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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.
# ==============================================================================
"""Tests for reading and writing variables."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.compiler.tests.xla_test import XLATestCase
from tensorflow.python.framework import constant_op
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 math_ops
from tensorflow.python.ops import resource_variable_ops
from tensorflow.python.ops import state_ops
from tensorflow.python.ops import variable_scope
from tensorflow.python.ops import variables
from tensorflow.python.platform import googletest
from tensorflow.python.training.gradient_descent import GradientDescentOptimizer
class VariableOpsTest(XLATestCase):
"""Test cases for resource variable operators."""
def testOneWriteOneOutput(self):
# Regression test for a bug where computations with one non-constant
# output and one variable update were mishandled.
for dtype in self.numeric_types:
init = np.array([[1, 2], [3, 4]], dtype=dtype)
with self.test_session() as sess, self.test_scope():
v = resource_variable_ops.ResourceVariable(init)
sess.run(variables.variables_initializer([v]))
p = array_ops.placeholder(dtype)
x = v.assign_add(p)
with ops.control_dependencies([x]):
y = v.read_value()
self.assertAllClose(np.array([[2, 3], [4, 5]], dtype=dtype),
sess.run(y, {p: 1}))
def testSparseRead0DIndices(self):
for dtype in self.numeric_types:
init = np.array([[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]], dtype=dtype)
with self.test_session() as sess, self.test_scope():
v = resource_variable_ops.ResourceVariable(init)
sess.run(variables.variables_initializer([v]))
x = v.sparse_read(2)
self.assertAllClose(np.array([8, 9, 10, 11], dtype=dtype), sess.run(x))
def testSparseRead1DIndices(self):
for dtype in self.numeric_types:
init = np.array([[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]], dtype=dtype)
with self.test_session() as sess, self.test_scope():
v = resource_variable_ops.ResourceVariable(init)
sess.run(variables.variables_initializer([v]))
x = v.sparse_read([2, 1])
self.assertAllClose(
np.array([[8, 9, 10, 11], [4, 5, 6, 7]], dtype=dtype), sess.run(x))
def testSparseRead2DIndices(self):
for dtype in self.numeric_types:
init = np.array([[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]], dtype=dtype)
with self.test_session() as sess, self.test_scope():
v = resource_variable_ops.ResourceVariable(init)
sess.run(variables.variables_initializer([v]))
x = v.sparse_read([[2, 1], [0, 2]])
self.assertAllClose(
np.array(
[[[8, 9, 10, 11], [4, 5, 6, 7]], [[0, 1, 2, 3], [8, 9, 10,
11]]],
dtype=dtype), sess.run(x))
def testSparseRead2DIndices3DTensor(self):
for dtype in self.numeric_types:
init = np.array(
[[[0, 1, 2], [3, 4, 5]], [[10, 11, 12], [13, 14, 15]],
[[20, 21, 22], [23, 24, 25]], [[30, 31, 32], [33, 34, 35]]],
dtype=dtype)
with self.test_session() as sess, self.test_scope():
v = resource_variable_ops.ResourceVariable(init)
sess.run(variables.variables_initializer([v]))
x = v.sparse_read([[2, 1], [3, 0]])
self.assertAllClose(
np.array(
[[[[20, 21, 22], [23, 24, 25]], [[10, 11, 12], [13, 14, 15]]],
[[[30, 31, 32], [33, 34, 35]], [[0, 1, 2], [3, 4, 5]]]],
dtype=dtype), sess.run(x))
def testReadWrite(self):
"""Tests initialization, reading, and writing a resource variable."""
with self.test_session() as session:
with self.test_scope():
with variable_scope.variable_scope("ascope", use_resource=True):
x = variable_scope.get_variable(
"x",
shape=[],
dtype=dtypes.float32,
initializer=init_ops.constant_initializer(2))
a = x.read_value()
with ops.control_dependencies([a]):
b = state_ops.assign(x, 47)
with ops.control_dependencies([b]):
c = x.read_value()
with ops.control_dependencies([c]):
d = state_ops.assign_add(x, 3)
with ops.control_dependencies([d]):
e = x.read_value()
session.run(variables.global_variables_initializer())
v1, v2, v3 = session.run([a, c, e])
self.assertAllClose(2.0, v1)
self.assertAllClose(47.0, v2)
self.assertAllClose(50.0, v3)
def testTraining(self):
"""Tests a gradient descent step for a simple model."""
