Source code for monai.networks.blocks.activation

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import torch
from torch import nn


[docs]class Swish(nn.Module): r"""Applies the element-wise function: .. math:: \text{Swish}(x) = x * \text{Sigmoid}(\alpha * x) for constant value alpha. Citation: Searching for Activation Functions, Ramachandran et al., 2017, https://arxiv.org/abs/1710.05941. Shape: - Input: :math:`(N, *)` where `*` means, any number of additional dimensions - Output: :math:`(N, *)`, same shape as the input Examples:: >>> m = Act['swish']() >>> input = torch.randn(2) >>> output = m(input) """ def __init__(self, alpha=1.0): super().__init__() self.alpha = alpha
[docs] def forward(self, input: torch.Tensor) -> torch.Tensor: return input * torch.sigmoid(self.alpha * input)
[docs]class Mish(nn.Module): r"""Applies the element-wise function: .. math:: \text{Mish}(x) = x * tanh(\text{softplus}(x)). Citation: Mish: A Self Regularized Non-Monotonic Activation Function, Diganta Misra, 2019, https://arxiv.org/abs/1908.08681. Shape: - Input: :math:`(N, *)` where `*` means, any number of additional dimensions - Output: :math:`(N, *)`, same shape as the input Examples:: >>> m = Act['mish']() >>> input = torch.randn(2) >>> output = m(input) """
[docs] def forward(self, input: torch.Tensor) -> torch.Tensor: return input * torch.tanh(torch.nn.functional.softplus(input))