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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.
# ==============================================================================
"""Tests for tensorflow.ops.reverse_sequence_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 dtypes
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gradient_checker
from tensorflow.python.platform import test
class ReverseSequenceTest(test.TestCase):
def _testReverseSequence(self,
x,
batch_axis,
seq_axis,
seq_lengths,
truth,
use_gpu=False,
expected_err_re=None):
with self.test_session(use_gpu=use_gpu):
ans = array_ops.reverse_sequence(
x, batch_axis=batch_axis, seq_axis=seq_axis, seq_lengths=seq_lengths)
if expected_err_re is None:
tf_ans = ans.eval()
self.assertAllClose(tf_ans, truth, atol=1e-10)
self.assertShapeEqual(truth, ans)
else:
with self.assertRaisesOpError(expected_err_re):
ans.eval()
def _testBothReverseSequence(self,
x,
batch_axis,
seq_axis,
seq_lengths,
truth,
expected_err_re=None):
self._testReverseSequence(x, batch_axis, seq_axis, seq_lengths, truth, True,
expected_err_re)
self._testReverseSequence(x, batch_axis, seq_axis, seq_lengths, truth,
False, expected_err_re)
def _testBasic(self, dtype, len_dtype=np.int64):
x = np.asarray(
[[[1, 2, 3, 4], [5, 6, 7, 8]], [[9, 10, 11, 12], [13, 14, 15, 16]],
[[17, 18, 19, 20], [21, 22, 23, 24]]],
dtype=dtype)
x = x.reshape(3, 2, 4, 1, 1)
x = x.transpose([2, 1, 0, 3, 4]) # permute axes 0 <=> 2
# reverse dim 2 up to (0:3, none, 0:4) along dim=0
seq_lengths = np.asarray([3, 0, 4], dtype=len_dtype)
truth_orig = np.asarray(
[
[[3, 2, 1, 4], [7, 6, 5, 8]], # reverse 0:3
[[9, 10, 11, 12], [13, 14, 15, 16]], # reverse none
[[20, 19, 18, 17], [24, 23, 22, 21]]
], # reverse 0:4 (all)
dtype=dtype)
truth_orig = truth_orig.reshape(3, 2, 4, 1, 1)
truth = truth_orig.transpose([2, 1, 0, 3, 4]) # permute axes 0 <=> 2
seq_axis = 0 # permute seq_axis and batch_axis (originally 2 and 0, resp.)
batch_axis = 2
self._testBothReverseSequence(x, batch_axis, seq_axis, seq_lengths, truth)
def testSeqLengthInt32(self):
self._testBasic(np.float32, np.int32)
def testFloatBasic(self):
self._testBasic(np.float32)
def testDoubleBasic(self):
self._testBasic(np.float64)
def testInt32Basic(self):
self._testBasic(np.int32)
def testInt64Basic(self):
self._testBasic(np.int64)
def testComplex64Basic(self):
self._testBasic(np.complex64)
def testComplex128Basic(self):
self._testBasic(np.complex128)
def testFloatReverseSequenceGrad(self):
x = np.asarray(
[[[1, 2, 3, 4], [5, 6, 7, 8]], [[9, 10, 11, 12], [13, 14, 15, 16]],
[[17, 18, 19, 20], [21, 22, 23, 24]]],
dtype=np.float)
x = x.reshape(3, 2, 4, 1, 1)
x = x.transpose([2, 1, 0, 3, 4]) # transpose axes 0 <=> 2
# reverse dim 0 up to (0:3, none, 0:4) along dim=2
seq_axis = 0
batch_axis = 2
seq_lengths = np.asarray([3, 0, 4], dtype=np.int64)
with self.cached_session():
input_t = constant_op.constant(x, shape=x.shape)
seq_lengths_t = constant_op.constant(seq_lengths, shape=seq_lengths.shape)
reverse_sequence_out = array_ops.reverse_sequence(
input_t,
batch_axis=batch_axis,
seq_axis=seq_axis,
seq_lengths=seq_lengths_t)
err = gradient_checker.compute_gradient_error(
input_t, x.shape, reverse_sequence_out, x.shape, x_init_value=x)
print("ReverseSequence gradient error = %g" % err)
self.assertLess(err, 1e-8)
def testShapeFunctionEdgeCases(self):
t = array_ops.reverse_sequence(
array_ops.placeholder(
dtypes.float32, shape=None),
seq_lengths=array_ops.placeholder(
dtypes.int64, shape=(32,)),
batch_axis=0,
seq_axis=1)
self.assertIs(t.get_shape().ndims, None)
# Batch size mismatched between input and seq_lengths.
with self.assertRaises(ValueError):
array_ops.reverse_sequence(
array_ops.placeholder(
dtypes.float32, shape=(32, 2, 3)),
seq_lengths=array_ops.placeholder(
dtypes.int64, shape=(33,)),
seq_axis=3)
# seq_axis out of bounds.
with self.assertRaisesRegexp(ValueError, "seq_dim must be < input rank"):
array_ops.reverse_sequence(
array_ops.placeholder(
dtypes.float32, shape=(32, 2, 3)),
seq_lengths=array_ops.placeholder(
dtypes.int64, shape=(32,)),
seq_axis=3)
# batch_axis out of bounds.
with self.assertRaisesRegexp(ValueError, "batch_dim must be < input rank"):
array_ops.reverse_sequence(
array_ops.placeholder(
dtypes.float32, shape=(32, 2, 3)),
seq_lengths=array_ops.placeholder(
dtypes.int64, shape=(32,)),
seq_axis=0,
batch_axis=3)
with self.cached_session():
inputs = array_ops.placeholder(dtypes.float32, shape=(32, 2, 3))
seq_lengths = array_ops.placeholder(dtypes.int64, shape=(32,))
output = array_ops.reverse_sequence(
inputs, seq_lengths=seq_lengths,
seq_axis=0) # batch_axis default is 0
with self.assertRaisesOpError("batch_dim == seq_dim"):
output.eval(feed_dict={
inputs: np.random.rand(32, 2, 3),
seq_lengths: xrange(32)
})
if __name__ == "__main__":
test.main()
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