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loss.py
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loss.py
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import torch
import torch.nn as nn
class BatchHardTripLoss(nn.Module):
"""Triplet losses 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, batch_size, margin=0.3):
super(BatchHardTripLoss, 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().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 losses
y = torch.ones_like(dist_an)
loss = self.ranking_loss(dist_an, dist_ap, y)
# compute accuracy
correct = torch.ge(dist_an, dist_ap).sum().item()
return loss, correct