diff --git a/README.md b/README.md index 7b95401dc46245ac339fc25059d4a56d90b4cde5..47813c9d3ac5331d288d91453872cdbd43870e62 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,113 @@ ---- -license: apache-2.0 ---- +# SAE: Sustainable Adversarial Example Evaluation Framework for Class-Incremental Learning + +## 🌟 Overview + +_**News**: This work has been accepted as a poster by [AAAI 2026](https://aaai.org/conference/aaai/aaai-26/)._ + +**SAE (Sustainable Adversarial Example)** is a *universal adversarial attack framework* targeting **Class-Incremental Learning (CIL)**. This repository provides a comprehensive pipeline for both CIL training and benchmarking multiple attack methods, including our proposed SAE approach. + +The project integrates with [PyCIL: A Python Toolbox for Class-Incremental Learning](https://github.com/LAMDA-CL/PyCIL) for CIL model training. It also supports benchmarking several attack baselines alongside SAE, enabling fair and reproducible evaluations of adversarial robustness across CIL methods. + +If you are interested in our work, please refer to: +``` +@inproceedings{liu2026SAE, + title={Improving Sustainability of Adversarial Examples in Class-Incremental Learning}, + author={Taifeng Liu, Xinjing Liu, Liangqiu Dong, Yang Liu, Yilong Yang, Zhuo Ma}, + booktitle={Proceedings of the AAAI Conference on Artificial Intelligence}, + year={2026} +} +``` + +--- + +## ⚙️ Environment Setup + +### Project Layout + +* `attacks/`: Implementations of all attack baselines including **MIFGSM**, **Gaker**, **AIM**, **CGNC**, **CleanSheet**, **UnivIntruder**, and **SAE**. +* `convs/`: Backbone definitions for CIL models and CLIP model, including `resnet32`, `resnet50`, `cosine_resnet32`, and `cosine_resnet50`. + * **Pre-trained CLIP** is downloaded from [HuggingFace](https://huggingface.co/laion/CLIP-ViT-B-32-laion2B-s34B-b79K). +* `datasets/`: Dataset management for CIFAR-100 (32x32) and ImageNet-100 (224x224). + * **CIFAR-100** is automatically downloaded at runtime. + * **ImageNet-100** should be manually extracted from ImageNet-1K using `create_imagenet100_from_imagenet.py`, which parses `train.txt` and `eval.txt` to extract relevant classes. +* `exps/`: JSON configuration files for various CIL training methods. +* `logs/`: Stores trained CIL model checkpoints and evaluation results of attacks. +* `models/`: Contains implementations of **9** CIL algorithms: **BiC**, **DER**, **Finetune**, **Foster**, **iCaRL**, **MEMO**, **PodNet**, **Replay**, **WA**. +* `scripts/`: Shell scripts for CIL training and attack benchmarking. +* `utils/`: Utility functions for augmentation, logging, dataset processing, visualization, etc. +* `attack.py`: Main entry point to run adversarial attacks. +* `trainCIL.py`: Main entry point to train CIL models. + +### Dependency Requirements + +* **OS**: Ubuntu 22.04 +* **Python**: 3.12 +* **PyTorch**: ≥ 2.1 +* **GPU**: NVIDIA RTX 4090 (24GB VRAM) + +To set up the environment: + +```bash +conda env create -f environment.yml +conda activate SAE +``` + +--- + +## 🚀 Result Reproduction + +### Step 1: CIL Training + +First, you need to train the target CIL models follow the instruction below. + +Example: training a single CIL method (e.g., **iCaRL**) on **CIFAR-100**: + +```bash +python trainCIL.py --config exps/icarl.json +``` + +Example: training a single CIL method (e.g., **iCaRL**) on **ImageNet-100**: + +```bash +python trainCIL.py --config exps/icarl-imagenet100.json +``` + +We also provide scripts to train all 9 CIL methods on **CIFAR-100**: + +```bash +./scripts/trainCIL-CIFAR100.sh +``` + +Train all 9 CIL methods on **ImageNet-100**: + +```bash +./scripts/trainCIL-ImageNet100.sh +``` + +All model checkpoints will be saved under the `logs/` directory, organized by method and dataset. + + +--- + +### Step 2: Adversarial Attack Benchmarking + +_Note: assume that you have already prepared the dataset, the CIL model, and the CLIP model._ + +To launch an **SAE** attack targeting class **0** on a CIL model trained on **CIFAR-100**: + +```bash +python attack.py --config exps/icarl.json --attack_method SAE --target_class 0 +``` + +To test a different attack baseline, simply change the `--attack_method` argument. + +You can reproduce the overall evaluation results by runing the below scripts (referring to the **Table 1** in our paper). + +```bash +./scripts/attacks-CIFAR100.sh +./scripts/attacks-ImageNet100.sh +``` + +#### Benchmark Results + +All benchmark results are available in the `appendix.pdf` of Supplementary Material. \ No newline at end of file diff --git a/attack.py b/attack.py new file mode 100644 index 0000000000000000000000000000000000000000..9daabd2e45bb16df44a7994ff23bbfba095f732e --- /dev/null +++ b/attack.py @@ -0,0 +1,140 @@ +import json +import argparse +import sys +import logging +import copy +import torch +import os + +from trainCIL import _set_random, _set_device, print_args +from attacks.AIM.AIMAttack import AIM +from attacks.BASEAttack import BASEAttack +from attacks.Gaker.GAKERAttack import Gaker +from attacks.CGNC.CGNCAttack import CGNC +from attacks.CleanSheet.CleanSheetAttack import CleanSheet +from attacks.UnivIntruder.UnivIntruderAttack import UnivIntruder +from attacks.SAE.SAEAttack import SAE + + +def main(): + args = setup_parser().parse_args() + param = load_json(args.config) + args = vars(args) # Converting argparse Namespace to a dict. + args.update(param) # Add parameters from json + + evaluate(args) + + +def evaluate(args): + seed_list = copy.deepcopy(args["seed"]) + device = copy.deepcopy(args["device"]) + + for seed in seed_list: + args["seed"] = seed + args["device"] = device + _evaluate(args) + + +def _evaluate(args): + # For attacks + args["attack"] = True + + device = 'cuda' if torch.cuda.is_available() else 'cpu' + init_cls = 0 if args["init_cls"] == args["increment"] else args["init_cls"] + logs_name = "logs/{}/{}/init_cls_{}/per_classes_{}/{}".format(args["model_name"], args["dataset"], init_cls, + args['increment'], args["convnet_type"]) + if not os.path.exists(logs_name): + raise "Model is not trained yet." + logs_eval_name = "logs/{}/{}/init_cls_{}/per_classes_{}/{}/eval/{}".format(args["model_name"], args["dataset"], init_cls, + args['increment'], args["convnet_type"], args['attack_method']) + if not os.path.exists(logs_eval_name): + os.makedirs(logs_eval_name) + + logfilename = "logs/{}/{}/init_cls_{}/per_classes_{}/{}/eval/{}/adv_log".format( + args["model_name"], + args["dataset"], + init_cls, + args["increment"], + args["convnet_type"], + args['attack_method'] + ) + logging.basicConfig( + level=logging.INFO, + format="%(asctime)s [%(filename)s] => %(message)s", + handlers=[ + logging.FileHandler(filename=logfilename + ".log"), + logging.StreamHandler(sys.stdout), + ], + ) + + args['epsilons'] = [0.01, 0.015, 0.03, 0.06, 0.1, 0.2] + args['logs_name'] = logs_name + args['logs_eval_name'] = logs_eval_name + _set_random() + _set_device(args) + print_args(args) + + # Init the attack + if args['attack_method'] in NEW_ATTACKS: + adv = init_new_attack(args, device=device) + if args['attack_method'] == 'AIM' or args['attack_method'] == 'Gaker' or args['attack_method'] == 'CGNC': + adv.train_generator() + elif args['attack_method'] == 'SAE' or args['attack_method'] == 'UnivIntruder' or args['attack_method'] == 'CleanSheet': + adv.train_adv() + else: + adv = init_foolbox_attack(args, device=device) + + # Conduct attack + adv.run_test() + + +NEW_ATTACKS = ['AIM', 'Gaker', 'CGNC', 'CleanSheet', 'UnivIntruder', 'SAE'] +FOOLBOX_ATTACKS = ['L2FGM', 'FGSM', 'MIFGSM', + 'L1PGD', 'L2PGD', 'LinfPGD', + 'L2DeepFool', 'LinfDeepFool', 'BoundaryAttack', + 'CarliniWagnerL2', 'GaussianNoise', 'UniformNoise'] + +def init_new_attack(args, device='cuda', **kwargs): + attack_name = args['attack_method'] + models = kwargs.get('models', None) + + if attack_name == 'AIM': + adv = AIM(args=args, device=device) + elif attack_name == 'Gaker': + adv = Gaker(args=args, device=device) + elif attack_name == 'CGNC': + adv = CGNC(args=args, device=device) + elif attack_name == 'CleanSheet': + adv = CleanSheet(args=args, device=device) + elif attack_name == 'UnivIntruder': + adv = UnivIntruder(args=args, device=device) + elif attack_name == 'SAE': + adv = SAE(args=args, device=device) + else: + raise ValueError(f"Unknown attack method: {attack_name}") + + return adv + +def init_foolbox_attack(args, device='cuda', **kwargs): + attack = BASEAttack(args=args, device=device) + return attack + +def load_json(settings_path): + with open(settings_path) as data_file: + param = json.load(data_file) + + return param + + +def setup_parser(): + parser = argparse.ArgumentParser(description='Reproduce of multiple continual learning algorithms.') + parser.add_argument('--config', type=str, default='exps/finetune.json', help='Json file of settings.') + parser.add_argument('--batch_size', type=int, default=128, help='set the batch size.') + parser.add_argument('--attack_method', type=str, default='AIM', help='set the attack method, e.g., LinfPGD, MIFGSM, AIM, Gaker, CGNC, CleanSheet, UnivIntruder, SAE.') + parser.add_argument('--target_class', type=int, default=0, help='the target class, None indicates untargeted attack.') + parser.add_argument('--eval', action='store_true', help='evaluation only') + return parser + + +if __name__ == '__main__': + main() diff --git a/attacks/AIM/AIMAttack.py b/attacks/AIM/AIMAttack.py new file mode 100644 index 0000000000000000000000000000000000000000..1531bc4aef56ed4283574137e76e864c96103b4a --- /dev/null +++ b/attacks/AIM/AIMAttack.py @@ -0,0 +1,226 @@ +import os.path +import torch +from foolbox.attacks.base import * +from foolbox.attacks.gradient_descent_base import * +from tqdm import tqdm +from attacks.AIM.src.gat.models.attack import AIMAttack, ContrastiveLoss +from attacks.AIM.src.gat.models.surrogate import midlayer_dict, register_collecter, register_collecter_cl +from attacks.attack_config import SustainableAttack +from utils.plot import plot_asr_per_target, save_grad_cam +import logging +import pandas as pd +import foolbox as fb +from foolbox import PyTorchModel +import numpy as np +from utils import factory +from utils.data_manager import get_dataloader + + +class AIM(SustainableAttack): + def __init__(self, args, device='cuda'): + super().__init__(args, device) + self.device = device + self.args = args + self.surrogate_model = None + + self.adv_generator = AIMAttack(device=device) + self.adv_generator.set_mode('train') + self.lr = 0.001 + self.betas = (0.5, 0.999) + self.num_epoch = 100 + self.optim = torch.optim.Adam(self.adv_generator.get_params(), lr=self.lr, betas=self.betas) + self.contrastive_loss = ContrastiveLoss(0.2) + self.sim_loss = torch.nn.functional.cosine_similarity + self.eval_batch_szie = 128 + + self.surrogate_model_name = 'resnet32_cl' + self.layer = midlayer_dict[self.surrogate_model_name] + self.prefix = (f'adv_generator_{self.surrogate_model_name}' + f'_{self.layer}' + f'_tclass{self.target_class}') + self.save_path = os.path.join(self.args['logs_eval_name'], f'target{str(self.target_class)}') + if not os.path.exists(self.save_path): + os.makedirs(self.save_path) + + self.plot_gradcam = False + + def train_generator(self): + if 'cl' in self.surrogate_model_name: + s_model = factory.get_model(self.args["model_name"], self.args) + s_model.incremental_train(self.data_manager) + s_model._network.load_state_dict( + torch.load(self.ckpt_paths[0], map_location=self.device)['model_state_dict']) + s_model._network.to(self.device) + s_model._network.eval() + self.surrogate_model = s_model._network + del s_model + torch.cuda.empty_cache() + self.feat_collecter = [] + self.feat_collecter_handler, self.feat_collecter = register_collecter_cl(self.surrogate_model, + self.layer, + self.feat_collecter, + self.args["model_name"]) + else: + self.surrogate_model = torch.hub.load("chenyaofo/pytorch-cifar-models", 'cifar100_resnet32', pretrained=True) + self.surrogate_model.to(self.device) + self.surrogate_model.eval() + self.feat_collecter = [] + self.feat_collecter_handler, self.feat_collecter = register_collecter(self.surrogate_model, + self.layer, + self.feat_collecter) + self.file_path = os.path.join(self.save_path, f'{self.prefix}.pth') + if os.path.exists(self.file_path): + self.adv_generator.load_ckpt(self.file_path) + self.adv_generator.set_mode('eval') + else: + loaders = get_dataloader(self.data_manager, batch_size=self.batch_size, + start_class=0, end_class=10, + train=True, shuffle=True, num_workers=0) + + target_images = [] + target_labels = [] + for data in loaders: + _, image_batch, label_batch = data + mask = label_batch == self.target_class + selected_images = image_batch[mask] + selected_labels = label_batch[mask] + target_images.append(selected_images) + target_labels.append(selected_labels) + del loaders + target_images = torch.cat(target_images, dim=0).to(self.device) + target_labels = torch.cat(target_labels, dim=0).to(self.device) + target_images, target_labels = ep.astensors(*(target_images[:self.batch_size], target_labels[:self.batch_size])) + + total_loss = [] + for epoch in range(1, self.num_epoch + 1): + laoder_tqdm = tqdm(self.loader, total=len(self.loader), desc=f'Epoch {epoch}') + loss_np = 0 + for i, (_, x, y) in enumerate(laoder_tqdm): + x_f = x[y != self.target_class].to(self.device) + del x, y + if len(x_f) > len(target_images): + x_f = x_f[:len(target_images)].to(self.device) + else: + random_indices = torch.randperm(len(target_images))[:len(x_f)].to(self.device) + target_images = target_images[random_indices] + + x_adv = self.adv_generator(x_f, target_images.raw.to(self.device)) + + logits_nat = self.surrogate_model(self.norm(x_f)) + feat_nat = self.feat_collecter.pop() + logits_tar = self.surrogate_model(self.norm(target_images.raw)) + feat_tar = self.feat_collecter.pop() + logits_adv = self.surrogate_model(self.norm(x_adv)) + feat_adv = self.feat_collecter.pop() + + loss = (self.contrastive_loss(logits_adv, logits_nat, logits_tar) + + self.sim_loss(feat_nat, feat_adv) - + self.sim_loss(feat_tar, feat_adv)).mean() + # print(loss.item()) + loss_np = loss_np + loss.item() + + self.optim.zero_grad() + loss.backward() + self.optim.step() + del x_f, x_adv, logits_nat, logits_adv, logits_tar, feat_nat, feat_tar, feat_adv + torch.cuda.empty_cache() + total_loss.append(loss_np/(i+1)) + logging.info(f'Epoch {epoch} loss: {loss_np/(len(self.loader))}') + logging.info(f'Total loss: {total_loss}') + self.feat_collecter_handler.remove() + self.adv_generator.save_ckpt(self.file_path) + + + def run_test(self): + # Load Batch Data + self.adv_generator.set_mode('eval') + self.adv_generator.adv_gen.to(self.device) + self.loader = get_dataloader(self.data_manager, batch_size=self.eval_batch_szie, + start_class=0, end_class=10, + train=False, shuffle=False, num_workers=0) + target_images = [] + target_labels = [] + for data in self.loader: + _, image_batch, label_batch = data + mask = label_batch == self.target_class + selected_images = image_batch[mask] + selected_labels = label_batch[mask] + target_images.append(selected_images) + target_labels.append(selected_labels) + target_imgs = torch.cat(target_images, dim=0).to(self.device) + target_labels = torch.cat(target_labels, dim=0).to(self.device) + target_imgs, target_labels = ep.astensors(*(target_imgs, target_labels)) + for i, (_, imgs, labels) in enumerate(tqdm(self.loader, total=len(self.loader), + desc=f'Loading Data with Batch Size of {self.batch_size}) :')): + if i > 0: + break + + imgs, labels = ep.astensors(*(imgs.to(self.device), labels.to(self.device))) + + imgs_f = imgs[labels != self.target_class] + labels_f = labels[labels != self.target_class] + labels_t_f = ep.full_like(labels_f, fill_value=self.target_class) + + self.attacks(i, imgs_f, labels_f, labels_t_f, target_imgs[:20], target_labels[:20]) + + + def attacks(self, i_batch, imgs, labels, labels_t, target_imgs=None, target_labels=None): + asr_matrix = np.ones((10, len(target_imgs))) + self.model = factory.get_model(self.args["model_name"], self.args) + for task in range(10): + logging.info("***** Starting attack on task [{}]. *****".format(task)) + self.model.incremental_train(self.data_manager) + self.model._network.load_state_dict(torch.load(self.ckpt_paths[task], map_location=self.device)['model_state_dict']) + self.model._network.to(self.device) + self.model._network.eval() + + # Run attack on ecah target image + criterion = fb.criteria.Misclassification( + labels) if self.target_class is None else fb.criteria.TargetedMisclassification( + labels_t) + current_model = PyTorchModel(self.model._network, bounds=(0, 1), preprocessing=self.preprocessing) + verify_input_bounds(imgs, current_model) + criterion = get_criterion(criterion) + is_adversarial = get_is_adversarial(criterion, current_model) + + logging.info("Eval attack on each target images.") + for i, target_image in enumerate(target_imgs): + advs = ep.astensor(self.adv_generator(imgs.raw.to(self.device), target_image.raw.repeat(len(imgs), 1, 1, 1).to(self.device))) + is_adv = is_adversarial(advs)[0] + asr_matrix[task, i] = (is_adv.bool().sum().raw.item() / len(imgs)) + if self.plot_gradcam: + save_grad_cam(self.args, torch.clip(advs.raw.detach(),0,1), labels_t.raw, + self.model._network, self.save_path + "/GradCam" + f"targetimg{i}", prefix=f'task{task}', + layer_name='stage_3', save_num=100, save_raw=True) + + del advs, is_adv, target_image + torch.cuda.empty_cache() + + del criterion, current_model, is_adversarial + torch.cuda.empty_cache() + + self.model.after_task() + + # Save all target images info: everage asr, + asr_matrix = np.mean(asr_matrix, axis=1, keepdims=True) + prefix = f'batch{i_batch}_{self.prefix}' + plot_asr_per_target(asr_matrix, self.save_path, prefix, self.args) + df = pd.DataFrame(asr_matrix, columns=['ASR']) + df.to_excel(os.path.join(self.save_path, f"{prefix}.xlsx"), index=False) + + del asr_matrix, imgs, labels, labels_t, target_imgs + torch.cuda.empty_cache() + + def __call__( + self, + model: Model, + inputs: T, + criterion: Any, + *, + epsilons: Union[Sequence[Union[float, None]], float, None], + **kwargs: Any, + ) -> Union[Tuple[List[T], List[T], T], Tuple[T, T, T]]: + ... + + def repeat(self, times: int) -> "AIM": + ... \ No newline at end of file diff --git a/attacks/AIM/examples/aim_attack.py b/attacks/AIM/examples/aim_attack.py new file mode 100644 index 0000000000000000000000000000000000000000..ab0cb6459d07061396f4661e5beb5146b912a370 --- /dev/null +++ b/attacks/AIM/examples/aim_attack.py @@ -0,0 +1,237 @@ +#!/usr/bin/env python3 +# Usage: +# ./examples/aim_attack.py -h +import argparse +import json +from pathlib import Path +from pprint import pformat +from typing import List, Union + +import torch +from tqdm import tqdm + +from attacks.GAT.src.gat.datasets import build_dataset, list_datasets +from attacks.GAT.src.gat.datasets.transforms import norm +from attacks.GAT.src.gat.models.attack import AIMAttack, ContrastiveLoss +from attacks.GAT.src.gat.models.surrogate import (build_surrogate, list_surrogates, + midlayer_dict, register_collecter) +from attacks.GAT.src.gat.runtime import AverageMeter, calc_cls_accuracy, fix_random, randid + + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument('-v', '--verbose', action='store_true') + parser.add_argument('--seed', type=int, default=0) + parser.add_argument('--expid', type=str, default=randid(4)) + parser.add_argument('--workdir', type=str, default='workdirs') + parser.add_argument('--device', type=str, default='cuda') + parser.add_argument('--tar-classes', type=int, default=24) + parser.add_argument('--batch-size', type=int, default=16) + parser.add_argument('--dataset', + type=str, + default='imagenet', + choices=list_datasets()) + parser.add_argument('--data-root', + type=str, + default=Path(__file__).parent / '../data' / 'in_1k') + sub_parsers = parser.add_subparsers(dest='command') + train_parser = sub_parsers.add_parser('train') + train_parser.add_argument('--surrogate-id', + type=str, + default='resnet152', + choices=list_surrogates()) + train_parser.add_argument('--num-epoch', type=int, default=10) + train_parser.add_argument('--lr', type=float, default=0.0002) + train_parser.add_argument('--betas', + type=float, + nargs=2, + default=(0.5, 0.999)) + sub_parsers.add_parser('evaluate') + args = parser.parse_args() + + args.workdir = Path(args.workdir) / args.expid + args.workdir.mkdir(parents=True, exist_ok=True) + args.device = torch.device(args.device) + + args.ckpt = args.workdir / 'model.pth' + + fix_random(args.seed) + + with open(args.workdir / 'args.txt', 'w') as f: + f.write(pformat(vars(args))) + + return args + + +def init_loader(dataset: str, + data_root: Union[str, Path], + tar_classes: Union[int, List[int]], + batch_size: int = 16, + command: str = 'train') -> List[torch.utils.data.DataLoader]: + train_ds = build_dataset(dataset, + data_root=data_root, + is_train=(command == 'train')) + train_loader = torch.utils.data.DataLoader( + train_ds, + batch_size=batch_size, + shuffle=True, + num_workers=4, + pin_memory=True, + ) + target_ds = build_dataset(dataset, + data_root=data_root, + is_train=True, + filter_class=tar_classes) + target_loader = torch.utils.data.DataLoader( + target_ds, + batch_size=batch_size, + sampler=torch.utils.data.RandomSampler(target_ds, + replacement=True, + num_samples=len(train_ds)), + num_workers=4, + pin_memory=True, + ) + return train_loader, target_loader + + +def train( + surrogate_id: str, + dataset: str, + data_root: Union[str, Path], + tar_classes: Union[int, List[int]] = 24, + num_epoch: int = 10, + batch_size: int = 16, + lr: float = 0.0002, + betas: Union[float, List[float]] = (0.5, 0.999), + device: Union[str, torch.device] = torch.device('cuda'), + command: str = 'train', + workdir: Union[str, + Path] = Path(__file__).parents[1] / 'workdirs') -> None: + + train_loader, target_loader = init_loader(dataset, data_root, tar_classes, + batch_size, command) + normalizer = norm(dataset, _callable=True) + + surrogate = build_surrogate(surrogate_id, pretrain=True).to(device) + surrogate.eval() + feat_collecter_handler, feat_collecter = register_collecter( + surrogate, midlayer_dict[surrogate_id]) + + attack = AIMAttack(device=device) + attack.set_mode('train') + optim = torch.optim.Adam(attack.get_params(), lr=lr, betas=betas) + + contrastive_loss = ContrastiveLoss(0.2) + sim_loss = torch.nn.functional.cosine_similarity + + for epoch in range(1, num_epoch + 1): + attack.set_mode('train') + enumerator = enumerate(zip(train_loader, target_loader)) + enumerator = tqdm(enumerator, + total=len(train_loader), + desc=f'Epoch {epoch}') + for batch_idx, ((x_nat, y_nat), (x_tar, y_tar)) in enumerator: + if torch.any(y_nat == y_tar): + continue + x_nat, x_tar = x_nat.to(device), x_tar.to(device) + y_nat, y_tar = y_nat.to(device), y_tar.to(device) + x_adv = attack(x_nat, x_tar) + + logits_nat = surrogate(normalizer(x_nat)) + feat_nat = feat_collecter.pop() + logits_tar = surrogate(normalizer(x_tar)) + feat_tar = feat_collecter.pop() + logits_adv = surrogate(normalizer(x_adv)) + feat_adv = feat_collecter.pop() + + loss = (contrastive_loss(logits_adv, logits_nat, logits_tar) + + sim_loss(feat_nat, feat_adv) - + sim_loss(feat_tar, feat_adv)).mean() + + optim.zero_grad() + loss.backward() + optim.step() + + feat_collecter_handler.remove() + + attack.save_ckpt(workdir / 'model.pth') + + +@torch.no_grad() +def evaluate( + ckpt: Union[str, Path], + dataset: str, + data_root: Union[str, Path], + tar_classes: Union[int, List[int]] = 24, + batch_size: int = 16, + device: Union[str, torch.device] = torch.device('cuda'), + command: str = 'train', + workdir: Union[str, + Path] = Path(__file__).parents[1] / 'workdirs') -> None: + # init dataloader + eval_loader, target_loader = init_loader(dataset, data_root, tar_classes, + batch_size, command) + normalizer = norm(dataset, _callable=True) + # init attack method + attack = AIMAttack(device=device) + attack.load_ckpt(ckpt) + attack.set_mode('eval') + # init evaluate models + models = { + surrogate_id: build_surrogate(surrogate_id, pretrain=True).to(device) + for surrogate_id in list_surrogates() + } + for surrogate_id in models.keys(): + models[surrogate_id].eval() + model_meters = { + surrogate_id: [AverageMeter() for _ in range(2)] + for surrogate_id in models.keys() + } + # evaluate + enumerator = enumerate(zip(eval_loader, target_loader)) + enumerator = tqdm(enumerator, total=len(eval_loader), desc='Eval') + for batch_idx, ((x_nat, y_nat), (x_tar, y_tar)) in enumerator: + x_nat, y_nat = x_nat.to(device), y_nat.to(device) + x_tar, y_tar = x_tar.to(device), y_tar.to(device) + x_adv = attack(x_nat, x_tar) + for surrogate_id, model in models.items(): + logits_nat = model(normalizer(x_nat)) + logits_adv = model(normalizer(x_adv)) + # collect metrics + acc = calc_cls_accuracy(logits_nat, y_nat) + asr = calc_cls_accuracy(logits_adv, y_tar) + model_meters[surrogate_id][0].update(acc[0].item(), x_nat.size(0)) + model_meters[surrogate_id][1].update(asr[0].item(), x_nat.size(0)) + # print result + results = { + surrogate_id: { + 'acc': meters[0].avg, + 'asr': meters[1].avg + } + for surrogate_id, meters in model_meters.items() + } + print(pformat(results)) + with open(workdir / 'results.json', 'w') as f: + json.dump(results, f) + + +def main() -> None: + args = parse_args() + args.command = 'train' + args.surrogate_id = 'resnet152' + args.num_epoch = 10 + args.lr = 0.0002 + args.betas = (0.5, 0.999) + if args.command == 'train': + train(args.surrogate_id, args.dataset, args.data_root, + args.tar_classes, args.num_epoch, args.batch_size, args.lr, + args.betas, args.device, args.command, args.workdir) + elif args.command == 'evaluate': + evaluate(args.ckpt, args.dataset, args.data_root, args.tar_classes, + args.batch_size, args.device, args.command, args.workdir) + else: + raise NotImplementedError + + +if __name__ == '__main__': + main() diff --git a/attacks/AIM/examples/ens-gen.py b/attacks/AIM/examples/ens-gen.py new file mode 100644 index 0000000000000000000000000000000000000000..3c504694c2aa7d604dfaff6411a43fca8ae5dcdb --- /dev/null +++ b/attacks/AIM/examples/ens-gen.py @@ -0,0 +1,523 @@ +#!/usr/bin/env python3 +""" +Usage +----- +./examples/ens-gen.py -d -v -s 0 \ + --dataset imagenet -b 16 --eps 8 --workdir "workdirs" \ + --device "cuda:0" \ + train --n-ep 1 \ + --surrogate-model-ids vgg19 inception_v3 resnet152 densenet169 \ + --lr 0.0002 --beta 0.5 0.999 \ + --use-logit-loss --use-logit-weights --use-logit-softmax-weights +""" +import argparse +import json +from pathlib import Path +from pprint import pformat +from typing import List, Union + +import torch +import torchvision +from torch.nn import functional as F +from torch.utils.tensorboard import SummaryWriter +from tqdm import tqdm + +from gat.datasets import build_dataset, list_datasets +from gat.datasets.transforms import norm +from gat.models.attack import CDAAttack +from gat.models.attack.optim import (SAM, disable_running_stats, + enable_running_stats) +from gat.models.surrogate import (build_surrogate, feat_col, list_surrogates, + midlayer_dict) +from gat.runtime import AverageMeter, calc_cls_accuracy, fix_random, randid + + +class CLIParser: + + @staticmethod + def init_basic_parser(p: argparse.ArgumentParser): + g_basic = p.add_argument_group('Basic Settings') + g_basic.add_argument('-v', + '--verbose', + action='store_true', + default=False) + g_basic.add_argument('-d', '--dev', action='store_true', default=False) + g_basic.add_argument('-s', '--seed', type=int, default=0) + g_basic.add_argument('--expid', type=str, default=randid(4)) + g_basic.add_argument('--device', type=str, default='cuda') + + g_path = p.add_argument_group('Path Settings') + g_path.add_argument('--workdir', type=str, default='workdirs') + g_path.add_argument('--data-root', + type=str, + default=Path(__file__).parent / '../data' / + 'in_1k') + + g_ds = p.add_argument_group('Dataset Settings') + g_ds.add_argument('--dataset', + type=str, + default='imagenet', + choices=list_datasets()) + g_ds.add_argument('-b', '--batch-size', type=int, default=16) + + g_at_basic = p.add_argument_group('General Attack Settings') + g_at_basic.add_argument('--eps', + '--epsilon', + dest='epsilon', + type=int, + default=8, + choices=[1, 2, 4, 8, 16]) + + @staticmethod + def post_basic_parser(args: argparse.Namespace): + if args.dev: + args.workdir = args.workdir.replace('workdirs', 'workdirs-dev') + args.workdir = Path(args.workdir) / args.expid + args.workdir.mkdir(parents=True, exist_ok=True) + args.device = torch.device(args.device) + + args.ckpt = args.workdir / 'model.pth' + args.tf_logger = SummaryWriter(args.workdir / 'tf_log') + + args.epsilon /= 255.0 + if args.command == 'evaluate-pgd': + args.alpha /= 255.0 + + fix_random(args.seed) + + if args.verbose: + print(pformat(vars(args))) + with open(args.workdir / f'args-{args.command}.txt', 'w') as f: + f.write(pformat(vars(args))) + + @staticmethod + def init_train_parser(p: argparse.ArgumentParser): + g_at = p.add_argument_group('Attack Settings') + g_at.add_argument('--sur-ids', + '--surrogate-model-ids', + dest='surrogate_model_ids', + type=str, + default=['resnet152'], + nargs='+', + choices=list_surrogates()) + g_at.add_argument('--n-ep', + '--num-epoch', + dest='num_epoch', + type=int, + default=10) + + g_optim = p.add_argument_group('Optimization Settings') + g_optim.add_argument('--use-sam', action='store_true', default=False) + g_optim.add_argument('--lr', type=float, default=0.0002) + g_optim.add_argument('--betas', + type=float, + nargs=2, + default=(0.5, 0.999)) + + g_loss = p.add_argument_group('Loss Func Settings') + g_loss.add_argument('--use-logit-loss', + action='store_true', + default=False) + g_loss.add_argument('--use-logit-kl', + action='store_true', + default=False) + g_loss.add_argument('--use-logit-weights', + action='store_true', + default=False) + g_loss.add_argument('--use-logit-softmax-weights', + action='store_true', + default=False) + g_loss.add_argument('--use-feat-loss', + action='store_true', + default=False) + g_loss.add_argument('--use-feat-attn', + action='store_true', + default=False) + + @staticmethod + def post_train_parser(args: argparse.Namespace): + if args.command == 'train': + assert args.use_logit_loss ^ args.use_feat_loss + if args.use_logit_kl: + assert not args.use_feat_loss + if args.use_feat_attn: + assert not args.use_logit_loss + if args.use_logit_weights: + assert args.use_logit_loss + if args.use_logit_softmax_weights: + assert args.use_logit_loss + + @staticmethod + def init_evaluate_parser(p: argparse.ArgumentParser): + pass + + @staticmethod + def post_evaluate_parser(args: argparse.Namespace): + pass + + @staticmethod + def init_evaluate_pgd_parser(p: argparse.ArgumentParser): + g_at = p.add_argument_group('Attack Settings') + g_at.add_argument('--surrogate-model-ids', + type=str, + default=['resnet152'], + nargs='+', + choices=list_surrogates()) + + g_optim = p.add_argument_group('Optimization Settings') + g_optim.add_argument('--num-step', type=int, default=100) + g_optim.add_argument('--alpha', + type=int, + default=2, + choices=[1, 2, 4, 8, 16]) + + g_loss = p.add_argument_group('Loss Func Settings') + g_loss.add_argument('--use-loss-avg', + action='store_true', + default=False) + g_loss.add_argument('--use-logit-avg', + action='store_true', + default=False) + + @staticmethod + def post_evaluate_pgd_parser(args: argparse.Namespace): + if args.command == 'evaluate-pgd': + assert args.use_loss_avg ^ args.use_logit_avg + + @staticmethod + def parse_args(): + p = argparse.ArgumentParser() + CLIParser.init_basic_parser(p) + sub_p = p.add_subparsers(dest='command') + + CLIParser.init_train_parser(sub_p.add_parser('train')) + CLIParser.init_evaluate_parser(sub_p.add_parser('evaluate')) + CLIParser.init_evaluate_pgd_parser(sub_p.add_parser('evaluate-pgd')) + args = p.parse_args() + CLIParser.post_train_parser(args) + CLIParser.post_evaluate_parser(args) + CLIParser.post_evaluate_pgd_parser(args) + + CLIParser.post_basic_parser(args) + + return args + + +def init_loader(dataset: str, + data_root: Union[str, Path], + num_epoch: int = 1, + batch_size: int = 16, + command: str = 'train') -> List[torch.utils.data.DataLoader]: + ds = build_dataset(dataset, + data_root=data_root, + is_train=(command == 'train')) + dataloader = torch.utils.data.DataLoader( + ds, + batch_size=batch_size, + sampler=torch.utils.data.RandomSampler(ds, + replacement=True, + num_samples=len(ds) * + num_epoch), + num_workers=4, + pin_memory=True, + ) + normalizer = norm(dataset, _callable=True) + return dataloader, normalizer + + +def init_models(model_ids: Union[str, List[str]], + device: Union[str, torch.device] = torch.device('cuda')): + if isinstance(model_ids, str): + model_ids = [model_ids] + models = [ + build_surrogate(_surrogate_id, pretrain=True).to(device) + for _surrogate_id in model_ids + ] + for _ in models: + _.eval() + return models + + +def calc_loss(x_nat: torch.Tensor, + y_nat: torch.Tensor, + x_adv: torch.Tensor, + feat_collecter: List, + surrogate_models: List[torch.nn.Module], + normalizer: torchvision.transforms.Compose, + use_logit_loss: bool, + use_logit_kl: bool, + use_logit_weights: bool, + use_logit_softmax_weights: bool, + use_feat_loss: bool, + use_feat_attn: bool, + device: Union[str, torch.device] = torch.device('cuda')): + loss_sur = [] + for surrogate_model in surrogate_models: + logit_nat = surrogate_model(normalizer(x_nat)) + feat_nat = feat_collecter.pop() + logit_adv = surrogate_model(normalizer(x_adv)) + feat_adv = feat_collecter.pop() + if use_logit_loss: + if use_logit_kl: + loss_sur.append(-(F.kl_div(F.log_softmax(logit_adv, dim=1), + F.softmax(logit_nat, dim=1)) + + F.kl_div(F.log_softmax(logit_nat, dim=1), + F.softmax(logit_adv, dim=1)))) + else: + loss_sur.append(-(F.cross_entropy(logit_adv, y_nat).mean())) + elif use_feat_loss: + if use_feat_attn: + attn = torch.abs(torch.mean(feat_nat, dim=1, keepdim=True)) + else: + attn = torch.ones_like(feat_nat) + loss_sur.append(1 + F.cosine_similarity(attn * feat_nat, attn * + feat_adv).mean()) + else: + raise NotImplementedError + loss_sur = torch.stack(loss_sur) + if use_logit_weights: + if use_logit_softmax_weights: + loss_weights = torch.nn.functional.softmax(loss_sur) + else: + loss_weights = torch.nn.functional.softmin(loss_sur) + loss_all = torch.sum(loss_weights * loss_sur) + else: + loss_all = loss_sur.mean() + + return loss_all + + +def train(surrogate_model_ids: Union[str, List[str]], + epsilon: float = 16.0 / 255.0, + num_epoch: int = 10, + dataset: str = 'imagenet', + batch_size: int = 16, + use_sam: bool = False, + lr: float = 0.0002, + betas: Union[float, List[float]] = (0.5, 0.999), + use_logit_loss: bool = False, + use_logit_kl: bool = False, + use_logit_weights: bool = False, + use_logit_softmax_weights: bool = False, + use_feat_loss: bool = False, + use_feat_attn: bool = False, + device: Union[str, torch.device] = torch.device('cuda'), + workdir: Union[str, Path] = Path(__file__).parents[1] / 'workdirs', + data_root: Union[str, + Path] = Path(__file__).parent / '../data' / 'in_1k', + tf_logger: SummaryWriter = None) -> None: + """ + Train the attack model with the given surrogate models. + """ + loader, normalizer = init_loader(dataset, data_root, num_epoch, batch_size, + 'train') + surrogate_models = init_models(surrogate_model_ids, device) + + attack = CDAAttack(device=device, epsilon=epsilon) + attack.set_mode('train') + if use_sam: + optim = SAM(attack.get_params(), torch.optim.Adam, lr=lr, betas=betas) + else: + optim = torch.optim.Adam(attack.get_params(), lr=lr, betas=betas) + + with feat_col(surrogate_models, + [midlayer_dict[_] + for _ in surrogate_model_ids]) as feat_collecter: + attack.set_mode('train') + enumerator = tqdm(enumerate(loader), total=len(loader), desc='') + for step, (x_nat, y_nat) in enumerator: + x_nat, y_nat = x_nat.to(device), y_nat.to(device) + + if use_sam: + # 1 + enable_running_stats(attack.get_model()) + loss_v = calc_loss(x_nat, y_nat, attack(x_nat), feat_collecter, + surrogate_models, normalizer, + use_logit_loss, use_logit_kl, + use_logit_weights, + use_logit_softmax_weights, use_feat_loss, + use_feat_attn, device) + loss_v.backward() + optim.first_step(zero_grad=True) + # 2 + disable_running_stats(attack.get_model()) + calc_loss(x_nat, y_nat, attack(x_nat), feat_collecter, + surrogate_models, normalizer, use_logit_loss, + use_logit_kl, use_logit_weights, + use_logit_softmax_weights, use_feat_loss, + use_feat_attn, device).backward() + optim.second_step(zero_grad=True) + else: + x_adv = attack(x_nat) + loss_v = calc_loss(x_nat, y_nat, x_adv, feat_collecter, + surrogate_models, normalizer, + use_logit_loss, use_logit_kl, + use_logit_weights, + use_logit_softmax_weights, use_feat_loss, + use_feat_attn, device) + optim.zero_grad() + loss_v.backward() + optim.step() + + if tf_logger: + tf_logger.add_scalar('loss', loss_v.item(), step) + tf_logger.add_scalar('lr', optim.param_groups[0]['lr'], step) + + attack.save_ckpt(workdir / 'model.pth') + + +@torch.no_grad() +def evaluate( + ckpt: Union[str, Path], + epsilon: float = 16.0 / 255.0, + dataset: str = 'imagenet', + batch_size: int = 16, + device: Union[str, torch.device] = torch.device('cuda'), + workdir: Union[str, Path] = Path(__file__).parents[1] / 'workdirs', + data_root: Union[str, Path] = Path(__file__).parent / '../data' / 'in_1k', +) -> None: + """ + Evaluate the attack model with the given surrogate models + """ + loader, normalizer = init_loader(dataset, data_root, 1, batch_size, + 'evaluate') + target_models = { + k: v + for k, v in zip(list_surrogates(), + init_models(list_surrogates(), device)) + } + target_acc_meters = { + target_model_id: [AverageMeter() for _ in range(2)] + for target_model_id in target_models.keys() + } + # init attack method + attack = CDAAttack(device=device, epsilon=epsilon) + attack.load_ckpt(ckpt) + attack.set_mode('eval') + # evaluate + enumerator = tqdm(enumerate(loader), total=len(loader), desc='Eval') + for step, (x_nat, y_nat) in enumerator: + x_nat, y_nat = x_nat.to(device), y_nat.to(device) + x_adv = attack(x_nat) + for target_model_id, target_model in target_models.items(): + logit_nat = target_model(normalizer(x_nat)) + logit_adv = target_model(normalizer(x_adv)) + # collect metrics + target_acc = calc_cls_accuracy(logit_nat, y_nat) + target_asr = calc_cls_accuracy(logit_adv, y_nat) + target_acc_meters[target_model_id][0].update( + target_acc[0].item(), x_nat.size(0)) + target_acc_meters[target_model_id][1].update( + target_asr[0].item(), x_nat.size(0)) + results = { + target_model_id: { + 'nat_acc': target_acc_meter[0].avg, + 'adv_acc': target_acc_meter[1].avg + } + for target_model_id, target_acc_meter in target_acc_meters.items() + } + print(pformat(results)) + with open(workdir / 'results.json', 'w') as f: + json.dump(results, f) + + +def evaluate_pgd( + surrogate_model_ids: Union[str, List[str]], + epsilon: float = 16.0 / 255.0, + num_step: int = 1000, + alpha: float = 2.0 / 255.0, + dataset: str = 'imagenet', + batch_size: int = 16, + use_loss_avg: bool = False, + use_logit_avg: bool = False, + device: Union[str, torch.device] = torch.device('cuda'), + workdir: Union[str, Path] = Path(__file__).parents[1] / 'workdirs', + data_root: Union[str, Path] = Path(__file__).parent / '../data' / 'in_1k', +): + loader, normalizer = init_loader(dataset, data_root, 1, batch_size, + 'evaluate') + surrogate_models = init_models(surrogate_model_ids, device) + target_models = { + k: v + for k, v in zip(list_surrogates(), + init_models(list_surrogates(), device)) + } + target_acc_meters = { + target_model_id: [AverageMeter() for _ in range(2)] + for target_model_id in target_models.keys() + } + # evaluate + enumerator = tqdm(enumerate(loader), total=len(loader), desc='') + for step, (x_nat, y_nat) in enumerator: + x_nat, y_nat = x_nat.to(device), y_nat.to(device) + # attack + x_nat_ori = x_nat.data + for _ in range(num_step): + x_nat.requires_grad = True + if use_loss_avg: + loss_all = 0.0 + for surrogate_model in surrogate_models: + logit = surrogate_model(x_nat) + surrogate_model.zero_grad() + loss_all += F.cross_entropy(logit, y_nat) + elif use_logit_avg: + logit = torch.stack([ + surrogate_model(x_nat) + for surrogate_model in surrogate_models + ]).mean(dim=0) + loss_all = F.cross_entropy(logit, y_nat) + else: + raise NotADirectoryError + loss_all.backward() + x_adv_ = x_nat + alpha * x_nat.grad.sign() + eta = torch.clamp(x_adv_ - x_nat_ori, min=-epsilon, max=epsilon) + x_nat = torch.clamp(x_nat_ori + eta, min=0.0, max=1.0).detach_() + x_adv = x_nat + x_nat = x_nat_ori + # eval + with torch.no_grad(): + for target_model_id, target_model in target_models.items(): + logit_nat = target_model(normalizer(x_nat)) + logit_adv = target_model(normalizer(x_adv)) + # collect + target_acc_ = calc_cls_accuracy(logit_nat, y_nat) + target_asr_ = calc_cls_accuracy(logit_adv, y_nat) + target_acc_meters[target_model_id][0].update( + target_acc_[0].item(), x_nat.size(0)) + target_acc_meters[target_model_id][1].update( + target_asr_[0].item(), x_nat.size(0)) + results = { + target_model_id: { + 'nat_acc': target_acc_meter[0].avg, + 'adv_acc': target_acc_meter[1].avg + } + for target_model_id, target_acc_meter in target_acc_meters.items() + } + print(pformat(results)) + with open(workdir / 'results-pgd.json', 'w') as f: + json.dump(results, f) + + +def main() -> None: + args = CLIParser.parse_args() + if args.command == 'train': + train(args.surrogate_model_ids, args.epsilon, args.num_epoch, + args.dataset, args.batch_size, args.use_sam, args.lr, args.betas, + args.use_logit_loss, args.use_logit_kl, args.use_logit_weights, + args.use_logit_softmax_weights, args.use_feat_loss, + args.use_feat_attn, args.device, args.workdir, args.data_root, + args.tf_logger) + elif args.command == 'evaluate': + evaluate(args.ckpt, args.epsilon, args.dataset, args.batch_size, + args.device, args.workdir, args.data_root) + elif args.command == 'evaluate-pgd': + evaluate_pgd(args.surrogate_model_ids, args.epsilon, args.num_step, + args.alpha, args.dataset, args.batch_size, + args.use_loss_avg, args.use_logit_avg, args.device, + args.workdir, args.data_root) + else: + raise NotImplementedError + + +if __name__ == '__main__': + main() diff --git a/attacks/AIM/examples/workdirs/1MsK/args.txt b/attacks/AIM/examples/workdirs/1MsK/args.txt new file mode 100644 index 0000000000000000000000000000000000000000..ba489366d0bc87a31368a1fcb0cdffad2da2656d --- /dev/null +++ b/attacks/AIM/examples/workdirs/1MsK/args.txt @@ -0,0 +1,11 @@ +{'batch_size': 16, + 'ckpt': WindowsPath('workdirs/1MsK/model.pth'), + 'command': None, + 'data_root': WindowsPath('D:/Sharing/Programs/3-Durable-Adv/PyCIL-master/attacks/GAT/examples/../data/in_1k'), + 'dataset': 'imagenet', + 'device': device(type='cuda'), + 'expid': '1MsK', + 'seed': 0, + 'tar_classes': 24, + 'verbose': False, + 'workdir': WindowsPath('workdirs/1MsK')} \ No newline at end of file diff --git a/attacks/AIM/examples/workdirs/CGKQ/args.txt b/attacks/AIM/examples/workdirs/CGKQ/args.txt new file mode 100644 index 0000000000000000000000000000000000000000..74af11a62efcaee6b0d3bdfc32267755baea9115 --- /dev/null +++ b/attacks/AIM/examples/workdirs/CGKQ/args.txt @@ -0,0 +1,11 @@ +{'batch_size': 16, + 'ckpt': WindowsPath('workdirs/CGKQ/model.pth'), + 'command': None, + 'data_root': WindowsPath('D:/Sharing/Programs/3-Durable-Adv/PyCIL-master/attacks/GAT/examples/../data/in_1k'), + 'dataset': 'imagenet', + 'device': device(type='cuda'), + 'expid': 'CGKQ', + 'seed': 0, + 'tar_classes': 24, + 'verbose': False, + 'workdir': WindowsPath('workdirs/CGKQ')} \ No newline at end of file diff --git a/attacks/AIM/examples/workdirs/Fvu1/args.txt b/attacks/AIM/examples/workdirs/Fvu1/args.txt new file mode 100644 index 0000000000000000000000000000000000000000..9ece1315da52160ebee49151274c61bb836d4814 --- /dev/null +++ b/attacks/AIM/examples/workdirs/Fvu1/args.txt @@ -0,0 +1,11 @@ +{'batch_size': 16, + 'ckpt': WindowsPath('workdirs/Fvu1/model.pth'), + 'command': None, + 'data_root': WindowsPath('D:/Sharing/Programs/3-Durable-Adv/PyCIL-master/attacks/GAT/examples/../data/in_1k'), + 'dataset': 'imagenet', + 'device': device(type='cuda'), + 'expid': 'Fvu1', + 'seed': 0, + 'tar_classes': 24, + 'verbose': False, + 'workdir': WindowsPath('workdirs/Fvu1')} \ No newline at end of file diff --git a/attacks/AIM/examples/workdirs/Qonx/args.txt b/attacks/AIM/examples/workdirs/Qonx/args.txt new file mode 100644 index 0000000000000000000000000000000000000000..4f2abd87f03263c4d6b5545a33d932fefa839379 --- /dev/null +++ b/attacks/AIM/examples/workdirs/Qonx/args.txt @@ -0,0 +1,11 @@ +{'batch_size': 16, + 'ckpt': WindowsPath('workdirs/Qonx/model.pth'), + 'command': None, + 'data_root': WindowsPath('D:/Sharing/Programs/3-Durable-Adv/PyCIL-master/attacks/GAT/examples/../data/in_1k'), + 'dataset': 'imagenet', + 'device': device(type='cuda'), + 'expid': 'Qonx', + 'seed': 0, + 'tar_classes': 24, + 'verbose': False, + 'workdir': WindowsPath('workdirs/Qonx')} \ No newline at end of file diff --git a/attacks/AIM/examples/workdirs/fnNs/args.txt b/attacks/AIM/examples/workdirs/fnNs/args.txt new file mode 100644 index 0000000000000000000000000000000000000000..ac36479657e93504b31288e35520ad69c34302b8 --- /dev/null +++ b/attacks/AIM/examples/workdirs/fnNs/args.txt @@ -0,0 +1,11 @@ +{'batch_size': 16, + 'ckpt': WindowsPath('workdirs/fnNs/model.pth'), + 'command': None, + 'data_root': WindowsPath('D:/Sharing/Programs/3-Durable-Adv/PyCIL-master/attacks/GAT/examples/../data/in_1k'), + 'dataset': 'imagenet', + 'device': device(type='cuda'), + 'expid': 'fnNs', + 'seed': 0, + 'tar_classes': 24, + 'verbose': False, + 'workdir': WindowsPath('workdirs/fnNs')} \ No newline at end of file diff --git a/attacks/AIM/examples/workdirs/jtMb/args.txt b/attacks/AIM/examples/workdirs/jtMb/args.txt new file mode 100644 index 0000000000000000000000000000000000000000..87c703eb3f0f278dbff5104dfd4417a367c1d1d9 --- /dev/null +++ b/attacks/AIM/examples/workdirs/jtMb/args.txt @@ -0,0 +1,11 @@ +{'batch_size': 16, + 'ckpt': WindowsPath('workdirs/jtMb/model.pth'), + 'command': None, + 'data_root': WindowsPath('D:/Sharing/Programs/3-Durable-Adv/PyCIL-master/attacks/GAT/examples/../data/in_1k'), + 'dataset': 'imagenet', + 'device': device(type='cuda'), + 'expid': 'jtMb', + 'seed': 0, + 'tar_classes': 24, + 'verbose': False, + 'workdir': WindowsPath('workdirs/jtMb')} \ No newline at end of file diff --git a/attacks/AIM/examples/workdirs/krhX/args.txt b/attacks/AIM/examples/workdirs/krhX/args.txt new file mode 100644 index 0000000000000000000000000000000000000000..d448b6e2f205d1f4443db224a37d6b516c009e07 --- /dev/null +++ b/attacks/AIM/examples/workdirs/krhX/args.txt @@ -0,0 +1,11 @@ +{'batch_size': 16, + 'ckpt': WindowsPath('workdirs/krhX/model.pth'), + 'command': None, + 'data_root': WindowsPath('D:/Sharing/Programs/3-Durable-Adv/PyCIL-master/attacks/GAT/examples/../data/in_1k'), + 'dataset': 'imagenet', + 'device': device(type='cuda'), + 'expid': 'krhX', + 'seed': 0, + 'tar_classes': 24, + 'verbose': False, + 'workdir': WindowsPath('workdirs/krhX')} \ No newline at end of file diff --git a/attacks/AIM/setup.py b/attacks/AIM/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..03cbf50596b06c5e3a7135bbab45da0e1a8b7355 --- /dev/null +++ b/attacks/AIM/setup.py @@ -0,0 +1,47 @@ +#!/usr/bin/env python3 +import sys +from pathlib import Path +from typing import Union + +from setuptools import find_packages, setup + +try: + sys.path.append(str(Path(__file__).parent / 'src')) + import gat +except ImportError: + print('Please install aim_attack package') + sys.exit(1) + + +def read(path: Union[str, Path]) -> str: + with open(path, 'r') as f: + return f.read() + + +setup(name='pygat', + version=gat.VERSION, + description='GAT: Generative Attack Toolbox', + long_description=read('README.md'), + long_description_content_type='text/markdown', + author='Terry Li', + url='https://terrytengli.com/GAT/', + classifiers=[ + 'Programming Language :: Python :: 3', + 'License :: OSI Approved :: MIT License' + ], + python_requires='>=3.10', + packages=find_packages('src'), + package_dir={'': 'src'}, + entry_points={ + 'console_scripts': [ + 'aim-api=gat.runtime.api.aim_attack:main', + ], + }, + install_requires=[ + 'tqdm', 'tabulate', 'torch', 'torchvision', 'tensorboard', 'jupyter' + ], + extras_require={ + 'cli': ['python-multipart', 'fastapi', 'uvicorn'], + 'ens-gen': [] + }, + include_package_data=True) diff --git a/attacks/AIM/src/gat/__init__.py b/attacks/AIM/src/gat/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..876d6e35899b3d897168297e74bf02fc6db4713f --- /dev/null +++ b/attacks/AIM/src/gat/__init__.py @@ -0,0 +1 @@ +VERSION = '202411.2' diff --git a/attacks/AIM/src/gat/datasets/__init__.py b/attacks/AIM/src/gat/datasets/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9bbb57e463f9af26964d749ee623c448a28e979c --- /dev/null +++ b/attacks/AIM/src/gat/datasets/__init__.py @@ -0,0 +1,5 @@ +from .builder import build_dataset, list_datasets +from .cub import cub +from .imagenet import imagenet + +__all__ = ['build_dataset', 'list_datasets', 'imagenet', 'cub'] diff --git a/attacks/AIM/src/gat/datasets/builder.py b/attacks/AIM/src/gat/datasets/builder.py new file mode 100644 index 0000000000000000000000000000000000000000..54d7d5eea00794bb0508a9123ab187d2443b6be9 --- /dev/null +++ b/attacks/AIM/src/gat/datasets/builder.py @@ -0,0 +1,11 @@ +from ..runtime.factory import Registry + +DATASET_REGISTRY = Registry('DATASET') + + +def build_dataset(_type: str, *args, **kwargs) -> object: + return DATASET_REGISTRY.get(_type)(*args, **kwargs) + + +def list_datasets(): + return list(DATASET_REGISTRY._obj_map.keys()) diff --git a/attacks/AIM/src/gat/datasets/cub.py b/attacks/AIM/src/gat/datasets/cub.py new file mode 100644 index 0000000000000000000000000000000000000000..5cec9e2e1f71630c00df3b60cf2ad383e3cf683c --- /dev/null +++ b/attacks/AIM/src/gat/datasets/cub.py @@ -0,0 +1,6 @@ +from .builder import DATASET_REGISTRY + + +@DATASET_REGISTRY.register() +def cub(): + raise NotADirectoryError diff --git a/attacks/AIM/src/gat/datasets/env.py b/attacks/AIM/src/gat/datasets/env.py new file mode 100644 index 0000000000000000000000000000000000000000..efb40a13d426574f6d1e522f56656ecac9e8b8c1 --- /dev/null +++ b/attacks/AIM/src/gat/datasets/env.py @@ -0,0 +1,6 @@ +CIFAR10_DEFAULT_MEAN = (0.4914, 0.4822, 0.4465) +CIFAR10_DEFAULT_STD = (0.2470, 0.2435, 0.2616) +CIFAR100_DEFAULT_MEAN = (0.5071, 0.4865, 0.4409) +CIFAR100_DEFAULT_STD = (0.2673, 0.2564, 0.2762) +IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406) +IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225) diff --git a/attacks/AIM/src/gat/datasets/imagenet.py b/attacks/AIM/src/gat/datasets/imagenet.py new file mode 100644 index 0000000000000000000000000000000000000000..c2d34582fa2c5f4f131a74dbb66c3142fda9a6f2 --- /dev/null +++ b/attacks/AIM/src/gat/datasets/imagenet.py @@ -0,0 +1,36 @@ +from pathlib import Path +from typing import List, Union + +import torch +import torchvision + +from .builder import DATASET_REGISTRY +from .transforms import resize_256_224, to_color, to_ts + + +@DATASET_REGISTRY.register() +def imagenet( + data_root: Union[str, Path], + is_train: bool = True, + filter_class: Union[int, List[int]] = None, +) -> torch.utils.data.Dataset: + if isinstance(data_root, str): + data_root = Path(data_root) + if is_train: + data_root = data_root / 'train' + else: + data_root = data_root / 'val' + + _transforms = resize_256_224() + to_color() + to_ts() + + _ds = torchvision.datasets.ImageFolder( + data_root, + transform=torchvision.transforms.Compose(_transforms), + ) + + if isinstance(filter_class, int): + filter_class = [filter_class] + if filter_class: + _ds.samples = list(filter(lambda x: x[1] in filter_class, _ds.samples)) + + return _ds diff --git a/attacks/AIM/src/gat/datasets/transforms.py b/attacks/AIM/src/gat/datasets/transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..6317a4b9bbf7bf2b9514213699c68462a6d74fcf --- /dev/null +++ b/attacks/AIM/src/gat/datasets/transforms.py @@ -0,0 +1,55 @@ +from typing import List + +import torchvision + +from . import env + + +def resize_256_224() -> List: + return [ + torchvision.transforms.Resize(size=256), + torchvision.transforms.CenterCrop(size=(224, 224)), + ] + + +def resize_512_448() -> List: + return [ + torchvision.transforms.Resize(size=512), + torchvision.transforms.CenterCrop(size=(448, 448)), + ] + + +def resize_224() -> List: + return [torchvision.transforms.Resize(size=224)] + + +def hflip(p: float = 0.5) -> List: + assert 0 <= p <= 1 + return [torchvision.transforms.RandomHorizontalFlip(p)] + + +def to_ts() -> List: + return [torchvision.transforms.ToTensor()] + + +def to_pil() -> List: + return [torchvision.transforms.ToPILImage()] + + +def to_color() -> List: + return [ + torchvision.transforms.Lambda(lambda x: x.convert('RGB') + if x.mode != 'RGB' else x) + ] + + +def norm(dataset: str = 'IMAGENET', _callable: bool = False) -> List: + dataset = dataset.upper() + mean_std = ( + getattr(env, dataset + '_DEFAULT_MEAN'), + getattr(env, dataset + '_DEFAULT_STD'), + ) + transforms = [torchvision.transforms.Normalize(*mean_std)] + if _callable: + return torchvision.transforms.Compose(transforms) + return transforms diff --git a/attacks/AIM/src/gat/models/__init__.py b/attacks/AIM/src/gat/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/attacks/AIM/src/gat/models/attack/__init__.py b/attacks/AIM/src/gat/models/attack/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..86ba1376a3509896f8715e7b66cc852912ec1901 --- /dev/null +++ b/attacks/AIM/src/gat/models/attack/__init__.py @@ -0,0 +1,5 @@ +from .aim_attack import AIMAttack +from .cda_attack import CDAAttack +from .loss.logits import ContrastiveLoss + +__all__ = ['AIMAttack', 'CDAAttack', 'ContrastiveLoss'] diff --git a/attacks/AIM/src/gat/models/attack/aim_attack.py b/attacks/AIM/src/gat/models/attack/aim_attack.py new file mode 100644 index 0000000000000000000000000000000000000000..89335e2ae48043aed31b3c0ae56b66606113c00b --- /dev/null +++ b/attacks/AIM/src/gat/models/attack/aim_attack.py @@ -0,0 +1,14 @@ +import torch + +from .base_attack import BaseGenerativeAttack +from .generator.aim import AIMGenerator + + +class AIMAttack(BaseGenerativeAttack): + + def set_adv_gen(self): + self.adv_gen = AIMGenerator().to(self.device) + + def attack(self, x_nat, *extra_inputs) -> torch.Tensor: + x_guid = extra_inputs[0].to(self.device) + return self.adv_gen(x_nat, x_guid) diff --git a/attacks/AIM/src/gat/models/attack/base_attack.py b/attacks/AIM/src/gat/models/attack/base_attack.py new file mode 100644 index 0000000000000000000000000000000000000000..04c0a568713e517465c581312873deb04d04d114 --- /dev/null +++ b/attacks/AIM/src/gat/models/attack/base_attack.py @@ -0,0 +1,60 @@ +import abc +from collections import OrderedDict +from pathlib import Path +from typing import Union + +import torch + + +class BaseGenerativeAttack(abc.ABC): + + def __init__(self, + device: Union[str, torch.device], + epsilon: float = 32 / 255) -> None: + if isinstance(device, str): + device = torch.device(device) + self.device = device + self.set_adv_gen() + self.set_mode('eval') + self.epsilon = epsilon + + @abc.abstractmethod + def set_adv_gen(self): + pass + + def load_ckpt(self, ckpt: Union[str, Path, OrderedDict]) -> None: + if isinstance(ckpt, str): + ckpt = Path(ckpt) + if isinstance(ckpt, Path): + if not ckpt.exists(): + raise FileNotFoundError(f'File not found: {ckpt}') + ckpt = torch.load(ckpt, map_location=self.device) + self.adv_gen.load_state_dict(ckpt) + self.adv_gen.to(self.device) + + def save_ckpt(self, ckpt: Union[str, Path]) -> None: + if isinstance(ckpt, str): + ckpt = Path(ckpt) + _adv_gen_cpu = self.adv_gen.to('cpu') + torch.save(_adv_gen_cpu.state_dict(), ckpt) + + def get_params(self) -> torch.nn.Parameter: + return self.adv_gen.parameters() + + def get_model(self) -> torch.nn.Module: + return self.adv_gen + + def set_mode(self, mode: str) -> None: + assert mode in ['train', 'eval'] + self.adv_gen.train() if mode == 'train' else self.adv_gen.eval() + + @abc.abstractmethod + def attack(self, *args) -> torch.Tensor: + pass + + def __call__(self, x_nat: torch.Tensor, *extra_inputs) -> torch.Tensor: + x_adv = self.attack(x_nat, *extra_inputs) + x_adv = torch.min(torch.max(x_adv, x_nat - self.epsilon), + x_nat + self.epsilon) + torch.clamp_(x_adv, 0.0, 1.0) + return x_adv diff --git a/attacks/AIM/src/gat/models/attack/cda_attack.py b/attacks/AIM/src/gat/models/attack/cda_attack.py new file mode 100644 index 0000000000000000000000000000000000000000..0fdf6573340bfeb51846cb3d7252d549427480d9 --- /dev/null +++ b/attacks/AIM/src/gat/models/attack/cda_attack.py @@ -0,0 +1,13 @@ +import torch + +from .base_attack import BaseGenerativeAttack +from .generator.cda import CDAGenerator + + +class CDAAttack(BaseGenerativeAttack): + + def set_adv_gen(self): + self.adv_gen = CDAGenerator().to(self.device) + + def attack(self, x_nat, *extra_inputs) -> torch.Tensor: + return self.adv_gen(x_nat) diff --git a/attacks/AIM/src/gat/models/attack/generator/__init__.py b/attacks/AIM/src/gat/models/attack/generator/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/attacks/AIM/src/gat/models/attack/generator/aim.py b/attacks/AIM/src/gat/models/attack/generator/aim.py new file mode 100644 index 0000000000000000000000000000000000000000..5a8fa0f1b6e5699d3d0bf4cdd0a3189c2679d7f5 --- /dev/null +++ b/attacks/AIM/src/gat/models/attack/generator/aim.py @@ -0,0 +1,179 @@ +import torch +from torch import nn + + +class EnhancedBN(nn.Module): + def __init__(self, nc: int, sty_nc: int = 3, sty_nhidden: int = 128): + super(EnhancedBN, self).__init__() + self.bn = nn.BatchNorm2d(nc) + self.mapping = nn.Conv2d( + in_channels=sty_nc, + out_channels=sty_nhidden, + kernel_size=3, + padding=1, + stride=1, + ) + self.gamma = nn.Conv2d( + in_channels=sty_nhidden, + out_channels=nc, + kernel_size=3, + padding=1, + stride=1, + ) + self.beta = nn.Conv2d( + in_channels=sty_nhidden, + out_channels=nc, + kernel_size=3, + padding=1, + stride=1, + ) + self.init_weight() + + def init_weight(self): + nn.init.kaiming_normal_(self.mapping.weight) + nn.init.kaiming_normal_(self.gamma.weight) + nn.init.kaiming_normal_(self.beta.weight) + + def forward(self, base, sty): + bn = self.bn(base) + sty_resized = torch.nn.functional.interpolate( + sty, size=bn.size()[2:], mode='bilinear' + ) + actv = torch.nn.functional.relu(self.mapping(sty_resized)) + # style injection + bn = bn * (1 + self.gamma(actv)) + self.beta(actv) + return bn + + +class ResidualBlock(nn.Module): + def __init__(self, num_filters): + super(ResidualBlock, self).__init__() + self.block1 = nn.Sequential( + nn.ReflectionPad2d(1), + nn.Conv2d( + in_channels=num_filters, + out_channels=num_filters, + kernel_size=3, + stride=1, + padding=0, + bias=False, + ), + ) + self.bn1 = EnhancedBN(num_filters) + self.block2 = nn.Sequential( + nn.ReLU(True), + nn.Dropout(0.5), + nn.ReflectionPad2d(1), + nn.Conv2d( + in_channels=num_filters, + out_channels=num_filters, + kernel_size=3, + stride=1, + padding=0, + bias=False, + ), + ) + self.bn2 = EnhancedBN(num_filters) + + def forward(self, x, sty): + residual = self.block1(x) + residual = self.bn1(residual, sty) + residual = self.block2(residual) + residual = self.bn2(residual, sty) + return x + residual + + +ngf = 64 + + +class ResNetGenerator(nn.Module): + def __init__(self): + super(ResNetGenerator, self).__init__() + self.block1 = nn.Sequential( + nn.ReflectionPad2d(3), + nn.Conv2d(3, ngf, kernel_size=7, padding=0, bias=False), + ) + self.bn1 = EnhancedBN(ngf) + # Input size = 3, n, n + self.block2 = nn.Sequential( + nn.Conv2d( + ngf, ngf * 2, kernel_size=3, stride=2, padding=1, bias=False + ), + ) + self.bn2 = EnhancedBN(ngf * 2) + # Input size = 3, n/2, n/2 + self.block3 = nn.Sequential( + nn.Conv2d( + ngf * 2, + ngf * 4, + kernel_size=3, + stride=2, + padding=1, + bias=False, + ), + ) + self.bn3 = EnhancedBN(ngf * 4) + # Input size = 3, n/4, n/4 + # Residual Blocks: 6 + self.resblock1 = ResidualBlock(ngf * 4) + self.resblock2 = ResidualBlock(ngf * 4) + self.resblock3 = ResidualBlock(ngf * 4) + self.resblock4 = ResidualBlock(ngf * 4) + self.resblock5 = ResidualBlock(ngf * 4) + self.resblock6 = ResidualBlock(ngf * 4) + # Input size = 3, n/4, n/4 + self.upsampl1 = nn.ConvTranspose2d( + ngf * 4, + ngf * 2, + kernel_size=3, + stride=2, + padding=1, + output_padding=1, + bias=False, + ) + self.ubn1 = EnhancedBN(ngf * 2) + # Input size = 3, n/2, n/2 + self.upsampl2 = nn.ConvTranspose2d( + ngf * 2, + ngf, + kernel_size=3, + stride=2, + padding=1, + output_padding=1, + bias=False, + ) + self.ubn2 = EnhancedBN(ngf) + # Input size = 3, n, n + self.blockf = nn.Sequential( + nn.ReflectionPad2d(3), nn.Conv2d(ngf, 3, kernel_size=7, padding=0) + ) + + def forward(self, input, sty): + x = self.block1(input) + x = self.bn1(x, sty) + x = torch.nn.functional.relu(x) + x = self.block2(x) + x = self.bn2(x, sty) + x = torch.nn.functional.relu(x) + x = self.block3(x) + x = self.bn3(x, sty) + x = torch.nn.functional.relu(x) + # ============================= + x = self.resblock1(x, sty) + x = self.resblock2(x, sty) + x = self.resblock3(x, sty) + x = self.resblock4(x, sty) + x = self.resblock5(x, sty) + x = self.resblock6(x, sty) + # ============================= + x = self.upsampl1(x) + x = self.ubn1(x, sty) + x = torch.nn.functional.relu(x) + x = self.upsampl2(x) + x = self.ubn2(x, sty) + x = torch.nn.functional.relu(x) + x = self.blockf(x) + return (torch.tanh(x) + 1) / 2 + + +AIMGenerator = ResNetGenerator diff --git a/attacks/AIM/src/gat/models/attack/generator/cda.py b/attacks/AIM/src/gat/models/attack/generator/cda.py new file mode 100644 index 0000000000000000000000000000000000000000..d67dc94f5c5fba0db304870713004c571bc01900 --- /dev/null +++ b/attacks/AIM/src/gat/models/attack/generator/cda.py @@ -0,0 +1,146 @@ +# Code is copied from +# github.com/Alibaba-AAIG/Beyond-ImageNet-Attack:generator.py@863b7 +import torch +from torch import nn + + +class ResidualBlock(nn.Module): + def __init__(self, num_filters): + super(ResidualBlock, self).__init__() + self.block = nn.Sequential( + nn.ReflectionPad2d(1), + nn.Conv2d( + in_channels=num_filters, + out_channels=num_filters, + kernel_size=3, + stride=1, + padding=0, + bias=False, + ), + nn.BatchNorm2d(num_filters), + nn.ReLU(True), + nn.Dropout(0.5), + nn.ReflectionPad2d(1), + nn.Conv2d( + in_channels=num_filters, + out_channels=num_filters, + kernel_size=3, + stride=1, + padding=0, + bias=False, + ), + nn.BatchNorm2d(num_filters), + ) + + def forward(self, x): + residual = self.block(x) + return x + residual + + +ngf = 64 + + +class ResNetGenerator(nn.Module): + """ + https://github.com/Alibaba-AAIG/Beyond-ImageNet-Attack/blob/863b758ee4f4a6d3d4e7777c5f94f457fa449f73/generator.py#L14 + + Test Case: + >>> netG = ResNetGenerator() + >>> test_sample = torch.rand(1, 3, 224, 224) + >>> print("Generator output:", netG(test_sample).size()) + >>> print( + >>> "Generator parameters:", + >>> sum(p.numel() for p in netG.parameters() if p.requires_grad), + >>> ) + """ + + def __init__(self, inception=False): + super(ResNetGenerator, self).__init__() + self.inception = inception + self.block1 = nn.Sequential( + nn.ReflectionPad2d(3), + nn.Conv2d(3, ngf, kernel_size=7, padding=0, bias=False), + nn.BatchNorm2d(ngf), + nn.ReLU(True), + ) + # output: (ngf) x (n) x (n) + self.block2 = nn.Sequential( + nn.Conv2d( + ngf, ngf * 2, kernel_size=3, stride=2, padding=1, bias=False + ), + nn.BatchNorm2d(ngf * 2), + nn.ReLU(True), + ) + # output: (ngf*2) x (n/2) x (n/2) + self.block3 = nn.Sequential( + nn.Conv2d( + ngf * 2, + ngf * 4, + kernel_size=3, + stride=2, + padding=1, + bias=False, + ), + nn.BatchNorm2d(ngf * 4), + nn.ReLU(True), + ) + # output: (ngf*4) x (n/4) x (n/4) + self.resblock1 = ResidualBlock(ngf * 4) + self.resblock2 = ResidualBlock(ngf * 4) + self.resblock3 = ResidualBlock(ngf * 4) + self.resblock4 = ResidualBlock(ngf * 4) + self.resblock5 = ResidualBlock(ngf * 4) + self.resblock6 = ResidualBlock(ngf * 4) + # output: (ngf*4) x (n/4) x (n/4) + self.upsampl1 = nn.Sequential( + nn.ConvTranspose2d( + ngf * 4, + ngf * 2, + kernel_size=3, + stride=2, + padding=1, + output_padding=1, + bias=False, + ), + nn.BatchNorm2d(ngf * 2), + nn.ReLU(True), + ) + # output: (ngf*2) x (n/2) x (n/2) + self.upsampl2 = nn.Sequential( + nn.ConvTranspose2d( + ngf * 2, + ngf, + kernel_size=3, + stride=2, + padding=1, + output_padding=1, + bias=False, + ), + nn.BatchNorm2d(ngf), + nn.ReLU(True), + ) + # output: (ngf) x (n) x (n) + self.blockf = nn.Sequential( + nn.ReflectionPad2d(3), nn.Conv2d(ngf, 3, kernel_size=7, padding=0) + ) + self.crop = nn.ConstantPad2d((0, -1, -1, 0), 0) + + def forward(self, input): + x = self.block1(input) + x = self.block2(x) + x = self.block3(x) + x = self.resblock1(x) + x = self.resblock2(x) + x = self.resblock3(x) + x = self.resblock4(x) + x = self.resblock5(x) + x = self.resblock6(x) + x = self.upsampl1(x) + x = self.upsampl2(x) + x = self.blockf(x) + if self.inception: + x = self.crop(x) + return (torch.tanh(x) + 1) / 2 + + +CDAGenerator = ResNetGenerator diff --git a/attacks/AIM/src/gat/models/attack/loss/__init__.py b/attacks/AIM/src/gat/models/attack/loss/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/attacks/AIM/src/gat/models/attack/loss/logits.py b/attacks/AIM/src/gat/models/attack/loss/logits.py new file mode 100644 index 0000000000000000000000000000000000000000..2c4da3dac2bdec72d4322862d55c1e046fd6d079 --- /dev/null +++ b/attacks/AIM/src/gat/models/attack/loss/logits.py @@ -0,0 +1,27 @@ +import torch + + +class ContrastiveLoss(torch.nn.Module): + """ + Contrastive loss + Adapted from: (OnlineContrastiveLoss) + https://github.com/adambielski/siamese-triplet/blob/master/losses.py + Based on: http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf + """ + + def __init__(self, margin): + super(ContrastiveLoss, self).__init__() + self.margin = margin + + def forward(self, anchors, negatives, positives): + anchors = anchors / anchors.norm(dim=-1, keepdim=True) + negatives = negatives / negatives.norm(dim=-1, keepdim=True) + positives = positives / positives.norm(dim=-1, keepdim=True) + + positive_loss = (anchors - positives).pow(2).sum(1) + negative_loss = torch.nn.functional.relu( + self.margin - (anchors - negatives).pow(2).sum(1).sqrt()).pow(2) + + loss = 0.5 * torch.cat([positive_loss, negative_loss], dim=0) + + return loss.mean() diff --git a/attacks/AIM/src/gat/models/attack/optim/__init__.py b/attacks/AIM/src/gat/models/attack/optim/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b77878771aae903fcea88edb421c8c07c748728e --- /dev/null +++ b/attacks/AIM/src/gat/models/attack/optim/__init__.py @@ -0,0 +1,3 @@ +from .sam import SAM, disable_running_stats, enable_running_stats + +__all__ = ['SAM', 'enable_running_stats', 'disable_running_stats'] diff --git a/attacks/AIM/src/gat/models/attack/optim/sam.py b/attacks/AIM/src/gat/models/attack/optim/sam.py new file mode 100644 index 0000000000000000000000000000000000000000..5e7448644322aa1a09078cbebb691906152d6db9 --- /dev/null +++ b/attacks/AIM/src/gat/models/attack/optim/sam.py @@ -0,0 +1,100 @@ +# Code is copied from +# github.com/davda54/sam/sam.py@3c3afdb +import torch +from torch.nn.modules.batchnorm import _BatchNorm + + +class SAM(torch.optim.Optimizer): + + def __init__(self, + params, + base_optimizer, + rho=0.05, + adaptive=False, + **kwargs): + assert rho >= 0.0, f"Invalid rho, should be non-negative: {rho}" + + defaults = dict(rho=rho, adaptive=adaptive, **kwargs) + super(SAM, self).__init__(params, defaults) + + self.base_optimizer = base_optimizer(self.param_groups, **kwargs) + self.param_groups = self.base_optimizer.param_groups + self.defaults.update(self.base_optimizer.defaults) + + @torch.no_grad() + def first_step(self, zero_grad=False): + grad_norm = self._grad_norm() + for group in self.param_groups: + scale = group['rho'] / (grad_norm + 1e-12) + + for p in group['params']: + if p.grad is None: + continue + self.state[p]['old_p'] = p.data.clone() + e_w = (torch.pow(p, 2) + if group['adaptive'] else 1.0) * p.grad * scale.to(p) + p.add_(e_w) # climb to the local maximum "w + e(w)" + + if zero_grad: + self.zero_grad() + + @torch.no_grad() + def second_step(self, zero_grad=False): + for group in self.param_groups: + for p in group['params']: + if p.grad is None: + continue + p.data = self.state[p][ + 'old_p'] # get back to "w" from "w + e(w)" + + self.base_optimizer.step() # do the actual "sharpness-aware" update + + if zero_grad: + self.zero_grad() + + @torch.no_grad() + def step(self, closure=None): + assert closure is not None, \ + 'Sharpness Aware Minimization requires closure, ' \ + 'but it was not provided' + closure = torch.enable_grad()( + closure) # the closure should do a full forward-backward pass + + self.first_step(zero_grad=True) + closure() + self.second_step() + + def _grad_norm(self): + # put everything on the same device, in case of model parallelism + shared_device = self.param_groups[0]['params'][0].device + norm = torch.norm(torch.stack([ + ((torch.abs(p) if group['adaptive'] else 1.0) * + p.grad).norm(p=2).to(shared_device) for group in self.param_groups + for p in group['params'] if p.grad is not None + ]), + p=2) + return norm + + def load_state_dict(self, state_dict): + super().load_state_dict(state_dict) + self.base_optimizer.param_groups = self.param_groups + + +def disable_running_stats(model): + + def _disable(module): + if isinstance(module, _BatchNorm): + module.backup_momentum = module.momentum + module.momentum = 0 + + model.apply(_disable) + + +def enable_running_stats(model): + + def _enable(module): + if isinstance(module, _BatchNorm) and hasattr(module, + 'backup_momentum'): + module.momentum = module.backup_momentum + + model.apply(_enable) diff --git a/attacks/AIM/src/gat/models/surrogate/__init__.py b/attacks/AIM/src/gat/models/surrogate/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e5cbf6fd413bc3f05ba3fe6d9787fe485ad05104 --- /dev/null +++ b/attacks/AIM/src/gat/models/surrogate/__init__.py @@ -0,0 +1,10 @@ +from .builder import build_surrogate, list_surrogates +from .hooks import feat_col, midlayer_dict, register_collecter, register_collecter_cl +from .tv import (densenet121, densenet169, inception_v3, resnet50, resnet152, + swin_b, vgg16, vgg19, vit_b_16, vit_b_32) + +__all__ = [ + 'build_surrogate', 'list_surrogates', 'inception_v3', 'vgg16', 'vgg19', + 'resnet50', 'resnet152', 'densenet121', 'densenet169', 'vit_b_16', + 'vit_b_32', 'swin_b', 'midlayer_dict', 'register_collecter', 'feat_col', 'register_collecter_cl' +] diff --git a/attacks/AIM/src/gat/models/surrogate/builder.py b/attacks/AIM/src/gat/models/surrogate/builder.py new file mode 100644 index 0000000000000000000000000000000000000000..c20ccf8020695014a893649d1de59fee58b45dae --- /dev/null +++ b/attacks/AIM/src/gat/models/surrogate/builder.py @@ -0,0 +1,11 @@ +from ...runtime.factory import Registry + +SURROGATE_REGISTRY = Registry('SURROGATE') + + +def build_surrogate(_type: str, *args, **kwargs) -> object: + return SURROGATE_REGISTRY.get(_type)(*args, **kwargs) + + +def list_surrogates(): + return list(SURROGATE_REGISTRY._obj_map.keys()) diff --git a/attacks/AIM/src/gat/models/surrogate/hooks.py b/attacks/AIM/src/gat/models/surrogate/hooks.py new file mode 100644 index 0000000000000000000000000000000000000000..fb29d59d5c76d2da4306fe98105899d5fbebb454 --- /dev/null +++ b/attacks/AIM/src/gat/models/surrogate/hooks.py @@ -0,0 +1,59 @@ +from contextlib import contextmanager +from typing import List, Union + +import torch + +midlayer_dict = { + 'vgg16': 'features.16', + 'vgg19': 'features.18', + 'resnet152': 'layer1', + 'densenet169': 'features.denseblock2', + 'inception_v3': 'Mixed_6c', + 'resnet50': 'conv1', # conv1, layer1, layer2, layer3, layer4 + 'resnet50_cl': 'layer4', + 'resnet32': 'conv_1_3x3', # conv_1_3x3, stage_1, stage_2, stage_3 + 'resnet32_cl': 'conv_1_3x3', +} + + +def register_collecter(m: torch.nn.Module, layer: str, feat_collecter: List): + + def _hook(m, i, o): + feat_collecter.append(o) + + _handler = m.get_submodule(layer).register_forward_hook(_hook) #m.convnets[0].get_submodule(layer).register_forward_hook(_hook) + return _handler, feat_collecter + +def register_collecter_cl(m: torch.nn.Module, layer: str, feat_collecter: List, cl_methods: str): + + def _hook(m, i, o): + feat_collecter.append(o) + + if cl_methods == 'icarl' or cl_methods == 'finetune' or cl_methods == 'wa' or cl_methods == 'replay' or cl_methods == 'podnet' or cl_methods == 'bic': + _handler = m.convnet.get_submodule(layer).register_forward_hook(_hook) # For ewc, icarl methods + elif cl_methods == 'foster' or cl_methods == 'der': + _handler = m.convnets[0].get_submodule(layer).register_forward_hook(_hook) # For foster and der methods + elif cl_methods == 'memo': + _handler = m.TaskAgnosticExtractor.get_submodule(layer).register_forward_hook(_hook) # For foster and der methods + return _handler, feat_collecter + + +@contextmanager +def feat_col(m: Union[torch.nn.Module, List[torch.nn.Module]], + layer: Union[str, List[str]]): + if isinstance(m, torch.nn.Module): + m = [m] + if isinstance(layer, str): + layer = [layer] + assert len(m) == len(layer) + handlers = [] + feat_collecter = [] + for _m, _layer in zip(m, layer): + handler, feat_collecter = register_collecter(_m, _layer, + feat_collecter) + handlers.append(handler) + yield feat_collecter + for handler in handlers: + handler.remove() + feat_collecter.clear() + del feat_collecter diff --git a/attacks/AIM/src/gat/models/surrogate/tv.py b/attacks/AIM/src/gat/models/surrogate/tv.py new file mode 100644 index 0000000000000000000000000000000000000000..4f309acfd1ae6e1dc14cb61563a1b5feb3571072 --- /dev/null +++ b/attacks/AIM/src/gat/models/surrogate/tv.py @@ -0,0 +1,206 @@ +from torchvision import models as tv_models + +from .builder import SURROGATE_REGISTRY + +weights_dict = dict( + alexnet=tv_models.AlexNet_Weights.IMAGENET1K_V1, + googlenet=tv_models.GoogLeNet_Weights.IMAGENET1K_V1, + vgg16=tv_models.VGG16_Weights.IMAGENET1K_V1, + vgg19=tv_models.VGG19_Weights.IMAGENET1K_V1, + inception_v3=tv_models.Inception_V3_Weights.IMAGENET1K_V1, + resnet18=tv_models.ResNet18_Weights.IMAGENET1K_V1, + resnet34=tv_models.ResNet34_Weights.IMAGENET1K_V1, + resnet50=tv_models.ResNet50_Weights.IMAGENET1K_V1, + resnet152=tv_models.ResNet152_Weights.IMAGENET1K_V1, + wide_resnet50_2=tv_models.Wide_ResNet50_2_Weights.IMAGENET1K_V1, + wide_resnet101_2=tv_models.Wide_ResNet101_2_Weights.IMAGENET1K_V1, + densenet121=tv_models.DenseNet121_Weights.IMAGENET1K_V1, + densenet169=tv_models.DenseNet169_Weights.IMAGENET1K_V1, + mobilenet_v2=tv_models.MobileNet_V2_Weights.IMAGENET1K_V1, + mobilenet_v3_small=tv_models.MobileNet_V3_Small_Weights.IMAGENET1K_V1, + mobilenet_v3_large=tv_models.MobileNet_V3_Large_Weights.IMAGENET1K_V1, + squeezenet1_0=tv_models.SqueezeNet1_0_Weights.IMAGENET1K_V1, + squeezenet1_1=tv_models.SqueezeNet1_1_Weights.IMAGENET1K_V1, + shufflenet_v2_x0_5=tv_models.ShuffleNet_V2_X0_5_Weights.IMAGENET1K_V1, + shufflenet_v2_x1_0=tv_models.ShuffleNet_V2_X1_0_Weights.IMAGENET1K_V1, + shufflenet_v2_x1_5=tv_models.ShuffleNet_V2_X1_5_Weights.IMAGENET1K_V1, + shufflenet_v2_x2_0=tv_models.ShuffleNet_V2_X2_0_Weights.IMAGENET1K_V1, + efficientnet_b0=tv_models.EfficientNet_B0_Weights.IMAGENET1K_V1, + efficientnet_v2_s=tv_models.EfficientNet_V2_S_Weights.IMAGENET1K_V1, + efficientnet_v2_m=tv_models.EfficientNet_V2_M_Weights.IMAGENET1K_V1, + efficientnet_v2_l=tv_models.EfficientNet_V2_L_Weights.IMAGENET1K_V1, + vit_b_16=tv_models.ViT_B_16_Weights.IMAGENET1K_V1, + vit_b_32=tv_models.ViT_B_32_Weights.IMAGENET1K_V1, + swin_b=tv_models.Swin_B_Weights.IMAGENET1K_V1) + + +@SURROGATE_REGISTRY.register() +def alexnet(pretrain=True): + return tv_models.alexnet( + weights=weights_dict['alexnet'] if pretrain else None) + + +@SURROGATE_REGISTRY.register() +def googlenet(pretrain=True): + return tv_models.googlenet( + weights=weights_dict['googlenet'] if pretrain else None) + + +@SURROGATE_REGISTRY.register() +def vgg16(pretrain=True): + return tv_models.vgg16(weights=weights_dict['vgg16'] if pretrain else None) + + +@SURROGATE_REGISTRY.register() +def vgg19(pretrain=True): + return tv_models.vgg19(weights=weights_dict['vgg19'] if pretrain else None) + + +@SURROGATE_REGISTRY.register() +def inception_v3(pretrain=True): + return tv_models.inception_v3( + weights=weights_dict['inception_v3'] if pretrain else None) + + +@SURROGATE_REGISTRY.register() +def resnet18(pretrain=True): + return tv_models.resnet18( + weights=weights_dict['resnet18'] if pretrain else None) + + +@SURROGATE_REGISTRY.register() +def resnet34(pretrain=True): + return tv_models.resnet34( + weights=weights_dict['resnet34'] if pretrain else None) + + +@SURROGATE_REGISTRY.register() +def resnet50(pretrain=True): + return tv_models.resnet50( + weights=weights_dict['resnet50'] if pretrain else None) + + +@SURROGATE_REGISTRY.register() +def resnet152(pretrain=True): + return tv_models.resnet152( + weights=weights_dict['resnet152'] if pretrain else None) + + +@SURROGATE_REGISTRY.register() +def wide_resnet50_2(pretrain=True): + return tv_models.wide_resnet50_2( + weights=weights_dict['wide_resnet50_2'] if pretrain else None) + + +@SURROGATE_REGISTRY.register() +def wide_resnet101_2(pretrain=True): + return tv_models.wide_resnet101_2( + weights=weights_dict['wide_resnet101_2'] if pretrain else None) + + +@SURROGATE_REGISTRY.register() +def densenet121(pretrain=True): + return tv_models.densenet121( + weights=weights_dict['densenet121'] if pretrain else None) + + +@SURROGATE_REGISTRY.register() +def densenet169(pretrain=True): + return tv_models.densenet169( + weights=weights_dict['densenet169'] if pretrain else None) + + +@SURROGATE_REGISTRY.register() +def mobilenet_v2(pretrain=True): + return tv_models.mobilenet_v2( + weights=weights_dict['mobilenet_v2'] if pretrain else None) + + +@SURROGATE_REGISTRY.register() +def mobilenet_v3_small(pretrain=True): + return tv_models.mobilenet_v3_small( + weights=weights_dict['mobilenet_v3_small'] if pretrain else None) + + +@SURROGATE_REGISTRY.register() +def mobilenet_v3_large(pretrain=True): + return tv_models.mobilenet_v3_large( + weights=weights_dict['mobilenet_v3_large'] if pretrain else None) + + +@SURROGATE_REGISTRY.register() +def squeezenet1_0(pretrain=True): + return tv_models.squeezenet1_0( + weights=weights_dict['squeezenet1_0'] if pretrain else None) + + +@SURROGATE_REGISTRY.register() +def squeezenet1_1(pretrain=True): + return tv_models.squeezenet1_1( + weights=weights_dict['squeezenet1_1'] if pretrain else None) + + +@SURROGATE_REGISTRY.register() +def shufflenet_v2_x0_5(pretrain=True): + return tv_models.shufflenet_v2_x0_5( + weights=weights_dict['shufflenet_v2_x0_5'] if pretrain else None) + + +@SURROGATE_REGISTRY.register() +def shufflenet_v2_x1_0(pretrain=True): + return tv_models.shufflenet_v2_x1_0( + weights=weights_dict['shufflenet_v2_x1_0'] if pretrain else None) + + +@SURROGATE_REGISTRY.register() +def shufflenet_v2_x1_5(pretrain=True): + return tv_models.shufflenet_v2_x1_5( + weights=weights_dict['shufflenet_v2_x1_5'] if pretrain else None) + + +@SURROGATE_REGISTRY.register() +def shufflenet_v2_x2_0(pretrain=True): + return tv_models.shufflenet_v2_x2_0( + weights=weights_dict['shufflenet_v2_x2_0'] if pretrain else None) + + +@SURROGATE_REGISTRY.register() +def efficientnet_b0(pretrain=True): + return tv_models.efficientnet_b0( + weights=weights_dict['efficientnet_b0'] if pretrain else None) + + +@SURROGATE_REGISTRY.register() +def efficientnet_v2_s(pretrain=True): + return tv_models.efficientnet_v2_s( + weights=weights_dict['efficientnet_v2_s'] if pretrain else None) + + +@SURROGATE_REGISTRY.register() +def efficientnet_v2_m(pretrain=True): + return tv_models.efficientnet_v2_m( + weights=weights_dict['efficientnet_v2_m'] if pretrain else None) + + +@SURROGATE_REGISTRY.register() +def efficientnet_v2_l(pretrain=True): + return tv_models.efficientnet_v2_l( + weights=weights_dict['efficientnet_v2_l'] if pretrain else None) + + +@SURROGATE_REGISTRY.register() +def vit_b_16(pretrain=True): + return tv_models.vit_b_16( + weights=weights_dict['vit_b_16'] if pretrain else None) + + +@SURROGATE_REGISTRY.register() +def vit_b_32(pretrain=True): + return tv_models.vit_b_32( + weights=weights_dict['vit_b_32'] if pretrain else None) + + +@SURROGATE_REGISTRY.register() +def swin_b(pretrain=True): + return tv_models.swin_b( + weights=weights_dict['swin_b'] if pretrain else None) diff --git a/attacks/AIM/src/gat/runtime/__init__.py b/attacks/AIM/src/gat/runtime/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..1272d9884c26c2473612ab0055ee83fd681c623b --- /dev/null +++ b/attacks/AIM/src/gat/runtime/__init__.py @@ -0,0 +1,4 @@ +from .meter import AverageMeter, calc_cls_accuracy +from .utils import fix_random, randid + +__all__ = ['fix_random', 'randid', 'AverageMeter', 'calc_cls_accuracy'] diff --git a/attacks/AIM/src/gat/runtime/api/__init__.py b/attacks/AIM/src/gat/runtime/api/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/attacks/AIM/src/gat/runtime/api/aim_attack.py b/attacks/AIM/src/gat/runtime/api/aim_attack.py new file mode 100644 index 0000000000000000000000000000000000000000..a84cf8d4e30faa3383f656f37461e071f102d15d --- /dev/null +++ b/attacks/AIM/src/gat/runtime/api/aim_attack.py @@ -0,0 +1,58 @@ +#!/usr/bin/env python3 +import argparse +import io +from pathlib import Path +from typing import Union + +import torchvision +import uvicorn +from fastapi import FastAPI, File, UploadFile +from fastapi.responses import StreamingResponse +from PIL import Image + +from ...datasets.transforms import resize_256_224, to_color, to_pil, to_ts +from ...models.attack import AIMAttack + + +def init_attack(ckpt: Union[str, Path] = None): + attack = AIMAttack(device='cpu') + if ckpt: + attack.load_ckpt(ckpt) + attack.set_mode('eval') + attack_preproc = torchvision.transforms.Compose(resize_256_224() + + to_color() + to_ts()) + return attack, attack_preproc + + +def main(): + parser = argparse.ArgumentParser(description='AIM Attack API') + parser.add_argument('--host', type=str, default='0.0.0.0', help='host') + parser.add_argument('--port', type=int, default=8000, help='port') + parser.add_argument('--ckpt', + type=str, + default=None, + help='path to the checkpoint') + args = parser.parse_args() + + attack, attack_preproc = init_attack(args.ckpt) + app = FastAPI() + + @app.get('/attack/aim/') + async def aim_attack(x_nat: UploadFile = File(...), + x_guid: UploadFile = File(...)): + io_x_nat = await x_nat.read() + io_x_guid = await x_guid.read() + pil_x_nat = Image.open(io.BytesIO(io_x_nat)) + pil_x_guid = Image.open(io.BytesIO(io_x_guid)) + ts_x_nat = attack_preproc(pil_x_nat).unsqueeze(0) + ts_x_guid = attack_preproc(pil_x_guid).unsqueeze(0) + ts_x_adv = attack(ts_x_nat, ts_x_guid) + pil_x_adv = torchvision.transforms.Compose(to_pil())(ts_x_adv[0]) + + img_byte_array = io.BytesIO() + pil_x_adv.save(img_byte_array, format='PNG') + img_byte_array.seek(0) + + return StreamingResponse(img_byte_array, media_type='image/png') + + uvicorn.run(app, host=args.host, port=args.port) diff --git a/attacks/AIM/src/gat/runtime/factory.py b/attacks/AIM/src/gat/runtime/factory.py new file mode 100644 index 0000000000000000000000000000000000000000..9f26a90fce142f5fefeffb8883fb124e39cf7df5 --- /dev/null +++ b/attacks/AIM/src/gat/runtime/factory.py @@ -0,0 +1,97 @@ +# Code is copied from +# github.com/facebookresearch/fvcore:common/registry.py@242366 +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +# pyre-strict +# pyre-ignore-all-errors[2,3] +from typing import Any, Dict, Iterable, Iterator, Tuple + +from tabulate import tabulate + + +class Registry(Iterable[Tuple[str, Any]]): + """ + The registry that provides name -> object mapping, to support third-party + users' custom modules. + + To create a registry (e.g. a backbone registry): + + .. code-block:: python + + BACKBONE_REGISTRY = Registry('BACKBONE') + + To register an object: + + .. code-block:: python + + @BACKBONE_REGISTRY.register() + class MyBackbone(): + ... + + Or: + + .. code-block:: python + + BACKBONE_REGISTRY.register(MyBackbone) + """ + + def __init__(self, name: str) -> None: + """ + Args: + name (str): the name of this registry + """ + self._name: str = name + self._obj_map: Dict[str, Any] = {} + + def _do_register(self, name: str, obj: Any) -> None: + assert ( + name not in self._obj_map + ), "An object named '{}' was already registered in '{}' registry!".format( # noqa: E501 + name, self._name + ) + self._obj_map[name] = obj + + def register(self, obj: Any = None) -> Any: + """ + Register the given object under the the name `obj.__name__`. + Can be used as either a decorator or not. See docstring of this class + for usage. + """ + if obj is None: + # used as a decorator + def deco(func_or_class: Any) -> Any: + name = func_or_class.__name__ + self._do_register(name, func_or_class) + return func_or_class + + return deco + + # used as a function call + name = obj.__name__ + self._do_register(name, obj) + + def get(self, name: str) -> Any: + ret = self._obj_map.get(name) + if ret is None: + raise KeyError( + "No object named '{}' found in '{}' registry!".format( + name, self._name + ) + ) + return ret + + def __contains__(self, name: str) -> bool: + return name in self._obj_map + + def __repr__(self) -> str: + table_headers = ['Names', 'Objects'] + table = tabulate( + self._obj_map.items(), headers=table_headers, tablefmt='fancy_grid' + ) + return 'Registry of {}:\n'.format(self._name) + table + + def __iter__(self) -> Iterator[Tuple[str, Any]]: + return iter(self._obj_map.items()) + + # pyre-fixme[4]: Attribute must be annotated. + __str__ = __repr__ diff --git a/attacks/AIM/src/gat/runtime/meter.py b/attacks/AIM/src/gat/runtime/meter.py new file mode 100644 index 0000000000000000000000000000000000000000..30cf2c28318b8b4c1c99ad7f2672d72fd4de407d --- /dev/null +++ b/attacks/AIM/src/gat/runtime/meter.py @@ -0,0 +1,27 @@ +class AverageMeter: + def __init__(self): + self.reset() + + def reset(self): + self.val = 0 + self.avg = 0 + self.sum = 0 + self.count = 0 + + def update(self, val, n=1): + self.val = val + self.sum += val * n + self.count += n + self.avg = self.sum / self.count + + +def calc_cls_accuracy(output, target, topk=(1,)): + maxk = min(max(topk), output.size()[1]) + batch_size = target.size(0) + _, pred = output.topk(maxk, 1, True, True) + pred = pred.t() + correct = pred.eq(target.reshape(1, -1).expand_as(pred)) + return [ + correct[: min(k, maxk)].reshape(-1).float().sum(0) * 100.0 / batch_size + for k in topk + ] diff --git a/attacks/AIM/src/gat/runtime/utils.py b/attacks/AIM/src/gat/runtime/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..671305b182f2fdb6ae391fc14dfdcab0474db136 --- /dev/null +++ b/attacks/AIM/src/gat/runtime/utils.py @@ -0,0 +1,18 @@ +import random + +import numpy as np +import torch + + +def fix_random(seed: int = 0) -> None: + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + + +def randid(k: int = 4): + charset = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789' + return ''.join(random.choices(charset, k=k)) diff --git a/attacks/AIM/tests/__init__.py b/attacks/AIM/tests/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/attacks/AIM/tests/test_datasets/__init__.py b/attacks/AIM/tests/test_datasets/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/attacks/AIM/tests/test_datasets/test_datasets.py b/attacks/AIM/tests/test_datasets/test_datasets.py new file mode 100644 index 0000000000000000000000000000000000000000..3b71135b3d6b6d9ea69d31a1fa6ccde44123a9b8 --- /dev/null +++ b/attacks/AIM/tests/test_datasets/test_datasets.py @@ -0,0 +1,33 @@ +import os +import unittest +from pathlib import Path + +from gat.datasets import build_dataset + +in1k_data_root = '/root/workspace/proj/transfer-at/data/in_1k' +in1k_data_root = os.environ.get('DATA_ROOT', + Path(__file__).parents[2] / 'data' / 'in_1k') + + +class TestImageNet(unittest.TestCase): + + def test_in1k(self): + ds = build_dataset( + 'imagenet', + data_root=in1k_data_root, + is_train=True, + ) + self.assertEqual(len(ds), 1281167) + + def test_in1k_filter(self): + ds = build_dataset( + 'imagenet', + data_root=in1k_data_root, + is_train=False, + filter_class=0, + ) + self.assertEqual(len(ds), 50) + + +if __name__ == '__main__': + unittest.main() diff --git a/attacks/AIM/tests/test_datasets/test_transforms.py b/attacks/AIM/tests/test_datasets/test_transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..3f471d1b105c8b9fa114698dc7cd60b6baa69d84 --- /dev/null +++ b/attacks/AIM/tests/test_datasets/test_transforms.py @@ -0,0 +1,80 @@ +import unittest + +import torch +from PIL import Image +from torchvision.transforms import Compose + +from gat.datasets.transforms import (hflip, norm, resize_224, resize_256_224, + resize_512_448, to_color, to_pil, to_ts) + + +class TestResize(unittest.TestCase): + + def test_resize_256_224(self): + inputs = torch.rand(3, 256, 256) + outputs = Compose(resize_256_224())(inputs) + self.assertEqual(outputs.shape, (3, 224, 224)) + + def test_resize_512_448(self): + inputs = torch.rand(3, 512, 512) + outputs = Compose(resize_512_448())(inputs) + self.assertEqual(outputs.shape, (3, 448, 448)) + + def test_resize_224(self): + inputs = torch.rand(3, 224, 224) + outputs = Compose(resize_224())(inputs) + self.assertEqual(outputs.shape, (3, 224, 224)) + + +class TestAug(unittest.TestCase): + + def test_probability_invalid(self): + with self.assertRaises(AssertionError): + hflip(-0.1) + + def test_probability_valid(self): + for valid_p in [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]: + result = hflip(valid_p) + self.assertEqual(result[0].p, valid_p) + + +class TestTypeTransforms(unittest.TestCase): + + def test_to_ts(self): + pil_image = Image.new('RGB', (224, 224)) + outputs = Compose(to_ts())(pil_image) + self.assertIsInstance(outputs, torch.Tensor) + + def test_to_pil(self): + inputs = torch.rand(3, 224, 224) + outputs = Compose(to_pil())(inputs) + self.assertIsInstance(outputs, Image.Image) + + def test_to_color_grayscale(self): + inputs = Image.new('L', (224, 224)) + outputs = Compose(to_color())(inputs) + self.assertEqual(outputs.mode, 'RGB') + + def test_to_color_rgb(self): + inputs = Image.new('RGB', (224, 224)) + outputs = Compose(to_color())(inputs) + self.assertEqual(outputs.mode, 'RGB') + + +class TestNorm(unittest.TestCase): + + def test_default(self): + self.assertEqual(norm()[0].mean, (0.485, 0.456, 0.406)) + self.assertEqual(norm()[0].std, (0.229, 0.224, 0.225)) + + def test_imagenet(self): + self.assertEqual(norm('IMAGENET')[0].mean, (0.485, 0.456, 0.406)) + self.assertEqual(norm('IMAGENET')[0].std, (0.229, 0.224, 0.225)) + + def test_invalid_ds(self): + with self.assertRaises(AttributeError): + norm('imagenets') + + +if __name__ == '__main__': + unittest.main() diff --git a/attacks/AIM/tests/test_models/__init__.py b/attacks/AIM/tests/test_models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/attacks/AIM/tests/test_models/test_attack.py b/attacks/AIM/tests/test_models/test_attack.py new file mode 100644 index 0000000000000000000000000000000000000000..e2989a611768393cd49ce09cde068649b76497f5 --- /dev/null +++ b/attacks/AIM/tests/test_models/test_attack.py @@ -0,0 +1,110 @@ +import unittest + +import torch + +from gat.models.attack.aim_attack import AIMAttack +from gat.models.attack.cda_attack import CDAAttack +from gat.models.attack.generator.aim import AIMGenerator +from gat.models.attack.generator.cda import CDAGenerator +from gat.models.attack.loss.logits import ContrastiveLoss + + +class TestGenerator(unittest.TestCase): + + @torch.no_grad() + def test_cda(self): + generator = CDAGenerator() + inputs = torch.rand(1, 3, 224, 224) + outputs = generator(inputs) + self.assertEqual(outputs.shape, (1, 3, 224, 224)) + + @torch.no_grad() + def test_aim(self): + generator = AIMGenerator() + inputs = (torch.rand(1, 3, 224, 224), torch.rand(1, 3, 224, 224)) + outputs = generator(*inputs) + self.assertEqual(outputs.shape, (1, 3, 224, 224)) + + +class TestContrastiveLoss(unittest.TestCase): + + def setUp(self): + self.margin = 1.0 + self.loss_fn = ContrastiveLoss(margin=self.margin) + + @torch.no_grad() + def test_forward_shape(self): + anchors = torch.tensor([[1.0, 0.0], [0.0, 1.0]]) + positives = torch.tensor([[1.0, 0.0], [0.0, 1.0]]) + negatives = torch.tensor([[0.0, 0.0], [1.0, 1.0]]) + + loss = self.loss_fn(anchors, negatives, positives) + self.assertEqual(loss.shape, torch.Size([])) + + @torch.no_grad() + def test_loss_outputs(self): + anchors = torch.tensor([[1.0, 0.0], [0.0, 1.0]]) + positives = torch.tensor([[1.0, 0.0], [0.0, 1.0]]) + negatives = torch.tensor([[2.0, 0.0], [0.0, 2.0]]) + + loss = self.loss_fn(anchors, negatives, positives) + self.assertAlmostEqual(loss.item(), 0.25) + + @torch.no_grad() + def test_non_zero_loss(self): + anchors = torch.tensor([[1.0, 0.0], [0.0, 1.0]]) + positives = torch.tensor([[0.0, 1.0], [1.0, 0.0]]) + negatives = torch.tensor([[2.0, 0.0], [0.0, 2.0]]) + + loss = self.loss_fn(anchors, negatives, positives) + self.assertAlmostEqual(loss.item(), 0.75) + + +class TestCDAAttack(unittest.TestCase): + + def setUp(self): + self.device = torch.device('cpu') + self.epsilon = 16. / 255. + self.attack = CDAAttack(self.device, self.epsilon) + + @torch.no_grad() + def test_outputs_shape(self): + x_nat = torch.rand(1, 3, 224, 224) + x_adv = self.attack(x_nat) + self.assertEqual(x_adv.shape, (1, 3, 224, 224)) + + @torch.no_grad() + def test_outputs_bound(self): + x_nat = torch.rand(1, 3, 224, 224) + x_adv = self.attack(x_nat) + self.assertTrue((x_adv >= 0.0).all()) + self.assertTrue((x_adv <= 1.0).all()) + self.assertTrue((x_adv - x_nat).abs().max() <= self.epsilon) + + +class TestAIMAttack(unittest.TestCase): + + def setUp(self): + self.device = torch.device('cpu') + self.epsilon = 16. / 255. + self.attack = AIMAttack(self.device, self.epsilon) + + @torch.no_grad() + def test_outputs_shape(self): + x_nat = torch.rand(1, 3, 224, 224) + x_guid = torch.rand(1, 3, 224, 224) + x_adv = self.attack(x_nat, x_guid) + self.assertEqual(x_adv.shape, (1, 3, 224, 224)) + + @torch.no_grad() + def test_outputs_bound(self): + x_nat = torch.rand(1, 3, 224, 224) + x_guid = torch.rand(1, 3, 224, 224) + x_adv = self.attack(x_nat, x_guid) + self.assertTrue((x_adv >= 0.0).all()) + self.assertTrue((x_adv <= 1.0).all()) + self.assertTrue((x_adv - x_nat).abs().max() <= self.epsilon) + + +if __name__ == '__main__': + unittest.main() diff --git a/attacks/AIM/tests/test_models/test_surrogate.py b/attacks/AIM/tests/test_models/test_surrogate.py new file mode 100644 index 0000000000000000000000000000000000000000..e6bb6dfc3c495033451c9c4c34ded364537b149f --- /dev/null +++ b/attacks/AIM/tests/test_models/test_surrogate.py @@ -0,0 +1,59 @@ +import unittest + +import torch + +from gat.models.surrogate import build_surrogate, list_surrogates +from gat.models.surrogate.hooks import feat_col + + +class TestTVModels(unittest.TestCase): + + @torch.no_grad() + def test_outputs_shape(self): + inputs = torch.rand(1, 3, 224, 224) + for surrogate_id in list_surrogates(): + if surrogate_id not in ['inception_v3']: + outputs = build_surrogate(surrogate_id, pretrain=False)(inputs) + self.assertEqual(outputs.shape, (1, 1000)) + + +class TestFeatCol(unittest.TestCase): + + @torch.no_grad() + def test_feat_col(self): + testcases = [{ + 'surrogate_id': 'vgg16', + 'feat_layer': 'features.16', + 'input_shape': (1, 3, 224, 224), + 'feat_shape': (1, 256, 28, 28) + }, { + 'surrogate_id': 'vgg19', + 'feat_layer': 'features.18', + 'input_shape': (1, 3, 224, 224), + 'feat_shape': (1, 256, 28, 28) + }, { + 'surrogate_id': 'resnet152', + 'feat_layer': 'layer2', + 'input_shape': (1, 3, 224, 224), + 'feat_shape': (1, 512, 28, 28) + }, { + 'surrogate_id': 'densenet169', + 'feat_layer': 'features.denseblock2', + 'input_shape': (1, 3, 224, 224), + 'feat_shape': (1, 512, 28, 28) + }] + for testcase in testcases: + model = build_surrogate(testcase['surrogate_id'], pretrain=False) + with feat_col(model, testcase['feat_layer']) as _feat_collecter: + inputs = torch.rand(testcase['input_shape']) + model(inputs) + model(inputs) + self.assertEqual(len(_feat_collecter), 2) + self.assertEqual(_feat_collecter.pop().shape, + testcase['feat_shape']) + self.assertEqual(_feat_collecter.pop().shape, + testcase['feat_shape']) + + +if __name__ == '__main__': + unittest.main() diff --git a/attacks/AIM/tests/test_runtime/__init__.py b/attacks/AIM/tests/test_runtime/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/attacks/AIM/tests/test_runtime/test_meter.py b/attacks/AIM/tests/test_runtime/test_meter.py new file mode 100644 index 0000000000000000000000000000000000000000..810d602fd30cd8393a2958087a139486c3f1e337 --- /dev/null +++ b/attacks/AIM/tests/test_runtime/test_meter.py @@ -0,0 +1,55 @@ +import unittest + +import torch + +from gat.runtime.meter import AverageMeter, calc_cls_accuracy + + +class TestAverageMeter(unittest.TestCase): + + def test_initialization(self): + avg_meter = AverageMeter() + self.assertEqual(avg_meter.val, 0) + self.assertEqual(avg_meter.avg, 0) + self.assertEqual(avg_meter.sum, 0) + self.assertEqual(avg_meter.count, 0) + + def test_update(self): + avg_meter = AverageMeter() + avg_meter.update(10) + self.assertEqual(avg_meter.val, 10) + self.assertEqual(avg_meter.sum, 10) + self.assertEqual(avg_meter.count, 1) + self.assertEqual(avg_meter.avg, 10) + + avg_meter.update(20, n=2) + self.assertEqual(avg_meter.val, 20) + self.assertEqual(avg_meter.sum, 50) + self.assertEqual(avg_meter.count, 3) + self.assertAlmostEqual(avg_meter.avg, 16.67, delta=0.01) + + def test_reset(self): + avg_meter = AverageMeter() + avg_meter.update(10) + avg_meter.reset() + self.assertEqual(avg_meter.val, 0) + self.assertEqual(avg_meter.avg, 0) + self.assertEqual(avg_meter.sum, 0) + self.assertEqual(avg_meter.count, 0) + + +class TestCalcClsAccuracy(unittest.TestCase): + + def test_calc_cls_accuracy(self): + output = torch.tensor([[0.1, 0.7, 0.2], [0.8, 0.1, 0.1]]) + target = torch.tensor([2, 0]) + + acc1 = calc_cls_accuracy(output, target, topk=(1, )) + self.assertAlmostEqual(acc1[0].item(), 50.0) + + acc2 = calc_cls_accuracy(output, target, topk=(2, )) + self.assertAlmostEqual(acc2[0].item(), 100.0) + + +if __name__ == '__main__': + unittest.main() diff --git a/attacks/AIM/tests/test_runtime/test_utils.py b/attacks/AIM/tests/test_runtime/test_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..92fa917f2f8b46ee9ba68c1d4d85fc05040bbadf --- /dev/null +++ b/attacks/AIM/tests/test_runtime/test_utils.py @@ -0,0 +1,22 @@ +import unittest + +from gat.runtime.utils import fix_random, randid + + +class TestUtils(unittest.TestCase): + + def test_fix_random(self): + try: + fix_random(0) + self.assertTrue(True) + except Exception as e: + self.fail(f'fix_random raised an exception: {e}') + + def test_randid(self): + length = 4 + result = randid(length) + self.assertEqual(len(result), length) + + +if __name__ == '__main__': + unittest.main() diff --git a/attacks/BASEAttack.py b/attacks/BASEAttack.py new file mode 100644 index 0000000000000000000000000000000000000000..c1ee2128c49ecb087e9d0054371e3437cdb01c59 --- /dev/null +++ b/attacks/BASEAttack.py @@ -0,0 +1,141 @@ +import torch +from foolbox.attacks.base import * +from foolbox.attacks.gradient_descent_base import * +from attacks.attack_config import SustainableAttack +from tqdm import tqdm +import logging +import foolbox as fb +from foolbox import PyTorchModel, accuracy +import numpy as np +from utils import factory +from utils.data_manager import get_dataloader +import os +from utils.plot import plot_asr_per_target, save_grad_cam +import pandas as pd + + +class BASEAttack(SustainableAttack): + def __init__(self, args, device='cuda'): + super().__init__(args, device) + attack_name = args['attack_method'] + if attack_name == 'L2FGM': + self.attack = fb.attacks.FGM() + elif attack_name == 'FGSM': + self.attack = fb.attacks.FGSM() + elif attack_name == 'MIFGSM': + self.attack = fb.attacks.MIFGSM(momentum=0.9, steps=10) + elif attack_name == 'L1PGD': + self.attack = fb.attacks.L1PGD(steps=10) + elif attack_name == 'L2PGD': + self.attack = fb.attacks.L2PGD(steps=10) + elif attack_name == 'LinfPGD': + self.attack = fb.attacks.LinfPGD(steps=10) + elif attack_name == 'L2DeepFool': + self.attack = fb.attacks.L2DeepFoolAttack(steps=10) + elif attack_name == 'LinfDeepFool': + self.attack = fb.attacks.LinfDeepFoolAttack(steps=10) + elif attack_name == 'BoundaryAttack': + self.attack = fb.attacks.BoundaryAttack(steps=10) + elif attack_name == 'CarliniWagnerL2': + self.attack = fb.attacks.L2CarliniWagnerAttack(steps=10) + elif attack_name == 'GaussianNoise': + self.attack = fb.attacks.LinfRepeatedAdditiveUniformNoiseAttack() + elif attack_name == 'UniformNoise': + self.attack = fb.attacks.LinfAdditiveUniformNoiseAttack() + else: + raise ValueError(f"Unknown attack method: {attack_name}") + + self.test_mode = 'BaseAttacks' + self.epsilon = 16 + self.eval = args['eval'] + self.prefix = f'eps{self.epsilon}' + self.save_path = os.path.join(self.args['logs_eval_name']) + self.out_path = os.path.join(self.args['logs_eval_name'], f'gradcam_t{self.target_class}') + if not os.path.exists(self.out_path): + os.makedirs(self.out_path) + + self.plot_gradcam = True + + def run_test(self): + # Load Batch Data + eval_batch_szie = 1024 + self.loader = get_dataloader(self.data_manager, batch_size=eval_batch_szie, + start_class=0, end_class=10, + train=False, shuffle=False, num_workers=0) + for i, (_, imgs, labels) in enumerate(tqdm(self.loader, total=len(self.loader), + desc=f'Loading Data with Batch Size of {self.batch_size}) :')): + if i> 0: + break + + imgs, labels = ep.astensors(*(imgs.to(self.device), labels.to(self.device))) + target_imgs = imgs[labels == self.target_class] + target_labels = labels[labels == self.target_class] + imgs_f = imgs[labels != self.target_class] + labels_f = labels[labels != self.target_class] + labels_t_f = ep.full_like(labels_f, fill_value=self.target_class) + + self.attacks(i, imgs_f, labels_f, labels_t_f, target_imgs, target_labels) + + def attacks(self, i_batch, imgs, labels, labels_t): + clean_acc_matrix = [] + asr_matrix = np.ones((10, 1)) + self.model = factory.get_model(self.args["model_name"], self.args) + for task in range(10): + logging.info("***** Starting attack on task [{}]. *****".format(task)) + self.model.incremental_train(self.data_manager) + self.model._network.load_state_dict(torch.load(self.ckpt_paths[task], map_location=self.device)['model_state_dict']) + self.model._network.to(self.device) + self.model._network.eval() + + current_model = PyTorchModel(self.model._network, bounds=(0, 1), preprocessing=self.preprocessing) + acc, acc_bool = accuracy(current_model, imgs, labels)[:2] + clean_acc_matrix.append(acc) + if task == 0: + imgs = imgs[acc_bool] + labels = labels[acc_bool] + labels_t = labels_t[acc_bool] + verify_input_bounds(imgs, current_model) + criterion = fb.criteria.Misclassification( + labels) if self.target_class is None else fb.criteria.TargetedMisclassification( + labels_t) + if task == 0: + adv, adv_clip, asr_bool = self.attack(current_model, imgs, criterion=criterion, epsilons=self.epsilon/255) + asr_matrix[task] = asr_bool.sum().raw.item() / len(imgs) + + else: + asr = accuracy(current_model, adv_clip, labels_t)[0] + asr_matrix[task] = asr + + if self.plot_gradcam: + save_grad_cam(self.args, torch.clip(adv_clip.raw, 0, 1), labels_t.raw, + self.model._network, self.out_path + "/GradCam", prefix=f'task{task}', + layer_name='stage_3', save_num=100, save_raw=True) + + del criterion, current_model + torch.cuda.empty_cache() + + self.model.after_task() + + # Save all target images info: everage asr, + prefix = f'batch{i_batch}_{self.prefix}_tc{self.target_class}' + plot_asr_per_target(asr_matrix, self.save_path, prefix, self.args, clean_acc_matrix) + df = pd.DataFrame(asr_matrix, columns=['ASR']) + df.to_excel(os.path.join(self.save_path, f"{prefix}.xlsx"), index=False) + + del asr_matrix, imgs, labels, labels_t + torch.cuda.empty_cache() + + + def __call__( + self, + model: Model, + inputs: T, + criterion: Any, + *, + epsilons: Sequence[Union[float, None]], + **kwargs: Any, + ) -> Tuple[List[T], List[T], T]: + ... + + def repeat(self, times: int) -> "BASEAttack": + ... \ No newline at end of file diff --git a/attacks/CGNC/CGNCAttack.py b/attacks/CGNC/CGNCAttack.py new file mode 100644 index 0000000000000000000000000000000000000000..2110e5af42b657326d5d893882250df467ebbe5f --- /dev/null +++ b/attacks/CGNC/CGNCAttack.py @@ -0,0 +1,207 @@ +import os.path +from foolbox.attacks.base import * +from foolbox.attacks.gradient_descent_base import * +from tqdm import tqdm +import torch.optim as optim +from attacks.CGNC.models.generator import CrossAttenGenerator +from attacks.CGNC.utils_ import * +from attacks.CGNC.image_transformer import rotation +from attacks.attack_config import SustainableAttack +from utils.plot import plot_asr_per_target +import logging +import foolbox as fb +from foolbox import PyTorchModel +import numpy as np +from utils import factory +from utils.data_manager import get_dataloader +from attacks.AIM.src.gat.models.surrogate import build_surrogate + +class CGNC(SustainableAttack): + def __init__(self, args, device='cuda'): + super().