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""" |
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This implementation is used to create adversarial dataset. |
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""" |
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import numpy as np |
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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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import torch.optim as optim |
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from torchvision import datasets,models,transforms |
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from PIL import Image |
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from deeprobust.image.attack.pgd import PGD |
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import deeprobust.image.netmodels.resnet as resnet |
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import deeprobust.image.netmodels.CNN as CNN |
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from deeprobust.image.config import attack_params |
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import matplotlib.pyplot as plt |
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from deeprobust.image.config import attack_params |
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def main(args): |
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model = resnet.ResNet18().to('cuda') |
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print("Load network") |
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model.load_state_dict(torch.load("~/Documents/deeprobust_model/cifar_res18_120.pt")) |
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model.eval() |
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transform_val = transforms.Compose([ |
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transforms.ToTensor(), |
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]) |
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train_loader = torch.utils.data.DataLoader( |
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datasets.MNIST('deeprobust/image/defense/data', train=True, download=True, |
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transform=transforms.Compose([transforms.ToTensor()])), |
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batch_size=128, |
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shuffle=True) |
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test_loader = torch.utils.data.DataLoader( |
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datasets.CIFAR10('deeprobust/image/data', train = False, download=True, |
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transform = transform_val), |
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batch_size = 128, shuffle=True) |
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normal_data, adv_data = None, None |
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adversary = PGD(model) |
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for x, y in train_loader: |
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x, y = x.cuda(), t.cuda() |
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y_pred = model(x) |
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train_acc += accuracy(y_pred, y) |
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x_adv = adversary.generate(x, y, **attack_params['PGD_CIFAR10']).float() |
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y_adv = model(x_adv) |
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adv_acc += accuracy(y_adv, y) |
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train_n += y.size(0) |
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x, x_adv = x.data, x_adv.data |
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if normal_data is None: |
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normal_data, adv_data = x, x_adv |
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else: |
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normal_data = torch.cat((normal_data, x)) |
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adv_data = torch.cat((adv_data, x_adv)) |
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print("Accuracy(normal) {:.6f}, Accuracy(FGSM) {:.6f}".format(train_acc / train_n * 100, adv_acc / train_n * 100)) |
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torch.save({"normal": normal_data, "adv": adv_data}, "data.tar") |
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torch.save({"state_dict": model.state_dict()}, "cnn.tar") |
