58 lines
1.8 KiB
Python
58 lines
1.8 KiB
Python
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import numpy as np
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import torch
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from torch import nn
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from torch.nn import init
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class SpatialGroupEnhance(nn.Module):
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def __init__(self, groups):
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super().__init__()
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self.groups = groups
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self.avg_pool = nn.AdaptiveAvgPool2d(1)
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self.weight = nn.Parameter(torch.zeros(1, groups, 1, 1))
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self.bias = nn.Parameter(torch.zeros(1, groups, 1, 1))
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self.sig = nn.Sigmoid()
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self.init_weights()
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def init_weights(self):
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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init.kaiming_normal_(m.weight, mode='fan_out')
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if m.bias is not None:
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init.constant_(m.bias, 0)
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elif isinstance(m, nn.BatchNorm2d):
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init.constant_(m.weight, 1)
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init.constant_(m.bias, 0)
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elif isinstance(m, nn.Linear):
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init.normal_(m.weight, std=0.001)
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if m.bias is not None:
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init.constant_(m.bias, 0)
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def forward(self, x):
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b, c, h, w = x.shape
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x = x.view(b * self.groups, -1, h, w) # bs*g,dim//g,h,w
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xn = x * self.avg_pool(x) # bs*g,dim//g,h,w
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xn = xn.sum(dim=1, keepdim=True) # bs*g,1,h,w
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t = xn.view(b * self.groups, -1) # bs*g,h*w
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t = t - t.mean(dim=1, keepdim=True) # bs*g,h*w
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std = t.std(dim=1, keepdim=True) + 1e-5
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t = t / std # bs*g,h*w
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t = t.view(b, self.groups, h, w) # bs,g,h*w
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t = t * self.weight + self.bias # bs,g,h*w
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t = t.view(b * self.groups, 1, h, w) # bs*g,1,h*w
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x = x * self.sig(t)
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x = x.view(b, c, h, w)
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return x
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# 输入 N C H W, 输出 N C H W
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if __name__ == '__main__':
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input = torch.randn(50, 512, 7, 7)
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sge = SpatialGroupEnhance(groups=8)
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output = sge(input)
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print(output.shape)
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