put losses in separate files (to ease extension)
KaiyangZhou
parent
bd18a9489e
commit
366784361d
161
losses.py
161
losses.py
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@ -1,161 +0,0 @@
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from __future__ import absolute_import
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from __future__ import division
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import sys
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import torch
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from torch import nn
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def DeepSupervision(criterion, xs, y):
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"""
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Args:
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- criterion: loss function
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- xs: tuple of inputs
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- y: ground truth
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"""
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loss = 0.
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for x in xs:
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loss += criterion(x, y)
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loss /= len(xs)
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return loss
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class CrossEntropyLabelSmooth(nn.Module):
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"""Cross entropy loss with label smoothing regularizer.
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Reference:
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Szegedy et al. Rethinking the Inception Architecture for Computer Vision. CVPR 2016.
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Equation: y = (1 - epsilon) * y + epsilon / K.
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Args:
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- num_classes (int): number of classes.
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- epsilon (float): weight.
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"""
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def __init__(self, num_classes, epsilon=0.1, use_gpu=True):
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super(CrossEntropyLabelSmooth, self).__init__()
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self.num_classes = num_classes
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self.epsilon = epsilon
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self.use_gpu = use_gpu
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self.logsoftmax = nn.LogSoftmax(dim=1)
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def forward(self, inputs, targets):
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"""
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Args:
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- inputs: prediction matrix (before softmax) with shape (batch_size, num_classes)
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- targets: ground truth labels with shape (num_classes)
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"""
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log_probs = self.logsoftmax(inputs)
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targets = torch.zeros(log_probs.size()).scatter_(1, targets.unsqueeze(1).data.cpu(), 1)
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if self.use_gpu: targets = targets.cuda()
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targets = (1 - self.epsilon) * targets + self.epsilon / self.num_classes
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loss = (- targets * log_probs).mean(0).sum()
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return loss
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class TripletLoss(nn.Module):
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"""Triplet loss with hard positive/negative mining.
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Reference:
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Hermans et al. In Defense of the Triplet Loss for Person Re-Identification. arXiv:1703.07737.
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Code imported from https://github.com/Cysu/open-reid/blob/master/reid/loss/triplet.py.
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Args:
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- margin (float): margin for triplet.
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"""
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def __init__(self, margin=0.3):
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super(TripletLoss, self).__init__()
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self.margin = margin
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self.ranking_loss = nn.MarginRankingLoss(margin=margin)
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def forward(self, inputs, targets):
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"""
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Args:
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- inputs: feature matrix with shape (batch_size, feat_dim)
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- targets: ground truth labels with shape (num_classes)
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"""
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n = inputs.size(0)
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# Compute pairwise distance, replace by the official when merged
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dist = torch.pow(inputs, 2).sum(dim=1, keepdim=True).expand(n, n)
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dist = dist + dist.t()
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dist.addmm_(1, -2, inputs, inputs.t())
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dist = dist.clamp(min=1e-12).sqrt() # for numerical stability
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# For each anchor, find the hardest positive and negative
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mask = targets.expand(n, n).eq(targets.expand(n, n).t())
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dist_ap, dist_an = [], []
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for i in range(n):
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dist_ap.append(dist[i][mask[i]].max().unsqueeze(0))
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dist_an.append(dist[i][mask[i] == 0].min().unsqueeze(0))
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dist_ap = torch.cat(dist_ap)
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dist_an = torch.cat(dist_an)
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# Compute ranking hinge loss
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y = torch.ones_like(dist_an)
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loss = self.ranking_loss(dist_an, dist_ap, y)
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return loss
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class CenterLoss(nn.Module):
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"""Center loss.
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Reference:
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Wen et al. A Discriminative Feature Learning Approach for Deep Face Recognition. ECCV 2016.
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Args:
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- num_classes (int): number of classes.
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- feat_dim (int): feature dimension.
