github/DeepFaceLab
1
0
Fork 0
mirror of https://github.com/iperov/DeepFaceLab.git synced 2025-07-06 13:02:15 -07:00
Code Issues Projects Releases Packages Wiki Activity Actions

change 'epoch' to 'iter',

Browse source 
added timestamp prefix to training string
This commit is contained in:
iperov 2019-03-12 19:23:52 +04:00
parent 69174a48e0
commit 97b6fabaab
7 changed files with 93 additions and 87 deletions
Download patch file Download diff file Expand all files Collapse all files

57
mainscripts/Trainer.py
View file

@ -40,30 +40,33 @@ def trainerThread (s2c, c2s, args, device_args):
debug=debug, debug=debug,
device_args=device_args) device_args=device_args)
is_reached_goal = model.is_reached_epoch_goal() is_reached_goal = model.is_reached_iter_goal()
is_upd_save_time_after_train = False is_upd_save_time_after_train = False
loss_string = ""
def model_save(): def model_save():
if not debug and not is_reached_goal: if not debug and not is_reached_goal:
io.log_info ("Saving....", end='\r')
model.save() model.save()
io.log_info(loss_string)
is_upd_save_time_after_train = True is_upd_save_time_after_train = True
def send_preview(): def send_preview():
if not debug: if not debug:
previews = model.get_previews() previews = model.get_previews()
c2s.put ( {'op':'show', 'previews': previews, 'epoch':model.get_epoch(), 'loss_history': model.get_loss_history().copy() } ) c2s.put ( {'op':'show', 'previews': previews, 'iter':model.get_iter(), 'loss_history': model.get_loss_history().copy() } )
else: else:
previews = [( 'debug, press update for new', model.debug_one_epoch())] previews = [( 'debug, press update for new', model.debug_one_iter())]
c2s.put ( {'op':'show', 'previews': previews} ) c2s.put ( {'op':'show', 'previews': previews} )
if model.is_first_run(): if model.is_first_run():
model_save() model_save()
if model.get_target_epoch() != 0: if model.get_target_iter() != 0:
if is_reached_goal: if is_reached_goal:
io.log_info('Model already trained to target epoch. You can use preview.') io.log_info('Model already trained to target iteration. You can use preview.')
else: else:
io.log_info('Starting. Target epoch: %d. Press "Enter" to stop training and save model.' % ( model.get_target_epoch() ) ) io.log_info('Starting. Target iteration: %d. Press "Enter" to stop training and save model.' % ( model.get_target_iter() ) )
else: else:
io.log_info('Starting. Press "Enter" to stop training and save model.') io.log_info('Starting. Press "Enter" to stop training and save model.')
@ -72,14 +75,14 @@ def trainerThread (s2c, c2s, args, device_args):
for i in itertools.count(0,1): for i in itertools.count(0,1):
if not debug: if not debug:
if not is_reached_goal: if not is_reached_goal:
loss_string = model.train_one_epoch() loss_string = model.train_one_iter()
if is_upd_save_time_after_train: if is_upd_save_time_after_train:
#save resets plaidML programs, so upd last_save_time only after plaidML rebuild them #save resets plaidML programs, so upd last_save_time only after plaidML rebuild them
last_save_time = time.time() last_save_time = time.time()
io.log_info (loss_string, end='\r') io.log_info (loss_string, end='\r')
if model.get_target_epoch() != 0 and model.is_reached_epoch_goal(): if model.get_target_iter() != 0 and model.is_reached_iter_goal():
io.log_info ('Reached target epoch.') io.log_info ('Reached target iteration.')
model_save() model_save()
is_reached_goal = True is_reached_goal = True
io.log_info ('You can use preview now.') io.log_info ('You can use preview now.')
