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efficientnet_v2_l.py
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import torch
import torch.nn
import torch.functional
import torch.nn.functional
class efficientnet_v2_l(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, 32, 1, 1, 0, bias=False)
self.features_1_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(32)
self.features_2_conv_0 = torch.nn.modules.conv.Conv2d(32, 32, 3, 1, 1, bias=False)
self.features_2_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(32)
self.features_2_conv_3 = torch.nn.modules.conv.Conv2d(32, 32, 1, 1, 0, bias=False)
self.features_2_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(32)
self.features_3_conv_0 = torch.nn.modules.conv.Conv2d(32, 32, 3, 1, 1, bias=False)
self.features_3_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(32)
self.features_3_conv_3 = torch.nn.modules.conv.Conv2d(32, 32, 1, 1, 0, bias=False)
self.features_3_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(32)
self.features_4_conv_0 = torch.nn.modules.conv.Conv2d(32, 32, 3, 1, 1, bias=False)
self.features_4_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(32)
self.features_4_conv_3 = torch.nn.modules.conv.Conv2d(32, 32, 1, 1, 0, bias=False)
self.features_4_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(32)
self.features_5_conv_0 = torch.nn.modules.conv.Conv2d(32, 128, 3, 2, 1, bias=False)
self.features_5_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(128)
self.features_5_conv_3 = torch.nn.modules.conv.Conv2d(128, 64, 1, 1, 0, bias=False)
self.features_5_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(64)
self.features_6_conv_0 = torch.nn.modules.conv.Conv2d(64, 256, 3, 1, 1, bias=False)
self.features_6_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_6_conv_3 = torch.nn.modules.conv.Conv2d(256, 64, 1, 1, 0, bias=False)
self.features_6_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(64)
self.features_7_conv_0 = torch.nn.modules.conv.Conv2d(64, 256, 3, 1, 1, bias=False)
self.features_7_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_7_conv_3 = torch.nn.modules.conv.Conv2d(256, 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, 3, 1, 1, bias=False)
self.features_11_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_11_conv_3 = torch.nn.modules.conv.Conv2d(256, 64, 1, 1, 0, bias=False)
self.features_11_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(64)
self.features_12_conv_0 = torch.nn.modules.conv.Conv2d(64, 256, 3, 2, 1, bias=False)
self.features_12_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_12_conv_3 = torch.nn.modules.conv.Conv2d(256, 96, 1, 1, 0, bias=False)
self.features_12_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(96)
self.features_13_conv_0 = torch.nn.modules.conv.Conv2d(96, 384, 3, 1, 1, bias=False)
self.features_13_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(384)
self.features_13_conv_3 = torch.nn.modules.conv.Conv2d(384, 96, 1, 1, 0, bias=False)
self.features_13_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(96)
self.features_14_conv_0 = torch.nn.modules.conv.Conv2d(96, 384, 3, 1, 1, bias=False)
self.features_14_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(384)
self.features_14_conv_3 = torch.nn.modules.conv.Conv2d(384, 96, 1, 1, 0, bias=False)
self.features_14_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(96)
self.features_15_conv_0 = torch.nn.modules.conv.Conv2d(96, 384, 3, 1, 1, bias=False)
self.features_15_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(384)
self.features_15_conv_3 = torch.nn.modules.conv.Conv2d(384, 96, 1, 1, 0, bias=False)
self.features_15_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(96)
self.features_16_conv_0 = torch.nn.modules.conv.Conv2d(96, 384, 3, 1, 1, bias=False)
self.features_16_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(384)
self.features_16_conv_3 = torch.nn.modules.conv.Conv2d(384, 96, 1, 1, 0, bias=False)
self.features_16_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(96)
self.features_17_conv_0 = torch.nn.modules.conv.Conv2d(96, 384, 3, 1, 1, bias=False)
self.features_17_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(384)
self.features_17_conv_3 = torch.nn.modules.conv.Conv2d(384, 96, 1, 1, 0, bias=False)
self.features_17_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(96)
self.features_18_conv_0 = torch.nn.modules.conv.Conv2d(96, 384, 3, 1, 1, bias=False)
self.features_18_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(384)
self.features_18_conv_3 = torch.nn.modules.conv.Conv2d(384, 96, 1, 1, 0, bias=False)
self.features_18_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(96)
self.features_19_conv_0 = torch.nn.modules.conv.Conv2d(96, 384, 1, 1, 0, bias=False)
self.features_19_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(384)
self.features_19_conv_3 = torch.nn.modules.conv.Conv2d(384, 384, 3, 2, 1, groups=384, bias=False)
self.features_19_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(384)
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(384, 24)
self.features_19_conv_6_fc_2 = torch.nn.modules.linear.Linear(24, 384)
self.features_19_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_19_conv_7 = torch.nn.modules.conv.Conv2d(384, 192, 1, 1, 0, bias=False)
self.features_19_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_20_conv_0 = torch.nn.modules.conv.Conv2d(192, 768, 1, 1, 0, bias=False)
self.features_20_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_20_conv_3 = torch.nn.modules.conv.Conv2d(768, 768, 3, 1, 1, groups=768, bias=False)
self.features_20_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(768)
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(768, 48)
self.features_20_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 768)
self.features_20_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_20_conv_7 = torch.nn.modules.conv.Conv2d(768, 192, 1, 1, 0, bias=False)
self.features_20_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_21_conv_0 = torch.nn.modules.conv.Conv2d(192, 768, 1, 1, 0, bias=False)
self.features_21_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_21_conv_3 = torch.nn.modules.conv.Conv2d(768, 768, 3, 1, 1, groups=768, bias=False)
self.features_21_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(768)
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(768, 48)
self.features_21_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 768)
self.features_21_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_21_conv_7 = torch.nn.modules.conv.Conv2d(768, 192, 1, 1, 0, bias=False)
self.features_21_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_22_conv_0 = torch.nn.modules.conv.Conv2d(192, 768, 1, 1, 0, bias=False)
self.features_22_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_22_conv_3 = torch.nn.modules.conv.Conv2d(768, 768, 3, 1, 1, groups=768, bias=False)
self.features_22_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(768)
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(768, 48)
self.features_22_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 768)
self.features_22_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_22_conv_7 = torch.nn.modules.conv.Conv2d(768, 192, 1, 1, 0, bias=False)
self.features_22_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_23_conv_0 = torch.nn.modules.conv.Conv2d(192, 768, 1, 1, 0, bias=False)
self.features_23_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_23_conv_3 = torch.nn.modules.conv.Conv2d(768, 768, 3, 1, 1, groups=768, bias=False)
