in models/efficientnet_v2_m.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, 24, 1, 1, 0, bias=False)
self.features_1_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(24)
self.features_2_conv_0 = torch.nn.modules.conv.Conv2d(24, 24, 3, 1, 1, bias=False)
self.features_2_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(24)
self.features_2_conv_3 = torch.nn.modules.conv.Conv2d(24, 24, 1, 1, 0, bias=False)
self.features_2_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(24)
self.features_3_conv_0 = torch.nn.modules.conv.Conv2d(24, 24, 3, 1, 1, bias=False)
self.features_3_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(24)
self.features_3_conv_3 = torch.nn.modules.conv.Conv2d(24, 24, 1, 1, 0, bias=False)
self.features_3_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(24)
self.features_4_conv_0 = torch.nn.modules.conv.Conv2d(24, 96, 3, 2, 1, bias=False)
self.features_4_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(96)
self.features_4_conv_3 = torch.nn.modules.conv.Conv2d(96, 48, 1, 1, 0, bias=False)
self.features_4_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(48)
self.features_5_conv_0 = torch.nn.modules.conv.Conv2d(48, 192, 3, 1, 1, bias=False)
self.features_5_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_5_conv_3 = torch.nn.modules.conv.Conv2d(192, 48, 1, 1, 0, bias=False)
self.features_5_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(48)
self.features_6_conv_0 = torch.nn.modules.conv.Conv2d(48, 192, 3, 1, 1, bias=False)
self.features_6_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_6_conv_3 = torch.nn.modules.conv.Conv2d(192, 48, 1, 1, 0, bias=False)
self.features_6_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(48)
self.features_7_conv_0 = torch.nn.modules.conv.Conv2d(48, 192, 3, 1, 1, bias=False)
self.features_7_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_7_conv_3 = torch.nn.modules.conv.Conv2d(192, 48, 1, 1, 0, bias=False)
self.features_7_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(48)
self.features_8_conv_0 = torch.nn.modules.conv.Conv2d(48, 192, 3, 1, 1, bias=False)
self.features_8_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_8_conv_3 = torch.nn.modules.conv.Conv2d(192, 48, 1, 1, 0, bias=False)
self.features_8_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(48)
self.features_9_conv_0 = torch.nn.modules.conv.Conv2d(48, 192, 3, 2, 1, bias=False)
self.features_9_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_9_conv_3 = torch.nn.modules.conv.Conv2d(192, 80, 1, 1, 0, bias=False)
self.features_9_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(80)
self.features_10_conv_0 = torch.nn.modules.conv.Conv2d(80, 320, 3, 1, 1, bias=False)
self.features_10_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(320)
self.features_10_conv_3 = torch.nn.modules.conv.Conv2d(320, 80, 1, 1, 0, bias=False)
self.features_10_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(80)
self.features_11_conv_0 = torch.nn.modules.conv.Conv2d(80, 320, 3, 1, 1, bias=False)
self.features_11_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(320)
self.features_11_conv_3 = torch.nn.modules.conv.Conv2d(320, 80, 1, 1, 0, bias=False)
self.features_11_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(80)
self.features_12_conv_0 = torch.nn.modules.conv.Conv2d(80, 320, 3, 1, 1, bias=False)
self.features_12_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(320)
self.features_12_conv_3 = torch.nn.modules.conv.Conv2d(320, 80, 1, 1, 0, bias=False)
self.features_12_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(80)
self.features_13_conv_0 = torch.nn.modules.conv.Conv2d(80, 320, 3, 1, 1, bias=False)
self.features_13_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(320)
self.features_13_conv_3 = torch.nn.modules.conv.Conv2d(320, 80, 1, 1, 0, bias=False)
self.features_13_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(80)
self.features_14_conv_0 = torch.nn.modules.conv.Conv2d(80, 320, 1, 1, 0, bias=False)
self.features_14_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(320)
self.features_14_conv_3 = torch.nn.modules.conv.Conv2d(320, 320, 3, 2, 1, groups=320, bias=False)
self.features_14_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(320)
self.features_14_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_14_conv_6_fc_0 = torch.nn.modules.linear.Linear(320, 24)
self.features_14_conv_6_fc_2 = torch.nn.modules.linear.Linear(24, 320)
self.features_14_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_14_conv_7 = torch.nn.modules.conv.Conv2d(320, 160, 1, 1, 0, bias=False)
self.features_14_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(160)
self.features_15_conv_0 = torch.nn.modules.conv.Conv2d(160, 640, 1, 1, 0, bias=False)
self.features_15_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(640)
