def load_models(dataset): archs = [] model_path_list = [] if dataset == "CIFAR-10" or dataset == "CIFAR-100": for arch in ["resnet-110","WRN-28-10","WRN-34-10","resnext-8x64d","resnext-16x64d"]: test_model_path = "{}/train_pytorch_model/real_image_model/{}-pretrained/{}/checkpoint.pth.tar".format( PY_ROOT, dataset, arch) if os.path.exists(test_model_path): archs.append(arch) model_path_list.append(test_model_path) else: log.info(test_model_path + " does not exist!") elif dataset == "TinyImageNet": # for arch in ["vgg11_bn","resnet18","vgg16_bn","resnext64_4","densenet121"]: for arch in MODELS_TEST_STANDARD[dataset]: test_model_path = "{}/train_pytorch_model/real_image_model/{}@{}@*.pth.tar".format( PY_ROOT, dataset, arch) test_model_path = list(glob.glob(test_model_path))[0] if os.path.exists(test_model_path): archs.append(arch) model_path_list.append(test_model_path) else: log.info(test_model_path + "does not exist!") else: for arch in ["inceptionv3","inceptionv4", "inceptionresnetv2","resnet101", "resnet152"]: test_model_list_path = "{}/train_pytorch_model/real_image_model/{}-pretrained/checkpoints/{}*.pth".format( PY_ROOT, dataset, arch) test_model_path = list(glob.glob(test_model_list_path)) if len(test_model_path) == 0: # this arch does not exists in args.dataset continue archs.append(arch) model_path_list.append(test_model_path[0]) models = [] print("begin construct model") if dataset == "TinyImageNet": for idx, arch in enumerate(archs): model = MetaLearnerModelBuilder.construct_tiny_imagenet_model(arch, dataset) model_path = model_path_list[idx] model.load_state_dict(torch.load(model_path, map_location=lambda storage, location: storage)["state_dict"]) model.cuda() model.eval() models.append(model) else: for arch in archs: model = StandardModel(dataset, arch, no_grad=True) model.cuda() model.eval() models.append(model) print("end construct model") return models
def main(args, arch): model = StandardModel(args["dataset"], arch, False) model.cuda() model.eval() # attack related settings if args["attack_method"] == "zoo" or args[ "attack_method"] == "autozoom_bilin": if args["img_resize"] is None: args["img_resize"] = model.input_size[-1] log.info( "Argument img_resize is not set and not using autoencoder, set to image original size:{}" .format(args["img_resize"])) codec = None if args["attack_method"] == "zoo_ae" or args[ "attack_method"] == "autozoom_ae": codec = Codec(model.input_size[-1], IN_CHANNELS[args["dataset"]], args["compress_mode"], args["resize"], use_tanh=args["use_tanh"]) codec.load_codec(args["codec_path"]) codec.cuda() decoder = codec.decoder args["img_resize"] = decoder.input_shape[1] log.info( "Loading autoencoder: {}, set the attack image size to:{}".format( args["codec_path"], args["img_resize"])) # setup attack if args["attack_method"] == "zoo": blackbox_attack = ZOO(model, args["dataset"], args) elif args["attack_method"] == "zoo_ae": blackbox_attack = ZOO_AE(model, args["dataset"], args, decoder) elif args["attack_method"] == "autozoom_bilin": blackbox_attack = AutoZOOM_BiLIN(model, args["dataset"], args) elif args["attack_method"] == "autozoom_ae": blackbox_attack = AutoZOOM_AE(model, args["dataset"], args, decoder) target_str = "untargeted" if args[ "attack_type"] != "targeted" else "targeted_{}".format( args["target_type"]) save_result_path = args[ "exp_dir"] + "/data_{}@arch_{}@attack_{}@{}_result.json".format( args["dataset"], arch, args["attack_method"], target_str) attack_framework = AutoZoomAttackFramework(args) attack_framework.attack_dataset_images(args, blackbox_attack, arch, model, codec, save_result_path) model.cpu()
def test_attack(threshold, arch, dataset, test_loader): target_model = StandardModel(dataset, arch, no_grad=False) if torch.cuda.is_available(): target_model = target_model.cuda() target_model.eval() attack = LinfPGDAttack(target_model, loss_fn=nn.CrossEntropyLoss(reduction="sum"), eps=threshold, nb_iter=30, eps_iter=0.01, rand_init=True, clip_min=0.0, clip_max=1.0, targeted=False) all_count = 0 success_count = 0 all_adv_images = [] all_true_labels = [] for idx, (img, true_label) in enumerate(test_loader): img = img.cuda() true_label = true_label.cuda().long() adv_image = attack.perturb(img, true_label) # (3, 224, 224), float if adv_image is None: continue adv_label = target_model.forward(adv_image).max(1)[1].detach().cpu().numpy().astype(np.int32) # adv_image = np.transpose(adv_image, (0, 2, 3, 1)) # N,C,H,W -> (N, H, W, 3), float all_count += len(img) true_label_np = true_label.detach().cpu().numpy().astype(np.int32) success_count+= len(np.where(true_label_np != adv_label)[0]) all_adv_images.append(adv_image.cpu().detach().numpy()) all_true_labels.append(true_label_np) attack_success_rate = success_count / float(all_count) log.info("Before train. Attack success rate is {:.3f}".format(attack_success_rate)) return target_model, np.concatenate(all_adv_images,0), np.concatenate(all_true_labels, 0) # N,224,224,3
