import torch import torch.nn import torch.functional import torch.nn.functional class efficientnet_v2_s(torch.nn.Module): def __init__(self): super().__init__() self.features_0_0 = torch.nn.modules.conv.Conv2d(3, 24, 3, 2, 1, bias=False) self.features_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(24) self.features_1_conv_0 = torch.nn.modules.conv.Conv2d(24, 24, 3, 1, 1, bias=False) self.features_1_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(24) self.features_1_conv_3 = torch.nn.modules.conv.Conv2d(24, 24, 1, 1, 0, bias=False) self.features_1_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(24) self.features_2_conv_0 = torch.nn.modules.conv.Conv2d(24, 24, 3, 1, 1, bias=False) self.features_2_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(24) self.features_2_conv_3 = torch.nn.modules.conv.Conv2d(24, 24, 1, 1, 0, bias=False) self.features_2_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(24) self.features_3_conv_0 = torch.nn.modules.conv.Conv2d(24, 96, 3, 2, 1, bias=False) self.features_3_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(96) self.features_3_conv_3 = torch.nn.modules.conv.Conv2d(96, 48, 1, 1, 0, bias=False) self.features_3_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(48) self.features_4_conv_0 = torch.nn.modules.conv.Conv2d(48, 192, 3, 1, 1, bias=False) self.features_4_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(192) self.features_4_conv_3 = torch.nn.modules.conv.Conv2d(192, 48, 1, 1, 0, bias=False) self.features_4_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(48) self.features_5_conv_0 = torch.nn.modules.conv.Conv2d(48, 192, 3, 1, 1, bias=False) self.features_5_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(192) self.features_5_conv_3 = torch.nn.modules.conv.Conv2d(192, 48, 1, 1, 0, bias=False) self.features_5_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(48) self.features_6_conv_0 = torch.nn.modules.conv.Conv2d(48, 192, 3, 1, 1, bias=False) self.features_6_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(192) self.features_6_conv_3 = torch.nn.modules.conv.Conv2d(192, 48, 1, 1, 0, bias=False) self.features_6_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(48) self.features_7_conv_0 = torch.nn.modules.conv.Conv2d(48, 192, 3, 2, 1, bias=False) self.features_7_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(192) self.features_7_conv_3 = torch.nn.modules.conv.Conv2d(192, 64, 1, 1, 0, bias=False) self.features_7_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(64) self.features_8_conv_0 = torch.nn.modules.conv.Conv2d(64, 256, 3, 1, 1, bias=False) self.features_8_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(256) self.features_8_conv_3 = torch.nn.modules.conv.Conv2d(256, 64, 1, 1, 0, bias=False) self.features_8_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(64) self.features_9_conv_0 = torch.nn.modules.conv.Conv2d(64, 256, 3, 1, 1, bias=False) self.features_9_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(256) self.features_9_conv_3 = torch.nn.modules.conv.Conv2d(256, 64, 1, 1, 0, bias=False) self.features_9_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(64) self.features_10_conv_0 = torch.nn.modules.conv.Conv2d(64, 256, 3, 1, 1, bias=False) self.features_10_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(256) self.features_10_conv_3 = torch.nn.modules.conv.Conv2d(256, 64, 1, 1, 0, bias=False) self.features_10_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(64) self.features_11_conv_0 = torch.nn.modules.conv.Conv2d(64, 256, 1, 1, 0, bias=False) self.features_11_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(256) self.features_11_conv_3 = torch.nn.modules.conv.Conv2d(256, 256, 3, 2, 1, groups=256, bias=False) self.features_11_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(256) self.features_11_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1) self.features_11_conv_6_fc_0 = torch.nn.modules.linear.Linear(256, 16) self.features_11_conv_6_fc_2 = torch.nn.modules.linear.Linear(16, 256) self.features_11_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid() self.features_11_conv_7 = torch.nn.modules.conv.Conv2d(256, 128, 1, 1, 0, bias=False) self.features_11_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(128) self.features_12_conv_0 = torch.nn.modules.conv.Conv2d(128, 512, 1, 1, 0, bias=False) self.features_12_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(512) self.features_12_conv_3 = torch.nn.modules.conv.Conv2d(512, 512, 3, 1, 1, groups=512, bias=False) self.features_12_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(512) self.features_12_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1) self.features_12_conv_6_fc_0 = torch.nn.modules.linear.Linear(512, 32) self.features_12_conv_6_fc_2 = torch.nn.modules.linear.Linear(32, 512) self.features_12_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid() self.features_12_conv_7 = torch.nn.modules.conv.Conv2d(512, 128, 1, 1, 0, bias=False) self.features_12_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(128) self.features_13_conv_0 = torch.nn.modules.conv.Conv2d(128, 512, 1, 1, 0, bias=False) self.features_13_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(512) self.features_13_conv_3 = torch.nn.modules.conv.Conv2d(512, 512, 3, 1, 1, groups=512, bias=False) self.features_13_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(512) self.features_13_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1) self.features_13_conv_6_fc_0 = torch.nn.modules.linear.Linear(512, 32) self.features_13_conv_6_fc_2 = torch.nn.modules.linear.Linear(32, 512) self.features_13_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid() self.features_13_conv_7 = torch.nn.modules.conv.Conv2d(512, 128, 1, 1, 0, bias=False) self.features_13_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(128) self.features_14_conv_0 = torch.nn.modules.conv.Conv2d(128, 512, 1, 1, 0, bias=False) self.features_14_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(512) self.features_14_conv_3 = torch.nn.modules.conv.Conv2d(512, 512, 3, 1, 1, groups=512, bias=False) self.features_14_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(512) self.features_14_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1) self.features_14_conv_6_fc_0 = torch.nn.modules.linear.Linear(512, 32) self.features_14_conv_6_fc_2 = torch.nn.modules.linear.Linear(32, 512) self.features_14_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid() self.features_14_conv_7 = torch.nn.modules.conv.Conv2d(512, 128, 1, 1, 0, bias=False) self.features_14_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(128) self.features_15_conv_0 = torch.nn.modules.conv.Conv2d(128, 512, 1, 1, 0, bias=False) self.features_15_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(512) self.features_15_conv_3 = torch.nn.modules.conv.Conv2d(512, 512, 3, 1, 1, groups=512, bias=False) self.features_15_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(512) self.features_15_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1) self.features_15_conv_6_fc_0 = torch.nn.modules.linear.Linear(512, 32) self.features_15_conv_6_fc_2 = torch.nn.modules.linear.Linear(32, 512) self.features_15_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid() self.features_15_conv_7 = torch.nn.modules.conv.Conv2d(512, 128, 1, 1, 0, bias=False) self.features_15_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(128) self.features_16_conv_0 = torch.nn.modules.conv.Conv2d(128, 512, 1, 1, 0, bias=False) self.features_16_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(512) self.features_16_conv_3 = torch.nn.modules.conv.Conv2d(512, 512, 3, 1, 1, groups=512, bias=False) self.features_16_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(512) self.features_16_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1) self.features_16_conv_6_fc_0 = torch.nn.modules.linear.Linear(512, 32) self.features_16_conv_6_fc_2 = torch.nn.modules.linear.Linear(32, 512) self.features_16_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid() self.features_16_conv_7 = torch.nn.modules.conv.Conv2d(512, 128, 1, 1, 0, bias=False) self.features_16_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(128) self.features_17_conv_0 = torch.nn.modules.conv.Conv2d(128, 