def load_net(attack_model, filename, path):
if(attack_model == "CNN"):
from deeprobust.image.netmodels.CNN import Net
model = Net()
if(attack_model == "ResNet18"):
import deeprobust.image.netmodels.resnet as Net
model = Net.ResNet18()
model.load_state_dict(torch.load(path + filename))
model.eval()
return model
import numpy as np
import torch
import torch.nn as nn
from torchvision import datasets, models, transforms
from deeprobust.image.attack.Nattack import NATTACK
from deeprobust.image.netmodels.CNN import Net
#initialize model
model = Net()
model.load_state_dict(
torch.load("defense_model/mnist_pgdtraining_0.3.pt",
map_location=torch.device('cuda')))
model.eval()
print("----------model_parameters-----------")
for names, parameters in model.named_parameters():
print(names, ',', parameters.type())
print("-------------------------------------")
data_loader = torch.utils.data.DataLoader(datasets.MNIST(
'deeprobust/image/data',
train=True,
download=True,
transform=transforms.Compose([transforms.ToTensor()])),
batch_size=1,
shuffle=True)
attack = NATTACK(model)
attack.generate(dataloader=data_loader, classnum=10)