self.features_15_conv_3 = torch.nn.modules.conv.Conv2d(640, 640, 3, 1, 1, groups=640, bias=False)
self.features_15_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(640)
self.features_15_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_15_conv_6_fc_0 = torch.nn.modules.linear.Linear(640, 40)
self.features_15_conv_6_fc_2 = torch.nn.modules.linear.Linear(40, 640)
self.features_15_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_15_conv_7 = torch.nn.modules.conv.Conv2d(640, 160, 1, 1, 0, bias=False)
self.features_15_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(160)
self.features_16_conv_0 = torch.nn.modules.conv.Conv2d(160, 640, 1, 1, 0, bias=False)
self.features_16_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(640)
self.features_16_conv_3 = torch.nn.modules.conv.Conv2d(640, 640, 3, 1, 1, groups=640, bias=False)
self.features_16_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(640)
self.features_16_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_16_conv_6_fc_0 = torch.nn.modules.linear.Linear(640, 40)
self.features_16_conv_6_fc_2 = torch.nn.modules.linear.Linear(40, 640)
self.features_16_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_16_conv_7 = torch.nn.modules.conv.Conv2d(640, 160, 1, 1, 0, bias=False)
self.features_16_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(160)
self.features_17_conv_0 = torch.nn.modules.conv.Conv2d(160, 640, 1, 1, 0, bias=False)
self.features_17_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(640)
self.features_17_conv_3 = torch.nn.modules.conv.Conv2d(640, 640, 3, 1, 1, groups=640, bias=False)
self.features_17_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(640)
self.features_17_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_17_conv_6_fc_0 = torch.nn.modules.linear.Linear(640, 40)
self.features_17_conv_6_fc_2 = torch.nn.modules.linear.Linear(40, 640)
self.features_17_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_17_conv_7 = torch.nn.modules.conv.Conv2d(640, 160, 1, 1, 0, bias=False)
self.features_17_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(160)
self.features_18_conv_0 = torch.nn.modules.conv.Conv2d(160, 640, 1, 1, 0, bias=False)
self.features_18_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(640)
self.features_18_conv_3 = torch.nn.modules.conv.Conv2d(640, 640, 3, 1, 1, groups=640, bias=False)
self.features_18_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(640)
self.features_18_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_18_conv_6_fc_0 = torch.nn.modules.linear.Linear(640, 40)
self.features_18_conv_6_fc_2 = torch.nn.modules.linear.Linear(40, 640)
self.features_18_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_18_conv_7 = torch.nn.modules.conv.Conv2d(640, 160, 1, 1, 0, bias=False)
self.features_18_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(160)
self.features_19_conv_0 = torch.nn.modules.conv.Conv2d(160, 640, 1, 1, 0, bias=False)
self.features_19_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(640)
self.features_19_conv_3 = torch.nn.modules.conv.Conv2d(640, 640, 3, 1, 1, groups=640, bias=False)
self.features_19_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(640)
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(640, 40)
self.features_19_conv_6_fc_2 = torch.nn.modules.linear.Linear(40, 640)
self.features_19_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_19_conv_7 = torch.nn.modules.conv.Conv2d(640, 160, 1, 1, 0, bias=False)
self.features_19_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(160)
self.features_20_conv_0 = torch.nn.modules.conv.Conv2d(160, 640, 1, 1, 0, bias=False)
self.features_20_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(640)
self.features_20_conv_3 = torch.nn.modules.conv.Conv2d(640, 640, 3, 1, 1, groups=640, bias=False)
self.features_20_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(640)
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(640, 40)
self.features_20_conv_6_fc_2 = torch.nn.modules.linear.Linear(40, 640)
self.features_20_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_20_conv_7 = torch.nn.modules.conv.Conv2d(640, 160, 1, 1, 0, bias=False)
self.features_20_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(160)
self.features_21_conv_0 = torch.nn.modules.conv.Conv2d(160, 960, 1, 1, 0, bias=False)
self.features_21_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(960)
self.features_21_conv_3 = torch.nn.modules.conv.Conv2d(960, 960, 3, 1, 1, groups=960, bias=False)
self.features_21_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(960)
self.features_21_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_21_conv_6_fc_0 = torch.nn.modules.linear.Linear(960, 40)
self.features_21_conv_6_fc_2 = torch.nn.modules.linear.Linear(40, 960)
self.features_21_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_21_conv_7 = torch.nn.modules.conv.Conv2d(960, 176, 1, 1, 0, bias=False)
