Python official.utils.logs.metric_hook.LoggingMetricHook() Examples

The following are 30 code examples of official.utils.logs.metric_hook.LoggingMetricHook(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module official.utils.logs.metric_hook , or try the search function .
Example #1
Source File: hooks_helper.py    From multilabel-image-classification-tensorflow with MIT License 6 votes vote down vote up 
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.

  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)


# A dictionary to map one hook name and its corresponding function
Example #2
Source File: hooks_helper.py    From models with Apache License 2.0 6 votes vote down vote up 
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.

  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)


# A dictionary to map one hook name and its corresponding function
Example #3
Source File: metric_hook_test.py    From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 6 votes vote down vote up 
def test_print_at_end_only(self):
    with tf.Graph().as_default(), tf.compat.v1.Session() as sess:
      tf.compat.v1.train.get_or_create_global_step()
      t = tf.constant(42.0, name="foo")
      train_op = tf.constant(3)
      hook = metric_hook.LoggingMetricHook(
          tensors=[t.name], 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), 1)
      metric = self._logger.logged_metric[0]
      self.assertRegexpMatches(metric["name"], "foo")
      self.assertEqual(metric["value"], 42.0)
      self.assertEqual(metric["unit"], None)
      self.assertEqual(metric["global_step"], 0)
Example #4
Source File: metric_hook_test.py    From models with Apache License 2.0 6 votes vote down vote up 
def test_print_at_end_only(self):
    with tf.Graph().as_default(), tf.Session() as sess:
      tf.train.get_or_create_global_step()
      t = tf.constant(42.0, name="foo")
      train_op = tf.constant(3)
      hook = metric_hook.LoggingMetricHook(
          tensors=[t.name], at_end=True, metric_logger=self._logger)
      hook.begin()
      mon_sess = monitored_session._HookedSession(sess, [hook])  # pylint: disable=protected-access
      sess.run(tf.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), 1)
      metric = self._logger.logged_metric[0]
      self.assertRegexpMatches(metric["name"], "foo")
      self.assertEqual(metric["value"], 42.0)
      self.assertEqual(metric["unit"], None)
      self.assertEqual(metric["global_step"], 0)
Example #5
Source File: hooks_helper.py    From models with Apache License 2.0 6 votes vote down vote up 
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.

  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)


# A dictionary to map one hook name and its corresponding function
Example #6
Source File: hooks_helper.py    From cloudml-samples with Apache License 2.0 6 votes vote down vote up 
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.

  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)


# A dictionary to map one hook name and its corresponding function
Example #7
Source File: metric_hook_test.py    From models with Apache License 2.0 6 votes vote down vote up 
def test_print_at_end_only(self):
    with tf.Graph().as_default(), tf.Session() as sess:
      tf.train.get_or_create_global_step()
      t = tf.constant(42.0, name="foo")
      train_op = tf.constant(3)
      hook = metric_hook.LoggingMetricHook(
          tensors=[t.name], at_end=True, metric_logger=self._logger)
      hook.begin()
      mon_sess = monitored_session._HookedSession(sess, [hook])  # pylint: disable=protected-access
      sess.run(tf.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), 1)
      metric = self._logger.logged_metric[0]
      self.assertRegexpMatches(metric["name"], "foo")
      self.assertEqual(metric["value"], 42.0)
      self.assertEqual(metric["unit"], None)
      self.assertEqual(metric["global_step"], 0)
Example #8
Source File: hooks_helper.py    From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 6 votes vote down vote up 
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)
Example #9
Source File: hooks_helper.py    From models with Apache License 2.0 6 votes vote down vote up 
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.

