def onInitialize(self, batch_size=-1, **in_options):
exec(nnlib.code_import_all, locals(), globals())
self.set_vram_batch_requirements({2:1,3:1,4:4,5:8,6:16})
resolution = self.options['resolution']
face_type = self.face_type = FaceType.FULL if self.options['face_type'] == 'f' else FaceType.HALF
self.model = FUNIT( face_type_str=FaceType.toString(face_type),
batch_size=self.batch_size,
encoder_nf=64,
encoder_downs=2,
encoder_res_blk=2,
class_downs=4,
class_nf=64,
class_latent=64,
mlp_nf=256,
mlp_blks=2,
dis_nf=64,
dis_res_blks=10,
num_classes=2,
subpixel_decoder=True,
initialize_weights=self.is_first_run(),
is_training=self.is_training_mode,
tf_cpu_mode=self.options['optimizer_mode']-1
)
if not self.is_first_run():
self.load_weights_safe(self.model.get_model_filename_list())
t = SampleProcessor.Types
face_type = t.FACE_TYPE_FULL if self.options['face_type'] == 'f' else t.FACE_TYPE_HALF
if self.is_training_mode:
output_sample_types=[ {'types': (t.IMG_TRANSFORMED, face_type, t.MODE_BGR), 'resolution':resolution, 'normalize_tanh':True},
]
self.set_training_data_generators ([
SampleGeneratorFace(self.training_data_src_path, debug=self.is_debug(), batch_size=self.batch_size,
sample_process_options=SampleProcessor.Options(random_flip=True),
output_sample_types=output_sample_types ),
SampleGeneratorFace(self.training_data_dst_path, debug=self.is_debug(), batch_size=self.batch_size,
sample_process_options=SampleProcessor.Options(random_flip=True),
output_sample_types=output_sample_types )
])
else:
generator = SampleGeneratorFace(self.training_data_src_path, batch_size=1,
sample_process_options=SampleProcessor.Options(),
output_sample_types=[ {'types': (t.IMG_SOURCE, face_type, t.MODE_BGR), 'resolution':resolution, 'normalize_tanh':True} ] )
io.log_info("Calculating average src face style...")
codes = []
for i in io.progress_bar_generator(range(generator.get_total_sample_count())):
codes += self.model.get_average_class_code( generator.generate_next() )
self.average_class_code = np.mean ( np.array(codes), axis=0 )[None,...]
def onInitialize(self, batch_size=-1, **in_options):
exec(nnlib.code_import_all, locals(), globals())
self.set_vram_batch_requirements({4:16})
resolution = self.options['resolution']
face_type = FaceType.FULL if self.options['face_type'] == 'f' else FaceType.HALF
person_id_max_count = SampleGeneratorFace.get_person_id_max_count(self.training_data_src_path)
self.model = FUNIT( face_type_str=FaceType.toString(face_type),
batch_size=self.batch_size,
encoder_nf=64,
encoder_downs=2,
encoder_res_blk=2,
class_downs=4,
class_nf=64,
class_latent=64,
mlp_nf=256,
mlp_blks=2,
dis_nf=64,
dis_res_blks=10,
num_classes=person_id_max_count,
subpixel_decoder=True,
initialize_weights=self.is_first_run(),
is_training=self.is_training_mode
)
if not self.is_first_run():
self.load_weights_safe(self.model.get_model_filename_list())
if self.is_training_mode:
t = SampleProcessor.Types
face_type = t.FACE_TYPE_FULL if self.options['face_type'] == 'f' else t.FACE_TYPE_HALF
output_sample_types=[ {'types': (t.IMG_TRANSFORMED, face_type, t.MODE_BGR), 'resolution':128, 'normalize_tanh':True} ]
self.set_training_data_generators ([
SampleGeneratorFace(self.training_data_src_path, debug=self.is_debug(), batch_size=self.batch_size,
sample_process_options=SampleProcessor.Options(random_flip=True),
