IVGCVSW-5962 Remove boost::multi_array
* Replaced all instances of boost::multi_array with flat vectors.
* Updated LayerTestResult struct with new member variables.
* Updated CompareTensor function to compare flat vectors and the shape.
* Removed MakeTensor function from TensorHelpers.hpp.
* Removed GetTensorShapeAsArray function from LayerTestResult.hpp.
* Removed boost::array usage.
* Removed boost::extents usages.
* Removed boost::random usages.
Signed-off-by: Matthew Sloyan <matthew.sloyan@arm.com>
Signed-off-by: Sadik Armagan <sadik.armagan@arm.com>
Change-Id: Iccde9d6640b534940292ff048fb80c00b38c4743
diff --git a/src/backends/backendsCommon/test/layerTests/LstmTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/LstmTestImpl.cpp
index 1c63542..11003a2 100644
--- a/src/backends/backendsCommon/test/layerTests/LstmTestImpl.cpp
+++ b/src/backends/backendsCommon/test/layerTests/LstmTestImpl.cpp
@@ -20,18 +20,17 @@
#include <test/TensorHelpers.hpp>
-#include <boost/multi_array.hpp>
-
namespace
{
template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
void LstmUtilsVectorBatchVectorAddTestImpl(
- boost::multi_array<float, 1>& vec,
- boost::multi_array<float, 2>& batchVec,
+ std::vector<float>& vec,
+ std::vector<float>& batchVec,
uint32_t vSize,
uint32_t nBatch,
- boost::multi_array<float, 2>& expectedOutput )
+ std::vector<float>& expectedOutput,
+ armnn::TensorShape& expectedShape)
{
float qScale = 0.0f;
int32_t qOffset = 0;
@@ -45,19 +44,20 @@
VectorBatchVectorAdd(*vecDecoder, vSize, *batchVecDecoder, nBatch, *batchVecEncoder);
// check shape and compare values
- auto result = CompareTensors(batchVec, expectedOutput);
+ auto result = CompareTensors(batchVec, expectedOutput, expectedShape, expectedShape);
BOOST_TEST(result.m_Result, result.m_Message.str());
// check if iterator is back at start position
batchVecEncoder->Set(1.0f);
- BOOST_TEST(batchVec[0][0] == 1.0f);
+ BOOST_TEST(batchVec[0] == 1.0f);
}
template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
void LstmUtilsZeroVectorTestImpl(
- boost::multi_array<float, 1>& input,
+ std::vector<float>& input,
uint32_t vSize,
- boost::multi_array<float, 1>& expectedOutput)
+ std::vector<float>& expectedOutput,
+ armnn::TensorShape& expectedShape)
{
float qScale = 0.0f;
int32_t qOffset = 0;
@@ -71,7 +71,7 @@
ZeroVector(*outputEncoder, vSize);
// check shape and compare values
- auto result = CompareTensors(input, expectedOutput);
+ auto result = CompareTensors(input, expectedOutput, expectedShape, expectedShape);
BOOST_TEST(result.m_Result, result.m_Message.str());
// check if iterator is back at start position
@@ -82,10 +82,11 @@
template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
void LstmUtilsMeanStddevNormalizationTestImpl(
- boost::multi_array<float, 2>& input,
+ std::vector<float>& input,
uint32_t vSize,
uint32_t nBatch,
- boost::multi_array<float, 2>& expectedOutput)
+ std::vector<float>& expectedOutput,
+ armnn::TensorShape& expectedShape)
{
float qScale = 0.0f;
int32_t qOffset = 0;
@@ -98,21 +99,22 @@
MeanStddevNormalization(*inputDecoder, *outputEncoder, vSize, nBatch, 1e-8f);
// check shape and compare values
- auto result = CompareTensors(input, expectedOutput);
+ auto result = CompareTensors(input, expectedOutput, expectedShape, expectedShape);
BOOST_TEST(result.m_Result, result.m_Message.str());
// check if iterator is back at start position
outputEncoder->Set(1.0f);
- BOOST_TEST(input[0][0] == 1.0f);
+ BOOST_TEST(input[0] == 1.0f);
}
template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
void LstmUtilsVectorBatchVectorCwiseProductTestImpl(
- boost::multi_array<float, 1>& vec,
- boost::multi_array<float, 2>& batchVec,
+ std::vector<float>& vec,
+ std::vector<float>& batchVec,
uint32_t vSize,
uint32_t nBatch,
- boost::multi_array<float, 2>& expectedOutput)
+ std::vector<float>& expectedOutput,
+ armnn::TensorShape& expectedShape)
{
float qScale = 0.0f;
int32_t qOffset = 0;
@@ -126,12 +128,12 @@
VectorBatchVectorCwiseProduct(*vecDecoder, vSize, *batchVecDecoder, nBatch, *batchVecEncoder);
// check shape and compare values
- auto result = CompareTensors(batchVec, expectedOutput);
+ auto result = CompareTensors(batchVec, expectedOutput, expectedShape, expectedShape);
BOOST_TEST(result.m_Result, result.m_Message.str());
// check if iterator is back at start position
batchVecEncoder->Set(1.0f);
- BOOST_TEST(batchVec[0][0] == 1.0f);
+ BOOST_TEST(batchVec[0] == 1.0f);
}
// Lstm Layer tests:
@@ -142,16 +144,18 @@
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
const armnn::ITensorHandleFactory& tensorHandleFactory,
- const boost::multi_array<T, 2>& input,
- const boost::multi_array<T, 2>& outputExpected,
+ const std::vector<T>& input,
+ const std::vector<T>& outputExpected,
+ const armnn::TensorShape& inputShape,
+ const armnn::TensorShape& outputExpectedShape,
float qScale = 0.0f,
int32_t qOffset = 0,
armnn::DataType constantDataType = armnn::DataType::Float32)
{
IgnoreUnused(memoryManager);
- unsigned int batchSize = armnn::numeric_cast<unsigned int>(input.shape()[0]);
- unsigned int inputSize = armnn::numeric_cast<unsigned int>(input.shape()[1]);
- unsigned int outputSize = armnn::numeric_cast<unsigned int>(outputExpected.shape()[1]);
+ unsigned int batchSize = armnn::numeric_cast<unsigned int>(inputShape[0]);
+ unsigned int inputSize = armnn::numeric_cast<unsigned int>(inputShape[1]);
+ unsigned int outputSize = armnn::numeric_cast<unsigned int>(outputExpectedShape[1]);
// cellSize and outputSize have the same size when there is no projection.
unsigned numUnits = outputSize;
@@ -164,30 +168,19 @@
armnn::TensorInfo outputStateOutTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset);
armnn::TensorInfo outputTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset);
- LayerTestResult<T, 2> ret(outputTensorInfo);
-
std::vector<T> inputVector;
inputVector.assign(input.data(), input.data() + (batchSize * inputSize));
- auto inputTensor = MakeTensor<T,2>(inputTensorInfo, inputVector);
std::vector<T> cellStateInVector(batchSize * numUnits, T());
- auto cellStateInTensor = MakeTensor<T,2>(cellStateInTensorInfo, cellStateInVector);
-
std::vector<T> outputStateInVector(batchSize * outputSize, T());
- auto outputStateInTensor = MakeTensor<T,2>(outputStateInTensorInfo, outputStateInVector);
-
std::vector<T> scratchBufferVector(batchSize * numUnits * 4, T());
- auto scratchBufferTensor = MakeTensor<T,2>(scratchBufferTensorInfo, scratchBufferVector);
-
std::vector<T> outputStateOutVector(batchSize * outputSize, T());
- auto outputStateOutTensor = MakeTensor<T,2>(outputStateOutTensorInfo, outputStateOutVector);
-
std::vector<T> cellStateOutVector(batchSize * numUnits, T());
- auto cellStateOutTensor = MakeTensor<T,2>(cellStateOutTensorInfo, cellStateOutVector);
+
+ std::vector<T> actualOutput(outputTensorInfo.GetNumElements());
std::vector<T> outputVector;
outputVector.assign(outputExpected.data(), outputExpected.data() + (batchSize * outputSize));
- ret.outputExpected = MakeTensor<T, 2>(outputTensorInfo, outputVector);
std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
@@ -219,59 +212,59 @@
armnn::TensorInfo tensorInfo8({numUnits, 2}, constantDataType, qScale, qOffset);
armnn::TensorInfo tensorInfo16({numUnits, 4}, constantDataType, qScale, qOffset);
- auto inputToInputWeights = MakeTensor<float, 2>(tensorInfo8, {-0.45018822f, -0.02338299f, -0.0870589f,
- -0.34550029f, 0.04266912f, -0.15680569f,
- -0.34856534f, 0.43890524f});
+ std::vector<float> inputToInputWeights = {-0.45018822f, -0.02338299f, -0.0870589f,
+ -0.34550029f, 0.04266912f, -0.15680569f,
+ -0.34856534f, 0.43890524f};
- auto inputToForgetWeights = MakeTensor<float, 2>(tensorInfo8, {0.09701663f, 0.20334584f, -0.50592935f,
- -0.31343272f, -0.40032279f, 0.44781327f,
- 0.01387155f, -0.35593212f});
+ std::vector<float> inputToForgetWeights = { 0.09701663f, 0.20334584f, -0.50592935f,
+ -0.31343272f, -0.40032279f, 0.44781327f,
+ 0.01387155f, -0.35593212f};
- auto inputToCellWeights = MakeTensor<float, 2>(tensorInfo8, {-0.50013041f, 0.1370284f, 0.11810488f, 0.2013163f,
- -0.20583314f, 0.44344562f, 0.22077113f,
- -0.29909778f});
+ std::vector<float> inputToCellWeights = { -0.50013041f, 0.1370284f, 0.11810488f, 0.2013163f,
+ -0.20583314f, 0.44344562f, 0.22077113f,
+ -0.29909778f};
- auto inputToOutputWeights = MakeTensor<float, 2>(tensorInfo8, {-0.25065863f, -0.28290087f, 0.04613829f,
- 0.40525138f, 0.44272184f, 0.03897077f,
- -0.1556896f, 0.19487578f});
+ std::vector<float> inputToOutputWeights = { -0.25065863f, -0.28290087f, 0.04613829f,
+ 0.40525138f, 0.44272184f, 0.03897077f,
+ -0.1556896f, 0.19487578f};
- auto recurrentToInputWeights = MakeTensor<float, 2>(tensorInfo16, {-0.0063535f, -0.2042388f, 0.31454784f,
- -0.35746509f, 0.28902304f, 0.08183324f,
- -0.16555229f, 0.02286911f, -0.13566875f,
- 0.03034258f, 0.48091322f, -0.12528998f,
- 0.24077177f, -0.51332325f, -0.33502164f,
- 0.10629296f});
+ std::vector<float> recurrentToInputWeights = {-0.0063535f, -0.2042388f, 0.31454784f,
+ -0.35746509f, 0.28902304f, 0.08183324f,
+ -0.16555229f, 0.02286911f, -0.13566875f,
+ 0.03034258f, 0.48091322f, -0.12528998f,
+ 0.24077177f, -0.51332325f, -0.33502164f,
+ 0.10629296f};
- auto recurrentToForgetWeights = MakeTensor<float, 2>(tensorInfo16, {-0.48684245f, -0.06655136f, 0.42224967f,
- 0.2112639f, 0.27654213f, 0.20864892f,
- -0.07646349f, 0.45877004f, 0.00141793f,
- -0.14609534f, 0.36447752f, 0.09196436f,
- 0.28053468f, 0.01560611f, -0.20127171f,
- -0.01140004f});
+ std::vector<float> recurrentToForgetWeights = { -0.48684245f, -0.06655136f, 0.42224967f,
+ 0.2112639f, 0.27654213f, 0.20864892f,
+ -0.07646349f, 0.45877004f, 0.00141793f,
+ -0.14609534f, 0.36447752f, 0.09196436f,
+ 0.28053468f, 0.01560611f, -0.20127171f,
+ -0.01140004f};
- auto recurrentToCellWeights = MakeTensor<float, 2>(tensorInfo16, {-0.3407414f, 0.24443203f, -0.2078532f,
- 0.26320225f, 0.05695659f, -0.00123841f,
- -0.4744786f, -0.35869038f, -0.06418842f,
- -0.13502428f, -0.501764f, 0.22830659f,
- -0.46367589f, 0.26016325f, -0.03894562f,
- -0.16368064f});
+ std::vector<float> recurrentToCellWeights = { -0.3407414f, 0.24443203f, -0.2078532f,
+ 0.26320225f, 0.05695659f, -0.00123841f,
+ -0.4744786f, -0.35869038f, -0.06418842f,
+ -0.13502428f, -0.501764f, 0.22830659f,
+ -0.46367589f, 0.26016325f, -0.03894562f,
+ -0.16368064f};
- auto recurrentToOutputWeights = MakeTensor<float, 2>(tensorInfo16, {0.43385774f, -0.17194885f, 0.2718237f,
- 0.09215671f, 0.24107647f, -0.39835793f,
- 0.18212086f, 0.01301402f, 0.48572797f,
- -0.50656658f, 0.20047462f, -0.20607421f,
- -0.51818722f, -0.15390486f, 0.0468148f,
- 0.39922136f});
+ std::vector<float> recurrentToOutputWeights = { 0.43385774f, -0.17194885f, 0.2718237f,
+ 0.09215671f, 0.24107647f, -0.39835793f,
+ 0.18212086f, 0.01301402f, 0.48572797f,
+ -0.50656658f, 0.20047462f, -0.20607421f,
+ -0.51818722f, -0.15390486f, 0.0468148f,
+ 0.39922136f};
- auto cellToInputWeights = MakeTensor<float, 1>(tensorInfo4, {0., 0., 0., 0.});
+ std::vector<float> cellToInputWeights = {0., 0., 0., 0.};
- auto inputGateBias = MakeTensor<float, 1>(tensorInfo4, {0., 0., 0., 0.});
+ std::vector<float> inputGateBias = {0., 0., 0., 0.};
