mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-07-06 13:02:15 -07:00
181 lines
No EOL
8 KiB
Python
181 lines
No EOL
8 KiB
Python
import numpy as np
|
|
|
|
from nnlib import nnlib
|
|
from models import ModelBase
|
|
from facelib import FaceType
|
|
from samples import *
|
|
from utils.console_utils import *
|
|
|
|
class Model(ModelBase):
|
|
|
|
#override
|
|
def onInitializeOptions(self, is_first_run, ask_override):
|
|
if is_first_run or ask_override:
|
|
def_pixel_loss = self.options.get('pixel_loss', False)
|
|
self.options['pixel_loss'] = input_bool ("Use pixel loss? (y/n, ?:help skip: n/default ) : ", def_pixel_loss, help_message="Default DSSIM loss good for initial understanding structure of faces. Use pixel loss after 20k epochs to enhance fine details and decrease face jitter.")
|
|
else:
|
|
self.options['pixel_loss'] = self.options.get('pixel_loss', False)
|
|
|
|
#override
|
|
def onInitialize(self, **in_options):
|
|
exec(nnlib.import_all(), locals(), globals())
|
|
self.set_vram_batch_requirements( {4.5:4} )
|
|
|
|
ae_input_layer = Input(shape=(128, 128, 3))
|
|
mask_layer = Input(shape=(128, 128, 1)) #same as output
|
|
|
|
self.encoder, self.decoder, self.inter_B, self.inter_AB = self.Build(ae_input_layer)
|
|
|
|
if not self.is_first_run():
|
|
weights_to_load = [ [self.encoder, 'encoder.h5'],
|
|
[self.decoder, 'decoder.h5'],
|
|
[self.inter_B, 'inter_B.h5'],
|
|
[self.inter_AB, 'inter_AB.h5']
|
|
]
|
|
self.load_weights_safe(weights_to_load)
|
|
|
|
code = self.encoder(ae_input_layer)
|
|
AB = self.inter_AB(code)
|
|
B = self.inter_B(code)
|
|
self.autoencoder_src = Model([ae_input_layer,mask_layer], self.decoder(Concatenate()([AB, AB])) )
|
|
self.autoencoder_dst = Model([ae_input_layer,mask_layer], self.decoder(Concatenate()([B, AB])) )
|
|
|
|
self.autoencoder_src.compile(optimizer=Adam(lr=5e-5, beta_1=0.5, beta_2=0.999), loss=[DSSIMMSEMaskLoss(mask_layer, is_mse=self.options['pixel_loss']), 'mse'] )
|
|
self.autoencoder_dst.compile(optimizer=Adam(lr=5e-5, beta_1=0.5, beta_2=0.999), loss=[DSSIMMSEMaskLoss(mask_layer, is_mse=self.options['pixel_loss']), 'mse'] )
|
|
|
|
if self.is_training_mode:
|
|
f = SampleProcessor.TypeFlags
|
|
self.set_training_data_generators ([
|
|
|
|
|
|
SampleGeneratorFace(self.training_data_src_path, sort_by_yaw_target_samples_path=self.training_data_dst_path if self.sort_by_yaw else None,
|
|
debug=self.is_debug(), batch_size=self.batch_size,
|
|
sample_process_options=SampleProcessor.Options(random_flip=self.random_flip, scale_range=np.array([-0.05, 0.05])+self.src_scale_mod / 100.0 ),
|
|
output_sample_types=[ [f.WARPED_TRANSFORMED | f.FACE_ALIGN_FULL | f.MODE_BGR, 128],
|
|
[f.TRANSFORMED | f.FACE_ALIGN_FULL | f.MODE_BGR, 128],
|
|
[f.TRANSFORMED | f.FACE_ALIGN_FULL | f.MODE_M | f.FACE_MASK_FULL, 128] ] ),
|
|
|
|
SampleGeneratorFace(self.training_data_dst_path, debug=self.is_debug(), batch_size=self.batch_size,
|
|
sample_process_options=SampleProcessor.Options(random_flip=self.random_flip),
|
|
output_sample_types=[ [f.WARPED_TRANSFORMED | f.FACE_ALIGN_FULL | f.MODE_BGR, 128],
|
|
[f.TRANSFORMED | f.FACE_ALIGN_FULL | f.MODE_BGR, 128],
|
|
[f.TRANSFORMED | f.FACE_ALIGN_FULL | f.MODE_M | f.FACE_MASK_FULL, 128] ] )
|
|
])
|
|
|
|
#override
|
|
def onSave(self):
|
|
self.save_weights_safe( [[self.encoder, 'encoder.h5'],
|
|