self.features_23_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(768)
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(768, 48)
self.features_23_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 768)
self.features_23_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_23_conv_7 = torch.nn.modules.conv.Conv2d(768, 192, 1, 1, 0, bias=False)
self.features_23_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_24_conv_0 = torch.nn.modules.conv.Conv2d(192, 768, 1, 1, 0, bias=False)
self.features_24_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_24_conv_3 = torch.nn.modules.conv.Conv2d(768, 768, 3, 1, 1, groups=768, bias=False)
self.features_24_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(768)
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(768, 48)
self.features_24_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 768)
self.features_24_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_24_conv_7 = torch.nn.modules.conv.Conv2d(768, 192, 1, 1, 0, bias=False)
self.features_24_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_25_conv_0 = torch.nn.modules.conv.Conv2d(192, 768, 1, 1, 0, bias=False)
self.features_25_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_25_conv_3 = torch.nn.modules.conv.Conv2d(768, 768, 3, 1, 1, groups=768, bias=False)
self.features_25_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(768)
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(768, 48)
self.features_25_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 768)
self.features_25_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_25_conv_7 = torch.nn.modules.conv.Conv2d(768, 192, 1, 1, 0, bias=False)
self.features_25_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_26_conv_0 = torch.nn.modules.conv.Conv2d(192, 768, 1, 1, 0, bias=False)
self.features_26_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_26_conv_3 = torch.nn.modules.conv.Conv2d(768, 768, 3, 1, 1, groups=768, bias=False)
self.features_26_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(768)
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(768, 48)
self.features_26_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 768)
self.features_26_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_26_conv_7 = torch.nn.modules.conv.Conv2d(768, 192, 1, 1, 0, bias=False)
self.features_26_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_27_conv_0 = torch.nn.modules.conv.Conv2d(192, 768, 1, 1, 0, bias=False)
self.features_27_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_27_conv_3 = torch.nn.modules.conv.Conv2d(768, 768, 3, 1, 1, groups=768, bias=False)
self.features_27_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(768)
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(768, 48)
self.features_27_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 768)
self.features_27_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_27_conv_7 = torch.nn.modules.conv.Conv2d(768, 192, 1, 1, 0, bias=False)
self.features_27_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_28_conv_0 = torch.nn.modules.conv.Conv2d(192, 768, 1, 1, 0, bias=False)
self.features_28_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_28_conv_3 = torch.nn.modules.conv.Conv2d(768, 768, 3, 1, 1, groups=768, bias=False)
self.features_28_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(768)
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(768, 48)
self.features_28_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 768)
self.features_28_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_28_conv_7 = torch.nn.modules.conv.Conv2d(768, 192, 1, 1, 0, bias=False)
self.features_28_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_29_conv_0 = torch.nn.modules.conv.Conv2d(192, 1152, 1, 1, 0, bias=False)
self.features_29_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1152)
self.features_29_conv_3 = torch.nn.modules.conv.Conv2d(1152, 1152, 3, 1, 1, groups=1152, bias=False)
self.features_29_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1152)
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(1152, 48)
self.features_29_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 1152)
self.features_29_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_29_conv_7 = torch.nn.modules.conv.Conv2d(1152, 224, 1, 1, 0, bias=False)
self.features_29_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(224)
self.features_30_conv_0 = torch.nn.modules.conv.Conv2d(224, 1344, 1, 1, 0, bias=False)
self.features_30_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1344)
self.features_30_conv_3 = torch.nn.modules.conv.Conv2d(1344, 1344, 3, 1, 1, groups=1344, bias=False)
self.features_30_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1344)
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(1344, 56)
self.features_30_conv_6_fc_2 = torch.nn.modules.linear.Linear(56, 1344)
self.features_30_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_30_conv_7 = torch.nn.modules.conv.Conv2d(1344, 224, 1, 1, 0, bias=False)
self.features_30_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(224)
self.features_31_conv_0 = torch.nn.modules.conv.Conv2d(224, 1344, 1, 1, 0, bias=False)
self.features_31_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1344)
self.features_31_conv_3 = torch.nn.modules.conv.Conv2d(1344, 1344, 3, 1, 1, groups=1344, bias=False)
self.features_31_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1344)
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(1344, 56)
self.features_31_conv_6_fc_2 = torch.nn.modules.linear.Linear(56, 1344)
self.features_31_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_31_conv_7 = torch.nn.modules.conv.Conv2d(1344, 224, 1, 1, 0, bias=False)
self.features_31_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(224)
self.features_32_conv_0 = torch.nn.modules.conv.Conv2d(224, 1344, 1, 1, 0, bias=False)
self.features_32_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1344)
self.features_32_conv_3 = torch.nn.modules.conv.Conv2d(1344, 1344, 3, 1, 1, groups=1344, bias=False)
self.features_32_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1344)
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(1344, 56)
self.features_32_conv_6_fc_2 = torch.nn.modules.linear.Linear(56, 1344)
self.features_32_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_32_conv_7 = torch.nn.modules.conv.Conv2d(1344, 224, 1, 1, 0, bias=False)
self.features_32_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(224)
self.features_33_conv_0 = torch.nn.modules.conv.Conv2d(224, 1344, 1, 1, 0, bias=False)
self.features_33_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1344)
self.features_33_conv_3 = torch.nn.modules.conv.Conv2d(1344, 1344, 3, 1, 1, groups=1344, bias=False)
self.features_33_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1344)
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(1344, 56)
self.features_33_conv_6_fc_2 = torch.nn.modules.linear.Linear(56, 1344)
self.features_33_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_33_conv_7 = torch.nn.modules.conv.Conv2d(1344, 224, 1, 1, 0, bias=False)
self.features_33_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(224)
self.features_34_conv_0 = torch.nn.modules.conv.Conv2d(224, 1344, 1, 1, 0, bias=False)
self.features_34_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1344)
self.features_34_conv_3 = torch.nn.modules.conv.Conv2d(1344, 1344, 3, 1, 1, groups=1344, bias=False)
self.features_34_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1344)
