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- import torch
- import torch.nn as nn
- import torch.nn.functional as F
-
-
- # Swish https://arxiv.org/pdf/1905.02244.pdf ---------------------------------------------------------------------------
- class Swish(nn.Module): #
- @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)
- self.bn = nn.BatchNorm2d(c1)
-
- def forward(self, x):
- return torch.max(x, self.bn(self.conv(x)))
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