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other_attacks.py
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other_attacks.py
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import torch.nn as nn
import torchvision.models as models
import numpy as np
import os
from art.estimators.classification import PyTorchClassifier
import timm
from torch_nets import (
tf2torch_adv_inception_v3,
tf2torch_ens3_adv_inc_v3,
tf2torch_ens4_adv_inc_v3,
tf2torch_ens_adv_inc_res_v2,
)
import warnings
import pytorch_fid.fid_score as fid_score
from Finegrained_model import model as otherModel
warnings.filterwarnings("ignore")
def model_selection(name):
if name == "convnext":
model = models.convnext_base(pretrained=True)
elif name == "resnet":
model = models.resnet50(pretrained=True)
elif name == "vit":
model = models.vit_b_16(pretrained=True)
elif name == "swin":
model = models.swin_b(weights=models.Swin_B_Weights.IMAGENET1K_V1)
elif name == "vgg":
model = models.vgg19(pretrained=True)
elif name == "mobile":
model = models.mobilenet_v2(pretrained=True)
elif name == "inception":
model = models.inception_v3(pretrained=True)
elif name == "deit-b":
model = timm.create_model(
'deit_base_patch16_224',
pretrained=True
)
elif name == "deit-s":
model = timm.create_model(
'deit_small_patch16_224',
pretrained=True
)
elif name == "mixer-b":
model = timm.create_model(
'mixer_b16_224',
pretrained=True
)
elif name == "mixer-l":
model = timm.create_model(
'mixer_l16_224',
pretrained=True
)
elif name == 'tf2torch_adv_inception_v3':
net = tf2torch_adv_inception_v3
model_path = os.path.join("pretrained_models", name + '.npy')
model = net.KitModel(model_path)
elif name == 'tf2torch_ens3_adv_inc_v3':
net = tf2torch_ens3_adv_inc_v3
model_path = os.path.join("pretrained_models", name + '.npy')
model = net.KitModel(model_path)
elif name == 'tf2torch_ens4_adv_inc_v3':
net = tf2torch_ens4_adv_inc_v3
model_path = os.path.join("pretrained_models", name + '.npy')
model = net.KitModel(model_path)
elif name == 'tf2torch_ens_adv_inc_res_v2':
net = tf2torch_ens_adv_inc_res_v2
model_path = os.path.join("pretrained_models", name + '.npy')
model = net.KitModel(model_path)
elif name == 'cubResnet50':
model = otherModel.CUB()[0]
elif name == 'cubSEResnet154':
model = otherModel.CUB()[1]
elif name == 'cubSEResnet101':
model = otherModel.CUB()[2]
elif name == 'carResnet50':
model = otherModel.CAR()[0]
elif name == 'carSEResnet154':
model = otherModel.CAR()[1]
elif name == 'carSEResnet101':
model = otherModel.CAR()[2]
else:
raise NotImplementedError("No such model!")
return model.cuda()
def model_transfer(clean_img, adv_img, label, res, save_path=r"C:\Users\PC\Desktop\output", fid_path=None, args=None):
log = open(os.path.join(save_path, "log.txt"), mode="w", encoding="utf-8")
if args.dataset_name == "imagenet_compatible":
models_transfer_name = ["resnet", "vgg", "mobile", "inception", "convnext", "vit", "swin", 'deit-b', 'deit-s',
'mixer-b', 'mixer-l', 'tf2torch_adv_inception_v3', 'tf2torch_ens3_adv_inc_v3',
'tf2torch_ens4_adv_inc_v3', 'tf2torch_ens_adv_inc_res_v2']
nb_classes = 1000
elif args.dataset_name == "cub_200_2011":
models_transfer_name = ["cubResnet50", "cubSEResnet154", "cubSEResnet101"]
nb_classes = 200
elif args.dataset_name == "standford_car":
models_transfer_name = ["carResnet50", "carSEResnet154", "carSEResnet101"]
nb_classes = 196
else:
raise NotImplementedError
all_clean_accuracy = []
all_adv_accuracy = []
for name in models_transfer_name:
print("\n*********Transfer to {}********".format(name))
print("\n*********Transfer to {}********".format(name), file=log)
model = model_selection(name)
model.eval()
f_model = PyTorchClassifier(
model=model,
clip_values=(0, 1),
loss=nn.CrossEntropyLoss(),
input_shape=(3, res, res),
nb_classes=nb_classes,
preprocessing=(np.array([0.5, 0.5, 0.5]), np.array([0.5, 0.5, 0.5])) if "adv" in name else (
np.array([0.485, 0.456, 0.406]), np.array([0.229, 0.224, 0.225])),
device_type='gpu',
)
clean_pred = f_model.predict(clean_img, batch_size=50)
accuracy = np.sum((np.argmax(clean_pred, axis=1) - 1) == label) / len(label) if "adv" in name else np.sum(
np.argmax(clean_pred, axis=1) == label) / len(label)
print("Accuracy on benign examples: {}%".format(accuracy * 100))
print("Accuracy on benign examples: {}%".format(accuracy * 100), file=log)
all_clean_accuracy.append(accuracy * 100)
adv_pred = f_model.predict(adv_img, batch_size=50)
accuracy = np.sum((np.argmax(adv_pred, axis=1) - 1) == label) / len(label) if "adv" in name else np.sum(
np.argmax(adv_pred, axis=1) == label) / len(label)
print("Accuracy on adversarial examples: {}%".format(accuracy * 100))
print("Accuracy on adversarial examples: {}%".format(accuracy * 100), file=log)
all_adv_accuracy.append(accuracy * 100)
print("clean_accuracy: ", "\t".join([str(x) for x in all_clean_accuracy]), file=log)
print("adv_accuracy: ", "\t".join([str(x) for x in all_adv_accuracy]), file=log)
fid = fid_score.main(save_path if fid_path is None else fid_path, args.dataset_name)
print("\n*********fid: {}********".format(fid))
print("\n*********fid: {}********".format(fid), file=log)
log.close()