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# Copyright 2018 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 numpy
from tensorflow.python.eager import context
from tensorflow.python.eager import test
from tensorflow.python.framework import test_util
from tensorflow.python.keras.engine import training
from tensorflow.python.keras.layers import core
from tensorflow.python.keras.layers import normalization
from tensorflow.python.layers import core as non_keras_core
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import resource_variable_ops
from tensorflow.python.training.checkpointable import data_structures
class HasList(training.Model):
def __init__(self):
super(HasList, self).__init__()
self.layer_list = data_structures.List([core.Dense(3)])
self.layer_list.append(core.Dense(4))
self.layer_list.extend(
[core.Dense(5),
core.Dense(6, kernel_regularizer=math_ops.reduce_sum)])
self.layer_list += [
core.Dense(7, bias_regularizer=math_ops.reduce_sum),
core.Dense(8)
]
self.layer_list += (
data_structures.List([core.Dense(9)]) + data_structures.List(
[core.Dense(10)]))
self.layer_list.extend(
data_structures.List(
list(sequence=[core.Dense(11)]) + [core.Dense(12)]))
self.layers_with_updates = data_structures.List(
sequence=(normalization.BatchNormalization(),))
def call(self, x):
aggregation = 0.
for l in self.layer_list:
x = l(x)
aggregation += math_ops.reduce_sum(x)
bn, = self.layers_with_updates
return bn(x) / aggregation
class ListTests(test.TestCase):
@test_util.run_in_graph_and_eager_modes
def testTracking(self):
model = HasList()
output = model(array_ops.ones([32, 2]))
self.assertAllEqual([32, 12], output.shape)
self.assertEqual(2, len(model.layers))
self.assertIs(model.layer_list, model.layers[0])
self.assertEqual(10, len(model.layers[0].layers))
for index in range(10):
self.assertEqual(3 + index, model.layers[0].layers[index].units)
self.assertEqual(2, len(model._checkpoint_dependencies))
self.assertIs(model.layer_list, model._checkpoint_dependencies[0].ref)
self.assertIs(model.layers_with_updates,
model._checkpoint_dependencies[1].ref)
self.assertEqual(
10, len(model._checkpoint_dependencies[0].ref._checkpoint_dependencies))
self.evaluate([v.initializer for v in model.variables])
self.evaluate(model.variables[0].assign([[1., 2., 3.], [4., 5., 6.]]))
save_path = os.path.join(self.get_temp_dir(), "ckpt")
model.save_weights(save_path)
self.evaluate(model.variables[0].assign(array_ops.zeros([2, 3])))
model.load_weights(save_path)
self.assertAllEqual([[1., 2., 3.], [4., 5., 6.]],
self.evaluate(model.variables[0]))
def testUpdatesForwarded(self):
with context.graph_mode():
model = HasList()
model_input = array_ops.ones([32, 2])
model(model_input)
self.assertGreater(len(model.layers_with_updates[0].updates), 0)
self.assertEqual(set(model.layers_with_updates[0].updates),
set(model.updates))
with context.eager_mode():
model = HasList()
model_input = array_ops.ones([32, 2])
model(model_input)
self.assertEqual(0, len(model.updates))
@test_util.run_in_graph_and_eager_modes
def testLossesForwarded(self):
model = HasList()
model_input = array_ops.ones([32, 2])
model(model_input)
self.assertEqual(2, len(model.losses))
def testNotCheckpointable(self):
class NotCheckpointable(object):
pass
with self.assertRaises(ValueError):
data_structures.List([NotCheckpointable()])
def testCallNotImplemented(self):
with self.assertRaisesRegexp(TypeError, "not callable"):
data_structures.List()(1.)
