def testGetNodeInGraph(self):
g = tf.Graph()
with g.as_default():
apple = tf.constant(1.0)
meta_graph_def = meta_graph_pb2.MetaGraphDef()
meta_graph_def.collection_def['fruit_node'].any_list.value.extend(
[encoding.encode_tensor_node(apple)])
self.assertEqual(
apple, graph_ref.get_node_in_graph(meta_graph_def, 'fruit_node', g))
def _load_and_parse_graph(self):
"""Actually load and parse the graph.
This is factored out from __init__ in case we want to support delayed-loads
in the future.
"""
meta_graph_def = tf.saved_model.loader.load(
self._session, [tf.saved_model.tag_constants.SERVING], self._path)
with self._graph.as_default():
# Get references to "named" nodes.
self._input_example_node = graph_ref.get_node_in_graph(
meta_graph_def, encoding.INPUT_EXAMPLE_COLLECTION, self._graph)
self._labels_map = graph_ref.get_node_map_in_graph(
meta_graph_def, encoding.LABELS_COLLECTION,
[encoding.NODE_SUFFIX], self._graph)
self._predictions_map = graph_ref.get_node_map_in_graph(
meta_graph_def, encoding.PREDICTIONS_COLLECTION,
[encoding.NODE_SUFFIX], self._graph)
self._features_map = graph_ref.get_node_map_in_graph(
meta_graph_def, encoding.FEATURES_COLLECTION,
[encoding.NODE_SUFFIX], self._graph)
metrics = graph_ref.get_node_map_in_graph(
meta_graph_def, encoding.METRICS_COLLECTION,
[encoding.VALUE_OP_SUFFIX, encoding.UPDATE_OP_SUFFIX],
self._graph)
metric_ops = {}
for metric_name, ops in metrics.items():
metric_ops[metric_name] = (ops[encoding.VALUE_OP_SUFFIX],
ops[encoding.UPDATE_OP_SUFFIX])
self._metric_names = []
self._metric_value_ops = []
self._metric_update_ops = []
self._metric_variable_nodes = []
self._metric_variable_placeholders = []
self._metric_variable_assign_ops = []
self.register_additional_metric_ops(metric_ops)
def _load_and_parse_graph(self):
"""Actually load and parse the graph.
This is factored out from __init__ in case we want to support delayed-loads
in the future.
Raises:
ValueError: Could not find signature keyed with EVAL_TAG; or
signature_def did not have exactly one input; or there was a signature
output with the metric prefix but an unrecognised suffix.
"""
meta_graph_def = tf.saved_model.loader.load(self._session,
[constants.EVAL_TAG],
self._path)
self._check_version(meta_graph_def)
with self._graph.as_default():
signature_def = meta_graph_def.signature_def.get(
constants.EVAL_TAG)
if signature_def is None:
raise ValueError(
'could not find signature with name %s. signature_def '
'was %s' % (constants.EVAL_TAG, signature_def))
# Note that there are two different encoding schemes in use here:
#
# 1. The scheme used by TFMA for the TFMA-specific extra collections
# for the features and labels.
# 2. The scheme used by TensorFlow Estimators in the SignatureDefs for the
# input example node, predictions, metrics and so on.
# Features and labels are in TFMA-specific extra collections.
#
# We use OrderedDict because the ordering of the keys matters:
# we need to fix a canonical ordering for passing feed_list arguments
# into make_callable.
self._features_map = collections.OrderedDict(
graph_ref.get_node_map_in_graph(meta_graph_def,
encoding.FEATURES_COLLECTION,
[encoding.NODE_SUFFIX],
self._graph))
self._labels_map = collections.OrderedDict(
graph_ref.get_node_map_in_graph(meta_graph_def,
encoding.LABELS_COLLECTION,
[encoding.NODE_SUFFIX],
self._graph))
if len(signature_def.inputs) != 1:
raise ValueError(
'there should be exactly one input. signature_def '
'was: %s' % signature_def)
