[bugfix] auto convert tensor type when batching

fastNLP/core/batch.py

@@ -69,13 +69,20 @@ class DataSetGetter:
 
         def may_to_tensor(data):
             dtype, dim = _get_ele_type_and_dim(data)
-            print(dtype, type(dtype))
+            # print(dtype, type(dtype), str(dtype))
             if not self.as_numpy:
                 try:
                     data, flag = _to_tensor(data, dtype)
                 except TypeError as e:
                     logger.error(f"Field {n} cannot be converted to torch.tensor.")
                     raise e
+            # if torch.is_tensor(data):
+            #     str_dtype = str(dtype)
+            #     if 'float' in str_dtype:
+            #         data = data.float()
+            #     elif 'int' in str_dtype:
+            #         data = data.long()
+            # print(data.dtype)
             return data
 
         def pad(batch_dict):
@@ -293,14 +300,16 @@ def _to_tensor(batch, field_dtype):
         if field_dtype is not None and isinstance(field_dtype, type)\
                 and issubclass(field_dtype, Number) \
                 and not isinstance(batch, torch.Tensor):
-            if issubclass(field_dtype, np.floating):
-                new_batch = torch.as_tensor(batch).float()  # 默认使用float32
-            elif issubclass(field_dtype, np.integer):
-                new_batch = torch.as_tensor(batch).long()  # 复用内存地址，避免复制
-            else:
-                new_batch = torch.as_tensor(batch)
-            return new_batch, True
+            new_batch = torch.as_tensor(batch)
+            flag = True
         else:
-            return batch, False
+            new_batch = batch
+            flag = False
+        if torch.is_tensor(new_batch):
+            if 'float' in new_batch.dtype.__repr__():
+                new_batch = new_batch.float()
+            elif 'int' in new_batch.dtype.__repr__():
+                new_batch = new_batch.long()
+        return new_batch, flag
     except Exception as e:
         raise e