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

层名称 类型 输入大小(HWC) 输出大小(HWC) 核尺寸 步长 参数数量
输入层 输入 227x227x3 - - - 0
Conv1 卷积层 227x227x3 55x55x96 11x11 4 961111*3 + 96 = 34944
MaxPool1 最大池化层 55x55x96 27x27x96 3x3 2 0
LRN1 局部响应归一化 27x27x96 27x27x96 - - -
Conv2 卷积层 27x27x96 27x27x256 5x5 1 25655*96 + 256 = 614656
MaxPool2 最大池化层 27x27x256 13x13x256 3x3 2 0
LRN2 局部响应归一化 13x13x256 13x13x256 - - -
Conv3 卷积层 13x13x256 13x13x384 3x3 1 38433*256 + 384 = 885120
Conv4 卷积层 13x13x384 13x13x384 3x3 1 38433*384 + 384 = 1327488
Conv5 卷积层 13x13x384 13x13x256 3x3 1 25633*384 + 256 = 884992
MaxPool3 最大池化层 13x13x256 6x6x256 3x3 2 0
FC6 全连接层 6x6x256 4096 - - 66256*4096 + 4096 = 37752832
FC7 全连接层 4096 4096 - - 4096*4096 + 4096 = 16781312
FC8 全连接层 4096 1000 - - 4096*1000 + 1000 = 4194304

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

层名称 类型 输入大小 (HWC) 输出大小 (HWC) 核尺寸 步长 参数数量
输入层 输入 32x32x1 32x32x1 - - 0
C1 卷积层 32x32x1 28x28x6 5x5 1 (5x5x1+1)x6 = 156
S2 下采样层 28x28x6 14x14x6 2x2 2 0
C3 卷积层 14x14x6 10x10x16 5x5 1 (5x5x6+1)x16 = 2416
S4 下采样层 10x10x16 5x5x16 2x2 2 0
C5 卷积层 5x5x16 1x1x120 5x5 1 (5x5x16+1)x120 = 48120
F6 全连接层 1x1x120 1x1x84 - - 120x84 + 84 = 10164
输出层 全连接层 1x1x84 1x1x10 - - 84x10 + 10 = 850

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

层名称 类型 输入大小 (HWC) 输出大小 (HWC) 核尺寸 步长 参数数量
Input - 224x224x3 - - - 0
Conv1_1 Conv2D 224x224x3 224x224x64 3x3 1 1792
Conv1_2 Conv2D 224x224x64 224x224x64 3x3 1 36928
MaxPool1 MaxPooling2D 224x224x64 112x112x64 2x2 2 0
Conv2_1 Conv2D 112x112x64 112x112x128 3x3 1 73856
Conv2_2 Conv2D 112x112x128 112x112x128 3x3 1 147584
MaxPool2 MaxPooling2D 112x112x128 56x56x128 2x2 2 0
Conv3_1 Conv2D 56x56x128 56x56x256 3x3 1 295168
Conv3_2 Conv2D 56x56x256 56x56x256 3x3 1 590080
Conv3_3 Conv2D 56x56x256 56x56x256 3x3 1 590080
MaxPool3 MaxPooling2D 56x56x256 28x28x256 2x2 2 0
Conv4_1 Conv2D 28x28x256 28x28x512 3x3 1 1180160
Conv4_2 Conv2D 28x28x512 28x28x512 3x3 1 2359808
Conv4_3 Conv2D 28x28x512 28x28x512 3x3 1 2359808
MaxPool4 MaxPooling2D 28x28x512 14x14x512 2x2 2 0
Conv5_1 Conv2D 14x14x512 14x14x512 3x3 1 2359808
Conv5_2 Conv2D 14x14x512 14x14x512 3x3 1 2359808
Conv5_3 Conv2D 14x14x512 14x14x512 3x3 1 2359808
MaxPool5 MaxPooling2D 14x14x512 7x7x512 2x2 2 0
Flatten Flatten 7x7x512 25088 - - 0
FC6 Dense 25088 4096 - - 102760448
FC7 Dense 4096 4096 - -

