class_docstr: DetailAggregateLoss's implementation based on PaddlePaddle.
  
  The original article refers to Meituan
  Fan, Mingyuan, et al. "Rethinking BiSeNet For Real-time Semantic Segmentation."
  (https://arxiv.org/abs/2104.13188)
  
  Args:
      ignore_index (int64, optional): Specifies a target value that is ignored
          and does not contribute to the input gradient. Default ``255``.
docstr: Args:
      logits (Tensor): Logit tensor, the data type is float32, float64. Shape is
          (N, C), where C is number of classes, and if shape is more than 2D, this
          is (N, C, D1, D2,..., Dk), k >= 1.
      label (Tensor): Label tensor, the data type is int64. Shape is (N), where each
          value is 0 <= label[i] <= C-1, and if shape is more than 2D, this is
          (N, D1, D2,..., Dk), k >= 1.
  Returns: loss
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code: def forward(self, logits, label):
      """
      Args:
          logits (Tensor): Logit tensor, the data type is float32, float64. Shape is
              (N, C), where C is number of classes, and if shape is more than 2D, this
              is (N, C, D1, D2,..., Dk), k >= 1.
          label (Tensor): Label tensor, the data type is int64. Shape is (N), where each
              value is 0 <= label[i] <= C-1, and if shape is more than 2D, this is
              (N, D1, D2,..., Dk), k >= 1.
      Returns: loss
      """
      boundary_targets = F.conv2d(
          paddle.unsqueeze(label, axis=1).astype("float32"),
          self.laplacian_kernel,
          padding=1,
      )
      boundary_targets = paddle.clip(boundary_targets, min=0)
      boundary_targets = boundary_targets > 0.1
      boundary_targets = boundary_targets.astype("float32")
  
      boundary_targets_x2 = F.conv2d(
          paddle.unsqueeze(label, axis=1).astype("float32"),
          self.laplacian_kernel,
          stride=2,
          padding=1,
      )
      boundary_targets_x2 = paddle.clip(boundary_targets_x2, min=0)
      boundary_targets_x4 = F.conv2d(
          paddle.unsqueeze(label, axis=1).astype("float32"),
          self.laplacian_kernel,
          stride=4,
          padding=1,
      )
      boundary_targets_x4 = paddle.clip(boundary_targets_x4, min=0)
  
      boundary_targets_x8 = F.conv2d(
          paddle.unsqueeze(label, axis=1).astype("float32"),
          self.laplacian_kernel,
          stride=8,
          padding=1,
      )
      boundary_targets_x8 = paddle.clip(boundary_targets_x8, min=0)
  
      boundary_targets_x8_up = F.interpolate(
          boundary_targets_x8,
          boundary_targets.shape[2:],
          mode="nearest",
      )
      boundary_targets_x4_up = F.interpolate(
          boundary_targets_x4,
          boundary_targets.shape[2:],
          mode="nearest",
      )
      boundary_targets_x2_up = F.interpolate(
          boundary_targets_x2,
          boundary_targets.shape[2:],
          mode="nearest",
      )
  
      boundary_targets_x2_up = boundary_targets_x2_up > 0.1
      boundary_targets_x2_up = boundary_targets_x2_up.astype("float32")
  
      boundary_targets_x4_up = boundary_targets_x4_up > 0.1
      boundary_targets_x4_up = boundary_targets_x4_up.astype("float32")
  
      boundary_targets_x8_up = boundary_targets_x8_up > 0.1
      boundary_targets_x8_up = boundary_targets_x8_up.astype("float32")
  
      boudary_targets_pyramids = paddle.stack(
          (
              boundary_targets,
              boundary_targets_x2_up,
              boundary_targets_x4_up,
          ),
          axis=1,
      )
  
      boudary_targets_pyramids = paddle.squeeze(
          boudary_targets_pyramids, axis=2
      )
      boudary_targets_pyramid = F.conv2d(
          boudary_targets_pyramids, self.fuse_kernel
      )
  
      boudary_targets_pyramid = boudary_targets_pyramid > 0.1
      boudary_targets_pyramid = boudary_targets_pyramid.astype(
          "float32"
      )
  
      if logits.shape[-1] != boundary_targets.shape[-1]:
          logits = F.interpolate(
              logits,
              boundary_targets.shape[2:],
              mode="bilinear",
              align_corners=True,
          )
  
      bce_loss = F.binary_cross_entropy_with_logits(
          logits, boudary_targets_pyramid
      )
      dice_loss = self.fixed_dice_loss_func(
          F.sigmoid(logits), boudary_targets_pyramid
      )
      detail_loss = bce_loss + dice_loss
  
      label.stop_gradient = True
      return detail_loss
  
file_path: PaddleSeg/paddleseg/models/losses/detail_aggregate_loss.py
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager
label_desc: Use image node_id 2 for calling the DetailAggregateLoss obj's underlying member method code with example usage: obj.forward(logits, label) and returns: detail_loss