57 lines
2.2 KiB
Python
57 lines
2.2 KiB
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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"""
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Unified activation function module from AGLU.
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This class implements a parameterized activation function with learnable parameters lambda and kappa, based on the
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AGLU (Adaptive Gated Linear Unit) approach.
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Attributes:
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act (nn.Softplus): Softplus activation function with negative beta.
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lambd (nn.Parameter): Learnable lambda parameter initialized with uniform distribution.
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kappa (nn.Parameter): Learnable kappa parameter initialized with uniform distribution.
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Methods:
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forward: Compute the forward pass of the Unified activation function.
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Examples:
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>>> import torch
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>>> m = AGLU()
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>>> input = torch.randn(2)
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>>> output = m(input)
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>>> print(output.shape)
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torch.Size([2])
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References:
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https://github.com/kostas1515/AGLU
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"""
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def __init__(self, device=None, dtype=None) -> None:
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"""Initialize the Unified activation function with learnable parameters."""
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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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"""
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Apply the Adaptive Gated Linear Unit (AGLU) activation function.
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This forward method implements the AGLU activation function with learnable parameters lambda and kappa.
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The function applies a transformation that adaptively combines linear and non-linear components.
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Args:
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x (torch.Tensor): Input tensor to apply the activation function to.
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Returns:
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(torch.Tensor): Output tensor after applying the AGLU activation function, with the same shape as the input.
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"""
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lam = torch.clamp(self.lambd, min=0.0001) # Clamp lambda to avoid division by zero
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return torch.exp((1 / lam) * self.act((self.kappa * x) - torch.log(lam)))
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