From df1e8679d96bb401d4a76103d4c5859753088603 Mon Sep 17 00:00:00 2001 From: Mert <101130780+mertalev@users.noreply.github.com> Date: Tue, 27 Jun 2023 19:21:33 -0400 Subject: [PATCH] chore(ml): added testing and github workflow (#2969) * added testing * github action for python, made mypy happy * formatted with black * minor fixes and styling * test model cache * cache test dependencies * narrowed model cache tests * moved endpoint tests to their own class * cleaned up fixtures * formatting * removed unused dep --- .github/workflows/test.yml | 30 +++ machine-learning/app/config.py | 1 + machine-learning/app/conftest.py | 119 +++++++++ machine-learning/app/main.py | 20 +- machine-learning/app/models/base.py | 24 +- machine-learning/app/models/cache.py | 31 ++- .../app/models/facial_recognition.py | 4 +- .../app/models/image_classification.py | 16 +- machine-learning/app/schemas.py | 5 +- machine-learning/app/test_main.py | 183 +++++++++++++ machine-learning/poetry.lock | 249 +++++++++++++++--- machine-learning/pyproject.toml | 35 ++- 12 files changed, 622 insertions(+), 95 deletions(-) create mode 100644 machine-learning/app/conftest.py create mode 100644 machine-learning/app/test_main.py diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 2db37ae85..5672316cf 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -121,6 +121,36 @@ jobs: working-directory: ./mobile run: flutter test -j 1 + ml-unit-tests: + name: Run ML unit tests and checks + runs-on: ubuntu-latest + defaults: + run: + working-directory: ./machine-learning + steps: + - uses: actions/checkout@v3 + - name: Install poetry + run: pipx install poetry + - uses: actions/setup-python@v4 + with: + python-version: 3.11 + cache: "poetry" + - name: Install dependencies + run: | + poetry install --with dev + - name: Lint with ruff + run: | + poetry run ruff check --format=github app + - name: Check black formatting + run: | + poetry run black --check app + - name: Run mypy type checking + run: | + poetry run mypy --install-types --non-interactive app/ + - name: Run tests and coverage + run: | + poetry run pytest --cov app + generated-api-up-to-date: name: Check generated files are up-to-date runs-on: ubuntu-latest diff --git a/machine-learning/app/config.py b/machine-learning/app/config.py index 3845cf1f3..70520b27c 100644 --- a/machine-learning/app/config.py +++ b/machine-learning/app/config.py @@ -18,6 +18,7 @@ class Settings(BaseSettings): port: int = 3003 workers: int = 1 min_face_score: float = 0.7 + test_full: bool = False class Config(BaseSettings.Config): env_prefix = "MACHINE_LEARNING_" diff --git a/machine-learning/app/conftest.py b/machine-learning/app/conftest.py new file mode 100644 index 000000000..aa73049ec --- /dev/null +++ b/machine-learning/app/conftest.py @@ -0,0 +1,119 @@ +from types import SimpleNamespace +from typing import Any, Iterator, TypeAlias +from unittest import mock + +import numpy as np +import pytest +from fastapi.testclient import TestClient +from PIL import Image + +from .main import app, init_state + +ndarray: TypeAlias = np.ndarray[int, np.dtype[np.float32]] + + +@pytest.fixture +def pil_image() -> Image.Image: + return Image.new("RGB", (600, 800)) + + +@pytest.fixture +def cv_image(pil_image: Image.Image) -> ndarray: + return np.asarray(pil_image)[:, :, ::-1] # PIL uses RGB while cv2 uses BGR + + +@pytest.fixture +def mock_classifier_pipeline() -> Iterator[mock.Mock]: + with mock.patch("app.models.image_classification.pipeline") as model: + classifier_preds = [ + {"label": "that's an image alright", "score": 0.8}, + {"label": "well it ends with .jpg", "score": 0.1}, + {"label": "idk, im just seeing bytes", "score": 0.05}, + {"label": "not sure", "score": 