- auto forgetGateBias = MakeTensor<float, 1>(tensorInfo4, {1., 1., 1., 1.});
+ std::vector<float> forgetGateBias = {1., 1., 1., 1.};
- auto cellBias = MakeTensor<float, 1>(tensorInfo4, {0., 0., 0., 0.});
+ std::vector<float> cellBias = {0., 0., 0., 0.};
- auto outputGateBias = MakeTensor<float, 1>(tensorInfo4, {0., 0., 0., 0.});
+ std::vector<float> outputGateBias = {0., 0., 0., 0.};
armnn::ScopedTensorHandle inputToInputWeightsTensor(tensorInfo8);
armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfo8);
@@ -287,19 +280,19 @@
armnn::ScopedTensorHandle cellBiasTensor(tensorInfo4);
armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfo4);
- AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, &inputToInputWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, &inputToForgetWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, &inputToCellWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, &inputToOutputWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, &recurrentToInputWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, &recurrentToForgetWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, &recurrentToCellWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, &recurrentToOutputWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&cellToInputWeightsTensor, &cellToInputWeights[0]);
- AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, &inputGateBias[0]);
- AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, &forgetGateBias[0]);
- AllocateAndCopyDataToITensorHandle(&cellBiasTensor, &cellBias[0]);
- AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, &outputGateBias[0]);
+ AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, inputToInputWeights.data());
+ AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data());
+ AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data());
+ AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data());
+ AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, recurrentToInputWeights.data());
+ AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data());
+ AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data());
+ AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data());
+ AllocateAndCopyDataToITensorHandle(&cellToInputWeightsTensor, cellToInputWeights.data());
+ AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, inputGateBias.data());
+ AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data());
+ AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data());
+ AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data());
data.m_InputToInputWeights = &inputToInputWeightsTensor;
data.m_InputToForgetWeights = &inputToForgetWeightsTensor;
@@ -330,15 +323,18 @@
cellStateOutHandle->Allocate();
outputHandle->Allocate();
- CopyDataToITensorHandle(inputHandle.get(), &inputTensor[0][0]);
- CopyDataToITensorHandle(outputStateInHandle.get(), &outputStateInTensor[0][0]);
- CopyDataToITensorHandle(cellStateInHandle.get(), &cellStateInTensor[0][0]);
+ CopyDataToITensorHandle(inputHandle.get(), inputVector.data());
+ CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data());
+ CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data());
workload->Execute();
- CopyDataFromITensorHandle(&ret.output[0][0], outputHandle.get());
+ CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get());
- return ret;
+ return LayerTestResult<T, 2>(actualOutput,
+ outputVector,
+ outputHandle->GetShape(),
+ outputTensorInfo.GetShape());
}
template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
@@ -346,8 +342,8 @@
LstmLayerNoCifgWithPeepholeWithProjectionTestImpl(armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
const armnn::ITensorHandleFactory& tensorHandleFactory,
- const boost::multi_array<T, 2>& input,
- const boost::multi_array<T, 2>& outputExpected,
+ const std::vector<T>& input,
+ const std::vector<T>& outputExpected,
float qScale = 0.0f,
int32_t qOffset = 0,
armnn::DataType constantDataType = armnn::DataType::Float32)
@@ -368,30 +364,19 @@
armnn::TensorInfo outputStateOutTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset);
armnn::TensorInfo outputTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset);
- LayerTestResult<T, 2> ret(outputTensorInfo);
-
std::vector<T> inputVector;
inputVector.assign(input.data(), input.data() + (batchSize * inputSize));
- auto inputTensor = MakeTensor<T,2>(inputTensorInfo, inputVector);
std::vector<T> cellStateInVector(batchSize * numUnits, T());
- auto cellStateInTensor = MakeTensor<T,2>(cellStateInTensorInfo, cellStateInVector);
-
std::vector<T> outputStateInVector(batchSize * outputSize, T());
- auto outputStateInTensor = MakeTensor<T,2>(outputStateInTensorInfo, outputStateInVector);
-
std::vector<T> scratchBufferVector(batchSize * numUnits * 4, T());
- auto scratchBufferTensor = MakeTensor<T,2>(scratchBufferTensorInfo, scratchBufferVector);
-
std::vector<T> outputStateOutVector(batchSize * outputSize, T());
- auto outputStateOutTensor = MakeTensor<T,2>(outputStateOutTensorInfo, outputStateOutVector);
-
std::vector<T> cellStateOutVector(batchSize * numUnits, T());
- auto cellStateOutTensor = MakeTensor<T,2>(cellStateOutTensorInfo, cellStateOutVector);
+
+ std::vector<T> actualOutput(outputTensorInfo.GetNumElements());
std::vector<T> outputVector;
outputVector.assign(outputExpected.data(), outputExpected.data() + (batchSize * outputSize));
- ret.outputExpected = MakeTensor<T, 2>(outputTensorInfo, outputVector);
std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
@@ -425,135 +410,118 @@
armnn::TensorInfo tensorInfo20x16({numUnits, outputSize}, constantDataType, qScale, qOffset);
armnn::TensorInfo tensorInfo16x20({outputSize, numUnits}, constantDataType, qScale, qOffset);
- auto inputToInputWeights =
- MakeTensor<float, 2>(tensorInfo20x5, {0.021393683f,0.06124551f, 0.046905167f,-0.014657677f,-0.03149463f,
- 0.09171803f, 0.14647801f,0.10797193f, -0.0057968358f,0.0019193048f,
- -0.2726754f, 0.10154029f, -0.018539885f, 0.080349885f, -0.10262385f,
- -0.022599787f,-0.09121155f, -0.008675967f, -0.045206103f,-0.0821282f,
- -0.008045952f,0.015478081f, 0.055217247f, 0.038719587f, 0.044153627f,
- -0.06453243f,0.05031825f, -0.046935108f, -0.008164439f, 0.014574226f,
- -0.1671009f, -0.15519552f, -0.16819797f,-0.13971269f,-0.11953059f,
- 0.25005487f, -0.22790983f, 0.009855087f, -0.028140958f, -0.11200698f,
- 0.11295408f, -0.0035217577f, 0.054485075f, 0.05184695f, 0.064711206f,
- 0.10989193f, 0.11674786f, 0.03490607f, 0.07727357f, 0.11390585f,
- -0.1863375f, -0.1034451f, -0.13945189f, -0.049401227f, -0.18767063f,
- 0.042483903f, 0.14233552f, 0.13832581f, 0.18350165f, 0.14545603f,
- -0.028545704f,0.024939531f,0.050929718f,0.0076203286f,-0.0029723682f,
- -0.042484224f, -0.11827596f, -0.09171104f, -0.10808628f,-0.16327988f,
- -0.2273378f, -0.0993647f, -0.017155107f,0.0023917493f,0.049272764f,
- 0.0038534778f, 0.054764505f, 0.089753784f, 0.06947234f, 0.08014476f,
- -0.04544234f, -0.0497073f,-0.07135631f, -0.048929106f,-0.004042012f,
- -0.009284026f, 0.018042054f, 0.0036860977f,-0.07427302f, -0.11434604f,
- -0.018995456f, 0.031487543f, 0.012834908f,0.019977754f,0.044256654f,
- -0.39292613f, -0.18519334f, -0.11651281f,-0.06809892f, 0.011373677f
- });
+ std::vector<float> inputToInputWeights = {0.021393683f,0.06124551f, 0.046905167f,-0.014657677f,-0.03149463f,
+ 0.09171803f, 0.14647801f,0.10797193f, -0.0057968358f,0.0019193048f,
+ -0.2726754f, 0.10154029f, -0.018539885f, 0.080349885f, -0.10262385f,
+ -0.022599787f,-0.09121155f, -0.008675967f, -0.045206103f,-0.0821282f,
+ -0.008045952f,0.015478081f, 0.055217247f, 0.038719587f, 0.044153627f,
+ -0.06453243f,0.05031825f, -0.046935108f, -0.008164439f, 0.014574226f,
+ -0.1671009f, -0.15519552f, -0.16819797f,-0.13971269f,-0.11953059f,
+ 0.25005487f, -0.22790983f, 0.009855087f, -0.028140958f, -0.11200698f,
+ 0.11295408f, -0.0035217577f, 0.054485075f, 0.05184695f, 0.064711206f,
+ 0.10989193f, 0.11674786f, 0.03490607f, 0.07727357f, 0.11390585f,
+ -0.1863375f, -0.1034451f, -0.13945189f, -0.049401227f, -0.18767063f,
+ 0.042483903f, 0.14233552f, 0.13832581f, 0.18350165f, 0.14545603f,
+ -0.028545704f,0.024939531f,0.050929718f,0.0076203286f,-0.0029723682f,
+ -0.042484224f, -0.11827596f, -0.09171104f, -0.10808628f,-0.16327988f,
+ -0.2273378f, -0.0993647f, -0.017155107f,0.0023917493f,0.049272764f,
+ 0.0038534778f, 0.054764505f, 0.089753784f, 0.06947234f, 0.08014476f,
+ -0.04544234f, -0.0497073f,-0.07135631f, -0.048929106f,-0.004042012f,
+ -0.009284026f, 0.018042054f, 0.0036860977f,-0.07427302f, -0.11434604f,
+ -0.018995456f, 0.031487543f, 0.012834908f,0.019977754f,0.044256654f,
+ -0.39292613f, -0.18519334f, -0.11651281f,-0.06809892f, 0.011373677f };
- auto inputToForgetWeights =
- MakeTensor<float, 2>(tensorInfo20x5, {-0.0018401089f, -0.004852237f,0.03698424f, 0.014181704f,0.028273236f,
- -0.016726194f, -0.05249759f,-0.10204261f, 0.00861066f,-0.040979505f,
- -0.009899187f,0.01923892f,-0.028177269f, -0.08535103f,-0.14585495f,
- 0.10662567f,-0.01909731f,-0.017883534f,-0.0047269356f,-0.045103323f,
- 0.0030784295f,0.076784775f,0.07463696f, 0.094531395f,0.0814421f,
- -0.12257899f, -0.033945758f,-0.031303465f, 0.045630626f,0.06843887f,
- -0.13492945f, -0.012480007f,-0.0811829f, -0.07224499f,-0.09628791f,
- 0.045100946f,0.0012300825f, 0.013964662f, 0.099372394f,0.02543059f,
- 0.06958324f, 0.034257296f, 0.0482646f, 0.06267997f,0.052625068f,
- 0.12784666f, 0.07077897f, 0.025725935f, 0.04165009f,0.07241905f,
- 0.018668644f, -0.037377294f,-0.06277783f,-0.08833636f,-0.040120605f,
- -0.011405586f,-0.007808335f,-0.010301386f,-0.005102167f,0.027717464f,
- 0.05483423f, 0.11449111f, 0.11289652f,0.10939839f, 0.13396506f,
- -0.08402166f,-0.01901462f, -0.044678304f,-0.07720565f,0.014350063f,
- -0.11757958f, -0.0652038f, -0.08185733f,-0.076754324f,-0.092614375f,
- 0.10405491f, 0.052960336f, 0.035755895f,0.035839386f,-0.012540553f,
- 0.036881298f, 0.02913376f, 0.03420159f,0.05448447f,-0.054523353f,
- 0.02582715f, 0.02327355f, -0.011857179f,-0.0011980024f,-0.034641717f,
- -0.026125094f,-0.17582615f,-0.15923657f,-0.27486774f,-0.0006143371f,
- 0.0001771948f, -8.470171e-05f, 0.02651807f,0.045790765f,0.06956496f
- });
+ std::vector<float> inputToForgetWeights = {-0.0018401089f, -0.004852237f,0.03698424f, 0.014181704f,0.028273236f,
+ -0.016726194f, -0.05249759f,-0.10204261f, 0.00861066f,-0.040979505f,
+ -0.009899187f,0.01923892f,-0.028177269f, -0.08535103f,-0.14585495f,
+ 0.10662567f,-0.01909731f,-0.017883534f,-0.0047269356f,-0.045103323f,
+ 0.0030784295f,0.076784775f,0.07463696f, 0.094531395f,0.0814421f,
+ -0.12257899f, -0.033945758f,-0.031303465f, 0.045630626f,0.06843887f,
+ -0.13492945f, -0.012480007f,-0.0811829f, -0.07224499f,-0.09628791f,
+ 0.045100946f,0.0012300825f, 0.013964662f, 0.099372394f,0.02543059f,
+ 0.06958324f, 0.034257296f, 0.0482646f, 0.06267997f,0.052625068f,
+ 0.12784666f, 0.07077897f, 0.025725935f, 0.04165009f,0.07241905f,
+ 0.018668644f, -0.037377294f,-0.06277783f,-0.08833636f,-0.040120605f,
+ -0.011405586f,-0.007808335f,-0.010301386f,-0.005102167f,0.027717464f,
+ 0.05483423f, 0.11449111f, 0.11289652f,0.10939839f, 0.13396506f,
+ -0.08402166f,-0.01901462f, -0.044678304f,-0.07720565f,0.014350063f,
