。

fastNLP/core/losses.py

@@ -169,6 +169,8 @@ class LossFunc(LossBase):
 
 class CrossEntropyLoss(LossBase):
     def __init__(self, pred=None, target=None):
+        # TODO 需要做一些检查，F.cross_entropy在计算时，如果pred是(16, 10 ,4), target的形状按道理应该是(16, 10), 但实际却需要
+        # TODO  （16， 4）
         super(CrossEntropyLoss, self).__init__()
         self.get_loss = F.cross_entropy
         self._init_param_map(input=pred, target=target)

test/core/test_dataset.py

@@ -125,6 +125,13 @@ class TestDataSet(unittest.TestCase):
         ds = DataSet({"x": [[1, 2, 3, 4]] * 10, "y": [[5, 6]] * 10})
         self.assertEqual(ds.get_target_name(), [_ for _ in ds.field_arrays if ds.field_arrays[_].is_target])
 
+    def test_apply2(self):
+        def split_sent(ins):
+            return ins['raw_sentence'].split()
+        dataset = DataSet.read_csv('../../sentence.csv', headers=('raw_sentence', 'label'), sep='\t')
+        dataset.apply(split_sent, new_field_name='words')
+        # print(dataset)
+
 
 class TestDataSetIter(unittest.TestCase):
     def test__repr__(self):

test/core/test_loss.py

@@ -311,9 +311,17 @@ class TestLosserError(unittest.TestCase):
         print(los(pred_dict=pred_dict, target_dict=target_dict))
 
     #
-    def test_AccuracyMetric2(self):
+    def test_losser2(self):
         # (2) with corrupted size
-        pred_dict = {"pred": torch.zeros(16, 3, 4)}
+        pred_dict = {"pred": torch.zeros(16, 3)}
+        target_dict = {'target': torch.zeros(16, 3).long()}
+        los = loss.CrossEntropyLoss()
+
+        print(los(pred_dict=pred_dict, target_dict=target_dict))
+
+    def test_losser3(self):
+        # (2) with corrupted size
+        pred_dict = {"pred": torch.zeros(16, 3), 'stop_fast_param':0}
         target_dict = {'target': torch.zeros(16, 3).long()}
         los = loss.CrossEntropyLoss()