官方給出的例子：
target output size of 5x7
m = nn.AdaptiveMaxPool2d((5,7))
input = torch.randn(1, 64, 8, 9)
output = m(input)
output.size()
torch.Size([1, 64, 5, 7])
target output size of 7x7 (square)
m = nn.AdaptiveMaxPool2d(7)
input = torch.randn(1, 64, 10, 9)
output = m(input)
output.size()
torch.Size([1, 64, 7, 7])
target output size of 10x7
m = nn.AdaptiveMaxPool2d((None, 7))
input = torch.randn(1, 64, 10, 9)
output = m(input)
output.size()
torch.Size([1, 64, 10, 7])
Adaptive Pooling特殊性在於，輸出張量的大小都是給定的output_size output_sizeoutput_size。例如輸入張量大小為(1, 64, 8, 9)，設定輸出大小為(5,7)，通過Adaptive Pooling層，可以得到大小為(1, 64, 5, 7)的張量。
inputsize = 9
outputsize = 4
input = torch.randn(1, 1, inputsize)
input
tensor([[[ 1.5695, -0.4357, 1.5179, 0.9639, -0.4226, 0.5312, -0.5689, 0.4945, 0.1421]]])
m1 = nn.AdaptiveMaxPool1d(outputsize)
m2 = nn.MaxPool1d(kernel_size=math.ceil(inputsize / outputsize), stride=math.floor(inputsize / outputsize), padding=0)
output1 = m1(input)
output2 = m2(input)
output1
tensor([[[1.5695, 1.5179, 0.5312, 0.4945]]]) torch.Size([1, 1, 4])output2
tensor([[[1.5695, 1.5179, 0.5312, 0.4945]]]) torch.Size([1, 1, 4])
通過實驗發現：
