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
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what is packing and why
packing is the form of storing multiple short-sized values as one long-sized value.
element packing is well mapped with the underlying simd register, which usually use one very wide register to store different types of values.
C | elemsize | elempack |
---|---|---|
double | 8 | 1 |
float | 4 | 1 |
int | 4 | 1 |
short | 2 | 1 |
signed char | 1 | 1 |
arm neon | elemsize | elempack |
---|---|---|
float64x2_t | 16 | 2 |
float32x4_t | 16 | 4 |
int32x4_t | 16 | 4 |
float16x4_t | 8 | 4 |
int8x8_t | 8 | 8 |
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.
dims | w | h | c | cstep | elemsize | elempack |
---|---|---|---|---|---|---|
1 | 40 | 1 | 1 | 40 | 4 | 1 |
1 | 10 | 1 | 1 | 10 | 16 | 4 |
packing style convention
In practice, elempack 1, 4, 8 are the most common cases. It is possible to use any other packing style in theory.
The following table show the packing axis used in ncnn for different dimension.
dims | packing axis | shape before packing | shape after packing |
---|---|---|---|
1 | w | w | w/elempack |
2 | h | w, h | w, h/elempack |
3 | c | w, h, c | w, h, c/elempack |
If the packing axis dim is not evenly divisible by elempack, zero padding may be used.
outw = (w + elempack - 1) / elempack;
The following snippet shows the memory layout after elempack=4 on 3-dim Mat
// w=2 h=3 c=4 elempack=1
0 1
2 3
4 5
6 7
8 9
10 11
12 13
14 15
16 17
18 19
20 21
22 23
// w=2 h=3 c=1 elempack=4
(0,6,12,18) (1,7,13,19)
(2,8,14,20) (3,9,15,21)
(4,10,16,22) (5,11,17,23)
how to convert elempack
There is a convenient wrapper function provided
// convert to elempack 4 if packing axis dim is evenly divisible by elempack
// return the identity Mat otherwise
ncnn::Mat a;
ncnn::Mat a_packed;
ncnn::convert_packing(a, a_packed, 4);
if (a_packed.elempack == 4)
{
// check if packing is successful
}
// convert to packing 1, aka unpacking, shall be always successful
ncnn::Mat b;
ncnn::Mat b_unpacked;
ncnn::convert_packing(b, b_unpacked, 1);
handle general interleaved data
Here is an example of using convert packing to convert RGB interleaved data to planar
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()
// rgb_interleaved_u8 is RGB RGB RGB ...
// rgb_interleaved_u8.w = w;
// rgb_interleaved_u8.h = h;
// rgb_interleaved_u8.c = 1;
// rgb_interleaved_u8.elemsize = 3;
// rgb_interleaved_u8.elempack = 3;
ncnn::Mat rgb_interleaved_u8(w, h, 1, 3, 3);
ncnn::Mat rgb_planar_u8;
ncnn::convert_packing(rgb_interleaved_u8, rgb_planar_u8, 1);
// rgb_planar_u8 is now RRR ... GGG ... BBB ...
// rgb_planar_u8.w = w;
// rgb_planar_u8.h = h;
// rgb_planar_u8.c = 3;
// rgb_planar_u8.elemsize = 1;
// rgb_planar_u8.elempack = 1;