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
This commit is contained in:
Mert
GitHub
parent
2f26a7edae
commit
c73832bd9c
10 changed files with 350 additions and 274 deletions
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@ -20,7 +20,7 @@ class Settings(BaseSettings):
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min_face_score: float = 0.7
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test_full: bool = False
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class Config(BaseSettings.Config):
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class Config:
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env_prefix = "MACHINE_LEARNING_"
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case_sensitive = False
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@ -1,5 +1,4 @@
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from types import SimpleNamespace
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from typing import Any, Iterator, TypeAlias
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from typing import Iterator, TypeAlias
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from unittest import mock
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import numpy as np
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@ -22,91 +21,6 @@ def cv_image(pil_image: Image.Image) -> ndarray:
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return np.asarray(pil_image)[:, :, ::-1] # PIL uses RGB while cv2 uses BGR
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@pytest.fixture
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def mock_classifier_pipeline() -> Iterator[mock.Mock]:
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with mock.patch("app.models.image_classification.pipeline") as model:
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classifier_preds = [
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{"label": "that's an image alright", "score": 0.8},
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{"label": "well it ends with .jpg", "score": 0.1},
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{"label": "idk, im just seeing bytes", "score": 0.05},
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{"label": "not sure", "score": 0.04},
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{"label": "probably a virus", "score": 0.01},
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]
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def forward(
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inputs: Image.Image | list[Image.Image], **kwargs: Any
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) -> list[dict[str, Any]] | list[list[dict[str, Any]]]:
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if isinstance(inputs, list) and not all([isinstance(img, Image.Image) for img in inputs]):
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raise TypeError
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elif not isinstance(inputs, Image.Image):
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raise TypeError
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if isinstance(inputs, list):
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return [classifier_preds] * len(inputs)
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return classifier_preds
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model.return_value = forward
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yield model
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@pytest.fixture
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def mock_st() -> Iterator[mock.Mock]:
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with mock.patch("app.models.clip.SentenceTransformer") as model:
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embedding = np.random.rand(512).astype(np.float32)
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def encode(inputs: Image.Image | list[Image.Image], **kwargs: Any) -> ndarray | list[ndarray]:
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# mypy complains unless isinstance(inputs, list) is used explicitly
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img_batch = isinstance(inputs, list) and all([isinstance(inst, Image.Image) for inst in inputs])
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text_batch = isinstance(inputs, list) and all([isinstance(inst, str) for inst in inputs])
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if isinstance(inputs, list) and not any([img_batch, text_batch]):
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raise TypeError
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if isinstance(inputs, list):
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return np.stack([embedding] * len(inputs))
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return embedding
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mocked = mock.Mock()
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mocked.encode = encode
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model.return_value = mocked
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yield model
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@pytest.fixture
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def mock_faceanalysis() -> Iterator[mock.Mock]:
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with mock.patch("app.models.facial_recognition.FaceAnalysis") as model:
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face_preds = [
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SimpleNamespace( # this is so these fields can be accessed through dot notation
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**{
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"bbox": np.random.rand(4).astype(np.float32),
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"kps": np.random.rand(5, 2).astype(np.float32),
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"det_score": np.array([0.67]).astype(np.float32),
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"normed_embedding": np.random.rand(512).astype(np.float32),
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}
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),
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SimpleNamespace(
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**{
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"bbox": np.random.rand(4).astype(np.float32),
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"kps": np.random.rand(5, 2).astype(np.float32),
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"det_score": np.array([0.4]).astype(np.float32),
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"normed_embedding": np.random.rand(512).astype(np.float32),
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}
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),
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]
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def get(image: np.ndarray[int, np.dtype[np.float32]], **kwargs: Any) -> list[SimpleNamespace]:
