利用pytorch搭建Ghost卷积模块
时间: 2023-07-04 14:31:10 浏览: 98
好的,利用PyTorch搭建Ghost卷积模块的步骤如下:
1. 导入需要的模块
```python
import torch.nn as nn
import torch.nn.functional as F
```
2. 定义Ghost卷积层
```python
class GhostConv(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding=0, groups=1, ratio=2):
super(GhostConv, self).__init__()
self.primary_conv = nn.Conv2d(in_channels, out_channels // ratio, kernel_size, stride, padding, groups=groups, bias=False)
self.cheap_conv = nn.Conv2d(out_channels // ratio, out_channels - in_channels, kernel_size=1, stride=1, padding=0, bias=False)
self.bn = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
x1 = self.primary_conv(x)
x2 = self.cheap_conv(x1)
out = torch.cat([x1, x2], dim=1)
out = self.bn(out)
out = self.relu(out)
return out
```
3. 测试Ghost卷积层
```python
ghost_conv = GhostConv(3, 6, kernel_size=3, stride=1, padding=1, groups=1, ratio=2)
x = torch.randn(1, 3, 32, 32)
out = ghost_conv(x)
print(out.shape)
```
以上就是利用PyTorch搭建Ghost卷积模块的步骤,其中`GhostConv`类中的`ratio`参数表示primary convolution输出通道数与cheap convolution输出通道数的比例,通常为2。
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