mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-07-06 04:52:13 -07:00
SAE: new options face_style_power, bg_style_power instead of style_power. Zero - means dont use style.
SAE: new option 'lighter_encoder'. Now model settings can be overrided by pressing enter in 2 seconds while model loading (works on Windows). Removed all MultiGPU models, because keras multi_gpu in fact doesn't work.
This commit is contained in:
iperov
parent
c3f175862a
commit
48d0123f0b
9 changed files with 158 additions and 139 deletions
|
|
@ -18,14 +18,7 @@ You can implement your own model. Check examples.
|
|||
class ModelBase(object):
|
||||
|
||||
#DONT OVERRIDE
|
||||
def __init__(self, model_path, training_data_src_path=None, training_data_dst_path=None,
|
||||
ask_for_session_options=False,
|
||||
session_write_preview_history = None,
|
||||
session_target_epoch=0,
|
||||
session_batch_size=0,
|
||||
|
||||
debug = False, **in_options
|
||||
):
|
||||
def __init__(self, model_path, training_data_src_path=None, training_data_dst_path=None, debug = False, **in_options):
|
||||
print ("Loading model...")
|
||||
self.model_path = model_path
|
||||
self.model_data_path = Path( self.get_strpath_storage_for_file('data.dat') )
|
||||
|
|
@ -56,50 +49,52 @@ class ModelBase(object):
|
|||
self.loss_history = model_data['loss_history'] if 'loss_history' in model_data.keys() else []
|
||||
self.sample_for_preview = model_data['sample_for_preview'] if 'sample_for_preview' in model_data.keys() else None
|
||||
|
||||
ask_override = self.epoch != 0 and input_in_time ("Press enter during 2 seconds to override some model settings.", 2)
|
||||
|
||||
if self.epoch == 0:
|
||||
print ("\nModel first run. Enter model options as default for each run.")
|
||||
|
||||
if self.epoch == 0 or ask_override:
|
||||
self.options['write_preview_history'] = input_bool("Write preview history? (y/n ?:help skip:n) : ", False, help_message="Preview history will be writed to <ModelName>_history folder.")
|
||||
self.options['target_epoch'] = max(0, input_int("Target epoch (skip:unlimited) : ", 0))
|
||||
self.options['batch_size'] = max(0, input_int("Batch_size (?:help skip:model choice) : ", 0, help_message="Larger batch size is always better for NN's generalization, but it can cause Out of Memory error. Tune this value for your videocard manually."))
|
||||
self.options['sort_by_yaw'] = input_bool("Feed faces to network sorted by yaw? (y/n ?:help skip:n) : ", False, help_message="NN will not learn src face directions that don't match dst face directions." )
|
||||
self.options['random_flip'] = input_bool("Flip faces randomly? (y/n ?:help skip:y) : ", True, help_message="Predicted face will look more naturally without this option, but src faceset should cover all face directions as dst faceset.")
|
||||
self.options['src_scale_mod'] = np.clip( input_int("Src face scale modifier % ( -30...30, ?:help skip:0) : ", 0, help_message="If src face shape is wider than dst, try to decrease this value to get a better result."), -30, 30)
|
||||
#self.options['use_fp16'] = use_fp16 = input_bool("Use float16? (y/n skip:n) : ", False)
|
||||
else:
|
||||
self.options['write_preview_history'] = self.options.get('write_preview_history', False)
|
||||
self.options['target_epoch'] = self.options.get('target_epoch', 0)
|
||||
self.options['batch_size'] = self.options.get('batch_size', 0)
|
||||
self.options['sort_by_yaw'] = self.options.get('sort_by_yaw', False)
|
||||
self.options['random_flip'] = self.options.get('random_flip', True)
|
||||
self.options['src_scale_mod'] = self.options.get('src_scale_mod', 0)
|
||||
#self.options['use_fp16'] = use_fp16 = self.options['use_fp16'] if 'use_fp16' in self.options.keys() else False
|
||||
|
||||
use_fp16 = False #currently models fails with fp16
|
||||
|
||||
if ask_for_session_options:
|
||||
print ("Override options for current session:")
|
||||
session_write_preview_history = input_bool("Write preview history? (y/n skip:default) : ", None )
|
||||
session_target_epoch = input_int("Target epoch (skip:default) : ", 0)
|
||||
session_batch_size = input_int("Batch_size (skip:default) : ", 0)
|
||||
|
||||
if self.options['write_preview_history']:
|
||||
if session_write_preview_history is None:
|
||||
session_write_preview_history = self.options['write_preview_history']
|
||||
else:
|
||||
self.options['write_preview_history'] = self.options.get('write_preview_history', False)
|
||||
|
||||
if self.epoch == 0 or ask_override:
|
||||
self.options['target_epoch'] = max(0, input_int("Target epoch (skip:unlimited) : ", 0))
|
||||
else:
|
||||
self.options['target_epoch'] = self.options.get('target_epoch', 0)
|
||||
|
||||
if self.epoch == 0 or ask_override:
|
||||
default_batch_size = 0 if self.epoch == 0 else self.options['batch_size']
|
||||
self.options['batch_size'] = max(0, input_int("Batch_size (?:help skip:default) : ", default_batch_size, help_message="Larger batch size is always better for NN's generalization, but it can cause Out of Memory error. Tune this value for your videocard manually."))
