-
Notifications
You must be signed in to change notification settings - Fork 558
/
model.py
210 lines (163 loc) · 8.26 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
202
203
204
205
206
207
208
209
210
# Copyright 2019-2020 Stanislav Pidhorskyi
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
import random
import losses
from net import *
import numpy as np
class DLatent(nn.Module):
def __init__(self, dlatent_size, layer_count):
super(DLatent, self).__init__()
buffer = torch.zeros(layer_count, dlatent_size, dtype=torch.float32)
self.register_buffer('buff', buffer)
class Model(nn.Module):
def __init__(self, startf=32, maxf=256, layer_count=3, latent_size=128, mapping_layers=5, dlatent_avg_beta=None,
truncation_psi=None, truncation_cutoff=None, style_mixing_prob=None, channels=3, generator="",
encoder="", z_regression=False):
super(Model, self).__init__()
self.layer_count = layer_count
self.z_regression = z_regression
self.mapping_d = MAPPINGS["MappingD"](
latent_size=latent_size,
dlatent_size=latent_size,
mapping_fmaps=latent_size,
mapping_layers=3)
self.mapping_f = MAPPINGS["MappingF"](
num_layers=2 * layer_count,
latent_size=latent_size,
dlatent_size=latent_size,
mapping_fmaps=latent_size,
mapping_layers=mapping_layers)
self.decoder = GENERATORS[generator](
startf=startf,
layer_count=layer_count,
maxf=maxf,
latent_size=latent_size,
channels=channels)
self.encoder = ENCODERS[encoder](
startf=startf,
layer_count=layer_count,
maxf=maxf,
latent_size=latent_size,
channels=channels)
self.dlatent_avg = DLatent(latent_size, self.mapping_f.num_layers)
self.latent_size = latent_size
self.dlatent_avg_beta = dlatent_avg_beta
self.truncation_psi = truncation_psi
self.style_mixing_prob = style_mixing_prob
self.truncation_cutoff = truncation_cutoff
def generate(self, lod, blend_factor, z=None, count=32, mixing=True, noise=True, return_styles=False, no_truncation=False):
if z is None:
z = torch.randn(count, self.latent_size)
styles = self.mapping_f(z)[:, 0]
s = styles.view(styles.shape[0], 1, styles.shape[1])
styles = s.repeat(1, self.mapping_f.num_layers, 1)
if self.dlatent_avg_beta is not None:
with torch.no_grad():
batch_avg = styles.mean(dim=0)
self.dlatent_avg.buff.data.lerp_(batch_avg.data, 1.0 - self.dlatent_avg_beta)
if mixing and self.style_mixing_prob is not None:
if random.random() < self.style_mixing_prob:
z2 = torch.randn(count, self.latent_size)
styles2 = self.mapping_f(z2)[:, 0]
styles2 = styles2.view(styles2.shape[0], 1, styles2.shape[1]).repeat(1, self.mapping_f.num_layers, 1)
layer_idx = torch.arange(self.mapping_f.num_layers)[np.newaxis, :, np.newaxis]
cur_layers = (lod + 1) * 2
mixing_cutoff = random.randint(1, cur_layers)
styles = torch.where(layer_idx < mixing_cutoff, styles, styles2)
if (self.truncation_psi is not None) and not no_truncation:
layer_idx = torch.arange(self.mapping_f.num_layers)[np.newaxis, :, np.newaxis]
dtype=torch.float32)
coefs = torch.where(layer_idx < self.truncation_cutoff, self.truncation_psi * ones, ones)
styles = torch.lerp(self.dlatent_avg.buff.data, styles, coefs)
rec = self.decoder.forward(styles, lod, blend_factor, noise)
if return_styles:
return s, rec
else:
return rec
def encode(self, x, lod, blend_factor):
Z = self.encoder(x, lod, blend_factor)
discriminator_prediction = self.mapping_d(Z)
return Z[:, :1], discriminator_prediction
def forward(self, x, lod, blend_factor, d_train, ae):
if ae:
self.encoder.requires_grad_(True)
z = torch.randn(x.shape[0], self.latent_size)
s, rec = self.generate(lod, blend_factor, z=z, mixing=False, noise=True, return_styles=True)
Z, d_result_real = self.encode(rec, lod, blend_factor)
assert Z.shape == s.shape
if self.z_regression:
Lae = torch.mean(((Z[:, 0] - z)**2))
else:
Lae = torch.mean(((Z - s.detach())**2))
return Lae
elif d_train:
with torch.no_grad():
Xp = self.generate(lod, blend_factor, count=x.shape[0], noise=True)
self.encoder.requires_grad_(True)
_, d_result_real = self.encode(x, lod, blend_factor)
_, d_result_fake = self.encode(Xp, lod, blend_factor)
loss_d = losses.discriminator_logistic_simple_gp(d_result_fake, d_result_real, x)
return loss_d
else:
with torch.no_grad():
z = torch.randn(x.shape[0], self.latent_size)
self.encoder.requires_grad_(False)
rec = self.generate(lod, blend_factor, count=x.shape[0], z=z.detach(), noise=True)
_, d_result_fake = self.encode(rec, lod, blend_factor)
loss_g = losses.generator_logistic_non_saturating(d_result_fake)
return loss_g
def lerp(self, other, betta):
if hasattr(other, 'module'):
other = other.module
with torch.no_grad():
params = list(self.mapping_d.parameters()) + list(self.mapping_f.parameters()) + list(self.decoder.parameters()) + list(self.encoder.parameters()) + list(self.dlatent_avg.parameters())
other_param = list(other.mapping_d.parameters()) + list(other.mapping_f.parameters()) + list(other.decoder.parameters()) + list(other.encoder.parameters()) + list(other.dlatent_avg.parameters())
for p, p_other in zip(params, other_param):
p.data.lerp_(p_other.data, 1.0 - betta)
class GenModel(nn.Module):
def __init__(self, startf=32, maxf=256, layer_count=3, latent_size=128, mapping_layers=5, dlatent_avg_beta=None,
truncation_psi=None, truncation_cutoff=None, style_mixing_prob=None, channels=3, generator="", encoder="", z_regression=False):
super(GenModel, self).__init__()
self.layer_count = layer_count
self.mapping_f = MAPPINGS["MappingF"](
num_layers=2 * layer_count,
latent_size=latent_size,
dlatent_size=latent_size,
mapping_fmaps=latent_size,
mapping_layers=mapping_layers)
self.decoder = GENERATORS[generator](
startf=startf,
layer_count=layer_count,
maxf=maxf,
latent_size=latent_size,
channels=channels)
self.dlatent_avg = DLatent(latent_size, self.mapping_f.num_layers)
self.latent_size = latent_size
self.dlatent_avg_beta = dlatent_avg_beta
self.truncation_psi = truncation_psi
self.style_mixing_prob = style_mixing_prob
self.truncation_cutoff = truncation_cutoff
def generate(self, lod, blend_factor, z=None):
styles = self.mapping_f(z)[:, 0]
s = styles.view(styles.shape[0], 1, styles.shape[1])
styles = s.repeat(1, self.mapping_f.num_layers, 1)
layer_idx = torch.arange(self.mapping_f.num_layers)[np.newaxis, :, np.newaxis]
dtype=torch.float32)
coefs = torch.where(layer_idx < self.truncation_cutoff, self.truncation_psi * ones, ones)
styles = torch.lerp(self.dlatent_avg.buff.data, styles, coefs)
rec = self.decoder.forward(styles, lod, blend_factor, True)
return rec
def forward(self, x):
return self.generate(self.layer_count-1, 1.0, z=x)