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plot_ua.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Mar 13 10:50:25 2019
@author: Keshik
"""
import argparse
import torch
import numpy as np
from torchvision import transforms
import torchvision.models as models
from torch.utils.data import DataLoader
from dataset import PascalVOC_Dataset
# from randaugment import RandAugment
from utils import encode_labels
import os
import torch.utils.model_zoo as model_zoo
from models.inception import Inception3
from matplotlib import pyplot as plt
os.environ['TORCH_HOME'] = '.'
# os.environ["CUDA_VISIBLE_DEVICES"] = '1'
def main(args):
data_dir = args.data
model_name = args.arch
num = args.num
batch_size = args.batch_size
download_data = args.download_data
model_dir = os.path.join(args.results, args.arch)
if not os.path.isdir(model_dir):
os.makedirs(model_dir)
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
'inception_v3': 'https://download.pytorch.org/models/inception_v3_google-1a9a5a14.pth'
}
model_collections_dict = {
"resnet18": models.resnet18(),
"resnet34": models.resnet34(),
"resnet50": models.resnet50(),
"inception_v3": models.inception_v3()
}
# Initialize cuda parameters
use_cuda = torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
print("Available device = ", device)
if model_name in ['resnet18', 'resnet34', 'resnet50', 'inception_v3']:
model = model_collections_dict[model_name]
model.avgpool = torch.nn.AdaptiveAvgPool2d(1)
model.load_state_dict(model_zoo.load_url(model_urls[model_name]))
num_ftrs = model.fc.in_features
model.fc = torch.nn.Linear(num_ftrs, args.num_classes)
else:
model = Inception3(num_classes=args.num_classes)
model.to(device)
if args.normalize == 'mean_std':
mean = [0.457342265910642, 0.4387686270106377, 0.4073427106250871]
std = [0.26753769276329037, 0.2638145880487105, 0.2776826934044154]
elif args.normalize == 'boxmaxmin':
if args.boxmax == 1 and args.boxmin == 0:
mean = [0, 0, 0]
std = [1.0, 1.0, 1.0]
elif args.boxmax == -(args.boxmin):
mean = [0.5, 0.5, 0.5]
std = [0.5 / args.boxmax, 0.5 / args.boxmax, 0.5 / args.boxmax]
else:
return
# VOC validation dataloader
transformations_valid = transforms.Compose([transforms.Resize(330),
transforms.CenterCrop(300),
transforms.ToTensor(),
transforms.Normalize(mean = mean, std = std),
])
# transformations_valid = transforms.Compose([transforms.ToTensor(),
# ])
VOC_dataset_valid = PascalVOC_Dataset(data_dir,
year='2012',
image_set='val',
download=download_data,
transform=transformations_valid,
target_transform=encode_labels)
valid_loader = DataLoader(VOC_dataset_valid, batch_size=batch_size, num_workers=4)
#---------------Test your model here---------------------------------------
# Load the best weights before testing
weights_file_path = os.path.join(model_dir, "model-{}.pth".format(num))
if os.path.isfile(weights_file_path):
print("Loading best weights")
model.load_state_dict(torch.load(weights_file_path))
return model, device, valid_loader
def plot(args, model, device, valid_loader):
model_name = args.arch
model_dir = os.path.join(args.results, args.arch)
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
'inception_v3': 'https://download.pytorch.org/models/inception_v3_google-1a9a5a14.pth'
}
model_collections_dict = {
"resnet18": models.resnet18(),
"resnet34": models.resnet34(),
"resnet50": models.resnet50(),
"inception_v3": models.inception_v3()
}
# Initialize cuda parameters
use_cuda = torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
print("Available device = ", device)