  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)


# A dictionary to map one hook name and its corresponding function
Example #10
Source File: metric_hook_test.py    From g-tensorflow-models with Apache License 2.0 6 votes vote down vote up 
def test_print_at_end_only(self):
    with tf.Graph().as_default(), tf.Session() as sess:
      tf.train.get_or_create_global_step()
      t = tf.constant(42.0, name="foo")
      train_op = tf.constant(3)
      hook = metric_hook.LoggingMetricHook(
          tensors=[t.name], at_end=True, metric_logger=self._logger)
      hook.begin()
      mon_sess = monitored_session._HookedSession(sess, [hook])  # pylint: disable=protected-access
      sess.run(tf.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), 1)
      metric = self._logger.logged_metric[0]
      self.assertRegexpMatches(metric["name"], "foo")
      self.assertEqual(metric["value"], 42.0)
      self.assertEqual(metric["unit"], None)
      self.assertEqual(metric["global_step"], 0)
Example #11
Source File: metric_hook_test.py    From models with Apache License 2.0 6 votes vote down vote up 
def test_print_at_end_only(self):
    with tf.Graph().as_default(), tf.Session() as sess:
      tf.train.get_or_create_global_step()
      t = tf.constant(42.0, name="foo")
      train_op = tf.constant(3)
      hook = metric_hook.LoggingMetricHook(
          tensors=[t.name], at_end=True, metric_logger=self._logger)
      hook.begin()
      mon_sess = monitored_session._HookedSession(sess, [hook])  # pylint: disable=protected-access
      sess.run(tf.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), 1)
      metric = self._logger.logged_metric[0]
      self.assertRegexpMatches(metric["name"], "foo")
      self.assertEqual(metric["value"], 42.0)
      self.assertEqual(metric["unit"], None)
      self.assertEqual(metric["global_step"], 0)
Example #12
Source File: hooks_helper.py    From ml-on-gcp with Apache License 2.0 6 votes vote down vote up 
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.

  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)


# A dictionary to map one hook name and its corresponding function
Example #13
Source File: hooks_helper.py    From ml-on-gcp with Apache License 2.0 6 votes vote down vote up 
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.

  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)


# A dictionary to map one hook name and its corresponding function
Example #14
Source File: hooks_helper.py    From ml-on-gcp with Apache License 2.0 6 votes vote down vote up 
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.

  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)


# A dictionary to map one hook name and its corresponding function
Example #15
Source File: hooks_helper.py    From ml-on-gcp with Apache License 2.0 6 votes vote down vote up 
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.

  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)


# A dictionary to map one hook name and its corresponding function
Example #16
Source File: hooks_helper.py    From g-tensorflow-models with Apache License 2.0 6 votes vote down vote up 
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.

  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)


