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feat: Add additional condition for loading batches
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When the first layer is Dense, the batch is only one matrix, where
each row is one example.
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@IlievskiV @lmoneta
IlievskiV authored and lmoneta committed Apr 8, 2018
1 parent d13e778 commit a94c617
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Showing 2 changed files with 142 additions and 60 deletions.
108 changes: 76 additions & 32 deletions tmva/tmva/src/DNN/Architectures/Cpu/CpuBuffer.cxx
View file
Original file line number Diff line number Diff line change
Expand Up @@ -301,15 +301,26 @@ void TTensorDataLoader<TensorInput, TCpu<Real_t>>::CopyTensorInput(TCpuBuffer<Re
{
const std::vector<TMatrixT<Double_t>> &inputTensor = std::get<0>(fData);

for (size_t i = 0; i < fBatchSize; i++) {
size_t sampleIndex = *sampleIterator;
for (size_t j = 0; j < fBatchHeight; j++) {
for (size_t k = 0; k < fBatchWidth; k++) {
size_t bufferIndex = i * fBatchHeight * fBatchWidth + k * fBatchHeight + j;
buffer[bufferIndex] = static_cast<Real_t>(inputTensor[sampleIndex](j, k));
if (fBatchDepth == 1) {
for (size_t i = 0; i < fBatchHeight; i++) {
size_t sampleIndex = *sampleIterator;
for (size_t j = 0; j < fBatchWidth; j++) {
size_t bufferIndex = j * fBatchHeight + i;
buffer[bufferIndex] = static_cast<Real_t>(inputTensor[0](sampleIndex, j));
}
sampleIterator++;
}
} else {
for (size_t i = 0; i < fBatchDepth; i++) {
size_t sampleIndex = *sampleIterator;
for (size_t j = 0; j < fBatchHeight; j++) {
for (size_t k = 0; k < fBatchWidth; k++) {
size_t bufferIndex = i * fBatchHeight * fBatchWidth + k * fBatchHeight + j;
buffer[bufferIndex] = static_cast<Real_t>(inputTensor[sampleIndex](j, k));
}
}
sampleIterator++;
}
sampleIterator++;
}
}

Expand Down Expand Up @@ -352,16 +363,26 @@ void TTensorDataLoader<TensorInput, TCpu<Double_t>>::CopyTensorInput(TCpuBuffer<
{
const std::vector<TMatrixT<Double_t>> &inputTensor = std::get<0>(fData);

for (size_t i = 0; i < fBatchSize; i++) {
size_t sampleIndex = *sampleIterator;
for (size_t j = 0; j < fBatchHeight; j++) {
for (size_t k = 0; k < fBatchWidth; k++) {
// because of the column-major ordering
size_t bufferIndex = i * fBatchHeight * fBatchWidth + k * fBatchHeight + j;
buffer[bufferIndex] = inputTensor[sampleIndex](j, k);
if (fBatchDepth == 1) {
for (size_t i = 0; i < fBatchHeight; i++) {
size_t sampleIndex = *sampleIterator;
for (size_t j = 0; j < fBatchWidth; j++) {
size_t bufferIndex = j * fBatchHeight + i;
buffer[bufferIndex] = inputTensor[0](sampleIndex, j);
}
sampleIterator++;
}
} else {
for (size_t i = 0; i < fBatchDepth; i++) {
size_t sampleIndex = *sampleIterator;
for (size_t j = 0; j < fBatchHeight; j++) {
for (size_t k = 0; k < fBatchWidth; k++) {
size_t bufferIndex = i * fBatchHeight * fBatchWidth + k * fBatchHeight + j;
buffer[bufferIndex] = inputTensor[sampleIndex](j, k);
}
}
sampleIterator++;
}
sampleIterator++;
}
}

