refactor(ml): model downloading (#3545)

* download facial recognition models

* download hf models

* simplified logic

* updated `predict` for facial recognition

* ensure download method is called

* fixed repo_id for clip

* fixed download destination

* use st's own `snapshot_download`

* conditional download

* fixed predict method

* check if loaded

* minor fixes

* updated mypy overrides

* added pytest-mock

* updated tests

* updated lock

machine-learning/app/config.py

@@ -20,7 +20,7 @@ class Settings(BaseSettings):
     min_face_score: float = 0.7
     test_full: bool = False
 
-    class Config(BaseSettings.Config):
+    class Config:
         env_prefix = "MACHINE_LEARNING_"
         case_sensitive = False
 

machine-learning/app/conftest.py

@@ -1,5 +1,4 @@
-from types import SimpleNamespace
-from typing import Any, Iterator, TypeAlias
+from typing import Iterator, TypeAlias
 from unittest import mock
 
 import numpy as np
@@ -22,91 +21,6 @@ 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:

machine-learning/app/main.py

@@ -9,7 +9,6 @@ from fastapi import Body, Depends, FastAPI
 from PIL import Image
 
 from .config import settings
-from .models.base import InferenceModel
 from .models.cache import ModelCache
 from .schemas import (
     EmbeddingResponse,
@@ -38,10 +37,7 @@ async def load_models() -> None:
 
     # Get all models
     for model_name, model_type in models:
-        if settings.eager_startup:
-            await app.state.model_cache.get(model_name, model_type)
-        else:
-            InferenceModel.from_model_type(model_type, model_name)
+        await app.state.model_cache.get(model_name, model_type, eager=settings.eager_startup)
 
 
 @app.on_event("startup")

machine-learning/app/models/base.py

@@ -14,22 +14,43 @@ from ..schemas import ModelType
 class InferenceModel(ABC):
     _model_type: ModelType
 
-    def __init__(self, model_name: str, cache_dir: Path | str | None = None, **model_kwargs: Any) -> None:
+    def __init__(
+        self, model_name: str, cache_dir: Path | str | None = None, eager: bool = True, **model_kwargs: Any
+    ) -> None:
         self.model_name = model_name
+        self._loaded = False
         self._cache_dir = Path(cache_dir) if cache_dir is not None else get_cache_dir(model_name, self.model_type)
-
+        loader = self.load if eager else self.download
         try:
-            self.load(**model_kwargs)
+            loader(**model_kwargs)
         except (OSError, InvalidProtobuf):
             self.clear_cache()
-            self.load(**model_kwargs)
+            loader(**model_kwargs)
+
+    def download(self, **model_kwargs: Any) -> None:
+        if not self.cached:
+            self._download(**model_kwargs)
+
+    def load(self, **model_kwargs: Any) -> None:
+        self.download(**model_kwargs)
+        self._load(**model_kwargs)
+        self._loaded = True
+
+    def predict(self, inputs: Any) -> Any:
+        if not self._loaded:
+            self.load()
+        return self._predict(inputs)
 
     @abstractmethod
-    def load(self, **model_kwargs: Any) -> None:
+    def _predict(self, inputs: Any) -> Any:
         ...
 
     @abstractmethod
-    def predict(self, inputs: Any) -> Any:
+    def _download(self, **model_kwargs: Any) -> None:
+        ...
+
+    @abstractmethod
+    def _load(self, **model_kwargs: Any) -> None:
         ...
 
     @property
@@ -44,6 +65,10 @@ class InferenceModel(ABC):
     def cache_dir(self, cache_dir: Path) -> None:
         self._cache_dir = cache_dir
 
+    @property
+    def cached(self) -> bool:
+        return self.cache_dir.exists() and any(self.cache_dir.iterdir())
+
     @classmethod
     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__()}
@@ -55,7 +80,11 @@ class InferenceModel(ABC):
     def clear_cache(self) -> None:
         if not self.cache_dir.exists():
             return
-        elif not rmtree.avoids_symlink_attacks:
+        if not rmtree.avoids_symlink_attacks:
             raise RuntimeError("Attempted to clear cache, but rmtree is not safe on this platform.")
 