@ -91,7 +94,7 @@ def trainerThread (s2c, c2s, args, device_args):
if i==0: if i==0:
if is_reached_goal: if is_reached_goal:
model.pass_one_epoch() model.pass_one_iter()
send_preview() send_preview()
if debug: if debug:
@ -104,7 +107,7 @@ def trainerThread (s2c, c2s, args, device_args):
model_save() model_save()
elif op == 'preview': elif op == 'preview':
if is_reached_goal: if is_reached_goal:
model.pass_one_epoch() model.pass_one_iter()
send_preview() send_preview()
elif op == 'close': elif op == 'close':
model_save() model_save()
@ -156,8 +159,8 @@ def main(args, device_args):
update_preview = False update_preview = False
is_showing = False is_showing = False
is_waiting_preview = False is_waiting_preview = False
show_last_history_epochs_count = 0 show_last_history_iters_count = 0
epoch = 0 iter = 0
while True: while True:
if not c2s.empty(): if not c2s.empty():
input = c2s.get() input = c2s.get()
@ -166,7 +169,7 @@ def main(args, device_args):
is_waiting_preview = False is_waiting_preview = False
loss_history = input['loss_history'] if 'loss_history' in input.keys() else None loss_history = input['loss_history'] if 'loss_history' in input.keys() else None
previews = input['previews'] if 'previews' in input.keys() else None previews = input['previews'] if 'previews' in input.keys() else None
epoch = input['epoch'] if 'epoch' in input.keys() else 0 iter = input['iter'] if 'iter' in input.keys() else 0
if previews is not None: if previews is not None:
max_w = 0 max_w = 0
max_h = 0 max_h = 0
@ -217,12 +220,12 @@ def main(args, device_args):
final = head final = head
if loss_history is not None: if loss_history is not None:
if show_last_history_epochs_count == 0: if show_last_history_iters_count == 0:
loss_history_to_show = loss_history loss_history_to_show = loss_history
else: else:
loss_history_to_show = loss_history[-show_last_history_epochs_count:] loss_history_to_show = loss_history[-show_last_history_iters_count:]
lh_img = models.ModelBase.get_loss_history_preview(loss_history_to_show, epoch, w, c) lh_img = models.ModelBase.get_loss_history_preview(loss_history_to_show, iter, w, c)
final = np.concatenate ( [final, lh_img], axis=0 ) final = np.concatenate ( [final, lh_img], axis=0 )
final = np.concatenate ( [final, selected_preview_rgb], axis=0 ) final = np.concatenate ( [final, selected_preview_rgb], axis=0 )
@ -243,16 +246,16 @@ def main(args, device_args):
is_waiting_preview = True is_waiting_preview = True
s2c.put ( {'op': 'preview'} ) s2c.put ( {'op': 'preview'} )
elif key == ord('l'): elif key == ord('l'):
if show_last_history_epochs_count == 0: if show_last_history_iters_count == 0:
show_last_history_epochs_count = 5000 show_last_history_iters_count = 5000
elif show_last_history_epochs_count == 5000: elif show_last_history_iters_count == 5000:
show_last_history_epochs_count = 10000 show_last_history_iters_count = 10000
elif show_last_history_epochs_count == 10000: elif show_last_history_iters_count == 10000:
show_last_history_epochs_count = 50000 show_last_history_iters_count = 50000
elif show_last_history_epochs_count == 50000: elif show_last_history_iters_count == 50000:
show_last_history_epochs_count = 100000 show_last_history_iters_count = 100000
elif show_last_history_epochs_count == 100000: elif show_last_history_iters_count == 100000:
show_last_history_epochs_count = 0 show_last_history_iters_count = 0
update_preview = True update_preview = True
elif key == ord(' '): elif key == ord(' '):
selected_preview = (selected_preview + 1) % len(previews) selected_preview = (selected_preview + 1) % len(previews)

