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ML_students/proj_transferLearn_PV_classification/inception/slim/variables_test.py
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variables_test.py
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# Copyright 2016 Google Inc. 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 slim.variables."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from inception.slim import scopes
from inception.slim import variables
class VariablesTest(tf.test.TestCase):
def testCreateVariable(self):
with self.test_session():
with tf.variable_scope('A'):
a = variables.variable('a', [5])
self.assertEquals(a.op.name, 'A/a')
self.assertListEqual(a.get_shape().as_list(), [5])
def testGetVariables(self):
with self.test_session():
with tf.variable_scope('A'):
a = variables.variable('a', [5])
with tf.variable_scope('B'):
b = variables.variable('a', [5])
self.assertEquals([a, b], variables.get_variables())
self.assertEquals([a], variables.get_variables('A'))
self.assertEquals([b], variables.get_variables('B'))
def testGetVariablesSuffix(self):
with self.test_session():
with tf.variable_scope('A'):
a = variables.variable('a', [5])
with tf.variable_scope('A'):
b = variables.variable('b', [5])
self.assertEquals([a], variables.get_variables(suffix='a'))
self.assertEquals([b], variables.get_variables(suffix='b'))
def testGetVariableWithSingleVar(self):
with self.test_session():
with tf.variable_scope('parent'):
a = variables.variable('child', [5])
self.assertEquals(a, variables.get_unique_variable('parent/child'))
def testGetVariableWithDistractors(self):
with self.test_session():
with tf.variable_scope('parent'):
a = variables.variable('child', [5])
with tf.variable_scope('child'):
variables.variable('grandchild1', [7])
variables.variable('grandchild2', [9])
self.assertEquals(a, variables.get_unique_variable('parent/child'))
def testGetVariableThrowsExceptionWithNoMatch(self):
var_name = 'cant_find_me'
with self.test_session():
with self.assertRaises(ValueError):
variables.get_unique_variable(var_name)
def testGetThrowsExceptionWithChildrenButNoMatch(self):
var_name = 'parent/child'
with self.test_session():
with tf.variable_scope(var_name):
variables.variable('grandchild1', [7])
variables.variable('grandchild2', [9])
with self.assertRaises(ValueError):
variables.get_unique_variable(var_name)
def testGetVariablesToRestore(self):
with self.test_session():
with tf.variable_scope('A'):
a = variables.variable('a', [5])
with tf.variable_scope('B'):
b = variables.variable('a', [5])
self.assertEquals([a, b], variables.get_variables_to_restore())
def testNoneGetVariablesToRestore(self):
with self.test_session():
with tf.variable_scope('A'):
a = variables.variable('a', [5], restore=False)
with tf.variable_scope('B'):
b = variables.variable('a', [5], restore=False)
self.assertEquals([], variables.get_variables_to_restore())
self.assertEquals([a, b], variables.get_variables())
def testGetMixedVariablesToRestore(self):
with self.test_session():
with tf.variable_scope('A'):
a = variables.variable('a', [5])
b = variables.variable('b', [5], restore=False)
with tf.variable_scope('B'):
c = variables.variable('c', [5])
d = variables.variable('d', [5], restore=False)
self.assertEquals([a, b, c, d], variables.get_variables())
self.assertEquals([a, c], variables.get_variables_to_restore())
def testReuseVariable(self):
with self.test_session():
with tf.variable_scope('A'):
a = variables.variable('a', [])
with tf.variable_scope('A', reuse=True):
b = variables.variable('a', [])
self.assertEquals(a, b)
self.assertListEqual([a], variables.get_variables())
def testVariableWithDevice(self):
with self.test_session():
with tf.variable_scope('A'):
a = variables.variable('a', [], device='cpu:0')
b = variables.variable('b', [], device='cpu:1')
self.assertDeviceEqual(a.device, 'cpu:0')
self.assertDeviceEqual(b.device, 'cpu:1')
def testVariableWithDeviceFromScope(self):
with self.test_session():
with tf.device('/cpu:0'):
a = variables.variable('a', [])
b = variables.variable('b', [], device='cpu:1')
self.assertDeviceEqual(a.device, 'cpu:0')
self.assertDeviceEqual(b.device, 'cpu:1')
def testVariableWithDeviceFunction(self):
class DevFn(object):
def __init__(self):
self.counter = -1
def __call__(self, op):
self.counter += 1
return 'cpu:%d' % self.counter
with self.test_session():
with scopes.arg_scope([variables.variable], device=DevFn()):
a = variables.variable('a', [])
b = variables.variable('b', [])
c = variables.variable('c', [], device='cpu:12')
d = variables.variable('d', [])
with tf.device('cpu:99'):
e_init = tf.constant(12)
e = variables.variable('e', initializer=e_init)
self.assertDeviceEqual(a.device, 'cpu:0')
self.assertDeviceEqual(a.initial_value.device, 'cpu:0')
self.assertDeviceEqual(b.device, 'cpu:1')
self.assertDeviceEqual(b.initial_value.device, 'cpu:1')
self.assertDeviceEqual(c.device, 'cpu:12')
self.assertDeviceEqual(c.initial_value.device, 'cpu:12')
self.assertDeviceEqual(d.device, 'cpu:2')
self.assertDeviceEqual(d.initial_value.device, 'cpu:2')
self.assertDeviceEqual(e.device, 'cpu:3')
self.assertDeviceEqual(e.initial_value.device, 'cpu:99')
def testVariableWithReplicaDeviceSetter(self):
with self.test_session():
with tf.device(tf.train.replica_device_setter(ps_tasks=2)):
a = variables.variable('a', [])
b = variables.variable('b', [])
c = variables.variable('c', [], device='cpu:12')
d = variables.variable('d', [])
with tf.device('cpu:99'):
e_init = tf.constant(12)
e = variables.variable('e', initializer=e_init)
