You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
296 lines
8.9 KiB
C++
296 lines
8.9 KiB
C++
#include "UnaryTestsF32.h"
|
|
#include <stdio.h>
|
|
#include "Error.h"
|
|
|
|
#define SNR_THRESHOLD 120
|
|
|
|
/*
|
|
|
|
Reference patterns are generated with
|
|
a double precision computation.
|
|
|
|
*/
|
|
#define REL_ERROR (1.0e-6)
|
|
#define ABS_ERROR (1.0e-5)
|
|
|
|
/*
|
|
|
|
Comparisons for inverse
|
|
|
|
*/
|
|
|
|
/* Not very accurate for big matrix.
|
|
But big matrix needed for checking the vectorized code */
|
|
|
|
#define SNR_THRESHOLD_INV 70
|
|
#define REL_ERROR_INV (1.0e-3)
|
|
#define ABS_ERROR_INV (1.0e-3)
|
|
|
|
/* Upper bound of maximum matrix dimension used by Python */
|
|
#define MAXMATRIXDIM 40
|
|
|
|
#define LOADDATA2() \
|
|
const float32_t *inp1=input1.ptr(); \
|
|
const float32_t *inp2=input2.ptr(); \
|
|
\
|
|
float32_t *ap=a.ptr(); \
|
|
float32_t *bp=b.ptr(); \
|
|
\
|
|
float32_t *outp=output.ptr(); \
|
|
int16_t *dimsp = dims.ptr(); \
|
|
int nbMatrixes = dims.nbSamples() >> 1;\
|
|
int rows,columns; \
|
|
int i;
|
|
|
|
#define LOADDATA1() \
|
|
const float32_t *inp1=input1.ptr(); \
|
|
\
|
|
float32_t *ap=a.ptr(); \
|
|
\
|
|
float32_t *outp=output.ptr(); \
|
|
int16_t *dimsp = dims.ptr(); \
|
|
int nbMatrixes = dims.nbSamples() >> 1;\
|
|
int rows,columns; \
|
|
int i;
|
|
|
|
#define PREPAREDATA2() \
|
|
in1.numRows=rows; \
|
|
in1.numCols=columns; \
|
|
memcpy((void*)ap,(const void*)inp1,sizeof(float32_t)*rows*columns);\
|
|
in1.pData = ap; \
|
|
\
|
|
in2.numRows=rows; \
|
|
in2.numCols=columns; \
|
|
memcpy((void*)bp,(const void*)inp2,sizeof(float32_t)*rows*columns);\
|
|
in2.pData = bp; \
|
|
\
|
|
out.numRows=rows; \
|
|
out.numCols=columns; \
|
|
out.pData = outp;
|
|
|
|
#define PREPAREDATA1(TRANSPOSED) \
|
|
in1.numRows=rows; \
|
|
in1.numCols=columns; \
|
|
memcpy((void*)ap,(const void*)inp1,sizeof(float32_t)*rows*columns);\
|
|
in1.pData = ap; \
|
|
\
|
|
if (TRANSPOSED) \
|
|
{ \
|
|
out.numRows=columns; \
|
|
out.numCols=rows; \
|
|
} \
|
|
else \
|
|
{ \
|
|
out.numRows=rows; \
|
|
out.numCols=columns; \
|
|
} \
|
|
out.pData = outp;
|
|
|
|
|
|
void UnaryTestsF32::test_mat_add_f32()
|
|
{
|
|
LOADDATA2();
|
|
|
|
for(i=0;i < nbMatrixes ; i ++)
|
|
{
|
|
rows = *dimsp++;
|
|
columns = *dimsp++;
|
|
|
|
PREPAREDATA2();
|
|
|
|
