31 lines
926 B
Python
31 lines
926 B
Python
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# Simam: A simple, parameter-free attention module for convolutional neural networks (ICML 2021)
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import torch
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import torch.nn as nn
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from thop import profile
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class Simam_module(torch.nn.Module):
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def __init__(self, e_lambda=1e-4):
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super(Simam_module, self).__init__()
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self.act = nn.Sigmoid()
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self.e_lambda = e_lambda
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def forward(self, x):
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b, c, h, w = x.size()
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n = w * h - 1
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x_minus_mu_square = (x - x.mean(dim=[2, 3], keepdim=True)).pow(2)
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y = x_minus_mu_square / (4 * (x_minus_mu_square.sum(dim=[2, 3], keepdim=True) / n + self.e_lambda)) + 0.5
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return x * self.act(y)
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# 无参注意力机制 输入 N C H W, 输出 N C H W
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if __name__ == '__main__':
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model = Simam_module().cuda()
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x = torch.randn(1, 3, 64, 64).cuda()
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y = model(x)
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print(y.size())
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flops, params = profile(model, (x,))
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print(flops / 1e9)
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print(params)
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