更新 cifar10.py
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from PIL import Image
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import numpy as np
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import timm
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torchvision.transforms as transforms
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from torch.utils.data import Dataset, DataLoader
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from torch.optim.lr_scheduler import MultiStepLR, StepLR
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from art.estimators.classification import PyTorchClassifier
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from art.data_generators import PyTorchDataGenerator
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from art.defences.trainer import AdversarialTrainer
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from art.attacks.evasion import ProjectedGradientDescent
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from datasets import load_dataset
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from torchvision.transforms import (CenterCrop,
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Compose,
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Normalize,
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RandomHorizontalFlip,
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RandomResizedCrop,
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Resize,
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ToTensor)
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from tensorflow.keras.utils import to_categorical
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from transformers import ViTImageProcessor
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processor = ViTImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k")
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IMAGENET_DEFAULT_MEAN = processor.image_mean
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IMAGENET_DEFAULT_STD = processor.image_std
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size = processor.size["height"]
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"""
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For this example we choose the ResNet18 model as used in the paper (https://proceedings.mlr.press/v97/zhang19p.html)
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The code for the model architecture has been adopted from
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https://github.com/yaodongyu/TRADES/blob/master/models/resnet.py
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"""
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model = timm.create_model("timm/vit_base_patch16_224.orig_in21k_ft_in1k", pretrained=False)
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model.head = nn.Linear(model.head.in_features, 10)
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model.load_state_dict(
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torch.hub.load_state_dict_from_url(
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"https://huggingface.co/edadaltocg/vit_base_patch16_224_in21k_ft_cifar10/resolve/main/pytorch_model.bin",
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map_location="cuda",
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file_name="vit_base_patch16_224_in21k_ft_cifar10.pth",
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)
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)
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# Step 1: Load the CIFAR10 dataset
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train_ds, test_ds = load_dataset('cifar10', split=['train[:5000]', 'test[:2000]'])
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splits = train_ds.train_test_split(test_size=0.1)
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train_ds = splits['train']
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val_ds = splits['test']
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train_size=len(train_ds)
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test_size=len(test_ds)
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normalize = Normalize(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD)
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_train_transforms = Compose(
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[
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RandomResizedCrop(size),
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RandomHorizontalFlip(),
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ToTensor(),
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normalize,
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]
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)
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_val_transforms = Compose(
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[
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Resize(size),
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CenterCrop(size),
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ToTensor(),
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normalize,
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]
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)
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def train_transforms(examples):
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examples['pixel_values'] = [_train_transforms(image.convert("RGB")) for image in examples['img']]
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return examples
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def val_transforms(examples):
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examples['pixel_values'] = [_val_transforms(image.convert("RGB")) for image in examples['img']]
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return examples
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train_ds.set_transform(train_transforms)
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val_ds.set_transform(val_transforms)
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test_ds.set_transform(val_transforms)
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def collate_fn(examples):
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pixel_values = torch.stack([example["pixel_values"] for example in examples])
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labels = torch.tensor([example["label"] for example in examples])
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return pixel_values,labels
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train_batch_size = 32
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eval_batch_size = 32
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def dataset2np(dataset):
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X = []
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Y = []
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for i in range(int(2000)):
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x,y = dataset[i]["pixel_values"], dataset[i]["label"]
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y=to_categorical(y,num_classes=10)
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X.append(x.detach().numpy())
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Y.append(y)
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X = np.array(X).astype("float32")
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Y = np.array(Y).astype("float32")
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return X,Y
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train_dataloader = DataLoader(train_ds, shuffle=True, collate_fn=collate_fn, batch_size=train_batch_size)
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val_dataloader = DataLoader(val_ds, collate_fn=collate_fn, batch_size=eval_batch_size)
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test_dataloader = DataLoader(test_ds, collate_fn=collate_fn, batch_size=eval_batch_size)
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x_test, y_test=dataset2np(test_ds)
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opt = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9, weight_decay=2e-4)
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lr_scheduler = StepLR(opt, step_size=3, gamma=0.1)
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criterion = nn.CrossEntropyLoss()
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# Step 3: Create the ART classifier
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classifier = PyTorchClassifier(
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model=model,
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clip_values=(0.0, 1.0),
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loss=criterion,
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optimizer=opt,
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input_shape=(3, size, size),
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nb_classes=10,
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)
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attack = ProjectedGradientDescent(
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classifier,
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norm=np.inf,
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eps=8.0 / 255.0,
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eps_step=2.0 / 255.0,
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max_iter=10,
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targeted=False,
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num_random_init=1,
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batch_size=128,
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verbose=False,
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)
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x_test_clean_pred=np.argmax(classifier.predict(x_test), axis=1)
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print(
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"Accuracy on clean samples before adversarial training: %.2f%%"
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% (np.sum(x_test_clean_pred == np.argmax(y_test, axis=1)) / x_test.shape[0] * 100)
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)
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# Step 4: Create the trainer object - AdversarialTrainerTRADESPyTorch
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trainer = AdversarialTrainer(
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classifier, attack
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)
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# Build a Keras image augmentation object and wrap it in ART
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art_datagen = PyTorchDataGenerator(iterator=train_dataloader, size=train_size, batch_size=128)
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# Step 5: fit the trainer