Expand Down Expand Up @@ -405,17 +426,29 @@ void TTensorDataLoader<TMVAInput_t, TCpu<Double_t>>::CopyTensorInput(TCpuBuffer<
// one event, one example in the batch
Event *event = fData.front();

for (size_t i = 0; i < fBatchSize; i++) {
size_t sampleIndex = *sampleIterator;
for (size_t j = 0; j < fBatchHeight; j++) {
for (size_t k = 0; k < fBatchWidth; k++) {
if (fBatchDepth == 1) {
for (size_t i = 0; i < fBatchHeight; i++) {
size_t sampleIndex = *sampleIterator;
for (size_t j = 0; j < fBatchWidth; j++) {
event = fData[sampleIndex];
// because of the column-major ordering
size_t bufferIndex = i * fBatchHeight * fBatchWidth + k * fBatchHeight + j;
buffer[bufferIndex] = event->GetValue(j * fBatchHeight + k);
size_t bufferIndex = j * fBatchHeight + i;
buffer[bufferIndex] = event->GetValue(j);
}
sampleIterator++;
}
} else {
for (size_t i = 0; i < fBatchDepth; i++) {
size_t sampleIndex = *sampleIterator;
for (size_t j = 0; j < fBatchHeight; j++) {
for (size_t k = 0; k < fBatchWidth; k++) {
event = fData[sampleIndex];
// because of the column-major ordering
size_t bufferIndex = i * fBatchHeight * fBatchWidth + k * fBatchHeight + j;
buffer[bufferIndex] = event->GetValue(j * fBatchHeight + k);
}
}
sampleIterator++;
}
sampleIterator++;
}
}

Expand Down Expand Up @@ -473,20 +506,31 @@ template <>
void TTensorDataLoader<TMVAInput_t, TCpu<Real_t>>::CopyTensorInput(TCpuBuffer<Real_t> &buffer,
IndexIterator_t sampleIterator)
{

Event *event = fData.front();

for (size_t i = 0; i < fBatchSize; i++) {
size_t sampleIndex = *sampleIterator;
for (size_t j = 0; j < fBatchHeight; j++) {
for (size_t k = 0; k < fBatchWidth; k++) {
if (fBatchDepth == 1) {
for (size_t i = 0; i < fBatchHeight; i++) {
size_t sampleIndex = *sampleIterator;
for (size_t j = 0; j < fBatchWidth; j++) {
event = fData[sampleIndex];
// because of the column-major ordering
size_t bufferIndex = i * fBatchHeight * fBatchWidth + k * fBatchHeight + j;
buffer[bufferIndex] = static_cast<Real_t>(event->GetValue(j * fBatchHeight + k));
size_t bufferIndex = j * fBatchHeight + i;
buffer[bufferIndex] = static_cast<Real_t>(event->GetValue(j));
}
sampleIterator++;
}
} else {
for (size_t i = 0; i < fBatchDepth; i++) {
size_t sampleIndex = *sampleIterator;
for (size_t j = 0; j < fBatchHeight; j++) {
for (size_t k = 0; k < fBatchWidth; k++) {
event = fData[sampleIndex];
// because of the column-major ordering
size_t bufferIndex = i * fBatchHeight * fBatchWidth + k * fBatchHeight + j;
buffer[bufferIndex] = static_cast<Real_t>(event->GetValue(j * fBatchHeight + k));
}
}
sampleIterator++;
}
sampleIterator++;
}
}

Expand Down
94 changes: 66 additions & 28 deletions tmva/tmva/src/DNN/Architectures/Reference/TensorDataLoader.cxx
View file
Original file line number Diff line number Diff line change
Expand Up @@ -41,15 +41,24 @@ void TTensorDataLoader<TensorInput, TReference<Real_t>>::CopyTensorInput(std::ve
{
const std::vector<TMatrixT<Double_t>> &inputTensor = std::get<0>(fData);

for (size_t i = 0; i < fBatchSize; i++) {
size_t sampleIndex = *sampleIterator;
for (size_t j = 0; j < fBatchHeight; j++) {
for (size_t k = 0; k < fBatchWidth; k++) {
tensor[i](j, k) = static_cast<Real_t>(inputTensor[sampleIndex](j, k));
if (fBatchDepth == 1) {
for (size_t i = 0; i < fBatchHeight; i++) {
size_t sampleIndex = *sampleIterator;
for (size_t j = 0; j < fBatchWidth; j++) {
tensor[0](i, j) = static_cast<Real_t>(inputTensor[0](sampleIndex, j));
}
sampleIterator++;
}
} else {
for (size_t i = 0; i < fBatchDepth; i++) {
size_t sampleIndex = *sampleIterator;
for (size_t j = 0; j < fBatchHeight; j++) {
for (size_t k = 0; k < fBatchWidth; k++) {
tensor[i](j, k) = static_cast<Real_t>(inputTensor[sampleIndex](j, k));
}
}
sampleIterator++;
}

