mirror of
https://github.com/immich-app/immich.git
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753 lines
32 KiB
Python
753 lines
32 KiB
Python
import json
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import os
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from io import BytesIO
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from pathlib import Path
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from random import randint
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from types import SimpleNamespace
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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 onnxruntime as ort
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import pytest
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from fastapi import HTTPException
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from fastapi.testclient import TestClient
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from PIL import Image
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from pytest import MonkeyPatch
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from pytest_mock import MockerFixture
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from app.main import load, preload_models
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from app.models.clip.textual import MClipTextualEncoder, OpenClipTextualEncoder
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from app.models.clip.visual import OpenClipVisualEncoder
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from app.models.facial_recognition.detection import FaceDetector
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from app.models.facial_recognition.recognition import FaceRecognizer
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from .config import Settings, log, settings
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from .models.base import InferenceModel
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from .models.cache import ModelCache
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from .schemas import ModelFormat, ModelTask, ModelType
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class TestBase:
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CPU_EP = ["CPUExecutionProvider"]
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CUDA_EP = ["CUDAExecutionProvider", "CPUExecutionProvider"]
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OV_EP = ["OpenVINOExecutionProvider", "CPUExecutionProvider"]
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CUDA_EP_OUT_OF_ORDER = ["CPUExecutionProvider", "CUDAExecutionProvider"]
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TRT_EP = ["TensorrtExecutionProvider", "CUDAExecutionProvider", "CPUExecutionProvider"]
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@pytest.mark.providers(CPU_EP)
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def test_sets_cpu_provider(self, providers: list[str]) -> None:
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encoder = OpenClipTextualEncoder("ViT-B-32__openai")
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assert encoder.providers == self.CPU_EP
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@pytest.mark.providers(CUDA_EP)
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def test_sets_cuda_provider_if_available(self, providers: list[str]) -> None:
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encoder = OpenClipTextualEncoder("ViT-B-32__openai")
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assert encoder.providers == self.CUDA_EP
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@pytest.mark.providers(OV_EP)
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def test_sets_openvino_provider_if_available(self, providers: list[str], mocker: MockerFixture) -> None:
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mocked = mocker.patch("app.models.base.ort.capi._pybind_state")
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mocked.get_available_openvino_device_ids.return_value = ["GPU.0", "CPU"]
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encoder = OpenClipTextualEncoder("ViT-B-32__openai")
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assert encoder.providers == self.OV_EP
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@pytest.mark.providers(OV_EP)
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def test_avoids_openvino_if_gpu_not_available(self, providers: list[str], mocker: MockerFixture) -> None:
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mocked = mocker.patch("app.models.base.ort.capi._pybind_state")
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mocked.get_available_openvino_device_ids.return_value = ["CPU"]
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encoder = OpenClipTextualEncoder("ViT-B-32__openai")
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assert encoder.providers == self.CPU_EP
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@pytest.mark.providers(CUDA_EP_OUT_OF_ORDER)
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def test_sets_providers_in_correct_order(self, providers: list[str]) -> None:
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encoder = OpenClipTextualEncoder("ViT-B-32__openai")
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assert encoder.providers == self.CUDA_EP
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@pytest.mark.providers(TRT_EP)
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def test_ignores_unsupported_providers(self, providers: list[str]) -> None:
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encoder = OpenClipTextualEncoder("ViT-B-32__openai")
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assert encoder.providers == self.CUDA_EP
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def test_sets_provider_kwarg(self) -> None:
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providers = ["CUDAExecutionProvider"]
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encoder = OpenClipTextualEncoder("ViT-B-32__openai", providers=providers)
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assert encoder.providers == providers
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def test_sets_default_provider_options(self, mocker: MockerFixture) -> None:
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mocked = mocker.patch("app.models.base.ort.capi._pybind_state")
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mocked.get_available_openvino_device_ids.return_value = ["GPU.0", "CPU"]
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encoder = OpenClipTextualEncoder(
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"ViT-B-32__openai", providers=["OpenVINOExecutionProvider", "CPUExecutionProvider"]
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)
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assert encoder.provider_options == [
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{"device_type": "GPU_FP32", "cache_dir": (encoder.cache_dir / "openvino").as_posix()},
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{"arena_extend_strategy": "kSameAsRequested"},
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]
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def test_sets_provider_options_kwarg(self) -> None:
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encoder = OpenClipTextualEncoder(
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"ViT-B-32__openai",
