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1ec9a60e41
* configurable batch size, default openvino to 1 * update docs * don't add a new dependency for two lines * fix typing
87 lines
4.0 KiB
Python
87 lines
4.0 KiB
Python
from pathlib import Path
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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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from onnx.tools.update_model_dims import update_inputs_outputs_dims
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from PIL import Image
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from app.config import log, settings
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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, ModelFormat, ModelSession, ModelTask, ModelType
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class FaceRecognizer(InferenceModel):
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depends = [(ModelType.DETECTION, ModelTask.FACIAL_RECOGNITION)]
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identity = (ModelType.RECOGNITION, ModelTask.FACIAL_RECOGNITION)
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def __init__(self, model_name: str, min_score: float = 0.7, **model_kwargs: Any) -> None:
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super().__init__(model_name, **model_kwargs)
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self.min_score = model_kwargs.pop("minScore", min_score)
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max_batch_size = settings.max_batch_size.facial_recognition if settings.max_batch_size else None
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self.batch_size = max_batch_size if max_batch_size else self._batch_size_default
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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.batch_size or self.batch_size > 1) and str(session.get_inputs()[0].shape[0]) != "batch":
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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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session=session,
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)
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return session
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def _predict(
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self, inputs: NDArray[np.uint8] | bytes | Image.Image, faces: FaceDetectionOutput, **kwargs: Any
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) -> FacialRecognitionOutput:
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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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cropped_faces = self._crop(inputs, faces)
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embeddings = self._predict_batch(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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if not self.batch_size or len(cropped_faces) <= self.batch_size:
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embeddings: NDArray[np.float32] = self.model.get_feat(cropped_faces)
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return embeddings
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batch_embeddings: list[NDArray[np.float32]] = []
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for i in range(0, len(cropped_faces), self.batch_size):
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batch_embeddings.append(self.model.get_feat(cropped_faces[i : i + self.batch_size]))
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return np.concatenate(batch_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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"boundingBox": {"x1": x1, "y1": y1, "x2": x2, "y2": y2},
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"embedding": embedding,
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"score": score,
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}
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for (x1, y1, x2, y2), embedding, score in zip(faces["boxes"], embeddings, faces["scores"])
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]
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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 _add_batch_axis(self, model_path: Path) -> None:
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log.debug(f"Adding batch axis to model {model_path}")
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proto = onnx.load(model_path)
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static_input_dims = [shape.dim_value for shape in proto.graph.input[0].type.tensor_type.shape.dim[1:]]
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static_output_dims = [shape.dim_value for shape in proto.graph.output[0].type.tensor_type.shape.dim[1:]]
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input_dims = {proto.graph.input[0].name: ["batch"] + static_input_dims}
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output_dims = {proto.graph.output[0].name: ["batch"] + static_output_dims}
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updated_proto = update_inputs_outputs_dims(proto, input_dims, output_dims)
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onnx.save(updated_proto, model_path)
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@property
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def _batch_size_default(self) -> int | None:
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providers = ort.get_available_providers()
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return None if self.model_format == ModelFormat.ONNX and "OpenVINOExecutionProvider" not in providers else 1
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