+        if self.cache_dir.is_dir():
             rmtree(self.cache_dir)
+        else:
+            self.cache_dir.unlink()
+        self.cache_dir.mkdir(parents=True, exist_ok=True)

machine-learning/app/models/clip.py

@@ -1,8 +1,8 @@
-from pathlib import Path
 from typing import Any
 
 from PIL.Image import Image
 from sentence_transformers import SentenceTransformer
+from sentence_transformers.util import snapshot_download
 
 from ..schemas import ModelType
 from .base import InferenceModel
@@ -11,12 +11,21 @@ from .base import InferenceModel
 class CLIPSTEncoder(InferenceModel):
     _model_type = ModelType.CLIP
 
-    def load(self, **model_kwargs: Any) -> None:
+    def _download(self, **model_kwargs: Any) -> None:
+        repo_id = self.model_name if "/" in self.model_name else f"sentence-transformers/{self.model_name}"
+        snapshot_download(
+            cache_dir=self.cache_dir,
+            repo_id=repo_id,
+            library_name="sentence-transformers",
+            ignore_files=["flax_model.msgpack", "rust_model.ot", "tf_model.h5"],
+        )
+
+    def _load(self, **model_kwargs: Any) -> None:
         self.model = SentenceTransformer(
             self.model_name,
             cache_folder=self.cache_dir.as_posix(),
             **model_kwargs,
         )
 
-    def predict(self, image_or_text: Image | str) -> list[float]:
+    def _predict(self, image_or_text: Image | str) -> list[float]:
         return self.model.encode(image_or_text).tolist()

machine-learning/app/models/facial_recognition.py

@@ -1,8 +1,12 @@
+import zipfile
 from pathlib import Path
 from typing import Any
 
 import cv2
-from insightface.app import FaceAnalysis
+import numpy as np
+from insightface.model_zoo import ArcFaceONNX, RetinaFace
+from insightface.utils.face_align import norm_crop
+from insightface.utils.storage import BASE_REPO_URL, download_file
 
 from ..config import settings
 from ..schemas import ModelType
@@ -22,39 +26,62 @@ class FaceRecognizer(InferenceModel):
         self.min_score = min_score
         super().__init__(model_name, cache_dir, **model_kwargs)
 
-    def load(self, **model_kwargs: Any) -> None:
-        self.model = FaceAnalysis(
-            name=self.model_name,
-            root=self.cache_dir.as_posix(),
-            allowed_modules=["detection", "recognition"],
-            **model_kwargs,
-        )
-        self.model.prepare(
-            ctx_id=0,
+    def _download(self, **model_kwargs: Any) -> None:
+        zip_file = self.cache_dir / f"{self.model_name}.zip"
+        download_file(f"{BASE_REPO_URL}/{self.model_name}.zip", zip_file)
+        with zipfile.ZipFile(zip_file, "r") as zip:
+            members = zip.namelist()
+            det_file = next(model for model in members if model.startswith("det_"))
+            rec_file = next(model for model in members if model.startswith("w600k_"))
+            zip.extractall(self.cache_dir, members=[det_file, rec_file])
+        zip_file.unlink()
+
+    def _load(self, **model_kwargs: Any) -> None:
+        try:
+            det_file = next(self.cache_dir.glob("det_*.onnx"))
+            rec_file = next(self.cache_dir.glob("w600k_*.onnx"))
+        except StopIteration:
+            raise FileNotFoundError("Facial recognition models not found in cache directory")
+        self.det_model = RetinaFace(det_file.as_posix())
+        self.rec_model = ArcFaceONNX(rec_file.as_posix())
+
+        self.det_model.prepare(
+            ctx_id=-1,
             det_thresh=self.min_score,
-            det_size=(640, 640),
+            input_size=(640, 640),
         )
+        self.rec_model.prepare(ctx_id=-1)
+
+    def _predict(self, image: cv2.Mat) -> list[dict[str, Any]]:
+        bboxes, kpss = self.det_model.detect(image)
+        if bboxes.size == 0:
+            return []
+        assert isinstance(kpss, np.ndarray)
+
+        scores = bboxes[:, 4].tolist()
+        bboxes = bboxes[:, :4].round().tolist()
 
