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| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from modules import devices | |
| # see https://github.com/AUTOMATIC1111/TorchDeepDanbooru for more | |
| 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'}) | |