CoCalc -- deepbooru

GitHub Repository: automatic1111/stable-diffusion-webui
Path: blob/master/modules/deepbooru_model.py
³⁰⁵⁵ views
1
import torch
2
import torch.nn as nn
3
import torch.nn.functional as F
4

5
from modules import devices
6

7
# see https://github.com/AUTOMATIC1111/TorchDeepDanbooru for more
8

9

10
class DeepDanbooruModel(nn.Module):
11
    def __init__(self):
12
        super(DeepDanbooruModel, self).__init__()
13

14
        self.tags = []
15

16
        self.n_Conv_0 = nn.Conv2d(kernel_size=(7, 7), in_channels=3, out_channels=64, stride=(2, 2))
17
        self.n_MaxPool_0 = nn.MaxPool2d(kernel_size=(3, 3), stride=(2, 2))
18
        self.n_Conv_1 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256)
19
        self.n_Conv_2 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=64)
20
        self.n_Conv_3 = nn.Conv2d(kernel_size=(3, 3), in_channels=64, out_channels=64)
21
        self.n_Conv_4 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256)
22
        self.n_Conv_5 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=64)
23
        self.n_Conv_6 = nn.Conv2d(kernel_size=(3, 3), in_channels=64, out_channels=64)
24
        self.n_Conv_7 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256)
25
        self.n_Conv_8 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=64)
26
        self.n_Conv_9 = nn.Conv2d(kernel_size=(3, 3), in_channels=64, out_channels=64)
27
        self.n_Conv_10 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256)
28
        self.n_Conv_11 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=512, stride=(2, 2))
29
        self.n_Conv_12 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=128)
30
        self.n_Conv_13 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128, stride=(2, 2))
31
        self.n_Conv_14 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
32
        self.n_Conv_15 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
33
        self.n_Conv_16 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
34
        self.n_Conv_17 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
35
        self.n_Conv_18 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
36
        self.n_Conv_19 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
37
        self.n_Conv_20 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
38
        self.n_Conv_21 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
39
        self.n_Conv_22 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
40
        self.n_Conv_23 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
41
        self.n_Conv_24 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
42
        self.n_Conv_25 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
43
        self.n_Conv_26 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
44
        self.n_Conv_27 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
45
        self.n_Conv_28 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
46
        self.n_Conv_29 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
47
        self.n_Conv_30 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
48
        self.n_Conv_31 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
49
        self.n_Conv_32 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
50
        self.n_Conv_33 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
51
        self.n_Conv_34 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
52
        self.n_Conv_35 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
53
        self.n_Conv_36 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=1024, stride=(2, 2))
54
        self.n_Conv_37 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=256)
55
        self.n_Conv_38 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256, stride=(2, 2))
56
        self.n_Conv_39 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
57
        self.n_Conv_40 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
58
        self.n_Conv_41 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
59
        self.n_Conv_42 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
60
        self.n_Conv_43 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
61
        self.n_Conv_44 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
62
        self.n_Conv_45 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
63
        self.n_Conv_46 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
64
        self.n_Conv_47 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
