From bfdca9306eae2d7d9a3524be2e61bdbc8a6738c7 Mon Sep 17 00:00:00 2001 From: Luca Demetrio Date: Mon, 6 Mar 2023 16:38:25 +0100 Subject: [PATCH 01/18] Reverting example script to previous one --- src/examples/run_evasion_attack.py | 51 +++++++----------------------- 1 file changed, 11 insertions(+), 40 deletions(-) diff --git a/src/examples/run_evasion_attack.py b/src/examples/run_evasion_attack.py index dc6f625..6dfff2a 100644 --- a/src/examples/run_evasion_attack.py +++ b/src/examples/run_evasion_attack.py @@ -1,4 +1,3 @@ -import torch import torchvision.datasets from torch.utils.data import DataLoader, Subset from src.adv.backends import Backends @@ -11,13 +10,12 @@ from robustbench.utils import load_model net = load_model(model_name="Rony2019Decoupling", dataset="cifar10", threat_model="L2") -device = "mps" -net.to(device) +net.to("mps") test_dataset = torchvision.datasets.CIFAR10( transform=torchvision.transforms.ToTensor(), train=False, root=".", download=True ) test_dataset = Subset(test_dataset, list(range(5))) -test_data_loader = DataLoader(test_dataset, batch_size=5, shuffle=False) +test_data_loader = DataLoader(test_dataset, batch_size=64, shuffle=False) # Wrap model model = BasePytorchClassifier(net) @@ -27,44 +25,17 @@ print(accuracy) # Create and run attack -epsilon = 0.5 -num_steps = 50 -step_size = 0.05 -perturbation_model = PerturbationModels.LINF -y_target = None -native_attack = PGD( - perturbation_model=perturbation_model, - epsilon=epsilon, - num_steps=num_steps, - step_size=step_size, - random_start=False, - y_target=y_target, - backend=Backends.NATIVE, -) -native_adv_ds = native_attack(model, test_data_loader) - -# Test accuracy on adversarial examples -n_robust_accuracy = Accuracy()(model, native_adv_ds) -print(n_robust_accuracy) - -# Create and run attack -foolbox_attack = PGD( - perturbation_model=perturbation_model, - epsilon=epsilon, - num_steps=num_steps, - step_size=step_size, +attack = PGD( + perturbation_model=PerturbationModels.LINF, + epsilon=0.5, + num_steps=50, + step_size=0.05, random_start=False, y_target=None, - backend=Backends.FOOLBOX, + backend=Backends.NATIVE, ) -f_adv_ds = foolbox_attack(model, test_data_loader) +adv_ds = attack(model, test_data_loader) # Test accuracy on adversarial examples -f_robust_accuracy = Accuracy()(model, f_adv_ds) -print(f_robust_accuracy) - -native_data, native_labels = next(iter(native_adv_ds)) -f_data, f_labels = next(iter(f_adv_ds)) - -distance = torch.linalg.norm(native_data.flatten(start_dim=1).to(device) - f_data.flatten(start_dim=1), ord=2, dim=1) -print(distance) +robust_accuracy = Accuracy()(model, adv_ds) +print(robust_accuracy) From d66799b39519a3ccba5e9e9eb63a611e8391b9aa Mon Sep 17 00:00:00 2001 From: Luca Demetrio Date: Mon, 6 Mar 2023 16:40:14 +0100 Subject: [PATCH 02/18] Debug composite attack with SGD --- src/adv/evasion/composite_attack.py | 9 +- src/examples/run_evasion_attack.py | 58 +++++++++--- .../run_evasion_attack_pure_pytorch.py | 92 +++++++++++++++++++ 3 files changed, 144 insertions(+), 15 deletions(-) create mode 100644 src/examples/run_evasion_attack_pure_pytorch.py diff --git a/src/adv/evasion/composite_attack.py b/src/adv/evasion/composite_attack.py index a4904f0..95cb929 100644 --- a/src/adv/evasion/composite_attack.py +++ b/src/adv/evasion/composite_attack.py @@ -106,10 +106,11 @@ def __call__(self, model: BaseModel, data_loader: DataLoader) -> DataLoader: x_adv = self.manipulation_function(samples, delta) for constraint in self.domain_constraints: x_adv.data = constraint(x_adv.data) - adversarials.append(x_adv) - original_labels.append(labels) - # print('NORM : ', delta.flatten(start_dim=1).norm(p=float('inf'))) - #TODO check best according to custom metric + + adversarials.append(x_adv) + original_labels.append(labels) + # print('NORM : ', delta.flatten(start_dim=1).norm(p=float('inf'))) + #TODO check best according to custom metric adversarials = torch.vstack(adversarials) original_labels = torch.hstack(original_labels) diff --git a/src/examples/run_evasion_attack.py b/src/examples/run_evasion_attack.py index 6dfff2a..3ddde99 100644 --- a/src/examples/run_evasion_attack.py +++ b/src/examples/run_evasion_attack.py @@ -1,3 +1,4 @@ +import torch import torchvision.datasets from torch.utils.data import DataLoader, Subset from src.adv.backends import Backends @@ -10,12 +11,13 @@ from robustbench.utils import load_model net = load_model(model_name="Rony2019Decoupling", dataset="cifar10", threat_model="L2") -net.to("mps") +device = "mps" +net.to(device) test_dataset = torchvision.datasets.CIFAR10( transform=torchvision.transforms.ToTensor(), train=False, root=".", download=True ) test_dataset = Subset(test_dataset, list(range(5))) -test_data_loader = DataLoader(test_dataset, batch_size=64, shuffle=False) +test_data_loader = DataLoader(test_dataset, batch_size=5, shuffle=False) # Wrap model model = BasePytorchClassifier(net) @@ -25,17 +27,51 @@ print(accuracy) # Create and run attack -attack = PGD( - perturbation_model=PerturbationModels.LINF, - epsilon=0.5, - num_steps=50, - step_size=0.05, +epsilon = 0.5 +num_steps = 50 +step_size = 0.05 +perturbation_model = PerturbationModels.LINF +y_target = None +native_attack = PGD( + perturbation_model=perturbation_model, + epsilon=epsilon, + num_steps=num_steps, + step_size=step_size, random_start=False, - y_target=None, + y_target=y_target, backend=Backends.NATIVE, ) -adv_ds = attack(model, test_data_loader) +native_adv_ds = native_attack(model, test_data_loader) + +# Test accuracy on adversarial examples +n_robust_accuracy = Accuracy()(model, native_adv_ds) +print(n_robust_accuracy) + +# Create and run attack +foolbox_attack = PGD( + perturbation_model=perturbation_model, + epsilon=epsilon, + num_steps=num_steps, + step_size=step_size, + random_start=False, + y_target=None, + backend=Backends.FOOLBOX, +) +f_adv_ds = foolbox_attack(model, test_data_loader) # Test accuracy on adversarial examples -robust_accuracy = Accuracy()(model, adv_ds) -print(robust_accuracy) +f_robust_accuracy = Accuracy()(model, f_adv_ds) +print(f_robust_accuracy) + +native_data, native_labels = next(iter(native_adv_ds)) +f_data, f_labels = next(iter(f_adv_ds)) +real_data, real_labels = next(iter(test_data_loader)) + +distance = torch.linalg.norm(native_data.flatten(start_dim=1).to(device) - f_data.flatten(start_dim=1), + ord=float('inf'), dim=1) +print("Solutions are :", distance, "linf distant") + +real_native = torch.linalg.norm(torch.flatten(real_data.to(device) - native_data.to(device), start_dim=1), ord=float('inf'), dim=1) +real_fb = torch.linalg.norm(torch.flatten(real_data.to(device) - f_data.to(device), start_dim=1), ord=float('inf'), dim=1) + +print(real_native, real_fb) diff --git a/src/examples/run_evasion_attack_pure_pytorch.py b/src/examples/run_evasion_attack_pure_pytorch.py new file mode 100644 index 0000000..9a11e23 --- /dev/null +++ b/src/examples/run_evasion_attack_pure_pytorch.py @@ -0,0 +1,92 @@ +import torch +import torchvision.datasets +from torch.optim import SGD +from torch.utils.data import DataLoader, Subset, TensorDataset +from src.adv.backends import Backends +from src.adv.evasion.pgd import PGD +from src.adv.evasion.perturbation_models import PerturbationModels + +from src.metrics.classification import Accuracy +from src.models.pytorch.base_pytorch_nn import BasePytorchClassifier + +from robustbench.utils import load_model + + +def attack_linf_pytorch_optim(model, samples, labels, optimizer, steps=100, + step_size=0.05, eps=0.3, device='cpu'): + x_adv = samples.clone().detach().to(device).requires_grad_() + optimizer = optimizer([x_adv], lr=step_size) + for _ in range(steps): + out = model(x_adv) + loss = -torch.nn.functional.cross_entropy(out, labels) + optimizer.zero_grad() + loss.backward() + print(loss) + x_adv.grad = x_adv.grad.sign() + optimizer.step() + + diff = x_adv.data - samples + diff = (diff).clamp_(-eps, +eps) + x_adv.detach().copy_((diff + samples).clamp_(0, 1)) + return x_adv + + +net = load_model(model_name="Rony2019Decoupling", dataset="cifar10", threat_model="L2") +device = "mps" +net.to(device) +test_dataset = torchvision.datasets.CIFAR10( + transform=torchvision.transforms.ToTensor(), train=False, root=".", download=True +) +test_dataset = Subset(test_dataset, list(range(1))) +test_data_loader = DataLoader(test_dataset, batch_size=5, shuffle=False) + +# Wrap model +model = BasePytorchClassifier(net) + +# Test accuracy on original data +accuracy = Accuracy()(model, test_data_loader) +print(accuracy) + +# Create and run attack +epsilon = 0.5 +num_steps = 50 +step_size = 0.05 +perturbation_model = PerturbationModels.LINF +y_target = None +native_attack = PGD( + perturbation_model=perturbation_model, + epsilon=epsilon, + num_steps=num_steps, + step_size=step_size, + random_start=False, + y_target=y_target, + backend=Backends.NATIVE, +) +native_adv_ds = native_attack(model, test_data_loader) + +# Test accuracy on adversarial examples +n_robust_accuracy = Accuracy()(model, native_adv_ds) +print(n_robust_accuracy) + +print('OTHER ATTACK') + +advs = [] +advlb = [] +for data, labels in test_data_loader: + x_adv = attack_linf_pytorch_optim(model, data.to(device), labels.to(device), SGD, num_steps, step_size, epsilon, + 'mps') + advs.append(x_adv) + advlb.append(labels) +advlb = torch.cat(advlb) +advs = torch.cat(advs) +f_adv_ds = DataLoader(TensorDataset(advs, advlb), batch_size=5, shuffle=False) +# Test accuracy on adversarial examples +f_robust_accuracy = Accuracy()(model, f_adv_ds) +print(f_robust_accuracy) + +native_data, native_labels = next(iter(native_adv_ds)) +f_data, f_labels = next(iter(f_adv_ds)) + +distance = torch.linalg.norm(native_data.flatten(start_dim=1).to(device) - f_data.flatten(start_dim=1), + ord=float('inf'), dim=1) +print(distance) From 9635c548e9acbfdd0667244fe58b0bf70983b2ef Mon Sep 17 00:00:00 2001 From: Luca Demetrio Date: Mon, 6 Mar 2023 17:52:08 +0100 Subject: [PATCH 03/18] Composite attack not working, results diverge from pure-pytorch results (look at final print in run_evasion_attack_pure_pytorch.py) Problem with METAL, crashes for TARGETED attacks --- src/adv/evasion/composite_attack.py | 5 +-- src/examples/run_evasion_attack.py | 4 +-- .../run_evasion_attack_pure_pytorch.py | 5 ++- src/optimization/constraints.py | 34 +++++++------------ 4 files changed, 19 insertions(+), 29 deletions(-) diff --git a/src/adv/evasion/composite_attack.py b/src/adv/evasion/composite_attack.py index 95cb929..c86b9ee 100644 --- a/src/adv/evasion/composite_attack.py +++ b/src/adv/evasion/composite_attack.py @@ -81,7 +81,7 @@ def __call__(self, model: BaseModel, data_loader: DataLoader) -> DataLoader: multiplier = 1 if self.y_target is not None else -1 for samples, labels in data_loader: target = ( - torch.zeros_like(labels) + self.y_target + torch.zeros_like(labels).to(self.y_target) + self.y_target if self.y_target is not None else labels ) @@ -94,7 +94,8 @@ def __call__(self, model: BaseModel, data_loader: DataLoader) -> DataLoader: for i in range(self.num_steps): scores = model.decision_function(x_adv) target = target.to(scores.device) - loss = self.loss_function(scores, target) * multiplier + loss = self.loss_function(scores, target) + loss = loss * multiplier optimizer.zero_grad() loss.backward() gradient = delta.grad diff --git a/src/examples/run_evasion_attack.py b/src/examples/run_evasion_attack.py index 3ddde99..1a4f28f 100644 --- a/src/examples/run_evasion_attack.py +++ b/src/examples/run_evasion_attack.py @@ -31,7 +31,7 @@ num_steps = 50 step_size = 0.05 perturbation_model = PerturbationModels.LINF -y_target = None +y_target = torch.Tensor([9]).to(device) native_attack = PGD( perturbation_model=perturbation_model, epsilon=epsilon, @@ -54,7 +54,7 @@ num_steps=num_steps, step_size=step_size, random_start=False, - y_target=None, + y_target=y_target, backend=Backends.FOOLBOX, ) f_adv_ds = foolbox_attack(model, test_data_loader) diff --git a/src/examples/run_evasion_attack_pure_pytorch.py b/src/examples/run_evasion_attack_pure_pytorch.py index 9a11e23..1fea7bc 100644 --- a/src/examples/run_evasion_attack_pure_pytorch.py +++ b/src/examples/run_evasion_attack_pure_pytorch.py @@ -21,12 +21,11 @@ def attack_linf_pytorch_optim(model, samples, labels, optimizer, steps=100, loss = -torch.nn.functional.cross_entropy(out, labels) optimizer.zero_grad() loss.backward() - print(loss) x_adv.grad = x_adv.grad.sign() optimizer.step() diff = x_adv.data - samples - diff = (diff).clamp_(-eps, +eps) + diff = diff.clamp_(-eps, +eps) x_adv.detach().copy_((diff + samples).clamp_(0, 1)) return x_adv @@ -48,7 +47,7 @@ def attack_linf_pytorch_optim(model, samples, labels, optimizer, steps=100, print(accuracy) # Create and run attack -epsilon = 0.5 +epsilon = 100 num_steps = 50 step_size = 0.05 perturbation_model = PerturbationModels.LINF diff --git a/src/optimization/constraints.py b/src/optimization/constraints.py index 6f435cd..8381103 100644 --- a/src/optimization/constraints.py +++ b/src/optimization/constraints.py @@ -30,23 +30,12 @@ def project(self, x: torch.Tensor) -> torch.Tensor: def __call__(self, x: torch.Tensor, *args, **kwargs) -> torch.Tensor: # x = x + self.center - norm = torch.linalg.norm(x.flatten(start_dim=1), ord=self.p, dim=1) - to_normalize = (norm > self.radius).view(-1, 1) - delta = self.project(x).flatten(start_dim=1) * to_normalize + x.flatten(start_dim=1) * torch.logical_not( - to_normalize) - delta = delta.view(x.shape) - - # with torch.no_grad(): - # diff = evasion.x_adv.data - samples - # - # diff = diff.flatten(start_dim=1) - # - # diff_norm = diff.norm(p=2, dim=1, keepdim=True).clamp_(min=1e-12) - # diff.mul_(eps.unsqueeze(1) / diff_norm).clamp_(max=1) - # diff = diff.reshape(evasion.x_adv.shape) - # - # evasion.x_adv.copy_((diff + samples).clamp_(0, 1)) - + with torch.no_grad(): + norm = torch.linalg.norm(x.flatten(start_dim=1), ord=self.p, dim=1) + to_normalize = (norm > self.radius).view(-1, 1) + proj_delta = self.project(x).flatten(start_dim=1) + delta = proj_delta * to_normalize + x.flatten(start_dim=1) * torch.logical_not(to_normalize) + delta = delta.view(x.shape) return delta @@ -57,7 +46,8 @@ def __init__(self, center, radius): def project(self, x): flat_x = x.flatten(start_dim=1) diff_norm = flat_x.norm(p=2, dim=1, keepdim=True).clamp_(min=1e-12) - flat_x.mul_(self.radius / diff_norm).clamp_(max=1) + flat_x = flat_x / diff_norm * self.radius + # flat_x.mul_(self.radius / diff_norm).clamp_(max=1) x = flat_x.reshape(x.shape) return x @@ -67,9 +57,9 @@ def __init__(self, center, radius): super().