from __future__ import absolute_import
import torch
from torch import nn
from torch.autograd import Variable
"""
Shorthands for loss:
- CrossEntropyLabelSmooth: xent
- TripletLoss: htri
- CenterLoss: cent
"""
__all__ = ['CrossEntropyLabelSmooth', 'TripletLoss']
class CrossEntropyLabelSmooth(nn.Module):
"""Cross entropy loss with label smoothing regularizer.
Reference:
Szegedy et al. Rethinking the Inception Architecture for Computer Vision. CVPR 2016.
Equation: y = (1 - epsilon) * y + epsilon / K.
Args:
num_classes (int): number of classes.
epsilon (float): weight.
"""
def __init__(self, num_classes, epsilon=0.1, use_gpu=True):
super(CrossEntropyLabelSmooth, self).__init__()
self.num_classes = num_classes
self.epsilon = epsilon
self.use_gpu = use_gpu
self.logsoftmax = nn.LogSoftmax(dim=1)
def forward(self, inputs, targets):
"""
Args:
inputs: prediction matrix (before softmax) with shape (batch_size, num_classes)
targets: ground truth labels with shape (num_classes)
"""
log_probs = self.logsoftmax(inputs)
targets = torch.zeros(log_probs.size()).scatter_(
1, targets.unsqueeze(1).data.cpu(), 1)
if self.use_gpu:
targets = targets.cuda()
targets = Variable(targets, requires_grad=False)
targets = (1 - self.epsilon) * targets + \
self.epsilon / self.num_classes
loss = (- targets * log_probs).mean(0).sum()
return loss
class TripletLoss(nn.Module):
"""Triplet loss with hard positive/negative mining.
Reference:
Hermans et al. In Defense of the Triplet Loss for Person Re-Identification. arXiv:1703.07737.
Code imported from https://github.com/Cysu/open-reid/blob/master/reid/loss/triplet.py.
Args:
margin (float): margin for triplet.
"""
def __init__(self, margin=0.3):
super(TripletLoss, self).__init__()
self.margin = margin
self.ranking_loss = nn.MarginRankingLoss(margin=margin)
def forward(self, inputs, targets):
"""
Args:
inputs: feature matrix with shape (batch_size, feat_dim)
targets: ground truth labels with shape (num_classes)
"""
n = inputs.size(0)
# Compute pairwise distance, replace by the official when merged
dist = torch.pow(inputs, 2).sum(dim=1, keepdim=True).expand(n, n)
dist = dist + dist.t()
dist.addmm_(1, -2, inputs, inputs.t())
dist = dist.clamp(min=1e-12).sqrt() # for numerical stability
# For each anchor, find the hardest positive and negative
mask = targets.expand(n, n).eq(targets.expand(n, n).t())
dist_ap, dist_an = [], []
for i in range(n):
dist_ap.append(dist[i][mask[i]].max())
dist_an.append(dist[i][mask[i] == 0].min())
dist_ap = torch.stack(dist_ap)
dist_an = torch.stack(dist_an)
# Compute ranking hinge loss
y = dist_an.data.new()
y.resize_as_(dist_an.data)
y.fill_(1)
y = Variable(y)
loss = self.ranking_loss(dist_an, dist_ap, y)
return loss
if __name__ == '__main__':
pass