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

层名称 类型 输入大小(HWC) 输出大小(HWC) 核尺寸 步长 参数数量
Conv2d_1a_3x3 卷积层 299x299x3 149x149x32 3x3 2 864
Conv2d_2a_3x3 卷积层 149x149x32 147x147x32 3x3 1 9216
Conv2d_2b_3x3 卷积层 147x147x32 147x147x64 3x3 1 18432
MaxPool_3a_3x3 最大池化层 147x147x64 73x73x64 3x3 2 0
Conv2d_3b_1x1 卷积层 73x73x64 73x73x80 1x1 1 5120
Conv2d_4a_3x3 卷积层 73x73x80 71x71x192 3x3 1 138240
MaxPool_5a_3x3 最大池化层 71x71x192 35x35x192 3x3 2 0
Mixed_5b Inception模块 35x35x192 35x35x256 - - -
Mixed_5c Inception模块 35x35x256 35x35x288 - - -
Mixed_5d Inception模块 35x35x288 35x35x288 - - -
Mixed_6a Inception模块 35x35x288 17x17x768 - 2 -
Mixed_6b Inception模块 17x17x768 17x17x768 - - -
Mixed_6c Inception模块 17x17x768 17x17x768 - - -
Mixed_6d Inception模块 17x17x768 17x17x768 - - -
Mixed_6e Inception模块 17x17x768 17x17x768 - - -
Mixed_7a Inception模块 17x17x768 8x8x1280 - 2 -
Mixed_7b Inception模块 8x8x1280 8x8x2048 - - -
Mixed_7c Inception模块 8x8x2048 8x8x2048 - - -

以Mixed_5b为例,列出其内部结构。

层名称 类型 输入大小(HWC) 输出大小(HWC) 核尺寸 步长 参数数量
Mixed_5b/1x1 卷积层 35x35x192 35x35x64 1x1 1 12288
Mixed_5b/3x3/1x1 卷积层 35x35x192 35x35x64 1x1 1 12288
Mixed_5b/3x3/3x3 卷积层 35x35x64 35x35x96 3x3 1 63360
Mixed_5b/5x5/1x1 卷积层 35x35x192 35x35x16 1x1 1 3072
Mixed_5b/5x5/5x5 卷积层 35x35x16 35x35x16 5x5 1 3072
Mixed_5b/pool 池化层 35x35x192 35x35x32 - 1 0
Mixed_5b/output Concatenate - 35x35x256 - - -

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

层名称 类型 输入大小(HWC) 输出大小(HWC) 核尺寸 步长 参数数量
Conv1 卷积层 224x224x3 112x112x64 7x7 2 9472
BatchNorm1 批归一化层 112x112x64 112x112x64 - - 256
ReLU1 激活层 112x112x64 112x112x64 - - 0
MaxPool1 最大池化层 112x112x64 56x56x64 3x3 2 0
ResidualBlock1_1 残差块 56x56x64 56x56x64 - - 8448
ResidualBlock1_2 残差块 56x56x64 56x56x64 - - 8448
ResidualBlock2_1 残差块 56x56x64 28x28x128 - 2 43008
ResidualBlock2_2 残差块 28x28x128 28x28x128 - - 43008
ResidualBlock3_1 残差块 28x28x128 14x14x256 - 2 172448
ResidualBlock3_2 残差块 14x14x256 14x14x256 - - 172448
AvgPool 平均池化层 14x14x256 7x7x256 7x7 2 0
Flatten 展平层 7x7x256 12544 - - 0
FC 全连接层 12544 1000 - - 12545000
Softmax Softmax层 1000 1000 - - 0

每个残差块的结构:

阶段 残差块 层名称 类型 输入大小(HWC) 输出大小(HWC) 核尺寸 步长 参数数量
1 1 conv1 卷积 224x224x64 112x112x64 7x7 2 9408
conv2 卷积 112x112x64 112x112x64 3x3 1 18432
skip1 卷积 224x224x64 112x112x64 1x1 2 256
1 2 conv1 卷积 112x112x64 112x112x64 3x3 1 18432
conv2 卷积 112x112x64 112x112x64 3x3 1 18432
2 1 conv1 卷积 112x112x64 56x56x128 3x3 2 73984
conv2 卷积 56x56x128 56x56x128 3x3 1 147584
skip1 卷积 112x112x64 56x56x128 1x1 2 832
2 2 conv1 卷积 56x56x128 56x56x128 3x3 1 147584
conv2 卷积 56x56x128 56x56x128 3x3 1 147584
3 1 conv1 卷积 56x56x128 28x28x256 3x3 2 295168
conv2 卷积 28x28x256 28x28x256 3x3 1 589824
skip1 卷积 56x56x128 28x28x256 1x1 2 3328
3 2 conv1 卷积 28x28x256 28x28x256 3x3 1 589824
conv2 卷积 28x28x256 28x28x256 3x3 1 589824
4 1 conv1 卷积 28x28x256 14x14x512 3x3 2 1180928
conv2 卷积 14x14x512 14x14x512 3x3 1 2359296

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)
posted @ 2024-11-14 11:04  绝不原创的飞龙  阅读(11)  评论(0编辑  收藏  举报