in models/efficientnet_v2_xl.py [0:0]
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, 1, 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, 64, 1, 1, 0, bias=False)
self.features_12_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(64)
self.features_13_conv_0 = torch.nn.modules.conv.Conv2d(64, 256, 3, 2, 1, bias=False)
self.features_13_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_13_conv_3 = torch.nn.modules.conv.Conv2d(256, 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, 3, 1, 1, bias=False)
self.features_19_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(384)
self.features_19_conv_3 = torch.nn.modules.conv.Conv2d(384, 96, 1, 1, 0, bias=False)
self.features_19_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(96)
self.features_20_conv_0 = torch.nn.modules.conv.Conv2d(96, 384, 3, 1, 1, bias=False)
self.features_20_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(384)
self.features_20_conv_3 = torch.nn.modules.conv.Conv2d(384, 96, 1, 1, 0, bias=False)
self.features_20_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(96)
self.features_21_conv_0 = torch.nn.modules.conv.Conv2d(96, 384, 1, 1, 0, bias=False)
self.features_21_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(384)
self.features_21_conv_3 = torch.nn.modules.conv.Conv2d(384, 384, 3, 2, 1, groups=384, bias=False)
self.features_21_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(384)
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(384, 24)
self.features_21_conv_6_fc_2 = torch.nn.modules.linear.Linear(24, 384)
self.features_21_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_21_conv_7 = torch.nn.modules.conv.Conv2d(384, 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, 768, 1, 1, 0, bias=False)
self.features_29_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_29_conv_3 = torch.nn.modules.conv.Conv2d(768, 768, 3, 1, 1, groups=768, bias=False)
self.features_29_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(768)
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(768, 48)
self.features_29_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 768)
self.features_29_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_29_conv_7 = torch.nn.modules.conv.Conv2d(768, 192, 1, 1, 0, bias=False)
self.features_29_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_30_conv_0 = torch.nn.modules.conv.Conv2d(192, 768, 1, 1, 0, bias=False)
self.features_30_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_30_conv_3 = torch.nn.modules.conv.Conv2d(768, 768, 3, 1, 1, groups=768, bias=False)
self.features_30_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(768)
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(768, 48)
self.features_30_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 768)
self.features_30_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_30_conv_7 = torch.nn.modules.conv.Conv2d(768, 192, 1, 1, 0, bias=False)
self.features_30_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_31_conv_0 = torch.nn.modules.conv.Conv2d(192, 768, 1, 1, 0, bias=False)
self.features_31_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_31_conv_3 = torch.nn.modules.conv.Conv2d(768, 768, 3, 1, 1, groups=768, bias=False)
self.features_31_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(768)
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(768, 48)
self.features_31_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 768)
self.features_31_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_31_conv_7 = torch.nn.modules.conv.Conv2d(768, 192, 1, 1, 0, bias=False)
self.features_31_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_32_conv_0 = torch.nn.modules.conv.Conv2d(192, 768, 1, 1, 0, bias=False)
self.features_32_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_32_conv_3 = torch.nn.modules.conv.Conv2d(768, 768, 3, 1, 1, groups=768, bias=False)
self.features_32_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(768)
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(768, 48)
self.features_32_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 768)
self.features_32_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_32_conv_7 = torch.nn.modules.conv.Conv2d(768, 192, 1, 1, 0, bias=False)
self.features_32_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_33_conv_0 = torch.nn.modules.conv.Conv2d(192, 768, 1, 1, 0, bias=False)
self.features_33_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_33_conv_3 = torch.nn.modules.conv.Conv2d(768, 768, 3, 1, 1, groups=768, bias=False)
self.features_33_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(768)
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(768, 48)
self.features_33_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 768)
self.features_33_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_33_conv_7 = torch.nn.modules.conv.Conv2d(768, 192, 1, 1, 0, bias=False)
self.features_33_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_34_conv_0 = torch.nn.modules.conv.Conv2d(192, 768, 1, 1, 0, bias=False)
self.features_34_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_34_conv_3 = torch.nn.modules.conv.Conv2d(768, 768, 3, 1, 1, groups=768, bias=False)
self.features_34_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(768)
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(768, 48)
self.features_34_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 768)
self.features_34_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_34_conv_7 = torch.nn.modules.conv.Conv2d(768, 192, 1, 1, 0, bias=False)
self.features_34_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_35_conv_0 = torch.nn.modules.conv.Conv2d(192, 768, 1, 1, 0, bias=False)
self.features_35_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_35_conv_3 = torch.nn.modules.conv.Conv2d(768, 768, 3, 1, 1, groups=768, bias=False)
self.features_35_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(768)
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(768, 48)
self.features_35_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 768)
self.features_35_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_35_conv_7 = torch.nn.modules.conv.Conv2d(768, 192, 1, 1, 0, bias=False)
self.features_35_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_36_conv_0 = torch.nn.modules.conv.Conv2d(192, 768, 1, 1, 0, bias=False)
self.features_36_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_36_conv_3 = torch.nn.modules.conv.Conv2d(768, 768, 3, 1, 1, groups=768, bias=False)
self.features_36_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(768)
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(768, 48)
self.features_36_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 768)
self.features_36_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_36_conv_7 = torch.nn.modules.conv.Conv2d(768, 192, 1, 1, 0, bias=False)
self.features_36_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_37_conv_0 = torch.nn.modules.conv.Conv2d(192, 1152, 1, 1, 0, bias=False)
self.features_37_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1152)
self.features_37_conv_3 = torch.nn.modules.conv.Conv2d(1152, 1152, 3, 1, 1, groups=1152, bias=False)
self.features_37_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1152)
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(1152, 48)
self.features_37_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 1152)
self.features_37_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_37_conv_7 = torch.nn.modules.conv.Conv2d(1152, 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.features_41_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_41_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_41_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_41_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
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(1536, 64)
self.features_41_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_41_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_41_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_41_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_42_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_42_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_42_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_42_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
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(1536, 64)
self.features_42_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_42_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_42_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_42_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_43_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_43_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_43_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_43_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
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(1536, 64)
self.features_43_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_43_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_43_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_43_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_44_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_44_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_44_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_44_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
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(1536, 64)
self.features_44_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_44_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_44_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_44_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_45_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_45_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_45_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_45_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
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(1536, 64)
self.features_45_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_45_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_45_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_45_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_46_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_46_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_46_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_46_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
