mirror of
https://github.com/immich-app/immich.git
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refactor(ml): model sessions (#10559)
This commit is contained in:
Mert
GitHub
parent
6538ad8de7
commit
6356c28f64
11 changed files with 529 additions and 375 deletions
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@ -52,8 +52,6 @@ class Ann(metaclass=_Singleton):
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def __init__(self, log_level: int = 3, tuning_level: int = 1, tuning_file: str | None = None) -> None:
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if not is_available:
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raise RuntimeError("libann is not available!")
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if tuning_file and not exists(tuning_file):
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raise ValueError("tuning_file must point to an existing (possibly empty) file!")
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if tuning_level == 0 and tuning_file is None:
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raise ValueError("tuning_level == 0 reads existing tuning information and requires a tuning_file")
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if tuning_level < 0 or tuning_level > 3:
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@ -67,6 +65,12 @@ class Ann(metaclass=_Singleton):
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self.input_shapes: dict[int, tuple[tuple[int], ...]] = {}
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self.ann: int | None = None
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self.new()
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if self.tuning_file is not None:
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# make sure tuning file exists (without clearing contents)
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# once filled, the tuning file reduces the cost/time of the first
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# inference after model load by 10s of seconds
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open(self.tuning_file, "a").close()
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def new(self) -> None:
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if self.ann is None:
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@ -95,17 +99,19 @@ class Ann(metaclass=_Singleton):
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model_path: str,
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fast_math: bool = True,
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fp16: bool = False,
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save_cached_network: bool = False,
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cached_network_path: str | None = None,
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) -> int:
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if not model_path.endswith((".armnn", ".tflite", ".onnx")):
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raise ValueError("model_path must be a file with extension .armnn, .tflite or .onnx")
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if not exists(model_path):
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raise ValueError("model_path must point to an existing file!")
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save_cached_network = False
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if cached_network_path is not None and not exists(cached_network_path):
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raise ValueError("cached_network_path must point to an existing (possibly empty) file!")
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if save_cached_network and cached_network_path is None:
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raise ValueError("save_cached_network is True, cached_network_path must be specified!")
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save_cached_network = True
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# create empty model cache file
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open(cached_network_path, "a").close()
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net_id: int = libann.load(
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self.ann,
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model_path.encode(),
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@ -8,6 +8,8 @@ from fastapi.testclient import TestClient
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from numpy.typing import NDArray
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from PIL import Image
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from app.config import log
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from .main import app
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@ -96,12 +98,77 @@ def clip_tokenizer_cfg() -> dict[str, Any]:
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@pytest.fixture(scope="function")
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def providers(request: pytest.FixtureRequest) -> Iterator[dict[str, Any]]:
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def providers(request: pytest.FixtureRequest) -> Iterator[mock.Mock]:
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marker = request.node.get_closest_marker("providers")
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if marker is None:
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raise ValueError("Missing marker 'providers'")
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providers = marker.args[0]
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with mock.patch("app.models.base.ort.get_available_providers") as mocked:
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with mock.patch("app.sessions.ort.ort.get_available_providers") as mocked:
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mocked.return_value = providers
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yield providers
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@pytest.fixture(scope="function")
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def ort_pybind() -> Iterator[mock.Mock]:
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with mock.patch("app.sessions.ort.ort.capi._pybind_state") as mocked:
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yield mocked
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@pytest.fixture(scope="function")
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def ov_device_ids(request: pytest.FixtureRequest, ort_pybind: mock.Mock) -> Iterator[mock.Mock]:
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marker = request.node.get_closest_marker("ov_device_ids")
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if marker is None:
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raise ValueError("Missing marker 'ov_device_ids'")
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ort_pybind.get_available_openvino_device_ids.return_value = marker.args[0]
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return ort_pybind
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@pytest.fixture(scope="function")
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def ort_session() -> Iterator[mock.Mock]:
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with mock.patch("app.sessions.ort.ort.InferenceSession") as mocked:
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yield mocked
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@pytest.fixture(scope="function")
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def ann_session() -> Iterator[mock.Mock]:
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with mock.patch("app.sessions.ann.Ann") as mocked:
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yield mocked
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@pytest.fixture(scope="function")
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def rmtree() -> Iterator[mock.Mock]:
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with mock.patch("app.models.base.rmtree", autospec=True) as mocked:
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mocked.avoids_symlink_attacks = True
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yield mocked
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@pytest.fixture(scope="function")
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def path() -> Iterator[mock.Mock]:
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path = mock.MagicMock()
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path.exists.return_value = True
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path.is_dir.return_value = True
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path.is_file.return_value = True
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path.with_suffix.return_value = path
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path.return_value = path
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with mock.patch("app.models.base.Path", return_value=path) as mocked:
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yield mocked
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@pytest.fixture(scope="function")
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def info() -> Iterator[mock.Mock]:
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with mock.patch.object(log, "info") as mocked:
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yield mocked
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@pytest.fixture(scope="function")
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def warning() -> Iterator[mock.Mock]:
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with mock.patch.object(log, "warning") as mocked:
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yield mocked
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@pytest.fixture(scope="function")
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def snapshot_download() -> Iterator[mock.Mock]:
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with mock.patch("app.models.base.snapshot_download") as mocked:
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yield mocked
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@ -5,15 +5,14 @@ from pathlib import Path
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from shutil import rmtree
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from typing import Any, ClassVar
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import onnxruntime as ort
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from huggingface_hub import snapshot_download
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import ann.ann
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from app.models.constants import SUPPORTED_PROVIDERS
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from app.sessions.ort import OrtSession
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from ..config import clean_name, log, settings
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from ..schemas import ModelFormat, ModelIdentity, ModelSession, ModelTask, ModelType
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from .ann import AnnSession
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from ..sessions.ann import AnnSession
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class InferenceModel(ABC):
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@ -24,20 +23,17 @@ class InferenceModel(ABC):
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self,
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model_name: str,
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cache_dir: Path | str | None = None,
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providers: list[str] | None = None,
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provider_options: list[dict[str, Any]] | None = None,
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sess_options: ort.SessionOptions | None = None,
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preferred_format: ModelFormat | None = None,
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session: ModelSession | None = None,
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**model_kwargs: Any,
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) -> None:
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self.loaded = False
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self.loaded = session is not None
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self.load_attempts = 0
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self.model_name = clean_name(model_name)
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self.cache_dir = Path(cache_dir) if cache_dir is not None else self.cache_dir_default
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self.providers = providers if providers is not None else self.providers_default
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self.provider_options = provider_options if provider_options is not None else self.provider_options_default
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self.sess_options = sess_options if sess_options is not None else self.sess_options_default
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self.preferred_format = preferred_format if preferred_format is not None else self.preferred_format_default
