| | import torch |
| | import torch.nn as nn |
| | import torch.nn.functional as F |
| |
|
| | from modules import devices |
| |
|
| | |
| |
|
| |
|
| | class DeepDanbooruModel(nn.Module): |
| | def __init__(self): |
| | super(DeepDanbooruModel, self).__init__() |
| |
|
| | self.tags = [] |
| |
|
| | self.n_Conv_0 = nn.Conv2d(kernel_size=(7, 7), in_channels=3, out_channels=64, stride=(2, 2)) |
| | self.n_MaxPool_0 = nn.MaxPool2d(kernel_size=(3, 3), stride=(2, 2)) |
| | self.n_Conv_1 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256) |
| | self.n_Conv_2 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=64) |
| | self.n_Conv_3 = nn.Conv2d(kernel_size=(3, 3), in_channels=64, out_channels=64) |
| | self.n_Conv_4 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256) |
| | self.n_Conv_5 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=64) |
| | self.n_Conv_6 = nn.Conv2d(kernel_size=(3, 3), in_channels=64, out_channels=64) |
| | self.n_Conv_7 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256) |
| | self.n_Conv_8 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=64) |
| | self.n_Conv_9 = nn.Conv2d(kernel_size=(3, 3), in_channels=64, out_channels=64) |
| | self.n_Conv_10 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256) |
| | self.n_Conv_11 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=512, stride=(2, 2)) |
| | self.n_Conv_12 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=128) |
| | self.n_Conv_13 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128, stride=(2, 2)) |
| | self.n_Conv_14 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512) |
| | self.n_Conv_15 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128) |
| | self.n_Conv_16 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128) |
| | self.n_Conv_17 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512) |
| | self.n_Conv_18 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128) |
| | self.n_Conv_19 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128) |
| | self.n_Conv_20 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512) |
| | self.n_Conv_21 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128) |
| | self.n_Conv_22 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128) |
| | self.n_Conv_23 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512) |
| | self.n_Conv_24 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128) |
| | self.n_Conv_25 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128) |
| | self.n_Conv_26 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512) |
| | self.n_Conv_27 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128) |
| | self.n_Conv_28 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128) |
| | self.n_Conv_29 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512) |
| | self.n_Conv_30 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128) |
| | self.n_Conv_31 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128) |
| | self.n_Conv_32 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512) |
| | self.n_Conv_33 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128) |
| | self.n_Conv_34 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128) |
| | self.n_Conv_35 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512) |
| | self.n_Conv_36 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=1024, stride=(2, 2)) |
| | self.n_Conv_37 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=256) |
| | self.n_Conv_38 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256, stride=(2, 2)) |
| | self.n_Conv_39 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) |
| | self.n_Conv_40 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) |
| | self.n_Conv_41 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) |
| | self.n_Conv_42 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) |
| | self.n_Conv_43 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) |
| | self.n_Conv_44 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) |
| | self.n_Conv_45 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) |
