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feat(ml): configurable batch size for facial recognition (#13689)

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* configurable batch size, default openvino to 1

* update docs

* don't add a new dependency for two lines

* fix typing
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Mert 2024-10-23 08:50:28 -04:00 committed by GitHub
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commit 1ec9a60e41
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4 changed files with 70 additions and 31 deletions
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5
docs/docs/install/environment-variables.md
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@ -149,7 +149,7 @@ 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 |
@ -158,13 +158,14 @@ Redis (Sentinel) URL example JSON before encoding:
| `MACHINE_LEARNING_MODEL_INTRA_OP_THREADS` | Number of threads for each model operation | `2` | machine learning |
| `MACHINE_LEARNING_WORKERS`<sup>\*2</sup> | Number of worker processes to spawn | `1` | machine learning |
| `MACHINE_LEARNING_HTTP_KEEPALIVE_TIMEOUT_S`<sup>\*3</sup> | 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_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`<sup>\*4</sup> | 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.

5
machine-learning/app/config.py
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@ -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:

25
machine-learning/app/models/facial_recognition/recognition.py
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@ -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]:
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

32
machine-learning/app/test_main.py
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@ -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()