From 1ec9a60e4117205cff2556ff5ae7989c09395ab8 Mon Sep 17 00:00:00 2001 From: Mert <101130780+mertalev@users.noreply.github.com> Date: Wed, 23 Oct 2024 08:50:28 -0400 Subject: [PATCH] feat(ml): configurable batch size for facial recognition (#13689) * configurable batch size, default openvino to 1 * update docs * don't add a new dependency for two lines * fix typing --- docs/docs/install/environment-variables.md | 35 ++++++++++--------- machine-learning/app/config.py | 5 +++ .../models/facial_recognition/recognition.py | 29 +++++++++------ machine-learning/app/test_main.py | 32 +++++++++++++++-- 4 files changed, 70 insertions(+), 31 deletions(-) diff --git a/docs/docs/install/environment-variables.md b/docs/docs/install/environment-variables.md index bb9b4d434c..e86199dc74 100644 --- a/docs/docs/install/environment-variables.md +++ b/docs/docs/install/environment-variables.md @@ -148,23 +148,24 @@ Redis (Sentinel) URL example JSON before encoding: ## Machine Learning -| Variable | Description | Default | Containers | -| :-------------------------------------------------------- | :-------------------------------------------------------------------------------------------------- | :-----------------------------------: | :--------------- | -| `MACHINE_LEARNING_MODEL_TTL` | Inactivity time (s) before a model is unloaded (disabled if \<= 0) | `300` | machine learning | -| `MACHINE_LEARNING_MODEL_TTL_POLL_S` | Interval (s) between checks for the model TTL (disabled if \<= 0) | `10` | machine learning | -| `MACHINE_LEARNING_CACHE_FOLDER` | Directory where models are downloaded | `/cache` | machine learning | -| `MACHINE_LEARNING_REQUEST_THREADS`\*1 | Thread count of the request thread pool (disabled if \<= 0) | number of CPU cores | machine learning | -| `MACHINE_LEARNING_MODEL_INTER_OP_THREADS` | Number of parallel model operations | `1` | machine learning | -| `MACHINE_LEARNING_MODEL_INTRA_OP_THREADS` | Number of threads for each model operation | `2` | machine learning | -| `MACHINE_LEARNING_WORKERS`\*2 | Number of worker processes to spawn | `1` | machine learning | -| `MACHINE_LEARNING_HTTP_KEEPALIVE_TIMEOUT_S`\*3 | HTTP Keep-alive time in seconds | `2` | machine learning | -| `MACHINE_LEARNING_WORKER_TIMEOUT` | Maximum time (s) of unresponsiveness before a worker is killed | `120` (`300` if using OpenVINO image) | machine learning | -| `MACHINE_LEARNING_PRELOAD__CLIP` | Name of a CLIP model to be preloaded and kept in cache | | machine learning | -| `MACHINE_LEARNING_PRELOAD__FACIAL_RECOGNITION` | Name of a facial recognition model to be preloaded and kept in cache | | machine learning | -| `MACHINE_LEARNING_ANN` | Enable ARM-NN hardware acceleration if supported | `True` | machine learning | -| `MACHINE_LEARNING_ANN_FP16_TURBO` | Execute operations in FP16 precision: increasing speed, reducing precision (applies only to ARM-NN) | `False` | machine learning | -| `MACHINE_LEARNING_ANN_TUNING_LEVEL` | ARM-NN GPU tuning level (1: rapid, 2: normal, 3: exhaustive) | `2` | machine learning | -| `MACHINE_LEARNING_DEVICE_IDS`\*4 | Device IDs to use in multi-GPU environments | `0` | machine learning | +| Variable | Description | Default | Containers | +| :-------------------------------------------------------- | :-------------------------------------------------------------------------------------------------- | :-----------------------------: | :--------------- | +| `MACHINE_LEARNING_MODEL_TTL` | Inactivity time (s) before a model is unloaded (disabled if \<= 0) | `300` | machine learning | +| `MACHINE_LEARNING_MODEL_TTL_POLL_S` | Interval (s) between checks for the model TTL (disabled if \<= 0) | `10` | machine learning | +| `MACHINE_LEARNING_CACHE_FOLDER` | Directory where models are downloaded | `/cache` | machine learning | +| `MACHINE_LEARNING_REQUEST_THREADS`\*1 | Thread