mirror of
https://github.com/immich-app/immich.git
synced 2024-12-23 02:06:15 +02:00
df1e8679d9
* 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
120 lines
4.0 KiB
Python
120 lines
4.0 KiB
Python
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)
|