modify logger.warn to logger.warning

fastNLP/embeddings/bert_embedding.py

@@ -72,8 +72,8 @@ class BertEmbedding(ContextualEmbedding):
 
         if model_dir_or_name.lower() in PRETRAINED_BERT_MODEL_DIR:
             if 'cn' in model_dir_or_name.lower() and pool_method not in ('first', 'last'):
-                logger.warn("For Chinese bert, pooled_method should choose from 'first', 'last' in order to achieve"
-                            " faster speed.")
+                logger.warning("For Chinese bert, pooled_method should choose from 'first', 'last' in order to achieve"
+                               " faster speed.")
                 warnings.warn("For Chinese bert, pooled_method should choose from 'first', 'last' in order to achieve"
                               " faster speed.")
         

fastNLP/io/file_reader.py

@@ -111,7 +111,7 @@ def _read_conll(path, encoding='utf-8', indexes=None, dropna=True):
                         yield line_idx, res
                     except Exception as e:
                         if dropna:
-                            logger.warn('Invalid instance which ends at line: {} has been dropped.'.format(line_idx))
+                            logger.warning('Invalid instance which ends at line: {} has been dropped.'.format(line_idx))
                             continue
                         raise ValueError('Invalid instance which ends at line: {}'.format(line_idx))
             elif line.startswith('#'):

fastNLP/io/pipe/classification.py

@@ -387,7 +387,7 @@ class SST2Pipe(_CLSPipe):
                        f" in {[name for name in data_bundle.datasets.keys() if 'train' not in name]} " \
                        f"data set but not in train data set!."
             warnings.warn(warn_msg)
-            logger.warn(warn_msg)
+            logger.warning(warn_msg)
         datasets = []
         for name, dataset in data_bundle.datasets.items():
             if dataset.has_field(Const.TARGET):

fastNLP/io/pipe/matching.py

@@ -121,7 +121,7 @@ class MatchingBertPipe(Pipe):
                        f" in {[name for name in data_bundle.datasets.keys() if 'train' not in name]} " \
                        f"data set but not in train data set!."
             warnings.warn(warn_msg)
-            logger.warn(warn_msg)
+            logger.warning(warn_msg)
 
         has_target_datasets = [dataset for name, dataset in data_bundle.datasets.items() if
                                dataset.has_field(Const.TARGET)]
@@ -258,7 +258,7 @@ class MatchingPipe(Pipe):
                        f" in {[name for name in data_bundle.datasets.keys() if 'train' not in name]} " \
                        f"data set but not in train data set!."
             warnings.warn(warn_msg)
-            logger.warn(warn_msg)
+            logger.warning(warn_msg)
 
         has_target_datasets = [dataset for name, dataset in data_bundle.datasets.items() if
                                dataset.has_field(Const.TARGET)]

fastNLP/io/pipe/utils.py

@@ -130,11 +130,12 @@ def _indexize(data_bundle, input_field_names=Const.INPUT, target_field_names=Con
                                                         if ('train' not in name) and (ds.has_field(target_field_name))]
                                )
         if len(tgt_vocab._no_create_word) > 0:
-            warn_msg = f"There are {len(tgt_vocab._no_create_word)} target labels" \
+            warn_msg = f"There are {len(tgt_vocab._no_create_word)} `{target_field_name}` labels" \
                        f" in {[name for name in data_bundle.datasets.keys() if 'train' not in name]} " \
-                       f"data set but not in train data set!."
+                       f"data set but not in train data set!.\n" \
+                       f"These label(s) are {tgt_vocab._no_create_word}"
             warnings.warn(warn_msg)
-            logger.warn(warn_msg)
+            logger.warning(warn_msg)
         tgt_vocab.index_dataset(*data_bundle.datasets.values(), field_name=target_field_name)
         data_bundle.set_vocab(tgt_vocab, target_field_name)
     

