acnet可以替换yolov5中的卷积,代码
时间: 2024-02-11 17:07:27 浏览: 36
是的,Acnet 可以替换 YOLOv5 中的卷积层。下面是如何在 YOLOv5 中使用 Acnet 的代码实现。
1.首先,在 `models/common.py` 文件中定义 Acnet 模块:
```python
import torch.nn.functional as F
class ACBlock(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride, padding):
super(ACBlock, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, bias=False)
self.bn = nn.BatchNorm2d(out_channels)
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
x = F.relu(x)
x = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
return x
```
2.然后,在 `models/yolo.py` 文件中找到需要替换的卷积层,例如 `Conv`,将其替换为 `ACBlock`:
```python
from models.common import Conv, BottleneckCSP, ACBlock
class CSPBlock(nn.Module):
def __init__(self, in_channels, out_channels, bottleneck=1, groups=1, expansion=0.5, shortcut=True):
super(CSPBlock, self).__init__()
hidden_channels = int(out_channels * expansion)
self.conv1 = ACBlock(in_channels, hidden_channels, kernel_size=1, stride=1, padding=0)
self.conv2 = Conv(in_channels, hidden_channels, kernel_size=1, stride=1, padding=0)
self.conv3 = Conv(hidden_channels, hidden_channels, kernel_size=3, stride=1, padding=1, groups=groups)
self.conv4 = ACBlock(hidden_channels, hidden_channels, kernel_size=1, stride=1, padding=0)
self.concat = Concat(1)
if shortcut and in_channels == out_channels:
self.shortcut = nn.Sequential()
else:
self.shortcut = ACBlock(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
def forward(self, x):
shortcut = self.shortcut(x)
x = self.conv1(x)
x = self.concat([self.conv4(self.conv3(self.conv2(x))), x])
return shortcut + x
```
然后将 `Conv` 替换为 `ACBlock`,就可以使用 Acnet 了。
```python
from models.common import Conv, BottleneckCSP, ACBlock
class CSPBlock(nn.Module):
def __init__(self, in_channels, out_channels, bottleneck=1, groups=1, expansion=0.5, shortcut=True):
super(CSPBlock, self).__init__()
hidden_channels = int(out_channels * expansion)
self.conv1 = ACBlock(in_channels, hidden_channels, kernel_size=1, stride=1, padding=0)
self.conv2 = ACBlock(in_channels, hidden_channels, kernel_size=1, stride=1, padding=0)
self.conv3 = ACBlock(hidden_channels, hidden_channels, kernel_size=3, stride=1, padding=1)
self.conv4 = ACBlock(hidden_channels, hidden_channels, kernel_size=1, stride=1, padding=0)
self.concat = Concat(1)
if shortcut and in_channels == out_channels:
self.shortcut = nn.Sequential()
else:
self.shortcut = ACBlock(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
def forward(self, x):
shortcut = self.shortcut(x)
x = self.conv1(x)
x = self.concat([self.conv4(self.conv3(self.conv2(x))), x])
return shortcut + x
```
这样就可以将 Acnet 替换 YOLOv5 中的卷积层了。
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