# The values below highlight how the replica_device_setter puts initial
# values on the worker job, and how it merges explicit devices.
self.assertDeviceEqual(a.device, '/job:ps/task:0/cpu:0')
self.assertDeviceEqual(a.initial_value.device, '/job:worker/cpu:0')
self.assertDeviceEqual(b.device, '/job:ps/task:1/cpu:0')
self.assertDeviceEqual(b.initial_value.device, '/job:worker/cpu:0')
self.assertDeviceEqual(c.device, '/job:ps/task:0/cpu:12')
self.assertDeviceEqual(c.initial_value.device, '/job:worker/cpu:12')
self.assertDeviceEqual(d.device, '/job:ps/task:1/cpu:0')
self.assertDeviceEqual(d.initial_value.device, '/job:worker/cpu:0')
self.assertDeviceEqual(e.device, '/job:ps/task:0/cpu:0')
self.assertDeviceEqual(e.initial_value.device, '/job:worker/cpu:99')
def testVariableWithVariableDeviceChooser(self):
with tf.Graph().as_default():
device_fn = variables.VariableDeviceChooser(num_parameter_servers=2)
with scopes.arg_scope([variables.variable], device=device_fn):
a = variables.variable('a', [])
b = variables.variable('b', [])
c = variables.variable('c', [], device='cpu:12')
d = variables.variable('d', [])
with tf.device('cpu:99'):
e_init = tf.constant(12)
e = variables.variable('e', initializer=e_init)
# The values below highlight how the VariableDeviceChooser puts initial
# values on the same device as the variable job.
self.assertDeviceEqual(a.device, '/job:ps/task:0/cpu:0')
self.assertDeviceEqual(a.initial_value.device, a.device)
self.assertDeviceEqual(b.device, '/job:ps/task:1/cpu:0')
self.assertDeviceEqual(b.initial_value.device, b.device)
self.assertDeviceEqual(c.device, '/cpu:12')
self.assertDeviceEqual(c.initial_value.device, c.device)
self.assertDeviceEqual(d.device, '/job:ps/task:0/cpu:0')
self.assertDeviceEqual(d.initial_value.device, d.device)
self.assertDeviceEqual(e.device, '/job:ps/task:1/cpu:0')
self.assertDeviceEqual(e.initial_value.device, '/cpu:99')
def testVariableGPUPlacement(self):
with tf.Graph().as_default():
device_fn = variables.VariableDeviceChooser(placement='gpu:0')
with scopes.arg_scope([variables.variable], device=device_fn):
a = variables.variable('a', [])
b = variables.variable('b', [])
c = variables.variable('c', [], device='cpu:12')
d = variables.variable('d', [])
with tf.device('cpu:99'):
e_init = tf.constant(12)
e = variables.variable('e', initializer=e_init)
