deb87d8ce3
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
21 lines
923 B
Python
21 lines
923 B
Python
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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"""Activation modules."""
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import torch
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import torch.nn as nn
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class AGLU(nn.Module):
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"""Unified activation function module from https://github.com/kostas1515/AGLU."""
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def __init__(self, device=None, dtype=None) -> None:
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"""Initialize the Unified activation function."""
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super().__init__()
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self.act = nn.Softplus(beta=-1.0)
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self.lambd = nn.Parameter(nn.init.uniform_(torch.empty(1, device=device, dtype=dtype))) # lambda parameter
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self.kappa = nn.Parameter(nn.init.uniform_(torch.empty(1, device=device, dtype=dtype))) # kappa parameter
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""Compute the forward pass of the Unified activation function."""
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lam = torch.clamp(self.lambd, min=0.0001)
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return torch.exp((1 / lam) * self.act((self.kappa * x) - torch.log(lam)))
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