PNNX is an open standard for PyTorch model interoperability #3262

nihui · 2021-10-01T08:39:57Z

欢迎留言交流 w
welcome to leave your idea here

fixed compile error

iyangzy · 2021-11-22T08:35:26Z

前段时间在忙秋招的事，今天试了一下编译通过啦，比心比心
就是install的位置比较尴尬
-- Installing: /usr/local/bin/pnnx
况且我还有些强迫症。。。
希望nihui大大最终merge到master的可以解决一下

nihui · 2021-11-22T08:36:07Z

前段时间在忙秋招的事，今天试了一下编译通过啦，比心比心就是install的位置比较尴尬 -- Installing: /usr/local/bin/pnnx 况且我还有些强迫症。。。希望nihui大大最终merge到master的可以解决一下

cmake -DCMAKE_INSTALL_PREFIX=<your target dir> ..

iyangzy · 2021-11-22T08:39:54Z

收到

zhu-zhaofei · 2021-12-19T15:09:14Z

大佬好，我尝试转换一个GAN模 Operand* old_output_operand = Operand* new_output_operand = 转换后的pnnx.param文件为：
**
7767517
676 682
pnnx.Input pnnx_input_0 pnnx.Expression pnnx_expr_2438 pnnx.Expression pnnx_expr_2436 nn.Conv2d conv_body_first aten::leaky_relu pnnx_6 pnnx.Expression pnnx_expr_2431 nn.Conv2d conv_body_down.0.conv1 aten::leaky_relu_ pnnx_12 F.upsample F.upsample_38 nn.Conv2d conv_body_down.0.conv2 pnnx.Expression pnnx_expr_2427 aten::leaky_relu_ pnnx_17 F.upsample F.upsample_39 nn.Conv2d conv_body_down.0.skip pnnx.Expression pnnx_expr_2423 pnnx.Expression pnnx_expr_2421 nn.Conv2d conv_body_down.1.conv1 aten::leaky_relu_ pnnx_30 F.upsample F.upsample_40 nn.Conv2d conv_body_down.1.conv2 pnnx.Expression pnnx_expr_2417 aten::leaky_relu_ pnnx_35 F.upsample F.upsample_41 nn.Conv2d conv_body_down.1.skip pnnx.Expression pnnx_expr_2413 pnnx.Expression pnnx_expr_2411 nn.Conv2d conv_body_down.2.conv1 aten::leaky_relu_ pnnx_48 F.upsample F.upsample_42 nn.Conv2d conv_body_down.2.conv2 pnnx.Expression pnnx_expr_2407 aten::leaky_relu_ pnnx_53 F.upsample F.upsample_43 nn.Conv2d conv_body_down.2.skip pnnx.Expression pnnx_expr_2403 pnnx.Expression pnnx_expr_2401 nn.Conv2d conv_body_down.3.conv1 aten::leaky_relu_ pnnx_66 F.upsample F.upsample_44 nn.Conv2d conv_body_down.3.conv2 pnnx.Expression pnnx_expr_2397 aten::leaky_relu_ pnnx_71 F.upsample F.upsample_45 nn.Conv2d conv_body_down.3.skip pnnx.Expression pnnx_expr_2393 pnnx.Expression pnnx_expr_2391 nn.Conv2d conv_body_down.4.conv1 aten::leaky_relu_ pnnx_84 F.upsample F.upsample_46 nn.Conv2d conv_body_down.4.conv2 pnnx.Expression pnnx_expr_2387 aten::leaky_relu_ pnnx_89 F.upsample F.upsample_47 nn.Conv2d conv_body_down.4.skip pnnx.Expression pnnx_expr_2383 pnnx.Expression pnnx_expr_2381 nn.Conv2d conv_body_down.5.conv1 aten::leaky_relu_ pnnx_102 F.upsample F.upsample_48 nn.Conv2d conv_body_down.5.conv2 pnnx.Expression pnnx_expr_2377 aten::leaky_relu_ pnnx_107 F.upsample F.upsample_49 nn.Conv2d conv_body_down.5.skip pnnx.Expression pnnx_expr_2373 pnnx.Expression pnnx_expr_2371 nn.Conv2d conv_body_down.6.conv1 aten::leaky_relu_ pnnx_120 F.upsample F.upsample_50 nn.Conv2d conv_body_down.6.conv2 pnnx.Expression pnnx_expr_2367 aten::leaky_relu_ pnnx_125 F.upsample F.upsample_51 nn.Conv2d conv_body_down.6.skip pnnx.Expression pnnx_expr_2363 nn.Conv2d final_conv pnnx.Expression pnnx_expr_2362 aten::leaky_relu_ pnnx_134 Tensor.view Tensor.view_156 nn.Linear final_linear pnnx.Expression pnnx_expr_2351 pnnx.Expression pnnx_expr_2349 nn.Conv2d conv_body_up.0.conv1 aten::leaky_relu_ pnnx_153 F.upsample F.upsample_52 nn.Conv2d conv_body_up.0.conv2 pnnx.Expression pnnx_expr_2345 aten::leaky_relu_ pnnx_158 F.upsample F.upsample_53 nn.Conv2d conv_body_up.0.skip pnnx.Expression pnnx_expr_2341 nn.Conv2d condition_scale.0.0 nn.LeakyReLU condition_scale.0.1 nn.Conv2d condition_scale.0.2 aten::clone pnnx_166 nn.Conv2d condition_shift.0.0 nn.LeakyReLU condition_shift.0.1 nn.Conv2d condition_shift.0.2 pnnx.Expression pnnx_expr_2340 aten::clone pnnx_168 nn.Conv2d toRGB.0 pnnx.Expression pnnx_expr_2338 pnnx.Expression pnnx_expr_2336 nn.Conv2d conv_body_up.1.conv1 aten::leaky_relu_ pnnx_176 F.upsample F.upsample_54 nn.Conv2d conv_body_up.1.conv2 pnnx.Expression pnnx_expr_2332 aten::leaky_relu_ pnnx_181 F.upsample F.upsample_55 nn.Conv2d conv_body_up.1.skip pnnx.Expression pnnx_expr_2328 nn.Conv2d condition_scale.1.0 nn.LeakyReLU condition_scale.1.1 nn.Conv2d condition_scale.1.2 pnnx.Expression pnnx_expr_2327 aten::clone pnnx_190 nn.Conv2d condition_shift.1.0 nn.LeakyReLU condition_shift.1.1 nn.Conv2d condition_shift.1.2 pnnx.Expression pnnx_expr_2326 aten::clone pnnx_192 nn.Conv2d toRGB.1 pnnx.Expression pnnx_expr_2324 pnnx.Expression pnnx_expr_2322 nn.Conv2d conv_body_up.2.conv1 aten::leaky_relu_ pnnx_200 F.upsample F.upsample_56 nn.Conv2d conv_body_up.2.conv2 pnnx.Expression pnnx_expr_2318 aten::leaky_relu_ pnnx_205 F.upsample F.upsample_57 nn.Conv2d conv_body_up.2.skip pnnx.Expression pnnx_expr_2314 nn.Conv2d condition_scale.2.0 nn.LeakyReLU condition_scale.2.1 nn.Conv2d condition_scale.2.2 pnnx.Expression pnnx_expr_2313 aten::clone pnnx_214 nn.Conv2d condition_shift.2.0 nn.LeakyReLU condition_shift.2.1 nn.Conv2d condition_shift.2.2 pnnx.Expression pnnx_expr_2312 aten::clone pnnx_216 nn.Conv2d toRGB.2 pnnx.Expression pnnx_expr_2310 pnnx.Expression pnnx_expr_2308 nn.Conv2d conv_body_up.3.conv1 aten::leaky_relu_ pnnx_224 F.upsample F.upsample_58 nn.Conv2d conv_body_up.3.conv2 pnnx.Expression pnnx_expr_2304 aten::leaky_relu_ pnnx_229 F.upsample F.upsample_59 nn.Conv2d conv_body_up.3.skip pnnx.Expression pnnx_expr_2300 nn.Conv2d condition_scale.3.0 nn.LeakyReLU condition_scale.3.1 nn.Conv2d condition_scale.3.2 pnnx.Expression pnnx_expr_2299 aten::clone pnnx_238 nn.Conv2d condition_shift.3.0 nn.LeakyReLU condition_shift.3.1 nn.Conv2d condition_shift.3.2 pnnx.Expression pnnx_expr_2298 aten::clone pnnx_240 nn.Conv2d toRGB.3 pnnx.Expression pnnx_expr_2296 pnnx.Expression pnnx_expr_2294 nn.Conv2d conv_body_up.4.conv1 aten::leaky_relu_ pnnx_248 F.upsample F.upsample_60 nn.Conv2d conv_body_up.4.conv2 pnnx.Expression pnnx_expr_2290 aten::leaky_relu_ pnnx_253 F.upsample F.upsample_61 nn.Conv2d conv_body_up.4.skip pnnx.Expression pnnx_expr_2286 nn.Conv2d condition_scale.4.0 nn.LeakyReLU condition_scale.4.1 nn.Conv2d condition_scale.4.2 pnnx.Expression pnnx_expr_2285 aten::clone pnnx_262 nn.Conv2d condition_shift.4.0 nn.LeakyReLU condition_shift.4.1 nn.Conv2d condition_shift.4.2 pnnx.Expression pnnx_expr_2284 aten::clone pnnx_264 nn.Conv2d toRGB.4 pnnx.Expression pnnx_expr_2282 pnnx.Expression pnnx_expr_2280 nn.Conv2d conv_body_up.5.conv1 aten::leaky_relu_ pnnx_272 F.upsample F.upsample_62 nn.Conv2d conv_body_up.5.conv2 pnnx.Expression pnnx_expr_2276 aten::leaky_relu_ pnnx_277 F.upsample F.upsample_63 nn.Conv2d conv_body_up.5.skip pnnx.Expression pnnx_expr_2272 nn.Conv2d condition_scale.5.0 nn.LeakyReLU condition_scale.5.1 nn.Conv2d condition_scale.5.2 pnnx.Expression pnnx_expr_2271 aten::clone pnnx_286 nn.Conv2d condition_shift.5.0 nn.LeakyReLU condition_shift.5.1 nn.Conv2d condition_shift.5.2 pnnx.Expression pnnx_expr_2270 aten::clone pnnx_288 nn.Conv2d toRGB.5 pnnx.Expression pnnx_expr_2268 pnnx.Expression pnnx_expr_2266 nn.Conv2d conv_body_up.6.conv1 aten::leaky_relu_ pnnx_296 F.upsample F.upsample_64 nn.Conv2d conv_body_up.6.conv2 pnnx.Expression pnnx_expr_2262 aten::leaky_relu_ pnnx_301 F.upsample F.upsample_65 nn.Conv2d conv_body_up.6.skip pnnx.Expression pnnx_expr_2258 nn.Conv2d condition_scale.6.0 nn.LeakyReLU condition_scale.6.1 nn.Conv2d condition_scale.6.2 pnnx.Expression pnnx_expr_2257 aten::clone pnnx_310 nn.Conv2d condition_shift.6.0 nn.LeakyReLU condition_shift.6.1 nn.Conv2d condition_shift.6.2 pnnx.Expression pnnx_expr_2256 aten::clone pnnx_312 nn.Conv2d toRGB.6 Tensor.view Tensor.view_157 pnnx.Attribute stylegan_decod pnnx.Expression pnnx_expr_2223 pnnx.Expression pnnx_expr_2222 pnnx.Expression pnnx_expr_2221 pnnx.Attribute stylegan_decod pnnx.Attribute stylegan_decod pnnx.Attribute stylegan_decod Tensor.repeat Tensor.repeat_80 Tensor.select Tensor.select_81 nn.Linear stylegan_decod Tensor.view Tensor.view_158 pnnx.Expression pnnx_expr_2195 pnnx.Expression pnnx_expr_2194 torch.sum torch.sum_111 pnnx.Expression pnnx_expr_2189 Tensor.view Tensor.view_159 pnnx.Expression pnnx_expr_2184 Tensor.view Tensor.view_160 F.conv2d F.conv2d_15 pnnx.Expression pnnx_expr_2138 Tensor.new_empty Tensor.new_empty_0 aten::normal_ pnnx_466 pnnx.Expression pnnx_expr_2123 nn.LeakyReLU stylegan_decod pnnx.Attribute stylegan_decoder.to_rgb1 pnnx.Attribute stylegan_decod Tensor.select Tensor.select_82 nn.Linear stylegan_decod Tensor.view Tensor.view_163 pnnx.Expression pnnx_expr_2098 Tensor.view Tensor.view_164 F.conv2d F.conv2d_16 pnnx.Expression pnnx_expr_2055 pnnx.Expression pnnx_expr_2048 pnnx.Expression pnnx_expr_2047 pnnx.Expression pnnx_expr_2046 pnnx.Attribute stylegan_decod pnnx.Attribute stylegan_decod pnnx.Attribute stylegan_decod Tensor.select Tensor.select_83 nn.Linear stylegan_decod Tensor.view Tensor.view_167 pnnx.Expression pnnx_expr_2019 pnnx.Expression pnnx_expr_2018 torch.sum torch.sum_112 pnnx.Expression pnnx_expr_2013 Tensor.view Tensor.view_168 pnnx.Expression pnnx_expr_2008 F.upsample F.upsample_66 Tensor.view Tensor.view_169 F.conv2d F.conv2d_17 pnnx.Expression pnnx_expr_1960 Tensor.new_empty Tensor.new_empty_1 aten::normal_ pnnx_687 pnnx.Expression pnnx_expr_1945 nn.LeakyReLU stylegan_decod torch.split torch.split_126 pnnx.Expression pnnx_expr_1941 pnnx.Expression pnnx_expr_1934 pnnx.Expression pnnx_expr_1933 pnnx.Expression pnnx_expr_1932 pnnx.Attribute stylegan_decod pnnx.Attribute stylegan_decod pnnx.Attribute stylegan_decod torch.cat torch.cat_104 Tensor.select Tensor.select_84 nn.Linear stylegan_decod Tensor.view Tensor.view_172 pnnx.Expression pnnx_expr_1906 pnnx.Expression pnnx_expr_1905 torch.sum torch.sum_113 pnnx.Expression pnnx_expr_1900 Tensor.view Tensor.view_173 pnnx.Expression pnnx_expr_1895 Tensor.view Tensor.view_174 F.conv2d F.conv2d_18 pnnx.Expression pnnx_expr_1849 Tensor.new_empty Tensor.new_empty_2 aten::normal_ pnnx_827 pnnx.Expression pnnx_expr_1834 nn.LeakyReLU stylegan_decod pnnx.Attribute stylegan_decoder.to_rgbs.0 pnnx.Attribute stylegan_decod Tensor.select Tensor.select_85 nn.Linear stylegan_decod Tensor.view Tensor.view_177 pnnx.Expression pnnx_expr_1809 Tensor.view Tensor.view_178 F.conv2d F.conv2d_19 F.upsample F.upsample_67 pnnx.Expression pnnx_expr_1762 pnnx.Expression pnnx_expr_1755 pnnx.Expression pnnx_expr_1754 pnnx.Expression pnnx_expr_1753 pnnx.Attribute stylegan_decod pnnx.Attribute stylegan_decod pnnx.Attribute stylegan_decod Tensor.select Tensor.select_86 nn.Linear stylegan_decod Tensor.view Tensor.view_181 pnnx.Expression pnnx_expr_1726 pnnx.Expression pnnx_expr_1725 torch.sum torch.sum_114 pnnx.Expression pnnx_expr_1720 Tensor.view Tensor.view_182 pnnx.Expression pnnx_expr_1715 F.upsample F.upsample_68 Tensor.view Tensor.view_183 F.conv2d F.conv2d_20 pnnx.Expression pnnx_expr_1667 Tensor.new_empty Tensor.new_empty_3 aten::normal_ pnnx_1055 pnnx.Expression pnnx_expr_1652 nn.LeakyReLU stylegan_decod torch.split torch.split_127 pnnx.Expression pnnx_expr_1647 pnnx.Expression pnnx_expr_1640 pnnx.Expression pnnx_expr_1639 pnnx.Expression pnnx_expr_1638 pnnx.Attribute stylegan_decod pnnx.Attribute stylegan_decod pnnx.Attribute stylegan_decod torch.cat torch.cat_105 Tensor.select Tensor.select_87 nn.Linear stylegan_decod Tensor.view Tensor.view_186 pnnx.Expression pnnx_expr_1612 pnnx.Expression pnnx_expr_1611 torch.sum torch.sum_115 pnnx.Expression pnnx_expr_1606 Tensor.view Tensor.view_187 pnnx.Expression pnnx_expr_1601 Tensor.view Tensor.view_188 F.conv2d F.conv2d_21 pnnx.Expression pnnx_expr_1555 Tensor.new_empty Tensor.new_empty_4 aten::normal_ pnnx_1196 pnnx.Expression pnnx_expr_1540 nn.LeakyReLU stylegan_decod pnnx.Attribute stylegan_decoder.to_rgbs.1 pnnx.Attribute stylegan_decod Tensor.select Tensor.select_88 nn.Linear stylegan_decod Tensor.view Tensor.view_191 pnnx.Expression pnnx_expr_1515 Tensor.view Tensor.view_192 F.conv2d F.conv2d_22 F.upsample F.upsample_69 pnnx.Expression pnnx_expr_1468 pnnx.Expression pnnx_expr_1461 pnnx.Expression pnnx_expr_1460 pnnx.Expression pnnx_expr_1459 pnnx.Attribute stylegan_decod pnnx.Attribute stylegan_decod pnnx.Attribute stylegan_decod Tensor.select Tensor.select_89 nn.Linear stylegan_decod Tensor.view Tensor.view_195 pnnx.Expression pnnx_expr_1432 pnnx.Expression pnnx_expr_1431 torch.sum torch.sum_116 pnnx.Expression pnnx_expr_1426 Tensor.view Tensor.view_196 pnnx.Expression pnnx_expr_1421 F.upsample F.upsample_70 Tensor.view Tensor.view_197 F.conv2d F.conv2d_23 pnnx.Expression pnnx_expr_1373 Tensor.new_empty Tensor.new_empty_5 aten::normal_ pnnx_1424 pnnx.Expression pnnx_expr_1358 nn.LeakyReLU stylegan_decod torch.split torch.split_128 pnnx.Expression pnnx_expr_1353 pnnx.Expression pnnx_expr_1346 pnnx.Expression pnnx_expr_1345 pnnx.Expression pnnx_expr_1344 pnnx.Attribute stylegan_decod pnnx.Attribute stylegan_decod pnnx.Attribute stylegan_decod torch.cat torch.cat_106 Tensor.select Tensor.select_90 nn.Linear stylegan_decod Tensor.view Tensor.view_200 pnnx.Expression pnnx_expr_1318 pnnx.Expression pnnx_expr_1317 torch.sum torch.sum_117 pnnx.Expression pnnx_expr_1312 Tensor.view Tensor.view_201 pnnx.Expression pnnx_expr_1307 Tensor.view Tensor.view_202 F.conv2d F.conv2d_24 pnnx.Expression pnnx_expr_1261 Tensor.new_empty Tensor.new_empty_6 aten::normal_ pnnx_1565 pnnx.Expression pnnx_expr_1246 nn.LeakyReLU stylegan_decod pnnx.Attribute stylegan_decoder.to_rgbs.2 pnnx.Attribute stylegan_decod Tensor.select Tensor.select_91 nn.Linear stylegan_decod Tensor.view Tensor.view_205 pnnx.Expression pnnx_expr_1221 Tensor.view Tensor.view_206 F.conv2d F.conv2d_25 F.upsample F.upsample_71 pnnx.Expression pnnx_expr_1174 pnnx.Expression pnnx_expr_1167 pnnx.Expression pnnx_expr_1166 pnnx.Expression pnnx_expr_1165 pnnx.Attribute stylegan_decod pnnx.Attribute stylegan_decod pnnx.Attribute stylegan_decod Tensor.select Tensor.select_92 nn.Linear stylegan_decod Tensor.view Tensor.view_209 pnnx.Expression pnnx_expr_1138 pnnx.Expression pnnx_expr_1137 torch.sum torch.sum_118 pnnx.Expression pnnx_expr_1132 Tensor.view Tensor.view_210 pnnx.Expression pnnx_expr_1127 F.upsample F.upsample_72 Tensor.view Tensor.view_211 F.conv2d F.conv2d_26 pnnx.Expression pnnx_expr_1079 Tensor.new_empty Tensor.new_empty_7 aten::normal_ pnnx_1793 pnnx.Expression pnnx_expr_1064 nn.LeakyReLU stylegan_decod torch.split torch.split_129 pnnx.Expression pnnx_expr_1059 pnnx.Expression pnnx_expr_1052 pnnx.Expression pnnx_expr_1051 pnnx.Expression pnnx_expr_1050 pnnx.Attribute stylegan_decod pnnx.Attribute stylegan_decod pnnx.Attribute stylegan_decod torch.cat torch.cat_107 Tensor.select Tensor.select_93 nn.Linear stylegan_decod Tensor.view Tensor.view_214 pnnx.Expression pnnx_expr_1024 pnnx.Expression pnnx_expr_1023 torch.sum torch.sum_119 pnnx.Expression pnnx_expr_1018 Tensor.view Tensor.view_215 pnnx.Expression pnnx_expr_1013 Tensor.view Tensor.view_216 F.conv2d F.conv2d_27 pnnx.Expression pnnx_expr_967 Tensor.new_empty Tensor.new_empty_8 aten::normal_ pnnx_1934 pnnx.Expression pnnx_expr_952 nn.LeakyReLU stylegan_decod pnnx.Attribute stylegan_decoder.to_rgbs.3 pnnx.Attribute stylegan_decod Tensor.select Tensor.select_94 nn.Linear stylegan_decod Tensor.view Tensor.view_219 pnnx.Expression pnnx_expr_927 Tensor.view Tensor.view_220 F.conv2d F.conv2d_28 F.upsample F.upsample_73 pnnx.Expression pnnx_expr_880 pnnx.Expression pnnx_expr_873 pnnx.Expression pnnx_expr_872 pnnx.Expression pnnx_expr_871 pnnx.Attribute stylegan_decod pnnx.Attribute stylegan_decod pnnx.Attribute stylegan_decod Tensor.select Tensor.select_95 nn.Linear stylegan_decod Tensor.view Tensor.view_223 pnnx.Expression pnnx_expr_844 pnnx.Expression pnnx_expr_843 torch.sum torch.sum_120 pnnx.Expression pnnx_expr_838 Tensor.view Tensor.view_224 pnnx.Expression pnnx_expr_833 F.upsample F.upsample_74 Tensor.view Tensor.view_225 F.conv2d F.conv2d_29 pnnx.Expression pnnx_expr_785 Tensor.new_empty Tensor.new_empty_9 aten::normal_ pnnx_2162 pnnx.Expression pnnx_expr_770 nn.LeakyReLU stylegan_decod torch.split torch.split_130 pnnx.Expression pnnx_expr_766 pnnx.Expression pnnx_expr_759 pnnx.Expression pnnx_expr_758 pnnx.Expression pnnx_expr_757 pnnx.Attribute stylegan_decod pnnx.Attribute stylegan_decod pnnx.Attribute stylegan_decod torch.cat torch.cat_108 Tensor.select Tensor.select_96 nn.Linear stylegan_decod Tensor.view Tensor.view_228 pnnx.Expression pnnx_expr_731 pnnx.Expression pnnx_expr_730 torch.sum torch.sum_121 pnnx.Expression pnnx_expr_725 Tensor.view Tensor.view_229 pnnx.Expression pnnx_expr_720 Tensor.view Tensor.view_230 F.conv2d F.conv2d_30 pnnx.Expression pnnx_expr_674 Tensor.new_empty Tensor.new_empty_10 aten::normal_ pnnx_2302 pnnx.Expression pnnx_expr_659 nn.LeakyReLU stylegan_decod pnnx.Attribute stylegan_decoder.to_rgbs.4 pnnx.Attribute stylegan_decod Tensor.select Tensor.select_97 nn.Linear stylegan_decod Tensor.view Tensor.view_233 pnnx.Expression pnnx_expr_634 Tensor.view Tensor.view_234 F.conv2d F.conv2d_31 F.upsample F.upsample_75 pnnx.Expression pnnx_expr_587 pnnx.Expression pnnx_expr_580 pnnx.Expression pnnx_expr_579 pnnx.Expression pnnx_expr_578 pnnx.Attribute stylegan_decod pnnx.Attribute stylegan_decod pnnx.Attribute stylegan_decod Tensor.select Tensor.select_98 nn.Linear stylegan_decod Tensor.view Tensor.view_237 pnnx.Expression pnnx_expr_551 pnnx.Expression pnnx_expr_550 torch.sum torch.sum_122 pnnx.Expression pnnx_expr_545 Tensor.view Tensor.view_238 pnnx.Expression pnnx_expr_540 F.upsample F.upsample_76 Tensor.view Tensor.view_239 F.conv2d F.conv2d_32 pnnx.Expression pnnx_expr_492 Tensor.new_empty Tensor.new_empty_11 aten::normal_ pnnx_2530 pnnx.Expression pnnx_expr_477 nn.LeakyReLU stylegan_decod torch.split torch.split_131 pnnx.Expression pnnx_expr_473 pnnx.Expression pnnx_expr_466 pnnx.Expression pnnx_expr_465 pnnx.Expression pnnx_expr_464 pnnx.Attribute stylegan_decod pnnx.Attribute stylegan_decod pnnx.Attribute stylegan_decod torch.cat torch.cat_109 Tensor.select Tensor.select_99 nn.Linear stylegan_decod Tensor.view Tensor.view_242 pnnx.Expression pnnx_expr_438 pnnx.Expression pnnx_expr_437 torch.sum torch.sum_123 pnnx.Expression pnnx_expr_432 Tensor.view Tensor.view_243 pnnx.Expression pnnx_expr_427 Tensor.view Tensor.view_244 F.conv2d F.conv2d_33 pnnx.Expression pnnx_expr_381 Tensor.new_empty Tensor.new_empty_12 aten::normal_ pnnx_2670 pnnx.Expression pnnx_expr_366 nn.LeakyReLU stylegan_decod pnnx.Attribute stylegan_decoder.to_rgbs.5 pnnx.Attribute stylegan_decod Tensor.select Tensor.select_100 nn.Linear stylegan_decod Tensor.view Tensor.view_247 pnnx.Expression pnnx_expr_341 Tensor.view Tensor.view_248 F.conv2d F.conv2d_34 F.upsample F.upsample_77 pnnx.Expression pnnx_expr_294 pnnx.Expression pnnx_expr_287 pnnx.Expression pnnx_expr_286 pnnx.Expression pnnx_expr_285 pnnx.Attribute stylegan_decod pnnx.Attribute stylegan_decod pnnx.Attribute stylegan_decod Tensor.select Tensor.select_101 nn.Linear stylegan_decod Tensor.view Tensor.view_251 pnnx.Expression pnnx_expr_258 pnnx.Expression pnnx_expr_257 torch.sum torch.sum_124 pnnx.Expression pnnx_expr_252 Tensor.view Tensor.view_252 pnnx.Expression pnnx_expr_247 F.upsample F.upsample_78 Tensor.view Tensor.view_253 F.conv2d F.conv2d_35 pnnx.Expression pnnx_expr_199 Tensor.new_empty Tensor.new_empty_13 aten::normal_ pnnx_2898 pnnx.Expression pnnx_expr_184 nn.LeakyReLU stylegan_decod torch.split torch.split_132 pnnx.Expression pnnx_expr_180 pnnx.Expression pnnx_expr_173 pnnx.Expression pnnx_expr_172 pnnx.Expression pnnx_expr_171 pnnx.Attribute stylegan_decod pnnx.Attribute stylegan_decod pnnx.Attribute stylegan_decod torch.cat torch.cat_110 Tensor.select Tensor.select_102 nn.Linear stylegan_decod Tensor.view Tensor.view_256 pnnx.Expression pnnx_expr_145 pnnx.Expression pnnx_expr_144 torch.sum torch.sum_125 pnnx.Expression pnnx_expr_139 Tensor.view Tensor.view_257 pnnx.Expression pnnx_expr_134 Tensor.view Tensor.view_258 F.conv2d F.conv2d_36 pnnx.Expression pnnx_expr_88 Tensor.new_empty Tensor.new_empty_14 aten::normal_ pnnx_3038 pnnx.Expression pnnx_expr_73 nn.LeakyReLU stylegan_decod pnnx.Attribute stylegan_decoder.to_rgbs.6 pnnx.Attribute stylegan_decod Tensor.select Tensor.select_103 nn.Linear stylegan_decod Tensor.view Tensor.view_261 pnnx.Expression pnnx_expr_48 Tensor.view Tensor.view_262 F.conv2d F.conv2d_37 F.upsample F.upsample_79 pnnx.Expression pnnx_expr_1 pnnx.Expression pnnx_expr_0 prim::TupleConstruct pnnx_3135 pnnx.Output pnnx_output_0 ** 型，能生成pnnx.bin,pnnx.param,pnnx.py,debug.bin,debug.param,debug2.param.debug2.bin,但并不能生成ncnn.param和ncnn.bin。调试后发现main函数中，pnnx::pass_ncnn(pnnx_graph)失败，进入pass_ncnn.cpp中模块ncnn::expand_expression(g)不能通过，进入ncnn::expand_expression(g)函数发现
op->outputs[0];
graph.get_operand(op->name + "" + [outname);中指针变量new_output_operand会出现nullptr，导致崩溃，麻烦大佬帮忙看下。多谢。
0 1 0 #0=(1,3,512,512)f32
0 1 1 expr=None
0 1 2 expr=2.000000e-01
1 1 0 3 bias=True dilation=(1,1) groups=1 in_channels=3 kernel_size=(1,1) out_channels=32 padding=(0,0) padding_mode=zeros stride=(1,1) @bias=(32)f32 @weight=(32,3,1,1)f32 #0=(1,3,512,512)f32 #3=(1,32,512,512)f32
2 1 3 2 4 #3=(1,32,512,512)f32 #4=(1,32,512,512)f32
0 1 5 expr=2.000000e-01
1 1 4 6 bias=True dilation=(1,1) groups=1 in_channels=32 kernel_size=(3,3) out_channels=32 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(32)f32 @weight=(32,32,3,3)f32 #4=(1,32,512,512)f32 #6=(1,32,512,512)f32
2 1 6 5 7 #6=(1,32,512,512)f32 #7=(1,32,512,512)f32
1 1 7 8 align_corners=False mode=bilinear scale_factor=(5.000000e-01,5.000000e-01) $input=7 #7=(1,32,512,512)f32 #8=(1,32,256,256)f32
1 1 8 9 bias=True dilation=(1,1) groups=1 in_channels=32 kernel_size=(3,3) out_channels=64 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(64)f32 @weight=(64,32,3,3)f32 #8=(1,32,256,256)f32 #9=(1,64,256,256)f32
0 1 10 expr=2.000000e-01
2 1 9 10 11 #9=(1,64,256,256)f32 #11=(1,64,256,256)f32
1 1 4 12 align_corners=False mode=bilinear scale_factor=(5.000000e-01,5.000000e-01) $input=4 #4=(1,32,512,512)f32 #12=(1,32,256,256)f32
1 1 12 13 bias=False dilation=(1,1) groups=1 in_channels=32 kernel_size=(1,1) out_channels=64 padding=(0,0) padding_mode=zeros stride=(1,1) @weight=(64,32,1,1)f32 #12=(1,32,256,256)f32 #13=(1,64,256,256)f32
2 1 11 13 14 expr=add(@0,@1) #11=(1,64,256,256)f32 #13=(1,64,256,256)f32 #14=(1,64,256,256)f32
