ResearchPapers/fawkes
ResearchPapers/fawkes
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fix eval

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Shawn-Shan 2020-07-21 01:13:28 -05:00
parent 5e1b8fd2fc
commit 2a8d4f0e51
1 changed files with 15 additions and 10 deletions
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25
evaluation/eval_local.py
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@ -8,7 +8,7 @@ import keras
import numpy as np import numpy as np
random.seed(1000) random.seed(1000)
from fawkes.utils import init_gpu, load_extractor, load_victim_model, get_file, preprocess, Faces from fawkes.utils import init_gpu, load_extractor, load_victim_model, get_file, preprocess, Faces, filter_image_paths
from keras.preprocessing import image from keras.preprocessing import image
from keras.utils import to_categorical from keras.utils import to_categorical
from fawkes.align_face import aligner from fawkes.align_face import aligner
@ -115,7 +115,6 @@ class CallbackGenerator(keras.callbacks.Callback):
def on_epoch_end(self, epoch, logs=None): def on_epoch_end(self, epoch, logs=None):
_, original_acc = self.model.evaluate(self.original_imgs, self.original_y, verbose=0) _, original_acc = self.model.evaluate(self.original_imgs, self.original_y, verbose=0)
print("Epoch: {} - Protection Success Rate {:.4f}".format(epoch, 1 - original_acc)) print("Epoch: {} - Protection Success Rate {:.4f}".format(epoch, 1 - original_acc))
@ -124,16 +123,22 @@ def main():
ali = aligner(sess) ali = aligner(sess)
print("Build attacker's model") print("Build attacker's model")
image_paths = glob.glob(os.path.join(args.directory, "*")) image_paths = glob.glob(os.path.join(args.directory, "*"))
cloak_file_name = "low_cloaked" cloak_file_name = "_cloaked"
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 cloak_file_name in path.split("/")[-1]])
original_faces = Faces(original_image_paths, ali, verbose=1, eval_local=True) original_image_paths = sorted([path for path in image_paths if "cloaked" not in path.split("/")[-1]])
original_image_paths, original_loaded_images = filter_image_paths(original_image_paths)
protect_image_paths = sorted([path for path in image_paths if cloak_file_name in path.split("/")[-1]])
protect_image_paths, protected_loaded_images = filter_image_paths(protect_image_paths)
print("Find {} original image and {} cloaked images".format(len(original_image_paths), len(protect_image_paths)))
original_faces = Faces(original_image_paths, original_loaded_images, ali, verbose=1, eval_local=True)
original_faces = original_faces.cropped_faces original_faces = original_faces.cropped_faces
cloaked_faces = Faces(protect_image_paths, ali, verbose=1, eval_local=True) cloaked_faces = Faces(protect_image_paths, protected_loaded_images, ali, verbose=1, eval_local=True)
cloaked_faces = cloaked_faces.cropped_faces cloaked_faces = cloaked_faces.cropped_faces
if len(original_faces) <= 10: if len(original_faces) <= 10 or len(protect_image_paths) <= 10:
raise Exception("Must have more than 10 protected images to run the evaluation") raise Exception("Must have more than 10 protected images to run the evaluation")
num_classes = args.num_other_classes + 1 num_classes = args.num_other_classes + 1
@ -152,7 +157,7 @@ def main():
model.fit_generator(train_generator, steps_per_epoch=num_classes * 10 // 32, model.fit_generator(train_generator, steps_per_epoch=num_classes * 10 // 32,
epochs=args.n_epochs, epochs=args.n_epochs,
verbose=2, verbose=1,
callbacks=[cb] callbacks=[cb]
) )
@ -169,7 +174,7 @@ def parse_arguments(argv):
parser.add_argument('--dataset', type=str, parser.add_argument('--dataset', type=str,
help='name of dataset', default='scrub') help='name of dataset', default='scrub')
parser.add_argument('--num_other_classes', type=int, parser.add_argument('--num_other_classes', type=int,
help='name of dataset', default=1000) help='name of dataset', default=500)
parser.add_argument('--directory', '-d', type=str, parser.add_argument('--directory', '-d', type=str,
help='name of the cloak result directory', required=True) help='name of the cloak result directory', required=True)