class_docstr: The dual task loss implement of GSCNN
  
  Args:
      ignore_index (int64): Specifies a target value that is ignored
          and does not contribute to the input gradient. Default ``255``.
      tau (float): the tau of gumbel softmax sample.
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docstr: Convolves an image by a 2D triangle filter (the 1D triangle filter f is
  [1:r r+1 r:-1:1]/(r+1)^2, the 2D version is simply conv2(f,f'))
docstr_tok: ["Convolves","an","image","by","a","2D","triangle","filter","(","the","1D","triangle","filter","f","is","[","1",":","r","r+1","r",":","-1:1","]","\/","(","r+1",")","^2",",","the","2D","version","is","simply","conv2","(","f",",","f","'",")",")"]
code: def conv_tri(self, input, r):
      """
      Convolves an image by a 2D triangle filter (the 1D triangle filter f is
      [1:r r+1 r:-1:1]/(r+1)^2, the 2D version is simply conv2(f,f'))
      """
      if r <= 1:
          raise ValueError(
              "`r` should be greater than 1, but it is {}.".format(r)
          )
  
      kernel = [
          list(range(1, r + 1))
          + [r + 1]
          + list(reversed(range(1, r + 1)))
      ]
      kernel = paddle.to_tensor(kernel).astype("float32")
      kernel = kernel / (r + 1) ** 2
      input_ = F.pad(input, [1, 1, 0, 0], mode="replicate")
      input_ = F.pad(input_, [r, r, 0, 0], mode="reflect")
      input_ = [input_[:, :, :, :r], input, input_[:, :, :, -r:]]
      input_ = paddle.concat(input_, axis=3)
      tem = input_.clone()
  
      input_ = F.pad(input_, [0, 0, 1, 1], mode="replicate")
      input_ = F.pad(input_, [0, 0, r, r], mode="reflect")
      input_ = [input_[:, :, :r, :], tem, input_[:, :, -r:, :]]
      input_ = paddle.concat(input_, axis=2)
  
      c = input.shape[1]
      kernel_x = paddle.concat([kernel.unsqueeze((0, 1))] * c, axis=0)
      output = F.conv2d(input_, kernel_x, padding=0, groups=c)
      kernel_y = paddle.concat(
          [kernel.t().unsqueeze((0, 1))] * c, axis=0
      )
      output = F.conv2d(output, kernel_y, padding=0, groups=c)
      return output
  
file_path: PaddleSeg/paddleseg/models/losses/gscnn_dual_task_loss.py
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager
label_desc: Use image node_id 4 for calling the DualTaskLoss obj's underlying member method code with example usage: obj.conv_tri(input, r) and returns: output