text: 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)))
  