| | self.n_Conv_46 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) |
| | self.n_Conv_47 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) |
| | self.n_Conv_48 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) |
| | self.n_Conv_49 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) |
| | self.n_Conv_50 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) |
| | self.n_Conv_51 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) |
| | self.n_Conv_52 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) |
| | self.n_Conv_53 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) |
| | self.n_Conv_54 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) |
| | self.n_Conv_55 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) |
| | self.n_Conv_56 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) |
| | self.n_Conv_57 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) |
| | self.n_Conv_58 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) |
| | self.n_Conv_59 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) |
| | self.n_Conv_60 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) |
| | self.n_Conv_61 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) |
| | self.n_Conv_62 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) |
| | self.n_Conv_63 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) |
| | self.n_Conv_64 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) |
| | self.n_Conv_65 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) |
| | self.n_Conv_66 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) |
| | self.n_Conv_67 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) |
| | self.n_Conv_68 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) |
| | self.n_Conv_69 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) |
| | self.n_Conv_70 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) |
| | self.n_Conv_71 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) |
| | self.n_Conv_72 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) |
| | self.n_Conv_73 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) |
| | self.n_Conv_74 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) |
| | self.n_Conv_75 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) |
| | self.n_Conv_76 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) |
| | self.n_Conv_77 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) |
| | self.n_Conv_78 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) |
| | self.n_Conv_79 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) |
| | self.n_Conv_80 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) |
| | self.n_Conv_81 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) |
| | self.n_Conv_82 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) |
| | self.n_Conv_83 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) |
| | self.n_Conv_84 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) |
| | self.n_Conv_85 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) |
| | self.n_Conv_86 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) |
| | self.n_Conv_87 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) |
| | self.n_Conv_88 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) |
| | self.n_Conv_89 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) |
| | self.n_Conv_90 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) |
| | self.n_Conv_91 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) |
| | self.n_Conv_92 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) |
| | self.n_Conv_93 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) |
| | self.n_Conv_94 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) |
| | self.n_Conv_95 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) |
| | self.n_Conv_96 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) |
| | self.n_Conv_97 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) |
| | self.n_Conv_98 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256, stride=(2, 2)) |