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providers=["OpenVINOExecutionProvider", "CPUExecutionProvider"],
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provider_options=[],
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)
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assert encoder.provider_options == []
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def test_sets_default_sess_options(self) -> None:
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encoder = OpenClipTextualEncoder("ViT-B-32__openai")
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assert encoder.sess_options.execution_mode == ort.ExecutionMode.ORT_SEQUENTIAL
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assert encoder.sess_options.inter_op_num_threads == 1
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assert encoder.sess_options.intra_op_num_threads == 2
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assert encoder.sess_options.enable_cpu_mem_arena is False
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def test_sets_default_sess_options_does_not_set_threads_if_non_cpu_and_default_threads(self) -> None:
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encoder = OpenClipTextualEncoder(
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"ViT-B-32__openai", providers=["CUDAExecutionProvider", "CPUExecutionProvider"]
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)
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assert encoder.sess_options.inter_op_num_threads == 0
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assert encoder.sess_options.intra_op_num_threads == 0
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def test_sets_default_sess_options_sets_threads_if_non_cpu_and_set_threads(self, mocker: MockerFixture) -> None:
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mock_settings = mocker.patch("app.models.base.settings", autospec=True)
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mock_settings.model_inter_op_threads = 2
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mock_settings.model_intra_op_threads = 4
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encoder = OpenClipTextualEncoder(
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"ViT-B-32__openai", providers=["CUDAExecutionProvider", "CPUExecutionProvider"]
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)
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assert encoder.sess_options.inter_op_num_threads == 2
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assert encoder.sess_options.intra_op_num_threads == 4
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def test_sets_sess_options_kwarg(self) -> None:
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sess_options = ort.SessionOptions()
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encoder = OpenClipTextualEncoder(
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"ViT-B-32__openai",
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providers=["OpenVINOExecutionProvider", "CPUExecutionProvider"],
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provider_options=[],
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sess_options=sess_options,
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)
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assert sess_options is encoder.sess_options
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def test_sets_default_cache_dir(self) -> None:
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encoder = OpenClipTextualEncoder("ViT-B-32__openai")
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assert encoder.cache_dir == Path(settings.cache_folder) / "clip" / "ViT-B-32__openai"
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def test_sets_cache_dir_kwarg(self) -> None:
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cache_dir = Path("/test_cache")
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encoder = OpenClipTextualEncoder("ViT-B-32__openai", cache_dir=cache_dir)
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assert encoder.cache_dir == cache_dir
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def test_sets_default_preferred_format(self, mocker: MockerFixture) -> None:
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mocker.patch.object(settings, "ann", True)
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mocker.patch("ann.ann.is_available", False)
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encoder = OpenClipTextualEncoder("ViT-B-32__openai")
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assert encoder.preferred_format == ModelFormat.ONNX
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def test_sets_default_preferred_format_to_armnn_if_available(self, mocker: MockerFixture) -> None:
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mocker.patch.object(settings, "ann", True)
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mocker.patch("ann.ann.is_available", True)
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encoder = OpenClipTextualEncoder("ViT-B-32__openai")
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assert encoder.preferred_format == ModelFormat.ARMNN
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def test_sets_preferred_format_kwarg(self, mocker: MockerFixture) -> None:
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mocker.patch.object(settings, "ann", False)
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mocker.patch("ann.ann.is_available", False)
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encoder = OpenClipTextualEncoder("ViT-B-32__openai", preferred_format=ModelFormat.ARMNN)
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assert encoder.preferred_format == ModelFormat.ARMNN
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def test_casts_cache_dir_string_to_path(self) -> None:
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cache_dir = "/test_cache"
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encoder = OpenClipTextualEncoder("ViT-B-32__openai", cache_dir=cache_dir)
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assert encoder.cache_dir == Path(cache_dir)
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def test_clear_cache(self, mocker: MockerFixture) -> None:
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mock_rmtree = mocker.patch("app.models.base.rmtree", autospec=True)
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mock_rmtree.avoids_symlink_attacks = True
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mock_cache_dir = mocker.Mock()
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mock_cache_dir.exists.return_value = True
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mock_cache_dir.is_dir.return_value = True
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mocker.patch("app.models.base.Path", return_value=mock_cache_dir)
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info = mocker.spy(log, "info")
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encoder = OpenClipTextualEncoder("ViT-B-32__openai", cache_dir=mock_cache_dir)
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encoder.clear_cache()
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mock_rmtree.assert_called_once_with(encoder.cache_dir)
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info.assert_called_with(f"Cleared cache directory for model '{encoder.model_name}'.")
