PyTorch的自适应池化Adaptive Pooling实例_Python

简介

自适应池化Adaptive Pooling是PyTorch含有的一种池化层，在PyTorch的中有六种形式：

自适应最大池化Adaptive Max Pooling：

torch.nn.AdaptiveMaxPool1d(output_size)
torch.nn.AdaptiveMaxPool2d(output_size)
torch.nn.AdaptiveMaxPool3d(output_size)

自适应平均池化Adaptive Average Pooling：

torch.nn.AdaptiveAvgPool1d(output_size)
torch.nn.AdaptiveAvgPool2d(output_size)
torch.nn.AdaptiveAvgPool3d(output_size)

具体可见官方文档。

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									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 output\_sizeoutput_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。

原理

									>>> 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：

PyTorch的自适应池化Adaptive Pooling实例下面是Adaptive Average Pooling的c++源码部分。

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									template <typename scalar_t>

									 static void adaptive_avg_pool2d_out_frame(

									      scalar_t *input_p,

									      scalar_t *output_p,

									      int64_t sizeD,

									      int64_t isizeH,

									      int64_t isizeW,

									      int64_t osizeH,

									      int64_t osizeW,

									      int64_t istrideD,

									      int64_t istrideH,

									      int64_t istrideW)

									 {

									  int64_t d;

									 #pragma omp parallel for private(d)

									  for (d = 0; d < sizeD; d++)

									  {

									   /* loop over output */

									   int64_t oh, ow;

									   for(oh = 0; oh < osizeH; oh++)

									   {

									    int istartH = start_index(oh, osizeH, isizeH);

									    int iendH  = end_index(oh, osizeH, isizeH);

									    int kH = iendH - istartH;

									    for(ow = 0; ow < osizeW; ow++)

									    {

									     int istartW = start_index(ow, osizeW, isizeW);

									     int iendW  = end_index(ow, osizeW, isizeW);

									     int kW = iendW - istartW;

									     /* local pointers */

									     scalar_t *ip = input_p  + d*istrideD + istartH*istrideH + istartW*istrideW;

									     scalar_t *op = output_p + d*osizeH*osizeW + oh*osizeW + ow;

									     /* compute local average: */

									     scalar_t sum = 0;

									     int ih, iw;

									     for(ih = 0; ih < kH; ih++)

									     {

									      for(iw = 0; iw < kW; iw++)

									      {

									       scalar_t val = *(ip + ih*istrideH + iw*istrideW);

									       sum += val;

									      }

									     }

									     /* set output to local average */

									     *op = sum / kW / kH;

									    }

									   }

									  }

									}

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原文链接：https://blog.csdn.net/u013382233/article/details/85948695

PyTorch的自适应池化Adaptive Pooling实例

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