0 1 15 expr=2.000000e-01
1 1 14 16 bias=True dilation=(1,1) groups=1 in_channels=64 kernel_size=(3,3) out_channels=64 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(64)f32 @weight=(64,64,3,3)f32 #14=(1,64,256,256)f32 #16=(1,64,256,256)f32
2 1 16 15 17 #16=(1,64,256,256)f32 #17=(1,64,256,256)f32
1 1 17 18 align_corners=False mode=bilinear scale_factor=(5.000000e-01,5.000000e-01) $input=17 #17=(1,64,256,256)f32 #18=(1,64,128,128)f32
1 1 18 19 bias=True dilation=(1,1) groups=1 in_channels=64 kernel_size=(3,3) out_channels=128 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(128)f32 @weight=(128,64,3,3)f32 #18=(1,64,128,128)f32 #19=(1,128,128,128)f32
0 1 20 expr=2.000000e-01
2 1 19 20 21 #19=(1,128,128,128)f32 #21=(1,128,128,128)f32
1 1 14 22 align_corners=False mode=bilinear scale_factor=(5.000000e-01,5.000000e-01) $input=14 #14=(1,64,256,256)f32 #22=(1,64,128,128)f32
1 1 22 23 bias=False dilation=(1,1) groups=1 in_channels=64 kernel_size=(1,1) out_channels=128 padding=(0,0) padding_mode=zeros stride=(1,1) @weight=(128,64,1,1)f32 #22=(1,64,128,128)f32 #23=(1,128,128,128)f32
2 1 21 23 24 expr=add(@0,@1) #21=(1,128,128,128)f32 #23=(1,128,128,128)f32 #24=(1,128,128,128)f32
0 1 25 expr=2.000000e-01
1 1 24 26 bias=True dilation=(1,1) groups=1 in_channels=128 kernel_size=(3,3) out_channels=128 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(128)f32 @weight=(128,128,3,3)f32 #24=(1,128,128,128)f32 #26=(1,128,128,128)f32
2 1 26 25 27 #26=(1,128,128,128)f32 #27=(1,128,128,128)f32
1 1 27 28 align_corners=False mode=bilinear scale_factor=(5.000000e-01,5.000000e-01) $input=27 #27=(1,128,128,128)f32 #28=(1,128,64,64)f32
1 1 28 29 bias=True dilation=(1,1) groups=1 in_channels=128 kernel_size=(3,3) out_channels=256 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,128,3,3)f32 #28=(1,128,64,64)f32 #29=(1,256,64,64)f32
0 1 30 expr=2.000000e-01
2 1 29 30 31 #29=(1,256,64,64)f32 #31=(1,256,64,64)f32
1 1 24 32 align_corners=False mode=bilinear scale_factor=(5.000000e-01,5.000000e-01) $input=24 #24=(1,128,128,128)f32 #32=(1,128,64,64)f32
1 1 32 33 bias=False dilation=(1,1) groups=1 in_channels=128 kernel_size=(1,1) out_channels=256 padding=(0,0) padding_mode=zeros stride=(1,1) @weight=(256,128,1,1)f32 #32=(1,128,64,64)f32 #33=(1,256,64,64)f32
2 1 31 33 34 expr=add(@0,@1) #31=(1,256,64,64)f32 #33=(1,256,64,64)f32 #34=(1,256,64,64)f32
0 1 35 expr=2.000000e-01
1 1 34 36 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(3,3) out_channels=256 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,256,3,3)f32 #34=(1,256,64,64)f32 #36=(1,256,64,64)f32
2 1 36 35 37 #36=(1,256,64,64)f32 #37=(1,256,64,64)f32
1 1 37 38 align_corners=False mode=bilinear scale_factor=(5.000000e-01,5.000000e-01) $input=37 #37=(1,256,64,64)f32 #38=(1,256,32,32)f32
1 1 38 39 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(3,3) out_channels=256 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,256,3,3)f32 #38=(1,256,32,32)f32 #39=(1,256,32,32)f32
0 1 40 expr=2.000000e-01
2 1 39 40 41 #39=(1,256,32,32)f32 #41=(1,256,32,32)f32
1 1 34 42 align_corners=False mode=bilinear scale_factor=(5.000000e-01,5.000000e-01) $input=34 #34=(1,256,64,64)f32 #42=(1,256,32,32)f32
1 1 42 43 bias=False dilation=(1,1) groups=1 in_channels=256 kernel_size=(1,1) out_channels=256 padding=(0,0) padding_mode=zeros stride=(1,1) @weight=(256,256,1,1)f32 #42=(1,256,32,32)f32 #43=(1,256,32,32)f32
2 1 41 43 44 expr=add(@0,@1) #41=(1,256,32,32)f32 #43=(1,256,32,32)f32 #44=(1,256,32,32)f32
0 1 45 expr=2.000000e-01
1 1 44 46 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(3,3) out_channels=256 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,256,3,3)f32 #44=(1,256,32,32)f32 #46=(1,256,32,32)f32
2 1 46 45 47 #46=(1,256,32,32)f32 #47=(1,256,32,32)f32
1 1 47 48 align_corners=False mode=bilinear scale_factor=(5.000000e-01,5.000000e-01) $input=47 #47=(1,256,32,32)f32 #48=(1,256,16,16)f32
1 1 48 49 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(3,3) out_channels=256 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,256,3,3)f32 #48=(1,256,16,16)f32 #49=(1,256,16,16)f32
0 1 50 expr=2.000000e-01
2 1 49 50 51 #49=(1,256,16,16)f32 #51=(1,256,16,16)f32
1 1 44 52 align_corners=False mode=bilinear scale_factor=(5.000000e-01,5.000000e-01) $input=44 #44=(1,256,32,32)f32 #52=(1,256,16,16)f32
1 1 52 53 bias=False dilation=(1,1) groups=1 in_channels=256 kernel_size=(1,1) out_channels=256 padding=(0,0) padding_mode=zeros stride=(1,1) @weight=(256,256,1,1)f32 #52=(1,256,16,16)f32 #53=(1,256,16,16)f32
2 1 51 53 54 expr=add(@0,@1) #51=(1,256,16,16)f32 #53=(1,256,16,16)f32 #54=(1,256,16,16)f32
0 1 55 expr=2.000000e-01
1 1 54 56 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(3,3) out_channels=256 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,256,3,3)f32 #54=(1,256,16,16)f32 #56=(1,256,16,16)f32
2 1 56 55 57 #56=(1,256,16,16)f32 #57=(1,256,16,16)f32
1 1 57 58 align_corners=False mode=bilinear scale_factor=(5.000000e-01,5.000000e-01) $input=57 #57=(1,256,16,16)f32 #58=(1,256,8,8)f32
1 1 58 59 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(3,3) out_channels=256 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,256,3,3)f32 #58=(1,256,8,8)f32 #59=(1,256,8,8)f32
0 1 60 expr=2.000000e-01
2 1 59 60 61 #59=(1,256,8,8)f32 #61=(1,256,8,8)f32
1 1 54 62 align_corners=False mode=bilinear scale_factor=(5.000000e-01,5.000000e-01) $input=54 #54=(1,256,16,16)f32 #62=(1,256,8,8)f32
1 1 62 63 bias=False dilation=(1,1) groups=1 in_channels=256 kernel_size=(1,1) out_channels=256 padding=(0,0) padding_mode=zeros stride=(1,1) @weight=(256,256,1,1)f32 #62=(1,256,8,8)f32 #63=(1,256,8,8)f32
2 1 61 63 64 expr=add(@0,@1) #61=(1,256,8,8)f32 #63=(1,256,8,8)f32 #64=(1,256,8,8)f32
0 1 65 expr=2.000000e-01
1 1 64 66 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(3,3) out_channels=256 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,256,3,3)f32 #64=(1,256,8,8)f32 #66=(1,256,8,8)f32
2 1 66 65 67 #66=(1,256,8,8)f32 #67=(1,256,8,8)f32
1 1 67 68 align_corners=False mode=bilinear scale_factor=(5.000000e-01,5.000000e-01) $input=67 #67=(1,256,8,8)f32 #68=(1,256,4,4)f32
1 1 68 69 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(3,3) out_channels=256 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,256,3,3)f32 #68=(1,256,4,4)f32 #69=(1,256,4,4)f32
0 1 70 expr=2.000000e-01
2 1 69 70 71 #69=(1,256,4,4)f32 #71=(1,256,4,4)f32
1 1 64 72 align_corners=False mode=bilinear scale_factor=(5.000000e-01,5.000000e-01) $input=64 #64=(1,256,8,8)f32 #72=(1,256,4,4)f32
1 1 72 73 bias=False dilation=(1,1) groups=1 in_channels=256 kernel_size=(1,1) out_channels=256 padding=(0,0) padding_mode=zeros stride=(1,1) @weight=(256,256,1,1)f32 #72=(1,256,4,4)f32 #73=(1,256,4,4)f32
2 1 71 73 74 expr=add(@0,@1) #71=(1,256,4,4)f32 #73=(1,256,4,4)f32 #74=(1,256,4,4)f32
1 1 74 75 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(3,3) out_channels=256 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,256,3,3)f32 #74=(1,256,4,4)f32 #75=(1,256,4,4)f32
0 1 76 expr=2.000000e-01
2 1 75 76 77 #75=(1,256,4,4)f32 #77=(1,256,4,4)f32
1 1 77 78 shape=(1,-1) $input=77 #77=(1,256,4,4)f32 #78=(1,4096)f32
1 1 78 79 bias=True in_features=4096 out_features=8192 @bias=(8192)f32 @weight=(8192,4096)f32 #78=(1,4096)f32 #79=(1,8192)f32
2 1 77 74 80 expr=add(@0,@1) #77=(1,256,4,4)f32 #74=(1,256,4,4)f32 #80=(1,256,4,4)f32
0 1 81 expr=2.000000e-01
1 1 80 82 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(3,3) out_channels=256 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,256,3,3)f32 #80=(1,256,4,4)f32 #82=(1,256,4,4)f32
2 1 82 81 83 #82=(1,256,4,4)f32 #83=(1,256,4,4)f32
1 1 83 84 align_corners=False mode=bilinear scale_factor=(2.000000e+00,2.000000e+00) $input=83 #83=(1,256,4,4)f32 #84=(1,256,8,8)f32
1 1 84 85 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(3,3) out_channels=256 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,256,3,3)f32 #84=(1,256,8,8)f32 #85=(1,256,8,8)f32
0 1 86 expr=2.000000e-01
2 1 85 86 87 #85=(1,256,8,8)f32 #87=(1,256,8,8)f32
1 1 80 88 align_corners=False mode=bilinear scale_factor=(2.000000e+00,2.000000e+00) $input=80 #80=(1,256,4,4)f32 #88=(1,256,8,8)f32
1 1 88 89 bias=False dilation=(1,1) groups=1 in_channels=256 kernel_size=(1,1) out_channels=256 padding=(0,0) padding_mode=zeros stride=(1,1) @weight=(256,256,1,1)f32 #88=(1,256,8,8)f32 #89=(1,256,8,8)f32
2 1 87 89 90 expr=add(@0,@1) #87=(1,256,8,8)f32 #89=(1,256,8,8)f32 #90=(1,256,8,8)f32
1 1 90 91 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(3,3) out_channels=256 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,256,3,3)f32 #90=(1,256,8,8)f32 #91=(1,256,8,8)f32
1 1 91 92 negative_slope=2.000000e-01 #91=(1,256,8,8)f32 #92=(1,256,8,8)f32
1 1 92 93 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(3,3) out_channels=256 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,256,3,3)f32 #92=(1,256,8,8)f32 #93=(1,256,8,8)f32
2 1 93 1 94 #93=(1,256,8,8)f32 #94=(1,256,8,8)f32
1 1 90 95 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(3,3) out_channels=256 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,256,3,3)f32 #90=(1,256,8,8)f32 #95=(1,256,8,8)f32
1 1 95 96 negative_slope=2.000000e-01 #95=(1,256,8,8)f32 #96=(1,256,8,8)f32
1 1 96 97 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(3,3) out_channels=256 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,256,3,3)f32 #96=(1,256,8,8)f32 #97=(1,256,8,8)f32
0 1 98 expr=None
2 1 97 98 99 #97=(1,256,8,8)f32 #99=(1,256,8,8)f32
1 1 90 100 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(1,1) out_channels=3 padding=(0,0) padding_mode=zeros stride=(1,1) @bias=(3)f32 @weight=(3,256,1,1)f32 #90=(1,256,8,8)f32 #100=(1,3,8,8)f32
2 1 90 64 101 expr=add(@0,@1) #90=(1,256,8,8)f32 #64=(1,256,8,8)f32 #101=(1,256,8,8)f32
0 1 102 expr=2.000000e-01
1 1 101 103 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(3,3) out_channels=256 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,256,3,3)f32 #101=(1,256,8,8)f32 #103=(1,256,8,8)f32
2 1 103 102 104 #103=(1,256,8,8)f32 #104=(1,256,8,8)f32
1 1 104 105 align_corners=False mode=bilinear scale_factor=(2.000000e+00,2.000000e+00) $input=104 #104=(1,256,8,8)f32 #105=(1,256,16,16)f32
1 1 105 106 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(3,3) out_channels=256 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,256,3,3)f32 #105=(1,256,16,16)f32 #106=(1,256,16,16)f32
0 1 107 expr=2.000000e-01
2 1 106 107 108 #106=(1,256,16,16)f32 #108=(1,256,16,16)f32
1 1 101 109 align_corners=False mode=bilinear scale_factor=(2.000000e+00,2.000000e+00) $input=101 #101=(1,256,8,8)f32 #109=(1,256,16,16)f32
1 1 109 110 bias=False dilation=(1,1) groups=1 in_channels=256 kernel_size=(1,1) out_channels=256 padding=(0,0) padding_mode=zeros stride=(1,1) @weight=(256,256,1,1)f32 #109=(1,256,16,16)f32 #110=(1,256,16,16)f32
2 1 108 110 111 expr=add(@0,@1) #108=(1,256,16,16)f32 #110=(1,256,16,16)f32 #111=(1,256,16,16)f32
1 1 111 112 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(3,3) out_channels=256 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,256,3,3)f32 #111=(1,256,16,16)f32 #112=(1,256,16,16)f32
1 1 112 113 negative_slope=2.000000e-01 #112=(1,256,16,16)f32 #113=(1,256,16,16)f32
1 1 113 114 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(3,3) out_channels=256 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,256,3,3)f32 #113=(1,256,16,16)f32 #114=(1,256,16,16)f32
0 1 115 expr=None
2 1 114 115 116 #114=(1,256,16,16)f32 #116=(1,256,16,16)f32
1 1 111 117 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(3,3) out_channels=256 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,256,3,3)f32 #111=(1,256,16,16)f32 #117=(1,256,16,16)f32
1 1 117 118 negative_slope=2.000000e-01 #117=(1,256,16,16)f32 #118=(1,256,16,16)f32
1 1 118 119 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(3,3) out_channels=256 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,256,3,3)f32 #118=(1,256,16,16)f32 #119=(1,256,16,16)f32
0 1 120 expr=None
2 1 119 120 121 #119=(1,256,16,16)f32 #121=(1,256,16,16)f32
1 1 111 122 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(1,1) out_channels=3 padding=(0,0) padding_mode=zeros stride=(1,1) @bias=(3)f32 @weight=(3,256,1,1)f32 #111=(1,256,16,16)f32 #122=(1,3,16,16)f32
2 1 111 54 123 expr=add(@0,@1) #111=(1,256,16,16)f32 #54=(1,256,16,16)f32 #123=(1,256,16,16)f32
0 1 124 expr=2.000000e-01
1 1 123 125 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(3,3) out_channels=256 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,256,3,3)f32 #123=(1,256,16,16)f32 #125=(1,256,16,16)f32
2 1 125 124 126 #125=(1,256,16,16)f32 #126=(1,256,16,16)f32
1 1 126 127 align_corners=False mode=bilinear scale_factor=(2.000000e+00,2.000000e+00) $input=126 #126=(1,256,16,16)f32 #127=(1,256,32,32)f32
1 1 127 128 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(3,3) out_channels=256 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,256,3,3)f32 #127=(1,256,32,32)f32 #128=(1,256,32,32)f32
0 1 129 expr=2.000000e-01
2 1 128 129 130 #128=(1,256,32,32)f32 #130=(1,256,32,32)f32
1 1 123 131 align_corners=False mode=bilinear scale_factor=(2.000000e+00,2.000000e+00) $input=123 #123=(1,256,16,16)f32 #131=(1,256,32,32)f32
1 1 131 132 bias=False dilation=(1,1) groups=1 in_channels=256 kernel_size=(1,1) out_channels=256 padding=(0,0) padding_mode=zeros stride=(1,1) @weight=(256,256,1,1)f32 #131=(1,256,32,32)f32 #132=(1,256,32,32)f32
2 1 130 132 133 expr=add(@0,@1) #130=(1,256,32,32)f32 #132=(1,256,32,32)f32 #133=(1,256,32,32)f32
1 1 133 134 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(3,3) out_channels=256 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,256,3,3)f32 #133=(1,256,32,32)f32 #134=(1,256,32,32)f32
1 1 134 135 negative_slope=2.000000e-01 #134=(1,256,32,32)f32 #135=(1,256,32,32)f32
1 1 135 136 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(3,3) out_channels=256 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,256,3,3)f32 #135=(1,256,32,32)f32 #136=(1,256,32,32)f32
0 1 137 expr=None
2 1 136 137 138 #136=(1,256,32,32)f32 #138=(1,256,32,32)f32
1 1 133 139 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(3,3) out_channels=256 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,256,3,3)f32 #133=(1,256,32,32)f32 #139=(1,256,32,32)f32
1 1 139 140 negative_slope=2.000000e-01 #139=(1,256,32,32)f32 #140=(1,256,32,32)f32
1 1 140 141 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(3,3) out_channels=256 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,256,3,3)f32 #140=(1,256,32,32)f32 #141=(1,256,32,32)f32
0 1 142 expr=None
2 1 141 142 143 #141=(1,256,32,32)f32 #143=(1,256,32,32)f32
1 1 133 144 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(1,1) out_channels=3 padding=(0,0) padding_mode=zeros stride=(1,1) @bias=(3)f32 @weight=(3,256,1,1)f32 #133=(1,256,32,32)f32 #144=(1,3,32,32)f32
2 1 133 44 145 expr=add(@0,@1) #133=(1,256,32,32)f32 #44=(1,256,32,32)f32 #145=(1,256,32,32)f32
0 1 146 expr=2.000000e-01
1 1 145 147 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(3,3) out_channels=256 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,256,3,3)f32 #145=(1,256,32,32)f32 #147=(1,256,32,32)f32
2 1 147 146 148 #147=(1,256,32,32)f32 #148=(1,256,32,32)f32
1 1 148 149 align_corners=False mode=bilinear scale_factor=(2.000000e+00,2.000000e+00) $input=148 #148=(1,256,32,32)f32 #149=(1,256,64,64)f32
1 1 149 150 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(3,3) out_channels=256 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,256,3,3)f32 #149=(1,256,64,64)f32 #150=(1,256,64,64)f32
0 1 151 expr=2.000000e-01
2 1 150 151 152 #150=(1,256,64,64)f32 #152=(1,256,64,64)f32
1 1 145 153 align_corners=False mode=bilinear scale_factor=(2.000000e+00,2.000000e+00) $input=145 #145=(1,256,32,32)f32 #153=(1,256,64,64)f32
1 1 153 154 bias=False dilation=(1,1) groups=1 in_channels=256 kernel_size=(1,1) out_channels=256 padding=(0,0) padding_mode=zeros stride=(1,1) @weight=(256,256,1,1)f32 #153=(1,256,64,64)f32 #154=(1,256,64,64)f32
2 1 152 154 155 expr=add(@0,@1) #152=(1,256,64,64)f32 #154=(1,256,64,64)f32 #155=(1,256,64,64)f32
1 1 155 156 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(3,3) out_channels=256 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,256,3,3)f32 #155=(1,256,64,64)f32 #156=(1,256,64,64)f32
1 1 156 157 negative_slope=2.000000e-01 #156=(1,256,64,64)f32 #157=(1,256,64,64)f32
1 1 157 158 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(3,3) out_channels=256 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,256,3,3)f32 #157=(1,256,64,64)f32 #158=(1,256,64,64)f32
0 1 159 expr=None
2 1 158 159 160 #158=(1,256,64,64)f32 #160=(1,256,64,64)f32
1 1 155 161 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(3,3) out_channels=256 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,256,3,3)f32 #155=(1,256,64,64)f32 #161=(1,256,64,64)f32
1 1 161 162 negative_slope=2.000000e-01 #161=(1,256,64,64)f32 #162=(1,256,64,64)f32
1 1 162 163 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(3,3) out_channels=256 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,256,3,3)f32 #162=(1,256,64,64)f32 #163=(1,256,64,64)f32
0 1 164 expr=None
2 1 163 164 165 #163=(1,256,64,64)f32 #165=(1,256,64,64)f32
1 1 155 166 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(1,1) out_channels=3 padding=(0,0) padding_mode=zeros stride=(1,1) @bias=(3)f32 @weight=(3,256,1,1)f32 #155=(1,256,64,64)f32 #166=(1,3,64,64)f32
2 1 155 34 167 expr=add(@0,@1) #155=(1,256,64,64)f32 #34=(1,256,64,64)f32 #167=(1,256,64,64)f32
0 1 168 expr=2.000000e-01
1 1 167 169 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(3,3) out_channels=256 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,256,3,3)f32 #167=(1,256,64,64)f32 #169=(1,256,64,64)f32
2 1 169 168 170 #169=(1,256,64,64)f32 #170=(1,256,64,64)f32
1 1 170 171 align_corners=False mode=bilinear scale_factor=(2.000000e+00,2.000000e+00) $input=170 #170=(1,256,64,64)f32 #171=(1,256,128,128)f32
1 1 171 172 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(3,3) out_channels=128 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(128)f32 @weight=(128,256,3,3)f32 #171=(1,256,128,128)f32 #172=(1,128,128,128)f32
0 1 173 expr=2.000000e-01
2 1 172 173 174 #172=(1,128,128,128)f32 #174=(1,128,128,128)f32
1 1 167 175 align_corners=False mode=bilinear scale_factor=(2.000000e+00,2.000000e+00) $input=167 #167=(1,256,64,64)f32 #175=(1,256,128,128)f32
1 1 175 176 bias=False dilation=(1,1) groups=1 in_channels=256 kernel_size=(1,1) out_channels=128 padding=(0,0) padding_mode=zeros stride=(1,1) @weight=(128,256,1,1)f32 #175=(1,256,128,128)f32 #176=(1,128,128,128)f32
2 1 174 176 177 expr=add(@0,@1) #174=(1,128,128,128)f32 #176=(1,128,128,128)f32 #177=(1,128,128,128)f32
1 1 177 178 bias=True dilation=(1,1) groups=1 in_channels=128 kernel_size=(3,3) out_channels=128 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(128)f32 @weight=(128,128,3,3)f32 #177=(1,128,128,128)f32 #178=(1,128,128,128)f32
1 1 178 179 negative_slope=2.000000e-01 #178=(1,128,128,128)f32 #179=(1,128,128,128)f32
1 1 179 180 bias=True dilation=(1,1) groups=1 in_channels=128 kernel_size=(3,3) out_channels=128 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(128)f32 @weight=(128,128,3,3)f32 #179=(1,128,128,128)f32 #180=(1,128,128,128)f32
0 1 181 expr=None
2 1 180 181 182 #180=(1,128,128,128)f32 #182=(1,128,128,128)f32
1 1 177 183 bias=True dilation=(1,1) groups=1 in_channels=128 kernel_size=(3,3) out_channels=128 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(128)f32 @weight=(128,128,3,3)f32 #177=(1,128,128,128)f32 #183=(1,128,128,128)f32
1 1 183 184 negative_slope=2.000000e-01 #183=(1,128,128,128)f32 #184=(1,128,128,128)f32
1 1 184 185 bias=True dilation=(1,1) groups=1 in_channels=128 kernel_size=(3,3) out_channels=128 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(128)f32 @weight=(128,128,3,3)f32 #184=(1,128,128,128)f32 #185=(1,128,128,128)f32
0 1 186 expr=None
2 1 185 186 187 #185=(1,128,128,128)f32 #187=(1,128,128,128)f32
1 1 177 188 bias=True dilation=(1,1) groups=1 in_channels=128 kernel_size=(1,1) out_channels=3 padding=(0,0) padding_mode=zeros stride=(1,1) @bias=(3)f32 @weight=(3,128,1,1)f32 #177=(1,128,128,128)f32 #188=(1,3,128,128)f32
2 1 177 24 189 expr=add(@0,@1) #177=(1,128,128,128)f32 #24=(1,128,128,128)f32 #189=(1,128,128,128)f32
0 1 190 expr=2.000000e-01
1 1 189 191 bias=True dilation=(1,1) groups=1 in_channels=128 kernel_size=(3,3) out_channels=128 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(128)f32 @weight=(128,128,3,3)f32 #189=(1,128,128,128)f32 #191=(1,128,128,128)f32
2 1 191 190 192 #191=(1,128,128,128)f32 #192=(1,128,128,128)f32
1 1 192 193 align_corners=False mode=bilinear scale_factor=(2.000000e+00,2.000000e+00) $input=192 #192=(1,128,128,128)f32 #193=(1,128,256,256)f32
1 1 193 194 bias=True dilation=(1,1) groups=1 in_channels=128 kernel_size=(3,3) out_channels=64 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(64)f32 @weight=(64,128,3,3)f32 #193=(1,128,256,256)f32 #194=(1,64,256,256)f32
0 1 195 expr=2.000000e-01
2 1 194 195 196 #194=(1,64,256,256)f32 #196=(1,64,256,256)f32
1 1 189 197 align_corners=False mode=bilinear scale_factor=(2.000000e+00,2.000000e+00) $input=189 #189=(1,128,128,128)f32 #197=(1,128,256,256)f32
1 1 197 198 bias=False dilation=(1,1) groups=1 in_channels=128 kernel_size=(1,1) out_channels=64 padding=(0,0) padding_mode=zeros stride=(1,1) @weight=(64,128,1,1)f32 #197=(1,128,256,256)f32 #198=(1,64,256,256)f32
2 1 196 198 199 expr=add(@0,@1) #196=(1,64,256,256)f32 #198=(1,64,256,256)f32 #199=(1,64,256,256)f32
1 1 199 200 bias=True dilation=(1,1) groups=1 in_channels=64 kernel_size=(3,3) out_channels=64 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(64)f32 @weight=(64,64,3,3)f32 #199=(1,64,256,256)f32 #200=(1,64,256,256)f32
1 1 200 201 negative_slope=2.000000e-01 #200=(1,64,256,256)f32 #201=(1,64,256,256)f32
1 1 201 202 bias=True dilation=(1,1) groups=1 in_channels=64 kernel_size=(3,3) out_channels=64 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(64)f32 @weight=(64,64,3,3)f32 #201=(1,64,256,256)f32 #202=(1,64,256,256)f32
0 1 203 expr=None
2 1 202 203 204 #202=(1,64,256,256)f32 #204=(1,64,256,256)f32
1 1 199 205 bias=True dilation=(1,1) groups=1 in_channels=64 kernel_size=(3,3) out_channels=64 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(64)f32 @weight=(64,64,3,3)f32 #199=(1,64,256,256)f32 #205=(1,64,256,256)f32
1 1 205 206 negative_slope=2.000000e-01 #205=(1,64,256,256)f32 #206=(1,64,256,256)f32
1 1 206 207 bias=True dilation=(1,1) groups=1 in_channels=64 kernel_size=(3,3) out_channels=64 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(64)f32 @weight=(64,64,3,3)f32 #206=(1,64,256,256)f32 #207=(1,64,256,256)f32
0 1 208 expr=None
2 1 207 208 209 #207=(1,64,256,256)f32 #209=(1,64,256,256)f32
1 1 199 210 bias=True dilation=(1,1) groups=1 in_channels=64 kernel_size=(1,1) out_channels=3 padding=(0,0) padding_mode=zeros stride=(1,1) @bias=(3)f32 @weight=(3,64,1,1)f32 #199=(1,64,256,256)f32 #210=(1,3,256,256)f32
2 1 199 14 211 expr=add(@0,@1) #199=(1,64,256,256)f32 #14=(1,64,256,256)f32 #211=(1,64,256,256)f32
0 1 212 expr=2.000000e-01
1 1 211 213 bias=True dilation=(1,1) groups=1 in_channels=64 kernel_size=(3,3) out_channels=64 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(64)f32 @weight=(64,64,3,3)f32 #211=(1,64,256,256)f32 #213=(1,64,256,256)f32
2 1 213 212 214 #213=(1,64,256,256)f32 #214=(1,64,256,256)f32
1 1 214 215 align_corners=False mode=bilinear scale_factor=(2.000000e+00,2.000000e+00) $input=214 #214=(1,64,256,256)f32 #215=(1,64,512,512)f32
1 1 215 216 bias=True dilation=(1,1) groups=1 in_channels=64 kernel_size=(3,3) out_channels=32 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(32)f32 @weight=(32,64,3,3)f32 #215=(1,64,512,512)f32 #216=(1,32,512,512)f32
0 1 217 expr=2.000000e-01
2 1 216 217 218 #216=(1,32,512,512)f32 #218=(1,32,512,512)f32
1 1 211 219 align_corners=False mode=bilinear scale_factor=(2.000000e+00,2.000000e+00) $input=211 #211=(1,64,256,256)f32 #219=(1,64,512,512)f32
1 1 219 220 bias=False dilation=(1,1) groups=1 in_channels=64 kernel_size=(1,1) out_channels=32 padding=(0,0) padding_mode=zeros stride=(1,1) @weight=(32,64,1,1)f32 #219=(1,64,512,512)f32 #220=(1,32,512,512)f32
2 1 218 220 221 expr=add(@0,@1) #218=(1,32,512,512)f32 #220=(1,32,512,512)f32 #221=(1,32,512,512)f32
1 1 221 222 bias=True dilation=(1,1) groups=1 in_channels=32 kernel_size=(3,3) out_channels=32 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(32)f32 @weight=(32,32,3,3)f32 #221=(1,32,512,512)f32 #222=(1,32,512,512)f32
1 1 222 223 negative_slope=2.000000e-01 #222=(1,32,512,512)f32 #223=(1,32,512,512)f32
1 1 223 224 bias=True dilation=(1,1) groups=1 in_channels=32 kernel_size=(3,3) out_channels=32 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(32)f32 @weight=(32,32,3,3)f32 #223=(1,32,512,512)f32 #224=(1,32,512,512)f32
0 1 225 expr=None
2 1 224 225 226 #224=(1,32,512,512)f32 #226=(1,32,512,512)f32
1 1 221 227 bias=True dilation=(1,1) groups=1 in_channels=32 kernel_size=(3,3) out_channels=32 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(32)f32 @weight=(32,32,3,3)f32 #221=(1,32,512,512)f32 #227=(1,32,512,512)f32
1 1 227 228 negative_slope=2.000000e-01 #227=(1,32,512,512)f32 #228=(1,32,512,512)f32
1 1 228 229 bias=True dilation=(1,1) groups=1 in_channels=32 kernel_size=(3,3) out_channels=32 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(32)f32 @weight=(32,32,3,3)f32 #228=(1,32,512,512)f32 #229=(1,32,512,512)f32
0 1 230 expr=None
2 1 229 230 231 #229=(1,32,512,512)f32 #231=(1,32,512,512)f32
1 1 221 232 bias=True dilation=(1,1) groups=1 in_channels=32 kernel_size=(1,1) out_channels=3 padding=(0,0) padding_mode=zeros stride=(1,1) @bias=(3)f32 @weight=(3,32,1,1)f32 #221=(1,32,512,512)f32 #232=(1,3,512,512)f32
1 1 79 233 shape=(1,-1,512) $input=79 #79=(1,8192)f32 #233=(1,16,512)f32
er.constant_input 0 1 234 @weight=(1,512,4,4)f32 #234=(1,512,4,4)f32
0 1 235 expr=None
0 1 236 expr=1.000000e+00
0 1 237 expr=0.000000e+00
er.style_conv1 0 1 238 @bias=(1,512,1,1)f32 #238=(1,512,1,1)f32
er.style_conv1 0 1 239 @weight=(1)f32 #239=(1)f32
er.style_conv1.modulated_conv 0 1 240 @weight=(1,512,512,3,3)f32 #240=(1,512,512,3,3)f32
1 1 234 241 sizes=(1,1,1,1) $input=234 #234=(1,512,4,4)f32 #241=(1,512,4,4)f32
1 1 233 242 dim=1 index=0 $input=233 #233=(1,16,512)f32 #242=(1,512)f32
er.style_conv1.modulated_conv.modulation 1 1 242 243 bias=True in_features=512 out_features=512 @bias=(512)f32 @weight=(512,512)f32 #242=(1,512)f32 #243=(1,512)f32
1 1 243 244 shape=(1,1,512,1,1) $input=243 #243=(1,512)f32 #244=(1,1,512,1,1)f32
2 1 240 244 245 expr=mul(@0,@1) #240=(1,512,512,3,3)f32 #244=(1,1,512,1,1)f32 #245=(1,512,512,3,3)f32
1 1 245 246 expr=pow(@0,2) #245=(1,512,512,3,3)f32 #246=(1,512,512,3,3)f32
1 1 246 247 dim=(2,3,4) keepdim=False $input=246 #246=(1,512,512,3,3)f32 #247=(1,512)f32
1 1 247 248 expr=rsqrt(add(@0,1.000000e-08)) #247=(1,512)f32 #248=(1,512)f32
1 1 248 249 shape=(1,512,1,1,1) $input=248 #248=(1,512)f32 #249=(1,512,1,1,1)f32
2 1 245 249 250 expr=mul(@0,@1) #245=(1,512,512,3,3)f32 #249=(1,512,1,1,1)f32 #250=(1,512,512,3,3)f32
1 1 250 251 shape=(512,512,3,3) $input=250 #250=(1,512,512,3,3)f32 #251=(512,512,3,3)f32
2 1 241 251 252 bias=None dilation=(1,1) groups=1 padding=(1,1) stride=(1,1) $input=241 $weight=251 #241=(1,512,4,4)f32 #251=(512,512,3,3)f32 #252=(1,512,4,4)f32
1 1 252 253 expr=mul(@0,1.414214e+00) #252=(1,512,4,4)f32 #253=(1,512,4,4)f32
1 1 253 254 size=(1,1,4,4) $input=253 #253=(1,512,4,4)f32 #254=(1,1,4,4)f32
4 1 254 237 236 235 255 #254=(1,1,4,4)f32 #255=(1,1,4,4)f32
4 1 253 239 255 238 256 expr=add(add(@0,mul(@1,@2)),@3) #253=(1,512,4,4)f32 #239=(1)f32 #255=(1,1,4,4)f32 #238=(1,512,1,1)f32 #256=(1,512,4,4)f32
er.style_conv1.activate 1 1 256 257 negative_slope=2.000000e-01 #256=(1,512,4,4)f32 #257=(1,512,4,4)f32
0 1 258 @bias=(1,3,1,1)f32 #258=(1,3,1,1)f32
er.to_rgb1.modulated_conv 0 1 259 @weight=(1,3,512,1,1)f32 #259=(1,3,512,1,1)f32
1 1 233 260 dim=1 index=1 $input=233 #233=(1,16,512)f32 #260=(1,512)f32
er.to_rgb1.modulated_conv.modulation 1 1 260 261 bias=True in_features=512 out_features=512 @bias=(512)f32 @weight=(512,512)f32 #260=(1,512)f32 #261=(1,512)f32
1 1 261 262 shape=(1,1,512,1,1) $input=261 #261=(1,512)f32 #262=(1,1,512,1,1)f32
2 1 259 262 263 expr=mul(@0,@1) #259=(1,3,512,1,1)f32 #262=(1,1,512,1,1)f32 #263=(1,3,512,1,1)f32
1 1 263 264 shape=(3,512,1,1) $input=263 #263=(1,3,512,1,1)f32 #264=(3,512,1,1)f32
2 1 257 264 265 bias=None dilation=(1,1) groups=1 padding=(0,0) stride=(1,1) $input=257 $weight=264 #257=(1,512,4,4)f32 #264=(3,512,1,1)f32 #265=(1,3,4,4)f32
2 1 265 258 266 expr=add(@0,@1) #265=(1,3,4,4)f32 #258=(1,3,1,1)f32 #266=(1,3,4,4)f32
0 1 267 expr=None
0 1 268 expr=1.000000e+00
0 1 269 expr=0.000000e+00
er.style_convs.0 0 1 270 @bias=(1,512,1,1)f32 #270=(1,512,1,1)f32
er.style_convs.0 0 1 271 @weight=(1)f32 #271=(1)f32
er.style_convs.0.modulated_conv 0 1 272 @weight=(1,512,512,3,3)f32 #272=(1,512,512,3,3)f32
1 1 233 273 dim=1 index=1 $input=233 #233=(1,16,512)f32 #273=(1,512)f32
er.style_convs.0.modulated_conv.modulation 1 1 273 274 bias=True in_features=512 out_features=512 @bias=(512)f32 @weight=(512,512)f32 #273=(1,512)f32 #274=(1,512)f32
1 1 274 275 shape=(1,1,512,1,1) $input=274 #274=(1,512)f32 #275=(1,1,512,1,1)f32
2 1 272 275 276 expr=mul(@0,@1) #272=(1,512,512,3,3)f32 #275=(1,1,512,1,1)f32 #276=(1,512,512,3,3)f32
1 1 276 277 expr=pow(@0,2) #276=(1,512,512,3,3)f32 #277=(1,512,512,3,3)f32
1 1 277 278 dim=(2,3,4) keepdim=False $input=277 #277=(1,512,512,3,3)f32 #278=(1,512)f32
1 1 278 279 expr=rsqrt(add(@0,1.000000e-08)) #278=(1,512)f32 #279=(1,512)f32
1 1 279 280 shape=(1,512,1,1,1) $input=279 #279=(1,512)f32 #280=(1,512,1,1,1)f32
2 1 276 280 281 expr=mul(@0,@1) #276=(1,512,512,3,3)f32 #280=(1,512,1,1,1)f32 #281=(1,512,512,3,3)f32
1 1 257 282 align_corners=False mode=bilinear scale_factor=(2.000000e+00,2.000000e+00) $input=257 #257=(1,512,4,4)f32 #282=(1,512,8,8)f32
1 1 281 283 shape=(512,512,3,3) $input=281 #281=(1,512,512,3,3)f32 #283=(512,512,3,3)f32
2 1 282 283 284 bias=None dilation=(1,1) groups=1 padding=(1,1) stride=(1,1) $input=282 $weight=283 #282=(1,512,8,8)f32 #283=(512,512,3,3)f32 #284=(1,512,8,8)f32
1 1 284 285 expr=mul(@0,1.414214e+00) #284=(1,512,8,8)f32 #285=(1,512,8,8)f32
1 1 285 286 size=(1,1,8,8) $input=285 #285=(1,512,8,8)f32 #286=(1,1,8,8)f32
4 1 286 269 268 267 287 #286=(1,1,8,8)f32 #287=(1,1,8,8)f32
4 1 285 271 287 270 288 expr=add(add(@0,mul(@1,@2)),@3) #285=(1,512,8,8)f32 #271=(1)f32 #287=(1,1,8,8)f32 #270=(1,512,1,1)f32 #288=(1,512,8,8)f32
er.style_convs.0.activate 1 1 288 289 negative_slope=2.000000e-01 #288=(1,512,8,8)f32 #289=(1,512,8,8)f32
1 2 289 290 291 dim=1 split_size_or_sections=256 $tensor=289 #289=(1,512,8,8)f32 #290=(1,256,8,8)f32 #291=(1,256,8,8)f32
3 1 291 94 99 292 expr=add(mul(@0,@1),@2) #291=(1,256,8,8)f32 #94=(1,256,8,8)f32 #99=(1,256,8,8)f32 #292=(1,256,8,8)f32
0 1 293 expr=None
0 1 294 expr=1.000000e+00
0 1 295 expr=0.000000e+00
er.style_convs.1 0 1 296 @bias=(1,512,1,1)f32 #296=(1,512,1,1)f32
er.style_convs.1 0 1 297 @weight=(1)f32 #297=(1)f32
er.style_convs.1.modulated_conv 0 1 298 @weight=(1,512,512,3,3)f32 #298=(1,512,512,3,3)f32
2 1 290 292 299 dim=1 #290=(1,256,8,8)f32 #292=(1,256,8,8)f32 #299=(1,512,8,8)f32
1 1 233 300 dim=1 index=2 $input=233 #233=(1,16,512)f32 #300=(1,512)f32
er.style_convs.1.modulated_conv.modulation 1 1 300 301 bias=True in_features=512 out_features=512 @bias=(512)f32 @weight=(512,512)f32 #300=(1,512)f32 #301=(1,512)f32
1 1 301 302 shape=(1,1,512,1,1) $input=301 #301=(1,512)f32 #302=(1,1,512,1,1)f32
2 1 298 302 303 expr=mul(@0,@1) #298=(1,512,512,3,3)f32 #302=(1,1,512,1,1)f32 #303=(1,512,512,3,3)f32
1 1 303 304 expr=pow(@0,2) #303=(1,512,512,3,3)f32 #304=(1,512,512,3,3)f32
1 1 304 305 dim=(2,3,4) keepdim=False $input=304 #304=(1,512,512,3,3)f32 #305=(1,512)f32
1 1 305 306 expr=rsqrt(add(@0,1.000000e-08)) #305=(1,512)f32 #306=(1,512)f32
1 1 306 307 shape=(1,512,1,1,1) $input=306 #306=(1,512)f32 #307=(1,512,1,1,1)f32
2 1 303 307 308 expr=mul(@0,@1) #303=(1,512,512,3,3)f32 #307=(1,512,1,1,1)f32 #308=(1,512,512,3,3)f32
1 1 308 309 shape=(512,512,3,3) $input=308 #308=(1,512,512,3,3)f32 #309=(512,512,3,3)f32
2 1 299 309 310 bias=None dilation=(1,1) groups=1 padding=(1,1) stride=(1,1) $input=299 $weight=309 #299=(1,512,8,8)f32 #309=(512,512,3,3)f32 #310=(1,512,8,8)f32
1 1 310 311 expr=mul(@0,1.414214e+00) #310=(1,512,8,8)f32 #311=(1,512,8,8)f32
1 1 311 312 size=(1,1,8,8) $input=311 #311=(1,512,8,8)f32 #312=(1,1,8,8)f32
4 1 312 295 294 293 313 #312=(1,1,8,8)f32 #313=(1,1,8,8)f32
4 1 311 297 313 296 314 expr=add(add(@0,mul(@1,@2)),@3) #311=(1,512,8,8)f32 #297=(1)f32 #313=(1,1,8,8)f32 #296=(1,512,1,1)f32 #314=(1,512,8,8)f32
er.style_convs.1.activate 1 1 314 315 negative_slope=2.000000e-01 #314=(1,512,8,8)f32 #315=(1,512,8,8)f32
0 1 316 @bias=(1,3,1,1)f32 #316=(1,3,1,1)f32
er.to_rgbs.0.modulated_conv 0 1 317 @weight=(1,3,512,1,1)f32 #317=(1,3,512,1,1)f32
1 1 233 318 dim=1 index=3 $input=233 #233=(1,16,512)f32 #318=(1,512)f32
er.to_rgbs.0.modulated_conv.modulation 1 1 318 319 bias=True in_features=512 out_features=512 @bias=(512)f32 @weight=(512,512)f32 #318=(1,512)f32 #319=(1,512)f32
1 1 319 320 shape=(1,1,512,1,1) $input=319 #319=(1,512)f32 #320=(1,1,512,1,1)f32
2 1 317 320 321 expr=mul(@0,@1) #317=(1,3,512,1,1)f32 #320=(1,1,512,1,1)f32 #321=(1,3,512,1,1)f32
1 1 321 322 shape=(3,512,1,1) $input=321 #321=(1,3,512,1,1)f32 #322=(3,512,1,1)f32
2 1 315 322 323 bias=None dilation=(1,1) groups=1 padding=(0,0) stride=(1,1) $input=315 $weight=322 #315=(1,512,8,8)f32 #322=(3,512,1,1)f32 #323=(1,3,8,8)f32
1 1 266 324 align_corners=False mode=bilinear scale_factor=(2.000000e+00,2.000000e+00) $input=266 #266=(1,3,4,4)f32 #324=(1,3,8,8)f32
3 1 323 316 324 325 expr=add(add(@0,@1),@2) #323=(1,3,8,8)f32 #316=(1,3,1,1)f32 #324=(1,3,8,8)f32 #325=(1,3,8,8)f32
0 1 326 expr=None
0 1 327 expr=1.000000e+00
0 1 328 expr=0.000000e+00
er.style_convs.2 0 1 329 @bias=(1,512,1,1)f32 #329=(1,512,1,1)f32
er.style_convs.2 0 1 330 @weight=(1)f32 #330=(1)f32
er.style_convs.2.modulated_conv 0 1 331 @weight=(1,512,512,3,3)f32 #331=(1,512,512,3,3)f32
1 1 233 332 dim=1 index=3 $input=233 #233=(1,16,512)f32 #332=(1,512)f32
er.style_convs.2.modulated_conv.modulation 1 1 332 333 bias=True in_features=512 out_features=512 @bias=(512)f32 @weight=(512,512)f32 #332=(1,512)f32 #333=(1,512)f32
1 1 333 334 shape=(1,1,512,1,1) $input=333 #333=(1,512)f32 #334=(1,1,512,1,1)f32
2 1 331 334 335 expr=mul(@0,@1) #331=(1,512,512,3,3)f32 #334=(1,1,512,1,1)f32 #335=(1,512,512,3,3)f32
1 1 335 336 expr=pow(@0,2) #335=(1,512,512,3,3)f32 #336=(1,512,512,3,3)f32
1 1 336 337 dim=(2,3,4) keepdim=False $input=336 #336=(1,512,512,3,3)f32 #337=(1,512)f32
1 1 337 338 expr=rsqrt(add(@0,1.000000e-08)) #337=(1,512)f32 #338=(1,512)f32
1 1 338 339 shape=(1,512,1,1,1) $input=338 #338=(1,512)f32 #339=(1,512,1,1,1)f32
2 1 335 339 340 expr=mul(@0,@1) #335=(1,512,512,3,3)f32 #339=(1,512,1,1,1)f32 #340=(1,512,512,3,3)f32
1 1 315 341 align_corners=False mode=bilinear scale_factor=(2.000000e+00,2.000000e+00) $input=315 #315=(1,512,8,8)f32 #341=(1,512,16,16)f32
1 1 340 342 shape=(512,512,3,3) $input=340 #340=(1,512,512,3,3)f32 #342=(512,512,3,3)f32
2 1 341 342 343 bias=None dilation=(1,1) groups=1 padding=(1,1) stride=(1,1) $input=341 $weight=342 #341=(1,512,16,16)f32 #342=(512,512,3,3)f32 #343=(1,512,16,16)f32
1 1 343 344 expr=mul(@0,1.414214e+00) #343=(1,512,16,16)f32 #344=(1,512,16,16)f32
1 1 344 345 size=(1,1,16,16) $input=344 #344=(1,512,16,16)f32 #345=(1,1,16,16)f32
4 1 345 328 327 326 346 #345=(1,1,16,16)f32 #346=(1,1,16,16)f32
4 1 344 330 346 329 347 expr=add(add(@0,mul(@1,@2)),@3) #344=(1,512,16,16)f32 #330=(1)f32 #346=(1,1,16,16)f32 #329=(1,512,1,1)f32 #347=(1,512,16,16)f32
er.style_convs.2.activate 1 1 347 348 negative_slope=2.000000e-01 #347=(1,512,16,16)f32 #348=(1,512,16,16)f32
1 2 348 349 350 dim=1 split_size_or_sections=256 $tensor=348 #348=(1,512,16,16)f32 #349=(1,256,16,16)f32 #350=(1,256,16,16)f32
3 1 350 116 121 351 expr=add(mul(@0,@1),@2) #350=(1,256,16,16)f32 #116=(1,256,16,16)f32 #121=(1,256,16,16)f32 #351=(1,256,16,16)f32
0 1 352 expr=None
0 1 353 expr=1.000000e+00
0 1 354 expr=0.000000e+00
er.style_convs.3 0 1 355 @bias=(1,512,1,1)f32 #355=(1,512,1,1)f32
er.style_convs.3 0 1 356 @weight=(1)f32 #356=(1)f32
er.style_convs.3.modulated_conv 0 1 357 @weight=(1,512,512,3,3)f32 #357=(1,512,512,3,3)f32
2 1 349 351 358 dim=1 #349=(1,256,16,16)f32 #351=(1,256,16,16)f32 #358=(1,512,16,16)f32
1 1 233 359 dim=1 index=4 $input=233 #233=(1,16,512)f32 #359=(1,512)f32
er.style_convs.3.modulated_conv.modulation 1 1 359 360 bias=True in_features=512 out_features=512 @bias=(512)f32 @weight=(512,512)f32 #359=(1,512)f32 #360=(1,512)f32
1 1 360 361 shape=(1,1,512,1,1) $input=360 #360=(1,512)f32 #361=(1,1,512,1,1)f32
2 1 357 361 362 expr=mul(@0,@1) #357=(1,512,512,3,3)f32 #361=(1,1,512,1,1)f32 #362=(1,512,512,3,3)f32
1 1 362 363 expr=pow(@0,2) #362=(1,512,512,3,3)f32 #363=(1,512,512,3,3)f32
1 1 363 364 dim=(2,3,4) keepdim=False $input=363 #363=(1,512,512,3,3)f32 #364=(1,512)f32
1 1 364 365 expr=rsqrt(add(@0,1.000000e-08)) #364=(1,512)f32 #365=(1,512)f32
1 1 365 366 shape=(1,512,1,1,1) $input=365 #365=(1,512)f32 #366=(1,512,1,1,1)f32
2 1 362 366 367 expr=mul(@0,@1) #362=(1,512,512,3,3)f32 #366=(1,512,1,1,1)f32 #367=(1,512,512,3,3)f32
1 1 367 368 shape=(512,512,3,3) $input=367 #367=(1,512,512,3,3)f32 #368=(512,512,3,3)f32
2 1 358 368 369 bias=None dilation=(1,1) groups=1 padding=(1,1) stride=(1,1) $input=358 $weight=368 #358=(1,512,16,16)f32 #368=(512,512,3,3)f32 #369=(1,512,16,16)f32
1 1 369 370 expr=mul(@0,1.414214e+00) #369=(1,512,16,16)f32 #370=(1,512,16,16)f32
1 1 370 371 size=(1,1,16,16) $input=370 #370=(1,512,16,16)f32 #371=(1,1,16,16)f32
4 1 371 354 353 352 372 #371=(1,1,16,16)f32 #372=(1,1,16,16)f32
4 1 370 356 372 355 373 expr=add(add(@0,mul(@1,@2)),@3) #370=(1,512,16,16)f32 #356=(1)f32 #372=(1,1,16,16)f32 #355=(1,512,1,1)f32 #373=(1,512,16,16)f32
er.style_convs.3.activate 1 1 373 374 negative_slope=2.000000e-01 #373=(1,512,16,16)f32 #374=(1,512,16,16)f32
0 1 375 @bias=(1,3,1,1)f32 #375=(1,3,1,1)f32
er.to_rgbs.1.modulated_conv 0 1 376 @weight=(1,3,512,1,1)f32 #376=(1,3,512,1,1)f32
1 1 233 377 dim=1 index=5 $input=233 #233=(1,16,512)f32 #377=(1,512)f32
er.to_rgbs.1.modulated_conv.modulation 1 1 377 378 bias=True in_features=512 out_features=512 @bias=(512)f32 @weight=(512,512)f32 #377=(1,512)f32 #378=(1,512)f32
1 1 378 379 shape=(1,1,512,1,1) $input=378 #378=(1,512)f32 #379=(1,1,512,1,1)f32
2 1 376 379 380 expr=mul(@0,@1) #376=(1,3,512,1,1)f32 #379=(1,1,512,1,1)f32 #380=(1,3,512,1,1)f32
1 1 380 381 shape=(3,512,1,1) $input=380 #380=(1,3,512,1,1)f32 #381=(3,512,1,1)f32
2 1 374 381 382 bias=None dilation=(1,1) groups=1 padding=(0,0) stride=(1,1) $input=374 $weight=381 #374=(1,512,16,16)f32 #381=(3,512,1,1)f32 #382=(1,3,16,16)f32
1 1 325 383 align_corners=False mode=bilinear scale_factor=(2.000000e+00,2.000000e+00) $input=325 #325=(1,3,8,8)f32 #383=(1,3,16,16)f32
3 1 382 375 383 384 expr=add(add(@0,@1),@2) #382=(1,3,16,16)f32 #375=(1,3,1,1)f32 #383=(1,3,16,16)f32 #384=(1,3,16,16)f32
0 1 385 expr=None
0 1 386 expr=1.000000e+00
0 1 387 expr=0.000000e+00
er.style_convs.4 0 1 388 @bias=(1,512,1,1)f32 #388=(1,512,1,1)f32
er.style_convs.4 0 1 389 @weight=(1)f32 #389=(1)f32
er.style_convs.4.modulated_conv 0 1 390 @weight=(1,512,512,3,3)f32 #390=(1,512,512,3,3)f32
1 1 233 391 dim=1 index=5 $input=233 #233=(1,16,512)f32 #391=(1,512)f32
er.style_convs.4.modulated_conv.modulation 1 1 391 392 bias=True in_features=512 out_features=512 @bias=(512)f32 @weight=(512,512)f32 #391=(1,512)f32 #392=(1,512)f32
1 1 392 393 shape=(1,1,512,1,1) $input=392 #392=(1,512)f32 #393=(1,1,512,1,1)f32
2 1 390 393 394 expr=mul(@0,@1) #390=(1,512,512,3,3)f32 #393=(1,1,512,1,1)f32 #394=(1,512,512,3,3)f32
1 1 394 395 expr=pow(@0,2) #394=(1,512,512,3,3)f32 #395=(1,512,512,3,3)f32
1 1 395 396 dim=(2,3,4) keepdim=False $input=395 #395=(1,512,512,3,3)f32 #396=(1,512)f32
1 1 396 397 expr=rsqrt(add(@0,1.000000e-08)) #396=(1,512)f32 #397=(1,512)f32
1 1 397 398 shape=(1,512,1,1,1) $input=397 #397=(1,512)f32 #398=(1,512,1,1,1)f32
2 1 394 398 399 expr=mul(@0,@1) #394=(1,512,512,3,3)f32 #398=(1,512,1,1,1)f32 #399=(1,512,512,3,3)f32
1 1 374 400 align_corners=False mode=bilinear scale_factor=(2.000000e+00,2.000000e+00) $input=374 #374=(1,512,16,16)f32 #400=(1,512,32,32)f32
1 1 399 401 shape=(512,512,3,3) $input=399 #399=(1,512,512,3,3)f32 #401=(512,512,3,3)f32
2 1 400 401 402 bias=None dilation=(1,1) groups=1 padding=(1,1) stride=(1,1) $input=400 $weight=401 #400=(1,512,32,32)f32 #401=(512,512,3,3)f32 #402=(1,512,32,32)f32
1 1 402 403 expr=mul(@0,1.414214e+00) #402=(1,512,32,32)f32 #403=(1,512,32,32)f32
1 1 403 404 size=(1,1,32,32) $input=403 #403=(1,512,32,32)f32 #404=(1,1,32,32)f32
4 1 404 387 386 385 405 #404=(1,1,32,32)f32 #405=(1,1,32,32)f32
4 1 403 389 405 388 406 expr=add(add(@0,mul(@1,@2)),@3) #403=(1,512,32,32)f32 #389=(1)f32 #405=(1,1,32,32)f32 #388=(1,512,1,1)f32 #406=(1,512,32,32)f32
er.style_convs.4.activate 1 1 406 407 negative_slope=2.000000e-01 #406=(1,512,32,32)f32 #407=(1,512,32,32)f32
1 2 407 408 409 dim=1 split_size_or_sections=256 $tensor=407 #407=(1,512,32,32)f32 #408=(1,256,32,32)f32 #409=(1,256,32,32)f32
3 1 409 138 143 410 expr=add(mul(@0,@1),@2) #409=(1,256,32,32)f32 #138=(1,256,32,32)f32 #143=(1,256,32,32)f32 #410=(1,256,32,32)f32
0 1 411 expr=None
0 1 412 expr=1.000000e+00
0 1 413 expr=0.000000e+00
er.style_convs.5 0 1 414 @bias=(1,512,1,1)f32 #414=(1,512,1,1)f32
er.style_convs.5 0 1 415 @weight=(1)f32 #415=(1)f32
er.style_convs.5.modulated_conv 0 1 416 @weight=(1,512,512,3,3)f32 #416=(1,512,512,3,3)f32
2 1 408 410 417 dim=1 #408=(1,256,32,32)f32 #410=(1,256,32,32)f32 #417=(1,512,32,32)f32
1 1 233 418 dim=1 index=6 $input=233 #233=(1,16,512)f32 #418=(1,512)f32
er.style_convs.5.modulated_conv.modulation 1 1 418 419 bias=True in_features=512 out_features=512 @bias=(512)f32 @weight=(512,512)f32 #418=(1,512)f32 #419=(1,512)f32
1 1 419 420 shape=(1,1,512,1,1) $input=419 #419=(1,512)f32 #420=(1,1,512,1,1)f32
2 1 416 420 421 expr=mul(@0,@1) #416=(1,512,512,3,3)f32 #420=(1,1,512,1,1)f32 #421=(1,512,512,3,3)f32
1 1 421 422 expr=pow(@0,2) #421=(1,512,512,3,3)f32 #422=(1,512,512,3,3)f32
1 1 422 423 dim=(2,3,4) keepdim=False $input=422 #422=(1,512,512,3,3)f32 #423=(1,512)f32
1 1 423 424 expr=rsqrt(add(@0,1.000000e-08)) #423=(1,512)f32 #424=(1,512)f32
1 1 424 425 shape=(1,512,1,1,1) $input=424 #424=(1,512)f32 #425=(1,512,1,1,1)f32
2 1 421 425 426 expr=mul(@0,@1) #421=(1,512,512,3,3)f32 #425=(1,512,1,1,1)f32 #426=(1,512,512,3,3)f32
1 1 426 427 shape=(512,512,3,3) $input=426 #426=(1,512,512,3,3)f32 #427=(512,512,3,3)f32
2 1 417 427 428 bias=None dilation=(1,1) groups=1 padding=(1,1) stride=(1,1) $input=417 $weight=427 #417=(1,512,32,32)f32 #427=(512,512,3,3)f32 #428=(1,512,32,32)f32
1 1 428 429 expr=mul(@0,1.414214e+00) #428=(1,512,32,32)f32 #429=(1,512,32,32)f32
1 1 429 430 size=(1,1,32,32) $input=429 #429=(1,512,32,32)f32 #430=(1,1,32,32)f32
4 1 430 413 412 411 431 #430=(1,1,32,32)f32 #431=(1,1,32,32)f32
4 1 429 415 431 414 432 expr=add(add(@0,mul(@1,@2)),@3) #429=(1,512,32,32)f32 #415=(1)f32 #431=(1,1,32,32)f32 #414=(1,512,1,1)f32 #432=(1,512,32,32)f32
er.style_convs.5.activate 1 1 432 433 negative_slope=2.000000e-01 #432=(1,512,32,32)f32 #433=(1,512,32,32)f32
0 1 434 @bias=(1,3,1,1)f32 #434=(1,3,1,1)f32
er.to_rgbs.2.modulated_conv 0 1 435 @weight=(1,3,512,1,1)f32 #435=(1,3,512,1,1)f32
1 1 233 436 dim=1 index=7 $input=233 #233=(1,16,512)f32 #436=(1,512)f32
er.to_rgbs.2.modulated_conv.modulation 1 1 436 437 bias=True in_features=512 out_features=512 @bias=(512)f32 @weight=(512,512)f32 #436=(1,512)f32 #437=(1,512)f32
1 1 437 438 shape=(1,1,512,1,1) $input=437 #437=(1,512)f32 #438=(1,1,512,1,1)f32
2 1 435 438 439 expr=mul(@0,@1) #435=(1,3,512,1,1)f32 #438=(1,1,512,1,1)f32 #439=(1,3,512,1,1)f32
1 1 439 440 shape=(3,512,1,1) $input=439 #439=(1,3,512,1,1)f32 #440=(3,512,1,1)f32
