activations.py

# Activation functions
# refer to https://github.com/ultralytics/yolov5/blob/master/utils/activations.py

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()
    @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 Swish(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)

class Mish(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, bias=False)
        self.bn = nn.BatchNorm2d(c1)

    def forward(self, x):
        return torch.max(x, self.bn(self.conv(x)))


def get_activation(activation, c1=1, k=3):
    """
    Args:
        norm (str): either one of SiLU, Hardswish, swish, Mish, FReLU;.

    Returns:
        nn.Module or None: the activation layer
    """
    if activation is None:
        return None
    if isinstance(activation, str):
        if len(activation) == 0:
            return None
        activation = {
            "ReLU": nn.ReLU(),
            "SiLU": SiLU(),
            "Hardswish": Hardswish(),
            "Swish": Swish(),
            "Mish": Mish(),
            "FReLU": FReLU(c1, k),
        }[activation]
    return activation