__init__(args, device) + self.device = device + self.args = args + self.surrogate_model = None + + self.adv_generator = CrossAttenGenerator(nz=16, device=device) + self.adv_generator = self.adv_generator.to(device) + self.lr = 0.001 # 2e-4 + self.betas = (0.5, 0.999) + self.num_epoch = 100 + self.optim = optim.Adam(self.adv_generator.parameters(), lr=self.lr, betas=self.betas) + self.criterion = nn.CrossEntropyLoss() + self.text_cond_dict = torch.load("attacks/CGNC/text_feature.pth") + self.label_set = get_classes("CL") + self.eps = 32 /255 + self.eval_batch_szie = 128 + self.surrogate_model_name = f'resnet32_cl' + self.prefix = f'{self.surrogate_model_name}_{len(self.label_set)}classes_eps{int(self.eps * 255)}' + self.save_path = os.path.join(self.args['logs_eval_name']) + os.makedirs(self.save_path, exist_ok=True) + + def train_generator(self): + if 'cl' in self.surrogate_model_name: + s_model = factory.get_model(self.args["model_name"], self.args) + s_model.incremental_train(self.data_manager) + s_model._network.load_state_dict( + torch.load(self.ckpt_paths[0], map_location=self.device)['model_state_dict']) + s_model._network.to(self.device) + s_model._network.eval() + self.surrogate_model = s_model._network + del s_model + torch.cuda.empty_cache() + else: + self.surrogate_model = build_surrogate(self.surrogate_model_name, pretrain=True).to(self.device) + self.surrogate_model.eval() + + file_path = os.path.join(self.save_path, f'{self.prefix}.pth') + if os.path.exists(file_path): + self.adv_generator.load_state_dict(torch.load(file_path, map_location=self.device)) + self.adv_generator.eval() + else: + self.loader = get_dataloader(self.data_manager, batch_size=self.batch_size, + start_class=0, end_class=10, + train=True, shuffle=True, num_workers=0) + for epoch in range(1, self.num_epoch + 1): + running_loss = 0 + laoder_tqdm = tqdm(self.loader, total=len(self.loader), desc=f'Epoch {epoch}') + loss_np = 0 + for i, (_, x, y) in enumerate(laoder_tqdm): + imgs = x.to(self.device) + imgs_rot = rotation(x)[0].to(self.device) + color_jitter = transforms.ColorJitter(0.8, 0.8, 0.8, 0.2) + aug = transforms.Compose([transforms.ToPILImage(), + transforms.RandomResizedCrop(size=imgs.size(-1)), + transforms.RandomHorizontalFlip(), + transforms.RandomApply([color_jitter], p=0.8), + transforms.RandomGrayscale(p=0.2), + transforms.ToTensor()]) + imgs_aug = torch.stack([aug(img) for img in x]).to(self.device) + del x, y + + label_map = {self.label_set[i]: i for i in range(len(self.label_set))} + np.random.shuffle(self.label_set) + label = np.random.choice(self.label_set, imgs.size(0)) + cond = torch.stack([self.text_cond_dict[j] for j in label], dim=0) + label = torch.from_numpy(label).long().to(self.device) + for i in range(len(label)): + label[i] = label_map.get(label[i].item(), label[i].item()) + self.adv_generator.train() + self.optim.zero_grad() + + # generate img + noise = self.adv_generator(input=imgs, cond=cond, eps=self.eps) + noise_rot = self.adv_generator(input=imgs_rot, cond=cond, eps=self.eps) + noise_aug = self.adv_generator(input=imgs_aug, cond=cond, eps=self.eps) + + adv = noise + imgs + adv = torch.clamp(adv, 0.0, 1.0) + + adv_rot = noise_rot + imgs_rot + adv_rot = torch.clamp(adv_rot, 0.0, 1.0) + + adv_aug = noise_aug + imgs_aug + adv_aug = torch.clamp(adv_aug, 0.0, 1.0) + + adv_out = self.surrogate_model(normalize(adv)) + adv_rot_out = self.surrogate_model(normalize(adv_rot)) + adv_aug_out = self.surrogate_model(normalize(adv_aug)) + + loss = self.criterion(adv_out, label) + self.criterion(adv_rot_out, label) + self.criterion(adv_aug_out, label) + loss.backward() + self.optim.step() + + if i % 10 == 9: + running_loss = 0 + running_loss += abs(loss.item()) + loss_np += loss.item() + + del imgs, imgs_rot, imgs_aug, adv, label, cond, noise, noise_rot, noise_aug, adv_rot, adv_aug, adv_out, adv_rot_out, adv_aug_out + torch.cuda.empty_cache() + logging.info(f'Epoch {epoch} loss: {loss_np / (len(self.loader))}') + torch.save(self.adv_generator.state_dict(), file_path) + + def run_test(self): + # Load Batch Data + self.adv_generator.eval() + self.loader = get_dataloader(self.data_manager, batch_size=self.eval_batch_szie, + start_class=0, end_class=10, + train=False, shuffle=True, num_workers=0) + for i, (_, imgs, labels) in enumerate(tqdm(self.loader, total=len(self.loader), + desc=f'Loading Data with Batch Size of {self.batch_size}) :')): + if i > 0: + break + + imgs, labels = ep.astensors(*(imgs.to(self.device), labels.to(self.device))) + + target_imgs = imgs[labels == self.target_class] + target_labels = labels[labels == self.target_class] + imgs_f = imgs[labels != self.target_class] + labels_f = labels[labels != self.target_class] + labels_t_f = ep.full_like(labels_f, fill_value=self.target_class) + + if self.args["model_name"] != 'finetune': + imgs_f, labels_f, labels_t_f = self.to_alls(imgs_f, labels_f, + labels_t_f, + target_imgs, + target_labels)[:3] + self.attacks(i, imgs_f, labels_f, labels_t_f) + + + def attacks(self, i_batch, imgs, labels, labels_t): + asr_matrix = np.ones((10, len(self.label_set))) + self.model = factory.get_model(self.args["model_name"], self.args) + for task in range(10): + logging.info("***** Starting attack on task [{}]. *****".format(task)) + self.model.incremental_train(self.data_manager) + self.model._network.load_state_dict(torch.load(self.ckpt_paths[task], map_location=self.device)['model_state_dict']) + self.model._network.to(self.device) + self.model._network.eval() + + # Run attack on ecah target image + criterion = fb.criteria.Misclassification( + labels) if self.target_class is None else fb.criteria.TargetedMisclassification( + labels_t) + current_model = PyTorchModel(self.model._network, bounds=(0, 1), preprocessing=self.preprocessing) + verify_input_bounds(imgs, current_model) + criterion = get_criterion(criterion) + is_adversarial = get_is_adversarial(criterion, current_model) + + logging.info("Eval attack on each target images.") + for idx in range(len(self.label_set)): + cond = torch.tile(self.text_cond_dict[self.label_set[idx]], (len(imgs), 1)).to(torch.float).to(self.device) + noises = self.adv_generator(imgs.raw, cond, eps=self.eps) + advs = noises + imgs.raw + advs = torch.clamp(advs, 0.0, 1.0) + is_adv = is_adversarial(ep.astensor(advs))[0] + asr_matrix[task, idx] = (is_adv.bool().sum().raw.item() / len(imgs)) + + del advs, noises, cond, is_adv + torch.cuda.empty_cache() + del criterion, current_model, is_adversarial + torch.cuda.empty_cache() + + self.model.after_task() + + prefix = f'batch{i_batch}_{self.prefix}' + plot_asr_per_target(asr_matrix, self.save_path, prefix, self.args) + for i in range(len(self.label_set)): + df = pd.DataFrame(asr_matrix[:, i], columns=['ASR']) + df.to_excel(os.path.join(self.save_path, f"{prefix}_class{i}.xlsx"), index=False) + + del asr_matrix, imgs, labels, labels_t + torch.cuda.empty_cache() + + def __call__( + self, + model: Model, + inputs: T, + criterion: Any, + *, + epsilons: Union[Sequence[Union[float, None]], float, None], + **kwargs: Any, + ) -> Union[Tuple[List[T], List[T], T], Tuple[T, T, T]]: + ... + + def repeat(self, times: int) -> "CGNC": + ... \ No newline at end of file diff --git a/attacks/CGNC/get_text_feature.py b/attacks/CGNC/get_text_feature.py new file mode 100644 index 0000000000000000000000000000000000000000..a82ceff3346240efc6247728fc6cd951500be5e3 --- /dev/null +++ b/attacks/CGNC/get_text_feature.py @@ -0,0 +1,67 @@ +import argparse +import os +from attacks.UnivIntruder.utils_.text_templates import imagenet_templates +import open_clip + +parser = argparse.ArgumentParser(description='Text Condition') +parser.add_argument('--gpu_id', type=str, default='0', help='GPU id') +parser.add_argument('--label_flag', type=str, default='CL', help='label nums: N8, D1,...,D20') +parser.add_argument('--save_path', type=str, default='text_feature_10.pth', help='save path') +args = parser.parse_args() +print(args) + +os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id + +import clip +import torch +from utils_ import getImageNetClassIndex, getCIFAR100ClassIndex, get_classes +from utils.plot import load_cifar100_classes + +use_gpu = torch.cuda.is_available() +if use_gpu: + torch.manual_seed(1) + torch.cuda.manual_seed(1) + torch.cuda.manual_seed_all(1) + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + +device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") +print(device) + +clip_model = 'ViT-B-32' +model, _, clip_preprocess = open_clip.create_model_and_transforms( + clip_model, + pretrained= 'D:\\Sharing\\Programs\\3-Durable-Adv\\PyCIL-master\\CLIP-ViT-B-32-laion2B-s34B-b79K\\open_clip_pytorch_model.bin', # pretrained, + jit=True, + device=device, + ) +model.to(device) +tokenizer = open_clip.get_tokenizer(clip_model) +label_set = get_classes(args.label_flag) +all_classes = load_cifar100_classes() +class_str = [all_classes[i] for i in label_set] +text_class_features = dict() +templates = imagenet_templates +for classes in class_str: + if all_classes.index(classes) not in text_class_features: + template_text = [template.format(classes) for template in templates] + tokens = tokenizer(template_text).to(device) + text_features = model.encode_text(tokens).detach() + text_features = text_features.mean(dim=0) + if True: + text_features /= text_features.norm(dim=-1, keepdim=True) + text_class_features[all_classes.index(classes)] = text_features +torch.save(text_class_features, f'text_feature_10.pth') + +# clip_model, _ = clip.load('ViT-B/16') +# clip_model = clip_model.to(device) +# class_list = getCIFAR100ClassIndex() +# class_text = torch.cat([clip.tokenize(f"a photo of a {class_list[c][1]}") for c in range(len(class_list))]).to(device) +# text_embed = clip_model.encode_text(class_text) +# text_embed = text_embed / text_embed.norm(dim=-1, keepdim=True) +# label_set = get_classes(args.label_flag) +# text_cond_dict = dict() +# for label in label_set: +# text_cond_dict[label] = text_embed[label] +# print(text_cond_dict) +# torch.save(text_cond_dict, args.save_path) \ No newline at end of file diff --git a/attacks/CGNC/image_transformer.py b/attacks/CGNC/image_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..f194902353e0d0edc9e2ac5c118d04fc6f566c35 --- /dev/null +++ b/attacks/CGNC/image_transformer.py @@ -0,0 +1,31 @@ +import numpy as np +import torch +import torchvision.transforms as transforms + + +# Transformations +class TwoCropTransform: + def __init__(self, transform, img_size): + self.transform = transform + self.img_size = img_size + color_jitter = transforms.ColorJitter(0.8, 0.8, 0.8, 0.2) + self.data_transforms = transforms.Compose([transforms.RandomResizedCrop(size=self.img_size), + transforms.RandomHorizontalFlip(), + transforms.RandomApply([color_jitter], p=0.8), + transforms.RandomGrayscale(p=0.2), + transforms.ToTensor()]) + + def __call__(self, x): + return [self.transform(x), self.data_transforms(x)] + + +def rotation(input): + batch = input.shape[0] + target = torch.tensor(np.random.permutation([0,1,2,3] * (int(batch / 4) + 1)), device = input.device)[:batch] + target = target.long() + image = torch.zeros_like(input) + image.copy_(input) + for i in range(batch): + image[i, :, :, :] = torch.rot90(input[i, :, :, :], target[i], [1, 2]) + + return image, target diff --git a/attacks/CGNC/imagenet_class_index.json b/attacks/CGNC/imagenet_class_index.json new file mode 100644 index 0000000000000000000000000000000000000000..5fe0dfefcd3dca3b1d169c7ab51b93de327e07e2 --- /dev/null +++ b/attacks/CGNC/imagenet_class_index.json @@ -0,0 +1 @@ +{"0": ["n01440764", "tench"], "1": ["n01443537", "goldfish"], "2": ["n01484850", "great_white_shark"], "3": ["n01491361", "tiger_shark"], "4": ["n01494475", "hammerhead"], "5": ["n01496331", "electric_ray"], "6": ["n01498041", "stingray"], "7": ["n01514668", "cock"], "8": ["n01514859", "hen"], "9": ["n01518878", "ostrich"], "10": ["n01530575", "brambling"], "11": ["n01531178", "goldfinch"], "12": ["n01532829", "house_finch"], "13": ["n01534433", "junco"], "14": ["n01537544", "indigo_bunting"], "15": ["n01558993", "robin"], "16": ["n01560419", "bulbul"], "17": ["n01580077", "jay"], "18": ["n01582220", "magpie"], "19": ["n01592084", "chickadee"], "20": ["n01601694", "water_ouzel"], "21": ["n01608432", "kite"], "22": ["n01614925", "bald_eagle"], "23": ["n01616318", "vulture"], "24": ["n01622779", "great_grey_owl"], "25": ["n01629819", "European_fire_salamander"], "26": ["n01630670", "common_newt"], "27": ["n01631663", "eft"], "28": ["n01632458", "spotted_salamander"], "29": ["n01632777", "axolotl"], "30": ["n01641577", "bullfrog"], "31": ["n01644373", "tree_frog"], "32": ["n01644900", "tailed_frog"], "33": ["n01664065", "loggerhead"], "34": ["n01665541", "leatherback_turtle"], "35": ["n01667114", "mud_turtle"], "36": ["n01667778", "terrapin"], "37": ["n01669191", "box_turtle"], "38": ["n01675722", "banded_gecko"], "39": ["n01677366", "common_iguana"], "40": ["n01682714", "American_chameleon"], "41": ["n01685808", "whiptail"], "42": ["n01687978", "agama"], "43": ["n01688243", "frilled_lizard"], "44": ["n01689811", "alligator_lizard"], "45": ["n01692333", "Gila_monster"], "46": ["n01693334", "green_lizard"], "47": ["n01694178", "African_chameleon"], "48": ["n01695060", "Komodo_dragon"], "49": ["n01697457", "African_crocodile"], "50": ["n01698640", "American_alligator"], "51": ["n01704323", "triceratops"], "52": ["n01728572", "thunder_snake"], "53": ["n01728920", "ringneck_snake"], "54": ["n01729322", "hognose_snake"], "55": ["n01729977", "green_snake"], "56": ["n01734418", "king_snake"], "57": ["n01735189", "garter_snake"], "58": ["n01737021", "water_snake"], "59": ["n01739381", "vine_snake"], "60": ["n01740131", "night_snake"], "61": ["n01742172", "boa_constrictor"], "62": ["n01744401", "rock_python"], "63": ["n01748264", "Indian_cobra"], "64": ["n01749939", "green_mamba"], "65": ["n01751748", "sea_snake"], "66": ["n01753488", "horned_viper"], "67": ["n01755581", "diamondback"], "68": ["n01756291", "sidewinder"], "69": ["n01768244", "trilobite"], "70": ["n01770081", "harvestman"], "71": ["n01770393", "scorpion"], "72": ["n01773157", "black_and_gold_garden_spider"], "73": ["n01773549", "barn_spider"], "74": ["n01773797", "garden_spider"], "75": ["n01774384", "black_widow"], "76": ["n01774750", "tarantula"], "77": ["n01775062", "wolf_spider"], "78": ["n01776313", "tick"], "79": ["n01784675", "centipede"], "80": ["n01795545", "black_grouse"], "81": ["n01796340", "ptarmigan"], "82": ["n01797886", "ruffed_grouse"], "83": ["n01798484", "prairie_chicken"], "84": ["n01806143", "peacock"], "85": ["n01806567", "quail"], "86": ["n01807496", "partridge"], "87": ["n01817953", "African_grey"], "88": ["n01818515", "macaw"], "89": ["n01819313", "sulphur-crested_cockatoo"], "90": ["n01820546", "lorikeet"], "91": ["n01824575", "coucal"], "92": ["n01828970", "bee_eater"], "93": ["n01829413", "hornbill"], "94": ["n01833805", "hummingbird"], "95": ["n01843065", "jacamar"], "96": ["n01843383", "toucan"], "97": ["n01847000", "drake"], "98": ["n01855032", "red-breasted_merganser"], "99": ["n01855672", "goose"], "100": ["n01860187", "black_swan"], "101": ["n01871265", "tusker"], "102": ["n01872401", "echidna"], "103": ["n01873310", "platypus"], "104": ["n01877812", "wallaby"], "105": ["n01882714", "koala"], "106": ["n01883070", "wombat"], "107": ["n01910747", "jellyfish"], "108": ["n01914609", "sea_anemone"], "109": ["n01917289", "brain_coral"], "110": ["n01924916", "flatworm"], "111": ["n01930112", "nematode"], "112": ["n01943899", "conch"], "113": ["n01944390", "snail"], "114": ["n01945685", "slug"], "115": ["n01950731", "sea_slug"], "116": ["n01955084", "chiton"], "117": ["n01968897", "chambered_nautilus"], "118": ["n01978287", "Dungeness_crab"], "119": ["n01978455", "rock_crab"], "120": ["n01980166", "fiddler_crab"], "121": ["n01981276", "king_crab"], "122": ["n01983481", "American_lobster"], "123": ["n01984695", "spiny_lobster"], "124": ["n01985128", "crayfish"], "125": ["n01986214", "hermit_crab"], "126": ["n01990800", "isopod"], "127": ["n02002556", "white_stork"], "128": ["n02002724", "black_stork"], "129": ["n02006656", "spoonbill"], "130": ["n02007558", "flamingo"], "131": ["n02009229", "little_blue_heron"], "132": ["n02009912", "American_egret"], "133": ["n02011460", "bittern"], "134": ["n02012849", "crane"], "135": ["n02013706", "limpkin"], "136": ["n02017213", "European_gallinule"], "137": ["n02018207", "American_coot"], "138": ["n02018795", "bustard"], "139": ["n02025239", "ruddy_turnstone"], "140": ["n02027492", "red-backed_sandpiper"], "141": ["n02028035", "redshank"], "142": ["n02033041", "dowitcher"], "143": ["n02037110", "oystercatcher"], "144": ["n02051845", "pelican"], "145": ["n02056570", "king_penguin"], "146": ["n02058221", "albatross"], "147": ["n02066245", "grey_whale"], "148": ["n02071294", "killer_whale"], "149": ["n02074367", "dugong"], "150": ["n02077923", "sea_lion"], "151": ["n02085620", "Chihuahua"], "152": ["n02085782", "Japanese_spaniel"], "153": ["n02085936", "Maltese_dog"], "154": ["n02086079", "Pekinese"], "155": ["n02086240", "Shih-Tzu"], "156": ["n02086646", "Blenheim_spaniel"], "157": ["n02086910", "papillon"], "158": ["n02087046", "toy_terrier"], "159": ["n02087394", "Rhodesian_ridgeback"], "160": ["n02088094", "Afghan_hound"], "161": ["n02088238", "basset"], "162": ["n02088364", "beagle"], "163": ["n02088466", "bloodhound"], "164": ["n02088632", "bluetick"], "165": ["n02089078", "black-and-tan_coonhound"], "166": ["n02089867", "Walker_hound"], "167": ["n02089973", "English_foxhound"], "168": ["n02090379", "redbone"], "169": ["n02090622", "borzoi"], "170": ["n02090721", "Irish_wolfhound"], "171": ["n02091032", "Italian_greyhound"], "172": ["n02091134", "whippet"], "173": ["n02091244", "Ibizan_hound"], "174": ["n02091467", "Norwegian_elkhound"], "175": ["n02091635", "otterhound"], "176": ["n02091831", "Saluki"], "177": ["n02092002", "Scottish_deerhound"], "178": ["n02092339", "Weimaraner"], "179": ["n02093256", "Staffordshire_bullterrier"], "180": ["n02093428", "American_Staffordshire_terrier"], "181": ["n02093647", "Bedlington_terrier"], "182": ["n02093754", "Border_terrier"], "183": ["n02093859", "Kerry_blue_terrier"], "184": ["n02093991", "Irish_terrier"], "185": ["n02094114", "Norfolk_terrier"], "186": ["n02094258", "Norwich_terrier"], "187": ["n02094433", "Yorkshire_terrier"], "188": ["n02095314", "wire-haired_fox_terrier"], "189": ["n02095570", "Lakeland_terrier"], "190": ["n02095889", "Sealyham_terrier"], "191": ["n02096051", "Airedale"], "192": ["n02096177", "cairn"], "193": ["n02096294", "Australian_terrier"], "194": ["n02096437", "Dandie_Dinmont"], "195": ["n02096585", "Boston_bull"], "196": ["n02097047", "miniature_schnauzer"], "197": ["n02097130", "giant_schnauzer"], "198": ["n02097209", "standard_schnauzer"], "199": ["n02097298", "Scotch_terrier"], "200": ["n02097474", "Tibetan_terrier"], "201": ["n02097658", "silky_terrier"], "202": ["n02098105", "soft-coated_wheaten_terrier"], "203": ["n02098286", "West_Highland_white_terrier"], "204": ["n02098413", "Lhasa"], "205": ["n02099267", "flat-coated_retriever"], "206": ["n02099429", "curly-coated_retriever"], "207": ["n02099601", "golden_retriever"], "208": ["n02099712", "Labrador_retriever"], "209": ["n02099849", "Chesapeake_Bay_retriever"], "210": ["n02100236", "German_short-haired_pointer"], "211": ["n02100583", "vizsla"], "212": ["n02100735", "English_setter"], "213": ["n02100877", "Irish_setter"], "214": ["n02101006", "Gordon_setter"], "215": ["n02101388", "Brittany_spaniel"], "216": ["n02101556", "clumber"], "217": ["n02102040", "English_springer"], "218": ["n02102177", "Welsh_springer_spaniel"], "219": ["n02102318", "cocker_spaniel"], "220": ["n02102480", "Sussex_spaniel"], "221": ["n02102973", "Irish_water_spaniel"], "222": ["n02104029", "kuvasz"], "223": ["n02104365", "schipperke"], "224": ["n02105056", "groenendael"], "225": ["n02105162", "malinois"], "226": ["n02105251", "briard"], "227": ["n02105412", "kelpie"], "228": ["n02105505", "komondor"], "229": ["n02105641", "Old_English_sheepdog"], "230": ["n02105855", "Shetland_sheepdog"], "231": ["n02106030", "collie"], "232": ["n02106166", "Border_collie"], "233": ["n02106382", "Bouvier_des_Flandres"], "234": ["n02106550", "Rottweiler"], "235": ["n02106662", "German_shepherd"], "236": ["n02107142", "Doberman"], "237": ["n02107312", "miniature_pinscher"], "238": ["n02107574", "Greater_Swiss_Mountain_dog"], "239": ["n02107683", "Bernese_mountain_dog"], "240": ["n02107908", "Appenzeller"], "241": ["n02108000", "EntleBucher"], "242": ["n02108089", "boxer"], "243": ["n02108422", "bull_mastiff"], "244": ["n02108551", "Tibetan_mastiff"], "245": ["n02108915", "French_bulldog"], "246": ["n02109047", "Great_Dane"], "247": ["n02109525", "Saint_Bernard"], "248": ["n02109961", "Eskimo_dog"], "249": ["n02110063", "malamute"], "250": ["n02110185", "Siberian_husky"], "251": ["n02110341", "dalmatian"], "252": ["n02110627", "affenpinscher"], "253": ["n02110806", "basenji"], "254": ["n02110958", "pug"], "255": ["n02111129", "Leonberg"], "256": ["n02111277", "Newfoundland"], "257": ["n02111500", "Great_Pyrenees"], "258": ["n02111889", "Samoyed"], "259": ["n02112018", "Pomeranian"], "260": ["n02112137", "chow"], "261": ["n02112350", "keeshond"], "262": ["n02112706", "Brabancon_griffon"], "263": ["n02113023", "Pembroke"], "264": ["n02113186", "Cardigan"], "265": ["n02113624", "toy_poodle"], "266": ["n02113712", "miniature_poodle"], "267": ["n02113799", "standard_poodle"], "268": ["n02113978", "Mexican_hairless"], "269": ["n02114367", "timber_wolf"], "270": ["n02114548", "white_wolf"], "271": ["n02114712", "red_wolf"], "272": ["n02114855", "coyote"], "273": ["n02115641", "dingo"], "274": ["n02115913", "dhole"], "275": ["n02116738", "African_hunting_dog"], "276": ["n02117135", "hyena"], "277": ["n02119022", "red_fox"], "278": ["n02119789", "kit_fox"], "279": ["n02120079", "Arctic_fox"], "280": ["n02120505", "grey_fox"], "281": ["n02123045", "tabby"], "282": ["n02123159", "tiger_cat"], "283": ["n02123394", "Persian_cat"], "284": ["n02123597", "Siamese_cat"], "285": ["n02124075", "Egyptian_cat"], "286": ["n02125311", "cougar"], "287": ["n02127052", "lynx"], "288": ["n02128385", "leopard"], "289": ["n02128757", "snow_leopard"], "290": ["n02128925", "jaguar"], "291": ["n02129165", "lion"], "292": ["n02129604", "tiger"], "293": ["n02130308", "cheetah"], "294": ["n02132136", "brown_bear"], "295": ["n02133161", "American_black_bear"], "296": ["n02134084", "ice_bear"], "297": ["n02134418", "sloth_bear"], "298": ["n02137549", "mongoose"], "299": ["n02138441", "meerkat"], "300": ["n02165105", "tiger_beetle"], "301": ["n02165456", "ladybug"], "302": ["n02167151", "ground_beetle"], "303": ["n02168699", "long-horned_beetle"], "304": ["n02169497", "leaf_beetle"], "305": ["n02172182", "dung_beetle"], "306": ["n02174001", "rhinoceros_beetle"], "307": ["n02177972", "weevil"], "308": ["n02190166", "fly"], "309": ["n02206856", "bee"], "310": ["n02219486", "ant"], "311": ["n02226429", "grasshopper"], "312": ["n02229544", "cricket"], "313": ["n02231487", "walking_stick"], "314": ["n02233338", "cockroach"], "315": ["n02236044", "mantis"], "316": ["n02256656", "cicada"], "317": ["n02259212", "leafhopper"], "318": ["n02264363", "lacewing"], "319": ["n02268443", "dragonfly"], "320": ["n02268853", "damselfly"], "321": ["n02276258", "admiral"], "322": ["n02277742", "ringlet"], "323": ["n02279972", "monarch"], "324": ["n02280649", "cabbage_butterfly"], "325": ["n02281406", "sulphur_butterfly"], "326": ["n02281787", "lycaenid"], "327": ["n02317335", "starfish"], "328": ["n02319095", "sea_urchin"], "329": ["n02321529", "sea_cucumber"], "330": ["n02325366", "wood_rabbit"], "331": ["n02326432", "hare"], "332": ["n02328150", "Angora"], "333": ["n02342885", "hamster"], "334": ["n02346627", "porcupine"], "335": ["n02356798", "fox_squirrel"], "336": ["n02361337", "marmot"], "337": ["n02363005", "beaver"], "338": ["n02364673", "guinea_pig"], "339": ["n02389026", "sorrel"], "340": ["n02391049", "zebra"], "341": ["n02395406", "hog"], "342": ["n02396427", "wild_boar"], "343": ["n02397096", "warthog"], "344": ["n02398521", "hippopotamus"], "345": ["n02403003", "ox"], "346": ["n02408429", "water_buffalo"], "347": ["n02410509", "bison"], "348": ["n02412080", "ram"], "349": ["n02415577", "bighorn"], "350": ["n02417914", "ibex"], "351": ["n02422106", "hartebeest"], "352": ["n02422699", "impala"], "353": ["n02423022", "gazelle"], "354": ["n02437312", "Arabian_camel"], "355": ["n02437616", "llama"], "356": ["n02441942", "weasel"], "357": ["n02442845", "mink"], "358": ["n02443114", "polecat"], "359": ["n02443484", "black-footed_ferret"], "360": ["n02444819", "otter"], "361": ["n02445715", "skunk"], "362": ["n02447366", "badger"], "363": ["n02454379", "armadillo"], "364": ["n02457408", "three-toed_sloth"], "365": ["n02480495", "orangutan"], "366": ["n02480855", "gorilla"], "367": ["n02481823", "chimpanzee"], "368": ["n02483362", "gibbon"], "369": ["n02483708", "siamang"], "370": ["n02484975", "guenon"], "371": ["n02486261", "patas"], "372": ["n02486410", "baboon"], "373": ["n02487347", "macaque"], "374": ["n02488291", "langur"], "375": ["n02488702", "colobus"], "376": ["n02489166", "proboscis_monkey"], "377": ["n02490219", "marmoset"], "378": ["n02492035", "capuchin"], "379": ["n02492660", "howler_monkey"], "380": ["n02493509", "titi"], "381": ["n02493793", "spider_monkey"], "382": ["n02494079", "squirrel_monkey"], "383": ["n02497673", "Madagascar_cat"], "384": ["n02500267", "indri"], "385": ["n02504013", "Indian_elephant"], "386": ["n02504458", "African_elephant"], "387": ["n02509815", "lesser_panda"], "388": ["n02510455", "giant_panda"], "389": ["n02514041", "barracouta"], "390": ["n02526121", "eel"], "391": ["n02536864", "coho"], "392": ["n02606052", "rock_beauty"], "393": ["n02607072", "anemone_fish"], "394": ["n02640242", "sturgeon"], "395": ["n02641379", "gar"], "396": ["n02643566", "lionfish"], "397": ["n02655020", "puffer"], "398": ["n02666196", "abacus"], "399": ["n02667093", "abaya"], "400": ["n02669723", "academic_gown"], "401": ["n02672831", "accordion"], "402": ["n02676566", "acoustic_guitar"], "403": ["n02687172", "aircraft_carrier"], "404": ["n02690373", "airliner"], "405": ["n02692877", "airship"], "406": ["n02699494", "altar"], "407": ["n02701002", "ambulance"], "408": ["n02704792", "amphibian"], "409": ["n02708093", "analog_clock"], "410": ["n02727426", "apiary"], "411": ["n02730930", "apron"], "412": ["n02747177", "ashcan"], "413": ["n02749479", "assault_rifle"], "414": ["n02769748", "backpack"], "415": ["n02776631", "bakery"], "416": ["n02777292", "balance_beam"], "417": ["n02782093", "balloon"], "418": ["n02783161", "ballpoint"], "419": ["n02786058", "Band_Aid"], "420": ["n02787622", "banjo"], "421": ["n02788148", "bannister"], "422": ["n02790996", "barbell"], "423": ["n02791124", "barber_chair"], "424": ["n02791270", "barbershop"], "425": ["n02793495", "barn"], "426": ["n02794156", "barometer"], "427": ["n02795169", "barrel"], "428": ["n02797295", "barrow"], "429": ["n02799071", "baseball"], "430": ["n02802426", "basketball"], "431": ["n02804414", "bassinet"], "432": ["n02804610", "bassoon"], "433": ["n02807133", "bathing_cap"], "434": ["n02808304", "bath_towel"], "435": ["n02808440", "bathtub"], "436": ["n02814533", "beach_wagon"], "437": ["n02814860", "beacon"], "438": ["n02815834", "beaker"], "439": ["n02817516", "bearskin"], "440": ["n02823428", "beer_bottle"], "441": ["n02823750", "beer_glass"], "442": ["n02825657", "bell_cote"], "443": ["n02834397", "bib"], "444": ["n02835271", "bicycle-built-for-two"], "445": ["n02837789", "bikini"], "446": ["n02840245", "binder"], "447": ["n02841315", "binoculars"], "448": ["n02843684", "birdhouse"], "449": ["n02859443", "boathouse"], "450": ["n02860847", "bobsled"], "451": ["n02865351", "bolo_tie"], "452": ["n02869837", "bonnet"], "453": ["n02870880", "bookcase"], "454": ["n02871525", "bookshop"], "455": ["n02877765", "bottlecap"], "456": ["n02879718", "bow"], "457": ["n02883205", "bow_tie"], "458": ["n02892201", "brass"], "459": ["n02892767", "brassiere"], "460": ["n02894605", "breakwater"], "461": ["n02895154", "breastplate"], "462": ["n02906734", "broom"], "463": ["n02909870", "bucket"], "464": ["n02910353", "buckle"], "465": ["n02916936", "bulletproof_vest"], "466": ["n02917067", "bullet_train"], "467": ["n02927161", "butcher_shop"], "468": ["n02930766", "cab"], "469": ["n02939185", "caldron"], "470": ["n02948072", "candle"], "471": ["n02950826", "cannon"], "472": ["n02951358", "canoe"], "473": ["n02951585", "can_opener"], "474": ["n02963159", "cardigan"], "475": ["n02965783", "car_mirror"], "476": ["n02966193", "carousel"], "477": ["n02966687", "carpenter's_kit"], "478": ["n02971356", "carton"], "479": ["n02974003", "car_wheel"], "480": ["n02977058", "cash_machine"], "481": ["n02978881", "cassette"], "482": ["n02979186", "cassette_player"], "483": ["n02980441", "castle"], "484": ["n02981792", "catamaran"], "485": ["n02988304", "CD_player"], "486": ["n02992211", "cello"], "487": ["n02992529", "cellular_telephone"], "488": ["n02999410", "chain"], "489": ["n03000134", "chainlink_fence"], "490": ["n03000247", "chain_mail"], "491": ["n03000684", "chain_saw"], "492": ["n03014705", "chest"], "493": ["n03016953", "chiffonier"], "494": ["n03017168", "chime"], "495": ["n03018349", "china_cabinet"], "496": ["n03026506", "Christmas_stocking"], "497": ["n03028079", "church"], "498": ["n03032252", "cinema"], "499": ["n03041632", "cleaver"], "500": ["n03042490", "cliff_dwelling"], "501": ["n03045698", "cloak"], "502": ["n03047690", "clog"], "503": ["n03062245", "cocktail_shaker"], "504": ["n03063599", "coffee_mug"], "505": ["n03063689", "coffeepot"], "506": ["n03065424", "coil"], "507": ["n03075370", "combination_lock"], "508": ["n03085013", "computer_keyboard"], "509": ["n03089624", "confectionery"], "510": ["n03095699", "container_ship"], "511": ["n03100240", "convertible"], "512": ["n03109150", "corkscrew"], "513": ["n03110669", "cornet"], "514": ["n03124043", "cowboy_boot"], "515": ["n03124170", "cowboy_hat"], "516": ["n03125729", "cradle"], "517": ["n03126707", "crane"], "518": ["n03127747", "crash_helmet"], "519": ["n03127925", "crate"], "520": ["n03131574", "crib"], "521": ["n03133878", "Crock_Pot"], "522": ["n03134739", "croquet_ball"], "523": ["n03141823", "crutch"], "524": ["n03146219", "cuirass"], "525": ["n03160309", "dam"], "526": ["n03179701", "desk"], "527": ["n03180011", "desktop_computer"], "528": ["n03187595", "dial_telephone"], "529": ["n03188531", "diaper"], "530": ["n03196217", "digital_clock"], "531": ["n03197337", "digital_watch"], "532": ["n03201208", "dining_table"], "533": ["n03207743", "dishrag"], "534": ["n03207941", "dishwasher"], "535": ["n03208938", "disk_brake"], "536": ["n03216828", "dock"], "537": ["n03218198", "dogsled"], "538": ["n03220513", "dome"], "539": ["n03223299", "doormat"], "540": ["n03240683", "drilling_platform"], "541": ["n03249569", "drum"], "542": ["n03250847", "drumstick"], "543": ["n03255030", "dumbbell"], "544": ["n03259280", "Dutch_oven"], "545": ["n03271574", "electric_fan"], "546": ["n03272010", "electric_guitar"], "547": ["n03272562", "electric_locomotive"], "548": ["n03290653", "entertainment_center"], "549": ["n03291819", "envelope"], "550": ["n03297495", "espresso_maker"], "551": ["n03314780", "face_powder"], "552": ["n03325584", "feather_boa"], "553": ["n03337140", "file"], "554": ["n03344393", "fireboat"], "555": ["n03345487", "fire_engine"], "556": ["n03347037", "fire_screen"], "557": ["n03355925", "flagpole"], "558": ["n03372029", "flute"], "559": ["n03376595", "folding_chair"], "560": ["n03379051", "football_helmet"], "561": ["n03384352", "forklift"], "562": ["n03388043", "fountain"], "563": ["n03388183", "fountain_pen"], "564": ["n03388549", "four-poster"], "565": ["n03393912", "freight_car"], "566": ["n03394916", "French_horn"], "567": ["n03400231", "frying_pan"], "568": ["n03404251", "fur_coat"], "569": ["n03417042", "garbage_truck"], "570": ["n03424325", "gasmask"], "571": ["n03425413", "gas_pump"], "572": ["n03443371", "goblet"], "573": ["n03444034", "go-kart"], "574": ["n03445777", "golf_ball"], "575": ["n03445924", "golfcart"], "576": ["n03447447", "gondola"], "577": ["n03447721", "gong"], "578": ["n03450230", "gown"], "579": ["n03452741", "grand_piano"], "580": ["n03457902", "greenhouse"], "581": ["n03459775", "grille"], "582": ["n03461385", "grocery_store"], "583": ["n03467068", "guillotine"], "584": ["n03476684", "hair_slide"], "585": ["n03476991", "hair_spray"], "586": ["n03478589", "half_track"], "587": ["n03481172", "hammer"], "588": ["n03482405", "hamper"], "589": ["n03483316", "hand_blower"], "590": ["n03485407", "hand-held_computer"], "591": ["n03485794", "handkerchief"], "592": ["n03492542", "hard_disc"], "593": ["n03494278", "harmonica"], "594": ["n03495258", "harp"], "595": ["n03496892", "harvester"], "596": ["n03498962", "hatchet"], "597": ["n03527444", "holster"], "598": ["n03529860", "home_theater"], "599": ["n03530642", "honeycomb"], "600": ["n03532672", "hook"], "601": ["n03534580", "hoopskirt"], "602": ["n03535780", "horizontal_bar"], "603": ["n03538406", "horse_cart"], "604": ["n03544143", "hourglass"], "605": ["n03584254", "iPod"], "606": ["n03584829", "iron"], "607": ["n03590841", "jack-o'-lantern"], "608": ["n03594734", "jean"], "609": ["n03594945", "jeep"], "610": ["n03595614", "jersey"], "611": ["n03598930", "jigsaw_puzzle"], "612": ["n03599486", "jinrikisha"], "613": ["n03602883", "joystick"], "614": ["n03617480", "kimono"], "615": ["n03623198", "knee_pad"], "616": ["n03627232", "knot"], "617": ["n03630383", "lab_coat"], "618": ["n03633091", "ladle"], "619": ["n03637318", "lampshade"], "620": ["n03642806", "laptop"], "621": ["n03649909", "lawn_mower"], "622": ["n03657121", "lens_cap"], "623": ["n03658185", "letter_opener"], "624": ["n03661043", "library"], "625": ["n03662601", "lifeboat"], "626": ["n03666591", "lighter"], "627": ["n03670208", "limousine"], "628": ["n03673027", "liner"], "629": ["n03676483", "lipstick"], "630": ["n03680355", "Loafer"], "631": ["n03690938", "lotion"], "632": ["n03691459", "loudspeaker"], "633": ["n03692522", "loupe"], "634": ["n03697007", "lumbermill"], "635": ["n03706229", "magnetic_compass"], "636": ["n03709823", "mailbag"], "637": ["n03710193", "mailbox"], "638": ["n03710637", "maillot"], "639": ["n03710721", "maillot"], "640": ["n03717622", "manhole_cover"], "641": ["n03720891", "maraca"], "642": ["n03721384", "marimba"], "643": ["n03724870", "mask"], "644": ["n03729826", "matchstick"], "645": ["n03733131", "maypole"], "646": ["n03733281", "maze"], "647": ["n03733805", "measuring_cup"], "648": ["n03742115", "medicine_chest"], "649": ["n03743016", "megalith"], "650": ["n03759954", "microphone"], "651": ["n03761084", "microwave"], "652": ["n03763968", "military_uniform"], "653": ["n03764736", "milk_can"], "654": ["n03769881", "minibus"], "655": ["n03770439", "miniskirt"], "656": ["n03770679", "minivan"], "657": ["n03773504", "missile"], "658": ["n03775071", "mitten"], "659": ["n03775546", "mixing_bowl"], "660": ["n03776460", "mobile_home"], "661": ["n03777568", "Model_T"], "662": ["n03777754", "modem"], "663": ["n03781244", "monastery"], "664": ["n03782006", "monitor"], "665": ["n03785016", "moped"], "666": ["n03786901", "mortar"], "667": ["n03787032", "mortarboard"], "668": ["n03788195", "mosque"], "669": ["n03788365", "mosquito_net"], "670": ["n03791053", "motor_scooter"], "671": ["n03792782", "mountain_bike"], "672": ["n03792972", "mountain_tent"], "673": ["n03793489", "mouse"], "674": ["n03794056", "mousetrap"], "675": ["n03796401", "moving_van"], "676": ["n03803284", "muzzle"], "677": ["n03804744", "nail"], "678": ["n03814639", "neck_brace"], "679": ["n03814906", "necklace"], "680": ["n03825788", "nipple"], "681": ["n03832673", "notebook"], "682": ["n03837869", "obelisk"], "683": ["n03838899", "oboe"], "684": ["n03840681", "ocarina"], "685": ["n03841143", "odometer"], "686": ["n03843555", "oil_filter"], "687": ["n03854065", "organ"], "688": ["n03857828", "oscilloscope"], "689": ["n03866082", "overskirt"], "690": ["n03868242", "oxcart"], "691": ["n03868863", "oxygen_mask"], "692": ["n03871628", "packet"], "693": ["n03873416", "paddle"], "694": ["n03874293", "paddlewheel"], "695": ["n03874599", "padlock"], "696": ["n03876231", "paintbrush"], "697": ["n03877472", "pajama"], "698": ["n03877845", "palace"], "699": ["n03884397", "panpipe"], "700": ["n03887697", "paper_towel"], "701": ["n03888257", "parachute"], "702": ["n03888605", "parallel_bars"], "703": ["n03891251", "park_bench"], "704": ["n03891332", "parking_meter"], "705": ["n03895866", "passenger_car"], "706": ["n03899768", "patio"], "707": ["n03902125", "pay-phone"], "708": ["n03903868", "pedestal"], "709": ["n03908618", "pencil_box"], "710": ["n03908714", "pencil_sharpener"], "711": ["n03916031", "perfume"], "712": ["n03920288", "Petri_dish"], "713": ["n03924679", "photocopier"], "714": ["n03929660", "pick"], "715": ["n03929855", "pickelhaube"], "716": ["n03930313", "picket_fence"], "717": ["n03930630", "pickup"], "718": ["n03933933", "pier"], "719": ["n03935335", "piggy_bank"], "720": ["n03937543", "pill_bottle"], "721": ["n03938244", "pillow"], "722": ["n03942813", "ping-pong_ball"], "723": ["n03944341", "pinwheel"], "724": ["n03947888", "pirate"], "725": ["n03950228", "pitcher"], "726": ["n03954731", "plane"], "727": ["n03956157", "planetarium"], "728": ["n03958227", "plastic_bag"], "729": ["n03961711", "plate_rack"], "730": ["n03967562", "plow"], "731": ["n03970156", "plunger"], "732": ["n03976467", "Polaroid_camera"], "733": ["n03976657", "pole"], "734": ["n03977966", "police_van"], "735": ["n03980874", "poncho"], "736": ["n03982430", "pool_table"], "737": ["n03983396", "pop_bottle"], "738": ["n03991062", "pot"], "739": ["n03992509", "potter's_wheel"], "740": ["n03995372", "power_drill"], "741": ["n03998194", "prayer_rug"], "742": ["n04004767", "printer"], "743": ["n04005630", "prison"], "744": ["n04008634", "projectile"], "745": ["n04009552", "projector"], "746": ["n04019541", "puck"], "747": ["n04023962", "punching_bag"], "748": ["n04026417", "purse"], "749": ["n04033901", "quill"], "750": ["n04033995", "quilt"], "751": ["n04037443", "racer"], "752": ["n04039381", "racket"], "753": ["n04040759", "radiator"], "754": ["n04041544", "radio"], "755": ["n04044716", "radio_telescope"], "756": ["n04049303", "rain_barrel"], "757": ["n04065272", "recreational_vehicle"], "758": ["n04067472", "reel"], "759": ["n04069434", "reflex_camera"], "760": ["n04070727", "refrigerator"], "761": ["n04074963", "remote_control"], "762": ["n04081281", "restaurant"], "763": ["n04086273", "revolver"], "764": ["n04090263", "rifle"], "765": ["n04099969", "rocking_chair"], "766": ["n04111531", "rotisserie"], "767": ["n04116512", "rubber_eraser"], "768": ["n04118538", "rugby_ball"], "769": ["n04118776", "rule"], "770": ["n04120489", "running_shoe"], "771": ["n04125021", "safe"], "772": ["n04127249", "safety_pin"], "773": ["n04131690", "saltshaker"], "774": ["n04133789", "sandal"], "775": ["n04136333", "sarong"], "776": ["n04141076", "sax"], "777": ["n04141327", "scabbard"], "778": ["n04141975", "scale"], "779": ["n04146614", "school_bus"], "780": ["n04147183", "schooner"], "781": ["n04149813", "scoreboard"], "782": ["n04152593", "screen"], "783": ["n04153751", "screw"], "784": ["n04154565", "screwdriver"], "785": ["n04162706", "seat_belt"], "786": ["n04179913", "sewing_machine"], "787": ["n04192698", "shield"], "788": ["n04200800", "shoe_shop"], "789": ["n04201297", "shoji"], "790": ["n04204238", "shopping_basket"], "791": ["n04204347", "shopping_cart"], "792": ["n04208210", "shovel"], "793": ["n04209133", "shower_cap"], "794": ["n04209239", "shower_curtain"], "795": ["n04228054", "ski"], "796": ["n04229816", "ski_mask"], "797": ["n04235860", "sleeping_bag"], "798": ["n04238763", "slide_rule"], "799": ["n04239074", "sliding_door"], "800": ["n04243546", "slot"], "801": ["n04251144", "snorkel"], "802": ["n04252077", "snowmobile"], "803": ["n04252225", "snowplow"], "804": ["n04254120", "soap_dispenser"], "805": ["n04254680", "soccer_ball"], "806": ["n04254777", "sock"], "807": ["n04258138", "solar_dish"], "808": ["n04259630", "sombrero"], "809": ["n04263257", "soup_bowl"], "810": ["n04264628", "space_bar"], "811": ["n04265275", "space_heater"], "812": ["n04266014", "space_shuttle"], "813": ["n04270147", "spatula"], "814": ["n04273569", "speedboat"], "815": ["n04275548", "spider_web"], "816": ["n04277352", "spindle"], "817": ["n04285008", "sports_car"], "818": ["n04286575", "spotlight"], "819": ["n04296562", "stage"], "820": ["n04310018", "steam_locomotive"], "821": ["n04311004", "steel_arch_bridge"], "822": ["n04311174", "steel_drum"], "823": ["n04317175", "stethoscope"], "824": ["n04325704", "stole"], "825": ["n04326547", "stone_wall"], "826": ["n04328186", "stopwatch"], "827": ["n04330267", "stove"], "828": ["n04332243", "strainer"], "829": ["n04335435", "streetcar"], "830": ["n04336792", "stretcher"], "831": ["n04344873", "studio_couch"], "832": ["n04346328", "stupa"], "833": ["n04347754", "submarine"], "834": ["n04350905", "suit"], "835": ["n04355338", "sundial"], "836": ["n04355933", "sunglass"], "837": ["n04356056", "sunglasses"], "838": ["n04357314", "sunscreen"], "839": ["n04366367", "suspension_bridge"], "840": ["n04367480", "swab"], "841": ["n04370456", "sweatshirt"], "842": ["n04371430", "swimming_trunks"], "843": ["n04371774", "swing"], "844": ["n04372370", "switch"], "845": ["n04376876", "syringe"], "846": ["n04380533", "table_lamp"], "847": ["n04389033", "tank"], "848": ["n04392985", "tape_player"], "849": ["n04398044", "teapot"], "850": ["n04399382", "teddy"], "851": ["n04404412", "television"], "852": ["n04409515", "tennis_ball"], "853": ["n04417672", "thatch"], "854": ["n04418357", "theater_curtain"], "855": ["n04423845", "thimble"], "856": ["n04428191", "thresher"], "857": ["n04429376", "throne"], "858": ["n04435653", "tile_roof"], "859": ["n04442312", "toaster"], "860": ["n04443257", "tobacco_shop"], "861": ["n04447861", "toilet_seat"], "862": ["n04456115", "torch"], "863": ["n04458633", "totem_pole"], "864": ["n04461696", "tow_truck"], "865": ["n04462240", "toyshop"], "866": ["n04465501", "tractor"], "867": ["n04467665", "trailer_truck"], "868": ["n04476259", "tray"], "869": ["n04479046", "trench_coat"], "870": ["n04482393", "tricycle"], "871": ["n04483307", "trimaran"], "872": ["n04485082", "tripod"], "873": ["n04486054", "triumphal_arch"], "874": ["n04487081", "trolleybus"], "875": ["n04487394", "trombone"], "876": ["n04493381", "tub"], "877": ["n04501370", "turnstile"], "878": ["n04505470", "typewriter_keyboard"], "879": ["n04507155", "umbrella"], "880": ["n04509417", "unicycle"], "881": ["n04515003", "upright"], "882": ["n04517823", "vacuum"], "883": ["n04522168", "vase"], "884": ["n04523525", "vault"], "885": ["n04525038", "velvet"], "886": ["n04525305", "vending_machine"], "887": ["n04532106", "vestment"], "888": ["n04532670", "viaduct"], "889": ["n04536866", "violin"], "890": ["n04540053", "volleyball"], "891": ["n04542943", "waffle_iron"], "892": ["n04548280", "wall_clock"], "893": ["n04548362", "wallet"], "894": ["n04550184", "wardrobe"], "895": ["n04552348", "warplane"], "896": ["n04553703", "washbasin"], "897": ["n04554684", "washer"], "898": ["n04557648", "water_bottle"], "899": ["n04560804", "water_jug"], "900": ["n04562935", "water_tower"], "901": ["n04579145", "whiskey_jug"], "902": ["n04579432", "whistle"], "903": ["n04584207", "wig"], "904": ["n04589890", "window_screen"], "905": ["n04590129", "window_shade"], "906": ["n04591157", "Windsor_tie"], "907": ["n04591713", "wine_bottle"], "908": ["n04592741", "wing"], "909": ["n04596742", "wok"], "910": ["n04597913", "wooden_spoon"], "911": ["n04599235", "wool"], "912": ["n04604644", "worm_fence"], "913": ["n04606251", "wreck"], "914": ["n04612504", "yawl"], "915": ["n04613696", "yurt"], "916": ["n06359193", "web_site"], "917": ["n06596364", "comic_book"], "918": ["n06785654", "crossword_puzzle"], "919": ["n06794110", "street_sign"], "920": ["n06874185", "traffic_light"], "921": ["n07248320", "book_jacket"], "922": ["n07565083", "menu"], "923": ["n07579787", "plate"], "924": ["n07583066", "guacamole"], "925": ["n07584110", "consomme"], "926": ["n07590611", "hot_pot"], "927": ["n07613480", "trifle"], "928": ["n07614500", "ice_cream"], "929": ["n07615774", "ice_lolly"], "930": ["n07684084", "French_loaf"], "931": ["n07693725", "bagel"], "932": ["n07695742", "pretzel"], "933": ["n07697313", "cheeseburger"], "934": ["n07697537", "hotdog"], "935": ["n07711569", "mashed_potato"], "936": ["n07714571", "head_cabbage"], "937": ["n07714990", "broccoli"], "938": ["n07715103", "cauliflower"], "939": ["n07716358", "zucchini"], "940": ["n07716906", "spaghetti_squash"], "941": ["n07717410", "acorn_squash"], "942": ["n07717556", "butternut_squash"], "943": ["n07718472", "cucumber"], "944": ["n07718747", "artichoke"], "945": ["n07720875", "bell_pepper"], "946": ["n07730033", "cardoon"], "947": ["n07734744", "mushroom"], "948": ["n07742313", "Granny_Smith"], "949": ["n07745940", "strawberry"], "950": ["n07747607", "orange"], "951": ["n07749582", "lemon"], "952": ["n07753113", "fig"], "953": ["n07753275", "pineapple"], "954": ["n07753592", "banana"], "955": ["n07754684", "jackfruit"], "956": ["n07760859", "custard_apple"], "957": ["n07768694", "pomegranate"], "958": ["n07802026", "hay"], "959": ["n07831146", "carbonara"], "960": ["n07836838", "chocolate_sauce"], "961": ["n07860988", "dough"], "962": ["n07871810", "meat_loaf"], "963": ["n07873807", "pizza"], "964": ["n07875152", "potpie"], "965": ["n07880968", "burrito"], "966": ["n07892512", "red_wine"], "967": ["n07920052", "espresso"], "968": ["n07930864", "cup"], "969": ["n07932039", "eggnog"], "970": ["n09193705", "alp"], "971": ["n09229709", "bubble"], "972": ["n09246464", "cliff"], "973": ["n09256479", "coral_reef"], "974": ["n09288635", "geyser"], "975": ["n09332890", "lakeside"], "976": ["n09399592", "promontory"], "977": ["n09421951", "sandbar"], "978": ["n09428293", "seashore"], "979": ["n09468604", "valley"], "980": ["n09472597", "volcano"], "981": ["n09835506", "ballplayer"], "982": ["n10148035", "groom"], "983": ["n10565667", "scuba_diver"], "984": ["n11879895", "rapeseed"], "985": ["n11939491", "daisy"], "986": ["n12057211", "yellow_lady's_slipper"], "987": ["n12144580", "corn"], "988": ["n12267677", "acorn"], "989": ["n12620546", "hip"], "990": ["n12768682", "buckeye"], "991": ["n12985857", "coral_fungus"], "992": ["n12998815", "agaric"], "993": ["n13037406", "gyromitra"], "994": ["n13040303", "stinkhorn"], "995": ["n13044778", "earthstar"], "996": ["n13052670", "hen-of-the-woods"], "997": ["n13054560", "bolete"], "998": ["n13133613", "ear"], "999": ["n15075141", "toilet_tissue"]} \ No newline at end of file diff --git a/attacks/CGNC/inference.py b/attacks/CGNC/inference.py new file mode 100644 index 0000000000000000000000000000000000000000..12bb29984671e136060d13b2221bf6ed391b878b --- /dev/null +++ b/attacks/CGNC/inference.py @@ -0,0 +1,73 @@ +import argparse +from torch.utils.data import DataLoader +from utils_ import * + +parser = argparse.ArgumentParser(description='Clip-based Generative Networks') +parser.add_argument('--test_dir', default='', help='Testing Data') +parser.add_argument('--batch_size', type=int, default=10, help='Batch Size') +parser.add_argument('--model_t',type=str, default= 'all', help ='Model under attack : vgg16, vgg19, ..., dense121') +parser.add_argument('--label_flag', type=str, default='N8', help='Label nums: N8, C20, C50, ...') +parser.add_argument('--finetune', action='store_true', help='Finetune for single class attack') +parser.add_argument('--finetune_class', type=int, help='Class id to be finetuned') +args = parser.parse_args() +print(args) + +n_class = 1000 +# GPU +device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') +print(device) + +dic = dict() + +if args.model_t == 'all': + model_name_list = ['vgg16', 'googlenet', 'incv3', 'res152', 'dense121', 'incv4', 'inc_res_v2', 'adv_incv3', 'ens_inc_res_v2', 'res50_sin', 'res50_sin_in', 'res50_sin_fine_in'] +elif args.model_t == 'robust': + model_name_list = ['adv_incv3', 'ens_inc_res_v2', 'res50_sin', 'res50_sin_in', 'res50_sin_fine_in'] +elif args.model_t == 'normal': + model_name_list = ['vgg16', 'googlenet', 'incv3', 'res152', 'dense121', 'incv4', 'inc_res_v2'] +else: + model_name_list = [args.model_t] + +for model_name in model_name_list: + model_t = load_model(model_name) + + model_t = model_t.to(device) + model_t.eval() + + # Input dimensions: Inception takes 3x299x299 + if model_name in ['incv3', 'incv4', 'inc_res_v2', 'adv_incv3', 'ens_inc_res_v2']: + img_size = 299 + else: + img_size = 224 + + # Setup-Data + data_transform = transforms.Compose([ + transforms.Resize(img_size), + transforms.ToTensor(), + ]) + + if args.finetune: + class_ids = np.array([args.finetune_class]) + else: + class_ids = get_classes(args.label_flag) + + # Evaluation + sr = np.zeros(len(class_ids)) + for idx in range(len(class_ids)): + test_dir = '{}_t{}'.format(args.test_dir, class_ids[idx]) + + target_acc = 0. + target_test_size = 0. + test_set = datasets.ImageFolder(test_dir, data_transform) + test_loader = torch.utils.data.DataLoader(test_set, batch_size=args.batch_size, shuffle=False, num_workers=4, + pin_memory=True) + for i, (img, _) in enumerate(test_loader): + img = img.to(device) + adv_out = model_t(normalize(img.clone().detach())) + target_acc += torch.sum(adv_out.argmax(dim=-1) == (class_ids[idx])).item() + target_test_size += img.size(0) + sr[idx] = target_acc / target_test_size + print('sr: {}'.format(sr)) + print('model:{} \t target acc:{:.2%}\t target_test_size:{}'.format(model_name, sr.mean(), target_test_size)) + dic[model_name] = sr.mean() * 100 +print(dic) diff --git a/attacks/CGNC/models/attention.py b/attacks/CGNC/models/attention.py new file mode 100644 index 0000000000000000000000000000000000000000..819605b232a051254d392c97fe67ceb96eccd107 --- /dev/null +++ b/attacks/CGNC/models/attention.py @@ -0,0 +1,260 @@ +from inspect import isfunction +import math +import torch +import torch.nn.functional as F +from torch import nn, einsum +from einops import rearrange, repeat + + +def exists(val): + return val is not None + + +def uniq(arr): + return{el: True for el in arr}.keys() + + +def default(val, d): + if exists(val): + return val + return d() if isfunction(d) else d + + +def max_neg_value(t): + return -torch.finfo(t.dtype).max + + +def init_(tensor): + dim = tensor.shape[-1] + std = 1 / math.sqrt(dim) + tensor.uniform_(-std, std) + return tensor + + +# feedforward +class GEGLU(nn.Module): + def __init__(self, dim_in, dim_out): + super().__init__() + self.proj = nn.Linear(dim_in, dim_out * 2) + + def forward(self, x): + x, gate = self.proj(x).chunk(2, dim=-1) + return x * F.gelu(gate) + + +class FeedForward(nn.Module): + def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.): + super().__init__() + inner_dim = int(dim * mult) + dim_out = default(dim_out, dim) + project_in = nn.Sequential( + nn.Linear(dim, inner_dim), + nn.GELU() + ) if not glu else GEGLU(dim, inner_dim) + + self.net = nn.Sequential( + project_in, + nn.Dropout(dropout), + nn.Linear(inner_dim, dim_out) + ) + + def forward(self, x): + return self.net(x) + + +def zero_module(module): + """ + Zero out the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().zero_() + return module + + +def Normalize(in_channels): + return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) + + +class LinearAttention(nn.Module): + def __init__(self, dim, heads=4, dim_head=32): + super().__init__() + self.heads = heads + hidden_dim = dim_head * heads + self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False) + self.to_out = nn.Conv2d(hidden_dim, dim, 1) + + def forward(self, x): + b, c, h, w = x.shape + qkv = self.to_qkv(x) + q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3) + k = k.softmax(dim=-1) + context = torch.einsum('bhdn,bhen->bhde', k, v) + out = torch.einsum('bhde,bhdn->bhen', context, q) + out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w) + return self.to_out(out) + + +class SpatialSelfAttention(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.in_channels = in_channels + + self.norm = Normalize(in_channels) + self.q = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.k = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.v = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.proj_out = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + + def forward(self, x): + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + # compute attention + b,c,h,w = q.shape + q = rearrange(q, 'b c h w -> b (h w) c') + k = rearrange(k, 'b c h w -> b c (h w)') + w_ = torch.einsum('bij,bjk->bik', q, k) + + w_ = w_ * (int(c)**(-0.5)) + w_ = torch.nn.functional.softmax(w_, dim=2) + + # attend to values + v = rearrange(v, 'b c h w -> b c (h w)') + w_ = rearrange(w_, 'b i j -> b j i') + h_ = torch.einsum('bij,bjk->bik', v, w_) + h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h) + h_ = self.proj_out(h_) + + return x+h_ + + +class CrossAttention(nn.Module): + def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.): + super().__init__() + inner_dim = dim_head * heads + context_dim = default(context_dim, query_dim) + + self.scale = dim_head ** -0.5 + self.heads = heads + + self.to_q = nn.Linear(query_dim, inner_dim, bias=False) + self.to_k = nn.Linear(context_dim, inner_dim, bias=False) + self.to_v = nn.Linear(context_dim, inner_dim, bias=False) + + self.to_out = nn.Sequential( + nn.Linear(inner_dim, query_dim), + nn.Dropout(dropout) + ) + + def forward(self, x, context=None, mask=None): + h = self.heads + + q = self.to_q(x) + context = default(context, x) + k = self.to_k(context) + v = self.to_v(context) + + q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v)) + + sim = einsum('b i d, b j d -> b i j', q, k) * self.scale + + if exists(mask): + mask = rearrange(mask, 'b ... -> b (...)') + max_neg_value = -torch.finfo(sim.dtype).max + mask = repeat(mask, 'b j -> (b h) () j', h=h) + sim.masked_fill_(~mask, max_neg_value) + + # attention, what we cannot get enough of + attn = sim.softmax(dim=-1) + + out = einsum('b i j, b j d -> b i d', attn, v) + out = rearrange(out, '(b h) n d -> b n (h d)', h=h) + return self.to_out(out) + + +class BasicTransformerBlock(nn.Module): + def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True): + super().__init__() + self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout) # is a self-attention + self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff) + self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim, + heads=n_heads, dim_head=d_head, dropout=dropout) # is self-attn if context is none + self.norm1 = nn.LayerNorm(dim) + self.norm2 = nn.LayerNorm(dim) + self.norm3 = nn.LayerNorm(dim) + self.checkpoint = checkpoint + + 'Cancel the checkpointing operation.' + # def forward(self, x, context=None): + # return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint) + + def forward(self, x, context=None): + x = self.attn1(self.norm1(x)) + x + x = self.attn2(self.norm2(x), context=context) + x + x = self.ff(self.norm3(x)) + x + return x + + +class SpatialTransformer(nn.Module): + """ + Transformer block for image-like data. + First, project the input (aka embedding) + and reshape to b, t, d. + Then apply standard transformer action. + Finally, reshape to image + """ + def __init__(self, in_channels, n_heads, d_head, + depth=1, dropout=0., context_dim=None): + super().__init__() + self.in_channels = in_channels + inner_dim = n_heads * d_head + self.norm = Normalize(in_channels) + + self.proj_in = nn.Conv2d(in_channels, + inner_dim, + kernel_size=1, + stride=1, + padding=0) + + self.transformer_blocks = nn.ModuleList( + [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim) + for d in range(depth)] + ) + + self.proj_out = zero_module(nn.Conv2d(inner_dim, + in_channels, + kernel_size=1, + stride=1, + padding=0)) + + def forward(self, x, context=None): + # note: if no context is given, cross-attention defaults to self-attention + b, c, h, w = x.shape + x_in = x + x = self.norm(x) + x = self.proj_in(x) + x = rearrange(x, 'b c h w -> b (h w) c') + for block in self.transformer_blocks: + x = block(x, context=context) + x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w) + x = self.proj_out(x) + return x + x_in \ No newline at end of file diff --git a/attacks/CGNC/models/generator.py b/attacks/CGNC/models/generator.py new file mode 100644 index 0000000000000000000000000000000000000000..bf0645cc841c8d42fb3b543934ee8ffcabad0cfb --- /dev/null +++ b/attacks/CGNC/models/generator.py @@ -0,0 +1,154 @@ +import torch +import torch.nn as nn +import math +from attacks.CGNC.models.attention import SpatialTransformer + +# To control feature map in generator +ngf = 64 + + +def snconv2d(eps=1e-12, **kwargs): + return nn.utils.spectral_norm(nn.Conv2d(**kwargs), eps=eps) + + +def snlinear(eps=1e-12, **kwargs): + return nn.utils.spectral_norm(nn.Linear(**kwargs), eps=eps) + + +def get_gaussian_kernel(kernel_size=3, pad=2, sigma=1, channels=3): + x_coord = torch.arange(kernel_size) + x_grid = x_coord.repeat(kernel_size).view(kernel_size, kernel_size) + y_grid = x_grid.t() + xy_grid = torch.stack([x_grid, y_grid], dim=-1).float() + + mean = (kernel_size - 1) / 2. + variance = sigma ** 2. + gaussian_kernel = (1. / (2. * math.pi * variance)) * \ + torch.exp( + -torch.sum((xy_grid - mean) ** 2., dim=-1) / \ + (2 * variance) + ) + gaussian_kernel = gaussian_kernel / torch.sum(gaussian_kernel) + gaussian_kernel = gaussian_kernel.view(1, 1, kernel_size, kernel_size) + gaussian_kernel = gaussian_kernel.repeat(channels, 1, 1, 1) + + gaussian_filter = torch.nn.Conv2d(in_channels=channels, out_channels=channels, + kernel_size=kernel_size, groups=channels, padding=kernel_size - pad, bias=False) + + gaussian_filter.weight.data = gaussian_kernel + gaussian_filter.weight.requires_grad = False + + return gaussian_filter + + +class CrossAttenGenerator(nn.Module): + def __init__(self, inception=False, device='cuda', num_head=1, nz=16, loc=[1, 1, 1], context_dim=512): + ''' + :param inception: if True crop layer will be added to go from 3x300x300 to 3x299x299. + ''' + super(CrossAttenGenerator, self).__init__() + self.inception = inception + self.device = device + self.snlinear = snlinear(in_features=512, out_features=nz, bias=False) + self.loc = loc + + self.block1 = nn.Sequential( + nn.ReflectionPad2d(3), + nn.Conv2d(3 + nz * self.loc[0], ngf, kernel_size=7, padding=0, bias=False), + nn.BatchNorm2d(ngf), + nn.ReLU(True) + ) + self.block2 = nn.Sequential( + nn.Conv2d(ngf + nz * self.loc[1], ngf * 2, kernel_size=3, stride=2, padding=1, bias=False), + nn.BatchNorm2d(ngf * 2), + nn.ReLU(True) + + ) + self.block3 = nn.Sequential( + nn.Conv2d(ngf * 2 + nz * self.loc[2], ngf * 4, kernel_size=3, stride=2, padding=1, bias=False), + nn.BatchNorm2d(ngf * 4), + nn.ReLU(True) + ) + self.resblock1 = ResidualBlock(ngf * 4) + self.resblock2 = ResidualBlock(ngf * 4) + self.cross_att2 = SpatialTransformer(ngf * 4, num_head, ngf * 4 // num_head, depth=1, context_dim=context_dim) + self.resblock3 = ResidualBlock(ngf * 4) + self.resblock4 = ResidualBlock(ngf * 4) + self.cross_att4 = SpatialTransformer(ngf * 4, num_head, ngf * 4 // num_head, depth=1, context_dim=context_dim) + self.resblock5 = ResidualBlock(ngf * 4) + self.resblock6 = ResidualBlock(ngf * 4) + + self.upsampl1 = nn.Sequential( + nn.ConvTranspose2d(ngf * 4, ngf * 2, kernel_size=3, stride=2, padding=1, output_padding=1, bias=False), + nn.BatchNorm2d(ngf * 2), + nn.ReLU(True) + ) + self.upsampl2 = nn.Sequential( + nn.ConvTranspose2d(ngf * 2, ngf, kernel_size=3, stride=2, padding=1, output_padding=1, bias=False), + nn.BatchNorm2d(ngf), + nn.ReLU(True) + ) + self.blockf = nn.Sequential( + nn.ReflectionPad2d(3), + nn.Conv2d(ngf, 3, kernel_size=7, padding=0) + ) + self.crop = nn.ConstantPad2d((0, -1, -1, 0), 0) + self.alf_layer = get_gaussian_kernel(kernel_size=3, pad=2, sigma=1) + + def forward(self, input, cond, eps=16): + text_cond = cond.unsqueeze(1).to(torch.float) + z_cond = self.snlinear(cond.float()) + + # loc 0 + z_img = z_cond[:, :, None, None].expand(z_cond.size(0), z_cond.size(1), input.size(2), input.size(3)) + x = self.block1(torch.cat((input, z_img), dim=1)) + + # loc 1 + z_img = z_cond[:, :, None, None].expand(z_cond.size(0), z_cond.size(1), x.size(2), x.size(3)) + x = self.block2(torch.cat((x, z_img), dim=1)) if self.loc[1] else self.block2(x) + + # loc 2 + z_img = z_cond[:, :, None, None].expand(z_cond.size(0), z_cond.size(1), x.size(2), x.size(3)) + x = self.block3(torch.cat((x, z_img), dim=1)) if self.loc[2] else self.block3(x) + + x = self.resblock1(x) + x = self.resblock2(x) + x = self.cross_att2(x, text_cond) + x = self.resblock3(x) + x = self.resblock4(x) + x = self.cross_att4(x, text_cond) + x = self.resblock5(x) + x = self.resblock6(x) + + x = self.upsampl1(x) + x = self.upsampl2(x) + x = self.blockf(x) + if self.inception: + x = self.crop(x) + x = torch.tanh(x) + x = self.alf_layer(x) + + return x * eps + + +class ResidualBlock(nn.Module): + def __init__(self, num_filters): + super(ResidualBlock, self).__init__() + self.block = nn.Sequential( + nn.ReflectionPad2d(1), + nn.Conv2d(in_channels=num_filters, out_channels=num_filters, kernel_size=3, stride=1, padding=0, + bias=False), + nn.BatchNorm2d(num_filters), + nn.ReLU(True), + + nn.Dropout(0.5), + + nn.ReflectionPad2d(1), + nn.Conv2d(in_channels=num_filters, out_channels=num_filters, kernel_size=3, stride=1, padding=0, + bias=False), + nn.BatchNorm2d(num_filters) + ) + + def forward(self, x): + residual = self.block(x) + return x + residual diff --git a/attacks/CGNC/models/generator.sync-conflict-20250703-173917-PJCGIIU.py b/attacks/CGNC/models/generator.sync-conflict-20250703-173917-PJCGIIU.py new file mode 100644 index 0000000000000000000000000000000000000000..193c9f30e6c133b0443b6d5a1ee45d4e99de2ed8 --- /dev/null +++ b/attacks/CGNC/models/generator.sync-conflict-20250703-173917-PJCGIIU.py @@ -0,0 +1,154 @@ +import torch +import torch.nn as nn +import math +from attacks.CGNC.models.attention import SpatialTransformer + +# To control feature map in generator +ngf = 64 + + +def snconv2d(eps=1e-12, **kwargs): + return nn.utils.spectral_norm(nn.Conv2d(**kwargs), eps=eps) + + +def snlinear(eps=1e-12, **kwargs): + return nn.utils.spectral_norm(nn.Linear(**kwargs), eps=eps) + + +def get_gaussian_kernel(kernel_size=3, pad=2, sigma=1, channels=3): + x_coord = torch.arange(kernel_size) + x_grid = x_coord.repeat(kernel_size).view(kernel_size, kernel_size) + y_grid = x_grid.t() + xy_grid = torch.stack([x_grid, y_grid], dim=-1).float() + + mean = (kernel_size - 1) / 2. + variance = sigma ** 2. + gaussian_kernel = (1. / (2. * math.pi * variance)) * \ + torch.exp( + -torch.sum((xy_grid - mean) ** 2., dim=-1) / \ + (2 * variance) + ) + gaussian_kernel = gaussian_kernel / torch.sum(gaussian_kernel) + gaussian_kernel = gaussian_kernel.view(1, 1, kernel_size, kernel_size) + gaussian_kernel = gaussian_kernel.repeat(channels, 1, 1, 1) + + gaussian_filter = torch.nn.Conv2d(in_channels=channels, out_channels=channels, + kernel_size=kernel_size, groups=channels, padding=kernel_size - pad, bias=False) + + gaussian_filter.weight.data = gaussian_kernel + gaussian_filter.weight.requires_grad = False + + return gaussian_filter + + +class CrossAttenGenerator(nn.Module): + def __init__(self, inception=False, device='cuda', num_head=1, nz=16, loc=[1, 1, 1], context_dim=512): + ''' + :param inception: if True crop layer will be added to go from 3x300x300 to 3x299x299. + ''' + super(CrossAttenGenerator, self).__init__() + self.inception = inception + self.device = device + self.snlinear = snlinear(in_features=512, out_features=nz, bias=False) + self.loc = loc + + self.block1 = nn.Sequential( + nn.ReflectionPad2d(3), + nn.Conv2d(3 + nz * self.loc[0], ngf, kernel_size=7, padding=0, bias=False), + nn.BatchNorm2d(ngf), + nn.ReLU(True) + ) + self.block2 = nn.Sequential( + nn.Conv2d(ngf + nz * self.loc[1], ngf * 2, kernel_size=3, stride=2, padding=1, bias=False), + nn.BatchNorm2d(ngf * 2), + nn.ReLU(True) + + ) + self.block3 = nn.Sequential( + nn.Conv2d(ngf * 2 + nz * self.loc[2], ngf * 4, kernel_size=3, stride=2, padding=1, bias=False), + nn.BatchNorm2d(ngf * 4), + nn.ReLU(True) + ) + self.resblock1 = ResidualBlock(ngf * 4) + self.resblock2 = ResidualBlock(ngf * 4) + self.cross_att2 = SpatialTransformer(ngf * 4, num_head, ngf * 4 // num_head, depth=1, context_dim=context_dim) + self.resblock3 = ResidualBlock(ngf * 4) + self.resblock4 = ResidualBlock(ngf * 4) + self.cross_att4 = SpatialTransformer(ngf * 4, num_head, ngf * 4 // num_head, depth=1, context_dim=context_dim) + self.resblock5 = ResidualBlock(ngf * 4) + self.resblock6 = ResidualBlock(ngf * 4) + + self.upsampl1 = nn.Sequential( + nn.ConvTranspose2d(ngf * 4, ngf * 2, kernel_size=3, stride=2, padding=1, output_padding=1, bias=False), + nn.BatchNorm2d(ngf * 2), + nn.ReLU(True) + ) + self.upsampl2 = nn.Sequential( + nn.ConvTranspose2d(ngf * 2, ngf, kernel_size=3, stride=2, padding=1, output_padding=1, bias=False), + nn.BatchNorm2d(ngf), + nn.ReLU(True) + ) + self.blockf = nn.Sequential( + nn.ReflectionPad2d(3), + nn.Conv2d(ngf, 3, kernel_size=7, padding=0) + ) + self.crop = nn.ConstantPad2d((0, -1, -1, 0), 0) + self.alf_layer = get_gaussian_kernel(kernel_size=3, pad=2, sigma=1) + + def forward(self, input, cond, eps=16): + text_cond = cond.unsqueeze(1).to(torch.float) + z_cond = self.snlinear(cond.float()) + + # loc 0 + z_img = z_cond[:, :, None, None].expand(z_cond.size(0), z_cond.size(1), input.size(2), input.size(3)) + x = self.block1(torch.cat((input, z_img), dim=1)) + + # loc 1 + z_img = z_cond[:, :, None, None].expand(z_cond.size(0), z_cond.size(1), x.size(2), x.size(3)) + x = self.block2(torch.cat((x, z_img), dim=1)) if self.loc[1] else self.block2(x) + + # loc 2 + z_img = z_cond[:, :, None, None].expand(z_cond.size(0), z_cond.size(1), x.size(2), x.size(3)) + x = self.block3(torch.cat((x, z_img), dim=1)) if self.loc[2] else self.block3(x) + + x = self.resblock1(x) + x = self.resblock2(x) + x = self.cross_att2(x, text_cond) + x = self.resblock3(x) + x = self.resblock4(x) + x = self.cross_att4(x, text_cond) + x = self.resblock5(x) + x = self.resblock6(x) + + x = self.upsampl1(x) + x = self.upsampl2(x) + x = self.blockf(x) + if self.inception: + x = self.crop(x) + x = torch.tanh(x) + x = self.alf_layer(x) + + return x * eps #torch.clip(x, -eps, eps) + + +class ResidualBlock(nn.Module): + def __init__(self, num_filters): + super(ResidualBlock, self).__init__() + self.block = nn.Sequential( + nn.ReflectionPad2d(1), + nn.Conv2d(in_channels=num_filters, out_channels=num_filters, kernel_size=3, stride=1, padding=0, + bias=False), + nn.BatchNorm2d(num_filters), + nn.ReLU(True), + + nn.Dropout(0.5), + + nn.ReflectionPad2d(1), + nn.Conv2d(in_channels=num_filters, out_channels=num_filters, kernel_size=3, stride=1, padding=0, + bias=False), + nn.BatchNorm2d(num_filters) + ) + + def forward(self, x): + residual = self.block(x) + return x + residual diff --git a/attacks/CGNC/text_feature.pth b/attacks/CGNC/text_feature.pth new file mode 100644 index 0000000000000000000000000000000000000000..e4003d17a8e20431eae41ef649f5ae2ad0f831bc --- /dev/null +++ b/attacks/CGNC/text_feature.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6da1b4774875ce00f303b83a70ec81d6479b5cba1c32c1a8908f8c46b69980ae +size 24040 diff --git a/attacks/CGNC/train.py b/attacks/CGNC/train.py new file mode 100644 index 0000000000000000000000000000000000000000..c1340adf7232db6b58a3001e0dde3379d0dfeb9a --- /dev/null +++ b/attacks/CGNC/train.py @@ -0,0 +1,150 @@ +from torch.utils.data import DataLoader +from tqdm import tqdm +from utils_ import * +from models.generator import CrossAttenGenerator +from image_transformer import rotation +from PIL import Image, ImageFile +ImageFile.LOAD_TRUNCATED_IMAGES = True +import argparse +Image.MAX_IMAGE_PIXELS = None +import torch.optim as optim + +parser = argparse.ArgumentParser(description='Clip-based Generative Networks') +parser.add_argument('--train_dir', default='./dataset/ImageNet/train', help='imagenet') +parser.add_argument('--batch_size', type=int, default=20, help='Number of training samples/batch') +parser.add_argument('--epochs', type=int, default=10, help='Number of training epochs') +parser.add_argument('--lr', type=float, default=2e-4, help='Initial learning rate') +parser.add_argument('--eps', type=int, default=16, help='Perturbation budget') +parser.add_argument('--model_type', type=str, default='res152', help='Source model') +parser.add_argument('--start_epoch', type=int, default=0, help='Start epoch') +parser.add_argument('--label_flag', type=str, default='N8', help='Label nums: N8, C20,...,C200') +parser.add_argument('--nz', type=int, default=16, help='nz') +parser.add_argument('--save_dir', type=str, default='checkpoints', help='Dictionary to save the model') +parser.add_argument('--load_path', type=str, help='Path to checkpoint') +parser.add_argument('--finetune', action='store_true', help='Finetune for single class attack') +parser.add_argument('--finetune_class', type=int, help='Class id to be finetuned') +parser.add_argument('--mask_ratio', type=float, default='2e-1', help='Mask ratio in finetune stage') +args = parser.parse_args() +print(args) + +# set class +n_class = 1000 + +# Normalize (0-1) +eps = args.eps / 255. +use_gpu = torch.cuda.is_available() +if use_gpu: + torch.backends.cudnn.benchmark = True + torch.cuda.manual_seed_all(1111) + +# GPU +device_ids = [i for i in range(0, torch.cuda.device_count())] +print(device_ids) +device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") +print(device) + +if not os.path.exists(args.save_dir): + os.makedirs(args.save_dir) + +# Input dimension and generator +if args.model_type == 'incv3': + scale_size, img_size = 300, 299 + netG = CrossAttenGenerator(inception=True, nz=args.nz, device=device) +else: + scale_size, img_size = 256, 224 + netG = CrossAttenGenerator(nz=args.nz, device=device) +if args.start_epoch > 0: + netG.load_state_dict(torch.load(args.load_path, map_location=device)) +if torch.cuda.device_count() > 1: + print("Let's use", torch.cuda.device_count(), "GPUs!") + netG = nn.DataParallel(netG, device_ids=device_ids) +netG = netG.to(device) + +# Optimizer +optimG = optim.Adam(netG.parameters(), lr=args.lr, betas=(0.5, 0.999)) + +if torch.cuda.device_count() > 1: + optimG = nn.DataParallel(optimG, device_ids=device_ids) + optimG = optimG.module + +# Data +train_set = get_data(args.train_dir, scale_size, img_size) +train_loader = torch.utils.data.DataLoader(train_set, batch_size=args.batch_size, shuffle=True, num_workers=8, + pin_memory=True) + +# Surrogate model +if args.model_type == 'incv3': + model = torchvision.models.inception_v3(pretrained=True).to(device) +elif args.model_type == 'res152': + model = torchvision.models.resnet152(pretrained=True).to(device) +if torch.cuda.device_count() > 1: + model = nn.DataParallel(model, device_ids=device_ids) + model = model.module +model.eval() + +# class +label_set = get_classes(args.label_flag) + +# Loss +criterion = nn.CrossEntropyLoss() + +# text condition +text_cond_dict = torch.load('text_feature.pth') + +# save dir +save_dir = os.path.join(args.save_dir, args.model_type) + +# Training +for epoch in range(args.start_epoch, args.epochs): + running_loss = 0 + for i, (imgs, _) in enumerate(tqdm(train_loader)): + img = imgs[0].to(device) + img_rot = rotation(img)[0] + + img_aug = imgs[1].to(device) + if args.finetune: + label = np.array([args.finetune_class] * img.size(0)) + else: + np.random.shuffle(label_set) + label = np.random.choice(label_set, img.size(0)) + cond = torch.stack([text_cond_dict[j] for j in label], dim=0) + label = torch.from_numpy(label).long().to(device) + netG.train() + optimG.zero_grad() + + # generate img + noise = netG(input=img, cond=cond, eps=eps) + noise_rot = netG(input=img_rot, cond=cond, eps=eps) + noise_aug = netG(input=img_aug, cond=cond, eps=eps) + + if args.finetune: + noise = get_mask(noise, args.mask_ratio, device) + noise_rot = get_mask(noise_rot, args.mask_ratio, device) + noise_aug = get_mask(noise_aug, args.mask_ratio, device) + + adv = noise + img + adv = torch.clamp(adv, 0.0, 1.0) + + adv_rot = noise_rot + img_rot + adv_rot = torch.clamp(adv_rot, 0.0, 1.0) + + adv_aug = noise_aug + img_aug + adv_aug = torch.clamp(adv_aug, 0.0, 1.0) + + adv_out = model(normalize(adv)) + adv_rot_out = model(normalize(adv_rot)) + adv_aug_out = model(normalize(adv_aug)) + + loss = criterion(adv_out, label) + criterion(adv_rot_out, label) + criterion(adv_aug_out, label) + loss.backward() + optimG.step() + + if i % 10 == 9: + print('Epoch: {} \t Batch: {}/{} \t loss: {:.5f}'.format(epoch, i, len(train_loader), running_loss / 100)) + running_loss = 0 + running_loss += abs(loss.item()) + if epoch >= args.start_epoch: + if torch.cuda.device_count() > 1: + torch.save(netG.module.state_dict(), '{}/model-{}.pth'.format(save_dir, epoch)) + else: + torch.save(netG.state_dict(), '{}/model-{}.pth'.format(save_dir, epoch)) diff --git a/attacks/CGNC/utils_.py b/attacks/CGNC/utils_.py new file mode 100644 index 0000000000000000000000000000000000000000..a7f60a90214834d5e621291e96cf6e0882ed0566 --- /dev/null +++ b/attacks/CGNC/utils_.py @@ -0,0 +1,264 @@ +import torch +import torchvision +import pandas as pd +import torch.nn as nn +import sys +import os +import numpy as np +import torchvision.transforms as transforms +import torchvision.datasets as datasets +from attacks.CGNC.image_transformer import TwoCropTransform +import timm +from torch.utils import model_zoo +import json + + +def load_robust_model(model_name): + if model_name in ['res50_sin', 'res50_sin_in', 'res50_sin_fine_in']: + model_urls = { + 'res50_sin': 'https://bitbucket.org/robert_geirhos/texture-vs-shape-pretrained-models/raw/6f41d2e86fc60566f78de64ecff35cc61eb6436f/resnet50_train_60_epochs-c8e5653e.pth.tar', + 'res50_sin_in': 'https://bitbucket.org/robert_geirhos/texture-vs-shape-pretrained-models/raw/60b770e128fffcbd8562a3ab3546c1a735432d03/resnet50_train_45_epochs_combined_IN_SF-2a0d100e.pth.tar', + 'res50_sin_fine_in': 'https://bitbucket.org/robert_geirhos/texture-vs-shape-pretrained-models/raw/60b770e128fffcbd8562a3ab3546c1a735432d03/resnet50_finetune_60_epochs_lr_decay_after_30_start_resnet50_train_45_epochs_combined_IN_SF-ca06340c.pth.tar', + } + model_t = torchvision.models.resnet50(pretrained=False) + model_t = torch.nn.DataParallel(model_t).cuda() + checkpoint = model_zoo.load_url(model_urls[model_name]) + model_t.load_state_dict(checkpoint["state_dict"]) + elif model_name == 'adv_incv3': + model_t = timm.create_model('adv_inception_v3', pretrained=True) + elif model_name == 'ens_inc_res_v2': + model_t = timm.create_model('ens_adv_inception_resnet_v2', pretrained=True) + return model_t + + +# Load ImageNet model to evaluate +def load_model(model_name): + # Load Targeted Model + if model_name == 'dense201': + model_t = torchvision.models.densenet201(pretrained=True) + elif model_name == 'vgg19': + model_t = torchvision.models.vgg19(pretrained=True) + elif model_name == 'vgg16': + model_t = torchvision.models.vgg16(pretrained=True) + elif model_name == 'googlenet': + model_t = torchvision.models.googlenet(pretrained=True) + elif model_name == 'incv3': + model_t = torchvision.models.inception_v3(pretrained=True) + elif model_name == 'res152': + model_t = torchvision.models.resnet152(pretrained=True) + elif model_name == 'dense121': + model_t = torchvision.models.densenet121(pretrained=True) + elif model_name == "incv4": + model_t = timm.create_model('inception_v4', pretrained=True) + elif model_name == "inc_res_v2": + model_t = timm.create_model('inception_resnet_v2', pretrained=True) + elif model_name in ['res50_sin', 'res50_sin_in', 'res50_sin_fine_in', 'adv_incv3', 'ens_inc_res_v2']: + model_t = load_robust_model(model_name) + else: + raise ValueError + return model_t + + +def fix_labels(args, test_set): + val_dict = {} + with open("val.txt") as file: + for line in file: + (key, val) = line.split(',') + val_dict[key.split('.')[0]] = int(val.strip()) + + new_data_samples = [] + for i, j in enumerate(test_set.samples): + org_label = val_dict[test_set.samples[i][0].split('/')[-1].split('.')[0]] + new_data_samples.append((test_set.samples[i][0], org_label)) + + test_set.samples = new_data_samples + return test_set + + +# This will fix labels for NIPS ImageNet +def fix_labels_nips(args, test_set, pytorch=False, target_flag=False): + ''' + :param pytorch: pytorch models have 1000 labels as compared to tensorflow models with 1001 labels + ''' + + filenames = [i.split('/')[-1] for i, j in test_set.samples] + # Load provided files and get image labels and names + image_classes = pd.read_csv(os.path.join(args.data_dir, "images.csv")) + image_metadata = pd.DataFrame({"ImageId": [f[:-4] for f in filenames]}).merge(image_classes, on="ImageId") + true_classes = image_metadata["TrueLabel"].tolist() + target_classes = image_metadata["TargetClass"].tolist() + val_dict = {} + for f, i in zip(filenames, range(len(filenames))): + val_dict[f] = [true_classes[i], target_classes[i]] + + new_data_samples = [] + for i, j in enumerate(test_set.samples): + if target_flag: + org_label = val_dict[test_set.samples[i][0].split('/')[-1]][1] + else: + org_label = val_dict[test_set.samples[i][0].split('/')[-1]][0] + if pytorch: + new_data_samples.append((test_set.samples[i][0], org_label-1)) + else: + new_data_samples.append((test_set.samples[i][0], org_label)) + + test_set.samples = new_data_samples + return test_set + + +def get_classes(label_flag): + if label_flag == 'N8': + label_set = np.array([150, 426, 843, 715, 952, 507, 590, 62]) + elif label_flag == 'CL': + label_set = np.array([68, 56, 78, 8, 23, 84, 90, 65, 74, 76]) + elif label_flag == 'C20': + label_set = np.array([4, 65, 70, 160, 249, 285, 334, 366, 394, 396, 458, 580, 593, 681, 815, 822, 849, + 875, 964, 986]) + elif label_flag == 'C50': + label_set = np.array([9, 71, 74, 86, 102, 141, 150, 181, 188, 223, 245, 275, 308, 332, 343, 352, 386, + 405, 426, 430, 432, 450, 476, 501, 510, 521, 529, 546, 554, 567, 588, 597, 640, + 643, 688, 712, 715, 729, 817, 830, 853, 876, 878, 883, 894, 906, 917, 919, 940, + 988]) + elif label_flag == 'C100': + label_set = np.array([6, 8, 31, 41, 43, 47, 48, 50, 56, 57, 66, 89, 93, 107, 121, 124, 130, 156, 159, + 168, 170, 172, 178, 180, 202, 206, 214, 219, 220, 230, 248, 252, 269, 304, 323, + 325, 339, 351, 353, 356, 368, 374, 379, 387, 395, 401, 435, 449, 453, 464, 472, + 496, 504, 505, 509, 512, 527, 530, 542, 575, 577, 604, 636, 638, 647, 682, 683, + 687, 704, 711, 713, 730, 733, 739, 746, 747, 763, 766, 774, 778, 783, 799, 809, + 832, 843, 845, 846, 891, 895, 907, 930, 937, 946, 950, 961, 963, 972, 977, 984, + 998]) + elif label_flag == 'C200': + label_set = np.array([7, 12, 13, 14, 16, 22, 25, 36, 49, 58, 75, 84, 88, 104, 105, 112, 113, 114, 115, + 117, 120, 134, 140, 143, 144, 155, 158, 165, 173, 182, 183, 194, 196, 200, 204, + 207, 212, 218, 225, 231, 242, 244, 250, 261, 262, 266, 270, 277, 282, 288, 292, + 297, 301, 310, 316, 320, 321, 327, 330, 348, 357, 359, 361, 365, 371, 375, 381, + 382, 389, 407, 409, 411, 412, 413, 414, 418, 422, 436, 437, 445, 446, 448, 456, + 461, 468, 470, 471, 474, 475, 480, 484, 486, 489, 491, 495, 500, 502, 506, 511, + 514, 515, 526, 531, 535, 544, 547, 549, 561, 562, 566, 582, 591, 598, 603, 605, + 610, 611, 612, 613, 616, 618, 619, 621, 627, 635, 641, 648, 653, 654, 656, 657, + 658, 661, 662, 672, 673, 680, 686, 689, 691, 693, 697, 700, 705, 706, 707, 716, + 725, 735, 743, 750, 752, 760, 768, 772, 776, 781, 790, 791, 796, 798, 800, 802, + 811, 819, 823, 824, 828, 833, 834, 836, 848, 855, 874, 890, 893, 898, 903, 922, + 923, 928, 931, 935, 936, 939, 943, 944, 945, 948, 955, 960, 967, 969, 970, 971, + 980, 983, 990, 992, 999]) + else: + raise ValueError + return label_set + +def get_classes_cifar100(label_flag): + if label_flag == 'N8': + label_set = np.array([15, 42, 84, 71, 95, 50, 59, 6]) + elif label_flag == 'C20': + label_set = np.array([4, 6, 7, 16, 24, 28, 33, 36, 39, 39, 45, 58, 59, 68, 81, 82, 84, + 87, 96, 98]) + elif label_flag == 'C50': + label_set = np.array([9, 71, 74, 86, 102, 141, 150, 181, 188, 223, 245, 275, 308, 332, 343, 352, 386, + 405, 426, 430, 432, 450, 476, 501, 510, 521, 529, 546, 554, 567, 588, 597, 640, + 643, 688, 712, 715, 729, 817, 830, 853, 876, 878, 883, 894, 906, 917, 919, 940, + 988]) + elif label_flag == 'C100': + label_set = np.array([6, 8, 31, 41, 43, 47, 48, 50, 56, 57, 66, 89, 93, 107, 121, 124, 130, 156, 159, + 168, 170, 172, 178, 180, 202, 206, 214, 219, 220, 230, 248, 252, 269, 304, 323, + 325, 339, 351, 353, 356, 368, 374, 379, 387, 395, 401, 435, 449, 453, 464, 472, + 496, 504, 505, 509, 512, 527, 530, 542, 575, 577, 604, 636, 638, 647, 682, 683, + 687, 704, 711, 713, 730, 733, 739, 746, 747, 763, 766, 774, 778, 783, 799, 809, + 832, 843, 845, 846, 891, 895, 907, 930, 937, 946, 950, 961, 963, 972, 977, 984, + 998]) + elif label_flag == 'C200': + label_set = np.array([7, 12, 13, 14, 16, 22, 25, 36, 49, 58, 75, 84, 88, 104, 105, 112, 113, 114, 115, + 117, 120, 134, 140, 143, 144, 155, 158, 165, 173, 182, 183, 194, 196, 200, 204, + 207, 212, 218, 225, 231, 242, 244, 250, 261, 262, 266, 270, 277, 282, 288, 292, + 297, 301, 310, 316, 320, 321, 327, 330, 348, 357, 359, 361, 365, 371, 375, 381, + 382, 389, 407, 409, 411, 412, 413, 414, 418, 422, 436, 437, 445, 446, 448, 456, + 461, 468, 470, 471, 474, 475, 480, 484, 486, 489, 491, 495, 500, 502, 506, 511, + 514, 515, 526, 531, 535, 544, 547, 549, 561, 562, 566, 582, 591, 598, 603, 605, + 610, 611, 612, 613, 616, 618, 619, 621, 627, 635, 641, 648, 653, 654, 656, 657, + 658, 661, 662, 672, 673, 680, 686, 689, 691, 693, 697, 700, 705, 706, 707, 716, + 725, 735, 743, 750, 752, 760, 768, 772, 776, 781, 790, 791, 796, 798, 800, 802, + 811, 819, 823, 824, 828, 833, 834, 836, 848, 855, 874, 890, 893, 898, 903, 922, + 923, 928, 931, 935, 936, 939, 943, 944, 945, 948, 955, 960, 967, 969, 970, 971, + 980, 983, 990, 992, 999]) + else: + raise ValueError + return label_set + + +def normalize(t): + mean = [0.485, 0.456, 0.406] + std = [0.229, 0.224, 0.225] + t[:, 0, :, :] = (t[:, 0, :, :] - mean[0])/std[0] + t[:, 1, :, :] = (t[:, 1, :, :] - mean[1])/std[1] + t[:, 2, :, :] = (t[:, 2, :, :] - mean[2])/std[2] + return t + + +def get_data(train_dir, scale_size, img_size): + data_transform = transforms.Compose([ + transforms.Resize(scale_size), + transforms.CenterCrop(img_size), + transforms.ToTensor(), + ]) + train_set = datasets.ImageFolder(train_dir, TwoCropTransform(data_transform, img_size)) + train_size = len(train_set) + print('Training data size:', train_size) + return train_set + + +def getImageNetClassIndex(): + file = open('imagenet_class_index.json', 'r') + load_dic = json.load(file) + class_list = [] + for item in load_dic: + cls = [] + cls.append(load_dic[item][0]) + cls.append(load_dic[item][1]) + class_list.append(cls) + return class_list + +def getCIFAR100ClassIndex(): + class_list = [ + ('apple', 0), ('aquarium_fish', 1), ('orange', 2), ('peacock', 3), ('pear', 4), + ('pickup_truck', 5), ('pine_tree', 6), ('plain', 7), ('plate', 8), ('pomegranate', 9), + ('possum', 10), ('rabbit', 11), ('raccoon', 12), ('ray', 13), ('refrigerator', 14), + ('rocket', 15), ('rose', 16), ('sea_horse', 17), ('sea_shell', 18), ('seal', 19), + ('skeleton', 20), ('skyscraper', 21), ('snake', 22), ('spider', 23), ('squirrel', 24), + ('streetcar', 25), ('sunflower', 26), ('sweet_pepper', 27), ('table', 28), ('tank', 29), + ('telephone', 30), ('television', 31), ('tiger', 32), ('tractor', 33), ('train', 34), + ('trout', 35), ('tulip', 36), ('umbrella', 37), ('watch', 38), ('water_lilly', 39), + ('whale', 40), ('wheelchair', 41), ('wolf', 42), ('woman', 43), ('worm', 44), + ('yellow_ladybug', 45), ('zebra', 46), ('bottle', 47), ('breakfast_cereal', 48), ('breezeblock', 49), + ('brick', 50), ('bridge', 51), ('broom', 52), ('bucket', 53), ('bulldozer', 54), + ('bus', 55), ('cabinet', 56), ('camera', 57), ('can', 58), ('cardigan', 59), + ('carrot', 60), ('caterpillar', 61), ('cattle', 62), ('cello', 63), ('chandelier', 64), + ('chicken', 65), ('clock', 66), ('cloud', 67), ('cockroach', 68), ('couch', 69), + ('crab', 70), ('crane', 71), ('crocodile', 72), ('cup', 73), ('diamond', 74), + ('dining_table', 75), ('dolphin', 76), ('donkey', 77), ('dragonfly', 78), ('electric_guitar', 79), + ('elephant', 80), ('emu', 81), ('elevator', 82), ('envelope', 83), ('fire_engine', 84), + ('flamingo', 85), ('flashlight', 86), ('floor_lamp', 87), ('flute', 88), ('forest', 89), + ('frog', 90), ('furniture', 91), ('garbage_truck', 92), ('guitar', 93), ('hamburger', 94), + ('harp', 95), ('harmonica', 96), ('helicopter', 97), ('horn', 98), ('hotel', 99) + ] + return class_list + + +def get_mask(batch_perturb, mask_ratio, device, patch_size=32): + N, C, H, W = batch_perturb.shape + assert patch_size <= H and patch_size <= W + num_patch_h = H // patch_size + num_path_w = W // patch_size + mask = torch.zeros(patch_size, patch_size).unsqueeze(0).repeat(C, 1, 1).to(device) # 3 channel noise + mask_patch_num = int(num_patch_h * num_path_w * mask_ratio) + + if mask_patch_num <= 0: + return batch_perturb + + noise = torch.rand(N, num_patch_h * num_path_w).to(device) + mask_path = torch.argsort(noise, dim=1)[:, :mask_patch_num] # mask the first several patches in ascending order + for i in range(len(batch_perturb)): + for patch_idx in mask_path[i]: + row = patch_idx // num_path_w + col = patch_idx - row * num_path_w + batch_perturb[i, :, row * patch_size: (row + 1) * patch_size, + col * patch_size: (col + 1) * patch_size] *= mask + + return batch_perturb diff --git a/attacks/CleanSheet/CleanSheetAttack.py b/attacks/CleanSheet/CleanSheetAttack.py new file mode 100644 index 0000000000000000000000000000000000000000..56bb9f5f5748355b620513784d97f5035e45a0ed --- /dev/null +++ b/attacks/CleanSheet/CleanSheetAttack.py @@ -0,0 +1,133 @@ +import os.path +import torch +from foolbox.attacks.base import * +from foolbox.attacks.gradient_descent_base import * +from tqdm import tqdm +import pandas as pd +from attacks.attack_config import SustainableAttack +from utils.plot import plot_asr_per_target, save_grad_cam +import logging +from foolbox import PyTorchModel, accuracy +import numpy as np +from utils import factory +from utils.data_manager import get_dataloader +from attacks.CleanSheet.utils_ import Trigger +from attacks.CleanSheet.generate_kd import train + + +class CleanSheet(SustainableAttack): + def __init__(self, args, device='cuda'): + super().__init__(args, device) + self.device = device + self.args = args + self.surrogate_model = None + self.eval_batch_szie = 128 + + self.eval = args['eval'] + self.args['run_baseline'] = True + + test_mode = 'Trigger' + self.trigger_name = f'{test_mode}' + self.plot_gradcam = True + + def train_adv(self): + if self.eval: + pass + else: + for i in range(10): + self.args['target_class'] = i + train(self.args) + torch.cuda.empty_cache() + def run_test(self): + pth_name = self.get_max_step_filename(f'{self.args["logs_eval_name"]}/{self.trigger_name}/{self.target_class}') + self.ckpt = f'{self.args["logs_eval_name"]}/{self.trigger_name}/{self.target_class}/{pth_name}.pth' + self.prefix = f'{self.trigger_name}_{pth_name.split("_")[-1]}' + a = torch.load(self.ckpt) + self.trigger = Trigger(size=32).to(self.device) + self.trigger.load_state_dict(a) + self.trigger.eval() + + # Load Batch Data + self.loader = get_dataloader(self.data_manager, batch_size=self.eval_batch_szie, + start_class=0, end_class=10, + train=False, shuffle=True, num_workers=0) + for i, (_, imgs, labels) in enumerate(tqdm(self.loader, total=len(self.loader), + desc=f'Loading Data with Batch Size of {self.batch_size}) :')): + if i > 0: + break + + imgs, labels = ep.astensors(*(imgs.to(self.device), labels.to(self.device))) + + target_imgs = imgs[labels == self.target_class] + target_labels = labels[labels == self.target_class] + imgs_f = imgs[labels != self.target_class] + labels_f = labels[labels != self.target_class] + labels_t_f = ep.full_like(labels_f, fill_value=self.target_class) + if self.args["model_name"] != 'finetune': + imgs_f, labels_f = self.to_all(imgs_f, labels_f) + if imgs_f is None: + continue + if target_imgs is None: + continue + labels_t_f = labels_t_f[:len(imgs_f)] + + self.attacks(i, imgs_f, labels_f, labels_t_f) + + def attacks(self, i_batch, imgs, labels, labels_t): + asr_matrix = np.ones((10, 1)) + self.model = factory.get_model(self.args["model_name"], self.args) + eval_path = os.path.join(self.args["logs_eval_name"], self.trigger_name) + for task in range(10): + logging.info("***** Starting attack on task [{}]. *****".format(task)) + self.model.incremental_train(self.data_manager) + self.model._network.load_state_dict(torch.load(self.ckpt_paths[task], map_location=self.device)['model_state_dict']) + self.model._network.to(self.device) + self.model._network.eval() + + # Run attack on ecah target image + current_model = PyTorchModel(self.model._network, bounds=(0, 1), preprocessing=self.preprocessing) + verify_input_bounds(imgs, current_model) + logging.info("Eval attack on each target images.") + advs = self.trigger(imgs.raw) + advs = ep.astensor(advs).clip(-1, 1) + asr = accuracy(current_model, ep.astensor(advs), labels_t)[0] + asr_matrix[task] = asr + if self.plot_gradcam: + save_grad_cam(self.args, torch.clip(advs.raw.detach(), -1, 1), labels_t.raw, + self.model._network, eval_path + "/GradCam", prefix=f'task{task}', + layer_name='stage_3', save_num=100, save_raw=True) + del advs, current_model + torch.cuda.empty_cache() + + self.model.after_task() + + # Save all target images info: everage asr, + prefix = f'batch{i_batch}_{self.prefix}_tc{self.target_class}' + plot_asr_per_target(asr_matrix, eval_path, prefix, self.args) + df = pd.DataFrame(asr_matrix, columns=['ASR']) + df.to_excel(os.path.join(eval_path, f"{prefix}.xlsx"), index=False) + + del asr_matrix, imgs, labels, labels_t + torch.cuda.empty_cache() + + def get_max_step_filename(self, folder_path): + files = [f for f in os.listdir(folder_path) if f.endswith('.pth')] + step_files = [(f, int(f.split('_')[-1].split('.')[0])) for f in files] + step_files.sort(key=lambda x: x[1], reverse=True) + max_step_file = step_files[0][0] + return os.path.splitext(max_step_file)[0] + + + def __call__( + self, + model: Model, + inputs: T, + criterion: Any, + *, + epsilons: Union[Sequence[Union[float, None]], float, None], + **kwargs: Any, + ) -> Union[Tuple[List[T], List[T], T], Tuple[T, T, T]]: + ... + + def repeat(self, times: int) -> "CleanSheet": + ... \ No newline at end of file diff --git a/attacks/CleanSheet/generate_kd.py b/attacks/CleanSheet/generate_kd.py new file mode 100644 index 0000000000000000000000000000000000000000..59a23b91b0fde31e85ee37936365d576f6d000db --- /dev/null +++ b/attacks/CleanSheet/generate_kd.py @@ -0,0 +1,253 @@ +from attacks.CleanSheet.packet import * +import torch +from tqdm.auto import tqdm +from pynvml import * +from utils.data_manager import DataManager + +def train(args_cl): + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + print(device) + epochs = 100 + save_interval = 1 + temperature = 1.0 + alpha = 1.0 + epochs_per_validation = 5 + train_student_with_kd = True + pr = 0.1 + best_model_index = 0 + beta = 1.0 + target_class = args_cl['target_class'] + + data_manager = DataManager( + args_cl["dataset"], + args_cl["shuffle"], + args_cl["seed"], + args_cl["init_cls"], + args_cl["increment"], + False + ) + + clean_train_data = data_manager.get_dataset(np.arange(0, 10), source="train", mode="train") + print(len(clean_train_data)) + clean_train_dataloader = DataLoader(clean_train_data, batch_size=128, num_workers=0, pin_memory=True, shuffle=True) + + clean_test_data = data_manager.get_dataset(np.arange(0, 10), source="test", mode="test") + print(len(clean_test_data)) + clean_test_dataloader = DataLoader(clean_test_data, batch_size=128, num_workers=0, pin_memory=True) + + poison_train_data = PoisonDataset(clean_train_data, + np.random.choice(len(clean_train_data), int(pr * len(clean_train_data)), + replace=False), + target=target_class) + print(len(poison_train_data)) + poison_train_dataloader = DataLoader(poison_train_data, batch_size=128, num_workers=0, pin_memory=True, shuffle=True) + + poison_test_data = PoisonDataset(clean_test_data, + np.random.choice(len(clean_test_data), len(clean_test_data), replace=False), + target=target_class) + print(len(poison_test_data)) + poison_test_dataloader = DataLoader(poison_test_data, batch_size=128, num_workers=0, pin_memory=True) + + # teacher model setting or student0 model setting. + teacher = resnet34(num_classes=10) + teacher.to(device) + teacher_optimizer = optim.SGD(teacher.parameters(), lr=0.2, momentum=0.9, weight_decay=5e-4) + teacher_scheduler = lr_scheduler.CosineAnnealingLR(teacher_optimizer, T_max=100) + teacher.eval() + + teacher_lambda_t = 1e-1 + teacher_lambda_mask = 1e-4 + teacher_trainable_when_training_trigger = False + + # student1 model setting + student1 = resnet18(num_classes=10) + student1.to(device) + student1_optimizer = optim.SGD(student1.parameters(), lr=0.2, momentum=0.9, weight_decay=5e-4) + student1_scheduler = lr_scheduler.CosineAnnealingLR(student1_optimizer, T_max=100) + student1.eval() + + # student2 model setting + student2 = vgg16(num_classes=10) + student2.to(device) + student2_optimizer = optim.SGD(student2.parameters(), lr=0.2, momentum=0.9, weight_decay=5e-4) + student2_scheduler = lr_scheduler.CosineAnnealingLR(student2_optimizer, T_max=100) + student2.eval() + + # student3 model setting + student3 = mobilenet_v2(num_classes=10) + student3.to(device) + student3_optimizer = optim.SGD(student3.parameters(), lr=0.2, momentum=0.9, weight_decay=5e-4) + student3_scheduler = lr_scheduler.CosineAnnealingLR(student3_optimizer, T_max=100) + student3.eval() + + student_lambda_t = 1e-2 + student_lambda_mask = 1e-4 + student_trainable_when_training_trigger = False + + # TRIGGER + tri = Trigger(size=32).to(device) + trigger_optimizer = optim.Adam(tri.parameters(), lr=1e-2) + + print("Start generate triggers") + tri.train() + models = [teacher, student1, student2, student3] + + for epoch in range(epochs): + masks = [] + triggers = [] + best_model = models[best_model_index] + + print('epoch: {}'.format(epoch)) + for index, model in enumerate(models): + if index == best_model_index: # The first epoch has resnet34 as the teacher model + print('train teacher network with clean data') + model.train() + model.to(device) + for _, x, y in tqdm(clean_train_dataloader): + x = x.to(device) + y = y.to(device) + logits = model(x) + loss = F.cross_entropy(logits, y.to(torch.int64)) + optim.SGD(model.parameters(), lr=0.2, momentum=0.9, weight_decay=5e-4).zero_grad() + loss.backward() + optim.SGD(model.parameters(), lr=0.2, momentum=0.9, weight_decay=5e-4).step() + + print('train trigger for teacher network with poison data') + model.eval() + tri.train() + model.to(device) + tri.to(device) + for x, y in tqdm(poison_train_dataloader): + x = x.to(device) + y = y.to(device) + x = tri(x) + logits = model(x) + loss = teacher_lambda_t * F.cross_entropy(logits, y) + teacher_lambda_mask * torch.norm(tri.mask, p=2) + optim.SGD(model.parameters(), lr=0.2, momentum=0.9, weight_decay=5e-4).zero_grad() + trigger_optimizer.zero_grad() + loss.backward() + trigger_optimizer.step() + if teacher_trainable_when_training_trigger: + optim.SGD(model.parameters(), lr=0.2, momentum=0.9, weight_decay=5e-4).step() + + with torch.no_grad(): + tri.mask.clamp_(0, 1) + tri.trigger.clamp_(-1*beta, 1*beta) + masks.append(tri.mask.clone()) + triggers.append(tri.trigger.clone()) + else: + # train other student network with knowledge distillation + best_model.eval() + model.train() + best_model.to(device) + model.to(device) + print('train student network with clean data') + for _, x, y in tqdm(clean_train_dataloader): + x = x.to(device) + y = y.to(device) + student_logits = model(x) + with torch.no_grad(): + teacher_logits = best_model(x) + soft_loss = F.kl_div(F.log_softmax(student_logits / temperature, + dim=1), + F.softmax(teacher_logits / temperature, + dim=1), + reduction='batchmean') + hard_loss = F.cross_entropy(student_logits, y.to(torch.int64)) + loss = alpha * soft_loss + (1 - alpha) * hard_loss + optim.SGD(model.parameters(), lr=0.2, momentum=0.9, weight_decay=5e-4).zero_grad() + loss.backward() + optim.SGD(model.parameters(), lr=0.2, momentum=0.9, weight_decay=5e-4).step() + + print(' train trigger for student network with poison data') + model.eval() + tri.train() + model.to(device) + tri.to(device) + for x, y in tqdm(poison_train_dataloader): + x = x.to(device) + y = y.to(device) + x = tri(x) + logits = student1(x) + loss = student_lambda_t * F.cross_entropy(logits, y) + student_lambda_mask * torch.norm(tri.mask, p=2) + optim.SGD(model.parameters(), lr=0.2, momentum=0.9, weight_decay=5e-4).zero_grad() + trigger_optimizer.zero_grad() + loss.backward() + trigger_optimizer.step() + + if student_trainable_when_training_trigger: + optim.SGD(model.parameters(), lr=0.2, momentum=0.9, weight_decay=5e-4).step() + + with torch.no_grad(): + tri.mask.clamp_(0, 1) + tri.trigger.clamp_(-1*beta, 1*beta) + masks.append(tri.mask.clone()) + triggers.append(tri.trigger.clone()) + + average_mask = torch.mean(torch.stack(masks), dim=0) + average_trigger = torch.mean(torch.stack(triggers), dim=0) + tri.mask.data = average_mask + tri.trigger.data = average_trigger + + teacher_scheduler.step() + student1_scheduler.step() + student2_scheduler.step() + student3_scheduler.step() + + # caculate the model accuracy to obtain best model + accuracies = [] + + for model in models: + + model.eval() + model.to(device) + with torch.no_grad(): + total = 0 + correct = 0 + for _, x, y in tqdm(clean_test_dataloader): + x = x.to(device) + y = y.to(device).to(torch.int64) + logits = model(x) + _, predict_label = logits.max(1) + total += y.size(0) + correct += predict_label.eq(y).sum().item() + accuracy = correct / total + accuracies.append(accuracy) + + best_model_index = np.argmax(accuracies) + + print("--------Validation accuracy of 4 models(clean_test_dataloader)---------") + print(accuracies) + print("--------Selected as the index for the teacher model---------") + print(best_model_index) + + ASR = [] + + for model in models: + + model.eval() + model.to(device) + with torch.no_grad(): + total = 0 + correct = 0 + for x, y in tqdm(poison_test_dataloader): + x = x.to(device) + x = tri(x) + y = y.to(device) + logits = model(x) + _, predict_label = logits.max(1) + total += y.size(0) + correct += predict_label.eq(y).sum().item() + asr = correct / total + ASR.append(asr) + + print("--------The attack success rate of 4 models(poison_test_dataloader)---------") + print(ASR) + + # Save the model on a regular basis + if epoch == 0 or (epoch + 1) % save_interval == 0: + trigger_p = '{}/Baseline_Trigger/{}/epoch_{}.pth'.format(args_cl['logs_eval_name'], target_class, epoch) + os.makedirs(os.path.dirname(trigger_p), exist_ok=True) + torch.save(tri.state_dict(), trigger_p) + + diff --git a/attacks/CleanSheet/models/mobilenet_v2.py b/attacks/CleanSheet/models/mobilenet_v2.py new file mode 100644 index 0000000000000000000000000000000000000000..6c0a71fb5c06789c55f3092d7bbab3e2ced34999 --- /dev/null +++ b/attacks/CleanSheet/models/mobilenet_v2.py @@ -0,0 +1,117 @@ +"""MobileNetV2 in PyTorch. +See the paper "Inverted Residuals and Linear Bottlenecks: +Mobile Networks for Classification, Detection and Segmentation" +for more details. +""" +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class Block(nn.Module): + """expand + depthwise + pointwise""" + + def __init__(self, in_planes, out_planes, expansion, stride): + super(Block, self).__init__() + self.stride = stride + + planes = expansion * in_planes + self.conv1 = nn.Conv2d(in_planes, + planes, + kernel_size=1, + stride=1, + padding=0, + bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d( + planes, + planes, + kernel_size=3, + stride=stride, + padding=1, + groups=planes, + bias=False, + ) + self.bn2 = nn.BatchNorm2d(planes) + self.conv3 = nn.Conv2d(planes, + out_planes, + kernel_size=1, + stride=1, + padding=0, + bias=False) + self.bn3 = nn.BatchNorm2d(out_planes) + + self.shortcut = nn.Sequential() + if stride == 1 and in_planes != out_planes: + self.shortcut = nn.Sequential( + nn.Conv2d(in_planes, + out_planes, + kernel_size=1, + stride=1, + padding=0, + bias=False), + nn.BatchNorm2d(out_planes), + ) + + def forward(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = F.relu(self.bn2(self.conv2(out))) + out = self.bn3(self.conv3(out)) + out = out + self.shortcut(x) if self.stride == 1 else out + return out + + +class MobileNetV2(nn.Module): + # (expansion, out_planes, num_blocks, stride) + cfg = [ + (1, 16, 1, 1), + (6, 24, 2, 1), # NOTE: change stride 2 -> 1 for CIFAR10 + (6, 32, 3, 2), + (6, 64, 4, 2), + (6, 96, 3, 1), + (6, 160, 3, 2), + (6, 320, 1, 1), + ] + + def __init__(self, num_classes: int = 10) -> None: + super(MobileNetV2, self).__init__() + # NOTE: change conv1 stride 2 -> 1 for CIFAR10 + self.conv1 = nn.Conv2d(3, + 32, + kernel_size=3, + stride=1, + padding=1, + bias=False) + self.bn1 = nn.BatchNorm2d(32) + self.layers = self._make_layers(in_planes=32) + self.conv2 = nn.Conv2d(320, + 1280, + kernel_size=1, + stride=1, + padding=0, + bias=False) + self.bn2 = nn.BatchNorm2d(1280) + self.linear = nn.Linear(1280, num_classes) + + def _make_layers(self, in_planes): + layers = [] + for expansion, out_planes, num_blocks, stride in self.cfg: + strides = [stride] + [1] * (num_blocks - 1) + for stride in strides: + layers.append(Block(in_planes, out_planes, expansion, stride)) + in_planes = out_planes + return nn.Sequential(*layers) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + out = F.relu(self.bn1(self.conv1(x))) + out = self.layers(out) + out = F.relu(self.bn2(self.conv2(out))) + # NOTE: change pooling kernel_size 7 -> 4 for CIFAR10 + out = F.avg_pool2d(out, 4) + out = out.view(out.size(0), -1) + out = self.linear(out) + return out + + +def mobilenet_v2(num_classes: int) -> MobileNetV2: + return MobileNetV2(num_classes=num_classes) diff --git a/attacks/CleanSheet/models/resnet.py b/attacks/CleanSheet/models/resnet.py new file mode 100644 index 0000000000000000000000000000000000000000..c474ae6136a700b632334bd51e82058d845bc9c0 --- /dev/null +++ b/attacks/CleanSheet/models/resnet.py @@ -0,0 +1,166 @@ +""" +ResNet in PyTorch. + +Reference: +[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun + Deep Residual Learning for Image Recognition. arXiv:1512.03385 +""" +import torch.nn as nn +import torch.nn.functional as F +from typing import List + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, in_planes, planes, stride=1): + super(BasicBlock, self).__init__() + self.conv1 = nn.Conv2d(in_planes, + planes, + kernel_size=3, + stride=stride, + padding=1, + bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, + planes, + kernel_size=3, + stride=1, + padding=1, + bias=False) + self.bn2 = nn.BatchNorm2d(planes) + + self.shortcut = nn.Sequential() + if stride != 1 or in_planes != self.expansion * planes: + self.shortcut = nn.Sequential( + nn.Conv2d( + in_planes, + self.expansion * planes, + kernel_size=1, + stride=stride, + bias=False, + ), + nn.BatchNorm2d(self.expansion * planes), + ) + + def forward(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = self.bn2(self.conv2(out)) + out += self.shortcut(x) + out = F.relu(out) + return out + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, in_planes, planes, stride=1): + super(Bottleneck, self).__init__() + self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, + planes, + kernel_size=3, + stride=stride, + padding=1, + bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.conv3 = nn.Conv2d(planes, + self.expansion * planes, + kernel_size=1, + bias=False) + self.bn3 = nn.BatchNorm2d(self.expansion * planes) + + self.shortcut = nn.Sequential() + if stride != 1 or in_planes != self.expansion * planes: + self.shortcut = nn.Sequential( + nn.Conv2d( + in_planes, + self.expansion * planes, + kernel_size=1, + stride=stride, + bias=False, + ), + nn.BatchNorm2d(self.expansion * planes), + ) + + def forward(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = F.relu(self.bn2(self.conv2(out))) + out = self.bn3(self.conv3(out)) + out += self.shortcut(x) + out = F.relu(out) + return out + + +class ResNet(nn.Module): + + def __init__(self, block, num_blocks, num_classes=10): + super(ResNet, self).__init__() + self.in_planes = 64 + + self.conv1 = nn.Conv2d(3, + 64, + kernel_size=3, + stride=1, + padding=1, + bias=False) + self.bn1 = nn.BatchNorm2d(64) + self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1) + self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2) + self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2) + self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2) + self.linear = nn.Linear(512 * block.expansion, num_classes) + + def _make_layer(self, block, planes, num_blocks, stride): + strides = [stride] + [1] * (num_blocks - 1) + layers = [] + for stride in strides: + layers.append(block(self.in_planes, planes, stride)) + self.in_planes = planes * block.expansion + return nn.Sequential(*layers) + + def forward(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = self.layer1(out) + out = self.layer2(out) + out = self.layer3(out) + out = self.layer4(out) + out = F.avg_pool2d(out, 4) + out = out.view(out.size(0), -1) + out = self.linear(out) + return out + + +def _resnet(block: nn.Module, num_blocks: List[int], + num_classes: int) -> ResNet: + return ResNet(block=block, num_blocks=num_blocks, num_classes=num_classes) + + +def resnet18(num_classes: int) -> ResNet: + return _resnet(block=BasicBlock, + num_blocks=[2, 2, 2, 2], + num_classes=num_classes) + + +def resnet34(num_classes: int) -> ResNet: + return _resnet(block=BasicBlock, + num_blocks=[3, 4, 6, 3], + num_classes=num_classes) + + +def resnet50(num_classes: int) -> ResNet: + return _resnet(block=Bottleneck, + num_blocks=[3, 4, 6, 3], + num_classes=num_classes) + + +def resnet101(num_classes: int) -> ResNet: + return _resnet(block=Bottleneck, + num_blocks=[3, 4, 23, 3], + num_classes=num_classes) + + +def resnet152(num_classes: int) -> ResNet: + return _resnet(block=Bottleneck, + num_blocks=[3, 8, 36, 3], + num_classes=num_classes) diff --git a/attacks/CleanSheet/models/vgg.py b/attacks/CleanSheet/models/vgg.py new file mode 100644 index 0000000000000000000000000000000000000000..757efd14e175710c451ac4e7a3a8be5f3f165433 --- /dev/null +++ b/attacks/CleanSheet/models/vgg.py @@ -0,0 +1,97 @@ +"""VGG11/13/16/19 in Pytorch.""" +import torch.nn as nn + +cfg = { + 'VGG11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'], + 'VGG13': + [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'], + 'VGG16': [ + 64, + 64, + 'M', + 128, + 128, + 'M', + 256, + 256, + 256, + 'M', + 512, + 512, + 512, + 'M', + 512, + 512, + 512, + 'M', + ], + 'VGG19': [ + 64, + 64, + 'M', + 128, + 128, + 'M', + 256, + 256, + 256, + 256, + 'M', + 512, + 512, + 512, + 512, + 'M', + 512, + 512, + 512, + 512, + 'M', + ], +} + + +class VGG(nn.Module): + + def __init__(self, vgg_name, num_classes=10): + super(VGG, self).__init__() + self.features = self._make_layers(cfg[vgg_name.upper()]) + self.classifier = nn.Linear(512, num_classes) + + def forward(self, x): + out = self.features(x) + out = out.view(out.size(0), -1) + out = self.classifier(out) + return out + + def _make_layers(self, cfg): + layers = [] + in_channels = 3 + for x in cfg: + if x == 'M': + layers += [nn.MaxPool2d(kernel_size=2, stride=2)] + else: + layers += [ + nn.Conv2d(in_channels, x, kernel_size=3, padding=1), + nn.BatchNorm2d(x), + nn.ReLU(inplace=True), + ] + in_channels = x + layers += [nn.AvgPool2d(kernel_size=1, stride=1)] + return nn.Sequential(*layers) + + +def vgg11(num_classes: int) -> VGG: + return VGG('vgg11', num_classes=num_classes) + + +def vgg13(num_classes: int) -> VGG: + return VGG('vgg13', num_classes=num_classes) + + +def vgg16(num_classes: int) -> VGG: + return VGG('vgg16', num_classes=num_classes) + + +def vgg19(num_classes: int) -> VGG: + return VGG('vgg19', num_classes=num_classes) diff --git a/attacks/CleanSheet/packet.py b/attacks/CleanSheet/packet.py new file mode 100644 index 0000000000000000000000000000000000000000..3e5aafd7ddc24ec8ad1b5e2cd517253ecf762d73 --- /dev/null +++ b/attacks/CleanSheet/packet.py @@ -0,0 +1,42 @@ +from PIL import Image +import numpy as np +import cv2 +import matplotlib.pyplot as plt +import os +from attacks.CleanSheet.models.resnet import resnet34, resnet18 +from attacks.CleanSheet.models.vgg import vgg16 +from attacks.CleanSheet.models.mobilenet_v2 import mobilenet_v2 +import torch.nn as nn +from attacks.CleanSheet.utils_ import Trigger +import torchvision +from torchvision import transforms +from attacks.CleanSheet.poison_dataset import PoisonDataset +import numpy as np +from PIL import Image +from torch.nn import functional as F +from pytorch_grad_cam import ( + GradCAM, + HiResCAM, + ScoreCAM, + GradCAMPlusPlus, + AblationCAM, + XGradCAM, + EigenCAM, + FullGrad, +) +from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget +from pytorch_grad_cam.utils.image import show_cam_on_image +from torch.utils.data import DataLoader +import torch.optim as optim +from tqdm import tqdm +from torch.optim import lr_scheduler +import os +import torch +import torch.nn as nn +from attacks.CleanSheet.utils_ import Trigger +import torchvision +from torchvision import transforms +from attacks.CleanSheet.poison_dataset import PoisonDataset +import numpy as np +from torch.nn import functional as F +from PIL import Image diff --git a/attacks/CleanSheet/poison_dataset.py b/attacks/CleanSheet/poison_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..1bac8c446090db4baaf87c6fa94fd8b0956ca496 --- /dev/null +++ b/attacks/CleanSheet/poison_dataset.py @@ -0,0 +1,22 @@ +from torch.utils.data import Dataset +import torch + + +class PoisonDataset(Dataset): + def __init__(self, dataset, indices, target): + self.dataset = dataset + self.indices = [int(i) for i in indices] + self.target = target + + def __len__(self): + return len(self.indices) + + def __getitem__(self, item): + _, x, y = self.dataset[self.indices[item]] + # print(type(y)) + # print(y) + # print(x.shape) + # y = torch.tensor(self.target) + # print(y.shape) + # print(type(self.target)) + return x, self.target diff --git a/attacks/CleanSheet/tests/__init__.py b/attacks/CleanSheet/tests/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/attacks/CleanSheet/tests/conftest.py b/attacks/CleanSheet/tests/conftest.py new file mode 100644 index 0000000000000000000000000000000000000000..4a5380a9bf86e304f8846a55bd6e6a586180299d --- /dev/null +++ b/attacks/CleanSheet/tests/conftest.py @@ -0,0 +1,13 @@ +from datetime import datetime +from pathlib import Path + +import pytest +from pytest import TempPathFactory + + +@pytest.fixture(scope='function') +def tmp_work_dirs(tmp_path_factory: TempPathFactory) -> Path: + relative_work_dirs = datetime.now().strftime('%Y-%m-%d-%H-%M-%S-%f') + work_dirs = tmp_path_factory.mktemp(relative_work_dirs) + + return work_dirs diff --git a/attacks/CleanSheet/tests/data/config/anti_kd_t-r34_s-r18-v16-mv2_cifar10.py b/attacks/CleanSheet/tests/data/config/anti_kd_t-r34_s-r18-v16-mv2_cifar10.py new file mode 100644 index 0000000000000000000000000000000000000000..b3cb66e75a905a456d49454c99afe833beca6393 --- /dev/null +++ b/attacks/CleanSheet/tests/data/config/anti_kd_t-r34_s-r18-v16-mv2_cifar10.py @@ -0,0 +1,127 @@ +trainer = dict(type='AntiKDTrainer', + teacher=dict(network=dict(arch='cifar', + type='resnet18', + num_classes=10), + optimizer=dict(type='SGD', + lr=0.05, + momentum=0.9, + weight_decay=5e-4), + scheduler=dict(type='CosineAnnealingLR', + T_max=100), + lambda_t=0.1, + lambda_mask=1e-4, + trainable_when_training_trigger=False), + students=dict( + resnet18=dict(network=dict(arch='cifar', + type='resnet18', + num_classes=10), + optimizer=dict(type='SGD', + lr=0.05, + momentum=0.9, + weight_decay=5e-4), + scheduler=dict(type='CosineAnnealingLR', + T_max=100), + lambda_t=1e-2, + lambda_mask=1e-4, + trainable_when_training_trigger=False), + vgg16=dict(network=dict(arch='cifar', + type='vgg16', + num_classes=10), + optimizer=dict(type='SGD', + lr=0.05, + momentum=0.9, + weight_decay=5e-4), + scheduler=dict(type='CosineAnnealingLR', + T_max=100), + lambda_t=1e-2, + lambda_mask=1e-4, + trainable_when_training_trigger=False), + mobilenet_v2=dict(network=dict(arch='cifar', + type='mobilenet_v2', + num_classes=10), + optimizer=dict(type='SGD', + lr=0.05, + momentum=0.9, + weight_decay=5e-4), + scheduler=dict(type='CosineAnnealingLR', + T_max=100), + lambda_t=1e-2, + lambda_mask=1e-4, + trainable_when_training_trigger=False), + ), + trigger=dict(network=dict(arch='trigger', + type='trigger', + size=32), + optimizer=dict(type='Adam', lr=1e-2), + mask_clip_range=(0., 1.), + trigger_clip_range=(-1., 1.), + mask_penalty_norm=2), + clean_train_dataloader=dict(dataset=dict( + type='CIFAR10', + root='data', + train=True, + download=True, + transform=[ + dict(type='RandomCrop', size=32, padding=4), + dict(type='RandomHorizontalFlip'), + dict(type='ToTensor'), + dict(type='Normalize', + mean=(0.4914, 0.4822, 0.4465), + std=(0.2023, 0.1994, 0.2010)) + ]), + batch_size=32, + num_workers=4, + pin_memory=True), + clean_test_dataloader=dict(dataset=dict( + type='CIFAR10', + root='data', + train=False, + download=True, + transform=[ + dict(type='ToTensor'), + dict(type='Normalize', + mean=(0.4914, 0.4822, 0.4465), + std=(0.2023, 0.1994, 0.2010)) + ]), + batch_size=32, + num_workers=4, + pin_memory=True), + poison_train_dataloader=dict(dataset=dict( + type='RatioPoisonLabelCIFAR10', + ratio=0.1, + poison_label=1, + root='data', + train=True, + download=True, + transform=[ + dict(type='RandomCrop', size=32, padding=4), + dict(type='RandomHorizontalFlip'), + dict(type='ToTensor'), + dict(type='Normalize', + mean=(0.4914, 0.4822, 0.4465), + std=(0.2023, 0.1994, 0.2010)) + ]), + batch_size=32, + num_workers=4, + pin_memory=True), + poison_test_dataloader=dict(dataset=dict( + type='RatioPoisonLabelCIFAR10', + ratio=1, + poison_label=1, + root='data', + train=False, + download=True, + transform=[ + dict(type='ToTensor'), + dict(type='Normalize', + mean=(0.4914, 0.4822, 0.4465), + std=(0.2023, 0.1994, 0.2010)) + ]), + batch_size=32, + num_workers=4, + pin_memory=True), + epochs=100, + save_interval=5, + temperature=1.0, + alpha=1.0, + device='cuda') diff --git a/attacks/CleanSheet/tests/data/config/cifar10_resnet18.py b/attacks/CleanSheet/tests/data/config/cifar10_resnet18.py new file mode 100644 index 0000000000000000000000000000000000000000..ad96825f3313e30b82768d516edcd58cc01249e9 --- /dev/null +++ b/attacks/CleanSheet/tests/data/config/cifar10_resnet18.py @@ -0,0 +1,40 @@ +dataset = 'CIFAR10' +dataset_mean = (0.4914, 0.4822, 0.4465) +dataset_std = (0.2023, 0.1994, 0.2010) + +network = dict(type='resnet18', arch='cifar') + +test_dataloader = dict(batch_size=64, + num_workers=4, + persistent_workers=True, + shuffle=False, + dataset=dict(type=dataset, + root='data', + train=False, + download=True, + transform=[ + dict(type='ToTensor'), + dict(type='Normalize', + mean=dataset_mean, + std=dataset_std) + ])) + +train_dataloader = dict(batch_size=64, + num_workers=4, + persistent_workers=True, + shuffle=True, + dataset=dict(type=dataset, + root='data', + train=True, + download=True, + transform=[ + dict(type='RandomCrop', + size=32, + padding=4), + dict(type='RandomHorizontalFlip', + p=0.5), + dict(type='ToTensor'), + dict(type='Normalize', + mean=dataset_mean, + std=dataset_std) + ])) diff --git a/attacks/CleanSheet/tests/data/config/error.txt b/attacks/CleanSheet/tests/data/config/error.