+ -0.11757958f, -0.0652038f, -0.08185733f,-0.076754324f,-0.092614375f,
+ 0.10405491f, 0.052960336f, 0.035755895f,0.035839386f,-0.012540553f,
+ 0.036881298f, 0.02913376f, 0.03420159f,0.05448447f,-0.054523353f,
+ 0.02582715f, 0.02327355f, -0.011857179f,-0.0011980024f,-0.034641717f,
+ -0.026125094f,-0.17582615f,-0.15923657f,-0.27486774f,-0.0006143371f,
+ 0.0001771948f, -8.470171e-05f, 0.02651807f,0.045790765f,0.06956496f };
- auto inputToCellWeights =
- MakeTensor<float, 2>(tensorInfo20x5, {-0.04580283f, -0.09549462f, -0.032418985f, -0.06454633f,
- -0.043528453f, 0.043018587f, -0.049152344f, -0.12418144f,
- -0.078985475f, -0.07596889f, 0.019484362f, -0.11434962f,
- -0.0074034138f, -0.06314844f, -0.092981495f, 0.0062155537f,
- -0.025034338f, -0.0028890965f, 0.048929527f, 0.06235075f,
- 0.10665918f, -0.032036792f, -0.08505916f, -0.10843358f,
- -0.13002433f, -0.036816437f, -0.02130134f, -0.016518239f,
- 0.0047691227f, -0.0025825808f, 0.066017866f, 0.029991534f,
- -0.10652836f, -0.1037554f, -0.13056071f, -0.03266643f,
- -0.033702414f, -0.006473424f, -0.04611692f, 0.014419339f,
- -0.025174323f, 0.0396852f, 0.081777506f, 0.06157468f,
- 0.10210095f, -0.009658194f, 0.046511717f, 0.03603906f,
- 0.0069369148f, 0.015960095f, -0.06507666f, 0.09551598f,
- 0.053568836f, 0.06408714f, 0.12835667f, -0.008714329f,
- -0.20211966f, -0.12093674f, 0.029450472f, 0.2849013f,
- -0.029227901f, 0.1164364f, -0.08560263f, 0.09941786f,
- -0.036999565f, -0.028842626f, -0.0033637602f, -0.017012902f,
- -0.09720865f, -0.11193351f, -0.029155117f, -0.017936034f,
- -0.009768936f, -0.04223324f, -0.036159635f, 0.06505112f,
- -0.021742892f, -0.023377212f, -0.07221364f, -0.06430552f,
- 0.05453865f, 0.091149814f, 0.06387331f, 0.007518393f,
- 0.055960953f, 0.069779344f, 0.046411168f, 0.10509911f,
- 0.07463894f, 0.0075130584f, 0.012850982f, 0.04555431f,
- 0.056955688f, 0.06555285f, 0.050801456f, -0.009862683f,
- 0.00826772f, -0.026555609f, -0.0073611983f, -0.0014897042f
- });
+ std::vector<float> inputToCellWeights = { -0.04580283f, -0.09549462f, -0.032418985f, -0.06454633f,
+ -0.043528453f, 0.043018587f, -0.049152344f, -0.12418144f,
+ -0.078985475f, -0.07596889f, 0.019484362f, -0.11434962f,
+ -0.0074034138f, -0.06314844f, -0.092981495f, 0.0062155537f,
+ -0.025034338f, -0.0028890965f, 0.048929527f, 0.06235075f,
+ 0.10665918f, -0.032036792f, -0.08505916f, -0.10843358f,
+ -0.13002433f, -0.036816437f, -0.02130134f, -0.016518239f,
+ 0.0047691227f, -0.0025825808f, 0.066017866f, 0.029991534f,
+ -0.10652836f, -0.1037554f, -0.13056071f, -0.03266643f,
+ -0.033702414f, -0.006473424f, -0.04611692f, 0.014419339f,
+ -0.025174323f, 0.0396852f, 0.081777506f, 0.06157468f,
+ 0.10210095f, -0.009658194f, 0.046511717f, 0.03603906f,
+ 0.0069369148f, 0.015960095f, -0.06507666f, 0.09551598f,
+ 0.053568836f, 0.06408714f, 0.12835667f, -0.008714329f,
+ -0.20211966f, -0.12093674f, 0.029450472f, 0.2849013f,
+ -0.029227901f, 0.1164364f, -0.08560263f, 0.09941786f,
+ -0.036999565f, -0.028842626f, -0.0033637602f, -0.017012902f,
+ -0.09720865f, -0.11193351f, -0.029155117f, -0.017936034f,
+ -0.009768936f, -0.04223324f, -0.036159635f, 0.06505112f,
+ -0.021742892f, -0.023377212f, -0.07221364f, -0.06430552f,
+ 0.05453865f, 0.091149814f, 0.06387331f, 0.007518393f,
+ 0.055960953f, 0.069779344f, 0.046411168f, 0.10509911f,
+ 0.07463894f, 0.0075130584f, 0.012850982f, 0.04555431f,
+ 0.056955688f, 0.06555285f, 0.050801456f, -0.009862683f,
+ 0.00826772f, -0.026555609f, -0.0073611983f, -0.0014897042f };
- auto inputToOutputWeights =
- MakeTensor<float, 2>(tensorInfo20x5, {-0.0998932f, -0.07201956f, -0.052803773f,-0.15629593f,-0.15001918f,
- -0.07650751f,0.02359855f, -0.075155355f, -0.08037709f, -0.15093534f,
- 0.029517552f, -0.04751393f, 0.010350531f,-0.02664851f, -0.016839722f,
- -0.023121163f, 0.0077019283f, 0.012851257f, -0.05040649f,-0.0129761f,
- -0.021737747f,-0.038305793f,-0.06870586f, -0.01481247f,-0.001285394f,
- 0.10124236f, 0.083122835f, 0.053313006f,-0.062235646f,-0.075637154f,
- -0.027833903f, 0.029774971f, 0.1130802f, 0.09218906f, 0.09506135f,
- -0.086665764f,-0.037162706f,-0.038880914f,-0.035832845f,-0.014481564f,
- -0.09825003f,-0.12048569f,-0.097665586f,-0.05287633f, -0.0964047f,
- -0.11366429f, 0.035777505f, 0.13568819f, 0.052451383f,0.050649304f,
- 0.05798951f, -0.021852335f,-0.099848844f,0.014740475f,-0.078897946f,
- 0.04974699f, 0.014160473f, 0.06973932f, 0.04964942f, 0.033364646f,
- 0.08190124f, 0.025535367f, 0.050893165f, 0.048514254f,0.06945813f,
- -0.078907564f,-0.06707616f, -0.11844508f, -0.09986688f,-0.07509403f,
- 0.06263226f, 0.14925587f, 0.20188436f, 0.12098451f,0.14639415f,
- 0.0015017595f, -0.014267382f, -0.03417257f,0.012711468f,0.0028300495f,
- -0.024758482f, -0.05098548f,-0.0821182f, 0.014225672f, 0.021544158f,
- 0.08949725f, 0.07505268f, -0.0020780868f, 0.04908258f,0.06476295f,
- -0.022907063f,0.027562456f,0.040185735f, 0.019567577f,-0.015598739f,
- -0.049097303f, -0.017121866f, -0.083368234f,-0.02332002f,-0.0840956f
- });
+ std::vector<float> inputToOutputWeights ={-0.0998932f, -0.07201956f, -0.052803773f,-0.15629593f,-0.15001918f,
+ -0.07650751f,0.02359855f, -0.075155355f, -0.08037709f, -0.15093534f,
+ 0.029517552f, -0.04751393f, 0.010350531f,-0.02664851f, -0.016839722f,
+ -0.023121163f, 0.0077019283f, 0.012851257f, -0.05040649f,-0.0129761f,
+ -0.021737747f,-0.038305793f,-0.06870586f, -0.01481247f,-0.001285394f,
+ 0.10124236f, 0.083122835f, 0.053313006f,-0.062235646f,-0.075637154f,
+ -0.027833903f, 0.029774971f, 0.1130802f, 0.09218906f, 0.09506135f,
+ -0.086665764f,-0.037162706f,-0.038880914f,-0.035832845f,-0.014481564f,
+ -0.09825003f,-0.12048569f,-0.097665586f,-0.05287633f, -0.0964047f,
+ -0.11366429f, 0.035777505f, 0.13568819f, 0.052451383f,0.050649304f,
+ 0.05798951f, -0.021852335f,-0.099848844f,0.014740475f,-0.078897946f,
+ 0.04974699f, 0.014160473f, 0.06973932f, 0.04964942f, 0.033364646f,
+ 0.08190124f, 0.025535367f, 0.050893165f, 0.048514254f,0.06945813f,
+ -0.078907564f,-0.06707616f, -0.11844508f, -0.09986688f,-0.07509403f,
+ 0.06263226f, 0.14925587f, 0.20188436f, 0.12098451f,0.14639415f,
+ 0.0015017595f, -0.014267382f, -0.03417257f,0.012711468f,0.0028300495f,
+ -0.024758482f, -0.05098548f,-0.0821182f, 0.014225672f, 0.021544158f,
+ 0.08949725f, 0.07505268f, -0.0020780868f, 0.04908258f,0.06476295f,
+ -0.022907063f,0.027562456f,0.040185735f, 0.019567577f,-0.015598739f,
+ -0.049097303f, -0.017121866f, -0.083368234f,-0.02332002f,-0.0840956f };
- auto inputGateBias =
- MakeTensor<float, 1>(tensorInfo20, {0.02234832f, 0.14757581f, 0.18176508f, 0.10380666f, 0.053110216f,
- -0.06928846f, -0.13942584f, -0.11816189f, 0.19483899f, 0.03652339f,
- -0.10250295f, 0.036714908f, -0.18426876f, 0.036065217f, 0.21810818f,
- 0.02383196f, -0.043370757f, 0.08690144f, -0.04444982f, 0.00030581196f
- });
+ std::vector<float> inputGateBias = {0.02234832f, 0.14757581f, 0.18176508f, 0.10380666f, 0.053110216f,
+ -0.06928846f, -0.13942584f, -0.11816189f, 0.19483899f, 0.03652339f,
+ -0.10250295f, 0.036714908f, -0.18426876f, 0.036065217f, 0.21810818f,
+ 0.02383196f, -0.043370757f, 0.08690144f, -0.04444982f, 0.00030581196f };
- auto forgetGateBias =
- MakeTensor<float, 1>(tensorInfo20, {0.035185695f, -0.042891346f, -0.03032477f, 0.23027696f,
- 0.11098921f, 0.15378423f, 0.09263801f, 0.09790885f,
- 0.09508917f, 0.061199076f, 0.07665568f, -0.015443159f,
- -0.03499149f, 0.046190713f, 0.08895977f, 0.10899629f,
- 0.40694186f, 0.06030037f, 0.012413437f, -0.06108739f
- });
+ std::vector<float> forgetGateBias ={0.035185695f, -0.042891346f, -0.03032477f, 0.23027696f,
+ 0.11098921f, 0.15378423f, 0.09263801f, 0.09790885f,
+ 0.09508917f, 0.061199076f, 0.07665568f, -0.015443159f,
+ -0.03499149f, 0.046190713f, 0.08895977f, 0.10899629f,
+ 0.40694186f, 0.06030037f, 0.012413437f, -0.06108739f };
- auto cellBias =
- MakeTensor<float, 1>(tensorInfo20, {-0.024379363f, 0.0055531194f, 0.23377132f, 0.033463873f,
- -0.1483596f, -0.10639995f, -0.091433935f, 0.058573797f,
- -0.06809782f, -0.07889636f, -0.043246906f, -0.09829136f,
- -0.4279842f, 0.034901652f, 0.18797937f, 0.0075234566f,
- 0.016178843f, 0.1749513f, 0.13975595f, 0.92058027f
- });
+ std::vector<float> cellBias = { -0.024379363f, 0.0055531194f, 0.23377132f, 0.033463873f,
+ -0.1483596f, -0.10639995f, -0.091433935f, 0.058573797f,
+ -0.06809782f, -0.07889636f, -0.043246906f, -0.09829136f,
+ -0.4279842f, 0.034901652f, 0.18797937f, 0.0075234566f,
+ 0.016178843f, 0.1749513f, 0.13975595f, 0.92058027f };
- auto outputGateBias =
- MakeTensor<float, 1>(tensorInfo20, {0.046159424f, -0.0012809046f, 0.03563469f, 0.12648113f, 0.027195795f,
- 0.35373217f, -0.018957434f, 0.008907322f, -0.0762701f, 0.12018895f,
- 0.04216877f, 0.0022856654f, 0.040952638f, 0.3147856f, 0.08225149f,
- -0.057416286f, -0.14995944f, -0.008040261f, 0.13208859f, 0.029760877f
- });
+ std::vector<float> outputGateBias ={0.046159424f, -0.0012809046f, 0.03563469f, 0.12648113f, 0.027195795f,
+ 0.35373217f, -0.018957434f, 0.008907322f, -0.0762701f, 0.12018895f,
+ 0.04216877f, 0.0022856654f, 0.040952638f, 0.3147856f, 0.08225149f,
+ -0.057416286f, -0.14995944f, -0.008040261f, 0.13208859f, 0.029760877f};
- auto recurrentToInputWeights =
- MakeTensor<float, 2>(tensorInfo20x16, {-0.001374326f, -0.078856036f, 0.10672688f, 0.029162422f,
+ std::vector<float> recurrentToInputWeights = { -0.001374326f, -0.078856036f, 0.10672688f, 0.029162422f,
-0.11585556f, 0.02557986f, -0.13446963f, -0.035785314f,
-0.01244275f, 0.025961924f, -0.02337298f, -0.044228926f,
-0.055839065f, -0.046598054f, -0.010546039f, -0.06900766f,
@@ -632,11 +600,9 @@
-0.014512694f, -0.08251313f, 0.08861942f, 0.13589665f,
0.026351685f, 0.012641483f, 0.07466548f, 0.044301085f,
-0.045414884f, -0.051112458f, 0.03444247f, -0.08502782f,
- -0.04106223f, -0.028126027f, 0.028473156f, 0.10467447f
- });
+ -0.04106223f, -0.028126027f, 0.028473156f, 0.10467447f };
- auto recurrentToForgetWeights =
- MakeTensor<float, 2>(tensorInfo20x16, {-0.057784554f, -0.026057621f, -0.068447545f, -0.022581743f,
+ std::vector<float> recurrentToForgetWeights = {-0.057784554f, -0.026057621f, -0.068447545f, -0.022581743f,
0.14811787f, 0.10826372f, 0.09471067f, 0.03987225f,
-0.0039523416f, 0.00030638507f, 0.053185795f, 0.10572994f,
0.08414449f, -0.022036452f, -0.00066928595f, -0.09203576f,
@@ -715,11 +681,9 @@
-0.081302024f, 0.017264642f, -0.009585969f, 0.09491168f,
-0.051313367f, 0.054532815f, -0.014298593f, 0.10657464f,
0.007076659f, 0.10964551f, 0.0409152f, 0.008275321f,
- -0.07283536f, 0.07937492f, 0.04192024f, -0.1075027f
- });
+ -0.07283536f, 0.07937492f, 0.04192024f, -0.1075027f };
- auto recurrentToCellWeights =
- MakeTensor<float, 2>(tensorInfo20x16, {-0.037322544f, 0.018592842f, 0.0056175636f, -0.06253426f,
+ std::vector<float> recurrentToCellWeights = { -0.037322544f, 0.018592842f, 0.0056175636f, -0.06253426f,
0.055647098f, -0.05713207f, -0.05626563f, 0.005559383f,
0.03375411f, -0.025757805f, -0.088049285f, 0.06017052f,
-0.06570978f, 0.007384076f, 0.035123326f, -0.07920549f,
@@ -798,12 +762,10 @@
0.031502828f, 0.036232427f, -0.031581745f, 0.023051167f,
-0.05325106f, -0.03421577f, 0.028793324f, -0.034633752f,
-0.009881397f, -0.043551125f, -0.018609839f, 0.0019097115f,
- -0.008799762f, 0.056595087f, 0.0022273948f, 0.055752404f
