models/efficientnet_v2_s.py [323:353]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 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) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - models/efficientnet_v2_xl.py [263:293]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 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) - 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