mirror of
https://github.com/immich-app/immich.git
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259 lines
10 KiB
Python
259 lines
10 KiB
Python
import json
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import pickle
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from io import BytesIO
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from pathlib import Path
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from typing import Any, Callable
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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.base import PicklableSessionOptions
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from .models.cache import ModelCache
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from .models.clip import OpenCLIPEncoder
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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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class TestImageClassifier:
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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 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)
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assert classifier.min_score == 0.0
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classifier.model = mock.Mock()
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classifier.model.return_value = self.classifier_preds
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all_labels = classifier.predict(pil_image)
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classifier.min_score = 0.5
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filtered_labels = classifier.predict(pil_image)
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assert all_labels == [
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"that's an image alright",
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"well it ends with .jpg",
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"idk",
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"im just seeing bytes",
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"not sure",
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"probably a virus",
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]
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assert filtered_labels == ["that's an image alright"]
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class TestCLIP:
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embedding = np.random.rand(512).astype(np.float32)
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cache_dir = Path("test_cache")
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def test_basic_image(
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self,
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pil_image: Image.Image,
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mocker: MockerFixture,
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clip_model_cfg: dict[str, Any],
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clip_preprocess_cfg: Callable[[Path], dict[str, Any]],
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) -> None:
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mocker.patch.object(OpenCLIPEncoder, "download")
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mocker.patch.object(OpenCLIPEncoder, "model_cfg", clip_model_cfg)
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mocker.patch.object(OpenCLIPEncoder, "preprocess_cfg", clip_preprocess_cfg)
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mocker.patch("app.models.clip.AutoTokenizer.from_pretrained", autospec=True)
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mocked = mocker.patch("app.models.clip.ort.InferenceSession", autospec=True)
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mocked.return_value.run.return_value = [[self.embedding]]
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clip_encoder = OpenCLIPEncoder("ViT-B-32::openai", cache_dir="test_cache", mode="vision")
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embedding = clip_encoder.predict(pil_image)
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assert clip_encoder.mode == "vision"
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assert isinstance(embedding, np.ndarray)
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assert embedding.shape[0] == clip_model_cfg["embed_dim"]
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assert embedding.dtype == np.float32
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clip_encoder.vision_model.run.assert_called_once()
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def test_basic_text(
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self,
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mocker: MockerFixture,
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clip_model_cfg: dict[str, Any],
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clip_preprocess_cfg: Callable[[Path], dict[str, Any]],
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) -> None:
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mocker.patch.object(OpenCLIPEncoder, "download")
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mocker.patch.object(OpenCLIPEncoder, "model_cfg", clip_model_cfg)
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mocker.patch.object(OpenCLIPEncoder, "preprocess_cfg", clip_preprocess_cfg)
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mocker.patch("app.models.clip.AutoTokenizer.from_pretrained", autospec=True)
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mocked = mocker.patch("app.models.clip.ort.InferenceSession", autospec=True)
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mocked.return_value.run.return_value = [[self.embedding]]
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clip_encoder = OpenCLIPEncoder("ViT-B-32::openai", cache_dir="test_cache", mode="text")
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embedding = clip_encoder.predict("test search query")
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assert clip_encoder.mode == "text"
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assert isinstance(embedding, np.ndarray)
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assert embedding.shape[0] == clip_model_cfg["embed_dim"]
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assert embedding.dtype == np.float32
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clip_encoder.text_model.run.assert_called_once()
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class TestFaceRecognition:
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def test_set_min_score(self, mocker: MockerFixture) -> None:
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mocker.patch.object(FaceRecognizer, "load")
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face_recognizer = FaceRecognizer("buffalo_s", cache_dir="test_cache", min_score=0.5)
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assert face_recognizer.min_score == 0.5
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def test_basic(self, cv_image: cv2.Mat, mocker: MockerFixture) -> None:
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mocker.patch.object(FaceRecognizer, "load")
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face_recognizer = FaceRecognizer("buffalo_s", min_score=0.0, cache_dir="test_cache")
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det_model = mock.Mock()
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num_faces = 2
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bbox = np.random.rand(num_faces, 4).astype(np.float32)
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score = np.array([[0.67]] * num_faces).astype(np.float32)
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kpss = np.random.rand(num_faces, 5, 2).astype(np.float32)
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det_model.detect.return_value = (np.concatenate([bbox, score], axis=-1), kpss)
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face_recognizer.det_model = det_model
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rec_model = mock.Mock()
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embedding = np.random.rand(num_faces, 512).astype(np.float32)
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rec_model.get_feat.return_value = embedding
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face_recognizer.rec_model = rec_model
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faces = face_recognizer.predict(cv_image)
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assert len(faces) == num_faces
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for face in faces:
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assert face["imageHeight"] == 800
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assert face["imageWidth"] == 600
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assert isinstance(face["embedding"], np.ndarray)
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assert face["embedding"].shape[0] == 512
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assert face["embedding"].dtype == np.float32
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det_model.detect.assert_called_once()
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assert rec_model.get_feat.call_count == num_faces
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@pytest.mark.asyncio
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class TestCache:
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async def test_caches(self, mock_get_model: mock.Mock) -> None:
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model_cache = ModelCache()
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await model_cache.get("test_model_name", ModelType.IMAGE_CLASSIFICATION)
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await model_cache.get("test_model_name", ModelType.IMAGE_CLASSIFICATION)
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assert len(model_cache.cache._cache) == 1
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mock_get_model.assert_called_once()
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async def test_kwargs_used(self, mock_get_model: mock.Mock) -> None:
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model_cache = ModelCache()
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await model_cache.get("test_model_name", ModelType.IMAGE_CLASSIFICATION, cache_dir="test_cache")
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mock_get_model.assert_called_once_with(
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ModelType.IMAGE_CLASSIFICATION, "test_model_name", cache_dir="test_cache"
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)
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async def test_different_clip(self, mock_get_model: mock.Mock) -> None:
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model_cache = ModelCache()
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await model_cache.get("test_image_model_name", ModelType.CLIP)
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await model_cache.get("test_text_model_name", ModelType.CLIP)
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mock_get_model.assert_has_calls(
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[
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mock.call(ModelType.CLIP, "test_image_model_name"),
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mock.call(ModelType.CLIP, "test_text_model_name"),
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]
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)
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assert len(model_cache.cache._cache) == 2
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@mock.patch("app.models.cache.OptimisticLock", autospec=True)
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async def test_model_ttl(self, mock_lock_cls: mock.Mock, mock_get_model: mock.Mock) -> None:
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model_cache = ModelCache(ttl=100)
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await model_cache.get("test_model_name", ModelType.IMAGE_CLASSIFICATION)
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mock_lock_cls.return_value.__aenter__.return_value.cas.assert_called_with(mock.ANY, ttl=100)
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@mock.patch("app.models.cache.SimpleMemoryCache.expire")
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async def test_revalidate(self, mock_cache_expire: mock.Mock, mock_get_model: mock.Mock) -> None:
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model_cache = ModelCache(ttl=100, revalidate=True)
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await model_cache.get("test_model_name", ModelType.IMAGE_CLASSIFICATION)
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await model_cache.get("test_model_name", ModelType.IMAGE_CLASSIFICATION)
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mock_cache_expire.assert_called_once_with(mock.ANY, 100)
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@pytest.mark.skipif(
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not settings.test_full,
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reason="More time-consuming since it deploys the app and loads models.",
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)
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class TestEndpoints:
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def test_tagging_endpoint(
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self, pil_image: Image.Image, responses: dict[str, Any], deployed_app: TestClient
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) -> None:
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byte_image = BytesIO()
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pil_image.save(byte_image, format="jpeg")
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response = deployed_app.post(
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"http://localhost:3003/predict",
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data={
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"modelName": "microsoft/resnet-50",
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"modelType": "image-classification",
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"options": json.dumps({"minScore": 0.0}),
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},
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files={"image": byte_image.getvalue()},
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)
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assert response.status_code == 200
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assert response.json() == responses["image-classification"]
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def test_clip_image_endpoint(
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self, pil_image: Image.Image, responses: dict[str, Any], deployed_app: TestClient
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) -> None:
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byte_image = BytesIO()
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pil_image.save(byte_image, format="jpeg")
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response = deployed_app.post(
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"http://localhost:3003/predict",
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data={"modelName": "ViT-B-32::openai", "modelType": "clip", "options": json.dumps({"mode": "vision"})},
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files={"image": byte_image.getvalue()},
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)
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assert response.status_code == 200
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assert response.json() == responses["clip"]["image"]
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def test_clip_text_endpoint(self, responses: dict[str, Any], deployed_app: TestClient) -> None:
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response = deployed_app.post(
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"http://localhost:3003/predict",
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data={
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"modelName": "ViT-B-32::openai",
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"modelType": "clip",
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"text": "test search query",
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"options": json.dumps({"mode": "text"}),
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},
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)
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assert response.status_code == 200
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assert response.json() == responses["clip"]["text"]
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def test_face_endpoint(self, pil_image: Image.Image, responses: dict[str, Any], deployed_app: TestClient) -> None:
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byte_image = BytesIO()
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pil_image.save(byte_image, format="jpeg")
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response = deployed_app.post(
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"http://localhost:3003/predict",
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data={
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"modelName": "buffalo_l",
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"modelType": "facial-recognition",
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"options": json.dumps({"minScore": 0.034}),
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},
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files={"image": byte_image.getvalue()},
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)
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assert response.status_code == 200
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assert response.json() == responses["facial-recognition"]
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def test_sess_options() -> None:
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sess_options = PicklableSessionOptions()
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sess_options.intra_op_num_threads = 1
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sess_options.inter_op_num_threads = 1
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pickled = pickle.dumps(sess_options)
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unpickled = pickle.loads(pickled)
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assert unpickled.intra_op_num_threads == 1
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assert unpickled.inter_op_num_threads == 1
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