self.features_21_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(176)
self.features_22_conv_0 = torch.nn.modules.conv.Conv2d(176, 1056, 1, 1, 0, bias=False)
self.features_22_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
self.features_22_conv_3 = torch.nn.modules.conv.Conv2d(1056, 1056, 3, 1, 1, groups=1056, bias=False)
self.features_22_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
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(1056, 48)
self.features_22_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 1056)
self.features_22_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_22_conv_7 = torch.nn.modules.conv.Conv2d(1056, 176, 1, 1, 0, bias=False)
self.features_22_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(176)
self.features_23_conv_0 = torch.nn.modules.conv.Conv2d(176, 1056, 1, 1, 0, bias=False)
self.features_23_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
self.features_23_conv_3 = torch.nn.modules.conv.Conv2d(1056, 1056, 3, 1, 1, groups=1056, bias=False)
self.features_23_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
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(1056, 48)
self.features_23_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 1056)
self.features_23_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_23_conv_7 = torch.nn.modules.conv.Conv2d(1056, 176, 1, 1, 0, bias=False)
self.features_23_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(176)
self.features_24_conv_0 = torch.nn.modules.conv.Conv2d(176, 1056, 1, 1, 0, bias=False)
self.features_24_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
self.features_24_conv_3 = torch.nn.modules.conv.Conv2d(1056, 1056, 3, 1, 1, groups=1056, bias=False)
self.features_24_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
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(1056, 48)
self.features_24_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 1056)
self.features_24_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_24_conv_7 = torch.nn.modules.conv.Conv2d(1056, 176, 1, 1, 0, bias=False)
self.features_24_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(176)
self.features_25_conv_0 = torch.nn.modules.conv.Conv2d(176, 1056, 1, 1, 0, bias=False)
self.features_25_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
self.features_25_conv_3 = torch.nn.modules.conv.Conv2d(1056, 1056, 3, 1, 1, groups=1056, bias=False)
self.features_25_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
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(1056, 48)
self.features_25_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 1056)
self.features_25_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_25_conv_7 = torch.nn.modules.conv.Conv2d(1056, 176, 1, 1, 0, bias=False)
self.features_25_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(176)
self.features_26_conv_0 = torch.nn.modules.conv.Conv2d(176, 1056, 1, 1, 0, bias=False)
self.features_26_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
self.features_26_conv_3 = torch.nn.modules.conv.Conv2d(1056, 1056, 3, 1, 1, groups=1056, bias=False)
self.features_26_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
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(1056, 48)
self.features_26_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 1056)
self.features_26_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_26_conv_7 = torch.nn.modules.conv.Conv2d(1056, 176, 1, 1, 0, bias=False)
self.features_26_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(176)
self.features_27_conv_0 = torch.nn.modules.conv.Conv2d(176, 1056, 1, 1, 0, bias=False)
self.features_27_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
self.features_27_conv_3 = torch.nn.modules.conv.Conv2d(1056, 1056, 3, 1, 1, groups=1056, bias=False)
self.features_27_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
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(1056, 48)
self.features_27_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 1056)
self.features_27_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_27_conv_7 = torch.nn.modules.conv.Conv2d(1056, 176, 1, 1, 0, bias=False)
self.features_27_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(176)
self.features_28_conv_0 = torch.nn.modules.conv.Conv2d(176, 1056, 1, 1, 0, bias=False)
self.features_28_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
self.features_28_conv_3 = torch.nn.modules.conv.Conv2d(1056, 1056, 3, 1, 1, groups=1056, bias=False)
self.features_28_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
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(1056, 48)
self.features_28_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 1056)
