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| # YOLOv5 🚀 by Ultralytics, GPL-3.0 license | |
| """ | |
| Activation functions | |
| """ | |
| 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 | |
| def forward(x): | |
| return x * torch.sigmoid(x) | |
| class Hardswish(nn.Module): | |
| # Hard-SiLU activation | |
| 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 | |
| def forward(x): | |
| return x * F.softplus(x).tanh() | |
| class MemoryEfficientMish(nn.Module): | |
| # Mish activation memory-efficient | |
| class F(torch.autograd.Function): | |
| def forward(ctx, x): | |
| ctx.save_for_backward(x) | |
| return x.mul(torch.tanh(F.softplus(x))) # x * tanh(ln(1 + exp(x))) | |
| 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" <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 | |