import json from io import BytesIO from pathlib import Path from random import randint from types import SimpleNamespace from typing import Any, Callable from unittest import mock import cv2 import numpy as np import onnxruntime as ort import pytest from fastapi.testclient import TestClient from PIL import Image from pytest_mock import MockerFixture from app.main import load from .config import log, settings from .models.base import InferenceModel from .models.cache import ModelCache from .models.clip import MCLIPEncoder, OpenCLIPEncoder from .models.facial_recognition import FaceRecognizer from .schemas import ModelRuntime, ModelType class TestBase: CPU_EP = ["CPUExecutionProvider"] CUDA_EP = ["CUDAExecutionProvider", "CPUExecutionProvider"] OV_EP = ["OpenVINOExecutionProvider", "CPUExecutionProvider"] CUDA_EP_OUT_OF_ORDER = ["CPUExecutionProvider", "CUDAExecutionProvider"] TRT_EP = ["TensorrtExecutionProvider", "CUDAExecutionProvider", "CPUExecutionProvider"] @pytest.mark.providers(CPU_EP) def test_sets_cpu_provider(self, providers: list[str]) -> None: encoder = OpenCLIPEncoder("ViT-B-32__openai") assert encoder.providers == self.CPU_EP @pytest.mark.providers(CUDA_EP) def test_sets_cuda_provider_if_available(self, providers: list[str]) -> None: encoder = OpenCLIPEncoder("ViT-B-32__openai") assert encoder.providers == self.CUDA_EP @pytest.mark.providers(OV_EP) def test_sets_openvino_provider_if_available(self, providers: list[str]) -> None: encoder = OpenCLIPEncoder("ViT-B-32__openai") assert encoder.providers == self.OV_EP @pytest.mark.providers(CUDA_EP_OUT_OF_ORDER) def test_sets_providers_in_correct_order(self, providers: list[str]) -> None: encoder = OpenCLIPEncoder("ViT-B-32__openai") assert encoder.providers == self.CUDA_EP @pytest.mark.providers(TRT_EP) def test_ignores_unsupported_providers(self, providers: list[str]) -> None: encoder = OpenCLIPEncoder("ViT-B-32__openai") assert encoder.providers == self.CUDA_EP def test_sets_provider_kwarg(self) -> None: providers = ["CUDAExecutionProvider"] encoder = OpenCLIPEncoder("ViT-B-32__openai", providers=providers) assert encoder.providers == providers def test_sets_default_provider_options(self) -> None: encoder = OpenCLIPEncoder("ViT-B-32__openai", providers=["OpenVINOExecutionProvider", "CPUExecutionProvider"]) assert encoder.provider_options == [ {}, {"arena_extend_strategy": "kSameAsRequested"}, ] def test_sets_openvino_device_id_if_possible(self, mocker: MockerFixture) -> None: mocked = mocker.patch("app.models.base.ort.capi._pybind_state") mocked.get_available_openvino_device_ids.return_value = ["GPU.0", "CPU"] encoder = OpenCLIPEncoder("ViT-B-32__openai", providers=["OpenVINOExecutionProvider", "CPUExecutionProvider"]) assert encoder.provider_options == [ {"device_id": "GPU.0"}, {"arena_extend_strategy": "kSameAsRequested"}, ] def test_sets_provider_options_kwarg(self) -> None: encoder = OpenCLIPEncoder( "ViT-B-32__openai", providers=["OpenVINOExecutionProvider", "CPUExecutionProvider"], provider_options=[], ) assert encoder.provider_options == [] def test_sets_default_sess_options(self) -> None: encoder = OpenCLIPEncoder("ViT-B-32__openai") assert encoder.sess_options.execution_mode == ort.ExecutionMode.ORT_SEQUENTIAL assert encoder.sess_options.inter_op_num_threads == 1 assert encoder.sess_options.intra_op_num_threads == 2 assert encoder.sess_options.enable_cpu_mem_arena is False def