__init__(center=center, radius=radius, p=float('inf')) def project(self, x): - x = x + self.center - x = x.clamp_(-self.radius, self.radius) - x = x - self.center + # x = x + self.center + x = x.clamp(min=-self.radius, max=self.radius) + # x = x - self.center return x @@ -110,7 +100,7 @@ def project(self, x): John Duchi, Shai Shalev-Shwartz, Yoram Singer, and Tushar Chandra. International Conference on Machine Learning (ICML 2008) """ - x = x + self.center + # x = x + self.center original_shape = x.shape x = x.view(x.shape[0], -1) mask = (torch.norm(x, p=1, dim=1) < self.radius).float().unsqueeze(1) From 2ca6f2eb0598e8e34cb0555b575e39217de4a3c5 Mon Sep 17 00:00:00 2001 From: maurapintor Date: Mon, 6 Mar 2023 19:18:16 +0100 Subject: [PATCH 04/18] fixed (?) composite pgd --- src/adv/evasion/composite_attack.py | 12 +++++------- src/adv/evasion/pgd.py | 4 ++-- src/optimization/constraints.py | 17 +++++++++++------ 3 files changed, 18 insertions(+), 15 deletions(-) diff --git a/src/adv/evasion/composite_attack.py b/src/adv/evasion/composite_attack.py index c86b9ee..5044266 100644 --- a/src/adv/evasion/composite_attack.py +++ b/src/adv/evasion/composite_attack.py @@ -89,7 +89,7 @@ def __call__(self, model: BaseModel, data_loader: DataLoader) -> DataLoader: delta = self.initializer(samples.data) delta.requires_grad = True optimizer = self.optimizer_cls([delta], lr=self.step_size) - perturbation_constraints = self.init_perturbation_constraints(samples) + perturbation_constraints = self.init_perturbation_constraints() x_adv = self.manipulation_function(samples, delta) for i in range(self.num_steps): scores = model.decision_function(x_adv) @@ -98,19 +98,17 @@ def __call__(self, model: BaseModel, data_loader: DataLoader) -> DataLoader: loss = loss * multiplier optimizer.zero_grad() loss.backward() - gradient = delta.grad - gradient = self.gradient_processing(gradient) - delta.grad.data = gradient.data + delta.grad.data = self.gradient_processing(delta.grad.data) optimizer.step() for constraint in perturbation_constraints: delta.data = constraint(delta.data) - x_adv = self.manipulation_function(samples, delta) + x_adv.data = self.manipulation_function(samples.data, delta.data) for constraint in self.domain_constraints: x_adv.data = constraint(x_adv.data) - + delta.data = x_adv.data - samples.data + adversarials.append(x_adv) original_labels.append(labels) - # print('NORM : ', delta.flatten(start_dim=1).norm(p=float('inf'))) #TODO check best according to custom metric adversarials = torch.vstack(adversarials) diff --git a/src/adv/evasion/pgd.py b/src/adv/evasion/pgd.py index c41d9c0..e2c866f 100644 --- a/src/adv/evasion/pgd.py +++ b/src/adv/evasion/pgd.py @@ -132,5 +132,5 @@ def __init__( gradient_processing=gradient_processing, initializer=initializer) - def init_perturbation_constraints(self, center: torch.Tensor) -> List[Constraint]: - return [p(center, self.epsilon) for p in self.perturbation_constraints] + def init_perturbation_constraints(self) -> List[Constraint]: + return [p(radius=self.epsilon) for p in self.perturbation_constraints] diff --git a/src/optimization/constraints.py b/src/optimization/constraints.py index 8381103..23430f2 100644 --- a/src/optimization/constraints.py +++ b/src/optimization/constraints.py @@ -9,6 +9,7 @@ def __call__(self, x: torch.Tensor, *args, **kwargs): ... + class ClipConstraint(Constraint): def __init__(self, lb=0, ub=1): self.lb = lb @@ -19,7 +20,8 @@ def __call__(self, x: torch.Tensor, *args, **kwargs) -> torch.Tensor: class LpConstraint(Constraint): - def __init__(self, center, radius, p): + def __init__(self, center=0, radius=0, p=torch.inf): + # TODO consider swapping order of the parameters to get eps as first arg self.p = p self.center = center self.radius = radius @@ -29,18 +31,21 @@ def project(self, x: torch.Tensor) -> torch.Tensor: ... def __call__(self, x: torch.Tensor, *args, **kwargs) -> torch.Tensor: - # x = x + self.center + x = x + self.center with torch.no_grad(): norm = torch.linalg.norm(x.flatten(start_dim=1), ord=self.p, dim=1) to_normalize = (norm > self.radius).view(-1, 1) proj_delta = self.project(x).flatten(start_dim=1) - delta = proj_delta * to_normalize + x.flatten(start_dim=1) * torch.logical_not(to_normalize) + delta = torch.where(to_normalize, proj_delta, x.flatten(start_dim=1)) delta = delta.view(x.shape) return delta + def __repr__(self) -> str: + return f"L{self.p} constraint with {'nonzero' if self.center!=0 else 'zero'} center with radius {self.radius}." class L2Constraint(LpConstraint): - def __init__(self, center, radius): + def __init__(self, center=0, radius=0): + # TODO consider swapping to accept radius as first positional arg super().__init__(center=center, radius=radius, p=2) def project(self, x): @@ -53,7 +58,7 @@ def project(self, x): class LInfConstraint(LpConstraint): - def __init__(self, center, radius): + def __init__(self, center=0, radius=0): super().__init__(center=center, radius=radius, p=float('inf')) def project(self, x): @@ -64,7 +69,7 @@ def project(self, x): class L1Constraint(LpConstraint): - def __init__(self, center, radius) -> None: + def __init__(self, center=0, radius=0) -> None: super().__init__(center=center, radius=radius, p=1) def project(self, x): From 75bace37a50c20f410cc182b35581872d8add260 Mon Sep 17 00:00:00 2001 From: maurapintor Date: Tue, 7 Mar 2023 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delta.data = x_adv.data - samples.data + delta.data = self.manipulation_function.invert(samples.data, x_adv.data) adversarials.append(x_adv) original_labels.append(labels) diff --git a/src/manipulations/manipulation.py b/src/manipulations/manipulation.py index e13e4bf..f7fa47b 100644 --- a/src/manipulations/manipulation.py +++ b/src/manipulations/manipulation.py @@ -5,8 +5,15 @@ class Manipulation: def __call__(self, x: torch.Tensor, delta: torch.Tensor) -> torch.Tensor: raise NotImplementedError("Abstract manipulation.") + def invert(self, x: torch.Tensor, x_adv: torch.Tensor) -> torch.Tensor: + raise NotImplementedError("Abstract inversion.") + class AdditiveManipulation(Manipulation): def __call__(self, x: torch.Tensor, delta: torch.Tensor) -> torch.Tensor: x_adv = x + delta return x_adv + + def invert(self, x: torch.Tensor, x_adv: torch.Tensor) -> torch.Tensor: + delta = x_adv - x + return delta From b7984d4d632a8b4b7a486cd78a2a23948527e4a2 Mon Sep 17 00:00:00 2001 From: maurapintor Date: Wed, 14 Jun 2023 15:19:00 +0200 Subject: [PATCH 07/18] added postprocessing and refactor preprocessing as data processing --- src/models/base_model.py | 26 ++++++++++++++----- .../__init__.py | 0 .../data_processing.py} | 6 ++--- .../identity_data_processing.py | 11 ++++++++ .../test/__init__.py | 0 .../preprocessing/identity_preprocessing.py | 11 -------- src/models/pytorch/base_pytorch_nn.py | 9 ++++--- src/models/sklearn/base_sklearn_model.py | 11 +++++--- src/models/tensorflow/base_tensorflow_nn.py | 9 ++++--- 9 files changed, 51 insertions(+), 32 deletions(-) rename src/models/{preprocessing => data_processing}/__init__.py (100%) rename src/models/{preprocessing/preprocessing.py => data_processing/data_processing.py} (63%) create mode 100644 src/models/data_processing/identity_data_processing.py rename src/models/{preprocessing => data_processing}/test/__init__.py (100%) delete mode 100644 src/models/preprocessing/identity_preprocessing.py diff --git a/src/models/base_model.py b/src/models/base_model.py index fc8c4e3..7b78c46 100644 --- a/src/models/base_model.py +++ b/src/models/base_model.py @@ -4,29 +4,43 @@ import torch from torch.utils.data import DataLoader -from src.models.preprocessing.identity_preprocessing import IdentityPreprocessing -from src.models.preprocessing.preprocessing import Preprocessing +from src.models.data_processing.identity_data_processing import IdentityDataProcessing +from src.models.data_processing.data_processing import DataProcessing class BaseModel(ABC): - def __init__(self, preprocessing: Preprocessing = None): + def __init__( + self, + preprocessing: DataProcessing = None, + postprocessing: DataProcessing = None, + ): """ Create base abstract model Parameters ---------- - preprocessing : Preprocessing + preprocessing : DataProcessing + postprocessing: DataProcessing """ self._preprocessing = ( - preprocessing if preprocessing is not None else IdentityPreprocessing() + preprocessing if preprocessing is not None else IdentityDataProcessing() + ) + self._postprocessing = ( + postprocessing if postprocessing is not None else IdentityDataProcessing() ) @abstractmethod def predict(self, x: torch.Tensor) -> torch.Tensor: ... - @abstractmethod def decision_function(self, x: torch.Tensor) -> torch.Tensor: + x = self._preprocessing(x) + x = self._decision_function(x) + x = self._postprocessing(x) + return x + + @abstractmethod + def _decision_function(self, x: torch.Tensor) -> torch.Tensor: ... @abstractmethod diff --git a/src/models/preprocessing/__init__.py b/src/models/data_processing/__init__.py similarity index 100% rename from src/models/preprocessing/__init__.py rename to src/models/data_processing/__init__.py diff --git a/src/models/preprocessing/preprocessing.py b/src/models/data_processing/data_processing.py similarity index 63% rename from src/models/preprocessing/preprocessing.py rename to src/models/data_processing/data_processing.py index f3371ef..335b609 100644 --- a/src/models/preprocessing/preprocessing.py +++ b/src/models/data_processing/data_processing.py @@ -3,13 +3,13 @@ import torch -class Preprocessing(ABC): +class DataProcessing(ABC): @abstractmethod - def preprocess(self, x: torch.Tensor) -> torch.Tensor: + def process(self, x: torch.Tensor) -> torch.Tensor: ... def invert(self, x: torch.Tensor) -> torch.Tensor: ... def __call__(self, x: torch.Tensor) -> torch.Tensor: - return self.preprocess(x) + return self.process(x) diff --git a/src/models/data_processing/identity_data_processing.py b/src/models/data_processing/identity_data_processing.py new file mode 100644 index 0000000..f02ffd8 --- /dev/null +++ b/src/models/data_processing/identity_data_processing.py @@ -0,0 +1,11 @@ +import torch + +from src.models.data_processing.data_processing import DataProcessing + + +class IdentityDataProcessing(DataProcessing): + def process(self, x: torch.Tensor) -> torch.Tensor: + return x + + def invert(self, x: torch.Tensor) -> torch.Tensor: + return x diff --git a/src/models/preprocessing/test/__init__.py b/src/models/data_processing/test/__init__.py similarity index 100% rename from src/models/preprocessing/test/__init__.py rename to src/models/data_processing/test/__init__.py diff --git a/src/models/preprocessing/identity_preprocessing.py b/src/models/preprocessing/identity_preprocessing.py deleted file mode 100644 index fb41088..0000000 --- a/src/models/preprocessing/identity_preprocessing.py +++ /dev/null @@ -1,11 +0,0 @@ -import torch - -from src.models.preprocessing.preprocessing import Preprocessing - - -class IdentityPreprocessing(Preprocessing): - def preprocess(self, x: torch.Tensor) -> torch.Tensor: - return x - - def invert(self, x: torch.Tensor) -> torch.Tensor: - return x diff --git a/src/models/pytorch/base_pytorch_nn.py b/src/models/pytorch/base_pytorch_nn.py index 611abd4..eca9075 100644 --- a/src/models/pytorch/base_pytorch_nn.py +++ b/src/models/pytorch/base_pytorch_nn.py @@ -2,7 +2,7 @@ from torch.utils.data import DataLoader from src.models.base_model import BaseModel -from src.models.preprocessing.preprocessing import Preprocessing +from src.models.data_processing.data_processing import DataProcessing from src.models.pytorch.base_pytorch_trainer import BasePyTorchTrainer @@ -10,10 +10,11 @@ class BasePytorchClassifier(BaseModel): def __init__( self, model: torch.nn.Module, - preprocessing: Preprocessing = None, + preprocessing: DataProcessing = None, + postprocessing: DataProcessing = None, trainer: BasePyTorchTrainer = None, ): - super().__init__(preprocessing=preprocessing) + super().__init__(preprocessing=preprocessing, postprocessing=postprocessing) self._model: torch.nn.Module = model self._trainer = trainer @@ -29,7 +30,7 @@ def predict(self, x: torch.Tensor) -> torch.Tensor: labels = torch.argmax(scores, dim=-1) return labels - def decision_function(self, x: torch.Tensor) -> torch.Tensor: + def _decision_function(self, x: torch.Tensor) -> torch.Tensor: """ Returns the decision function of the model. Parameters diff --git a/src/models/sklearn/base_sklearn_model.py b/src/models/sklearn/base_sklearn_model.py index 4a70561..7e7830f 100644 --- a/src/models/sklearn/base_sklearn_model.py +++ b/src/models/sklearn/base_sklearn_model.py @@ -7,17 +7,20 @@ from src.data.sklearn_dataset import SklearnDataset from src.models.base_model import BaseModel -from src.models.preprocessing.preprocessing import Preprocessing +from src.models.data_processing.data_processing import DataProcessing class BaseSklearnModel(BaseModel): def __init__( - self, model: sklearn.base.BaseEstimator, preprocessing: Preprocessing = None + self, + model: sklearn.base.BaseEstimator, + preprocessing: DataProcessing = None, + postprocessing: DataProcessing = None, ): - super().