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multi_similarity_loss.py
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multi_similarity_loss.py
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# Copyright (c) Malong Technologies Co., Ltd.
# All rights reserved.
#
# Contact: github@malong.com
#
# This source code is licensed under the LICENSE file in the root directory of this source tree.
import torch
from torch import nn
from ret_benchmark.losses.registry import LOSS
@LOSS.register('ms_loss')
class MultiSimilarityLoss(nn.Module):
def __init__(self, cfg):
super(MultiSimilarityLoss, self).__init__()
self.thresh = 0.5
self.margin = 0.1
self.scale_pos = cfg.LOSSES.MULTI_SIMILARITY_LOSS.SCALE_POS
self.scale_neg = cfg.LOSSES.MULTI_SIMILARITY_LOSS.SCALE_NEG
def forward(self, feats, labels):
assert feats.size(0) == labels.size(0), \
f"feats.size(0): {feats.size(0)} is not equal to labels.size(0): {labels.size(0)}"
batch_size = feats.size(0)
sim_mat = torch.matmul(feats, torch.t(feats))
epsilon = 1e-5
loss = list()
for i in range(batch_size):
pos_pair_ = sim_mat[i][labels == labels[i]]
pos_pair_ = pos_pair_[pos_pair_ < 1 - epsilon]
neg_pair_ = sim_mat[i][labels != labels[i]]
neg_pair = neg_pair_[neg_pair_ + self.margin > min(pos_pair_)]
pos_pair = pos_pair_[pos_pair_ - self.margin < max(neg_pair_)]
if len(neg_pair) < 1 or len(pos_pair) < 1:
continue
# weighting step
pos_loss = 1.0 / self.scale_pos * torch.log(
1 + torch.sum(torch.exp(-self.scale_pos * (pos_pair - self.thresh))))
neg_loss = 1.0 / self.scale_neg * torch.log(
1 + torch.sum(torch.exp(self.scale_neg * (neg_pair - self.thresh))))
loss.append(pos_loss + neg_loss)
if len(loss) == 0:
return torch.zeros([], requires_grad=True)
loss = sum(loss) / batch_size
return loss