o W h@s.dZddlZddlmZGdddejZdS)zActivation modules.Ncs:eZdZdZd d fdd ZdejdejfddZZS) AGLUa4 Unified activation function module from AGLU. This class implements a parameterized activation function with learnable parameters lambda and kappa, based on the AGLU (Adaptive Gated Linear Unit) approach (https://github.com/kostas1515/AGLU). Attributes: act (nn.Softplus): Softplus activation function with negative beta. lambd (nn.Parameter): Learnable lambda parameter initialized with uniform distribution. kappa (nn.Parameter): Learnable kappa parameter initialized with uniform distribution. Methods: forward: Compute the forward pass of the Unified activation function. Examples: >>> import torch >>> m = AGLU() >>> input = torch.randn(2) >>> output = m(input) >>> print(output.shape) torch.Size([2]) Nreturnc s\ttjdd|_ttjtj d||d|_ ttjtj d||d|_ dS)zEInitialize the Unified activation function with learnable parameters.g)beta)devicedtypeN) super__init__nnSoftplusact Parameterinituniform_torchemptylambdkappa)selfrr __class__U/var/www/vscode/kcb/lib/python3.10/site-packages/ultralytics/nn/modules/activation.pyr s  $z AGLU.__init__xc Cs8tj|jdd}td|||j|t|S)a Apply the Adaptive Gated Linear Unit (AGLU) activation function. This forward method implements the AGLU activation function with learnable parameters lambda and kappa. The function applies a transformation that adaptively combines linear and non-linear components. Args: x (torch.Tensor): Input tensor to apply the activation function to. Returns: (torch.Tensor): Output tensor after applying the AGLU activation function, with the same shape as the input. g-C6?)minr)rclamprexpr rlog)rrlamrrrforward's (z AGLU.forward)NN)rN) __name__ __module__ __qualname____doc__r rTensorr __classcell__rrrrrsr)r#rtorch.nnr Modulerrrrrs