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(1344, 56)
self.features_34_conv_6_fc_2 = torch.nn.modules.linear.Linear(56, 1344)
self.features_34_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_34_conv_7 = torch.nn.modules.conv.Conv2d(1344, 224, 1, 1, 0, bias=False)
self.features_34_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(224)
self.features_35_conv_0 = torch.nn.modules.conv.Conv2d(224, 1344, 1, 1, 0, bias=False)
self.features_35_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1344)
self.features_35_conv_3 = torch.nn.modules.conv.Conv2d(1344, 1344, 3, 1, 1, groups=1344, bias=False)
self.features_35_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1344)
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(1344, 56)
self.features_35_conv_6_fc_2 = torch.nn.modules.linear.Linear(56, 1344)
self.features_35_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_35_conv_7 = torch.nn.modules.conv.Conv2d(1344, 224, 1, 1, 0, bias=False)
self.features_35_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(224)
self.features_36_conv_0 = torch.nn.modules.conv.Conv2d(224, 1344, 1, 1, 0, bias=False)
self.features_36_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1344)
self.features_36_conv_3 = torch.nn.modules.conv.Conv2d(1344, 1344, 3, 1, 1, groups=1344, bias=False)
self.features_36_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1344)
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(1344, 56)
self.features_36_conv_6_fc_2 = torch.nn.modules.linear.Linear(56, 1344)
self.features_36_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_36_conv_7 = torch.nn.modules.conv.Conv2d(1344, 224, 1, 1, 0, bias=False)
self.features_36_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(224)
self.features_37_conv_0 = torch.nn.modules.conv.Conv2d(224, 1344, 1, 1, 0, bias=False)
self.features_37_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1344)
self.features_37_conv_3 = torch.nn.modules.conv.Conv2d(1344, 1344, 3, 1, 1, groups=1344, bias=False)
self.features_37_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1344)
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(1344, 56)
self.features_37_conv_6_fc_2 = torch.nn.modules.linear.Linear(56, 1344)
self.features_37_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_37_conv_7 = torch.nn.modules.conv.Conv2d(1344, 224, 1, 1, 0, bias=False)
self.features_37_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(224)
self.features_38_conv_0 = torch.nn.modules.conv.Conv2d(224, 1344, 1, 1, 0, bias=False)
self.features_38_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1344)
self.features_38_conv_3 = torch.nn.modules.conv.Conv2d(1344, 1344, 3, 1, 1, groups=1344, bias=False)
self.features_38_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1344)
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(1344, 56)
self.features_38_conv_6_fc_2 = torch.nn.modules.linear.Linear(56, 1344)
self.features_38_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_38_conv_7 = torch.nn.modules.conv.Conv2d(1344, 224, 1, 1, 0, bias=False)
self.features_38_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(224)
self.features_39_conv_0 = torch.nn.modules.conv.Conv2d(224, 1344, 1, 1, 0, bias=False)
self.features_39_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1344)
self.features_39_conv_3 = torch.nn.modules.conv.Conv2d(1344, 1344, 3, 1, 1, groups=1344, bias=False)
self.features_39_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1344)
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(1344, 56)
self.features_39_conv_6_fc_2 = torch.nn.modules.linear.Linear(56, 1344)
self.features_39_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_39_conv_7 = torch.nn.modules.conv.Conv2d(1344, 224, 1, 1, 0, bias=False)
self.features_39_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(224)
self.features_40_conv_0 = torch.nn.modules.conv.Conv2d(224, 1344, 1, 1, 0, bias=False)
self.features_40_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1344)
self.features_40_conv_3 = torch.nn.modules.conv.Conv2d(1344, 1344, 3, 1, 1, groups=1344, bias=False)
self.features_40_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1344)
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(1344, 56)
self.features_40_conv_6_fc_2 = torch.nn.modules.linear.Linear(56, 1344)
self.features_40_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_40_conv_7 = torch.nn.modules.conv.Conv2d(1344, 224, 1, 1, 0, bias=False)
self.features_40_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(224)
self.features_41_conv_0 = torch.nn.modules.conv.Conv2d(224, 1344, 1, 1, 0, bias=False)
self.features_41_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1344)
self.features_41_conv_3 = torch.nn.modules.conv.Conv2d(1344, 1344, 3, 1, 1, groups=1344, bias=False)
self.features_41_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1344)
self.features_41_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_41_conv_6_fc_0 = torch.nn.modules.linear.Linear(1344, 56)
self.features_41_conv_6_fc_2 = torch.nn.modules.linear.Linear(56, 1344)
self.features_41_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_41_conv_7 = torch.nn.modules.conv.Conv2d(1344, 224, 1, 1, 0, bias=False)
self.features_41_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(224)
self.features_42_conv_0 = torch.nn.modules.conv.Conv2d(224, 1344, 1, 1, 0, bias=False)
self.features_42_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1344)
self.features_42_conv_3 = torch.nn.modules.conv.Conv2d(1344, 1344, 3, 1, 1, groups=1344, bias=False)
self.features_42_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1344)
self.features_42_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_42_conv_6_fc_0 = torch.nn.modules.linear.Linear(1344, 56)
self.features_42_conv_6_fc_2 = torch.nn.modules.linear.Linear(56, 1344)
self.features_42_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_42_conv_7 = torch.nn.modules.conv.Conv2d(1344, 224, 1, 1, 0, bias=False)
self.features_42_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(224)
self.features_43_conv_0 = torch.nn.modules.conv.Conv2d(224, 1344, 1, 1, 0, bias=False)
self.features_43_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1344)
self.features_43_conv_3 = torch.nn.modules.conv.Conv2d(1344, 1344, 3, 1, 1, groups=1344, bias=False)
self.features_43_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1344)
self.features_43_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_43_conv_6_fc_0 = torch.nn.modules.linear.Linear(1344, 56)
self.features_43_conv_6_fc_2 = torch.nn.modules.linear.Linear(56, 1344)
self.features_43_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_43_conv_7 = torch.nn.modules.conv.Conv2d(1344, 224, 1, 1, 0, bias=False)
self.features_43_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(224)
self.features_44_conv_0 = torch.nn.modules.conv.Conv2d(224, 1344, 1, 1, 0, bias=False)
self.features_44_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1344)
self.features_44_conv_3 = torch.nn.modules.conv.Conv2d(1344, 1344, 3, 1, 1, groups=1344, bias=False)
self.features_44_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1344)
self.features_44_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_44_conv_6_fc_0 = torch.nn.modules.linear.Linear(1344, 56)
self.features_44_conv_6_fc_2 = torch.nn.modules.linear.Linear(56, 1344)
self.features_44_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_44_conv_7 = torch.nn.modules.conv.Conv2d(1344, 224, 1, 1, 0, bias=False)
self.features_44_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(224)
