Closed Ric92 closed 2 years ago
First, the conversion command you ran is wrong. You are trying to do a 3-channel normalization, and it looks like the image in the calibration dataset has 3 channels. The input is expected to be two channels.
tmp_image = eval(string_formulas_for_normalization) # Default: (data - [127.5,127.5,127.5]) / [127.5,127.5,127.5]
↓
ValueError: operands could not be broadcast together with shapes (1,120,160,2) (3,)
Secondly, even if the calibration is done correctly, the conversion to EdgeTPU is not possible at this time. Before converting to TPU, there are two types of operations that are not supported by TensorFlow Lite. These are StridedSlice
in 6D and Transpose
in 7D.
I have asked the TensorFlow team to see if they can solve this problem for me. However, I think they are very busy and it will be many years before they will heed my wishes. https://github.com/tensorflow/tensorflow/issues/53702
Thank you very much for your answer, we will wait for the TensorFlow team then.
Congratulations for your work.
Issue Type
Support
OS
Ubuntu
OS architecture
x86_64
Programming Language
Python
Framework
TensorFlowLite
Description
I have a problem converting the HITNET model to edgeTPU in particular when converting the saved_model to full_integer_quant. I have already seen that it is not by default converted and I don't know if it is something that definitely can't be done. I tested eth3d, flyingthings finalpass xl, and middlebury d400.
Thanks in advance
Relevant Log Output
URL or source code for simple inference testing code
No response