修复CRF为负数的bug

fastNLP/core/dataset.py

@@ -243,6 +243,8 @@ class DataSet(object):
         :param padder: PadderBase类型或None. 设置为None即删除padder。即对该field不进行padding操作.
         :return:
         """
+        if field_name not in self.field_arrays:
+            raise KeyError("There is no field named {}.".format(field_name))
         self.field_arrays[field_name].set_padder(padder)
 
     def set_pad_val(self, field_name, pad_val):
@@ -253,6 +255,8 @@ class DataSet(object):
         :param pad_val: int，该field的padder会以pad_val作为padding index
         :return:
         """
+        if field_name not in self.field_arrays:
+            raise KeyError("There is no field named {}.".format(field_name))
         self.field_arrays[field_name].set_pad_val(pad_val)
 
     def get_input_name(self):

fastNLP/core/fieldarray.py

@@ -206,7 +206,7 @@ class FieldArray(object):
         if list in type_set:
             if len(type_set) > 1:
                 # list 跟 非list 混在一起
-                raise RuntimeError("Mixed data types in Field {}: {}".format(self.name, type_set))
+                raise RuntimeError("Mixed data types in Field {}: {}".format(self.name, list(type_set)))
             # >1维list
             inner_type_set = set()
             for l in content:
@@ -229,7 +229,7 @@ class FieldArray(object):
                     return self._basic_type_detection(inner_inner_type_set)
                 else:
                     # list 跟 非list 混在一起
-                    raise RuntimeError("Mixed data types in Field {}: {}".format(self.name, inner_type_set))
+                    raise RuntimeError("Mixed data types in Field {}: {}".format(self.name, list(inner_type_set)))
         else:
             # 一维list
             for content_type in type_set:
@@ -253,17 +253,17 @@ class FieldArray(object):
                 return float
             else:
                 # str 跟 int 或者 float 混在一起
-                raise RuntimeError("Mixed data types in Field {}: {}".format(self.name, type_set))
+                raise RuntimeError("Mixed data types in Field {}: {}".format(self.name, list(type_set)))
         else:
             # str, int, float混在一起
-            raise RuntimeError("Mixed data types in Field {}: {}".format(self.name, type_set))
+            raise RuntimeError("Mixed data types in Field {}: {}".format(self.name, list(type_set)))
 
     def _1d_list_check(self, val):
         """如果不是1D list就报错
         """
         type_set = set((type(obj) for obj in val))
         if any(obj not in self.BASIC_TYPES for obj in type_set):
-            raise ValueError("Mixed data types in Field {}: {}".format(self.name, type_set))
+            raise ValueError("Mixed data types in Field {}: {}".format(self.name, list(type_set)))
         self._basic_type_detection(type_set)
         # otherwise: _basic_type_detection will raise error
         return True

fastNLP/modules/decoder/CRF.py

@@ -192,7 +192,7 @@ class ConditionalRandomField(nn.Module):
         seq_len, batch_size, n_tags = logits.size()
         alpha = logits[0]
         if self.include_start_end_trans:
-            alpha += self.start_scores.view(1, -1)
+            alpha = alpha + self.start_scores.view(1, -1)
 
         flip_mask = mask.eq(0)
 
@@ -204,7 +204,7 @@ class ConditionalRandomField(nn.Module):
                     alpha.masked_fill(mask[i].byte().view(batch_size, 1), 0)
 
         if self.include_start_end_trans:
-            alpha += self.end_scores.view(1, -1)
+            alpha = alpha + self.end_scores.view(1, -1)
 
         return log_sum_exp(alpha, 1)
 
@@ -233,7 +233,7 @@ class ConditionalRandomField(nn.Module):
             st_scores = self.start_scores.view(1, -1).repeat(batch_size, 1)[batch_idx, tags[0]]
             last_idx = mask.long().sum(0) - 1
             ed_scores = self.end_scores.view(1, -1).repeat(batch_size, 1)[batch_idx, tags[last_idx, batch_idx]]
-            score += st_scores + ed_scores
+            score = score + st_scores + ed_scores
         # return [B,]
         return score
 

test/core/test_dataset.py

@@ -216,6 +216,11 @@ class TestDataSetMethods(unittest.TestCase):
         self.assertTrue(isinstance(ds, DataSet))
         self.assertTrue(len(ds) > 0)
 
+    def test_add_null(self):
+        ds = DataSet()
+        ds.add_field('test', [])
+        ds.set_target('test')
+
 
 class TestDataSetIter(unittest.TestCase):
     def test__repr__(self):

test/modules/decoder/test_CRF.py

@@ -101,4 +101,28 @@ class TestCRF(unittest.TestCase):
         # # seq equal
         # self.assertListEqual([_ for _, score in allen_res], fast_res[0])
 
+    def test_case3(self):
+        # 测试crf的loss不会出现负数
+        import torch
+        from fastNLP.modules.decoder.CRF import ConditionalRandomField
+        from fastNLP.core.utils import seq_lens_to_masks
+        from torch import optim
+        from torch import nn
 
+        num_tags, include_start_end_trans = 4, True
+        num_samples = 4
+        lengths = torch.randint(3, 50, size=(num_samples, )).long()
+        max_len = lengths.max()
+        tags = torch.randint(num_tags, size=(num_samples, max_len))
+        masks = seq_lens_to_masks(lengths)
+        feats = nn.Parameter(torch.randn(num_samples, max_len, num_tags))
+        crf = ConditionalRandomField(num_tags, include_start_end_trans)
+        optimizer = optim.SGD([param for param in crf.parameters() if param.requires_grad] + [feats], lr=0.1)
+        for _ in range(10000):
+            loss = crf(feats, tags, masks).mean()
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+            if _%1000==0:
+                print(loss)
+            assert loss.item()>0, "CRF loss cannot be less than 0."