diff --git a/mmseg/apis/inference.py b/mmseg/apis/inference.py
index 95f80b6..41d508d 100644
--- a/mmseg/apis/inference.py
+++ b/mmseg/apis/inference.py
@@ -61,7 +61,9 @@ def init_segmentor_kn(config, checkpoint=None, device='cuda:0'):
         device_id = int(device_id)
     except Exception:
         device_id = None if device == 'cpu' else 0
-    model = ONNXRuntimeSegmentorKN(checkpoint, cfg=config, device_id=device_id).eval()
+    model = ONNXRuntimeSegmentorKN(
+        checkpoint, cfg=config, device_id=device_id
+    ).eval()
     return model
 
 
@@ -124,9 +126,17 @@ def inference_segmentor(model, img):
     return result
 
 
+@torch.no_grad()
 def inference_segmentor_kn(model, img):
     if model.endswith(".onnx"):
-        pass
+        cfg = model.cfg
+        test_pipeline = [LoadImage()] + cfg.data.test.pipeline[1:]
+        test_pipeline = Compose(test_pipeline)
+        data = dict(img=img)
+        data = test_pipeline(data)
+        data = collate([data], samples_per_gpu=1)
+        data['img_metas'] = [i.data[0] for i in data['img_metas']]
+        return model(return_loss=False, rescale=True, **data)
     else:
         return inference_segmentor(model, img)
 
diff --git a/mmseg/models/segmentors/base.py b/mmseg/models/segmentors/base.py
index b4f3e39..257d4fa 100644
--- a/mmseg/models/segmentors/base.py
+++ b/mmseg/models/segmentors/base.py
@@ -10,6 +10,7 @@ import numpy as np
 import torch
 import torch.distributed as dist
 from mmcv.runner import BaseModule, auto_fp16
+from mmseg.core import get_classes, get_palette
 from mmseg.ops import resize
 
 
@@ -335,6 +336,25 @@ class ONNXRuntimeSegmentorKN(BaseSegmentor):
         self.test_mode = self.test_cfg.mode  # NOTE: should be 'whole' or 'slide'
         self.is_cuda_available = is_cuda_available
         self.count_mat = None
+        try:
+            if 'test' in cfg.data:
+                dataset_name = cfg.data.test['type']
+            else:
+                dataset_name = cfg.data.train['type']
+            dataset_name = dataset_name.lower()[:-7]
+            self.CLASSES = get_classes(dataset_name)
+            self.PALETTE = get_palette(dataset_name)
+        except (AttributeError, KeyError):
+            warnings.warn(
+                "Failed to fetch dataset name from config; no CLASSES "
+                "and PALETTE for this ONNX model"
+            )
+        except ValueError:
+            warnings.warn(
+                "Failed to fetch CLASSES and PALETTE from dataset "
+                f"{dataset_name}; no CLASSES and PALETTE for this "
+                "ONNX MODEL."
+            )
 
     def extract_feat(self, imgs):
         raise NotImplementedError('This method is not implemented.')
@@ -406,6 +426,7 @@ class ONNXRuntimeSegmentorKN(BaseSegmentor):
             seg_pred = self.sess.run(
                 self.output_name_list, {self.input_name: img}
             )[0]
+        print(img.shape, seg_pred.shape)
         if img_meta is not None:
             ori_shape = img_meta[0]['ori_shape']
             if not (ori_shape[0] == seg_pred.shape[-2]
@@ -414,6 +435,11 @@ class ONNXRuntimeSegmentorKN(BaseSegmentor):
                 seg_pred = resize(
                     seg_pred, size=tuple(ori_shape[:2]), mode='bilinear')
                 seg_pred = seg_pred.numpy()
+        elif img.shape[2:] != seg_pred.shape[2:]:
+            seg_pred = torch.from_numpy(seg_pred).float()
+            seg_pred = resize(
+                seg_pred, size=(img.shape[3], img.shape[2]), mode='bilinear')
+            seg_pred = seg_pred.numpy()
         seg_pred = seg_pred.argmax(1)
         return list(seg_pred)