#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::data_management;
using namespace daal::services;
string datasetFileName = "../data/batch/layer.csv";
int main(int argc, char *argv[])
{
checkArguments(argc, argv, 1, &datasetFileName);
Collection<size_t> inDims;
inDims.push_back(1);
inDims.push_back(2);
inDims.push_back(4);
inDims.push_back(4);
TensorPtr tensorData = TensorPtr(new HomogenTensor<>(inDims, Tensor::doAllocate, 1.0f));
transposed_conv2d::forward::Batch<> transposedConv2dLayerForward;
transposedConv2dLayerForward.input.set(forward::data, tensorData);
transposedConv2dLayerForward.compute();
transposed_conv2d::forward::ResultPtr forwardResult = transposedConv2dLayerForward.getResult();
printTensor(forwardResult->get(forward::value), "Two-dimensional transposed convolution layer result (first 5 rows):", 5, 15);
printTensor(forwardResult->get(transposed_conv2d::auxWeights), "Two-dimensional transposed convolution layer weights (first 5 rows):", 5, 15);
const Collection<size_t> &gDims = forwardResult->get(forward::value)->getDimensions();
TensorPtr tensorDataBack = TensorPtr(new HomogenTensor<>(gDims, Tensor::doAllocate, 0.01f));
transposed_conv2d::backward::Batch<> transposedConv2dLayerBackward;
transposedConv2dLayerBackward.input.set(backward::inputGradient, tensorDataBack);
transposedConv2dLayerBackward.input.set(backward::inputFromForward, forwardResult->get(forward::resultForBackward));
transposedConv2dLayerBackward.compute();
backward::ResultPtr backwardResult = transposedConv2dLayerBackward.getResult();
printTensor(backwardResult->get(backward::gradient),
"Two-dimensional transposed convolution layer backpropagation gradient result (first 5 rows):", 5, 15);
printTensor(backwardResult->get(backward::weightDerivatives),
"Two-dimensional transposed convolution layer backpropagation weightDerivative result (first 5 rows):", 5, 15);
printTensor(backwardResult->get(backward::biasDerivatives),
"Two-dimensional transposed convolution layer backpropagation biasDerivative result (first 5 rows):", 5, 15);
return 0;
}