count of the request thread pool (disabled if \<= 0) | number of CPU cores | machine learning | +| `MACHINE_LEARNING_MODEL_INTER_OP_THREADS` | Number of parallel model operations | `1` | machine learning | +| `MACHINE_LEARNING_MODEL_INTRA_OP_THREADS` | Number of threads for each model operation | `2` | machine learning | +| `MACHINE_LEARNING_WORKERS`\*2 | Number of worker processes to spawn | `1` | machine learning | +| `MACHINE_LEARNING_HTTP_KEEPALIVE_TIMEOUT_S`\*3 | HTTP Keep-alive time in seconds | `2` | machine learning | +| `MACHINE_LEARNING_WORKER_TIMEOUT` | Maximum time (s) of unresponsiveness before a worker is killed | `120` (`300` if using OpenVINO) | machine learning | +| `MACHINE_LEARNING_PRELOAD__CLIP` | Name of a CLIP model to be preloaded and kept in cache | | machine learning | +| `MACHINE_LEARNING_PRELOAD__FACIAL_RECOGNITION` | Name of a facial recognition model to be preloaded and kept in cache | | machine learning | +| `MACHINE_LEARNING_ANN` | Enable ARM-NN hardware acceleration if supported | `True` | machine learning | +| `MACHINE_LEARNING_ANN_FP16_TURBO` | Execute operations in FP16 precision: increasing speed, reducing precision (applies only to ARM-NN) | `False` | machine learning | +| `MACHINE_LEARNING_ANN_TUNING_LEVEL` | ARM-NN GPU tuning level (1: rapid, 2: normal, 3: exhaustive) | `2` | machine learning | +| `MACHINE_LEARNING_DEVICE_IDS`\*4 | Device IDs to use in multi-GPU environments | `0` | machine learning | +| `MACHINE_LEARNING_MAX_BATCH_SIZE__FACIAL_RECOGNITION` | Set the maximum number of faces that will be processed at once by the facial recognition model | None (`1` if using OpenVINO) | machine learning | \*1: It is recommended to begin with this parameter when changing the concurrency levels of the machine learning service and then tune the other ones. diff --git a/machine-learning/app/config.py b/machine-learning/app/config.py index 828dee15f0..92799ac692 100644 --- a/machine-learning/app/config.py +++ b/machine-learning/app/config.py @@ -19,6 +19,10 @@ class PreloadModelData(BaseModel): facial_recognition: str | None = None +class MaxBatchSize(BaseModel): + facial_recognition: int | None = None + + class Settings(BaseSettings): model_config = SettingsConfigDict( env_prefix="MACHINE_LEARNING_", @@ -41,6 +45,7 @@ class Settings(BaseSettings): ann_fp16_turbo: bool = False ann_tuning_level: int = 2 preload: PreloadModelData | None = None + max_batch_size: MaxBatchSize | None = None @property def device_id(self) -> str: diff --git a/machine-learning/app/models/facial_recognition/recognition.py b/machine-learning/app/models/facial_recognition/recognition.py index c060bdd616..dcfb6b530e 100644 --- a/machine-learning/app/models/facial_recognition/recognition.py +++ b/machine-learning/app/models/facial_recognition/recognition.py @@ -3,13 +3,14 @@ from typing import Any import numpy as np import onnx +import onnxruntime as ort from insightface.model_zoo import ArcFaceONNX from insightface.utils.face_align import norm_crop from numpy.typing import NDArray from onnx.tools.update_model_dims import update_inputs_outputs_dims from PIL import Image -from app.config import log +from app.config import log, settings from app.models.base import InferenceModel from app.models.transforms import decode_cv2 from app.schemas import FaceDetectionOutput, FacialRecognitionOutput, ModelFormat, ModelSession, ModelTask, ModelType @@ -22,11 +23,12 @@ class FaceRecognizer(InferenceModel): def __init__(self, model_name: str, min_score: float = 0.7, **model_kwargs: Any) -> None: super().