fix utils

Former-commit-id: 366e3dc5c64ebf707640a2f969ca3bb867e9665f [formerly 9cef0f9c15d5956f586af5d9059a71b3a999824a]
Former-commit-id: 3ae67acd4d630ea937abc41ad125471f527ef617

fawkes/utils.py

@@ -8,7 +8,6 @@ import shutil
 import sys
 import tarfile
 import zipfile
-
 import six
 from six.moves.urllib.error import HTTPError, URLError
 
@@ -21,7 +20,7 @@ import keras.backend as K
 import numpy as np
 import tensorflow as tf
 from PIL import Image, ExifTags
-from keras.layers import Dense, Activation
+from keras.layers import Dense, Activation, Dropout
 from keras.models import Model
 from keras.preprocessing import image
 from skimage.transform import resize
@@ -63,7 +62,9 @@ def clip_img(X, preprocessing='raw'):
 
 
 def load_image(path):
+
     img = Image.open(path)
+
     if img._getexif() is not None:
         for orientation in ExifTags.TAGS.keys():
             if ExifTags.TAGS[orientation] == 'Orientation':
@@ -184,10 +185,12 @@ def fix_gpu_memory(mem_fraction=1):
     return sess
 
 
-def load_victim_model(number_classes, teacher_model=None, end2end=False):
+def load_victim_model(number_classes, teacher_model=None, end2end=False, dropout=0):
     for l in teacher_model.layers:
         l.trainable = end2end
     x = teacher_model.layers[-1].output
+    if dropout > 0:
+        x = Dropout(dropout)(x)
     x = Dense(number_classes)(x)
     x = Activation('softmax', name="act")(x)
     model = Model(teacher_model.input, x)

