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triplet_loss.py
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triplet_loss.py
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from torch import nn
import torch
from torch.autograd import Variable
class TripletLoss(object):
def __init__(self, margin=0.3):
self.margin = margin
if margin is not None:
self.ranking_loss = nn.MarginRankingLoss(margin=margin)
else:
self.ranking_loss = nn.SoftMarginLoss()
def __call__(self, dist_ap, dist_an):
y = Variable(dist_an.data.new().resize_as_(dist_an.data).fill_(1))
if self.margin is not None:
loss = self.ranking_loss(dist_an, dist_ap, y)
else:
loss = self.ranking_loss(dist_an - dist_ap, y)
return loss
def euclidean_dist(x, y):
m, n = x.size(0), y.size(0)
xx = torch.pow(x, 2).sum(1, keepdim=True).expand(m, n)
yy = torch.pow(y, 2).sum(1, keepdim=True).expand(n, m).t()
dist = xx + yy
dist.addmm_(1, -2, x, y.t())
dist = dist.clamp(min=1e-12).sqrt() # for numerical stability
return dist
def get_dist(imgs, labels):
dist_mat = euclidean_dist(imgs, imgs)
assert len(dist_mat.size()) == 2
assert dist_mat.size(0) == dist_mat.size(1)
N = dist_mat.size(0)
is_pos = labels.expand(N, N).eq(labels.expand(N, N).t())
is_neg = labels.expand(N, N).ne(labels.expand(N, N).t())
pr3 = dist_mat[is_pos].contiguous()
pr4 = dist_mat[is_neg].contiguous()
if(len(pr3) % N != 0):
rem = len(pr3) % N
pr3 = pr3[0:(len(pr3)-rem)]
if (len(pr4) % N != 0):
rem = len(pr4) % N
pr4 = pr4[0:(len(pr4) - rem)]
dist_ap, relative_p_inds = torch.max(
pr3.view(N, -1), 1, keepdim=True)
dist_an, relative_n_inds = torch.min(
pr4.view(N, -1), 1, keepdim=True)
# shape [N]
dist_ap = dist_ap.squeeze(1)
dist_an = dist_an.squeeze(1)
return dist_ap, dist_an
def local_dist(x, y):
M, m, d = x.size()
N, n, d = y.size()
x = x.contiguous().view(M * m, d)
y = y.contiguous().view(N * n, d)
# shape [M * m, N * n]
dist_mat = euclidean_dist(x, y)
dist_mat = (torch.exp(dist_mat) - 1.) / (torch.exp(dist_mat) + 1.)
# shape [M * m, N * n] -> [M, m, N, n] -> [m, n, M, N]
dist_mat = dist_mat.contiguous().view(M, m, N, n).permute(1, 3, 0, 2)
# shape [M, N]
dist_mat = shortest_dist(dist_mat)
return dist_mat
def shortest_dist(dist_mat):
m, n = dist_mat.size()[:2]
# Just offering some reference for accessing intermediate distance.
dist = [[0 for _ in range(n)] for _ in range(m)]
for i in range(m):
for j in range(n):
if (i == 0) and (j == 0):
dist[i][j] = dist_mat[i, j]
elif (i == 0) and (j > 0):
dist[i][j] = dist[i][j - 1] + dist_mat[i, j]
elif (i > 0) and (j == 0):
dist[i][j] = dist[i - 1][j] + dist_mat[i, j]
else:
dist[i][j] = torch.min(dist[i - 1][j], dist[i][j - 1]) + dist_mat[i, j]
dist = dist[-1][-1]
return dist
def get_dist_local(local_feat,labels):
dist_mat = local_dist(local_feat, local_feat)
assert len(dist_mat.size()) == 2
assert dist_mat.size(0) == dist_mat.size(1)
N = dist_mat.size(0)
is_pos = labels.expand(N, N).eq(labels.expand(N, N).t())
is_neg = labels.expand(N, N).ne(labels.expand(N, N).t())
pr3 = dist_mat[is_pos].contiguous()
pr4 = dist_mat[is_neg].contiguous()
if len(pr3) % N != 0:
rem = len(pr3) % N
pr3 = pr3[0:(len(pr3) - rem)]
if len(pr4) % N != 0:
rem = len(pr4) % N
pr4 = pr4[0:(len(pr4) - rem)]
dist_ap, relative_p_inds = torch.max(
pr3.view(N, -1), 1, keepdim=True)
dist_an, relative_n_inds = torch.min(
pr4.view(N, -1), 1, keepdim=True)
# shape [N]
dist_ap = dist_ap.squeeze(1)
dist_an = dist_an.squeeze(1)
return dist_ap, dist_an