768, 1, 1, 0, bias=False) self.features_17_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(768) self.features_17_conv_3 = torch.nn.modules.conv.Conv2d(768, 768, 3, 1, 1, groups=768, bias=False) self.features_17_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(768) self.features_17_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1) self.features_17_conv_6_fc_0 = torch.nn.modules.linear.Linear(768, 32) self.features_17_conv_6_fc_2 = torch.nn.modules.linear.Linear(32, 768) self.features_17_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid() self.features_17_conv_7 = torch.nn.modules.conv.Conv2d(768, 160, 1, 1, 0, bias=False) self.features_17_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(160) self.features_18_conv_0 = torch.nn.modules.conv.Conv2d(160, 960, 1, 1, 0, bias=False) self.features_18_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(960) self.features_18_conv_3 = torch.nn.modules.conv.Conv2d(960, 960, 3, 1, 1, groups=960, bias=False) self.features_18_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(960) self.features_18_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1) self.features_18_conv_6_fc_0 = torch.nn.modules.linear.Linear(960, 40) self.features_18_conv_6_fc_2 = torch.nn.modules.linear.Linear(40, 960) self.features_18_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid() self.features_18_conv_7 = torch.nn.modules.conv.Conv2d(960, 160, 1, 1, 0, bias=False) self.features_18_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(160) self.features_19_conv_0 = torch.nn.modules.conv.Conv2d(160, 960, 1, 1, 0, bias=False) self.features_19_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(960) self.features_19_conv_3 = torch.nn.modules.conv.Conv2d(960, 960, 3, 1, 1, groups=960, bias=False) self.features_19_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(960) self.features_19_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1) self.features_19_conv_6_fc_0 = torch.nn.modules.linear.Linear(960, 40) self.features_19_conv_6_fc_2 = torch.nn.modules.linear.Linear(40, 960) self.features_19_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid() self.features_19_conv_7 = torch.nn.modules.conv.Conv2d(960, 160, 1, 1, 0, bias=False) self.features_19_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(160) self.features_20_conv_0 = torch.nn.modules.conv.Conv2d(160, 960, 1, 1, 0, bias=False) self.features_20_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(960) self.features_20_conv_3 = torch.nn.modules.conv.Conv2d(960, 960, 3, 1, 1, groups=960, bias=False) self.features_20_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(960) self.features_20_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1) self.features_20_conv_6_fc_0 = torch.nn.modules.linear.Linear(960, 40) self.features_20_conv_6_fc_2 = torch.nn.modules.linear.Linear(40, 960) self.features_20_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid() self.features_20_conv_7 = torch.nn.modules.conv.Conv2d(960, 160, 1, 1, 0, bias=False) self.features_20_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(160) self.features_21_conv_0 = torch.nn.modules.conv.Conv2d(160, 960, 1, 1, 0, bias=False) self.features_21_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(960) self.features_21_conv_3 = torch.nn.modules.conv.Conv2d(960, 960, 3, 1, 1, groups=960, bias=False) self.features_21_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(960) self.features_21_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1) self.features_21_conv_6_fc_0 = torch.nn.modules.linear.Linear(960, 40) self.features_21_conv_6_fc_2 = torch.nn.modules.linear.Linear(40, 960) self.features_21_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid() self.features_21_conv_7 = torch.nn.modules.conv.Conv2d(960, 160, 1, 1, 0, bias=False) self.features_21_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(160) self.features_22_conv_0 = torch.nn.modules.conv.Conv2d(160, 960, 1, 1, 0, bias=False) self.features_22_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(960) self.features_22_conv_3 = torch.nn.modules.conv.Conv2d(960, 960, 3, 1, 1, groups=960, bias=False) self.features_22_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(960) self.features_22_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1) self.features_22_conv_6_fc_0 = torch.nn.modules.linear.Linear(960, 40) self.features_22_conv_6_fc_2 = torch.nn.modules.linear.Linear(40, 960) self.features_22_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid() self.features_22_conv_7 = torch.nn.modules.conv.Conv2d(960, 160, 1, 1, 0, bias=False) self.features_22_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(160) self.features_23_conv_0 = torch.nn.modules.conv.Conv2d(160, 960, 1, 1, 0, bias=False) self.features_23_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(960) self.features_23_conv_3 = torch.nn.modules.conv.Conv2d(960, 960, 3, 1, 1, groups=960, bias=False) self.features_23_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(960) self.features_23_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1) self.features_23_conv_6_fc_0 = torch.nn.modules.linear.Linear(960, 40) self.features_23_conv_6_fc_2 = torch.nn.modules.linear.Linear(40, 960) self.features_23_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid() self.features_23_conv_7 = torch.nn.modules.conv.Conv2d(960, 160, 1, 1, 0, bias=False) self.features_23_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(160) self.features_24_conv_0 = torch.nn.modules.conv.Conv2d(160, 960, 1, 1, 0, bias=False) self.features_24_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(960) self.features_24_conv_3 = torch.nn.modules.conv.Conv2d(960, 960, 3, 1, 1, groups=960, bias=False) self.features_24_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(960) self.features_24_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1) self.features_24_conv_6_fc_0 = torch.nn.modules.linear.Linear(960, 40) self.features_24_conv_6_fc_2 = torch.nn.modules.linear.Linear(40, 960) self.features_24_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid() self.features_24_conv_7 = torch.nn.modules.conv.Conv2d(960, 160, 1, 1, 0, bias=False) self.features_24_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(160) self.features_25_conv_0 = torch.nn.modules.conv.Conv2d(160, 960, 1, 1, 0, bias=False) self.features_25_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(960) self.features_25_conv_3 = torch.nn.modules.conv.Conv2d(960, 960, 3, 1, 1, groups=960, bias=False) self.features_25_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(960) self.features_25_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1) self.features_25_conv_6_fc_0 = torch.nn.modules.linear.Linear(960, 40) self.features_25_conv_6_fc_2 = torch.nn.modules.linear.Linear(40, 960) self.features_25_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid() self.features_25_conv_7 = torch.nn.modules.conv.Conv2d(960, 160, 1, 1, 0, bias=False) self.features_25_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(160) self.features_26_conv_0 = torch.nn.modules.conv.Conv2d(160, 960, 1, 1, 0, bias=False) self.features_26_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(960) self.features_26_conv_3 = torch.nn.modules.conv.Conv2d(960, 960, 3, 2, 1, groups=960, bias=False) self.features_26_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(960) self.features_26_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1) self.features_26_conv_6_fc_0 = torch.nn.modules.linear.Linear(960, 40) self.features_26_conv_6_fc_2 = torch.nn.modules.linear.Linear(40, 960) self.features_26_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid() self.features_26_conv_7 = torch.nn.modules.conv.Conv2d(960, 256, 1, 1, 