2 1 433 440 441 bias=None dilation=(1,1) groups=1 padding=(0,0) stride=(1,1) $input=433 $weight=440 #433=(1,512,32,32)f32 #440=(3,512,1,1)f32 #441=(1,3,32,32)f32
1 1 384 442 align_corners=False mode=bilinear scale_factor=(2.000000e+00,2.000000e+00) $input=384 #384=(1,3,16,16)f32 #442=(1,3,32,32)f32
3 1 441 434 442 443 expr=add(add(@0,@1),@2) #441=(1,3,32,32)f32 #434=(1,3,1,1)f32 #442=(1,3,32,32)f32 #443=(1,3,32,32)f32
0 1 444 expr=None
0 1 445 expr=1.000000e+00
0 1 446 expr=0.000000e+00
er.style_convs.6 0 1 447 @bias=(1,512,1,1)f32 #447=(1,512,1,1)f32
er.style_convs.6 0 1 448 @weight=(1)f32 #448=(1)f32
er.style_convs.6.modulated_conv 0 1 449 @weight=(1,512,512,3,3)f32 #449=(1,512,512,3,3)f32
1 1 233 450 dim=1 index=7 $input=233 #233=(1,16,512)f32 #450=(1,512)f32
er.style_convs.6.modulated_conv.modulation 1 1 450 451 bias=True in_features=512 out_features=512 @bias=(512)f32 @weight=(512,512)f32 #450=(1,512)f32 #451=(1,512)f32
1 1 451 452 shape=(1,1,512,1,1) $input=451 #451=(1,512)f32 #452=(1,1,512,1,1)f32
2 1 449 452 453 expr=mul(@0,@1) #449=(1,512,512,3,3)f32 #452=(1,1,512,1,1)f32 #453=(1,512,512,3,3)f32
1 1 453 454 expr=pow(@0,2) #453=(1,512,512,3,3)f32 #454=(1,512,512,3,3)f32
1 1 454 455 dim=(2,3,4) keepdim=False $input=454 #454=(1,512,512,3,3)f32 #455=(1,512)f32
1 1 455 456 expr=rsqrt(add(@0,1.000000e-08)) #455=(1,512)f32 #456=(1,512)f32
1 1 456 457 shape=(1,512,1,1,1) $input=456 #456=(1,512)f32 #457=(1,512,1,1,1)f32
2 1 453 457 458 expr=mul(@0,@1) #453=(1,512,512,3,3)f32 #457=(1,512,1,1,1)f32 #458=(1,512,512,3,3)f32
1 1 433 459 align_corners=False mode=bilinear scale_factor=(2.000000e+00,2.000000e+00) $input=433 #433=(1,512,32,32)f32 #459=(1,512,64,64)f32
1 1 458 460 shape=(512,512,3,3) $input=458 #458=(1,512,512,3,3)f32 #460=(512,512,3,3)f32
2 1 459 460 461 bias=None dilation=(1,1) groups=1 padding=(1,1) stride=(1,1) $input=459 $weight=460 #459=(1,512,64,64)f32 #460=(512,512,3,3)f32 #461=(1,512,64,64)f32
1 1 461 462 expr=mul(@0,1.414214e+00) #461=(1,512,64,64)f32 #462=(1,512,64,64)f32
1 1 462 463 size=(1,1,64,64) $input=462 #462=(1,512,64,64)f32 #463=(1,1,64,64)f32
4 1 463 446 445 444 464 #463=(1,1,64,64)f32 #464=(1,1,64,64)f32
4 1 462 448 464 447 465 expr=add(add(@0,mul(@1,@2)),@3) #462=(1,512,64,64)f32 #448=(1)f32 #464=(1,1,64,64)f32 #447=(1,512,1,1)f32 #465=(1,512,64,64)f32
er.style_convs.6.activate 1 1 465 466 negative_slope=2.000000e-01 #465=(1,512,64,64)f32 #466=(1,512,64,64)f32
1 2 466 467 468 dim=1 split_size_or_sections=256 $tensor=466 #466=(1,512,64,64)f32 #467=(1,256,64,64)f32 #468=(1,256,64,64)f32
3 1 468 160 165 469 expr=add(mul(@0,@1),@2) #468=(1,256,64,64)f32 #160=(1,256,64,64)f32 #165=(1,256,64,64)f32 #469=(1,256,64,64)f32
0 1 470 expr=None
0 1 471 expr=1.000000e+00
0 1 472 expr=0.000000e+00
er.style_convs.7 0 1 473 @bias=(1,512,1,1)f32 #473=(1,512,1,1)f32
er.style_convs.7 0 1 474 @weight=(1)f32 #474=(1)f32
er.style_convs.7.modulated_conv 0 1 475 @weight=(1,512,512,3,3)f32 #475=(1,512,512,3,3)f32
2 1 467 469 476 dim=1 #467=(1,256,64,64)f32 #469=(1,256,64,64)f32 #476=(1,512,64,64)f32
1 1 233 477 dim=1 index=8 $input=233 #233=(1,16,512)f32 #477=(1,512)f32
er.style_convs.7.modulated_conv.modulation 1 1 477 478 bias=True in_features=512 out_features=512 @bias=(512)f32 @weight=(512,512)f32 #477=(1,512)f32 #478=(1,512)f32
1 1 478 479 shape=(1,1,512,1,1) $input=478 #478=(1,512)f32 #479=(1,1,512,1,1)f32
2 1 475 479 480 expr=mul(@0,@1) #475=(1,512,512,3,3)f32 #479=(1,1,512,1,1)f32 #480=(1,512,512,3,3)f32
1 1 480 481 expr=pow(@0,2) #480=(1,512,512,3,3)f32 #481=(1,512,512,3,3)f32
1 1 481 482 dim=(2,3,4) keepdim=False $input=481 #481=(1,512,512,3,3)f32 #482=(1,512)f32
1 1 482 483 expr=rsqrt(add(@0,1.000000e-08)) #482=(1,512)f32 #483=(1,512)f32
1 1 483 484 shape=(1,512,1,1,1) $input=483 #483=(1,512)f32 #484=(1,512,1,1,1)f32
2 1 480 484 485 expr=mul(@0,@1) #480=(1,512,512,3,3)f32 #484=(1,512,1,1,1)f32 #485=(1,512,512,3,3)f32
1 1 485 486 shape=(512,512,3,3) $input=485 #485=(1,512,512,3,3)f32 #486=(512,512,3,3)f32
2 1 476 486 487 bias=None dilation=(1,1) groups=1 padding=(1,1) stride=(1,1) $input=476 $weight=486 #476=(1,512,64,64)f32 #486=(512,512,3,3)f32 #487=(1,512,64,64)f32
1 1 487 488 expr=mul(@0,1.414214e+00) #487=(1,512,64,64)f32 #488=(1,512,64,64)f32
1 1 488 489 size=(1,1,64,64) $input=488 #488=(1,512,64,64)f32 #489=(1,1,64,64)f32
4 1 489 472 471 470 490 #489=(1,1,64,64)f32 #490=(1,1,64,64)f32
4 1 488 474 490 473 491 expr=add(add(@0,mul(@1,@2)),@3) #488=(1,512,64,64)f32 #474=(1)f32 #490=(1,1,64,64)f32 #473=(1,512,1,1)f32 #491=(1,512,64,64)f32
er.style_convs.7.activate 1 1 491 492 negative_slope=2.000000e-01 #491=(1,512,64,64)f32 #492=(1,512,64,64)f32
0 1 493 @bias=(1,3,1,1)f32 #493=(1,3,1,1)f32
er.to_rgbs.3.modulated_conv 0 1 494 @weight=(1,3,512,1,1)f32 #494=(1,3,512,1,1)f32
1 1 233 495 dim=1 index=9 $input=233 #233=(1,16,512)f32 #495=(1,512)f32
er.to_rgbs.3.modulated_conv.modulation 1 1 495 496 bias=True in_features=512 out_features=512 @bias=(512)f32 @weight=(512,512)f32 #495=(1,512)f32 #496=(1,512)f32
1 1 496 497 shape=(1,1,512,1,1) $input=496 #496=(1,512)f32 #497=(1,1,512,1,1)f32
2 1 494 497 498 expr=mul(@0,@1) #494=(1,3,512,1,1)f32 #497=(1,1,512,1,1)f32 #498=(1,3,512,1,1)f32
1 1 498 499 shape=(3,512,1,1) $input=498 #498=(1,3,512,1,1)f32 #499=(3,512,1,1)f32
2 1 492 499 500 bias=None dilation=(1,1) groups=1 padding=(0,0) stride=(1,1) $input=492 $weight=499 #492=(1,512,64,64)f32 #499=(3,512,1,1)f32 #500=(1,3,64,64)f32
1 1 443 501 align_corners=False mode=bilinear scale_factor=(2.000000e+00,2.000000e+00) $input=443 #443=(1,3,32,32)f32 #501=(1,3,64,64)f32
3 1 500 493 501 502 expr=add(add(@0,@1),@2) #500=(1,3,64,64)f32 #493=(1,3,1,1)f32 #501=(1,3,64,64)f32 #502=(1,3,64,64)f32
0 1 503 expr=None
0 1 504 expr=1.000000e+00
0 1 505 expr=0.000000e+00
er.style_convs.8 0 1 506 @bias=(1,256,1,1)f32 #506=(1,256,1,1)f32
er.style_convs.8 0 1 507 @weight=(1)f32 #507=(1)f32
er.style_convs.8.modulated_conv 0 1 508 @weight=(1,256,512,3,3)f32 #508=(1,256,512,3,3)f32
1 1 233 509 dim=1 index=9 $input=233 #233=(1,16,512)f32 #509=(1,512)f32
er.style_convs.8.modulated_conv.modulation 1 1 509 510 bias=True in_features=512 out_features=512 @bias=(512)f32 @weight=(512,512)f32 #509=(1,512)f32 #510=(1,512)f32
1 1 510 511 shape=(1,1,512,1,1) $input=510 #510=(1,512)f32 #511=(1,1,512,1,1)f32
2 1 508 511 512 expr=mul(@0,@1) #508=(1,256,512,3,3)f32 #511=(1,1,512,1,1)f32 #512=(1,256,512,3,3)f32
1 1 512 513 expr=pow(@0,2) #512=(1,256,512,3,3)f32 #513=(1,256,512,3,3)f32
1 1 513 514 dim=(2,3,4) keepdim=False $input=513 #513=(1,256,512,3,3)f32 #514=(1,256)f32
1 1 514 515 expr=rsqrt(add(@0,1.000000e-08)) #514=(1,256)f32 #515=(1,256)f32
1 1 515 516 shape=(1,256,1,1,1) $input=515 #515=(1,256)f32 #516=(1,256,1,1,1)f32
2 1 512 516 517 expr=mul(@0,@1) #512=(1,256,512,3,3)f32 #516=(1,256,1,1,1)f32 #517=(1,256,512,3,3)f32
1 1 492 518 align_corners=False mode=bilinear scale_factor=(2.000000e+00,2.000000e+00) $input=492 #492=(1,512,64,64)f32 #518=(1,512,128,128)f32
1 1 517 519 shape=(256,512,3,3) $input=517 #517=(1,256,512,3,3)f32 #519=(256,512,3,3)f32
2 1 518 519 520 bias=None dilation=(1,1) groups=1 padding=(1,1) stride=(1,1) $input=518 $weight=519 #518=(1,512,128,128)f32 #519=(256,512,3,3)f32 #520=(1,256,128,128)f32
1 1 520 521 expr=mul(@0,1.414214e+00) #520=(1,256,128,128)f32 #521=(1,256,128,128)f32
1 1 521 522 size=(1,1,128,128) $input=521 #521=(1,256,128,128)f32 #522=(1,1,128,128)f32
4 1 522 505 504 503 523 #522=(1,1,128,128)f32 #523=(1,1,128,128)f32
4 1 521 507 523 506 524 expr=add(add(@0,mul(@1,@2)),@3) #521=(1,256,128,128)f32 #507=(1)f32 #523=(1,1,128,128)f32 #506=(1,256,1,1)f32 #524=(1,256,128,128)f32
er.style_convs.8.activate 1 1 524 525 negative_slope=2.000000e-01 #524=(1,256,128,128)f32 #525=(1,256,128,128)f32
1 2 525 526 527 dim=1 split_size_or_sections=128 $tensor=525 #525=(1,256,128,128)f32 #526=(1,128,128,128)f32 #527=(1,128,128,128)f32
3 1 527 182 187 528 expr=add(mul(@0,@1),@2) #527=(1,128,128,128)f32 #182=(1,128,128,128)f32 #187=(1,128,128,128)f32 #528=(1,128,128,128)f32
0 1 529 expr=None
0 1 530 expr=1.000000e+00
0 1 531 expr=0.000000e+00
er.style_convs.9 0 1 532 @bias=(1,256,1,1)f32 #532=(1,256,1,1)f32
er.style_convs.9 0 1 533 @weight=(1)f32 #533=(1)f32
er.style_convs.9.modulated_conv 0 1 534 @weight=(1,256,256,3,3)f32 #534=(1,256,256,3,3)f32
2 1 526 528 535 dim=1 #526=(1,128,128,128)f32 #528=(1,128,128,128)f32 #535=(1,256,128,128)f32
1 1 233 536 dim=1 index=10 $input=233 #233=(1,16,512)f32 #536=(1,512)f32
er.style_convs.9.modulated_conv.modulation 1 1 536 537 bias=True in_features=512 out_features=256 @bias=(256)f32 @weight=(256,512)f32 #536=(1,512)f32 #537=(1,256)f32
1 1 537 538 shape=(1,1,256,1,1) $input=537 #537=(1,256)f32 #538=(1,1,256,1,1)f32
2 1 534 538 539 expr=mul(@0,@1) #534=(1,256,256,3,3)f32 #538=(1,1,256,1,1)f32 #539=(1,256,256,3,3)f32
1 1 539 540 expr=pow(@0,2) #539=(1,256,256,3,3)f32 #540=(1,256,256,3,3)f32
1 1 540 541 dim=(2,3,4) keepdim=False $input=540 #540=(1,256,256,3,3)f32 #541=(1,256)f32
1 1 541 542 expr=rsqrt(add(@0,1.000000e-08)) #541=(1,256)f32 #542=(1,256)f32
1 1 542 543 shape=(1,256,1,1,1) $input=542 #542=(1,256)f32 #543=(1,256,1,1,1)f32
2 1 539 543 544 expr=mul(@0,@1) #539=(1,256,256,3,3)f32 #543=(1,256,1,1,1)f32 #544=(1,256,256,3,3)f32
1 1 544 545 shape=(256,256,3,3) $input=544 #544=(1,256,256,3,3)f32 #545=(256,256,3,3)f32
2 1 535 545 546 bias=None dilation=(1,1) groups=1 padding=(1,1) stride=(1,1) $input=535 $weight=545 #535=(1,256,128,128)f32 #545=(256,256,3,3)f32 #546=(1,256,128,128)f32
1 1 546 547 expr=mul(@0,1.414214e+00) #546=(1,256,128,128)f32 #547=(1,256,128,128)f32
1 1 547 548 size=(1,1,128,128) $input=547 #547=(1,256,128,128)f32 #548=(1,1,128,128)f32
4 1 548 531 530 529 549 #548=(1,1,128,128)f32 #549=(1,1,128,128)f32
4 1 547 533 549 532 550 expr=add(add(@0,mul(@1,@2)),@3) #547=(1,256,128,128)f32 #533=(1)f32 #549=(1,1,128,128)f32 #532=(1,256,1,1)f32 #550=(1,256,128,128)f32
er.style_convs.9.activate 1 1 550 551 negative_slope=2.000000e-01 #550=(1,256,128,128)f32 #551=(1,256,128,128)f32
0 1 552 @bias=(1,3,1,1)f32 #552=(1,3,1,1)f32
er.to_rgbs.4.modulated_conv 0 1 553 @weight=(1,3,256,1,1)f32 #553=(1,3,256,1,1)f32
1 1 233 554 dim=1 index=11 $input=233 #233=(1,16,512)f32 #554=(1,512)f32
er.to_rgbs.4.modulated_conv.modulation 1 1 554 555 bias=True in_features=512 out_features=256 @bias=(256)f32 @weight=(256,512)f32 #554=(1,512)f32 #555=(1,256)f32
1 1 555 556 shape=(1,1,256,1,1) $input=555 #555=(1,256)f32 #556=(1,1,256,1,1)f32
2 1 553 556 557 expr=mul(@0,@1) #553=(1,3,256,1,1)f32 #556=(1,1,256,1,1)f32 #557=(1,3,256,1,1)f32
1 1 557 558 shape=(3,256,1,1) $input=557 #557=(1,3,256,1,1)f32 #558=(3,256,1,1)f32
2 1 551 558 559 bias=None dilation=(1,1) groups=1 padding=(0,0) stride=(1,1) $input=551 $weight=558 #551=(1,256,128,128)f32 #558=(3,256,1,1)f32 #559=(1,3,128,128)f32
1 1 502 560 align_corners=False mode=bilinear scale_factor=(2.000000e+00,2.000000e+00) $input=502 #502=(1,3,64,64)f32 #560=(1,3,128,128)f32
3 1 559 552 560 561 expr=add(add(@0,@1),@2) #559=(1,3,128,128)f32 #552=(1,3,1,1)f32 #560=(1,3,128,128)f32 #561=(1,3,128,128)f32
0 1 562 expr=None
0 1 563 expr=1.000000e+00
0 1 564 expr=0.000000e+00
er.style_convs.10 0 1 565 @bias=(1,128,1,1)f32 #565=(1,128,1,1)f32
er.style_convs.10 0 1 566 @weight=(1)f32 #566=(1)f32
er.style_convs.10.modulated_conv 0 1 567 @weight=(1,128,256,3,3)f32 #567=(1,128,256,3,3)f32
1 1 233 568 dim=1 index=11 $input=233 #233=(1,16,512)f32 #568=(1,512)f32
er.style_convs.10.modulated_conv.modulation 1 1 568 569 bias=True in_features=512 out_features=256 @bias=(256)f32 @weight=(256,512)f32 #568=(1,512)f32 #569=(1,256)f32
1 1 569 570 shape=(1,1,256,1,1) $input=569 #569=(1,256)f32 #570=(1,1,256,1,1)f32
2 1 567 570 571 expr=mul(@0,@1) #567=(1,128,256,3,3)f32 #570=(1,1,256,1,1)f32 #571=(1,128,256,3,3)f32
1 1 571 572 expr=pow(@0,2) #571=(1,128,256,3,3)f32 #572=(1,128,256,3,3)f32
1 1 572 573 dim=(2,3,4) keepdim=False $input=572 #572=(1,128,256,3,3)f32 #573=(1,128)f32
1 1 573 574 expr=rsqrt(add(@0,1.000000e-08)) #573=(1,128)f32 #574=(1,128)f32
1 1 574 575 shape=(1,128,1,1,1) $input=574 #574=(1,128)f32 #575=(1,128,1,1,1)f32
2 1 571 575 576 expr=mul(@0,@1) #571=(1,128,256,3,3)f32 #575=(1,128,1,1,1)f32 #576=(1,128,256,3,3)f32
1 1 551 577 align_corners=False mode=bilinear scale_factor=(2.000000e+00,2.000000e+00) $input=551 #551=(1,256,128,128)f32 #577=(1,256,256,256)f32
1 1 576 578 shape=(128,256,3,3) $input=576 #576=(1,128,256,3,3)f32 #578=(128,256,3,3)f32
2 1 577 578 579 bias=None dilation=(1,1) groups=1 padding=(1,1) stride=(1,1) $input=577 $weight=578 #577=(1,256,256,256)f32 #578=(128,256,3,3)f32 #579=(1,128,256,256)f32
1 1 579 580 expr=mul(@0,1.414214e+00) #579=(1,128,256,256)f32 #580=(1,128,256,256)f32
1 1 580 581 size=(1,1,256,256) $input=580 #580=(1,128,256,256)f32 #581=(1,1,256,256)f32
4 1 581 564 563 562 582 #581=(1,1,256,256)f32 #582=(1,1,256,256)f32
4 1 580 566 582 565 583 expr=add(add(@0,mul(@1,@2)),@3) #580=(1,128,256,256)f32 #566=(1)f32 #582=(1,1,256,256)f32 #565=(1,128,1,1)f32 #583=(1,128,256,256)f32
er.style_convs.10.activate 1 1 583 584 negative_slope=2.000000e-01 #583=(1,128,256,256)f32 #584=(1,128,256,256)f32
1 2 584 585 586 dim=1 split_size_or_sections=64 $tensor=584 #584=(1,128,256,256)f32 #585=(1,64,256,256)f32 #586=(1,64,256,256)f32
3 1 586 204 209 587 expr=add(mul(@0,@1),@2) #586=(1,64,256,256)f32 #204=(1,64,256,256)f32 #209=(1,64,256,256)f32 #587=(1,64,256,256)f32
0 1 588 expr=None
0 1 589 expr=1.000000e+00
0 1 590 expr=0.000000e+00
er.style_convs.11 0 1 591 @bias=(1,128,1,1)f32 #591=(1,128,1,1)f32
er.style_convs.11 0 1 592 @weight=(1)f32 #592=(1)f32
er.style_convs.11.modulated_conv 0 1 593 @weight=(1,128,128,3,3)f32 #593=(1,128,128,3,3)f32
2 1 585 587 594 dim=1 #585=(1,64,256,256)f32 #587=(1,64,256,256)f32 #594=(1,128,256,256)f32
1 1 233 595 dim=1 index=12 $input=233 #233=(1,16,512)f32 #595=(1,512)f32
er.style_convs.11.modulated_conv.modulation 1 1 595 596 bias=True in_features=512 out_features=128 @bias=(128)f32 @weight=(128,512)f32 #595=(1,512)f32 #596=(1,128)f32
1 1 596 597 shape=(1,1,128,1,1) $input=596 #596=(1,128)f32 #597=(1,1,128,1,1)f32
2 1 593 597 598 expr=mul(@0,@1) #593=(1,128,128,3,3)f32 #597=(1,1,128,1,1)f32 #598=(1,128,128,3,3)f32
1 1 598 599 expr=pow(@0,2) #598=(1,128,128,3,3)f32 #599=(1,128,128,3,3)f32
1 1 599 600 dim=(2,3,4) keepdim=False $input=599 #599=(1,128,128,3,3)f32 #600=(1,128)f32
1 1 600 601 expr=rsqrt(add(@0,1.000000e-08)) #600=(1,128)f32 #601=(1,128)f32
1 1 601 602 shape=(1,128,1,1,1) $input=601 #601=(1,128)f32 #602=(1,128,1,1,1)f32
2 1 598 602 603 expr=mul(@0,@1) #598=(1,128,128,3,3)f32 #602=(1,128,1,1,1)f32 #603=(1,128,128,3,3)f32
1 1 603 604 shape=(128,128,3,3) $input=603 #603=(1,128,128,3,3)f32 #604=(128,128,3,3)f32
2 1 594 604 605 bias=None dilation=(1,1) groups=1 padding=(1,1) stride=(1,1) $input=594 $weight=604 #594=(1,128,256,256)f32 #604=(128,128,3,3)f32 #605=(1,128,256,256)f32
1 1 605 606 expr=mul(@0,1.414214e+00) #605=(1,128,256,256)f32 #606=(1,128,256,256)f32
1 1 606 607 size=(1,1,256,256) $input=606 #606=(1,128,256,256)f32 #607=(1,1,256,256)f32
4 1 607 590 589 588 608 #607=(1,1,256,256)f32 #608=(1,1,256,256)f32
4 1 606 592 608 591 609 expr=add(add(@0,mul(@1,@2)),@3) #606=(1,128,256,256)f32 #592=(1)f32 #608=(1,1,256,256)f32 #591=(1,128,1,1)f32 #609=(1,128,256,256)f32
er.style_convs.11.activate 1 1 609 610 negative_slope=2.000000e-01 #609=(1,128,256,256)f32 #610=(1,128,256,256)f32
0 1 611 @bias=(1,3,1,1)f32 #611=(1,3,1,1)f32
er.to_rgbs.5.modulated_conv 0 1 612 @weight=(1,3,128,1,1)f32 #612=(1,3,128,1,1)f32
1 1 233 613 dim=1 index=13 $input=233 #233=(1,16,512)f32 #613=(1,512)f32
er.to_rgbs.5.modulated_conv.modulation 1 1 613 614 bias=True in_features=512 out_features=128 @bias=(128)f32 @weight=(128,512)f32 #613=(1,512)f32 #614=(1,128)f32
1 1 614 615 shape=(1,1,128,1,1) $input=614 #614=(1,128)f32 #615=(1,1,128,1,1)f32
2 1 612 615 616 expr=mul(@0,@1) #612=(1,3,128,1,1)f32 #615=(1,1,128,1,1)f32 #616=(1,3,128,1,1)f32
1 1 616 617 shape=(3,128,1,1) $input=616 #616=(1,3,128,1,1)f32 #617=(3,128,1,1)f32
2 1 610 617 618 bias=None dilation=(1,1) groups=1 padding=(0,0) stride=(1,1) $input=610 $weight=617 #610=(1,128,256,256)f32 #617=(3,128,1,1)f32 #618=(1,3,256,256)f32
1 1 561 619 align_corners=False mode=bilinear scale_factor=(2.000000e+00,2.000000e+00) $input=561 #561=(1,3,128,128)f32 #619=(1,3,256,256)f32
3 1 618 611 619 620 expr=add(add(@0,@1),@2) #618=(1,3,256,256)f32 #611=(1,3,1,1)f32 #619=(1,3,256,256)f32 #620=(1,3,256,256)f32
0 1 621 expr=None
0 1 622 expr=1.000000e+00
0 1 623 expr=0.000000e+00
er.style_convs.12 0 1 624 @bias=(1,64,1,1)f32 #624=(1,64,1,1)f32
er.style_convs.12 0 1 625 @weight=(1)f32 #625=(1)f32
er.style_convs.12.modulated_conv 0 1 626 @weight=(1,64,128,3,3)f32 #626=(1,64,128,3,3)f32
1 1 233 627 dim=1 index=13 $input=233 #233=(1,16,512)f32 #627=(1,512)f32
er.style_convs.12.modulated_conv.modulation 1 1 627 628 bias=True in_features=512 out_features=128 @bias=(128)f32 @weight=(128,512)f32 #627=(1,512)f32 #628=(1,128)f32
1 1 628 629 shape=(1,1,128,1,1) $input=628 #628=(1,128)f32 #629=(1,1,128,1,1)f32
2 1 626 629 630 expr=mul(@0,@1) #626=(1,64,128,3,3)f32 #629=(1,1,128,1,1)f32 #630=(1,64,128,3,3)f32
1 1 630 631 expr=pow(@0,2) #630=(1,64,128,3,3)f32 #631=(1,64,128,3,3)f32
1 1 631 632 dim=(2,3,4) keepdim=False $input=631 #631=(1,64,128,3,3)f32 #632=(1,64)f32
1 1 632 633 expr=rsqrt(add(@0,1.000000e-08)) #632=(1,64)f32 #633=(1,64)f32
1 1 633 634 shape=(1,64,1,1,1) $input=633 #633=(1,64)f32 #634=(1,64,1,1,1)f32
2 1 630 634 635 expr=mul(@0,@1) #630=(1,64,128,3,3)f32 #634=(1,64,1,1,1)f32 #635=(1,64,128,3,3)f32
1 1 610 636 align_corners=False mode=bilinear scale_factor=(2.000000e+00,2.000000e+00) $input=610 #610=(1,128,256,256)f32 #636=(1,128,512,512)f32
1 1 635 637 shape=(64,128,3,3) $input=635 #635=(1,64,128,3,3)f32 #637=(64,128,3,3)f32
2 1 636 637 638 bias=None dilation=(1,1) groups=1 padding=(1,1) stride=(1,1) $input=636 $weight=637 #636=(1,128,512,512)f32 #637=(64,128,3,3)f32 #638=(1,64,512,512)f32
1 1 638 639 expr=mul(@0,1.414214e+00) #638=(1,64,512,512)f32 #639=(1,64,512,512)f32
1 1 639 640 size=(1,1,512,512) $input=639 #639=(1,64,512,512)f32 #640=(1,1,512,512)f32
4 1 640 623 622 621 641 #640=(1,1,512,512)f32 #641=(1,1,512,512)f32
4 1 639 625 641 624 642 expr=add(add(@0,mul(@1,@2)),@3) #639=(1,64,512,512)f32 #625=(1)f32 #641=(1,1,512,512)f32 #624=(1,64,1,1)f32 #642=(1,64,512,512)f32
er.style_convs.12.activate 1 1 642 643 negative_slope=2.000000e-01 #642=(1,64,512,512)f32 #643=(1,64,512,512)f32
1 2 643 644 645 dim=1 split_size_or_sections=32 $tensor=643 #643=(1,64,512,512)f32 #644=(1,32,512,512)f32 #645=(1,32,512,512)f32
3 1 645 226 231 646 expr=add(mul(@0,@1),@2) #645=(1,32,512,512)f32 #226=(1,32,512,512)f32 #231=(1,32,512,512)f32 #646=(1,32,512,512)f32
0 1 647 expr=None
0 1 648 expr=1.000000e+00
0 1 649 expr=0.000000e+00
er.style_convs.13 0 1 650 @bias=(1,64,1,1)f32 #650=(1,64,1,1)f32
er.style_convs.13 0 1 651 @weight=(1)f32 #651=(1)f32
er.style_convs.13.modulated_conv 0 1 652 @weight=(1,64,64,3,3)f32 #652=(1,64,64,3,3)f32
2 1 644 646 653 dim=1 #644=(1,32,512,512)f32 #646=(1,32,512,512)f32 #653=(1,64,512,512)f32
1 1 233 654 dim=1 index=14 $input=233 #233=(1,16,512)f32 #654=(1,512)f32
er.style_convs.13.modulated_conv.modulation 1 1 654 655 bias=True in_features=512 out_features=64 @bias=(64)f32 @weight=(64,512)f32 #654=(1,512)f32 #655=(1,64)f32
1 1 655 656 shape=(1,1,64,1,1) $input=655 #655=(1,64)f32 #656=(1,1,64,1,1)f32
2 1 652 656 657 expr=mul(@0,@1) #652=(1,64,64,3,3)f32 #656=(1,1,64,1,1)f32 #657=(1,64,64,3,3)f32
1 1 657 658 expr=pow(@0,2) #657=(1,64,64,3,3)f32 #658=(1,64,64,3,3)f32
1 1 658 659 dim=(2,3,4) keepdim=False $input=658 #658=(1,64,64,3,3)f32 #659=(1,64)f32
1 1 659 660 expr=rsqrt(add(@0,1.000000e-08)) #659=(1,64)f32 #660=(1,64)f32
1 1 660 661 shape=(1,64,1,1,1) $input=660 #660=(1,64)f32 #661=(1,64,1,1,1)f32
2 1 657 661 662 expr=mul(@0,@1) #657=(1,64,64,3,3)f32 #661=(1,64,1,1,1)f32 #662=(1,64,64,3,3)f32
1 1 662 663 shape=(64,64,3,3) $input=662 #662=(1,64,64,3,3)f32 #663=(64,64,3,3)f32
2 1 653 663 664 bias=None dilation=(1,1) groups=1 padding=(1,1) stride=(1,1) $input=653 $weight=663 #653=(1,64,512,512)f32 #663=(64,64,3,3)f32 #664=(1,64,512,512)f32
1 1 664 665 expr=mul(@0,1.414214e+00) #664=(1,64,512,512)f32 #665=(1,64,512,512)f32
1 1 665 666 size=(1,1,512,512) $input=665 #665=(1,64,512,512)f32 #666=(1,1,512,512)f32
4 1 666 649 648 647 667 #666=(1,1,512,512)f32 #667=(1,1,512,512)f32
4 1 665 651 667 650 668 expr=add(add(@0,mul(@1,@2)),@3) #665=(1,64,512,512)f32 #651=(1)f32 #667=(1,1,512,512)f32 #650=(1,64,1,1)f32 #668=(1,64,512,512)f32
er.style_convs.13.activate 1 1 668 669 negative_slope=2.000000e-01 #668=(1,64,512,512)f32 #669=(1,64,512,512)f32
0 1 670 @bias=(1,3,1,1)f32 #670=(1,3,1,1)f32
er.to_rgbs.6.modulated_conv 0 1 671 @weight=(1,3,64,1,1)f32 #671=(1,3,64,1,1)f32
1 1 233 672 dim=1 index=15 $input=233 #233=(1,16,512)f32 #672=(1,512)f32
er.to_rgbs.6.modulated_conv.modulation 1 1 672 673 bias=True in_features=512 out_features=64 @bias=(64)f32 @weight=(64,512)f32 #672=(1,512)f32 #673=(1,64)f32
1 1 673 674 shape=(1,1,64,1,1) $input=673 #673=(1,64)f32 #674=(1,1,64,1,1)f32
2 1 671 674 675 expr=mul(@0,@1) #671=(1,3,64,1,1)f32 #674=(1,1,64,1,1)f32 #675=(1,3,64,1,1)f32
1 1 675 676 shape=(3,64,1,1) $input=675 #675=(1,3,64,1,1)f32 #676=(3,64,1,1)f32
2 1 669 676 677 bias=None dilation=(1,1) groups=1 padding=(0,0) stride=(1,1) $input=669 $weight=676 #669=(1,64,512,512)f32 #676=(3,64,1,1)f32 #677=(1,3,512,512)f32
1 1 620 678 align_corners=False mode=bilinear scale_factor=(2.000000e+00,2.000000e+00) $input=620 #620=(1,3,256,256)f32 #678=(1,3,512,512)f32
3 1 677 670 678 679 expr=add(add(@0,@1),@2) #677=(1,3,512,512)f32 #670=(1,3,1,1)f32 #678=(1,3,512,512)f32 #679=(1,3,512,512)f32
7 1 100 122 144 166 188 210 232 680 expr=[@0,@1,@2,@3,@4,@5,@6] #100=(1,3,8,8)f32 #122=(1,3,16,16)f32 #144=(1,3,32,32)f32 #166=(1,3,64,64)f32 #188=(1,3,128,128)f32 #210=(1,3,256,256)f32 #232=(1,3,512,512)f32
2 1 679 680 681 #679=(1,3,512,512)f32
1 0 681

