A more consistent and multi-functional global level of verbosity control,
suggest an enhancement that will see print(...) in project be converted to using the python logging. module

import logging
#Then instead of print() use either
logging.info(......)
#or
logging.debug(.....)
#or
logging.warning(....)
#or
#logging.error()

In that way verbosity can be globally

The 2x down-sampling is one of the important operations in reference models. But, a convolution or a pooling with stride=2, padding='SAME' may result in different outputs over different deep learning libraries (e.g., TensorFlow, CNTK, Theano, Caffe, Torch, ...) due to their different padding behaviors.

For example (TensorNets syntax; but can be regarded as pseudo codes for other libraries),

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pip insatll tensorflow==2.0.0 or pip install tensorflow==2.0.0-beta1，tensorflow2.0.0or tensorflow2.0.0-beta1,but when I run train.py, the error of "ValueError: This converter can only convert a single ConcreteFunction. Converting multiple functions is under development" appears,why?How to do?