0, bias=False) self.features_26_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256) self.features_27_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False) self.features_27_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536) self.features_27_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False) self.features_27_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536) self.features_27_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1) self.features_27_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64) self.features_27_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536) self.features_27_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid() self.features_27_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False) self.features_27_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256) self.features_28_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False) self.features_28_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536) self.features_28_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False) self.features_28_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536) self.features_28_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1) self.features_28_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64) self.features_28_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536) self.features_28_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid() self.features_28_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False) self.features_28_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256) self.features_29_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False) self.features_29_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536) self.features_29_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False) self.features_29_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536) self.features_29_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1) self.features_29_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64) self.features_29_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536) self.features_29_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid() self.features_29_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False) self.features_29_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256) self.features_30_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False) self.features_30_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536) self.features_30_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False) self.features_30_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536) self.features_30_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1) self.features_30_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64) self.features_30_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536) self.features_30_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid() self.features_30_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False) self.features_30_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256) self.features_31_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False) self.features_31_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536) self.features_31_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False) self.features_31_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536) self.features_31_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1) self.features_31_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64) self.features_31_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536) self.features_31_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid() self.features_31_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False) self.features_31_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256) self.features_32_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False) self.features_32_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536) self.features_32_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False) self.features_32_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536) self.features_32_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1) self.features_32_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64) self.features_32_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536) self.features_32_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid() self.features_32_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False) self.features_32_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256) self.features_33_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False) self.features_33_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536) self.features_33_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False) self.features_33_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536) self.features_33_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1) self.features_33_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64) self.features_33_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536) self.features_33_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid() self.features_33_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False) self.features_33_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256) self.features_34_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False) self.features_34_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536) self.features_34_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False) self.features_34_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536) self.features_34_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1) self.features_34_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64) self.features_34_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536) self.features_34_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid() self.features_34_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False) self.features_34_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256) self.features_35_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False) self.features_35_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536) self.features_35_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False) self.features_35_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536) self.features_35_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1) self.features_35_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64) self.features_35_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536) self.features_35_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid() self.features_35_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False) self.features_35_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256) self.features_36_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False) self.features_36_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536) self.features_36_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False) self.features_36_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536) self.features_36_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1) self.features_36_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64) self.features_36_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536) self.features_36_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid() self.features_36_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False) self.features_36_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256) self.features_37_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False) self.features_37_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536) self.features_37_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False) self.features_37_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536) self.features_37_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1) self.features_37_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64) self.features_37_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536) self.features_37_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid() self.features_37_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False) self.features_37_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256) self.features_38_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False) self.features_38_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536) self.features_38_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False) self.features_38_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536) self.features_38_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1) self.features_38_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64) self.features_38_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536) self.features_38_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid() self.features_38_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False) self.features_38_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256) self.features_39_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False) self.features_39_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536) self.features_39_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False) self.features_39_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536) self.features_39_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1) self.features_39_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64) self.features_39_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536) self.features_39_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid() self.features_39_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False) self.features_39_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256) self.features_40_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False) self.features_40_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536) self.features_40_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False) self.features_40_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536) self.features_40_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1) self.features_40_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64) self.features_40_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536) self.features_40_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid() self.features_40_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False) self.features_40_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256) self.conv_0 = torch.nn.modules.conv.Conv2d(256, 1792, 1, 1, 0, bias=False) self.conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1792) self.avgpool = torch.nn.modules.pooling.AdaptiveAvgPool2d((1, 1)) self.classifier = torch.nn.modules.linear.Linear(1792, 1000) def forward(self, input_1): features_0_0 = self.features_0_0(input_1) features_0_1 = self.features_0_1(features_0_0) sigmoid_1 = torch.sigmoid(features_0_1) mul_1 = features_0_1.__mul__(sigmoid_1) features_1_conv_0 = self.features_1_conv_0(mul_1) features_1_conv_1 = self.features_1_conv_1(features_1_conv_0) sigmoid_2 = torch.sigmoid(features_1_conv_1) mul_2 = features_1_conv_1.__mul__(sigmoid_2) features_1_conv_3 = self.features_1_conv_3(mul_2) features_1_conv_4 = self.features_1_conv_4(features_1_conv_3) add_1 = mul_1.__add__(features_1_conv_4) features_2_conv_0 = self.features_2_conv_0(add_1) features_2_conv_1 = self.features_2_conv_1(features_2_conv_0) sigmoid_3 = torch.sigmoid(features_2_conv_1) mul_3 = features_2_conv_1.__mul__(sigmoid_3) features_2_conv_3 = self.features_2_conv_3(mul_3) features_2_conv_4 = self.features_2_conv_4(features_2_conv_3) add_2 = add_1.__add__(features_2_conv_4) features_3_conv_0 = self.features_3_conv_0(add_2) features_3_conv_1 = self.features_3_conv_1(features_3_conv_0) sigmoid_4 = torch.sigmoid(features_3_conv_1) mul_4 = features_3_conv_1.__mul__(sigmoid_4) features_3_conv_3 = self.features_3_conv_3(mul_4) features_3_conv_4 = self.features_3_conv_4(features_3_conv_3) features_4_conv_0 = self.features_4_conv_0(features_3_conv_4) features_4_conv_1 = self.features_4_conv_1(features_4_conv_0) sigmoid_5 = torch.sigmoid(features_4_conv_1) mul_5 = features_4_conv_1.__mul__(sigmoid_5) features_4_conv_3 = self.features_4_conv_3(mul_5) features_4_conv_4 = self.features_4_conv_4(features_4_conv_3) add_3 = features_3_conv_4.__add__(features_4_conv_4) features_5_conv_0 = self.features_5_conv_0(add_3) features_5_conv_1 = self.features_5_conv_1(features_5_conv_0) sigmoid_6 = torch.sigmoid(features_5_conv_1) mul_6 = features_5_conv_1.__mul__(sigmoid_6) features_5_conv_3 = self.features_5_conv_3(mul_6) features_5_conv_4 = self.features_5_conv_4(features_5_conv_3) add_4 = add_3.__add__(features_5_conv_4) features_6_conv_0 = self.features_6_conv_0(add_4) features_6_conv_1 = self.features_6_conv_1(features_6_conv_0) sigmoid_7 = torch.sigmoid(features_6_conv_1) mul_7 = features_6_conv_1.__mul__(sigmoid_7) features_6_conv_3 = self.features_6_conv_3(mul_7) features_6_conv_4 = self.features_6_conv_4(features_6_conv_3) add_5 = add_4.__add__(features_6_conv_4) features_7_conv_0 = self.features_7_conv_0(add_5) features_7_conv_1 = self.features_7_conv_1(features_7_conv_0) sigmoid_8 = torch.sigmoid(features_7_conv_1) mul_8 = features_7_conv_1.__mul__(sigmoid_8) features_7_conv_3 = self.features_7_conv_3(mul_8) features_7_conv_4 = self.features_7_conv_4(features_7_conv_3) features_8_conv_0 = self.features_8_conv_0(features_7_conv_4) features_8_conv_1 = self.features_8_conv_1(features_8_conv_0) sigmoid_9 = torch.sigmoid(features_8_conv_1) mul_9 = features_8_conv_1.__mul__(sigmoid_9) features_8_conv_3 = self.features_8_conv_3(mul_9) features_8_conv_4 = self.features_8_conv_4(features_8_conv_3) add_6 = features_7_conv_4.__add__(features_8_conv_4) features_9_conv_0 = self.features_9_conv_0(add_6) features_9_conv_1 = self.features_9_conv_1(features_9_conv_0) sigmoid_10 = torch.sigmoid(features_9_conv_1) mul_10 = features_9_conv_1.__mul__(sigmoid_10) features_9_conv_3 = self.features_9_conv_3(mul_10) features_9_conv_4 = self.features_9_conv_4(features_9_conv_3) add_7 = add_6.__add__(features_9_conv_4) features_10_conv_0 = self.features_10_conv_0(add_7) features_10_conv_1 = self.features_10_conv_1(features_10_conv_0) sigmoid_11 = torch.sigmoid(features_10_conv_1) mul_11 = features_10_conv_1.__mul__(sigmoid_11) features_10_conv_3 = self.features_10_conv_3(mul_11) features_10_conv_4 = self.features_10_conv_4(features_10_conv_3) add_8 = add_7.__add__(features_10_conv_4) features_11_conv_0 = self.features_11_conv_0(add_8) features_11_conv_1 = self.features_11_conv_1(features_11_conv_0) sigmoid_12 = torch.sigmoid(features_11_conv_1) mul_12 = features_11_conv_1.__mul__(sigmoid_12) features_11_conv_3 = self.features_11_conv_3(mul_12) features_11_conv_4 = self.features_11_conv_4(features_11_conv_3) sigmoid_13 = torch.sigmoid(features_11_conv_4) mul_13 = features_11_conv_4.__mul__(sigmoid_13) size_1 = mul_13.size() features_11_conv_6_avg_pool = self.features_11_conv_6_avg_pool(mul_13) view_1 = features_11_conv_6_avg_pool.view(size_1[0], size_1[1]) features_11_conv_6_fc_0 = self.features_11_conv_6_fc_0(view_1) sigmoid_14 = torch.sigmoid(features_11_conv_6_fc_0) mul_14 = features_11_conv_6_fc_0.__mul__(sigmoid_14) features_11_conv_6_fc_2 = self.features_11_conv_6_fc_2(mul_14) features_11_conv_6_fc_3 = self.features_11_conv_6_fc_3(features_11_conv_6_fc_2) view_2 = features_11_conv_6_fc_3.view(size_1[0], size_1[1], 1, 1) mul_15 = mul_13.__mul__(view_2) features_11_conv_7 = self.features_11_conv_7(mul_15) features_11_conv_8 = self.features_11_conv_8(features_11_conv_7) features_12_conv_0 = self.features_12_conv_0(features_11_conv_8) features_12_conv_1 = self.features_12_conv_1(features_12_conv_0) sigmoid_15 = torch.sigmoid(features_12_conv_1) mul_16 = features_12_conv_1.__mul__(sigmoid_15) features_12_conv_3 = self.features_12_conv_3(mul_16) features_12_conv_4 = self.features_12_conv_4(features_12_conv_3) sigmoid_16 = torch.sigmoid(features_12_conv_4) mul_17 = features_12_conv_4.__mul__(sigmoid_16) size_2 = mul_17.size() features_12_conv_6_avg_pool = self.features_12_conv_6_avg_pool(mul_17) view_3 = features_12_conv_6_avg_pool.view(size_2[0], size_2[1]) features_12_conv_6_fc_0 = self.features_12_conv_6_fc_0(view_3) sigmoid_17 = torch.sigmoid(features_12_conv_6_fc_0) mul_18 = features_12_conv_6_fc_0.__mul__(sigmoid_17) features_12_conv_6_fc_2 = self.features_12_conv_6_fc_2(mul_18) features_12_conv_6_fc_3 = self.features_12_conv_6_fc_3(features_12_conv_6_fc_2) view_4 = features_12_conv_6_fc_3.view(size_2[0], size_2[1], 1, 1) mul_19 = mul_17.__mul__(view_4) features_12_conv_7 = self.features_12_conv_7(mul_19) features_12_conv_8 = self.features_12_conv_8(features_12_conv_7) add_9 = features_11_conv_8.