if model_name in ['resnet18', 'resnet34', 'resnet50', 'inception_v3']:
model = model_collections_dict[model_name]
model.avgpool = torch.nn.AdaptiveAvgPool2d(1)
model.load_state_dict(model_zoo.load_url(model_urls[model_name]))
num_ftrs = model.fc.in_features
model.fc = torch.nn.Linear(num_ftrs, args.num_classes)
else:
model = Inception3(num_classes=args.num_classes)
model.to(device)
model.eval()
target_attack_result = np.load('./result/{}/{}/{}/eps_{}/k_{}.npy'.format(args.dataset, args.label_difficult, 'none_target_attack', args.eps, args.k_value), allow_pickle=True)
baseline_rank_result = np.load('./result/{}/{}/{}/eps_{}/k_{}.npy'.format(args.dataset, args.label_difficult, 'baseline_kfool', args.eps, args.k_value), allow_pickle=True)
ta_norm_list = target_attack_result[0]
ta_index = target_attack_result[1]
base_index = baseline_rank_result[0]
ta_dic = {}
sample_list = np.load('ap_{}_list.npy'.format(args.dataset))
for i in range(len(ta_index)):
ta_dic[ta_index[i]]=ta_norm_list[i]
a = {}
if args.app =='target_attack':
a['best_3'] = [1118, 488, 460]
a['best_5'] = [310, 316, 814]
a['best_10'] = [858,896,316]
a['random_3'] = [309, 860, 828]
a['random_5'] = [721,390,603]
a['random_10'] = [1067,978,896]
a['worst_3'] = [858,859,721]
a['worst_5'] = [942,870,978]
a['worst_10'] = [1137,521,858]
elif args.app =='none_target_attack':
a['best_3'] = [5] # 30, 521, 874, 448,5
# a['best_3'] = [521]
a['best_5'] = [5]
a['best_10'] = [5]
# mean = [0.5, 0.5, 0.5]
# std = [0.5, 0.5, 0.5]
mean = 0.5
std = 0.5
index = 0
labels=['Aeroplane', 'Bicycle', 'Bird', 'Boat', 'Bottle', 'Bus', 'Car', 'Cat', 'Chair', 'Cow', \
'Diningtable', 'Dog', 'Horse', 'Motorbike', 'Person', 'Pottedplant', 'Sheep', 'Sofa', 'Train', 'Tvmonitor']
for ith in a['{}_{}'.format(args.label_difficult, args.k_value)]:
index = index +1
if index <= 1:
ta_3 = np.load('./plot_result/{}/{}/{}/eps_{}/images_result_k_{}_ith_{}.npy'.format(args.dataset, args.label_difficult, 'none_target_attack', args.eps,
args.k_value,ith), allow_pickle=True)
bs_3 = np.load('./plot_result/{}/{}/{}/eps_{}/images_result_k_{}_ith_{}.npy'.format(args.dataset, args.label_difficult, 'baseline_kfool',
args.eps, args.k_value, ith), allow_pickle=True)
os.makedirs('./plot_fig/{}/{}/{}/eps_{}'.format(args.dataset, args.label_difficult, args.app, args.eps),
exist_ok=True)
########GT
fig = plt.figure(constrained_layout=True)
plt.imshow(ta_3[0][0].transpose((1, 2, 0)) * std + mean)
plt.axis('off')
plt.tight_layout()
plt.show()
if args.k_value ==3:
fig.savefig('./plot_fig/{}/{}/{}/eps_{}/{}_UA_result_{}_k_{}_ith_{}_original.jpg'.format( \
args.dataset, args.label_difficult, args.app, args.eps, args.dataset, args.label_difficult,
args.k_value, ith),
bbox_inches='tight')
GT = np.asarray(labels)[ta_3[1][0] == 1]
GT_str = ''
for i in range(GT.size):
GT_str += GT[i]
if i < GT.size - 1:
GT_str += ','
print('GT:{}'.format(GT_str))
###########kFool
fig = plt.figure(constrained_layout=True)
plt.imshow(bs_3[3][0].transpose((1, 2, 0)) * std + mean)
plt.axis('off')
plt.tight_layout()
plt.show()
fig.savefig('./plot_fig/{}/{}/{}/eps_{}/{}_UA_result_{}_k_{}_ith_{}_kFool.jpg'.format( \
args.dataset, args.label_difficult, args.app, args.eps, args.dataset, args.label_difficult,
args.k_value, ith),
bbox_inches='tight')
ML_AP_TA = np.asarray(labels)[bs_3[5][0] == 1]
ML_AP_TA_str = ''
for j in range(ML_AP_TA.size):
ML_AP_TA_str += ML_AP_TA[j]
if j < ML_AP_TA.size - 1:
ML_AP_TA_str += ','
print('Top-kFool-{}:{}'.format(args.k_value, ML_AP_TA_str))
fig = plt.figure(constrained_layout=True)