def main(args, arch): model = StandardModel(args.dataset, arch, args.solver != "fake_zero") model.cuda() model.eval() if args.init_size is None: args.init_size = model.input_size[-1] log.info( "Argument init_size is not set and not using autoencoder, set to image original size:{}" .format(args.init_size)) target_str = "untargeted" if not args.targeted else "targeted_{}".format( args.target_type) save_result_path = args.exp_dir + "/data_{}@arch_{}@solver_{}@{}_result.json".format( args.dataset, arch, args.solver, target_str) if os.path.exists(save_result_path): model.cpu() return attack_framework = ZooAttackFramework(args) attack_framework.attack_dataset_images(args, arch, model, save_result_path) model.cpu()
def main(): model = StandardModel(args.dataset, args.arch, no_grad=False, load_pretrained=False) model.cuda() model.train() device = torch.device("cuda") optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay) model_path = '{}/train_pytorch_model/adversarial_train/TRADES/{}@{}@epoch_{}@batch_{}.pth.tar'.format( PY_ROOT, args.dataset, args.arch, args.epochs, args.batch_size) os.makedirs(os.path.dirname(model_path), exist_ok=True) print("After trained, the model will save to {}".format(model_path)) for epoch in range(1, args.epochs + 1): # adjust learning rate for SGD adjust_learning_rate(optimizer, epoch) # adversarial training train(args, model, device, train_loader, optimizer, epoch) # evaluation on natural examples print( '================================================================') eval_train(model, device, train_loader) eval_test(model, device, test_loader) print( '================================================================') # save checkpoint if epoch % args.save_freq == 0: state = { 'state_dict': model.state_dict(), 'epoch': epoch, 'optimizer': optimizer.state_dict() } torch.save(state, os.path.join(model_dir, model_path))
def generate(datasetname, batch_size): save_dir_path = "{}/data_adv_defense/guided_denoiser".format(PY_ROOT) os.makedirs(save_dir_path, exist_ok=True) set_log_file(save_dir_path + "/generate_{}.log".format(datasetname)) data_loader = DataLoaderMaker.get_img_label_data_loader(datasetname, batch_size, is_train=True) attackers = [] for model_name in MODELS_TRAIN_STANDARD[datasetname] + MODELS_TEST_STANDARD[datasetname]: model = StandardModel(datasetname, model_name, no_grad=False) model = model.cuda().eval() linf_PGD_attack =LinfPGDAttack(model, loss_fn=nn.CrossEntropyLoss(reduction="sum"), eps=0.031372, nb_iter=30, eps_iter=0.01, rand_init=True, clip_min=0.0, clip_max=1.0, targeted=False) l2_PGD_attack = L2PGDAttack(model, loss_fn=nn.CrossEntropyLoss(reduction="sum"),eps=4.6, nb_iter=30,clip_min=0.0, clip_max=1.0, targeted=False) FGSM_attack = FGSM(model, loss_fn=nn.CrossEntropyLoss(reduction="sum")) momentum_attack = MomentumIterativeAttack(model, loss_fn=nn.CrossEntropyLoss(reduction="sum"), eps=0.031372, nb_iter=30, eps_iter=0.01, clip_min=0.0, clip_max=1.0, targeted=False) attackers.append(linf_PGD_attack) attackers.append(l2_PGD_attack) attackers.append(FGSM_attack) attackers.append(momentum_attack) log.info("Create model {} done!".format(model_name)) generate_and_save_adv_examples(datasetname, data_loader, attackers, save_dir_path)
PY_ROOT, args.dataset, arch) test_model_list_path = list(glob.glob(test_model_list_path)) if len(test_model_list_path ) == 0: # this arch does not exists in args.dataset continue archs.append(arch) else: assert args.arch is not None archs = [args.arch] args.arch = ", ".join(archs) log.info('Command line is: {}'.format(' '.join(sys.argv))) log.info("Log file is written in {}".format(log_file_path)) log.info('Called with args:') print_args(args) surrogate_model = StandardModel(args.dataset, args.surrogate_arch, False) surrogate_model.cuda() surrogate_model.eval() attacker = SwitchNeg(args.dataset, args.batch_size, args.targeted, args.target_type, args.epsilon, args.norm, 0.0, 1.0, args.max_queries) for arch in archs: if args.attack_defense: save_result_path = args.exp_dir + "/{}_{}_result.json".format( arch, args.defense_model) else: save_result_path = args.exp_dir + "/{}_result.json".format(arch) if os.path.exists(save_result_path): continue log.info("Begin attack {} on {}, result will be saved to {}".format( arch, args.dataset, save_result_path))
