in models/efficientnet_v2_s.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, 96, 3, 2, 1, bias=False)
self.features_3_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(96)
self.features_3_conv_3 = torch.nn.modules.conv.Conv2d(96, 48, 1, 1, 0, bias=False)
self.features_3_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(48)
self.features_4_conv_0 = torch.nn.modules.conv.Conv2d(48, 192, 3, 1, 1, bias=False)
self.features_4_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_4_conv_3 = torch.nn.modules.conv.Conv2d(192, 48, 1, 1, 0, bias=False)
self.features_4_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(48)
self.features_5_conv_0 = torch.nn.modules.conv.Conv2d(48, 192, 3, 1, 1, bias=False)
self.features_5_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_5_conv_3 = torch.nn.modules.conv.Conv2d(192, 48, 1, 1, 0, bias=False)
self.features_5_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(48)
self.features_6_conv_0 = torch.nn.modules.conv.Conv2d(48, 192, 3, 1, 1, bias=False)
self.features_6_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_6_conv_3 = torch.nn.modules.conv.Conv2d(192, 48, 1, 1, 0, bias=False)
self.features_6_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(48)
self.features_7_conv_0 = torch.nn.modules.conv.Conv2d(48, 192, 3, 2, 1, bias=False)
self.features_7_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_7_conv_3 = torch.nn.modules.conv.Conv2d(192, 64, 1, 1, 0, bias=False)
self.features_7_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(64)
self.features_8_conv_0 = torch.nn.modules.conv.Conv2d(64, 256, 3, 1, 1, bias=False)
self.features_8_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_8_conv_3 = torch.nn.modules.conv.Conv2d(256, 64, 1, 1, 0, bias=False)
self.features_8_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(64)
self.features_9_conv_0 = torch.nn.modules.conv.Conv2d(64, 256, 3, 1, 1, bias=False)
self.features_9_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_9_conv_3 = torch.nn.modules.conv.Conv2d(256, 64, 1, 1, 0, bias=False)
self.features_9_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(64)
self.features_10_conv_0 = torch.nn.modules.conv.Conv2d(64, 256, 3, 1, 1, bias=False)
self.features_10_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_10_conv_3 = torch.nn.modules.conv.Conv2d(256, 64, 1, 1, 0, bias=False)
self.features_10_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(64)
self.features_11_conv_0 = torch.nn.modules.conv.Conv2d(64, 256, 1, 1, 0, bias=False)
self.features_11_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_11_conv_3 = torch.nn.modules.conv.Conv2d(256, 256, 3, 2, 1, groups=256, bias=False)
self.features_11_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_11_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_11_conv_6_fc_0 = torch.nn.modules.linear.Linear(256, 16)
self.features_11_conv_6_fc_2 = torch.nn.modules.linear.Linear(16, 256)
self.features_11_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_11_conv_7 = torch.nn.modules.conv.Conv2d(256, 128, 1, 1, 0, bias=False)
self.features_11_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(128)
self.features_12_conv_0 = torch.nn.modules.conv.Conv2d(128, 512, 1, 1, 0, bias=False)
self.features_12_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_12_conv_3 = torch.nn.modules.conv.Conv2d(512, 512, 3, 1, 1, groups=512, bias=False)
self.features_12_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_12_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_12_conv_6_fc_0 = torch.nn.modules.linear.Linear(512, 32)
self.features_12_conv_6_fc_2 = torch.nn.modules.linear.Linear(32, 512)
self.features_12_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_12_conv_7 = torch.nn.modules.conv.Conv2d(512, 128, 1, 1, 0, bias=False)
self.features_12_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(128)
self.features_13_conv_0 = torch.nn.modules.conv.Conv2d(128, 512, 1, 1, 0, bias=False)
self.features_13_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_13_conv_3 = torch.nn.modules.conv.Conv2d(512, 512, 3, 1, 1, groups=512, bias=False)
self.features_13_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_13_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_13_conv_6_fc_0 = torch.nn.modules.linear.Linear(512, 32)
self.features_13_conv_6_fc_2 = torch.nn.modules.linear.Linear(32, 512)
self.features_13_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_13_conv_7 = torch.nn.modules.conv.Conv2d(512, 128, 1, 1, 0, bias=False)
self.features_13_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(128)
self.features_14_conv_0 = torch.nn.modules.conv.Conv2d(128, 512, 1, 1, 0, bias=False)
self.features_14_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_14_conv_3 = torch.nn.modules.conv.Conv2d(512, 512, 3, 1, 1, groups=512, bias=False)
self.features_14_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_14_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_14_conv_6_fc_0 = torch.nn.modules.linear.Linear(512, 32)
self.features_14_conv_6_fc_2 = torch.nn.modules.linear.Linear(32, 512)
self.features_14_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_14_conv_7 = torch.nn.modules.conv.Conv2d(512, 128, 1, 1, 0, bias=False)
self.features_14_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(128)
self.features_15_conv_0 = torch.nn.modules.conv.Conv2d(128, 512, 1, 1, 0, bias=False)
self.features_15_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_15_conv_3 = torch.nn.modules.conv.Conv2d(512, 512, 3, 1, 1, groups=512, bias=False)
self.features_15_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_15_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_15_conv_6_fc_0 = torch.nn.modules.linear.Linear(512, 32)
self.features_15_conv_6_fc_2 = torch.nn.modules.linear.Linear(32, 512)
self.features_15_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_15_conv_7 = torch.nn.modules.conv.Conv2d(512, 128, 1, 1, 0, bias=False)
self.features_15_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(128)
self.features_16_conv_0 = torch.nn.modules.conv.Conv2d(128, 512, 1, 1, 0, bias=False)
self.features_16_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_16_conv_3 = torch.nn.modules.conv.Conv2d(512, 512, 3, 1, 1, groups=512, bias=False)
self.features_16_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_16_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_16_conv_6_fc_0 = torch.nn.modules.linear.Linear(512, 32)
self.features_16_conv_6_fc_2 = torch.nn.modules.linear.Linear(32, 512)
self.features_16_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_16_conv_7 = torch.nn.modules.conv.Conv2d(512, 128, 1, 1, 0, bias=False)
self.features_16_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(128)
self.features_17_conv_0 = torch.nn.modules.conv.Conv2d(128, 768, 1, 1, 0, bias=False)
self.features_17_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_17_conv_3 = torch.nn.modules.conv.Conv2d(768, 768, 3, 1, 1, groups=768, bias=False)
self.features_17_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_17_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_17_conv_6_fc_0 = torch.nn.modules.linear.Linear(768, 32)
self.features_17_conv_6_fc_2 = torch.nn.modules.linear.Linear(32, 768)
self.features_17_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_17_conv_7 = torch.nn.modules.conv.Conv2d(768, 160, 1, 1, 0, bias=False)
self.features_17_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(160)
self.features_18_conv_0 = torch.nn.modules.conv.Conv2d(160, 960, 1, 1, 0, bias=False)
self.features_18_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(960)
