remove UserWarning: masked_fill_

main.py

@@ -225,7 +225,7 @@ def batchify_sequence_labeling_with_label(input_batch_list, gpu, if_train=True):
     feature_seq_tensors = []
     for idx in range(feature_num):
         feature_seq_tensors.append(torch.zeros((batch_size, max_seq_len),requires_grad =  if_train).long())
-    mask = torch.zeros((batch_size, max_seq_len), requires_grad =  if_train).byte()
+    mask = torch.zeros((batch_size, max_seq_len), requires_grad =  if_train).bool()
     for idx, (seq, label, seqlen) in enumerate(zip(words, labels, word_seq_lengths)):
         seqlen = seqlen.item()
         word_seq_tensor[idx, :seqlen] = torch.LongTensor(seq)
@@ -304,7 +304,7 @@ def batchify_sentence_classification_with_label(input_batch_list, gpu, if_train=
     feature_seq_tensors = []
     for idx in range(feature_num):
         feature_seq_tensors.append(torch.zeros((batch_size, max_seq_len),requires_grad =  if_train).long())
-    mask = torch.zeros((batch_size, max_seq_len), requires_grad =  if_train).byte()
+    mask = torch.zeros((batch_size, max_seq_len), requires_grad =  if_train).bool()
     label_seq_tensor = torch.LongTensor(labels)
     # exit(0)
     for idx, (seq,  seqlen) in enumerate(zip(words,  word_seq_lengths)):

main_parse.py

@@ -233,7 +233,7 @@ def batchify_with_label(input_batch_list, gpu, volatile_flag=False):
     feature_seq_tensors = []
     for idx in range(feature_num):
         feature_seq_tensors.append(autograd.Variable(torch.zeros((batch_size, max_seq_len)),volatile =  volatile_flag).long())
-    mask = autograd.Variable(torch.zeros((batch_size, max_seq_len)),volatile =  volatile_flag).byte()
+    mask = autograd.Variable(torch.zeros((batch_size, max_seq_len)),volatile =  volatile_flag).bool()
     for idx, (seq, label, seqlen) in enumerate(zip(words, labels, word_seq_lengths)):
         word_seq_tensor[idx, :seqlen] = torch.LongTensor(seq)
         label_seq_tensor[idx, :seqlen] = torch.LongTensor(label)

model/crf.py

@@ -133,7 +133,7 @@ def _viterbi_decode(self, feats, mask):
         partition_history = list()
         ##  reverse mask (bug for mask = 1- mask, use this as alternative choice)
         # mask = 1 + (-1)*mask
-        mask =  (1 - mask.long()).byte()
+        mask =  (1 - mask.long()).bool()
         _, inivalues = next(seq_iter)  # bat_size * from_target_size * to_target_size
         # only need start from start_tag
         partition = inivalues[:, START_TAG, :].clone().view(batch_size, tag_size)  # bat_size * to_target_size
@@ -297,7 +297,7 @@ def _viterbi_decode_nbest(self, feats, mask, nbest):
         partition_history = list()
         ##  reverse mask (bug for mask = 1- mask, use this as alternative choice)
         # mask = 1 + (-1)*mask
-        mask =  (1 - mask.long()).byte()
+        mask =  (1 - mask.long()).bool()
         _, inivalues = next(seq_iter)  # bat_size * from_target_size * to_target_size
         # only need start from start_tag
         partition = inivalues[:, START_TAG, :].clone()  # bat_size * to_target_size