self.features_45_conv_0 = torch.nn.modules.conv.Conv2d(224, 1344, 1, 1, 0, bias=False)
self.features_45_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1344)
self.features_45_conv_3 = torch.nn.modules.conv.Conv2d(1344, 1344, 3, 1, 1, groups=1344, bias=False)
self.features_45_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1344)
self.features_45_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_45_conv_6_fc_0 = torch.nn.modules.linear.Linear(1344, 56)
self.features_45_conv_6_fc_2 = torch.nn.modules.linear.Linear(56, 1344)
self.features_45_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_45_conv_7 = torch.nn.modules.conv.Conv2d(1344, 224, 1, 1, 0, bias=False)
self.features_45_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(224)
self.features_46_conv_0 = torch.nn.modules.conv.Conv2d(224, 1344, 1, 1, 0, bias=False)
self.features_46_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1344)
self.features_46_conv_3 = torch.nn.modules.conv.Conv2d(1344, 1344, 3, 1, 1, groups=1344, bias=False)
self.features_46_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1344)
self.features_46_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_46_conv_6_fc_0 = torch.nn.modules.linear.Linear(1344, 56)
self.features_46_conv_6_fc_2 = torch.nn.modules.linear.Linear(56, 1344)
self.features_46_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_46_conv_7 = torch.nn.modules.conv.Conv2d(1344, 224, 1, 1, 0, bias=False)
self.features_46_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(224)
self.features_47_conv_0 = torch.nn.modules.conv.Conv2d(224, 1344, 1, 1, 0, bias=False)
self.features_47_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1344)
self.features_47_conv_3 = torch.nn.modules.conv.Conv2d(1344, 1344, 3, 1, 1, groups=1344, bias=False)
self.features_47_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1344)
self.features_47_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_47_conv_6_fc_0 = torch.nn.modules.linear.Linear(1344, 56)
self.features_47_conv_6_fc_2 = torch.nn.modules.linear.Linear(56, 1344)
self.features_47_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_47_conv_7 = torch.nn.modules.conv.Conv2d(1344, 224, 1, 1, 0, bias=False)
self.features_47_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(224)
self.features_48_conv_0 = torch.nn.modules.conv.Conv2d(224, 1344, 1, 1, 0, bias=False)
self.features_48_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1344)
self.features_48_conv_3 = torch.nn.modules.conv.Conv2d(1344, 1344, 3, 2, 1, groups=1344, bias=False)
self.features_48_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1344)
self.features_48_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_48_conv_6_fc_0 = torch.nn.modules.linear.Linear(1344, 56)
self.features_48_conv_6_fc_2 = torch.nn.modules.linear.Linear(56, 1344)
self.features_48_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_48_conv_7 = torch.nn.modules.conv.Conv2d(1344, 384, 1, 1, 0, bias=False)
self.features_48_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(384)
self.features_49_conv_0 = torch.nn.modules.conv.Conv2d(384, 2304, 1, 1, 0, bias=False)
self.features_49_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_49_conv_3 = torch.nn.modules.conv.Conv2d(2304, 2304, 3, 1, 1, groups=2304, bias=False)
self.features_49_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_49_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_49_conv_6_fc_0 = torch.nn.modules.linear.Linear(2304, 96)
self.features_49_conv_6_fc_2 = torch.nn.modules.linear.Linear(96, 2304)
self.features_49_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_49_conv_7 = torch.nn.modules.conv.Conv2d(2304, 384, 1, 1, 0, bias=False)
self.features_49_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(384)
self.features_50_conv_0 = torch.nn.modules.conv.Conv2d(384, 2304, 1, 1, 0, bias=False)
self.features_50_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_50_conv_3 = torch.nn.modules.conv.Conv2d(2304, 2304, 3, 1, 1, groups=2304, bias=False)
self.features_50_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_50_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_50_conv_6_fc_0 = torch.nn.modules.linear.Linear(2304, 96)
self.features_50_conv_6_fc_2 = torch.nn.modules.linear.Linear(96, 2304)
self.features_50_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_50_conv_7 = torch.nn.modules.conv.Conv2d(2304, 384, 1, 1, 0, bias=False)
self.features_50_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(384)
self.features_51_conv_0 = torch.nn.modules.conv.Conv2d(384, 2304, 1, 1, 0, bias=False)
self.features_51_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_51_conv_3 = torch.nn.modules.conv.Conv2d(2304, 2304, 3, 1, 1, groups=2304, bias=False)
self.features_51_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_51_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_51_conv_6_fc_0 = torch.nn.modules.linear.Linear(2304, 96)
self.features_51_conv_6_fc_2 = torch.nn.modules.linear.Linear(96, 2304)
self.features_51_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_51_conv_7 = torch.nn.modules.conv.Conv2d(2304, 384, 1, 1, 0, bias=False)
self.features_51_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(384)
self.features_52_conv_0 = torch.nn.modules.conv.Conv2d(384, 2304, 1, 1, 0, bias=False)
self.features_52_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_52_conv_3 = torch.nn.modules.conv.Conv2d(2304, 2304, 3, 1, 1, groups=2304, bias=False)
self.features_52_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_52_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_52_conv_6_fc_0 = torch.nn.modules.linear.Linear(2304, 96)
self.features_52_conv_6_fc_2 = torch.nn.modules.linear.Linear(96, 2304)
self.features_52_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_52_conv_7 = torch.nn.modules.conv.Conv2d(2304, 384, 1, 1, 0, bias=False)
self.features_52_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(384)
self.features_53_conv_0 = torch.nn.modules.conv.Conv2d(384, 2304, 1, 1, 0, bias=False)
self.features_53_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_53_conv_3 = torch.nn.modules.conv.Conv2d(2304, 2304, 3, 1, 1, groups=2304, bias=False)
self.features_53_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_53_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_53_conv_6_fc_0 = torch.nn.modules.linear.Linear(2304, 96)
self.features_53_conv_6_fc_2 = torch.nn.modules.linear.Linear(96, 2304)
self.features_53_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_53_conv_7 = torch.nn.modules.conv.Conv2d(2304, 384, 1, 1, 0, bias=False)
self.features_53_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(384)
self.features_54_conv_0 = torch.nn.modules.conv.Conv2d(384, 2304, 1, 1, 0, bias=False)
self.features_54_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_54_conv_3 = torch.nn.modules.conv.Conv2d(2304, 2304, 3, 1, 1, groups=2304, bias=False)
self.features_54_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_54_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_54_conv_6_fc_0 = torch.nn.modules.linear.Linear(2304, 96)
self.features_54_conv_6_fc_2 = torch.nn.modules.linear.Linear(96, 2304)
self.features_54_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_54_conv_7 = torch.nn.modules.conv.Conv2d(2304, 384, 1, 1, 0, bias=False)
self.features_54_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(384)
self.features_55_conv_0 = torch.nn.modules.conv.Conv2d(384, 2304, 1, 1, 0, bias=False)