-    def predict(self, image: cv2.Mat) -> list[dict[str, Any]]:
-        height, width, _ = image.shape
         results = []
-        faces = self.model.get(image)
-
-        for face in faces:
-            x1, y1, x2, y2 = face.bbox
-
+        height, width, _ = image.shape
+        for (x1, y1, x2, y2), score, kps in zip(bboxes, scores, kpss):
+            cropped_img = norm_crop(image, kps)
+            embedding = self.rec_model.get_feat(cropped_img)[0].tolist()
             results.append(
                 {
                     "imageWidth": width,
                     "imageHeight": height,
                     "boundingBox": {
-                        "x1": round(x1),
-                        "y1": round(y1),
-                        "x2": round(x2),
-                        "y2": round(y2),
+                        "x1": x1,
+                        "y1": y1,
+                        "x2": x2,
+                        "y2": y2,
                     },
-                    "score": face.det_score.item(),
-                    "embedding": face.normed_embedding.tolist(),
+                    "score": score,
+                    "embedding": embedding,
                 }
             )
         return results
+
+    @property
+    def cached(self) -> bool:
+        return self.cache_dir.is_dir() and any(self.cache_dir.glob("*.onnx"))

machine-learning/app/models/image_classification.py

@@ -1,6 +1,7 @@
 from pathlib import Path
 from typing import Any
 
+from huggingface_hub import snapshot_download
 from PIL.Image import Image
 from transformers.pipelines import pipeline
 
@@ -22,14 +23,19 @@ class ImageClassifier(InferenceModel):
         self.min_score = min_score
         super().__init__(model_name, cache_dir, **model_kwargs)
 
-    def load(self, **model_kwargs: Any) -> None:
+    def _download(self, **model_kwargs: Any) -> None:
+        snapshot_download(
+            cache_dir=self.cache_dir, repo_id=self.model_name, allow_patterns=["*.bin", "*.json", "*.txt"]
+        )
+
+    def _load(self, **model_kwargs: Any) -> None:
         self.model = pipeline(
             self.model_type.value,
             self.model_name,
             model_kwargs={"cache_dir": self.cache_dir, **model_kwargs},
         )
 
-    def predict(self, image: Image) -> list[str]:
+    def _predict(self, image: Image) -> list[str]:
         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]
 

machine-learning/app/test_main.py

@@ -1,11 +1,13 @@
 from io import BytesIO
-from pathlib import Path
+from typing import TypeAlias
 from unittest import mock
 
 import cv2
+import numpy as np
 import pytest
 from fastapi.testclient import TestClient
 from PIL import Image
+from pytest_mock import MockerFixture
 
 from .config import settings
 from .models.cache import ModelCache
@@ -14,22 +16,43 @@ from .models.facial_recognition import FaceRecognizer
 from .models.image_classification import ImageClassifier
 from .schemas import ModelType
 
+ndarray: TypeAlias = np.ndarray[int, np.dtype[np.float32]]
+
 
 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)
+    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},
+    ]
 
-        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_eager_init(self, mocker: MockerFixture) -> None:
+        mocker.patch.object(ImageClassifier, "download")
+        mock_load = mocker.patch.object(ImageClassifier, "load")
+        classifier = ImageClassifier("test_model_name", cache_dir="test_cache", eager=True, test_arg="test_arg")
 
-    def test_min_score(self, pil_image: Image.Image, mock_classifier_pipeline: mock.Mock) -> None:
+        assert classifier.model_name == "test_model_name"
+        mock_load.assert_called_once_with(test_arg="test_arg")
+
+    def test_lazy_init(self, mocker: MockerFixture) -> None:
+        mock_download = mocker.patch.object(ImageClassifier, "download")
+        mock_load = mocker.patch.object(ImageClassifier, "load")
+        face_model = ImageClassifier("test_model_name", cache_dir="test_cache", eager=False, test_arg="test_arg")
+
+        assert face_model.model_name == "test_model_name"
+        mock_download.assert_called_once_with(test_arg="test_arg")
+        mock_load.assert_not_called()
+
+    def test_min_score(self, pil_image: Image.Image, mocker: MockerFixture) -> None:
+        mocker.patch.object(ImageClassifier, "load")
         classifier = ImageClassifier("test_model_name", min_score=0.0)
-        classifier.min_score = 0.0
+        assert classifier.min_score == 0.0
+
+        classifier.model = mock.Mock()
+        classifier.model.return_value = self.classifier_preds
+
         all_labels = classifier.predict(pil_image)
         classifier.min_score = 0.5
         filtered_labels = classifier.predict(pil_image)
@@ -46,45 +69,94 @@ class TestImageClassifier:
 
 
 class TestCLIP:
-    def test_init(self, mock_st: mock.Mock) -> None:
-        CLIPSTEncoder("test_model_name", cache_dir="test_cache")
+    embedding = np.random.rand(512).astype(np.float32)
 
-        mock_st.assert_called_once_with("test_model_name", cache_folder="test_cache")
+    def test_eager_init(self, mocker: MockerFixture) -> None:
+        mocker.patch.object(CLIPSTEncoder, "download")
+        mock_load = mocker.patch.object(CLIPSTEncoder, "load")
+        clip_model = CLIPSTEncoder("test_model_name", cache_dir="test_cache", eager=True, test_arg="test_arg")
 
-    def test_basic_image(self, pil_image: Image.Image, mock_st: mock.Mock) -> None:
+        assert clip_model.model_name == "test_model_name"
+        mock_load.assert_called_once_with(test_arg="test_arg")
+
+    def test_lazy_init(self, mocker: MockerFixture) -> None:
+        mock_download = mocker.patch.object(CLIPSTEncoder, "download")
+        mock_load = mocker.patch.object(CLIPSTEncoder, "load")
+        clip_model = CLIPSTEncoder("test_model_name", cache_dir="test_cache", eager=False, test_arg="test_arg")
+
+        assert clip_model.model_name == "test_model_name"
+        mock_download.assert_called_once_with(test_arg="test_arg")
+        mock_load.assert_not_called()
+
+    def test_basic_image(self, pil_image: Image.Image, mocker: MockerFixture) -> None:
+        mocker.patch.object(CLIPSTEncoder, "load")
         clip_encoder = CLIPSTEncoder("test_model_name", cache_dir="test_cache")
+        clip_encoder.model = mock.Mock()
+        clip_encoder.model.encode.return_value = self.embedding
         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()
+        clip_encoder.model.encode.assert_called_once()
 
-    def test_basic_text(self, mock_st: mock.Mock) -> None:
+    def test_basic_text(self, mocker: MockerFixture) -> None:
+        mocker.patch.object(CLIPSTEncoder, "load")
         clip_encoder = CLIPSTEncoder("test_model_name", cache_dir="test_cache")
+        clip_encoder.model = mock.Mock()
+        clip_encoder.model.encode.return_value = self.embedding
         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()
+        clip_encoder.model.encode.assert_called_once()
 
 
 class TestFaceRecognition:
-    def test_init(self, mock_faceanalysis: mock.Mock) -> None:
-        FaceRecognizer("test_model_name", cache_dir="test_cache")
+    def test_eager_init(self, mocker: MockerFixture) -> None:
+        mocker.patch.object(FaceRecognizer, "download")
+        mock_load = mocker.patch.object(FaceRecognizer, "load")
+        face_model = FaceRecognizer("test_model_name", cache_dir="test_cache", eager=True, test_arg="test_arg")
 
-        mock_faceanalysis.assert_called_once_with(
-            name="test_model_name",
-            root="test_cache",
-            allowed_modules=["detection", "recognition"],
-        )
+        assert face_model.model_name == "test_model_name"
+        mock_load.assert_called_once_with(test_arg="test_arg")
 
-    def test_basic(self, cv_image: cv2.Mat, mock_faceanalysis: mock.Mock) -> None:
+    def test_lazy_init(self, mocker: MockerFixture) -> None:
+        mock_download = mocker.patch.object(FaceRecognizer, "download")
+        mock_load = mocker.patch.object(FaceRecognizer, "load")
+        face_model = FaceRecognizer("test_model_name", cache_dir="test_cache", eager=False, test_arg="test_arg")
+
+        assert face_model.model_name == "test_model_name"
+        mock_download.assert_called_once_with(test_arg="test_arg")
+        mock_load.assert_not_called()
+
+    def test_set_min_score(self, mocker: MockerFixture) -> None:
+        mocker.patch.object(FaceRecognizer, "load")
+        face_recognizer = FaceRecognizer("test_model_name", cache_dir="test_cache", min_score=0.5)
+
+        assert face_recognizer.min_score == 0.5
+
+    def test_basic(self, cv_image: cv2.Mat, mocker: MockerFixture) -> None:
+        mocker.patch.object(FaceRecognizer, "load")
         face_recognizer = FaceRecognizer("test_model_name", min_score=0.0, cache_dir="test_cache")
+
+        det_model = mock.Mock()
+        num_faces = 2
+        bbox = np.random.rand(num_faces, 4).astype(np.float32)
+        score = np.array([[0.67]] * num_faces).astype(np.float32)
+        kpss = np.random.rand(num_faces, 5, 2).astype(np.float32)
+        det_model.detect.return_value = (np.concatenate([bbox, score], axis=-1), kpss)
+        face_recognizer.det_model = det_model
+
+        rec_model = mock.Mock()
+        embedding = np.random.rand(num_faces, 512).astype(np.float32)
+        rec_model.get_feat.return_value = embedding
+        face_recognizer.rec_model = rec_model
+
         faces = face_recognizer.predict(cv_image)
 
-        assert len(faces) == 2
+        assert len(faces) == num_faces
         for face in faces:
             assert face["imageHeight"] == 800
             assert face["imageWidth"] == 600
@@ -92,7 +164,8 @@ class TestFaceRecognition:
             assert len(face["embedding"]) == 512
             assert all([isinstance(num, float) for num in face["embedding"]])
 
-        mock_faceanalysis.assert_called_once()
+        det_model.detect.assert_called_once()
+        assert rec_model.get_feat.call_count == num_faces
 
 
 @pytest.mark.asyncio

machine-learning/poetry.lock

@@ -421,13 +421,13 @@ cron = ["capturer (>=2.4)"]
 
 [[package]]
 name = "configargparse"
-version = "1.5.5"
+version = "1.7"
 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.*"
+python-versions = ">=3.5"
 files = [
-    {file = "ConfigArgParse-1.5.5-py3-none-any.whl", hash = "sha256:541360ddc1b15c517f95c0d02d1fca4591266628f3667acdc5d13dccc78884ca"},
-    {file = "ConfigArgParse-1.5.5.tar.gz", hash = "sha256:363d80a6d35614bd446e2f2b1b216f3b33741d03ac6d0a92803306f40e555b58"},
+    {file = "ConfigArgParse-1.7-py3-none-any.whl", hash = "sha256:d249da6591465c6c26df64a9f73d2536e743be2f244eb3ebe61114af2f94f86b"},
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 ]
 
 [package.dependencies]
@@ -2346,6 +2346,23 @@ pytest = ">=4.6"
 [package.extras]
 testing = ["fields", "hunter", "process-tests", "pytest-xdist", "six", "virtualenv"]
 
+[[package]]
+name = "pytest-mock"
+version = "3.11.1"
+description = "Thin-wrapper around the mock package for easier use with pytest"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "pytest-mock-3.11.1.tar.gz", hash = "sha256:7f6b125602ac6d743e523ae0bfa71e1a697a2f5534064528c6ff84c2f7c2fc7f"},
+    {file = "pytest_mock-3.11.1-py3-none-any.whl", hash = "sha256:21c279fff83d70763b05f8874cc9cfb3fcacd6d354247a976f9529d19f9acf39"},
+]
+
+[package.dependencies]
+pytest = ">=5.0"
+
+[package.extras]
+dev = ["pre-commit", "pytest-asyncio", "tox"]
+
 [[package]]
 name = "python-dateutil"
 version = "2.8.2"
@@ -3664,4 +3681,4 @@ testing = ["coverage (>=5.0.3)", "zope.event", "zope.testing"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.11"
-content-hash = "4a06d26614d016bfdbb290ad93b3c71378ad03b249a8f06cb53c82465862977f"
+content-hash = "0a4f26164e0dd32ce9d63da9322739c0812e56a5bdfb4148c973e22434344032"

machine-learning/pyproject.toml

@@ -33,6 +33,7 @@ httpx = "^0.24.1"
 pytest-asyncio = "^0.21.0"
 pytest-cov = "^4.1.0"
 ruff = "^0.0.272"
+pytest-mock = "^3.11.1"
 
 [[tool.poetry.source]]
 name = "pytorch-cpu"
@@ -60,10 +61,14 @@ warn_untyped_fields = true
 
 [[tool.mypy.overrides]]
 module = [
+    "huggingface_hub",
     "transformers.pipelines",
     "cv2",
-    "insightface.app",
+    "insightface.model_zoo",
+    "insightface.utils.face_align",
+    "insightface.utils.storage",
     "sentence_transformers",
+    "sentence_transformers.util",
     "aiocache.backends.memory",
     "aiocache.lock",
     "aiocache.plugins"