__add__(features_12_conv_8) features_13_conv_0 = self.features_13_conv_0(add_9) features_13_conv_1 = self.features_13_conv_1(features_13_conv_0) sigmoid_18 = torch.sigmoid(features_13_conv_1) mul_20 = features_13_conv_1.__mul__(sigmoid_18) features_13_conv_3 = self.features_13_conv_3(mul_20) features_13_conv_4 = self.features_13_conv_4(features_13_conv_3) sigmoid_19 = torch.sigmoid(features_13_conv_4) mul_21 = features_13_conv_4.__mul__(sigmoid_19) size_3 = mul_21.size() features_13_conv_6_avg_pool = self.features_13_conv_6_avg_pool(mul_21) view_5 = features_13_conv_6_avg_pool.view(size_3[0], size_3[1]) features_13_conv_6_fc_0 = self.features_13_conv_6_fc_0(view_5) sigmoid_20 = torch.sigmoid(features_13_conv_6_fc_0) mul_22 = features_13_conv_6_fc_0.__mul__(sigmoid_20) features_13_conv_6_fc_2 = self.features_13_conv_6_fc_2(mul_22) features_13_conv_6_fc_3 = self.features_13_conv_6_fc_3(features_13_conv_6_fc_2) view_6 = features_13_conv_6_fc_3.view(size_3[0], size_3[1], 1, 1) mul_23 = mul_21.__mul__(view_6) features_13_conv_7 = self.features_13_conv_7(mul_23) features_13_conv_8 = self.features_13_conv_8(features_13_conv_7) add_10 = add_9.__add__(features_13_conv_8) features_14_conv_0 = self.features_14_conv_0(add_10) features_14_conv_1 = self.features_14_conv_1(features_14_conv_0) sigmoid_21 = torch.sigmoid(features_14_conv_1) mul_24 = features_14_conv_1.__mul__(sigmoid_21) features_14_conv_3 = self.features_14_conv_3(mul_24) features_14_conv_4 = self.features_14_conv_4(features_14_conv_3) sigmoid_22 = torch.sigmoid(features_14_conv_4) mul_25 = features_14_conv_4.__mul__(sigmoid_22) size_4 = mul_25.size() features_14_conv_6_avg_pool = self.features_14_conv_6_avg_pool(mul_25) view_7 = features_14_conv_6_avg_pool.view(size_4[0], size_4[1]) features_14_conv_6_fc_0 = self.features_14_conv_6_fc_0(view_7) sigmoid_23 = torch.sigmoid(features_14_conv_6_fc_0) mul_26 = features_14_conv_6_fc_0.__mul__(sigmoid_23) features_14_conv_6_fc_2 = self.features_14_conv_6_fc_2(mul_26) features_14_conv_6_fc_3 = self.features_14_conv_6_fc_3(features_14_conv_6_fc_2) view_8 = features_14_conv_6_fc_3.view(size_4[0], size_4[1], 1, 1) mul_27 = mul_25.__mul__(view_8) features_14_conv_7 = self.features_14_conv_7(mul_27) features_14_conv_8 = self.features_14_conv_8(features_14_conv_7) add_11 = add_10.__add__(features_14_conv_8) features_15_conv_0 = self.features_15_conv_0(add_11) features_15_conv_1 = self.features_15_conv_1(features_15_conv_0) sigmoid_24 = torch.sigmoid(features_15_conv_1) mul_28 = features_15_conv_1.__mul__(sigmoid_24) features_15_conv_3 = self.features_15_conv_3(mul_28) features_15_conv_4 = self.features_15_conv_4(features_15_conv_3) sigmoid_25 = torch.sigmoid(features_15_conv_4) mul_29 = features_15_conv_4.__mul__(sigmoid_25) size_5 = mul_29.size() features_15_conv_6_avg_pool = self.features_15_conv_6_avg_pool(mul_29) view_9 = features_15_conv_6_avg_pool.view(size_5[0], size_5[1]) features_15_conv_6_fc_0 = self.features_15_conv_6_fc_0(view_9) sigmoid_26 = torch.sigmoid(features_15_conv_6_fc_0) mul_30 = features_15_conv_6_fc_0.__mul__(sigmoid_26) features_15_conv_6_fc_2 = self.features_15_conv_6_fc_2(mul_30) features_15_conv_6_fc_3 = self.features_15_conv_6_fc_3(features_15_conv_6_fc_2) view_10 = features_15_conv_6_fc_3.view(size_5[0], size_5[1], 1, 1) mul_31 = mul_29.__mul__(view_10) features_15_conv_7 = self.features_15_conv_7(mul_31) features_15_conv_8 = self.features_15_conv_8(features_15_conv_7) add_12 = add_11.__add__(features_15_conv_8) features_16_conv_0 = self.features_16_conv_0(add_12) features_16_conv_1 = self.features_16_conv_1(features_16_conv_0) sigmoid_27 = torch.sigmoid(features_16_conv_1) mul_32 = features_16_conv_1.__mul__(sigmoid_27) features_16_conv_3 = self.features_16_conv_3(mul_32) features_16_conv_4 = self.features_16_conv_4(features_16_conv_3) sigmoid_28 = torch.sigmoid(features_16_conv_4) mul_33 = features_16_conv_4.__mul__(sigmoid_28) size_6 = mul_33.size() features_16_conv_6_avg_pool = self.features_16_conv_6_avg_pool(mul_33) view_11 = features_16_conv_6_avg_pool.view(size_6[0], size_6[1]) features_16_conv_6_fc_0 = self.features_16_conv_6_fc_0(view_11) sigmoid_29 = torch.sigmoid(features_16_conv_6_fc_0) mul_34 = features_16_conv_6_fc_0.__mul__(sigmoid_29) features_16_conv_6_fc_2 = self.features_16_conv_6_fc_2(mul_34) features_16_conv_6_fc_3 = self.features_16_conv_6_fc_3(features_16_conv_6_fc_2) view_12 = features_16_conv_6_fc_3.view(size_6[0], size_6[1], 1, 1) mul_35 = mul_33.__mul__(view_12) features_16_conv_7 = self.features_16_conv_7(mul_35) features_16_conv_8 = self.features_16_conv_8(features_16_conv_7) add_13 = add_12.__add__(features_16_conv_8) features_17_conv_0 = self.features_17_conv_0(add_13) features_17_conv_1 = self.features_17_conv_1(features_17_conv_0) sigmoid_30 = torch.sigmoid(features_17_conv_1) mul_36 = features_17_conv_1.__mul__(sigmoid_30) features_17_conv_3 = self.features_17_conv_3(mul_36) features_17_conv_4 = self.features_17_conv_4(features_17_conv_3) sigmoid_31 = torch.sigmoid(features_17_conv_4) mul_37 = features_17_conv_4.__mul__(sigmoid_31) size_7 = mul_37.size() features_17_conv_6_avg_pool = self.features_17_conv_6_avg_pool(mul_37) view_13 = features_17_conv_6_avg_pool.view(size_7[0], size_7[1]) features_17_conv_6_fc_0 = self.features_17_conv_6_fc_0(view_13) sigmoid_32 = torch.sigmoid(features_17_conv_6_fc_0) mul_38 = features_17_conv_6_fc_0.__mul__(sigmoid_32) features_17_conv_6_fc_2 = self.features_17_conv_6_fc_2(mul_38) features_17_conv_6_fc_3 = self.features_17_conv_6_fc_3(features_17_conv_6_fc_2) view_14 = features_17_conv_6_fc_3.view(size_7[0], size_7[1], 1, 1) mul_39 = mul_37.__mul__(view_14) features_17_conv_7 = self.features_17_conv_7(mul_39) features_17_conv_8 = self.features_17_conv_8(features_17_conv_7) features_18_conv_0 = self.features_18_conv_0(features_17_conv_8) features_18_conv_1 = self.features_18_conv_1(features_18_conv_0) sigmoid_33 = torch.sigmoid(features_18_conv_1) mul_40 = features_18_conv_1.__mul__(sigmoid_33) features_18_conv_3 = self.features_18_conv_3(mul_40) features_18_conv_4 = self.features_18_conv_4(features_18_conv_3) sigmoid_34 = torch.sigmoid(features_18_conv_4) mul_41 = features_18_conv_4.__mul__(sigmoid_34) size_8 = mul_41.size() features_18_conv_6_avg_pool = self.features_18_conv_6_avg_pool(mul_41) view_15 = features_18_conv_6_avg_pool.view(size_8[0], size_8[1]) features_18_conv_6_fc_0 = self.features_18_conv_6_fc_0(view_15) sigmoid_35 = torch.sigmoid(features_18_conv_6_fc_0) mul_42 = features_18_conv_6_fc_0.__mul__(sigmoid_35) features_18_conv_6_fc_2 = self.features_18_conv_6_fc_2(mul_42) features_18_conv_6_fc_3 = self.features_18_conv_6_fc_3(features_18_conv_6_fc_2) view_16 = features_18_conv_6_fc_3.view(size_8[0], size_8[1], 1, 1) mul_43 = mul_41.__mul__(view_16) features_18_conv_7 = self.features_18_conv_7(mul_43) features_18_conv_8 = self.features_18_conv_8(features_18_conv_7) add_14 = features_17_conv_8.__add__(features_18_conv_8) features_19_conv_0 = self.features_19_conv_0(add_14) features_19_conv_1 = self.features_19_conv_1(features_19_conv_0) sigmoid_36 = torch.sigmoid(features_19_conv_1) mul_44 = features_19_conv_1.__mul__(sigmoid_36) features_19_conv_3 = self.features_19_conv_3(mul_44) features_19_conv_4 = self.features_19_conv_4(features_19_conv_3) sigmoid_37 = torch.sigmoid(features_19_conv_4) mul_45 = features_19_conv_4.__mul__(sigmoid_37) size_9 = mul_45.size() features_19_conv_6_avg_pool = self.features_19_conv_6_avg_pool(mul_45) view_17 = features_19_conv_6_avg_pool.view(size_9[0], size_9[1]) features_19_conv_6_fc_0 = self.features_19_conv_6_fc_0(view_17) sigmoid_38 = torch.sigmoid(features_19_conv_6_fc_0) mul_46 = features_19_conv_6_fc_0.__mul__(sigmoid_38) features_19_conv_6_fc_2 = self.features_19_conv_6_fc_2(mul_46) features_19_conv_6_fc_3 = self.features_19_conv_6_fc_3(features_19_conv_6_fc_2) view_18 = features_19_conv_6_fc_3.view(size_9[0], size_9[1], 1, 1) mul_47 = mul_45.__mul__(view_18) features_19_conv_7 = self.features_19_conv_7(mul_47) features_19_conv_8 = self.features_19_conv_8(features_19_conv_7) add_15 = add_14.__add__(features_19_conv_8) features_20_conv_0 = self.features_20_conv_0(add_15) features_20_conv_1 = self.features_20_conv_1(features_20_conv_0) sigmoid_39 = torch.sigmoid(features_20_conv_1) mul_48 = features_20_conv_1.__mul__(sigmoid_39) features_20_conv_3 = self.features_20_conv_3(mul_48) features_20_conv_4 = self.features_20_conv_4(features_20_conv_3) sigmoid_40 = torch.sigmoid(features_20_conv_4) mul_49 = features_20_conv_4.__mul__(sigmoid_40) size_10 = mul_49.size() features_20_conv_6_avg_pool = self.features_20_conv_6_avg_pool(mul_49) view_19 = features_20_conv_6_avg_pool.view(size_10[0], size_10[1]) features_20_conv_6_fc_0 = self.features_20_conv_6_fc_0(view_19) sigmoid_41 = torch.sigmoid(features_20_conv_6_fc_0) mul_50 = features_20_conv_6_fc_0.__mul__(sigmoid_41) features_20_conv_6_fc_2 = self.features_20_conv_6_fc_2(mul_50) features_20_conv_6_fc_3 = self.features_20_conv_6_fc_3(features_20_conv_6_fc_2) view_20 = features_20_conv_6_fc_3.view(size_10[0], size_10[1], 1, 1) mul_51 = mul_49.__mul__(view_20) features_20_conv_7 = self.features_20_conv_7(mul_51) features_20_conv_8 = self.features_20_conv_8(features_20_conv_7) add_16 = add_15.__add__(features_20_conv_8) features_21_conv_0 = self.features_21_conv_0(add_16) features_21_conv_1 = self.features_21_conv_1(features_21_conv_0) sigmoid_42 = torch.sigmoid(features_21_conv_1) mul_52 = features_21_conv_1.__mul__(sigmoid_42) features_21_conv_3 = self.features_21_conv_3(mul_52) features_21_conv_4 = self.features_21_conv_4(features_21_conv_3) sigmoid_43 = torch.sigmoid(features_21_conv_4) mul_53 = features_21_conv_4.__mul__(sigmoid_43) size_11 = mul_53.size() features_21_conv_6_avg_pool = self.features_21_conv_6_avg_pool(mul_53) view_21 = features_21_conv_6_avg_pool.view(size_11[0], size_11[1]) features_21_conv_6_fc_0 = self.features_21_conv_6_fc_0(view_21) sigmoid_44 = torch.sigmoid(features_21_conv_6_fc_0) mul_54 = features_21_conv_6_fc_0.__mul__(sigmoid_44) features_21_conv_6_fc_2 = self.features_21_conv_6_fc_2(mul_54) features_21_conv_6_fc_3 = self.features_21_conv_6_fc_3(features_21_conv_6_fc_2) view_22 = features_21_conv_6_fc_3.view(size_11[0], size_11[1], 1, 1) mul_55 = mul_53.__mul__(view_22) features_21_conv_7 = self.features_21_conv_7(mul_55) features_21_conv_8 = self.features_21_conv_8(features_21_conv_7) add_17 = add_16.__add__(features_21_conv_8) features_22_conv_0 = self.features_22_conv_0(add_17) features_22_conv_1 = self.features_22_conv_1(features_22_conv_0) sigmoid_45 = torch.sigmoid(features_22_conv_1) mul_56 = features_22_conv_1.__mul__(sigmoid_45) features_22_conv_3 = self.features_22_conv_3(mul_56) features_22_conv_4 = self.features_22_conv_4(features_22_conv_3) sigmoid_46 = torch.sigmoid(features_22_conv_4) mul_57 = features_22_conv_4.__mul__(sigmoid_46) size_12 = mul_57.size() features_22_conv_6_avg_pool = self.features_22_conv_6_avg_pool(mul_57) view_23 = features_22_conv_6_avg_pool.view(size_12[0], size_12[1]) features_22_conv_6_fc_0 = self.features_22_conv_6_fc_0(view_23) sigmoid_47 = torch.sigmoid(features_22_conv_6_fc_0) mul_58 = features_22_conv_6_fc_0.__mul__(sigmoid_47) features_22_conv_6_fc_2 = self.features_22_conv_6_fc_2(mul_58) features_22_conv_6_fc_3 = self.features_22_conv_6_fc_3(features_22_conv_6_fc_2) view_24 = features_22_conv_6_fc_3.view(size_12[0], size_12[1], 1, 1) mul_59 = mul_57.__mul__(view_24) features_22_conv_7 = self.features_22_conv_7(mul_59) features_22_conv_8 = self.features_22_conv_8(features_22_conv_7) add_18 = add_17.__add__(features_22_conv_8) features_23_conv_0 = self.features_23_conv_0(add_18) features_23_conv_1 = self.features_23_conv_1(features_23_conv_0) sigmoid_48 = torch.sigmoid(features_23_conv_1) mul_60 = features_23_conv_1.__mul__(sigmoid_48) features_23_conv_3 = self.features_23_conv_3(mul_60) features_23_conv_4 = self.features_23_conv_4(features_23_conv_3) sigmoid_49 = torch.sigmoid(features_23_conv_4) mul_61 = features_23_conv_4.__mul__(sigmoid_49) size_13 = mul_61.size() features_23_conv_6_avg_pool = self.features_23_conv_6_avg_pool(mul_61) view_25 = features_23_conv_6_avg_pool.view(size_13[0], size_13[1]) features_23_conv_6_fc_0 = self.features_23_conv_6_fc_0(view_25) sigmoid_50 = torch.sigmoid(features_23_conv_6_fc_0) mul_62 = features_23_conv_6_fc_0.__mul__(sigmoid_50) features_23_conv_6_fc_2 = self.features_23_conv_6_fc_2(mul_62) features_23_conv_6_fc_3 = self.features_23_conv_6_fc_3(features_23_conv_6_fc_2) view_26 = features_23_conv_6_fc_3.view(size_13[0], size_13[1], 1, 1) mul_63 = mul_61.__mul__(view_26) features_23_conv_7 = self.features_23_conv_7(mul_63) features_23_conv_8 = self.features_23_conv_8(features_23_conv_7) add_19 = add_18.__add__(features_23_conv_8) features_24_conv_0 = self.features_24_conv_0(add_19) features_24_conv_1 = self.features_24_conv_1(features_24_conv_0) sigmoid_51 = torch.sigmoid(features_24_conv_1) mul_64 = features_24_conv_1.__mul__(sigmoid_51) features_24_conv_3 = self.features_24_conv_3(mul_64) features_24_conv_4 = self.features_24_conv_4(features_24_conv_3) sigmoid_52 = torch.sigmoid(features_24_conv_4) mul_65 = features_24_conv_4.__mul__(sigmoid_52) size_14 = mul_65.size() features_24_conv_6_avg_pool = self.features_24_conv_6_avg_pool(mul_65) view_27 = features_24_conv_6_avg_pool.view(size_14[0], size_14[1]) features_24_conv_6_fc_0 = self.features_24_conv_6_fc_0(view_27) sigmoid_53 = torch.sigmoid(features_24_conv_6_fc_0) mul_66 = features_24_conv_6_fc_0.__mul__(sigmoid_53) features_24_conv_6_fc_2 = self.features_24_conv_6_fc_2(mul_66) features_24_conv_6_fc_3 = self.features_24_conv_6_fc_3(features_24_conv_6_fc_2) view_28 = features_24_conv_6_fc_3.view(size_14[0], size_14[1], 1, 1) mul_67 = mul_65.__mul__(view_28) features_24_conv_7 = self.features_24_conv_7(mul_67) features_24_conv_8 = self.features_24_conv_8(features_24_conv_7) add_20 = add_19.__add__(features_24_conv_8) features_25_conv_0 = self.features_25_conv_0(add_20) features_25_conv_1 = self.features_25_conv_1(features_25_conv_0) sigmoid_54 = torch.sigmoid(features_25_conv_1) mul_68 = features_25_conv_1.__mul__(sigmoid_54) features_25_conv_3 = self.features_25_conv_3(mul_68) features_25_conv_4 = self.features_25_conv_4(features_25_conv_3) sigmoid_55 = torch.sigmoid(features_25_conv_4) mul_69 = features_25_conv_4.__mul__(sigmoid_55) size_15 = mul_69.size() features_25_conv_6_avg_pool = self.features_25_conv_6_avg_pool(mul_69) view_29 = features_25_conv_6_avg_pool.view(size_15[0], size_15[1]) features_25_conv_6_fc_0 = self.features_25_conv_6_fc_0(view_29) sigmoid_56 = torch.sigmoid(features_25_conv_6_fc_0) mul_70 = features_25_conv_6_fc_0.__mul__(sigmoid_56) features_25_conv_6_fc_2 = self.features_25_conv_6_fc_2(mul_70) features_25_conv_6_fc_3 = self.features_25_conv_6_fc_3(features_25_conv_6_fc_2) view_30 = features_25_conv_6_fc_3.view(size_15[0], size_15[1], 1, 1) mul_71 = mul_69.__mul__(view_30) features_25_conv_7 = self.features_25_conv_7(mul_71) features_25_conv_8 = self.features_25_conv_8(features_25_conv_7) add_21 = add_20.__add__(features_25_conv_8) features_26_conv_0 = self.features_26_conv_0(add_21) features_26_conv_1 = self.features_26_conv_1(features_26_conv_0) sigmoid_57 = torch.sigmoid(features_26_conv_1) mul_72 = features_26_conv_1.__mul__(sigmoid_57) features_26_conv_3 = self.features_26_conv_3(mul_72) features_26_conv_4 = self.features_26_conv_4(features_26_conv_3) sigmoid_58 = torch.sigmoid(features_26_conv_4) mul_73 = features_26_conv_4.__mul__(sigmoid_58) size_16 = mul_73.size() features_26_conv_6_avg_pool = self.features_26_conv_6_avg_pool(mul_73) view_31 = features_26_conv_6_avg_pool.view(size_16[0], size_16[1]) features_26_conv_6_fc_0 = self.features_26_conv_6_fc_0(view_31) sigmoid_59 = torch.sigmoid(features_26_conv_6_fc_0) mul_74 = features_26_conv_6_fc_0.__mul__(sigmoid_59) features_26_conv_6_fc_2 = self.features_26_conv_6_fc_2(mul_74) features_26_conv_6_fc_3 = self.features_26_conv_6_fc_3(features_26_conv_6_fc_2) view_32 = features_26_conv_6_fc_3.view(size_16[0], size_16[1], 1, 1) mul_75 = mul_73.__mul__(view_32) features_26_conv_7 = self.features_26_conv_7(mul_75) features_26_conv_8 = self.features_26_conv_8(features_26_conv_7) features_27_conv_0 = self.features_27_conv_0(features_26_conv_8) features_27_conv_1 = self.features_27_conv_1(features_27_conv_0) sigmoid_60 = torch.sigmoid(features_27_conv_1) mul_76 = features_27_conv_1.__mul__(sigmoid_60) features_27_conv_3 = self.features_27_conv_3(mul_76) features_27_conv_4 = self.features_27_conv_4(features_27_conv_3) sigmoid_61 = torch.sigmoid(features_27_conv_4) mul_77 = features_27_conv_4.