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def test_clear_cache_warns_if_path_does_not_exist(self, mocker: MockerFixture) -> None:
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mock_rmtree = mocker.patch("app.models.base.rmtree", autospec=True)
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mock_rmtree.avoids_symlink_attacks = True
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mock_cache_dir = mocker.Mock()
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mock_cache_dir.exists.return_value = False
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mock_cache_dir.is_dir.return_value = True
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mocker.patch("app.models.base.Path", return_value=mock_cache_dir)
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warning = mocker.spy(log, "warning")
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encoder = OpenClipTextualEncoder("ViT-B-32__openai", cache_dir=mock_cache_dir)
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encoder.clear_cache()
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mock_rmtree.assert_not_called()
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warning.assert_called_once()
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def test_clear_cache_raises_exception_if_vulnerable_to_symlink_attack(self, mocker: MockerFixture) -> None:
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mock_rmtree = mocker.patch("app.models.base.rmtree", autospec=True)
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mock_rmtree.avoids_symlink_attacks = False
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mock_cache_dir = mocker.Mock()
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mock_cache_dir.exists.return_value = True
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mock_cache_dir.is_dir.return_value = True
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mocker.patch("app.models.base.Path", return_value=mock_cache_dir)
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encoder = OpenClipTextualEncoder("ViT-B-32__openai", cache_dir=mock_cache_dir)
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with pytest.raises(RuntimeError):
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encoder.clear_cache()
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mock_rmtree.assert_not_called()
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def test_clear_cache_replaces_file_with_dir_if_path_is_file(self, mocker: MockerFixture) -> None:
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mock_rmtree = mocker.patch("app.models.base.rmtree", autospec=True)
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mock_rmtree.avoids_symlink_attacks = True
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mock_cache_dir = mocker.Mock()
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mock_cache_dir.exists.return_value = True
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mock_cache_dir.is_dir.return_value = False
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mocker.patch("app.models.base.Path", return_value=mock_cache_dir)
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warning = mocker.spy(log, "warning")
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encoder = OpenClipTextualEncoder("ViT-B-32__openai", cache_dir=mock_cache_dir)
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encoder.clear_cache()
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mock_rmtree.assert_not_called()
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mock_cache_dir.unlink.assert_called_once()
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mock_cache_dir.mkdir.assert_called_once()
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warning.assert_called_once()
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def test_make_session_return_ann_if_available(self, mocker: MockerFixture) -> None:
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mock_model_path = mocker.Mock()
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mock_model_path.is_file.return_value = True
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mock_model_path.suffix = ".armnn"
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mock_model_path.with_suffix.return_value = mock_model_path
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mock_ann = mocker.patch("app.models.base.AnnSession")
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encoder = OpenClipTextualEncoder("ViT-B-32__openai")
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encoder._make_session(mock_model_path)
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mock_ann.assert_called_once()
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def test_make_session_return_ort_if_available_and_ann_is_not(self, mocker: MockerFixture) -> None:
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mock_armnn_path = mocker.Mock()
