# 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. # reference: https://arxiv.org/abs/1801.07698 import paddle import paddle.nn as nn import math class ArcMargin(nn.Layer): def __init__(self, embedding_size, class_num, margin=0.5, scale=80.0, easy_margin=False): super().__init__() self.embedding_size = embedding_size self.class_num = class_num self.margin = margin self.scale = scale self.easy_margin = easy_margin 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=None): 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 sin = paddle.sqrt(1.0 - paddle.square(cos) + 1e-6) cos_m = math.cos(self.margin) sin_m = math.sin(self.margin) phi = cos * cos_m - sin * sin_m th = math.cos(self.margin) * (-1) mm = math.sin(self.margin) * self.margin if self.easy_margin: phi = self._paddle_where_more_than(cos, 0, phi, cos) else: phi = self._paddle_where_more_than(cos, th, phi, cos - mm) one_hot = paddle.nn.functional.one_hot(label, self.class_num) one_hot = paddle.squeeze(one_hot, axis=[1]) output = paddle.multiply(one_hot, phi) + paddle.multiply( (1.0 - one_hot), cos) output = output * self.scale return output def _paddle_where_more_than(self, target, limit, x, y): mask = paddle.cast(x=(target > limit), dtype='float32') output = paddle.multiply(mask, x) + paddle.multiply((1.0 - mask), y) return output