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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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Preamble
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The GNU General Public License is a free, copyleft license for
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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>.
|
||||
@@ -0,0 +1,48 @@
|
||||
<img src='http://www.albertpumarola.com/images/2018/GANimation/face1_cyc.gif' align="right" width=90>
|
||||
|
||||
# GANimation: Anatomically-aware Facial Animation from a Single Image
|
||||
### [[Project]](http://www.albertpumarola.com/research/GANimation/index.html)[ [Paper]](https://rdcu.be/bPuaJ)
|
||||
Official implementation of [GANimation](http://www.albertpumarola.com/research/GANimation/index.html). In this work we introduce a novel GAN conditioning scheme based on Action Units (AU) annotations, which describe in a continuous manifold the anatomical facial movements defining a human expression. Our approach permits controlling the magnitude of activation of each AU and combine several of them. For more information please refer to the [paper](https://arxiv.org/abs/1807.09251).
|
||||
|
||||
This code was made public to share our research for the benefit of the scientific community. Do NOT use it for immoral purposes.
|
||||
|
||||
|
||||
|
||||
## Prerequisites
|
||||
- Install PyTorch (version 0.3.1), Torch Vision and dependencies from http://pytorch.org
|
||||
- Install requirements.txt (```pip install -r requirements.txt```)
|
||||
|
||||
## Data Preparation
|
||||
The code requires a directory containing the following files:
|
||||
- `imgs/`: folder with all image
|
||||
- `aus_openface.pkl`: dictionary containing the images action units.
|
||||
- `train_ids.csv`: file containing the images names to be used to train.
|
||||
- `test_ids.csv`: file containing the images names to be used to test.
|
||||
|
||||
An example of this directory is shown in `sample_dataset/`.
|
||||
|
||||
To generate the `aus_openface.pkl` extract each image Action Units with [OpenFace](https://github.com/TadasBaltrusaitis/OpenFace/wiki/Action-Units) and store each output in a csv file the same name as the image. Then run:
|
||||
```
|
||||
python data/prepare_au_annotations.py
|
||||
```
|
||||
|
||||
## Run
|
||||
To train:
|
||||
```
|
||||
bash launch/run_train.sh
|
||||
```
|
||||
To test:
|
||||
```
|
||||
python test --input_path path/to/img
|
||||
```
|
||||
|
||||
## Citation
|
||||
If you use this code or ideas from the paper for your research, please cite our paper:
|
||||
```
|
||||
@article{Pumarola_ijcv2019,
|
||||
title={GANimation: One-Shot Anatomically Consistent Facial Animation},
|
||||
author={A. Pumarola and A. Agudo and A.M. Martinez and A. Sanfeliu and F. Moreno-Noguer},
|
||||
booktitle={International Journal of Computer Vision (IJCV)},
|
||||
year={2019}
|
||||
}
|
||||
```
|
||||
@@ -0,0 +1,25 @@
|
||||
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)
|
||||
@@ -0,0 +1,68 @@
|
||||
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
|
||||
@@ -0,0 +1,117 @@
|
||||
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
|
||||
@@ -0,0 +1,39 @@
|
||||
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()
|
||||
@@ -0,0 +1,6 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
python train.py \
|
||||
--data_dir path/to/dataset/ \
|
||||
--name experiment_1 \
|
||||
--batch_size 25 \
|
||||
@@ -0,0 +1,405 @@
|
||||
import torch
|
||||
from collections import OrderedDict
|
||||
from torch.autograd import Variable
|
||||
import utils.util as util
|
||||
import utils.plots as plot_utils
|
||||
from .models import BaseModel
|
||||
from networks.networks import NetworksFactory
|
||||
import os
|
||||
import numpy as np
|
||||
|
||||
|
||||
class GANimation(BaseModel):
|
||||
def __init__(self, opt):
|
||||
super(GANimation, self).__init__(opt)
|
||||
self._name = 'GANimation'
|
||||
|
||||
# create networks
|
||||
self._init_create_networks()
|
||||
|
||||
# init train variables
|
||||
if self._is_train:
|
||||
self._init_train_vars()
|
||||
|
||||
# load networks and optimizers
|
||||
if not self._is_train or self._opt.load_epoch > 0:
|
||||
self.load()
|
||||
|
||||
# prefetch variables
|
||||
self._init_prefetch_inputs()
|
||||
|
||||
# init
|
||||
self._init_losses()
|
||||
|
||||
def _init_create_networks(self):