test_sets_default_sess_options_does_not_set_threads_if_non_cpu_and_default_threads(self) -> None: encoder = OpenCLIPEncoder("ViT-B-32__openai", providers=["CUDAExecutionProvider", "CPUExecutionProvider"]) assert encoder.sess_options.inter_op_num_threads == 0 assert encoder.sess_options.intra_op_num_threads == 0 def test_sets_default_sess_options_sets_threads_if_non_cpu_and_set_threads(self, mocker: MockerFixture) -> None: mock_settings = mocker.patch("app.models.base.settings", autospec=True) mock_settings.model_inter_op_threads = 2 mock_settings.model_intra_op_threads = 4 encoder = OpenCLIPEncoder("ViT-B-32__openai", providers=["CUDAExecutionProvider", "CPUExecutionProvider"]) assert encoder.sess_options.inter_op_num_threads == 2 assert encoder.sess_options.intra_op_num_threads == 4 def test_sets_sess_options_kwarg(self) -> None: sess_options = ort.SessionOptions() encoder = OpenCLIPEncoder( "ViT-B-32__openai", providers=["OpenVINOExecutionProvider", "CPUExecutionProvider"], provider_options=[], sess_options=sess_options, ) assert sess_options is encoder.sess_options def test_sets_default_cache_dir(self) -> None: encoder = OpenCLIPEncoder("ViT-B-32__openai") assert encoder.cache_dir == Path(settings.cache_folder) / "clip" / "ViT-B-32__openai" def test_sets_cache_dir_kwarg(self) -> None: cache_dir = Path("/test_cache") encoder = OpenCLIPEncoder("ViT-B-32__openai", cache_dir=cache_dir) assert encoder.cache_dir == cache_dir def test_sets_default_preferred_runtime(self, mocker: MockerFixture) -> None: mocker.patch.object(settings, "ann", True) mocker.patch("ann.ann.is_available", False) encoder = OpenCLIPEncoder("ViT-B-32__openai") assert encoder.preferred_runtime == ModelRuntime.ONNX def test_sets_default_preferred_runtime_to_armnn_if_available(self, mocker: MockerFixture) -> None: mocker.patch.object(settings, "ann", True) mocker.patch("ann.ann.is_available", True) encoder = OpenCLIPEncoder("ViT-B-32__openai") assert encoder.preferred_runtime == ModelRuntime.ARMNN def test_sets_preferred_runtime_kwarg(self, mocker: MockerFixture) -> None: mocker.patch.object(settings, "ann", False) mocker.patch("ann.ann.is_available", False) encoder = OpenCLIPEncoder("ViT-B-32__openai", preferred_runtime=ModelRuntime.ARMNN) assert encoder.preferred_runtime == ModelRuntime.ARMNN def test_casts_cache_dir_string_to_path(self) -> None: cache_dir = "/test_cache" encoder = OpenCLIPEncoder("ViT-B-32__openai", cache_dir=cache_dir) assert encoder.cache_dir == Path(cache_dir) def test_clear_cache(self, mocker: MockerFixture) -> None: mock_rmtree = mocker.patch("app.models.base.rmtree", autospec=True) mock_rmtree.avoids_symlink_attacks = True mock_cache_dir = mocker.Mock() mock_cache_dir.exists.return_value = True mock_cache_dir.is_dir.return_value = True mocker.patch("app.models.base.Path", return_value=mock_cache_dir) info = mocker.spy(log, "info") encoder = OpenCLIPEncoder("ViT-B-32__openai", cache_dir=mock_cache_dir) encoder.clear_cache() mock_rmtree.assert_called_once_with(encoder.cache_dir) info.assert_called_with(f"Cleared cache directory for model '{encoder.model_name}'.") def test_clear_cache_warns_if_path_does_not_exist(self, mocker: MockerFixture) -> None: mock_rmtree = mocker.patch("app.models.base.rmtree", autospec=True) mock_rmtree.avoids_symlink_attacks = True mock_cache_dir = mocker.Mock() mock_cache_dir.exists.return_value = False mock_cache_dir.is_dir.return_value = True mocker.patch("app.models.base.Path", return_value=mock_cache_dir) warning = mocker.spy(log, "warning") encoder = OpenCLIPEncoder("ViT-B-32__openai", cache_dir=mock_cache_dir) encoder.clear_cache() mock_rmtree.assert_not_called() warning.assert_called_once() def test_clear_cache_raises_exception_if_vulnerable_to_symlink_attack(self, mocker: MockerFixture) -> None: mock_rmtree = mocker.patch("app.models.base.rmtree", autospec=True) mock_rmtree.avoids_symlink_attacks = False mock_cache_dir = mocker.Mock() mock_cache_dir.exists.return_value = True mock_cache_dir.is_dir.return_value = True mocker.patch("app.models.base.Path", return_value=mock_cache_dir) encoder = OpenCLIPEncoder("ViT-B-32__openai", cache_dir=mock_cache_dir) with pytest.raises(RuntimeError): encoder.clear_cache() mock_rmtree.assert_not_called() def test_clear_cache_replaces_file_with_dir_if_path_is_file(self, mocker: MockerFixture) -> None: mock_rmtree = mocker.patch("app.models.base.rmtree", autospec=True) mock_rmtree.avoids_symlink_attacks = True mock_cache_dir = mocker.Mock() mock_cache_dir.exists.return_value = True mock_cache_dir.is_dir.return_value = False mocker.patch("app.models.base.Path", return_value=mock_cache_dir) warning = mocker.spy(log, "warning") encoder = OpenCLIPEncoder("ViT-B-32__openai", cache_dir=mock_cache_dir) encoder.clear_cache() mock_rmtree.assert_not_called() mock_cache_dir.unlink.assert_called_once() mock_cache_dir.mkdir.assert_called_once() warning.assert_called_once() def test_make_session_return_ann_if_available(self, mocker: MockerFixture) -> None: mock_model_path = mocker.Mock() mock_model_path.is_file.return_value = True mock_model_path.suffix = ".armnn" mock_model_path.with_suffix.return_value = mock_model_path mock_session = mocker.patch("app.models.base.AnnSession") encoder = OpenCLIPEncoder("ViT-B-32__openai") encoder._make_session(mock_model_path) mock_session.assert_called_once() def test_make_session_return_ort_if_available_and_ann_is_not(self, mocker: MockerFixture) -> None: mock_armnn_path = mocker.Mock() mock_armnn_path.is_file.return_value = False mock_armnn_path.suffix = ".armnn" mock_onnx_path = mocker.Mock() mock_onnx_path.is_file.return_value = True mock_onnx_path.suffix = ".onnx" mock_armnn_path.with_suffix.return_value = mock_onnx_path mock_ann = mocker.patch("app.models.base.AnnSession") mock_ort = mocker.patch("app.models.base.ort.InferenceSession") encoder = OpenCLIPEncoder("ViT-B-32__openai") encoder._make_session(mock_armnn_path) mock_ort.assert_called_once() mock_ann.assert_not_called() def test_make_session_raises_exception_if_path_does_not_exist(self, mocker: MockerFixture) -> None: mock_model_path = mocker.Mock() mock_model_path.is_file.return_value = False mock_model_path.suffix = ".onnx" mock_model_path.with_suffix.return_value = mock_model_path mock_ann = mocker.patch("app.models.base.AnnSession") mock_ort = mocker.patch("app.models.base.ort.InferenceSession") encoder = OpenCLIPEncoder("ViT-B-32__openai") with pytest.raises(ValueError): encoder._make_session(mock_model_path) mock_ann.assert_not_called() mock_ort.assert_not_called() def test_download(self, mocker: MockerFixture) -> None: mock_snapshot_download = mocker.patch("app.models.base.snapshot_download") encoder = OpenCLIPEncoder("ViT-B-32__openai", cache_dir="/path/to/cache") encoder.download() mock_snapshot_download.assert_called_once_with( "immich-app/ViT-B-32__openai", cache_dir=encoder.cache_dir, local_dir=encoder.cache_dir, local_dir_use_symlinks=False, ignore_patterns=["*.armnn"], ) def