text: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  
  class FlowHead(nn.Module):
      def __init__(self, input_dim=128, hidden_dim=256):
          super(FlowHead, self).__init__()
          self.conv1 = nn.Conv2d(input_dim, hidden_dim, 3, padding=1)
          self.conv2 = nn.Conv2d(hidden_dim, 2, 3, padding=1)
          self.relu = nn.ReLU(inplace=True)
  
      def forward(self, x):
          return self.conv2(self.relu(self.conv1(x)))
  
  class ConvGRU(nn.Module):
      def __init__(self, hidden_dim=128, input_dim=192+128):
          super(ConvGRU, self).__init__()
          self.convz = nn.Conv2d(hidden_dim+input_dim, hidden_dim, 3, padding=1)
          self.convr = nn.Conv2d(hidden_dim+input_dim, hidden_dim, 3, padding=1)
          self.convq = nn.Conv2d(hidden_dim+input_dim, hidden_dim, 3, padding=1)
  
      def forward(self, h, x):
          hx = torch.cat([h, x], dim=1)
  
          z = torch.sigmoid(self.convz(hx))
          r = torch.sigmoid(self.convr(hx))
          q = torch.tanh(self.convq(torch.cat([r*h, x], dim=1)))
  
          h = (1-z) * h + z * q
          return h
  
  class SepConvGRU(nn.Module):
      def __init__(self, hidden_dim=128, input_dim=192+128):
          super(SepConvGRU, self).__init__()
          self.convz1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2))
          self.convr1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2))
          self.convq1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2))
  
          self.convz2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0))
          self.convr2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0))
          self.convq2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0))
  
  
      def forward(self, h, x):
          # horizontal
          hx = torch.cat([h, x], dim=1)
          z = torch.sigmoid(self.convz1(hx))
          r = torch.sigmoid(self.convr1(hx))
          q = torch.tanh(self.convq1(torch.cat([r*h, x], dim=1)))        
          h = (1-z) * h + z * q
  
          # vertical
          hx = torch.cat([h, x], dim=1)
          z = torch.sigmoid(self.convz2(hx))
          r = torch.sigmoid(self.convr2(hx))
          q = torch.tanh(self.convq2(torch.cat([r*h, x], dim=1)))       
          h = (1-z) * h + z * q
  
          return h
  
  class SmallMotionEncoder(nn.Module):
      def __init__(self, args):
          super(SmallMotionEncoder, self).__init__()
          cor_planes = args.corr_levels * (2*args.corr_radius + 1)**2
          self.convc1 = nn.Conv2d(cor_planes, 96, 1, padding=0)
          self.convf1 = nn.Conv2d(2, 64, 7, padding=3)
          self.convf2 = nn.Conv2d(64, 32, 3, padding=1)
          self.conv = nn.Conv2d(128, 80, 3, padding=1)
  
      def forward(self, flow, corr):
          cor = F.relu(self.convc1(corr))
          flo = F.relu(self.convf1(flow))
          flo = F.relu(self.convf2(flo))
          cor_flo = torch.cat([cor, flo], dim=1)
          out = F.relu(self.conv(cor_flo))
          return torch.cat([out, flow], dim=1)
  
  class BasicMotionEncoder(nn.Module):
      def __init__(self):
          super(BasicMotionEncoder, self).__init__()
          # cor_planes = args.corr_levels * (2*args.corr_radius + 1)**2
          cor_planes = 4 * (2*4 + 1)**2
          self.convc1 = nn.Conv2d(cor_planes, 256, 1, padding=0)
          self.convc2 = nn.Conv2d(256, 192, 3, padding=1)
          self.convf1 = nn.Conv2d(2, 128, 7, padding=3)
          self.convf2 = nn.Conv2d(128, 64, 3, padding=1)
          self.conv = nn.Conv2d(64+192, 128-2, 3, padding=1)
  
      def forward(self, flow, corr):
          cor = F.relu(self.convc1(corr))
          cor = F.relu(self.convc2(cor))
          flo = F.relu(self.convf1(flow))
          flo = F.relu(self.convf2(flo))
  
          cor_flo = torch.cat([cor, flo], dim=1)
          out = F.relu(self.conv(cor_flo))
          return torch.cat([out, flow], dim=1)
  
  class SmallUpdateBlock(nn.Module):
      def __init__(self, args, hidden_dim=96):
          super(SmallUpdateBlock, self).__init__()
          self.encoder = SmallMotionEncoder(args)
          self.gru = ConvGRU(hidden_dim=hidden_dim, input_dim=82+64)
          self.flow_head = FlowHead(hidden_dim, hidden_dim=128)
  
      def forward(self, net, inp, corr, flow):
          motion_features = self.encoder(flow, corr)
          inp = torch.cat([inp, motion_features], dim=1)
          net = self.gru(net, inp)
          delta_flow = self.flow_head(net)
  
          return net, None, delta_flow
  
  class BasicUpdateBlock(nn.Module):
      def __init__(self, hidden_dim=128, input_dim=128):
          super(BasicUpdateBlock, self).__init__()
          # self.args = args
          self.encoder = BasicMotionEncoder()
          self.gru = SepConvGRU(hidden_dim=hidden_dim, input_dim=128+hidden_dim)
          self.flow_head = FlowHead(hidden_dim, hidden_dim=256)
  
          self.mask = nn.Sequential(
              nn.Conv2d(128, 256, 3, padding=1),
              nn.ReLU(inplace=True),
              nn.Conv2d(256, 64*9, 1, padding=0))
  
      def forward(self, net, inp, corr, flow, upsample=True):
          motion_features = self.encoder(flow, corr)
          inp = torch.cat([inp, motion_features], dim=1)
  
          net = self.gru(net, inp)
          delta_flow = self.flow_head(net)
  
          # scale mask to balence gradients
          mask = .25 * self.mask(net)
          return net, mask, delta_flow
  
  
  
  