|
||||
# generator network
|
||||
self._G = self._create_generator()
|
||||
self._G.init_weights()
|
||||
if len(self._gpu_ids) > 1:
|
||||
self._G = torch.nn.DataParallel(self._G, device_ids=self._gpu_ids)
|
||||
self._G.cuda()
|
||||
|
||||
# discriminator network
|
||||
self._D = self._create_discriminator()
|
||||
self._D.init_weights()
|
||||
if len(self._gpu_ids) > 1:
|
||||
self._D = torch.nn.DataParallel(self._D, device_ids=self._gpu_ids)
|
||||
self._D.cuda()
|
||||
|
||||
def _create_generator(self):
|
||||
return NetworksFactory.get_by_name('generator_wasserstein_gan', c_dim=self._opt.cond_nc)
|
||||
|
||||
def _create_discriminator(self):
|
||||
return NetworksFactory.get_by_name('discriminator_wasserstein_gan', c_dim=self._opt.cond_nc)
|
||||
|
||||
def _init_train_vars(self):
|
||||
self._current_lr_G = self._opt.lr_G
|
||||
self._current_lr_D = self._opt.lr_D
|
||||
|
||||
# initialize optimizers
|
||||
self._optimizer_G = torch.optim.Adam(self._G.parameters(), lr=self._current_lr_G,
|
||||
betas=[self._opt.G_adam_b1, self._opt.G_adam_b2])
|
||||
self._optimizer_D = torch.optim.Adam(self._D.parameters(), lr=self._current_lr_D,
|
||||
betas=[self._opt.D_adam_b1, self._opt.D_adam_b2])
|
||||
|
||||
def _init_prefetch_inputs(self):
|
||||
self._input_real_img = self._Tensor(self._opt.batch_size, 3, self._opt.image_size, self._opt.image_size)
|
||||
self._input_real_cond = self._Tensor(self._opt.batch_size, self._opt.cond_nc)
|
||||
self._input_desired_cond = self._Tensor(self._opt.batch_size, self._opt.cond_nc)
|
||||
self._input_real_img_path = None
|
||||
self._input_real_cond_path = None
|
||||
|
||||
def _init_losses(self):
|
||||
# define loss functions
|
||||
self._criterion_cycle = torch.nn.L1Loss().cuda()
|
||||
self._criterion_D_cond = torch.nn.MSELoss().cuda()
|
||||
|
||||
# init losses G
|
||||
self._loss_g_fake = Variable(self._Tensor([0]))
|
||||
self._loss_g_cond = Variable(self._Tensor([0]))
|
||||
self._loss_g_cyc = Variable(self._Tensor([0]))
|
||||
self._loss_g_mask_1 = Variable(self._Tensor([0]))
|
||||
self._loss_g_mask_2 = Variable(self._Tensor([0]))
|
||||
self._loss_g_idt = Variable(self._Tensor([0]))
|
||||
self._loss_g_masked_fake = Variable(self._Tensor([0]))
|
||||
self._loss_g_masked_cond = Variable(self._Tensor([0]))
|
||||
self._loss_g_mask_1_smooth = Variable(self._Tensor([0]))
|
||||
self._loss_g_mask_2_smooth = Variable(self._Tensor([0]))
|
||||
self._loss_rec_real_img_rgb = Variable(self._Tensor([0]))
|
||||
self._loss_g_fake_imgs_smooth = Variable(self._Tensor([0]))
|
||||
self._loss_g_unmasked_rgb = Variable(self._Tensor([0]))
|
||||
|
||||
# init losses D
|
||||
self._loss_d_real = Variable(self._Tensor([0]))
|
||||
self._loss_d_cond = Variable(self._Tensor([0]))
|
||||
self._loss_d_fake = Variable(self._Tensor([0]))
|
||||
self._loss_d_gp = Variable(self._Tensor([0]))
|
||||
|
||||
def set_input(self, input):
|
||||
self._input_real_img.resize_(input['real_img'].size()).copy_(input['real_img'])
|
||||
self._input_real_cond.resize_(input['real_cond'].size()).copy_(input['real_cond'])
|
||||
self._input_desired_cond.resize_(input['desired_cond'].size()).copy_(input['desired_cond'])
|
||||
self._input_real_id = input['sample_id']
|
||||
self._input_real_img_path = input['real_img_path']
|
||||
|
||||
if len(self._gpu_ids) > 0:
|
||||
self._input_real_img = self._input_real_img.cuda(self._gpu_ids[0], async=True)
|
||||
self._input_real_cond = self._input_real_cond.cuda(self._gpu_ids[0], async=True)
|
||||
self._input_desired_cond = self._input_desired_cond.cuda(self._gpu_ids[0], async=True)
|
||||
|
||||
def set_train(self):
|
||||
self._G.train()
|
||||
self._D.train()
|
||||
self._is_train = True
|
||||
|
||||
def set_eval(self):
|
||||
self._G.eval()
|
||||
self._is_train = False
|
||||
|
||||
# get image paths
|
||||
def get_image_paths(self):
|
||||
return OrderedDict([('real_img', self._input_real_img_path)])
|
||||
|
||||
def forward(self, keep_data_for_visuals=False, return_estimates=False):
|
||||
if not self._is_train:
|
||||
# convert tensor to variables
|
||||
real_img = Variable(self._input_real_img, volatile=True)
|
||||
real_cond = Variable(self._input_real_cond, volatile=True)
|
||||
desired_cond = Variable(self._input_desired_cond, volatile=True)
|
||||
|
||||
# generate fake images
|
||||
fake_imgs, fake_img_mask = self._G.forward(real_img, desired_cond)
|
||||