__init__(preprocessing=preprocessing) + super().__init__(preprocessing=preprocessing, postprocessing=postprocessing) self._model: sklearn.base.BaseEstimator = model - def decision_function(self, x: torch.Tensor) -> torch.Tensor: + def _decision_function(self, x: torch.Tensor) -> torch.Tensor: if hasattr(self._model, "decision_function"): return self.to_tensor(self._model.decision_function(self.to_2d_numpy(x))) elif hasattr(self._model, "predict_proba"): diff --git a/src/models/tensorflow/base_tensorflow_nn.py b/src/models/tensorflow/base_tensorflow_nn.py index 90d0ca8..87f67c5 100644 --- a/src/models/tensorflow/base_tensorflow_nn.py +++ b/src/models/tensorflow/base_tensorflow_nn.py @@ -2,7 +2,7 @@ import torch from src.models.base_model import BaseModel -from src.models.preprocessing.preprocessing import Preprocessing +from src.models.data_processing.data_processing import DataProcessing from src.models.tensorflow.base_tensorflow_trainer import BaseTensorflowTrainer @@ -10,10 +10,11 @@ class BaseTensorflowClassifier(BaseModel): def __init__( self, model: tf.keras.Model, - preprocessing: Preprocessing = None, + preprocessing: DataProcessing = None, + postprocessing: DataProcessing = None, trainer: BaseTensorflowTrainer = None, ): - super().__init__(preprocessing=preprocessing) + super().__init__(preprocessing=preprocessing, postprocessing=postprocessing) self._model = model self._trainer = trainer @@ -30,7 +31,7 @@ def predict(self, x: torch.Tensor) -> torch.Tensor: """ pass - def decision_function(self, x: torch.Tensor) -> torch.Tensor: + def _decision_function(self, x: torch.Tensor) -> torch.Tensor: """ TODO Parameters From bf875961772abd041b256fbd25ed59fc9fa94a99 Mon Sep 17 00:00:00 2001 From: maurapintor Date: Wed, 14 Jun 2023 15:31:28 +0200 Subject: [PATCH 08/18] added black github action --- .github/workflows/black.yml | 10 ++++++++++ 1 file changed, 10 insertions(+) create mode 100644 .github/workflows/black.yml diff --git a/.github/workflows/black.yml b/.github/workflows/black.yml new file mode 100644 index 0000000..0a8b8e9 --- /dev/null +++ b/.github/workflows/black.yml @@ -0,0 +1,10 @@ +name: Lint + +on: [push, pull_request] + +jobs: + lint: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + - uses: psf/black@stable \ No newline at end of file From 5e589fcfcd4a3e64cd53ddb83e4967bb671d29f9 Mon Sep 17 00:00:00 2001 From: maurapintor Date: Wed, 14 Jun 2023 15:32:35 +0200 Subject: [PATCH 09/18] black formatted --- src/adv/evasion/composite_attack.py | 43 ++++++------ src/adv/evasion/pgd.py | 90 ++++++++++++++----------- src/optimization/constraints.py | 7 +- src/optimization/gradient_processing.py | 7 +- 4 files changed, 80 insertions(+), 67 deletions(-) diff --git a/src/adv/evasion/composite_attack.py b/src/adv/evasion/composite_attack.py index a4904f0..4259578 100644 --- a/src/adv/evasion/composite_attack.py +++ b/src/adv/evasion/composite_attack.py @@ -12,35 +12,32 @@ from src.optimization.gradient_processing import GradientProcessing from src.optimization.initializer import Initializer -CE_LOSS = 'ce_loss' -LOGITS_LOSS = 'logits_loss' +CE_LOSS = "ce_loss" +LOGITS_LOSS = "logits_loss" LOSS_FUNCTIONS = { CE_LOSS: CrossEntropyLoss, } -ADAM = 'adam' -StochasticGD = 'sgd' +ADAM = "adam" +StochasticGD = "sgd" -OPTIMIZERS = { - ADAM: Adam, - StochasticGD: SGD -} +OPTIMIZERS = {ADAM: Adam, StochasticGD: SGD} class CompositeEvasionAttack(BaseEvasionAttack): def __init__( - self, - y_target: Union[int, None], - num_steps: int, - step_size: float, - loss_function: Union[str, torch.nn.Module], - optimizer_cls: Union[str, Type[torch.nn.Module]], - manipulation_function: Manipulation, - domain_constraints: List[Constraint], - perturbation_constraints: List[Type[Constraint]], - initializer: Initializer, - gradient_processing: GradientProcessing, + self, + y_target: Union[int, None], + num_steps: int, + step_size: float, + loss_function: Union[str, torch.nn.Module], + optimizer_cls: Union[str, Type[torch.nn.Module]], + manipulation_function: Manipulation, + domain_constraints: List[Constraint], + perturbation_constraints: List[Type[Constraint]], + initializer: Initializer, + gradient_processing: GradientProcessing, ): self.y_target = y_target self.num_steps = num_steps @@ -51,7 +48,8 @@ def __init__( self.loss_function = LOSS_FUNCTIONS[loss_function]() else: raise ValueError( - f"{loss_function} not in list of init from string. Use one among {LOSS_FUNCTIONS.values()}") + f"{loss_function} not in list of init from string. Use one among {LOSS_FUNCTIONS.values()}" + ) else: self.loss_function = loss_function @@ -60,7 +58,8 @@ def __init__( self.optimizer_cls = OPTIMIZERS[optimizer_cls] else: raise ValueError( - f"{optimizer_cls} not in list of init from string. Use one among {OPTIMIZERS.values()}") + f"{optimizer_cls} not in list of init from string. Use one among {OPTIMIZERS.values()}" + ) else: self.optimizer_cls = optimizer_cls @@ -109,7 +108,7 @@ def __call__(self, model: BaseModel, data_loader: DataLoader) -> DataLoader: adversarials.append(x_adv) original_labels.append(labels) # print('NORM : ', delta.flatten(start_dim=1).norm(p=float('inf'))) - #TODO check best according to custom metric + # TODO check best according to custom metric adversarials = torch.vstack(adversarials) original_labels = torch.hstack(original_labels) diff --git a/src/adv/evasion/pgd.py b/src/adv/evasion/pgd.py index c41d9c0..ada4df7 100644 --- a/src/adv/evasion/pgd.py +++ b/src/adv/evasion/pgd.py @@ -21,22 +21,28 @@ LinfProjectedGradientDescentAttack, ) -from src.optimization.constraints import ClipConstraint, L1Constraint, L2Constraint, LInfConstraint, Constraint +from src.optimization.constraints import ( + ClipConstraint, + L1Constraint, + L2Constraint, + LInfConstraint, + Constraint, +) class PGD(BaseEvasionAttackCreator): def __new__( - cls, - perturbation_model: str, - epsilon: float, - num_steps: int, - step_size: float, - random_start: bool, - y_target: Optional[int] = None, - lb: float = 0.0, - ub: float = 1.0, - backend: str = Backends.FOOLBOX, - **kwargs + cls, + perturbation_model: str, + epsilon: float, + num_steps: int, + step_size: float, + random_start: bool, + y_target: Optional[int] = None, + lb: float = 0.0, + ub: float = 1.0, + backend: str = Backends.FOOLBOX, + **kwargs ): cls.check_perturbation_model_available(perturbation_model) implementation = cls.get_implementation(backend) @@ -63,16 +69,16 @@ def get_native_implementation(): class PGDFoolbox(BaseFoolboxEvasionAttack): def __init__( - self, - perturbation_model: str, - epsilon: float, - num_steps: int, - step_size: float, - random_start: bool, - y_target: Optional[int] = None, - lb: float = 0.0, - ub: float = 1.0, - **kwargs + self, + perturbation_model: str, + epsilon: float, + num_steps: int, + step_size: float, + random_start: bool, + y_target: Optional[int] = None, + lb: float = 0.0, + ub: float = 1.0, + **kwargs ) -> None: perturbation_models = { PerturbationModels.L1: L1ProjectedGradientDescentAttack, @@ -100,21 +106,21 @@ def __init__( class PGDNative(CompositeEvasionAttack): def __init__( - self, - perturbation_model: str, - epsilon: float, - num_steps: int, - step_size: float, - random_start: bool, - y_target: Optional[int] = None, - lb: float = 0.0, - ub: float = 1.0, - **kwargs + self, + perturbation_model: str, + epsilon: float, + num_steps: int, + step_size: float, + random_start: bool, + y_target: Optional[int] = None, + lb: float = 0.0, + ub: float = 1.0, + **kwargs ) -> None: perturbation_models = { PerturbationModels.L1: L1Constraint, PerturbationModels.L2: L2Constraint, - PerturbationModels.LINF: LInfConstraint + PerturbationModels.LINF: LInfConstraint, } initializer = Initializer() if random_start: @@ -125,12 +131,18 @@ def __init__( perturbation_constraints = [perturbation_models[perturbation_model]] domain_constraints = [ClipConstraint(lb=lb, ub=ub)] manipulation_function = AdditiveManipulation() - super().__init__(y_target=y_target, num_steps=num_steps, step_size=step_size, loss_function=CE_LOSS, - optimizer_cls=SGD, manipulation_function=manipulation_function, - domain_constraints=domain_constraints, - perturbation_constraints=perturbation_constraints, - gradient_processing=gradient_processing, - initializer=initializer) + super().__init__( + y_target=y_target, + num_steps=num_steps, + step_size=step_size, + loss_function=CE_LOSS, + optimizer_cls=SGD, + manipulation_function=manipulation_function, + domain_constraints=domain_constraints, + perturbation_constraints=perturbation_constraints, + gradient_processing=gradient_processing, + initializer=initializer, + ) def init_perturbation_constraints(self, center: torch.Tensor) -> List[Constraint]: return [p(center, self.epsilon) for p in self.perturbation_constraints] diff --git a/src/optimization/constraints.py b/src/optimization/constraints.py index 6f435cd..091e287 100644 --- a/src/optimization/constraints.py +++ b/src/optimization/constraints.py @@ -32,8 +32,9 @@ def __call__(self, x: torch.Tensor, *args, **kwargs) -> torch.Tensor: # x = x + self.center norm = torch.linalg.norm(x.flatten(start_dim=1), ord=self.p, dim=1) to_normalize = (norm > self.radius).view(-1, 1) - delta = self.project(x).flatten(start_dim=1) * to_normalize + x.flatten(start_dim=1) * torch.logical_not( - to_normalize) + delta = self.project(x).flatten(start_dim=1) * to_normalize + x.flatten( + start_dim=1 + ) * torch.logical_not(to_normalize) delta = delta.view(x.shape) # with torch.no_grad(): @@ -64,7 +65,7 @@ def project(self, x): class LInfConstraint(LpConstraint): def __init__(self, center, radius): - super().__init__(center=center, radius=radius, p=float('inf')) + super().__init__(center=center, radius=radius, p=float("inf")) def project(self, x): x = x + self.center diff --git a/src/optimization/gradient_processing.py b/src/optimization/gradient_processing.py index 57d43ca..af42932 100644 --- a/src/optimization/gradient_processing.py +++ b/src/optimization/gradient_processing.py @@ -16,17 +16,18 @@ def __init__(self, perturbation_model: str = PerturbationModels.L2): perturbations_models = { PerturbationModels.L1: 1, PerturbationModels.L2: 2, - PerturbationModels.LINF: float('inf') + PerturbationModels.LINF: float("inf"), } if perturbation_model not in perturbations_models: raise ValueError( - f"{perturbation_model} not included in normalizers. Available: {perturbations_models.values()}") + f"{perturbation_model} not included in normalizers. Available: {perturbations_models.values()}" + ) self.p = perturbations_models[perturbation_model] def __call__(self, grad: torch.Tensor) -> torch.Tensor: if self.p == 2: grad = normalize(grad.data, p=self.p, dim=0) return grad - if self.p == float('inf'): + if self.p == float("inf"): return torch.sign(grad) raise NotImplementedError("Only L2 and LInf norms implemented now") From 2c5b9a423fe75cac8240f698d3ff79fbbf65599e Mon Sep 17 00:00:00 2001 From: Luca Demetrio Date: Wed, 14 Jun 2023 15:52:29 +0200 Subject: [PATCH 10/18] Update README.md --- README.md | 33 +++++++++++++++++++++++++++++++++ 1 file changed, 33 insertions(+) diff --git a/README.md b/README.md index 487e138..145993c 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,35 @@ # SecML 2 New version of SecML + +### Implement new functionalities +1. Create a card inside the project, by specifying what will be implemented (not how), and convert it to an issue (Fig.1). *Please be clear and concise*. +2. Create a new branch from main that will be used to implement the new functionality. Please provide a meaningful name while creating the new branch. +Do it from the GitHub web interface to avoid creating non-updated branches. +**If you create the branch locally, remember to pull the latest commit before branching!** +3. Checkout the new branch on your local machine, and implement unit tests and the intended functionality. Check out the “code style” section before pushing any commits. +4. Once the implementation of both unit testing and new functionality is completed, open a pull request to merge the newly created branch and main. +Please, write inside the description of the pull request a detailed changelog of the implemented functionalities and refactoring. Move the card you created at step 1 inside the “Review” column. +5. Now that the pull request has been open, wait until the maintainers review and approve your changes. DO NOT click on “Merge Pull Request” until somebody reviews your changes. +**Please remember that modified file outside the scope of the pull request will not be approved.** +6. Once this is done, you can move the card to “Done”. +7. Congrats! You have included a new functionality in SecML2! + +### Code style +We leverage “Black” (https://github.com/psf/black) as default format for SecML2. +Before pushing to any branch of SecML2, execute: + +``` +black . +``` + +from the source of the repository. +If you work with an IDE, follow this guide to configure “Black”: https://black.readthedocs.io/en/stable/integrations/editors.html + + + + + + + + + From 78cd3ed8b0cae1489014191a19128bef26e948a2 Mon Sep 17 00:00:00 2001 From: Luca Demetrio Date: Wed, 14 Jun 2023 15:52:54 +0200 Subject: [PATCH 11/18] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 145993c..1536a7c 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ New version of SecML ### Implement new functionalities -1. Create a card inside the project, by specifying what will be implemented (not how), and convert it to an issue (Fig.1). *Please be clear and concise*. +1. Create a card inside the project, by specifying what will be implemented (not how), and convert it to an issue. *Please be clear and concise*. 