self.features_55_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_55_conv_3 = torch.nn.modules.conv.Conv2d(2304, 2304, 3, 1, 1, groups=2304, bias=False)
self.features_55_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_55_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_55_conv_6_fc_0 = torch.nn.modules.linear.Linear(2304, 96)
self.features_55_conv_6_fc_2 = torch.nn.modules.linear.Linear(96, 2304)
self.features_55_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_55_conv_7 = torch.nn.modules.conv.Conv2d(2304, 384, 1, 1, 0, bias=False)
self.features_55_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(384)
self.features_56_conv_0 = torch.nn.modules.conv.Conv2d(384, 2304, 1, 1, 0, bias=False)
self.features_56_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_56_conv_3 = torch.nn.modules.conv.Conv2d(2304, 2304, 3, 1, 1, groups=2304, bias=False)
self.features_56_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_56_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_56_conv_6_fc_0 = torch.nn.modules.linear.Linear(2304, 96)
self.features_56_conv_6_fc_2 = torch.nn.modules.linear.Linear(96, 2304)
self.features_56_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_56_conv_7 = torch.nn.modules.conv.Conv2d(2304, 384, 1, 1, 0, bias=False)
self.features_56_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(384)
self.features_57_conv_0 = torch.nn.modules.conv.Conv2d(384, 2304, 1, 1, 0, bias=False)
self.features_57_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_57_conv_3 = torch.nn.modules.conv.Conv2d(2304, 2304, 3, 1, 1, groups=2304, bias=False)
self.features_57_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_57_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_57_conv_6_fc_0 = torch.nn.modules.linear.Linear(2304, 96)
self.features_57_conv_6_fc_2 = torch.nn.modules.linear.Linear(96, 2304)
self.features_57_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_57_conv_7 = torch.nn.modules.conv.Conv2d(2304, 384, 1, 1, 0, bias=False)
self.features_57_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(384)
self.features_58_conv_0 = torch.nn.modules.conv.Conv2d(384, 2304, 1, 1, 0, bias=False)
self.features_58_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_58_conv_3 = torch.nn.modules.conv.Conv2d(2304, 2304, 3, 1, 1, groups=2304, bias=False)
self.features_58_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_58_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_58_conv_6_fc_0 = torch.nn.modules.linear.Linear(2304, 96)
self.features_58_conv_6_fc_2 = torch.nn.modules.linear.Linear(96, 2304)
self.features_58_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_58_conv_7 = torch.nn.modules.conv.Conv2d(2304, 384, 1, 1, 0, bias=False)
self.features_58_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(384)
self.features_59_conv_0 = torch.nn.modules.conv.Conv2d(384, 2304, 1, 1, 0, bias=False)
self.features_59_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_59_conv_3 = torch.nn.modules.conv.Conv2d(2304, 2304, 3, 1, 1, groups=2304, bias=False)
self.features_59_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_59_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_59_conv_6_fc_0 = torch.nn.modules.linear.Linear(2304, 96)
self.features_59_conv_6_fc_2 = torch.nn.modules.linear.Linear(96, 2304)
self.features_59_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_59_conv_7 = torch.nn.modules.conv.Conv2d(2304, 384, 1, 1, 0, bias=False)
self.features_59_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(384)
self.features_60_conv_0 = torch.nn.modules.conv.Conv2d(384, 2304, 1, 1, 0, bias=False)
self.features_60_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_60_conv_3 = torch.nn.modules.conv.Conv2d(2304, 2304, 3, 1, 1, groups=2304, bias=False)
self.features_60_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_60_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_60_conv_6_fc_0 = torch.nn.modules.linear.Linear(2304, 96)
self.features_60_conv_6_fc_2 = torch.nn.modules.linear.Linear(96, 2304)
self.features_60_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_60_conv_7 = torch.nn.modules.conv.Conv2d(2304, 384, 1, 1, 0, bias=False)
self.features_60_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(384)
self.features_61_conv_0 = torch.nn.modules.conv.Conv2d(384, 2304, 1, 1, 0, bias=False)
self.features_61_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_61_conv_3 = torch.nn.modules.conv.Conv2d(2304, 2304, 3, 1, 1, groups=2304, bias=False)
self.features_61_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_61_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_61_conv_6_fc_0 = torch.nn.modules.linear.Linear(2304, 96)
self.features_61_conv_6_fc_2 = torch.nn.modules.linear.Linear(96, 2304)
self.features_61_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_61_conv_7 = torch.nn.modules.conv.Conv2d(2304, 384, 1, 1, 0, bias=False)
self.features_61_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(384)
self.features_62_conv_0 = torch.nn.modules.conv.Conv2d(384, 2304, 1, 1, 0, bias=False)
self.features_62_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_62_conv_3 = torch.nn.modules.conv.Conv2d(2304, 2304, 3, 1, 1, groups=2304, bias=False)
self.features_62_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_62_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_62_conv_6_fc_0 = torch.nn.modules.linear.Linear(2304, 96)
self.features_62_conv_6_fc_2 = torch.nn.modules.linear.Linear(96, 2304)
self.features_62_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_62_conv_7 = torch.nn.modules.conv.Conv2d(2304, 384, 1, 1, 0, bias=False)
self.features_62_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(384)
self.features_63_conv_0 = torch.nn.modules.conv.Conv2d(384, 2304, 1, 1, 0, bias=False)
self.features_63_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_63_conv_3 = torch.nn.modules.conv.Conv2d(2304, 2304, 3, 1, 1, groups=2304, bias=False)
self.features_63_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_63_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_63_conv_6_fc_0 = torch.nn.modules.linear.Linear(2304, 96)
self.features_63_conv_6_fc_2 = torch.nn.modules.linear.Linear(96, 2304)
self.features_63_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_63_conv_7 = torch.nn.modules.conv.Conv2d(2304, 384, 1, 1, 0, bias=False)
self.features_63_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(384)
self.features_64_conv_0 = torch.nn.modules.conv.Conv2d(384, 2304, 1, 1, 0, bias=False)
self.features_64_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_64_conv_3 = torch.nn.modules.conv.Conv2d(2304, 2304, 3, 1, 1, groups=2304, bias=False)
self.features_64_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_64_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_64_conv_6_fc_0 = torch.nn.modules.linear.Linear(2304, 96)
self.features_64_conv_6_fc_2 = torch.nn.modules.linear.Linear(96, 2304)
self.features_64_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_64_conv_7 = torch.nn.modules.conv.Conv2d(2304, 384, 1, 1, 0, bias=False)
self.features_64_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(384)
self.features_65_conv_0 = torch.nn.modules.conv.Conv2d(384, 2304, 1, 1, 0, bias=False)
self.features_65_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_65_conv_3 = torch.nn.modules.conv.Conv2d(2304, 2304, 3, 1, 1, groups=2304, bias=False)
self.features_65_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_65_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_65_conv_6_fc_0 = torch.nn.modules.linear.Linear(2304, 96)
