Replaces custom _lengths_to_masks function with the official, more efficient sequence_mask function that supersedes it.

tensorflower-gardener · tensorflower-gardener · commit d4091eec522e · 2017-12-22T15:54:28.000-08:00
PiperOrigin-RevId: 179971521
diff --git a/tensorflow/contrib/crf/__init__.py b/tensorflow/contrib/crf/__init__.py
@@ -16,32 +16,31 @@
 
 See the @{$python/contrib.crf} guide.
 
-@@crf_sequence_score
-@@crf_log_norm
-@@crf_log_likelihood
-@@crf_unary_score
 @@crf_binary_score
 @@crf_decode
-@@CrfForwardRnnCell
-@@CrfDecodeForwardRnnCell
+@@crf_log_likelihood
+@@crf_log_norm
+@@crf_sequence_score
+@@crf_unary_score
 @@CrfDecodeBackwardRnnCell
+@@CrfDecodeForwardRnnCell
+@@CrfForwardRnnCell
 @@viterbi_decode
 """
 
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from tensorflow.contrib.crf.python.ops.crf import _lengths_to_masks
 from tensorflow.contrib.crf.python.ops.crf import crf_binary_score
 from tensorflow.contrib.crf.python.ops.crf import crf_decode
 from tensorflow.contrib.crf.python.ops.crf import crf_log_likelihood
 from tensorflow.contrib.crf.python.ops.crf import crf_log_norm
 from tensorflow.contrib.crf.python.ops.crf import crf_sequence_score
 from tensorflow.contrib.crf.python.ops.crf import crf_unary_score
-from tensorflow.contrib.crf.python.ops.crf import CrfForwardRnnCell
-from tensorflow.contrib.crf.python.ops.crf import CrfDecodeForwardRnnCell
 from tensorflow.contrib.crf.python.ops.crf import CrfDecodeBackwardRnnCell
+from tensorflow.contrib.crf.python.ops.crf import CrfDecodeForwardRnnCell
+from tensorflow.contrib.crf.python.ops.crf import CrfForwardRnnCell
 from tensorflow.contrib.crf.python.ops.crf import viterbi_decode
 
 from tensorflow.python.util.all_util import remove_undocumented
diff --git a/tensorflow/contrib/crf/python/kernel_tests/crf_test.py b/tensorflow/contrib/crf/python/kernel_tests/crf_test.py
@@ -179,17 +179,6 @@ def testCrfLogLikelihood(self):
       tf_total_log_likelihood = sess.run(total_log_likelihood)
       self.assertAllClose(tf_total_log_likelihood, 0.0)
 
-  def testLengthsToMasks(self):
-    with self.test_session() as sess:
-      sequence_lengths = [4, 1, 8, 2]
-      max_sequence_length = max(sequence_lengths)
-      mask = crf._lengths_to_masks(sequence_lengths, max_sequence_length)
-      tf_mask = sess.run(mask)
-      self.assertEqual(len(tf_mask), len(sequence_lengths))
-      for m, l in zip(tf_mask, sequence_lengths):
-        self.assertAllEqual(m[:l], [1] * l)
-        self.assertAllEqual(m[l:], [0] * (len(m) - l))
-
   def testViterbiDecode(self):
     inputs = np.array(
         [[4, 5, -3], [3, -1, 3], [-1, 2, 1], [0, 0, 0]], dtype=np.float32)
diff --git a/tensorflow/contrib/crf/python/ops/crf.py b/tensorflow/contrib/crf/python/ops/crf.py
@@ -70,25 +70,6 @@
 ]
 
 
-def _lengths_to_masks(lengths, max_length):
-  """Creates a binary matrix that can be used to mask away padding.
-
-  Args:
-    lengths: A vector of integers representing lengths.
-    max_length: An integer indicating the maximum length. All values in
-      lengths should be less than max_length.
-  Returns:
-    masks: Masks that can be used to get rid of padding.
-  """
-  tiled_ranges = array_ops.tile(
-      array_ops.expand_dims(math_ops.range(max_length), 0),
-      [array_ops.shape(lengths)[0], 1])
-  lengths = array_ops.expand_dims(lengths, 1)
-  masks = math_ops.to_float(
-      math_ops.to_int64(tiled_ranges) < math_ops.to_int64(lengths))
-  return masks
-
-
 def crf_sequence_score(inputs, tag_indices, sequence_lengths,
                        transition_params):
   """Computes the unnormalized score for a tag sequence.
@@ -234,7 +215,9 @@ def crf_unary_score(tag_indices, sequence_lengths, inputs):
       array_ops.gather(flattened_inputs, flattened_tag_indices),
       [batch_size, max_seq_len])
 
-  masks = _lengths_to_masks(sequence_lengths, array_ops.shape(tag_indices)[1])
+  masks = array_ops.sequence_mask(sequence_lengths,
+                                  maxlen=array_ops.shape(tag_indices)[1],
+                                  dtype=dtypes.float32)
 
   unary_scores = math_ops.reduce_sum(unary_scores * masks, 1)
   return unary_scores
@@ -268,7 +251,9 @@ def crf_binary_score(tag_indices, sequence_lengths, transition_params):
   binary_scores = array_ops.gather(flattened_transition_params,
                                    flattened_transition_indices)
 
-  masks = _lengths_to_masks(sequence_lengths, array_ops.shape(tag_indices)[1])
+  masks = array_ops.sequence_mask(sequence_lengths,
+                                  maxlen=array_ops.shape(tag_indices)[1],
+                                  dtype=dtypes.float32)
   truncated_masks = array_ops.slice(masks, [0, 1], [-1, -1])
   binary_scores = math_ops.reduce_sum(binary_scores * truncated_masks, 1)
   return binary_scores
