Tensorflow Object Detection API quantization with output tensor as TFLite_Detection_PostProcess

Question

I am in the process of performing a full 8-bit quantization of ssd_mobilenet_v2_320x320_coco17 using Object detection API. For this I run the following notebook.

In my case my final hardware requires that the output tensors are TFLite_Detection_PostProcess (they actually can be float32). However, when performing post-training quantization, with the Object detection API, the final tflite output tensors are StatefulPartitonedCall, which are not accepted by the application running on the mcu. So somehow I need to convert to TFLite_Detection_PostProcess.

Some searching reveales that some are using converter = tf.lite.TFLiteConverter.from_frozen_graph(... output_array=output_array...) for convertion where they use

output_arrays = ['TFLite_Detection_PostProcess', 
           'TFLite_Detection_PostProcess:1', 
           'TFLite_Detection_PostProcess:2', 
           'TFLite_Detection_PostProcess:3']

In my case the notebook is using converter = tf.lite.TFLiteConverter.from_saved_model(...) which does not accept an parameter such as output_array.

So, can I quantize using tf.lite.TFLiteConverter.from_saved_model and define my output tensors to TFLite_Detection_PostProcess?

See also TF Object detection API issue tracker TF2 Detection API Models on mobile TFLite_Detection_PostProcess #10130