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import numpy as np
from torch.autograd import Variable
import torch
import torch.nn as nn
import torch.nn.functional as F
from robust_loss.sat import ctx_noparamgrad_and_eval
def hat_loss(model, x, y, optimizer, step_size=0.007, epsilon=0.031, perturb_steps=10, beta=1.0,
attack='linf',natural_criterion= None ,h=3.5, gamma=1.0, hr_model=None):
"""
TRADES + Helper-based adversarial training.
"""
criterion_kl = nn.KLDivLoss(reduction='sum')
model.eval()
x_adv = x.detach() + 0.001 * torch.randn(x.shape).cuda().detach()
p_natural = F.softmax(model(x), dim=1)
if attack == 'l_inf':
for _ in range(perturb_steps):
x_adv.requires_grad_()
with torch.enable_grad():
loss_kl = criterion_kl(F.log_softmax(model(x_adv), dim=1), p_natural)
grad = torch.autograd.grad(loss_kl, [x_adv])[0]
x_adv = x_adv.detach() + step_size * torch.sign(grad.detach())
x_adv = torch.min(torch.max(x_adv, x - epsilon), x + epsilon)
x_adv = torch.clamp(x_adv, 0.0, 1.0)
elif attack == 'l2':
delta = 0.001 * torch.randn(x.shape).cuda().detach()
delta = Variable(delta.data, requires_grad=True)
batch_size = len(x)
optimizer_delta = torch.optim.SGD([delta], lr=step_size)
for _ in range(perturb_steps):
adv = x + delta
optimizer_delta.zero_grad()
with torch.enable_grad():
loss = (-1) * criterion_kl(F.log_softmax(model(adv), dim=1), p_natural)
loss.backward(retain_graph=True)
grad_norms = delta.grad.view(batch_size, -1).norm(p=2, dim=1)
delta.grad.div_(grad_norms.view(-1, 1, 1, 1))
if (grad_norms == 0).any():
delta.grad[grad_norms == 0] = torch.randn_like(delta.grad[grad_norms == 0])
optimizer_delta.step()
delta.data.add_(x)
delta.data.clamp_(0, 1).sub_(x)
delta.data.renorm_(p=2, dim=0, maxnorm=epsilon)
x_adv = Variable(x + delta, requires_grad=False)
else:
raise ValueError(f'Attack={attack} not supported for TRADES training!')
model.train()
x_adv = Variable(torch.clamp(x_adv, 0.0, 1.0), requires_grad=False)
x_hr = x + h * (x_adv - x)
if hr_model == None:
with ctx_noparamgrad_and_eval(model):
y_hr = model(x_adv).argmax(dim=1)
else:
with ctx_noparamgrad_and_eval(hr_model):
y_hr = hr_model(x_adv).argmax(dim=1)
optimizer.zero_grad()
out_clean, out_adv, out_help = model(x), model(x_adv), model(x_hr)
loss_clean = F.cross_entropy(out_clean, y, reduction='mean')
loss_adv = (1/len(x)) * criterion_kl(F.log_softmax(out_adv, dim=1), F.softmax(out_clean, dim=1))
loss_help = F.cross_entropy(out_help, y_hr, reduction='mean')
loss = loss_clean + beta * loss_adv + gamma * loss_help
return loss |