self.features_28_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_28_conv_7 = torch.nn.modules.conv.Conv2d(1056, 176, 1, 1, 0, bias=False)
self.features_28_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(176)
self.features_29_conv_0 = torch.nn.modules.conv.Conv2d(176, 1056, 1, 1, 0, bias=False)
self.features_29_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
self.features_29_conv_3 = torch.nn.modules.conv.Conv2d(1056, 1056, 3, 1, 1, groups=1056, bias=False)
self.features_29_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
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(1056, 48)
self.features_29_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 1056)
self.features_29_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_29_conv_7 = torch.nn.modules.conv.Conv2d(1056, 176, 1, 1, 0, bias=False)
self.features_29_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(176)
self.features_30_conv_0 = torch.nn.modules.conv.Conv2d(176, 1056, 1, 1, 0, bias=False)
self.features_30_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
self.features_30_conv_3 = torch.nn.modules.conv.Conv2d(1056, 1056, 3, 1, 1, groups=1056, bias=False)
self.features_30_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
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(1056, 48)
self.features_30_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 1056)
self.features_30_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_30_conv_7 = torch.nn.modules.conv.Conv2d(1056, 176, 1, 1, 0, bias=False)
self.features_30_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(176)
self.features_31_conv_0 = torch.nn.modules.conv.Conv2d(176, 1056, 1, 1, 0, bias=False)
self.features_31_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
self.features_31_conv_3 = torch.nn.modules.conv.Conv2d(1056, 1056, 3, 1, 1, groups=1056, bias=False)
self.features_31_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
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(1056, 48)
self.features_31_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 1056)
self.features_31_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_31_conv_7 = torch.nn.modules.conv.Conv2d(1056, 176, 1, 1, 0, bias=False)
self.features_31_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(176)
self.features_32_conv_0 = torch.nn.modules.conv.Conv2d(176, 1056, 1, 1, 0, bias=False)
self.features_32_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
self.features_32_conv_3 = torch.nn.modules.conv.Conv2d(1056, 1056, 3, 1, 1, groups=1056, bias=False)
self.features_32_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
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(1056, 48)
self.features_32_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 1056)
self.features_32_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_32_conv_7 = torch.nn.modules.conv.Conv2d(1056, 176, 1, 1, 0, bias=False)
self.features_32_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(176)
self.features_33_conv_0 = torch.nn.modules.conv.Conv2d(176, 1056, 1, 1, 0, bias=False)
self.features_33_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
self.features_33_conv_3 = torch.nn.modules.conv.Conv2d(1056, 1056, 3, 1, 1, groups=1056, bias=False)
self.features_33_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
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(1056, 48)
self.features_33_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 1056)
self.features_33_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_33_conv_7 = torch.nn.modules.conv.Conv2d(1056, 176, 1, 1, 0, bias=False)
self.features_33_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(176)
self.features_34_conv_0 = torch.nn.modules.conv.Conv2d(176, 1056, 1, 1, 0, bias=False)
self.features_34_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
self.features_34_conv_3 = torch.nn.modules.conv.Conv2d(1056, 1056, 3, 1, 1, groups=1056, bias=False)
self.features_34_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
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(1056, 48)
self.features_34_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 1056)
self.features_34_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_34_conv_7 = torch.nn.modules.conv.Conv2d(1056, 176, 1, 1, 0, bias=False)
self.features_34_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(176)
self.features_35_conv_0 = torch.nn.modules.conv.Conv2d(176, 1056, 1, 1, 0, bias=False)
self.features_35_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
self.features_35_conv_3 = torch.nn.modules.conv.Conv2d(1056, 1056, 3, 2, 1, groups=1056, bias=False)
self.features_35_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
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(1056, 48)
self.features_35_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 1056)
