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loss.py
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import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
from util.distance import distance
def pairwise_loss(output, label, alpha=1.0, class_num=1.0, l_threshold=15.0):
'''https://github.com/thuml/HashNet/issues/27#issuecomment-494265209'''
bits = output.shape[1]
similarity = Variable(torch.mm(label.data.float(), label.data.float().t()) > 0).float()
dot_product = alpha * torch.mm(output, output.t()) / bits
mask_dot = dot_product.data > l_threshold
mask_exp = dot_product.data <= l_threshold
mask_positive = similarity.data > 0
mask_negative = similarity.data <= 0
mask_dp = mask_dot & mask_positive
mask_dn = mask_dot & mask_negative
mask_ep = mask_exp & mask_positive
mask_en = mask_exp & mask_negative
dot_loss = dot_product * (1-similarity)
exp_loss = torch.log(1+torch.exp(dot_product)) - similarity * dot_product
loss = (torch.sum(torch.masked_select(exp_loss, Variable(mask_ep))) + torch.sum(torch.masked_select(dot_loss, Variable(mask_dp)))) * class_num + \
torch.sum(torch.masked_select(exp_loss, Variable(mask_en))) + torch.sum(torch.masked_select(dot_loss, Variable(mask_dn)))
return loss / (torch.sum(mask_positive.float()) * class_num + torch.sum(mask_negative.float()))
def pairwise_loss_debug(output, label, alpha=5.0):
'''https://github.com/thuml/HashNet/issues/17#issuecomment-443137529'''
bits = output.shape[0]
similarity = Variable(torch.mm(label.data.float(), label.data.float().t()) > 0).float()
dot_product = alpha * torch.mm(output, output.t()) / bits
mask_positive = similarity.data > 0
mask_negative = similarity.data <= 0
#weight
S1 = torch.sum(mask_positive.float())
S0 = torch.sum(mask_negative.float())
S = S0 + S1
exp_loss = torch.log(1+torch.exp(dot_product)) - similarity * dot_product
exp_loss[similarity.data > 0] = exp_loss[similarity.data > 0] * (S / S1)
exp_loss[similarity.data <= 0] = exp_loss[similarity.data <= 0] * (S / S0)
loss = torch.sum(exp_loss) / S
return loss
def contrastive_loss(output, label, margin=16):
'''contrastive loss
- Deep Supervised Hashing for Fast Image Retrieval
'''
batch_size = output.shape[0]
S = torch.mm(label.float(), label.float().t())
dist = distance(output)
loss_1 = S * dist + (1 - S) * torch.max(margin - dist, torch.zeros_like(dist))
loss = torch.sum(loss_1) / (batch_size*(batch_size-1))
return loss
def exp_loss(output, label, alpha, balanced=False):
'''exponential loss
'''
batch_size, bit = output.shape
mask = (torch.eye(batch_size) == 0).to(torch.device("cuda"))
S = torch.mm(label.float(), label.float().t())
S_m = torch.masked_select(S, mask)
# sigmoid
D = distance(output, dist_type='cosine')
E = torch.log(1 + torch.exp(-alpha * (1-2*D)))
E_m = torch.masked_select(E, mask)
loss_1 = 10 * S_m * E_m + (1 - S_m) * (E_m - torch.log((torch.exp(E_m) - 1).clamp(1e-6)))
if balanced:
S_all = batch_size * (batch_size - 1)
S_1 = torch.sum(S)
balance_param = (S_all / S_1) * S + (1 - S)
B_m= torch.masked_select(balance_param, mask)
loss_1 = B_m * loss_1
loss = torch.mean(loss_1)
return loss
def quantization_loss(output):
loss = torch.mean((torch.abs(output) - 1) ** 2)
return loss