76 lines
2.3 KiB
Python
76 lines
2.3 KiB
Python
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 PSA(nn.Module):
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def __init__(self, channel=512, reduction=4, S=4):
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super().__init__()
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self.S = S
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self.convs = []
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for i in range(S):
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self.convs.append(nn.Conv2d(channel // S, channel // S, kernel_size=2 * (i + 1) + 1, padding=i + 1))
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self.se_blocks = []
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for i in range(S):
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self.se_blocks.append(nn.Sequential(
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nn.AdaptiveAvgPool2d(1),
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nn.Conv2d(channel // S, channel // (S * reduction), kernel_size=1, bias=False),
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nn.ReLU(inplace=True),
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nn.Conv2d(channel // (S * reduction), channel // S, kernel_size=1, bias=False),
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nn.Sigmoid()
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))
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self.softmax = nn.Softmax(dim=1)
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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.size()
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# Step1:SPC module
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SPC_out = x.view(b, self.S, c // self.S, h, w) # bs,s,ci,h,w
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for idx, conv in enumerate(self.convs):
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SPC_out[:, idx, :, :, :] = conv(SPC_out[:, idx, :, :, :])
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# Step2:SE weight
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se_out = []
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for idx, se in enumerate(self.se_blocks):
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se_out.append(se(SPC_out[:, idx, :, :, :]))
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SE_out = torch.stack(se_out, dim=1)
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SE_out = SE_out.expand_as(SPC_out)
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# Step3:Softmax
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softmax_out = self.softmax(SE_out)
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# Step4:SPA
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PSA_out = SPC_out * softmax_out
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PSA_out = PSA_out.view(b, -1, h, w)
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return PSA_out
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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(3, 512, 64, 64)
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psa = PSA(channel=512, reduction=8)
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output = psa(input)
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a = output.view(-1).sum()
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a.backward()
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print(output.shape)
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