PaddleClas/ppcls/arch/gears/arcmargin.py

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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 paddle
import paddle.nn as nn
import math
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class ArcMargin(nn.Layer):
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def __init__(self,
embedding_size,
class_num,
margin=0.5,
scale=80.0,
easy_margin=False):
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super(ArcMargin, self).__init__()
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self.embedding_size = embedding_size
self.class_num = class_num
self.margin = margin
self.scale = scale
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self.easy_margin = easy_margin
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weight_attr = paddle.ParamAttr(
initializer=paddle.nn.initializer.XavierNormal())
self.fc = nn.Linear(
self.embedding_size,
self.class_num,
weight_attr=weight_attr,
bias_attr=False)
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def forward(self, input, label=None):
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input_norm = paddle.sqrt(
paddle.sum(paddle.square(input), axis=1, keepdim=True))
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input = paddle.divide(input, input_norm)
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weight = self.fc.weight
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weight_norm = paddle.sqrt(
paddle.sum(paddle.square(weight), axis=0, keepdim=True))
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weight = paddle.divide(weight, weight_norm)
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cos = paddle.matmul(input, weight)
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if not self.training or label is None:
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return cos
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sin = paddle.sqrt(1.0 - paddle.square(cos) + 1e-6)
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cos_m = math.cos(self.margin)
sin_m = math.sin(self.margin)
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phi = cos * cos_m - sin * sin_m
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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)
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one_hot = paddle.nn.functional.one_hot(label, self.class_num)
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one_hot = paddle.squeeze(one_hot, axis=[1])
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output = paddle.multiply(one_hot, phi) + paddle.multiply(
(1.0 - one_hot), cos)
output = output * self.scale
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return output
def _paddle_where_more_than(self, target, limit, x, y):
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mask = paddle.cast(x=(target > limit), dtype='float32')
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output = paddle.multiply(mask, x) + paddle.multiply((1.0 - mask), y)
return output