ECANet注意力机制学习 （附代码）_eca注意力机制

论文地址：ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks

1.是什么？

ECA注意力模块是在CVPR 2020的论文"ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks"中提出的。

ECA（Efficient Channel Attention）是一种轻量级的通道注意力机制，它通过一个1D卷积层来学习通道注意力，并减少计算复杂度。ECA注意力机制避免了降维，而是利用1维卷积实现了局部跨通道交互，从而提取通道间的依赖关系。

2.为什么？

神经网络在提取特征时，所获取的特征图中并不是每一个特征层的贡献都一样，不同的特征层对于结果的作用权重是不相同。

ECA注意力机制将通道注意力机制引入卷积神经网络，通过对每个通道的特征图进行全局自适应加权，提升了特征的表达能力。

3.怎么样？

3.2 避免降维

实证分析表明降维会对渠道关注度的预测产生副作用，而且对所有渠道的相关性进行捕获是低效且不必要的。SE块使用两个FC层计算权重。与之不同的是，ECA通过执行大小为k的快速一维卷积来生成通道权值，其中k通过通道维C的函数自适应地确定。

3.2 跨通道交互

虽然SEVar2和SE-Var3都保持通道维数不变，但后者的性能更好。主要的区别是SE-Var3捕获跨通道交互，而SEVar2不捕获。这说明跨通道互动有助于学习有效注意。但是SE-Var3涉及大量的参数，导致模型复杂度过高。从有效卷积的角度来看，SE-Var2可视为深度可分离卷积(Chollet 2017)。自然，组卷积作为另一种有效的卷积，也可以用来捕获跨通道交互。给定一个FC层，组卷积将它分成多个组，并在每个组中独立地执行线性变换。

公式推理过程：

对于不降维的聚合特征 y ∈ RC，可以学习通道注意 ：

W 为 C x C 的参数矩阵 ；

Wvar2 是一个对角矩阵，包含C个参数 ；
Wvar3 是一个完整的矩阵，包含 C×C 的参数 ；
关键的区别在于：SE-var3考虑了跨通道交互，而SE-var2没有考虑，因此SE-V ar3的性能更好 ；

在 ECA-Net 中，探索了另一种获取 局部跨通道交互 的方法，以保证效率和有效性，使用一个 波段矩阵Wk 来学习通道注意力：

其中，C1D 表示一维卷积 ；

3.3自适应卷积核

由于使用1D卷积来捕获局部的跨通道交互，k决定了交互的覆盖范围，不同的通道数和不同的CNN架构的卷积块可能会有所不同。尽管k可以手动调优，但它将消耗大量计算资源。k与通道维数c有关，这是合理的。一般认为，通道尺寸越大，长期交互作用越强，而通道尺寸越小，短期交互作用越强。 

3.4 开发方向

大致可以分为两个方向：

（1）增强特征聚合；
（2）通道与空间注意的结合 ；

4.代码 

4.1eca_module.py

import torch
from torch import nn
from torch.nn.parameter import Parameter
 
class eca_layer(nn.Module):
    """Constructs a ECA module.
    Args:
        channel: Number of channels of the input feature map
        k_size: Adaptive selection of kernel size
    """
    def __init__(self, channel, k_size=3):
        super(eca_layer, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.conv = nn.Conv1d(1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False) 
        self.sigmoid = nn.Sigmoid()
 
    def forward(self, x):
        # feature descriptor on the global spatial information
        y = self.avg_pool(x)
 
        # Two different branches of ECA module
        y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1)
 
        # Multi-scale information fusion
        y = self.sigmoid(y)
 
        return x * y.expand_as(x)

