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## Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for SavedModel."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import tensorflow as tf
from tensorflow.core.protobuf import config_pb2
from tensorflow.core.protobuf import meta_graph_pb2
from tensorflow.python.framework import errors
from tensorflow.python.lib.io import file_io
from tensorflow.python.saved_model import builder as saved_model_builder
from tensorflow.python.saved_model import constants
from tensorflow.python.saved_model import loader
from tensorflow.python.saved_model import utils
from tensorflow.python.util import compat
def tearDownModule():
file_io.delete_recursively(tf.test.get_temp_dir())
class SavedModelTest(tf.test.TestCase):
def testSequence(self):
export_dir = os.path.join(tf.test.get_temp_dir(), "sequence")
builder = saved_model_builder.SavedModelBuilder(export_dir)
# Expect an assertion error since add_meta_graph_and_variables() should be
# invoked before any add_meta_graph() calls.
with self.test_session(graph=tf.Graph()) as sess:
self.assertRaises(AssertionError, builder.add_meta_graph, ["foo"])
# Expect an assertion error for multiple calls of
# add_meta_graph_and_variables() since weights should be saved exactly once.
with self.test_session(graph=tf.Graph()) as sess:
v = tf.Variable(42, name="v")
sess.run(tf.initialize_all_variables())
self.assertEqual(42, v.eval())
builder.add_meta_graph_and_variables(sess, ["bar"])
self.assertRaises(AssertionError, builder.add_meta_graph_and_variables,
sess, ["baz"])
def testTags(self):
export_dir = os.path.join(
compat.as_bytes(tf.test.get_temp_dir()), compat.as_bytes("tags"))
builder = saved_model_builder.SavedModelBuilder(export_dir)
# Graph with a single variable. SavedModel invoked to:
# - add with weights.
# - a single tag (from predefined constants).
with self.test_session(graph=tf.Graph()) as sess:
v = tf.Variable(42, name="v")
sess.run(tf.initialize_all_variables())
self.assertEqual(42, v.eval())
builder.add_meta_graph_and_variables(sess, [constants.TAG_TRAINING])
# Graph that updates the single variable. SavedModel invoked to:
# - simply add the model (weights are not updated).
# - a single tag (from predefined constants).
with self.test_session(graph=tf.Graph()) as sess:
v = tf.Variable(43, name="v")
sess.run(tf.initialize_all_variables())
self.assertEqual(43, v.eval())
builder.add_meta_graph([constants.TAG_SERVING])
# Graph that updates the single variable. SavedModel is invoked:
# - to add the model (weights are not updated).
# - multiple custom tags.
with self.test_session(graph=tf.Graph()) as sess:
v = tf.Variable(44, name="v")
sess.run(tf.initialize_all_variables())
self.assertEqual(44, v.eval())
builder.add_meta_graph(["foo", "bar"])
# Save the SavedModel to disk.
builder.save()
# Restore the graph with a single predefined tag whose variables were saved.
with self.test_session(graph=tf.Graph()) as sess:
loader.load(sess, [constants.TAG_TRAINING], export_dir)
self.assertEqual(42, tf.get_collection(tf.GraphKeys.VARIABLES)[0].eval())
# Restore the graph with a single predefined tag whose variables were not
# saved.
with self.test_session(graph=tf.Graph()) as sess:
loader.load(sess, [constants.TAG_SERVING], export_dir)
self.assertEqual(42, tf.get_collection(tf.GraphKeys.VARIABLES)[0].eval())
# Restore the graph with multiple tags. Provide duplicate tags to test set
# semantics.
with self.test_session(graph=tf.Graph()) as sess:
loader.load(sess, ["foo", "bar", "foo"], export_dir)
self.assertEqual(42, tf.get_collection(tf.GraphKeys.VARIABLES)[0].eval())
# Try restoring a graph with a non-existent tag. This should yield a runtime
# error.
with self.test_session(graph=tf.Graph()) as sess:
self.assertRaises(RuntimeError, loader.load, sess, ["INVALID"],
export_dir)