65
        self.n_Conv_48 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
66
        self.n_Conv_49 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
67
        self.n_Conv_50 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
68
        self.n_Conv_51 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
69
        self.n_Conv_52 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
70
        self.n_Conv_53 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
71
        self.n_Conv_54 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
72
        self.n_Conv_55 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
73
        self.n_Conv_56 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
74
        self.n_Conv_57 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
75
        self.n_Conv_58 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
76
        self.n_Conv_59 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
77
        self.n_Conv_60 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
78
        self.n_Conv_61 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
79
        self.n_Conv_62 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
80
        self.n_Conv_63 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
81
        self.n_Conv_64 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
82
        self.n_Conv_65 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
83
        self.n_Conv_66 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
84
        self.n_Conv_67 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
85
        self.n_Conv_68 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
86
        self.n_Conv_69 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
87
        self.n_Conv_70 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
88
        self.n_Conv_71 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
89
        self.n_Conv_72 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
90
        self.n_Conv_73 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
91
        self.n_Conv_74 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
92
        self.n_Conv_75 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
93
        self.n_Conv_76 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
94
        self.n_Conv_77 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
95
        self.n_Conv_78 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
96
        self.n_Conv_79 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
97
        self.n_Conv_80 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
98
        self.n_Conv_81 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
99
        self.n_Conv_82 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
100
        self.n_Conv_83 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
101
        self.n_Conv_84 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
102
        self.n_Conv_85 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
103
        self.n_Conv_86 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
104
        self.n_Conv_87 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
105
        self.n_Conv_88 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
106
        self.n_Conv_89 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
107
        self.n_Conv_90 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
108
        self.n_Conv_91 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
109
        self.n_Conv_92 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
110
        self.n_Conv_93 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
111
        self.n_Conv_94 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
112
        self.n_Conv_95 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
113
        self.n_Conv_96 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
114
        self.n_Conv_97 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
115
        self.n_Conv_98 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256, stride=(2, 2))
116
        self.n_Conv_99 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
117
        self.n_Conv_100 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=1024, stride=(2, 2))
118
        self.n_Conv_101 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
119
        self.n_Conv_102 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
120
        self.n_Conv_103 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