2. Create a new branch from main that will be used to implement the new functionality. Please provide a meaningful name while creating the new branch. Do it from the GitHub web interface to avoid creating non-updated branches. **If you create the branch locally, remember to pull the latest commit before branching!** From 5f223e47e524d906d9ef86951a76203a66c58f39 Mon Sep 17 00:00:00 2001 From: zangobot Date: Wed, 14 Jun 2023 16:38:04 +0200 Subject: [PATCH 12/18] Fixed TODOs from review, proceeding to completing pull request. --- src/adv/evasion/composite_attack.py | 53 +++++++-------- src/adv/evasion/foolbox.py | 7 +- src/adv/evasion/pgd.py | 1 - src/examples/mnist_example.py | 68 ++++++++++--------- src/examples/run_evasion_attack.py | 27 +++++--- .../run_evasion_attack_pure_pytorch.py | 26 +++++-- src/manipulations/manipulation.py | 4 +- src/optimization/constraints.py | 23 +++---- 8 files changed, 116 insertions(+), 93 deletions(-) diff --git a/src/adv/evasion/composite_attack.py b/src/adv/evasion/composite_attack.py index 6355b85..0c24846 100644 --- a/src/adv/evasion/composite_attack.py +++ b/src/adv/evasion/composite_attack.py @@ -12,35 +12,32 @@ from src.optimization.gradient_processing import GradientProcessing from src.optimization.initializer import Initializer -CE_LOSS = 'ce_loss' -LOGITS_LOSS = 'logits_loss' +CE_LOSS = "ce_loss" +LOGITS_LOSS = "logits_loss" LOSS_FUNCTIONS = { CE_LOSS: CrossEntropyLoss, } -ADAM = 'adam' -StochasticGD = 'sgd' +ADAM = "adam" +StochasticGD = "sgd" -OPTIMIZERS = { - ADAM: Adam, - StochasticGD: SGD -} +OPTIMIZERS = {ADAM: Adam, StochasticGD: SGD} class CompositeEvasionAttack(BaseEvasionAttack): def __init__( - self, - y_target: Union[int, None], - num_steps: int, - step_size: float, - loss_function: Union[str, torch.nn.Module], - optimizer_cls: Union[str, Type[torch.nn.Module]], - manipulation_function: Manipulation, - domain_constraints: List[Constraint], - perturbation_constraints: List[Type[Constraint]], - initializer: Initializer, - gradient_processing: GradientProcessing, + self, + y_target: Union[int, None], + num_steps: int, + step_size: float, + loss_function: Union[str, torch.nn.Module], + optimizer_cls: Union[str, Type[torch.nn.Module]], + manipulation_function: Manipulation, + domain_constraints: List[Constraint], + perturbation_constraints: List[Type[Constraint]], + initializer: Initializer, + gradient_processing: GradientProcessing, ): self.y_target = y_target self.num_steps = num_steps @@ -51,7 +48,8 @@ def __init__( self.loss_function = LOSS_FUNCTIONS[loss_function]() else: raise ValueError( - f"{loss_function} not in list of init from string. Use one among {LOSS_FUNCTIONS.values()}") + f"{loss_function} not in list of init from string. Use one among {LOSS_FUNCTIONS.values()}" + ) else: self.loss_function = loss_function @@ -60,7 +58,8 @@ def __init__( self.optimizer_cls = OPTIMIZERS[optimizer_cls] else: raise ValueError( - f"{optimizer_cls} not in list of init from string. Use one among {OPTIMIZERS.values()}") + f"{optimizer_cls} not in list of init from string. Use one among {OPTIMIZERS.values()}" + ) else: self.optimizer_cls = optimizer_cls @@ -72,24 +71,23 @@ def __init__( super().__init__() - def init_perturbation_constraints(self, center: torch.Tensor) -> List[Constraint]: + def init_perturbation_constraints(self) -> List[Constraint]: raise NotImplementedError("Must be implemented accordingly") def __call__(self, model: BaseModel, data_loader: DataLoader) -> DataLoader: adversarials = [] original_labels = [] multiplier = 1 if self.y_target is not None else -1 + perturbation_constraints = self.init_perturbation_constraints() for samples, labels in data_loader: target = ( - torch.zeros_like(labels).to(self.y_target) + self.y_target + torch.zeros_like(labels) + self.y_target if self.y_target is not None else labels - ) - + ).type(labels.dtype) delta = self.initializer(samples.data) delta.requires_grad = True optimizer = self.optimizer_cls([delta], lr=self.step_size) - perturbation_constraints = self.init_perturbation_constraints() x_adv = self.manipulation_function(samples, delta) for i in range(self.num_steps): scores = model.decision_function(x_adv) @@ -106,10 +104,9 @@ def __call__(self, model: BaseModel, data_loader: DataLoader) -> DataLoader: for constraint in self.domain_constraints: x_adv.data = constraint(x_adv.data) delta.data = self.manipulation_function.invert(samples.data, x_adv.data) - + adversarials.append(x_adv) original_labels.append(labels) - #TODO check best according to custom metric adversarials = torch.vstack(adversarials) original_labels = torch.hstack(original_labels) diff --git a/src/adv/evasion/foolbox.py b/src/adv/evasion/foolbox.py index 90caf78..faf6d68 100644 --- a/src/adv/evasion/foolbox.py +++ b/src/adv/evasion/foolbox.py @@ -40,7 +40,12 @@ def __call__(self, model: BaseModel, data_loader: DataLoader) -> DataLoader: if self.y_target is None: criterion = Misclassification(labels) else: - criterion = TargetedMisclassification(self.y_target) + target = ( + torch.zeros_like(labels) + self.y_target + if self.y_target is not None + else labels + ).type(labels.dtype) + criterion = TargetedMisclassification(target) _, advx, _ = self.foolbox_attack( model=foolbox_model, inputs=samples, diff --git a/src/adv/evasion/pgd.py b/src/adv/evasion/pgd.py index c018bdc..7e9be52 100644 --- a/src/adv/evasion/pgd.py +++ b/src/adv/evasion/pgd.py @@ -125,7 +125,6 @@ def __init__( initializer = Initializer() if random_start: raise NotImplementedError("Random start in LP ball not yet implemented.") - # TODO add random init with different LP norms self.epsilon = epsilon gradient_processing = LinearProjectionGradientProcessing(perturbation_model) perturbation_constraints = [perturbation_models[perturbation_model]] diff --git a/src/examples/mnist_example.py b/src/examples/mnist_example.py index 33efb70..8045532 100644 --- a/src/examples/mnist_example.py +++ b/src/examples/mnist_example.py @@ -9,9 +9,9 @@ from src.metrics.classification import Accuracy from src.models.pytorch.base_pytorch_nn import BasePytorchClassifier -from robustbench.utils import load_model from torch import nn + class SmallCNN(nn.Module): def __init__(self, drop=0.5): super(SmallCNN, self).__init__() @@ -21,27 +21,35 @@ def __init__(self, drop=0.5): activ = nn.ReLU(True) - self.feature_extractor = nn.Sequential(OrderedDict([ - ('conv1', nn.Conv2d(self.num_channels, 32, 3)), - ('relu1', activ), - ('conv2', nn.Conv2d(32, 32, 3)), - ('relu2', activ), - ('maxpool1', nn.MaxPool2d(2, 2)), - ('conv3', nn.Conv2d(32, 64, 3)), - ('relu3', activ), - ('conv4', nn.Conv2d(64, 64, 3)), - ('relu4', activ), - ('maxpool2', nn.MaxPool2d(2, 2)), - ])) - - self.classifier = nn.Sequential(OrderedDict([ - ('fc1', nn.Linear(64 * 4 * 4, 200)), - ('relu1', activ), - ('drop', nn.Dropout(drop)), - ('fc2', nn.Linear(200, 200)), - ('relu2', activ), - ('fc3', nn.Linear(200, self.num_labels)), - ])) + self.feature_extractor = nn.Sequential( + OrderedDict( + [ + ("conv1", nn.Conv2d(self.num_channels, 32, 3)), + ("relu1", activ), + ("conv2", nn.Conv2d(32, 32, 3)), + ("relu2", activ), + ("maxpool1", nn.MaxPool2d(2, 2)), + ("conv3", nn.Conv2d(32, 64, 3)), + ("relu3", activ), + ("conv4", nn.Conv2d(64, 64, 3)), + ("relu4", activ), + ("maxpool2", nn.MaxPool2d(2, 2)), + ] + ) + ) + + self.classifier = nn.Sequential( + OrderedDict( + [ + ("fc1", nn.Linear(64 * 4 * 4, 200)), + ("relu1", activ), + ("drop", nn.Dropout(drop)), + ("fc2", nn.Linear(200, 200)), + ("relu2", activ), + ("fc3", nn.Linear(200, self.num_labels)), + ] + ) + ) for m in self.modules(): if isinstance(m, (nn.Conv2d)): @@ -59,6 +67,7 @@ def forward(self, input): logits = self.classifier(features.view(-1, 64 * 4 * 4)) return logits + net = SmallCNN() model_weigths = torch.load("models/mnist/mnist_smallcnn_standard.pth") net.eval() @@ -83,7 +92,7 @@ def forward(self, input): num_steps = 1 step_size = 0.05 perturbation_model = PerturbationModels.LINF -y_target = None # torch.tensor([9]).to(device) +y_target = None native_attack = PGD( perturbation_model=perturbation_model, epsilon=epsilon, @@ -119,12 +128,9 @@ def forward(self, input): f_data, f_labels = next(iter(f_adv_ds)) real_data, real_labels = next(iter(test_data_loader)) -distance = torch.linalg.norm(native_data.flatten(start_dim=1).to(device) - f_data.flatten(start_dim=1), - ord=float('inf'), dim=1) +distance = torch.linalg.norm( + native_data.flatten(start_dim=1).to(device) - f_data.flatten(start_dim=1), + ord=float("inf"), + dim=1, +) print("Solutions are :", distance, "linf distant") - -# real_native = torch.linalg.norm(torch.flatten(real_data.to(device) - native_data.to(device), start_dim=1), ord=float('inf'), dim=1) -# real_fb = torch.linalg.norm(torch.flatten(real_data.to(device) - f_data.to(device), start_dim=1), ord=float('inf'), dim=1) - - -# print(real_native, real_fb) diff --git a/src/examples/run_evasion_attack.py b/src/examples/run_evasion_attack.py index 1a4f28f..b937a6a 100644 --- a/src/examples/run_evasion_attack.py +++ b/src/examples/run_evasion_attack.py @@ -11,7 +11,7 @@ from robustbench.utils import load_model net = load_model(model_name="Rony2019Decoupling", dataset="cifar10", threat_model="L2") -device = "mps" +device = "cpu" net.to(device) test_dataset = torchvision.datasets.CIFAR10( transform=torchvision.transforms.ToTensor(), train=False, root=".", download=True @@ -28,10 +28,10 @@ # Create and run attack epsilon = 0.5 -num_steps = 50 -step_size = 0.05 +num_steps = 10 +step_size = 0.005 perturbation_model = PerturbationModels.LINF -y_target = torch.Tensor([9]).to(device) +y_target = None native_attack = PGD( perturbation_model=perturbation_model, epsilon=epsilon, @@ -67,11 +67,22 @@ f_data, f_labels = next(iter(f_adv_ds)) real_data, real_labels = next(iter(test_data_loader)) -distance = torch.linalg.norm(native_data.flatten(start_dim=1).to(device) - f_data.flatten(start_dim=1), - ord=float('inf'), dim=1) +distance = torch.linalg.norm( + native_data.flatten(start_dim=1).to(device) - f_data.flatten(start_dim=1), + ord=float("inf"), + dim=1, +) print("Solutions are :", distance, "linf distant") -real_native = torch.linalg.norm(torch.flatten(real_data.to(device) - native_data.to(device), start_dim=1), ord=float('inf'), dim=1) -real_fb = torch.linalg.norm(torch.flatten(real_data.to(device) - f_data.to(device), start_dim=1), ord=float('inf'), dim=1) +real_native = torch.linalg.norm( + torch.flatten(real_data.to(device) - native_data.to(device), start_dim=1), + ord=float("inf"), + dim=1, +) +real_fb = torch.linalg.norm( + torch.flatten(real_data.to(device) - f_data.to(device), start_dim=1), + ord=float("inf"), + dim=1, +) print(real_native, real_fb) diff --git a/src/examples/run_evasion_attack_pure_pytorch.py b/src/examples/run_evasion_attack_pure_pytorch.py index 1fea7bc..2110c35 100644 --- a/src/examples/run_evasion_attack_pure_pytorch.py +++ b/src/examples/run_evasion_attack_pure_pytorch.py @@ -12,8 +12,9 @@ from robustbench.utils import load_model -def attack_linf_pytorch_optim(model, samples, labels, optimizer, steps=100, - step_size=0.05, eps=0.3, device='cpu'): +def attack_linf_pytorch_optim( + model, samples, labels, optimizer, steps=100, step_size=0.05, eps=0.3, device="cpu" +): x_adv = samples.clone().detach().to(device).requires_grad_() optimizer = optimizer([x_adv], lr=step_size) for _ in range(steps): @@ -67,13 +68,21 @@ def attack_linf_pytorch_optim(model, samples, labels, optimizer, steps=100, n_robust_accuracy = Accuracy()(model, native_adv_ds) print(n_robust_accuracy) -print('OTHER ATTACK') +print("OTHER ATTACK") advs = [] advlb = [] for data, labels in test_data_loader: - x_adv = attack_linf_pytorch_optim(model, data.to(device), labels.to(device), SGD, num_steps, step_size, epsilon, - 'mps') + x_adv = attack_linf_pytorch_optim( + model, + data.to(device), + labels.to(device), + SGD, + num_steps, + step_size, + epsilon, + "mps", + ) advs.append(x_adv) advlb.append(labels) advlb = torch.cat(advlb) @@ -86,6 +95,9 @@ def attack_linf_pytorch_optim(model, samples, labels, optimizer, steps=100, native_data, native_labels = next(iter(native_adv_ds)) f_data, f_labels = next(iter(f_adv_ds)) -distance = torch.linalg.norm(native_data.flatten(start_dim=1).to(device) - f_data.flatten(start_dim=1), - ord=float('inf'), dim=1) +distance = torch.linalg.norm( + native_data.flatten(start_dim=1).to(device) - f_data.flatten(start_dim=1), + ord=float("inf"), + dim=1, +) print(distance) diff --git a/src/manipulations/manipulation.py b/src/manipulations/manipulation.py index f7fa47b..b815b42 100644 --- a/src/manipulations/manipulation.py +++ b/src/manipulations/manipulation.py @@ -3,10 +3,10 @@ class Manipulation: def __call__(self, x: torch.Tensor, delta: torch.Tensor) -> torch.Tensor: - raise NotImplementedError("Abstract manipulation.") + raise NotImplementedError("Manipulation not implemented.") def invert(self, x: torch.Tensor, x_adv: torch.Tensor) -> torch.Tensor: - raise NotImplementedError("Abstract inversion.") + raise NotImplementedError("Inversion not available.") class AdditiveManipulation(Manipulation): diff --git a/src/optimization/constraints.py b/src/optimization/constraints.py index 23430f2..2ca9bff 100644 --- a/src/optimization/constraints.py +++ b/src/optimization/constraints.py @@ -9,7 +9,6 @@ def __call__(self, x: torch.Tensor, *args, **kwargs): ... - class ClipConstraint(Constraint): def __init__(self, lb=0, ub=1): self.lb = lb @@ -20,8 +19,7 @@ def __call__(self, x: torch.Tensor, *args, **kwargs) -> torch.Tensor: class LpConstraint(Constraint): - def __init__(self, center=0, radius=0, p=torch.inf): - # TODO consider swapping order of the parameters to get eps as first arg + def __init__(self, radius=0, center=0, p=torch.inf): self.p = p self.center = center self.radius = radius @@ -43,38 +41,34 @@ def __call__(self, x: torch.Tensor, *args, **kwargs) -> torch.Tensor: def __repr__(self) -> str: return f"L{self.p} constraint with {'nonzero' if self.center!