From 2d8ca064fe41389cd3b6edfc419a3bf0ddb06bca Mon Sep 17 00:00:00 2001 From: Anil Ozyalcin Date: Sat, 28 Jan 2023 20:52:35 -0800 Subject: [PATCH] Initial commit to enable Yolox models with OpenVINO in Frigate --- frigate/detectors/detector_config.py | 6 ++ frigate/detectors/plugins/openvino.py | 105 +++++++++++++++++++++----- 2 files changed, 92 insertions(+), 19 deletions(-) diff --git a/frigate/detectors/detector_config.py b/frigate/detectors/detector_config.py index 747a12de4..59d0a4751 100644 --- a/frigate/detectors/detector_config.py +++ b/frigate/detectors/detector_config.py @@ -22,6 +22,9 @@ class InputTensorEnum(str, Enum): nchw = "nchw" nhwc = "nhwc" +class ModelTypeEnum(str, Enum): + ssd = "ssd" + yolox = "yolox" class ModelConfig(BaseModel): path: Optional[str] = Field(title="Custom Object detection model path.") @@ -37,6 +40,9 @@ class ModelConfig(BaseModel): input_pixel_format: PixelFormatEnum = Field( default=PixelFormatEnum.rgb, title="Model Input Pixel Color Format" ) + model_type: ModelTypeEnum = Field( + default=ModelTypeEnum.ssd, title="Object Detection Model Type" + ) _merged_labelmap: Optional[Dict[int, str]] = PrivateAttr() _colormap: Dict[int, Tuple[int, int, int]] = PrivateAttr() diff --git a/frigate/detectors/plugins/openvino.py b/frigate/detectors/plugins/openvino.py index 93f3cf6a6..85b33b3da 100644 --- a/frigate/detectors/plugins/openvino.py +++ b/frigate/detectors/plugins/openvino.py @@ -4,6 +4,7 @@ import openvino.runtime as ov from frigate.detectors.detection_api import DetectionApi from frigate.detectors.detector_config import BaseDetectorConfig +from frigate.config import ModelTypeEnum from typing import Literal from pydantic import Extra, Field @@ -24,6 +25,14 @@ class OvDetector(DetectionApi): def __init__(self, detector_config: OvDetectorConfig): self.ov_core = ov.Core() self.ov_model = self.ov_core.read_model(detector_config.model.path) + self.ov_model_type = detector_config.model.model_type + + self.num_classes = 80 # TODO + self.h = detector_config.model.height # 416 + self.w = detector_config.model.width # 416 + logger.info(self.ov_model_type) + if(self.ov_model_type == ModelTypeEnum.yolox): + self.set_strides_grids() self.interpreter = self.ov_core.compile_model( model=self.ov_model, device_name=detector_config.device @@ -39,28 +48,86 @@ class OvDetector(DetectionApi): logger.info(f"Model has {self.output_indexes} Output Tensors") break - def detect_raw(self, tensor_input): + def set_strides_grids(self): + grids = [] + expanded_strides = [] + strides = [8, 16, 32] + + hsizes = [self.h // stride for stride in strides] + wsizes = [self.w // stride for stride in strides] + + for hsize, wsize, stride in zip(hsizes, wsizes, strides): + xv, yv = np.meshgrid(np.arange(wsize), np.arange(hsize)) + grid = np.stack((xv, yv), 2).reshape(1, -1, 2) + grids.append(grid) + shape = grid.shape[:2] + expanded_strides.append(np.full((*shape, 1), stride)) + + self.grids = np.concatenate(grids, 1) + self.expanded_strides = np.concatenate(expanded_strides, 1) + + def detect_raw(self, tensor_input): infer_request = self.interpreter.create_infer_request() infer_request.infer([tensor_input]) - results = infer_request.get_output_tensor() + if(self.ov_model_type == ModelTypeEnum.ssd): + results = infer_request.get_output_tensor() - detections = np.zeros((20, 6), np.float32) - i = 0 - for object_detected in results.data[0, 0, :]: - if object_detected[0] != -1: - logger.debug(object_detected) - if object_detected[2] < 0.1 or i == 20: - break - detections[i] = [ - object_detected[1], # Label ID - float(object_detected[2]), # Confidence - object_detected[4], # y_min - object_detected[3], # x_min - object_detected[6], # y_max - object_detected[5], # x_max - ] - i += 1 + detections = np.zeros((20, 6), np.float32) + i = 0 + for object_detected in results.data[0, 0, :]: + if object_detected[0] != -1: + logger.debug(object_detected) + if object_detected[2] < 0.1 or i == 20: + break + detections[i] = [ + object_detected[1], # Label ID + float(object_detected[2]), # Confidence + object_detected[4], # y_min + object_detected[3], # x_min + object_detected[6], # y_max + object_detected[5], # x_max + ] + i += 1 + return detections - return detections + elif(self.ov_model_type == ModelTypeEnum.yolox): + out_tensor = infer_request.get_output_tensor() + results = out_tensor.data + results[..., :2] = (results[..., :2] + self.grids) * self.expanded_strides + results[..., 2:4] = np.exp(results[..., 2:4]) * self.expanded_strides + image_pred = results[0, ...] + + class_conf = np.max(image_pred[:, 5:5+self.num_classes], axis=1, keepdims=True) + class_pred = np.argmax(image_pred[: , 5:5+self.num_classes], axis=1) + class_pred = np.expand_dims(class_pred, axis=1) + + conf_mask = (image_pred[:, 4] * class_conf.squeeze() >= 0.3).squeeze() + # Detections ordered as (x1, y1, x2, y2, obj_conf, class_conf, class_pred) + dets = np.concatenate((image_pred[:, :5], class_conf, class_pred), axis=1) + dets = dets[conf_mask] + + ordered = dets[dets[:, 5].argsort()[::-1]][:20] + + detections = np.zeros((20, 6), np.float32) + i = 0 + + for object_detected in ordered: + if i < 20: + # [x, y, h, w, box_score, class_no_1, ..., class_no_80], + detections[i] = [ + object_detected[6], # Label ID + object_detected[5], # Confidence + (object_detected[1]-(object_detected[3]/2))/self.h, # y_min + (object_detected[0]-(object_detected[2]/2))/self.w, # x_min + (object_detected[1]+(object_detected[3]/2))/self.h, # y_max + (object_detected[0]+(object_detected[2]/2))/self.w, # x_max + ] + #logger.info(object_detected) + #logger.info(detections[i]) + i += 1 + else: + break + + return detections