First commit.
This commit is contained in:
Nataniel Ruiz Gutierrez
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*.jpeg
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*.pth
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*.npy
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@@ -1,674 +0,0 @@
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GNU GENERAL PUBLIC LICENSE
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Version 3, 29 June 2007
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Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
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Everyone is permitted to copy and distribute verbatim copies
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|
||||
You are not required to accept this License in order to receive or
|
||||
run a copy of the Program. Ancillary propagation of a covered work
|
||||
occurring solely as a consequence of using peer-to-peer transmission
|
||||
to receive a copy likewise does not require acceptance. However,
|
||||
nothing other than this License grants you permission to propagate or
|
||||
modify any covered work. These actions infringe copyright if you do
|
||||
not accept this License. Therefore, by modifying or propagating a
|
||||
covered work, you indicate your acceptance of this License to do so.
|
||||
|
||||
10. Automatic Licensing of Downstream Recipients.
|
||||
|
||||
Each time you convey a covered work, the recipient automatically
|
||||
receives a license from the original licensors, to run, modify and
|
||||
propagate that work, subject to this License. You are not responsible
|
||||
for enforcing compliance by third parties with this License.
|
||||
|
||||
An "entity transaction" is a transaction transferring control of an
|
||||
organization, or substantially all assets of one, or subdividing an
|
||||
organization, or merging organizations. If propagation of a covered
|
||||
work results from an entity transaction, each party to that
|
||||
transaction who receives a copy of the work also receives whatever
|
||||
licenses to the work the party's predecessor in interest had or could
|
||||
give under the previous paragraph, plus a right to possession of the
|
||||
Corresponding Source of the work from the predecessor in interest, if
|
||||
the predecessor has it or can get it with reasonable efforts.
|
||||
|
||||
You may not impose any further restrictions on the exercise of the
|
||||
rights granted or affirmed under this License. For example, you may
|
||||
not impose a license fee, royalty, or other charge for exercise of
|
||||
rights granted under this License, and you may not initiate litigation
|
||||
(including a cross-claim or counterclaim in a lawsuit) alleging that
|
||||
any patent claim is infringed by making, using, selling, offering for
|
||||
sale, or importing the Program or any portion of it.
|
||||
|
||||
11. Patents.
|
||||
|
||||
A "contributor" is a copyright holder who authorizes use under this
|
||||
License of the Program or a work on which the Program is based. The
|
||||
work thus licensed is called the contributor's "contributor version".
|
||||
|
||||
A contributor's "essential patent claims" are all patent claims
|
||||
owned or controlled by the contributor, whether already acquired or
|
||||
hereafter acquired, that would be infringed by some manner, permitted
|
||||
by this License, of making, using, or selling its contributor version,
|
||||
but do not include claims that would be infringed only as a
|
||||
consequence of further modification of the contributor version. For
|
||||
purposes of this definition, "control" includes the right to grant
|
||||
patent sublicenses in a manner consistent with the requirements of
|
||||
this License.
|
||||
|
||||
Each contributor grants you a non-exclusive, worldwide, royalty-free
|
||||
patent license under the contributor's essential patent claims, to
|
||||
make, use, sell, offer for sale, import and otherwise run, modify and
|
||||
propagate the contents of its contributor version.
|
||||
|
||||
In the following three paragraphs, a "patent license" is any express
|
||||
agreement or commitment, however denominated, not to enforce a patent
|
||||
(such as an express permission to practice a patent or covenant not to
|
||||
sue for patent infringement). To "grant" such a patent license to a
|
||||
party means to make such an agreement or commitment not to enforce a
|
||||
patent against the party.
|
||||
|
||||
If you convey a covered work, knowingly relying on a patent license,
|
||||
and the Corresponding Source of the work is not available for anyone
|
||||
to copy, free of charge and under the terms of this License, through a
|
||||
publicly available network server or other readily accessible means,
|
||||
then you must either (1) cause the Corresponding Source to be so
|
||||
available, or (2) arrange to deprive yourself of the benefit of the
|
||||
patent license for this particular work, or (3) arrange, in a manner
|
||||
consistent with the requirements of this License, to extend the patent
|
||||
license to downstream recipients. "Knowingly relying" means you have
|
||||
actual knowledge that, but for the patent license, your conveying the
|
||||
covered work in a country, or your recipient's use of the covered work
|
||||
in a country, would infringe one or more identifiable patents in that
|
||||
country that you have reason to believe are valid.
|
||||
|
||||
If, pursuant to or in connection with a single transaction or
|
||||
arrangement, you convey, or propagate by procuring conveyance of, a
|
||||
covered work, and grant a patent license to some of the parties
|
||||
receiving the covered work authorizing them to use, propagate, modify
|
||||
or convey a specific copy of the covered work, then the patent license
|
||||
you grant is automatically extended to all recipients of the covered
|
||||
work and works based on it.
|
||||
|
||||
A patent license is "discriminatory" if it does not include within
|
||||
the scope of its coverage, prohibits the exercise of, or is
|
||||
conditioned on the non-exercise of one or more of the rights that are
|
||||
specifically granted under this License. You may not convey a covered
|
||||
work if you are a party to an arrangement with a third party that is
|
||||
in the business of distributing software, under which you make payment
|
||||
to the third party based on the extent of your activity of conveying
|
||||
the work, and under which the third party grants, to any of the
|
||||
parties who would receive the covered work from you, a discriminatory
|
||||
patent license (a) in connection with copies of the covered work
|
||||
conveyed by you (or copies made from those copies), or (b) primarily
|
||||
for and in connection with specific products or compilations that
|
||||
contain the covered work, unless you entered into that arrangement,
|
||||
or that patent license was granted, prior to 28 March 2007.
|
||||
|
||||
Nothing in this License shall be construed as excluding or limiting
|
||||
any implied license or other defenses to infringement that may
|
||||
otherwise be available to you under applicable patent law.
|
||||
|
||||
12. No Surrender of Others' Freedom.
|
||||
|
||||
If conditions are imposed on you (whether by court order, agreement or
|
||||
otherwise) that contradict the conditions of this License, they do not
|
||||
excuse you from the conditions of this License. If you cannot convey a
|
||||
covered work so as to satisfy simultaneously your obligations under this
|
||||
License and any other pertinent obligations, then as a consequence you may
|
||||
not convey it at all. For example, if you agree to terms that obligate you
|
||||
to collect a royalty for further conveying from those to whom you convey
|
||||
the Program, the only way you could satisfy both those terms and this
|
||||
License would be to refrain entirely from conveying the Program.
|
||||
|
||||
13. Use with the GNU Affero General Public License.
|
||||
|
||||
Notwithstanding any other provision of this License, you have
|
||||
permission to link or combine any covered work with a work licensed
|
||||
under version 3 of the GNU Affero General Public License into a single
|
||||
combined work, and to convey the resulting work. The terms of this
|
||||
License will continue to apply to the part which is the covered work,
|
||||
but the special requirements of the GNU Affero General Public License,
|
||||
section 13, concerning interaction through a network will apply to the
|
||||
combination as such.
|
||||
|
||||
14. Revised Versions of this License.
|
||||
|
||||
The Free Software Foundation may publish revised and/or new versions of
|
||||
the GNU General Public License from time to time. Such new versions will
|
||||
be similar in spirit to the present version, but may differ in detail to
|
||||
address new problems or concerns.
|
||||
|
||||
Each version is given a distinguishing version number. If the
|
||||
Program specifies that a certain numbered version of the GNU General
|
||||
Public License "or any later version" applies to it, you have the
|
||||
option of following the terms and conditions either of that numbered
|
||||
version or of any later version published by the Free Software
|
||||
Foundation. If the Program does not specify a version number of the
|
||||
GNU General Public License, you may choose any version ever published
|
||||
by the Free Software Foundation.
|
||||
|
||||
If the Program specifies that a proxy can decide which future
|
||||
versions of the GNU General Public License can be used, that proxy's
|
||||
public statement of acceptance of a version permanently authorizes you
|
||||
to choose that version for the Program.
|
||||
|
||||
Later license versions may give you additional or different
|
||||
permissions. However, no additional obligations are imposed on any
|
||||
author or copyright holder as a result of your choosing to follow a
|
||||
later version.
|
||||
|
||||
15. Disclaimer of Warranty.
|
||||
|
||||
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
|
||||
APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
|
||||
HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
|
||||
OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
|
||||
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
|
||||
PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
|
||||
IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
|
||||
ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
|
||||
|
||||
16. Limitation of Liability.
|
||||
|
||||
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
|
||||
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
|
||||
THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
|
||||
GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
|
||||
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
|
||||
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
|
||||
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
|
||||
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
|
||||
SUCH DAMAGES.
|
||||
|
||||
17. Interpretation of Sections 15 and 16.
|
||||
|
||||
If the disclaimer of warranty and limitation of liability provided
|
||||
above cannot be given local legal effect according to their terms,
|
||||
reviewing courts shall apply local law that most closely approximates
|
||||
an absolute waiver of all civil liability in connection with the
|
||||
Program, unless a warranty or assumption of liability accompanies a
|
||||
copy of the Program in return for a fee.
|
||||
|
||||
END OF TERMS AND CONDITIONS
|
||||
|
||||
How to Apply These Terms to Your New Programs
|
||||
|
||||
If you develop a new program, and you want it to be of the greatest
|
||||
possible use to the public, the best way to achieve this is to make it
|
||||
free software which everyone can redistribute and change under these terms.
|
||||
|
||||
To do so, attach the following notices to the program. It is safest
|
||||
to attach them to the start of each source file to most effectively
|
||||
state the exclusion of warranty; and each file should have at least
|
||||
the "copyright" line and a pointer to where the full notice is found.
|
||||
|
||||
<one line to give the program's name and a brief idea of what it does.>
|
||||
Copyright (C) <year> <name of author>
|
||||
|
||||
This program is free software: you can redistribute it and/or modify
|
||||
it under the terms of the GNU General Public License as published by
|
||||
the Free Software Foundation, either version 3 of the License, or
|
||||
(at your option) any later version.
|
||||
|
||||
This program is distributed in the hope that it will be useful,
|
||||
but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
GNU General Public License for more details.
|
||||
|
||||
You should have received a copy of the GNU General Public License
|
||||
along with this program. If not, see <http://www.gnu.org/licenses/>.
|
||||
|
||||
Also add information on how to contact you by electronic and paper mail.
|
||||
|
||||
If the program does terminal interaction, make it output a short
|
||||
notice like this when it starts in an interactive mode:
|
||||
|
||||
<program> Copyright (C) <year> <name of author>
|
||||
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
|
||||
This is free software, and you are welcome to redistribute it
|
||||
under certain conditions; type `show c' for details.
|
||||
|
||||
The hypothetical commands `show w' and `show c' should show the appropriate
|
||||
parts of the General Public License. Of course, your program's commands
|
||||
might be different; for a GUI interface, you would use an "about box".
|
||||
|
||||
You should also get your employer (if you work as a programmer) or school,
|
||||
if any, to sign a "copyright disclaimer" for the program, if necessary.
|
||||
For more information on this, and how to apply and follow the GNU GPL, see
|
||||
<http://www.gnu.org/licenses/>.
|
||||
|
||||
The GNU General Public License does not permit incorporating your program
|
||||
into proprietary programs. If your program is a subroutine library, you
|
||||
may consider it more useful to permit linking proprietary applications with
|
||||
the library. If this is what you want to do, use the GNU Lesser General
|
||||
Public License instead of this License. But first, please read
|
||||
<http://www.gnu.org/philosophy/why-not-lgpl.html>.
|
||||
@@ -1,25 +0,0 @@
|
||||
import torch.utils.data
|
||||
from data.dataset import DatasetFactory
|
||||
|
||||
|
||||
class CustomDatasetDataLoader:
|
||||
def __init__(self, opt, is_for_train=True):
|
||||
self._opt = opt
|
||||
self._is_for_train = is_for_train
|
||||
self._num_threds = opt.n_threads_train if is_for_train else opt.n_threads_test
|
||||
self._create_dataset()
|
||||
|
||||
def _create_dataset(self):
|
||||
self._dataset = DatasetFactory.get_by_name(self._opt.dataset_mode, self._opt, self._is_for_train)
|
||||
self._dataloader = torch.utils.data.DataLoader(
|
||||
self._dataset,
|
||||
batch_size=self._opt.batch_size,
|
||||
shuffle=not self._opt.serial_batches,
|
||||
num_workers=int(self._num_threds),
|
||||
drop_last=True)
|
||||
|
||||
def load_data(self):
|
||||
return self._dataloader
|
||||
|
||||
def __len__(self):
|
||||
return len(self._dataset)
|
||||
@@ -1,68 +0,0 @@
|
||||
import torch.utils.data as data
|
||||
from PIL import Image
|
||||
import torchvision.transforms as transforms
|
||||
import os
|
||||
import os.path
|
||||
|
||||
|
||||
class DatasetFactory:
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
@staticmethod
|
||||
def get_by_name(dataset_name, opt, is_for_train):
|
||||
if dataset_name == 'aus':
|
||||
from data.dataset_aus import AusDataset
|
||||
dataset = AusDataset(opt, is_for_train)
|
||||
else:
|
||||
raise ValueError("Dataset [%s] not recognized." % dataset_name)
|
||||
|
||||
print('Dataset {} was created'.format(dataset.name))
|
||||
return dataset
|
||||
|
||||
|
||||
class DatasetBase(data.Dataset):
|
||||
def __init__(self, opt, is_for_train):
|
||||
super(DatasetBase, self).__init__()
|
||||
self._name = 'BaseDataset'
|
||||
self._root = None
|
||||
self._opt = opt
|
||||
self._is_for_train = is_for_train
|
||||
self._create_transform()
|
||||
|
||||
self._IMG_EXTENSIONS = [
|
||||
'.jpg', '.JPG', '.jpeg', '.JPEG',
|
||||
'.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP',
|
||||
]
|
||||
|
||||
@property
|
||||
def name(self):
|
||||
return self._name
|
||||
|
||||
@property
|
||||
def path(self):
|
||||
return self._root
|
||||
|
||||
def _create_transform(self):
|
||||
self._transform = transforms.Compose([])
|
||||
|
||||
def get_transform(self):
|
||||
return self._transform
|
||||
|
||||
def _is_image_file(self, filename):
|
||||
return any(filename.endswith(extension) for extension in self._IMG_EXTENSIONS)
|
||||
|
||||
def _is_csv_file(self, filename):
|
||||
return filename.endswith('.csv')
|
||||
|
||||
def _get_all_files_in_subfolders(self, dir, is_file):
|
||||
images = []
|
||||
assert os.path.isdir(dir), '%s is not a valid directory' % dir
|
||||
|
||||
for root, _, fnames in sorted(os.walk(dir)):
|
||||
for fname in fnames:
|
||||
if is_file(fname):
|
||||
path = os.path.join(root, fname)
|
||||
images.append(path)
|
||||
|
||||
return images
|
||||
@@ -1,117 +0,0 @@
|
||||
import os.path
|
||||
import torchvision.transforms as transforms
|
||||
from data.dataset import DatasetBase
|
||||
from PIL import Image
|
||||
import random
|
||||
import numpy as np
|
||||
import pickle
|
||||
from utils import cv_utils
|
||||
|
||||
|
||||
class AusDataset(DatasetBase):
|
||||
def __init__(self, opt, is_for_train):
|
||||
super(AusDataset, self).__init__(opt, is_for_train)
|
||||
self._name = 'AusDataset'
|
||||
|
||||
# read dataset
|
||||
self._read_dataset_paths()
|
||||
|
||||
def __getitem__(self, index):
|
||||
assert (index < self._dataset_size)
|
||||
|
||||
# start_time = time.time()
|
||||
real_img = None
|
||||
real_cond = None
|
||||
while real_img is None or real_cond is None:
|
||||
# if sample randomly: overwrite index
|
||||
if not self._opt.serial_batches:
|
||||
index = random.randint(0, self._dataset_size - 1)
|
||||
|
||||
# get sample data
|
||||
sample_id = self._ids[index]
|
||||
|
||||
real_img, real_img_path = self._get_img_by_id(sample_id)
|
||||
real_cond = self._get_cond_by_id(sample_id)
|
||||
|
||||
if real_img is None:
|
||||
print 'error reading image %s, skipping sample' % sample_id
|
||||
if real_cond is None:
|
||||
print 'error reading aus %s, skipping sample' % sample_id
|
||||
|
||||
desired_cond = self._generate_random_cond()
|
||||
|
||||
# transform data
|
||||
img = self._transform(Image.fromarray(real_img))
|
||||
|
||||
# pack data
|
||||
sample = {'real_img': img,
|
||||
'real_cond': real_cond,
|
||||
'desired_cond': desired_cond,
|
||||
'sample_id': sample_id,
|
||||
'real_img_path': real_img_path
|
||||
}
|
||||
|
||||
# print (time.time() - start_time)
|
||||
|
||||
return sample
|
||||
|
||||
def __len__(self):
|
||||
return self._dataset_size
|
||||
|
||||
def _read_dataset_paths(self):
|
||||
self._root = self._opt.data_dir
|
||||
self._imgs_dir = os.path.join(self._root, self._opt.images_folder)
|
||||
|
||||
# read ids
|
||||
use_ids_filename = self._opt.train_ids_file if self._is_for_train else self._opt.test_ids_file
|
||||
use_ids_filepath = os.path.join(self._root, use_ids_filename)
|
||||
self._ids = self._read_ids(use_ids_filepath)
|
||||
|
||||
# read aus
|
||||
conds_filepath = os.path.join(self._root, self._opt.aus_file)
|
||||
self._conds = self._read_conds(conds_filepath)
|
||||
|
||||
self._ids = list(set(self._ids).intersection(set(self._conds.keys())))
|
||||
|
||||
# dataset size
|
||||
self._dataset_size = len(self._ids)
|
||||
|
||||
def _create_transform(self):
|
||||
if self._is_for_train:
|
||||
transform_list = [transforms.RandomHorizontalFlip(),
|
||||
transforms.ToTensor(),
|
||||
transforms.Normalize(mean=[0.5, 0.5, 0.5],
|
||||
std=[0.5, 0.5, 0.5]),
|
||||
]
|
||||
else:
|
||||
transform_list = [transforms.ToTensor(),
|
||||
transforms.Normalize(mean=[0.5, 0.5, 0.5],
|
||||
std=[0.5, 0.5, 0.5]),
|
||||
]
|
||||
self._transform = transforms.Compose(transform_list)
|
||||
|
||||
def _read_ids(self, file_path):
|
||||
ids = np.loadtxt(file_path, delimiter='\t', dtype=np.str)
|
||||
return [id[:-4] for id in ids]
|
||||
|
||||
def _read_conds(self, file_path):
|
||||
with open(file_path, 'rb') as f:
|
||||
return pickle.load(f)
|
||||
|
||||
def _get_cond_by_id(self, id):
|
||||
if id in self._conds:
|
||||
return self._conds[id]/5.0
|
||||
else:
|
||||
return None
|
||||
|
||||
def _get_img_by_id(self, id):
|
||||
filepath = os.path.join(self._imgs_dir, id+'.jpg')
|
||||
return cv_utils.read_cv2_img(filepath), filepath
|
||||
|
||||
def _generate_random_cond(self):
|
||||
cond = None
|
||||
while cond is None:
|
||||
rand_sample_id = self._ids[random.randint(0, self._dataset_size - 1)]
|
||||
cond = self._get_cond_by_id(rand_sample_id)
|
||||
cond += np.random.uniform(-0.1, 0.1, cond.shape)
|
||||
return cond
|
||||
@@ -1,39 +0,0 @@
|
||||
import numpy as np
|
||||
import os
|
||||
from tqdm import tqdm
|
||||
import argparse
|
||||
import glob
|
||||
import re
|
||||
import pickle
|
||||
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('-ia', '--input_aus_filesdir', type=str, help='Dir with imgs aus files')
|
||||
parser.add_argument('-op', '--output_path', type=str, help='Output path')
|
||||
args = parser.parse_args()
|
||||
|
||||
def get_data(filepaths):
|
||||
data = dict()
|
||||
for filepath in tqdm(filepaths):
|
||||
content = np.loadtxt(filepath, delimiter=', ', skiprows=1)
|
||||
data[os.path.basename(filepath[:-4])] = content[2:19]
|
||||
|
||||
return data
|
||||
|
||||
def save_dict(data, name):
|
||||
with open(name + '.pkl', 'wb') as f:
|
||||
pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)
|
||||
|
||||
def main():
|
||||
filepaths = glob.glob(os.path.join(args.input_aus_filesdir, '*.csv'))
|
||||
filepaths.sort()
|
||||
|
||||
# create aus file
|
||||
data = get_data(filepaths)
|
||||
|
||||
if not os.path.isdir(args.output_path):
|
||||
os.makedirs(args.output_path)
|
||||
save_dict(data, os.path.join(args.output_path, "aus"))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
@@ -1,6 +0,0 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
python train.py \
|
||||
--data_dir path/to/dataset/ \
|
||||
--name experiment_1 \
|
||||
--batch_size 25 \
|
||||
@@ -1,30 +0,0 @@
|
||||
import torch.nn as nn
|
||||
import numpy as np
|
||||
from .networks import NetworkBase
|
||||
|
||||
class Discriminator(NetworkBase):
|
||||
"""Discriminator. PatchGAN."""
|
||||
def __init__(self, image_size=128, conv_dim=64, c_dim=5, repeat_num=6):
|
||||
super(Discriminator, self).__init__()
|
||||
self._name = 'discriminator_wgan'
|
||||
|
||||
layers = []
|
||||
layers.append(nn.Conv2d(3, conv_dim, kernel_size=4, stride=2, padding=1))
|
||||
layers.append(nn.LeakyReLU(0.01, inplace=True))
|
||||
|
||||
curr_dim = conv_dim
|
||||
for i in range(1, repeat_num):
|
||||
layers.append(nn.Conv2d(curr_dim, curr_dim*2, kernel_size=4, stride=2, padding=1))
|
||||
layers.append(nn.LeakyReLU(0.01, inplace=True))
|
||||
curr_dim = curr_dim * 2
|
||||
|
||||
k_size = int(image_size / np.power(2, repeat_num))
|
||||
self.main = nn.Sequential(*layers)
|
||||
self.conv1 = nn.Conv2d(curr_dim, 1, kernel_size=3, stride=1, padding=1, bias=False)
|
||||
self.conv2 = nn.Conv2d(curr_dim, c_dim, kernel_size=k_size, bias=False)
|
||||
|
||||
def forward(self, x):
|
||||
h = self.main(x)
|
||||
out_real = self.conv1(h)
|
||||
out_aux = self.conv2(h)
|
||||
return out_real.squeeze(), out_aux.squeeze()
|
||||
@@ -1,68 +0,0 @@
|
||||
import torch.nn as nn
|
||||
import numpy as np
|
||||
from .networks import NetworkBase
|
||||
import torch
|
||||
|
||||
class Generator(NetworkBase):
|
||||
"""Generator. Encoder-Decoder Architecture."""
