def forward(self, logits, label):
        """
        Forward computation.

        Args:
            logits (tuple|list): (seg_logit, edge_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. C =1 of edge_logit .
            label (Tensor): Label tensor, the data type is int64. Shape is (N, C), where each
                value is 0 <= label[i] <= C-1, and if shape is more than 2D, this is
                (N, C, D1, D2,..., Dk), k >= 1.
        """
        seg_logit, edge_logit = logits[0], logits[1]
        if len(label.shape) != len(seg_logit.shape):
            label = paddle.unsqueeze(label, 1)
        if edge_logit.shape != label.shape:
            raise ValueError(
                'The shape of edge_logit should equal to the label, but they are {} != {}'
                .format(edge_logit.shape, label.shape))

        # Filter out edge
        filler = paddle.ones_like(label) * self.ignore_index
        label = paddle.where(edge_logit > self.edge_threshold, label, filler)

        # ohem
        n, c, h, w = seg_logit.shape
        label = label.reshape((-1, ))
        valid_mask = (label != self.ignore_index).astype('int64')
        num_valid = valid_mask.sum()
        label = label * valid_mask

        prob = F.softmax(seg_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).astype(prob.dtype)

            # 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()
                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(seg_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

        Forward computation.

        Args:
            logits (tuple|list): (seg_logit, edge_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. C =1 of edge_logit .
            label (Tensor): Label tensor, the data type is int64. Shape is (N, C), where each
                value is 0 <= label[i] <= C-1, and if shape is more than 2D, this is
                (N, C, D1, D2,..., Dk), k >= 1.
        
paddleseg/models/losses/ohem_edge_attention_loss.py