# The input node, predictions and metrics are in the signature.
input_node = list(signature_def.inputs.values())[0]
self._input_example_node = (
tf.saved_model.utils.get_tensor_from_tensor_info(
input_node, self._graph))
# The example reference node. If not defined in the graph, use the
# input examples as example references.
try:
self._example_ref_tensor = graph_ref.get_node_in_graph(
meta_graph_def, encoding.EXAMPLE_REF_COLLECTION,
self._graph)
except KeyError:
# If we can't find the ExampleRef collection, then this is probably a
# model created before we introduced the ExampleRef parameter to
# EvalInputReceiver. In that case, we default to a tensor of range(0,
# len(input_example)).
self._example_ref_tensor = tf.range(
tf.size(self._input_example_node))
# We use OrderedDict because the ordering of the keys matters:
# we need to fix a canonical ordering for passing feed_dict arguments
# into make_callable.
#
# The canonical ordering we use here is simply the ordering we get
# from the predictions collection.
predictions = graph_ref.extract_signature_outputs_with_prefix(
constants.PREDICTIONS_NAME, signature_def.outputs)
predictions_map = collections.OrderedDict()
for k, v in predictions.items():
# Extract to dictionary with a single key for consistency with
# how features and labels are extracted.
predictions_map[k] = {
encoding.NODE_SUFFIX:
tf.saved_model.utils.get_tensor_from_tensor_info(
v, self._graph)
}
self._predictions_map = predictions_map
metrics = graph_ref.extract_signature_outputs_with_prefix(
constants.METRICS_NAME, signature_def.outputs)
metrics_map = collections.defaultdict(dict)
for k, v in metrics.items():
node = tf.saved_model.utils.get_tensor_from_tensor_info(
v, self._graph)
if k.endswith('/' + constants.METRIC_VALUE_SUFFIX):
key = k[:-len(constants.METRIC_VALUE_SUFFIX) - 1]
metrics_map[key][encoding.VALUE_OP_SUFFIX] = node
elif k.endswith('/' + constants.METRIC_UPDATE_SUFFIX):
key = k[:-len(constants.METRIC_UPDATE_SUFFIX) - 1]
metrics_map[key][encoding.UPDATE_OP_SUFFIX] = node
else:
raise ValueError(
'unrecognised suffix for metric. key was: %s' % k)
metric_ops = {}
for metric_name, ops in metrics_map.items():
metric_ops[metric_name] = (ops[encoding.VALUE_OP_SUFFIX],
ops[encoding.UPDATE_OP_SUFFIX])
# Create feed_list for metrics_reset_update_get_fn
#
# We need to save this because we need to update the
# metrics_reset_update_get_fn when additional metric ops are registered
# (the feed_list will stay the same though).
feed_list = []
feed_list_keys = []
for which_map, key, map_dict in (
self._iterate_fpl_maps_in_canonical_order()):
feed_list.append(map_dict[encoding.NODE_SUFFIX])
feed_list_keys.append((which_map, key))
self._metrics_reset_update_get_fn_feed_list = feed_list
# We also keep the associated keys for better error messages.
self._metrics_reset_update_get_fn_feed_list_keys = feed_list_keys
self._metric_names = []
self._metric_value_ops = []
self._metric_update_ops = []
self._metric_variable_nodes = []
self._metric_variable_placeholders = []
self._metric_variable_assign_ops = []
self.register_additional_metric_ops(metric_ops)
# Make callable for predict_list. The callable for
# metrics_reset_update_get is updated in register_additional_metric_ops.
# Repeated calls to a callable made using make_callable are faster than
# doing repeated calls to session.run.
self._predict_list_fn = self._session.make_callable(
fetches=(self._features_map, self._predictions_map,
self._labels_map, self._example_ref_tensor),
feed_list=[self._input_example_node])
