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b/experiments//gnn=7359b40603e0f13826ddb6555290b9ccc0003624e7352e853c79d9d63997c923/gnn_model_manager=af10d66cd7cb2f91e34105afd48bea10ddb6d6e3d6dccefb67530aac40632afe/poison_attacker=622e4c6269431d33eb901d7cc5341741fef989f67a75cdc2877617a6fd83a4c9/poison_defender=45c803bdfed6f7a15539b63fb93956b616540230082e12f9a692e98aa1eed52f/mi_defender=31f5cea6d3199b289e1d575ac077837708309a2c273757ab15da85ea09440609/evasion_defender=f1c43a91a2ee19f2a0b25e63e36426915c875a7657d06cde7fef8b6204107581/evasion_attacker=8cc41a7527fc594256b1b27e003a576880c45b5c68eb14815d780b3f3211a97d/mi_attacker=10fca640b40550bacb64003c293885316558340faf9e3a19681903533c09e75d/epochs=100/model_ver_ind=0/model differ diff --git a/experiments/attack_defense_metric_test.py b/experiments/attack_defense_metric_test.py index 02f80a6..b9ec28e 100644 --- a/experiments/attack_defense_metric_test.py +++ b/experiments/attack_defense_metric_test.py @@ -57,18 +57,24 @@ def attack_defense_metrics(): } ) - steps_epochs = 200 - gnn_model_manager = FrameworkGNNModelManager( - gnn=gnn, - dataset_path=results_dataset_path, - manager_config=manager_config, - modification=ModelModificationConfig(model_ver_ind=0, epochs=steps_epochs) - ) + steps_epochs = 1 + for i in range(2): + modification = ModelModificationConfig( + model_ver_ind=i, + epochs=steps_epochs + ) + + gnn_model_manager = FrameworkGNNModelManager( + gnn=gnn, + dataset_path=results_dataset_path, + manager_config=manager_config, + modification=modification, + ) - # save_model_flag = False - save_model_flag = True + # save_model_flag = False + save_model_flag = True - gnn_model_manager.gnn.to(my_device) + gnn_model_manager.gnn.to(my_device) # random_poison_attack_config = ConfigPattern( # _class_name="RandomPoisonAttack", @@ -79,144 +85,144 @@ def attack_defense_metrics(): # } # ) - metafull_poison_attack_config = ConfigPattern( - _class_name="MetaAttackFull", - _import_path=POISON_ATTACK_PARAMETERS_PATH, - _config_class="PoisonAttackConfig", - _config_kwargs={ - "num_nodes": dataset.dataset.x.shape[0], - "lambda": 0, - } - ) - - gnnguard_poison_defense_config = ConfigPattern( - _class_name="GNNGuard", - _import_path=POISON_DEFENSE_PARAMETERS_PATH, - _config_class="PoisonDefenseConfig", - _config_kwargs={ - "lr": 0.01, - "train_iters": 100, - # "model": gnn_model_manager.gnn - } - ) - - jaccard_poison_defense_config = ConfigPattern( - _class_name="JaccardDefender", - _import_path=POISON_DEFENSE_PARAMETERS_PATH, - _config_class="PoisonDefenseConfig", - _config_kwargs={ - "threshold": 0.4, - } - ) - - fgsm_evasion_attack_config = ConfigPattern( - _class_name="FGSM", - _import_path=EVASION_ATTACK_PARAMETERS_PATH, - _config_class="EvasionAttackConfig", - _config_kwargs={ - "epsilon": 0.001 * 5, - } - ) - - gradientregularization_evasion_defense_config = ConfigPattern( - _class_name="GradientRegularizationDefender", - _import_path=EVASION_DEFENSE_PARAMETERS_PATH, - _config_class="EvasionDefenseConfig", - _config_kwargs={ - "regularization_strength": 0.1 * 500 - } - ) - - fgsm_evasion_attack_config1 = ConfigPattern( - _class_name="FGSM", - _import_path=EVASION_ATTACK_PARAMETERS_PATH, - _config_class="EvasionAttackConfig", - _config_kwargs={ - "epsilon": 0.01, - } - ) - at_evasion_defense_config = ConfigPattern( - _class_name="AdvTraining", - _import_path=EVASION_DEFENSE_PARAMETERS_PATH, - _config_class="EvasionDefenseConfig", - _config_kwargs={ - "attack_name": None, - "attack_config": fgsm_evasion_attack_config1 - } - ) - - # gnn_model_manager.set_poison_attacker(poison_attack_config=metafull_poison_attack_config) - gnn_model_manager.set_poison_defender(poison_defense_config=gnnguard_poison_defense_config) - gnn_model_manager.set_evasion_attacker(evasion_attack_config=fgsm_evasion_attack_config) - gnn_model_manager.set_evasion_defender(evasion_defense_config=gradientregularization_evasion_defense_config) - - warnings.warn("Start training") - dataset.train_test_split() - - # try: - # raise FileNotFoundError() - # # gnn_model_manager.load_model_executor() - # except FileNotFoundError: - # gnn_model_manager.epochs = gnn_model_manager.modification.epochs = 0 - # train_test_split_path = gnn_model_manager.train_model(gen_dataset=dataset, steps=steps_epochs, - # save_model_flag=save_model_flag, - # metrics=[Metric("F1", mask='train', average=None), - # Metric("Accuracy", mask="train")]) - # - # if train_test_split_path is not None: - # dataset.save_train_test_mask(train_test_split_path) - # train_mask, val_mask, test_mask, train_test_sizes = torch.load(train_test_split_path / 'train_test_split')[ - # :] - # dataset.train_mask, dataset.val_mask, dataset.test_mask = train_mask, val_mask, test_mask - # data.percent_train_class, data.percent_test_class = train_test_sizes - # - # warnings.warn("Training was successful") - # - # # mask_loc = Metric.create_mask_by_target_list(y_true=dataset.labels, target_list=node_idxs) - # - # metric_loc = gnn_model_manager.evaluate_model( - # gen_dataset=dataset, metrics=[Metric("F1", mask='train', average='macro'), - # Metric("Accuracy", mask='train')], - # save_flag=True - # ) - # print("TRAIN", metric_loc) - # - # metric_loc = gnn_model_manager.evaluate_model( - # gen_dataset=dataset, metrics=[Metric("F1", mask='test', average='macro'), - # Metric("Accuracy", mask='test')]) - # print("TEST", metric_loc) - - adm = FrameworkAttackDefenseManager( - gen_dataset=copy.deepcopy(dataset), - gnn_manager=gnn_model_manager, - ) - # adm.evasion_attack_pipeline( - # steps=steps_epochs, - # save_model_flag=save_model_flag, - # metrics_attack=[AttackMetric("ASR")], - # mask='test' - # ) - # adm.poison_attack_pipeline( - # steps=steps_epochs, - # save_model_flag=save_model_flag, - # metrics_attack=[AttackMetric("ASR")], - # mask='test' - # ) - # adm.evasion_defense_pipeline( - # steps=steps_epochs, - # save_model_flag=save_model_flag, - # metrics_attack=[AttackMetric("ASR"), AttackMetric("AuccAttackDiff"),], - # metrics_defense=[DefenseMetric("AuccDefenseCleanDiff"), DefenseMetric("AuccDefenseAttackDiff"), ], - # mask='test' - # ) - - adm.full_pipeline_model_metrics_only( - # steps=1, - steps=steps_epochs, - save_model_flag=save_model_flag, - model_metrics=[Metric("Accuracy", mask="test")], - task="tttttt", - ) + metafull_poison_attack_config = ConfigPattern( + _class_name="MetaAttackFull", + _import_path=POISON_ATTACK_PARAMETERS_PATH, + _config_class="PoisonAttackConfig", + _config_kwargs={ + "num_nodes": dataset.dataset.x.shape[0], + "lambda": 0, + } + ) + + gnnguard_poison_defense_config = ConfigPattern( + _class_name="GNNGuard", + _import_path=POISON_DEFENSE_PARAMETERS_PATH, + _config_class="PoisonDefenseConfig", + _config_kwargs={ + "lr": 0.01, + "train_iters": 100, + # "model": gnn_model_manager.gnn + } + ) + + jaccard_poison_defense_config = ConfigPattern( + _class_name="JaccardDefender", + _import_path=POISON_DEFENSE_PARAMETERS_PATH, + _config_class="PoisonDefenseConfig", + _config_kwargs={ + "threshold": 0.4, + } + ) + + fgsm_evasion_attack_config = ConfigPattern( + _class_name="FGSM", + _import_path=EVASION_ATTACK_PARAMETERS_PATH, + _config_class="EvasionAttackConfig", + _config_kwargs={ + "epsilon": 0.001 * 5, + } + ) + + gradientregularization_evasion_defense_config = ConfigPattern( + _class_name="GradientRegularizationDefender", + _import_path=EVASION_DEFENSE_PARAMETERS_PATH, + _config_class="EvasionDefenseConfig", + _config_kwargs={ + "regularization_strength": 0.1 * 500 + } + ) + + fgsm_evasion_attack_config1 = ConfigPattern( + _class_name="FGSM", + _import_path=EVASION_ATTACK_PARAMETERS_PATH, + _config_class="EvasionAttackConfig", + _config_kwargs={ + "epsilon": 0.01, + } + ) + at_evasion_defense_config = ConfigPattern( + _class_name="AdvTraining", + _import_path=EVASION_DEFENSE_PARAMETERS_PATH, + _config_class="EvasionDefenseConfig", + _config_kwargs={ + "attack_name": None, + "attack_config": fgsm_evasion_attack_config1 + } + ) + + # gnn_model_manager.set_poison_attacker(poison_attack_config=metafull_poison_attack_config) + gnn_model_manager.set_poison_defender(poison_defense_config=gnnguard_poison_defense_config) + gnn_model_manager.set_evasion_attacker(evasion_attack_config=fgsm_evasion_attack_config) + gnn_model_manager.set_evasion_defender(evasion_defense_config=gradientregularization_evasion_defense_config) + + warnings.warn("Start training") + dataset.train_test_split() + + # try: + # raise FileNotFoundError() + # # gnn_model_manager.load_model_executor() + # except FileNotFoundError: + # gnn_model_manager.epochs = gnn_model_manager.modification.epochs = 0 + # train_test_split_path = gnn_model_manager.train_model(gen_dataset=dataset, steps=steps_epochs, + # save_model_flag=save_model_flag, + # metrics=[Metric("F1", mask='train', average=None), + # Metric("Accuracy", mask="train")]) + # + # if train_test_split_path is not None: + # dataset.save_train_test_mask(train_test_split_path) + # train_mask, val_mask, test_mask, train_test_sizes = torch.load(train_test_split_path / 'train_test_split')[ + # :] + # dataset.train_mask, dataset.val_mask, dataset.test_mask = train_mask, val_mask, test_mask + # data.percent_train_class, data.percent_test_class = train_test_sizes + # + # warnings.warn("Training was successful") + # + # # mask_loc = Metric.create_mask_by_target_list(y_true=dataset.labels, target_list=node_idxs) + # + # metric_loc = gnn_model_manager.evaluate_model( + # gen_dataset=dataset, metrics=[Metric("F1", mask='train', average='macro'), + # Metric("Accuracy", mask='train')], + # save_flag=True + # ) + # print("TRAIN", metric_loc) + # + # metric_loc = gnn_model_manager.evaluate_model( + # gen_dataset=dataset, metrics=[Metric("F1", mask='test', average='macro'), + # Metric("Accuracy", mask='test')]) + # print("TEST", metric_loc) + + adm = FrameworkAttackDefenseManager( + gen_dataset=copy.deepcopy(dataset), + gnn_manager=gnn_model_manager, + ) + # adm.evasion_attack_pipeline( + # steps=steps_epochs, + # save_model_flag=save_model_flag, + # metrics_attack=[AttackMetric("ASR")], + # mask='test' + # ) + # adm.poison_attack_pipeline( + # steps=steps_epochs, + # save_model_flag=save_model_flag, + # metrics_attack=[AttackMetric("ASR")], + # mask='test' + # ) + # adm.evasion_defense_pipeline( + # steps=steps_epochs, + # save_model_flag=save_model_flag, + # metrics_attack=[AttackMetric("ASR"), AttackMetric("AuccAttackDiff"),], + # metrics_defense=[DefenseMetric("AuccDefenseCleanDiff"), DefenseMetric("AuccDefenseAttackDiff"), ], + # mask='test' + # ) + + adm.full_pipeline_model_metrics_only( + # steps=1, + steps=steps_epochs, + save_model_flag=save_model_flag, + model_metrics=[Metric("Accuracy", mask="test")], + task="tt*f*f", + ) if __name__ == '__main__': diff --git a/experiments/attack_defense_test.py b/experiments/attack_defense_test.py index 441e6b3..dd7ef37 100644 --- a/experiments/attack_defense_test.py +++ b/experiments/attack_defense_test.py @@ -21,7 +21,14 @@ from defense.GNNGuard import gnnguard -def test_attack_defense(d='Cora', m='gin_2', a_e=None, d_e=None, a_p=None, d_p=None): +def test_attack_defense( + d='Cora', + m='gin_2', + a_e=None, + d_e=None, + a_p=None, + d_p=None +): # my_device = device('cuda' if torch.cuda.is_available() else 'cpu') my_device = device('cpu') @@ -107,219 +114,217 @@ def test_attack_defense(d='Cora', m='gin_2', a_e=None, d_e=None, a_p=None, d_p=N # train_test_split = [0.8, 0.2] # train_test_split = [0.6, 0.4] steps_epochs = 200 - gnn_model_manager = FrameworkGNNModelManager( - gnn=gnn, - dataset_path=results_dataset_path, - manager_config=manager_config, - modification=ModelModificationConfig(model_ver_ind=0, epochs=steps_epochs) - ) - - # save_model_flag = False - save_model_flag = True - - # data.x = data.x.float() - gnn_model_manager.gnn.to(my_device) - data = data.to(my_device) - dataset.dataset.data.to(my_device) - - # poison_attack_config = ConfigPattern( - # _class_name="RandomPoisonAttack", - # _import_path=POISON_ATTACK_PARAMETERS_PATH, - # _config_class="PoisonAttackConfig", - # _config_kwargs={ - # "n_edges_percent": 0.1, - # } - # ) + for i in range(2): + modification = ModelModificationConfig( + model_ver_ind=i, + epochs=steps_epochs + ) - metafull_poison_attack_config = ConfigPattern( - _class_name="MetaAttackApprox", - _import_path=POISON_ATTACK_PARAMETERS_PATH, - _config_class="PoisonAttackConfig", - _config_kwargs={ - "num_nodes": dataset.dataset.x.shape[0], - "lambda_": 0, - } - ) + gnn_model_manager = FrameworkGNNModelManager( + gnn=gnn, + dataset_path=results_dataset_path, + manager_config=manager_config, + modification=modification, + ) - random_poison_attack_config = ConfigPattern( - _class_name="RandomPoisonAttack", - _import_path=POISON_ATTACK_PARAMETERS_PATH, - _config_class="PoisonAttackConfig", - _config_kwargs={ - "n_edges_percent": 1.0, - } - ) + # save_model_flag = False + save_model_flag = True + + # data.x = data.x.float() + gnn_model_manager.gnn.to(my_device) + data = data.to(my_device) + dataset.dataset.data.to(my_device) + + # poison_attack_config = ConfigPattern( + # _class_name="RandomPoisonAttack", + # _import_path=POISON_ATTACK_PARAMETERS_PATH, + # _config_class="PoisonAttackConfig", + # _config_kwargs={ + # "n_edges_percent": 0.1, + # } + # ) - gnnguard_poison_defense_config = ConfigPattern( - _class_name="GNNGuard", - _import_path=POISON_DEFENSE_PARAMETERS_PATH, - _config_class="PoisonDefenseConfig", - _config_kwargs={ - "lr": 0.01, - "train_iters": 100, - # "model": gnn_model_manager.gnn - } - ) + metafull_poison_attack_config = ConfigPattern( + _class_name="MetaAttackApprox", + _import_path=POISON_ATTACK_PARAMETERS_PATH, + _config_class="PoisonAttackConfig", + _config_kwargs={ + "num_nodes": dataset.dataset.x.shape[0], + "lambda_": 0, + } + ) - jaccard_poison_defense_config = ConfigPattern( - _class_name="JaccardDefender", - _import_path=POISON_DEFENSE_PARAMETERS_PATH, - _config_class="PoisonDefenseConfig", - _config_kwargs={ - "threshold": 0.4, - } - ) + random_poison_attack_config = ConfigPattern( + _class_name="RandomPoisonAttack", + _import_path=POISON_ATTACK_PARAMETERS_PATH, + _config_class="PoisonAttackConfig", + _config_kwargs={ + "n_edges_percent": 1.0, + } + ) - qattack_evasion_attack_config = ConfigPattern( - _class_name="QAttack", - _import_path=EVASION_ATTACK_PARAMETERS_PATH, - _config_class="EvasionAttackConfig", - _config_kwargs={ - "population_size": 500, - "individual_size": 100, - "generations": 100, - "prob_cross": 0.5, - "prob_mutate": 0.02 - } - ) + gnnguard_poison_defense_config = ConfigPattern( + _class_name="GNNGuard", + _import_path=POISON_DEFENSE_PARAMETERS_PATH, + _config_class="PoisonDefenseConfig", + _config_kwargs={ + "lr": 0.01, + "train_iters": 100, + # "model": gnn_model_manager.gnn + } + ) - fgsm_evasion_attack_config = ConfigPattern( - _class_name="FGSM", - _import_path=EVASION_ATTACK_PARAMETERS_PATH, - _config_class="EvasionAttackConfig", - _config_kwargs={ - "epsilon": 0.001 * 12, - } - ) + jaccard_poison_defense_config = ConfigPattern( + _class_name="JaccardDefender", + _import_path=POISON_DEFENSE_PARAMETERS_PATH, + _config_class="PoisonDefenseConfig", + _config_kwargs={ + "threshold": 0.4, + } + ) - netattack_evasion_attack_config = ConfigPattern( - _class_name="NettackEvasionAttacker", - _import_path=EVASION_ATTACK_PARAMETERS_PATH, - _config_class="EvasionAttackConfig", - _config_kwargs={ - "node_idx": 0, # Node for attack - "n_perturbations": 20, - "perturb_features": True, - "perturb_structure": True, - "direct": True, - "n_influencers": 3 - } - ) + qattack_evasion_attack_config = ConfigPattern( + _class_name="QAttack", + _import_path=EVASION_ATTACK_PARAMETERS_PATH, + _config_class="EvasionAttackConfig", + _config_kwargs={ + "population_size": 500, + "individual_size": 100, + "generations": 100, + "prob_cross": 0.5, + "prob_mutate": 0.02 + } + ) - netattackgroup_evasion_attack_config = ConfigPattern( - _class_name="NettackGroupEvasionAttacker", - _import_path=EVASION_ATTACK_PARAMETERS_PATH, - _config_class="EvasionAttackConfig", - _config_kwargs={ - "node_idxs": [random.randint(0, 500) for _ in range(20)], # Nodes for attack - "n_perturbations": 50, - "perturb_features": True, - "perturb_structure": True, - "direct": True, - "n_influencers": 10 - } - ) + fgsm_evasion_attack_config = ConfigPattern( + _class_name="FGSM", + _import_path=EVASION_ATTACK_PARAMETERS_PATH, + _config_class="EvasionAttackConfig", + _config_kwargs={ + "epsilon": 0.005, + } + ) - gradientregularization_evasion_defense_config = ConfigPattern( - _class_name="GradientRegularizationDefender", - _import_path=EVASION_DEFENSE_PARAMETERS_PATH, - _config_class="EvasionDefenseConfig", - _config_kwargs={ - "regularization_strength": 0.1 * 1000 - } - ) + netattack_evasion_attack_config = ConfigPattern( + _class_name="NettackEvasionAttacker", + _import_path=EVASION_ATTACK_PARAMETERS_PATH, + _config_class="EvasionAttackConfig", + _config_kwargs={ + "node_idx": 0, # Node for attack + "n_perturbations": 20, + "perturb_features": True, + "perturb_structure": True, + "direct": True, + "n_influencers": 3 + } + ) - quantization_evasion_defense_config = ConfigPattern( - _class_name="QuantizationDefender", - _import_path=EVASION_DEFENSE_PARAMETERS_PATH, - _config_class="EvasionDefenseConfig", - _config_kwargs={ - "num_levels": 2 - } - ) + netattackgroup_evasion_attack_config = ConfigPattern( + _class_name="NettackGroupEvasionAttacker", + _import_path=EVASION_ATTACK_PARAMETERS_PATH, + _config_class="EvasionAttackConfig", + _config_kwargs={ + "node_idxs": [random.randint(0, 500) for _ in range(20)], # Nodes for attack + "n_perturbations": 50, + "perturb_features": True, + "perturb_structure": True, + "direct": True, + "n_influencers": 10 + } + ) - distillation_evasion_defense_config = ConfigPattern( - _class_name="DistillationDefender", - _import_path=EVASION_DEFENSE_PARAMETERS_PATH, - _config_class="EvasionDefenseConfig", - _config_kwargs={ - "temperature": 0.5 * 20 - } - ) + gradientregularization_evasion_defense_config = ConfigPattern( + _class_name="GradientRegularizationDefender", + _import_path=EVASION_DEFENSE_PARAMETERS_PATH, + _config_class="EvasionDefenseConfig", + _config_kwargs={ + "regularization_strength": 0.1 * 500 + } + ) - autoencoder_evasion_defense_config = ConfigPattern( - _class_name="AutoEncoderDefender", - _import_path=EVASION_DEFENSE_PARAMETERS_PATH, - _config_class="EvasionDefenseConfig", - _config_kwargs={ - "hidden_dim": 300, - "bottleneck_dim": 100, - "reconstruction_loss_weight": 0.1, - } - ) + quantization_evasion_defense_config = ConfigPattern( + _class_name="QuantizationDefender", + _import_path=EVASION_DEFENSE_PARAMETERS_PATH, + _config_class="EvasionDefenseConfig", + _config_kwargs={ + "num_levels": 2 + } + ) - fgsm_evasion_attack_config0 = ConfigPattern( - _class_name="FGSM", - _import_path=EVASION_ATTACK_PARAMETERS_PATH, - _config_class="EvasionAttackConfig", - _config_kwargs={ - "epsilon": 0.01, - } - ) + distillation_evasion_defense_config = ConfigPattern( + _class_name="DistillationDefender", + _import_path=EVASION_DEFENSE_PARAMETERS_PATH, + _config_class="EvasionDefenseConfig", + _config_kwargs={ + "temperature": 0.5 * 20 + } + ) - clga_poison_attack_config = ConfigPattern( - _class_name="CLGAAttack", - _import_path=POISON_ATTACK_PARAMETERS_PATH, - _config_class="PoisonAttackConfig", - _config_kwargs={ - "num_nodes": dataset.dataset.x.shape[0], - "feature_shape": dataset.dataset.x.shape[1] - } - ) + autoencoder_evasion_defense_config = ConfigPattern( + _class_name="AutoEncoderDefender", + _import_path=EVASION_DEFENSE_PARAMETERS_PATH, + _config_class="EvasionDefenseConfig", + _config_kwargs={ + "hidden_dim": 300, + "bottleneck_dim": 100, + "reconstruction_loss_weight": 0.1, + } + ) - fgsm_evasion_attack_config1 = ConfigPattern( - _class_name="FGSM", - _import_path=EVASION_ATTACK_PARAMETERS_PATH, - _config_class="EvasionAttackConfig", - _config_kwargs={ - "epsilon": 0.1, - } - ) - at_evasion_defense_config = ConfigPattern( - _class_name="AdvTraining", - _import_path=EVASION_DEFENSE_PARAMETERS_PATH, - _config_class="EvasionDefenseConfig", - _config_kwargs={ - "attack_name": None, - "attack_config": fgsm_evasion_attack_config1 - } - ) + clga_poison_attack_config = ConfigPattern( + _class_name="CLGAAttack", + _import_path=POISON_ATTACK_PARAMETERS_PATH, + _config_class="PoisonAttackConfig", + _config_kwargs={ + "num_nodes": dataset.dataset.x.shape[0], + "feature_shape": dataset.dataset.x.shape[1] + } + ) - # gnn_model_manager.set_poison_attacker(poison_attack_config=random_poison_attack_config) - # gnn_model_manager.set_poison_defender(poison_defense_config=gnnguard_poison_defense_config) - if a_p is not None: - if a_p == 'metaattack': - gnn_model_manager.set_poison_attacker(poison_attack_config=metafull_poison_attack_config) - elif a_p == 'clga': - gnn_model_manager.set_poison_attacker(poison_attack_config=clga_poison_attack_config) - if d_p is not None: - if d_p == 'gnnguard': - gnn_model_manager.set_poison_defender(poison_defense_config=gnnguard_poison_defense_config) - elif d_p == 'jaccard': - gnn_model_manager.set_poison_defender(poison_defense_config=jaccard_poison_defense_config) - if a_e is not None: - if a_e == 'fgsm': - gnn_model_manager.set_evasion_attacker(evasion_attack_config=fgsm_evasion_attack_config) - elif a_e == 'nettack': - gnn_model_manager.set_evasion_attacker(evasion_attack_config=netattack_evasion_attack_config) - if d_e is not None: - if d_e == 'at': - gnn_model_manager.set_evasion_defender(evasion_defense_config=at_evasion_defense_config) + fgsm_evasion_attack_config1 = ConfigPattern( + _class_name="FGSM", + _import_path=EVASION_ATTACK_PARAMETERS_PATH, + _config_class="EvasionAttackConfig", + _config_kwargs={ + "epsilon": 0.01, + } + ) + at_evasion_defense_config = ConfigPattern( + _class_name="AdvTraining", + _import_path=EVASION_DEFENSE_PARAMETERS_PATH, + _config_class="EvasionDefenseConfig", + _config_kwargs={ + "attack_name": None, + "attack_config": fgsm_evasion_attack_config1 + } + ) - warnings.warn("Start training") - dataset.train_test_split() + # gnn_model_manager.set_poison_attacker(poison_attack_config=random_poison_attack_config) + # gnn_model_manager.set_poison_defender(poison_defense_config=gnnguard_poison_defense_config) + if a_p is not None: + if a_p == 'metaattack': + gnn_model_manager.set_poison_attacker(poison_attack_config=metafull_poison_attack_config) + elif a_p == 'clga': + gnn_model_manager.set_poison_attacker(poison_attack_config=clga_poison_attack_config) + if d_p is not None: + if d_p == 'gnnguard': + gnn_model_manager.set_poison_defender(poison_defense_config=gnnguard_poison_defense_config) + elif d_p == 'jaccard': + gnn_model_manager.set_poison_defender(poison_defense_config=jaccard_poison_defense_config) + if a_e is not None: + if a_e == 'fgsm': + gnn_model_manager.set_evasion_attacker(evasion_attack_config=fgsm_evasion_attack_config) + elif a_e == 'nettack': + gnn_model_manager.set_evasion_attacker(evasion_attack_config=netattack_evasion_attack_config) + if d_e is not None: + if d_e == 'at': + gnn_model_manager.set_evasion_defender(evasion_defense_config=at_evasion_defense_config) + elif d_e == 'gr': + gnn_model_manager.set_evasion_defender(evasion_defense_config=gradientregularization_evasion_defense_config) + + warnings.warn("Start training") + dataset.train_test_split() - for i in range(2): adm = FrameworkAttackDefenseManager( gen_dataset=copy.deepcopy(dataset), gnn_manager=gnn_model_manager, @@ -331,12 +336,21 @@ def test_attack_defense(d='Cora', m='gin_2', a_e=None, d_e=None, a_p=None, d_p=N # metrics_defense=[DefenseMetric("AuccDefenseCleanDiff"), DefenseMetric("AuccDefenseAttackDiff"), ], # mask='test' # ) - adm.poison_defense_pipeline( + # adm.poison_defense_pipeline( + # steps=steps_epochs, + # save_model_flag=save_model_flag, + # metrics_attack=[AttackMetric("ASR"), AttackMetric("AuccAttackDiff"), ], + # metrics_defense=[DefenseMetric("AuccDefenseCleanDiff"), DefenseMetric("AuccDefenseAttackDiff"), ], + # mask='test' + # ) + + adm.full_pipeline_model_metrics_only( + # steps=1, steps=steps_epochs, save_model_flag=save_model_flag, - metrics_attack=[AttackMetric("ASR"), AttackMetric("AuccAttackDiff"), ], - metrics_defense=[DefenseMetric("AuccDefenseCleanDiff"), DefenseMetric("AuccDefenseAttackDiff"), ], - mask='test' + model_metrics=[Metric("Accuracy", mask="test")], + # task="*t*ftf", + task="*t*ftf", ) # @@ -1119,20 +1133,21 @@ def test_pgd(): def exp_pipeline(): - dataset_grid = ['Cora'] + dataset_grid = ['Cora', 'Photo'] # model_grid = ['gcn_2', 'gcn_3', 'gin_2'] model_grid = ['gin_2'] - attack_grid_evasion = ['fgsm', 'nettack'] + attack_grid_evasion = ['fgsm'] attack_grid_poison = ['clga'] - defense_grid_evasion = [] + defense_grid_evasion = ['at', 'gr'] defense_grid_poison = ['jaccard'] for d in dataset_grid: for m in model_grid: for a_p in attack_grid_poison: for d_p in defense_grid_poison: - test_attack_defense(d, m, a_p=a_p, d_p=d_p) - + for d_e in defense_grid_evasion: + for a_e in attack_grid_evasion: + test_attack_defense(d, m, a_p=a_p, d_e=d_e, a_e=a_e, d_p=d_p) if __name__ == '__main__': import random diff --git a/src/models_builder/attack_defense_manager.py b/src/models_builder/attack_defense_manager.py index 7cfbf7b..cdeaefd 100644 --- a/src/models_builder/attack_defense_manager.py +++ b/src/models_builder/attack_defense_manager.py @@ -82,7 +82,7 @@ def full_pipeline_model_metrics_only( steps: int, save_model_flag: bool = True, model_metrics=None, - task: str = "tttttt", + task: str = "******", ): if model_metrics is None: from models_builder.gnn_models import Metric @@ -123,6 +123,7 @@ def run_experiments( flags = [] if position == len(task): + # print(f"Task: {task}; Flags: {flags}") self.start( flags=flags, steps=steps, @@ -141,6 +142,15 @@ def run_experiments( position=position + 1 ) elif task[position] == 't': + self.run_experiments( + steps=steps, + save_model_flag=save_model_flag, + model_metrics=model_metrics, + task=task, + flags=flags + [True], + position=position + 1 + ) + elif task[position] == '*': self.run_experiments( steps=steps, save_model_flag=save_model_flag, @@ -173,13 +183,13 @@ def start( if flags[1]: self.gnn_manager.poison_defense_flag = True if flags[2]: - self.gnn_manager.evasion_attack_flag = True + self.gnn_manager.evasion_defense_flag = True if flags[3]: - self.gnn_manager.mi_attack_flag = True + self.gnn_manager.mi_defense_flag = True if flags[4]: - self.gnn_manager.evasion_defense_flag = True + self.gnn_manager.evasion_attack_flag = True if flags[5]: - self.gnn_manager.mi_defense_flag = True + self.gnn_manager.mi_attack_flag = True self.gnn_manager.epochs = self.gnn_manager.modification.epochs = 0 from models_builder.gnn_models import Metric train_test_split_path = self.gnn_manager.train_model(