output_sample_types=output_sample_types, person_id_mode=True ),
SampleGeneratorFace(self.training_data_src_path, debug=self.is_debug(), batch_size=self.batch_size,
sample_process_options=SampleProcessor.Options(random_flip=True),
output_sample_types=output_sample_types, person_id_mode=True ),
SampleGeneratorFace(self.training_data_dst_path, debug=self.is_debug(), batch_size=self.batch_size,
sample_process_options=SampleProcessor.Options(random_flip=True),
output_sample_types=output_sample_types, person_id_mode=True ),
SampleGeneratorFace(self.training_data_dst_path, debug=self.is_debug(), batch_size=self.batch_size,
sample_process_options=SampleProcessor.Options(random_flip=True),
output_sample_types=output_sample_types, person_id_mode=True ),
])
def onInitialize(self, batch_size=-1, **in_options):
exec(nnlib.code_import_all, locals(), globals())
self.set_vram_batch_requirements({4: 16, 11: 24})
resolution = self.options['resolution']
face_type = FaceType.FULL if self.options[
'face_type'] == 'f' else FaceType.HALF
person_id_max_count = SampleGeneratorFacePerson.get_person_id_max_count(
self.training_data_src_path)
self.model = FUNIT(
face_type_str=FaceType.toString(face_type),
batch_size=self.batch_size,
encoder_nf=64,
encoder_downs=2,
encoder_res_blk=2,
class_downs=4,
class_nf=64,
class_latent=64,
mlp_blks=2,
dis_nf=64,
dis_res_blks=8, #10
num_classes=person_id_max_count,
subpixel_decoder=True,
initialize_weights=self.is_first_run(),
is_training=self.is_training_mode,
tf_cpu_mode=self.options['optimizer_mode'] - 1)
if not self.is_first_run():
self.load_weights_safe(self.model.get_model_filename_list())
if self.is_training_mode:
t = SampleProcessor.Types
if self.options['face_type'] == 'h':
face_type = t.FACE_TYPE_HALF
elif self.options['face_type'] == 'mf':
face_type = t.FACE_TYPE_MID_FULL
elif self.options['face_type'] == 'f':
face_type = t.FACE_TYPE_FULL
output_sample_types = [{
'types': (t.IMG_TRANSFORMED, face_type, t.MODE_BGR),
'resolution':
resolution,
'normalize_tanh':
True
}]
output_sample_types1 = [{
'types': (t.IMG_SOURCE, face_type, t.MODE_BGR),
'resolution':
resolution,
'normalize_tanh':
True
}]
self.set_training_data_generators([
SampleGeneratorFacePerson(
self.training_data_src_path,
debug=self.is_debug(),
batch_size=self.batch_size,
sample_process_options=SampleProcessor.Options(
random_flip=True, rotation_range=[0, 0]),
output_sample_types=output_sample_types,
person_id_mode=1,
use_caching=True,
generators_count=1),
SampleGeneratorFacePerson(
self.training_data_src_path,
debug=self.is_debug(),
batch_size=self.batch_size,
sample_process_options=SampleProcessor.Options(
random_flip=True, rotation_range=[0, 0]),
output_sample_types=output_sample_types,
person_id_mode=1,
use_caching=True,
generators_count=1),
SampleGeneratorFacePerson(
self.training_data_dst_path,
debug=self.is_debug(),
batch_size=self.batch_size,
sample_process_options=SampleProcessor.Options(
random_flip=True, rotation_range=[0, 0]),
output_sample_types=output_sample_types1,
person_id_mode=1,
use_caching=True,
generators_count=1),
SampleGeneratorFacePerson(
self.training_data_dst_path,
debug=self.is_debug(),
batch_size=self.batch_size,
sample_process_options=SampleProcessor.Options(
random_flip=True, rotation_range=[0, 0]),
output_sample_types=output_sample_types1,
person_id_mode=1,
use_caching=True,
generators_count=1),
])