f.write(socket.gethostname() + ":" + args.results_folder + "\n") def onehot(ind, num_classes): vector = np.zeros([num_classes]) vector[ind] = 1 return vector.astype(np.float32) logger.info("build dataloader") train_loader = DataLoaderMaker.get_img_label_data_loader( args.dataset, args.batch_size, True) val_loader = DataLoaderMaker.get_img_label_data_loader(args.dataset, args.batch_size, False) model = StandardModel(args.dataset, args.arch, no_grad=False) model.cuda() model.train() def anneal_lr(epoch): if epoch < 100: return 1. elif epoch < 150: return 0.1 else: return 0.01 pgd_kwargs = { "eps": 16. / 255., "eps_iter": 4. / 255.,
def attack_all_images(self, args, arch, tmp_dump_path, result_dump_path): # subset_pos用于回调函数汇报汇总统计结果 model = StandardModel(args.dataset, arch, no_grad=True) model.cuda() model.eval() # 带有缩减功能的,攻击成功的图片自动删除掉 for data_idx, data_tuple in enumerate(self.dataset_loader): if os.path.exists(tmp_dump_path): with open(tmp_dump_path, "r") as file_obj: json_content = json.load(file_obj) resume_batch_idx = int(json_content["batch_idx"]) # resume for key in [ 'query_all', 'correct_all', 'not_done_all', 'success_all', 'success_query_all' ]: if key in json_content: setattr( self, key, torch.from_numpy(np.asarray( json_content[key])).float()) if data_idx < resume_batch_idx: # resume continue if args.dataset == "ImageNet": if model.input_size[-1] >= 299: images, true_labels = data_tuple[1], data_tuple[2] else: images, true_labels = data_tuple[0], data_tuple[2] else: images, true_labels = data_tuple[0], data_tuple[1] if images.size(-1) != model.input_size[-1]: images = F.interpolate(images, size=model.input_size[-1], mode='bilinear', align_corners=True) # skip_batch_index_list = np.nonzero(np.asarray(chunk_skip_indexes[data_idx]))[0].tolist() selected = torch.arange( data_idx * args.batch_size, min((data_idx + 1) * args.batch_size, self.total_images)) # 选择这个batch的所有图片的index img_idx_to_batch_idx = ImageIdxToOrigBatchIdx(args.batch_size) images, true_labels = images.cuda(), true_labels.cuda() first_finetune = True finetune_queue = FinetuneQueue(args.batch_size, args.meta_seq_len, img_idx_to_batch_idx) prior_size = model.input_size[ -1] if not args.tiling else args.tile_size assert args.tiling == (args.dataset == "ImageNet") if args.tiling: upsampler = Upsample(size=(model.input_size[-2], model.input_size[-1])) else: upsampler = lambda x: x with torch.no_grad(): logit = model(images) pred = logit.argmax(dim=1) query = torch.zeros(images.size(0)).cuda() correct = pred.eq(true_labels).float() # shape = (batch_size,) not_done = correct.clone() # shape = (batch_size,) if args.targeted: if args.target_type == 'random': target_labels = torch.randint( low=0, high=CLASS_NUM[args.dataset], size=true_labels.size()).long().cuda() invalid_target_index = target_labels.eq(true_labels) while invalid_target_index.sum().item() > 0: target_labels[invalid_target_index] = torch.randint( low=0, high=logit.shape[1], size=target_labels[invalid_target_index].shape ).long().cuda() invalid_target_index = target_labels.eq(true_labels) elif args.target_type == 'least_likely': target_labels = logit.argmin(dim=1) elif args.target_type == "increment": target_labels = torch.fmod(true_labels + 1, CLASS_NUM[args.dataset]) else: raise NotImplementedError('Unknown target_type: {}'.format( args.target_type)) else: target_labels = None prior = torch.zeros(images.size(0), IN_CHANNELS[args.dataset], prior_size, prior_size).cuda() prior_step = self.gd_prior_step if args.norm == 'l2' else self.eg_prior_step image_step = self.l2_image_step if args.norm == 'l2' else self.linf_step proj_step = self.l2_proj_step if args.norm == 'l2' else self.linf_proj_step # 调用proj_maker返回的是一个函数 criterion = self.cw_loss if args.data_loss == "cw" else self.xent_loss adv_images = images.clone() for step_index in range(1, args.max_queries + 1): # Create noise for exporation, estimate the gradient, and take a PGD step dim = prior.nelement() / images.size( 0) # nelement() --> total number of elements exp_noise = args.exploration * torch.randn_like(prior) / ( dim**0.5 ) # parameterizes the exploration to be done around the prior exp_noise = exp_noise.cuda() q1 = upsampler( prior + exp_noise ) # 这就是Finite