def _create_train_loop_fn(train_step_fn, options: StandardTrainerOptions):
"""Creates a training loop from the given step function and options."""
if options.use_tf_while_loop:
loop_fn = loop_fns.create_tf_while_loop_fn(train_step_fn)
if options.use_tpu_summary_optimization:
loop_fn = loop_fns.LoopFnWithSummaries(loop_fn)
else:
loop_fn = tf.function(loop_fn)
else:
if options.use_tf_function:
train_step_fn = tf.function(train_step_fn)
loop_fn = loop_fns.create_loop_fn(train_step_fn)
return loop_fn
def create_train_loop_fn(self):
"""Creates a training loop from the current step function and options.
Returns:
The train loop function, i.e. wrapper of multiple train steps.
"""
train_step_fn = self.train_step
if self._train_options.use_tf_while_loop:
loop_fn = loop_fns.create_tf_while_loop_fn(train_step_fn)
if self._train_options.use_tpu_summary_optimization:
loop_fn = loop_fns.LoopFnWithSummaries(loop_fn)
else:
loop_fn = tf.function(loop_fn)
else:
if self._train_options.use_tf_function:
train_step_fn = tf.function(train_step_fn)
loop_fn = loop_fns.create_loop_fn(train_step_fn)
return loop_fn