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"""
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def __init__(self, num_classes=10, feat_dim=2, use_gpu=True):
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super(CenterLoss, self).__init__()
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self.num_classes = num_classes
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self.feat_dim = feat_dim
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self.use_gpu = use_gpu
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if self.use_gpu:
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self.centers = nn.Parameter(torch.randn(self.num_classes, self.feat_dim).cuda())
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else:
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self.centers = nn.Parameter(torch.randn(self.num_classes, self.feat_dim))
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def forward(self, x, labels):
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"""
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Args:
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- x: feature matrix with shape (batch_size, feat_dim).
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- labels: ground truth labels with shape (num_classes).
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"""
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batch_size = x.size(0)
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distmat = torch.pow(x, 2).sum(dim=1, keepdim=True).expand(batch_size, self.num_classes) + \
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torch.pow(self.centers, 2).sum(dim=1, keepdim=True).expand(self.num_classes, batch_size).t()
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distmat.addmm_(1, -2, x, self.centers.t())
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classes = torch.arange(self.num_classes).long()
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if self.use_gpu: classes = classes.cuda()
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labels = labels.unsqueeze(1).expand(batch_size, self.num_classes)
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mask = labels.eq(classes.expand(batch_size, self.num_classes))
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dist = []
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for i in range(batch_size):
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value = distmat[i][mask[i]]
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value = value.clamp(min=1e-12, max=1e+12) # for numerical stability
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dist.append(value)
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dist = torch.cat(dist)
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loss = dist.mean()
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return loss
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class RingLoss(nn.Module):
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"""Ring loss.
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Reference:
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Zheng et al. Ring loss: Convex Feature Normalization for Face Recognition. CVPR 2018.
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"""
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def __init__(self):
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super(RingLoss, self).__init__()
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self.radius = nn.Parameter(torch.ones(1, dtype=torch.float))
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def forward(self, x):
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loss = ((x.norm(p=2, dim=1) - self.radius)**2).mean()
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return loss
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@ -0,0 +1,22 @@
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from .cross_entropy_loss import CrossEntropyLabelSmooth
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from .hard_mine_triplet_loss import TripletLoss
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from .center_loss import CenterLoss
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from .ring_loss import RingLoss
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def DeepSupervision(criterion, xs, y):
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"""
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Args:
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- criterion: loss function
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- xs: tuple of inputs
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- y: ground truth
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"""
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loss = 0.
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for x in xs:
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loss += criterion(x, y)
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loss /= len(xs)
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return loss
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@ -0,0 +1,53 @@
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from __future__ import absolute_import
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from __future__ import division
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import torch
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import torch.nn as nn
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class CenterLoss(nn.Module):
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"""Center loss.
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Reference:
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Wen et al. A Discriminative Feature Learning Approach for Deep Face Recognition. ECCV 2016.
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Args:
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- num_classes (int): number of classes.
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- feat_dim (int): feature dimension.
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"""
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def __init__(self, num_classes=10, feat_dim=2, use_gpu=True):
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super(CenterLoss, self).__init__()
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self.num_classes = num_classes
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self.feat_dim = feat_dim
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self.use_gpu = use_gpu
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if self.use_gpu:
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self.centers = nn.Parameter(torch.randn(self.num_classes, self.feat_dim).cuda())
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else:
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self.centers = nn.Parameter(torch.randn(self.num_classes, self.feat_dim))
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def forward(self, x, labels):
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"""
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Args:
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- x: feature matrix with shape (batch_size, feat_dim).
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- labels: ground truth labels with shape (num_classes).
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"""
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batch_size = x.size(0)
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distmat = torch.pow(x, 2).sum(dim=1, keepdim=True).expand(batch_size, self.num_classes) + \
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torch.pow(self.centers, 2).sum(dim=1, keepdim=True).expand(self.num_classes, batch_size).t()
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distmat.addmm_(1, -2, x, self.centers.t())
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classes = torch.arange(self.num_classes).long()
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if self.use_gpu: classes = classes.cuda()
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labels = labels.unsqueeze(1).expand(batch_size, self.num_classes)
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mask = labels.eq(classes.expand(batch_size, self.num_classes))
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dist = []
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for i in range(batch_size):
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value = distmat[i][mask[i]]
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value = value.clamp(min=1e-12, max=1e+12) # for numerical stability
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dist.append(value)
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dist = torch.cat(dist)
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loss = dist.mean()
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return loss
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@ -0,0 +1,37 @@
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from __future__ import absolute_import
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from __future__ import division
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import torch
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import torch.nn as nn
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class CrossEntropyLabelSmooth(nn.Module):
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"""Cross entropy loss with label smoothing regularizer.
