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main_unlearn_cifar100_mixed_label_resnet50.py
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from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.optim.lr_scheduler import StepLR
from advertorch.attacks import L2PGDAttack
from torch.utils.data import DataLoader, Dataset
from torch.utils.data import Subset
from typing import *
from scipy.io import savemat
import copy
import itertools
from itertools import cycle
from resnet_cifar100 import resnet18, resnet34, resnet50
import numpy as np
import random
from utils import JointDataset, NormalizeLayer, naive_train, train, adv_attack, test, estimate_parameter_importance
def main():
# Training settings
parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
parser.add_argument('--batch-size', type=int, default=128, metavar='N',
help='input batch size for training (default: 64)')
parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
help='input batch size for testing (default: 1000)')
parser.add_argument('--epochs', type=int, default=15, metavar='N',
help='number of epochs to train (default: 14)')
parser.add_argument('--lr', type=float, default=1.0, metavar='LR',
help='learning rate (default: 1.0)')
parser.add_argument('--gamma', type=float, default=0.7, metavar='M',
help='Learning rate step gamma (default: 0.7)')
parser.add_argument('--no-cuda', action='store_true', default=False,
help='disables CUDA training')
parser.add_argument('--dry-run', action='store_true', default=False,
help='quickly check a single pass')
parser.add_argument('--seed', type=int, default=0, metavar='S',
help='random seed (default: 1)')
parser.add_argument('--log-interval', type=int, default=10, metavar='N',
help='how many batches to wait before logging training status')
parser.add_argument('--save-model', action='store_true', default=False,
help='For Saving the current Model')
parser.add_argument('--pgd-eps', type=float, default=2.0, metavar='M',
help='Learning rate step gamma (default: 0.7)')
parser.add_argument('--pgd-alpha', type=float, default=0.1, metavar='M',
help='Learning rate step gamma (default: 0.7)')
parser.add_argument('--pgd-iter', type=int, default=100, metavar='M',
help='Learning rate step gamma (default: 0.7)')
parser.add_argument('--unlearn-label', type=int, default=9, metavar='M',
help='Learning rate step gamma (default: 0.7)')
parser.add_argument('--unlearn-k', type=int, default=10, metavar='M',
help='Learning rate step gamma (default: 0.7)')
parser.add_argument('--unlearn-lr', type=float, default=0.01, metavar='LR',
help='learning rate (default: 1.0)')
parser.add_argument('--num-adv-images', type=int, default=None, metavar='M',
help='Learning rate step gamma (default: 0.7)')
parser.add_argument('--reg-lamb', type=float, default=10.0, metavar='LR',
help='learning rate (default: 1.0)')
args = parser.parse_args()
use_cuda = not args.no_cuda and torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
eps = args.pgd_eps
iters = args.pgd_iter
alpha = args.pgd_alpha
k_arr = [16]
# k_arr = [16, 64, 128, 256]
D_r_acc = []
D_f_acc = []
D_test_acc = []
case1_D_r = []
case2_D_r = []
case3_D_r = []
case1_D_f = []
case2_D_f = []
case3_D_f = []
case1_D_test = []
case2_D_test = []
case3_D_test = []
train_kwargs = {'batch_size': 256}
test_kwargs = {'batch_size': 1024}
naiive_unlearn_kwargs = {'batch_size': 32}
transform=transforms.Compose([
transforms.ToTensor(),
])
dataset1 = datasets.CIFAR100('../data', train=True, download=True,
transform=transform)
dataset2 = datasets.CIFAR100('../data', train=False,
transform=transform)
if use_cuda:
cuda_kwargs = {'num_workers': 0,
'pin_memory': True,
'shuffle': False}
train_kwargs.update(cuda_kwargs)
test_kwargs.update(cuda_kwargs)
for unlearn_k in k_arr:
random_seed = 0
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed) # if use multi-GPU
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
np.random.seed(args.seed)
random.seed(args.seed)
unlearn_label = args.unlearn_label
train_labels = dataset1.targets
train_labels = torch.from_numpy(np.array(train_labels))
indices_k_unlearn = torch.randperm(train_labels.shape[0])[:unlearn_k]