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mock_armnn_path.is_file.return_value = False
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mock_armnn_path.suffix = ".armnn"
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mock_onnx_path = mocker.Mock()
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mock_onnx_path.is_file.return_value = True
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mock_onnx_path.suffix = ".onnx"
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mock_armnn_path.with_suffix.return_value = mock_onnx_path
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mock_ann = mocker.patch("app.models.base.AnnSession")
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mock_ort = mocker.patch("app.models.base.ort.InferenceSession")
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encoder = OpenClipTextualEncoder("ViT-B-32__openai")
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encoder._make_session(mock_armnn_path)
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mock_ort.assert_called_once()
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mock_ann.assert_not_called()
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def test_make_session_raises_exception_if_path_does_not_exist(self, mocker: MockerFixture) -> None:
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mock_model_path = mocker.Mock()
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mock_model_path.is_file.return_value = False
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mock_model_path.suffix = ".onnx"
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mock_model_path.with_suffix.return_value = mock_model_path
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mock_ann = mocker.patch("app.models.base.AnnSession")
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mock_ort = mocker.patch("app.models.base.ort.InferenceSession")
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encoder = OpenClipTextualEncoder("ViT-B-32__openai")
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with pytest.raises(ValueError):
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encoder._make_session(mock_model_path)
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mock_ann.assert_not_called()
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mock_ort.assert_not_called()
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def test_download(self, mocker: MockerFixture) -> None:
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mock_snapshot_download = mocker.patch("app.models.base.snapshot_download")
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encoder = OpenClipTextualEncoder("ViT-B-32__openai", cache_dir="/path/to/cache")
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encoder.download()
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mock_snapshot_download.assert_called_once_with(
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"immich-app/ViT-B-32__openai",
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cache_dir=encoder.cache_dir,
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local_dir=encoder.cache_dir,
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local_dir_use_symlinks=False,
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ignore_patterns=["*.armnn"],
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)
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def test_download_downloads_armnn_if_preferred_format(self, mocker: MockerFixture) -> None:
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mock_snapshot_download = mocker.patch("app.models.base.snapshot_download")
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encoder = OpenClipTextualEncoder("ViT-B-32__openai", preferred_format=ModelFormat.ARMNN)
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encoder.download()
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mock_snapshot_download.assert_called_once_with(
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"immich-app/ViT-B-32__openai",
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cache_dir=encoder.cache_dir,
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local_dir=encoder.cache_dir,
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local_dir_use_symlinks=False,
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ignore_patterns=[],
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)
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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(OpenClipVisualEncoder, "download")
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mocker.patch.object(OpenClipVisualEncoder, "model_cfg", clip_model_cfg)
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mocker.patch.object(OpenClipVisualEncoder, "preprocess_cfg", clip_preprocess_cfg)
