update eval.py

data.py

@@ -225,6 +225,10 @@ def data_generator(self, batch_size, seq_len, data_type='train', file_prefix=Non
 
             ep += 1
 
+        # leftover
+        if len(batch_src) > 0:
+            yield np.array(batch_src).copy(), np.array(batch_tgt).copy()
+
 
 def recover_sentence(sent_ids, id2word):
     """Convert a list of word ids back to a sentence string.

eval.py

@@ -7,7 +7,7 @@
 """
 import click
 import numpy as np
-from data import DatasetManager, recover_sentence
+from data import DatasetManager, recover_sentence, PAD_ID
 from transformer import Transformer
 from nltk.translate.bleu_score import corpus_bleu
 
@@ -21,20 +21,30 @@ def eval(model_name, file_prefix):
 
     cfg = transformer.config
 
+    batch_size = cfg['train_params']['batch_size']
+    seq_len = cfg['train_params']['seq_len'] + 1
+
     dm = DatasetManager(cfg['dataset'])
     dm.maybe_download_data_files()
     data_iter = dm.data_generator(
-        cfg['train_params']['batch_size'],
-        cfg['train_params']['seq_len'] + 1,
-        data_type='test', file_prefix=file_prefix, epoch=1,
-    )
+        batch_size, seq_len, data_type='test', file_prefix=file_prefix, epoch=1)
 
     refs = []
     hypos = []
     for source_ids, target_ids in data_iter:
+        valid_size = len(source_ids)
+        print(source_ids.shape, target_ids.shape)
+
+        if valid_size < batch_size:
+            source_ids = np.array(list(source_ids) + [[PAD_ID] * seq_len] * (batch_size - source_ids.shape[0]))
+            target_ids = np.array(list(target_ids) + [[PAD_ID] * seq_len] * (batch_size - target_ids.shape[0]))
+
         pred_ids = transformer.predict(source_ids)
-        refs += [[recover_sentence(sent_ids, dm.target_id2word)] for sent_ids in target_ids]
-        hypos += [recover_sentence(sent_ids, dm.target_id2word) for sent_ids in pred_ids]
+
+        refs += [[recover_sentence(sent_ids, dm.target_id2word)]
+                 for sent_ids in target_ids[:valid_size]]
+        hypos += [recover_sentence(sent_ids, dm.target_id2word)
+                  for sent_ids in pred_ids[:valid_size]]
         print(f"Num. sentences processed: {len(hypos)}", end='\r', flush=True)
 
     print()

utils.py

@@ -5,7 +5,6 @@
 
 REPO_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__)))
 
-
 class BaseModelMixin:
     """Abstract object representing an Reader model.
     Code borrowed from: https://github.com/devsisters/DQN-tensorflow/blob/master/dqn/base.py