Difference算法, prior相当于论文里的v,这个prior也会更新,把梯度累积上去 q2 = upsampler( prior - exp_noise) # prior 相当于累积的更新量,用这个更新量,再去修改image,就会变得非常准 # Loss points for finite difference estimator q1_images = adv_images + args.fd_eta * q1 / self.norm(q1) q2_images = adv_images + args.fd_eta * q2 / self.norm(q2) predict_by_target_model = False if (step_index <= args.warm_up_steps or (step_index - args.warm_up_steps) % args.meta_predict_steps == 0): log.info("predict from target model") predict_by_target_model = True with torch.no_grad(): q1_logits = model(q1_images) q2_logits = model(q2_images) q1_logits = q1_logits / torch.norm( q1_logits, p=2, dim=-1, keepdim=True) # 加入normalize q2_logits = q2_logits / torch.norm( q2_logits, p=2, dim=-1, keepdim=True) finetune_queue.append(q1_images.detach(), q2_images.detach(), q1_logits.detach(), q2_logits.detach()) if step_index >= args.warm_up_steps: q1_images_seq, q2_images_seq, q1_logits_seq, q2_logits_seq = finetune_queue.stack_history_track( ) finetune_times = args.finetune_times if first_finetune else random.randint( 3, 5) # FIXME log.info("begin finetune for {} times".format( finetune_times)) self.meta_finetuner.finetune( q1_images_seq, q2_images_seq, q1_logits_seq, q2_logits_seq, finetune_times, first_finetune, img_idx_to_batch_idx) first_finetune = False else: with torch.no_grad(): q1_logits, q2_logits = self.meta_finetuner.predict( q1_images, q2_images, img_idx_to_batch_idx) q1_logits = q1_logits / torch.norm( q1_logits, p=2, dim=-1, keepdim=True) q2_logits = q2_logits / torch.norm( q2_logits, p=2, dim=-1, keepdim=True) l1 = criterion(q1_logits, true_labels, target_labels) l2 = criterion(q2_logits, true_labels, target_labels) # Finite differences estimate of directional derivative est_deriv = (l1 - l2) / (args.fd_eta * args.exploration ) # 方向导数 , l1和l2是loss # 2-query gradient estimate est_grad = est_deriv.view(-1, 1, 1, 1) * exp_noise # B, C, H, W, # Update the prior with the estimated gradient prior = prior_step( prior, est_grad, args.online_lr) # 注意,修正的是prior,这就是bandit算法的精髓 grad = upsampler(prior) # prior相当于梯度 ## Update the image: adv_images = image_step( adv_images, grad * correct.view(-1, 1, 1, 1), # 注意correct也是删减过的 args.image_lr) # prior放大后相当于累积的更新量,可以用来更新 adv_images = proj_step(images, args.epsilon, adv_images) adv_images = torch.clamp(adv_images, 0, 1) with torch.no_grad(): adv_logit = model(adv_images) # adv_pred = adv_logit.argmax(dim=1) adv_prob = F.softmax(adv_logit, dim=1) adv_loss = criterion(adv_logit, true_labels, target_labels) ## Continue query count if predict_by_target_model: query = query + 2 * not_done if args.targeted: not_done = not_done * ( 1 - adv_pred.eq(target_labels).float() ).float() # not_done初始化为 correct, shape = (batch_size,) else: not_done = not_done * adv_pred.eq( true_labels).float() # 只要是跟原始label相等的,就还需要query,还没有成功 success = (1 - not_done) * correct success_query = success * query not_done_loss = adv_loss * not_done not_done_prob = adv_prob[torch.arange(adv_images.size(0)), true_labels] * not_done log.info('Attacking image {} - {} / {}, step {}'.format( data_idx * args.batch_size, (data_idx + 1) * args.batch_size, self.total_images, step_index)) log.info(' not_done: {:.4f}'.format( len( np.where(not_done.detach().cpu().numpy().astype( np.int32) == 1)[0]) / float(args.batch_size))) log.info(' fd_scalar: {:.9f}'.format( (l1 - l2).mean().item())) if success.sum().item() > 0: log.info(' mean_query: {:.4f}'.format( success_query[success.byte()].mean().item())) log.info(' median_query: {:.4f}'.format( success_query[success.byte()].median().item())) if not_done.sum().item() > 0: log.info(' not_done_loss: {:.4f}'.format( not_done_loss[not_done.byte()].mean().item())) log.info(' not_done_prob: {:.4f}'.format( not_done_prob[not_done.byte()].mean().item())) not_done_np = not_done.detach().cpu().numpy().astype(np.int32) done_img_idx_list = np.where(not_done_np == 0)[0].tolist() delete_all = False if done_img_idx_list: for skip_index in done_img_idx_list: # 两次循环,第一次循环先汇报出去,第二次循环删除 batch_idx = img_idx_to_batch_idx[skip_index] pos = selected[batch_idx].item() # 先汇报被删减的值self.query_all for key in [ 'query', 'correct', 'not_done', 'success', 'success_query', 'not_done_loss', 'not_done_prob' ]: value_all = getattr(self, key + "_all") value = eval(key)[skip_index].item() value_all[pos] = value images, adv_images, prior, query, true_labels, target_labels, correct, not_done = \ self.delete_tensor_by_index_list(done_img_idx_list, images, adv_images, prior, query, true_labels, target_labels, correct, not_done) img_idx_to_batch_idx.del_by_index_list(done_img_idx_list) delete_all = images is None if delete_all: break # report to all stats the rest unsuccess for key in [ 'query', 'correct', 'not_done', 'success', 'success_query', 'not_done_loss', 'not_done_prob' ]: for img_idx, batch_idx in img_idx_to_batch_idx.proj_dict.items( ): pos = selected[batch_idx].item() value_all = getattr(self, key + "_all") value = eval(key)[img_idx].item() value_all[ pos] = value # 由于value_all是全部图片都放在一个数组里,当前batch选择出来 img_idx_to_batch_idx.proj_dict.clear() tmp_info_dict = { "batch_idx": data_idx + 1, "batch_size": args.batch_size } for key in [ 'query_all', 'correct_all', 'not_done_all', 'success_all', 'success_query_all' ]: value_all = getattr(self, key).detach().cpu().numpy().tolist() tmp_info_dict[key] = value_all with open(tmp_dump_path, "w") as result_file_obj: json.dump(tmp_info_dict, result_file_obj, sort_keys=True) log.info('Saving results to {}'.format(result_dump_path)) meta_info_dict = { "avg_correct": self.correct_all.mean().item(), "avg_not_done": self.not_done_all[self.correct_all.byte()].mean().item(), "mean_query": self.success_query_all[self.success_all.byte()].mean().item(), "median_query": self.success_query_all[self.success_all.byte()].median().item(), "max_query": self.success_query_all[self.success_all.byte()].max().item(), "correct_all": self.correct_all.detach().cpu().numpy().astype(np.int32).tolist(), "not_done_all": self.not_done_all.detach().cpu().numpy().astype(np.int32).tolist(), "query_all": self.query_all.detach().cpu().numpy().astype(np.int32).tolist(), "not_done_loss": self.not_done_loss_all[self.not_done_all.byte()].mean().item(), "not_done_prob": self.not_done_prob_all[self.not_done_all.byte()].mean().item(), "args": vars(args) } with open(result_dump_path, "w") as result_file_obj: json.dump(meta_info_dict, result_file_obj, sort_keys=True) log.info("done, write stats info to {}".format(result_dump_path)) self.query_all.fill_(0) self.correct_all.fill_(0) self.not_done_all.fill_(0) self.success_all.fill_(0) self.success_query_all.fill_(0) self.not_done_loss_all.fill_(0) self.not_done_prob_all.fill_(0) model.cpu()
transform=train_preprocessor) elif dataset == "TinyImageNet": train_dataset = TinyImageNet(IMAGE_DATA_ROOT[dataset], train_preprocessor, train=True) batch_size = args.batch_size train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=0) print('==> Building model..') arch_list = MODELS_TRAIN_STANDARD[args.dataset] model_dict = {} for arch in arch_list: if StandardModel.check_arch(arch, args.dataset): print("begin use arch {}".format(arch)) model = StandardModel(args.dataset, arch, no_grad=True) model_dict[arch] = model.eval() print("use arch {} done".format(arch)) print("==> Save gradient..") for arch, model in model_dict.items(): dump_path = "{}/benign_images_logits_pair/{}/{}_images.npy".format( PY_ROOT, args.dataset, arch) os.makedirs(os.path.dirname(dump_path), exist_ok=True) model = model.cuda() save_image_logits_pairs(model, train_loader, dump_path, args.batch_size, args.max_items) model.cpu()
def __init__(self, dataset, batch_size, meta_arch, meta_train_type, distill_loss, data_loss, norm, targeted, use_softmax, mode="meta"): if mode == "meta": target_str = "targeted_attack_random" if targeted else "untargeted_attack" # 2Q_DISTILLATION@CIFAR-100@TRAIN_I_TEST_II@model_resnet34@loss_pair_mse@dataloss_cw_l2_untargeted_attack@epoch_4@meta_batch_size_30@num_support_50@num_updates_12@lr_0.001@inner_lr_0.01.pth.tar self.meta_model_path = "{root}/train_pytorch_model/meta_simulator/{meta_train_type}@{dataset}@{split}@model_{meta_arch}@loss_{loss}@dataloss_{data_loss}_{norm}_{target_str}*inner_lr_0.01.pth.tar".format( root=PY_ROOT, meta_train_type=meta_train_type.upper(), dataset=dataset, split=SPLIT_DATA_PROTOCOL.TRAIN_I_TEST_II, meta_arch=meta_arch, loss=distill_loss, data_loss=data_loss, norm=norm, target_str=target_str) log.info("start using {}".format(self.meta_model_path)) self.meta_model_path = glob.glob(self.meta_model_path) pattern = re.compile(".*model_(.*?)@.*inner_lr_(.*?)\.pth.