diff --git a/mmocr/models/textdet/detectors/base.py b/mmocr/models/textdet/detectors/base.py
index d88713cb..dd0adab2 100644
--- a/mmocr/models/textdet/detectors/base.py
+++ b/mmocr/models/textdet/detectors/base.py
@@ -36,8 +36,8 @@ class BaseTextDetector(BaseModel, metaclass=ABCMeta):
         return hasattr(self, 'neck') and self.neck is not None
 
     def forward(self,
-                batch_inputs: torch.Tensor,
-                batch_data_samples: OptDetSampleList = None,
+                inputs: torch.Tensor,
+                data_samples: OptDetSampleList = None,
                 mode: str = 'tensor') -> ForwardResults:
         """The unified entry for a forward process in both training and test.
 
@@ -54,10 +54,11 @@ class BaseTextDetector(BaseModel, metaclass=ABCMeta):
         optimizer updating, which are done in the :meth:`train_step`.
 
         Args:
-            batch_inputs (torch.Tensor): The input tensor with shape
+            inputs (torch.Tensor): The input tensor with shape
                 (N, C, ...) in general.
-            batch_data_samples (list[:obj:`TextDetDataSample`], optional): The
-                annotation data of every samples. Defaults to None.
+            data_samples (list[:obj:`TextDetDataSample`], optional): A batch of
+                data samples that contain annotations and predictions.
+                Defaults to None.
             mode (str): Return what kind of value. Defaults to 'tensor'.
 
         Returns:
@@ -68,32 +69,30 @@ class BaseTextDetector(BaseModel, metaclass=ABCMeta):
             - If ``mode="loss"``, return a dict of tensor.
         """
         if mode == 'loss':
-            return self.loss(batch_inputs, batch_data_samples)
+            return self.loss(inputs, data_samples)
         elif mode == 'predict':
-            return self.predict(batch_inputs, batch_data_samples)
+            return self.predict(inputs, data_samples)
         elif mode == 'tensor':
-            return self._forward(batch_inputs, batch_data_samples)
+            return self._forward(inputs, data_samples)
         else:
             raise RuntimeError(f'Invalid mode "{mode}". '
                                'Only supports loss, predict and tensor mode')
 
     @abstractmethod
-    def loss(self, batch_inputs: Tensor,
-             batch_data_samples: DetSampleList) -> Union[dict, tuple]:
+    def loss(self, inputs: Tensor,
+             data_samples: DetSampleList) -> Union[dict, tuple]:
         """Calculate losses from a batch of inputs and data samples."""
         pass
 
     @abstractmethod
-    def predict(self, batch_inputs: Tensor,
-                batch_data_samples: DetSampleList) -> DetSampleList:
+    def predict(self, inputs: Tensor,
+                data_samples: DetSampleList) -> DetSampleList:
         """Predict results from a batch of inputs and data samples with post-
         processing."""
         pass
 
     @abstractmethod
-    def _forward(self,
-                 batch_inputs: Tensor,
-                 batch_data_samples: OptDetSampleList = None):
+    def _forward(self, inputs: Tensor, data_samples: OptDetSampleList = None):
         """Network forward process.
 
         Usually includes backbone, neck and head forward without any post-
@@ -102,6 +101,6 @@ class BaseTextDetector(BaseModel, metaclass=ABCMeta):
         pass
 
     @abstractmethod
-    def extract_feat(self, batch_inputs: Tensor):
+    def extract_feat(self, inputs: Tensor):
         """Extract features from images."""
         pass