fake_img_mask = self._do_if_necessary_saturate_mask(fake_img_mask, saturate=self._opt.do_saturate_mask)
|
||||
fake_imgs_masked = fake_img_mask * real_img + (1 - fake_img_mask) * fake_imgs
|
||||
|
||||
rec_real_img_rgb, rec_real_img_mask = self._G.forward(fake_imgs_masked, real_cond)
|
||||
rec_real_img_mask = self._do_if_necessary_saturate_mask(rec_real_img_mask, saturate=self._opt.do_saturate_mask)
|
||||
rec_real_imgs = rec_real_img_mask * fake_imgs_masked + (1 - rec_real_img_mask) * rec_real_img_rgb
|
||||
|
||||
imgs = None
|
||||
data = None
|
||||
if return_estimates:
|
||||
# normalize mask for better visualization
|
||||
fake_img_mask_max = fake_imgs_masked.view(fake_img_mask.size(0), -1).max(-1)[0]
|
||||
fake_img_mask_max = torch.unsqueeze(torch.unsqueeze(torch.unsqueeze(fake_img_mask_max, -1), -1), -1)
|
||||
# fake_img_mask_norm = fake_img_mask / fake_img_mask_max
|
||||
fake_img_mask_norm = fake_img_mask
|
||||
|
||||
# generate images
|
||||
im_real_img = util.tensor2im(real_img.data)
|
||||
im_fake_imgs = util.tensor2im(fake_imgs.data)
|
||||
im_fake_img_mask_norm = util.tensor2maskim(fake_img_mask_norm.data)
|
||||
im_fake_imgs_masked = util.tensor2im(fake_imgs_masked.data)
|
||||
im_rec_imgs = util.tensor2im(rec_real_img_rgb.data)
|
||||
im_rec_img_mask_norm = util.tensor2maskim(rec_real_img_mask.data)
|
||||
im_rec_imgs_masked = util.tensor2im(rec_real_imgs.data)
|
||||
im_concat_img = np.concatenate([im_real_img, im_fake_imgs_masked, im_fake_img_mask_norm, im_fake_imgs,
|
||||
im_rec_imgs, im_rec_img_mask_norm, im_rec_imgs_masked],
|
||||
1)
|
||||
|
||||
im_real_img_batch = util.tensor2im(real_img.data, idx=-1, nrows=1)
|
||||
im_fake_imgs_batch = util.tensor2im(fake_imgs.data, idx=-1, nrows=1)
|
||||
im_fake_img_mask_norm_batch = util.tensor2maskim(fake_img_mask_norm.data, idx=-1, nrows=1)
|
||||
im_fake_imgs_masked_batch = util.tensor2im(fake_imgs_masked.data, idx=-1, nrows=1)
|
||||
im_concat_img_batch = np.concatenate([im_real_img_batch, im_fake_imgs_masked_batch,
|
||||
im_fake_img_mask_norm_batch, im_fake_imgs_batch],
|
||||
1)
|
||||
|
||||
imgs = OrderedDict([('real_img', im_real_img),
|
||||
('fake_imgs', im_fake_imgs),
|
||||
('fake_img_mask', im_fake_img_mask_norm),
|
||||
('fake_imgs_masked', im_fake_imgs_masked),
|
||||
('concat', im_concat_img),
|
||||
('real_img_batch', im_real_img_batch),
|
||||
('fake_imgs_batch', im_fake_imgs_batch),
|
||||
('fake_img_mask_batch', im_fake_img_mask_norm_batch),
|
||||
('fake_imgs_masked_batch', im_fake_imgs_masked_batch),
|
||||
('concat_batch', im_concat_img_batch),
|
||||
])
|
||||
|
||||
data = OrderedDict([('real_path', self._input_real_img_path),
|
||||
('desired_cond', desired_cond.data[0, ...].cpu().numpy().astype('str'))
|
||||
])
|
||||
|
||||
# keep data for visualization
|
||||
if keep_data_for_visuals:
|
||||
self._vis_real_img = util.tensor2im(self._input_real_img)
|
||||
self._vis_fake_img_unmasked = util.tensor2im(fake_imgs.data)
|
||||
self._vis_fake_img = util.tensor2im(fake_imgs_masked.data)
|
||||
self._vis_fake_img_mask = util.tensor2maskim(fake_img_mask.data)
|
||||
self._vis_real_cond = self._input_real_cond.cpu()[0, ...].numpy()
|
||||
self._vis_desired_cond = self._input_desired_cond.cpu()[0, ...].numpy()
|
||||
self._vis_batch_real_img = util.tensor2im(self._input_real_img, idx=-1)
|
||||
self._vis_batch_fake_img_mask = util.tensor2maskim(fake_img_mask.data, idx=-1)
|
||||
self._vis_batch_fake_img = util.tensor2im(fake_imgs_masked.data, idx=-1)
|
||||
|
||||
return imgs, data
|
||||
|
||||
def optimize_parameters(self, train_generator=True, keep_data_for_visuals=False):
|
||||
if self._is_train:
|
||||
# convert tensor to variables
|
||||
self._B = self._input_real_img.size(0)
|
||||
self._real_img = Variable(self._input_real_img)
|
||||
self._real_cond = Variable(self._input_real_cond)
|
||||
self._desired_cond = Variable(self._input_desired_cond)
|
||||
|
||||
# train D
|
||||
loss_D, fake_imgs_masked = self._forward_D()
|
||||
self._optimizer_D.zero_grad()
|
||||
loss_D.backward()
|
||||
self._optimizer_D.step()
|
||||
|
||||
loss_D_gp= self._gradinet_penalty_D(fake_imgs_masked)
|
||||
self._optimizer_D.zero_grad()
|
||||
loss_D_gp.backward()
|
||||
self._optimizer_D.step()
|
||||
|
||||
# train G
|
||||
if train_generator:
|
||||
loss_G = self._forward_G(keep_data_for_visuals)
|
||||
self._optimizer_G.zero_grad()
|
||||
loss_G.backward()
|
||||
self._optimizer_G.step()
