mmd.py

#!/usr/bin/env python
# encoding: utf-8

import torch
from torch.autograd import Variable

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 mmd(source, target, kernel_mul=2.0, kernel_num=5, fix_sigma=None):
    batch_size = int(source.size()[0])
    kernels = guassian_kernel(source, target,
                              kernel_mul=kernel_mul, kernel_num=kernel_num, fix_sigma=fix_sigma)
    XX = kernels[:batch_size, :batch_size]
    YY = kernels[batch_size:, batch_size:]
    XY = kernels[:batch_size, batch_size:]
    YX = kernels[batch_size:, :batch_size]
    loss = torch.mean(XX + YY - XY -YX)
   # import pdb
   # pdb.set_trace()
    return loss
#