arm_mat_add_f32(&this->in1,&this->in2,&this->out);
|
|
|
|
outp += (rows * columns);
|
|
|
|
}
|
|
|
|
ASSERT_EMPTY_TAIL(output);
|
|
|
|
ASSERT_SNR(output,ref,(float32_t)SNR_THRESHOLD);
|
|
|
|
ASSERT_CLOSE_ERROR(output,ref,ABS_ERROR,REL_ERROR);
|
|
|
|
}
|
|
|
|
void UnaryTestsF32::test_mat_sub_f32()
|
|
{
|
|
LOADDATA2();
|
|
|
|
for(i=0;i < nbMatrixes ; i ++)
|
|
{
|
|
rows = *dimsp++;
|
|
columns = *dimsp++;
|
|
|
|
PREPAREDATA2();
|
|
|
|
arm_mat_sub_f32(&this->in1,&this->in2,&this->out);
|
|
|
|
outp += (rows * columns);
|
|
|
|
}
|
|
|
|
ASSERT_EMPTY_TAIL(output);
|
|
|
|
ASSERT_SNR(output,ref,(float32_t)SNR_THRESHOLD);
|
|
|
|
ASSERT_CLOSE_ERROR(output,ref,ABS_ERROR,REL_ERROR);
|
|
|
|
}
|
|
|
|
void UnaryTestsF32::test_mat_scale_f32()
|
|
{
|
|
LOADDATA1();
|
|
|
|
for(i=0;i < nbMatrixes ; i ++)
|
|
{
|
|
rows = *dimsp++;
|
|
columns = *dimsp++;
|
|
|
|
PREPAREDATA1(false);
|
|
|
|
arm_mat_scale_f32(&this->in1,0.5f,&this->out);
|
|
|
|
outp += (rows * columns);
|
|
|
|
}
|
|
|
|
ASSERT_EMPTY_TAIL(output);
|
|
|
|
ASSERT_SNR(output,ref,(float32_t)SNR_THRESHOLD);
|
|
|
|
ASSERT_CLOSE_ERROR(output,ref,ABS_ERROR,REL_ERROR);
|
|
|
|
}
|
|
|
|
void UnaryTestsF32::test_mat_trans_f32()
|
|
{
|
|
LOADDATA1();
|
|
|
|
for(i=0;i < nbMatrixes ; i ++)
|
|
{
|
|
rows = *dimsp++;
|
|
columns = *dimsp++;
|
|
|
|
PREPAREDATA1(true);
|
|
|
|
arm_mat_trans_f32(&this->in1,&this->out);
|
|
|
|
outp += (rows * columns);
|
|
|
|
}
|
|
|
|
ASSERT_EMPTY_TAIL(output);
|
|
|
|
ASSERT_SNR(output,ref,(float32_t)SNR_THRESHOLD);
|
|
|
|
ASSERT_CLOSE_ERROR(output,ref,ABS_ERROR,REL_ERROR);
|
|
|
|
}
|
|
|
|
void UnaryTestsF32::test_mat_inverse_f32()
|
|
{
|
|
const float32_t *inp1=input1.ptr();
|
|
|
|
float32_t *ap=a.ptr();
|
|
|
|
float32_t *outp=output.ptr();
|
|
int16_t *dimsp = dims.ptr();
|
|
int nbMatrixes = dims.nbSamples();
|
|
int rows,columns;
|
|
int i;
|
|
arm_status status;
|
|
|
|
for(i=0;i < nbMatrixes ; i ++)
|
|
{
|
|
rows = *dimsp++;
|
|
columns = rows;
|
|
|
|
PREPAREDATA1(false);
|
|
|
|
status=arm_mat_inverse_f32(&this->in1,&this->out);
|
|
ASSERT_TRUE(status==ARM_MATH_SUCCESS);
|
|
|
|
outp += (rows * columns);
|
|
inp1 += (rows * columns);
|
|
|
|
}
|
|
|
|
ASSERT_EMPTY_TAIL(output);
|
|
|
|
ASSERT_SNR(output,ref,(float32_t)SNR_THRESHOLD_INV);
|
|
|
|
ASSERT_CLOSE_ERROR(output,ref,ABS_ERROR_INV,REL_ERROR_INV);
