class_docstr: Implements the ohem cross entropy loss function.
  
  Args:
      thresh (float, optional): The threshold of ohem. Default: 0.7.
      min_kept (int, optional): The min number to keep in loss computation. Default: 10000.
      ignore_index (int64, optional): Specifies a target value that is ignored
          and does not contribute to the input gradient. Default ``255``.
docstr: Forward computation.
  
  Args:
      logit (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.
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code: def forward(self, logit, label):
      """
      Forward computation.
  
      Args:
          logit (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.
      """
      if len(label.shape) != len(logit.shape):
          label = paddle.unsqueeze(label, 1)
  
      # get the label after ohem
      n, c, h, w = logit.shape
      label = label.reshape((-1,)).astype("int64")
      valid_mask = (label != self.ignore_index).astype("int64")
      num_valid = valid_mask.sum()
      label = label * valid_mask
  
      prob = F.softmax(logit, axis=1)
      prob = prob.transpose((1, 0, 2, 3)).reshape((c, -1))
  
      if self.min_kept < num_valid and num_valid > 0:
          # let the value which ignored greater than 1
          prob = prob + (1 - valid_mask)
  
          # get the prob of relevant label
          label_onehot = F.one_hot(label, c)
          label_onehot = label_onehot.transpose((1, 0))
          prob = prob * label_onehot
          prob = paddle.sum(prob, axis=0)
  
          threshold = self.thresh
          if self.min_kept > 0:
              index = prob.argsort()
              if hasattr(paddle.Tensor, "contiguous"):
                  threshold_index = index[
                      min(len(index), self.min_kept) - 1
                  ].contiguous()
              else:
                  threshold_index = index[
                      min(len(index), self.min_kept) - 1
                  ]
              threshold_index = int(threshold_index)
              if prob[threshold_index] > self.thresh:
                  threshold = prob[threshold_index]
              kept_mask = (prob < threshold).astype("int64")
              label = label * kept_mask
              valid_mask = valid_mask * kept_mask
  
      # make the invalid region as ignore
      label = label + (1 - valid_mask) * self.ignore_index
  
      label = label.reshape((n, 1, h, w))
      valid_mask = valid_mask.reshape((n, 1, h, w)).astype("float32")
      loss = F.softmax_with_cross_entropy(
          logit, label, ignore_index=self.ignore_index, axis=1
      )
      loss = loss * valid_mask
      avg_loss = paddle.mean(loss) / (
          paddle.mean(valid_mask) + self.EPS
      )
  
      label.stop_gradient = True
      valid_mask.stop_gradient = True
      return avg_loss
  
file_path: PaddleSeg/paddleseg/models/losses/ohem_cross_entropy_loss.py
imports: import paddle
  from paddle import nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager
label_desc: Use image node_id 2 for calling the OhemCrossEntropyLoss obj's underlying member method code with example usage: obj.forward(logit, label) and returns: avg_loss