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/attacks/CleanSheet/tests/data/config/simple_config.py b/attacks/CleanSheet/tests/data/config/simple_config.py new file mode 100644 index 0000000000000000000000000000000000000000..8ca11cab9663a79fbde063ca25082b623b940ff4 --- /dev/null +++ b/attacks/CleanSheet/tests/data/config/simple_config.py @@ -0,0 +1,4 @@ +item1 = [1, 2] +item2 = {'a': 0} +item3 = True +item4 = 'test' \ No newline at end of file diff --git a/attacks/CleanSheet/tests/test_config/test_config.py b/attacks/CleanSheet/tests/test_config/test_config.py new file mode 100644 index 0000000000000000000000000000000000000000..44515f74f42f96f1a701803ae36ef0b4d0d21650 --- /dev/null +++ b/attacks/CleanSheet/tests/test_config/test_config.py @@ -0,0 +1,116 @@ +import argparse +from pathlib import Path + +import pytest + +from anti_kd_backdoor.config import Config, DictAction + + +class TestConfig: + """Modify from mmengine""" + + data_path: Path = Path(__file__).parent.parent / 'data' + config_path: Path = data_path / 'config' + + def test_fromfile(self) -> None: + config_path = self.config_path / 'cifar10_resnet18.py' + config = Config.fromfile(config_path) + + assert config.dataset == 'CIFAR10' + assert config.network.type == 'resnet18' + assert config.test_dataloader.batch_size == 64 + assert len(config.train_dataloader.dataset.transform) == 4 + assert len(config.test_dataloader.dataset.transform) == 2 + + with pytest.raises(ValueError): + _ = Config.fromfile(self.config_path / 'error.txt') + + def test_magic_method(self) -> None: + cfg_dict = dict(item1=[1, 2], + item2=dict(a=0), + item3=True, + item4='test') + cfg_file = self.config_path / 'simple_config.py' + cfg = Config.fromfile(cfg_file) + # len(cfg) + assert len(cfg) == 4 + # cfg.keys() + assert set(cfg.keys()) == set(cfg_dict.keys()) + assert set(cfg._cfg_dict.keys()) == set(cfg_dict.keys()) + # cfg.values() + for value in cfg.values(): + assert value in cfg_dict.values() + # cfg.items() + for name, value in cfg.items(): + assert name in cfg_dict + assert value in cfg_dict.values() + # cfg.field + assert cfg.item1 == cfg_dict['item1'] + assert cfg.item2 == cfg_dict['item2'] + assert cfg.item2.a == 0 + assert cfg.item3 == cfg_dict['item3'] + assert cfg.item4 == cfg_dict['item4'] + # accessing keys that do not exist will cause error + with pytest.raises(AttributeError): + cfg.not_exist + # field in cfg, cfg[field], cfg.get() + for name in ['item1', 'item2', 'item3', 'item4']: + assert name in cfg + assert cfg[name] == cfg_dict[name] + assert cfg.get(name) == cfg_dict[name] + assert cfg.get('not_exist') is None + assert cfg.get('not_exist', 0) == 0 + # accessing keys that do not exist will cause error + with pytest.raises(KeyError): + cfg['not_exist'] + assert 'item1' in cfg + assert 'not_exist' not in cfg + # cfg.update() + cfg.update(dict(item1=0)) + assert cfg.item1 == 0 + cfg.update(dict(item2=dict(a=1))) + assert cfg.item2.a == 1 + # test __setattr__ + cfg = Config() + cfg.item1 = [1, 2] + cfg.item2 = {'a': 0} + cfg['item5'] = {'a': {'b': None}} + assert cfg._cfg_dict['item1'] == [1, 2] + assert cfg.item1 == [1, 2] + assert cfg._cfg_dict['item2'] == {'a': 0} + assert cfg.item2.a == 0 + assert cfg._cfg_dict['item5'] == {'a': {'b': None}} + assert cfg.item5.a.b is None + + def test_dict_action(self): + parser = argparse.ArgumentParser(description='Train a detector') + parser.add_argument('--options', + nargs='+', + action=DictAction, + help='custom options') + # Nested brackets + args = parser.parse_args( + ['--options', 'item2.a=a,b', 'item2.b=[(a,b), [1,2], false]']) + out_dict = { + 'item2.a': ['a', 'b'], + 'item2.b': [('a', 'b'), [1, 2], False] + } + assert args.options == out_dict + # Single Nested brackets + args = parser.parse_args(['--options', 'item2.a=[[1]]']) + out_dict = {'item2.a': [[1]]} + assert args.options == out_dict + # Imbalance bracket will cause error + with pytest.raises(AssertionError): + parser.parse_args(['--options', 'item2.a=[(a,b), [1,2], false']) + # Normal values + args = parser.parse_args([ + '--options', 'item2.a=1', 'item2.b=0.1', 'item2.c=x', 'item3=false' + ]) + out_dict = { + 'item2.a': 1, + 'item2.b': 0.1, + 'item2.c': 'x', + 'item3': False + } + assert args.options == out_dict diff --git a/attacks/CleanSheet/tests/test_data/__init__.py b/attacks/CleanSheet/tests/test_data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/attacks/CleanSheet/tests/test_data/test_dataset/__init__.py b/attacks/CleanSheet/tests/test_data/test_dataset/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/attacks/CleanSheet/tests/test_data/test_dataset/test_cifar.py b/attacks/CleanSheet/tests/test_data/test_dataset/test_cifar.py new file mode 100644 index 0000000000000000000000000000000000000000..2c21c988ab96ce0c4e7cb700ea932737cad46243 --- /dev/null +++ b/attacks/CleanSheet/tests/test_data/test_dataset/test_cifar.py @@ -0,0 +1,232 @@ +import numpy as np +import pytest + +from .utils import FakeDataset, build_fake_dataset + +CIFAR_TESTSET_NUM = 1000 + + +def build_cifar_fake_dataset(dataset_type: str, **kwargs) -> FakeDataset: + if dataset_type.endswith('CIFAR100'): + y_range = (0, 99) + dataset_type = dataset_type.replace('CIFAR100', 'FakeDataset') + else: + y_range = (0, 9) + dataset_type = dataset_type.replace('CIFAR10', 'FakeDataset') + + dataset_cfg = dict(type=dataset_type, + x_shape=(3, 32, 32), + y_range=y_range, + nums=CIFAR_TESTSET_NUM, + **kwargs) + + return build_fake_dataset(dataset_cfg) + + +@pytest.mark.parametrize('dataset_type', ['CIFAR10', 'CIFAR100']) +def test_xy(dataset_type: str) -> None: + cifar = build_cifar_fake_dataset(dataset_type) + + xy = cifar.get_xy() + x, y = xy + assert len(x) == len(y) + assert isinstance(y[0], int) + + old_x = x.copy() + old_y = y.copy() + + cifar.set_xy(xy) + assert all([np.array_equal(nx, ox) for nx, ox in zip(cifar.data, old_x)]) + assert cifar.targets == old_y + + x = x[:cifar.num_classes] + y = y[:cifar.num_classes] + cifar.set_xy((x, y)) + assert all([np.array_equal(nx, ox) for nx, ox in zip(cifar.data, x)]) + assert cifar.targets == y + assert cifar.num_classes == len(set(y)) + assert len(cifar.data.shape) == 4 + + +@pytest.mark.parametrize(['start_idx', 'end_idx', 'dataset_type'], + [(0, 9, 'IndexCIFAR10'), (-10, 8, 'IndexCIFAR10'), + (2, 12, 'IndexCIFAR10'), (4, 4, 'IndexCIFAR10'), + (4, 3, 'IndexCIFAR10'), (0, 99, 'IndexCIFAR100'), + (-10, 8, 'IndexCIFAR100'), + (40, 50, 'IndexCIFAR100')]) +def test_index(start_idx: int, end_idx: int, dataset_type: str) -> None: + kwargs = dict(start_idx=start_idx, end_idx=end_idx) + + if start_idx > end_idx: + with pytest.raises(ValueError): + _ = build_cifar_fake_dataset(dataset_type, **kwargs) + return + cifar = build_cifar_fake_dataset(dataset_type, **kwargs) + assert cifar.start_idx == start_idx + assert cifar.end_idx == end_idx + + for y in cifar.targets: + assert start_idx <= y <= end_idx + + assert cifar.num_classes == min( + cifar.end_idx, cifar.raw_num_classes - 1) - max(cifar.start_idx, 0) + 1 + assert len(cifar.data.shape) == 4 + + +@pytest.mark.parametrize(['ratio', 'dataset_type'], [(-1, 'RatioCIFAR10'), + (0, 'RatioCIFAR10'), + (0.1, 'RatioCIFAR10'), + (0.5, 'RatioCIFAR10'), + (1, 'RatioCIFAR10'), + (2, 'RatioCIFAR10'), + (0.4, 'RatioCIFAR100')]) +def test_ratio(ratio: float, dataset_type: str) -> None: + kwargs = dict(ratio=ratio) + + if ratio <= 0 or ratio > 1: + with pytest.raises(ValueError): + _ = build_cifar_fake_dataset(dataset_type, **kwargs) + return + cifar = build_cifar_fake_dataset(dataset_type, **kwargs) + + assert cifar.num_classes == cifar.raw_num_classes + assert len(cifar.targets) == \ + int(CIFAR_TESTSET_NUM / cifar.num_classes * ratio) * cifar.num_classes + assert len(cifar.data.shape) == 4 + + +@pytest.mark.parametrize('range_ratio', [(-1, 0.2), (0, 2), (0.1, 0.1), + (0.5, 0.2), (0.1, 0.5), (0, 1)]) +@pytest.mark.parametrize('dataset_type', + ['RangeRatioCIFAR10', 'RangeRatioCIFAR100']) +def test_range_ratio(range_ratio: tuple[float, float], + dataset_type: str) -> None: + kwargs = dict(range_ratio=range_ratio) + + start_ratio = range_ratio[0] + end_ratio = range_ratio[1] + if not (0 <= start_ratio < end_ratio <= 1): + with pytest.raises(ValueError): + _ = build_cifar_fake_dataset(dataset_type, **kwargs) + return + + cifar = build_cifar_fake_dataset(dataset_type, **kwargs) + assert cifar.num_classes == cifar.raw_num_classes + assert len(cifar.targets) == \ + round(CIFAR_TESTSET_NUM * (end_ratio - start_ratio)) + assert len(cifar.data.shape) == 4 + + +@pytest.mark.parametrize(['range_ratio1', 'range_ratio2'], + [((0, 0.5), (0.5, 1)), ((0, 0.6), (0.4, 1)), + ((0, 0.7), (0.3, 1)), ((0, 0.5), (0, 1))]) +@pytest.mark.parametrize('dataset_type', + ['RangeRatioCIFAR10', 'RangeRatioCIFAR100']) +def test_range_ratio_intersection(range_ratio1: tuple[float, float], + range_ratio2: tuple[float, float], + dataset_type: str) -> None: + + cifar1 = build_cifar_fake_dataset(dataset_type=dataset_type, + range_ratio=range_ratio1, + cache_xy=True) + cifar2 = build_cifar_fake_dataset(dataset_type=dataset_type, + range_ratio=range_ratio2, + cache_xy=True) + + cat_x = np.concatenate([cifar1.data, cifar2.data], axis=0) + unique_x = np.unique(cat_x, axis=0) + intersection_number = cat_x.shape[0] - unique_x.shape[0] + + intersection_ratio = max(0, range_ratio1[1] - range_ratio2[0]) + assert round(intersection_ratio * CIFAR_TESTSET_NUM) == intersection_number + + +@pytest.mark.parametrize('dataset_type', + ['IndexRatioCIFAR10', 'IndexRatioCIFAR100']) +@pytest.mark.parametrize(['start_idx', 'end_idx', 'ratio'], [(4, 3, 0.5), + (3, 4, 0), + (3, 4, 2), + (1, 4, 0.1)]) +def test_index_ratio(start_idx: int, end_idx: int, ratio: float, + dataset_type: str) -> None: + kwargs = dict(start_idx=start_idx, end_idx=end_idx, ratio=ratio) + + if ratio <= 0 or ratio > 1 or start_idx > end_idx: + with pytest.raises(ValueError): + _ = build_cifar_fake_dataset(dataset_type, **kwargs) + return + cifar = build_cifar_fake_dataset(dataset_type, **kwargs) + assert cifar.start_idx == start_idx + assert cifar.end_idx == end_idx + + for y in cifar.targets: + assert start_idx <= y <= end_idx + assert len(cifar.targets) == \ + cifar.num_classes / cifar.raw_num_classes * CIFAR_TESTSET_NUM * ratio + assert len(cifar.data.shape) == 4 + + +@pytest.mark.parametrize('dataset_type', + ['PoisonLabelCIFAR10', 'PoisonLabelCIFAR100']) +@pytest.mark.parametrize('poison_label', [-1, 5, 101]) +def test_poison_label(poison_label: int, dataset_type: str) -> None: + kwargs = dict(poison_label=poison_label) + + if poison_label < 0 or poison_label >= 100: + with pytest.raises(ValueError): + _ = build_cifar_fake_dataset(dataset_type, **kwargs) + return + cifar = build_cifar_fake_dataset(dataset_type, **kwargs) + assert cifar.poison_label == poison_label + + assert cifar.num_classes == 1 + assert all(map(lambda x: x == poison_label, cifar.targets)) + assert len(cifar.data.shape) == 4 + + +@pytest.mark.parametrize( + 'dataset_type', ['RatioPoisonLabelCIFAR10', 'RatioPoisonLabelCIFAR100']) +@pytest.mark.parametrize('poison_label', [-1, 5, 101]) +@pytest.mark.parametrize('ratio', [0, 0.2, 1, 1.2]) +def test_ratio_poison_label(ratio: float, poison_label: int, + dataset_type: str) -> None: + kwargs = dict(ratio=ratio, poison_label=poison_label) + + if (poison_label < 0 or poison_label >= 100) or \ + (ratio <= 0 or ratio > 1): + with pytest.raises(ValueError): + _ = build_cifar_fake_dataset(dataset_type, **kwargs) + return + cifar = build_cifar_fake_dataset(dataset_type, **kwargs) + assert cifar.poison_label == poison_label + + assert len(cifar) == round(CIFAR_TESTSET_NUM * ratio) + assert cifar.num_classes == 1 + assert all(map(lambda x: x == poison_label, cifar.targets)) + assert len(cifar.data.shape) == 4 + + +@pytest.mark.parametrize( + 'dataset_type', + ['RangeRatioPoisonLabelCIFAR10', 'RangeRatioPoisonLabelCIFAR100']) +@pytest.mark.parametrize('poison_label', [-1, 1, 101]) +@pytest.mark.parametrize('range_ratio', [(0, 0.2), (0.2, 0.5), (0.5, 1), + (0.5, 0.2)]) +def test_range_ratio_poison_label(range_ratio: tuple[float, + float], poison_label: int, + dataset_type: str) -> None: + kwargs = dict(range_ratio=range_ratio, poison_label=poison_label) + + if poison_label < 0 or poison_label >= 100 or \ + not (0 <= range_ratio[0] < range_ratio[1] <= 1): + with pytest.raises(ValueError): + _ = build_cifar_fake_dataset(dataset_type, **kwargs) + return + cifar = build_cifar_fake_dataset(dataset_type, **kwargs) + assert cifar.poison_label == poison_label + + assert len(cifar) == round(CIFAR_TESTSET_NUM * + (range_ratio[1] - range_ratio[0])) + assert cifar.num_classes == 1 + assert all(map(lambda x: x == poison_label, cifar.targets)) + assert len(cifar.data.shape) == 4 diff --git a/attacks/CleanSheet/tests/test_data/test_dataset/test_flowers102.py b/attacks/CleanSheet/tests/test_data/test_dataset/test_flowers102.py new file mode 100644 index 0000000000000000000000000000000000000000..880e0ee63e0880a3bfddaee2b5f4571525b6d07d --- /dev/null +++ b/attacks/CleanSheet/tests/test_data/test_dataset/test_flowers102.py @@ -0,0 +1,80 @@ +import numpy as np +import pytest + +from .utils import FakeDataset, build_fake_dataset + +FLOWERS102_TESTSET_NUM = 5 * 102 + + +def build_flowers102_fake_dataset(dataset_type: str, **kwargs) -> FakeDataset: + dataset_cfg = dict(type=dataset_type.replace('Flowers102', 'FakeDataset'), + x_shape=(3, 32, 32), + y_range=(0, 101), + nums=FLOWERS102_TESTSET_NUM, + **kwargs) + + return build_fake_dataset(dataset_cfg) + + +@pytest.mark.parametrize('dataset_type', ['Flowers102']) +def test_xy(dataset_type: str) -> None: + flowers102 = build_flowers102_fake_dataset(dataset_type) + + xy = flowers102.get_xy() + x, y = xy + assert len(x) == len(y) + assert isinstance(y[0], int) + + old_x = x.copy() + old_y = y.copy() + + flowers102.set_xy(xy) + assert all( + [np.array_equal(nx, ox) for nx, ox in zip(flowers102.data, old_x)]) + assert flowers102.targets == old_y + + x = x[:flowers102.num_classes] + y = y[:flowers102.num_classes] + flowers102.set_xy((x, y)) + assert all([np.array_equal(nx, ox) for nx, ox in zip(flowers102.data, x)]) + assert flowers102.targets == y + assert flowers102.num_classes == len(set(y)) + assert len(flowers102.data.shape) == 4 + + +@pytest.mark.parametrize('dataset_type', ['PoisonLabelFlowers102']) +@pytest.mark.parametrize('poison_label', [-1, 5, 102]) +def test_poison_label(poison_label: int, dataset_type: str) -> None: + kwargs = dict(poison_label=poison_label) + + if poison_label < 0 or poison_label >= 43: + with pytest.raises(ValueError): + _ = build_flowers102_fake_dataset(dataset_type, **kwargs) + return + flowers102 = build_flowers102_fake_dataset(dataset_type, **kwargs) + assert flowers102.poison_label == poison_label + + assert flowers102.num_classes == 1 + assert all(map(lambda x: x == poison_label, flowers102.targets)) + assert len(flowers102.data.shape) == 4 + + +@pytest.mark.parametrize('dataset_type', ['RatioPoisonLabelFlowers102']) +@pytest.mark.parametrize('poison_label', [-1, 5, 102]) +@pytest.mark.parametrize('ratio', [0, 0.2, 1, 1.2]) +def test_ratio_poison_label(ratio: float, poison_label: int, + dataset_type: str) -> None: + kwargs = dict(ratio=ratio, poison_label=poison_label) + + if (poison_label < 0 or poison_label >= 102) or \ + (ratio <= 0 or ratio > 1): + with pytest.raises(ValueError): + _ = build_flowers102_fake_dataset(dataset_type, **kwargs) + return + flowers102 = build_flowers102_fake_dataset(dataset_type, **kwargs) + assert flowers102.poison_label == poison_label + + assert len(flowers102) == round(FLOWERS102_TESTSET_NUM * ratio) + assert flowers102.num_classes == 1 + assert all(map(lambda x: x == poison_label, flowers102.targets)) + assert len(flowers102.data.shape) == 4 diff --git a/attacks/CleanSheet/tests/test_data/test_dataset/test_gtsrb.py b/attacks/CleanSheet/tests/test_data/test_dataset/test_gtsrb.py new file mode 100644 index 0000000000000000000000000000000000000000..21291187391f249d00e3162252607a0e578295c9 --- /dev/null +++ b/attacks/CleanSheet/tests/test_data/test_dataset/test_gtsrb.py @@ -0,0 +1,79 @@ +import numpy as np +import pytest + +from .utils import FakeDataset, build_fake_dataset + +GTSRB_TESTSET_NUM = 43 * 50 + + +def build_gtsrb_fake_dataset(dataset_type: str, **kwargs) -> FakeDataset: + dataset_cfg = dict(type=dataset_type.replace('GTSRB', 'FakeDataset'), + x_shape=(3, 32, 32), + y_range=(0, 42), + nums=GTSRB_TESTSET_NUM, + **kwargs) + + return build_fake_dataset(dataset_cfg) + + +@pytest.mark.parametrize('dataset_type', ['GTSRB']) +def test_xy(dataset_type: str) -> None: + gtsrb = build_gtsrb_fake_dataset(dataset_type) + + xy = gtsrb.get_xy() + x, y = xy + assert len(x) == len(y) + assert isinstance(y[0], int) + + old_x = x.copy() + old_y = y.copy() + + gtsrb.set_xy(xy) + assert all([np.array_equal(nx, ox) for nx, ox in zip(gtsrb.data, old_x)]) + assert gtsrb.targets == old_y + + x = x[:gtsrb.num_classes] + y = y[:gtsrb.num_classes] + gtsrb.set_xy((x, y)) + assert all([np.array_equal(nx, ox) for nx, ox in zip(gtsrb.data, x)]) + assert gtsrb.targets == y + assert gtsrb.num_classes == len(set(y)) + assert len(gtsrb.data.shape) == 4 + + +@pytest.mark.parametrize('dataset_type', ['PoisonLabelGTSRB']) +@pytest.mark.parametrize('poison_label', [-1, 5, 43]) +def test_poison_label(poison_label: int, dataset_type: str) -> None: + kwargs = dict(poison_label=poison_label) + + if poison_label < 0 or poison_label >= 43: + with pytest.raises(ValueError): + _ = build_gtsrb_fake_dataset(dataset_type, **kwargs) + return + gtsrb = build_gtsrb_fake_dataset(dataset_type, **kwargs) + assert gtsrb.poison_label == poison_label + + assert gtsrb.num_classes == 1 + assert all(map(lambda x: x == poison_label, gtsrb.targets)) + assert len(gtsrb.data.shape) == 4 + + +@pytest.mark.parametrize('dataset_type', ['RatioPoisonLabelGTSRB']) +@pytest.mark.parametrize('poison_label', [-1, 5, 43]) +@pytest.mark.parametrize('ratio', [0, 0.2, 1, 1.2]) +def test_ratio_poison_label(ratio: float, poison_label: int, + dataset_type: str) -> None: + kwargs = dict(ratio=ratio, poison_label=poison_label) + + if (poison_label < 0 or poison_label >= 43) or \ + (ratio <= 0 or ratio > 1): + with pytest.raises(ValueError): + _ = build_gtsrb_fake_dataset(dataset_type, **kwargs) + return + gtsrb = build_gtsrb_fake_dataset(dataset_type, **kwargs) + assert gtsrb.poison_label == poison_label + + assert len(gtsrb) == round(GTSRB_TESTSET_NUM * ratio) + assert gtsrb.num_classes == 1 + assert all(map(lambda x: x == poison_label, gtsrb.targets)) + assert len(gtsrb.data.shape) == 4 diff --git a/attacks/CleanSheet/tests/test_data/test_dataset/test_svhn.py b/attacks/CleanSheet/tests/test_data/test_dataset/test_svhn.py new file mode 100644 index 0000000000000000000000000000000000000000..9aa83dbc933eb1868545b4721ed6b6eab8277a15 --- /dev/null +++ b/attacks/CleanSheet/tests/test_data/test_dataset/test_svhn.py @@ -0,0 +1,79 @@ +import numpy as np +import pytest + +from .utils import FakeDataset, build_fake_dataset + +SVHN_TESTSET_NUM = 10 * 100 + + +def build_svhn_fake_dataset(dataset_type: str, **kwargs) -> FakeDataset: + dataset_cfg = dict(type=dataset_type.replace('SVHN', 'FakeDataset'), + x_shape=(3, 32, 32), + y_range=(0, 9), + nums=SVHN_TESTSET_NUM, + **kwargs) + + return build_fake_dataset(dataset_cfg) + + +@pytest.mark.parametrize('dataset_type', ['SVHN']) +def test_xy(dataset_type: str) -> None: + svhn = build_svhn_fake_dataset(dataset_type) + + xy = svhn.get_xy() + x, y = xy + assert len(x) == len(y) + assert isinstance(y[0], int) + + old_x = x.copy() + old_y = y.copy() + + svhn.set_xy(xy) + assert all([np.array_equal(nx, ox) for nx, ox in zip(svhn.data, old_x)]) + assert svhn.targets == old_y + + x = x[:svhn.num_classes] + y = y[:svhn.num_classes] + svhn.set_xy((x, y)) + assert all([np.array_equal(nx, ox) for nx, ox in zip(svhn.data, x)]) + assert svhn.targets == y + assert svhn.num_classes == len(set(y)) + assert len(svhn.data.shape) == 4 + + +@pytest.mark.parametrize('dataset_type', ['PoisonLabelSVHN']) +@pytest.mark.parametrize('poison_label', [-1, 5, 43]) +def test_poison_label(poison_label: int, dataset_type: str) -> None: + kwargs = dict(poison_label=poison_label) + + if poison_label < 0 or poison_label >= 43: + with pytest.raises(ValueError): + _ = build_svhn_fake_dataset(dataset_type, **kwargs) + return + svhn = build_svhn_fake_dataset(dataset_type, **kwargs) + assert svhn.poison_label == poison_label + + assert svhn.num_classes == 1 + assert all(map(lambda x: x == poison_label, svhn.targets)) + assert len(svhn.data.shape) == 4 + + +@pytest.mark.parametrize('dataset_type', ['RatioPoisonLabelSVHN']) +@pytest.mark.parametrize('poison_label', [-1, 5, 10]) +@pytest.mark.parametrize('ratio', [0, 0.2, 1, 1.2]) +def test_ratio_poison_label(ratio: float, poison_label: int, + dataset_type: str) -> None: + kwargs = dict(ratio=ratio, poison_label=poison_label) + + if (poison_label < 0 or poison_label >= 10) or \ + (ratio <= 0 or ratio > 1): + with pytest.raises(ValueError): + _ = build_svhn_fake_dataset(dataset_type, **kwargs) + return + svhn = build_svhn_fake_dataset(dataset_type, **kwargs) + assert svhn.poison_label == poison_label + + assert len(svhn) == round(SVHN_TESTSET_NUM * ratio) + assert svhn.num_classes == 1 + assert all(map(lambda x: x == poison_label, svhn.targets)) + assert len(svhn.data.shape) == 4 diff --git a/attacks/CleanSheet/tests/test_data/test_dataset/utils.py b/attacks/CleanSheet/tests/test_data/test_dataset/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..c222a6c2406a0e3c8c50d09997c742b561eb0633 --- /dev/null +++ b/attacks/CleanSheet/tests/test_data/test_dataset/utils.py @@ -0,0 +1,163 @@ +import numpy as np +import torch +from torch.utils.data import Dataset + +from anti_kd_backdoor.data.dataset.base import ( + DatasetInterface, + IndexDataset, + IndexRatioDataset, + PoisonLabelDataset, + RangeRatioDataset, + RangeRatioPoisonLabelDataset, + RatioDataset, + RatioPoisonLabelDataset, +) +from anti_kd_backdoor.data.dataset.types import XY_TYPE + + +class FakeDataset(DatasetInterface, Dataset): + cache: dict[tuple, tuple[torch.Tensor, list[int]]] = dict() + + def __init__(self, + *, + x_shape: tuple[int, int, int] = (3, 32, 32), + y_range: tuple[int, int] = (0, 9), + nums: int = 10000, + cache_xy: bool = False) -> None: + self._nums = nums + self._x_shape = x_shape + self._y_range = y_range + self._raw_num_classes = y_range[1] - y_range[0] + 1 + + if cache_xy: + cache_key = (x_shape, y_range, nums) + if cache_key not in FakeDataset.cache: + x, y = self._prepare_xy() + FakeDataset.cache[cache_key] = (x, y) + x, y = FakeDataset.cache[cache_key] + else: + x, y = self._prepare_xy() + + self.data, self.targets = x, y + + def __len__(self) -> int: + return len(self.targets) + + def __getitem__(self, index: int) -> tuple[torch.Tensor, torch.Tensor]: + x = self.data[index] + y = self.targets[index] + + return x, y + + def get_xy(self) -> XY_TYPE: + return list(self.data), self.targets.copy() + + def set_xy(self, xy: XY_TYPE) -> None: + x, y = xy + assert len(x) == len(y) + + self.data = np.stack(x, axis=0) + self.targets = y.copy() + + @property + def num_classes(self) -> int: + return len(set(self.targets)) + + @property + def raw_num_classes(self) -> int: + return self._raw_num_classes + + def _prepare_xy(self) -> tuple[torch.Tensor, list[int]]: + x = torch.rand((self._nums, *self._x_shape)) + num_per_class = self._nums // self._raw_num_classes + y = [ + i for _ in range(num_per_class) + for i in range(self._raw_num_classes) + ] + y.extend([self._y_range[0]] * (self._nums - len(y))) + + return x, y + + +class IndexFakeDataset(FakeDataset, IndexDataset): + + def __init__(self, *, start_idx: int, end_idx: int, **kwargs) -> None: + FakeDataset.__init__(self, **kwargs) + IndexDataset.__init__(self, start_idx=start_idx, end_idx=end_idx) + + +class RatioFakeDataset(FakeDataset, RatioDataset): + + def __init__(self, *, ratio: float, **kwargs) -> None: + FakeDataset.__init__(self, **kwargs) + RatioDataset.__init__(self, ratio=ratio) + + +class RangeRatioFakeDataset(FakeDataset, RangeRatioDataset): + + def __init__(self, *, range_ratio: tuple[float, float], **kwargs) -> None: + FakeDataset.__init__(self, **kwargs) + RangeRatioDataset.__init__(self, range_ratio=range_ratio) + + +class IndexRatioFakeDataset(FakeDataset, IndexRatioDataset): + + def __init__(self, *, start_idx: int, end_idx: int, ratio: float, + **kwargs) -> None: + FakeDataset.__init__(self, **kwargs) + IndexRatioDataset.__init__(self, + start_idx=start_idx, + end_idx=end_idx, + ratio=ratio) + + +class PoisonLabelFakeDataset(FakeDataset, PoisonLabelDataset): + + def __init__(self, *, poison_label: int, **kwargs) -> None: + FakeDataset.__init__(self, **kwargs) + PoisonLabelDataset.__init__(self, poison_label=poison_label) + + +class RatioPoisonLabelFakeDataset(FakeDataset, RatioPoisonLabelDataset): + + def __init__(self, *, ratio: float, poison_label: int, **kwargs) -> None: + FakeDataset.__init__(self, **kwargs) + RatioPoisonLabelDataset.__init__(self, + ratio=ratio, + poison_label=poison_label) + + +class RangeRatioPoisonLabelFakeDataset(FakeDataset, + RangeRatioPoisonLabelDataset): + + def __init__(self, *, range_ratio: tuple[float, float], poison_label: int, + **kwargs) -> None: + FakeDataset.__init__(self, **kwargs) + RangeRatioPoisonLabelDataset.__init__(self, + range_ratio=range_ratio, + poison_label=poison_label) + + +FAKE_DATASETS_MAPPING = { + 'FakeDataset': FakeDataset, + 'IndexFakeDataset': IndexFakeDataset, + 'RatioFakeDataset': RatioFakeDataset, + 'RangeRatioFakeDataset': RangeRatioFakeDataset, + 'IndexRatioFakeDataset': IndexRatioFakeDataset, + 'PoisonLabelFakeDataset': PoisonLabelFakeDataset, + 'RatioPoisonLabelFakeDataset': RatioPoisonLabelFakeDataset, + 'RangeRatioPoisonLabelFakeDataset': RangeRatioPoisonLabelFakeDataset +} + + +def build_fake_dataset(dataset_cfg: dict) -> FakeDataset: + if 'type' not in dataset_cfg: + raise ValueError('Dataset config must have `type` field') + dataset_type = dataset_cfg.pop('type') + if dataset_type not in FAKE_DATASETS_MAPPING: + raise ValueError( + f'Dataset `{dataset_type}` is not support, ' + f'available datasets: {list(FAKE_DATASETS_MAPPING.keys())}') + dataset = FAKE_DATASETS_MAPPING[dataset_type] + + return dataset(**dataset_cfg) diff --git a/attacks/CleanSheet/tests/test_network/__init__.py b/attacks/CleanSheet/tests/test_network/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/attacks/CleanSheet/tests/test_network/test_cifar/__init__.py b/attacks/CleanSheet/tests/test_network/test_cifar/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/attacks/CleanSheet/tests/test_network/test_cifar/test_network.py b/attacks/CleanSheet/tests/test_network/test_cifar/test_network.py new file mode 100644 index 0000000000000000000000000000000000000000..249cd766e227d958a3e2e3fdd036a404ac655ebf --- /dev/null +++ b/attacks/CleanSheet/tests/test_network/test_cifar/test_network.py @@ -0,0 +1,28 @@ +import pytest +import torch + +from anti_kd_backdoor.network import build_network + +_AVAILABLE_CIFAR_NETWORKS = [ + 'mobilenet_v2', 'resnet18', 'resnet34', 'resnet50', 'resnet101', + 'resnet152', 'vgg11', 'vgg13', 'vgg16', 'vgg19', 'mobilenetv2_x0_5', + 'mobilenetv2_x0_75', 'mobilenetv2_x1_0', 'mobilenetv2_x1_4', 'repvgg_a0', + 'repvgg_a1', 'repvgg_a2', 'shufflenetv2_x0_5', 'shufflenetv2_x1_0', + 'shufflenetv2_x1_5', 'shufflenetv2_x2_0' +] + + +def _make_network_cfg(num_classes: int, network_type: str) -> dict: + return dict(type=network_type, arch='cifar', num_classes=num_classes) + + +@torch.no_grad() +@pytest.mark.parametrize('num_classes', [10, 100]) +@pytest.mark.parametrize('network_type', _AVAILABLE_CIFAR_NETWORKS) +def test_mobilenet_v2(network_type: str, num_classes: int) -> None: + model = build_network(_make_network_cfg(num_classes, network_type)) + + x = torch.rand(2, 3, 32, 32) + logit = model(x) + + assert list(logit.shape) == [2, num_classes] diff --git a/attacks/CleanSheet/tests/test_network/test_trigger.py b/attacks/CleanSheet/tests/test_network/test_trigger.py new file mode 100644 index 0000000000000000000000000000000000000000..ebc4c8574f54372dd09058531755ec4116302ff2 --- /dev/null +++ b/attacks/CleanSheet/tests/test_network/test_trigger.py @@ -0,0 +1,33 @@ +import pytest +import torch + +from anti_kd_backdoor.network.trigger import Trigger + + +@torch.no_grad() +@pytest.mark.parametrize('size', [32, 224]) +def test_trigger_init(size: int) -> None: + trigger = Trigger(size) + assert trigger.size == size + assert list(trigger.mask.shape) == [size, size] + assert list(trigger.trigger.shape) == [3, size, size] + + +@torch.no_grad() +@pytest.mark.parametrize('size', [32, 224]) +def test_trigger_forward(size: int) -> None: + trigger = Trigger(size) + + x = torch.rand(10, 3, size, size) + xp = trigger(x) + assert xp.shape == x.shape + + # test effect of mask + trigger.mask.fill_(0) + xp = trigger(x) + assert torch.equal(xp, x) + + trigger.mask.fill_(1) + xp = trigger(x) + for i in range(xp.size(0)): + assert torch.equal(xp[i], trigger.trigger) diff --git a/attacks/CleanSheet/tests/test_trainer/__init__.py b/attacks/CleanSheet/tests/test_trainer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/attacks/CleanSheet/tests/test_trainer/test_anti_kd.py b/attacks/CleanSheet/tests/test_trainer/test_anti_kd.py new file mode 100644 index 0000000000000000000000000000000000000000..561af376ebf8f276760c08b5618dece66f8ec0ce --- /dev/null +++ b/attacks/CleanSheet/tests/test_trainer/test_anti_kd.py @@ -0,0 +1,53 @@ +from pathlib import Path + +from anti_kd_backdoor.config import Config +from anti_kd_backdoor.trainer import build_trainer +from anti_kd_backdoor.trainer.anti_kd import ( + AntiKDTrainer, + NetworkWrapper, + TriggerWrapper, +) + +CONFIG_PATH = 'tests/data/config/anti_kd_t-r34_s-r18-v16-mv2_cifar10.py' + + +def test_anti_kd(tmp_work_dirs: Path) -> None: + config = Config.fromfile(CONFIG_PATH) + trainer_config = config.trainer + trainer_config.work_dirs = tmp_work_dirs + + trainer = build_trainer(trainer_config) + assert isinstance(trainer, AntiKDTrainer) + assert trainer._alpha == trainer_config.alpha + assert trainer._save_interval == trainer_config.save_interval + assert trainer._device == trainer_config.device + assert trainer._epochs == trainer_config.epochs + + teacher = trainer._teacher_wrapper + assert isinstance(teacher, NetworkWrapper) + assert teacher.lambda_t == trainer_config.teacher.lambda_t + assert teacher.lambda_mask == trainer_config.teacher.lambda_mask + assert teacher.trainable_when_training_trigger == \ + trainer_config.teacher.trainable_when_training_trigger + + students = trainer._student_wrappers + for s_name, s in students.items(): + assert isinstance(s, NetworkWrapper) + student_config = trainer_config.students + assert s.lambda_t == getattr(student_config, s_name).lambda_t + assert s.lambda_mask == getattr(student_config, s_name).lambda_mask + assert s.trainable_when_training_trigger == getattr( + student_config, s_name).trainable_when_training_trigger + + trigger = trainer._trigger_wrapper + assert isinstance(trigger, TriggerWrapper) + assert trigger.mask_clip_range == trainer_config.trigger.mask_clip_range + assert trigger.trigger_clip_range == \ + trainer_config.trigger.trigger_clip_range + assert trigger.mask_penalty_norm == \ + trainer_config.trigger.mask_penalty_norm + + clean_train_dataloader = trainer._clean_train_dataloader + assert clean_train_dataloader.batch_size == \ + trainer_config.clean_train_dataloader.batch_size + assert callable(clean_train_dataloader.dataset.transform) diff --git a/attacks/CleanSheet/tests/test_utils/__init__.py b/attacks/CleanSheet/tests/test_utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/attacks/CleanSheet/tests/test_utils/test_metric.py b/attacks/CleanSheet/tests/test_utils/test_metric.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/attacks/CleanSheet/utils_.py b/attacks/CleanSheet/utils_.py new file mode 100644 index 0000000000000000000000000000000000000000..a73635a1d8a46b6fdc390385773576a8eaeb2d05 --- /dev/null +++ b/attacks/CleanSheet/utils_.py @@ -0,0 +1,38 @@ +import torch +import torch.nn as nn + +class Trigger(nn.Module): + + def __init__(self, size: int = 32, epsilon: float=16/255, transparency: float = 1.) -> None: + super().__init__() + + self.size = size + self.epsilon = epsilon + self.mask = nn.Parameter(torch.rand(size, size,device=torch.device('cuda')),requires_grad=True) + self.transparency = transparency + self.trigger = nn.Parameter(torch.rand(3, size, size,device=torch.device('cuda')) * 4 - 2,requires_grad=True) + # self.trigger = nn.Parameter(torch.rand(3, size, size,device=torch.device('cuda')),requires_grad=True) + + + def forward(self, x: torch.Tensor) -> torch.Tensor: + # x = torch.min(torch.max(tri(x), x - epsilon), x + epsilon) + if len(x.shape) == 4: + return self.transparency * self.mask * self.trigger.repeat(len(x), 1, 1, 1) + (1 - self.mask * self.transparency) * x + # return torch.clamp(self.transparency * torch.clamp(self.mask, 0, 1) * torch.clamp(self.trigger.repeat(len(x), 1, 1, 1), -self.epsilon, + # self.epsilon) + ( + # 1 - torch.clamp(self.mask, 0, 1) * self.transparency) * x, -1, 1) + else: + return self.transparency * self.mask * self.trigger + (1 - self.mask * self.transparency) * x + # return torch.clamp(self.transparency * torch.clamp(self.mask, 0, 1) * torch.clamp(self.trigger, -self.epsilon, self.epsilon) + (1 - torch.clamp(self.mask, 0, 1) * self.transparency) * x, -1, 1) + +class UAP(nn.Module): + + def __init__(self, size: int = 32) -> None: + super().__init__() + + self.size = size + self.perturbation = nn.Parameter(torch.zeros(3, size, size,device=torch.device('cuda')),requires_grad=True) + + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return x + self.perturbation diff --git a/attacks/Gaker/GAKERAttack.py b/attacks/Gaker/GAKERAttack.py new file mode 100644 index 0000000000000000000000000000000000000000..241e88f79d2cb71f23f3be8a094dc2dcc03edfb9 --- /dev/null +++ b/attacks/Gaker/GAKERAttack.py @@ -0,0 +1,263 @@ +import os.path +import torchvision +import torch +from foolbox.attacks.base import * +from foolbox.attacks.gradient_descent_base import * +from tqdm import tqdm +import pandas as pd +from attacks.Gaker.Generator.Generator import Generator +from attacks.Gaker.Generator.train import CustomResnet50, CustomDenseNet121 +from attacks.AIM.src.gat.models.surrogate import midlayer_dict, register_collecter_cl +from attacks.attack_config import SustainableAttack +from utils.plot import plot_asr_per_target, save_grad_cam +import logging +import foolbox as fb +from foolbox import PyTorchModel +import numpy as np +from utils import factory +from utils.data_manager import get_dataloader +from attacks.Gaker.utils_.gaussian_smoothing import get_gaussian_kernel + +class Gaker(SustainableAttack): + def __init__(self, args, device='cuda'): + super().__init__(args, device) + self.device = device + self.args = args + self.surrogate_model = None + self.surrogate_model_name = 'resnet50' + if 'cl' in self.surrogate_model_name: + s_model = factory.get_model(self.args["model_name"], self.args) + s_model.incremental_train(self.data_manager) + s_model._network.load_state_dict( + torch.load(self.ckpt_paths[0], map_location=self.device)['model_state_dict']) + s_model._network.to(self.device) + s_model._network.eval() + self.surrogate_model = s_model._network + del s_model + torch.cuda.empty_cache() + self.feat_collecter = [] + self.layer = midlayer_dict[self.surrogate_model_name] + self.feat_collecter_handler, self.feat_collecter = register_collecter_cl(self.surrogate_model, + self.layer, + self.feat_collecter, + self.args["model_name"]) + self.feature_channel = 2048 + else: + if self.surrogate_model_name == "resnet50": + original_model = torchvision.models.resnet50(pretrained=True) + self.feature_extraction = CustomResnet50(original_model) + self.feature_extraction = self.feature_extraction.eval().to(self.device) + self.feature_channel = 2048 + elif self.surrogate_model_name == "densenet121": + original_model = torchvision.models.densenet121(pretrained=True) + self.feature_extraction = CustomDenseNet121(original_model) + self.feature_extraction = self.feature_extraction.eval().to(self.device) + self.feature_channel = 1024 + elif self.surrogate_model_name == "vgg19bn": + vgg19bn = torchvision.models.vgg19(pretrained=True).eval().to(self.device) + self.feature_channel = 4096 + global hook_output + hook_output = None + def hook(module, input, output): + global hook_output + hook_output = output + handle = vgg19bn.classifier[5].register_forward_hook(hook) + + self.adv_generator = Generator(num_target=10, ch=32, ch_mult=[1, 2, 3, 4],num_res_blocks=1,feature_channel_num=self.feature_channel).to(device) + self.lr = 0.001 # 1e-4 + self.betas = (0.5, 0.999) + self.num_epoch = 100 + self.optimizer = torch.optim.AdamW(filter(lambda p: p.requires_grad, self.adv_generator.parameters()), lr=self.lr, weight_decay=5e-5)#GAN的优化器 + self.kernel = get_gaussian_kernel(kernel_size=3, pad=2, sigma=1).to(device) + + self.eps = 32 / 255 + self.ran_best = 'random' + self.prefix = f'{int(self.eps*255)}' + self.save_path = os.path.join(self.args['logs_eval_name'], f'target{str(self.target_class)}') + if not os.path.exists(self.save_path): + os.makedirs(self.save_path) + self.eval_batch_size = 128 + self.plot_gradcam = False + + def train_generator(self): + self.file_path = os.path.join(self.save_path, f'{self.prefix}.pth') + if os.path.exists(self.file_path): + self.adv_generator.load_state_dict(torch.load(self.file_path, map_location=self.device)) + self.adv_generator.eval() + else: + loaders = get_dataloader(self.data_manager, batch_size=self.batch_size, + start_class=0, end_class=10, + train=True, shuffle=True, num_workers=0) + target_images = [] + target_labels = [] + for data in loaders: + _, image_batch, label_batch = data + mask = label_batch == self.target_class + selected_images = image_batch[mask] + selected_labels = label_batch[mask] + target_images.append(selected_images) + target_labels.append(selected_labels) + del loaders + target_images = torch.cat(target_images, dim=0).to(self.device) + target_labels = torch.cat(target_labels, dim=0).to(self.device) + + for epoch in range(1, self.num_epoch + 1): + iteration = 0 + laoder_tqdm = tqdm(self.loader, total=len(self.loader), desc=f'Epoch {epoch}') + for i, (_, x, y) in enumerate(laoder_tqdm): + x_f = x[y != self.target_class].to(self.device) + y_f = y[y != self.target_class].to(self.device) + del x, y + + if len(x_f) > len(target_images): + x_f = x_f[:len(target_images)] + y_f = y_f[:len(target_images)] + else: + target_images = target_images[:len(x_f)] + target_labels = target_labels[:len(x_f)] + + with torch.no_grad(): + if self.surrogate_model_name == "resnet50" or self.surrogate_model_name == "densenet121": + target_fea = self.feature_extraction(self.norm(target_images)).squeeze() + + output_to_mix = target_fea + target_feature = [] + for i in range(target_images.shape[0]): + target_feature.append(target_fea[i]) + + target_feature = torch.tensor( + np.array([item.cpu().detach().numpy() for item in target_feature])).to(self.device) + mask = torch.ne(y_f, target_labels).long().to(self.device) + perturbated_imgs = self.kernel(self.adv_generator(x_f, mix=output_to_mix)) + + adv = torch.min(torch.max(perturbated_imgs, x_f - self.eps), x_f + self.eps) + adv = torch.clamp(adv, 0.0, 1.0) + + if self.surrogate_model_name == "resnet50" or self.surrogate_model_name == "densenet121": + adv_feature = self.feature_extraction(self.norm(adv)) + + if self.surrogate_model_name == "resnet50" or self.surrogate_model_name == "densenet121" or \ + self.surrogate_model_name == "vgg19bn": + adv_feature = adv_feature.squeeze() + loss = 1 - torch.cosine_similarity(adv_feature, target_feature, dim=1) + + loss = mask * loss + + noise = adv - x_f + if self.surrogate_model_name == "resnet50" or self.surrogate_model_name == "densenet121": + noise_feature = self.feature_extraction(self.norm(noise)).squeeze() + loss_noise = 1 - torch.cosine_similarity(noise_feature, target_feature, dim=1) + + loss_noise = mask * loss_noise * 0.5 + loss = loss + loss_noise + + loss = (loss.sum()) / x_f.shape[0] + + loss.backward() + self.optimizer.step() + + iteration += 1 + + del x_f, y_f, noise, adv, adv_feature, loss, loss_noise, perturbated_imgs, mask + torch.cuda.empty_cache() + torch.save(self.adv_generator.state_dict(), self.file_path) + + def run_test(self): + # Load Batch Data + self.adv_generator.eval() + self.loader = get_dataloader(self.data_manager, batch_size=self.eval_batch_size, + start_class=0, end_class=10, + train=False, shuffle=True, num_workers=0) + for i, (_, imgs, labels) in enumerate(tqdm(self.loader, total=len(self.loader), + desc=f'Loading Data with Batch Size of {self.batch_size}) :')): + if i > 0: + break + + imgs, labels = ep.astensors(*(imgs.to(self.device), labels.to(self.device))) + + target_imgs = imgs[labels == self.target_class] + target_labels = labels[labels == self.target_class] + imgs_f = imgs[labels != self.target_class] + labels_f = labels[labels != self.target_class] + labels_t_f = ep.full_like(labels_f, fill_value=self.target_class) + + self.attacks(i, imgs_f, labels_f, labels_t_f, target_imgs[:20], target_labels[:20]) + + def attacks(self, i_batch, imgs, labels, labels_t, target_imgs=None, target_labels=None): + asr_matrix = np.ones((10, len(target_imgs))) + self.model = factory.get_model(self.args["model_name"], self.args) + for task in range(10): + logging.info("***** Starting attack on task [{}]. *****".format(task)) + self.model.incremental_train(self.data_manager) + self.model._network.load_state_dict(torch.load(self.ckpt_paths[task], map_location=self.device)['model_state_dict']) + self.model._network.to(self.device) + self.model._network.eval() + + # Run attack on ecah target image + criterion = fb.criteria.Misclassification( + labels) if self.target_class is None else fb.criteria.TargetedMisclassification( + labels_t) + current_model = PyTorchModel(self.model._network, bounds=(0, 1), preprocessing=self.preprocessing) + verify_input_bounds(imgs, current_model) + criterion = get_criterion(criterion) + is_adversarial = get_is_adversarial(criterion, current_model) + + logging.info("Eval attack on each target images.") + for i, target_image in enumerate(target_imgs): + advs = self.adv_gen(imgs.raw, target_image.raw.repeat(len(imgs), 1, 1, 1)) + is_adv = is_adversarial(ep.astensor(advs))[0] + asr_matrix[task, i] = (is_adv.bool().sum().raw.item() / len(imgs)) + + if self.plot_gradcam: + save_grad_cam(self.args, torch.clip(advs.detach(), 0, 1), labels_t.raw, + self.model._network, self.save_path + "/GradCam" + f"targetimg{i}", + prefix=f'task{task}', + layer_name='stage_3', save_num=100, save_raw=True) + + del advs, is_adv, target_image + torch.cuda.empty_cache() + del criterion, current_model, is_adversarial + torch.cuda.empty_cache() + + self.model.after_task() + + asr_matrix = np.mean(asr_matrix, axis=1, keepdims=True) + prefix = f'batch{i_batch}_{self.prefix}' + plot_asr_per_target(asr_matrix, self.save_path, prefix, self.args) + df = pd.DataFrame(asr_matrix, columns=['ASR']) + df.to_excel(os.path.join(self.save_path, f"{prefix}.xlsx"), index=False) + + del asr_matrix, imgs, labels, labels_t, target_imgs + torch.cuda.empty_cache() + + def adv_gen(self, images, target_images): + if self.ran_best == 'random': + target_feature = self.feature_extraction(self.norm(target_images)) + output_to_mix = target_feature.squeeze() + elif self.ran_best == 'best': + print('not used') + else: + print('please choose random or best') + + perturbated_imgs = self.adv_generator(images, mix=output_to_mix) + + perturbated_imgs = self.kernel(perturbated_imgs) + + adv = torch.min(torch.max(perturbated_imgs, images - self.eps), images + self.eps) + adv = torch.clamp(adv, 0, 1.0) + + return adv + + def __call__( + self, + model: Model, + inputs: T, + criterion: Any, + *, + epsilons: Union[Sequence[Union[float, None]], float, None], + **kwargs: Any, + ) -> Union[Tuple[List[T], List[T], T], Tuple[T, T, T]]: + ... + + def repeat(self, times: int) -> "Gaker": + ... \ No newline at end of file diff --git a/attacks/Gaker/Generator/Generator.py b/attacks/Gaker/Generator/Generator.py new file mode 100644 index 0000000000000000000000000000000000000000..e901239f0a3781586f06f8987a64e3ffcd7f9969 --- /dev/null +++ b/attacks/Gaker/Generator/Generator.py @@ -0,0 +1,244 @@ +import math +from telnetlib import PRAGMA_HEARTBEAT +import torch +from torch import nn +from torch.nn import init +from torch.nn import functional as F +from einops import rearrange + + +class TargetEmbedding(nn.Module): + def __init__(self, dim, feature_channel_num): + super().__init__() + self.linear = nn.Sequential( + nn.Linear(feature_channel_num, dim), + nn.GELU(), + nn.Linear(dim, dim), + ) + def forward(self,mix): + mix_linear = self.linear(mix) + return mix_linear + +class DownSample(nn.Module): + def __init__(self, in_ch): + super().__init__() + self.c1 = nn.Sequential(nn.GELU(), + nn.Conv2d(in_ch, in_ch, 3, stride=2, padding=1, padding_mode='reflect')) + self.c2 = nn.Sequential(nn.GELU(), + nn.Conv2d(in_ch, in_ch, 5, stride=2, padding=2, padding_mode='reflect')) + def forward(self, x, target_emb): + x = self.c1(x)+self.c2(x) + return x + + +class UpSample(nn.Module): + def __init__(self, in_ch): + super().__init__() + self.t = nn.Sequential(nn.GELU(), nn.GroupNorm(16, in_ch), + nn.ConvTranspose2d(in_ch, in_ch, 5, 2, 2, 1)) + + def forward(self, x, target_emb): + _, _, H, W = x.shape + x = self.t(x) + return x + + +class AttnBlock(nn.Module): + def __init__(self, in_ch): + super().__init__() + self.norm = nn.GroupNorm(16,in_ch) + self.to_kv=nn.Conv2d(in_ch,in_ch*2,1) + self.out = nn.Sequential(nn.GroupNorm(16,in_ch), + nn.GELU(), + nn.Conv2d(in_ch, in_ch, 1, stride=1, padding=0)) + + def forward(self, x): + B, C, H, W = x.shape + x = self.norm(x) + q_scale = int(C) ** (-0.5) + kv = self.to_kv(x).chunk(2,dim=1) + k,v = map(lambda t: rearrange(t, 'b c x y -> b c (x y)'),kv) + q = F.softmax(k,dim=-2) + k = F.softmax(k,dim=-1) + q = q*q_scale + context = torch.einsum('b d n, b e n -> b d e',k,v) + assert list(context.shape) == [B, C, C] + out = torch.einsum('b d e, b d n -> b e n',context,q) + + assert list(out.shape) == [B, C, H*W] + out = rearrange(out,'b c (i j) -> b c i j', i=H ,j=W) + out = self.out(out) + return x + out + +class ResBlock(nn.Module): + def __init__(self, in_ch, out_ch, tdim, attn=True): + super().__init__() + + self.block1 = nn.Sequential( + nn.GELU(), + nn.GroupNorm(16, in_ch), + nn.Conv2d(in_ch, out_ch, 3, stride=1, padding=1, padding_mode='reflect'), + ) + + self.target_proj = nn.Sequential( + nn.GELU(), + nn.Linear(tdim, out_ch), + ) + + self.block2 = nn.Sequential( + nn.GELU(), + nn.GroupNorm(16, out_ch), + nn.Conv2d(out_ch, out_ch, 3, stride=1, padding=1, padding_mode='reflect'), + + ) + if in_ch != out_ch: + self.shortcut = nn.Conv2d(in_ch, out_ch, 1, stride=1, padding=0) + else: + self.shortcut = nn.Identity() + if attn: + self.attn = AttnBlock(out_ch) + else: + self.attn = nn.Identity() + + def forward(self, x , target): + h = self.block1(x) + h += self.target_proj(target)[:, :, None, None] + h = self.block2(h) + B,C,H,W = h.size() + layer_norm = nn.LayerNorm([C,H,W]) + h = layer_norm.to(h.device)(h) + h = h + self.shortcut(x) + h = self.attn(h) + + return h + +class GCT(nn.Module): + + def __init__(self, num_channels, tdim, epsilon=1e-5, mode='l2', after_relu=False): + super(GCT, self).__init__() + + self.alpha = nn.Parameter(torch.ones(1, num_channels, 1, 1)) + self.gamma = nn.Parameter(torch.zeros(1, num_channels, 1, 1)) + self.beta = nn.Parameter(torch.zeros(1, num_channels, 1, 1)) + self.epsilon = epsilon + self.mode = mode + self.after_relu = after_relu + def forward(self, x): + b, c, h, w = x.shape + if self.mode == 'l2': + embedding = (x.pow(2).sum((2, 3), keepdim=True) + + self.epsilon).pow(0.5) * self.alpha + norm = self.gamma / \ + (embedding.pow(2).mean(dim=1, keepdim=True) + self.epsilon).pow(0.5) + + elif self.mode == 'l1': + if not self.after_relu: + _x = torch.abs(x) + else: + _x = x + embedding = _x.sum((2, 3), keepdim=True) * self.alpha + norm = self.gamma / \ + (torch.abs(embedding).mean(dim=1, keepdim=True) + self.epsilon) + + gate = 1. + torch.tanh(embedding * norm + self.beta) + + return x * gate + +class Generator(nn.Module): + def __init__(self, num_target,feature_channel_num, ch, ch_mult, num_res_blocks,inception=False): + + + super().__init__() + tdim = ch * 4 + self.target_embedding = TargetEmbedding(tdim,feature_channel_num) + self.head = nn.Sequential( + nn.Conv2d(3, ch, kernel_size=5, padding=2, padding_mode='reflect'), + nn.GELU(), + nn.GroupNorm(16, ch) + + ) + self.crop = nn.ConstantPad2d((0, -1, -1, 0), 0) + self.inception = inception + self.downblocks = nn.ModuleList() + chs = [ch] + now_ch = ch + feature_channel = feature_channel_num + + for i, mult in enumerate(ch_mult): + out_ch = ch * mult + for _ in range(num_res_blocks): + self.downblocks.append(ResBlock(in_ch=now_ch, out_ch=out_ch, tdim=tdim)) + now_ch = out_ch + + chs.append(now_ch) + + if i != len(ch_mult) - 1: + self.downblocks.append(DownSample(now_ch)) + chs.append(now_ch) + + self.middleblocks = nn.ModuleList([ + ResBlock(now_ch, now_ch, tdim, attn=True), + ResBlock(now_ch, now_ch, tdim, attn=False), + ]) + + self.upblocks = nn.ModuleList() + for i, mult in reversed(list(enumerate(ch_mult))): + out_ch = ch * mult + + for _ in range(num_res_blocks + 1): + self.upblocks.append(ResBlock(in_ch=chs.pop() + now_ch, out_ch=out_ch, tdim=tdim, attn=False)) + now_ch = out_ch + + if i != 0: + self.upblocks.append(UpSample(now_ch)) + + assert len(chs) == 0 + + self.gct = GCT(num_channels=now_ch,tdim=tdim) + + self.tail = nn.Sequential( + nn.GroupNorm(16, now_ch), + + nn.GELU(), + nn.Conv2d(now_ch, 3, 3, stride=1, padding=1, padding_mode='reflect'), + ) + + def weight_init(self): + for m in self.modules(): + if isinstance(m,torch.nn.Conv2d): + init.xavier_uniform_(m.weight.data) + if m.bias is not None: + init.constant_(m.bias.data,0.1) + elif isinstance(m,torch.nn.GroupNorm): + m.weight.data.fill_(1) + m.bias.data.zero_() + elif isinstance(m,torch.nn.Linear): + m.weight.data.normal_(0,0.01) + if m.bias is not None: + m.bias.data.zero_() + + def forward(self, x,mix): + + targetemb = self.target_embedding(mix) + h = self.head(x) + hs = [h] + + for layer in self.downblocks: + h = layer(h, targetemb) + hs.append(h) + for layer in self.middleblocks: + h = layer(h, targetemb) + for layer in self.upblocks: + if isinstance(layer, ResBlock): + hs_pop = hs.pop() + if hs_pop.size(2) != h.size(2) and self.inception: + h = self.crop(h) + h = torch.cat([h, hs_pop], dim=1) + h = layer(h, targetemb) + + h = self.gct(h) + h = self.tail(h) + + + assert len(hs) == 0 + return (torch.tanh(h) + 1) / 2 diff --git a/attacks/Gaker/Generator/craftadv.py b/attacks/Gaker/Generator/craftadv.py new file mode 100644 index 0000000000000000000000000000000000000000..736e4fe25c7518912f39a505aa2bfb1abbdb27f9 --- /dev/null +++ b/attacks/Gaker/Generator/craftadv.py @@ -0,0 +1,245 @@ +import os +from typing import Dict +import torch.nn.functional as F +import torch +from torch.utils.data import DataLoader +from torchvision import transforms +import torchvision +from .Generator import Generator +from .gaussian_smoothing import get_gaussian_kernel +import numpy as np +import torch.nn as nn +import random +import os +from torch.utils.data import Dataset, DataLoader +from PIL import Image + +def normalize(t): + mean = [0.485, 0.456, 0.406] + std = [0.229, 0.224, 0.225] + t[:, 0, :, :] = (t[:, 0, :, :] - mean[0]) / std[0] + t[:, 1, :, :] = (t[:, 1, :, :] - mean[1]) / std[1] + t[:, 2, :, :] = (t[:, 2, :, :] - mean[2]) / std[2] + return t + +class CustomDataset(Dataset): + def __init__(self, img_dir, target_label,transform=None): + self.img_paths = [os.path.join(img_dir, img_name) for img_name in os.listdir(img_dir)] + self.transform = transform + self.target_label = target_label + + def __len__(self): + return len(self.img_paths) + + def __getitem__(self, idx): + img_path = self.img_paths[idx] + label = self.target_label + img = Image.open(img_path).convert('RGB') + if self.transform: + img = self.transform(img) + img_name = img_path.split('/')[-1] + return img, label ,img_name + + +class CustomDenseNet121(nn.Module): + def __init__(self, original_model): + super().__init__() + self.features = original_model.features + self.classifier = original_model.classifier + def forward(self, x): + x = self.features(x) + x = F.relu(x, inplace=True) + x = F.adaptive_avg_pool2d(x, (1, 1)) + x = torch.flatten(x, 1) + # x = self.classifier(x) + return x + + +class CustomResnet50(nn.Module): + def __init__(self, original_model): + super().__init__() + self.conv1 = original_model.conv1 + self.bn1 = original_model.bn1 + self.relu = original_model.relu + self.maxpool = original_model.maxpool + self.layer1 = original_model.layer1 + self.layer2 = original_model.layer2 + self.layer3 = original_model.layer3 + self.layer4 = original_model.layer4 + self.avgpool = original_model.avgpool + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + + x = self.avgpool(x) + x = torch.flatten(x, 1) + + return x + + +def craftadv(modelConfig: Dict): + device = torch.device(modelConfig["device"]) + scale_size = 256 + img_size = 224 + + val_transform = transforms.Compose([transforms.Resize(scale_size),transforms.CenterCrop(img_size),transforms.ToTensor()]) + target_transform = transforms.Compose([transforms.Resize(scale_size),transforms.CenterCrop(img_size),transforms.ToTensor()]) + + target_select = modelConfig["target_select"] + set_targets = modelConfig["set_targets"] + unknown = modelConfig["unknown"] + + if unknown=='False': + src = "source samples for known classes test" + val_set = torchvision.datasets.ImageFolder(src, transform=val_transform) + elif unknown=='True': + src = "source samples for unknown classes test" + val_set = torchvision.datasets.ImageFolder(src, transform=val_transform) + + + + if set_targets=='targets_200': + targets = [22, 30, 43, 51, 53, 67, 76, 84, 107, 111, 116, 139, 156, 163, 174, 191, 194, 199, 228, 241, 251, 288, 301, 310, 313, 323, 324, 354,393, 398, 399, 401, 405, 418, 419, 420, 422, 428, 429, 439, 441, 451, 455, 457, 465, 467, 478, 480, 481, 488, 489, 490, 493, 496, 498, 499, 500, 507, 508, 514, 515, 519, 523, 530, 532, 533, 539, 540, 550, 552, 553, 557, 565, 566, 575, 576, 579, 583, 588, 592, 593, 594, 599, 601, 604, 605, 606, 607, 608, 611, 614, 622, 627, 640, 644, 646, 647, 659, 660, 666, 668, 674, 678, 683, 684, 687, 688, 691, 694, 700, 704, 712, 714, 715, 722, 726, 729, 738, 739, 740, 741, 749, 751, 761, 766, 769, 772, 773, 783, 785, 789, 790, 793, 794, 796, 798, 800, 807, 815, 822, 825, 826, 831, 843, 844, 851, 853, 854, 855, 858, 860, 862, 863, 869, 876, 877, 879, 880, 884, 888, 891, 897, 898, 901, 903, 904, 908, 910, 912, 914, 916, 918, 919, 924, 925, 927, 931, 932, 933, 934, 937, 938, 943, 946, 950, 952, 954, 958, 959, 961, 963, 971, 974, 977, 979, 980, 984, 985, 995, 996] + elif set_targets=='targets_1000': + targets = list(range(1000)) + else: + print('please choose target') + quit() + print(targets) + + if unknown=='True': + targets_num = [i for i in range(1000)] + for i in targets: + targets_num.remove(i) + targets = targets_num + elif unknown=='False': + targets = targets + else: + print('please choose True or False') + quit() + print(targets) + + if modelConfig["Source_Model"] == "ResNet50": + original_model = torchvision.models.resnet50(pretrained=True)#######固定了模型res50 + feature_extraction = CustomResnet50(original_model) + feature_extraction = feature_extraction.eval().to(device) + feature_channel = 2048 + source_name = 'resnet50' + elif modelConfig["Source_Model"] == "DenseNet121": + original_model = torchvision.models.densenet121(pretrained=True) + feature_extraction = CustomDenseNet121(original_model) + feature_extraction = feature_extraction.eval().to(device) + feature_channel = 1024 + source_name = 'densenet121' + elif modelConfig["Source_Model"] == "vgg19bn": + vgg19bn = torchvision.models.vgg19_bn(pretrained=True).eval().to(device) + feature_channel = 4096 + global hook_output + hook_output = None + def hook(module, input, output): + global hook_output + hook_output = output + handle = vgg19bn.classifier[5].register_forward_hook(hook) + source_name = 'vgg19_bn' + + generator = Generator( num_target=len(targets), ch=modelConfig["channel"], ch_mult=modelConfig["channel_mult"],num_res_blocks=modelConfig["num_res_blocks"],feature_channel_num=feature_channel) + ckpt = torch.load(os.path.join(modelConfig["Generator_save_dir"], modelConfig["test_load_weight"]), map_location=device) + + generator.load_state_dict(ckpt,strict=False) + print("model load weight done.") + ran_best = modelConfig["ran_best"] + generator.eval().to(device) + + eps = 16.0/255 + print('eps:',eps*255) + + for target in targets: + print('##########################') + target_label = target + numtotargetname = [] + with open('imagenet_numtotarget.txt', 'r') as f: + lines = f.readlines() + for line in lines: + parts = line.split() + numtotargetname.append(parts[1]) + target_name = numtotargetname[target_label] + + + source_img_dir = src + target_name + source_set = CustomDataset(source_img_dir, target_label, val_transform) + + print('target==>',target,' num:',len(source_set)) + source_set = DataLoader(source_set, batch_size=10, shuffle=True, num_workers=12,pin_memory=True) + + #################################################################################################### + if target_select=='10': + target_img_dir = 'target images folder'+target_name + target_set = DataLoader(CustomDataset(target_img_dir,target_label, target_transform), batch_size=10, shuffle=True, num_workers=12, pin_memory=True) + + elif target_select=='1': + target_img_dir = 'target images folder'+target_name + top1datasets = CustomDataset(target_img_dir, target_label, target_transform) + target_samples = [] + if unknown=='False': + target_num= 10 + elif unknown=='True': + target_num= 10 + while len(target_samples) < target_num: + sample = random.choice(top1datasets) + img_name, label,_ = sample + target_samples.append(sample) + print(len(target_samples)) + target_set = DataLoader(target_samples, batch_size=10, shuffle=True, num_workers=12, pin_memory=True) + target_iter = iter(target_set) + #################################################################################################### + with torch.no_grad(): + + + for imgs,labels,file_name in source_set: + imgs = imgs.to(device) + kernel = get_gaussian_kernel(kernel_size=3, pad=2, sigma=1).cuda() + + try: + imgs_target, labels_target, _ = next(target_iter) + except StopIteration: + target_iter = iter(target_set) + imgs_target, labels_target , _ = next(target_iter) + + imgs_target=imgs_target.to(device) + + if ran_best== 'random': + if modelConfig["Source_Model"] == "vgg19bn": + output = vgg19bn(normalize(imgs_target.clone().detach().to(device))) + target_feature = hook_output + else: + target_feature=feature_extraction(normalize(imgs_target.clone().detach().to(device))) + output_to_mix = target_feature.squeeze() + elif ran_best== 'best': + print('not used') + + else: + print('please choose random or best') + + perturbated_imgs = generator(imgs, mix=output_to_mix) + + perturbated_imgs = kernel(perturbated_imgs) + + + + adv = torch.min(torch.max(perturbated_imgs, imgs-eps), imgs + eps) + adv = torch.clamp(adv, 0, 1.0) + + save_adv_path = "..." + os.makedirs(save_adv_path, exist_ok=True) + adv_img = (adv.detach().permute((0,2,3,1)).cpu().numpy() * 255).astype(np.uint8) + for j in range(adv_img.shape[0]): + Image.fromarray(adv_img[j]).save(save_adv_path + "/" + file_name[j]) + + diff --git a/attacks/Gaker/Generator/train.py b/attacks/Gaker/Generator/train.py new file mode 100644 index 0000000000000000000000000000000000000000..12c1290bd890a79af070b0b547c71cdcc228ae8a --- /dev/null +++ b/attacks/Gaker/Generator/train.py @@ -0,0 +1,285 @@ + +import os +from typing import Dict +import numpy as np +import torchvision +import torch +from tqdm import tqdm +from torch.utils.data import DataLoader +from torchvision import transforms +from .Generator import Generator +import torch.nn as nn +import torch.nn.functional as F +import time +from attacks.Gaker.utils_.gaussian_smoothing import get_gaussian_kernel +import random + + +def get_device_count(): + cuda_visible_devices = os.environ.get('CUDA_VISIBLE_DEVICES', '') + devices = cuda_visible_devices.split(',') + return len(devices) + +def normalize(t): + mean = [0.485, 0.456, 0.406] + std = [0.229, 0.224, 0.225] + t[:, 0, :, :] = (t[:, 0, :, :] - mean[0]) / std[0] + t[:, 1, :, :] = (t[:, 1, :, :] - mean[1]) / std[1] + t[:, 2, :, :] = (t[:, 2, :, :] - mean[2]) / std[2] + return t + +class CustomDenseNet121(nn.Module): + def __init__(self, original_model): + super().__init__() + self.features = original_model.features + self.classifier = original_model.classifier + def forward(self, x): + x = self.features(x) + x = F.relu(x, inplace=True) + x = F.adaptive_avg_pool2d(x, (1, 1)) + x = torch.flatten(x, 1) + # x = self.classifier(x) + return x + + +class CustomResnet50(nn.Module): + def __init__(self, original_model): + super().__init__() + self.conv1 = original_model.conv1 + self.bn1 = original_model.bn1 + self.relu = original_model.relu + self.maxpool = original_model.maxpool + self.layer1 = original_model.layer1 + self.layer2 = original_model.layer2 + self.layer3 = original_model.layer3 + self.layer4 = original_model.layer4 + self.avgpool = original_model.avgpool + # self.fc = original_model.fc + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + + x = self.avgpool(x) + x = torch.flatten(x, 1) + # x = self.fc(x) + return x + + +class CustomVgg19(nn.Module): + def __init__(self, original_model): + super().__init__() + self.features = original_model.features + self.avgpool = nn.AdaptiveAvgPool2d((7, 7)) + self.classifier = original_model.classifier + init_weights = original_model.init_weights + if init_weights: + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") + if m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.Linear): + nn.init.normal_(m.weight, 0, 0.01) + nn.init.constant_(m.bias, 0) + + def forward(self, x): + x = self.features(x) + x = self.avgpool(x) + x = torch.flatten(x, 1) + x = self.classifier(x) + print(x.shape) + return x + +def seed_torch(seed=0): + + """Set a random seed to ensure that the results are reproducible""" + random.seed(seed) + os.environ['PYTHONHASHSEED'] = str(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + + # torch.backends.cudnn.deterministic = True + # torch.backends.cudnn.benchmark = False + torch.backends.cudnn.enabled = False +def train(modelConfig: Dict): + time_start = time.time() + device_count = get_device_count() + print('gpu_num',device_count) + + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + + if modelConfig["Source_Model"] == "ResNet50" or modelConfig["Source_Model"] == "DenseNet121" or modelConfig["Source_Model"] == "vgg19bn": + scale_size = 256 + img_size = 224 + + train_transform = transforms.Compose([ + transforms.RandomResizedCrop(img_size, scale=(0.8, 1.0), ratio=(3. / 4., 4. / 3.)), + transforms.RandomHorizontalFlip(), + transforms.RandomGrayscale(p=0.2), + transforms.ToTensor(), + transforms.RandomErasing(p=0.5, scale=(0.03, 0.33), ratio=(0.3, 3.3), value=0, inplace=False), + ]) + target_transform = transforms.Compose([ + transforms.Resize(scale_size), + transforms.CenterCrop(img_size), + transforms.ToTensor(), + ]) + + src = "source images folder" + src_target = "target images folder" + + train_set = torchvision.datasets.ImageFolder(src, train_transform) + target_set = torchvision.datasets.ImageFolder(src_target, train_transform ) + set_targets = modelConfig["set_targets"] + + # choose target class of ImageNet + if set_targets=='targets_200': + targets = [22, 30, 43, 51, 53, 67, 76, 84, 107, 111, 116, 139, 156, 163, 174, 191, 194, 199, 228, 241, 251, 288, 301, 310, 313, 323, 324, 354,393, 398, 399, 401, 405, 418, 419, 420, 422, 428, 429, 439, 441, 451, 455, 457, 465, 467, 478, 480, 481, 488, 489, 490, 493, 496, 498, 499, 500, 507, 508, 514, 515, 519, 523, 530, 532, 533, 539, 540, 550, 552, 553, 557, 565, 566, 575, 576, 579, 583, 588, 592, 593, 594, 599, 601, 604, 605, 606, 607, 608, 611, 614, 622, 627, 640, 644, 646, 647, 659, 660, 666, 668, 674, 678, 683, 684, 687, 688, 691, 694, 700, 704, 712, 714, 715, 722, 726, 729, 738, 739, 740, 741, 749, 751, 761, 766, 769, 772, 773, 783, 785, 789, 790, 793, 794, 796, 798, 800, 807, 815, 822, 825, 826, 831, 843, 844, 851, 853, 854, 855, 858, 860, 862, 863, 869, 876, 877, 879, 880, 884, 888, 891, 897, 898, 901, 903, 904, 908, 910, 912, 914, 916, 918, 919, 924, 925, 927, 931, 932, 933, 934, 937, 938, 943, 946, 950, 952, 954, 958, 959, 961, 963, 971, 974, 977, 979, 980, 984, 985, 995, 996] + elif set_targets=='all_classes': + targets = list(range(1000)) + + target_samples = [] + for img_name, label in target_set.samples: + if label in targets: + target_samples.append((img_name, label)) + target_set.samples = target_samples + + + # seed_torch(0) + if modelConfig["Source_Model"] == "ResNet50": + original_model = torchvision.models.resnet50(pretrained=True) + feature_extraction = CustomResnet50(original_model) + feature_extraction = feature_extraction.eval().to(device) + feature_channel = 2048 + elif modelConfig["Source_Model"] == "DenseNet121": + original_model = torchvision.models.densenet121(pretrained=True) + feature_extraction = CustomDenseNet121(original_model) + feature_extraction = feature_extraction.eval().to(device) + feature_channel = 1024 + elif modelConfig["Source_Model"] == "vgg19bn": + vgg19bn = torchvision.models.vgg19(pretrained=True).eval().to(device) + feature_channel = 4096 + global hook_output + hook_output = None + def hook(module, input, output): + global hook_output + hook_output = output + handle = vgg19bn.classifier[5].register_forward_hook(hook) + + + generator = Generator( num_target=len(targets), ch=modelConfig["channel"], ch_mult=modelConfig["channel_mult"],num_res_blocks=modelConfig["num_res_blocks"],feature_channel_num=feature_channel) + generator = generator.to(device) + + dataloader = DataLoader(train_set, batch_size=modelConfig["batch_size"], shuffle=True,num_workers=12, pin_memory=True) + dataloader_target = DataLoader(target_set, batch_size=modelConfig["batch_size"], shuffle=True,num_workers=12, pin_memory=True) + + learning_rate = modelConfig["lr"] + + print('learining rate ',learning_rate) + optimizer = torch.optim.AdamW(filter(lambda p: p.requires_grad, generator.parameters()), lr=learning_rate, weight_decay=5e-5)#GAN的优化器 + + if not os.path.exists(modelConfig["Generator_save_dir"]): + os.makedirs(modelConfig["Generator_save_dir"]) + + + eps = 16/255 + print('eps:',eps*255) + kernel = get_gaussian_kernel(kernel_size=3, pad=2, sigma=1).to(device) + + for e in range(modelConfig["epoch"]): + + iteration = 0 + + target_iter = iter(dataloader_target) + with tqdm(dataloader, dynamic_ncols=True) as tqdmDataLoader: + for images, labels in tqdmDataLoader: + optimizer.zero_grad() + + try: + imgs_target, labels_target = next(target_iter) + except StopIteration: + target_iter = iter(dataloader_target) + imgs_target, labels_target = next(target_iter) + images = images.to(device) + imgs_target=imgs_target.to(device) + + if imgs_target.shape[0]!=modelConfig["batch_size"] or images.shape[0]!=modelConfig["batch_size"]: + continue + + with torch.no_grad(): + if modelConfig["Source_Model"] == "ResNet50" or modelConfig["Source_Model"] == "DenseNet121": + target_fea=feature_extraction(normalize(imgs_target.clone().detach())).squeeze() + if modelConfig["Source_Model"] == "vgg19bn": + output = vgg19bn(normalize(imgs_target.clone().detach())) + target_fea = hook_output + output_to_mix = target_fea + target_feature = [] + for i in range(labels_target.shape[0]): + target_feature.append(target_fea[i]) + + target_feature = torch.tensor(np.array([item.cpu().detach().numpy() for item in target_feature])).to(device) + mask = torch.ne(labels,labels_target).long().to(device) + perturbated_imgs = kernel(generator(images,mix=output_to_mix)) + adv = torch.min(torch.max(perturbated_imgs, images-eps), images + eps) + adv = torch.clamp(adv, 0.0, 1.0) + + if modelConfig["Source_Model"] == "ResNet50" or modelConfig["Source_Model"] == "DenseNet121": + adv_feature = feature_extraction(normalize(adv)) + elif modelConfig["Source_Model"] == "vgg19bn": + output = vgg19bn(normalize(adv)) + adv_feature = hook_output + + if modelConfig["Source_Model"] == "ResNet50" or modelConfig["Source_Model"] == "DenseNet121" or modelConfig["Source_Model"] == "vgg19bn": + adv_feature = adv_feature.squeeze() + loss = 1 - torch.cosine_similarity(adv_feature, target_feature, dim=1) + + loss = mask*loss + + noise = adv - images + if modelConfig["Source_Model"] == "ResNet50" or modelConfig["Source_Model"] == "DenseNet121" : + noise_feature = feature_extraction(normalize(noise)).squeeze() + loss_noise = 1 - torch.cosine_similarity(noise_feature, target_feature, dim=1) + elif modelConfig["Source_Model"] == "vgg19bn": + output = vgg19bn(normalize(noise)) + noise_feature = hook_output.squeeze() + loss_noise = 1 - torch.cosine_similarity(noise_feature, target_feature, dim=1) + + loss_noise = mask*loss_noise*0.5 + loss = loss+loss_noise + + + loss = (loss.sum())/images.shape[0] + + loss.backward() + optimizer.step() + + tqdmDataLoader.set_postfix(ordered_dict={ + "epoch": e, + "loss: ": loss.item() + }) + if iteration % 1000 == 0: + with open(os.path.join(modelConfig["Generator_save_dir"], 'loss' + ".txt"), 'a') as f: + f.write(f'epoch {e}: iter {iteration}: loss {loss.item()}\n') + iteration += 1 + + torch.cuda.empty_cache() + if (e+1)%1==0: + torch.save(generator.state_dict(), os.path.join(modelConfig["Generator_save_dir"], 'ckpt_' + str(e) + "_" + modelConfig["Source_Model"] +"_.pt")) + time_end = time.time() + print('time cost'+': ',time_end-time_start,'s') + + diff --git a/attacks/Gaker/imagenet_classes.txt b/attacks/Gaker/imagenet_classes.txt new file mode 100644 index 0000000000000000000000000000000000000000..722c984560c36a6014113deb8106a6cf50050ad5 --- /dev/null +++ b/attacks/Gaker/imagenet_classes.txt @@ -0,0 +1,1000 @@ +tench, Tinca tinca +goldfish, Carassius auratus +great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias +tiger shark, Galeocerdo cuvieri +hammerhead, hammerhead shark +electric ray, crampfish, numbfish, torpedo +stingray +cock +hen +ostrich, Struthio camelus +brambling, Fringilla montifringilla +goldfinch, Carduelis carduelis +house finch, linnet, Carpodacus mexicanus +junco, snowbird +indigo bunting, indigo finch, indigo bird, Passerina cyanea +robin, American robin, Turdus migratorius +bulbul +jay +magpie +chickadee +water ouzel, dipper +kite +bald eagle, American eagle, Haliaeetus leucocephalus +vulture +great grey owl, great gray owl, Strix nebulosa +European fire salamander, Salamandra salamandra +common newt, Triturus vulgaris +eft +spotted salamander, Ambystoma maculatum +axolotl, mud puppy, Ambystoma mexicanum +bullfrog, Rana catesbeiana +tree frog, tree-frog +tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui +loggerhead, loggerhead turtle, Caretta caretta +leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea +mud turtle +terrapin +box turtle, box tortoise +banded gecko +common iguana, iguana, Iguana iguana +American chameleon, anole, Anolis carolinensis +whiptail, whiptail lizard +agama +frilled lizard, Chlamydosaurus kingi +alligator lizard +Gila monster, Heloderma suspectum +green lizard, Lacerta viridis +African chameleon, Chamaeleo chamaeleon +Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis +African crocodile, Nile crocodile, Crocodylus niloticus +American alligator, Alligator mississipiensis +triceratops +thunder snake, worm snake, Carphophis amoenus +ringneck snake, ring-necked snake, ring snake +hognose snake, puff adder, sand viper +green snake, grass snake +king snake, kingsnake +garter snake, grass snake +water snake +vine snake +night snake, Hypsiglena torquata +boa constrictor, Constrictor constrictor +rock python, rock snake, Python sebae +Indian cobra, Naja naja +green mamba +sea snake +horned viper, cerastes, sand viper, horned asp, Cerastes cornutus +diamondback, diamondback rattlesnake, Crotalus adamanteus +sidewinder, horned rattlesnake, Crotalus cerastes +trilobite +harvestman, daddy longlegs, Phalangium opilio +scorpion +black and gold garden spider, Argiope aurantia +barn spider, Araneus cavaticus +garden spider, Aranea diademata +black widow, Latrodectus mactans +tarantula +wolf spider, hunting spider +tick +centipede +black grouse +ptarmigan +ruffed grouse, partridge, Bonasa umbellus +prairie chicken, prairie grouse, prairie fowl +peacock +quail +partridge +African grey, African gray, Psittacus erithacus +macaw +sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita +lorikeet +coucal +bee eater +hornbill +hummingbird +jacamar +toucan +drake +red-breasted merganser, Mergus serrator +goose +black swan, Cygnus atratus +tusker +echidna, spiny anteater, anteater +platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus +wallaby, brush kangaroo +koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus +wombat +jellyfish +sea anemone, anemone +brain coral +flatworm, platyhelminth +nematode, nematode worm, roundworm +conch +snail +slug +sea slug, nudibranch +chiton, coat-of-mail shell, sea cradle, polyplacophore +chambered nautilus, pearly nautilus, nautilus +Dungeness crab, Cancer magister +rock crab, Cancer irroratus +fiddler crab +king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica +American lobster, Northern lobster, Maine lobster, Homarus americanus +spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish +crayfish, crawfish, crawdad, crawdaddy +hermit crab +isopod +white stork, Ciconia ciconia +black stork, Ciconia nigra +spoonbill +flamingo +little blue heron, Egretta caerulea +American egret, great white heron, Egretta albus +bittern +crane +limpkin, Aramus pictus +European gallinule, Porphyrio porphyrio +American coot, marsh hen, mud hen, water hen, Fulica americana +bustard +ruddy turnstone, Arenaria interpres +red-backed sandpiper, dunlin, Erolia alpina +redshank, Tringa totanus +dowitcher +oystercatcher, oyster catcher +pelican +king penguin, Aptenodytes patagonica +albatross, mollymawk +grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus +killer whale, killer, orca, grampus, sea wolf, Orcinus orca +dugong, Dugong dugon +sea lion +Chihuahua +Japanese spaniel +Maltese dog, Maltese terrier, Maltese +Pekinese, Pekingese, Peke +Shih-Tzu +Blenheim spaniel +papillon +toy terrier +Rhodesian ridgeback +Afghan hound, Afghan +basset, basset hound +beagle +bloodhound, sleuthhound +bluetick +black-and-tan coonhound +Walker hound, Walker foxhound +English foxhound +redbone +borzoi, Russian wolfhound +Irish wolfhound +Italian greyhound +whippet +Ibizan hound, Ibizan Podenco +Norwegian elkhound, elkhound +otterhound, otter hound +Saluki, gazelle hound +Scottish deerhound, deerhound +Weimaraner +Staffordshire bullterrier, Staffordshire bull terrier +American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier +Bedlington terrier +Border terrier +Kerry blue terrier +Irish terrier +Norfolk terrier +Norwich terrier +Yorkshire terrier +wire-haired fox terrier +Lakeland terrier +Sealyham terrier, Sealyham +Airedale, Airedale terrier +cairn, cairn terrier +Australian terrier +Dandie Dinmont, Dandie Dinmont terrier +Boston bull, Boston terrier +miniature schnauzer +giant schnauzer +standard schnauzer +Scotch terrier, Scottish terrier, Scottie +Tibetan terrier, chrysanthemum dog +silky terrier, Sydney silky +soft-coated wheaten terrier +West Highland white terrier +Lhasa, Lhasa apso +flat-coated retriever +curly-coated retriever +golden retriever +Labrador retriever +Chesapeake Bay retriever +German short-haired pointer +vizsla, Hungarian pointer +English setter +Irish setter, red setter +Gordon setter +Brittany spaniel +clumber, clumber spaniel +English springer, English springer spaniel +Welsh springer spaniel +cocker spaniel, English cocker spaniel, cocker +Sussex spaniel +Irish water spaniel +kuvasz +schipperke +groenendael +malinois +briard +kelpie +komondor +Old English sheepdog, bobtail +Shetland sheepdog, Shetland sheep dog, Shetland +collie +Border collie +Bouvier des Flandres, Bouviers des Flandres +Rottweiler +German shepherd, German shepherd dog, German police dog, alsatian +Doberman, Doberman pinscher +miniature pinscher +Greater Swiss Mountain dog +Bernese mountain dog +Appenzeller +EntleBucher +boxer +bull mastiff +Tibetan mastiff +French bulldog +Great Dane +Saint Bernard, St Bernard +Eskimo dog, husky +malamute, malemute, Alaskan malamute +Siberian husky +dalmatian, coach dog, carriage dog +affenpinscher, monkey pinscher, monkey dog +basenji +pug, pug-dog +Leonberg +Newfoundland, Newfoundland dog +Great Pyrenees +Samoyed, Samoyede +Pomeranian +chow, chow chow +keeshond +Brabancon griffon +Pembroke, Pembroke Welsh corgi +Cardigan, Cardigan Welsh corgi +toy poodle +miniature poodle +standard poodle +Mexican hairless +timber wolf, grey wolf, gray wolf, Canis lupus +white wolf, Arctic wolf, Canis lupus tundrarum +red wolf, maned wolf, Canis rufus, Canis niger +coyote, prairie wolf, brush wolf, Canis latrans +dingo, warrigal, warragal, Canis dingo +dhole, Cuon alpinus +African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus +hyena, hyaena +red fox, Vulpes vulpes +kit fox, Vulpes macrotis +Arctic fox, white fox, Alopex lagopus +grey fox, gray fox, Urocyon cinereoargenteus +tabby, tabby cat +tiger cat +Persian cat +Siamese cat, Siamese +Egyptian cat +cougar, puma, catamount, mountain lion, painter, panther, Felis concolor +lynx, catamount +leopard, Panthera pardus +snow leopard, ounce, Panthera uncia +jaguar, panther, Panthera onca, Felis onca +lion, king of beasts, Panthera leo +tiger, Panthera tigris +cheetah, chetah, Acinonyx jubatus +brown bear, bruin, Ursus arctos +American black bear, black bear, Ursus americanus, Euarctos americanus +ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus +sloth bear, Melursus ursinus, Ursus ursinus +mongoose +meerkat, mierkat +tiger beetle +ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle +ground beetle, carabid beetle +long-horned beetle, longicorn, longicorn beetle +leaf beetle, chrysomelid +dung beetle +rhinoceros beetle +weevil +fly +bee +ant, emmet, pismire +grasshopper, hopper +cricket +walking stick, walkingstick, stick insect +cockroach, roach +mantis, mantid +cicada, cicala +leafhopper +lacewing, lacewing fly +dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk +damselfly +admiral +ringlet, ringlet butterfly +monarch, monarch butterfly, milkweed butterfly, Danaus plexippus +cabbage butterfly +sulphur butterfly, sulfur butterfly +lycaenid, lycaenid butterfly +starfish, sea star +sea urchin +sea cucumber, holothurian +wood rabbit, cottontail, cottontail rabbit +hare +Angora, Angora rabbit +hamster +porcupine, hedgehog +fox squirrel, eastern fox squirrel, Sciurus niger +marmot +beaver +guinea pig, Cavia cobaya +sorrel +zebra +hog, pig, grunter, squealer, Sus scrofa +wild boar, boar, Sus scrofa +warthog +hippopotamus, hippo, river horse, Hippopotamus amphibius +ox +water buffalo, water ox, Asiatic buffalo, Bubalus bubalis +bison +ram, tup +bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis +ibex, Capra ibex +hartebeest +impala, Aepyceros melampus +gazelle +Arabian camel, dromedary, Camelus dromedarius +llama +weasel +mink +polecat, fitch, foulmart, foumart, Mustela putorius +black-footed ferret, ferret, Mustela nigripes +otter +skunk, polecat, wood pussy +badger +armadillo +three-toed sloth, ai, Bradypus tridactylus +orangutan, orang, orangutang, Pongo pygmaeus +gorilla, Gorilla gorilla +chimpanzee, chimp, Pan troglodytes +gibbon, Hylobates lar +siamang, Hylobates syndactylus, Symphalangus syndactylus +guenon, guenon monkey +patas, hussar monkey, Erythrocebus patas +baboon +macaque +langur +colobus, colobus monkey +proboscis monkey, Nasalis larvatus +marmoset +capuchin, ringtail, Cebus capucinus +howler monkey, howler +titi, titi monkey +spider monkey, Ateles geoffroyi +squirrel monkey, Saimiri sciureus +Madagascar cat, ring-tailed lemur, Lemur catta +indri, indris, Indri indri, Indri brevicaudatus +Indian elephant, Elephas maximus +African elephant, Loxodonta africana +lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens +giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca +barracouta, snoek +eel +coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch +rock beauty, Holocanthus tricolor +anemone fish +sturgeon +gar, garfish, garpike, billfish, Lepisosteus osseus +lionfish +puffer, pufferfish, blowfish, globefish +abacus +abaya +academic gown, academic robe, judge's robe +accordion, piano accordion, squeeze box +acoustic guitar +aircraft carrier, carrier, flattop, attack aircraft carrier +airliner +airship, dirigible +altar +ambulance +amphibian, amphibious vehicle +analog clock +apiary, bee house +apron +ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin +assault rifle, assault gun +backpack, back pack, knapsack, packsack, rucksack, haversack +bakery, bakeshop, bakehouse +balance beam, beam +balloon +ballpoint, ballpoint pen, ballpen, Biro +Band Aid +banjo +bannister, banister, balustrade, balusters, handrail +barbell +barber chair +barbershop +barn +barometer +barrel, cask +barrow, garden cart, lawn cart, wheelbarrow +baseball +basketball +bassinet +bassoon +bathing cap, swimming cap +bath towel +bathtub, bathing tub, bath, tub +beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon +beacon, lighthouse, beacon light, pharos +beaker +bearskin, busby, shako +beer bottle +beer glass +bell cote, bell cot +bib +bicycle-built-for-two, tandem bicycle, tandem +bikini, two-piece +binder, ring-binder +binoculars, field glasses, opera glasses +birdhouse +boathouse +bobsled, bobsleigh, bob +bolo tie, bolo, bola tie, bola +bonnet, poke bonnet +bookcase +bookshop, bookstore, bookstall +bottlecap +bow +bow tie, bow-tie, bowtie +brass, memorial tablet, plaque +brassiere, bra, bandeau +breakwater, groin, groyne, mole, bulwark, seawall, jetty +breastplate, aegis, egis +broom +bucket, pail +buckle +bulletproof vest +bullet train, bullet +butcher shop, meat market +cab, hack, taxi, taxicab +caldron, cauldron +candle, taper, wax light +cannon +canoe +can opener, tin opener +cardigan +car mirror +carousel, carrousel, merry-go-round, roundabout, whirligig +carpenter's kit, tool kit +carton +car wheel +cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM +cassette +cassette player +castle +catamaran +CD player +cello, violoncello +cellular telephone, cellular phone, cellphone, cell, mobile phone +chain +chainlink fence +chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour +chain saw, chainsaw +chest +chiffonier, commode +chime, bell, gong +china cabinet, china closet +Christmas stocking +church, church building +cinema, movie theater, movie theatre, movie house, picture palace +cleaver, meat cleaver, chopper +cliff dwelling +cloak +clog, geta, patten, sabot +cocktail shaker +coffee mug +coffeepot +coil, spiral, volute, whorl, helix +combination lock +computer keyboard, keypad +confectionery, confectionary, candy store +container ship, containership, container vessel +convertible +corkscrew, bottle screw +cornet, horn, trumpet, trump +cowboy boot +cowboy hat, ten-gallon hat +cradle +crane +crash helmet +crate +crib, cot +Crock Pot +croquet ball +crutch +cuirass +dam, dike, dyke +desk +desktop computer +dial telephone, dial phone +diaper, nappy, napkin +digital clock +digital watch +dining table, board +dishrag, dishcloth +dishwasher, dish washer, dishwashing machine +disk brake, disc brake +dock, dockage, docking facility +dogsled, dog sled, dog sleigh +dome +doormat, welcome mat +drilling platform, offshore rig +drum, membranophone, tympan +drumstick +dumbbell +Dutch oven +electric fan, blower +electric guitar +electric locomotive +entertainment center +envelope +espresso maker +face powder +feather boa, boa +file, file cabinet, filing cabinet +fireboat +fire engine, fire truck +fire screen, fireguard +flagpole, flagstaff +flute, transverse flute +folding chair +football helmet +forklift +fountain +fountain pen +four-poster +freight car +French horn, horn +frying pan, frypan, skillet +fur coat +garbage truck, dustcart +gasmask, respirator, gas helmet +gas pump, gasoline pump, petrol pump, island dispenser +goblet +go-kart +golf ball +golfcart, golf cart +gondola +gong, tam-tam +gown +grand piano, grand +greenhouse, nursery, glasshouse +grille, radiator grille +grocery store, grocery, food market, market +guillotine +hair slide +hair spray +half track +hammer +hamper +hand blower, blow dryer, blow drier, hair dryer, hair drier +hand-held computer, hand-held microcomputer +handkerchief, hankie, hanky, hankey +hard disc, hard disk, fixed disk +harmonica, mouth organ, harp, mouth harp +harp +harvester, reaper +hatchet +holster +home theater, home theatre +honeycomb +hook, claw +hoopskirt, crinoline +horizontal bar, high bar +horse cart, horse-cart +hourglass +iPod +iron, smoothing iron +jack-o'-lantern +jean, blue jean, denim +jeep, landrover +jersey, T-shirt, tee shirt +jigsaw puzzle +jinrikisha, ricksha, rickshaw +joystick +kimono +knee pad +knot +lab coat, laboratory coat +ladle +lampshade, lamp shade +laptop, laptop computer +lawn mower, mower +lens cap, lens cover +letter opener, paper knife, paperknife +library +lifeboat +lighter, light, igniter, ignitor +limousine, limo +liner, ocean liner +lipstick, lip rouge +Loafer +lotion +loudspeaker, speaker, speaker unit, loudspeaker system, speaker system +loupe, jeweler's loupe +lumbermill, sawmill +magnetic compass +mailbag, postbag +mailbox, letter box +maillot +maillot, tank suit +manhole cover +maraca +marimba, xylophone +mask +matchstick +maypole +maze, labyrinth +measuring cup +medicine chest, medicine cabinet +megalith, megalithic structure +microphone, mike +microwave, microwave oven +military uniform +milk can +minibus +miniskirt, mini +minivan +missile +mitten +mixing bowl +mobile home, manufactured home +Model T +modem +monastery +monitor +moped +mortar +mortarboard +mosque +mosquito net +motor scooter, scooter +mountain bike, all-terrain bike, off-roader +mountain tent +mouse, computer mouse +mousetrap +moving van +muzzle +nail +neck brace +necklace +nipple +notebook, notebook computer +obelisk +oboe, hautboy, hautbois +ocarina, sweet potato +odometer, hodometer, mileometer, milometer +oil filter +organ, pipe organ +oscilloscope, scope, cathode-ray oscilloscope, CRO +overskirt +oxcart +oxygen mask +packet +paddle, boat paddle +paddlewheel, paddle wheel +padlock +paintbrush +pajama, pyjama, pj's, jammies +palace +panpipe, pandean pipe, syrinx +paper towel +parachute, chute +parallel bars, bars +park bench +parking meter +passenger car, coach, carriage +patio, terrace +pay-phone, pay-station +pedestal, plinth, footstall +pencil box, pencil case +pencil sharpener +perfume, essence +Petri dish +photocopier +pick, plectrum, plectron +pickelhaube +picket fence, paling +pickup, pickup truck +pier +piggy bank, penny bank +pill bottle +pillow +ping-pong ball +pinwheel +pirate, pirate ship +pitcher, ewer +plane, carpenter's plane, woodworking plane +planetarium +plastic bag +plate rack +plow, plough +plunger, plumber's helper +Polaroid camera, Polaroid Land camera +pole +police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria +poncho +pool table, billiard table, snooker table +pop bottle, soda bottle +pot, flowerpot +potter's wheel +power drill +prayer rug, prayer mat +printer +prison, prison house +projectile, missile +projector +puck, hockey puck +punching bag, punch bag, punching ball, punchball +purse +quill, quill pen +quilt, comforter, comfort, puff +racer, race car, racing car +racket, racquet +radiator +radio, wireless +radio telescope, radio reflector +rain barrel +recreational vehicle, RV, R.V. +reel +reflex camera +refrigerator, icebox +remote control, remote +restaurant, eating house, eating place, eatery +revolver, six-gun, six-shooter +rifle +rocking chair, rocker +rotisserie +rubber eraser, rubber, pencil eraser +rugby ball +rule, ruler +running shoe +safe +safety pin +saltshaker, salt shaker +sandal +sarong +sax, saxophone +scabbard +scale, weighing machine +school bus +schooner +scoreboard +screen, CRT screen +screw +screwdriver +seat belt, seatbelt +sewing machine +shield, buckler +shoe shop, shoe-shop, shoe store +shoji +shopping basket +shopping cart +shovel +shower cap +shower curtain +ski +ski mask +sleeping bag +slide rule, slipstick +sliding door +slot, one-armed bandit +snorkel +snowmobile +snowplow, snowplough +soap dispenser +soccer ball +sock +solar dish, solar collector, solar furnace +sombrero +soup bowl +space bar +space heater +space shuttle +spatula +speedboat +spider web, spider's web +spindle +sports car, sport car +spotlight, spot +stage +steam locomotive +steel arch bridge +steel drum +stethoscope +stole +stone wall +stopwatch, stop watch +stove +strainer +streetcar, tram, tramcar, trolley, trolley car +stretcher +studio couch, day bed +stupa, tope +submarine, pigboat, sub, U-boat +suit, suit of clothes +sundial +sunglass +sunglasses, dark glasses, shades +sunscreen, sunblock, sun blocker +suspension bridge +swab, swob, mop +sweatshirt +swimming trunks, bathing trunks +swing +switch, electric switch, electrical switch +syringe +table lamp +tank, army tank, armored combat vehicle, armoured combat vehicle +tape player +teapot +teddy, teddy bear +television, television system +tennis ball +thatch, thatched roof +theater curtain, theatre curtain +thimble +thresher, thrasher, threshing machine +throne +tile roof +toaster +tobacco shop, tobacconist shop, tobacconist +toilet seat +torch +totem pole +tow truck, tow car, wrecker +toyshop +tractor +trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi +tray +trench coat +tricycle, trike, velocipede +trimaran +tripod +triumphal arch +trolleybus, trolley coach, trackless trolley +trombone +tub, vat +turnstile +typewriter keyboard +umbrella +unicycle, monocycle +upright, upright piano +vacuum, vacuum cleaner +vase +vault +velvet +vending machine +vestment +viaduct +violin, fiddle +volleyball +waffle iron +wall clock +wallet, billfold, notecase, pocketbook +wardrobe, closet, press +warplane, military plane +washbasin, handbasin, washbowl, lavabo, wash-hand basin +washer, automatic washer, washing machine +water bottle +water jug +water tower +whiskey jug +whistle +wig +window screen +window shade +Windsor tie +wine bottle +wing +wok +wooden spoon +wool, woolen, woollen +worm fence, snake fence, snake-rail fence, Virginia fence +wreck +yawl +yurt +web site, website, internet site, site +comic book +crossword puzzle, crossword +street sign +traffic light, traffic signal, stoplight +book jacket, dust cover, dust jacket, dust wrapper +menu +plate +guacamole +consomme +hot pot, hotpot +trifle +ice cream, icecream +ice lolly, lolly, lollipop, popsicle +French loaf +bagel, beigel +pretzel +cheeseburger +hotdog, hot dog, red hot +mashed potato +head cabbage +broccoli +cauliflower +zucchini, courgette +spaghetti squash +acorn squash +butternut squash +cucumber, cuke +artichoke, globe artichoke +bell pepper +cardoon +mushroom +Granny Smith +strawberry +orange +lemon +fig +pineapple, ananas +banana +jackfruit, jak, jack +custard apple +pomegranate +hay +carbonara +chocolate sauce, chocolate syrup +dough +meat loaf, meatloaf +pizza, pizza pie +potpie +burrito +red wine +espresso +cup +eggnog +alp +bubble +cliff, drop, drop-off +coral reef +geyser +lakeside, lakeshore +promontory, headland, head, foreland +sandbar, sand bar +seashore, coast, seacoast, sea-coast +valley, vale +volcano +ballplayer, baseball player +groom, bridegroom +scuba diver +rapeseed +daisy +yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum +corn +acorn +hip, rose hip, rosehip +buckeye, horse chestnut, conker +coral fungus +agaric +gyromitra +stinkhorn, carrion fungus +earthstar +hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa +bolete +ear, spike, capitulum +toilet tissue, toilet paper, bathroom tissue \ No newline at end of file diff --git a/attacks/Gaker/imagenet_numtotarget.txt b/attacks/Gaker/imagenet_numtotarget.txt new file mode 100644 index 0000000000000000000000000000000000000000..95c3c313cecec9133297529a15db3dd05f5e675b --- /dev/null +++ b/attacks/Gaker/imagenet_numtotarget.txt @@ -0,0 +1,1000 @@ +0 n01440764 鱼, tench, Tinca tinca +1 n01443537 鱼, goldfish, Carassius auratus +2 n01484850 鱼, great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias +3 n01491361 鱼, tiger shark, Galeocerdo cuvieri +4 n01494475 鱼, hammerhead, hammerhead shark +5 n01496331 鱼, electric ray, crampfish, numbfish, torpedo +6 n01498041 鱼, stingray +7 n01514668 鸡, cock +8 n01514859 鸡, hen +9 n01518878 鸵鸟, ostrich, Struthio camelus +10 n01530575 鸟, brambling, Fringilla montifringilla +11 n01531178 鸟, goldfinch, Carduelis carduelis +12 n01532829 鸟, house finch, linnet, Carpodacus mexicanus +13 n01534433 鸟, junco, snowbird +14 n01537544 鸟, indigo bunting, indigo finch, indigo bird, Passerina cyanea +15 n01558993 鸟, robin, American robin, Turdus migratorius +16 n01560419 鸟, bulbul +17 n01580077 鸟, jay +18 n01582220 鸟, magpie +19 n01592084 鸟, chickadee +20 n01601694 鸟, water ouzel, dipper +21 n01608432 鸟, kite +22 n01614925 鹰, bald eagle, American eagle, Haliaeetus leucocephalus +23 n01616318 鹰, vulture +24 n01622779 猫头鹰, great grey owl, great gray owl, Strix nebulosa +25 n01629819 壁虎, European fire salamander, Salamandra salamandra +26 n01630670 壁虎, common newt, Triturus vulgaris +27 n01631663 壁虎, eft +28 n01632458 壁虎, spotted salamander, Ambystoma maculatum +29 n01632777 壁虎, axolotl, mud puppy, Ambystoma mexicanum +30 n01641577 蛤蟆, bullfrog, Rana catesbeiana +31 n01644373 青蛙, tree frog, tree-frog +32 n01644900 青蛙, tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui +33 n01664065 龟, loggerhead, loggerhead turtle, Caretta caretta +34 n01665541 龟, leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea +35 n01667114 龟, mud turtle +36 n01667778 龟, terrapin +37 n01669191 龟, box turtle, box tortoise +38 n01675722 蜥蜴, banded gecko +39 n01677366 蜥蜴, common iguana, iguana, Iguana iguana +40 n01682714 蜥蜴, American chameleon, anole, Anolis carolinensis +41 n01685808 蜥蜴, whiptail, whiptail lizard +42 n01687978 蜥蜴, agama +43 n01688243 蜥蜴, frilled lizard, Chlamydosaurus kingi +44 n01689811 蜥蜴, alligator lizard +45 n01692333 蜥蜴, Gila monster, Heloderma suspectum +46 n01693334 蜥蜴, green lizard, Lacerta viridis +47 n01694178 蜥蜴, African chameleon, Chamaeleo chamaeleon +48 n01695060 蜥蜴, Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis +49 n01697457 鳄鱼, African crocodile, Nile crocodile, Crocodylus niloticus +50 n01698640 鳄鱼, American alligator, Alligator mississipiensis +51 n01704323 恐龙, triceratops +52 n01728572 蛇, thunder snake, worm snake, Carphophis amoenus +53 n01728920 蛇, ringneck snake, ring-necked snake, ring snake +54 n01729322 蛇, hognose snake, puff adder, sand viper +55 n01729977 蛇, green snake, grass snake +56 n01734418 蛇, king snake, kingsnake +57 n01735189 蛇, garter snake, grass snake +58 n01737021 蛇, water snake +59 n01739381 蛇, vine snake +60 n01740131 蛇, night snake, Hypsiglena torquata +61 n01742172 蛇, boa constrictor, Constrictor constrictor +62 n01744401 蛇, rock python, rock snake, Python sebae +63 n01748264 蛇, Indian cobra, Naja naja +64 n01749939 蛇, green mamba +65 n01751748 蛇, sea snake +66 n01753488 蛇, horned viper, cerastes, sand viper, horned asp, Cerastes cornutus +67 n01755581 蛇, diamondback, diamondback rattlesnake, Crotalus adamanteus +68 n01756291 蛇, sidewinder, horned rattlesnake, Crotalus cerastes +69 n01768244 化石, trilobite +70 n01770081 蜘蛛, harvestman, daddy longlegs, Phalangium opilio +71 n01770393 蝎子, scorpion +72 n01773157 蜘蛛, black and gold garden spider, Argiope aurantia +73 n01773549 蜘蛛, barn spider, Araneus cavaticus +74 n01773797 蜘蛛, garden spider, Aranea diademata +75 n01774384 蜘蛛, black widow, Latrodectus mactans +76 n01774750 蜘蛛, tarantula +77 n01775062 蜘蛛, wolf spider, hunting spider +78 n01776313 蜘蛛, tick +79 n01784675 蜈蚣, centipede +80 n01795545 鸟, black grouse +81 n01796340 鸟, ptarmigan +82 n01797886 鸟, ruffed grouse, partridge, Bonasa umbellus +83 n01798484 鸟, prairie chicken, prairie grouse, prairie fowl +84 n01806143 孔雀, peacock +85 n01806567 鸟, quail +86 n01807496 鸟, partridge +87 n01817953 鸟, African grey, African gray, Psittacus erithacus +88 n01818515 鸟, macaw +89 n01819313 鸟, sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita +90 n01820546 鸟, lorikeet +91 n01824575 鸟, coucal +92 n01828970 鸟, bee eater +93 n01829413 鸟, hornbill +94 n01833805 鸟, hummingbird +95 n01843065 鸟, jacamar +96 n01843383 鸟, toucan +97 n01847000 鸭子, drake +98 n01855032 鹅, red-breasted merganser, Mergus serrator +99 n01855672 鹅, goose +100 n01860187 鹅, black swan, Cygnus atratus +101 n01871265 大象, tusker +102 n01872401 刺猬, echidna, spiny anteater, anteater +103 n01873310 鸭嘴兽, platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus +104 n01877812 袋鼠, wallaby, brush kangaroo +105 n01882714 考拉, koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus +106 n01883070 土拨鼠, wombat +107 n01910747 水母, jellyfish +108 n01914609 珊瑚, sea anemone, anemone +109 n01917289 珊瑚, brain coral +110 n01924916 海洋生物, flatworm, platyhelminth +111 n01930112 海蛇, nematode, nematode worm, roundworm +112 n01943899 海螺, conch +113 n01944390 蜗牛, snail +114 n01945685 蜗牛, slug +115 n01950731 海洋生物, sea slug, nudibranch +116 n01955084 海洋生物, chiton, coat-of-mail shell, sea cradle, polyplacophore +117 n01968897 海螺, chambered nautilus, pearly nautilus, nautilus +118 n01978287 螃蟹, Dungeness crab, Cancer magister +119 n01978455 螃蟹, rock crab, Cancer irroratus +120 n01980166 螃蟹, fiddler crab +121 n01981276 螃蟹, king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica +122 n01983481 龙虾, American lobster, Northern lobster, Maine lobster, Homarus americanus +123 n01984695 龙虾, spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish +124 n01985128 龙虾, crayfish, crawfish, crawdad, crawdaddy +125 n01986214 寄居蟹, hermit crab +126 n01990800 海洋生物, isopod +127 n02002556 鸟, white stork, Ciconia ciconia +128 n02002724 鸟, black stork, Ciconia nigra +129 n02006656 鸟, spoonbill +130 n02007558 鸟, flamingo +131 n02009229 鸟, little blue heron, Egretta caerulea +132 n02009912 鸟, American egret, great white heron, Egretta albus +133 n02011460 鸟, bittern +134 n02012849 鸟, crane +135 n02013706 鸟, limpkin, Aramus pictus +136 n02017213 鸟, European gallinule, Porphyrio porphyrio +137 n02018207 鸟, American coot, marsh hen, mud hen, water hen, Fulica americana +138 n02018795 鸟, bustard +139 n02025239 鸟, ruddy turnstone, Arenaria interpres +140 n02027492 鸟, red-backed sandpiper, dunlin, Erolia alpina +141 n02028035 鸟, redshank, Tringa totanus +142 n02033041 鸟, dowitcher +143 n02037110 鸟, oystercatcher, oyster catcher +144 n02051845 鸟, pelican +145 n02056570 企鹅, king penguin, Aptenodytes patagonica +146 n02058221 鸟, albatross, mollymawk +147 n02066245 鲸鱼, grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus +148 n02071294 鲸鱼, killer whale, killer, orca, grampus, sea wolf, Orcinus orca +149 n02074367 海象, dugong, Dugong dugon +150 n02077923 海狮, sea lion +151 n02085620 狗, Chihuahua +152 n02085782 狗, Japanese spaniel +153 n02085936 狗, Maltese dog, Maltese terrier, Maltese +154 n02086079 狗, Pekinese, Pekingese, Peke +155 n02086240 狗, Shih-Tzu +156 n02086646 狗, Blenheim spaniel +157 n02086910 狗, papillon +158 n02087046 狗, toy terrier +159 n02087394 狗, Rhodesian ridgeback +160 n02088094 狗, Afghan hound, Afghan +161 n02088238 狗, basset, basset hound +162 n02088364 狗, beagle +163 n02088466 狗, bloodhound, sleuthhound +164 n02088632 狗, bluetick +165 n02089078 狗, black-and-tan coonhound +166 n02089867 狗, Walker hound, Walker foxhound +167 n02089973 狗, English foxhound +168 n02090379 狗, redbone +169 n02090622 狗, borzoi, Russian wolfhound +170 n02090721 狗, Irish wolfhound +171 n02091032 狗, Italian greyhound +172 n02091134 狗, whippet +173 n02091244 狗, Ibizan hound, Ibizan Podenco +174 n02091467 狗, Norwegian elkhound, elkhound +175 n02091635 狗, otterhound, otter hound +176 n02091831 狗, Saluki, gazelle hound +177 n02092002 狗, Scottish deerhound, deerhound +178 n02092339 狗, Weimaraner +179 n02093256 狗, Staffordshire bullterrier, Staffordshire bull terrier +180 n02093428 狗, American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier +181 n02093647 狗, Bedlington terrier +182 n02093754 狗, Border terrier +183 n02093859 狗, Kerry blue terrier +184 n02093991 狗, Irish terrier +185 n02094114 狗, Norfolk terrier +186 n02094258 狗, Norwich terrier +187 n02094433 狗, Yorkshire terrier +188 n02095314 狗, wire-haired fox terrier +189 n02095570 狗, Lakeland terrier +190 n02095889 狗, Sealyham terrier, Sealyham +191 n02096051 狗, Airedale, Airedale terrier +192 n02096177 狗, cairn, cairn terrier +193 n02096294 狗, Australian terrier +194 n02096437 狗, Dandie Dinmont, Dandie Dinmont terrier +195 n02096585 狗, Boston bull, Boston terrier +196 n02097047 狗, miniature schnauzer +197 n02097130 狗, giant schnauzer +198 n02097209 狗, standard schnauzer +199 n02097298 狗, Scotch terrier, Scottish terrier, Scottie +200 n02097474 狗, Tibetan terrier, chrysanthemum dog +201 n02097658 狗, silky terrier, Sydney silky +202 n02098105 狗, soft-coated wheaten terrier +203 n02098286 狗, West Highland white terrier +204 n02098413 狗, Lhasa, Lhasa apso +205 n02099267 狗, flat-coated retriever +206 n02099429 狗, curly-coated retriever +207 n02099601 狗, golden retriever +208 n02099712 狗, Labrador retriever +209 n02099849 狗, Chesapeake Bay retriever +210 n02100236 狗, German short-haired pointer +211 n02100583 狗, vizsla, Hungarian pointer +212 n02100735 狗, English setter +213 n02100877 狗, Irish setter, red setter +214 n02101006 狗, Gordon setter +215 n02101388 狗, Brittany spaniel +216 n02101556 狗, clumber, clumber spaniel +217 n02102040 狗, English springer, English springer spaniel +218 n02102177 狗, Welsh springer spaniel +219 n02102318 狗, cocker spaniel, English cocker spaniel, cocker +220 n02102480 狗, Sussex spaniel +221 n02102973 狗, Irish water spaniel +222 n02104029 狗, kuvasz +223 n02104365 狗, schipperke +224 n02105056 狗, groenendael +225 n02105162 狗, malinois +226 n02105251 狗, briard +227 n02105412 狗, kelpie +228 n02105505 狗, komondor +229 n02105641 狗, Old English sheepdog, bobtail +230 n02105855 狗, Shetland sheepdog, Shetland sheep dog, Shetland +231 n02106030 狗, collie +232 n02106166 狗, Border collie +233 n02106382 狗, Bouvier des Flandres, Bouviers des Flandres +234 n02106550 狗, Rottweiler +235 n02106662 狗, German shepherd, German shepherd dog, German police dog, alsatian +236 n02107142 狗, Doberman, Doberman pinscher +237 n02107312 狗, miniature pinscher +238 n02107574 狗, Greater Swiss Mountain dog +239 n02107683 狗, Bernese mountain dog +240 n02107908 狗, Appenzeller +241 n02108000 狗, EntleBucher +242 n02108089 狗, boxer +243 n02108422 狗, bull mastiff +244 n02108551 狗, Tibetan mastiff +245 n02108915 狗, French bulldog +246 n02109047 狗, Great Dane +247 n02109525 狗, Saint Bernard, St Bernard +248 n02109961 狗, Eskimo dog, husky +249 n02110063 狗, malamute, malemute, Alaskan malamute +250 n02110185 狗, Siberian husky +251 n02110341 狗, dalmatian, coach dog, carriage dog +252 n02110627 狗, affenpinscher, monkey pinscher, monkey dog +253 n02110806 狗, basenji +254 n02110958 狗, pug, pug-dog +255 n02111129 狗, Leonberg +256 n02111277 狗, Newfoundland, Newfoundland dog +257 n02111500 狗, Great Pyrenees +258 n02111889 狗, Samoyed, Samoyede +259 n02112018 狗, Pomeranian +260 n02112137 狗, chow, chow chow +261 n02112350 狗, keeshond +262 n02112706 狗, Brabancon griffon +263 n02113023 狗, Pembroke, Pembroke Welsh corgi +264 n02113186 狗, Cardigan, Cardigan Welsh corgi +265 n02113624 狗, toy poodle +266 n02113712 狗, miniature poodle +267 n02113799 狗, standard poodle +268 n02113978 狗, Mexican hairless +269 n02114367 狼, timber wolf, grey wolf, gray wolf, Canis lupus +270 n02114548 狼, white wolf, Arctic wolf, Canis lupus tundrarum +271 n02114712 狼, red wolf, maned wolf, Canis rufus, Canis niger +272 n02114855 狼, coyote, prairie wolf, brush wolf, Canis latrans +273 n02115641 狼, dingo, warrigal, warragal, Canis dingo +274 n02115913 狼, dhole, Cuon alpinus +275 n02116738 狼, African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus +276 n02117135 狼, hyena, hyaena +277 n02119022 狐狸, red fox, Vulpes vulpes +278 n02119789 狐狸, kit fox, Vulpes macrotis +279 n02120079 狐狸, Arctic fox, white fox, Alopex lagopus +280 n02120505 狐狸, grey fox, gray fox, Urocyon cinereoargenteus +281 n02123045 猫, tabby, tabby cat +282 n02123159 猫, tiger cat +283 n02123394 猫, Persian cat +284 n02123597 猫, Siamese cat, Siamese +285 n02124075 猫, Egyptian cat +286 n02125311 猫, cougar, puma, catamount, mountain lion, painter, panther, Felis concolor +287 n02127052 猫, lynx, catamount +288 n02128385 豹, leopard, Panthera pardus +289 n02128757 豹, snow leopard, ounce, Panthera uncia +290 n02128925 豹, jaguar, panther, Panthera onca, Felis onca +291 n02129165 狮子, lion, king of beasts, Panthera leo +292 n02129604 老虎, tiger, Panthera tigris +293 n02130308 豹, cheetah, chetah, Acinonyx jubatus +294 n02132136 熊, brown bear, bruin, Ursus arctos +295 n02133161 熊, American black bear, black bear, Ursus americanus, Euarctos americanus +296 n02134084 熊, ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus +297 n02134418 熊, sloth bear, Melursus ursinus, Ursus ursinus +298 n02137549 猫鼬, mongoose +299 n02138441 猫鼬, meerkat, mierkat +300 n02165105 昆虫, tiger beetle +301 n02165456 昆虫, ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle +302 n02167151 昆虫, ground beetle, carabid beetle +303 n02168699 昆虫, long-horned beetle, longicorn, longicorn beetle +304 n02169497 昆虫, leaf beetle, chrysomelid +305 n02172182 昆虫, dung beetle +306 n02174001 昆虫, rhinoceros beetle +307 n02177972 昆虫, weevil +308 n02190166 昆虫, fly +309 n02206856 昆虫, bee +310 n02219486 昆虫, ant, emmet, pismire +311 n02226429 昆虫, grasshopper, hopper +312 n02229544 昆虫, cricket +313 n02231487 昆虫, walking stick, walkingstick, stick insect +314 n02233338 昆虫, cockroach, roach +315 n02236044 昆虫, mantis, mantid +316 n02256656 昆虫, cicada, cicala +317 n02259212 昆虫, leafhopper +318 n02264363 昆虫, lacewing, lacewing fly +319 n02268443 蜻蜓, dragonfly, darning needle, devil’s darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk +320 n02268853 蜻蜓, damselfly +321 n02276258 蝴蝶, admiral +322 n02277742 蝴蝶, ringlet, ringlet butterfly +323 n02279972 蝴蝶, monarch, monarch butterfly, milkweed butterfly, Danaus plexippus +324 n02280649 蝴蝶, cabbage butterfly +325 n02281406 蝴蝶, sulphur butterfly, sulfur butterfly +326 n02281787 蝴蝶, lycaenid, lycaenid butterfly +327 n02317335 海星, starfish, sea star +328 n02319095 海胆, sea urchin +329 n02321529 海洋生物, sea cucumber, holothurian +330 n02325366 兔子, wood rabbit, cottontail, cottontail rabbit +331 n02326432 兔子, hare +332 n02328150 兔子, Angora, Angora rabbit +333 n02342885 鼠, hamster +334 n02346627 鼠, porcupine, hedgehog +335 n02356798 松鼠, fox squirrel, eastern fox squirrel, Sciurus niger +336 n02361337 鼠, marmot +337 n02363005 鼠, beaver +338 n02364673 鼠, guinea pig, Cavia cobaya +339 n02389026 马, sorrel +340 n02391049 斑马, zebra +341 n02395406 猪, hog, pig, grunter, squealer, Sus scrofa +342 n02396427 猪, wild boar, boar, Sus scrofa +343 n02397096 猪, warthog +344 n02398521 河马, hippopotamus, hippo, river horse, Hippopotamus amphibius +345 n02403003 牛, ox +346 n02408429 牛, water buffalo, water ox, Asiatic buffalo, Bubalus bubalis +347 n02410509 牛, bison +348 n02412080 羊, ram, tup +349 n02415577 羊, bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis +350 n02417914 羊, ibex, Capra ibex +351 n02422106 羊, hartebeest +352 n02422699 羊, impala, Aepyceros melampus +353 n02423022 羊, gazelle +354 n02437312 骆驼, Arabian camel, dromedary, Camelus dromedarius +355 n02437616 羊驼, llama +356 n02441942 狸, weasel +357 n02442845 狸, mink +358 n02443114 狸, polecat, fitch, foulmart, foumart, Mustela putorius +359 n02443484 狸, black-footed ferret, ferret, Mustela nigripes +360 n02444819 狸, otter +361 n02445715 狸, skunk, polecat, wood pussy +362 n02447366 狸, badger +363 n02454379 穿山甲, armadillo +364 n02457408 树懒, three-toed sloth, ai, Bradypus tridactylus +365 n02480495 狒狒, orangutan, orang, orangutang, Pongo pygmaeus +366 n02480855 猩猩, gorilla, Gorilla gorilla +367 n02481823 猴子, chimpanzee, chimp, Pan troglodytes +368 n02483362 猴子, gibbon, Hylobates lar +369 n02483708 猴子, siamang, Hylobates syndactylus, Symphalangus syndactylus +370 n02484975 猴子, guenon, guenon monkey +371 n02486261 猴子, patas, hussar monkey, Erythrocebus patas +372 n02486410 猴子, baboon +373 n02487347 猴子, macaque +374 n02488291 猴子, langur +375 n02488702 猴子, colobus, colobus monkey +376 n02489166 猴子, proboscis monkey, Nasalis larvatus +377 n02490219 猴子, marmoset +378 n02492035 猴子, capuchin, ringtail, Cebus capucinus +379 n02492660 猴子, howler monkey, howler +380 n02493509 猴子, titi, titi monkey +381 n02493793 猴子, spider monkey, Ateles geoffroyi +382 n02494079 猴子, squirrel monkey, Saimiri sciureus +383 n02497673 猴子, Madagascar cat, ring-tailed lemur, Lemur catta +384 n02500267 猴子, indri, indris, Indri indri, Indri brevicaudatus +385 n02504013 大象, Indian elephant, Elephas maximus +386 n02504458 大象, African elephant, Loxodonta africana +387 n02509815 浣熊, lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens +388 n02510455 熊猫, giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca +389 n02514041 鱼, barracouta, snoek +390 n02526121 鱼, eel +391 n02536864 鱼, coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch +392 n02606052 鱼, rock beauty, Holocanthus tricolor +393 n02607072 鱼, anemone fish +394 n02640242 鱼, sturgeon +395 n02641379 鱼, gar, garfish, garpike, billfish, Lepisosteus osseus +396 n02643566 鱼, lionfish +397 n02655020 鱼, puffer, pufferfish, blowfish, globefish +398 n02666196 算盘, abacus +399 n02667093 穆斯林, abaya +400 n02669723 学士服, academic gown, academic robe, judge’s robe +401 n02672831 手风琴, accordion, piano accordion, squeeze box +402 n02676566 吉他, acoustic guitar +403 n02687172 航空母舰, aircraft carrier, carrier, flattop, attack aircraft carrier +404 n02690373 飞机, airliner +405 n02692877 飞艇, airship, dirigible +406 n02699494 教堂, altar +407 n02701002 救护车 check, ambulance +408 n02704792 水陆两用车 check, amphibian, amphibious vehicle +409 n02708093 钟, analog clock +410 n02727426 箱子, apiary, bee house +411 n02730930 围裙, apron +412 n02747177 垃圾箱, ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin +413 n02749479 枪, assault rifle, assault gun +414 n02769748 背包, backpack, back pack, knapsack, packsack, rucksack, haversack +415 n02776631 面包柜, bakery, bakeshop, bakehouse +416 n02777292 体操, balance beam, beam +417 n02782093 热气球, balloon +418 n02783161 钢笔, ballpoint, ballpoint pen, ballpen, Biro +419 n02786058 邦迪, Band Aid +420 n02787622 乐器, banjo +421 n02788148 楼梯, bannister, banister, balustrade, balusters, handrail +422 n02790996 杠铃, barbell +423 n02791124 座椅, barber chair +424 n02791270 理发, barbershop +425 n02793495 木屋, barn +426 n02794156 表, barometer +427 n02795169 酒桶, barrel, cask +428 n02797295 手推车, barrow, garden cart, lawn cart, wheelbarrow +429 n02799071 棒球, baseball +430 n02802426 篮球, basketball +431 n02804414 婴儿, bassinet +432 n02804610 乐器, bassoon +433 n02807133 游泳, bathing cap, swimming cap +434 n02808304 婴儿毛巾, bath towel +435 n02808440 浴缸, bathtub, bathing tub, bath, tub +436 n02814533 轿车 check, beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon +437 n02814860 灯塔, beacon, lighthouse, beacon light, pharos +438 n02815834 烧杯, beaker +439 n02817516 守卫, bearskin, busby, shako +440 n02823428 啤酒, beer bottle +441 n02823750 啤酒, beer glass +442 n02825657 建筑, bell cote, bell cot +443 n02834397 围兜, bib +444 n02835271 双人自行车, bicycle-built-for-two, tandem bicycle, tandem +445 n02837789 比基尼, bikini, two-piece +446 n02840245 笔记本, binder, ring-binder +447 n02841315 望远镜, binoculars, field glasses, opera glasses +448 n02843684 信箱, birdhouse +449 n02859443 小屋, boathouse +450 n02860847 雪橇, bobsled, bobsleigh, bob +451 n02865351 项链, bolo tie, bolo, bola tie, bola +452 n02869837 帽子, bonnet, poke bonnet +453 n02870880 书柜, bookcase +454 n02871525 书店, bookshop, bookstore, bookstall +455 n02877765 瓶盖, bottlecap +456 n02879718 弓箭, bow +457 n02883205 领结, bow tie, bow-tie, bowtie +458 n02892201 墓碑, brass, memorial tablet, plaque +459 n02892767 胸罩, brassiere, bra, bandeau +460 n02894605 海岸, breakwater, groin, groyne, mole, bulwark, seawall, jetty +461 n02895154 盔甲, breastplate, aegis, egis +462 n02906734 扫帚, broom +463 n02909870 水桶, bucket, pail +464 n02910353 皮带, buckle +465 n02916936 防弹背心, bulletproof vest +466 n02917067 火车, bullet train, bullet +467 n02927161 肉铺, butcher shop, meat market +468 n02930766 出租车 check, cab, hack, taxi, taxicab +469 n02939185 锅, caldron, cauldron +470 n02948072 蜡烛, candle, taper, wax light +471 n02950826 炮, cannon +472 n02951358 艇, canoe +473 n02951585 订书机, can opener, tin opener +474 n02963159 毛衣, cardigan +475 n02965783 反光镜, car mirror +476 n02966193 旋转木马, carousel, carrousel, merry-go-round, roundabout, whirligig +477 n02966687 工具箱, carpenter’s kit, tool kit +478 n02971356 盒子, carton +479 n02974003 轮胎, car wheel +480 n02977058 取款机, cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM +481 n02978881 磁带, cassette +482 n02979186 磁带, cassette player +483 n02980441 城堡, castle +484 n02981792 帆船, catamaran +485 n02988304 cd播放器, CD player +486 n02992211 大提琴, cello, violoncello +487 n02992529 手机, cellular telephone, cellular phone, cellphone, cell, mobile phone +488 n02999410 铁链, chain +489 n03000134 铁丝网, chainlink fence +490 n03000247 铁丝网, chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour +491 n03000684 电锯, chain saw, chainsaw +492 n03014705 木箱, chest +493 n03016953 木柜, chiffonier, commode +494 n03017168 锣鼓, chime, bell, gong +495 n03018349 柜子, china cabinet, china closet +496 n03026506 袜子, Christmas stocking +497 n03028079 教堂, church, church building +498 n03032252 剧院, cinema, movie theater, movie theatre, movie house, picture palace +499 n03041632 刀, cleaver, meat cleaver, chopper +500 n03042490 堡垒, cliff dwelling +501 n03045698 斗篷, cloak +502 n03047690 鞋子, clog, geta, patten, sabot +503 n03062245 瓶子, cocktail shaker +504 n03063599 杯子, coffee mug +505 n03063689 壶, coffeepot +506 n03065424 螺旋, coil, spiral, volute, whorl, helix +507 n03075370 锁, combination lock +508 n03085013 键盘, computer keyboard, keypad +509 n03089624 糖果, confectionery, confectionary, candy store +510 n03095699 船, container ship, containership, container vessel +511 n03100240 轿车 check, convertible +512 n03109150 开瓶器, corkscrew, bottle screw +513 n03110669 号（乐器）, cornet, horn, trumpet, trump +514 n03124043 靴子, cowboy boot +515 n03124170 帽子, cowboy hat, ten-gallon hat +516 n03125729 婴儿床, cradle +517 n03126707 起重机, crane +518 n03127747 头盔, crash helmet +519 n03127925 木箱, crate +520 n03131574 婴儿床, crib, cot +521 n03133878 电饭锅, Crock Pot +522 n03134739 推球, croquet ball +523 n03141823 拐杖, crutch +524 n03146219 盔甲, cuirass +525 n03160309 水库, dam, dike, dyke +526 n03179701 办公桌, desk +527 n03180011 电脑, desktop computer +528 n03187595 电话机, dial telephone, dial phone +529 n03188531 尿布, diaper, nappy, napkin +530 n03196217 闹钟, digital clock +531 n03197337 手表, digital watch +532 n03201208 餐桌, dining table, board +533 n03207743 垫子, dishrag, dishcloth +534 n03207941 洗碗柜, dishwasher, dish washer, dishwashing machine +535 n03208938 车轮, disk brake, disc brake +536 n03216828 港口, dock, dockage, docking facility +537 n03218198 雪橇, dogsled, dog sled, dog sleigh +538 n03220513 穹顶, dome +539 n03223299 地毯, doormat, welcome mat +540 n03240683 油田, drilling platform, offshore rig +541 n03249569 鼓, drum, membranophone, tympan +542 n03250847 鼓棒, drumstick +543 n03255030 哑铃, dumbbell +544 n03259280 锅, Dutch oven +545 n03271574 风扇, electric fan, blower +546 n03272010 电吉他, electric guitar +547 n03272562 火车 check, electric locomotive +548 n03290653 电视机, entertainment center +549 n03291819 信, envelope +550 n03297495 咖啡机, espresso maker +551 n03314780 化妆品, face powder +552 n03325584 绒毛, feather boa, boa +553 n03337140 柜子, file, file cabinet, filing cabinet +554 n03344393 喷泉, fireboat +555 n03345487 消防车 check, fire engine, fire truck +556 n03347037 壁炉, fire screen, fireguard +557 n03355925 旗杆, flagpole, flagstaff +558 n03372029 笛子, flute, transverse flute +559 n03376595 座椅, folding chair +560 n03379051 橄榄球, football helmet +561 n03384352 叉车, forklift +562 n03388043 喷泉, fountain +563 n03388183 钢笔, fountain pen +564 n03388549 床, four-poster +565 n03393912 火车, freight car +566 n03394916 圆号, French horn, horn +567 n03400231 平底锅, frying pan, frypan, skillet +568 n03404251 裘皮, fur coat +569 n03417042 卡车 check, garbage truck, dustcart +570 n03424325 面具, gasmask, respirator, gas helmet +571 n03425413 加油, gas pump, gasoline pump, petrol pump, island dispenser +572 n03443371 酒杯, goblet +573 n03444034 卡丁车, go-kart +574 n03445777 高尔夫, golf ball +575 n03445924 高尔夫车, golfcart, golf cart +576 n03447447 小船, gondola +577 n03447721 锣鼓, gong, tam-tam +578 n03450230 婚纱, gown +579 n03452741 钢琴, grand piano, grand +580 n03457902 大棚, greenhouse, nursery, glasshouse +581 n03459775 轿车 车标 check, grille, radiator grille +582 n03461385 菜场, grocery store, grocery, food market, market +583 n03467068 断头台, guillotine +584 n03476684 发饰, hair slide +585 n03476991 发蜡, hair spray +586 n03478589 坦克, half track +587 n03481172 榔头, hammer +588 n03482405 竹筒, hamper +589 n03483316 吹风机, hand blower, blow dryer, blow drier, hair dryer, hair drier +590 n03485407 pos机, hand-held computer, hand-held microcomputer +591 n03485794 手帕, handkerchief, hankie, hanky, hankey +592 n03492542 硬盘, hard disc, hard disk, fixed disk +593 n03494278 口风琴, harmonica, mouth organ, harp, mouth harp +594 n03495258 竖琴, harp +595 n03496892 起重机, harvester, reaper +596 n03498962 斧头, hatchet +597 n03527444 手枪, holster +598 n03529860 电视机, home theater, home theatre +599 n03530642 蜂巢, honeycomb +600 n03532672 钩子, hook, claw +601 n03534580 裙子, hoopskirt, crinoline +602 n03535780 体操, horizontal bar, high bar +603 n03538406 马车, horse cart, horse-cart +604 n03544143 沙漏, hourglass +605 n03584254 音乐播放器, iPod +606 n03584829 电熨斗, iron, smoothing iron +607 n03590841 南瓜灯, jack-o’-lantern +608 n03594734 牛仔裤, jean, blue jean, denim +609 n03594945 吉普车 check, jeep, landrover +610 n03595614 T恤, jersey, T-shirt, tee shirt +611 n03598930 拼图, jigsaw puzzle +612 n03599486 黄包车, jinrikisha, ricksha, rickshaw +613 n03602883 操纵杆, joystick +614 n03617480 和服, kimono +615 n03623198 护具, knee pad +616 n03627232 绳结, knot +617 n03630383 医生, lab coat, laboratory coat +618 n03633091 勺子, ladle +619 n03637318 灯, lampshade, lamp shade +620 n03642806 笔记本电脑, laptop, laptop computer +621 n03649909 割草机, lawn mower, mower +622 n03657121 镜头盖, lens cap, lens cover +623 n03658185 小刀, letter opener, paper knife, paperknife +624 n03661043 图书馆, library +625 n03662601 救生船, lifeboat +626 n03666591 打火机, lighter, light, igniter, ignitor +627 n03670208 加长车 check, limousine, limo +628 n03673027 轮船, liner, ocean liner +629 n03676483 口红, lipstick, lip rouge +630 n03680355 鞋子, Loafer +631 n03690938 护肤品, lotion +632 n03691459 音响, loudspeaker, speaker, speaker unit, loudspeaker system, speaker system +633 n03692522 放大镜, loupe, jeweler’s loupe +634 n03697007 原木, lumbermill, sawmill +635 n03706229 指南针, magnetic compass +636 n03709823 包, mailbag, postbag +637 n03710193 邮箱, mailbox, letter box +638 n03710637 泳衣, maillot +639 n03710721 泳衣, maillot, tank suit +640 n03717622 窨井盖, manhole cover +641 n03720891 手摇铃, maraca +642 n03721384 木琴, marimba, xylophone +643 n03724870 面具, mask +644 n03729826 火柴, matchstick +645 n03733131 绳子, maypole +646 n03733281 迷宫, maze, labyrinth +647 n03733805 烧杯, measuring cup +648 n03742115 冰箱, medicine chest, medicine cabinet +649 n03743016 石柱, megalith, megalithic structure +650 n03759954 话筒, microphone, mike +651 n03761084 微波炉, microwave, microwave oven +652 n03763968 军人, military uniform +653 n03764736 水壶, milk can +654 n03769881 小客车 check, minibus +655 n03770439 短裙, miniskirt, mini +656 n03770679 面包车 check, minivan +657 n03773504 导弹, missile +658 n03775071 手套, mitten +659 n03775546 碗, mixing bowl +660 n03776460 房车, mobile home, manufactured home +661 n03777568 老爷车, Model T +662 n03777754 路由器, modem +663 n03781244 建筑, monastery +664 n03782006 显示器, monitor +665 n03785016 摩托车 check, moped +666 n03786901 砚, mortar +667 n03787032 学士帽, mortarboard +668 n03788195 建筑, mosque +669 n03788365 蚊帐, mosquito net +670 n03791053 助动车 check, motor scooter, scooter +671 n03792782 自行车 check, mountain bike, all-terrain bike, off-roader +672 n03792972 帐篷, mountain tent +673 n03793489 键盘鼠标, mouse, computer mouse +674 n03794056 捕鼠夹, mousetrap +675 n03796401 货车 卡车 check, moving van +676 n03803284 狗嘴套, muzzle +677 n03804744 钉子, nail +678 n03814639 颈托, neck brace +679 n03814906 项链, necklace +680 n03825788 奶瓶, nipple +681 n03832673 笔记本电脑, notebook, notebook computer +682 n03837869 建筑, obelisk +683 n03838899 黑管, oboe, hautboy, hautbois +684 n03840681 埙, ocarina, sweet potato +685 n03841143 仪表盘, odometer, hodometer, mileometer, milometer +686 n03843555 机油滤清器, oil filter +687 n03854065 管风琴, organ, pipe organ +688 n03857828 示波器, oscilloscope, scope, cathode-ray oscilloscope, CRO +689 n03866082 礼服, overskirt +690 n03868242 牛车, oxcart +691 n03868863 呼吸器, oxygen mask +692 n03871628 零食, packet +693 n03873416 划桨, paddle, boat paddle +694 n03874293 水轮, paddlewheel, paddle wheel +695 n03874599 锁, padlock +696 n03876231 刷子, paintbrush +697 n03877472 睡衣, pajama, pyjama, pj’s, jammies +698 n03877845 建筑, palace +699 n03884397 乐器, panpipe, pandean pipe, syrinx +700 n03887697 纸巾, paper towel +701 n03888257 降落伞, parachute, chute +702 n03888605 体操, parallel bars, bars +703 n03891251 长椅, park bench +704 n03891332 停车缴费器, parking meter +705 n03895866 火车 check, passenger car, coach, carriage +706 n03899768 院子, patio, terrace +707 n03902125 公用电话, pay-phone, pay-station +708 n03903868 柱子, pedestal, plinth, footstall +709 n03908618 文具袋, pencil box, pencil case +710 n03908714 卷笔刀, pencil sharpener +711 n03916031 香水, perfume, essence +712 n03920288 培养皿, Petri dish +713 n03924679 打印机, photocopier +714 n03929660 吉他拨片, pick, plectrum, plectron +715 n03929855 头盔, pickelhaube +716 n03930313 栅栏, picket fence, paling +717 n03930630 轿车 check, pickup, pickup truck +718 n03933933 桥, pier +719 n03935335 储蓄罐, piggy bank, penny bank +720 n03937543 药丸, pill bottle +721 n03938244 枕头, pillow +722 n03942813 乒乓球, ping-pong ball +723 n03944341 风车, pinwheel +724 n03947888 帆船, pirate, pirate ship +725 n03950228 茶壶, pitcher, ewer +726 n03954731 刨子, plane, carpenter’s plane, woodworking plane +727 n03956157 建筑, planetarium +728 n03958227 塑料袋, plastic bag +729 n03961711 碗架, plate rack +730 n03967562 推土机, plow, plough +731 n03970156 搋子, plunger, plumber’s helper +732 n03976467 相机, Polaroid camera, Polaroid Land camera +733 n03976657 杆子, pole +734 n03977966 警车 check, police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria +735 n03980874 披风, poncho +736 n03982430 桌球, pool table, billiard table, snooker table +737 n03983396 瓶子, pop bottle, soda bottle +738 n03991062 盆栽, pot, flowerpot +739 n03992509 陶艺, potter’s wheel +740 n03995372 钻机, power drill +741 n03998194 毯子, prayer rug, prayer mat +742 n04004767 打印机, printer +743 n04005630 监狱, prison, prison house +744 n04008634 导弹, projectile, missile +745 n04009552 投影仪, projector +746 n04019541 冰球, puck, hockey puck +747 n04023962 拳击, punching bag, punch bag, punching ball, punchball +748 n04026417 手提袋, purse +749 n04033901 羽毛笔, quill, quill pen +750 n04033995 床 被子, quilt, comforter, comfort, puff +751 n04037443 赛车, racer, race car, racing car +752 n04039381 网球, racket, racquet +753 n04040759 加热器, radiator +754 n04041544 收音机, radio, wireless +755 n04044716 卫星接收器, radio telescope, radio reflector +756 n04049303 酒桶, rain barrel +757 n04065272 房车, recreational vehicle, RV, R.V. +758 n04067472 鱼竿, reel +759 n04069434 相机, reflex camera +760 n04070727 冰箱, refrigerator, icebox +761 n04074963 遥控器, remote control, remote +762 n04081281 餐厅, restaurant, eating house, eating place, eatery +763 n04086273 手枪, revolver, six-gun, six-shooter +764 n04090263 狙击枪, rifle +765 n04099969 摇椅, rocking chair, rocker +766 n04111531 烤箱, rotisserie +767 n04116512 橡皮, rubber eraser, rubber, pencil eraser +768 n04118538 橄榄球, rugby ball +769 n04118776 尺, rule, ruler +770 n04120489 运动鞋, running shoe +771 n04125021 保险箱, safe +772 n04127249 回形针, safety pin +773 n04131690 调料瓶, saltshaker, salt shaker +774 n04133789 拖鞋, sandal +775 n04136333 长裙, sarong +776 n04141076 萨克斯, sax, saxophone +777 n04141327 剑, scabbard +778 n04141975 秤, scale, weighing machine +779 n04146614 校车, school bus +780 n04147183 帆船, schooner +781 n04149813 计分板, scoreboard +782 n04152593 显示器, screen, CRT screen +783 n04153751 螺丝, screw +784 n04154565 螺丝刀, screwdriver +785 n04162706 安全带, seat belt, seatbelt +786 n04179913 缝纫机, sewing machine +787 n04192698 盾牌, shield, buckler +788 n04200800 鞋店, shoe shop, shoe-shop, shoe store +789 n04201297 榻榻米, shoji +790 n04204238 购物篮, shopping basket +791 n04204347 购物车, shopping cart +792 n04208210 铲子, shovel +793 n04209133 浴帽, shower cap +794 n04209239 浴帘, shower curtain +795 n04228054 滑雪, ski +796 n04229816 面罩, ski mask +797 n04235860 睡袋, sleeping bag +798 n04238763 游标卡尺, slide rule, slipstick +799 n04239074 移门, sliding door +800 n04243546 老虎机, slot, one-armed bandit +801 n04251144 游泳眼镜, snorkel +802 n04252077 滑雪车, snowmobile +803 n04252225 铲雪车, snowplow, snowplough +804 n04254120 洗手液, soap dispenser +805 n04254680 足球, soccer ball +806 n04254777 袜子, sock +807 n04258138 太阳能板, solar dish, solar collector, solar furnace +808 n04259630 帽子, sombrero +809 n04263257 碗, soup bowl +810 n04264628 键盘, space bar +811 n04265275 电热器, space heater +812 n04266014 航天飞船, space shuttle +813 n04270147 锅铲, spatula +814 n04273569 快艇, speedboat +815 n04275548 蜘蛛网, spider web, spider’s web +816 n04277352 毛线, spindle +817 n04285008 运动型轿车 check, sports car, sport car +818 n04286575 探照灯, spotlight, spot +819 n04296562 乐队, stage +820 n04310018 蒸汽机车, steam locomotive +821 n04311004 桥, steel arch bridge +822 n04311174 鼓, steel drum +823 n04317175 听诊器, stethoscope +824 n04325704 担架, stole +825 n04326547 石堆, stone wall +826 n04328186 秒表, stopwatch, stop watch +827 n04330267 火炉, stove +828 n04332243 滤网, strainer +829 n04335435 公交车 check, streetcar, tram, tramcar, trolley, trolley car +830 n04336792 担架, stretcher +831 n04344873 沙发, studio couch, day bed +832 n04346328 皇宫, stupa, tope +833 n04347754 轮船, submarine, pigboat, sub, U-boat +834 n04350905 西装, suit, suit of clothes +835 n04355338 日晷, sundial +836 n04355933 墨镜, sunglass +837 n04356056 墨镜, sunglasses, dark glasses, shades +838 n04357314 防晒霜, sunscreen, sunblock, sun blocker +839 n04366367 桥, suspension bridge +840 n04367480 拖把, swab, swob, mop +841 n04370456 连帽衫, sweatshirt +842 n04371430 沙滩裤, swimming trunks, bathing trunks +843 n04371774 秋千, swing +844 n04372370 开关, switch, electric switch, electrical switch +845 n04376876 针筒, syringe +846 n04380533 台灯, table lamp +847 n04389033 坦克, tank, army tank, armored combat vehicle, armoured combat vehicle +848 n04392985 磁带播放器, tape player +849 n04398044 茶壶, teapot +850 n04399382 毛绒玩具, teddy, teddy bear +851 n04404412 电视机, television, television system +852 n04409515 网球, tennis ball +853 n04417672 草屋, thatch, thatched roof +854 n04418357 幕布, theater curtain, theatre curtain +855 n04423845 指套, thimble +856 n04428191 装甲车, thresher, thrasher, threshing machine +857 n04429376 皇位, throne +858 n04435653 瓦片, tile roof +859 n04442312 面包机, toaster +860 n04443257 烟酒店, tobacco shop, tobacconist shop, tobacconist +861 n04447861 马桶, toilet seat +862 n04456115 火炬, torch +863 n04458633 图腾, totem pole +864 n04461696 大卡车 check, tow truck, tow car, wrecker +865 n04462240 玩具店, toyshop +866 n04465501 拖拉机, tractor +867 n04467665 大货车 check, trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi +868 n04476259 碟子 盘子, tray +869 n04479046 风衣, trench coat +870 n04482393 儿童自行车, tricycle, trike, velocipede +871 n04483307 船, trimaran +872 n04485082 三脚架, tripod +873 n04486054 拱门, triumphal arch +874 n04487081 巴士 check, trolleybus, trolley coach, trackless trolley +875 n04487394 长号, trombone +876 n04493381 浴缸, tub, vat +877 n04501370 闸机, turnstile +878 n04505470 打字机, typewriter keyboard +879 n04507155 伞, umbrella +880 n04509417 独轮车, unicycle, monocycle +881 n04515003 钢琴, upright, upright piano +882 n04517823 吸尘器, vacuum, vacuum cleaner +883 n04522168 花瓶, vase +884 n04523525 拱廊, vault +885 n04525038 珊瑚绒, velvet +886 n04525305 自动贩卖机, vending machine +887 n04532106 教皇袍, vestment +888 n04532670 桥, viaduct +889 n04536866 小提琴, violin, fiddle +890 n04540053 排球, volleyball +891 n04542943 煎饼锅, waffle iron +892 n04548280 挂钟, wall clock +893 n04548362 钱夹, wallet, billfold, notecase, pocketbook +894 n04550184 柜子, wardrobe, closet, press +895 n04552348 飞机, warplane, military plane +896 n04553703 台盆, washbasin, handbasin, washbowl, lavabo, wash-hand basin +897 n04554684 洗衣机, washer, automatic washer, washing machine +898 n04557648 水瓶, water bottle +899 n04560804 水壶, water jug +900 n04562935 煤气包, water tower +901 n04579145 水壶, whiskey jug +902 n04579432 哨子, whistle +903 n04584207 头发, wig +904 n04589890 窗户, window screen +905 n04590129 百叶窗, window shade +906 n04591157 领带, Windsor tie +907 n04591713 葡萄酒, wine bottle +908 n04592741 飞机, wing +909 n04596742 炒锅, wok +910 n04597913 勺子, wooden spoon +911 n04599235 围巾, wool, woolen, woollen +912 n04604644 栅栏, worm fence, snake fence, snake-rail fence, Virginia fence +913 n04606251 沉船, wreck +914 n04612504 帆船, yawl +915 n04613696 蒙古包, yurt +916 n06359193 网页, web site, website, internet site, site +917 n06596364 海报, comic book +918 n06785654 填字游戏, crossword puzzle, crossword +919 n06794110 交通标志, street sign +920 n06874185 交通灯, traffic light, traffic signal, stoplight +921 n07248320 书, book jacket, dust cover, dust jacket, dust wrapper +922 n07565083 菜单, menu +923 n07579787 菜, plate +924 n07583066 菜, guacamole +925 n07584110 菜, consomme +926 n07590611 菜, hot pot, hotpot +927 n07613480 蛋糕, trifle +928 n07614500 冰激凌, ice cream, icecream +929 n07615774 棒冰, ice lolly, lolly, lollipop, popsicle +930 n07684084 面包, French loaf +931 n07693725 甜甜圈, bagel, beigel +932 n07695742 面包, pretzel +933 n07697313 汉堡, cheeseburger +934 n07697537 热狗, hotdog, hot dog, red hot +935 n07711569 焗饭, mashed potato +936 n07714571 蔬菜, head cabbage +937 n07714990 西蓝花, broccoli +938 n07715103 花椰菜, cauliflower +939 n07716358 蔬菜, zucchini, courgette +940 n07716906 金瓜, spaghetti squash +941 n07717410 南瓜, acorn squash +942 n07717556 南瓜, butternut squash +943 n07718472 黄瓜, cucumber, cuke +944 n07718747 蔬菜, artichoke, globe artichoke +945 n07720875 青椒 黄椒 红椒, bell pepper +946 n07730033 花, cardoon +947 n07734744 蘑菇, mushroom +948 n07742313 苹果, Granny Smith +949 n07745940 草莓, strawberry +950 n07747607 橙子, orange +951 n07749582 柠檬, lemon +952 n07753113 水果, fig +953 n07753275 菠萝, pineapple, ananas +954 n07753592 香蕉, banana +955 n07754684 榴莲, jackfruit, jak, jack +956 n07760859 水果, custard apple +957 n07768694 石榴, pomegranate +958 n07802026 草垛, hay +959 n07831146 意大利面, carbonara +960 n07836838 甜品, chocolate sauce, chocolate syrup +961 n07860988 面团, dough +962 n07871810 肉酱, meat loaf, meatloaf +963 n07873807 披萨, pizza, pizza pie +964 n07875152 派, potpie +965 n07880968 肉卷, burrito +966 n07892512 红酒, red wine +967 n07920052 咖啡, espresso +968 n07930864 茶, cup +969 n07932039 饮料杯, eggnog +970 n09193705 雪山, alp +971 n09229709 泡泡, bubble +972 n09246464 悬崖, cliff, drop, drop-off +973 n09256479 珊瑚, coral reef +974 n09288635 温泉, geyser +975 n09332890 风景, lakeside, lakeshore +976 n09399592 小岛, promontory, headland, head, foreland +977 n09421951 沙滩, sandbar, sand bar +978 n09428293 海滩, seashore, coast, seacoast, sea-coast +979 n09468604 瀑布 溪流, valley, vale +980 n09472597 火山, volcano +981 n09835506 棒球, ballplayer, baseball player +982 n10148035 婚礼, groom, bridegroom +983 n10565667 潜水, scuba diver +984 n11879895 油菜花, rapeseed +985 n11939491 菊花, daisy +986 n12057211 植物, yellow lady’s slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum +987 n12144580 玉米, corn +988 n12267677 松果, acorn +989 n12620546 植物, hip, rose hip, rosehip +990 n12768682 栗子, buckeye, horse chestnut, conker +991 n12985857 菌菇, coral fungus +992 n12998815 菌菇, agaric +993 n13037406 菌菇, gyromitra +994 n13040303 菌菇, stinkhorn, carrion fungus +995 n13044778 菌菇, earthstar +996 n13052670 菌菇, hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa +997 n13054560 菌菇, bolete +998 n13133613 玉米, ear, spike, capitulum +999 n15075141 卷筒纸, toilet tissue, toilet paper, bathroom tissue \ No newline at end of file diff --git a/attacks/Gaker/tools/classfication_templates.json b/attacks/Gaker/tools/classfication_templates.json new file mode 100644 index 0000000000000000000000000000000000000000..d66a534fed5be818b7771b1631282654c852dad8 --- /dev/null +++ b/attacks/Gaker/tools/classfication_templates.json @@ -0,0 +1,84 @@ +{ + "imagenet1k": [ + "a bad photo of a {c}.", + "a photo of many {c}.", + "a sculpture of a {c}.", + "a photo of the hard to see {c}.", + "a low resolution photo of the {c}.", + "a rendering of a {c}.", + "graffiti of a {c}.", + "a bad photo of the {c}.", + "a cropped photo of the {c}.", + "a tattoo of a {c}.", + "the embroidered {c}.", + "a photo of a hard to see {c}.", + "a bright photo of a {c}.", + "a photo of a clean {c}.", + "a photo of a dirty {c}.", + "a dark photo of the {c}.", + "a drawing of a {c}.", + "a photo of my {c}.", + "the plastic {c}.", + "a photo of the cool {c}.", + "a close-up photo of a {c}.", + "a black and white photo of the {c}.", + "a painting of the {c}.", + "a painting of a {c}.", + "a pixelated photo of the {c}.", + "a sculpture of the {c}.", + "a bright photo of the {c}.", + "a cropped photo of a {c}.", + "a plastic {c}.", + "a photo of the dirty {c}.", + "a jpeg corrupted photo of a {c}.", + "a blurry photo of the {c}.", + "a photo of the {c}.", + "a good photo of the {c}.", + "a rendering of the {c}.", + "a {c} in a video game.", + "a photo of one {c}.", + "a doodle of a {c}.", + "a close-up photo of the {c}.", + "a photo of a {c}.", + "the origami {c}.", + "the {c} in a video game.", + "a sketch of a {c}.", + "a doodle of the {c}.", + "a origami {c}.", + "a low resolution photo of a {c}.", + "the toy {c}.", + "a rendition of the {c}.", + "a photo of the clean {c}.", + "a photo of a large {c}.", + "a rendition of a {c}.", + "a photo of a nice {c}.", + "a photo of a weird {c}.", + "a blurry photo of a {c}.", + "a cartoon {c}.", + "art of a {c}.", + "a sketch of the {c}.", + "a embroidered {c}.", + "a pixelated photo of a {c}.", + "itap of the {c}.", + "a jpeg corrupted photo of the {c}.", + "a good photo of a {c}.", + "a plushie {c}.", + "a photo of the nice {c}.", + "a photo of the small {c}.", + "a photo of the weird {c}.", + "the cartoon {c}.", + "art of the {c}.", + "a drawing of the {c}.", + "a photo of the large {c}.", + "a black and white photo of a {c}.", + "the plushie {c}.", + "a dark photo of a {c}.", + "itap of a {c}.", + "graffiti of the {c}.", + "a toy {c}.", + "itap of my {c}.", + "a photo of a cool {c}.", + "a photo of a small {c}.", + "a tattoo of the {c}." + ] +} \ No newline at end of file diff --git a/attacks/Gaker/tools/imagenet_classes.txt b/attacks/Gaker/tools/imagenet_classes.txt new file mode 100644 index 0000000000000000000000000000000000000000..722c984560c36a6014113deb8106a6cf50050ad5 --- /dev/null +++ b/attacks/Gaker/tools/imagenet_classes.txt @@ -0,0 +1,1000 @@ +tench, Tinca tinca +goldfish, Carassius auratus +great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias +tiger shark, Galeocerdo cuvieri +hammerhead, hammerhead shark +electric ray, crampfish, numbfish, torpedo +stingray +cock +hen +ostrich, Struthio camelus +brambling, Fringilla montifringilla +goldfinch, Carduelis carduelis +house finch, linnet, Carpodacus mexicanus +junco, snowbird +indigo bunting, indigo finch, indigo bird, Passerina cyanea +robin, American robin, Turdus migratorius +bulbul +jay +magpie +chickadee +water ouzel, dipper +kite +bald eagle, American eagle, Haliaeetus leucocephalus +vulture +great grey owl, great gray owl, Strix nebulosa +European fire salamander, Salamandra salamandra +common newt, Triturus vulgaris +eft +spotted salamander, Ambystoma maculatum +axolotl, mud puppy, Ambystoma mexicanum +bullfrog, Rana catesbeiana +tree frog, tree-frog +tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui +loggerhead, loggerhead turtle, Caretta caretta +leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea +mud turtle +terrapin +box turtle, box tortoise +banded gecko +common iguana, iguana, Iguana iguana +American chameleon, anole, Anolis carolinensis +whiptail, whiptail lizard +agama +frilled lizard, Chlamydosaurus kingi +alligator lizard +Gila monster, Heloderma suspectum +green lizard, Lacerta viridis +African chameleon, Chamaeleo chamaeleon +Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis +African crocodile, Nile crocodile, Crocodylus niloticus +American alligator, Alligator mississipiensis +triceratops +thunder snake, worm snake, Carphophis amoenus +ringneck snake, ring-necked snake, ring snake +hognose snake, puff adder, sand viper +green snake, grass snake +king snake, kingsnake +garter snake, grass snake +water snake +vine snake +night snake, Hypsiglena torquata +boa constrictor, Constrictor constrictor +rock python, rock snake, Python sebae +Indian cobra, Naja naja +green mamba +sea snake +horned viper, cerastes, sand viper, horned asp, Cerastes cornutus +diamondback, diamondback rattlesnake, Crotalus adamanteus +sidewinder, horned rattlesnake, Crotalus cerastes +trilobite +harvestman, daddy longlegs, Phalangium opilio +scorpion +black and gold garden spider, Argiope aurantia +barn spider, Araneus cavaticus +garden spider, Aranea diademata +black widow, Latrodectus mactans +tarantula +wolf spider, hunting spider +tick +centipede +black grouse +ptarmigan +ruffed grouse, partridge, Bonasa umbellus +prairie chicken, prairie grouse, prairie fowl +peacock +quail +partridge +African grey, African gray, Psittacus erithacus +macaw +sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita +lorikeet +coucal +bee eater +hornbill +hummingbird +jacamar +toucan +drake +red-breasted merganser, Mergus serrator +goose +black swan, Cygnus atratus +tusker +echidna, spiny anteater, anteater +platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus +wallaby, brush kangaroo +koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus +wombat +jellyfish +sea anemone, anemone +brain coral +flatworm, platyhelminth +nematode, nematode worm, roundworm +conch +snail +slug +sea slug, nudibranch +chiton, coat-of-mail shell, sea cradle, polyplacophore +chambered nautilus, pearly nautilus, nautilus +Dungeness crab, Cancer magister +rock crab, Cancer irroratus +fiddler crab +king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica +American lobster, Northern lobster, Maine lobster, Homarus americanus +spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish +crayfish, crawfish, crawdad, crawdaddy +hermit crab +isopod +white stork, Ciconia ciconia +black stork, Ciconia nigra +spoonbill +flamingo +little blue heron, Egretta caerulea +American egret, great white heron, Egretta albus +bittern +crane +limpkin, Aramus pictus +European gallinule, Porphyrio porphyrio +American coot, marsh hen, mud hen, water hen, Fulica americana +bustard +ruddy turnstone, Arenaria interpres +red-backed sandpiper, dunlin, Erolia alpina +redshank, Tringa totanus +dowitcher +oystercatcher, oyster catcher +pelican +king penguin, Aptenodytes patagonica +albatross, mollymawk +grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus +killer whale, killer, orca, grampus, sea wolf, Orcinus orca +dugong, Dugong dugon +sea lion +Chihuahua +Japanese spaniel +Maltese dog, Maltese terrier, Maltese +Pekinese, Pekingese, Peke +Shih-Tzu +Blenheim spaniel +papillon +toy terrier +Rhodesian ridgeback +Afghan hound, Afghan +basset, basset hound +beagle +bloodhound, sleuthhound +bluetick +black-and-tan coonhound +Walker hound, Walker foxhound +English foxhound +redbone +borzoi, Russian wolfhound +Irish wolfhound +Italian greyhound +whippet +Ibizan hound, Ibizan Podenco +Norwegian elkhound, elkhound +otterhound, otter hound +Saluki, gazelle hound +Scottish deerhound, deerhound +Weimaraner +Staffordshire bullterrier, Staffordshire bull terrier +American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier +Bedlington terrier +Border terrier +Kerry blue terrier +Irish terrier +Norfolk terrier +Norwich terrier +Yorkshire terrier +wire-haired fox terrier +Lakeland terrier +Sealyham terrier, Sealyham +Airedale, Airedale terrier +cairn, cairn terrier +Australian terrier +Dandie Dinmont, Dandie Dinmont terrier +Boston bull, Boston terrier +miniature schnauzer +giant schnauzer +standard schnauzer +Scotch terrier, Scottish terrier, Scottie +Tibetan terrier, chrysanthemum dog +silky terrier, Sydney silky +soft-coated wheaten terrier +West Highland white terrier +Lhasa, Lhasa apso +flat-coated retriever +curly-coated retriever +golden retriever +Labrador retriever +Chesapeake Bay retriever +German short-haired pointer +vizsla, Hungarian pointer +English setter +Irish setter, red setter +Gordon setter +Brittany spaniel +clumber, clumber spaniel +English springer, English springer spaniel +Welsh springer spaniel +cocker spaniel, English cocker spaniel, cocker +Sussex spaniel +Irish water spaniel +kuvasz +schipperke +groenendael +malinois +briard +kelpie +komondor +Old English sheepdog, bobtail +Shetland sheepdog, Shetland sheep dog, Shetland +collie +Border collie +Bouvier des Flandres, Bouviers des Flandres +Rottweiler +German shepherd, German shepherd dog, German police dog, alsatian +Doberman, Doberman pinscher +miniature pinscher +Greater Swiss Mountain dog +Bernese mountain dog +Appenzeller +EntleBucher +boxer +bull mastiff +Tibetan mastiff +French bulldog +Great Dane +Saint Bernard, St Bernard +Eskimo dog, husky +malamute, malemute, Alaskan malamute +Siberian husky +dalmatian, coach dog, carriage dog +affenpinscher, monkey pinscher, monkey dog +basenji +pug, pug-dog +Leonberg +Newfoundland, Newfoundland dog +Great Pyrenees +Samoyed, Samoyede +Pomeranian +chow, chow chow +keeshond +Brabancon griffon +Pembroke, Pembroke Welsh corgi +Cardigan, Cardigan Welsh corgi +toy poodle +miniature poodle +standard poodle +Mexican hairless +timber wolf, grey wolf, gray wolf, Canis lupus +white wolf, Arctic wolf, Canis lupus tundrarum +red wolf, maned wolf, Canis rufus, Canis niger +coyote, prairie wolf, brush wolf, Canis latrans +dingo, warrigal, warragal, Canis dingo +dhole, Cuon alpinus +African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus +hyena, hyaena +red fox, Vulpes vulpes +kit fox, Vulpes macrotis +Arctic fox, white fox, Alopex lagopus +grey fox, gray fox, Urocyon cinereoargenteus +tabby, tabby cat +tiger cat +Persian cat +Siamese cat, Siamese +Egyptian cat +cougar, puma, catamount, mountain lion, painter, panther, Felis concolor +lynx, catamount +leopard, Panthera pardus +snow leopard, ounce, Panthera uncia +jaguar, panther, Panthera onca, Felis onca +lion, king of beasts, Panthera leo +tiger, Panthera tigris +cheetah, chetah, Acinonyx jubatus +brown bear, bruin, Ursus arctos +American black bear, black bear, Ursus americanus, Euarctos americanus +ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus +sloth bear, Melursus ursinus, Ursus ursinus +mongoose +meerkat, mierkat +tiger beetle +ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle +ground beetle, carabid beetle +long-horned beetle, longicorn, longicorn beetle +leaf beetle, chrysomelid +dung beetle +rhinoceros beetle +weevil +fly +bee +ant, emmet, pismire +grasshopper, hopper +cricket +walking stick, walkingstick, stick insect +cockroach, roach +mantis, mantid +cicada, cicala +leafhopper +lacewing, lacewing fly +dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk +damselfly +admiral +ringlet, ringlet butterfly +monarch, monarch butterfly, milkweed butterfly, Danaus plexippus +cabbage butterfly +sulphur butterfly, sulfur butterfly +lycaenid, lycaenid butterfly +starfish, sea star +sea urchin +sea cucumber, holothurian +wood rabbit, cottontail, cottontail rabbit +hare +Angora, Angora rabbit +hamster +porcupine, hedgehog +fox squirrel, eastern fox squirrel, Sciurus niger +marmot +beaver +guinea pig, Cavia cobaya +sorrel +zebra +hog, pig, grunter, squealer, Sus scrofa +wild boar, boar, Sus scrofa +warthog +hippopotamus, hippo, river horse, Hippopotamus amphibius +ox +water buffalo, water ox, Asiatic buffalo, Bubalus bubalis +bison +ram, tup +bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis +ibex, Capra ibex +hartebeest +impala, Aepyceros melampus +gazelle +Arabian camel, dromedary, Camelus dromedarius +llama +weasel +mink +polecat, fitch, foulmart, foumart, Mustela putorius +black-footed ferret, ferret, Mustela nigripes +otter +skunk, polecat, wood pussy +badger +armadillo +three-toed sloth, ai, Bradypus tridactylus +orangutan, orang, orangutang, Pongo pygmaeus +gorilla, Gorilla gorilla +chimpanzee, chimp, Pan troglodytes +gibbon, Hylobates lar +siamang, Hylobates syndactylus, Symphalangus syndactylus +guenon, guenon monkey +patas, hussar monkey, Erythrocebus patas +baboon +macaque +langur +colobus, colobus monkey +proboscis monkey, Nasalis larvatus +marmoset +capuchin, ringtail, Cebus capucinus +howler monkey, howler +titi, titi monkey +spider monkey, Ateles geoffroyi +squirrel monkey, Saimiri sciureus +Madagascar cat, ring-tailed lemur, Lemur catta +indri, indris, Indri indri, Indri brevicaudatus +Indian elephant, Elephas maximus +African elephant, Loxodonta africana +lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens +giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca +barracouta, snoek +eel +coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch +rock beauty, Holocanthus tricolor +anemone fish +sturgeon +gar, garfish, garpike, billfish, Lepisosteus osseus +lionfish +puffer, pufferfish, blowfish, globefish +abacus +abaya +academic gown, academic robe, judge's robe +accordion, piano accordion, squeeze box +acoustic guitar +aircraft carrier, carrier, flattop, attack aircraft carrier +airliner +airship, dirigible +altar +ambulance +amphibian, amphibious vehicle +analog clock +apiary, bee house +apron +ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin +assault rifle, assault gun +backpack, back pack, knapsack, packsack, rucksack, haversack +bakery, bakeshop, bakehouse +balance beam, beam +balloon +ballpoint, ballpoint pen, ballpen, Biro +Band Aid +banjo +bannister, banister, balustrade, balusters, handrail +barbell +barber chair +barbershop +barn +barometer +barrel, cask +barrow, garden cart, lawn cart, wheelbarrow +baseball +basketball +bassinet +bassoon +bathing cap, swimming cap +bath towel +bathtub, bathing tub, bath, tub +beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon +beacon, lighthouse, beacon light, pharos +beaker +bearskin, busby, shako +beer bottle +beer glass +bell cote, bell cot +bib +bicycle-built-for-two, tandem bicycle, tandem +bikini, two-piece +binder, ring-binder +binoculars, field glasses, opera glasses +birdhouse +boathouse +bobsled, bobsleigh, bob +bolo tie, bolo, bola tie, bola +bonnet, poke bonnet +bookcase +bookshop, bookstore, bookstall +bottlecap +bow +bow tie, bow-tie, bowtie +brass, memorial tablet, plaque +brassiere, bra, bandeau +breakwater, groin, groyne, mole, bulwark, seawall, jetty +breastplate, aegis, egis +broom +bucket, pail +buckle +bulletproof vest +bullet train, bullet +butcher shop, meat market +cab, hack, taxi, taxicab +caldron, cauldron +candle, taper, wax light +cannon +canoe +can opener, tin opener +cardigan +car mirror +carousel, carrousel, merry-go-round, roundabout, whirligig +carpenter's kit, tool kit +carton +car wheel +cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM +cassette +cassette player +castle +catamaran +CD player +cello, violoncello +cellular telephone, cellular phone, cellphone, cell, mobile phone +chain +chainlink fence +chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour +chain saw, chainsaw +chest +chiffonier, commode +chime, bell, gong +china cabinet, china closet +Christmas stocking +church, church building +cinema, movie theater, movie theatre, movie house, picture palace +cleaver, meat cleaver, chopper +cliff dwelling +cloak +clog, geta, patten, sabot +cocktail shaker +coffee mug +coffeepot +coil, spiral, volute, whorl, helix +combination lock +computer keyboard, keypad +confectionery, confectionary, candy store +container ship, containership, container vessel +convertible +corkscrew, bottle screw +cornet, horn, trumpet, trump +cowboy boot +cowboy hat, ten-gallon hat +cradle +crane +crash helmet +crate +crib, cot +Crock Pot +croquet ball +crutch +cuirass +dam, dike, dyke +desk +desktop computer +dial telephone, dial phone +diaper, nappy, napkin +digital clock +digital watch +dining table, board +dishrag, dishcloth +dishwasher, dish washer, dishwashing machine +disk brake, disc brake +dock, dockage, docking facility +dogsled, dog sled, dog sleigh +dome +doormat, welcome mat +drilling platform, offshore rig +drum, membranophone, tympan +drumstick +dumbbell +Dutch oven +electric fan, blower +electric guitar +electric locomotive +entertainment center +envelope +espresso maker +face powder +feather boa, boa +file, file cabinet, filing cabinet +fireboat +fire engine, fire truck +fire screen, fireguard +flagpole, flagstaff +flute, transverse flute +folding chair +football helmet +forklift +fountain +fountain pen +four-poster +freight car +French horn, horn +frying pan, frypan, skillet +fur coat +garbage truck, dustcart +gasmask, respirator, gas helmet +gas pump, gasoline pump, petrol pump, island dispenser +goblet +go-kart +golf ball +golfcart, golf cart +gondola +gong, tam-tam +gown +grand piano, grand +greenhouse, nursery, glasshouse +grille, radiator grille +grocery store, grocery, food market, market +guillotine +hair slide +hair spray +half track +hammer +hamper +hand blower, blow dryer, blow drier, hair dryer, hair drier +hand-held computer, hand-held microcomputer +handkerchief, hankie, hanky, hankey +hard disc, hard disk, fixed disk +harmonica, mouth organ, harp, mouth harp +harp +harvester, reaper +hatchet +holster +home theater, home theatre +honeycomb +hook, claw +hoopskirt, crinoline +horizontal bar, high bar +horse cart, horse-cart +hourglass +iPod +iron, smoothing iron +jack-o'-lantern +jean, blue jean, denim +jeep, landrover +jersey, T-shirt, tee shirt +jigsaw puzzle +jinrikisha, ricksha, rickshaw +joystick +kimono +knee pad +knot +lab coat, laboratory coat +ladle +lampshade, lamp shade +laptop, laptop computer +lawn mower, mower +lens cap, lens cover +letter opener, paper knife, paperknife +library +lifeboat +lighter, light, igniter, ignitor +limousine, limo +liner, ocean liner +lipstick, lip rouge +Loafer +lotion +loudspeaker, speaker, speaker unit, loudspeaker system, speaker system +loupe, jeweler's loupe +lumbermill, sawmill +magnetic compass +mailbag, postbag +mailbox, letter box +maillot +maillot, tank suit +manhole cover +maraca +marimba, xylophone +mask +matchstick +maypole +maze, labyrinth +measuring cup +medicine chest, medicine cabinet +megalith, megalithic structure +microphone, mike +microwave, microwave oven +military uniform +milk can +minibus +miniskirt, mini +minivan +missile +mitten +mixing bowl +mobile home, manufactured home +Model T +modem +monastery +monitor +moped +mortar +mortarboard +mosque +mosquito net +motor scooter, scooter +mountain bike, all-terrain bike, off-roader +mountain tent +mouse, computer mouse +mousetrap +moving van +muzzle +nail +neck brace +necklace +nipple +notebook, notebook computer +obelisk +oboe, hautboy, hautbois +ocarina, sweet potato +odometer, hodometer, mileometer, milometer +oil filter +organ, pipe organ +oscilloscope, scope, cathode-ray oscilloscope, CRO +overskirt +oxcart +oxygen mask +packet +paddle, boat paddle +paddlewheel, paddle wheel +padlock +paintbrush +pajama, pyjama, pj's, jammies +palace +panpipe, pandean pipe, syrinx +paper towel +parachute, chute +parallel bars, bars +park bench +parking meter +passenger car, coach, carriage +patio, terrace +pay-phone, pay-station +pedestal, plinth, footstall +pencil box, pencil case +pencil sharpener +perfume, essence +Petri dish +photocopier +pick, plectrum, plectron +pickelhaube +picket fence, paling +pickup, pickup truck +pier +piggy bank, penny bank +pill bottle +pillow +ping-pong ball +pinwheel +pirate, pirate ship +pitcher, ewer +plane, carpenter's plane, woodworking plane +planetarium +plastic bag +plate rack +plow, plough +plunger, plumber's helper +Polaroid camera, Polaroid Land camera +pole +police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria +poncho +pool table, billiard table, snooker table +pop bottle, soda bottle +pot, flowerpot +potter's wheel +power drill +prayer rug, prayer mat +printer +prison, prison house +projectile, missile +projector +puck, hockey puck +punching bag, punch bag, punching ball, punchball +purse +quill, quill pen +quilt, comforter, comfort, puff +racer, race car, racing car +racket, racquet +radiator +radio, wireless +radio telescope, radio reflector +rain barrel +recreational vehicle, RV, R.V. +reel +reflex camera +refrigerator, icebox +remote control, remote +restaurant, eating house, eating place, eatery +revolver, six-gun, six-shooter +rifle +rocking chair, rocker +rotisserie +rubber eraser, rubber, pencil eraser +rugby ball +rule, ruler +running shoe +safe +safety pin +saltshaker, salt shaker +sandal +sarong +sax, saxophone +scabbard +scale, weighing machine +school bus +schooner +scoreboard +screen, CRT screen +screw +screwdriver +seat belt, seatbelt +sewing machine +shield, buckler +shoe shop, shoe-shop, shoe store +shoji +shopping basket +shopping cart +shovel +shower cap +shower curtain +ski +ski mask +sleeping bag +slide rule, slipstick +sliding door +slot, one-armed bandit +snorkel +snowmobile +snowplow, snowplough +soap dispenser +soccer ball +sock +solar dish, solar collector, solar furnace +sombrero +soup bowl +space bar +space heater +space shuttle +spatula +speedboat +spider web, spider's web +spindle +sports car, sport car +spotlight, spot +stage +steam locomotive +steel arch bridge +steel drum +stethoscope +stole +stone wall +stopwatch, stop watch +stove +strainer +streetcar, tram, tramcar, trolley, trolley car +stretcher +studio couch, day bed +stupa, tope +submarine, pigboat, sub, U-boat +suit, suit of clothes +sundial +sunglass +sunglasses, dark glasses, shades +sunscreen, sunblock, sun blocker +suspension bridge +swab, swob, mop +sweatshirt +swimming trunks, bathing trunks +swing +switch, electric switch, electrical switch +syringe +table lamp +tank, army tank, armored combat vehicle, armoured combat vehicle +tape player +teapot +teddy, teddy bear +television, television system +tennis ball +thatch, thatched roof +theater curtain, theatre curtain +thimble +thresher, thrasher, threshing machine +throne +tile roof +toaster +tobacco shop, tobacconist shop, tobacconist +toilet seat +torch +totem pole +tow truck, tow car, wrecker +toyshop +tractor +trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi +tray +trench coat +tricycle, trike, velocipede +trimaran +tripod +triumphal arch +trolleybus, trolley coach, trackless trolley +trombone +tub, vat +turnstile +typewriter keyboard +umbrella +unicycle, monocycle +upright, upright piano +vacuum, vacuum cleaner +vase +vault +velvet +vending machine +vestment +viaduct +violin, fiddle +volleyball +waffle iron +wall clock +wallet, billfold, notecase, pocketbook +wardrobe, closet, press +warplane, military plane +washbasin, handbasin, washbowl, lavabo, wash-hand basin +washer, automatic washer, washing machine +water bottle +water jug +water tower +whiskey jug +whistle +wig +window screen +window shade +Windsor tie +wine bottle +wing +wok +wooden spoon +wool, woolen, woollen +worm fence, snake fence, snake-rail fence, Virginia fence +wreck +yawl +yurt +web site, website, internet site, site +comic book +crossword puzzle, crossword +street sign +traffic light, traffic signal, stoplight +book jacket, dust cover, dust jacket, dust wrapper +menu +plate +guacamole +consomme +hot pot, hotpot +trifle +ice cream, icecream +ice lolly, lolly, lollipop, popsicle +French loaf +bagel, beigel +pretzel +cheeseburger +hotdog, hot dog, red hot +mashed potato +head cabbage +broccoli +cauliflower +zucchini, courgette +spaghetti squash +acorn squash +butternut squash +cucumber, cuke +artichoke, globe artichoke +bell pepper +cardoon +mushroom +Granny Smith +strawberry +orange +lemon +fig +pineapple, ananas +banana +jackfruit, jak, jack +custard apple +pomegranate +hay +carbonara +chocolate sauce, chocolate syrup +dough +meat loaf, meatloaf +pizza, pizza pie +potpie +burrito +red wine +espresso +cup +eggnog +alp +bubble +cliff, drop, drop-off +coral reef +geyser +lakeside, lakeshore +promontory, headland, head, foreland +sandbar, sand bar +seashore, coast, seacoast, sea-coast +valley, vale +volcano +ballplayer, baseball player +groom, bridegroom +scuba diver +rapeseed +daisy +yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum +corn +acorn +hip, rose hip, rosehip +buckeye, horse chestnut, conker +coral fungus +agaric +gyromitra +stinkhorn, carrion fungus +earthstar +hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa +bolete +ear, spike, capitulum +toilet tissue, toilet paper, bathroom tissue \ No newline at end of file diff --git a/attacks/Gaker/tools/imagenet_numtotarget.txt b/attacks/Gaker/tools/imagenet_numtotarget.txt new file mode 100644 index 0000000000000000000000000000000000000000..95c3c313cecec9133297529a15db3dd05f5e675b --- /dev/null +++ b/attacks/Gaker/tools/imagenet_numtotarget.txt @@ -0,0 +1,1000 @@ +0 n01440764 鱼, tench, Tinca tinca +1 n01443537 鱼, goldfish, Carassius auratus +2 n01484850 鱼, great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias +3 n01491361 鱼, tiger shark, Galeocerdo cuvieri +4 n01494475 鱼, hammerhead, hammerhead shark +5 n01496331 鱼, electric ray, crampfish, numbfish, torpedo +6 n01498041 鱼, stingray +7 n01514668 鸡, cock +8 n01514859 鸡, hen +9 n01518878 鸵鸟, ostrich, Struthio camelus +10 n01530575 鸟, brambling, Fringilla montifringilla +11 n01531178 鸟, goldfinch, Carduelis carduelis +12 n01532829 鸟, house finch, linnet, Carpodacus mexicanus +13 n01534433 鸟, junco, snowbird +14 n01537544 鸟, indigo bunting, indigo finch, indigo bird, Passerina cyanea +15 n01558993 鸟, robin, American robin, Turdus migratorius +16 n01560419 鸟, bulbul +17 n01580077 鸟, jay +18 n01582220 鸟, magpie +19 n01592084 鸟, chickadee +20 n01601694 鸟, water ouzel, dipper +21 n01608432 鸟, kite +22 n01614925 鹰, bald eagle, American eagle, Haliaeetus leucocephalus +23 n01616318 鹰, vulture +24 n01622779 猫头鹰, great grey owl, great gray owl, Strix nebulosa +25 n01629819 壁虎, European fire salamander, Salamandra salamandra +26 n01630670 壁虎, common newt, Triturus vulgaris +27 n01631663 壁虎, eft +28 n01632458 壁虎, spotted salamander, Ambystoma maculatum +29 n01632777 壁虎, axolotl, mud puppy, Ambystoma mexicanum +30 n01641577 蛤蟆, bullfrog, Rana catesbeiana +31 n01644373 青蛙, tree frog, tree-frog +32 n01644900 青蛙, tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui +33 n01664065 龟, loggerhead, loggerhead turtle, Caretta caretta +34 n01665541 龟, leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea +35 n01667114 龟, mud turtle +36 n01667778 龟, terrapin +37 n01669191 龟, box turtle, box tortoise +38 n01675722 蜥蜴, banded gecko +39 n01677366 蜥蜴, common iguana, iguana, Iguana iguana +40 n01682714 蜥蜴, American chameleon, anole, Anolis carolinensis +41 n01685808 蜥蜴, whiptail, whiptail lizard +42 n01687978 蜥蜴, agama +43 n01688243 蜥蜴, frilled lizard, Chlamydosaurus kingi +44 n01689811 蜥蜴, alligator lizard +45 n01692333 蜥蜴, Gila monster, Heloderma suspectum +46 n01693334 蜥蜴, green lizard, Lacerta viridis +47 n01694178 蜥蜴, African chameleon, Chamaeleo chamaeleon +48 n01695060 蜥蜴, Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis +49 n01697457 鳄鱼, African crocodile, Nile crocodile, Crocodylus niloticus +50 n01698640 鳄鱼, American alligator, Alligator mississipiensis +51 n01704323 恐龙, triceratops +52 n01728572 蛇, thunder snake, worm snake, Carphophis amoenus +53 n01728920 蛇, ringneck snake, ring-necked snake, ring snake +54 n01729322 蛇, hognose snake, puff adder, sand viper +55 n01729977 蛇, green snake, grass snake +56 n01734418 蛇, king snake, kingsnake +57 n01735189 蛇, garter snake, grass snake +58 n01737021 蛇, water snake +59 n01739381 蛇, vine snake +60 n01740131 蛇, night snake, Hypsiglena torquata +61 n01742172 蛇, boa constrictor, Constrictor constrictor +62 n01744401 蛇, rock python, rock snake, Python sebae +63 n01748264 蛇, Indian cobra, Naja naja +64 n01749939 蛇, green mamba +65 n01751748 蛇, sea snake +66 n01753488 蛇, horned viper, cerastes, sand viper, horned asp, Cerastes cornutus +67 n01755581 蛇, diamondback, diamondback rattlesnake, Crotalus adamanteus +68 n01756291 蛇, sidewinder, horned rattlesnake, Crotalus cerastes +69 n01768244 化石, trilobite +70 n01770081 蜘蛛, harvestman, daddy longlegs, Phalangium opilio +71 n01770393 蝎子, scorpion +72 n01773157 蜘蛛, black and gold garden spider, Argiope aurantia +73 n01773549 蜘蛛, barn spider, Araneus cavaticus +74 n01773797 蜘蛛, garden spider, Aranea diademata +75 n01774384 蜘蛛, black widow, Latrodectus mactans +76 n01774750 蜘蛛, tarantula +77 n01775062 蜘蛛, wolf spider, hunting spider +78 n01776313 蜘蛛, tick +79 n01784675 蜈蚣, centipede +80 n01795545 鸟, black grouse +81 n01796340 鸟, ptarmigan +82 n01797886 鸟, ruffed grouse, partridge, Bonasa umbellus +83 n01798484 鸟, prairie chicken, prairie grouse, prairie fowl +84 n01806143 孔雀, peacock +85 n01806567 鸟, quail +86 n01807496 鸟, partridge +87 n01817953 鸟, African grey, African gray, Psittacus erithacus +88 n01818515 鸟, macaw +89 n01819313 鸟, sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita +90 n01820546 鸟, lorikeet +91 n01824575 鸟, coucal +92 n01828970 鸟, bee eater +93 n01829413 鸟, hornbill +94 n01833805 鸟, hummingbird +95 n01843065 鸟, jacamar +96 n01843383 鸟, toucan +97 n01847000 鸭子, drake +98 n01855032 鹅, red-breasted merganser, Mergus serrator +99 n01855672 鹅, goose +100 n01860187 鹅, black swan, Cygnus atratus +101 n01871265 大象, tusker +102 n01872401 刺猬, echidna, spiny anteater, anteater +103 n01873310 鸭嘴兽, platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus +104 n01877812 袋鼠, wallaby, brush kangaroo +105 n01882714 考拉, koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus +106 n01883070 土拨鼠, wombat +107 n01910747 水母, jellyfish +108 n01914609 珊瑚, sea anemone, anemone +109 n01917289 珊瑚, brain coral +110 n01924916 海洋生物, flatworm, platyhelminth +111 n01930112 海蛇, nematode, nematode worm, roundworm +112 n01943899 海螺, conch +113 n01944390 蜗牛, snail +114 n01945685 蜗牛, slug +115 n01950731 海洋生物, sea slug, nudibranch +116 n01955084 海洋生物, chiton, coat-of-mail shell, sea cradle, polyplacophore +117 n01968897 海螺, chambered nautilus, pearly nautilus, nautilus +118 n01978287 螃蟹, Dungeness crab, Cancer magister +119 n01978455 螃蟹, rock crab, Cancer irroratus +120 n01980166 螃蟹, fiddler crab +121 n01981276 螃蟹, king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica +122 n01983481 龙虾, American lobster, Northern lobster, Maine lobster, Homarus americanus +123 n01984695 龙虾, spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish +124 n01985128 龙虾, crayfish, crawfish, crawdad, crawdaddy +125 n01986214 寄居蟹, hermit crab +126 n01990800 海洋生物, isopod +127 n02002556 鸟, white stork, Ciconia ciconia +128 n02002724 鸟, black stork, Ciconia nigra +129 n02006656 鸟, spoonbill +130 n02007558 鸟, flamingo +131 n02009229 鸟, little blue heron, Egretta caerulea +132 n02009912 鸟, American egret, great white heron, Egretta albus +133 n02011460 鸟, bittern +134 n02012849 鸟, crane +135 n02013706 鸟, limpkin, Aramus pictus +136 n02017213 鸟, European gallinule, Porphyrio porphyrio +137 n02018207 鸟, American coot, marsh hen, mud hen, water hen, Fulica americana +138 n02018795 鸟, bustard +139 n02025239 鸟, ruddy turnstone, Arenaria interpres +140 n02027492 鸟, red-backed sandpiper, dunlin, Erolia alpina +141 n02028035 鸟, redshank, Tringa totanus +142 n02033041 鸟, dowitcher +143 n02037110 鸟, oystercatcher, oyster catcher +144 n02051845 鸟, pelican +145 n02056570 企鹅, king penguin, Aptenodytes patagonica +146 n02058221 鸟, albatross, mollymawk +147 n02066245 鲸鱼, grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus +148 n02071294 鲸鱼, killer whale, killer, orca, grampus, sea wolf, Orcinus orca +149 n02074367 海象, dugong, Dugong dugon +150 n02077923 海狮, sea lion +151 n02085620 狗, Chihuahua +152 n02085782 狗, Japanese spaniel +153 n02085936 狗, Maltese dog, Maltese terrier, Maltese +154 n02086079 狗, Pekinese, Pekingese, Peke +155 n02086240 狗, Shih-Tzu +156 n02086646 狗, Blenheim spaniel +157 n02086910 狗, papillon +158 n02087046 狗, toy terrier +159 n02087394 狗, Rhodesian ridgeback +160 n02088094 狗, Afghan hound, Afghan +161 n02088238 狗, basset, basset hound +162 n02088364 狗, beagle +163 n02088466 狗, bloodhound, sleuthhound +164 n02088632 狗, bluetick +165 n02089078 狗, black-and-tan coonhound +166 n02089867 狗, Walker hound, Walker foxhound +167 n02089973 狗, English foxhound +168 n02090379 狗, redbone +169 n02090622 狗, borzoi, Russian wolfhound +170 n02090721 狗, Irish wolfhound +171 n02091032 狗, Italian greyhound +172 n02091134 狗, whippet +173 n02091244 狗, Ibizan hound, Ibizan Podenco +174 n02091467 狗, Norwegian elkhound, elkhound +175 n02091635 狗, otterhound, otter hound +176 n02091831 狗, Saluki, gazelle hound +177 n02092002 狗, Scottish deerhound, deerhound +178 n02092339 狗, Weimaraner +179 n02093256 狗, Staffordshire bullterrier, Staffordshire bull terrier +180 n02093428 狗, American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier +181 n02093647 狗, Bedlington terrier +182 n02093754 狗, Border terrier +183 n02093859 狗, Kerry blue terrier +184 n02093991 狗, Irish terrier +185 n02094114 狗, Norfolk terrier +186 n02094258 狗, Norwich terrier +187 n02094433 狗, Yorkshire terrier +188 n02095314 狗, wire-haired fox terrier +189 n02095570 狗, Lakeland terrier +190 n02095889 狗, Sealyham terrier, Sealyham +191 n02096051 狗, Airedale, Airedale terrier +192 n02096177 狗, cairn, cairn terrier +193 n02096294 狗, Australian terrier +194 n02096437 狗, Dandie Dinmont, Dandie Dinmont terrier +195 n02096585 狗, Boston bull, Boston terrier +196 n02097047 狗, miniature schnauzer +197 n02097130 狗, giant schnauzer +198 n02097209 狗, standard schnauzer +199 n02097298 狗, Scotch terrier, Scottish terrier, Scottie +200 n02097474 狗, Tibetan terrier, chrysanthemum dog +201 n02097658 狗, silky terrier, Sydney silky +202 n02098105 狗, soft-coated wheaten terrier +203 n02098286 狗, West Highland white terrier +204 n02098413 狗, Lhasa, Lhasa apso +205 n02099267 狗, flat-coated retriever +206 n02099429 狗, curly-coated retriever +207 n02099601 狗, golden retriever +208 n02099712 狗, Labrador retriever +209 n02099849 狗, Chesapeake Bay retriever +210 n02100236 狗, German short-haired pointer +211 n02100583 狗, vizsla, Hungarian pointer +212 n02100735 狗, English setter +213 n02100877 狗, Irish setter, red setter +214 n02101006 狗, Gordon setter +215 n02101388 狗, Brittany spaniel +216 n02101556 狗, clumber, clumber spaniel +217 n02102040 狗, English springer, English springer spaniel +218 n02102177 狗, Welsh springer spaniel +219 n02102318 狗, cocker spaniel, English cocker spaniel, cocker +220 n02102480 狗, Sussex spaniel +221 n02102973 狗, Irish water spaniel +222 n02104029 狗, kuvasz +223 n02104365 狗, schipperke +224 n02105056 狗, groenendael +225 n02105162 狗, malinois +226 n02105251 狗, briard +227 n02105412 狗, kelpie +228 n02105505 狗, komondor +229 n02105641 狗, Old English sheepdog, bobtail +230 n02105855 狗, Shetland sheepdog, Shetland sheep dog, Shetland +231 n02106030 狗, collie +232 n02106166 狗, Border collie +233 n02106382 狗, Bouvier des Flandres, Bouviers des Flandres +234 n02106550 狗, Rottweiler +235 n02106662 狗, German shepherd, German shepherd dog, German police dog, alsatian +236 n02107142 狗, Doberman, Doberman pinscher +237 n02107312 狗, miniature pinscher +238 n02107574 狗, Greater Swiss Mountain dog +239 n02107683 狗, Bernese mountain dog +240 n02107908 狗, Appenzeller +241 n02108000 狗, EntleBucher +242 n02108089 狗, boxer +243 n02108422 狗, bull mastiff +244 n02108551 狗, Tibetan mastiff +245 n02108915 狗, French bulldog +246 n02109047 狗, Great Dane +247 n02109525 狗, Saint Bernard, St Bernard +248 n02109961 狗, Eskimo dog, husky +249 n02110063 狗, malamute, malemute, Alaskan malamute +250 n02110185 狗, Siberian husky +251 n02110341 狗, dalmatian, coach dog, carriage dog +252 n02110627 狗, affenpinscher, monkey pinscher, monkey dog +253 n02110806 狗, basenji +254 n02110958 狗, pug, pug-dog +255 n02111129 狗, Leonberg +256 n02111277 狗, Newfoundland, Newfoundland dog +257 n02111500 狗, Great Pyrenees +258 n02111889 狗, Samoyed, Samoyede +259 n02112018 狗, Pomeranian +260 n02112137 狗, chow, chow chow +261 n02112350 狗, keeshond +262 n02112706 狗, Brabancon griffon +263 n02113023 狗, Pembroke, Pembroke Welsh corgi +264 n02113186 狗, Cardigan, Cardigan Welsh corgi +265 n02113624 狗, toy poodle +266 n02113712 狗, miniature poodle +267 n02113799 狗, standard poodle +268 n02113978 狗, Mexican hairless +269 n02114367 狼, timber wolf, grey wolf, gray wolf, Canis lupus +270 n02114548 狼, white wolf, Arctic wolf, Canis lupus tundrarum +271 n02114712 狼, red wolf, maned wolf, Canis rufus, Canis niger +272 n02114855 狼, coyote, prairie wolf, brush wolf, Canis latrans +273 n02115641 狼, dingo, warrigal, warragal, Canis dingo +274 n02115913 狼, dhole, Cuon alpinus +275 n02116738 狼, African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus +276 n02117135 狼, hyena, hyaena +277 n02119022 狐狸, red fox, Vulpes vulpes +278 n02119789 狐狸, kit fox, Vulpes macrotis +279 n02120079 狐狸, Arctic fox, white fox, Alopex lagopus +280 n02120505 狐狸, grey fox, gray fox, Urocyon cinereoargenteus +281 n02123045 猫, tabby, tabby cat +282 n02123159 猫, tiger cat +283 n02123394 猫, Persian cat +284 n02123597 猫, Siamese cat, Siamese +285 n02124075 猫, Egyptian cat +286 n02125311 猫, cougar, puma, catamount, mountain lion, painter, panther, Felis concolor +287 n02127052 猫, lynx, catamount +288 n02128385 豹, leopard, Panthera pardus +289 n02128757 豹, snow leopard, ounce, Panthera uncia +290 n02128925 豹, jaguar, panther, Panthera onca, Felis onca +291 n02129165 狮子, lion, king of beasts, Panthera leo +292 n02129604 老虎, tiger, Panthera tigris +293 n02130308 豹, cheetah, chetah, Acinonyx jubatus +294 n02132136 熊, brown bear, bruin, Ursus arctos +295 n02133161 熊, American black bear, black bear, Ursus americanus, Euarctos americanus +296 n02134084 熊, ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus +297 n02134418 熊, sloth bear, Melursus ursinus, Ursus ursinus +298 n02137549 猫鼬, mongoose +299 n02138441 猫鼬, meerkat, mierkat +300 n02165105 昆虫, tiger beetle +301 n02165456 昆虫, ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle +302 n02167151 昆虫, ground beetle, carabid beetle +303 n02168699 昆虫, long-horned beetle, longicorn, longicorn beetle +304 n02169497 昆虫, leaf beetle, chrysomelid +305 n02172182 昆虫, dung beetle +306 n02174001 昆虫, rhinoceros beetle +307 n02177972 昆虫, weevil +308 n02190166 昆虫, fly +309 n02206856 昆虫, bee +310 n02219486 昆虫, ant, emmet, pismire +311 n02226429 昆虫, grasshopper, hopper +312 n02229544 昆虫, cricket +313 n02231487 昆虫, walking stick, walkingstick, stick insect +314 n02233338 昆虫, cockroach, roach +315 n02236044 昆虫, mantis, mantid +316 n02256656 昆虫, cicada, cicala +317 n02259212 昆虫, leafhopper +318 n02264363 昆虫, lacewing, lacewing fly +319 n02268443 蜻蜓, dragonfly, darning needle, devil’s darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk +320 n02268853 蜻蜓, damselfly +321 n02276258 蝴蝶, admiral +322 n02277742 蝴蝶, ringlet, ringlet butterfly +323 n02279972 蝴蝶, monarch, monarch butterfly, milkweed butterfly, Danaus plexippus +324 n02280649 蝴蝶, cabbage butterfly +325 n02281406 蝴蝶, sulphur butterfly, sulfur butterfly +326 n02281787 蝴蝶, lycaenid, lycaenid butterfly +327 n02317335 海星, starfish, sea star +328 n02319095 海胆, sea urchin +329 n02321529 海洋生物, sea cucumber, holothurian +330 n02325366 兔子, wood rabbit, cottontail, cottontail rabbit +331 n02326432 兔子, hare +332 n02328150 兔子, Angora, Angora rabbit +333 n02342885 鼠, hamster +334 n02346627 鼠, porcupine, hedgehog +335 n02356798 松鼠, fox squirrel, eastern fox squirrel, Sciurus niger +336 n02361337 鼠, marmot +337 n02363005 鼠, beaver +338 n02364673 鼠, guinea pig, Cavia cobaya +339 n02389026 马, sorrel +340 n02391049 斑马, zebra +341 n02395406 猪, hog, pig, grunter, squealer, Sus scrofa +342 n02396427 猪, wild boar, boar, Sus scrofa +343 n02397096 猪, warthog +344 n02398521 河马, hippopotamus, hippo, river horse, Hippopotamus amphibius +345 n02403003 牛, ox +346 n02408429 牛, water buffalo, water ox, Asiatic buffalo, Bubalus bubalis +347 n02410509 牛, bison +348 n02412080 羊, ram, tup +349 n02415577 羊, bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis +350 n02417914 羊, ibex, Capra ibex +351 n02422106 羊, hartebeest +352 n02422699 羊, impala, Aepyceros melampus +353 n02423022 羊, gazelle +354 n02437312 骆驼, Arabian camel, dromedary, Camelus dromedarius +355 n02437616 羊驼, llama +356 n02441942 狸, weasel +357 n02442845 狸, mink +358 n02443114 狸, polecat, fitch, foulmart, foumart, Mustela putorius +359 n02443484 狸, black-footed ferret, ferret, Mustela nigripes +360 n02444819 狸, otter +361 n02445715 狸, skunk, polecat, wood pussy +362 n02447366 狸, badger +363 n02454379 穿山甲, armadillo +364 n02457408 树懒, three-toed sloth, ai, Bradypus tridactylus +365 n02480495 狒狒, orangutan, orang, orangutang, Pongo pygmaeus +366 n02480855 猩猩, gorilla, Gorilla gorilla +367 n02481823 猴子, chimpanzee, chimp, Pan troglodytes +368 n02483362 猴子, gibbon, Hylobates lar +369 n02483708 猴子, siamang, Hylobates syndactylus, Symphalangus syndactylus +370 n02484975 猴子, guenon, guenon monkey +371 n02486261 猴子, patas, hussar monkey, Erythrocebus patas +372 n02486410 猴子, baboon +373 n02487347 猴子, macaque +374 n02488291 猴子, langur +375 n02488702 猴子, colobus, colobus monkey +376 n02489166 猴子, proboscis monkey, Nasalis larvatus +377 n02490219 猴子, marmoset +378 n02492035 猴子, capuchin, ringtail, Cebus capucinus +379 n02492660 猴子, howler monkey, howler +380 n02493509 猴子, titi, titi monkey +381 n02493793 猴子, spider monkey, Ateles geoffroyi +382 n02494079 猴子, squirrel monkey, Saimiri sciureus +383 n02497673 猴子, Madagascar cat, ring-tailed lemur, Lemur catta +384 n02500267 猴子, indri, indris, Indri indri, Indri brevicaudatus +385 n02504013 大象, Indian elephant, Elephas maximus +386 n02504458 大象, African elephant, Loxodonta africana +387 n02509815 浣熊, lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens +388 n02510455 熊猫, giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca +389 n02514041 鱼, barracouta, snoek +390 n02526121 鱼, eel +391 n02536864 鱼, coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch +392 n02606052 鱼, rock beauty, Holocanthus tricolor +393 n02607072 鱼, anemone fish +394 n02640242 鱼, sturgeon +395 n02641379 鱼, gar, garfish, garpike, billfish, Lepisosteus osseus +396 n02643566 鱼, lionfish +397 n02655020 鱼, puffer, pufferfish, blowfish, globefish +398 n02666196 算盘, abacus +399 n02667093 穆斯林, abaya +400 n02669723 学士服, academic gown, academic robe, judge’s robe +401 n02672831 手风琴, accordion, piano accordion, squeeze box +402 n02676566 吉他, acoustic guitar +403 n02687172 航空母舰, aircraft carrier, carrier, flattop, attack aircraft carrier +404 n02690373 飞机, airliner +405 n02692877 飞艇, airship, dirigible +406 n02699494 教堂, altar +407 n02701002 救护车 check, ambulance +408 n02704792 水陆两用车 check, amphibian, amphibious vehicle +409 n02708093 钟, analog clock +410 n02727426 箱子, apiary, bee house +411 n02730930 围裙, apron +412 n02747177 垃圾箱, ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin +413 n02749479 枪, assault rifle, assault gun +414 n02769748 背包, backpack, back pack, knapsack, packsack, rucksack, haversack +415 n02776631 面包柜, bakery, bakeshop, bakehouse +416 n02777292 体操, balance beam, beam +417 n02782093 热气球, balloon +418 n02783161 钢笔, ballpoint, ballpoint pen, ballpen, Biro +419 n02786058 邦迪, Band Aid +420 n02787622 乐器, banjo +421 n02788148 楼梯, bannister, banister, balustrade, balusters, handrail +422 n02790996 杠铃, barbell +423 n02791124 座椅, barber chair +424 n02791270 理发, barbershop +425 n02793495 木屋, barn +426 n02794156 表, barometer +427 n02795169 酒桶, barrel, cask +428 n02797295 手推车, barrow, garden cart, lawn cart, wheelbarrow +429 n02799071 棒球, baseball +430 n02802426 篮球, basketball +431 n02804414 婴儿, bassinet +432 n02804610 乐器, bassoon +433 n02807133 游泳, bathing cap, swimming cap +434 n02808304 婴儿毛巾, bath towel +435 n02808440 浴缸, bathtub, bathing tub, bath, tub +436 n02814533 轿车 check, beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon +437 n02814860 灯塔, beacon, lighthouse, beacon light, pharos +438 n02815834 烧杯, beaker +439 n02817516 守卫, bearskin, busby, shako +440 n02823428 啤酒, beer bottle +441 n02823750 啤酒, beer glass +442 n02825657 建筑, bell cote, bell cot +443 n02834397 围兜, bib +444 n02835271 双人自行车, bicycle-built-for-two, tandem bicycle, tandem +445 n02837789 比基尼, bikini, two-piece +446 n02840245 笔记本, binder, ring-binder +447 n02841315 望远镜, binoculars, field glasses, opera glasses +448 n02843684 信箱, birdhouse +449 n02859443 小屋, boathouse +450 n02860847 雪橇, bobsled, bobsleigh, bob +451 n02865351 项链, bolo tie, bolo, bola tie, bola +452 n02869837 帽子, bonnet, poke bonnet +453 n02870880 书柜, bookcase +454 n02871525 书店, bookshop, bookstore, bookstall +455 n02877765 瓶盖, bottlecap +456 n02879718 弓箭, bow +457 n02883205 领结, bow tie, bow-tie, bowtie +458 n02892201 墓碑, brass, memorial tablet, plaque +459 n02892767 胸罩, brassiere, bra, bandeau +460 n02894605 海岸, breakwater, groin, groyne, mole, bulwark, seawall, jetty +461 n02895154 盔甲, breastplate, aegis, egis +462 n02906734 扫帚, broom +463 n02909870 水桶, bucket, pail +464 n02910353 皮带, buckle +465 n02916936 防弹背心, bulletproof vest +466 n02917067 火车, bullet train, bullet +467 n02927161 肉铺, butcher shop, meat market +468 n02930766 出租车 check, cab, hack, taxi, taxicab +469 n02939185 锅, caldron, cauldron +470 n02948072 蜡烛, candle, taper, wax light +471 n02950826 炮, cannon +472 n02951358 艇, canoe +473 n02951585 订书机, can opener, tin opener +474 n02963159 毛衣, cardigan +475 n02965783 反光镜, car mirror +476 n02966193 旋转木马, carousel, carrousel, merry-go-round, roundabout, whirligig +477 n02966687 工具箱, carpenter’s kit, tool kit +478 n02971356 盒子, carton +479 n02974003 轮胎, car wheel +480 n02977058 取款机, cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM +481 n02978881 磁带, cassette +482 n02979186 磁带, cassette player +483 n02980441 城堡, castle +484 n02981792 帆船, catamaran +485 n02988304 cd播放器, CD player +486 n02992211 大提琴, cello, violoncello +487 n02992529 手机, cellular telephone, cellular phone, cellphone, cell, mobile phone +488 n02999410 铁链, chain +489 n03000134 铁丝网, chainlink fence +490 n03000247 铁丝网, chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour +491 n03000684 电锯, chain saw, chainsaw +492 n03014705 木箱, chest +493 n03016953 木柜, chiffonier, commode +494 n03017168 锣鼓, chime, bell, gong +495 n03018349 柜子, china cabinet, china closet +496 n03026506 袜子, Christmas stocking +497 n03028079 教堂, church, church building +498 n03032252 剧院, cinema, movie theater, movie theatre, movie house, picture palace +499 n03041632 刀, cleaver, meat cleaver, chopper +500 n03042490 堡垒, cliff dwelling +501 n03045698 斗篷, cloak +502 n03047690 鞋子, clog, geta, patten, sabot +503 n03062245 瓶子, cocktail shaker +504 n03063599 杯子, coffee mug +505 n03063689 壶, coffeepot +506 n03065424 螺旋, coil, spiral, volute, whorl, helix +507 n03075370 锁, combination lock +508 n03085013 键盘, computer keyboard, keypad +509 n03089624 糖果, confectionery, confectionary, candy store +510 n03095699 船, container ship, containership, container vessel +511 n03100240 轿车 check, convertible +512 n03109150 开瓶器, corkscrew, bottle screw +513 n03110669 号（乐器）, cornet, horn, trumpet, trump +514 n03124043 靴子, cowboy boot +515 n03124170 帽子, cowboy hat, ten-gallon hat +516 n03125729 婴儿床, cradle +517 n03126707 起重机, crane +518 n03127747 头盔, crash helmet +519 n03127925 木箱, crate +520 n03131574 婴儿床, crib, cot +521 n03133878 电饭锅, Crock Pot +522 n03134739 推球, croquet ball +523 n03141823 拐杖, crutch +524 n03146219 盔甲, cuirass +525 n03160309 水库, dam, dike, dyke +526 n03179701 办公桌, desk +527 n03180011 电脑, desktop computer +528 n03187595 电话机, dial telephone, dial phone +529 n03188531 尿布, diaper, nappy, napkin +530 n03196217 闹钟, digital clock +531 n03197337 手表, digital watch +532 n03201208 餐桌, dining table, board +533 n03207743 垫子, dishrag, dishcloth +534 n03207941 洗碗柜, dishwasher, dish washer, dishwashing machine +535 n03208938 车轮, disk brake, disc brake +536 n03216828 港口, dock, dockage, docking facility +537 n03218198 雪橇, dogsled, dog sled, dog sleigh +538 n03220513 穹顶, dome +539 n03223299 地毯, doormat, welcome mat +540 n03240683 油田, drilling platform, offshore rig +541 n03249569 鼓, drum, membranophone, tympan +542 n03250847 鼓棒, drumstick +543 n03255030 哑铃, dumbbell +544 n03259280 锅, Dutch oven +545 n03271574 风扇, electric fan, blower +546 n03272010 电吉他, electric guitar +547 n03272562 火车 check, electric locomotive +548 n03290653 电视机, entertainment center +549 n03291819 信, envelope +550 n03297495 咖啡机, espresso maker +551 n03314780 化妆品, face powder +552 n03325584 绒毛, feather boa, boa +553 n03337140 柜子, file, file cabinet, filing cabinet +554 n03344393 喷泉, fireboat +555 n03345487 消防车 check, fire engine, fire truck +556 n03347037 壁炉, fire screen, fireguard +557 n03355925 旗杆, flagpole, flagstaff +558 n03372029 笛子, flute, transverse flute +559 n03376595 座椅, folding chair +560 n03379051 橄榄球, football helmet +561 n03384352 叉车, forklift +562 n03388043 喷泉, fountain +563 n03388183 钢笔, fountain pen +564 n03388549 床, four-poster +565 n03393912 火车, freight car +566 n03394916 圆号, French horn, horn +567 n03400231 平底锅, frying pan, frypan, skillet +568 n03404251 裘皮, fur coat +569 n03417042 卡车 check, garbage truck, dustcart +570 n03424325 面具, gasmask, respirator, gas helmet +571 n03425413 加油, gas pump, gasoline pump, petrol pump, island dispenser +572 n03443371 酒杯, goblet +573 n03444034 卡丁车, go-kart +574 n03445777 高尔夫, golf ball +575 n03445924 高尔夫车, golfcart, golf cart +576 n03447447 小船, gondola +577 n03447721 锣鼓, gong, tam-tam +578 n03450230 婚纱, gown +579 n03452741 钢琴, grand piano, grand +580 n03457902 大棚, greenhouse, nursery, glasshouse +581 n03459775 轿车 车标 check, grille, radiator grille +582 n03461385 菜场, grocery store, grocery, food market, market +583 n03467068 断头台, guillotine +584 n03476684 发饰, hair slide +585 n03476991 发蜡, hair spray +586 n03478589 坦克, half track +587 n03481172 榔头, hammer +588 n03482405 竹筒, hamper +589 n03483316 吹风机, hand blower, blow dryer, blow drier, hair dryer, hair drier +590 n03485407 pos机, hand-held computer, hand-held microcomputer +591 n03485794 手帕, handkerchief, hankie, hanky, hankey +592 n03492542 硬盘, hard disc, hard disk, fixed disk +593 n03494278 口风琴, harmonica, mouth organ, harp, mouth harp +594 n03495258 竖琴, harp +595 n03496892 起重机, harvester, reaper +596 n03498962 斧头, hatchet +597 n03527444 手枪, holster +598 n03529860 电视机, home theater, home theatre +599 n03530642 蜂巢, honeycomb +600 n03532672 钩子, hook, claw +601 n03534580 裙子, hoopskirt, crinoline +602 n03535780 体操, horizontal bar, high bar +603 n03538406 马车, horse cart, horse-cart +604 n03544143 沙漏, hourglass +605 n03584254 音乐播放器, iPod +606 n03584829 电熨斗, iron, smoothing iron +607 n03590841 南瓜灯, jack-o’-lantern +608 n03594734 牛仔裤, jean, blue jean, denim +609 n03594945 吉普车 check, jeep, landrover +610 n03595614 T恤, jersey, T-shirt, tee shirt +611 n03598930 拼图, jigsaw puzzle +612 n03599486 黄包车, jinrikisha, ricksha, rickshaw +613 n03602883 操纵杆, joystick +614 n03617480 和服, kimono +615 n03623198 护具, knee pad +616 n03627232 绳结, knot +617 n03630383 医生, lab coat, laboratory coat +618 n03633091 勺子, ladle +619 n03637318 灯, lampshade, lamp shade +620 n03642806 笔记本电脑, laptop, laptop computer +621 n03649909 割草机, lawn mower, mower +622 n03657121 镜头盖, lens cap, lens cover +623 n03658185 小刀, letter opener, paper knife, paperknife +624 n03661043 图书馆, library +625 n03662601 救生船, lifeboat +626 n03666591 打火机, lighter, light, igniter, ignitor +627 n03670208 加长车 check, limousine, limo +628 n03673027 轮船, liner, ocean liner +629 n03676483 口红, lipstick, lip rouge +630 n03680355 鞋子, Loafer +631 n03690938 护肤品, lotion +632 n03691459 音响, loudspeaker, speaker, speaker unit, loudspeaker system, speaker system +633 n03692522 放大镜, loupe, jeweler’s loupe +634 n03697007 原木, lumbermill, sawmill +635 n03706229 指南针, magnetic compass +636 n03709823 包, mailbag, postbag +637 n03710193 邮箱, mailbox, letter box +638 n03710637 泳衣, maillot +639 n03710721 泳衣, maillot, tank suit +640 n03717622 窨井盖, manhole cover +641 n03720891 手摇铃, maraca +642 n03721384 木琴, marimba, xylophone +643 n03724870 面具, mask +644 n03729826 火柴, matchstick +645 n03733131 绳子, maypole +646 n03733281 迷宫, maze, labyrinth +647 n03733805 烧杯, measuring cup +648 n03742115 冰箱, medicine chest, medicine cabinet +649 n03743016 石柱, megalith, megalithic structure +650 n03759954 话筒, microphone, mike +651 n03761084 微波炉, microwave, microwave oven +652 n03763968 军人, military uniform +653 n03764736 水壶, milk can +654 n03769881 小客车 check, minibus +655 n03770439 短裙, miniskirt, mini +656 n03770679 面包车 check, minivan +657 n03773504 导弹, missile +658 n03775071 手套, mitten +659 n03775546 碗, mixing bowl +660 n03776460 房车, mobile home, manufactured home +661 n03777568 老爷车, Model T +662 n03777754 路由器, modem +663 n03781244 建筑, monastery +664 n03782006 显示器, monitor +665 n03785016 摩托车 check, moped +666 n03786901 砚, mortar +667 n03787032 学士帽, mortarboard +668 n03788195 建筑, mosque +669 n03788365 蚊帐, mosquito net +670 n03791053 助动车 check, motor scooter, scooter +671 n03792782 自行车 check, mountain bike, all-terrain bike, off-roader +672 n03792972 帐篷, mountain tent +673 n03793489 键盘鼠标, mouse, computer mouse +674 n03794056 捕鼠夹, mousetrap +675 n03796401 货车 卡车 check, moving van +676 n03803284 狗嘴套, muzzle +677 n03804744 钉子, nail +678 n03814639 颈托, neck brace +679 n03814906 项链, necklace +680 n03825788 奶瓶, nipple +681 n03832673 笔记本电脑, notebook, notebook computer +682 n03837869 建筑, obelisk +683 n03838899 黑管, oboe, hautboy, hautbois +684 n03840681 埙, ocarina, sweet potato +685 n03841143 仪表盘, odometer, hodometer, mileometer, milometer +686 n03843555 机油滤清器, oil filter +687 n03854065 管风琴, organ, pipe organ +688 n03857828 示波器, oscilloscope, scope, cathode-ray oscilloscope, CRO +689 n03866082 礼服, overskirt +690 n03868242 牛车, oxcart +691 n03868863 呼吸器, oxygen mask +692 n03871628 零食, packet +693 n03873416 划桨, paddle, boat paddle +694 n03874293 水轮, paddlewheel, paddle wheel +695 n03874599 锁, padlock +696 n03876231 刷子, paintbrush +697 n03877472 睡衣, pajama, pyjama, pj’s, jammies +698 n03877845 建筑, palace +699 n03884397 乐器, panpipe, pandean pipe, syrinx +700 n03887697 纸巾, paper towel +701 n03888257 降落伞, parachute, chute +702 n03888605 体操, parallel bars, bars +703 n03891251 长椅, park bench +704 n03891332 停车缴费器, parking meter +705 n03895866 火车 check, passenger car, coach, carriage +706 n03899768 院子, patio, terrace +707 n03902125 公用电话, pay-phone, pay-station +708 n03903868 柱子, pedestal, plinth, footstall +709 n03908618 文具袋, pencil box, pencil case +710 n03908714 卷笔刀, pencil sharpener +711 n03916031 香水, perfume, essence +712 n03920288 培养皿, Petri dish +713 n03924679 打印机, photocopier +714 n03929660 吉他拨片, pick, plectrum, plectron +715 n03929855 头盔, pickelhaube +716 n03930313 栅栏, picket fence, paling +717 n03930630 轿车 check, pickup, pickup truck +718 n03933933 桥, pier +719 n03935335 储蓄罐, piggy bank, penny bank +720 n03937543 药丸, pill bottle +721 n03938244 枕头, pillow +722 n03942813 乒乓球, ping-pong ball +723 n03944341 风车, pinwheel +724 n03947888 帆船, pirate, pirate ship +725 n03950228 茶壶, pitcher, ewer +726 n03954731 刨子, plane, carpenter’s plane, woodworking plane +727 n03956157 建筑, planetarium +728 n03958227 塑料袋, plastic bag +729 n03961711 碗架, plate rack +730 n03967562 推土机, plow, plough +731 n03970156 搋子, plunger, plumber’s helper +732 n03976467 相机, Polaroid camera, Polaroid Land camera +733 n03976657 杆子, pole +734 n03977966 警车 check, police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria +735 n03980874 披风, poncho +736 n03982430 桌球, pool table, billiard table, snooker table +737 n03983396 瓶子, pop bottle, soda bottle +738 n03991062 盆栽, pot, flowerpot +739 n03992509 陶艺, potter’s wheel +740 n03995372 钻机, power drill +741 n03998194 毯子, prayer rug, prayer mat +742 n04004767 打印机, printer +743 n04005630 监狱, prison, prison house +744 n04008634 导弹, projectile, missile +745 n04009552 投影仪, projector +746 n04019541 冰球, puck, hockey puck +747 n04023962 拳击, punching bag, punch bag, punching ball, punchball +748 n04026417 手提袋, purse +749 n04033901 羽毛笔, quill, quill pen +750 n04033995 床 被子, quilt, comforter, comfort, puff +751 n04037443 赛车, racer, race car, racing car +752 n04039381 网球, racket, racquet +753 n04040759 加热器, radiator +754 n04041544 收音机, radio, wireless +755 n04044716 卫星接收器, radio telescope, radio reflector +756 n04049303 酒桶, rain barrel +757 n04065272 房车, recreational vehicle, RV, R.V. +758 n04067472 鱼竿, reel +759 n04069434 相机, reflex camera +760 n04070727 冰箱, refrigerator, icebox +761 n04074963 遥控器, remote control, remote +762 n04081281 餐厅, restaurant, eating house, eating place, eatery +763 n04086273 手枪, revolver, six-gun, six-shooter +764 n04090263 狙击枪, rifle +765 n04099969 摇椅, rocking chair, rocker +766 n04111531 烤箱, rotisserie +767 n04116512 橡皮, rubber eraser, rubber, pencil eraser +768 n04118538 橄榄球, rugby ball +769 n04118776 尺, rule, ruler +770 n04120489 运动鞋, running shoe +771 n04125021 保险箱, safe +772 n04127249 回形针, safety pin +773 n04131690 调料瓶, saltshaker, salt shaker +774 n04133789 拖鞋, sandal +775 n04136333 长裙, sarong +776 n04141076 萨克斯, sax, saxophone +777 n04141327 剑, scabbard +778 n04141975 秤, scale, weighing machine +779 n04146614 校车, school bus +780 n04147183 帆船, schooner +781 n04149813 计分板, scoreboard +782 n04152593 显示器, screen, CRT screen +783 n04153751 螺丝, screw +784 n04154565 螺丝刀, screwdriver +785 n04162706 安全带, seat belt, seatbelt +786 n04179913 缝纫机, sewing machine +787 n04192698 盾牌, shield, buckler +788 n04200800 鞋店, shoe shop, shoe-shop, shoe store +789 n04201297 榻榻米, shoji +790 n04204238 购物篮, shopping basket +791 n04204347 购物车, shopping cart +792 n04208210 铲子, shovel +793 n04209133 浴帽, shower cap +794 n04209239 浴帘, shower curtain +795 n04228054 滑雪, ski +796 n04229816 面罩, ski mask +797 n04235860 睡袋, sleeping bag +798 n04238763 游标卡尺, slide rule, slipstick +799 n04239074 移门, sliding door +800 n04243546 老虎机, slot, one-armed bandit +801 n04251144 游泳眼镜, snorkel +802 n04252077 滑雪车, snowmobile +803 n04252225 铲雪车, snowplow, snowplough +804 n04254120 洗手液, soap dispenser +805 n04254680 足球, soccer ball +806 n04254777 袜子, sock +807 n04258138 太阳能板, solar dish, solar collector, solar furnace +808 n04259630 帽子, sombrero +809 n04263257 碗, soup bowl +810 n04264628 键盘, space bar +811 n04265275 电热器, space heater +812 n04266014 航天飞船, space shuttle +813 n04270147 锅铲, spatula +814 n04273569 快艇, speedboat +815 n04275548 蜘蛛网, spider web, spider’s web +816 n04277352 毛线, spindle +817 n04285008 运动型轿车 check, sports car, sport car +818 n04286575 探照灯, spotlight, spot +819 n04296562 乐队, stage +820 n04310018 蒸汽机车, steam locomotive +821 n04311004 桥, steel arch bridge +822 n04311174 鼓, steel drum +823 n04317175 听诊器, stethoscope +824 n04325704 担架, stole +825 n04326547 石堆, stone wall +826 n04328186 秒表, stopwatch, stop watch +827 n04330267 火炉, stove +828 n04332243 滤网, strainer +829 n04335435 公交车 check, streetcar, tram, tramcar, trolley, trolley car +830 n04336792 担架, stretcher +831 n04344873 沙发, studio couch, day bed +832 n04346328 皇宫, stupa, tope +833 n04347754 轮船, submarine, pigboat, sub, U-boat +834 n04350905 西装, suit, suit of clothes +835 n04355338 日晷, sundial +836 n04355933 墨镜, sunglass +837 n04356056 墨镜, sunglasses, dark glasses, shades +838 n04357314 防晒霜, sunscreen, sunblock, sun blocker +839 n04366367 桥, suspension bridge +840 n04367480 拖把, swab, swob, mop +841 n04370456 连帽衫, sweatshirt +842 n04371430 沙滩裤, swimming trunks, bathing trunks +843 n04371774 秋千, swing +844 n04372370 开关, switch, electric switch, electrical switch +845 n04376876 针筒, syringe +846 n04380533 台灯, table lamp +847 n04389033 坦克, tank, army tank, armored combat vehicle, armoured combat vehicle +848 n04392985 磁带播放器, tape player +849 n04398044 茶壶, teapot +850 n04399382 毛绒玩具, teddy, teddy bear +851 n04404412 电视机, television, television system +852 n04409515 网球, tennis ball +853 n04417672 草屋, thatch, thatched roof +854 n04418357 幕布, theater curtain, theatre curtain +855 n04423845 指套, thimble +856 n04428191 装甲车, thresher, thrasher, threshing machine +857 n04429376 皇位, throne +858 n04435653 瓦片, tile roof +859 n04442312 面包机, toaster +860 n04443257 烟酒店, tobacco shop, tobacconist shop, tobacconist +861 n04447861 马桶, toilet seat +862 n04456115 火炬, torch +863 n04458633 图腾, totem pole +864 n04461696 大卡车 check, tow truck, tow car, wrecker +865 n04462240 玩具店, toyshop +866 n04465501 拖拉机, tractor +867 n04467665 大货车 check, trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi +868 n04476259 碟子 盘子, tray +869 n04479046 风衣, trench coat +870 n04482393 儿童自行车, tricycle, trike, velocipede +871 n04483307 船, trimaran +872 n04485082 三脚架, tripod +873 n04486054 拱门, triumphal arch +874 n04487081 巴士 check, trolleybus, trolley coach, trackless trolley +875 n04487394 长号, trombone +876 n04493381 浴缸, tub, vat +877 n04501370 闸机, turnstile +878 n04505470 打字机, typewriter keyboard +879 n04507155 伞, umbrella +880 n04509417 独轮车, unicycle, monocycle +881 n04515003 钢琴, upright, upright piano +882 n04517823 吸尘器, vacuum, vacuum cleaner +883 n04522168 花瓶, vase +884 n04523525 拱廊, vault +885 n04525038 珊瑚绒, velvet +886 n04525305 自动贩卖机, vending machine +887 n04532106 教皇袍, vestment +888 n04532670 桥, viaduct +889 n04536866 小提琴, violin, fiddle +890 n04540053 排球, volleyball +891 n04542943 煎饼锅, waffle iron +892 n04548280 挂钟, wall clock +893 n04548362 钱夹, wallet, billfold, notecase, pocketbook +894 n04550184 柜子, wardrobe, closet, press +895 n04552348 飞机, warplane, military plane +896 n04553703 台盆, washbasin, handbasin, washbowl, lavabo, wash-hand basin +897 n04554684 洗衣机, washer, automatic washer, washing machine +898 n04557648 水瓶, water bottle +899 n04560804 水壶, water jug +900 n04562935 煤气包, water tower +901 n04579145 水壶, whiskey jug +902 n04579432 哨子, whistle +903 n04584207 头发, wig +904 n04589890 窗户, window screen +905 n04590129 百叶窗, window shade +906 n04591157 领带, Windsor tie +907 n04591713 葡萄酒, wine bottle +908 n04592741 飞机, wing +909 n04596742 炒锅, wok +910 n04597913 勺子, wooden spoon +911 n04599235 围巾, wool, woolen, woollen +912 n04604644 栅栏, worm fence, snake fence, snake-rail fence, Virginia fence +913 n04606251 沉船, wreck +914 n04612504 帆船, yawl +915 n04613696 蒙古包, yurt +916 n06359193 网页, web site, website, internet site, site +917 n06596364 海报, comic book +918 n06785654 填字游戏, crossword puzzle, crossword +919 n06794110 交通标志, street sign +920 n06874185 交通灯, traffic light, traffic signal, stoplight +921 n07248320 书, book jacket, dust cover, dust jacket, dust wrapper +922 n07565083 菜单, menu +923 n07579787 菜, plate +924 n07583066 菜, guacamole +925 n07584110 菜, consomme +926 n07590611 菜, hot pot, hotpot +927 n07613480 蛋糕, trifle +928 n07614500 冰激凌, ice cream, icecream +929 n07615774 棒冰, ice lolly, lolly, lollipop, popsicle +930 n07684084 面包, French loaf +931 n07693725 甜甜圈, bagel, beigel +932 n07695742 面包, pretzel +933 n07697313 汉堡, cheeseburger +934 n07697537 热狗, hotdog, hot dog, red hot +935 n07711569 焗饭, mashed potato +936 n07714571 蔬菜, head cabbage +937 n07714990 西蓝花, broccoli +938 n07715103 花椰菜, cauliflower +939 n07716358 蔬菜, zucchini, courgette +940 n07716906 金瓜, spaghetti squash +941 n07717410 南瓜, acorn squash +942 n07717556 南瓜, butternut squash +943 n07718472 黄瓜, cucumber, cuke +944 n07718747 蔬菜, artichoke, globe artichoke +945 n07720875 青椒 黄椒 红椒, bell pepper +946 n07730033 花, cardoon +947 n07734744 蘑菇, mushroom +948 n07742313 苹果, Granny Smith +949 n07745940 草莓, strawberry +950 n07747607 橙子, orange +951 n07749582 柠檬, lemon +952 n07753113 水果, fig +953 n07753275 菠萝, pineapple, ananas +954 n07753592 香蕉, banana +955 n07754684 榴莲, jackfruit, jak, jack +956 n07760859 水果, custard apple +957 n07768694 石榴, pomegranate +958 n07802026 草垛, hay +959 n07831146 意大利面, carbonara +960 n07836838 甜品, chocolate sauce, chocolate syrup +961 n07860988 面团, dough +962 n07871810 肉酱, meat loaf, meatloaf +963 n07873807 披萨, pizza, pizza pie +964 n07875152 派, potpie +965 n07880968 肉卷, burrito +966 n07892512 红酒, red wine +967 n07920052 咖啡, espresso +968 n07930864 茶, cup +969 n07932039 饮料杯, eggnog +970 n09193705 雪山, alp +971 n09229709 泡泡, bubble +972 n09246464 悬崖, cliff, drop, drop-off +973 n09256479 珊瑚, coral reef +974 n09288635 温泉, geyser +975 n09332890 风景, lakeside, lakeshore +976 n09399592 小岛, promontory, headland, head, foreland +977 n09421951 沙滩, sandbar, sand bar +978 n09428293 海滩, seashore, coast, seacoast, sea-coast +979 n09468604 瀑布 溪流, valley, vale +980 n09472597 火山, volcano +981 n09835506 棒球, ballplayer, baseball player +982 n10148035 婚礼, groom, bridegroom +983 n10565667 潜水, scuba diver +984 n11879895 油菜花, rapeseed +985 n11939491 菊花, daisy +986 n12057211 植物, yellow lady’s slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum +987 n12144580 玉米, corn +988 n12267677 松果, acorn +989 n12620546 植物, hip, rose hip, rosehip +990 n12768682 栗子, buckeye, horse chestnut, conker +991 n12985857 菌菇, coral fungus +992 n12998815 菌菇, agaric +993 n13037406 菌菇, gyromitra +994 n13040303 菌菇, stinkhorn, carrion fungus +995 n13044778 菌菇, earthstar +996 n13052670 菌菇, hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa +997 n13054560 菌菇, bolete +998 n13133613 玉米, ear, spike, capitulum +999 n15075141 卷筒纸, toilet tissue, toilet paper, bathroom tissue \ No newline at end of file diff --git a/attacks/Gaker/utils_/gaussian_smoothing.py b/attacks/Gaker/utils_/gaussian_smoothing.py new file mode 100644 index 0000000000000000000000000000000000000000..1bf8b501c3065eba86c54b0228049374a75ec10f --- /dev/null +++ b/attacks/Gaker/utils_/gaussian_smoothing.py @@ -0,0 +1,39 @@ +import torch +import torch.nn as nn +import math + + +# From https://discuss.pytorch.org/t/is-there-anyway-to-do-gaussian-filtering-for-an-image-2d-3d-in-pytorch/12351/3 +def get_gaussian_kernel(kernel_size=3, pad=2, sigma=2, channels=3): + # Create a x, y coordinate grid of shape (kernel_size, kernel_size, 2) + x_coord = torch.arange(kernel_size) + x_grid = x_coord.repeat(kernel_size).view(kernel_size, kernel_size) + y_grid = x_grid.t() + xy_grid = torch.stack([x_grid, y_grid], dim=-1).float() + + mean = (kernel_size - 1) / 2. + variance = sigma ** 2. + + # Calculate the 2-dimensional gaussian kernel which is + # the product of two gaussian distributions for two different + # variables (in this case called x and y) + gaussian_kernel = (1. / (2. * math.pi * variance)) * \ + torch.exp( + -torch.sum((xy_grid - mean) ** 2., dim=-1) / \ + (2 * variance) + ) + + # Make sure sum of values in gaussian kernel equals 1. + gaussian_kernel = gaussian_kernel / torch.sum(gaussian_kernel) + + # Reshape to 2d depthwise convolutional weight + gaussian_kernel = gaussian_kernel.view(1, 1, kernel_size, kernel_size) + gaussian_kernel = gaussian_kernel.repeat(channels, 1, 1, 1) + + gaussian_filter = nn.Conv2d(in_channels=channels, out_channels=channels, + kernel_size=kernel_size, groups=channels, padding=kernel_size-pad, bias=False) + + gaussian_filter.weight.data = gaussian_kernel + gaussian_filter.weight.requires_grad = False + + return gaussian_filter diff --git a/attacks/Gaker/utils_/metrics.py b/attacks/Gaker/utils_/metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..7367b3678a61edbbb4d513552ce022771dee9c6d --- /dev/null +++ b/attacks/Gaker/utils_/metrics.py @@ -0,0 +1,484 @@ +import torch +import numpy as np +import torch.nn.functional as F +from torch import nn + +def KL(P,Q,mask=None): + eps = 0.0000001 + d = (P+eps).log()-(Q+eps).log() + d = P*d + if mask !=None: + d = d*mask + return torch.sum(d) +def CE(P,Q,mask=None): + return KL(P,Q,mask)+KL(1-P,1-Q,mask) + +def algorithm2(P,Q,Y): + eps = 0.0000001 + mean = P.mean(dim=1) + mask1 = P>=mean + mask2 = Y == Y.t() + mask = mask1 == mask2 + loss =torch.mean(P * torch.log((P + eps) / (Q + eps))) + return loss + +def umap(output_net, target_net, eps=0.0000001): + # Normalize each vector by its norm + (n, d) = output_net.shape + output_net_norm = torch.sqrt(torch.sum(output_net ** 2, dim=1, keepdim=True)) + output_net = output_net / (output_net_norm + eps) + output_net[output_net != output_net] = 0 + target_net_norm = torch.sqrt(torch.sum(target_net ** 2, dim=1, keepdim=True)) + target_net = target_net / (target_net_norm + eps) + target_net[target_net != target_net] = 0 + # Calculate the cosine similarity + model_similarity = torch.mm(output_net, output_net.transpose(0, 1)) + model_distance = 1-model_similarity #[0,2] + model_distance[range(n), range(n)] = 3 + model_distance = model_distance - torch.min(model_distance, dim=1)[0].view(-1, 1) + model_distance[range(n), range(n)] = 0 + model_similarity = 1-model_distance + target_similarity = torch.mm(target_net, target_net.transpose(0, 1)) + target_distance = 1-target_similarity + target_distance[range(n), range(n)] = 3 + target_distance = target_distance - torch.min(target_distance,dim=1)[0].view(-1,1) + target_distance[range(n), range(n)] = 0 + target_similarity = 1 - target_distance + # Scale cosine similarity to 0..1 + model_similarity = (model_similarity + 1.0) / 2.0 + target_similarity = (target_similarity + 1.0) / 2.0 + # Transform them into probabilities + model_similarity = model_similarity / torch.sum(model_similarity, dim=1, keepdim=True) + target_similarity = target_similarity / torch.sum(target_similarity, dim=1, keepdim=True) + # Calculate the KL-divergence + loss = CE(target_similarity,model_similarity) + return loss + + +def supervised_umap(output_net, target_net,y,sample_weight=0, eps=0.0000001): + # Normalize each vector by its norm + (n, d) = output_net.shape + distance = 2.0 + tahn = nn.Tanh() + sample_weight = sample_weight.view(-1,n) + sample_weight_matrix = (sample_weight+sample_weight.t())/32.0 + sample_weight_matrix = tahn(sample_weight_matrix) + y = y.view(-1,n) + # print("y",y) + mask =1- (y==y.t()).float() + mask[mask == 0] = -1 + distance = distance*mask*sample_weight_matrix + output_net_norm = torch.sqrt(torch.sum(output_net ** 2, dim=1, keepdim=True)) + output_net = output_net / (output_net_norm + eps) + output_net[output_net != output_net] = 0 + + target_net_norm = torch.sqrt(torch.sum(target_net ** 2, dim=1, keepdim=True)) + target_net = target_net / (target_net_norm + eps) + target_net[target_net != target_net] = 0 + + # Calculate the cosine similarity + model_similarity = torch.mm(output_net, output_net.transpose(0, 1)) + model_distance = 1 - model_similarity # [0,2] + model_distance[range(n), range(n)] = 100000 + model_distance = model_distance - torch.min(model_distance, dim=1)[0].view(-1, 1) + model_distance[range(n), range(n)] = 0 + model_distance = torch.clamp(model_distance, 0+eps, 2.0-eps) + + model_similarity = 1 - model_distance + + target_similarity = torch.mm(target_net, target_net.transpose(0, 1)) + target_distance = 1 - target_similarity + target_distance[range(n), range(n)] = 100000 + p = torch.min(target_distance, dim=1) + target_distance = target_distance - p[0].view(-1, 1) + target_distance[range(n), range(n)] = 0 + target_distance = (1 - sample_weight_matrix) * target_distance + distance + target_distance = torch.clamp(target_distance, 0+eps, 2.0-eps) + target_similarity = 1 - target_distance + + # Scale cosine similarity to 0..1 + model_similarity = (model_similarity + 1.0) / 2.0 + target_similarity = (target_similarity + 1.0) / 2.0 + + # # Transform them into probabilities + model_similarity = model_similarity / torch.sum(model_similarity, dim=1, keepdim=True) + target_similarity = target_similarity / torch.sum(target_similarity, dim=1, keepdim=True) + # Calculate the CE-Loss + loss = CE(target_similarity, model_similarity) + return loss + +def cosine_similarity_loss(output_net, target_net, eps=0.0000001): + # Normalize each vector by its norm + output_net_norm = torch.sqrt(torch.sum(output_net ** 2, dim=1, keepdim=True)) + output_net = output_net / (output_net_norm + eps) + output_net[output_net != output_net] = 0 + + target_net_norm = torch.sqrt(torch.sum(target_net ** 2, dim=1, keepdim=True)) + target_net = target_net / (target_net_norm + eps) + target_net[target_net != target_net] = 0 + + # Calculate the cosine similarity + model_similarity = torch.mm(output_net, output_net.transpose(0, 1)) + target_similarity = torch.mm(target_net, target_net.transpose(0, 1)) + + # Scale cosine similarity to 0..1 + model_similarity = (model_similarity + 1.0) / 2.0 + target_similarity = (target_similarity + 1.0) / 2.0 + + # Transform them into probabilities + model_similarity = model_similarity / torch.sum(model_similarity, dim=1, keepdim=True) + target_similarity = target_similarity / torch.sum(target_similarity, dim=1, keepdim=True) + + # Calculate the KL-divergence + loss = CE(target_similarity, model_similarity) + + return loss + + + +class dual_softmax_loss(nn.Module): + def __init__(self, ): + super(dual_softmax_loss, self).__init__() + + def forward(self, sim_matrix, temp=1000): + sim_matrix = sim_matrix * F.softmax(sim_matrix / temp, dim=0) * len( + sim_matrix) # With an appropriate temperature parameter, the model achieves higher performance + logpt = F.log_softmax(sim_matrix, dim=-1) # row softmax and column softmax + logpt = torch.diag(logpt) + loss = -logpt + return loss + + +def log_sum_exp(x): + '''Utility function for computing log-sun-exp while determining + This will be used to determine unaveraged confidence loss across all examples in a batch. + ''' + x_max = x.data.max() + return torch.log(torch.sum(torch.exp(x - x_max), -1, keepdim=True)) + x_max + + +def l2norm(X, dim, eps=1e-8): + """L2-normalize columns of X + """ + norm = torch.pow(X, 2).sum(dim=dim, keepdim=True).sqrt() + eps + X = torch.div(X, norm) + return X + + +def calcdist(img, txt): + ''' + Input img = (batch,dim), txt = (batch,dim) + Output Euclid Distance Matrix = Tensor(batch,batch), and dist[i,j] = d(img_i,txt_j) + ''' + dist = img.unsqueeze(1) - txt.unsqueeze(0) + dist = torch.sum(torch.pow(dist, 2), dim=2) + return torch.sqrt(dist) + + +def calcmatch(label): + ''' + Input label = (batch,) + Output Match Matrix =Tensor(batch,batch) and match[i,j] == 1 iff. label[i]==label[j] + ''' + match = label.unsqueeze(1) - label.unsqueeze(0) + match[match != 0] = 1 + return 1 - match + + +def calcneg(dist, label, anchor, positive): + ''' + Input dist = (batch,batch), label = (batch,), anchor = index, positive = index + Output chosen negative sample index + ''' + + standard = dist[anchor, positive] # positive distance + dist = dist[anchor] - standard # distance of other samples + if max(dist[label != label[anchor]]) >= 0: # there exists valid negative + dist[dist < 0] = max(dist) + 2 # delete negative samples below standard + dist[label == label[anchor]] = max(dist) + 2 # delete positive samples + return int(torch.argmin(dist).cpu()) # return the closest negative sample + else: # choose argmax + dist[label == label[anchor]] = min(dist) - 2 # delete positive samples + return int(torch.argmax(dist).cpu()) + + +def calcneg_dot(img, txt, match, anchor, positive): + ''' + Input img = (batch,dim), txt = (batch,dim), match = (batch,batch), anchor = index, positive = index + Output chosen negative sample index + ''' + distdot = torch.sum(torch.mul(img.unsqueeze(1), txt.unsqueeze(0)), 2) + distdot[match == 1] = -66666 + return int(torch.argmax(distdot[anchor]).cpu()) + + +def Triplet(img, txt, label): + ''' + Input img = (batch,dim), txt = (batch,dim), label = (batch,) + Output dist = (batch,batch),match = (batch,batch), triplets = List with shape(pairs,3) + ''' + triplet_list = [] + batch = img.shape[0] + dist = calcdist(img, txt) + match = calcmatch(label) + match_n = match.cpu().numpy() + positive_list = np.argwhere(match_n == 1).tolist() # the index list of all positive samples + for positive in positive_list: + negative = calcneg(dist, label, positive[0], positive[1]) # calculate negatives + # negative = calcneg_dot(img, txt, match, anchor, positive) # calculate negative with dot 效果很差 + triplet_list.append([positive[0], int(positive[1].cuda()), negative]) + + return dist, match, triplet_list + + +def Positive(img, txt, label): + ''' + Input img = (batch,dim), txt = (batch,dim), label = (batch,) + Output dist = (batch,batch),match = (batch,batch), positives = List with shape(pairs,2) + Remark: return (anchor,positive) without finding triplets + ''' + batch = img.shape[0] + dist = calcdist(img, txt) + match = calcmatch(label) + sample_list = torch.tensor([x for x in range(batch)]).int().cuda() + positive_list = [[i, int(j.cpu())] for i in range(batch) for j in sample_list[label == label[i]]] + return dist, match, positive_list + + +def Modality_invariant_Loss(img, txt, label, margin=0.2): + ''' + Input img = (batch,dim), txt = (batch,dim), label = (batch,), margin = parameter + Calculate invariant loss between images and texts belonging to the same class + ''' + batch = img.shape[0] + dist = calcdist(img, txt) + dist = torch.pow(dist, 2) + match = calcmatch(label) # similar is 1, dissimilar is 0 + pos = torch.mul(dist, match) + loss = torch.sum(pos) + + return loss / batch + + +def Contrastive_Loss(img, txt, label, margin=0.2): + ''' + Input img = (batch,dim), txt = (batch,dim), label = (batch,), margin = parameter + Calculate triplet loss + ''' + batch = img.shape[0] + dist = calcdist(img, txt) + dist = torch.pow(dist, 2) + match = calcmatch(label) # similar is 1, dissimilar is 0 + pos = torch.mul(dist, match) + neg = margin - torch.mul(dist, 1 - match) + neg = torch.clamp(neg, 0) + loss = torch.sum(pos) + torch.sum(neg) + + return loss / batch + + +def Triplet_Loss(img, txt, label, margin=0.2, semi_hard=True): + ''' + Input img = (batch,dim), txt = (batch,dim), label = (batch,), margin = parameter + Calculate triplet loss + ''' + loss = 0 + dist = calcdist(img, txt) + dist = torch.pow(dist, 2) + match = calcmatch(label) + match_n = match.cpu().numpy() + positive = np.argwhere(match_n == 1).tolist() # the index list of all positive samples + for x in positive: + # # Semi-Hard Negative Mining + if semi_hard: + neg_index = torch.where( + match[x[0]] == 0) # the index list of all negative samples (shared by image and text) + neg_dis = dist[x[0]][neg_index] + tmp = dist[x[0], x[1]] - neg_dis + margin + tmp = torch.clamp(tmp, 0) + loss = loss + torch.sum(tmp, dim=-1) + else: + # Hard Negative Mining + negative = calcneg(dist, label, x[0], x[1]) # calculate hard negative + tmp = dist[x[0], x[1]] - dist[x[0], negative] + margin + if tmp > 0: + loss = loss + tmp + + return loss / len(positive) + + +def Lifted_Loss(img, txt, label, margin=1): # the margin is set to be 1 as the original paper + ''' + Input img = (batch,dim), txt = (batch,dim), label = (batch,), margin = parameter + Calculate lifted structured embedding loss + ''' + # dist, match, positive = Positive(img, txt, label) + dist = calcdist(img, txt) + match = calcmatch(label) + match_n = match.cpu().numpy() + positive = np.argwhere(match_n == 1).tolist() # the index list of all positive samples + loss = 0 + for x in positive: + neg_index = torch.where(match[x[0]] == 0) # the index list of all negative samples (shared by image and text) + neg_dis_anchor = dist[x[0]][neg_index] + neg_dis_postive = dist[x[1]][neg_index] + tmp = dist[x[0], x[1]] + log_sum_exp(margin - neg_dis_postive) + log_sum_exp(margin - neg_dis_anchor) + loss = loss + tmp + + return loss / (2 * len(positive)) + + +def Npairs(img, txt, label, margin=0.2, alpha=0.1): + ''' + Input img = (batch,dim), txt = (batch,dim), label = (batch,), margin = parameter, alpha = parameter + Calculate N-pairs loss + ''' + # dist, match, positive = Positive(img, txt, label) + batch = img.shape[0] + distdot_it = torch.exp(F.linear(img, txt)) + # distdot_ti = torch.t(distdot) + # !!!!!!!!!!!!! + distdot_ti = torch.t(distdot_it) + # !!!!!!!!!!!!! + match = calcmatch(label) + match_n = match.cpu().numpy() + positive = np.argwhere(match_n == 1).tolist() # the index list of all positive samples + loss = 0 + for x in positive: + neg_index = torch.where(match[x[0]] == 0) # the index list of all negative samples + tmp_i2t = distdot_it[x[0], x[1]] - log_sum_exp(distdot_it[x[0]][neg_index]) + tmp_t2i = distdot_ti[x[0], x[1]] - log_sum_exp(distdot_ti[x[0]][neg_index]) + loss = loss + (tmp_i2t + tmp_t2i) / 2 + loss = -loss / len(positive) + for x in range(batch): + loss = loss + alpha * (torch.norm(img[x]) + torch.norm(txt[x])) / batch + + return loss + + +def Supervised_Contrastive_Loss(img, txt, label): + ''' + Input img = (batch,dim), txt = (batch,dim), label = (batch,), margin = parameter + An unofficial implementation of supervised contrastive loss for multimodal learning + ''' + loss = 0 + batch = img.shape[0] + dist = calcdist(img, txt) + dist = torch.pow(dist, 2) + dist = dist / (torch.sum(dist) / (batch * batch)) # scale the metric + match = calcmatch(label) # 相似为1，不相似为0 + match_n = match.cpu().numpy() + positive = np.argwhere(match_n == 1).tolist() # the index list of all positive samples + loss = 0 + for x in positive: + neg_index = torch.where(match[x[0]] == 0) # the index list of all negative samples + pos_sim = -dist[x[0], x[1]] + neg_sims = -dist[x[0]][neg_index] + tmp = pos_sim - log_sum_exp(neg_sims) + + loss = loss + tmp + + loss = -loss / len(positive) + + return loss + + +def regularization(features, centers, labels): + # features = l2norm(features, dim=-1) + distance = (features - centers[labels]) + distance = torch.sum(torch.pow(distance, 2), 1, keepdim=True) + distance = (torch.sum(distance, 0, keepdim=True)) / features.shape[0] + + return distance + + +def PAN(features, centers, labels, add_regularization=False): + """The prototype contrastive loss and regularization loss in + PAN(https://dl.acm.org/doi/abs/10.1145/3404835.3462867)""" + batch = features.shape[0] + features_square = torch.sum(torch.pow(features, 2), 1, keepdim=True) # 在第一个维度上平方 + centers_square = torch.sum(torch.pow(torch.t(centers), 2), 0, keepdim=True) + features_into_centers = 2 * torch.matmul(features, torch.t(centers)) + dist = -(features_square + centers_square - features_into_centers) + output = F.log_softmax(dist, dim=1) + dce_loss = F.nll_loss(output, labels) + + if add_regularization: + reg_loss = regularization(features, centers, labels) + loss = dce_loss + reg_loss + + loss = dce_loss + + return loss / batch + + +def Label_Regression_Loss(view1_predict, view2_predict, label_onehot): + loss = ((view1_predict - label_onehot.float()) ** 2).sum(1).sqrt().mean() + ( + (view2_predict - label_onehot.float()) ** 2).sum(1).sqrt().mean() + + return loss + + +def Proxy_NCA(features, label, proxies, mrg=0.1, alpha=1): + """ + Input: + :param feature: [2*batch, dim] concat image and text features + :param label: [2*batch] + :param proxies: [feature_dim, n_classes] + :return: Proxy Anchor loss + """ + P = torch.t(proxies) # [feature_dim, n_classes]-->[n_classes, feature_dim] + n_classes = P.shape[0] + # similar to Proxc-NCA and Normlized Softmax + cos = F.linear(features, P) # Calcluate cosine similarity [batch, n_classes] + + # Proxy-NCA loss (similar to Normlized Softmax and PAN，while the denominator does not contain positive prototype) + loss = 0 + for x in range(features.shape[0]): + pos = torch.exp(cos[x, label[x]]) + neg = torch.exp(cos[x]).sum(dim=-1) - pos + loss = loss + torch.log(pos / neg) + loss = -loss / features.shape[0] + + return loss + + +def Proxy_Anchor(features, label, proxies, mrg=0.1, alpha=1): + """ + Input: + :param + feature: [2 * batch, dim] + concat + image and text + features + :param + label: [2 * batch] + :param + proxies: [feature_dim, n_classes] + :return: Proxy + Anchor + loss + """ + P = torch.t(proxies) # [feature_dim, n_classes]-->[n_classes, feature_dim] + n_classes = P.shape[0] + # similar to Proxc-NCA and Normlized Softmax + cos = F.linear(features, P) # Calcluate cosine similarity [batch, n_classes] + + P_one_hot = label # [batch, n_classes] + N_one_hot = 1 - P_one_hot + + pos_exp = torch.exp(-alpha * (cos - mrg)) # [batch, n_class] + neg_exp = torch.exp(alpha * (cos + mrg)) # 出现了e+30导致nan + + with_pos_proxies = torch.nonzero(P_one_hot.sum(dim=0) != 0).squeeze( + dim=1) # The set of positive proxies of data in the batch + num_valid_proxies = len(with_pos_proxies) # The number of positive proxies + + P_sim_sum = torch.where(P_one_hot == 1, pos_exp, torch.zeros_like(pos_exp)).sum(dim=0) + N_sim_sum = torch.where(N_one_hot == 1, neg_exp, torch.zeros_like(neg_exp)).sum(dim=0) + + pos_term = torch.log(1 + P_sim_sum).sum() / num_valid_proxies + neg_term = torch.log(1 + N_sim_sum).sum() / n_classes + loss = pos_term + neg_term + + return loss diff --git a/attacks/Gaker/utils_/nce.py b/attacks/Gaker/utils_/nce.py new file mode 100644 index 0000000000000000000000000000000000000000..1dd105ba0219737c1b7713d381ff3dd9735efb19 --- /dev/null +++ b/attacks/Gaker/utils_/nce.py @@ -0,0 +1,126 @@ +import torch +import torch.nn.functional as F +from torch import nn + +__all__ = ['InfoNCE', 'info_nce'] + + +class InfoNCE(nn.Module): + """ + Calculates the InfoNCE loss for self-supervised learning. + This contrastive loss enforces the embeddings of similar (positive) samples to be close + and those of different (negative) samples to be distant. + A query embedding is compared with one positive key and with one or more negative keys. + + References: + https://arxiv.org/abs/1807.03748v2 + https://arxiv.org/abs/2010.05113 + + Args: + temperature: Logits are divided by temperature before calculating the cross entropy. + reduction: Reduction method applied to the output. + Value must be one of ['none', 'sum', 'mean']. + See torch.nn.functional.cross_entropy for more details about each option. + negative_mode: Determines how the (optional) negative_keys are handled. + Value must be one of ['paired', 'unpaired']. + If 'paired', then each query sample is paired with a number of negative keys. + Comparable to a triplet loss, but with multiple negatives per sample. + If 'unpaired', then the set of negative keys are all unrelated to any positive key. + + Input shape: + query: (N, D) Tensor with query samples (e.g. embeddings of the input). + positive_key: (N, D) Tensor with positive samples (e.g. embeddings of augmented input). + negative_keys (optional): Tensor with negative samples (e.g. embeddings of other inputs) + If negative_mode = 'paired', then negative_keys is a (N, M, D) Tensor. + If negative_mode = 'unpaired', then negative_keys is a (M, D) Tensor. + If None, then the negative keys for a sample are the positive keys for the other samples. + + Returns: + Value of the InfoNCE Loss. + + Examples: + >>> loss = InfoNCE() + >>> batch_size, num_negative, embedding_size = 32, 48, 128 + >>> query = torch.randn(batch_size, embedding_size) + >>> positive_key = torch.randn(batch_size, embedding_size) + >>> negative_keys = torch.randn(num_negative, embedding_size) + >>> output = loss(query, positive_key, negative_keys) + """ + + def __init__(self, temperature=0.1, reduction='mean', negative_mode='unpaired'): + super().__init__() + self.temperature = temperature + self.reduction = reduction + self.negative_mode = negative_mode + + def forward(self, query, positive_key, negative_keys=None): + return info_nce(query, positive_key, negative_keys, + temperature=self.temperature, + reduction=self.reduction, + negative_mode=self.negative_mode) + + +def info_nce(query, positive_key, negative_keys=None, temperature=0.1, reduction='mean', negative_mode='unpaired'): + # Check input dimensionality. + if query.dim() != 2: + raise ValueError(' must have 2 dimensions.') + if positive_key.dim() != 2: + raise ValueError(' must have 2 dimensions.') + if negative_keys is not None: + if negative_mode == 'unpaired' and negative_keys.dim() != 2: + raise ValueError(" must have 2 dimensions if == 'unpaired'.") + if negative_mode == 'paired' and negative_keys.dim() != 3: + raise ValueError(" must have 3 dimensions if == 'paired'.") + + # Check matching number of samples. + if len(query) != len(positive_key): + raise ValueError(' and must must have the same number of samples.') + if negative_keys is not None: + if negative_mode == 'paired' and len(query) != len(negative_keys): + raise ValueError("If negative_mode == 'paired', then must have the same number of samples as .") + + # Embedding vectors should have same number of components. + if query.shape[-1] != positive_key.shape[-1]: + raise ValueError('Vectors of and should have the same number of components.') + if negative_keys is not None: + if query.shape[-1] != negative_keys.shape[-1]: + raise ValueError('Vectors of and should have the same number of components.') + + # Normalize to unit vectors + query, positive_key, negative_keys = normalize(query, positive_key, negative_keys) + if negative_keys is not None: + # Explicit negative keys + + # Cosine between positive pairs + positive_logit = torch.sum(query * positive_key, dim=1, keepdim=True) + + if negative_mode == 'unpaired': + # Cosine between all query-negative combinations + negative_logits = query @ transpose(negative_keys) + + elif negative_mode == 'paired': + query = query.unsqueeze(1) + negative_logits = query @ transpose(negative_keys) + negative_logits = negative_logits.squeeze(1) + + # First index in last dimension are the positive samples + logits = torch.cat([positive_logit, negative_logits], dim=1) + labels = torch.zeros(len(logits), dtype=torch.long, device=query.device) + else: + # Negative keys are implicitly off-diagonal positive keys. + + # Cosine between all combinations + logits = query @ transpose(positive_key) + + # Positive keys are the entries on the diagonal + labels = torch.arange(len(query), device=query.device) + + return F.cross_entropy(logits / temperature, labels, reduction=reduction) + + +def transpose(x): + return x.transpose(-2, -1) + + +def normalize(*xs): + return [None if x is None else F.normalize(x, dim=-1) for x in xs] diff --git a/attacks/SAE/SAEAttack.py b/attacks/SAE/SAEAttack.py new file mode 100644 index 0000000000000000000000000000000000000000..7f065f9e1e77a91288c4e4d9dd266271e27b16c5 --- /dev/null +++ b/attacks/SAE/SAEAttack.py @@ -0,0 +1,295 @@ +import os.path +import torch +import torch.nn as nn +from torchvision import transforms +from foolbox.attacks.base import * +from foolbox.attacks.gradient_descent_base import * +from tqdm import tqdm +import torch.optim as optim +from torch.utils.data import DataLoader +import pandas as pd +from attacks.attack_config import SustainableAttack +from utils.plot import save_grad_cam, plot_asr_per_target, save_batch_images +import logging +from foolbox import PyTorchModel, accuracy +import numpy as np +from utils import factory +from utils.data_manager import get_dataloader +from utils.plot import save_images +from attacks.SAE.data_util import GetDatasetMeta, InMemoryDataset +from attacks.SAE.model_util import TrainableAffineTransform, UniversalPerturbation, NoTargetDataset +from attacks.SAE.clip_correction import CLIPSemantic + + +class SAE(SustainableAttack): + def __init__(self, args, device='cuda'): + super().