self.features_35_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_35_conv_7 = torch.nn.modules.conv.Conv2d(1056, 304, 1, 1, 0, bias=False)
self.features_35_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(304)
self.features_36_conv_0 = torch.nn.modules.conv.Conv2d(304, 1824, 1, 1, 0, bias=False)
self.features_36_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_36_conv_3 = torch.nn.modules.conv.Conv2d(1824, 1824, 3, 1, 1, groups=1824, bias=False)
self.features_36_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
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(1824, 80)
self.features_36_conv_6_fc_2 = torch.nn.modules.linear.Linear(80, 1824)
self.features_36_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_36_conv_7 = torch.nn.modules.conv.Conv2d(1824, 304, 1, 1, 0, bias=False)
self.features_36_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(304)
self.features_37_conv_0 = torch.nn.modules.conv.Conv2d(304, 1824, 1, 1, 0, bias=False)
self.features_37_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_37_conv_3 = torch.nn.modules.conv.Conv2d(1824, 1824, 3, 1, 1, groups=1824, bias=False)
self.features_37_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
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(1824, 80)
self.features_37_conv_6_fc_2 = torch.nn.modules.linear.Linear(80, 1824)
self.features_37_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_37_conv_7 = torch.nn.modules.conv.Conv2d(1824, 304, 1, 1, 0, bias=False)
self.features_37_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(304)
self.features_38_conv_0 = torch.nn.modules.conv.Conv2d(304, 1824, 1, 1, 0, bias=False)
self.features_38_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_38_conv_3 = torch.nn.modules.conv.Conv2d(1824, 1824, 3, 1, 1, groups=1824, bias=False)
self.features_38_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
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(1824, 80)
self.features_38_conv_6_fc_2 = torch.nn.modules.linear.Linear(80, 1824)
self.features_38_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_38_conv_7 = torch.nn.modules.conv.Conv2d(1824, 304, 1, 1, 0, bias=False)
self.features_38_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(304)
self.features_39_conv_0 = torch.nn.modules.conv.Conv2d(304, 1824, 1, 1, 0, bias=False)
self.features_39_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_39_conv_3 = torch.nn.modules.conv.Conv2d(1824, 1824, 3, 1, 1, groups=1824, bias=False)
self.features_39_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
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(1824, 80)
self.features_39_conv_6_fc_2 = torch.nn.modules.linear.Linear(80, 1824)
self.features_39_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_39_conv_7 = torch.nn.modules.conv.Conv2d(1824, 304, 1, 1, 0, bias=False)
self.features_39_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(304)
self.features_40_conv_0 = torch.nn.modules.conv.Conv2d(304, 1824, 1, 1, 0, bias=False)
self.features_40_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_40_conv_3 = torch.nn.modules.conv.Conv2d(1824, 1824, 3, 1, 1, groups=1824, bias=False)
self.features_40_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
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(1824, 80)
self.features_40_conv_6_fc_2 = torch.nn.modules.linear.Linear(80, 1824)
self.features_40_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_40_conv_7 = torch.nn.modules.conv.Conv2d(1824, 304, 1, 1, 0, bias=False)
self.features_40_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(304)
self.features_41_conv_0 = torch.nn.modules.conv.Conv2d(304, 1824, 1, 1, 0, bias=False)
self.features_41_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_41_conv_3 = torch.nn.modules.conv.Conv2d(1824, 1824, 3, 1, 1, groups=1824, bias=False)
self.features_41_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
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(1824, 80)
self.features_41_conv_6_fc_2 = torch.nn.modules.linear.Linear(80, 1824)
self.features_41_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_41_conv_7 = torch.nn.modules.conv.Conv2d(1824, 304, 1, 1, 0, bias=False)
self.features_41_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(304)
self.features_42_conv_0 = torch.nn.modules.conv.Conv2d(304, 1824, 1, 1, 0, bias=False)
self.features_42_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_42_conv_3 = torch.nn.modules.conv.Conv2d(1824, 1824, 3, 1, 1, groups=1824, bias=False)
self.features_42_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
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(1824, 80)
self.features_42_conv_6_fc_2 = torch.nn.modules.linear.Linear(80, 1824)
self.features_42_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_42_conv_7 = torch.nn.modules.conv.Conv2d(1824, 304, 1, 1, 0, bias=False)
self.features_42_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(304)
