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SAE, SAEHD: random flip and learn mask options now can be overridden

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This commit is contained in:
Colombo 2019-10-10 22:56:57 +04:00
parent 1f27d13f61
commit e15f846d08
3 changed files with 17 additions and 9 deletions
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7
models/ModelBase.py
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@ -123,10 +123,11 @@ class ModelBase(object):
self.options['sort_by_yaw'] = self.options.get('sort_by_yaw', False)
if ask_random_flip:
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.")
default_random_flip = self.options.get('random_flip', True)
if (self.iter == 0 or ask_override):
self.options['random_flip'] = io.input_bool(f"Flip faces randomly? (y/n ?:help skip:{yn_str[default_random_flip]}) : ", 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)
self.options['random_flip'] = self.options.get('random_flip', default_random_flip)
if ask_src_scale_mod:
if (self.iter == 0):

8
models/Model_SAE/Model.py
View file

@ -20,7 +20,7 @@ class SAEModel(ModelBase):
default_resolution = 128
default_archi = 'df'
default_face_type = 'f'
default_learn_mask = True
if is_first_run:
resolution = io.input_int("Resolution ( 64-256 ?:help skip:128) : ", default_resolution, help_message="More resolution requires more VRAM and time to train. Value will be adjusted to multiple of 16.")
@ -30,10 +30,14 @@ class SAEModel(ModelBase):
self.options['resolution'] = resolution
self.options['face_type'] = io.input_str ("Half or Full face? (h/f, ?:help skip:f) : ", default_face_type, ['h','f'], help_message="Half face has better resolution, but covers less area of cheeks.").lower()
self.options['learn_mask'] = io.input_bool ( f"Learn mask? (y/n, ?:help skip:{yn_str[default_learn_mask]} ) : " , default_learn_mask, help_message="Learning mask can help model to recognize face directions. Learn without mask can reduce model size, in this case converter forced to use 'not predicted mask' that is not smooth as predicted. Model with style values can be learned without mask and produce same quality result.")
else:
self.options['resolution'] = self.options.get('resolution', default_resolution)
self.options['face_type'] = self.options.get('face_type', default_face_type)
default_learn_mask = self.options.get('learn_mask', True)
if is_first_run or ask_override:
self.options['learn_mask'] = io.input_bool ( f"Learn mask? (y/n, ?:help skip:{yn_str[default_learn_mask]} ) : " , default_learn_mask, help_message="Learning mask can help model to recognize face directions. Learn without mask can reduce model size, in this case converter forced to use 'not predicted mask' that is not smooth as predicted. Model with style values can be learned without mask and produce same quality result.")
else:
self.options['learn_mask'] = self.options.get('learn_mask', default_learn_mask)
if (is_first_run or ask_override) and 'tensorflow' in self.device_config.backend:

9
models/Model_SAEHD/Model.py
View file

@ -20,7 +20,7 @@ class SAEv2Model(ModelBase):
default_resolution = 128
default_archi = 'df'
default_face_type = 'f'
default_learn_mask = True
if is_first_run:
resolution = io.input_int("Resolution ( 64-256 ?:help skip:128) : ", default_resolution, help_message="More resolution requires more VRAM and time to train. Value will be adjusted to multiple of 16.")
@ -28,12 +28,15 @@ class SAEv2Model(ModelBase):
while np.modf(resolution / 16)[0] != 0.0:
resolution -= 1
self.options['resolution'] = resolution
self.options['face_type'] = io.input_str ("Half, mid full, or full face? (h/mf/f, ?:help skip:f) : ", default_face_type, ['h','mf','f'], help_message="Half face has better resolution, but covers less area of cheeks. Mid face is 30% wider than half face.").lower()
self.options['learn_mask'] = io.input_bool ( f"Learn mask? (y/n, ?:help skip:{yn_str[default_learn_mask]} ) : " , default_learn_mask, help_message="Learning mask can help model to recognize face directions. Learn without mask can reduce model size, in this case converter forced to use 'not predicted mask' that is not smooth as predicted.")
else:
self.options['resolution'] = self.options.get('resolution', default_resolution)
self.options['face_type'] = self.options.get('face_type', default_face_type)
default_learn_mask = self.options.get('learn_mask', True)
if is_first_run or ask_override:
self.options['learn_mask'] = io.input_bool ( f"Learn mask? (y/n, ?:help skip:{yn_str[default_learn_mask]} ) : " , default_learn_mask, help_message="Learning mask can help model to recognize face directions. Learn without mask can reduce model size, in this case converter forced to use 'not predicted mask' that is not smooth as predicted.")
else:
self.options['learn_mask'] = self.options.get('learn_mask', default_learn_mask)
if (is_first_run or ask_override) and 'tensorflow' in self.device_config.backend: