def test_raise_in_none_secs_and_steps(self):
with self.assertRaises(ValueError):
hooks.ExamplesPerSecondHook(
batch_size=256,
every_n_steps=None,
every_n_secs=None,
metric_logger=self._logger)
def _validate_log_every_n_steps(self, sess, every_n_steps, warm_steps):
hook = hooks.ExamplesPerSecondHook(
batch_size=256,
every_n_steps=every_n_steps,
warm_steps=warm_steps)
hook.begin()
mon_sess = monitored_session._HookedSession(sess, [hook]) # pylint: disable=protected-access
sess.run(tf.global_variables_initializer())
self.logged_message = ''
for _ in range(every_n_steps):
mon_sess.run(self.train_op)
self.assertEqual(str(self.logged_message).find('exp/sec'), -1)
mon_sess.run(self.train_op)
global_step_val = sess.run(self.global_step)
# assertNotRegexpMatches is not supported by python 3.1 and later
if global_step_val > warm_steps:
self.assertRegexpMatches(str(self.logged_message), 'exp/sec')
else:
self.assertEqual(str(self.logged_message).find('exp/sec'), -1)
# Add additional run to verify proper reset when called multiple times.
self.logged_message = ''
mon_sess.run(self.train_op)
global_step_val = sess.run(self.global_step)
if every_n_steps == 1 and global_step_val > warm_steps:
self.assertRegexpMatches(str(self.logged_message), 'exp/sec')
else:
self.assertEqual(str(self.logged_message).find('exp/sec'), -1)
hook.end(sess)
def _validate_log_every_n_secs(self, sess, every_n_secs):
hook = hooks.ExamplesPerSecondHook(batch_size=256,
every_n_steps=None,
every_n_secs=every_n_secs)
hook.begin()
mon_sess = monitored_session._HookedSession(sess, [hook]) # pylint: disable=protected-access
sess.run(tf.global_variables_initializer())
self.logged_message = ''
mon_sess.run(self.train_op)
self.assertEqual(str(self.logged_message).find('exp/sec'), -1)
time.sleep(every_n_secs)
self.logged_message = ''
mon_sess.run(self.train_op)
self.assertRegexpMatches(str(self.logged_message), 'exp/sec')
hook.end(sess)
def _validate_log_every_n_secs(self, every_n_secs):
hook = hooks.ExamplesPerSecondHook(batch_size=256,
every_n_steps=None,
every_n_secs=every_n_secs,
metric_logger=self._logger)
with tf.train.MonitoredSession(tf.train.ChiefSessionCreator(),
[hook]) as mon_sess:
# Explicitly run global_step after train_op to get the accurate
# global_step value
mon_sess.run(self.train_op)
mon_sess.run(self.global_step)
# Nothing should be in the list yet
self.assertFalse(self._logger.logged_metric)
time.sleep(every_n_secs)
mon_sess.run(self.train_op)
mon_sess.run(self.global_step)
self._assert_metrics()
def get_examples_per_second_hook(every_n_steps=100,
batch_size=128,
warm_steps=5,
**kwargs): # pylint: disable=unused-argument
"""Function to get ExamplesPerSecondHook.
Args:
every_n_steps: `int`, print current and average examples per second every
N steps.
batch_size: `int`, total batch size used to calculate examples/second from
global time.
warm_steps: skip this number of steps before logging and running average.
**kwargs: a dictionary of arguments to ExamplesPerSecondHook.
Returns:
Returns a ProfilerHook that writes out timelines that can be loaded into
profiling tools like chrome://tracing.
"""
return hooks.ExamplesPerSecondHook(every_n_steps=every_n_steps,
batch_size=batch_size,
warm_steps=warm_steps)
def _validate_log_every_n_steps(self, every_n_steps, warm_steps):
hook = hooks.ExamplesPerSecondHook(
batch_size=256,
every_n_steps=every_n_steps,
warm_steps=warm_steps,
metric_logger=self._logger)
with tf.train.MonitoredSession(
tf.train.ChiefSessionCreator(), [hook]) as mon_sess:
for _ in range(every_n_steps):
# Explicitly run global_step after train_op to get the accurate
# global_step value
mon_sess.run(self.train_op)
mon_sess.run(self.global_step)
# Nothing should be in the list yet
self.assertFalse(self._logger.logged_metric)
mon_sess.run(self.train_op)
global_step_val = mon_sess.run(self.global_step)
if global_step_val > warm_steps:
self._assert_metrics()
else:
# Nothing should be in the list yet
self.assertFalse(self._logger.logged_metric)
# Add additional run to verify proper reset when called multiple times.
prev_log_len = len(self._logger.logged_metric)
mon_sess.run(self.train_op)
global_step_val = mon_sess.run(self.global_step)
if every_n_steps == 1 and global_step_val > warm_steps:
# Each time, we log two additional metrics. Did exactly 2 get added?
self.assertEqual(len(self._logger.logged_metric), prev_log_len + 2)
else:
# No change in the size of the metric list.
self.assertEqual(len(self._logger.logged_metric), prev_log_len)
def test_raise_in_both_secs_and_steps(self):
with self.assertRaises(ValueError):
hooks.ExamplesPerSecondHook(
batch_size=256,
every_n_steps=10,
every_n_secs=20)