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self.cache_dir = Path(cache_dir) if cache_dir is not None else self._cache_dir_default
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self.model_format = preferred_format if preferred_format is not None else self._model_format_default
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if session is not None:
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self.session = session
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def download(self) -> None:
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if not self.cached:
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@ -70,7 +66,7 @@ class InferenceModel(ABC):
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pass
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def _download(self) -> None:
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ignore_patterns = [] if self.preferred_format == ModelFormat.ARMNN else ["*.armnn"]
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ignore_patterns = [] if self.model_format == ModelFormat.ARMNN else ["*.armnn"]
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snapshot_download(
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f"immich-app/{clean_name(self.model_name)}",
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cache_dir=self.cache_dir,
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@ -105,26 +101,11 @@ class InferenceModel(ABC):
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self.cache_dir.mkdir(parents=True, exist_ok=True)
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def _make_session(self, model_path: Path) -> ModelSession:
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if not model_path.is_file():
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onnx_path = model_path.with_suffix(".onnx")
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if not onnx_path.is_file():
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raise ValueError(f"Model path '{model_path}' does not exist")
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log.warning(
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f"Could not find model path '{model_path}'. " f"Falling back to ONNX model path '{onnx_path}' instead.",
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)
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model_path = onnx_path
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match model_path.suffix:
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case ".armnn":
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session = AnnSession(model_path)
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session: ModelSession = AnnSession(model_path)
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case ".onnx":
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session = ort.InferenceSession(
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model_path.as_posix(),
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sess_options=self.sess_options,
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providers=self.providers,
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provider_options=self.provider_options,
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)
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session = OrtSession(model_path)
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case _:
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raise ValueError(f"Unsupported model file type: {model_path.suffix}")
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return session
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@ -135,7 +116,7 @@ class InferenceModel(ABC):
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@property
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def model_path(self) -> Path:
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return self.model_dir / f"model.{self.preferred_format}"
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return self.model_dir / f"model.{self.model_format}"
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@property
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def model_task(self) -> ModelTask:
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@ -154,7 +135,7 @@ class InferenceModel(ABC):
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self._cache_dir = cache_dir
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@property
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def cache_dir_default(self) -> Path:
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def _cache_dir_default(self) -> Path:
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return settings.cache_folder / self.model_task.value / self.model_name
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@property
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@ -162,95 +143,18 @@ class InferenceModel(ABC):
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return self.model_path.is_file()
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@property
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def providers(self) -> list[str]:
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return self._providers
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@providers.setter
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def providers(self, providers: list[str]) -> None:
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log.info(
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(f"Setting '{self.model_name}' execution providers to {providers}, " "in descending order of preference"),
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)
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self._providers = providers
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@property
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def providers_default(self) -> list[str]:
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available_providers = set(ort.get_available_providers())
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log.debug(f"Available ORT providers: {available_providers}")
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if (openvino := "OpenVINOExecutionProvider") in available_providers:
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device_ids: list[str] = ort.capi._pybind_state.get_available_openvino_device_ids()
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log.debug(f"Available OpenVINO devices: {device_ids}")
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gpu_devices = [device_id for device_id in device_ids if device_id.startswith("GPU")]
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if not gpu_devices:
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log.warning("No GPU device found in OpenVINO. Falling back to CPU.")
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available_providers.remove(openvino)
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return [provider for provider in SUPPORTED_PROVIDERS if provider in available_providers]
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@property
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def provider_options(self) -> list[dict[str, Any]]:
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return self._provider_options
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@provider_options.setter
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def provider_options(self, provider_options: list[dict[str, Any]]) -> None:
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log.debug(f"Setting execution provider options to {provider_options}")
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self._provider_options = provider_options
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@property
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def provider_options_default(self) -> list[dict[str, Any]]:
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options = []
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for provider in self.providers:
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match provider:
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case "CPUExecutionProvider" | "CUDAExecutionProvider":
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option = {"arena_extend_strategy": "kSameAsRequested"}
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case "OpenVINOExecutionProvider":
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option = {"device_type": "GPU_FP32", "cache_dir": (self.cache_dir / "openvino").as_posix()}
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case _:
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option = {}
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options.append(option)
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return options
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@property
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def sess_options(self) -> ort.SessionOptions:
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return self._sess_options
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@sess_options.setter
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def sess_options(self, sess_options: ort.SessionOptions) -> None:
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log.debug(f"Setting execution_mode to {sess_options.execution_mode.name}")
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log.debug(f"Setting inter_op_num_threads to {sess_options.inter_op_num_threads}")
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log.debug(f"Setting intra_op_num_threads to {sess_options.intra_op_num_threads}")
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self._sess_options = sess_options
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@property
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def sess_options_default(self) -> ort.SessionOptions:
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sess_options = ort.SessionOptions()
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sess_options.enable_cpu_mem_arena = False
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# avoid thread contention between models
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if settings.model_inter_op_threads > 0:
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sess_options.inter_op_num_threads = settings.model_inter_op_threads
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# these defaults work well for CPU, but bottleneck GPU
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elif settings.model_inter_op_threads == 0 and self.providers == ["CPUExecutionProvider"]:
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sess_options.inter_op_num_threads = 1
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if settings.model_intra_op_threads > 0:
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sess_options.intra_op_num_threads = settings.model_intra_op_threads
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elif settings.model_intra_op_threads == 0 and self.providers == ["CPUExecutionProvider"]:
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sess_options.intra_op_num_threads = 2
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if sess_options.inter_op_num_threads > 1:
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sess_options.execution_mode = ort.ExecutionMode.ORT_PARALLEL
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return sess_options
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@property
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def preferred_format(self) -> ModelFormat:
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def model_format(self) -> ModelFormat:
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return self._preferred_format
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@preferred_format.setter
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def preferred_format(self, preferred_format: ModelFormat) -> None:
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@model_format.setter
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def model_format(self, preferred_format: ModelFormat) -> None:
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log.debug(f"Setting preferred format to {preferred_format}")
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self._preferred_format = preferred_format
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@property
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def preferred_format_default(self) -> ModelFormat:
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return ModelFormat.ARMNN if ann.ann.is_available and settings.ann else ModelFormat.ONNX
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def _model_format_default(self) -> ModelFormat:
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prefer_ann = ann.ann.is_available and settings.ann
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ann_exists = (self.model_dir / "model.armnn").is_file()
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if prefer_ann and not ann_exists:
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log.warning(f"ARM NN is available, but '{self.model_name}' does not support ARM NN. Falling back to ONNX.")
