73 lines
2.2 KiB
Python
73 lines
2.2 KiB
Python
|
# Activation functions
|
||
|
|
|
||
|
|
import torch
|
||
|
|
import torch.nn as nn
|
||
|
|
import torch.nn.functional as F
|
||
|
|
|
||
|
|
|
||
|
|
# SiLU https://arxiv.org/pdf/1905.02244.pdf ----------------------------------------------------------------------------
|
||
|
|
class SiLU(nn.Module): # export-friendly version of nn.SiLU()
|
||
|
|
@staticmethod
|
||
|
|
def forward(x):
|
||
|
|
return x * torch.sigmoid(x)
|
||
|
|
|
||
|
|
|
||
|
|
class Hardswish(nn.Module): # export-friendly version of nn.Hardswish()
|
||
|
|
@staticmethod
|
||
|
|
def forward(x):
|
||
|
|
# return x * F.hardsigmoid(x) # for torchscript and CoreML
|
||
|
|
return x * F.hardtanh(x + 3, 0., 6.) / 6. # for torchscript, CoreML and ONNX
|
||
|
|
|
||
|
|
|
||
|
|
class MemoryEfficientSwish(nn.Module):
|
||
|
|
class F(torch.autograd.Function):
|
||
|
|
@staticmethod
|
||
|
|
def forward(ctx, x):
|
||
|
|
ctx.save_for_backward(x)
|
||
|
|
return x * torch.sigmoid(x)
|
||
|
|
|
||
|
|
@staticmethod
|
||
|
|
def backward(ctx, grad_output):
|
||
|
|
x = ctx.saved_tensors[0]
|
||
|
|
sx = torch.sigmoid(x)
|
||
|
|
return grad_output * (sx * (1 + x * (1 - sx)))
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
return self.F.apply(x)
|
||
|
|
|
||
|
|
|
||
|
|
# Mish https://github.com/digantamisra98/Mish --------------------------------------------------------------------------
|
||
|
|
class Mish(nn.Module):
|
||
|
|
@staticmethod
|
||
|
|
def forward(x):
|
||
|
|
return x * F.softplus(x).tanh()
|
||
|
|
|
||
|
|
|
||
|
|
class MemoryEfficientMish(nn.Module):
|
||
|
|
class F(torch.autograd.Function):
|
||
|
|
@staticmethod
|
||
|
|
def forward(ctx, x):
|
||
|
|
ctx.save_for_backward(x)
|
||
|
|
return x.mul(torch.tanh(F.softplus(x))) # x * tanh(ln(1 + exp(x)))
|
||
|
|
|
||
|
|
@staticmethod
|
||
|
|
def backward(ctx, grad_output):
|
||
|
|
x = ctx.saved_tensors[0]
|
||
|
|
sx = torch.sigmoid(x)
|
||
|
|
fx = F.softplus(x).tanh()
|
||
|
|
return grad_output * (fx + x * sx * (1 - fx * fx))
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
return self.F.apply(x)
|
||
|
|
|
||
|
|
|
||
|
|
# FReLU https://arxiv.org/abs/2007.11824 -------------------------------------------------------------------------------
|
||
|
|
class FReLU(nn.Module):
|
||
|
|
def __init__(self, c1, k=3): # ch_in, kernel
|
||
|
|
super().__init__()
|
||
|
|
self.conv = nn.Conv2d(c1, c1, k, 1, 1, groups=c1, bias=False)
|
||
|
|
self.bn = nn.BatchNorm2d(c1)
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
return torch.max(x, self.bn(self.conv(x)))
|