PyTorch的自適應(yīng)池化Adaptive Pooling實(shí)例

更新時(shí)間：2020年01月03日 11:22:30 作者：冷月葬婲魂

今天小編就為大家分享一篇PyTorch的自適應(yīng)池化Adaptive Pooling實(shí)例，具有很好的參考價(jià)值，希望對(duì)大家有所幫助。一起跟隨小編過(guò)來(lái)看看吧

簡(jiǎn)介

自適應(yīng)池化Adaptive Pooling是PyTorch含有的一種池化層，在PyTorch的中有六種形式：

自適應(yīng)最大池化Adaptive Max Pooling：

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

自適應(yīng)平均池化Adaptive Average Pooling：

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

具體可見(jiàn)官方文檔。

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特殊性在于，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(shè)定輸出大小為(5,7)，通過(guò)Adaptive Pooling層，可以得到大小為(1, 64, 5, 7)的張量。

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])

通過(guò)實(shí)驗(yàn)發(fā)現(xiàn)：

下面是Adaptive Average Pooling的c++源碼部分。

 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;
    }
   }
  }
}

以上這篇PyTorch的自適應(yīng)池化Adaptive Pooling實(shí)例就是小編分享給大家的全部?jī)?nèi)容了，希望能給大家一個(gè)參考，也希望大家多多支持腳本之家。

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