import torch
import torch.nn as nn
import torch.nn.functional as F
class SimpleCNN(nn.Module):
"""简单的CNN模型，包含两个卷积层和两个全连接层"""
def __init__(self):
super(SimpleCNN, self).__init__()
self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1)
self.conv2 = nn.Conv2d(16, 32, kernel_size=3, padding=1)
self.pool = nn.MaxPool2d(2, 2)
self.fc1 = nn.Linear(32 * 8 * 8, 128)
self.fc2 = nn.Linear(128, 10)
self.relu = nn.ReLU()
def forward(self, x):
x = self.pool(self.relu(self.conv1(x))) # 输出大小: 16x16x16
x = self.pool(self.relu(self.conv2(x))) # 输出大小: 8x8x32
x = x.view(-1, 32 * 8 * 8) # 展平
x = self.relu(self.fc1(x))
x = self.fc2(x)
return x
class MediumCNN(nn.Module):
"""中等复杂度的CNN模型，包含批标准化和Dropout"""
def __init__(self, use_bn=True):
super(MediumCNN, self).__init__()
self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)
self.bn1 = nn.BatchNorm2d(32) if use_bn else nn.Identity()
self.conv2 = nn.Conv2d(32, 32, kernel_size=3, padding=1)
self.bn2 = nn.BatchNorm2d(32) if use_bn else nn.Identity()
self.pool1 = nn.MaxPool2d(2, 2)
self.dropout1 = nn.Dropout(0.25)
self.conv3 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
self.bn3 = nn.BatchNorm2d(64) if use_bn else nn.Identity()
self.conv4 = nn.Conv2d(64, 64, kernel_size=3, padding=1)
self.bn4 = nn.BatchNorm2d(64) if use_bn else nn.Identity()
self.pool2 = nn.MaxPool2d(2, 2)
self.dropout2 = nn.Dropout(0.25)
self.flatten = nn.Flatten()
self.fc1 = nn.Linear(64 * 8 * 8, 512)
self.bn5 = nn.BatchNorm1d(512) if use_bn else nn.Identity()
self.dropout3 = nn.Dropout(0.5)
self.fc2 = nn.Linear(512, 10)
self.relu = nn.ReLU()
def forward(self, x):
x = self.relu(self.bn1(self.conv1(x)))
x = self.relu(self.bn2(self.conv2(x)))
x = self.pool1(x)
x = self.dropout1(x)
x = self.relu(self.bn3(self.conv3(x)))
x = self.relu(self.bn4(self.conv4(x)))
x = self.pool2(x)
x = self.dropout2(x)
x = self.flatten(x)
x = self.relu(self.bn5(self.fc1(x)))
x = self.dropout3(x)
x = self.fc2(x)
return x
class VGGStyleNet(nn.Module):
"""VGG风格的CNN网络，使用连续的3x3卷积和池化"""
def __init__(self):
super(VGGStyleNet, self).__init__()
# VGG风格：连续的3x3卷积 + 池化
self.features = nn.Sequential(
# 第一块：64通道
nn.Conv2d(3, 64, kernel_size=3, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.Conv2d(64, 64, kernel_size=3, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Dropout(0.25),
# 第二块：128通道
nn.Conv2d(64, 128, kernel_size=3, padding=1),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
nn.Conv2d(128, 128, kernel_size=3, padding=1),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Dropout(0.25),
# 第三块：256通道
nn.Conv2d(128, 256, kernel_size=3, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Dropout(0.25),
)
# 分类器
self.classifier = nn.Sequential(
nn.Flatten(),
nn.Linear(256 * 4 * 4, 512),
nn.BatchNorm1d(512),
nn.ReLU(inplace=True),
nn.Dropout(0.5),
nn.Linear(512, 10)
)
# 权重初始化
self._initialize_weights()
def forward(self, x):
x = self.features(x)
x = self.classifier(x)
return x
def _initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
nn.init.constant_(m.bias, 0)
class ResidualBlock(nn.Module):
"""卷积神经网络的残差块"""
def __init__(self, in_channels, out_channels, stride=1):
super(ResidualBlock, 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.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3,
stride=1, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU(inplace=True)
self.shortcut = nn.Sequential()
if stride != 1 or in_channels != out_channels:
self.shortcut = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=1,
stride=stride, bias=False),
nn.BatchNorm2d(out_channels)
)
def forward(self, x):
residual = x
out = self.relu(self.bn1(self.conv1(x)))
out = self.bn2(self.conv2(out))
out += self.shortcut(residual)
out = self.relu(out)
return out
class SimpleResNet(nn.Module):
"""简化版ResNet模型"""
def __init__(self, num_blocks=[2, 2, 2], num_classes=10):
super(SimpleResNet, self).__init__()
self.in_channels = 16
self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1,
padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(16)
self.relu = nn.ReLU(inplace=True)
self.layer1 = self._make_layer(16, num_blocks[0], stride=1)
self.layer2 = self._make_layer(32, num_blocks[1], stride=2)
self.layer3 = self._make_layer(64, num_blocks[2], stride=2)
self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(64, num_classes)
def _make_layer(self, out_channels, num_blocks, stride):
strides = [stride] + [1] * (num_blocks - 1)
layers = []
for stride in strides:
layers.append(ResidualBlock(self.in_channels, out_channels, stride))
self.in_channels = out_channels
return nn.Sequential(*layers)
def forward(self, x):
x = self.relu(self.bn1(self.conv1(x)))
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.avg_pool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x