ResNet改進綜述

一、ResNet改進版

ResNet（Residual Network）是一種深度神經網路結構，它在ImageNet數據集上取得了巨大的成功。然而，由於殘差網路需要大量的計算資源，同時仍存在著一些問題，因此研究者對殘差網路進行了改進。其中，一種改進版的ResNet是對殘差塊進行了改進。

改進後的ResNet使用了可分離卷積，這種卷積可以將卷積分為兩步：depthwise和pointwise卷積。它們可以減少模型參數和計算量，同時具有更好的表示能力。

以下是改進版的ResNet代碼實現：

import torch
import torch.nn as nn

class SeparableConv2d(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, bias=False):
        super(SeparableConv2d, self).__init__()
        
        self.conv1 = nn.Conv2d(in_channels, in_channels, kernel_size, stride, padding, dilation, groups=in_channels, bias=bias)
        self.pointwise = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0, dilation=1, groups=1, bias=bias)

    def forward(self, x):
        x = self.conv1(x)
        x = self.pointwise(x)
        return x

class BasicBlock(nn.Module):
    def __init__(self, in_planes, out_planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.stride = stride
        self.downsample = downsample
        
        self.conv1 = nn.Sequential(
            nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False),
            nn.BatchNorm2d(out_planes),
            nn.ReLU(inplace=True),
            SeparableConv2d(out_planes, out_planes, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(out_planes),
        )
        self.conv2 = nn.Sequential(
            SeparableConv2d(out_planes, out_planes, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(out_planes),
        )
        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.conv2(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):
        super(ResNet, self).__init__()
        
        self.in_planes = 64
        
        self.conv1 = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
        )
        self.layer1 = self._make_layer(block, 64, layers[0], stride=1)
        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, num_classes)

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.in_planes != planes:
            downsample = nn.Sequential(
                nn.Conv2d(self.in_planes, planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes),
            )
        
        layers = []
        layers.append(block(self.in_planes, planes, stride, downsample))
        self.in_planes = planes
        
        for i in range(1, blocks):
            layers.append(block(self.in_planes, planes))

        return nn.Sequential(*layers)
    
    def forward(self, x):
        x = self.conv1(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

二、ResNet改進CRNN

CRNN（Convolutional Recurrent Neural Network）是一種基於深度神經網路的語音識別模型，其主要結構由卷積神經網路和循環神經網路組成。對於CRNN，研究者們對ResNet的改進主要是引入來自空間和時間領域的特徵結構，提升模型對語音信號的建模能力和特徵表示能力。

具體來說，這種改進是通過將殘差塊中的1×1卷積替換為包含多項式函數的卷積實現的，以增加對空間特徵的提取能力；同時，還引入了一種「多通道門控一維卷積」結構，將時間領域的信息引入模型中。

以下是ResNet改進CRNN的代碼實現：

import torch
import torch.nn as nn

class MultiBranchBlock(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size=(3,1), stride=(1,1), padding=(1,0)):
        super(MultiBranchBlock, self).__init__()

        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding, bias=False)
        self.bn = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)
        
    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        x = self.relu(x)
        
        return x

class ChannelGate(nn.Module):
    def __init__(self, gate_channels, reduction_ratio=16, pool_types=['avg', 'max']):
        super(ChannelGate, self).__init__()
        
        self.gate_channels = gate_channels
        self.mlp = nn.Sequential(
            nn.Linear(gate_channels, gate_channels // reduction_ratio),
            nn.ReLU(),
            nn.Linear(gate_channels // reduction_ratio, gate_channels)
        )   
        
        self.pool_types = pool_types
        
    def forward(self, x):
        channel_att_sum = None
        channel_att_max = None
        
        for pool_type in self.pool_types:
            if pool_type == 'avg':
                avg_channel_att = torch.mean(x, dim=(2,3), keepdim=True)
                channel_att = self.mlp(avg_channel_att.view(x.size(0), self.gate_channels))
            elif pool_type == 'max':
                max_channel_att = torch.max(x, dim=(2,3), keepdim=True)[0]
                channel_att = self.mlp(max_channel_att.view(x.size(0), self.gate_channels))
                