- });
+ -0.008799762f, 0.056595087f, 0.0022273948f, 0.055752404f };
- auto recurrentToOutputWeights =
- MakeTensor<float, 2>(tensorInfo20x16, {0.025825322f, -0.05813119f, 0.09495884f,-0.045984812f, -0.01255415f,
- -0.0026479573f,-0.08196161f,-0.054914974f,-0.0046604523f,
+ std::vector<float> recurrentToOutputWeights = { 0.025825322f, -0.05813119f, 0.09495884f,-0.045984812f, -0.01255415f,
+ -0.0026479573f,-0.08196161f,-0.054914974f,-0.0046604523f,
-0.029587349f, -0.044576716f, -0.07480124f, -0.082868785f,
0.023254942f, 0.027502948f, -0.0039728214f, -0.08683098f,
-0.08116779f, -0.014675607f, -0.037924774f, -0.023314456f,
@@ -879,101 +841,90 @@
-0.05347844f, -0.11768019f, 0.085926116f, -0.08251791f,
-0.045081906f, 0.0948852f, 0.068401024f, 0.024856757f,
0.06978981f, -0.057309967f, -0.012775832f, -0.0032452994f,
- 0.01977615f, -0.041040014f, -0.024264973f,0.063464895f, 0.05431621f
- });
+ 0.01977615f, -0.041040014f, -0.024264973f,0.063464895f, 0.05431621f};
- auto cellToInputWeights =
- MakeTensor<float, 1>(tensorInfo20, {0.040369894f, 0.030746894f, 0.24704495f, 0.018586371f, -0.037586458f,
- -0.15312155f, -0.11812848f, -0.11465643f, 0.20259799f, 0.11418174f,
- -0.10116027f, -0.011334949f, 0.12411352f, -0.076769054f,-0.052169047f,
- 0.21198851f, -0.38871562f, -0.09061183f, -0.09683246f, -0.21929175f
- });
+ std::vector<float> cellToInputWeights = {0.040369894f, 0.030746894f, 0.24704495f, 0.018586371f, -0.037586458f,
+ -0.15312155f, -0.11812848f, -0.11465643f, 0.20259799f, 0.11418174f,
+ -0.10116027f, -0.011334949f, 0.12411352f, -0.076769054f,-0.052169047f,
+ 0.21198851f, -0.38871562f, -0.09061183f, -0.09683246f, -0.21929175f};
- auto cellToForgetWeights =
- MakeTensor<float, 1>(tensorInfo20, {-0.01998659f,-0.15568835f,-0.24248174f, -0.012770197f, 0.041331276f,
- -0.072311886f, -0.052123554f,-0.0066330447f,-0.043891653f,0.036225766f,
- -0.047248036f, 0.021479502f,0.033189066f, 0.11952997f, -0.020432774f,
- 0.64658105f, -0.06650122f, -0.03467612f, 0.095340036f, 0.23647355f
- });
+ std::vector<float> cellToForgetWeights = {-0.01998659f,-0.15568835f,-0.24248174f, -0.012770197f, 0.041331276f,
+ -0.072311886f, -0.052123554f,-0.0066330447f,-0.043891653f,0.036225766f,
+ -0.047248036f, 0.021479502f,0.033189066f, 0.11952997f, -0.020432774f,
+ 0.64658105f, -0.06650122f, -0.03467612f, 0.095340036f, 0.23647355f};
- auto cellToOutputWeights =
- MakeTensor<float, 1>(tensorInfo20, {0.08286371f, -0.08261836f, -0.51210177f, 0.002913762f, 0.17764764f,
- -0.5495371f, -0.08460716f, -0.24552552f, 0.030037103f, 0.04123544f,
- -0.11940523f, 0.007358328f, 0.1890978f, 0.4833202f, -0.34441817f,
- 0.36312827f, -0.26375428f, 0.1457655f, -0.19724406f, 0.15548733f
- });
+ std::vector<float> cellToOutputWeights = { 0.08286371f, -0.08261836f, -0.51210177f, 0.002913762f, 0.17764764f,
+ -0.5495371f, -0.08460716f, -0.24552552f, 0.030037103f, 0.04123544f,
+ -0.11940523f, 0.007358328f, 0.1890978f, 0.4833202f, -0.34441817f,
+ 0.36312827f, -0.26375428f, 0.1457655f, -0.19724406f, 0.15548733f};
- auto projectionWeights =
- MakeTensor<float, 2>(tensorInfo16x20,
- {-0.009802181f, 0.09401916f, 0.0717386f, -0.13895074f, 0.09641832f,
- 0.060420845f, 0.08539281f, 0.054285463f, 0.061395317f, 0.034448683f,
- -0.042991187f, 0.019801661f, -0.16840284f, -0.015726732f, -0.23041931f,
- -0.024478018f, -0.10959692f, -0.013875541f, 0.18600968f, -0.061274476f,
- 0.0138165f, -0.08160894f, -0.07661644f, 0.032372914f, 0.16169067f,
- 0.22465782f, -0.03993472f, -0.004017731f, 0.08633481f, -0.28869787f,
- 0.08682067f, 0.17240396f, 0.014975425f, 0.056431185f, 0.031037588f,
- 0.16702051f, 0.0077946745f, 0.15140012f, 0.29405436f, 0.120285f,
- -0.188994f, -0.027265169f, 0.043389652f, -0.022061434f, 0.014777949f,
- -0.20203483f, 0.094781205f, 0.19100232f, 0.13987629f, -0.036132768f,
- -0.06426278f, -0.05108664f, 0.13221376f, 0.009441198f, -0.16715929f,
- 0.15859416f, -0.040437475f, 0.050779544f, -0.022187516f, 0.012166504f,
- 0.027685808f, -0.07675938f, -0.0055694645f, -0.09444123f, 0.0046453946f,
- 0.050794356f, 0.10770313f, -0.20790008f, -0.07149004f, -0.11425117f,
- 0.008225835f, -0.035802525f, 0.14374903f, 0.15262283f, 0.048710253f,
- 0.1847461f, -0.007487823f, 0.11000021f, -0.09542012f, 0.22619456f,
- -0.029149994f, 0.08527916f, 0.009043713f, 0.0042746216f, 0.016261552f,
- 0.022461696f, 0.12689082f, -0.043589946f, -0.12035478f, -0.08361797f,
- -0.050666027f, -0.1248618f, -0.1275799f, -0.071875185f, 0.07377272f,
- 0.09944291f, -0.18897448f, -0.1593054f, -0.06526116f, -0.040107165f,
- -0.004618631f, -0.067624845f, -0.007576253f, 0.10727444f, 0.041546922f,
- -0.20424393f, 0.06907816f, 0.050412357f, 0.00724631f, 0.039827548f,
- 0.12449835f, 0.10747581f, 0.13708383f, 0.09134148f, -0.12617786f,
- -0.06428341f, 0.09956831f, 0.1208086f, -0.14676677f, -0.0727722f,
- 0.1126304f, 0.010139365f, 0.015571211f, -0.038128063f, 0.022913318f,
- -0.042050496f, 0.16842307f, -0.060597885f, 0.10531834f, -0.06411776f,
- -0.07451711f, -0.03410368f, -0.13393489f, 0.06534304f, 0.003620307f,
- 0.04490757f, 0.05970546f, 0.05197996f, 0.02839995f, 0.10434969f,
- -0.013699693f, -0.028353551f, -0.07260381f, 0.047201227f, -0.024575593f,
- -0.036445823f, 0.07155557f, 0.009672501f, -0.02328883f, 0.009533515f,
- -0.03606021f, -0.07421458f, -0.028082801f, -0.2678904f, -0.13221288f,
- 0.18419984f, -0.13012612f, -0.014588381f, -0.035059117f, -0.04824723f,
- 0.07830115f, -0.056184657f, 0.03277091f, 0.025466874f, 0.14494097f,
- -0.12522776f, -0.098633975f, -0.10766018f, -0.08317623f, 0.08594209f,
- 0.07749552f, 0.039474737f, 0.1776665f, -0.07409566f, -0.0477268f,
- 0.29323658f, 0.10801441f, 0.1154011f, 0.013952499f, 0.10739139f,
- 0.10708251f, -0.051456142f, 0.0074137426f, -0.10430189f, 0.10034707f,
- 0.045594677f, 0.0635285f, -0.0715442f, -0.089667566f, -0.10811871f,
- 0.00026344223f, 0.08298446f, -0.009525053f, 0.006585689f, -0.24567553f,
- -0.09450807f, 0.09648481f, 0.026996298f, -0.06419476f, -0.04752702f,
- -0.11063944f, -0.23441927f, -0.17608605f, -0.052156363f, 0.067035615f,
- 0.19271925f, -0.0032889997f, -0.043264326f, 0.09663576f, -0.057112187f,
- -0.10100678f, 0.0628376f, 0.04447668f, 0.017961001f, -0.10094388f,
- -0.10190601f, 0.18335468f, 0.10494553f, -0.052095775f, -0.0026118709f,
- 0.10539724f, -0.04383912f, -0.042349473f, 0.08438151f, -0.1947263f,
- 0.02251204f, 0.11216432f, -0.10307853f, 0.17351969f, -0.039091777f,
- 0.08066188f, -0.00561982f, 0.12633002f, 0.11335965f, -0.0088127935f,
- -0.019777594f, 0.06864014f, -0.059751723f, 0.016233567f, -0.06894641f,
- -0.28651384f, -0.004228674f, 0.019708522f, -0.16305895f, -0.07468996f,
- -0.0855457f, 0.099339016f, -0.07580735f, -0.13775392f, 0.08434318f,
- 0.08330512f, -0.12131499f, 0.031935584f, 0.09180414f, -0.08876437f,
- -0.08049874f, 0.008753825f, 0.03498998f, 0.030215185f, 0.03907079f,
- 0.089751154f, 0.029194152f, -0.03337423f, -0.019092513f, 0.04331237f,
- 0.04299654f, -0.036394123f, -0.12915532f, 0.09793732f, 0.07512415f,
- -0.11319543f, -0.032502122f, 0.15661901f, 0.07671967f, -0.005491124f,
- -0.19379048f, -0.218606f, 0.21448623f, 0.017840758f, 0.1416943f,
- -0.07051762f, 0.19488361f, 0.02664691f, -0.18104725f, -0.09334311f,
- 0.15026465f, -0.15493552f, -0.057762887f, -0.11604192f, -0.262013f,
- -0.01391798f, 0.012185008f, 0.11156489f, -0.07483202f, 0.06693364f,
- -0.26151478f, 0.046425626f, 0.036540434f, -0.16435726f, 0.17338543f,
- -0.21401681f, -0.11385144f, -0.08283257f, -0.069031075f, 0.030635102f,
- 0.010969227f, 0.11109743f, 0.010919218f, 0.027526086f, 0.13519906f,
- 0.01891392f, -0.046839405f, -0.040167913f, 0.017953383f, -0.09700955f,
- 0.0061885654f, -0.07000971f, 0.026893595f, -0.038844477f, 0.14543656f
- });
+ std::vector<float> projectionWeights={-0.009802181f, 0.09401916f, 0.0717386f, -0.13895074f, 0.09641832f,
+ 0.060420845f, 0.08539281f, 0.054285463f, 0.061395317f, 0.034448683f,
+ -0.042991187f, 0.019801661f, -0.16840284f, -0.015726732f, -0.23041931f,
+ -0.024478018f, -0.10959692f, -0.013875541f, 0.18600968f, -0.061274476f,
+ 0.0138165f, -0.08160894f, -0.07661644f, 0.032372914f, 0.16169067f,
+ 0.22465782f, -0.03993472f, -0.004017731f, 0.08633481f, -0.28869787f,
+ 0.08682067f, 0.17240396f, 0.014975425f, 0.056431185f, 0.031037588f,
+ 0.16702051f, 0.0077946745f, 0.15140012f, 0.29405436f, 0.120285f,
+ -0.188994f, -0.027265169f, 0.043389652f, -0.022061434f, 0.014777949f,
+ -0.20203483f, 0.094781205f, 0.19100232f, 0.13987629f, -0.036132768f,
+ -0.06426278f, -0.05108664f, 0.13221376f, 0.009441198f, -0.16715929f,
+ 0.15859416f, -0.040437475f, 0.050779544f, -0.022187516f, 0.012166504f,
+ 0.027685808f, -0.07675938f, -0.0055694645f, -0.09444123f, 0.0046453946f,
+ 0.050794356f, 0.10770313f, -0.20790008f, -0.07149004f, -0.11425117f,
+ 0.008225835f, -0.035802525f, 0.14374903f, 0.15262283f, 0.048710253f,
+ 0.1847461f, -0.007487823f, 0.11000021f, -0.09542012f, 0.22619456f,
+ -0.029149994f, 0.08527916f, 0.009043713f, 0.0042746216f, 0.016261552f,
+ 0.022461696f, 0.12689082f, -0.043589946f, -0.12035478f, -0.08361797f,
+ -0.050666027f, -0.1248618f, -0.1275799f, -0.071875185f, 0.07377272f,
+ 0.09944291f, -0.18897448f, -0.1593054f, -0.06526116f, -0.040107165f,
+ -0.004618631f, -0.067624845f, -0.007576253f, 0.10727444f, 0.041546922f,
+ -0.20424393f, 0.06907816f, 0.050412357f, 0.00724631f, 0.039827548f,
+ 0.12449835f, 0.10747581f, 0.13708383f, 0.09134148f, -0.12617786f,
+ -0.06428341f, 0.09956831f, 0.1208086f, -0.14676677f, -0.0727722f,
+ 0.1126304f, 0.010139365f, 0.015571211f, -0.038128063f, 0.022913318f,
+ -0.042050496f, 0.16842307f, -0.060597885f, 0.10531834f, -0.06411776f,
+ -0.07451711f, -0.03410368f, -0.13393489f, 0.06534304f, 0.003620307f,
+ 0.04490757f, 0.05970546f, 0.05197996f, 0.02839995f, 0.10434969f,
+ -0.013699693f, -0.028353551f, -0.07260381f, 0.047201227f, -0.024575593f,
+ -0.036445823f, 0.07155557f, 0.009672501f, -0.02328883f, 0.009533515f,
+ -0.03606021f, -0.07421458f, -0.028082801f, -0.2678904f, -0.13221288f,
+ 0.18419984f, -0.13012612f, -0.014588381f, -0.035059117f, -0.04824723f,
+ 0.07830115f, -0.056184657f, 0.03277091f, 0.025466874f, 0.14494097f,
+ -0.12522776f, -0.098633975f, -0.10766018f, -0.08317623f, 0.08594209f,
+ 0.07749552f, 0.039474737f, 0.1776665f, -0.07409566f, -0.0477268f,
+ 0.29323658f, 0.10801441f, 0.1154011f, 0.013952499f, 0.10739139f,
+ 0.10708251f, -0.051456142f, 0.0074137426f, -0.10430189f, 0.10034707f,
+ 0.045594677f, 0.0635285f, -0.0715442f, -0.089667566f, -0.10811871f,
+ 0.00026344223f, 0.08298446f, -0.009525053f, 0.006585689f, -0.24567553f,
+ -0.09450807f, 0.09648481f, 0.026996298f, -0.06419476f, -0.04752702f,
+ -0.11063944f, -0.23441927f, -0.17608605f, -0.052156363f, 0.067035615f,
+ 0.19271925f, -0.0032889997f, -0.043264326f, 0.09663576f, -0.057112187f,
+ -0.10100678f, 0.0628376f, 0.04447668f, 0.017961001f, -0.10094388f,
+ -0.10190601f, 0.18335468f, 0.10494553f, -0.052095775f, -0.0026118709f,
+ 0.10539724f, -0.04383912f, -0.042349473f, 0.08438151f, -0.1947263f,
+ 0.02251204f, 0.11216432f, -0.10307853f, 0.17351969f, -0.039091777f,
+ 0.08066188f, -0.00561982f, 0.12633002f, 0.11335965f, -0.0088127935f,
+ -0.019777594f, 0.06864014f, -0.059751723f, 0.016233567f, -0.06894641f,
+ -0.28651384f, -0.004228674f, 0.019708522f, -0.16305895f, -0.07468996f,
