forked from Github/frigate
YOLOX support for OpenVINO Detector (#5285)
* Initial commit to enable Yolox models with OpenVINO in Frigate * Fix ModelEnumtType import error in openvino.py * Initial edit of the docs to include verbage about yolox * Initial edit of the docs to include verbage about yolox * Elaborate configuration and limitations in docs. * Add capability to dynamically determine number of classes in yolox model * Further refinements * Removed unnecesarry comments, improved documentation, addressed PR items * Fixed lint formatting issues
This commit is contained in:
Anil Ozyalcin
@@ -3,7 +3,7 @@ import numpy as np
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import openvino.runtime as ov
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from frigate.detectors.detection_api import DetectionApi
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from frigate.detectors.detector_config import BaseDetectorConfig
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from frigate.detectors.detector_config import BaseDetectorConfig, ModelTypeEnum
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from typing import Literal
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from pydantic import Extra, Field
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@@ -24,12 +24,18 @@ class OvDetector(DetectionApi):
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def __init__(self, detector_config: OvDetectorConfig):
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self.ov_core = ov.Core()
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self.ov_model = self.ov_core.read_model(detector_config.model.path)
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self.ov_model_type = detector_config.model.model_type
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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.interpreter = self.ov_core.compile_model(
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model=self.ov_model, device_name=detector_config.device
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)
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logger.info(f"Model Input Shape: {self.interpreter.input(0).shape}")
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self.output_indexes = 0
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while True:
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try:
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tensor_shape = self.interpreter.output(self.output_indexes).shape
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@@ -38,29 +44,92 @@ class OvDetector(DetectionApi):
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except:
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logger.info(f"Model has {self.output_indexes} Output Tensors")
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break
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if self.ov_model_type == ModelTypeEnum.yolox:
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self.num_classes = tensor_shape[2] - 5
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logger.info(f"YOLOX model has {self.num_classes} classes")
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self.set_strides_grids()
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def set_strides_grids(self):
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grids = []
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expanded_strides = []
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strides = [8, 16, 32]
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hsizes = [self.h // stride for stride in strides]
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wsizes = [self.w // stride for stride in strides]
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for hsize, wsize, stride in zip(hsizes, wsizes, strides):
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xv, yv = np.meshgrid(np.arange(wsize), np.arange(hsize))
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grid = np.stack((xv, yv), 2).reshape(1, -1, 2)
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grids.append(grid)
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shape = grid.shape[:2]
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expanded_strides.append(np.full((*shape, 1), stride))
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self.grids = np.concatenate(grids, 1)
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self.expanded_strides = np.concatenate(expanded_strides, 1)
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def detect_raw(self, tensor_input):
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infer_request = self.interpreter.create_infer_request()
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infer_request.infer([tensor_input])
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results = infer_request.get_output_tensor()
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if self.ov_model_type == ModelTypeEnum.ssd:
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results = infer_request.get_output_tensor()
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detections = np.zeros((20, 6), np.float32)
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i = 0
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for object_detected in results.data[0, 0, :]:
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if object_detected[0] != -1:
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logger.debug(object_detected)
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if object_detected[2] < 0.1 or i == 20:
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break
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detections[i] = [
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object_detected[1], # Label ID
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float(object_detected[2]), # Confidence
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object_detected[4], # y_min
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object_detected[3], # x_min
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object_detected[6], # y_max
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object_detected[5], # x_max
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]
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i += 1
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detections = np.zeros((20, 6), np.float32)
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i = 0
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for object_detected in results.data[0, 0, :]:
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if object_detected[0] != -1:
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logger.debug(object_detected)
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if object_detected[2] < 0.1 or i == 20:
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break
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detections[i] = [
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object_detected[1], # Label ID
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float(object_detected[2]), # Confidence
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object_detected[4], # y_min
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object_detected[3], # x_min
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object_detected[6], # y_max
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object_detected[5], # x_max
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]
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i += 1
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return detections
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elif self.ov_model_type == ModelTypeEnum.yolox:
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out_tensor = infer_request.get_output_tensor()
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# [x, y, h, w, box_score, class_no_1, ..., class_no_80],
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results = out_tensor.data
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results[..., :2] = (results[..., :2] + self.grids) * self.expanded_strides
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results[..., 2:4] = np.exp(results[..., 2:4]) * self.expanded_strides
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image_pred = results[0, ...]
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return detections
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class_conf = np.max(
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image_pred[:, 5 : 5 + self.num_classes], axis=1, keepdims=True
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)
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class_pred = np.argmax(image_pred[:, 5 : 5 + self.num_classes], axis=1)
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class_pred = np.expand_dims(class_pred, axis=1)
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conf_mask = (image_pred[:, 4] * class_conf.squeeze() >= 0.3).squeeze()
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# Detections ordered as (x1, y1, x2, y2, obj_conf, class_conf, class_pred)
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dets = np.concatenate((image_pred[:, :5], class_conf, class_pred), axis=1)
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dets = dets[conf_mask]
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ordered = dets[dets[:, 5].argsort()[::-1]][:20]
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detections = np.zeros((20, 6), np.float32)
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i = 0
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for object_detected in ordered:
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if i < 20:
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detections[i] = [
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object_detected[6], # Label ID
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object_detected[5], # Confidence
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(object_detected[1] - (object_detected[3] / 2))
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/ self.h, # y_min
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(object_detected[0] - (object_detected[2] / 2))
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/ self.w, # x_min
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(object_detected[1] + (object_detected[3] / 2))
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/ self.h, # y_max
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(object_detected[0] + (object_detected[2] / 2))
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/ self.w, # x_max
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]
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i += 1
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else:
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break
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return detections
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