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refactor: migrate person repository to kysely ()

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* refactor: migrate person repository to kysely

* `asVector` begone

* linting

* fix metadata faces

* update test

---------

Co-authored-by: Alex <alex.tran1502@gmail.com>
Co-authored-by: mertalev <101130780+mertalev@users.noreply.github.com>
This commit is contained in:
Daniel Dietzler 2025-01-21 19:12:28 +01:00 • committed by GitHub
parent 0c152366ec
commit 332a865ce6
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29 changed files with 715 additions and 747 deletions
machine-learning/app
test_main.py

45
machine-learning/app/test_main.py
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@ -10,6 +10,7 @@ from unittest import mock
import cv2
import numpy as np
import onnxruntime as ort
import orjson
import pytest
from fastapi import HTTPException
from fastapi.testclient import TestClient
@ -346,11 +347,11 @@ class TestCLIP:
mocked.run.return_value = [[self.embedding]]
clip_encoder = OpenClipVisualEncoder("ViT-B-32__openai", cache_dir="test_cache")
embedding = clip_encoder.predict(pil_image)
assert isinstance(embedding, np.ndarray)
assert embedding.shape[0] == clip_model_cfg["embed_dim"]
assert embedding.dtype == np.float32
embedding_str = clip_encoder.predict(pil_image)
assert isinstance(embedding_str, str)
embedding = orjson.loads(embedding_str)
assert isinstance(embedding, list)
assert len(embedding) == clip_model_cfg["embed_dim"]
mocked.run.assert_called_once()
def test_basic_text(
@ -368,11 +369,11 @@ class TestCLIP:
mocker.patch("app.models.clip.textual.Tokenizer.from_file", autospec=True)
clip_encoder = OpenClipTextualEncoder("ViT-B-32__openai", cache_dir="test_cache")
embedding = clip_encoder.predict("test search query")
assert isinstance(embedding, np.ndarray)
assert embedding.shape[0] == clip_model_cfg["embed_dim"]
assert embedding.dtype == np.float32
embedding_str = clip_encoder.predict("test search query")
assert isinstance(embedding_str, str)
embedding = orjson.loads(embedding_str)
assert isinstance(embedding, list)
assert len(embedding) == clip_model_cfg["embed_dim"]
mocked.run.assert_called_once()
def test_openclip_tokenizer(
@ -508,8 +509,11 @@ class TestFaceRecognition:
assert isinstance(face.get("boundingBox"), dict)
assert set(face["boundingBox"]) == {"x1", "y1", "x2", "y2"}
assert all(isinstance(val, np.float32) for val in face["boundingBox"].values())
assert isinstance(face.get("embedding"), np.ndarray)
assert face["embedding"].shape[0] == 512
embedding_str = face.get("embedding")
assert isinstance(embedding_str, str)
embedding = orjson.loads(embedding_str)
assert isinstance(embedding, list)
assert len(embedding) == 512
assert isinstance(face.get("score", None), np.float32)
rec_model.get_feat.assert_called_once()
@ -880,8 +884,10 @@ class TestPredictionEndpoints:
actual = response.json()
assert response.status_code == 200
assert isinstance(actual, dict)
assert isinstance(actual.get("clip", None), list)
assert np.allclose(expected, actual["clip"])
embedding = actual.get("clip", None)
assert isinstance(embedding, str)
parsed_embedding = orjson.loads(embedding)
assert np.allclose(expected, parsed_embedding)
def test_clip_text_endpoint(self, responses: dict[str, Any], deployed_app: TestClient) -> None:
expected = responses["clip"]["text"]
@ -901,8 +907,10 @@ class TestPredictionEndpoints:
actual = response.json()
assert response.status_code == 200
assert isinstance(actual, dict)
assert isinstance(actual.get("clip", None), list)
assert np.allclose(expected, actual["clip"])
embedding = actual.get("clip", None)
assert isinstance(embedding, str)
parsed_embedding = orjson.loads(embedding)
assert np.allclose(expected, parsed_embedding)
def test_face_endpoint(self, pil_image: Image.Image, responses: dict[str, Any], deployed_app: TestClient) -> None:
byte_image = BytesIO()
@ -933,5 +941,8 @@ class TestPredictionEndpoints:
for expected_face, actual_face in zip(responses["facial-recognition"], actual["facial-recognition"]):
assert expected_face["boundingBox"] == actual_face["boundingBox"]
assert np.allclose(expected_face["embedding"], actual_face["embedding"])
embedding = actual_face.get("embedding", None)
assert isinstance(embedding, str)
parsed_embedding = orjson.loads(embedding)
assert np.allclose(expected_face["embedding"], parsed_embedding)
assert np.allclose(expected_face["score"], actual_face["score"])