Added sorting algorithm for the input sentences based on the length, …

nmt/inference.py

@@ -129,6 +129,18 @@ def single_worker_inference(infer_model,
 
   # Read data
   infer_data = load_data(inference_input_file, hparams)
+  infer_data_feed = infer_data
+
+  #sort the input file if no hparams.inference_indices is defined
+  index_pair = {}
+  new_input =[]
+  if hparams.inference_indices is None:
+    input_length = [(len(line.split()), i) for i, line in enumerate(infer_data)]
+    sorted_input_bylens = sorted(input_length)
+    for ni, (_, oi) in enumerate(sorted_input_bylens):
+      new_input.append(infer_data[oi])
+      index_pair[oi] = ni
+    infer_data_feed = new_input
 
   with tf.Session(
       graph=infer_model.graph, config=utils.get_config_proto()) as sess:
@@ -137,7 +149,7 @@ def single_worker_inference(infer_model,
     sess.run(
         infer_model.iterator.initializer,
         feed_dict={
-            infer_model.src_placeholder: infer_data,
+            infer_model.src_placeholder: infer_data_feed,
             infer_model.batch_size_placeholder: hparams.infer_batch_size
         })
     # Decode
@@ -162,7 +174,8 @@ def single_worker_inference(infer_model,
           subword_option=hparams.subword_option,
           beam_width=hparams.beam_width,
           tgt_eos=hparams.eos,
-          num_translations_per_input=hparams.num_translations_per_input)
+          num_translations_per_input=hparams.num_translations_per_input,
+          index_pair=index_pair)
 
 
 def multi_worker_inference(infer_model,

nmt/utils/nmt_utils.py

@@ -37,7 +37,8 @@ def decode_and_evaluate(name,
                         beam_width,
                         tgt_eos,
                         num_translations_per_input=1,
-                        decode=True):
+                        decode=True,
+                        index_pair=[]):
   """Decode a test set and compute a score according to the evaluation task."""
   # Decode
   if decode:
@@ -51,6 +52,7 @@ def decode_and_evaluate(name,
 
       num_translations_per_input = max(
           min(num_translations_per_input, beam_width), 1)
+      translation = []
       while True:
         try:
           nmt_outputs, _ = model.decode(sess)
@@ -62,17 +64,22 @@ def decode_and_evaluate(name,
 
           for sent_id in range(batch_size):
             for beam_id in range(num_translations_per_input):
-              translation = get_translation(
+              translation.append(get_translation(
                   nmt_outputs[beam_id],
                   sent_id,
                   tgt_eos=tgt_eos,
-                  subword_option=subword_option)
-              trans_f.write((translation + b"\n").decode("utf-8"))
+                  subword_option=subword_option))
         except tf.errors.OutOfRangeError:
           utils.print_time(
               "  done, num sentences %d, num translations per input %d" %
               (num_sentences, num_translations_per_input), start_time)
           break
+      if len(index_pair) is 0:
+        for sentence in translation:
+          trans_f.write(sentence + b"\n").decode("utf-8")
+      else:
+        for i in index_pair:
+          trans_f.write((translation[index_pair[i]] + b"\n").decode("utf-8"))
 
   # Evaluation
   evaluation_scores = {}