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: added option 'Pretrain the model?',

Browse source 
Pretrain the model with large amount of various faces. This technique may help to train the fake with overly different face shapes and light conditions of src/dst data. Face will be look more like a morphed. To reduce the morph effect, some model files will be initialized but not be updated after pretrain: LIAE: inter_AB.h5 DF: both decoders.h5. The longer you pretrain the model the more morphed face will look. After that, save and run the training again.
This commit is contained in:
iperov 2019-05-01 19:55:27 +04:00
parent 659aa5705a
commit 2a8dd788dc
8 changed files with 78 additions and 44 deletions
Download patch file Download diff file Expand all files Collapse all files

15
models/ModelBase.py
View file

@ -20,9 +20,13 @@ 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, debug = False, device_args = None,
ask_write_preview_history=True, ask_target_iter=True, ask_batch_size=True, ask_sort_by_yaw=True,
ask_random_flip=True, ask_src_scale_mod=True):
def __init__(self, model_path, training_data_src_path=None, training_data_dst_path=None, pretraining_data_path=None, debug = False, device_args = None,
ask_write_preview_history=True,
ask_target_iter=True,
ask_batch_size=True,
ask_sort_by_yaw=True,
ask_random_flip=True,
ask_src_scale_mod=True):
device_args['force_gpu_idx'] = device_args.get('force_gpu_idx',-1)
device_args['cpu_only'] = device_args.get('cpu_only',False)
@ -46,7 +50,8 @@ class ModelBase(object):
self.training_data_src_path = training_data_src_path
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
@ -85,7 +90,7 @@ class ModelBase(object):
else:
self.options['write_preview_history'] = self.options.get('write_preview_history', False)
if self.iter == 0 and self.options['write_preview_history'] and io.is_support_windows():
if (self.iter == 0 or ask_override) and self.options['write_preview_history'] and io.is_support_windows():
choose_preview_history = io.input_bool("Choose image for the preview history? (y/n skip:%s) : " % (yn_str[False]) , False)
else:
choose_preview_history = False