转后的pnnx,py 文件为：
**
import os
import numpy as np
import tempfile, zipfile
import torch
import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
def init(self):
super(Model, self).init()

self.conv_body_first = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=3, kernel_size=(1,1), out_channels=32, padding=(0,0), padding_mode='zeros', stride=(1,1)) self.conv_body_down_0_conv1 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=32, kernel_size=(3,3), out_channels=32, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.conv_body_down_0_conv2 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=32, kernel_size=(3,3), out_channels=64, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.conv_body_down_0_skip = nn.Conv2d(bias=False, dilation=(1,1), groups=1, in_channels=32, kernel_size=(1,1), out_channels=64, padding=(0,0), padding_mode='zeros', stride=(1,1)) self.conv_body_down_1_conv1 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=64, kernel_size=(3,3), out_channels=64, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.conv_body_down_1_conv2 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=64, kernel_size=(3,3), out_channels=128, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.conv_body_down_1_skip = nn.Conv2d(bias=False, dilation=(1,1), groups=1, in_channels=64, kernel_size=(1,1), out_channels=128, padding=(0,0), padding_mode='zeros', stride=(1,1)) self.conv_body_down_2_conv1 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=128, kernel_size=(3,3), out_channels=128, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.conv_body_down_2_conv2 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=128, kernel_size=(3,3), out_channels=256, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.conv_body_down_2_skip = nn.Conv2d(bias=False, dilation=(1,1), groups=1, in_channels=128, kernel_size=(1,1), out_channels=256, padding=(0,0), padding_mode='zeros', stride=(1,1)) self.conv_body_down_3_conv1 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=256, kernel_size=(3,3), out_channels=256, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.conv_body_down_3_conv2 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=256, kernel_size=(3,3), out_channels=256, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.conv_body_down_3_skip = nn.Conv2d(bias=False, dilation=(1,1), groups=1, in_channels=256, kernel_size=(1,1), out_channels=256, padding=(0,0), padding_mode='zeros', stride=(1,1)) self.conv_body_down_4_conv1 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=256, kernel_size=(3,3), out_channels=256, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.conv_body_down_4_conv2 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=256, kernel_size=(3,3), out_channels=256, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.conv_body_down_4_skip = nn.Conv2d(bias=False, dilation=(1,1), groups=1, in_channels=256, kernel_size=(1,1), out_channels=256, padding=(0,0), padding_mode='zeros', stride=(1,1)) self.conv_body_down_5_conv1 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=256, kernel_size=(3,3), out_channels=256, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.conv_body_down_5_conv2 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=256, kernel_size=(3,3), out_channels=256, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.conv_body_down_5_skip = nn.Conv2d(bias=False, dilation=(1,1), groups=1, in_channels=256, kernel_size=(1,1), out_channels=256, padding=(0,0), padding_mode='zeros', stride=(1,1)) self.conv_body_down_6_conv1 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=256, kernel_size=(3,3), out_channels=256, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.conv_body_down_6_conv2 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=256, kernel_size=(3,3), out_channels=256, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.conv_body_down_6_skip = nn.Conv2d(bias=False, dilation=(1,1), groups=1, in_channels=256, kernel_size=(1,1), out_channels=256, padding=(0,0), padding_mode='zeros', stride=(1,1)) self.final_conv = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=256, kernel_size=(3,3), out_channels=256, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.final_linear = nn.Linear(bias=True, in_features=4096, out_features=8192) self.conv_body_up_0_conv1 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=256, kernel_size=(3,3), out_channels=256, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.conv_body_up_0_conv2 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=256, kernel_size=(3,3), out_channels=256, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.conv_body_up_0_skip = nn.Conv2d(bias=False, dilation=(1,1), groups=1, in_channels=256, kernel_size=(1,1), out_channels=256, padding=(0,0), padding_mode='zeros', stride=(1,1)) self.condition_scale_0_0 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=256, kernel_size=(3,3), out_channels=256, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.condition_scale_0_1 = nn.LeakyReLU(negative_slope=0.200000) self.condition_scale_0_2 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=256, kernel_size=(3,3), out_channels=256, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.condition_shift_0_0 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=256, kernel_size=(3,3), out_channels=256, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.condition_shift_0_1 = nn.LeakyReLU(negative_slope=0.200000) self.condition_shift_0_2 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=256, kernel_size=(3,3), out_channels=256, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.toRGB_0 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=256, kernel_size=(1,1), out_channels=3, padding=(0,0), padding_mode='zeros', stride=(1,1)) self.conv_body_up_1_conv1 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=256, kernel_size=(3,3), out_channels=256, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.conv_body_up_1_conv2 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=256, kernel_size=(3,3), out_channels=256, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.conv_body_up_1_skip = nn.Conv2d(bias=False, dilation=(1,1), groups=1, in_channels=256, kernel_size=(1,1), out_channels=256, padding=(0,0), padding_mode='zeros', stride=(1,1)) self.condition_scale_1_0 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=256, kernel_size=(3,3), out_channels=256, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.condition_scale_1_1 = nn.LeakyReLU(negative_slope=0.200000) self.condition_scale_1_2 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=256, kernel_size=(3,3), out_channels=256, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.condition_shift_1_0 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=256, kernel_size=(3,3), out_channels=256, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.condition_shift_1_1 = nn.LeakyReLU(negative_slope=0.200000) self.condition_shift_1_2 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=256, kernel_size=(3,3), out_channels=256, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.toRGB_1 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=256, kernel_size=(1,1), out_channels=3, padding=(0,0), padding_mode='zeros', stride=(1,1)) self.conv_body_up_2_conv1 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=256, kernel_size=(3,3), out_channels=256, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.conv_body_up_2_conv2 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=256, kernel_size=(3,3), out_channels=256, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.conv_body_up_2_skip = nn.Conv2d(bias=False, dilation=(1,1), groups=1, in_channels=256, kernel_size=(1,1), out_channels=256, padding=(0,0), padding_mode='zeros', stride=(1,1)) self.condition_scale_2_0 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=256, kernel_size=(3,3), out_channels=256, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.condition_scale_2_1 = nn.LeakyReLU(negative_slope=0.200000) self.condition_scale_2_2 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=256, kernel_size=(3,3), out_channels=256, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.condition_shift_2_0 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=256, kernel_size=(3,3), out_channels=256, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.condition_shift_2_1 = nn.LeakyReLU(negative_slope=0.200000) self.condition_shift_2_2 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=256, kernel_size=(3,3), out_channels=256, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.toRGB_2 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=256, kernel_size=(1,1), out_channels=3, padding=(0,0), padding_mode='zeros', stride=(1,1)) self.conv_body_up_3_conv1 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=256, kernel_size=(3,3), out_channels=256, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.conv_body_up_3_conv2 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=256, kernel_size=(3,3), out_channels=256, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.conv_body_up_3_skip = nn.Conv2d(bias=False, dilation=(1,1), groups=1, in_channels=256, kernel_size=(1,1), out_channels=256, padding=(0,0), padding_mode='zeros', stride=(1,1)) self.condition_scale_3_0 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=256, kernel_size=(3,3), out_channels=256, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.condition_scale_3_1 = nn.LeakyReLU(negative_slope=0.200000) self.condition_scale_3_2 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=256, kernel_size=(3,3), out_channels=256, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.condition_shift_3_0 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=256, kernel_size=(3,3), out_channels=256, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.condition_shift_3_1 = nn.LeakyReLU(negative_slope=0.200000) self.condition_shift_3_2 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=256, kernel_size=(3,3), out_channels=256, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.toRGB_3 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=256, kernel_size=(1,1), out_channels=3, padding=(0,0), padding_mode='zeros', stride=(1,1)) self.conv_body_up_4_conv1 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=256, kernel_size=(3,3), out_channels=256, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.conv_body_up_4_conv2 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=256, kernel_size=(3,3), out_channels=128, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.conv_body_up_4_skip = nn.Conv2d(bias=False, dilation=(1,1), groups=1, in_channels=256, kernel_size=(1,1), out_channels=128, padding=(0,0), padding_mode='zeros', stride=(1,1)) self.condition_scale_4_0 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=128, kernel_size=(3,3), out_channels=128, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.condition_scale_4_1 = nn.LeakyReLU(negative_slope=0.200000) self.condition_scale_4_2 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=128, kernel_size=(3,3), out_channels=128, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.condition_shift_4_0 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=128, kernel_size=(3,3), out_channels=128, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.condition_shift_4_1 = nn.LeakyReLU(negative_slope=0.200000) self.condition_shift_4_2 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=128, kernel_size=(3,3), out_channels=128, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.toRGB_4 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=128, kernel_size=(1,1), out_channels=3, padding=(0,0), padding_mode='zeros', stride=(1,1)) self.conv_body_up_5_conv1 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=128, kernel_size=(3,3), out_channels=128, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.conv_body_up_5_conv2 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=128, kernel_size=(3,3), out_channels=64, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.conv_body_up_5_skip = nn.Conv2d(bias=False, dilation=(1,1), groups=1, in_channels=128, kernel_size=(1,1), out_channels=64, padding=(0,0), padding_mode='zeros', stride=(1,1)) self.condition_scale_5_0 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=64, kernel_size=(3,3), out_channels=64, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.condition_scale_5_1 = nn.LeakyReLU(negative_slope=0.200000) self.condition_scale_5_2 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=64, kernel_size=(3,3), out_channels=64, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.condition_shift_5_0 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=64, kernel_size=(3,3), out_channels=64, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.condition_shift_5_1 = nn.LeakyReLU(negative_slope=0.200000) self.condition_shift_5_2 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=64, kernel_size=(3,3), out_channels=64, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.toRGB_5 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=64, kernel_size=(1,1), out_channels=3, padding=(0,0), padding_mode='zeros', stride=(1,1)) self.conv_body_up_6_conv1 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=64, kernel_size=(3,3), out_channels=64, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.conv_body_up_6_conv2 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=64, kernel_size=(3,3), out_channels=32, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.conv_body_up_6_skip = nn.Conv2d(bias=False, dilation=(1,1), groups=1, in_channels=64, kernel_size=(1,1), out_channels=32, padding=(0,0), padding_mode='zeros', stride=(1,1)) self.condition_scale_6_0 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=32, kernel_size=(3,3), out_channels=32, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.condition_scale_6_1 = nn.LeakyReLU(negative_slope=0.200000) self.condition_scale_6_2 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=32, kernel_size=(3,3), out_channels=32, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.condition_shift_6_0 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=32, kernel_size=(3,3), out_channels=32, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.condition_shift_6_1 = nn.LeakyReLU(negative_slope=0.200000) self.condition_shift_6_2 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=32, kernel_size=(3,3), out_channels=32, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.toRGB_6 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=32, kernel_size=(1,1), out_channels=3, padding=(0,0), padding_mode='zeros', stride=(1,1)) self.stylegan_decoder_style_conv1_modulated_conv_modulation = nn.Linear(bias=True, in_features=512, out_features=512) self.stylegan_decoder_style_conv1_activate = nn.LeakyReLU(negative_slope=0.200000) self.stylegan_decoder_to_rgb1_modulated_conv_modulation = nn.Linear(bias=True, in_features=512, out_features=512) self.stylegan_decoder_style_convs_0_modulated_conv_modulation = nn.Linear(bias=True, in_features=512, out_features=512) self.stylegan_decoder_style_convs_0_activate = nn.LeakyReLU(negative_slope=0.200000) self.stylegan_decoder_style_convs_1_modulated_conv_modulation = nn.Linear(bias=True, in_features=512, out_features=512) self.stylegan_decoder_style_convs_1_activate = nn.LeakyReLU(negative_slope=0.200000) self.stylegan_decoder_to_rgbs_0_modulated_conv_modulation = nn.Linear(bias=True, in_features=512, out_features=512) self.stylegan_decoder_style_convs_2_modulated_conv_modulation = nn.Linear(bias=True, in_features=512, out_features=512) self.stylegan_decoder_style_convs_2_activate = nn.LeakyReLU(negative_slope=0.200000) self.stylegan_decoder_style_convs_3_modulated_conv_modulation = nn.Linear(bias=True, in_features=512, out_features=512) self.stylegan_decoder_style_convs_3_activate = nn.LeakyReLU(negative_slope=0.200000) self.stylegan_decoder_to_rgbs_1_modulated_conv_modulation = nn.Linear(bias=True, in_features=512, out_features=512) self.stylegan_decoder_style_convs_4_modulated_conv_modulation = nn.Linear(bias=True, in_features=512, out_features=512) self.stylegan_decoder_style_convs_4_activate = nn.LeakyReLU(negative_slope=0.200000) self.stylegan_decoder_style_convs_5_modulated_conv_modulation = nn.Linear(bias=True, in_features=512, out_features=512) self.stylegan_decoder_style_convs_5_activate = nn.LeakyReLU(negative_slope=0.200000) self.stylegan_decoder_to_rgbs_2_modulated_conv_modulation = nn.Linear(bias=True, in_features=512, out_features=512) self.stylegan_decoder_style_convs_6_modulated_conv_modulation = nn.Linear(bias=True, in_features=512, out_features=512) self.stylegan_decoder_style_convs_6_activate = nn.LeakyReLU(negative_slope=0.200000) self.stylegan_decoder_style_convs_7_modulated_conv_modulation = nn.Linear(bias=True, in_features=512, out_features=512) self.stylegan_decoder_style_convs_7_activate = nn.LeakyReLU(negative_slope=0.200000) self.stylegan_decoder_to_rgbs_3_modulated_conv_modulation = nn.Linear(bias=True, in_features=512, out_features=512) self.stylegan_decoder_style_convs_8_modulated_conv_modulation = nn.Linear(bias=True, in_features=512, out_features=512) self.stylegan_decoder_style_convs_8_activate = nn.LeakyReLU(negative_slope=0.200000) self.stylegan_decoder_style_convs_9_modulated_conv_modulation = nn.Linear(bias=True, in_features=512, out_features=256) self.stylegan_decoder_style_convs_9_activate = nn.LeakyReLU(negative_slope=0.200000) self.stylegan_decoder_to_rgbs_4_modulated_conv_modulation = nn.Linear(bias=True, in_features=512, out_features=256) self.stylegan_decoder_style_convs_10_modulated_conv_modulation = nn.Linear(bias=True, in_features=512, out_features=256) self.stylegan_decoder_style_convs_10_activate = nn.LeakyReLU(negative_slope=0.200000) self.stylegan_decoder_style_convs_11_modulated_conv_modulation = nn.Linear(bias=True, in_features=512, out_features=128) self.stylegan_decoder_style_convs_11_activate = nn.LeakyReLU(negative_slope=0.200000) self.stylegan_decoder_to_rgbs_5_modulated_conv_modulation = nn.Linear(bias=True, in_features=512, out_features=128) self.stylegan_decoder_style_convs_12_modulated_conv_modulation = nn.Linear(bias=True, in_features=512, out_features=128) self.stylegan_decoder_style_convs_12_activate = nn.LeakyReLU(negative_slope=0.200000) self.stylegan_decoder_style_convs_13_modulated_conv_modulation = nn.Linear(bias=True, in_features=512, out_features=64) self.stylegan_decoder_style_convs_13_activate = nn.LeakyReLU(negative_slope=0.200000) self.stylegan_decoder_to_rgbs_6_modulated_conv_modulation = nn.Linear(bias=True, in_features=512, out_features=64) archive = zipfile.ZipFile('E://opt//ncnn//tools//pnnx//build_debug//install//bin//GFPGANCleanv1-NoCE-C2.pnnx.bin', 'r') self.conv_body_first.bias = self.load_pnnx_bin_as_parameter(archive, 'conv_body_first.bias', (32), 'float32') self.conv_body_first.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_body_first.weight', (32,3,1,1), 'float32') self.conv_body_down_0_conv1.bias = self.load_pnnx_bin_as_parameter(archive, 'conv_body_down.0.conv1.bias', (32), 'float32') self.conv_body_down_0_conv1.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_body_down.0.conv1.weight', (32,32,3,3), 'float32') self.conv_body_down_0_conv2.bias = self.load_pnnx_bin_as_parameter(archive, 'conv_body_down.0.conv2.bias', (64), 'float32') self.conv_body_down_0_conv2.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_body_down.0.conv2.weight', (64,32,3,3), 'float32') self.conv_body_down_0_skip.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_body_down.0.skip.weight', (64,32,1,1), 'float32') self.conv_body_down_1_conv1.bias = self.load_pnnx_bin_as_parameter(archive, 'conv_body_down.1.conv1.bias', (64), 'float32') self.conv_body_down_1_conv1.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_body_down.1.conv1.weight', (64,64,3,3), 'float32') self.conv_body_down_1_conv2.bias = self.load_pnnx_bin_as_parameter(archive, 'conv_body_down.1.conv2.bias', (128), 'float32') self.conv_body_down_1_conv2.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_body_down.1.conv2.weight', (128,64,3,3), 'float32') self.conv_body_down_1_skip.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_body_down.1.skip.weight', (128,64,1,1), 'float32') self.conv_body_down_2_conv1.bias = self.load_pnnx_bin_as_parameter(archive, 'conv_body_down.2.conv1.bias', (128), 'float32') self.conv_body_down_2_conv1.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_body_down.2.conv1.weight', (128,128,3,3), 'float32') self.conv_body_down_2_conv2.bias = self.load_pnnx_bin_as_parameter(archive, 'conv_body_down.2.conv2.bias', (256), 'float32') self.conv_body_down_2_conv2.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_body_down.2.conv2.weight', (256,128,3,3), 'float32') self.conv_body_down_2_skip.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_body_down.2.skip.weight', (256,128,1,1), 'float32') self.conv_body_down_3_conv1.bias = self.load_pnnx_bin_as_parameter(archive, 'conv_body_down.3.conv1.bias', (256), 'float32') self.conv_body_down_3_conv1.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_body_down.3.conv1.weight', (256,256,3,3), 'float32') self.conv_body_down_3_conv2.bias = self.load_pnnx_bin_as_parameter(archive, 'conv_body_down.3.conv2.bias', (256), 'float32') self.conv_body_down_3_conv2.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_body_down.3.conv2.weight', (256,256,3,3), 'float32') self.conv_body_down_3_skip.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_body_down.3.skip.weight', (256,256,1,1), 'float32') self.conv_body_down_4_conv1.bias = self.load_pnnx_bin_as_parameter(archive, 'conv_body_down.4.conv1.bias', (256), 'float32') self.conv_body_down_4_conv1.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_body_down.4.conv1.weight', (256,256,3,3), 'float32') self.conv_body_down_4_conv2.bias = self.load_pnnx_bin_as_parameter(archive, 'conv_body_down.4.conv2.bias', (256), 'float32') self.conv_body_down_4_conv2.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_body_down.4.conv2.weight', (256,256,3,3), 'float32') self.conv_body_down_4_skip.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_body_down.4.skip.weight', (256,256,1,1), 'float32') self.conv_body_down_5_conv1.bias = self.load_pnnx_bin_as_parameter(archive, 'conv_body_down.5.conv1.bias', (256), 'float32') self.conv_body_down_5_conv1.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_body_down.5.conv1.weight', (256,256,3,3), 'float32') self.conv_body_down_5_conv2.bias = self.load_pnnx_bin_as_parameter(archive, 'conv_body_down.5.conv2.bias', (256), 'float32') self.conv_body_down_5_conv2.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_body_down.5.conv2.weight', (256,256,3,3), 'float32') self.conv_body_down_5_skip.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_body_down.5.skip.weight', (256,256,1,1), 'float32') self.conv_body_down_6_conv1.bias = self.load_pnnx_bin_as_parameter(archive, 'conv_body_down.6.conv1.bias', (256), 'float32') self.conv_body_down_6_conv1.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_body_down.6.conv1.weight', (256,256,3,3), 'float32') self.conv_body_down_6_conv2.bias = self.load_pnnx_bin_as_parameter(archive, 'conv_body_down.6.conv2.bias', (256), 'float32') self.conv_body_down_6_conv2.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_body_down.6.conv2.weight', (256,256,3,3), 'float32') self.conv_body_down_6_skip.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_body_down.6.skip.weight', (256,256,1,1), 'float32') self.final_conv.bias = self.load_pnnx_bin_as_parameter(archive, 'final_conv.bias', (256), 'float32') self.final_conv.weight = self.load_pnnx_bin_as_parameter(archive, 'final_conv.weight', (256,256,3,3), 'float32') self.final_linear.bias = self.load_pnnx_bin_as_parameter(archive, 'final_linear.bias', (8192), 'float32') self.final_linear.weight = self.load_pnnx_bin_as_parameter(archive, 'final_linear.weight', (8192,4096), 'float32') self.conv_body_up_0_conv1.bias = self.load_pnnx_bin_as_parameter(archive, 'conv_body_up.0.conv1.bias', (256), 'float32') self.conv_body_up_0_conv1.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_body_up.0.conv1.weight', (256,256,3,3), 'float32') self.conv_body_up_0_conv2.bias = self.load_pnnx_bin_as_parameter(archive, 'conv_body_up.0.conv2.bias', (256), 'float32') self.conv_body_up_0_conv2.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_body_up.0.conv2.weight', (256,256,3,3), 'float32') self.conv_body_up_0_skip.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_body_up.0.skip.weight', (256,256,1,1), 'float32') self.condition_scale_0_0.bias = self.load_pnnx_bin_as_parameter(archive, 'condition_scale.0.0.bias', (256), 'float32') self.condition_scale_0_0.weight = self.load_pnnx_bin_as_parameter(archive, 'condition_scale.0.0.weight', (256,256,3,3), 'float32') self.condition_scale_0_2.bias = self.load_pnnx_bin_as_parameter(archive, 'condition_scale.0.2.bias', (256), 'float32') self.condition_scale_0_2.weight = self.load_pnnx_bin_as_parameter(archive, 'condition_scale.0.2.weight', (256,256,3,3), 'float32') self.condition_shift_0_0.bias = self.load_pnnx_bin_as_parameter(archive, 'condition_shift.0.0.bias', (256), 'float32') self.condition_shift_0_0.weight = self.load_pnnx_bin_as_parameter(archive, 'condition_shift.0.0.weight', (256,256,3,3), 'float32') self.condition_shift_0_2.bias = self.load_pnnx_bin_as_parameter(archive, 'condition_shift.0.2.bias', (256), 'float32') self.condition_shift_0_2.weight = self.load_pnnx_bin_as_parameter(archive, 'condition_shift.0.2.weight', (256,256,3,3), 'float32') self.toRGB_0.bias = self.load_pnnx_bin_as_parameter(archive, 'toRGB.0.bias', (3), 'float32') self.toRGB_0.weight = self.load_pnnx_bin_as_parameter(archive, 'toRGB.0.weight', (3,256,1,1), 'float32') self.conv_body_up_1_conv1.bias = self.load_pnnx_bin_as_parameter(archive, 'conv_body_up.1.conv1.bias', (256), 'float32') self.conv_body_up_1_conv1.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_body_up.1.conv1.weight', (256,256,3,3), 'float32') self.conv_body_up_1_conv2.bias = self.load_pnnx_bin_as_parameter(archive, 'conv_body_up.1.conv2.bias', (256), 'float32') self.conv_body_up_1_conv2.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_body_up.1.conv2.weight', (256,256,3,3), 'float32') self.conv_body_up_1_skip.