self.features_43_conv_0 = torch.nn.modules.conv.Conv2d(304, 1824, 1, 1, 0, bias=False)
self.features_43_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_43_conv_3 = torch.nn.modules.conv.Conv2d(1824, 1824, 3, 1, 1, groups=1824, bias=False)
self.features_43_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
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(1824, 80)
self.features_43_conv_6_fc_2 = torch.nn.modules.linear.Linear(80, 1824)
self.features_43_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_43_conv_7 = torch.nn.modules.conv.Conv2d(1824, 304, 1, 1, 0, bias=False)
self.features_43_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(304)
self.features_44_conv_0 = torch.nn.modules.conv.Conv2d(304, 1824, 1, 1, 0, bias=False)
self.features_44_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_44_conv_3 = torch.nn.modules.conv.Conv2d(1824, 1824, 3, 1, 1, groups=1824, bias=False)
self.features_44_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
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(1824, 80)
self.features_44_conv_6_fc_2 = torch.nn.modules.linear.Linear(80, 1824)
self.features_44_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_44_conv_7 = torch.nn.modules.conv.Conv2d(1824, 304, 1, 1, 0, bias=False)
self.features_44_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(304)
self.features_45_conv_0 = torch.nn.modules.conv.Conv2d(304, 1824, 1, 1, 0, bias=False)
self.features_45_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_45_conv_3 = torch.nn.modules.conv.Conv2d(1824, 1824, 3, 1, 1, groups=1824, bias=False)
self.features_45_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
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(1824, 80)
self.features_45_conv_6_fc_2 = torch.nn.modules.linear.Linear(80, 1824)
self.features_45_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_45_conv_7 = torch.nn.modules.conv.Conv2d(1824, 304, 1, 1, 0, bias=False)
self.features_45_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(304)
self.features_46_conv_0 = torch.nn.modules.conv.Conv2d(304, 1824, 1, 1, 0, bias=False)
self.features_46_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_46_conv_3 = torch.nn.modules.conv.Conv2d(1824, 1824, 3, 1, 1, groups=1824, bias=False)
self.features_46_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
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(1824, 80)
self.features_46_conv_6_fc_2 = torch.nn.modules.linear.Linear(80, 1824)
self.features_46_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_46_conv_7 = torch.nn.modules.conv.Conv2d(1824, 304, 1, 1, 0, bias=False)
self.features_46_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(304)
self.features_47_conv_0 = torch.nn.modules.conv.Conv2d(304, 1824, 1, 1, 0, bias=False)
self.features_47_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_47_conv_3 = torch.nn.modules.conv.Conv2d(1824, 1824, 3, 1, 1, groups=1824, bias=False)
self.features_47_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
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(1824, 80)
self.features_47_conv_6_fc_2 = torch.nn.modules.linear.Linear(80, 1824)
self.features_47_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_47_conv_7 = torch.nn.modules.conv.Conv2d(1824, 304, 1, 1, 0, bias=False)
self.features_47_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(304)
self.features_48_conv_0 = torch.nn.modules.conv.Conv2d(304, 1824, 1, 1, 0, bias=False)
self.features_48_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_48_conv_3 = torch.nn.modules.conv.Conv2d(1824, 1824, 3, 1, 1, groups=1824, bias=False)
self.features_48_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
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(1824, 80)
self.features_48_conv_6_fc_2 = torch.nn.modules.linear.Linear(80, 1824)
self.features_48_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_48_conv_7 = torch.nn.modules.conv.Conv2d(1824, 304, 1, 1, 0, bias=False)
self.features_48_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(304)
self.features_49_conv_0 = torch.nn.modules.conv.Conv2d(304, 1824, 1, 1, 0, bias=False)
self.features_49_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_49_conv_3 = torch.nn.modules.conv.Conv2d(1824, 1824, 3, 1, 1, groups=1824, bias=False)
self.features_49_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
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(1824, 80)
self.features_49_conv_6_fc_2 = torch.nn.modules.linear.Linear(80, 1824)
self.features_49_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_49_conv_7 = torch.nn.modules.conv.Conv2d(1824, 304, 1, 1, 0, bias=False)
self.features_49_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(304)
self.features_50_conv_0 = torch.nn.modules.conv.Conv2d(304, 1824, 1, 1, 0, bias=False)
