YOLOv7中Bottleneck层代码是什么
时间: 2024-05-15 19:12:34 浏览: 13
在YOLOv7中,Bottleneck层的代码如下所示:
```python
class Bottleneck(nn.Module):
# Standard bottleneck
def __init__(self, c1, c2, shortcut=True, g=1, e=0.5):
super(Bottleneck, self).__init__()
self.conv1 = Conv(c1, c2//2, 1, 1)
self.conv2 = Conv(c2//2, c2, 3, 1, g=g, e=e)
self.add = shortcut and c1 == c2
def forward(self, x):
out = self.conv1(x)
out = self.conv2(out)
if self.add:
out += x
return out
```
这个Bottleneck层是YOLOv7网络中的标准瓶颈层,它包含两个卷积层,一个1x1的卷积层和一个3x3的卷积层。其中,1x1的卷积层用于降维,3x3的卷积层用于提取特征。此外,Bottleneck层还包含一个可选的残差连接(shortcut),用于加速训练和提高精度。
相关问题
yolov5网络结构代码
以下是YOLOv5网络结构的代码示例:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class Conv(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding=None, groups=1, bias=False):
super(Conv, self).__init__()
if padding is None:
padding = kernel_size // 2 if stride == 1 else 0
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, groups=groups, bias=bias)
self.bn = nn.BatchNorm2d(out_channels)
self.activation = nn.ReLU()
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
x = self.activation(x)
return x
class Bottleneck(nn.Module):
def __init__(self, in_channels, out_channels, shortcut=True, groups=1):
super(Bottleneck, self).__init__()
mid_channels = int(in_channels * 0.5)
self.conv1 = Conv(in_channels, mid_channels, kernel_size=1)
self.conv2 = Conv(mid_channels, in_channels, kernel_size=3, padding=1)
self.conv3 = Conv(in_channels, mid_channels, kernel_size=1)
self.conv4 = Conv(mid_channels, out_channels, kernel_size=3, padding=1)
self.shortcut = shortcut and in_channels == out_channels
if self.shortcut:
self.shortcut_conv = Conv(in_channels, out_channels, kernel_size=1)
def forward(self, x):
identity = x
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
x = self.conv4(x)
if self.shortcut:
identity = self.shortcut_conv(identity)
x += identity
return x
class YOLOv5(nn.Module):
def __init__(self, num_classes=80):
super(YOLOv5, self).__init__()
self.backbone = nn.Sequential(
Conv(3, 32, kernel_size=3, stride=1),
Bottleneck(32, 64),
nn.MaxPool2d(2, 2),
Bottleneck(64, 128),
nn.MaxPool2d(2, 2),
Bottleneck(128, 256),
nn.MaxPool2d(2, 2),
Bottleneck(256, 512),
nn.MaxPool2d(2, 2),
Bottleneck(512, 1024),
)
self.neck = nn.Sequential(
Conv(1024, 512, kernel_size=1),
Conv(512, 1024, kernel_size=3, padding=1),
Conv(1024, 512, kernel_size=1),
Conv(512, 1024, kernel_size=3, padding=1),
Conv(1024, 512, kernel_size=1),
)
self.head = nn.Sequential(
Conv(512, 1024, kernel_size=3, padding=1),
nn.Conv2d(1024, num_classes, kernel_size=1)
)
def forward(self, x):
x = self.backbone(x)
x = self.neck(x)
x = self.head(x)
x = F.adaptive_avg_pool2d(x, (1, 1))
x = torch.flatten(x, 1)
return x
model = YOLOv5()
```
请注意,这只是YOLOv5的基本网络结构示例,可能与实际的YOLOv5网络结构代码有所不同。实际的YOLOv5网络结构可能包含其他组件和层。这里的代码仅提供了一个大致的框架。
yolov7 c3str模块
引用\[1\]中提到,在新版yolov5中,作者将BottleneckCSP(瓶颈层)模块转变为了C3模块,其结构作用基本相同均为CSP架构,只是在修正单元的选择上有所不同,其包含了3个标准卷积层以及多个Bottleneck模块。引用\[2\]中给出了C3模块的具体实现代码,其中包括了两支结构,一支使用了多个Bottleneck堆叠和3个标准卷积层,另一支仅经过一个基本卷积模块,最后将两支进行concat操作。引用\[3\]提供了一个GitHub链接,其中包含了TPH-YOLOv5项目,该项目对yolov5进行了修改,添加了小目标检测头和TensorRT部署。根据提供的信息,没有提到yolov7 c3str模块的相关内容。
#### 引用[.reference_title]
- *1* *2* [深度学习Week9-YOLOv5-C3模块实现(Pytorch)](https://blog.csdn.net/m0_62237233/article/details/128135260)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^insert_down1,239^v3^insert_chatgpt"}} ] [.reference_item]
- *3* [yolov5-6.0网络添加小目标检测头 TensorRT部署](https://blog.csdn.net/niuyuanye/article/details/114286950)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^insert_down1,239^v3^insert_chatgpt"}} ] [.reference_item]
[ .reference_list ]
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