# The values below highlight how the VariableDeviceChooser puts initial
# values on the same device as the variable job.
self.assertDeviceEqual(a.device, '/gpu:0')
self.assertDeviceEqual(a.initial_value.device, a.device)
self.assertDeviceEqual(b.device, '/gpu:0')
self.assertDeviceEqual(b.initial_value.device, b.device)
self.assertDeviceEqual(c.device, '/cpu:12')
self.assertDeviceEqual(c.initial_value.device, c.device)
self.assertDeviceEqual(d.device, '/gpu:0')
self.assertDeviceEqual(d.initial_value.device, d.device)
self.assertDeviceEqual(e.device, '/gpu:0')
self.assertDeviceEqual(e.initial_value.device, '/cpu:99')
def testVariableCollection(self):
with self.test_session():
a = variables.variable('a', [], collections='A')
b = variables.variable('b', [], collections='B')
self.assertEquals(a, tf.get_collection('A')[0])
self.assertEquals(b, tf.get_collection('B')[0])
def testVariableCollections(self):
with self.test_session():
a = variables.variable('a', [], collections=['A', 'C'])
b = variables.variable('b', [], collections=['B', 'C'])
self.assertEquals(a, tf.get_collection('A')[0])
self.assertEquals(b, tf.get_collection('B')[0])
def testVariableCollectionsWithArgScope(self):
with self.test_session():
with scopes.arg_scope([variables.variable], collections='A'):
a = variables.variable('a', [])
b = variables.variable('b', [])
self.assertListEqual([a, b], tf.get_collection('A'))
def testVariableCollectionsWithArgScopeNested(self):
with self.test_session():
with scopes.arg_scope([variables.variable], collections='A'):
a = variables.variable('a', [])
with scopes.arg_scope([variables.variable], collections='B'):
b = variables.variable('b', [])
self.assertEquals(a, tf.get_collection('A')[0])
self.assertEquals(b, tf.get_collection('B')[0])
def testVariableCollectionsWithArgScopeNonNested(self):
with self.test_session():
with scopes.arg_scope([variables.variable], collections='A'):
a = variables.variable('a', [])
with scopes.arg_scope([variables.variable], collections='B'):
b = variables.variable('b', [])
variables.variable('c', [])
self.assertListEqual([a], tf.get_collection('A'))
self.assertListEqual([b], tf.get_collection('B'))
def testVariableRestoreWithArgScopeNested(self):
with self.test_session():
with scopes.arg_scope([variables.variable], restore=True):
a = variables.variable('a', [])
with scopes.arg_scope([variables.variable],
trainable=False,
collections=['A', 'B']):
b = variables.variable('b', [])
c = variables.variable('c', [])
self.assertListEqual([a, b, c], variables.get_variables_to_restore())
self.assertListEqual([a, c], tf.trainable_variables())
self.assertListEqual([b], tf.get_collection('A'))
self.assertListEqual([b], tf.get_collection('B'))
class GetVariablesByNameTest(tf.test.TestCase):
def testGetVariableGivenNameScoped(self):
with self.test_session():
with tf.variable_scope('A'):
a = variables.variable('a', [5])
b = variables.variable('b', [5])
self.assertEquals([a], variables.get_variables_by_name('a'))
self.assertEquals([b], variables.get_variables_by_name('b'))
def testGetVariablesByNameReturnsByValueWithScope(self):
with self.test_session():
with tf.variable_scope('A'):
a = variables.variable('a', [5])
matched_variables = variables.get_variables_by_name('a')
# If variables.get_variables_by_name returns the list by reference, the
# following append should persist, and be returned, in subsequent calls
# to variables.get_variables_by_name('a').
matched_variables.append(4)
matched_variables = variables.get_variables_by_name('a')
self.assertEquals([a], matched_variables)
def testGetVariablesByNameReturnsByValueWithoutScope(self):
with self.test_session():
a = variables.variable('a', [5])
matched_variables = variables.get_variables_by_name('a')
# If variables.get_variables_by_name returns the list by reference, the
# following append should persist, and be returned, in subsequent calls
# to variables.get_variables_by_name('a').
matched_variables.append(4)
matched_variables = variables.get_variables_by_name('a')
self.assertEquals([a], matched_variables)
class GlobalStepTest(tf.test.TestCase):
def testStable(self):
with tf.Graph().as_default():
gs = variables.global_step()
gs2 = variables.global_step()
self.assertTrue(gs is gs2)
def testDevice(self):
with tf.Graph().as_default():
with scopes.arg_scope([variables.global_step], device='/gpu:0'):
gs = variables.global_step()
self.assertDeviceEqual(gs.device, '/gpu:0')
def testDeviceFn(self):
class DevFn(object):
def __init__(self):
self.counter = -1
def __call__(self, op):
self.counter += 1
return '/cpu:%d' % self.counter
with tf.Graph().as_default():
with scopes.arg_scope([variables.global_step], device=DevFn()):
gs = variables.global_step()
gs2 = variables.global_step()
self.assertDeviceEqual(gs.device, '/cpu:0')
self.assertEquals(gs, gs2)
self.assertDeviceEqual(gs2.device, '/cpu:0')
def testReplicaDeviceSetter(self):
device_fn = tf.train.replica_device_setter(2)
with tf.Graph().as_default():
with scopes.arg_scope([variables.global_step], device=device_fn):
gs = variables.global_step()
gs2 = variables.global_step()
self.assertEquals(gs, gs2)
self.assertDeviceEqual(gs.device, '/job:ps/task:0')
self.assertDeviceEqual(gs.initial_value.device, '/job:ps/task:0')
self.assertDeviceEqual(gs2.device, '/job:ps/task:0')
self.assertDeviceEqual(gs2.initial_value.device, '/job:ps/task:0')
def testVariableWithVariableDeviceChooser(self):
with tf.Graph().as_default():
device_fn = variables.VariableDeviceChooser()
with scopes.arg_scope([variables.global_step], device=device_fn):
gs = variables.global_step()
gs2 = variables.global_step()
self.assertEquals(gs, gs2)
self.assertDeviceEqual(gs.device, 'cpu:0')
self.assertDeviceEqual(gs.initial_value.device, gs.device)
self.assertDeviceEqual(gs2.device, 'cpu:0')
self.assertDeviceEqual(gs2.initial_value.device, gs2.device)
if __name__ == '__main__':
tf.test.main()
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