mirror of
https://github.com/blakeblackshear/frigate.git
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0de928703f
* initial implementation of D-FINE model * revert docker-compose * add docs for D-FINE * remove weird auto-format issue
104 lines
3.4 KiB
Python
104 lines
3.4 KiB
Python
import logging
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import numpy as np
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from pydantic import Field
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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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)
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from frigate.util.model import (
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get_ort_providers,
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post_process_dfine,
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post_process_yolov9,
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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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device: str = Field(default="AUTO", title="Device Type")
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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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providers, options = get_ort_providers(
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detector_config.device == "CPU", detector_config.device
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)
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self.model = ort.InferenceSession(
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path, providers=providers, provider_options=options
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)
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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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self.onnx_model_shape = detector_config.model.input_tensor
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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: np.ndarray):
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if self.onnx_model_type == ModelTypeEnum.dfine:
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tensor_output = self.model.run(
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None,
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{
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"images": tensor_input,
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"orig_target_sizes": np.array([[self.h, self.w]], dtype=np.int64),
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},
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)
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return post_process_dfine(tensor_output, self.w, self.h)
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model_input_name = self.model.get_inputs()[0].name
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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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elif self.onnx_model_type == ModelTypeEnum.yolov9:
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predictions: np.ndarray = tensor_output[0]
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return post_process_yolov9(predictions, self.w, self.h)
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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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