Hello! Our static bug checker has found a performance issue in TF2/utils.py: trainer is repeatedly called in a for loop, but there is a tf.function decorated function optimize_one_step defined and called in trainer.
In that case, when is called in a loop, the function will create a new graph every time, and that can trigger tf.function retracing warning.
Here is the tensorflow document to support it.
Briefly, for better efficiency, it's better to use:
@tf.function
def inner():
pass
def outer():
inner() than:
def outer():
@tf.function
def inner():
pass
inner()Looking forward to your reply.
Hello! Our static bug checker has found a performance issue in TF2/utils.py:
traineris repeatedly called in a for loop, but there is a tf.function decorated functionoptimize_one_stepdefined and called intrainer.In that case, when
is called in a loop, the functionwill create a new graph every time, and that can trigger tf.function retracing warning.Here is the tensorflow document to support it.
Briefly, for better efficiency, it's better to use:
than:
Looking forward to your reply.