73 lines
2.2 KiB
Python
73 lines
2.2 KiB
Python
# Activation functions
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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# SiLU https://arxiv.org/pdf/1606.08415.pdf ----------------------------------------------------------------------------
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class SiLU(nn.Module): # export-friendly version of nn.SiLU()
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@staticmethod
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def forward(x):
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return x * torch.sigmoid(x)
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class Hardswish(nn.Module): # export-friendly version of nn.Hardswish()
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@staticmethod
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def forward(x):
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# return x * F.hardsigmoid(x) # for torchscript and CoreML
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return x * F.hardtanh(x + 3, 0., 6.) / 6. # for torchscript, CoreML and ONNX
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class MemoryEfficientSwish(nn.Module):
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class F(torch.autograd.Function):
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@staticmethod
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def forward(ctx, x):
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ctx.save_for_backward(x)
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return x * torch.sigmoid(x)
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@staticmethod
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def backward(ctx, grad_output):
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x = ctx.saved_tensors[0]
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sx = torch.sigmoid(x)
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return grad_output * (sx * (1 + x * (1 - sx)))
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def forward(self, x):
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return self.F.apply(x)
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# Mish https://github.com/digantamisra98/Mish --------------------------------------------------------------------------
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class Mish(nn.Module):
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@staticmethod
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def forward(x):
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return x * F.softplus(x).tanh()
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class MemoryEfficientMish(nn.Module):
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class F(torch.autograd.Function):
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@staticmethod
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def forward(ctx, x):
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ctx.save_for_backward(x)
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return x.mul(torch.tanh(F.softplus(x))) # x * tanh(ln(1 + exp(x)))
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@staticmethod
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def backward(ctx, grad_output):
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x = ctx.saved_tensors[0]
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sx = torch.sigmoid(x)
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fx = F.softplus(x).tanh()
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return grad_output * (fx + x * sx * (1 - fx * fx))
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def forward(self, x):
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return self.F.apply(x)
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# FReLU https://arxiv.org/abs/2007.11824 -------------------------------------------------------------------------------
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class FReLU(nn.Module):
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def __init__(self, c1, k=3): # ch_in, kernel
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super().__init__()
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self.conv = nn.Conv2d(c1, c1, k, 1, 1, groups=c1, bias=False)
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self.bn = nn.BatchNorm2d(c1)
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def forward(self, x):
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return torch.max(x, self.bn(self.conv(x)))
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