test_download_downloads_armnn_if_preferred_runtime(self, mocker: MockerFixture) -> None: mock_snapshot_download = mocker.patch("app.models.base.snapshot_download") encoder = OpenCLIPEncoder("ViT-B-32__openai", preferred_runtime=ModelRuntime.ARMNN) encoder.download() mock_snapshot_download.assert_called_once_with( "immich-app/ViT-B-32__openai", cache_dir=encoder.cache_dir, local_dir=encoder.cache_dir, local_dir_use_symlinks=False, ignore_patterns=[], ) class TestCLIP: embedding = np.random.rand(512).astype(np.float32) cache_dir = Path("test_cache") def test_basic_image( self, pil_image: Image.Image, mocker: MockerFixture, clip_model_cfg: dict[str, Any], clip_preprocess_cfg: Callable[[Path], dict[str, Any]], clip_tokenizer_cfg: Callable[[Path], dict[str, Any]], ) -> None: mocker.patch.object(OpenCLIPEncoder, "download") mocker.patch.object(OpenCLIPEncoder, "model_cfg", clip_model_cfg) mocker.patch.object(OpenCLIPEncoder, "preprocess_cfg", clip_preprocess_cfg) mocker.patch.object(OpenCLIPEncoder, "tokenizer_cfg", clip_tokenizer_cfg) mocked = mocker.patch.object(InferenceModel, "_make_session", autospec=True).return_value mocked.run.return_value = [[self.embedding]] mocker.patch("app.models.clip.Tokenizer.from_file", autospec=True) clip_encoder = OpenCLIPEncoder("ViT-B-32__openai", cache_dir="test_cache", mode="vision") embedding = clip_encoder.predict(pil_image) assert clip_encoder.mode == "vision" assert isinstance(embedding, np.ndarray) assert embedding.shape[0] == clip_model_cfg["embed_dim"] assert embedding.dtype == np.float32 mocked.run.assert_called_once() def test_basic_text( self, mocker: MockerFixture, clip_model_cfg: dict[str, Any], clip_preprocess_cfg: Callable[[Path], dict[str, Any]], clip_tokenizer_cfg: Callable[[Path], dict[str, Any]], ) -> None: mocker.patch.object(OpenCLIPEncoder, "download") mocker.patch.object(OpenCLIPEncoder, "model_cfg", clip_model_cfg) mocker.patch.object(OpenCLIPEncoder, "preprocess_cfg", clip_preprocess_cfg) mocker.patch.object(OpenCLIPEncoder, "tokenizer_cfg", clip_tokenizer_cfg) mocked = mocker.patch.object(InferenceModel, "_make_session", autospec=True).return_value mocked.run.return_value = [[self.embedding]] mocker.patch("app.models.clip.Tokenizer.from_file", autospec=True) clip_encoder = OpenCLIPEncoder("ViT-B-32__openai", cache_dir="test_cache", mode="text") embedding = clip_encoder.predict("test search query") assert clip_encoder.mode == "text" assert isinstance(embedding, np.ndarray) assert embedding.shape[0] == clip_model_cfg["embed_dim"] assert embedding.dtype == np.float32 mocked.run.assert_called_once() def test_openclip_tokenizer( self, mocker: MockerFixture, clip_model_cfg: dict[str, Any], clip_preprocess_cfg: Callable[[Path], dict[str, Any]], clip_tokenizer_cfg: Callable[[Path], dict[str, Any]], ) -> None: mocker.patch.object(OpenCLIPEncoder, "download") mocker.patch.object(OpenCLIPEncoder, "model_cfg", clip_model_cfg) mocker.patch.object(OpenCLIPEncoder, "preprocess_cfg", clip_preprocess_cfg) mocker.patch.object(OpenCLIPEncoder, "tokenizer_cfg", clip_tokenizer_cfg) mock_tokenizer = mocker.patch("app.models.clip.Tokenizer.from_file", autospec=True).return_value mock_ids = [randint(0, 50000) for _ in range(77)] mock_tokenizer.encode.return_value = SimpleNamespace(ids=mock_ids) clip_encoder = OpenCLIPEncoder("ViT-B-32__openai", cache_dir="test_cache", mode="text") clip_encoder._load_tokenizer() tokens = clip_encoder.tokenize("test search query") assert "text" in tokens assert isinstance(tokens["text"], np.ndarray) assert tokens["text"].shape == (1, 77) assert tokens["text"].dtype == np.int32 assert np.allclose(tokens["text"], np.array([mock_ids], dtype=np.int32), atol=0) def test_mclip_tokenizer( self, mocker: MockerFixture, clip_model_cfg: dict[str, Any], clip_preprocess_cfg: Callable[[Path], dict[str, Any]], clip_tokenizer_cfg: Callable[[Path], dict[str, Any]], ) -> None: mocker.patch.object(OpenCLIPEncoder, "download") mocker.patch.object(OpenCLIPEncoder, "model_cfg", clip_model_cfg) mocker.patch.object(OpenCLIPEncoder, "preprocess_cfg", clip_preprocess_cfg) mocker.patch.object(OpenCLIPEncoder, "tokenizer_cfg", clip_tokenizer_cfg) mock_tokenizer = mocker.patch("app.models.clip.Tokenizer.from_file", autospec=True).return_value mock_ids = [randint(0, 50000) for _ in range(77)] mock_attention_mask = [randint(0, 1) for _ in range(77)] mock_tokenizer.encode.return_value = SimpleNamespace(ids=mock_ids, attention_mask=mock_attention_mask) clip_encoder = MCLIPEncoder("ViT-B-32__openai", cache_dir="test_cache", mode="text") clip_encoder._load_tokenizer() tokens = clip_encoder.tokenize("test search query") assert "input_ids" in tokens assert "attention_mask" in tokens assert isinstance(tokens["input_ids"], np.ndarray) assert isinstance(tokens["attention_mask"], np.ndarray) assert tokens["input_ids"].shape == (1, 77) assert tokens["attention_mask"].shape == (1, 77) assert np.allclose(tokens["input_ids"], np.array([mock_ids], dtype=np.int32), atol=0) assert np.allclose(tokens["attention_mask"], np.array([mock_attention_mask], dtype=np.int32), atol=0) class TestFaceRecognition: def test_set_min_score(self, mocker: MockerFixture) -> None: mocker.patch.object(FaceRecognizer, "load") face_recognizer = FaceRecognizer("buffalo_s", 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("buffalo_s", 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) == num_faces for face in faces: assert face["imageHeight"] == 800 assert face["imageWidth"] == 600 assert isinstance(face["embedding"], np.ndarray) assert face["embedding"].shape[0] == 512 assert face["embedding"].dtype == np.float32 det_model.detect.assert_called_once() assert rec_model.get_feat.call_count == num_faces @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.FACIAL_RECOGNITION) await model_cache.get("test_model_name", ModelType.FACIAL_RECOGNITION) 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.FACIAL_RECOGNITION, cache_dir="test_cache") mock_get_model.assert_called_once_with(ModelType.FACIAL_RECOGNITION, "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.FACIAL_RECOGNITION) 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_get(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.FACIAL_RECOGNITION) await model_cache.get("test_model_name", ModelType.FACIAL_RECOGNITION) mock_cache_expire.assert_called_once_with(mock.ANY, 100) async def test_profiling(self, mock_get_model: mock.Mock) -> None: model_cache = ModelCache(ttl=100, profiling=True) await model_cache.get("test_model_name", ModelType.FACIAL_RECOGNITION) profiling = await model_cache.get_profiling() assert isinstance(profiling, dict) assert profiling == model_cache.cache.profiling async def test_loads_mclip(self) -> None: model_cache = ModelCache() model = await model_cache.get("XLM-Roberta-Large-Vit-B-32", ModelType.CLIP, mode="text") assert isinstance(model, MCLIPEncoder) assert