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mocked = mocker.patch.object(InferenceModel, "_make_session", autospec=True).return_value
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mocked.run.return_value = [[self.embedding]]
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clip_encoder = OpenClipVisualEncoder("ViT-B-32__openai", cache_dir="test_cache")
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embedding = clip_encoder.predict(pil_image)
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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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mocked.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_tokenizer_cfg: Callable[[Path], dict[str, Any]],
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) -> None:
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mocker.patch.object(OpenClipTextualEncoder, "download")
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mocker.patch.object(OpenClipTextualEncoder, "model_cfg", clip_model_cfg)
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mocker.patch.object(OpenClipTextualEncoder, "tokenizer_cfg", clip_tokenizer_cfg)
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mocked = mocker.patch.object(InferenceModel, "_make_session", autospec=True).return_value
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mocked.run.return_value = [[self.embedding]]
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mocker.patch("app.models.clip.textual.Tokenizer.from_file", autospec=True)
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clip_encoder = OpenClipTextualEncoder("ViT-B-32__openai", cache_dir="test_cache")
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embedding = clip_encoder.predict("test search query")
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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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mocked.run.assert_called_once()
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def test_openclip_tokenizer(
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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_tokenizer_cfg: Callable[[Path], dict[str, Any]],
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) -> None:
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mocker.patch.object(OpenClipTextualEncoder, "download")
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mocker.patch.object(OpenClipTextualEncoder, "model_cfg", clip_model_cfg)
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mocker.patch.object(OpenClipTextualEncoder, "tokenizer_cfg", clip_tokenizer_cfg)
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mocker.patch.object(InferenceModel, "_make_session", autospec=True).return_value
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mock_tokenizer = mocker.patch("app.models.clip.textual.Tokenizer.from_file", autospec=True).return_value
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mock_ids = [randint(0, 50000) for _ in range(77)]
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mock_tokenizer.encode.return_value = SimpleNamespace(ids=mock_ids)
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clip_encoder = OpenClipTextualEncoder("ViT-B-32__openai", cache_dir="test_cache")
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clip_encoder._load()
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tokens = clip_encoder.tokenize("test search query")
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assert "text" in tokens
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assert isinstance(tokens["text"], np.ndarray)
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assert tokens["text"].shape == (1, 77)
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assert tokens["text"].dtype == np.int32
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assert np.allclose(tokens["text"], np.array([mock_ids], dtype=np.int32), atol=0)
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def test_mclip_tokenizer(
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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_tokenizer_cfg: Callable[[Path], dict[str, Any]],
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) -> None:
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mocker.patch.object(MClipTextualEncoder, "download")
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mocker.patch.object(MClipTextualEncoder, "model_cfg", clip_model_cfg)
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mocker.patch.object(MClipTextualEncoder, "tokenizer_cfg", clip_tokenizer_cfg)
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mocker.patch.object(InferenceModel, "_make_session", autospec=True).return_value
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mock_tokenizer = mocker.patch("app.models.clip.textual.Tokenizer.from_file", autospec=True).return_value
