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""" Activations (jit) | |
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. | |
Copyright 2020 Ross Wightman | |
""" | |
import torch | |
from torch import nn as nn | |
from torch.nn import functional as F | |
__all__ = ['swish_jit', 'SwishJit', 'mish_jit', 'MishJit', | |
'hard_sigmoid_jit', 'HardSigmoidJit', 'hard_swish_jit', 'HardSwishJit'] | |
def swish_jit(x, inplace: bool = False): | |
"""Swish - Described originally as SiLU (https://arxiv.org/abs/1702.03118v3) | |
and also as Swish (https://arxiv.org/abs/1710.05941). | |
TODO Rename to SiLU with addition to PyTorch | |
""" | |
return x.mul(x.sigmoid()) | |
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) | |
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) | |
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) | |