PyTorch的自适应池化Adaptive Pooling实例
时间：2022-07-14 08:18:03 | 栏目：Python代码 | 点击：次
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) >>> 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：
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;
}
}
}
}