[FR] Enable YOLOv4 engine about berrynet HOT 5 OPEN

pi@raspberrypi:~/codes/tensorflow-yolov4-tflite $ python3 detect.py --weights ./checkpoints/yolov4-tiny-416.tflite --size 416 --model yolov4 --image ./data/kite.jpg --framework tflite
Traceback (most recent call last):
  File "detect.py", line 90, in <module>
    app.run(main)
  File "/usr/local/lib/python3.7/dist-packages/absl/app.py", line 300, in run
    _run_main(main, args)
  File "/usr/local/lib/python3.7/dist-packages/absl/app.py", line 251, in _run_main
    sys.exit(main(argv))
  File "detect.py", line 49, in main
    interpreter = tf.lite.Interpreter(model_path=FLAGS.weights)
  File "/usr/local/lib/python3.7/dist-packages/tensorflow/lite/python/interpreter.py", line 77, in __init__
    model_path))
ValueError: Didn't find op for builtin opcode 'RESIZE_BILINEAR' version '3'
Registration failed.

pi@raspberrypi:~/codes/tensorflow-yolov4-tflite $ python3 -m pip freeze | grep tensorflow
tensorflow==1.14.0
tensorflow-estimator==1.14.0

pi@raspberrypi:~/codes/tensorflow-yolov4-tflite $ python3 detect.py --weights ./checkpoints/yolov4-tiny-416.tflite --size 416 --model yolov4 --image ./data/kite.jpg --framework tflite                   
[{'name': 'input_1', 'index': 0, 'shape': array([  1, 416, 416,   3]), 'shape_signature': array([  1, 416, 416,   3]), 'dtype': <class 'numpy.float32'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}]
[{'name': 'Identity', 'index': 224, 'shape': array([   1, 2535,    4]), 'shape_signature': array([   1, 2535,    4]), 'dtype': <class 'numpy.float32'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}, {'name': 'Identity_1', 'index': 206, 'shape': array([   1, 2535,   80]), 'shape_signature': array([   1, 2535,   80]), 'dtype': <class 'numpy.float32'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}]
Inference takes 0.8501577377319336 s