self.features_65_conv_6_fc_2 = torch.nn.modules.linear.Linear(96, 2304)
self.features_65_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_65_conv_7 = torch.nn.modules.conv.Conv2d(2304, 384, 1, 1, 0, bias=False)
self.features_65_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(384)
self.features_66_conv_0 = torch.nn.modules.conv.Conv2d(384, 2304, 1, 1, 0, bias=False)
self.features_66_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_66_conv_3 = torch.nn.modules.conv.Conv2d(2304, 2304, 3, 1, 1, groups=2304, bias=False)
self.features_66_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_66_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_66_conv_6_fc_0 = torch.nn.modules.linear.Linear(2304, 96)
self.features_66_conv_6_fc_2 = torch.nn.modules.linear.Linear(96, 2304)
self.features_66_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_66_conv_7 = torch.nn.modules.conv.Conv2d(2304, 384, 1, 1, 0, bias=False)
self.features_66_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(384)
self.features_67_conv_0 = torch.nn.modules.conv.Conv2d(384, 2304, 1, 1, 0, bias=False)
self.features_67_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_67_conv_3 = torch.nn.modules.conv.Conv2d(2304, 2304, 3, 1, 1, groups=2304, bias=False)
self.features_67_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_67_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_67_conv_6_fc_0 = torch.nn.modules.linear.Linear(2304, 96)
self.features_67_conv_6_fc_2 = torch.nn.modules.linear.Linear(96, 2304)
self.features_67_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_67_conv_7 = torch.nn.modules.conv.Conv2d(2304, 384, 1, 1, 0, bias=False)
self.features_67_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(384)
self.features_68_conv_0 = torch.nn.modules.conv.Conv2d(384, 2304, 1, 1, 0, bias=False)
self.features_68_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_68_conv_3 = torch.nn.modules.conv.Conv2d(2304, 2304, 3, 1, 1, groups=2304, bias=False)
self.features_68_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_68_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_68_conv_6_fc_0 = torch.nn.modules.linear.Linear(2304, 96)
self.features_68_conv_6_fc_2 = torch.nn.modules.linear.Linear(96, 2304)
self.features_68_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_68_conv_7 = torch.nn.modules.conv.Conv2d(2304, 384, 1, 1, 0, bias=False)
self.features_68_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(384)
self.features_69_conv_0 = torch.nn.modules.conv.Conv2d(384, 2304, 1, 1, 0, bias=False)
self.features_69_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_69_conv_3 = torch.nn.modules.conv.Conv2d(2304, 2304, 3, 1, 1, groups=2304, bias=False)
self.features_69_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_69_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_69_conv_6_fc_0 = torch.nn.modules.linear.Linear(2304, 96)
self.features_69_conv_6_fc_2 = torch.nn.modules.linear.Linear(96, 2304)
self.features_69_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_69_conv_7 = torch.nn.modules.conv.Conv2d(2304, 384, 1, 1, 0, bias=False)
self.features_69_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(384)
self.features_70_conv_0 = torch.nn.modules.conv.Conv2d(384, 2304, 1, 1, 0, bias=False)
self.features_70_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_70_conv_3 = torch.nn.modules.conv.Conv2d(2304, 2304, 3, 1, 1, groups=2304, bias=False)
self.features_70_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_70_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_70_conv_6_fc_0 = torch.nn.modules.linear.Linear(2304, 96)
self.features_70_conv_6_fc_2 = torch.nn.modules.linear.Linear(96, 2304)
self.features_70_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_70_conv_7 = torch.nn.modules.conv.Conv2d(2304, 384, 1, 1, 0, bias=False)
self.features_70_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(384)
self.features_71_conv_0 = torch.nn.modules.conv.Conv2d(384, 2304, 1, 1, 0, bias=False)
self.features_71_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_71_conv_3 = torch.nn.modules.conv.Conv2d(2304, 2304, 3, 1, 1, groups=2304, bias=False)
self.features_71_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_71_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_71_conv_6_fc_0 = torch.nn.modules.linear.Linear(2304, 96)
self.features_71_conv_6_fc_2 = torch.nn.modules.linear.Linear(96, 2304)
self.features_71_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_71_conv_7 = torch.nn.modules.conv.Conv2d(2304, 384, 1, 1, 0, bias=False)
self.features_71_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(384)
self.features_72_conv_0 = torch.nn.modules.conv.Conv2d(384, 2304, 1, 1, 0, bias=False)
self.features_72_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_72_conv_3 = torch.nn.modules.conv.Conv2d(2304, 2304, 3, 1, 1, groups=2304, bias=False)
self.features_72_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_72_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_72_conv_6_fc_0 = torch.nn.modules.linear.Linear(2304, 96)
self.features_72_conv_6_fc_2 = torch.nn.modules.linear.Linear(96, 2304)
self.features_72_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_72_conv_7 = torch.nn.modules.conv.Conv2d(2304, 384, 1, 1, 0, bias=False)
self.features_72_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(384)
self.features_73_conv_0 = torch.nn.modules.conv.Conv2d(384, 2304, 1, 1, 0, bias=False)
self.features_73_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_73_conv_3 = torch.nn.modules.conv.Conv2d(2304, 2304, 3, 1, 1, groups=2304, bias=False)
self.features_73_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(2304)
self.features_73_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_73_conv_6_fc_0 = torch.nn.modules.linear.Linear(2304, 96)
self.features_73_conv_6_fc_2 = torch.nn.modules.linear.Linear(96, 2304)
self.features_73_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_73_conv_7 = torch.nn.modules.conv.Conv2d(2304, 640, 1, 1, 0, bias=False)
self.features_73_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(640)
self.features_74_conv_0 = torch.nn.modules.conv.Conv2d(640, 3840, 1, 1, 0, bias=False)
self.features_74_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3840)
self.features_74_conv_3 = torch.nn.modules.conv.Conv2d(3840, 3840, 3, 1, 1, groups=3840, bias=False)
self.features_74_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3840)
self.features_74_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_74_conv_6_fc_0 = torch.nn.modules.linear.Linear(3840, 160)
self.features_74_conv_6_fc_2 = torch.nn.modules.linear.Linear(160, 3840)
self.features_74_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_74_conv_7 = torch.nn.modules.conv.Conv2d(3840, 640, 1, 1, 0, bias=False)
self.features_74_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(640)
self.features_75_conv_0 = torch.nn.modules.conv.Conv2d(640, 3840, 1, 1, 0, bias=False)
self.features_75_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3840)
self.features_75_conv_3 = torch.nn.modules.conv.Conv2d(3840, 3840, 3, 1, 1, groups=3840, bias=False)
self.features_75_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3840)
self.features_75_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_75_conv_6_fc_0 = torch.nn.modules.linear.Linear(3840, 160)
self.features_75_conv_6_fc_2 = torch.nn.modules.linear.Linear(160, 3840)
self.features_75_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_75_conv_7 = torch.nn.modules.conv.Conv2d(3840, 640, 1, 1, 0, bias=False)