|
||||
|
||||
def _forward_G(self, keep_data_for_visuals):
|
||||
# generate fake images
|
||||
fake_imgs, fake_img_mask = self._G.forward(self._real_img, self._desired_cond)
|
||||
fake_img_mask = self._do_if_necessary_saturate_mask(fake_img_mask, saturate=self._opt.do_saturate_mask)
|
||||
fake_imgs_masked = fake_img_mask * self._real_img + (1 - fake_img_mask) * fake_imgs
|
||||
|
||||
# D(G(Ic1, c2)*M) masked
|
||||
d_fake_desired_img_masked_prob, d_fake_desired_img_masked_cond = self._D.forward(fake_imgs_masked)
|
||||
self._loss_g_masked_fake = self._compute_loss_D(d_fake_desired_img_masked_prob, True) * self._opt.lambda_D_prob
|
||||
self._loss_g_masked_cond = self._criterion_D_cond(d_fake_desired_img_masked_cond, self._desired_cond) / self._B * self._opt.lambda_D_cond
|
||||
|
||||
# G(G(Ic1,c2), c1)
|
||||
rec_real_img_rgb, rec_real_img_mask = self._G.forward(fake_imgs_masked, self._real_cond)
|
||||
rec_real_img_mask = self._do_if_necessary_saturate_mask(rec_real_img_mask, saturate=self._opt.do_saturate_mask)
|
||||
rec_real_imgs = rec_real_img_mask * fake_imgs_masked + (1 - rec_real_img_mask) * rec_real_img_rgb
|
||||
|
||||
# l_cyc(G(G(Ic1,c2), c1)*M)
|
||||
self._loss_g_cyc = self._criterion_cycle(rec_real_imgs, self._real_img) * self._opt.lambda_cyc
|
||||
|
||||
# loss mask
|
||||
self._loss_g_mask_1 = torch.mean(fake_img_mask) * self._opt.lambda_mask
|
||||
self._loss_g_mask_2 = torch.mean(rec_real_img_mask) * self._opt.lambda_mask
|
||||
self._loss_g_mask_1_smooth = self._compute_loss_smooth(fake_img_mask) * self._opt.lambda_mask_smooth
|
||||
self._loss_g_mask_2_smooth = self._compute_loss_smooth(rec_real_img_mask) * self._opt.lambda_mask_smooth
|
||||
|
||||
# keep data for visualization
|
||||
if keep_data_for_visuals:
|
||||
self._vis_real_img = util.tensor2im(self._input_real_img)
|
||||
self._vis_fake_img_unmasked = util.tensor2im(fake_imgs.data)
|
||||
self._vis_fake_img = util.tensor2im(fake_imgs_masked.data)
|
||||
self._vis_fake_img_mask = util.tensor2maskim(fake_img_mask.data)
|
||||
self._vis_real_cond = self._input_real_cond.cpu()[0, ...].numpy()
|
||||
self._vis_desired_cond = self._input_desired_cond.cpu()[0, ...].numpy()
|
||||
self._vis_batch_real_img = util.tensor2im(self._input_real_img, idx=-1)
|
||||
self._vis_batch_fake_img_mask = util.tensor2maskim(fake_img_mask.data, idx=-1)
|
||||
self._vis_batch_fake_img = util.tensor2im(fake_imgs_masked.data, idx=-1)
|
||||
self._vis_rec_img_unmasked = util.tensor2im(rec_real_img_rgb.data)
|
||||
self._vis_rec_real_img = util.tensor2im(rec_real_imgs.data)
|
||||
self._vis_rec_real_img_mask = util.tensor2maskim(rec_real_img_mask.data)
|
||||
self._vis_batch_rec_real_img = util.tensor2im(rec_real_imgs.data, idx=-1)
|
||||
|
||||
# combine losses
|
||||
return self._loss_g_masked_fake + self._loss_g_masked_cond + \
|
||||
self._loss_g_cyc + \
|
||||
self._loss_g_mask_1 + self._loss_g_mask_2 + \
|
||||
self._loss_g_mask_1_smooth + self._loss_g_mask_2_smooth
|
||||
|
||||
def _forward_D(self):
|
||||
# generate fake images
|
||||
fake_imgs, fake_img_mask = self._G.forward(self._real_img, self._desired_cond)
|
||||
fake_img_mask = self._do_if_necessary_saturate_mask(fake_img_mask, saturate=self._opt.do_saturate_mask)
|
||||
fake_imgs_masked = fake_img_mask * self._real_img + (1 - fake_img_mask) * fake_imgs
|
||||
|
||||
# D(real_I)
|
||||
d_real_img_prob, d_real_img_cond = self._D.forward(self._real_img)
|
||||
self._loss_d_real = self._compute_loss_D(d_real_img_prob, True) * self._opt.lambda_D_prob
|
||||
self._loss_d_cond = self._criterion_D_cond(d_real_img_cond, self._real_cond) / self._B * self._opt.lambda_D_cond
|
||||
|
||||
# D(fake_I)
|
||||
d_fake_desired_img_prob, _ = self._D.forward(fake_imgs_masked.detach())
|
||||
self._loss_d_fake = self._compute_loss_D(d_fake_desired_img_prob, False) * self._opt.lambda_D_prob
|
||||
|
||||
# combine losses
|
||||
return self._loss_d_real + self._loss_d_cond + self._loss_d_fake, fake_imgs_masked
|
||||
|
||||
def _gradinet_penalty_D(self, fake_imgs_masked):
|
||||
# interpolate sample
|
||||
alpha = torch.rand(self._B, 1, 1, 1).cuda().expand_as(self._real_img)
|
||||
interpolated = Variable(alpha * self._real_img.data + (1 - alpha) * fake_imgs_masked.data, requires_grad=True)
|
||||
interpolated_prob, _ = self._D(interpolated)
|
||||
|
||||
# compute gradients
|
||||
grad = torch.autograd.grad(outputs=interpolated_prob,
|
||||