self.features_50_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_50_conv_3 = torch.nn.modules.conv.Conv2d(1824, 1824, 3, 1, 1, groups=1824, bias=False)
self.features_50_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
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(1824, 80)
self.features_50_conv_6_fc_2 = torch.nn.modules.linear.Linear(80, 1824)
self.features_50_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_50_conv_7 = torch.nn.modules.conv.Conv2d(1824, 304, 1, 1, 0, bias=False)
self.features_50_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(304)
self.features_51_conv_0 = torch.nn.modules.conv.Conv2d(304, 1824, 1, 1, 0, bias=False)
self.features_51_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_51_conv_3 = torch.nn.modules.conv.Conv2d(1824, 1824, 3, 1, 1, groups=1824, bias=False)
self.features_51_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
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(1824, 80)
self.features_51_conv_6_fc_2 = torch.nn.modules.linear.Linear(80, 1824)
self.features_51_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_51_conv_7 = torch.nn.modules.conv.Conv2d(1824, 304, 1, 1, 0, bias=False)
self.features_51_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(304)
self.features_52_conv_0 = torch.nn.modules.conv.Conv2d(304, 1824, 1, 1, 0, bias=False)
self.features_52_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_52_conv_3 = torch.nn.modules.conv.Conv2d(1824, 1824, 3, 1, 1, groups=1824, bias=False)
self.features_52_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
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(1824, 80)
self.features_52_conv_6_fc_2 = torch.nn.modules.linear.Linear(80, 1824)
self.features_52_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_52_conv_7 = torch.nn.modules.conv.Conv2d(1824, 304, 1, 1, 0, bias=False)
self.features_52_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(304)
self.features_53_conv_0 = torch.nn.modules.conv.Conv2d(304, 1824, 1, 1, 0, bias=False)
self.features_53_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_53_conv_3 = torch.nn.modules.conv.Conv2d(1824, 1824, 3, 1, 1, groups=1824, bias=False)
self.features_53_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
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(1824, 80)
self.features_53_conv_6_fc_2 = torch.nn.modules.linear.Linear(80, 1824)
self.features_53_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_53_conv_7 = torch.nn.modules.conv.Conv2d(1824, 512, 1, 1, 0, bias=False)
self.features_53_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_54_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_54_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_54_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_54_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
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(3072, 128)
self.features_54_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_54_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_54_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_54_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_55_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_55_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_55_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_55_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
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(3072, 128)
self.features_55_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_55_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_55_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_55_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_56_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_56_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_56_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_56_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
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(3072, 128)
self.features_56_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_56_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_56_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_56_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_57_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_57_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_57_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_57_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
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(3072, 128)
self.features_57_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_57_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_57_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_57_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.conv_0 = torch.nn.modules.conv.Conv2d(512, 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)