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if not isinstance(image, np.ndarray):
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raise TypeError
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return face_preds
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mocked = mock.Mock()
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mocked.get = get
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model.return_value = mocked
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yield model
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@pytest.fixture
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def mock_get_model() -> Iterator[mock.Mock]:
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with mock.patch("app.models.cache.InferenceModel.from_model_type", autospec=True) as mocked:
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@ -9,7 +9,6 @@ from fastapi import Body, Depends, FastAPI
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from PIL import Image
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from .config import settings
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from .models.base import InferenceModel
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from .models.cache import ModelCache
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from .schemas import (
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EmbeddingResponse,
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@ -38,10 +37,7 @@ async def load_models() -> None:
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# Get all models
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for model_name, model_type in models:
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if settings.eager_startup:
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await app.state.model_cache.get(model_name, model_type)
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else:
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InferenceModel.from_model_type(model_type, model_name)
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await app.state.model_cache.get(model_name, model_type, eager=settings.eager_startup)
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@app.on_event("startup")
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@ -14,22 +14,43 @@ from ..schemas import ModelType
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class InferenceModel(ABC):
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_model_type: ModelType
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def __init__(self, model_name: str, cache_dir: Path | str | None = None, **model_kwargs: Any) -> None:
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def __init__(
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self, model_name: str, cache_dir: Path | str | None = None, eager: bool = True, **model_kwargs: Any
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) -> None:
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self.model_name = model_name
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self._loaded = False
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self._cache_dir = Path(cache_dir) if cache_dir is not None else get_cache_dir(model_name, self.model_type)
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loader = self.load if eager else self.download
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try:
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self.load(**model_kwargs)
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loader(**model_kwargs)
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except (OSError, InvalidProtobuf):
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self.clear_cache()
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self.load(**model_kwargs)
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loader(**model_kwargs)
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def download(self, **model_kwargs: Any) -> None:
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if not self.cached:
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self._download(**model_kwargs)
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def load(self, **model_kwargs: Any) -> None:
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self.download(**model_kwargs)
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self._load(**model_kwargs)
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self._loaded = True
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def predict(self, inputs: Any) -> Any:
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if not self._loaded:
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self.load()
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return self._predict(inputs)
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@abstractmethod
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def load(self, **model_kwargs: Any) -> None:
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def _predict(self, inputs: Any) -> Any:
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...
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@abstractmethod
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def predict(self, inputs: Any) -> Any:
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def _download(self, **model_kwargs: Any) -> None:
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...
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@abstractmethod
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def _load(self, **model_kwargs: Any) -> None:
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...
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@property
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@ -44,6 +65,10 @@ class InferenceModel(ABC):
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def cache_dir(self, cache_dir: Path) -> None:
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self._cache_dir = cache_dir
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@property
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def cached(self) -> bool:
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return self.cache_dir.exists() and any(self.cache_dir.iterdir())
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@classmethod
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def from_model_type(cls, model_type: ModelType, model_name: str, **model_kwargs: Any) -> InferenceModel:
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subclasses = {subclass._model_type: subclass for subclass in cls.__subclasses__()}
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@ -55,7 +80,11 @@ class InferenceModel(ABC):
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def clear_cache(self) -> None:
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if not self.cache_dir.exists():
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return
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elif not rmtree.avoids_symlink_attacks:
