YOLOv5代码详细注释
时间: 2023-11-12 17:25:32 浏览: 49
YOLOv5是一种目标检测算法,是对YOLO系列的最新改进。它采用了一种新的架构设计,具有更高的检测精度和更快的检测速度。下面是YOLOv5的代码详细注释:
1.导入必要的库
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
```
2.定义Conv和Bottleneck块
```python
# Conv块
def conv(in_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=False, groups=1):
return nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, bias=bias, groups=groups)
# Bottleneck块
class Bottleneck(nn.Module):
# 构造函数
def __init__(self, in_channels, out_channels, shortcut=True, groups=1, expansion=0.5):
super(Bottleneck, self).__init__()
mid_channels = int(out_channels * expansion)
self.conv1 = conv(in_channels, mid_channels, kernel_size=1, stride=1, padding=0, bias=False, groups=groups)
self.bn1 = nn.BatchNorm2d(mid_channels)
self.act1 = nn.SiLU(inplace=True)
self.conv2 = conv(mid_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False, groups=groups)
self.bn2 = nn.BatchNorm2d(out_channels)
self.act2 = nn.SiLU(inplace=True)
self.shortcut = shortcut and in_channels == out_channels
if self.shortcut:
self.conv_shortcut = conv(in_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=False, groups=groups)
self.bn_shortcut = nn.BatchNorm2d(out_channels)
# 前向传播函数
def forward(self, x):
shortcut = x
x = self.conv1(x)
x = self.bn1(x)
x = self.act1(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.act2(x)
if self.shortcut:
shortcut = self.conv_shortcut(shortcut)
shortcut = self.bn_shortcut(shortcut)
x += shortcut
x = self.act2(x)
return x
```
3.定义CSPDarknet53主干网络
```python
class CSPDarknet53(nn.Module):
# 构造函数
def __init__(self, layers):
super(CSPDarknet53, self).__init__()
self.stem = nn.Sequential(
conv(3, 32, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(32),
nn.SiLU(inplace=True)
)
self.layer1 = nn.Sequential(
conv(32, 64, kernel_size=3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(64),
nn.SiLU(inplace=True),
Bottleneck(64, 64 * 2),
nn.BatchNorm2d(64 * 2),
nn.SiLU(inplace=True),
Bottleneck(64 * 2, 64)
)
self.layer2 = nn.Sequential(
conv(64, 128, kernel_size=3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(128),
nn.SiLU(inplace=True),
Bottleneck(128, 128 * 2),
nn.BatchNorm2d(128 * 2),
nn.SiLU(inplace=True),
Bottleneck(128 * 2, 128),
nn.BatchNorm2d(128),
nn.SiLU(inplace=True),
Bottleneck(128, 128 * 2),
nn.BatchNorm2d(128 * 2),
nn.SiLU(inplace=True),
Bottleneck(128 * 2, 128)
)
self.layer3 = nn.Sequential(
conv(128, 256, kernel_size=3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(256),
nn.SiLU(inplace=True),
Bottleneck(256, 256 * 2),
nn.BatchNorm2d(256 * 2),
nn.SiLU(inplace=True),
Bottleneck(256 * 2, 256),
nn.BatchNorm2d(256),
nn.SiLU(inplace=True),
Bottleneck(256, 256 * 2),
nn.BatchNorm2d(256 * 2),
nn.SiLU(inplace=True),
Bottleneck(256 * 2, 256),
nn.BatchNorm2d(256),
nn.SiLU(inplace=True),
Bottleneck(256, 256 * 2),
nn.BatchNorm2d(256 * 2),
nn.SiLU(inplace=True),
Bottleneck(256 * 2, 256)
)
self.layer4 = nn.Sequential(
conv(256, 512, kernel_size=3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(512),
nn.SiLU(inplace=True),
Bottleneck(512, 512 * 2),
nn.BatchNorm2d(512 * 2),
nn.SiLU(inplace=True),
Bottleneck(512 * 2, 512),
nn.BatchNorm2d(512),
nn.SiLU(inplace=True),
Bottleneck(512, 512 * 2),
nn.BatchNorm2d(512 * 2),
nn.SiLU(inplace=True),
Bottleneck(512 * 2, 512),
nn.BatchNorm2d(512),
nn.SiLU(inplace=True),
Bottleneck(512, 512 * 2),
nn.BatchNorm2d(512 * 2),
nn.SiLU(inplace=True),
Bottleneck(512 * 2, 512)
)
self.layers = nn.ModuleList([self.layer1, self.layer2, self.layer3, self.layer4])
self.out_channels = [64, 128, 256, 512]