fastNLP/models/bert.py

@@ -65,7 +65,7 @@ class BertForSequenceClassification(BaseModel):
             self.bert.model.include_cls_sep = True
             warn_msg = "Bert for sequence classification excepts BertEmbedding `include_cls_sep` True, " \
                        "but got False. FastNLP has changed it to True."
-            logger.warn(warn_msg)
+            logger.warning(warn_msg)
             warnings.warn(warn_msg)
 
     def forward(self, words):
@@ -110,7 +110,7 @@ class BertForSentenceMatching(BaseModel):
             self.bert.model.include_cls_sep = True
             warn_msg = "Bert for sentence matching excepts BertEmbedding `include_cls_sep` True, " \
                        "but got False. FastNLP has changed it to True."
-            logger.warn(warn_msg)
+            logger.warning(warn_msg)
             warnings.warn(warn_msg)
 
     def forward(self, words):
@@ -156,7 +156,7 @@ class BertForMultipleChoice(BaseModel):
             self.bert.model.include_cls_sep = True
             warn_msg = "Bert for multiple choice excepts BertEmbedding `include_cls_sep` True, " \
                        "but got False. FastNLP has changed it to True."
-            logger.warn(warn_msg)
+            logger.warning(warn_msg)
             warnings.warn(warn_msg)
 
     def forward(self, words):
@@ -206,7 +206,7 @@ class BertForTokenClassification(BaseModel):
             self.bert.model.include_cls_sep = False
             warn_msg = "Bert for token classification excepts BertEmbedding `include_cls_sep` False, " \
                        "but got True. FastNLP has changed it to False."
-            logger.warn(warn_msg)
+            logger.warning(warn_msg)
             warnings.warn(warn_msg)
 
     def forward(self, words):
@@ -250,7 +250,7 @@ class BertForQuestionAnswering(BaseModel):
             self.bert.model.include_cls_sep = True
             warn_msg = "Bert for question answering excepts BertEmbedding `include_cls_sep` True, " \
                        "but got False. FastNLP has changed it to True."
-            logger.warn(warn_msg)
+            logger.warning(warn_msg)
             warnings.warn(warn_msg)
 
     def forward(self, words):

fastNLP/modules/encoder/bert.py

@@ -488,10 +488,10 @@ class BertModel(nn.Module):
 
         load(model, prefix='' if hasattr(model, 'bert') else 'bert.')
         if len(missing_keys) > 0:
-            logger.warn("Weights of {} not initialized from pretrained model: {}".format(
+            logger.warning("Weights of {} not initialized from pretrained model: {}".format(
                 model.__class__.__name__, missing_keys))
         if len(unexpected_keys) > 0:
-            logger.warn("Weights from pretrained model not used in {}: {}".format(
+            logger.warning("Weights from pretrained model not used in {}: {}".format(
                 model.__class__.__name__, unexpected_keys))
 
         logger.info(f"Load pre-trained BERT parameters from file {weights_path}.")
@@ -800,7 +800,7 @@ class BertTokenizer(object):
         for token in tokens:
             ids.append(self.vocab[token])
         if len(ids) > self.max_len:
-            logger.warn(
+            logger.warning(
                 "Token indices sequence length is longer than the specified maximum "
                 " sequence length for this BERT model ({} > {}). Running this"
                 " sequence through BERT will result in indexing errors".format(len(ids), self.max_len)
@@ -824,8 +824,8 @@ class BertTokenizer(object):
         with open(vocab_file, "w", encoding="utf-8") as writer:
             for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]):
                 if index != token_index:
-                    logger.warn("Saving vocabulary to {}: vocabulary indices are not consecutive."
-                          " Please check that the vocabulary is not corrupted!".format(vocab_file))
+                    logger.warning("Saving vocabulary to {}: vocabulary indices are not consecutive."
+                                   " Please check that the vocabulary is not corrupted!".format(vocab_file))
                     index = token_index
                 writer.write(token + u'\n')
                 index += 1