如何实现对特征图分割成空间金字塔利用全局平均池化,代码演示
时间: 2024-03-24 12:39:40 浏览: 14
可以使用PyTorch实现特征图分割成空间金字塔利用全局平均池化的方法,以下是代码演示:
```python
import torch.nn as nn
import torch.nn.functional as F
class SpatialPyramidPooling(nn.Module):
def __init__(self, num_pools, pool_size):
super(SpatialPyramidPooling, self).__init__()
self.num_pools = num_pools
self.pool_size = pool_size
def forward(self, x):
num_channels = x.size(1)
pool_sizes = [x.size(2) // i for i in range(1, self.num_pools+1)]
pools = []
for pool_size in pool_sizes:
pool = F.avg_pool2d(x, kernel_size=(pool_size, pool_size))
pool = F.interpolate(pool, size=(self.pool_size, self.pool_size), mode='nearest')
pools.append(pool)
out = torch.cat(pools, dim=1)
return out
class Net(nn.Module):
def __init__(self, num_pools, pool_size):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
self.conv3 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
self.pool = SpatialPyramidPooling(num_pools, pool_size)
self.fc = nn.Linear(256*num_pools*pool_size*pool_size, 10)
def forward(self, x):
x = F.relu(self.conv1(x))
x = F.relu(self.conv2(x))
x = F.relu(self.conv3(x))
x = self.pool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
```
以上代码中,我们定义了一个`SpatialPyramidPooling`类来实现特征图分割成空间金字塔利用全局平均池化的操作。在`Net`类中,我们先定义了三个卷积层,然后使用`SpatialPyramidPooling`类对特征图进行分割和池化操作,最后使用一个全连接层输出结果。可以通过调整`num_pools`和`pool_size`来控制分割的层数和每层池化后的大小。