0.04}, + {"label": "probably a virus", "score": 0.01}, + ] + + def forward( + inputs: Image.Image | list[Image.Image], **kwargs: Any + ) -> list[dict[str, Any]] | list[list[dict[str, Any]]]: + if isinstance(inputs, list) and not all([isinstance(img, Image.Image) for img in inputs]): + raise TypeError + elif not isinstance(inputs, Image.Image): + raise TypeError + + if isinstance(inputs, list): + return [classifier_preds] * len(inputs) + + return classifier_preds + + model.return_value = forward + yield model + + +@pytest.fixture +def mock_st() -> Iterator[mock.Mock]: + with mock.patch("app.models.clip.SentenceTransformer") as model: + embedding = np.random.rand(512).astype(np.float32) + + def encode(inputs: Image.Image | list[Image.Image], **kwargs: Any) -> ndarray | list[ndarray]: + # mypy complains unless isinstance(inputs, list) is used explicitly + img_batch = isinstance(inputs, list) and all([isinstance(inst, Image.Image) for inst in inputs]) + text_batch = isinstance(inputs, list) and all([isinstance(inst, str) for inst in inputs]) + if isinstance(inputs, list) and not any([img_batch, text_batch]): + raise TypeError + + if isinstance(inputs, list): + return np.stack([embedding] * len(inputs)) + + return embedding + + mocked = mock.Mock() + mocked.encode = encode + model.return_value = mocked + yield model + + +@pytest.fixture +def mock_faceanalysis() -> Iterator[mock.Mock]: + with mock.patch("app.models.facial_recognition.FaceAnalysis") as model: + face_preds = [ + SimpleNamespace( # this is so these fields can be accessed through dot notation + **{ + "bbox": np.random.rand(4).astype(np.float32), + "kps": np.random.rand(5, 2).astype(np.float32), + "det_score": np.array([0.67]).astype(np.float32), + "normed_embedding": np.random.rand(512).astype(np.float32), + } + ), + SimpleNamespace( + **{ + "bbox": np.random.rand(4).astype(np.float32), + "kps": np.random.rand(5, 2).astype(np.float32), + "det_score": np.array([0.4]).astype(np.float32), + "normed_embedding": np.random.rand(512).astype(np.float32), + } + ), + ] + + def get(image: np.ndarray[int, np.dtype[np.float32]], **kwargs: Any) -> list[SimpleNamespace]: + if not isinstance(image, np.ndarray): + raise TypeError + + return face_preds + + mocked = mock.Mock() + mocked.get = get + model.return_value = mocked + yield model + + +@pytest.fixture +def mock_get_model() -> Iterator[mock.Mock]: + with mock.patch("app.models.cache.InferenceModel.from_model_type", autospec=True) as mocked: + yield mocked + + +@pytest.fixture(scope="session") +def deployed_app() -> TestClient: + init_state() + return TestClient(app) diff --git a/machine-learning/app/main.py b/machine-learning/app/main.py index e59d0d838..35ee27204 100644 --- a/machine-learning/app/main.py +++ b/machine-learning/app/main.py @@ -24,9 +24,11 @@ from .schemas import ( app = FastAPI() -@app.on_event("startup") -async def startup_event() -> None: +def init_state() -> None: app.state.model_cache = ModelCache(ttl=settings.model_ttl, revalidate=True) + + +async def load_models() -> None: models = [ (settings.classification_model, ModelType.IMAGE_CLASSIFICATION), (settings.clip_image_model, ModelType.CLIP), @@ -42,6 +44,12 @@ async def startup_event() -> None: InferenceModel.from_model_type(model_type, model_name) +@app.on_event("startup") +async def startup_event() -> None: + init_state() + await load_models() + + def dep_pil_image(byte_image: bytes = Body(...)) -> Image.Image: return Image.open(BytesIO(byte_image)) @@ -69,9 +77,7 @@ def ping() -> str: async def image_classification( image: Image.Image = Depends(dep_pil_image), ) -> list[str]: - model = await app.state.model_cache.get( - settings.classification_model, ModelType.IMAGE_CLASSIFICATION - ) + model = await app.state.model_cache.get(settings.classification_model, ModelType.IMAGE_CLASSIFICATION) labels = model.predict(image) return labels @@ -108,9 +114,7 @@ async def clip_encode_text(payload: TextModelRequest) -> list[float]: async def facial_recognition( image: cv2.Mat = Depends(dep_cv_image), ) -> list[dict[str, Any]]: - model = await app.state.model_cache.get( - settings.facial_recognition_model, ModelType.FACIAL_RECOGNITION - ) + model = await app.state.model_cache.get(settings.facial_recognition_model, ModelType.FACIAL_RECOGNITION) faces = model.predict(image) return faces diff --git a/machine-learning/app/models/base.py b/machine-learning/app/models/base.py index a62d7730c..98d6fb834 100644 --- a/machine-learning/app/models/base.py +++ b/machine-learning/app/models/base.py @@ -5,7 +5,7 @@ from pathlib import Path from shutil import rmtree from typing import Any -from onnxruntime.capi.onnxruntime_pybind11_state import InvalidProtobuf +from onnxruntime.capi.onnxruntime_pybind11_state import InvalidProtobuf # type: ignore from ..config import get_cache_dir from ..schemas import ModelType @@ -14,15 +14,9 @@ from ..schemas import ModelType class InferenceModel(ABC): _model_type: ModelType - def __init__( - self, model_name: str, cache_dir: Path | None = None, **model_kwargs - ) -> None: + def __init__(self, model_name: str, cache_dir: Path | str | None = None, **model_kwargs: Any) -> None: self.model_name = model_name - self._cache_dir = ( - cache_dir - if cache_dir is not None - else get_cache_dir(model_name, self.model_type) - ) + self._cache_dir = Path(cache_dir) if cache_dir is not None else get_cache_dir(model_name, self.model_type) try: self.load(**model_kwargs) @@ -51,12 +45,8 @@ class InferenceModel(ABC): self._cache_dir = cache_dir @classmethod - def from_model_type( - cls, model_type: ModelType, model_name, **model_kwargs - ) -> InferenceModel: - subclasses = { - subclass._model_type: subclass for subclass in cls.__subclasses__() - } + def from_model_type(cls, model_type: ModelType, model_name: str, **model_kwargs: Any) -> InferenceModel: + subclasses = {subclass._model_type: subclass for subclass in cls.__subclasses__()} if model_type not in subclasses: raise ValueError(f"Unsupported model type: {model_type}") @@ -66,8 +56,6 @@ class InferenceModel(ABC): if not self.cache_dir.exists(): return elif not rmtree.avoids_symlink_attacks: - raise RuntimeError( - "Attempted to clear cache, but rmtree is not safe on this platform." - ) + raise RuntimeError("Attempted to clear cache, but rmtree is not safe on this platform.") rmtree(self.cache_dir) diff --git a/machine-learning/app/models/cache.py b/machine-learning/app/models/cache.py index bcf959fe7..086a57c5a 100644 --- a/machine-learning/app/models/cache.py +++ b/machine-learning/app/models/cache.py @@ -1,4 +1,5 @@ import asyncio +from typing import Any from aiocache.backends.memory import SimpleMemoryCache from aiocache.lock import OptimisticLock @@ -34,13 +35,9 @@ class ModelCache: if profiling: plugins.append(TimingPlugin()) - self.cache = SimpleMemoryCache( - ttl=ttl, timeout=timeout, plugins=plugins, namespace=None - ) + self.cache = SimpleMemoryCache(ttl=ttl, timeout=timeout, plugins=plugins, namespace=None) - async def get( - self, model_name: str, model_type: ModelType, **model_kwargs - ) -> InferenceModel: + async def get(self, model_name: str, model_type: ModelType, **model_kwargs: Any) -> InferenceModel: """ Args: model_name: Name of model in the model hub used for the task. @@ -56,9 +53,7 @@ class ModelCache: async with OptimisticLock(self.cache, key) as lock: model = await asyncio.get_running_loop().run_in_executor( None, - lambda: InferenceModel.from_model_type( - model_type, model_name, **model_kwargs - ), + lambda: InferenceModel.from_model_type(model_type, model_name, **model_kwargs), ) await lock.cas(model, ttl=self.ttl) return model @@ -73,7 +68,14 @@ class ModelCache: class RevalidationPlugin(BasePlugin): """Revalidates cache item's TTL after cache hit.""" - async def post_get(self, client, key, ret=None, namespace=None, **kwargs): + async def post_get( + self, + client: SimpleMemoryCache, + key: str, + ret: Any | None = None, + namespace: str | None = None, + **kwargs: Any, + ) -> None: if ret is None: return if namespace is not None: @@ -81,7 +83,14 @@ class RevalidationPlugin(BasePlugin): if key in client._handlers: await client.expire(key, client.ttl) - async def post_multi_get(self, client, keys, ret=None, namespace=None, **kwargs): + async def post_multi_get( + self, + client: SimpleMemoryCache, + keys: list[str], + ret: list[Any] | None = None, + namespace: str | None = None, + **kwargs: Any, + ) -> None: if ret is None: return diff --git a/machine-learning/app/models/facial_recognition.py b/machine-learning/app/models/facial_recognition.py index 99349409f..b9f96b7b4 100644 --- a/machine-learning/app/models/facial_recognition.py +++ b/machine-learning/app/models/facial_recognition.py @@ -16,8 +16,8 @@ class FaceRecognizer(InferenceModel): self, model_name: str, min_score: float = settings.min_face_score, - cache_dir: Path | None = None, - **model_kwargs, + cache_dir: Path | str | None = None, + **model_kwargs: Any, ) -> None: self.min_score = min_score super().__init__(model_name, cache_dir, **model_kwargs) diff --git a/machine-learning/app/models/image_classification.py b/machine-learning/app/models/image_classification.py index 9f7e4cfb6..0b5887f53 100644 --- a/machine-learning/app/models/image_classification.py +++ b/machine-learning/app/models/image_classification.py @@ -16,8 +16,8 @@ class ImageClassifier(InferenceModel): self, model_name: str, min_score: float = settings.min_tag_score, - cache_dir: Path | None = None, - **model_kwargs, + cache_dir: Path | str | None = None, + **model_kwargs: Any, ) -> None: self.min_score = min_score super().__init__(model_name, cache_dir, **model_kwargs) @@ -30,13 +30,7 @@ class ImageClassifier(InferenceModel): ) def predict(self, image: Image) -> list[str]: - predictions = self.model(image) - tags = list( - { - tag - for pred in predictions - for tag in pred["label"].split(", ") - if pred["score"] >= self.min_score - } - ) + predictions: list[dict[str, Any]] = self.model(image) # type: ignore + tags = [tag for pred in predictions for tag in pred["label"].split(", ") if pred["score"] >= self.min_score] + return tags diff --git a/machine-learning/app/schemas.py b/machine-learning/app/schemas.py index 16618faa6..b19b49873 100644 --- a/machine-learning/app/schemas.py +++ b/machine-learning/app/schemas.py @@ -4,10 +4,7 @@ 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("_")) - ] + tokens = [token.capitalize() if i > 0 else token for i, token in enumerate(string.split("_"))] return "".join(tokens) diff --git a/machine-learning/app/test_main.py b/machine-learning/app/test_main.py new file mode 100644 index 000000000..11a0466c8 --- /dev/null +++ b/machine-learning/app/test_main.py @@ -0,0 +1,183 @@ +from io import BytesIO +from pathlib import Path +from unittest import mock + +import cv2 +import pytest +from fastapi.testclient import TestClient +from PIL import Image + +from .config import settings +from .models.cache import ModelCache +from .models.clip import CLIPSTEncoder +from .models.facial_recognition import FaceRecognizer +from .models.image_classification import ImageClassifier +from .schemas import ModelType + + +class TestImageClassifier: + def