__mul__(sigmoid_61) size_17 = mul_77.size() features_27_conv_6_avg_pool = self.features_27_conv_6_avg_pool(mul_77) view_33 = features_27_conv_6_avg_pool.view(size_17[0], size_17[1]) features_27_conv_6_fc_0 = self.features_27_conv_6_fc_0(view_33) sigmoid_62 = torch.sigmoid(features_27_conv_6_fc_0) mul_78 = features_27_conv_6_fc_0.__mul__(sigmoid_62) features_27_conv_6_fc_2 = self.features_27_conv_6_fc_2(mul_78) features_27_conv_6_fc_3 = self.features_27_conv_6_fc_3(features_27_conv_6_fc_2) view_34 = features_27_conv_6_fc_3.view(size_17[0], size_17[1], 1, 1) mul_79 = mul_77.__mul__(view_34) features_27_conv_7 = self.features_27_conv_7(mul_79) features_27_conv_8 = self.features_27_conv_8(features_27_conv_7) add_22 = features_26_conv_8.__add__(features_27_conv_8) features_28_conv_0 = self.features_28_conv_0(add_22) features_28_conv_1 = self.features_28_conv_1(features_28_conv_0) sigmoid_63 = torch.sigmoid(features_28_conv_1) mul_80 = features_28_conv_1.__mul__(sigmoid_63) features_28_conv_3 = self.features_28_conv_3(mul_80) features_28_conv_4 = self.features_28_conv_4(features_28_conv_3) sigmoid_64 = torch.sigmoid(features_28_conv_4) mul_81 = features_28_conv_4.__mul__(sigmoid_64) size_18 = mul_81.size() features_28_conv_6_avg_pool = self.features_28_conv_6_avg_pool(mul_81) view_35 = features_28_conv_6_avg_pool.view(size_18[0], size_18[1]) features_28_conv_6_fc_0 = self.features_28_conv_6_fc_0(view_35) sigmoid_65 = torch.sigmoid(features_28_conv_6_fc_0) mul_82 = features_28_conv_6_fc_0.__mul__(sigmoid_65) features_28_conv_6_fc_2 = self.features_28_conv_6_fc_2(mul_82) features_28_conv_6_fc_3 = self.features_28_conv_6_fc_3(features_28_conv_6_fc_2) view_36 = features_28_conv_6_fc_3.view(size_18[0], size_18[1], 1, 1) mul_83 = mul_81.__mul__(view_36) features_28_conv_7 = self.features_28_conv_7(mul_83) features_28_conv_8 = self.features_28_conv_8(features_28_conv_7) add_23 = add_22.__add__(features_28_conv_8) features_29_conv_0 = self.features_29_conv_0(add_23) features_29_conv_1 = self.features_29_conv_1(features_29_conv_0) sigmoid_66 = torch.sigmoid(features_29_conv_1) mul_84 = features_29_conv_1.__mul__(sigmoid_66) features_29_conv_3 = self.features_29_conv_3(mul_84) features_29_conv_4 = self.features_29_conv_4(features_29_conv_3) sigmoid_67 = torch.sigmoid(features_29_conv_4) mul_85 = features_29_conv_4.__mul__(sigmoid_67) size_19 = mul_85.size() features_29_conv_6_avg_pool = self.features_29_conv_6_avg_pool(mul_85) view_37 = features_29_conv_6_avg_pool.view(size_19[0], size_19[1]) features_29_conv_6_fc_0 = self.features_29_conv_6_fc_0(view_37) sigmoid_68 = torch.sigmoid(features_29_conv_6_fc_0) mul_86 = features_29_conv_6_fc_0.__mul__(sigmoid_68) features_29_conv_6_fc_2 = self.features_29_conv_6_fc_2(mul_86) features_29_conv_6_fc_3 = self.features_29_conv_6_fc_3(features_29_conv_6_fc_2) view_38 = features_29_conv_6_fc_3.view(size_19[0], size_19[1], 1, 1) mul_87 = mul_85.__mul__(view_38) features_29_conv_7 = self.features_29_conv_7(mul_87) features_29_conv_8 = self.features_29_conv_8(features_29_conv_7) add_24 = add_23.__add__(features_29_conv_8) features_30_conv_0 = self.features_30_conv_0(add_24) features_30_conv_1 = self.features_30_conv_1(features_30_conv_0) sigmoid_69 = torch.sigmoid(features_30_conv_1) mul_88 = features_30_conv_1.__mul__(sigmoid_69) features_30_conv_3 = self.features_30_conv_3(mul_88) features_30_conv_4 = self.features_30_conv_4(features_30_conv_3) sigmoid_70 = torch.sigmoid(features_30_conv_4) mul_89 = features_30_conv_4.__mul__(sigmoid_70) size_20 = mul_89.size() features_30_conv_6_avg_pool = self.features_30_conv_6_avg_pool(mul_89) view_39 = features_30_conv_6_avg_pool.view(size_20[0], size_20[1]) features_30_conv_6_fc_0 = self.features_30_conv_6_fc_0(view_39) sigmoid_71 = torch.sigmoid(features_30_conv_6_fc_0) mul_90 = features_30_conv_6_fc_0.__mul__(sigmoid_71) features_30_conv_6_fc_2 = self.features_30_conv_6_fc_2(mul_90) features_30_conv_6_fc_3 = self.features_30_conv_6_fc_3(features_30_conv_6_fc_2) view_40 = features_30_conv_6_fc_3.view(size_20[0], size_20[1], 1, 1) mul_91 = mul_89.__mul__(view_40) features_30_conv_7 = self.features_30_conv_7(mul_91) features_30_conv_8 = self.features_30_conv_8(features_30_conv_7) add_25 = add_24.__add__(features_30_conv_8) features_31_conv_0 = self.features_31_conv_0(add_25) features_31_conv_1 = self.features_31_conv_1(features_31_conv_0) sigmoid_72 = torch.sigmoid(features_31_conv_1) mul_92 = features_31_conv_1.__mul__(sigmoid_72) features_31_conv_3 = self.features_31_conv_3(mul_92) features_31_conv_4 = self.features_31_conv_4(features_31_conv_3) sigmoid_73 = torch.sigmoid(features_31_conv_4) mul_93 = features_31_conv_4.__mul__(sigmoid_73) size_21 = mul_93.size() features_31_conv_6_avg_pool = self.features_31_conv_6_avg_pool(mul_93) view_41 = features_31_conv_6_avg_pool.view(size_21[0], size_21[1]) features_31_conv_6_fc_0 = self.features_31_conv_6_fc_0(view_41) sigmoid_74 = torch.sigmoid(features_31_conv_6_fc_0) mul_94 = features_31_conv_6_fc_0.__mul__(sigmoid_74) features_31_conv_6_fc_2 = self.features_31_conv_6_fc_2(mul_94) features_31_conv_6_fc_3 = self.features_31_conv_6_fc_3(features_31_conv_6_fc_2) view_42 = features_31_conv_6_fc_3.view(size_21[0], size_21[1], 1, 1) mul_95 = mul_93.__mul__(view_42) features_31_conv_7 = self.features_31_conv_7(mul_95) features_31_conv_8 = self.features_31_conv_8(features_31_conv_7) add_26 = add_25.__add__(features_31_conv_8) features_32_conv_0 = self.features_32_conv_0(add_26) features_32_conv_1 = self.features_32_conv_1(features_32_conv_0) sigmoid_75 = torch.sigmoid(features_32_conv_1) mul_96 = features_32_conv_1.__mul__(sigmoid_75) features_32_conv_3 = self.features_32_conv_3(mul_96) features_32_conv_4 = self.features_32_conv_4(features_32_conv_3) sigmoid_76 = torch.sigmoid(features_32_conv_4) mul_97 = features_32_conv_4.__mul__(sigmoid_76) size_22 = mul_97.size() features_32_conv_6_avg_pool = self.features_32_conv_6_avg_pool(mul_97) view_43 = features_32_conv_6_avg_pool.view(size_22[0], size_22[1]) features_32_conv_6_fc_0 = self.features_32_conv_6_fc_0(view_43) sigmoid_77 = torch.sigmoid(features_32_conv_6_fc_0) mul_98 = features_32_conv_6_fc_0.__mul__(sigmoid_77) features_32_conv_6_fc_2 = self.features_32_conv_6_fc_2(mul_98) features_32_conv_6_fc_3 = self.features_32_conv_6_fc_3(features_32_conv_6_fc_2) view_44 = features_32_conv_6_fc_3.view(size_22[0], size_22[1], 1, 1) mul_99 = mul_97.__mul__(view_44) features_32_conv_7 = self.features_32_conv_7(mul_99) features_32_conv_8 = self.features_32_conv_8(features_32_conv_7) add_27 = add_26.__add__(features_32_conv_8) features_33_conv_0 = self.features_33_conv_0(add_27) features_33_conv_1 = self.features_33_conv_1(features_33_conv_0) sigmoid_78 = torch.sigmoid(features_33_conv_1) mul_100 = features_33_conv_1.__mul__(sigmoid_78) features_33_conv_3 = self.features_33_conv_3(mul_100) features_33_conv_4 = self.features_33_conv_4(features_33_conv_3) sigmoid_79 = torch.sigmoid(features_33_conv_4) mul_101 = features_33_conv_4.__mul__(sigmoid_79) size_23 = mul_101.size() features_33_conv_6_avg_pool = self.features_33_conv_6_avg_pool(mul_101) view_45 = features_33_conv_6_avg_pool.view(size_23[0], size_23[1]) features_33_conv_6_fc_0 = self.features_33_conv_6_fc_0(view_45) sigmoid_80 = torch.sigmoid(features_33_conv_6_fc_0) mul_102 = features_33_conv_6_fc_0.__mul__(sigmoid_80) features_33_conv_6_fc_2 = self.features_33_conv_6_fc_2(mul_102) features_33_conv_6_fc_3 = self.features_33_conv_6_fc_3(features_33_conv_6_fc_2) view_46 = features_33_conv_6_fc_3.view(size_23[0], size_23[1], 1, 1) mul_103 = mul_101.__mul__(view_46) features_33_conv_7 = self.features_33_conv_7(mul_103) features_33_conv_8 = self.features_33_conv_8(features_33_conv_7) add_28 = add_27.__add__(features_33_conv_8) features_34_conv_0 = self.features_34_conv_0(add_28) features_34_conv_1 = self.features_34_conv_1(features_34_conv_0) sigmoid_81 = torch.sigmoid(features_34_conv_1) mul_104 = features_34_conv_1.__mul__(sigmoid_81) features_34_conv_3 = self.features_34_conv_3(mul_104) features_34_conv_4 = self.features_34_conv_4(features_34_conv_3) sigmoid_82 = torch.sigmoid(features_34_conv_4) mul_105 = features_34_conv_4.__mul__(sigmoid_82) size_24 = mul_105.size() features_34_conv_6_avg_pool = self.features_34_conv_6_avg_pool(mul_105) view_47 = features_34_conv_6_avg_pool.view(size_24[0], size_24[1]) features_34_conv_6_fc_0 = self.features_34_conv_6_fc_0(view_47) sigmoid_83 = torch.sigmoid(features_34_conv_6_fc_0) mul_106 = features_34_conv_6_fc_0.__mul__(sigmoid_83) features_34_conv_6_fc_2 = self.features_34_conv_6_fc_2(mul_106) features_34_conv_6_fc_3 = self.features_34_conv_6_fc_3(features_34_conv_6_fc_2) view_48 = features_34_conv_6_fc_3.view(size_24[0], size_24[1], 1, 1) mul_107 = mul_105.__mul__(view_48) features_34_conv_7 = self.features_34_conv_7(mul_107) features_34_conv_8 = self.features_34_conv_8(features_34_conv_7) add_29 = add_28.__add__(features_34_conv_8) features_35_conv_0 = self.features_35_conv_0(add_29) features_35_conv_1 = self.features_35_conv_1(features_35_conv_0) sigmoid_84 = torch.sigmoid(features_35_conv_1) mul_108 = features_35_conv_1.__mul__(sigmoid_84) features_35_conv_3 = self.features_35_conv_3(mul_108) features_35_conv_4 = self.features_35_conv_4(features_35_conv_3) sigmoid_85 = torch.sigmoid(features_35_conv_4) mul_109 = features_35_conv_4.__mul__(sigmoid_85) size_25 = mul_109.size() features_35_conv_6_avg_pool = self.features_35_conv_6_avg_pool(mul_109) view_49 = features_35_conv_6_avg_pool.view(size_25[0], size_25[1]) features_35_conv_6_fc_0 = self.features_35_conv_6_fc_0(view_49) sigmoid_86 = torch.sigmoid(features_35_conv_6_fc_0) mul_110 = features_35_conv_6_fc_0.__mul__(sigmoid_86) features_35_conv_6_fc_2 = self.features_35_conv_6_fc_2(mul_110) features_35_conv_6_fc_3 = self.features_35_conv_6_fc_3(features_35_conv_6_fc_2) view_50 = features_35_conv_6_fc_3.view(size_25[0], size_25[1], 1, 1) mul_111 = mul_109.__mul__(view_50) features_35_conv_7 = self.features_35_conv_7(mul_111) features_35_conv_8 = self.features_35_conv_8(features_35_conv_7) add_30 = add_29.__add__(features_35_conv_8) features_36_conv_0 = self.features_36_conv_0(add_30) features_36_conv_1 = self.features_36_conv_1(features_36_conv_0) sigmoid_87 = torch.sigmoid(features_36_conv_1) mul_112 = features_36_conv_1.__mul__(sigmoid_87) features_36_conv_3 = self.features_36_conv_3(mul_112) features_36_conv_4 = self.features_36_conv_4(features_36_conv_3) sigmoid_88 = torch.sigmoid(features_36_conv_4) mul_113 = features_36_conv_4.__mul__(sigmoid_88) size_26 = mul_113.size() features_36_conv_6_avg_pool = self.features_36_conv_6_avg_pool(mul_113) view_51 = features_36_conv_6_avg_pool.view(size_26[0], size_26[1]) features_36_conv_6_fc_0 = self.features_36_conv_6_fc_0(view_51) sigmoid_89 = torch.sigmoid(features_36_conv_6_fc_0) mul_114 = features_36_conv_6_fc_0.__mul__(sigmoid_89) features_36_conv_6_fc_2 = self.features_36_conv_6_fc_2(mul_114) features_36_conv_6_fc_3 = self.features_36_conv_6_fc_3(features_36_conv_6_fc_2) view_52 = features_36_conv_6_fc_3.view(size_26[0], size_26[1], 1, 1) mul_115 = mul_113.__mul__(view_52) features_36_conv_7 = self.features_36_conv_7(mul_115) features_36_conv_8 = self.features_36_conv_8(features_36_conv_7) add_31 = add_30.__add__(features_36_conv_8) features_37_conv_0 = self.features_37_conv_0(add_31) features_37_conv_1 = self.features_37_conv_1(features_37_conv_0) sigmoid_90 = torch.sigmoid(features_37_conv_1) mul_116 = features_37_conv_1.__mul__(sigmoid_90) features_37_conv_3 = self.features_37_conv_3(mul_116) features_37_conv_4 = self.features_37_conv_4(features_37_conv_3) sigmoid_91 = torch.sigmoid(features_37_conv_4) mul_117 = features_37_conv_4.__mul__(sigmoid_91) size_27 = mul_117.size() features_37_conv_6_avg_pool = self.features_37_conv_6_avg_pool(mul_117) view_53 = features_37_conv_6_avg_pool.view(size_27[0], size_27[1]) features_37_conv_6_fc_0 = self.features_37_conv_6_fc_0(view_53) sigmoid_92 = torch.sigmoid(features_37_conv_6_fc_0) mul_118 = features_37_conv_6_fc_0.__mul__(sigmoid_92) features_37_conv_6_fc_2 = self.features_37_conv_6_fc_2(mul_118) features_37_conv_6_fc_3 = self.features_37_conv_6_fc_3(features_37_conv_6_fc_2) view_54 = features_37_conv_6_fc_3.view(size_27[0], size_27[1], 1, 1) mul_119 = mul_117.__mul__(view_54) features_37_conv_7 = self.features_37_conv_7(mul_119) features_37_conv_8 = self.features_37_conv_8(features_37_conv_7) add_32 = add_31.__add__(features_37_conv_8) features_38_conv_0 = self.features_38_conv_0(add_32) features_38_conv_1 = self.features_38_conv_1(features_38_conv_0) sigmoid_93 = torch.sigmoid(features_38_conv_1) mul_120 = features_38_conv_1.__mul__(sigmoid_93) features_38_conv_3 = self.features_38_conv_3(mul_120) features_38_conv_4 = self.features_38_conv_4(features_38_conv_3) sigmoid_94 = torch.sigmoid(features_38_conv_4) mul_121 = features_38_conv_4.__mul__(sigmoid_94) size_28 = mul_121.size() features_38_conv_6_avg_pool = self.features_38_conv_6_avg_pool(mul_121) view_55 = features_38_conv_6_avg_pool.view(size_28[0], size_28[1]) features_38_conv_6_fc_0 = self.features_38_conv_6_fc_0(view_55) sigmoid_95 = torch.sigmoid(features_38_conv_6_fc_0) mul_122 = features_38_conv_6_fc_0.__mul__(sigmoid_95) features_38_conv_6_fc_2 = self.features_38_conv_6_fc_2(mul_122) features_38_conv_6_fc_3 = self.features_38_conv_6_fc_3(features_38_conv_6_fc_2) view_56 = features_38_conv_6_fc_3.view(size_28[0], size_28[1], 1, 1) mul_123 = mul_121.__mul__(view_56) features_38_conv_7 = self.features_38_conv_7(mul_123) features_38_conv_8 = self.features_38_conv_8(features_38_conv_7) add_33 = add_32.__add__(features_38_conv_8) features_39_conv_0 = self.features_39_conv_0(add_33) features_39_conv_1 = self.features_39_conv_1(features_39_conv_0) sigmoid_96 = torch.sigmoid(features_39_conv_1) mul_124 = features_39_conv_1.__mul__(sigmoid_96) features_39_conv_3 = self.features_39_conv_3(mul_124) features_39_conv_4 = self.features_39_conv_4(features_39_conv_3) sigmoid_97 = torch.sigmoid(features_39_conv_4) mul_125 = features_39_conv_4.__mul__(sigmoid_97) size_29 = mul_125.size() features_39_conv_6_avg_pool = self.features_39_conv_6_avg_pool(mul_125) view_57 = features_39_conv_6_avg_pool.view(size_29[0], size_29[1]) features_39_conv_6_fc_0 = self.features_39_conv_6_fc_0(view_57) sigmoid_98 = torch.sigmoid(features_39_conv_6_fc_0) mul_126 = features_39_conv_6_fc_0.__mul__(sigmoid_98) features_39_conv_6_fc_2 = self.features_39_conv_6_fc_2(mul_126) features_39_conv_6_fc_3 = self.features_39_conv_6_fc_3(features_39_conv_6_fc_2) view_58 = features_39_conv_6_fc_3.view(size_29[0], size_29[1], 1, 1) mul_127 = mul_125.__mul__(view_58) features_39_conv_7 = self.features_39_conv_7(mul_127) features_39_conv_8 = self.features_39_conv_8(features_39_conv_7) add_34 = add_33.__add__(features_39_conv_8) features_40_conv_0 = self.features_40_conv_0(add_34) features_40_conv_1 = self.features_40_conv_1(features_40_conv_0) sigmoid_99 = torch.sigmoid(features_40_conv_1) mul_128 = features_40_conv_1.__mul__(sigmoid_99) features_40_conv_3 = self.features_40_conv_3(mul_128) features_40_conv_4 = self.features_40_conv_4(features_40_conv_3) sigmoid_100 = torch.sigmoid(features_40_conv_4) mul_129 = features_40_conv_4.__mul__(sigmoid_100) size_30 = mul_129.size() features_40_conv_6_avg_pool = self.features_40_conv_6_avg_pool(mul_129) view_59 = features_40_conv_6_avg_pool.view(size_30[0], size_30[1]) features_40_conv_6_fc_0 = self.features_40_conv_6_fc_0(view_59) sigmoid_101 = torch.sigmoid(features_40_conv_6_fc_0) mul_130 = features_40_conv_6_fc_0.__mul__(sigmoid_101) features_40_conv_6_fc_2 = self.features_40_conv_6_fc_2(mul_130) features_40_conv_6_fc_3 = self.features_40_conv_6_fc_3(features_40_conv_6_fc_2) view_60 = features_40_conv_6_fc_3.view(size_30[0], size_30[1], 1, 1) mul_131 = mul_129.__mul__(view_60) features_40_conv_7 = self.features_40_conv_7(mul_131) features_40_conv_8 = self.features_40_conv_8(features_40_conv_7) add_35 = add_34.__add__(features_40_conv_8) conv_0 = self.conv_0(add_35) conv_1 = self.conv_1(conv_0) sigmoid_102 = torch.sigmoid(conv_1) mul_132 = conv_1.__mul__(sigmoid_102) avgpool = self.avgpool(mul_132) size_31 = avgpool.size(0) view_61 = avgpool.view(size_31, -1) classifier = self.classifier(view_61) return classifier if __name__ == "__main__": model = efficientnet_v2_s() model.eval() model.cpu() dummy_input_0 = torch.ones((1, 3, 224, 224), dtype=torch.float32) output = model(dummy_input_0) print(output)