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create_clean_set.py
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create_clean_set.py
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import numpy as np
import os
import torch
from torchvision import datasets, transforms
from torchvision.utils import save_image
import argparse
import random
import config
from utils import default_args, tools
"""
<Datasets>
GTSRB, CIFAR10, Imagenette, Imagenet, Ember
"""
parser = argparse.ArgumentParser()
parser.add_argument('-dataset', type=str, required=False, default=default_args.parser_default['dataset'],
choices=default_args.parser_choices['dataset'])
parser.add_argument('-clean_budget', type=int, default=2000)
# by defaut : we assume 2000 clean samples for defensive purpose
args = parser.parse_args()
tools.setup_seed(0)
"""
Get Data Set
"""
data_dir = './data' # directory to save standard clean set
if args.dataset == 'gtsrb':
data_transform = transforms.Compose([
transforms.Resize((32, 32)),
transforms.ToTensor(),
])
clean_set = datasets.GTSRB(os.path.join(data_dir, 'gtsrb'), split='test',
transform=data_transform, download=True)
img_size = 32
num_classes = 43
elif args.dataset == 'cifar10':
data_transform = transforms.Compose([
transforms.ToTensor()
])
clean_set = datasets.CIFAR10(os.path.join(data_dir, 'cifar10'), train=False,
download=True, transform=data_transform)
img_size = 32
num_classes = 10
elif args.dataset == 'imagenette':
data_transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
])
clean_set = datasets.ImageFolder(os.path.join(os.path.join(data_dir, 'imagenette2'), 'val'), data_transform)
img_size = 224
num_classes = 10
elif args.dataset == 'imagenet':
pass
elif args.dataset == 'ember':
import ember
EMBER_DATA_DIR = os.path.join(data_dir, 'ember')
# Perform feature vectorization only if necessary.
try:
x_train, y_train, x_test, y_test = ember.read_vectorized_features(
EMBER_DATA_DIR,
feature_version=1
)
except:
ember.create_vectorized_features(
EMBER_DATA_DIR,
feature_version=1
)
x_train, y_train, x_test, y_test = ember.read_vectorized_features(
EMBER_DATA_DIR,
feature_version=1
)
# x_train = x_train.astype(dtype='float64')
x_test = x_test.astype(np.float)
y_test = y_test.astype(np.long)
# Get rid of unknown labels
# x_train = x_train[y_train != -1]
# y_train = y_train[y_train != -1]
x_test = x_test[y_test != -1]
y_test = y_test[y_test != -1]
else:
print('<Undefined> Dataset = %s' % args.dataset)
exit(0)
"""
Generate Clean Split
"""
root_dir = 'clean_set'
if not os.path.exists(root_dir):
os.mkdir(root_dir)
root_dir = os.path.join(root_dir, args.dataset)
if not os.path.exists(root_dir):
os.mkdir(root_dir)
clean_split_dir = os.path.join(root_dir, 'clean_split') # clean samples at hand for defensive purpose
if not os.path.exists(clean_split_dir):
os.mkdir(clean_split_dir)
clean_split_img_dir = os.path.join(clean_split_dir, 'data') # to save img
if not os.path.exists(clean_split_img_dir):
os.mkdir(clean_split_img_dir)
test_split_dir = os.path.join(root_dir, 'test_split') # test samples for evaluation & debug purpose
if not os.path.exists(test_split_dir):
os.mkdir(test_split_dir)
test_split_img_dir = os.path.join(test_split_dir, 'data') # to save img
if not os.path.exists(test_split_img_dir):
os.mkdir(test_split_img_dir)
if args.dataset != 'ember' and args.dataset != 'imagenet':
# randomly sample from a clean test set to simulate the clean samples at hand
num_img = len(clean_set)
id_set = list(range(0, num_img))
random.shuffle(id_set)
clean_split_indices = id_set[:args.clean_budget]
test_indices = id_set[args.clean_budget:]
# Construct Shift Set for Defensive Purpose
clean_split_set = torch.utils.data.Subset(clean_set, clean_split_indices)
num = len(clean_split_set)
clean_label_set = []
for i in range(num):
img, gt = clean_split_set[i]
img_file_name = '%d.png' % i
img_file_path = os.path.join(clean_split_img_dir, img_file_name)
save_image(img, img_file_path)
print('[Generate Clean Split] Save %s' % img_file_path)
clean_label_set.append(gt)
clean_label_set = torch.LongTensor(clean_label_set)
clean_label_path = os.path.join(clean_split_dir, 'clean_labels')
torch.save(clean_label_set, clean_label_path)
print('[Generate Clean Split Set] Save %s' % clean_label_path)
# Take the rest clean samples as the test set for debug & evaluation
test_set = torch.utils.data.Subset(clean_set, test_indices)
num = len(test_set)
label_set = []
for i in range(num):
img, gt = test_set[i]
img_file_name = '%d.png' % i
img_file_path = os.path.join(test_split_img_dir, img_file_name)
save_image(img, img_file_path)
print('[Generate Test Set] Save %s' % img_file_path)
label_set.append(gt)
label_set = torch.LongTensor(label_set)
label_path = os.path.join(test_split_dir, 'labels')
torch.save(label_set, label_path)
print('[Generate Test Set] Save %s' % label_path)
elif args.dataset == 'imagenet':
# randomly sample from a clean test set to simulate the clean samples at hand
num_img = 50000
id_set = list(range(0, num_img))
random.shuffle(id_set)
clean_split_indices = id_set[:args.clean_budget]
test_indices = id_set[args.clean_budget:]
print('[Generate Clean Split Set] Save %s' % os.path.join(clean_split_dir, 'clean_split_indices'))
torch.save(clean_split_indices, os.path.join(clean_split_dir, 'clean_split_indices'))
print('[Generate Test Set] Save %s' % os.path.join(test_split_dir, 'test_indices'))
torch.save(test_indices, os.path.join(test_split_dir, 'test_indices'))
else:
num_samples = len(y_test)
id_set = list(range(0, num_samples))
random.shuffle(id_set)
clean_split_indices = id_set[:args.clean_budget]
test_indices = id_set[args.clean_budget:]
x_clean_split = x_test[clean_split_indices]
y_clean_split = y_test[clean_split_indices]
x_test_split = x_test[test_indices]
y_test_split = y_test[test_indices]
np.save(os.path.join(clean_split_dir, 'X'), x_clean_split)
np.save(os.path.join(clean_split_dir, 'Y'), y_clean_split)
print(
'[Generate Clean Split Set] %s, %s' % (os.path.join(clean_split_dir, 'X'), os.path.join(clean_split_dir, 'Y')))
np.save(os.path.join(test_split_dir, 'X'), x_test_split)
np.save(os.path.join(test_split_dir, 'Y'), y_test_split)
print('[Generate Test Set] %s, %s' % (os.path.join(test_split_dir, 'X'), os.path.join(test_split_dir, 'Y')))