39 lines
1.1 KiB
Python
39 lines
1.1 KiB
Python
import torch
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class SoftmaxPower(torch.nn.Module):
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dim: int | None
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power: float
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mean_mode: bool
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def __init__(
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self, power: float = 2.0, dim: int | None = None, mean_mode: bool = False
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) -> None:
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super().__init__()
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self.dim = dim
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self.power = power
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self.mean_mode = mean_mode
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def __setstate__(self, state):
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super().__setstate__(state)
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if not hasattr(self, "dim"):
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self.dim = None
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if not hasattr(self, "power"):
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self.power = 2.0
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if not hasattr(self, "mean_mode"):
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self.mean_mode = False
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def forward(self, input: torch.Tensor) -> torch.Tensor:
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output: torch.Tensor = torch.abs(input).pow(self.power)
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if self.dim is None:
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output = output / output.sum()
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else:
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output = output / output.sum(dim=self.dim, keepdim=True)
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if self.mean_mode:
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return torch.abs(input).mean(dim=1, keepdim=True) * output
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else:
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return input * output
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def extra_repr(self) -> str:
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return f"dim={self.dim} ; power={self.power}"
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