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main_forget_imagenet.py
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main_forget_imagenet.py
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import copy
import os
from collections import OrderedDict
import torch
import torch.nn as nn
import torch.optim
import torch.utils.data
import arg_parser
import evaluation
import pruner
import unlearn
import utils
from imagenet import get_x_y_from_data_dict
from trainer import validate
def main():
args = arg_parser.parse_args()
if torch.cuda.is_available():
torch.cuda.set_device(int(args.gpu))
device = torch.device(f"cuda:{int(args.gpu)}")
else:
device = torch.device("cpu")
os.makedirs(args.save_dir, exist_ok=True)
if args.seed:
utils.setup_seed(args.seed)
seed = args.seed
# prepare dataset
model, retain_loader, forget_loader, val_loader = utils.setup_model_dataset(args)
print(len(retain_loader.dataset))
print(len(forget_loader.dataset))
model.cuda()
unlearn_data_loaders = OrderedDict(
retain=retain_loader, forget=forget_loader, val=val_loader, test=val_loader
)
criterion = nn.CrossEntropyLoss()
evaluation_result = None
if args.resume:
checkpoint = unlearn.load_unlearn_checkpoint(model, device, args)
if args.resume and checkpoint is not None:
model, evaluation_result = checkpoint
else:
checkpoint = torch.load(args.mask, map_location=device)
if "state_dict" in checkpoint.keys():
checkpoint = checkpoint["state_dict"]
current_mask = pruner.extract_mask(checkpoint)
pruner.prune_model_custom(model, current_mask)
pruner.check_sparsity(model)
if args.unlearn != "retrain":
model.load_state_dict(checkpoint, strict=False)
unlearn_method = unlearn.get_unlearn_method(args.unlearn)
unlearn_method(unlearn_data_loaders, model, criterion, args)
unlearn.save_unlearn_checkpoint(model, None, args)
if evaluation_result is None:
evaluation_result = {}
if "accuracy" not in evaluation_result:
accuracy = {}
unlearn_data_loaders = dict(reversed(list(unlearn_data_loaders.items())))
for name, loader in unlearn_data_loaders.items():
print("start testing")
# utils.dataset_convert_to_test(loader.dataset,args)
val_acc = validate(loader, model, criterion, args)
accuracy[name] = val_acc
print(f"{name} acc: {val_acc}")
evaluation_result["accuracy"] = accuracy
unlearn.save_unlearn_checkpoint(model, evaluation_result, args)
# if 'new_accuracy' not in evaluation_result:
# accuracy = {}
# for name, loader in unlearn_data_loaders.items():
# print("start testing")
# # utils.dataset_convert_to_test(loader.dataset,args)
# val_acc = validate(loader, model, criterion, args)
# accuracy[name] = val_acc
# print(f"{name} acc: {val_acc}")
# evaluation_result['accuracy'] = accuracy
# unlearn.save_unlearn_checkpoint(model, evaluation_result, args)
# for deprecated in ['MIA', 'SVC_MIA', 'SVC_MIA_forget']:
# if deprecated in evaluation_result:
# evaluation_result.pop(deprecated)
"""forget efficacy MIA:
in distribution: retain
out of distribution: test
target: (, forget)"""
if "SVC_MIA_forget_efficacy" not in evaluation_result:
test_len = len(val_loader.dataset)
N = 10000
print(test_len)
forget_dataset = forget_loader.dataset
retain_dataset = retain_loader.dataset
forget_len = len(forget_dataset)
retain_len = len(retain_dataset)
val_dataset = torch.utils.data.Subset(val_loader.dataset, list(range(N)))
# utils.dataset_convert_to_test(retain_dataset,args)
# utils.dataset_convert_to_test(forget_loader,args)
# utils.dataset_convert_to_test(test_loader,args)
shadow_train = torch.utils.data.Subset(retain_dataset, list(range(N)))
shadow_train_loader = torch.utils.data.DataLoader(
shadow_train, batch_size=args.batch_size, shuffle=False
)
val_loader = torch.utils.data.DataLoader(
val_dataset, batch_size=args.batch_size, shuffle=False
)
evaluation_result["SVC_MIA_forget_efficacy"] = evaluation.SVC_MIA(
shadow_train=shadow_train_loader,
shadow_test=val_loader,
target_train=None,
target_test=forget_loader,
model=model,
)
unlearn.save_unlearn_checkpoint(model, evaluation_result, args)
"""training privacy MIA:
in distribution: retain
out of distribution: test
target: (retain, test)"""
# if 'SVC_MIA_training_privacy' not in evaluation_result:
# test_len = len(test_loader.dataset)
# retain_len = len(retain_dataset)
# num = test_len // 2
# # utils.dataset_convert_to_test(retain_dataset,args)
# # utils.dataset_convert_to_test(forget_loader,args)
# # utils.dataset_convert_to_test(test_loader,args)
# shadow_train = torch.utils.data.Subset(
# retain_dataset, list(range(num)))
# target_train = torch.utils.data.Subset(
# retain_dataset, list(range(num, retain_len)))
# shadow_test = torch.utils.data.Subset(
# test_loader.dataset, list(range(num)))
# target_test = torch.utils.data.Subset(
# test_loader.dataset, list(range(num, test_len)))
# shadow_train_loader = torch.utils.data.DataLoader(
# shadow_train, batch_size=args.batch_size, shuffle=False)
# shadow_test_loader = torch.utils.data.DataLoader(
# shadow_test, batch_size=args.batch_size, shuffle=False)
# target_train_loader = torch.utils.data.DataLoader(
# target_train, batch_size=args.batch_size, shuffle=False)
# target_test_loader = torch.utils.data.DataLoader(
# target_test, batch_size=args.batch_size, shuffle=False)
# evaluation_result['SVC_MIA_training_privacy'] = evaluation.SVC_MIA(
# shadow_train=shadow_train_loader, shadow_test=shadow_test_loader,
# target_train=target_train_loader, target_test=target_test_loader,
# model=model)
# unlearn.save_unlearn_checkpoint(model, evaluation_result, args)
unlearn.save_unlearn_checkpoint(model, evaluation_result, args)
if __name__ == "__main__":
main()