caffe之upsample实现

message UpsampleParameter{
  optional int32 scale = 1 [default = 1];
}
message LayerParameter{
...
 optional UpsampleParameter upsample_param = 149;  //序号不冲突即可
}
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https://github.com/SeanQ88/caffe_upsample
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int N = 1, C =1, H = 4, H = 4;
for (int n = 0; n 

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4. 反向传播

由于bottom_diff是NCHW=(1,1,2,2), top_diff(1,1,4,4)
并且scale是常数，不参数梯度，即可认为是f = x,导数为f’=1
从整个表达来说f = scale * x，top_diff * scale 应该也是可以的，然后再做如下运算，传递梯度到下一层。如有理解误的，望指正。

  for (int n = 0; n < N; n++) {
    for (int c = 0; c < C; c++) {
      for (int h = 0; h < H; h++) {
        for (int w = 0; w < W; w++) {
          for (int i = 0; i < scale_; i++) {
            for (int j = 0; j < scale_; j++) {
              int nw = w * scale_ + i;
              int nh = h * scale_ + j;
              int out_idx = (((n * C + c) * H) + h) * W + w;
              int in_idx = (((n * C + c) * (H * scale_))
                  + nh) * (W * scale_) + nw;
              bottom_diff[out_idx] += top_diff[in_idx];
            }
          }
        }
      }
    }
  }
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