4.2 eca_resnet.py

import torch.nn as nn
import math
# import torch.utils.model_zoo as model_zoo
from eca_module import eca_layer
 
 
def conv3x3(in_planes, out_planes, stride=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=1, bias=False)
 
 
class ECABasicBlock(nn.Module):
    expansion = 1
 
    def __init__(self, inplanes, planes, stride=1, downsample=None, k_size=3):
        super(ECABasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes, 1)
        self.bn2 = nn.BatchNorm2d(planes)
        self.eca = eca_layer(planes, k_size)
        self.downsample = downsample
        self.stride = stride
 
    def forward(self, x):
        residual = x
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
 
        out = self.conv2(out)
        out = self.bn2(out)
        out = self.eca(out)
 
        if self.downsample is not None:
            residual = self.downsample(x)
 
        out += residual
        out = self.relu(out)
 
        return out
 
 
class ECABottleneck(nn.Module):
    expansion = 4
 
    def __init__(self, inplanes, planes, stride=1, downsample=None, k_size=3):
        super(ECABottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                               padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)
        self.relu = nn.ReLU(inplace=True)
        self.eca = eca_layer(planes * 4, k_size)
        self.downsample = downsample
        self.stride = stride
 
    def forward(self, x):
        residual = x
 
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
 
        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)
 
        out = self.conv3(out)
        out = self.bn3(out)
        out = self.eca(out)
 
        if self.downsample is not None:
            residual = self.downsample(x)
 
        out += residual
        out = self.relu(out)
 
        return out
 
 
class ResNet(nn.Module):
 
    def __init__(self, block, layers, num_classes=1000, k_size=[3, 3, 3, 3]):
        self.inplanes = 64
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0], int(k_size[0]))
        self.layer2 = self._make_layer(block, 128, layers[1], int(k_size[1]), stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], int(k_size[2]), stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], int(k_size[3]), stride=2)
        self.avgpool = nn.AvgPool2d(7, stride=1)
        self.fc = nn.Linear(512 * block.expansion, num_classes)
 
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
 
    def _make_layer(self, block, planes, blocks, k_size, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )
 
        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample, k_size))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes, k_size=k_size))
 
        return nn.Sequential(*layers)
 
    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)
 
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
 
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
 
        return x
 
 
def eca_resnet18(k_size=[3, 3, 3, 3], num_classes=1_000, pretrained=False):
    """Constructs a ResNet-18 model.
    Args:
        k_size: Adaptive selection of kernel size
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        num_classes:The classes of classification
    """
    model = ResNet(ECABasicBlock, [2, 2, 2, 2], num_classes=num_classes, k_size=k_size)
    model.avgpool = nn.AdaptiveAvgPool2d(1)
    return model
 
 
def eca_resnet34(k_size=[3, 3, 3, 3], num_classes=1_000, pretrained=False):
    """Constructs a ResNet-34 model.
    Args:
        k_size: Adaptive selection of kernel size
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        num_classes:The classes of classification
    """
    model = ResNet(ECABasicBlock, [3, 4, 6, 3], num_classes=num_classes, k_size=k_size)
    model.avgpool = nn.AdaptiveAvgPool2d(1)
    return model
 
 
def eca_resnet50(k_size=[3, 3, 3, 3], num_classes=1000, pretrained=False):
    """Constructs a ResNet-50 model.
    Args:
        k_size: Adaptive selection of kernel size
        num_classes:The classes of classification
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    print("Constructing eca_resnet50......")
    model = ResNet(ECABottleneck, [3, 4, 6, 3], num_classes=num_classes, k_size=k_size)
    model.avgpool = nn.AdaptiveAvgPool2d(1)
    return model
 
 
def eca_resnet101(k_size=[3, 3, 3, 3], num_classes=1_000, pretrained=False):
    """Constructs a ResNet-101 model.
    Args:
        k_size: Adaptive selection of kernel size
        num_classes:The classes of classification
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(ECABottleneck, [3, 4, 23, 3], num_classes=num_classes, k_size=k_size)
    model.avgpool = nn.AdaptiveAvgPool2d(1)
    return model
 
 
def eca_resnet152(k_size=[3, 3, 3, 3], num_classes=1_000, pretrained=False):
    """Constructs a ResNet-152 model.
    Args:
        k_size: Adaptive selection of kernel size
        num_classes:The classes of classification
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(ECABottleneck, [3, 8, 36, 3], num_classes=num_classes, k_size=k_size)
    model.avgpool = nn.AdaptiveAvgPool2d(1)
    return model

参考：

【pytorch】ECA-NET注意力机制应用于ResNet的代码实现

[ 注意力机制 ] 经典网络模型3——ECANet 详解与复现

论文翻译：ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks