def download(model_path): pth_path = os.path.join(model_path, 'cifar_resnet_2px.pth') if not os.path.exists(pth_path): if not os.path.exists(os.path.dirname(pth_path)): os.makedirs(os.path.dirname(pth_path)) url = 'https://github.com/locuslab/convex_adversarial/blob/master/models_scaled/cifar_resnet_2px.pth?raw=true' download_res(url, pth_path)
def download(model_path): mat_name = 'cifar10-convnet-15742544.mat' mat_path = os.path.abspath(os.path.join(model_path, mat_name)) tar_path = os.path.abspath( os.path.join(model_path, 'mnist-cifar10-data-model.tar')) if not os.path.exists(mat_path): if not os.path.exists(os.path.dirname(tar_path)): os.makedirs(os.path.dirname(tar_path), exist_ok=True) if not os.path.exists(tar_path): print( 'Please download "mnist-cifar10-data-model.tar" from ' + '"https://drive.google.com/open?id=15xoZ-LUbc9GZpTlxmCJmvL_DR2qYEu2J", ' + 'and save it to "{}".'.format(tar_path)) return import tarfile with tarfile.TarFile(tar_path) as f: mat_file = f.extractfile('data/' + mat_name) with open(mat_path, 'wb+') as t: t.write(mat_file.read()) os.remove(tar_path) stats_path = os.path.abspath(os.path.join(model_path, 'stats.mat')) if not os.path.exists(stats_path): download_res( 'http://ml.cs.tsinghua.edu.cn/~xiaoyang/downloads/realsafe/DeepDefense/stats.mat', stats_path)
def download(model_path): if not os.path.exists(model_path): import tarfile os.makedirs(model_path) download_res('http://download.tensorflow.org/models/ens4_adv_inception_v3_2017_08_18.tar.gz', os.path.join(model_path, 'ens4_adv_inception_v3_2017_08_18.tar.gz')) tar = tarfile.open(os.path.join(model_path, 'ens4_adv_inception_v3_2017_08_18.tar.gz')) file_names = tar.getnames() for file_name in file_names: tar.extract(file_name, model_path) session = tf.Session(config=tf.ConfigProto(device_count={'GPU': 0})) reader = tf.train.load_checkpoint(os.path.join(model_path, 'ens4_adv_inception_v3.ckpt')) var_list = list(reader.get_variable_to_dtype_map().keys()) for var in var_list: tf.Variable(reader.get_tensor(var), name=var.replace('InceptionV3', 'Ens4InceptionV3')) session.run(tf.global_variables_initializer()) import shutil shutil.rmtree(model_path) os.makedirs(model_path) saver = tf.train.Saver() saver.save(session, os.path.join(model_path, 'ens4_adv_inception_v3.ckpt'))
def download(model_path): if not os.path.exists(model_path): if not os.path.exists(os.path.dirname(model_path)): os.makedirs(os.path.dirname(model_path)) download_res( 'http://ml.cs.tsinghua.edu.cn/~xiaoyang/downloads/resnet56-cifar.ckpt', model_path)
def download(model_path): url = 'http://download.tensorflow.org/models/adversarial_logit_pairing/imagenet64_alp025_2018_06_26.ckpt.tar.gz' if not os.path.exists(model_path + '.meta'): if not os.path.exists(os.path.dirname(model_path)): os.makedirs(os.path.dirname(model_path)) import tarfile download_res(url, model_path + '.tar.gz') t = tarfile.open(model_path + '.tar.gz') t.extractall(os.path.dirname(model_path)) os.remove(model_path + '.tar.gz')
def download(model_path): if not os.path.exists(os.path.join(model_path, 'models/adv_trained/checkpoint')): zip_path = os.path.join(model_path, 'adv_trained.zip') if not os.path.exists(zip_path): if not os.path.exists(os.path.dirname(zip_path)): os.makedirs(os.path.dirname(zip_path)) download_res('https://www.dropbox.com/s/g4b6ntrp8zrudbz/adv_trained.zip?dl=1', zip_path) import zipfile zipfile.ZipFile(zip_path).extractall(model_path) os.remove(zip_path)
def download(model_path): if not os.path.exists(model_path): if not os.path.exists(os.path.dirname(model_path)): os.makedirs(os.path.dirname(model_path), exist_ok=True) gz_path = os.path.join(os.path.dirname(model_path), 'inception_v3_2016_08_28.tar.gz') if not os.path.exists(gz_path): download_res('http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz', gz_path) import tarfile with tarfile.open(gz_path) as tar: tar.extractall(os.path.dirname(model_path)) os.remove(gz_path)
def download(model_path): if not os.path.exists(model_path): os.makedirs(model_path) h5_name = 'cifar10_ResNet110v2_model.200.h5' h5_path = os.path.join(model_path, h5_name) if not os.path.exists(h5_path): url = 'http://ml.cs.tsinghua.edu.cn/~tianyu/ADP/pretrained_models/ADP_standard_3networks/' + h5_name download_res(url, h5_path) npy_path = os.path.join(model_path, 'mean.npy') if not os.path.exists(npy_path): download_res( 'http://ml.cs.tsinghua.edu.cn/~yinpeng/downloads/adp_datamean.npy', npy_path)
r_filenames, r_labels, r_targets = [], [], [] if targets is None: for filename, label in zip(filenames, labels): if label == target_label: r_filenames.append(filename) r_labels.append(label) return r_filenames, r_labels, None else: for filename, label, target in zip(filenames, labels, targets): if label == target_label: r_filenames.append(filename) r_labels.append(label) r_targets.append(target) return r_filenames, r_labels, r_targets if __name__ == '__main__': if not os.path.exists(PATH_VAL_TXT): os.makedirs(os.path.dirname(PATH_VAL_TXT), exist_ok=True) download_res('http://ml.cs.tsinghua.edu.cn/~yinpeng/downloads/val.txt', PATH_VAL_TXT) if not os.path.exists(PATH_TARGET_TXT): os.makedirs(os.path.dirname(PATH_TARGET_TXT), exist_ok=True) download_res( 'http://ml.cs.tsinghua.edu.cn/~yinpeng/downloads/target.txt', PATH_TARGET_TXT) if not os.path.exists(PATH_IMGS): print( 'Please download "ILSVRC2012_img_val.tar" from "http://www.image-net.org/download-images", ' + 'and extract it to "{}".'.format(PATH_IMGS))
def download(model_path): url = 'https://github.com/facebookresearch/ImageNet-Adversarial-Training/releases/download/v0.1/R152-Denoise.npz' if not os.path.exists(model_path): if not os.path.exists(os.path.dirname(model_path)): os.makedirs(os.path.dirname(model_path), exist_ok=True) download_res(url, model_path)
xs_test[offset:]), tf.data.Dataset.from_tensor_slices(ys_test[offset:]) ys = ys.map(lambda y: tf.cast(y, label_dtype)) ts = ts.map(lambda t: tf.cast(t, label_dtype)) ids = tf.data.Dataset.range(offset, len(ys_test) - offset) if target_label is not None: if load_target: return tf.data.Dataset.zip( (ids, xs, ys, ts)).filter(lambda *ps: tf.math.equal(ps[2], target_label)) else: return tf.data.Dataset.zip( (ids, xs, ys)).filter(lambda *ps: tf.math.equal(ps[2], target_label)) if load_target: return tf.data.Dataset.zip((ids, xs, ys, ts)) else: return tf.data.Dataset.zip((ids, xs, ys)) if __name__ == '__main__': import os from realsafe.utils import download_res _ = load_data() if not os.path.exists(PATH_TARGET): os.makedirs(os.path.dirname(PATH_TARGET), exist_ok=True) download_res('https://ml.cs.tsinghua.edu.cn/~qian/realsafe/target.npy', PATH_TARGET)