PyTorch的自适应池化Adaptive Pooling实例

Python  /  管理员 发布于 5年前   441

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。

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)>>> inputtensor([[[ 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)>>> output1tensor([[[1.5695, 1.5179, 0.5312, 0.4945]]]) torch.Size([1, 1, 4])>>> output2tensor([[[1.5695, 1.5179, 0.5312, 0.4945]]]) torch.Size([1, 1, 4])

通过实验发现：

下面是Adaptive Average Pooling的c++源码部分。

 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;    }   }  }}

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