from functools import partial
import cv2
import numpy as np
from facelib import FaceType
from interact import interact as io
from mathlib import get_power_of_two
from models import ModelBase
from nnlib import nnlib, FUNIT
from samplelib import *
class FUNITModel(ModelBase):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs,
ask_random_flip=False)
#override
def onInitializeOptions(self, is_first_run, ask_override):
default_resolution = 64
if is_first_run:
self.options['resolution'] = io.input_int(f"Resolution ( 64,96,128,224 ?:help skip:{default_resolution}) : ", default_resolution, [64,96,128,224])
else:
self.options['resolution'] = self.options.get('resolution', default_resolution)
default_face_type = 'mf'
if is_first_run:
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)
if (is_first_run or ask_override) and 'tensorflow' in self.device_config.backend:
def_optimizer_mode = self.options.get('optimizer_mode', 1)
self.options['optimizer_mode'] = io.input_int ("Optimizer mode? ( 1,2,3 ?:help skip:%d) : " % (def_optimizer_mode), def_optimizer_mode, help_message="1 - no changes. 2 - allows you to train x2 bigger network consuming RAM. 3 - allows you to train x3 bigger network consuming huge amount of RAM and slower, depends on CPU power.")
else:
self.options['optimizer_mode'] = self.options.get('optimizer_mode', 1)
#override
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, ),
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, ),
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, ),
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, ),
])
#override
def get_model_filename_list(self):
return self.model.get_model_filename_list()
#override
def onSave(self):
self.save_weights_safe(self.model.get_model_filename_list())
#override
def onTrainOneIter(self, generators_samples, generators_list):
xa,la = generators_samples[0]
xb,lb = generators_samples[1]
G_loss, D_loss = self.model.train(xa,la,xb,lb)
return ( ('G_loss', G_loss), ('D_loss', D_loss), )
#override
def onGetPreview(self, generators_samples):
xa = generators_samples[0][0]
xb = generators_samples[1][0]
ta = generators_samples[2][0]
tb = generators_samples[3][0]
view_samples = min(4, xa.shape[0])
lines_train = []
lines_test = []
for i in range(view_samples):
s_xa = self.model.get_average_class_code([ xa[i:i+1] ])[0][None,...]
s_xb = self.model.get_average_class_code([ xb[i:i+1] ])[0][None,...]
s_ta = self.model.get_average_class_code([ ta[i:i+1] ])[0][None,...]
s_tb = self.model.get_average_class_code([ tb[i:i+1] ])[0][None,...]
xaxa = self.model.convert ([ xa[i:i+1], s_xa ] )[0][0]
xbxb = self.model.convert ([ xb[i:i+1], s_xb ] )[0][0]
xaxb = self.model.convert ([ xa[i:i+1], s_xb ] )[0][0]
xbxa = self.model.convert ([ xb[i:i+1], s_xa ] )[0][0]
tata = self.model.convert ([ ta[i:i+1], s_ta ] )[0][0]
tbtb = self.model.convert ([ tb[i:i+1], s_tb ] )[0][0]
tatb = self.model.convert ([ ta[i:i+1], s_tb ] )[0][0]
tbta = self.model.convert ([ tb[i:i+1], s_ta ] )[0][0]
line_train = [ xa[i], xaxa, xb[i], xbxb, xaxb, xbxa ]
line_test = [ ta[i], tata, tb[i], tbtb, tatb, tbta ]
lines_train += [ np.concatenate([ np.clip(x/2+0.5,0,1) for x in line_train], axis=1) ]
lines_test += [ np.concatenate([ np.clip(x/2+0.5,0,1) for x in line_test ], axis=1) ]
lines_train = np.concatenate ( lines_train, axis=0 )
lines_test = np.concatenate ( lines_test, axis=0 )
return [ ('TRAIN', lines_train ), ('TEST', lines_test) ]
def predictor_func (self, face=None, dummy_predict=False):
if dummy_predict:
self.model.convert ([ np.zeros ( (1, self.options['resolution'], self.options['resolution'], 3), dtype=np.float32 ), self.average_class_code ])
else:
bgr, = self.model.convert ([ face[np.newaxis,...]*2-1, self.average_class_code ])
return bgr[0] / 2 + 0.5
#override
def get_ConverterConfig(self):
face_type = FaceType.FULL
import converters
return self.predictor_func, (self.options['resolution'], self.options['resolution'], 3), converters.ConverterConfigMasked(face_type=face_type,
default_mode = 1,
clip_hborder_mask_per=0.0625 if (face_type == FaceType.FULL) else 0,
)
Model = FUNITModel