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mock_ids = [randint(0, 50000) for _ in range(77)]
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mock_attention_mask = [randint(0, 1) for _ in range(77)]
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mock_tokenizer.encode.return_value = SimpleNamespace(ids=mock_ids, attention_mask=mock_attention_mask)
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clip_encoder = MClipTextualEncoder("ViT-B-32__openai", cache_dir="test_cache")
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clip_encoder._load()
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tokens = clip_encoder.tokenize("test search query")
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assert "input_ids" in tokens
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assert "attention_mask" in tokens
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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_detection(self, cv_image: cv2.Mat, mocker: MockerFixture) -> None:
|
|
mocker.patch.object(FaceDetector, "load")
|
|
face_detector = FaceDetector("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)
|
|
scores = 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, scores], axis=-1), kpss)
|
|
face_detector.model = det_model
|
|
|
|
faces = face_detector.predict(cv_image)
|
|
|
|
assert isinstance(faces, dict)
|
|
assert isinstance(faces.get("boxes", None), np.ndarray)
|
|
assert isinstance(faces.get("landmarks", None), np.ndarray)
|
|
assert isinstance(faces.get("scores", None), np.ndarray)
|
|
assert np.equal(faces["boxes"], bbox.round()).all()
|
|
assert np.equal(faces["landmarks"], kpss).all()
|
|
assert np.equal(faces["scores"], scores).all()
|
|
det_model.detect.assert_called_once()
|
|
|
|
def test_recognition(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")
|
|
|
|
num_faces = 2
|
|
bbox = np.random.rand(num_faces, 4).astype(np.float32)
|
|
scores = np.array([0.67] * num_faces).astype(np.float32)
|
|
kpss = np.random.rand(num_faces, 5, 2).astype(np.float32)
|
|
faces = {"boxes": bbox, "landmarks": kpss, "scores": scores}
|
|
|
|
rec_model = mock.Mock()
|
|
embedding = np.random.rand(num_faces, 512).astype(np.float32)
|
|
rec_model.get_feat.return_value = embedding
|
|
face_recognizer.model = rec_model
|
|
|
|
faces = face_recognizer.predict(cv_image, faces)
|
|
|
|
assert isinstance(faces, list)
|
|
assert len(faces) == num_faces
|
|
for face in faces:
|
|
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
|
|
assert isinstance(face.get("score", None), np.float32)
|
|
|
|
rec_model.get_feat.assert_called_once()
|
|
call_args = rec_model.get_feat.call_args_list[0].args
|
|
assert len(call_args) == 1
|
|
assert isinstance(call_args[0], list)
|
|
assert isinstance(call_args[0][0], np.ndarray)
|
|
assert call_args[0][0].shape == (112, 112, 3)
|
|
|
|
|
|
@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.RECOGNITION, ModelTask.FACIAL_RECOGNITION)
|
|
await model_cache.get("test_model_name", ModelType.RECOGNITION, ModelTask.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.RECOGNITION, ModelTask.FACIAL_RECOGNITION, cache_dir="test_cache"
|
|
)
|
|
mock_get_model.assert_called_once_with(
|
|
"test_model_name", ModelType.RECOGNITION, ModelTask.FACIAL_RECOGNITION, cache_dir="test_cache"
|
|
)
|
|
|
|
async def test_different_clip(self, mock_get_model: mock.Mock) -> None:
|
|
model_cache = ModelCache()
|
|
await model_cache.get("test_model_name", ModelType.VISUAL, ModelTask.SEARCH)
|
|
await model_cache.get("test_model_name", ModelType.TEXTUAL, ModelTask.SEARCH)
|
|
mock_get_model.assert_has_calls(
|
|
[
|
|
mock.call("test_model_name", ModelType.VISUAL, ModelTask.SEARCH),
|
|
mock.call("test_model_name", ModelType.TEXTUAL, ModelTask.SEARCH),
|
|
]
|
|
)
|
|
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()
|
|
await model_cache.get("test_model_name", ModelType.RECOGNITION, ModelTask.FACIAL_RECOGNITION, ttl=100)
|
|
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(revalidate=True)
|
|
await model_cache.get("test_model_name", ModelType.RECOGNITION, ModelTask.FACIAL_RECOGNITION, ttl=100)
|
|
await model_cache.get("test_model_name", ModelType.RECOGNITION, ModelTask.FACIAL_RECOGNITION, ttl=100)
|
|
mock_cache_expire.assert_called_once_with(mock.ANY, 100)
|
|
|
|
async def test_profiling(self, mock_get_model: mock.Mock) -> None:
|
|
model_cache = ModelCache(profiling=True)
|
|
await model_cache.get("test_model_name", ModelType.RECOGNITION, ModelTask.FACIAL_RECOGNITION, ttl=100)
|
|
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.TEXTUAL, ModelTask.SEARCH)
|
|
|
|
assert isinstance(model, MClipTextualEncoder)
|
|
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", ModelType.TEXTUAL, invalid)
|
|
|
|
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.TEXTUAL, ModelTask.SEARCH)
|
|
|
|
async def test_preloads_clip_models(self, monkeypatch: MonkeyPatch, mock_get_model: mock.Mock) -> None:
