84 lines
3.0 KiB
Python
84 lines
3.0 KiB
Python
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from __future__ import absolute_import
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import torch
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from torch import nn
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from torch.autograd import Variable
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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 = Variable(targets, requires_grad=False)
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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())
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dist_an.append(dist[i][mask[i] == 0].min())
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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 = dist_an.data.new()
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y.resize_as_(dist_an.data)
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y.fill_(1)
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y = Variable(y)
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loss = self.ranking_loss(dist_an, dist_ap, y)
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return loss
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if __name__ == '__main__':
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pass
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