=0 else 'zero'} center with radius {self.radius}." + class L2Constraint(LpConstraint): - def __init__(self, center=0, radius=0): - # TODO consider swapping to accept radius as first positional arg - super().__init__(center=center, radius=radius, p=2) + def __init__(self, radius=0, center=0): + super().__init__(radius=radius, center=center, p=2) def project(self, x): flat_x = x.flatten(start_dim=1) diff_norm = flat_x.norm(p=2, dim=1, keepdim=True).clamp_(min=1e-12) flat_x = flat_x / diff_norm * self.radius - # flat_x.mul_(self.radius / diff_norm).clamp_(max=1) x = flat_x.reshape(x.shape) return x class LInfConstraint(LpConstraint): - def __init__(self, center=0, radius=0): - super().__init__(center=center, radius=radius, p=float('inf')) + def __init__(self, radius=0, center=0): + super().__init__(radius=radius, center=center, p=float("inf")) def project(self, x): - # x = x + self.center x = x.clamp(min=-self.radius, max=self.radius) - # x = x - self.center return x class L1Constraint(LpConstraint): - def __init__(self, center=0, radius=0) -> None: - super().__init__(center=center, radius=radius, p=1) + def __init__(self, radius=0, center=0) -> None: + super().__init__(radius=radius, center=center, p=1) def project(self, x): """ - TODO fix docstring to our format Compute Euclidean projection onto the L1 ball for a batch. Source: https://gist.github.com/tonyduan/1329998205d88c566588e57e3e2c0c55 @@ -105,7 +99,6 @@ def project(self, x): John Duchi, Shai Shalev-Shwartz, Yoram Singer, and Tushar Chandra. International Conference on Machine Learning (ICML 2008) """ - # x = x + self.center original_shape = x.shape x = x.view(x.shape[0], -1) mask = (torch.norm(x, p=1, dim=1) < self.radius).float().unsqueeze(1) From 2d98b5c076972646a72e88b3e7fdfb36007c57eb Mon Sep 17 00:00:00 2001 From: zangobot Date: Wed, 14 Jun 2023 16:41:44 +0200 Subject: [PATCH 13/18] Removed unused example. --- .../run_evasion_attack_pure_pytorch.py | 103 ------------------ 1 file changed, 103 deletions(-) delete mode 100644 src/examples/run_evasion_attack_pure_pytorch.py diff --git a/src/examples/run_evasion_attack_pure_pytorch.py b/src/examples/run_evasion_attack_pure_pytorch.py deleted file mode 100644 index 2110c35..0000000 --- a/src/examples/run_evasion_attack_pure_pytorch.py +++ /dev/null @@ -1,103 +0,0 @@ -import torch -import torchvision.datasets -from torch.optim import SGD -from torch.utils.data import DataLoader, Subset, TensorDataset -from src.adv.backends import Backends -from src.adv.evasion.pgd import PGD -from src.adv.evasion.perturbation_models import PerturbationModels - -from src.metrics.classification import Accuracy -from src.models.pytorch.base_pytorch_nn import BasePytorchClassifier - -from robustbench.utils import load_model - - -def attack_linf_pytorch_optim( - model, samples, labels, optimizer, steps=100, step_size=0.05, eps=0.3, device="cpu" -): - x_adv = samples.clone().detach().to(device).requires_grad_() - optimizer = optimizer([x_adv], lr=step_size) - for _ in range(steps): - out = model(x_adv) - loss = -torch.nn.functional.cross_entropy(out, labels) - optimizer.zero_grad() - loss.backward() - x_adv.grad = x_adv.grad.sign() - optimizer.step() - - diff = x_adv.data - samples - diff = diff.clamp_(-eps, +eps) - x_adv.detach().copy_((diff + samples).clamp_(0, 1)) - return x_adv - - -net = load_model(model_name="Rony2019Decoupling", dataset="cifar10", threat_model="L2") -device = "mps" -net.to(device) -test_dataset = torchvision.datasets.CIFAR10( - transform=torchvision.transforms.ToTensor(), train=False, root=".", download=True -) -test_dataset = Subset(test_dataset, list(range(1))) -test_data_loader = DataLoader(test_dataset, batch_size=5, shuffle=False) - -# Wrap model -model = BasePytorchClassifier(net) - -# Test accuracy on original data -accuracy = Accuracy()(model, test_data_loader) -print(accuracy) - -# Create and run attack -epsilon = 100 -num_steps = 50 -step_size = 0.05 -perturbation_model = PerturbationModels.LINF -y_target = None -native_attack = PGD( - perturbation_model=perturbation_model, - epsilon=epsilon, - num_steps=num_steps, - step_size=step_size, - random_start=False, - y_target=y_target, - backend=Backends.NATIVE, -) -native_adv_ds = native_attack(model, test_data_loader) - -# Test accuracy on adversarial examples -n_robust_accuracy = Accuracy()(model, native_adv_ds) -print(n_robust_accuracy) - -print("OTHER ATTACK") - -advs = [] -advlb = [] -for data, labels in test_data_loader: - x_adv = attack_linf_pytorch_optim( - model, - data.to(device), - labels.to(device), - SGD, - num_steps, - step_size, - epsilon, - "mps", - ) - advs.append(x_adv) - advlb.append(labels) -advlb = torch.cat(advlb) -advs = torch.cat(advs) -f_adv_ds = DataLoader(TensorDataset(advs, advlb), batch_size=5, shuffle=False) -# Test accuracy on adversarial examples -f_robust_accuracy = Accuracy()(model, f_adv_ds) -print(f_robust_accuracy) - -native_data, native_labels = next(iter(native_adv_ds)) -f_data, f_labels = next(iter(f_adv_ds)) - -distance = torch.linalg.norm( - native_data.flatten(start_dim=1).to(device) - f_data.flatten(start_dim=1), - ord=float("inf"), - dim=1, -) -print(distance) From 6fc530976d2aec950eae028c36d6c52047c63245 Mon Sep 17 00:00:00 2001 From: zangobot Date: Wed, 14 Jun 2023 16:44:24 +0200 Subject: [PATCH 14/18] Removed unused model, updated gitignore --- .gitignore | 2 ++ models/mnist/mnist_smallcnn_standard.pth | Bin 1250812 -> 0 bytes 2 files changed, 2 insertions(+) delete mode 100644 models/mnist/mnist_smallcnn_standard.pth diff --git a/.gitignore b/.gitignore index eb55b51..c325131 100644 --- a/.gitignore +++ b/.gitignore @@ -245,3 +245,5 @@ $RECYCLE.BIN/ # Windows shortcuts *.lnk + +models/ \ No newline at end of file diff --git a/models/mnist/mnist_smallcnn_standard.pth b/models/mnist/mnist_smallcnn_standard.pth deleted file mode 100644 index 177f30628dfe481c7b43029bc6ad92c635bf9767..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1250812 zcmaHS2UL{HvNbtMP9jJWQ3REormHc6ih?4F2@nAj2o4~aC5RwN0VByAP{0HtOjiSf z3W@k!b5fg@gJm=o~zxVEYcV^A3H8tPt+WYJ3>h9{E1gUhTh#!R>-%J)BoTxt} zL2ANFY4Q6ip~S#x>JkzXi=(4rqe7x0g5tuWV-`e33MIuCOj8km6R{vRHXG=~Y6RfzxEg#+dn0(Xm2VFR5wL;!g`AV}){FGSg)K zU_?dE6UuwZELkG<86tKQ5f&0F_7bDy6CD~B9TqxjK}f8UP{PtzC^>MNk@y#L!-8X% zM2p=njg1ZtiH(XL5)u^|XFDW5Y{9(wu|laorjYqVf|kTCh=@@NiVmBzWI;q|P;6La zOjLAGoSl+Tdf>om@_$%~uMD0SCY1U6CrTa>QNgj3|28TKWv8i%uW@y-bGCDGv~hN@ zcXDtM%KarEWbqQA`~(w8?^vP2Brl19UM60WUeaFDu|maxUW($Uw3lS8P-(sRdu3l? 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z;7|s>kQCr!j12TdrBkz}512SE!^WEOaCoH+#2*i5k1W|qlZ4}Nf4>VGGcBB6`@EQ& zuO{JeRt-}oeczguKf-xWpOePvwrKcVihXd(hw`@Fz{ Date: Sun, 9 Jul 2023 19:00:01 +0200 Subject: [PATCH 15/18] Changing name of the package from src to secml2 (so it can be imported as secml2 instead of src) --- {src => secml2}/__init__.py | 0 {src => secml2}/adv/backends.py | 0 .../adv/evasion/base_evasion_attack.py | 6 +- .../adv/evasion/composite_attack.py | 16 ++--- {src => secml2}/adv/evasion/foolbox.py | 8 +-- .../adv/evasion/perturbation_models.py | 0 {src => secml2}/adv/evasion/pgd.py | 20 +++--- {src => secml2}/data/__init__.py | 0 {src => secml2}/data/sklearn_dataset.py | 0 {src => secml2}/examples/mnist_example.py | 10 +-- .../examples/run_evasion_attack.py | 10 +-- {src => secml2}/examples/train_model.py | 6 +- {src => secml2}/manipulations/__init__.py | 0 {src => secml2}/manipulations/manipulation.py | 0 {src => secml2}/metrics/__init__.py | 0 {src => secml2}/metrics/classification.py | 2 +- {src => secml2}/metrics/test/__init__.py | 0 .../optimization}/__init__.py | 0 {src => secml2}/optimization/constraints.py | 0 .../optimization/gradient_processing.py | 2 +- {src => secml2}/optimization/initializer.py | 2 +- src/models/base_model.py | 55 -------------- src/models/base_trainer.py | 7 -- src/models/data_processing/__init__.py | 0 src/models/data_processing/data_processing.py | 15 ---- .../identity_data_processing.py | 11 --- src/models/data_processing/test/__init__.py | 0 src/models/pytorch/__init__.py | 0 src/models/pytorch/base_pytorch_nn.py | 72 ------------------- src/models/pytorch/base_pytorch_trainer.py | 34 --------- src/models/pytorch/test/__init__.py | 0 src/models/pytorch/test/base_pytorch.py | 33 --------- src/models/pytorch/test/test_pytorch_nn.py | 25 ------- .../pytorch/test/test_pytorch_trainer.py | 22 ------ src/models/sklearn/__init__.py | 0 src/models/sklearn/base_sklearn_model.py | 63 ---------------- src/models/sklearn/test/__init__.py | 0 src/models/tensorflow/__init__.py | 0 src/models/tensorflow/base_tensorflow_nn.py | 71 ------------------ .../tensorflow/base_tensorflow_trainer.py | 9 --- src/models/test/__init__.py | 0 src/optimization/__init__.py | 0 42 files changed, 41 insertions(+), 458 deletions(-) rename {src => secml2}/__init__.py (100%) rename {src => secml2}/adv/backends.py (100%) rename {src => secml2}/adv/evasion/base_evasion_attack.py (91%) rename {src => secml2}/adv/evasion/composite_attack.py (90%) rename {src => secml2}/adv/evasion/foolbox.py (90%) rename {src => secml2}/adv/evasion/perturbation_models.py (100%) rename {src => secml2}/adv/evasion/pgd.py (87%) rename {src => secml2}/data/__init__.py (100%) rename {src => secml2}/data/sklearn_dataset.py (100%) rename {src => secml2}/examples/mnist_example.py (93%) rename {src => secml2}/examples/run_evasion_attack.py (89%) rename {src => secml2}/examples/train_model.py (86%) rename {src => secml2}/manipulations/__init__.py (100%) rename {src => secml2}/manipulations/manipulation.py (100%) rename {src => secml2}/metrics/__init__.py (100%) rename {src => secml2}/metrics/classification.py (95%) rename {src => secml2}/metrics/test/__init__.py (100%) rename {src/models => secml2/optimization}/__init__.py (100%) rename {src => secml2}/optimization/constraints.py (100%) rename {src => secml2}/optimization/gradient_processing.py (93%) rename {src => secml2}/optimization/initializer.py (87%) delete mode 100644 src/models/base_model.py delete mode 100644 src/models/base_trainer.py delete mode 100644 src/models/data_processing/__init__.py delete mode 100644 src/models/data_processing/data_processing.py delete mode 100644 src/models/data_processing/identity_data_processing.py delete mode 100644 src/models/data_processing/test/__init__.py delete mode 100644 src/models/pytorch/__init__.py delete mode 100644 src/models/pytorch/base_pytorch_nn.py delete mode 100644 src/models/pytorch/base_pytorch_trainer.py delete mode 100644 src/models/pytorch/test/__init__.py delete mode 100644 src/models/pytorch/test/base_pytorch.py delete mode 100644 src/models/pytorch/test/test_pytorch_nn.py delete mode 100644 src/models/pytorch/test/test_pytorch_trainer.py delete mode 100644 src/models/sklearn/__init__.py delete mode 100644 src/models/sklearn/base_sklearn_model.py delete mode 100644 src/models/sklearn/test/__init__.py delete mode 100644 src/models/tensorflow/__init__.py delete mode 100644 src/models/tensorflow/base_tensorflow_nn.py delete mode 100644 src/models/tensorflow/base_tensorflow_trainer.py delete mode 100644 src/models/test/__init__.py delete mode 100644 src/optimization/__init__.py diff --git a/src/__init__.py b/secml2/__init__.py similarity index 100% rename from src/__init__.py rename to secml2/__init__.py diff --git a/src/adv/backends.py b/secml2/adv/backends.py similarity index 100% rename from src/adv/backends.py rename to secml2/adv/backends.py diff --git a/src/adv/evasion/base_evasion_attack.py b/secml2/adv/evasion/base_evasion_attack.py similarity index 91% rename from src/adv/evasion/base_evasion_attack.py rename to secml2/adv/evasion/base_evasion_attack.py index 68e11d0..91b5431 100644 --- a/src/adv/evasion/base_evasion_attack.py +++ b/secml2/adv/evasion/base_evasion_attack.py @@ -3,9 +3,9 @@ from torch.utils.data import DataLoader -from src.adv.backends import Backends -from src.adv.evasion.perturbation_models import PerturbationModels -from src.models.base_model import BaseModel +from secml2.adv.backends import Backends +from secml2.adv.evasion.perturbation_models import PerturbationModels +from secml2.models.base_model import BaseModel class BaseEvasionAttackCreator: diff --git a/src/adv/evasion/composite_attack.py b/secml2/adv/evasion/composite_attack.py similarity index 90% rename from src/adv/evasion/composite_attack.py rename to secml2/adv/evasion/composite_attack.py index 0c24846..a7beb2f 100644 --- a/src/adv/evasion/composite_attack.py +++ b/secml2/adv/evasion/composite_attack.py @@ -2,15 +2,15 @@ import torch.nn from torch.nn import CrossEntropyLoss -from torch.optim import Adam, SGD +from torch.optim import Adam, SGD, Optimizer from torch.utils.data import DataLoader, TensorDataset -from src.adv.evasion.base_evasion_attack import BaseEvasionAttack -from src.manipulations.manipulation import Manipulation -from src.models.base_model import BaseModel -from src.optimization.constraints import Constraint -from src.optimization.gradient_processing import GradientProcessing -from src.optimization.initializer import Initializer +from secml2.adv.evasion.base_evasion_attack import BaseEvasionAttack +from secml2.manipulations.manipulation import Manipulation +from secml2.models.base_model import BaseModel +from secml2.optimization.constraints import Constraint +from secml2.optimization.gradient_processing import GradientProcessing +from secml2.optimization.initializer import Initializer CE_LOSS = "ce_loss" LOGITS_LOSS = "logits_loss" @@ -32,7 +32,7 @@ def __init__( num_steps: int, step_size: float, loss_function: Union[str, torch.nn.Module], - optimizer_cls: Union[str, Type[torch.nn.Module]], + optimizer_cls: Union[str, Type[Optimizer]], manipulation_function: Manipulation, domain_constraints: