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utils.py
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utils.py
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import torch
import numpy as np
import torchvision
import copy
import torch.nn as nn
import torch.nn.functional as F
import os
from PIL import Image
from torchvision import transforms
from generate_poisoned_dataset.TinyImageNet_load import TinyImageNet_load
from torchtoolbox.transform import Cutout
from torchvision.datasets import CIFAR10, CIFAR100, ImageFolder
class CIFAR10PoisonIndex(CIFAR10):
def __init__(self, delta: torch.FloatTensor = None, ratio=1.0, **kwargs):
super(CIFAR10PoisonIndex, self).__init__(**kwargs)
self.delta = delta
assert ratio <= 1.0 and ratio > 0
if self.delta is not None:
if len(delta) == 10:
self.delta = self.delta[torch.tensor(self.targets)]
if delta.shape != self.data.shape:
self.delta = self.delta.permute(0, 2, 3, 1)
assert self.delta.shape == self.data.shape
set_size = int(len(self.data) * ratio)
if set_size < len(self.data):
self.delta[set_size:] = 0.0
self.delta = self.delta.mul(255).cpu().numpy()
self.data = np.clip(self.data.astype(np.float32) + self.delta, 0, 255).astype(np.uint8)
def __getitem__(self, idx):
img, target = self.data[idx], self.targets[idx]
img = Image.fromarray(img)
img = self.transform(img)
return img, target, idx
class CIFAR100PoisonIndex(CIFAR100):
def __init__(self, delta: torch.FloatTensor = None, ratio=1.0, **kwargs):
super(CIFAR100PoisonIndex, self).__init__(**kwargs)
self.delta = delta
assert ratio <= 1.0 and ratio > 0
if self.delta is not None:
if len(delta) == 100:
self.delta = self.delta[torch.tensor(self.targets)]
if delta.shape != self.data.shape:
self.delta = self.delta.permute(0, 2, 3, 1)
assert self.delta.shape == self.data.shape
set_size = int(len(self.data) * ratio)
if set_size < len(self.data):
self.delta[set_size:] = 0.0
self.delta = self.delta.mul(255).cpu().numpy()
self.data = np.clip(self.data.astype(np.float32) + self.delta, 0, 255).astype(np.uint8)
def __getitem__(self, idx):
img, target = self.data[idx], self.targets[idx]
img = Image.fromarray(img)
img = self.transform(img)
return img, target, idx
class TinyImageNetPoisonIndex(ImageFolder):
def __init__(self, root, train=True, transform=None, delta: torch.FloatTensor = None):
if train:
root = os.path.join(root, 'train')
else:
root = os.path.join(root, 'val')
super().__init__(root, transform=transform)
self.delta = delta
def __getitem__(self, index: int):
path, target = self.samples[index]
sample = self.loader(path)
if self.delta is not None:
if len(self.delta) == 200:
delta = self.delta[target]
else:
delta = self.delta[index]
delta = delta.mul(255).numpy().transpose(1, 2, 0)
sample = np.asarray(sample)
sample = np.clip(sample.astype(np.float32) + delta, 0, 255).astype(np.uint8)
sample = Image.fromarray(sample, mode='RGB')
sample = self.transform(sample)
return sample, target, index
def Normalize(x, mean, std, device):
x = x.to(device)
for i in range(len(x)):
for j in range(len(x[0])):
s, m = torch.tensor(std[j]).to(device), torch.tensor(mean[j]).to(device)
x[i][j] = (x[i][j] - m) / s
return x
def mixup_data(data, target, device, alpha=1.0):
if alpha > 0:
lam = np.random.beta(alpha, alpha)
else:
lam = 1
batch_size = data.size()[0]
index = torch.randperm(batch_size).to(device)
mixed_data = lam * data + (1 - lam) * data[index, :]
target_a, target_b = target, target[index]
return mixed_data, target_a, target_b, lam
def mixup_criterion(criterion, pred, target_a, target_b, lam):
return lam * criterion(pred, target_a) + (1 - lam) * criterion(pred, target_b)
def CW(output, y):
x_target = output[:, y]
logit, label = torch.sort(output, dim=1)
ind = (label[:, -1] == y).float()
u = torch.arange(output.shape[0])
# lossvalue = ((-output[u, y] + logit[:, -2] * ind +logit[:, -1] * (1. - ind))/\
# (logit[:, -1] - logit[:, -3] + 1e-12)).sum()
lossvalue = -output[u, y] + logit[:, -2] * ind + logit[:, -1] * (1. - ind)
return lossvalue
def quantized(x, device, smaller, larger):
x = x.to(device)
#x: one image for CIFAR10 is 3 * 32 * 32
assert torch.min(x) >= 0 and torch.max(x) <= 1
assert 0 <= smaller <= 0.5 and 0.5 <= larger <= 1
x = torch.where(x > 0.5, larger, smaller)
return x.cpu()
def mask(x,p=0.9):
y = copy.deepcopy(x)