+ -0.0855457f, 0.099339016f, -0.07580735f, -0.13775392f, 0.08434318f,
+ 0.08330512f, -0.12131499f, 0.031935584f, 0.09180414f, -0.08876437f,
+ -0.08049874f, 0.008753825f, 0.03498998f, 0.030215185f, 0.03907079f,
+ 0.089751154f, 0.029194152f, -0.03337423f, -0.019092513f, 0.04331237f,
+ 0.04299654f, -0.036394123f, -0.12915532f, 0.09793732f, 0.07512415f,
+ -0.11319543f, -0.032502122f, 0.15661901f, 0.07671967f, -0.005491124f,
+ -0.19379048f, -0.218606f, 0.21448623f, 0.017840758f, 0.1416943f,
+ -0.07051762f, 0.19488361f, 0.02664691f, -0.18104725f, -0.09334311f,
+ 0.15026465f, -0.15493552f, -0.057762887f, -0.11604192f, -0.262013f,
+ -0.01391798f, 0.012185008f, 0.11156489f, -0.07483202f, 0.06693364f,
+ -0.26151478f, 0.046425626f, 0.036540434f, -0.16435726f, 0.17338543f,
+ -0.21401681f, -0.11385144f, -0.08283257f, -0.069031075f, 0.030635102f,
+ 0.010969227f, 0.11109743f, 0.010919218f, 0.027526086f, 0.13519906f,
+ 0.01891392f, -0.046839405f, -0.040167913f, 0.017953383f, -0.09700955f,
+ 0.0061885654f, -0.07000971f, 0.026893595f, -0.038844477f, 0.14543656f};
std::vector<float> projectionBiasVector(outputSize, 0.f);
- auto projectionBias = MakeTensor<float,1>(tensorInfo16, projectionBiasVector);
armnn::ScopedTensorHandle inputToInputWeightsTensor(tensorInfo20x5);
armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfo20x5);
@@ -993,23 +944,23 @@
armnn::ScopedTensorHandle projectionWeightsTensor(tensorInfo16x20);
armnn::ScopedTensorHandle projectionBiasTensor(tensorInfo16);
- AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, &inputToInputWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, &inputToForgetWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, &inputToCellWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, &inputToOutputWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, &recurrentToInputWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, &recurrentToForgetWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, &recurrentToCellWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, &recurrentToOutputWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&cellToInputWeightsTensor, &cellToInputWeights[0]);
- AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, &inputGateBias[0]);
- AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, &forgetGateBias[0]);
- AllocateAndCopyDataToITensorHandle(&cellBiasTensor, &cellBias[0]);
- AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, &outputGateBias[0]);
- AllocateAndCopyDataToITensorHandle(&cellToForgetWeightsTensor, &cellToForgetWeights[0]);
- AllocateAndCopyDataToITensorHandle(&cellToOutputWeightsTensor, &cellToOutputWeights[0]);
- AllocateAndCopyDataToITensorHandle(&projectionWeightsTensor, &projectionWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&projectionBiasTensor, &projectionBias[0]);
+ AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, inputToInputWeights.data());
+ AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data());
+ AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data());
+ AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data());
+ AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, recurrentToInputWeights.data());
+ AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data());
+ AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data());
+ AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data());
+ AllocateAndCopyDataToITensorHandle(&cellToInputWeightsTensor, cellToInputWeights.data());
+ AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, inputGateBias.data());
+ AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data());
+ AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data());
+ AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data());
+ AllocateAndCopyDataToITensorHandle(&cellToForgetWeightsTensor, cellToForgetWeights.data());
+ AllocateAndCopyDataToITensorHandle(&cellToOutputWeightsTensor, cellToOutputWeights.data());
+ AllocateAndCopyDataToITensorHandle(&projectionWeightsTensor, projectionWeights.data());
+ AllocateAndCopyDataToITensorHandle(&projectionBiasTensor, projectionBiasVector.data());
data.m_InputToInputWeights = &inputToInputWeightsTensor;
data.m_InputToForgetWeights = &inputToForgetWeightsTensor;
@@ -1035,7 +986,6 @@
data.m_Parameters.m_PeepholeEnabled = true;
data.m_Parameters.m_ProjectionEnabled = true;
-
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateLstm(data, info);
inputHandle->Allocate();
outputStateInHandle->Allocate();
@@ -1046,16 +996,18 @@
cellStateOutHandle->Allocate();
outputHandle->Allocate();
- CopyDataToITensorHandle(inputHandle.get(), &inputTensor[0][0]);
- CopyDataToITensorHandle(outputStateInHandle.get(), &outputStateInTensor[0][0]);
- CopyDataToITensorHandle(cellStateInHandle.get(), &cellStateInTensor[0][0]);
+ CopyDataToITensorHandle(inputHandle.get(), inputVector.data());
+ CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data());
+ CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data());
workload->Execute();
- CopyDataFromITensorHandle(&ret.output[0][0], outputHandle.get());
+ CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get());
- return ret;
-
+ return LayerTestResult<T, 2>(actualOutput,
+ outputVector,
+ outputHandle->GetShape(),
+ outputTensorInfo.GetShape());
}
template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
@@ -1063,8 +1015,10 @@
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
const armnn::ITensorHandleFactory& tensorHandleFactory,
- const boost::multi_array<T, 2>& input,
- const boost::multi_array<T, 2>& outputExpected,
+ const std::vector<T>& input,
+ const std::vector<T>& outputExpected,
+ const armnn::TensorShape& inputShape,
+ const armnn::TensorShape& outputExpectedShape,
float qScale = 0.0f,
int32_t qOffset = 0,
armnn::DataType constantDataType = armnn::DataType::Float32)
@@ -1074,10 +1028,10 @@
bool peepholeEnabled = true;
bool projectionEnabled = false;
// These are not the input and the output of Lstm yet
- unsigned int batchSize = armnn::numeric_cast<unsigned int>(input.shape()[0]);
- unsigned int inputSize = armnn::numeric_cast<unsigned int>(input.shape()[1]);
+ unsigned int batchSize = armnn::numeric_cast<unsigned int>(inputShape[0]);
+ unsigned int inputSize = armnn::numeric_cast<unsigned int>(inputShape[1]);
- unsigned int outputSize = armnn::numeric_cast<unsigned int>(outputExpected.shape()[1]);
+ unsigned int outputSize = armnn::numeric_cast<unsigned int>(outputExpectedShape[1]);
const unsigned int cellSize = outputSize;
@@ -1095,14 +1049,10 @@
// List of inputs
std::vector<float> inputData;
inputData.assign(input.data(), input.data() + batchSize*inputSize);
- auto inputTensor = MakeTensor<float,2>(inputTensorInfo, inputData);
std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);
- auto outputStateInTensor = MakeTensor<float, 2>(outputStateInTensorInfo, outputStateInVector);
std::vector<float> cellStateInVector(batchSize * cellSize, 0.f);
- auto cellStateInTensor = MakeTensor<float, 2>(cellStateInTensorInfo, cellStateInVector);
-
// Prepare all the weights in the descriptor for LSTM
armnn::LstmQueueDescriptor data;
@@ -1110,41 +1060,51 @@
armnn::TensorInfo tensorInfoOutput({cellSize, outputSize}, constantDataType, qScale, qOffset);
armnn::TensorInfo tensorInfoNumUnits({cellSize}, constantDataType, qScale, qOffset);
- auto inputToCellWeights = MakeTensor<float, 2>(tensorInfoInput,
- {-0.49770179f, -0.27711356f, -0.09624726f, 0.05100781f,
- 0.04717243f, 0.48944736f, -0.38535351f,
- -0.17212132f});
- auto inputToForgetWeights = MakeTensor<float, 2>(tensorInfoInput,
- {-0.55291498f, -0.42866567f, 0.13056988f,
- -0.3633365f, -0.22755712f, 0.28253698f, 0.24407166f,
- 0.33826375f});
- auto inputToOutputWeights = MakeTensor<float, 2>(tensorInfoInput,
- {0.10725588f, -0.02335852f, -0.55932593f,
- -0.09426838f, -0.44257352f, 0.54939759f,
- 0.01533556f, 0.42751634f});
- auto cellBias = MakeTensor<float, 1>(tensorInfoNumUnits, {0.f, 0.f, 0.f, 0.f});
- auto forgetGateBias = MakeTensor<float, 1>(tensorInfoNumUnits, {1.f, 1.f, 1.f, 1.f});
- auto outputGateBias = MakeTensor<float, 1>(tensorInfoNumUnits, {0.f, 0.f, 0.f, 0.f});
+ std::vector<float> inputToCellWeights =
+ {
+ -0.49770179f, -0.27711356f, -0.09624726f, 0.05100781f,
+ 0.04717243f, 0.48944736f, -0.38535351f,
+ -0.17212132f
+ };
+ std::vector<float> inputToForgetWeights =
+ {
+ -0.55291498f, -0.42866567f, 0.13056988f,
+ -0.3633365f, -0.22755712f, 0.28253698f, 0.24407166f,
+ 0.33826375f
+ };
+ std::vector<float> inputToOutputWeights =
+ {
+ 0.10725588f, -0.02335852f, -0.55932593f,
+ -0.09426838f, -0.44257352f, 0.54939759f,
+ 0.01533556f, 0.42751634f
+ };
+ std::vector<float> cellBias = {0.f, 0.f, 0.f, 0.f};
+ std::vector<float> forgetGateBias = {1.f, 1.f, 1.f, 1.f};
+ std::vector<float> outputGateBias = {0.f, 0.f, 0.f, 0.f};
- auto recurrentToCellWeights = MakeTensor<float, 2>(tensorInfoOutput,
- {0.54066205f, -0.32668582f, -0.43562764f, -0.56094903f, 0.42957711f,
- 0.01841056f, -0.32764608f, -0.33027974f, -0.10826075f, 0.20675004f,
- 0.19069612f, -0.03026325f, -0.54532051f, 0.33003211f, 0.44901288f,
- 0.21193194f});
- auto recurrentToForgetWeights = MakeTensor<float, 2>(tensorInfoOutput,
- {-0.13832897f, -0.0515101f, -0.2359007f, -0.16661474f, -0.14340827f,
- 0.36986142f, 0.23414481f, 0.55899f, 0.10798943f, -0.41174671f, 0.17751795f,
- -0.34484994f, -0.35874045f, -0.11352962f, 0.27268326f, 0.54058349f});
+ std::vector<float> recurrentToCellWeights =
+ {
+ 0.54066205f, -0.32668582f, -0.43562764f, -0.56094903f, 0.42957711f,
+ 0.01841056f, -0.32764608f, -0.33027974f, -0.10826075f, 0.20675004f,
+ 0.19069612f, -0.03026325f, -0.54532051f, 0.33003211f, 0.44901288f,
+ 0.21193194f
+ };
+ std::vector<float> recurrentToForgetWeights =
+ {
+ -0.13832897f, -0.0515101f, -0.2359007f, -0.16661474f, -0.14340827f,
+ 0.36986142f, 0.23414481f, 0.55899f, 0.10798943f, -0.41174671f, 0.17751795f,
+ -0.34484994f, -0.35874045f, -0.11352962f, 0.27268326f, 0.54058349f
+ };
- auto recurrentToOutputWeights = MakeTensor<float, 2>(tensorInfoOutput,
- {0.41613156f, 0.42610586f, -0.16495961f, -0.5663873f, 0.30579174f, -0.05115908f,
- -0.33941799f, 0.23364776f, 0.11178309f, 0.09481031f, -0.26424935f, 0.46261835f,
- 0.50248802f, 0.26114327f, -0.43736315f, 0.33149987f});
+ std::vector<float> recurrentToOutputWeights =
+ {
+ 0.41613156f, 0.42610586f, -0.16495961f, -0.5663873f, 0.30579174f, -0.05115908f,
+ -0.33941799f, 0.23364776f, 0.11178309f, 0.09481031f, -0.26424935f, 0.46261835f,
+ 0.50248802f, 0.26114327f, -0.43736315f, 0.33149987f
+ };
- auto cellToForgetWeights = MakeTensor<float, 1>(tensorInfoNumUnits,
- {0.47485286f, -0.51955009f, -0.24458408f, 0.31544167f});
- auto cellToOutputWeights = MakeTensor<float, 1>(tensorInfoNumUnits,
- {-0.17135078f, 0.82760304f, 0.85573703f, -0.77109635f});
+ std::vector<float> cellToForgetWeights = {0.47485286f, -0.51955009f, -0.24458408f, 0.31544167f};
+ std::vector<float> cellToOutputWeights = {-0.17135078f, 0.82760304f, 0.85573703f, -0.77109635f};
armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfoInput);
armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfoInput);
@@ -1158,25 +1118,23 @@
armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfoOutput);
armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfoOutput);
-
armnn::ScopedTensorHandle cellToForgetWeightsTensor(tensorInfoNumUnits);
armnn::ScopedTensorHandle cellToOutputWeightsTensor(tensorInfoNumUnits);
- AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, &inputToCellWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, &inputToForgetWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, &inputToOutputWeights[0][0]);
+ AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data());
+ AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data());
+ AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data());
- AllocateAndCopyDataToITensorHandle(&cellBiasTensor, &cellBias[0]);
- AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, &forgetGateBias[0]);
- AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, &outputGateBias[0]);
+ AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data());
+ AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data());
+ AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data());
- AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, &recurrentToCellWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, &recurrentToForgetWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, &recurrentToOutputWeights[0][0]);
+ AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data());
+ AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data());
+ AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data());
- AllocateAndCopyDataToITensorHandle(&cellToForgetWeightsTensor, &cellToForgetWeights[0]);
- AllocateAndCopyDataToITensorHandle(&cellToOutputWeightsTensor, &cellToOutputWeights[0]);
-
+ AllocateAndCopyDataToITensorHandle(&cellToForgetWeightsTensor, cellToForgetWeights.data());
+ AllocateAndCopyDataToITensorHandle(&cellToOutputWeightsTensor, cellToOutputWeights.data());
data.m_InputToCellWeights = &inputToCellWeightsTensor;
data.m_InputToForgetWeights = &inputToForgetWeightsTensor;
@@ -1202,29 +1160,28 @@
data.m_Parameters.m_ClippingThresProj = 0.0;
data.m_Parameters.m_ClippingThresCell = 0.0;
-
// List of outputs
std::vector<T> scratchBufferVector(batchSize * scratchBufferSize, T());
- auto scratchBufferTensor = MakeTensor<T,2>(scratchBufferTensorInfo, scratchBufferVector);
LayerTestResult<T, 2> ret0(scratchBufferTensorInfo);
// Output state for a certain time step
std::vector<T> outputStateOutVector(batchSize * outputSize, T());
- auto outputStateOutTensor = MakeTensor<T,2>(outputStateOutTensorInfo, outputStateOutVector);
LayerTestResult<T, 2> ret1(outputStateOutTensorInfo);
// Cell state for a certain time step
std::vector<T> cellStateOutVector(batchSize * cellSize, T());
- auto cellStateOutTensor = MakeTensor<T,2>(cellStateOutTensorInfo, cellStateOutVector);
LayerTestResult<T, 2> ret2(cellStateOutTensorInfo);
// Output for a certain time step
- std::vector<T> outputVector(batchSize * outputSize, T());
- auto outputTensor = MakeTensor<T, 2>(outputTensorInfo, outputVector);
std::vector<T> outputData;
outputData.assign(outputExpected.data(), outputExpected.data() + batchSize*outputSize);
LayerTestResult<T, 2> ret3(outputTensorInfo);
- ret3.outputExpected = MakeTensor<T, 2>(outputTensorInfo, outputData);
+ ret3.m_ExpectedData = outputData;
+
+ std::vector<T> actualScratchBufferOutput(scratchBufferTensorInfo.GetNumElements());
+ std::vector<T> actualOutputStateOutput(outputStateOutTensorInfo.GetNumElements());
+ std::vector<T> actualCellStateOutput(cellStateOutTensorInfo.GetNumElements());
+ std::vector<T> actualOutput(outputTensorInfo.GetNumElements());
// Prepare the inputs and outputs for the workload
std::unique_ptr<armnn::ITensorHandle> inputHandle =
@@ -1255,7 +1212,6 @@
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateLstm(data, info);
-
inputHandle->Allocate();
outputStateInHandle->Allocate();
cellStateInHandle->Allocate();
@@ -1265,21 +1221,25 @@
cellStateOutHandle->Allocate();
outputHandle->Allocate();
+ CopyDataToITensorHandle(inputHandle.get(), inputData.data());
+ CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data());
+ CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data());
- CopyDataToITensorHandle(inputHandle.get(), &inputTensor[0][0]);
- CopyDataToITensorHandle(outputStateInHandle.get(), &outputStateInTensor[0][0]);
- CopyDataToITensorHandle(cellStateInHandle.get(), &cellStateInTensor[0][0]);
-
- CopyDataToITensorHandle(scratchBufferHandle.get(), &scratchBufferTensor[0][0]);
- CopyDataToITensorHandle(outputStateOutHandle.get(), &outputStateOutTensor[0][0]);
- CopyDataToITensorHandle(cellStateOutHandle.get(), &cellStateOutTensor[0][0]);
+ CopyDataToITensorHandle(scratchBufferHandle.get(), scratchBufferVector.data());
+ CopyDataToITensorHandle(outputStateOutHandle.get(), outputStateOutVector.data());
+ CopyDataToITensorHandle(cellStateOutHandle.get(), cellStateOutVector.data());
workload->Execute();
- CopyDataFromITensorHandle(&ret0.output[0][0], scratchBufferHandle.get());
- CopyDataFromITensorHandle(&ret1.output[0][0], outputStateOutHandle.get());
- CopyDataFromITensorHandle(&ret2.output[0][0], cellStateOutHandle.get());
- CopyDataFromITensorHandle(&ret3.output[0][0], outputHandle.get());
+ CopyDataFromITensorHandle(actualScratchBufferOutput.data(), scratchBufferHandle.get());
+ CopyDataFromITensorHandle(actualOutputStateOutput.data(), outputStateOutHandle.get());
+ CopyDataFromITensorHandle(actualCellStateOutput.data(), cellStateOutHandle.get());
+ CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get());
+
+ ret0.m_ActualData = actualScratchBufferOutput;
+ ret1.m_ActualData = actualOutputStateOutput;
+ ret2.m_ActualData = actualCellStateOutput;
+ ret3.m_ActualData = actualOutput;
return ret3;
}
@@ -1289,8 +1249,8 @@
LstmLayerNoCifgWithPeepholeWithProjectionWithLayerNormTestImpl(armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
const armnn::ITensorHandleFactory& tensorHandleFactory,
- const boost::multi_array<T, 2>& input,
- const boost::multi_array<T, 2>& outputExpected,
+ const std::vector<T>& input,
+ const std::vector<T>& outputExpected,
float qScale = 0.0f,
int32_t qOffset = 0,
armnn::DataType constantDataType = armnn::DataType::Float32)
@@ -1311,30 +1271,19 @@
armnn::TensorInfo outputStateOutTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset);
armnn::TensorInfo outputTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset);
- LayerTestResult<T, 2> ret(outputTensorInfo);
-
std::vector<float> inputVector;
inputVector.assign(input.data(), input.data() + (batchSize * inputSize));
- auto inputTensor = MakeTensor<float,2>(inputTensorInfo, inputVector);
std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);
- auto cellStateInTensor = MakeTensor<float,2>(cellStateInTensorInfo, cellStateInVector);
-
std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);
- auto outputStateInTensor = MakeTensor<float,2>(outputStateInTensorInfo, outputStateInVector);
-
std::vector<float> scratchBufferVector(batchSize * numUnits * 4, 0.f);
- auto scratchBufferTensor = MakeTensor<float,2>(scratchBufferTensorInfo, scratchBufferVector);
-
std::vector<float> outputStateOutVector(batchSize * outputSize, 0.f);
- auto outputStateOutTensor = MakeTensor<float,2>(outputStateOutTensorInfo, outputStateOutVector);
-
std::vector<float> cellStateOutVector(batchSize * numUnits, 0.f);
- auto cellStateOutTensor = MakeTensor<float,2>(cellStateOutTensorInfo, cellStateOutVector);
+
+ std::vector<float> actualOutput(outputTensorInfo.GetNumElements());
std::vector<float> outputVector;
outputVector.assign(outputExpected.data(), outputExpected.data() + (batchSize * outputSize));
- ret.outputExpected = MakeTensor<float, 2>(outputTensorInfo, outputVector);
std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
@@ -1368,95 +1317,73 @@
armnn::TensorInfo tensorInfo4x3({numUnits, outputSize}, constantDataType, qScale, qOffset);
armnn::TensorInfo tensorInfo3x4({outputSize, numUnits}, constantDataType, qScale, qOffset);
- auto inputToInputWeights =
- MakeTensor<float, 2>(tensorInfo4x5, { 0.5f, 0.6f, 0.7f, -0.8f, -0.9f,
- 0.1f, 0.2f, 0.3f, -0.4f, 0.5f,
- -0.8f, 0.7f, -0.6f, 0.5f, -0.4f,
- -0.5f, -0.4f, -0.3f, -0.2f, -0.1f}); //{numUnits, inputSize}
+ std::vector<float> inputToInputWeights = {0.5f, 0.6f, 0.7f, -0.8f, -0.9f,
+ 0.1f, 0.2f, 0.3f, -0.4f, 0.5f,
+ -0.8f, 0.7f, -0.6f, 0.5f, -0.4f,
+ -0.5f, -0.4f, -0.3f, -0.2f, -0.1f}; //{numUnits, inputSize}
- auto inputToForgetWeights =
- MakeTensor<float, 2>(tensorInfo4x5, {-0.6f, -0.1f, 0.3f, 0.2f, 0.9f,
- -0.5f, -0.2f, -0.4f, 0.3f, -0.8f,
- -0.4f, 0.3f, -0.5f, -0.4f, -0.6f,
- 0.3f, -0.4f, -0.6f, -0.5f, -0.5f}); //{numUnits, inputSize}
+ std::vector<float> inputToForgetWeights = { -0.6f, -0.1f, 0.3f, 0.2f, 0.9f,
+ -0.5f, -0.2f, -0.4f, 0.3f, -0.8f,
+ -0.4f, 0.3f, -0.5f, -0.4f, -0.6f,
+ 0.3f, -0.4f, -0.6f, -0.5f, -0.5f}; //{numUnits, inputSize}
- auto inputToCellWeights =
- MakeTensor<float, 2>(tensorInfo4x5, {-0.4f, -0.3f, -0.2f, -0.1f, -0.5f,
- 0.5f, -0.2f, -0.3f, -0.2f, -0.6f,
- 0.6f, -0.1f, -0.4f, -0.3f, -0.7f,
- 0.7f, -0.9f, -0.5f, 0.8f, 0.6f}); //{numUnits, inputSize}
+ std::vector<float> inputToCellWeights = {-0.4f, -0.3f, -0.2f, -0.1f, -0.5f,
+ 0.5f, -0.2f, -0.3f, -0.2f, -0.6f,
+ 0.6f, -0.1f, -0.4f, -0.3f, -0.7f,
+ 0.7f, -0.9f, -0.5f, 0.8f, 0.6f}; //{numUnits, inputSize}
- auto inputToOutputWeights =
- MakeTensor<float, 2>(tensorInfo4x5, {-0.8f, -0.4f, -0.2f, -0.9f, -0.1f,
- -0.7f, 0.3f, -0.3f, -0.8f, -0.2f,
- 0.6f, -0.2f, 0.4f, -0.7f, -0.3f,
- -0.5f, 0.1f, 0.5f, -0.6f, -0.4f}); //{numUnits, inputSize}
+ std::vector<float> inputToOutputWeights = {-0.8f, -0.4f, -0.2f, -0.9f, -0.1f,
+ -0.7f, 0.3f, -0.3f, -0.8f, -0.2f,
+ 0.6f, -0.2f, 0.4f, -0.7f, -0.3f,
+ -0.5f, 0.1f, 0.5f, -0.6f, -0.4f}; //{numUnits, inputSize}
- auto inputGateBias =
- MakeTensor<float, 1>(tensorInfo4, {0.03f, 0.15f, 0.22f, 0.38f}); //{numUnits}
+ std::vector<float> inputGateBias = {0.03f, 0.15f, 0.22f, 0.38f}; //{numUnits}
- auto forgetGateBias =
- MakeTensor<float, 1>(tensorInfo4, {0.1f, -0.3f, -0.2f, 0.1f}); //{numUnits}
+ std::vector<float> forgetGateBias = {0.1f, -0.3f, -0.2f, 0.1f}; //{numUnits}
- auto cellBias =
- MakeTensor<float, 1>(tensorInfo4, {-0.05f, 0.72f, 0.25f, 0.08f}); //{numUnits}
+ std::vector<float> cellBias = {-0.05f, 0.72f, 0.25f, 0.08f}; //{numUnits}
- auto outputGateBias =
- MakeTensor<float, 1>(tensorInfo4, {0.05f, -0.01f, 0.2f, 0.1f}); //{numUnits}
+ std::vector<float> outputGateBias = {0.05f, -0.01f, 0.2f, 0.1f}; //{numUnits}
- auto recurrentToInputWeights =
- MakeTensor<float, 2>(tensorInfo4x3, {-0.2f, -0.3f, 0.4f,
+ std::vector<float> recurrentToInputWeights ={-0.2f, -0.3f, 0.4f,
0.1f, -0.5f, 0.9f,
-0.2f, -0.3f, -0.7f,
- 0.05f, -0.2f, -0.6f}); //{numUnits, outputSize}
+ 0.05f, -0.2f, -0.6f}; //{numUnits, outputSize}
- auto recurrentToCellWeights =
- MakeTensor<float, 2>(tensorInfo4x3, {-0.3f, 0.2f, 0.1f,
+ std::vector<float> recurrentToCellWeights = {-0.3f, 0.2f, 0.1f,
-0.3f, 0.8f, -0.08f,
-0.2f, 0.3f, 0.8f,
- -0.6f, -0.1f, 0.2f}); //{numUnits, outputSize}
+ -0.6f, -0.1f, 0.2f}; //{numUnits, outputSize}
- auto recurrentToForgetWeights =
- MakeTensor<float, 2>(tensorInfo4x3, {-0.5f, -0.3f, -0.5f,
