3f277c18dee8a6470cdda8a397ce71f56dabdc9e,Losses.py,,loss_HardNet,#,15

Before Change

    elif batch_reduce == "average":
        min_neg = torch.mean(dist_without_min_on_diag,1)[0]
        if anchor_swap:
            min_neg2 = torch.t(torch.mean(dist_without_min_on_diag,0)[0])
            min_neg = torch.min(min_neg,min_neg2)
        min_neg = torch.t(min_neg).squeeze(0)
    elif batch_reduce == "random":
        idxs = torch.randperm(anchor.size()[0]).long()
        min_neg = dist_without_min_on_diag.gather(1,idxs.view(-1,1))
        if anchor_swap:
            min_neg2 = torch.t(dist_without_min_on_diag.gather(0,idxs.view(-1,1))) 
            min_neg = torch.min(min_neg,min_neg2)
        min_neg = torch.t(min_neg).squeeze(0)
    else: 
        print ("Unknown batch reduce mode. Try min, average or random")
        sys.exit(1)

    if loss_type == "triplet_margin":
        loss = torch.clamp(margin + pos - min_neg, min=0.0)
    elif loss_type == "softmax":
        exp_pos = torch.exp(2.0 - pos);
        exp_den = exp_pos + torch.exp(2.0 - min_neg) + eps;
        loss = - torch.log( exp_pos / exp_den )
    elif loss_type == "contrastive":
        loss = torch.clamp(margin - min_neg, min=0.0) + pos;
    else: 

After Change

    elif batch_reduce == "average":
        min_neg = torch.mean(dist_without_min_on_diag,1)
        if anchor_swap:
            min_neg2 = torch.t(torch.mean(dist_without_min_on_diag,0))
            min_neg = torch.min(min_neg,min_neg2)
        min_neg = torch.t(min_neg).squeeze(0)
    elif batch_reduce == "random":
        idxs = torch.autograd.Variable(torch.randperm(anchor.size()[0]).long()).cuda()
        min_neg = dist_without_min_on_diag.gather(1,idxs.view(-1,1))
        if anchor_swap:
            min_neg2 = torch.t(dist_without_min_on_diag).gather(1,idxs.view(-1,1)) 
            min_neg = torch.min(min_neg,min_neg2)
        min_neg = torch.t(min_neg).squeeze(0)
    else: 
        print ("Unknown batch reduce mode. Try min, average or random")
        sys.exit(1)

    if loss_type == "triplet_margin":
        loss = torch.clamp(margin + pos - min_neg, min=0.0)
    elif loss_type == "softmax":
        exp_pos = torch.exp(2.0 - pos);
        exp_den = exp_pos + torch.exp(2.0 - min_neg) + eps;
        loss = - torch.log( exp_pos / exp_den )
    elif loss_type == "contrastive":
        loss = torch.clamp(margin - min_neg, min=0.0) + pos;
    else: 

Instances