content: import torch
  import torch.nn as nn
  from torch.nn import init
  
  import math
  import numpy as np
  
  from .submodules import *
  'Parameter count = 45,371,666'
  
  class FlowNetSD(nn.Module):
      def __init__(self, args, batchNorm=True):
          super(FlowNetSD,self).__init__()
  
          self.batchNorm = batchNorm
          self.conv0   = conv(self.batchNorm,  6,   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.conv3   = conv(self.batchNorm, 128,  256, stride=2)
          self.conv3_1 = conv(self.batchNorm, 256,  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.inter_conv5 = i_conv(self.batchNorm,  1026,   512)
          self.inter_conv4 = i_conv(self.batchNorm,  770,   256)
          self.inter_conv3 = i_conv(self.batchNorm,  386,   128)
          self.inter_conv2 = i_conv(self.batchNorm,  194,   64)
  
          self.predict_flow6 = predict_flow(1024)
          self.predict_flow5 = predict_flow(512)
          self.predict_flow4 = predict_flow(256)
          self.predict_flow3 = predict_flow(128)
          self.predict_flow2 = predict_flow(64)
  
          self.upsampled_flow6_to_5 = nn.ConvTranspose2d(2, 2, 4, 2, 1)
          self.upsampled_flow5_to_4 = nn.ConvTranspose2d(2, 2, 4, 2, 1)
          self.upsampled_flow4_to_3 = nn.ConvTranspose2d(2, 2, 4, 2, 1)
          self.upsampled_flow3_to_2 = 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)
          self.upsample1 = nn.Upsample(scale_factor=4, mode='bilinear')
  
  
  
      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))
  
          out_conv3 = self.conv3_1(self.conv3(out_conv2))
          out_conv4 = self.conv4_1(self.conv4(out_conv3))
          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)
          out_interconv5 = self.inter_conv5(concat5)
          flow5       = self.predict_flow5(out_interconv5)
  
          flow5_up    = self.upsampled_flow5_to_4(flow5)
          out_deconv4 = self.deconv4(concat5)
          
          concat4 = torch.cat((out_conv4,out_deconv4,flow5_up),1)
          out_interconv4 = self.inter_conv4(concat4)
          flow4       = self.predict_flow4(out_interconv4)
          flow4_up    = self.upsampled_flow4_to_3(flow4)
          out_deconv3 = self.deconv3(concat4)
          
          concat3 = torch.cat((out_conv3,out_deconv3,flow4_up),1)
          out_interconv3 = self.inter_conv3(concat3)
          flow3       = self.predict_flow3(out_interconv3)
          flow3_up    = self.upsampled_flow3_to_2(flow3)
          out_deconv2 = self.deconv2(concat3)
  
          concat2 = torch.cat((out_conv2,out_deconv2,flow3_up),1)
          out_interconv2 = self.inter_conv2(concat2)
          flow2 = self.predict_flow2(out_interconv2)
  
          if self.training:
              return flow2,flow3,flow4,flow5,flow6
          else:
              return flow2,
  