diff --git a/configs/cgnet/cgnet_512x1024_60k_cityscapes.py b/configs/cgnet/cgnet_512x1024_60k_cityscapes.py
index dcabaf01c..fc9ad7c1c 100644
--- a/configs/cgnet/cgnet_512x1024_60k_cityscapes.py
+++ b/configs/cgnet/cgnet_512x1024_60k_cityscapes.py
@@ -22,7 +22,8 @@ train_cfg = dict(
     type='IterBasedTrainLoop', max_iters=total_iters, val_interval=4000)
 val_cfg = dict(type='ValLoop')
 test_cfg = dict(type='TestLoop')
-default_hooks = dict(checkpoint=dict(by_epoch=False, interval=4000))
+default_hooks = dict(
+    checkpoint=dict(type='CheckpointHook', by_epoch=False, interval=4000))
 
 crop_size = (512, 1024)
 data_preprocessor = dict(size=crop_size)
diff --git a/configs/cgnet/cgnet_680x680_60k_cityscapes.py b/configs/cgnet/cgnet_680x680_60k_cityscapes.py
index 93457fe51..4854b481e 100644
--- a/configs/cgnet/cgnet_680x680_60k_cityscapes.py
+++ b/configs/cgnet/cgnet_680x680_60k_cityscapes.py
@@ -22,7 +22,8 @@ train_cfg = dict(
     type='IterBasedTrainLoop', max_iters=total_iters, val_interval=4000)
 val_cfg = dict(type='ValLoop')
 test_cfg = dict(type='TestLoop')
-default_hooks = dict(checkpoint=dict(by_epoch=False, interval=4000))
+default_hooks = dict(
+    checkpoint=dict(type='CheckpointHook', by_epoch=False, interval=4000))
 
 crop_size = (680, 680)
 data_preprocessor = dict(size=crop_size)
diff --git a/mmseg/models/decode_heads/enc_head.py b/mmseg/models/decode_heads/enc_head.py
index 0915d35fb..05eaa8582 100644
--- a/mmseg/models/decode_heads/enc_head.py
+++ b/mmseg/models/decode_heads/enc_head.py
@@ -8,6 +8,7 @@ from mmcv.cnn import ConvModule, build_norm_layer
 from torch import Tensor
 
 from mmseg.core.utils import SampleList
+from mmseg.core.utils.typing import ConfigType
 from mmseg.ops import Encoding, resize
 from mmseg.registry import MODELS
 from ..builder import build_loss
@@ -155,13 +156,13 @@ class EncHead(BaseDecodeHead):
             return output
 
     def predict(self, inputs: Tuple[Tensor], batch_img_metas: List[dict],
-                **kwargs):
+                test_cfg: ConfigType):
         """Forward function for testing, ignore se_loss."""
         if self.use_se_loss:
             seg_logits = self.forward(inputs)[0]
         else:
             seg_logits = self.forward(inputs)
-        return self.predict_by_feat(seg_logits, batch_img_metas, **kwargs)
+        return self.predict_by_feat(seg_logits, batch_img_metas)
 
     @staticmethod
     def _convert_to_onehot_labels(seg_label, num_classes):
@@ -188,7 +189,8 @@ class EncHead(BaseDecodeHead):
         """Compute segmentation and semantic encoding loss."""
         seg_logit, se_seg_logit = seg_logit
         loss = dict()
-        loss.update(super(EncHead, self).losses(seg_logit, batch_data_samples))
+        loss.update(
+            super(EncHead, self).loss_by_feat(seg_logit, batch_data_samples))
 
         seg_label = self._stack_batch_gt(batch_data_samples)
         se_loss = self.loss_se_decode(