inputs=interpolated,
|
||||
grad_outputs=torch.ones(interpolated_prob.size()).cuda(),
|
||||
retain_graph=True,
|
||||
create_graph=True,
|
||||
only_inputs=True)[0]
|
||||
|
||||
# penalize gradients
|
||||
grad = grad.view(grad.size(0), -1)
|
||||
grad_l2norm = torch.sqrt(torch.sum(grad ** 2, dim=1))
|
||||
self._loss_d_gp = torch.mean((grad_l2norm - 1) ** 2) * self._opt.lambda_D_gp
|
||||
|
||||
return self._loss_d_gp
|
||||
|
||||
def _compute_loss_D(self, estim, is_real):
|
||||
return -torch.mean(estim) if is_real else torch.mean(estim)
|
||||
|
||||
def _compute_loss_smooth(self, mat):
|
||||
return torch.sum(torch.abs(mat[:, :, :, :-1] - mat[:, :, :, 1:])) + \
|
||||
torch.sum(torch.abs(mat[:, :, :-1, :] - mat[:, :, 1:, :]))
|
||||
|
||||
def get_current_errors(self):
|
||||
loss_dict = OrderedDict([('g_fake', self._loss_g_fake.data[0]),
|
||||
('g_cond', self._loss_g_cond.data[0]),
|
||||
('g_mskd_fake', self._loss_g_masked_fake.data[0]),
|
||||
('g_mskd_cond', self._loss_g_masked_cond.data[0]),
|
||||
('g_cyc', self._loss_g_cyc.data[0]),
|
||||
('g_rgb', self._loss_rec_real_img_rgb.data[0]),
|
||||
('g_rgb_un', self._loss_g_unmasked_rgb.data[0]),
|
||||
('g_rgb_s', self._loss_g_fake_imgs_smooth.data[0]),
|
||||
('g_m1', self._loss_g_mask_1.data[0]),
|
||||
('g_m2', self._loss_g_mask_2.data[0]),
|
||||
('g_m1_s', self._loss_g_mask_1_smooth.data[0]),
|
||||
('g_m2_s', self._loss_g_mask_2_smooth.data[0]),
|
||||
('g_idt', self._loss_g_idt.data[0]),
|
||||
('d_real', self._loss_d_real.data[0]),
|
||||
('d_cond', self._loss_d_cond.data[0]),
|
||||
('d_fake', self._loss_d_fake.data[0]),
|
||||
('d_gp', self._loss_d_gp.data[0])])
|
||||
|
||||
return loss_dict
|
||||
|
||||
def get_current_scalars(self):
|
||||
return OrderedDict([('lr_G', self._current_lr_G), ('lr_D', self._current_lr_D)])
|
||||
|
||||
def get_current_visuals(self):
|
||||
# visuals return dictionary
|
||||
visuals = OrderedDict()
|
||||
|
||||
# input visuals
|
||||
title_input_img = os.path.basename(self._input_real_img_path[0])
|
||||
visuals['1_input_img'] = plot_utils.plot_au(self._vis_real_img, self._vis_real_cond, title=title_input_img)
|
||||
visuals['2_fake_img'] = plot_utils.plot_au(self._vis_fake_img, self._vis_desired_cond)
|
||||
visuals['3_rec_real_img'] = plot_utils.plot_au(self._vis_rec_real_img, self._vis_real_cond)
|
||||
visuals['4_fake_img_unmasked'] = self._vis_fake_img_unmasked
|
||||
visuals['5_fake_img_mask'] = self._vis_fake_img_mask
|
||||
visuals['6_rec_real_img_mask'] = self._vis_rec_real_img_mask
|
||||
visuals['7_cyc_img_unmasked'] = self._vis_fake_img_unmasked
|
||||
# visuals['8_fake_img_mask_sat'] = self._vis_fake_img_mask_saturated
|
||||
# visuals['9_rec_real_img_mask_sat'] = self._vis_rec_real_img_mask_saturated
|
||||
visuals['10_batch_real_img'] = self._vis_batch_real_img
|
||||
visuals['11_batch_fake_img'] = self._vis_batch_fake_img
|
||||
visuals['12_batch_fake_img_mask'] = self._vis_batch_fake_img_mask
|
||||
# visuals['11_idt_img'] = self._vis_idt_img
|
||||
|
||||
return visuals
|
||||
|
||||
def save(self, label):
|
||||
# save networks
|
||||
self._save_network(self._G, 'G', label)
|
||||
self._save_network(self._D, 'D', label)
|
||||
|
||||
# save optimizers
|
||||
self._save_optimizer(self._optimizer_G, 'G', label)
|
||||
self._save_optimizer(self._optimizer_D, 'D', label)
|
||||
|
||||
def load(self):
|
||||
load_epoch = self._opt.load_epoch
|
||||
|
||||
# load G
|
||||
self._load_network(self._G, 'G', load_epoch)
|
||||
|
||||
if self._is_train:
|
||||
# load D
|
||||
self._load_network(self._D, 'D', load_epoch)
|
||||
|
||||
# load optimizers
|
||||
self._load_optimizer(self._optimizer_G, 'G', load_epoch)
|
||||
self._load_optimizer(self._optimizer_D, 'D', load_epoch)
|
||||
|
||||
def update_learning_rate(self):
|
||||
# updated learning rate G
|
||||
lr_decay_G = self._opt.lr_G / self._opt.nepochs_decay
|
||||
self._current_lr_G -= lr_decay_G
|
||||
for param_group in self._optimizer_G.param_groups:
|
||||
param_group['lr'] = self._current_lr_G
|
||||
print('update G learning rate: %f -> %f' % (self._current_lr_G + lr_decay_G, self._current_lr_G))
|
||||
|
||||
# update learning rate D
|
||||
lr_decay_D = self._opt.lr_D / self._opt.nepochs_decay
|
||||
self._current_lr_D -= lr_decay_D
|
||||
for param_group in self._optimizer_D.param_groups:
|
||||
param_group['lr'] = self._current_lr_D
|
||||