            channel_att = torch.sigmoid(channel_att).view(x.size(0), x.size(1), 1, 1)
            if channel_att_sum is None:
                channel_att_sum = channel_att
            else:
                channel_att_sum += channel_att
            if channel_att_max is None:
                channel_att_max = channel_att
            else:
                channel_att_max = torch.max(channel_att_max, channel_att)
        
        return channel_att_sum, channel_att_max

class CRNN(nn.Module):
    def __init__(self, in_channels, num_classes):
        super(CRNN, self).__init__()

        self.cnn = nn.Sequential(
            nn.Conv2d(in_channels, 32, kernel_size=(41,11), stride=(2,2), padding=(20,5), bias=False),
            nn.BatchNorm2d(32),
            nn.Hardtanh(min_val=0, max_val=20, inplace=True),

            nn.Conv2d(32, 32, kernel_size=(21,11), stride=(2,1), padding=(10,5), bias=False),
            nn.BatchNorm2d(32),
            nn.Hardtanh(min_val=0, max_val=20, inplace=True),
            
            nn.Conv2d(32, 32, kernel_size=(21,11), stride=(2,1), padding=(10,5), bias=False),
            nn.BatchNorm2d(32),
            nn.Hardtanh(min_val=0, max_val=20, inplace=True),
        )
        
        self.resblocks = nn.Sequential(
            nn.Sequential(
                ChannelGate(32, reduction_ratio=16),
                nn.Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False),
                nn.BatchNorm2d(32),
                nn.ReLU(inplace=True),
            ),
            nn.Sequential(
                ChannelGate(32, reduction_ratio=16),
                nn.Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False),
                nn.BatchNorm2d(32),
                nn.ReLU(inplace=True),
            ),
            nn.Sequential(
                ChannelGate(32, reduction_ratio=16),
                nn.Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False),
                nn.BatchNorm2d(32),
                nn.ReLU(inplace=True),
            ),
            nn.Sequential(
                ChannelGate(32, reduction_ratio=16),
                nn.Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False),
                nn.BatchNorm2d(32),
                nn.ReLU(inplace=True),
            ),
            nn.Sequential(
                ChannelGate(32, reduction_ratio=16),
                nn.Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False),
                nn.BatchNorm2d(32),
                nn.ReLU(inplace=True),
            ),
        )
        
        self.rnn = nn.LSTM(input_size=32, hidden_size=1024, num_layers=5, dropout=0.2, bidirectional=True)
        self.fc = nn.Linear(2048, num_classes)

    def forward(self, x):
        x = self.cnn(x)
        x = self.resblocks(x)
        x = x.permute(3,0,1,2).contiguous()
        x = x.view(x.size(0), x.size(1), -1).permute(1,0,2).contiguous()
        x, _ = self.rnn(x)
        x = self.fc(x.mean(0))
        return x

三、ResNet改進方法

除了改進結構之外，還有一些其他的方法可以提升ResNet的性能。

其中，一種重要的方法是使用更加廣泛的數據增強，例如Mixup和CutMix。這些方法可以使模型更加健壯，同時減輕過擬合的程度。

此外，還有一種方法是使用自監督學習來預訓練ResNet。自監督學習能夠使用無標註的數據來提取更好的特徵，從而提升模型性能。此外，還可以使用半監督學習來利用有標註和無標註的數據來訓練模型，提升模型性能。

四、ResNet改進太難了

儘管ResNet的改進方法非常多，但是實際上改進ResNet是非常困難的。首先，深度神經網路的結構非常複雜，難以理解；其次，改進ResNet需要大量的計算資源和數據資源，需要進行耗時的實驗。

因此，研究者們需要在理論和實踐方面花費大量的時間和精力來進行改進。同時，他們需要積極探索新的方法和技術，以尋找更加高效和有效的ResNet改進方法。

五、ResNet改進網路

網路的深度和寬度是影響ResNet性能的兩個重要因素。儘管在一定程度上增加深度和寬度可以提升模型性能，但是這也帶來了更多的計算資源和訓練難度。

因此，研究者們提出了一些改進ResNet網路的方法。其中，一種方法是使用ResNeXt網路，這種網路通過在殘差塊中使用多條路徑來增加網路的寬度。

另一種方法是使用DenseNet網路，這種網路結構可以通過連接不同層之