List[Constraint], perturbation_constraints: List[Type[Constraint]], diff --git a/src/adv/evasion/foolbox.py b/secml2/adv/evasion/foolbox.py similarity index 90% rename from src/adv/evasion/foolbox.py rename to secml2/adv/evasion/foolbox.py index faf6d68..889f11b 100644 --- a/src/adv/evasion/foolbox.py +++ b/secml2/adv/evasion/foolbox.py @@ -1,10 +1,10 @@ from typing import Optional -from src.adv.evasion.base_evasion_attack import BaseEvasionAttack +from secml2.adv.evasion.base_evasion_attack import BaseEvasionAttack from foolbox.attacks.base import Attack from torch.utils.data import DataLoader -from src.models.base_model import BaseModel -from src.models.pytorch.base_pytorch_nn import BasePytorchClassifier -from src.models.base_model import BaseModel +from secml2.models.base_model import BaseModel +from secml2.models.pytorch.base_pytorch_nn import BasePytorchClassifier +from secml2.models.base_model import BaseModel from foolbox.models.pytorch import PyTorchModel from foolbox.criteria import Misclassification, TargetedMisclassification import torch diff --git a/src/adv/evasion/perturbation_models.py b/secml2/adv/evasion/perturbation_models.py similarity index 100% rename from src/adv/evasion/perturbation_models.py rename to secml2/adv/evasion/perturbation_models.py diff --git a/src/adv/evasion/pgd.py b/secml2/adv/evasion/pgd.py similarity index 87% rename from src/adv/evasion/pgd.py rename to secml2/adv/evasion/pgd.py index 7e9be52..f7c24df 100644 --- a/src/adv/evasion/pgd.py +++ b/secml2/adv/evasion/pgd.py @@ -3,15 +3,15 @@ import torch from torch.optim import Adam -from src.adv.evasion.composite_attack import CompositeEvasionAttack, CE_LOSS, SGD -from src.manipulations.manipulation import AdditiveManipulation -from src.optimization.initializer import Initializer -from src.optimization.gradient_processing import LinearProjectionGradientProcessing -from src.adv.evasion.foolbox import BaseFoolboxEvasionAttack - -from src.adv.evasion.perturbation_models import PerturbationModels -from src.adv.backends import Backends -from src.adv.evasion.base_evasion_attack import ( +from secml2.adv.evasion.composite_attack import CompositeEvasionAttack, CE_LOSS, SGD +from secml2.manipulations.manipulation import AdditiveManipulation +from secml2.optimization.initializer import Initializer +from secml2.optimization.gradient_processing import LinearProjectionGradientProcessing +from secml2.adv.evasion.foolbox import BaseFoolboxEvasionAttack + +from secml2.adv.evasion.perturbation_models import PerturbationModels +from secml2.adv.backends import Backends +from secml2.adv.evasion.base_evasion_attack import ( BaseEvasionAttackCreator, ) @@ -21,7 +21,7 @@ LinfProjectedGradientDescentAttack, ) -from src.optimization.constraints import ( +from secml2.optimization.constraints import ( ClipConstraint, L1Constraint, L2Constraint, diff --git a/src/data/__init__.py b/secml2/data/__init__.py similarity index 100% rename from src/data/__init__.py rename to secml2/data/__init__.py diff --git a/src/data/sklearn_dataset.py b/secml2/data/sklearn_dataset.py similarity index 100% rename from src/data/sklearn_dataset.py rename to secml2/data/sklearn_dataset.py diff --git a/src/examples/mnist_example.py b/secml2/examples/mnist_example.py similarity index 93% rename from src/examples/mnist_example.py rename to secml2/examples/mnist_example.py index 8045532..c3136a3 100644 --- a/src/examples/mnist_example.py +++ b/secml2/examples/mnist_example.py @@ -2,12 +2,12 @@ import torch import torchvision.datasets from torch.utils.data import DataLoader, Subset -from src.adv.backends import Backends -from src.adv.evasion.pgd import PGD -from src.adv.evasion.perturbation_models import PerturbationModels +from secml2.adv.backends import Backends +from secml2.adv.evasion.pgd import PGD +from secml2.adv.evasion.perturbation_models import PerturbationModels -from src.metrics.classification import Accuracy -from src.models.pytorch.base_pytorch_nn import BasePytorchClassifier +from secml2.metrics.classification import Accuracy +from secml2.models.pytorch.base_pytorch_nn import BasePytorchClassifier from torch import nn diff --git a/src/examples/run_evasion_attack.py b/secml2/examples/run_evasion_attack.py similarity index 89% rename from src/examples/run_evasion_attack.py rename to secml2/examples/run_evasion_attack.py index b937a6a..7c43a80 100644 --- a/src/examples/run_evasion_attack.py +++ b/secml2/examples/run_evasion_attack.py @@ -1,12 +1,12 @@ import torch import torchvision.datasets from torch.utils.data import DataLoader, Subset -from src.adv.backends import Backends -from src.adv.evasion.pgd import PGD -from src.adv.evasion.perturbation_models import PerturbationModels +from secml2.adv.backends import Backends +from secml2.adv.evasion.pgd import PGD +from secml2.adv.evasion.perturbation_models import PerturbationModels -from src.metrics.classification import Accuracy -from src.models.pytorch.base_pytorch_nn import BasePytorchClassifier +from secml2.metrics.classification import Accuracy +from secml2.models.pytorch.base_pytorch_nn import BasePytorchClassifier from robustbench.utils import load_model diff --git a/src/examples/train_model.py b/secml2/examples/train_model.py similarity index 86% rename from src/examples/train_model.py rename to secml2/examples/train_model.py index 08650f3..c6615ad 100644 --- a/src/examples/train_model.py +++ b/secml2/examples/train_model.py @@ -3,9 +3,9 @@ from torch.optim import Adam from torch.utils.data import DataLoader -from src.metrics.classification import Accuracy -from src.models.pytorch.base_pytorch_nn import BasePytorchClassifier -from src.models.pytorch.base_pytorch_trainer import BasePyTorchTrainer +from secml2.metrics.classification import Accuracy +from secml2.models.pytorch.base_pytorch_nn import BasePytorchClassifier +from secml2.models.pytorch.base_pytorch_trainer import BasePyTorchTrainer class MNISTNet(torch.nn.Module): diff --git a/src/manipulations/__init__.py b/secml2/manipulations/__init__.py similarity index 100% rename from src/manipulations/__init__.py rename to secml2/manipulations/__init__.py diff --git a/src/manipulations/manipulation.py b/secml2/manipulations/manipulation.py similarity index 100% rename from src/manipulations/manipulation.py rename to secml2/manipulations/manipulation.py diff --git a/src/metrics/__init__.py b/secml2/metrics/__init__.py similarity index 100% rename from src/metrics/__init__.py rename to secml2/metrics/__init__.py diff --git a/src/metrics/classification.py b/secml2/metrics/classification.py similarity index 95% rename from src/metrics/classification.py rename to secml2/metrics/classification.py index 2e32254..b2d51fd 100644 --- a/src/metrics/classification.py +++ b/secml2/metrics/classification.py @@ -1,7 +1,7 @@ import torch from torch.utils.data import DataLoader -from src.models.base_model import BaseModel +from secml2.models.base_model import BaseModel def accuracy(y_pred: torch.Tensor, y_true: torch.Tensor) -> torch.Tensor: diff --git a/src/metrics/test/__init__.py b/secml2/metrics/test/__init__.py similarity index 100% rename from src/metrics/test/__init__.py rename to secml2/metrics/test/__init__.py diff --git a/src/models/__init__.py b/secml2/optimization/__init__.py similarity index 100% rename from src/models/__init__.py rename to secml2/optimization/__init__.py diff --git a/src/optimization/constraints.py b/secml2/optimization/constraints.py similarity index 100% rename from src/optimization/constraints.py rename to secml2/optimization/constraints.py diff --git a/src/optimization/gradient_processing.py b/secml2/optimization/gradient_processing.py similarity index 93% rename from src/optimization/gradient_processing.py rename to secml2/optimization/gradient_processing.py index af42932..b77f0df 100644 --- a/src/optimization/gradient_processing.py +++ b/secml2/optimization/gradient_processing.py @@ -3,7 +3,7 @@ import torch.linalg from torch.nn.functional import normalize -from src.adv.evasion.perturbation_models import PerturbationModels +from secml2.adv.evasion.perturbation_models import PerturbationModels class GradientProcessing: diff --git a/src/optimization/initializer.py b/secml2/optimization/initializer.py similarity index 87% rename from src/optimization/initializer.py rename to secml2/optimization/initializer.py index aff9094..9fff9b3 100644 --- a/src/optimization/initializer.py +++ b/secml2/optimization/initializer.py @@ -1,6 +1,6 @@ import torch -from src.adv.evasion.perturbation_models import PerturbationModels +from secml2.adv.evasion.perturbation_models import PerturbationModels class Initializer: diff --git a/src/models/base_model.py b/src/models/base_model.py deleted file mode 100644 index 7b78c46..0000000 --- a/src/models/base_model.py +++ /dev/null @@ -1,55 +0,0 @@ -from abc import ABC, abstractmethod -from typing import Callable - -import torch -from torch.utils.data import DataLoader - -from src.models.data_processing.identity_data_processing import IdentityDataProcessing -from src.models.data_processing.data_processing import DataProcessing - - -class BaseModel(ABC): - def __init__( - self, - preprocessing: DataProcessing = None, - postprocessing: DataProcessing = None, - ): - """ - Create base abstract model - Parameters - ---------- - preprocessing : DataProcessing - postprocessing: DataProcessing - - """ - self._preprocessing = ( - preprocessing if preprocessing is not None else IdentityDataProcessing() - ) - self._postprocessing = ( - postprocessing if postprocessing is not None else IdentityDataProcessing() - ) - - @abstractmethod - def predict(self, x: torch.Tensor) -> torch.Tensor: - ... - - def decision_function(self, x: torch.Tensor) -> torch.Tensor: - x = self._preprocessing(x) - x = self._decision_function(x) - x = self._postprocessing(x) - return x - - @abstractmethod - def _decision_function(self, x: torch.Tensor) -> torch.Tensor: - ... - - @abstractmethod - def gradient(self, x: torch.Tensor, y: int) -> torch.Tensor: - ... - - @abstractmethod - def train(self, dataloader: DataLoader): - ... - - def __call__(self, x: torch.Tensor) -> torch.Tensor: - return self.decision_function(x) diff --git a/src/models/base_trainer.py b/src/models/base_trainer.py deleted file mode 100644 index 32bd71d..0000000 --- a/src/models/base_trainer.py +++ /dev/null @@ -1,7 +0,0 @@ -from abc import ABCMeta, abstractmethod - - -class BaseTrainer(metaclass=ABCMeta): - @abstractmethod - def train(self, model, dataloader): - pass diff --git a/src/models/data_processing/__init__.py b/src/models/data_processing/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/src/models/data_processing/data_processing.py b/src/models/data_processing/data_processing.py deleted file mode 100644 index 335b609..0000000 --- a/src/models/data_processing/data_processing.py +++ /dev/null @@ -1,15 +0,0 @@ -from abc import ABC, abstractmethod - -import torch - - -class DataProcessing(ABC): - @abstractmethod - def process(self, x: torch.Tensor) -> torch.Tensor: - ... - - def invert(self, x: torch.Tensor) -> torch.Tensor: - ... - - def __call__(self, x: torch.Tensor) -> torch.Tensor: - return self.process(x) diff --git a/src/models/data_processing/identity_data_processing.py b/src/models/data_processing/identity_data_processing.py deleted file mode 100644 index f02ffd8..0000000 --- a/src/models/data_processing/identity_data_processing.py +++ /dev/null @@ -1,11 +0,0 @@ -import torch - -from src.models.data_processing.data_processing import DataProcessing - - -class IdentityDataProcessing(DataProcessing): - def process(self, x: torch.Tensor) -> torch.Tensor: - return x - - def invert(self, x: torch.Tensor) -> torch.Tensor: - return x diff --git a/src/models/data_processing/test/__init__.py b/src/models/data_processing/test/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/src/models/pytorch/__init__.py b/src/models/pytorch/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/src/models/pytorch/base_pytorch_nn.py b/src/models/pytorch/base_pytorch_nn.py deleted file mode 100644 index eca9075..0000000 --- a/src/models/pytorch/base_pytorch_nn.py +++ /dev/null @@ -1,72 +0,0 @@ -import torch -from torch.utils.data import DataLoader - -from src.models.base_model import BaseModel -from src.models.data_processing.data_processing import DataProcessing -from src.models.pytorch.base_pytorch_trainer import BasePyTorchTrainer - - -class BasePytorchClassifier(BaseModel): - def __init__( - self, - model: torch.nn.Module, - preprocessing: DataProcessing = None, - postprocessing: DataProcessing = None, - trainer: BasePyTorchTrainer = None, - ): - super().