121
        self.n_Conv_104 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
122
        self.n_Conv_105 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
123
        self.n_Conv_106 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
124
        self.n_Conv_107 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
125
        self.n_Conv_108 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
126
        self.n_Conv_109 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
127
        self.n_Conv_110 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
128
        self.n_Conv_111 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
129
        self.n_Conv_112 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
130
        self.n_Conv_113 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
131
        self.n_Conv_114 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
132
        self.n_Conv_115 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
133
        self.n_Conv_116 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
134
        self.n_Conv_117 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
135
        self.n_Conv_118 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
136
        self.n_Conv_119 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
137
        self.n_Conv_120 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
138
        self.n_Conv_121 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
139
        self.n_Conv_122 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
140
        self.n_Conv_123 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
141
        self.n_Conv_124 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
142
        self.n_Conv_125 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
143
        self.n_Conv_126 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
144
        self.n_Conv_127 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
145
        self.n_Conv_128 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
146
        self.n_Conv_129 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
147
        self.n_Conv_130 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
148
        self.n_Conv_131 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
149
        self.n_Conv_132 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
150
        self.n_Conv_133 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
151
        self.n_Conv_134 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
152
        self.n_Conv_135 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
153
        self.n_Conv_136 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
154
        self.n_Conv_137 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
155
        self.n_Conv_138 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
156
        self.n_Conv_139 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
157
        self.n_Conv_140 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
158
        self.n_Conv_141 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
159
        self.n_Conv_142 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
160
        self.n_Conv_143 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
161
        self.n_Conv_144 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
162
        self.n_Conv_145 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
163
        self.n_Conv_146 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
164
        self.n_Conv_147 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
165
        self.n_Conv_148 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
166
        self.n_Conv_149 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
167
        self.n_Conv_150 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
168
        self.n_Conv_151 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
169
        self.n_Conv_152 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
170
        self.n_Conv_153 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
171
        self.n_Conv_154 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
172
        self.n_Conv_155 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
173
        self.n_Conv_156 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
174
        self.n_Conv_157 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
175
        self.n_Conv_158 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=2048, stride=(2, 2))