|
||||
def __init__(self, conv_dim=64, c_dim=5, repeat_num=6):
|
||||
super(Generator, self).__init__()
|
||||
self._name = 'generator_wgan'
|
||||
|
||||
layers = []
|
||||
layers.append(nn.Conv2d(3+c_dim, conv_dim, kernel_size=7, stride=1, padding=3, bias=False))
|
||||
layers.append(nn.InstanceNorm2d(conv_dim, affine=True))
|
||||
layers.append(nn.ReLU(inplace=True))
|
||||
|
||||
# Down-Sampling
|
||||
curr_dim = conv_dim
|
||||
for i in range(2):
|
||||
layers.append(nn.Conv2d(curr_dim, curr_dim*2, kernel_size=4, stride=2, padding=1, bias=False))
|
||||
layers.append(nn.InstanceNorm2d(curr_dim*2, affine=True))
|
||||
layers.append(nn.ReLU(inplace=True))
|
||||
curr_dim = curr_dim * 2
|
||||
|
||||
# Bottleneck
|
||||
for i in range(repeat_num):
|
||||
layers.append(ResidualBlock(dim_in=curr_dim, dim_out=curr_dim))
|
||||
|
||||
# Up-Sampling
|
||||
for i in range(2):
|
||||
layers.append(nn.ConvTranspose2d(curr_dim, curr_dim//2, kernel_size=4, stride=2, padding=1, bias=False))
|
||||
layers.append(nn.InstanceNorm2d(curr_dim//2, affine=True))
|
||||
layers.append(nn.ReLU(inplace=True))
|
||||
curr_dim = curr_dim // 2
|
||||
|
||||
self.main = nn.Sequential(*layers)
|
||||
|
||||
layers = []
|
||||
layers.append(nn.Conv2d(curr_dim, 3, kernel_size=7, stride=1, padding=3, bias=False))
|
||||
layers.append(nn.Tanh())
|
||||
self.img_reg = nn.Sequential(*layers)
|
||||
|
||||
layers = []
|
||||
layers.append(nn.Conv2d(curr_dim, 1, kernel_size=7, stride=1, padding=3, bias=False))
|
||||
layers.append(nn.Sigmoid())
|
||||
self.attetion_reg = nn.Sequential(*layers)
|
||||
|
||||
def forward(self, x, c):
|
||||
# replicate spatially and concatenate domain information
|
||||
c = c.unsqueeze(2).unsqueeze(3)
|
||||
c = c.expand(c.size(0), c.size(1), x.size(2), x.size(3))
|
||||
x = torch.cat([x, c], dim=1)
|
||||
features = self.main(x)
|
||||
return self.img_reg(features), self.attetion_reg(features)
|
||||
|
||||
class ResidualBlock(nn.Module):
|
||||
"""Residual Block."""
|
||||
def __init__(self, dim_in, dim_out):
|
||||
super(ResidualBlock, self).__init__()
|
||||
self.main = nn.Sequential(
|
||||
nn.Conv2d(dim_in, dim_out, kernel_size=3, stride=1, padding=1, bias=False),
|
||||
nn.InstanceNorm2d(dim_out, affine=True),
|
||||
nn.ReLU(inplace=True),
|
||||
nn.Conv2d(dim_out, dim_out, kernel_size=3, stride=1, padding=1, bias=False),
|
||||
nn.InstanceNorm2d(dim_out, affine=True))
|
||||
|
||||
def forward(self, x):
|
||||
return x + self.main(x)
|
||||
@@ -1,57 +0,0 @@
|
||||
import torch.nn as nn
|
||||
import functools
|
||||
|
||||
class NetworksFactory:
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
@staticmethod
|
||||
def get_by_name(network_name, *args, **kwargs):
|
||||
|
||||
if network_name == 'generator_wasserstein_gan':
|
||||
from .generator_wasserstein_gan import Generator
|
||||
network = Generator(*args, **kwargs)
|
||||
elif network_name == 'discriminator_wasserstein_gan':
|
||||
from .discriminator_wasserstein_gan import Discriminator
|
||||
network = Discriminator(*args, **kwargs)
|
||||
else:
|
||||
raise ValueError("Network %s not recognized." % network_name)
|
||||
|
||||
print "Network %s was created" % network_name
|
||||
|
||||
return network
|
||||
|
||||
|
||||
class NetworkBase(nn.Module):
|
||||
def __init__(self):
|
||||
super(NetworkBase, self).__init__()
|
||||
self._name = 'BaseNetwork'
|
||||
|
||||
@property
|
||||
def name(self):
|
||||
return self._name
|
||||
|
||||
def init_weights(self):
|
||||
self.apply(self._weights_init_fn)
|
||||
|
||||
def _weights_init_fn(self, m):
|
||||
classname = m.__class__.__name__
|
||||
if classname.find('Conv') != -1:
|
||||
m.weight.data.normal_(0.0, 0.02)
|
||||
if hasattr(m.bias, 'data'):
|
||||
m.bias.data.fill_(0)
|
||||
elif classname.find('BatchNorm2d') != -1:
|
||||
m.weight.data.normal_(1.0, 0.02)
|
||||
m.bias.data.fill_(0)
|
||||
|
||||
def _get_norm_layer(self, norm_type='batch'):
|
||||
if norm_type == 'batch':
|
||||
norm_layer = functools.partial(nn.BatchNorm2d, affine=True)
|
||||
elif norm_type == 'instance':
|
||||
norm_layer = functools.partial(nn.InstanceNorm2d, affine=False)
|
||||
elif norm_type =='batchnorm2d':
|
||||
norm_layer = nn.BatchNorm2d
|
||||
else:
|
||||
raise NotImplementedError('normalization layer [%s] is not found' % norm_type)
|
||||
|
||||
return norm_layer
|
||||
@@ -1,108 +0,0 @@
|
||||
import argparse
|
||||
import os
|
||||
from utils import util
|
||||
import torch
|
||||
|
||||
class BaseOptions():
|
||||
def __init__(self):
|
||||
self._parser = argparse.ArgumentParser()
|
||||
self._initialized = False
|
||||
|
||||
def initialize(self):
|
||||
self._parser.add_argument('--data_dir', type=str, help='path to dataset')
|
||||
self._parser.add_argument('--train_ids_file', type=str, default='train_ids.csv', help='file containing train ids')
|
||||
self._parser.add_argument('--test_ids_file', type=str, default='test_ids.csv', help='file containing test ids')
|
||||
self._parser.add_argument('--images_folder', type=str, default='imgs', help='images folder')
|
||||
self._parser.add_argument('--aus_file', type=str, default='aus_openface.pkl', help='file containing samples aus')
|
||||
|
||||
self._parser.add_argument('--load_epoch', type=int, default=-1, help='which epoch to load? set to -1 to use latest cached model')
|
||||
self._parser.add_argument('--batch_size', type=int, default=4, help='input batch size')
|
||||
self._parser.add_argument('--image_size', type=int, default=128, help='input image size')
|
||||
self._parser.add_argument('--cond_nc', type=int, default=17, help='# of conditions')
|
||||
self._parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0 0,1,2, 0,2. use -1 for CPU')
|
||||
self._parser.add_argument('--name', type=str, default='experiment_1', help='name of the experiment. It decides where to store samples and models')
|
||||
self._parser.add_argument('--dataset_mode', type=str, default='aus', help='chooses dataset to be used')
|
||||
self._parser.add_argument('--model', type=str, default='ganimation', help='model to run[au_net_model]')
|
||||
self._parser.add_argument('--n_threads_test', default=1, type=int, help='# threads for loading data')
|
||||
self._parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here')
|
||||
self._parser.add_argument('--serial_batches', action='store_true', help='if true, takes images in order to make batches, otherwise takes them randomly')
|
||||
self._parser.add_argument('--do_saturate_mask', action="store_true", default=False, help='do use mask_fake for mask_cyc')
|
||||
|
||||
|
||||
|
||||
|
||||
self._initialized = True
|
||||
|
||||
def parse(self):
|
||||
if not self._initialized:
|
||||
self.initialize()
|
||||
self._opt = self._parser.parse_args()
|
||||
|
||||
# set is train or set
|
||||
self._opt.is_train = self.is_train
|
||||
|
||||
# set and check load_epoch
|
||||
self._set_and_check_load_epoch()
|
||||
|
||||
# get and set gpus
|
||||
self._get_set_gpus()
|
||||
|
||||
args = vars(self._opt)
|
||||
|
||||
# print in terminal args
|
||||
self._print(args)
|
||||
|
||||
# save args to file
|
||||
self._save(args)
|
||||
|
||||
return self._opt
|
||||
|
||||
def _set_and_check_load_epoch(self):
|
||||
models_dir = os.path.join(self._opt.checkpoints_dir, self._opt.name)
|
||||
if os.path.exists(models_dir):
|
||||
if self._opt.load_epoch == -1:
|
||||
load_epoch = 0
|
||||
for file in os.listdir(models_dir):
|
||||
if file.startswith("net_epoch_"):
|
||||
load_epoch = max(load_epoch, int(file.split('_')[2]))
|
||||
self._opt.load_epoch = load_epoch
|
||||
else:
|
||||
found = False
|
||||
for file in os.listdir(models_dir):
|
||||
if file.startswith("net_epoch_"):
|
||||
found = int(file.split('_')[2]) == self._opt.load_epoch
|
||||
if found: break
|
||||
assert found, 'Model for epoch %i not found' % self._opt.load_epoch
|
||||
else:
|
||||
assert self._opt.load_epoch < 1, 'Model for epoch %i not found' % self._opt.load_epoch
|
||||
self._opt.load_epoch = 0
|
||||
|
||||
def _get_set_gpus(self):
|
||||
# get gpu ids
|
||||
str_ids = self._opt.gpu_ids.split(',')
|
||||
self._opt.gpu_ids = []
|
||||
for str_id in str_ids:
|
||||
id = int(str_id)
|
||||
if id >= 0:
|
||||
self._opt.gpu_ids.append(id)
|
||||
|
||||
# set gpu ids
|
||||
if len(self._opt.gpu_ids) > 0:
|
||||
torch.cuda.set_device(self._opt.gpu_ids[0])
|
||||
|
||||
def _print(self, args):
|
||||
print('------------ Options -------------')
|
||||
for k, v in sorted(args.items()):
|
||||
print('%s: %s' % (str(k), str(v)))
|
||||
print('-------------- End ----------------')
|
||||
|
||||
def _save(self, args):
|
||||
expr_dir = os.path.join(self._opt.checkpoints_dir, self._opt.name)
|
||||
print(expr_dir)
|
||||
util.mkdirs(expr_dir)
|
||||
file_name = os.path.join(expr_dir, 'opt_%s.txt' % ('train' if self.is_train else 'test'))
|
||||
with open(file_name, 'wt') as opt_file:
|
||||
opt_file.write('------------ Options -------------\n')
|
||||
for k, v in sorted(args.items()):
|
||||
opt_file.write('%s: %s\n' % (str(k), str(v)))
|
||||
opt_file.write('-------------- End ----------------\n')
|
||||
@@ -1,9 +0,0 @@
|
||||
from .base_options import BaseOptions
|
||||
|
||||
|
||||
class TestOptions(BaseOptions):
|
||||
def initialize(self):
|
||||
BaseOptions.initialize(self)
|
||||
self._parser.add_argument('--input_path', type=str, help='path to image')
|
||||
self._parser.add_argument('--output_dir', type=str, default='./output', help='output path')
|
||||
self.is_train = False
|
||||
@@ -1,31 +0,0 @@
|
||||
from .base_options import BaseOptions
|
||||
|
||||
|
||||
class TrainOptions(BaseOptions):
|
||||
def initialize(self):
|
||||
BaseOptions.initialize(self)
|
||||
self._parser.add_argument('--n_threads_train', default=4, type=int, help='# threads for loading data')
|
||||
self._parser.add_argument('--num_iters_validate', default=1, type=int, help='# batches to use when validating')
|
||||
self._parser.add_argument('--print_freq_s', type=int, default=60, help='frequency of showing training results on console')
|
||||
self._parser.add_argument('--display_freq_s', type=int, default=300, help='frequency [s] of showing training results on screen')
|
||||
self._parser.add_argument('--save_latest_freq_s', type=int, default=3600, help='frequency of saving the latest results')
|
||||
|
||||
self._parser.add_argument('--nepochs_no_decay', type=int, default=20, help='# of epochs at starting learning rate')
|
||||
self._parser.add_argument('--nepochs_decay', type=int, default=10, help='# of epochs to linearly decay learning rate to zero')
|
||||
|
||||
self._parser.add_argument('--train_G_every_n_iterations', type=int, default=5, help='train G every n interations')
|
||||
self._parser.add_argument('--poses_g_sigma', type=float, default=0.06, help='initial learning rate for adam')
|
||||
self._parser.add_argument('--lr_G', type=float, default=0.0001, help='initial learning rate for G adam')
|
||||
self._parser.add_argument('--G_adam_b1', type=float, default=0.5, help='beta1 for G adam')
|
||||
self._parser.add_argument('--G_adam_b2', type=float, default=0.999, help='beta2 for G adam')
|
||||
self._parser.add_argument('--lr_D', type=float, default=0.0001, help='initial learning rate for D adam')
|
||||
self._parser.add_argument('--D_adam_b1', type=float, default=0.5, help='beta1 for D adam')
|
||||
self._parser.add_argument('--D_adam_b2', type=float, default=0.999, help='beta2 for D adam')
|
||||
self._parser.add_argument('--lambda_D_prob', type=float, default=1, help='lambda for real/fake discriminator loss')
|
||||
self._parser.add_argument('--lambda_D_cond', type=float, default=4000, help='lambda for condition discriminator loss')
|
||||
self._parser.add_argument('--lambda_cyc', type=float, default=10, help='lambda cycle loss')
|
||||
self._parser.add_argument('--lambda_mask', type=float, default=0.1, help='lambda mask loss')
|
||||
self._parser.add_argument('--lambda_D_gp', type=float, default=10, help='lambda gradient penalty loss')
|
||||
self._parser.add_argument('--lambda_mask_smooth', type=float, default=1e-5, help='lambda mask smooth loss')
|
||||
|
||||
self.is_train = True
|
||||
@@ -1,6 +0,0 @@
|
||||
numpy
|
||||
matplotlib
|
||||
tqdm
|
||||
dlib
|
||||
face_recognition
|
||||
opencv-contrib-python
|
||||
Binary file not shown.
