class_docstr: SOLOv2Loss
  Args:
      ins_loss_weight (float): Weight of instance loss.
      focal_loss_gamma (float): Gamma parameter for focal loss.
      focal_loss_alpha (float): Alpha parameter for focal loss.
class_docstr_tok: ["SOLOv2Loss","Args",":","ins_loss_weight","(","float",")",":","Weight","of","instance","loss",".","focal_loss_gamma","(","float",")",":","Gamma","parameter","for","focal","loss",".","focal_loss_alpha","(","float",")",":","Alpha","parameter","for","focal","loss","."]
code: def _dice_loss(self, input, target):
      input = paddle.reshape(input, shape=(paddle.shape(input)[0], -1))
      target = paddle.reshape(
          target, shape=(paddle.shape(target)[0], -1)
      )
      a = paddle.sum(input * target, axis=1)
      b = paddle.sum(input * input, axis=1) + 0.001
      c = paddle.sum(target * target, axis=1) + 0.001
      d = (2 * a) / (b + c)
      return 1 - d
  
file_path: PaddleDetection/ppdet/modeling/losses/solov2_loss.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  import paddle.nn.functional
  from ppdet.core.workspace import register, serializable
label_desc: Use image node_id 2 for calling the SOLOv2Loss obj's underlying member method code with example usage: obj._dice_loss(input, target) and returns: unknown