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Reference:
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Szegedy et al. Rethinking the Inception Architecture for Computer Vision. CVPR 2016.
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Equation: y = (1 - epsilon) * y + epsilon / K.
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Args:
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- num_classes (int): number of classes.
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- epsilon (float): weight.
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"""
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def __init__(self, num_classes, epsilon=0.1, use_gpu=True):
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super(CrossEntropyLabelSmooth, self).__init__()
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self.num_classes = num_classes
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self.epsilon = epsilon
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self.use_gpu = use_gpu
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self.logsoftmax = nn.LogSoftmax(dim=1)
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def forward(self, inputs, targets):
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"""
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Args:
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- inputs: prediction matrix (before softmax) with shape (batch_size, num_classes)
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- targets: ground truth labels with shape (num_classes)
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"""
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log_probs = self.logsoftmax(inputs)
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targets = torch.zeros(log_probs.size()).scatter_(1, targets.unsqueeze(1).data.cpu(), 1)
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if self.use_gpu: targets = targets.cuda()
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targets = (1 - self.epsilon) * targets + self.epsilon / self.num_classes
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loss = (- targets * log_probs).mean(0).sum()
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return loss
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@ -0,0 +1,49 @@
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from __future__ import absolute_import
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from __future__ import division
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import torch
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import torch.nn as nn
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class TripletLoss(nn.Module):
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"""Triplet loss with hard positive/negative mining.
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Reference:
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Hermans et al. In Defense of the Triplet Loss for Person Re-Identification. arXiv:1703.07737.
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Code imported from https://github.com/Cysu/open-reid/blob/master/reid/loss/triplet.py.
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Args:
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- margin (float): margin for triplet.
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"""
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def __init__(self, margin=0.3):
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super(TripletLoss, self).__init__()
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self.margin = margin
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self.ranking_loss = nn.MarginRankingLoss(margin=margin)
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def forward(self, inputs, targets):
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"""
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Args:
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- inputs: feature matrix with shape (batch_size, feat_dim)
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- targets: ground truth labels with shape (num_classes)
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"""
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n = inputs.size(0)
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# Compute pairwise distance, replace by the official when merged
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dist = torch.pow(inputs, 2).sum(dim=1, keepdim=True).expand(n, n)
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dist = dist + dist.t()
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dist.addmm_(1, -2, inputs, inputs.t())
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dist = dist.clamp(min=1e-12).sqrt() # for numerical stability
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# For each anchor, find the hardest positive and negative
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mask = targets.expand(n, n).eq(targets.expand(n, n).t())
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dist_ap, dist_an = [], []
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for i in range(n):
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dist_ap.append(dist[i][mask[i]].max().unsqueeze(0))
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dist_an.append(dist[i][mask[i] == 0].min().unsqueeze(0))
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dist_ap = torch.cat(dist_ap)
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dist_an = torch.cat(dist_an)
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# Compute ranking hinge loss
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y = torch.ones_like(dist_an)
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loss = self.ranking_loss(dist_an, dist_ap, y)
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return loss
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@ -0,0 +1,20 @@
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from __future__ import absolute_import
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from __future__ import division
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import torch
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import torch.nn as nn
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class RingLoss(nn.Module):
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"""Ring loss.
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Reference:
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Zheng et al. Ring loss: Convex Feature Normalization for Face Recognition. CVPR 2018.
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"""
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def __init__(self):
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super(RingLoss, self).__init__()
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self.radius = nn.Parameter(torch.ones(1, dtype=torch.float))
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def forward(self, x):
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loss = ((x.norm(p=2, dim=1) - self.radius)**2).mean()
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return loss
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