__init__(model_name, **model_kwargs) self.min_score = model_kwargs.pop("minScore", min_score) - self.batch = self.model_format == ModelFormat.ONNX + max_batch_size = settings.max_batch_size.facial_recognition if settings.max_batch_size else None + self.batch_size = max_batch_size if max_batch_size else self._batch_size_default def _load(self) -> ModelSession: session = self._make_session(self.model_path) - if self.batch and str(session.get_inputs()[0].shape[0]) != "batch": + if (not self.batch_size or self.batch_size > 1) and str(session.get_inputs()[0].shape[0]) != "batch": self._add_batch_axis(self.model_path) session = self._make_session(self.model_path) self.model = ArcFaceONNX( @@ -42,18 +44,18 @@ class FaceRecognizer(InferenceModel): return [] inputs = decode_cv2(inputs) cropped_faces = self._crop(inputs, faces) - embeddings = self._predict_batch(cropped_faces) if self.batch else self._predict_single(cropped_faces) + embeddings = self._predict_batch(cropped_faces) return self.postprocess(faces, embeddings) def _predict_batch(self, cropped_faces: list[NDArray[np.uint8]]) -> NDArray[np.float32]: - embeddings: NDArray[np.float32] = self.model.get_feat(cropped_faces) - return embeddings + if not self.batch_size or len(cropped_faces) <= self.batch_size: + embeddings: NDArray[np.float32] = self.model.get_feat(cropped_faces) + return embeddings - def _predict_single(self, cropped_faces: list[NDArray[np.uint8]]) -> NDArray[np.float32]: - embeddings: list[NDArray[np.float32]] = [] - for face in cropped_faces: - embeddings.append(self.model.get_feat(face)) - return np.concatenate(embeddings, axis=0) + batch_embeddings: list[NDArray[np.float32]] = [] + for i in range(0, len(cropped_faces), self.batch_size): + batch_embeddings.append(self.model.get_feat(cropped_faces[i : i + self.batch_size])) + return np.concatenate(batch_embeddings, axis=0) def postprocess(self, faces: FaceDetectionOutput, embeddings: NDArray[np.float32]) -> FacialRecognitionOutput: return [ @@ -77,3 +79,8 @@ class FaceRecognizer(InferenceModel): output_dims = {proto.graph.output[0].name: ["batch"] + static_output_dims} updated_proto = update_inputs_outputs_dims(proto, input_dims, output_dims) onnx.save(updated_proto, model_path) + + @property + def _batch_size_default(self) -> int | None: + providers = ort.get_available_providers() + return None if self.model_format == ModelFormat.ONNX and "OpenVINOExecutionProvider" not in providers else 1 diff --git a/machine-learning/app/test_main.py b/machine-learning/app/test_main.py index 50ec188aa4..e5cb63997c 100644 --- a/machine-learning/app/test_main.py +++ b/machine-learning/app/test_main.py @@ -549,7 +549,7 @@ class TestFaceRecognition: face_recognizer = FaceRecognizer("buffalo_s", cache_dir=path) face_recognizer.load() - assert face_recognizer.batch is True + assert face_recognizer.batch_size is None 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) @@ -572,7 +572,7 @@ class TestFaceRecognition: face_recognizer = FaceRecognizer("buffalo_s", cache_dir=path) face_recognizer.load() - assert face_recognizer.batch is True + assert face_recognizer.batch_size is None update_dims.assert_not_called() onnx.load.assert_not_called() onnx.save.assert_not_called() @@ -596,7 +596,33 @@ class TestFaceRecognition: face_recognizer = FaceRecognizer("buffalo_s", model_format=ModelFormat.ARMNN, cache_dir=path) face_recognizer.load() - assert face_recognizer.batch is False + assert face_recognizer.batch_size == 1 + update_dims.assert_not_called() + onnx.load.assert_not_called() + onnx.save.assert_not_called() + + def test_recognition_does_not_add_batch_axis_for_openvino( + self, ort_session: mock.Mock, path: 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") + path.return_value.__truediv__.return_value.__truediv__.return_value.suffix = ".onnx" + + 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", model_format=ModelFormat.ARMNN, cache_dir=path, providers=["OpenVINOExecutionProvider"] + ) + face_recognizer.load() + + assert face_recognizer.batch_size == 1 update_dims.assert_not_called() onnx.load.assert_not_called() onnx.save.assert_not_called()