diff --git a/mmedit/models/backbones/encoder_decoders/decoders/plain_decoder.py b/mmedit/models/backbones/encoder_decoders/decoders/plain_decoder.py
index a07a829dd1..4265734830 100644
--- a/mmedit/models/backbones/encoder_decoders/decoders/plain_decoder.py
+++ b/mmedit/models/backbones/encoder_decoders/decoders/plain_decoder.py
@@ -1,5 +1,3 @@
-import warnings
-
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
@@ -36,9 +34,62 @@ def forward(ctx, input, indices, kernel_size, stride, padding,
 
     @staticmethod
     def symbolic(g, input, indices, kernel_size, stride, padding, output_size):
-        warnings.warn(
-            'The definitions of indices are different between Pytorch and ONNX'
-            ', so the outputs between Pytorch and ONNX maybe different')
+        # get shape
+        input_shape = g.op('Shape', input)
+        const_0 = g.op('Constant', value_t=torch.tensor(0))
+        const_1 = g.op('Constant', value_t=torch.tensor(1))
+        batch_size = g.op('Gather', input_shape, const_0, axis_i=0)
+        channel = g.op('Gather', input_shape, const_1, axis_i=0)
+
+        # height = (height - 1) * stride + kernel_size
+        height = g.op(
+            'Gather',
+            input_shape,
+            g.op('Constant', value_t=torch.tensor(2)),
+            axis_i=0)
+        height = g.op('Sub', height, const_1)
+        height = g.op('Mul', height,
+                      g.op('Constant', value_t=torch.tensor(stride[1])))
+        height = g.op('Add', height,
+                      g.op('Constant', value_t=torch.tensor(kernel_size[1])))
+
+        # width = (width - 1) * stride + kernel_size
+        width = g.op(
+            'Gather',
+            input_shape,
+            g.op('Constant', value_t=torch.tensor(3)),
+            axis_i=0)
+        width = g.op('Sub', width, const_1)
+        width = g.op('Mul', width,
+                     g.op('Constant', value_t=torch.tensor(stride[0])))
+        width = g.op('Add', width,
+                     g.op('Constant', value_t=torch.tensor(kernel_size[0])))
+
+        # step of channel
+        channel_step = g.op('Mul', height, width)
+        # step of batch
+        batch_step = g.op('Mul', channel_step, channel)
+
+        # channel offset
+        range_channel = g.op('Range', const_0, channel, const_1)
+        range_channel = g.op(
+            'Reshape', range_channel,
+            g.op('Constant', value_t=torch.tensor([1, -1, 1, 1])))
+        range_channel = g.op('Mul', range_channel, channel_step)
+        range_channel = g.op('Cast', range_channel, to_i=7)  # 7 is int64
+
+        # batch offset
+        range_batch = g.op('Range', const_0, batch_size, const_1)
+        range_batch = g.op(
+            'Reshape', range_batch,
+            g.op('Constant', value_t=torch.tensor([-1, 1, 1, 1])))
+        range_batch = g.op('Mul', range_batch, batch_step)
+        range_batch = g.op('Cast', range_batch, to_i=7)  # 7 is int64
+
+        # update indices
+        indices = g.op('Add', indices, range_channel)
+        indices = g.op('Add', indices, range_batch)
+
         return g.op(
             'MaxUnpool',
             input,