|  | 
| init_runtime应该在build或load_rknn完成之后调用,参考 
 if __name__ == '__main__':
    # Create RKNN object
    rknn = RKNN()
    # Config for Model Input PreProcess
    rknn.config(channel_mean_value='128 128 128 128', reorder_channel='0 1 2', batch_size=5)
    # Load TensorFlow Model
    print('--> Loading model')
    rknn.load_tensorflow(tf_pb='./ssd_mobilenet_v1_coco_2017_11_17.pb',
                         inputs=['FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/BatchNorm/batchnorm/mul_1'],
                         outputs=['concat', 'concat_1'],
                         input_size_list=[[INPUT_SIZE, INPUT_SIZE, 3]])
    print('done')
    # Build Model
    print('--> Building model')
    rknn.build(do_quantization=True, dataset='./dataset.txt')
    print('done')
    # Export RKNN Model
    rknn.export_rknn('./ssd_mobilenet_v1_coco.rknn')
    # Direct Load RKNN Model
    # rknn.load_rknn('./ssd_mobilenet_v1_coco.rknn')
    # Set inputs
    orig_img = cv2.imread('./road.bmp')
    img = cv2.cvtColor(orig_img, cv2.COLOR_BGR2RGB)
    img = cv2.resize(img, (INPUT_SIZE, INPUT_SIZE), interpolation=cv2.INTER_CUBIC)
    # init runtime environment
    print('--> Init runtime environment')
    ret = rknn.init_runtime()
    if ret != 0:
        print('Init runtime environment failed')
        exit(ret)
    print('done')
    # Inference
    print('--> Running model')
    outputs = rknn.inference(inputs=[img])
    print('done')
    print('inference result: ', outputs)
 | 
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