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160 lines
4.4 KiB
160 lines
4.4 KiB
""" Activations
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A collection of activations fn and modules with a common interface so that they can
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easily be swapped. All have an `inplace` arg even if not used.
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Hacked together by / Copyright 2020 Ross Wightman
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"""
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import torch
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from torch import nn as nn
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from torch.nn import functional as F
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def swish(x, inplace: bool = False):
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"""Swish - Described in: https://arxiv.org/abs/1710.05941
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"""
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return x.mul_(x.sigmoid()) if inplace else x.mul(x.sigmoid())
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class Swish(nn.Module):
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def __init__(self, inplace: bool = False):
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super(Swish, self).__init__()
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self.inplace = inplace
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def forward(self, x):
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return swish(x, self.inplace)
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def mish(x, inplace: bool = False):
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"""Mish: A Self Regularized Non-Monotonic Neural Activation Function - https://arxiv.org/abs/1908.08681
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NOTE: I don't have a working inplace variant
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"""
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return x.mul(F.softplus(x).tanh())
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class Mish(nn.Module):
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"""Mish: A Self Regularized Non-Monotonic Neural Activation Function - https://arxiv.org/abs/1908.08681
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"""
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def __init__(self, inplace: bool = False):
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super(Mish, self).__init__()
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def forward(self, x):
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return mish(x)
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def sigmoid(x, inplace: bool = False):
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return x.sigmoid_() if inplace else x.sigmoid()
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# PyTorch has this, but not with a consistent inplace argmument interface
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class Sigmoid(nn.Module):
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def __init__(self, inplace: bool = False):
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super(Sigmoid, self).__init__()
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self.inplace = inplace
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def forward(self, x):
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return x.sigmoid_() if self.inplace else x.sigmoid()
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def tanh(x, inplace: bool = False):
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return x.tanh_() if inplace else x.tanh()
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# PyTorch has this, but not with a consistent inplace argmument interface
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class Tanh(nn.Module):
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def __init__(self, inplace: bool = False):
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super(Tanh, self).__init__()
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self.inplace = inplace
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def forward(self, x):
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return x.tanh_() if self.inplace else x.tanh()
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def hard_swish(x, inplace: bool = False):
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inner = F.relu6(x + 3.).div_(6.)
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return x.mul_(inner) if inplace else x.mul(inner)
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class HardSwish(nn.Module):
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def __init__(self, inplace: bool = False):
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super(HardSwish, self).__init__()
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self.inplace = inplace
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def forward(self, x):
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return hard_swish(x, self.inplace)
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def hard_sigmoid(x, inplace: bool = False):
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if inplace:
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return x.add_(3.).clamp_(0., 6.).div_(6.)
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else:
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return F.relu6(x + 3.) / 6.
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class HardSigmoid(nn.Module):
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def __init__(self, inplace: bool = False):
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super(HardSigmoid, self).__init__()
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self.inplace = inplace
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def forward(self, x):
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return hard_sigmoid(x, self.inplace)
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def hard_mish(x, inplace: bool = False):
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""" Hard Mish
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Experimental, based on notes by Mish author Diganta Misra at
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https://github.com/digantamisra98/H-Mish/blob/0da20d4bc58e696b6803f2523c58d3c8a82782d0/README.md
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"""
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if inplace:
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return x.mul_(0.5 * (x + 2).clamp(min=0, max=2))
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else:
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return 0.5 * x * (x + 2).clamp(min=0, max=2)
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class HardMish(nn.Module):
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def __init__(self, inplace: bool = False):
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super(HardMish, self).__init__()
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self.inplace = inplace
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def forward(self, x):
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return hard_mish(x, self.inplace)
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class PReLU(nn.PReLU):
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"""Applies PReLU (w/ dummy inplace arg)
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"""
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def __init__(self, num_parameters: int = 1, init: float = 0.25, inplace: bool = False) -> None:
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super(PReLU, self).__init__(num_parameters=num_parameters, init=init)
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def forward(self, input: torch.Tensor) -> torch.Tensor:
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return F.prelu(input, self.weight)
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def gelu(x: torch.Tensor, inplace: bool = False) -> torch.Tensor:
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return F.gelu(x)
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class GELU(nn.Module):
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"""Applies the Gaussian Error Linear Units function (w/ dummy inplace arg)
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"""
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def __init__(self, inplace: bool = False):
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super(GELU, self).__init__()
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def forward(self, input: torch.Tensor) -> torch.Tensor:
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return F.gelu(input)
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def gelu_tanh(x: torch.Tensor, inplace: bool = False) -> torch.Tensor:
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return F.gelu(x, approximate='tanh')
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class GELUTanh(nn.Module):
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"""Applies the Gaussian Error Linear Units function (w/ dummy inplace arg)
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"""
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def __init__(self, inplace: bool = False):
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super(GELUTanh, self).__init__()
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def forward(self, input: torch.Tensor) -> torch.Tensor:
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return F.gelu(input, approximate='tanh')
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