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_body_up.1.skip.weight', (256,256,1,1), 'float32') self.condition_scale_1_0.bias = self.load_pnnx_bin_as_parameter(archive, 'condition_scale.1.0.bias', (256), 'float32') self.condition_scale_1_0.weight = self.load_pnnx_bin_as_parameter(archive, 'condition_scale.1.0.weight', (256,256,3,3), 'float32') self.condition_scale_1_2.bias = self.load_pnnx_bin_as_parameter(archive, 'condition_scale.1.2.bias', (256), 'float32') self.condition_scale_1_2.weight = self.load_pnnx_bin_as_parameter(archive, 'condition_scale.1.2.weight', (256,256,3,3), 'float32') self.condition_shift_1_0.bias = self.load_pnnx_bin_as_parameter(archive, 'condition_shift.1.0.bias', (256), 'float32') self.condition_shift_1_0.weight = self.load_pnnx_bin_as_parameter(archive, 'condition_shift.1.0.weight', (256,256,3,3), 'float32') self.condition_shift_1_2.bias = self.load_pnnx_bin_as_parameter(archive, 'condition_shift.1.2.bias', (256), 'float32') self.condition_shift_1_2.weight = self.load_pnnx_bin_as_parameter(archive, 'condition_shift.1.2.weight', (256,256,3,3), 'float32') self.toRGB_1.bias = self.load_pnnx_bin_as_parameter(archive, 'toRGB.1.bias', (3), 'float32') self.toRGB_1.weight = self.load_pnnx_bin_as_parameter(archive, 'toRGB.1.weight', (3,256,1,1), 'float32') self.conv_body_up_2_conv1.bias = self.load_pnnx_bin_as_parameter(archive, 'conv_body_up.2.conv1.bias', (256), 'float32') self.conv_body_up_2_conv1.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_body_up.2.conv1.weight', (256,256,3,3), 'float32') self.conv_body_up_2_conv2.bias = self.load_pnnx_bin_as_parameter(archive, 'conv_body_up.2.conv2.bias', (256), 'float32') self.conv_body_up_2_conv2.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_body_up.2.conv2.weight', (256,256,3,3), 'float32') self.conv_body_up_2_skip.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_body_up.2.skip.weight', (256,256,1,1), 'float32') self.condition_scale_2_0.bias = self.load_pnnx_bin_as_parameter(archive, 'condition_scale.2.0.bias', (256), 'float32') self.condition_scale_2_0.weight = self.load_pnnx_bin_as_parameter(archive, 'condition_scale.2.0.weight', (256,256,3,3), 'float32') self.condition_scale_2_2.bias = self.load_pnnx_bin_as_parameter(archive, 'condition_scale.2.2.bias', (256), 'float32') self.condition_scale_2_2.weight = self.load_pnnx_bin_as_parameter(archive, 'condition_scale.2.2.weight', (256,256,3,3), 'float32') self.condition_shift_2_0.bias = self.load_pnnx_bin_as_parameter(archive, 'condition_shift.2.0.bias', (256), 'float32') self.condition_shift_2_0.weight = self.load_pnnx_bin_as_parameter(archive, 'condition_shift.2.0.weight', (256,256,3,3), 'float32') self.condition_shift_2_2.bias = self.load_pnnx_bin_as_parameter(archive, 'condition_shift.2.2.bias', (256), 'float32') self.condition_shift_2_2.weight = self.load_pnnx_bin_as_parameter(archive, 'condition_shift.2.2.weight', (256,256,3,3), 'float32') self.toRGB_2.bias = self.load_pnnx_bin_as_parameter(archive, 'toRGB.2.bias', (3), 'float32') self.toRGB_2.weight = self.load_pnnx_bin_as_parameter(archive, 'toRGB.2.weight', (3,256,1,1), 'float32') self.conv_body_up_3_conv1.bias = self.load_pnnx_bin_as_parameter(archive, 'conv_body_up.3.conv1.bias', (256), 'float32') self.conv_body_up_3_conv1.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_body_up.3.conv1.weight', (256,256,3,3), 'float32') self.conv_body_up_3_conv2.bias = self.load_pnnx_bin_as_parameter(archive, 'conv_body_up.3.conv2.bias', (256), 'float32') self.conv_body_up_3_conv2.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_body_up.3.conv2.weight', (256,256,3,3), 'float32') self.conv_body_up_3_skip.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_body_up.3.skip.weight', (256,256,1,1), 'float32') self.condition_scale_3_0.bias = self.load_pnnx_bin_as_parameter(archive, 'condition_scale.3.0.bias', (256), 'float32') self.condition_scale_3_0.weight = self.load_pnnx_bin_as_parameter(archive, 'condition_scale.3.0.weight', (256,256,3,3), 'float32') self.condition_scale_3_2.bias = self.load_pnnx_bin_as_parameter(archive, 'condition_scale.3.2.bias', (256), 'float32') self.condition_scale_3_2.weight = self.load_pnnx_bin_as_parameter(archive, 'condition_scale.3.2.weight', (256,256,3,3), 'float32') self.condition_shift_3_0.bias = self.load_pnnx_bin_as_parameter(archive, 'condition_shift.3.0.bias', (256), 'float32') self.condition_shift_3_0.weight = self.load_pnnx_bin_as_parameter(archive, 'condition_shift.3.0.weight', (256,256,3,3), 'float32') self.condition_shift_3_2.bias = self.load_pnnx_bin_as_parameter(archive, 'condition_shift.3.2.bias', (256), 'float32') self.condition_shift_3_2.weight = self.load_pnnx_bin_as_parameter(archive, 'condition_shift.3.2.weight', (256,256,3,3), 'float32') self.toRGB_3.bias = self.load_pnnx_bin_as_parameter(archive, 'toRGB.3.bias', (3), 'float32') self.toRGB_3.weight = self.load_pnnx_bin_as_parameter(archive, 'toRGB.3.weight', (3,256,1,1), 'float32') self.conv_body_up_4_conv1.bias = self.load_pnnx_bin_as_parameter(archive, 'conv_body_up.4.conv1.bias', (256), 'float32') self.conv_body_up_4_conv1.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_body_up.4.conv1.weight', (256,256,3,3), 'float32') self.conv_body_up_4_conv2.bias = self.load_pnnx_bin_as_parameter(archive, 'conv_body_up.4.conv2.bias', (128), 'float32') self.conv_body_up_4_conv2.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_body_up.4.conv2.weight', (128,256,3,3), 'float32') self.conv_body_up_4_skip.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_body_up.4.skip.weight', (128,256,1,1), 'float32') self.condition_scale_4_0.bias = self.load_pnnx_bin_as_parameter(archive, 'condition_scale.4.0.bias', (128), 'float32') self.condition_scale_4_0.weight = self.load_pnnx_bin_as_parameter(archive, 'condition_scale.4.0.weight', (128,128,3,3), 'float32') self.condition_scale_4_2.bias = self.load_pnnx_bin_as_parameter(archive, 'condition_scale.4.2.bias', (128), 'float32') self.condition_scale_4_2.weight = self.load_pnnx_bin_as_parameter(archive, 'condition_scale.4.2.weight', (128,128,3,3), 'float32') self.condition_shift_4_0.bias = self.load_pnnx_bin_as_parameter(archive, 'condition_shift.4.0.bias', (128), 'float32') self.condition_shift_4_0.weight = self.load_pnnx_bin_as_parameter(archive, 'condition_shift.4.0.weight', (128,128,3,3), 'float32') self.condition_shift_4_2.bias = self.load_pnnx_bin_as_parameter(archive, 'condition_shift.4.2.bias', (128), 'float32') self.condition_shift_4_2.weight = self.load_pnnx_bin_as_parameter(archive, 'condition_shift.4.2.weight', (128,128,3,3), 'float32') self.toRGB_4.bias = self.load_pnnx_bin_as_parameter(archive, 'toRGB.4.bias', (3), 'float32') self.toRGB_4.weight = self.load_pnnx_bin_as_parameter(archive, 'toRGB.4.weight', (3,128,1,1), 'float32') self.conv_body_up_5_conv1.bias = self.load_pnnx_bin_as_parameter(archive, 'conv_body_up.5.conv1.bias', (128), 'float32') self.conv_body_up_5_conv1.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_body_up.5.conv1.weight', (128,128,3,3), 'float32') self.conv_body_up_5_conv2.bias = self.load_pnnx_bin_as_parameter(archive, 'conv_body_up.5.conv2.bias', (64), 'float32') self.conv_body_up_5_conv2.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_body_up.5.conv2.weight', (64,128,3,3), 'float32') self.conv_body_up_5_skip.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_body_up.5.skip.weight', (64,128,1,1), 'float32') self.condition_scale_5_0.bias = self.load_pnnx_bin_as_parameter(archive, 'condition_scale.5.0.bias', (64), 'float32') self.condition_scale_5_0.weight = self.load_pnnx_bin_as_parameter(archive, 'condition_scale.5.0.weight', (64,64,3,3), 'float32') self.condition_scale_5_2.bias = self.load_pnnx_bin_as_parameter(archive, 'condition_scale.5.2.bias', (64), 'float32') self.condition_scale_5_2.weight = self.load_pnnx_bin_as_parameter(archive, 'condition_scale.5.2.weight', (64,64,3,3), 'float32') self.condition_shift_5_0.bias = self.load_pnnx_bin_as_parameter(archive, 'condition_shift.5.0.bias', (64), 'float32') self.condition_shift_5_0.weight = self.load_pnnx_bin_as_parameter(archive, 'condition_shift.5.0.weight', (64,64,3,3), 'float32') self.condition_shift_5_2.bias = self.load_pnnx_bin_as_parameter(archive, 'condition_shift.5.2.bias', (64), 'float32') self.condition_shift_5_2.weight = self.load_pnnx_bin_as_parameter(archive, 'condition_shift.5.2.weight', (64,64,3,3), 'float32') self.toRGB_5.bias = self.load_pnnx_bin_as_parameter(archive, 'toRGB.5.bias', (3), 'float32') self.toRGB_5.weight = self.load_pnnx_bin_as_parameter(archive, 'toRGB.5.weight', (3,64,1,1), 'float32') self.conv_body_up_6_conv1.bias = self.load_pnnx_bin_as_parameter(archive, 'conv_body_up.6.conv1.bias', (64), 'float32') self.conv_body_up_6_conv1.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_body_up.6.conv1.weight', (64,64,3,3), 'float32') self.conv_body_up_6_conv2.bias = self.load_pnnx_bin_as_parameter(archive, 'conv_body_up.6.conv2.bias', (32), 'float32') self.conv_body_up_6_conv2.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_body_up.6.conv2.weight', (32,64,3,3), 'float32') self.conv_body_up_6_skip.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_body_up.6.skip.weight', (32,64,1,1), 'float32') self.condition_scale_6_0.bias = self.load_pnnx_bin_as_parameter(archive, 'condition_scale.6.0.bias', (32), 'float32') self.condition_scale_6_0.weight = self.load_pnnx_bin_as_parameter(archive, 'condition_scale.6.0.weight', (32,32,3,3), 'float32') self.condition_scale_6_2.bias = self.load_pnnx_bin_as_parameter(archive, 'condition_scale.6.2.bias', (32), 'float32') self.condition_scale_6_2.weight = self.load_pnnx_bin_as_parameter(archive, 'condition_scale.6.2.weight', (32,32,3,3), 'float32') self.condition_shift_6_0.bias = self.load_pnnx_bin_as_parameter(archive, 'condition_shift.6.0.bias', (32), 'float32') self.condition_shift_6_0.weight = self.load_pnnx_bin_as_parameter(archive, 'condition_shift.6.0.weight', (32,32,3,3), 'float32') self.condition_shift_6_2.bias = self.load_pnnx_bin_as_parameter(archive, 'condition_shift.6.2.bias', (32), 'float32') self.condition_shift_6_2.weight = self.load_pnnx_bin_as_parameter(archive, 'condition_shift.6.2.weight', (32,32,3,3), 'float32') self.toRGB_6.bias = self.load_pnnx_bin_as_parameter(archive, 'toRGB.6.bias', (3), 'float32') self.toRGB_6.weight = self.load_pnnx_bin_as_parameter(archive, 'toRGB.6.weight', (3,32,1,1), 'float32') self.stylegan_decoder_style_conv1_modulated_conv_modulation.bias = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder.style_conv1.modulated_conv.modulation.bias', (512), 'float32') self.stylegan_decoder_style_conv1_modulated_conv_modulation.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder.style_conv1.modulated_conv.modulation.weight', (512,512), 'float32') self.stylegan_decoder_to_rgb1_modulated_conv_modulation.bias = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder.to_rgb1.modulated_conv.modulation.bias', (512), 'float32') self.stylegan_decoder_to_rgb1_modulated_conv_modulation.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder.to_rgb1.modulated_conv.modulation.weight', (512,512), 'float32') self.stylegan_decoder_style_convs_0_modulated_conv_modulation.bias = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder.style_convs.0.modulated_conv.modulation.bias', (512), 'float32') self.stylegan_decoder_style_convs_0_modulated_conv_modulation.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder.style_convs.0.modulated_conv.modulation.weight', (512,512), 'float32') self.stylegan_decoder_style_convs_1_modulated_conv_modulation.bias = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder.style_convs.1.modulated_conv.modulation.bias', (512), 'float32') self.stylegan_decoder_style_convs_1_modulated_conv_modulation.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder.style_convs.1.modulated_conv.modulation.weight', (512,512), 'float32') self.stylegan_decoder_to_rgbs_0_modulated_conv_modulation.bias = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder.to_rgbs.0.modulated_conv.modulation.bias', (512), 'float32') self.stylegan_decoder_to_rgbs_0_modulated_conv_modulation.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder.to_rgbs.0.modulated_conv.modulation.weight', (512,512), 'float32') self.stylegan_decoder_style_convs_2_modulated_conv_modulation.bias = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder.style_convs.2.modulated_conv.modulation.bias', (512), 'float32') self.stylegan_decoder_style_convs_2_modulated_conv_modulation.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder.style_convs.2.modulated_conv.modulation.weight', (512,512), 'float32') self.stylegan_decoder_style_convs_3_modulated_conv_modulation.bias = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder.style_convs.3.modulated_conv.modulation.bias', (512), 'float32') self.stylegan_decoder_style_convs_3_modulated_conv_modulation.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder.style_convs.3.modulated_conv.modulation.weight', (512,512), 'float32') self.stylegan_decoder_to_rgbs_1_modulated_conv_modulation.bias = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder.to_rgbs.1.modulated_conv.modulation.bias', (512), 'float32') self.stylegan_decoder_to_rgbs_1_modulated_conv_modulation.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder.to_rgbs.1.modulated_conv.modulation.weight', (512,512), 'float32') self.stylegan_decoder_style_convs_4_modulated_conv_modulation.bias = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder.style_convs.4.modulated_conv.modulation.bias', (512), 'float32') self.stylegan_decoder_style_convs_4_modulated_conv_modulation.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder.style_convs.4.modulated_conv.modulation.weight', (512,512), 'float32') self.stylegan_decoder_style_convs_5_modulated_conv_modulation.bias = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder.style_convs.5.modulated_conv.modulation.bias', (512), 'float32') self.stylegan_decoder_style_convs_5_modulated_conv_modulation.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder.style_convs.5.modulated_conv.modulation.weight', (512,512), 'float32') self.stylegan_decoder_to_rgbs_2_modulated_conv_modulation.bias = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder.to_rgbs.2.modulated_conv.modulation.bias', (512), 'float32') self.stylegan_decoder_to_rgbs_2_modulated_conv_modulation.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder.to_rgbs.2.modulated_conv.modulation.weight', (512,512), 'float32') self.stylegan_decoder_style_convs_6_modulated_conv_modulation.bias = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder.style_convs.6.modulated_conv.modulation.bias', (512), 'float32') self.stylegan_decoder_style_convs_6_modulated_conv_modulation.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder.style_convs.6.modulated_conv.modulation.weight', (512,512), 'float32') self.stylegan_decoder_style_convs_7_modulated_conv_modulation.bias = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder.style_convs.7.modulated_conv.modulation.bias', (512), 'float32') self.stylegan_decoder_style_convs_7_modulated_conv_modulation.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder.style_convs.7.modulated_conv.modulation.weight', (512,512), 'float32') self.stylegan_decoder_to_rgbs_3_modulated_conv_modulation.bias = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder.to_rgbs.3.modulated_conv.modulation.bias', (512), 'float32') self.stylegan_decoder_to_rgbs_3_modulated_conv_modulation.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder.to_rgbs.3.modulated_conv.modulation.weight', (512,512), 'float32') self.stylegan_decoder_style_convs_8_modulated_conv_modulation.bias = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder.style_convs.8.modulated_conv.modulation.bias', (512), 'float32') self.stylegan_decoder_style_convs_8_modulated_conv_modulation.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder.style_convs.8.modulated_conv.modulation.weight', (512,512), 'float32') self.stylegan_decoder_style_convs_9_modulated_conv_modulation.bias = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder.style_convs.9.modulated_conv.modulation.bias', (256), 'float32') self.stylegan_decoder_style_convs_9_modulated_conv_modulation.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder.style_convs.9.modulated_conv.modulation.weight', (256,512), 'float32') self.stylegan_decoder_to_rgbs_4_modulated_conv_modulation.bias = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder.to_rgbs.4.modulated_conv.modulation.bias', (256), 'float32') self.stylegan_decoder_to_rgbs_4_modulated_conv_modulation.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder.to_rgbs.4.modulated_conv.modulation.weight', (256,512), 'float32') self.stylegan_decoder_style_convs_10_modulated_conv_modulation.bias = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder.style_convs.10.modulated_conv.modulation.bias', (256), 'float32') self.stylegan_decoder_style_convs_10_modulated_conv_modulation.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder.style_convs.10.modulated_conv.modulation.weight', (256,512), 'float32') self.stylegan_decoder_style_convs_11_modulated_conv_modulation.bias = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder.style_convs.11.modulated_conv.modulation.bias', (128), 'float32') self.stylegan_decoder_style_convs_11_modulated_conv_modulation.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder.style_convs.11.modulated_conv.modulation.weight', (128,512), 'float32') self.stylegan_decoder_to_rgbs_5_modulated_conv_modulation.bias = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder.to_rgbs.5.modulated_conv.modulation.bias', (128), 'float32') self.stylegan_decoder_to_rgbs_5_modulated_conv_modulation.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder.to_rgbs.5.modulated_conv.modulation.weight', (128,512), 'float32') self.stylegan_decoder_style_convs_12_modulated_conv_modulation.bias = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder.style_convs.12.modulated_conv.modulation.bias', (128), 'float32') self.stylegan_decoder_style_convs_12_modulated_conv_modulation.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder.style_convs.12.modulated_conv.modulation.weight', (128,512), 'float32') self.stylegan_decoder_style_convs_13_modulated_conv_modulation.bias = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder.style_convs.13.modulated_conv.modulation.bias', (64), 'float32') self.stylegan_decoder_style_convs_13_modulated_conv_modulation.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder.style_convs.13.modulated_conv.modulation.weight', (64,512), 'float32') self.stylegan_decoder_to_rgbs_6_modulated_conv_modulation.bias = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder.to_rgbs.6.modulated_conv.modulation.bias', (64), 'float32') self.stylegan_decoder_to_rgbs_6_modulated_conv_modulation.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder.to_rgbs.6.modulated_conv.modulation.weight', (64,512), 'float32') self.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_constant_input.weight', (1,512,4,4), 'float32') self.bias = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_style_conv1.bias', (1,512,1,1), 'float32') self.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_style_conv1.weight', (1), 'float32') self.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_style_conv1_modulated_conv.weight', (1,512,512,3,3), 'float32') self.bias = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_to_rgb1.bias', (1,3,1,1), 'float32') self.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_to_rgb1_modulated_conv.weight', (1,3,512,1,1), 'float32') self.bias = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_style_convs_0.bias', (1,512,1,1), 'float32') self.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_style_convs_0.weight', (1), 'float32') self.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_style_convs_0_modulated_conv.weight', (1,512,512,3,3), 'float32') self.bias = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_style_convs_1.bias', (1,512,1,1), 'float32') self.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_style_convs_1.weight', (1), 'float32') self.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_style_convs_1_modulated_conv.weight', (1,512,512,3,3), 'float32') self.bias = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_to_rgbs_0.bias', (1,3,1,1), 'float32') self.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_to_rgbs_0_modulated_conv.weight', (1,3,512,1,1), 'float32') self.bias = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_style_convs_2.bias', (1,512,1,1), 'float32') self.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_style_convs_2.weight', (1), 'float32') self.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_style_convs_2_modulated_conv.weight', (1,512,512,3,3), 'float32') self.bias = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_style_convs_3.bias', (1,512,1,1), 'float32') self.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_style_convs_3.weight', (1), 'float32') self.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_style_convs_3_modulated_conv.weight', (1,512,512,3,3), 'float32') self.bias = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_to_rgbs_1.bias', (1,3,1,1), 'float32') self.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_to_rgbs_1_modulated_conv.weight', (1,3,512,1,1), 'float32') self.bias = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_style_convs_4.bias', (1,512,1,1), 'float32') self.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_style_convs_4.weight', (1), 'float32') self.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_style_convs_4_modulated_conv.weight', (1,512,512,3,3), 'float32') self.bias = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_style_convs_5.bias', (1,512,1,1), 'float32') self.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_style_convs_5.weight', (1), 'float32') self.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_style_convs_5_modulated_conv.weight', (1,512,512,3,3), 'float32') self.bias = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_to_rgbs_2.bias', (1,3,1,1), 'float32') self.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_to_rgbs_2_modulated_conv.weight', (1,3,512,1,1), 'float32') self.bias = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_style_convs_6.bias', (1,512,1,1), 'float32') self.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_style_convs_6.weight', (1), 'float32') self.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_style_convs_6_modulated_conv.weight', (1,512,512,3,3), 'float32') self.bias = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_style_convs_7.bias', (1,512,1,1), 'float32') self.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_style_convs_7.weight', (1), 'float32') self.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_style_convs_7_modulated_conv.weight', (1,512,512,3,3), 'float32') self.bias = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_to_rgbs_3.bias', (1,3,1,1), 'float32') self.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_to_rgbs_3_modulated_conv.weight', (1,3,512,1,1), 'float32') self.bias = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_style_convs_8.bias', (1,256,1,1), 'float32') self.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_style_convs_8.weight', (1), 'float32') self.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_style_convs_8_modulated_conv.weight', (1,256,512,3,3), 'float32') self.bias = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_style_convs_9.bias', (1,256,1,1), 'float32') self.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_style_convs_9.weight', (1), 'float32') self.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_style_convs_9_modulated_conv.weight', (1,256,256,3,3), 'float32') self.bias = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_to_rgbs_4.bias', (1,3,1,1), 'float32') self.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_to_rgbs_4_modulated_conv.weight', (1,3,256,1,1), 'float32') self.bias = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_style_convs_10.bias', (1,128,1,1), 'float32') self.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_style_convs_10.weight', (1), 'float32') self.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_style_convs_10_modulated_conv.weight', (1,128,256,3,3), 'float32') self.bias = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_style_convs_11.bias', (1,128,1,1), 'float32') self.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_style_convs_11.weight', (1), 'float32') self.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_style_convs_11_modulated_conv.weight', (1,128,128,3,3), 'float32') self.bias = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_to_rgbs_5.bias', (1,3,1,1), 'float32') self.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_to_rgbs_5_modulated_conv.weight', (1,3,128,1,1), 'float32') self.bias = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_style_convs_12.bias', (1,64,1,1), 'float32') self.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_style_convs_12.weight', (1), 'float32') self.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_style_convs_12_modulated_conv.weight', (1,64,128,3,3), 'float32') self.bias = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_style_convs_13.bias', (1,64,1,1), 'float32') self.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_style_convs_13.weight', (1), 'float32') self.