EfficientNet網路結構

一、介紹

EfficientNet是一種高度可擴展和高效的神經網路結構，它是由Google Brain團隊發表在ICML2019上的論文《EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks》中提出的。EfficientNet基於網路深度、寬度和解析度等因素的平衡來優化模型效率和準確度，可在資源有限的情況下獲得更好的性能。該結構已經在許多計算機視覺任務上取得了最先進的性能，並且被廣泛應用於圖像分類、對象檢測、分割和OCR等領域。

二、深度、寬度和解析度的平衡

EfficientNet通過控制網路的深度、寬度和解析度達到高效的目的，這些參數的平衡對於最終的網路性能至關重要。具體來說，EfficientNet使用一種稱為複合縮放（Compound Scaling）的方法，即同時增加深度、寬度和解析度，以獲得更好的性能。

在具體實現中，EfficientNet首先使用一個基準模型，該模型的深度、寬度和解析度均為1，然後根據一個參數phi進行縮放，得到phi深、phi寬、phi解析度的網路，其中phi是一個控制模型大小的超參數。

def compound_coefficient(phi):
    # 根據phi計算深度、寬度和解析度的縮放係數
    alpha, beta, gamma = phi**2.0, phi, phi**0.5
    return alpha, beta, gamma

三、網路結構

EfficientNet沿用了Inception結構中的基本思想，即使用多個卷積分支並行地提取特徵，然後通過一個混合層將這些特徵進行混合。但是與Inception結構不同的是，EfficientNet中使用了一種稱為輕量化Inception模塊（MBConv）的新型卷積塊，MBConv塊能夠很好地平衡增加深度和寬度帶來的計算負擔，從而提高模型性能。

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、模型架構

通過複合縮放，EfficientNet得到了8個不同大小的模型，分別命名為EfficientNetB0~B7，其中B0是最小的模型，B7是最大的模型。下面給出EfficientNetB0的網路結構圖和代碼示例：

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、結論

EfficientNet是一種高度可擴展和高效的神經網路結構，它通過平衡網路的深度、寬度和解析度，以優化模型效率和準確度。實驗表明，EfficientNet能夠在計算資源有限的情況下達到最先進的性能，被廣泛應用於圖像分類、對象檢測、分割和OCR等領域。該網路結構已經成為計算機視覺領域的重要研究方向，其進一步的探究和優化將有望推動計算機視覺技術的發展。