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""" Activations
A collection of jit-scripted activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
All jit scripted activations are lacking in-place variations on purpose, scripted kernel fusion does not
currently work across in-place op boundaries, thus performance is equal to or less than the non-scripted
versions if they contain in-place ops.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from torch.nn import functional as F
@torch.jit.script
def swish_jit(x, inplace: bool = False):
"""Swish - Described in: https://arxiv.org/abs/1710.05941
"""
return x.mul(x.sigmoid())
@torch.jit.script
def mish_jit(x, _inplace: bool = False):
"""Mish: A Self Regularized Non-Monotonic Neural Activation Function - https://arxiv.org/abs/1908.08681
"""
return x.mul(F.softplus(x).tanh())
class SwishJit(nn.Module):
def __init__(self, inplace: bool = False):
super(SwishJit, self).__init__()
def forward(self, x):
return swish_jit(x)
class MishJit(nn.Module):
def __init__(self, inplace: bool = False):
super(MishJit, self).__init__()
def forward(self, x):
return mish_jit(x)
@torch.jit.script
def hard_sigmoid_jit(x, inplace: bool = False):
# return F.relu6(x + 3.) / 6.
return (x + 3).clamp(min=0, max=6).div(6.) # clamp seems ever so slightly faster?
class HardSigmoidJit(nn.Module):
def __init__(self, inplace: bool = False):
super(HardSigmoidJit, self).__init__()
def forward(self, x):
return hard_sigmoid_jit(x)
@torch.jit.script
def hard_swish_jit(x, inplace: bool = False):
# return x * (F.relu6(x + 3.) / 6)
return x * (x + 3).clamp(min=0, max=6).div(6.) # clamp seems ever so slightly faster?
class HardSwishJit(nn.Module):
def __init__(self, inplace: bool = False):
super(HardSwishJit, self).__init__()
def forward(self, x):
return hard_swish_jit(x)
@torch.jit.script
def hard_mish_jit(x, inplace: bool = False):
""" Hard Mish
Experimental, based on notes by Mish author Diganta Misra at
https://github.com/digantamisra98/H-Mish/blob/0da20d4bc58e696b6803f2523c58d3c8a82782d0/README.md
"""
return 0.5 * x * (x + 2).clamp(min=0, max=2)
class HardMishJit(nn.Module):
def __init__(self, inplace: bool = False):
super(HardMishJit, self).__init__()
def forward(self, x):
return hard_mish_jit(x)
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