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| # Activation functions | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| # SiLU https://arxiv.org/pdf/1606.08415.pdf ---------------------------------------------------------------------------- | |
| class SiLU(nn.Module): # export-friendly version of nn.SiLU() | |
| def forward(x): | |
| return x * torch.sigmoid(x) | |
| class Hardswish(nn.Module): # export-friendly version of nn.Hardswish() | |
| 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): | |
| def forward(ctx, x): | |
| ctx.save_for_backward(x) | |
| return x * torch.sigmoid(x) | |
| 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): | |
| def forward(x): | |
| return x * F.softplus(x).tanh() | |
| class MemoryEfficientMish(nn.Module): | |
| class F(torch.autograd.Function): | |
| def forward(ctx, x): | |
| ctx.save_for_backward(x) | |
| return x.mul(torch.tanh(F.softplus(x))) # x * tanh(ln(1 + exp(x))) | |
| 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))) | |