diff --git a/mmseg/metrics/iou_metric.py b/mmseg/metrics/iou_metric.py
index a7c0524e6..f0dd45a4c 100644
--- a/mmseg/metrics/iou_metric.py
+++ b/mmseg/metrics/iou_metric.py
@@ -1,8 +1,7 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from collections import OrderedDict
-from typing import Dict, List, Optional, Sequence, Union
+from typing import Dict, List, Optional, Sequence
 
-import mmcv
 import numpy as np
 import torch
 from mmengine.evaluator import BaseMetric
@@ -59,15 +58,12 @@ class IoUMetric(BaseMetric):
             predictions (Sequence[dict]): A batch of outputs from the model.
         """
         num_classes = len(self.dataset_meta['classes'])
-        label_map = self.dataset_meta['label_map']
-        reduce_zero_label = self.dataset_meta['reduce_zero_label']
         for data, pred in zip(data_batch, predictions):
-            label = data['data_sample']['gt_sem_seg']['data'][0].cpu().numpy()
-            pred_label = pred['pred_sem_seg']['data'][0].cpu().numpy()
+            label = data['data_sample']['gt_sem_seg']['data'][0].cpu()
+            pred_label = pred['pred_sem_seg']['data'][0].cpu()
             self.results.append(
                 self.intersect_and_union(pred_label, label, num_classes,
-                                         self.ignore_index, label_map,
-                                         reduce_zero_label))
+                                         self.ignore_index))
 
     def compute_metrics(self, results: list) -> Dict[str, float]:
         """Compute the metrics from processed results.
@@ -129,25 +125,17 @@ class IoUMetric(BaseMetric):
         return metrics
 
     @staticmethod
-    def intersect_and_union(pred_label: Union[np.ndarray, str],
-                            label: Union[np.ndarray, str],
-                            num_classes: int,
-                            ignore_index: int,
-                            label_map: dict = dict(),
-                            reduce_zero_label: bool = False):
+    def intersect_and_union(pred_label: torch.tensor, label: torch.tensor,
+                            num_classes: int, ignore_index: int):
         """Calculate intersection and Union.
 
         Args:
-            pred_label (ndarray | str): Prediction segmentation map
+            pred_label (torch.tensor): Prediction segmentation map
                 or predict result filename. The shape is (H, W).
-            label (ndarray | str): Ground truth segmentation map
+            label (torch.tensor): Ground truth segmentation map
                 or label filename. The shape is (H, W).
             num_classes (int): Number of categories.
             ignore_index (int): Index that will be ignored in evaluation.
-            label_map (dict): Mapping old labels to new labels. The parameter
-                will work only when label is str. Default: dict().
-            reduce_zero_label (bool): Whether ignore zero label. The parameter
-                will work only when label is str. Default: False.
 
         Returns:
             torch.Tensor: The intersection of prediction and ground truth
@@ -158,25 +146,6 @@ class IoUMetric(BaseMetric):
             torch.Tensor: The ground truth histogram on all classes.
         """
 
-        if isinstance(pred_label, str):
-            pred_label = torch.from_numpy(np.load(pred_label))
-        else:
-            pred_label = torch.from_numpy((pred_label))
-
-        if isinstance(label, str):
-            label = torch.from_numpy(
-                mmcv.imread(label, flag='unchanged', backend='pillow'))
-        else:
-            label = torch.from_numpy(label)
-
-        if label_map is not None:
-            for old_id, new_id in label_map.items():
-                label[label == old_id] = new_id
-        if reduce_zero_label:
-            label[label == 0] = 255
-            label = label - 1
-            label[label == 254] = 255
-
         mask = (label != ignore_index)
         pred_label = pred_label[mask]
         label = label[mask]