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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""Activation modules."""
import torch
import torch.nn as nn
class AGLU(nn.Module):
"""
Unified activation function module from https://github.com/kostas1515/AGLU.
This class implements a parameterized activation function with learnable parameters lambda and kappa.
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.
"""
def __init__(self, device=None, dtype=None) -> None:
"""Initialize the Unified activation function with learnable parameters."""
super().__init__()
self.act = nn.Softplus(beta=-1.0)
self.lambd = nn.Parameter(nn.init.uniform_(torch.empty(1, device=device, dtype=dtype))) # lambda parameter
self.kappa = nn.Parameter(nn.init.uniform_(torch.empty(1, device=device, dtype=dtype))) # kappa parameter
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Compute the forward pass of the Unified activation function."""
lam = torch.clamp(self.lambd, min=0.0001) # Clamp lambda to avoid division by zero
return torch.exp((1 / lam) * self.act((self.kappa * x) - torch.log(lam)))
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