100
models/ModelBase.py
View file

@ -51,53 +51,57 @@ class ModelBase(object):
self.debug = debug 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 = (training_data_src_path is not None and training_data_dst_path is not None)
self.epoch = 0 self.iter = 0
self.options = {} self.options = {}
self.loss_history = [] self.loss_history = []
self.sample_for_preview = None self.sample_for_preview = None
if self.model_data_path.exists(): if self.model_data_path.exists():
model_data = pickle.loads ( self.model_data_path.read_bytes() ) model_data = pickle.loads ( self.model_data_path.read_bytes() )
self.epoch = model_data['epoch'] self.iter = max( model_data.get('iter',0), model_data.get('epoch',0) )
if self.epoch != 0: if 'epoch' in self.options:
self.options.pop('epoch')
if self.iter != 0:
self.options = model_data['options'] self.options = model_data['options']
self.loss_history = model_data['loss_history'] if 'loss_history' in model_data.keys() else [] 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 self.sample_for_preview = model_data['sample_for_preview'] if 'sample_for_preview' in model_data.keys() else None
ask_override = self.is_training_mode and self.epoch != 0 and io.input_in_time ("Press enter in 2 seconds to override model settings.", 2) ask_override = self.is_training_mode and self.iter != 0 and io.input_in_time ("Press enter in 2 seconds to override model settings.", 2)
yn_str = {True:'y',False:'n'} yn_str = {True:'y',False:'n'}
if self.epoch == 0: if self.iter == 0:
io.log_info ("\nModel first run. Enter model options as default for each run.") io.log_info ("\nModel first run. Enter model options as default for each run.")
if self.epoch == 0 or ask_override: if self.iter == 0 or ask_override:
default_write_preview_history = False if self.epoch == 0 else self.options.get('write_preview_history',False) default_write_preview_history = False if self.iter == 0 else self.options.get('write_preview_history',False)
self.options['write_preview_history'] = io.input_bool("Write preview history? (y/n ?:help skip:%s) : " % (yn_str[default_write_preview_history]) , default_write_preview_history, help_message="Preview history will be writed to <ModelName>_history folder.") self.options['write_preview_history'] = io.input_bool("Write preview history? (y/n ?:help skip:%s) : " % (yn_str[default_write_preview_history]) , default_write_preview_history, help_message="Preview history will be writed to <ModelName>_history folder.")
else: else:
self.options['write_preview_history'] = self.options.get('write_preview_history', False) self.options['write_preview_history'] = self.options.get('write_preview_history', False)
if self.epoch == 0 or ask_override: if self.iter == 0 or ask_override:
self.options['target_epoch'] = max(0, io.input_int("Target epoch (skip:unlimited/default) : ", 0)) self.options['target_iter'] = max(0, io.input_int("Target iteration (skip:unlimited/default) : ", 0))
else: else:
self.options['target_epoch'] = self.options.get('target_epoch', 0) self.options['target_iter'] = max(model_data.get('target_iter',0), self.options.get('target_epoch',0))
if 'target_epoch' in self.options:
self.options.pop('target_epoch')
if self.epoch == 0 or ask_override: if self.iter == 0 or ask_override:
default_batch_size = 0 if self.epoch == 0 else self.options.get('batch_size',0) default_batch_size = 0 if self.iter == 0 else self.options.get('batch_size',0)
self.options['batch_size'] = max(0, io.input_int("Batch_size (?:help skip:0/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.")) self.options['batch_size'] = max(0, io.input_int("Batch_size (?:help skip:0/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: else:
self.options['batch_size'] = self.options.get('batch_size', 0) self.options['batch_size'] = self.options.get('batch_size', 0)
if self.epoch == 0: if self.iter == 0:
self.options['sort_by_yaw'] = io.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['sort_by_yaw'] = io.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: else:
self.options['sort_by_yaw'] = self.options.get('sort_by_yaw', False) self.options['sort_by_yaw'] = self.options.get('sort_by_yaw', False)
if self.epoch == 0: if self.iter == 0:
self.options['random_flip'] = io.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['random_flip'] = io.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: else:
self.options['random_flip'] = self.options.get('random_flip', True) self.options['random_flip'] = self.options.get('random_flip', True)
if self.epoch == 0: if self.iter == 0:
self.options['src_scale_mod'] = np.clip( io.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['src_scale_mod'] = np.clip( io.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: else:
self.options['src_scale_mod'] = self.options.get('src_scale_mod', 0) self.options['src_scale_mod'] = self.options.get('src_scale_mod', 0)
@ -106,9 +110,9 @@ class ModelBase(object):
if not self.options['write_preview_history']: if not self.options['write_preview_history']:
self.options.pop('write_preview_history') self.options.pop('write_preview_history')
self.target_epoch = self.options['target_epoch'] self.target_iter = self.options['target_iter']
if self.options['target_epoch'] == 0: if self.options['target_iter'] == 0:
self.options.pop('target_epoch') self.options.pop('target_iter')
self.batch_size = self.options['batch_size'] self.batch_size = self.options['batch_size']
self.sort_by_yaw = self.options['sort_by_yaw'] self.sort_by_yaw = self.options['sort_by_yaw']
@ -118,7 +122,7 @@ class ModelBase(object):
if self.src_scale_mod == 0: if self.src_scale_mod == 0:
self.options.pop('src_scale_mod') self.options.pop('src_scale_mod')
self.onInitializeOptions(self.epoch == 0, ask_override) self.onInitializeOptions(self.iter == 0, ask_override)
nnlib.import_all ( nnlib.DeviceConfig(allow_growth=False, **self.device_args) ) nnlib.import_all ( nnlib.DeviceConfig(allow_growth=False, **self.device_args) )
self.device_config = nnlib.active_DeviceConfig self.device_config = nnlib.active_DeviceConfig
@ -142,7 +146,7 @@ class ModelBase(object):
if not self.preview_history_path.exists(): if not self.preview_history_path.exists():
self.preview_history_path.mkdir(exist_ok=True) self.preview_history_path.mkdir(exist_ok=True)
else: else:
if self.epoch == 0: if self.iter == 0:
for filename in Path_utils.get_image_paths(self.preview_history_path): for filename in Path_utils.get_image_paths(self.preview_history_path):
Path(filename).unlink() Path(filename).unlink()
@ -153,7 +157,7 @@ class ModelBase(object):
if not isinstance(generator, SampleGeneratorBase): if not isinstance(generator, SampleGeneratorBase):
raise ValueError('training data generator is not subclass of SampleGeneratorBase') raise ValueError('training data generator is not subclass of SampleGeneratorBase')
if (self.sample_for_preview is None) or (self.epoch == 0): if (self.sample_for_preview is None) or (self.iter == 0):
self.sample_for_preview = self.generate_next_sample() self.sample_for_preview = self.generate_next_sample()
model_summary_text = [] model_summary_text = []
@ -161,7 +165,7 @@ class ModelBase(object):
model_summary_text += ["===== Model summary ====="] model_summary_text += ["===== Model summary ====="]
model_summary_text += ["== Model name: " + self.get_model_name()] model_summary_text += ["== Model name: " + self.get_model_name()]
model_summary_text += ["=="] model_summary_text += ["=="]
model_summary_text += ["== Current epoch: " + str(self.epoch)] model_summary_text += ["== Current iteration: " + str(self.iter)]
model_summary_text += ["=="] model_summary_text += ["=="]
model_summary_text += ["== Model options:"] model_summary_text += ["== Model options:"]
for key in self.options.keys(): for key in self.options.keys():
@ -210,7 +214,7 @@ class ModelBase(object):
pass pass
#overridable #overridable
def onTrainOneEpoch(self, sample, generator_list): def onTrainOneIter(self, sample, generator_list):
#train your keras models here #train your keras models here
#return array of losses #return array of losses
@ -231,11 +235,11 @@ class ModelBase(object):
raise NotImplementeError raise NotImplementeError
#return existing or your own converter which derived from base #return existing or your own converter which derived from base
def get_target_epoch(self): def get_target_iter(self):
return self.target_epoch return self.target_iter
def is_reached_epoch_goal(self): def is_reached_iter_goal(self):
return self.target_epoch != 0 and self.epoch >= self.target_epoch return self.target_iter != 0 and self.iter >= self.target_iter
#multi gpu in keras actually is fake and doesn't work for training https://github.com/keras-team/keras/issues/11976 #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): #def to_multi_gpu_model_if_possible (self, models_list):
@ -263,13 +267,13 @@ class ModelBase(object):
return self.onGetPreview (self.sample_for_preview)[0][1] #first preview, and bgr return self.onGetPreview (self.sample_for_preview)[0][1] #first preview, and bgr
def save(self): def save(self):
io.log_info ("Saving...") io.log_info ("Saving....", end='\r')
Path( self.get_strpath_storage_for_file('summary.txt') ).write_text(self.model_summary_text) Path( self.get_strpath_storage_for_file('summary.txt') ).write_text(self.model_summary_text)
