text: # Copyright (c) 2020, Huawei Technologies.All rights reserved.
  #
  # Licensed under the BSD 3-Clause License  (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  # https://opensource.org/licenses/BSD-3-Clause
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  import torch
  import torch.nn as nn
  
  
  class LabelSmoothingCrossEntropy(nn.Module):
      """CrossEntropy with LabelSmoothing using npu api.
  
      Paper: [Rethinking the Inception Architecture for Computer Vision]
      https://arxiv.org/pdf/1512.00567.pdf
  
      Args:
          smooth_factor (float): default 0. If label_smoothing using, using 0.1([0, 1]) instead.
          num_classes (float): classes numbers using for onehot.
  
      Returns:
          float: tensors of shape (k, 5) and (k, 1). Labels are 0-based.
      """
  
      def __init__(self, num_classes=1000, smooth_factor=0.):
          super(LabelSmoothingCrossEntropy, self).__init__()
          self.on_value = 1.0 - smooth_factor
          self.off_value = 1.0 * smooth_factor / (num_classes - 1)
  
      def forward(self, pred, target):
          one_hot_label = torch.npu_one_hot(target.int(), -1, pred.size(1), self.on_value, self.off_value)
          loss = torch.npu_softmax_cross_entropy_with_logits(pred, one_hot_label)
  
          loss = torch.mean(loss, [0], keepdim=False, dtype=torch.float32)
          return loss
  
  
  if __name__ == '__main__':
      x = torch.randn(2, 10)
      x.requires_grad = True
      y = torch.randint(0, 10, size=(2,))
  
      torch.npu.set_device(0)
      x = x.npu()
      y = y.npu()
      m = LabelSmoothingCrossEntropy(10)
      l = m(x, y)
      l.backward()
      print('test ce ok, loss is ', l)
  
      m = LabelSmoothingCrossEntropy(10, 0.1)
      l = m(x, y)
      l.backward()
      print('test lsce ok, loss is ', l)
  