self.features_18_conv_3 = torch.nn.modules.conv.Conv2d(960, 960, 3, 1, 1, groups=960, bias=False)
self.features_18_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(960)
self.features_18_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_18_conv_6_fc_0 = torch.nn.modules.linear.Linear(960, 40)
self.features_18_conv_6_fc_2 = torch.nn.modules.linear.Linear(40, 960)
self.features_18_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_18_conv_7 = torch.nn.modules.conv.Conv2d(960, 160, 1, 1, 0, bias=False)
self.features_18_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(160)
self.features_19_conv_0 = torch.nn.modules.conv.Conv2d(160, 960, 1, 1, 0, bias=False)
self.features_19_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(960)
self.features_19_conv_3 = torch.nn.modules.conv.Conv2d(960, 960, 3, 1, 1, groups=960, bias=False)
self.features_19_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(960)
self.features_19_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_19_conv_6_fc_0 = torch.nn.modules.linear.Linear(960, 40)
self.features_19_conv_6_fc_2 = torch.nn.modules.linear.Linear(40, 960)
self.features_19_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_19_conv_7 = torch.nn.modules.conv.Conv2d(960, 160, 1, 1, 0, bias=False)
self.features_19_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(160)
self.features_20_conv_0 = torch.nn.modules.conv.Conv2d(160, 960, 1, 1, 0, bias=False)
self.features_20_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(960)
self.features_20_conv_3 = torch.nn.modules.conv.Conv2d(960, 960, 3, 1, 1, groups=960, bias=False)
self.features_20_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(960)
self.features_20_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_20_conv_6_fc_0 = torch.nn.modules.linear.Linear(960, 40)
self.features_20_conv_6_fc_2 = torch.nn.modules.linear.Linear(40, 960)
self.features_20_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_20_conv_7 = torch.nn.modules.conv.Conv2d(960, 160, 1, 1, 0, bias=False)
self.features_20_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(160)
self.features_21_conv_0 = torch.nn.modules.conv.Conv2d(160, 960, 1, 1, 0, bias=False)
self.features_21_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(960)
self.features_21_conv_3 = torch.nn.modules.conv.Conv2d(960, 960, 3, 1, 1, groups=960, bias=False)
self.features_21_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(960)
self.features_21_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_21_conv_6_fc_0 = torch.nn.modules.linear.Linear(960, 40)
self.features_21_conv_6_fc_2 = torch.nn.modules.linear.Linear(40, 960)
self.features_21_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_21_conv_7 = torch.nn.modules.conv.Conv2d(960, 160, 1, 1, 0, bias=False)
self.features_21_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(160)
self.features_22_conv_0 = torch.nn.modules.conv.Conv2d(160, 960, 1, 1, 0, bias=False)
self.features_22_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(960)
self.features_22_conv_3 = torch.nn.modules.conv.Conv2d(960, 960, 3, 1, 1, groups=960, bias=False)
self.features_22_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(960)
self.features_22_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_22_conv_6_fc_0 = torch.nn.modules.linear.Linear(960, 40)
self.features_22_conv_6_fc_2 = torch.nn.modules.linear.Linear(40, 960)
self.features_22_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_22_conv_7 = torch.nn.modules.conv.Conv2d(960, 160, 1, 1, 0, bias=False)
self.features_22_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(160)
self.features_23_conv_0 = torch.nn.modules.conv.Conv2d(160, 960, 1, 1, 0, bias=False)
self.features_23_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(960)
self.features_23_conv_3 = torch.nn.modules.conv.Conv2d(960, 960, 3, 1, 1, groups=960, bias=False)
self.features_23_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(960)
self.features_23_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_23_conv_6_fc_0 = torch.nn.modules.linear.Linear(960, 40)
self.features_23_conv_6_fc_2 = torch.nn.modules.linear.Linear(40, 960)
self.features_23_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_23_conv_7 = torch.nn.modules.conv.Conv2d(960, 160, 1, 1, 0, bias=False)
self.features_23_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(160)
self.features_24_conv_0 = torch.nn.modules.conv.Conv2d(160, 960, 1, 1, 0, bias=False)
self.features_24_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(960)
self.features_24_conv_3 = torch.nn.modules.conv.Conv2d(960, 960, 3, 1, 1, groups=960, bias=False)
self.features_24_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(960)
self.features_24_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_24_conv_6_fc_0 = torch.nn.modules.linear.Linear(960, 40)
self.features_24_conv_6_fc_2 = torch.nn.modules.linear.Linear(40, 960)
self.features_24_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_24_conv_7 = torch.nn.modules.conv.Conv2d(960, 160, 1, 1, 0, bias=False)
self.features_24_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(160)
self.features_25_conv_0 = torch.nn.modules.conv.Conv2d(160, 960, 1, 1, 0, bias=False)
self.features_25_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(960)
self.features_25_conv_3 = torch.nn.modules.conv.Conv2d(960, 960, 3, 1, 1, groups=960, bias=False)
self.features_25_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(960)
self.features_25_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_25_conv_6_fc_0 = torch.nn.modules.linear.Linear(960, 40)
self.features_25_conv_6_fc_2 = torch.nn.modules.linear.Linear(40, 960)
self.features_25_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_25_conv_7 = torch.nn.modules.conv.Conv2d(960, 160, 1, 1, 0, bias=False)
self.features_25_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(160)
self.features_26_conv_0 = torch.nn.modules.conv.Conv2d(160, 960, 1, 1, 0, bias=False)
self.features_26_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(960)
self.features_26_conv_3 = torch.nn.modules.conv.Conv2d(960, 960, 3, 2, 1, groups=960, bias=False)
self.features_26_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(960)
self.features_26_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_26_conv_6_fc_0 = torch.nn.modules.linear.Linear(960, 40)
self.features_26_conv_6_fc_2 = torch.nn.modules.linear.Linear(40, 960)
self.features_26_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_26_conv_7 = torch.nn.modules.conv.Conv2d(960, 256, 1, 1, 0, bias=False)
self.features_26_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_27_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_27_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_27_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_27_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_27_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_27_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64)
self.features_27_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_27_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_27_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_27_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_28_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_28_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_28_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_28_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_28_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_28_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64)