# Try restoring a graph where a subset of the tags match. Since tag matching
# for meta graph defs follows "all" semantics, this should yield a runtime
# error.
with self.test_session(graph=tf.Graph()) as sess:
self.assertRaises(RuntimeError, loader.load, sess, ["foo", "baz"],
export_dir)
def testVariables(self):
export_dir = os.path.join(
compat.as_bytes(tf.test.get_temp_dir()), compat.as_bytes("variables"))
builder = saved_model_builder.SavedModelBuilder(export_dir)
# Graph with two variables. SavedModel invoked to:
# - add with weights.
with self.test_session(graph=tf.Graph()) as sess:
v1 = tf.Variable(1, name="v1")
v2 = tf.Variable(2, name="v2")
sess.run(tf.initialize_all_variables())
self.assertEqual(1, v1.eval())
self.assertEqual(2, v2.eval())
builder.add_meta_graph_and_variables(sess, ["foo"])
# Graph with a single variable (subset of the variables from the previous
# graph whose weights were saved). SavedModel invoked to:
# - simply add the model (weights are not updated).
with self.test_session(graph=tf.Graph()) as sess:
v2 = tf.Variable(3, name="v2")
sess.run(tf.initialize_all_variables())
self.assertEqual(3, v2.eval())
builder.add_meta_graph(["bar"])
# Graph with a single variable (disjoint set of variables from the previous
# graph whose weights were saved). SavedModel invoked to:
# - simply add the model (weights are not updated).
with self.test_session(graph=tf.Graph()) as sess:
v3 = tf.Variable(4, name="v3")
sess.run(tf.initialize_all_variables())
self.assertEqual(4, v3.eval())
builder.add_meta_graph(["baz"])
# Save the SavedModel to disk.
builder.save()
# Restore the graph with tag "foo", whose variables were saved.
with self.test_session(graph=tf.Graph()) as sess:
loader.load(sess, ["foo"], export_dir)
collection_vars = tf.get_collection(tf.GraphKeys.VARIABLES)
self.assertEqual(len(collection_vars), 2)
self.assertEqual(1, collection_vars[0].eval())
self.assertEqual(2, collection_vars[1].eval())
# Restore the graph with tag "bar", whose variables were not saved. Only the
# subset of the variables added to the graph will be restored with the
# checkpointed value.
with self.test_session(graph=tf.Graph()) as sess:
loader.load(sess, ["bar"], export_dir)
collection_vars = tf.get_collection(tf.GraphKeys.VARIABLES)
self.assertEqual(len(collection_vars), 1)
self.assertEqual(2, collection_vars[0].eval())
# Try restoring the graph with tag "baz", whose variables were not saved.
# Since this graph has a disjoint set of variables from the set that was
# saved, this should raise an error.
with self.test_session(graph=tf.Graph()) as sess:
self.assertRaises(errors.NotFoundError, loader.load, sess, ["baz"],
export_dir)
def testSaveAsText(self):
export_dir = os.path.join(
compat.as_bytes(tf.test.get_temp_dir()), compat.as_bytes("astext"))
builder = saved_model_builder.SavedModelBuilder(export_dir)
# Graph with a single variable. SavedModel invoked to:
# - add with weights.
with self.test_session(graph=tf.Graph()) as sess:
v = tf.Variable(42, name="v")
sess.run(tf.initialize_all_variables())
self.assertEqual(42, v.eval())
builder.add_meta_graph_and_variables(sess, ["foo"])
# Graph with the same single variable. SavedModel invoked to:
# - simply add the model (weights are not updated).
with self.test_session(graph=tf.Graph()) as sess:
v = tf.Variable(43, name="v")
sess.run(tf.initialize_all_variables())
self.assertEqual(43, v.eval())
builder.add_meta_graph(["bar"])
# Save the SavedModel to disk in text format.
builder.save(as_text=True)
# Restore the graph with tag "foo", whose variables were saved.
with self.test_session(graph=tf.Graph()) as sess:
loader.load(sess, ["foo"], export_dir)
self.assertEqual(42, tf.get_collection(tf.GraphKeys.VARIABLES)[0].eval())
# Restore the graph with tag "bar", whose variables were not saved.
with self.test_session(graph=tf.Graph()) as sess:
loader.load(sess, ["bar"], export_dir)
self.assertEqual(42, tf.get_collection(tf.GraphKeys.VARIABLES)[0].eval())
def testCollections(self):
export_dir = os.path.join(
compat.as_bytes(tf.test.get_temp_dir()), compat.as_bytes("collections"))
builder = saved_model_builder.SavedModelBuilder(export_dir)
# Graph with a single variable added to a collection. SavedModel invoked to:
# - add with weights.
with self.test_session(graph=tf.Graph()) as sess:
v = tf.Variable(42, name="v")
tf.add_to_collection("foo_vars", v)
sess.run(tf.initialize_all_variables())
self.assertEqual(42, v.eval())
builder.add_meta_graph_and_variables(sess, ["foo"])
