Importing Bidirectional LSTM model via onnx shows an error

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Hi,

I'm trying to export model from easyOCR to ONNX and import it through openCV. I successfully exported it to ONNX, checked that it runs well, but failed to import the model through openCV using readNetFromONNX. Below is my model export code and the error:

batch_size=1
x = torch.rand(batch_size,1,64,256).float().cpu()
torch.onnx.export(model, (x,''), "ocr0807_0.onnx")
net = cv2.dnn.readNetFromONNX('ocr0807_0.onnx') <- where error occurs

 error: (-215:Assertion failed) (int)_numAxes == inputs[0].size() in function 'getMemoryShapes'

The error occurs at Bidirectional LSTM layer of the model even though I'm using openCV 4.4.0 and python 3.7.

Below is the part of model code from easyOCR, which include Bidirectional LSTM:

""" Sequence modeling"""
        self.SequenceModeling = nn.Sequential(
            BidirectionalLSTM(self.FeatureExtraction_output, hidden_size, hidden_size),
            BidirectionalLSTM(hidden_size, hidden_size, hidden_size))
        self.SequenceModeling_output = hidden_size

Would you please help me with this problem? Thank you.

(NEWLY ADDED) The full error message is:

error msg is cv2.error: OpenCV(4.4.0) ../modules/dnn/src/layers/permute_layer.cpp:134: error: (-215:Assertion failed) (int)_numAxes == inputs[0].size() in function 'getMemoryShapes'

Also, here is the error reproducing minimal test case code:

import torch.nn as nn
import torch

class BidirectionalLSTM(nn.Module):
   def __init__(self, input_size, hidden_size, output_size):
       super(BidirectionalLSTM, self).__init__()
       self.rnn = nn.LSTM(input_size, hidden_size, bidirectional=True, batch_first=True)
       self.linear = nn.Linear(hidden_size * 2, output_size)

   def forward(self, input):
       self.rnn.flatten_parameters()
       recurrent, _ = self.rnn(input)
       output = self.linear(recurrent)
       return output


class Model(nn.Module):
   def __init__(self):
       super(Model, self).__init__()
       self.SequenceModeling = nn.Sequential(
           BidirectionalLSTM(512, 512, 512),
           BidirectionalLSTM(512, 512, 512))

   def forward(self, input, text):
       contextual_feature = self.SequenceModeling(input)
       return contextual_feature


x = torch.rand(1, 65, 512).float().cpu()
model = Model()
model.eval()
torch.onnx.export(model, x, "ocr0811_1.onnx")

import cv2
net = cv2.dnn.readNetFromONNX('ocr0811_1.onnx')

Thank you.