# 前向传播函数
def forward(self, x):
x = self.stem(x)
outs = []
for layer in self.layers:
x = layer(x)
outs.append(x)
return tuple(outs)
```
4.定义SPP和PANet模块
```python
# SPP模块
class SPP(nn.Module):
# 构造函数
def __init__(self, in_channels, out_channels):
super(SPP, self).__init__()
self.conv1 = conv(in_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=False)
self.bn1 = nn.BatchNorm2d(out_channels)
self.act1 = nn.SiLU(inplace=True)
self.pool2 = nn.MaxPool2d(kernel_size=5, stride=1, padding=2)
self.conv2 = conv(in_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=False)
self.bn2 = nn.BatchNorm2d(out_channels)
self.act2 = nn.SiLU(inplace=True)
self.pool3 = nn.MaxPool2d(kernel_size=9, stride=1, padding=4)
self.conv3 = conv(in_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=False)
self.bn3 = nn.BatchNorm2d(out_channels)
self.act3 = nn.SiLU(inplace=True)
self.conv4 = conv(out_channels * 4, out_channels, kernel_size=1, stride=1, padding=0, bias=False)
self.bn4 = nn.BatchNorm2d(out_channels)
self.act4 = nn.SiLU(inplace=True)
# 前向传播函数
def forward(self, x):
x1 = self.conv1(x)
x1 = self.bn1(x1)
x1 = self.act1(x1)
x2 = self.pool2(x)
x2 = self.conv2(x2)
x2 = self.bn2(x2)
x2 = self.act2(x2)
x3 = self.pool3(x)
x3 = self.conv3(x3)
x3 = self.bn3(x3)
x3 = self.act3(x3)
x = torch.cat([x1, x2, x3, x], dim=1)
x = self.conv4(x)
x = self.bn4(x)
x = self.act4(x)
return x
# PANet模块
class PANet(nn.Module):
# 构造函数
def __init__(self, in_channels, out_channels):
super(PANet, self).__init__()
self.upsample = nn.Upsample(scale_factor=2, mode='nearest')
self.conv1 = conv(in_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=False)
self.bn1 = nn.BatchNorm2d(out_channels)
self.act1 = nn.SiLU(inplace=True)
self.conv2 = conv(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(out_channels)
self.act2 = nn.SiLU(inplace=True)
# 前向传播函数
def forward(self, x, y):
x = self.upsample(x)
if y.shape[2] != x.shape[2] or y.shape[3] != x.shape[3]:
y = F.interpolate(y, size=x.shape[2:], mode='nearest')
x = torch.cat([x, y], dim=1)
x = self.conv1(x)
x = self.bn1(x)
x = self.act1(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.act2(x)
return x
```
5.定义YOLOv5头部网络
```python
class YOLOv5Head(nn.Module):
# 构造函数
def __init__(self, in_channels, num_classes, anchors):
super(YOLOv5Head, self).__init__()
self.num_anchors = len(anchors)
self.num_classes = num_classes
self.conv1 = conv(in_channels, in_channels * 2, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(in_channels * 2)
self.act1 = nn.SiLU(inplace=True)
self.conv2 = conv(in_channels * 2, self.num_anchors * (self.num_classes + 5), kernel_size=1, stride=1, padding=0)
# 前向传播函数
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.act1(x)
x = self.conv2(x)
return x
```
6.定义YOLOv5整体网络
```python
class YOLOv5(nn.Module):
# 构造函数
def __init__(self, num_classes, anchors):
super(YOLOv5, self).__init__()
self.backbone = CSPDarknet53([1, 2, 8, 8])
self.spp = SPP(512, 512)
self.panet1 = PANet(512, 256)
self.panet2 = PANet(256, 128)
self.heads = nn.ModuleList([
YOLOv5Head(512, num_classes, anchors[0]),
YOLOv5Head(256, num_classes, anchors[1]),
YOLOv5Head(128, num_classes, anchors[2])
])
self.out_channels = self.backbone.out_channels
# 前向传播函数
def forward(self, x):
x = self.backbone(x)
x = self.spp(x[-1])
x = self.panet1(x, x[-2])
x = self.panet2(x, x[-3])
outputs = []
for i, x in enumerate(x[::-1]):
outputs.append(self.heads[i](x))
return tuple(outputs)
```
以上是YOLOv5的代码详细注释。该代码实现了CSPDarknet53主干网络,SPP和PANet模块以及YOLOv5头部网络,最终实现了一个完整的YOLOv5目标检测网络。
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