yolov7添加p2特征层代码
时间: 2023-07-11 17:50:26 浏览: 82
以下是在YoloV7模型中添加P2特征层的代码示例:
首先,在`models/yolo.py`中添加`P2Layer`类:
```
class P2Layer(nn.Module):
def __init__(self, in_channels, out_channels, k=3, s=2, p=1):
super(P2Layer, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=k, stride=s, padding=p)
self.bn = nn.BatchNorm2d(out_channels)
self.act = nn.LeakyReLU(0.1)
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
x = self.act(x)
return x
```
然后,在`models/yolo.py`中的`__init__`函数中,在`self.backbone`中添加`P2Layer`:
```
# YOLOv7 backbone
self.backbone = nn.Sequential(
# stage 1
Focus(3, 64, k=3),
Conv(64, 128, k=3, s=2),
C3(128, 128, shortcut=True),
# stage 2
Conv(128, 256, k=3, s=2),
C3(256, 256, shortcut=True),
# stage 3
Conv(256, 512, k=3, s=2),
SPP(512, 512, k=(5, 9, 13)),
C3(512, 512, shortcut=True),
P2Layer(512, 256),
# stage 4
Conv(768, 512, k=1),
nn.Upsample(scale_factor=2),
C3(512, 512, shortcut=True),
P2Layer(512, 256),
# stage 5
Conv(768, 512, k=1),
nn.Upsample(scale_factor=2),
C3(512, 512, shortcut=True),
P2Layer(512, 256),
# stage 6
Conv(768, 512, k=1),
C3(512, 512, shortcut=True),
P2Layer(512, 256),
# stage 7
Conv(1024, 1024, k=1),
SPP(1024, 1024, k=(5, 9, 13)),
C3(1024, 1024, shortcut=False),
)
```
最后,在`models/yolo.py`中的`forward`函数中,将`C3`层后面添加的`P2Layer`与`C3`层的输出连接:
```
x = self.backbone(x)
# stage 7
x = self.spp(x)
x = self.conv7(x)
# stage 6
x = self.conv6(x)
p6 = x
x = torch.cat((self.upsample(x), self.backbone[5][-1].conv_bn_act[0].conv.weight), 1)
x = self.conv6d(x)
# stage 5
x = torch.cat((x, p5), 1)
x = self.conv5(x)
p5 = x
x = torch.cat((self.upsample(x), self.backbone[4][-1].conv_bn_act[0].conv.weight), 1)
x = self.conv5d(x)
# stage 4
x = torch.cat((x, p4), 1)
x = self.conv4(x)
p4 = x
x = torch.cat((self.upsample(x), self.backbone[3][-1].conv_bn_act[0].conv.weight), 1)
x = self.conv4d(x)
# stage 3
x = torch.cat((x, p3), 1)
x = self.conv3(x)
p3 = x
x = torch.cat((self.upsample(x), self.backbone[2][-1].conv_bn_act[0].conv.weight), 1)
x = self.conv3d(x)
# stage 2
x = torch.cat((x, p2), 1)
x = self.conv2(x)
# stage 1
x = torch.cat((x, p1), 1)
x = self.conv1(x)
```
这样,就成功在YoloV7模型中添加了P2特征层。
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