diff --git a/svhn.py b/svhn.py
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+++ b/svhn.py
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+import warnings
+
+import torchvision.datasets
+
+warnings.filterwarnings('ignore')
+
+from PIL import Image
+import torch
+import timm
+import requests
+import numpy as np
+import torchvision.transforms as transforms
+from torch import nn
+from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
+from torch.utils.data import Dataset, DataLoader
+import copy
+
+from art.estimators.classification import PyTorchClassifier
+from art.data_generators import PyTorchDataGenerator
+from art.utils import load_cifar10
+from art.attacks.evasion import ProjectedGradientDescent
+from art.defences.trainer import AdversarialTrainer
+
+model = timm.create_model("timm/vit_base_patch16_224.orig_in21k_ft_in1k", pretrained=False)
+model.head = nn.Linear(model.head.in_features, 10)
+state_dict = torch.load('/home/leewlving/.cache/torch/hub/checkpoints/vit_base_patch16_224_in21k_ft_cifar10.pth')
+model.load_state_dict(state_dict)
+# model.load_state_dict(
+#     torch.hub.load_state_dict_from_url(
+#         "https://huggingface.co/edadaltocg/vit_base_patch16_224_in21k_ft_cifar10/resolve/main/pytorch_model.bin",
+#         map_location="cuda",
+#         file_name="vit_base_patch16_224_in21k_ft_cifar10.pth",
+#     )
+# )
+model.eval()
+
+DEFAULT_MEAN = (0.485, 0.456, 0.406)
+DEFAULT_STD = (0.229, 0.224, 0.225)
+
+transform = transforms.Compose([
+    transforms.Resize(256, interpolation=3),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize(DEFAULT_MEAN, DEFAULT_STD),
+])
+
+
+class CIFAR10_dataset(Dataset):
+    def __init__(self, data, targets, transform=None):
+        self.data = data
+        self.targets = torch.LongTensor(targets)
+        self.transform = transform
+
+    def __getitem__(self, index):
+        x = Image.fromarray(((self.data[index] * 255).round()).astype(np.uint8).transpose(1, 2, 0))
+        x = self.transform(x)
+        y = self.targets[index]
+        return x, y
+
+    def __len__(self):
+        return len(self.data)
+
+
+# (x_train, y_train), (x_test, y_test), min_pixel_value, max_pixel_value = load_cifar10()
+# print(max_pixel_value)
+# x_train = x_train.transpose(0, 3, 1, 2).astype("float32")
+# x_test = x_test.transpose(0, 3, 1, 2).astype("float32")
+train_dataset = torchvision.datasets.SVHN(root='./svhn',split='train',download=True,transform=transform)
+test_dataset= torchvision.datasets.SVHN(root='./svhn',split='test',download=True,transform=transform)
+# dataset = CIFAR10_dataset(x_train, y_train, transform=transform)
+dataloader = DataLoader(train_dataset, batch_size=64, shuffle=True)
+test_dataloader =DataLoader(test_dataset, batch_size=64, shuffle=False)
+
+opt = torch.optim.Adam(model.parameters(), lr=0.01)
+
+
+criterion = nn.CrossEntropyLoss()
+
+classifier = PyTorchClassifier(
+    model=model,
+    clip_values=(0.0, 1.0),
+    loss=criterion,
+    optimizer=opt,
+    input_shape=(3, 224, 224),
+    nb_classes=10,
+)
+
+
+attack = ProjectedGradientDescent(
+    classifier,
+    norm=np.inf,
+    eps=8.0 / 255.0,
+    eps_step=2.0 / 255.0,
+    max_iter=10,
+    targeted=False,
+    num_random_init=1,
+    batch_size=64,
+    verbose=False,
+)
+
+trainer = AdversarialTrainer(
+    classifier, attack
+)
+art_datagen = PyTorchDataGenerator(iterator=dataloader, size=len(train_dataset), batch_size=64)
+
+trainer.fit_generator(art_datagen, nb_epochs=1)
+
+# for i, data in enumerate(test_dataloader):
+#     x, y = data
+#     x = x.numpy()
+#     y = y.numpy()
+#     # print(x.shape)
+#     # print(y.shape)
+#     x_test_pred = np.argmax(classifier.predict(x), axis=1)
+#     print(
+#         "Accuracy on benign test samples after adversarial training: %.2f%%"
+#         % (np.sum(x_test_pred == np.argmax(y, axis=1)) / x.shape[0] * 100)
+#     )
+
+# trainer.classifier.save('AT-cifar10.pth')
+torch.save(trainer.classifier.model.state_dict(), 'AT-svhn.pth')
+