ECAAttention3d.py

import numpy as np
import torch
from torch import nn
from torch.nn import init
from collections import OrderedDict



class ECAAttention(nn.Module):

    def __init__(self, kernel_size=3):
        super().__init__()
        self.gap=nn.AdaptiveAvgPool3d(1)
        self.conv=nn.Conv1d(1,1,kernel_size=kernel_size,padding=(kernel_size-1)//2)
        self.sigmoid=nn.Sigmoid()

    def init_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv3d):
                init.kaiming_normal_(m.weight, mode='fan_out')
                if m.bias is not None:
                    init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm3d):
                init.constant_(m.weight, 1)
                init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                init.normal_(m.weight, std=0.001)
                if m.bias is not None:
                    init.constant_(m.bias, 0)

    def forward(self, x):
        y=self.gap(x) #bs,c,1,1,1
        y=y.squeeze(-1).squeeze(-1).permute(0,2,1) #bs,1,c
        y=self.conv(y) #bs,1,c
        y=self.sigmoid(y) #bs,1,c
        y=y.permute(0,2,1).unsqueeze(-1).unsqueeze(-1) #bs,c,1,1,1
        return x*y.expand_as(x)


if __name__ == '__main__':
    input=torch.randn(1,64,32,20,24)
    eca = ECAAttention(kernel_size=3)
    output=eca(input)
    print(output.shape)