self.onSave() self.onSave()
model_data = { model_data = {
'epoch': self.epoch, 'iter': self.iter,
'options': self.options, 'options': self.options,
'loss_history': self.loss_history, 'loss_history': self.loss_history,
'sample_for_preview' : self.sample_for_preview 'sample_for_preview' : self.sample_for_preview
@ -336,7 +340,7 @@ class ModelBase(object):
source_filename.rename ( str(target_filename) ) source_filename.rename ( str(target_filename) )
def debug_one_epoch(self): def debug_one_iter(self):
images = [] images = []
for generator in self.generator_list: for generator in self.generator_list:
for i,batch in enumerate(next(generator)): for i,batch in enumerate(next(generator)):
@ -348,42 +352,42 @@ class ModelBase(object):
def generate_next_sample(self): def generate_next_sample(self):
return [next(generator) for generator in self.generator_list] return [next(generator) for generator in self.generator_list]
def train_one_epoch(self): def train_one_iter(self):
sample = self.generate_next_sample() sample = self.generate_next_sample()
epoch_time = time.time() iter_time = time.time()
losses = self.onTrainOneEpoch(sample, self.generator_list) losses = self.onTrainOneIter(sample, self.generator_list)
epoch_time = time.time() - epoch_time iter_time = time.time() - iter_time
self.last_sample = sample self.last_sample = sample
self.loss_history.append ( [float(loss[1]) for loss in losses] ) self.loss_history.append ( [float(loss[1]) for loss in losses] )
if self.write_preview_history: if self.write_preview_history:
if self.epoch % 10 == 0: if self.iter % 10 == 0:
preview = self.get_static_preview() preview = self.get_static_preview()
preview_lh = ModelBase.get_loss_history_preview(self.loss_history, self.epoch, preview.shape[1], preview.shape[2]) preview_lh = ModelBase.get_loss_history_preview(self.loss_history, self.iter, preview.shape[1], preview.shape[2])
img = (np.concatenate ( [preview_lh, preview], axis=0 ) * 255).astype(np.uint8) img = (np.concatenate ( [preview_lh, preview], axis=0 ) * 255).astype(np.uint8)
cv2_imwrite ( str (self.preview_history_path / ('%.6d.jpg' %( self.epoch) )), img ) cv2_imwrite ( str (self.preview_history_path / ('%.6d.jpg' %( self.iter) )), img )
self.epoch += 1 self.iter += 1
if epoch_time >= 10: time_str = time.strftime("[%H:%M:%S]")
#............."Saving... if iter_time >= 10:
loss_string = "Training [#{0:06d}][{1:.5s}s]".format ( self.epoch, '{:0.4f}'.format(epoch_time) ) loss_string = "{0}[#{1:06d}][{2:.5s}s]".format ( time_str, self.iter, '{:0.4f}'.format(iter_time) )
else: else:
loss_string = "Training [#{0:06d}][{1:04d}ms]".format ( self.epoch, int(epoch_time*1000) ) loss_string = "{0}[#{1:06d}][{2:04d}ms]".format ( time_str, self.iter, int(iter_time*1000) )
for (loss_name, loss_value) in losses: for (loss_name, loss_value) in losses:
loss_string += " %s:%.3f" % (loss_name, loss_value) loss_string += " %s:%.3f" % (loss_name, loss_value)
return loss_string return loss_string
def pass_one_epoch(self): def pass_one_iter(self):
self.last_sample = self.generate_next_sample() self.last_sample = self.generate_next_sample()
def finalize(self): def finalize(self):
nnlib.finalize_all() nnlib.finalize_all()
def is_first_run(self): def is_first_run(self):
return self.epoch == 0 return self.iter == 0
def is_debug(self): def is_debug(self):
return self.debug return self.debug
@ -394,8 +398,8 @@ class ModelBase(object):
def get_batch_size(self): def get_batch_size(self):
return self.batch_size return self.batch_size
def get_epoch(self): def get_iter(self):
return self.epoch return self.iter
def get_loss_history(self): def get_loss_history(self):
return self.loss_history return self.loss_history
@ -430,7 +434,7 @@ class ModelBase(object):
self.batch_size = d[ keys[-1] ] self.batch_size = d[ keys[-1] ]
@staticmethod @staticmethod
def get_loss_history_preview(loss_history, epoch, w, c): def get_loss_history_preview(loss_history, iter, w, c):
loss_history = np.array (loss_history.copy()) loss_history = np.array (loss_history.copy())
lh_height = 100 lh_height = 100
@ -483,7 +487,7 @@ class ModelBase(object):
last_line_t = int((lh_lines-1)*lh_line_height) last_line_t = int((lh_lines-1)*lh_line_height)
last_line_b = int(lh_lines*lh_line_height) last_line_b = int(lh_lines*lh_line_height)
lh_text = 'Epoch: %d' % (epoch) if epoch != 0 else '' lh_text = 'Iter: %d' % (iter) if iter != 0 else ''
lh_img[last_line_t:last_line_b, 0:w] += image_utils.get_text_image ( (w,last_line_b-last_line_t,c), lh_text, color=[0.8]*c ) lh_img[last_line_t:last_line_b, 0:w] += image_utils.get_text_image ( (w,last_line_b-last_line_t,c), lh_text, color=[0.8]*c )
return lh_img return lh_img

