56 lines
1.9 KiB
Python
56 lines
1.9 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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import warnings
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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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warnings.warn('This method is deprecated')
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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 |