EfficientNet网络结构

一、介绍

EfficientNet是一种高度可扩展和高效的神经网络结构，它是由Google Brain团队发表在ICML2019上的论文《EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks》中提出的。EfficientNet基于网络深度、宽度和分辨率等因素的平衡来优化模型效率和准确度，可在资源有限的情况下获得更好的性能。该结构已经在许多计算机视觉任务上取得了最先进的性能，并且被广泛应用于图像分类、对象检测、分割和OCR等领域。

二、深度、宽度和分辨率的平衡

EfficientNet通过控制网络的深度、宽度和分辨率达到高效的目的，这些参数的平衡对于最终的网络性能至关重要。具体来说，EfficientNet使用一种称为复合缩放（Compound Scaling）的方法，即同时增加深度、宽度和分辨率，以获得更好的性能。

在具体实现中，EfficientNet首先使用一个基准模型，该模型的深度、宽度和分辨率均为1，然后根据一个参数phi进行缩放，得到phi深、phi宽、phi分辨率的网络，其中phi是一个控制模型大小的超参数。

def compound_coefficient(phi):
    # 根据phi计算深度、宽度和分辨率的缩放系数
    alpha, beta, gamma = phi**2.0, phi, phi**0.5
    return alpha, beta, gamma

三、网络结构

EfficientNet沿用了Inception结构中的基本思想，即使用多个卷积分支并行地提取特征，然后通过一个混合层将这些特征进行混合。但是与Inception结构不同的是，EfficientNet中使用了一种称为轻量化Inception模块（MBConv）的新型卷积块，MBConv块能够很好地平衡增加深度和宽度带来的计算负担，从而提高模型性能。

import torch.nn as nn

class MBConv(nn.Module):
    def __init__(self, in_channels, out_channels, expand_ratio, kernel_size, stride, se_ratio):
        super(MBConv, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.expand_ratio = expand_ratio
        self.kernel_size = kernel_size
        self.stride = stride
        self.se_ratio = se_ratio

        self.use_se = (self.se_ratio is not None) and (0 < self.se_ratio <= 1)

        expand_channels = int(round(self.in_channels * self.expand_ratio))
        self.expansion_conv = None
        if self.expand_ratio != 1:
            self.expansion_conv = nn.Conv2d(self.in_channels, expand_channels, kernel_size=1, stride=1, padding=0, bias=False)
            self.bn0 = nn.BatchNorm2d(expand_channels)
            self.relu0 = nn.ReLU(inplace=True)

        depthwise_conv_padding = (kernel_size - 1) // 2
        self.depthwise_conv = nn.Conv2d(expand_channels, expand_channels, kernel_size=kernel_size, stride=stride,
                                        padding=depthwise_conv_padding, groups=expand_channels, bias=False)
        self.bn1 = nn.BatchNorm2d(expand_channels)
        self.relu1 = nn.ReLU(inplace=True)

        self.linear_conv = nn.Conv2d(expand_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels)

    def forward(self, x):
        out = x
        if self.expand_ratio != 1:
            out = self.expansion_conv(out)
            out = self.bn0(out)
            out = self.relu0(out)

        out = self.depthwise_conv(out)
        out = self.bn1(out)
        out = self.relu1(out)

        if self.use_se:
            out = self.squeeze_excitation(out)

        out = self.linear_conv(out)
        out = self.bn2(out)

        if self.stride == 1 and self.in_channels == self.out_channels:
            out = out + x

        return out

四、模型架构

通过复合缩放，EfficientNet得到了8个不同大小的模型，分别命名为EfficientNetB0~B7，其中B0是最小的模型，B7是最大的模型。下面给出EfficientNetB0的网络结构图和代码示例：

import torch.nn as nn

class EfficientNet(nn.Module):
    def __init__(self, phi):
        super(EfficientNet, self).__init__()
        self.alpha, self.beta, self.gamma = compound_coefficient(phi)

        # stem
        self.conv1 = nn.Conv2d(3, int(32*self.beta), kernel_size=3, stride=2, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(int(32*self.beta))
        self.relu1 = nn.ReLU(inplace=True)

        # blocks
        self.blocks = nn.Sequential(
            MBConv(int(32*self.beta), int(16*self.beta), expand_ratio=1, kernel_size=3, stride=1, se_ratio=0.25),
            MBConv(int(16*self.beta), int(24*self.beta), expand_ratio=6, kernel_size=3, stride=2, se_ratio=0.25),
            MBConv(int(24*self.beta), int(40*self.beta), expand_ratio=6, kernel_size=5, stride=2, se_ratio=0.25),
            MBConv(int(40*self.beta), int(80*self.beta), expand_ratio=6, kernel_size=3, stride=2, se_ratio=0.25),
            MBConv(int(80*self.beta), int(112*self.beta), expand_ratio=6, kernel_size=5, stride=1, se_ratio=0.25),
            MBConv(int(112*self.beta), int(192*self.beta), expand_ratio=6, kernel_size=5, stride=2, se_ratio=0.25),
            MBConv(int(192*self.beta), int(320*self.beta), expand_ratio=6, kernel_size=3, stride=1, se_ratio=0.25),
        )

        # head
        self.conv2 = nn.Conv2d(int(320*self.beta), int(1280*self.beta), kernel_size=1, stride=1, padding=0, bias=False)
        self.bn2 = nn.BatchNorm2d(int(1280*self.beta))
        self.relu2 = nn.ReLU(inplace=True)
        self.avgpool = nn.AdaptiveAvgPool2d(1)
        self.dropout = nn.Dropout(p=0.2)
        self.fc = nn.Linear(int(1280*self.beta), 1000)

        # initialize parameters
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.ones_(m.weight)
                nn.init.zeros_(m.bias)

    def forward(self, x):
        # stem
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu1(out)

        # blocks
        out = self.blocks(out)

        # head
        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu2(out)
        out = self.avgpool(out)
        out = out.view(out.size(0), -1)
        out = self.dropout(out)
        out = self.fc(out)

        return out

五、结论

EfficientNet是一种高度可扩展和高效的神经网络结构，它通过平衡网络的深度、宽度和分辨率，以优化模型效率和准确度。实验表明，EfficientNet能够在计算资源有限的情况下达到最先进的性能，被广泛应用于图像分类、对象检测、分割和OCR等领域。该网络结构已经成为计算机视觉领域的重要研究方向，其进一步的探究和优化将有望推动计算机视觉技术的发展。