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# Copyright 2016 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.ctc_ops.ctc_loss_op."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import itertools
import numpy as np
from six.moves import zip_longest
from tensorflow.python.framework import errors
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import ctc_ops
from tensorflow.python.platform import test
def grouper(iterable, n, fillvalue=None):
"""Collect data into fixed-length chunks or blocks."""
# grouper('ABCDEFG', 3, 'x') --> ABC DEF Gxx
args = [iter(iterable)] * n
return zip_longest(fillvalue=fillvalue, *args)
def flatten(list_of_lists):
"""Flatten one level of nesting."""
return itertools.chain.from_iterable(list_of_lists)
class CTCGreedyDecoderTest(test.TestCase):
def _testCTCDecoder(self,
decoder,
inputs,
seq_lens,
log_prob_truth,
decode_truth,
expected_err_re=None,
**decoder_args):
inputs_t = [ops.convert_to_tensor(x) for x in inputs]
# convert inputs_t into a [max_time x batch_size x depth] tensor
# from a len time python list of [batch_size x depth] tensors
inputs_t = array_ops.stack(inputs_t)
with self.test_session(use_gpu=False) as sess:
decoded_list, log_probability = decoder(
inputs_t, sequence_length=seq_lens, **decoder_args)
decoded_unwrapped = list(
flatten([(st.indices, st.values, st.dense_shape) for st in
decoded_list]))
if expected_err_re is None:
outputs = sess.run(decoded_unwrapped + [log_probability])
# Group outputs into (ix, vals, shape) tuples
output_sparse_tensors = list(grouper(outputs[:-1], 3))
output_log_probability = outputs[-1]
# Check the number of decoded outputs (top_paths) match
self.assertEqual(len(output_sparse_tensors), len(decode_truth))
# For each SparseTensor tuple, compare (ix, vals, shape)
for out_st, truth_st, tf_st in zip(output_sparse_tensors, decode_truth,
decoded_list):
self.assertAllEqual(out_st[0], truth_st[0]) # ix
self.assertAllEqual(out_st[1], truth_st[1]) # vals
self.assertAllEqual(out_st[2], truth_st[2]) # shape
# Compare the shapes of the components with the truth. The
# `None` elements are not known statically.
self.assertEqual([None, truth_st[0].shape[1]],
tf_st.indices.get_shape().as_list())
self.assertEqual([None], tf_st.values.get_shape().as_list())
self.assertShapeEqual(truth_st[2], tf_st.dense_shape)
# Make sure decoded probabilities match
self.assertAllClose(output_log_probability, log_prob_truth, atol=1e-6)
else:
with self.assertRaisesOpError(expected_err_re):
sess.run(decoded_unwrapped + [log_probability])
def testCTCGreedyDecoder(self):
"""Test two batch entries - best path decoder."""
max_time_steps = 6
# depth == 4
seq_len_0 = 4
input_prob_matrix_0 = np.asarray(
[[1.0, 0.0, 0.0, 0.0], # t=0
[0.0, 0.0, 0.4, 0.6], # t=1
[0.0, 0.0, 0.4, 0.6], # t=2
[0.0, 0.9, 0.1, 0.0], # t=3
[0.0, 0.0, 0.0, 0.0], # t=4 (ignored)
[0.0, 0.0, 0.0, 0.0]], # t=5 (ignored)
dtype=np.float32)
input_log_prob_matrix_0 = np.log(input_prob_matrix_0)
seq_len_1 = 5
# dimensions are time x depth
input_prob_matrix_1 = np.asarray(
[
[0.1, 0.9, 0.0, 0.0], # t=0
[0.0, 0.9, 0.1, 0.0], # t=1
[0.0, 0.0, 0.1, 0.9], # t=2
[0.0, 0.9, 0.1, 0.1], # t=3
[0.9, 0.1, 0.0, 0.0], # t=4
[0.0, 0.0, 0.0, 0.0]
], # t=5 (ignored)
dtype=np.float32)
input_log_prob_matrix_1 = np.log(input_prob_matrix_1)
# len max_time_steps array of batch_size x depth matrices
inputs = [
np.vstack(
[input_log_prob_matrix_0[t, :], input_log_prob_matrix_1[t, :]])
for t in range(max_time_steps)
]
# batch_size length vector of sequence_lengths
seq_lens = np.array([seq_len_0, seq_len_1], dtype=np.int32)
# batch_size length vector of negative log probabilities
log_prob_truth = np.array([
np.sum(-np.log([1.0, 0.6, 0.6, 0.9])),
np.sum(-np.log([0.9, 0.9, 0.9, 0.9, 0.9]))
], np.float32)[:, np.newaxis]
# decode_truth: one SparseTensor (ix, vals, shape)
decode_truth = [
(
np.array(
[
[0, 0], # batch 0, 2 outputs
[0, 1],
[1, 0], # batch 1, 3 outputs
[1, 1],
[1, 2]
],
dtype=np.int64),
np.array(
[
0,
1, # batch 0
1,
1,
0
], # batch 1
dtype=np.int64),
# shape is batch x max_decoded_length
np.array(
[2, 3], dtype=np.int64)),
]
self._testCTCDecoder(ctc_ops.ctc_greedy_decoder, inputs, seq_lens,
log_prob_truth, decode_truth)
def testCTCDecoderBeamSearch(self):
"""Test one batch, two beams - hibernating beam search."""
# max_time_steps == 8
depth = 6
seq_len_0 = 5
input_prob_matrix_0 = np.asarray(
[
[0.30999, 0.309938, 0.0679938, 0.0673362, 0.0708352, 0.173908],
[0.215136, 0.439699, 0.0370931, 0.0393967, 0.0381581, 0.230517],
[0.199959, 0.489485, 0.0233221, 0.0251417, 0.0233289, 0.238763],
[0.279611, 0.452966, 0.0204795, 0.0209126, 0.0194803, 0.20655],
[0.51286, 0.288951, 0.0243026, 0.0220788, 0.0219297, 0.129878],
# Random entry added in at time=5
[0.155251, 0.164444, 0.173517, 0.176138, 0.169979, 0.160671]
],
dtype=np.float32)
# Add arbitrary offset - this is fine
input_prob_matrix_0 = input_prob_matrix_0 + 2.0
# len max_time_steps array of batch_size x depth matrices
inputs = ([
input_prob_matrix_0[t, :][np.newaxis, :] for t in range(seq_len_0)
] # Pad to max_time_steps = 8
+ 2 * [np.zeros(
(1, depth), dtype=np.float32)])
# batch_size length vector of sequence_lengths
seq_lens = np.array([seq_len_0], dtype=np.int32)
# batch_size length vector of log probabilities
log_prob_truth = np.array(
[
-5.811451, # output beam 0
-6.63339 # output beam 1
],
np.float32)[np.newaxis, :]
# decode_truth: two SparseTensors, (ix, values, shape)
decode_truth = [
# beam 0, batch 0, two outputs decoded
(np.array(
[[0, 0], [0, 1]], dtype=np.int64), np.array(
[1, 0], dtype=np.int64), np.array(
[1, 2], dtype=np.int64)),
# beam 1, batch 0, one output decoded
(np.array(
[[0, 0]], dtype=np.int64), np.array(
[1], dtype=np.int64), np.array(
[1, 1], dtype=np.int64)),
]
# Test correct decoding.
self._testCTCDecoder(
ctc_ops.ctc_beam_search_decoder,
inputs,
seq_lens,
log_prob_truth,
decode_truth,
beam_width=2,
top_paths=2)
# Requesting more paths than the beam width allows.
with self.assertRaisesRegexp(errors.InvalidArgumentError,
(".*requested more paths than the beam "
"width.*")):
self._testCTCDecoder(
ctc_ops.ctc_beam_search_decoder,
inputs,
seq_lens,
log_prob_truth,
decode_truth,
beam_width=2,
top_paths=3)
if __name__ == "__main__":
test.main()
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