| | self.n_Conv_99 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) |
| | self.n_Conv_100 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=1024, stride=(2, 2)) |
| | self.n_Conv_101 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) |
| | self.n_Conv_102 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) |
| | self.n_Conv_103 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) |
| | self.n_Conv_104 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) |
| | self.n_Conv_105 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) |
| | self.n_Conv_106 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) |
| | self.n_Conv_107 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) |
| | self.n_Conv_108 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) |
| | self.n_Conv_109 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) |
| | self.n_Conv_110 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) |
| | self.n_Conv_111 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) |
| | self.n_Conv_112 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) |
| | self.n_Conv_113 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) |
| | self.n_Conv_114 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) |
| | self.n_Conv_115 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) |
| | self.n_Conv_116 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) |
| | self.n_Conv_117 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) |
| | self.n_Conv_118 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) |
| | self.n_Conv_119 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) |
| | self.n_Conv_120 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) |
| | self.n_Conv_121 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) |
| | self.n_Conv_122 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) |
| | self.n_Conv_123 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) |
| | self.n_Conv_124 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) |
| | self.n_Conv_125 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) |
| | self.n_Conv_126 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) |
| | self.n_Conv_127 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) |
| | self.n_Conv_128 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) |
| | self.n_Conv_129 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) |
| | self.n_Conv_130 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) |
| | self.n_Conv_131 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) |
| | self.n_Conv_132 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) |
| | self.n_Conv_133 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) |
| | self.n_Conv_134 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) |
| | self.n_Conv_135 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) |
| | self.n_Conv_136 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) |
| | self.n_Conv_137 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) |
| | self.n_Conv_138 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) |
| | self.n_Conv_139 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) |
| | self.n_Conv_140 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) |
| | self.n_Conv_141 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) |
| | self.n_Conv_142 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) |
| | self.n_Conv_143 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) |
| | self.n_Conv_144 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) |
| | self.n_Conv_145 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) |
| | self.n_Conv_146 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) |
| | self.n_Conv_147 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) |
| | self.n_Conv_148 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) |
| | self.n_Conv_149 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) |
| | self.n_Conv_150 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) |
