神经网络架构参考：2-1 卷积篇

提示词：

给出{xxx}的网络结构表格，包含层名称、类型、输入大小（HWC），输出大小（HWC）、核尺寸、步长、参数数量

AlexNet

PyTorch 源码

import torch
import torch.nn as nn
import torch.nn.functional as F
class AlexNet(nn.Module):
    def __init__(self, num_classes=1000):
        super(AlexNet, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 96, kernel_size=11, stride=4, padding=2),  # Conv1
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),  # MaxPool1
            nn.LocalResponseNorm(size=5, alpha=0.0001, beta=0.75, k=2),  # LRN1
            nn.Conv2d(96, 256, kernel_size=5, padding=2),  # Conv2
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),  # MaxPool2
            nn.LocalResponseNorm(size=5, alpha=0.0001, beta=0.75, k=2),  # LRN2
            nn.Conv2d(256, 384, kernel_size=3, padding=1),  # Conv3
            nn.ReLU(inplace=True),
            nn.Conv2d(384, 384, kernel_size=3, padding=1),  # Conv4
            nn.ReLU(inplace=True),
            nn.Conv2d(384, 256, kernel_size=3, padding=1),  # Conv5
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),  # MaxPool3
        )
        self.classifier = nn.Sequential(
            nn.Dropout(),
            nn.Linear(256 * 6 * 6, 4096),  # FC6
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(4096, 4096),  # FC7
            nn.ReLU(inplace=True),
            nn.Linear(4096, num_classes),  # FC8
        )
    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), 256 * 6 * 6)
        x = self.classifier(x)
        return x
# 创建AlexNet模型实例
model = AlexNet(num_classes=1000)
print(model)

LENET5

网络结构

PyTorch 代码

import torch
import torch.nn as nn
import torch.nn.functional as F
class LeNet5(nn.Module):
    def __init__(self, num_classes=10):
        super(LeNet5, self).__init__()
        # Convolutional layer (C1)
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=6, kernel_size=5, padding=2)
        # Subsampling layer (S2)
        self.pool1 = nn.AvgPool2d(kernel_size=2, stride=2)
        # Convolutional layer (C3)
        self.conv2 = nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5)
        # Subsampling layer (S4)
        self.pool2 = nn.AvgPool2d(kernel_size=2, stride=2)
        # Convolutional layer (C5)
        self.conv3 = nn.Conv2d(in_channels=16, out_channels=120, kernel_size=5)
        # Fully connected layer (F6)
        self.fc1 = nn.Linear(in_features=120, out_features=84)
        # Output layer
        self.fc2 = nn.Linear(in_features=84, out_features=num_classes)
    def forward(self, x):
        # C1
        x = self.conv1(x)
        x = F.relu(x)
        # S2
        x = self.pool1(x)
        # C3
        x = self.conv2(x)
        x = F.relu(x)
        # S4
        x = self.pool2(x)
        # C5
        x = self.conv3(x)
        x = F.relu(x)
        # Flatten the output for the fully connected layer
        x = x.view(-1, self.num_flat_features(x))
        # F6
        x = self.fc1(x)
        x = F.relu(x)
        # Output layer
        x = self.fc2(x)
        return x
    def num_flat_features(self, x):
        size = x.size()[1:]  # all dimensions except the batch dimension
        num_features = 1
        for s in size:
            num_features *= s
        return num_features
# Example of creating the LeNet5 model
model = LeNet5(num_classes=10)
print(model)
# Example input tensor (batch size of 1, 1 channel, 32x32 image)
input_tensor = torch.randn(1, 1, 32, 32)
# Forward pass through the model
output = model(input_tensor)
print(output)

VGG16

PyTorch 代码

import torch
import torch.nn as nn
class VGG16(nn.Module):
    def __init__(self, num_classes=1000):
        super(VGG16, self).__init__()
        self.features = nn.Sequential(
            # Conv1
            nn.Conv2d(3, 64, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(64, 64, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            
            # Conv2
            nn.Conv2d(64, 128, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(128, 128, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            
            # Conv3
            nn.Conv2d(128, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            
            # Conv4
            nn.Conv2d(256, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            
            # Conv5
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )
        
        self.classifier = nn.Sequential(
            nn.Linear(512 * 7 * 7, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(4096, num_classes)
        )
        
    def forward(self, x):
        x = self.features(x)
        x = torch.flatten(x, 1)
        x = self.classifier(x)
        return x
# 实例化模型
model = VGG16(num_classes=1000)
print(model)