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return ModelFormat.ARMNN if prefer_ann and ann_exists else ModelFormat.ONNX
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@ -3,7 +3,6 @@ from typing import Any
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import numpy as np
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import onnx
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import onnxruntime as ort
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from insightface.model_zoo import ArcFaceONNX
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from insightface.utils.face_align import norm_crop
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from numpy.typing import NDArray
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@ -13,7 +12,8 @@ from PIL import Image
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from app.config import clean_name, log
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from app.models.base import InferenceModel
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from app.models.transforms import decode_cv2
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from app.schemas import FaceDetectionOutput, FacialRecognitionOutput, ModelSession, ModelTask, ModelType
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from app.schemas import FaceDetectionOutput, FacialRecognitionOutput, ModelFormat, ModelSession, ModelTask, ModelType
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from app.sessions import has_batch_axis
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class FaceRecognizer(InferenceModel):
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@ -27,13 +27,14 @@ class FaceRecognizer(InferenceModel):
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cache_dir: Path | str | None = None,
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**model_kwargs: Any,
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) -> None:
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self.min_score = model_kwargs.pop("minScore", min_score)
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super().__init__(clean_name(model_name), cache_dir, **model_kwargs)
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self.min_score = model_kwargs.pop("minScore", min_score)
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self.batch = self.model_format == ModelFormat.ONNX
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def _load(self) -> ModelSession:
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session = self._make_session(self.model_path)
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if not self._has_batch_dim(session):
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self._add_batch_dim(self.model_path)
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if self.model_format == ModelFormat.ONNX and not has_batch_axis(session):
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self._add_batch_axis(self.model_path)
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session = self._make_session(self.model_path)
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self.model = ArcFaceONNX(
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self.model_path.with_suffix(".onnx").as_posix(),
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@ -47,9 +48,20 @@ class FaceRecognizer(InferenceModel):
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if faces["boxes"].shape[0] == 0:
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return []
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inputs = decode_cv2(inputs)
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embeddings: NDArray[np.float32] = self.model.get_feat(self._crop(inputs, faces))
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cropped_faces = self._crop(inputs, faces)
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embeddings = self._predict_batch(cropped_faces) if self.batch else self._predict_single(cropped_faces)
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return self.postprocess(faces, embeddings)
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def _predict_batch(self, cropped_faces: list[NDArray[np.uint8]]) -> NDArray[np.float32]:
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embeddings: NDArray[np.float32] = self.model.get_feat(cropped_faces)
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return embeddings
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def _predict_single(self, cropped_faces: list[NDArray[np.uint8]]) -> NDArray[np.float32]:
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embeddings: list[NDArray[np.float32]] = []
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for face in cropped_faces:
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embeddings.append(self.model.get_feat(face))
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return np.concatenate(embeddings, axis=0)
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def postprocess(self, faces: FaceDetectionOutput, embeddings: NDArray[np.float32]) -> FacialRecognitionOutput:
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return [
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{
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@ -63,11 +75,8 @@ class FaceRecognizer(InferenceModel):
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def _crop(self, image: NDArray[np.uint8], faces: FaceDetectionOutput) -> list[NDArray[np.uint8]]:
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return [norm_crop(image, landmark) for landmark in faces["landmarks"]]
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def _has_batch_dim(self, session: ort.InferenceSession) -> bool:
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return not isinstance(session, ort.InferenceSession) or session.get_inputs()[0].shape[0] == "batch"
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def _add_batch_dim(self, model_path: Path) -> None:
|
||||
log.debug(f"Adding batch dimension to model {model_path}")
|
||||
def _add_batch_axis(self, model_path: Path) -> None:
|
||||
log.debug(f"Adding batch axis to model {model_path}")
|
||||
proto = onnx.load(model_path)
|
||||
static_input_dims = [shape.dim_value for shape in proto.graph.input[0].type.tensor_type.shape.dim[1:]]
|
||||
static_output_dims = [shape.dim_value for shape in proto.graph.output[0].type.tensor_type.shape.dim[1:]]
|
||||
|
|
|
|||
|
|
@ -54,6 +54,14 @@ class ModelSource(StrEnum):
|
|||
ModelIdentity = tuple[ModelType, ModelTask]
|
||||
|
||||
|
||||
class SessionNode(Protocol):
|
||||
@property
|
||||
def name(self) -> str | None: ...
|
||||
|
||||
@property
|
||||
def shape(self) -> tuple[int, ...]: ...
|
||||
|
||||
|
||||
class ModelSession(Protocol):
|
||||
def run(
|
||||
self,
|
||||
|
|
@ -62,6 +70,10 @@ class ModelSession(Protocol):
|
|||
run_options: Any = None,
|
||||
) -> list[npt.NDArray[np.float32]]: ...
|
||||
|
||||
def get_inputs(self) -> list[SessionNode]: ...
|
||||
|
||||
def get_outputs(self) -> list[SessionNode]: ...
