在yolov7中添加P2特征层,代码
时间: 2023-12-18 21:24:40 浏览: 57
在 YOLOv7 中添加 P2 特征层的代码如下:
首先,在 `models/yolo.py` 文件中,找到 `__init__` 函数,并添加如下代码:
```python
# 添加 P2 特征层
self.p2 = nn.Conv2d(256, 256, kernel_size=3, stride=2, padding=1)
```
接着,在 `forward` 函数中,找到 `x_36` 的计算代码,添加如下代码:
```python
# 计算 P2 特征层
x = self.conv5(x)
x = self.conv6(x)
x = self.conv7(x)
p2 = self.p2(x)
```
最后,在 `__init__` 函数中,找到 `self.m` 的初始化代码,并将 `nn.Sequential` 的层数修改为 16:
```python
# 修改层数为 16
self.m = nn.Sequential(*layers[:16])
```
这样就添加了 P2 特征层到 YOLOv7 中了。完整代码如下:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
from models.common import Conv, DWConv
class YOLOv7(nn.Module):
def __init__(self, nc, anchors, ch=256):
super(YOLOv7, self).__init__()
self.backbone = nn.Sequential(
Conv(3, 32, 3, 1),
Conv(32, 64, 3, 2),
Conv(64, 64, 1, 1),
Conv(64, 64, 3, 1),
nn.MaxPool2d(2, 2),
Conv(64, 128, 3, 1),
Conv(128, 128, 1, 1),
Conv(128, 128, 3, 1),
nn.MaxPool2d(2, 2),
Conv(128, 256, 3, 1),
Conv(256, 256, 1, 1),
Conv(256, 256, 3, 1),
nn.MaxPool2d(2, 2),
Conv(256, 512, 3, 1),
Conv(512, 512, 1, 1),
Conv(512, 512, 3, 1),
nn.MaxPool2d(2, 2),
Conv(512, 1024, 3, 1),
Conv(1024, 1024, 3, 1),
Conv(1024, 1024, 3, 1),
)
self.neck = nn.Sequential(
Conv(1024, ch, 1, 1),
nn.ConvTranspose2d(ch, ch, 4, 2, 1),
Conv(ch, ch, 1, 1),
Conv(ch, ch, 3, 1),
Conv(ch, ch, 1, 1),
Conv(ch, ch, 3, 1),
)
# 添加 P2 特征层
self.p2 = nn.Conv2d(256, 256, kernel_size=3, stride=2, padding=1)
self.head = nn.Sequential(
Conv(ch + 1024, ch, 1, 1),
Conv(ch, ch * 2, 3, 1),
Conv(ch * 2, ch, 1, 1),
Conv(ch, ch * 2, 3, 1),
Conv(ch * 2, ch, 1, 1),
)
self.conv = nn.Conv2d(ch, 3 * (nc + 5), 1, 1, 0)
self.stride = [8, 16, 32]
self._initialize_biases()
self.register_buffer("anchors", torch.tensor(anchors).float().view(3, -1, 2))
def _initialize_biases(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.normal_(m.bias, 0, 0.01)
def forward(self, x):
x_8, x_16, x_32 = self.backbone(x)
x = self.neck(x_32)
# 计算 P2 特征层
x = self.conv5(x)
x = self.conv6(x)
x = self.conv7(x)
p2 = self.p2(x)
x = self.head(torch.cat([p2, x_32], 1))
out = self.conv(x)
return (
out.sigmoid()
.view(out.shape[0], 3, -1, out.shape[-2], out.shape[-1])
.permute(0, 1, 3, 4, 2)
)
```
这份代码是在 YOLOv5 的基础上进行修改得到的,如果你需要使用的话,需要做一些调整以适应你的数据集和训练方式。