[self.decoder, 'decoder.h5'],
|
|
[self.inter_B, 'inter_B.h5'],
|
|
[self.inter_AB, 'inter_AB.h5']] )
|
|
|
|
#override
|
|
def onTrainOneEpoch(self, sample, generators_list):
|
|
warped_src, target_src, target_src_mask = sample[0]
|
|
warped_dst, target_dst, target_dst_mask = sample[1]
|
|
|
|
loss_src = self.autoencoder_src.train_on_batch( [warped_src, target_src_mask], [target_src, target_src_mask] )
|
|
loss_dst = self.autoencoder_dst.train_on_batch( [warped_dst, target_dst_mask], [target_dst, target_dst_mask] )
|
|
|
|
return ( ('loss_src', loss_src[0]), ('loss_dst', loss_dst[0]) )
|
|
|
|
|
|
#override
|
|
def onGetPreview(self, sample):
|
|
test_A = sample[0][1][0:4] #first 4 samples
|
|
test_A_m = sample[0][2][0:4] #first 4 samples
|
|
test_B = sample[1][1][0:4]
|
|
test_B_m = sample[1][2][0:4]
|
|
|
|
AA, mAA = self.autoencoder_src.predict([test_A, test_A_m])
|
|
AB, mAB = self.autoencoder_src.predict([test_B, test_B_m])
|
|
BB, mBB = self.autoencoder_dst.predict([test_B, test_B_m])
|
|
|
|
mAA = np.repeat ( mAA, (3,), -1)
|
|
mAB = np.repeat ( mAB, (3,), -1)
|
|
mBB = np.repeat ( mBB, (3,), -1)
|
|
|
|
st = []
|
|
for i in range(0, len(test_A)):
|
|
st.append ( np.concatenate ( (
|
|
test_A[i,:,:,0:3],
|
|
AA[i],
|
|
#mAA[i],
|
|
test_B[i,:,:,0:3],
|
|
BB[i],
|
|
#mBB[i],
|
|
AB[i],
|
|
#mAB[i]
|
|
), axis=1) )
|
|
|
|
return [ ('LIAEF128', np.concatenate ( st, axis=0 ) ) ]
|
|
|
|
def predictor_func (self, face):
|
|
|
|
face_128_bgr = face[...,0:3]
|
|
face_128_mask = np.expand_dims(face[...,3],-1)
|
|
|
|
x, mx = self.autoencoder_src.predict ( [ np.expand_dims(face_128_bgr,0), np.expand_dims(face_128_mask,0) ] )
|
|
x, mx = x[0], mx[0]
|
|
|
|
return np.concatenate ( (x,mx), -1 )
|
|
|
|
#override
|
|
def get_converter(self, **in_options):
|
|
from models import ConverterMasked
|
|
return ConverterMasked(self.predictor_func,
|
|
predictor_input_size=128,
|
|
output_size=128,
|
|
face_type=FaceType.FULL,
|
|
base_erode_mask_modifier=30,
|
|
base_blur_mask_modifier=0,
|
|
**in_options)
|
|
|
|
def Build(self, input_layer):
|
|
exec(nnlib.code_import_all, locals(), globals())
|
|
|
|
def downscale (dim):
|
|
def func(x):
|
|
return LeakyReLU(0.1)(Conv2D(dim, 5, strides=2, padding='same')(x))
|
|
return func
|
|
|
|
def upscale (dim):
|
|
def func(x):
|
|
return PixelShuffler()(LeakyReLU(0.1)(Conv2D(dim * 4, 3, strides=1, padding='same')(x)))
|
|
return func
|
|
|
|
def Encoder():
|
|
x = input_layer
|
|
x = downscale(128)(x)
|
|
x = downscale(256)(x)
|
|
x = downscale(512)(x)
|
|
x = downscale(1024)(x)
|
|
x = Flatten()(x)
|
|
return Model(input_layer, x)
|
|
|
|
def Intermediate():
|
|
input_layer = Input(shape=(None, 8 * 8 * 1024))
|
|
x = input_layer
|
|
x = Dense(256)(x)
|
|
x = Dense(8 * 8 * 512)(x)
|
|
x = Reshape((8, 8, 512))(x)
|
|
x = upscale(512)(x)
|
|
return Model(input_layer, x)
|
|
|
|
def Decoder():
|
|
input_ = Input(shape=(16, 16, 1024))
|
|
x = input_
|
|
x = upscale(512)(x)
|
|
x = upscale(256)(x)
|
|
x = upscale(128)(x)
|
|
x = Conv2D(3, kernel_size=5, padding='same', activation='sigmoid')(x)
|
|
|
|
y = input_ #mask decoder
|
|
y = upscale(512)(y)
|
|
y = upscale(256)(y)
|
|
y = upscale(128)(y)
|
|
y = Conv2D(1, kernel_size=5, padding='same', activation='sigmoid' )(y)
|
|
|
|
return Model(input_, [x,y])
|
|
|
|
return Encoder(), Decoder(), Intermediate(), Intermediate() |