Explicit reshape dimension in case the alignment vector is empty (#383)

CHANGELOG.md

@@ -18,6 +18,7 @@ OpenNMT-tf follows [semantic versioning 2.0.0](https://semver.org/). The API cov
 
 * Fix compatibility issue with legacy TensorFlow 1.4
 * Fix inference of language models
+* Fix inference error when using `replace_unknown_target` and the alignment vector was empty
 
 ## [1.21.4](https://github.com/OpenNMT/OpenNMT-tf/releases/tag/v1.21.4) (2019-03-07)
 

opennmt/models/sequence_to_sequence.py

@@ -10,7 +10,7 @@
 from opennmt.models.model import Model
 from opennmt.utils import compat
 from opennmt.utils.losses import cross_entropy_sequence_loss
-from opennmt.utils.misc import print_bytes, format_translation_output, merge_dict
+from opennmt.utils.misc import print_bytes, format_translation_output, merge_dict, shape_list
 from opennmt.decoders.decoder import get_sampling_probability
 
 
@@ -277,7 +277,9 @@ def _call(self, features, labels, params, mode):
         # Merge batch and beam dimensions.
         original_shape = tf.shape(target_tokens)
         target_tokens = tf.reshape(target_tokens, [-1, original_shape[-1]])
-        attention = tf.reshape(alignment, [-1, tf.shape(alignment)[2], tf.shape(alignment)[3]])
+        align_shape = shape_list(alignment)
+        attention = tf.reshape(
+            alignment, [align_shape[0] * align_shape[1], align_shape[2], align_shape[3]])
         # We don't have attention for </s> but ensure that the attention time dimension matches
         # the tokens time dimension.
         attention = reducer.align_in_time(attention, tf.shape(target_tokens)[1])