176
        self.n_Conv_159 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=512)
177
        self.n_Conv_160 = nn.Conv2d(kernel_size=(3, 3), in_channels=512, out_channels=512, stride=(2, 2))
178
        self.n_Conv_161 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=2048)
179
        self.n_Conv_162 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=512)
180
        self.n_Conv_163 = nn.Conv2d(kernel_size=(3, 3), in_channels=512, out_channels=512)
181
        self.n_Conv_164 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=2048)
182
        self.n_Conv_165 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=512)
183
        self.n_Conv_166 = nn.Conv2d(kernel_size=(3, 3), in_channels=512, out_channels=512)
184
        self.n_Conv_167 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=2048)
185
        self.n_Conv_168 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=4096, stride=(2, 2))
186
        self.n_Conv_169 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=1024)
187
        self.n_Conv_170 = nn.Conv2d(kernel_size=(3, 3), in_channels=1024, out_channels=1024, stride=(2, 2))
188
        self.n_Conv_171 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=4096)
189
        self.n_Conv_172 = nn.Conv2d(kernel_size=(1, 1), in_channels=4096, out_channels=1024)
190
        self.n_Conv_173 = nn.Conv2d(kernel_size=(3, 3), in_channels=1024, out_channels=1024)
191
        self.n_Conv_174 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=4096)
192
        self.n_Conv_175 = nn.Conv2d(kernel_size=(1, 1), in_channels=4096, out_channels=1024)
193
        self.n_Conv_176 = nn.Conv2d(kernel_size=(3, 3), in_channels=1024, out_channels=1024)
194
        self.n_Conv_177 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=4096)
195
        self.n_Conv_178 = nn.Conv2d(kernel_size=(1, 1), in_channels=4096, out_channels=9176, bias=False)
196

197
    def forward(self, *inputs):
198
        t_358, = inputs
199
        t_359 = t_358.permute(*[0, 3, 1, 2])
200
        t_359_padded = F.pad(t_359, [2, 3, 2, 3], value=0)
201
        t_360 = self.n_Conv_0(t_359_padded.to(self.n_Conv_0.bias.dtype) if devices.unet_needs_upcast else t_359_padded)
202
        t_361 = F.relu(t_360)
203
        t_361 = F.pad(t_361, [0, 1, 0, 1], value=float('-inf'))
204
        t_362 = self.n_MaxPool_0(t_361)
205
        t_363 = self.n_Conv_1(t_362)
206
        t_364 = self.n_Conv_2(t_362)
207
        t_365 = F.relu(t_364)
208
        t_365_padded = F.pad(t_365, [1, 1, 1, 1], value=0)
209
        t_366 = self.n_Conv_3(t_365_padded)
210
        t_367 = F.relu(t_366)
211
        t_368 = self.n_Conv_4(t_367)
212
        t_369 = torch.add(t_368, t_363)
213
        t_370 = F.relu(t_369)
214
        t_371 = self.n_Conv_5(t_370)
215
        t_372 = F.relu(t_371)
216
        t_372_padded = F.pad(t_372, [1, 1, 1, 1], value=0)
217
        t_373 = self.n_Conv_6(t_372_padded)
218
        t_374 = F.relu(t_373)
219
        t_375 = self.n_Conv_7(t_374)
220
        t_376 = torch.add(t_375, t_370)
221
        t_377 = F.relu(t_376)
222
        t_378 = self.n_Conv_8(t_377)
223
        t_379 = F.relu(t_378)
224
        t_379_padded = F.pad(t_379, [1, 1, 1, 1], value=0)
225
        t_380 = self.n_Conv_9(t_379_padded)
226
        t_381 = F.relu(t_380)
227
        t_382 = self.n_Conv_10(t_381)
228
        t_383 = torch.add(t_382, t_377)
229
        t_384 = F.relu(t_383)
230
        t_385 = self.n_Conv_11(t_384)
231
        t_386 = self.n_Conv_12(t_384)
232
        t_387 = F.relu(t_386)
233
        t_387_padded = F.pad(t_387, [0, 1, 0, 1], value=0)
234
        t_388 = self.n_Conv_13(t_387_padded)
235
        t_389 = F.relu(t_388)
236
        t_390 = self.n_Conv_14(t_389)
237
        t_391 = torch.add(t_390, t_385)
238
        t_392 = F.relu(t_391)
239
        t_393 = self.n_Conv_15(t_392)
240
        t_394 = F.relu(t_393)
241
        t_394_padded = F.pad(t_394, [1, 1, 1, 1], value=0)
242
        t_395 = self.n_Conv_16(t_394_padded)
243
        t_396 = F.relu(t_395)
244
        t_397 = self.n_Conv_17(t_396)
245
        t_398 = torch.add(t_397, t_392)
246
        t_399 = F.relu(t_398)
247
        t_400 = self.n_Conv_18(t_399)
248
        t_401 = F.relu(t_400)
249
        t_401_padded = F.pad(t_401, [1, 1, 1, 1], value=0)
250
        t_402 = self.n_Conv_19(t_401_padded)
251
        t_403 = F.relu(t_402)
252
        t_404 = self.n_Conv_20(t_403)
253
        t_405 = torch.add(t_404, t_399)
254
        t_406 = F.relu(t_405)
255
        t_407 = self.n_Conv_21(t_406)
256
        t_408 = F.relu(t_407)
