Pytorch AdaptivePooing操作转Pooling操作

Pytorch AdaptivePooing操作转Pooling操作

多数的前向推理框架不支持AdaptivePooing操作，此时需要将AdaptivePooing操作转换为普通的Pooling操作。AdaptivePooling与Max/AvgPooling相互转换提供了一种转换方法，但我在Pytorch1.6中的测试结果是错误的。通过查看Pytorch源码（pytorch-master\aten\src\ATen\native\AdaptiveAveragePooling.cpp）我找出了正确的转换方式。

  inline int start_index(int a, int b, int c) {
    return (int)std::floor((float)(a * c) / b);
  }

  inline int end_index(int a, int b, int c) {
    return (int)std::ceil((float)((a + 1) * c) / b);
  }

  template 
  static void adaptive_avg_pool2d_single_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)
  {
    at::parallel_for(0, sizeD, 0, [&](int64_t start, int64_t end) {
      for (auto d = start; d < end; 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;
          }
        }
      }
    });
  }

上述代码段中isizeH，isizeW分别表示输入张量的宽高osizeH，osizeW则表示输出宽高。关注第二个for循环for(oh = 0; oh < osizeH; oh++){.....}中的内容。假设输入的宽高均为223isizeH = isizeW = 223，输出的宽高均为7osizeH = osizeW = 224,然后简单分析一下oh=0,1,2时的情况：

• oh=0, istartH = 0, iendH = ceil(223/7)=32, kH = 32
• oh=1, istartH = floor(223/7) = 31, iendH = ceil(223*2/7)=64, kH = 33
• oh=2, istartH = floor(223*2/7) = 63, iendH = ceil(223*3/7)=96, kH = 33

这里的kH就是kernel_size的大小. oh=0时的kernel_size比其他情况要小，所以需要在输入上添加padding，让oh=0时的kernel_size与其他情况相同。添加的padding大小为1，等价于让istartH从-1开始，即kH = 32-(-1) = 33. 下一个需要获取的参数是stride，stride = istartH[oh=i]-istartH[oh=i-1], 在上述例子中即为32。按照上述的例子分析输入宽高为224的情况可以发现padding=0，所以padding也是一个需要转换的参数。下面给出3个参数的转换公式：

• stride = ceil(input_size / output_size)
• kernel_size = ceil(2 * input_size / output_size) - floor(input_size / output_size)
• padding = ceil(input_size / output_size) - floor(input_size / output_size)

在上述的代码中最后部分，可以看见均值使用*op = sum / kW / kH计算得到的。这表明在边缘部分计算均值没有考虑padding，所以对应的AvgPool中的count_include_pad应该设为False。下面贴出我的测试代码：

def test(size):
    import numpy as np
    import torch
    
    x = torch.randn(1,1,size,size)
    
    input_size = np.array(x.shape[2:])
    output_size = np.array([7,7])

    # stride = ceil(input_size / output_size)
    # kernel_size = ceil(2 * input_size / output_size) - floor(input_size / output_size)
    # padding = ceil(input_size / output_size) - floor(input_size / output_size)

    stride = numpy.ceil(input_size / output_size).astype(int)
    kernel_size = (numpy.ceil(2 * input_size / output_size) - numpy.floor(input_size / output_size)).astype(int)
    padding = (numpy.ceil(input_size / output_size) - numpy.floor(input_size / output_size)).astype(int)
    print(stride)
    print(kernel_size)
    print(padding)
    avg1 = nn.AdaptiveAvgPool2d(list(output_size))
    avg2 = nn.AvgPool2d(kernel_size=kernel_size.tolist(), stride=stride.tolist(), padding=padding.tolist(), ceil_mode=False, count_include_pad=False)
    max1 = nn.AdaptiveMaxPool2d(list(output_size))
    max2 = nn.MaxPool2d(kernel_size=kernel_size.tolist(), stride=stride.tolist(), padding=padding.tolist(), ceil_mode=False )


    avg1_out = avg1(x)
    avg2_out = avg2(x)
    max1_out = max1(x)
    max2_out = max2(x)
    print(avg1_out-avg2_out)
    print(max1_out-max2_out)
    print(torch.__version__)

• inH = inW=224时的输出

• inH = inW=223时的输出