robust_loss/mart.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from utils.criterion import CrossEntropyWithLabelSmooth


def mart_loss(model, x_natural, y, optimizer, step_size=0.007, epsilon=0.031, perturb_steps=10, beta=6.0, 
              attack='l_inf',natural_criterion= nn.CrossEntropyLoss()):
    """
    MART training (Wang et al, 2020).
    """
  
    kl = nn.KLDivLoss(reduction='none')
    model.eval()
    batch_size = len(x_natural)
    
    # generate adversarial example
    x_adv = x_natural.detach() + 0.001 * torch.randn(x_natural.shape).cuda().detach()
    if attack == 'l_inf':
        for _ in range(perturb_steps):
            x_adv.requires_grad_()
            with torch.enable_grad():
                loss_ce = natural_criterion(model(x_adv), y)
            grad = torch.autograd.grad(loss_ce, [x_adv])[0]
            x_adv = x_adv.detach() + step_size * torch.sign(grad.detach())
            x_adv = torch.min(torch.max(x_adv, x_natural - epsilon), x_natural + epsilon)
            x_adv = torch.clamp(x_adv, 0.0, 1.0)
    else:
        raise ValueError(f'Attack={attack} not supported for MART training!')
    model.train()
    
    x_adv = Variable(torch.clamp(x_adv, 0.0, 1.0), requires_grad=False)
    # zero gradient
    optimizer.zero_grad()

    logits = model(x_natural)
    logits_adv = model(x_adv)
    
    adv_probs = F.softmax(logits_adv, dim=1)
    tmp1 = torch.argsort(adv_probs, dim=1)[:, -2:]
    new_y = torch.where(tmp1[:, -1] == y, tmp1[:, -2], tmp1[:, -1])
    loss_adv = natural_criterion(logits_adv, y) + F.nll_loss(torch.log(1.0001 - adv_probs + 1e-12), new_y)

    nat_probs = F.softmax(logits, dim=1)
    true_probs = torch.gather(nat_probs, 1, (y.unsqueeze(1)).long()).squeeze()

    loss_robust = (1.0 / batch_size) * torch.sum(
        torch.sum(kl(torch.log(adv_probs + 1e-12), nat_probs), dim=1) * (1.0000001 - true_probs))
    loss = loss_adv + float(beta) * loss_robust


        
    return loss