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import os
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
from torch.autograd import Variable
import torch.optim as optim
from torchvision import datasets, transforms
import numpy as np
def PGD_Revise(model, img, gt, epsilon, step_size, num_steps, device):
model.eval()
x_adv = img.detach() + torch.from_numpy(np.random.uniform(-epsilon, epsilon, img.shape)).float().to(device)
x_adv = torch.clamp(x_adv, 0.0, 1.0)
for k in range(num_steps):
x_adv.requires_grad_()
output = model(x_adv)
model.zero_grad()
with torch.enable_grad():
loss_adv = nn.CrossEntropyLoss()(output, gt)
loss_adv.backward()
eta = step_size * x_adv.grad.sign()
# Update adversarial img
x_adv = x_adv.detach() + eta
x_adv = torch.min(torch.max(x_adv, img - epsilon), img + epsilon)
x_adv = torch.clamp(x_adv, 0.0, 1.0)
if k == (num_steps//2) :
x_adv_mid = x_adv.detach().clone()
x_adv = Variable(x_adv, requires_grad=False)
x_adv_mid = Variable(x_adv_mid, requires_grad = False)
return x_adv, x_adv_mid
def PGD_Revise2(model, img, gt, epsilon, step_size, num_steps, device):
model.eval()
x_adv = img.detach() + torch.from_numpy(np.random.uniform(-epsilon, epsilon, img.shape)).float().to(device)
x_adv = torch.clamp(x_adv, 0.0, 1.0)
for k in range(num_steps):
x_adv.requires_grad_()
output = model(x_adv)
model.zero_grad()
with torch.enable_grad():
loss_adv = nn.CrossEntropyLoss()(output, gt)
loss_adv.backward()
eta = step_size * x_adv.grad.sign()
# Update adversarial img
x_adv = x_adv.detach() + eta
x_adv = torch.min(torch.max(x_adv, img - epsilon), img + epsilon)
x_adv = torch.clamp(x_adv, 0.0, 1.0)
if k == (num_steps//2 -1) :
x_adv_now = x_adv.detach().clone()
x_adv = Variable(x_adv, requires_grad=False)
x_adv_now = Variable(x_adv_now, requires_grad = False)
return x_adv_now, x_adv |