- -0.2f, 0.6f, 0.4f,
- 0.9f, 0.3f, -0.1f,
- 0.2f, 0.5f, 0.2f}); //{numUnits, outputSize}
+ std::vector<float> recurrentToForgetWeights = { -0.5f, -0.3f, -0.5f,
+ -0.2f, 0.6f, 0.4f,
+ 0.9f, 0.3f, -0.1f,
+ 0.2f, 0.5f, 0.2f}; //{numUnits, outputSize}
- auto recurrentToOutputWeights =
- MakeTensor<float, 2>(tensorInfo4x3, { 0.3f, -0.1f, 0.1f,
- -0.2f, -0.5f, -0.7f,
- -0.2f, -0.6f, -0.1f,
- -0.4f, -0.7f, -0.2f}); //{numUnits, outputSize}
+ std::vector<float> recurrentToOutputWeights = { 0.3f, -0.1f, 0.1f,
+ -0.2f, -0.5f, -0.7f,
+ -0.2f, -0.6f, -0.1f,
+ -0.4f, -0.7f, -0.2f}; //{numUnits, outputSize}
- auto cellToInputWeights =
- MakeTensor<float, 1>(tensorInfo4, {0.05f, 0.1f, 0.25f, 0.15f}); //{numUnits}
+ std::vector<float> cellToInputWeights = {0.05f, 0.1f, 0.25f, 0.15f}; //{numUnits}
- auto cellToForgetWeights =
- MakeTensor<float, 1>(tensorInfo4, {-0.02f, -0.15f, -0.25f, -0.03f}); //{numUnits}
+ std::vector<float> cellToForgetWeights = {-0.02f, -0.15f, -0.25f, -0.03f}; //{numUnits}
- auto cellToOutputWeights =
- MakeTensor<float, 1>(tensorInfo4, {0.1f, -0.1f, -0.5f, 0.05f}); //{numUnits}
+ std::vector<float> cellToOutputWeights = {0.1f, -0.1f, -0.5f, 0.05f}; //{numUnits}
- auto projectionWeights =
- MakeTensor<float, 2>(tensorInfo3x4,
- {-0.1f, 0.2f, 0.01f, -0.2f,
- 0.1f, 0.5f, 0.3f, 0.08f,
- 0.07f, 0.2f, -0.4f, 0.2f}); //{outputSize, numUnits}
+ std::vector<float> projectionWeights = {-0.1f, 0.2f, 0.01f, -0.2f,
+ 0.1f, 0.5f, 0.3f, 0.08f,
+ 0.07f, 0.2f, -0.4f, 0.2f}; //{outputSize, numUnits}
- std::vector<float> projectionBiasVector(outputSize, 0.f);
- auto projectionBias = MakeTensor<float,1>(tensorInfo3, projectionBiasVector); //{outputSize}
+ std::vector<float> projectionBiasVector(outputSize, 0.f); //{outputSize}
- auto inputLayerNormWeights =
- MakeTensor<float, 1>(tensorInfo4, {0.1f, 0.2f, 0.3f, 0.5f}); //{numUnits}
+ std::vector<float> inputLayerNormWeights = {0.1f, 0.2f, 0.3f, 0.5f}; //{numUnits}
- auto forgetLayerNormWeights =
- MakeTensor<float, 1>(tensorInfo4, {0.2f, 0.2f, 0.4f, 0.3f}); //{numUnits}
+ std::vector<float> forgetLayerNormWeights = {0.2f, 0.2f, 0.4f, 0.3f}; //{numUnits}
- auto cellLayerNormWeights =
- MakeTensor<float, 1>(tensorInfo4, {0.7f, 0.2f, 0.3f, 0.8f}); //{numUnits}
+ std::vector<float> cellLayerNormWeights = {0.7f, 0.2f, 0.3f, 0.8f}; //{numUnits}
- auto outputLayerNormWeights =
- MakeTensor<float, 1>(tensorInfo4, {0.6f, 0.2f, 0.2f, 0.5f}); //{numUnits}
+ std::vector<float> outputLayerNormWeights = {0.6f, 0.2f, 0.2f, 0.5f}; //{numUnits}
armnn::ScopedTensorHandle inputToInputWeightsTensor(tensorInfo4x5);
@@ -1482,28 +1409,28 @@
armnn::ScopedTensorHandle cellLayerNormWeightsTensor(tensorInfo4);
armnn::ScopedTensorHandle outputLayerNormWeightsTensor(tensorInfo4);
- AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, &inputToInputWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, &inputToForgetWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, &inputToCellWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, &inputToOutputWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, &recurrentToInputWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, &recurrentToForgetWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, &recurrentToCellWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, &recurrentToOutputWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&cellToInputWeightsTensor, &cellToInputWeights[0]);
- AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, &inputGateBias[0]);
- AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, &forgetGateBias[0]);
- AllocateAndCopyDataToITensorHandle(&cellBiasTensor, &cellBias[0]);
- AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, &outputGateBias[0]);
- AllocateAndCopyDataToITensorHandle(&cellToForgetWeightsTensor, &cellToForgetWeights[0]);
- AllocateAndCopyDataToITensorHandle(&cellToOutputWeightsTensor, &cellToOutputWeights[0]);
- AllocateAndCopyDataToITensorHandle(&projectionWeightsTensor, &projectionWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&projectionBiasTensor, &projectionBias[0]);
+ AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, inputToInputWeights.data());
+ AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data());
+ AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data());
+ AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data());
+ AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, recurrentToInputWeights.data());
+ AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data());
+ AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data());
+ AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data());
+ AllocateAndCopyDataToITensorHandle(&cellToInputWeightsTensor, cellToInputWeights.data());
+ AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, inputGateBias.data());
+ AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data());
+ AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data());
+ AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data());
+ AllocateAndCopyDataToITensorHandle(&cellToForgetWeightsTensor, cellToForgetWeights.data());
+ AllocateAndCopyDataToITensorHandle(&cellToOutputWeightsTensor, cellToOutputWeights.data());
+ AllocateAndCopyDataToITensorHandle(&projectionWeightsTensor, projectionWeights.data());
+ AllocateAndCopyDataToITensorHandle(&projectionBiasTensor, projectionBiasVector.data());
- AllocateAndCopyDataToITensorHandle(&inputLayerNormWeightsTensor, &inputLayerNormWeights[0]);
- AllocateAndCopyDataToITensorHandle(&forgetLayerNormWeightsTensor, &forgetLayerNormWeights[0]);
- AllocateAndCopyDataToITensorHandle(&cellLayerNormWeightsTensor, &cellLayerNormWeights[0]);
- AllocateAndCopyDataToITensorHandle(&outputLayerNormWeightsTensor, &outputLayerNormWeights[0]);
+ AllocateAndCopyDataToITensorHandle(&inputLayerNormWeightsTensor, inputLayerNormWeights.data());
+ AllocateAndCopyDataToITensorHandle(&forgetLayerNormWeightsTensor, forgetLayerNormWeights.data());
+ AllocateAndCopyDataToITensorHandle(&cellLayerNormWeightsTensor, cellLayerNormWeights.data());
+ AllocateAndCopyDataToITensorHandle(&outputLayerNormWeightsTensor, outputLayerNormWeights.data());
data.m_InputToInputWeights = &inputToInputWeightsTensor;
data.m_InputToForgetWeights = &inputToForgetWeightsTensor;
@@ -1546,28 +1473,33 @@
cellStateOutHandle->Allocate();
outputHandle->Allocate();
- CopyDataToITensorHandle(inputHandle.get(), &inputTensor[0][0]);
- CopyDataToITensorHandle(outputStateInHandle.get(), &outputStateInTensor[0][0]);
- CopyDataToITensorHandle(cellStateInHandle.get(), &cellStateInTensor[0][0]);
+ CopyDataToITensorHandle(inputHandle.get(), inputVector.data());
+ CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data());
+ CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data());
workload->Execute();
- CopyDataFromITensorHandle(&ret.output[0][0], outputHandle.get());
+ CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get());
- return ret;
+ return LayerTestResult<T, 2>(actualOutput,
+ outputVector,
+ outputHandle->GetShape(),
+ outputTensorInfo.GetShape());
}
LayerTestResult<uint8_t, 2> QuantizedLstmTestImpl(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
const armnn::ITensorHandleFactory& tensorHandleFactory,
- const boost::multi_array<uint8_t, 2>& input,
- const boost::multi_array<uint8_t, 2>& outputExpected)
+ const std::vector<uint8_t>& input,
+ const std::vector<uint8_t>& outputExpected,
+ const armnn::TensorShape& inputShape,
+ const armnn::TensorShape& outputExpectedShape)
{
IgnoreUnused(memoryManager);
- auto numBatches = armnn::numeric_cast<unsigned int>(input.shape()[0]);
- auto inputSize = armnn::numeric_cast<unsigned int>(input.shape()[1]);
- auto outputSize = armnn::numeric_cast<unsigned int>(outputExpected.shape()[1]);
+ auto numBatches = armnn::numeric_cast<unsigned int>(inputShape[0]);
+ auto inputSize = armnn::numeric_cast<unsigned int>(inputShape[1]);
+ auto outputSize = armnn::numeric_cast<unsigned int>(outputExpectedShape[1]);
// Scale/Offset for input/output, cellState In/Out, weights, bias
float inputOutputScale = 0.0078125f;
@@ -1598,29 +1530,23 @@
inputOutputScale,
inputOutputOffset);
- LayerTestResult<uint8_t, 2> ret(outputStateInfo);
-
// Input0
std::vector<uint8_t> inputVector;
inputVector.assign(input.data(), input.data() + (numBatches * inputSize));
- auto inputTensor = MakeTensor<uint8_t, 2>(inputInfo, inputVector);
// Input1
std::vector<int16_t> cellStateInVector = {876, 1034, 955, -909, 761, 1029, 796, -1036}; // 13
- auto cellStateInTensor = MakeTensor<int16_t, 2>(cellStateInfo, cellStateInVector);
-
// Input2
std::vector<uint8_t> outputStateInVector = {136, 150, 140, 115, 135, 152, 138, 112}; // 14
- auto outputStateInTensor = MakeTensor<uint8_t, 2>(outputStateInfo, outputStateInVector);
// Output0
std::vector<int16_t> cellStateOutVector = {1485, 1177, 1373, -1023, 1019, 1355, 1097, -1235}; // 0
- auto cellStateOutTensor = MakeTensor<int16_t, 2>(cellStateInfo, cellStateOutVector);
// Output1
std::vector<uint8_t> outputVector; // 1
outputVector.assign(outputExpected.data(), outputExpected.data() + (numBatches * outputSize));
- ret.outputExpected = MakeTensor<uint8_t, 2>(outputStateInfo, outputVector);
+
+ std::vector<uint8_t> actualOutput(outputStateInfo.GetNumElements());
// Create tensor handles
std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputInfo);
@@ -1658,24 +1584,24 @@
armnn::TensorInfo biasInfo({outputSize}, armnn::DataType::Signed32, biasScale, biasOffset);
// Weights and bias tensor data
- auto inputToInputWeights = MakeTensor<uint8_t, 2>(inputWeightsInfo, {146, 250, 235, 171, 10, 218, 171, 108});
- auto inputToForgetWeights = MakeTensor<uint8_t, 2>(inputWeightsInfo, {24, 50, 132, 179, 158, 110, 3, 169});
- auto inputToCellWeights = MakeTensor<uint8_t, 2>(inputWeightsInfo, {133, 34, 29, 49, 206, 109, 54, 183});
- auto inputToOutputWeights = MakeTensor<uint8_t, 2>(inputWeightsInfo, {195, 187, 11, 99, 109, 10, 218, 48});
+ std::vector<uint8_t> inputToInputWeights = {146, 250, 235, 171, 10, 218, 171, 108};
+ std::vector<uint8_t> inputToForgetWeights = {24, 50, 132, 179, 158, 110, 3, 169};
+ std::vector<uint8_t> inputToCellWeights = {133, 34, 29, 49, 206, 109, 54, 183};
+ std::vector<uint8_t> inputToOutputWeights = {195, 187, 11, 99, 109, 10, 218, 48};
- auto recurrentToInputWeights = MakeTensor<uint8_t, 2>(recurrentWeightsInfo,
- {254, 206, 77, 168, 71, 20, 215, 6, 223, 7, 118, 225, 59, 130, 174, 26});
- auto recurrentToForgetWeights = MakeTensor<uint8_t, 2>(recurrentWeightsInfo,
- {137, 240, 103, 52, 68, 51, 237, 112, 0, 220, 89, 23, 69, 4, 207, 253});
- auto recurrentToCellWeights = MakeTensor<uint8_t, 2>(recurrentWeightsInfo,
- {172, 60, 205, 65, 14, 0, 140, 168, 240, 223, 133, 56, 142, 64, 246, 216});
- auto recurrentToOutputWeights = MakeTensor<uint8_t, 2>(recurrentWeightsInfo,
- {106, 214, 67, 23, 59, 158, 45, 3, 119, 132, 49, 205, 129, 218, 11, 98});
+ std::vector<uint8_t> recurrentToInputWeights =
+ {254, 206, 77, 168, 71, 20, 215, 6, 223, 7, 118, 225, 59, 130, 174, 26};
+ std::vector<uint8_t> recurrentToForgetWeights =
+ {137, 240, 103, 52, 68, 51, 237, 112, 0, 220, 89, 23, 69, 4, 207, 253};
