# Compute MMD distance using pytorch
import torch
import torch.nn as nn
class MMD_loss(nn.Module):
def __init__(self, kernel_type='rbf', kernel_mul=2.0, kernel_num=5):
super(MMD_loss, self).__init__()
self.kernel_num = kernel_num
self.kernel_mul = kernel_mul
self.fix_sigma = None
self.kernel_type = kernel_type
def guassian_kernel(self, 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)
def linear_mmd2(self, f_of_X, f_of_Y):
loss = 0.0
delta = f_of_X.float().mean(0) - f_of_Y.float().mean(0)
loss = delta.dot(delta.T)
return loss
def forward(self, source, target):
if self.kernel_type == 'linear':
return self.linear_mmd2(source, target)
elif self.kernel_type == 'rbf':
batch_size = int(source.size()[0])
kernels = self.guassian_kernel(
source, target, kernel_mul=self.kernel_mul, kernel_num=self.kernel_num, fix_sigma=self.fix_sigma)
with torch.no_grad():
XX = torch.mean(kernels[:batch_size, :batch_size])
YY = torch.mean(kernels[batch_size:, batch_size:])
XY = torch.mean(kernels[:batch_size, batch_size:])
YX = torch.mean(kernels[batch_size:, :batch_size])
loss = torch.mean(XX + YY - XY - YX)
torch.cuda.empty_cache()
return loss