test_init(self, mock_classifier_pipeline: mock.Mock) -> None: + cache_dir = Path("test_cache") + classifier = ImageClassifier("test_model_name", 0.5, cache_dir=cache_dir) + + assert classifier.min_score == 0.5 + mock_classifier_pipeline.assert_called_once_with( + "image-classification", + "test_model_name", + model_kwargs={"cache_dir": cache_dir}, + ) + + def test_min_score(self, pil_image: Image.Image, mock_classifier_pipeline: mock.Mock) -> None: + classifier = ImageClassifier("test_model_name", min_score=0.0) + classifier.min_score = 0.0 + all_labels = classifier.predict(pil_image) + classifier.min_score = 0.5 + filtered_labels = classifier.predict(pil_image) + + assert all_labels == [ + "that's an image alright", + "well it ends with .jpg", + "idk", + "im just seeing bytes", + "not sure", + "probably a virus", + ] + assert filtered_labels == ["that's an image alright"] + + +class TestCLIP: + def test_init(self, mock_st: mock.Mock) -> None: + CLIPSTEncoder("test_model_name", cache_dir="test_cache") + + mock_st.assert_called_once_with("test_model_name", cache_folder="test_cache") + + def test_basic_image(self, pil_image: Image.Image, mock_st: mock.Mock) -> None: + clip_encoder = CLIPSTEncoder("test_model_name", cache_dir="test_cache") + embedding = clip_encoder.predict(pil_image) + + assert isinstance(embedding, list) + assert len(embedding) == 512 + assert all([isinstance(num, float) for num in embedding]) + mock_st.assert_called_once() + + def test_basic_text(self, mock_st: mock.Mock) -> None: + clip_encoder = CLIPSTEncoder("test_model_name", cache_dir="test_cache") + embedding = clip_encoder.predict("test search query") + + assert isinstance(embedding, list) + assert len(embedding) == 512 + assert all([isinstance(num, float) for num in embedding]) + mock_st.assert_called_once() + + +class TestFaceRecognition: + def test_init(self, mock_faceanalysis: mock.Mock) -> None: + FaceRecognizer("test_model_name", cache_dir="test_cache") + + mock_faceanalysis.assert_called_once_with( + name="test_model_name", + root="test_cache", + allowed_modules=["detection", "recognition"], + ) + + def test_basic(self, cv_image: cv2.Mat, mock_faceanalysis: mock.Mock) -> None: + face_recognizer = FaceRecognizer("test_model_name", min_score=0.0, cache_dir="test_cache") + faces = face_recognizer.predict(cv_image) + + assert len(faces) == 2 + for face in faces: + assert face["imageHeight"] == 800 + assert face["imageWidth"] == 600 + assert isinstance(face["embedding"], list) + assert len(face["embedding"]) == 512 + assert all([isinstance(num, float) for num in face["embedding"]]) + + mock_faceanalysis.assert_called_once() + + +@pytest.mark.asyncio +class TestCache: + async def test_caches(self, mock_get_model: mock.Mock) -> None: + model_cache = ModelCache() + await model_cache.get("test_model_name", ModelType.IMAGE_CLASSIFICATION) + await model_cache.get("test_model_name", ModelType.IMAGE_CLASSIFICATION) + assert len(model_cache.cache._cache) == 1 + mock_get_model.assert_called_once() + + async def test_kwargs_used(self, mock_get_model: mock.Mock) -> None: + model_cache = ModelCache() + await model_cache.get("test_model_name", ModelType.IMAGE_CLASSIFICATION, cache_dir="test_cache") + mock_get_model.assert_called_once_with( + ModelType.IMAGE_CLASSIFICATION, "test_model_name", cache_dir="test_cache" + ) + + async def test_different_clip(self, mock_get_model: mock.Mock) -> None: + model_cache = ModelCache() + await model_cache.get("test_image_model_name", ModelType.CLIP) + await model_cache.get("test_text_model_name", ModelType.CLIP) + mock_get_model.assert_has_calls( + [ + mock.call(ModelType.CLIP, "test_image_model_name"), + mock.call(ModelType.CLIP, "test_text_model_name"), + ] + ) + assert len(model_cache.cache._cache) == 2 + + @mock.patch("app.models.cache.OptimisticLock", autospec=True) + async def test_model_ttl(self, mock_lock_cls: mock.Mock, mock_get_model: mock.Mock) -> None: + model_cache = ModelCache(ttl=100) + await model_cache.get("test_model_name", ModelType.IMAGE_CLASSIFICATION) + mock_lock_cls.return_value.