hFSrSSKrSSKJr "SS\R5rg)zActivation modules.Ncn^\rSrSrSrSS U4SjjjrS\RS\R4SjrSr U=r $) AGLUa Unified activation function module from AGLU. This class implements a parameterized activation function with learnable parameters lambda and kappa, based on the AGLU (Adaptive Gated Linear Unit) approach. Attributes: act (nn.Softplus): Softplus activation function with negative beta. lambd (nn.Parameter): Learnable lambda parameter initialized with uniform distribution. kappa (nn.Parameter): Learnable kappa parameter initialized with uniform distribution. Methods: forward: Compute the forward pass of the Unified activation function. Examples: >>> import torch >>> m = AGLU() >>> input = torch.randn(2) >>> output = m(input) >>> print(output.shape) torch.Size([2]) References: https://github.com/kostas1515/AGLU returnc >[TU]5 [R"SS9Ul[R "[R R[R"SXS955Ul [R "[R R[R"SXS955Ul g)zEInitialize the Unified activation function with learnable parameters.g)beta)devicedtypeN) super__init__nnSoftplusact Parameterinituniform_torchemptylambdkappa)selfr r __class__s [/home/james-whalen/.local/lib/python3.13/site-packages/ultralytics/nn/modules/activation.pyr AGLU.__init__#sr ;;D)\\"''"2"25;;q3]"^_ \\"''"2"25;;q3]"^_ xc [R"URSS9n[R"SU- UR UR U-[R "U5- 5-5$)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 )rclamprexprrlog)rrlams rforward AGLU.forward*sNkk$**&1yy!c'TXXtzzA~3.O%PPQQr)rrr)NN)rN) __name__ __module__ __qualname____firstlineno____doc__r rTensorr$__static_attributes__ __classcell__)rs@rrrs84``RR%,,RRrr)r*rtorch.nnrModulerrrr1s  0R2990Rr