__init__(args, device) + self.device = device + self.args = args + self.surrogate_model = None # surrogate__model.to(device).eval() + + # cifar100 and imagenet100 class: 68, 56, 78, 8, 23, 84, 90, 65, 74, 76 + class_map = {68: 0, 56: 1, 78: 2, 8: 3, 23: 4, 84: 5, 90: 6, 65: 7, 74: 8, 76: 9} + self.target_class = args['target_class'] + self.epochs = 100 + self.epsilon = 32 + self.image_size = 32 if self.args['dataset'] == 'cifar100' else 224 + self.data_path = 'datasets' + self.weights = 1 + self.filter = 1 + self.aug = 1 + self.data_path = 'datasets' + if self.args['dataset'] == 'cifar100': + self.src_dataset = 'TinyImageNet' + self.tgt_dataset = 'CIFAR100' + self.eval_batch_szie = 128 + elif self.args['dataset'] == 'imagenet100': + self.src_dataset = 'ImageNet' + self.tgt_dataset = 'ImageNet100' + self.eval_batch_szie = 64 + + self.adv_name = f'adv_eps{self.epsilon}_tc{self.target_class}_filter{self.filter}_aug{self.aug}' + self.out_path = f'{self.args["logs_eval_name"]}/{self.adv_name}' + os.makedirs(self.out_path, exist_ok=True) + + self.eval = args['eval'] + self.plot_gradcam = False + self.ckpt_num = None # None + + def train_adv(self): + if self.eval: + pass + else: + s_model = factory.get_model(self.args["model_name"], self.args) + s_model.incremental_train(self.data_manager) + s_model._network.load_state_dict( + torch.load(self.ckpt_paths[0], map_location=self.device)['model_state_dict']) + s_model._network.to(self.device) + s_model._network.eval() + self.gen_adv(cl_model=s_model._network) + + def gen_adv(self, cl_model=None): + class_map = self.args['target_class_dict'] + target_class = list(class_map.keys())[list(class_map.values()).index(self.args['target_class'])] + + transform = transforms.Compose([ + transforms.Resize((self.image_size, self.image_size)), + transforms.ToTensor(), + ]) + + src_data_meta = GetDatasetMeta(self.data_path, self.src_dataset) + tgt_data_meta = GetDatasetMeta(self.data_path, self.tgt_dataset) + train_set = src_data_meta.get_dataset(transform=transform, + train=True if self.src_dataset != 'ImageNet' else False, + download=True) + test_set = tgt_data_meta.get_dataset(transform=transform, + train=True if self.tgt_dataset != 'ImageNet100' else False, + download=True) + test_set = NoTargetDataset(test_set, target_class) + test_set, _ = torch.utils.data.random_split(test_set, + [len(test_set) // 1, len(test_set) - len(test_set) // 1]) + src_label_text_dict = src_data_meta.get_dataset_label_names() + tgt_label_text_dict = tgt_data_meta.get_dataset_label_names() + + y_negative = set(tgt_label_text_dict.values()) + y_negative.discard(tgt_label_text_dict[target_class]) + + train_set = NoTargetDataset(train_set, target=next( + (k for k, v in src_label_text_dict.items() if v == tgt_label_text_dict[target_class]), None)) + train_set = InMemoryDataset([i for i in train_set]) + + train_loader = DataLoader(train_set, batch_size=self.batch_size, shuffle=True, drop_last=True, num_workers=0) + + adv = UniversalPerturbation((3, int(self.image_size), int(self.image_size)), self.epsilon / 255, + initialization=None, + device=self.device) + + criterion = nn.CrossEntropyLoss() + + # Optimizer + optimizer = optim.Adam(adv.parameters(), lr=0.01, weight_decay=1e-5) + + # CLIP-based Semantic Correction + clip_func = CLIPSemantic( + self.device, + lambda_direction=1, + clip_model='ViT-B-32', + pretrained='laion2b_s34b_b79k' + ) + + clip_func.precompute_text_features(list(src_label_text_dict.values()), templates=src_data_meta.get_template()) + clip_func.precompute_text_features(list(tgt_label_text_dict.values()), templates=tgt_data_meta.get_template()) + + # Training loop + for epoch in range(self.epochs): + average_epoch_loss = [] + univ_loss = 0 + cl_loss = 0 + total_loss = 0 + for i, (x, y) in enumerate(tqdm(train_loader, total=len(train_loader), + desc=f'Epoch {epoch}, Training Perturbation: ')): + bs = x.size(0) + x = x.to(self.device).to(torch.float32) + + if self.filter: + y_t = torch.full_like(y, fill_value=self.target_class, device=self.device) + current_model = PyTorchModel(cl_model, bounds=(0, 1), preprocessing=self.preprocessing) + acc_bool = accuracy(current_model, ep.astensor(x), ep.astensor(y_t))[1] + filter_index = ep.logical_not(acc_bool) + x_f = x[filter_index.raw] + y_f = y[filter_index.raw.cpu()] + logging.info("Filtering {} OOD dataset under batch size of {}".format(len(x), self.batch_size)) + del current_model, acc_bool, filter_index, y_t + + x_hat = adv(self.norm(x)) # Apply the perturbation + grad_transform = TrainableAffineTransform(bs, 0.25, flip=True) + if self.filter: + x_f_hat = adv(self.norm(x_f)) + x_f_hat_trans = grad_transform(x_f_hat) + + if src_label_text_dict: + y_source = [src_label_text_dict[int(y_i)] for y_i in y] + else: + y_source = None + y_target = [tgt_label_text_dict[int(target_class)] for _ in y] + + # Compute the loss + if self.aug: + loss1 = clip_func.clip_directional_aug(x, grad_transform(x), y_source, + grad_transform(x_hat), y_target, y_negative) + else: + loss1 = clip_func(x, y_source, grad_transform(x_hat), y_target, y_negative) + + if self.filter: + target_labels = torch.full((len(x_f_hat_trans),), class_map[int(target_class)]).to(self.device) + loss2 = criterion(cl_model(x_f_hat), target_labels) + loss3 = criterion(cl_model(x_f_hat_trans), target_labels) + else: + target_labels = torch.full((len(x_hat),), class_map[int(target_class)]).to(self.device) + loss2 = criterion(cl_model(x_hat), target_labels) + loss3 = criterion(cl_model(grad_transform(x_hat)), target_labels) + loss = self.weights * loss1 + (loss2 + loss3) + + # Backward and optimize + optimizer.zero_grad() + loss.backward() + optimizer.step() + + with torch.no_grad(): + adv.adv.clamp_(-1, 1) + + average_epoch_loss.append(loss.item()) + univ_loss += loss1.item() + cl_loss += (loss2.item() + loss3.item()) + total_loss += loss.item() + + a = adv.adv.detach().cpu() + os.makedirs(os.path.join(self.out_path, 'ckpts'), exist_ok=True) + torch.save(a, os.path.join(self.out_path, f'ckpts/adv_{epoch}.pth')) + + # self.run_test() + torch.cuda.empty_cache() + + adv = adv.to(self.device) + adv.train() + + def run_test(self): + pth_name = self.get_max_step_filename(f'{self.args["logs_eval_name"]}/{self.adv_name}/ckpts') + + self.ckpt = f'{self.args["logs_eval_name"]}/{self.adv_name}/ckpts/{pth_name}.pth' + self.prefix = f'{self.adv_name}_{pth_name.split("_")[-1]}' + a = torch.load(self.ckpt) + self.adv = UniversalPerturbation((3, self.image_size, self.image_size), self.epsilon / 255, + initialization=a, device=self.device) + self.adv.eval() + + # Load Batch Data + self.loader = get_dataloader(self.data_manager, batch_size=self.eval_batch_szie, + start_class=0, end_class=10, + train=False, shuffle=False, num_workers=0) + for i, (_, imgs, labels) in enumerate(tqdm(self.loader, total=len(self.loader), + desc=f'Loading Data with Batch Size of {self.batch_size}) :')): + if i > 0: + break + + imgs, labels = ep.astensors(*(imgs.to(self.device), labels.to(self.device))) + imgs_f = imgs[labels != self.target_class] + labels_f = labels[labels != self.target_class] + labels_t_f = ep.full_like(labels_f, fill_value=self.target_class) + + self.attacks(i, imgs_f, labels_f, labels_t_f) + + + def attacks(self, i_batch, imgs, labels, labels_t): + if self.args["model_name"] != 'finetune': + imgs_f, labels_f = self.to_all(imgs, labels) + labels_t = labels_t[:len(imgs_f)] + else: + imgs_f, labels_f = imgs, labels + clean_acc_matrix = [] + asr_matrix = np.ones((self.data_manager.nb_tasks, 1)) + self.model = factory.get_model(self.args["model_name"], self.args) + eval_path = os.path.join(self.args["logs_eval_name"], self.adv_name) + cnn_matrix, nme_matrix = [], [] + for task in range(self.data_manager.nb_tasks): + logging.info("***** Starting attack on task [{}]. *****".format(task)) + self.model.incremental_train(self.data_manager) + self.model._network.load_state_dict( + torch.load(self.ckpt_paths[task], map_location=self.device)['model_state_dict']) + self.model._network.to(self.device) + self.model._network.eval() + + current_model = PyTorchModel(self.model._network, bounds=(0, 1), preprocessing=self.preprocessing) + verify_input_bounds(imgs_f, current_model) + + # Evaluate the model perfromance with clean data + acc = accuracy(current_model, imgs, labels)[0] + logging.info("Clean accuracy on task {}: {}%".format(task, acc * 100)) + clean_acc_matrix.append(acc) + + advs = self.adv(imgs_f.raw) + asr = accuracy(current_model, ep.astensor(advs), labels_t)[0] + asr_matrix[task] = asr + if self.plot_gradcam: + save_grad_cam(self.args, torch.clip(advs.detach(), 0, 1), labels_t.raw, + self.model._network, self.out_path + "/GradCam", prefix=f'task{task}', + layer_name='stage_3', save_num=100, save_raw=True) + save_images(imgs_f.raw, advs, len(imgs_f), eval_path, f'batch{i_batch}_{self.prefix}_imgs', test=True) + save_batch_images(self.adv.adv.detach().cpu().unsqueeze(0), eval_path, + prefix=f'batch{i_batch}_{self.prefix}') + + del advs, current_model + torch.cuda.empty_cache() + + self.model.after_task() + + # Save all target images info: everage asr, + prefix = f'batch{i_batch}_{self.prefix}' + plot_asr_per_target(asr_matrix, eval_path, prefix, self.args, clean_acc_matrix) + df = pd.DataFrame(asr_matrix, columns=['ASR']) + df.to_excel(os.path.join(eval_path, f"{prefix}.xlsx"), index=False) + + del asr_matrix, imgs, labels, labels_t, imgs_f, labels_f + torch.cuda.empty_cache() + + def get_max_step_filename(self, folder_path): + files = [f for f in os.listdir(folder_path) if f.endswith('.pth')] + step_files = [(f, int(f.split('_')[-1].split('.')[0])) for f in files] + step_files.sort(key=lambda x: x[1], reverse=True) + max_step_file = step_files[0][0] + return os.path.splitext(max_step_file)[0] + + + def __call__( + self, + model: Model, + inputs: T, + criterion: Any, + *, + epsilons: Union[Sequence[Union[float, None]], float, None], + **kwargs: Any, + ) -> Union[Tuple[List[T], List[T], T], Tuple[T, T, T]]: + ... + + def repeat(self, times: int) -> "SAE": + ... \ No newline at end of file diff --git a/attacks/SAE/clip_correction.py b/attacks/SAE/clip_correction.py new file mode 100644 index 0000000000000000000000000000000000000000..9c29b39271619a62ec60c0cb6d9062a4fc4d69e4 --- /dev/null +++ b/attacks/SAE/clip_correction.py @@ -0,0 +1,150 @@ +import torch +import torchvision.transforms as transforms +from typing import Union, List +import open_clip + + +class CLIPSemantic(torch.nn.Module): + def __init__(self, device, lambda_direction=1., direction_loss_type='cosine', clip_model='ViT-B/32', + pretrained=None, class_names=None): + super(CLIPSemantic, self).__init__() + + self.device = device + self.model, _, clip_preprocess = open_clip.create_model_and_transforms( + clip_model, + pretrained='convs/CLIP-ViT-B-32-laion2B-s34B-b79K/open_clip_pytorch_model.bin', + # pretrained, + jit=True, + device=device, + ) + self.tokenizer = open_clip.get_tokenizer(clip_model) + self.clip_preprocess = clip_preprocess + self.preprocess = transforms.Compose([transforms.Normalize(mean=[-1.0, -1.0, -1.0], std=[2.0, 2.0,2.0])] + + clip_preprocess.transforms[:2] + + clip_preprocess.transforms[4:]) + + self.target_direction = None + self.patch_text_directions = None + + self.patch_direction_loss = torch.nn.CosineSimilarity(dim=2) + self.cosine_sim = torch.nn.CosineSimilarity(dim=1) + self.lambda_direction = lambda_direction + self.src_text_features = None + self.target_text_features = None + self.angle_loss = torch.nn.L1Loss() + + self.text_class_features = dict() + self.image_class_features = dict() + self.predicted_classes = class_names + self.mse = torch.nn.MSELoss() + + def tokenize(self, strings: list): + return self.tokenizer(strings).to(self.device) + + def encode_text(self, tokens: list) -> torch.Tensor: + return self.model.encode_text(tokens) + + def encode_images(self, images: torch.Tensor) -> torch.Tensor: + images = self.preprocess(images).to(self.device) + return self.model.encode_image(images) + + def get_text_features(self, class_str: str) -> torch.Tensor: + text_features = [self.text_class_features[i] for i in class_str] + return torch.stack(text_features, dim=0) + + def precompute_text_features(self, class_str: str, templates=None, norm: bool = True) -> torch.Tensor: + for classes in class_str: + if classes not in self.text_class_features: + template_text = self.compose_text_with_templates(classes, templates) + tokens = self.tokenizer(template_text).to(self.device) + text_features = self.encode_text(tokens).detach() + text_features = text_features.mean(dim=0) + if norm: + text_features /= text_features.norm(dim=-1, keepdim=True) + self.text_class_features[classes] = text_features + + def get_image_features(self, img: torch.Tensor, norm: bool = True) -> torch.Tensor: + image_features = self.encode_images(img) + + if norm: + image_features /= image_features.clone().norm(dim=-1, keepdim=True) + + return image_features + + def compute_text_direction(self, source_class: Union[str, List[str]], target_class: Union[str, List[str]], + broadcast=False) -> torch.Tensor: + source_features = self.get_text_features(source_class) + target_features = self.get_text_features(target_class) + + if broadcast: + text_direction = (target_features.T.unsqueeze(0) - source_features.unsqueeze(-1)) + else: + text_direction = (target_features - source_features) + text_direction /= text_direction.norm(dim=-1, keepdim=True) + + return text_direction + + def compose_text_with_templates(self, text: str, templates=None) -> list: + return [template.format(text) for template in templates] + + def clip_directional(self, src_img: torch.Tensor, source_class: Union[str, List[str]], + target_img: torch.Tensor, target_class: Union[str, List[str]], + negative_class: Union[str, List[str]]) -> torch.Tensor: + self.target_direction = self.compute_text_direction(source_class, target_class) + self.negative_direction = self.compute_text_direction(source_class, negative_class, broadcast=True) + + src_encoding = self.get_image_features(src_img) + target_encoding = self.get_image_features(target_img) + + edit_direction = (target_encoding - src_encoding) + edit_direction /= (edit_direction.clone().norm(dim=-1, keepdim=True) + 1e-7) + + logit_target = self.cosine_sim(self.target_direction, edit_direction) + logit_negative = self.cosine_sim(self.negative_direction, edit_direction.unsqueeze(-1)) + pp = torch.exp(logit_target) + pn = torch.sum(torch.exp(logit_negative), dim=-1) + p = pp / (pp + pn) + return -torch.log(p).mean() + + def clip_directional_aug(self, src_img: torch.Tensor, aug_img: torch.Tensor, + source_class: Union[str, List[str]], target_img: torch.Tensor, + target_class: Union[str, List[str]], + negative_class: Union[str, List[str]]) -> torch.Tensor: + self.target_direction = self.compute_text_direction(source_class, target_class) + self.negative_direction = self.compute_text_direction(source_class, negative_class, broadcast=True) + + src_encoding = self.get_image_features(src_img) + src_encoding_aug = self.get_image_features(aug_img) + target_encoding = self.get_image_features(target_img) + + edit_direction1 = (target_encoding - src_encoding) + edit_direction1 /= (edit_direction1.clone().norm(dim=-1, keepdim=True) + 1e-7) + + edit_direction2 = (target_encoding - src_encoding_aug) + edit_direction2 /= (edit_direction2.clone().norm(dim=-1, keepdim=True) + 1e-7) + + logit_target1 = self.cosine_sim(self.target_direction, edit_direction1) + logit_negative1 = self.cosine_sim(self.negative_direction, edit_direction1.unsqueeze(-1)) + + logit_target2 = self.cosine_sim(self.target_direction, edit_direction2) + logit_negative2 = self.cosine_sim(self.negative_direction, edit_direction2.unsqueeze(-1)) + + pp1 = torch.exp(logit_target1) + pn1 = torch.sum(torch.exp(logit_negative1), dim=-1) + + pp2 = torch.exp(logit_target2) + pn2 = torch.sum(torch.exp(logit_negative2), dim=-1) + + p = (pp1 + pp2) / (pp1 + pp2 + pn1 + pn2) + return -torch.log(p).mean() + + def forward(self, src_img: torch.Tensor, source_class: Union[str, List[str]], target_img: torch.Tensor, + target_class: Union[str, List[str]], negative_class: Union[str, List[str]], + texture_image: torch.Tensor = None): + clip_loss = 0.0 + + if self.lambda_direction: + direction_loss = self.clip_directional(src_img, source_class, target_img, target_class, negative_class) + clip_loss += self.lambda_direction * direction_loss + + return clip_loss diff --git a/attacks/SAE/data_util.py b/attacks/SAE/data_util.py new file mode 100644 index 0000000000000000000000000000000000000000..012662766e942eaee83c6432e9e7dad20a62f829 --- /dev/null +++ b/attacks/SAE/data_util.py @@ -0,0 +1,394 @@ +import os +from torchvision.datasets import CIFAR100 +import torch +import torchvision +from torchvision import transforms +from attacks.SAE.text_templates import imagenet_templates +import os +import torch +from PIL import Image +from torch.utils.data import Dataset +from typing import Optional, Callable +from torchvision.datasets.utils import download_and_extract_archive +import shutil + +def reverse_dict(label2class): + # Use dictionary comprehension to reverse the key-value pairs + class2label = {value: key for key, value in label2class.items()} + return class2label + + +class GetDatasetMeta(): + def __init__(self, root, dataset_name) -> None: + self.dataset_name = dataset_name + self.root = root + + def get_dataset_label_names(self): + if self.dataset_name == "CIFAR100": + tmp = CIFAR100(root=self.root) + label_dict = reverse_dict(tmp.class_to_idx) + + elif self.dataset_name == "TinyImageNet" or self.dataset_name == "ImageNet" or self.dataset_name == "ImageNet100": + with open(os.path.join('attacks/SAE/map_clsloc.txt'), 'r') as file: + lines = file.readlines() + + label_dict = {} + + for line in lines: + parts = line.strip().split(',') + label_dict[int(parts[1])] = parts[2] + + else: + label_dict = None + + return label_dict + + def get_transformation(self): + if self.dataset_name == "CIFAR100": + size = 32 + normalize = [[0.5070751592371323, 0.48654887331495095, 0.4409178433670343], + [0.2673342858792401, 0.2564384629170883, 0.27615047132568404]] + elif self.dataset_name == "TinyImageNet" or self.dataset_name == "ImageNet" or self.dataset_name == "ImageNet100": + size = 224 + # normalize = [0.4802, 0.4481, 0.3975], [0.2302, 0.2265, 0.2262] + normalize = [[0.485, 0.456, 0.406], [0.229, 0.224, 0.225]] + else: + return None + preprocess = transforms.Compose([ + transforms.Resize((size, size)), + transforms.Normalize(mean=normalize[0], std=normalize[1]), + ]) + return preprocess + + def get_template(self): + return imagenet_templates + + def get_dataset(self, train=False, download=False, transform=None, **kwargs): + if self.dataset_name == "CIFAR100": + target_dataset = CIFAR100(root=self.root, train=train, download=download, transform=transform) + elif self.dataset_name == "TinyImageNet": + target_dataset = TinyImageNet(root=self.root, split='train' if train else 'test', download=download, + transform=transform) + elif self.dataset_name == "ImageNet": + target_dataset = ImageNet(root=self.root, split='train' if train else 'val', download=download, + transform=transform) + elif self.dataset_name == "ImageNet100": + target_dataset = ImageNet100(root=self.root, split='train' if train else 'val', download=download, + transform=transform) + return target_dataset + + def get_clean_model(self): + if self.dataset_name == "CIFAR100": + model_visual = torch.hub.load("chenyaofo/pytorch-cifar-models", 'cifar100_resnet44', pretrained=True) + elif self.dataset_name == "TinyImageNet" or self.dataset_name == "ImageNet": + model_visual = torchvision.models.resnet50(pretrained=True) + return model_visual + + def n_classes(self): + if self.dataset_name == "CIFAR100": + n = 100 + elif self.dataset_name == "TinyImageNet": + n = 500 + elif self.dataset_name == "ImageNet": + n = 1000 + elif self.dataset_name == "ImageNet100": + n = 100 + return n + + +class InMemoryDataset(torch.utils.data.Dataset): + def __init__(self, data_list): + self.data_list = data_list + + def __len__(self): + return len(self.data_list) + + def __getitem__(self, idx): + return self.data_list[idx] + + +class TransformedDataset(torch.utils.data.Dataset): + def __init__(self, args_cl, original_dataset, transform=None): + self.original_dataset = original_dataset + self.transform = transform + self.args_cl = args_cl + + def __getitem__(self, index): + if self.args_cl['approach'] == 0: + image, label = self.original_dataset[index] + else: + _, image, label = self.original_dataset[index] + # _, image, label = self.original_dataset[index] + if self.transform: + image = self.transform(image) + return image, label + + def __len__(self): + return len(self.original_dataset) + + +class ImageNet(Dataset): + def __init__( + self, + root: str, + split: str = "train", + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + download: bool = False, + ) -> None: + super(ImageNet, self).__init__() + self.root = root + self.split = split + self.transform = transform + self.target_transform = target_transform + self.data = [] + self.targets = [] + + with open(os.path.join('attacks/SAE/map_clsloc.txt'), 'r') as file: + lines = file.readlines() + + # Create the dictionary + self.data_dict = {} + + # Iterate over each line + for line in lines: + # Strip any surrounding whitespace and split by comma + parts = line.strip().split(',') + # Extract the key and value + key = parts[0] + value = [int(parts[1]), parts[2]] + # Add to the dictionary + self.data_dict[key] = value + + # Assuming the structure is root/split/images, e.g., root/train/images + split_dir = os.path.join(self.root, 'imagenet', self.split) + + # Load images and labels + for class_name in os.listdir(split_dir): + class_dir = os.path.join(split_dir, class_name) + class_idx = self.data_dict[class_name][0] + for image_name in os.listdir(class_dir): + with open(os.path.join(class_dir, image_name), 'rb') as f: + img = Image.open(f).convert('RGB') + self.data.append(img) + self.targets.append(class_idx) + + if download: + # Code to download the dataset (if required) + pass + + def __len__(self): + return len(self.data) + + def __getitem__(self, idx): + img = self.data[idx] + target = self.targets[idx] + + # Apply transformations + if self.transform is not None: + img = self.transform(img) + + if self.target_transform is not None: + target = self.target_transform(target) + + return img, target + + +class ImageNet100(Dataset): + def __init__( + self, + root: str, + split: str = "train", + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + download: bool = False, + ) -> None: + super(ImageNet100, self).__init__() + self.root = root + self.split = split + self.transform = transform + self.target_transform = target_transform + self.data = [] + self.targets = [] + + with open(os.path.join('attacks/SAE/map_clsloc.txt'), 'r') as file: + lines = file.readlines() + + # Create the dictionary + self.data_dict = {} + + # Iterate over each line + for line in lines: + # Strip any surrounding whitespace and split by comma + parts = line.strip().split(',') + # Extract the key and value + key = parts[0] + value = [int(parts[1]), parts[2]] + # Add to the dictionary + self.data_dict[key] = value + + # Assuming the structure is root/split/images, e.g., root/train/images + split_dir = os.path.join(self.root, 'imagenet100', self.split) + + # Load images and labels + for class_name in os.listdir(split_dir): + class_dir = os.path.join(split_dir, class_name) + class_idx = self.data_dict[class_name][0] + for image_name in os.listdir(class_dir): + with open(os.path.join(class_dir, image_name), 'rb') as f: + img = Image.open(f).convert('RGB') + self.data.append(img) + self.targets.append(class_idx) + + if download: + # Code to download the dataset (if required) + pass + + def __len__(self): + return len(self.data) + + def __getitem__(self, idx): + img = self.data[idx] + target = self.targets[idx] + + # Apply transformations + if self.transform is not None: + img = self.transform(img) + + if self.target_transform is not None: + target = self.target_transform(target) + + return img, target + + +def normalize_tin_val_folder_structure(path, + images_folder='images', + annotations_file='val_annotations.txt'): + # Check if files/annotations are still there to see + # if we already run reorganize the folder structure. + images_folder = os.path.join(path, images_folder) + annotations_file = os.path.join(path, annotations_file) + + # Exists + if not os.path.exists(images_folder) \ + and not os.path.exists(annotations_file): + if not os.listdir(path): + raise RuntimeError('Validation folder is empty.') + return + + # Parse the annotations + with open(annotations_file) as f: + for line in f: + values = line.split() + img = values[0] + label = values[1] + img_file = os.path.join(images_folder, values[0]) + label_folder = os.path.join(path, label) + os.makedirs(label_folder, exist_ok=True) + try: + shutil.move(img_file, os.path.join(label_folder, img)) + except FileNotFoundError: + continue + + os.fsync() + assert not os.listdir(images_folder) + shutil.rmtree(images_folder) + os.remove(annotations_file) + os.fsync() + + +class TinyImageNet(Dataset): + """Dataset for TinyImageNet-200""" + base_folder = 'tiny-imagenet-200' + zip_md5 = '90528d7ca1a48142e341f4ef8d21d0de' + splits = ('train', 'val') + filename = 'tiny-imagenet-200.zip' + url = 'http://cs231n.stanford.edu/tiny-imagenet-200.zip' + + def __init__( + self, + root: str, + split: str = "train", + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + download: bool = False, + ) -> None: + super(TinyImageNet, self).__init__() + self.data_root = root + self.split = split + self.transform = transform + self.target_transform = target_transform + self.data = [] + self.targets = [] + + if download: + self.download() + + with open(os.path.join('attacks/UnivIntruder/utils_/map_clsloc.txt'), 'r') as file: + lines = file.readlines() + + # Create the dictionary + self.data_dict = {} + + # Iterate over each line + for line in lines: + # Strip any surrounding whitespace and split by comma + parts = line.strip().split(',') + # Extract the key and value + key = parts[0] + value = [int(parts[1]), parts[2]] + # Add to the dictionary + self.data_dict[key] = value + + # Assuming the structure is root/split/images, e.g., root/train/images + split_dir = os.path.join(self.data_root, 'tiny-imagenet-200', self.split) + + # Load images and labels + for class_name in os.listdir(split_dir): + class_dir = os.path.join(split_dir, class_name, 'images') + class_idx = self.data_dict[class_name][0] + for image_name in os.listdir(class_dir): + with open(os.path.join(class_dir, image_name), 'rb') as f: + img = Image.open(f).convert('RGB') + self.data.append(img) + self.targets.append(class_idx) + + def __len__(self): + return len(self.data) + + def __getitem__(self, idx): + img = self.data[idx] + target = self.targets[idx] + + # Apply transformations + if self.transform is not None: + img = self.transform(img) + + if self.target_transform is not None: + target = self.target_transform(target) + + return img, target + + @property + def dataset_folder(self): + return os.path.join(self.data_root, self.base_folder) + + @property + def split_folder(self): + return os.path.join(self.dataset_folder, self.split) + + def _check_exists(self): + return os.path.exists(self.split_folder) + + def extra_repr(self): + return "Split: {split}".format(**self.__dict__) + + def download(self): + if self._check_exists(): + return + download_and_extract_archive( + self.url, self.data_root, filename=self.filename, + remove_finished=True, md5=self.zip_md5) + assert 'val' in self.splits + normalize_tin_val_folder_structure( + os.path.join(self.dataset_folder, 'val')) \ No newline at end of file diff --git a/attacks/SAE/map_clsloc.txt b/attacks/SAE/map_clsloc.txt new file mode 100644 index 0000000000000000000000000000000000000000..58e9be47d7f2970a787cd39e5ed77a6f517db897 --- /dev/null +++ b/attacks/SAE/map_clsloc.txt @@ -0,0 +1,1000 @@ +n01440764,0,tench +n01443537,1,goldfish +n01484850,2,great white shark +n01491361,3,tiger shark +n01494475,4,hammerhead shark +n01496331,5,electric ray +n01498041,6,stingray +n01514668,7,rooster +n01514859,8,hen +n01518878,9,ostrich +n01530575,10,brambling +n01531178,11,goldfinch +n01532829,12,house finch +n01534433,13,junco +n01537544,14,indigo bunting +n01558993,15,American robin +n01560419,16,bulbul +n01580077,17,jay +n01582220,18,magpie +n01592084,19,chickadee +n01601694,20,American dipper +n01608432,21,kite (bird of prey) +n01614925,22,bald eagle +n01616318,23,vulture +n01622779,24,great grey owl +n01629819,25,fire salamander +n01630670,26,smooth newt +n01631663,27,newt +n01632458,28,spotted salamander +n01632777,29,axolotl +n01641577,30,American bullfrog +n01644373,31,tree frog +n01644900,32,tailed frog +n01664065,33,loggerhead sea turtle +n01665541,34,leatherback sea turtle +n01667114,35,mud turtle +n01667778,36,terrapin +n01669191,37,box turtle +n01675722,38,banded gecko +n01677366,39,green iguana +n01682714,40,Carolina anole +n01685808,41,desert grassland whiptail lizard +n01687978,42,agama +n01688243,43,frilled-necked lizard +n01689811,44,alligator lizard +n01692333,45,Gila monster +n01693334,46,European green lizard +n01694178,47,chameleon +n01695060,48,Komodo dragon +n01697457,49,Nile crocodile +n01698640,50,American alligator +n01704323,51,triceratops +n01728572,52,worm snake +n01728920,53,ring-necked snake +n01729322,54,eastern hog-nosed snake +n01729977,55,smooth green snake +n01734418,56,kingsnake +n01735189,57,garter snake +n01737021,58,water snake +n01739381,59,vine snake +n01740131,60,night snake +n01742172,61,boa constrictor +n01744401,62,African rock python +n01748264,63,Indian cobra +n01749939,64,green mamba +n01751748,65,sea snake +n01753488,66,Saharan horned viper +n01755581,67,eastern diamondback rattlesnake +n01756291,68,sidewinder rattlesnake +n01768244,69,trilobite +n01770081,70,harvestman +n01770393,71,scorpion +n01773157,72,yellow garden spider +n01773549,73,barn spider +n01773797,74,European garden spider +n01774384,75,southern black widow +n01774750,76,tarantula +n01775062,77,wolf spider +n01776313,78,tick +n01784675,79,centipede +n01795545,80,black grouse +n01796340,81,ptarmigan +n01797886,82,ruffed grouse +n01798484,83,prairie grouse +n01806143,84,peafowl +n01806567,85,quail +n01807496,86,partridge +n01817953,87,african grey parrot +n01818515,88,macaw +n01819313,89,sulphur-crested cockatoo +n01820546,90,lorikeet +n01824575,91,coucal +n01828970,92,bee eater +n01829413,93,hornbill +n01833805,94,hummingbird +n01843065,95,jacamar +n01843383,96,toucan +n01847000,97,duck +n01855032,98,red-breasted merganser +n01855672,99,goose +n01860187,100,black swan +n01871265,101,tusker +n01872401,102,echidna +n01873310,103,platypus +n01877812,104,wallaby +n01882714,105,koala +n01883070,106,wombat +n01910747,107,jellyfish +n01914609,108,sea anemone +n01917289,109,brain coral +n01924916,110,flatworm +n01930112,111,nematode +n01943899,112,conch +n01944390,113,snail +n01945685,114,slug +n01950731,115,sea slug +n01955084,116,chiton +n01968897,117,chambered nautilus +n01978287,118,Dungeness crab +n01978455,119,rock crab +n01980166,120,fiddler crab +n01981276,121,red king crab +n01983481,122,American lobster +n01984695,123,spiny lobster +n01985128,124,crayfish +n01986214,125,hermit crab +n01990800,126,isopod +n02002556,127,white stork +n02002724,128,black stork +n02006656,129,spoonbill +n02007558,130,flamingo +n02009229,131,little blue heron +n02009912,132,great egret +n02011460,133,bittern bird +n02012849,134,crane bird +n02013706,135,limpkin +n02017213,136,common gallinule +n02018207,137,American coot +n02018795,138,bustard +n02025239,139,ruddy turnstone +n02027492,140,dunlin +n02028035,141,common redshank +n02033041,142,dowitcher +n02037110,143,oystercatcher +n02051845,144,pelican +n02056570,145,king penguin +n02058221,146,albatross +n02066245,147,grey whale +n02071294,148,killer whale +n02074367,149,dugong +n02077923,150,sea lion +n02085620,151,Chihuahua +n02085782,152,Japanese Chin +n02085936,153,Maltese +n02086079,154,Pekingese +n02086240,155,Shih Tzu +n02086646,156,King Charles Spaniel +n02086910,157,Papillon +n02087046,158,toy terrier +n02087394,159,Rhodesian Ridgeback +n02088094,160,Afghan Hound +n02088238,161,Basset Hound +n02088364,162,Beagle +n02088466,163,Bloodhound +n02088632,164,Bluetick Coonhound +n02089078,165,Black and Tan Coonhound +n02089867,166,Treeing Walker Coonhound +n02089973,167,English foxhound +n02090379,168,Redbone Coonhound +n02090622,169,borzoi +n02090721,170,Irish Wolfhound +n02091032,171,Italian Greyhound +n02091134,172,Whippet +n02091244,173,Ibizan Hound +n02091467,174,Norwegian Elkhound +n02091635,175,Otterhound +n02091831,176,Saluki +n02092002,177,Scottish Deerhound +n02092339,178,Weimaraner +n02093256,179,Staffordshire Bull Terrier +n02093428,180,American Staffordshire Terrier +n02093647,181,Bedlington Terrier +n02093754,182,Border Terrier +n02093859,183,Kerry Blue Terrier +n02093991,184,Irish Terrier +n02094114,185,Norfolk Terrier +n02094258,186,Norwich Terrier +n02094433,187,Yorkshire Terrier +n02095314,188,Wire Fox Terrier +n02095570,189,Lakeland Terrier +n02095889,190,Sealyham Terrier +n02096051,191,Airedale Terrier +n02096177,192,Cairn Terrier +n02096294,193,Australian Terrier +n02096437,194,Dandie Dinmont Terrier +n02096585,195,Boston Terrier +n02097047,196,Miniature Schnauzer +n02097130,197,Giant Schnauzer +n02097209,198,Standard Schnauzer +n02097298,199,Scottish Terrier +n02097474,200,Tibetan Terrier +n02097658,201,Australian Silky Terrier +n02098105,202,Soft-coated Wheaten Terrier +n02098286,203,West Highland White Terrier +n02098413,204,Lhasa Apso +n02099267,205,Flat-Coated Retriever +n02099429,206,Curly-coated Retriever +n02099601,207,Golden Retriever +n02099712,208,Labrador Retriever +n02099849,209,Chesapeake Bay Retriever +n02100236,210,German Shorthaired Pointer +n02100583,211,Vizsla +n02100735,212,English Setter +n02100877,213,Irish Setter +n02101006,214,Gordon Setter +n02101388,215,Brittany dog +n02101556,216,Clumber Spaniel +n02102040,217,English Springer Spaniel +n02102177,218,Welsh Springer Spaniel +n02102318,219,Cocker Spaniel +n02102480,220,Sussex Spaniel +n02102973,221,Irish Water Spaniel +n02104029,222,Kuvasz +n02104365,223,Schipperke +n02105056,224,Groenendael dog +n02105162,225,Malinois +n02105251,226,Briard +n02105412,227,Australian Kelpie +n02105505,228,Komondor +n02105641,229,Old English Sheepdog +n02105855,230,Shetland Sheepdog +n02106030,231,collie +n02106166,232,Border Collie +n02106382,233,Bouvier des Flandres dog +n02106550,234,Rottweiler +n02106662,235,German Shepherd Dog +n02107142,236,Dobermann +n02107312,237,Miniature Pinscher +n02107574,238,Greater Swiss Mountain Dog +n02107683,239,Bernese Mountain Dog +n02107908,240,Appenzeller Sennenhund +n02108000,241,Entlebucher Sennenhund +n02108089,242,Boxer +n02108422,243,Bullmastiff +n02108551,244,Tibetan Mastiff +n02108915,245,French Bulldog +n02109047,246,Great Dane +n02109525,247,St. Bernard +n02109961,248,husky +n02110063,249,Alaskan Malamute +n02110185,250,Siberian Husky +n02110341,251,Dalmatian +n02110627,252,Affenpinscher +n02110806,253,Basenji +n02110958,254,pug +n02111129,255,Leonberger +n02111277,256,Newfoundland dog +n02111500,257,Great Pyrenees dog +n02111889,258,Samoyed +n02112018,259,Pomeranian +n02112137,260,Chow Chow +n02112350,261,Keeshond +n02112706,262,brussels griffon +n02113023,263,Pembroke Welsh Corgi +n02113186,264,Cardigan Welsh Corgi +n02113624,265,Toy Poodle +n02113712,266,Miniature Poodle +n02113799,267,Standard Poodle +n02113978,268,Mexican hairless dog (xoloitzcuintli) +n02114367,269,grey wolf +n02114548,270,Alaskan tundra wolf +n02114712,271,red wolf or maned wolf +n02114855,272,coyote +n02115641,273,dingo +n02115913,274,dhole +n02116738,275,African wild dog +n02117135,276,hyena +n02119022,277,red fox +n02119789,278,kit fox +n02120079,279,Arctic fox +n02120505,280,grey fox +n02123045,281,tabby cat +n02123159,282,tiger cat +n02123394,283,Persian cat +n02123597,284,Siamese cat +n02124075,285,Egyptian Mau +n02125311,286,cougar +n02127052,287,lynx +n02128385,288,leopard +n02128757,289,snow leopard +n02128925,290,jaguar +n02129165,291,lion +n02129604,292,tiger +n02130308,293,cheetah +n02132136,294,brown bear +n02133161,295,American black bear +n02134084,296,polar bear +n02134418,297,sloth bear +n02137549,298,mongoose +n02138441,299,meerkat +n02165105,300,tiger beetle +n02165456,301,ladybug +n02167151,302,ground beetle +n02168699,303,longhorn beetle +n02169497,304,leaf beetle +n02172182,305,dung beetle +n02174001,306,rhinoceros beetle +n02177972,307,weevil +n02190166,308,fly +n02206856,309,bee +n02219486,310,ant +n02226429,311,grasshopper +n02229544,312,cricket insect +n02231487,313,stick insect +n02233338,314,cockroach +n02236044,315,praying mantis +n02256656,316,cicada +n02259212,317,leafhopper +n02264363,318,lacewing +n02268443,319,dragonfly +n02268853,320,damselfly +n02276258,321,red admiral butterfly +n02277742,322,ringlet butterfly +n02279972,323,monarch butterfly +n02280649,324,small white butterfly +n02281406,325,sulphur butterfly +n02281787,326,gossamer-winged butterfly +n02317335,327,starfish +n02319095,328,sea urchin +n02321529,329,sea cucumber +n02325366,330,cottontail rabbit +n02326432,331,hare +n02328150,332,Angora rabbit +n02342885,333,hamster +n02346627,334,porcupine +n02356798,335,fox squirrel +n02361337,336,marmot +n02363005,337,beaver +n02364673,338,guinea pig +n02389026,339,common sorrel horse +n02391049,340,zebra +n02395406,341,pig +n02396427,342,wild boar +n02397096,343,warthog +n02398521,344,hippopotamus +n02403003,345,ox +n02408429,346,water buffalo +n02410509,347,bison +n02412080,348,ram (adult male sheep) +n02415577,349,bighorn sheep +n02417914,350,Alpine ibex +n02422106,351,hartebeest +n02422699,352,impala (antelope) +n02423022,353,gazelle +n02437312,354,arabian camel +n02437616,355,llama +n02441942,356,weasel +n02442845,357,mink +n02443114,358,European polecat +n02443484,359,black-footed ferret +n02444819,360,otter +n02445715,361,skunk +n02447366,362,badger +n02454379,363,armadillo +n02457408,364,three-toed sloth +n02480495,365,orangutan +n02480855,366,gorilla +n02481823,367,chimpanzee +n02483362,368,gibbon +n02483708,369,siamang +n02484975,370,guenon +n02486261,371,patas monkey +n02486410,372,baboon +n02487347,373,macaque +n02488291,374,langur +n02488702,375,black-and-white colobus +n02489166,376,proboscis monkey +n02490219,377,marmoset +n02492035,378,white-headed capuchin +n02492660,379,howler monkey +n02493509,380,titi monkey +n02493793,381,Geoffroy's spider monkey +n02494079,382,common squirrel monkey +n02497673,383,ring-tailed lemur +n02500267,384,indri +n02504013,385,Asian elephant +n02504458,386,African bush elephant +n02509815,387,red panda +n02510455,388,giant panda +n02514041,389,snoek fish +n02526121,390,eel +n02536864,391,silver salmon +n02606052,392,rock beauty fish +n02607072,393,clownfish +n02640242,394,sturgeon +n02641379,395,gar fish +n02643566,396,lionfish +n02655020,397,pufferfish +n02666196,398,abacus +n02667093,399,abaya +n02669723,400,academic gown +n02672831,401,accordion +n02676566,402,acoustic guitar +n02687172,403,aircraft carrier +n02690373,404,airliner +n02692877,405,airship +n02699494,406,altar +n02701002,407,ambulance +n02704792,408,amphibious vehicle +n02708093,409,analog clock +n02727426,410,apiary +n02730930,411,apron +n02747177,412,trash can +n02749479,413,assault rifle +n02769748,414,backpack +n02776631,415,bakery +n02777292,416,balance beam +n02782093,417,balloon +n02783161,418,ballpoint pen +n02786058,419,Band-Aid +n02787622,420,banjo +n02788148,421,baluster / handrail +n02790996,422,barbell +n02791124,423,barber chair +n02791270,424,barbershop +n02793495,425,barn +n02794156,426,barometer +n02795169,427,barrel +n02797295,428,wheelbarrow +n02799071,429,baseball +n02802426,430,basketball +n02804414,431,bassinet +n02804610,432,bassoon +n02807133,433,swimming cap +n02808304,434,bath towel +n02808440,435,bathtub +n02814533,436,station wagon +n02814860,437,lighthouse +n02815834,438,beaker +n02817516,439,military hat (bearskin or shako) +n02823428,440,beer bottle +n02823750,441,beer glass +n02825657,442,bell tower +n02834397,443,baby bib +n02835271,444,tandem bicycle +n02837789,445,bikini +n02840245,446,ring binder +n02841315,447,binoculars +n02843684,448,birdhouse +n02859443,449,boathouse +n02860847,450,bobsleigh +n02865351,451,bolo tie +n02869837,452,poke bonnet +n02870880,453,bookcase +n02871525,454,bookstore +n02877765,455,bottle cap +n02879718,456,hunting bow +n02883205,457,bow tie +n02892201,458,brass memorial plaque +n02892767,459,bra +n02894605,460,breakwater +n02895154,461,breastplate +n02906734,462,broom +n02909870,463,bucket +n02910353,464,buckle +n02916936,465,bulletproof vest +n02917067,466,high-speed train +n02927161,467,butcher shop +n02930766,468,taxicab +n02939185,469,cauldron +n02948072,470,candle +n02950826,471,cannon +n02951358,472,canoe +n02951585,473,can opener +n02963159,474,cardigan +n02965783,475,car mirror +n02966193,476,carousel +n02966687,477,tool kit +n02971356,478,cardboard box / carton +n02974003,479,car wheel +n02977058,480,automated teller machine +n02978881,481,cassette +n02979186,482,cassette player +n02980441,483,castle +n02981792,484,catamaran +n02988304,485,CD player +n02992211,486,cello +n02992529,487,mobile phone +n02999410,488,chain +n03000134,489,chain-link fence +n03000247,490,chain mail +n03000684,491,chainsaw +n03014705,492,storage chest +n03016953,493,chiffonier +n03017168,494,bell or wind chime +n03018349,495,china cabinet +n03026506,496,Christmas stocking +n03028079,497,church +n03032252,498,movie theater +n03041632,499,cleaver +n03042490,500,cliff dwelling +n03045698,501,cloak +n03047690,502,clogs +n03062245,503,cocktail shaker +n03063599,504,coffee mug +n03063689,505,coffeemaker +n03065424,506,spiral or coil +n03075370,507,combination lock +n03085013,508,computer keyboard +n03089624,509,candy store +n03095699,510,container ship +n03100240,511,convertible +n03109150,512,corkscrew +n03110669,513,cornet +n03124043,514,cowboy boot +n03124170,515,cowboy hat +n03125729,516,cradle +n03126707,517,construction crane +n03127747,518,crash helmet +n03127925,519,crate +n03131574,520,infant bed +n03133878,521,Crock Pot +n03134739,522,croquet ball +n03141823,523,crutch +n03146219,524,cuirass +n03160309,525,dam +n03179701,526,desk +n03180011,527,desktop computer +n03187595,528,rotary dial telephone +n03188531,529,diaper +n03196217,530,digital clock +n03197337,531,digital watch +n03201208,532,dining table +n03207743,533,dishcloth +n03207941,534,dishwasher +n03208938,535,disc brake +n03216828,536,dock +n03218198,537,dog sled +n03220513,538,dome +n03223299,539,doormat +n03240683,540,drilling rig +n03249569,541,drum +n03250847,542,drumstick +n03255030,543,dumbbell +n03259280,544,Dutch oven +n03271574,545,electric fan +n03272010,546,electric guitar +n03272562,547,electric locomotive +n03290653,548,entertainment center +n03291819,549,envelope +n03297495,550,espresso machine +n03314780,551,face powder +n03325584,552,feather boa +n03337140,553,filing cabinet +n03344393,554,fireboat +n03345487,555,fire truck +n03347037,556,fire screen +n03355925,557,flagpole +n03372029,558,flute +n03376595,559,folding chair +n03379051,560,football helmet +n03384352,561,forklift +n03388043,562,fountain +n03388183,563,fountain pen +n03388549,564,four-poster bed +n03393912,565,freight car +n03394916,566,French horn +n03400231,567,frying pan +n03404251,568,fur coat +n03417042,569,garbage truck +n03424325,570,gas mask or respirator +n03425413,571,gas pump +n03443371,572,goblet +n03444034,573,go-kart +n03445777,574,golf ball +n03445924,575,golf cart +n03447447,576,gondola +n03447721,577,gong +n03450230,578,gown +n03452741,579,grand piano +n03457902,580,greenhouse +n03459775,581,radiator grille +n03461385,582,grocery store +n03467068,583,guillotine +n03476684,584,hair clip +n03476991,585,hair spray +n03478589,586,half-track +n03481172,587,hammer +n03482405,588,hamper +n03483316,589,hair dryer +n03485407,590,hand-held computer +n03485794,591,handkerchief +n03492542,592,hard disk drive +n03494278,593,harmonica +n03495258,594,harp +n03496892,595,combine harvester +n03498962,596,hatchet +n03527444,597,holster +n03529860,598,home theater +n03530642,599,honeycomb +n03532672,600,hook +n03534580,601,hoop skirt +n03535780,602,gymnastic horizontal bar +n03538406,603,horse-drawn vehicle +n03544143,604,hourglass +n03584254,605,iPod +n03584829,606,clothes iron +n03590841,607,carved pumpkin +n03594734,608,jeans +n03594945,609,jeep +n03595614,610,T-shirt +n03598930,611,jigsaw puzzle +n03599486,612,rickshaw +n03602883,613,joystick +n03617480,614,kimono +n03623198,615,knee pad +n03627232,616,knot +n03630383,617,lab coat +n03633091,618,ladle +n03637318,619,lampshade +n03642806,620,laptop computer +n03649909,621,lawn mower +n03657121,622,lens cap +n03658185,623,letter opener +n03661043,624,library +n03662601,625,lifeboat +n03666591,626,lighter +n03670208,627,limousine +n03673027,628,ocean liner +n03676483,629,lipstick +n03680355,630,slip-on shoe +n03690938,631,lotion +n03691459,632,music speaker +n03692522,633,loupe magnifying glass +n03697007,634,sawmill +n03706229,635,magnetic compass +n03709823,636,messenger bag +n03710193,637,mailbox +n03710637,638,tights +n03710721,639,one-piece bathing suit +n03717622,640,manhole cover +n03720891,641,maraca +n03721384,642,marimba +n03724870,643,mask +n03729826,644,matchstick +n03733131,645,maypole +n03733281,646,maze +n03733805,647,measuring cup +n03742115,648,medicine cabinet +n03743016,649,megalith +n03759954,650,microphone +n03761084,651,microwave oven +n03763968,652,military uniform +n03764736,653,milk can +n03769881,654,minibus +n03770439,655,miniskirt +n03770679,656,minivan +n03773504,657,missile +n03775071,658,mitten +n03775546,659,mixing bowl +n03776460,660,mobile home +n03777568,661,ford model t +n03777754,662,modem +n03781244,663,monastery +n03782006,664,monitor +n03785016,665,moped +n03786901,666,mortar and pestle +n03787032,667,graduation cap +n03788195,668,mosque +n03788365,669,mosquito net +n03791053,670,vespa +n03792782,671,mountain bike +n03792972,672,tent +n03793489,673,computer mouse +n03794056,674,mousetrap +n03796401,675,moving van +n03803284,676,muzzle +n03804744,677,metal nail +n03814639,678,neck brace +n03814906,679,necklace +n03825788,680,baby pacifier +n03832673,681,notebook computer +n03837869,682,obelisk +n03838899,683,oboe +n03840681,684,ocarina +n03841143,685,odometer +n03843555,686,oil filter +n03854065,687,pipe organ +n03857828,688,oscilloscope +n03866082,689,overskirt +n03868242,690,bullock cart +n03868863,691,oxygen mask +n03871628,692,product packet / packaging +n03873416,693,paddle +n03874293,694,paddle wheel +n03874599,695,padlock +n03876231,696,paintbrush +n03877472,697,pajamas +n03877845,698,palace +n03884397,699,pan flute +n03887697,700,paper towel +n03888257,701,parachute +n03888605,702,parallel bars +n03891251,703,park bench +n03891332,704,parking meter +n03895866,705,railroad car +n03899768,706,patio +n03902125,707,payphone +n03903868,708,pedestal +n03908618,709,pencil case +n03908714,710,pencil sharpener +n03916031,711,perfume +n03920288,712,Petri dish +n03924679,713,photocopier +n03929660,714,plectrum +n03929855,715,Pickelhaube +n03930313,716,picket fence +n03930630,717,pickup truck +n03933933,718,pier +n03935335,719,piggy bank +n03937543,720,pill bottle +n03938244,721,pillow +n03942813,722,ping-pong ball +n03944341,723,pinwheel +n03947888,724,pirate ship +n03950228,725,drink pitcher +n03954731,726,block plane +n03956157,727,planetarium +n03958227,728,plastic bag +n03961711,729,plate rack +n03967562,730,farm plow +n03970156,731,plunger +n03976467,732,Polaroid camera +n03976657,733,pole +n03977966,734,police van +n03980874,735,poncho +n03982430,736,pool table +n03983396,737,soda bottle +n03991062,738,plant pot +n03992509,739,potter's wheel +n03995372,740,power drill +n03998194,741,prayer rug +n04004767,742,printer +n04005630,743,prison +n04008634,744,missile +n04009552,745,projector +n04019541,746,hockey puck +n04023962,747,punching bag +n04026417,748,purse +n04033901,749,quill +n04033995,750,quilt +n04037443,751,race car +n04039381,752,racket +n04040759,753,radiator +n04041544,754,radio +n04044716,755,radio telescope +n04049303,756,rain barrel +n04065272,757,recreational vehicle +n04067472,758,fishing casting reel +n04069434,759,reflex camera +n04070727,760,refrigerator +n04074963,761,remote control +n04081281,762,restaurant +n04086273,763,revolver +n04090263,764,rifle +n04099969,765,rocking chair +n04111531,766,rotisserie +n04116512,767,eraser +n04118538,768,rugby ball +n04118776,769,ruler measuring stick +n04120489,770,sneaker +n04125021,771,safe +n04127249,772,safety pin +n04131690,773,salt shaker +n04133789,774,sandal +n04136333,775,sarong +n04141076,776,saxophone +n04141327,777,scabbard +n04141975,778,weighing scale +n04146614,779,school bus +n04147183,780,schooner +n04149813,781,scoreboard +n04152593,782,CRT monitor +n04153751,783,screw +n04154565,784,screwdriver +n04162706,785,seat belt +n04179913,786,sewing machine +n04192698,787,shield +n04200800,788,shoe store +n04201297,789,shoji screen / room divider +n04204238,790,shopping basket +n04204347,791,shopping cart +n04208210,792,shovel +n04209133,793,shower cap +n04209239,794,shower curtain +n04228054,795,ski +n04229816,796,balaclava ski mask +n04235860,797,sleeping bag +n04238763,798,slide rule +n04239074,799,sliding door +n04243546,800,slot machine +n04251144,801,snorkel +n04252077,802,snowmobile +n04252225,803,snowplow +n04254120,804,soap dispenser +n04254680,805,soccer ball +n04254777,806,sock +n04258138,807,solar thermal collector +n04259630,808,sombrero +n04263257,809,soup bowl +n04264628,810,keyboard space bar +n04265275,811,space heater +n04266014,812,space shuttle +n04270147,813,spatula +n04273569,814,motorboat +n04275548,815,spider web +n04277352,816,spindle +n04285008,817,sports car +n04286575,818,spotlight +n04296562,819,stage +n04310018,820,steam locomotive +n04311004,821,through arch bridge +n04311174,822,steel drum +n04317175,823,stethoscope +n04325704,824,scarf +n04326547,825,stone wall +n04328186,826,stopwatch +n04330267,827,stove +n04332243,828,strainer +n04335435,829,tram +n04336792,830,stretcher +n04344873,831,couch +n04346328,832,stupa +n04347754,833,submarine +n04350905,834,suit +n04355338,835,sundial +n04355933,836,sunglasses +n04356056,837,sunglasses +n04357314,838,sunscreen +n04366367,839,suspension bridge +n04367480,840,mop +n04370456,841,sweatshirt +n04371430,842,swim trunks / shorts +n04371774,843,swing +n04372370,844,electrical switch +n04376876,845,syringe +n04380533,846,table lamp +n04389033,847,tank +n04392985,848,tape player +n04398044,849,teapot +n04399382,850,teddy bear +n04404412,851,television +n04409515,852,tennis ball +n04417672,853,thatched roof +n04418357,854,front curtain +n04423845,855,thimble +n04428191,856,threshing machine +n04429376,857,throne +n04435653,858,tile roof +n04442312,859,toaster +n04443257,860,tobacco shop +n04447861,861,toilet seat +n04456115,862,torch +n04458633,863,totem pole +n04461696,864,tow truck +n04462240,865,toy store +n04465501,866,tractor +n04467665,867,semi-trailer truck +n04476259,868,tray +n04479046,869,trench coat +n04482393,870,tricycle +n04483307,871,trimaran +n04485082,872,tripod +n04486054,873,triumphal arch +n04487081,874,trolleybus +n04487394,875,trombone +n04493381,876,hot tub +n04501370,877,turnstile +n04505470,878,typewriter keyboard +n04507155,879,umbrella +n04509417,880,unicycle +n04515003,881,upright piano +n04517823,882,vacuum cleaner +n04522168,883,vase +n04523525,884,vaulted or arched ceiling +n04525038,885,velvet fabric +n04525305,886,vending machine +n04532106,887,vestment +n04532670,888,viaduct +n04536866,889,violin +n04540053,890,volleyball +n04542943,891,waffle iron +n04548280,892,wall clock +n04548362,893,wallet +n04550184,894,wardrobe +n04552348,895,military aircraft +n04553703,896,sink +n04554684,897,washing machine +n04557648,898,water bottle +n04560804,899,water jug +n04562935,900,water tower +n04579145,901,whiskey jug +n04579432,902,whistle +n04584207,903,hair wig +n04589890,904,window screen +n04590129,905,window shade +n04591157,906,Windsor tie +n04591713,907,wine bottle +n04592741,908,airplane wing +n04596742,909,wok +n04597913,910,wooden spoon +n04599235,911,wool +n04604644,912,split-rail fence +n04606251,913,shipwreck +n04612504,914,sailboat +n04613696,915,yurt +n06359193,916,website +n06596364,917,comic book +n06785654,918,crossword +n06794110,919,traffic or street sign +n06874185,920,traffic light +n07248320,921,dust jacket +n07565083,922,menu +n07579787,923,plate +n07583066,924,guacamole +n07584110,925,consomme +n07590611,926,hot pot +n07613480,927,trifle +n07614500,928,ice cream +n07615774,929,popsicle +n07684084,930,baguette +n07693725,931,bagel +n07695742,932,pretzel +n07697313,933,cheeseburger +n07697537,934,hot dog +n07711569,935,mashed potatoes +n07714571,936,cabbage +n07714990,937,broccoli +n07715103,938,cauliflower +n07716358,939,zucchini +n07716906,940,spaghetti squash +n07717410,941,acorn squash +n07717556,942,butternut squash +n07718472,943,cucumber +n07718747,944,artichoke +n07720875,945,bell pepper +n07730033,946,cardoon +n07734744,947,mushroom +n07742313,948,Granny Smith apple +n07745940,949,strawberry +n07747607,950,orange +n07749582,951,lemon +n07753113,952,fig +n07753275,953,pineapple +n07753592,954,banana +n07754684,955,jackfruit +n07760859,956,cherimoya (custard apple) +n07768694,957,pomegranate +n07802026,958,hay +n07831146,959,carbonara +n07836838,960,chocolate syrup +n07860988,961,dough +n07871810,962,meatloaf +n07873807,963,pizza +n07875152,964,pot pie +n07880968,965,burrito +n07892512,966,red wine +n07920052,967,espresso +n07930864,968,tea cup +n07932039,969,eggnog +n09193705,970,mountain +n09229709,971,bubble +n09246464,972,cliff +n09256479,973,coral reef +n09288635,974,geyser +n09332890,975,lakeshore +n09399592,976,promontory +n09421951,977,sandbar +n09428293,978,beach +n09468604,979,valley +n09472597,980,volcano +n09835506,981,baseball player +n10148035,982,bridegroom +n10565667,983,scuba diver +n11879895,984,rapeseed +n11939491,985,daisy +n12057211,986,yellow lady's slipper +n12144580,987,corn +n12267677,988,acorn +n12620546,989,rose hip +n12768682,990,horse chestnut seed +n12985857,991,coral fungus +n12998815,992,agaric +n13037406,993,gyromitra +n13040303,994,stinkhorn mushroom +n13044778,995,earth star fungus +n13052670,996,hen of the woods mushroom +n13054560,997,bolete +n13133613,998,corn cob +n15075141,999,toilet paper \ No newline at end of file diff --git a/attacks/SAE/map_clsloc_imagenet100.txt b/attacks/SAE/map_clsloc_imagenet100.txt new file mode 100644 index 0000000000000000000000000000000000000000..fbdd765cfbbda4d0c4f0468b4de517466ed97d06 --- /dev/null +++ b/attacks/SAE/map_clsloc_imagenet100.txt @@ -0,0 +1,100 @@ +n01514668,7,rooster +n01531178,11,goldfinch +n01537544,14,indigo bunting +n01689811,44,alligator lizard +n01729322,54,eastern hog-nosed snake +n01744401,62,African rock python +n01774384,75,southern black widow +n01775062,77,wolf spider +n01806567,85,quail +n01882714,105,koala +n01914609,108,sea anemone +n01984695,123,spiny lobster +n01990800,126,isopod +n02002556,127,white stork +n02011460,133,bittern bird +n02033041,142,dowitcher +n02088094,160,Afghan Hound +n02091467,174,Norwegian Elkhound +n02094258,186,Norwich Terrier +n02096177,192,Cairn Terrier +n02101388,215,Brittany dog +n02102318,219,Cocker Spaniel +n02105056,224,Groenendael dog +n02107574,238,Greater Swiss Mountain Dog +n02107908,240,Appenzeller Sennenhund +n02112137,260,Chow Chow +n02112706,262,brussels griffon +n02115913,274,dhole +n02116738,275,African wild dog +n02120079,279,Arctic fox +n02123597,284,Siamese cat +n02128385,288,leopard +n02128757,289,snow leopard +n02168699,303,longhorn beetle +n02229544,312,cricket insect +n02279972,323,monarch butterfly +n02280649,324,small white butterfly +n02363005,337,beaver +n02526121,390,eel +n02536864,391,silver salmon +n02640242,394,sturgeon +n02669723,400,academic gown +n02749479,413,assault rifle +n02786058,419,Band-Aid +n02814860,437,lighthouse +n02840245,446,ring binder +n02917067,466,high-speed train +n02948072,470,candle +n02950826,471,cannon +n02951585,473,can opener +n02971356,478,cardboard box / carton +n03000247,490,chain mail +n03016953,493,chiffonier +n03017168,494,bell or wind chime +n03075370,507,combination lock +n03085013,508,computer keyboard +n03089624,509,candy store +n03109150,512,corkscrew +n03141823,523,crutch +n03223299,539,doormat +n03250847,542,drumstick +n03290653,548,entertainment center +n03485407,590,hand-held computer +n03527444,597,holster +n03532672,600,hook +n03657121,622,lens cap +n03691459,632,music speaker +n03697007,634,sawmill +n03710193,637,mailbox +n03759954,650,microphone +n03775071,658,mitten +n03785016,665,moped +n03884397,699,pan flute +n03891332,704,parking meter +n03942813,722,ping-pong ball +n03967562,730,farm plow +n04026417,748,purse +n04074963,761,remote control +n04152593,782,CRT monitor +n04153751,783,screw +n04266014,812,space shuttle +n04355933,836,sunglasses +n04371430,842,swim trunks / shorts +n04399382,850,teddy bear +n04409515,852,tennis ball +n04428191,856,threshing machine +n04442312,859,toaster +n04525305,886,vending machine +n04550184,894,wardrobe +n04591157,906,Windsor tie +n04599235,911,wool +n04604644,912,split-rail fence +n07614500,928,ice cream +n07831146,959,carbonara +n07892512,966,red wine +n09468604,979,valley +n12267677,988,acorn +n12620546,989,rose hip +n12768682,990,horse chestnut seed +n15075141,999,toilet paper \ No newline at end of file diff --git a/attacks/SAE/model_util.py b/attacks/SAE/model_util.py new file mode 100644 index 0000000000000000000000000000000000000000..92762461e26a58b7900c3edc69c5e3f6947d0bc4 --- /dev/null +++ b/attacks/SAE/model_util.py @@ -0,0 +1,134 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np + + +def generate_points(w): + return np.random.randint(0, w, size=(6, 2)) + + +# Function to calculate the Euclidean distance between two points +def euclidean_distance(p1, p2): + return np.sqrt((p1[0] - p2[0]) ** 2 + (p1[1] - p2[1]) ** 2) + + +# Function to drop rectangles in the image +def drop_blocks(image): + c, w, h = image.shape + points = generate_points(w) + pairs = [] + + # Repeat three times to find three pairs + for _ in range(3): + distances = {} + + # Calculate distances between each pair of points that haven't been paired yet + for i in range(len(points)): + for j in range(i + 1, len(points)): + if {i, j}.isdisjoint(set(sum(pairs, ()))): + dist = euclidean_distance(points[i], points[j]) + distances[(i, j)] = dist + + # Find the pair of points with the smallest distance + nearest_pair = min(distances, key=distances.get) + pairs.append(nearest_pair) + + # Function to mask a block in the image + def mask_block(image, start_point, end_point): + x1, y1 = points[start_point] + x2, y2 = points[end_point] + x_min, x_max = min(x1, x2), max(x1, x2) + y_min, y_max = min(y1, y2), max(y1, y2) + image[:, x_min:x_max + 1, y_min:y_max + 1] = 0 + + # Apply masking to the three blocks + for pair in pairs: + mask_block(image, *pair) + + return image + + +class NoTargetDataset(torch.utils.data.Dataset): + def __init__(self, dataset, target): + self.dataset = dataset + self.indices = [] + self.target = target + if target: + if hasattr(self.dataset, 'targets') and len(self.dataset.targets) == len(self.dataset): + for idx, y in enumerate(self.dataset.targets): + if y != target: + self.indices.append(int(idx)) + else: + for idx, (_, X, y) in enumerate(self.dataset): + if y != target: + self.indices.append(int(idx)) + else: + self.indices = [i for i in range(len(dataset))] + + def __len__(self): + return len(self.indices) + + def __getitem__(self, item): + return self.dataset[self.indices[item]] + + +class UniversalPerturbation(nn.Module): + def __init__(self, size, epsilon=32 / 255, initialization=None, device='cuda'): + super(UniversalPerturbation, self).__init__() + # Initialize the adv as a learnable parameter + self.device = device + if isinstance(initialization, torch.Tensor): + self.adv = nn.Parameter(initialization.clone().detach().to(self.device)) + else: + self.adv = nn.Parameter(torch.empty(size, requires_grad=True, device=self.device)) + nn.init.normal_(self.adv, mean=0.0, std=0.01) + self.epsilon = epsilon + + def forward(self, x): + x = x.to(self.adv.device) + x_hat = x + torch.clamp(self.adv, -self.epsilon * 2, self.epsilon * 2) + return torch.clamp(x_hat, -1, 1) + + + +class TrainableAffineTransform(nn.Module): + def __init__(self, batch_size=1, scale=0.05, flip=True): + super(TrainableAffineTransform, self).__init__() + # Initialize the parameters for the affine transformation with small random values + theta = torch.eye(2, 3).unsqueeze(0).repeat(batch_size, 1, 1) + + # Apply small random transformations for rotation, translation, and scaling + random_rotation = (torch.rand(batch_size) - 0.5) * 2 * scale # Rotation angle between -5 and 5 degrees + random_translation = (torch.rand(batch_size, + 2) - 0.5) * 2 * scale # Translation between -5% and 5% of image size + random_scaling = 1 + (torch.rand(batch_size, 2) - 0.5) * 2 * scale # Scaling between 0.95 and 1.05 + + for i in range(batch_size): + rotation_matrix = torch.tensor([ + [torch.cos(random_rotation[i]), -torch.sin(random_rotation[i]), random_translation[i, 0]], + [torch.sin(random_rotation[i]), torch.cos(random_rotation[i]), random_translation[i, 1]] + ], dtype=torch.float) + + scale_matrix = torch.diag(torch.cat([random_scaling[i], torch.tensor([1.0])])).unsqueeze(0) + + theta[i, :, :] = torch.mm(rotation_matrix, scale_matrix.squeeze()) + + self.theta = nn.Parameter(theta, requires_grad=False) # Set requires_grad=False as these are for augmentation + self.flip = flip + + def forward(self, x): + batch_size = x.size(0) + theta = self.theta[:batch_size].to(x.device) # Ensure theta is the correct shape and on the correct device + grid = F.affine_grid(theta, x.size(), align_corners=False) + x = F.grid_sample(x, grid, align_corners=False) + + if self.flip: + flip_indices = torch.randperm(batch_size)[:batch_size // 2] + x[flip_indices] = torch.flip(x[flip_indices], [3]) + + # Drop two rectangular blocks + for i in range(batch_size): + x[i] = drop_blocks(x[i]) + + return x diff --git a/attacks/SAE/text_templates.py b/attacks/SAE/text_templates.py new file mode 100644 index 0000000000000000000000000000000000000000..8045ba57e7bf5a750c19e1185c66bedb51583470 --- /dev/null +++ b/attacks/SAE/text_templates.py @@ -0,0 +1,129 @@ +imagenet_templates = [ + 'a bad photo of a {}.', + 'a sculpture of a {}.', + 'a photo of the hard to see {}.', + 'a low resolution photo of the {}.', + 'a rendering of a {}.', + 'graffiti of a {}.', + 'a bad photo of the {}.', + 'a cropped photo of the {}.', + 'a tattoo of a {}.', + 'the embroidered {}.', + 'a photo of a hard to see {}.', + 'a bright photo of a {}.', + 'a photo of a clean {}.', + 'a photo of a dirty {}.', + 'a dark photo of the {}.', + 'a drawing of a {}.', + 'a photo of my {}.', + 'the plastic {}.', + 'a photo of the cool {}.', + 'a close-up photo of a {}.', + 'a black and white photo of the {}.', + 'a painting of the {}.', + 'a painting of a {}.', + 'a pixelated photo of the {}.', + 'a sculpture of the {}.', + 'a bright photo of the {}.', + 'a cropped photo of a {}.', + 'a plastic {}.', + 'a photo of the dirty {}.', + 'a jpeg corrupted photo of a {}.', + 'a blurry photo of the {}.', + 'a photo of the {}.', + 'a good photo of the {}.', + 'a rendering of the {}.', + 'a {} in a video game.', + 'a photo of one {}.', + 'a doodle of a {}.', + 'a close-up photo of the {}.', + 'a photo of a {}.', + 'the origami {}.', + 'the {} in a video game.', + 'a sketch of a {}.', + 'a doodle of the {}.', + 'a origami {}.', + 'a low resolution photo of a {}.', + 'the toy {}.', + 'a rendition of the {}.', + 'a photo of the clean {}.', + 'a photo of a large {}.', + 'a rendition of a {}.', + 'a photo of a nice {}.', + 'a photo of a weird {}.', + 'a blurry photo of a {}.', + 'a cartoon {}.', + 'art of a {}.', + 'a sketch of the {}.', + 'a embroidered {}.', + 'a pixelated photo of a {}.', + 'itap of the {}.', + 'a jpeg corrupted photo of the {}.', + 'a good photo of a {}.', + 'a plushie {}.', + 'a photo of the nice {}.', + 'a photo of the small {}.', + 'a photo of the weird {}.', + 'the cartoon {}.', + 'art of the {}.', + 'a drawing of the {}.', + 'a photo of the large {}.', + 'a black and white photo of a {}.', + 'the plushie {}.', + 'a dark photo of a {}.', + 'itap of a {}.', + 'graffiti of the {}.', + 'a toy {}.', + 'itap of my {}.', + 'a photo of a cool {}.', + 'a photo of a small {}.', + 'a tattoo of the {}.', +] + +part_templates = [ + 'the paw of a {}.', + 'the nose of a {}.', + 'the eye of the {}.', + 'the ears of a {}.', + 'an eye of a {}.', + 'the tongue of a {}.', + 'the fur of the {}.', + 'colorful {} fur.', + 'a snout of a {}.', + 'the teeth of the {}.', + 'the {}s fangs.', + 'a claw of the {}.', + 'the face of the {}', + 'a neck of a {}', + 'the head of the {}', +] + +imagenet_templates_small = [ + 'a photo of a {}.', + 'a rendering of a {}.', + 'a cropped photo of the {}.', + 'the photo of a {}.', + 'a photo of a clean {}.', + 'a photo of a dirty {}.', + 'a dark photo of the {}.', + 'a photo of my {}.', + 'a photo of the cool {}.', + 'a close-up photo of a {}.', + 'a bright photo of the {}.', + 'a cropped photo of a {}.', + 'a photo of the {}.', + 'a good photo of the {}.', + 'a photo of one {}.', + 'a close-up photo of the {}.', + 'a rendition of the {}.', + 'a photo of the clean {}.', + 'a rendition of a {}.', + 'a photo of a nice {}.', + 'a good photo of a {}.', + 'a photo of the nice {}.', + 'a photo of the small {}.', + 'a photo of the weird {}.', + 'a photo of the large {}.', + 'a photo of a cool {}.', + 'a photo of a small {}.', +] \ No newline at end of file diff --git a/attacks/UnivIntruder/UnivIntruderAttack.py b/attacks/UnivIntruder/UnivIntruderAttack.py new file mode 100644 index 0000000000000000000000000000000000000000..a58cd40f1b4aba50e633b2ba5ab328b6a7c5ceb1 --- /dev/null +++ b/attacks/UnivIntruder/UnivIntruderAttack.py @@ -0,0 +1,140 @@ +import os.path +import torch +from foolbox.attacks.base import * +from foolbox.attacks.gradient_descent_base import * +from tqdm import tqdm +import pandas as pd +from attacks.attack_config import SustainableAttack +from utils.plot import save_grad_cam, plot_asr_per_target +import logging +from foolbox import PyTorchModel, accuracy +import numpy as np +from utils import factory +from utils.data_manager import DataManager, get_dataloader +from attacks.UnivIntruder.att import train +from attacks.UnivIntruder.loss import UniversalPerturbation + + +class UnivIntruder(SustainableAttack): + def __init__(self, args, device='cuda'): + super().__init__(args, device) + self.device = device + self.args = args + self.surrogate_model = None # surrogate__model.to(device).eval() + + self.target_class = args['target_class'] + self.epsilon = 32 + self.image_size = 32 if self.args['dataset'] == 'cifar100' else 224 + self.eval_batch_szie = 128 + + self.adv_name = f'adv_eps{self.epsilon}_tc{self.target_class}' + self.out_path = f'{self.args["logs_eval_name"]}/{self.adv_name}' + os.makedirs(self.out_path, exist_ok=True) + + self.eval = args['eval'] + self.plot_gradcam = True + self.ckpt_num = None # None + + def train_adv(self): + if self.eval: + pass + else: + train(self.args) + + def run_test(self): + pth_name = self.get_max_step_filename(f'{self.args["logs_eval_name"]}/{self.adv_name}/ckpts') + + self.ckpt = f'{self.args["logs_eval_name"]}/{self.adv_name}/ckpts/{pth_name}.pth' + self.prefix = f'{self.adv_name}_{pth_name.split("_")[-1]}' + a = torch.load(self.ckpt) + self.adv = UniversalPerturbation((3, self.image_size, self.image_size), self.epsilon / 255, + initialization=a, device=self.device) + self.adv.eval() + + # Load Batch Data + self.loader = get_dataloader(self.data_manager, batch_size=self.eval_batch_szie, + start_class=0, end_class=10, + train=False, shuffle=False, num_workers=0) + for i, (_, imgs, labels) in enumerate(tqdm(self.loader, total=len(self.loader), + desc=f'Loading Data with Batch Size of {self.batch_size}) :')): + if i > 0: + break + + imgs, labels = ep.astensors(*(imgs.to(self.device), labels.to(self.device))) + + imgs_f = imgs[labels != self.target_class] + labels_f = labels[labels != self.target_class] + labels_t_f = ep.full_like(labels_f, fill_value=self.target_class) + + self.attacks(i, imgs_f, labels_f, labels_t_f) + + def attacks(self, i_batch, imgs, labels, labels_t): + if self.args["model_name"] != 'finetune': + imgs_f, labels_f = self.to_all(imgs, labels) + labels_t = labels_t[:len(imgs_f)] + else: + imgs_f, labels_f = imgs, labels + clean_acc_matrix = [] + asr_matrix = np.ones((self.data_manager.nb_tasks, 1)) + self.model = factory.get_model(self.args["model_name"], self.args) + eval_path = os.path.join(self.args["logs_eval_name"], self.adv_name) + cnn_matrix, nme_matrix = [], [] + for task in range(self.data_manager.nb_tasks): + logging.info("***** Starting attack on task [{}]. *****".format(task)) + self.model.incremental_train(self.data_manager) + self.model._network.load_state_dict( + torch.load(self.ckpt_paths[task], map_location=self.device)['model_state_dict']) + self.model._network.to(self.device) + self.model._network.eval() + + # Run attack on ecah target image + current_model = PyTorchModel(self.model._network, bounds=(0, 1), preprocessing=self.preprocessing) + verify_input_bounds(imgs_f, current_model) + + # Evaluate the model perfromance with clean data + acc = accuracy(current_model, imgs, labels)[0] + logging.info("Clean accuracy on task {}: {}%".format(task, acc * 100)) + clean_acc_matrix.append(acc) + + advs = self.adv(imgs_f.raw) + asr = accuracy(current_model, ep.astensor(advs), labels_t)[0] + asr_matrix[task] = asr + if self.plot_gradcam: + save_grad_cam(self.args, torch.clip(advs.detach(), 0, 1), labels_t.raw, + self.model._network, self.out_path + "/GradCam", prefix=f'task{task}', + layer_name='stage_3', save_num=100, save_raw=True) + + del advs, current_model + torch.cuda.empty_cache() + + self.model.after_task() + + # Save all target images info: everage asr, + prefix = f'batch{i_batch}_{self.prefix}' + plot_asr_per_target(asr_matrix, eval_path, prefix, self.args, clean_acc_matrix) + df = pd.DataFrame(asr_matrix, columns=['ASR']) + df.to_excel(os.path.join(eval_path, f"{prefix}.xlsx"), index=False) + + del asr_matrix, imgs, labels, labels_t, imgs_f, labels_f + torch.cuda.empty_cache() + + def get_max_step_filename(self, folder_path): + files = [f for f in os.listdir(folder_path) if f.endswith('.pth')] + step_files = [(f, int(f.split('_')[-1].split('.')[0])) for f in files] + step_files.sort(key=lambda x: x[1], reverse=True) + max_step_file = step_files[0][0] + return os.path.splitext(max_step_file)[0] + + def __call__( + self, + model: Model, + inputs: T, + criterion: Any, + *, + epsilons: Union[Sequence[Union[float, None]], float, None], + **kwargs: Any, + ) -> Union[Tuple[List[T], List[T], T], Tuple[T, T, T]]: + ... + + def repeat(self, times: int) -> "UnivIntruder": + ... \ No newline at end of file diff --git a/attacks/UnivIntruder/att.py b/attacks/UnivIntruder/att.py new file mode 100644 index 0000000000000000000000000000000000000000..d9a2c508fb110638835db566786a01a364a14f52 --- /dev/null +++ b/attacks/UnivIntruder/att.py @@ -0,0 +1,171 @@ +import torch +import torch.optim as optim +import torchvision +from torchvision import datasets, transforms +from torch.utils.data import DataLoader +import os +import numpy as np +import random +import torch.nn.functional as F +from attacks.UnivIntruder.dataloader import GetDatasetMeta, TransformedDataset, InMemoryDataset +from attacks.UnivIntruder.loss import TrainableAffineTransform, UniversalPerturbation, BackdoorEval, \ + NoTargetDataset +from attacks.UnivIntruder.loss_function.clip_loss import CLIPLoss +from tqdm import tqdm + + +# Fix random seeds for reproducibility +def fix_random_seeds(seed=42): + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) # if you are using multi-GPU. + np.random.seed(seed) + random.seed(seed) + torch.backends.cudnn.benchmark = False + torch.backends.cudnn.deterministic = True + +def save_images(x, x_hat, render_num=64, output_dir='rendered_images', step=0, test=False): + os.makedirs(output_dir, exist_ok=True) + + num_rows = int(render_num ** 0.5 / 2) * 2 + + img_lst = [] + for i in range(int(render_num / 2)): + img_lst.append(x[i]) + img_lst.append(x_hat[i]) + + grid = torchvision.utils.make_grid(img_lst, nrow=num_rows, padding=2) + if test: + torchvision.utils.save_image(grid, os.path.join(output_dir, str(step) + '.png'), nrow=num_rows) + else: + torchvision.utils.save_image(grid * 0.5 + 0.5, os.path.join(output_dir, str(step) + '.png'), nrow=num_rows) + + + +def train(args_cl): + # fix_random_seeds(1) + + # Parameters + device = 'cuda:0' # args.device + class_map = args_cl['target_class_dict'] + target_class = list(class_map.keys())[list(class_map.values()).index(args_cl['target_class'])] + eps = 32 # args.eps + epsilon = eps / 255 + batch_size = args_cl['batch_size'] # args.batch_sizes + + data_path = 'datasets' # args.data_path + if args_cl['dataset'] == 'cifar100': + src_dataset = 'TinyImageNet' + tgt_dataset = 'CIFAR100' + elif args_cl['dataset'] == 'imagenet100': + src_dataset = 'ImageNet' + tgt_dataset = 'ImageNet100' + image_size = 32 if tgt_dataset == 'CIFAR100' else 224 # args.image_size + + max_step = 1 # args.max_step + out_path = f'{args_cl["logs_eval_name"]}/adv_eps{eps}_tc{class_map[target_class]}' + os.makedirs(out_path, exist_ok=True) + pretrain = None + flip = True + download = True + simple_out = False + top5 = True + split = 1 # args.split + + transform = transforms.Compose([ + transforms.Resize((image_size, image_size)), + transforms.ToTensor(), + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) + ]) + + src_data_meta = GetDatasetMeta(data_path, src_dataset) + tgt_data_meta = GetDatasetMeta(data_path, tgt_dataset) + + train_set = src_data_meta.get_dataset(transform=transform, train=True if src_dataset != 'ImageNet' else False, + download=download) + test_set = tgt_data_meta.get_dataset(transform=transform, train=True if tgt_dataset != 'ImageNet100' else False, + download=download) + test_set = NoTargetDataset(test_set, target_class) + test_set, _ = torch.utils.data.random_split(test_set, + [len(test_set) // split, len(test_set) - len(test_set) // split]) + src_label_text_dict = src_data_meta.get_dataset_label_names() + tgt_label_text_dict = tgt_data_meta.get_dataset_label_names() + + tgt_transform = tgt_data_meta.get_transformation() + y_negative = set(tgt_label_text_dict.values()) + y_negative.discard(tgt_label_text_dict[target_class]) + + train_set = NoTargetDataset(train_set, target=next( + (k for k, v in src_label_text_dict.items() if v == tgt_label_text_dict[target_class]), None)) + train_set = InMemoryDataset([i for i in train_set]) + + train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True, drop_last=True, num_workers=0) + + if pretrain: + input_tensor = torch.load(pretrain) + output_tensor = F.interpolate(input_tensor.unsqueeze(0), size=(image_size, image_size), mode='bilinear', + align_corners=False).squeeze(0) + trigger_model = UniversalPerturbation((3, image_size, image_size), epsilon, initialization=output_tensor, + device=device) + else: + trigger_model = UniversalPerturbation((3, int(image_size), int(image_size)), epsilon, initialization=None, + device=device) + + visual_model = tgt_data_meta.get_clean_model() + evaluator = BackdoorEval(visual_model, len(list(tgt_label_text_dict.keys())), device, target_class, simple_out, + top5) + + # Optimizer + optimizer = optim.Adam(trigger_model.parameters(), lr=0.01, weight_decay=1e-5) + + # Loss function + clip_loss_func = CLIPLoss(device, lambda_direction=1, clip_model='ViT-B-32', pretrained='laion2b_s34b_b79k' + ) + + clip_loss_func.precompute_text_features(list(src_label_text_dict.values()), templates=src_data_meta.get_template()) + clip_loss_func.precompute_text_features(list(tgt_label_text_dict.values()), templates=tgt_data_meta.get_template()) + + # Training loop + global_step = 0 + while global_step <= max_step: + average_epoch_loss = [] + univ_loss = 0 + cl_loss = 0 + total_loss = 0 + for i, (x, y) in enumerate(tqdm(train_loader, total=len(train_loader), + desc=f'Training Trigger: ')): + if global_step > max_step: + break + bs = x.size(0) + x = x.to(device).to(torch.float32) + x_hat = trigger_model(x) # Apply the perturbation + + grad_transform = TrainableAffineTransform(bs, 0.25, flip=flip) + + x_hat_trans = grad_transform(x_hat) + + if src_label_text_dict: + y_source = [src_label_text_dict[int(y_i)] for y_i in y] + else: + y_source = None + y_target = [tgt_label_text_dict[int(target_class)] for _ in y] + + # Compute the loss + loss = clip_loss_func(x, y_source, x_hat_trans, y_target, y_negative) + + # Backward and optimize + optimizer.zero_grad() + loss.backward() + optimizer.step() + + with torch.no_grad(): + trigger_model.trigger.clamp_(-1, 1) + + average_epoch_loss.append(loss.item()) + global_step += 1 + univ_loss += loss.item() + + os.makedirs(os.path.join(out_path, 'ckpts'), exist_ok=True) + torch.save(trigger_model.trigger.detach().cpu(), os.path.join(out_path, f'ckpts/trigger_{global_step}.pth')) + trigger_model = trigger_model.to(device) + trigger_model.train() diff --git a/attacks/UnivIntruder/dataloader.py b/attacks/UnivIntruder/dataloader.py new file mode 100644 index 0000000000000000000000000000000000000000..fb062b0a9814fc96837b864808ae9a059fa57113 --- /dev/null +++ b/attacks/UnivIntruder/dataloader.py @@ -0,0 +1,146 @@ +import os +from torchvision.datasets import CIFAR10, CIFAR100 +import torch +import torchvision +from torchvision import transforms +from attacks.UnivIntruder.dataset import TinyImageNet, ImageNet, Caltech101, ImageNet100 +from attacks.UnivIntruder.utils_.text_templates import imagenet_templates + + +def reverse_dict(label2class): + # Use dictionary comprehension to reverse the key-value pairs + class2label = {value: key for key, value in label2class.items()} + return class2label + + +class GetDatasetMeta(): + def __init__(self, root, dataset_name) -> None: + self.dataset_name = dataset_name + self.root = root + + def get_dataset_label_names(self): + if self.dataset_name == "CIFAR10": + tmp = CIFAR10(root=self.root) + label_dict = reverse_dict(tmp.class_to_idx) + + elif self.dataset_name == "CIFAR100": + tmp = CIFAR100(root=self.root) + label_dict = reverse_dict(tmp.class_to_idx) + + elif self.dataset_name == "TinyImageNet" or self.dataset_name == "ImageNet" or self.dataset_name == "ImageNet100": + with open(os.path.join('attacks/UnivIntruder/utils_/map_clsloc.txt'), 'r') as file: + lines = file.readlines() + + label_dict = {} + + for line in lines: + parts = line.strip().split(',') + label_dict[int(parts[1])] = parts[2] + + elif self.dataset_name == "Caltech101": + label_list = self.get_dataset().dataset.categories + label_dict = {i: label_list[i] for i in range(101)} + + else: + label_dict = None + + return label_dict + + def get_transformation(self): + if self.dataset_name == "CIFAR10": + size = 32 + normalize = [[0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]] + elif self.dataset_name == "CIFAR100": + size = 32 + normalize = [[0.5070751592371323, 0.48654887331495095, 0.4409178433670343], + [0.2673342858792401, 0.2564384629170883, 0.27615047132568404]] + elif self.dataset_name == "TinyImageNet" or self.dataset_name == "ImageNet" or self.dataset_name == "ImageNet100": + size = 224 + # normalize = [0.4802, 0.4481, 0.3975], [0.2302, 0.2265, 0.2262] + normalize = [[0.485, 0.456, 0.406], [0.229, 0.224, 0.225]] + elif self.dataset_name == "Caltech101": + size = 224 + normalize = [[0.485, 0.456, 0.406], [0.229, 0.224, 0.225]] + else: + return None + preprocess = transforms.Compose([ + transforms.Resize((size, size)), + transforms.Normalize(mean=normalize[0], std=normalize[1]), + ]) + return preprocess + + def get_template(self): + return imagenet_templates + + def get_dataset(self, train=False, download=False, transform=None, **kwargs): + if self.dataset_name == "CIFAR10": + target_dataset = CIFAR10(root=self.root, train=train, download=download, transform=transform) + elif self.dataset_name == "CIFAR100": + target_dataset = CIFAR100(root=self.root, train=train, download=download, transform=transform) + elif self.dataset_name == "TinyImageNet": + target_dataset = TinyImageNet(root=self.root, split='train' if train else 'test', download=download, + transform=transform) + elif self.dataset_name == "ImageNet": + target_dataset = ImageNet(root=self.root, split='train' if train else 'val', download=download, + transform=transform) + elif self.dataset_name == "ImageNet100": + target_dataset = ImageNet100(root=self.root, split='train' if train else 'val', download=download, + transform=transform) + elif self.dataset_name == "Caltech101": + target_dataset = Caltech101(root=self.root, transform=transform, train=train) + return target_dataset + + def get_clean_model(self): + if self.dataset_name == "CIFAR10": + model_visual = torch.hub.load("chenyaofo/pytorch-cifar-models", 'cifar10_resnet44', pretrained=True) + elif self.dataset_name == "CIFAR100": + model_visual = torch.hub.load("chenyaofo/pytorch-cifar-models", 'cifar100_resnet44', pretrained=True) + elif self.dataset_name == "TinyImageNet" or self.dataset_name == "ImageNet": + model_visual = torchvision.models.resnet50(pretrained=True) + return model_visual + + def n_classes(self): + if self.dataset_name == "CIFAR10": + n = 10 + elif self.dataset_name == "CIFAR100": + n = 100 + elif self.dataset_name == "TinyImageNet": + n = 500 + elif self.dataset_name == "ImageNet": + n = 1000 + elif self.dataset_name == "ImageNet100": + n = 100 + elif self.dataset_name == "Caltech101": + n = 101 + return n + + +class InMemoryDataset(torch.utils.data.Dataset): + def __init__(self, data_list): + self.data_list = data_list + + def __len__(self): + return len(self.data_list) + + def __getitem__(self, idx): + return self.data_list[idx] + + +class TransformedDataset(torch.utils.data.Dataset): + def __init__(self, args_cl, original_dataset, transform=None): + self.original_dataset = original_dataset + self.transform = transform + self.args_cl = args_cl + + def __getitem__(self, index): + if self.args_cl['approach'] == 0: + image, label = self.original_dataset[index] + else: + _, image, label = self.original_dataset[index] + # _, image, label = self.original_dataset[index] + if self.transform: + image = self.transform(image) + return image, label + + def __len__(self): + return len(self.original_dataset) \ No newline at end of file diff --git a/attacks/UnivIntruder/dataset/__init__.py b/attacks/UnivIntruder/dataset/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f867c0f4d420d6165b852368f449bec7d736108d --- /dev/null +++ b/attacks/UnivIntruder/dataset/__init__.py @@ -0,0 +1,4 @@ +from .tiny_imagenet import TinyImageNet +from .imagenet import ImageNet +from .imagenet100 import ImageNet100 +from .caltech101 import CustomCaltech101 as Caltech101 \ No newline at end of file diff --git a/attacks/UnivIntruder/dataset/caltech101.py b/attacks/UnivIntruder/dataset/caltech101.py new file mode 100644 index 0000000000000000000000000000000000000000..9652c96f61ef1e7304866d704cc6b7f901dc23c4 --- /dev/null +++ b/attacks/UnivIntruder/dataset/caltech101.py @@ -0,0 +1,70 @@ +import os +import torch +from torchvision.datasets import Caltech101 +from torchvision import transforms +from torch.utils.data import Dataset +from sklearn.model_selection import train_test_split +from PIL import Image +from typing import Optional, Callable, Tuple + +class CustomCaltech101(Dataset): + def __init__(self, root: str, train: bool = True, transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, download: bool = False, + test_size: float = 0.2, random_state: int = 42): + + # Preload images and labels + self.dataset = Caltech101(root, download=download) + self.data = [] + self.targets = [] + self.transform = transform + self.target_transform = target_transform + + for idx, (image, target) in enumerate(self.dataset): + self.data.append(image.convert('RGB')) + self.targets.append(target) + + # Split the data into train and test sets + self.train = train + train_data, test_data, train_targets, test_targets = train_test_split( + self.data, self.targets, test_size=test_size, stratify=self.targets, random_state=random_state + ) + + if self.train: + self.data, self.targets = train_data, train_targets + else: + self.data, self.targets = test_data, test_targets + + def __len__(self) -> int: + return len(self.data) + + def __getitem__(self, index: int) -> Tuple[torch.Tensor, int]: + img = self.data[index] + target = self.targets[index] + + if self.transform is not None: + img = self.transform(img) + if self.target_transform is not None: + target = self.target_transform(target) + + return img, target + +# Example usage +if __name__ == "__main__": + # Define the root directory where the dataset will be stored + root_dir = "/data/datasets" + + # Define the transform to apply to the images + transform = transforms.Compose([ + transforms.Resize((224, 224)), + transforms.ToTensor(), + ]) + + # Create an instance of the custom dataset for training + train_dataset = CustomCaltech101(root=root_dir, train=True, transform=transform, download=True) + + # Create an instance of the custom dataset for testing + test_dataset = CustomCaltech101(root=root_dir, train=False, transform=transform, download=True) + + # Print some information + print(f"Number of training samples: {len(train_dataset)}") + print(f"Number of testing samples: {len(test_dataset)}") \ No newline at end of file diff --git a/attacks/UnivIntruder/dataset/imagenet.py b/attacks/UnivIntruder/dataset/imagenet.py new file mode 100644 index 0000000000000000000000000000000000000000..1fa85de0ea65f6a373a8eee46ac2a02d382bd9f2 --- /dev/null +++ b/attacks/UnivIntruder/dataset/imagenet.py @@ -0,0 +1,71 @@ +import os +import torch +from PIL import Image +from torch.utils.data import Dataset +from typing import Optional, Callable + +class ImageNet(Dataset): + def __init__( + self, + root: str, + split: str = "train", + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + download: bool = False, + ) -> None: + super(ImageNet, self).__init__() + self.root = root + self.split = split + self.transform = transform + self.target_transform = target_transform + self.data = [] + self.targets = [] + + with open(os.path.join('attacks/UnivIntruder/utils_/map_clsloc.txt'), 'r') as file: + lines = file.readlines() + + # Create the dictionary + self.data_dict = {} + + # Iterate over each line + for line in lines: + # Strip any surrounding whitespace and split by comma + parts = line.strip().split(',') + # Extract the key and value + key = parts[0] + value = [int(parts[1]), parts[2]] + # Add to the dictionary + self.data_dict[key] = value + + # Assuming the structure is root/split/images, e.g., root/train/images + split_dir = os.path.join(self.root, 'imagenet', self.split) + + # Load images and labels + for class_name in os.listdir(split_dir): + class_dir = os.path.join(split_dir, class_name) + class_idx = self.data_dict[class_name][0] + for image_name in os.listdir(class_dir): + with open(os.path.join(class_dir, image_name), 'rb') as f: + img = Image.open(f).convert('RGB') + self.data.append(img) + self.targets.append(class_idx) + + if download: + # Code to download the dataset (if required) + pass + + def __len__(self): + return len(self.data) + + def __getitem__(self, idx): + img = self.data[idx] + target = self.targets[idx] + + # Apply transformations + if self.transform is not None: + img = self.transform(img) + + if self.target_transform is not None: + target = self.target_transform(target) + + return img, target diff --git a/attacks/UnivIntruder/dataset/imagenet100.py b/attacks/UnivIntruder/dataset/imagenet100.py new file mode 100644 index 0000000000000000000000000000000000000000..524bee1d5cdd9054ea098941a6e7f7fed209a08a --- /dev/null +++ b/attacks/UnivIntruder/dataset/imagenet100.py @@ -0,0 +1,72 @@ +import os +import torch +from PIL import Image +from torch.utils.data import Dataset +from typing import Optional, Callable + + +class ImageNet100(Dataset): + def __init__( + self, + root: str, + split: str = "train", + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + download: bool = False, + ) -> None: + super(ImageNet100, self).__init__() + self.root = root + self.split = split + self.transform = transform + self.target_transform = target_transform + self.data = [] + self.targets = [] + + with open(os.path.join('attacks/UnivIntruder/utils_/map_clsloc.txt'), 'r') as file: + lines = file.readlines() + + # Create the dictionary + self.data_dict = {} + + # Iterate over each line + for line in lines: + # Strip any surrounding whitespace and split by comma + parts = line.strip().split(',') + # Extract the key and value + key = parts[0] + value = [int(parts[1]), parts[2]] + # Add to the dictionary + self.data_dict[key] = value + + # Assuming the structure is root/split/images, e.g., root/train/images + split_dir = os.path.join(self.root, 'imagenet100', self.split) + + # Load images and labels + for class_name in os.listdir(split_dir): + class_dir = os.path.join(split_dir, class_name) + class_idx = self.data_dict[class_name][0] + for image_name in os.listdir(class_dir): + with open(os.path.join(class_dir, image_name), 'rb') as f: + img = Image.open(f).convert('RGB') + self.data.append(img) + self.targets.append(class_idx) + + if download: + # Code to download the dataset (if required) + pass + + def __len__(self): + return len(self.data) + + def __getitem__(self, idx): + img = self.data[idx] + target = self.targets[idx] + + # Apply transformations + if self.transform is not None: + img = self.transform(img) + + if self.target_transform is not None: + target = self.target_transform(target) + + return img, target diff --git a/attacks/UnivIntruder/dataset/tiny_imagenet.py b/attacks/UnivIntruder/dataset/tiny_imagenet.py new file mode 100644 index 0000000000000000000000000000000000000000..2893928e3d6f1f20b87ce9217daa0a6abbe2b638 --- /dev/null +++ b/attacks/UnivIntruder/dataset/tiny_imagenet.py @@ -0,0 +1,139 @@ +import os +import torch +from PIL import Image +from torch.utils.data import Dataset +from typing import Optional, Callable +from torchvision.datasets.utils import verify_str_arg +from torchvision.datasets.utils import download_and_extract_archive +import shutil + +def normalize_tin_val_folder_structure(path, + images_folder='images', + annotations_file='val_annotations.txt'): + # Check if files/annotations are still there to see + # if we already run reorganize the folder structure. + images_folder = os.path.join(path, images_folder) + annotations_file = os.path.join(path, annotations_file) + + # Exists + if not os.path.exists(images_folder) \ + and not os.path.exists(annotations_file): + if not os.listdir(path): + raise RuntimeError('Validation folder is empty.') + return + + # Parse the annotations + with open(annotations_file) as f: + for line in f: + values = line.split() + img = values[0] + label = values[1] + img_file = os.path.join(images_folder, values[0]) + label_folder = os.path.join(path, label) + os.makedirs(label_folder, exist_ok=True) + try: + shutil.move(img_file, os.path.join(label_folder, img)) + except FileNotFoundError: + continue + + os.fsync() + assert not os.listdir(images_folder) + shutil.rmtree(images_folder) + os.remove(annotations_file) + os.fsync() + +class TinyImageNet(Dataset): + """Dataset for TinyImageNet-200""" + base_folder = 'tiny-imagenet-200' + zip_md5 = '90528d7ca1a48142e341f4ef8d21d0de' + splits = ('train', 'val') + filename = 'tiny-imagenet-200.zip' + url = 'http://cs231n.stanford.edu/tiny-imagenet-200.zip' + + def __init__( + self, + root: str, + split: str = "train", + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + download: bool = False, + ) -> None: + super(TinyImageNet, self).__init__() + self.data_root = root + self.split = split + self.transform = transform + self.target_transform = target_transform + self.data = [] + self.targets = [] + + if download: + self.download() + + with open(os.path.join('attacks/UnivIntruder/utils_/map_clsloc.txt'), 'r') as file: + lines = file.readlines() + + # Create the dictionary + self.data_dict = {} + + # Iterate over each line + for line in lines: + # Strip any surrounding whitespace and split by comma + parts = line.strip().split(',') + # Extract the key and value + key = parts[0] + value = [int(parts[1]), parts[2]] + # Add to the dictionary + self.data_dict[key] = value + + # Assuming the structure is root/split/images, e.g., root/train/images + split_dir = os.path.join(self.data_root, 'tiny-imagenet-200', self.split) + + # Load images and labels + for class_name in os.listdir(split_dir): + class_dir = os.path.join(split_dir, class_name, 'images') + class_idx = self.data_dict[class_name][0] + for image_name in os.listdir(class_dir): + with open(os.path.join(class_dir, image_name), 'rb') as f: + img = Image.open(f).convert('RGB') + self.data.append(img) + self.targets.append(class_idx) + + def __len__(self): + return len(self.data) + + def __getitem__(self, idx): + img = self.data[idx] + target = self.targets[idx] + + # Apply transformations + if self.transform is not None: + img = self.transform(img) + + if self.target_transform is not None: + target = self.target_transform(target) + + return img, target + + @property + def dataset_folder(self): + return os.path.join(self.data_root, self.base_folder) + + @property + def split_folder(self): + return os.path.join(self.dataset_folder, self.split) + + def _check_exists(self): + return os.path.exists(self.split_folder) + + def extra_repr(self): + return "Split: {split}".format(**self.__dict__) + + def download(self): + if self._check_exists(): + return + download_and_extract_archive( + self.url, self.data_root, filename=self.filename, + remove_finished=True, md5=self.zip_md5) + assert 'val' in self.splits + normalize_tin_val_folder_structure( + os.path.join(self.dataset_folder, 'val')) \ No newline at end of file diff --git a/attacks/UnivIntruder/loss.py b/attacks/UnivIntruder/loss.py new file mode 100644 index 0000000000000000000000000000000000000000..42bf6683432678ceb9f55c37342d35e6d6242e40 --- /dev/null +++ b/attacks/UnivIntruder/loss.py @@ -0,0 +1,191 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np + + +def generate_points(w): + return np.random.randint(0, w, size=(6, 2)) + + +# Function to calculate the Euclidean distance between two points +def euclidean_distance(p1, p2): + return np.sqrt((p1[0] - p2[0]) ** 2 + (p1[1] - p2[1]) ** 2) + + +# Function to drop rectangles in the image +def drop_blocks(image): + c, w, h = image.shape + points = generate_points(w) + pairs = [] + + # Repeat three times to find three pairs + for _ in range(3): + distances = {} + + # Calculate distances between each pair of points that haven't been paired yet + for i in range(len(points)): + for j in range(i + 1, len(points)): + if {i, j}.isdisjoint(set(sum(pairs, ()))): + dist = euclidean_distance(points[i], points[j]) + distances[(i, j)] = dist + + # Find the pair of points with the smallest distance + nearest_pair = min(distances, key=distances.get) + pairs.append(nearest_pair) + + # Function to mask a block in the image + def mask_block(image, start_point, end_point): + x1, y1 = points[start_point] + x2, y2 = points[end_point] + x_min, x_max = min(x1, x2), max(x1, x2) + y_min, y_max = min(y1, y2), max(y1, y2) + image[:, x_min:x_max + 1, y_min:y_max + 1] = 0 + + # Apply masking to the three blocks + for pair in pairs: + mask_block(image, *pair) + + return image + + +class NoTargetDataset(torch.utils.data.Dataset): + def __init__(self, dataset, target): + self.dataset = dataset + self.indices = [] + self.target = target + if target: + if hasattr(self.dataset, 'targets') and len(self.dataset.targets) == len(self.dataset): + for idx, y in enumerate(self.dataset.targets): + if y != target: + self.indices.append(int(idx)) + else: + for idx, (_, X, y) in enumerate(self.dataset): + if y != target: + self.indices.append(int(idx)) + else: + self.indices = [i for i in range(len(dataset))] + + def __len__(self): + return len(self.indices) + + def __getitem__(self, item): + return self.dataset[self.indices[item]] + + +class UniversalPerturbation(nn.Module): + def __init__(self, size, epsilon=32 / 255, initialization=None, device='cuda'): + super(UniversalPerturbation, self).__init__() + # Initialize the trigger as a learnable parameter + self.device = device + if isinstance(initialization, torch.Tensor): + self.trigger = nn.Parameter(initialization.clone().detach().to(self.device)) + else: + self.trigger = nn.Parameter(torch.empty(size, requires_grad=True, device=self.device)) + nn.init.normal_(self.trigger, mean=0.0, std=0.01) + self.epsilon = epsilon + + def forward(self, x): + x = x.to(self.trigger.device) + x_hat = x + torch.clamp(self.trigger, -self.epsilon * 2, self.epsilon * 2) + return torch.clamp(x_hat, -1, 1) + + +class BackdoorEval(): + def __init__(self, predictor, num_classes, device, target_class, target_only=True, top5=False): + self.device = device + self.num_classes = num_classes + self.target_class = target_class + self.target_only = target_only + self.predict = predictor.to(self.device) + self.predict.eval() + self.top5 = top5 + + def __call__(self, data_loader): + total_predictions = torch.zeros(self.num_classes) + top1_correct = 0 + top5_correct = 0 + total_samples = 0 + + for i, (inputs, labels, *other_info) in enumerate(data_loader): + inputs = inputs.to(self.device) + outputs = self.predict(inputs)[:, :self.num_classes] + preds = torch.argmax(outputs, dim=1).detach().cpu() + total_predictions += torch.bincount(preds, minlength=self.num_classes) + + if self.top5: + # Top-1 accuracy + top1_correct += (preds == self.target_class).sum().item() + + # Top-5 accuracy + top5_preds = torch.topk(outputs, 5, dim=1).indices + top5_correct += sum([self.target_class in top5_preds[i].detach().cpu() for i in range(inputs.size(0))]) + + total_samples += inputs.size(0) + + if self.target_only: + # Calculate the percentage for the target class only + total = total_predictions.sum() + target_percentage = (total_predictions[self.target_class] / total) * 100 + if self.top5: + top1_acc = (top1_correct / total_samples) * 100 + top5_acc = (top5_correct / total_samples) * 100 + return { + 'Top-1 Accuracy': top1_acc, + 'Top-5 Accuracy': top5_acc + } + return target_percentage.item() + else: + # Normalize counts to percentages for all classes + total_predictions = (total_predictions / total_predictions.sum()) * 100 + if self.top5: + top1_acc = (top1_correct / total_samples) * 100 + top5_acc = (top5_correct / total_samples) * 100 + return { + 'Class Percentages': {f'Class {i}': p.item() for i, p in enumerate(total_predictions)}, + 'Top-1 Accuracy': top1_acc, + 'Top-5 Accuracy': top5_acc + } + return {f'Class {i}': p.item() for i, p in enumerate(total_predictions)} + + +class TrainableAffineTransform(nn.Module): + def __init__(self, batch_size=1, scale=0.05, flip=True): + super(TrainableAffineTransform, self).__init__() + # Initialize the parameters for the affine transformation with small random values + theta = torch.eye(2, 3).unsqueeze(0).repeat(batch_size, 1, 1) + + # Apply small random transformations for rotation, translation, and scaling + random_rotation = (torch.rand(batch_size) - 0.5) * 2 * scale # Rotation angle between -5 and 5 degrees + random_translation = (torch.rand(batch_size, + 2) - 0.5) * 2 * scale # Translation between -5% and 5% of image size + random_scaling = 1 + (torch.rand(batch_size, 2) - 0.5) * 2 * scale # Scaling between 0.95 and 1.05 + + for i in range(batch_size): + rotation_matrix = torch.tensor([ + [torch.cos(random_rotation[i]), -torch.sin(random_rotation[i]), random_translation[i, 0]], + [torch.sin(random_rotation[i]), torch.cos(random_rotation[i]), random_translation[i, 1]] + ], dtype=torch.float) + + scale_matrix = torch.diag(torch.cat([random_scaling[i], torch.tensor([1.0])])).unsqueeze(0) + + theta[i, :, :] = torch.mm(rotation_matrix, scale_matrix.squeeze()) + + self.theta = nn.Parameter(theta, requires_grad=False) # Set requires_grad=False as these are for augmentation + self.flip = flip + + def forward(self, x): + batch_size = x.size(0) + theta = self.theta[:batch_size].to(x.device) # Ensure theta is the correct shape and on the correct device + grid = F.affine_grid(theta, x.size(), align_corners=False) + x = F.grid_sample(x, grid, align_corners=False) + + if self.flip: + flip_indices = torch.randperm(batch_size)[:batch_size // 2] + x[flip_indices] = torch.flip(x[flip_indices], [3]) + + # Drop two rectangular blocks + for i in range(batch_size): + x[i] = drop_blocks(x[i]) + + return x diff --git a/attacks/UnivIntruder/loss_function/clip_loss.py b/attacks/UnivIntruder/loss_function/clip_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..5066a35e9b3edadb35e10dc17074b1932487a922 --- /dev/null +++ b/attacks/UnivIntruder/loss_function/clip_loss.py @@ -0,0 +1,123 @@ +import torch +import torchvision.transforms as transforms +from typing import Union, List +import open_clip + + +class CLIPLoss(torch.nn.Module): + def __init__(self, device, lambda_direction=1., direction_loss_type='cosine', clip_model='ViT-B/32', + pretrained=None, class_names=None): + super(CLIPLoss, self).__init__() + + self.device = device + self.model, _, clip_preprocess = open_clip.create_model_and_transforms( + clip_model, + pretrained='convs/CLIP-ViT-B-32-laion2B-s34B-b79K/open_clip_pytorch_model.bin', + # pretrained, + jit=True, + device=device, + ) + self.tokenizer = open_clip.get_tokenizer(clip_model) + + self.clip_preprocess = clip_preprocess + + self.preprocess = transforms.Compose([transforms.Normalize(mean=[-1.0, -1.0, -1.0], std=[2.0, 2.0, + 2.0])] + # Un-normalize from [-1.0, 1.0] (GAN output) to [0, 1]. + clip_preprocess.transforms[:2] + # to match CLIP input scale assumptions + clip_preprocess.transforms[4:]) # + skip convert PIL to tensor + + self.target_direction = None + self.patch_text_directions = None + + self.patch_direction_loss = torch.nn.CosineSimilarity(dim=2) + self.cosine_sim = torch.nn.CosineSimilarity(dim=1) + + self.lambda_direction = lambda_direction + + self.src_text_features = None + self.target_text_features = None + self.angle_loss = torch.nn.L1Loss() + + self.text_class_features = dict() + self.image_class_features = dict() + self.predicted_classes = class_names + self.mse = torch.nn.MSELoss() + + def tokenize(self, strings: list): + return self.tokenizer(strings).to(self.device) + + def encode_text(self, tokens: list) -> torch.Tensor: + return self.model.encode_text(tokens) + + def encode_images(self, images: torch.Tensor) -> torch.Tensor: + images = self.preprocess(images).to(self.device) + return self.model.encode_image(images) + + def get_text_features(self, class_str: str) -> torch.Tensor: + text_features = [self.text_class_features[i] for i in class_str] + return torch.stack(text_features, dim=0) + + def precompute_text_features(self, class_str: str, templates=None, norm: bool = True) -> torch.Tensor: + for classes in class_str: + if classes not in self.text_class_features: + template_text = self.compose_text_with_templates(classes, templates) + tokens = self.tokenizer(template_text).to(self.device) + text_features = self.encode_text(tokens).detach() + text_features = text_features.mean(dim=0) + if norm: + text_features /= text_features.norm(dim=-1, keepdim=True) + self.text_class_features[classes] = text_features + + def get_image_features(self, img: torch.Tensor, norm: bool = True) -> torch.Tensor: + image_features = self.encode_images(img) + + if norm: + image_features /= image_features.clone().norm(dim=-1, keepdim=True) + + return image_features + + def compute_text_direction(self, source_class: Union[str, List[str]], target_class: Union[str, List[str]], + broadcast=False) -> torch.Tensor: + source_features = self.get_text_features(source_class) + target_features = self.get_text_features(target_class) + + if broadcast: + text_direction = (target_features.T.unsqueeze(0) - source_features.unsqueeze(-1)) + else: + text_direction = (target_features - source_features) + text_direction /= text_direction.norm(dim=-1, keepdim=True) + + return text_direction + + def compose_text_with_templates(self, text: str, templates=None) -> list: + return [template.format(text) for template in templates] + + def clip_loss(self, src_img: torch.Tensor, source_class: Union[str, List[str]], + target_img: torch.Tensor, target_class: Union[str, List[str]], + negative_class: Union[str, List[str]]) -> torch.Tensor: + self.target_direction = self.compute_text_direction(source_class, target_class) + self.negative_direction = self.compute_text_direction(source_class, negative_class, broadcast=True) + + src_encoding = self.get_image_features(src_img) + target_encoding = self.get_image_features(target_img) + + edit_direction = (target_encoding - src_encoding) + edit_direction /= (edit_direction.clone().norm(dim=-1, keepdim=True) + 1e-7) + + logit_target = self.cosine_sim(self.target_direction, edit_direction) + logit_negative = self.cosine_sim(self.negative_direction, edit_direction.unsqueeze(-1)) + pp = torch.exp(logit_target) + pn = torch.sum(torch.exp(logit_negative), dim=-1) + p = pp / (pp + pn) + return -torch.log(p).mean() + + def forward(self, src_img: torch.Tensor, source_class: Union[str, List[str]], target_img: torch.Tensor, + target_class: Union[str, List[str]], negative_class: Union[str, List[str]], + texture_image: torch.Tensor = None): + clip_loss = 0.0 + + if self.lambda_direction: + direction_loss = self.clip_loss(src_img, source_class, target_img, target_class, negative_class) + clip_loss += self.lambda_direction * direction_loss + + return clip_loss \ No newline at end of file diff --git a/attacks/UnivIntruder/loss_function/loss_image_bind.py b/attacks/UnivIntruder/loss_function/loss_image_bind.py new file mode 100644 index 0000000000000000000000000000000000000000..9bff64bfe2a43a5ae3b9f3fcec74c8988dfee4f6 --- /dev/null +++ b/attacks/UnivIntruder/loss_function/loss_image_bind.py @@ -0,0 +1,220 @@ +import torch +import torchvision.transforms as transforms +import torchvision.transforms.functional as TF +import numpy as np +from typing import Union, List +import os +from imagebind import data +from imagebind.models import imagebind_model +from imagebind.models.imagebind_model import ModalityType + + +class DirectionLoss(torch.nn.Module): + def __init__(self, loss_type='mse'): + super(DirectionLoss, self).__init__() + self.loss_type = loss_type + self.loss_func = { + 'mse': torch.nn.MSELoss, + 'cosine': torch.nn.CosineSimilarity, + 'mae': torch.nn.L1Loss + }[loss_type]() + + def forward(self, x, y): + if self.loss_type == "cosine": + return 1. - self.loss_func(x, y) + return self.loss_func(x, y) + + +class ImageBindLoss(torch.nn.Module): + def __init__(self, device, lambda_direction=1., lambda_naive=0., direction_loss_type='cosine', pretrained=True, class_names=None): + super(ImageBindLoss, self).__init__() + self.device = device + + # Load ImageBind model + self.model = imagebind_model.imagebind_huge(pretrained=pretrained) + self.model.eval() # Eval mode, but gradients will still flow for adversarial attacks + self.model.to(device) + + # Loss components (unchanged from CLIPLoss) + self.direction_loss = DirectionLoss(direction_loss_type) + self.patch_direction_loss = torch.nn.CosineSimilarity(dim=2) + self.cosine_sim = torch.nn.CosineSimilarity(dim=1) + + self.lambda_naive = lambda_naive + self.lambda_direction = lambda_direction + + self.src_text_features = None + self.target_text_features = None + self.angle_loss = torch.nn.L1Loss() + + self.text_class_features = dict() + self.image_class_features = dict() + self.predicted_classes = class_names + self.mse = torch.nn.MSELoss() + + def tokenize(self, strings: list): + """Tokenize text strings using ImageBind's data.load_and_transform_text.""" + return data.load_and_transform_text(strings, self.device) + + def encode_text(self, tokens: torch.Tensor) -> torch.Tensor: + """Encode tokenized text using ImageBind model, without gradients.""" + inputs = {ModalityType.TEXT: tokens} + with torch.no_grad(): # Text features are typically fixed, no gradients needed + embeddings = self.model(inputs) + return embeddings[ModalityType.TEXT] + + def preprocess(self, x: torch.Tensor) -> torch.Tensor: + """Preprocess tensor images from [-1, 1] to ImageBind's expected format.""" + x = (x + 1) / 2 # Convert from [-1, 1] to [0, 1] + x = TF.resize(x, 224, interpolation=TF.InterpolationMode.BICUBIC) + x = TF.center_crop(x, 224) + x = TF.normalize( + x, + mean=(0.48145466, 0.4578275, 0.40821073), + std=(0.26862954, 0.26130258, 0.27577711) + ) + return x + + def encode_images(self, images: torch.Tensor) -> torch.Tensor: + """Encode images using ImageBind model, preserving gradients.""" + images = self.preprocess(images).to(self.device) + inputs = {ModalityType.VISION: images} + embeddings = self.model(inputs) # No torch.no_grad() to allow gradients + return embeddings[ModalityType.VISION] + + def get_text_features(self, class_str: Union[str, List[str]]) -> torch.Tensor: + """Retrieve precomputed text features for given classes.""" + if isinstance(class_str, str): + class_str = [class_str] + text_features = [self.text_class_features[i] for i in class_str] + return torch.stack(text_features, dim=0) + + def precompute_text_features(self, class_str: Union[str, List[str]], templates=None, norm: bool = True) -> None: + """Precompute and store text features for given classes.""" + if isinstance(class_str, str): + class_str = [class_str] + for classes in class_str: + if classes not in self.text_class_features: + template_text = self.compose_text_with_templates(classes, templates) + tokens = self.tokenize(template_text) + text_features = self.encode_text(tokens) + text_features = text_features.mean(dim=0) + if norm: # ImageBind embeddings are already normalized, but kept for consistency + text_features /= text_features.norm(dim=-1, keepdim=True) + self.text_class_features[classes] = text_features + + def precompute_image_features(self, images: dict, norm: bool = True) -> None: + """Precompute and store image features for given classes.""" + for classes in images: + if classes not in self.image_class_features: + class_images = images[classes] + all_images = class_images + batch_size = 16 + num_batches = (all_images.size(0) + batch_size - 1) // batch_size + + batched_image_features = [] + for i in range(num_batches): + start_idx = i * batch_size + end_idx = min((i + 1) * batch_size, all_images.size(0)) + batch = all_images[start_idx:end_idx] + batch_features = self.encode_images(batch) + batched_image_features.append(batch_features.detach()) # Detach to save memory + + image_features = torch.cat(batched_image_features, dim=0) + image_features = image_features.mean(dim=0) + if norm: + image_features /= image_features.norm(dim=-1, keepdim=True) + self.image_class_features[classes] = image_features + + def get_image_features(self, img: torch.Tensor, norm: bool = True) -> torch.Tensor: + """Get image features with optional normalization.""" + image_features = self.encode_images(img) + if norm: + image_features /= image_features.clone().norm(dim=-1, keepdim=True) + return image_features + + def compute_text_direction(self, source_class: Union[str, List[str]], target_class: Union[str, List[str]], broadcast=False) -> torch.Tensor: + """Compute the direction between source and target text features.""" + source_features = self.get_text_features(source_class) + target_features = self.get_text_features(target_class) + if broadcast: + text_direction = (target_features.T.unsqueeze(0) - source_features.unsqueeze(-1)) + else: + text_direction = (target_features - source_features) + text_direction /= text_direction.norm(dim=-1, keepdim=True) + return text_direction + + def compose_text_with_templates(self, text: str, templates=None) -> list: + """Compose text with templates (assumes templates are provided).""" + if templates is None: + templates = ["{}"] # Default template if none provided + return [template.format(text) for template in templates] + + def clip_directional_loss(self, src_img: torch.Tensor, source_class: Union[str, List[str]], target_img: torch.Tensor, target_class: Union[str, List[str]], negative_class: Union[str, List[str]]) -> torch.Tensor: + """Compute directional loss using ImageBind embeddings.""" + self.target_direction = self.compute_text_direction(source_class, target_class).detach() + self.negative_direction = self.compute_text_direction(source_class, negative_class, broadcast=True).detach() + + src_encoding = self.get_image_features(src_img) + target_encoding = self.get_image_features(target_img) + + edit_direction = (target_encoding - src_encoding) + edit_direction /= (edit_direction.clone().norm(dim=-1, keepdim=True) + 1e-7) + + logit_target = self.cosine_sim(self.target_direction, edit_direction) + logit_negative = self.cosine_sim(self.negative_direction, edit_direction.unsqueeze(-1)) + pp = torch.exp(logit_target) + pn = torch.sum(torch.exp(logit_negative), dim=-1) + p = pp / (pp + pn) + return -torch.log(p).mean() + + def clip_class_loss(self, target_img: torch.Tensor, target_class: Union[str, List[str]], negative_class: Union[str, List[str]]) -> torch.Tensor: + """Compute class loss using ImageBind embeddings.""" + text_features = self.get_text_features(target_class) + negative_text_features = self.get_text_features(negative_class) + image_features = self.get_image_features(target_img) + + logit_target = self.cosine_sim(text_features, image_features) + logit_negative = self.cosine_sim( + negative_text_features.unsqueeze(0).expand(len(target_img), -1, -1).permute(0, 2, 1), + image_features.unsqueeze(-1) + ) + pp = torch.exp(logit_target) + pn = torch.sum(torch.exp(logit_negative), dim=-1) + p = pp / (pp + pn) + return -torch.log(p).mean() + + def forward(self, src_img: torch.Tensor, source_class: Union[str, List[str]], target_img: torch.Tensor, target_class: Union[str, List[str]], negative_class: Union[str, List[str]], texture_image: torch.Tensor = None) -> torch.Tensor: + """Forward pass combining naive and directional losses.""" + clip_loss = 0.0 + # if self.lambda_naive: + # naive_loss = self.clip_class_loss(target_img, target_class, negative_class) + # clip_loss += self.lambda_naive * naive_loss + if self.lambda_direction: + direction_loss = self.clip_directional_loss(src_img, source_class, target_img, target_class, negative_class) + clip_loss += self.lambda_direction * direction_loss + return clip_loss + + def predict(self, img: torch.Tensor) -> torch.Tensor: + """Predict class probabilities using ImageBind embeddings.""" + image_features = self.get_image_features(img) + text_features = self.get_text_features(self.predicted_classes) + similarity = (100.0 * image_features @ text_features.T).softmax(dim=-1) + return similarity + + +if __name__ == '__main__': + device = 'cuda' if torch.cuda.is_available() else 'cpu' + imagebind_loss = ImageBindLoss(device) + + # Test with small batch + src_img = torch.zeros(2, 3, 32, 32, device=device) + tgt_img = torch.zeros(2, 3, 32, 32, device=device) + loss = imagebind_loss(src_img, ['dog', 'cat'], tgt_img, ['boy', 'cat'], ['bird', 'fish']) + print(f"Loss with batch size 2: {loss.item()}") + + # Test with larger batch + src_img = torch.zeros(64, 3, 32, 32, device=device) + tgt_img = torch.zeros(64, 3, 32, 32, device=device) + loss = imagebind_loss(src_img, 'dog', tgt_img, 'cat', 'bird') + print(f"Loss with batch size 64: {loss.item()}") \ No newline at end of file diff --git a/attacks/UnivIntruder/loss_function/loss_siglip.py b/attacks/UnivIntruder/loss_function/loss_siglip.py new file mode 100644 index 0000000000000000000000000000000000000000..9f8d4de9b064fa14b551cde8d4782c879e6759ee --- /dev/null +++ b/attacks/UnivIntruder/loss_function/loss_siglip.py @@ -0,0 +1,189 @@ +import torch +import torch.nn.functional as F +from transformers import AutoProcessor, AutoModel +from torchvision import transforms +import numpy as np +from typing import Union, List +import os +import random + +class DirectionLoss(torch.nn.Module): + def __init__(self, loss_type='mse'): + super(DirectionLoss, self).