|
||||
else:
|
||||
self.options['batch_size'] = self.options.get('batch_size', 0)
|
||||
|
||||
if self.epoch == 0:
|
||||
self.options['sort_by_yaw'] = input_bool("Feed faces to network sorted by yaw? (y/n ?:help skip:n) : ", False, help_message="NN will not learn src face directions that don't match dst face directions." )
|
||||
else:
|
||||
self.options['sort_by_yaw'] = self.options.get('sort_by_yaw', False)
|
||||
|
||||
if self.epoch == 0:
|
||||
self.options['random_flip'] = input_bool("Flip faces randomly? (y/n ?:help skip:y) : ", True, help_message="Predicted face will look more naturally without this option, but src faceset should cover all face directions as dst faceset.")
|
||||
else:
|
||||
self.options['random_flip'] = self.options.get('random_flip', True)
|
||||
|
||||
if self.epoch == 0:
|
||||
self.options['src_scale_mod'] = np.clip( input_int("Src face scale modifier % ( -30...30, ?:help skip:0) : ", 0, help_message="If src face shape is wider than dst, try to decrease this value to get a better result."), -30, 30)
|
||||
else:
|
||||
self.options['src_scale_mod'] = self.options.get('src_scale_mod', 0)
|
||||
|
||||
self.write_preview_history = self.options['write_preview_history']
|
||||
if not self.options['write_preview_history']:
|
||||
self.options.pop('write_preview_history')
|
||||
|
||||
if self.options['target_epoch'] != 0:
|
||||
if session_target_epoch == 0:
|
||||
session_target_epoch = self.options['target_epoch']
|
||||
else:
|
||||
self.target_epoch = self.options['target_epoch']
|
||||
if self.options['target_epoch'] == 0:
|
||||
self.options.pop('target_epoch')
|
||||
|
||||
if self.options['batch_size'] != 0:
|
||||
if session_batch_size == 0:
|
||||
session_batch_size = self.options['batch_size']
|
||||
else:
|
||||
self.options.pop('batch_size')
|
||||
|
||||
self.batch_size = self.options['batch_size']
|
||||
|
||||
self.sort_by_yaw = self.options['sort_by_yaw']
|
||||
if not self.sort_by_yaw:
|
||||
self.options.pop('sort_by_yaw')
|
||||
|
|
@ -112,18 +107,17 @@ class ModelBase(object):
|
|||
if self.src_scale_mod == 0:
|
||||
self.options.pop('src_scale_mod')
|
||||
|
||||
self.write_preview_history = session_write_preview_history
|
||||
self.target_epoch = session_target_epoch
|
||||
self.batch_size = session_batch_size
|
||||
self.onInitializeOptions(self.epoch == 0, ask_for_session_options)
|
||||
self.onInitializeOptions(self.epoch == 0, ask_override)
|
||||
|
||||
nnlib.import_all ( nnlib.DeviceConfig(allow_growth=False, use_fp16=use_fp16, **in_options) )
|
||||
nnlib.import_all ( nnlib.DeviceConfig(allow_growth=False, **in_options) )
|
||||
self.device_config = nnlib.active_DeviceConfig
|
||||
|
||||
self.created_vram_gb = self.options['created_vram_gb'] if 'created_vram_gb' in self.options.keys() else self.device_config.gpu_total_vram_gb
|
||||
|
||||
self.onInitialize(**in_options)
|
||||
|
||||
self.options['batch_size'] = self.batch_size
|
||||
|
||||
if self.debug or self.batch_size == 0:
|
||||
self.batch_size = 1
|
||||
|
||||
|
|
@ -155,16 +149,10 @@ class ModelBase(object):
|
|||
print ("==")
|
||||
print ("== Model options:")
|
||||
for key in self.options.keys():
|
||||
print ("== |== %s : %s" % (key, self.options[key]) )
|
||||
print ("== Session options:")
|
||||
if self.write_preview_history:
|
||||
print ("== |== write_preview_history : True ")
|
||||
if self.target_epoch != 0:
|
||||
print ("== |== target_epoch : %s " % (self.target_epoch) )
|
||||
print ("== |== batch_size : %s " % (self.batch_size) )
|
||||
print ("== |== %s : %s" % (key, self.options[key]) )
|
||||
|
||||
if self.device_config.multi_gpu:
|
||||
print ("== |== multi_gpu : True ")
|
||||
|
||||
print ("== |== multi_gpu : True ")
|
||||
|
||||
print ("== Running on:")
|
||||
if self.device_config.cpu_only:
|
||||
|
|
@ -183,7 +171,7 @@ class ModelBase(object):
|
|||
print ("=========================")
|
||||
|
||||
#overridable
|
||||
def onInitializeOptions(self, is_first_run, ask_for_session_options):
|
||||
def onInitializeOptions(self, is_first_run, ask_override):
|
||||
pass
|
||||
|
||||
#overridable
|
||||
|
|
@ -231,23 +219,24 @@ class ModelBase(object):
|
|||
def is_reached_epoch_goal(self):
|
||||
return self.target_epoch != 0 and self.epoch >= self.target_epoch
|
||||
|
||||
def to_multi_gpu_model_if_possible (self, models_list):
|
||||
if len(self.device_config.gpu_idxs) > 1:
|
||||
#make batch_size to divide on GPU count without remainder
|
||||
self.batch_size = int( self.batch_size / len(self.device_config.gpu_idxs) )
|
||||
if self.batch_size == 0:
|
||||
self.batch_size = 1
|
||||
self.batch_size *= len(self.device_config.gpu_idxs)
|
||||
|
||||
result = []
|
||||
for model in models_list:
|
||||
for i in range( len(model.output_names) ):
|
||||
model.output_names = 'output_%d' % (i)
|
||||
result += [ nnlib.keras.utils.multi_gpu_model( model, self.device_config.gpu_idxs ) ]
|
||||
|
||||
return result
|
||||
else:
|
||||
return models_list
|
||||
#multi gpu in keras actually is fake and doesn't work for training https://github.com/keras-team/keras/issues/11976
|
||||
#def to_multi_gpu_model_if_possible (self, models_list):
|
||||
# if len(self.device_config.gpu_idxs) > 1:
|
||||
# #make batch_size to divide on GPU count without remainder
|
||||
# self.batch_size = int( self.batch_size / len(self.device_config.gpu_idxs) )
|
||||
# if self.batch_size == 0:
|
||||
# self.batch_size = 1
|
||||
# self.batch_size *= len(self.device_config.gpu_idxs)
|
||||
#
|
||||
# result = []
|
||||
# for model in models_list:
|
||||
# for i in range( len(model.output_names) ):
|
||||
# model.output_names = 'output_%d' % (i)
|
||||
# result += [ nnlib.keras.utils.multi_gpu_model( model, self.device_config.gpu_idxs ) ]
|
||||
#
|
||||
# return result
|
||||
# else:
|
||||
# return models_list
|
||||
|
||||
def get_previews(self):
|
||||
return self.onGetPreview ( self.last_sample )
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue