{

public:

virtual void SetUp() {}

{

public:

virtual void SetUp() {}

{

if (noDoubleTests<T>()) return;

using af::dim4;

vector<dim4> numDims;

vector<vector<T> > in;

vector<vector<T> > tests;

readTests<T, T, int>(pTestFile, numDims, in, tests);

dim4 sDims = numDims[0];

dim4 fDims = numDims[1];

af_array signal = 0;

af_array filter = 0;

af_array outArray = 0;

af_dtype in_type =(af_dtype)af::dtype_traits<T>::af_type;

ASSERT_EQ(AF_SUCCESS, af_create_array(&signal, &(in[0].front()),

sDims.ndims(), sDims.get(), in_type));

ASSERT_EQ(AF_SUCCESS, af_create_array(&filter, &(in[1].front()),

fDims.ndims(), fDims.get(), in_type));

af_conv_mode mode = expand ? AF_CONV_EXPAND : AF_CONV_DEFAULT;

switch(baseDim) {

case 1: ASSERT_EQ(AF_SUCCESS, af_fftconvolve1(&outArray, signal, filter, mode)); break;

case 2: ASSERT_EQ(AF_SUCCESS, af_fftconvolve2(&outArray, signal, filter, mode)); break;

case 3: ASSERT_EQ(AF_SUCCESS, af_fftconvolve3(&outArray, signal, filter, mode)); break;

}

vector<T> currGoldBar = tests[0];

size_t nElems = currGoldBar.size();

dim_t out_elems = 0;

ASSERT_EQ(AF_SUCCESS, af_get_elements(&out_elems, outArray));

ASSERT_EQ(nElems, (size_t)out_elems);

T *outData = new T[nElems];

ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));

for (size_t elIter=0; elIter<nElems; ++elIter) {

ASSERT_NEAR(

get_real(currGoldBar[elIter]),

get_real(outData[elIter])

, 1e-2)<< "at: " << elIter<< std::endl;

}

delete[] outData;

ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));

ASSERT_EQ(AF_SUCCESS, af_release_array(signal));

ASSERT_EQ(AF_SUCCESS, af_release_array(filter));

{

if (noDoubleTests<T>()) return;

using af::dim4;

using af::seq;

using af::array;

int outDim = sDim + fDim - 1;

int fftDim = (int)pow(2, ceil(log2(outDim)));

int sd[4], fd[4];

for (int k = 0; k < 4; k++) {

if (k < baseDim) {

sd[k] = sDim;

fd[k] = fDim;

}

else if (k == baseDim) {

sd[k] = sBatch;

fd[k] = fBatch;

}

else {

sd[k] = 1;

fd[k] = 1;

}

}

const dim4 signalDims(sd[0], sd[1], sd[2], sd[3]);

const dim4 filterDims(fd[0], fd[1], fd[2], fd[3]);

array signal = randu(signalDims, (af_dtype) af::dtype_traits<T>::af_type);

array filter = randu(filterDims, (af_dtype) af::dtype_traits<T>::af_type);

array out = fftconvolve(signal, filter, expand ? AF_CONV_EXPAND : AF_CONV_DEFAULT);

array gold;

switch(baseDim) {

case 1:

gold = real(af::ifft(af::fft(signal, fftDim) * af::fft(filter, fftDim)));

break;

case 2:

gold = real(af::ifft2(af::fft2(signal, fftDim, fftDim) * af::fft2(filter, fftDim, fftDim)));

break;

case 3:

gold = real(af::ifft3(af::fft3(signal, fftDim, fftDim, fftDim) * af::fft3(filter, fftDim, fftDim, fftDim)));

break;

default:

ASSERT_LT(baseDim, 4);

}

int cropMin = 0, cropMax = 0;

if (expand) {

cropMin = 0;

cropMax = outDim - 1;

}

else {

cropMin = fDim/2;

cropMax = outDim - fDim/2 - 1;

}

switch(baseDim) {

case 1:

gold = gold(seq(cropMin, cropMax));

break;

case 2:

gold = gold(seq(cropMin, cropMax), seq(cropMin, cropMax));

break;

case 3:

gold = gold(seq(cropMin, cropMax), seq(cropMin, cropMax), seq(cropMin, cropMax));

break;

