Pytorch(2) maxpool的ceil_mode

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。

???

举两个例子就明白了。

# 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)

在上面的代码中,无论ceil_mode是True or False,结果都是一样
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。而floor模式则是直接把不足square_size的边给舍弃了。

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