self.features_28_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_28_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_28_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_28_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_29_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_29_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_29_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_29_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_29_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_29_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64)
self.features_29_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_29_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_29_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_29_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_30_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_30_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_30_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_30_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_30_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_30_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64)
self.features_30_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_30_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_30_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_30_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_31_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_31_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_31_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_31_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_31_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_31_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64)
self.features_31_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_31_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_31_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_31_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_32_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_32_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_32_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_32_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_32_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_32_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64)
self.features_32_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_32_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_32_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_32_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_33_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_33_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_33_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_33_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_33_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_33_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64)
self.features_33_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_33_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_33_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_33_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_34_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_34_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_34_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_34_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_34_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_34_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64)
self.features_34_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_34_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_34_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_34_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_35_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_35_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_35_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_35_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_35_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_35_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64)
self.features_35_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_35_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_35_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_35_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_36_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_36_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_36_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_36_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_36_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_36_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64)
self.features_36_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_36_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_36_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_36_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_37_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_37_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_37_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_37_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_37_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_37_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64)
self.features_37_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_37_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_37_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_37_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_38_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_38_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_38_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_38_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_38_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_38_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64)
self.features_38_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_38_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_38_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_38_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_39_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_39_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_39_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_39_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_39_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_39_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64)
self.features_39_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_39_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_39_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_39_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_40_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_40_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_40_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_40_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_40_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_40_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64)
self.features_40_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_40_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_40_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_40_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.conv_0 = torch.nn.modules.conv.Conv2d(256, 1792, 1, 1, 0, bias=False)
self.conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1792)
self.avgpool = torch.nn.modules.pooling.AdaptiveAvgPool2d((1, 1))
self.classifier = torch.nn.modules.linear.Linear(1792, 1000)