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