def _perform_metrics_update_list(self, examples_list: List[Any]) -> None:
"""Run a metrics update on a list of examples."""
try:
self._perform_metrics_update_fn(*[examples_list])
except (RuntimeError, TypeError, ValueError,
tf.errors.OpError) as exception:
general_util.reraise_augmented(exception,
'raw_input = %s' % (examples_list))
def __init__(self, model_agnostic_config: ModelAgnosticConfig):
self._graph = tf.Graph()
self._session = tf.compat.v1.Session(graph=self._graph)
self._config = model_agnostic_config
try:
self._create_graph()
except (RuntimeError, ValueError) as exception:
general_util.reraise_augmented(
exception, 'Failed to initialize agnostic model')
def __init__(self, path):
self._path = path
self._graph = tf.Graph()
self._session = tf.Session(graph=self._graph)
try:
self._load_and_parse_graph()
except (RuntimeError, ValueError) as exception:
general_util.reraise_augmented(
exception, 'for saved_model at path %s' % self._path)
def perform_metrics_update(self, features_predictions_labels):
"""Run a single metrics update step on a single FPL."""
feed_dict = self._create_feed_for_features_predictions_labels(
features_predictions_labels)
try:
self._session.run(fetches=self._all_metric_update_ops,
feed_dict=feed_dict)
except (RuntimeError, TypeError, ValueError) as exception:
general_util.reraise_augmented(
exception, 'features_predictions_labels = %s, feed_dict = %s' %
(features_predictions_labels, feed_dict))
def metrics_reset_update_get(self, features_predictions_labels):
"""Run the metrics reset, update, get operations on a single FPL."""
self.reset_metric_variables()
feed_dict = self._create_feed_for_features_predictions_labels(
features_predictions_labels)
try:
[_, result] = self._session.run(fetches=[
self._all_metric_update_ops, self._metric_variable_nodes
],
feed_dict=feed_dict)
except (RuntimeError, TypeError, ValueError) as exception:
general_util.reraise_augmented(
exception, 'features_predictions_labels = %s, feed_dict = %s' %
(features_predictions_labels, feed_dict))
return result
def __init__(self):
"""Initializes this class and attempts to create the graph.
This method attempts to create the graph through _construct_graph and
also creates all class variables that need to be populated by the override
function _construct_graph.
"""
self._graph = tf.Graph()
self._session = tf.Session(graph=self._graph)
# Variables that need to be populated.
# The names of the metric.
self._metric_names = []
# Ops associated with reading and writing the metric variables.
self._metric_value_ops = []
self._metric_update_ops = []
self._metric_variable_assign_ops = []
# Nodes associated with the metric variables.
self._metric_variable_nodes = []
# Placeholders and feed input for the metric variables.
self._metric_variable_placeholders = []
self._metrics_reset_update_get_fn_feed_list = []
self._metrics_reset_update_get_fn_feed_list_keys = []
# Dict that maps Features Predictions Label keys to their tensors.
self._features_map = {}
self._predictions_map = {}
self._labels_map = {}
# Ops to update/reset all metric variables.
self._all_metric_update_ops = None
self._reset_variables_op = None
# Callables to perform the above ops.
self._perform_metrics_update_fn = None
self._metrics_reset_update_get_fn = None
try:
self._construct_graph()
except (RuntimeError, TypeError, ValueError,
tf.errors.OpError) as exception:
general_util.reraise_augmented(exception,
'Failed to create graph.')
def _perform_metrics_update_list(self, features_predictions_labels_list):
"""Run a metrics update on a list of FPLs."""
feed_list = self._create_feed_for_features_predictions_labels_list(
features_predictions_labels_list)
try:
self._perform_metrics_update_fn(*feed_list)
except (RuntimeError, TypeError, ValueError,
tf.errors.OpError) as exception:
feed_dict = dict(
zip(self._perform_metrics_update_fn_feed_list_keys, feed_list))
self._log_debug_message_for_tracing_feed_errors(
fetches=[self._all_metric_update_ops] +
self._metric_variable_nodes,
feed_list=self._perform_metrics_update_fn_feed_list)
general_util.reraise_augmented(
exception,
'features_predictions_labels_list = %s, feed_dict = %s' %
(features_predictions_labels_list, feed_dict))
def metrics_reset_update_get(self, features_predictions_labels):
"""Run the metrics reset, update, get operations on a single FPL."""
self.reset_metric_variables()
feed_list = self._create_feed_for_features_predictions_labels(
features_predictions_labels)
try:
[_, result] = self._metrics_reset_update_get_fn(*feed_list)
except (RuntimeError, TypeError, ValueError,
tf.errors.OpError) as exception:
feed_dict = dict(
zip(self._metrics_reset_update_get_fn_feed_list_keys,
feed_list))
self._log_debug_message_for_tracing_feed_errors(
fetches=[self._all_metric_update_ops],
feed_list=self._metrics_reset_update_get_fn_feed_list)
general_util.reraise_augmented(
exception, 'features_predictions_labels = %s, feed_dict = %s' %
(features_predictions_labels, feed_dict))
return result
def __init__(self):
"""Initializes this class and attempts to create the graph.
This method attempts to create the graph through _construct_graph and
also creates all class variables that need to be populated by the override
function _construct_graph.
"""
self._graph = tf.Graph()
self._session = tf.compat.v1.Session(graph=self._graph)
# This lock is for multi-threaded contexts where multiple threads
# share the same EvalSavedModel.
#
# Locking is required in the case where there are multiple threads using
# the same EvalMetricsGraph. Because the metrics variables are part of the
# session, and all threads share the same session, without a lock, the
# "reset-update-get" steps may not be atomic and there can be races.
#
# Having each thread have its own session would also work, but would
# require a bigger refactor.
# TODO(b/131727905): Investigate whether it's possible / better to have
# each thread have its own session.
self._lock = threading.Lock()
# Variables that need to be populated.
# The names of the metric.
self._metric_names = []
# Ops associated with reading and writing the metric variables.
self._metric_value_ops = []
self._metric_update_ops = []
self._metric_variable_assign_ops = []
# Nodes associated with the metric variables.
self._metric_variable_nodes = []
# Placeholders and feed input for the metric variables.
self._metric_variable_placeholders = []
self._perform_metrics_update_fn_feed_list = []
self._perform_metrics_update_fn_feed_list_keys = []
# OrderedDicts that map features, predictions, and labels keys to their
# tensors.
self._features_map = {}
self._predictions_map = {}
self._labels_map = {}
# Ops to set/update/reset all metric variables.
self._all_metric_variable_assign_ops = None
self._all_metric_update_ops = None
self._reset_variables_op = None
# Callable to perform metric update.
self._perform_metrics_update_fn = None
# OrderedDict produced by graph_ref's load_(legacy_)inputs, mapping input
# key to tensor value.
self._input_map = None
self._batch_size = (
beam.metrics.Metrics.distribution(constants.METRICS_NAMESPACE,
'batch_size'))
self._batch_size_failed = (
beam.metrics.Metrics.distribution(constants.METRICS_NAMESPACE,
'batch_size_failed'))
try:
self._construct_graph()
except (RuntimeError, TypeError, ValueError,
tf.errors.OpError) as exception:
general_util.reraise_augmented(exception, 'Failed to create graph.')
