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64 lines
1.6 KiB
64 lines
1.6 KiB
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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import torch.nn as nn
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# Swish ------------------------------------------------------------------------
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class SwishImplementation(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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class MemoryEfficientSwish(nn.Module):
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@staticmethod
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def forward(x):
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return SwishImplementation.apply(x)
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class HardSwish(nn.Module): # https://arxiv.org/pdf/1905.02244.pdf
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@staticmethod
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def forward(x):
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return x * F.hardtanh(x + 3, 0., 6., True) / 6.
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class Swish(nn.Module):
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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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# Mish ------------------------------------------------------------------------
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class MishImplementation(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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class MemoryEfficientMish(nn.Module):
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@staticmethod
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def forward(x):
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return MishImplementation.apply(x)
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class Mish(nn.Module): # https://github.com/digantamisra98/Mish
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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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