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added new model U-net Face Morpher.

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removed AVATAR - useless model was just for demo
removed MIAEF128 - use UFM insted
removed LIAEF128YAW - use model option sort by yaw on start for any model
All models now ask some options on start.
Session options (such as target epoch, batch_size, write_preview_history etc) can be overrided by special command arg.
Converter now always ask options and no more support to define options via command line.
fix bug when ConverterMasked always used not predicted mask.
SampleGenerator now always generate samples with replicated border, exclude mask samples.
refactorings
This commit is contained in:
iperov 2019-01-02 17:26:12 +04:00
parent f3782a012b
commit 7b70e7eec1
29 changed files with 673 additions and 1013 deletions
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18
models/Model_RecycleGAN/Model.py
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@ -25,7 +25,7 @@ class Model(ModelBase):
if self.epoch == 0:
#first run
print ("\nModel first run. Enter options.")
try:
created_resolution = int ( input ("Resolution (default:64, valid: 64,128,256) : ") )
@ -68,9 +68,9 @@ class Model(ModelBase):
self.set_batch_size(created_batch_size)
use_batch_norm = created_batch_size > 1
self.GA = modelify(ResNet (bgr_shape[2], use_batch_norm, n_blocks=6, ngf=ngf, use_dropout=False))(Input(bgr_shape))
self.GB = modelify(ResNet (bgr_shape[2], use_batch_norm, n_blocks=6, ngf=ngf, use_dropout=False))(Input(bgr_shape))
use_batch_norm = False #created_batch_size > 1
self.GA = modelify(ResNet (bgr_shape[2], use_batch_norm, n_blocks=6, ngf=ngf, use_dropout=True))(Input(bgr_shape))
self.GB = modelify(ResNet (bgr_shape[2], use_batch_norm, n_blocks=6, ngf=ngf, use_dropout=True))(Input(bgr_shape))
#self.GA = modelify(UNet (bgr_shape[2], use_batch_norm, num_downs=get_power_of_two(resolution)-1, ngf=ngf, use_dropout=True))(Input(bgr_shape))
#self.GB = modelify(UNet (bgr_shape[2], use_batch_norm, num_downs=get_power_of_two(resolution)-1, ngf=ngf, use_dropout=True))(Input(bgr_shape))
@ -211,7 +211,7 @@ class Model(ModelBase):
loss_G, = self.G_train ( feed )
loss_DA, = self.DA_train( feed )
loss_DB, = self.DB_train( feed )
#return ( ('G', loss_G), )
return ( ('G', loss_G), ('DA', loss_DA), ('DB', loss_DB) )
#override
@ -242,7 +242,9 @@ class Model(ModelBase):
#override
def get_converter(self, **in_options):
from models import ConverterImage
return ConverterImage(self.predictor_func, predictor_input_size=self.options['created_resolution'], output_size=self.options['created_resolution'], **in_options)
from models import ConverterImage
return ConverterImage(self.predictor_func,
predictor_input_size=self.options['created_resolution'],
output_size=self.options['created_resolution'],
**in_options)