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91 lines
2.5 KiB
91 lines
2.5 KiB
""" Activations
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A collection of jit-scripted 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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All jit scripted activations are lacking in-place variations on purpose, scripted kernel fusion does not
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currently work across in-place op boundaries, thus performance is equal to or less than the non-scripted
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versions if they contain in-place ops.
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Hacked together by 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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@torch.jit.script
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def swish_jit(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())
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@torch.jit.script
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def mish_jit(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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"""
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return x.mul(F.softplus(x).tanh())
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class SwishJit(nn.Module):
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def __init__(self, inplace: bool = False):
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super(SwishJit, self).__init__()
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def forward(self, x):
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return swish_jit(x)
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class MishJit(nn.Module):
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def __init__(self, inplace: bool = False):
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super(MishJit, self).__init__()
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def forward(self, x):
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return mish_jit(x)
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@torch.jit.script
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def hard_sigmoid_jit(x, inplace: bool = False):
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# return F.relu6(x + 3.) / 6.
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return (x + 3).clamp(min=0, max=6).div(6.) # clamp seems ever so slightly faster?
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class HardSigmoidJit(nn.Module):
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def __init__(self, inplace: bool = False):
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super(HardSigmoidJit, self).__init__()
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def forward(self, x):
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return hard_sigmoid_jit(x)
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@torch.jit.script
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def hard_swish_jit(x, inplace: bool = False):
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# return x * (F.relu6(x + 3.) / 6)
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return x * (x + 3).clamp(min=0, max=6).div(6.) # clamp seems ever so slightly faster?
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class HardSwishJit(nn.Module):
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def __init__(self, inplace: bool = False):
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super(HardSwishJit, self).__init__()
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def forward(self, x):
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return hard_swish_jit(x)
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@torch.jit.script
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def hard_mish_jit(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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return 0.5 * x * (x + 2).clamp(min=0, max=2)
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class HardMishJit(nn.Module):
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def __init__(self, inplace: bool = False):
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super(HardMishJit, self).__init__()
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def forward(self, x):
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return hard_mish_jit(x)
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