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Searched refs:numOutputs (Results 1 – 8 of 8) sorted by relevance

/packages/modules/NeuralNetworks/common/types/operations/src/
DBidirectionalSequenceRNN.cpp30 const uint32_t numOutputs = context->getNumOutputs(); in validate() local
31 NN_RET_CHECK(numOutputs == kNumOutputs || numOutputs == kNumOutputsMerged || in validate()
32 numOutputs == kNumOutputsWithState || numOutputs == kNumOutputsMergedWithState); in validate()
44 std::vector<OperandType> outExpectedTypes(numOutputs, inputType); in validate()
48 if (numOutputs == kNumOutputsWithState || numOutputs == kNumOutputsMergedWithState) { in validate()
DUnidirectionalSequenceRNN.cpp28 const int numOutputs = context->getNumOutputs(); in validate() local
29 NN_RET_CHECK(numOutputs == kNumOutputs || numOutputs == kNumOutputsWithState); in validate()
39 if (numOutputs == kNumOutputsWithState) { in validate()
DUnidirectionalSequenceLSTM.cpp28 const uint32_t numOutputs = context->getNumOutputs(); in validate() local
29 NN_RET_CHECK(numOutputs == kNumOutputs || numOutputs == kNumOutputsWithState); in validate()
/packages/modules/NeuralNetworks/common/cpu_operations/
DBidirectionalSequenceRNN.cpp290 const int32_t numOutputs = context->getNumOutputs(); in prepare() local
292 NN_RET_CHECK(numOutputs == kNumOutputsMerged || numOutputs == kNumOutputsMergedWithState); in prepare()
294 NN_RET_CHECK(numOutputs == kNumOutputs || numOutputs == kNumOutputsWithState); in prepare()
395 (numOutputs == kNumOutputsWithState || numOutputs == kNumOutputsMergedWithState); in prepare()
/packages/modules/NeuralNetworks/common/
DCpuExecutor.cpp1376 const int32_t numOutputs = getScalarData<int32_t>(operands[ins[2]]); in executeOperation() local
1378 if (static_cast<size_t>(numOutputs) != outs.size()) { in executeOperation()
1382 std::vector<Shape> outputShapes(numOutputs); in executeOperation()
1383 for (int i = 0; i < numOutputs; ++i) { in executeOperation()
1387 success = splitPrepare(input.shape(), axis, numOutputs, &outputShapes); in executeOperation()
1388 for (int i = 0; i < numOutputs; ++i) { in executeOperation()
1394 std::vector<_Float16*> outputDataPtrs(numOutputs); in executeOperation()
1395 for (int i = 0; i < numOutputs; ++i) { in executeOperation()
1403 std::vector<float*> outputDataPtrs(numOutputs); in executeOperation()
1404 for (int i = 0; i < numOutputs; ++i) { in executeOperation()
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DOperationsExecutionUtils.cpp598 bool splitPrepare(const Shape& input, int32_t axis, int32_t numOutputs, in splitPrepare() argument
603 NN_OPS_CHECK(sizeOfAxisToSplit % numOutputs == 0); in splitPrepare()
604 const int32_t sliceSize = sizeOfAxisToSplit / numOutputs; in splitPrepare()
606 for (int i = 0; i < numOutputs; ++i) { in splitPrepare()
/packages/modules/NeuralNetworks/shim_and_sl/
DShimPreparedModel.cpp339 int numOutputs, ExecutionResult* executionResult) { in executeSynchronouslyInternal() argument
347 outputShapes.reserve(numOutputs); in executeSynchronouslyInternal()
349 for (int i = 0; i < numOutputs; ++i) { in executeSynchronouslyInternal()
/packages/modules/NeuralNetworks/common/include/
DOperationsExecutionUtils.h259 bool splitPrepare(const Shape& input, int32_t axis, int32_t numOutputs, std::vector<Shape>* output);