__init__() + self.loss_type = loss_type + self.loss_func = { + 'mse': torch.nn.MSELoss, + 'cosine': torch.nn.CosineSimilarity, + 'mae': torch.nn.L1Loss + }[loss_type]() + + def forward(self, x, y): + if self.loss_type == "cosine": + return 1. - self.loss_func(x, y) + return self.loss_func(x, y) + +class SigLIPLoss(torch.nn.Module): + def __init__(self, device, lambda_direction=1., lambda_naive=0., direction_loss_type='cosine', model_name='google/siglip-base-patch16-224', class_names=None): + super(SigLIPLoss, self).__init__() + self.device = device + + # 加载 SigLIP 模型和处理器 + self.processor = AutoProcessor.from_pretrained(model_name) + self.model = AutoModel.from_pretrained(model_name).to(device) + self.model.eval() # 确保模型在评估模式 + + # 手动图像预处理（保持梯度可计算） + self.image_transform = transforms.Compose([ + transforms.Resize(224, interpolation=transforms.InterpolationMode.BICUBIC), + transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) # 归一化到 [-1, 1] + ]) + + # 初始化变量 + self.target_direction = None + self.patch_text_directions = None + self.direction_loss = DirectionLoss(direction_loss_type) + self.patch_direction_loss = torch.nn.CosineSimilarity(dim=2) + self.cosine_sim = torch.nn.CosineSimilarity(dim=1) + self.lambda_naive = lambda_naive + self.lambda_direction = lambda_direction + self.src_text_features = None + self.target_text_features = None + self.angle_loss = torch.nn.L1Loss() + self.text_class_features = dict() + self.image_class_features = dict() + self.predicted_classes = class_names + self.mse = torch.nn.MSELoss() + + def tokenize(self, strings: list): + """将文本字符串转换为 token""" + return self.processor(text=strings, padding="max_length", return_tensors="pt").to(self.device) + + def encode_text(self, tokens) -> torch.Tensor: + """编码文本 token 为特征""" + with torch.no_grad(): # 文本特征通常不需要梯度 + return self.model.get_text_features(**tokens) + + def encode_images(self, images: torch.Tensor) -> torch.Tensor: + """编码图像为特征""" + images = images.to(self.device) + return self.model.get_image_features(images) # 注意：这里不使用 no_grad，以便保留梯度 + + def get_text_features(self, class_str: Union[str, List[str]]) -> torch.Tensor: + """获取文本特征，如果未预计算则计算""" + if isinstance(class_str, str): + class_str = [class_str] + text_features = [] + for cls in class_str: + if cls not in self.text_class_features: + self.precompute_text_features([cls]) + text_features.append(self.text_class_features[cls]) + return torch.stack(text_features, dim=0) + + def precompute_text_features(self, class_str: List[str], templates=None, norm: bool = True): + """预计算文本特征""" + for classes in class_str: + if classes not in self.text_class_features: + if templates: + template_text = self.compose_text_with_templates(classes, templates) + else: + template_text = [classes] + tokens = self.tokenize(template_text) + text_features = self.encode_text(tokens) + text_features = text_features.mean(dim=0) + if norm: + text_features /= text_features.norm(dim=-1, keepdim=True) + self.text_class_features[classes] = text_features + + def get_image_features(self, img: torch.Tensor, norm: bool = True) -> torch.Tensor: + """获取图像特征""" + img = img.to(self.device) + img = self.image_transform(img) # 手动预处理 + image_features = self.encode_images(img) + if norm: + image_features /= image_features.clone().norm(dim=-1, keepdim=True) + return image_features + + def compute_text_direction(self, source_class: Union[str, List[str]], target_class: Union[str, List[str]], broadcast=False) -> torch.Tensor: + """计算文本方向""" + source_features = self.get_text_features(source_class) + target_features = self.get_text_features(target_class) + if broadcast: + text_direction = (target_features.T.unsqueeze(0) - source_features.unsqueeze(-1)) + else: + text_direction = (target_features - source_features) + text_direction /= text_direction.norm(dim=-1, keepdim=True) + return text_direction + + def compose_text_with_templates(self, text: str, templates=None) -> list: + """使用模板生成文本""" + return [template.format(text) for template in templates] + + def clip_directional_loss(self, src_img: torch.Tensor, source_class: Union[str, List[str]], target_img: torch.Tensor, target_class: Union[str, List[str]], negative_class: Union[str, List[str]]) -> torch.Tensor: + """计算方向损失""" + self.target_direction = self.compute_text_direction(source_class, target_class) + self.negative_direction = self.compute_text_direction(source_class, negative_class, broadcast=True) + + src_encoding = self.get_image_features(src_img) + target_encoding = self.get_image_features(target_img) + + edit_direction = (target_encoding - src_encoding) + edit_direction /= (edit_direction.clone().norm(dim=-1, keepdim=True) + 1e-7) + + logit_target = self.cosine_sim(self.target_direction, edit_direction) + logit_negative = self.cosine_sim(self.negative_direction, edit_direction.unsqueeze(-1)) + pp = torch.exp(logit_target) + pn = torch.sum(torch.exp(logit_negative), dim=-1) + p = pp / (pp + pn) + return -torch.log(p).mean() + + def clip_class_loss(self, target_img: torch.Tensor, target_class: Union[str, List[str]], negative_class: Union[str, List[str]]) -> torch.Tensor: + """计算分类损失""" + text_features = self.get_text_features(target_class) + negative_text_features = self.get_text_features(negative_class) + image_features = self.get_image_features(target_img) + + logit_target = self.cosine_sim(text_features, image_features) + logit_negative = self.cosine_sim(negative_text_features.unsqueeze(0).expand(len(target_img), -1, -1).permute(0, 2, 1), image_features.unsqueeze(-1)) + pp = torch.exp(logit_target) + pn = torch.sum(torch.exp(logit_negative), dim=-1) + p = pp / (pp + pn) + return -torch.log(p).mean() + + def forward(self, src_img: torch.Tensor, source_class: Union[str, List[str]], target_img: torch.Tensor, target_class: Union[str, List[str]], negative_class: Union[str, List[str]], texture_image: torch.Tensor = None): + """前向传播""" + clip_loss = 0.0 + + if self.lambda_naive: + naive_loss = self.clip_class_loss(target_img, target_class, negative_class) + clip_loss += self.lambda_naive * naive_loss + + if self.lambda_direction: + direction_loss = self.clip_directional_loss(src_img, source_class, target_img, target_class, negative_class) + clip_loss += self.lambda_direction * direction_loss + + return clip_loss + + def predict(self, img: torch.Tensor): + """预测相似度""" + image_features = self.get_image_features(img) + text_features = self.get_text_features(self.predicted_classes) + similarity = (100.0 * image_features @ text_features.T).softmax(dim=-1) + return similarity + +if __name__ == '__main__': + device = 'cuda' + siglip_loss = SigLIPLoss(device, class_names=['dog', 'cat', 'boy']) + src_img = torch.zeros(2, 3, 32, 32, device=device, requires_grad=True) + tgt_img = torch.zeros(2, 3, 32, 32, device=device, requires_grad=True) + loss = siglip_loss(src_img, ['dog', 'cat'], tgt_img, ['boy', 'cat'], ['negative']) + print(f"Loss: {loss.item()}") + + # 检查梯度 + loss.backward() + print(f"Gradient of src_img: {src_img.grad is not None}") + print(f"Gradient of tgt_img: {tgt_img.grad is not None}") + + src_img = torch.zeros(64, 3, 32, 32, device=device, requires_grad=True) + tgt_img = torch.zeros(64, 3, 32, 32, device=device, requires_grad=True) + loss = siglip_loss(src_img, 'dog', tgt_img, 'cat', 'negative') + print(f"Loss: {loss.item()}") \ No newline at end of file diff --git a/attacks/UnivIntruder/utils_/eval_path.py b/attacks/UnivIntruder/utils_/eval_path.py new file mode 100644 index 0000000000000000000000000000000000000000..6411b01e78b443b7ca8f867162d194150feaca8b --- /dev/null +++ b/attacks/UnivIntruder/utils_/eval_path.py @@ -0,0 +1,43 @@ +import torchvision.models as models + +imagenet_models = [ + models.resnet18, models.resnet34, models.resnet50, models.resnet101, + models.wide_resnet50_2, models.wide_resnet101_2, + models.vgg11, models.vgg11_bn, models.vgg16, models.vgg16_bn, + models.mobilenet_v2, models.shufflenet_v2_x1_0, + models.densenet121, models.densenet169, models.densenet201, + models.vit_b_32, models.vit_b_16, models.vit_l_32, models.vit_l_16, + models.swin_t, models.swin_s, models.swin_v2_t, models.swin_v2_s, +] + +cifar10_models = [ + 'cifar10_resnet20', 'cifar10_resnet32', 'cifar10_resnet44', 'cifar10_resnet56', + 'cifar10_vgg11_bn', 'cifar10_vgg13_bn', 'cifar10_vgg16_bn', 'cifar10_vgg19_bn', + 'cifar10_mobilenetv2_x0_5', 'cifar10_mobilenetv2_x0_75', 'cifar10_mobilenetv2_x1_0', 'cifar10_mobilenetv2_x1_4', + 'cifar10_shufflenetv2_x0_5', 'cifar10_shufflenetv2_x1_0', 'cifar10_shufflenetv2_x1_5', 'cifar10_shufflenetv2_x2_0', + 'cifar10_repvgg_a0', 'cifar10_repvgg_a1', 'cifar10_repvgg_a2', + 'binyxuCUHK/cifar10-vit-b-32', + 'binyxuCUHK/cifar10-vit-b-16', + 'binyxuCUHK/cifar10-vit-l-32', + 'binyxuCUHK/cifar10-vit-l-16', + 'binyxuCUHK/cifar10-swin-b', + 'binyxuCUHK/cifar10-swin-l', + 'binyxuCUHK/cifar10-swin-v2-b', + 'binyxuCUHK/cifar10-swin-v2-l', +] + +cifar100_models = [ + 'cifar100_resnet20', 'cifar100_resnet32', 'cifar100_resnet44', 'cifar100_resnet56', + 'cifar100_vgg11_bn', 'cifar100_vgg13_bn', 'cifar100_vgg16_bn', 'cifar100_vgg19_bn', + 'cifar100_mobilenetv2_x0_5', 'cifar100_mobilenetv2_x0_75', 'cifar100_mobilenetv2_x1_0', 'cifar100_mobilenetv2_x1_4', + 'cifar100_shufflenetv2_x0_5', 'cifar100_shufflenetv2_x1_0', 'cifar100_shufflenetv2_x1_5', 'cifar100_shufflenetv2_x2_0', + 'cifar100_repvgg_a0', 'cifar100_repvgg_a1', 'cifar100_repvgg_a2', + 'binyxuCUHK/cifar100-vit-b-32', + 'binyxuCUHK/cifar100-vit-b-16', + 'binyxuCUHK/cifar100-vit-l-32', + 'binyxuCUHK/cifar100-vit-l-16', + 'binyxuCUHK/cifar100-swin-b', + 'binyxuCUHK/cifar100-swin-l', + 'binyxuCUHK/cifar100-swin-v2-b', + 'binyxuCUHK/cifar100-swin-v2-l', +] diff --git a/attacks/UnivIntruder/utils_/map_clsloc.txt b/attacks/UnivIntruder/utils_/map_clsloc.txt new file mode 100644 index 0000000000000000000000000000000000000000..58e9be47d7f2970a787cd39e5ed77a6f517db897 --- /dev/null +++ b/attacks/UnivIntruder/utils_/map_clsloc.txt @@ -0,0 +1,1000 @@ +n01440764,0,tench +n01443537,1,goldfish +n01484850,2,great white shark +n01491361,3,tiger shark +n01494475,4,hammerhead shark +n01496331,5,electric ray +n01498041,6,stingray +n01514668,7,rooster +n01514859,8,hen +n01518878,9,ostrich +n01530575,10,brambling +n01531178,11,goldfinch +n01532829,12,house finch +n01534433,13,junco +n01537544,14,indigo bunting +n01558993,15,American robin +n01560419,16,bulbul +n01580077,17,jay +n01582220,18,magpie +n01592084,19,chickadee +n01601694,20,American dipper +n01608432,21,kite (bird of prey) +n01614925,22,bald eagle +n01616318,23,vulture +n01622779,24,great grey owl +n01629819,25,fire salamander +n01630670,26,smooth newt +n01631663,27,newt +n01632458,28,spotted salamander +n01632777,29,axolotl +n01641577,30,American bullfrog +n01644373,31,tree frog +n01644900,32,tailed frog +n01664065,33,loggerhead sea turtle +n01665541,34,leatherback sea turtle +n01667114,35,mud turtle +n01667778,36,terrapin +n01669191,37,box turtle +n01675722,38,banded gecko +n01677366,39,green iguana +n01682714,40,Carolina anole +n01685808,41,desert grassland whiptail lizard +n01687978,42,agama +n01688243,43,frilled-necked lizard +n01689811,44,alligator lizard +n01692333,45,Gila monster +n01693334,46,European green lizard +n01694178,47,chameleon +n01695060,48,Komodo dragon +n01697457,49,Nile crocodile +n01698640,50,American alligator +n01704323,51,triceratops +n01728572,52,worm snake +n01728920,53,ring-necked snake +n01729322,54,eastern hog-nosed snake +n01729977,55,smooth green snake +n01734418,56,kingsnake +n01735189,57,garter snake +n01737021,58,water snake +n01739381,59,vine snake +n01740131,60,night snake +n01742172,61,boa constrictor +n01744401,62,African rock python +n01748264,63,Indian cobra +n01749939,64,green mamba +n01751748,65,sea snake +n01753488,66,Saharan horned viper +n01755581,67,eastern diamondback rattlesnake +n01756291,68,sidewinder rattlesnake +n01768244,69,trilobite +n01770081,70,harvestman +n01770393,71,scorpion +n01773157,72,yellow garden spider +n01773549,73,barn spider +n01773797,74,European garden spider +n01774384,75,southern black widow +n01774750,76,tarantula +n01775062,77,wolf spider +n01776313,78,tick +n01784675,79,centipede +n01795545,80,black grouse +n01796340,81,ptarmigan +n01797886,82,ruffed grouse +n01798484,83,prairie grouse +n01806143,84,peafowl +n01806567,85,quail +n01807496,86,partridge +n01817953,87,african grey parrot +n01818515,88,macaw +n01819313,89,sulphur-crested cockatoo +n01820546,90,lorikeet +n01824575,91,coucal +n01828970,92,bee eater +n01829413,93,hornbill +n01833805,94,hummingbird +n01843065,95,jacamar +n01843383,96,toucan +n01847000,97,duck +n01855032,98,red-breasted merganser +n01855672,99,goose +n01860187,100,black swan +n01871265,101,tusker +n01872401,102,echidna +n01873310,103,platypus +n01877812,104,wallaby +n01882714,105,koala +n01883070,106,wombat +n01910747,107,jellyfish +n01914609,108,sea anemone +n01917289,109,brain coral +n01924916,110,flatworm +n01930112,111,nematode +n01943899,112,conch +n01944390,113,snail +n01945685,114,slug +n01950731,115,sea slug +n01955084,116,chiton +n01968897,117,chambered nautilus +n01978287,118,Dungeness crab +n01978455,119,rock crab +n01980166,120,fiddler crab +n01981276,121,red king crab +n01983481,122,American lobster +n01984695,123,spiny lobster +n01985128,124,crayfish +n01986214,125,hermit crab +n01990800,126,isopod +n02002556,127,white stork +n02002724,128,black stork +n02006656,129,spoonbill +n02007558,130,flamingo +n02009229,131,little blue heron +n02009912,132,great egret +n02011460,133,bittern bird +n02012849,134,crane bird +n02013706,135,limpkin +n02017213,136,common gallinule +n02018207,137,American coot +n02018795,138,bustard +n02025239,139,ruddy turnstone +n02027492,140,dunlin +n02028035,141,common redshank +n02033041,142,dowitcher +n02037110,143,oystercatcher +n02051845,144,pelican +n02056570,145,king penguin +n02058221,146,albatross +n02066245,147,grey whale +n02071294,148,killer whale +n02074367,149,dugong +n02077923,150,sea lion +n02085620,151,Chihuahua +n02085782,152,Japanese Chin +n02085936,153,Maltese +n02086079,154,Pekingese +n02086240,155,Shih Tzu +n02086646,156,King Charles Spaniel +n02086910,157,Papillon +n02087046,158,toy terrier +n02087394,159,Rhodesian Ridgeback +n02088094,160,Afghan Hound +n02088238,161,Basset Hound +n02088364,162,Beagle +n02088466,163,Bloodhound +n02088632,164,Bluetick Coonhound +n02089078,165,Black and Tan Coonhound +n02089867,166,Treeing Walker Coonhound +n02089973,167,English foxhound +n02090379,168,Redbone Coonhound +n02090622,169,borzoi +n02090721,170,Irish Wolfhound +n02091032,171,Italian Greyhound +n02091134,172,Whippet +n02091244,173,Ibizan Hound +n02091467,174,Norwegian Elkhound +n02091635,175,Otterhound +n02091831,176,Saluki +n02092002,177,Scottish Deerhound +n02092339,178,Weimaraner +n02093256,179,Staffordshire Bull Terrier +n02093428,180,American Staffordshire Terrier +n02093647,181,Bedlington Terrier +n02093754,182,Border Terrier +n02093859,183,Kerry Blue Terrier +n02093991,184,Irish Terrier +n02094114,185,Norfolk Terrier +n02094258,186,Norwich Terrier +n02094433,187,Yorkshire Terrier +n02095314,188,Wire Fox Terrier +n02095570,189,Lakeland Terrier +n02095889,190,Sealyham Terrier +n02096051,191,Airedale Terrier +n02096177,192,Cairn Terrier +n02096294,193,Australian Terrier +n02096437,194,Dandie Dinmont Terrier +n02096585,195,Boston Terrier +n02097047,196,Miniature Schnauzer +n02097130,197,Giant Schnauzer +n02097209,198,Standard Schnauzer +n02097298,199,Scottish Terrier +n02097474,200,Tibetan Terrier +n02097658,201,Australian Silky Terrier +n02098105,202,Soft-coated Wheaten Terrier +n02098286,203,West Highland White Terrier +n02098413,204,Lhasa Apso +n02099267,205,Flat-Coated Retriever +n02099429,206,Curly-coated Retriever +n02099601,207,Golden Retriever +n02099712,208,Labrador Retriever +n02099849,209,Chesapeake Bay Retriever +n02100236,210,German Shorthaired Pointer +n02100583,211,Vizsla +n02100735,212,English Setter +n02100877,213,Irish Setter +n02101006,214,Gordon Setter +n02101388,215,Brittany dog +n02101556,216,Clumber Spaniel +n02102040,217,English Springer Spaniel +n02102177,218,Welsh Springer Spaniel +n02102318,219,Cocker Spaniel +n02102480,220,Sussex Spaniel +n02102973,221,Irish Water Spaniel +n02104029,222,Kuvasz +n02104365,223,Schipperke +n02105056,224,Groenendael dog +n02105162,225,Malinois +n02105251,226,Briard +n02105412,227,Australian Kelpie +n02105505,228,Komondor +n02105641,229,Old English Sheepdog +n02105855,230,Shetland Sheepdog +n02106030,231,collie +n02106166,232,Border Collie +n02106382,233,Bouvier des Flandres dog +n02106550,234,Rottweiler +n02106662,235,German Shepherd Dog +n02107142,236,Dobermann +n02107312,237,Miniature Pinscher +n02107574,238,Greater Swiss Mountain Dog +n02107683,239,Bernese Mountain Dog +n02107908,240,Appenzeller Sennenhund +n02108000,241,Entlebucher Sennenhund +n02108089,242,Boxer +n02108422,243,Bullmastiff +n02108551,244,Tibetan Mastiff +n02108915,245,French Bulldog +n02109047,246,Great Dane +n02109525,247,St. Bernard +n02109961,248,husky +n02110063,249,Alaskan Malamute +n02110185,250,Siberian Husky +n02110341,251,Dalmatian +n02110627,252,Affenpinscher +n02110806,253,Basenji +n02110958,254,pug +n02111129,255,Leonberger +n02111277,256,Newfoundland dog +n02111500,257,Great Pyrenees dog +n02111889,258,Samoyed +n02112018,259,Pomeranian +n02112137,260,Chow Chow +n02112350,261,Keeshond +n02112706,262,brussels griffon +n02113023,263,Pembroke Welsh Corgi +n02113186,264,Cardigan Welsh Corgi +n02113624,265,Toy Poodle +n02113712,266,Miniature Poodle +n02113799,267,Standard Poodle +n02113978,268,Mexican hairless dog (xoloitzcuintli) +n02114367,269,grey wolf +n02114548,270,Alaskan tundra wolf +n02114712,271,red wolf or maned wolf +n02114855,272,coyote +n02115641,273,dingo +n02115913,274,dhole +n02116738,275,African wild dog +n02117135,276,hyena +n02119022,277,red fox +n02119789,278,kit fox +n02120079,279,Arctic fox +n02120505,280,grey fox +n02123045,281,tabby cat +n02123159,282,tiger cat +n02123394,283,Persian cat +n02123597,284,Siamese cat +n02124075,285,Egyptian Mau +n02125311,286,cougar +n02127052,287,lynx +n02128385,288,leopard +n02128757,289,snow leopard +n02128925,290,jaguar +n02129165,291,lion +n02129604,292,tiger +n02130308,293,cheetah +n02132136,294,brown bear +n02133161,295,American black bear +n02134084,296,polar bear +n02134418,297,sloth bear +n02137549,298,mongoose +n02138441,299,meerkat +n02165105,300,tiger beetle +n02165456,301,ladybug +n02167151,302,ground beetle +n02168699,303,longhorn beetle +n02169497,304,leaf beetle +n02172182,305,dung beetle +n02174001,306,rhinoceros beetle +n02177972,307,weevil +n02190166,308,fly +n02206856,309,bee +n02219486,310,ant +n02226429,311,grasshopper +n02229544,312,cricket insect +n02231487,313,stick insect +n02233338,314,cockroach +n02236044,315,praying mantis +n02256656,316,cicada +n02259212,317,leafhopper +n02264363,318,lacewing +n02268443,319,dragonfly +n02268853,320,damselfly +n02276258,321,red admiral butterfly +n02277742,322,ringlet butterfly +n02279972,323,monarch butterfly +n02280649,324,small white butterfly +n02281406,325,sulphur butterfly +n02281787,326,gossamer-winged butterfly +n02317335,327,starfish +n02319095,328,sea urchin +n02321529,329,sea cucumber +n02325366,330,cottontail rabbit +n02326432,331,hare +n02328150,332,Angora rabbit +n02342885,333,hamster +n02346627,334,porcupine +n02356798,335,fox squirrel +n02361337,336,marmot +n02363005,337,beaver +n02364673,338,guinea pig +n02389026,339,common sorrel horse +n02391049,340,zebra +n02395406,341,pig +n02396427,342,wild boar +n02397096,343,warthog +n02398521,344,hippopotamus +n02403003,345,ox +n02408429,346,water buffalo +n02410509,347,bison +n02412080,348,ram (adult male sheep) +n02415577,349,bighorn sheep +n02417914,350,Alpine ibex +n02422106,351,hartebeest +n02422699,352,impala (antelope) +n02423022,353,gazelle +n02437312,354,arabian camel +n02437616,355,llama +n02441942,356,weasel +n02442845,357,mink +n02443114,358,European polecat +n02443484,359,black-footed ferret +n02444819,360,otter +n02445715,361,skunk +n02447366,362,badger +n02454379,363,armadillo +n02457408,364,three-toed sloth +n02480495,365,orangutan +n02480855,366,gorilla +n02481823,367,chimpanzee +n02483362,368,gibbon +n02483708,369,siamang +n02484975,370,guenon +n02486261,371,patas monkey +n02486410,372,baboon +n02487347,373,macaque +n02488291,374,langur +n02488702,375,black-and-white colobus +n02489166,376,proboscis monkey +n02490219,377,marmoset +n02492035,378,white-headed capuchin +n02492660,379,howler monkey +n02493509,380,titi monkey +n02493793,381,Geoffroy's spider monkey +n02494079,382,common squirrel monkey +n02497673,383,ring-tailed lemur +n02500267,384,indri +n02504013,385,Asian elephant +n02504458,386,African bush elephant +n02509815,387,red panda +n02510455,388,giant panda +n02514041,389,snoek fish +n02526121,390,eel +n02536864,391,silver salmon +n02606052,392,rock beauty fish +n02607072,393,clownfish +n02640242,394,sturgeon +n02641379,395,gar fish +n02643566,396,lionfish +n02655020,397,pufferfish +n02666196,398,abacus +n02667093,399,abaya +n02669723,400,academic gown +n02672831,401,accordion +n02676566,402,acoustic guitar +n02687172,403,aircraft carrier +n02690373,404,airliner +n02692877,405,airship +n02699494,406,altar +n02701002,407,ambulance +n02704792,408,amphibious vehicle +n02708093,409,analog clock +n02727426,410,apiary +n02730930,411,apron +n02747177,412,trash can +n02749479,413,assault rifle +n02769748,414,backpack +n02776631,415,bakery +n02777292,416,balance beam +n02782093,417,balloon +n02783161,418,ballpoint pen +n02786058,419,Band-Aid +n02787622,420,banjo +n02788148,421,baluster / handrail +n02790996,422,barbell +n02791124,423,barber chair +n02791270,424,barbershop +n02793495,425,barn +n02794156,426,barometer +n02795169,427,barrel +n02797295,428,wheelbarrow +n02799071,429,baseball +n02802426,430,basketball +n02804414,431,bassinet +n02804610,432,bassoon +n02807133,433,swimming cap +n02808304,434,bath towel +n02808440,435,bathtub +n02814533,436,station wagon +n02814860,437,lighthouse +n02815834,438,beaker +n02817516,439,military hat (bearskin or shako) +n02823428,440,beer bottle +n02823750,441,beer glass +n02825657,442,bell tower +n02834397,443,baby bib +n02835271,444,tandem bicycle +n02837789,445,bikini +n02840245,446,ring binder +n02841315,447,binoculars +n02843684,448,birdhouse +n02859443,449,boathouse +n02860847,450,bobsleigh +n02865351,451,bolo tie +n02869837,452,poke bonnet +n02870880,453,bookcase +n02871525,454,bookstore +n02877765,455,bottle cap +n02879718,456,hunting bow +n02883205,457,bow tie +n02892201,458,brass memorial plaque +n02892767,459,bra +n02894605,460,breakwater +n02895154,461,breastplate +n02906734,462,broom +n02909870,463,bucket +n02910353,464,buckle +n02916936,465,bulletproof vest +n02917067,466,high-speed train +n02927161,467,butcher shop +n02930766,468,taxicab +n02939185,469,cauldron +n02948072,470,candle +n02950826,471,cannon +n02951358,472,canoe +n02951585,473,can opener +n02963159,474,cardigan +n02965783,475,car mirror +n02966193,476,carousel +n02966687,477,tool kit +n02971356,478,cardboard box / carton +n02974003,479,car wheel +n02977058,480,automated teller machine +n02978881,481,cassette +n02979186,482,cassette player +n02980441,483,castle +n02981792,484,catamaran +n02988304,485,CD player +n02992211,486,cello +n02992529,487,mobile phone +n02999410,488,chain +n03000134,489,chain-link fence +n03000247,490,chain mail +n03000684,491,chainsaw +n03014705,492,storage chest +n03016953,493,chiffonier +n03017168,494,bell or wind chime +n03018349,495,china cabinet +n03026506,496,Christmas stocking +n03028079,497,church +n03032252,498,movie theater +n03041632,499,cleaver +n03042490,500,cliff dwelling +n03045698,501,cloak +n03047690,502,clogs +n03062245,503,cocktail shaker +n03063599,504,coffee mug +n03063689,505,coffeemaker +n03065424,506,spiral or coil +n03075370,507,combination lock +n03085013,508,computer keyboard +n03089624,509,candy store +n03095699,510,container ship +n03100240,511,convertible +n03109150,512,corkscrew +n03110669,513,cornet +n03124043,514,cowboy boot +n03124170,515,cowboy hat +n03125729,516,cradle +n03126707,517,construction crane +n03127747,518,crash helmet +n03127925,519,crate +n03131574,520,infant bed +n03133878,521,Crock Pot +n03134739,522,croquet ball +n03141823,523,crutch +n03146219,524,cuirass +n03160309,525,dam +n03179701,526,desk +n03180011,527,desktop computer +n03187595,528,rotary dial telephone +n03188531,529,diaper +n03196217,530,digital clock +n03197337,531,digital watch +n03201208,532,dining table +n03207743,533,dishcloth +n03207941,534,dishwasher +n03208938,535,disc brake +n03216828,536,dock +n03218198,537,dog sled +n03220513,538,dome +n03223299,539,doormat +n03240683,540,drilling rig +n03249569,541,drum +n03250847,542,drumstick +n03255030,543,dumbbell +n03259280,544,Dutch oven +n03271574,545,electric fan +n03272010,546,electric guitar +n03272562,547,electric locomotive +n03290653,548,entertainment center +n03291819,549,envelope +n03297495,550,espresso machine +n03314780,551,face powder +n03325584,552,feather boa +n03337140,553,filing cabinet +n03344393,554,fireboat +n03345487,555,fire truck +n03347037,556,fire screen +n03355925,557,flagpole +n03372029,558,flute +n03376595,559,folding chair +n03379051,560,football helmet +n03384352,561,forklift +n03388043,562,fountain +n03388183,563,fountain pen +n03388549,564,four-poster bed +n03393912,565,freight car +n03394916,566,French horn +n03400231,567,frying pan +n03404251,568,fur coat +n03417042,569,garbage truck +n03424325,570,gas mask or respirator +n03425413,571,gas pump +n03443371,572,goblet +n03444034,573,go-kart +n03445777,574,golf ball +n03445924,575,golf cart +n03447447,576,gondola +n03447721,577,gong +n03450230,578,gown +n03452741,579,grand piano +n03457902,580,greenhouse +n03459775,581,radiator grille +n03461385,582,grocery store +n03467068,583,guillotine +n03476684,584,hair clip +n03476991,585,hair spray +n03478589,586,half-track +n03481172,587,hammer +n03482405,588,hamper +n03483316,589,hair dryer +n03485407,590,hand-held computer +n03485794,591,handkerchief +n03492542,592,hard disk drive +n03494278,593,harmonica +n03495258,594,harp +n03496892,595,combine harvester +n03498962,596,hatchet +n03527444,597,holster +n03529860,598,home theater +n03530642,599,honeycomb +n03532672,600,hook +n03534580,601,hoop skirt +n03535780,602,gymnastic horizontal bar +n03538406,603,horse-drawn vehicle +n03544143,604,hourglass +n03584254,605,iPod +n03584829,606,clothes iron +n03590841,607,carved pumpkin +n03594734,608,jeans +n03594945,609,jeep +n03595614,610,T-shirt +n03598930,611,jigsaw puzzle +n03599486,612,rickshaw +n03602883,613,joystick +n03617480,614,kimono +n03623198,615,knee pad +n03627232,616,knot +n03630383,617,lab coat +n03633091,618,ladle +n03637318,619,lampshade +n03642806,620,laptop computer +n03649909,621,lawn mower +n03657121,622,lens cap +n03658185,623,letter opener +n03661043,624,library +n03662601,625,lifeboat +n03666591,626,lighter +n03670208,627,limousine +n03673027,628,ocean liner +n03676483,629,lipstick +n03680355,630,slip-on shoe +n03690938,631,lotion +n03691459,632,music speaker +n03692522,633,loupe magnifying glass +n03697007,634,sawmill +n03706229,635,magnetic compass +n03709823,636,messenger bag +n03710193,637,mailbox +n03710637,638,tights +n03710721,639,one-piece bathing suit +n03717622,640,manhole cover +n03720891,641,maraca +n03721384,642,marimba +n03724870,643,mask +n03729826,644,matchstick +n03733131,645,maypole +n03733281,646,maze +n03733805,647,measuring cup +n03742115,648,medicine cabinet +n03743016,649,megalith +n03759954,650,microphone +n03761084,651,microwave oven +n03763968,652,military uniform +n03764736,653,milk can +n03769881,654,minibus +n03770439,655,miniskirt +n03770679,656,minivan +n03773504,657,missile +n03775071,658,mitten +n03775546,659,mixing bowl +n03776460,660,mobile home +n03777568,661,ford model t +n03777754,662,modem +n03781244,663,monastery +n03782006,664,monitor +n03785016,665,moped +n03786901,666,mortar and pestle +n03787032,667,graduation cap +n03788195,668,mosque +n03788365,669,mosquito net +n03791053,670,vespa +n03792782,671,mountain bike +n03792972,672,tent +n03793489,673,computer mouse +n03794056,674,mousetrap +n03796401,675,moving van +n03803284,676,muzzle +n03804744,677,metal nail +n03814639,678,neck brace +n03814906,679,necklace +n03825788,680,baby pacifier +n03832673,681,notebook computer +n03837869,682,obelisk +n03838899,683,oboe +n03840681,684,ocarina +n03841143,685,odometer +n03843555,686,oil filter +n03854065,687,pipe organ +n03857828,688,oscilloscope +n03866082,689,overskirt +n03868242,690,bullock cart +n03868863,691,oxygen mask +n03871628,692,product packet / packaging +n03873416,693,paddle +n03874293,694,paddle wheel +n03874599,695,padlock +n03876231,696,paintbrush +n03877472,697,pajamas +n03877845,698,palace +n03884397,699,pan flute +n03887697,700,paper towel +n03888257,701,parachute +n03888605,702,parallel bars +n03891251,703,park bench +n03891332,704,parking meter +n03895866,705,railroad car +n03899768,706,patio +n03902125,707,payphone +n03903868,708,pedestal +n03908618,709,pencil case +n03908714,710,pencil sharpener +n03916031,711,perfume +n03920288,712,Petri dish +n03924679,713,photocopier +n03929660,714,plectrum +n03929855,715,Pickelhaube +n03930313,716,picket fence +n03930630,717,pickup truck +n03933933,718,pier +n03935335,719,piggy bank +n03937543,720,pill bottle +n03938244,721,pillow +n03942813,722,ping-pong ball +n03944341,723,pinwheel +n03947888,724,pirate ship +n03950228,725,drink pitcher +n03954731,726,block plane +n03956157,727,planetarium +n03958227,728,plastic bag +n03961711,729,plate rack +n03967562,730,farm plow +n03970156,731,plunger +n03976467,732,Polaroid camera +n03976657,733,pole +n03977966,734,police van +n03980874,735,poncho +n03982430,736,pool table +n03983396,737,soda bottle +n03991062,738,plant pot +n03992509,739,potter's wheel +n03995372,740,power drill +n03998194,741,prayer rug +n04004767,742,printer +n04005630,743,prison +n04008634,744,missile +n04009552,745,projector +n04019541,746,hockey puck +n04023962,747,punching bag +n04026417,748,purse +n04033901,749,quill +n04033995,750,quilt +n04037443,751,race car +n04039381,752,racket +n04040759,753,radiator +n04041544,754,radio +n04044716,755,radio telescope +n04049303,756,rain barrel +n04065272,757,recreational vehicle +n04067472,758,fishing casting reel +n04069434,759,reflex camera +n04070727,760,refrigerator +n04074963,761,remote control +n04081281,762,restaurant +n04086273,763,revolver +n04090263,764,rifle +n04099969,765,rocking chair +n04111531,766,rotisserie +n04116512,767,eraser +n04118538,768,rugby ball +n04118776,769,ruler measuring stick +n04120489,770,sneaker +n04125021,771,safe +n04127249,772,safety pin +n04131690,773,salt shaker +n04133789,774,sandal +n04136333,775,sarong +n04141076,776,saxophone +n04141327,777,scabbard +n04141975,778,weighing scale +n04146614,779,school bus +n04147183,780,schooner +n04149813,781,scoreboard +n04152593,782,CRT monitor +n04153751,783,screw +n04154565,784,screwdriver +n04162706,785,seat belt +n04179913,786,sewing machine +n04192698,787,shield +n04200800,788,shoe store +n04201297,789,shoji screen / room divider +n04204238,790,shopping basket +n04204347,791,shopping cart +n04208210,792,shovel +n04209133,793,shower cap +n04209239,794,shower curtain +n04228054,795,ski +n04229816,796,balaclava ski mask +n04235860,797,sleeping bag +n04238763,798,slide rule +n04239074,799,sliding door +n04243546,800,slot machine +n04251144,801,snorkel +n04252077,802,snowmobile +n04252225,803,snowplow +n04254120,804,soap dispenser +n04254680,805,soccer ball +n04254777,806,sock +n04258138,807,solar thermal collector +n04259630,808,sombrero +n04263257,809,soup bowl +n04264628,810,keyboard space bar +n04265275,811,space heater +n04266014,812,space shuttle +n04270147,813,spatula +n04273569,814,motorboat +n04275548,815,spider web +n04277352,816,spindle +n04285008,817,sports car +n04286575,818,spotlight +n04296562,819,stage +n04310018,820,steam locomotive +n04311004,821,through arch bridge +n04311174,822,steel drum +n04317175,823,stethoscope +n04325704,824,scarf +n04326547,825,stone wall +n04328186,826,stopwatch +n04330267,827,stove +n04332243,828,strainer +n04335435,829,tram +n04336792,830,stretcher +n04344873,831,couch +n04346328,832,stupa +n04347754,833,submarine +n04350905,834,suit +n04355338,835,sundial +n04355933,836,sunglasses +n04356056,837,sunglasses +n04357314,838,sunscreen +n04366367,839,suspension bridge +n04367480,840,mop +n04370456,841,sweatshirt +n04371430,842,swim trunks / shorts +n04371774,843,swing +n04372370,844,electrical switch +n04376876,845,syringe +n04380533,846,table lamp +n04389033,847,tank +n04392985,848,tape player +n04398044,849,teapot +n04399382,850,teddy bear +n04404412,851,television +n04409515,852,tennis ball +n04417672,853,thatched roof +n04418357,854,front curtain +n04423845,855,thimble +n04428191,856,threshing machine +n04429376,857,throne +n04435653,858,tile roof +n04442312,859,toaster +n04443257,860,tobacco shop +n04447861,861,toilet seat +n04456115,862,torch +n04458633,863,totem pole +n04461696,864,tow truck +n04462240,865,toy store +n04465501,866,tractor +n04467665,867,semi-trailer truck +n04476259,868,tray +n04479046,869,trench coat +n04482393,870,tricycle +n04483307,871,trimaran +n04485082,872,tripod +n04486054,873,triumphal arch +n04487081,874,trolleybus +n04487394,875,trombone +n04493381,876,hot tub +n04501370,877,turnstile +n04505470,878,typewriter keyboard +n04507155,879,umbrella +n04509417,880,unicycle +n04515003,881,upright piano +n04517823,882,vacuum cleaner +n04522168,883,vase +n04523525,884,vaulted or arched ceiling +n04525038,885,velvet fabric +n04525305,886,vending machine +n04532106,887,vestment +n04532670,888,viaduct +n04536866,889,violin +n04540053,890,volleyball +n04542943,891,waffle iron +n04548280,892,wall clock +n04548362,893,wallet +n04550184,894,wardrobe +n04552348,895,military aircraft +n04553703,896,sink +n04554684,897,washing machine +n04557648,898,water bottle +n04560804,899,water jug +n04562935,900,water tower +n04579145,901,whiskey jug +n04579432,902,whistle +n04584207,903,hair wig +n04589890,904,window screen +n04590129,905,window shade +n04591157,906,Windsor tie +n04591713,907,wine bottle +n04592741,908,airplane wing +n04596742,909,wok +n04597913,910,wooden spoon +n04599235,911,wool +n04604644,912,split-rail fence +n04606251,913,shipwreck +n04612504,914,sailboat +n04613696,915,yurt +n06359193,916,website +n06596364,917,comic book +n06785654,918,crossword +n06794110,919,traffic or street sign +n06874185,920,traffic light +n07248320,921,dust jacket +n07565083,922,menu +n07579787,923,plate +n07583066,924,guacamole +n07584110,925,consomme +n07590611,926,hot pot +n07613480,927,trifle +n07614500,928,ice cream +n07615774,929,popsicle +n07684084,930,baguette +n07693725,931,bagel +n07695742,932,pretzel +n07697313,933,cheeseburger +n07697537,934,hot dog +n07711569,935,mashed potatoes +n07714571,936,cabbage +n07714990,937,broccoli +n07715103,938,cauliflower +n07716358,939,zucchini +n07716906,940,spaghetti squash +n07717410,941,acorn squash +n07717556,942,butternut squash +n07718472,943,cucumber +n07718747,944,artichoke +n07720875,945,bell pepper +n07730033,946,cardoon +n07734744,947,mushroom +n07742313,948,Granny Smith apple +n07745940,949,strawberry +n07747607,950,orange +n07749582,951,lemon +n07753113,952,fig +n07753275,953,pineapple +n07753592,954,banana +n07754684,955,jackfruit +n07760859,956,cherimoya (custard apple) +n07768694,957,pomegranate +n07802026,958,hay +n07831146,959,carbonara +n07836838,960,chocolate syrup +n07860988,961,dough +n07871810,962,meatloaf +n07873807,963,pizza +n07875152,964,pot pie +n07880968,965,burrito +n07892512,966,red wine +n07920052,967,espresso +n07930864,968,tea cup +n07932039,969,eggnog +n09193705,970,mountain +n09229709,971,bubble +n09246464,972,cliff +n09256479,973,coral reef +n09288635,974,geyser +n09332890,975,lakeshore +n09399592,976,promontory +n09421951,977,sandbar +n09428293,978,beach +n09468604,979,valley +n09472597,980,volcano +n09835506,981,baseball player +n10148035,982,bridegroom +n10565667,983,scuba diver +n11879895,984,rapeseed +n11939491,985,daisy +n12057211,986,yellow lady's slipper +n12144580,987,corn +n12267677,988,acorn +n12620546,989,rose hip +n12768682,990,horse chestnut seed +n12985857,991,coral fungus +n12998815,992,agaric +n13037406,993,gyromitra +n13040303,994,stinkhorn mushroom +n13044778,995,earth star fungus +n13052670,996,hen of the woods mushroom +n13054560,997,bolete +n13133613,998,corn cob +n15075141,999,toilet paper \ No newline at end of file diff --git a/attacks/UnivIntruder/utils_/text_templates.py b/attacks/UnivIntruder/utils_/text_templates.py new file mode 100644 index 0000000000000000000000000000000000000000..8045ba57e7bf5a750c19e1185c66bedb51583470 --- /dev/null +++ b/attacks/UnivIntruder/utils_/text_templates.py @@ -0,0 +1,129 @@ +imagenet_templates = [ + 'a bad photo of a {}.', + 'a sculpture of a {}.', + 'a photo of the hard to see {}.', + 'a low resolution photo of the {}.', + 'a rendering of a {}.', + 'graffiti of a {}.', + 'a bad photo of the {}.', + 'a cropped photo of the {}.', + 'a tattoo of a {}.', + 'the embroidered {}.', + 'a photo of a hard to see {}.', + 'a bright photo of a {}.', + 'a photo of a clean {}.', + 'a photo of a dirty {}.', + 'a dark photo of the {}.', + 'a drawing of a {}.', + 'a photo of my {}.', + 'the plastic {}.', + 'a photo of the cool {}.', + 'a close-up photo of a {}.', + 'a black and white photo of the {}.', + 'a painting of the {}.', + 'a painting of a {}.', + 'a pixelated photo of the {}.', + 'a sculpture of the {}.', + 'a bright photo of the {}.', + 'a cropped photo of a {}.', + 'a plastic {}.', + 'a photo of the dirty {}.', + 'a jpeg corrupted photo of a {}.', + 'a blurry photo of the {}.', + 'a photo of the {}.', + 'a good photo of the {}.', + 'a rendering of the {}.', + 'a {} in a video game.', + 'a photo of one {}.', + 'a doodle of a {}.', + 'a close-up photo of the {}.', + 'a photo of a {}.', + 'the origami {}.', + 'the {} in a video game.', + 'a sketch of a {}.', + 'a doodle of the {}.', + 'a origami {}.', + 'a low resolution photo of a {}.', + 'the toy {}.', + 'a rendition of the {}.', + 'a photo of the clean {}.', + 'a photo of a large {}.', + 'a rendition of a {}.', + 'a photo of a nice {}.', + 'a photo of a weird {}.', + 'a blurry photo of a {}.', + 'a cartoon {}.', + 'art of a {}.', + 'a sketch of the {}.', + 'a embroidered {}.', + 'a pixelated photo of a {}.', + 'itap of the {}.', + 'a jpeg corrupted photo of the {}.', + 'a good photo of a {}.', + 'a plushie {}.', + 'a photo of the nice {}.', + 'a photo of the small {}.', + 'a photo of the weird {}.', + 'the cartoon {}.', + 'art of the {}.', + 'a drawing of the {}.', + 'a photo of the large {}.', + 'a black and white photo of a {}.', + 'the plushie {}.', + 'a dark photo of a {}.', + 'itap of a {}.', + 'graffiti of the {}.', + 'a toy {}.', + 'itap of my {}.', + 'a photo of a cool {}.', + 'a photo of a small {}.', + 'a tattoo of the {}.', +] + +part_templates = [ + 'the paw of a {}.', + 'the nose of a {}.', + 'the eye of the {}.', + 'the ears of a {}.', + 'an eye of a {}.', + 'the tongue of a {}.', + 'the fur of the {}.', + 'colorful {} fur.', + 'a snout of a {}.', + 'the teeth of the {}.', + 'the {}s fangs.', + 'a claw of the {}.', + 'the face of the {}', + 'a neck of a {}', + 'the head of the {}', +] + +imagenet_templates_small = [ + 'a photo of a {}.', + 'a rendering of a {}.', + 'a cropped photo of the {}.', + 'the photo of a {}.', + 'a photo of a clean {}.', + 'a photo of a dirty {}.', + 'a dark photo of the {}.', + 'a photo of my {}.', + 'a photo of the cool {}.', + 'a close-up photo of a {}.', + 'a bright photo of the {}.', + 'a cropped photo of a {}.', + 'a photo of the {}.', + 'a good photo of the {}.', + 'a photo of one {}.', + 'a close-up photo of the {}.', + 'a rendition of the {}.', + 'a photo of the clean {}.', + 'a rendition of a {}.', + 'a photo of a nice {}.', + 'a good photo of a {}.', + 'a photo of the nice {}.', + 'a photo of the small {}.', + 'a photo of the weird {}.', + 'a photo of the large {}.', + 'a photo of a cool {}.', + 'a photo of a small {}.', +] \ No newline at end of file diff --git a/attacks/attack_config.py b/attacks/attack_config.py new file mode 100644 index 0000000000000000000000000000000000000000..4d008a197bccb4b04506444c26d786aeadb9a642 --- /dev/null +++ b/attacks/attack_config.py @@ -0,0 +1,141 @@ +import os +from foolbox import PyTorchModel, accuracy +from foolbox.attacks.base import * +from foolbox.attacks.gradient_descent_base import * +from torchvision import transforms +from utils.data_manager import DataManager, get_dataloader +import torch +import logging +import eagerpy as ep +from utils.data_manager import load_all_task_models + + + +class SustainableAttack(Attack): + def __init__(self, args, device='cuda'): + super().__init__() + self.device = device + self.args = args + + # Only init the first 10 classes + self.data_manager = DataManager( + args["dataset"], + args["shuffle"], + args["seed"], + args["init_cls"], + args["increment"], + args["attack"] + ) + self.args['target_class_list'] = self.data_manager._class_order[:self.data_manager._increments[0]] + self.args['target_class_dict'] = dict(zip(self.args['target_class_list'], range(len(self.args['target_class_list'])))) + self.img_s = 32 if args["dataset"] == 'cifar100' else 224 + self.batch_size = args['batch_size'] + self.loader = get_dataloader(self.data_manager, batch_size=self.batch_size, + start_class=0, end_class=10, + train=True, shuffle=True, num_workers=0) + + ckpts = sorted([f for f in os.listdir(args['logs_name']) if f.endswith('.pkl')]) + self.ckpt_paths = [os.path.join(args['logs_name'], ckpt_file) for ckpt_file in ckpts] + self.model = None + self.model0 = None + self.attack = None + + self.target_class = args['target_class'] + if args["dataset"] == "cifar100": + self.norm = transforms.Normalize(mean=[0.5071, 0.4867, 0.4408], + std=[0.2675, 0.2565, 0.2761]) + self.preprocessing = dict(mean=[0.5071, 0.4867, 0.4408], + std=[0.2675, 0.2565, 0.2761], axis=-3) + else: + self.norm = transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225]) + self.preprocessing = dict(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225], axis=-3) + + def run_attack(self): + pass + + + def to_alls(self, imgs, labels, labels_t=None, + target_imgs=None, target_labels=None, return_index=False): + correct_index = ep.full_like(ep.astensors(torch.ones((len(imgs),), dtype=bool, device=self.device))[0], fill_value=True) + correct_index_t = ep.full_like(ep.astensors(torch.ones((len(target_imgs),), dtype=bool, device=self.device))[0], fill_value=True) + models = load_all_task_models(self.args, self.args['logs_name'], self.data_manager, + batch_size=self.batch_size, + train=True, + load_type='model')[0] + for task in range(len(models)): + model = PyTorchModel(models[task]._network, bounds=(0, 1), preprocessing=self.preprocessing) + acc_bool = accuracy(model, imgs, labels)[1] + if task == 0: + acc_bool_t, target_logits = accuracy(model, target_imgs, target_labels)[1:] + else: + acc_bool_t = accuracy(model, target_imgs, target_labels)[1] + correct_index = ep.logical_and(correct_index, acc_bool) + correct_index_t = ep.logical_and(correct_index_t, acc_bool_t) + del model, acc_bool, acc_bool_t + if correct_index.any(): + imgs = imgs[correct_index] + labels = labels[correct_index] + if self.target_class is not None: + labels_t = labels_t[correct_index] + logging.info( + f"Filtering {len(labels)} Correct samples for all CL models.") + else: + print("No valid samples found for IMGS, skipping this batch.") + imgs, labels, labels_t = None, None, None + + if correct_index_t.any(): + target_imgs = target_imgs[correct_index_t] + target_labels = target_labels[correct_index_t] + target_logits = target_logits[correct_index_t] + logging.info( + f"Filtering {len(target_labels)} Target samples for all CL models.") + else: + logging.info("No valid samples found for TARGET IMGS, skipping this batch.") + target_imgs, target_labels, target_logits = None, None, None + if return_index: + return correct_index, correct_index_t + del models, correct_index, correct_index_t + return imgs, labels, labels_t, target_imgs, target_labels, target_logits + + def to_all(self, imgs, labels, return_index=False): + # Filtering Correct Samples for All CL Models + correct_index = ep.full_like(ep.astensors(torch.ones((len(imgs),), dtype=bool, device=self.device))[0], fill_value=True) + models = load_all_task_models(self.args, self.args['logs_name'], self.data_manager, + batch_size=self.batch_size, + train=True, + load_type='model')[0] + for task in range(len(models)): + model = PyTorchModel(models[task]._network, bounds=(0, 1), preprocessing=self.preprocessing) + acc_bool = accuracy(model, imgs, labels)[1] + correct_index = ep.logical_and(correct_index, acc_bool) + del model, acc_bool + if correct_index.any(): + imgs = imgs[correct_index] + labels = labels[correct_index] + logging.info( + f"Filtering {len(labels)} Correct samples for all CL models.") + else: + logging.info("No valid samples found for IMGS, skipping this batch.") + imgs, labels = None, None + if return_index: + return correct_index + del models, correct_index + return imgs, labels + + + def __call__( + self, + model: Model, + inputs: T, + criterion: Any, + *, + epsilons: Sequence[Union[float, None]], + **kwargs: Any, + ) -> Tuple[List[T], List[T], T]: + ... + + def repeat(self, times: int) -> "SustainableAttack": + ... + diff --git a/convs/ACL_buffer.py b/convs/ACL_buffer.py new file mode 100644 index 0000000000000000000000000000000000000000..2b05a00b8089e8fb7b6cdc997ddd22b9b680399e --- /dev/null +++ b/convs/ACL_buffer.py @@ -0,0 +1,86 @@ +# -*- coding: utf-8 -*- +""" +Buffer layers for the analytic continual learning (ACL) [1-3]. + +This implementation refers to the official implementation https://github.com/ZHUANGHP/Analytic-continual-learning. + +References: +[1] Zhuang, Huiping, et al. + "ACIL: Analytic class-incremental learning with absolute memorization and privacy protection." + Advances in Neural Information Processing Systems 35 (2022): 11602-11614. +[2] Zhuang, Huiping, et al. + "GKEAL: Gaussian Kernel Embedded Analytic Learning for Few-Shot Class Incremental Task." + Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023. +[3] Zhuang, Huiping, et al. + "DS-AL: A Dual-Stream Analytic Learning for Exemplar-Free Class-Incremental Learning." + Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 38. No. 15. 2024. +""" + +import torch +from typing import Optional, Union, Callable +from abc import ABCMeta, abstractmethod + +__all__ = [ + "Buffer", + "RandomBuffer", + "activation_t", +] + +activation_t = Union[Callable[[torch.Tensor], torch.Tensor], torch.nn.Module] + + +class Buffer(torch.nn.Module, metaclass=ABCMeta): + def __init__(self) -> None: + super().__init__() + + @abstractmethod + def forward(self, X: torch.Tensor) -> torch.Tensor: + raise NotImplementedError() + + +class RandomBuffer(torch.nn.Linear, Buffer): + """ + Random buffer layer for the ACIL [1] and DS-AL [2]. + + This implementation refers to the official implementation https://github.com/ZHUANGHP/Analytic-continual-learning. + + References: + [1] Zhuang, Huiping, et al. + "ACIL: Analytic class-incremental learning with absolute memorization and privacy protection." + Advances in Neural Information Processing Systems 35 (2022): 11602-11614. + [2] Zhuang, Huiping, et al. + "DS-AL: A Dual-Stream Analytic Learning for Exemplar-Free Class-Incremental Learning." + Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 38. No. 15. 2024. + """ + + def __init__( + self, + in_features: int, + out_features: int, + bias: bool = False, + device=None, + dtype=torch.float, + activation: Optional[activation_t] = torch.relu_, + ) -> None: + super(torch.nn.Linear, self).__init__() + factory_kwargs = {"device": device, "dtype": dtype} + self.in_features = in_features + self.out_features = out_features + self.activation: activation_t = ( + torch.nn.Identity() if activation is None else activation + ) + + W = torch.empty((out_features, in_features), **factory_kwargs) + b = torch.empty(out_features, **factory_kwargs) if bias else None + + # Using buffer instead of parameter + self.register_buffer("weight", W) + self.register_buffer("bias", b) + + # Random Initialization + self.reset_parameters() + + @torch.no_grad() + def forward(self, X: torch.Tensor) -> torch.Tensor: + X = X.to(self.weight) + return self.activation(super().forward(X)) diff --git a/convs/__init__.py b/convs/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/convs/cifar_resnet.py b/convs/cifar_resnet.py new file mode 100644 index 0000000000000000000000000000000000000000..a2668dd45ccd05b6a881b544b6008abd8d8b58af --- /dev/null +++ b/convs/cifar_resnet.py @@ -0,0 +1,207 @@ +''' +Reference: +https://github.com/khurramjaved96/incremental-learning/blob/autoencoders/model/resnet32.py +''' +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class DownsampleA(nn.Module): + def __init__(self, nIn, nOut, stride): + super(DownsampleA, self).__init__() + assert stride == 2 + self.avg = nn.AvgPool2d(kernel_size=1, stride=stride) + + def forward(self, x): + x = self.avg(x) + return torch.cat((x, x.mul(0)), 1) + + +class DownsampleB(nn.Module): + def __init__(self, nIn, nOut, stride): + super(DownsampleB, self).__init__() + self.conv = nn.Conv2d(nIn, nOut, kernel_size=1, stride=stride, padding=0, bias=False) + self.bn = nn.BatchNorm2d(nOut) + + def forward(self, x): + x = self.conv(x) + x = self.bn(x) + return x + + +class DownsampleC(nn.Module): + def __init__(self, nIn, nOut, stride): + super(DownsampleC, self).__init__() + assert stride != 1 or nIn != nOut + self.conv = nn.Conv2d(nIn, nOut, kernel_size=1, stride=stride, padding=0, bias=False) + + def forward(self, x): + x = self.conv(x) + return x + + +class DownsampleD(nn.Module): + def __init__(self, nIn, nOut, stride): + super(DownsampleD, self).__init__() + assert stride == 2 + self.conv = nn.Conv2d(nIn, nOut, kernel_size=2, stride=stride, padding=0, bias=False) + self.bn = nn.BatchNorm2d(nOut) + + def forward(self, x): + x = self.conv(x) + x = self.bn(x) + return x + + +class ResNetBasicblock(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(ResNetBasicblock, self).__init__() + + self.conv_a = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False) + self.bn_a = nn.BatchNorm2d(planes) + + self.conv_b = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False) + self.bn_b = nn.BatchNorm2d(planes) + + self.downsample = downsample + + def forward(self, x): + residual = x + + basicblock = self.conv_a(x) + basicblock = self.bn_a(basicblock) + basicblock = F.relu(basicblock, inplace=True) + + basicblock = self.conv_b(basicblock) + basicblock = self.bn_b(basicblock) + + if self.downsample is not None: + residual = self.downsample(x) + + return F.relu(residual + basicblock, inplace=True) + + +class CifarResNet(nn.Module): + """ + ResNet optimized for the Cifar Dataset, as specified in + https://arxiv.org/abs/1512.03385.pdf + """ + + def __init__(self, block, depth, channels=3): + super(CifarResNet, self).__init__() + + # Model type specifies number of layers for CIFAR-10 and CIFAR-100 model + assert (depth - 2) % 6 == 0, 'depth should be one of 20, 32, 44, 56, 110' + layer_blocks = (depth - 2) // 6 + + self.conv_1_3x3 = nn.Conv2d(channels, 16, kernel_size=3, stride=1, padding=1, bias=False) + self.bn_1 = nn.BatchNorm2d(16) + + self.inplanes = 16 + self.stage_1 = self._make_layer(block, 16, layer_blocks, 1) + self.stage_2 = self._make_layer(block, 32, layer_blocks, 2) + self.stage_3 = self._make_layer(block, 64, layer_blocks, 2) + self.avgpool = nn.AvgPool2d(8) + self.out_dim = 64 * block.expansion + self.fc = nn.Linear(64*block.expansion, 10) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2. / n)) + # m.bias.data.zero_() + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + elif isinstance(m, nn.Linear): + nn.init.kaiming_normal_(m.weight) + m.bias.data.zero_() + + def _make_layer(self, block, planes, blocks, stride=1): + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = DownsampleA(self.inplanes, planes * block.expansion, stride) + + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample)) + self.inplanes = planes * block.expansion + for i in range(1, blocks): + layers.append(block(self.inplanes, planes)) + + return nn.Sequential(*layers) + + def forward(self, x): + x = self.conv_1_3x3(x) # [bs, 16, 32, 32] + x = F.relu(self.bn_1(x), inplace=True) + + x_1 = self.stage_1(x) # [bs, 16, 32, 32] + x_2 = self.stage_2(x_1) # [bs, 32, 16, 16] + x_3 = self.stage_3(x_2) # [bs, 64, 8, 8] + + pooled = self.avgpool(x_3) # [bs, 64, 1, 1] + features = pooled.view(pooled.size(0), -1) # [bs, 64] + + return { + 'fmaps': [x_1, x_2, x_3], + 'features': features + } + + @property + def last_conv(self): + return self.stage_3[-1].conv_b + + +def resnet20mnist(): + """Constructs a ResNet-20 model for MNIST.""" + model = CifarResNet(ResNetBasicblock, 20, 1) + return model + + +def resnet32mnist(): + """Constructs a ResNet-32 model for MNIST.""" + model = CifarResNet(ResNetBasicblock, 32, 1) + return model + + +def resnet20(): + """Constructs a ResNet-20 model for CIFAR-10.""" + model = CifarResNet(ResNetBasicblock, 20) + return model + + +def resnet32(): + """Constructs a ResNet-32 model for CIFAR-10.""" + model = CifarResNet(ResNetBasicblock, 32) + return model + + +def resnet44(): + """Constructs a ResNet-44 model for CIFAR-10.""" + model = CifarResNet(ResNetBasicblock, 44) + return model + + +def resnet56(): + """Constructs a ResNet-56 model for CIFAR-10.""" + model = CifarResNet(ResNetBasicblock, 56) + return model + + +def resnet110(): + """Constructs a ResNet-110 model for CIFAR-10.""" + model = CifarResNet(ResNetBasicblock, 110) + return model + +# for auc +def resnet14(): + model = CifarResNet(ResNetBasicblock, 14) + return model + +def resnet26(): + model = CifarResNet(ResNetBasicblock, 26) + return model \ No newline at end of file diff --git a/convs/conv_cifar.py b/convs/conv_cifar.py new file mode 100644 index 0000000000000000000000000000000000000000..2c337270b92312b0dc7ed37b2f7c937345f40696 --- /dev/null +++ b/convs/conv_cifar.py @@ -0,0 +1,77 @@ +''' +For MEMO implementations of CIFAR-ConvNet +Reference: +https://github.com/wangkiw/ICLR23-MEMO/blob/main/convs/conv_cifar.py +''' +import torch +import torch.nn as nn +import torch.nn.functional as F + +# for cifar +def conv_block(in_channels, out_channels): + return nn.Sequential( + nn.Conv2d(in_channels, out_channels, 3, padding=1), + nn.BatchNorm2d(out_channels), + nn.ReLU(), + nn.MaxPool2d(2) + ) + +class ConvNet2(nn.Module): + def __init__(self, x_dim=3, hid_dim=64, z_dim=64): + super().__init__() + self.out_dim = 64 + self.avgpool = nn.AvgPool2d(8) + self.encoder = nn.Sequential( + conv_block(x_dim, hid_dim), + conv_block(hid_dim, z_dim), + ) + + def forward(self, x): + x = self.encoder(x) + x = self.avgpool(x) + features = x.view(x.shape[0], -1) + return { + "features":features + } + +class GeneralizedConvNet2(nn.Module): + def __init__(self, x_dim=3, hid_dim=64, z_dim=64): + super().__init__() + self.encoder = nn.Sequential( + conv_block(x_dim, hid_dim), + ) + + def forward(self, x): + base_features = self.encoder(x) + return base_features + +class SpecializedConvNet2(nn.Module): + def __init__(self,hid_dim=64,z_dim=64): + super().__init__() + self.feature_dim = 64 + self.avgpool = nn.AvgPool2d(8) + self.AdaptiveBlock = conv_block(hid_dim,z_dim) + + def forward(self,x): + base_features = self.AdaptiveBlock(x) + pooled = self.avgpool(base_features) + features = pooled.view(pooled.size(0),-1) + return features + +def conv2(): + return ConvNet2() + +def get_conv_a2fc(): + basenet = GeneralizedConvNet2() + adaptivenet = SpecializedConvNet2() + return basenet,adaptivenet + +if __name__ == '__main__': + a, b = get_conv_a2fc() + _base = sum(p.numel() for p in a.parameters()) + _adap = sum(p.numel() for p in b.parameters()) + print(f"conv :{_base+_adap}") + + conv2 = conv2() + conv2_sum = sum(p.numel() for p in conv2.parameters()) + print(f"conv2 :{conv2_sum}") \ No newline at end of file diff --git a/convs/conv_imagenet.py b/convs/conv_imagenet.py new file mode 100644 index 0000000000000000000000000000000000000000..59793b2184b938250d0a55fff1786d1fc724ed73 --- /dev/null +++ b/convs/conv_imagenet.py @@ -0,0 +1,82 @@ +''' +For MEMO implementations of ImageNet-ConvNet +Reference: +https://github.com/wangkiw/ICLR23-MEMO/blob/main/convs/conv_imagenet.py +''' +import torch.nn as nn +import torch + +# for imagenet +def first_block(in_channels, out_channels): + return nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=7, stride=2, padding=3), + nn.BatchNorm2d(out_channels), + nn.ReLU(), + nn.MaxPool2d(2) + ) + +def conv_block(in_channels, out_channels): + return nn.Sequential( + nn.Conv2d(in_channels, out_channels, 3, padding=1), + nn.BatchNorm2d(out_channels), + nn.ReLU(), + nn.MaxPool2d(2) + ) + +class ConvNet(nn.Module): + def __init__(self, x_dim=3, hid_dim=128, z_dim=512): + super().__init__() + self.block1 = first_block(x_dim, hid_dim) + self.block2 = conv_block(hid_dim, hid_dim) + self.block3 = conv_block(hid_dim, hid_dim) + self.block4 = conv_block(hid_dim, z_dim) + self.avgpool = nn.AvgPool2d(7) + self.out_dim = 512 + + def forward(self, x): + x = self.block1(x) + x = self.block2(x) + x = self.block3(x) + x = self.block4(x) + + x = self.avgpool(x) + features = x.view(x.shape[0], -1) + + return { + "features": features + } + +class GeneralizedConvNet(nn.Module): + def __init__(self, x_dim=3, hid_dim=128, z_dim=512): + super().__init__() + self.block1 = first_block(x_dim, hid_dim) + self.block2 = conv_block(hid_dim, hid_dim) + self.block3 = conv_block(hid_dim, hid_dim) + + def forward(self, x): + x = self.block1(x) + x = self.block2(x) + x = self.block3(x) + return x + +class SpecializedConvNet(nn.Module): + def __init__(self, hid_dim=128,z_dim=512): + super().__init__() + self.block4 = conv_block(hid_dim, z_dim) + self.avgpool = nn.AvgPool2d(7) + self.feature_dim = 512 + + def forward(self, x): + x = self.block4(x) + x = self.avgpool(x) + features = x.view(x.shape[0], -1) + return features + +def conv4(): + model = ConvNet() + return model + +def conv_a2fc_imagenet(): + _base = GeneralizedConvNet() + _adaptive_net = SpecializedConvNet() + return _base, _adaptive_net \ No newline at end of file diff --git a/convs/linears.py b/convs/linears.py new file mode 100644 index 0000000000000000000000000000000000000000..2d3a1ac0a616288adbfec703fc39caa7214e0090 --- /dev/null +++ b/convs/linears.py @@ -0,0 +1,302 @@ +''' +Reference: +https://github.com/hshustc/CVPR19_Incremental_Learning/blob/master/cifar100-class-incremental/modified_linear.py +''' +import math +import torch +from torch import nn +from torch.nn import functional as F +from typing import Union, Optional, Dict +from abc import ABCMeta, abstractmethod + + +class SimpleLinear(nn.Module): + ''' + Reference: + https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/linear.py + ''' + def __init__(self, in_features, out_features, bias=True): + super(SimpleLinear, self).__init__() + self.in_features = in_features + self.out_features = out_features + self.weight = nn.Parameter(torch.Tensor(out_features, in_features)) + if bias: + self.bias = nn.Parameter(torch.Tensor(out_features)) + else: + self.register_parameter('bias', None) + self.reset_parameters() + + def reset_parameters(self): + nn.init.kaiming_uniform_(self.weight, nonlinearity='linear') + nn.init.constant_(self.bias, 0) + + def forward(self, input): + return {'logits': F.linear(input, self.weight, self.bias)} + + +class CosineLinear(nn.Module): + def __init__(self, in_features, out_features, nb_proxy=1, to_reduce=False, sigma=True): + super(CosineLinear, self).__init__() + self.in_features = in_features + self.out_features = out_features * nb_proxy + self.nb_proxy = nb_proxy + self.to_reduce = to_reduce + self.weight = nn.Parameter(torch.Tensor(self.out_features, in_features)) + if sigma: + self.sigma = nn.Parameter(torch.Tensor(1)) + else: + self.register_parameter('sigma', None) + self.reset_parameters() + + def reset_parameters(self): + stdv = 1. / math.sqrt(self.weight.size(1)) + self.weight.data.uniform_(-stdv, stdv) + if self.sigma is not None: + self.sigma.data.fill_(1) + + def forward(self, input): + out = F.linear(F.normalize(input, p=2, dim=1), F.normalize(self.weight, p=2, dim=1)) + + if self.to_reduce: + # Reduce_proxy + out = reduce_proxies(out, self.nb_proxy) + + if self.sigma is not None: + out = self.sigma * out + + return {'logits': out} + + +class SplitCosineLinear(nn.Module): + def __init__(self, in_features, out_features1, out_features2, nb_proxy=1, sigma=True): + super(SplitCosineLinear, self).__init__() + self.in_features = in_features + self.out_features = (out_features1 + out_features2) * nb_proxy + self.nb_proxy = nb_proxy + self.fc1 = CosineLinear(in_features, out_features1, nb_proxy, False, False) + self.fc2 = CosineLinear(in_features, out_features2, nb_proxy, False, False) + if sigma: + self.sigma = nn.Parameter(torch.Tensor(1)) + self.sigma.data.fill_(1) + else: + self.register_parameter('sigma', None) + + def forward(self, x): + out1 = self.fc1(x) + out2 = self.fc2(x) + + out = torch.cat((out1['logits'], out2['logits']), dim=1) # concatenate along the channel + + # Reduce_proxy + out = reduce_proxies(out, self.nb_proxy) + + if self.sigma is not None: + out = self.sigma * out + + return { + 'old_scores': reduce_proxies(out1['logits'], self.nb_proxy), + 'new_scores': reduce_proxies(out2['logits'], self.nb_proxy), + 'logits': out + } + + +class AnalyticLinear(torch.nn.Linear, metaclass=ABCMeta): + """ + Abstract linear module for the analytic continual learning [1-3]. + + This implementation refers to the official implementation https://github.com/ZHUANGHP/Analytic-continual-learning. + + References: + [1] Zhuang, Huiping, et al. + "ACIL: Analytic class-incremental learning with absolute memorization and privacy protection." + Advances in Neural Information Processing Systems 35 (2022): 11602-11614. + [2] Zhuang, Huiping, et al. + "GKEAL: Gaussian Kernel Embedded Analytic Learning for Few-Shot Class Incremental Task." + Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023. + [3] Zhuang, Huiping, et al. + "DS-AL: A Dual-Stream Analytic Learning for Exemplar-Free Class-Incremental Learning." + Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 38. No. 15. 2024. + """ + + def __init__( + self, + in_features: int, + gamma: float = 1e-1, + bias: bool = False, + device: Optional[Union[torch.device, str, int]] = None, + dtype=torch.double, + ) -> None: + super(torch.nn.Linear, self).__init__() # Skip the Linear class + factory_kwargs = {"device": device, "dtype": dtype} + self.gamma: float = gamma + self.bias: bool = bias + self.dtype = dtype + + # Linear Layer + if bias: + in_features += 1 + weight = torch.zeros((in_features, 0), **factory_kwargs) + self.register_buffer("weight", weight) + + @torch.no_grad() + def forward(self, X: torch.Tensor) -> Dict[str, torch.Tensor]: + X = X.to(self.weight) + if self.bias: + X = torch.cat((X, torch.ones(X.shape[0], 1).to(X)), dim=-1) + return {"logits": X @ self.weight} + + @property + def in_features(self) -> int: + if self.bias: + return self.weight.shape[0] - 1 + return self.weight.shape[0] + + @property + def out_features(self) -> int: + return self.weight.shape[1] + + def reset_parameters(self) -> None: + # Following the equation (4) of ACIL, self.weight is set to \hat{W}_{FCN}^{-1} + self.weight = torch.zeros((self.weight.shape[0], 0)).to(self.weight) + + @abstractmethod + def fit(self, X: torch.Tensor, Y: torch.Tensor) -> None: + raise NotImplementedError() + + def after_task(self) -> None: + assert torch.isfinite(self.weight).all(), ( + "Pay attention to the numerical stability! " + "A possible solution is to increase the value of gamma. " + "Setting self.dtype=torch.double also helps." + ) + + +class RecursiveLinear(AnalyticLinear): + """ + Recursive analytic linear (ridge regression) modules for the analytic continual learning [1-3]. + + This implementation refers to the official implementation https://github.com/ZHUANGHP/Analytic-continual-learning. + + References: + [1] Zhuang, Huiping, et al. + "ACIL: Analytic class-incremental learning with absolute memorization and privacy protection." + Advances in Neural Information Processing Systems 35 (2022): 11602-11614. + [2] Zhuang, Huiping, et al. + "GKEAL: Gaussian Kernel Embedded Analytic Learning for Few-Shot Class Incremental Task." + Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023. + [3] Zhuang, Huiping, et al. + "DS-AL: A Dual-Stream Analytic Learning for Exemplar-Free Class-Incremental Learning." + Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 38. No. 15. 2024. + """ + + def __init__( + self, + in_features: int, + gamma: float = 1e-1, + bias: bool = False, + device: Optional[Union[torch.device, str, int]] = None, + dtype=torch.double, + ) -> None: + super().__init__(in_features, gamma, bias, device, dtype) + factory_kwargs = {"device": device, "dtype": dtype} + + # Regularized Feature Autocorrelation Matrix (RFAuM) + self.R: torch.Tensor + R = torch.eye(self.weight.shape[0], **factory_kwargs) / self.gamma + self.register_buffer("R", R) + + def update_fc(self, nb_classes: int) -> None: + increment_size = nb_classes - self.out_features + assert increment_size >= 0, "The number of classes should be increasing." + tail = torch.zeros((self.weight.shape[0], increment_size)).to(self.weight) + self.weight = torch.cat((self.weight, tail), dim=1) + + @torch.no_grad() + def fit(self, X: torch.Tensor, Y: torch.Tensor) -> None: + """The core code of the ACIL [1]. + This implementation, which is different but equivalent to the equations shown in the paper, + which supports mini-batch learning. + """ + X, Y = X.to(self.weight), Y.to(self.weight) + if self.bias: + X = torch.cat((X, torch.ones(X.shape[0], 1).to(X)), dim=-1) + + # ACIL + # Please update your PyTorch & CUDA if the `cusolver error` occurs. + # If you insist on using this version, doing the `torch.inverse` on CPUs might help. + # >>> K_inv = torch.eye(X.shape[0]).to(X) + X @ self.R @ X.T + # >>> K = torch.inverse(K_inv.cpu()).to(self.weight.device) + K = torch.inverse(torch.eye(X.shape[0]).to(X) + X @ self.R @ X.T) + # Equation (10) of ACIL + self.R -= self.R @ X.T @ K @ X @ self.R + # Equation (9) of ACIL + self.weight += self.R @ X.T @ (Y - X @ self.weight) + + +def reduce_proxies(out, nb_proxy): + if nb_proxy == 1: + return out + bs = out.shape[0] + nb_classes = out.shape[1] / nb_proxy + assert nb_classes.is_integer(), 'Shape error' + nb_classes = int(nb_classes) + + simi_per_class = out.view(bs, nb_classes, nb_proxy) + attentions = F.softmax(simi_per_class, dim=-1) + + return (attentions * simi_per_class).sum(-1) + + +''' +class CosineLinear(nn.Module): + def __init__(self, in_features, out_features, sigma=True): + super(CosineLinear, self).__init__() + self.in_features = in_features + self.out_features = out_features + self.weight = nn.Parameter(torch.Tensor(out_features, in_features)) + if sigma: + self.sigma = nn.Parameter(torch.Tensor(1)) + else: + self.register_parameter('sigma', None) + self.reset_parameters() + + def reset_parameters(self): + stdv = 1. / math.sqrt(self.weight.size(1)) + self.weight.data.uniform_(-stdv, stdv) + if self.sigma is not None: + self.sigma.data.fill_(1) + + def forward(self, input): + out = F.linear(F.normalize(input, p=2, dim=1), F.normalize(self.weight, p=2, dim=1)) + if self.sigma is not None: + out = self.sigma * out + return {'logits': out} + + +class SplitCosineLinear(nn.Module): + def __init__(self, in_features, out_features1, out_features2, sigma=True): + super(SplitCosineLinear, self).__init__() + self.in_features = in_features + self.out_features = out_features1 + out_features2 + self.fc1 = CosineLinear(in_features, out_features1, False) + self.fc2 = CosineLinear(in_features, out_features2, False) + if sigma: + self.sigma = nn.Parameter(torch.Tensor(1)) + self.sigma.data.fill_(1) + else: + self.register_parameter('sigma', None) + + def forward(self, x): + out1 = self.fc1(x) + out2 = self.fc2(x) + + out = torch.cat((out1['logits'], out2['logits']), dim=1) # concatenate along the channel + if self.sigma is not None: + out = self.sigma * out + + return { + 'old_scores': out1['logits'], + 'new_scores': out2['logits'], + 'logits': out + } +''' diff --git a/convs/memo_cifar_resnet.py b/convs/memo_cifar_resnet.py new file mode 100644 index 0000000000000000000000000000000000000000..b9116f9c15ba4b093b3f3b85d7264c458a9ec04f --- /dev/null +++ b/convs/memo_cifar_resnet.py @@ -0,0 +1,169 @@ +''' +For MEMO implementations of CIFAR-ResNet +Reference: +https://github.com/khurramjaved96/incremental-learning/blob/autoencoders/model/resnet32.py +''' +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +class DownsampleA(nn.Module): + def __init__(self, nIn, nOut, stride): + super(DownsampleA, self).__init__() + assert stride == 2 + self.avg = nn.AvgPool2d(kernel_size=1, stride=stride) + + def forward(self, x): + x = self.avg(x) + return torch.cat((x, x.mul(0)), 1) + +class ResNetBasicblock(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(ResNetBasicblock, self).__init__() + + self.conv_a = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False) + self.bn_a = nn.BatchNorm2d(planes) + + self.conv_b = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False) + self.bn_b = nn.BatchNorm2d(planes) + + self.downsample = downsample + + def forward(self, x): + residual = x + + basicblock = self.conv_a(x) + basicblock = self.bn_a(basicblock) + basicblock = F.relu(basicblock, inplace=True) + + basicblock = self.conv_b(basicblock) + basicblock = self.bn_b(basicblock) + + if self.downsample is not None: + residual = self.downsample(x) + + return F.relu(residual + basicblock, inplace=True) + + + +class GeneralizedResNet_cifar(nn.Module): + def __init__(self, block, depth, channels=3): + super(GeneralizedResNet_cifar, self).__init__() + assert (depth - 2) % 6 == 0, 'depth should be one of 20, 32, 44, 56, 110' + layer_blocks = (depth - 2) // 6 + self.conv_1_3x3 = nn.Conv2d(channels, 16, kernel_size=3, stride=1, padding=1, bias=False) + self.bn_1 = nn.BatchNorm2d(16) + + self.inplanes = 16 + self.stage_1 = self._make_layer(block, 16, layer_blocks, 1) + self.stage_2 = self._make_layer(block, 32, layer_blocks, 2) + + self.out_dim = 64 * block.expansion + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2. / n)) + # m.bias.data.zero_() + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + elif isinstance(m, nn.Linear): + nn.init.kaiming_normal_(m.weight) + m.bias.data.zero_() + + def _make_layer(self, block, planes, blocks, stride=1): + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = DownsampleA(self.inplanes, planes * block.expansion, stride) + + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample)) + self.inplanes = planes * block.expansion + for i in range(1, blocks): + layers.append(block(self.inplanes, planes)) + + return nn.Sequential(*layers) + + def forward(self, x): + x = self.conv_1_3x3(x) # [bs, 16, 32, 32] + x = F.relu(self.bn_1(x), inplace=True) + + x_1 = self.stage_1(x) # [bs, 16, 32, 32] + x_2 = self.stage_2(x_1) # [bs, 32, 16, 16] + return x_2 + +class SpecializedResNet_cifar(nn.Module): + def __init__(self, block, depth, inplanes=32, feature_dim=64): + super(SpecializedResNet_cifar, self).__init__() + self.inplanes = inplanes + self.feature_dim = feature_dim + layer_blocks = (depth - 2) // 6 + self.final_stage = self._make_layer(block, 64, layer_blocks, 2) + self.avgpool = nn.AvgPool2d(8) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2. / n)) + # m.bias.data.zero_() + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + elif isinstance(m, nn.Linear): + nn.init.kaiming_normal_(m.weight) + m.bias.data.zero_() + + def _make_layer(self, block, planes, blocks, stride=2): + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = DownsampleA(self.inplanes, planes * block.expansion, stride) + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample)) + self.inplanes = planes * block.expansion + for i in range(1, blocks): + layers.append(block(self.inplanes, planes)) + return nn.Sequential(*layers) + + def forward(self, base_feature_map): + final_feature_map = self.final_stage(base_feature_map) + pooled = self.avgpool(final_feature_map) + features = pooled.view(pooled.size(0), -1) #bs x 64 + return features + +#For cifar & MEMO +def get_resnet8_a2fc(): + basenet = GeneralizedResNet_cifar(ResNetBasicblock,8) + adaptivenet = SpecializedResNet_cifar(ResNetBasicblock,8) + return basenet,adaptivenet + +def get_resnet14_a2fc(): + basenet = GeneralizedResNet_cifar(ResNetBasicblock,14) + adaptivenet = SpecializedResNet_cifar(ResNetBasicblock,14) + return basenet,adaptivenet + +def get_resnet20_a2fc(): + basenet = GeneralizedResNet_cifar(ResNetBasicblock,20) + adaptivenet = SpecializedResNet_cifar(ResNetBasicblock,20) + return basenet,adaptivenet + +def get_resnet26_a2fc(): + basenet = GeneralizedResNet_cifar(ResNetBasicblock,26) + adaptivenet = SpecializedResNet_cifar(ResNetBasicblock,26) + return basenet,adaptivenet + +def get_resnet32_a2fc(): + basenet = GeneralizedResNet_cifar(ResNetBasicblock,32) + adaptivenet = SpecializedResNet_cifar(ResNetBasicblock,32) + return basenet,adaptivenet + +def get_resnet50_a2fc(): + basenet = GeneralizedResNet_cifar(ResNetBasicblock,32) + adaptivenet = SpecializedResNet_cifar(ResNetBasicblock,32) + return basenet,adaptivenet + + diff --git a/convs/memo_resnet.py b/convs/memo_resnet.py new file mode 100644 index 0000000000000000000000000000000000000000..507b0bd60e35528b93a5820af373b1a1a06d2aff --- /dev/null +++ b/convs/memo_resnet.py @@ -0,0 +1,322 @@ +''' +For MEMO implementations of ImageNet-ResNet +Reference: +https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py +''' +import torch +import torch.nn as nn +try: + from torchvision.models.utils import load_state_dict_from_url +except: + from torch.hub import load_state_dict_from_url + +__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', + 'resnet152', 'resnext50_32x4d', 'resnext101_32x8d', + 'wide_resnet50_2', 'wide_resnet101_2'] + + +model_urls = { + 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', + 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth', + 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth', + 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth', + 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth', + 'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth', + 'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth', + 'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth', + 'wide_resnet101_2': 'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth', +} + + +def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=dilation, groups=groups, bias=False, dilation=dilation) + + +def conv1x1(in_planes, out_planes, stride=1): + """1x1 convolution""" + return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) + + +class BasicBlock(nn.Module): + expansion = 1 + __constants__ = ['downsample'] + + def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, + base_width=64, dilation=1, norm_layer=None): + super(BasicBlock, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + if groups != 1 or base_width != 64: + raise ValueError('BasicBlock only supports groups=1 and base_width=64') + if dilation > 1: + raise NotImplementedError("Dilation > 1 not supported in BasicBlock") + # Both self.conv1 and self.downsample layers downsample the input when stride != 1 + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = norm_layer(planes) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = norm_layer(planes) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + identity = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.relu(out) + + return out + + +class Bottleneck(nn.Module): + expansion = 4 + __constants__ = ['downsample'] + + def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, + base_width=64, dilation=1, norm_layer=None): + super(Bottleneck, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + width = int(planes * (base_width / 64.)) * groups + # Both self.conv2 and self.downsample layers downsample the input when stride != 1 + self.conv1 = conv1x1(inplanes, width) + self.bn1 = norm_layer(width) + self.conv2 = conv3x3(width, width, stride, groups, dilation) + self.bn2 = norm_layer(width) + self.conv3 = conv1x1(width, planes * self.expansion) + self.bn3 = norm_layer(planes * self.expansion) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + identity = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.relu(out) + + return out + + +class GeneralizedResNet_imagenet(nn.Module): + def __init__(self, block, layers, num_classes=1000, zero_init_residual=False, + groups=1, width_per_group=64, replace_stride_with_dilation=None, + norm_layer=None): + super(GeneralizedResNet_imagenet, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + self._norm_layer = norm_layer + + self.inplanes = 64 + self.dilation = 1 + if replace_stride_with_dilation is None: + # each element in the tuple indicates if we should replace + # the 2x2 stride with a dilated convolution instead + replace_stride_with_dilation = [False, False, False] + if len(replace_stride_with_dilation) != 3: + raise ValueError("replace_stride_with_dilation should be None " + "or a 3-element tuple, got {}".format(replace_stride_with_dilation)) + self.groups = groups + self.base_width = width_per_group + self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, # stride=2 -> stride=1 for cifar + bias=False) + self.bn1 = norm_layer(self.inplanes) + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) # Removed in _forward_impl for cifar + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer(block, 128, layers[1], stride=2, + dilate=replace_stride_with_dilation[0]) + self.layer3 = self._make_layer(block, 256, layers[2], stride=2, + dilate=replace_stride_with_dilation[1]) + self.out_dim = 512 * block.expansion + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + # Zero-initialize the last BN in each residual branch, + # so that the residual branch starts with zeros, and each residual block behaves like an identity. + # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677 + if zero_init_residual: + for m in self.modules(): + if isinstance(m, Bottleneck): + nn.init.constant_(m.bn3.weight, 0) + elif isinstance(m, BasicBlock): + nn.init.constant_(m.bn2.weight, 0) + + def _make_layer(self, block, planes, blocks, stride=1, dilate=False): + norm_layer = self._norm_layer + downsample = None + previous_dilation = self.dilation + if dilate: + self.dilation *= stride + stride = 1 + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + conv1x1(self.inplanes, planes * block.expansion, stride), + norm_layer(planes * block.expansion), + ) + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample, self.groups, + self.base_width, previous_dilation, norm_layer)) + self.inplanes = planes * block.expansion + for _ in range(1, blocks): + layers.append(block(self.inplanes, planes, groups=self.groups, + base_width=self.base_width, dilation=self.dilation, + norm_layer=norm_layer)) + return nn.Sequential(*layers) + def _forward_impl(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + x_1 = self.layer1(x) + x_2 = self.layer2(x_1) + x_3 = self.layer3(x_2) + return x_3 + + def forward(self, x): + return self._forward_impl(x) + +class SpecializedResNet_imagenet(nn.Module): + def __init__(self, block, layers, num_classes=1000, zero_init_residual=False, + groups=1, width_per_group=64, replace_stride_with_dilation=None, + norm_layer=None): + super(SpecializedResNet_imagenet, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + self._norm_layer = norm_layer + self.feature_dim = 512 * block.expansion + self.inplanes = 256 * block.expansion + self.dilation = 1 + if replace_stride_with_dilation is None: + replace_stride_with_dilation = [False, False, False] + if len(replace_stride_with_dilation) != 3: + raise ValueError("replace_stride_with_dilation should be None " + "or a 3-element tuple, got {}".format(replace_stride_with_dilation)) + self.groups = groups + self.base_width = width_per_group + + self.layer4 = self._make_layer(block, 512, layers[3], stride=2, + dilate=replace_stride_with_dilation[2]) + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + self.out_dim = 512 * block.expansion + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + def _make_layer(self, block, planes, blocks, stride=1, dilate=False): + norm_layer = self._norm_layer + downsample = None + previous_dilation = self.dilation + if dilate: + self.dilation *= stride + stride = 1 + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + conv1x1(self.inplanes, planes * block.expansion, stride), + norm_layer(planes * block.expansion), + ) + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample, self.groups, + self.base_width, previous_dilation, norm_layer)) + self.inplanes = planes * block.expansion + for _ in range(1, blocks): + layers.append(block(self.inplanes, planes, groups=self.groups, + base_width=self.base_width, dilation=self.dilation, + norm_layer=norm_layer)) + + return nn.Sequential(*layers) + + def forward(self,x): + x_4 = self.layer4(x) # [bs, 512, 4, 4] + pooled = self.avgpool(x_4) # [bs, 512, 1, 1] + features = torch.flatten(pooled, 1) # [bs, 512] + return features + +def get_resnet10_imagenet(): + basenet = GeneralizedResNet_imagenet(BasicBlock,[1, 1, 1, 1]) + adaptivenet = SpecializedResNet_imagenet(BasicBlock, [1, 1, 1, 1]) + return basenet,adaptivenet + +def get_resnet18_imagenet(): + basenet = GeneralizedResNet_imagenet(BasicBlock,[2, 2, 2, 2]) + adaptivenet = SpecializedResNet_imagenet(BasicBlock, [2, 2, 2, 2]) + return basenet,adaptivenet + +def get_resnet26_imagenet(): + basenet = GeneralizedResNet_imagenet(Bottleneck,[2, 2, 2, 2]) + adaptivenet = SpecializedResNet_imagenet(Bottleneck, [2, 2, 2, 2]) + return basenet,adaptivenet + +def get_resnet34_imagenet(): + basenet = GeneralizedResNet_imagenet(BasicBlock,[3, 4, 6, 3]) + adaptivenet = SpecializedResNet_imagenet(BasicBlock, [3, 4, 6, 3]) + return basenet,adaptivenet + +def get_resnet50_imagenet(): + basenet = GeneralizedResNet_imagenet(Bottleneck,[3, 4, 6, 3]) + adaptivenet = SpecializedResNet_imagenet(Bottleneck, [3, 4, 6, 3]) + return basenet,adaptivenet + + +if __name__ == '__main__': + model2imagenet = 3*224*224 + + a, b = get_resnet10_imagenet() + _base = sum(p.numel() for p in a.parameters()) + _adap = sum(p.numel() for p in b.parameters()) + print(f"resnet10 #params:{_base+_adap}") + + a, b = get_resnet18_imagenet() + _base = sum(p.numel() for p in a.parameters()) + _adap = sum(p.numel() for p in b.parameters()) + print(f"resnet18 #params:{_base+_adap}") + + a, b = get_resnet26_imagenet() + _base = sum(p.numel() for p in a.parameters()) + _adap = sum(p.numel() for p in b.parameters()) + print(f"resnet26 #params:{_base+_adap}") + + a, b = get_resnet34_imagenet() + _base = sum(p.numel() for p in a.parameters()) + _adap = sum(p.numel() for p in b.parameters()) + print(f"resnet34 #params:{_base+_adap}") + + a, b = get_resnet50_imagenet() + _base = sum(p.numel() for p in a.parameters()) + _adap = sum(p.numel() for p in b.parameters()) + print(f"resnet50 #params:{_base+_adap}") \ No newline at end of file diff --git a/convs/modified_represnet.py b/convs/modified_represnet.py new file mode 100644 index 0000000000000000000000000000000000000000..99d2854d077c9023d1bde69a3cde77168caf4b61 --- /dev/null +++ b/convs/modified_represnet.py @@ -0,0 +1,177 @@ +import torch +import torch.nn as nn +import math +import torch.utils.model_zoo as model_zoo +import torch.nn.functional as F + +__all__ = ['ResNet', 'resnet18_rep', 'resnet34_rep' ] + + +def conv3x3(in_planes, out_planes, stride=1): + "3x3 convolution with padding" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=1, bias=True) + + +def conv1x1(in_planes, out_planes, stride=1): + """1x1 convolution""" + return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=True) + +class conv_block(nn.Module): + + def __init__(self, in_planes, planes, mode, stride=1): + super(conv_block, self).__init__() + self.conv = conv3x3(in_planes, planes, stride) + self.mode = mode + if mode == 'parallel_adapters': + self.adapter = conv1x1(in_planes, planes, stride) + + + def re_init_conv(self): + nn.init.kaiming_normal_(self.adapter.weight, mode='fan_out', nonlinearity='relu') + return + def forward(self, x): + y = self.conv(x) + if self.mode == 'parallel_adapters': + y = y + self.adapter(x) + + return y + + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, mode, stride=1, downsample=None): + super(BasicBlock, self).__init__() + self.conv1 = conv_block(inplanes, planes, mode, stride) + self.norm1 = nn.BatchNorm2d(planes) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv_block(planes, planes, mode) + self.norm2 = nn.BatchNorm2d(planes) + self.mode = mode + + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + out = self.conv1(x) + out = self.norm1(out) + out = self.relu(out) + out = self.conv2(out) + out = self.norm2(out) + if self.downsample is not None: + residual = self.downsample(x) + out += residual + out = self.relu(out) + return out + + +class ResNet(nn.Module): + + def __init__(self, block, layers, num_classes=100, args = None): + self.inplanes = 64 + super(ResNet, self).__init__() + assert args is not None + self.mode = args["mode"] + + if 'cifar' in args["dataset"]: + self.conv1 = nn.Sequential(nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False), + nn.BatchNorm2d(self.inplanes), nn.ReLU(inplace=True)) + print("use cifar") + elif 'imagenet' in args["dataset"]: + if args["init_cls"] == args["increment"]: + self.conv1 = nn.Sequential( + nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False), + nn.BatchNorm2d(self.inplanes), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=3, stride=2, padding=1), + ) + else: + # Following PODNET implmentation + self.conv1 = nn.Sequential( + nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False), + nn.BatchNorm2d(self.inplanes), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=3, stride=2, padding=1), + ) + + + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer(block, 128, layers[1], stride=2) + self.layer3 = self._make_layer(block, 256, layers[2], stride=2) + self.layer4 = self._make_layer(block, 512, layers[3], stride=2) + self.feature = nn.AvgPool2d(4, stride=1) + self.out_dim = 512 + + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + def _make_layer(self, block, planes, blocks, stride=1): + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + nn.Conv2d(self.inplanes, planes * block.expansion, + kernel_size=1, stride=stride, bias=True), + ) + layers = [] + layers.append(block(self.inplanes, planes, self.mode, stride, downsample)) + self.inplanes = planes * block.expansion + for i in range(1, blocks): + layers.append(block(self.inplanes, planes, self.mode)) + + return nn.Sequential(*layers) + + def switch(self, mode='normal'): + for name, module in self.named_modules(): + if hasattr(module, 'mode'): + module.mode = mode + def re_init_params(self): + for name, module in self.named_modules(): + if hasattr(module, 're_init_conv'): + module.re_init_conv() + def forward(self, x): + x = self.conv1(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + dim = x.size()[-1] + pool = nn.AvgPool2d(dim, stride=1) + x = pool(x) + x = x.view(x.size(0), -1) + return {"features": x} + + +def resnet18_rep(pretrained=False, **kwargs): + """Constructs a ResNet-18 model. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + """ + model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) + if pretrained: + pretrained_state_dict = model_zoo.load_url(model_urls['resnet18']) + now_state_dict = model.state_dict() + now_state_dict.update(pretrained_state_dict) + model.load_state_dict(now_state_dict) + return model + + +def resnet34_rep(pretrained=False, **kwargs): + """Constructs a ResNet-34 model. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + """ + model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs) + if pretrained: + pretrained_state_dict = model_zoo.load_url(model_urls['resnet34']) + now_state_dict = model.state_dict() + now_state_dict.update(pretrained_state_dict) + model.load_state_dict(now_state_dict) + return model \ No newline at end of file diff --git a/convs/resnet.py b/convs/resnet.py new file mode 100644 index 0000000000000000000000000000000000000000..30ff240d25b61de847b33dbf3cc6d56039f8c986 --- /dev/null +++ b/convs/resnet.py @@ -0,0 +1,395 @@ +''' +Reference: +https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py +''' +import torch +import torch.nn as nn +try: + from torchvision.models.utils import load_state_dict_from_url +except: + from torch.hub import load_state_dict_from_url + +__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', + 'resnet152', 'resnext50_32x4d', 'resnext101_32x8d', + 'wide_resnet50_2', 'wide_resnet101_2'] + + +model_urls = { + 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', + 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth', + 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth', + 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth', + 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth', + 'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth', + 'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth', + 'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth', + 'wide_resnet101_2': 'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth', +} + + +def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=dilation, groups=groups, bias=False, dilation=dilation) + + +def conv1x1(in_planes, out_planes, stride=1): + """1x1 convolution""" + return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) + + +class BasicBlock(nn.Module): + expansion = 1 + __constants__ = ['downsample'] + + def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, + base_width=64, dilation=1, norm_layer=None): + super(BasicBlock, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + if groups != 1 or base_width != 64: + raise ValueError('BasicBlock only supports groups=1 and base_width=64') + if dilation > 1: + raise NotImplementedError("Dilation > 1 not supported in BasicBlock") + # Both self.conv1 and self.downsample layers downsample the input when stride != 1 + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = norm_layer(planes) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = norm_layer(planes) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + identity = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.relu(out) + + return out + + +class Bottleneck(nn.Module): + expansion = 4 + __constants__ = ['downsample'] + + def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, + base_width=64, dilation=1, norm_layer=None): + super(Bottleneck, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + width = int(planes * (base_width / 64.)) * groups + # Both self.conv2 and self.downsample layers downsample the input when stride != 1 + self.conv1 = conv1x1(inplanes, width) + self.bn1 = norm_layer(width) + self.conv2 = conv3x3(width, width, stride, groups, dilation) + self.bn2 = norm_layer(width) + self.conv3 = conv1x1(width, planes * self.expansion) + self.bn3 = norm_layer(planes * self.expansion) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + identity = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.relu(out) + + return out + + + + + +class ResNet(nn.Module): + + def __init__(self, block, layers, num_classes=1000, zero_init_residual=False, + groups=1, width_per_group=64, replace_stride_with_dilation=None, + norm_layer=None,args=None): + super(ResNet, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + self._norm_layer = norm_layer + + self.inplanes = 64 + self.dilation = 1 + if replace_stride_with_dilation is None: + # each element in the tuple indicates if we should replace + # the 2x2 stride with a dilated convolution instead + replace_stride_with_dilation = [False, False, False] + if len(replace_stride_with_dilation) != 3: + raise ValueError("replace_stride_with_dilation should be None " + "or a 3-element tuple, got {}".format(replace_stride_with_dilation)) + self.groups = groups + self.base_width = width_per_group + + assert args is not None, "you should pass args to resnet" + if 'cifar' in args["dataset"]: + if args["model_name"] == "memo": + self.conv1 = nn.Sequential( + nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False), + nn.BatchNorm2d(self.inplanes), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=3, stride=2, padding=1), + ) + else: + self.conv1 = nn.Sequential( + nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False), + nn.BatchNorm2d(self.inplanes), + nn.ReLU(inplace=True)) + elif 'imagenet' in args["dataset"]: + if args["init_cls"] == args["increment"]: + self.conv1 = nn.Sequential( + nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False), + nn.BatchNorm2d(self.inplanes), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=3, stride=2, padding=1), + ) + else: + self.conv1 = nn.Sequential( + nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False), + nn.BatchNorm2d(self.inplanes), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=3, stride=2, padding=1), + ) + + + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer(block, 128, layers[1], stride=2, + dilate=replace_stride_with_dilation[0]) + self.layer3 = self._make_layer(block, 256, layers[2], stride=2, + dilate=replace_stride_with_dilation[1]) + self.layer4 = self._make_layer(block, 512, layers[3], stride=2, + dilate=replace_stride_with_dilation[2]) + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + self.out_dim = 512 * block.expansion + # self.fc = nn.Linear(512 * block.expansion, num_classes) # Removed in _forward_impl + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + # Zero-initialize the last BN in each residual branch, + # so that the residual branch starts with zeros, and each residual block behaves like an identity. + # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677 + if zero_init_residual: + for m in self.modules(): + if isinstance(m, Bottleneck): + nn.init.constant_(m.bn3.weight, 0) + elif isinstance(m, BasicBlock): + nn.init.constant_(m.bn2.weight, 0) + + def _make_layer(self, block, planes, blocks, stride=1, dilate=False): + norm_layer = self._norm_layer + downsample = None + previous_dilation = self.dilation + if dilate: + self.dilation *= stride + stride = 1 + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + conv1x1(self.inplanes, planes * block.expansion, stride), + norm_layer(planes * block.expansion), + ) + + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample, self.groups, + self.base_width, previous_dilation, norm_layer)) + self.inplanes = planes * block.expansion + for _ in range(1, blocks): + layers.append(block(self.inplanes, planes, groups=self.groups, + base_width=self.base_width, dilation=self.dilation, + norm_layer=norm_layer)) + + return nn.Sequential(*layers) + + def _forward_impl(self, x): + # See note [TorchScript super()] + x = self.conv1(x) # [bs, 64, 32, 32] + + x_1 = self.layer1(x) # [bs, 128, 32, 32] + x_2 = self.layer2(x_1) # [bs, 256, 16, 16] + x_3 = self.layer3(x_2) # [bs, 512, 8, 8] + x_4 = self.layer4(x_3) # [bs, 512, 4, 4] + + pooled = self.avgpool(x_4) # [bs, 512, 1, 1] + features = torch.flatten(pooled, 1) # [bs, 512] + # x = self.fc(x) + + return { + 'fmaps': [x_1, x_2, x_3, x_4], + 'features': features + } + + def forward(self, x): + return self._forward_impl(x) + + @property + def last_conv(self): + if hasattr(self.layer4[-1], 'conv3'): + return self.layer4[-1].conv3 + else: + return self.layer4[-1].conv2 + + +def _resnet(arch, block, layers, pretrained, progress, **kwargs): + model = ResNet(block, layers, **kwargs) + if pretrained: + state_dict = load_state_dict_from_url(model_urls[arch], + progress=progress) + model.load_state_dict(state_dict) + return model + +def resnet10(pretrained=False, progress=True, **kwargs): + """ + For MEMO implementations of ResNet-10 + """ + return _resnet('resnet10', BasicBlock, [1, 1, 1, 1], pretrained, progress, + **kwargs) + +def resnet26(pretrained=False, progress=True, **kwargs): + """ + For MEMO implementations of ResNet-26 + """ + return _resnet('resnet26', Bottleneck, [2, 2, 2, 2], pretrained, progress, + **kwargs) + +def resnet18(pretrained=False, progress=True, **kwargs): + r"""ResNet-18 model from + `"Deep Residual Learning for Image Recognition" `_ + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress, + **kwargs) + + +def resnet34(pretrained=False, progress=True, **kwargs): + r"""ResNet-34 model from + `"Deep Residual Learning for Image Recognition" `_ + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress, + **kwargs) + + +def resnet50(pretrained=False, progress=True, **kwargs): + r"""ResNet-50 model from + `"Deep Residual Learning for Image Recognition" `_ + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress, + **kwargs) + + +def resnet101(pretrained=False, progress=True, **kwargs): + r"""ResNet-101 model from + `"Deep Residual Learning for Image Recognition" `_ + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress, + **kwargs) + + +def resnet152(pretrained=False, progress=True, **kwargs): + r"""ResNet-152 model from + `"Deep Residual Learning for Image Recognition" `_ + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _resnet('resnet152', Bottleneck, [3, 8, 36, 3], pretrained, progress, + **kwargs) + + +def resnext50_32x4d(pretrained=False, progress=True, **kwargs): + r"""ResNeXt-50 32x4d model from + `"Aggregated Residual Transformation for Deep Neural Networks" `_ + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + kwargs['groups'] = 32 + kwargs['width_per_group'] = 4 + return _resnet('resnext50_32x4d', Bottleneck, [3, 4, 6, 3], + pretrained, progress, **kwargs) + + +def resnext101_32x8d(pretrained=False, progress=True, **kwargs): + r"""ResNeXt-101 32x8d model from + `"Aggregated Residual Transformation for Deep Neural Networks" `_ + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + kwargs['groups'] = 32 + kwargs['width_per_group'] = 8 + return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3], + pretrained, progress, **kwargs) + + +def wide_resnet50_2(pretrained=False, progress=True, **kwargs): + r"""Wide ResNet-50-2 model from + `"Wide Residual Networks" `_ + The model is the same as ResNet except for the bottleneck number of channels + which is twice larger in every block. The number of channels in outer 1x1 + convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048 + channels, and in Wide ResNet-50-2 has 2048-1024-2048. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + kwargs['width_per_group'] = 64 * 2 + return _resnet('wide_resnet50_2', Bottleneck, [3, 4, 6, 3], + pretrained, progress, **kwargs) + + +def wide_resnet101_2(pretrained=False, progress=True, **kwargs): + r"""Wide ResNet-101-2 model from + `"Wide Residual Networks" `_ + The model is the same as ResNet except for the bottleneck number of channels + which is twice larger in every block. The number of channels in outer 1x1 + convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048 + channels, and in Wide ResNet-50-2 has 2048-1024-2048. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + kwargs['width_per_group'] = 64 * 2 + return _resnet('wide_resnet101_2', Bottleneck, [3, 4, 23, 3], + pretrained, progress, **kwargs) diff --git a/convs/resnet_cbam.py b/convs/resnet_cbam.py new file mode 100644 index 0000000000000000000000000000000000000000..dbf59f686e9a7eb64c2572d28f3995eb5eb9bc0f --- /dev/null +++ b/convs/resnet_cbam.py @@ -0,0 +1,267 @@ +import torch +import torch.nn as nn +import math +import torch.utils.model_zoo as model_zoo +import torch.nn.functional as F + +__all__ = ['ResNet', 'resnet18_cbam', 'resnet34_cbam', 'resnet50_cbam', 'resnet101_cbam', + 'resnet152_cbam'] + + +model_urls = { + 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', + 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth', + 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth', + 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth', + 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth', +} + + +def conv3x3(in_planes, out_planes, stride=1): + "3x3 convolution with padding" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=1, bias=False) + + +class ChannelAttention(nn.Module): + def __init__(self, in_planes, ratio=16): + super(ChannelAttention, self).__init__() + self.avg_pool = nn.AdaptiveAvgPool2d(1) + self.max_pool = nn.AdaptiveMaxPool2d(1) + + self.fc1 = nn.Conv2d(in_planes, in_planes // 16, 1, bias=False) + self.relu1 = nn.ReLU() + self.fc2 = nn.Conv2d(in_planes // 16, in_planes, 1, bias=False) + + self.sigmoid = nn.Sigmoid() + + def forward(self, x): + avg_out = self.fc2(self.relu1(self.fc1(self.avg_pool(x)))) + max_out = self.fc2(self.relu1(self.fc1(self.max_pool(x)))) + out = avg_out + max_out + return self.sigmoid(out) + + +class SpatialAttention(nn.Module): + def __init__(self, kernel_size=7): + super(SpatialAttention, self).__init__() + + assert kernel_size in (3, 7), 'kernel size must be 3 or 7' + padding = 3 if kernel_size == 7 else 1 + + self.conv1 = nn.Conv2d(2, 1, kernel_size, padding=padding, bias=False) + self.sigmoid = nn.Sigmoid() + + def forward(self, x): + avg_out = torch.mean(x, dim=1, keepdim=True) + max_out, _ = torch.max(x, dim=1, keepdim=True) + x = torch.cat([avg_out, max_out], dim=1) + x = self.conv1(x) + return self.sigmoid(x) + + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(BasicBlock, self).__init__() + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = nn.BatchNorm2d(planes) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = nn.BatchNorm2d(planes) + + self.ca = ChannelAttention(planes) + self.sa = SpatialAttention() + + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + out = self.conv2(out) + out = self.bn2(out) + if self.downsample is not None: + residual = self.downsample(x) + out += residual + out = self.relu(out) + return out + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(Bottleneck, self).__init__() + self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, + padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) + self.bn3 = nn.BatchNorm2d(planes * 4) + self.relu = nn.ReLU(inplace=True) + self.ca = ChannelAttention(planes * 4) + self.sa = SpatialAttention() + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + out = self.conv3(out) + out = self.bn3(out) + out = self.ca(out) * out + out = self.sa(out) * out + if self.downsample is not None: + residual = self.downsample(x) + out += residual + out = self.relu(out) + return out + + +class ResNet(nn.Module): + + def __init__(self, block, layers, num_classes=100, args=None): + self.inplanes = 64 + super(ResNet, self).__init__() + assert args is not None, "you should pass args to resnet" + if 'cifar' in args["dataset"]: + self.conv1 = nn.Sequential(nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False), + nn.BatchNorm2d(self.inplanes), nn.ReLU(inplace=True)) + elif 'imagenet' in args["dataset"]: + if args["init_cls"] == args["increment"]: + self.conv1 = nn.Sequential( + nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False), + nn.BatchNorm2d(self.inplanes), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=3, stride=2, padding=1), + ) + else: + self.conv1 = nn.Sequential( + nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False), + nn.BatchNorm2d(self.inplanes), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=3, stride=2, padding=1), + ) + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer(block, 128, layers[1], stride=2) + self.layer3 = self._make_layer(block, 256, layers[2], stride=2) + self.layer4 = self._make_layer(block, 512, layers[3], stride=2) + self.feature = nn.AvgPool2d(4, stride=1) + # self.fc = nn.Linear(512 * block.expansion, num_classes) + self.out_dim = 512 * block.expansion + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2. / n)) + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + + def _make_layer(self, block, planes, blocks, stride=1): + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + nn.Conv2d(self.inplanes, planes * block.expansion, + kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(planes * block.expansion), + ) + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample)) + self.inplanes = planes * block.expansion + for i in range(1, blocks): + layers.append(block(self.inplanes, planes)) + + return nn.Sequential(*layers) + + def forward(self, x): + x = self.conv1(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + dim = x.size()[-1] + pool = nn.AvgPool2d(dim, stride=1) + x = pool(x) + x = x.view(x.size(0), -1) + return {"features": x} + +def resnet18_cbam(pretrained=False, **kwargs): + """Constructs a ResNet-18 model. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + """ + model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) + if pretrained: + pretrained_state_dict = model_zoo.load_url(model_urls['resnet18']) + now_state_dict = model.state_dict() + now_state_dict.update(pretrained_state_dict) + model.load_state_dict(now_state_dict) + return model + + +def resnet34_cbam(pretrained=False, **kwargs): + """Constructs a ResNet-34 model. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + """ + model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs) + if pretrained: + pretrained_state_dict = model_zoo.load_url(model_urls['resnet34']) + now_state_dict = model.state_dict() + now_state_dict.update(pretrained_state_dict) + model.load_state_dict(now_state_dict) + return model + + +def resnet50_cbam(pretrained=False, **kwargs): + """Constructs a ResNet-50 model. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + """ + model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs) + if pretrained: + pretrained_state_dict = model_zoo.load_url(model_urls['resnet50']) + now_state_dict = model.state_dict() + now_state_dict.update(pretrained_state_dict) + model.load_state_dict(now_state_dict) + return model + + +def resnet101_cbam(pretrained=False, **kwargs): + """Constructs a ResNet-101 model. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + """ + model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs) + if pretrained: + pretrained_state_dict = model_zoo.load_url(model_urls['resnet101']) + now_state_dict = model.state_dict() + now_state_dict.update(pretrained_state_dict) + model.load_state_dict(now_state_dict) + return model + + +def resnet152_cbam(pretrained=False, **kwargs): + """Constructs a ResNet-152 model. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + """ + model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs) + if pretrained: + pretrained_state_dict = model_zoo.load_url(model_urls['resnet152']) + now_state_dict = model.state_dict() + now_state_dict.update(pretrained_state_dict) + model.load_state_dict(now_state_dict) + return model \ No newline at end of file diff --git a/convs/ucir_cifar_resnet.py b/convs/ucir_cifar_resnet.py new file mode 100644 index 0000000000000000000000000000000000000000..9e71b742f56925d3a717228a8450c43e59dca39f --- /dev/null +++ b/convs/ucir_cifar_resnet.py @@ -0,0 +1,204 @@ +''' +Reference: +https://github.com/khurramjaved96/incremental-learning/blob/autoencoders/model/resnet32.py +https://github.com/hshustc/CVPR19_Incremental_Learning/blob/master/cifar100-class-incremental/modified_resnet_cifar.py +''' +import torch +import torch.nn as nn +import torch.nn.functional as F +# from convs.modified_linear import CosineLinear + + +class DownsampleA(nn.Module): + def __init__(self, nIn, nOut, stride): + super(DownsampleA, self).__init__() + assert stride == 2 + self.avg = nn.AvgPool2d(kernel_size=1, stride=stride) + + def forward(self, x): + x = self.avg(x) + return torch.cat((x, x.mul(0)), 1) + + +class DownsampleB(nn.Module): + def __init__(self, nIn, nOut, stride): + super(DownsampleB, self).__init__() + self.conv = nn.Conv2d(nIn, nOut, kernel_size=1, stride=stride, padding=0, bias=False) + self.bn = nn.BatchNorm2d(nOut) + + def forward(self, x): + x = self.conv(x) + x = self.bn(x) + return x + + +class DownsampleC(nn.Module): + def __init__(self, nIn, nOut, stride): + super(DownsampleC, self).__init__() + assert stride != 1 or nIn != nOut + self.conv = nn.Conv2d(nIn, nOut, kernel_size=1, stride=stride, padding=0, bias=False) + + def forward(self, x): + x = self.conv(x) + return x + + +class DownsampleD(nn.Module): + def __init__(self, nIn, nOut, stride): + super(DownsampleD, self).__init__() + assert stride == 2 + self.conv = nn.Conv2d(nIn, nOut, kernel_size=2, stride=stride, padding=0, bias=False) + self.bn = nn.BatchNorm2d(nOut) + + def forward(self, x): + x = self.conv(x) + x = self.bn(x) + return x + + +class ResNetBasicblock(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, downsample=None, last=False): + super(ResNetBasicblock, self).__init__() + + self.conv_a = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False) + self.bn_a = nn.BatchNorm2d(planes) + + self.conv_b = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False) + self.bn_b = nn.BatchNorm2d(planes) + + self.downsample = downsample + self.last = last + + def forward(self, x): + residual = x + + basicblock = self.conv_a(x) + basicblock = self.bn_a(basicblock) + basicblock = F.relu(basicblock, inplace=True) + + basicblock = self.conv_b(basicblock) + basicblock = self.bn_b(basicblock) + + if self.downsample is not None: + residual = self.downsample(x) + + out = residual + basicblock + if not self.last: + out = F.relu(out, inplace=True) + + return out + + +class CifarResNet(nn.Module): + """ + ResNet optimized for the Cifar Dataset, as specified in + https://arxiv.org/abs/1512.03385.pdf + """ + + def __init__(self, block, depth, channels=3): + super(CifarResNet, self).