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# Copyright 2015 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.
# ==============================================================================
"""Functional tests for Unstack Op."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from six.moves import xrange # pylint: disable=redefined-builtin
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import test_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gradient_checker
from tensorflow.python.platform import test
def np_split_squeeze(array, axis):
axis_len = array.shape[axis]
return [
np.squeeze(
arr, axis=(axis,)) for arr in np.split(
array, axis_len, axis=axis)
]
class UnstackOpTest(test.TestCase):
def testSimple(self):
np.random.seed(7)
with self.test_session(use_gpu=True):
for shape in (2,), (3,), (2, 3), (3, 2), (4, 3, 2):
for dtype in [
np.bool, np.float16, np.float32, np.float64, np.int32, np.int64
]:
data = np.random.randn(*shape).astype(dtype)
# Convert data to a single tensorflow tensor
x = constant_op.constant(data)
# Unstack into a list of tensors
cs = array_ops.unstack(x, num=shape[0])
self.assertEqual(type(cs), list)
self.assertEqual(len(cs), shape[0])
cs = [c.eval() for c in cs]
self.assertAllEqual(cs, data)
def testSimpleGpu(self):
if not test_util.is_gpu_available():
self.skipTest('No GPU available')
np.random.seed(7)
with self.test_session(use_gpu=True, force_gpu=True):
for shape in (2,), (3,), (2, 3), (3, 2), (4, 3, 2):
for dtype in [np.float16, np.float32, np.float64, np.int32, np.int64]:
data = np.random.randn(*shape).astype(dtype)
# Convert data to a single tensorflow tensor
x = constant_op.constant(data)
# Unstack into a list of tensors
cs = array_ops.unstack(x, num=shape[0])
self.assertEqual(type(cs), list)
self.assertEqual(len(cs), shape[0])
cs = [c.eval() for c in cs]
self.assertAllEqual(cs, data)
def testGradientsAxis0(self):
for shape in (2,), (3,), (2, 3), (3, 2), (4, 3, 2):
data = np.random.randn(*shape)
shapes = [shape[1:]] * shape[0]
for i in xrange(shape[0]):
with self.test_session(use_gpu=True):
x = constant_op.constant(data)
cs = array_ops.unstack(x, num=shape[0])
err = gradient_checker.compute_gradient_error(x, shape, cs[i],
shapes[i])
self.assertLess(err, 1e-6)
def testGradientsAxis1(self):
for shape in (2, 3), (3, 2), (4, 3, 2):
data = np.random.randn(*shape)
out_shape = list(shape)
del out_shape[1]
for i in xrange(shape[1]):
with self.test_session(use_gpu=True):
x = constant_op.constant(data)
cs = array_ops.unstack(x, num=shape[1], axis=1)
err = gradient_checker.compute_gradient_error(x, shape, cs[i],
out_shape)
self.assertLess(err, 1e-6)
def testInferNum(self):
with self.cached_session():
for shape in (2,), (3,), (2, 3), (3, 2), (4, 3, 2):
x = array_ops.placeholder(np.float32, shape=shape)
cs = array_ops.unstack(x)
self.assertEqual(type(cs), list)
self.assertEqual(len(cs), shape[0])
def testCannotInferNumFromUnknownShape(self):
x = array_ops.placeholder(np.float32)
with self.assertRaisesRegexp(ValueError,
r'Cannot infer num from shape <unknown>'):
array_ops.unstack(x)
def testUnknownShapeOkWithNum(self):
x = array_ops.placeholder(np.float32)
array_ops.unstack(x, num=2)
def testCannotInferNumFromNoneShape(self):
x = array_ops.placeholder(np.float32, shape=(None,))
with self.assertRaisesRegexp(ValueError,
r'Cannot infer num from shape \(\?,\)'):
array_ops.unstack(x)
def testAgainstNumpy(self):
# For 1 to 5 dimensions.
for i in range(1, 6):
a = np.random.random(np.random.permutation(i) + 1)
# For all the possible axis to split it, including negative indices.
for j in range(-i, i):
expected = np_split_squeeze(a, j)
with self.cached_session() as sess:
actual_unstack = sess.run(array_ops.unstack(a, axis=j))
self.assertAllEqual(expected, actual_unstack)
def testAxis0Default(self):
with self.cached_session() as sess:
a = constant_op.constant([[1, 2, 3], [4, 5, 6]], name='a')
unstacked = sess.run(array_ops.unstack(a))
self.assertEqual(len(unstacked), 2)
self.assertAllEqual(unstacked[0], [1, 2, 3])
self.assertAllEqual(unstacked[1], [4, 5, 6])
def testAxisOutOfRange(self):
a = constant_op.constant([[1, 2, 3], [4, 5, 6]], name='a')
with self.assertRaisesRegexp(ValueError, r'axis = 2 not in \[-2, 2\)'):
array_ops.unstack(a, axis=2)
def testAxisOutOfNegativeRange(self):
a = constant_op.constant([[1, 2, 3], [4, 5, 6]], name='a')
with self.assertRaisesRegexp(ValueError, r'axis = -3 not in \[-2, 2\)'):
array_ops.unstack(a, axis=-3)
def testZeroLengthDim(self):
with self.cached_session():
x = array_ops.zeros(shape=(0, 1, 2))
y = array_ops.unstack(x, axis=1)[0].eval()
self.assertEqual(y.shape, (0, 2))
if __name__ == '__main__':
test.main()
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