4
models/Model_DF/Model.py
View file

@ -12,7 +12,7 @@ class Model(ModelBase):
def onInitializeOptions(self, is_first_run, ask_override): def onInitializeOptions(self, is_first_run, ask_override):
if is_first_run or ask_override: if is_first_run or ask_override:
def_pixel_loss = self.options.get('pixel_loss', False) def_pixel_loss = self.options.get('pixel_loss', False)
self.options['pixel_loss'] = io.input_bool ("Use pixel loss? (y/n, ?:help skip: n/default ) : ", def_pixel_loss, help_message="Default DSSIM loss good for initial understanding structure of faces. Use pixel loss after 20k epochs to enhance fine details and decrease face jitter.") self.options['pixel_loss'] = io.input_bool ("Use pixel loss? (y/n, ?:help skip: n/default ) : ", def_pixel_loss, help_message="Default DSSIM loss good for initial understanding structure of faces. Use pixel loss after 20k iters to enhance fine details and decrease face jitter.")
else: else:
self.options['pixel_loss'] = self.options.get('pixel_loss', False) self.options['pixel_loss'] = self.options.get('pixel_loss', False)
@ -62,7 +62,7 @@ class Model(ModelBase):
[self.decoder_dst, 'decoder_dst.h5']] ) [self.decoder_dst, 'decoder_dst.h5']] )
#override #override
def onTrainOneEpoch(self, sample, generators_list): def onTrainOneIter(self, sample, generators_list):
warped_src, target_src, target_src_mask = sample[0] warped_src, target_src, target_src_mask = sample[0]
warped_dst, target_dst, target_dst_mask = sample[1] warped_dst, target_dst, target_dst_mask = sample[1]