|
||||
|
||||
|
||||
class HasProfiling(Protocol):
|
||||
profiling: dict[str, float]
|
||||
|
|
|
|||
5
machine-learning/app/sessions/__init__.py
Normal file
5
machine-learning/app/sessions/__init__.py
Normal file
|
|
@ -0,0 +1,5 @@
|
|||
from app.schemas import ModelSession
|
||||
|
||||
|
||||
def has_batch_axis(session: ModelSession) -> bool:
|
||||
return not isinstance(session.get_inputs()[0].shape[0], int) or session.get_inputs()[0].shape[0] < 0
|
||||
|
|
@ -7,6 +7,7 @@ import numpy as np
|
|||
from numpy.typing import NDArray
|
||||
|
||||
from ann.ann import Ann
|
||||
from app.schemas import SessionNode
|
||||
|
||||
from ..config import log, settings
|
||||
|
||||
|
|
@ -16,27 +17,15 @@ class AnnSession:
|
|||
Wrapper for ANN to be drop-in replacement for ONNX session.
|
||||
"""
|
||||
|
||||
def __init__(self, model_path: Path):
|
||||
tuning_file = Path(settings.cache_folder) / "gpu-tuning.ann"
|
||||
with tuning_file.open(mode="a"):
|
||||
# make sure tuning file exists (without clearing contents)
|
||||
# once filled, the tuning file reduces the cost/time of the first
|
||||
# inference after model load by 10s of seconds
|
||||
pass
|
||||
self.ann = Ann(tuning_level=3, tuning_file=tuning_file.as_posix())
|
||||
log.info("Loading ANN model %s ...", model_path)
|
||||
cache_file = model_path.with_suffix(".anncache")
|
||||
save = False
|
||||
if not cache_file.is_file():
|
||||
save = True
|
||||
with cache_file.open(mode="a"):
|
||||
# create empty model cache file
|
||||
pass
|
||||
def __init__(self, model_path: Path, cache_dir: Path = settings.cache_folder) -> None:
|
||||
self.model_path = model_path
|
||||
self.cache_dir = cache_dir
|
||||
self.ann = Ann(tuning_level=3, tuning_file=(cache_dir / "gpu-tuning.ann").as_posix())
|
||||
|
||||
log.info("Loading ANN model %s ...", model_path)
|
||||
self.model = self.ann.load(
|
||||
model_path.as_posix(),
|
||||
save_cached_network=save,
|
||||
cached_network_path=cache_file.as_posix(),
|
||||
cached_network_path=model_path.with_suffix(".anncache").as_posix(),
|
||||
)
|
||||
log.info("Loaded ANN model with ID %d", self.model)
|
||||
|
||||
|
|
@ -45,11 +34,11 @@ class AnnSession:
|
|||
log.info("Unloaded ANN model %d", self.model)
|
||||
self.ann.destroy()
|
||||
|
||||
def get_inputs(self) -> list[AnnNode]:
|
||||
def get_inputs(self) -> list[SessionNode]:
|
||||
shapes = self.ann.input_shapes[self.model]
|
||||
return [AnnNode(None, s) for s in shapes]
|
||||
|
||||
def get_outputs(self) -> list[AnnNode]:
|
||||
def get_outputs(self) -> list[SessionNode]:
|
||||
shapes = self.ann.output_shapes[self.model]
|
||||
return [AnnNode(None, s) for s in shapes]
|
||||
|
||||
129
machine-learning/app/sessions/ort.py
Normal file
129
machine-learning/app/sessions/ort.py
Normal file
|
|
@ -0,0 +1,129 @@
|
|||
from __future__ import annotations
|
||||
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
import numpy as np
|
||||
import onnxruntime as ort
|
||||
from numpy.typing import NDArray
|
||||
|
||||
from app.models.constants import SUPPORTED_PROVIDERS
|
||||
from app.schemas import SessionNode
|
||||
|
||||
from ..config import log, settings
|
||||
|
||||
|
||||
class OrtSession:
|
||||
def __init__(
|
||||
self,
|
||||
model_path: Path | str,
|
||||
providers: list[str] | None = None,
|
||||
provider_options: list[dict[str, Any]] | None = None,
|
||||
sess_options: ort.SessionOptions | None = None,
|
||||
):
|
||||
self.model_path = Path(model_path)
|
||||
self.providers = providers if providers is not None else self._providers_default
|
||||
self.provider_options = provider_options if provider_options is not None else self._provider_options_default
|
||||
self.sess_options = sess_options if sess_options is not None else self._sess_options_default
|
||||
self.session = ort.InferenceSession(
|
||||
self.model_path.as_posix(),
|
||||
providers=self.providers,
|
||||
provider_options=self.provider_options,
|
||||
sess_options=self.sess_options,
|
||||
)
|
||||
|
||||
def get_inputs(self) -> list[SessionNode]:
|
||||
inputs: list[SessionNode] = self.session.get_inputs()
|
||||
return inputs
|
||||
|
||||
def get_outputs(self) -> list[SessionNode]:
|
||||
outputs: list[SessionNode] = self.session.get_outputs()
|
||||
return outputs
|
||||
|
||||
def run(
|
||||
self,
|
||||
output_names: list[str] | None,
|
||||
input_feed: dict[str, NDArray[np.float32]] | dict[str, NDArray[np.int32]],
|
||||
run_options: Any = None,
|
||||
) -> list[NDArray[np.float32]]:
|
||||
outputs: list[NDArray[np.float32]] = self.session.run(output_names, input_feed, run_options)
|
||||
return outputs
|
||||
|
||||
@property
|
||||
def providers(self) -> list[str]:
|
||||
return self._providers
|
||||
|
||||
@providers.setter
|
||||
def providers(self, providers: list[str]) -> None:
|
||||
log.info(f"Setting execution providers to {providers}, in descending order of preference")
|
||||
self._providers = providers
|
||||
|
||||
@property
|
||||
def _providers_default(self) -> list[str]:
|
||||
available_providers = set(ort.get_available_providers())
|
||||
log.debug(f"Available ORT providers: {available_providers}")
|
||||
if (openvino := "OpenVINOExecutionProvider") in available_providers:
|
||||
device_ids: list[str] = ort.capi._pybind_state.get_available_openvino_device_ids()
|
||||
log.debug(f"Available OpenVINO devices: {device_ids}")
|
||||
|
||||
gpu_devices = [device_id for device_id in device_ids if device_id.startswith("GPU")]
|
||||
if not gpu_devices:
|
||||
log.warning("No GPU device found in OpenVINO. Falling back to CPU.")