print('update D learning rate: %f -> %f' % (self._current_lr_D + lr_decay_D, self._current_lr_D))
|
||||
|
||||
def _l1_loss_with_target_gradients(self, input, target):
|
||||
return torch.sum(torch.abs(input - target)) / input.data.nelement()
|
||||
|
||||
def _do_if_necessary_saturate_mask(self, m, saturate=False):
|
||||
return torch.clamp(0.55*torch.tanh(3*(m-0.5))+0.5, 0, 1) if saturate else m
|
||||
@@ -0,0 +1,132 @@
|
||||
import os
|
||||
import torch
|
||||
from torch.optim import lr_scheduler
|
||||
|
||||
class ModelsFactory:
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
@staticmethod
|
||||
def get_by_name(model_name, *args, **kwargs):
|
||||
model = None
|
||||
|
||||
if model_name == 'ganimation':
|
||||
from .ganimation import GANimation
|
||||
model = GANimation(*args, **kwargs)
|
||||
else:
|
||||
raise ValueError("Model %s not recognized." % model_name)
|
||||
|
||||
print("Model %s was created" % model.name)
|
||||
return model
|
||||
|
||||
|
||||
class BaseModel(object):
|
||||
|
||||
def __init__(self, opt):
|
||||
self._name = 'BaseModel'
|
||||
|
||||
self._opt = opt
|
||||
self._gpu_ids = opt.gpu_ids
|
||||
self._is_train = opt.is_train
|
||||
|
||||
self._Tensor = torch.cuda.FloatTensor if self._gpu_ids else torch.Tensor
|
||||
self._save_dir = os.path.join(opt.checkpoints_dir, opt.name)
|
||||
|
||||
|
||||
@property
|
||||
def name(self):
|
||||
return self._name
|
||||
|
||||
@property
|
||||
def is_train(self):
|
||||
return self._is_train
|
||||
|
||||
def set_input(self, input):
|
||||
assert False, "set_input not implemented"
|
||||
|
||||
def set_train(self):
|
||||
assert False, "set_train not implemented"
|
||||
|
||||
def set_eval(self):
|
||||
assert False, "set_eval not implemented"
|
||||
|
||||
def forward(self, keep_data_for_visuals=False):
|
||||
assert False, "forward not implemented"
|
||||
|
||||
# used in test time, no backprop
|
||||
def test(self):
|
||||
assert False, "test not implemented"
|
||||
|
||||
def get_image_paths(self):
|
||||
return {}
|
||||
|
||||
def optimize_parameters(self):
|
||||
assert False, "optimize_parameters not implemented"
|
||||
|
||||
def get_current_visuals(self):
|
||||
return {}
|
||||
|
||||
def get_current_errors(self):
|
||||
return {}
|
||||
|
||||
def get_current_scalars(self):
|
||||
return {}
|
||||
|
||||
def save(self, label):
|
||||
assert False, "save not implemented"
|
||||
|
||||
def load(self):
|
||||
assert False, "load not implemented"
|
||||
|
||||
def _save_optimizer(self, optimizer, optimizer_label, epoch_label):
|
||||
save_filename = 'opt_epoch_%s_id_%s.pth' % (epoch_label, optimizer_label)
|
||||
save_path = os.path.join(self._save_dir, save_filename)
|
||||
torch.save(optimizer.state_dict(), save_path)
|
||||
|
||||
def _load_optimizer(self, optimizer, optimizer_label, epoch_label):
|
||||
load_filename = 'opt_epoch_%s_id_%s.pth' % (epoch_label, optimizer_label)
|
||||
load_path = os.path.join(self._save_dir, load_filename)
|
||||
assert os.path.exists(
|
||||
load_path), 'Weights file not found. Have you trained a model!? We are not providing one' % load_path
|
||||
|
||||
optimizer.load_state_dict(torch.load(load_path))
|
||||
print 'loaded optimizer: %s' % load_path
|
||||
|
||||
def _save_network(self, network, network_label, epoch_label):
|
||||
save_filename = 'net_epoch_%s_id_%s.pth' % (epoch_label, network_label)
|
||||
save_path = os.path.join(self._save_dir, save_filename)
|
||||
torch.save(network.state_dict(), save_path)
|
||||
print 'saved net: %s' % save_path
|
||||
|
||||
def _load_network(self, network, network_label, epoch_label):
|
||||
load_filename = 'net_epoch_%s_id_%s.pth' % (epoch_label, network_label)
|
||||
load_path = os.path.join(self._save_dir, load_filename)
|
||||
assert os.path.exists(
|
||||
load_path), 'Weights file not found. Have you trained a model!? We are not providing one' % load_path
|
||||
|
||||
network.load_state_dict(torch.load(load_path))
|
||||
print 'loaded net: %s' % load_path
|
||||
|
||||
def update_learning_rate(self):
|
||||
pass
|
||||
|
||||
def print_network(self, network):
|
||||
num_params = 0
|
||||
for param in network.parameters():
|
||||
num_params += param.numel()
|
||||
print(network)
|
||||
print('Total number of parameters: %d' % num_params)
|
||||
|
||||
def _get_scheduler(self, optimizer, opt):
|
||||
if opt.lr_policy == 'lambda':
|
||||
def lambda_rule(epoch):
|
||||
