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# Copyright 2017 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.
# ==============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import six
from tensorflow.contrib.checkpoint.python import containers
from tensorflow.python.framework import test_util
from tensorflow.python.keras import layers
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import resource_variable_ops
from tensorflow.python.platform import test
from tensorflow.python.training.checkpointable import data_structures
from tensorflow.python.training.checkpointable import tracking
from tensorflow.python.training.checkpointable import util
class UniqueNameTrackerTests(test.TestCase):
@test_util.run_in_graph_and_eager_modes
def testNames(self):
checkpoint_directory = self.get_temp_dir()
checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
x1 = resource_variable_ops.ResourceVariable(2.)
x2 = resource_variable_ops.ResourceVariable(3.)
x3 = resource_variable_ops.ResourceVariable(4.)
y = resource_variable_ops.ResourceVariable(5.)
slots = containers.UniqueNameTracker()
slots.track(x1, "x")
slots.track(x2, "x")
slots.track(x3, "x_1")
slots.track(y, "y")
self.evaluate((x1.initializer, x2.initializer, x3.initializer,
y.initializer))
save_root = util.Checkpoint(slots=slots)
save_path = save_root.save(checkpoint_prefix)
restore_slots = tracking.Checkpointable()
restore_root = util.Checkpoint(
slots=restore_slots)
status = restore_root.restore(save_path)
restore_slots.x = resource_variable_ops.ResourceVariable(0.)
restore_slots.x_1 = resource_variable_ops.ResourceVariable(0.)
restore_slots.x_1_1 = resource_variable_ops.ResourceVariable(0.)
restore_slots.y = resource_variable_ops.ResourceVariable(0.)
status.assert_consumed().run_restore_ops()
self.assertEqual(2., self.evaluate(restore_slots.x))
self.assertEqual(3., self.evaluate(restore_slots.x_1))
self.assertEqual(4., self.evaluate(restore_slots.x_1_1))
self.assertEqual(5., self.evaluate(restore_slots.y))
@test_util.run_in_graph_and_eager_modes
def testExample(self):
class SlotManager(tracking.Checkpointable):
def __init__(self):
self.slotdeps = containers.UniqueNameTracker()
slotdeps = self.slotdeps
slots = []
slots.append(slotdeps.track(
resource_variable_ops.ResourceVariable(3.), "x"))
slots.append(slotdeps.track(
resource_variable_ops.ResourceVariable(4.), "y"))
slots.append(slotdeps.track(
resource_variable_ops.ResourceVariable(5.), "x"))
self.slots = data_structures.NoDependency(slots)
manager = SlotManager()
self.evaluate([v.initializer for v in manager.slots])
checkpoint = util.Checkpoint(slot_manager=manager)
checkpoint_directory = self.get_temp_dir()
checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
save_path = checkpoint.save(checkpoint_prefix)
metadata = util.object_metadata(save_path)
dependency_names = []
for node in metadata.nodes:
for child in node.children:
dependency_names.append(child.local_name)
six.assertCountEqual(
self,
dependency_names,
["x", "x_1", "y", "slot_manager", "slotdeps", "save_counter"])
@test_util.run_in_graph_and_eager_modes
def testLayers(self):
tracker = containers.UniqueNameTracker()
tracker.track(layers.Dense(3), "dense")
tracker.layers[0](array_ops.zeros([1, 1]))
self.assertEqual(2, len(tracker.trainable_weights))
if __name__ == "__main__":
test.main()
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