*") assert len(self.meta_model_path) > 0 self.meta_model_path = self.meta_model_path[0] log.info("load meta model {}".format(self.meta_model_path)) ma = pattern.match(os.path.basename(self.meta_model_path)) arch = ma.group(1) self.inner_lr = float(ma.group(2)) meta_backbone = self.construct_model(arch, dataset) self.meta_network = MetaNetwork(meta_backbone) self.pretrained_weights = torch.load(self.meta_model_path, map_location=lambda storage, location: storage) self.meta_network.load_state_dict(self.pretrained_weights["state_dict"]) log.info("Load model in epoch {}.".format(self.pretrained_weights["epoch"])) self.pretrained_weights = self.pretrained_weights["state_dict"] elif mode == "vanilla": target_str = "targeted" if targeted else "untargeted" arch = meta_arch # 2Q_DISTILLATION@CIFAR-100@TRAIN_I_TEST_II@model_resnet34@loss_pair_mse@dataloss_cw_l2_untargeted_attack@epoch_4@meta_batch_size_30@num_support_50@num_updates_12@lr_0.001@inner_lr_0.01.pth.tar self.meta_model_path = "{root}/train_pytorch_model/vanilla_simulator/{dataset}@{norm}_norm_{target_str}@{meta_arch}*.tar".format( root=PY_ROOT, dataset=dataset, meta_arch=meta_arch,norm=norm, target_str=target_str) log.info("start using {}".format(self.meta_model_path)) self.meta_model_path = glob.glob(self.meta_model_path) assert len(self.meta_model_path) > 0 self.meta_model_path = self.meta_model_path[0] log.info("load meta model {}".format(self.meta_model_path)) self.inner_lr = 0.01 self.meta_network = self.construct_model(meta_arch, dataset) self.pretrained_weights = torch.load(self.meta_model_path, map_location=lambda storage, location: storage) log.info("Load model in epoch {}.".format(self.pretrained_weights["epoch"])) self.pretrained_weights = self.pretrained_weights["state_dict"] elif mode == "deep_benign_images": arch = "resnet34" self.inner_lr = 0.01 self.meta_network = self.construct_model(arch, dataset) self.meta_model_path = "{root}/train_pytorch_model/real_image_model/{dataset}@{arch}@epoch_200@lr_0.1@batch_200.pth.tar".format( root=PY_ROOT, dataset=dataset, arch=arch) assert os.path.exists(self.meta_model_path), "{} does not exists!".format(self.meta_model_path) self.pretrained_weights = torch.load(self.meta_model_path, map_location=lambda storage, location: storage)[ "state_dict"] elif mode == "random_init": arch = "resnet34" self.inner_lr = 0.01 self.meta_network = self.construct_model(arch, dataset) self.pretrained_weights = self.meta_network.state_dict() elif mode == 'ensemble_avg': self.inner_lr = 0.01 self.archs = ["densenet-bc-100-12","resnet-110","vgg19_bn"] self.meta_network = [] # meta_network和pretrained_weights都改成list self.pretrained_weights = [] for arch in self.archs: model = StandardModel(dataset, arch, no_grad=False, load_pretrained=True) model.eval() model.cuda() self.meta_network.append(model) self.pretrained_weights.append(model.state_dict()) elif mode == "benign_images": self.inner_lr = 0.01 self.meta_model_path = "{root}/train_pytorch_model/meta_simulator_on_benign_images/{dataset}@{split}*@inner_lr_0.01.pth.tar".format( root=PY_ROOT, meta_train_type=meta_train_type.upper(), dataset=dataset, split=SPLIT_DATA_PROTOCOL.TRAIN_I_TEST_II) self.meta_model_path = glob.glob(self.meta_model_path) pattern = re.compile(".*model_(.*?)@.*") assert len(self.meta_model_path) > 0 self.meta_model_path = self.meta_model_path[0] ma = pattern.match(os.path.basename(self.meta_model_path)) log.info("Loading meta model from {}".format(self.meta_model_path)) arch = ma.group(1) self.pretrained_weights = torch.load(self.meta_model_path, map_location=lambda storage, location: storage)["state_dict"] meta_backbone = self.construct_model(arch, dataset) self.meta_network = MetaNetwork(meta_backbone) self.meta_network.load_state_dict(self.pretrained_weights) self.meta_network.eval() self.meta_network.cuda() elif mode == "reptile_on_benign_images": self.inner_lr = 0.01 self.meta_model_path = "{root}/train_pytorch_model/meta_simulator_reptile_on_benign_images/{dataset}@{split}*@inner_lr_0.01.pth.tar".format( root=PY_ROOT, meta_train_type=meta_train_type.upper(), dataset=dataset, split=SPLIT_DATA_PROTOCOL.TRAIN_I_TEST_II) self.meta_model_path = glob.glob(self.meta_model_path) pattern = re.compile(".*model_(.*?)@.*") assert len(self.meta_model_path) > 0 self.meta_model_path = self.meta_model_path[0] log.info("Loading meta model from {}".format(self.meta_model_path)) ma = pattern.match(os.path.basename(self.meta_model_path)) arch = ma.group(1) self.pretrained_weights = torch.load(self.meta_model_path, map_location=lambda storage, location: storage)["state_dict"] meta_backbone = self.construct_model(arch, dataset) self.meta_network = MetaNetwork(meta_backbone) self.meta_network.load_state_dict(self.pretrained_weights) self.meta_network.eval() self.meta_network.cuda() self.arch = arch self.dataset = dataset self.need_pair_distance = (distill_loss.lower()=="pair_mse") # self.need_pair_distance = False self.softmax = nn.Softmax(dim=1) self.mse_loss = nn.MSELoss(reduction="mean") self.pair_wise_distance = nn.PairwiseDistance(p=2) self.use_softmax = use_softmax if mode != "ensemble_avg": self.meta_network.load_state_dict(self.pretrained_weights) self.meta_network.eval() self.meta_network.cuda() self.batch_size = batch_size if mode == 'ensemble_avg': self.batch_weights = defaultdict(dict) for idx in range(len(self.pretrained_weights)): for i in range(batch_size): self.batch_weights[idx][i] = self.pretrained_weights[idx] else: self.batch_weights = dict() for i in range(batch_size): self.batch_weights[i] = self.pretrained_weights
