Pytorch---maxpool的ceil_mode

https://blog.csdn.net/GZHermit/article/details/79351803

 

 

pytorch里面的maxpool,有一个属性叫ceil_mode,这个属性在api里面的解释是

ceil_mode: when True, will use ceil instead of floor to compute the output shape

也就是说,在计算输出的shape的时候,
如果ceil_mode的值为True,那么则用天花板模式
否则用地板模式。

举例子:
square_size=6
在下面的代码中,无论ceil_mode是True or False,结果都是一样 

# coding:utf-8
import torch
import torch.nn as nn
from torch.autograd import Variable


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.maxp = nn.MaxPool2d(kernel_size=2, ceil_mode=False)

    def forward(self, x):
        x = self.maxp(x)
        return x

square_size = 6
inputs = torch.randn(1, 1, square_size, square_size)
for i in range(square_size):
    inputs[0][0][i] = i * torch.ones(square_size)
inputs = Variable(inputs)
print(inputs)

net = Net()
outputs = net(inputs)
print(outputs.size())
print(outputs)

但是如果设置square_size=5,那么

当ceil_mode=True

Variable containing: 
(0 ,0 ,.,.) = 
0 0 0 0 0 
1 1 1 1 1 
2 2 2 2 2 
3 3 3 3 3 
4 4 4 4 4 
[torch.FloatTensor of size 1x1x5x5] 
torch.Size([1, 1, 3, 3]) 
Variable containing: 
(0 ,0 ,.,.) = 
1 1 1 
3 3 3 
4 4 4 
[torch.FloatTensor of size 1x1x3x3]

当ceil_mode=False

Variable containing: 
(0 ,0 ,.,.) = 
0 0 0 0 0 
1 1 1 1 1 
2 2 2 2 2 
3 3 3 3 3 
4 4 4 4 4 
[torch.FloatTensor of size 1x1x5x5] 
torch.Size([1, 1, 2, 2]) 
Variable containing: 
(0 ,0 ,.,.) = 
1 1 
3 3 
[torch.FloatTensor of size 1x1x2x2]

所以ceil模式就是会把不足square_size的边给保留下来,单独另算,
或者也可以理解为在原来的数据上补充了值为-NAN的边
floor模式则是直接把不足square_size的边给舍弃了。

 

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