__init__() + + # Model type specifies number of layers for CIFAR-10 and CIFAR-100 model + assert (depth - 2) % 6 == 0, 'depth should be one of 20, 32, 44, 56, 110' + layer_blocks = (depth - 2) // 6 + + self.conv_1_3x3 = nn.Conv2d(channels, 16, kernel_size=3, stride=1, padding=1, bias=False) + self.bn_1 = nn.BatchNorm2d(16) + + self.inplanes = 16 + self.stage_1 = self._make_layer(block, 16, layer_blocks, 1) + self.stage_2 = self._make_layer(block, 32, layer_blocks, 2) + self.stage_3 = self._make_layer(block, 64, layer_blocks, 2, last_phase=True) + self.avgpool = nn.AvgPool2d(8) + self.out_dim = 64 * block.expansion + # self.fc = CosineLinear(64*block.expansion, 10) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + def _make_layer(self, block, planes, blocks, stride=1, last_phase=False): + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = DownsampleB(self.inplanes, planes * block.expansion, stride) # DownsampleA => DownsampleB + + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample)) + self.inplanes = planes * block.expansion + if last_phase: + for i in range(1, blocks-1): + layers.append(block(self.inplanes, planes)) + layers.append(block(self.inplanes, planes, last=True)) + else: + for i in range(1, blocks): + layers.append(block(self.inplanes, planes)) + + return nn.Sequential(*layers) + + def forward(self, x): + x = self.conv_1_3x3(x) # [bs, 16, 32, 32] + x = F.relu(self.bn_1(x), inplace=True) + + x_1 = self.stage_1(x) # [bs, 16, 32, 32] + x_2 = self.stage_2(x_1) # [bs, 32, 16, 16] + x_3 = self.stage_3(x_2) # [bs, 64, 8, 8] + + pooled = self.avgpool(x_3) # [bs, 64, 1, 1] + features = pooled.view(pooled.size(0), -1) # [bs, 64] + # out = self.fc(vector) + + return { + 'fmaps': [x_1, x_2, x_3], + 'features': features + } + + @property + def last_conv(self): + return self.stage_3[-1].conv_b + + +def resnet20mnist(): + """Constructs a ResNet-20 model for MNIST.""" + model = CifarResNet(ResNetBasicblock, 20, 1) + return model + + +def resnet32mnist(): + """Constructs a ResNet-32 model for MNIST.""" + model = CifarResNet(ResNetBasicblock, 32, 1) + return model + + +def resnet20(): + """Constructs a ResNet-20 model for CIFAR-10.""" + model = CifarResNet(ResNetBasicblock, 20) + return model + + +def resnet32(): + """Constructs a ResNet-32 model for CIFAR-10.""" + model = CifarResNet(ResNetBasicblock, 32) + return model + + +def resnet44(): + """Constructs a ResNet-44 model for CIFAR-10.""" + model = CifarResNet(ResNetBasicblock, 44) + return model + + +def resnet56(): + """Constructs a ResNet-56 model for CIFAR-10.""" + model = CifarResNet(ResNetBasicblock, 56) + return model + + +def resnet110(): + """Constructs a ResNet-110 model for CIFAR-10.""" + model = CifarResNet(ResNetBasicblock, 110) + return model diff --git a/convs/ucir_resnet.py b/convs/ucir_resnet.py new file mode 100644 index 0000000000000000000000000000000000000000..14d424590eb5fbedae9f41e8693b3d3d6eb1fd19 --- /dev/null +++ b/convs/ucir_resnet.py @@ -0,0 +1,299 @@ +''' +Reference: +https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py +''' +import torch +import torch.nn as nn +try: + from torchvision.models.utils import load_state_dict_from_url +except: + from torch.hub import load_state_dict_from_url + +__all__ = ['resnet50'] + + +model_urls = { + 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', + 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth', + 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth', + 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth', + 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth', + 'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth', + 'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth', + 'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth', + 'wide_resnet101_2': 'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth', +} + + +def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=dilation, groups=groups, bias=False, dilation=dilation) + + +def conv1x1(in_planes, out_planes, stride=1): + """1x1 convolution""" + return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) + + +class BasicBlock(nn.Module): + expansion = 1 + __constants__ = ['downsample'] + + def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, + base_width=64, dilation=1, norm_layer=None, last=False): + super(BasicBlock, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + if groups != 1 or base_width != 64: + raise ValueError('BasicBlock only supports groups=1 and base_width=64') + if dilation > 1: + raise NotImplementedError("Dilation > 1 not supported in BasicBlock") + # Both self.conv1 and self.downsample layers downsample the input when stride != 1 + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = norm_layer(planes) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = norm_layer(planes) + self.downsample = downsample + self.stride = stride + self.last = last + + def forward(self, x): + identity = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + if not self.last: + out = self.relu(out) + + return out + + +class Bottleneck(nn.Module): + expansion = 4 + __constants__ = ['downsample'] + + def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, + base_width=64, dilation=1, norm_layer=None, last=False): + super(Bottleneck, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + width = int(planes * (base_width / 64.)) * groups + # Both self.conv2 and self.downsample layers downsample the input when stride != 1 + self.conv1 = conv1x1(inplanes, width) + self.bn1 = norm_layer(width) + self.conv2 = conv3x3(width, width, stride, groups, dilation) + self.bn2 = norm_layer(width) + self.conv3 = conv1x1(width, planes * self.expansion) + self.bn3 = norm_layer(planes * self.expansion) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + self.last = last + + def forward(self, x): + identity = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + if not self.last: + out = self.relu(out) + + return out + + +class ResNet(nn.Module): + + def __init__(self, block, layers, num_classes=1000, zero_init_residual=False, + groups=1, width_per_group=64, replace_stride_with_dilation=None, + norm_layer=None, args=None): + super(ResNet, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + self._norm_layer = norm_layer + + self.inplanes = 64 + self.dilation = 1 + if replace_stride_with_dilation is None: + # each element in the tuple indicates if we should replace + # the 2x2 stride with a dilated convolution instead + replace_stride_with_dilation = [False, False, False] + if len(replace_stride_with_dilation) != 3: + raise ValueError("replace_stride_with_dilation should be None " + "or a 3-element tuple, got {}".format(replace_stride_with_dilation)) + self.groups = groups + self.base_width = width_per_group + + assert args is not None, "you should pass args to resnet" + if 'cifar' in args["dataset"]: + self.conv1 = nn.Sequential(nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False), + nn.BatchNorm2d(self.inplanes), nn.ReLU(inplace=True)) + elif 'imagenet' in args["dataset"]: + if args["init_cls"] == args["increment"]: + self.conv1 = nn.Sequential( + nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False), + nn.BatchNorm2d(self.inplanes), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=3, stride=2, padding=1), + ) + else: + self.conv1 = nn.Sequential( + nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False), + nn.BatchNorm2d(self.inplanes), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=3, stride=2, padding=1), + ) + + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer(block, 128, layers[1], stride=2, + dilate=replace_stride_with_dilation[0]) + self.layer3 = self._make_layer(block, 256, layers[2], stride=2, + dilate=replace_stride_with_dilation[1]) + self.layer4 = self._make_layer(block, 512, layers[3], stride=2, + dilate=replace_stride_with_dilation[2], last_phase=True) + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + self.out_dim = 512 * block.expansion + self.fc = nn.Linear(512 * block.expansion, num_classes) # Removed in _forward_impl + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + # Zero-initialize the last BN in each residual branch, + # so that the residual branch starts with zeros, and each residual block behaves like an identity. + # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677 + if zero_init_residual: + for m in self.modules(): + if isinstance(m, Bottleneck): + nn.init.constant_(m.bn3.weight, 0) + elif isinstance(m, BasicBlock): + nn.init.constant_(m.bn2.weight, 0) + + def _make_layer(self, block, planes, blocks, stride=1, dilate=False, last_phase=False): + norm_layer = self._norm_layer + downsample = None + previous_dilation = self.dilation + if dilate: + self.dilation *= stride + stride = 1 + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + conv1x1(self.inplanes, planes * block.expansion, stride), + norm_layer(planes * block.expansion), + ) + + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample, self.groups, + self.base_width, previous_dilation, norm_layer)) + self.inplanes = planes * block.expansion + if last_phase: + for _ in range(1, blocks-1): + layers.append(block(self.inplanes, planes, groups=self.groups, + base_width=self.base_width, dilation=self.dilation, + norm_layer=norm_layer)) + layers.append(block(self.inplanes, planes, groups=self.groups, + base_width=self.base_width, dilation=self.dilation, + norm_layer=norm_layer, last=True)) + else: + for _ in range(1, blocks): + layers.append(block(self.inplanes, planes, groups=self.groups, + base_width=self.base_width, dilation=self.dilation, + norm_layer=norm_layer)) + + return nn.Sequential(*layers) + + def _forward_impl(self, x): + # See note [TorchScript super()] + x = self.conv1(x) # [bs, 64, 32, 32] + + x_1 = self.layer1(x) # [bs, 128, 32, 32] + x_2 = self.layer2(x_1) # [bs, 256, 16, 16] + x_3 = self.layer3(x_2) # [bs, 512, 8, 8] + x_4 = self.layer4(x_3) # [bs, 512, 4, 4] + + pooled = self.avgpool(x_4) # [bs, 512, 1, 1] + features = torch.flatten(pooled, 1) # [bs, 512] + # x = self.fc(x) + + return { + 'fmaps': [x_1, x_2, x_3, x_4], + 'features': features + } + + def forward(self, x): + return self._forward_impl(x) + + @property + def last_conv(self): + if hasattr(self.layer4[-1], 'conv3'): + return self.layer4[-1].conv3 + else: + return self.layer4[-1].conv2 + + +def _resnet(arch, block, layers, pretrained, progress, **kwargs): + model = ResNet(block, layers, **kwargs) + if pretrained: + state_dict = load_state_dict_from_url(model_urls[arch], + progress=progress) + model.load_state_dict(state_dict) + return model + + +def resnet18(pretrained=False, progress=True, **kwargs): + r"""ResNet-18 model from + `"Deep Residual Learning for Image Recognition" `_ + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress, + **kwargs) + + +def resnet34(pretrained=False, progress=True, **kwargs): + r"""ResNet-34 model from + `"Deep Residual Learning for Image Recognition" `_ + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress, + **kwargs) + + +def resnet50(pretrained=False, progress=True, **kwargs): + r"""ResNet-50 model from + `"Deep Residual Learning for Image Recognition" `_ + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress, + **kwargs) diff --git a/datasets/create imagenet100 from imagenet.py b/datasets/create imagenet100 from imagenet.py new file mode 100644 index 0000000000000000000000000000000000000000..296d06d8e29b15600d18a84c898983457ce2c355 --- /dev/null +++ b/datasets/create imagenet100 from imagenet.py @@ -0,0 +1,28 @@ +import os +import shutil + +# Please Provide ImageNet File Path +IMAGENET_TRAIN_ROOT = "you_path/Imagenet/train" +IMAGENET_VAL_ROOT ="you_path/Imagenet/val" +TARGET_ROOT = "you_path/Imagenet100" + +def copy_files_from_txt(txt_path, source_root, split_name): + with open(txt_path, 'r') as f: + lines = f.read().splitlines() + + current_class = "" + for line in lines: + if '/' not in line: # class name + current_class = line + else: + src_path = os.path.join(source_root, line) + tgt_path = os.path.join(TARGET_ROOT, split_name, line) + os.makedirs(os.path.dirname(tgt_path), exist_ok=True) + try: + shutil.copyfile(src_path, tgt_path) + print(f"✅ Copied: {line}") + except Exception as e: + print(f"❌ Failed to copy {line}: {e}") + +copy_files_from_txt("train.txt", IMAGENET_TRAIN_ROOT, "train") +copy_files_from_txt("eval.txt", IMAGENET_VAL_ROOT, "val") diff --git a/datasets/eval.txt b/datasets/eval.txt new file mode 100644 index 0000000000000000000000000000000000000000..fe8d495216f409be3e7762c2d39830770c370e09 --- /dev/null +++ b/datasets/eval.txt @@ -0,0 +1,5100 @@ +n01514668 +n01514668/ILSVRC2012_val_00004306.JPEG 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file mode 100644 index 0000000000000000000000000000000000000000..4b135dbda80632551739949fdcf651670458b0e1 --- /dev/null +++ b/datasets/imagenet100label.py @@ -0,0 +1,25 @@ +# There is an iamgenet mapping of classes and filename +imagenet_label_path = "you_path/map_clsloc.txt" # ImageNet class mapping file +train_file_path = "you_path/train.txt" # ImageNet100 train classes +eval_file_path = "you_path/train.txt" # ImageNet100 eval classes + +label_dict = {} +with open(imagenet_label_path, 'r') as f: + for line in f: + folder_name, label, class_name = line.strip().split(',') + label_dict[folder_name] = (label, class_name) + +def update_file(input_file_path, output_file_path): + with open(input_file_path, 'r') as infile, open(output_file_path, 'w') as outfile: + folder_name = None + for line in infile: + line = line.strip() + if line in label_dict: + folder_name = line + label, class_name = label_dict[folder_name] + outfile.write(f"{folder_name},{label},{class_name}\n") + +update_file(train_file_path, "you_path/map_clsloc_imagenet100_train.txt") +update_file(eval_file_path, "you_path/map_clsloc_imagenet100_eval.txt") + +print("Files have been updated.") diff --git a/datasets/train.txt b/datasets/train.txt new file mode 100644 index 0000000000000000000000000000000000000000..1a9aa51e9d43c81dc974a1d8edcb6449af85eaec --- /dev/null +++ b/datasets/train.txt @@ -0,0 +1,128956 @@ +n01514668 +n01514668/n01514668_23056.JPEG +n01514668/n01514668_17195.JPEG +n01514668/n01514668_10014.JPEG +n01514668/n01514668_14771.JPEG +n01514668/n01514668_18848.JPEG +n01514668/n01514668_21677.JPEG +n01514668/n01514668_16474.JPEG +n01514668/n01514668_22626.JPEG +n01514668/n01514668_16964.JPEG +n01514668/n01514668_18144.JPEG +n01514668/n01514668_10294.JPEG +n01514668/n01514668_4238.JPEG +n01514668/n01514668_19234.JPEG +n01514668/n01514668_16851.JPEG +n01514668/n01514668_18073.JPEG +n01514668/n01514668_13435.JPEG +n01514668/n01514668_23404.JPEG +n01514668/n01514668_21975.JPEG 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- conda=24.4.0=py312h06a4308_0 + - conda-content-trust=0.2.0=py312h06a4308_1 + - conda-libmamba-solver=24.1.0=pyhd3eb1b0_0 + - conda-package-handling=2.2.0=py312h06a4308_1 + - conda-package-streaming=0.9.0=py312h06a4308_0 + - cryptography=42.0.5=py312hdda0065_1 + - distro=1.9.0=py312h06a4308_0 + - expat=2.6.2=h6a678d5_0 + - fmt=9.1.0=hdb19cb5_1 + - icu=73.1=h6a678d5_0 + - idna=3.7=py312h06a4308_0 + - jsonpatch=1.33=py312h06a4308_1 + - jsonpointer=2.1=pyhd3eb1b0_0 + - krb5=1.20.1=h143b758_1 + - ld_impl_linux-64=2.38=h1181459_1 + - libarchive=3.6.2=h6ac8c49_3 + - libcurl=8.7.1=h251f7ec_0 + - libedit=3.1.20230828=h5eee18b_0 + - libev=4.33=h7f8727e_1 + - libffi=3.4.4=h6a678d5_1 + - libgcc-ng=11.2.0=h1234567_1 + - libgomp=11.2.0=h1234567_1 + - libmamba=1.5.8=hfe524e5_2 + - libmambapy=1.5.8=py312h2dafd23_2 + - libnghttp2=1.57.0=h2d74bed_0 + - libsolv=0.7.24=he621ea3_1 + - libssh2=1.11.0=h251f7ec_0 + - libstdcxx-ng=11.2.0=h1234567_1 + - libuuid=1.41.5=h5eee18b_0 + - libxml2=2.10.4=hfdd30dd_2 + - lz4-c=1.9.4=h6a678d5_1 + - menuinst=2.0.2=py312h06a4308_1 + - ncurses=6.4=h6a678d5_0 + - openssl=3.0.13=h7f8727e_1 + - packaging=23.2=py312h06a4308_0 + - pcre2=10.42=hebb0a14_1 + - pip=24.0=py312h06a4308_0 + - platformdirs=3.10.0=py312h06a4308_0 + - pluggy=1.0.0=py312h06a4308_1 + - pybind11-abi=5=hd3eb1b0_0 + - pycosat=0.6.6=py312h5eee18b_1 + - pycparser=2.21=pyhd3eb1b0_0 + - pysocks=1.7.1=py312h06a4308_0 + - python=3.12.3=h996f2a0_1 + - readline=8.2=h5eee18b_0 + - reproc=14.2.4=h6a678d5_2 + - reproc-cpp=14.2.4=h6a678d5_2 + - requests=2.31.0=py312h06a4308_1 + - ruamel.yaml=0.17.21=py312h5eee18b_0 + - setuptools=69.5.1=py312h06a4308_0 + - sqlite=3.45.3=h5eee18b_0 + - tk=8.6.14=h39e8969_0 + - tqdm=4.66.2=py312he106c6f_0 + - truststore=0.8.0=py312h06a4308_0 + - urllib3=2.1.0=py312h06a4308_1 + - wheel=0.43.0=py312h06a4308_0 + - xz=5.4.6=h5eee18b_1 + - yaml-cpp=0.8.0=h6a678d5_1 + - zlib=1.2.13=h5eee18b_1 + - zstandard=0.22.0=py312h2c38b39_0 + - zstd=1.5.5=hc292b87_2 + - pip: + - absl-py==2.1.0 + - anyio==4.6.2.post1 + - argon2-cffi==23.1.0 + - argon2-cffi-bindings==21.2.0 + - arrow==1.3.0 + - asttokens==2.4.1 + - async-lru==2.0.4 + - attrs==24.2.0 + - babel==2.16.0 + - beautifulsoup4==4.12.3 + - bleach==6.2.0 + - clip==0.2.0 + - comm==0.2.2 + - contourpy==1.3.1 + - cycler==0.12.1 + - debugpy==1.8.9 + - decorator==5.1.1 + - defusedxml==0.7.1 + - diffusers==0.34.0 + - eagerpy==0.30.0 + - einops==0.8.1 + - et-xmlfile==2.0.0 + - executing==2.1.0 + - fastjsonschema==2.20.0 + - filelock==3.16.1 + - fonttools==4.55.0 + - foolbox==3.3.4 + - fqdn==1.5.1 + - fsspec==2024.10.0 + - ftfy==6.3.1 + - gitdb==4.0.12 + - gitpython==3.1.44 + - grad-cam==1.5.5 + - grpcio==1.68.0 + - h11==0.14.0 + - hf-xet==1.1.5 + - httpcore==1.0.7 + - httpx==0.27.2 + - huggingface-hub==0.33.1 + - importlib-metadata==8.7.0 + - ipykernel==6.29.5 + - ipython==8.29.0 + - ipywidgets==8.1.5 + - isoduration==20.11.0 + - jedi==0.19.2 + - jinja2==3.1.4 + - joblib==1.5.1 + - json5==0.9.28 + - jsonschema==4.23.0 + - jsonschema-specifications==2024.10.1 + - jupyter-client==8.6.3 + - jupyter-core==5.7.2 + - jupyter-events==0.10.0 + - jupyter-lsp==2.2.5 + - jupyter-server==2.14.2 + - jupyter-server-terminals==0.5.3 + - jupyterlab==4.3.1 + - jupyterlab-language-pack-zh-cn==4.2.post3 + - jupyterlab-pygments==0.3.0 + - jupyterlab-server==2.27.3 + - jupyterlab-widgets==3.0.13 + - kiwisolver==1.4.7 + - llvmlite==0.44.0 + - markdown==3.7 + - markupsafe==3.0.2 + - matplotlib==3.9.2 + - matplotlib-inline==0.1.7 + - mistune==3.0.2 + - mpmath==1.3.0 + - munch==4.0.0 + - nbclient==0.10.0 + - nbconvert==7.16.4 + - nbformat==5.10.4 + - nest-asyncio==1.6.0 + - networkx==3.4.2 + - notebook-shim==0.2.4 + - numba==0.61.2 + - numpy==2.1.3 + - nvidia-cublas-cu12==12.4.5.8 + - nvidia-cuda-cupti-cu12==12.4.127 + - nvidia-cuda-nvrtc-cu12==12.4.127 + - nvidia-cuda-runtime-cu12==12.4.127 + - nvidia-cudnn-cu12==9.1.0.70 + - nvidia-cufft-cu12==11.2.1.3 + - nvidia-curand-cu12==10.3.5.147 + - nvidia-cusolver-cu12==11.6.1.9 + - nvidia-cusparse-cu12==12.3.1.170 + - nvidia-ml-py==12.575.51 + - nvidia-nccl-cu12==2.21.5 + - nvidia-nvjitlink-cu12==12.4.127 + - nvidia-nvtx-cu12==12.4.127 + - open-clip-torch==2.32.0 + - opencv-python==4.11.0.86 + - openpyxl==3.1.5 + - overrides==7.7.0 + - pandas==2.3.0 + - pandocfilters==1.5.1 + - parso==0.8.4 + - pexpect==4.9.0 + - pillow==11.0.0 + - pretrainedmodels==0.7.4 + - prometheus-client==0.21.0 + - prompt-toolkit==3.0.48 + - protobuf==5.28.3 + - psutil==6.1.0 + - ptyprocess==0.7.0 + - pure-eval==0.2.3 + - pygments==2.18.0 + - pynvml==12.0.0 + - pyparsing==3.2.0 + - python-dateutil==2.9.0.post0 + - python-json-logger==2.0.7 + - pytz==2025.2 + - pywavelets==1.8.0 + - pyyaml==6.0.2 + - pyzmq==26.2.0 + - referencing==0.35.1 + - regex==2024.11.6 + - rfc3339-validator==0.1.4 + - rfc3986-validator==0.1.1 + - rpds-py==0.21.0 + - safetensors==0.5.3 + - scikit-learn==1.7.0 + - scipy==1.15.2 + - send2trash==1.8.3 + - six==1.16.0 + - smmap==5.0.2 + - sniffio==1.3.1 + - soupsieve==2.6 + - stack-data==0.6.3 + - supervisor==4.2.5 + - sympy==1.13.1 + - tabulate==0.9.0 + - tensorboard==2.18.0 + - tensorboard-data-server==0.7.2 + - terminado==0.18.1 + - threadpoolctl==3.6.0 + - timm==1.0.16 + - tinycss2==1.4.0 + - tokenizers==0.21.2 + - torch==2.5.1+cu124 + - torchvision==0.20.1+cu124 + - tornado==6.4.2 + - traitlets==5.14.3 + - transformers==4.53.0 + - triton==3.1.0 + - ttach==0.0.3 + - types-python-dateutil==2.9.0.20241003 + - typing-extensions==4.12.2 + - tzdata==2025.2 + - umap==0.1.1 + - uri-template==1.3.0 + - wcwidth==0.2.13 + - webcolors==24.11.1 + - webencodings==0.5.1 + - websocket-client==1.8.0 + - werkzeug==3.1.3 + - widgetsnbextension==4.0.13 + - zipp==3.23.0 +prefix: /root/miniconda3 diff --git a/exps/bic-imagenet100.json b/exps/bic-imagenet100.json new file mode 100644 index 0000000000000000000000000000000000000000..6fbe887d8e83488eb116ae823e3c539a8bdb5072 --- /dev/null +++ b/exps/bic-imagenet100.json @@ -0,0 +1,16 @@ +{ + "prefix": "reproduce", + "dataset": "imagenet100", + "memory_size": 2000, + "memory_per_class": 20, + "fixed_memory": false, + "shuffle": true, + "init_cls": 10, + "increment": 10, + "model_name": "bic", + "convnet_type": "resnet50", + "device": ["0"], + "seed": [1993], + + "attack": false +} diff --git a/exps/bic.json b/exps/bic.json new file mode 100644 index 0000000000000000000000000000000000000000..95009f78b90abfd08c2d443070c8e2577636c8a4 --- /dev/null +++ b/exps/bic.json @@ -0,0 +1,16 @@ +{ + "prefix": "reproduce", + "dataset": "cifar100", + "memory_size": 2000, + "memory_per_class": 20, + "fixed_memory": false, + "shuffle": true, + "init_cls": 10, + "increment": 10, + "model_name": "bic", + "convnet_type": "resnet32", + "device": ["0"], + "seed": [1993], + + "attack": false +} diff --git a/exps/der-imagenet100.json b/exps/der-imagenet100.json new file mode 100644 index 0000000000000000000000000000000000000000..1bce1b7dc29aa33b7e679032e2e04a14d35f0bc2 --- /dev/null +++ b/exps/der-imagenet100.json @@ -0,0 +1,16 @@ +{ + "prefix": "reproduce", + "dataset": "imagenet100", + "memory_size": 2000, + "memory_per_class": 20, + "fixed_memory": false, + "shuffle": true, + "init_cls": 10, + "increment": 10, + "model_name": "der", + "convnet_type": "resnet50", + "device": ["0"], + "seed": [1993], + + "attack": false +} \ No newline at end of file diff --git a/exps/der.json b/exps/der.json new file mode 100644 index 0000000000000000000000000000000000000000..f6d57c941b3554ae078a2cb179c1423ea577736a --- /dev/null +++ b/exps/der.json @@ -0,0 +1,16 @@ +{ + "prefix": "reproduce", + "dataset": "cifar100", + "memory_size": 2000, + "memory_per_class": 20, + "fixed_memory": false, + "shuffle": true, + "init_cls": 10, + "increment": 10, + "model_name": "der", + "convnet_type": "resnet32", + "device": ["0"], + "seed": [1993], + + "attack": false +} \ No newline at end of file diff --git a/exps/finetune-imagenet100.json b/exps/finetune-imagenet100.json new file mode 100644 index 0000000000000000000000000000000000000000..65341412ee0162512851612bb360af0c772eb435 --- /dev/null +++ b/exps/finetune-imagenet100.json @@ -0,0 +1,16 @@ +{ + "prefix": "reproduce", + "dataset": "imagenet100", + "memory_size": 2000, + "memory_per_class": 20, + "fixed_memory": false, + "shuffle": true, + "init_cls": 10, + "increment": 10, + "model_name": "finetune", + "convnet_type": "resnet50", + "device": ["0"], + "seed": [1993], + + "attack": false +} \ No newline at end of file diff --git a/exps/finetune.json b/exps/finetune.json new file mode 100644 index 0000000000000000000000000000000000000000..cc76e1fd8b0ac2b6c031c61912a8a16a979ab17c --- /dev/null +++ b/exps/finetune.json @@ -0,0 +1,16 @@ +{ + "prefix": "reproduce", + "dataset": "cifar100", + "memory_size": 2000, + "memory_per_class": 20, + "fixed_memory": false, + "shuffle": true, + "init_cls": 10, + "increment": 10, + "model_name": "finetune", + "convnet_type": "resnet32", + "device": ["0"], + "seed": [1993], + + "attack": false +} \ No newline at end of file diff --git a/exps/foster-imagenet100.json b/exps/foster-imagenet100.json new file mode 100644 index 0000000000000000000000000000000000000000..e1e6309689c829468bd0b69fba6369210ad06a6c --- /dev/null +++ b/exps/foster-imagenet100.json @@ -0,0 +1,33 @@ +{ + "prefix": "cil", + "dataset": "imagenet100", + "memory_size": 2000, + "memory_per_class": 20, + "fixed_memory": true, + "shuffle": true, + "init_cls": 10, + "increment": 10, + "model_name": "foster", + "convnet_type": "resnet50", + "device": ["0"], + "seed": [1993], + "beta1":0.96, + "beta2":0.97, + "oofc":"ft", + "is_teacher_wa":false, + "is_student_wa":false, + "lambda_okd":1, + "wa_value":1, + "init_epochs": 200, + "init_lr" : 0.1, + "batch_size": 128, + "init_weight_decay" : 5e-4, + "boosting_epochs" : 170, + "compression_epochs" : 130, + "lr" : 0.1, + "weight_decay" : 5e-4, + "num_workers" : 0, + "T" : 2, + + "attack": false +} \ No newline at end of file diff --git a/exps/foster.json b/exps/foster.json new file mode 100644 index 0000000000000000000000000000000000000000..0402aa34fa39cf369a94723d017213be6038bf1f --- /dev/null +++ b/exps/foster.json @@ -0,0 +1,33 @@ +{ + "prefix": "cil", + "dataset": "cifar100", + "memory_size": 2000, + "memory_per_class": 20, + "fixed_memory": true, + "shuffle": true, + "init_cls": 10, + "increment": 10, + "model_name": "foster", + "convnet_type": "resnet32", + "device": ["0"], + "seed": [1993], + "beta1":0.96, + "beta2":0.97, + "oofc":"ft", + "is_teacher_wa":false, + "is_student_wa":false, + "lambda_okd":1, + "wa_value":1, + "init_epochs": 200, + "init_lr" : 0.1, + "batch_size": 256, + "init_weight_decay" : 5e-4, + "boosting_epochs" : 170, + "compression_epochs" : 130, + "lr" : 0.1, + "weight_decay" : 5e-4, + "num_workers" : 0, + "T" : 2, + + "attack": false +} \ No newline at end of file diff --git a/exps/icarl-imagenet100.json b/exps/icarl-imagenet100.json new file mode 100644 index 0000000000000000000000000000000000000000..1ad7364384d09332ed035d6c88d0e74d53d7ac97 --- /dev/null +++ b/exps/icarl-imagenet100.json @@ -0,0 +1,16 @@ +{ + "prefix": "reproduce", + "dataset": "imagenet100", + "memory_size": 2000, + "memory_per_class": 20, + "fixed_memory": false, + "shuffle": true, + "init_cls": 10, + "increment": 10, + "model_name": "icarl", + "convnet_type": "resnet50", + "device": ["0"], + "seed": [1993], + + "attack": false +} diff --git a/exps/icarl.json b/exps/icarl.json new file mode 100644 index 0000000000000000000000000000000000000000..6f2dbb4ec6b861645e9f4b552774eabb8c544c8a --- /dev/null +++ b/exps/icarl.json @@ -0,0 +1,16 @@ +{ + "prefix": "reproduce", + "dataset": "cifar100", + "memory_size": 2000, + "memory_per_class": 20, + "fixed_memory": false, + "shuffle": true, + "init_cls": 10, + "increment": 10, + "model_name": "icarl", + "convnet_type": "resnet32", + "device": ["0"], + "seed": [1993], + + "attack": false +} diff --git a/exps/memo-imagenet100.json b/exps/memo-imagenet100.json new file mode 100644 index 0000000000000000000000000000000000000000..c023673f529585245061b6b2774b20e899aed8ef --- /dev/null +++ b/exps/memo-imagenet100.json @@ -0,0 +1,34 @@ +{ + "prefix": "benchmark", + "dataset": "imagenet100", + "memory_size": 2000, + "memory_per_class":20, + "fixed_memory": false, + "shuffle": true, + "init_cls": 10, + "increment": 10, + "model_name": "memo", + "convnet_type": "memo_resnet18", + "train_base": true, + "train_adaptive": false, + "debug": false, + "skip": false, + "device": ["0"], + "seed":[1993], + "scheduler": "steplr", + "init_epoch": 200, + "t_max": null, + "init_lr" : 0.1, + "init_weight_decay" : 5e-4, + "init_lr_decay" : 0.1, + "init_milestones" : [60,120,170], + "milestones" : [80,120,150], + "epochs": 170, + "lrate" : 0.1, + "weight_decay" : 2e-4, + "lrate_decay" : 0.1, + "alpha_aux" : 1.0, + "batch_size": 128, + + "attack": false +} \ No newline at end of file diff --git a/exps/memo.json b/exps/memo.json new file mode 100644 index 0000000000000000000000000000000000000000..66cc4b821c5cb48d4f3b8380086720a1771a813d --- /dev/null +++ b/exps/memo.json @@ -0,0 +1,34 @@ +{ + "prefix": "benchmark", + "dataset": "cifar100", + "memory_size": 2000, + "memory_per_class":20, + "fixed_memory": false, + "shuffle": true, + "init_cls": 10, + "increment": 10, + "model_name": "memo", + "convnet_type": "memo_resnet32", + "train_base": true, + "train_adaptive": false, + "debug": false, + "skip": false, + "device": ["0"], + "seed":[1993], + "scheduler": "steplr", + "init_epoch": 200, + "t_max": null, + "init_lr" : 0.1, + "init_weight_decay" : 5e-4, + "init_lr_decay" : 0.1, + "init_milestones" : [60,120,170], + "milestones" : [80,120,150], + "epochs": 170, + "lrate" : 0.1, + "weight_decay" : 2e-4, + "lrate_decay" : 0.1, + "alpha_aux" : 1.0, + "batch_size": 256, + + "attack": false +} \ No newline at end of file diff --git a/exps/podnet-imagenet100.json b/exps/podnet-imagenet100.json new file mode 100644 index 0000000000000000000000000000000000000000..f72c86a1580cfd335e08c90286dc0ef26a7642c3 --- /dev/null +++ b/exps/podnet-imagenet100.json @@ -0,0 +1,16 @@ +{ + "prefix": "reproduce", + "dataset": "imagenet100", + "memory_size": 2000, + "memory_per_class": 20, + "fixed_memory": false, + "shuffle": true, + "init_cls": 10, + "increment": 10, + "model_name": "podnet", + "convnet_type": "cosine_resnet50", + "device": ["0"], + "seed": [1993], + + "attack": false +} diff --git a/exps/podnet.json b/exps/podnet.json new file mode 100644 index 0000000000000000000000000000000000000000..3830aab8a1e670d60bc418ec3dda037a8fbcc985 --- /dev/null +++ b/exps/podnet.json @@ -0,0 +1,16 @@ +{ + "prefix": "reproduce", + "dataset": "cifar100", + "memory_size": 2000, + "memory_per_class": 20, + "fixed_memory": false, + "shuffle": true, + "init_cls": 10, + "increment": 10, + "model_name": "podnet", + "convnet_type": "cosine_resnet32", + "device": ["0"], + "seed": [1993], + + "attack": false +} diff --git a/exps/replay-imagenet100.json b/exps/replay-imagenet100.json new file mode 100644 index 0000000000000000000000000000000000000000..31519e9f51bdcd93f3899d11e6f3e15536385c3d --- /dev/null +++ b/exps/replay-imagenet100.json @@ -0,0 +1,16 @@ +{ + "prefix": "reproduce", + "dataset": "imagenet100", + "memory_size": 2000, + "memory_per_class": 20, + "fixed_memory": false, + "shuffle": true, + "init_cls": 10, + "increment": 10, + "model_name": "replay", + "convnet_type": "resnet50", + "device": ["0"], + "seed": [1993], + + "attack": false +} \ No newline at end of file diff --git a/exps/replay.json b/exps/replay.json new file mode 100644 index 0000000000000000000000000000000000000000..c06fd1fed39218f22ff3580be0f64701684d3cd8 --- /dev/null +++ b/exps/replay.json @@ -0,0 +1,16 @@ +{ + "prefix": "reproduce", + "dataset": "cifar100", + "memory_size": 2000, + "memory_per_class": 20, + "fixed_memory": false, + "shuffle": true, + "init_cls": 10, + "increment": 10, + "model_name": "replay", + "convnet_type": "resnet32", + "device": ["0"], + "seed": [1993], + + "attack": false +} \ No newline at end of file diff --git a/exps/wa-imagenet100.json b/exps/wa-imagenet100.json new file mode 100644 index 0000000000000000000000000000000000000000..3f1a35280863cef30815274f7170d52850084e99 --- /dev/null +++ b/exps/wa-imagenet100.json @@ -0,0 +1,16 @@ +{ + "prefix": "reproduce", + "dataset": "imagenet100", + "memory_size": 2000, + "memory_per_class": 20, + "fixed_memory": false, + "shuffle": true, + "init_cls": 10, + "increment": 10, + "model_name": "wa", + "convnet_type": "resnet50", + "device": ["0"], + "seed": [1993], + + "attack": false +} \ No newline at end of file diff --git a/exps/wa.json b/exps/wa.json new file mode 100644 index 0000000000000000000000000000000000000000..96884e0eedb63863a17acf4e2c45ca83ef8e1090 --- /dev/null +++ b/exps/wa.json @@ -0,0 +1,16 @@ +{ + "prefix": "reproduce", + "dataset": "cifar100", + "memory_size": 2000, + "memory_per_class": 20, + "fixed_memory": false, + "shuffle": true, + "init_cls": 10, + "increment": 10, + "model_name": "wa", + "convnet_type": "resnet32", + "device": ["0"], + "seed": [1993], + + "attack": false +} \ No newline at end of file diff --git a/models/__init__.py b/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/base.py b/models/base.py new file mode 100644 index 0000000000000000000000000000000000000000..4afc143e3bf92e9df47a9d021c3fb47aa765f94a --- /dev/null +++ b/models/base.py @@ -0,0 +1,399 @@ +import copy +import logging +import numpy as np +import torch +from torch import nn +from torch.utils.data import DataLoader +from utils.toolkit import tensor2numpy, accuracy +from scipy.spatial.distance import cdist +import os + +EPSILON = 1e-8 +batch_size = 64 + + +class BaseLearner(object): + def __init__(self, args): + self.args = args + self._cur_task = -1 + self._known_classes = 0 + self._total_classes = 0 + self._network = None + self._snet = None + self._old_network = None + self._data_memory, self._targets_memory = np.array([]), np.array([]) + self.topk = 5 + + self._memory_size = args["memory_size"] + self._memory_per_class = args.get("memory_per_class", None) + self._fixed_memory = args.get("fixed_memory", False) + self._device = args["device"][0] + self._multiple_gpus = args["device"] + + @property + def exemplar_size(self): + assert len(self._data_memory) == len( + self._targets_memory + ), "Exemplar size error." + return len(self._targets_memory) + + @property + def samples_per_class(self): + if self._fixed_memory: + return self._memory_per_class + else: + assert self._total_classes != 0, "Total classes is 0" + return self._memory_size // self._total_classes + + @property + def feature_dim(self): + if isinstance(self._network, nn.DataParallel): + return self._network.module.feature_dim + else: + return self._network.feature_dim + + def build_rehearsal_memory(self, data_manager, per_class): + if self._fixed_memory: + self._construct_exemplar_unified(data_manager, per_class) + else: + self._reduce_exemplar(data_manager, per_class) + self._construct_exemplar(data_manager, per_class) + + def save_checkpoint(self, dir, filename): + self._network.cpu() + # if self.args["model_name"] == "foster" and self._cur_task > 1: + # save_dict = { + # "tasks": self._cur_task, + # "model_state_dict": self._network.state_dict(), + # "snet_state_dict": self._snet.state_dict(), + # } + # else: + save_dict = { + "tasks": self._cur_task, + "model_state_dict": self._network.state_dict(), + } + torch.save(save_dict, "{}/{}_task{}.pkl".format(dir, filename, self._cur_task)) + + def after_task(self): + pass + + def _evaluate(self, y_pred, y_true): + ret = {} + grouped = accuracy(y_pred.T[0], y_true, self._known_classes) + ret["grouped"] = grouped + ret["top1"] = grouped["total"] + ret["top{}".format(self.topk)] = np.around( + (y_pred.T == np.tile(y_true, (self.topk, 1))).sum() * 100 / len(y_true), + decimals=2, + ) + + return ret + + def eval_task(self, save_conf=False): + y_pred, y_true = self._eval_cnn(self.test_loader) + cnn_accy = self._evaluate(y_pred, y_true) + + if hasattr(self, "_class_means"): + y_pred, y_true = self._eval_nme(self.test_loader, self._class_means) + nme_accy = self._evaluate(y_pred, y_true) + else: + nme_accy = None + + if save_conf: + _pred = y_pred.T[0] + _pred_path = os.path.join(self.args['logfilename'], "pred.npy") + _target_path = os.path.join(self.args['logfilename'], "target.npy") + np.save(_pred_path, _pred) + np.save(_target_path, y_true) + + _save_dir = os.path.join(f"./results/conf_matrix/{self.args['prefix']}") + os.makedirs(_save_dir, exist_ok=True) + _save_path = os.path.join(_save_dir, f"{self.args['csv_name']}.csv") + with open(_save_path, "a+") as f: + f.write(f"{self.args['time_str']},{self.args['model_name']},{_pred_path},{_target_path} \n") + + return cnn_accy, nme_accy + + def incremental_train(self): + pass + + def _train(self): + pass + + def _get_memory(self): + if len(self._data_memory) == 0: + return None + else: + return (self._data_memory, self._targets_memory) + + def _compute_accuracy(self, model, loader): + if not self.args["attack"]: + model.eval() + correct, total = 0, 0 + for i, (_, inputs, targets) in enumerate(loader): + inputs = inputs.to(self._device) + with torch.no_grad(): + if self.args["attack"]: + outputs = model(inputs) + else: + outputs = model(inputs)["logits"] + predicts = torch.max(outputs, dim=1)[1] + correct += (predicts.cpu() == targets).sum() + total += len(targets) + + return np.around(tensor2numpy(correct) * 100 / total, decimals=2) + + def _eval_cnn(self, loader): + self._network.eval() + y_pred, y_true = [], [] + for _, (_, inputs, targets) in enumerate(loader): + inputs = inputs.to(self._device) + with torch.no_grad(): + if self.args["attack"]: + outputs = self._network(inputs) + else: + outputs = self._network(inputs)["logits"] + predicts = torch.topk( + outputs, k=self.topk, dim=1, largest=True, sorted=True + )[ + 1 + ] # [bs, topk] + y_pred.append(predicts.cpu().numpy()) + y_true.append(targets.cpu().numpy()) + + return np.concatenate(y_pred), np.concatenate(y_true) # [N, topk] + + def _eval_nme(self, loader, class_means): + self._network.eval() + vectors, y_true = self._extract_vectors(loader) + vectors = (vectors.T / (np.linalg.norm(vectors.T, axis=0) + EPSILON)).T + + dists = cdist(class_means, vectors, "sqeuclidean") # [nb_classes, N] + scores = dists.T # [N, nb_classes], choose the one with the smallest distance + + return np.argsort(scores, axis=1)[:, : self.topk], y_true # [N, topk] + + def _extract_vectors(self, loader): + self._network.eval() + vectors, targets = [], [] + for _, _inputs, _targets in loader: + _targets = _targets.numpy() + if isinstance(self._network, nn.DataParallel): + _vectors = tensor2numpy( + self._network.module.extract_vector(_inputs.to(self._device)) + ) + else: + _vectors = tensor2numpy( + self._network.extract_vector(_inputs.to(self._device)) + ) + + vectors.append(_vectors) + targets.append(_targets) + + return np.concatenate(vectors), np.concatenate(targets) + + def _reduce_exemplar(self, data_manager, m): + logging.info("Reducing exemplars...({} per classes)".format(m)) + dummy_data, dummy_targets = copy.deepcopy(self._data_memory), copy.deepcopy( + self._targets_memory + ) + self._class_means = np.zeros((self._total_classes, self.feature_dim)) + self._data_memory, self._targets_memory = np.array([]), np.array([]) + + for class_idx in range(self._known_classes): + mask = np.where(dummy_targets == class_idx)[0] + dd, dt = dummy_data[mask][:m], dummy_targets[mask][:m] + self._data_memory = ( + np.concatenate((self._data_memory, dd)) + if len(self._data_memory) != 0 + else dd + ) + self._targets_memory = ( + np.concatenate((self._targets_memory, dt)) + if len(self._targets_memory) != 0 + else dt + ) + + # Exemplar mean + idx_dataset = data_manager.get_dataset( + [], source="train", mode="test", appendent=(dd, dt) + ) + idx_loader = DataLoader( + idx_dataset, batch_size=batch_size, shuffle=False, num_workers=4 + ) + vectors, _ = self._extract_vectors(idx_loader) + vectors = (vectors.T / (np.linalg.norm(vectors.T, axis=0) + EPSILON)).T + mean = np.mean(vectors, axis=0) + mean = mean / np.linalg.norm(mean) + + self._class_means[class_idx, :] = mean + + def _construct_exemplar(self, data_manager, m): + logging.info("Constructing exemplars...({} per classes)".format(m)) + for class_idx in range(self._known_classes, self._total_classes): + data, targets, idx_dataset = data_manager.get_dataset( + np.arange(class_idx, class_idx + 1), + source="train", + mode="test", + ret_data=True, + ) + idx_loader = DataLoader( + idx_dataset, batch_size=batch_size, shuffle=False, num_workers=4 + ) + vectors, _ = self._extract_vectors(idx_loader) + vectors = (vectors.T / (np.linalg.norm(vectors.T, axis=0) + EPSILON)).T + class_mean = np.mean(vectors, axis=0) + + # Select + selected_exemplars = [] + exemplar_vectors = [] # [n, feature_dim] + for k in range(1, m + 1): + S = np.sum( + exemplar_vectors, axis=0 + ) # [feature_dim] sum of selected exemplars vectors + mu_p = (vectors + S) / k # [n, feature_dim] sum to all vectors + i = np.argmin(np.sqrt(np.sum((class_mean - mu_p) ** 2, axis=1))) + selected_exemplars.append( + np.array(data[i]) + ) # New object to avoid passing by inference + exemplar_vectors.append( + np.array(vectors[i]) + ) # New object to avoid passing by inference + + vectors = np.delete( + vectors, i, axis=0 + ) # Remove it to avoid duplicative selection + data = np.delete( + data, i, axis=0 + ) # Remove it to avoid duplicative selection + + # uniques = np.unique(selected_exemplars, axis=0) + # print('Unique elements: {}'.format(len(uniques))) + selected_exemplars = np.array(selected_exemplars) + exemplar_targets = np.full(m, class_idx) + self._data_memory = ( + np.concatenate((self._data_memory, selected_exemplars)) + if len(self._data_memory) != 0 + else selected_exemplars + ) + self._targets_memory = ( + np.concatenate((self._targets_memory, exemplar_targets)) + if len(self._targets_memory) != 0 + else exemplar_targets + ) + + # Exemplar mean + idx_dataset = data_manager.get_dataset( + [], + source="train", + mode="test", + appendent=(selected_exemplars, exemplar_targets), + ) + idx_loader = DataLoader( + idx_dataset, batch_size=batch_size, shuffle=False, num_workers=4 + ) + vectors, _ = self._extract_vectors(idx_loader) + vectors = (vectors.T / (np.linalg.norm(vectors.T, axis=0) + EPSILON)).T + mean = np.mean(vectors, axis=0) + mean = mean / np.linalg.norm(mean) + + self._class_means[class_idx, :] = mean + + def _construct_exemplar_unified(self, data_manager, m): + logging.info( + "Constructing exemplars for new classes...({} per classes)".format(m) + ) + _class_means = np.zeros((self._total_classes, self.feature_dim)) + + # Calculate the means of old classes with newly trained network + for class_idx in range(self._known_classes): + mask = np.where(self._targets_memory == class_idx)[0] + class_data, class_targets = ( + self._data_memory[mask], + self._targets_memory[mask], + ) + + class_dset = data_manager.get_dataset( + [], source="train", mode="test", appendent=(class_data, class_targets) + ) + class_loader = DataLoader( + class_dset, batch_size=batch_size, shuffle=False, num_workers=4 + ) + vectors, _ = self._extract_vectors(class_loader) + vectors = (vectors.T / (np.linalg.norm(vectors.T, axis=0) + EPSILON)).T + mean = np.mean(vectors, axis=0) + mean = mean / np.linalg.norm(mean) + + _class_means[class_idx, :] = mean + + # Construct exemplars for new classes and calculate the means + for class_idx in range(self._known_classes, self._total_classes): + data, targets, class_dset = data_manager.get_dataset( + np.arange(class_idx, class_idx + 1), + source="train", + mode="test", + ret_data=True, + ) + class_loader = DataLoader( + class_dset, batch_size=batch_size, shuffle=False, num_workers=4 + ) + + vectors, _ = self._extract_vectors(class_loader) + vectors = (vectors.T / (np.linalg.norm(vectors.T, axis=0) + EPSILON)).T + class_mean = np.mean(vectors, axis=0) + + # Select + selected_exemplars = [] + exemplar_vectors = [] + for k in range(1, m + 1): + S = np.sum( + exemplar_vectors, axis=0 + ) # [feature_dim] sum of selected exemplars vectors + mu_p = (vectors + S) / k # [n, feature_dim] sum to all vectors + i = np.argmin(np.sqrt(np.sum((class_mean - mu_p) ** 2, axis=1))) + + selected_exemplars.append( + np.array(data[i]) + ) # New object to avoid passing by inference + exemplar_vectors.append( + np.array(vectors[i]) + ) # New object to avoid passing by inference + + vectors = np.delete( + vectors, i, axis=0 + ) # Remove it to avoid duplicative selection + data = np.delete( + data, i, axis=0 + ) # Remove it to avoid duplicative selection + + selected_exemplars = np.array(selected_exemplars) + exemplar_targets = np.full(m, class_idx) + self._data_memory = ( + np.concatenate((self._data_memory, selected_exemplars)) + if len(self._data_memory) != 0 + else selected_exemplars + ) + self._targets_memory = ( + np.concatenate((self._targets_memory, exemplar_targets)) + if len(self._targets_memory) != 0 + else exemplar_targets + ) + + # Exemplar mean + exemplar_dset = data_manager.get_dataset( + [], + source="train", + mode="test", + appendent=(selected_exemplars, exemplar_targets), + ) + exemplar_loader = DataLoader( + exemplar_dset, batch_size=batch_size, shuffle=False, num_workers=4 + ) + vectors, _ = self._extract_vectors(exemplar_loader) + vectors = (vectors.T / (np.linalg.norm(vectors.T, axis=0) + EPSILON)).T + mean = np.mean(vectors, axis=0) + mean = mean / np.linalg.norm(mean) + + _class_means[class_idx, :] = mean + + self._class_means = _class_means diff --git a/models/bic.py b/models/bic.py new file mode 100644 index 0000000000000000000000000000000000000000..438ec94f0de8202294182feb204082bddffaa9bf --- /dev/null +++ b/models/bic.py @@ -0,0 +1,207 @@ +import logging +import numpy as np +import torch +from torch import nn +from torch import optim +from torch.nn import functional as F +from torch.utils.data import DataLoader +from models.base import BaseLearner +from utils.inc_net import IncrementalNetWithBias + + +epochs = 170 +lrate = 0.1 +milestones = [60, 100, 140] +lrate_decay = 0.1 +batch_size = 128 +split_ratio = 0.1 +T = 2 +weight_decay = 2e-4 +num_workers = 8 + + +class BiC(BaseLearner): + def __init__(self, args): + super().__init__(args) + self._network = IncrementalNetWithBias( + args, False, bias_correction=True + ) + self._class_means = None + + def after_task(self): + self._old_network = self._network.copy().freeze() + self._known_classes = self._total_classes + logging.info("Exemplar size: {}".format(self.exemplar_size)) + + def incremental_train(self, data_manager): + self._cur_task += 1 + self._total_classes = self._known_classes + data_manager.get_task_size( + self._cur_task + ) + self._network.update_fc(self._total_classes) + logging.info( + "Learning on {}-{}".format(self._known_classes, self._total_classes) + ) + + if not self.args["attack"]: + if self._cur_task >= 1: + train_dset, val_dset = data_manager.get_dataset_with_split( + np.arange(self._known_classes, self._total_classes), + source="train", + mode="train", + appendent=self._get_memory(), + val_samples_per_class=int( + split_ratio * self._memory_size / self._known_classes + ), + ) + self.val_loader = DataLoader( + val_dset, batch_size=batch_size, shuffle=True, num_workers=num_workers + ) + logging.info( + "Stage1 dset: {}, Stage2 dset: {}".format( + len(train_dset), len(val_dset) + ) + ) + self.lamda = self._known_classes / self._total_classes + logging.info("Lambda: {:.3f}".format(self.lamda)) + else: + train_dset = data_manager.get_dataset( + np.arange(self._known_classes, self._total_classes), + source="train", + mode="train", + appendent=self._get_memory(), + ) + test_dset = data_manager.get_dataset( + np.arange(0, self._total_classes), source="test", mode="test" + ) + + self.train_loader = DataLoader( + train_dset, batch_size=batch_size, shuffle=True, num_workers=num_workers + ) + self.test_loader = DataLoader( + test_dset, batch_size=batch_size, shuffle=False, num_workers=num_workers + ) + + self._log_bias_params() + self._stage1_training(self.train_loader, self.test_loader) + if self._cur_task >= 1: + self._stage2_bias_correction(self.val_loader, self.test_loader) + + self.build_rehearsal_memory(data_manager, self.samples_per_class) + + if len(self._multiple_gpus) > 1: + self._network = self._network.module + self._log_bias_params() + + def _run(self, train_loader, test_loader, optimizer, scheduler, stage): + for epoch in range(1, epochs + 1): + self._network.train() + losses = 0.0 + for i, (_, inputs, targets) in enumerate(train_loader): + inputs, targets = inputs.to(self._device), targets.to(self._device) + logits = self._network(inputs)["logits"] + + if stage == "training": + clf_loss = F.cross_entropy(logits, targets.to(torch.int64)) + if self._old_network is not None: + old_logits = self._old_network(inputs)["logits"].detach() + hat_pai_k = F.softmax(old_logits / T, dim=1) + log_pai_k = F.log_softmax( + logits[:, : self._known_classes] / T, dim=1 + ) + distill_loss = -torch.mean( + torch.sum(hat_pai_k * log_pai_k, dim=1) + ) + loss = distill_loss * self.lamda + clf_loss * (1 - self.lamda) + else: + loss = clf_loss + elif stage == "bias_correction": + loss = F.cross_entropy(torch.softmax(logits, dim=1), targets.to(torch.int64)) + else: + raise NotImplementedError() + + optimizer.zero_grad() + loss.backward() + optimizer.step() + losses += loss.item() + + scheduler.step() + train_acc = self._compute_accuracy(self._network, train_loader) + test_acc = self._compute_accuracy(self._network, test_loader) + info = "{} => Task {}, Epoch {}/{} => Loss {:.3f}, Train_accy {:.3f}, Test_accy {:.3f}".format( + stage, + self._cur_task, + epoch, + epochs, + losses / len(train_loader), + train_acc, + test_acc, + ) + logging.info(info) + + def _stage1_training(self, train_loader, test_loader): + """ + if self._cur_task == 0: + loaded_dict = torch.load('./dict_0.pkl') + self._network.load_state_dict(loaded_dict['model_state_dict']) + self._network.to(self._device) + return + """ + + ignored_params = list(map(id, self._network.bias_layers.parameters())) + base_params = filter( + lambda p: id(p) not in ignored_params, self._network.parameters() + ) + network_params = [ + {"params": base_params, "lr": lrate, "weight_decay": weight_decay}, + { + "params": self._network.bias_layers.parameters(), + "lr": 0, + "weight_decay": 0, + }, + ] + optimizer = optim.SGD( + network_params, lr=lrate, momentum=0.9, weight_decay=weight_decay + ) + scheduler = optim.lr_scheduler.MultiStepLR( + optimizer=optimizer, milestones=milestones, gamma=lrate_decay + ) + + if len(self._multiple_gpus) > 1: + self._network = nn.DataParallel(self._network, self._multiple_gpus) + self._network.to(self._device) + if self._old_network is not None: + self._old_network.to(self._device) + + self._run(train_loader, test_loader, optimizer, scheduler, stage="training") + + def _stage2_bias_correction(self, val_loader, test_loader): + if isinstance(self._network, nn.DataParallel): + self._network = self._network.module + network_params = [ + { + "params": self._network.bias_layers[-1].parameters(), + "lr": lrate, + "weight_decay": weight_decay, + } + ] + optimizer = optim.SGD( + network_params, lr=lrate, momentum=0.9, weight_decay=weight_decay + ) + scheduler = optim.lr_scheduler.MultiStepLR( + optimizer=optimizer, milestones=milestones, gamma=lrate_decay + ) + + if len(self._multiple_gpus) > 1: + self._network = nn.DataParallel(self._network, self._multiple_gpus) + self._network.to(self._device) + + self._run( + val_loader, test_loader, optimizer, scheduler, stage="bias_correction" + ) + + def _log_bias_params(self): + logging.info("Parameters of bias layer:") + params = self._network.get_bias_params() + for i, param in enumerate(params): + logging.info("{} => {:.3f}, {:.3f}".format(i, param[0], param[1])) diff --git a/models/der.py b/models/der.py new file mode 100644 index 0000000000000000000000000000000000000000..ad2922db2771be3d1d502afa501b7507f88153c4 --- /dev/null +++ b/models/der.py @@ -0,0 +1,238 @@ +# Please note that the current implementation of DER only contains the dynamic expansion process, since masking and pruning are not implemented by the source repo. +import logging +import numpy as np +from tqdm import tqdm +import torch +from torch import nn +from torch import optim +from torch.nn import functional as F +from torch.utils.data import DataLoader +from models.base import BaseLearner +from utils.inc_net import DERNet, IncrementalNet +from utils.toolkit import count_parameters, target2onehot, tensor2numpy + +EPSILON = 1e-8 + +init_epoch = 200 +init_lr = 0.1 +init_milestones = [60, 120, 170] +init_lr_decay = 0.1 +init_weight_decay = 0.0005 + + +epochs = 170 +lrate = 0.1 +milestones = [80, 120, 150] +lrate_decay = 0.1 +batch_size = 128 +weight_decay = 2e-4 +num_workers = 0 +T = 2 + + +class DER(BaseLearner): + def __init__(self, args): + super().__init__(args) + self.args = args + self._network = DERNet(args, False) + + def after_task(self): + self._known_classes = self._total_classes + logging.info("Exemplar size: {}".format(self.exemplar_size)) + + def incremental_train(self, data_manager): + self._cur_task += 1 + self._total_classes = self._known_classes + data_manager.get_task_size( + self._cur_task + ) + self._network.update_fc(self._total_classes) + logging.info( + "Learning on {}-{}".format(self._known_classes, self._total_classes) + ) + if not self.args["attack"]: + if self._cur_task > 0: + for i in range(self._cur_task): + for p in self._network.convnets[i].parameters(): + p.requires_grad = False + + logging.info("All params: {}".format(count_parameters(self._network))) + logging.info( + "Trainable params: {}".format(count_parameters(self._network, True)) + ) + + train_dataset = data_manager.get_dataset( + np.arange(self._known_classes, self._total_classes), + source="train", + mode="train", + appendent=self._get_memory(), + ) + self.train_loader = DataLoader( + train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers + ) + test_dataset = data_manager.get_dataset( + np.arange(0, self._total_classes), source="test", mode="test" + ) + self.test_loader = DataLoader( + test_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers + ) + + if len(self._multiple_gpus) > 1: + self._network = nn.DataParallel(self._network, self._multiple_gpus) + self._train(self.train_loader, self.test_loader) + self.build_rehearsal_memory(data_manager, self.samples_per_class) + if len(self._multiple_gpus) > 1: + self._network = self._network.module + else: + test_dataset = data_manager.get_dataset( + np.arange(0, self._total_classes), source="test", mode="test" + ) + self.test_loader = DataLoader( + test_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers + ) + + def train(self): + self._network.train() + if len(self._multiple_gpus) > 1 : + self._network_module_ptr = self._network.module + else: + self._network_module_ptr = self._network + self._network_module_ptr.convnets[-1].train() + if self._cur_task >= 1: + for i in range(self._cur_task): + self._network_module_ptr.convnets[i].eval() + + def _train(self, train_loader, test_loader): + self._network.to(self._device) + if self._cur_task == 0: + optimizer = optim.SGD( + filter(lambda p: p.requires_grad, self._network.parameters()), + momentum=0.9, + lr=init_lr, + weight_decay=init_weight_decay, + ) + scheduler = optim.lr_scheduler.MultiStepLR( + optimizer=optimizer, milestones=init_milestones, gamma=init_lr_decay + ) + self._init_train(train_loader, test_loader, optimizer, scheduler) + else: + optimizer = optim.SGD( + filter(lambda p: p.requires_grad, self._network.parameters()), + lr=lrate, + momentum=0.9, + weight_decay=weight_decay, + ) + scheduler = optim.lr_scheduler.MultiStepLR( + optimizer=optimizer, milestones=milestones, gamma=lrate_decay + ) + self._update_representation(train_loader, test_loader, optimizer, scheduler) + if len(self._multiple_gpus) > 1: + self._network.module.weight_align( + self._total_classes - self._known_classes + ) + else: + self._network.weight_align(self._total_classes - self._known_classes) + + def _init_train(self, train_loader, test_loader, optimizer, scheduler): + prog_bar = tqdm(range(init_epoch)) + for _, epoch in enumerate(prog_bar): + self.train() + losses = 0.0 + correct, total = 0, 0 + for i, (_, inputs, targets) in enumerate(train_loader): + inputs, targets = inputs.to(self._device), targets.to(self._device) + logits = self._network(inputs)["logits"] + + loss = F.cross_entropy(logits, targets) + optimizer.zero_grad() + loss.backward() + optimizer.step() + losses += loss.item() + + _, preds = torch.max(logits, dim=1) + correct += preds.eq(targets.expand_as(preds)).cpu().sum() + total += len(targets) + + scheduler.step() + train_acc = np.around(tensor2numpy(correct) * 100 / total, decimals=2) + + if epoch % 5 == 0: + test_acc = self._compute_accuracy(self._network, test_loader) + info = "Task {}, Epoch {}/{} => Loss {:.3f}, Train_accy {:.2f}, Test_accy {:.2f}".format( + self._cur_task, + epoch + 1, + init_epoch, + losses / len(train_loader), + train_acc, + test_acc, + ) + else: + info = "Task {}, Epoch {}/{} => Loss {:.3f}, Train_accy {:.2f}".format( + self._cur_task, + epoch + 1, + init_epoch, + losses / len(train_loader), + train_acc, + ) + prog_bar.set_description(info) + + logging.info(info) + + def _update_representation(self, train_loader, test_loader, optimizer, scheduler): + prog_bar = tqdm(range(epochs)) + for _, epoch in enumerate(prog_bar): + self.train() + losses = 0.0 + losses_clf = 0.0 + losses_aux = 0.0 + correct, total = 0, 0 + for i, (_, inputs, targets) in enumerate(train_loader): + inputs, targets = inputs.to(self._device), targets.to(self._device) + outputs = self._network(inputs) + logits, aux_logits = outputs["logits"], outputs["aux_logits"] + loss_clf = F.cross_entropy(logits, targets) + aux_targets = targets.clone() + aux_targets = torch.where( + aux_targets - self._known_classes + 1 > 0, + aux_targets - self._known_classes + 1, + 0, + ) + loss_aux = F.cross_entropy(aux_logits, aux_targets) + loss = loss_clf + loss_aux + + optimizer.zero_grad() + loss.backward() + optimizer.step() + losses += loss.item() + losses_aux += loss_aux.item() + losses_clf += loss_clf.item() + + _, preds = torch.max(logits, dim=1) + correct += preds.eq(targets.expand_as(preds)).cpu().sum() + total += len(targets) + + scheduler.step() + train_acc = np.around(tensor2numpy(correct) * 100 / total, decimals=2) + if epoch % 5 == 0: + test_acc = self._compute_accuracy(self._network, test_loader) + info = "Task {}, Epoch {}/{} => Loss {:.3f}, Loss_clf {:.3f}, Loss_aux {:.3f}, Train_accy {:.2f}, Test_accy {:.2f}".format( + self._cur_task, + epoch + 1, + epochs, + losses / len(train_loader), + losses_clf / len(train_loader), + losses_aux / len(train_loader), + train_acc, + test_acc, + ) + else: + info = "Task {}, Epoch {}/{} => Loss {:.3f}, Loss_clf {:.3f}, Loss_aux {:.3f}, Train_accy {:.2f}".format( + self._cur_task, + epoch + 1, + epochs, + losses / len(train_loader), + losses_clf / len(train_loader), + losses_aux / len(train_loader), + train_acc, + ) + prog_bar.set_description(info) + logging.info(info) diff --git a/models/finetune.py b/models/finetune.py new file mode 100644 index 0000000000000000000000000000000000000000..3bfd220c1c5f5e60f761f3e1b9e4020afce85148 --- /dev/null +++ b/models/finetune.py @@ -0,0 +1,190 @@ +import logging +import numpy as np +import torch +from torch import nn +from torch.serialization import load +from tqdm import tqdm +from torch import optim +from torch.nn import functional as F +from torch.utils.data import DataLoader +from utils.inc_net import IncrementalNet +from models.base import BaseLearner +from utils.toolkit import target2onehot, tensor2numpy + + +init_epoch = 200 +init_lr = 0.1 +init_milestones = [60, 120, 170] +init_lr_decay = 0.1 +init_weight_decay = 0.0005 + + +epochs = 80 +lrate = 0.1 +milestones = [40, 70] +lrate_decay = 0.1 +batch_size = 256 +weight_decay = 2e-4 +num_workers = 0 + + +class Finetune(BaseLearner): + def __init__(self, args): + super().__init__(args) + self.args = args + self._network = IncrementalNet(args, False) + + def after_task(self): + self._known_classes = self._total_classes + + def incremental_train(self, data_manager): + self._cur_task += 1 + self._total_classes = self._known_classes + data_manager.get_task_size( + self._cur_task + ) + self._network.update_fc(self._total_classes) + logging.info( + "Learning on {}-{}".format(self._known_classes, self._total_classes) + ) + if not self.args["attack"]: + train_dataset = data_manager.get_dataset( + np.arange(self._known_classes, self._total_classes), + source="train", + mode="train", + ) + self.train_loader = DataLoader( + train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers + ) + test_dataset = data_manager.get_dataset( + np.arange(0, self._total_classes), source="test", mode="test" + ) + self.test_loader = DataLoader( + test_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers + ) + + if len(self._multiple_gpus) > 1: + self._network = nn.DataParallel(self._network, self._multiple_gpus) + self._train(self.train_loader, self.test_loader) + if len(self._multiple_gpus) > 1: + self._network = self._network.module + + def _train(self, train_loader, test_loader): + self._network.to(self._device) + if self._cur_task == 0: + optimizer = optim.SGD( + self._network.parameters(), + momentum=0.9, + lr=init_lr, + weight_decay=init_weight_decay, + ) + scheduler = optim.lr_scheduler.MultiStepLR( + optimizer=optimizer, milestones=init_milestones, gamma=init_lr_decay + ) + self._init_train(train_loader, test_loader, optimizer, scheduler) + else: + optimizer = optim.SGD( + self._network.parameters(), + lr=lrate, + momentum=0.9, + weight_decay=weight_decay, + ) # 1e-5 + scheduler = optim.lr_scheduler.MultiStepLR( + optimizer=optimizer, milestones=milestones, gamma=lrate_decay + ) + self._update_representation(train_loader, test_loader, optimizer, scheduler) + + def _init_train(self, train_loader, test_loader, optimizer, scheduler): + prog_bar = tqdm(range(init_epoch)) + for _, epoch in enumerate(prog_bar): + self._network.train() + losses = 0.0 + correct, total = 0, 0 + for i, (_, inputs, targets) in enumerate(train_loader): + inputs, targets = inputs.to(self._device), targets.to(self._device) + logits = self._network(inputs)["logits"] + + loss = F.cross_entropy(logits, targets) + optimizer.zero_grad() + loss.backward() + optimizer.step() + losses += loss.item() + + _, preds = torch.max(logits, dim=1) + correct += preds.eq(targets.expand_as(preds)).cpu().sum() + total += len(targets) + + scheduler.step() + train_acc = np.around(tensor2numpy(correct) * 100 / total, decimals=2) + + if epoch % 5 == 0: + test_acc = self._compute_accuracy(self._network, test_loader) + info = "Task {}, Epoch {}/{} => Loss {:.3f}, Train_accy {:.2f}, Test_accy {:.2f}".format( + self._cur_task, + epoch + 1, + init_epoch, + losses / len(train_loader), + train_acc, + test_acc, + ) + else: + info = "Task {}, Epoch {}/{} => Loss {:.3f}, Train_accy {:.2f}".format( + self._cur_task, + epoch + 1, + init_epoch, + losses / len(train_loader), + train_acc, + ) + + prog_bar.set_description(info) + + logging.info(info) + + def _update_representation(self, train_loader, test_loader, optimizer, scheduler): + + prog_bar = tqdm(range(epochs)) + for _, epoch in enumerate(prog_bar): + self._network.train() + losses = 0.0 + correct, total = 0, 0 + for i, (_, inputs, targets) in enumerate(train_loader): + inputs, targets = inputs.to(self._device), targets.to(self._device) + logits = self._network(inputs)["logits"] + + fake_targets = targets - self._known_classes + loss_clf = F.cross_entropy( + logits[:, self._known_classes :], fake_targets + ) + + loss = loss_clf + + optimizer.zero_grad() + loss.backward() + optimizer.step() + losses += loss.item() + + _, preds = torch.max(logits, dim=1) + correct += preds.eq(targets.expand_as(preds)).cpu().sum() + total += len(targets) + + scheduler.step() + train_acc = np.around(tensor2numpy(correct) * 100 / total, decimals=2) + if epoch % 5 == 0: + test_acc = self._compute_accuracy(self._network, test_loader) + info = "Task {}, Epoch {}/{} => Loss {:.3f}, Train_accy {:.2f}, Test_accy {:.2f}".format( + self._cur_task, + epoch + 1, + epochs, + losses / len(train_loader), + train_acc, + test_acc, + ) + else: + info = "Task {}, Epoch {}/{} => Loss {:.3f}, Train_accy {:.2f}".format( + self._cur_task, + epoch + 1, + epochs, + losses / len(train_loader), + train_acc, + ) + prog_bar.set_description(info) + logging.info(info) diff --git a/models/foster.py b/models/foster.py new file mode 100644 index 0000000000000000000000000000000000000000..36ec513533c3f24009442401603a3603e2b3367e --- /dev/null +++ b/models/foster.py @@ -0,0 +1,428 @@ +import logging +import numpy as np +from tqdm import tqdm +import torch +from torch import nn +from torch import optim +from torch.nn import functional as F +from torch.utils.data import DataLoader +from models.base import BaseLearner +from utils.inc_net import FOSTERNet +from utils.toolkit import count_parameters, target2onehot, tensor2numpy + +# Please refer to https://github.com/G-U-N/ECCV22-FOSTER for the full source code to reproduce foster. + +EPSILON = 1e-8 + + +class FOSTER(BaseLearner): + def __init__(self, args): + super().__init__(args) + self.args = args + self._network = FOSTERNet(args, False) + self._snet = None + self.beta1 = args["beta1"] + self.beta2 = args["beta2"] + self.per_cls_weights = None + self.is_teacher_wa = args["is_teacher_wa"] + self.is_student_wa = args["is_student_wa"] + self.lambda_okd = args["lambda_okd"] + self.wa_value = args["wa_value"] + self.oofc = args["oofc"].lower() + + def after_task(self): + self._known_classes = self._total_classes + logging.info("Exemplar size: {}".format(self.exemplar_size)) + + def incremental_train(self, data_manager=None): + self.data_manager = data_manager + self._cur_task += 1 + if self._cur_task > 1: + self._network = self._snet + self._total_classes = self._known_classes + data_manager.get_task_size( + self._cur_task + ) + self._network.update_fc(self._total_classes) + self._network_module_ptr = self._network + logging.info( + "Learning on {}-{}".format(self._known_classes, self._total_classes) + ) + if not self.args["attack"]: + if self._cur_task > 0: + for p in self._network.convnets[0].parameters(): + p.requires_grad = False + for p in self._network.oldfc.parameters(): + p.requires_grad = False + + logging.info("All params: {}".format(count_parameters(self._network))) + logging.info( + "Trainable params: {}".format(count_parameters(self._network, True)) + ) + + train_dataset = data_manager.get_dataset( + np.arange(self._known_classes, self._total_classes), + source="train", + mode="train", + appendent=self._get_memory(), + ) + self.train_loader = DataLoader( + train_dataset, + batch_size=self.args["batch_size"], + shuffle=True, + num_workers=self.args["num_workers"], + pin_memory=True, + ) + test_dataset = data_manager.get_dataset( + np.arange(0, self._total_classes), source="test", mode="test" + ) + self.test_loader = DataLoader( + test_dataset, + batch_size=self.args["batch_size"], + shuffle=False, + num_workers=self.args["num_workers"], + ) + + if len(self._multiple_gpus) > 1: + self._network = nn.DataParallel(self._network, self._multiple_gpus) + else: + self.train_loader, self.test_loader = None, None + + self._train(self.train_loader, self.test_loader) + if not self.args["attack"]: + self.build_rehearsal_memory(data_manager, self.samples_per_class) + if len(self._multiple_gpus) > 1: + self._network = self._network.module + + def train(self): + self._network_module_ptr.train() + self._network_module_ptr.convnets[-1].train() + if self._cur_task >= 1: + self._network_module_ptr.convnets[0].eval() + + def _train(self, train_loader=None, test_loader=None): + self._network.to(self._device) + if self.args["attack"]: + self._network.eval() + + if hasattr(self._network, "module"): + self._network_module_ptr = self._network.module + if self._cur_task == 0: + if self.args["attack"]: + optimizer, scheduler = None, None + else: + optimizer = optim.SGD( + filter(lambda p: p.requires_grad, self._network.parameters()), + momentum=0.9, + lr=self.args["init_lr"], + weight_decay=self.args["init_weight_decay"], + ) + scheduler = optim.lr_scheduler.CosineAnnealingLR( + optimizer=optimizer, T_max=self.args["init_epochs"] + ) + self._init_train(train_loader, test_loader, optimizer, scheduler) + else: + if self.args["attack"]: + train_loader, test_loader = None, None + else: + cls_num_list = [self.samples_old_class] * self._known_classes + [ + self.samples_new_class(i) + for i in range(self._known_classes, self._total_classes) + ] + + effective_num = 1.0 - np.power(self.beta1, cls_num_list) + per_cls_weights = (1.0 - self.beta1) / np.array(effective_num) + per_cls_weights = ( + per_cls_weights / np.sum(per_cls_weights) * len(cls_num_list) + ) + + logging.info("per cls weights : {}".format(per_cls_weights)) + self.per_cls_weights = torch.FloatTensor(per_cls_weights).to(self._device) + + optimizer = optim.SGD( + filter(lambda p: p.requires_grad, self._network.parameters()), + lr=self.args["lr"], + momentum=0.9, + weight_decay=self.args["weight_decay"], + ) + scheduler = optim.lr_scheduler.CosineAnnealingLR( + optimizer=optimizer, T_max=self.args["boosting_epochs"] + ) + if self.oofc == "az": + for i, p in enumerate(self._network_module_ptr.fc.parameters()): + if i == 0: + p.data[ + self._known_classes :, : self._network_module_ptr.out_dim + ] = torch.tensor(0.0) + elif self.oofc != "ft": + assert 0, "not implemented" + self._feature_boosting(train_loader, test_loader, optimizer, scheduler) + if self.is_teacher_wa: + self._network_module_ptr.weight_align( + self._known_classes, + self._total_classes - self._known_classes, + self.wa_value, + ) + else: + logging.info("do not weight align teacher!") + + cls_num_list = [self.samples_old_class] * self._known_classes + [ + self.samples_new_class(i) + for i in range(self._known_classes, self._total_classes) + ] + effective_num = 1.0 - np.power(self.beta2, cls_num_list) + per_cls_weights = (1.0 - self.beta2) / np.array(effective_num) + per_cls_weights = ( + per_cls_weights / np.sum(per_cls_weights) * len(cls_num_list) + ) + logging.info("per cls weights : {}".format(per_cls_weights)) + self.per_cls_weights = torch.FloatTensor(per_cls_weights).to(self._device) + + self._feature_compression(train_loader, test_loader) + + def _init_train(self, train_loader, test_loader, optimizer, scheduler): + prog_bar = tqdm(range(self.args["init_epochs"])) + for _, epoch in enumerate(prog_bar): + self.train() + losses = 0.0 + correct, total = 0, 0 + for i, (_, inputs, targets) in enumerate(train_loader): + inputs, targets = inputs.to( + self._device, non_blocking=True + ), targets.to(self._device, non_blocking=True) + logits = self._network(inputs)["logits"] + loss = F.cross_entropy(logits, targets) + optimizer.zero_grad() + loss.backward() + optimizer.step() + losses += loss.item() + _, preds = torch.max(logits, dim=1) + correct += preds.eq(targets.expand_as(preds)).cpu().sum() + total += len(targets) + scheduler.step() + train_acc = np.around(tensor2numpy(correct) * 100 / total, decimals=2) + + if epoch % 5 == 0: + test_acc = self._compute_accuracy(self._network, test_loader) + info = "Task {}, Epoch {}/{} => Loss {:.3f}, Train_accy {:.2f}, Test_accy {:.2f}".format( + self._cur_task, + epoch + 1, + self.args["init_epochs"], + losses / len(train_loader), + train_acc, + test_acc, + ) + else: + info = "Task {}, Epoch {}/{} => Loss {:.3f}, Train_accy {:.2f}".format( + self._cur_task, + epoch + 1, + self.args["init_epochs"], + losses / len(train_loader), + train_acc, + ) + + prog_bar.set_description(info) + logging.info(info) + + def _feature_boosting(self, train_loader, test_loader, optimizer, scheduler): + prog_bar = tqdm(range(self.args["boosting_epochs"])) + for _, epoch in enumerate(prog_bar): + self.train() + losses = 0.0 + losses_clf = 0.0 + losses_fe = 0.0 + losses_kd = 0.0 + correct, total = 0, 0 + for i, (_, inputs, targets) in enumerate(train_loader): + inputs, targets = inputs.to( + self._device, non_blocking=True + ), targets.to(self._device, non_blocking=True) + outputs = self._network(inputs) + logits, fe_logits, old_logits = ( + outputs["logits"], + outputs["fe_logits"], + outputs["old_logits"].detach(), + ) + loss_clf = F.cross_entropy(logits / self.per_cls_weights, targets) + loss_fe = F.cross_entropy(fe_logits, targets) + loss_kd = self.lambda_okd * _KD_loss( + logits[:, : self._known_classes], old_logits, self.args["T"] + ) + loss = loss_clf + loss_fe + loss_kd + optimizer.zero_grad() + loss.backward() + if self.oofc == "az": + for i, p in enumerate(self._network_module_ptr.fc.parameters()): + if i == 0: + p.grad.data[ + self._known_classes :, + : self._network_module_ptr.out_dim, + ] = torch.tensor(0.0) + elif self.oofc != "ft": + assert 0, "not implemented" + optimizer.step() + losses += loss.item() + losses_fe += loss_fe.item() + losses_clf += loss_clf.item() + losses_kd += ( + self._known_classes / self._total_classes + ) * loss_kd.item() + _, preds = torch.max(logits, dim=1) + correct += preds.eq(targets.expand_as(preds)).cpu().sum() + total += len(targets) + scheduler.step() + train_acc = np.around(tensor2numpy(correct) * 100 / total, decimals=2) + if epoch % 5 == 0: + test_acc = self._compute_accuracy(self._network, test_loader) + info = "Task {}, Epoch {}/{} => Loss {:.3f}, Loss_clf {:.3f}, Loss_fe {:.3f}, Loss_kd {:.3f}, Train_accy {:.2f}, Test_accy {:.2f}".format( + self._cur_task, + epoch + 1, + self.args["boosting_epochs"], + losses / len(train_loader), + losses_clf / len(train_loader), + losses_fe / len(train_loader), + losses_kd / len(train_loader), + train_acc, + test_acc, + ) + else: + info = "Task {}, Epoch {}/{} => Loss {:.3f}, Loss_clf {:.3f}, Loss_fe {:.3f}, Loss_kd {:.3f}, Train_accy {:.2f}".format( + self._cur_task, + epoch + 1, + self.args["boosting_epochs"], + losses / len(train_loader), + losses_clf / len(train_loader), + losses_fe / len(train_loader), + losses_kd / len(train_loader), + train_acc, + ) + prog_bar.set_description(info) + logging.info(info) + + def _feature_compression(self, train_loader, test_loader): + self._snet = FOSTERNet(self.args, False) + self._snet.update_fc(self._total_classes) + if len(self._multiple_gpus) > 1: + self._snet = nn.DataParallel(self._snet, self._multiple_gpus) + if hasattr(self._snet, "module"): + self._snet_module_ptr = self._snet.module + else: + self._snet_module_ptr = self._snet + self._snet.to(self._device) + self._snet_module_ptr.convnets[0].load_state_dict( + self._network_module_ptr.convnets[0].state_dict() + ) + self._snet_module_ptr.copy_fc(self._network_module_ptr.oldfc) + + if not self.args["attack"]: + optimizer = optim.SGD( + filter(lambda p: p.requires_grad, self._snet.parameters()), + lr=self.args["lr"], + momentum=0.9, + ) + scheduler = optim.lr_scheduler.CosineAnnealingLR( + optimizer=optimizer, T_max=self.args["compression_epochs"] + ) + self._network.eval() + prog_bar = tqdm(range(self.args["compression_epochs"])) + for _, epoch in enumerate(prog_bar): + self._snet.train() + losses = 0.0 + correct, total = 0, 0 + for i, (_, inputs, targets) in enumerate(train_loader): + inputs, targets = inputs.to( + self._device, non_blocking=True + ), targets.to(self._device, non_blocking=True) + dark_logits = self._snet(inputs)["logits"] + with torch.no_grad(): + outputs = self._network(inputs) + logits, old_logits, fe_logits = ( + outputs["logits"], + outputs["old_logits"], + outputs["fe_logits"], + ) + loss_dark = self.BKD(dark_logits, logits, self.args["T"]) + loss = loss_dark + optimizer.zero_grad() + loss.backward() + optimizer.step() + losses += loss.item() + _, preds = torch.max(dark_logits[: targets.shape[0]], dim=1) + correct += preds.eq(targets.expand_as(preds)).cpu().sum() + total += len(targets) + scheduler.step() + train_acc = np.around(tensor2numpy(correct) * 100 / total, decimals=2) + if epoch % 5 == 0: + test_acc = self._compute_accuracy(self._snet, test_loader) + info = "SNet: Task {}, Epoch {}/{} => Loss {:.3f}, Train_accy {:.2f}, Test_accy {:.2f}".format( + self._cur_task, + epoch + 1, + self.args["compression_epochs"], + losses / len(train_loader), + train_acc, + test_acc, + ) + else: + info = "SNet: Task {}, Epoch {}/{} => Loss {:.3f}, Train_accy {:.2f}".format( + self._cur_task, + epoch + 1, + self.args["compression_epochs"], + losses / len(train_loader), + train_acc, + ) + prog_bar.set_description(info) + logging.info(info) + if len(self._multiple_gpus) > 1: + self._snet = self._snet.module + if self.is_student_wa: + self._snet.weight_align( + self._known_classes, + self._total_classes - self._known_classes, + self.wa_value, + ) + else: + logging.info("do not weight align student!") + + self._snet.eval() + y_pred, y_true = [], [] + for _, (_, inputs, targets) in enumerate(test_loader): + inputs = inputs.to(self._device, non_blocking=True) + with torch.no_grad(): + outputs = self._snet(inputs)["logits"] + predicts = torch.topk( + outputs, k=self.topk, dim=1, largest=True, sorted=True + )[1] + y_pred.append(predicts.cpu().numpy()) + y_true.append(targets.cpu().numpy()) + y_pred = np.concatenate(y_pred) + y_true = np.concatenate(y_true) + cnn_accy = self._evaluate(y_pred, y_true) + logging.info("darknet eval: ") + logging.info("CNN top1 curve: {}".format(cnn_accy["top1"])) + logging.info("CNN top5 curve: {}".format(cnn_accy["top5"])) + + @property + def samples_old_class(self): + if self._fixed_memory: + return self._memory_per_class + else: + assert self._total_classes != 0, "Total classes is 0" + return self._memory_size // self._known_classes + + def samples_new_class(self, index): + if self.args["dataset"] == "cifar100": + return 500 + else: + return self.data_manager.getlen(index) + + def BKD(self, pred, soft, T): + pred = torch.log_softmax(pred / T, dim=1) + soft = torch.softmax(soft / T, dim=1) + soft = soft * self.per_cls_weights + soft = soft / soft.sum(1)[:, None] + return -1 * torch.mul(soft, pred).sum() / pred.shape[0] + + +def _KD_loss(pred, soft, T): + pred = torch.log_softmax(pred / T, dim=1) + soft = torch.softmax(soft / T, dim=1) + return -1 * torch.mul(soft, pred).sum() / pred.shape[0] diff --git a/models/icarl.py b/models/icarl.py new file mode 100644 index 0000000000000000000000000000000000000000..fff8c9fe4b84297ee05ba22670637cc3ddb33532 --- /dev/null +++ b/models/icarl.py @@ -0,0 +1,223 @@ +import logging +import numpy as np +from tqdm import tqdm +import torch +from torch import nn +from torch import optim +from torch.nn import functional as F +from models.base import BaseLearner +from utils.inc_net import IncrementalNet +from utils.inc_net import CosineIncrementalNet +from utils.toolkit import target2onehot, tensor2numpy +from torch.utils.data import DataLoader + +EPSILON = 1e-8 + +init_epoch = 200 +init_lr = 0.1 +init_milestones = [60, 120, 170] +init_lr_decay = 0.1 +init_weight_decay = 0.0005 + + +epochs = 170 +lrate = 0.1 +milestones = [80, 120] +lrate_decay = 0.1 +batch_size = 128 +weight_decay = 2e-4 +num_workers = 0 +T = 2 + + +class iCaRL(BaseLearner): + def __init__(self, args): + super().__init__(args) + self.args = args + self._network = IncrementalNet(args, False) + + def after_task(self): + self._old_network = self._network.copy().freeze() + self._known_classes = self._total_classes + logging.info("Exemplar size: {}".format(self.exemplar_size)) + + def incremental_train(self, data_manager=None): + self._cur_task += 1 + self._total_classes = self._known_classes + data_manager.get_task_size( + self._cur_task + ) + self._network.update_fc(self._total_classes) + logging.info( + "Learning on {}-{}".format(self._known_classes, self._total_classes) + ) + if not self.args["attack"]: + + train_dataset = data_manager.get_dataset( + np.arange(self._known_classes, self._total_classes), + source="train", + mode="train", + appendent=self._get_memory(), + ) + self.train_loader = DataLoader( + train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers + ) + test_dataset = data_manager.get_dataset( + np.arange(0, self._total_classes), source="test", mode="test" + ) + self.test_loader = DataLoader( + test_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers + ) + + if len(self._multiple_gpus) > 1: + self._network = nn.DataParallel(self._network, self._multiple_gpus) + else: + self.train_loader, self.test_loader = None, None + test_dataset = data_manager.get_dataset( + np.arange(0, self._total_classes), source="test", mode="test" + ) + self.test_loader = DataLoader( + test_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers + ) + + self._train(self.train_loader, self.test_loader) + if not self.args["attack"]: + self.build_rehearsal_memory(data_manager, self.samples_per_class) + if len(self._multiple_gpus) > 1: + self._network = self._network.module + + def _train(self, train_loader=None, test_loader=None): + self._network.to(self._device) + if self.args["attack"]: + self._network.eval() + if self._old_network is not None: + self._old_network.to(self._device) + if self.args["attack"]: + self._old_network.eval() + + if self._cur_task == 0: + if not self.args["attack"]: + optimizer = optim.SGD( + self._network.parameters(), + momentum=0.9, + lr=init_lr, + weight_decay=init_weight_decay, + ) + scheduler = optim.lr_scheduler.MultiStepLR( + optimizer=optimizer, milestones=init_milestones, gamma=init_lr_decay + ) + self._init_train(train_loader, test_loader, optimizer, scheduler) + else: + if not self.args["attack"]: + optimizer = optim.SGD( + self._network.parameters(), + lr=lrate, + momentum=0.9, + weight_decay=weight_decay, + ) # 1e-5 + scheduler = optim.lr_scheduler.MultiStepLR( + optimizer=optimizer, milestones=milestones, gamma=lrate_decay + ) + self._update_representation(train_loader, test_loader, optimizer, scheduler) + + def _init_train(self, train_loader, test_loader, optimizer, scheduler): + prog_bar = tqdm(range(init_epoch)) + for _, epoch in enumerate(prog_bar): + self._network.train() + losses = 0.0 + correct, total = 0, 0 + for i, (_, inputs, targets) in enumerate(train_loader): + inputs, targets = inputs.to(self._device), targets.to(self._device).to(torch.int64) + logits = self._network(inputs)["logits"] + + loss = F.cross_entropy(logits, targets) + optimizer.zero_grad() + loss.backward() + optimizer.step() + losses += loss.item() + + _, preds = torch.max(logits, dim=1) + correct += preds.eq(targets.expand_as(preds)).cpu().sum() + total += len(targets) + + scheduler.step() + train_acc = np.around(tensor2numpy(correct) * 100 / total, decimals=2) + + if epoch % 5 == 0: + test_acc = self._compute_accuracy(self._network, test_loader) + info = "Task {}, Epoch {}/{} => Loss {:.3f}, Train_accy {:.2f}, Test_accy {:.2f}".format( + self._cur_task, + epoch + 1, + init_epoch, + losses / len(train_loader), + train_acc, + test_acc, + ) + else: + info = "Task {}, Epoch {}/{} => Loss {:.3f}, Train_accy {:.2f}".format( + self._cur_task, + epoch + 1, + init_epoch, + losses / len(train_loader), + train_acc, + ) + + prog_bar.set_description(info) + + logging.info(info) + + def _update_representation(self, train_loader, test_loader, optimizer, scheduler): + prog_bar = tqdm(range(epochs)) + for _, epoch in enumerate(prog_bar): + self._network.train() + losses = 0.0 + correct, total = 0, 0 + for i, (_, inputs, targets) in enumerate(train_loader): + inputs, targets = inputs.to(self._device), targets.to(self._device).to(torch.int64) + logits = self._network(inputs)["logits"] + + loss_clf = F.cross_entropy(logits, targets) + loss_kd = _KD_loss( + logits[:, : self._known_classes], + self._old_network(inputs)["logits"], + T, + ) + + loss = loss_clf + loss_kd + + optimizer.zero_grad() + loss.backward() + optimizer.step() + losses += loss.item() + + _, preds = torch.max(logits, dim=1) + correct += preds.eq(targets.expand_as(preds)).cpu().sum() + total += len(targets) + + scheduler.step() + train_acc = np.around(tensor2numpy(correct) * 100 / total, decimals=2) + if epoch % 5 == 0: + test_acc = self._compute_accuracy(self._network, test_loader) + info = "Task {}, Epoch {}/{} => Loss {:.3f}, Train_accy {:.2f}, Test_accy {:.2f}".format( + self._cur_task, + epoch + 1, + epochs, + losses / len(train_loader), + train_acc, + test_acc, + ) + else: + info = "Task {}, Epoch {}/{} => Loss {:.3f}, Train_accy {:.2f}".format( + self._cur_task, + epoch + 1, + epochs, + losses / len(train_loader), + train_acc, + ) + prog_bar.set_description(info) + logging.info(info) + + +def _KD_loss(pred, soft, T): + pred = torch.log_softmax(pred / T, dim=1) + soft = torch.softmax(soft / T, dim=1) + return -1 * torch.mul(soft, pred).sum() / pred.shape[0] diff --git a/models/memo.py b/models/memo.py new file mode 100644 index 0000000000000000000000000000000000000000..4e2055c514e6f56a7710681c735633c6831589aa --- /dev/null +++ b/models/memo.py @@ -0,0 +1,338 @@ +import logging +import numpy as np +from tqdm import tqdm +import torch +from torch import nn +import copy +from torch import optim +from torch.nn import functional as F +from torch.utils.data import DataLoader +from models.base import BaseLearner +from utils.inc_net import AdaptiveNet +from utils.toolkit import count_parameters, target2onehot, tensor2numpy + +num_workers=0 +EPSILON = 1e-8 +batch_size = 64 + +class MEMO(BaseLearner): + + def __init__(self, args): + super().__init__(args) + self.args = args + self._old_base = None + self._network = AdaptiveNet(args, False) + logging.info(f'>>> train generalized blocks:{self.args["train_base"]} train_adaptive:{self.args["train_adaptive"]}') + + def after_task(self): + self._known_classes = self._total_classes + if self._cur_task == 0: + if self.args['train_base']: + logging.info("Train Generalized Blocks...") + self._network.TaskAgnosticExtractor.train() + for param in self._network.TaskAgnosticExtractor.parameters(): + param.requires_grad = True + else: + logging.info("Fix Generalized Blocks...") + self._network.TaskAgnosticExtractor.eval() + for param in self._network.TaskAgnosticExtractor.parameters(): + param.requires_grad = False + + logging.info('Exemplar size: {}'.format(self.exemplar_size)) + + def incremental_train(self, data_manager): + self._cur_task += 1 + self._total_classes = self._known_classes + data_manager.get_task_size(self._cur_task) + self._network.update_fc(self._total_classes) + + logging.info('Learning on {}-{}'.format(self._known_classes, self._total_classes)) + + if not self.args["attack"]: + if self._cur_task>0: + for i in range(self._cur_task): + for p in self._network.AdaptiveExtractors[i].parameters(): + if self.args['train_adaptive']: + p.requires_grad = True + else: + p.requires_grad = False + + logging.info('All params: {}'.format(count_parameters(self._network))) + logging.info('Trainable params: {}'.format(count_parameters(self._network, True))) + train_dataset = data_manager.get_dataset( + np.arange(self._known_classes, self._total_classes), + source='train', + mode='train', + appendent=self._get_memory() + ) + self.train_loader = DataLoader( + train_dataset, + batch_size=self.args["batch_size"], + shuffle=True, + num_workers=num_workers + ) + + test_dataset = data_manager.get_dataset( + np.arange(0, self._total_classes), + source='test', + mode='test' + ) + self.test_loader = DataLoader( + test_dataset, + batch_size=self.args["batch_size"], + shuffle=False, + num_workers=num_workers + ) + + if len(self._multiple_gpus) > 1: + self._network = nn.DataParallel(self._network, self._multiple_gpus) + self._train(self.train_loader, self.test_loader) + self.build_rehearsal_memory(data_manager, self.samples_per_class) + if len(self._multiple_gpus) > 1: + self._network = self._network.module + + def set_network(self): + if len(self._multiple_gpus) > 1: + self._network = self._network.module + self._network.train() #All status from eval to train + if self.args['train_base']: + self._network.TaskAgnosticExtractor.train() + else: + self._network.TaskAgnosticExtractor.eval() + + # set adaptive extractor's status + self._network.AdaptiveExtractors[-1].train() + if self._cur_task >= 1: + for i in range(self._cur_task): + if self.args['train_adaptive']: + self._network.AdaptiveExtractors[i].train() + else: + self._network.AdaptiveExtractors[i].eval() + if len(self._multiple_gpus) > 1: + self._network = nn.DataParallel(self._network, self._multiple_gpus) + + def _train(self, train_loader, test_loader): + self._network.to(self._device) + if self._cur_task==0: + optimizer = optim.SGD( + filter(lambda p: p.requires_grad, self._network.parameters()), + momentum=0.9, + lr=self.args["init_lr"], + weight_decay=self.args["init_weight_decay"] + ) + if self.args['scheduler'] == 'steplr': + scheduler = optim.lr_scheduler.MultiStepLR( + optimizer=optimizer, + milestones=self.args['init_milestones'], + gamma=self.args['init_lr_decay'] + ) + elif self.args['scheduler'] == 'cosine': + scheduler = optim.lr_scheduler.CosineAnnealingLR( + optimizer=optimizer, + T_max=self.args['init_epoch'] + ) + else: + raise NotImplementedError + + if not self.args['skip']: + self._init_train(train_loader, test_loader, optimizer, scheduler) + else: + if isinstance(self._network, nn.DataParallel): + self._network = self._network.module + load_acc = self._network.load_checkpoint(self.args) + self._network.to(self._device) + + if len(self._multiple_gpus) > 1: + self._network = nn.DataParallel(self._network, self._multiple_gpus) + + cur_test_acc = self._compute_accuracy(self._network, self.test_loader) + logging.info(f"Loaded_Test_Acc:{load_acc} Cur_Test_Acc:{cur_test_acc}") + else: + optimizer = optim.SGD( + filter(lambda p: p.requires_grad, self._network.parameters()), + lr=self.args['lrate'], + momentum=0.9, + weight_decay=self.args['weight_decay'] + ) + if self.args['scheduler'] == 'steplr': + scheduler = optim.lr_scheduler.MultiStepLR( + optimizer=optimizer, + milestones=self.args['milestones'], + gamma=self.args['lrate_decay'] + ) + elif self.args['scheduler'] == 'cosine': + assert self.args['t_max'] is not None + scheduler = optim.lr_scheduler.CosineAnnealingLR( + optimizer=optimizer, + T_max=self.args['t_max'] + ) + else: + raise NotImplementedError + self._update_representation(train_loader, test_loader, optimizer, scheduler) + if len(self._multiple_gpus) > 1: + self._network.module.weight_align(self._total_classes-self._known_classes) + else: + self._network.weight_align(self._total_classes-self._known_classes) + + + def _init_train(self,train_loader,test_loader,optimizer,scheduler): + prog_bar = tqdm(range(self.args["init_epoch"])) + for _, epoch in enumerate(prog_bar): + self._network.train() + losses = 0. + correct, total = 0, 0 + for i, (_, inputs, targets) in enumerate(train_loader): + inputs, targets = inputs.to(self._device), targets.to(self._device) + logits = self._network(inputs)['logits'] + + loss=F.cross_entropy(logits,targets) + optimizer.zero_grad() + loss.backward() + optimizer.step() + losses += loss.item() + + _, preds = torch.max(logits, dim=1) + correct += preds.eq(targets.expand_as(preds)).cpu().sum() + total += len(targets) + + scheduler.step() + train_acc = np.around(tensor2numpy(correct)*100 / total, decimals=2) + if epoch%5==0: + test_acc = self._compute_accuracy(self._network, test_loader) + info = 'Task {}, Epoch {}/{} => Loss {:.3f}, Train_accy {:.2f}, Test_accy {:.2f}'.format( + self._cur_task, epoch+1, self.args['init_epoch'], losses/len(train_loader), train_acc, test_acc) + else: + info = 'Task {}, Epoch {}/{} => Loss {:.3f}, Train_accy {:.2f}'.format( + self._cur_task, epoch+1, self.args['init_epoch'], losses/len(train_loader), train_acc) + # prog_bar.set_description(info) + logging.info(info) + + def _update_representation(self, train_loader, test_loader, optimizer, scheduler): + prog_bar = tqdm(range(self.args["epochs"])) + for _, epoch in enumerate(prog_bar): + self.set_network() + losses = 0. + losses_clf=0. + losses_aux=0. + correct, total = 0, 0 + for i, (_, inputs, targets) in enumerate(train_loader): + inputs, targets = inputs.to(self._device), targets.to(self._device) + + outputs= self._network(inputs) + logits,aux_logits=outputs["logits"],outputs["aux_logits"] + loss_clf=F.cross_entropy(logits,targets) + aux_targets = targets.clone() + aux_targets=torch.where(aux_targets-self._known_classes+1>0, aux_targets-self._known_classes+1,0) + loss_aux=F.cross_entropy(aux_logits,aux_targets) + loss=loss_clf+self.args['alpha_aux']*loss_aux + + optimizer.zero_grad() + loss.backward() + optimizer.step() + losses += loss.item() + losses_aux+=loss_aux.item() + losses_clf+=loss_clf.item() + + _, preds = torch.max(logits, dim=1) + correct += preds.eq(targets.expand_as(preds)).cpu().sum() + total += len(targets) + + scheduler.step() + train_acc = np.around(tensor2numpy(correct)*100 / total, decimals=2) + if epoch%5==0: + test_acc = self._compute_accuracy(self._network, test_loader) + info = 'Task {}, Epoch {}/{} => Loss {:.3f}, Loss_clf {:.3f}, Loss_aux {:.3f}, Train_accy {:.2f}, Test_accy {:.2f}'.format( + self._cur_task, epoch+1, self.args["epochs"], losses/len(train_loader),losses_clf/len(train_loader),losses_aux/len(train_loader),train_acc, test_acc) + else: + info = 'Task {}, Epoch {}/{} => Loss {:.3f}, Loss_clf {:.3f}, Loss_aux {:.3f}, Train_accy {:.2f}'.format( + self._cur_task, epoch+1, self.args["epochs"], losses/len(train_loader), losses_clf/len(train_loader),losses_aux/len(train_loader),train_acc) + prog_bar.set_description(info) + logging.info(info) + + # def save_checkpoint(self, test_acc): + # assert self.args['model_name'] == 'finetune' + # checkpoint_name = f"checkpoints/finetune_{self.args['csv_name']}" + # _checkpoint_cpu = copy.deepcopy(self._network) + # if isinstance(_checkpoint_cpu, nn.DataParallel): + # _checkpoint_cpu = _checkpoint_cpu.module + # _checkpoint_cpu.cpu() + # save_dict = { + # "tasks": self._cur_task, + # "convnet": _checkpoint_cpu.convnet.state_dict(), + # "fc":_checkpoint_cpu.fc.state_dict(), + # "test_acc": test_acc + # } + # torch.save(save_dict, "{}_{}.pkl".format(checkpoint_name, self._cur_task)) + + def _construct_exemplar(self, data_manager, m): + logging.info("Constructing exemplars...({} per classes)".format(m)) + for class_idx in range(self._known_classes, self._total_classes): + data, targets, idx_dataset = data_manager.get_dataset( + np.arange(class_idx, class_idx + 1), + source="train", + mode="test", + ret_data=True, + ) + idx_loader = DataLoader( + idx_dataset, batch_size=batch_size, shuffle=False, num_workers=4 + ) + vectors, _ = self._extract_vectors(idx_loader) + vectors = (vectors.T / (np.linalg.norm(vectors.T, axis=0) + EPSILON)).T + class_mean = np.mean(vectors, axis=0) + + # Select + selected_exemplars = [] + exemplar_vectors = [] # [n, feature_dim] + for k in range(1, m + 1): + S = np.sum( + exemplar_vectors, axis=0 + ) # [feature_dim] sum of selected exemplars vectors + mu_p = (vectors + S) / k # [n, feature_dim] sum to all vectors + i = np.argmin(np.sqrt(np.sum((class_mean - mu_p) ** 2, axis=1))) + selected_exemplars.append( + np.array(data[i]) + ) # New object to avoid passing by inference + exemplar_vectors.append( + np.array(vectors[i]) + ) # New object to avoid passing by inference + + vectors = np.delete( + vectors, i, axis=0 + ) # Remove it to avoid duplicative selection + data = np.delete( + data, i, axis=0 + ) # Remove it to avoid duplicative selection + + if len(vectors) == 0: + break + # uniques = np.unique(selected_exemplars, axis=0) + # print('Unique elements: {}'.format(len(uniques))) + selected_exemplars = np.array(selected_exemplars) + # exemplar_targets = np.full(m, class_idx) + exemplar_targets = np.full(selected_exemplars.shape[0], class_idx) + self._data_memory = ( + np.concatenate((self._data_memory, selected_exemplars)) + if len(self._data_memory) != 0 + else selected_exemplars + ) + self._targets_memory = ( + np.concatenate((self._targets_memory, exemplar_targets)) + if len(self._targets_memory) != 0 + else exemplar_targets + ) + + # Exemplar mean + idx_dataset = data_manager.get_dataset( + [], + source="train", + mode="test", + appendent=(selected_exemplars, exemplar_targets), + ) + idx_loader = DataLoader( + idx_dataset, batch_size=batch_size, shuffle=False, num_workers=4 + ) + vectors, _ = self._extract_vectors(idx_loader) + vectors = (vectors.T / (np.linalg.norm(vectors.T, axis=0) + EPSILON)).T + mean = np.mean(vectors, axis=0) + mean = mean / np.linalg.norm(mean) + + self._class_means[class_idx, :] = mean \ No newline at end of file diff --git a/models/podnet.py b/models/podnet.py new file mode 100644 index 0000000000000000000000000000000000000000..59926d3fdda1089a4234d1f97b16583e9ff3bc30 --- /dev/null +++ b/models/podnet.py @@ -0,0 +1,325 @@ +import math +import logging +import numpy as np +import torch +from torch import optim +from torch.nn import functional as F +from torch.utils.data import DataLoader +from models.base import BaseLearner +from utils.inc_net import CosineIncrementalNet +from utils.toolkit import tensor2numpy + +epochs = 160 +lrate = 0.1 +ft_epochs = 20 +ft_lrate = 0.005 +batch_size = 128 +lambda_c_base = 5 +lambda_f_base = 1 +nb_proxy = 10 +weight_decay = 5e-4 +num_workers = 4 + +""" +Distillation losses: POD-flat (lambda_f=1) + POD-spatial (lambda_c=5) +NME results are shown. +The reproduced results are not in line with the reported results. +Maybe I missed something... ++--------------------+--------------------+--------------------+--------------------+ +| Classifier | Steps | Reported (%) | Reproduced (%) | ++--------------------+--------------------+--------------------+--------------------+ +| Cosine (k=1) | 50 | 56.69 | 55.49 | ++--------------------+--------------------+--------------------+--------------------+ +| LSC-CE (k=10) | 50 | 59.86 | 55.69 | ++--------------------+--------------------+--------------------+--------------------+ +| LSC-NCA (k=10) | 50 | 61.40 | 56.50 | ++--------------------+--------------------+--------------------+--------------------+ +| LSC-CE (k=10) | 25 | ----- | 59.16 | ++--------------------+--------------------+--------------------+--------------------+ +| LSC-NCA (k=10) | 25 | 62.71 | 59.79 | ++--------------------+--------------------+--------------------+--------------------+ +| LSC-CE (k=10) | 10 | ----- | 62.59 | ++--------------------+--------------------+--------------------+--------------------+ +| LSC-NCA (k=10) | 10 | 64.03 | 62.81 | ++--------------------+--------------------+--------------------+--------------------+ +| LSC-CE (k=10) | 5 | ----- | 64.16 | ++--------------------+--------------------+--------------------+--------------------+ +| LSC-NCA (k=10) | 5 | 64.48 | 64.37 | ++--------------------+--------------------+--------------------+--------------------+ +""" + + +class PODNet(BaseLearner): + def __init__(self, args): + super().__init__(args) + self._network = CosineIncrementalNet( + args, pretrained=False, nb_proxy=nb_proxy + ) + self._class_means = None + + def after_task(self): + self._old_network = self._network.copy().freeze() + self._known_classes = self._total_classes + logging.info("Exemplar size: {}".format(self.exemplar_size)) + + def incremental_train(self, data_manager): + self._cur_task += 1 + self._total_classes = self._known_classes + data_manager.get_task_size( + self._cur_task + ) + self.task_size = self._total_classes - self._known_classes + self._network.update_fc(self._total_classes, self._cur_task) + logging.info( + "Learning on {}-{}".format(self._known_classes, self._total_classes) + ) + + if not self.args["attack"]: + train_dset = data_manager.get_dataset( + np.arange(self._known_classes, self._total_classes), + source="train", + mode="train", + appendent=self._get_memory(), + ) + test_dset = data_manager.get_dataset( + np.arange(0, self._total_classes), source="test", mode="test" + ) + self.train_loader = DataLoader( + train_dset, batch_size=batch_size, shuffle=True, num_workers=num_workers + ) + self.test_loader = DataLoader( + test_dset, batch_size=batch_size, shuffle=False, num_workers=num_workers + ) + + self._train(data_manager, self.train_loader, self.test_loader) + self.build_rehearsal_memory(data_manager, self.samples_per_class) + + def _train(self, data_manager, train_loader, test_loader): + if self._cur_task == 0: + self.factor = 0 + else: + self.factor = math.sqrt( + self._total_classes / (self._total_classes - self._known_classes) + ) + logging.info("Adaptive factor: {}".format(self.factor)) + + self._network.to(self._device) + if self._old_network is not None: + self._old_network.to(self._device) + + if self._cur_task == 0: + network_params = self._network.parameters() + else: + ignored_params = list(map(id, self._network.fc.fc1.parameters())) + base_params = filter( + lambda p: id(p) not in ignored_params, self._network.parameters() + ) + network_params = [ + {"params": base_params, "lr": lrate, "weight_decay": weight_decay}, + { + "params": self._network.fc.fc1.parameters(), + "lr": 0, + "weight_decay": 0, + }, + ] + optimizer = optim.SGD( + network_params, lr=lrate, momentum=0.9, weight_decay=weight_decay + ) + scheduler = optim.lr_scheduler.CosineAnnealingLR( + optimizer=optimizer, T_max=epochs + ) + self._run(train_loader, test_loader, optimizer, scheduler, epochs) + + if self._cur_task == 0: + return + logging.info( + "Finetune the network (classifier part) with the undersampled dataset!" + ) + if self._fixed_memory: + finetune_samples_per_class = self._memory_per_class + self._construct_exemplar_unified(data_manager, finetune_samples_per_class) + else: + finetune_samples_per_class = self._memory_size // self._known_classes + self._reduce_exemplar(data_manager, finetune_samples_per_class) + self._construct_exemplar(data_manager, finetune_samples_per_class) + + finetune_train_dataset = data_manager.get_dataset( + [], source="train", mode="train", appendent=self._get_memory() + ) + finetune_train_loader = DataLoader( + finetune_train_dataset, + batch_size=batch_size, + shuffle=True, + num_workers=num_workers, + ) + logging.info( + "The size of finetune dataset: {}".format(len(finetune_train_dataset)) + ) + + ignored_params = list(map(id, self._network.fc.fc1.parameters())) + base_params = filter( + lambda p: id(p) not in ignored_params, self._network.parameters() + ) + network_params = [ + {"params": base_params, "lr": ft_lrate, "weight_decay": weight_decay}, + {"params": self._network.fc.fc1.parameters(), "lr": 0, "weight_decay": 0}, + ] + optimizer = optim.SGD( + network_params, lr=ft_lrate, momentum=0.9, weight_decay=weight_decay + ) + scheduler = optim.lr_scheduler.CosineAnnealingLR( + optimizer=optimizer, T_max=ft_epochs + ) + self._run(finetune_train_loader, test_loader, optimizer, scheduler, ft_epochs) + + if self._fixed_memory: + self._data_memory = self._data_memory[ + : -self._memory_per_class * self.task_size + ] + self._targets_memory = self._targets_memory[ + : -self._memory_per_class * self.task_size + ] + assert ( + len( + np.setdiff1d( + self._targets_memory, np.arange(0, self._known_classes) + ) + ) + == 0 + ), "Exemplar error!" + + def _run(self, train_loader, test_loader, optimizer, scheduler, epk): + for epoch in range(1, epk + 1): + self._network.train() + lsc_losses = 0.0 + spatial_losses = 0.0 + flat_losses = 0.0 + correct, total = 0, 0 + for i, (_, inputs, targets) in enumerate(train_loader): + inputs, targets = inputs.to(self._device), targets.to(self._device) + outputs = self._network(inputs) + logits = outputs["logits"] + features = outputs["features"] + fmaps = outputs["fmaps"] + lsc_loss = nca(logits, targets) + + spatial_loss = 0.0 + flat_loss = 0.0 + if self._old_network is not None: + with torch.no_grad(): + old_outputs = self._old_network(inputs) + old_features = old_outputs["features"] + old_fmaps = old_outputs["fmaps"] + flat_loss = ( + F.cosine_embedding_loss( + features, + old_features.detach(), + torch.ones(inputs.shape[0]).to(self._device), + ) + * self.factor + * lambda_f_base + ) + spatial_loss = ( + pod_spatial_loss(fmaps, old_fmaps) * self.factor * lambda_c_base + ) + + loss = lsc_loss + flat_loss + spatial_loss + optimizer.zero_grad() + loss.backward() + optimizer.step() + + lsc_losses += lsc_loss.item() + spatial_losses += ( + spatial_loss.item() if self._cur_task != 0 else spatial_loss + ) + flat_losses += flat_loss.item() if self._cur_task != 0 else flat_loss + + _, preds = torch.max(logits, dim=1) + correct += preds.eq(targets.expand_as(preds)).cpu().sum() + total += len(targets) + + if scheduler is not None: + scheduler.step() + train_acc = np.around(tensor2numpy(correct) * 100 / total, decimals=2) + test_acc = self._compute_accuracy(self._network, test_loader) + info1 = "Task {}, Epoch {}/{} (LR {:.5f}) => ".format( + self._cur_task, epoch, epk, optimizer.param_groups[0]["lr"] + ) + info2 = "LSC_loss {:.2f}, Spatial_loss {:.2f}, Flat_loss {:.2f}, Train_acc {:.2f}, Test_acc {:.2f}".format( + lsc_losses / (i + 1), + spatial_losses / (i + 1), + flat_losses / (i + 1), + train_acc, + test_acc, + ) + logging.info(info1 + info2) + + +def pod_spatial_loss(old_fmaps, fmaps, normalize=True): + """ + a, b: list of [bs, c, w, h] + """ + loss = torch.tensor(0.0).to(fmaps[0].device) + for i, (a, b) in enumerate(zip(old_fmaps, fmaps)): + assert a.shape == b.shape, "Shape error" + + a = torch.pow(a, 2) + b = torch.pow(b, 2) + + a_h = a.sum(dim=3).view(a.shape[0], -1) # [bs, c*w] + b_h = b.sum(dim=3).view(b.shape[0], -1) # [bs, c*w] + a_w = a.sum(dim=2).view(a.shape[0], -1) # [bs, c*h] + b_w = b.sum(dim=2).view(b.shape[0], -1) # [bs, c*h] + + a = torch.cat([a_h, a_w], dim=-1) + b = torch.cat([b_h, b_w], dim=-1) + + if normalize: + a = F.normalize(a, dim=1, p=2) + b = F.normalize(b, dim=1, p=2) + + layer_loss = torch.mean(torch.frobenius_norm(a - b, dim=-1)) + loss += layer_loss + + return loss / len(fmaps) + + +def nca( + similarities, + targets, + class_weights=None, + focal_gamma=None, + scale=1.0, + margin=0.6, + exclude_pos_denominator=True, + hinge_proxynca=False, + memory_flags=None, +): + margins = torch.zeros_like(similarities) + margins[torch.arange(margins.shape[0]), targets] = margin + similarities = scale * (similarities - margin) + + if exclude_pos_denominator: + similarities = similarities - similarities.max(1)[0].view(-1, 1) + + disable_pos = torch.zeros_like(similarities) + disable_pos[torch.arange(len(similarities)), targets] = similarities[ + torch.arange(len(similarities)), targets + ] + + numerator = similarities[torch.arange(similarities.shape[0]), targets] + denominator = similarities - disable_pos + + losses = numerator - torch.log(torch.exp(denominator).sum(-1)) + if class_weights is not None: + losses = class_weights[targets] * losses + + losses = -losses + if hinge_proxynca: + losses = torch.clamp(losses, min=0.0) + + loss = torch.mean(losses) + return loss + + return F.cross_entropy( + similarities, targets, weight=class_weights, reduction="mean" + ) diff --git a/models/replay.py b/models/replay.py new file mode 100644 index 0000000000000000000000000000000000000000..c7efdc270ba580c17ae2c733e466587dc55c96b8 --- /dev/null +++ b/models/replay.py @@ -0,0 +1,196 @@ +import logging +import numpy as np +from tqdm import tqdm +import torch +from torch import nn +from torch import optim +from torch.nn import functional as F +from torch.utils.data import DataLoader +from models.base import BaseLearner +from utils.inc_net import IncrementalNet +from utils.toolkit import target2onehot, tensor2numpy + +EPSILON = 1e-8 + + +init_epoch = 200 +init_lr = 0.1 +init_milestones = [60, 120, 170] +init_lr_decay = 0.1 +init_weight_decay = 0.0005 + + +epochs = 70 +lrate = 0.1 +milestones = [30, 50] +lrate_decay = 0.1 +batch_size = 128 +weight_decay = 2e-4 +num_workers = 4 +T = 2 + + +class Replay(BaseLearner): + def __init__(self, args): + super().__init__(args) + self._network = IncrementalNet(args, False) + + def after_task(self): + self._known_classes = self._total_classes + logging.info("Exemplar size: {}".format(self.exemplar_size)) + + def incremental_train(self, data_manager): + self._cur_task += 1 + self._total_classes = self._known_classes + data_manager.get_task_size( + self._cur_task + ) + self._network.update_fc(self._total_classes) + logging.info( + "Learning on {}-{}".format(self._known_classes, self._total_classes) + ) + + if not self.args["attack"]: + # Loader + train_dataset = data_manager.get_dataset( + np.arange(self._known_classes, self._total_classes), + source="train", + mode="train", + appendent=self._get_memory(), + ) + self.train_loader = DataLoader( + train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers + ) + test_dataset = data_manager.get_dataset( + np.arange(0, self._total_classes), source="test", mode="test" + ) + self.test_loader = DataLoader( + test_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers + ) + + # Procedure + if len(self._multiple_gpus) > 1: + self._network = nn.DataParallel(self._network, self._multiple_gpus) + self._train(self.train_loader, self.test_loader) + self.build_rehearsal_memory(data_manager, self.samples_per_class) + else: + self.train_loader, self.test_loader = None, None + + if len(self._multiple_gpus) > 1: + self._network = self._network.module + + def _train(self, train_loader, test_loader): + self._network.to(self._device) + if self._cur_task == 0: + optimizer = optim.SGD( + self._network.parameters(), + momentum=0.9, + lr=init_lr, + weight_decay=init_weight_decay, + ) + scheduler = optim.lr_scheduler.MultiStepLR( + optimizer=optimizer, milestones=init_milestones, gamma=init_lr_decay + ) + self._init_train(train_loader, test_loader, optimizer, scheduler) + else: + optimizer = optim.SGD( + self._network.parameters(), + lr=lrate, + momentum=0.9, + weight_decay=weight_decay, + ) # 1e-5 + scheduler = optim.lr_scheduler.MultiStepLR( + optimizer=optimizer, milestones=milestones, gamma=lrate_decay + ) + self._update_representation(train_loader, test_loader, optimizer, scheduler) + + def _init_train(self, train_loader, test_loader, optimizer, scheduler): + prog_bar = tqdm(range(init_epoch)) + for _, epoch in enumerate(prog_bar): + self._network.train() + losses = 0.0 + correct, total = 0, 0 + for i, (_, inputs, targets) in enumerate(train_loader): + inputs, targets = inputs.to(self._device), targets.to(self._device) + logits = self._network(inputs)["logits"] + + loss = F.cross_entropy(logits, targets) + optimizer.zero_grad() + loss.backward() + optimizer.step() + losses += loss.item() + + _, preds = torch.max(logits, dim=1) + correct += preds.eq(targets.expand_as(preds)).cpu().sum() + total += len(targets) + + scheduler.step() + train_acc = np.around(tensor2numpy(correct) * 100 / total, decimals=2) + + if epoch % 5 == 0: + test_acc = self._compute_accuracy(self._network, test_loader) + info = "Task {}, Epoch {}/{} => Loss {:.3f}, Train_accy {:.2f}, Test_accy {:.2f}".format( + self._cur_task, + epoch + 1, + init_epoch, + losses / len(train_loader), + train_acc, + test_acc, + ) + else: + info = "Task {}, Epoch {}/{} => Loss {:.3f}, Train_accy {:.2f}".format( + self._cur_task, + epoch + 1, + init_epoch, + losses / len(train_loader), + train_acc, + ) + + prog_bar.set_description(info) + + logging.info(info) + + def _update_representation(self, train_loader, test_loader, optimizer, scheduler): + prog_bar = tqdm(range(epochs)) + for _, epoch in enumerate(prog_bar): + self._network.train() + losses = 0.0 + correct, total = 0, 0 + for i, (_, inputs, targets) in enumerate(train_loader): + inputs, targets = inputs.to(self._device), targets.to(self._device) + logits = self._network(inputs)["logits"] + + loss_clf = F.cross_entropy(logits, targets) + loss = loss_clf + + optimizer.zero_grad() + loss.backward() + optimizer.step() + losses += loss.item() + + # acc + _, preds = torch.max(logits, dim=1) + correct += preds.eq(targets.expand_as(preds)).cpu().sum() + total += len(targets) + + scheduler.step() + train_acc = np.around(tensor2numpy(correct) * 100 / total, decimals=2) + if epoch % 5 == 0: + test_acc = self._compute_accuracy(self._network, test_loader) + info = "Task {}, Epoch {}/{} => Loss {:.3f}, Train_accy {:.2f}, Test_accy {:.2f}".format( + self._cur_task, + epoch + 1, + epochs, + losses / len(train_loader), + train_acc, + test_acc, + ) + else: + info = "Task {}, Epoch {}/{} => Loss {:.3f}, Train_accy {:.2f}".format( + self._cur_task, + epoch + 1, + epochs, + losses / len(train_loader), + train_acc, + ) + prog_bar.set_description(info) + logging.info(info) diff --git a/models/wa.py b/models/wa.py new file mode 100644 index 0000000000000000000000000000000000000000..e7298268e5d1c2b6e0dcab43c30e8abb1150d8cf --- /dev/null +++ b/models/wa.py @@ -0,0 +1,219 @@ +import logging +import numpy as np +from tqdm import tqdm +import torch +from torch import nn +from torch import optim +from torch.nn import functional as F +from torch.utils.data import DataLoader +from models.base import BaseLearner +from utils.inc_net import IncrementalNet +from utils.toolkit import target2onehot, tensor2numpy + +EPSILON = 1e-8 + + +init_epoch = 200 +init_lr = 0.1 +init_milestones = [60, 120, 170] +init_lr_decay = 0.1 +init_weight_decay = 0.0005 + + +epochs = 170 +lrate = 0.1 +milestones = [60, 100, 140] +lrate_decay = 0.1 +batch_size = 128 +weight_decay = 2e-4 +num_workers = 8 +T = 2 + + +class WA(BaseLearner): + def __init__(self, args): + super().