__aenter__.return_value.cas.assert_called_with(mock.ANY, ttl=100) + + @mock.patch("app.models.cache.SimpleMemoryCache.expire") + async def test_revalidate(self, mock_cache_expire: mock.Mock, mock_get_model: mock.Mock) -> None: + model_cache = ModelCache(ttl=100, revalidate=True) + await model_cache.get("test_model_name", ModelType.IMAGE_CLASSIFICATION) + await model_cache.get("test_model_name", ModelType.IMAGE_CLASSIFICATION) + mock_cache_expire.assert_called_once_with(mock.ANY, 100) + + +@pytest.mark.skipif( + not settings.test_full, + reason="More time-consuming since it deploys the app and loads models.", +) +class TestEndpoints: + def test_tagging_endpoint(self, pil_image: Image.Image, deployed_app: TestClient) -> None: + byte_image = BytesIO() + pil_image.save(byte_image, format="jpeg") + headers = {"Content-Type": "image/jpg"} + response = deployed_app.post( + "http://localhost:3003/image-classifier/tag-image", + content=byte_image.getvalue(), + headers=headers, + ) + assert response.status_code == 200 + + def test_clip_image_endpoint(self, pil_image: Image.Image, deployed_app: TestClient) -> None: + byte_image = BytesIO() + pil_image.save(byte_image, format="jpeg") + headers = {"Content-Type": "image/jpg"} + response = deployed_app.post( + "http://localhost:3003/sentence-transformer/encode-image", + content=byte_image.getvalue(), + headers=headers, + ) + assert response.status_code == 200 + + def test_clip_text_endpoint(self, deployed_app: TestClient) -> None: + response = deployed_app.post( + "http://localhost:3003/sentence-transformer/encode-text", + json={"text": "test search query"}, + ) + assert response.status_code == 200 + + def test_face_endpoint(self, pil_image: Image.Image, deployed_app: TestClient) -> None: + byte_image = BytesIO() + pil_image.save(byte_image, format="jpeg") + headers = {"Content-Type": "image/jpg"} + response = deployed_app.post( + "http://localhost:3003/facial-recognition/detect-faces", + content=byte_image.getvalue(), + headers=headers, + ) + assert response.status_code == 200 diff --git a/machine-learning/poetry.lock b/machine-learning/poetry.lock index c829e02b6..ea6a955f8 100644 --- a/machine-learning/poetry.lock +++ b/machine-learning/poetry.lock @@ -424,13 +424,13 @@ cron = ["capturer (>=2.4)"] [[package]] name = "configargparse" -version = "1.5.3" +version = "1.5.5" description = "A drop-in replacement for argparse that allows options to also be set via config files and/or environment variables." optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" files = [ - {file = "ConfigArgParse-1.5.3-py3-none-any.whl", hash = "sha256:18f6535a2db9f6e02bd5626cc7455eac3e96b9ab3d969d366f9aafd5c5c00fe7"}, - {file = "ConfigArgParse-1.5.3.tar.gz", 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true init_typed = true warn_required_dynamic_aliases = true -warn_untyped_fields = true \ No newline at end of file +warn_untyped_fields = true + +[[tool.mypy.overrides]] +module = [ + "transformers.pipelines", + "cv2", + "insightface.app", + "sentence_transformers", + "aiocache.backends.memory", + "aiocache.lock", + "aiocache.plugins" +] +ignore_missing_imports = true + +[tool.ruff] +line-length = 120 +target-version = "py311" +select = ["E", "F", "I"] +ignore = ["F401"] + +[tool.ruff.per-file-ignores] +"test_main.py" = ["F403"] + +[tool.black] +line-length = 120 +target-version = ['py311']