def __init__(self, tot_num_tasks, dataset, inner_batch_size, protocol): """ Args: num_samples_per_class: num samples to generate "per class" in one batch batch_size: size of meta batch size (e.g. number of functions) """ self.img_size = IMAGE_SIZE[dataset] self.dataset = dataset if protocol == SPLIT_DATA_PROTOCOL.TRAIN_I_TEST_II: self.model_names = MODELS_TRAIN_STANDARD[self.dataset] elif protocol == SPLIT_DATA_PROTOCOL.TRAIN_II_TEST_I: self.model_names = MODELS_TEST_STANDARD[self.dataset] elif protocol == SPLIT_DATA_PROTOCOL.TRAIN_ALL_TEST_ALL: self.model_names = MODELS_TRAIN_STANDARD[ self.dataset] + MODELS_TEST_STANDARD[self.dataset] self.model_dict = {} for arch in self.model_names: if StandardModel.check_arch(arch, dataset): model = StandardModel(dataset, arch, no_grad=False).eval() if dataset != "ImageNet": model = model.cuda() self.model_dict[arch] = model is_train = True preprocessor = DataLoaderMaker.get_preprocessor( IMAGE_SIZE[dataset], is_train) if dataset == "CIFAR-10": train_dataset = CIFAR10(IMAGE_DATA_ROOT[dataset], train=is_train, transform=preprocessor) elif dataset == "CIFAR-100": train_dataset = CIFAR100(IMAGE_DATA_ROOT[dataset], train=is_train, transform=preprocessor) elif dataset == "MNIST": train_dataset = MNIST(IMAGE_DATA_ROOT[dataset], train=is_train, transform=preprocessor) elif dataset == "FashionMNIST": train_dataset = FashionMNIST(IMAGE_DATA_ROOT[dataset], train=is_train, transform=preprocessor) elif dataset == "TinyImageNet": train_dataset = TinyImageNet(IMAGE_DATA_ROOT[dataset], preprocessor, train=is_train) elif dataset == "ImageNet": preprocessor = DataLoaderMaker.get_preprocessor( IMAGE_SIZE[dataset], is_train, center_crop=True) sub_folder = "/train" if is_train else "/validation" # Note that ImageNet uses pretrainedmodels.utils.TransformImage to apply transformation train_dataset = ImageFolder(IMAGE_DATA_ROOT[dataset] + sub_folder, transform=preprocessor) self.train_dataset = train_dataset self.total_num_images = len(train_dataset) self.all_tasks = dict() all_images_indexes = np.arange(self.total_num_images).tolist() for i in range(tot_num_tasks): self.all_tasks[i] = { "image": random.sample(all_images_indexes, inner_batch_size), "arch": random.choice(list(self.model_dict.keys())) }
if __name__ == "__main__": args = get_parse_args() if args.norm == "l2": args.epsilon = 4.6 elif args.norm == "linf": args.epsilon = 0.031372 if args.dataset == "ImageNet": args.epsilon = 0.05 data_loader = get_img_label_data_loader(args.dataset, args.batch_size, True) test_data_loader = get_img_label_data_loader(args.dataset, args.batch_size, False) os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu) target_model = StandardModel(args.dataset, args.model, no_grad=True) target_model = target_model.cuda() current_state = State( (args.batch_size, IN_CHANNELS[args.dataset], IMAGE_SIZE[args.dataset][0], IMAGE_SIZE[args.dataset][1]), args.norm, args.epsilon) fcn = MyFCN((IN_CHANNELS[args.dataset], IMAGE_SIZE[args.dataset][0], IMAGE_SIZE[args.dataset][1]), args.n_actions) fcn.apply(fcn.init_weights) optimizer = Adam(fcn.parameters(), lr=args.learning_rate) agent = PixelWiseA3C(fcn, optimizer, args.episode_len, args.gamma) agent.model.cuda() agent.shared_model.cuda() i = 0 episode = 0 save_model_path = "{}/train_pytorch_model/sign_player/{}_untargeted_{}_attack_on_{}.pth.tar".format( PY_ROOT, args.dataset, args.norm, args.model)