| | self.n_Conv_151 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) |
| | self.n_Conv_152 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) |
| | self.n_Conv_153 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) |
| | self.n_Conv_154 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) |
| | self.n_Conv_155 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) |
| | self.n_Conv_156 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) |
| | self.n_Conv_157 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) |
| | self.n_Conv_158 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=2048, stride=(2, 2)) |
| | self.n_Conv_159 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=512) |
| | self.n_Conv_160 = nn.Conv2d(kernel_size=(3, 3), in_channels=512, out_channels=512, stride=(2, 2)) |
| | self.n_Conv_161 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=2048) |
| | self.n_Conv_162 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=512) |
| | self.n_Conv_163 = nn.Conv2d(kernel_size=(3, 3), in_channels=512, out_channels=512) |
| | self.n_Conv_164 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=2048) |
| | self.n_Conv_165 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=512) |
| | self.n_Conv_166 = nn.Conv2d(kernel_size=(3, 3), in_channels=512, out_channels=512) |
| | self.n_Conv_167 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=2048) |
| | self.n_Conv_168 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=4096, stride=(2, 2)) |
| | self.n_Conv_169 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=1024) |
| | self.n_Conv_170 = nn.Conv2d(kernel_size=(3, 3), in_channels=1024, out_channels=1024, stride=(2, 2)) |
| | self.n_Conv_171 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=4096) |
| | self.n_Conv_172 = nn.Conv2d(kernel_size=(1, 1), in_channels=4096, out_channels=1024) |
| | self.n_Conv_173 = nn.Conv2d(kernel_size=(3, 3), in_channels=1024, out_channels=1024) |
| | self.n_Conv_174 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=4096) |
| | self.n_Conv_175 = nn.Conv2d(kernel_size=(1, 1), in_channels=4096, out_channels=1024) |
| | self.n_Conv_176 = nn.Conv2d(kernel_size=(3, 3), in_channels=1024, out_channels=1024) |
| | self.n_Conv_177 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=4096) |
| | self.n_Conv_178 = nn.Conv2d(kernel_size=(1, 1), in_channels=4096, out_channels=9176, bias=False) |
| |
|
| | def forward(self, *inputs): |
| | t_358, = inputs |
| | t_359 = t_358.permute(*[0, 3, 1, 2]) |
| | t_359_padded = F.pad(t_359, [2, 3, 2, 3], value=0) |
| | 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) |
| | t_361 = F.relu(t_360) |
| | t_361 = F.pad(t_361, [0, 1, 0, 1], value=float('-inf')) |
| | t_362 = self.n_MaxPool_0(t_361) |
| | t_363 = self.n_Conv_1(t_362) |
| | t_364 = self.n_Conv_2(t_362) |
| | t_365 = F.relu(t_364) |
| | t_365_padded = F.pad(t_365, [1, 1, 1, 1], value=0) |
| | t_366 = self.n_Conv_3(t_365_padded) |
| | t_367 = F.relu(t_366) |
| | t_368 = self.n_Conv_4(t_367) |
| | t_369 = torch.add(t_368, t_363) |
| | t_370 = F.relu(t_369) |
| | t_371 = self.n_Conv_5(t_370) |
| | t_372 = F.relu(t_371) |
| | t_372_padded = F.pad(t_372, [1, 1, 1, 1], value=0) |
| | t_373 = self.n_Conv_6(t_372_padded) |
| | t_374 = F.relu(t_373) |
| | t_375 = self.n_Conv_7(t_374) |
| | t_376 = torch.add(t_375, t_370) |
| | t_377 = F.relu(t_376) |
| | t_378 = self.n_Conv_8(t_377) |
| | t_379 = F.relu(t_378) |
| | t_379_padded = F.pad(t_379, [1, 1, 1, 1], value=0) |
| | t_380 = self.n_Conv_9(t_379_padded) |
| | t_381 = F.relu(t_380) |
| | t_382 = self.n_Conv_10(t_381) |
| | t_383 = torch.add(t_382, t_377) |
| | t_384 = F.relu(t_383) |
| | t_385 = self.n_Conv_11(t_384) |
| | t_386 = self.n_Conv_12(t_384) |
| | t_387 = F.relu(t_386) |
| | t_387_padded = F.pad(t_387, [0, 1, 0, 1], value=0) |
| | t_388 = self.n_Conv_13(t_387_padded) |
| | t_389 = F.relu(t_388) |
| | t_390 = self.n_Conv_14(t_389) |
| | t_391 = torch.add(t_390, t_385) |
| | t_392 = F.relu(t_391) |
| | t_393 = self.n_Conv_15(t_392) |
| | t_394 = F.relu(t_393) |
| | t_394_padded = F.pad(t_394, [1, 1, 1, 1], value=0) |
| | t_395 = self.n_Conv_16(t_394_padded) |
| | t_396 = F.relu(t_395) |
| | t_397 = self.n_Conv_17(t_396) |
| | t_398 = torch.add(t_397, t_392) |
| | t_399 = F.relu(t_398) |
| | t_400 = self.n_Conv_18(t_399) |
| | t_401 = F.relu(t_400) |
| | t_401_padded = F.pad(t_401, [1, 1, 1, 1], value=0) |
| | t_402 = self.n_Conv_19(t_401_padded) |
| | t_403 = F.relu(t_402) |
| | t_404 = self.n_Conv_20(t_403) |
| | t_405 = torch.add(t_404, t_399) |
| | t_406 = F.relu(t_405) |
| | t_407 = self.n_Conv_21(t_406) |
| | t_408 = F.relu(t_407) |
| | t_408_padded = F.pad(t_408, [1, 1, 1, 1], value=0) |
| | t_409 = self.n_Conv_22(t_408_padded) |
| | t_410 = F.relu(t_409) |
| | t_411 = self.n_Conv_23(t_410) |
| | t_412 = torch.add(t_411, t_406) |
| | t_413 = F.relu(t_412) |
| | t_414 = self.n_Conv_24(t_413) |
| | t_415 = F.relu(t_414) |
| | t_415_padded = F.pad(t_415, [1, 1, 1, 1], value=0) |
| | t_416 = self.n_Conv_25(t_415_padded) |
| | t_417 = F.relu(t_416) |
| | t_418 = self.n_Conv_26(t_417) |
| | t_419 = torch.add(t_418, t_413) |
| | t_420 = F.relu(t_419) |
| | t_421 = self.n_Conv_27(t_420) |
| | t_422 = F.relu(t_421) |
| | t_422_padded = F.pad(t_422, [1, 1, 1, 1], value=0) |
| | t_423 = self.n_Conv_28(t_422_padded) |
| | t_424 = F.relu(t_423) |
| | t_425 = self.n_Conv_29(t_424) |
| | t_426 = torch.add(t_425, t_420) |
| | t_427 = F.relu(t_426) |
| | t_428 = self.n_Conv_30(t_427) |
| | t_429 = F.relu(t_428) |
| | t_429_padded = F.pad(t_429, [1, 1, 1, 1], value=0) |
| | t_430 = self.n_Conv_31(t_429_padded) |
| | t_431 = F.relu(t_430) |
| | t_432 = self.n_Conv_32(t_431) |
| | t_433 = torch.add(t_432, t_427) |
| | t_434 = F.relu(t_433) |
| | t_435 = self.n_Conv_33(t_434) |
| | t_436 = F.relu(t_435) |
| | t_436_padded = F.pad(t_436, [1, 1, 1, 1], value=0) |
| | t_437 = self.n_Conv_34(t_436_padded) |
| | t_438 = F.relu(t_437) |
| | t_439 = self.n_Conv_35(t_438) |
| | t_440 = torch.add(t_439, t_434) |
| | t_441 = F.relu(t_440) |
| | t_442 = self.n_Conv_36(t_441) |
| | t_443 = self.n_Conv_37(t_441) |
| | t_444 = F.relu(t_443) |
| | t_444_padded = F.pad(t_444, [0, 1, 0, 1], value=0) |
| | t_445 = self.n_Conv_38(t_444_padded) |
| | t_446 = F.relu(t_445) |
| | t_447 = self.n_Conv_39(t_446) |
| | t_448 = torch.add(t_447, t_442) |
| | t_449 = F.relu(t_448) |
| | t_450 = self.n_Conv_40(t_449) |
| | t_451 = F.relu(t_450) |
| | t_451_padded = F.pad(t_451, [1, 1, 1, 1], value=0) |
| | t_452 = self.n_Conv_41(t_451_padded) |
| | t_453 = F.relu(t_452) |
| | t_454 = self.n_Conv_42(t_453) |
| | t_455 = torch.add(t_454, t_449) |
| | t_456 = F.relu(t_455) |
| | t_457 = self.n_Conv_43(t_456) |
| | t_458 = F.relu(t_457) |
| | t_458_padded = F.pad(t_458, [1, 1, 1, 1], value=0) |
| | t_459 = self.n_Conv_44(t_458_padded) |
| | t_460 = F.relu(t_459) |
| | t_461 = self.n_Conv_45(t_460) |
| | t_462 = torch.add(t_461, t_456) |
| | t_463 = F.relu(t_462) |
| | t_464 = self.n_Conv_46(t_463) |
| | t_465 = F.relu(t_464) |
| | t_465_padded = F.pad(t_465, [1, 1, 1, 1], value=0) |
| | t_466 = self.n_Conv_47(t_465_padded) |
| | t_467 = F.relu(t_466) |
| | t_468 = self.n_Conv_48(t_467) |
| | t_469 = torch.add(t_468, t_463) |
| | t_470 = F.relu(t_469) |
| | t_471 = self.n_Conv_49(t_470) |
| | t_472 = F.relu(t_471) |
| | t_472_padded = F.pad(t_472, [1, 1, 1, 1], value=0) |
| | t_473 = self.n_Conv_50(t_472_padded) |
| | t_474 = F.relu(t_473) |
| | t_475 = self.n_Conv_51(t_474) |
| | t_476 = torch.add(t_475, t_470) |
| | t_477 = F.relu(t_476) |
| | t_478 = self.n_Conv_52(t_477) |
| | t_479 = F.relu(t_478) |
| | t_479_padded = F.pad(t_479, [1, 1, 1, 1], value=0) |
| | t_480 = self.n_Conv_53(t_479_padded) |
| | t_481 = F.relu(t_480) |
| | t_482 = self.n_Conv_54(t_481) |
| | t_483 = torch.add(t_482, t_477) |
| | t_484 = F.relu(t_483) |
| | t_485 = self.n_Conv_55(t_484) |
| | t_486 = F.relu(t_485) |
| | t_486_padded = F.pad(t_486, [1, 1, 1, 1], value=0) |
| | t_487 = self.n_Conv_56(t_486_padded) |
| | t_488 = F.relu(t_487) |
| | t_489 = self.n_Conv_57(t_488) |
| | t_490 = torch.add(t_489, t_484) |
