content: import torch
  import torch.nn as nn
  from torch.nn import init
  
  import math
  import numpy as np
  
  from .resample2d_package.modules.resample2d import Resample2d
  from .channelnorm_package.modules.channelnorm import ChannelNorm
  
  from . import FlowNetC
  from . import FlowNetS
  from . import FlowNetSD
  from . import FlowNetFusion
  
  from .submodules import *
  'Parameter count = 162,518,834'
  
  class FlowNet2(nn.Module):
  
      def __init__(self, args, batchNorm=False, div_flow = 20., requires_grad=False):
          super(FlowNet2,self).__init__()
          self.batchNorm = batchNorm
          self.div_flow = div_flow
          self.rgb_max = args.rgb_max
          self.args = args
  
          self.channelnorm = ChannelNorm()
  
          # First Block (FlowNetC)
          self.flownetc = FlowNetC.FlowNetC(args, batchNorm=self.batchNorm)
          self.upsample1 = nn.Upsample(scale_factor=4, mode='bilinear')
  
          if args.fp16:
              self.resample1 = nn.Sequential(
                              tofp32(), 
                              Resample2d(),
                              tofp16()) 
          else:
              self.resample1 = Resample2d()
  
          # Block (FlowNetS1)
          self.flownets_1 = FlowNetS.FlowNetS(args, batchNorm=self.batchNorm)
          self.upsample2 = nn.Upsample(scale_factor=4, mode='bilinear')
          if args.fp16:
              self.resample2 = nn.Sequential(
                              tofp32(), 
                              Resample2d(),
                              tofp16()) 
          else:
              self.resample2 = Resample2d()
  
  
          # Block (FlowNetS2)
          self.flownets_2 = FlowNetS.FlowNetS(args, batchNorm=self.batchNorm)
  
          # Block (FlowNetSD)
          self.flownets_d = FlowNetSD.FlowNetSD(args, batchNorm=self.batchNorm) 
          self.upsample3 = nn.Upsample(scale_factor=4, mode='nearest') 
          self.upsample4 = nn.Upsample(scale_factor=4, mode='nearest') 
  
          if args.fp16:
              self.resample3 = nn.Sequential(
                              tofp32(), 
                              Resample2d(),
                              tofp16()) 
          else:
              self.resample3 = Resample2d()
  
          if args.fp16:
              self.resample4 = nn.Sequential(
                              tofp32(), 
                              Resample2d(),
                              tofp16()) 
          else:
              self.resample4 = Resample2d()
  
          # Block (FLowNetFusion)
          self.flownetfusion = FlowNetFusion.FlowNetFusion(args, batchNorm=self.batchNorm)
  
          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)
  
          if not requires_grad:
              for param in self.parameters():
                  param.requires_grad = False
                  
  
      def init_deconv_bilinear(self, weight):
          f_shape = weight.size()
          heigh, width = f_shape[-2], f_shape[-1]
          f = np.ceil(width/2.0)
          c = (2 * f - 1 - f % 2) / (2.0 * f)
          bilinear = np.zeros([heigh, width])
          for x in range(width):
              for y in range(heigh):
                  value = (1 - abs(x / f - c)) * (1 - abs(y / f - c))
                  bilinear[x, y] = value
          min_dim = min(f_shape[0], f_shape[1])
          weight.data.fill_(0.)
          for i in range(min_dim):
              weight.data[i,i,:,:] = torch.from_numpy(bilinear)
          return 
  
      def forward(self, img1, img2):
  
          ### normaize input
          sz = img1.size()
          img1 = img1.view(sz[0], sz[1], 1, sz[2], sz[3] )
          img2 = img2.view(sz[0], sz[1], 1, sz[2], sz[3] )
  
          inputs = torch.cat((img1, img2), dim=2)
  
          rgb_mean = inputs.contiguous().view(inputs.size()[:2]+(-1,)).mean(dim=-1).view(inputs.size()[:2] + (1,1,1,))
          
          x = (inputs - rgb_mean) / self.rgb_max
          x1 = x[:,:,0,:,:]
          x2 = x[:,:,1,:,:]
          x = torch.cat((x1,x2), dim = 1)
  
