RepVgg实战：图像分类新秀，VGG的现代复兴

2024-02-13 12:40:52

1. RepVgg 简介

RepVgg（Resnet-inspired VGG）是一种由旷视科技提出的新型神经网络架构。它通过结构重参数化让VGG再次伟大。所谓“VGG式”指的是：没有任何分支，网络结构简单，层数深。VGG的简单性和有效性使其成为图像分类任务中的经典模型。然而，VGG也存在一些问题，例如计算复杂性和参数冗余。RepVgg通过结构重参数化解决了这些问题，使VGG焕发了新的生机。

2. RepVgg 的原理

RepVgg的原理很简单，它通过结构重参数化来简化VGG的结构。具体来说，RepVgg将VGG中的每个卷积层都分解成了两个较小的卷积层。这样一来，VGG的层数就增加了一倍，但是参数的数量却减少了一半。这种结构重参数化不仅简化了VGG的结构，还提高了VGG的准确率。

3. RepVgg 的实现

RepVgg的实现非常简单，我们可以使用PyTorch或TensorFlow等深度学习框架轻松地实现它。下面是一个使用PyTorch实现RepVgg的示例代码：

import torch
import torch.nn as nn

class RepVgg(nn.Module):
    def __init__(self, num_classes=10):
        super(RepVgg, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu1 = nn.ReLU()
        self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
        self.bn2 = nn.BatchNorm2d(64)
        self.relu2 = nn.ReLU()
        self.maxpool1 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
        self.bn3 = nn.BatchNorm2d(128)
        self.relu3 = nn.ReLU()
        self.conv4 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1)
        self.bn4 = nn.BatchNorm2d(128)
        self.relu4 = nn.ReLU()
        self.maxpool2 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.conv5 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
        self.bn5 = nn.BatchNorm2d(256)
        self.relu5 = nn.ReLU()
        self.conv6 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
        self.bn6 = nn.BatchNorm2d(256)
        self.relu6 = nn.ReLU()
        self.maxpool3 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.conv7 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
        self.bn7 = nn.BatchNorm2d(512)
        self.relu7 = nn.ReLU()
        self.conv8 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
        self.bn8 = nn.BatchNorm2d(512)
        self.relu8 = nn.ReLU()
        self.maxpool4 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.conv9 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
        self.bn9 = nn.BatchNorm2d(512)
        self.relu9 = nn.ReLU()
        self.conv10 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
        self.bn10 = nn.BatchNorm2d(512)
        self.relu10 = nn.ReLU()
        self.maxpool5 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.fc1 = nn.Linear(512, 4096)
        self.relu11 = nn.ReLU()
        self.fc2 = nn.Linear(4096, 4096)
        self.relu12 = nn.ReLU()
        self.fc3 = nn.Linear(4096, num_classes)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu1(x)
        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu2(x)
        x = self.maxpool1(x)

        x = self.conv3(x)
        x = self.bn3(x)
        x = self.relu3(x)
        x = self.conv4(x)
        x = self.bn4(x)
        x = self.relu4(x)
        x = self.maxpool2(x)

        x = self.conv5(x)
        x = self.bn5(x)
        x = self.relu5(x)
        x = self.conv6(x)
        x = self.bn6(x)
        x = self.relu6(x)
        x = self.maxpool3(x)

        x = self.conv7(x)
        x = self.bn7(x)
        x = self.relu7(x)
        x = self.conv8(x)
        x = self.bn8(x)
        x = self.relu8(x)
        x = self.maxpool4(x)

        x = self.conv9(x)
        x = self.bn9(x)
        x = self.relu9(x)
        x = self.conv10(x)
        x = self.bn10(x)
        x = self.relu10(x)
        x = self.maxpool5(x)

        x = x.view(x.size(0), -1)
        x = self.fc1(x)
        x = self.relu11(x)
        x = self.fc2(x)
        x = self.relu12(x)
        x = self.fc3(x)
        return x