sampleIterator++;
}
}

Expand Down Expand Up @@ -92,15 +101,24 @@ void TTensorDataLoader<TensorInput, TReference<Double_t>>::CopyTensorInput(std::
{
const std::vector<TMatrixT<Double_t>> &inputTensor = std::get<0>(fData);

for (size_t i = 0; i < fBatchSize; i++) {
size_t sampleIndex = *sampleIterator;
for (size_t j = 0; j < fBatchHeight; j++) {
for (size_t k = 0; k < fBatchWidth; k++) {
tensor[i](j, k) = inputTensor[sampleIndex](j, k);
if (fBatchDepth == 1) {
for (size_t i = 0; i < fBatchHeight; i++) {
size_t sampleIndex = *sampleIterator;
for (size_t j = 0; j < fBatchWidth; j++) {
tensor[0](i, j) = inputTensor[0](sampleIndex, j);
}
sampleIterator++;
}
} else {
for (size_t i = 0; i < fBatchDepth; i++) {
size_t sampleIndex = *sampleIterator;
for (size_t j = 0; j < fBatchHeight; j++) {
for (size_t k = 0; k < fBatchWidth; k++) {
tensor[i](j, k) = inputTensor[sampleIndex](j, k);
}
}
sampleIterator++;
}

sampleIterator++;
}
}

Expand Down Expand Up @@ -144,16 +162,26 @@ void TTensorDataLoader<TMVAInput_t, TReference<Real_t>>::CopyTensorInput(std::ve
// one event, one example in the batch
Event *event = fData.front();

for (size_t i = 0; i < fBatchSize; i++) {
size_t sampleIndex = *sampleIterator;
for (size_t j = 0; j < fBatchHeight; j++) {
for (size_t k = 0; k < fBatchWidth; k++) {
if (fBatchDepth == 1) {
for (size_t i = 0; i < fBatchHeight; i++) {
size_t sampleIndex = *sampleIterator;
for (size_t j = 0; j < fBatchWidth; j++) {
event = fData[sampleIndex];
tensor[i](j, k) = static_cast<Real_t>(event->GetValue(j * fBatchHeight + k));
tensor[0](i, j) = static_cast<Real_t>(event->GetValue(j));
}
sampleIterator++;
}
} else {
for (size_t i = 0; i < fBatchDepth; i++) {
size_t sampleIndex = *sampleIterator;
for (size_t j = 0; j < fBatchHeight; j++) {
for (size_t k = 0; k < fBatchWidth; k++) {
event = fData[sampleIndex];
tensor[i](j, k) = static_cast<Real_t>(event->GetValue(j * fBatchHeight + k));
}
}
sampleIterator++;
}

sampleIterator++;
}
}

Expand Down Expand Up @@ -208,16 +236,26 @@ void TTensorDataLoader<TMVAInput_t, TReference<Double_t>>::CopyTensorInput(std::
// one event, one example in the batch
Event *event = fData.front();

for (size_t i = 0; i < fBatchSize; i++) {
size_t sampleIndex = *sampleIterator;
for (size_t j = 0; j < fBatchHeight; j++) {
for (size_t k = 0; k < fBatchWidth; k++) {
if (fBatchDepth == 1) {
for (size_t i = 0; i < fBatchHeight; i++) {
size_t sampleIndex = *sampleIterator;
for (size_t j = 0; j < fBatchWidth; j++) {
event = fData[sampleIndex];
tensor[i](j, k) = event->GetValue(j * fBatchHeight + k);
tensor[0](i, j) = event->GetValue(j);
}
sampleIterator++;
}
} else {
for (size_t i = 0; i < fBatchDepth; i++) {
size_t sampleIndex = *sampleIterator;
for (size_t j = 0; j < fBatchHeight; j++) {
for (size_t k = 0; k < fBatchWidth; k++) {
event = fData[sampleIndex];
tensor[i](j, k) = event->GetValue(j * fBatchHeight + k);
}
}
sampleIterator++;
}

sampleIterator++;
}
}

Expand Down

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