import torch import torch.nn as nn import torch.nn.functional as F class SiLU(nn.Module): # SiLU activation https://arxiv.org/pdf/1606.08415.pdf @staticmethod def forward(x): return x * torch.sigmoid(x) class Hardswish(nn.Module): # Hard-SiLU activation @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 class Mish(nn.Module): # Mish activation https://github.com/digantamisra98/Mish @staticmethod def forward(x): return x * F.softplus(x).tanh() class MemoryEfficientMish(nn.Module): # Mish activation memory-efficient 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) class FReLU(nn.Module): # FReLU activation https://arxiv.org/abs/2007.11824 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))) 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" . """ 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" . """ 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