Pytorch-PFE

[Ontheway361]

#!/usr/bin/env python3 #-- coding:utf-8 -- """ Created on 2020/04/23 author: lujie """ import torch import torch.nn as nn import torch.nn.functional as F from IPython import embed

class MLSLoss(nn.Module):

def __init__(self, mean = False):

    super(MLSLoss, self).__init__()
    self.mean = mean

def negMLS(self, mu_X, sigma_sq_X):

    if self.mean:
        XX = torch.mul(mu_X, mu_X).sum(dim=1, keepdim=True)
        YY = torch.mul(mu_X.T, mu_X.T).sum(dim=0, keepdim=True)
        XY = torch.mm(mu_X, mu_X.T)
        mu_diff = XX + YY - 2 * XY
        sig_sum = sigma_sq_X.mean(dim=1, keepdim=True) + sigma_sq_X.T.sum(dim=0, keepdim=True)
        diff    = mu_diff / (1e-8 + sig_sum) + mu_X.size(1) * torch.log(sig_sum)
        return diff
    else:
        mu_diff = mu_X.unsqueeze(1) - mu_X.unsqueeze(0)
        sig_sum = sigma_sq_X.unsqueeze(1) + sigma_sq_X.unsqueeze(0)
        diff    = torch.mul(mu_diff, mu_diff) / (1e-10 + sig_sum) + torch.log(sig_sum)
        diff    = diff.sum(dim=2, keepdim=False)
        return diff

def forward(self, mu_X, log_sigma_sq, gty):
    
    # mu_X     = F.normalize(mu_X) # TODO
    non_diag_mask = (1 - torch.eye(mu_X.size(0))).int()
    if gty.device.type == 'cuda':
        non_diag_mask = non_diag_mask.cuda(0)      
    sig_X    = torch.exp(log_sigma_sq)
    loss_mat = self.negMLS(mu_X, sig_X)
    gty_mask = (torch.eq(gty[:, None], gty[None, :])).int()
    pos_mask = (non_diag_mask * gty_mask) > 0
    pos_loss = loss_mat[pos_mask].mean()
    return pos_loss

if name == "main":

mls = MLSLoss(mean=False)
gty = torch.Tensor([1, 2, 3, 2, 3, 3, 2])
muX = torch.randn((7, 3))
siX = torch.rand((7,3))
diff = mls(gty, muX, siX)
print(diff)

this is my MLSLoss, is the anything wrong with MLSLoss ?