mirror of https://github.com/FFmpeg/FFmpeg.git
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
56 lines
2.2 KiB
56 lines
2.2 KiB
# Copyright (c) 2019 Guo Yejun |
|
# |
|
# This file is part of FFmpeg. |
|
# |
|
# FFmpeg is free software; you can redistribute it and/or |
|
# modify it under the terms of the GNU Lesser General Public |
|
# License as published by the Free Software Foundation; either |
|
# version 2.1 of the License, or (at your option) any later version. |
|
# |
|
# FFmpeg 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 |
|
# Lesser General Public License for more details. |
|
# |
|
# You should have received a copy of the GNU Lesser General Public |
|
# License along with FFmpeg; if not, write to the Free Software |
|
# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA |
|
# ============================================================================== |
|
|
|
# verified with Python 3.5.2 on Ubuntu 16.04 |
|
import argparse |
|
import os |
|
from convert_from_tensorflow import * |
|
|
|
def get_arguments(): |
|
parser = argparse.ArgumentParser(description='generate native mode model with weights from deep learning model') |
|
parser.add_argument('--outdir', type=str, default='./', help='where to put generated files') |
|
parser.add_argument('--infmt', type=str, default='tensorflow', help='format of the deep learning model') |
|
parser.add_argument('infile', help='path to the deep learning model with weights') |
|
parser.add_argument('--dump4tb', type=str, default='no', help='dump file for visualization in tensorboard') |
|
|
|
return parser.parse_args() |
|
|
|
def main(): |
|
args = get_arguments() |
|
|
|
if not os.path.isfile(args.infile): |
|
print('the specified input file %s does not exist' % args.infile) |
|
exit(1) |
|
|
|
if not os.path.exists(args.outdir): |
|
print('create output directory %s' % args.outdir) |
|
os.mkdir(args.outdir) |
|
|
|
basefile = os.path.split(args.infile)[1] |
|
basefile = os.path.splitext(basefile)[0] |
|
outfile = os.path.join(args.outdir, basefile) + '.model' |
|
dump4tb = False |
|
if args.dump4tb.lower() in ('yes', 'true', 't', 'y', '1'): |
|
dump4tb = True |
|
|
|
if args.infmt == 'tensorflow': |
|
convert_from_tensorflow(args.infile, outfile, dump4tb) |
|
|
|
if __name__ == '__main__': |
|
main()
|
|
|