def test_illegal_args(self):
with self.assertRaisesRegexp(ValueError, "nvalid every_n_iter"):
metric_hook.LoggingMetricHook(tensors=["t"], every_n_iter=0)
with self.assertRaisesRegexp(ValueError, "nvalid every_n_iter"):
metric_hook.LoggingMetricHook(tensors=["t"], every_n_iter=-10)
with self.assertRaisesRegexp(ValueError, "xactly one of"):
metric_hook.LoggingMetricHook(
tensors=["t"], every_n_iter=5, every_n_secs=5)
with self.assertRaisesRegexp(ValueError, "xactly one of"):
metric_hook.LoggingMetricHook(tensors=["t"])
with self.assertRaisesRegexp(ValueError, "metric_logger"):
metric_hook.LoggingMetricHook(tensors=["t"], every_n_iter=5)
def test_log_tensors(self):
with tf.Graph().as_default(), tf.compat.v1.Session() as sess:
tf.compat.v1.train.get_or_create_global_step()
t1 = tf.constant(42.0, name="foo")
t2 = tf.constant(43.0, name="bar")
train_op = tf.constant(3)
hook = metric_hook.LoggingMetricHook(
tensors=[t1, t2], at_end=True, metric_logger=self._logger)
hook.begin()
mon_sess = monitored_session._HookedSession(sess, [hook]) # pylint: disable=protected-access
sess.run(tf.compat.v1.global_variables_initializer())
for _ in range(3):
mon_sess.run(train_op)
self.assertEqual(self._logger.logged_metric, [])
hook.end(sess)
self.assertEqual(len(self._logger.logged_metric), 2)
metric1 = self._logger.logged_metric[0]
self.assertRegexpMatches(str(metric1["name"]), "foo")
self.assertEqual(metric1["value"], 42.0)
self.assertEqual(metric1["unit"], None)
self.assertEqual(metric1["global_step"], 0)
metric2 = self._logger.logged_metric[1]
self.assertRegexpMatches(str(metric2["name"]), "bar")
self.assertEqual(metric2["value"], 43.0)
self.assertEqual(metric2["unit"], None)
self.assertEqual(metric2["global_step"], 0)
def _validate_print_every_n_secs(self, sess, at_end):
t = tf.constant(42.0, name="foo")
train_op = tf.constant(3)
hook = metric_hook.LoggingMetricHook(
tensors=[t.name], every_n_secs=1.0, at_end=at_end,
metric_logger=self._logger)
hook.begin()
mon_sess = monitored_session._HookedSession(sess, [hook]) # pylint: disable=protected-access
sess.run(tf.compat.v1.global_variables_initializer())
mon_sess.run(train_op)
self.assertRegexpMatches(str(self._logger.logged_metric), t.name)
# assertNotRegexpMatches is not supported by python 3.1 and later
self._logger.logged_metric = []
mon_sess.run(train_op)
self.assertEqual(str(self._logger.logged_metric).find(t.name), -1)
time.sleep(1.0)
self._logger.logged_metric = []
mon_sess.run(train_op)
self.assertRegexpMatches(str(self._logger.logged_metric), t.name)
self._logger.logged_metric = []
hook.end(sess)
if at_end:
self.assertRegexpMatches(str(self._logger.logged_metric), t.name)
else:
# assertNotRegexpMatches is not supported by python 3.1 and later
self.assertEqual(str(self._logger.logged_metric).find(t.name), -1)
def _validate_print_every_n_steps(self, sess, at_end):
t = tf.constant(42.0, name="foo")
train_op = tf.constant(3)
hook = metric_hook.LoggingMetricHook(
tensors=[t.name], every_n_iter=10, at_end=at_end,
metric_logger=self._logger)
hook.begin()
mon_sess = monitored_session._HookedSession(sess, [hook]) # pylint: disable=protected-access
sess.run(tf.compat.v1.global_variables_initializer())
mon_sess.run(train_op)
self.assertRegexpMatches(str(self._logger.logged_metric), t.name)
for _ in range(3):
self._logger.logged_metric = []
for _ in range(9):
mon_sess.run(train_op)
# assertNotRegexpMatches is not supported by python 3.1 and later
self.assertEqual(str(self._logger.logged_metric).find(t.name), -1)
mon_sess.run(train_op)
self.assertRegexpMatches(str(self._logger.logged_metric), t.name)
# Add additional run to verify proper reset when called multiple times.
self._logger.logged_metric = []
mon_sess.run(train_op)
# assertNotRegexpMatches is not supported by python 3.1 and later
self.assertEqual(str(self._logger.logged_metric).find(t.name), -1)
self._logger.logged_metric = []
hook.end(sess)
if at_end:
self.assertRegexpMatches(str(self._logger.logged_metric), t.name)
else:
# assertNotRegexpMatches is not supported by python 3.1 and later
self.assertEqual(str(self._logger.logged_metric).find(t.name), -1)
def test_global_step_not_found(self):
with tf.Graph().as_default():
t = tf.constant(42.0, name="foo")
hook = metric_hook.LoggingMetricHook(
tensors=[t.name], at_end=True, metric_logger=self._logger)
with self.assertRaisesRegexp(
RuntimeError, "should be created to use LoggingMetricHook."):
hook.begin()
def get_logging_metric_hook(tensors_to_log=None,
every_n_secs=600,
**kwargs): # pylint: disable=unused-argument
"""Function to get LoggingMetricHook.
Args:
tensors_to_log: List of tensor names or dictionary mapping labels to tensor
names. If not set, log _TENSORS_TO_LOG by default.
every_n_secs: `int`, the frequency for logging the metric. Default to every
10 mins.
**kwargs: a dictionary of arguments.
Returns:
Returns a LoggingMetricHook that saves tensor values in a JSON format.
"""
if tensors_to_log is None:
tensors_to_log = _TENSORS_TO_LOG
return metric_hook.LoggingMetricHook(
tensors=tensors_to_log,
metric_logger=logger.get_benchmark_logger(),
every_n_secs=every_n_secs)