mirror of
https://github.com/blakeblackshear/frigate.git
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2f69f5afe6
* Add support for yolonas in onnx * Add correct deps * Set ld library path * Refactor cudnn to only be used in amd64 * Add onnx to docs and add explainer at the top * Undo change * Update comment * Remove uneccesary * Remove line change
90 lines
2.8 KiB
Python
90 lines
2.8 KiB
Python
import logging
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import cv2
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import numpy as np
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from typing_extensions import Literal
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from frigate.detectors.detection_api import DetectionApi
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from frigate.detectors.detector_config import (
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BaseDetectorConfig,
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ModelTypeEnum,
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PixelFormatEnum,
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)
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logger = logging.getLogger(__name__)
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DETECTOR_KEY = "onnx"
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class ONNXDetectorConfig(BaseDetectorConfig):
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type: Literal[DETECTOR_KEY]
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class ONNXDetector(DetectionApi):
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type_key = DETECTOR_KEY
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def __init__(self, detector_config: ONNXDetectorConfig):
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try:
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import onnxruntime as ort
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logger.info("ONNX: loaded onnxruntime module")
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except ModuleNotFoundError:
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logger.error(
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"ONNX: module loading failed, need 'pip install onnxruntime'?!?"
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)
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raise
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path = detector_config.model.path
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logger.info(f"ONNX: loading {detector_config.model.path}")
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self.model = ort.InferenceSession(path, providers=ort.get_available_providers())
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self.h = detector_config.model.height
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self.w = detector_config.model.width
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self.onnx_model_type = detector_config.model.model_type
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self.onnx_model_px = detector_config.model.input_pixel_format
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path = detector_config.model.path
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logger.info(f"ONNX: {path} loaded")
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def detect_raw(self, tensor_input):
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model_input_name = self.model.get_inputs()[0].name
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model_input_shape = self.model.get_inputs()[0].shape
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# adjust input shape
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if self.onnx_model_type == ModelTypeEnum.yolonas:
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tensor_input = cv2.dnn.blobFromImage(
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tensor_input[0],
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1.0,
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(model_input_shape[3], model_input_shape[2]),
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None,
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swapRB=self.onnx_model_px == PixelFormatEnum.bgr,
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).astype(np.uint8)
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tensor_output = self.model.run(None, {model_input_name: tensor_input})
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if self.onnx_model_type == ModelTypeEnum.yolonas:
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predictions = tensor_output[0]
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detections = np.zeros((20, 6), np.float32)
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for i, prediction in enumerate(predictions):
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if i == 20:
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break
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(_, x_min, y_min, x_max, y_max, confidence, class_id) = prediction
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# when running in GPU mode, empty predictions in the output have class_id of -1
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if class_id < 0:
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break
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detections[i] = [
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class_id,
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confidence,
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y_min / self.h,
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x_min / self.w,
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y_max / self.h,
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x_max / self.w,
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]
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return detections
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else:
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raise Exception(
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f"{self.onnx_model_type} is currently not supported for rocm. See the docs for more info on supported models."
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)
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