# Graph with the same single variable added to a different collection.
# SavedModel invoked to:
# - simply add the model (weights are not updated).
with self.test_session(graph=tf.Graph()) as sess:
v = tf.Variable(43, name="v")
tf.add_to_collection("bar_vars", v)
sess.run(tf.initialize_all_variables())
self.assertEqual(43, v.eval())
builder.add_meta_graph(["bar"])
# Save the SavedModel to disk.
builder.save()
# Restore the graph with tag "foo", whose variables were saved. The
# collection 'foo_vars' should contain a single element. The collection
# 'bar_vars' should not be found.
with self.test_session(graph=tf.Graph()) as sess:
loader.load(sess, ["foo"], export_dir)
collection_foo_vars = tf.get_collection("foo_vars")
self.assertEqual(len(collection_foo_vars), 1)
self.assertEqual(42, collection_foo_vars[0].eval())
self.assertEqual(len(tf.get_collection("bar_vars")), 0)
# Restore the graph with tag "bar", whose variables were not saved. The
# collection-def exported as part of the meta graph def is updated to
# reflect the new collection. The value of the variable in the
# collection-def corresponds to the saved value (from the previous graph
# with tag "foo").
with self.test_session(graph=tf.Graph()) as sess:
loader.load(sess, ["bar"], export_dir)
collection_bar_vars = tf.get_collection("bar_vars")
self.assertEqual(len(collection_bar_vars), 1)
self.assertEqual(42, collection_bar_vars[0].eval())
self.assertEqual(len(tf.get_collection("foo_vars")), 0)
def testSignatureDefs(self):
export_dir = os.path.join(
compat.as_bytes(tf.test.get_temp_dir()),
compat.as_bytes("signature_defs"))
builder = saved_model_builder.SavedModelBuilder(export_dir)
# Graph with a single variable and a single entry in the signature def map.
# SavedModel is invoked to add with weights.
with self.test_session(graph=tf.Graph()) as sess:
v = tf.Variable(42, name="v")
sess.run(tf.initialize_all_variables())
self.assertEqual(42, v.eval())
# Build and populate an empty SignatureDef for testing.
foo_signature = utils.build_signature_def(dict(), dict(), "foo")
builder.add_meta_graph_and_variables(
sess, ["foo"], signature_def_map={"foo_key": foo_signature})
# Graph with the same single variable and multiple entries in the signature
# def map. No weights are saved by SavedModel.
with self.test_session(graph=tf.Graph()) as sess:
v = tf.Variable(43, name="v")
sess.run(tf.initialize_all_variables())
self.assertEqual(43, v.eval())
# Build and populate a different SignatureDef for testing.
bar_signature = utils.build_signature_def(dict(), dict(), "bar")
# Also, build a different SignatureDef corresponding to "foo_key" defined
# in the previous graph.
foo_new_signature = utils.build_signature_def(dict(), dict(), "foo_new")
builder.add_meta_graph(
["bar"],
signature_def_map={"bar_key": bar_signature,
"foo_key": foo_new_signature})
# Save the SavedModel to disk.
builder.save()
# Restore the graph with tag "foo". The single entry in the SignatureDef map
# corresponding to "foo_key" should exist.
with self.test_session(graph=tf.Graph()) as sess:
foo_graph = loader.load(sess, ["foo"], export_dir)
self.assertEqual(42, tf.get_collection(tf.GraphKeys.VARIABLES)[0].eval())
foo_signature = foo_graph.signature_def
self.assertEqual(len(foo_signature), 1)
self.assertEqual("foo", foo_signature["foo_key"].method_name)
