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- # 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 math
- import paddle
- import paddle.nn as nn
- import paddle.nn.functional as F
- class CircleMargin(nn.Layer):
- def __init__(self, embedding_size, class_num, margin, scale):
- super(CircleMargin, 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):
- feat_norm = paddle.sqrt(
- paddle.sum(paddle.square(input), axis=1, keepdim=True))
- input = paddle.divide(input, feat_norm)
- weight_norm = paddle.sqrt(
- paddle.sum(paddle.square(self.weight), axis=0, keepdim=True))
- weight = paddle.divide(self.weight, weight_norm)
- logits = paddle.matmul(input, weight)
- if not self.training or label is None:
- return logits
- alpha_p = paddle.clip(-logits.detach() + 1 + self.margin, min=0.)
- alpha_n = paddle.clip(logits.detach() + self.margin, min=0.)
- delta_p = 1 - self.margin
- delta_n = self.margin
- m_hot = F.one_hot(label.reshape([-1]), num_classes=logits.shape[1])
- logits_p = alpha_p * (logits - delta_p)
- logits_n = alpha_n * (logits - delta_n)
- pre_logits = logits_p * m_hot + logits_n * (1 - m_hot)
- pre_logits = self.scale * pre_logits
- return pre_logits
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