self.features_75_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(640)
self.features_76_conv_0 = torch.nn.modules.conv.Conv2d(640, 3840, 1, 1, 0, bias=False)
self.features_76_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3840)
self.features_76_conv_3 = torch.nn.modules.conv.Conv2d(3840, 3840, 3, 1, 1, groups=3840, bias=False)
self.features_76_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3840)
self.features_76_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_76_conv_6_fc_0 = torch.nn.modules.linear.Linear(3840, 160)
self.features_76_conv_6_fc_2 = torch.nn.modules.linear.Linear(160, 3840)
self.features_76_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_76_conv_7 = torch.nn.modules.conv.Conv2d(3840, 640, 1, 1, 0, bias=False)
self.features_76_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(640)
self.features_77_conv_0 = torch.nn.modules.conv.Conv2d(640, 3840, 1, 1, 0, bias=False)
self.features_77_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3840)
self.features_77_conv_3 = torch.nn.modules.conv.Conv2d(3840, 3840, 3, 1, 1, groups=3840, bias=False)
self.features_77_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3840)
self.features_77_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_77_conv_6_fc_0 = torch.nn.modules.linear.Linear(3840, 160)
self.features_77_conv_6_fc_2 = torch.nn.modules.linear.Linear(160, 3840)
self.features_77_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_77_conv_7 = torch.nn.modules.conv.Conv2d(3840, 640, 1, 1, 0, bias=False)
self.features_77_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(640)
self.features_78_conv_0 = torch.nn.modules.conv.Conv2d(640, 3840, 1, 1, 0, bias=False)
self.features_78_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3840)
self.features_78_conv_3 = torch.nn.modules.conv.Conv2d(3840, 3840, 3, 1, 1, groups=3840, bias=False)
self.features_78_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3840)
self.features_78_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_78_conv_6_fc_0 = torch.nn.modules.linear.Linear(3840, 160)
self.features_78_conv_6_fc_2 = torch.nn.modules.linear.Linear(160, 3840)
self.features_78_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_78_conv_7 = torch.nn.modules.conv.Conv2d(3840, 640, 1, 1, 0, bias=False)
self.features_78_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(640)
self.features_79_conv_0 = torch.nn.modules.conv.Conv2d(640, 3840, 1, 1, 0, bias=False)
self.features_79_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3840)
self.features_79_conv_3 = torch.nn.modules.conv.Conv2d(3840, 3840, 3, 1, 1, groups=3840, bias=False)
self.features_79_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3840)
self.features_79_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_79_conv_6_fc_0 = torch.nn.modules.linear.Linear(3840, 160)
self.features_79_conv_6_fc_2 = torch.nn.modules.linear.Linear(160, 3840)
self.features_79_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_79_conv_7 = torch.nn.modules.conv.Conv2d(3840, 640, 1, 1, 0, bias=False)
self.features_79_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(640)
self.conv_0 = torch.nn.modules.conv.Conv2d(640, 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)
features_2_conv_0 = self.features_2_conv_0(features_1_conv_4)
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_1 = features_1_conv_4.__add__(features_2_conv_4)
features_3_conv_0 = self.features_3_conv_0(add_1)
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)
add_2 = add_1.__add__(features_3_conv_4)
features_4_conv_0 = self.features_4_conv_0(add_2)
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 = add_2.__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)
features_6_conv_0 = self.features_6_conv_0(features_5_conv_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_4 = features_5_conv_4.__add__(features_6_conv_4)
features_7_conv_0 = self.features_7_conv_0(add_4)
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)
add_5 = add_4.__add__(features_7_conv_4)
features_8_conv_0 = self.features_8_conv_0(add_5)
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 = add_5.__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)
add_9 = add_8.__add__(features_11_conv_4)
features_12_conv_0 = self.features_12_conv_0(add_9)
features_12_conv_1 = self.features_12_conv_1(features_12_conv_0)
sigmoid_13 = torch.sigmoid(features_12_conv_1)
mul_13 = features_12_conv_1.__mul__(sigmoid_13)
features_12_conv_3 = self.features_12_conv_3(mul_13)
features_12_conv_4 = self.features_12_conv_4(features_12_conv_3)
features_13_conv_0 = self.features_13_conv_0(features_12_conv_4)
features_13_conv_1 = self.features_13_conv_1(features_13_conv_0)
sigmoid_14 = torch.sigmoid(features_13_conv_1)
mul_14 = features_13_conv_1.__mul__(sigmoid_14)
features_13_conv_3 = self.features_13_conv_3(mul_14)
features_13_conv_4 = self.features_13_conv_4(features_13_conv_3)
add_10 = features_12_conv_4.__add__(features_13_conv_4)
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_15 = torch.sigmoid(features_14_conv_1)
mul_15 = features_14_conv_1.__mul__(sigmoid_15)
features_14_conv_3 = self.features_14_conv_3(mul_15)
features_14_conv_4 = self.features_14_conv_4(features_14_conv_3)
add_11 = add_10.__add__(features_14_conv_4)
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_16 = torch.sigmoid(features_15_conv_1)
mul_16 = features_15_conv_1.__mul__(sigmoid_16)
features_15_conv_3 = self.features_15_conv_3(mul_16)
features_15_conv_4 = self.features_15_conv_4(features_15_conv_3)
add_12 = add_11.__add__(features_15_conv_4)
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_17 = torch.sigmoid(features_16_conv_1)
mul_17 = features_16_conv_1.__mul__(sigmoid_17)
features_16_conv_3 = self.features_16_conv_3(mul_17)
features_16_conv_4 = self.features_16_conv_4(features_16_conv_3)
add_13 = add_12.__add__(features_16_conv_4)
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_18 = torch.sigmoid(features_17_conv_1)
mul_18 = features_17_conv_1.__mul__(sigmoid_18)
features_17_conv_3 = self.features_17_conv_3(mul_18)
features_17_conv_4 = self.features_17_conv_4(features_17_conv_3)
add_14 = add_13.__add__(features_17_conv_4)
features_18_conv_0 = self.features_18_conv_0(add_14)
features_18_conv_1 = self.features_18_conv_1(features_18_conv_0)
sigmoid_19 = torch.sigmoid(features_18_conv_1)
mul_19 = features_18_conv_1.__mul__(sigmoid_19)
features_18_conv_3 = self.features_18_conv_3(mul_19)
features_18_conv_4 = self.features_18_conv_4(features_18_conv_3)
add_15 = add_14.__add__(features_18_conv_4)
features_19_conv_0 = self.features_19_conv_0(add_15)
features_19_conv_1 = self.features_19_conv_1(features_19_conv_0)
sigmoid_20 = torch.sigmoid(features_19_conv_1)
mul_20 = features_19_conv_1.__mul__(sigmoid_20)
features_19_conv_3 = self.features_19_conv_3(mul_20)
features_19_conv_4 = self.features_19_conv_4(features_19_conv_3)
sigmoid_21 = torch.sigmoid(features_19_conv_4)
mul_21 = features_19_conv_4.__mul__(sigmoid_21)
size_1 = mul_21.size()
features_19_conv_6_avg_pool = self.features_19_conv_6_avg_pool(mul_21)
view_1 = features_19_conv_6_avg_pool.view(size_1[0], size_1[1])
features_19_conv_6_fc_0 = self.features_19_conv_6_fc_0(view_1)
sigmoid_22 = torch.sigmoid(features_19_conv_6_fc_0)
mul_22 = features_19_conv_6_fc_0.__mul__(sigmoid_22)
features_19_conv_6_fc_2 = self.features_19_conv_6_fc_2(mul_22)
features_19_conv_6_fc_3 = self.features_19_conv_6_fc_3(features_19_conv_6_fc_2)
view_2 = features_19_conv_6_fc_3.view(size_1[0], size_1[1], 1, 1)
