1.修复MNLILoader中的bug; 2.修复field中的tensor warning

fastNLP/core/field.py

@@ -595,7 +595,7 @@ class AutoPadder(Padder):
                     max_len = max(map(len, contents))
                     tensor = torch.full((len(contents), max_len), fill_value=self.pad_val, dtype=field_ele_dtype)
                     for i, content_i in enumerate(contents):
-                        tensor[i, :len(content_i)] = torch.tensor(content_i)
+                        tensor[i, :len(content_i)] = content_i.clone().detach()
                 elif dim == 2:
                     max_len = max(map(len, contents))
                     max_word_len = max([max([len(content_ii) for content_ii in content_i]) for
@@ -604,7 +604,7 @@ class AutoPadder(Padder):
                                         dtype=field_ele_dtype)
                     for i, content_i in enumerate(contents):
                         for j, content_ii in enumerate(content_i):
-                            tensor[i, j, :len(content_ii)] = torch.tensor(content_ii)
+                            tensor[i, j, :len(content_ii)] = content_ii.clone().detach()
                 else:
                     shapes = set([np.shape(content_i) for content_i in contents])
                     if len(shapes) > 1:
@@ -615,7 +615,7 @@ class AutoPadder(Padder):
                         tensor = torch.full([len(contents)] + list(shape), fill_value=self.pad_val,
                                             dtype=field_ele_dtype)
                         for i, content_i in enumerate(contents):
-                            tensor[i] = torch.tensor(content_i, dtype=field_ele_dtype)
+                            tensor[i] = content_i.clone().detach().to(field_ele_dtype)
                     else:
                         raise RuntimeError(
                             f"Field:{field_name} has 3 dimensions, every sample should have the same shape.")

fastNLP/core/vocabulary.py

@@ -253,7 +253,7 @@ class Vocabulary(object):
         if self.unknown is not None:
             return self.word2idx[self.unknown]
         else:
-            raise ValueError("word {} not in vocabulary".format(w))
+            raise ValueError("word `{}` not in vocabulary".format(w))
     
     @_check_build_vocab
     def index_dataset(self, *datasets, field_name, new_field_name=None):
@@ -360,7 +360,7 @@ class Vocabulary(object):
                 try:
                     dataset.apply(construct_vocab)
                 except BaseException as e:
-                    log("When processing the `{}` dataset, the following error occurred:".format(idx))
+                    logger.error("When processing the `{}` dataset, the following error occurred:".format(idx))
                     raise e
             else:
                 raise TypeError("Only DataSet type is allowed.")

fastNLP/io/loader/matching.py

@@ -41,7 +41,7 @@ class MNLILoader(Loader):
         ds = DataSet()
         with open(path, 'r', encoding='utf-8') as f:
             f.readline()  # 跳过header
-            if path.endswith("test.tsv"):
+            if path.endswith("test_matched.tsv") or path.endswith('test_mismatched.tsv'):
                 warnings.warn("RTE's test file has no target.")
                 for line in f:
                     line = line.strip()