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60 lines
2.1 KiB
60 lines
2.1 KiB
3 years ago
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import math
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import paddle
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import paddle.nn as nn
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import paddle.nn.functional as F
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class CircleMargin(nn.Layer):
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def __init__(self, embedding_size, class_num, margin, scale):
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super(CircleMargin, self).__init__()
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self.scale = scale
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self.margin = margin
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self.embedding_size = embedding_size
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self.class_num = class_num
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self.weight = self.create_parameter(
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shape=[self.embedding_size, self.class_num],
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is_bias=False,
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default_initializer=paddle.nn.initializer.XavierNormal())
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def forward(self, input, label):
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feat_norm = paddle.sqrt(
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paddle.sum(paddle.square(input), axis=1, keepdim=True))
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input = paddle.divide(input, feat_norm)
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weight_norm = paddle.sqrt(
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paddle.sum(paddle.square(self.weight), axis=0, keepdim=True))
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weight = paddle.divide(self.weight, weight_norm)
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logits = paddle.matmul(input, weight)
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if not self.training or label is None:
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return logits
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alpha_p = paddle.clip(-logits.detach() + 1 + self.margin, min=0.)
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alpha_n = paddle.clip(logits.detach() + self.margin, min=0.)
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delta_p = 1 - self.margin
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delta_n = self.margin
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m_hot = F.one_hot(label.reshape([-1]), num_classes=logits.shape[1])
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logits_p = alpha_p * (logits - delta_p)
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logits_n = alpha_n * (logits - delta_n)
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pre_logits = logits_p * m_hot + logits_n * (1 - m_hot)
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pre_logits = self.scale * pre_logits
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return pre_logits
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