mul_23 = mul_21.__mul__(view_2)
features_19_conv_7 = self.features_19_conv_7(mul_23)
features_19_conv_8 = self.features_19_conv_8(features_19_conv_7)
features_20_conv_0 = self.features_20_conv_0(features_19_conv_8)
features_20_conv_1 = self.features_20_conv_1(features_20_conv_0)
sigmoid_23 = torch.sigmoid(features_20_conv_1)
mul_24 = features_20_conv_1.__mul__(sigmoid_23)
features_20_conv_3 = self.features_20_conv_3(mul_24)
features_20_conv_4 = self.features_20_conv_4(features_20_conv_3)
sigmoid_24 = torch.sigmoid(features_20_conv_4)
mul_25 = features_20_conv_4.__mul__(sigmoid_24)
size_2 = mul_25.size()
features_20_conv_6_avg_pool = self.features_20_conv_6_avg_pool(mul_25)
view_3 = features_20_conv_6_avg_pool.view(size_2[0], size_2[1])
features_20_conv_6_fc_0 = self.features_20_conv_6_fc_0(view_3)
sigmoid_25 = torch.sigmoid(features_20_conv_6_fc_0)
mul_26 = features_20_conv_6_fc_0.__mul__(sigmoid_25)
features_20_conv_6_fc_2 = self.features_20_conv_6_fc_2(mul_26)
features_20_conv_6_fc_3 = self.features_20_conv_6_fc_3(features_20_conv_6_fc_2)
view_4 = features_20_conv_6_fc_3.view(size_2[0], size_2[1], 1, 1)
mul_27 = mul_25.__mul__(view_4)
features_20_conv_7 = self.features_20_conv_7(mul_27)
features_20_conv_8 = self.features_20_conv_8(features_20_conv_7)
add_16 = features_19_conv_8.__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_26 = torch.sigmoid(features_21_conv_1)
mul_28 = features_21_conv_1.__mul__(sigmoid_26)
features_21_conv_3 = self.features_21_conv_3(mul_28)
features_21_conv_4 = self.features_21_conv_4(features_21_conv_3)
sigmoid_27 = torch.sigmoid(features_21_conv_4)
mul_29 = features_21_conv_4.__mul__(sigmoid_27)
size_3 = mul_29.size()
features_21_conv_6_avg_pool = self.features_21_conv_6_avg_pool(mul_29)
view_5 = features_21_conv_6_avg_pool.view(size_3[0], size_3[1])
features_21_conv_6_fc_0 = self.features_21_conv_6_fc_0(view_5)
sigmoid_28 = torch.sigmoid(features_21_conv_6_fc_0)
mul_30 = features_21_conv_6_fc_0.__mul__(sigmoid_28)
features_21_conv_6_fc_2 = self.features_21_conv_6_fc_2(mul_30)
features_21_conv_6_fc_3 = self.features_21_conv_6_fc_3(features_21_conv_6_fc_2)
view_6 = features_21_conv_6_fc_3.view(size_3[0], size_3[1], 1, 1)
mul_31 = mul_29.__mul__(view_6)
features_21_conv_7 = self.features_21_conv_7(mul_31)
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_29 = torch.sigmoid(features_22_conv_1)
mul_32 = features_22_conv_1.__mul__(sigmoid_29)
features_22_conv_3 = self.features_22_conv_3(mul_32)
features_22_conv_4 = self.features_22_conv_4(features_22_conv_3)
sigmoid_30 = torch.sigmoid(features_22_conv_4)
mul_33 = features_22_conv_4.__mul__(sigmoid_30)
size_4 = mul_33.size()
features_22_conv_6_avg_pool = self.features_22_conv_6_avg_pool(mul_33)
view_7 = features_22_conv_6_avg_pool.view(size_4[0], size_4[1])
features_22_conv_6_fc_0 = self.features_22_conv_6_fc_0(view_7)
sigmoid_31 = torch.sigmoid(features_22_conv_6_fc_0)
mul_34 = features_22_conv_6_fc_0.__mul__(sigmoid_31)
features_22_conv_6_fc_2 = self.features_22_conv_6_fc_2(mul_34)
features_22_conv_6_fc_3 = self.features_22_conv_6_fc_3(features_22_conv_6_fc_2)
view_8 = features_22_conv_6_fc_3.view(size_4[0], size_4[1], 1, 1)
mul_35 = mul_33.__mul__(view_8)
features_22_conv_7 = self.features_22_conv_7(mul_35)
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_32 = torch.sigmoid(features_23_conv_1)
mul_36 = features_23_conv_1.__mul__(sigmoid_32)
features_23_conv_3 = self.features_23_conv_3(mul_36)
features_23_conv_4 = self.features_23_conv_4(features_23_conv_3)
sigmoid_33 = torch.sigmoid(features_23_conv_4)
mul_37 = features_23_conv_4.__mul__(sigmoid_33)
size_5 = mul_37.size()
features_23_conv_6_avg_pool = self.features_23_conv_6_avg_pool(mul_37)
view_9 = features_23_conv_6_avg_pool.view(size_5[0], size_5[1])
features_23_conv_6_fc_0 = self.features_23_conv_6_fc_0(view_9)
sigmoid_34 = torch.sigmoid(features_23_conv_6_fc_0)
mul_38 = features_23_conv_6_fc_0.__mul__(sigmoid_34)
features_23_conv_6_fc_2 = self.features_23_conv_6_fc_2(mul_38)
features_23_conv_6_fc_3 = self.features_23_conv_6_fc_3(features_23_conv_6_fc_2)
view_10 = features_23_conv_6_fc_3.view(size_5[0], size_5[1], 1, 1)
mul_39 = mul_37.__mul__(view_10)
features_23_conv_7 = self.features_23_conv_7(mul_39)
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_35 = torch.sigmoid(features_24_conv_1)
mul_40 = features_24_conv_1.__mul__(sigmoid_35)
features_24_conv_3 = self.features_24_conv_3(mul_40)
features_24_conv_4 = self.features_24_conv_4(features_24_conv_3)
sigmoid_36 = torch.sigmoid(features_24_conv_4)
mul_41 = features_24_conv_4.__mul__(sigmoid_36)
size_6 = mul_41.size()
features_24_conv_6_avg_pool = self.features_24_conv_6_avg_pool(mul_41)
view_11 = features_24_conv_6_avg_pool.view(size_6[0], size_6[1])
features_24_conv_6_fc_0 = self.features_24_conv_6_fc_0(view_11)
sigmoid_37 = torch.sigmoid(features_24_conv_6_fc_0)
mul_42 = features_24_conv_6_fc_0.__mul__(sigmoid_37)
features_24_conv_6_fc_2 = self.features_24_conv_6_fc_2(mul_42)
features_24_conv_6_fc_3 = self.features_24_conv_6_fc_3(features_24_conv_6_fc_2)
view_12 = features_24_conv_6_fc_3.view(size_6[0], size_6[1], 1, 1)
mul_43 = mul_41.__mul__(view_12)
features_24_conv_7 = self.features_24_conv_7(mul_43)
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_38 = torch.sigmoid(features_25_conv_1)
mul_44 = features_25_conv_1.__mul__(sigmoid_38)
features_25_conv_3 = self.features_25_conv_3(mul_44)
features_25_conv_4 = self.features_25_conv_4(features_25_conv_3)
sigmoid_39 = torch.sigmoid(features_25_conv_4)
mul_45 = features_25_conv_4.__mul__(sigmoid_39)
size_7 = mul_45.size()
features_25_conv_6_avg_pool = self.features_25_conv_6_avg_pool(mul_45)
view_13 = features_25_conv_6_avg_pool.view(size_7[0], size_7[1])
features_25_conv_6_fc_0 = self.features_25_conv_6_fc_0(view_13)
sigmoid_40 = torch.sigmoid(features_25_conv_6_fc_0)
mul_46 = features_25_conv_6_fc_0.__mul__(sigmoid_40)
features_25_conv_6_fc_2 = self.features_25_conv_6_fc_2(mul_46)
features_25_conv_6_fc_3 = self.features_25_conv_6_fc_3(features_25_conv_6_fc_2)
view_14 = features_25_conv_6_fc_3.view(size_7[0], size_7[1], 1, 1)
mul_47 = mul_45.__mul__(view_14)
features_25_conv_7 = self.features_25_conv_7(mul_47)
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_41 = torch.sigmoid(features_26_conv_1)
mul_48 = features_26_conv_1.__mul__(sigmoid_41)
features_26_conv_3 = self.features_26_conv_3(mul_48)
features_26_conv_4 = self.features_26_conv_4(features_26_conv_3)
sigmoid_42 = torch.sigmoid(features_26_conv_4)
mul_49 = features_26_conv_4.__mul__(sigmoid_42)
size_8 = mul_49.size()
features_26_conv_6_avg_pool = self.features_26_conv_6_avg_pool(mul_49)
view_15 = features_26_conv_6_avg_pool.view(size_8[0], size_8[1])
features_26_conv_6_fc_0 = self.features_26_conv_6_fc_0(view_15)
sigmoid_43 = torch.sigmoid(features_26_conv_6_fc_0)
mul_50 = features_26_conv_6_fc_0.__mul__(sigmoid_43)
features_26_conv_6_fc_2 = self.features_26_conv_6_fc_2(mul_50)
features_26_conv_6_fc_3 = self.features_26_conv_6_fc_3(features_26_conv_6_fc_2)
view_16 = features_26_conv_6_fc_3.view(size_8[0], size_8[1], 1, 1)
mul_51 = mul_49.__mul__(view_16)
features_26_conv_7 = self.features_26_conv_7(mul_51)
features_26_conv_8 = self.features_26_conv_8(features_26_conv_7)
add_22 = add_21.__add__(features_26_conv_8)
features_27_conv_0 = self.features_27_conv_0(add_22)
features_27_conv_1 = self.features_27_conv_1(features_27_conv_0)
sigmoid_44 = torch.sigmoid(features_27_conv_1)
mul_52 = features_27_conv_1.__mul__(sigmoid_44)
features_27_conv_3 = self.features_27_conv_3(mul_52)