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