257
        t_408_padded = F.pad(t_408, [1, 1, 1, 1], value=0)
258
        t_409 = self.n_Conv_22(t_408_padded)
259
        t_410 = F.relu(t_409)
260
        t_411 = self.n_Conv_23(t_410)
261
        t_412 = torch.add(t_411, t_406)
262
        t_413 = F.relu(t_412)
263
        t_414 = self.n_Conv_24(t_413)
264
        t_415 = F.relu(t_414)
265
        t_415_padded = F.pad(t_415, [1, 1, 1, 1], value=0)
266
        t_416 = self.n_Conv_25(t_415_padded)
267
        t_417 = F.relu(t_416)
268
        t_418 = self.n_Conv_26(t_417)
269
        t_419 = torch.add(t_418, t_413)
270
        t_420 = F.relu(t_419)
271
        t_421 = self.n_Conv_27(t_420)
272
        t_422 = F.relu(t_421)
273
        t_422_padded = F.pad(t_422, [1, 1, 1, 1], value=0)
274
        t_423 = self.n_Conv_28(t_422_padded)
275
        t_424 = F.relu(t_423)
276
        t_425 = self.n_Conv_29(t_424)
277
        t_426 = torch.add(t_425, t_420)
278
        t_427 = F.relu(t_426)
279
        t_428 = self.n_Conv_30(t_427)
280
        t_429 = F.relu(t_428)
281
        t_429_padded = F.pad(t_429, [1, 1, 1, 1], value=0)
282
        t_430 = self.n_Conv_31(t_429_padded)
283
        t_431 = F.relu(t_430)
284
        t_432 = self.n_Conv_32(t_431)
285
        t_433 = torch.add(t_432, t_427)
286
        t_434 = F.relu(t_433)
287
        t_435 = self.n_Conv_33(t_434)
288
        t_436 = F.relu(t_435)
289
        t_436_padded = F.pad(t_436, [1, 1, 1, 1], value=0)
290
        t_437 = self.n_Conv_34(t_436_padded)
291
        t_438 = F.relu(t_437)
292
        t_439 = self.n_Conv_35(t_438)
293
        t_440 = torch.add(t_439, t_434)
294
        t_441 = F.relu(t_440)
295
        t_442 = self.n_Conv_36(t_441)
296
        t_443 = self.n_Conv_37(t_441)
297
        t_444 = F.relu(t_443)
298
        t_444_padded = F.pad(t_444, [0, 1, 0, 1], value=0)
299
        t_445 = self.n_Conv_38(t_444_padded)
300
        t_446 = F.relu(t_445)
301
        t_447 = self.n_Conv_39(t_446)
302
        t_448 = torch.add(t_447, t_442)
303
        t_449 = F.relu(t_448)
304
        t_450 = self.n_Conv_40(t_449)
305
        t_451 = F.relu(t_450)
306
        t_451_padded = F.pad(t_451, [1, 1, 1, 1], value=0)
307
        t_452 = self.n_Conv_41(t_451_padded)
308
        t_453 = F.relu(t_452)
309
        t_454 = self.n_Conv_42(t_453)
310
        t_455 = torch.add(t_454, t_449)
311
        t_456 = F.relu(t_455)
312
        t_457 = self.n_Conv_43(t_456)
313
        t_458 = F.relu(t_457)
314
        t_458_padded = F.pad(t_458, [1, 1, 1, 1], value=0)
315
        t_459 = self.n_Conv_44(t_458_padded)
316
        t_460 = F.relu(t_459)
317
        t_461 = self.n_Conv_45(t_460)
318
        t_462 = torch.add(t_461, t_456)
319
        t_463 = F.relu(t_462)
320
        t_464 = self.n_Conv_46(t_463)
321
        t_465 = F.relu(t_464)
322
        t_465_padded = F.pad(t_465, [1, 1, 1, 1], value=0)
323
        t_466 = self.n_Conv_47(t_465_padded)
324
        t_467 = F.relu(t_466)
325
        t_468 = self.n_Conv_48(t_467)
326
        t_469 = torch.add(t_468, t_463)
327
        t_470 = F.relu(t_469)
328
        t_471 = self.n_Conv_49(t_470)
329
        t_472 = F.relu(t_471)
330
        t_472_padded = F.pad(t_472, [1, 1, 1, 1], value=0)
331
        t_473 = self.n_Conv_50(t_472_padded)
332
        t_474 = F.relu(t_473)
333
        t_475 = self.n_Conv_51(t_474)
334
        t_476 = torch.add(t_475, t_470)
335
        t_477 = F.relu(t_476)
336
        t_478 = self.n_Conv_52(t_477)
337
        t_479 = F.relu(t_478)
338
        t_479_padded = F.pad(t_479, [1, 1, 1, 1], value=0)
339
        t_480 = self.n_Conv_53(t_479_padded)
340
        t_481 = F.relu(t_480)
341
        t_482 = self.n_Conv_54(t_481)
342
        t_483 = torch.add(t_482, t_477)
343
        t_484 = F.relu(t_483)
344
        t_485 = self.n_Conv_55(t_484)
345
        t_486 = F.relu(t_485)
346
        t_486_padded = F.pad(t_486, [1, 1, 1, 1], value=0)
347
        t_487 = self.n_Conv_56(t_486_padded)
348
        t_488 = F.relu(t_487)
349
        t_489 = self.n_Conv_57(t_488)
350
        t_490 = torch.add(t_489, t_484)
351
        t_491 = F.relu(t_490)
352
        t_492 = self.n_Conv_58(t_491)
353
        t_493 = F.relu(t_492)
354
        t_493_padded = F.pad(t_493, [1, 1, 1, 1], value=0)
355
        t_494 = self.n_Conv_59(t_493_padded)
356
        t_495 = F.relu(t_494)
357
        t_496 = self.n_Conv_60(t_495)
358
        t_497 = torch.add(t_496, t_491)
359
        t_498 = F.relu(t_497)
360
        t_499 = self.n_Conv_61(t_498)
361
        t_500 = F.relu(t_499)
362
        t_500_padded = F.pad(t_500, [1, 1, 1, 1], value=0)