lr_l = 1.0 - max(0, epoch + 1 + opt.epoch_count - opt.niter) / float(opt.niter_decay + 1)
|
||||
return lr_l
|
||||
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda_rule)
|
||||
elif opt.lr_policy == 'step':
|
||||
scheduler = lr_scheduler.StepLR(optimizer, step_size=opt.lr_decay_iters, gamma=0.1)
|
||||
elif opt.lr_policy == 'plateau':
|
||||
scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.2, threshold=0.01, patience=5)
|
||||
else:
|
||||
return NotImplementedError('learning rate policy [%s] is not implemented', opt.lr_policy)
|
||||
return scheduler
|
||||
@@ -0,0 +1,30 @@
|
||||
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()
|
||||
@@ -0,0 +1,68 @@
|
||||
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)
|
||||
@@ -0,0 +1,57 @@
|
||||
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
|
||||
@@ -0,0 +1,108 @@
|
||||
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')
|
||||
@@ -0,0 +1,9 @@
|
||||
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
|
||||
@@ -0,0 +1,31 @@
|
||||
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
|
||||
@@ -0,0 +1,6 @@
|
||||
numpy
|
||||
matplotlib
|
||||
tqdm
|
||||
dlib
|
||||
face_recognition
|
||||
opencv-contrib-python
|
||||
Binary file not shown.
Binary file not shown.
|
After Width: | Height: | Size: 5.0 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 7.9 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 7.1 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 11 KiB |
@@ -0,0 +1,2 @@
|
||||
N_0000001507_00202.jpg
|
||||
N_0000001939_00054.jpg
|
||||
|
@@ -0,0 +1,2 @@
|
||||
N_0000000437_00540.jpg
|
||||
N_0000000356_00190.jpg
|
||||
|
@@ -0,0 +1,74 @@
|
||||
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()
|
||||
@@ -0,0 +1,141 @@
|
||||
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()
|
||||
@@ -0,0 +1,54 @@
|
||||
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()
|
||||
@@ -0,0 +1,71 @@
|
||||
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
|
||||
@@ -0,0 +1,67 @@
|
||||
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
|
||||
@@ -0,0 +1,66 @@
|
||||
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)
|
||||
@@ -0,0 +1,53 @@
|
||||
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 +1,6 @@
|
||||
data/*
|
||||
experiments/*
|
||||
__pycache__
|
||||
.vscode
|
||||
animations/eric_andre/pretrained_models/*
|
||||
animations/eric_andre/results/*
|
||||
@@ -0,0 +1,102 @@
|
||||
# GANimation
|
||||
|
||||
This repository contains an implementation of [GANimation](https://arxiv.org/pdf/1807.09251.pdf) by Pumarola et al. based on [StarGAN code](https://github.com/yunjey/stargan) by @yunjey. With this model they are able to modify in a continuous way facial expressions of single images.
|
||||
|
||||
[Pretrained models](https://www.dropbox.com/sh/108g19dk3gt1l7l/AAB4OJHHrMHlBDbNK8aFQVZSa?dl=0) and the [preprocessed CelebA dataset](https://www.dropbox.com/s/payjdk08292csra/celeba.zip?dl=0) are provided to facilitate the use of this model as well as the process for preparing other datasets for training this model.
|
||||
|
||||
<p align="center">
|
||||
<img width="170" height="170" src="https://github.com/vipermu/ganimation/blob/master/video_results/frida.gif">
|
||||
</p>
|
||||
|
||||
<p align="center">
|
||||
<img width="600" height="150" src="https://github.com/vipermu/ganimation/blob/master/video_results/eric_andre.gif">
|
||||
</p>
|
||||
|
||||
|
||||
## Setup
|
||||
|
||||
#### Conda environment
|
||||
Create your conda environment by just running the following command:
|
||||
`conda env create -f environment.yml`
|
||||
|
||||
|
||||
## Datasets
|
||||
|
||||
#### CelebA preprocessed dataset
|
||||
Download and unzip the *CelebA* preprocessed dataset uploaded to [this link](https://www.dropbox.com/s/payjdk08292csra/celeba.zip?dl=0) extracted from [MMLAB](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html). Here you can find a folder containing the aligned and resized 128x128 images as well as a _txt_ file containing their respective Action Units vectors computed using [OpenFace](https://github.com/TadasBaltrusaitis/OpenFace). By default, this code assumes that you have these two elements in _`./data/celeba/`_.
|
||||
|
||||
#### Use your own dataset
|
||||
If you want to use other datasets you will need to detect and crop bounding boxes around the face of each image, compute their corresponding Action Unit vectors and resize them to 128x128px.