model.model_name == "XLM-Roberta-Large-Vit-B-32" async def test_raises_exception_if_invalid_model_type(self) -> None: invalid: Any = SimpleNamespace(value="invalid") model_cache = ModelCache() with pytest.raises(ValueError): await model_cache.get("XLM-Roberta-Large-Vit-B-32", invalid, mode="text") async def test_raises_exception_if_unknown_model_name(self) -> None: model_cache = ModelCache() with pytest.raises(ValueError): await model_cache.get("test_model_name", ModelType.CLIP, mode="text") @pytest.mark.asyncio class TestLoad: async def test_load(self) -> None: mock_model = mock.Mock(spec=InferenceModel) mock_model.loaded = False res = await load(mock_model) assert res is mock_model mock_model.load.assert_called_once() mock_model.clear_cache.assert_not_called() async def test_load_returns_model_if_loaded(self) -> None: mock_model = mock.Mock(spec=InferenceModel) mock_model.loaded = True res = await load(mock_model) assert res is mock_model mock_model.load.assert_not_called() async def test_load_clears_cache_and_retries_if_os_error(self) -> None: mock_model = mock.Mock(spec=InferenceModel) mock_model.model_name = "test_model_name" mock_model.model_type = ModelType.CLIP mock_model.load.side_effect = [OSError, None] mock_model.loaded = False res = await load(mock_model) assert res is mock_model mock_model.clear_cache.assert_called_once() assert mock_model.load.call_count == 2 @pytest.mark.skipif( not settings.test_full, reason="More time-consuming since it deploys the app and loads models.", ) class TestEndpoints: def test_clip_image_endpoint( self, pil_image: Image.Image, responses: dict[str, Any], deployed_app: TestClient ) -> None: byte_image = BytesIO() pil_image.save(byte_image, format="jpeg") expected = responses["clip"]["image"] response = deployed_app.post( "http://localhost:3003/predict", data={"modelName": "ViT-B-32__openai", "modelType": "clip", "options": json.dumps({"mode": "vision"})}, files={"image": byte_image.getvalue()}, ) actual = response.json() assert response.status_code == 200 assert np.allclose(expected, actual) def test_clip_text_endpoint(self, responses: dict[str, Any], deployed_app: TestClient) -> None: expected = responses["clip"]["text"] response = deployed_app.post( "http://localhost:3003/predict", data={ "modelName": "ViT-B-32__openai", "modelType": "clip", "text": "test search query", "options": json.dumps({"mode": "text"}), }, ) actual = response.json() assert response.status_code == 200 assert np.allclose(expected, actual) def test_face_endpoint(self, pil_image: Image.Image, responses: dict[str, Any], deployed_app: TestClient) -> None: byte_image = BytesIO() pil_image.save(byte_image, format="jpeg") expected = responses["facial-recognition"] response = deployed_app.post( "http://localhost:3003/predict", data={ "modelName": "buffalo_l", "modelType": "facial-recognition", "options": json.dumps({"minScore": 0.034}), }, files={"image": byte_image.getvalue()}, ) actual = response.json() assert response.status_code == 200 assert len(expected) == len(actual) for expected_face, actual_face in zip(expected, actual): assert expected_face["imageHeight"] == actual_face["imageHeight"] assert expected_face["imageWidth"] == actual_face["imageWidth"] assert expected_face["boundingBox"] == actual_face["boundingBox"] assert np.allclose(expected_face["embedding"], actual_face["embedding"]) assert np.allclose(expected_face["score"], actual_face["score"])