363
        t_501 = self.n_Conv_62(t_500_padded)
364
        t_502 = F.relu(t_501)
365
        t_503 = self.n_Conv_63(t_502)
366
        t_504 = torch.add(t_503, t_498)
367
        t_505 = F.relu(t_504)
368
        t_506 = self.n_Conv_64(t_505)
369
        t_507 = F.relu(t_506)
370
        t_507_padded = F.pad(t_507, [1, 1, 1, 1], value=0)
371
        t_508 = self.n_Conv_65(t_507_padded)
372
        t_509 = F.relu(t_508)
373
        t_510 = self.n_Conv_66(t_509)
374
        t_511 = torch.add(t_510, t_505)
375
        t_512 = F.relu(t_511)
376
        t_513 = self.n_Conv_67(t_512)
377
        t_514 = F.relu(t_513)
378
        t_514_padded = F.pad(t_514, [1, 1, 1, 1], value=0)
379
        t_515 = self.n_Conv_68(t_514_padded)
380
        t_516 = F.relu(t_515)
381
        t_517 = self.n_Conv_69(t_516)
382
        t_518 = torch.add(t_517, t_512)
383
        t_519 = F.relu(t_518)
384
        t_520 = self.n_Conv_70(t_519)
385
        t_521 = F.relu(t_520)
386
        t_521_padded = F.pad(t_521, [1, 1, 1, 1], value=0)
387
        t_522 = self.n_Conv_71(t_521_padded)
388
        t_523 = F.relu(t_522)
389
        t_524 = self.n_Conv_72(t_523)
390
        t_525 = torch.add(t_524, t_519)
391
        t_526 = F.relu(t_525)
392
        t_527 = self.n_Conv_73(t_526)
393
        t_528 = F.relu(t_527)
394
        t_528_padded = F.pad(t_528, [1, 1, 1, 1], value=0)
395
        t_529 = self.n_Conv_74(t_528_padded)
396
        t_530 = F.relu(t_529)
397
        t_531 = self.n_Conv_75(t_530)
398
        t_532 = torch.add(t_531, t_526)
399
        t_533 = F.relu(t_532)
400
        t_534 = self.n_Conv_76(t_533)
401
        t_535 = F.relu(t_534)
402
        t_535_padded = F.pad(t_535, [1, 1, 1, 1], value=0)
403
        t_536 = self.n_Conv_77(t_535_padded)
404
        t_537 = F.relu(t_536)
405
        t_538 = self.n_Conv_78(t_537)
406
        t_539 = torch.add(t_538, t_533)
407
        t_540 = F.relu(t_539)
408
        t_541 = self.n_Conv_79(t_540)
409
        t_542 = F.relu(t_541)
410
        t_542_padded = F.pad(t_542, [1, 1, 1, 1], value=0)
411
        t_543 = self.n_Conv_80(t_542_padded)
412
        t_544 = F.relu(t_543)
413
        t_545 = self.n_Conv_81(t_544)
414
        t_546 = torch.add(t_545, t_540)
415
        t_547 = F.relu(t_546)
416
        t_548 = self.n_Conv_82(t_547)
417
        t_549 = F.relu(t_548)
418
        t_549_padded = F.pad(t_549, [1, 1, 1, 1], value=0)
419
        t_550 = self.n_Conv_83(t_549_padded)
420
        t_551 = F.relu(t_550)
421
        t_552 = self.n_Conv_84(t_551)
422
        t_553 = torch.add(t_552, t_547)
423
        t_554 = F.relu(t_553)
424
        t_555 = self.n_Conv_85(t_554)
425
        t_556 = F.relu(t_555)
426
        t_556_padded = F.pad(t_556, [1, 1, 1, 1], value=0)
427
        t_557 = self.n_Conv_86(t_556_padded)
428
        t_558 = F.relu(t_557)
429
        t_559 = self.n_Conv_87(t_558)
430
        t_560 = torch.add(t_559, t_554)
431
        t_561 = F.relu(t_560)
432
        t_562 = self.n_Conv_88(t_561)
433
        t_563 = F.relu(t_562)
434
        t_563_padded = F.pad(t_563, [1, 1, 1, 1], value=0)
435
        t_564 = self.n_Conv_89(t_563_padded)
436
        t_565 = F.relu(t_564)
437
        t_566 = self.n_Conv_90(t_565)
438
        t_567 = torch.add(t_566, t_561)
439
        t_568 = F.relu(t_567)
440
        t_569 = self.n_Conv_91(t_568)
441
        t_570 = F.relu(t_569)
442
        t_570_padded = F.pad(t_570, [1, 1, 1, 1], value=0)
443
        t_571 = self.n_Conv_92(t_570_padded)
444
        t_572 = F.relu(t_571)
445
        t_573 = self.n_Conv_93(t_572)
446
        t_574 = torch.add(t_573, t_568)
447
        t_575 = F.relu(t_574)
448
        t_576 = self.n_Conv_94(t_575)
449
        t_577 = F.relu(t_576)
450
        t_577_padded = F.pad(t_577, [1, 1, 1, 1], value=0)
451
        t_578 = self.n_Conv_95(t_577_padded)
452
        t_579 = F.relu(t_578)
453
        t_580 = self.n_Conv_96(t_579)
454
        t_581 = torch.add(t_580, t_575)
455
        t_582 = F.relu(t_581)
456
        t_583 = self.n_Conv_97(t_582)
457
        t_584 = F.relu(t_583)
458
        t_584_padded = F.pad(t_584, [0, 1, 0, 1], value=0)
459
        t_585 = self.n_Conv_98(t_584_padded)
460
        t_586 = F.relu(t_585)
461
        t_587 = self.n_Conv_99(t_586)
462
        t_588 = self.n_Conv_100(t_582)
463
        t_589 = torch.add(t_587, t_588)
464
        t_590 = F.relu(t_589)
465
        t_591 = self.n_Conv_101(t_590)
466
        t_592 = F.relu(t_591)
467
        t_592_padded = F.pad(t_592, [1, 1, 1, 1], value=0)
468
        t_593 = self.n_Conv_102(t_592_padded)