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if not rmtree.avoids_symlink_attacks:
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raise RuntimeError("Attempted to clear cache, but rmtree is not safe on this platform.")
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rmtree(self.cache_dir)
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if self.cache_dir.is_dir():
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rmtree(self.cache_dir)
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else:
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self.cache_dir.unlink()
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self.cache_dir.mkdir(parents=True, exist_ok=True)
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@ -1,8 +1,8 @@
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from pathlib import Path
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from typing import Any
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from PIL.Image import Image
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from sentence_transformers import SentenceTransformer
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from sentence_transformers.util import snapshot_download
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from ..schemas import ModelType
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from .base import InferenceModel
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@ -11,12 +11,21 @@ from .base import InferenceModel
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class CLIPSTEncoder(InferenceModel):
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_model_type = ModelType.CLIP
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def load(self, **model_kwargs: Any) -> None:
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def _download(self, **model_kwargs: Any) -> None:
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repo_id = self.model_name if "/" in self.model_name else f"sentence-transformers/{self.model_name}"
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snapshot_download(
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cache_dir=self.cache_dir,
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repo_id=repo_id,
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library_name="sentence-transformers",
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ignore_files=["flax_model.msgpack", "rust_model.ot", "tf_model.h5"],
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)
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def _load(self, **model_kwargs: Any) -> None:
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self.model = SentenceTransformer(
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self.model_name,
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cache_folder=self.cache_dir.as_posix(),
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**model_kwargs,
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)
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def predict(self, image_or_text: Image | str) -> list[float]:
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def _predict(self, image_or_text: Image | str) -> list[float]:
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return self.model.encode(image_or_text).tolist()
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@ -1,8 +1,12 @@
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import zipfile
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from pathlib import Path
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from typing import Any
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import cv2
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from insightface.app import FaceAnalysis
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import numpy as np
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from insightface.model_zoo import ArcFaceONNX, RetinaFace
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from insightface.utils.face_align import norm_crop
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from insightface.utils.storage import BASE_REPO_URL, download_file
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from ..config import settings
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from ..schemas import ModelType
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@ -22,39 +26,62 @@ class FaceRecognizer(InferenceModel):
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self.min_score = min_score
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super().__init__(model_name, cache_dir, **model_kwargs)
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def load(self, **model_kwargs: Any) -> None:
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self.model = FaceAnalysis(
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name=self.model_name,
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root=self.cache_dir.as_posix(),
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allowed_modules=["detection", "recognition"],
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**model_kwargs,
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)
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self.model.prepare(
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ctx_id=0,
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def _download(self, **model_kwargs: Any) -> None:
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zip_file = self.cache_dir / f"{self.model_name}.zip"
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download_file(f"{BASE_REPO_URL}/{self.model_name}.zip", zip_file)
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with zipfile.ZipFile(zip_file, "r") as zip:
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members = zip.namelist()
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det_file = next(model for model in members if model.startswith("det_"))
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rec_file = next(model for model in members if model.startswith("w600k_"))
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zip.extractall(self.cache_dir, members=[det_file, rec_file])
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zip_file.unlink()
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def _load(self, **model_kwargs: Any) -> None:
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try:
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det_file = next(self.cache_dir.glob("det_*.onnx"))
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rec_file = next(self.cache_dir.glob("w600k_*.onnx"))
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except StopIteration:
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raise FileNotFoundError("Facial recognition models not found in cache directory")
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self.det_model = RetinaFace(det_file.as_posix())
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self.rec_model = ArcFaceONNX(rec_file.as_posix())
