text: # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # 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 paddle
  import math
  import paddle.nn as nn
  
  
  class CosMargin(paddle.nn.Layer):
      def __init__(self, embedding_size, class_num, margin=0.35, scale=64.0):
          super(CosMargin, self).__init__()
          self.scale = scale
          self.margin = margin
          self.embedding_size = embedding_size
          self.class_num = class_num
  
          self.weight = self.create_parameter(
              shape=[self.embedding_size, self.class_num],
              is_bias=False,
              default_initializer=paddle.nn.initializer.XavierNormal())
  
      def forward(self, input, label):
          label.stop_gradient = True
  
          input_norm = paddle.sqrt(
              paddle.sum(paddle.square(input), axis=1, keepdim=True))
          input = paddle.divide(input, input_norm)
  
          weight_norm = paddle.sqrt(
              paddle.sum(paddle.square(self.weight), axis=0, keepdim=True))
          weight = paddle.divide(self.weight, weight_norm)
  
          cos = paddle.matmul(input, weight)
          if not self.training or label is None:
              return cos
  
          cos_m = cos - self.margin
  
          one_hot = paddle.nn.functional.one_hot(label, self.class_num)
          one_hot = paddle.squeeze(one_hot, axis=[1])
          output = paddle.multiply(one_hot, cos_m) + paddle.multiply(
              (1.0 - one_hot), cos)
          output = output * self.scale
          return output
  