|
|
os.environ["MACHINE_LEARNING_PRELOAD__CLIP"] = "ViT-B-32__openai"
|
|
|
|
settings = Settings()
|
|
assert settings.preload is not None
|
|
assert settings.preload.clip == "ViT-B-32__openai"
|
|
|
|
model_cache = ModelCache()
|
|
monkeypatch.setattr("app.main.model_cache", model_cache)
|
|
|
|
await preload_models(settings.preload)
|
|
mock_get_model.assert_has_calls(
|
|
[
|
|
mock.call("ViT-B-32__openai", ModelType.TEXTUAL, ModelTask.SEARCH),
|
|
mock.call("ViT-B-32__openai", ModelType.VISUAL, ModelTask.SEARCH),
|
|
],
|
|
any_order=True,
|
|
)
|
|
|
|
async def test_preloads_facial_recognition_models(
|
|
self, monkeypatch: MonkeyPatch, mock_get_model: mock.Mock
|
|
) -> None:
|
|
os.environ["MACHINE_LEARNING_PRELOAD__FACIAL_RECOGNITION"] = "buffalo_s"
|
|
|
|
settings = Settings()
|
|
assert settings.preload is not None
|
|
assert settings.preload.facial_recognition == "buffalo_s"
|
|
|
|
model_cache = ModelCache()
|
|
monkeypatch.setattr("app.main.model_cache", model_cache)
|
|
|
|
await preload_models(settings.preload)
|
|
mock_get_model.assert_has_calls(
|
|
[
|
|
mock.call("buffalo_s", ModelType.DETECTION, ModelTask.FACIAL_RECOGNITION),
|
|
mock.call("buffalo_s", ModelType.RECOGNITION, ModelTask.FACIAL_RECOGNITION),
|
|
],
|
|
any_order=True,
|
|
)
|
|
|
|
async def test_preloads_all_models(self, monkeypatch: MonkeyPatch, mock_get_model: mock.Mock) -> None:
|
|
os.environ["MACHINE_LEARNING_PRELOAD__CLIP"] = "ViT-B-32__openai"
|
|
os.environ["MACHINE_LEARNING_PRELOAD__FACIAL_RECOGNITION"] = "buffalo_s"
|
|
|
|
settings = Settings()
|
|
assert settings.preload is not None
|
|
assert settings.preload.clip == "ViT-B-32__openai"
|
|
assert settings.preload.facial_recognition == "buffalo_s"
|
|
|
|
model_cache = ModelCache()
|
|
monkeypatch.setattr("app.main.model_cache", model_cache)
|
|
|
|
await preload_models(settings.preload)
|
|
mock_get_model.assert_has_calls(
|
|
[
|
|
mock.call("ViT-B-32__openai", ModelType.TEXTUAL, ModelTask.SEARCH),
|
|
mock.call("ViT-B-32__openai", ModelType.VISUAL, ModelTask.SEARCH),
|
|
mock.call("buffalo_s", ModelType.DETECTION, ModelTask.FACIAL_RECOGNITION),
|
|
mock.call("buffalo_s", ModelType.RECOGNITION, ModelTask.FACIAL_RECOGNITION),
|
|
],
|
|
any_order=True,
|
|
)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
class TestLoad:
|
|
async def test_load(self) -> None:
|
|
mock_model = mock.Mock(spec=InferenceModel)
|
|
mock_model.loaded = False
|
|
mock_model.load_attempts = 0
|
|
|
|
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.VISUAL
|
|
mock_model.model_task = ModelTask.SEARCH
|
|
mock_model.load.side_effect = [OSError, None]
|
|
mock_model.loaded = False
|
|
mock_model.load_attempts = 0
|
|
|
|
res = await load(mock_model)
|
|
|
|
assert res is mock_model
|
|
mock_model.clear_cache.assert_called_once()
|
|
assert mock_model.load.call_count == 2
|
|
|
|
async def test_load_clears_cache_and_raises_if_os_error_and_already_retried(self) -> None:
|
|
mock_model = mock.Mock(spec=InferenceModel)
|
|
mock_model.model_name = "test_model_name"
|
|
mock_model.model_type = ModelType.VISUAL
|
|
mock_model.model_task = ModelTask.SEARCH
|
|
mock_model.loaded = False
|
|
mock_model.load_attempts = 2
|
|
|
|
with pytest.raises(HTTPException):
|
|
await load(mock_model)
|
|
|
|
mock_model.clear_cache.assert_not_called()
|
|
mock_model.load.assert_not_called()
|
|
|
|
|
|
@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={"entries": json.dumps({"clip": {"visual": {"modelName": "ViT-B-32__openai"}}})},
|
|
files={"image": byte_image.getvalue()},
|
|
)
|
|
|
|
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"])
|
|
|
|
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={
|
|
"entries": json.dumps(
|
|
{
|
|
"clip": {"textual": {"modelName": "ViT-B-32__openai"}},
|
|
},
|
|
),
|
|
"text": "test search query",
|
|
},
|
|
)
|
|
|
|
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"])
|
|
|
|
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")
|
|
|
|
response = deployed_app.post(
|
|
"http://localhost:3003/predict",
|
|
data={
|
|
"entries": json.dumps(
|
|
{
|
|
"facial-recognition": {
|
|
"detection": {"modelName": "buffalo_l", "options": {"minScore": 0.034}},
|
|
"recognition": {"modelName": "buffalo_l"},
|
|
}
|
|
}
|
|
)
|
|
},
|
|
files={"image": byte_image.getvalue()},
|
|
)
|
|
|
|
actual = response.json()
|
|
assert response.status_code == 200
|
|
assert isinstance(actual, dict)
|
|
assert actual.get("imageHeight", None) == responses["imageHeight"]
|
|
assert actual.get("imageWidth", None) == responses["imageWidth"]
|
|
assert "facial-recognition" in actual and isinstance(actual["facial-recognition"], list)
|
|
assert len(actual["facial-recognition"]) == len(responses["facial-recognition"])
|
|
|
|
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"])
|
|
assert np.allclose(expected_face["score"], actual_face["score"])
|