i > 0 else token + for i, token in enumerate(string.split("_")) + ] + 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 + + +class TextResponse(BaseModel): + __root__: str + + +class MessageResponse(BaseModel): + message: str + + +class TagResponse(BaseModel): + __root__: list[str] + + +class Embedding(BaseModel): + __root__: list[float] + + +class EmbeddingResponse(BaseModel): + __root__: Embedding + + +class BoundingBox(BaseModel): + x1: int + y1: int + x2: int + y2: int + + +class Face(BaseModel): + image_width: int + image_height: int + bounding_box: BoundingBox + score: float + embedding: Embedding + + class Config: + alias_generator = to_lower_camel + allow_population_by_field_name = True + + +class FaceResponse(BaseModel): + __root__: list[Face] diff --git a/machine-learning/poetry.lock b/machine-learning/poetry.lock new file mode 100644 index 000000000..4602bb6cb --- /dev/null +++ b/machine-learning/poetry.lock @@ -0,0 +1,2444 @@ +# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. + +[[package]] +name = "albumentations" +version = "1.3.0" +description = "Fast image augmentation library and easy to use wrapper around other libraries" +optional = false +python-versions = ">=3.6" +files = [ + {file = "albumentations-1.3.0-py3-none-any.whl", hash = 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version = "=2.0.1+cpu", source = "pytorch-cpu"} +] +transformers = "^4.29.2" +sentence-transformers = "^2.2.2" +onnxruntime = "^1.15.0" +insightface = "^0.7.3" +opencv-python-headless = "^4.7.0.72" +pillow = "^9.5.0" +fastapi = "^0.95.2" +uvicorn = {extras = ["standard"], version = "^0.22.0"} +pydantic = "^1.10.8" + +[tool.poetry.group.dev.dependencies] +mypy = "^1.3.0" +black = "^23.3.0" +pytest = "^7.3.1" + +[[tool.poetry.source]] +name = "pytorch-cpu" +url = "https://download.pytorch.org/whl/cpu" +priority = "explicit" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + +[tool.flake8] +max-line-length = 120 + +[tool.mypy] +python_version = "3.11" +plugins = "pydantic.mypy" +follow_imports = "silent" +warn_redundant_casts = true +disallow_any_generics = true +check_untyped_defs = true +no_implicit_reexport = true +disallow_untyped_defs = true + +[tool.pydantic-mypy] +init_forbid_extra = true +init_typed = true +warn_required_dynamic_aliases = true +warn_untyped_fields = true \ No newline at end of file diff --git a/misc/release/pump-version.sh b/misc/release/pump-version.sh index a0b2f0b6c..a23790971 100755 --- a/misc/release/pump-version.sh +++ b/misc/release/pump-version.sh @@ -63,6 +63,7 @@ if [ "$CURRENT_SERVER" != "$NEXT_SERVER" ]; then echo "Pumping Server: $CURRENT_SERVER => $NEXT_SERVER" npm --prefix server version $SERVER_PUMP npm --prefix server run api:generate + poetry --directory machine-learning version $SERVER_PUMP fi if [ "$CURRENT_MOBILE" != "$NEXT_MOBILE" ]; then diff --git a/server/libs/domain/src/facial-recognition/facial-recognition.service.spec.ts b/server/libs/domain/src/facial-recognition/facial-recognition.service.spec.ts index 4966a7fc2..6c4adbd37 100644 --- a/server/libs/domain/src/facial-recognition/facial-recognition.service.spec.ts +++ b/server/libs/domain/src/facial-recognition/facial-recognition.service.spec.ts @@ -175,7 +175,7 @@ describe(FacialRecognitionService.name, () => { assetMock.getByIds.mockResolvedValue([assetEntityStub.image]); await sut.handleRecognizeFaces({ id: assetEntityStub.image.id }); expect(machineLearningMock.detectFaces).toHaveBeenCalledWith({ - thumbnailPath: assetEntityStub.image.resizePath, + imagePath: assetEntityStub.image.resizePath, }); expect(faceMock.create).not.toHaveBeenCalled(); expect(jobMock.queue).not.toHaveBeenCalled(); diff --git a/server/libs/domain/src/facial-recognition/facial-recognition.services.ts b/server/libs/domain/src/facial-recognition/facial-recognition.services.ts index 622f1072b..a8559c6bc 100644 --- a/server/libs/domain/src/facial-recognition/facial-recognition.services.ts +++ b/server/libs/domain/src/facial-recognition/facial-recognition.services.ts @@ -54,7 +54,7 @@ export class FacialRecognitionService { return false; } - const faces = await this.machineLearning.detectFaces({ thumbnailPath: asset.resizePath }); + const faces = await this.machineLearning.detectFaces({ imagePath: asset.resizePath }); this.logger.debug(`${faces.length} faces detected in ${asset.resizePath}`); this.logger.verbose(faces.map((face) => ({ ...face, embedding: `float[${face.embedding.length}]` }))); diff --git a/server/libs/domain/src/smart-info/machine-learning.interface.ts b/server/libs/domain/src/smart-info/machine-learning.interface.ts index bbcf62adf..3f7b9b2d8 100644 --- a/server/libs/domain/src/smart-info/machine-learning.interface.ts +++ b/server/libs/domain/src/smart-info/machine-learning.interface.ts @@ -1,7 +1,7 @@ export const IMachineLearningRepository = 'IMachineLearningRepository'; export interface MachineLearningInput { - thumbnailPath: string; + imagePath: string; } export interface BoundingBox { diff --git a/server/libs/domain/src/smart-info/smart-info.service.spec.ts b/server/libs/domain/src/smart-info/smart-info.service.spec.ts index 2af80cc9f..404059c0b 100644 --- a/server/libs/domain/src/smart-info/smart-info.service.spec.ts +++ b/server/libs/domain/src/smart-info/smart-info.service.spec.ts @@ -84,7 +84,7 @@ describe(SmartInfoService.name, () => { await sut.handleClassifyImage({ id: asset.id }); - expect(machineMock.classifyImage).toHaveBeenCalledWith({ thumbnailPath: 'path/to/resize.ext' }); + expect(machineMock.classifyImage).toHaveBeenCalledWith({ imagePath: 'path/to/resize.ext' }); expect(smartMock.upsert).toHaveBeenCalledWith({ assetId: 'asset-1', tags: ['tag1', 'tag2', 'tag3'], @@ -143,7 +143,7 @@ describe(SmartInfoService.name, () => { await sut.handleEncodeClip({ id: asset.id }); - expect(machineMock.encodeImage).toHaveBeenCalledWith({ thumbnailPath: 'path/to/resize.ext' }); + expect(machineMock.encodeImage).toHaveBeenCalledWith({ imagePath: 'path/to/resize.ext' }); expect(smartMock.upsert).toHaveBeenCalledWith({ assetId: 'asset-1', clipEmbedding: [0.01, 0.02, 0.03], diff --git a/server/libs/domain/src/smart-info/smart-info.service.ts b/server/libs/domain/src/smart-info/smart-info.service.ts index a07ad0a6c..cc3a8a08e 100644 --- a/server/libs/domain/src/smart-info/smart-info.service.ts +++ b/server/libs/domain/src/smart-info/smart-info.service.ts @@ -40,7 +40,7 @@ export class SmartInfoService { return false; } - const tags = await this.machineLearning.classifyImage({ thumbnailPath: asset.resizePath }); + const tags = await this.machineLearning.classifyImage({ imagePath: asset.resizePath }); if (tags.length === 0) { return false; } @@ -73,7 +73,7 @@ export class SmartInfoService { return false; } - const clipEmbedding = await this.machineLearning.encodeImage({ thumbnailPath: asset.resizePath }); + const clipEmbedding = await this.machineLearning.encodeImage({ imagePath: asset.resizePath }); await this.repository.upsert({ assetId: asset.id, clipEmbedding: clipEmbedding }); return true;