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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
This commit is contained in:
Mert 2023-06-27 19:21:33 -04:00 committed by GitHub
parent 5e3bdc76b2
commit df1e8679d9
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12 changed files with 622 additions and 95 deletions

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@ -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

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@ -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_"

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@ -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)

View file

@ -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

View file

@ -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)

View file

@ -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

View file

@ -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)

View file

@ -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

View file

@ -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)

View file

@ -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

View file

@ -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", hash = "sha256:1b0b3cbf664ab59dada57123c81eff3d9737e0d11d8cf79e3d6eb10823f1739f"},
{file = "ConfigArgParse-1.5.5-py3-none-any.whl", hash = "sha256:541360ddc1b15c517f95c0d02d1fca4591266628f3667acdc5d13dccc78884ca"},
{file = "ConfigArgParse-1.5.5.tar.gz", hash = "sha256:363d80a6d35614bd446e2f2b1b216f3b33741d03ac6d0a92803306f40e555b58"},
]
[package.extras]
@ -495,6 +495,78 @@ mypy = ["contourpy[bokeh,docs]", "docutils-stubs", "mypy (==1.2.0)", "types-Pill
test = ["Pillow", "contourpy[test-no-images]", "matplotlib"]
test-no-images = ["pytest", "pytest-cov", "wurlitzer"]
[[package]]
name = "coverage"
version = "7.2.7"
description = "Code coverage measurement for Python"
optional = false
python-versions = ">=3.7"
files = [
{file = "coverage-7.2.7-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:d39b5b4f2a66ccae8b7263ac3c8170994b65266797fb96cbbfd3fb5b23921db8"},
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mypy-extensions = ">=1.0.0"
typing-extensions = ">=3.10"
typing-extensions = ">=4.1.0"
[package.extras]
dmypy = ["psutil (>=4.0)"]
@ -2133,6 +2248,42 @@ pluggy = ">=0.12,<2.0"
[package.extras]
testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
[[package]]
name = "pytest-asyncio"
version = "0.21.0"
description = "Pytest support for asyncio"
optional = false
python-versions = ">=3.7"
files = [
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pytest = ">=7.0.0"
[package.extras]
docs = ["sphinx (>=5.3)", "sphinx-rtd-theme (>=1.0)"]
testing = ["coverage (>=6.2)", "flaky (>=3.5.0)", "hypothesis (>=5.7.1)", "mypy (>=0.931)", "pytest-trio (>=0.7.0)"]
[[package]]
name = "pytest-cov"
version = "4.1.0"
description = "Pytest plugin for measuring coverage."
optional = false
python-versions = ">=3.7"
files = [
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[package.dependencies]
coverage = {version = ">=5.2.1", extras = ["toml"]}
pytest = ">=4.6"
[package.extras]
testing = ["fields", "hunter", "process-tests", "pytest-xdist", "six", "virtualenv"]
[[package]]
name = "python-dateutil"
version = "2.8.2"
@ -2504,6 +2655,32 @@ files = [
{file = "roundrobin-0.0.4.tar.gz", hash = "sha256:7e9d19a5bd6123d99993fb935fa86d25c88bb2096e493885f61737ed0f5e9abd"},
]
[[package]]
name = "ruff"
version = "0.0.272"
description = "An extremely fast Python linter, written in Rust."
optional = false
python-versions = ">=3.7"
files = [
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[[package]]
name = "safetensors"
version = "0.3.1"
@ -3425,4 +3602,4 @@ testing = ["coverage (>=5.0.3)", "zope.event", "zope.testing"]
[metadata]
lock-version = "2.0"
python-versions = "^3.11"
content-hash = "2981003c319d9990f05abec1e3d02dc1ea6680b0bf1590376c5e47801311d89f"
content-hash = "e0ac37404f0c11ee5b478d2c7113986a2d40d02e2b985ff18846374a65025a26"

View file

@ -22,6 +22,8 @@ fastapi = "^0.95.2"
uvicorn = {extras = ["standard"], version = "^0.22.0"}
pydantic = "^1.10.8"
aiocache = "^0.12.1"
pytest-cov = "^4.1.0"
ruff = "^0.0.272"
[tool.poetry.group.dev.dependencies]
mypy = "^1.3.0"
@ -29,6 +31,8 @@ black = "^23.3.0"
pytest = "^7.3.1"
locust = "^2.15.1"
gunicorn = "^20.1.0"
httpx = "^0.24.1"
pytest-asyncio = "^0.21.0"
[[tool.poetry.source]]
name = "pytorch-cpu"
@ -39,9 +43,6 @@ priority = "explicit"
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
[tool.flake8]
max-line-length = 120
[tool.mypy]
python_version = "3.11"
plugins = "pydantic.mypy"
@ -49,7 +50,6 @@ follow_imports = "silent"
warn_redundant_casts = true
disallow_any_generics = true
check_untyped_defs = true
no_implicit_reexport = true
disallow_untyped_defs = true
[tool.pydantic-mypy]
@ -57,3 +57,28 @@ init_forbid_extra = true
init_typed = true
warn_required_dynamic_aliases = true
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']