102 lines
3.7 KiB
Python
102 lines
3.7 KiB
Python
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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"""
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Activation functions
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"""
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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# SiLU https://arxiv.org/pdf/1606.08415.pdf ----------------------------------------------------------------------------
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class SiLU(nn.Module): # export-friendly version of nn.SiLU()
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@staticmethod
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def forward(x):
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return x * torch.sigmoid(x)
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class Hardswish(nn.Module): # export-friendly version of nn.Hardswish()
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@staticmethod
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def forward(x):
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# return x * F.hardsigmoid(x) # for torchscript and CoreML
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return x * F.hardtanh(x + 3, 0., 6.) / 6. # for torchscript, CoreML and ONNX
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# Mish https://github.com/digantamisra98/Mish --------------------------------------------------------------------------
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class Mish(nn.Module):
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@staticmethod
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def forward(x):
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return x * F.softplus(x).tanh()
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class MemoryEfficientMish(nn.Module):
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class F(torch.autograd.Function):
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@staticmethod
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def forward(ctx, x):
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ctx.save_for_backward(x)
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return x.mul(torch.tanh(F.softplus(x))) # x * tanh(ln(1 + exp(x)))
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@staticmethod
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def backward(ctx, grad_output):
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x = ctx.saved_tensors[0]
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sx = torch.sigmoid(x)
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fx = F.softplus(x).tanh()
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return grad_output * (fx + x * sx * (1 - fx * fx))
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def forward(self, x):
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return self.F.apply(x)
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# FReLU https://arxiv.org/abs/2007.11824 -------------------------------------------------------------------------------
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class FReLU(nn.Module):
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def __init__(self, c1, k=3): # ch_in, kernel
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super().__init__()
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self.conv = nn.Conv2d(c1, c1, k, 1, 1, groups=c1, bias=False)
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self.bn = nn.BatchNorm2d(c1)
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def forward(self, x):
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return torch.max(x, self.bn(self.conv(x)))
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# ACON https://arxiv.org/pdf/2009.04759.pdf ----------------------------------------------------------------------------
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class AconC(nn.Module):
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r""" ACON activation (activate or not).
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AconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is a learnable parameter
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according to "Activate or Not: Learning Customized Activation" <https://arxiv.org/pdf/2009.04759.pdf>.
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"""
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def __init__(self, c1):
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super().__init__()
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self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1))
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self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1))
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self.beta = nn.Parameter(torch.ones(1, c1, 1, 1))
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def forward(self, x):
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dpx = (self.p1 - self.p2) * x
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return dpx * torch.sigmoid(self.beta * dpx) + self.p2 * x
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class MetaAconC(nn.Module):
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r""" ACON activation (activate or not).
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MetaAconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is generated by a small network
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according to "Activate or Not: Learning Customized Activation" <https://arxiv.org/pdf/2009.04759.pdf>.
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"""
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def __init__(self, c1, k=1, s=1, r=16): # ch_in, kernel, stride, r
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super().__init__()
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c2 = max(r, c1 // r)
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self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1))
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self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1))
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self.fc1 = nn.Conv2d(c1, c2, k, s, bias=True)
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self.fc2 = nn.Conv2d(c2, c1, k, s, bias=True)
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# self.bn1 = nn.BatchNorm2d(c2)
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# self.bn2 = nn.BatchNorm2d(c1)
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def forward(self, x):
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y = x.mean(dim=2, keepdims=True).mean(dim=3, keepdims=True)
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# batch-size 1 bug/instabilities https://github.com/ultralytics/yolov5/issues/2891
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# beta = torch.sigmoid(self.bn2(self.fc2(self.bn1(self.fc1(y))))) # bug/unstable
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beta = torch.sigmoid(self.fc2(self.fc1(y))) # bug patch BN layers removed
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dpx = (self.p1 - self.p2) * x
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return dpx * torch.sigmoid(beta * dpx) + self.p2 * x
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