718c41634f
1.项目后端整体迁移至PaddleOCR-NCNN算法，已通过基本的兼容性测试 2.工程改为使用CMake组织，后续为了更好地兼容第三方库，不再提供QMake工程 3.重整权利声明文件，重整代码工程，确保最小化侵权风险 Log: 切换后端至PaddleOCR-NCNN，切换工程为CMake Change-Id: I4d5d2c5d37505a4a24b389b1a4c5d12f17bfa38c
120 lines
2.9 KiB
Markdown
120 lines
2.9 KiB
Markdown
### what is packing and why
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packing is the form of storing multiple short-sized values as one long-sized value.
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element packing is well mapped with the underlying simd register, which usually use one very wide register to store different types of values.
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|C|elemsize|elempack|
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|---|---|---|
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|double|8|1|
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|float|4|1|
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|int|4|1|
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|short|2|1|
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|signed char|1|1|
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|arm neon|elemsize|elempack|
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|---|---|---|
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|float64x2_t|16|2|
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|float32x4_t|16|4|
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|int32x4_t|16|4|
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|float16x4_t|8|4|
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|int8x8_t|8|8|
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Though the real count of values doubles when elempack is two, the wide-sized value is still treated as one value in the view of Mat structure. For example, we want to store 40 float values in Mat object, if elempack 1 is used, Mat width is then 40, while 10 if elempack 4 is used.
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|dims|w|h|c|cstep|elemsize|elempack|
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|---|---|---|---|---|---|---|
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|1|40|1|1|40|4|1|
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|1|10|1|1|10|16|4|
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### packing style convention
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In practice, elempack 1, 4, 8 are the most common cases. It is possible to use any other packing style in theory.
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The following table show the packing axis used in ncnn for different dimension.
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|dims|packing axis|shape before packing|shape after packing|
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|---|---|---|---|
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|1|w|w|w/elempack|
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|2|h|w, h|w, h/elempack|
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|3|c|w, h, c|w, h, c/elempack|
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If the packing axis dim is not evenly divisible by elempack, zero padding may be used.
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```
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outw = (w + elempack - 1) / elempack;
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```
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The following snippet shows the memory layout after elempack=4 on 3-dim Mat
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```
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// w=2 h=3 c=4 elempack=1
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0 1
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2 3
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4 5
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6 7
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8 9
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10 11
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12 13
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14 15
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16 17
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18 19
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20 21
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22 23
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// w=2 h=3 c=1 elempack=4
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(0,6,12,18) (1,7,13,19)
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(2,8,14,20) (3,9,15,21)
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(4,10,16,22) (5,11,17,23)
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```
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### how to convert elempack
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There is a convenient wrapper function provided
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```
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// convert to elempack 4 if packing axis dim is evenly divisible by elempack
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// return the identity Mat otherwise
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ncnn::Mat a;
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ncnn::Mat a_packed;
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ncnn::convert_packing(a, a_packed, 4);
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if (a_packed.elempack == 4)
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{
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// check if packing is successful
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}
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// convert to packing 1, aka unpacking, shall be always successful
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ncnn::Mat b;
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ncnn::Mat b_unpacked;
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ncnn::convert_packing(b, b_unpacked, 1);
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```
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### handle general interleaved data
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Here is an example of using convert packing to convert RGB interleaved data to planar
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**NOTE:** The following code is just presented to explain what packing is and the conversion process. Do not use it in production due to its poor performance. Do use ncnn::Mat::from_pixels()
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```cpp
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// rgb_interleaved_u8 is RGB RGB RGB ...
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// rgb_interleaved_u8.w = w;
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// rgb_interleaved_u8.h = h;
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// rgb_interleaved_u8.c = 1;
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// rgb_interleaved_u8.elemsize = 3;
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// rgb_interleaved_u8.elempack = 3;
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ncnn::Mat rgb_interleaved_u8(w, h, 1, 3, 3);
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ncnn::Mat rgb_planar_u8;
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ncnn::convert_packing(rgb_interleaved_u8, rgb_planar_u8, 1);
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// rgb_planar_u8 is now RRR ... GGG ... BBB ...
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// rgb_planar_u8.w = w;
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// rgb_planar_u8.h = h;
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// rgb_planar_u8.c = 3;
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// rgb_planar_u8.elemsize = 1;
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// rgb_planar_u8.elempack = 1;
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```
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