Pytorch MaxPool2d
先说说nn.functional和nn：只调用函数的话，其实是一回事。
再说说我学nn.MaxPool2d时遇到的问题：
import torch
import torch.nn as nn
m=nn.MaxPool2d(3,stride=2)
input=torch.randn(6,6)
output=m(input)
然后就会报这个错：
RuntimeError: non-empty 3D or 4D (batch mode) tensor expected for input
我寻思这不是maxpool2d么，为啥非得要3d或4d的输入数据啊。后来才明白，这个3d多出来的一维是指多个2d输入存到一个input里，然后用这个maxpool2d批量处理。把输入改一下：
input=torch.randn(6,6,6)
input
tensor([[[ 1.3651, -0.5862, 0.0039, 0.7669, -1.1744, -0.6920],
[-1.2498, 0.5902, 0.8022, 0.3453, 0.4135, -2.0394],
[-1.1461, -0.8356, 0.2398, -1.0064, -0.8885, 0.5996],
[-0.6647, 0.7676, -1.0845, -0.0285, -0.8607, -1.5220],
[-0.6462, 0.4841, -0.8933, -0.0123, 0.1304, -1.6382],
[-1.6518, 0.1764, -0.3518, 2.1585, 0.7927, -1.2119]],
[[-0.4033, -0.3612, -1.0288, 0.1967, 0.5706, -0.2084],
[-0.3500, -0.5760, 0.4434, -0.0729, -0.6940, 0.8788],
[ 0.3062, -0.3983, -1.0179, -0.0206, 1.6909, 1.3363],
[-0.5441, -0.3139, 0.3312, 0.0118, 0.6946, 1.0262],
[-0.7408, 1.8493, -0.3663, -0.7789, 0.6197, 1.3990],
[ 0.2292, 0.4398, 0.9694, 0.7889, -0.9330, 0.1868]],
[[-0.6787, -0.9165, -1.2320, -1.3221, 1.0197, 0.8859],
[ 0.3160, -0.6479, -1.8085, 0.0580, 1.6721, 0.8309],
[ 0.0744, -0.0405, -1.5407, 1.7720, 1.3292, 1.6349],
[-0.8554, -0.9928, 0.1849, 0.2057, 0.0687, 0.7527],
[ 0.9199, -0.7936, 0.3789, -0.1087, -0.4615, -0.9368],
[-0.1616, -0.0135, -0.0403, 0.0218, -1.7800, -0.3535]],
[[ 1.1523, -0.6533, -0.6440, -1.4352, -0.4556, -0.5540],
[ 1.8307, -0.3786, -0.0065, -0.9373, -1.3478, -0.9279],
[ 0.1462, -0.8618, 1.1309, -1.6756, -0.2423, 0.4219],
[-1.3056, 0.6921, 0.0219, -0.0710, -0.4203, 0.9639],
[ 0.5725, -1.2465, -0.4121, 1.0231, 1.3580, 0.7545],
[-0.8918, -0.1593, -1.7663, -0.7283, 0.9509, 1.6359]],
[[-0.8375, -0.8949, -1.7319, -0.9304, 2.3549, -0.3261],
[ 0.3865, -0.4016, 0.4349, 0.5395, 0.0925, -0.9209],
[ 0.5214, 0.3324, 0.9826, -0.4607, 0.5845, -0.4193],
[ 1.5872, -0.5236, -0.6116, -0.1904, -1.7557, -0.8296],
[-0.1711, 0.0454, -0.3158, 2.6561, 1.6830, 0.5439],
[-0.2289, -1.1473, -1.0294, 0.0683, -0.0164, 0.8971]],
[[-0.3294, 0.7784, 0.8532, -1.1260, -0.8300, -1.7882],
[-1.3112, 0.7504, 1.0123, -1.0672, 1.0213, -0.4506],
[-1.2506, 0.4484, -0.7977, 0.2568, -0.7581, 0.6981],
[-1.3001, 0.1575, -1.2660, 0.5367, -1.6008, 0.7765],
[ 1.3728, -1.2267, -0.1835, -0.8143, -0.1198, -0.3916],
[ 2.8967, 1.0502, 0.8877, 0.4246, -0.2282, 0.0319]]])
output=m(input)
output
tensor([[[1.3651, 0.8022],
[0.7676, 0.2398]],
[[0.4434, 1.6909],
[1.8493, 1.6909]],
[[0.3160, 1.7720],
[0.9199, 1.7720]],
[[1.8307, 1.1309],
[1.1309, 1.3580]],
[[0.9826, 2.3549],
[1.5872, 2.6561]],
[[1.0123, 1.0213],
[1.3728, 0.5367]]])
这回结果就出来了。