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_style_convs_13_modulated_conv.weight', (1,64,64,3,3), 'float32') self.bias = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_to_rgbs_6.bias', (1,3,1,1), 'float32') self.weight = self.load_pnnx_bin_as_parameter(archive, 'stylegan_decoder_to_rgbs_6_modulated_conv.weight', (1,3,64,1,1), 'float32') archive.close() def load_pnnx_bin_as_parameter(self, archive, key, shape, dtype, requires_grad=True): return nn.Parameter(self.load_pnnx_bin_as_tensor(archive, key, shape, dtype), requires_grad) def load_pnnx_bin_as_tensor(self, archive, key, shape, dtype): _, tmppath = tempfile.mkstemp() tmpf = open(tmppath, 'wb') with archive.open(key) as keyfile: tmpf.write(keyfile.read()) tmpf.close() m = np.memmap(tmppath, dtype=dtype, mode='r', shape=shape).copy() os.remove(tmppath) return torch.from_numpy(m) def forward(self, v_0): v_1 = None v_2 = 2.000000e-01 v_3 = self.conv_body_first(v_0) v_4 = aten::leaky_relu_(v_3, v_2) v_5 = 2.000000e-01 v_6 = self.conv_body_down_0_conv1(v_4) v_7 = aten::leaky_relu_(v_6, v_5) v_8 = F.upsample(input=v_7, align_corners=False, mode='bilinear', scale_factor=(0.500000,0.500000)) v_9 = self.conv_body_down_0_conv2(v_8) v_10 = 2.000000e-01 v_11 = aten::leaky_relu_(v_9, v_10) v_12 = F.upsample(input=v_4, align_corners=False, mode='bilinear', scale_factor=(0.500000,0.500000)) v_13 = self.conv_body_down_0_skip(v_12) v_14 = (v_11 + v_13) v_15 = 2.000000e-01 v_16 = self.conv_body_down_1_conv1(v_14) v_17 = aten::leaky_relu_(v_16, v_15) v_18 = F.upsample(input=v_17, align_corners=False, mode='bilinear', scale_factor=(0.500000,0.500000)) v_19 = self.conv_body_down_1_conv2(v_18) v_20 = 2.000000e-01 v_21 = aten::leaky_relu_(v_19, v_20) v_22 = F.upsample(input=v_14, align_corners=False, mode='bilinear', scale_factor=(0.500000,0.500000)) v_23 = self.conv_body_down_1_skip(v_22) v_24 = (v_21 + v_23) v_25 = 2.000000e-01 v_26 = self.conv_body_down_2_conv1(v_24) v_27 = aten::leaky_relu_(v_26, v_25) v_28 = F.upsample(input=v_27, align_corners=False, mode='bilinear', scale_factor=(0.500000,0.500000)) v_29 = self.conv_body_down_2_conv2(v_28) v_30 = 2.000000e-01 v_31 = aten::leaky_relu_(v_29, v_30) v_32 = F.upsample(input=v_24, align_corners=False, mode='bilinear', scale_factor=(0.500000,0.500000)) v_33 = self.conv_body_down_2_skip(v_32) v_34 = (v_31 + v_33) v_35 = 2.000000e-01 v_36 = self.conv_body_down_3_conv1(v_34) v_37 = aten::leaky_relu_(v_36, v_35) v_38 = F.upsample(input=v_37, align_corners=False, mode='bilinear', scale_factor=(0.500000,0.500000)) v_39 = self.conv_body_down_3_conv2(v_38) v_40 = 2.000000e-01 v_41 = aten::leaky_relu_(v_39, v_40) v_42 = F.upsample(input=v_34, align_corners=False, mode='bilinear', scale_factor=(0.500000,0.500000)) v_43 = self.conv_body_down_3_skip(v_42) v_44 = (v_41 + v_43) v_45 = 2.000000e-01 v_46 = self.conv_body_down_4_conv1(v_44) v_47 = aten::leaky_relu_(v_46, v_45) v_48 = F.upsample(input=v_47, align_corners=False, mode='bilinear', scale_factor=(0.500000,0.500000)) v_49 = self.conv_body_down_4_conv2(v_48) v_50 = 2.000000e-01 v_51 = aten::leaky_relu_(v_49, v_50) v_52 = F.upsample(input=v_44, align_corners=False, mode='bilinear', scale_factor=(0.500000,0.500000)) v_53 = self.conv_body_down_4_skip(v_52) v_54 = (v_51 + v_53) v_55 = 2.000000e-01 v_56 = self.conv_body_down_5_conv1(v_54) v_57 = aten::leaky_relu_(v_56, v_55) v_58 = F.upsample(input=v_57, align_corners=False, mode='bilinear', scale_factor=(0.500000,0.500000)) v_59 = self.conv_body_down_5_conv2(v_58) v_60 = 2.000000e-01 v_61 = aten::leaky_relu_(v_59, v_60) v_62 = F.upsample(input=v_54, align_corners=False, mode='bilinear', scale_factor=(0.500000,0.500000)) v_63 = self.conv_body_down_5_skip(v_62) v_64 = (v_61 + v_63) v_65 = 2.000000e-01 v_66 = self.conv_body_down_6_conv1(v_64) v_67 = aten::leaky_relu_(v_66, v_65) v_68 = F.upsample(input=v_67, align_corners=False, mode='bilinear', scale_factor=(0.500000,0.500000)) v_69 = self.conv_body_down_6_conv2(v_68) v_70 = 2.000000e-01 v_71 = aten::leaky_relu_(v_69, v_70) v_72 = F.upsample(input=v_64, align_corners=False, mode='bilinear', scale_factor=(0.500000,0.500000)) v_73 = self.conv_body_down_6_skip(v_72) v_74 = (v_71 + v_73) v_75 = self.final_conv(v_74) v_76 = 2.000000e-01 v_77 = aten::leaky_relu_(v_75, v_76) v_78 = v_77.view(1, -1) v_79 = self.final_linear(v_78) v_80 = (v_77 + v_74) v_81 = 2.000000e-01 v_82 = self.conv_body_up_0_conv1(v_80) v_83 = aten::leaky_relu_(v_82, v_81) v_84 = F.upsample(input=v_83, align_corners=False, mode='bilinear', scale_factor=(2.000000,2.000000)) v_85 = self.conv_body_up_0_conv2(v_84) v_86 = 2.000000e-01 v_87 = aten::leaky_relu_(v_85, v_86) v_88 = F.upsample(input=v_80, align_corners=False, mode='bilinear', scale_factor=(2.000000,2.000000)) v_89 = self.conv_body_up_0_skip(v_88) v_90 = (v_87 + v_89) v_91 = self.condition_scale_0_0(v_90) v_92 = self.condition_scale_0_1(v_91) v_93 = self.condition_scale_0_2(v_92) v_94 = aten::clone(v_93, v_1) v_95 = self.condition_shift_0_0(v_90) v_96 = self.condition_shift_0_1(v_95) v_97 = self.condition_shift_0_2(v_96) v_98 = None v_99 = aten::clone(v_97, v_98) v_100 = self.toRGB_0(v_90) v_101 = (v_90 + v_64) v_102 = 2.000000e-01 v_103 = self.conv_body_up_1_conv1(v_101) v_104 = aten::leaky_relu_(v_103, v_102) v_105 = F.upsample(input=v_104, align_corners=False, mode='bilinear', scale_factor=(2.000000,2.000000)) v_106 = self.conv_body_up_1_conv2(v_105) v_107 = 2.000000e-01 v_108 = aten::leaky_relu_(v_106, v_107) v_109 = F.upsample(input=v_101, align_corners=False, mode='bilinear', scale_factor=(2.000000,2.000000)) v_110 = self.conv_body_up_1_skip(v_109) v_111 = (v_108 + v_110) v_112 = self.condition_scale_1_0(v_111) v_113 = self.condition_scale_1_1(v_112) v_114 = self.condition_scale_1_2(v_113) v_115 = None v_116 = aten::clone(v_114, v_115) v_117 = self.condition_shift_1_0(v_111) v_118 = self.condition_shift_1_1(v_117) v_119 = self.condition_shift_1_2(v_118) v_120 = None v_121 = aten::clone(v_119, v_120) v_122 = self.toRGB_1(v_111) v_123 = (v_111 + v_54) v_124 = 2.000000e-01 v_125 = self.conv_body_up_2_conv1(v_123) v_126 = aten::leaky_relu_(v_125, v_124) v_127 = F.upsample(input=v_126, align_corners=False, mode='bilinear', scale_factor=(2.000000,2.000000)) v_128 = self.conv_body_up_2_conv2(v_127) v_129 = 2.000000e-01 v_130 = aten::leaky_relu_(v_128, v_129) v_131 = F.upsample(input=v_123, align_corners=False, mode='bilinear', scale_factor=(2.000000,2.000000)) v_132 = self.conv_body_up_2_skip(v_131) v_133 = (v_130 + v_132) v_134 = self.condition_scale_2_0(v_133) v_135 = self.condition_scale_2_1(v_134) v_136 = self.condition_scale_2_2(v_135) v_137 = None v_138 = aten::clone(v_136, v_137) v_139 = self.condition_shift_2_0(v_133) v_140 = self.condition_shift_2_1(v_139) v_141 = self.condition_shift_2_2(v_140) v_142 = None v_143 = aten::clone(v_141, v_142) v_144 = self.toRGB_2(v_133) v_145 = (v_133 + v_44) v_146 = 2.000000e-01 v_147 = self.conv_body_up_3_conv1(v_145) v_148 = aten::leaky_relu_(v_147, v_146) v_149 = F.upsample(input=v_148, align_corners=False, mode='bilinear', scale_factor=(2.000000,2.000000)) v_150 = self.conv_body_up_3_conv2(v_149) v_151 = 2.000000e-01 v_152 = aten::leaky_relu_(v_150, v_151) v_153 = F.upsample(input=v_145, align_corners=False, mode='bilinear', scale_factor=(2.000000,2.000000)) v_154 = self.conv_body_up_3_skip(v_153) v_155 = (v_152 + v_154) v_156 = self.condition_scale_3_0(v_155) v_157 = self.condition_scale_3_1(v_156) v_158 = self.condition_scale_3_2(v_157) v_159 = None v_160 = aten::clone(v_158, v_159) v_161 = self.condition_shift_3_0(v_155) v_162 = self.condition_shift_3_1(v_161) v_163 = self.condition_shift_3_2(v_162) v_164 = None v_165 = aten::clone(v_163, v_164) v_166 = self.toRGB_3(v_155) v_167 = (v_155 + v_34) v_168 = 2.000000e-01 v_169 = self.conv_body_up_4_conv1(v_167) v_170 = aten::leaky_relu_(v_169, v_168) v_171 = F.upsample(input=v_170, align_corners=False, mode='bilinear', scale_factor=(2.000000,2.000000)) v_172 = self.conv_body_up_4_conv2(v_171) v_173 = 2.000000e-01 v_174 = aten::leaky_relu_(v_172, v_173) v_175 = F.upsample(input=v_167, align_corners=False, mode='bilinear', scale_factor=(2.000000,2.000000)) v_176 = self.conv_body_up_4_skip(v_175) v_177 = (v_174 + v_176) v_178 = self.condition_scale_4_0(v_177) v_179 = self.condition_scale_4_1(v_178) v_180 = self.condition_scale_4_2(v_179) v_181 = None v_182 = aten::clone(v_180, v_181) v_183 = self.condition_shift_4_0(v_177) v_184 = self.condition_shift_4_1(v_183) v_185 = self.condition_shift_4_2(v_184) v_186 = None v_187 = aten::clone(v_185, v_186) v_188 = self.toRGB_4(v_177) v_189 = (v_177 + v_24) v_190 = 2.000000e-01 v_191 = self.conv_body_up_5_conv1(v_189) v_192 = aten::leaky_relu_(v_191, v_190) v_193 = F.upsample(input=v_192, align_corners=False, mode='bilinear', scale_factor=(2.000000,2.000000)) v_194 = self.conv_body_up_5_conv2(v_193) v_195 = 2.000000e-01 v_196 = aten::leaky_relu_(v_194, v_195) v_197 = F.upsample(input=v_189, align_corners=False, mode='bilinear', scale_factor=(2.000000,2.000000)) v_198 = self.conv_body_up_5_skip(v_197) v_199 = (v_196 + v_198) v_200 = self.condition_scale_5_0(v_199) v_201 = self.condition_scale_5_1(v_200) v_202 = self.condition_scale_5_2(v_201) v_203 = None v_204 = aten::clone(v_202, v_203) v_205 = self.condition_shift_5_0(v_199) v_206 = self.condition_shift_5_1(v_205) v_207 = self.condition_shift_5_2(v_206) v_208 = None v_209 = aten::clone(v_207, v_208) v_210 = self.toRGB_5(v_199) v_211 = (v_199 + v_14) v_212 = 2.000000e-01 v_213 = self.conv_body_up_6_conv1(v_211) v_214 = aten::leaky_relu_(v_213, v_212) v_215 = F.upsample(input=v_214, align_corners=False, mode='bilinear', scale_factor=(2.000000,2.000000)) v_216 = self.conv_body_up_6_conv2(v_215) v_217 = 2.000000e-01 v_218 = aten::leaky_relu_(v_216, v_217) v_219 = F.upsample(input=v_211, align_corners=False, mode='bilinear', scale_factor=(2.000000,2.000000)) v_220 = self.conv_body_up_6_skip(v_219) v_221 = (v_218 + v_220) v_222 = self.condition_scale_6_0(v_221) v_223 = self.condition_scale_6_1(v_222) v_224 = self.condition_scale_6_2(v_223) v_225 = None v_226 = aten::clone(v_224, v_225) v_227 = self.condition_shift_6_0(v_221) v_228 = self.condition_shift_6_1(v_227) v_229 = self.condition_shift_6_2(v_228) v_230 = None v_231 = aten::clone(v_229, v_230) v_232 = self.toRGB_6(v_221) v_233 = v_79.view(1, -1, 512) v_234 = self.weight v_235 = None v_236 = 1.000000e+00 v_237 = 0.000000e+00 v_238 = self.bias v_239 = self.weight v_240 = self.weight v_241 = v_234.repeat(1, 1, 1, 1) v_242 = v_233.select(dim=1, index=0) v_243 = self.stylegan_decoder_style_conv1_modulated_conv_modulation(v_242) v_244 = v_243.view(1, 1, 512, 1, 1) v_245 = (v_240 * v_244) v_246 = v_245.pow(2) v_247 = torch.sum(input=v_246, dim=(2,3,4), keepdim=False) v_248 = torch.rsqrt((v_247 + 1.000000e-08)) v_249 = v_248.view(1, 512, 1, 1, 1) v_250 = (v_245 * v_249) v_251 = v_250.view(512, 512, 3, 3) v_252 = F.conv2d(input=v_241, weight=v_251, bias=None, dilation=(1,1), groups=1, padding=(1,1), stride=(1,1)) v_253 = (v_252 * 1.414214e+00) v_254 = v_253.new_empty(size=(1,1,4,4)) v_255 = aten::normal_(v_254, v_237, v_236, v_235) v_256 = ((v_253 + (v_239 * v_255)) + v_238) v_257 = self.stylegan_decoder_style_conv1_activate(v_256) v_258 = self.bias v_259 = self.weight v_260 = v_233.select(dim=1, index=1) v_261 = self.stylegan_decoder_to_rgb1_modulated_conv_modulation(v_260) v_262 = v_261.view(1, 1, 512, 1, 1) v_263 = (v_259 * v_262) v_264 = v_263.view(3, 512, 1, 1) v_265 = F.conv2d(input=v_257, weight=v_264, bias=None, dilation=(1,1), groups=1, padding=(0,0), stride=(1,1)) v_266 = (v_265 + v_258) v_267 = None v_268 = 1.000000e+00 v_269 = 0.000000e+00 v_270 = self.bias v_271 = self.weight v_272 = self.weight v_273 = v_233.select(dim=1, index=1) v_274 = self.stylegan_decoder_style_convs_0_modulated_conv_modulation(v_273) v_275 = v_274.view(1, 1, 512, 1, 1) v_276 = (v_272 * v_275) v_277 = v_276.pow(2) v_278 = torch.sum(input=v_277, dim=(2,3,4), keepdim=False) v_279 = torch.rsqrt((v_278 + 1.000000e-08)) v_280 = v_279.view(1, 512, 1, 1, 1) v_281 = (v_276 * v_280) v_282 = F.upsample(input=v_257, align_corners=False, mode='bilinear', scale_factor=(2.000000,2.000000)) v_283 = v_281.view(512, 512, 3, 3) v_284 = F.conv2d(input=v_282, weight=v_283, bias=None, dilation=(1,1), groups=1, padding=(1,1), stride=(1,1)) v_285 = (v_284 * 1.414214e+00) v_286 = v_285.new_empty(size=(1,1,8,8)) v_287 = aten::normal_(v_286, v_269, v_268, v_267) v_288 = ((v_285 + (v_271 * v_287)) + v_270) v_289 = self.stylegan_decoder_style_convs_0_activate(v_288) v_290, v_291 = torch.split(tensor=v_289, dim=1, split_size_or_sections=256) v_292 = ((v_291 * v_94) + v_99) v_293 = None v_294 = 1.000000e+00 v_295 = 0.000000e+00 v_296 = self.bias v_297 = self.weight v_298 = self.weight v_299 = torch.cat((v_290, v_292), dim=1) v_300 = v_233.select(dim=1, index=2) v_301 = self.stylegan_decoder_style_convs_1_modulated_conv_modulation(v_300) v_302 = v_301.view(1, 1, 512, 1, 1) v_303 = (v_298 * v_302) v_304 = v_303.pow(2) v_305 = torch.sum(input=v_304, dim=(2,3,4), keepdim=False) v_306 = torch.rsqrt((v_305 + 1.000000e-08)) v_307 = v_306.view(1, 512, 1, 1, 1) v_308 = (v_303 * v_307) v_309 = v_308.view(512, 512, 3, 3) v_310 = F.conv2d(input=v_299, weight=v_309, bias=None, dilation=(1,1), groups=1, padding=(1,1), stride=(1,1)) v_311 = (v_310 * 1.414214e+00) v_312 = v_311.new_empty(size=(1,1,8,8)) v_313 = aten::normal_(v_312, v_295, v_294, v_293) v_314 = ((v_311 + (v_297 * v_313)) + v_296) v_315 = self.stylegan_decoder_style_convs_1_activate(v_314) v_316 = self.bias v_317 = self.weight v_318 = v_233.select(dim=1, index=3) v_319 = self.stylegan_decoder_to_rgbs_0_modulated_conv_modulation(v_318) v_320 = v_319.view(1, 1, 512, 1, 1) v_321 = (v_317 * v_320) v_322 = v_321.view(3, 512, 1, 1) v_323 = F.conv2d(input=v_315, weight=v_322, bias=None, dilation=(1,1), groups=1, padding=(0,0), stride=(1,1)) v_324 = F.upsample(input=v_266, align_corners=False, mode='bilinear', scale_factor=(2.000000,2.000000)) v_325 = ((v_323 + v_316) + v_324) v_326 = None v_327 = 1.000000e+00 v_328 = 0.000000e+00 v_329 = self.bias v_330 = self.weight v_331 = self.weight v_332 = v_233.select(dim=1, index=3) v_333 = self.stylegan_decoder_style_convs_2_modulated_conv_modulation(v_332) v_334 = v_333.view(1, 1, 512, 1, 1) v_335 = (v_331 * v_334) v_336 = v_335.pow(2) v_337 = torch.sum(input=v_336, dim=(2,3,4), keepdim=False) v_338 = torch.rsqrt((v_337 + 1.000000e-08)) v_339 = v_338.view(1, 512, 1, 1, 1) v_340 = (v_335 * v_339) v_341 = F.upsample(input=v_315, align_corners=False, mode='bilinear', scale_factor=(2.000000,2.000000)) v_342 = v_340.view(512, 512, 3, 3) v_343 = F.conv2d(input=v_341, weight=v_342, bias=None, dilation=(1,1), groups=1, padding=(1,1), stride=(1,1)) v_344 = (v_343 * 1.414214e+00) v_345 = v_344.new_empty(size=(1,1,16,16)) v_346 = aten::normal_(v_345, v_328, v_327, v_326) v_347 = ((v_344 + (v_330 * v_346)) + v_329) v_348 = self.stylegan_decoder_style_convs_2_activate(v_347) v_349, v_350 = torch.split(tensor=v_348, dim=1, split_size_or_sections=256) v_351 = ((v_350 * v_116) + v_121) v_352 = None v_353 = 1.000000e+00 v_354 = 0.000000e+00 v_355 = self.bias v_356 = self.weight v_357 = self.weight v_358 = torch.cat((v_349, v_351), dim=1) v_359 = v_233.select(dim=1, index=4) v_360 = self.stylegan_decoder_style_convs_3_modulated_conv_modulation(v_359) v_361 = v_360.view(1, 1, 512, 1, 1) v_362 = (v_357 * v_361) v_363 = v_362.pow(2) v_364 = torch.sum(input=v_363, dim=(2,3,4), keepdim=False) v_365 = torch.rsqrt((v_364 + 1.000000e-08)) v_366 = v_365.view(1, 512, 1, 1, 1) v_367 = (v_362 * v_366) v_368 = v_367.view(512, 512, 3, 3) v_369 = F.conv2d(input=v_358, weight=v_368, bias=None, dilation=(1,1), groups=1, padding=(1,1), stride=(1,1)) v_370 = (v_369 * 1.414214e+00) v_371 = v_370.new_empty(size=(1,1,16,16)) v_372 = aten::normal_(v_371, v_354, v_353, v_352) v_373 = ((v_370 + (v_356 * v_372)) + v_355) v_374 = self.stylegan_decoder_style_convs_3_activate(v_373) v_375 = self.bias v_376 = self.weight v_377 = v_233.select(dim=1, index=5) v_378 = self.stylegan_decoder_to_rgbs_1_modulated_conv_modulation(v_377) v_379 = v_378.view(1, 1, 512, 1, 1) v_380 = (v_376 * v_379) v_381 = v_380.view(3, 512, 1, 1) v_382 = F.conv2d(input=v_374, weight=v_381, bias=None, dilation=(1,1), groups=1, padding=(0,0), stride=(1,1)) v_383 = F.upsample(input=v_325, align_corners=False, mode='bilinear', scale_factor=(2.000000,2.000000)) v_384 = ((v_382 + v_375) + v_383) v_385 = None v_386 = 1.000000e+00 v_387 = 0.000000e+00 v_388 = self.bias v_389 = self.weight v_390 = self.weight v_391 = v_233.select(dim=1, index=5) v_392 = self.stylegan_decoder_style_convs_4_modulated_conv_modulation(v_391) v_393 = v_392.view(1, 1, 512, 1, 1) v_394 = (v_390 * v_393) v_395 = v_394.pow(2) v_396 = torch.sum(input=v_395, dim=(2,3,4), keepdim=False) v_397 = torch.rsqrt((v_396 + 1.000000e-08)) v_398 = v_397.view(1, 512, 1, 1, 1) v_399 = (v_394 * v_398) v_400 = F.upsample(input=v_374, align_corners=False, mode='bilinear', scale_factor=(2.000000,2.000000)) v_401 = v_399.view(512, 512, 3, 3) v_402 = F.conv2d(input=v_400, weight=v_401, bias=None, dilation=(1,1), groups=1, padding=(1,1), stride=(1,1)) v_403 = (v_402 * 1.414214e+00) v_404 = v_403.new_empty(size=(1,1,32,32)) v_405 = aten::normal_(v_404, v_387, v_386, v_385) v_406 = ((v_403 + (v_389 * v_405)) + v_388) v_407 = self.stylegan_decoder_style_convs_4_activate(v_406) v_408, v_409 = torch.split(tensor=v_407, dim=1, split_size_or_sections=256) v_410 = ((v_409 * v_138) + v_143) v_411 = None v_412 = 1.000000e+00 v_413 = 0.000000e+00 v_414 = self.bias v_415 = self.weight v_416 = self.weight v_417 = torch.cat((v_408, v_410), dim=1) v_418 = v_233.select(dim=1, index=6) v_419 = self.stylegan_decoder_style_convs_5_modulated_conv_modulation(v_418) v_420 = v_419.view(1, 1, 512, 1, 1) v_421 = (v_416 * v_420) v_422 = v_421.pow(2) v_423 = torch.sum(input=v_422, dim=(2,3,4), keepdim=False) v_424 = torch.rsqrt((v_423 + 1.000000e-08)) v_425 = v_424.view(1, 512, 1, 1, 1) v_426 = (v_421 * v_425) v_427 = v_426.view(512, 512, 3, 3) v_428 = F.conv2d(input=v_417, weight=v_427, bias=None, dilation=(1,1), groups=1, padding=(1,1), stride=(1,1)) v_429 = (v_428 * 1.414214e+00) v_430 = v_429.new_empty(size=(1,1,32,32)) v_431 = aten::normal_(v_430, v_413, v_412, v_411) v_432 = ((v_429 + (v_415 * v_431)) + v_414) v_433 = self.stylegan_decoder_style_convs_5_activate(v_432) v_434 = self.bias v_435 = self.weight v_436 = v_233.select(dim=1, index=7) v_437 = self.stylegan_decoder_to_rgbs_2_modulated_conv_modulation(v_436) v_438 = v_437.view(1, 1, 512, 1, 1) v_439 = (v_435 * v_438) v_440 = v_439.view(3, 512, 1, 1) v_441 = F.conv2d(input=v_433, weight=v_440, bias=None, dilation=(1,1), groups=1, padding=(0,0), stride=(1,1)) v_442 = F.upsample(input=v_384, align_corners=False, mode='bilinear', scale_factor=(2.000000,2.000000)) v_443 = ((v_441 + v_434) + v_442) v_444 = None v_445 = 1.000000e+00 v_446 = 0.000000e+00 v_447 = self.bias v_448 = self.weight v_449 = self.weight v_450 = v_233.select(dim=1, index=7) v_451 = self.stylegan_decoder_style_convs_6_modulated_conv_modulation(v_450) v_452 = v_451.view(1, 1, 512, 1, 1) v_453 = (v_449 * v_452) v_454 = v_453.pow(2) v_455 = torch.sum(input=v_454, dim=(2,3,4), keepdim=False) v_456 = torch.rsqrt((v_455 + 1.000000e-08)) v_457 = v_456.view(1, 512, 1, 1, 1) v_458 = (v_453 * v_457) v_459 = F.upsample(input=v_433, align_corners=False, mode='bilinear', scale_factor=(2.000000,2.000000)) v_460 = v_458.view(512, 512, 3, 3) v_461 = F.conv2d(input=v_459, weight=v_460, bias=None, dilation=(1,1), groups=1, padding=(1,1), stride=(1,1)) v_462 = (v_461 * 1.414214e+00) v_463 = v_462.new_empty(size=(1,1,64,64)) v_464 = aten::normal_(v_463, v_446, v_445, v_444) v_465 = ((v_462 + (v_448 * v_464)) + v_447) v_466 = self.stylegan_decoder_style_convs_6_activate(v_465) v_467, v_468 = torch.split(tensor=v_466, dim=1, split_size_or_sections=256) v_469 = ((v_468 * v_160) + v_165) v_470 = None v_471 = 1.000000e+00 v_472 = 0.000000e+00 v_473 = self.bias v_474 = self.weight v_475 = self.weight v_476 = torch.cat((v_467, v_469), dim=1) v_477 = v_233.select(dim=1, index=8) v_478 = self.stylegan_decoder_style_convs_7_modulated_conv_modulation(v_477) v_479 = v_478.view(1, 1, 512, 1, 1) v_480 = (v_475 * v_479) v_481 = v_480.pow(2) v_482 = torch.sum(input=v_481, dim=(2,3,4), keepdim=False) v_483 = torch.rsqrt((v_482 + 1.000000e-08)) v_484 = v_483.view(1, 512, 1, 1, 1) v_485 = (v_480 * v_484) v_486 = v_485.view(512, 512, 3, 3) v_487 = F.conv2d(input=v_476, weight=v_486, bias=None, dilation=(1,1), groups=1, padding=(1,1), stride=(1,1)) v_488 = (v_487 * 1.414214e+00) v_489 = v_488.new_empty(size=(1,1,64,64)) v_490 = aten::normal_(v_489, v_472, v_471, v_470) v_491 = ((v_488 + (v_474 * v_490)) + v_473) v_492 = self.stylegan_decoder_style_convs_7_activate(v_491) v_493 = self.bias v_494 = self.weight v_495 = v_233.select(dim=1, index=9) v_496 = self.stylegan_decoder_to_rgbs_3_modulated_conv_modulation(v_495) v_497 = v_496.view(1, 1, 512, 1, 1) v_498 = (v_494 * v_497) v_499 = v_498.view(3, 512, 1, 1) v_500 = F.conv2d(input=v_492, weight=v_499, bias=None, dilation=(1,1), groups=1, padding=(0,0), stride=(1,1)) v_501 = F.upsample(input=v_443, align_corners=False, mode='bilinear', scale_factor=(2.000000,2.000000)) v_502 = ((v_500 + v_493) + v_501) v_503 = None v_504 = 1.000000e+00 v_505 = 0.000000e+00 v_506 = self.bias v_507 = self.weight v_508 = self.weight v_509 = v_233.select(dim=1, index=9) v_510 = self.stylegan_decoder_style_convs_8_modulated_conv_modulation(v_509) v_511 = v_510.view(1, 1, 512, 1, 1) v_512 = (v_508 * v_511) v_513 = v_512.pow(2) v_514 = torch.sum(input=v_513, dim=(2,3,4), keepdim=False) v_515 = torch.rsqrt((v_514 + 1.000000e-08)) v_516 = v_515.view(1, 256, 1, 1, 1) v_517 = (v_512 * v_516) v_518 = F.upsample(input=v_492, align_corners=False, mode='bilinear', scale_factor=(2.000000,2.000000)) v_519 = v_517.view(256, 512, 3, 3) v_520 = F.conv2d(input=v_518, weight=v_519, bias=None, dilation=(1,1), groups=1, padding=(1,1), stride=(1,1)) v_521 = (v_520 * 1.414214e+00) v_522 = v_521.new_empty(size=(1,1,128,128)) v_523 = aten::normal_(v_522, v_505, v_504, v_503) v_524 = ((v_521 + (v_507 * v_523)) + v_506) v_525 = self.stylegan_decoder_style_convs_8_activate(v_524) v_526, v_527 = torch.split(tensor=v_525, dim=1, split_size_or_sections=128) v_528 = ((v_527 * v_182) + v_187) v_529 = None v_530 = 1.000000e+00 v_531 = 0.000000e+00 v_532 = self.bias v_533 = self.weight v_534 = self.weight v_535 = torch.cat((v_526, v_528), dim=1) v_536 = v_233.select(dim=1, index=10) v_537 = self.stylegan_decoder_style_convs_9_modulated_conv_modulation(v_536) v_538 = v_537.view(1, 1, 256, 1, 1) v_539 = (v_534 * v_538) v_540 = v_539.pow(2) v_541 = torch.sum(input=v_540, dim=(2,3,4), keepdim=False) v_542 = torch.rsqrt((v_541 + 1.000000e-08)) v_543 = v_542.view(1, 256, 1, 1, 1) v_544 = (v_539 * v_543) v_545 = v_544.view(256, 256, 3, 3) v_546 = F.conv2d(input=v_535, weight=v_545, bias=None, dilation=(1,1), groups=1, padding=(1,1), stride=(1,1)) v_547 = (v_546 * 1.414214e+00) v_548 = v_547.new_empty(size=(1,1,128,128)) v_549 = aten::normal_(v_548, v_531, v_530, v_529) v_550 = ((v_547 + (v_533 * v_549)) + v_532) v_551 = self.stylegan_decoder_style_convs_9_activate(v_550) v_552 = self.bias v_553 = self.weight v_554 = v_233.select(dim=1, index=11) v_555 = self.stylegan_decoder_to_rgbs_4_modulated_conv_modulation(v_554) v_556 = v_555.view(1, 1, 256, 1, 1) v_557 = (v_553 * v_556) v_558 = v_557.view(3, 256, 1, 1) v_559 = F.conv2d(input=v_551, weight=v_558, bias=None, dilation=(1,1), groups=1, padding=(0,0), stride=(1,1)) v_560 = F.upsample(input=v_502, align_corners=False, mode='bilinear', scale_factor=(2.000000,2.000000)) v_561 = ((v_559 + v_552) + v_560) v_562 = None v_563 = 1.000000e+00 v_564 = 0.000000e+00 v_565 = self.bias v_566 = self.weight v_567 = self.weight v_568 = v_233.select(dim=1, index=11) v_569 = self.stylegan_decoder_style_convs_10_modulated_conv_modulation(v_568) v_570 = v_569.view(1, 1, 256, 1, 1) v_571 = (v_567 * v_570) v_572 = v_571.pow(2) v_573 = torch.sum(input=v_572, dim=(2,3,4), keepdim=False) v_574 = torch.rsqrt((v_573 + 1.000000e-08)) v_575 = v_574.view(1, 128, 1, 1, 1) v_576 = (v_571 * v_575) v_577 = F.upsample(input=v_551, align_corners=False, mode='bilinear', scale_factor=(2.000000,2.000000)) v_578 = v_576.view(128, 256, 3, 3) v_579 = F.conv2d(input=v_577, weight=v_578, bias=None, dilation=(1,1), groups=1, padding=(1,1), stride=(1,1)) v_580 = (v_579 * 1.414214e+00) v_581 = v_580.new_empty(size=(1,1,256,256)) v_582 = aten::normal_(v_581, v_564, v_563, v_562) v_583 = ((v_580 + (v_566 * v_582)) + v_565) v_584 = self.stylegan_decoder_style_convs_10_activate(v_583) v_585, v_586 = torch.split(tensor=v_584, dim=1, split_size_or_sections=64) v_587 = ((v_586 * v_204) + v_209) v_588 = None v_589 = 1.000000e+00 v_590 = 0.000000e+00 v_591 = self.bias v_592 = self.weight v_593 = self.weight v_594 = torch.cat((v_585, v_587), dim=1) v_595 = v_233.select(dim=1, index=12) v_596 = self.stylegan_decoder_style_convs_11_modulated_conv_modulation(v_595) v_597 = v_596.view(1, 1, 128, 1, 1) v_598 = (v_593 * v_597) v_599 = v_598.pow(2) v_600 = torch.sum(input=v_599, dim=(2,3,4), keepdim=False) v_601 = torch.rsqrt((v_600 + 1.000000e-08)) v_602 = v_601.view(1, 128, 1, 1, 1) v_603 = (v_598 * v_602) v_604 = v_603.view(128, 128, 3, 3) v_605 = F.conv2d(input=v_594, weight=v_604, bias=None, dilation=(1,1), groups=1, padding=(1,1), stride=(1,1)) v_606 = (v_605 * 1.414214e+00) v_607 = v_606.new_empty(size=(1,1,256,256)) v_608 = aten::normal_(v_607, v_590, v_589, v_588) v_609 = ((v_606 + (v_592 * v_608)) + v_591) v_610 = self.stylegan_decoder_style_convs_11_activate(v_609) v_611 = self.bias v_612 = self.weight v_613 = v_233.select(dim=1, index=13) v_614 = self.stylegan_decoder_to_rgbs_5_modulated_conv_modulation(v_613) v_615 = v_614.view(1, 1, 128, 1, 1) v_616 = (v_612 * v_615) v_617 = v_616.view(3, 128, 1, 1) v_618 = F.conv2d(input=v_610, weight=v_617, bias=None, dilation=(1,1), groups=1, padding=(0,0), stride=(1,1)) v_619 = F.upsample(input=v_561, align_corners=False, mode='bilinear', scale_factor=(2.000000,2.000000)) v_620 = ((v_618 + v_611) + v_619) v_621 = None v_622 = 1.000000e+00 v_623 = 0.000000e+00 v_624 = self.bias v_625 = self.weight v_626 = self.weight v_627 = v_233.select(dim=1, index=13) v_628 = self.stylegan_decoder_style_convs_12_modulated_conv_modulation(v_627) v_629 = v_628.view(1, 1, 128, 1, 1) v_630 = (v_626 * v_629) v_631 = v_630.pow(2) v_632 = torch.sum(input=v_631, dim=(2,3,4), keepdim=False) v_633 = torch.rsqrt((v_632 + 1.000000e-08)) v_634 = v_633.view(1, 64, 1, 1, 1) v_635 = (v_630 * v_634) v_636 = F.upsample(input=v_610, align_corners=False, mode='bilinear', scale_factor=(2.000000,2.000000)) v_637 = v_635.view(64, 128, 3, 3) v_638 = F.conv2d(input=v_636, weight=v_637, bias=None, dilation=(1,1), groups=1, padding=(1,1), stride=(1,1)) v_639 = (v_638 * 1.414214e+00) v_640 = v_639.new_empty(size=(1,1,512,512)) v_641 = aten::normal_(v_640, v_623, v_622, v_621) v_642 = ((v_639 + (v_625 * v_641)) + v_624) v_643 = self.stylegan_decoder_style_convs_12_activate(v_642) v_644, v_645 = torch.split(tensor=v_643, dim=1, split_size_or_sections=32) v_646 = ((v_645 * v_226) + v_231) v_647 = None v_648 = 1.000000e+00 v_649 = 0.000000e+00 v_650 = self.bias v_651 = self.weight v_652 = self.weight v_653 = torch.cat((v_644, v_646), dim=1) v_654 = v_233.select(dim=1, index=14) v_655 = self.stylegan_decoder_style_convs_13_modulated_conv_modulation(v_654) v_656 = v_655.view(1, 1, 64, 1, 1) v_657 = (v_652 * v_656) v_658 = v_657.pow(2) v_659 = torch.sum(input=v_658, dim=(2,3,4), keepdim=False) v_660 = torch.rsqrt((v_659 + 1.000000e-08)) v_661 = v_660.view(1, 64, 1, 1, 1) v_662 = (v_657 * v_661) v_663 = v_662.view(64, 64, 3, 3) v_664 = F.conv2d(input=v_653, weight=v_663, bias=None, dilation=(1,1), groups=1, padding=(1,1), stride=(1,1)) v_665 = (v_664 * 1.414214e+00) v_666 = v_665.new_empty(size=(1,1,512,512)) v_667 = aten::normal_(v_666, v_649, v_648, v_647) v_668 = ((v_665 + (v_651 * v_667)) + v_650) v_669 = self.stylegan_decoder_style_convs_13_activate(v_668) v_670 = self.bias v_671 = self.weight v_672 = v_233.select(dim=1, index=15) v_673 = self.stylegan_decoder_to_rgbs_6_modulated_conv_modulation(v_672) v_674 = v_673.view(1, 1, 64, 1, 1) v_675 = (v_671 * v_674) v_676 = v_675.view(3, 64, 1, 1) v_677 = F.conv2d(input=v_669, weight=v_676, bias=None, dilation=(1,1), groups=1, padding=(0,0), stride=(1,1)) v_678 = F.upsample(input=v_620, align_corners=False, mode='bilinear', scale_factor=(2.000000,2.000000)) v_679 = ((v_677 + v_670) + v_678) v_680 = [v_100, v_122, v_144, v_166, v_188, v_210, v_232] v_681 = (v_679, v_680, ) return v_681