with self.test_session() as session:
with self.test_scope():
with variable_scope.variable_scope("ascope", use_resource=True):
w = variable_scope.get_variable(
"w",
shape=[4, 2],
dtype=dtypes.float32,
initializer=init_ops.constant_initializer(
np.array([[1, 2], [3, 4], [5, 6], [7, 8]], dtype=np.float32)))
b = variable_scope.get_variable(
"b",
shape=[2],
dtype=dtypes.float32,
initializer=init_ops.constant_initializer(
np.array([2, 3], dtype=np.float32)))
x = array_ops.placeholder(dtypes.float32, shape=[1, 4])
y = math_ops.matmul(x, w) + b
loss = math_ops.reduce_sum(y)
optimizer = GradientDescentOptimizer(0.1)
train = optimizer.minimize(loss)
session.run(variables.global_variables_initializer())
session.run(train, {x: np.array([[7, 3, 5, 9]], dtype=np.float32)})
vw, vb = session.run([w, b])
self.assertAllClose(
np.array(
[[0.3, 1.3], [2.7, 3.7], [4.5, 5.5], [6.1, 7.1]],
dtype=np.float32),
vw,
rtol=1e-4)
self.assertAllClose(np.array([1.9, 2.9], dtype=np.float32), vb, rtol=1e-4)
class StridedSliceAssignChecker(object):
"""Compares the results of a slice assignment using Tensorflow and numpy."""
def __init__(self, test, x, dtype):
self.dtype = dtype
self.test = test
self.x_np = np.array(x).astype(dtype)
def __setitem__(self, index, value):
value = np.array(value).astype(self.dtype)
with self.test.test_session() as sess, self.test.test_scope():
x = constant_op.constant(self.x_np, dtype=self.dtype)
var = resource_variable_ops.ResourceVariable(x)
sess.run(variables.variables_initializer([var]))
val = sess.run(var[index].assign(value))
# val_copy is used to check that tf.assign works equivalently to the
# assign method above.
val_copy = sess.run(state_ops.assign(var[index], value))
valnp = np.copy(self.x_np)
valnp[index] = np.array(value)
self.test.assertAllEqual(val, valnp)
self.test.assertAllEqual(val_copy, valnp)
class SliceAssignTest(XLATestCase):
def testSliceAssign(self):
for dtype in self.numeric_types:
checker = StridedSliceAssignChecker(self, [[1, 2, 3], [4, 5, 6]],
dtype=dtype)
# No-op assignment
checker[:] = [[10, 20, 30], [40, 50, 60]]
# Checks trivial (1,1) shape tensor
checker[1:2, 1:2] = [[66]]
# shrink shape changes
checker[1:2, 1] = [66]
checker[1, 1:2] = [66]
checker[1, 1] = 66
# newaxis shape changes
checker[:, None, :] = [[[10, 20, 30]], [[40, 50, 50]]]
# shrink and newaxis
checker[None, None, 0, 0:1] = [[[99]]]
# Non unit strides
checker[::1, 1::-1] = [[3, 33], [4, 44]]
# degenerate interval
checker[8:10, 0] = []
checker[8:10, 8:10] = [[]]
# Assign vector to scalar (rank-0) using newaxis
checker2 = StridedSliceAssignChecker(self, 222, dtype=dtype)
checker2[()] = 6 # no indices
checker2[...] = 6 # ellipsis
checker2[None] = [6] # new axis
def testUninitialized(self):
with self.assertRaisesRegexp(errors.InvalidArgumentError,
"uninitialized variable"):
with self.test_session() as sess, self.test_scope():
v = resource_variable_ops.ResourceVariable([1, 2])
sess.run(v[:].assign([1, 2]))
if __name__ == "__main__":
googletest.main()
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