text: # Copyright 2020 Toyota Research Institute.  All rights reserved.
  
  import torch
  import torch.nn as nn
  from packnet_sfm.networks.layers.packnet.layers01 import \
      PackLayerConv3d, UnpackLayerConv3d, Conv2D, ResidualBlock, InvDepth
  
  ########################################################################################################################
  
  class PackNet01(nn.Module):
      """
      PackNet network with 3d convolutions (version 01, from the CVPR paper).
  
      https://arxiv.org/abs/1905.02693
  
      Parameters
      ----------
      dropout : float
          Dropout value to use
      version : str
          Has a XY format, where:
          X controls upsampling variations (not used at the moment).
          Y controls feature stacking (A for concatenation and B for addition)
      kwargs : dict
          Extra parameters
      """
      def __init__(self, dropout=None, version=None, **kwargs):
          super().__init__()
          self.version = version[1:]
          # Input/output channels
          in_channels = 3
          out_channels = 1
          # Hyper-parameters
          ni, no = 64, out_channels
          n1, n2, n3, n4, n5 = 64, 64, 128, 256, 512
          num_blocks = [2, 2, 3, 3]
          pack_kernel = [5, 3, 3, 3, 3]
          unpack_kernel = [3, 3, 3, 3, 3]
          iconv_kernel = [3, 3, 3, 3, 3]
          # Initial convolutional layer
          self.pre_calc = Conv2D(in_channels, ni, 5, 1)
          # Support for different versions
          if self.version == 'A':
              n1o, n1i = n1, n1 + ni + no
              n2o, n2i = n2, n2 + n1 + no
              n3o, n3i = n3, n3 + n2 + no
              n4o, n4i = n4, n4 + n3
              n5o, n5i = n5, n5 + n4
          elif self.version == 'B':
              n1o, n1i = n1, n1 + no
              n2o, n2i = n2, n2 + no
              n3o, n3i = n3//2, n3//2 + no
              n4o, n4i = n4//2, n4//2
              n5o, n5i = n5//2, n5//2
          else:
              raise ValueError('Unknown PackNet version {}'.format(version))
  
          # Encoder
  
          self.pack1 = PackLayerConv3d(n1, pack_kernel[0])
          self.pack2 = PackLayerConv3d(n2, pack_kernel[1])
          self.pack3 = PackLayerConv3d(n3, pack_kernel[2])
          self.pack4 = PackLayerConv3d(n4, pack_kernel[3])
          self.pack5 = PackLayerConv3d(n5, pack_kernel[4])
  
          self.conv1 = Conv2D(ni, n1, 7, 1)
          self.conv2 = ResidualBlock(n1, n2, num_blocks[0], 1, dropout=dropout)
          self.conv3 = ResidualBlock(n2, n3, num_blocks[1], 1, dropout=dropout)
          self.conv4 = ResidualBlock(n3, n4, num_blocks[2], 1, dropout=dropout)
          self.conv5 = ResidualBlock(n4, n5, num_blocks[3], 1, dropout=dropout)
  
          # Decoder
  
          self.unpack5 = UnpackLayerConv3d(n5, n5o, unpack_kernel[0])
          self.unpack4 = UnpackLayerConv3d(n5, n4o, unpack_kernel[1])
          self.unpack3 = UnpackLayerConv3d(n4, n3o, unpack_kernel[2])
          self.unpack2 = UnpackLayerConv3d(n3, n2o, unpack_kernel[3])
          self.unpack1 = UnpackLayerConv3d(n2, n1o, unpack_kernel[4])
  
          self.iconv5 = Conv2D(n5i, n5, iconv_kernel[0], 1)
          self.iconv4 = Conv2D(n4i, n4, iconv_kernel[1], 1)
          self.iconv3 = Conv2D(n3i, n3, iconv_kernel[2], 1)
          self.iconv2 = Conv2D(n2i, n2, iconv_kernel[3], 1)
          self.iconv1 = Conv2D(n1i, n1, iconv_kernel[4], 1)
  
          # Depth Layers
  
          self.unpack_disps = nn.PixelShuffle(2)
          self.unpack_disp4 = nn.Upsample(scale_factor=2, mode='nearest', align_corners=None)
          self.unpack_disp3 = nn.Upsample(scale_factor=2, mode='nearest', align_corners=None)
          self.unpack_disp2 = nn.Upsample(scale_factor=2, mode='nearest', align_corners=None)
  
          self.disp4_layer = InvDepth(n4, out_channels=out_channels)
          self.disp3_layer = InvDepth(n3, out_channels=out_channels)
          self.disp2_layer = InvDepth(n2, out_channels=out_channels)
          self.disp1_layer = InvDepth(n1, out_channels=out_channels)
  
          self.init_weights()
  
      def init_weights(self):
          """Initializes network weights."""
          for m in self.modules():
              if isinstance(m, (nn.Conv2d, nn.Conv3d)):
                  nn.init.xavier_uniform_(m.weight)
                  if m.bias is not None:
                      m.bias.data.zero_()
  
      def forward(self, x):
          """
          Runs the network and returns inverse depth maps
          (4 scales if training and 1 if not).
          """
          x = self.pre_calc(x)
  
          # Encoder
  
          x1 = self.conv1(x)
          x1p = self.pack1(x1)
          x2 = self.conv2(x1p)
          x2p = self.pack2(x2)
          x3 = self.conv3(x2p)
          x3p = self.pack3(x3)
          x4 = self.conv4(x3p)
          x4p = self.pack4(x4)
          x5 = self.conv5(x4p)
          x5p = self.pack5(x5)
  
          # Skips
  
          skip1 = x
          skip2 = x1p
          skip3 = x2p
          skip4 = x3p
          skip5 = x4p
  
          # Decoder
  
          unpack5 = self.unpack5(x5p)
          if self.version == 'A':
              concat5 = torch.cat((unpack5, skip5), 1)
          else:
              concat5 = unpack5 + skip5
          iconv5 = self.iconv5(concat5)
  
          unpack4 = self.unpack4(iconv5)
          if self.version == 'A':
              concat4 = torch.cat((unpack4, skip4), 1)
          else:
              concat4 = unpack4 + skip4
          iconv4 = self.iconv4(concat4)
          disp4 = self.disp4_layer(iconv4)
          udisp4 = self.unpack_disp4(disp4)
  
          unpack3 = self.unpack3(iconv4)
          if self.version == 'A':
              concat3 = torch.cat((unpack3, skip3, udisp4), 1)
          else:
              concat3 = torch.cat((unpack3 + skip3, udisp4), 1)
          iconv3 = self.iconv3(concat3)
          disp3 = self.disp3_layer(iconv3)
          udisp3 = self.unpack_disp3(disp3)
  
          unpack2 = self.unpack2(iconv3)
          if self.version == 'A':
              concat2 = torch.cat((unpack2, skip2, udisp3), 1)
          else:
              concat2 = torch.cat((unpack2 + skip2, udisp3), 1)
          iconv2 = self.iconv2(concat2)
          disp2 = self.disp2_layer(iconv2)
          udisp2 = self.unpack_disp2(disp2)
  
          unpack1 = self.unpack1(iconv2)
          if self.version == 'A':
              concat1 = torch.cat((unpack1, skip1, udisp2), 1)
          else:
              concat1 = torch.cat((unpack1 +  skip1, udisp2), 1)
          iconv1 = self.iconv1(concat1)
          disp1 = self.disp1_layer(iconv1)
  
          if self.training:
              return [disp1, disp2, disp3, disp4]
          else:
              return disp1
  
  ########################################################################################################################
  