144 lines
4.7 KiB
Python
144 lines
4.7 KiB
Python
"""
|
|
模型定义文件 - 使用瓶颈结构 (Bottleneck) 的深度残差网络
|
|
目标：约50层，参数量约80M
|
|
author : yukun-hh
|
|
date : 2026-4-10
|
|
"""
|
|
import torch
|
|
from torch import nn
|
|
from torch.nn import functional as F
|
|
from torchsummary import summary
|
|
|
|
|
|
class Bottleneck(nn.Module):
|
|
"""
|
|
瓶颈残差块：1x1(降维) -> 3x3 -> 1x1(升维)
|
|
若需要下采样或通道变化，则在跳跃连接中使用1x1卷积
|
|
"""
|
|
expansion = 4 # 输出通道是中间通道的4倍
|
|
|
|
def __init__(self, in_channels, mid_channels, stride=1, downsample=None):
|
|
"""
|
|
:param in_channels: 输入通道数
|
|
:param mid_channels: 中间层通道数（1x1降维后的通道数）
|
|
:param stride: 步长，用于下采样
|
|
:param downsample: 下采样模块（当stride≠1或通道变化时使用）
|
|
"""
|
|
super().__init__()
|
|
self.conv1 = nn.Conv2d(in_channels, mid_channels, kernel_size=1, bias=False)
|
|
self.bn1 = nn.BatchNorm2d(mid_channels)
|
|
self.conv2 = nn.Conv2d(mid_channels, mid_channels, kernel_size=3, stride=stride, padding=1, bias=False)
|
|
self.bn2 = nn.BatchNorm2d(mid_channels)
|
|
self.conv3 = nn.Conv2d(mid_channels, mid_channels * self.expansion, kernel_size=1, bias=False)
|
|
self.bn3 = nn.BatchNorm2d(mid_channels * self.expansion)
|
|
self.relu = nn.ReLU(inplace=True)
|
|
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)
|
|
out = self.relu(out)
|
|
|
|
out = self.conv3(out)
|
|
out = self.bn3(out)
|
|
|
|
if self.downsample is not None:
|
|
identity = self.downsample(x)
|
|
|
|
out += identity
|
|
out = self.relu(out)
|
|
return out
|
|
|
|
|
|
class Net(nn.Module):
|
|
"""
|
|
基于 Bottleneck 的 ResNet 风格模型
|
|
各阶段配置仿照 ResNet-50，适当调整宽度以达到约80M参数
|
|
"""
|
|
|
|
def __init__(self, num_classes=4):
|
|
super().__init__()
|
|
|
|
# 第一阶段：7x7卷积 + 最大池化
|
|
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)
|
|
|
|
# 残差阶段定义
|
|
# 每个阶段的参数：[块数, 中间通道数, 步长]
|
|
# 为了达到80M参数，我们略微加宽网络（相比标准ResNet-50）
|
|
layers_config = [
|
|
(3, 64, 1), # stage2: 3个瓶颈块，输出通道 64*4=256
|
|
(4, 128, 2), # stage3: 4个瓶颈块，输出通道 128*4=512
|
|
(14, 256, 2), # stage4: 14个瓶颈块，输出通道 256*4=1024（加深至此阶段）
|
|
(3, 512, 2) # stage5: 3个瓶颈块，输出通道 512*4=2048
|
|
]
|
|
|
|
self.in_channels = 64
|
|
self.stage2 = self._make_layer(layers_config[0])
|
|
self.stage3 = self._make_layer(layers_config[1])
|
|
self.stage4 = self._make_layer(layers_config[2])
|
|
self.stage5 = self._make_layer(layers_config[3])
|
|
|
|
# 全局池化与分类层
|
|
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
|
|
self.fc = nn.Linear(2048, num_classes)
|
|
|
|
def _make_layer(self, config):
|
|
"""
|
|
构建一个残差阶段
|
|
:param config: (块数, 中间通道数, 第一阶段步长)
|
|
:return: nn.Sequential
|
|
"""
|
|
num_blocks, mid_channels, stride = config
|
|
downsample = None
|
|
layers = []
|
|
|
|
# 第一个块可能需要下采样和通道匹配
|
|
if stride != 1 or self.in_channels != mid_channels * Bottleneck.expansion:
|
|
downsample = nn.Sequential(
|
|
nn.Conv2d(self.in_channels, mid_channels * Bottleneck.expansion,
|
|
kernel_size=1, stride=stride, bias=False),
|
|
nn.BatchNorm2d(mid_channels * Bottleneck.expansion),
|
|
)
|
|
|
|
layers.append(
|
|
Bottleneck(self.in_channels, mid_channels, stride, downsample)
|
|
)
|
|
self.in_channels = mid_channels * Bottleneck.expansion
|
|
|
|
# 后续块
|
|
for _ in range(1, num_blocks):
|
|
layers.append(
|
|
Bottleneck(self.in_channels, mid_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.stage2(x)
|
|
x = self.stage3(x)
|
|
x = self.stage4(x)
|
|
x = self.stage5(x)
|
|
|
|
x = self.avgpool(x)
|
|
x = torch.flatten(x, 1)
|
|
x = self.fc(x)
|
|
return x
|
|
|
|
|
|
if __name__ == '__main__':
|
|
model = Net(num_classes=4)
|
|
summary(model, input_size=(3, 256, 256)) |