#print(y)
x = x.view(-1)
length = len(x.view(-1))
masked = torch.randperm(length)[:int(p*length)]
x[masked] = 0
x = x.view_as(y)
return x
def PGD_attack(model, X, y, device, epsilon=8/255, num_steps=10, step_size=1/255, normal=False, random=False):
model.eval()
criterion = nn.CrossEntropyLoss()
x_adv = X.detach()# + 0.001 * torch.randn(*X.shape).to(device).detach()
if random:
x_adv = X.detach() + torch.FloatTensor(*X.shape).uniform_(-epsilon, epsilon).to(device).detach()
for _ in range(num_steps):
optimizer = torch.optim.SGD([x_adv], lr=0.1)
optimizer.zero_grad()
x_adv.requires_grad_()
with torch.enable_grad():
loss = criterion(model(x_adv), y)
grad = torch.autograd.grad(loss, [x_adv],only_inputs=True)[0]
x_adv = x_adv.detach() + step_size * torch.sign(grad.detach())
if normal:
normal_epsilon = Normalize(epsilon * torch.ones(len(x_adv), 3, 32, 32),
mean=[0., 0., 0.], std=[0.2023, 0.1994, 0.2010], device=device)
x_adv = torch.min(torch.max(x_adv, X - normal_epsilon), X + normal_epsilon)
normal_zero = Normalize(torch.zeros(len(x_adv), 3, 32, 32),
mean=[0.4914, 0.4822, 0.4465], std=[0.2023, 0.1994, 0.2010], device=device)
normal_one = Normalize(torch.ones(len(x_adv), 3, 32, 32),
mean=[0.4914, 0.4822, 0.4465], std=[0.2023, 0.1994, 0.2010], device=device)
x_adv = torch.clamp(x_adv, normal_zero, normal_one)
else:
x_adv = torch.min(torch.max(x_adv, X - epsilon), X + epsilon)
x_adv = torch.clamp(x_adv, 0.0, 1.0) # clamp to [0,1] without normalize
optimizer.step()
#torch.cuda.empty_cache()
return x_adv
def CW_attack(model, X, y, device, epsilon=8/255, num_steps=10, step_size=1/255, normal=False, random=False):
model.eval()
x_adv = X.detach()# + 0.001 * torch.randn(*X.shape).to(device).detach()
if random:
x_adv = X.detach() + torch.FloatTensor(*X.shape).uniform_(-epsilon, epsilon).to(device).detach()
for _ in range(num_steps):
#optimizer = torch.optim.SGD([X_pgd], lr=1e-3)
#optimizer.zero_grad()
x_adv.requires_grad_()
with torch.enable_grad():
loss = CW(model(x_adv), y).mean()
grad = torch.autograd.grad(loss, [x_adv],only_inputs=True)[0]
x_adv = x_adv.detach() + step_size * torch.sign(grad.detach())
if normal:
normal_epsilon = Normalize(epsilon * torch.ones(len(x_adv), 3, 32, 32),
mean=[0., 0., 0.], std=[0.2023, 0.1994, 0.2010], device=device)
x_adv = torch.min(torch.max(x_adv, X - normal_epsilon), X + normal_epsilon)
normal_zero = Normalize(torch.zeros(len(x_adv), 3, 32, 32),
mean=[0.4914, 0.4822, 0.4465], std=[0.2023, 0.1994, 0.2010], device=device)
normal_one = Normalize(torch.ones(len(x_adv), 3, 32, 32),
mean=[0.4914, 0.4822, 0.4465], std=[0.2023, 0.1994, 0.2010], device=device)
x_adv = torch.clamp(x_adv, normal_zero, normal_one)
else:
x_adv = torch.min(torch.max(x_adv, X - epsilon), X + epsilon)
x_adv = torch.clamp(x_adv, 0.0, 1.0) # clamp to [0,1] without normalize
#torch.cuda.empty_cache()
return x_adv
def train(model, device, train_loader, optimizer, epoch, epsilon=8/255, num_steps=10, step_size=-1/255, attack='None',
normal=False, make_labels=False, mixup=False, quantize=False, masked=False, random=False):
criterion = nn.CrossEntropyLoss()
for batch_idx, (data, target, _) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
if make_labels:
target = (target + 1) % 10
if attack == 'PGD':
#data, target = Variable(data, requires_grad=True), Variable(target)
data = PGD_attack(model, data, target, device, epsilon=epsilon,
num_steps=num_steps, step_size=step_size, normal=normal,random=random)
elif attack == 'CW':
data = CW_attack(model, data, target, device, epsilon=epsilon,
num_steps=num_steps, step_size=step_size, normal=normal,random=random)
model.train()
optimizer.zero_grad()
if mixup:
mixed_data, target_a, target_b, lam = mixup_data(data, target, device, alpha=1.0)
output = model(mixed_data)
loss = mixup_criterion(criterion, output, target_a, target_b, lam)
elif quantize:
quantized_data = quantized(data,device,0.25,0.75).to(device)
small_data = torch.clamp(data-0.9,0,1)
big_data =torch.clamp(data+0.9,0,1)
output1 = model(quantized_data)
output2 = model(small_data)
output3 = model(big_data)
output4 = model(data)
loss = criterion(output1,target) + criterion(output2,target) + \
criterion(output3,target) + criterion(output4,target)
elif masked:
data = mask(data,p=0.7)
output = model(data)
loss = criterion(output, target)
