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DeepSoft-AI

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deepdetect

like 0

Sleeping

• "torch.nn.modules.activation.Sigmoid",
• "_codecs.encode",
• "models.rawnet.SincConv",
• "numpy.core.multiarray._reconstruct",
• "torch.LongStorage",
• "timm.models.efficientnet.EfficientNet",
• "torch.nn.modules.linear.Linear",
• "argparse.Namespace",
• "torch._utils._rebuild_tensor_v2",
• "torch.nn.modules.activation.LeakyReLU",
• "torch.nn.modules.activation.LogSoftmax",
• "__builtin__.set",
• "models.rawnet.Residual_block",
• "torch.nn.modules.dropout.Dropout",
• "torch.nn.modules.activation.SELU",
• "torch.nn.modules.linear.Identity",
• "torch.nn.modules.conv.Conv1d",
• "torch.nn.modules.container.Sequential",
• "numpy.core.multiarray.scalar",
• "torch.nn.modules.conv.Conv2d",
• "torch.nn.modules.activation.SiLU",
• "timm.layers.drop.DropPath",
• "torch.nn.modules.pooling.AdaptiveAvgPool2d",
• "torch.nn.modules.batchnorm.BatchNorm1d",
• "models.image.RawNet",
• "torch.nn.modules.pooling.AdaptiveAvgPool1d",
• "torch._utils._rebuild_parameter",
• "timm.models._efficientnet_blocks.SqueezeExcite",
• "timm.layers.conv2d_same.Conv2dSame",
• "timm.models._efficientnet_blocks.DepthwiseSeparableConv",
• "timm.layers.norm_act.BatchNormAct2d",
• "numpy.ndarray",
• "torch.nn.modules.pooling.MaxPool1d",
• "torch.nn.modules.flatten.Flatten",
• "numpy.dtype",
• "torch.FloatStorage",
• "timm.models._efficientnet_blocks.InvertedResidual",
• "timm.layers.adaptive_avgmax_pool.SelectAdaptivePool2d",
• "torch.nn.modules.rnn.GRU",
• "models.image.ImageEncoder",
• "collections.OrderedDict",
• "models.classifiers.DeepFakeClassifier"

How to fix it?

• "torch.nn.modules.conv.Conv2d",
• "timm.layers.drop.DropPath",
• "timm.models._efficientnet_blocks.InvertedResidual",
• "torch.nn.modules.activation.SELU",
• "__builtin__.set",
• "timm.models.efficientnet.EfficientNet",
• "torch.nn.modules.pooling.MaxPool1d",
• "models.TMC.ETMC",
• "torch.nn.modules.linear.Identity",
• "timm.models._efficientnet_blocks.SqueezeExcite",
• "timm.models._efficientnet_blocks.DepthwiseSeparableConv",
• "models.classifiers.DeepFakeClassifier",
• "numpy.core.multiarray.scalar",
• "numpy.core.multiarray._reconstruct",
• "torch.nn.modules.conv.Conv1d",
• "torch.LongStorage",
• "torch.nn.modules.container.Sequential",
• "torch.nn.modules.pooling.AdaptiveAvgPool2d",
• "models.rawnet.SincConv",
• "numpy.dtype",
• "torch.nn.modules.activation.Sigmoid",
• "timm.layers.adaptive_avgmax_pool.SelectAdaptivePool2d",
• "torch.nn.modules.activation.SiLU",
• "torch.nn.modules.activation.LeakyReLU",
• "torch.nn.modules.pooling.AdaptiveAvgPool1d",
• "torch.nn.modules.flatten.Flatten",
• "_codecs.encode",
• "numpy.ndarray",
• "torch.nn.modules.rnn.GRU",
• "torch.nn.modules.batchnorm.BatchNorm1d",
• "torch.nn.modules.dropout.Dropout",
• "collections.OrderedDict",
• "models.rawnet.Residual_block",
• "torch.FloatStorage",
• "timm.layers.conv2d_same.Conv2dSame",
• "models.image.RawNet",
• "torch.nn.modules.linear.Linear",
• "torch.nn.modules.activation.LogSoftmax",
• "models.image.ImageEncoder",
• "torch._utils._rebuild_tensor_v2",
• "timm.layers.norm_act.BatchNormAct2d",
• "torch._utils._rebuild_parameter",
• "argparse.Namespace",
• "torch.nn.modules.container.ModuleList"

How to fix it?