# Restore the graph with tag "bar". The SignatureDef map should have two
# entries. One corresponding to "bar_key" and another corresponding to the
# new value of "foo_key".
with self.test_session(graph=tf.Graph()) as sess:
bar_graph = loader.load(sess, ["bar"], export_dir)
self.assertEqual(42, tf.get_collection(tf.GraphKeys.VARIABLES)[0].eval())
bar_signature = bar_graph.signature_def
self.assertEqual(len(bar_signature), 2)
self.assertEqual("bar", bar_signature["bar_key"].method_name)
self.assertEqual("foo_new", bar_signature["foo_key"].method_name)
def testAssets(self):
export_dir = os.path.join(
compat.as_bytes(tf.test.get_temp_dir()), compat.as_bytes("with-assets"))
builder = saved_model_builder.SavedModelBuilder(export_dir)
with self.test_session(graph=tf.Graph()) as sess:
v = tf.Variable(42, name="v")
sess.run(tf.initialize_all_variables())
self.assertEqual(42, v.eval())
# Build an asset collection.
asset_filepath = os.path.join(
compat.as_bytes(tf.test.get_temp_dir()),
compat.as_bytes("hello42.txt"))
file_io.write_string_to_file(asset_filepath, "foo bar baz")
asset_file_tensor = tf.constant(asset_filepath, name="asset_file_tensor")
tf.add_to_collection(tf.GraphKeys.ASSET_FILEPATHS, asset_file_tensor)
ignored_filepath = os.path.join(
compat.as_bytes(tf.test.get_temp_dir()),
compat.as_bytes("ignored.txt"))
file_io.write_string_to_file(ignored_filepath, "will be ignored")
asset_collection = tf.get_collection(tf.GraphKeys.ASSET_FILEPATHS)
builder.add_meta_graph_and_variables(
sess, ["foo"], assets_collection=asset_collection)
# Save the SavedModel to disk.
builder.save()
with self.test_session(graph=tf.Graph()) as sess:
foo_graph = loader.load(sess, ["foo"], export_dir)
# Validate the assets.
collection_def = foo_graph.collection_def
assets_any = collection_def[constants.ASSETS_KEY].any_list.value
self.assertEqual(len(assets_any), 1)
asset = meta_graph_pb2.AssetFileDef()
assets_any[0].Unpack(asset)
assets_path = os.path.join(
compat.as_bytes(export_dir),
compat.as_bytes(constants.ASSETS_DIRECTORY),
compat.as_bytes("hello42.txt"))
asset_contents = file_io.read_file_to_string(assets_path)
self.assertEqual("foo bar baz", compat.as_text(asset_contents))
self.assertEqual("hello42.txt", asset.filename)
self.assertEqual("asset_file_tensor:0", asset.tensor_info.name)
ignored_asset_path = os.path.join(
compat.as_bytes(export_dir),
compat.as_bytes(constants.ASSETS_DIRECTORY),
compat.as_bytes("ignored.txt"))
self.assertFalse(file_io.file_exists(ignored_asset_path))
def testOp(self):
export_dir = os.path.join(
compat.as_bytes(tf.test.get_temp_dir()), compat.as_bytes("op"))
builder = saved_model_builder.SavedModelBuilder(export_dir)
with tf.Session(
graph=tf.Graph(),
config=config_pb2.ConfigProto(device_count={"CPU": 2})) as sess:
with sess.graph.device("/cpu:0"):
v1 = tf.Variable(1, name="v1")
with sess.graph.device("/cpu:1"):
v2 = tf.Variable(2, name="v2")
# v3 is an unsaved variable derived from v1 and v2. It is used to
# exercise the ability to run an init op when restoring a graph.
v3 = tf.Variable(1, name="v3", trainable=False, collections=[])
assign_v3 = tf.assign(v3, tf.add(v1, v2))
init_op = tf.group(assign_v3, name="init_op")
tf.add_to_collection("v", v1)
tf.add_to_collection("v", v2)
tf.add_to_collection("v", v3)
tf.add_to_collection("init_op", init_op)
sess.run(tf.initialize_all_variables())
self.assertEqual(1, tf.get_collection("v")[0].eval())
self.assertEqual(2, tf.get_collection("v")[1].eval())
builder.add_meta_graph_and_variables(sess, ["foo"])
# Save the SavedModel to disk.
builder.save()
with tf.Session(
graph=tf.Graph(),
config=config_pb2.ConfigProto(device_count={"CPU": 2})) as sess:
loader.load(sess, ["foo"], export_dir)
# Validate variables, run the init op and verify result.
self.assertEqual(1, tf.get_collection("v")[0].eval())
self.assertEqual(2, tf.get_collection("v")[1].eval())
tf.get_collection("init_op")[0].run()
self.assertEqual(3, tf.get_collection("v")[2].eval())
if __name__ == "__main__":
tf.test.main()