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)))