Binary file not shown.
|
Before Width: | Height: | Size: 5.0 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 7.9 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 7.1 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 11 KiB |
@@ -1,2 +0,0 @@
|
||||
N_0000001507_00202.jpg
|
||||
N_0000001939_00054.jpg
|
||||
|
@@ -1,2 +0,0 @@
|
||||
N_0000000437_00540.jpg
|
||||
N_0000000356_00190.jpg
|
||||
|
@@ -1,74 +0,0 @@
|
||||
import os
|
||||
import argparse
|
||||
import glob
|
||||
import cv2
|
||||
from utils import face_utils
|
||||
from utils import cv_utils
|
||||
import face_recognition
|
||||
from PIL import Image
|
||||
import torchvision.transforms as transforms
|
||||
import torch
|
||||
import pickle
|
||||
import numpy as np
|
||||
from models.models import ModelsFactory
|
||||
from options.test_options import TestOptions
|
||||
|
||||
class MorphFacesInTheWild:
|
||||
def __init__(self, opt):
|
||||
self._opt = opt
|
||||
self._model = ModelsFactory.get_by_name(self._opt.model, self._opt)
|
||||
self._model.set_eval()
|
||||
self._transform = transforms.Compose([transforms.ToTensor(),
|
||||
transforms.Normalize(mean=[0.5, 0.5, 0.5],
|
||||
std=[0.5, 0.5, 0.5])
|
||||
])
|
||||
|
||||
def morph_file(self, img_path, expresion):
|
||||
img = cv_utils.read_cv2_img(img_path)
|
||||
morphed_img = self._img_morph(img, expresion)
|
||||
output_name = '%s_out.png' % os.path.basename(img_path)
|
||||
self._save_img(morphed_img, output_name)
|
||||
|
||||
def _img_morph(self, img, expresion):
|
||||
bbs = face_recognition.face_locations(img)
|
||||
if len(bbs) > 0:
|
||||
y, right, bottom, x = bbs[0]
|
||||
bb = x, y, (right - x), (bottom - y)
|
||||
face = face_utils.crop_face_with_bb(img, bb)
|
||||
face = face_utils.resize_face(face)
|
||||
else:
|
||||
face = face_utils.resize_face(img)
|
||||
|
||||
morphed_face = self._morph_face(face, expresion)
|
||||
|
||||
return morphed_face
|
||||
|
||||
def _morph_face(self, face, expresion):
|
||||
face = torch.unsqueeze(self._transform(Image.fromarray(face)), 0)
|
||||
expresion = torch.unsqueeze(torch.from_numpy(expresion/5.0), 0)
|
||||
test_batch = {'real_img': face, 'real_cond': expresion, 'desired_cond': expresion, 'sample_id': torch.FloatTensor(), 'real_img_path': []}
|
||||
self._model.set_input(test_batch)
|
||||
imgs, _ = self._model.forward(keep_data_for_visuals=False, return_estimates=True)
|
||||
return imgs['concat']
|
||||
|
||||
def _save_img(self, img, filename):
|
||||
filepath = os.path.join(self._opt.output_dir, filename)
|
||||
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
|
||||
cv2.imwrite(filepath, img)
|
||||
|
||||
|
||||
def main():
|
||||
opt = TestOptions().parse()
|
||||
if not os.path.isdir(opt.output_dir):
|
||||
os.makedirs(opt.output_dir)
|
||||
|
||||
morph = MorphFacesInTheWild(opt)
|
||||
|
||||
image_path = opt.input_path
|
||||
expression = np.random.uniform(0, 1, opt.cond_nc)
|
||||
morph.morph_file(image_path, expression)
|
||||
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
@@ -1,141 +0,0 @@
|
||||
import time
|
||||
from options.train_options import TrainOptions
|
||||
from data.custom_dataset_data_loader import CustomDatasetDataLoader
|
||||
from models.models import ModelsFactory
|
||||
from utils.tb_visualizer import TBVisualizer
|
||||
from collections import OrderedDict
|
||||
import os
|
||||
|
||||
|
||||
class Train:
|
||||
def __init__(self):
|
||||
self._opt = TrainOptions().parse()
|
||||
data_loader_train = CustomDatasetDataLoader(self._opt, is_for_train=True)
|
||||
data_loader_test = CustomDatasetDataLoader(self._opt, is_for_train=False)
|
||||
|
||||
self._dataset_train = data_loader_train.load_data()
|
||||
self._dataset_test = data_loader_test.load_data()
|
||||
|
||||
self._dataset_train_size = len(data_loader_train)
|
||||
self._dataset_test_size = len(data_loader_test)
|
||||
print('#train images = %d' % self._dataset_train_size)
|
||||
print('#test images = %d' % self._dataset_test_size)
|
||||
|
||||
self._model = ModelsFactory.get_by_name(self._opt.model, self._opt)
|
||||
self._tb_visualizer = TBVisualizer(self._opt)
|
||||
|
||||
self._train()
|
||||
|
||||
def _train(self):
|
||||
self._total_steps = self._opt.load_epoch * self._dataset_train_size
|
||||
self._iters_per_epoch = self._dataset_train_size / self._opt.batch_size
|
||||
self._last_display_time = None
|
||||
self._last_save_latest_time = None
|
||||
self._last_print_time = time.time()
|
||||
|
||||
for i_epoch in range(self._opt.load_epoch + 1, self._opt.nepochs_no_decay + self._opt.nepochs_decay + 1):
|
||||
epoch_start_time = time.time()
|
||||
|
||||
# train epoch
|
||||
self._train_epoch(i_epoch)
|
||||
|
||||
# save model
|
||||
print('saving the model at the end of epoch %d, iters %d' % (i_epoch, self._total_steps))
|
||||
self._model.save(i_epoch)
|
||||
|
||||
# print epoch info
|
||||
time_epoch = time.time() - epoch_start_time
|
||||
print('End of epoch %d / %d \t Time Taken: %d sec (%d min or %d h)' %
|
||||
(i_epoch, self._opt.nepochs_no_decay + self._opt.nepochs_decay, time_epoch,
|
||||
time_epoch / 60, time_epoch / 3600))
|
||||
|
||||
# update learning rate
|
||||
if i_epoch > self._opt.nepochs_no_decay:
|
||||
self._model.update_learning_rate()
|
||||
|
||||
def _train_epoch(self, i_epoch):
|
||||
epoch_iter = 0
|
||||
self._model.set_train()
|
||||
for i_train_batch, train_batch in enumerate(self._dataset_train):
|
||||
iter_start_time = time.time()
|
||||
|
||||
# display flags
|
||||
do_visuals = self._last_display_time is None or time.time() - self._last_display_time > self._opt.display_freq_s
|
||||
do_print_terminal = time.time() - self._last_print_time > self._opt.print_freq_s or do_visuals
|
||||
|
||||
# train model
|
||||
self._model.set_input(train_batch)
|
||||
train_generator = ((i_train_batch+1) % self._opt.train_G_every_n_iterations == 0) or do_visuals
|
||||
self._model.optimize_parameters(keep_data_for_visuals=do_visuals, train_generator=train_generator)
|
||||
|
||||
# update epoch info
|
||||
self._total_steps += self._opt.batch_size
|
||||
epoch_iter += self._opt.batch_size
|
||||
|
||||
# display terminal
|
||||
if do_print_terminal:
|
||||
self._display_terminal(iter_start_time, i_epoch, i_train_batch, do_visuals)
|
||||
self._last_print_time = time.time()
|
||||
|
||||
# display visualizer
|
||||
if do_visuals:
|
||||
self._display_visualizer_train(self._total_steps)
|
||||
self._display_visualizer_val(i_epoch, self._total_steps)
|
||||
self._last_display_time = time.time()
|
||||
|
||||
# save model
|
||||
if self._last_save_latest_time is None or time.time() - self._last_save_latest_time > self._opt.save_latest_freq_s:
|
||||
print('saving the latest model (epoch %d, total_steps %d)' % (i_epoch, self._total_steps))
|
||||
self._model.save(i_epoch)
|
||||
self._last_save_latest_time = time.time()
|
||||
|
||||
def _display_terminal(self, iter_start_time, i_epoch, i_train_batch, visuals_flag):
|
||||
errors = self._model.get_current_errors()
|
||||
t = (time.time() - iter_start_time) / self._opt.batch_size
|
||||
self._tb_visualizer.print_current_train_errors(i_epoch, i_train_batch, self._iters_per_epoch, errors, t, visuals_flag)
|
||||
|
||||
def _display_visualizer_train(self, total_steps):
|
||||
self._tb_visualizer.display_current_results(self._model.get_current_visuals(), total_steps, is_train=True)
|
||||
self._tb_visualizer.plot_scalars(self._model.get_current_errors(), total_steps, is_train=True)
|
||||
self._tb_visualizer.plot_scalars(self._model.get_current_scalars(), total_steps, is_train=True)
|
||||
|
||||
def _display_visualizer_val(self, i_epoch, total_steps):
|
||||
val_start_time = time.time()
|
||||
|
||||
# set model to eval
|
||||
self._model.set_eval()
|
||||
|
||||
# evaluate self._opt.num_iters_validate epochs
|
||||
val_errors = OrderedDict()
|
||||
for i_val_batch, val_batch in enumerate(self._dataset_test):
|
||||
if i_val_batch == self._opt.num_iters_validate:
|
||||
break
|
||||
|
||||
# evaluate model
|
||||
self._model.set_input(val_batch)
|
||||
self._model.forward(keep_data_for_visuals=(i_val_batch == 0))
|
||||
errors = self._model.get_current_errors()
|
||||
|
||||
# store current batch errors
|
||||
for k, v in errors.iteritems():
|
||||
if k in val_errors:
|
||||
val_errors[k] += v
|
||||
else:
|
||||
val_errors[k] = v
|
||||
|
||||
# normalize errors
|
||||
for k in val_errors.iterkeys():
|
||||
val_errors[k] /= self._opt.num_iters_validate
|
||||
|
||||
# visualize
|
||||
t = (time.time() - val_start_time)
|
||||
self._tb_visualizer.print_current_validate_errors(i_epoch, val_errors, t)
|
||||
self._tb_visualizer.plot_scalars(val_errors, total_steps, is_train=False)