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self.det_model.prepare(
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ctx_id=-1,
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det_thresh=self.min_score,
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det_size=(640, 640),
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input_size=(640, 640),
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)
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self.rec_model.prepare(ctx_id=-1)
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def _predict(self, image: cv2.Mat) -> list[dict[str, Any]]:
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bboxes, kpss = self.det_model.detect(image)
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if bboxes.size == 0:
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return []
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assert isinstance(kpss, np.ndarray)
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scores = bboxes[:, 4].tolist()
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bboxes = bboxes[:, :4].round().tolist()
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def predict(self, image: cv2.Mat) -> list[dict[str, Any]]:
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height, width, _ = image.shape
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results = []
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faces = self.model.get(image)
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for face in faces:
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x1, y1, x2, y2 = face.bbox
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height, width, _ = image.shape
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for (x1, y1, x2, y2), score, kps in zip(bboxes, scores, kpss):
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cropped_img = norm_crop(image, kps)
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embedding = self.rec_model.get_feat(cropped_img)[0].tolist()
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results.append(
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{
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"imageWidth": width,
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"imageHeight": height,
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"boundingBox": {
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"x1": round(x1),
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"y1": round(y1),
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"x2": round(x2),
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"y2": round(y2),
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"x1": x1,
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"y1": y1,
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"x2": x2,
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"y2": y2,
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},
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"score": face.det_score.item(),
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"embedding": face.normed_embedding.tolist(),
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"score": score,
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"embedding": embedding,
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}
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)
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return results
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@property
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def cached(self) -> bool:
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return self.cache_dir.is_dir() and any(self.cache_dir.glob("*.onnx"))
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@ -1,6 +1,7 @@
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from pathlib import Path
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from typing import Any
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from huggingface_hub import snapshot_download
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from PIL.Image import Image
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from transformers.pipelines import pipeline
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@ -22,14 +23,19 @@ class ImageClassifier(InferenceModel):
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self.min_score = min_score
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super().__init__(model_name, cache_dir, **model_kwargs)
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def load(self, **model_kwargs: Any) -> None:
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def _download(self, **model_kwargs: Any) -> None:
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snapshot_download(
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cache_dir=self.cache_dir, repo_id=self.model_name, allow_patterns=["*.bin", "*.json", "*.txt"]
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)
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def _load(self, **model_kwargs: Any) -> None:
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self.model = pipeline(
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self.model_type.value,
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self.model_name,
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model_kwargs={"cache_dir": self.cache_dir, **model_kwargs},
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)
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def predict(self, image: Image) -> list[str]:
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def _predict(self, image: Image) -> list[str]:
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predictions: list[dict[str, Any]] = self.model(image) # type: ignore
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tags = [tag for pred in predictions for tag in pred["label"].split(", ") if pred["score"] >= self.min_score]
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@ -1,11 +1,13 @@
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from io import BytesIO
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from pathlib import Path
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from typing import TypeAlias
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from unittest import mock
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import cv2
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import numpy as np
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import pytest
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from fastapi.testclient import TestClient
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from PIL import Image
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from pytest_mock import MockerFixture
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from .config import settings
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from .models.cache import ModelCache
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@ -14,22 +16,43 @@ from .models.facial_recognition import FaceRecognizer
