content: import torch
  import torch.nn as nn
  from torch.nn import init
  
  import math
  import numpy as np
  
  from .submodules import *
  'Parameter count = 581,226'
  
  class FlowNetFusion(nn.Module):
      def __init__(self,args, batchNorm=True):
          super(FlowNetFusion,self).__init__()
  
          self.batchNorm = batchNorm
          self.conv0   = conv(self.batchNorm,  11,   64)
          self.conv1   = conv(self.batchNorm,  64,   64, stride=2)
          self.conv1_1 = conv(self.batchNorm,  64,   128)
          self.conv2   = conv(self.batchNorm,  128,  128, stride=2)
          self.conv2_1 = conv(self.batchNorm,  128,  128)
  
          self.deconv1 = deconv(128,32)
          self.deconv0 = deconv(162,16)
  
          self.inter_conv1 = i_conv(self.batchNorm,  162,   32)
          self.inter_conv0 = i_conv(self.batchNorm,  82,   16)
  
          self.predict_flow2 = predict_flow(128)
          self.predict_flow1 = predict_flow(32)
          self.predict_flow0 = predict_flow(16)
  
          self.upsampled_flow2_to_1 = nn.ConvTranspose2d(2, 2, 4, 2, 1)
          self.upsampled_flow1_to_0 = nn.ConvTranspose2d(2, 2, 4, 2, 1)
  
          for m in self.modules():
              if isinstance(m, nn.Conv2d):
                  if m.bias is not None:
                      init.uniform_(m.bias)
                  init.xavier_uniform_(m.weight)
  
              if isinstance(m, nn.ConvTranspose2d):
                  if m.bias is not None:
                      init.uniform_(m.bias)
                  init.xavier_uniform_(m.weight)
                  # init_deconv_bilinear(m.weight)
  
      def forward(self, x):
          out_conv0 = self.conv0(x)
          out_conv1 = self.conv1_1(self.conv1(out_conv0))
          out_conv2 = self.conv2_1(self.conv2(out_conv1))
  
          flow2       = self.predict_flow2(out_conv2)
          flow2_up    = self.upsampled_flow2_to_1(flow2)
          out_deconv1 = self.deconv1(out_conv2)
          
          concat1 = torch.cat((out_conv1,out_deconv1,flow2_up),1)
          out_interconv1 = self.inter_conv1(concat1)
          flow1       = self.predict_flow1(out_interconv1)
          flow1_up    = self.upsampled_flow1_to_0(flow1)
          out_deconv0 = self.deconv0(concat1)
          
          concat0 = torch.cat((out_conv0,out_deconv0,flow1_up),1)
          out_interconv0 = self.inter_conv0(concat0)
          flow0       = self.predict_flow0(out_interconv0)
  
          return flow0
  
  