SaELayer_torch.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.model_zoo import load_url

# 定义SE模块
class SELayer(nn.Module):
    def __init__(self, channel, reduction=16):
        super(SELayer, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.fc = nn.Sequential(
            nn.Linear(channel, channel // reduction, bias=False),
            nn.ReLU(inplace=True),
            nn.Linear(channel // reduction, channel, bias=False),
            nn.Sigmoid()
        )

    def forward(self, x):
        b, c, _, _ = x.size()
        y = self.avg_pool(x).view(b, c)
        y = self.fc(y).view(b, c, 1, 1)
        return x * y.expand_as(x)

# 定义SaE模块
class SaELayer(nn.Module):
    def __init__(self, in_channel, reduction=32):
        super(SaELayer, self).__init__()
        assert in_channel>=reduction and in_channel%reduction==0,'invalid in_channel in SaElayer'
        self.reduction = reduction
        self.cardinality=4
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        #cardinality 1
        self.fc1 = nn.Sequential(
            nn.Linear(in_channel,in_channel//self.reduction, bias=False),
            nn.ReLU(inplace=True)
        )
        # cardinality 2
        self.fc2 = nn.Sequential(
            nn.Linear(in_channel, in_channel // self.reduction, bias=False),
            nn.ReLU(inplace=True)
        )
        # cardinality 3
        self.fc3 = nn.Sequential(
            nn.Linear(in_channel, in_channel // self.reduction, bias=False),
            nn.ReLU(inplace=True)
        )
        # cardinality 4
        self.fc4 = nn.Sequential(
            nn.Linear(in_channel, in_channel // self.reduction, bias=False),
            nn.ReLU(inplace=True)
        )

        self.fc = nn.Sequential(
            nn.Linear(in_channel//self.reduction*self.cardinality, in_channel, bias=False),
            nn.Sigmoid()
        )

    def forward(self, x):
        b, c, _, _ = x.size()
        y = self.avg_pool(x).view(b, c)
        y1 = self.fc1(y)
        y2 = self.fc2(y)
        y3 = self.fc3(y)
        y4 = self.fc4(y)
        y_concate = torch.cat([y1,y2,y3,y4],dim=1)
        y_ex_dim = self.fc(y_concate).view(b,c,1,1)

        return x * y_ex_dim.expand_as(x)

# import ipdb
# ipdb.set_trace()
se_v2 = SaELayer(64)
# 示例输入
input = torch.randn(3, 64, 224, 224)
output = se_v2(input)

print(output.shape)#torch.Size([3, 64, 224, 224])