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from .models.image_classification import ImageClassifier
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from .schemas import ModelType
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ndarray: TypeAlias = np.ndarray[int, np.dtype[np.float32]]
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class TestImageClassifier:
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def test_init(self, mock_classifier_pipeline: mock.Mock) -> None:
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cache_dir = Path("test_cache")
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classifier = ImageClassifier("test_model_name", 0.5, cache_dir=cache_dir)
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classifier_preds = [
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{"label": "that's an image alright", "score": 0.8},
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{"label": "well it ends with .jpg", "score": 0.1},
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{"label": "idk, im just seeing bytes", "score": 0.05},
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{"label": "not sure", "score": 0.04},
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{"label": "probably a virus", "score": 0.01},
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]
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assert classifier.min_score == 0.5
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mock_classifier_pipeline.assert_called_once_with(
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"image-classification",
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"test_model_name",
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model_kwargs={"cache_dir": cache_dir},
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)
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def test_eager_init(self, mocker: MockerFixture) -> None:
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mocker.patch.object(ImageClassifier, "download")
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mock_load = mocker.patch.object(ImageClassifier, "load")
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classifier = ImageClassifier("test_model_name", cache_dir="test_cache", eager=True, test_arg="test_arg")
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def test_min_score(self, pil_image: Image.Image, mock_classifier_pipeline: mock.Mock) -> None:
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assert classifier.model_name == "test_model_name"
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mock_load.assert_called_once_with(test_arg="test_arg")
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def test_lazy_init(self, mocker: MockerFixture) -> None:
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mock_download = mocker.patch.object(ImageClassifier, "download")
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mock_load = mocker.patch.object(ImageClassifier, "load")
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face_model = ImageClassifier("test_model_name", cache_dir="test_cache", eager=False, test_arg="test_arg")
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assert face_model.model_name == "test_model_name"
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mock_download.assert_called_once_with(test_arg="test_arg")
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mock_load.assert_not_called()
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def test_min_score(self, pil_image: Image.Image, mocker: MockerFixture) -> None:
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mocker.patch.object(ImageClassifier, "load")
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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
|
||||
|
|
|
|||
245
machine-learning/poetry.lock
generated
245
machine-learning/poetry.lock
generated
|
|
@ -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"},
|
||||
{file = "ConfigArgParse-1.7.tar.gz", hash = "sha256:e7067471884de5478c58a511e529f0f9bd1c66bfef1dea90935438d6c23306d1"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
|
|
@ -750,45 +750,45 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "fonttools"
|
||||
version = "4.41.1"
|
||||
version = "4.42.0"
|
||||
description = "Tools to manipulate font files"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "fonttools-4.41.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:a7bbb290d13c6dd718ec2c3db46fe6c5f6811e7ea1e07f145fd8468176398224"},
|
||||
{file = "fonttools-4.41.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:ec453a45778524f925a8f20fd26a3326f398bfc55d534e37bab470c5e415caa1"},
|
||||
{file = "fonttools-4.41.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c2071267deaa6d93cb16288613419679c77220543551cbe61da02c93d92df72f"},
|
||||
{file = "fonttools-4.41.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4e3334d51f0e37e2c6056e67141b2adabc92613a968797e2571ca8a03bd64773"},
|
||||
{file = "fonttools-4.41.1-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:cac73bbef7734e78c60949da11c4903ee5837168e58772371bd42a75872f4f82"},
|
||||
{file = "fonttools-4.41.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:edee0900cf0eedb29d17c7876102d6e5a91ee333882b1f5abc83e85b934cadb5"},
|
||||
{file = "fonttools-4.41.1-cp310-cp310-win32.whl", hash = "sha256:2a22b2c425c698dcd5d6b0ff0b566e8e9663172118db6fd5f1941f9b8063da9b"},
|
||||
{file = "fonttools-4.41.1-cp310-cp310-win_amd64.whl", hash = "sha256:547ab36a799dded58a46fa647266c24d0ed43a66028cd1cd4370b246ad426cac"},
|
||||
{file = "fonttools-4.41.1-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:849ec722bbf7d3501a0e879e57dec1fc54919d31bff3f690af30bb87970f9784"},
|
||||
{file = "fonttools-4.41.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:38cdecd8f1fd4bf4daae7fed1b3170dfc1b523388d6664b2204b351820aa78a7"},
|
||||
{file = "fonttools-4.41.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3ae64303ba670f8959fdaaa30ba0c2dabe75364fdec1caeee596c45d51ca3425"},
|
||||
{file = "fonttools-4.41.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f14f3ccea4cc7dd1b277385adf3c3bf18f9860f87eab9c2fb650b0af16800f55"},
|
||||
{file = "fonttools-4.41.1-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:33191f062549e6bb1a4782c22a04ebd37009c09360e2d6686ac5083774d06d95"},
|
||||
{file = "fonttools-4.41.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:704bccd69b0abb6fab9f5e4d2b75896afa48b427caa2c7988792a2ffce35b441"},
|
||||
{file = "fonttools-4.41.1-cp311-cp311-win32.whl", hash = "sha256:4edc795533421e98f60acee7d28fc8d941ff5ac10f44668c9c3635ad72ae9045"},
|
||||
{file = "fonttools-4.41.1-cp311-cp311-win_amd64.whl", hash = "sha256:aaaef294d8e411f0ecb778a0aefd11bb5884c9b8333cc1011bdaf3b58ca4bd75"},
|
||||
{file = "fonttools-4.41.1-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:3d1f9471134affc1e3b1b806db6e3e2ad3fa99439e332f1881a474c825101096"},
|
||||
{file = "fonttools-4.41.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:59eba8b2e749a1de85760da22333f3d17c42b66e03758855a12a2a542723c6e7"},
|
||||
{file = "fonttools-4.41.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a9b3cc10dc9e0834b6665fd63ae0c6964c6bc3d7166e9bc84772e0edd09f9fa2"},
|
||||
{file = "fonttools-4.41.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:da2c2964bdc827ba6b8a91dc6de792620be4da3922c4cf0599f36a488c07e2b2"},
|
||||
{file = "fonttools-4.41.1-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:7763316111df7b5165529f4183a334aa24c13cdb5375ffa1dc8ce309c8bf4e5c"},
|
||||
{file = "fonttools-4.41.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:b2d1ee95be42b80d1f002d1ee0a51d7a435ea90d36f1a5ae331be9962ee5a3f1"},
|
||||
{file = "fonttools-4.41.1-cp38-cp38-win32.whl", hash = "sha256:f48602c0b3fd79cd83a34c40af565fe6db7ac9085c8823b552e6e751e3a5b8be"},
|
||||
{file = "fonttools-4.41.1-cp38-cp38-win_amd64.whl", hash = "sha256:b0938ebbeccf7c80bb9a15e31645cf831572c3a33d5cc69abe436e7000c61b14"},
|
||||
{file = "fonttools-4.41.1-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:e5c2b0a95a221838991e2f0e455dec1ca3a8cc9cd54febd68cc64d40fdb83669"},
|
||||
{file = "fonttools-4.41.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:891cfc5a83b0307688f78b9bb446f03a7a1ad981690ac8362f50518bc6153975"},
|
||||
{file = "fonttools-4.41.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:73ef0bb5d60eb02ba4d3a7d23ada32184bd86007cb2de3657cfcb1175325fc83"},
|
||||
{file = "fonttools-4.41.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f240d9adf0583ac8fc1646afe7f4ac039022b6f8fa4f1575a2cfa53675360b69"},
|
||||
{file = "fonttools-4.41.1-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:bdd729744ae7ecd7f7311ad25d99da4999003dcfe43b436cf3c333d4e68de73d"},
|
||||
{file = "fonttools-4.41.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:b927e5f466d99c03e6e20961946314b81d6e3490d95865ef88061144d9f62e38"},
|
||||
{file = "fonttools-4.41.1-cp39-cp39-win32.whl", hash = "sha256:afce2aeb80be72b4da7dd114f10f04873ff512793d13ce0b19d12b2a4c44c0f0"},
|
||||
{file = "fonttools-4.41.1-cp39-cp39-win_amd64.whl", hash = "sha256:1df1b6f4c7c4bc8201eb47f3b268adbf2539943aa43c400f84556557e3e109c0"},
|
||||
{file = "fonttools-4.41.1-py3-none-any.whl", hash = "sha256:952cb405f78734cf6466252fec42e206450d1a6715746013f64df9cbd4f896fa"},
|
||||
{file = "fonttools-4.41.1.tar.gz", hash = "sha256:e16a9449f21a93909c5be2f5ed5246420f2316e94195dbfccb5238aaa38f9751"},
|
||||
{file = "fonttools-4.42.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:9c456d1f23deff64ffc8b5b098718e149279abdea4d8692dba69172fb6a0d597"},
|
||||
{file = "fonttools-4.42.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:150122ed93127a26bc3670ebab7e2add1e0983d30927733aec327ebf4255b072"},
|
||||
{file = "fonttools-4.42.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:48e82d776d2e93f88ca56567509d102266e7ab2fb707a0326f032fe657335238"},
|
||||
{file = "fonttools-4.42.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:58c1165f9b2662645de9b19a8c8bdd636b36294ccc07e1b0163856b74f10bafc"},
|
||||
{file = "fonttools-4.42.0-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:2d6dc3fa91414ff4daa195c05f946e6a575bd214821e26d17ca50f74b35b0fe4"},
|
||||
{file = "fonttools-4.42.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:fae4e801b774cc62cecf4a57b1eae4097903fced00c608d9e2bc8f84cd87b54a"},
|
||||
{file = "fonttools-4.42.0-cp310-cp310-win32.whl", hash = "sha256:b8600ae7dce6ec3ddfb201abb98c9d53abbf8064d7ac0c8a0d8925e722ccf2a0"},
|
||||
{file = "fonttools-4.42.0-cp310-cp310-win_amd64.whl", hash = "sha256:57b68eab183fafac7cd7d464a7bfa0fcd4edf6c67837d14fb09c1c20516cf20b"},
|
||||
{file = "fonttools-4.42.0-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:0a1466713e54bdbf5521f2f73eebfe727a528905ff5ec63cda40961b4b1eea95"},
|
||||
{file = "fonttools-4.42.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:3fb2a69870bfe143ec20b039a1c8009e149dd7780dd89554cc8a11f79e5de86b"},
|
||||
{file = "fonttools-4.42.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ae881e484702efdb6cf756462622de81d4414c454edfd950b137e9a7352b3cb9"},
|
||||
{file = "fonttools-4.42.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:27ec3246a088555629f9f0902f7412220c67340553ca91eb540cf247aacb1983"},
|
||||
{file = "fonttools-4.42.0-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:8ece1886d12bb36c48c00b2031518877f41abae317e3a55620d38e307d799b7e"},
|
||||
{file = "fonttools-4.42.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:10dac980f2b975ef74532e2a94bb00e97a95b4595fb7f98db493c474d5f54d0e"},
|
||||
{file = "fonttools-4.42.0-cp311-cp311-win32.whl", hash = "sha256:83b98be5d291e08501bd4fc0c4e0f8e6e05b99f3924068b17c5c9972af6fff84"},
|
||||
{file = "fonttools-4.42.0-cp311-cp311-win_amd64.whl", hash = "sha256:e35bed436726194c5e6e094fdfb423fb7afaa0211199f9d245e59e11118c576c"},
|
||||
{file = "fonttools-4.42.0-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:c36c904ce0322df01e590ba814d5d69e084e985d7e4c2869378671d79662a7d4"},
|
||||
{file = "fonttools-4.42.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:d54e600a2bcfa5cdaa860237765c01804a03b08404d6affcd92942fa7315ffba"},
|
||||
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||||
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|
||||
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|
||||
|
||||
[package.extras]
|
||||
|
|
@ -1525,13 +1525,13 @@ test = ["pytest (>=7.4)", "pytest-cov (>=4.1)"]
|
|||
|
||||
[[package]]
|
||||
name = "locust"
|
||||
version = "2.15.1"
|
||||
version = "2.16.1"
|
||||
description = "Developer friendly load testing framework"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
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||||
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|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -1860,36 +1860,36 @@ twitter = ["twython"]
|
|||
|
||||
[[package]]
|
||||
name = "numpy"
|
||||
version = "1.25.1"
|
||||
version = "1.25.2"
|
||||
description = "Fundamental package for array computing in Python"
|
||||
optional = false
|
||||
python-versions = ">=3.9"
|
||||
files = [
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||||
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||||
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|
||||
{file = "numpy-1.25.2-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:1a1329e26f46230bf77b02cc19e900db9b52f398d6722ca853349a782d4cff55"},
|
||||
{file = "numpy-1.25.2-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4c3abc71e8b6edba80a01a52e66d83c5d14433cbcd26a40c329ec7ed09f37901"},
|
||||
{file = "numpy-1.25.2-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:1b9735c27cea5d995496f46a8b1cd7b408b3f34b6d50459d9ac8fe3a20cc17bf"},
|
||||
{file = "numpy-1.25.2.tar.gz", hash = "sha256:fd608e19c8d7c55021dffd43bfe5492fab8cc105cc8986f813f8c3c048b38760"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -2020,13 +2020,13 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "pathspec"
|
||||
version = "0.11.1"
|
||||
version = "0.11.2"
|
||||
description = "Utility library for gitignore style pattern matching of file paths."