| | t_491 = F.relu(t_490) |
| | t_492 = self.n_Conv_58(t_491) |
| | t_493 = F.relu(t_492) |
| | t_493_padded = F.pad(t_493, [1, 1, 1, 1], value=0) |
| | t_494 = self.n_Conv_59(t_493_padded) |
| | t_495 = F.relu(t_494) |
| | t_496 = self.n_Conv_60(t_495) |
| | t_497 = torch.add(t_496, t_491) |
| | t_498 = F.relu(t_497) |
| | t_499 = self.n_Conv_61(t_498) |
| | t_500 = F.relu(t_499) |
| | t_500_padded = F.pad(t_500, [1, 1, 1, 1], value=0) |
| | t_501 = self.n_Conv_62(t_500_padded) |
| | t_502 = F.relu(t_501) |
| | t_503 = self.n_Conv_63(t_502) |
| | t_504 = torch.add(t_503, t_498) |
| | t_505 = F.relu(t_504) |
| | t_506 = self.n_Conv_64(t_505) |
| | t_507 = F.relu(t_506) |
| | t_507_padded = F.pad(t_507, [1, 1, 1, 1], value=0) |
| | t_508 = self.n_Conv_65(t_507_padded) |
| | t_509 = F.relu(t_508) |
| | t_510 = self.n_Conv_66(t_509) |
| | t_511 = torch.add(t_510, t_505) |
| | t_512 = F.relu(t_511) |
| | t_513 = self.n_Conv_67(t_512) |
| | t_514 = F.relu(t_513) |
| | t_514_padded = F.pad(t_514, [1, 1, 1, 1], value=0) |
| | t_515 = self.n_Conv_68(t_514_padded) |
| | t_516 = F.relu(t_515) |
| | t_517 = self.n_Conv_69(t_516) |
| | t_518 = torch.add(t_517, t_512) |
| | t_519 = F.relu(t_518) |
| | t_520 = self.n_Conv_70(t_519) |
| | t_521 = F.relu(t_520) |
| | t_521_padded = F.pad(t_521, [1, 1, 1, 1], value=0) |
| | t_522 = self.n_Conv_71(t_521_padded) |
| | t_523 = F.relu(t_522) |
| | t_524 = self.n_Conv_72(t_523) |
| | t_525 = torch.add(t_524, t_519) |
| | t_526 = F.relu(t_525) |
| | t_527 = self.n_Conv_73(t_526) |
| | t_528 = F.relu(t_527) |
| | t_528_padded = F.pad(t_528, [1, 1, 1, 1], value=0) |
| | t_529 = self.n_Conv_74(t_528_padded) |
| | t_530 = F.relu(t_529) |
| | t_531 = self.n_Conv_75(t_530) |
| | t_532 = torch.add(t_531, t_526) |
| | t_533 = F.relu(t_532) |
| | t_534 = self.n_Conv_76(t_533) |
| | t_535 = F.relu(t_534) |
| | t_535_padded = F.pad(t_535, [1, 1, 1, 1], value=0) |
| | t_536 = self.n_Conv_77(t_535_padded) |
| | t_537 = F.relu(t_536) |
| | t_538 = self.n_Conv_78(t_537) |
| | t_539 = torch.add(t_538, t_533) |
| | t_540 = F.relu(t_539) |
| | t_541 = self.n_Conv_79(t_540) |
| | t_542 = F.relu(t_541) |
| | t_542_padded = F.pad(t_542, [1, 1, 1, 1], value=0) |
| | t_543 = self.n_Conv_80(t_542_padded) |
| | t_544 = F.relu(t_543) |
| | t_545 = self.n_Conv_81(t_544) |
| | t_546 = torch.add(t_545, t_540) |
| | t_547 = F.relu(t_546) |
| | t_548 = self.n_Conv_82(t_547) |
| | t_549 = F.relu(t_548) |
| | t_549_padded = F.pad(t_549, [1, 1, 1, 1], value=0) |
| | t_550 = self.n_Conv_83(t_549_padded) |
| | t_551 = F.relu(t_550) |
| | t_552 = self.n_Conv_84(t_551) |
| | t_553 = torch.add(t_552, t_547) |
| | t_554 = F.relu(t_553) |
| | t_555 = self.n_Conv_85(t_554) |
| | t_556 = F.relu(t_555) |
| | t_556_padded = F.pad(t_556, [1, 1, 1, 1], value=0) |
| | t_557 = self.n_Conv_86(t_556_padded) |
| | t_558 = F.relu(t_557) |
| | t_559 = self.n_Conv_87(t_558) |
| | t_560 = torch.add(t_559, t_554) |
| | t_561 = F.relu(t_560) |
| | t_562 = self.n_Conv_88(t_561) |
| | t_563 = F.relu(t_562) |
| | t_563_padded = F.pad(t_563, [1, 1, 1, 1], value=0) |
| | t_564 = self.n_Conv_89(t_563_padded) |
| | t_565 = F.relu(t_564) |
| | t_566 = self.n_Conv_90(t_565) |
| | t_567 = torch.add(t_566, t_561) |
| | t_568 = F.relu(t_567) |
| | t_569 = self.n_Conv_91(t_568) |
| | t_570 = F.relu(t_569) |
| | t_570_padded = F.pad(t_570, [1, 1, 1, 1], value=0) |
| | t_571 = self.n_Conv_92(t_570_padded) |
| | t_572 = F.relu(t_571) |
| | t_573 = self.n_Conv_93(t_572) |
| | t_574 = torch.add(t_573, t_568) |
| | t_575 = F.relu(t_574) |
| | t_576 = self.n_Conv_94(t_575) |
| | t_577 = F.relu(t_576) |
| | t_577_padded = F.pad(t_577, [1, 1, 1, 1], value=0) |
| | t_578 = self.n_Conv_95(t_577_padded) |
| | t_579 = F.relu(t_578) |
| | t_580 = self.n_Conv_96(t_579) |
| | t_581 = torch.add(t_580, t_575) |
| | t_582 = F.relu(t_581) |
| | t_583 = self.n_Conv_97(t_582) |
| | t_584 = F.relu(t_583) |
| | t_584_padded = F.pad(t_584, [0, 1, 0, 1], value=0) |
| | t_585 = self.n_Conv_98(t_584_padded) |
| | t_586 = F.relu(t_585) |
