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Quantized conversion from TF to TFLite

Hi,
I’m working on a project that uses Edge TPU and I need to use appropriate models[1] converted from Tensorflow form. I need a face recognition and decided to use FaceNet implementation and took the model from here: [2]. I have it working on my PC, but when I tried to convert the model to tflite and compile it for edgetpu (using steps presented in [3]), I ended up with all of the resulting embeddings (output tensors) to be the same. They are in proper form (128D vector with uint8 values (as opposed to float32 values of TF model)), but they are all the same.

Does anyone has any idea what might be the reason of that? Is such conversion impossible on already pre-trained model or am I missing something obvious?

References:

[1] https://coral.ai/docs/edgetpu/models-intro/

[2] https://github.com/nyoki-mtl/keras-facenet

[3] https://www.tensorflow.org/lite/performance/post_training_integer_quant

submitted by /u/surprajs
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