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "pathspec-0.11.1-py3-none-any.whl", hash = "sha256:d8af70af76652554bd134c22b3e8a1cc46ed7d91edcdd721ef1a0c51a84a5293"},
|
||||
{file = "pathspec-0.11.1.tar.gz", hash = "sha256:2798de800fa92780e33acca925945e9a19a133b715067cf165b8866c15a31687"},
|
||||
{file = "pathspec-0.11.2-py3-none-any.whl", hash = "sha256:1d6ed233af05e679efb96b1851550ea95bbb64b7c490b0f5aa52996c11e92a20"},
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{file = "pathspec-0.11.2.tar.gz", hash = "sha256:e0d8d0ac2f12da61956eb2306b69f9469b42f4deb0f3cb6ed47b9cce9996ced3"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -2110,18 +2110,18 @@ tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "pa
|
|||
|
||||
[[package]]
|
||||
name = "platformdirs"
|
||||
version = "3.9.1"
|
||||
version = "3.10.0"
|
||||
description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
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|
||||
]
|
||||
|
||||
[package.extras]
|
||||
docs = ["furo (>=2023.5.20)", "proselint (>=0.13)", "sphinx (>=7.0.1)", "sphinx-autodoc-typehints (>=1.23,!=1.23.4)"]
|
||||
test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.3.1)", "pytest-cov (>=4.1)", "pytest-mock (>=3.10)"]
|
||||
docs = ["furo (>=2023.7.26)", "proselint (>=0.13)", "sphinx (>=7.1.1)", "sphinx-autodoc-typehints (>=1.24)"]
|
||||
test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.4)", "pytest-cov (>=4.1)", "pytest-mock (>=3.11.1)"]
|
||||
|
||||
[[package]]
|
||||
name = "pluggy"
|
||||
|
|
@ -2215,47 +2215,47 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "pydantic"
|
||||
version = "1.10.11"
|
||||
version = "1.10.12"
|
||||
description = "Data validation and settings management using python type hints"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
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||||
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||||
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{file = "pydantic-1.10.12-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:1289c180abd4bd4555bb927c42ee42abc3aee02b0fb2d1223fb7c6e5bef87dbe"},
|
||||
{file = "pydantic-1.10.12-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:5d1197e462e0364906cbc19681605cb7c036f2475c899b6f296104ad42b9f5fb"},
|
||||
{file = "pydantic-1.10.12-cp39-cp39-win_amd64.whl", hash = "sha256:fdbdd1d630195689f325c9ef1a12900524dceb503b00a987663ff4f58669b93d"},
|
||||
{file = "pydantic-1.10.12-py3-none-any.whl", hash = "sha256:b749a43aa51e32839c9d71dc67eb1e4221bb04af1033a32e3923d46f9effa942"},
|
||||
{file = "pydantic-1.10.12.tar.gz", hash = "sha256:0fe8a415cea8f340e7a9af9c54fc71a649b43e8ca3cc732986116b3cb135d303"},
|
||||
]
|
||||
|
||||
[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"
|
||||
|
|
|
|||
|
|
@ -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"
|
||||
|
|
|
|||
Loading…
Reference in a new issue