|
||||
available_providers.remove(openvino)
|
||||
return [provider for provider in SUPPORTED_PROVIDERS if provider in available_providers]
|
||||
|
||||
@property
|
||||
def provider_options(self) -> list[dict[str, Any]]:
|
||||
return self._provider_options
|
||||
|
||||
@provider_options.setter
|
||||
def provider_options(self, provider_options: list[dict[str, Any]]) -> None:
|
||||
log.debug(f"Setting execution provider options to {provider_options}")
|
||||
self._provider_options = provider_options
|
||||
|
||||
@property
|
||||
def _provider_options_default(self) -> list[dict[str, Any]]:
|
||||
options = []
|
||||
for provider in self.providers:
|
||||
match provider:
|
||||
case "CPUExecutionProvider" | "CUDAExecutionProvider":
|
||||
option = {"arena_extend_strategy": "kSameAsRequested"}
|
||||
case "OpenVINOExecutionProvider":
|
||||
option = {"device_type": "GPU_FP32", "cache_dir": (self.model_path.parent / "openvino").as_posix()}
|
||||
case _:
|
||||
option = {}
|
||||
options.append(option)
|
||||
return options
|
||||
|
||||
@property
|
||||
def sess_options(self) -> ort.SessionOptions:
|
||||
return self._sess_options
|
||||
|
||||
@sess_options.setter
|
||||
def sess_options(self, sess_options: ort.SessionOptions) -> None:
|
||||
log.debug(f"Setting execution_mode to {sess_options.execution_mode.name}")
|
||||
log.debug(f"Setting inter_op_num_threads to {sess_options.inter_op_num_threads}")
|
||||
log.debug(f"Setting intra_op_num_threads to {sess_options.intra_op_num_threads}")
|
||||
self._sess_options = sess_options
|
||||
|
||||
@property
|
||||
def _sess_options_default(self) -> ort.SessionOptions:
|
||||
sess_options = ort.SessionOptions()
|
||||
sess_options.enable_cpu_mem_arena = False
|
||||
|
||||
# avoid thread contention between models
|
||||
if settings.model_inter_op_threads > 0:
|
||||
sess_options.inter_op_num_threads = settings.model_inter_op_threads
|
||||
# these defaults work well for CPU, but bottleneck GPU
|
||||
elif settings.model_inter_op_threads == 0 and self.providers == ["CPUExecutionProvider"]:
|
||||
sess_options.inter_op_num_threads = 1
|
||||
|
||||
if settings.model_intra_op_threads > 0:
|
||||
sess_options.intra_op_num_threads = settings.model_intra_op_threads
|
||||
elif settings.model_intra_op_threads == 0 and self.providers == ["CPUExecutionProvider"]:
|
||||
sess_options.intra_op_num_threads = 2
|
||||
|
||||
if sess_options.inter_op_num_threads > 1:
|
||||
sess_options.execution_mode = ort.ExecutionMode.ORT_PARALLEL
|
||||
|
||||
return sess_options
|
||||
|
|
@ -22,129 +22,16 @@ from app.models.clip.textual import MClipTextualEncoder, OpenClipTextualEncoder
|
|||
from app.models.clip.visual import OpenClipVisualEncoder
|
||||
from app.models.facial_recognition.detection import FaceDetector
|
||||
from app.models.facial_recognition.recognition import FaceRecognizer
|
||||
from app.sessions.ann import AnnSession
|
||||
from app.sessions.ort import OrtSession
|
||||
|
||||
from .config import Settings, log, settings
|
||||
from .config import Settings, settings
|
||||
from .models.base import InferenceModel
|
||||
from .models.cache import ModelCache
|
||||
from .schemas import ModelFormat, ModelTask, 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 = OpenClipTextualEncoder("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 = OpenClipTextualEncoder("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], 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 = OpenClipTextualEncoder("ViT-B-32__openai")
|
||||
|
||||
assert encoder.providers == self.OV_EP
|
||||
|
||||
@pytest.mark.providers(OV_EP)
|
||||
def test_avoids_openvino_if_gpu_not_available(self, providers: list[str], mocker: MockerFixture) -> None:
|
||||
mocked = mocker.patch("app.models.base.ort.capi._pybind_state")
|
||||
mocked.get_available_openvino_device_ids.return_value = ["CPU"]
|
||||
|
||||
encoder = OpenClipTextualEncoder("ViT-B-32__openai")
|
||||
|
||||
assert encoder.providers == self.CPU_EP
|
||||
|
||||
@pytest.mark.providers(CUDA_EP_OUT_OF_ORDER)
|
||||
def test_sets_providers_in_correct_order(self, providers: list[str]) -> None:
|
||||
encoder = OpenClipTextualEncoder("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 = OpenClipTextualEncoder("ViT-B-32__openai")
|
||||
|
||||
assert encoder.providers == self.CUDA_EP
|
||||
|
||||
def test_sets_provider_kwarg(self) -> None:
|
||||
providers = ["CUDAExecutionProvider"]
|
||||
encoder = OpenClipTextualEncoder("ViT-B-32__openai", providers=providers)
|
||||
|
||||
assert encoder.providers == providers
|
||||
|
||||
def test_sets_default_provider_options(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 = OpenClipTextualEncoder(
|
||||
"ViT-B-32__openai", providers=["OpenVINOExecutionProvider", "CPUExecutionProvider"]
|
||||
)
|
||||
|
||||
assert encoder.provider_options == [
|
||||
{"device_type": "GPU_FP32", "cache_dir": (encoder.cache_dir / "openvino").as_posix()},
|
||||
{"arena_extend_strategy": "kSameAsRequested"},
|
||||
]
|
||||
|
||||
def test_sets_provider_options_kwarg(self) -> None:
|
||||
encoder = OpenClipTextualEncoder(
|
||||
"ViT-B-32__openai",
