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()
                threshold_index = index[min(len(index), self.min_kept) - 1]
                threshold_index = int(threshold_index.numpy()[0])
                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)

        label.stop_gradient = True
        valid_mask.stop_gradient = True
        return avg_loss.clone()

        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.
        
contrib/CrossPseudoSupervision/models/losses/probohem_cross_entropy_loss.py