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Colombo 2019-10-25 11:19:23 +04:00
parent b9c41a269d
commit f17a54b23a
1 changed files with 19 additions and 12 deletions
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31
models/Model_DEV_FUNIT/Model.py
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@ -22,14 +22,16 @@ class FUNITModel(ModelBase):
#override
def onInitializeOptions(self, is_first_run, ask_override):
default_face_type = 'f'
default_resolution = 96
if is_first_run:
self.options['resolution'] = io.input_int("Resolution ( 128,224 ?:help skip:128) : ", 128, [128,224])
self.options['resolution'] = io.input_int(f"Resolution ( 96,128,224 ?:help skip:{default_resolution}) : ", default_resolution, [128,224])
else:
self.options['resolution'] = self.options.get('resolution', 128)
self.options['resolution'] = self.options.get('resolution', default_resolution)
default_face_type = 'mf'
if is_first_run:
self.options['face_type'] = io.input_str ("Half or Full face? (h/f, ?:help skip:f) : ", default_face_type, ['h','f'], help_message="").lower()
self.options['face_type'] = io.input_str (f"Half or Full face? (h/mf/f, ?:help skip:{default_face_type}) : ", default_face_type, ['h','mf','f'], help_message="").lower()
else:
self.options['face_type'] = self.options.get('face_type', default_face_type)
@ -59,7 +61,7 @@ class FUNITModel(ModelBase):
class_latent=64,
mlp_blks=2,
dis_nf=64,
dis_res_blks=10,
dis_res_blks=8,#10
num_classes=person_id_max_count,
subpixel_decoder=True,
initialize_weights=self.is_first_run(),
@ -72,26 +74,31 @@ class FUNITModel(ModelBase):
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
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':128, 'normalize_tanh':True} ]
output_sample_types1=[ {'types': (t.IMG_SOURCE, face_type, t.MODE_BGR), 'resolution':128, 'normalize_tanh':True} ]
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 ([
SampleGeneratorFace(self.training_data_src_path, debug=self.is_debug(), batch_size=self.batch_size,
sample_process_options=SampleProcessor.Options(random_flip=True),
sample_process_options=SampleProcessor.Options(random_flip=True, rotation_range=[0,0] ),
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),
sample_process_options=SampleProcessor.Options(random_flip=True, rotation_range=[0,0] ),
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),
sample_process_options=SampleProcessor.Options(random_flip=True, rotation_range=[0,0]),
output_sample_types=output_sample_types1, 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),
sample_process_options=SampleProcessor.Options(random_flip=True, rotation_range=[0,0]),
output_sample_types=output_sample_types1, person_id_mode=True ),
])