fawkes_dev/eval_cloak.py

@@ -1,169 +0,0 @@
-import argparse
-import os
-import sys
-
-import numpy as np
-
-sys.path.append("/home/shansixioing/fawkes/fawkes")
-from utils import extract_faces, get_dataset_path, init_gpu, load_extractor, load_victim_model
-
-import random
-import glob
-from keras.preprocessing import image
-from keras.utils import to_categorical
-from keras.applications.vgg16 import preprocess_input
-
-
-def select_samples(data_dir):
-    all_data_path = []
-    for cls in os.listdir(data_dir):
-        cls_dir = os.path.join(data_dir, cls)
-        for data_path in os.listdir(cls_dir):
-            all_data_path.append(os.path.join(cls_dir, data_path))
-    return all_data_path
-
-
-def generator_wrap(protect_images, test=False, validation_split=0.1):
-    train_data_dir, test_data_dir, num_classes, num_images = get_dataset_path(args.dataset)
-
-    idx = 0
-    path2class = {}
-    path2imgs_list = {}
-
-    for target_path in sorted(glob.glob(train_data_dir + "/*")):
-        path2class[target_path] = idx
-        path2imgs_list[target_path] = glob.glob(os.path.join(target_path, "*"))
-        idx += 1
-        if idx >= args.num_classes:
-            break
-
-    path2class["protected"] = idx
-
-    np.random.seed(12345)
-
-    while True:
-        batch_X = []
-        batch_Y = []
-        cur_batch_path = np.random.choice(list(path2class.keys()), args.batch_size)
-        for p in cur_batch_path:
-            cur_y = path2class[p]
-            if test and p == 'protected':
-                continue
-            # protect class images in train dataset
-            elif p == 'protected':
-                cur_x = random.choice(protect_images)
-            else:
-                cur_path = random.choice(path2imgs_list[p])
-                im = image.load_img(cur_path, target_size=(224, 224))
-                cur_x = image.img_to_array(im)
-
-            cur_x = preprocess_input(cur_x)
-            batch_X.append(cur_x)
-            batch_Y.append(cur_y)
-
-        batch_X = np.array(batch_X)
-        batch_Y = to_categorical(np.array(batch_Y), num_classes=args.num_classes + 1)
-
-        yield batch_X, batch_Y
-
-
-def eval_uncloaked_test_data(cloak_data, n_classes):
-    original_label = cloak_data.path2idx[list(cloak_data.protect_class_path)[0]]
-    protect_test_X = cloak_data.protect_test_X
-    original_Y = [original_label] * len(protect_test_X)
-    original_Y = to_categorical(original_Y, n_classes)
-    return protect_test_X, original_Y
-
-
-def eval_cloaked_test_data(cloak_data, n_classes, validation_split=0.1):
-    split = int(len(cloak_data.cloaked_protect_train_X) * (1 - validation_split))
-    cloaked_test_X = cloak_data.cloaked_protect_train_X[split:]
-    original_label = cloak_data.path2idx[list(cloak_data.protect_class_path)[0]]
-    original_Y = [original_label] * len(cloaked_test_X)
-    original_Y = to_categorical(original_Y, n_classes)
-    return cloaked_test_X, original_Y
-
-
-def main():
-    init_gpu(args.gpu)
-    #
-    # if args.dataset == 'pubfig':
-    #     N_CLASSES = 65
-    #     CLOAK_DIR = args.cloak_data
-    # elif args.dataset == 'scrub':
-    #     N_CLASSES = 530
-    #     CLOAK_DIR = args.cloak_data
-    # else:
-    #     raise ValueError
-
-    print("Build attacker's model")
-
-    image_paths = glob.glob(os.path.join(args.directory, "*"))
-    original_image_paths = sorted([path for path in image_paths if "_cloaked" not in path.split("/")[-1]])
-
-    protect_image_paths = sorted([path for path in image_paths if "_cloaked" in path.split("/")[-1]])
-
-    original_imgs = np.array([extract_faces(image.img_to_array(image.load_img(cur_path))) for cur_path in
-                     original_image_paths[:150]])
-    original_y = to_categorical([args.num_classes] * len(original_imgs), num_classes=args.num_classes + 1)
-
-    protect_imgs = [extract_faces(image.img_to_array(image.load_img(cur_path))) for cur_path in
-                    protect_image_paths]
-
-    train_generator = generator_wrap(protect_imgs,
-                                     validation_split=args.validation_split)
-    test_generator = generator_wrap(protect_imgs, test=True,
-                                    validation_split=args.validation_split)
-
-    base_model = load_extractor(args.transfer_model)
-    model = load_victim_model(teacher_model=base_model, number_classes=args.num_classes + 1)
-
-    # cloaked_test_X, cloaked_test_Y = eval_cloaked_test_data(cloak_data, args.num_classes,
-    #                                                         validation_split=args.validation_split)
-
-    # try:
-    train_data_dir, test_data_dir, num_classes, num_images = get_dataset_path(args.dataset)
-    model.fit_generator(train_generator, steps_per_epoch=num_images // 32,
-                        validation_data=(original_imgs, original_y),
-                        epochs=args.n_epochs,
-                        verbose=1,
-                        use_multiprocessing=True, workers=5)
-    # except KeyboardInterrupt:
-    #     pass
-
-    _, acc_original = model.evaluate(original_imgs, original_y, verbose=0)
-    print("Accuracy on uncloaked/original images TEST: {:.4f}".format(acc_original))
-    # EVAL_RES['acc_original'] = acc_original
-
-    _, other_acc = model.evaluate_generator(test_generator, verbose=0, steps=50)
-    print("Accuracy on other classes {:.4f}".format(other_acc))
-    # EVAL_RES['other_acc'] = other_acc
-    # dump_dictionary_as_json(EVAL_RES, os.path.join(CLOAK_DIR, "eval_seed{}.json".format(args.seed_idx)))
-
-
-def parse_arguments(argv):
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument('--gpu', type=str,
-                        help='GPU id', default='0')
-
-    parser.add_argument('--dataset', type=str,
-                        help='name of dataset', default='scrub')
-    parser.add_argument('--num_classes', type=int,
-                        help='name of dataset', default=520)
-
-    parser.add_argument('--directory', '-d', type=str,
-                        help='name of the cloak result directory',
-                        default='img/')
-
-    parser.add_argument('--transfer_model', type=str,
-                        help='the feature extractor used for tracker model training. ', default='low_extract')
-    parser.add_argument('--batch_size', type=int, default=32)
-    parser.add_argument('--validation_split', type=float, default=0.1)
-    parser.add_argument('--n_epochs', type=int, default=3)
-    return parser.parse_args(argv)
-
-
-if __name__ == '__main__':
-    args = parse_arguments(sys.argv[1:])
-    main()