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main_poison_projector.py
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main_poison_projector.py
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# Copyright 2021 solo-learn development team.
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to use,
# copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the
# Software, and to permit persons to whom the Software is furnished to do so,
# subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all copies
# or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
# INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR
# PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE
# FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
# OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
# DEALINGS IN THE SOFTWARE.
import os
import torch
import torch.nn as nn
from pytorch_lightning import Trainer
from pytorch_lightning.callbacks import LearningRateMonitor
from pytorch_lightning.loggers import WandbLogger
from torchvision.models import resnet18, resnet50
from solo.args.setup import parse_args_pretrain
from solo.methods import METHODS
from solo.methods.base import BaseMethod
from solo.utils.backbones import (
swin_base,
swin_large,
swin_small,
swin_tiny,
vit_base,
vit_large,
vit_small,
vit_tiny,
)
from solo.utils.classification_dataloader import prepare_data_no_aug
from poisoning_utils import *
def main_lfb(args):
assert args.backbone in BaseMethod._SUPPORTED_BACKBONES
backbone_model = {
"resnet18": resnet18,
"resnet50": resnet50,
"vit_tiny": vit_tiny,
"vit_small": vit_small,
"vit_base": vit_base,
"vit_large": vit_large,
"swin_tiny": swin_tiny,
"swin_small": swin_small,
"swin_base": swin_base,
"swin_large": swin_large,
}[args.backbone]
# initialize backbone
kwargs = args.backbone_args
# cifar = kwargs.pop("cifar", False)
# swin specific
# if "swin" in args.backbone and cifar:
# kwargs["window_size"] = 4
ckpt_path = args.pretrained_feature_extractor
state = torch.load(ckpt_path)["state_dict"]
MethodClass = METHODS[args.method]
model = MethodClass(**args.__dict__)
# import pdb; pdb.set_trace()
model.load_state_dict(state)
backbone = model
# backbone = lambda x: model(x)['feats']
backbone = model.cuda()
backbone.eval()
train_loader, _, train_dataset, _ = prepare_data_no_aug(
args.dataset,
data_dir=args.data_dir,
train_dir=args.train_dir,
val_dir=args.val_dir,
batch_size=args.batch_size,
num_workers=args.num_workers,
)
train_features = inference(backbone, train_loader)[0].cpu()
train_features = nn.functional.normalize(train_features, dim=1)
train_images, train_labels = train_dataset.data, np.array(train_dataset.targets)
# subset_indices = np.random.choice(len(train_features), 100*args.num_classes, replace=False)
# plot_tsne(train_features.cpu()[subset_indices], train_labels[subset_indices], args.num_classes)
num_poisons = int(args.poison_rate * len(train_features) / args.num_classes)
# step 1: get anchor
if args.target_class is None:
anchor_idx = untargeted_anchor_selection(train_features, num_poisons)
else:
anchor_idx = targeted_anchor_selection(train_features, train_labels, args.target_class, num_poisons)
# all_index = torch.arange(len(train_features))
# anchor_idx = all_index[train_labels == args.target_class][args.target_index]
anchor_feature = train_features[anchor_idx]
anchor_label = train_labels[anchor_idx]
anchor_image = train_images[anchor_idx]
# step 2: get poisoning subset by selecting KNN (including anchor itself)
poisoning_index = get_poisoning_indices(anchor_feature, train_features, num_poisons)
poisoning_index = poisoning_index.cpu()
# step 3: injecting triggers to the subset
pattern, mask = generate_trigger(trigger_type=args.trigger_type)
poison_images = add_trigger(train_images, pattern, mask, poisoning_index, args.trigger_alpha)
poisoning_labels = np.array(train_labels)[poisoning_index]
# import pdb; pdb.set_trace()
# anchor_label = poisoning_labels
acc = (poisoning_labels == anchor_label).astype(np.float).mean()
print('ratio of same-class (class {%d}) samples: %.4f ' % (
anchor_label, acc))
poisoning_data = {
'clean_data': train_images,
'poison_data': poison_images,
'targets': train_labels,
'poisoning_index': poisoning_index,
'anchor_data': anchor_image,
'anchor_label': anchor_label,
'pattern': pattern,
'mask': mask,
'acc': acc,
}
return poisoning_data
def main_clb(args):
train_loader, _, train_dataset, _ = prepare_data_no_aug(
args.dataset,
data_dir=args.data_dir,
train_dir=args.train_dir,
val_dir=args.val_dir,
batch_size=args.batch_size,
num_workers=args.num_workers,
)
train_images, train_labels = train_dataset.data, np.array(train_dataset.targets)
num_poisons = int(args.poison_rate * len(train_images) / args.num_classes)
assert args.target_class is not None
poisoning_index = torch.arange(len(train_images))[train_labels == args.target_class]
shuffle_idx = torch.randperm(len(poisoning_index))
poisoning_index = poisoning_index[shuffle_idx]
poisoning_index = poisoning_index[:num_poisons].cpu()
anchor_label = args.target_class
# step 3: injecting triggers to the subset
pattern, mask = generate_trigger(trigger_type=args.trigger_type)
poison_images = add_trigger(train_images, pattern, mask, poisoning_index, args.trigger_alpha)
poisoning_labels = np.array(train_labels)[poisoning_index]
acc = (poisoning_labels == anchor_label).astype(np.float).mean()
print('ratio of same-class (class {%d}) samples: %.4f ' % (
anchor_label, acc))
poisoning_data = {
'clean_data': train_images,
'poison_data': poison_images,
'targets': train_labels,
'poisoning_index': poisoning_index,
'anchor_data': None,
'anchor_label': anchor_label,
'pattern': pattern,
'mask': mask,
'acc': acc,
}
return poisoning_data
def test(model, data_loader):
model.eval()
device = torch.device('cuda')
total_correct = 0
total_loss = 0.0
with torch.no_grad():
for i, (images, labels) in enumerate(data_loader):
images, labels = images.to(device), labels.to(device)
output = model(images)
total_loss += nn.functional.cross_entropy(output, labels).item()
pred = output.data.max(1)[1]
total_correct += pred.eq(labels.data.view_as(pred)).sum()
loss = total_loss / len(data_loader)
acc = float(total_correct) / len(data_loader.dataset)
return loss, acc
if __name__ == "__main__":
args = parse_args_pretrain()
# args = parse_args_linear()
if args.pretrain_method == 'clb':
poison_data = main_clb(args)
else:
poison_data = main_lfb(args)
args.poison_data_name = "%s_%s_rate_%.2f_target_%s_trigger_%s_alpha_%.2f_class_%d_acc_%.4f" % (
args.dataset,
args.pretrain_method,
args.poison_rate,
args.target_class,
args.trigger_type,
args.trigger_alpha,
poison_data['anchor_label'],
poison_data['acc'])
args.save_dir = os.path.join(args.save_dir, args.dataset, args.pretrain_method, args.trigger_type)
os.makedirs(args.save_dir, exist_ok=True)
file_name = os.path.join(args.save_dir, args.poison_data_name + '.pt')
print('saving to %s' % file_name)
poison_data['args'] = args
# torch.save(poison_data, file_name)