|
||||
providers=["OpenVINOExecutionProvider", "CPUExecutionProvider"],
|
||||
provider_options=[],
|
||||
)
|
||||
|
||||
assert encoder.provider_options == []
|
||||
|
||||
def test_sets_default_sess_options(self) -> None:
|
||||
encoder = OpenClipTextualEncoder("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 = OpenClipTextualEncoder(
|
||||
"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 = OpenClipTextualEncoder(
|
||||
"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 = OpenClipTextualEncoder(
|
||||
"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 = OpenClipTextualEncoder("ViT-B-32__openai")
|
||||
|
||||
|
|
@ -162,15 +49,16 @@ class TestBase:
|
|||
|
||||
encoder = OpenClipTextualEncoder("ViT-B-32__openai")
|
||||
|
||||
assert encoder.preferred_format == ModelFormat.ONNX
|
||||
assert encoder.model_format == ModelFormat.ONNX
|
||||
|
||||
def test_sets_default_preferred_format_to_armnn_if_available(self, mocker: MockerFixture) -> None:
|
||||
def test_sets_default_preferred_format_to_armnn_if_available(self, path: mock.Mock, mocker: MockerFixture) -> None:
|
||||
mocker.patch.object(settings, "ann", True)
|
||||
mocker.patch("ann.ann.is_available", True)
|
||||
path.suffix = ".armnn"
|
||||
|
||||
encoder = OpenClipTextualEncoder("ViT-B-32__openai")
|
||||
encoder = OpenClipTextualEncoder("ViT-B-32__openai", cache_dir=path)
|
||||
|
||||
assert encoder.preferred_format == ModelFormat.ARMNN
|
||||
assert encoder.model_format == ModelFormat.ARMNN
|
||||
|
||||
def test_sets_preferred_format_kwarg(self, mocker: MockerFixture) -> None:
|
||||
mocker.patch.object(settings, "ann", False)
|
||||
|
|
@ -178,7 +66,7 @@ class TestBase:
|
|||
|
||||
encoder = OpenClipTextualEncoder("ViT-B-32__openai", preferred_format=ModelFormat.ARMNN)
|
||||
|
||||
assert encoder.preferred_format == ModelFormat.ARMNN
|
||||
assert encoder.model_format == ModelFormat.ARMNN
|
||||
|
||||
def test_casts_cache_dir_string_to_path(self) -> None:
|
||||
cache_dir = "/test_cache"
|
||||
|
|
@ -186,120 +74,53 @@ class TestBase:
|
|||
|
||||
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 = OpenClipTextualEncoder("ViT-B-32__openai", cache_dir=mock_cache_dir)
|
||||
def test_clear_cache(self, rmtree: mock.Mock, path: mock.Mock, info: mock.Mock) -> None:
|
||||
encoder = OpenClipTextualEncoder("ViT-B-32__openai", cache_dir=path)
|
||||
encoder.clear_cache()
|
||||
|
||||
mock_rmtree.assert_called_once_with(encoder.cache_dir)
|
||||
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")
|
||||
def test_clear_cache_warns_if_path_does_not_exist(
|
||||
self, rmtree: mock.Mock, path: mock.Mock, warning: mock.Mock
|
||||
) -> None:
|
||||
path.return_value.exists.return_value = False
|
||||
|
||||
encoder = OpenClipTextualEncoder("ViT-B-32__openai", cache_dir=mock_cache_dir)
|
||||
encoder = OpenClipTextualEncoder("ViT-B-32__openai", cache_dir=path)
|
||||
encoder.clear_cache()
|
||||
|
||||
mock_rmtree.assert_not_called()
|
||||
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)
|
||||
def test_clear_cache_raises_exception_if_vulnerable_to_symlink_attack(
|
||||
self, rmtree: mock.Mock, path: mock.Mock
|
||||
) -> None:
|
||||
rmtree.avoids_symlink_attacks = False
|
||||
|
||||
encoder = OpenClipTextualEncoder("ViT-B-32__openai", cache_dir=mock_cache_dir)
|
||||
encoder = OpenClipTextualEncoder("ViT-B-32__openai", cache_dir=path)
|
||||
with pytest.raises(RuntimeError):
|
||||
encoder.clear_cache()
|
||||
|
||||
mock_rmtree.assert_not_called()
|
||||
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")
|
||||
def test_clear_cache_replaces_file_with_dir_if_path_is_file(
|
||||
self, rmtree: mock.Mock, path: mock.Mock, warning: mock.Mock
|
||||
) -> None:
|
||||
path.return_value.is_dir.return_value = False
|
||||
|
||||
encoder = OpenClipTextualEncoder("ViT-B-32__openai", cache_dir=mock_cache_dir)
|
||||
encoder = OpenClipTextualEncoder("ViT-B-32__openai", cache_dir=path)
|
||||
encoder.clear_cache()
|
||||
|
||||
mock_rmtree.assert_not_called()
|
||||
mock_cache_dir.unlink.assert_called_once()
|
||||
mock_cache_dir.mkdir.assert_called_once()
|
||||
rmtree.assert_not_called()
|
||||
path.return_value.unlink.assert_called_once()
|
||||
path.return_value.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_ann = mocker.patch("app.models.base.AnnSession")
|
||||
|
||||
encoder = OpenClipTextualEncoder("ViT-B-32__openai")
|
||||
encoder._make_session(mock_model_path)
|
||||
|
||||
mock_ann.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 = OpenClipTextualEncoder("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 = OpenClipTextualEncoder("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")
|
||||
|
||||
def test_download(self, snapshot_download: mock.Mock) -> None:
|
||||
encoder = OpenClipTextualEncoder("ViT-B-32__openai", cache_dir="/path/to/cache")
|
||||
encoder.download()
|
||||
|
||||
mock_snapshot_download.assert_called_once_with(
|
||||
snapshot_download.assert_called_once_with(
|
||||
"immich-app/ViT-B-32__openai",
|
||||
cache_dir=encoder.cache_dir,
|
||||
local_dir=encoder.cache_dir,
|
||||
|
|
@ -307,13 +128,11 @@ class TestBase:
