diff --git a/mmseg/models/segmentors/base.py b/mmseg/models/segmentors/base.py
index 906c6fe56..944da0f2e 100644
--- a/mmseg/models/segmentors/base.py
+++ b/mmseg/models/segmentors/base.py
@@ -145,15 +145,22 @@ class BaseSegmentor(BaseModule, metaclass=ABCMeta):
 
         return outputs
 
-    def val_step(self, data_batch, **kwargs):
+    def val_step(self, data_batch, optimizer=None, **kwargs):
         """The iteration step during validation.
 
         This method shares the same signature as :func:`train_step`, but used
         during val epochs. Note that the evaluation after training epochs is
         not implemented with this method, but an evaluation hook.
         """
-        output = self(**data_batch, **kwargs)
-        return output
+        losses = self(**data_batch)
+        loss, log_vars = self._parse_losses(losses)
+
+        outputs = dict(
+            loss=loss,
+            log_vars=log_vars,
+            num_samples=len(data_batch['img_metas']))
+
+        return outputs
 
     @staticmethod
     def _parse_losses(losses):
diff --git a/tests/test_models/test_segmentors/utils.py b/tests/test_models/test_segmentors/utils.py
index 0f51a4b1f..1826dbf85 100644
--- a/tests/test_models/test_segmentors/utils.py
+++ b/tests/test_models/test_segmentors/utils.py
@@ -101,6 +101,26 @@ def _segmentor_forward_train_test(segmentor):
         imgs, img_metas, gt_semantic_seg=gt_semantic_seg, return_loss=True)
     assert isinstance(losses, dict)
 
+    # Test train_step
+    data_batch = dict(
+        img=imgs, img_metas=img_metas, gt_semantic_seg=gt_semantic_seg)
+    outputs = segmentor.train_step(data_batch, None)
+    assert isinstance(outputs, dict)
+    assert 'loss' in outputs
+    assert 'log_vars' in outputs
+    assert 'num_samples' in outputs
+
+    # Test val_step
+    with torch.no_grad():
+        segmentor.eval()
+        data_batch = dict(
+            img=imgs, img_metas=img_metas, gt_semantic_seg=gt_semantic_seg)
+        outputs = segmentor.val_step(data_batch, None)
+        assert isinstance(outputs, dict)
+        assert 'loss' in outputs
+        assert 'log_vars' in outputs
+        assert 'num_samples' in outputs
+
     # Test forward simple test
     with torch.no_grad():
         segmentor.eval()