#include "daal.h"
#include "service.h"
using namespace std;
using namespace daal;
using namespace daal::algorithms;
using namespace daal::algorithms::neural_networks::layers;
using namespace daal::algorithms::neural_networks;
using namespace daal::data_management;
using namespace daal::services;
static const size_t nDim = 4;
static const size_t dims[] = {2, 3, 2, 4};
static float dataArray[2][3][2][4] =
{{{{2, 4, 6, 8},
{10, 12, 14, 16}},
{{18, 20, 22, 24},
{26, 28, 30, 32}},
{{34, 36, 38, 40},
{42, 44, 46, 48}}},
{{{ -2, -4, -6, -8},
{ -10, -12, -14, -16}},
{{ -18, -20, -22, -24},
{ -26, -28, -30, -32}},
{{ -34, -36, -38, -40},
{ -42, -44, -46, -48}}}};
int main(int argc, char *argv[])
{
TensorPtr data(new HomogenTensor<>(nDim, dims, (float *)dataArray));
printTensor(data, "Forward two-dimensional spatial pyramid average pooling layer input (first 10 rows):", 10);
spatial_average_pooling2d::forward::Batch<> forwardLayer(2, nDim);
forwardLayer.input.set(forward::data, data);
forwardLayer.compute();
spatial_average_pooling2d::forward::ResultPtr forwardResult = forwardLayer.getResult();
printTensor(forwardResult->get(forward::value), "Forward two-dimensional spatial pyramid average pooling layer result (first 5 rows):", 5);
printNumericTable(forwardResult->get(spatial_average_pooling2d::auxInputDimensions), "Forward two-dimensional spatial pyramid average pooling layer input dimensions:");
spatial_average_pooling2d::backward::Batch<> backwardLayer(2, nDim);
backwardLayer.input.set(backward::inputGradient, forwardResult->get(forward::value));
backwardLayer.input.set(backward::inputFromForward, forwardResult->get(forward::resultForBackward));
backwardLayer.compute();
backward::ResultPtr backwardResult = backwardLayer.getResult();
printTensor(backwardResult->get(backward::gradient),
"Backward two-dimensional spatial pyramid average pooling layer result (first 10 rows):", 10);
return 0;
}