| | t_587 = self.n_Conv_99(t_586) |
| | t_588 = self.n_Conv_100(t_582) |
| | t_589 = torch.add(t_587, t_588) |
| | t_590 = F.relu(t_589) |
| | t_591 = self.n_Conv_101(t_590) |
| | t_592 = F.relu(t_591) |
| | t_592_padded = F.pad(t_592, [1, 1, 1, 1], value=0) |
| | t_593 = self.n_Conv_102(t_592_padded) |
| | t_594 = F.relu(t_593) |
| | t_595 = self.n_Conv_103(t_594) |
| | t_596 = torch.add(t_595, t_590) |
| | t_597 = F.relu(t_596) |
| | t_598 = self.n_Conv_104(t_597) |
| | t_599 = F.relu(t_598) |
| | t_599_padded = F.pad(t_599, [1, 1, 1, 1], value=0) |
| | t_600 = self.n_Conv_105(t_599_padded) |
| | t_601 = F.relu(t_600) |
| | t_602 = self.n_Conv_106(t_601) |
| | t_603 = torch.add(t_602, t_597) |
| | t_604 = F.relu(t_603) |
| | t_605 = self.n_Conv_107(t_604) |
| | t_606 = F.relu(t_605) |
| | t_606_padded = F.pad(t_606, [1, 1, 1, 1], value=0) |
| | t_607 = self.n_Conv_108(t_606_padded) |
| | t_608 = F.relu(t_607) |
| | t_609 = self.n_Conv_109(t_608) |
| | t_610 = torch.add(t_609, t_604) |
| | t_611 = F.relu(t_610) |
| | t_612 = self.n_Conv_110(t_611) |
| | t_613 = F.relu(t_612) |
| | t_613_padded = F.pad(t_613, [1, 1, 1, 1], value=0) |
| | t_614 = self.n_Conv_111(t_613_padded) |
| | t_615 = F.relu(t_614) |
| | t_616 = self.n_Conv_112(t_615) |
| | t_617 = torch.add(t_616, t_611) |
| | t_618 = F.relu(t_617) |
| | t_619 = self.n_Conv_113(t_618) |
| | t_620 = F.relu(t_619) |
| | t_620_padded = F.pad(t_620, [1, 1, 1, 1], value=0) |
| | t_621 = self.n_Conv_114(t_620_padded) |
| | t_622 = F.relu(t_621) |
| | t_623 = self.n_Conv_115(t_622) |
| | t_624 = torch.add(t_623, t_618) |
| | t_625 = F.relu(t_624) |
| | t_626 = self.n_Conv_116(t_625) |
| | t_627 = F.relu(t_626) |
| | t_627_padded = F.pad(t_627, [1, 1, 1, 1], value=0) |
| | t_628 = self.n_Conv_117(t_627_padded) |
| | t_629 = F.relu(t_628) |
| | t_630 = self.n_Conv_118(t_629) |
| | t_631 = torch.add(t_630, t_625) |
| | t_632 = F.relu(t_631) |
| | t_633 = self.n_Conv_119(t_632) |
| | t_634 = F.relu(t_633) |
| | t_634_padded = F.pad(t_634, [1, 1, 1, 1], value=0) |
| | t_635 = self.n_Conv_120(t_634_padded) |
| | t_636 = F.relu(t_635) |
| | t_637 = self.n_Conv_121(t_636) |
| | t_638 = torch.add(t_637, t_632) |
| | t_639 = F.relu(t_638) |
| | t_640 = self.n_Conv_122(t_639) |
| | t_641 = F.relu(t_640) |
| | t_641_padded = F.pad(t_641, [1, 1, 1, 1], value=0) |
| | t_642 = self.n_Conv_123(t_641_padded) |
| | t_643 = F.relu(t_642) |
| | t_644 = self.n_Conv_124(t_643) |
| | t_645 = torch.add(t_644, t_639) |
| | t_646 = F.relu(t_645) |
| | t_647 = self.n_Conv_125(t_646) |
| | t_648 = F.relu(t_647) |
| | t_648_padded = F.pad(t_648, [1, 1, 1, 1], value=0) |
| | t_649 = self.n_Conv_126(t_648_padded) |
| | t_650 = F.relu(t_649) |
| | t_651 = self.n_Conv_127(t_650) |
| | t_652 = torch.add(t_651, t_646) |
| | t_653 = F.relu(t_652) |
| | t_654 = self.n_Conv_128(t_653) |
| | t_655 = F.relu(t_654) |
| | t_655_padded = F.pad(t_655, [1, 1, 1, 1], value=0) |
| | t_656 = self.n_Conv_129(t_655_padded) |
| | t_657 = F.relu(t_656) |
| | t_658 = self.n_Conv_130(t_657) |
| | t_659 = torch.add(t_658, t_653) |
| | t_660 = F.relu(t_659) |
| | t_661 = self.n_Conv_131(t_660) |
| | t_662 = F.relu(t_661) |
| | t_662_padded = F.pad(t_662, [1, 1, 1, 1], value=0) |
| | t_663 = self.n_Conv_132(t_662_padded) |
| | t_664 = F.relu(t_663) |
| | t_665 = self.n_Conv_133(t_664) |
| | t_666 = torch.add(t_665, t_660) |
| | t_667 = F.relu(t_666) |
| | t_668 = self.n_Conv_134(t_667) |
| | t_669 = F.relu(t_668) |
| | t_669_padded = F.pad(t_669, [1, 1, 1, 1], value=0) |
| | t_670 = self.n_Conv_135(t_669_padded) |
| | t_671 = F.relu(t_670) |
| | t_672 = self.n_Conv_136(t_671) |
| | t_673 = torch.add(t_672, t_667) |
| | t_674 = F.relu(t_673) |
| | t_675 = self.n_Conv_137(t_674) |
| | t_676 = F.relu(t_675) |
| | t_676_padded = F.pad(t_676, [1, 1, 1, 1], value=0) |
| | t_677 = self.n_Conv_138(t_676_padded) |
| | t_678 = F.relu(t_677) |
| | t_679 = self.n_Conv_139(t_678) |
| | t_680 = torch.add(t_679, t_674) |
| | t_681 = F.relu(t_680) |
| | t_682 = self.n_Conv_140(t_681) |