def export_torchscript():
net = Model()
net.eval()

torch.manual_seed(0) v_0 = torch.rand(1, 3, 512, 512, dtype=torch.float) mod = torch.jit.trace(net, v_0) mod.save("E://opt//ncnn//tools//pnnx//build_debug//install//bin//a.pnnx.py.pt")

def test_inference():
net = Model()
net.eval()

torch.manual_seed(0) v_0 = torch.rand(1, 3, 512, 512, dtype=torch.float) return net(v_0)

**

os: Windosw 10 VS2017 X64 Debug Libtorch 1.10+cpu； Protobuf 3.4.0
cmd: pnnx.exe a.pt inputshape=[1,3,512,512]

麻烦大佬看下，不胜感激。
@nihui

zhu-zhaofei · 2022-01-11T06:14:49Z

ncnn/tools/pnnx/src/pass_ncnn/chain_multi_output.cpp文件中
`// Tencent is pleased to support the open source community by making ncnn available.
//
// Copyright (C) 2021 THL A29 Limited, a Tencent company. All rights reserved.
//
// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
// in compliance with the License. You may obtain a copy of the License at
//
// https://opensource.org/licenses/BSD-3-Clause
//
// Unless required by applicable law or agreed to in writing, software distributed
// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
// CONDITIONS OF ANY KIND, either express or implied. See the License for the
// specific language governing permissions and limitations under the License.

#include "chain_multi_output.h"

#include

namespace pnnx {

namespace ncnn {

void chain_multi_output(Graph& graph)
{
for (;;)
{
bool need_eliminate = false;

    for (int i = (int)graph.ops.size() - 1; i >= 0; i--)
    {
        Operator* op = graph.ops[i];

        if (op->type != "pnnx.Output")
            continue;

        // prim::TupleConstruct     pnnx_791                 2 1 a b out
        // pnnx.Expression          pnnx_expr_0              3 1 a b c out expr=[@0,@1,@2]
        // pnnx.Output              pnnx_output_0            1 0 out
        bool match_tuple_expr_output = false;
        for (int j = 0; j < (int)op->inputs.size(); j++)
        {
            Operand* r = op->inputs[j];

            if (r->consumers.size() != 1)
                continue;

            Operator* op0 = r->producer;

            if (op0->type == "prim::TupleConstruct")
            {
                match_tuple_expr_output = true;
            }
            else if (op0->type == "pnnx.Expression")
            {
                const int op_expr_input_count = (int)op0->inputs.size();
                const std::string& expr = op0->params.at("expr").s;

                std::string pattern_expr = "[";
                for (int k = 0; k < op_expr_input_count; k++)
                {
                    pattern_expr += std::string("@") + std::to_string(k);

                    if (k != op_expr_input_count - 1)
                        pattern_expr += ",";
                }
                pattern_expr += "]";

                if (expr == pattern_expr)
                {
                    match_tuple_expr_output = true;
                }
            }

            if (!match_tuple_expr_output)
                continue;

            // chain op0 as output and delete op0
            std::vector<Operand*> new_inputs;
            for (int k = 0; k < j; k++)
            {
                new_inputs.push_back(op->inputs[k]);
            }

            for (Operand* r : op0->inputs)
            {
                r->remove_consumer(op0);
                r->consumers.push_back(op);
                new_inputs.push_back(r);
            }

            for (int k = j + 1; k < (int)op->inputs.size(); k++)
            {
                new_inputs.push_back(op->inputs[k]);
            }

            op->inputs = new_inputs;

            op0->inputs.clear();
            op0->outputs.clear();

            Operand* op0_out = op0->outputs[0];
            op0_out->producer = 0;
            op0_out->consumers.clear();

            graph.operands.erase(std::find(graph.operands.begin(), graph.operands.end(), op0_out));
            delete op0_out;

            graph.ops.erase(std::find(graph.ops.begin(), graph.ops.end(), op0));
            delete op0;

            break;
        }

        if (match_tuple_expr_output)
            need_eliminate = true;

        break;
    }

    if (!need_eliminate)
        break;
}

}

} // namespace ncnn

} // namespace pnnx`
第99行 op0->outputs.clear();
而第101行 Operand* op0_out = op0->outputs[0];
这样op0_out指针就越界了？这段代码需要修改下

zhang0557kui · 2022-07-04T10:03:06Z

layer F.grid_sample not exists or registered

csukuangfj · 2022-08-06T04:21:29Z

https://github.com/nihui/ncnn/blob/a8992896f909d81cc1ba7d56bdc7298ebda7ecc9/tools/pnnx/README.md

这个 commit, 一下子就上传了一大堆的源代码。@nihui 可以分享 git history log 么?

LRY89757 · 2022-11-15T15:13:20Z

layer F.grid_sample not exists or registered

Now F.gridsample has been implemented. #4288

nihui added 30 commits August 15, 2021 21:05

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nihui and others added 5 commits November 15, 2021 18:59

ncnn interp-1d

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zhiqwang mentioned this pull request Nov 16, 2021

[RFC] Support exporting to torchscript open-mmlab/mmcv#1499

Closed

xiaooquanwu and others added 13 commits November 17, 2021 10:54

Update pass_level2.cpp (#29)

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code clean

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f72f2a3

move assets to ncnn-assets

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nihui merged commit e4c821a into Tencent:master Nov 22, 2021

zhu-zhaofei mentioned this pull request Dec 19, 2021

GAN模型PNNX转化问题 #3434

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zhu-zhaofei mentioned this pull request Jan 11, 2022

pnnx 模块转换出现指针越界 #3492

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PNNX is an open standard for PyTorch model interoperability #3262

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