github/DeepFaceLab
1
0
Fork 0
mirror of https://github.com/iperov/DeepFaceLab.git synced 2025-07-06 04:52:13 -07:00
Code Issues Projects Releases Packages Wiki Activity Actions

SAE : WARNING, RETRAIN IS REQUIRED !

Browse source 
fixed model sizes from previous update.
avoided bug in ML framework(keras) that forces to train the model on random noise.

Converter: added blur on the same keys as sharpness

Added new model 'TrueFace'. This is a GAN model ported from https://github.com/NVlabs/FUNIT
Model produces near zero morphing and high detail face.
Model has higher failure rate than other models.
Keep src and dst faceset in same lighting conditions.
This commit is contained in:
Colombo 2019-09-19 11:13:56 +04:00
parent 201b762541
commit dc11ec32be
26 changed files with 1308 additions and 250 deletions
Download patch file Download diff file Expand all files Collapse all files

70
models/ModelBase.py
View file

@ -23,7 +23,7 @@ You can implement your own model. Check examples.
class ModelBase(object):
def __init__(self, model_path, training_data_src_path=None, training_data_dst_path=None, pretraining_data_path=None, debug = False, device_args = None,
def __init__(self, model_path, training_data_src_path=None, training_data_dst_path=None, pretraining_data_path=None, is_training=False, debug = False, device_args = None,
ask_enable_autobackup=True,
ask_write_preview_history=True,
ask_target_iter=True,
@ -56,14 +56,8 @@ class ModelBase(object):
self.training_data_dst_path = training_data_dst_path
self.pretraining_data_path = pretraining_data_path
self.src_images_paths = None
self.dst_images_paths = None
self.src_yaw_images_paths = None
self.dst_yaw_images_paths = None
self.src_data_generator = None
self.dst_data_generator = None
self.debug = debug
self.is_training_mode = (training_data_src_path is not None and training_data_dst_path is not None)
self.is_training_mode = is_training
self.iter = 0
self.options = {}
@ -412,40 +406,60 @@ class ModelBase(object):
cv2_imwrite (filepath, img )
def load_weights_safe(self, model_filename_list, optimizer_filename_list=[]):
exec(nnlib.code_import_all, locals(), globals())
loaded = []
not_loaded = []
for mf in model_filename_list:
model, filename = mf
filename = self.get_strpath_storage_for_file(filename)
if Path(filename).exists():
loaded += [ mf ]
model.load_weights(filename)
if issubclass(model.__class__, keras.optimizers.Optimizer):
opt = model
try:
with open(filename, "rb") as f:
fd = pickle.loads(f.read())
weights = fd.get('weights', None)
if weights is not None:
opt.set_weights(weights)
except Exception as e:
print ("Unable to load ", filename)
else:
model.load_weights(filename)
else:
not_loaded += [ mf ]
if len(optimizer_filename_list) != 0:
opt_filename = self.get_strpath_storage_for_file('opt.h5')
if Path(opt_filename).exists():
try:
with open(opt_filename, "rb") as f:
d = pickle.loads(f.read())
for x in optimizer_filename_list:
opt, filename = x
if filename in d:
weights = d[filename].get('weights', None)
if weights:
opt.set_weights(weights)
print("set ok")
except Exception as e:
print ("Unable to load ", opt_filename)
return loaded, not_loaded
def save_weights_safe(self, model_filename_list):
exec(nnlib.code_import_all, locals(), globals())
for model, filename in model_filename_list:
filename = self.get_strpath_storage_for_file(filename)
model.save_weights( filename + '.tmp' )
filename = self.get_strpath_storage_for_file(filename) + '.tmp'
if issubclass(model.__class__, keras.optimizers.Optimizer):
opt = model
try:
fd = {}
symbolic_weights = getattr(opt, 'weights')
if symbolic_weights:
fd['weights'] = self.K.batch_get_value(symbolic_weights)
with open(filename, 'wb') as f:
f.write( pickle.dumps(fd) )
except Exception as e:
print ("Unable to save ", filename)
else:
model.save_weights( filename)
rename_list = model_filename_list