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import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn as nn
# Swish ------------------------------------------------------------------------
class SwishImplementation(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)))
class MemoryEfficientSwish(nn.Module):
@staticmethod
def forward(x):
return SwishImplementation.apply(x)
class HardSwish(nn.Module): # https://arxiv.org/pdf/1905.02244.pdf
@staticmethod
def forward(x):
return x * F.hardtanh(x + 3, 0., 6., True) / 6.
class Swish(nn.Module):
@staticmethod
def forward(x):
return x * torch.sigmoid(x)
# Mish ------------------------------------------------------------------------
class MishImplementation(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))
class MemoryEfficientMish(nn.Module):
@staticmethod
def forward(x):
return MishImplementation.apply(x)
class Mish(nn.Module): # https://github.com/digantamisra98/Mish
@staticmethod
def forward(x):
return x * F.softplus(x).tanh()
# 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)
self.bn = nn.BatchNorm2d(c1)
@staticmethod
def forward(self, x):
return torch.max(x, self.bn(self.conv(x)))
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