This repository has been archived by the owner on Nov 9, 2023. It is now read-only.
/
Model.py
201 lines (164 loc) · 8.68 KB
/
Model.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
import numpy as np
from nnlib import nnlib
from models import ModelBase
from facelib import FaceType
from samplelib import *
from interact import interact as io
class Model(ModelBase):
#override
def onInitializeOptions(self, is_first_run, ask_override):
if is_first_run:
self.options['lighter_ae'] = io.input_bool ("Use lightweight autoencoder? (y/n, ?:help skip:n) : ", False, help_message="Lightweight autoencoder is faster, requires less VRAM, sacrificing overall quality. If your GPU VRAM <= 4, you should to choose this option.")
else:
default_lighter_ae = self.options.get('created_vram_gb', 99) <= 4 #temporally support old models, deprecate in future
if 'created_vram_gb' in self.options.keys():
self.options.pop ('created_vram_gb')
self.options['lighter_ae'] = self.options.get('lighter_ae', default_lighter_ae)
if is_first_run or ask_override:
def_pixel_loss = self.options.get('pixel_loss', False)
self.options['pixel_loss'] = io.input_bool ("Use pixel loss? (y/n, ?:help skip: n/default ) : ", def_pixel_loss, help_message="Pixel loss may help to enhance fine details and stabilize face color. Use it only if quality does not improve over time.")
else:
self.options['pixel_loss'] = self.options.get('pixel_loss', False)
#override
def onInitialize(self):
exec(nnlib.import_all(), locals(), globals())
self.set_vram_batch_requirements( {2.5:4} )
bgr_shape, mask_shape, self.encoder, self.decoder_src, self.decoder_dst = self.Build( self.options['lighter_ae'] )
if not self.is_first_run():
weights_to_load = [ [self.encoder , 'encoder.h5'],
[self.decoder_src, 'decoder_src.h5'],
[self.decoder_dst, 'decoder_dst.h5']
]
self.load_weights_safe(weights_to_load)
input_src_bgr = Input(bgr_shape)
input_src_mask = Input(mask_shape)
input_dst_bgr = Input(bgr_shape)
input_dst_mask = Input(mask_shape)
rec_src_bgr, rec_src_mask = self.decoder_src( self.encoder(input_src_bgr) )
rec_dst_bgr, rec_dst_mask = self.decoder_dst( self.encoder(input_dst_bgr) )
self.ae = Model([input_src_bgr,input_src_mask,input_dst_bgr,input_dst_mask], [rec_src_bgr, rec_src_mask, rec_dst_bgr, rec_dst_mask] )
self.ae.compile(optimizer=Adam(lr=5e-5, beta_1=0.5, beta_2=0.999),
loss=[ DSSIMMSEMaskLoss(input_src_mask, is_mse=self.options['pixel_loss']), 'mae', DSSIMMSEMaskLoss(input_dst_mask, is_mse=self.options['pixel_loss']), 'mae' ] )
self.src_view = K.function([input_src_bgr],[rec_src_bgr, rec_src_mask])
self.dst_view = K.function([input_dst_bgr],[rec_dst_bgr, rec_dst_mask])
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_TYPE_HALF | f.MODE_BGR, 128],
[f.TRANSFORMED | f.FACE_TYPE_HALF | f.MODE_BGR, 128],
[f.TRANSFORMED | f.FACE_TYPE_HALF | 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_TYPE_HALF | f.MODE_BGR, 128],
[f.TRANSFORMED | f.FACE_TYPE_HALF | f.MODE_BGR, 128],
[f.TRANSFORMED | f.FACE_TYPE_HALF | f.MODE_M | f.FACE_MASK_FULL, 128] ] )
])
#override
def onSave(self):
self.save_weights_safe( [[self.encoder, 'encoder.h5'],
[self.decoder_src, 'decoder_src.h5'],
[self.decoder_dst, 'decoder_dst.h5']] )
#override
def onTrainOneIter(self, sample, generators_list):
warped_src, target_src, target_src_mask = sample[0]
warped_dst, target_dst, target_dst_mask = sample[1]
total, loss_src_bgr, loss_src_mask, loss_dst_bgr, loss_dst_mask = self.ae.train_on_batch( [warped_src, target_src_mask, warped_dst, target_dst_mask], [target_src, target_src_mask, target_dst, target_dst_mask] )
return ( ('loss_src', loss_src_bgr), ('loss_dst', loss_dst_bgr) )
#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.src_view([test_A])
AB, mAB = self.src_view([test_B])
BB, mBB = self.dst_view([test_B])
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 [ ('H128', np.concatenate ( st, axis=0 ) ) ]
def predictor_func (self, face):
x, mx = self.src_view ( [ face[np.newaxis,...] ] )
return x[0], mx[0][...,0]
#override
def get_converter(self):
from converters import ConverterMasked
return ConverterMasked(self.predictor_func,
predictor_input_size=128,
face_type=FaceType.HALF,
base_erode_mask_modifier=100,
base_blur_mask_modifier=100)
def Build(self, lighter_ae):
exec(nnlib.code_import_all, locals(), globals())
bgr_shape = (128, 128, 3)
mask_shape = (128, 128, 1)
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(input_shape):
input_layer = Input(input_shape)
x = input_layer
if not lighter_ae:
x = downscale(128)(x)
x = downscale(256)(x)
x = downscale(512)(x)
x = downscale(1024)(x)
x = Dense(512)(Flatten()(x))
x = Dense(8 * 8 * 512)(x)
x = Reshape((8, 8, 512))(x)
x = upscale(512)(x)
else:
x = downscale(128)(x)
x = downscale(256)(x)
x = downscale(512)(x)
x = downscale(1024)(x)
x = Dense(256)(Flatten()(x))
x = Dense(8 * 8 * 256)(x)
x = Reshape((8, 8, 256))(x)
x = upscale(256)(x)
return Model(input_layer, x)
def Decoder():
if not lighter_ae:
input_ = Input(shape=(16, 16, 512))
x = input_
x = upscale(512)(x)
x = upscale(256)(x)
x = upscale(128)(x)
y = input_ #mask decoder
y = upscale(512)(y)
y = upscale(256)(y)
y = upscale(128)(y)
else:
input_ = Input(shape=(16, 16, 256))
x = input_
x = upscale(256)(x)
x = upscale(128)(x)
x = upscale(64)(x)
y = input_ #mask decoder
y = upscale(256)(y)
y = upscale(128)(y)
y = upscale(64)(y)
x = Conv2D(3, kernel_size=5, padding='same', activation='sigmoid')(x)
y = Conv2D(1, kernel_size=5, padding='same', activation='sigmoid')(y)
return Model(input_, [x,y])
return bgr_shape, mask_shape, Encoder(bgr_shape), Decoder(), Decoder()