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(1536, 64)
self.features_46_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_46_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_46_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_46_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_47_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_47_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_47_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_47_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
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(1536, 64)
self.features_47_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_47_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_47_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_47_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_48_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_48_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_48_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_48_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
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(1536, 64)
self.features_48_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_48_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_48_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_48_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_49_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_49_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_49_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_49_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
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(1536, 64)
self.features_49_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_49_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_49_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_49_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_50_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_50_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_50_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_50_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
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(1536, 64)
self.features_50_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_50_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_50_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_50_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_51_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_51_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_51_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_51_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
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(1536, 64)
self.features_51_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_51_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_51_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_51_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_52_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_52_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_52_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_52_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
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(1536, 64)
self.features_52_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_52_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_52_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_52_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_53_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_53_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_53_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_53_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
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(1536, 64)
self.features_53_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_53_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_53_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_53_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_54_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_54_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_54_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_54_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
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(1536, 64)
self.features_54_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_54_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_54_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_54_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_55_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_55_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_55_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_55_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
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(1536, 64)
self.features_55_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_55_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_55_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_55_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_56_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_56_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_56_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_56_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
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(1536, 64)
self.features_56_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_56_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_56_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_56_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_57_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_57_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_57_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_57_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
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(1536, 64)
self.features_57_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_57_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_57_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_57_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_58_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_58_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_58_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_58_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
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(1536, 64)
self.features_58_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_58_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_58_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_58_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_59_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_59_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_59_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_59_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
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(1536, 64)
self.features_59_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_59_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_59_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_59_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_60_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_60_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_60_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_60_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
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(1536, 64)
self.features_60_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_60_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_60_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_60_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_61_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_61_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_61_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 2, 1, groups=1536, bias=False)
self.features_61_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
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(1536, 64)
self.features_61_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_61_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_61_conv_7 = torch.nn.modules.conv.Conv2d(1536, 512, 1, 1, 0, bias=False)
self.features_61_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_62_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_62_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_62_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_62_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
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(3072, 128)
self.features_62_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_62_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_62_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_62_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_63_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_63_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_63_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_63_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
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(3072, 128)
self.features_63_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_63_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_63_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_63_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_64_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_64_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_64_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_64_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
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(3072, 128)
self.features_64_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_64_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_64_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_64_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_65_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_65_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_65_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_65_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
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(3072, 128)
