# YOLOv5 🚀 by Ultralytics, GPL-3.0 license

"""

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()

    @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, 0.0, 6.0) / 6.0  # for torchscript, CoreML and ONNX

# 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, bias=False)

        self.bn = nn.BatchNorm2d(c1)

    def forward(self, x):

        return torch.max(x, self.bn(self.conv(x)))

# ACON https://arxiv.org/pdf/2009.04759.pdf ----------------------------------------------------------------------------

class AconC(nn.Module):

    r""" ACON activation (activate or not).

    AconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is a learnable parameter

    according to "Activate or Not: Learning Customized Activation" <https://arxiv.org/pdf/2009.04759.pdf>.

    """

    def __init__(self, c1):

        super().__init__()

        self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1))

        self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1))

        self.beta = nn.Parameter(torch.ones(1, c1, 1, 1))

    def forward(self, x):

        dpx = (self.p1 - self.p2) * x

        return dpx * torch.sigmoid(self.beta * dpx) + self.p2 * x

class MetaAconC(nn.Module):

    r""" ACON activation (activate or not).

    MetaAconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is generated by a small network

    according to "Activate or Not: Learning Customized Activation" <https://arxiv.org/pdf/2009.04759.pdf>.

    """

    def __init__(self, c1, k=1, s=1, r=16):  # ch_in, kernel, stride, r

        super().__init__()

        c2 = max(r, c1 // r)

        self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1))

        self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1))

        self.fc1 = nn.Conv2d(c1, c2, k, s, bias=True)

        self.fc2 = nn.Conv2d(c2, c1, k, s, bias=True)

        # self.bn1 = nn.BatchNorm2d(c2)

        # self.bn2 = nn.BatchNorm2d(c1)

    def forward(self, x):

        y = x.mean(dim=2, keepdims=True).mean(dim=3, keepdims=True)

        # batch-size 1 bug/instabilities https://github.com/ultralytics/yolov5/issues/2891

        # beta = torch.sigmoid(self.bn2(self.fc2(self.bn1(self.fc1(y)))))  # bug/unstable

        beta = torch.sigmoid(self.fc2(self.fc1(y)))  # bug patch BN layers removed

        dpx = (self.p1 - self.p2) * x

        return dpx * torch.sigmoid(beta * dpx) + self.p2 * x