Inception

以Mixed_5b为例，列出其内部结构。

PyTorch 源码

以下是使用PyTorch构建InceptionV3模型的一部分源码。这个源码展示了如何定义Inception模块和一些辅助函数，但不包括整个网络的所有细节。完整的InceptionV3模型定义会更长，这里只提供了核心部分。

import torch
import torch.nn as nn
import torch.nn.functional as F
class InceptionA(nn.Module):
    def __init__(self, in_channels):
        super(InceptionA, self).__init__()
        self.branch1x1 = BasicConv2d(in_channels, 64, kernel_size=1)
        self.branch5x5_1 = BasicConv2d(in_channels, 48, kernel_size=1)
        self.branch5x5_2 = BasicConv2d(48, 64, kernel_size=5, padding=2)
        self.branch3x3dbl_1 = BasicConv2d(in_channels, 64, kernel_size=1)
        self.branch3x3dbl_2 = BasicConv2d(64, 96, kernel_size=3, padding=1)
        self.branch3x3dbl_3 = BasicConv2d(96, 96, kernel_size=3, padding=1)
        self.branch_pool = BasicConv2d(in_channels, 32, kernel_size=1)
    def forward(self, x):
        branch1x1 = self.branch1x1(x)
        branch5x5 = self.branch5x5_1(x)
        branch5x5 = self.branch5x5_2(branch5x5)
        branch3x3dbl = self.branch3x3dbl_1(x)
        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
        branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
        branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
        branch_pool = self.branch_pool(branch_pool)
        outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool]
        return torch.cat(outputs, 1)
class BasicConv2d(nn.Module):
    def __init__(self, in_channels, out_channels, **kwargs):
        super(BasicConv2d, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs)
        self.bn = nn.BatchNorm2d(out_channels, eps=0.001)
    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        return F.relu(x, inplace=True)
class InceptionV3(nn.Module):
    def __init__(self, num_classes=1000):
        super(InceptionV3, self).__init__()
        # Stem
        self.Conv2d_1a_3x3 = BasicConv2d(3, 32, kernel_size=3, stride=2)
        self.Conv2d_2a_3x3 = BasicConv2d(32, 32, kernel_size=3)
        self.Conv2d_2b_3x3 = BasicConv2d(32, 64, kernel_size=3, padding=1)
        # ... additional stem layers ...
        # Inception modules
        self.Mixed_5b = InceptionA(256)
        self.Mixed_5c = InceptionA(288)
        # ... additional Inception modules ...
        # Auxiliary Logits
        self.AuxLogits = None
        # ... auxiliary logits layers ...
        # Final Logits
        self.Mixed_7c = InceptionA(768)
        # ... additional final layers ...
        self.fc = nn.Linear(2048, num_classes)
    def forward(self, x):
        # Stem
        x = self.Conv2d_1a_3x3(x)
        x = self.Conv2d_2a_3x3(x)
        x = self.Conv2d_2b_3x3(x)
        # ... additional stem layers ...
        # Inception modules
        x = self.Mixed_5b(x)
        x = self.Mixed_5c(x)
        # ... additional Inception modules ...
        # Auxiliary Logits
        if self.AuxLogits is not None:
            aux = self.AuxLogits(x)
        else:
            aux = None
        # Final Logits
        x = self.Mixed_7c(x)
        # ... additional final layers ...
        x = F.adaptive_avg_pool2d(x, (1, 1))
        x = torch.flatten(x, 1)
        x = self.fc(x)
        return x, aux
# Example usage:
# model = InceptionV3(num_classes=1000)

Resnet18

每个残差块的结构：

PyTorch 代码

import torch
import torch.nn as nn
import torch.nn.functional as F
# 定义基本残差块
class BasicBlock(nn.Module):
    expansion = 1
    def __init__(self, in_channels, out_channels, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, 
                               stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, 
                               stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.downsample = downsample
    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)
        if self.downsample is not None:
            identity = self.downsample(x)
        out += identity
        out = self.relu(out)
        return out
# 定义ResNet网络
class ResNet(nn.Module):
    def __init__(self, block, layers, num_classes=1000):
        super(ResNet, self).__init__()
        self.in_channels = 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)
        # 初始化权重
        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.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
    def _make_layer(self, block, out_channels, blocks, stride=1):
        downsample = None
        if stride != 1 or self.in_channels != out_channels * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.in_channels, out_channels * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(out_channels * block.expansion),
            )
        layers = []
        layers.append(block(self.in_channels, out_channels, stride, downsample))
        self.in_channels = out_channels * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.in_channels, out_channels))
        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
# 实例化ResNet-16模型
def resnet16(pretrained=False, **kwargs):
    model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
    if pretrained:
        # 这里没有预训练权重，如果需要预训练，可以在这里加载
        pass
    return model
# 创建模型实例
model = resnet16()
print(model)