text: import torch
  import torch.nn as nn
  from torch.nn import init
  
  import math
  import numpy as np
  
  from .correlation_package.correlation import Correlation
  
  from .submodules import *
  'Parameter count , 39,175,298 '
  
  class FlowNetC(nn.Module):
      def __init__(self,cfg, batchNorm=True, div_flow = 20):
          super(FlowNetC,self).__init__()
  
          self.batchNorm = batchNorm
          self.div_flow = div_flow
  
          self.conv1   = conv(self.batchNorm,   3,   64, kernel_size=7, stride=2)
          self.conv2   = conv(self.batchNorm,  64,  128, kernel_size=5, stride=2)
          self.conv3   = conv(self.batchNorm, 128,  256, kernel_size=5, stride=2)
          self.conv_redir  = conv(self.batchNorm, 256,   32, kernel_size=1, stride=1)
  
          if cfg.MODEL.auxiliary.optical_flow.fp16:
              self.corr = nn.Sequential(
                  tofp32(),
                  Correlation(pad_size=20, kernel_size=1, max_displacement=20, stride1=1, stride2=2, corr_multiply=1),
                  tofp16())
          else:
              self.corr = Correlation(pad_size=20, kernel_size=1, max_displacement=20, stride1=1, stride2=2, corr_multiply=1)
  
          self.corr_activation = nn.LeakyReLU(0.1,inplace=True)
          self.conv3_1 = conv(self.batchNorm, 473,  256)
          self.conv4   = conv(self.batchNorm, 256,  512, stride=2)
          self.conv4_1 = conv(self.batchNorm, 512,  512)
          self.conv5   = conv(self.batchNorm, 512,  512, stride=2)
          self.conv5_1 = conv(self.batchNorm, 512,  512)
          self.conv6   = conv(self.batchNorm, 512, 1024, stride=2)
          self.conv6_1 = conv(self.batchNorm,1024, 1024)
  
          self.deconv5 = deconv(1024,512)
          self.deconv4 = deconv(1026,256)
          self.deconv3 = deconv(770,128)
          self.deconv2 = deconv(386,64)
  
          self.predict_flow6 = predict_flow(1024)
          self.predict_flow5 = predict_flow(1026)
          self.predict_flow4 = predict_flow(770)
          self.predict_flow3 = predict_flow(386)
          self.predict_flow2 = predict_flow(194)
  
          self.upsampled_flow6_to_5 = nn.ConvTranspose2d(2, 2, 4, 2, 1, bias=True)
          self.upsampled_flow5_to_4 = nn.ConvTranspose2d(2, 2, 4, 2, 1, bias=True)
          self.upsampled_flow4_to_3 = nn.ConvTranspose2d(2, 2, 4, 2, 1, bias=True)
          self.upsampled_flow3_to_2 = nn.ConvTranspose2d(2, 2, 4, 2, 1, bias=True)
  
          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)
          self.upsample1 = nn.Upsample(scale_factor=4, mode='bilinear')
  
      def forward(self, x):
          x1 = x[:,0:3,:,:]
          x2 = x[:,3::,:,:]
  
          out_conv1a = self.conv1(x1)
          out_conv2a = self.conv2(out_conv1a)
          out_conv3a = self.conv3(out_conv2a)
  
          # FlownetC bottom input stream
          out_conv1b = self.conv1(x2)
          
          out_conv2b = self.conv2(out_conv1b)
          out_conv3b = self.conv3(out_conv2b)
  
          # Merge streams
          out_corr = self.corr(out_conv3a, out_conv3b) # False
          out_corr = self.corr_activation(out_corr)
  
          # Redirect top input stream and concatenate
          out_conv_redir = self.conv_redir(out_conv3a)
  
          in_conv3_1 = torch.cat((out_conv_redir, out_corr), 1)
  
          # Merged conv layers
          out_conv3_1 = self.conv3_1(in_conv3_1)
  
          out_conv4 = self.conv4_1(self.conv4(out_conv3_1))
  
          out_conv5 = self.conv5_1(self.conv5(out_conv4))
          out_conv6 = self.conv6_1(self.conv6(out_conv5))
  
          flow6       = self.predict_flow6(out_conv6)
          flow6_up    = self.upsampled_flow6_to_5(flow6)
          out_deconv5 = self.deconv5(out_conv6)
  
          concat5 = torch.cat((out_conv5,out_deconv5,flow6_up),1)
  
          flow5       = self.predict_flow5(concat5)
          flow5_up    = self.upsampled_flow5_to_4(flow5)
          out_deconv4 = self.deconv4(concat5)
          concat4 = torch.cat((out_conv4,out_deconv4,flow5_up),1)
  
          flow4       = self.predict_flow4(concat4)
          flow4_up    = self.upsampled_flow4_to_3(flow4)
          out_deconv3 = self.deconv3(concat4)
          concat3 = torch.cat((out_conv3_1,out_deconv3,flow4_up),1)
  
          flow3       = self.predict_flow3(concat3)
          flow3_up    = self.upsampled_flow3_to_2(flow3)
          out_deconv2 = self.deconv2(concat3)
          concat2 = torch.cat((out_conv2a,out_deconv2,flow3_up),1)
  
          flow2 = self.predict_flow2(concat2)
  
          if self.training:
              return flow2,flow3,flow4,flow5,flow6
          else:
              return flow2,
  