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"""Tests for eager mode Saver."""
# 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
from tensorflow.contrib.eager.python import saver as _saver
from tensorflow.python.eager import context
from tensorflow.python.eager import graph_callable
from tensorflow.python.eager import test
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import resource_variable_ops
from tensorflow.python.ops import variable_scope
from tensorflow.python.training import adam
from tensorflow.python.training import gradient_descent
from tensorflow.python.training import momentum
from tensorflow.python.training import rmsprop
class SaverTest(test.TestCase):
def _dev(self):
return '/device:GPU:0' if context.num_gpus() else '/device:CPU:0'
def testBasics(self):
with ops.device(self._dev()):
v1 = resource_variable_ops.ResourceVariable(1.0, name='v1')
def model():
return array_ops.constant(2.0) * v1
ckpt_prefix = os.path.join(test.get_temp_dir(), 'ckpt')
_ = model()
saver = _saver.Saver([v1])
saver.save(ckpt_prefix)
v1.assign(2.0)
self.assertEqual(v1.read_value().numpy(), 2.0)
saver.restore(ckpt_prefix)
self.assertEqual(v1.read_value().numpy(), 1.0)
def testSameNameNoClobbering(self):
with ops.device(self._dev()):
# Note that this test purposefully uses Graphs rather than
# IsolateTest. Users are more likely to accidentally create the same
# variable name this way.
first_graph = ops.Graph()
with first_graph.as_default():
v1_first_graph = resource_variable_ops.ResourceVariable(1.0, name='v1')
with ops.Graph().as_default():
v1_second_graph = resource_variable_ops.ResourceVariable(2.0, name='v1')
saver = _saver.Saver([v1_first_graph, v1_second_graph])
ckpt_prefix = os.path.join(test.get_temp_dir(), 'ckpt')
with self.assertRaisesRegexp(ValueError, 'v1'):
saver.save(ckpt_prefix)
def testSameObjectOK(self):
with ops.device(self._dev()):
v1 = resource_variable_ops.ResourceVariable(1.0, name='v1')
# While different objects with the same shared_name are not good, passing
# in the same object multiple times is fine.
saver = _saver.Saver([v1, v1])
ckpt_prefix = os.path.join(test.get_temp_dir(), 'ckpt')
saver.save(ckpt_prefix)
def testSaveByDict(self):
with ops.device(self._dev()):
v1 = resource_variable_ops.ResourceVariable(1.0, name='v1')
v2 = resource_variable_ops.ResourceVariable(1.0, name='v2')
def model():
return array_ops.constant(2.0) * v1 * v2
ckpt_prefix = os.path.join(test.get_temp_dir(), 'ckpt')
# Save the variables under different names.
_ = model()
saver = _saver.Saver({'ckpt/v1': v1, 'ckpt/v2': v2})
saver.save(ckpt_prefix)
v1.assign(2.0)
v2.assign(2.0)
self.assertEqual(v1.read_value().numpy(), 2.0)
self.assertEqual(v2.read_value().numpy(), 2.0)
# Can still restore it.
saver.restore(ckpt_prefix)
self.assertEqual(v1.read_value().numpy(), 1.0)
# However, cannot restore it with default name.
with self.assertRaisesOpError('not found in checkpoint'):
saver = _saver.Saver([v1, v2]).restore(ckpt_prefix)
# Can specify which variable in ckpt to restore to which variable.
def map_func(x):
return {'v3': 'ckpt/v1', 'v4': 'ckpt/v2'}.get(x, x)
with _saver.restore_variables_on_create(ckpt_prefix, map_func):
v3 = resource_variable_ops.ResourceVariable(2.0, name='v3')
v4 = resource_variable_ops.ResourceVariable(2.0, name='v4')
self.assertEqual(v3.read_value().numpy(), 1.0)
self.assertEqual(v4.read_value().numpy(), 1.0)
def testRestoreOnCreate(self):
with ops.device(self._dev()):
def model(init_val):
v1 = resource_variable_ops.ResourceVariable(init_val, name='v1')
return array_ops.constant(1.0) * v1, v1
ckpt_prefix = os.path.join(test.get_temp_dir(), 'ckpt')
_, v1 = model(1.0)
saver = _saver.Saver([v1])
saver.save(ckpt_prefix)
with ops.Graph().as_default():
saver = _saver.Saver([v1])
with _saver.restore_variables_on_create(ckpt_prefix):
