未验证 提交 6c4de88f 编写于 作者: C cuicheng01 提交者: GitHub

Merge pull request #778 from FredHuang16/patch-2

add pairwisecosface.py
...@@ -11,6 +11,7 @@ from .msmloss import MSMLoss ...@@ -11,6 +11,7 @@ from .msmloss import MSMLoss
from .npairsloss import NpairsLoss from .npairsloss import NpairsLoss
from .trihardloss import TriHardLoss from .trihardloss import TriHardLoss
from .triplet import TripletLoss, TripletLossV2 from .triplet import TripletLoss, TripletLossV2
from .pairwisecosface import PairwiseCosface
class CombinedLoss(nn.Layer): class CombinedLoss(nn.Layer):
......
# 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
class PairwiseCosface(nn.Layer):
def __init__(self, margin, gamma):
super(PairwiseCosface, self).__init__()
self.margin = margin
self.gamma = gamma
def forward(self, embedding, targets):
if isinstance(embedding, dict):
embedding = embedding['features']
# Normalize embedding features
embedding = F.normalize(embedding, axis=1)
dist_mat = paddle.matmul(embedding, embedding, transpose_y=True)
N = dist_mat.shape[0]
is_pos = targets.reshape([N,1]).expand([N,N]).equal(paddle.t(targets.reshape([N,1]).expand([N,N]))).astype('float')
is_neg = targets.reshape([N,1]).expand([N,N]).not_equal(paddle.t(targets.reshape([N,1]).expand([N,N]))).astype('float')
# Mask scores related to itself
is_pos = is_pos - paddle.eye(N, N)
s_p = dist_mat * is_pos
s_n = dist_mat * is_neg
logit_p = -self.gamma * s_p + (-99999999.) * (1 - is_pos)
logit_n = self.gamma * (s_n + self.margin) + (-99999999.) * (1 - is_neg)
loss = F.softplus(paddle.logsumexp(logit_p, axis=1) + paddle.logsumexp(logit_n, axis=1)).mean()
return {"PairwiseCosface": loss}
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