self.features_65_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_65_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_65_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_65_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_66_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_66_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_66_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_66_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
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(3072, 128)
self.features_66_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_66_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_66_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_66_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_67_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_67_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_67_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_67_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
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(3072, 128)
self.features_67_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_67_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_67_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_67_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_68_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_68_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_68_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_68_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
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(3072, 128)
self.features_68_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_68_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_68_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_68_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_69_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_69_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_69_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_69_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
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(3072, 128)
self.features_69_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_69_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_69_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_69_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_70_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_70_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_70_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_70_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
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(3072, 128)
self.features_70_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_70_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_70_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_70_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_71_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_71_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_71_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_71_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
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(3072, 128)
self.features_71_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_71_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_71_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_71_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_72_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_72_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_72_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_72_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
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(3072, 128)
self.features_72_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_72_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_72_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_72_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_73_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_73_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_73_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_73_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
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(3072, 128)
self.features_73_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_73_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_73_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_73_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_74_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_74_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_74_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_74_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
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(3072, 128)
self.features_74_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_74_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_74_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_74_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_75_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_75_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_75_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_75_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
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(3072, 128)
self.features_75_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_75_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_75_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_75_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_76_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_76_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_76_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_76_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
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(3072, 128)
self.features_76_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_76_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_76_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_76_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_77_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_77_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_77_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_77_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
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(3072, 128)
self.features_77_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_77_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_77_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_77_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_78_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_78_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_78_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_78_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
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(3072, 128)
self.features_78_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_78_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_78_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_78_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_79_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_79_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_79_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_79_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
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(3072, 128)
self.features_79_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_79_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_79_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_79_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_80_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_80_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_80_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_80_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_80_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_80_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_80_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_80_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_80_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_80_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_81_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_81_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_81_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_81_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_81_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_81_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_81_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_81_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_81_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_81_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_82_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_82_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_82_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_82_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_82_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_82_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_82_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_82_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_82_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_82_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_83_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_83_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_83_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_83_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_83_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_83_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_83_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_83_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_83_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_83_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_84_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_84_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_84_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_84_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_84_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_84_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_84_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_84_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_84_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_84_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_85_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