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trainer.fit_generator(art_datagen, nb_epochs=50)
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x_test_pred = np.argmax(classifier.predict(x_test), axis=1)
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print(
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"Accuracy on benign test samples after adversarial training: %.2f%%"
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% (np.sum(x_test_pred == np.argmax(y_test, axis=1)) / x_test.shape[0] * 100)
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)
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attack_test = ProjectedGradientDescent(
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classifier,
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norm=np.inf,
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eps=8.0 / 255.0,
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eps_step=2.0 / 255.0,
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max_iter=20,
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targeted=False,
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num_random_init=1,
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batch_size=128,
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verbose=False,
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)
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x_test_attack = attack_test.generate(x_test, y=y_test)
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x_test_attack_pred = np.argmax(classifier.predict(x_test_attack), axis=1)
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print(
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"Accuracy on original PGD adversarial samples after adversarial training: %.2f%%"
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% (np.sum(x_test_attack_pred == np.argmax(y_test, axis=1)) / x_test.shape[0] * 100)
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)
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torch.save(trainer.classifier.model.state_dict(), 'cifar10_pgd.pth')
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print(
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"Save the AT model! "
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)
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122
svhn.py
122
svhn.py
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import warnings
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import torchvision.datasets
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warnings.filterwarnings('ignore')
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from PIL import Image
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import torch
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import timm
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import requests
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import numpy as np
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import torchvision.transforms as transforms
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from torch import nn
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from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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from torch.utils.data import Dataset, DataLoader
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import copy
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from art.estimators.classification import PyTorchClassifier
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from art.data_generators import PyTorchDataGenerator
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from art.utils import load_cifar10
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from art.attacks.evasion import ProjectedGradientDescent
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from art.defences.trainer import AdversarialTrainer
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model = timm.create_model("timm/vit_base_patch16_224.orig_in21k_ft_in1k", pretrained=False)
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model.head = nn.Linear(model.head.in_features, 10)
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state_dict = torch.load('/home/leewlving/.cache/torch/hub/checkpoints/vit_base_patch16_224_in21k_ft_cifar10.pth')
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model.load_state_dict(state_dict)
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# model.load_state_dict(
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# torch.hub.load_state_dict_from_url(
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# "https://huggingface.co/edadaltocg/vit_base_patch16_224_in21k_ft_cifar10/resolve/main/pytorch_model.bin",
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# map_location="cuda",
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# file_name="vit_base_patch16_224_in21k_ft_cifar10.pth",
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# )
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# )
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model.eval()
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DEFAULT_MEAN = (0.485, 0.456, 0.406)
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DEFAULT_STD = (0.229, 0.224, 0.225)
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transform = transforms.Compose([
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transforms.Resize(256, interpolation=3),
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transforms.CenterCrop(224),
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transforms.ToTensor(),
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transforms.Normalize(DEFAULT_MEAN, DEFAULT_STD),
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])
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class CIFAR10_dataset(Dataset):
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def __init__(self, data, targets, transform=None):
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self.data = data
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self.targets = torch.LongTensor(targets)
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self.transform = transform
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def __getitem__(self, index):
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x = Image.fromarray(((self.data[index] * 255).round()).astype(np.uint8).transpose(1, 2, 0))
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x = self.transform(x)
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y = self.targets[index]
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return x, y
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def __len__(self):
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return len(self.data)
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# (x_train, y_train), (x_test, y_test), min_pixel_value, max_pixel_value = load_cifar10()
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# print(max_pixel_value)
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# x_train = x_train.transpose(0, 3, 1, 2).astype("float32")
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# x_test = x_test.transpose(0, 3, 1, 2).astype("float32")
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train_dataset = torchvision.datasets.SVHN(root='./svhn',split='train',download=True,transform=transform)
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test_dataset= torchvision.datasets.SVHN(root='./svhn',split='test',download=True,transform=transform)
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# dataset = CIFAR10_dataset(x_train, y_train, transform=transform)
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dataloader = DataLoader(train_dataset, batch_size=64, shuffle=True)
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test_dataloader =DataLoader(test_dataset, batch_size=64, shuffle=False)
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opt = torch.optim.Adam(model.parameters(), lr=0.01)
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criterion = nn.CrossEntropyLoss()
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classifier = PyTorchClassifier(
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model=model,
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clip_values=(0.0, 1.0),
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loss=criterion,
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optimizer=opt,
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input_shape=(3, 224, 224),
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nb_classes=10,
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)
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attack = ProjectedGradientDescent(
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classifier,
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norm=np.inf,
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eps=8.0 / 255.0,
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eps_step=2.0 / 255.0,
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max_iter=10,
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targeted=False,
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num_random_init=1,
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batch_size=64,
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verbose=False,
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)
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trainer = AdversarialTrainer(
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classifier, attack
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)
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art_datagen = PyTorchDataGenerator(iterator=dataloader, size=len(train_dataset), batch_size=64)
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trainer.fit_generator(art_datagen, nb_epochs=1)
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# for i, data in enumerate(test_dataloader):
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# x, y = data
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# x = x.numpy()
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# y = y.numpy()
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# # print(x.shape)
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# # print(y.shape)
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# x_test_pred = np.argmax(classifier.predict(x), axis=1)
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# print(
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# "Accuracy on benign test samples after adversarial training: %.2f%%"
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# % (np.sum(x_test_pred == np.argmax(y, axis=1)) / x.shape[0] * 100)
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# )
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# trainer.classifier.save('AT-cifar10.pth')
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torch.save(trainer.classifier.model.state_dict(), 'AT-svhn.pth')
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