          # flownetc
          flownetc_flow2 = self.flownetc(x)[0]
          flownetc_flow = self.upsample1(flownetc_flow2*self.div_flow)
          
          # warp img1 to img0; magnitude of diff between img0 and and warped_img1, 
          resampled_img1 = self.resample1(x[:,3:,:,:], flownetc_flow)
          diff_img0 = x[:,:3,:,:] - resampled_img1 
          norm_diff_img0 = self.channelnorm(diff_img0)
  
          # concat img0, img1, img1->img0, flow, diff-mag ; 
          concat1 = torch.cat((x, resampled_img1, flownetc_flow/self.div_flow, norm_diff_img0), dim=1)
          
          # flownets1
          flownets1_flow2 = self.flownets_1(concat1)[0]
          flownets1_flow = self.upsample2(flownets1_flow2*self.div_flow) 
  
          # warp img1 to img0 using flownets1; magnitude of diff between img0 and and warped_img1
          resampled_img1 = self.resample2(x[:,3:,:,:], flownets1_flow)
          diff_img0 = x[:,:3,:,:] - resampled_img1
          norm_diff_img0 = self.channelnorm(diff_img0)
  
          # concat img0, img1, img1->img0, flow, diff-mag
          concat2 = torch.cat((x, resampled_img1, flownets1_flow/self.div_flow, norm_diff_img0), dim=1)
  
          # flownets2
          flownets2_flow2 = self.flownets_2(concat2)[0]
          flownets2_flow = self.upsample4(flownets2_flow2 * self.div_flow)
          norm_flownets2_flow = self.channelnorm(flownets2_flow)
  
          diff_flownets2_flow = self.resample4(x[:,3:,:,:], flownets2_flow)
          #if not diff_flownets2_flow.volatile:
          #    diff_flownets2_flow.register_hook(save_grad(self.args.grads, 'diff_flownets2_flow'))
  
          diff_flownets2_img1 = self.channelnorm((x[:,:3,:,:]-diff_flownets2_flow))
          #if not diff_flownets2_img1.volatile:
          #    diff_flownets2_img1.register_hook(save_grad(self.args.grads, 'diff_flownets2_img1'))
  
          # flownetsd
          flownetsd_flow2 = self.flownets_d(x)[0]
          flownetsd_flow = self.upsample3(flownetsd_flow2 / self.div_flow)
          norm_flownetsd_flow = self.channelnorm(flownetsd_flow)
          
          diff_flownetsd_flow = self.resample3(x[:,3:,:,:], flownetsd_flow)
          #if not diff_flownetsd_flow.volatile:
          #    diff_flownetsd_flow.register_hook(save_grad(self.args.grads, 'diff_flownetsd_flow'))
  
          diff_flownetsd_img1 = self.channelnorm((x[:,:3,:,:]-diff_flownetsd_flow))
          #if not diff_flownetsd_img1.volatile:
          #    diff_flownetsd_img1.register_hook(save_grad(self.args.grads, 'diff_flownetsd_img1'))
  
          # concat img1 flownetsd, flownets2, norm_flownetsd, norm_flownets2, diff_flownetsd_img1, diff_flownets2_img1
          concat3 = torch.cat((x[:,:3,:,:], flownetsd_flow, flownets2_flow, norm_flownetsd_flow, norm_flownets2_flow, diff_flownetsd_img1, diff_flownets2_img1), dim=1)
          flownetfusion_flow = self.flownetfusion(concat3)
  