plt.imshow(20*(bs_3[6][0].transpose((1, 2, 0))))
plt.axis('off')
plt.tight_layout()
plt.show()
fig.savefig('./plot_fig/{}/{}/{}/eps_{}/{}_UA_result_{}_k_{}_ith_{}_kFool_pert.jpg'.format( \
args.dataset, args.label_difficult, args.app, args.eps, args.dataset, args.label_difficult,
args.k_value, ith),
bbox_inches='tight')
print('$||z||$={:.2f}'.format(np.linalg.norm(bs_3[6][0])))
###########TKML-UA
fig = plt.figure(constrained_layout=True)
plt.imshow(ta_3[3][0].transpose((1, 2, 0)) * std + mean)
plt.axis('off')
plt.tight_layout()
plt.show()
fig.savefig('./plot_fig/{}/{}/{}/eps_{}/{}_UA_result_{}_k_{}_ith_{}_TKML-UA.jpg'.format( \
args.dataset, args.label_difficult, args.app, args.eps, args.dataset, args.label_difficult,
args.k_value, ith),
bbox_inches='tight')
TKML_TA = np.asarray(labels)[ta_3[5][0] == 1]
TKML_TA_str = ''
for j in range(TKML_TA.size):
TKML_TA_str += TKML_TA[j]
if j < TKML_TA.size - 1:
TKML_TA_str += ','
print('Top-TKML-UA-{}:{}'.format(args.k_value, TKML_TA_str))
fig = plt.figure(constrained_layout=True)
plt.imshow(20 * (ta_3[6][0].transpose((1, 2, 0))))
plt.axis('off')
plt.tight_layout()
plt.show()
fig.savefig('./plot_fig/{}/{}/{}/eps_{}/{}_UA_result_{}_k_{}_ith_{}_TKML-UA_pert.jpg'.format( \
args.dataset, args.label_difficult, args.app, args.eps, args.dataset, args.label_difficult,
args.k_value, ith),
bbox_inches='tight')
print('$||z||$={:.2f}'.format(np.linalg.norm(ta_3[6][0])))
if index ==1:
break
# Execute main function here.
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--data', default='./data', help='path to dataset')
parser.add_argument('--dataset', default='VOC', type=str, choices={'VOC', 'COCO'}, help='path to dataset')
parser.add_argument('--results', default='woSigmoid-BCE-Adam-bs64-box_-1_1', help='path to dataset')
parser.add_argument('--num_classes', default=20, type=int, help='number of classes')
parser.add_argument('--arch', default='inception_v3',
help='model architecture: ' +
' (default: resnet18)')
parser.add_argument('-j', '--workers', default=1, type=int, metavar='N',
help='number of data loading workers (default: 4)')
parser.add_argument('--gpu', default=0, type=int,
help='GPU id to use.')
parser.add_argument('--normalize', default='boxmaxmin', type=str, choices={'mean_std', 'boxmaxmin'},
help='optimizer for training')
parser.add_argument('-b', '--batch-size', default=1, type=int,
metavar='N',
help='mini-batch size (default: 256), this is the total '
'batch size of all GPUs on the current node when '
'using Data Parallel or Distributed Data Parallel')
parser.add_argument('--download_data', default=False, type=bool, help='download data')
parser.add_argument('--num', default=1, type=int, help='num to resume')
parser.add_argument('--k_value', default=10, type=int, help='k-value')
parser.add_argument('--eps', default=10, type=int, help='eps')
parser.add_argument('--boxmax', default=1, type=float, help='max value of input')
parser.add_argument('--boxmin', default=-1, type=float, help='min value of input')
parser.add_argument('--label_difficult', default='best', type=str, choices={'best', 'random', 'worst'},
help='difficult types')
parser.add_argument('--app', default='none_target_attack', type=str,
choices={'target_attack', 'none_target_attack', 'UAP_attack', 'baseline_rank', 'baseline_kfool',
'baseline_kUAP', 'test', 'train'}, help='attack types')
args = parser.parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = '{}'.format(args.gpu)
model, device, valid_loader = main(args)
plot(args,model, device, valid_loader)