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在深度学习算法模型推理时，会遇到fp16类型，但是我们的c语言中没有这种类型，直接转成unsigned short又会丧失精度，因此我们首先将FP16转成float类型，再进行计算。
方法1：
typedef unsigned short ushort;//占用2个字节
typedef unsigned int uint; //占用4个字节
uint as_uint(const float x) {
return *(uint*)&x;
}
float as_float(const uint x) {
return *(float*)&x;
}
float half_to_float(const ushort x) { // IEEE-754 16-bit floating-point format (without infinity): 1-5-10, exp-15, +-131008.0, +-6.1035156E-5, +-5.9604645E-8, 3.311 digits
const uint e = (x&0x7C00)>>10; // exponent
const uint m = (x&0x03FF)<<13; // mantissa
const uint v = as_uint((float)m)>>23; // evil log2 bit hack to count leading zeros in denormalized format
return as_float((x&0x8000)<<16 | (e!=0)*((e+112)<<23|m) | ((e==0)&(m!=0))*((v-37)<<23|((m<<(150-v))&0x007FE000))); // sign : normalized : denormalized
}
ushort float_to_half(const float x) { // IEEE-754 16-bit floating-point format (without infinity): 1-5-10, exp-15, +-131008.0, +-6.1035156E-5, +-5.9604645E-8, 3.311 digits
const uint b = as_uint(x)+0x00001000; // round-to-nearest-even: add last bit after truncated mantissa
const uint e = (b&0x7F800000)>>23; // exponent
const uint m = b&0x007FFFFF; // mantissa; in line below: 0x007FF000 = 0x00800000-0x00001000 = decimal indicator flag - initial rounding
return (b&0x80000000)>>16 | (e>112)*((((e-112)<<10)&0x7C00)|m>>13) | ((e<113)&(e>101))*((((0x007FF000+m)>>(125-e))+1)>>1) | (e>143)*0x7FFF; // sign : normalized : denormalized : saturate
}
方法2：
float cpu_half2float(unsigned short x)
{
unsigned sign = ((x >> 15) & 1);
unsigned exponent = ((x >> 10) & 0x1f);
unsigned mantissa = ((x & 0x3ff) << 13);
if (exponent == 0x1f) { /* NaN or Inf */
mantissa = (mantissa ? (sign = 0, 0x7fffff) : 0);
exponent = 0xff;
} else if (!exponent) { /* Denorm or Zero */
if (mantissa) {
unsigned int msb;
exponent = 0x71;
do {
msb = (mantissa & 0x400000);
mantissa <<= 1; /* normalize */
--exponent;
} while (!msb);
mantissa &= 0x7fffff; /* 1.mantissa is implicit */
}
} else {
exponent += 0x70;
}
int temp = ((sign << 31) | (exponent << 23) | mantissa);
return *((float*)((void*)&temp));
}
3 demo
?下面的demo中，yolov5_outputs[0].buf是void *类型的，void *类型不能++，因此先转换成ushort*类型。
...
...
float *data0 = (float*)malloc(4 * output_attrs[0].n_elems);
float *data1 = (float*)malloc(4 * output_attrs[1].n_elems);
float *data2 = (float*)malloc(4 * output_attrs[2].n_elems);
unsigned short *temp0 = (ushort*)yolov5_outputs[0].buf;
unsigned short *temp1 = (ushort*)yolov5_outputs[1].buf;
unsigned short *temp2 = (ushort*)yolov5_outputs[2].buf;
for(int i=0; i < output_attrs[0].n_elems;i++)
{
data0[i] = half_to_float(temp0[i]);
}
for(int i=0; i < output_attrs[1].n_elems;i++)
{
data1[i] = half_to_float(temp1[i]);
}
for(int i=0; i < output_attrs[2].n_elems;i++)
{
data2[i] = half_to_float(temp2[i]);
}
...
...
参考文献：
https://github.com/PrincetonVision/marvin/blob/master/tools/tensorIO_matlab/half2float.cpp
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