4
models/Model_H128/Model.py
View file

@ -20,7 +20,7 @@ class Model(ModelBase):
if is_first_run or ask_override: if is_first_run or ask_override:
def_pixel_loss = self.options.get('pixel_loss', False) def_pixel_loss = self.options.get('pixel_loss', False)
self.options['pixel_loss'] = io.input_bool ("Use pixel loss? (y/n, ?:help skip: n/default ) : ", def_pixel_loss, help_message="Default DSSIM loss good for initial understanding structure of faces. Use pixel loss after 20k epochs to enhance fine details and decrease face jitter.") self.options['pixel_loss'] = io.input_bool ("Use pixel loss? (y/n, ?:help skip: n/default ) : ", def_pixel_loss, help_message="Default DSSIM loss good for initial understanding structure of faces. Use pixel loss after 20k iters to enhance fine details and decrease face jitter.")
else: else:
self.options['pixel_loss'] = self.options.get('pixel_loss', False) self.options['pixel_loss'] = self.options.get('pixel_loss', False)
@ -77,7 +77,7 @@ class Model(ModelBase):
[self.decoder_dst, 'decoder_dst.h5']] ) [self.decoder_dst, 'decoder_dst.h5']] )
#override #override
def onTrainOneEpoch(self, sample, generators_list): def onTrainOneIter(self, sample, generators_list):
warped_src, target_src, target_src_mask = sample[0] warped_src, target_src, target_src_mask = sample[0]
warped_dst, target_dst, target_dst_mask = sample[1] warped_dst, target_dst, target_dst_mask = sample[1]

4
models/Model_H64/Model.py
View file

@ -20,7 +20,7 @@ class Model(ModelBase):
if is_first_run or ask_override: if is_first_run or ask_override:
def_pixel_loss = self.options.get('pixel_loss', False) def_pixel_loss = self.options.get('pixel_loss', False)
self.options['pixel_loss'] = io.input_bool ("Use pixel loss? (y/n, ?:help skip: n/default ) : ", def_pixel_loss, help_message="Default DSSIM loss good for initial understanding structure of faces. Use pixel loss after 20k epochs to enhance fine details and decrease face jitter.") self.options['pixel_loss'] = io.input_bool ("Use pixel loss? (y/n, ?:help skip: n/default ) : ", def_pixel_loss, help_message="Default DSSIM loss good for initial understanding structure of faces. Use pixel loss after 20k iters to enhance fine details and decrease face jitter.")
else: else:
self.options['pixel_loss'] = self.options.get('pixel_loss', False) self.options['pixel_loss'] = self.options.get('pixel_loss', False)
@ -78,7 +78,7 @@ class Model(ModelBase):
[self.decoder_dst, 'decoder_dst.h5']] ) [self.decoder_dst, 'decoder_dst.h5']] )
#override #override
def onTrainOneEpoch(self, sample, generators_list): def onTrainOneIter(self, sample, generators_list):
warped_src, target_src, target_src_full_mask = sample[0] warped_src, target_src, target_src_full_mask = sample[0]
warped_dst, target_dst, target_dst_full_mask = sample[1] warped_dst, target_dst, target_dst_full_mask = sample[1]

4
models/Model_LIAEF128/Model.py
View file

@ -12,7 +12,7 @@ class Model(ModelBase):
def onInitializeOptions(self, is_first_run, ask_override): def onInitializeOptions(self, is_first_run, ask_override):
if is_first_run or ask_override: if is_first_run or ask_override:
def_pixel_loss = self.options.get('pixel_loss', False) def_pixel_loss = self.options.get('pixel_loss', False)
self.options['pixel_loss'] = io.input_bool ("Use pixel loss? (y/n, ?:help skip: n/default ) : ", def_pixel_loss, help_message="Default DSSIM loss good for initial understanding structure of faces. Use pixel loss after 20k epochs to enhance fine details and decrease face jitter.") self.options['pixel_loss'] = io.input_bool ("Use pixel loss? (y/n, ?:help skip: n/default ) : ", def_pixel_loss, help_message="Default DSSIM loss good for initial understanding structure of faces. Use pixel loss after 20k iters to enhance fine details and decrease face jitter.")
else: else:
self.options['pixel_loss'] = self.options.get('pixel_loss', False) self.options['pixel_loss'] = self.options.get('pixel_loss', False)
@ -70,7 +70,7 @@ class Model(ModelBase):
[self.inter_AB, 'inter_AB.h5']] ) [self.inter_AB, 'inter_AB.h5']] )
#override #override
def onTrainOneEpoch(self, sample, generators_list): def onTrainOneIter(self, sample, generators_list):
warped_src, target_src, target_src_mask = sample[0] warped_src, target_src, target_src_mask = sample[0]
warped_dst, target_dst, target_dst_mask = sample[1] warped_dst, target_dst, target_dst_mask = sample[1]

