from __future__ import absolute_import
from aligned.local_dist import *
import torch
from torch import nn
"""
Shorthands for loss:
- CrossEntropyLabelSmooth: xent
- TripletLoss: htri
- CenterLoss: cent
"""
__all__ = ['DeepSupervision', 'CrossEntropyLoss','CrossEntropyLabelSmooth', 'TripletLoss', 'CenterLoss', 'RingLoss']
def DeepSupervision(criterion, xs, y):
"""
Args:
criterion: loss function
xs: tuple of inputs
y: ground truth
"""
loss = 0.
for x in xs:
loss += criterion(x, y)
return loss
class CrossEntropyLoss(nn.Module):
"""Cross entropy loss.
"""
def __init__(self, use_gpu=True):
super(CrossEntropyLoss, self).__init__()
self.use_gpu = use_gpu
self.crossentropy_loss = nn.CrossEntropyLoss()
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)
"""
if self.use_gpu: targets = targets.cuda()
loss = self.crossentropy_loss(inputs, targets)
return loss
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 = (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, mutual_flag = False):
super(TripletLoss, self).__init__()
self.margin = margin
self.ranking_loss = nn.MarginRankingLoss(margin=margin)
self.mutual = mutual_flag
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)
# inputs = 1. * inputs / (torch.norm(inputs, 2, dim=-1, keepdim=True).expand_as(inputs) + 1e-12)
# 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().unsqueeze(0))
dist_an.append(dist[i][mask[i] == 0].min().unsqueeze(0))
dist_ap = torch.cat(dist_ap)
dist_an = torch.cat(dist_an)
# Compute ranking hinge loss
y = torch.ones_like(dist_an)
loss = self.ranking_loss(dist_an, dist_ap, y)
if self.mutual:
return loss, dist
return loss
class TripletLossAlignedReID(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, mutual_flag = False):
super(TripletLossAlignedReID, self).__init__()
self.margin = margin
self.ranking_loss = nn.MarginRankingLoss(margin=margin)
self.ranking_loss_local = nn.MarginRankingLoss(margin=margin)
self.mutual = mutual_flag
def forward(self, inputs, targets, local_features):
"""
Args:
inputs: feature matrix with shape (batch_size, feat_dim)
targets: ground truth labels with shape (num_classes)
"""
n = inputs.size(0)
#inputs = 1. * inputs / (torch.norm(inputs, 2, dim=-1, keepdim=True).expand_as(inputs) + 1e-12)
# 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
dist_ap,dist_an,p_inds,n_inds = hard_example_mining(dist,targets,return_inds=True)
local_features = local_features.permute(0,2,1)
p_local_features = local_features[p_inds]
n_local_features = local_features[n_inds]
local_dist_ap = batch_local_dist(local_features, p_local_features)
local_dist_an = batch_local_dist(local_features, n_local_features)
# Compute ranking hinge loss
y = torch.ones_like(dist_an)
global_loss = self.ranking_loss(dist_an, dist_ap, y)
local_loss = self.ranking_loss_local(local_dist_an,local_dist_ap, y)
if self.mutual:
return global_loss+local_loss,dist
return global_loss,local_loss
class CenterLoss(nn.Module):
"""Center loss.
Reference:
Wen et al. A Discriminative Feature Learning Approach for Deep Face Recognition. ECCV 2016.
Args:
num_classes (int): number of classes.
feat_dim (int): feature dimension.
"""
def __init__(self, num_classes=10, feat_dim=2, use_gpu=True):
super(CenterLoss, self).__init__()
self.num_classes = num_classes
self.feat_dim = feat_dim
self.use_gpu = use_gpu
if self.use_gpu:
self.centers = nn.Parameter(torch.randn(self.num_classes, self.feat_dim).cuda())
else:
self.centers = nn.Parameter(torch.randn(self.num_classes, self.feat_dim))
def forward(self, x, labels):
"""
Args:
x: feature matrix with shape (batch_size, feat_dim).
labels: ground truth labels with shape (num_classes).
"""
batch_size = x.size(0)
distmat = torch.pow(x, 2).sum(dim=1, keepdim=True).expand(batch_size, self.num_classes) + \
torch.pow(self.centers, 2).sum(dim=1, keepdim=True).expand(self.num_classes, batch_size).t()
distmat.addmm_(1, -2, x, self.centers.t())
classes = torch.arange(self.num_classes).long()
if self.use_gpu: classes = classes.cuda()
labels = labels.unsqueeze(1).expand(batch_size, self.num_classes)
mask = labels.eq(classes.expand(batch_size, self.num_classes))
dist = []
for i in range(batch_size):
value = distmat[i][mask[i]]
value = value.clamp(min=1e-12, max=1e+12) # for numerical stability
dist.append(value)
dist = torch.cat(dist)
loss = dist.mean()
return loss
class RingLoss(nn.Module):
"""Ring loss.
Reference:
Zheng et al. Ring loss: Convex Feature Normalization for Face Recognition. CVPR 2018.
"""
def __init__(self, weight_ring=1.):
super(RingLoss, self).__init__()
self.radius = nn.Parameter(torch.ones(1, dtype=torch.float))
self.weight_ring = weight_ring
def forward(self, x):
l = ((x.norm(p=2, dim=1) - self.radius)**2).mean()
return l * self.weight_ring
class KLMutualLoss(nn.Module):
def __init__(self):
super(KLMutualLoss,self).__init__()
self.kl_loss = nn.KLDivLoss(size_average=False)
self.log_softmax = nn.functional.log_softmax
self.softmax = nn.functional.softmax
def forward(self, pred1, pred2):
pred1 = self.log_softmax(pred1, dim=1)
pred2 = self.softmax(pred2, dim=1)
#loss = self.kl_loss(pred1, torch.autograd.Variable(pred2.data))
loss = self.kl_loss(pred1, pred2.detach())
# from IPython import embed
# embed()
#print(loss)
return loss
class MetricMutualLoss(nn.Module):
def __init__(self):
super(MetricMutualLoss, self).__init__()
self.l2_loss = nn.MSELoss()
def forward(self, dist1, dist2,pids):
loss = self.l2_loss(dist1, dist2)
# from IPython import embed
# embed()
print(loss)
return loss
if __name__ == '__main__':
pass
