Pool2D

1、torch.nn.AdaptiveMaxPool2d/ADAPTIVEAVGPOOL2D

官方给出的例子：

• 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特殊性在于，输出张量的大小都是给定的output_size。例如输入张量大小为(1, 64, 8, 9)，设定输出大小为(5,7)，通过Adaptive Pooling层，可以得到大小为(1, 64, 5, 7)的张量。

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])

通过实验发现：

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