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Fork the keras related saver_test to keras.

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PiperOrigin-RevId: 316475172
Change-Id: I9b38d7624ed01a724c15ae571c5be1a7f0e049ad
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Scott Zhu 2020-06-15 08:59:00 -07:00 committed by TensorFlower Gardener
parent 81601f9bb4
commit 7d36cebea4
3 changed files with 181 additions and 121 deletions
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23
tensorflow/python/keras/tests/BUILD
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@ -335,6 +335,29 @@ tf_py_test(
], ],
) )
cuda_py_test(
name = "saver_test",
size = "medium",
srcs = ["saver_test.py"],
python_version = "PY3",
deps = [
"//tensorflow/python:client_testlib",
"//tensorflow/python:constant_op",
"//tensorflow/python:errors",
"//tensorflow/python:framework_ops",
"//tensorflow/python:nn_grad",
"//tensorflow/python:resource_variable_ops",
"//tensorflow/python:saver",
"//tensorflow/python:training_lib",
"//tensorflow/python:training_util",
"//tensorflow/python/eager:context",
"//tensorflow/python/keras/engine",
"//tensorflow/python/keras/layers:core",
"//tensorflow/python/training/tracking",
"//tensorflow/python/training/tracking:util",
],
)
tf_py_test( tf_py_test(
name = "temporal_sample_weights_correctness_test", name = "temporal_sample_weights_correctness_test",
srcs = ["temporal_sample_weights_correctness_test.py"], srcs = ["temporal_sample_weights_correctness_test.py"],

158
tensorflow/python/keras/tests/saver_test.py Normal file
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@ -0,0 +1,158 @@
# 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 tensorflow.python.training.saver.py."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import functools
import os
from tensorflow.python.eager import context
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import errors
from tensorflow.python.framework import ops as ops_lib
from tensorflow.python.keras.engine import training
from tensorflow.python.keras.layers import core
from tensorflow.python.ops import resource_variable_ops
from tensorflow.python.platform import test
from tensorflow.python.training import adam
from tensorflow.python.training import saver as saver_module
from tensorflow.python.training import training_util
from tensorflow.python.training.tracking import tracking as trackable_tracking
from tensorflow.python.training.tracking import util as trackable_utils
class NonLayerTrackable(trackable_tracking.AutoTrackable):
def __init__(self):
super(NonLayerTrackable, self).__init__()
self.a_variable = trackable_utils.add_variable(
self, name="a_variable", shape=[])
class MyModel(training.Model):
"""A concrete Model for testing."""
def __init__(self):
super(MyModel, self).__init__()
self._named_dense = core.Dense(1, use_bias=True)
self._second = core.Dense(1, use_bias=False)
# We can still track Trackables which aren't Layers.
self._non_layer = NonLayerTrackable()
def call(self, values):
ret = self._second(self._named_dense(values))
return ret
class TrackableCompatibilityTests(test.TestCase):
def _initialized_model(self):
input_value = constant_op.constant([[3.]])
model = MyModel()
optimizer = adam.AdamOptimizer(0.001)
optimizer_step = training_util.get_or_create_global_step()
root_trackable = trackable_utils.Checkpoint(
optimizer=optimizer, model=model, optimizer_step=optimizer_step)
train_op = optimizer.minimize(
functools.partial(model, input_value),
global_step=optimizer_step)
self.evaluate(trackable_utils.gather_initializers(
root_trackable))
self.evaluate(train_op)
# A regular variable, a slot variable, and a non-slot Optimizer variable
# with known values to check when loading.
self.evaluate(model._named_dense.bias.assign([1.]))
self.evaluate(optimizer.get_slot(
var=model._named_dense.bias, name="m").assign([2.]))
beta1_power, _ = optimizer._get_beta_accumulators()
self.evaluate(beta1_power.assign(3.))
return root_trackable
def _set_sentinels(self, root_trackable):
self.evaluate(root_trackable.model._named_dense.bias.assign([101.]))
self.evaluate(
root_trackable.optimizer.get_slot(
var=root_trackable.model._named_dense.bias, name="m")
.assign([102.]))
beta1_power, _ = root_trackable.optimizer._get_beta_accumulators()
self.evaluate(beta1_power.assign(103.))