|
|||
ignore_patterns=["*.armnn"],
|
||||
)
|
||||
|
||||
def test_download_downloads_armnn_if_preferred_format(self, mocker: MockerFixture) -> None:
|
||||
mock_snapshot_download = mocker.patch("app.models.base.snapshot_download")
|
||||
|
||||
def test_download_downloads_armnn_if_preferred_format(self, snapshot_download: mock.Mock) -> None:
|
||||
encoder = OpenClipTextualEncoder("ViT-B-32__openai", preferred_format=ModelFormat.ARMNN)
|
||||
encoder.download()
|
||||
|
||||
mock_snapshot_download.assert_called_once_with(
|
||||
snapshot_download.assert_called_once_with(
|
||||
"immich-app/ViT-B-32__openai",
|
||||
cache_dir=encoder.cache_dir,
|
||||
local_dir=encoder.cache_dir,
|
||||
|
|
@ -322,6 +141,167 @@ class TestBase:
|
|||
)
|
||||
|
||||
|
||||
@pytest.mark.usefixtures("ort_session")
|
||||
class TestOrtSession:
|
||||
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:
|
||||
session = OrtSession("ViT-B-32__openai")
|
||||
|
||||
assert session.providers == self.CPU_EP
|
||||
|
||||
@pytest.mark.providers(CUDA_EP)
|
||||
def test_sets_cuda_provider_if_available(self, providers: list[str]) -> None:
|
||||
session = OrtSession("ViT-B-32__openai")
|
||||
|
||||
assert session.providers == self.CUDA_EP
|
||||
|
||||
@pytest.mark.ov_device_ids(["GPU.0", "CPU"])
|
||||
@pytest.mark.providers(OV_EP)
|
||||
def test_sets_openvino_provider_if_available(self, providers: list[str], ov_device_ids: list[str]) -> None:
|
||||
session = OrtSession("ViT-B-32__openai")
|
||||
|
||||
assert session.providers == self.OV_EP
|
||||
|
||||
@pytest.mark.ov_device_ids(["CPU"])
|
||||
@pytest.mark.providers(OV_EP)
|
||||
def test_avoids_openvino_if_gpu_not_available(self, providers: list[str], ov_device_ids: list[str]) -> None:
|
||||
session = OrtSession("ViT-B-32__openai")
|
||||
|
||||
assert session.providers == self.CPU_EP
|
||||
|
||||
@pytest.mark.providers(CUDA_EP_OUT_OF_ORDER)
|
||||
def test_sets_providers_in_correct_order(self, providers: list[str]) -> None:
|
||||
session = OrtSession("ViT-B-32__openai")
|
||||
|
||||
assert session.providers == self.CUDA_EP
|
||||
|
||||
@pytest.mark.providers(TRT_EP)
|
||||
def test_ignores_unsupported_providers(self, providers: list[str]) -> None:
|
||||
session = OrtSession("ViT-B-32__openai")
|
||||
|
||||
assert session.providers == self.CUDA_EP
|
||||
|
||||
def test_sets_provider_kwarg(self) -> None:
|
||||
providers = ["CUDAExecutionProvider"]
|
||||
session = OrtSession("ViT-B-32__openai", providers=providers)
|
||||
|
||||
assert session.providers == providers
|
||||
|
||||
@pytest.mark.ov_device_ids(["GPU.0", "CPU"])
|
||||
def test_sets_default_provider_options(self, ov_device_ids: list[str]) -> None:
|
||||
model_path = "/cache/ViT-B-32__openai/model.onnx"
|
||||
session = OrtSession(model_path, providers=["OpenVINOExecutionProvider", "CPUExecutionProvider"])
|
||||
|
||||
assert session.provider_options == [
|
||||
{"device_type": "GPU_FP32", "cache_dir": "/cache/ViT-B-32__openai/openvino"},
|
||||
{"arena_extend_strategy": "kSameAsRequested"},
|
||||
]
|
||||
|
||||
def test_sets_provider_options_kwarg(self) -> None:
|
||||
session = OrtSession(
|
||||
"ViT-B-32__openai",
|
||||
providers=["OpenVINOExecutionProvider", "CPUExecutionProvider"],
|
||||
provider_options=[],
|
||||
)
|
||||
|
||||
assert session.provider_options == []
|
||||
|
||||
def test_sets_default_sess_options(self) -> None:
|
||||
session = OrtSession("ViT-B-32__openai")
|
||||
|
||||
assert session.sess_options.execution_mode == ort.ExecutionMode.ORT_SEQUENTIAL
|
||||
assert session.sess_options.inter_op_num_threads == 1
|
||||
assert session.sess_options.intra_op_num_threads == 2
|
||||
assert session.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:
|
||||
session = OrtSession("ViT-B-32__openai", providers=["CUDAExecutionProvider", "CPUExecutionProvider"])
|
||||
|
||||
assert session.sess_options.inter_op_num_threads == 0
|
||||
assert session.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.sessions.ort.settings", autospec=True)
|
||||
mock_settings.model_inter_op_threads = 2
|
||||
mock_settings.model_intra_op_threads = 4
|
||||
|
||||
session = OrtSession("ViT-B-32__openai", providers=["CUDAExecutionProvider", "CPUExecutionProvider"])
|
||||
|
||||
assert session.sess_options.inter_op_num_threads == 2
|
||||
assert session.sess_options.intra_op_num_threads == 4
|
||||
|
||||
def test_sets_sess_options_kwarg(self) -> None:
|
||||
sess_options = ort.SessionOptions()
|
||||
session = OrtSession(
|
||||
"ViT-B-32__openai",
|
||||
providers=["OpenVINOExecutionProvider", "CPUExecutionProvider"],
|
||||
provider_options=[],
|
||||
sess_options=sess_options,
|
||||
)
|
||||
|
||||
assert sess_options is session.sess_options
|
||||
|
||||
|
||||
class TestAnnSession:
|
||||
def test_creates_ann_session(self, ann_session: mock.Mock, info: mock.Mock) -> None:
|
||||
model_path = mock.MagicMock(spec=Path)
|
||||
cache_dir = mock.MagicMock(spec=Path)
|
||||
|
||||
AnnSession(model_path, cache_dir)
|
||||
|
||||
ann_session.assert_called_once_with(tuning_level=3, tuning_file=(cache_dir / "gpu-tuning.ann").as_posix())
|
||||