|
||||
self._tb_visualizer.display_current_results(self._model.get_current_visuals(), total_steps, is_train=False)
|
||||
|
||||
# set model back to train
|
||||
self._model.set_train()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
Train()
|
||||
@@ -1,54 +0,0 @@
|
||||
import cv2
|
||||
from matplotlib import pyplot as plt
|
||||
import numpy as np
|
||||
|
||||
def read_cv2_img(path):
|
||||
'''
|
||||
Read color images
|
||||
:param path: Path to image
|
||||
:return: Only returns color images
|
||||
'''
|
||||
img = cv2.imread(path, -1)
|
||||
|
||||
if img is not None:
|
||||
if len(img.shape) != 3:
|
||||
return None
|
||||
|
||||
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
|
||||
|
||||
return img
|
||||
|
||||
def show_cv2_img(img, title='img'):
|
||||
'''
|
||||
Display cv2 image
|
||||
:param img: cv::mat
|
||||
:param title: title
|
||||
:return: None
|
||||
'''
|
||||
plt.imshow(img)
|
||||
plt.title(title)
|
||||
plt.axis('off')
|
||||
plt.show()
|
||||
|
||||
def show_images_row(imgs, titles, rows=1):
|
||||
'''
|
||||
Display grid of cv2 images image
|
||||
:param img: list [cv::mat]
|
||||
:param title: titles
|
||||
:return: None
|
||||
'''
|
||||
assert ((titles is None) or (len(imgs) == len(titles)))
|
||||
num_images = len(imgs)
|
||||
|
||||
if titles is None:
|
||||
titles = ['Image (%d)' % i for i in range(1, num_images + 1)]
|
||||
|
||||
fig = plt.figure()
|
||||
for n, (image, title) in enumerate(zip(imgs, titles)):
|
||||
ax = fig.add_subplot(rows, np.ceil(num_images / float(rows)), n + 1)
|
||||
if image.ndim == 2:
|
||||
plt.gray()
|
||||
plt.imshow(image)
|
||||
ax.set_title(title)
|
||||
plt.axis('off')
|
||||
plt.show()
|
||||
@@ -1,71 +0,0 @@
|
||||
import face_recognition
|
||||
import cv2
|
||||
import numpy as np
|
||||
import skimage
|
||||
import skimage.transform
|
||||
import warnings
|
||||
|
||||
def detect_faces(img):
|
||||
'''
|
||||
Detect faces in image
|
||||
:param img: cv::mat HxWx3 RGB
|
||||
:return: yield 4 <x,y,w,h>
|
||||
'''
|
||||
# detect faces
|
||||
bbs = face_recognition.face_locations(img)
|
||||
|
||||
for y, right, bottom, x in bbs:
|
||||
# Scale back up face bb
|
||||
yield x, y, (right - x), (bottom - y)
|
||||
|
||||
def detect_biggest_face(img):
|
||||
'''
|
||||
Detect biggest face in image
|
||||
:param img: cv::mat HxWx3 RGB
|
||||
:return: 4 <x,y,w,h>
|
||||
'''
|
||||
# detect faces
|
||||
bbs = face_recognition.face_locations(img)
|
||||
|
||||
max_area = float('-inf')
|
||||
max_area_i = 0
|
||||
for i, (y, right, bottom, x) in enumerate(bbs):
|
||||
area = (right - x) * (bottom - y)
|
||||
if max_area < area:
|
||||
max_area = area
|
||||
max_area_i = i
|
||||
|
||||
if max_area != float('-inf'):
|
||||
y, right, bottom, x = bbs[max_area_i]
|
||||
return x, y, (right - x), (bottom - y)
|
||||
|
||||
return None
|
||||
|
||||
def crop_face_with_bb(img, bb):
|
||||
'''
|
||||
Crop face in image given bb
|
||||
:param img: cv::mat HxWx3
|
||||
:param bb: 4 (<x,y,w,h>)
|
||||
:return: HxWx3
|
||||
'''
|
||||
x, y, w, h = bb
|
||||
return img[y:y+h, x:x+w, :]
|
||||
|
||||
def place_face(img, face, bb):
|
||||
x, y, w, h = bb
|
||||
face = resize_face(face, size=(w, h))
|
||||
img[y:y+h, x:x+w] = face
|
||||
return img
|
||||
|
||||
def resize_face(face_img, size=(128, 128)):
|
||||
'''
|
||||
Resize face to a given size
|
||||
:param face_img: cv::mat HxWx3
|
||||
:param size: new H and W (size x size). 128 by default.
|
||||
:return: cv::mat size x size x 3
|
||||
'''
|
||||
return cv2.resize(face_img, size)
|
||||
|
||||
def detect_landmarks(face_img):
|
||||
landmakrs = face_recognition.face_landmarks(face_img)
|
||||
return landmakrs[0] if len(landmakrs) > 0 else None
|
||||
@@ -1,67 +0,0 @@
|
||||
from __future__ import print_function
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
def plot_au(img, aus, title=None):
|
||||
'''
|
||||
Plot action units
|
||||
:param img: HxWx3
|
||||
:param aus: N
|
||||
:return:
|
||||
'''
|
||||
fig = plt.figure()
|
||||
ax = fig.add_subplot(1, 1, 1)
|
||||
ax.axis('off')
|
||||
fig.subplots_adjust(0, 0, 0.8, 1) # get rid of margins
|
||||
|
||||
# display img
|
||||
ax.imshow(img)
|
||||
|
||||
if len(aus) == 11:
|
||||
au_ids = ['1','2','4','5','6','9','12','17','20','25','26']
|
||||
x = 0.1
|
||||
y = 0.39
|
||||
i = 0
|
||||
for au, id in zip(aus, au_ids):
|
||||
if id == '9':
|
||||
x = 0.5
|
||||
y -= .15
|
||||
i = 0
|
||||
elif id == '12':
|
||||
x = 0.1
|
||||
y -= .15
|
||||
i = 0
|
||||
|
||||
ax.text(x + i * 0.2, y, id, horizontalalignment='center', verticalalignment='center',
|
||||
transform=ax.transAxes, color='r', fontsize=20)
|
||||
ax.text((x-0.001)+i*0.2, y-0.07, au, horizontalalignment='center', verticalalignment='center',
|
||||
transform=ax.transAxes, color='b', fontsize=20)
|
||||
i+=1
|
||||
|
||||
else:
|
||||
au_ids = ['1', '2', '4', '5', '6', '7', '9', '10', '12', '14', '15', '17', '20', '23', '25', '26', '45']
|
||||
x = 0.1
|
||||
y = 0.39
|
||||
i = 0
|
||||
for au, id in zip(aus, au_ids):
|
||||
if id == '9' or id == '20':
|
||||
x = 0.1
|
||||
y -= .15
|
||||
i = 0
|
||||
|
||||
ax.text(x + i * 0.2, y, id, horizontalalignment='center', verticalalignment='center',
|
||||
transform=ax.transAxes, color='r', fontsize=20)
|
||||
ax.text((x-0.001)+i*0.2, y-0.07, au, horizontalalignment='center', verticalalignment='center',
|
||||
transform=ax.transAxes, color='b', fontsize=20)
|
||||
i+=1
|
||||
|
||||
if title is not None:
|
||||
ax.text(0.5, 0.95, title, horizontalalignment='center', verticalalignment='center',
|
||||
transform=ax.transAxes, color='r', fontsize=20)
|
||||
|
||||
fig.canvas.draw()
|
||||
data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
|
||||
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
|
||||
plt.close(fig)
|
||||
|
||||
return data
|
||||
@@ -1,66 +0,0 @@
|
||||
import numpy as np
|
||||
import os
|
||||
import time
|
||||
from . import util
|
||||
from tensorboardX import SummaryWriter
|
||||
|
||||
|
||||
class TBVisualizer:
|
||||
def __init__(self, opt):
|
||||
self._opt = opt
|
||||
self._save_path = os.path.join(opt.checkpoints_dir, opt.name)
|
||||
|
||||
self._log_path = os.path.join(self._save_path, 'loss_log2.txt')
|
||||
self._tb_path = os.path.join(self._save_path, 'summary.json')
|
||||
self._writer = SummaryWriter(self._save_path)
|
||||
|
||||
with open(self._log_path, "a") as log_file:
|
||||
now = time.strftime("%c")
|
||||
log_file.write('================ Training Loss (%s) ================\n' % now)
|
||||
|
||||
def __del__(self):
|
||||
self._writer.close()
|
||||
|
||||
def display_current_results(self, visuals, it, is_train, save_visuals=False):
|
||||
for label, image_numpy in visuals.items():
|
||||
sum_name = '{}/{}'.format('Train' if is_train else 'Test', label)
|
||||
self._writer.add_image(sum_name, image_numpy, it)
|
||||
|
||||
if save_visuals:
|
||||
util.save_image(image_numpy,
|
||||
os.path.join(self._opt.checkpoints_dir, self._opt.name,
|
||||
'event_imgs', sum_name, '%08d.png' % it))
|
||||
|
||||
self._writer.export_scalars_to_json(self._tb_path)
|
||||
|
||||
def plot_scalars(self, scalars, it, is_train):
|
||||
for label, scalar in scalars.items():
|
||||
sum_name = '{}/{}'.format('Train' if is_train else 'Test', label)
|
||||
self._writer.add_scalar(sum_name, scalar, it)
|
||||
|
||||
def print_current_train_errors(self, epoch, i, iters_per_epoch, errors, t, visuals_were_stored):
|
||||
log_time = time.strftime("[%d/%m/%Y %H:%M:%S]")
|
||||
visuals_info = "v" if visuals_were_stored else ""
|
||||
message = '%s (T%s, epoch: %d, it: %d/%d, t/smpl: %.3fs) ' % (log_time, visuals_info, epoch, i, iters_per_epoch, t)
|
||||
for k, v in errors.items():
|
||||
message += '%s:%.3f ' % (k, v)
|
||||
|
||||
print(message)
|
||||
with open(self._log_path, "a") as log_file:
|
||||
log_file.write('%s\n' % message)
|
||||
|
||||
def print_current_validate_errors(self, epoch, errors, t):
|
||||
log_time = time.strftime("[%d/%m/%Y %H:%M:%S]")
|
||||
message = '%s (V, epoch: %d, time_to_val: %ds) ' % (log_time, epoch, t)
|
||||
for k, v in errors.items():
|
||||
message += '%s:%.3f ' % (k, v)
|
||||
|
||||
print(message)
|
||||
with open(self._log_path, "a") as log_file:
|
||||
log_file.write('%s\n' % message)
|
||||
|
||||
def save_images(self, visuals):
|
||||
for label, image_numpy in visuals.items():
|
||||
image_name = '%s.png' % label
|
||||
save_path = os.path.join(self._save_path, "samples", image_name)
|
||||
util.save_image(image_numpy, save_path)
|
||||
@@ -1,53 +0,0 @@
|
||||
from __future__ import print_function
|
||||
from PIL import Image
|
||||
import numpy as np
|
||||
import os
|
||||
import torchvision
|
||||
import math
|
||||
|
||||
|
||||
def tensor2im(img, imtype=np.uint8, unnormalize=True, idx=0, nrows=None):
|
||||
# select a sample or create grid if img is a batch
|
||||
if len(img.shape) == 4:
|
||||
nrows = nrows if nrows is not None else int(math.sqrt(img.size(0)))
|
||||
img = img[idx] if idx >= 0 else torchvision.utils.make_grid(img, nrows)
|
||||
|
||||
img = img.cpu().float()
|
||||
if unnormalize:
|
||||
mean = [0.5, 0.5, 0.5]
|
||||
std = [0.5, 0.5, 0.5]
|
||||
|
||||
for i, m, s in zip(img, mean, std):
|
||||
i.mul_(s).add_(m)
|
||||
|
||||
image_numpy = img.numpy()
|
||||
image_numpy_t = np.transpose(image_numpy, (1, 2, 0))
|
||||
image_numpy_t = image_numpy_t*254.0
|
||||
|
||||
return image_numpy_t.astype(imtype)
|
||||
|
||||
def tensor2maskim(mask, imtype=np.uint8, idx=0, nrows=1):
|
||||
im = tensor2im(mask, imtype=imtype, idx=idx, unnormalize=False, nrows=nrows)
|
||||
if im.shape[2] == 1:
|
||||
im = np.repeat(im, 3, axis=-1)
|
||||
return im
|
||||
|
||||
def mkdirs(paths):
|
||||
if isinstance(paths, list) and not isinstance(paths, str):
|
||||
for path in paths:
|
||||
mkdir(path)
|
||||
else:
|
||||
mkdir(paths)
|
||||
|
||||
def mkdir(path):
|
||||
if not os.path.exists(path):
|
||||
os.makedirs(path)
|
||||
|
||||
def save_image(image_numpy, image_path):
|
||||
mkdir(os.path.dirname(image_path))
|
||||
image_pil = Image.fromarray(image_numpy)
|
||||
image_pil.save(image_path)
|
||||
|
||||
def save_str_data(data, path):
|
||||
mkdir(os.path.dirname(path))
|
||||
np.savetxt(path, data, delimiter=",", fmt="%s")
|
||||
@@ -0,0 +1,26 @@
|
||||
# Disrupting Deepfakes: Adversarial Attacks on Conditional Image Translation Networks
|
||||
|
||||
## StarGAN Testing
|
||||
```
|
||||
python main.py --mode test --dataset CelebA --image_size 256 --c_dim 5 --selected_attrs Black_Hair Blond_Hair Brown_Hair Male Young --model_save_dir='stargan_celeba_256/models' --result_dir='stargan_celeba_256/results_test' --test_iters 200000 --batch_size 1
|
||||
```
|
||||
|
||||
## StarGAN Training
|
||||
```
|
||||
python main.py --mode train --dataset CelebA --image_size 256 --c_dim 5 --sample_dir stargan_both/samples --log_dir stargan_both/logs --model_save_dir stargan_both/models --result_dir stargan_both/results --selected_attrs Black_Hair Blond_Hair Brown_Hair Male Young
|
||||
```
|
||||
|
||||
## GANimation
|
||||
```
|
||||
python main.py --mode animation
|
||||
```
|
||||
|
||||
## pix2pixHD
|
||||
```
|
||||
python test.py --name label2city_1024p --netG local --ngf 32 --resize_or_crop none
|
||||
```
|
||||
|
||||
## CycleGAN
|
||||
```
|
||||
python test.py --dataroot datasets/horse2zebra/testA --name horse2zebra_pretrained --model test --no_dropout
|
||||
```
|
||||
Symlink
+1
@@ -0,0 +1 @@
|
||||
/home/grad3/nruiz9/research/fsynth/cyclegan/checkpoints
|
||||
Symlink
+1
@@ -0,0 +1 @@
|
||||
/scratch2/fsynth/cyclegan/datasets/horse2zebra
|
||||
Symlink
+1
@@ -0,0 +1 @@
|
||||
/scratch2/fsynth/cyclegan/datasets/monet2photo
|
||||
@@ -71,13 +71,12 @@ class TestModel(BaseModel):
|
||||
"""Run forward pass."""
|
||||
self.fake_noattack = self.netG(self.real) # G(real)
|
||||
|
||||
def attack(self):
|
||||
def attack(self, target):
|
||||
image = self.real
|
||||
|
||||
# Attack
|
||||
pgd_attack = attacks.LinfPGDAttack(model=self.netG)
|
||||
black = np.zeros((1, 3, image.size(2), image.size(3)))
|
||||
black = torch.FloatTensor(black).cuda()
|
||||
input_adv, perturb = pgd_attack.perturb(image, black)
|
||||
input_adv, perturb = pgd_attack.perturb(image, target)
|
||||
|
||||
return input_adv, perturb
|
||||
|
||||
@@ -92,8 +91,13 @@ class TestModel(BaseModel):
|
||||
l2 = F.mse_loss(generated, generated_noattack)
|
||||
l0 = (generated - generated_noattack).norm(0)
|
||||
d = (generated - generated_noattack).norm(float('-inf'))
|
||||
|
||||
if F.mse_loss(generated, generated_noattack) > 0.05:
|
||||
n_dist = 1
|
||||
else:
|
||||
n_dist = 0
|
||||
|
||||
return l1, l2, l0, d
|
||||
return l1, l2, l0, d, n_dist
|
||||
|
||||
def optimize_parameters(self):
|
||||
"""No optimization for test model."""
|
||||
|
||||
+14
-8
@@ -61,29 +61,35 @@ if __name__ == '__main__':
|
||||
torch.manual_seed(0)
|
||||
|
||||
# Initialize Metrics
|
||||
l1_error, l2_error, min_dist, l0_error, perceptual_error = 0.0, 0.0, 0.0, 0.0, 0.0
|
||||
n_samples = 0
|
||||
l1_error, l2_error, min_dist, l0_error = 0.0, 0.0, 0.0, 0.0
|
||||
n_dist, n_samples = 0, 0
|
||||
|
||||
for i, data in enumerate(dataset):
|
||||
if i >= opt.num_test: # only apply our model to opt.num_test images.
|
||||
break
|
||||
model.set_input(data) # unpack data from data loader
|
||||
|
||||
# Get ground-truth output
|
||||
with torch.no_grad():
|
||||
model.forward_noattack()
|
||||
if i == 0:
|
||||
input_adv, perturb = model.attack()
|
||||
# input_adv, perturb = model.attack()
|
||||
|
||||
# Attack
|
||||
input_adv, perturb = model.attack(target=model.fake_noattack)
|
||||
|
||||
# Get output from adversarial sample
|
||||
with torch.no_grad():
|
||||
model.forward_attack(perturb)
|
||||
model.compute_visuals()
|
||||
|
||||
# Compute metrics
|
||||
l1, l2, l0, d = model.compute_errors()
|
||||
l1, l2, l0, d, above = model.compute_errors()
|
||||
l1_error += l1
|
||||
l2_error += l2
|
||||
l0_error += l0
|
||||
min_dist += d
|
||||
n_dist += above
|
||||
n_samples += 1
|
||||
|
||||
# model.test() # run inference
|
||||
visuals = model.get_current_visuals() # get image results
|
||||
img_path = model.get_image_paths() # get image paths
|
||||
@@ -92,8 +98,8 @@ if __name__ == '__main__':
|
||||
save_images(webpage, visuals, img_path, aspect_ratio=opt.aspect_ratio, width=opt.display_winsize)
|
||||
|
||||
# Print metrics
|
||||
print('{} images. L1 error: {}. L2 error: {}. L0 error: {}. L_-inf error: {}. Perceptual error: {}.'.format(n_samples,
|
||||
l1_error / n_samples, l2_error / n_samples, l0_error / n_samples, min_dist / n_samples, perceptual_error / n_samples))
|
||||
print('{} images. L1 error: {}. L2 error: {}. prop_dist: {}. L0 error: {}. L_-inf error: {}.'.format(n_samples,
|
||||
l1_error / n_samples, l2_error / n_samples, float(n_dist) / n_samples, l0_error / n_samples, min_dist / n_samples))
|
||||
|
||||
webpage.save() # save the HTML
|
||||
|
||||
|
||||
@@ -7,26 +7,39 @@ import torch
|
||||
import torch.nn as nn
|
||||
|
||||
class LinfPGDAttack(object):
|
||||
def __init__(self, model=None, epsilon=0.05, k=1, a=0.05):
|
||||
def __init__(self, model=None, epsilon=0.05, k=10, a=0.01):
|
||||
"""
|
||||
FGSM, I-FGSM and PGD attacks
|
||||
epsilon: magnitude of attack
|
||||
k: iterations
|
||||
a: step size
|
||||
"""
|
||||
self.model = model
|
||||
self.epsilon = epsilon
|
||||
self.k = k
|
||||
self.a = a
|
||||
self.loss_fn = nn.MSELoss()
|
||||
|
||||
# PGD or I-FGSM?
|
||||
self.rand = True
|
||||
|
||||
def perturb(self, X_nat, y):
|
||||
"""
|
||||
Given examples (X_nat, y), returns adversarial
|
||||
examples within epsilon of X_nat in l_infinity norm.
|
||||
Vanilla Attack.
|
||||
"""
|
||||
X = X_nat.clone().detach_()
|
||||
if self.rand:
|
||||
X = X_nat.clone().detach_() + torch.tensor(np.random.uniform(-self.epsilon, self.epsilon, X_nat.shape).astype('float32')).cuda()
|
||||
else:
|
||||
X = X_nat.clone().detach_()
|
||||
# use the following if FGSM or I-FGSM and random seeds are fixed
|
||||
# X = X_nat.clone().detach_() + torch.tensor(np.random.uniform(-0.001, 0.001, X_nat.shape).astype('float32')).cuda()
|
||||
|
||||
for i in range(self.k):
|
||||
print('test', i)
|
||||
X.requires_grad = True
|
||||
output = self.model(X)
|
||||
|
||||
self.model.zero_grad()
|
||||
# Minus in the loss means "towards" and plus means "away from"
|
||||
loss = self.loss_fn(output, y)
|
||||
loss.backward()
|
||||
grad = X.grad
|
||||
|
||||
+30
-48
@@ -7,7 +7,13 @@ import torch
|
||||
import torch.nn as nn
|
||||
|
||||
class LinfPGDAttack(object):
|
||||
def __init__(self, model=None, device=None, epsilon=0.03, k=80, a=0.01):
|
||||
def __init__(self, model=None, device=None, epsilon=0.05, k=10, a=0.01):
|
||||
"""
|
||||
FGSM, I-FGSM and PGD attacks
|
||||
epsilon: magnitude of attack
|
||||
k: iterations
|
||||
a: step size
|
||||
"""
|
||||
self.model = model
|
||||
self.epsilon = epsilon
|
||||
self.k = k
|
||||
@@ -15,23 +21,34 @@ class LinfPGDAttack(object):
|
||||
self.loss_fn = nn.MSELoss().to(device)
|
||||
self.device = device
|
||||
|
||||
# PGD or I-FGSM?
|
||||
self.rand = True
|
||||
|
||||
def perturb(self, X_nat, y, c_trg):
|
||||
"""
|
||||
Given examples (X_nat, y), returns adversarial
|
||||
examples within epsilon of X_nat in l_infinity norm.