def testNoPop(self):
with self.assertRaises(AttributeError):
data_structures.List().pop()
def testNesting(self):
with context.graph_mode():
inner = data_structures.List()
outer = data_structures.List([inner])
inner.append(non_keras_core.Dense(1))
inner[0](array_ops.ones([2, 3]))
self.assertEqual(2, len(outer.variables))
self.assertIsInstance(
outer.variables[0],
resource_variable_ops.ResourceVariable)
def testNonLayerVariables(self):
v = resource_variable_ops.ResourceVariable([1.])
l = data_structures.List([v])
self.assertTrue(l.trainable)
self.assertEqual([], l.layers)
self.assertEqual([v], l.variables)
self.assertEqual([v], l.trainable_weights)
self.assertEqual([], l.non_trainable_variables)
l.trainable = False
self.assertEqual([v], l.variables)
self.assertEqual([], l.trainable_variables)
self.assertEqual([v], l.non_trainable_variables)
l.trainable = True
v2 = resource_variable_ops.ResourceVariable(1., trainable=False)
l.append(v2)
self.assertEqual([v, v2], l.weights)
self.assertEqual([v], l.trainable_weights)
self.assertEqual([v2], l.non_trainable_weights)
def testHashing(self):
has_sequences = set([data_structures.List(),
data_structures.List()])
self.assertEqual(2, len(has_sequences))
self.assertNotIn(data_structures.List(), has_sequences)
class HasMapping(training.Model):
def __init__(self):
super(HasMapping, self).__init__()
self.layer_dict = data_structures.Mapping(output=core.Dense(7))
self.layer_dict["norm"] = data_structures.List()
self.layer_dict["dense"] = data_structures.List()
self.layer_dict["dense"].extend(
[core.Dense(5),
core.Dense(6, kernel_regularizer=math_ops.reduce_sum)])
self.layer_dict["norm"].append(
normalization.BatchNormalization())
self.layer_dict["norm"].append(
normalization.BatchNormalization())
def call(self, x):
aggregation = 0.
for norm, dense in zip(self.layer_dict["norm"], self.layer_dict["dense"]):
x = norm(dense(x))
aggregation += math_ops.reduce_sum(x)
return self.layer_dict["output"](x) / aggregation
class MappingTests(test.TestCase):
@test_util.run_in_graph_and_eager_modes
def testTracking(self):
model = HasMapping()
output = model(array_ops.ones([32, 2]))
self.assertAllEqual([32, 7], output.shape)
self.assertEqual(1, len(model.layers))
self.assertIs(model.layer_dict, model.layers[0])
self.assertEqual(3, len(model.layers[0].layers))
self.assertEqual(1, len(model._checkpoint_dependencies))
self.assertIs(model.layer_dict, model._checkpoint_dependencies[0].ref)
self.evaluate([v.initializer for v in model.variables])
test_var = model.layer_dict["output"].kernel
self.evaluate(test_var.assign(array_ops.ones([6, 7])))
save_path = os.path.join(self.get_temp_dir(), "ckpt")
model.save_weights(save_path)
self.evaluate(test_var.assign(array_ops.zeros([6, 7])))
model.load_weights(save_path)
self.assertAllEqual(numpy.ones([6, 7]),
self.evaluate(test_var))
def testNoOverwrite(self):
mapping = data_structures.Mapping()
original = data_structures.List()
mapping["a"] = original
with self.assertRaises(ValueError):
mapping["a"] = data_structures.List()
self.assertIs(original, mapping["a"])
with self.assertRaises(AttributeError):
del mapping["a"]
mapping.update(b=data_structures.Mapping())
with self.assertRaises(ValueError):
mapping.update({"b": data_structures.Mapping()})
def testNonStringKeys(self):
mapping = data_structures.Mapping()
with self.assertRaises(TypeError):
mapping[1] = data_structures.List()
def testHashing(self):
has_mappings = set([data_structures.Mapping(),
data_structures.Mapping()])
self.assertEqual(2, len(has_mappings))
self.assertNotIn(data_structures.Mapping(), has_mappings)
if __name__ == "__main__":
test.main()
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