mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2025-10-31 01:54:44 +00:00
84d9ce30cb
This also handles type casting so that ROCm and MPS torch devices work correctly without --no-half. One cast is required for deepbooru in deepbooru_model.py, some explicit casting is required for img2img and inpainting. depth_model can't be converted to float16 or it won't work correctly on some systems (it's known to have issues on MPS) so in sd_models.py model.depth_model is removed for model.half().
679 lines
36 KiB
Python
679 lines
36 KiB
Python
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'})
|
|
|