7
models/Model_SAE/Model.py
View file

@ -77,11 +77,11 @@ class SAEModel(ModelBase):
default_bg_style_power = 0.0 default_bg_style_power = 0.0
if is_first_run or ask_override: if is_first_run or ask_override:
def_pixel_loss = self.options.get('pixel_loss', False) def_pixel_loss = self.options.get('pixel_loss', False)
self.options['pixel_loss'] = io.input_bool ("Use pixel loss? (y/n, ?:help skip: %s ) : " % (yn_str[def_pixel_loss]), def_pixel_loss, help_message="Default DSSIM loss good for initial understanding structure of faces. Use pixel loss after 15-25k epochs to enhance fine details and decrease face jitter.") self.options['pixel_loss'] = io.input_bool ("Use pixel loss? (y/n, ?:help skip: %s ) : " % (yn_str[def_pixel_loss]), def_pixel_loss, help_message="Default DSSIM loss good for initial understanding structure of faces. Use pixel loss after 15-25k iters to enhance fine details and decrease face jitter.")
default_face_style_power = default_face_style_power if is_first_run else self.options.get('face_style_power', default_face_style_power) default_face_style_power = default_face_style_power if is_first_run else self.options.get('face_style_power', default_face_style_power)
self.options['face_style_power'] = np.clip ( io.input_number("Face style power ( 0.0 .. 100.0 ?:help skip:%.2f) : " % (default_face_style_power), default_face_style_power, self.options['face_style_power'] = np.clip ( io.input_number("Face style power ( 0.0 .. 100.0 ?:help skip:%.2f) : " % (default_face_style_power), default_face_style_power,
help_message="Learn to transfer face style details such as light and color conditions. Warning: Enable it only after 10k epochs, when predicted face is clear enough to start learn style. Start from 0.1 value and check history changes."), 0.0, 100.0 ) help_message="Learn to transfer face style details such as light and color conditions. Warning: Enable it only after 10k iters, when predicted face is clear enough to start learn style. Start from 0.1 value and check history changes."), 0.0, 100.0 )
default_bg_style_power = default_bg_style_power if is_first_run else self.options.get('bg_style_power', default_bg_style_power) default_bg_style_power = default_bg_style_power if is_first_run else self.options.get('bg_style_power', default_bg_style_power)
self.options['bg_style_power'] = np.clip ( io.input_number("Background style power ( 0.0 .. 100.0 ?:help skip:%.2f) : " % (default_bg_style_power), default_bg_style_power, self.options['bg_style_power'] = np.clip ( io.input_number("Background style power ( 0.0 .. 100.0 ?:help skip:%.2f) : " % (default_bg_style_power), default_bg_style_power,
@ -107,7 +107,6 @@ class SAEModel(ModelBase):
masked_training = True masked_training = True
epoch_alpha = Input( (1,) )
warped_src = Input(bgr_shape) warped_src = Input(bgr_shape)
target_src = Input(bgr_shape) target_src = Input(bgr_shape)
target_srcm = Input(mask_shape) target_srcm = Input(mask_shape)
@ -395,7 +394,7 @@ class SAEModel(ModelBase):
#override #override
def onTrainOneEpoch(self, generators_samples, generators_list): def onTrainOneIter(self, generators_samples, generators_list):
src_samples = generators_samples[0] src_samples = generators_samples[0]
dst_samples = generators_samples[1] dst_samples = generators_samples[1]