__init__(args) + self._network = IncrementalNet(args, False) + + def after_task(self): + if self._cur_task > 0: + self._network.weight_align(self._total_classes - self._known_classes) + self._old_network = self._network.copy().freeze() + self._known_classes = self._total_classes + logging.info("Exemplar size: {}".format(self.exemplar_size)) + + def incremental_train(self, data_manager): + self._cur_task += 1 + self._total_classes = self._known_classes + data_manager.get_task_size( + self._cur_task + ) + self._network.update_fc(self._total_classes) + logging.info( + "Learning on {}-{}".format(self._known_classes, self._total_classes) + ) + + if not self.args["attack"]: + # Loader + train_dataset = data_manager.get_dataset( + np.arange(self._known_classes, self._total_classes), + source="train", + mode="train", + appendent=self._get_memory(), + ) + self.train_loader = DataLoader( + train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers + ) + test_dataset = data_manager.get_dataset( + np.arange(0, self._total_classes), source="test", mode="test" + ) + self.test_loader = DataLoader( + test_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers + ) + + # Procedure + if len(self._multiple_gpus) > 1: + self._network = nn.DataParallel(self._network, self._multiple_gpus) + self._train(self.train_loader, self.test_loader) + self.build_rehearsal_memory(data_manager, self.samples_per_class) + + if len(self._multiple_gpus) > 1: + self._network = self._network.module + + def _train(self, train_loader, test_loader): + self._network.to(self._device) + if self._old_network is not None: + self._old_network.to(self._device) + + if self._cur_task == 0: + optimizer = optim.SGD( + self._network.parameters(), + momentum=0.9, + lr=init_lr, + weight_decay=init_weight_decay, + ) + scheduler = optim.lr_scheduler.MultiStepLR( + optimizer=optimizer, milestones=init_milestones, gamma=init_lr_decay + ) + self._init_train(train_loader, test_loader, optimizer, scheduler) + else: + optimizer = optim.SGD( + self._network.parameters(), + lr=lrate, + momentum=0.9, + weight_decay=weight_decay, + ) # 1e-5 + scheduler = optim.lr_scheduler.MultiStepLR( + optimizer=optimizer, milestones=milestones, gamma=lrate_decay + ) + self._update_representation(train_loader, test_loader, optimizer, scheduler) + if len(self._multiple_gpus) > 1: + self._network.module.weight_align( + self._total_classes - self._known_classes + ) + else: + self._network.weight_align(self._total_classes - self._known_classes) + + def _init_train(self, train_loader, test_loader, optimizer, scheduler): + prog_bar = tqdm(range(init_epoch)) + for _, epoch in enumerate(prog_bar): + self._network.train() + losses = 0.0 + correct, total = 0, 0 + for i, (_, inputs, targets) in enumerate(train_loader): + inputs, targets = inputs.to(self._device), targets.to(self._device) + logits = self._network(inputs)["logits"] + + loss = F.cross_entropy(logits, targets) + optimizer.zero_grad() + loss.backward() + optimizer.step() + losses += loss.item() + + _, preds = torch.max(logits, dim=1) + correct += preds.eq(targets.expand_as(preds)).cpu().sum() + total += len(targets) + + scheduler.step() + train_acc = np.around(tensor2numpy(correct) * 100 / total, decimals=2) + + if epoch % 5 == 0: + test_acc = self._compute_accuracy(self._network, test_loader) + info = "Task {}, Epoch {}/{} => Loss {:.3f}, Train_accy {:.2f}, Test_accy {:.2f}".format( + self._cur_task, + epoch + 1, + init_epoch, + losses / len(train_loader), + train_acc, + test_acc, + ) + else: + info = "Task {}, Epoch {}/{} => Loss {:.3f}, Train_accy {:.2f}".format( + self._cur_task, + epoch + 1, + init_epoch, + losses / len(train_loader), + train_acc, + ) + + prog_bar.set_description(info) + + logging.info(info) + + def _update_representation(self, train_loader, test_loader, optimizer, scheduler): + kd_lambda = self._known_classes / self._total_classes + prog_bar = tqdm(range(epochs)) + for _, epoch in enumerate(prog_bar): + self._network.train() + losses = 0.0 + correct, total = 0, 0 + for i, (_, inputs, targets) in enumerate(train_loader): + inputs, targets = inputs.to(self._device), targets.to(self._device) + logits = self._network(inputs)["logits"] + + loss_clf = F.cross_entropy(logits, targets) + loss_kd = _KD_loss( + logits[:, : self._known_classes], + self._old_network(inputs)["logits"], + T, + ) + + loss = (1-kd_lambda) * loss_clf + kd_lambda * loss_kd + + optimizer.zero_grad() + loss.backward() + optimizer.step() + losses += loss.item() + + # acc + _, preds = torch.max(logits, dim=1) + correct += preds.eq(targets.expand_as(preds)).cpu().sum() + total += len(targets) + + scheduler.step() + train_acc = np.around(tensor2numpy(correct) * 100 / total, decimals=2) + if epoch % 5 == 0: + test_acc = self._compute_accuracy(self._network, test_loader) + info = "Task {}, Epoch {}/{} => Loss {:.3f}, Train_accy {:.2f}, Test_accy {:.2f}".format( + self._cur_task, + epoch + 1, + epochs, + losses / len(train_loader), + train_acc, + test_acc, + ) + else: + info = "Task {}, Epoch {}/{} => Loss {:.3f}, Train_accy {:.2f}".format( + self._cur_task, + epoch + 1, + epochs, + losses / len(train_loader), + train_acc, + ) + prog_bar.set_description(info) + logging.info(info) + + +def _KD_loss(pred, soft, T): + pred = torch.log_softmax(pred / T, dim=1) + soft = torch.softmax(soft / T, dim=1) + return -1 * torch.mul(soft, pred).sum() / pred.shape[0] diff --git a/scripts/attacks-CIFAR100.sh b/scripts/attacks-CIFAR100.sh new file mode 100644 index 0000000000000000000000000000000000000000..628bc71da1d875fb776e8860895e774b68895327 --- /dev/null +++ b/scripts/attacks-CIFAR100.sh @@ -0,0 +1,110 @@ +#!/bin/bash + +# PodNet +for i in {0..9} +do + python ./attack.py --config 'exps/podnet.json' --attack_method 'AIM' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/podnet.json' --attack_method 'CleanSheet' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/podnet.json' --attack_method 'Gaker' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/podnet.json' --attack_method 'SAE' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/podnet.json' --attack_method 'UnivIntruder' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/podnet.json' --attack_method 'MIFGSM' --target_class $i --batch_size 128 +done +python ./attack.py --config 'exps/podnet.json' --attack_method 'CGNC' --batch_size 128 + +# WA +for i in {0..9} +do + python ./attack.py --config 'exps/wa.json' --attack_method 'AIM' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/wa.json' --attack_method 'CleanSheet' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/wa.json' --attack_method 'Gaker' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/wa.json' --attack_method 'SAE' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/wa.json' --attack_method 'UnivIntruder' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/wa.json' --attack_method 'MIFGSM' --target_class $i --batch_size 128 +done +python ./attack.py --config 'exps/wa.json' --attack_method 'CGNC' --batch_size 128 + +# iCaRL +for i in {0..9} +do + python ./attack.py --config 'exps/icarl.json' --attack_method 'AIM' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/icarl.json' --attack_method 'CleanSheet' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/icarl.json' --attack_method 'Gaker' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/icarl.json' --attack_method 'SAE' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/icarl.json' --attack_method 'UnivIntruder' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/icarl.json' --attack_method 'MIFGSM' --target_class $i --batch_size 128 +done +python ./attack.py --config 'exps/icarl.json' --attack_method 'CGNC' --batch_size 128 + +# Replay +for i in {0..9} +do + python ./attack.py --config 'exps/replay.json' --attack_method 'AIM' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/replay.json' --attack_method 'CleanSheet' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/replay.json' --attack_method 'Gaker' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/replay.json' --attack_method 'SAE' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/replay.json' --attack_method 'UnivIntruder' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/replay.json' --attack_method 'MIFGSM' --target_class $i --batch_size 128 +done +python ./attack.py --config 'exps/replay.json' --attack_method 'CGNC' --batch_size 128 + +# MEMO +for i in {0..9} +do + python ./attack.py --config 'exps/memo.json' --attack_method 'AIM' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/memo.json' --attack_method 'CleanSheet' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/memo.json' --attack_method 'Gaker' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/memo.json' --attack_method 'SAE' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/memo.json' --attack_method 'UnivIntruder' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/memo.json' --attack_method 'MIFGSM' --target_class $i --batch_size 128 +done +python ./attack.py --config 'exps/memo.json' --attack_method 'CGNC' --batch_size 128 + +# DER +for i in {0..9} +do + python ./attack.py --config 'exps/der.json' --attack_method 'AIM' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/der.json' --attack_method 'CleanSheet' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/der.json' --attack_method 'Gaker' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/der.json' --attack_method 'SAE' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/der.json' --attack_method 'UnivIntruder' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/der.json' --attack_method 'MIFGSM' --target_class $i --batch_size 128 +done +python ./attack.py --config 'exps/der.json' --attack_method 'CGNC' --batch_size 128 + +# BiC +for i in {0..9} +do + python ./attack.py --config 'exps/bic.json' --attack_method 'AIM' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/bic.json' --attack_method 'CleanSheet' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/bic.json' --attack_method 'Gaker' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/bic.json' --attack_method 'SAE' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/bic.json' --attack_method 'UnivIntruder' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/bic.json' --attack_method 'MIFGSM' --target_class $i --batch_size 128 +done +python ./attack.py --config 'exps/bic.json' --attack_method 'CGNC' --batch_size 128 + +# Foster +for i in {0..9} +do + python ./attack.py --config 'exps/foster.json' --attack_method 'AIM' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/foster.json' --attack_method 'CleanSheet' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/foster.json' --attack_method 'Gaker' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/foster.json' --attack_method 'SAE' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/foster.json' --attack_method 'UnivIntruder' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/foster.json' --attack_method 'MIFGSM' --target_class $i --batch_size 128 +done +python ./attack.py --config 'exps/foster.json' --attack_method 'CGNC' --batch_size 128 + +# Finetune +for i in {0..9} +do + python ./attack.py --config 'exps/finetune.json' --attack_method 'AIM' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/finetune.json' --attack_method 'CleanSheet' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/finetune.json' --attack_method 'Gaker' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/finetune.json' --attack_method 'SAE' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/finetune.json' --attack_method 'UnivIntruder' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/finetune.json' --attack_method 'MIFGSM' --target_class $i --batch_size 128 +done +python ./attack.py --config 'exps/finetune.json' --attack_method 'CGNC' --batch_size 128 + diff --git a/scripts/attacks-ImageNet100.sh b/scripts/attacks-ImageNet100.sh new file mode 100644 index 0000000000000000000000000000000000000000..41c04083e506452cc61a9e0a3cb2e798a8ca1f73 --- /dev/null +++ b/scripts/attacks-ImageNet100.sh @@ -0,0 +1,110 @@ +#!/bin/bash + +# PodNet +for i in {0..9} +do + python ./attack.py --config 'exps/podnet-imagenet100.json' --attack_method 'AIM' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/podnet-imagenet100.json' --attack_method 'CleanSheet' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/podnet-imagenet100.json' --attack_method 'Gaker' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/podnet-imagenet100.json' --attack_method 'SAE' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/podnet-imagenet100.json' --attack_method 'UnivIntruder' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/podnet-imagenet100.json' --attack_method 'MIFGSM' --target_class $i --batch_size 128 +done +python ./attack.py --config 'exps/podnet-imagenet100.json' --attack_method 'CGNC' --batch_size 128 + +# WA +for i in {0..9} +do + python ./attack.py --config 'exps/wa-imagenet100.json' --attack_method 'AIM' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/wa-imagenet100.json' --attack_method 'CleanSheet' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/wa-imagenet100.json' --attack_method 'Gaker' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/wa-imagenet100.json' --attack_method 'SAE' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/wa-imagenet100.json' --attack_method 'UnivIntruder' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/wa-imagenet100.json' --attack_method 'MIFGSM' --target_class $i --batch_size 128 +done +python ./attack.py --config 'exps/wa-imagenet100.json' --attack_method 'CGNC' --batch_size 128 + +# iCaRL +for i in {0..9} +do + python ./attack.py --config 'exps/icarl-imagenet100.json' --attack_method 'AIM' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/icarl-imagenet100.json' --attack_method 'CleanSheet' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/icarl-imagenet100.json' --attack_method 'Gaker' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/icarl-imagenet100.json' --attack_method 'SAE' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/icarl-imagenet100.json' --attack_method 'UnivIntruder' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/icarl-imagenet100.json' --attack_method 'MIFGSM' --target_class $i --batch_size 128 +done +python ./attack.py --config 'exps/icarl-imagenet100.json' --attack_method 'CGNC' --batch_size 128 + +# Replay +for i in {0..9} +do + python ./attack.py --config 'exps/replay-imagenet100.json' --attack_method 'AIM' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/replay-imagenet100.json' --attack_method 'CleanSheet' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/replay-imagenet100.json' --attack_method 'Gaker' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/replay-imagenet100.json' --attack_method 'SAE' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/replay-imagenet100.json' --attack_method 'UnivIntruder' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/replay-imagenet100.json' --attack_method 'MIFGSM' --target_class $i --batch_size 128 +done +python ./attack.py --config 'exps/replay-imagenet100.json' --attack_method 'CGNC' --batch_size 128 + +# MEMO +for i in {0..9} +do + python ./attack.py --config 'exps/memo-imagenet100.json' --attack_method 'AIM' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/memo-imagenet100.json' --attack_method 'CleanSheet' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/memo-imagenet100.json' --attack_method 'Gaker' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/memo-imagenet100.json' --attack_method 'SAE' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/memo-imagenet100.json' --attack_method 'UnivIntruder' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/memo-imagenet100.json' --attack_method 'MIFGSM' --target_class $i --batch_size 128 +done +python ./attack.py --config 'exps/memo-imagenet100.json' --attack_method 'CGNC' --batch_size 128 + +# DER +for i in {0..9} +do + python ./attack.py --config 'exps/der-imagenet100.json' --attack_method 'AIM' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/der-imagenet100.json' --attack_method 'CleanSheet' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/der-imagenet100.json' --attack_method 'Gaker' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/der-imagenet100.json' --attack_method 'SAE' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/der-imagenet100.json' --attack_method 'UnivIntruder' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/der-imagenet100.json' --attack_method 'MIFGSM' --target_class $i --batch_size 128 +done +python ./attack.py --config 'exps/der-imagenet100.json' --attack_method 'CGNC' --batch_size 128 + +# BiC +for i in {0..9} +do + python ./attack.py --config 'exps/bic-imagenet100.json' --attack_method 'AIM' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/bic-imagenet100.json' --attack_method 'CleanSheet' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/bic-imagenet100.json' --attack_method 'Gaker' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/bic-imagenet100.json' --attack_method 'SAE' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/bic-imagenet100.json' --attack_method 'UnivIntruder' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/bic-imagenet100.json' --attack_method 'MIFGSM' --target_class $i --batch_size 128 +done +python ./attack.py --config 'exps/bic-imagenet100.json' --attack_method 'CGNC' --batch_size 128 + +# Foster +for i in {0..9} +do + python ./attack.py --config 'exps/foster-imagenet100.json' --attack_method 'AIM' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/foster-imagenet100.json' --attack_method 'CleanSheet' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/foster-imagenet100.json' --attack_method 'Gaker' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/foster-imagenet100.json' --attack_method 'SAE' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/foster-imagenet100.json' --attack_method 'UnivIntruder' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/foster-imagenet100.json' --attack_method 'MIFGSM' --target_class $i --batch_size 128 +done +python ./attack.py --config 'exps/foster-imagenet100.json' --attack_method 'CGNC' --batch_size 128 + +# Finetune +for i in {0..9} +do + python ./attack.py --config 'exps/finetune-imagenet100.json' --attack_method 'AIM' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/finetune-imagenet100.json' --attack_method 'CleanSheet' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/finetune-imagenet100.json' --attack_method 'Gaker' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/finetune-imagenet100.json' --attack_method 'SAE' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/finetune-imagenet100.json' --attack_method 'UnivIntruder' --target_class $i --batch_size 128 + python ./attack.py --config 'exps/finetune-imagenet100.json' --attack_method 'MIFGSM' --target_class $i --batch_size 128 +done +python ./attack.py --config 'exps/finetune-imagenet100.json' --attack_method 'CGNC' --batch_size 128 + diff --git a/scripts/trainCIL-CIFAR100.sh b/scripts/trainCIL-CIFAR100.sh new file mode 100644 index 0000000000000000000000000000000000000000..81a7a7c7bea21a0a05b4ee66ba7e9cb2cec1ab81 --- /dev/null +++ b/scripts/trainCIL-CIFAR100.sh @@ -0,0 +1,11 @@ +#!/bin/bash + +python trainCIL.py --config 'exps/podnet.json' +python trainCIL.py --config 'exps/wa.json' +python trainCIL.py --config 'exps/icarl.json' +python trainCIL.py --config 'exps/replay.json' +python trainCIL.py --config 'exps/memo.json' +python trainCIL.py --config 'exps/der.json' +python trainCIL.py --config 'exps/bic.json' +python trainCIL.py --config 'exps/foster.json' +python trainCIL.py --config 'exps/finetune.json' \ No newline at end of file diff --git a/scripts/trainCIL-ImageNet100.sh b/scripts/trainCIL-ImageNet100.sh new file mode 100644 index 0000000000000000000000000000000000000000..b9f9f787e49bd7bb724a1c0c567db0dda245ce24 --- /dev/null +++ b/scripts/trainCIL-ImageNet100.sh @@ -0,0 +1,11 @@ +#!/bin/bash + +python trainCIL.py --config 'exps/podnet-imagenet100.json' +python trainCIL.py --config 'exps/wa-imagenet100.json' +python trainCIL.py --config 'exps/icarl-imagenet100.json' +python trainCIL.py --config 'exps/replay-imagenet100.json' +python trainCIL.py --config 'exps/memo-imagenet100.json' +python trainCIL.py --config 'exps/der-imagenet100.json' +python trainCIL.py --config 'exps/bic-imagenet100.json' +python trainCIL.py --config 'exps/foster-imagenet100.json' +python trainCIL.py --config 'exps/finetune-imagenet100.json' \ No newline at end of file diff --git a/trainCIL.py b/trainCIL.py new file mode 100644 index 0000000000000000000000000000000000000000..506a410fe46d2babdf261a4cc5e55be8328a5f61 --- /dev/null +++ b/trainCIL.py @@ -0,0 +1,208 @@ +import json +import argparse +import sys +import logging +import copy +import torch +from utils import factory +from utils.data_manager import DataManager +from utils.toolkit import count_parameters +import os +import numpy as np + + +def main(): + args = setup_parser().parse_args() + param = load_json(args.config) + args = vars(args) # Converting argparse Namespace to a dict. + args.update(param) # Add parameters from json + + train(args) + + +def train(args): + seed_list = copy.deepcopy(args["seed"]) + device = copy.deepcopy(args["device"]) + + for seed in seed_list: + args["seed"] = seed + args["device"] = device + _train(args) + + +def _train(args): + init_cls = 0 if args["init_cls"] == args["increment"] else args["init_cls"] + logs_name = "logs/{}/{}/init_cls_{}/per_classes_{}/{}".format(args["model_name"], args["dataset"], init_cls, + args['increment'], args["convnet_type"]) + + if not os.path.exists(logs_name): + os.makedirs(logs_name) + + logfilename = "logs/{}/{}/init_cls_{}/per_classes_{}/{}/{}_{}_{}".format( + args["model_name"], + args["dataset"], + init_cls, + args["increment"], + args["convnet_type"], + args["prefix"], + args["seed"], + args["convnet_type"], + ) + logging.basicConfig( + level=logging.INFO, + format="%(asctime)s [%(filename)s] => %(message)s", + handlers=[ + logging.FileHandler(filename=logfilename + ".log"), + logging.StreamHandler(sys.stdout), + ], + ) + + _set_random() + _set_device(args) + print_args(args) + data_manager = DataManager( + args["dataset"], + args["shuffle"], + args["seed"], + args["init_cls"], + args["increment"], + ) + model = factory.get_model(args["model_name"], args) + + cnn_curve, nme_curve = {"top1": [], "top5": []}, {"top1": [], "top5": []} + cnn_matrix, nme_matrix = [], [] + + for task in range(data_manager.nb_tasks): + logging.info("All params: {}".format(count_parameters(model._network))) + logging.info( + "Trainable params: {}".format(count_parameters(model._network, True)) + ) + model.incremental_train(data_manager) + cnn_accy, nme_accy = model.eval_task() + model.save_checkpoint(logs_name, args["convnet_type"]) + model.after_task() + + if nme_accy is not None: + logging.info("CNN: {}".format(cnn_accy["grouped"])) + logging.info("NME: {}".format(nme_accy["grouped"])) + + cnn_keys = [key for key in cnn_accy["grouped"].keys() if '-' in key] + cnn_keys_sorted = sorted(cnn_keys) + cnn_values = [cnn_accy["grouped"][key] for key in cnn_keys_sorted] + cnn_matrix.append(cnn_values) + + nme_keys = [key for key in nme_accy["grouped"].keys() if '-' in key] + nme_keys_sorted = sorted(nme_keys) + nme_values = [nme_accy["grouped"][key] for key in nme_keys_sorted] + nme_matrix.append(nme_values) + + cnn_curve["top1"].append(cnn_accy["top1"]) + cnn_curve["top5"].append(cnn_accy["top5"]) + + nme_curve["top1"].append(nme_accy["top1"]) + nme_curve["top5"].append(nme_accy["top5"]) + + logging.info("CNN top1 curve: {}".format(cnn_curve["top1"])) + logging.info("CNN top5 curve: {}".format(cnn_curve["top5"])) + logging.info("NME top1 curve: {}".format(nme_curve["top1"])) + logging.info("NME top5 curve: {}\n".format(nme_curve["top5"])) + + print('Average Accuracy (CNN):', sum(cnn_curve["top1"]) / len(cnn_curve["top1"])) + print('Average Accuracy (NME):', sum(nme_curve["top1"]) / len(nme_curve["top1"])) + + logging.info("Average Accuracy (CNN): {}".format(sum(cnn_curve["top1"]) / len(cnn_curve["top1"]))) + logging.info("Average Accuracy (NME): {}".format(sum(nme_curve["top1"]) / len(nme_curve["top1"]))) + else: + logging.info("No NME accuracy.") + logging.info("CNN: {}".format(cnn_accy["grouped"])) + + cnn_keys = [key for key in cnn_accy["grouped"].keys() if '-' in key] + cnn_keys_sorted = sorted(cnn_keys) + cnn_values = [cnn_accy["grouped"][key] for key in cnn_keys_sorted] + cnn_matrix.append(cnn_values) + + cnn_curve["top1"].append(cnn_accy["top1"]) + cnn_curve["top5"].append(cnn_accy["top5"]) + + logging.info("CNN top1 curve: {}".format(cnn_curve["top1"])) + logging.info("CNN top5 curve: {}\n".format(cnn_curve["top5"])) + + print('Average Accuracy (CNN):', sum(cnn_curve["top1"]) / len(cnn_curve["top1"])) + logging.info("Average Accuracy (CNN): {}".format(sum(cnn_curve["top1"]) / len(cnn_curve["top1"]))) + + if len(cnn_matrix) > 0: + np_acctable = np.zeros([task + 1, task + 1]) + for idxx, line in enumerate(cnn_matrix): + idxy = len(line) + np_acctable[idxx, :idxy] = np.array(line) + np_acctable = np_acctable.T + forgetting = np.mean((np.max(np_acctable, axis=1) - np_acctable[:, task])[:task]) + print('Accuracy Matrix (CNN):') + print(np_acctable) + formatted_str = "\n".join( + ["\t[" + ", ".join("{:.1f}".format(float(val)) for val in row) + "]" for row in np_acctable]) + logging.info("Accuracy Matrix (CNN):\n{}".format(formatted_str)) + # logging.info('Accuracy Matrix (CNN): {}'.format(np_acctable.tolist())) + print('Forgetting (CNN):', forgetting) + logging.info('Forgetting (CNN): {}'.format(forgetting)) + if len(nme_matrix) > 0: + np_acctable = np.zeros([task + 1, task + 1]) + for idxx, line in enumerate(nme_matrix): + idxy = len(line) + np_acctable[idxx, :idxy] = np.array(line) + np_acctable = np_acctable.T + forgetting = np.mean((np.max(np_acctable, axis=1) - np_acctable[:, task])[:task]) + print('Accuracy Matrix (NME):') + print(np_acctable) + formatted_str = "\n".join( + ["\t[" + ", ".join("{:.1f}".format(float(val)) for val in row) + "]" for row in np_acctable]) + logging.info("Accuracy Matrix (NME):\n{}".format(formatted_str)) + print('Forgetting (NME):', forgetting) + logging.info('Forgetting (NME): {}'.format(forgetting)) + + +def _set_device(args): + device_type = args["device"] + gpus = [] + + for device in device_type: + if device_type == -1: + device = torch.device("cpu") + else: + device = torch.device("cuda:{}".format(device)) + + gpus.append(device) + + args["device"] = gpus + + +def _set_random(): + torch.manual_seed(1) + torch.cuda.manual_seed(1) + torch.cuda.manual_seed_all(1) + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + + +def print_args(args): + for key, value in args.items(): + logging.info("{}: {}".format(key, value)) + + +def load_json(settings_path): + with open(settings_path) as data_file: + param = json.load(data_file) + + return param + + +def setup_parser(): + parser = argparse.ArgumentParser(description='Reproduce of multiple continual learning algorithms.') + parser.add_argument('--config', type=str, default='./exps/gem.json', + help='Json file of settings.') + + return parser + + +if __name__ == '__main__': + main() diff --git a/utils/__init__.py b/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/utils/autoaugment.py b/utils/autoaugment.py new file mode 100644 index 0000000000000000000000000000000000000000..e2b3cb256e41c6c9d0dc13c8ea63be3f7bf99689 --- /dev/null +++ b/utils/autoaugment.py @@ -0,0 +1,215 @@ +import numpy as np +from .ops import * + + +class ImageNetPolicy(object): + """ Randomly choose one of the best 24 Sub-policies on ImageNet. + + Example: + >>> policy = ImageNetPolicy() + >>> transformed = policy(image) + + Example as a PyTorch Transform: + >>> transform = transforms.Compose([ + >>> transforms.Resize(256), + >>> ImageNetPolicy(), + >>> transforms.ToTensor()]) + """ + def __init__(self, fillcolor=(128, 128, 128)): + self.policies = [ + SubPolicy(0.4, "posterize", 8, 0.6, "rotate", 9, fillcolor), + SubPolicy(0.6, "solarize", 5, 0.6, "autocontrast", 5, fillcolor), + SubPolicy(0.8, "equalize", 8, 0.6, "equalize", 3, fillcolor), + SubPolicy(0.6, "posterize", 7, 0.6, "posterize", 6, fillcolor), + SubPolicy(0.4, "equalize", 7, 0.2, "solarize", 4, fillcolor), + + SubPolicy(0.4, "equalize", 4, 0.8, "rotate", 8, fillcolor), + SubPolicy(0.6, "solarize", 3, 0.6, "equalize", 7, fillcolor), + SubPolicy(0.8, "posterize", 5, 1.0, "equalize", 2, fillcolor), + SubPolicy(0.2, "rotate", 3, 0.6, "solarize", 8, fillcolor), + SubPolicy(0.6, "equalize", 8, 0.4, "posterize", 6, fillcolor), + + SubPolicy(0.8, "rotate", 8, 0.4, "color", 0, fillcolor), + SubPolicy(0.4, "rotate", 9, 0.6, "equalize", 2, fillcolor), + SubPolicy(0.0, "equalize", 7, 0.8, "equalize", 8, fillcolor), + SubPolicy(0.6, "invert", 4, 1.0, "equalize", 8, fillcolor), + SubPolicy(0.6, "color", 4, 1.0, "contrast", 8, fillcolor), + + SubPolicy(0.8, "rotate", 8, 1.0, "color", 2, fillcolor), + SubPolicy(0.8, "color", 8, 0.8, "solarize", 7, fillcolor), + SubPolicy(0.4, "sharpness", 7, 0.6, "invert", 8, fillcolor), + SubPolicy(0.6, "shearX", 5, 1.0, "equalize", 9, fillcolor), + SubPolicy(0.4, "color", 0, 0.6, "equalize", 3, fillcolor), + + SubPolicy(0.4, "equalize", 7, 0.2, "solarize", 4, fillcolor), + SubPolicy(0.6, "solarize", 5, 0.6, "autocontrast", 5, fillcolor), + SubPolicy(0.6, "invert", 4, 1.0, "equalize", 8, fillcolor), + SubPolicy(0.6, "color", 4, 1.0, "contrast", 8, fillcolor), + SubPolicy(0.8, "equalize", 8, 0.6, "equalize", 3, fillcolor) + ] + + def __call__(self, img): + policy_idx = random.randint(0, len(self.policies) - 1) + return self.policies[policy_idx](img) + + def __repr__(self): + return "AutoAugment ImageNet Policy" + + +class CIFAR10Policy(object): + """ Randomly choose one of the best 25 Sub-policies on CIFAR10. + + Example: + >>> policy = CIFAR10Policy() + >>> transformed = policy(image) + + Example as a PyTorch Transform: + >>> transform=transforms.Compose([ + >>> transforms.Resize(256), + >>> CIFAR10Policy(), + >>> transforms.ToTensor()]) + """ + def __init__(self, fillcolor=(128, 128, 128)): + self.policies = [ + SubPolicy(0.1, "invert", 7, 0.2, "contrast", 6, fillcolor), + SubPolicy(0.7, "rotate", 2, 0.3, "translateX", 9, fillcolor), + SubPolicy(0.8, "sharpness", 1, 0.9, "sharpness", 3, fillcolor), + SubPolicy(0.5, "shearY", 8, 0.7, "translateY", 9, fillcolor), + SubPolicy(0.5, "autocontrast", 8, 0.9, "equalize", 2, fillcolor), + + SubPolicy(0.2, "shearY", 7, 0.3, "posterize", 7, fillcolor), + SubPolicy(0.4, "color", 3, 0.6, "brightness", 7, fillcolor), + SubPolicy(0.3, "sharpness", 9, 0.7, "brightness", 9, fillcolor), + SubPolicy(0.6, "equalize", 5, 0.5, "equalize", 1, fillcolor), + SubPolicy(0.6, "contrast", 7, 0.6, "sharpness", 5, fillcolor), + + SubPolicy(0.7, "color", 7, 0.5, "translateX", 8, fillcolor), + SubPolicy(0.3, "equalize", 7, 0.4, "autocontrast", 8, fillcolor), + SubPolicy(0.4, "translateY", 3, 0.2, "sharpness", 6, fillcolor), + SubPolicy(0.9, "brightness", 6, 0.2, "color", 8, fillcolor), + SubPolicy(0.5, "solarize", 2, 0.0, "invert", 3, fillcolor), + + SubPolicy(0.2, "equalize", 0, 0.6, "autocontrast", 0, fillcolor), + SubPolicy(0.2, "equalize", 8, 0.6, "equalize", 4, fillcolor), + SubPolicy(0.9, "color", 9, 0.6, "equalize", 6, fillcolor), + SubPolicy(0.8, "autocontrast", 4, 0.2, "solarize", 8, fillcolor), + SubPolicy(0.1, "brightness", 3, 0.7, "color", 0, fillcolor), + + SubPolicy(0.4, "solarize", 5, 0.9, "autocontrast", 3, fillcolor), + SubPolicy(0.9, "translateY", 9, 0.7, "translateY", 9, fillcolor), + SubPolicy(0.9, "autocontrast", 2, 0.8, "solarize", 3, fillcolor), + SubPolicy(0.8, "equalize", 8, 0.1, "invert", 3, fillcolor), + SubPolicy(0.7, "translateY", 9, 0.9, "autocontrast", 1, fillcolor) + ] + + def __call__(self, img): + policy_idx = random.randint(0, len(self.policies) - 1) + return self.policies[policy_idx](img) + + def __repr__(self): + return "AutoAugment CIFAR10 Policy" + + +class SVHNPolicy(object): + """ Randomly choose one of the best 25 Sub-policies on SVHN. + + Example: + >>> policy = SVHNPolicy() + >>> transformed = policy(image) + + Example as a PyTorch Transform: + >>> transform=transforms.Compose([ + >>> transforms.Resize(256), + >>> SVHNPolicy(), + >>> transforms.ToTensor()]) + """ + def __init__(self, fillcolor=(128, 128, 128)): + self.policies = [ + SubPolicy(0.9, "shearX", 4, 0.2, "invert", 3, fillcolor), + SubPolicy(0.9, "shearY", 8, 0.7, "invert", 5, fillcolor), + SubPolicy(0.6, "equalize", 5, 0.6, "solarize", 6, fillcolor), + SubPolicy(0.9, "invert", 3, 0.6, "equalize", 3, fillcolor), + SubPolicy(0.6, "equalize", 1, 0.9, "rotate", 3, fillcolor), + + SubPolicy(0.9, "shearX", 4, 0.8, "autocontrast", 3, fillcolor), + SubPolicy(0.9, "shearY", 8, 0.4, "invert", 5, fillcolor), + SubPolicy(0.9, "shearY", 5, 0.2, "solarize", 6, fillcolor), + SubPolicy(0.9, "invert", 6, 0.8, "autocontrast", 1, fillcolor), + SubPolicy(0.6, "equalize", 3, 0.9, "rotate", 3, fillcolor), + + SubPolicy(0.9, "shearX", 4, 0.3, "solarize", 3, fillcolor), + SubPolicy(0.8, "shearY", 8, 0.7, "invert", 4, fillcolor), + SubPolicy(0.9, "equalize", 5, 0.6, "translateY", 6, fillcolor), + SubPolicy(0.9, "invert", 4, 0.6, "equalize", 7, fillcolor), + SubPolicy(0.3, "contrast", 3, 0.8, "rotate", 4, fillcolor), + + SubPolicy(0.8, "invert", 5, 0.0, "translateY", 2, fillcolor), + SubPolicy(0.7, "shearY", 6, 0.4, "solarize", 8, fillcolor), + SubPolicy(0.6, "invert", 4, 0.8, "rotate", 4, fillcolor), + SubPolicy(0.3, "shearY", 7, 0.9, "translateX", 3, fillcolor), + SubPolicy(0.1, "shearX", 6, 0.6, "invert", 5, fillcolor), + + SubPolicy(0.7, "solarize", 2, 0.6, "translateY", 7, fillcolor), + SubPolicy(0.8, "shearY", 4, 0.8, "invert", 8, fillcolor), + SubPolicy(0.7, "shearX", 9, 0.8, "translateY", 3, fillcolor), + SubPolicy(0.8, "shearY", 5, 0.7, "autocontrast", 3, fillcolor), + SubPolicy(0.7, "shearX", 2, 0.1, "invert", 5, fillcolor) + ] + + def __call__(self, img): + policy_idx = random.randint(0, len(self.policies) - 1) + return self.policies[policy_idx](img) + + def __repr__(self): + return "AutoAugment SVHN Policy" + + +class SubPolicy(object): + def __init__(self, p1, operation1, magnitude_idx1, p2, operation2, magnitude_idx2, fillcolor=(128, 128, 128)): + ranges = { + "shearX": np.linspace(0, 0.3, 10), + "shearY": np.linspace(0, 0.3, 10), + "translateX": np.linspace(0, 150 / 331, 10), + "translateY": np.linspace(0, 150 / 331, 10), + "rotate": np.linspace(0, 30, 10), + "color": np.linspace(0.0, 0.9, 10), + "posterize": np.round(np.linspace(8, 4, 10), 0).astype(np.int), + "solarize": np.linspace(256, 0, 10), + "contrast": np.linspace(0.0, 0.9, 10), + "sharpness": np.linspace(0.0, 0.9, 10), + "brightness": np.linspace(0.0, 0.9, 10), + "autocontrast": [0] * 10, + "equalize": [0] * 10, + "invert": [0] * 10 + } + + func = { + "shearX": ShearX(fillcolor=fillcolor), + "shearY": ShearY(fillcolor=fillcolor), + "translateX": TranslateX(fillcolor=fillcolor), + "translateY": TranslateY(fillcolor=fillcolor), + "rotate": Rotate(), + "color": Color(), + "posterize": Posterize(), + "solarize": Solarize(), + "contrast": Contrast(), + "sharpness": Sharpness(), + "brightness": Brightness(), + "autocontrast": AutoContrast(), + "equalize": Equalize(), + "invert": Invert() + } + + self.p1 = p1 + self.operation1 = func[operation1] + self.magnitude1 = ranges[operation1][magnitude_idx1] + self.p2 = p2 + self.operation2 = func[operation2] + self.magnitude2 = ranges[operation2][magnitude_idx2] + + def __call__(self, img): + if random.random() < self.p1: + img = self.operation1(img, self.magnitude1) + if random.random() < self.p2: + img = self.operation2(img, self.magnitude2) + return img diff --git a/utils/data.py b/utils/data.py new file mode 100644 index 0000000000000000000000000000000000000000..af4181613de7006d3e5f71adfd65cfe342124554 --- /dev/null +++ b/utils/data.py @@ -0,0 +1,217 @@ +import numpy as np +from torchvision import datasets, transforms +from utils.toolkit import split_images_labels +import os +import shutil +import torch +from PIL import Image +import logging +import json + + +class iData(object): + train_trsf = [] + test_trsf = [] + common_trsf = [] + class_order = None + + +class iCIFAR10(iData): + use_path = False + train_trsf = [ + transforms.RandomCrop(32, padding=4), + transforms.RandomHorizontalFlip(p=0.5), + transforms.ColorJitter(brightness=63 / 255), + transforms.ToTensor(), + ] + test_trsf = [transforms.ToTensor()] + common_trsf = [ + transforms.Normalize( + mean=(0.4914, 0.4822, 0.4465), std=(0.2023, 0.1994, 0.2010) + ), + ] + + class_order = np.arange(10).tolist() + + def download_data(self): + train_dataset = datasets.cifar.CIFAR10("./datasets", train=True, download=True) + test_dataset = datasets.cifar.CIFAR10("./datasets", train=False, download=True) + self.train_data, self.train_targets = train_dataset.data, np.array( + train_dataset.targets + ) + self.test_data, self.test_targets = test_dataset.data, np.array( + test_dataset.targets + ) + + +class iCIFAR100(iData): + use_path = False + train_trsf = [ + transforms.RandomCrop(32, padding=4), + transforms.RandomHorizontalFlip(), + transforms.ColorJitter(brightness=63 / 255), + transforms.ToTensor() + ] + test_trsf = [transforms.ToTensor()] + common_trsf = [ + transforms.Normalize( + mean=(0.5071, 0.4867, 0.4408), std=(0.2675, 0.2565, 0.2761) + ), + ] + + class_order = np.arange(100).tolist() + + def download_data(self): + train_dataset = datasets.cifar.CIFAR100("./datasets", train=True, download=True) + test_dataset = datasets.cifar.CIFAR100("./datasets", train=False, download=True) + self.train_data, self.train_targets = train_dataset.data, np.array( + train_dataset.targets + ) + self.test_data, self.test_targets = test_dataset.data, np.array( + test_dataset.targets + ) + + +class iImageNet1000(iData): + use_path = True + train_trsf = [ + transforms.RandomResizedCrop(224), + transforms.RandomHorizontalFlip(), + transforms.ColorJitter(brightness=63 / 255), + transforms.ToTensor(), + ] + test_trsf = [ + transforms.Resize(256), + transforms.CenterCrop(224), + transforms.ToTensor(), + ] + common_trsf = [ + # transforms.ToTensor(), + transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), + ] + + class_order = np.arange(1000).tolist() + + def download_data(self): + # assert 0, "You should specify the folder of your dataset" + train_dir = "you_path/Imagenet/train" + test_dir = "you_path/Imagenet/val" + + train_dset = datasets.ImageFolder(train_dir) + test_dset = datasets.ImageFolder(test_dir) + + self.train_data, self.train_targets = split_images_labels(train_dset.imgs) + self.test_data, self.test_targets = split_images_labels(test_dset.imgs) + + +class iImageNet100(iData): + use_path = True + train_trsf = [ + transforms.RandomResizedCrop(224), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + ] + test_trsf = [ + transforms.Resize(256), + transforms.CenterCrop(224), + transforms.ToTensor(), + ] + common_trsf = [ + # transforms.ToTensor(), + transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), + ] + + class_order = np.arange(1000).tolist() + + def download_data(self): + # assert 0, "You should specify the folder of your dataset" + train_dir = "you_path/Imagenet/train" + test_dir = "you_path/Imagenet/val" + + train_dset = datasets.ImageFolder(train_dir) + test_dset = datasets.ImageFolder(test_dir) + + self.train_data, self.train_targets = split_images_labels(train_dset.imgs) + self.test_data, self.test_targets = split_images_labels(test_dset.imgs) + + +def save_target_images_above_threshold(asr_matrix, target_imgs, threshold, save_path, target_class, alpha, + mode="aobve_threshold"): + if mode == "above_threshold": + avg_asr = asr_matrix.mean(axis=0) + selected_indices = np.where(avg_asr > threshold)[0] + mode = f"above_threshold_alpha{alpha}" + elif mode == 'top1': + avg_asr = asr_matrix.mean(axis=0) + selected_indices = [np.argmax(avg_asr)] + elif mode == 'top1_above_threshold': + avg_asr = asr_matrix.mean(axis=0) + selected_indices = np.where(avg_asr > threshold)[0] + if len(selected_indices) > 0: + selected_indices = [selected_indices[np.argmax(avg_asr[selected_indices])]] + mode = f"top1_above_threshold_alpha{alpha}" + elif mode == 'top1_for_task0': + asr_task0 = asr_matrix[0, :] + selected_indices = np.where(asr_task0 > threshold)[0] + if len(selected_indices) > 0: + selected_indices = [selected_indices[np.argmax(asr_task0[selected_indices])]] + + if len(selected_indices) == 0: + logging.info("No target images with average ASR above the threshold.") + return + + target_folder = os.path.join(save_path, f'target_dataset_{mode}', f"{target_class}_{threshold}") + os.makedirs(target_folder, exist_ok=True) + + existing_files = sorted(os.listdir(target_folder)) + next_index = len(existing_files) + + for idx in selected_indices: + target_image = target_imgs[idx] + + target_image_pil = Image.fromarray(np.moveaxis((target_image * 255).astype(np.uint8), 0, -1)) + target_image_name = f"{next_index}.png" + target_image_pil.save(os.path.join(target_folder, target_image_name)) + + logging.info(f"Saved target image {next_index} to {os.path.join(target_folder, target_image_name)}") + next_index += 1 + + logging.info(f"Target images saved to {target_folder}") + + +def load_target_imgs(logs_name, target_class, alpha, threshold): + target_folder = os.path.join(logs_name, f'target_dataset_alpha{alpha}', f"{target_class}_{threshold}") + + if not os.path.exists(target_folder): + logging.error(f"Target folder {target_folder} does not exist.") + return None, None + + target_imgs = [] + target_labels = [] + + for file_name in sorted(os.listdir(target_folder)): + if file_name.endswith(".png"): + target_image_path = os.path.join(target_folder, file_name) + target_image_pil = Image.open(target_image_path) + target_image_pil = target_image_pil.resize((32, 32)) + target_image = np.array(target_image_pil) + + target_image = np.moveaxis(target_image, -1, 0) + target_image_tensor = torch.from_numpy(target_image.astype(np.float32) / 255.0) + + target_imgs.append(target_image_tensor) + target_labels.append(target_class) + + # 将列表中的 target_imgs 转换为一个 eagerpy Tensor + target_imgs_tensor = torch.stack(target_imgs) + target_labels_tensor = torch.tensor(target_labels) + + logging.info(f"Loaded {len(target_imgs)} target images from {target_folder}") + return target_imgs_tensor, target_labels_tensor + + +def load_json(settings_path): + with open(settings_path) as data_file: + param = json.load(data_file) + + return param \ No newline at end of file diff --git a/utils/data_manager.py b/utils/data_manager.py new file mode 100644 index 0000000000000000000000000000000000000000..3ab3a194f140dab1c808cb4bfcd0848b3da10a5c --- /dev/null +++ b/utils/data_manager.py @@ -0,0 +1,509 @@ +import logging +import numpy as np +from PIL import Image +from torch.utils.data import Dataset +from torchvision import transforms +from utils.data import iCIFAR10, iCIFAR100, iImageNet100, iImageNet1000 +from tqdm import tqdm +from torch.utils.data import DataLoader +import os +import utils.inc_net +from utils import factory +import torch +import copy +import random + +class DataManager(object): + def __init__(self, dataset_name, shuffle, seed, init_cls, increment, attack=False): + self.dataset_name = dataset_name + self.attack = attack + self._setup_data(dataset_name, shuffle, seed, attack=self.attack) + assert init_cls <= len(self._class_order), "No enough classes." + self._increments = [init_cls] + while sum(self._increments) + increment < len(self._class_order): + self._increments.append(increment) + offset = len(self._class_order) - sum(self._increments) + if offset > 0: + self._increments.append(offset) + + @property + def nb_tasks(self): + return len(self._increments) + + def get_task_size(self, task): + return self._increments[task] + + def get_accumulate_tasksize(self,task): + return sum(self._increments[:task+1]) + + def get_total_classnum(self): + return len(self._class_order) + + def get_dataset( + self, indices, source, mode, appendent=None, ret_data=False, m_rate=None + ): + if source == "train": + x, y = self._train_data, self._train_targets + elif source == "test": + x, y = self._test_data, self._test_targets + else: + raise ValueError("Unknown data source {}.".format(source)) + + if mode == "train": + if self.attack: + trsf = transforms.Compose([*self._test_trsf,]) + else: + trsf = transforms.Compose([*self._train_trsf, *self._common_trsf]) + elif mode == "flip": + if self.attack: + trsf = transforms.Compose( + [ + *self._test_trsf, + transforms.RandomHorizontalFlip(p=1.0), + ] + ) + else: + trsf = transforms.Compose( + [ + *self._test_trsf, + transforms.RandomHorizontalFlip(p=1.0), + *self._common_trsf, + ] + ) + elif mode == "test": + if self.attack: + trsf = transforms.Compose([*self._test_trsf,]) + else: + trsf = transforms.Compose([*self._test_trsf, *self._common_trsf]) + else: + raise ValueError("Unknown mode {}.".format(mode)) + + data, targets = [], [] + for idx in indices: + if m_rate is None: + class_data, class_targets = self._select( + x, y, low_range=idx, high_range=idx + 1 + ) + else: + class_data, class_targets = self._select_rmm( + x, y, low_range=idx, high_range=idx + 1, m_rate=m_rate + ) + data.append(class_data) + targets.append(class_targets) + + if appendent is not None and len(appendent) != 0: + appendent_data, appendent_targets = appendent + data.append(appendent_data) + targets.append(appendent_targets) + + data, targets = np.concatenate(data), np.concatenate(targets) + + if ret_data: + return data, targets, DummyDataset(data, targets, trsf, self.use_path) + else: + return DummyDataset(data, targets, trsf, self.use_path) + + + def get_finetune_dataset(self,known_classes,total_classes,source,mode,appendent,type="ratio"): + if source == 'train': + x, y = self._train_data, self._train_targets + elif source == 'test': + x, y = self._test_data, self._test_targets + else: + raise ValueError('Unknown data source {}.'.format(source)) + + if mode == 'train': + trsf = transforms.Compose([*self._train_trsf, *self._common_trsf]) + elif mode == 'test': + trsf = transforms.Compose([*self._test_trsf, *self._common_trsf]) + else: + raise ValueError('Unknown mode {}.'.format(mode)) + val_data = [] + val_targets = [] + + old_num_tot = 0 + appendent_data, appendent_targets = appendent + + for idx in range(0, known_classes): + append_data, append_targets = self._select(appendent_data, appendent_targets, + low_range=idx, high_range=idx+1) + num=len(append_data) + if num == 0: + continue + old_num_tot += num + val_data.append(append_data) + val_targets.append(append_targets) + if type == "ratio": + new_num_tot = int(old_num_tot*(total_classes-known_classes)/known_classes) + elif type == "same": + new_num_tot = old_num_tot + else: + assert 0, "not implemented yet" + new_num_average = int(new_num_tot/(total_classes-known_classes)) + for idx in range(known_classes,total_classes): + class_data, class_targets = self._select(x, y, low_range=idx, high_range=idx+1) + val_indx = np.random.choice(len(class_data),new_num_average, replace=False) + val_data.append(class_data[val_indx]) + val_targets.append(class_targets[val_indx]) + val_data=np.concatenate(val_data) + val_targets = np.concatenate(val_targets) + return DummyDataset(val_data, val_targets, trsf, self.use_path) + + def get_dataset_with_split( + self, indices, source, mode, appendent=None, val_samples_per_class=0 + ): + if source == "train": + x, y = self._train_data, self._train_targets + elif source == "test": + x, y = self._test_data, self._test_targets + else: + raise ValueError("Unknown data source {}.".format(source)) + + if mode == "train": + trsf = transforms.Compose([*self._train_trsf, *self._common_trsf]) + elif mode == "test": + trsf = transforms.Compose([*self._test_trsf, *self._common_trsf]) + else: + raise ValueError("Unknown mode {}.".format(mode)) + + train_data, train_targets = [], [] + val_data, val_targets = [], [] + for idx in indices: + class_data, class_targets = self._select( + x, y, low_range=idx, high_range=idx + 1 + ) + val_indx = np.random.choice( + len(class_data), val_samples_per_class, replace=False + ) + train_indx = list(set(np.arange(len(class_data))) - set(val_indx)) + val_data.append(class_data[val_indx]) + val_targets.append(class_targets[val_indx]) + train_data.append(class_data[train_indx]) + train_targets.append(class_targets[train_indx]) + + if appendent is not None: + appendent_data, appendent_targets = appendent + for idx in range(0, int(np.max(appendent_targets)) + 1): + append_data, append_targets = self._select( + appendent_data, appendent_targets, low_range=idx, high_range=idx + 1 + ) + val_indx = np.random.choice( + len(append_data), val_samples_per_class, replace=False + ) + train_indx = list(set(np.arange(len(append_data))) - set(val_indx)) + val_data.append(append_data[val_indx]) + val_targets.append(append_targets[val_indx]) + train_data.append(append_data[train_indx]) + train_targets.append(append_targets[train_indx]) + + train_data, train_targets = np.concatenate(train_data), np.concatenate( + train_targets + ) + val_data, val_targets = np.concatenate(val_data), np.concatenate(val_targets) + + return DummyDataset( + train_data, train_targets, trsf, self.use_path + ), DummyDataset(val_data, val_targets, trsf, self.use_path) + + def _setup_data(self, dataset_name, shuffle, seed, attack=False): + idata = _get_idata(dataset_name) + idata.download_data() + + # Data + self._train_data, self._train_targets = idata.train_data, idata.train_targets + self._test_data, self._test_targets = idata.test_data, idata.test_targets + self.use_path = idata.use_path + + # Transforms + self._train_trsf = idata.train_trsf + self._test_trsf = idata.test_trsf + if attack: + self._common_trsf = None + else: + self._common_trsf = idata.common_trsf + + # Order + order = [i for i in range(len(np.unique(self._train_targets)))] + if shuffle: + np.random.seed(seed) + order = np.random.permutation(len(order)).tolist() + else: + order = idata.class_order + self._class_order = order + logging.info(self._class_order) + + # Map indices + self._train_targets = _map_new_class_index( + self._train_targets, self._class_order + ) + self._test_targets = _map_new_class_index(self._test_targets, self._class_order) + + def _select(self, x, y, low_range, high_range): + idxes = np.where(np.logical_and(y >= low_range, y < high_range))[0] + + if isinstance(x,np.ndarray): + x_return = x[idxes] + else: + x_return = [] + for id in idxes: + x_return.append(x[id]) + return x_return, y[idxes] + + def _select_rmm(self, x, y, low_range, high_range, m_rate): + assert m_rate is not None + if m_rate != 0: + idxes = np.where(np.logical_and(y >= low_range, y < high_range))[0] + selected_idxes = np.random.randint( + 0, len(idxes), size=int((1 - m_rate) * len(idxes)) + ) + new_idxes = idxes[selected_idxes] + new_idxes = np.sort(new_idxes) + else: + new_idxes = np.where(np.logical_and(y >= low_range, y < high_range))[0] + return x[new_idxes], y[new_idxes] + + def getlen(self, index): + y = self._train_targets + return np.sum(np.where(y == index)) + + +class DummyDataset(Dataset): + def __init__(self, images, labels, trsf, use_path=False): + assert len(images) == len(labels), "Data size error!" + self.images = images + self.labels = labels + self.trsf = trsf + self.use_path = use_path + + def __len__(self): + return len(self.images) + + def __getitem__(self, idx): + if self.use_path: + image = self.trsf(pil_loader(self.images[idx])) + else: + image = self.trsf(Image.fromarray(self.images[idx])) + label = self.labels[idx] + + return idx, image, label + + +def _map_new_class_index(y, order): + return np.array(list(map(lambda x: order.index(x), y))) + + +def _get_idata(dataset_name): + name = dataset_name.lower() + if name == "cifar10": + return iCIFAR10() + elif name == "cifar100": + return iCIFAR100() + elif name == "imagenet1000": + return iImageNet1000() + elif name == "imagenet100": + return iImageNet100() + else: + raise NotImplementedError("Unknown dataset {}.".format(dataset_name)) + + +def get_dataloader(data_manager, batch_size=32, + start_class=0, end_class=10, + train=False, shuffle=True, num_workers=0): + if train: + dataset = data_manager.get_dataset(np.arange(start_class, end_class), source="train", mode="train") + else: + dataset = data_manager.get_dataset(np.arange(start_class, end_class), source="test", mode="test") + loader = DataLoader(dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers) + + return loader + + +def pil_loader(path): + """ + Ref: + https://pytorch.org/docs/stable/_modules/torchvision/datasets/folder.html#ImageFolder + """ + # open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835) + with open(path, "rb") as f: + img = Image.open(f) + return img.convert("RGB") + + +def accimage_loader(path): + """ + Ref: + https://pytorch.org/docs/stable/_modules/torchvision/datasets/folder.html#ImageFolder + accimage is an accelerated Image loader and preprocessor leveraging Intel IPP. + accimage is available on conda-forge. + """ + import accimage + + try: + return accimage.Image(path) + except IOError: + # Potentially a decoding problem, fall back to PIL.Image + return pil_loader(path) + + +def default_loader(path): + """ + Ref: + https://pytorch.org/docs/stable/_modules/torchvision/datasets/folder.html#ImageFolder + """ + from torchvision import get_image_backend + + if get_image_backend() == "accimage": + return accimage_loader(path) + else: + return pil_loader(path) + +def load_all_task_models(args, checkpoint_dir, data_manager, batch_size, + device='cuda', train=False, weights=None, load_type='model_loader'): + if weights == None: + model_list = [] + # model = factory.get_model(args["model_name"], args) + loader_list = [] + ckpts = sorted([f for f in os.listdir(checkpoint_dir) if f.endswith('.pkl')]) + known_classes = 0 + + if 'model' in load_type: + model = factory.get_model(args["model_name"], args) + + for i, ckpt_file in enumerate(ckpts): + if 'model' in load_type: + ckpt_path = os.path.join(checkpoint_dir, ckpt_file) + ckpt = torch.load(ckpt_path, map_location=device) + + model.incremental_train(data_manager) + model._network.load_state_dict(ckpt['model_state_dict']) + model._network.to(device) + model._network.eval() + model_list.append(copy.deepcopy(model)) + model.after_task() + + if 'loader' in load_type: + cur_task = ckpt['tasks'] if 'tasks' in ckpt else int(ckpt_file.split('_')[-1].split('.')[0]) + total_classes = known_classes + data_manager.get_task_size(cur_task) + + if train: + dataset = data_manager.get_dataset(np.arange(0, total_classes), source="train", mode="train") + else: + dataset = data_manager.get_dataset(np.arange(0, total_classes), source="test", mode="test") + test_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=0) + loader_list.append(test_loader) + known_classes = total_classes + + return model_list, loader_list + + else: + model = factory.get_model(args["model_name"], args) + ckpt = torch.load(weights, map_location=device) + model.incremental_train(data_manager) + model._network.load_state_dict(ckpt['model_state_dict']) + model._network.to(device) + model._network.eval() + + total_classes = 10 + if train: + dataset = data_manager.get_dataset(np.arange(0, total_classes), source="train", mode="train") + else: + dataset = data_manager.get_dataset(np.arange(0, total_classes), source="test", mode="test") + loader = DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=0) + + return model, loader + +def load_src_model(model_name, checkpoint_dir, device='cuda'): + CL_model_dict = { + 'FOSTERNet': utils.inc_net.FOSTERNet + } + model = CL_model_dict["FOSTERNet"] + ckpt = torch.load(checkpoint_dir, map_location=device) + + total_classes = 10 + model.update_fc(total_classes) + model._network.load_state_dict(ckpt['model_state_dict']) + model._network.to(device) + return model + +def load_src_dataset(data_manager, batch_size): + total_classes = 10 + test_dataset = data_manager.get_dataset(np.arange(0, total_classes), source="train", mode="train") + test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=True, num_workers=0) + return test_loader + +def balanced_sample_from_loaders(loaders, total_batch_size): + num_loaders = len(loaders) + per_loader_sample = total_batch_size // num_loaders + remainder = total_batch_size % num_loaders + + x_batch, y_batch = [], [] + + for i, loader in enumerate(loaders): + batch_needed = per_loader_sample + (1 if i < remainder else 0) + data_iter = iter(loader) + current_count = 0 + while current_count < batch_needed: + x, y = next(data_iter) + needed = batch_needed - current_count + if x.shape[0] > needed: + x = x[:needed] + y = y[:needed] + x_batch.append(x) + y_batch.append(y) + current_count += x.shape[0] + + x_batch = torch.cat(x_batch, dim=0) + y_batch = torch.cat(y_batch, dim=0) + return x_batch, y_batch + + +class CustomDMDataset(Dataset): + def __init__(self, data_dir, transform=None, split='train', test_size=0.2): + self.data_dir = data_dir + self.transform = transform + self.split = split + self.test_size = test_size + + self.classes = sorted(os.listdir(data_dir)) + self.image_paths = [] + self.labels = [] + + for label, class_name in enumerate(self.classes): + class_folder = os.path.join(data_dir, class_name) + if os.path.isdir(class_folder): + for img_name in os.listdir(class_folder): + img_path = os.path.join(class_folder, img_name) + if img_name.endswith(".jpg") or img_name.endswith(".png"): # 根据文件类型选择 + self.image_paths.append(img_path) + self.labels.append(label) + + total_size = len(self.image_paths) + test_size = int(total_size * self.test_size) + train_size = total_size - test_size + + indices = list(range(total_size)) + random.shuffle(indices) + + train_indices = indices[:train_size] + test_indices = indices[train_size:] + + if self.split == 'train': + self.image_paths = [self.image_paths[i] for i in train_indices] + self.labels = [self.labels[i] for i in train_indices] + else: + self.image_paths = [self.image_paths[i] for i in test_indices] + self.labels = [self.labels[i] for i in test_indices] + + def __len__(self): + return len(self.image_paths) + + def __getitem__(self, idx): + img_path = self.image_paths[idx] + label = self.labels[idx] + img = Image.open(img_path) + + if self.transform: + img = self.transform(img) + + return img, label + diff --git a/utils/factory.py b/utils/factory.py new file mode 100644 index 0000000000000000000000000000000000000000..e528ee6bf03c4ccbfa473b3baf3cb1e4ff9c0004 --- /dev/null +++ b/utils/factory.py @@ -0,0 +1,76 @@ +def get_model(model_name, args): + name = model_name.lower() + if name == "icarl": + from models.icarl import iCaRL + return iCaRL(args) + elif name == "bic": + from models.bic import BiC + return BiC(args) + elif name == "podnet": + from models.podnet import PODNet + return PODNet(args) + elif name == "lwf": + from models.lwf import LwF + return LwF(args) + elif name == "ewc": + from models.ewc import EWC + return EWC(args) + elif name == "wa": + from models.wa import WA + return WA(args) + elif name == "der": + from models.der import DER + return DER(args) + elif name == "finetune": + from models.finetune import Finetune + return Finetune(args) + elif name == "replay": + from models.replay import Replay + return Replay(args) + elif name == "gem": + from models.gem import GEM + return GEM(args) + elif name == "coil": + from models.coil import COIL + return COIL(args) + elif name == "foster": + from models.foster import FOSTER + return FOSTER(args) + elif name == "rmm-icarl": + from models.rmm import RMM_FOSTER, RMM_iCaRL + return RMM_iCaRL(args) + elif name == "rmm-foster": + from models.rmm import RMM_FOSTER, RMM_iCaRL + return RMM_FOSTER(args) + elif name == "fetril": + from models.fetril import FeTrIL + return FeTrIL(args) + elif name == "pass": + from models.pa2s import PASS + return PASS(args) + elif name == "il2a": + from models.il2a import IL2A + return IL2A(args) + elif name == "ssre": + from models.ssre import SSRE + return SSRE(args) + elif name == "memo": + from models.memo import MEMO + return MEMO(args) + elif name == "beefiso": + from models.beef_iso import BEEFISO + return BEEFISO(args) + elif name == "simplecil": + from models.simplecil import SimpleCIL + return SimpleCIL(args) + elif name == "acil": + from models.acil import ACIL + return ACIL(args) + elif name == "ds-al": + from models.dsal import DSAL + return DSAL(args) + elif name == "aper_finetune": + from models.aper_finetune import APER_FINETUNE + return APER_FINETUNE(args) + else: + assert 0 diff --git a/utils/inc_net.py b/utils/inc_net.py new file mode 100644 index 0000000000000000000000000000000000000000..4a38f43d4cc7c0fd57e86aedfe31ff755cc64f89 --- /dev/null +++ b/utils/inc_net.py @@ -0,0 +1,1082 @@ +import copy +import logging +import torch +from torch import nn +from convs.cifar_resnet import resnet32 +from convs.resnet import resnet18, resnet34, resnet50, resnet101, resnet152 +from convs.ucir_cifar_resnet import resnet32 as cosine_resnet32 +from convs.ucir_resnet import resnet18 as cosine_resnet18 +from convs.ucir_resnet import resnet34 as cosine_resnet34 +from convs.ucir_resnet import resnet50 as cosine_resnet50 +from convs.linears import SimpleLinear, SplitCosineLinear, CosineLinear +from convs.modified_represnet import resnet18_rep, resnet34_rep +from convs.resnet_cbam import resnet18_cbam, resnet34_cbam, resnet50_cbam +from convs.memo_resnet import get_resnet18_imagenet as get_memo_resnet18 # for MEMO imagenet +from convs.memo_cifar_resnet import get_resnet32_a2fc as get_memo_resnet32 # for MEMO cifar +from convs.ACL_buffer import RandomBuffer, activation_t +from convs.linears import RecursiveLinear +from typing import Dict, Any + + +def get_convnet(args, pretrained=False): + name = args["convnet_type"].lower() + if name == "resnet32": + return resnet32() + elif name == "resnet18": + return resnet18(pretrained=pretrained, args=args) + elif name == "resnet34": + return resnet34(pretrained=pretrained, args=args) + elif name == "resnet50": + return resnet50(pretrained=pretrained, args=args) + elif name == "cosine_resnet18": + return cosine_resnet18(pretrained=pretrained, args=args) + elif name == "cosine_resnet32": + return cosine_resnet32() + elif name == "cosine_resnet34": + return cosine_resnet34(pretrained=pretrained, args=args) + elif name == "cosine_resnet50": + return cosine_resnet50(pretrained=pretrained, args=args) + elif name == "resnet18_rep": + return resnet18_rep(pretrained=pretrained, args=args) + elif name == "resnet18_cbam": + return resnet18_cbam(pretrained=pretrained, args=args) + elif name == "resnet34_cbam": + return resnet34_cbam(pretrained=pretrained, args=args) + elif name == "resnet50_cbam": + return resnet50_cbam(pretrained=pretrained, args=args) + + # MEMO benchmark backbone + elif name == 'memo_resnet18': + _basenet, _adaptive_net = get_memo_resnet18() + return _basenet, _adaptive_net + elif name == 'memo_resnet32': + _basenet, _adaptive_net = get_memo_resnet32() + return _basenet, _adaptive_net + + else: + raise NotImplementedError("Unknown type {}".format(name)) + + +class BaseNet(nn.Module): + def __init__(self, args, pretrained): + super(BaseNet, self).__init__() + + self.args = args + self.convnet = get_convnet(args, pretrained) + self.fc = None + + @property + def feature_dim(self): + return self.convnet.out_dim + + def extract_vector(self, x): + return self.convnet(x)["features"] + + def forward(self, x): + x = self.convnet(x) + out = self.fc(x["features"]) + """ + { + 'fmaps': [x_1, x_2, ..., x_n], + 'features': features + 'logits': logits + } + """ + out.update(x) + + if self.args["attack"]: + return out["logits"] + else: + return out + + def update_fc(self, nb_classes): + pass + + def generate_fc(self, in_dim, out_dim): + pass + + def copy(self): + return copy.deepcopy(self) + + def freeze(self): + for param in self.parameters(): + param.requires_grad = False + self.eval() + + return self + + def load_checkpoint(self, args): + if args["init_cls"] == 50: + pkl_name = "{}_{}_{}_B{}_Inc{}".format( + args["dataset"], + args["seed"], + args["convnet_type"], + 0, + args["init_cls"], + ) + checkpoint_name = f"checkpoints/finetune_{pkl_name}_0.pkl" + else: + checkpoint_name = f"checkpoints/finetune_{args['csv_name']}_0.pkl" + model_infos = torch.load(checkpoint_name) + self.convnet.load_state_dict(model_infos['convnet']) + self.fc.load_state_dict(model_infos['fc']) + test_acc = model_infos['test_acc'] + return test_acc + + +class IncrementalNet(BaseNet): + def __init__(self, args, pretrained, gradcam=False): + super().__init__(args, pretrained) + self.gradcam = gradcam + if hasattr(self, "gradcam") and self.gradcam: + self._gradcam_hooks = [None, None] + self.set_gradcam_hook() + + def update_fc(self, nb_classes): + fc = self.generate_fc(self.feature_dim, nb_classes) + if self.fc is not None: + nb_output = self.fc.out_features + weight = copy.deepcopy(self.fc.weight.data) + bias = copy.deepcopy(self.fc.bias.data) + fc.weight.data[:nb_output] = weight + fc.bias.data[:nb_output] = bias + + del self.fc + self.fc = fc + + def weight_align(self, increment): + weights = self.fc.weight.data + newnorm = torch.norm(weights[-increment:, :], p=2, dim=1) + oldnorm = torch.norm(weights[:-increment, :], p=2, dim=1) + meannew = torch.mean(newnorm) + meanold = torch.mean(oldnorm) + gamma = meanold / meannew + print("alignweights,gamma=", gamma) + self.fc.weight.data[-increment:, :] *= gamma + + def generate_fc(self, in_dim, out_dim): + fc = SimpleLinear(in_dim, out_dim) + + return fc + + def forward(self, x): + x = self.convnet(x) + out = self.fc(x["features"]) + out.update(x) + if hasattr(self, "gradcam") and self.gradcam: + out["gradcam_gradients"] = self._gradcam_gradients + out["gradcam_activations"] = self._gradcam_activations + + # Change for Foolbox attack + if self.args["attack"]: + return out["logits"] + else: + return out + + def unset_gradcam_hook(self): + self._gradcam_hooks[0].remove() + self._gradcam_hooks[1].remove() + self._gradcam_hooks[0] = None + self._gradcam_hooks[1] = None + self._gradcam_gradients, self._gradcam_activations = [None], [None] + + def set_gradcam_hook(self): + self._gradcam_gradients, self._gradcam_activations = [None], [None] + + def backward_hook(module, grad_input, grad_output): + self._gradcam_gradients[0] = grad_output[0] + return None + + def forward_hook(module, input, output): + self._gradcam_activations[0] = output + return None + + self._gradcam_hooks[0] = self.convnet.last_conv.register_backward_hook( + backward_hook + ) + self._gradcam_hooks[1] = self.convnet.last_conv.register_forward_hook( + forward_hook + ) + + +class IL2ANet(IncrementalNet): + + def update_fc(self, num_old, num_total, num_aux): + fc = self.generate_fc(self.feature_dim, num_total + num_aux) + if self.fc is not None: + weight = copy.deepcopy(self.fc.weight.data) + bias = copy.deepcopy(self.fc.bias.data) + fc.weight.data[:num_old] = weight[:num_old] + fc.bias.data[:num_old] = bias[:num_old] + del self.fc + self.fc = fc + + +class CosineIncrementalNet(BaseNet): + def __init__(self, args, pretrained, nb_proxy=1): + super().__init__(args, pretrained) + self.nb_proxy = nb_proxy + + def update_fc(self, nb_classes, task_num): + fc = self.generate_fc(self.feature_dim, nb_classes) + if self.fc is not None: + if task_num == 1: + fc.fc1.weight.data = self.fc.weight.data + fc.sigma.data = self.fc.sigma.data + else: + prev_out_features1 = self.fc.fc1.out_features + fc.fc1.weight.data[:prev_out_features1] = self.fc.fc1.weight.data + fc.fc1.weight.data[prev_out_features1:] = self.fc.fc2.weight.data + fc.sigma.data = self.fc.sigma.data + + del self.fc + self.fc = fc + + def generate_fc(self, in_dim, out_dim): + if self.fc is None: + fc = CosineLinear(in_dim, out_dim, self.nb_proxy, to_reduce=True) + else: + prev_out_features = self.fc.out_features // self.nb_proxy + # prev_out_features = self.fc.out_features + fc = SplitCosineLinear( + in_dim, prev_out_features, out_dim - prev_out_features, self.nb_proxy + ) + + return fc + + +class BiasLayer_BIC(nn.Module): + def __init__(self): + super(BiasLayer_BIC, self).__init__() + self.alpha = nn.Parameter(torch.ones(1, requires_grad=True)) + self.beta = nn.Parameter(torch.zeros(1, requires_grad=True)) + + def forward(self, x, low_range, high_range): + ret_x = x.clone() + ret_x[:, low_range:high_range] = ( + self.alpha * x[:, low_range:high_range] + self.beta + ) + return ret_x + + def get_params(self): + return (self.alpha.item(), self.beta.item()) + + +class IncrementalNetWithBias(BaseNet): + def __init__(self, args, pretrained, bias_correction=False): + super().__init__(args, pretrained) + + # Bias layer + self.bias_correction = bias_correction + self.bias_layers = nn.ModuleList([]) + self.task_sizes = [] + + def forward(self, x): + x = self.convnet(x) + out = self.fc(x["features"]) + if self.bias_correction: + logits = out["logits"] + for i, layer in enumerate(self.bias_layers): + logits = layer( + logits, sum(self.task_sizes[:i]), sum(self.task_sizes[: i + 1]) + ) + out["logits"] = logits + + out.update(x) + + if self.args["attack"]: + return out["logits"] + else: + return out + + def update_fc(self, nb_classes): + fc = self.generate_fc(self.feature_dim, nb_classes) + if self.fc is not None: + nb_output = self.fc.out_features + weight = copy.deepcopy(self.fc.weight.data) + bias = copy.deepcopy(self.fc.bias.data) + fc.weight.data[:nb_output] = weight + fc.bias.data[:nb_output] = bias + + del self.fc + self.fc = fc + + new_task_size = nb_classes - sum(self.task_sizes) + self.task_sizes.append(new_task_size) + self.bias_layers.append(BiasLayer_BIC()) + + def generate_fc(self, in_dim, out_dim): + fc = SimpleLinear(in_dim, out_dim) + + return fc + + def get_bias_params(self): + params = [] + for layer in self.bias_layers: + params.append(layer.get_params()) + + return params + + def unfreeze(self): + for param in self.parameters(): + param.requires_grad = True + + +class DERNet(nn.Module): + def __init__(self, args, pretrained): + super(DERNet, self).__init__() + self.convnet_type = args["convnet_type"] + self.convnets = nn.ModuleList() + self.pretrained = pretrained + self.out_dim = None + self.fc = None + self.aux_fc = None + self.task_sizes = [] + self.args = args + + @property + def feature_dim(self): + if self.out_dim is None: + return 0 + return self.out_dim * len(self.convnets) + + def extract_vector(self, x): + features = [convnet(x)["features"] for convnet in self.convnets] + features = torch.cat(features, 1) + return features + + def forward(self, x): + features = [convnet(x)["features"] for convnet in self.convnets] + features = torch.cat(features, 1) + + out = self.fc(features) # {logics: self.fc(features)} + + aux_logits = self.aux_fc(features[:, -self.out_dim:])["logits"] + + out.update({"aux_logits": aux_logits, "features": features}) + if self.args["attack"]: + return out["logits"] + else: + return out + """ + { + 'features': features + 'logits': logits + 'aux_logits':aux_logits + } + """ + + def update_fc(self, nb_classes): + if len(self.convnets) == 0: + self.convnets.append(get_convnet(self.args)) + else: + self.convnets.append(get_convnet(self.args)) + self.convnets[-1].load_state_dict(self.convnets[-2].state_dict()) + + if self.out_dim is None: + self.out_dim = self.convnets[-1].out_dim + fc = self.generate_fc(self.feature_dim, nb_classes) + if self.fc is not None: + nb_output = self.fc.out_features + weight = copy.deepcopy(self.fc.weight.data) + bias = copy.deepcopy(self.fc.bias.data) + fc.weight.data[:nb_output, : self.feature_dim - self.out_dim] = weight + fc.bias.data[:nb_output] = bias + + del self.fc + self.fc = fc + + new_task_size = nb_classes - sum(self.task_sizes) + self.task_sizes.append(new_task_size) + + self.aux_fc = self.generate_fc(self.out_dim, new_task_size + 1) + + def generate_fc(self, in_dim, out_dim): + fc = SimpleLinear(in_dim, out_dim) + + return fc + + def copy(self): + return copy.deepcopy(self) + + def freeze(self): + for param in self.parameters(): + param.requires_grad = False + self.eval() + + return self + + def freeze_conv(self): + for param in self.convnets.parameters(): + param.requires_grad = False + self.convnets.eval() + + def weight_align(self, increment): + weights = self.fc.weight.data + newnorm = torch.norm(weights[-increment:, :], p=2, dim=1) + oldnorm = torch.norm(weights[:-increment, :], p=2, dim=1) + meannew = torch.mean(newnorm) + meanold = torch.mean(oldnorm) + gamma = meanold / meannew + print("alignweights,gamma=", gamma) + self.fc.weight.data[-increment:, :] *= gamma + + def load_checkpoint(self, args): + checkpoint_name = f"checkpoints/finetune_{args['csv_name']}_0.pkl" + model_infos = torch.load(checkpoint_name) + assert len(self.convnets) == 1 + self.convnets[0].load_state_dict(model_infos['convnet']) + self.fc.load_state_dict(model_infos['fc']) + test_acc = model_infos['test_acc'] + return test_acc + + +class SimpleCosineIncrementalNet(BaseNet): + def __init__(self, args, pretrained): + super().__init__(args, pretrained) + + def update_fc(self, nb_classes, nextperiod_initialization=None): + fc = self.generate_fc(self.feature_dim, nb_classes).cuda() + if self.fc is not None: + nb_output = self.fc.out_features + weight = copy.deepcopy(self.fc.weight.data) + fc.sigma.data = self.fc.sigma.data + if nextperiod_initialization is not None: + + weight = torch.cat([weight, nextperiod_initialization]) + else: + weight = torch.cat([weight, torch.zeros(nb_classes - nb_output, self.feature_dim).cuda()]) + fc.weight = nn.Parameter(weight) + del self.fc + self.fc = fc + + def generate_fc(self, in_dim, out_dim): + fc = CosineLinear(in_dim, out_dim) + return fc + + def regenerate_fc(self, nb_classes): + fc = self.generate_fc(self.feature_dim, nb_classes).cuda() + del self.fc + self.fc = fc + return fc + + +class MultiBranchCosineIncrementalNet(BaseNet): + def __init__(self, args, pretrained): + super().__init__(args, pretrained) + + # no need the convnet. + + print( + 'Clear the convnet in MultiBranchCosineIncrementalNet, since we are using self.convnets with dual branches') + self.convnet = torch.nn.Identity() + for param in self.convnet.parameters(): + param.requires_grad = False + + self.convnets = nn.ModuleList() + self.args = args + + def update_fc(self, nb_classes, nextperiod_initialization=None): + fc = self.generate_fc(self._feature_dim, nb_classes).cuda() + if self.fc is not None: + nb_output = self.fc.out_features + weight = copy.deepcopy(self.fc.weight.data) + fc.sigma.data = self.fc.sigma.data + if nextperiod_initialization is not None: + weight = torch.cat([weight, nextperiod_initialization]) + else: + weight = torch.cat([weight, torch.zeros(nb_classes - nb_output, self._feature_dim).cuda()]) + fc.weight = nn.Parameter(weight) + del self.fc + self.fc = fc + + def generate_fc(self, in_dim, out_dim): + fc = CosineLinear(in_dim, out_dim) + return fc + + def forward(self, x): + features = [convnet(x)["features"] for convnet in self.convnets] + features = torch.cat(features, 1) + # import pdb; pdb.set_trace() + out = self.fc(features) + out.update({"features": features}) + if self.args["attack"]: + return out["logits"] + else: + return out + + def construct_dual_branch_network(self, trained_model, tuned_model, cls_num): + self.convnets.append(trained_model.convnet) + self.convnets.append(tuned_model.convnet) + + self._feature_dim = self.convnets[0].out_dim * len(self.convnets) + self.fc = self.generate_fc(self._feature_dim, cls_num) + + +class FOSTERNet(nn.Module): + def __init__(self, args, pretrained): + super(FOSTERNet, self).__init__() + self.convnet_type = args["convnet_type"] + self.convnets = nn.ModuleList() + self.pretrained = pretrained + self.out_dim = None + self.fc = None + self.fe_fc = None + self.task_sizes = [] + self.oldfc = None + self.args = args + + @property + def feature_dim(self): + if self.out_dim is None: + return 0 + return self.out_dim * len(self.convnets) + + def extract_vector(self, x): + features = [convnet(x)["features"] for convnet in self.convnets] + features = torch.cat(features, 1) + return features + + def forward(self, x): + features = [convnet(x)["features"] for convnet in self.convnets] + features = torch.cat(features, 1) + out = self.fc(features) + fe_logits = self.fe_fc(features[:, -self.out_dim:])["logits"] + + out.update({"fe_logits": fe_logits, "features": features}) + + if self.oldfc is not None: + old_logits = self.oldfc(features[:, : -self.out_dim])["logits"] + out.update({"old_logits": old_logits}) + + out.update({"eval_logits": out["logits"]}) + + # Change for Foolbox attack + if self.args["attack"]: + return out["logits"] + else: + return out + + def update_fc(self, nb_classes): + self.convnets.append(get_convnet(self.args)) + if self.out_dim is None: + self.out_dim = self.convnets[-1].out_dim + fc = self.generate_fc(self.feature_dim, nb_classes) + if self.fc is not None: + nb_output = self.fc.out_features + weight = copy.deepcopy(self.fc.weight.data) + bias = copy.deepcopy(self.fc.bias.data) + fc.weight.data[:nb_output, : self.feature_dim - self.out_dim] = weight + fc.bias.data[:nb_output] = bias + self.convnets[-1].load_state_dict(self.convnets[-2].state_dict()) + + self.oldfc = self.fc + self.fc = fc + new_task_size = nb_classes - sum(self.task_sizes) + self.task_sizes.append(new_task_size) + self.fe_fc = self.generate_fc(self.out_dim, nb_classes) + + def generate_fc(self, in_dim, out_dim): + fc = SimpleLinear(in_dim, out_dim) + return fc + + def copy(self): + return copy.deepcopy(self) + + def copy_fc(self, fc): + weight = copy.deepcopy(fc.weight.data) + bias = copy.deepcopy(fc.bias.data) + n, m = weight.shape[0], weight.shape[1] + self.fc.weight.data[:n, :m] = weight + self.fc.bias.data[:n] = bias + + def freeze(self): + for param in self.parameters(): + param.requires_grad = False + self.eval() + return self + + def freeze_conv(self): + for param in self.convnets.parameters(): + param.requires_grad = False + self.convnets.eval() + + def weight_align(self, old, increment, value): + weights = self.fc.weight.data + newnorm = torch.norm(weights[-increment:, :], p=2, dim=1) + oldnorm = torch.norm(weights[:-increment, :], p=2, dim=1) + meannew = torch.mean(newnorm) + meanold = torch.mean(oldnorm) + gamma = meanold / meannew * (value ** (old / increment)) + logging.info("align weights, gamma = {} ".format(gamma)) + self.fc.weight.data[-increment:, :] *= gamma + + def load_checkpoint(self, args): + if args["init_cls"] == 50: + pkl_name = "{}_{}_{}_B{}_Inc{}".format( + args["dataset"], + args["seed"], + args["convnet_type"], + 0, + args["init_cls"], + ) + checkpoint_name = f"checkpoints/finetune_{pkl_name}_0.pkl" + else: + checkpoint_name = f"checkpoints/finetune_{args['csv_name']}_0.pkl" + model_infos = torch.load(checkpoint_name) + assert len(self.convnets) == 1 + self.convnets[0].load_state_dict(model_infos['convnet']) + self.fc.load_state_dict(model_infos['fc']) + test_acc = model_infos['test_acc'] + return test_acc + + +class BiasLayer(nn.Module): + def __init__(self): + super(BiasLayer, self).__init__() + self.alpha = nn.Parameter(torch.zeros(1, requires_grad=True)) + self.beta = nn.Parameter(torch.zeros(1, requires_grad=True)) + + def forward(self, x, bias=True): + ret_x = x.clone() + ret_x = (self.alpha + 1) * x # + self.beta + if bias: + ret_x = ret_x + self.beta + return ret_x + + def get_params(self): + return (self.alpha.item(), self.beta.item()) + + +class BEEFISONet(nn.Module): + def __init__(self, args, pretrained): + super(BEEFISONet, self).__init__() + self.convnet_type = args["convnet_type"] + self.convnets = nn.ModuleList() + self.pretrained = pretrained + self.out_dim = None + self.old_fc = None + self.new_fc = None + self.task_sizes = [] + self.forward_prototypes = None + self.backward_prototypes = None + self.args = args + self.biases = nn.ModuleList() + + @property + def feature_dim(self): + if self.out_dim is None: + return 0 + return self.out_dim * len(self.convnets) + + def extract_vector(self, x): + features = [convnet(x)["features"] for convnet in self.convnets] + features = torch.cat(features, 1) + return features + + def forward(self, x): + features = [convnet(x)["features"] for convnet in self.convnets] + features = torch.cat(features, 1) + + if self.old_fc is None: + fc = self.new_fc + out = fc(features) + else: + ''' + merge the weights + ''' + new_task_size = self.task_sizes[-1] + fc_weight = torch.cat( + [self.old_fc.weight, torch.zeros((new_task_size, self.feature_dim - self.out_dim)).cuda()], dim=0) + new_fc_weight = self.new_fc.weight + new_fc_bias = self.new_fc.bias + for i in range(len(self.task_sizes) - 2, -1, -1): + new_fc_weight = torch.cat([*[self.biases[i](self.backward_prototypes.weight[i].unsqueeze(0), bias=False) + for _ in range(self.task_sizes[i])], new_fc_weight], dim=0) + new_fc_bias = torch.cat([*[self.biases[i](self.backward_prototypes.bias[i].unsqueeze(0), bias=True) for + _ in range(self.task_sizes[i])], new_fc_bias]) + fc_weight = torch.cat([fc_weight, new_fc_weight], dim=1) + fc_bias = torch.cat([self.old_fc.bias, torch.zeros(new_task_size).cuda()]) + fc_bias = +new_fc_bias + logits = features @ fc_weight.permute(1, 0) + fc_bias + out = {"logits": logits} + + new_fc_weight = self.new_fc.weight + new_fc_bias = self.new_fc.bias + for i in range(len(self.task_sizes) - 2, -1, -1): + new_fc_weight = torch.cat([self.backward_prototypes.weight[i].unsqueeze(0), new_fc_weight], dim=0) + new_fc_bias = torch.cat([self.backward_prototypes.bias[i].unsqueeze(0), new_fc_bias]) + out["train_logits"] = features[:, -self.out_dim:] @ new_fc_weight.permute(1, 0) + new_fc_bias + out.update({"eval_logits": out["logits"], + "energy_logits": self.forward_prototypes(features[:, -self.out_dim:])["logits"]}) + if self.args["attack"]: + return out["logits"] + else: + return out + + def update_fc_before(self, nb_classes): + new_task_size = nb_classes - sum(self.task_sizes) + self.biases = nn.ModuleList([BiasLayer() for i in range(len(self.task_sizes))]) + self.convnets.append(get_convnet(self.args)) + if self.out_dim is None: + self.out_dim = self.convnets[-1].out_dim + if self.new_fc is not None: + self.fe_fc = self.generate_fc(self.out_dim, nb_classes) + self.backward_prototypes = self.generate_fc(self.out_dim, len(self.task_sizes)) + self.convnets[-1].load_state_dict(self.convnets[0].state_dict()) + self.forward_prototypes = self.generate_fc(self.out_dim, nb_classes) + self.new_fc = self.generate_fc(self.out_dim, new_task_size) + self.task_sizes.append(new_task_size) + + def generate_fc(self, in_dim, out_dim): + fc = SimpleLinear(in_dim, out_dim) + return fc + + def update_fc_after(self): + if self.old_fc is not None: + old_fc = self.generate_fc(self.feature_dim, sum(self.task_sizes)) + new_task_size = self.task_sizes[-1] + old_fc.weight.data = torch.cat( + [self.old_fc.weight.data, torch.zeros((new_task_size, self.feature_dim - self.out_dim)).cuda()], dim=0) + new_fc_weight = self.new_fc.weight.data + new_fc_bias = self.new_fc.bias.data + for i in range(len(self.task_sizes) - 2, -1, -1): + new_fc_weight = torch.cat([*[ + self.biases[i](self.backward_prototypes.weight.data[i].unsqueeze(0), bias=False) for _ in + range(self.task_sizes[i])], new_fc_weight], dim=0) + new_fc_bias = torch.cat([*[self.biases[i](self.backward_prototypes.bias.data[i].unsqueeze(0), bias=True) + for _ in range(self.task_sizes[i])], new_fc_bias]) + old_fc.weight.data = torch.cat([old_fc.weight.data, new_fc_weight], dim=1) + old_fc.bias.data = torch.cat([self.old_fc.bias.data, torch.zeros(new_task_size).cuda()]) + old_fc.bias.data += new_fc_bias + self.old_fc = old_fc + else: + self.old_fc = self.new_fc + + def copy(self): + return copy.deepcopy(self) + + def copy_fc(self, fc): + weight = copy.deepcopy(fc.weight.data) + bias = copy.deepcopy(fc.bias.data) + n, m = weight.shape[0], weight.shape[1] + self.fc.weight.data[:n, :m] = weight + self.fc.bias.data[:n] = bias + + def freeze(self): + for param in self.parameters(): + param.requires_grad = False + self.eval() + return self + + def freeze_conv(self): + for param in self.convnets.parameters(): + param.requires_grad = False + self.convnets.eval() + + def weight_align(self, old, increment, value): + weights = self.fc.weight.data + newnorm = torch.norm(weights[-increment:, :], p=2, dim=1) + oldnorm = torch.norm(weights[:-increment, :], p=2, dim=1) + meannew = torch.mean(newnorm) + meanold = torch.mean(oldnorm) + gamma = meanold / meannew * (value ** (old / increment)) + logging.info("align weights, gamma = {} ".format(gamma)) + self.fc.weight.data[-increment:, :] *= gamma + + +class AdaptiveNet(nn.Module): + def __init__(self, args, pretrained): + super(AdaptiveNet, self).__init__() + self.convnet_type = args["convnet_type"] + self.TaskAgnosticExtractor, _ = get_convnet(args, pretrained) # Generalized blocks + self.TaskAgnosticExtractor.train() + self.AdaptiveExtractors = nn.ModuleList() # Specialized Blocks + self.pretrained = pretrained + self.out_dim = None + self.fc = None + self.aux_fc = None + self.task_sizes = [] + self.args = args + + @property + def feature_dim(self): + if self.out_dim is None: + return 0 + return self.out_dim * len(self.AdaptiveExtractors) + + def extract_vector(self, x): + base_feature_map = self.TaskAgnosticExtractor(x) + features = [extractor(base_feature_map) for extractor in self.AdaptiveExtractors] + features = torch.cat(features, 1) + return features + + def forward(self, x): + base_feature_map = self.TaskAgnosticExtractor(x) + features = [extractor(base_feature_map) for extractor in self.AdaptiveExtractors] + features = torch.cat(features, 1) + out = self.fc(features) # {logits: self.fc(features)} + + aux_logits = self.aux_fc(features[:, -self.out_dim:])["logits"] + + out.update({"aux_logits": aux_logits, "features": features}) + out.update({"base_features": base_feature_map}) + if self.args["attack"]: + return out["logits"] + else: + return out + ''' + { + 'features': features + 'logits': logits + 'aux_logits':aux_logits + } + ''' + + def update_fc(self, nb_classes): + _, _new_extractor = get_convnet(self.args) + if len(self.AdaptiveExtractors) == 0: + self.AdaptiveExtractors.append(_new_extractor) + else: + self.AdaptiveExtractors.append(_new_extractor) + self.AdaptiveExtractors[-1].load_state_dict(self.AdaptiveExtractors[-2].state_dict()) + + if self.out_dim is None: + logging.info(self.AdaptiveExtractors[-1]) + self.out_dim = self.AdaptiveExtractors[-1].feature_dim + fc = self.generate_fc(self.feature_dim, nb_classes) + if self.fc is not None: + nb_output = self.fc.out_features + weight = copy.deepcopy(self.fc.weight.data) + bias = copy.deepcopy(self.fc.bias.data) + fc.weight.data[:nb_output, :self.feature_dim - self.out_dim] = weight + fc.bias.data[:nb_output] = bias + + del self.fc + self.fc = fc + + new_task_size = nb_classes - sum(self.task_sizes) + self.task_sizes.append(new_task_size) + self.aux_fc = self.generate_fc(self.out_dim, new_task_size + 1) + + def generate_fc(self, in_dim, out_dim): + fc = SimpleLinear(in_dim, out_dim) + return fc + + def copy(self): + return copy.deepcopy(self) + + def weight_align(self, increment): + weights = self.fc.weight.data + newnorm = (torch.norm(weights[-increment:, :], p=2, dim=1)) + oldnorm = (torch.norm(weights[:-increment, :], p=2, dim=1)) + meannew = torch.mean(newnorm) + meanold = torch.mean(oldnorm) + gamma = meanold / meannew + print('alignweights,gamma=', gamma) + self.fc.weight.data[-increment:, :] *= gamma + + def load_checkpoint(self, args): + if args["init_cls"] == 50: + pkl_name = "{}_{}_{}_B{}_Inc{}".format( + args["dataset"], + args["seed"], + args["convnet_type"], + 0, + args["init_cls"], + ) + checkpoint_name = f"checkpoints/finetune_{pkl_name}_0.pkl" + else: + checkpoint_name = f"checkpoints/finetune_{args['csv_name']}_0.pkl" + checkpoint_name = checkpoint_name.replace("memo_", "") + model_infos = torch.load(checkpoint_name) + model_dict = model_infos['convnet'] + assert len(self.AdaptiveExtractors) == 1 + + base_state_dict = self.TaskAgnosticExtractor.state_dict() + adap_state_dict = self.AdaptiveExtractors[0].state_dict() + + pretrained_base_dict = { + k: v + for k, v in model_dict.items() + if k in base_state_dict + } + + pretrained_adap_dict = { + k: v + for k, v in model_dict.items() + if k in adap_state_dict + } + + base_state_dict.update(pretrained_base_dict) + adap_state_dict.update(pretrained_adap_dict) + + self.TaskAgnosticExtractor.load_state_dict(base_state_dict) + self.AdaptiveExtractors[0].load_state_dict(adap_state_dict) + self.fc.load_state_dict(model_infos['fc']) + test_acc = model_infos['test_acc'] + return test_acc + + +class ACILNet(BaseNet): + """ + Network structure of the ACIL [1]. + + This implementation refers to the official implementation https://github.com/ZHUANGHP/Analytic-continual-learning. + + References: + [1] Zhuang, Huiping, et al. + "ACIL: Analytic class-incremental learning with absolute memorization and privacy protection." + Advances in Neural Information Processing Systems 35 (2022): 11602-11614. + """ + + def __init__( + self, + args: Dict[str, Any], + buffer_size: int = 8192, + gamma: float = 0.1, + pretrained: bool = False, + device=None, + dtype=torch.double, + ) -> None: + super().__init__(args, pretrained) + assert isinstance( + self.convnet, torch.nn.Module + ), "The backbone network `convnet` must be a `torch.nn.Module`." + self.convnet: torch.nn.Module = self.convnet.to(device, non_blocking=True) + + self.args = args + self.buffer_size: int = buffer_size + self.gamma: float = gamma + self.device = device + self.dtype = dtype + + @torch.no_grad() + def forward(self, X: torch.Tensor) -> Dict[str, torch.Tensor]: + X = self.convnet(X)["features"] + X = self.buffer(X) + X = self.fc(X)["logits"] + return {"logits": X} + + def update_fc(self, nb_classes: int) -> None: + self.fc.update_fc(nb_classes) + + def generate_fc(self, *_) -> None: + self.fc = RecursiveLinear( + self.buffer_size, + self.gamma, + bias=False, + device=self.device, + dtype=self.dtype, + ) + + def generate_buffer(self) -> None: + self.buffer = RandomBuffer( + self.feature_dim, self.buffer_size, device=self.device, dtype=self.dtype + ) + + def after_task(self) -> None: + self.fc.after_task() + + @torch.no_grad() + def fit(self, X: torch.Tensor, y: torch.Tensor) -> None: + X = self.convnet(X)["features"] + X = self.buffer(X) + Y: torch.Tensor = torch.nn.functional.one_hot(y, self.fc.out_features) + self.fc.fit(X, Y) + + +class DSALNet(ACILNet): + """ + Network structure of the DS-AL [1]. + + This implementation refers to the official implementation https://github.com/ZHUANGHP/Analytic-continual-learning. + + References: + [1] Zhuang, Huiping, et al. + "DS-AL: A Dual-Stream Analytic Learning for Exemplar-Free Class-Incremental Learning." + Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 38. No. 15. 2024. + """ + + def __init__( + self, + args: Dict[str, Any], + buffer_size: int = 8192, + gamma_main: float = 1e-3, + gamma_comp: float = 1e-3, + C: float = 1, + activation_main: activation_t = torch.relu, + activation_comp: activation_t = torch.tanh, + pretrained: bool = False, + device=None, + dtype=torch.double, + ) -> None: + self.C = C + self.gamma_comp = gamma_comp + self.activation_main = activation_main + self.activation_comp = activation_comp + super().__init__(args, buffer_size, gamma_main, pretrained, device, dtype) + + @torch.no_grad() + def forward(self, X: torch.Tensor) -> Dict[str, torch.Tensor]: + X = self.buffer(self.convnet(X)["features"]) + X_main = self.fc(self.activation_main(X))["logits"] + X_comp = self.fc_comp(self.activation_comp(X))["logits"] + return {"logits": X_main + self.C * X_comp} + + @torch.no_grad() + def fit(self, X: torch.Tensor, y: torch.Tensor) -> None: + num_classes = max(self.fc.out_features, int(y.max().item()) + 1) + Y_main = torch.nn.functional.one_hot(y, num_classes=num_classes) + X = self.buffer(self.convnet(X)["features"]) + + # Train the main stream + X_main = self.activation_main(X) + self.fc.fit(X_main, Y_main) + self.fc.after_task() + + # Previous label cleansing (PLC) + Y_comp = Y_main - self.fc(X_main)["logits"] + Y_comp[:, : -self.increment_size] = 0 + + # Train the compensation stream + X_comp = self.activation_comp(X) + self.fc_comp.fit(X_comp, Y_comp) + + @torch.no_grad() + def after_task(self) -> None: + self.fc.after_task() + self.fc_comp.after_task() + + def generate_buffer(self) -> None: + self.buffer = RandomBuffer( + self.feature_dim, + self.buffer_size, + activation=None, + device=self.device, + dtype=self.dtype, + ) + + def generate_fc(self, *_) -> None: + # Main stream + self.fc = RecursiveLinear( + self.buffer_size, + self.gamma, + bias=False, + device=self.device, + dtype=self.dtype, + ) + + # Compensation stream + self.fc_comp = RecursiveLinear( + self.buffer_size, + self.gamma_comp, + bias=False, + device=self.device, + dtype=self.dtype, + ) + + def update_fc(self, nb_classes) -> None: + self.increment_size = nb_classes - self.fc.out_features + self.fc.update_fc(nb_classes) + self.fc_comp.update_fc(nb_classes) diff --git a/utils/ops.py b/utils/ops.py new file mode 100644 index 0000000000000000000000000000000000000000..66dcb775caed0ebd9105ac530acd3e70e138d72e --- /dev/null +++ b/utils/ops.py @@ -0,0 +1,121 @@ +from PIL import Image, ImageEnhance, ImageOps +import random +import torch +import numpy as np +class Cutout(object): + def __init__(self, n_holes, length): + self.n_holes = n_holes + self.length = length + + def __call__(self, img): + h = img.size(1) + w = img.size(2) + + mask = np.ones((h, w), np.float32) + + for n in range(self.n_holes): + y = np.random.randint(h) + x = np.random.randint(w) + + y1 = np.clip(y - self.length // 2, 0, h) + y2 = np.clip(y + self.length // 2, 0, h) + x1 = np.clip(x - self.length // 2, 0, w) + x2 = np.clip(x + self.length // 2, 0, w) + + mask[y1: y2, x1: x2] = 0. + + mask = torch.from_numpy(mask) + mask = mask.expand_as(img) + img = img * mask + + return img + +class ShearX(object): + def __init__(self, fillcolor=(128, 128, 128)): + self.fillcolor = fillcolor + + def __call__(self, x, magnitude): + return x.transform( + x.size, Image.AFFINE, (1, magnitude * random.choice([-1, 1]), 0, 0, 1, 0), + Image.BICUBIC, fillcolor=self.fillcolor) + + +class ShearY(object): + def __init__(self, fillcolor=(128, 128, 128)): + self.fillcolor = fillcolor + + def __call__(self, x, magnitude): + return x.transform( + x.size, Image.AFFINE, (1, 0, 0, magnitude * random.choice([-1, 1]), 1, 0), + Image.BICUBIC, fillcolor=self.fillcolor) + + +class TranslateX(object): + def __init__(self, fillcolor=(128, 128, 128)): + self.fillcolor = fillcolor + + def __call__(self, x, magnitude): + return x.transform( + x.size, Image.AFFINE, (1, 0, magnitude * x.size[0] * random.choice([-1, 1]), 0, 1, 0), + fillcolor=self.fillcolor) + + +class TranslateY(object): + def __init__(self, fillcolor=(128, 128, 128)): + self.fillcolor = fillcolor + + def __call__(self, x, magnitude): + return x.transform( + x.size, Image.AFFINE, (1, 0, 0, 0, 1, magnitude * x.size[1] * random.choice([-1, 1])), + fillcolor=self.fillcolor) + + +class Rotate(object): + def __call__(self, x, magnitude): + rot = x.convert("RGBA").rotate(magnitude * random.choice([-1, 1])) + return Image.composite(rot, Image.new("RGBA", rot.size, (128,) * 4), rot).convert(x.mode) + + +class Color(object): + def __call__(self, x, magnitude): + return ImageEnhance.Color(x).enhance(1 + magnitude * random.choice([-1, 1])) + + +class Posterize(object): + def __call__(self, x, magnitude): + return ImageOps.posterize(x, magnitude) + + +class Solarize(object): + def __call__(self, x, magnitude): + return ImageOps.solarize(x, magnitude) + + +class Contrast(object): + def __call__(self, x, magnitude): + return ImageEnhance.Contrast(x).enhance(1 + magnitude * random.choice([-1, 1])) + + +class Sharpness(object): + def __call__(self, x, magnitude): + return ImageEnhance.Sharpness(x).enhance(1 + magnitude * random.choice([-1, 1])) + + +class Brightness(object): + def __call__(self, x, magnitude): + return ImageEnhance.Brightness(x).enhance(1 + magnitude * random.choice([-1, 1])) + + +class AutoContrast(object): + def __call__(self, x, magnitude): + return ImageOps.autocontrast(x) + + +class Equalize(object): + def __call__(self, x, magnitude): + return ImageOps.equalize(x) + + +class Invert(object): + def __call__(self, x, magnitude): + return ImageOps.invert(x) diff --git a/utils/plot.py b/utils/plot.py new file mode 100644 index 0000000000000000000000000000000000000000..043539363e5273ae3e79d79d2de146b4d6a2fc90 --- /dev/null +++ b/utils/plot.py @@ -0,0 +1,191 @@ +import os +import numpy as np +import torch +import torch.nn.functional as F +import matplotlib.pyplot as plt +import pandas as pd +from pytorch_grad_cam.grad_cam import GradCAM +from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget +from pytorch_grad_cam.utils.image import show_cam_on_image +from torchvision import transforms +import cv2 +import torchvision +from pathlib import Path +from PIL import Image +from torchvision.utils import save_image + + +def save_images(x, x_hat, render_num=64, output_dir='rendered_images', step=0, test=False): + os.makedirs(output_dir, exist_ok=True) + + num_rows = int(render_num ** 0.5 / 2) * 2 + + img_lst = [] + for i in range(int(render_num / 2)): + img_lst.append(x[i]) + img_lst.append(x_hat[i]) + + grid = torchvision.utils.make_grid(img_lst, nrow=num_rows, padding=2) + if test: + torchvision.utils.save_image(grid, os.path.join(output_dir, str(step) + '.png'), nrow=num_rows) + else: + torchvision.utils.save_image(grid * 0.5 + 0.5, os.path.join(output_dir, str(step) + '.png'), nrow=num_rows) + +def load_imagenet_class_dict(): + with open(os.path.join('attacks/UnivIntruder/utils_/map_clsloc.txt'), 'r') as file: + lines = file.readlines() + label_dict = {} + for line in lines: + parts = line.strip().split(',') + label_dict[int(parts[1])] = parts[2] + return label_dict + +def load_imagenet100_class_dict(): + with open(os.path.join('attacks/SAE/map_clsloc_imagenet100.txt'), 'r') as file: + lines = file.readlines() + label_dict = {} + for line in lines: + parts = line.strip().split(',') + label_dict[int(parts[1])] = parts[2] + return label_dict + + +imagenet100_classes_dict = load_imagenet100_class_dict() +imagenet_classes_dict = load_imagenet_class_dict() +cifar100_classes_dict = { + 0: 'apple', 1: 'aquarium_fish', 2: 'baby', 3: 'bear', 4: 'beaver', 5: 'bed', + 6: 'bee', 7: 'beetle', 8: 'bicycle', 9: 'bottle', 10: 'bowl', 11: 'boy', 12: 'bridge', + 13: 'bus', 14: 'butterfly', 15: 'camel', 16: 'can', 17: 'castle', 18: 'caterpillar', + 19: 'cattle', 20: 'chair', 21: 'chimpanzee', 22: 'clock', 23: 'cloud', 24: 'cockroach', + 25: 'couch', 26: 'crab', 27: 'crocodile', 28: 'cup', 29: 'dinosaur', 30: 'dolphin', + 31: 'elephant', 32: 'flatfish', 33: 'forest', 34: 'fox', 35: 'girl', 36: 'hamster', + 37: 'house', 38: 'kangaroo', 39: 'computer_keyboard', 40: 'lamp', 41: 'lawn_mower', + 42: 'leopard', 43: 'lion', 44: 'lizard', 45: 'lobster', 46: 'man', 47: 'maple_tree', + 48: 'motorcycle', 49: 'mountain', 50: 'mouse', 51: 'mushroom', 52: 'oak_tree', + 53: 'orange', 54: 'orchid', 55: 'otter', 56: 'palm_tree', 57: 'pear', 58: 'pickup_truck', + 59: 'pine_tree', 60: 'plain', 61: 'plate', 62: 'poppy', 63: 'porcupine', 64: 'possum', + 65: 'rabbit', 66: 'raccoon', 67: 'ray', 68: 'road', 69: 'rocket', 70: 'rose', 71: 'sea', + 72: 'seal', 73: 'shark', 74: 'shrew', 75: 'skunk', 76: 'skyscraper', 77: 'snail', + 78: 'snake', 79: 'spider', 80: 'squirrel', 81: 'streetcar', 82: 'sunflower', + 83: 'sweet_pepper', 84: 'table', 85: 'tank', 86: 'telephone', 87: 'television', + 88: 'tiger', 89: 'tractor', 90: 'train', 91: 'trout', 92: 'tulip', 93: 'turtle', + 94: 'wardrobe', 95: 'whale', 96: 'willow_tree', 97: 'wolf', 98: 'woman', 99: 'worm' + } + +def load_cifar100_classes(): + return [ + 'apple', 'aquarium_fish', 'baby', 'bear', 'beaver', 'bed', 'bee', 'beetle', + 'bicycle', 'bottle', 'bowl', 'boy', 'bridge', 'bus', 'butterfly', 'camel', + 'can', 'castle', 'caterpillar', 'cattle', 'chair', 'chimpanzee', 'clock', + 'cloud', 'cockroach', 'couch', 'crab', 'crocodile', 'cup', 'dinosaur', 'dolphin', + 'elephant', 'flatfish', 'forest', 'fox', 'girl', 'hamster', 'house', 'kangaroo', + 'computer_keyboard', 'lamp', 'lawn_mower', 'leopard', 'lion', 'lizard', 'lobster', + 'man', 'maple_tree', 'motorcycle', 'mountain', 'mouse', 'mushroom', 'oak_tree', + 'orange', 'orchid', 'otter', 'palm_tree', 'pear', 'pickup_truck', 'pine_tree', + 'plain', 'plate', 'poppy', 'porcupine', 'possum', 'rabbit', 'raccoon', 'ray', + 'road', 'rocket', 'rose', 'sea', 'seal', 'shark', 'shrew', 'skunk', 'skyscraper', + 'snail', 'snake', 'spider', 'squirrel', 'streetcar', 'sunflower', 'sweet_pepper', + 'table', 'tank', 'telephone', 'television', 'tiger', 'tractor', 'train', + 'trout', 'tulip', 'turtle', 'wardrobe', 'whale', 'willow_tree', 'wolf', 'woman', + 'worm' + ] + + +def plot_asr_per_target(asr_matrix, save_path, prefix, args, acc_metric=None): + num_tasks, num_targets = asr_matrix.shape + tasks = np.arange(num_tasks) + + avg_asr = asr_matrix.mean(axis=0) + + fig, (ax_line, ax_bar) = plt.subplots(2, 1, figsize=(12, 10), gridspec_kw={'height_ratios': [3, 1]}) + + for i in range(num_targets): + ax_line.plot(tasks, asr_matrix[:, i], marker='o', label=f'Target Image {i} (ASR)') + + if acc_metric is not None: + ax_line.plot(tasks, acc_metric, marker='x', label='Clean Accuracy', color='red', linestyle='--') + + ax_line.set_xlabel('Task') + ax_line.set_ylabel('Attack Success Rate (ASR)' if acc_metric is None else 'ASR / Accuracy') + ax_line.set_title(f'ASR of each Target Image (Target Class: {args["target_class"]}) across Tasks') + ax_line.set_xticks(tasks) + ax_line.set_ylim(0, 1) + ax_line.grid(True) + ax_line.legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize='small', ncol=1) + + indices = np.arange(num_targets) + ax_bar.bar(indices, avg_asr, color='skyblue') + ax_bar.set_xlabel('Target Image') + ax_bar.set_ylabel('Average ASR') + ax_bar.set_title(f'Average ASR per Target Image (Target Class: {args["target_class"]})') + ax_bar.set_xticks(indices) + ax_bar.set_xticklabels([f'{i}' for i in range(num_targets)], rotation=45, fontsize='small') + ax_bar.set_ylim(0, 1) + ax_bar.grid(axis='y') + + plt.tight_layout(rect=[0, 0, 0.85, 1]) + os.makedirs(save_path, exist_ok=True) + plt.savefig(os.path.join(save_path, f"{prefix}.png"), bbox_inches='tight') + plt.close() + + + +def save_batch_images(batch_imgs, logs_eval_name, filename=None, prefix="adv", save_num=2): + if filename is not None: + target_folder = os.path.join(logs_eval_name, f'{filename}') + else: + target_folder = os.path.join(logs_eval_name) + os.makedirs(target_folder, exist_ok=True) + + for i, img_tensor in enumerate(batch_imgs): + if i + 1 > save_num: + break + img_name = f"{prefix}_{i}.png" + save_image(img_tensor, os.path.join(target_folder, img_name)) + + +def save_grad_cam(args, imgs, labels, model, save_path, prefix, layer_name="layer4", save_num=2, save_raw=False): + os.makedirs(save_path, exist_ok=True) + model.eval() + cl_methods = args['model_name'] + if cl_methods == 'icarl' or cl_methods == 'finetune' or cl_methods == 'wa' or cl_methods == 'replay' or cl_methods == 'podnet' or cl_methods == 'bic': + target_layers = [model.convnet.get_submodule(layer_name)[-1]] + elif cl_methods == 'foster' or cl_methods == 'der': + target_layers = [model.convnets[0].get_submodule(layer_name)[-1]] + elif cl_methods == 'memo': + target_layers = [model.TaskAgnosticExtractor.get_submodule(layer_name)[-1]] + cam = GradCAM(model=model, target_layers=target_layers) + + # cam_target = ClassifierOutputTarget(labels) + + # Iterate through the images + for i, img in enumerate(imgs): + if i + 1 > save_num: + break + + cam_target = ClassifierOutputTarget(labels[i]) + # grayscale_cam = cam(img.unsqueeze(0), [cam_target]) + grayscale_cam = cam(img.unsqueeze(0)) + grayscale_cam = grayscale_cam[0, :] + + img_np = np.array(img.cpu().permute(1, 2, 0)) + img_np = np.float32(img_np) + + cam_imgs = show_cam_on_image(img_np, grayscale_cam, use_rgb=False) + + # Save the result + output_path = Path(save_path) / f"{i}_{prefix}_grad_cam.png" + cv2.imwrite(str(output_path), cam_imgs) + + if save_raw: + output_path = Path(save_path) / f"{i}_{prefix}_grad_cam_raw.png" + cv2.imwrite(str(output_path), np.clip(img_np, 0.0, 1.0) * 255) + + # Clear GPU cache and gradients + torch.cuda.empty_cache() + # + # if i == 10: + # break + + print(f"Grad-CAM images saved to {save_path}") + diff --git a/utils/rl_utils/ddpg.py b/utils/rl_utils/ddpg.py new file mode 100644 index 0000000000000000000000000000000000000000..555e46645d91bc08e7e90e6b3d262248a8d2900f --- /dev/null +++ b/utils/rl_utils/ddpg.py @@ -0,0 +1,206 @@ +import logging +import torch +from torch import nn +import torch.nn.functional as F +import numpy as np + + +class PolicyNet(torch.nn.Module): + def __init__(self, state_dim, hidden_dim, action_dim, action_bound): + super(PolicyNet, self).__init__() + self.fc1 = torch.nn.Linear(state_dim, hidden_dim) + self.fc2 = torch.nn.Linear(hidden_dim, action_dim) + self.action_bound = action_bound + + def forward(self, x): + x = F.relu(self.fc1(x)) + return torch.tanh(self.fc2(x)) * self.action_bound + + +class RMMPolicyNet(torch.nn.Module): + def __init__(self, state_dim, hidden_dim, action_dim): + super(RMMPolicyNet, self).__init__() + self.fc1 = nn.Sequential( + nn.Linear(state_dim, hidden_dim), + nn.ReLU(inplace=True), + nn.Linear(hidden_dim, action_dim), + ) + self.fc2 = nn.Sequential( + nn.Linear(state_dim+action_dim, hidden_dim), + nn.ReLU(inplace=True), + nn.Linear(hidden_dim, action_dim), + ) + def forward(self, x): + a1 = torch.sigmoid(self.fc1(x)) + x = torch.cat([x,a1],dim=1) + a2 = torch.tanh(self.fc2(x)) + return torch.cat([a1,a2],dim=1) + +class QValueNet(torch.nn.Module): + def __init__(self, state_dim, hidden_dim, action_dim): + super(QValueNet, self).__init__() + self.fc1 = torch.nn.Linear(state_dim + action_dim, hidden_dim) + self.fc2 = torch.nn.Linear(hidden_dim, 1) + + def forward(self, x, a): + cat = torch.cat([x, a], dim=1) + x = F.relu(self.fc1(cat)) + return self.fc2(x) + + +class TwoLayerFC(torch.nn.Module): + def __init__( + self, num_in, num_out, hidden_dim, activation=F.relu, out_fn=lambda x: x + ): + super().__init__() + self.fc1 = nn.Linear(num_in, hidden_dim) + self.fc2 = nn.Linear(hidden_dim, hidden_dim) + self.fc3 = nn.Linear(hidden_dim, num_out) + + self.activation = activation + self.out_fn = out_fn + + def forward(self, x): + x = self.activation(self.fc1(x)) + x = self.activation(self.fc2(x)) + x = self.out_fn(self.fc3(x)) + return x + + +class DDPG: + """DDPG algo""" + + def __init__( + self, + num_in_actor, + num_out_actor, + num_in_critic, + hidden_dim, + discrete, + action_bound, + sigma, + actor_lr, + critic_lr, + tau, + gamma, + device, + use_rmm=True, + ): + + out_fn = (lambda x: x) if discrete else (lambda x: torch.tanh(x) * action_bound) + + if use_rmm: + self.actor = RMMPolicyNet( + num_in_actor, + hidden_dim, + num_out_actor, + ).to(device) + self.target_actor = RMMPolicyNet( + num_in_actor, + hidden_dim, + num_out_actor, + ).to(device) + else: + self.actor = TwoLayerFC( + num_in_actor, + num_out_actor, + hidden_dim, + activation=F.relu, + out_fn=out_fn, + ).to(device) + self.target_actor = TwoLayerFC( + num_in_actor, + num_out_actor, + hidden_dim, + activation=F.relu, + out_fn=out_fn, + ).to(device) + + self.critic = TwoLayerFC(num_in_critic, 1, hidden_dim).to(device) + self.target_critic = TwoLayerFC(num_in_critic, 1, hidden_dim).to(device) + self.target_critic.load_state_dict(self.critic.state_dict()) + self.target_actor.load_state_dict(self.actor.state_dict()) + self.actor_optimizer = torch.optim.Adam(self.actor.parameters(), lr=actor_lr) + self.critic_optimizer = torch.optim.Adam(self.critic.parameters(), lr=critic_lr) + self.gamma = gamma + self.sigma = sigma + self.action_bound = action_bound + self.tau = tau + self.action_dim = num_out_actor + self.device = device + + def take_action(self, state): + state = torch.tensor(np.expand_dims(state,0), dtype=torch.float).to(self.device) + action = self.actor(state)[0].detach().cpu().numpy() + + action = action + self.sigma * np.random.randn(self.action_dim) + action[0]=np.clip(action[0],0,1) + action[1]=np.clip(action[1],-1,1) + return action + def save_state_dict(self,name): + dicts = { + "critic":self.critic.state_dict(), + "target_critic":self.target_critic.state_dict(), + "actor":self.actor.state_dict(), + "target_actor":self.target_actor.state_dict() + } + torch.save(dicts,name) + def load_state_dict(self,name): + dicts = torch.load(name) + self.critic.load_state_dict(dicts["critic"]) + self.target_critic.load_state_dict(dicts["target_critic"]) + self.actor.load_state_dict(dicts["actor"]) + self.target_actor.load_state_dict(dicts["target_actor"]) + def soft_update(self, net, target_net): + for param_target, param in zip(target_net.parameters(), net.parameters()): + param_target.data.copy_( + param_target.data * (1.0 - self.tau) + param.data * self.tau + ) + + def update(self, transition_dict): + states = torch.tensor(transition_dict["states"], dtype=torch.float).to( + self.device + ) + actions = ( + torch.tensor(transition_dict["actions"], dtype=torch.float) + .to(self.device) + ) + rewards = ( + torch.tensor(transition_dict["rewards"], dtype=torch.float) + .view(-1, 1) + .to(self.device) + ) + next_states = torch.tensor( + transition_dict["next_states"], dtype=torch.float + ).to(self.device) + dones = ( + torch.tensor(transition_dict["dones"], dtype=torch.float) + .view(-1, 1) + .to(self.device) + ) + + next_q_values = self.target_critic( + torch.cat([next_states, self.target_actor(next_states)], dim=1) + ) + q_targets = rewards + self.gamma * next_q_values * (1 - dones) + critic_loss = torch.mean( + F.mse_loss( + self.critic(torch.cat([states, actions], dim=1)), + q_targets, + ) + ) + self.critic_optimizer.zero_grad() + critic_loss.backward() + self.critic_optimizer.step() + + actor_loss = -torch.mean( + self.critic( + torch.cat([states, self.actor(states)], dim=1) + ) + ) + self.actor_optimizer.zero_grad() + actor_loss.backward() + self.actor_optimizer.step() + logging.info(f"update DDPG: actor loss {actor_loss.item():.3f}, critic loss {critic_loss.item():.3f}, ") + self.soft_update(self.actor, self.target_actor) # soft-update the target policy net + self.soft_update(self.critic, self.target_critic) # soft-update the target Q value net diff --git a/utils/rl_utils/rl_utils.py b/utils/rl_utils/rl_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..33799bbd129890e2a8b14c48ff5086bfd51f7e97 --- /dev/null +++ b/utils/rl_utils/rl_utils.py @@ -0,0 +1,20 @@ +from tqdm import tqdm +import numpy as np +import torch +import collections +import random + +class ReplayBuffer: + def __init__(self, capacity): + self.buffer = collections.deque(maxlen=capacity) + + def add(self, state, action, reward, next_state, done): + self.buffer.append((state, action, reward, next_state, done)) + + def sample(self, batch_size): + transitions = random.sample(self.buffer, batch_size) + state, action, reward, next_state, done = zip(*transitions) + return np.array(state), np.array(action), reward, np.array(next_state), done + + def size(self): + return len(self.buffer) \ No newline at end of file diff --git a/utils/toolkit.py b/utils/toolkit.py new file mode 100644 index 0000000000000000000000000000000000000000..cde11a8aab6f526faf9d3d53f334d98ea1d29d31 --- /dev/null +++ b/utils/toolkit.py @@ -0,0 +1,111 @@ +import os +import numpy as np +import torch +import json +from enum import Enum + +class ConfigEncoder(json.JSONEncoder): + def default(self, o): + if isinstance(o, type): + return {'$class': o.__module__ + "." + o.__name__} + elif isinstance(o, Enum): + return { + '$enum': o.__module__ + "." + o.__class__.__name__ + '.' + o.name + } + elif callable(o): + return { + '$function': o.__module__ + "." + o.__name__ + } + return json.JSONEncoder.default(self, o) + +def count_parameters(model, trainable=False): + if trainable: + return sum(p.numel() for p in model.parameters() if p.requires_grad) + return sum(p.numel() for p in model.parameters()) + + +def tensor2numpy(x): + return x.cpu().data.numpy() if x.is_cuda else x.data.numpy() + + +def target2onehot(targets, n_classes): + onehot = torch.zeros(targets.shape[0], n_classes).to(targets.device) + onehot.scatter_(dim=1, index=targets.long().view(-1, 1), value=1.0) + return onehot + + +def makedirs(path): + if not os.path.exists(path): + os.makedirs(path) + + +def accuracy(y_pred, y_true, nb_old, increment=10): + assert len(y_pred) == len(y_true), "Data length error." + all_acc = {} + all_acc["total"] = np.around( + (y_pred == y_true).sum() * 100 / len(y_true), decimals=2 + ) + + # Grouped accuracy + for class_id in range(0, np.max(y_true), increment): + idxes = np.where( + np.logical_and(y_true >= class_id, y_true < class_id + increment) + )[0] + label = "{}-{}".format( + str(class_id).rjust(2, "0"), str(class_id + increment - 1).rjust(2, "0") + ) + all_acc[label] = np.around( + (y_pred[idxes] == y_true[idxes]).sum() * 100 / len(idxes), decimals=2 + ) + + # Old accuracy + idxes = np.where(y_true < nb_old)[0] + all_acc["old"] = ( + 0 + if len(idxes) == 0 + else np.around( + (y_pred[idxes] == y_true[idxes]).sum() * 100 / len(idxes), decimals=2 + ) + ) + + # New accuracy + idxes = np.where(y_true >= nb_old)[0] + all_acc["new"] = np.around( + (y_pred[idxes] == y_true[idxes]).sum() * 100 / len(idxes), decimals=2 + ) + + return all_acc + + +def split_images_labels(imgs): + # split trainset.imgs in ImageFolder + images = [] + labels = [] + for item in imgs: + images.append(item[0]) + labels.append(item[1]) + + return np.array(images), np.array(labels) + +def save_fc(args, model): + _path = os.path.join(args['logfilename'], "fc.pt") + if len(args['device']) > 1: + fc_weight = model._network.fc.weight.data + else: + fc_weight = model._network.fc.weight.data.cpu() + torch.save(fc_weight, _path) + + _save_dir = os.path.join(f"./results/fc_weights/{args['prefix']}") + os.makedirs(_save_dir, exist_ok=True) + _save_path = os.path.join(_save_dir, f"{args['csv_name']}.csv") + with open(_save_path, "a+") as f: + f.write(f"{args['time_str']},{args['model_name']},{_path} \n") + +def save_model(args, model): + #used in PODNet + _path = os.path.join(args['logfilename'], "model.pt") + if len(args['device']) > 1: + weight = model._network + else: + weight = model._network.cpu() + torch.save(weight, _path) \ No newline at end of file