深度學習網路resnet34詳解

一、介紹

殘差網路（ResNet）是由何凱明等人提出的一種深層神經網路。ResNet在網路加深的同時，可以解決由梯度消失或爆炸而導致的精度下降問題。其中resnet34是ResNet中比較簡單的一個版本，只有34層深度。ResNet34在計算機視覺領域應用廣泛，如圖像分類、物體檢測和分割等。

二、原理

ResNet的主要思想是通過引入殘差模塊（residual block）來加深網路，殘差模塊和普通的卷積模塊相比，其主要區別在於在每一個卷積層之後都有直接的連接（shortcut connection）來 bypass 原本的卷積輸出。

這種直接連接可以理解為是一條跨層連接的捷徑，保證了網路的信息流暢性。用公式表示如下：

其中H(X)代表residual block輸出，F(X)代表輸入經過兩個卷積之後的結果，W是權重，b是偏差（bias），後面的權重是可學習的。 X+H(X) 是shortcut connection。

ResNet 使用的 residual block 是基於兩個 3×3 卷積層和一個跨層連接的模塊，我們稱之為 Residual Unit 。如下圖所示：

其中的 shortcut connection，即跨層連接，可以有兩種形式：addition 和 projection shortcut。

addition shortcut 如圖：

而 projection shortcut 如圖：

三、代碼實現

下面是使用PyTorch實現resnet34的代碼。首先要導入必要的庫：

import torch.nn as nn
import torch.utils.model_zoo as model_zoo
import torch.nn.functional as F
import torch

__all__ = ['ResNet', 'resnet34']

model_urls = {
    'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
}

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)

def conv1x1(in_planes, out_planes, stride=1):
    "1x1 convolution"
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)

class BasicBlock(nn.Module):
    expansion = 1
    
    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride
    
    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        
        # 判斷是否需要downsample操作
        if self.downsample is not None:
            identity = self.downsample(x)
        
        out += identity
        out = self.relu(out)

        return out

class ResNet(nn.Module):
    def __init__(self, block, layers, num_classes=1000, zero_init_residual=False):
        super(ResNet, self).__init__()
        self.inplanes = 64
        
        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])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * block.expansion, num_classes)

        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.GroupNorm)):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
        
        # 零初始化最後一層的權重
        if zero_init_residual:
            for m in self.modules():
                if isinstance(m, BasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)
                elif isinstance(m, Bottleneck):
                    nn.init.constant_(m.bn3.weight, 0)
    
    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        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 = torch.flatten(x, 1)
        x = self.fc(x)

        return x

def _resnet(arch, block, layers, pretrained, progress, **kwargs):
    model = ResNet(block, layers, **kwargs)
    if pretrained:
        state_dict = model_zoo.load_url(model_urls[arch], progress=progress)
        model.load_state_dict(state_dict)
    return model

def resnet34(pretrained=False, progress=True, **kwargs):
    return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress, **kwargs)

四、應用與拓展

ResNet的應用非常廣泛，除了在計算機視覺領域，也被用作自然語言處理和語音識別等其他領域。除了resnet34，還有resnet50、resnet101等版本，可以根據任務的複雜度選擇適合的版本。在實際應用中，可以通過fine-tuning、數據增強等方法對ResNet進行拓展，提高其在特定任務上的表現。