}

size_t outElems = out.elements();

size_t goldElems = gold.elements();

ASSERT_EQ(goldElems, outElems);

T *goldData = new T[goldElems];

gold.host(goldData);

T *outData = new T[outElems];

out.host(outData);

for (size_t elIter=0; elIter<outElems; ++elIter) {

ASSERT_NEAR(goldData[elIter], outData[elIter], 5e-2) << "at: " << elIter << std::endl;

}

delete[] goldData;

delete[] outData;

{

if (noDoubleTests<float>()) return;

using af::dim4;

vector<dim4> numDims;

vector<vector<float> > in;

vector<vector<float> > tests;

readTests<float, float, int>(string(TEST_DIR"/convolve/vector.test"), numDims, in, tests);

//![ex_image_convolve1]

//vector<dim4> numDims;

//vector<vector<float> > in;

af::array signal(numDims[0], &(in[0].front()));

//signal dims = [32 1 1 1]

af::array filter(numDims[1], &(in[1].front()));

//filter dims = [4 1 1 1]

af::array output = fftconvolve1(signal, filter, AF_CONV_EXPAND);

//output dims = [32 1 1 1] - same as input since expand(3rd argument is false)

//None of the dimensions > 1 has lenght > 1, so no batch mode is activated.

//![ex_image_convolve1]

vector<float> currGoldBar = tests[0];

size_t nElems = output.elements();

float *outData = new float[nElems];

output.host(outData);

for (size_t elIter=0; elIter<nElems; ++elIter) {

ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1e-2)<< "at: " << elIter<< std::endl;

}

delete[] outData;

{

if (noDoubleTests<float>()) return;

using af::dim4;

vector<dim4> numDims;

vector<vector<float> > in;

vector<vector<float> > tests;

readTests<float, float, int>(string(TEST_DIR"/convolve/rectangle_one2many.test"), numDims, in, tests);

//![ex_image_convolve2]

//vector<dim4> numDims;

//vector<vector<float> > in;

af::array signal(numDims[0], &(in[0].front()));

//signal dims = [15 17 1 1]

af::array filter(numDims[1], &(in[1].front()));

//filter dims = [5 5 2 1]

af::array output = fftconvolve2(signal, filter, AF_CONV_EXPAND);

//output dims = [15 17 1 1] - same as input since expand(3rd argument is false)

//however, notice that the 3rd dimension of filter is > 1.

//So, one to many batch mode will be activated automatically

//where the 2d input signal is convolved with each 2d filter

//and the result will written corresponding slice in the output 3d array

//![ex_image_convolve2]

vector<float> currGoldBar = tests[0];

size_t nElems = output.elements();

float *outData = new float[nElems];

output.host(outData);

for (size_t elIter=0; elIter<nElems; ++elIter) {

ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1e-2)<< "at: " << elIter<< std::endl;

}

delete[] outData;

{

if (noDoubleTests<float>()) return;

using af::dim4;

vector<dim4> numDims;

vector<vector<float> > in;

vector<vector<float> > tests;

readTests<float, float, int>(string(TEST_DIR"/convolve/cuboid_many2many.test"), numDims, in, tests);

//![ex_image_convolve3]

//vector<dim4> numDims;

//vector<vector<float> > in;

af::array signal(numDims[0], &(in[0].front()));

//signal dims = [10 11 2 2]

af::array filter(numDims[1], &(in[1].front()));

//filter dims = [4 2 3 2]

af::array output = fftconvolve3(signal, filter, AF_CONV_EXPAND);

//output dims = [10 11 2 2] - same as input since expand(3rd argument is false)

//however, notice that the 4th dimension is > 1 for both signal

//and the filter, therefore many to many batch mode will be

//activated where each 3d signal is convolved with the corresponding 3d filter

//![ex_image_convolve3]

vector<float> currGoldBar = tests[0];

size_t nElems = output.elements();

float *outData = new float[nElems];

output.host(outData);

for (size_t elIter=0; elIter<nElems; ++elIter) {

ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1e-2)<< "at: " << elIter<< std::endl;

}

delete[] outData;

{

// This unit test doesn't necessarily need to function

// accuracy as af::convolve is merely a wrapper to

// af::convolve[1|2|3]

using af::array;

using af::dim4;

using af::randu;

using af::constant;

using af::convolve;

//![ex_image_convolve_1d]

array a = randu(10);

//af_print(a);

//a [10 1 1 1] = 0.0000 0.1315 0.7556 0.4587 0.5328 0.2190 0.0470 0.6789 0.6793 0.9347

array b = randu(4);

//af_print(b);

//b [4 1 1 1] = 0.3835 0.5194 0.8310 0.0346

array c = convolve(a, b);

//af_print(c);

//c [10 1 1 1] = 0.3581 0.6777 1.0750 0.7679 0.5903 0.4851 0.6598 1.2770 1.0734 0.8002

//![ex_image_convolve_1d]

//![ex_image_convolve_2d]

array d = constant(0.5, 5, 5);

//af_print(d);

//d [5 5 1 1]

// 0.5000 0.5000 0.5000 0.5000 0.5000

// 0.5000 0.5000 0.5000 0.5000 0.5000

// 0.5000 0.5000 0.5000 0.5000 0.5000

// 0.5000 0.5000 0.5000 0.5000 0.5000

// 0.5000 0.5000 0.5000 0.5000 0.5000

array e = constant(1, 2, 2);

//af_print(e);

//e [2 2 1 1]

// 1.0000 1.0000

// 1.0000 1.0000

array f = fftconvolve(d, e);

//af_print(f);

//f [5 5 1 1]

// 2.0000 2.0000 2.0000 2.0000 1.0000

// 2.0000 2.0000 2.0000 2.0000 1.0000

// 2.0000 2.0000 2.0000 2.0000 1.0000

// 2.0000 2.0000 2.0000 2.0000 1.0000

// 1.0000 1.0000 1.0000 1.0000 0.5000

//![ex_image_convolve_2d]

//![ex_image_convolve_3d]

array g = constant(1, 4, 4, 4);

//af_print(g);

//g [4 4 4 1]

// 1.0000 1.0000 1.0000 1.0000

// 1.0000 1.0000 1.0000 1.0000

// 1.0000 1.0000 1.0000 1.0000

// 1.0000 1.0000 1.0000 1.0000

// 1.0000 1.0000 1.0000 1.0000

// 1.0000 1.0000 1.0000 1.0000

// 1.0000 1.0000 1.0000 1.0000

// 1.0000 1.0000 1.0000 1.0000

// 1.0000 1.0000 1.0000 1.0000

// 1.0000 1.0000 1.0000 1.0000

// 1.0000 1.0000 1.0000 1.0000

// 1.0000 1.0000 1.0000 1.0000

// 1.0000 1.0000 1.0000 1.0000

// 1.0000 1.0000 1.0000 1.0000

// 1.0000 1.0000 1.0000 1.0000

// 1.0000 1.0000 1.0000 1.0000

array h = constant(0.5, 2, 2, 2);

//af_print(h);

//h [2 2 2 1]

// 0.5000 0.5000

// 0.5000 0.5000

// 0.5000 0.5000

// 0.5000 0.5000

array i = fftconvolve(g, h);

//af_print(i);

//i [4 4 4 1]

// 4.0000 4.0000 4.0000 2.0000

// 4.0000 4.0000 4.0000 2.0000

// 4.0000 4.0000 4.0000 2.0000

// 2.0000 2.0000 2.0000 1.0000

// 4.0000 4.0000 4.0000 2.0000

// 4.0000 4.0000 4.0000 2.0000

// 4.0000 4.0000 4.0000 2.0000

// 2.0000 2.0000 2.0000 1.0000

// 4.0000 4.0000 4.0000 2.0000

// 4.0000 4.0000 4.0000 2.0000

// 4.0000 4.0000 4.0000 2.0000

// 2.0000 2.0000 2.0000 1.0000

// 2.0000 2.0000 2.0000 1.0000

// 2.0000 2.0000 2.0000 1.0000

// 2.0000 2.0000 2.0000 1.0000

// 1.0000 1.0000 1.0000 0.5000

//![ex_image_convolve_3d]