__init__(preprocessing=preprocessing, postprocessing=postprocessing) - self._model: torch.nn.Module = model - self._trainer = trainer - - @property - def model(self): - return self._model - - def get_device(self): - return next(self._model.parameters()).device - - def predict(self, x: torch.Tensor) -> torch.Tensor: - scores = self.decision_function(x) - labels = torch.argmax(scores, dim=-1) - return labels - - def _decision_function(self, x: torch.Tensor) -> torch.Tensor: - """ - Returns the decision function of the model. - Parameters - ---------- - x : input samples - - Returns - ------- - output of decision function - """ - x = x.to(device=self.get_device()) - return self._model(x) - - def gradient(self, x: torch.Tensor, y: int) -> torch.Tensor: - """ - The functions computes gradients of class with label y w.r.t. x. - Gradients are computed in batch. - Parameters - ---------- - x : input samples - y : class label - - Returns - ------- - Gradient of class y w.r.t. input x - """ - x = x.clone().requires_grad_() - if x.grad is not None: - x.grad.zero_() - output = self.decision_function(x) - output = output[:, y].sum() - output.backward() - grad = x.grad - return grad - - def train(self, dataloader: DataLoader): - if self._trainer is None: - raise ValueError("Cannot train without a trainer.") - return self._trainer.train(self._model, dataloader) diff --git a/src/models/pytorch/base_pytorch_trainer.py b/src/models/pytorch/base_pytorch_trainer.py deleted file mode 100644 index af069cd..0000000 --- a/src/models/pytorch/base_pytorch_trainer.py +++ /dev/null @@ -1,34 +0,0 @@ -import torch.nn -from torch.optim.lr_scheduler import _LRScheduler # noqa -from torch.utils.data import DataLoader - -from src.models.base_trainer import BaseTrainer - - -class BasePyTorchTrainer(BaseTrainer): - def __init__( - self, - optimizer: torch.optim.Optimizer, - epochs: int = 5, - loss: torch.nn.Module = None, - scheduler: _LRScheduler = None, - ): - self._epochs = epochs - self._optimizer = optimizer - self._loss = loss if loss is not None else torch.nn.CrossEntropyLoss() - self._scheduler = scheduler - - def train(self, model: torch.nn.Module, dataloader: DataLoader): - device = next(model.parameters()).device - model = model.train() - for epoch in range(self._epochs): - for batch_idx, (x, y) in enumerate(dataloader): - x, y = x.to(device), y.to(device) - self._optimizer.zero_grad() - outputs = model(x) - loss = self._loss(outputs, y) - loss.backward() - self._optimizer.step() - if self._scheduler is not None: - self._scheduler.step() - return model diff --git a/src/models/pytorch/test/__init__.py b/src/models/pytorch/test/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/src/models/pytorch/test/base_pytorch.py b/src/models/pytorch/test/base_pytorch.py deleted file mode 100644 index 4d7a030..0000000 --- a/src/models/pytorch/test/base_pytorch.py +++ /dev/null @@ -1,33 +0,0 @@ -import unittest -from functools import reduce - -import torch.nn -from torch.nn import Module -from torch.utils.data import DataLoader -from torchvision.datasets import FakeData -from torchvision.transforms import ToTensor - - -class Net(Module): - def __init__(self, input_size, output_size): - super(Net, self).__init__() - self._l1 = torch.nn.Linear( - reduce(lambda x, y: x * y, input_size), out_features=output_size - ) - - def forward(self, x: torch.Tensor): - return self._l1(x.view(x.shape[0], -1)) - - -class BasePytorchTests(unittest.TestCase): - def setUp(self): - self.input_shape = (3, 224, 224) - self.output_shape = 2 - self._data = FakeData( - size=10, - num_classes=self.output_shape, - image_size=self.input_shape, - transform=ToTensor(), - ) - self._dataloader = DataLoader(self._data) - self._net = Net(self.input_shape, self.output_shape) diff --git a/src/models/pytorch/test/test_pytorch_nn.py b/src/models/pytorch/test/test_pytorch_nn.py deleted file mode 100644 index ba94e59..0000000 --- a/src/models/pytorch/test/test_pytorch_nn.py +++ /dev/null @@ -1,25 +0,0 @@ -import torch -from torch.optim import Adam - -from src.models.pytorch.base_pytorch_nn import BasePytorchClassifier -from src.models.pytorch.base_pytorch_trainer import BasePyTorchTrainer -from src.models.pytorch.test.base_pytorch import BasePytorchTests - - -class TestPytorchNN(BasePytorchTests): - def setUp(self): - super(TestPytorchNN, self).setUp() - self._trainer = BasePyTorchTrainer( - optimizer=Adam(lr=1e-3, params=self._net.parameters()), - loss=torch.nn.CrossEntropyLoss(), - ) - - def test_fit_raise_exception_no_trainer(self): - test_net = BasePytorchClassifier(self._net, trainer=None) - with self.assertRaises(ValueError): - test_net.train(self._dataloader) - - def test_fit_ok(self): - test_net = BasePytorchClassifier(self._net, trainer=self._trainer) - test_net.train(self._dataloader) - self.assertTrue(test_net) diff --git a/src/models/pytorch/test/test_pytorch_trainer.py b/src/models/pytorch/test/test_pytorch_trainer.py deleted file mode 100644 index 7835e8d..0000000 --- a/src/models/pytorch/test/test_pytorch_trainer.py +++ /dev/null @@ -1,22 +0,0 @@ -from torch.optim import Adam - -from src.models.pytorch.base_pytorch_trainer import BasePyTorchTrainer -from src.models.pytorch.test.base_pytorch import BasePytorchTests - - -class TestPytorchTrainer(BasePytorchTests): - def test_train_net(self): - old_weights = [t.clone() for t in list(self._net.parameters())] - trainer = BasePyTorchTrainer( - optimizer=Adam(lr=1e-3, params=self._net.parameters()) - ) - fit_net = trainer.train(self._net, self._dataloader) - - sum_old_weights = sum([t.sum().item() for t in old_weights]) - sum_new_weights = sum([t.sum().item() for t in fit_net.parameters()]) - - self.assertNotEqual(sum_old_weights, sum_new_weights) - - def test_test_accuracy(self): - # TODO: implement test with mock network - raise NotImplementedError("test yet to be implemented") diff --git a/src/models/sklearn/__init__.py b/src/models/sklearn/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/src/models/sklearn/base_sklearn_model.py b/src/models/sklearn/base_sklearn_model.py deleted file mode 100644 index 7e7830f..0000000 --- a/src/models/sklearn/base_sklearn_model.py +++ /dev/null @@ -1,63 +0,0 @@ -from typing import Callable - -import numpy as np -import sklearn -import torch -from torch.utils.data import DataLoader - -from src.data.sklearn_dataset import SklearnDataset -from src.models.base_model import BaseModel -from src.models.data_processing.data_processing import DataProcessing - - -class BaseSklearnModel(BaseModel): - def __init__( - self, - model: sklearn.base.BaseEstimator, - preprocessing: DataProcessing = None, - postprocessing: DataProcessing = None, - ): - super().__init__(preprocessing=preprocessing, postprocessing=postprocessing) - self._model: sklearn.base.BaseEstimator = model - - def _decision_function(self, x: torch.Tensor) -> torch.Tensor: - if hasattr(self._model, "decision_function"): - return self.to_tensor(self._model.decision_function(self.to_2d_numpy(x))) - elif hasattr(self._model, "predict_proba"): - return self.to_tensor(self._model.predict_proba(self.to_2d_numpy(x))) - raise AttributeError( - "This model has neither decision_function nor predict_proba." - ) - - def gradient(self, x: torch.Tensor, y: int) -> torch.Tensor: - raise NotImplementedError( - "Custom sklearn model do not implement gradients. " - "Use specific class or create subclass with custom definition." - ) - - def train(self, dataloader: DataLoader): - if not isinstance(dataloader.dataset, SklearnDataset): - raise ValueError( - f"Internal dataset is not SklearnDataset, but {type(dataloader.dataset)}" - ) - x, y = dataloader.dataset.x, dataloader.dataset.y - self._model.fit(x, y) - return self - - def predict(self, x: torch.Tensor) -> torch.Tensor: - x_numpy = self.to_2d_numpy(x) - y = self._model.predict(x_numpy) - y = self.to_tensor(y) - return y - - @classmethod - def to_numpy(cls, x: torch.Tensor) -> np.ndarray: - return x.detach().cpu().numpy() - - @classmethod - def to_tensor(cls, x: np.ndarray) -> torch.Tensor: - return torch.tensor(x) - - @classmethod - def to_2d_numpy(cls, x: torch.Tensor) -> np.ndarray: - return x.view(x.shape[0], -1).cpu().detach().numpy() diff --git a/src/models/sklearn/test/__init__.py b/src/models/sklearn/test/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/src/models/tensorflow/__init__.py b/src/models/tensorflow/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/src/models/tensorflow/base_tensorflow_nn.py b/src/models/tensorflow/base_tensorflow_nn.py deleted file mode 100644 index 87f67c5..0000000 --- a/src/models/tensorflow/base_tensorflow_nn.py +++ /dev/null @@ -1,71 +0,0 @@ -import tensorflow as tf -import torch - -from src.models.base_model import BaseModel -from src.models.data_processing.data_processing import DataProcessing -from src.models.tensorflow.base_tensorflow_trainer import BaseTensorflowTrainer - - -class BaseTensorflowClassifier(BaseModel): - def __init__( - self, - model: tf.keras.Model, - preprocessing: DataProcessing = None, - postprocessing: DataProcessing = None, - trainer: BaseTensorflowTrainer = None, - ): - super().__init__(preprocessing=preprocessing, postprocessing=postprocessing) - self._model = model - self._trainer = trainer - - def predict(self, x: torch.Tensor) -> torch.Tensor: - """ - TODO - Parameters - ---------- - x : - - Returns - ------- - - """ - pass - - def _decision_function(self, x: torch.Tensor) -> torch.Tensor: - """ - TODO - Parameters - ---------- - x : - - Returns - ------- - - """ - pass - - def gradient(self, x: torch.Tensor, y: int) -> torch.Tensor: - """ - TODO - Parameters - ---------- - x : - - Returns - ------- - - """ - pass - - def train(self, dataloader: torch.Tensor): - """ - TODO - Parameters - ---------- - dataloader : - - Returns - ------- - - """ - pass diff --git a/src/models/tensorflow/base_tensorflow_trainer.py b/src/models/tensorflow/base_tensorflow_trainer.py deleted file mode 100644 index 4bdf65f..0000000 --- a/src/models/tensorflow/base_tensorflow_trainer.py +++ /dev/null @@ -1,9 +0,0 @@ -from src.models.base_trainer import BaseTrainer - - -class BaseTensorflowTrainer(BaseTrainer): - def __init__(self): - pass - - def train(self, model, dataloader): - pass diff --git a/src/models/test/__init__.py b/src/models/test/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/src/optimization/__init__.py b/src/optimization/__init__.py deleted file mode 100644 index e69de29..0000000 From 67f8a1d1711be100b9f66d7933e485aa3fe4b1e8 Mon Sep 17 00:00:00 2001 From: zangobot Date: Thu, 13 Jul 2023 10:04:17 +0200 Subject: [PATCH 16/18] Problem with gitignore --- .gitignore | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/.gitignore b/.gitignore index c325131..446d990 100644 --- a/.gitignore +++ b/.gitignore @@ -244,6 +244,4 @@ $RECYCLE.BIN/ *.msp # Windows shortcuts -*.lnk - -models/ \ No newline at end of file +*.lnk \ No newline at end of file From c48649ce92ec297aaf77bb413024fb99fea1547b Mon Sep 17 00:00:00 2001 From: zangobot Date: Thu, 13 Jul 2023 10:04:42 +0200 Subject: [PATCH 17/18] Adding models back --- secml2/models/__init__.py | 0 secml2/models/base_model.py | 55 ++++++++++++++ secml2/models/base_trainer.py | 7 ++ secml2/models/data_processing/__init__.py | 0 .../models/data_processing/data_processing.py | 15 ++++ .../identity_data_processing.py | 11 +++ .../models/data_processing/test/__init__.py | 0 secml2/models/pytorch/__init__.py | 0 secml2/models/pytorch/base_pytorch_nn.py | 72 +++++++++++++++++++ secml2/models/pytorch/base_pytorch_trainer.py | 34 +++++++++ secml2/models/pytorch/test/__init__.py | 0 secml2/models/pytorch/test/base_pytorch.py | 33 +++++++++ secml2/models/pytorch/test/test_pytorch_nn.py | 25 +++++++ .../pytorch/test/test_pytorch_trainer.py | 22 ++++++ secml2/models/sklearn/__init__.py | 0 secml2/models/sklearn/base_sklearn_model.py | 63 ++++++++++++++++ secml2/models/sklearn/test/__init__.py | 0 secml2/models/tensorflow/__init__.py | 0 .../models/tensorflow/base_tensorflow_nn.py | 71 ++++++++++++++++++ .../tensorflow/base_tensorflow_trainer.py | 9 +++ secml2/models/test/__init__.py | 0 21 files changed, 417 insertions(+) create mode 100644 secml2/models/__init__.py create mode 100644 secml2/models/base_model.py create mode 100644 secml2/models/base_trainer.py create mode 100644 secml2/models/data_processing/__init__.py create mode 100644 secml2/models/data_processing/data_processing.py create mode 100644 secml2/models/data_processing/identity_data_processing.py create mode 100644 secml2/models/data_processing/test/__init__.py create mode 100644 secml2/models/pytorch/__init__.py create mode 100644 secml2/models/pytorch/base_pytorch_nn.py create mode 100644 secml2/models/pytorch/base_pytorch_trainer.py create mode 100644 secml2/models/pytorch/test/__init__.py create mode 100644 secml2/models/pytorch/test/base_pytorch.py create mode 100644 secml2/models/pytorch/test/test_pytorch_nn.py create mode 100644 secml2/models/pytorch/test/test_pytorch_trainer.py create mode 100644 secml2/models/sklearn/__init__.py create mode 100644 secml2/models/sklearn/base_sklearn_model.py create mode 100644 secml2/models/sklearn/test/__init__.py create mode 100644 secml2/models/tensorflow/__init__.py create mode 100644 secml2/models/tensorflow/base_tensorflow_nn.py create mode 100644 secml2/models/tensorflow/base_tensorflow_trainer.py create mode 100644 secml2/models/test/__init__.py diff --git a/secml2/models/__init__.py b/secml2/models/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/secml2/models/base_model.py b/secml2/models/base_model.py new file mode 100644 index 0000000..394e336 --- /dev/null +++ b/secml2/models/base_model.py @@ -0,0 +1,55 @@ +from abc import ABC, abstractmethod +from typing import Callable + +import torch +from torch.utils.data import DataLoader + +from secml2.models.data_processing.identity_data_processing import IdentityDataProcessing +from secml2.models.data_processing.data_processing import DataProcessing + + +class BaseModel(ABC): + def __init__( + self, + preprocessing: DataProcessing = None, + postprocessing: DataProcessing = None, + ): + """ + Create base abstract model + Parameters + ---------- + preprocessing : DataProcessing + postprocessing: DataProcessing + + """ + self._preprocessing = ( + preprocessing if preprocessing is not None else IdentityDataProcessing() + ) + self._postprocessing = ( + postprocessing if postprocessing is not None else IdentityDataProcessing() + ) + + @abstractmethod + def predict(self, x: torch.Tensor) -> torch.Tensor: + ... + + def decision_function(self, x: torch.Tensor) -> torch.Tensor: + x = self._preprocessing(x) + x = self._decision_function(x) + x = self._postprocessing(x) + return x + + @abstractmethod + def _decision_function(self, x: torch.Tensor) -> torch.Tensor: + ... + + @abstractmethod + def gradient(self, x: torch.Tensor, y: int) -> torch.Tensor: + ... + + @abstractmethod + def train(self, dataloader: DataLoader): + ... + + def __call__(self, x: torch.Tensor) -> torch.Tensor: + return self.decision_function(x) diff --git a/secml2/models/base_trainer.py b/secml2/models/base_trainer.py new file mode 100644 index 0000000..32bd71d --- /dev/null +++ b/secml2/models/base_trainer.py @@ -0,0 +1,7 @@ +from abc import ABCMeta, abstractmethod + + +class BaseTrainer(metaclass=ABCMeta): + @abstractmethod + def train(self, model, dataloader): + pass diff --git a/secml2/models/data_processing/__init__.py b/secml2/models/data_processing/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/secml2/models/data_processing/data_processing.py b/secml2/models/data_processing/data_processing.py new file mode 100644 index 0000000..335b609 --- /dev/null +++ b/secml2/models/data_processing/data_processing.py @@ -0,0 +1,15 @@ +from abc import ABC, abstractmethod + +import torch + + +class DataProcessing(ABC): + @abstractmethod + def process(self, x: torch.Tensor) -> torch.Tensor: + ... + + def invert(self, x: torch.Tensor) -> torch.Tensor: + ... + + def __call__(self, x: torch.Tensor) -> torch.Tensor: + return self.process(x) diff --git a/secml2/models/data_processing/identity_data_processing.py b/secml2/models/data_processing/identity_data_processing.py new file mode 100644 index 0000000..13452a6 --- /dev/null +++ b/secml2/models/data_processing/identity_data_processing.py @@ -0,0 +1,11 @@ +import torch + +from secml2.models.data_processing.data_processing import DataProcessing + + +class IdentityDataProcessing(DataProcessing): + def process(self, x: torch.Tensor) -> torch.Tensor: + return x + + def invert(self, x: torch.Tensor) -> torch.Tensor: + return x diff --git a/secml2/models/data_processing/test/__init__.py b/secml2/models/data_processing/test/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/secml2/models/pytorch/__init__.py b/secml2/models/pytorch/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/secml2/models/pytorch/base_pytorch_nn.py b/secml2/models/pytorch/base_pytorch_nn.py new file mode 100644 index 0000000..e542038 --- /dev/null +++ b/secml2/models/pytorch/base_pytorch_nn.py @@ -0,0 +1,72 @@ +import torch +from torch.utils.data import DataLoader + +from secml2.models.base_model import BaseModel +from secml2.models.data_processing.data_processing import DataProcessing +from secml2.models.pytorch.base_pytorch_trainer import BasePyTorchTrainer + + +class BasePytorchClassifier(BaseModel): + def __init__( + self, + model: torch.nn.Module, + preprocessing: DataProcessing = None, + postprocessing: DataProcessing = None, + trainer: BasePyTorchTrainer = None, + ): + super().__init__(preprocessing=preprocessing, postprocessing=postprocessing) + self._model: torch.nn.Module = model + self._trainer = trainer + + @property + def model(self): + return self._model + + def get_device(self): + return next(self._model.parameters()).device + + def predict(self, x: torch.Tensor) -> torch.Tensor: + scores = self.decision_function(x) + labels = torch.argmax(scores, dim=-1) + return labels + + def _decision_function(self, x: torch.Tensor) -> torch.Tensor: + """ + Returns the decision function of the model. + Parameters + ---------- + x : input samples + + Returns + ------- + output of decision function + """ + x = x.to(device=self.get_device()) + return self._model(x) + + def gradient(self, x: torch.Tensor, y: int) -> torch.Tensor: + """ + The functions computes gradients of class with label y w.r.t. x. + Gradients are computed in batch. + Parameters + ---------- + x : input samples + y : class label + + Returns + ------- + Gradient of class y w.r.t. input x + """ + x = x.clone().requires_grad_() + if x.grad is not None: + x.grad.zero_() + output = self.decision_function(x) + output = output[:, y].sum() + output.backward() + grad = x.grad + return grad + + def train(self, dataloader: DataLoader): + if self._trainer is None: + raise ValueError("Cannot train without a trainer.") + return self._trainer.train(self._model, dataloader) diff --git a/secml2/models/pytorch/base_pytorch_trainer.py b/secml2/models/pytorch/base_pytorch_trainer.py new file mode 100644 index 0000000..ad5aca5 --- /dev/null +++ b/secml2/models/pytorch/base_pytorch_trainer.py @@ -0,0 +1,34 @@ +import torch.nn +from torch.optim.lr_scheduler import _LRScheduler # noqa +from torch.utils.data import DataLoader + +from secml2.models.base_trainer import BaseTrainer + + +class BasePyTorchTrainer(BaseTrainer): + def __init__( + self, + optimizer: torch.optim.Optimizer, + epochs: int = 5, + loss: torch.nn.Module = None, + scheduler: _LRScheduler = None, + ): + self._epochs = epochs + self._optimizer = optimizer + self._loss = loss if loss is not None else torch.nn.CrossEntropyLoss() + self._scheduler = scheduler + + def train(self, model: torch.nn.Module, dataloader: DataLoader): + device = next(model.parameters()).device + model = model.train() + for epoch in range(self._epochs): + for batch_idx, (x, y) in enumerate(dataloader): + x, y = x.to(device), y.to(device) + self._optimizer.zero_grad() + outputs = model(x) + loss = self._loss(outputs, y) + loss.backward() + self._optimizer.step() + if self._scheduler is not None: + self._scheduler.step() + return model diff --git a/secml2/models/pytorch/test/__init__.py b/secml2/models/pytorch/test/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/secml2/models/pytorch/test/base_pytorch.py b/secml2/models/pytorch/test/base_pytorch.py new file mode 100644 index 0000000..4d7a030 --- /dev/null +++ b/secml2/models/pytorch/test/base_pytorch.py @@ -0,0 +1,33 @@ +import unittest +from functools import reduce + +import torch.nn +from torch.nn import Module +from torch.utils.data import DataLoader +from torchvision.datasets import FakeData +from torchvision.transforms import ToTensor + + +class Net(Module): + def __init__(self, input_size, output_size): + super(Net, self).__init__() + self._l1 = torch.nn.Linear( + reduce(lambda x, y: x * y, input_size), out_features=output_size + ) + + def forward(self, x: torch.Tensor): + return self._l1(x.view(x.shape[0], -1)) + + +class BasePytorchTests(unittest.TestCase): + def setUp(self): + self.input_shape = (3, 224, 224) + self.output_shape = 2 + self._data = FakeData( + size=10, + num_classes=self.output_shape, + image_size=self.input_shape, + transform=ToTensor(), + ) + self._dataloader = DataLoader(self._data) + self._net = Net(self.input_shape, self.output_shape) diff --git a/secml2/models/pytorch/test/test_pytorch_nn.py b/secml2/models/pytorch/test/test_pytorch_nn.py new file mode 100644 index 0000000..9800b7f --- /dev/null +++ b/secml2/models/pytorch/test/test_pytorch_nn.py @@ -0,0 +1,25 @@ +import torch +from torch.optim import Adam + +from secml2.models.pytorch.base_pytorch_nn import BasePytorchClassifier +from secml2.models.pytorch.base_pytorch_trainer import BasePyTorchTrainer +from secml2.models.pytorch.test.base_pytorch import BasePytorchTests + + +class TestPytorchNN(BasePytorchTests): + def setUp(self): + super(TestPytorchNN, self).setUp() + self._trainer = BasePyTorchTrainer( + optimizer=Adam(lr=1e-3, params=self._net.parameters()), + loss=torch.nn.CrossEntropyLoss(), + ) + + def test_fit_raise_exception_no_trainer(self): + test_net = BasePytorchClassifier(self._net, trainer=None) + with self.assertRaises(ValueError): + test_net.train(self._dataloader) + + def test_fit_ok(self): + test_net = BasePytorchClassifier(self._net, trainer=self._trainer) + test_net.train(self._dataloader) + self.assertTrue(test_net) diff --git a/secml2/models/pytorch/test/test_pytorch_trainer.py b/secml2/models/pytorch/test/test_pytorch_trainer.py new file mode 100644 index 0000000..a3f19b4 --- /dev/null +++ b/secml2/models/pytorch/test/test_pytorch_trainer.py @@ -0,0 +1,22 @@ +from torch.optim import Adam + +from secml2.models.pytorch.base_pytorch_trainer import BasePyTorchTrainer +from secml2.models.pytorch.test.base_pytorch import BasePytorchTests + + +class TestPytorchTrainer(BasePytorchTests): + def test_train_net(self): + old_weights = [t.clone() for t in list(self._net.parameters())] + trainer = BasePyTorchTrainer( + optimizer=Adam(lr=1e-3, params=self._net.parameters()) + ) + fit_net = trainer.train(self._net, self._dataloader) + + sum_old_weights = sum([t.sum().item() for t in old_weights]) + sum_new_weights = sum([t.sum().item() for t in fit_net.parameters()]) + + self.assertNotEqual(sum_old_weights, sum_new_weights) + + def test_test_accuracy(self): + # TODO: implement test with mock network + raise NotImplementedError("test yet to be implemented") diff --git a/secml2/models/sklearn/__init__.py b/secml2/models/sklearn/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/secml2/models/sklearn/base_sklearn_model.py b/secml2/models/sklearn/base_sklearn_model.py new file mode 100644 index 0000000..a5a3351 --- /dev/null +++ b/secml2/models/sklearn/base_sklearn_model.py @@ -0,0 +1,63 @@ +from typing import Callable + +import numpy as np +import sklearn +import torch +from torch.utils.data import DataLoader + +from secml2.data.sklearn_dataset import SklearnDataset +from secml2.models.base_model import BaseModel +from secml2.models.data_processing.data_processing import DataProcessing + + +class BaseSklearnModel(BaseModel): + def __init__( + self, + model: sklearn.base.BaseEstimator, + preprocessing: DataProcessing = None, + postprocessing: DataProcessing = None, + ): + super().__init__(preprocessing=preprocessing, postprocessing=postprocessing) + self._model: sklearn.base.BaseEstimator = model + + def _decision_function(self, x: torch.Tensor) -> torch.Tensor: + if hasattr(self._model, "decision_function"): + return self.to_tensor(self._model.decision_function(self.to_2d_numpy(x))) + elif hasattr(self._model, "predict_proba"): + return self.to_tensor(self._model.predict_proba(self.to_2d_numpy(x))) + raise AttributeError( + "This model has neither decision_function nor predict_proba." + ) + + def gradient(self, x: torch.Tensor, y: int) -> torch.Tensor: + raise NotImplementedError( + "Custom sklearn model do not implement gradients. " + "Use specific class or create subclass with custom definition." + ) + + def train(self, dataloader: DataLoader): + if not isinstance(dataloader.dataset, SklearnDataset): + raise ValueError( + f"Internal dataset is not SklearnDataset, but {type(dataloader.dataset)}" + ) + x, y = dataloader.dataset.x, dataloader.dataset.y + self._model.fit(x, y) + return self + + def predict(self, x: torch.Tensor) -> torch.Tensor: + x_numpy = self.to_2d_numpy(x) + y = self._model.predict(x_numpy) + y = self.to_tensor(y) + return y + + @classmethod + def to_numpy(cls, x: torch.Tensor) -> np.ndarray: + return x.detach().cpu().numpy() + + @classmethod + def to_tensor(cls, x: np.ndarray) -> torch.Tensor: + return torch.tensor(x) + + @classmethod + def to_2d_numpy(cls, x: torch.Tensor) -> np.ndarray: + return x.view(x.shape[0], -1).cpu().detach().numpy() diff --git a/secml2/models/sklearn/test/__init__.py b/secml2/models/sklearn/test/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/secml2/models/tensorflow/__init__.py b/secml2/models/tensorflow/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/secml2/models/tensorflow/base_tensorflow_nn.py b/secml2/models/tensorflow/base_tensorflow_nn.py new file mode 100644 index 0000000..0a41822 --- /dev/null +++ b/secml2/models/tensorflow/base_tensorflow_nn.py @@ -0,0 +1,71 @@ +import tensorflow as tf +import torch + +from secml2.models.base_model import BaseModel +from secml2.models.data_processing.data_processing import DataProcessing +from secml2.models.tensorflow.base_tensorflow_trainer import BaseTensorflowTrainer + + +class BaseTensorflowClassifier(BaseModel): + def __init__( + self, + model: tf.keras.Model, + preprocessing: DataProcessing = None, + postprocessing: DataProcessing = None, + trainer: BaseTensorflowTrainer = None, + ): + super().__init__(preprocessing=preprocessing, postprocessing=postprocessing) + self._model = model + self._trainer = trainer + + def predict(self, x: torch.Tensor) -> torch.Tensor: + """ + TODO + Parameters + ---------- + x : + + Returns + ------- + + """ + pass + + def _decision_function(self, x: torch.Tensor) -> torch.Tensor: + """ + TODO + Parameters + ---------- + x : + + Returns + ------- + + """ + pass + + def gradient(self, x: torch.Tensor, y: int) -> torch.Tensor: + """ + TODO + Parameters + ---------- + x : + + Returns + ------- + + """ + pass + + def train(self, dataloader: torch.Tensor): + """ + TODO + Parameters + ---------- + dataloader : + + Returns + ------- + + """ + pass diff --git a/secml2/models/tensorflow/base_tensorflow_trainer.py b/secml2/models/tensorflow/base_tensorflow_trainer.py new file mode 100644 index 0000000..ff484ac --- /dev/null +++ b/secml2/models/tensorflow/base_tensorflow_trainer.py @@ -0,0 +1,9 @@ +from secml2.models.base_trainer import BaseTrainer + + +class BaseTensorflowTrainer(BaseTrainer): + def __init__(self): + pass + + def train(self, model, dataloader): + pass diff --git a/secml2/models/test/__init__.py b/secml2/models/test/__init__.py new file mode 100644 index 0000000..e69de29 From ad2cb76ed253934f29d057167888fc282a27e790 Mon Sep 17 00:00:00 2001 From: zangobot Date: Thu, 21 Sep 2023 10:43:50 +0200 Subject: [PATCH 18/18] Black base_model --- secml2/models/base_model.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/secml2/models/base_model.py b/secml2/models/base_model.py index 394e336..4840be1 100644 --- a/secml2/models/base_model.py +++ b/secml2/models/base_model.py @@ -4,7 +4,9 @@ import torch from torch.utils.data import DataLoader -from secml2.models.data_processing.identity_data_processing import IdentityDataProcessing +from secml2.models.data_processing.identity_data_processing import ( + IdentityDataProcessing, +) from secml2.models.data_processing.data_processing import DataProcessing

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