_85_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_85_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_85_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_85_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_85_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_85_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_85_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_85_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_85_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_86_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_86_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_86_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_86_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_86_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_86_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_86_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_86_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_86_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_86_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_87_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_87_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_87_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_87_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_87_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_87_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_87_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_87_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_87_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_87_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_88_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_88_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_88_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_88_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_88_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_88_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_88_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_88_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_88_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_88_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_89_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_89_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_89_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_89_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_89_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_89_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_89_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_89_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_89_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_89_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_90_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_90_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_90_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_90_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_90_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_90_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_90_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_90_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_90_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_90_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_91_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_91_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_91_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_91_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_91_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_91_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_91_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_91_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_91_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_91_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_92_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_92_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_92_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_92_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_92_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_92_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_92_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_92_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_92_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_92_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_93_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_93_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_93_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_93_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_93_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_93_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_93_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_93_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_93_conv_7 = torch.nn.modules.conv.Conv2d(3072, 640, 1, 1, 0, bias=False)
self.features_93_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(640)
self.features_94_conv_0 = torch.nn.modules.conv.Conv2d(640, 3840, 1, 1, 0, bias=False)
self.features_94_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3840)
self.features_94_conv_3 = torch.nn.modules.conv.Conv2d(3840, 3840, 3, 1, 1, groups=3840, bias=False)
self.features_94_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3840)
self.features_94_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_94_conv_6_fc_0 = torch.nn.modules.linear.Linear(3840, 160)
self.features_94_conv_6_fc_2 = torch.nn.modules.linear.Linear(160, 3840)
self.features_94_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_94_conv_7 = torch.nn.modules.conv.Conv2d(3840, 640, 1, 1, 0, bias=False)
self.features_94_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(640)
self.features_95_conv_0 = torch.nn.modules.conv.Conv2d(640, 3840, 1, 1, 0, bias=False)
self.features_95_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3840)
self.features_95_conv_3 = torch.nn.modules.conv.Conv2d(3840, 3840, 3, 1, 1, groups=3840, bias=False)
self.features_95_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3840)
self.features_95_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_95_conv_6_fc_0 = torch.nn.modules.linear.Linear(3840, 160)
self.features_95_conv_6_fc_2 = torch.nn.modules.linear.Linear(160, 3840)
self.features_95_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_95_conv_7 = torch.nn.modules.conv.Conv2d(3840, 640, 1, 1, 0, bias=False)
self.features_95_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(640)
self.features_96_conv_0 = torch.nn.modules.conv.Conv2d(640, 3840, 1, 1, 0, bias=False)
self.features_96_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3840)
self.features_96_conv_3 = torch.nn.modules.conv.Conv2d(3840, 3840, 3, 1, 1, groups=3840, bias=False)
self.features_96_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3840)
self.features_96_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_96_conv_6_fc_0 = torch.nn.modules.linear.Linear(3840, 160)
self.features_96_conv_6_fc_2 = torch.nn.modules.linear.Linear(160, 3840)
self.features_96_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_96_conv_7 = torch.nn.modules.conv.Conv2d(3840, 640, 1, 1, 0, bias=False)
self.features_96_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(640)
self.features_97_conv_0 = torch.nn.modules.conv.Conv2d(640, 3840, 1, 1, 0, bias=False)
self.features_97_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3840)
self.features_97_conv_3 = torch.nn.modules.conv.Conv2d(3840, 3840, 3, 1, 1, groups=3840, bias=False)
self.features_97_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3840)
self.features_97_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_97_conv_6_fc_0 = torch.nn.modules.linear.Linear(3840, 160)
self.features_97_conv_6_fc_2 = torch.nn.modules.linear.Linear(160, 3840)
self.features_97_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_97_conv_7 = torch.nn.modules.conv.Conv2d(3840, 640, 1, 1, 0, bias=False)
self.features_97_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(640)
self.features_98_conv_0 = torch.nn.modules.conv.Conv2d(640, 3840, 1, 1, 0, bias=False)
self.features_98_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3840)
self.features_98_conv_3 = torch.nn.modules.conv.Conv2d(3840, 3840, 3, 1, 1, groups=3840, bias=False)
self.features_98_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3840)
self.features_98_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_98_conv_6_fc_0 = torch.nn.modules.linear.Linear(3840, 160)
self.features_98_conv_6_fc_2 = torch.nn.modules.linear.Linear(160, 3840)
self.features_98_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_98_conv_7 = torch.nn.modules.conv.Conv2d(3840, 640, 1, 1, 0, bias=False)
self.features_98_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(640)
self.features_99_conv_0 = torch.nn.modules.conv.Conv2d(640, 3840, 1, 1, 0, bias=False)
self.features_99_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3840)
self.features_99_conv_3 = torch.nn.modules.conv.Conv2d(3840, 3840, 3, 1, 1, groups=3840, bias=False)
self.features_99_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3840)
self.features_99_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_99_conv_6_fc_0 = torch.nn.modules.linear.Linear(3840, 160)
self.features_99_conv_6_fc_2 = torch.nn.modules.linear.Linear(160, 3840)
self.features_99_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_99_conv_7 = torch.nn.modules.conv.Conv2d(3840, 640, 1, 1, 0, bias=False)
self.features_99_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(640)
self.features_100_conv_0 = torch.nn.modules.conv.Conv2d(640, 3840, 1, 1, 0, bias=False)
self.features_100_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3840)
self.features_100_conv_3 = torch.nn.modules.conv.Conv2d(3840, 3840, 3, 1, 1, groups=3840, bias=False)
self.features_100_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3840)
self.features_100_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_100_conv_6_fc_0 = torch.nn.modules.linear.Linear(3840, 160)
self.features_100_conv_6_fc_2 = torch.nn.modules.linear.Linear(160, 3840)
self.features_100_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_100_conv_7 = torch.nn.modules.conv.Conv2d(3840, 640, 1, 1, 0, bias=False)
self.features_100_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)