|
||||
|
||||
You can perform all these steps using [OpenFace](https://github.com/TadasBaltrusaitis/OpenFace). First you will need to setup the project. They provide guides for [linux](https://github.com/TadasBaltrusaitis/OpenFace/wiki/Unix-Installation) and [windows](https://github.com/TadasBaltrusaitis/OpenFace/wiki/Windows-Installation). Once the models are compiled, read their [Action Unit wiki](https://github.com/TadasBaltrusaitis/OpenFace/wiki/Action-Units) and their [documentation](https://github.com/TadasBaltrusaitis/OpenFace/wiki/Command-line-arguments) on these models to find out which is the command that you need to execute.
|
||||
|
||||
In my case the command was the following: `./build/bin/FaceLandmarkImg -fdir datasets/my-dataset/ -out_dir processed/my-processed-dataset/ -aus -simalign -au_static -nobadaligned -simsize 128 -format_aligned jpg -nomask`
|
||||
|
||||
After computing these Action Units, depending on the command that you have used, you will obtain different output formats. With the command that I used, I obtained a _csv_ file for each image containing its corresponding Action Units vector among extra information, a folder for each image containing the resized and cropped image and a _txt_ file with extra details about each image. You can find in _openface_utils_ folder the code that I used to extract all the Action Unit information in a _txt_ file and to group all the images into a single folder.
|
||||
|
||||
After having the Action Unit _txt_ file and the image folder you can move them to the directory of this project. By default, this code assumes that you have these two elements in _`./data/celeba/`_.
|
||||
|
||||
## Generate animations
|
||||
Pretrained models can be downloaded from [this](https://www.dropbox.com/sh/108g19dk3gt1l7l/AAB4OJHHrMHlBDbNK8aFQVZSa?dl=0) link. This folder contains the weights of both models (the Generator and the Discriminator) after training the model for 37 epochs.
|
||||
|
||||
By running `python main.py --mode animation` the default animation will be executed. There are two different types of animations already implemented which can be selected with the parameter 'animation_mode'. It is presuposed that the following folders are present:
|
||||
|
||||
- **attribute_images**: images from which the Action Units that we want to use for the animation were computed.
|
||||
- **images_to_animate**: images that we want to animate.
|
||||
- **pretrained_models**: pretrained models (only the generator is needed, you can download it from [here](https://www.dropbox.com/home/data/pretrained_models)
|
||||
- **results**: folder where the resulting images will be stored.
|
||||
- **attributes.txt**: file with the action units from 'attribute_images' computed.
|
||||
|
||||
The two options already implemented are the following:
|
||||
- **animate_image**: applies the expressions from 'attributes.txt' to the images in 'images_to_animate'.
|
||||
- **animate_random_batch**: applies the expressions from 'attributes.txt' to random batches of images from the training dataset.
|
||||
|
||||
|
||||
## Train the model
|
||||
|
||||
#### Parameters
|
||||
|
||||
You can either modify these parameters in `main.py` or by calling them as command line arguments.
|
||||
|
||||
|
||||
##### Lambdas
|
||||
|
||||
- *lambda_cls*: classification lambda.
|
||||
- *lambda_rec*: lambda for the cycle consistency loss.
|
||||
- *lambda_gp*: gradient penalty lambda.
|
||||
- *lambda_sat*: lambda for attention saturation loss.
|
||||
- *lambda_smooth*: lambda for attention smoothing loss.
|
||||
|
||||
##### Training parameters
|
||||
|
||||
- *c_dim*: number of Action Units to use to train the model.
|
||||
- *batch_size*
|
||||
- *num_epochs*
|
||||
- *num_epochs_decay*: number of epochs to start decaying the learning rate.
|
||||
- *g_lr*: generator's learning rate.
|
||||
- *d_lr*: discriminator's learning rate.
|
||||
|
||||
##### Pretrained models parameters
|
||||
The weights are stored in the following format: `<epoch>-<iteration>-<G/D>.ckpt` where G and D represent the Generator and the Discriminator respectively. We save the state of thoptimizers in the same format and extension but add '_optim'.
|
||||
|
||||
- *resume_iters*: iteration numbre from which we want to start the training. Note that we will need to have a saved model corresponding to that exact iteration number.
|
||||
- *first_epoch*: initial epoch for when we train from pretrained models.
|
||||
|
||||
##### Miscellaneous:
|
||||
- *mode*: train/test.
|
||||
- *image_dir*: path to your image folder.
|
||||
- *attr_path*: path to your attributes _txt_ folder.
|
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- *outputs_dir*: name for the output folder.
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#### Virtual
|
||||
- *use_virtual*: this flag activates the use of _cycle consistency loss_ during the training.
|
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|
||||
## Virtual Cycle Consistency Loss
|
||||
The aim of this new component is to minimize the noise produced by the Action Unit regression. This idea was extracted from [Label-Noise Robust Multi-Domain Image-to-Image Translation](https://arxiv.org/abs/1905.02185) by Kaneko et al.. It is not proven that this new component improves the outcomes of the model but the masks seem to be darker when it is applied without losing realism on the output images.
|
||||
|
||||
## TODOs
|
||||
|
||||
- Clean Test function. (DONE)
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- Add an Action Units selector option for training.
|
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- Add multi-gpu support.
|
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- Smoother video generation.
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Reference in New Issue
Block a user