else:
output = model(data)
loss = criterion(output,target)
loss.backward()
optimizer.step()
#torch.cuda.empty_cache()
# print progress
if attack == 'PGD' or attack == 'CW':
if batch_idx % 10 == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch+1, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.item()))
else:
if batch_idx % 100 == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch+1, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.item()))
def eval_train(model, device, train_loader,attack_method='None',normal=False):
model.eval()
train_loss = 0
correct = 0
with torch.no_grad():
for data, target, _ in train_loader:
data, target = data.to(device), target.to(device)
if attack_method == 'PGD':
data = PGD_attack(model, data, target, device, epsilon=8 / 255, num_steps=20, step_size=1 / 255,
normal=normal)
elif attack_method == 'CW':
data = CW_attack(model, data, target, device, epsilon=8 / 255,
num_steps=20, step_size=1 / 255, normal=normal)
output = model(data)
train_loss += F.cross_entropy(output, target, size_average=False).item()
pred = output.max(1, keepdim=True)[1]
correct += pred.eq(target.view_as(pred)).sum().item()
train_loss /= len(train_loader.dataset)
train_accuracy = correct / len(train_loader.dataset)
return train_loss, train_accuracy
def eval_test(model, device, test_loader,attack_method='None',normal=False):
model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target, _ in test_loader:
data, target = data.to(device), target.to(device)
if attack_method == 'PGD':
data = PGD_attack(model, data, target, device, epsilon=8 / 255, num_steps=20, step_size=1 / 255,
normal=normal)
elif attack_method == 'CW':
data = CW_attack(model, data, target, device, epsilon=8 / 255,
num_steps=20, step_size=1 / 255, normal=normal)
output = model(data)
test_loss += F.cross_entropy(output, target, size_average=False).item()
pred = output.max(1, keepdim=True)[1]
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
test_accuracy = correct / len(test_loader.dataset)
return test_loss, test_accuracy
def adjust_learning_rate(optimizer, epoch,total_epoch,lr=0.1,schedule='cosine'):
if schedule == 'cosine':
lr = lr * 0.5 * (1 + np.cos(epoch / total_epoch * np.pi))
elif schedule == 'piecewise':
if epoch >= 75:
lr = lr * 0.1
if epoch >= 90:
lr = lr * 0.01
if epoch >= 100:
lr = lr * 0.001
elif schedule == 'adv_piecewise':
if epoch >= 100:
lr = lr * 0.1
if epoch >= 150:
lr = lr * 0.01
elif schedule == 'vit_piecewise':
if epoch == 0:
lr = lr * 0.1
if epoch >= 75:
lr = lr * 0.1
if epoch >= 90:
lr = lr * 0.01
if epoch >= 100:
lr = lr * 0.001
for param_group in optimizer.param_groups:
param_group['lr'] = lr
def data_utils(args):
if args.dataset == 'CIFAR-10':
width = 32
labels = 10
if args.data_augmentation:
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
])
trainset = CIFAR10PoisonIndex(root='../data', train=True, download=True,
transform=transform_train)
else:
trainset = CIFAR10PoisonIndex(root='../data', train=True, download=True,
transform=transforms.ToTensor())
testset = CIFAR10PoisonIndex(root='../data', train=False, download=True,
transform=transforms.ToTensor())
elif args.dataset == 'CIFAR-100':
width = 32
labels = 100
if args.data_augmentation:
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
])
trainset = CIFAR100PoisonIndex(root=args.data, train=True, download=True,
transform=transform_train)
else:
trainset = CIFAR100PoisonIndex(root=args.data, train=False, download=True,
transform=transforms.ToTensor())
testset = CIFAR100PoisonIndex(root=args.data, train=False, download=True,
transform=transforms.ToTensor())
elif args.dataset == 'TinyImageNet':
width = 64
labels = 200
if args.data_augmentation:
transform_train = transforms.Compose([
transforms.RandomResizedCrop(64),
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4, hue=0.2),
Cutout(),
transforms.ToTensor(),
])
trainset = TinyImageNetPoisonIndex(root='../data/tiny-imagenet-200/', train=True,
transform=transform_train)
else:
trainset = TinyImageNetPoisonIndex(root='../data/tiny-imagenet-200/', train=True,
transform=transforms.ToTensor())
testset = TinyImageNetPoisonIndex(root='../data/tiny-imagenet-200/', train=False,
transform=transforms.ToTensor())
else:
raise {'dataset error'}
return width, labels, trainset, testset