          #if not flownetfusion_flow.volatile:
          #    flownetfusion_flow.register_hook(save_grad(self.args.grads, 'flownetfusion_flow'))
  
          return flownetfusion_flow
  
  class FlowNet2C(FlowNetC.FlowNetC):
      def __init__(self, args, batchNorm=False, div_flow=20):
          super(FlowNet2C,self).__init__(args, batchNorm=batchNorm, div_flow=20)
          self.rgb_max = args.rgb_max
  
      def forward(self, inputs):
          rgb_mean = inputs.contiguous().view(inputs.size()[:2]+(-1,)).mean(dim=-1).view(inputs.size()[:2] + (1,1,1,))
          
          x = (inputs - rgb_mean) / self.rgb_max
          x1 = x[:,:,0,:,:]
          x2 = x[:,:,1,:,:]
  
          # FlownetC top input stream
          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 self.upsample1(flow2*self.div_flow)
  
  class FlowNet2S(FlowNetS.FlowNetS):
      def __init__(self, args, batchNorm=False, div_flow=20):
          super(FlowNet2S,self).__init__(args, input_channels = 6, batchNorm=batchNorm)
          self.rgb_max = args.rgb_max
          self.div_flow = div_flow
          
      def forward(self, inputs):
          rgb_mean = inputs.contiguous().view(inputs.size()[:2]+(-1,)).mean(dim=-1).view(inputs.size()[:2] + (1,1,1,))
          x = (inputs - rgb_mean) / self.rgb_max
          x = torch.cat( (x[:,:,0,:,:], x[:,:,1,:,:]), dim = 1)
  
          out_conv1 = self.conv1(x)
  
          out_conv2 = 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)
          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,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_conv2,out_deconv2,flow3_up),1)
          flow2 = self.predict_flow2(concat2)
  
          if self.training:
              return flow2,flow3,flow4,flow5,flow6
          else:
              return self.upsample1(flow2*self.div_flow)
  
  class FlowNet2SD(FlowNetSD.FlowNetSD):
      def __init__(self, args, batchNorm=False, div_flow=20):
          super(FlowNet2SD,self).__init__(args, batchNorm=batchNorm)
          self.rgb_max = args.rgb_max
          self.div_flow = div_flow
  
      def forward(self, inputs):
          rgb_mean = inputs.contiguous().view(inputs.size()[:2]+(-1,)).mean(dim=-1).view(inputs.size()[:2] + (1,1,1,))
          x = (inputs - rgb_mean) / self.rgb_max
          x = torch.cat( (x[:,:,0,:,:], x[:,:,1,:,:]), dim = 1)
  
          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 self.upsample1(flow2*self.div_flow)
  
  class FlowNet2CS(nn.Module):
  
      def __init__(self, args, batchNorm=False, div_flow = 20.):
          super(FlowNet2CS,self).__init__()
          self.batchNorm = batchNorm
          self.div_flow = div_flow
          self.rgb_max = args.rgb_max
          self.args = args
  
          self.channelnorm = ChannelNorm()
  
          # First Block (FlowNetC)
          self.flownetc = FlowNetC.FlowNetC(args, batchNorm=self.batchNorm)
          self.upsample1 = nn.Upsample(scale_factor=4, mode='bilinear')
  
          if args.fp16:
              self.resample1 = nn.Sequential(
                              tofp32(), 
                              Resample2d(),
                              tofp16()) 
          else:
              self.resample1 = Resample2d()
  
          # Block (FlowNetS1)
          self.flownets_1 = FlowNetS.FlowNetS(args, batchNorm=self.batchNorm)
          self.upsample2 = nn.Upsample(scale_factor=4, mode='bilinear')
  
          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, inputs):
          rgb_mean = inputs.contiguous().view(inputs.size()[:2]+(-1,)).mean(dim=-1).view(inputs.size()[:2] + (1,1,1,))
          
          x = (inputs - rgb_mean) / self.rgb_max
          x1 = x[:,:,0,:,:]
          x2 = x[:,:,1,:,:]
          x = torch.cat((x1,x2), dim = 1)
  
          # flownetc
          flownetc_flow2 = self.flownetc(x)[0]
          flownetc_flow = self.upsample1(flownetc_flow2*self.div_flow)
          