| | t_683 = F.relu(t_682) |
| | t_683_padded = F.pad(t_683, [1, 1, 1, 1], value=0) |
| | t_684 = self.n_Conv_141(t_683_padded) |
| | t_685 = F.relu(t_684) |
| | t_686 = self.n_Conv_142(t_685) |
| | t_687 = torch.add(t_686, t_681) |
| | t_688 = F.relu(t_687) |
| | t_689 = self.n_Conv_143(t_688) |
| | t_690 = F.relu(t_689) |
| | t_690_padded = F.pad(t_690, [1, 1, 1, 1], value=0) |
| | t_691 = self.n_Conv_144(t_690_padded) |
| | t_692 = F.relu(t_691) |
| | t_693 = self.n_Conv_145(t_692) |
| | t_694 = torch.add(t_693, t_688) |
| | t_695 = F.relu(t_694) |
| | t_696 = self.n_Conv_146(t_695) |
| | t_697 = F.relu(t_696) |
| | t_697_padded = F.pad(t_697, [1, 1, 1, 1], value=0) |
| | t_698 = self.n_Conv_147(t_697_padded) |
| | t_699 = F.relu(t_698) |
| | t_700 = self.n_Conv_148(t_699) |
| | t_701 = torch.add(t_700, t_695) |
| | t_702 = F.relu(t_701) |
| | t_703 = self.n_Conv_149(t_702) |
| | t_704 = F.relu(t_703) |
| | t_704_padded = F.pad(t_704, [1, 1, 1, 1], value=0) |
| | t_705 = self.n_Conv_150(t_704_padded) |
| | t_706 = F.relu(t_705) |
| | t_707 = self.n_Conv_151(t_706) |
| | t_708 = torch.add(t_707, t_702) |
| | t_709 = F.relu(t_708) |
| | t_710 = self.n_Conv_152(t_709) |
| | t_711 = F.relu(t_710) |
| | t_711_padded = F.pad(t_711, [1, 1, 1, 1], value=0) |
| | t_712 = self.n_Conv_153(t_711_padded) |
| | t_713 = F.relu(t_712) |
| | t_714 = self.n_Conv_154(t_713) |
| | t_715 = torch.add(t_714, t_709) |
| | t_716 = F.relu(t_715) |
| | t_717 = self.n_Conv_155(t_716) |
| | t_718 = F.relu(t_717) |
| | t_718_padded = F.pad(t_718, [1, 1, 1, 1], value=0) |
| | t_719 = self.n_Conv_156(t_718_padded) |
| | t_720 = F.relu(t_719) |
| | t_721 = self.n_Conv_157(t_720) |
| | t_722 = torch.add(t_721, t_716) |
| | t_723 = F.relu(t_722) |
| | t_724 = self.n_Conv_158(t_723) |
| | t_725 = self.n_Conv_159(t_723) |
| | t_726 = F.relu(t_725) |
| | t_726_padded = F.pad(t_726, [0, 1, 0, 1], value=0) |
| | t_727 = self.n_Conv_160(t_726_padded) |
| | t_728 = F.relu(t_727) |
| | t_729 = self.n_Conv_161(t_728) |
| | t_730 = torch.add(t_729, t_724) |
| | t_731 = F.relu(t_730) |
| | t_732 = self.n_Conv_162(t_731) |
| | t_733 = F.relu(t_732) |
| | t_733_padded = F.pad(t_733, [1, 1, 1, 1], value=0) |
| | t_734 = self.n_Conv_163(t_733_padded) |
| | t_735 = F.relu(t_734) |
| | t_736 = self.n_Conv_164(t_735) |
| | t_737 = torch.add(t_736, t_731) |
| | t_738 = F.relu(t_737) |
| | t_739 = self.n_Conv_165(t_738) |
| | t_740 = F.relu(t_739) |
| | t_740_padded = F.pad(t_740, [1, 1, 1, 1], value=0) |
| | t_741 = self.n_Conv_166(t_740_padded) |
| | t_742 = F.relu(t_741) |
| | t_743 = self.n_Conv_167(t_742) |
| | t_744 = torch.add(t_743, t_738) |
| | t_745 = F.relu(t_744) |
| | t_746 = self.n_Conv_168(t_745) |
| | t_747 = self.n_Conv_169(t_745) |
| | t_748 = F.relu(t_747) |
| | t_748_padded = F.pad(t_748, [0, 1, 0, 1], value=0) |
| | t_749 = self.n_Conv_170(t_748_padded) |
| | t_750 = F.relu(t_749) |
| | t_751 = self.n_Conv_171(t_750) |
| | t_752 = torch.add(t_751, t_746) |
| | t_753 = F.relu(t_752) |
| | t_754 = self.n_Conv_172(t_753) |
| | t_755 = F.relu(t_754) |
| | t_755_padded = F.pad(t_755, [1, 1, 1, 1], value=0) |
| | t_756 = self.n_Conv_173(t_755_padded) |
| | t_757 = F.relu(t_756) |
| | t_758 = self.n_Conv_174(t_757) |
| | t_759 = torch.add(t_758, t_753) |
| | t_760 = F.relu(t_759) |
| | t_761 = self.n_Conv_175(t_760) |
| | t_762 = F.relu(t_761) |
| | t_762_padded = F.pad(t_762, [1, 1, 1, 1], value=0) |
| | t_763 = self.n_Conv_176(t_762_padded) |
| | t_764 = F.relu(t_763) |
| | t_765 = self.n_Conv_177(t_764) |
| | t_766 = torch.add(t_765, t_760) |
| | t_767 = F.relu(t_766) |
| | t_768 = self.n_Conv_178(t_767) |
| | t_769 = F.avg_pool2d(t_768, kernel_size=t_768.shape[-2:]) |
| | t_770 = torch.squeeze(t_769, 3) |
| | t_770 = torch.squeeze(t_770, 2) |
| | t_771 = torch.sigmoid(t_770) |
| | return t_771 |
| |
|
| | def load_state_dict(self, state_dict, **kwargs): |
| | self.tags = state_dict.get('tags', []) |
| |
|
| | super(DeepDanbooruModel, self).load_state_dict({k: v for k, v in state_dict.items() if k != 'tags'}) |
| |
|
| |
|
