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- # Copyright (c) 2018 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.
- from __future__ import absolute_import
- from __future__ import division
- from __future__ import print_function
- import math
- import paddle
- import numpy as np
- from .comfunc import rerange_index
- class EmlLoss(paddle.nn.Layer):
- def __init__(self, batch_size=40, samples_each_class=2):
- super(EmlLoss, self).__init__()
- assert (batch_size % samples_each_class == 0)
- self.samples_each_class = samples_each_class
- self.batch_size = batch_size
- self.rerange_index = rerange_index(batch_size, samples_each_class)
- self.thresh = 20.0
- self.beta = 100000
- def surrogate_function(self, beta, theta, bias):
- x = theta * paddle.exp(bias)
- output = paddle.log(1 + beta * x) / math.log(1 + beta)
- return output
- def surrogate_function_approximate(self, beta, theta, bias):
- output = (
- paddle.log(theta) + bias + math.log(beta)) / math.log(1 + beta)
- return output
- def surrogate_function_stable(self, beta, theta, target, thresh):
- max_gap = paddle.to_tensor(thresh, dtype='float32')
- max_gap.stop_gradient = True
- target_max = paddle.maximum(target, max_gap)
- target_min = paddle.minimum(target, max_gap)
- loss1 = self.surrogate_function(beta, theta, target_min)
- loss2 = self.surrogate_function_approximate(beta, theta, target_max)
- bias = self.surrogate_function(beta, theta, max_gap)
- loss = loss1 + loss2 - bias
- return loss
- def forward(self, input, target=None):
- features = input["features"]
- samples_each_class = self.samples_each_class
- batch_size = self.batch_size
- rerange_index = self.rerange_index
- #calc distance
- diffs = paddle.unsqueeze(
- features, axis=1) - paddle.unsqueeze(
- features, axis=0)
- similary_matrix = paddle.sum(paddle.square(diffs), axis=-1)
- tmp = paddle.reshape(similary_matrix, shape=[-1, 1])
- rerange_index = paddle.to_tensor(rerange_index)
- tmp = paddle.gather(tmp, index=rerange_index)
- similary_matrix = paddle.reshape(tmp, shape=[-1, batch_size])
- ignore, pos, neg = paddle.split(
- similary_matrix,
- num_or_sections=[
- 1, samples_each_class - 1, batch_size - samples_each_class
- ],
- axis=1)
- ignore.stop_gradient = True
- pos_max = paddle.max(pos, axis=1, keepdim=True)
- pos = paddle.exp(pos - pos_max)
- pos_mean = paddle.mean(pos, axis=1, keepdim=True)
- neg_min = paddle.min(neg, axis=1, keepdim=True)
- neg = paddle.exp(neg_min - neg)
- neg_mean = paddle.mean(neg, axis=1, keepdim=True)
- bias = pos_max - neg_min
- theta = paddle.multiply(neg_mean, pos_mean)
- loss = self.surrogate_function_stable(self.beta, theta, bias,
- self.thresh)
- loss = paddle.mean(loss)
- return {"emlloss": loss}
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