469
        t_594 = F.relu(t_593)
470
        t_595 = self.n_Conv_103(t_594)
471
        t_596 = torch.add(t_595, t_590)
472
        t_597 = F.relu(t_596)
473
        t_598 = self.n_Conv_104(t_597)
474
        t_599 = F.relu(t_598)
475
        t_599_padded = F.pad(t_599, [1, 1, 1, 1], value=0)
476
        t_600 = self.n_Conv_105(t_599_padded)
477
        t_601 = F.relu(t_600)
478
        t_602 = self.n_Conv_106(t_601)
479
        t_603 = torch.add(t_602, t_597)
480
        t_604 = F.relu(t_603)
481
        t_605 = self.n_Conv_107(t_604)
482
        t_606 = F.relu(t_605)
483
        t_606_padded = F.pad(t_606, [1, 1, 1, 1], value=0)
484
        t_607 = self.n_Conv_108(t_606_padded)
485
        t_608 = F.relu(t_607)
486
        t_609 = self.n_Conv_109(t_608)
487
        t_610 = torch.add(t_609, t_604)
488
        t_611 = F.relu(t_610)
489
        t_612 = self.n_Conv_110(t_611)
490
        t_613 = F.relu(t_612)
491
        t_613_padded = F.pad(t_613, [1, 1, 1, 1], value=0)
492
        t_614 = self.n_Conv_111(t_613_padded)
493
        t_615 = F.relu(t_614)
494
        t_616 = self.n_Conv_112(t_615)
495
        t_617 = torch.add(t_616, t_611)
496
        t_618 = F.relu(t_617)
497
        t_619 = self.n_Conv_113(t_618)
498
        t_620 = F.relu(t_619)
499
        t_620_padded = F.pad(t_620, [1, 1, 1, 1], value=0)
500
        t_621 = self.n_Conv_114(t_620_padded)
501
        t_622 = F.relu(t_621)
502
        t_623 = self.n_Conv_115(t_622)
503
        t_624 = torch.add(t_623, t_618)
504
        t_625 = F.relu(t_624)
505
        t_626 = self.n_Conv_116(t_625)
506
        t_627 = F.relu(t_626)
507
        t_627_padded = F.pad(t_627, [1, 1, 1, 1], value=0)
508
        t_628 = self.n_Conv_117(t_627_padded)
509
        t_629 = F.relu(t_628)
510
        t_630 = self.n_Conv_118(t_629)
511
        t_631 = torch.add(t_630, t_625)
512
        t_632 = F.relu(t_631)
513
        t_633 = self.n_Conv_119(t_632)
514
        t_634 = F.relu(t_633)
515
        t_634_padded = F.pad(t_634, [1, 1, 1, 1], value=0)
516
        t_635 = self.n_Conv_120(t_634_padded)
517
        t_636 = F.relu(t_635)
518
        t_637 = self.n_Conv_121(t_636)
519
        t_638 = torch.add(t_637, t_632)
520
        t_639 = F.relu(t_638)
521
        t_640 = self.n_Conv_122(t_639)
522
        t_641 = F.relu(t_640)
523
        t_641_padded = F.pad(t_641, [1, 1, 1, 1], value=0)
524
        t_642 = self.n_Conv_123(t_641_padded)
525
        t_643 = F.relu(t_642)
526
        t_644 = self.n_Conv_124(t_643)
527
        t_645 = torch.add(t_644, t_639)
528
        t_646 = F.relu(t_645)
529
        t_647 = self.n_Conv_125(t_646)
530
        t_648 = F.relu(t_647)
531
        t_648_padded = F.pad(t_648, [1, 1, 1, 1], value=0)
532
        t_649 = self.n_Conv_126(t_648_padded)
533
        t_650 = F.relu(t_649)
534
        t_651 = self.n_Conv_127(t_650)
535
        t_652 = torch.add(t_651, t_646)
536
        t_653 = F.relu(t_652)
537
        t_654 = self.n_Conv_128(t_653)
538
        t_655 = F.relu(t_654)
539
        t_655_padded = F.pad(t_655, [1, 1, 1, 1], value=0)
540
        t_656 = self.n_Conv_129(t_655_padded)
541
        t_657 = F.relu(t_656)
542
        t_658 = self.n_Conv_130(t_657)
543
        t_659 = torch.add(t_658, t_653)
544
        t_660 = F.relu(t_659)
545
        t_661 = self.n_Conv_131(t_660)
546
        t_662 = F.relu(t_661)
547
        t_662_padded = F.pad(t_662, [1, 1, 1, 1], value=0)
548
        t_663 = self.n_Conv_132(t_662_padded)
549
        t_664 = F.relu(t_663)
550
        t_665 = self.n_Conv_133(t_664)
551
        t_666 = torch.add(t_665, t_660)
552
        t_667 = F.relu(t_666)
553
        t_668 = self.n_Conv_134(t_667)
554
        t_669 = F.relu(t_668)
555
        t_669_padded = F.pad(t_669, [1, 1, 1, 1], value=0)
556
        t_670 = self.n_Conv_135(t_669_padded)
557
        t_671 = F.relu(t_670)
558
        t_672 = self.n_Conv_136(t_671)
559
        t_673 = torch.add(t_672, t_667)
560
        t_674 = F.relu(t_673)
561
        t_675 = self.n_Conv_137(t_674)
562
        t_676 = F.relu(t_675)
563
        t_676_padded = F.pad(t_676, [1, 1, 1, 1], value=0)
564
        t_677 = self.n_Conv_138(t_676_padded)
565
        t_678 = F.relu(t_677)
566
        t_679 = self.n_Conv_139(t_678)
567
        t_680 = torch.add(t_679, t_674)
568
        t_681 = F.relu(t_680)
569
        t_682 = self.n_Conv_140(t_681)
570
        t_683 = F.relu(t_682)
571
        t_683_padded = F.pad(t_683, [1, 1, 1, 1], value=0)
572
        t_684 = self.n_Conv_141(t_683_padded)
573
        t_685 = F.relu(t_684)