# A dictionary to map one hook name and its corresponding function
Example #17
Source File: metric_hook_test.py    From models with Apache License 2.0 6 votes vote down vote up 
def test_print_at_end_only(self):
    with tf.Graph().as_default(), tf.compat.v1.Session() as sess:
      tf.compat.v1.train.get_or_create_global_step()
      t = tf.constant(42.0, name="foo")
      train_op = tf.constant(3)
      hook = metric_hook.LoggingMetricHook(
          tensors=[t.name], 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), 1)
      metric = self._logger.logged_metric[0]
      self.assertRegexpMatches(metric["name"], "foo")
      self.assertEqual(metric["value"], 42.0)
      self.assertEqual(metric["unit"], None)
      self.assertEqual(metric["global_step"], 0)
Example #18
Source File: metric_hook_test.py    From nsfw with Apache License 2.0 6 votes vote down vote up 
def test_print_at_end_only(self):
    with tf.Graph().as_default(), tf.Session() as sess:
      tf.train.get_or_create_global_step()
      t = tf.constant(42.0, name="foo")
      train_op = tf.constant(3)
      hook = metric_hook.LoggingMetricHook(
          tensors=[t.name], at_end=True, metric_logger=self._logger)
      hook.begin()
      mon_sess = monitored_session._HookedSession(sess, [hook])  # pylint: disable=protected-access
      sess.run(tf.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), 1)
      metric = self._logger.logged_metric[0]
      self.assertRegexpMatches(metric["name"], "foo")
      self.assertEqual(metric["value"], 42.0)
      self.assertEqual(metric["unit"], None)
      self.assertEqual(metric["global_step"], 0)
Example #19
Source File: metric_hook_test.py    From multilabel-image-classification-tensorflow with MIT License 6 votes vote down vote up 
def test_print_at_end_only(self):
    with tf.Graph().as_default(), tf.Session() as sess:
      tf.train.get_or_create_global_step()
      t = tf.constant(42.0, name="foo")
      train_op = tf.constant(3)
      hook = metric_hook.LoggingMetricHook(
          tensors=[t.name], at_end=True, metric_logger=self._logger)
      hook.begin()
      mon_sess = monitored_session._HookedSession(sess, [hook])  # pylint: disable=protected-access
      sess.run(tf.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), 1)
      metric = self._logger.logged_metric[0]
      self.assertRegexpMatches(metric["name"], "foo")
      self.assertEqual(metric["value"], 42.0)
      self.assertEqual(metric["unit"], None)
      self.assertEqual(metric["global_step"], 0)
Example #20
Source File: hooks_helper.py    From models with Apache License 2.0 6 votes vote down vote up 
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)
Example #21
Source File: metric_hook_test.py    From models with Apache License 2.0 5 votes vote down vote up 
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.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)
Example #22
Source File: hooks_helper.py    From models with Apache License 2.0 5 votes vote down vote up 
def get_logging_metric_hook(benchmark_log_dir=None,
                            tensors_to_log=None,
                            every_n_secs=600,
                            **kwargs):  # pylint: disable=unused-argument
  """Function to get LoggingMetricHook.

  Args:
    benchmark_log_dir: `string`, directory path to save the metric log.
    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.

  Returns:
    Returns a ProfilerHook that writes out timelines that can be loaded into
    profiling tools like chrome://tracing.
  """
  logger.config_benchmark_logger(benchmark_log_dir)
  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)


# A dictionary to map one hook name and its corresponding function
Example #23
Source File: metric_hook_test.py    From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 5 votes vote down vote up 
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)
Example #24
Source File: metric_hook_test.py    From multilabel-image-classification-tensorflow with MIT License 5 votes vote down vote up 
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.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)
Example #25
Source File: metric_hook_test.py    From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 5 votes vote down vote up 
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()
Example #26
Source File: metric_hook_test.py    From multilabel-image-classification-tensorflow with MIT License 5 votes vote down vote up 
def test_log_tensors(self):
    with tf.Graph().as_default(), tf.Session() as sess:
      tf.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.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)
Example #27
Source File: metric_hook_test.py    From multilabel-image-classification-tensorflow with MIT License 5 votes vote down vote up 
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()
Example #28
Source File: metric_hook_test.py    From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 5 votes vote down vote up 
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)
Example #29
Source File: metric_hook_test.py    From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 5 votes vote down vote up 
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)
Example #30
Source File: hooks_helper.py    From training with Apache License 2.0 5 votes vote down vote up 
def get_logging_metric_hook(benchmark_log_dir=None,
                            tensors_to_log=None,
                            every_n_secs=600,
                            **kwargs):  # pylint: disable=unused-argument
  """Function to get LoggingMetricHook.

  Args:
    benchmark_log_dir: `string`, directory path to save the metric log.
    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.

  Returns:
    Returns a ProfilerHook that writes out timelines that can be loaded into
    profiling tools like chrome://tracing.
  """
  if benchmark_log_dir is None:
    raise ValueError("metric_log_dir should be provided to use metric logger")
  if tensors_to_log is None:
    tensors_to_log = _TENSORS_TO_LOG
  return metric_hook.LoggingMetricHook(
      tensors=tensors_to_log,
      log_dir=benchmark_log_dir,
      every_n_secs=every_n_secs)


# A dictionary to map one hook name and its corresponding function