mirror of
https://github.com/deepfakes/faceswap.git
synced 2026-10-24 02:39:08 +08:00
torzdf
ca63242996
* Extraction - Speed improvements (#522) * Initial Plugin restructure * Detectors to plugins. Detector speed improvements * Re-implement dlib aligner, remove models, FAN to TF. Parallel processing * Update manual, update convert, implement parallel/serial switching * linting + fix cuda check (setup.py). requirements update keras 2.2.4 * Add extract size option. Fix dlib hog init * GUI: Increase tooltip width * Update alignment tool to support new DetectedFace * Add skip existing faces option * Fix sort tool to new plugin structure * remove old align plugin * fix convert -skip faces bug * Fix convert skipping no faces frames * Convert - draw onto transparent layer * Fix blur threshold bug * fix skip_faces convert bug * Fix training
60 lines
2.5 KiB
Python
60 lines
2.5 KiB
Python
# AutoEncoder base classes
|
|
|
|
from lib.utils import backup_file
|
|
|
|
hdf = {'encoderH5': 'lowmem_encoder.h5',
|
|
'decoder_AH5': 'lowmem_decoder_A.h5',
|
|
'decoder_BH5': 'lowmem_decoder_B.h5'}
|
|
|
|
#Part of Filename migration, should be remopved some reasonable time after first added
|
|
import os.path
|
|
old_encoderH5 = 'encoder.h5'
|
|
old_decoder_AH5 = 'decoder_A.h5'
|
|
old_decoder_BH5 = 'decoder_B.h5'
|
|
#End filename migration
|
|
|
|
class AutoEncoder:
|
|
def __init__(self, model_dir, gpus):
|
|
self.model_dir = model_dir
|
|
self.gpus = gpus
|
|
|
|
self.encoder = self.Encoder()
|
|
self.decoder_A = self.Decoder()
|
|
self.decoder_B = self.Decoder()
|
|
|
|
self.initModel()
|
|
|
|
def load(self, swapped):
|
|
(face_A,face_B) = (hdf['decoder_AH5'], hdf['decoder_BH5']) if not swapped else (hdf['decoder_BH5'], hdf['decoder_AH5'])
|
|
|
|
try:
|
|
#Part of Filename migration, should be remopved some reasonable time after first added
|
|
if os.path.isfile(str(self.model_dir / old_encoderH5)):
|
|
print('Migrating to new filenames: ', end='')
|
|
if os.path.isfile(str(self.model_dir / hdf['encoderH5'])) is not True:
|
|
os.rename(str(self.model_dir / old_decoder_AH5), str(self.model_dir / hdf['decoder_AH5']))
|
|
os.rename(str(self.model_dir / old_decoder_BH5), str(self.model_dir / hdf['decoder_BH5']))
|
|
os.rename(str(self.model_dir / old_encoderH5), str(self.model_dir / hdf['encoderH5']))
|
|
print('Complete')
|
|
else:
|
|
print('Failed due to existing files in folder. Loading already migrated files')
|
|
#End filename migration
|
|
self.encoder.load_weights(str(self.model_dir / hdf['encoderH5']))
|
|
self.decoder_A.load_weights(str(self.model_dir / face_A))
|
|
self.decoder_B.load_weights(str(self.model_dir / face_B))
|
|
print('loaded model weights')
|
|
return True
|
|
except Exception as e:
|
|
print('Failed loading existing training data.')
|
|
print(e)
|
|
return False
|
|
|
|
def save_weights(self):
|
|
model_dir = str(self.model_dir)
|
|
for model in hdf.values():
|
|
backup_file(model_dir, model)
|
|
self.encoder.save_weights(str(self.model_dir / hdf['encoderH5']))
|
|
self.decoder_A.save_weights(str(self.model_dir / hdf['decoder_AH5']))
|
|
self.decoder_B.save_weights(str(self.model_dir / hdf['decoder_BH5']))
|
|
print('saved model weights')
|