574
        t_686 = self.n_Conv_142(t_685)
575
        t_687 = torch.add(t_686, t_681)
576
        t_688 = F.relu(t_687)
577
        t_689 = self.n_Conv_143(t_688)
578
        t_690 = F.relu(t_689)
579
        t_690_padded = F.pad(t_690, [1, 1, 1, 1], value=0)
580
        t_691 = self.n_Conv_144(t_690_padded)
581
        t_692 = F.relu(t_691)
582
        t_693 = self.n_Conv_145(t_692)
583
        t_694 = torch.add(t_693, t_688)
584
        t_695 = F.relu(t_694)
585
        t_696 = self.n_Conv_146(t_695)
586
        t_697 = F.relu(t_696)
587
        t_697_padded = F.pad(t_697, [1, 1, 1, 1], value=0)
588
        t_698 = self.n_Conv_147(t_697_padded)
589
        t_699 = F.relu(t_698)
590
        t_700 = self.n_Conv_148(t_699)
591
        t_701 = torch.add(t_700, t_695)
592
        t_702 = F.relu(t_701)
593
        t_703 = self.n_Conv_149(t_702)
594
        t_704 = F.relu(t_703)
595
        t_704_padded = F.pad(t_704, [1, 1, 1, 1], value=0)
596
        t_705 = self.n_Conv_150(t_704_padded)
597
        t_706 = F.relu(t_705)
598
        t_707 = self.n_Conv_151(t_706)
599
        t_708 = torch.add(t_707, t_702)
600
        t_709 = F.relu(t_708)
601
        t_710 = self.n_Conv_152(t_709)
602
        t_711 = F.relu(t_710)
603
        t_711_padded = F.pad(t_711, [1, 1, 1, 1], value=0)
604
        t_712 = self.n_Conv_153(t_711_padded)
605
        t_713 = F.relu(t_712)
606
        t_714 = self.n_Conv_154(t_713)
607
        t_715 = torch.add(t_714, t_709)
608
        t_716 = F.relu(t_715)
609
        t_717 = self.n_Conv_155(t_716)
610
        t_718 = F.relu(t_717)
611
        t_718_padded = F.pad(t_718, [1, 1, 1, 1], value=0)
612
        t_719 = self.n_Conv_156(t_718_padded)
613
        t_720 = F.relu(t_719)
614
        t_721 = self.n_Conv_157(t_720)
615
        t_722 = torch.add(t_721, t_716)
616
        t_723 = F.relu(t_722)
617
        t_724 = self.n_Conv_158(t_723)
618
        t_725 = self.n_Conv_159(t_723)
619
        t_726 = F.relu(t_725)
620
        t_726_padded = F.pad(t_726, [0, 1, 0, 1], value=0)
621
        t_727 = self.n_Conv_160(t_726_padded)
622
        t_728 = F.relu(t_727)
623
        t_729 = self.n_Conv_161(t_728)
624
        t_730 = torch.add(t_729, t_724)
625
        t_731 = F.relu(t_730)
626
        t_732 = self.n_Conv_162(t_731)
627
        t_733 = F.relu(t_732)
628
        t_733_padded = F.pad(t_733, [1, 1, 1, 1], value=0)
629
        t_734 = self.n_Conv_163(t_733_padded)
630
        t_735 = F.relu(t_734)
631
        t_736 = self.n_Conv_164(t_735)
632
        t_737 = torch.add(t_736, t_731)
633
        t_738 = F.relu(t_737)
634
        t_739 = self.n_Conv_165(t_738)
635
        t_740 = F.relu(t_739)
636
        t_740_padded = F.pad(t_740, [1, 1, 1, 1], value=0)
637
        t_741 = self.n_Conv_166(t_740_padded)
638
        t_742 = F.relu(t_741)
639
        t_743 = self.n_Conv_167(t_742)
640
        t_744 = torch.add(t_743, t_738)
641
        t_745 = F.relu(t_744)
642
        t_746 = self.n_Conv_168(t_745)
643
        t_747 = self.n_Conv_169(t_745)
644
        t_748 = F.relu(t_747)
645
        t_748_padded = F.pad(t_748, [0, 1, 0, 1], value=0)
646
        t_749 = self.n_Conv_170(t_748_padded)
647
        t_750 = F.relu(t_749)
648
        t_751 = self.n_Conv_171(t_750)
649
        t_752 = torch.add(t_751, t_746)
650
        t_753 = F.relu(t_752)
651
        t_754 = self.n_Conv_172(t_753)
652
        t_755 = F.relu(t_754)
653
        t_755_padded = F.pad(t_755, [1, 1, 1, 1], value=0)
654
        t_756 = self.n_Conv_173(t_755_padded)
655
        t_757 = F.relu(t_756)
656
        t_758 = self.n_Conv_174(t_757)
657
        t_759 = torch.add(t_758, t_753)
658
        t_760 = F.relu(t_759)
659
        t_761 = self.n_Conv_175(t_760)
660
        t_762 = F.relu(t_761)
661
        t_762_padded = F.pad(t_762, [1, 1, 1, 1], value=0)
662
        t_763 = self.n_Conv_176(t_762_padded)
663
        t_764 = F.relu(t_763)
664
        t_765 = self.n_Conv_177(t_764)
665
        t_766 = torch.add(t_765, t_760)
666
        t_767 = F.relu(t_766)
667
        t_768 = self.n_Conv_178(t_767)
668
        t_769 = F.avg_pool2d(t_768, kernel_size=t_768.shape[-2:])
669
        t_770 = torch.squeeze(t_769, 3)
670
        t_770 = torch.squeeze(t_770, 2)
671
        t_771 = torch.sigmoid(t_770)
672
        return t_771
673

674
    def load_state_dict(self, state_dict, **kwargs):
675
        self.tags = state_dict.get('tags', [])
676

677
        super(DeepDanbooruModel, self).load_state_dict({k: v for k, v in state_dict.items() if k != 'tags'})
678

679

680
Product

Resources

Company