|
|
|
|
}
|
|
|
|
void UnaryTestsF32::setUp(Testing::testID_t id,std::vector<Testing::param_t>& params,Client::PatternMgr *mgr)
|
|
{
|
|
|
|
|
|
|
|
switch(id)
|
|
{
|
|
case TEST_MAT_ADD_F32_1:
|
|
input1.reload(UnaryTestsF32::INPUTS1_F32_ID,mgr);
|
|
input2.reload(UnaryTestsF32::INPUTS2_F32_ID,mgr);
|
|
dims.reload(UnaryTestsF32::DIMSUNARY1_S16_ID,mgr);
|
|
|
|
ref.reload(UnaryTestsF32::REFADD1_F32_ID,mgr);
|
|
|
|
output.create(ref.nbSamples(),UnaryTestsF32::OUT_F32_ID,mgr);
|
|
a.create(MAXMATRIXDIM*MAXMATRIXDIM,UnaryTestsF32::TMPA_F32_ID,mgr);
|
|
b.create(MAXMATRIXDIM*MAXMATRIXDIM,UnaryTestsF32::TMPB_F32_ID,mgr);
|
|
break;
|
|
|
|
case TEST_MAT_SUB_F32_2:
|
|
input1.reload(UnaryTestsF32::INPUTS1_F32_ID,mgr);
|
|
input2.reload(UnaryTestsF32::INPUTS2_F32_ID,mgr);
|
|
dims.reload(UnaryTestsF32::DIMSUNARY1_S16_ID,mgr);
|
|
|
|
ref.reload(UnaryTestsF32::REFSUB1_F32_ID,mgr);
|
|
|
|
output.create(ref.nbSamples(),UnaryTestsF32::OUT_F32_ID,mgr);
|
|
a.create(MAXMATRIXDIM*MAXMATRIXDIM,UnaryTestsF32::TMPA_F32_ID,mgr);
|
|
b.create(MAXMATRIXDIM*MAXMATRIXDIM,UnaryTestsF32::TMPB_F32_ID,mgr);
|
|
break;
|
|
|
|
case TEST_MAT_SCALE_F32_3:
|
|
input1.reload(UnaryTestsF32::INPUTS1_F32_ID,mgr);
|
|
dims.reload(UnaryTestsF32::DIMSUNARY1_S16_ID,mgr);
|
|
|
|
ref.reload(UnaryTestsF32::REFSCALE1_F32_ID,mgr);
|
|
|
|
output.create(ref.nbSamples(),UnaryTestsF32::OUT_F32_ID,mgr);
|
|
a.create(MAXMATRIXDIM*MAXMATRIXDIM,UnaryTestsF32::TMPA_F32_ID,mgr);
|
|
break;
|
|
|
|
case TEST_MAT_TRANS_F32_4:
|
|
input1.reload(UnaryTestsF32::INPUTS1_F32_ID,mgr);
|
|
dims.reload(UnaryTestsF32::DIMSUNARY1_S16_ID,mgr);
|
|
|
|
ref.reload(UnaryTestsF32::REFTRANS1_F32_ID,mgr);
|
|
|
|
output.create(ref.nbSamples(),UnaryTestsF32::OUT_F32_ID,mgr);
|
|
a.create(MAXMATRIXDIM*MAXMATRIXDIM,UnaryTestsF32::TMPA_F32_ID,mgr);
|
|
break;
|
|
|
|
case TEST_MAT_INVERSE_F32_5:
|
|
input1.reload(UnaryTestsF32::INPUTSINV_F32_ID,mgr);
|
|
dims.reload(UnaryTestsF32::DIMSINVERT1_S16_ID,mgr);
|
|
|
|
ref.reload(UnaryTestsF32::REFINV1_F32_ID,mgr);
|
|
|
|
output.create(ref.nbSamples(),UnaryTestsF32::OUT_F32_ID,mgr);
|
|
a.create(MAXMATRIXDIM*MAXMATRIXDIM,UnaryTestsF32::TMPA_F32_ID,mgr);
|
|
break;
|
|
}
|
|
|
|
|
|
|
|
}
|
|
|
|
void UnaryTestsF32::tearDown(Testing::testID_t id,Client::PatternMgr *mgr)
|
|
{
|
|
output.dump(mgr);
|
|
}
|