导读
ECA-Net是一种基于注意力机制的神经网络模型，它在图像分类和目标检测等计算机视觉任务中取得了非常好的表现。近年来，深度学习技术在计算机视觉领域得到了广泛应用，但是在处理大尺度高分辨率图像时，常规的卷积神经网络模型存在一些局限性。ECA-Net通过引入可变的全局上下文信息，解决了这个问题。
ECA讲解
一个人能否以更有效的方式学习有效的集中注意力?由于在SE-NET中使用了降维操作，对通道注意力带来了一定的负面影响，并且获取到的通道之间的互相关系是非必要的。为了消除这个负面影响提出了ECA。我们今天在ECA中将找到答案。
精髓
ECA模块的精髓在于引入了可扩展卷积注意力机制，可以自适应地对每个通道的特征图进行加权，以提高模型在处理大尺度高分辨率图像时的表现。相比于传统的卷积操作，ECA模块可以更好地捕捉图像中的细节信息，从而提高模型的准确率和鲁棒性。
步骤
ECA卷积模块是ECA-Net中引入的一种新型卷积操作，它利用注意力机制对特征图进行自适应的加权，以提高模型对图像中细节信息的提取能力。ECA卷积模块主要包括以下三个步骤：
- 全局平均池化（Global Average Pooling）：将每个通道的特征图进行平均池化，得到一个全局的特征向量，用来表示整张图像的上下文信息。
- 一维卷积（1D Convolution）：对全局特征向量进行一维卷积操作，以学习到通道之间的关联性。
- Sigmoid激活函数：将卷积输出进行Sigmoid激活，以得到每个通道的权重系数。这些权重系数将被用于对每个通道的特征图进行加权，以提高模型对图像中细节信息的提取能力。
在下图中我们的有效通道注意力(ECA)模块示意图。考虑到通过全球平均池化(GAP)获得的聚合特征，ECA通过执行大小为k的快速1D卷积来生成信道权重，其中k是通过信道维度C的映射自适应确定的。
代码
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import torch from torch import nn class eca_layer(nn.Module): def __init__(self, channel, k_size): super(eca_layer, self).__init__() self.avg_pool = nn.AdaptiveAvgPool2d(1) self.k_size = k_size self.conv = nn.Conv1d(channel, channel, kernel_size=k_size, bias=False, groups=channel) self.sigmoid = nn.Sigmoid() def forward(self, x): b, c, _, _ = x.size() y = self.avg_pool(x) y = nn.functional.unfold(y.transpose(-1, -3), kernel_size=(1, self.k_size), padding=(0, (self.k_size - 1) // 2)) y = self.conv(y.transpose(-1, -2)).unsqueeze(-1) y = self.sigmoid(y) x = x * y.expand_as(x) return x if __name__ == "__main__": x = torch.zeros(1,3,224,224) model = eca_layer(3,3) y = model(x) print(y.shape) ----torch.Size([1, 3, 224, 224])
ECA模块能够自适应地对每个通道的特征图进行加权，从而提高模型在处理大尺度高分辨率图像时的表现。实验证明，在多项计算机视觉任务中，ECA模块能够显著提高模型的准确率和鲁棒性。 ECA模块可以轻松地嵌入到现有的卷积神经网络模型中，而不会增加太多的计算和存储成本。这使得ECA模块的应用非常灵活和方便。
ECA嵌入
一般我们是将ECA模块嵌入到卷积层之后或者是激活函数之前，在嵌入的时候需要注意的是输入与输出的确定，卷积层的输出量即为ECA模块的输入输出部分。根据实际需要，选择ECA模块的超参数，包括卷积核大小、步长、填充方式等。这些超参数的选择可以通过实验来确定。 我们以RESNET为例子：
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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
在论文中我们可以看到一系列经过ECA模块改良后的acc值的对比：
结尾
又是一年毕业季，又到了各路学子们“写论文”的时候了，你是不是还在担心自己的论文无法过关？来看本文中提到的ECA-Net，顶刊级别的结构融入到你论文中还怕无法通过学校的检查么，这不直接吊炸天了啊
