TVLoss.py

import torch
import torch.nn as nn
from torch.autograd import Variable

class TVLoss(nn.Module):
    def __init__(self,TVLoss_weight=1):
        super(TVLoss,self).__init__()
        self.TVLoss_weight = TVLoss_weight

    def forward(self,x):
        batch_size = x.size()[0]
        h_x = x.size()[2]
        w_x = x.size()[3]
        count_h = self._tensor_size(x[:,:,1:,:])
        count_w = self._tensor_size(x[:,:,:,1:])
        h_tv = torch.pow((x[:,:,1:,:]-x[:,:,:h_x-1,:]),2).sum()
        w_tv = torch.pow((x[:,:,:,1:]-x[:,:,:,:w_x-1]),2).sum()
        return self.TVLoss_weight*2*(h_tv/count_h+w_tv/count_w)/batch_size

    def _tensor_size(self,t):
        return t.size()[1]*t.size()[2]*t.size()[3]

def main():
    # x = Variable(torch.FloatTensor([[[1,2],[2,3]],[[1,2],[2,3]]]).view(1,2,2,2), requires_grad=True)
    # x = Variable(torch.FloatTensor([[[3,1],[4,3]],[[3,1],[4,3]]]).view(1,2,2,2), requires_grad=True)
    # x = Variable(torch.FloatTensor([[[1,1,1], [2,2,2],[3,3,3]],[[1,1,1], [2,2,2],[3,3,3]]]).view(1, 2, 3, 3), requires_grad=True)
    x = Variable(torch.FloatTensor([[[1, 2, 3], [2, 3, 4], [3, 4, 5]], [[1, 2, 3], [2, 3, 4], [3, 4, 5]]]).view(1, 2, 3, 3),requires_grad=True)
    addition = TVLoss()
    z = addition(x)
    print x
    print z.data
    z.backward()
    print x.grad


if __name__ == '__main__':
    main()