          # warp img1 to img0; magnitude of diff between img0 and and warped_img1, 
          resampled_img1 = self.resample1(x[:,3:,:,:], flownetc_flow)
          diff_img0 = x[:,:3,:,:] - resampled_img1 
          norm_diff_img0 = self.channelnorm(diff_img0)
  
          # concat img0, img1, img1->img0, flow, diff-mag ; 
          concat1 = torch.cat((x, resampled_img1, flownetc_flow/self.div_flow, norm_diff_img0), dim=1)
          
          # flownets1
          flownets1_flow2 = self.flownets_1(concat1)[0]
          flownets1_flow = self.upsample2(flownets1_flow2*self.div_flow) 
  
          return flownets1_flow
  
  class FlowNet2CSS(nn.Module):
  
      def __init__(self, args, batchNorm=False, div_flow = 20.):
          super(FlowNet2CSS,self).__init__()
          self.batchNorm = batchNorm
          self.div_flow = div_flow
          self.rgb_max = args.rgb_max
          self.args = args
  
          self.channelnorm = ChannelNorm()
  
          # First Block (FlowNetC)
          self.flownetc = FlowNetC.FlowNetC(args, batchNorm=self.batchNorm)
          self.upsample1 = nn.Upsample(scale_factor=4, mode='bilinear')
  
          if args.fp16:
              self.resample1 = nn.Sequential(
                              tofp32(), 
                              Resample2d(),
                              tofp16()) 
          else:
              self.resample1 = Resample2d()
  
          # Block (FlowNetS1)
          self.flownets_1 = FlowNetS.FlowNetS(args, batchNorm=self.batchNorm)
          self.upsample2 = nn.Upsample(scale_factor=4, mode='bilinear')
          if args.fp16:
              self.resample2 = nn.Sequential(
                              tofp32(), 
                              Resample2d(),
                              tofp16()) 
          else:
              self.resample2 = Resample2d()
  
  
          # Block (FlowNetS2)
          self.flownets_2 = FlowNetS.FlowNetS(args, batchNorm=self.batchNorm)
          self.upsample3 = nn.Upsample(scale_factor=4, mode='nearest') 
  
          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, inputs):
          rgb_mean = inputs.contiguous().view(inputs.size()[:2]+(-1,)).mean(dim=-1).view(inputs.size()[:2] + (1,1,1,))
          
          x = (inputs - rgb_mean) / self.rgb_max
          x1 = x[:,:,0,:,:]
          x2 = x[:,:,1,:,:]
          x = torch.cat((x1,x2), dim = 1)
  
          # flownetc
          flownetc_flow2 = self.flownetc(x)[0]
          flownetc_flow = self.upsample1(flownetc_flow2*self.div_flow)
          
          # warp img1 to img0; magnitude of diff between img0 and and warped_img1, 
          resampled_img1 = self.resample1(x[:,3:,:,:], flownetc_flow)
          diff_img0 = x[:,:3,:,:] - resampled_img1 
          norm_diff_img0 = self.channelnorm(diff_img0)
  
          # concat img0, img1, img1->img0, flow, diff-mag ; 
          concat1 = torch.cat((x, resampled_img1, flownetc_flow/self.div_flow, norm_diff_img0), dim=1)
          
          # flownets1
          flownets1_flow2 = self.flownets_1(concat1)[0]
          flownets1_flow = self.upsample2(flownets1_flow2*self.div_flow) 
  
          # warp img1 to img0 using flownets1; magnitude of diff between img0 and and warped_img1
          resampled_img1 = self.resample2(x[:,3:,:,:], flownets1_flow)
          diff_img0 = x[:,:3,:,:] - resampled_img1
          norm_diff_img0 = self.channelnorm(diff_img0)
  
          # concat img0, img1, img1->img0, flow, diff-mag
          concat2 = torch.cat((x, resampled_img1, flownets1_flow/self.div_flow, norm_diff_img0), dim=1)
  
          # flownets2
          flownets2_flow2 = self.flownets_2(concat2)[0]
          flownets2_flow = self.upsample3(flownets2_flow2 * self.div_flow)
  
          return flownets2_flow
  
  