# Value is from checkpoint, but not from argument.
ret, _ = model(2.0)
self.assertEqual(ret.numpy(), 1.0)
def testRestoreNotFound(self):
with ops.device(self._dev()):
def model(v):
return array_ops.constant(1.0) * v
ckpt_prefix = os.path.join(test.get_temp_dir(), 'ckpt')
v = resource_variable_ops.ResourceVariable(1.0, name='v1')
_ = model(v)
saver = _saver.Saver([v])
saver.save(ckpt_prefix)
with self.assertRaisesRegexp(errors.NotFoundError,
'v2 not found in checkpoint'):
with _saver.restore_variables_on_create(ckpt_prefix):
_ = model(resource_variable_ops.ResourceVariable(1.0, name='v2'))
def testSaveRestoreGraphCallable(self):
with ops.device(self._dev()):
@graph_callable.graph_callable(
[graph_callable.ShapeAndDtype(shape=(), dtype=dtypes.float32)])
def model(x):
v = variable_scope.get_variable(
'v', initializer=init_ops.zeros_initializer(), shape=())
return v + x
# Default 2 + 0 = 2
self.assertEqual(
2, model(array_ops.constant(2, dtype=dtypes.float32)).numpy())
# Save the variable value 0.
ckpt_prefix = os.path.join(test.get_temp_dir(), 'ckpt')
_saver.Saver(model.variables).save(ckpt_prefix)
# update variable to 1, so that 2 + 1 = 3
model.variables[0].assign(1.)
self.assertEqual(
3, model(array_ops.constant(2, dtype=dtypes.float32)).numpy())
# load the variable value 0, so that 2 + 0 = 2
_saver.Saver(model.variables).restore(ckpt_prefix)
self.assertEqual(
2, model(array_ops.constant(2, dtype=dtypes.float32)).numpy())
# update checkpoint variable to 1 and memory value to 2.
model.variables[0].assign(1.)
_saver.Saver(model.variables).save(ckpt_prefix)
model.variables[0].assign(2.)
self.assertEqual(
4, model(array_ops.constant(2, dtype=dtypes.float32)).numpy())
# reset the graph and reload on create, so that 1 + 2 = 3
with ops.Graph().as_default():
with _saver.restore_variables_on_create(ckpt_prefix):
@graph_callable.graph_callable(
[graph_callable.ShapeAndDtype(shape=(), dtype=dtypes.float32)])
def model2(x):
v = variable_scope.get_variable(
'v', initializer=init_ops.zeros_initializer(), shape=())
return v + x
self.assertEqual(
3, model2(array_ops.constant(2, dtype=dtypes.float32)).numpy())
class GetOptimizerTests(test.TestCase):
def _optimizer_test_template(self, optimizer):
"""Checks save and restore. Returns the optimizer variables."""
v = resource_variable_ops.ResourceVariable([[2., 3.]], name='v')
loss_fn = lambda: v[0, 0] ** 2 + v[0, 1] ** 2
optimizer.minimize(loss_fn)
optimizer_variables = _saver.get_optimizer_variables(optimizer)
saver = _saver.Saver(optimizer_variables + [v])
checkpoint_path = saver.save(self.get_temp_dir())
optimizer.minimize(loss_fn)
after_first_minimize = v.numpy()
# After we restore, the next step should be exactly the same as the one we
# just did.
saver.restore(checkpoint_path)
optimizer.minimize(loss_fn)
self.assertAllEqual(after_first_minimize, v.numpy())
return optimizer_variables
def testAdam(self):
optimizer = adam.AdamOptimizer(0.1)
self._optimizer_test_template(optimizer)
def testGradientDescent(self):
optimizer = gradient_descent.GradientDescentOptimizer(0.02)
self.assertEqual(0, len(self._optimizer_test_template(optimizer)))
def testMomentum(self):
optimizer = momentum.MomentumOptimizer(
learning_rate=0.03,
momentum=0.5)
self._optimizer_test_template(optimizer)
def testRMSProp(self):
optimizer = rmsprop.RMSPropOptimizer(0.01)
self._optimizer_test_template(optimizer)
if __name__ == '__main__':
test.main()
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