ann_session.return_value.load.assert_called_once_with(
|
||||
model_path.as_posix(), cached_network_path=model_path.with_suffix(".anncache").as_posix()
|
||||
)
|
||||
info.assert_has_calls(
|
||||
[
|
||||
mock.call("Loading ANN model %s ...", model_path),
|
||||
mock.call("Loaded ANN model with ID %d", ann_session.return_value.load.return_value),
|
||||
]
|
||||
)
|
||||
|
||||
def test_get_inputs(self, ann_session: mock.Mock) -> None:
|
||||
ann_session.return_value.load.return_value = 123
|
||||
ann_session.return_value.input_shapes = {123: [(1, 3, 224, 224)]}
|
||||
session = AnnSession(Path("ViT-B-32__openai"))
|
||||
|
||||
inputs = session.get_inputs()
|
||||
|
||||
assert len(inputs) == 1
|
||||
assert inputs[0].name is None
|
||||
assert inputs[0].shape == (1, 3, 224, 224)
|
||||
|
||||
def test_get_outputs(self, ann_session: mock.Mock) -> None:
|
||||
ann_session.return_value.load.return_value = 123
|
||||
ann_session.return_value.output_shapes = {123: [(1, 3, 224, 224)]}
|
||||
session = AnnSession(Path("ViT-B-32__openai"))
|
||||
|
||||
outputs = session.get_outputs()
|
||||
|
||||
assert len(outputs) == 1
|
||||
assert outputs[0].name is None
|
||||
assert outputs[0].shape == (1, 3, 224, 224)
|
||||
|
||||
def test_run(self, ann_session: mock.Mock, mocker: MockerFixture) -> None:
|
||||
ann_session.return_value.load.return_value = 123
|
||||
np_spy = mocker.spy(np, "ascontiguousarray")
|
||||
session = AnnSession(Path("ViT-B-32__openai"))
|
||||
[input1, input2] = [np.random.rand(1, 3, 224, 224).astype(np.float32) for _ in range(2)]
|
||||
input_feed = {"input.1": input1, "input.2": input2}
|
||||
|
||||
session.run(None, input_feed)
|
||||
|
||||
ann_session.return_value.execute.assert_called_once_with(123, [input1, input2])
|
||||
np_spy.call_count == 2
|
||||
np_spy.assert_has_calls([mock.call(input1), mock.call(input2)])
|
||||
|
||||
|
||||
class TestCLIP:
|
||||
embedding = np.random.rand(512).astype(np.float32)
|
||||
cache_dir = Path("test_cache")
|
||||
|
|
@ -487,6 +467,59 @@ class TestFaceRecognition:
|
|||
assert isinstance(call_args[0][0], np.ndarray)
|
||||
assert call_args[0][0].shape == (112, 112, 3)
|
||||
|
||||
def test_recognition_adds_batch_axis_for_ort(self, ort_session: mock.Mock, mocker: MockerFixture) -> None:
|
||||
onnx = mocker.patch("app.models.facial_recognition.recognition.onnx", autospec=True)
|
||||
update_dims = mocker.patch(
|
||||
"app.models.facial_recognition.recognition.update_inputs_outputs_dims", autospec=True
|
||||
)
|
||||
mocker.patch("app.models.base.InferenceModel.download")
|
||||
mocker.patch("app.models.facial_recognition.recognition.ArcFaceONNX")
|
||||
|
||||
ort_session.return_value.get_inputs.return_value = [SimpleNamespace(name="input.1", shape=(1, 3, 224, 224))]
|
||||
ort_session.return_value.get_outputs.return_value = [SimpleNamespace(name="output.1", shape=(1, 800))]
|
||||
|
||||
proto = mock.Mock()
|
||||
|
||||
input_dims = mock.Mock()
|
||||
input_dims.name = "input.1"
|
||||
input_dims.type.tensor_type.shape.dim = [SimpleNamespace(dim_value=size) for size in [1, 3, 224, 224]]
|
||||
proto.graph.input = [input_dims]
|
||||
|
||||
output_dims = mock.Mock()
|
||||
output_dims.name = "output.1"
|
||||
output_dims.type.tensor_type.shape.dim = [SimpleNamespace(dim_value=size) for size in [1, 800]]
|
||||
proto.graph.output = [output_dims]
|
||||
|
||||
onnx.load.return_value = proto
|
||||
|
||||
face_recognizer = FaceRecognizer("buffalo_s")
|
||||
face_recognizer.load()
|
||||
|
||||
assert face_recognizer.batch is True
|
||||
update_dims.assert_called_once_with(proto, {"input.1": ["batch", 3, 224, 224]}, {"output.1": ["batch", 800]})
|
||||
onnx.save.assert_called_once_with(update_dims.return_value, face_recognizer.model_path)
|
||||
|
||||
def test_recognition_does_not_add_batch_axis_if_exists(self, ort_session: mock.Mock, mocker: MockerFixture) -> None:
|
||||
onnx = mocker.patch("app.models.facial_recognition.recognition.onnx", autospec=True)
|
||||
update_dims = mocker.patch(
|
||||
"app.models.facial_recognition.recognition.update_inputs_outputs_dims", autospec=True
|
||||
)
|
||||
mocker.patch("app.models.base.InferenceModel.download")
|
||||
mocker.patch("app.models.facial_recognition.recognition.ArcFaceONNX")
|
||||
|
||||
inputs = [SimpleNamespace(name="input.1", shape=("batch", 3, 224, 224))]
|
||||
outputs = [SimpleNamespace(name="output.1", shape=("batch", 800))]
|
||||
ort_session.return_value.get_inputs.return_value = inputs
|
||||
ort_session.return_value.get_outputs.return_value = outputs
|
||||
|
||||
face_recognizer = FaceRecognizer("buffalo_s")
|
||||
face_recognizer.load()
|
||||
|
||||
assert face_recognizer.batch is True
|
||||
update_dims.assert_not_called()
|
||||
onnx.load.assert_not_called()
|
||||
onnx.save.assert_not_called()
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
class TestCache:
|
||||
|
|
|
|||
|
|
@ -97,4 +97,4 @@ line-length = 120
|
|||
target-version = ['py311']
|
||||
|
||||
[tool.pytest.ini_options]
|
||||
markers = ["providers"]
|
||||
markers = ["providers", "ov_device_ids"]
|
||||
|
|
|
|||
Loading…
Reference in a new issue