batch-all loss

[liangbh6]

Hi, I try to implement the batch-all loss with this framework, modified from triplet.py, like: `from future import absolute_import

import torch from torch import nn from torch.autograd import Variable

class Improved_TripletLoss(nn.Module): def init(self, margin=0, num_instances=4): super(Improved_TripletLoss, self).init() self.margin = margin self.num_instances = num_instances self.ranking_loss = nn.MarginRankingLoss(margin=margin)

def forward(self, inputs, targets):
    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 its positives and negatives
    mask = targets.expand(n, n).eq(targets.expand(n, n).t())
    # to exclude the diagonal elements
    mask = mask + Variable(torch.eye(n)).type("torch.cuda.ByteTensor")
    dist_ap, dist_an = [], []

    indices = torch.nonzero((mask == 1).data)
    for l in range(indices.size(0)):
        dist_an.append(dist[indices[l, 0], ][mask[indices[l, 0], ] == 0])
        dist_an.append(dist[indices[l, 1], ][mask[indices[l, 1], ] == 0])
        dist_ap.append(dist[indices[l, 0], indices[l, 1]].repeat(2 * (n - self.num_instances), ))

    dist_ap = torch.cat(dist_ap)
    dist_an = torch.cat(dist_an)

    # Compute ranking hinge loss
    y = dist_an.data.new()
    y.resize_as_(dist_an.data)
    y.fill_(1)
    y = Variable(y)
    loss = self.ranking_loss(dist_an, dist_ap, y) / 2
    prec = (dist_an.data > dist_ap.data).sum() * 1. / y.size(0)
    return loss, prec`

but I got rank1 accuracy around 60% on Market1501 dataset, worse than softmax loss. I can't figure out why, is there anyone who do same try?