|
||||
Vanilla Attack.
|
||||
"""
|
||||
X = X_nat.clone().detach_()
|
||||
if self.rand:
|
||||
X = X_nat.clone().detach_() + torch.tensor(np.random.uniform(-self.epsilon, self.epsilon, X_nat.shape).astype('float32')).to(self.device)
|
||||
else:
|
||||
X = X_nat.clone().detach_()
|
||||
# use the following if FGSM or I-FGSM and random seeds are fixed
|
||||
# X = X_nat.clone().detach_() + torch.tensor(np.random.uniform(-0.001, 0.001, X_nat.shape).astype('float32')).cuda()
|
||||
|
||||
for i in range(self.k):
|
||||
# print(i)
|
||||
X.requires_grad = True
|
||||
output_att, output_img = self.model(X, c_trg)
|
||||
|
||||
out = imFromAttReg(output_att, output_img, X)
|
||||
|
||||
self.model.zero_grad()
|
||||
loss = self.loss_fn(output_att, y)
|
||||
# loss = -self.loss_fn(out, y)
|
||||
|
||||
# Attention attack
|
||||
# loss = self.loss_fn(output_att, y)
|
||||
|
||||
# Output attack
|
||||
# Minus in the loss means "towards" and plus means "away from"
|
||||
loss = self.loss_fn(out, y)
|
||||
loss.backward()
|
||||
grad = X.grad
|
||||
|
||||
@@ -40,41 +57,8 @@ class LinfPGDAttack(object):
|
||||
eta = torch.clamp(X_adv - X_nat, min=-self.epsilon, max=self.epsilon)
|
||||
X = torch.clamp(X_nat + eta, min=-1, max=1).detach_()
|
||||
|
||||
return X, eta
|
||||
|
||||
def perturb_iter_data(self, X_nat, X_all, y, c_trg):
|
||||
"""
|
||||
X_nat is a tensor with several different images.
|
||||
This does not work at all yet..
|
||||
"""
|
||||
X = X_nat.clone().detach_()
|
||||
# X_all_local = X_all.clone().detach_()
|
||||
|
||||
j = 0
|
||||
J = X_all.size(0)
|
||||
J = 1
|
||||
|
||||
for i in range(self.k):
|
||||
# print(i,j)
|
||||
X_j = X_all[j].unsqueeze(0)
|
||||
X_j.requires_grad = True
|
||||
output_att, output_img = self.model(X_j, c_trg)
|
||||
|
||||
out = imFromAttReg(output_att, output_img, X_j)
|
||||
|
||||
self.model.zero_grad()
|
||||
loss = -self.loss_fn(out, y)
|
||||
loss.backward()
|
||||
grad = X_j.grad
|
||||
|
||||
X_adv = X + self.a * grad.sign()
|
||||
|
||||
eta = torch.clamp(X_adv - X_nat, min=-self.epsilon, max=self.epsilon)
|
||||
X = torch.clamp(X_nat + eta, min=-1, max=1).detach_()
|
||||
|
||||
j += 1
|
||||
if j == J:
|
||||
j = 0
|
||||
# Debug
|
||||
# X_adv, loss, grad, output_att, output_img = None, None, None, None, None
|
||||
|
||||
return X, eta
|
||||
|
||||
@@ -88,7 +72,6 @@ class LinfPGDAttack(object):
|
||||
J = c_trg.size(0)
|
||||
|
||||
for i in range(self.k):
|
||||
# print(i)
|
||||
X.requires_grad = True
|
||||
output_att, output_img = self.model(X, c_trg[j,:].unsqueeze(0))
|
||||
|
||||
@@ -96,8 +79,8 @@ class LinfPGDAttack(object):
|
||||
|
||||
self.model.zero_grad()
|
||||
|
||||
loss = self.loss_fn(output_att, y)
|
||||
# loss = -self.loss_fn(out, y)
|
||||
# loss = self.loss_fn(output_att, y)
|
||||
loss = self.loss_fn(out, y)
|
||||
loss.backward()
|
||||
grad = X.grad
|
||||
|
||||
@@ -126,13 +109,12 @@ class LinfPGDAttack(object):
|
||||
self.model.zero_grad()
|
||||
|
||||
for j in range(J):
|
||||
# print(i, j)
|
||||
output_att, output_img = self.model(X, c_trg[j,:].unsqueeze(0))
|
||||
|
||||
out = imFromAttReg(output_att, output_img, X)
|
||||
|
||||
loss = self.loss_fn(output_att, y)
|
||||
# loss = -self.loss_fn(out, y)
|
||||
# loss = self.loss_fn(output_att, y)
|
||||
loss = self.loss_fn(out, y)
|
||||
full_loss += loss
|
||||
|
||||
full_loss.backward()
|
||||
|
||||
@@ -75,7 +75,7 @@ def get_config():
|
||||
# parser.add_argument('--animation_images_dir', type=str,
|
||||
# default='animations/eric_andre/images_to_animate')
|
||||
parser.add_argument('--animation_images_dir', type=str,
|
||||
default='data/celeba_small/')
|
||||
default='data/celeba/images_aligned/new_small')
|
||||
parser.add_argument('--animation_attribute_images_dir', type=str,
|
||||
default='animations/eric_andre/attribute_images')
|
||||
parser.add_argument('--animation_attributes_path', type=str,
|
||||
|
||||
+1
-1
@@ -1 +1 @@
|
||||
/scratch2/ganimation/models
|
||||
/home/grad3/nruiz9/research/fsynth/ganimation/models
|
||||
+46
-90
@@ -384,66 +384,51 @@ class Solver(Utils):
|
||||
reference_expression_images[target_idx]))
|
||||
|
||||
if mode == 'animate_image':
|
||||
|
||||
black = np.zeros((1,3,128,128))
|
||||
black = torch.FloatTensor(black).to(self.device)
|
||||
|
||||
# Initialize Metrics
|
||||
l1_error = 0.0
|
||||
l2_error = 0.0
|
||||
min_dist = 0.0
|
||||
l0_error = 0.0
|
||||
perceptual_error = 0.0
|
||||
n_samples = 0
|
||||
l1_error, l2_error, min_dist, l0_error = 0.0, 0.0, 0.0, 0.0
|
||||
n_dist, n_samples = 0, 0
|
||||
|
||||
pgd_attack = attacks.LinfPGDAttack(model=self.G, device=self.device)
|
||||
|
||||
images_to_animate_path = sorted(glob.glob(
|
||||
self.animation_images_dir + '/*'))
|
||||
|
||||
x_advs = []
|
||||
|
||||
for idx, image_path in enumerate(images_to_animate_path):
|
||||
image_to_animate = regular_image_transform(Image.open(image_path)).unsqueeze(0).cuda()
|
||||
|
||||
all_images = torch.cat([regular_image_transform(Image.open(path)).unsqueeze(0) for path in images_to_animate_path], dim=0).cuda()
|
||||
for target_idx in range(targets.size(0)-1):
|
||||
print('image', idx, 'AU', target_idx)
|
||||
|
||||
# Transfer to different images
|
||||
# if idx == 0:
|
||||
# for target_idx in range(targets.size(0)):
|
||||
# x_adv, perturb = pgd_attack.perturb(image_to_animate, black, targets[target_idx, :].unsqueeze(0).cuda())
|
||||
# x_advs.append((x_adv, perturb))
|
||||
|
||||
for target_idx in range(targets.size(0)):
|
||||
# Transfer to different classes
|
||||
# if target_idx == 0:
|
||||
# img = regular_image_transform(Image.open(images_to_animate_path[idx])).unsqueeze(0).cuda()
|
||||
|
||||
# Wrong Class
|
||||
# x_adv, perturb = pgd_attack.perturb(image_to_animate, black, targets[0, :].unsqueeze(0).cuda())
|
||||
|
||||
# Joint Class Conditional
|
||||
# x_adv, perturb = pgd_attack.perturb_joint_class(image_to_animate, black, targets[:, :].cuda())
|
||||
|
||||
# Iterative Class Conditional
|
||||
# x_adv, perturb = pgd_attack.perturb_iter_class(image_to_animate, black, targets[:, :].cuda())
|
||||
|
||||
# Iterative Data
|
||||
# _, perturb = pgd_attack.perturb_iter_data(image_to_animate, all_images, black, targets[68, :].unsqueeze(0).cuda())
|
||||
|
||||
targets_au = targets[target_idx, :].unsqueeze(0).cuda()
|
||||
|
||||
with torch.no_grad():
|
||||
resulting_images_att_noattack, resulting_images_reg_noattack = self.G(
|
||||
image_to_animate, targets_au)
|
||||
resulting_image_noattack = self.imFromAttReg(
|
||||
resulting_images_att_noattack, resulting_images_reg_noattack, image_to_animate).cuda()
|
||||
|
||||
# Transfer to different classes
|
||||
# if target_idx == 0:
|
||||
# Wrong Class
|
||||
# x_adv, perturb = pgd_attack.perturb(image_to_animate, image_to_animate, targets[0, :].unsqueeze(0).cuda())
|
||||
|
||||
# Joint Class Conditional
|
||||
# x_adv, perturb = pgd_attack.perturb_joint_class(image_to_animate, image_to_animate, targets[:, :].cuda())
|
||||
|
||||
# Iterative Class Conditional
|
||||
# x_adv, perturb = pgd_attack.perturb_iter_class(image_to_animate, image_to_animate, targets[:, :].cuda())
|
||||
|
||||
# Iterative Data
|
||||
# _, perturb = pgd_attack.perturb_iter_data(image_to_animate, all_images, image_to_animate, targets[68, :].unsqueeze(0).cuda())
|
||||
|
||||
# Normal Attack
|
||||
# x_adv, perturb = pgd_attack.perturb(image_to_animate, black, targets_au)
|
||||
x_adv, perturb = pgd_attack.perturb(image_to_animate, resulting_image_noattack, targets_au)
|
||||
|
||||
# x_adv, perturb = x_advs[target_idx]
|
||||
|
||||
# x_adv = image_to_animate + perturb
|
||||
# Use this line if transferring attacks
|
||||
x_adv = image_to_animate + perturb
|
||||
|
||||
# No Attack
|
||||
x_adv = image_to_animate
|
||||
|
||||
# print(image_to_animate.shape, x_adv.shape)
|
||||
# x_adv = image_to_animate
|
||||
|
||||
with torch.no_grad():
|
||||
resulting_images_att, resulting_images_reg = self.G(
|
||||
@@ -451,12 +436,6 @@ class Solver(Utils):
|
||||
resulting_image = self.imFromAttReg(
|
||||
resulting_images_att, resulting_images_reg, x_adv).cuda()
|
||||
|
||||
# with torch.no_grad():
|
||||
# resulting_images_att_noattack, resulting_images_reg_noattack = self.G(
|
||||
# image_to_animate, targets_au)
|
||||
# resulting_image_noattack = self.imFromAttReg(
|
||||
# resulting_images_att_noattack, resulting_images_reg_noattack, image_to_animate).cuda()
|
||||
|
||||
save_image((resulting_image+1)/2, os.path.join(self.animation_results_dir,
|
||||
image_path.split('/')[-1].split('.')[0]
|
||||
+ '_' + reference_expression_images[target_idx]))
|
||||
@@ -465,50 +444,27 @@ class Solver(Utils):
|
||||
image_path.split('/')[-1].split('.')[0]
|
||||
+ '_ref.jpg'))
|
||||
|
||||
# l1_error += F.l1_loss(resulting_image, resulting_image_noattack)
|
||||
# l2_error += F.mse_loss(resulting_image, resulting_image_noattack)
|
||||
# l0_error += (resulting_image - resulting_image_noattack).norm(0)
|
||||
# min_dist += (resulting_image - resulting_image_noattack).norm(float('-inf'))
|
||||
# Compare to ground-truth output
|
||||
l1_error += F.l1_loss(resulting_image, resulting_image_noattack)
|
||||
l2_error += F.mse_loss(resulting_image, resulting_image_noattack)
|
||||
l0_error += (resulting_image - resulting_image_noattack).norm(0)
|
||||
min_dist += (resulting_image - resulting_image_noattack).norm(float('-inf'))
|
||||
|
||||
# Compare to input image
|
||||
l1_error += F.l1_loss(resulting_image, image_to_animate)
|
||||
l2_error += F.mse_loss(resulting_image, image_to_animate)
|
||||
l0_error += (resulting_image - image_to_animate).norm(0)
|
||||
min_dist += (resulting_image - image_to_animate).norm(float('-inf'))
|
||||
# l1_error += F.l1_loss(resulting_image, x_adv)
|
||||
# l2_error += F.mse_loss(resulting_image, x_adv)
|
||||
# l0_error += (resulting_image - x_adv).norm(0)
|
||||
# min_dist += (resulting_image - x_adv).norm(float('-inf'))
|
||||
|
||||
if F.mse_loss(resulting_image, resulting_image_noattack) > 0.05:
|
||||
n_dist += 1
|
||||
n_samples += 1
|
||||
|
||||
# Debug
|
||||
# x_adv, targets_au, resulting_image, resulting_images_att, resulting_images_reg = None, None, None, None, None
|
||||
|
||||
image_to_animate = None
|
||||
|
||||
# Print metrics
|
||||
print('{} images. L1 error: {}. L2 error: {}. L0 error: {}. L_-inf error: {}. Perceptual error: {}.'.format(n_samples,
|
||||
l1_error / n_samples, l2_error / n_samples, l0_error / n_samples, min_dist / n_samples, perceptual_error / n_samples))
|
||||
|
||||
# """ Code to modify single Action Units """
|
||||
|
||||
# Set data loader.
|
||||
# self.data_loader = self.data_loader
|
||||
|
||||
# with torch.no_grad():
|
||||
# for i, (self.x_real, c_org) in enumerate(self.data_loader):
|
||||
|
||||
# # Prepare input images and target domain labels.
|
||||
# self.x_real = self.x_real.to(self.device)
|
||||
# c_org = c_org.to(self.device)
|
||||
|
||||
# # c_trg_list = self.create_labels(self.data_loader)
|
||||
|
||||
# crit, cl_regression = self.D(self.x_real)
|
||||
# # print(crit)
|
||||
# print("ORIGINAL", c_org[0])
|
||||
# print("REGRESSION", cl_regression[0])
|
||||
|
||||
# for au in range(17):
|
||||
# alpha = np.linspace(-0.3,0.3,10)
|
||||
# for j, a in enumerate(alpha):
|
||||
# new_emotion = c_org.clone()
|
||||
# new_emotion[:,au]=torch.clamp(new_emotion[:,au]+a, 0, 1)
|
||||
# attention, reg = self.G(self.x_real, new_emotion)
|
||||
# x_fake = self.imFromAttReg(attention, reg, self.x_real)
|
||||
# save_image((x_fake+1)/2, os.path.join(self.result_dir, '{}-{}-{}-images.jpg'.format(i,au,j)))
|
||||
|
||||
# if i >= 3:
|
||||
# break
|
||||
print('{} images. L1 error: {}. L2 error: {}. prop_dist: {}. L0 error: {}. L_-inf error: {}.'.format(n_samples,
|
||||
l1_error / n_samples, l2_error / n_samples, float(n_dist) / float(n_samples), l0_error / n_samples, min_dist / n_samples))
|
||||
|
||||
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@@ -1 +1 @@
|
||||
/scratch2/fsynth/checkpoints
|
||||
/home/grad3/nruiz9/research/fsynth/pix2pixHD_attack/checkpoints
|
||||
@@ -1,51 +0,0 @@
|
||||
import os
|
||||
import numpy as np
|
||||
from matplotlib import pyplot as plt
|
||||
from PIL import Image
|
||||
from data import landmarks
|
||||
from torchvision import transforms
|
||||
from torch.utils.data.dataset import Dataset
|
||||
import glob
|
||||
import random
|
||||
|
||||
class AVSpeech(Dataset):
|
||||
def __init__(self, transform):
|
||||
self.frame_folder = 'datasets/avspeech/frames'
|
||||
self.meta_folder = 'datasets/avspeech/meta'
|
||||
self.user_folders = glob.glob(os.path.join(self.frame_folder, '*'))
|
||||
self.users = [x.split('/')[-1] for x in self.user_folders]
|
||||
|
||||
self.transform = transform
|
||||
|
||||
self.length = len(self.users)
|
||||
|
||||
def __getitem__(self, index):
|
||||
# Get list of frames for user
|
||||
user = self.users[index]
|
||||
meta = dict(np.load(os.path.join(self.meta_folder, '{}.npz'.format(user))))
|
||||
|
||||
frame_list = glob.glob(os.path.join(self.frame_folder, '{}/*.png'.format(user)))
|
||||
frame_list = [int(x.split('/')[-1].split('.')[0]) for x in frame_list]
|
||||
|
||||
ref_frame = random.choice(frame_list)
|
||||
tgt_frame = random.choice(frame_list)
|
||||
|
||||
ref_img = Image.open(os.path.join(self.frame_folder, '{}/{}.png'.format(user, ref_frame)))
|
||||
tgt_img = Image.open(os.path.join(self.frame_folder, '{}/{}.png'.format(user, tgt_frame)))
|
||||
|
||||
# Make reference and target landmarks
|
||||
ref_lnd = landmarks.plot_landmarks(landmarks.get_relative_landmarks(meta, ref_frame))
|
||||
tgt_lnd = landmarks.plot_landmarks(landmarks.get_relative_landmarks(meta, tgt_frame))
|
||||
|
||||
ref_img = self.transform(ref_img)
|
||||
tgt_img = self.transform(tgt_img)
|
||||
ref_lnd = self.transform(ref_lnd)
|
||||
tgt_lnd = self.transform(tgt_lnd)
|
||||
|
||||
input_dict = {'ref_img': ref_img, 'tgt_img': tgt_img, 'ref_lnd': ref_lnd,
|
||||
'tgt_lnd': tgt_lnd, 'user': user}
|
||||
|
||||
return input_dict
|
||||
|
||||
def __len__(self):
|
||||
return self.length
|
||||
@@ -1,61 +0,0 @@
|
||||
import cv2
|
||||
from matplotlib import pyplot as plt
|
||||
from PIL import Image
|
||||
import numpy as np
|
||||
|
||||
def get_relative_landmarks(meta, frame_num):
|
||||
centerx, centery, l = meta['bbox'][frame_num - 1]
|
||||
orig_height = meta['length'].item()
|
||||
orig_width = meta['width'].item()
|
||||
landmarks = meta['landmarks_2d'][frame_num - 1]
|
||||
|
||||
# Go from frame landmarks to cropped and resized frame landmarks
|
||||
x_left = max(0, centerx-l)
|
||||
x_right = min(centerx+l, orig_height)
|
||||
y_up = max(0, centery-l)
|
||||
y_down = min(centery+l, orig_width)
|
||||
w = x_right - x_left
|
||||
h = y_down - y_up
|
||||
ar_h = 255. / h
|
||||
ar_w = 255. / w
|
||||
|
||||
landmarks[:,0] -= (centery - l)
|
||||
landmarks[:,1] -= (centerx - l)
|
||||
landmarks[:,0] *= ar_h
|
||||
landmarks[:,1] *= ar_w
|
||||
|
||||
return landmarks
|
||||
|
||||
def plot_landmarks(landmarks):
|
||||
fig = plt.figure(figsize=(256, 256), dpi=1)
|
||||
ax = fig.add_subplot(111)
|
||||
ax.axis('off')
|
||||
plt.imshow(np.ones((256, 256, 3)))
|
||||
plt.subplots_adjust(left=0, right=1, top=1, bottom=0)
|
||||
|
||||
lw = 100
|
||||
|
||||
# Head
|
||||
ax.plot(landmarks[0:17, 0], landmarks[0:17, 1], linestyle='-', color='green', lw=lw)
|
||||
# Eyebrows
|
||||
ax.plot(landmarks[17:22, 0], landmarks[17:22, 1], linestyle='-', color='orange', lw=lw)
|
||||
ax.plot(landmarks[22:27, 0], landmarks[22:27, 1], linestyle='-', color='orange', lw=lw)
|
||||
# Nose
|
||||
ax.plot(landmarks[27:31, 0], landmarks[27:31, 1], linestyle='-', color='blue', lw=lw)
|
||||
ax.plot(landmarks[31:36, 0], landmarks[31:36, 1], linestyle='-', color='blue', lw=lw)
|
||||
# Eyes
|
||||
ax.plot(landmarks[36:42, 0], landmarks[36:42, 1], linestyle='-', color='red', lw=lw)
|
||||
ax.plot(landmarks[42:48, 0], landmarks[42:48, 1], linestyle='-', color='red', lw=lw)
|
||||
ax.plot([landmarks[36, 0], landmarks[41, 0]], [landmarks[36, 1], landmarks[41, 1]],
|
||||
linestyle='-', color='red', lw=lw)
|
||||
ax.plot([landmarks[42, 0], landmarks[47, 0]], [landmarks[42, 1], landmarks[47, 1]],
|
||||
linestyle='-', color='red', lw=lw)
|
||||
# Mouth
|
||||
ax.plot(landmarks[48:60, 0], landmarks[48:60, 1], linestyle='-', color='purple', lw=lw)
|
||||
ax.plot([landmarks[48, 0], landmarks[59, 0]], [landmarks[48, 1], landmarks[59, 1]],
|
||||
linestyle='-', color='purple', lw=lw)
|
||||
|
||||
fig.canvas.draw()
|
||||
data = Image.frombuffer('RGB', fig.canvas.get_width_height(), fig.canvas.tostring_rgb(), 'raw', 'RGB', 0, 1)
|
||||
plt.close(fig)
|
||||
return data
|
||||
@@ -1 +0,0 @@
|
||||
/scratch2/avspeech_process/avspeech/
|
||||
Symlink
+1
@@ -0,0 +1 @@
|
||||
/home/grad3/nruiz9/research/fsynth/pix2pixHD_attack/datasets/cityscapes
|
||||
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Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user