diff --git a/tools/model_converters/pytorch2onnx.py b/tools/model_converters/pytorch2onnx.py
index 09919c2b6f..f0dabb00ad 100644
--- a/tools/model_converters/pytorch2onnx.py
+++ b/tools/model_converters/pytorch2onnx.py
@@ -1,18 +1,15 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import argparse
-import glob
 import os.path as osp
-import re
 import warnings
-from functools import reduce
 
 import cv2
-import mmcv
 import numpy as np
 import onnx
 import onnxruntime as rt
 import torch
 from mmcv.onnx import register_extra_symbolics
+from mmengine import Config
 from mmengine.dataset import Compose
 from mmengine.runner import load_checkpoint
 
@@ -63,7 +60,7 @@ def pytorch2onnx(model,
         img = input['inputs'].unsqueeze(0)
         data = torch.cat((img, masks), dim=1)
     elif model_type == 'video_restorer':
-        data = input['inputs'].unsqueeze(0)
+        data = input['inputs'].unsqueeze(0).float()
     data = data.to(device)
 
     # pytorch has some bug in pytorch1.3, we have to fix it
@@ -158,6 +155,8 @@ def parse_args():
         '--mask-path',
         default=None,
         help='path to input mask file, used in inpainting model')
+    parser.add_argument('--num-frames', type=int, default=None)
+    parser.add_argument('--sequence-length', type=int, default=None)
     parser.add_argument('--device', type=int, default=0, help='CUDA device id')
     parser.add_argument('--show', action='store_true', help='show onnx graph')
     parser.add_argument('--output-file', type=str, default='tmp.onnx')
@@ -188,7 +187,7 @@ def parse_args():
     else:
         device = torch.device('cuda', args.device)
 
-    config = mmcv.Config.fromfile(args.config)
+    config = Config.fromfile(args.config)
     delete_cfg(config, key='init_cfg')
 
     # ONNX does not support spectral norm
@@ -217,6 +216,8 @@ def parse_args():
         keys_to_remove = ['alpha', 'ori_alpha']
     elif model_type == 'image_restorer':
         keys_to_remove = ['gt', 'gt_path']
+    elif model_type == 'video_restorer':
+        keys_to_remove = ['gt', 'gt_path']
     else:
         keys_to_remove = []
     for key in keys_to_remove:
@@ -244,17 +245,15 @@ def parse_args():
                             f'"GenerateSegmentIndices", but got '
                             f'"{test_pipeline[0]["type"]}".')
         # prepare data
-        sequence_length = len(glob.glob(osp.join(args.img_path, '*')))
-        img_dir_split = re.split(r'[\\/]', args.img_path)
-        if img_dir_split[0] == '':
-            img_dir_split[0] = '/'
-        key = img_dir_split[-1]
-        lq_folder = reduce(osp.join, img_dir_split[:-1])
+        # sequence_length = len(glob.glob(osp.join(args.img_path, '*')))
+        lq_folder = osp.dirname(args.img_path)
+        key = osp.basename(args.img_path)
         data = dict(
             img_path=lq_folder,
             gt_path='',
             key=key,
-            sequence_length=sequence_length)
+            num_frames=args.num_frames,
+            sequence_length=args.sequence_length)
 
     # build the data pipeline
     test_pipeline = Compose(test_pipeline)