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Upgraded to TF version 1.13.2

Browse source 
Removed the wait at first launch for most graphics cards.

Increased speed of training by 10-20%, but you have to retrain all models from scratch.

SAEHD:

added option 'use float16'
	Experimental option. Reduces the model size by half.
	Increases the speed of training.
	Decreases the accuracy of the model.
	The model may collapse or not train.
	Model may not learn the mask in large resolutions.

true_face_training option is replaced by
"True face power". 0.0000 .. 1.0
Experimental option. Discriminates the result face to be more like the src face. Higher value - stronger discrimination.
Comparison - https://i.imgur.com/czScS9q.png
This commit is contained in:
Colombo 2020-01-25 21:58:19 +04:00
parent a3dfcb91b9
commit 76ca79216e
49 changed files with 1320 additions and 1297 deletions
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28
models/ModelBase.py
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@ -68,7 +68,7 @@ class ModelBase(object):
s = f"[{i}] : {model_name} "
if i == 0:
s += "- latest"
io.log_info (s)
io.log_info (s)
inp = io.input_str(f"", "0", show_default_value=False )
model_idx = -1
@ -81,27 +81,27 @@ class ModelBase(object):
if len(inp) == 1:
is_rename = inp[0] == 'r'
is_delete = inp[0] == 'd'
if is_rename or is_delete:
if len(saved_models_names) != 0:
if is_rename:
name = io.input_str(f"Enter the name of the model you want to rename")
elif is_delete:
name = io.input_str(f"Enter the name of the model you want to delete")
name = io.input_str(f"Enter the name of the model you want to delete")
if name in saved_models_names:
if is_rename:
new_model_name = io.input_str(f"Enter new name of the model")
for filepath in pathex.get_file_paths(saved_models_path):
filepath_name = filepath.name
model_filename, remain_filename = filepath_name.split('_', 1)
if model_filename == name:
if is_rename:
if is_rename:
new_filepath = filepath.parent / ( new_model_name + '_' + remain_filename )
filepath.rename (new_filepath)
elif is_delete:
@ -159,7 +159,7 @@ class ModelBase(object):
#####
io.input_skip_pending()
self.on_initialize_options()
if self.is_first_run():
# save as default options only for first run model initialize
@ -172,7 +172,7 @@ class ModelBase(object):
self.on_initialize()
self.options['batch_size'] = self.batch_size
if self.is_training:
self.preview_history_path = self.saved_models_path / ( f'{self.get_model_name()}_history' )
self.autobackups_path = self.saved_models_path / ( f'{self.get_model_name()}_autobackups' )
@ -326,7 +326,7 @@ class ModelBase(object):
def get_pretraining_data_path(self):
return self.pretraining_data_path
def get_target_iter(self):
return self.target_iter
@ -479,7 +479,7 @@ class ModelBase(object):
#Find the longest key name and value string. Used as column widths.
width_name = max([len(k) for k in self.options.keys()] + [17]) + 1 # Single space buffer to left edge. Minimum of 17, the length of the longest static string used "Current iteration"
width_value = max([len(str(x)) for x in self.options.values()] + [len(str(self.get_iter())), len(self.get_model_name())]) + 1 # Single space buffer to right edge
if not self.device_config.cpu_only: #Check length of GPU names
if len(self.device_config.devices) != 0: #Check length of GPU names
width_value = max([len(device.name)+1 for device in self.device_config.devices] + [width_value])
width_total = width_name + width_value + 2 #Plus 2 for ": "
@ -499,7 +499,7 @@ class ModelBase(object):
summary_text += [f'=={" Running On ":-^{width_total}}=='] # Training hardware info
summary_text += [f'=={" "*width_total}==']
if self.device_config.cpu_only:
if len(self.device_config.devices) == 0:
summary_text += [f'=={"Using device": >{width_name}}: {"CPU": <{width_value}}=='] # cpu_only
else:
for device in self.device_config.devices: