#!/usr/bin/env python
# encoding: utf-8
import torch
import numpy as np
from torch.autograd import Variable
min_var_est = 1e-8
def guassian_kernel(source, target, kernel_mul=2.0, kernel_num=5, fix_sigma=None):
n_samples = int(source.size()[0])+int(target.size()[0])
total = torch.cat([source, target], dim=0)
total0 = total.unsqueeze(0).expand(int(total.size(0)), int(total.size(0)), int(total.size(1)))
total1 = total.unsqueeze(1).expand(int(total.size(0)), int(total.size(0)), int(total.size(1)))
L2_distance = ((total0-total1)**2).sum(2)
if fix_sigma:
bandwidth = fix_sigma
else:
bandwidth = torch.sum(L2_distance.data) / (n_samples**2-n_samples)
bandwidth /= kernel_mul ** (kernel_num // 2)
bandwidth_list = [bandwidth * (kernel_mul**i) for i in range(kernel_num)]
kernel_val = [torch.exp(-L2_distance / bandwidth_temp) for bandwidth_temp in bandwidth_list]
return sum(kernel_val)#/len(kernel_val)
def cmmd(source, target, s_label, t_label, kernel_mul=2.0, kernel_num=5, fix_sigma=None):
s_label = s_label.cpu()
s_label = s_label.view(32,1)
s_label = torch.zeros(32, 31).scatter_(1, s_label.data, 1)
s_label = Variable(s_label).cuda()
t_label = t_label.cpu()
t_label = t_label.view(32, 1)
t_label = torch.zeros(32, 31).scatter_(1, t_label.data, 1)
t_label = Variable(t_label).cuda()
batch_size = int(source.size()[0])
kernels = guassian_kernel(source, target,
kernel_mul=kernel_mul, kernel_num=kernel_num, fix_sigma=fix_sigma)
loss = 0
XX = kernels[:batch_size, :batch_size]
YY = kernels[batch_size:, batch_size:]
XY = kernels[:batch_size, batch_size:]
loss += torch.mean(torch.mm(s_label, torch.transpose(s_label, 0, 1)) * XX +
torch.mm(t_label, torch.transpose(t_label, 0, 1)) * YY -
2 * torch.mm(s_label, torch.transpose(t_label, 0, 1)) * XY)
return loss