def main(): args = get_args() os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu) logger.info(args) model_path = '{}/train_pytorch_model/adversarial_train/fast_adv_train/{}@{}@epoch_{}.pth.tar'.format( PY_ROOT, args.dataset, args.arch, args.epochs) out_dir = os.path.dirname(model_path) os.makedirs(out_dir, exist_ok=True) np.random.seed(args.seed) torch.manual_seed(args.seed) torch.cuda.manual_seed(args.seed) start_start_time = time.time() train_loader = DataLoaderMaker.get_img_label_data_loader(args.dataset, args.batch_size, True) test_loader = DataLoaderMaker.get_img_label_data_loader(args.dataset, args.batch_size, False) epsilon = (args.epsilon / 255.) / std pgd_alpha = (args.pgd_alpha / 255.) / std model = StandardModel(args.dataset, args.arch, no_grad=False) model.apply(initialize_weights) model.cuda() model.train() opt = torch.optim.SGD(model.parameters(), lr=args.lr_max, momentum=0.9, weight_decay=5e-4) model, opt = amp.initialize(model, opt, opt_level="O2", loss_scale=1.0, master_weights=False) criterion = nn.CrossEntropyLoss() if args.attack == 'free': delta = torch.zeros(args.batch_size, 3, 32, 32).cuda() delta.requires_grad = True elif args.attack == 'fgsm' and args.fgsm_init == 'previous': delta = torch.zeros(args.batch_size, 3, 32, 32).cuda() delta.requires_grad = True if args.attack == 'free': assert args.epochs % args.attack_iters == 0 epochs = int(math.ceil(args.epochs / args.attack_iters)) else: epochs = args.epochs if args.lr_schedule == 'cyclic': lr_schedule = lambda t: np.interp([t], [0, args.epochs * 2 // 5, args.epochs], [0, args.lr_max, 0])[0] elif args.lr_schedule == 'piecewise': def lr_schedule(t): if t / args.epochs < 0.5: return args.lr_max elif t / args.epochs < 0.75: return args.lr_max / 10. else: return args.lr_max / 100. prev_robust_acc = 0. logger.info('Epoch \t Time \t LR \t \t Train Loss \t Train Acc') for epoch in range(epochs): start_time = time.time() train_loss = 0 train_acc = 0 train_n = 0 for i, (X, y) in enumerate(train_loader): X = X.cuda().float() y = y.cuda().long() if i == 0: first_batch = X, y lr = lr_schedule(epoch + (i + 1) / len(train_loader)) opt.param_groups[0].update(lr=lr) if args.attack == 'pgd': delta = attack_pgd(model, X, y, epsilon, pgd_alpha, args.attack_iters, args.restarts, opt) elif args.attack == 'fgsm': if args.fgsm_init == 'zero': delta = torch.zeros_like(X, requires_grad=True) delta.requires_grad = True elif args.fgsm_init == 'random': delta = torch.zeros_like(X).cuda() delta[:, 0, :, :].uniform_(-epsilon[0][0][0].item(), epsilon[0][0][0].item()) delta[:, 1, :, :].uniform_(-epsilon[1][0][0].item(), epsilon[1][0][0].item()) delta[:, 2, :, :].uniform_(-epsilon[2][0][0].item(), epsilon[2][0][0].item()) delta.requires_grad = True elif args.fgsm_init == 'previous': delta.requires_grad = True output = model(X + delta[:X.size(0)]) loss = F.cross_entropy(output, y) with amp.scale_loss(loss, opt) as scaled_loss: scaled_loss.backward() grad = delta.grad.detach() delta.data = clamp(delta + args.fgsm_alpha * epsilon * torch.sign(grad), -epsilon, epsilon) delta = delta.detach() elif args.attack == 'free': delta.requires_grad = True for j in range(args.attack_iters): epoch_iters = epoch * args.attack_iters + (i * args.attack_iters + j + 1) / len(train_loader) lr = lr_schedule(epoch_iters) opt.param_groups[0].update(lr=lr) output = model(clamp(X + delta[:X.size(0)], lower_limit, upper_limit)) loss = F.cross_entropy(output, y) opt.zero_grad() with amp.scale_loss(loss, opt) as scaled_loss: scaled_loss.backward() grad = delta.grad.detach() delta.data = clamp(delta + epsilon * torch.sign(grad), -epsilon, epsilon) nn.utils.clip_grad_norm_(model.parameters(), 0.5) opt.step() delta.grad.zero_() elif args.attack == 'none': delta = torch.zeros_like(X) output = model(clamp(X + delta[:X.size(0)], lower_limit, upper_limit)) loss = criterion(output, y) if args.attack != 'free': opt.zero_grad() with amp.scale_loss(loss, opt) as scaled_loss: scaled_loss.backward() nn.utils.clip_grad_norm_(model.parameters(), 0.5) opt.step() train_loss += loss.item() * y.size(0) train_acc += (output.max(1)[1] == y).sum().item() train_n += y.size(0) if args.overfit_check: # Check current PGD robustness of model using random minibatch X, y = first_batch['input'], first_batch['target'] pgd_delta = attack_pgd(model, X, y, epsilon, pgd_alpha, args.attack_iters, args.restarts, opt) with torch.no_grad(): output = model(clamp(X + pgd_delta[:X.size(0)], lower_limit, upper_limit)) robust_acc = (output.max(1)[1] == y).sum().item() / y.size(0) if robust_acc - prev_robust_acc < -0.5: break prev_robust_acc = robust_acc best_state_dict = copy.deepcopy(model.state_dict()) train_time = time.time() logger.info('%d \t %.1f \t %.4f \t %.4f \t %.4f', epoch, train_time - start_time, lr, train_loss/train_n, train_acc/train_n) torch.save(best_state_dict, model_path) logger.info('Total time: %.4f', train_time - start_start_time)