def _check_sentinels(self, root_trackable):
self.assertAllEqual(
[1.], self.evaluate(root_trackable.model._named_dense.bias))
self.assertAllEqual([2.], self.evaluate(
root_trackable.optimizer.get_slot(
var=root_trackable.model._named_dense.bias, name="m")))
beta1_power, _ = root_trackable.optimizer._get_beta_accumulators()
self.assertAllEqual(3., self.evaluate(beta1_power))
def testLoadFromObjectBasedGraph(self):
checkpoint_directory = self.get_temp_dir()
checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
save_graph = ops_lib.Graph()
with save_graph.as_default(), self.session(graph=save_graph) as sess:
root = self._initialized_model()
object_saver = trackable_utils.Checkpoint(root=root)
save_path = object_saver.save(file_prefix=checkpoint_prefix)
# An incompatible object-based checkpoint to check error messages
var = resource_variable_ops.ResourceVariable(1., name="a")
self.evaluate(var.initializer)
second_saver = trackable_utils.Checkpoint(v=var)
second_path = second_saver.save(file_prefix=os.path.join(
checkpoint_directory, "second"))
restore_graph = ops_lib.Graph()
with restore_graph.as_default(), self.session(
graph=restore_graph) as sess:
root = self._initialized_model()
self._set_sentinels(root)
saver = saver_module.Saver()
saver.restore(sess=sess, save_path=save_path)
self._check_sentinels(root)
before_second_restore_ops = restore_graph.get_operations()
# Test that multiple restores do not pollute the graph
saver.restore(sess=sess, save_path=save_path)
self.assertEqual(before_second_restore_ops,
restore_graph.get_operations())
with self.assertRaisesRegexp(errors.NotFoundError,
"Could not find some variables"):
saver.restore(sess=sess, save_path=second_path)
def testLoadFromObjectBasedEager(self):
checkpoint_directory = self.get_temp_dir()
checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
save_graph = ops_lib.Graph()
with save_graph.as_default(), self.session(graph=save_graph):
root = self._initialized_model()
object_saver = trackable_utils.Checkpoint(root=root)
save_path = object_saver.save(file_prefix=checkpoint_prefix)
with context.eager_mode():
root = self._initialized_model()
self._set_sentinels(root)
saver = saver_module.Saver(
root.model.variables + root.optimizer.variables())
saver.restore(sess=None, save_path=save_path)
self._check_sentinels(root)
if __name__ == "__main__":
test.main()

121
tensorflow/python/training/saver_test.py
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@ -18,7 +18,6 @@ from __future__ import absolute_import
from __future__ import division from __future__ import division
from __future__ import print_function from __future__ import print_function
import functools
import glob import glob
import math import math
import os import os
@ -48,8 +47,6 @@ from tensorflow.python.framework import graph_io
from tensorflow.python.framework import meta_graph from tensorflow.python.framework import meta_graph
from tensorflow.python.framework import ops as ops_lib from tensorflow.python.framework import ops as ops_lib
from tensorflow.python.framework import test_util from tensorflow.python.framework import test_util
from tensorflow.python.keras.engine import training
from tensorflow.python.keras.layers import core
from tensorflow.python.lib.io import file_io from tensorflow.python.lib.io import file_io
from tensorflow.python.ops import array_ops from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import control_flow_ops
@ -74,10 +71,7 @@ from tensorflow.python.training import py_checkpoint_reader
from tensorflow.python.training import queue_runner_impl from tensorflow.python.training import queue_runner_impl
from tensorflow.python.training import saver as saver_module from tensorflow.python.training import saver as saver_module
from tensorflow.python.training import saver_test_utils from tensorflow.python.training import saver_test_utils
from tensorflow.python.training import training_util
from tensorflow.python.training.tracking import base as trackable_base from tensorflow.python.training.tracking import base as trackable_base
from tensorflow.python.training.tracking import tracking as trackable_tracking
from tensorflow.python.training.tracking import util as trackable_utils
from tensorflow.python.util import compat from tensorflow.python.util import compat
@ -3024,29 +3018,6 @@ class _OwnsMirroredVariables(trackable_base.Trackable):
return self.non_dep_variable.name return self.non_dep_variable.name
class NonLayerTrackable(trackable_tracking.AutoTrackable):
def __init__(self):
super(NonLayerTrackable, self).__init__()
self.a_variable = trackable_utils.add_variable(
self, name="a_variable", shape=[])
class MyModel(training.Model):
"""A concrete Model for testing."""
def __init__(self):
super(MyModel, self).__init__()
self._named_dense = core.Dense(1, use_bias=True)
self._second = core.Dense(1, use_bias=False)
# We can still track Trackables which aren't Layers.
self._non_layer = NonLayerTrackable()
def call(self, values):
ret = self._second(self._named_dense(values))
return ret
class TrackableCompatibilityTests(test.TestCase): class TrackableCompatibilityTests(test.TestCase):
# TODO(allenl): Track down python3 reference cycles in these tests. # TODO(allenl): Track down python3 reference cycles in these tests.
@ -3112,46 +3083,6 @@ class TrackableCompatibilityTests(test.TestCase):
saver.restore(sess, save_path) saver.restore(sess, save_path)
self.assertEqual(1, v.eval_count) self.assertEqual(1, v.eval_count)
def _initialized_model(self):
input_value = constant_op.constant([[3.]])
model = MyModel()
optimizer = adam.AdamOptimizer(0.001)
optimizer_step = training_util.get_or_create_global_step()
root_trackable = trackable_utils.Checkpoint(
optimizer=optimizer, model=model, optimizer_step=optimizer_step)
train_op = optimizer.minimize(
functools.partial(model, input_value),
global_step=optimizer_step)
self.evaluate(trackable_utils.gather_initializers(
root_trackable))
self.evaluate(train_op)
# A regular variable, a slot variable, and a non-slot Optimizer variable
# with known values to check when loading.
self.evaluate(model._named_dense.bias.assign([1.]))
self.evaluate(optimizer.get_slot(
var=model._named_dense.bias, name="m").assign([2.]))
beta1_power, _ = optimizer._get_beta_accumulators()
self.evaluate(beta1_power.assign(3.))
return root_trackable
def _set_sentinels(self, root_trackable):
self.evaluate(root_trackable.model._named_dense.bias.assign([101.]))
self.evaluate(
root_trackable.optimizer.get_slot(
var=root_trackable.model._named_dense.bias, name="m")
.assign([102.]))
beta1_power, _ = root_trackable.optimizer._get_beta_accumulators()
self.evaluate(beta1_power.assign(103.))
def _check_sentinels(self, root_trackable):
self.assertAllEqual(
[1.], self.evaluate(root_trackable.model._named_dense.bias))
self.assertAllEqual([2.], self.evaluate(
root_trackable.optimizer.get_slot(
var=root_trackable.model._named_dense.bias, name="m")))
beta1_power, _ = root_trackable.optimizer._get_beta_accumulators()
self.assertAllEqual(3., self.evaluate(beta1_power))
def testVariableNotFoundErrorRaised(self): def testVariableNotFoundErrorRaised(self):
# Restore does some tricky exception handling to figure out if it should # Restore does some tricky exception handling to figure out if it should
# load an object-based checkpoint. Tests that the exception handling isn't # load an object-based checkpoint. Tests that the exception handling isn't
@ -3199,58 +3130,6 @@ class TrackableCompatibilityTests(test.TestCase):
"a mismatch between the current graph and the graph"): "a mismatch between the current graph and the graph"):
a_saver.restore(sess=sess, save_path=save_path) a_saver.restore(sess=sess, save_path=save_path)
def testLoadFromObjectBasedGraph(self):
checkpoint_directory = self.get_temp_dir()
checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
save_graph = ops_lib.Graph()
with save_graph.as_default(), self.session(graph=save_graph) as sess:
root = self._initialized_model()
object_saver = trackable_utils.Checkpoint(root=root)
save_path = object_saver.save(file_prefix=checkpoint_prefix)
# An incompatible object-based checkpoint to check error messages
var = resource_variable_ops.ResourceVariable(1., name="a")
self.evaluate(var.initializer)
second_saver = trackable_utils.Checkpoint(v=var)
second_path = second_saver.save(file_prefix=os.path.join(
checkpoint_directory, "second"))
restore_graph = ops_lib.Graph()
with restore_graph.as_default(), self.session(
graph=restore_graph) as sess:
root = self._initialized_model()
self._set_sentinels(root)
saver = saver_module.Saver()
saver.restore(sess=sess, save_path=save_path)
self._check_sentinels(root)
before_second_restore_ops = restore_graph.get_operations()
# Test that multiple restores do not pollute the graph
saver.restore(sess=sess, save_path=save_path)
self.assertEqual(before_second_restore_ops,
restore_graph.get_operations())
with self.assertRaisesRegexp(errors.NotFoundError,
"Could not find some variables"):
saver.restore(sess=sess, save_path=second_path)
def testLoadFromObjectBasedEager(self):
checkpoint_directory = self.get_temp_dir()
checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
save_graph = ops_lib.Graph()
with save_graph.as_default(), self.session(graph=save_graph):
root = self._initialized_model()
object_saver = trackable_utils.Checkpoint(root=root)
save_path = object_saver.save(file_prefix=checkpoint_prefix)
with context.eager_mode():
root = self._initialized_model()
self._set_sentinels(root)
saver = saver_module.Saver(
root.model.variables + root.optimizer.variables())
saver.restore(sess=None, save_path=save_path)
self._check_sentinels(root)
if __name__ == "__main__": if __name__ == "__main__":
test.main() test.main()