print ('indices_k_unlearn : ', indices_k_unlearn)
copy_train_labels = train_labels.clone()
copy_train_labels[indices_k_unlearn] = -10
indices_other_data = (copy_train_labels != -10).nonzero(as_tuple=False)
unlearn_dataset = Subset(dataset1, indices_k_unlearn.view(-1,))
unlearn_loader = torch.utils.data.DataLoader(unlearn_dataset,**naiive_unlearn_kwargs)
other_dataset = Subset(dataset1, indices_other_data.view(-1,))
other_loader = torch.utils.data.DataLoader(other_dataset,**test_kwargs)
cifar_test_loader = torch.utils.data.DataLoader(dataset2, **test_kwargs)
print ('len(unlearn_dataset) : ', len(unlearn_dataset), ' len(other_dataset) : ', len(other_dataset))
model = resnet50().to(device)
model.load_state_dict(torch.load('./cifar100_pretrained_models/resnet50.pt'))
normalize_layer = NormalizeLayer((0.5071, 0.4867, 0.4408), (0.2675, 0.2565, 0.2761))
model = torch.nn.Sequential(normalize_layer, model)
optimizer = optim.SGD(model.parameters(), lr=args.unlearn_lr, momentum=0.9, weight_decay=1e-4)
model.eval()
print ('Baseline 1: Naiive Appraoch - finetuning with D_forget (maximizing CE loss)')
other_loss, other_acc = test(model, device, other_loader)
unlearn_loss, unlearn_acc = test(model, device, unlearn_loader)
test_loss, test_acc = test(model, device, cifar_test_loader)
str_list = '\n Before | D_test - D_forget acc : ' + str(other_acc) + ', D_forget acc : ' + str(unlearn_acc)+ ', D_test acc : ' + str(test_acc)
D_test_acc.append(test_acc)
D_r_acc.append(other_acc)
D_f_acc.append(unlearn_acc)
print (str_list)
unlearn_acc = 100
max_iter = 100
j = 0
while unlearn_acc != 0:
naive_train(args, model, device, unlearn_loader, optimizer, 0)
model.eval()
unlearn_loss, unlearn_acc = test(model, device, unlearn_loader)
j += 1
if max_iter < j:
break
model.eval()
other_loss, other_acc = test(model, device, other_loader)
test_loss, test_acc = test(model, device, cifar_test_loader)
str_list = '\n After | D_test - D_forget acc : ' + str(other_acc) + ', D_forget acc : ' + str(unlearn_acc) + ', D_test acc : ' + str(test_acc)
print (str_list)
case1_D_test.append(test_acc)
case1_D_r.append(other_acc)
case1_D_f.append(unlearn_acc)
model = resnet50().to(device)
model.load_state_dict(torch.load('./cifar100_pretrained_models/resnet50.pt'))
normalize_layer = NormalizeLayer((0.5071, 0.4867, 0.4408), (0.2675, 0.2565, 0.2761))
model = torch.nn.Sequential(normalize_layer, model)
optimizer = optim.SGD(model.parameters(), lr=args.unlearn_lr, momentum=0.9, weight_decay=1e-4)
origin_params = {n: p.clone().detach() for n, p in model.named_parameters() if p.requires_grad}
print ()
print ('\n Baseline 2: Our Appraoch - using adversarial examples only')
unlearn_acc = 100
alpha = 0.0
model.eval()
other_loss, other_acc = test(model, device, other_loader)
unlearn_loss, unlearn_acc = test(model, device, unlearn_loader)
test_loss, test_acc = test(model, device, cifar_test_loader)
str_list = '\n Before | D_test - D_forget acc : ' + str(other_acc) + ', D_forget acc : ' + str(unlearn_acc)+ ', D_test acc : ' + str(test_acc)
print (str_list)
adversary = L2PGDAttack(model, eps=args.pgd_eps, eps_iter=args.pgd_alpha, nb_iter=args.pgd_iter,
rand_init=True, targeted=True)
adv_images, target_labels = adv_attack(args, model, device, unlearn_loader, adversary, unlearn_k, args.num_adv_images)
adv_dataset = JointDataset(adv_images, target_labels)
adv_loader = torch.utils.data.DataLoader(adv_dataset, **train_kwargs)
j = 0
unlearn_loader_cycle = cycle(unlearn_loader)
CE = nn.CrossEntropyLoss()
while unlearn_acc != 0:
model.train()
for i , data in enumerate(zip(adv_loader, unlearn_loader_cycle)):
model.train()
(adv_data, adv_target), (data, target) = data
optimizer.zero_grad()
output_adv = model(adv_data.to(device))
output = model(data.to(device))
loss_unlearn = -CE(output, target.to(device)) * (data.shape[0] / (adv_data.shape[0] + data.shape[0]))
loss_adv = CE(output_adv, adv_target.to(device)) * (adv_data.shape[0] / (adv_data.shape[0] + data.shape[0]))
loss = loss_unlearn + loss_adv
loss.backward()
optimizer.step()
model.eval()
unlearn_loss, unlearn_acc = test(model, device, unlearn_loader)
if unlearn_acc == 0:
print ('unlearn_acc == 0, Break at j = ', j, ' i = ', i)
break
j += 1
if max_iter < j:
break
model.eval()
unlearn_loss, unlearn_acc = test(model, device, unlearn_loader)
other_loss, other_acc = test(model, device, other_loader)
test_loss, test_acc = test(model, device, cifar_test_loader)
str_list = '\n After | D_test - D_forget acc : ' + str(other_acc) + ', D_forget acc : ' + str(unlearn_acc)+ ', D_test acc : ' + str(test_acc)
print (str_list)
case2_D_test.append(test_acc)
case2_D_r.append(other_acc)
case2_D_f.append(unlearn_acc)
print ()
print ('\n Baseline 3: Our Appraoch - using both adversarial examples and weight importance')
model = resnet50().to(device)
model.load_state_dict(torch.load('./cifar100_pretrained_models/resnet50.pt'))
normalize_layer = NormalizeLayer((0.5071, 0.4867, 0.4408), (0.2675, 0.2565, 0.2761))
model = torch.nn.Sequential(normalize_layer, model)
optimizer = optim.SGD(model.parameters(), lr=args.unlearn_lr, momentum=0.9, weight_decay=1e-4)
origin_params = {n: p.clone().detach() for n, p in model.named_parameters() if p.requires_grad}
model_for_importance = copy.deepcopy(model)
num_samples = len(unlearn_dataset)
importance = estimate_parameter_importance(unlearn_loader, model_for_importance, device, num_samples, optimizer)
for keys in importance.keys():
importance[keys] = (importance[keys] - importance[keys].min()) / (importance[keys].max() - importance[keys].min())
importance[keys] = (1 - importance[keys])
CE = nn.CrossEntropyLoss()
unlearn_acc = 100
model.eval()
other_loss, other_acc = test(model, device, other_loader)
unlearn_loss, unlearn_acc = test(model, device, unlearn_loader)
test_loss, test_acc = test(model, device, cifar_test_loader)
str_list = '\n Before | D_test - D_forget acc : ' + str(other_acc) + ', D_forget acc : ' + str(unlearn_acc)+ ', D_test acc : ' + str(test_acc)
print (str_list)
adv_dataset = JointDataset(adv_images, target_labels)
adv_loader = torch.utils.data.DataLoader(adv_dataset, **train_kwargs)
j = 0
unlearn_loader_cycle = cycle(unlearn_loader)
while unlearn_acc != 0:
for i , data in enumerate(zip(adv_loader, unlearn_loader_cycle)):
model.train()
(adv_data, adv_target), (data, target) = data
optimizer.zero_grad()
output_adv = model(adv_data.to(device))
output = model(data.to(device))
loss_unlearn = -CE(output, target.to(device)) * (data.shape[0] / (adv_data.shape[0] + data.shape[0]))
loss_adv = CE(output_adv, adv_target.to(device)) * (adv_data.shape[0] / (adv_data.shape[0] + data.shape[0]))
loss_reg = 0
for n, p in model.named_parameters():
if n in importance.keys():
loss_reg += torch.sum(importance[n] * (p - origin_params[n]).pow(2)) / 2
loss = loss_unlearn + loss_adv + loss_reg * args.reg_lamb
loss.backward()
optimizer.step()
model.eval()
unlearn_loss, unlearn_acc = test(model, device, unlearn_loader)
if unlearn_acc == 0:
print ('unlearn_acc == 0, Break at j = ', j, ' i = ', i)
break
j += 1
if max_iter < j:
break
model.eval()
unlearn_loss, unlearn_acc = test(model, device, unlearn_loader)
other_loss, other_acc = test(model, device, other_loader)
test_loss, test_acc = test(model, device, cifar_test_loader)
str_list = '\n After | D_test - D_forget acc : ' + str(other_acc) + ', D_forget acc : ' + str(unlearn_acc)+ ', D_test acc : ' + str(test_acc)
print (str_list)
case3_D_test.append(test_acc)
case3_D_r.append(other_acc)
case3_D_f.append(unlearn_acc)
save_file_name = 'cifar100_unlearning_label_mix_resnet50_unlearning_lr_' + str(args.unlearn_lr) + '_reg_lamb_' + str(args.reg_lamb) + '_num_adv_images_' + str(args.num_adv_images) + '_l2_pgd_eps_' + str(args.pgd_eps) + '_iter_' + str(args.pgd_iter) + '_alpha_' + str(args.pgd_alpha) + '_seed_' + str(args.seed)
savemat('./result_data/' + save_file_name + '.mat', {"k_arr": np.array(k_arr),"D_r_acc": np.array(D_r_acc),"D_test_acc": np.array(D_test_acc),"D_f_acc": np.array(D_f_acc),"case1_D_r": np.array(case1_D_r),"case2_D_r": np.array(case2_D_r),"case3_D_r": np.array(case3_D_r),"case1_D_f": np.array(case1_D_f),"case2_D_f": np.array(case2_D_f),"case3_D_f": np.array(case3_D_f),"case1_D_test": np.array(case1_D_test),"case2_D_test": np.array(case2_D_test),"case3_D_test": np.array(case3_D_test)})
if __name__ == '__main__':
main()