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。 而floor模式则是直接把不足square_size的边给舍弃了。
