import logging import os import numpy as np try: from tflite_runtime.interpreter import Interpreter, load_delegate except ModuleNotFoundError: from tensorflow.lite.python.interpreter import Interpreter, load_delegate logger = logging.getLogger(__name__) def tflite_init(self, interpreter): self.interpreter = interpreter self.interpreter.allocate_tensors() self.tensor_input_details = self.interpreter.get_input_details() self.tensor_output_details = self.interpreter.get_output_details() def tflite_detect_raw(self, tensor_input): self.interpreter.set_tensor(self.tensor_input_details[0]["index"], tensor_input) self.interpreter.invoke() boxes = self.interpreter.tensor(self.tensor_output_details[0]["index"])()[0] class_ids = self.interpreter.tensor(self.tensor_output_details[1]["index"])()[0] scores = self.interpreter.tensor(self.tensor_output_details[2]["index"])()[0] count = int(self.interpreter.tensor(self.tensor_output_details[3]["index"])()[0]) detections = np.zeros((20, 6), np.float32) for i in range(count): if scores[i] < 0.4 or i == 20: break detections[i] = [ class_ids[i], float(scores[i]), boxes[i][0], boxes[i][1], boxes[i][2], boxes[i][3], ] return detections def tflite_load_delegate_interpreter( delegate_library: str, detector_config, device_config ): try: logger.info("Attempting to load NPU") tf_delegate = load_delegate(delegate_library, device_config) logger.info("NPU found") interpreter = Interpreter( model_path=detector_config.model.path, experimental_delegates=[tf_delegate], ) return interpreter except ValueError: _, ext = os.path.splitext(detector_config.model.path) if ext and ext != ".tflite": logger.error( "Incorrect model used with NPU. Only .tflite models can be used with a TFLite delegate." ) else: logger.error( "No NPU was detected. If you do not have a TFLite device yet, you must configure CPU detectors." ) raise