+ std::vector<uint8_t> recurrentToCellWeights =
+ {172, 60, 205, 65, 14, 0, 140, 168, 240, 223, 133, 56, 142, 64, 246, 216};
+ std::vector<uint8_t> recurrentToOutputWeights =
+ {106, 214, 67, 23, 59, 158, 45, 3, 119, 132, 49, 205, 129, 218, 11, 98};
- auto inputGateBias = MakeTensor<int32_t, 1>(biasInfo, {-7876, 13488, -726, 32839});
- auto forgetGateBias = MakeTensor<int32_t, 1>(biasInfo, {9206, -46884, -11693, -38724});
- auto cellBias = MakeTensor<int32_t, 1>(biasInfo, {39481, 48624, 48976, -21419});
- auto outputGateBias = MakeTensor<int32_t, 1>(biasInfo, {-58999, -17050, -41852, -40538});
+ std::vector<int32_t> inputGateBias = {-7876, 13488, -726, 32839};
+ std::vector<int32_t> forgetGateBias = {9206, -46884, -11693, -38724};
+ std::vector<int32_t> cellBias = {39481, 48624, 48976, -21419};
+ std::vector<int32_t> outputGateBias = {-58999, -17050, -41852, -40538};
// ScopedTensorHandles
armnn::ScopedTensorHandle inputToInputWeightsTensor(inputWeightsInfo);
@@ -1694,20 +1620,20 @@
armnn::ScopedTensorHandle outputGateBiasTensor(biasInfo);
// Allocate and copy data
- AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, &inputToInputWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, &inputToForgetWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, &inputToCellWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, &inputToOutputWeights[0][0]);
+ AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, inputToInputWeights.data());
+ AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data());
+ AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data());
+ AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data());
- AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, &recurrentToInputWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, &recurrentToForgetWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, &recurrentToCellWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, &recurrentToOutputWeights[0][0]);
+ AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, recurrentToInputWeights.data());
+ AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data());
+ AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data());
+ AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data());
- AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, &inputGateBias[0]);
- AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, &forgetGateBias[0]);
- AllocateAndCopyDataToITensorHandle(&cellBiasTensor, &cellBias[0]);
- AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, &outputGateBias[0]);
+ AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, inputGateBias.data());
+ AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data());
+ AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data());
+ AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data());
// Setup queue descriptor
data.m_InputToInputWeights = &inputToInputWeightsTensor;
@@ -1734,15 +1660,18 @@
cellStateOutHandle->Allocate();
outputHandle->Allocate();
- CopyDataToITensorHandle(inputHandle.get(), &inputTensor[0][0]);
- CopyDataToITensorHandle(outputStateInHandle.get(), &outputStateInTensor[0][0]);
- CopyDataToITensorHandle(cellStateInHandle.get(), &cellStateInTensor[0][0]);
+ CopyDataToITensorHandle(inputHandle.get(), inputVector.data());
+ CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data());
+ CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data());
workload->Execute();
- CopyDataFromITensorHandle(&ret.output[0][0], outputHandle.get());
+ CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get());
- return ret;
+ return LayerTestResult<uint8_t, 2>(actualOutput,
+ outputVector,
+ outputHandle->GetShape(),
+ outputStateInfo.GetShape());
}
// QLSTM: CIFG, LayerNorm
@@ -1750,8 +1679,8 @@
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
const armnn::ITensorHandleFactory& tensorHandleFactory,
- const boost::multi_array<int8_t, 2>& input,
- const boost::multi_array<int8_t, 2>& outputExpected)
+ const std::vector<int8_t>& input,
+ const std::vector<int8_t>& outputExpected)
{
IgnoreUnused(memoryManager);
unsigned int numBatches = 2;
@@ -1816,21 +1745,18 @@
// Input tensors
std::vector<int8_t> inputVector;
inputVector.assign(input.data(), input.data() + (numBatches * inputSize));
- auto inputTensor = MakeTensor<int8_t, 2>(inputInfo, inputVector);
std::vector<int16_t> cellStateInVector = {0, 0, 0, 0, 0, 0, 0, 0};
- auto cellStateInTensor = MakeTensor<int16_t, 2>(cellStateInfo, cellStateInVector);
std::vector<int8_t> outputStateInVector = {0, 0, 0, 0, 0, 0, 0, 0};
- auto outputStateInTensor = MakeTensor<int8_t, 2>(outputStateInfo, outputStateInVector);
// Output tensors
- std::vector<int16_t> cellStateOutVector = {-11692, 9960, 5491, 8861, -9422, 7726, 2056, 13149};
- auto cellStateOutTensor = MakeTensor<int16_t, 2>(cellStateInfo, cellStateOutVector);
+ std::vector<int16_t> cellStateOutVector = {-11692, 9960, 5491, 8861, -9422, 7726, 2056, 13149};
std::vector<int8_t> outputVector;
outputVector.assign(outputExpected.data(), outputExpected.data() + (numBatches * outputSize));
- ret.outputExpected = MakeTensor<int8_t, 2>(outputStateInfo, outputVector);
+
+ std::vector<int8_t> actualOutput(outputStateInfo.GetNumElements());
// Create tensor handles
std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputInfo);
@@ -1873,27 +1799,27 @@
armnn::TensorInfo layerNormWeightsInfo({numUnits}, armnn::DataType::QSymmS16, layerNormScale, layerNormOffset);
// Weights and bias tensor data
- auto inputToForgetWeights = MakeTensor<int8_t, 2>(inputWeightsInfo,
- {-77, -13, 38, 25, 115, -64, -25, -51, 38, -102, -51, 38, -64, -51, -77, 38, -51, -77, -64, -64});
- auto inputToCellWeights = MakeTensor<int8_t, 2>(inputWeightsInfo,
- {-51, -38, -25, -13, -64, 64, -25, -38, -25, -77, 77, -13, -51, -38, -89, 89, -115, -64, 102, 77});
- auto inputToOutputWeights = MakeTensor<int8_t, 2>(inputWeightsInfo,
- {-102, -51, -25, -115, -13, -89, 38, -38, -102, -25, 77, -25, 51, -89, -38, -64, 13, 64, -77, -51});
+ std::vector<int8_t> inputToForgetWeights =
+ {-77, -13, 38, 25, 115, -64, -25, -51, 38, -102, -51, 38, -64, -51, -77, 38, -51, -77, -64, -64};
+ std::vector<int8_t> inputToCellWeights =
+ {-51, -38, -25, -13, -64, 64, -25, -38, -25, -77, 77, -13, -51, -38, -89, 89, -115, -64, 102, 77};
+ std::vector<int8_t> inputToOutputWeights =
+ {-102, -51, -25, -115, -13, -89, 38, -38, -102, -25, 77, -25, 51, -89, -38, -64, 13, 64, -77, -51};
- auto recurrentToForgetWeights = MakeTensor<int8_t, 2>(recurrentWeightsInfo,
- {-64, -38, -64, -25, 77, 51, 115, 38, -13, 25, 64, 25, 25, 38, -13, 51});
- auto recurrentToCellWeights = MakeTensor<int8_t, 2>(recurrentWeightsInfo,
- {-38, 25, 13, -38, 102, -10, -25, 38, 102, -77, -13, 25, 38, -13, 25, 64});
- auto recurrentToOutputWeights = MakeTensor<int8_t, 2>(recurrentWeightsInfo,
- {38, -13, 13, -25, -64, -89, -25, -77, -13, -51, -89, -25, 13, 64, 25, -38});
+ std::vector<int8_t> recurrentToForgetWeights =
+ {-64, -38, -64, -25, 77, 51, 115, 38, -13, 25, 64, 25, 25, 38, -13, 51};
+ std::vector<int8_t> recurrentToCellWeights =
+ {-38, 25, 13, -38, 102, -10, -25, 38, 102, -77, -13, 25, 38, -13, 25, 64};
+ std::vector<int8_t> recurrentToOutputWeights =
+ {38, -13, 13, -25, -64, -89, -25, -77, -13, -51, -89, -25, 13, 64, 25, -38};
- auto forgetGateBias = MakeTensor<int32_t, 1>(biasInfo, {2147484, -6442451, -4294968, 2147484});
- auto cellBias = MakeTensor<int32_t, 1>(biasInfo, {-1073742, 15461883, 5368709, 1717987});
- auto outputGateBias = MakeTensor<int32_t, 1>(biasInfo, {1073742, -214748, 4294968, 2147484});
+ std::vector<int32_t> forgetGateBias = {2147484, -6442451, -4294968, 2147484};
+ std::vector<int32_t> cellBias = {-1073742, 15461883, 5368709, 1717987};
+ std::vector<int32_t> outputGateBias = {1073742, -214748, 4294968, 2147484};
- auto forgetLayerNormWeights = MakeTensor<int16_t, 1>(layerNormWeightsInfo, {6553, 6553, 13107, 9830});
- auto cellLayerNormWeights = MakeTensor<int16_t, 1>(layerNormWeightsInfo, {22937, 6553, 9830, 26214});
- auto outputLayerNormWeights = MakeTensor<int16_t, 1>(layerNormWeightsInfo, {19660, 6553, 6553, 16384});
+ std::vector<int16_t> forgetLayerNormWeights = {6553, 6553, 13107, 9830};
+ std::vector<int16_t> cellLayerNormWeights = {22937, 6553, 9830, 26214};
+ std::vector<int16_t> outputLayerNormWeights = {19660, 6553, 6553, 16384};
// ScopedTensorHandles
armnn::ScopedTensorHandle inputToForgetWeightsTensor(inputWeightsInfo);
@@ -1913,21 +1839,21 @@
armnn::ScopedTensorHandle outputLayerNormWeightsTensor(layerNormWeightsInfo);
// Allocate and copy data
- AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, &inputToForgetWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, &inputToCellWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, &inputToOutputWeights[0][0]);
+ AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data());
+ AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data());
+ AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data());
- AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, &recurrentToForgetWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, &recurrentToCellWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, &recurrentToOutputWeights[0][0]);
+ AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data());
+ AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data());
+ AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data());
- AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, &forgetGateBias[0]);
- AllocateAndCopyDataToITensorHandle(&cellBiasTensor, &cellBias[0]);
- AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, &outputGateBias[0]);
+ AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data());
+ AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data());
+ AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data());
- AllocateAndCopyDataToITensorHandle(&forgetLayerNormWeightsTensor, &forgetLayerNormWeights[0]);
- AllocateAndCopyDataToITensorHandle(&cellLayerNormWeightsTensor, &cellLayerNormWeights[0]);
- AllocateAndCopyDataToITensorHandle(&outputLayerNormWeightsTensor, &outputLayerNormWeights[0]);
+ AllocateAndCopyDataToITensorHandle(&forgetLayerNormWeightsTensor, forgetLayerNormWeights.data());
+ AllocateAndCopyDataToITensorHandle(&cellLayerNormWeightsTensor, cellLayerNormWeights.data());
+ AllocateAndCopyDataToITensorHandle(&outputLayerNormWeightsTensor, outputLayerNormWeights.data());
// Setup queue descriptor
data.m_InputToForgetWeights = &inputToForgetWeightsTensor;
@@ -1972,15 +1898,18 @@
cellStateOutHandle->Allocate();
outputHandle->Allocate();
- CopyDataToITensorHandle(inputHandle.get(), &inputTensor[0][0]);
- CopyDataToITensorHandle(outputStateInHandle.get(), &outputStateInTensor[0][0]);
- CopyDataToITensorHandle(cellStateInHandle.get(), &cellStateInTensor[0][0]);
+ CopyDataToITensorHandle(inputHandle.get(), inputVector.data());
+ CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data());
+ CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data());
workload->Execute();
- CopyDataFromITensorHandle(&ret.output[0][0], outputHandle.get());
+ CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get());
- return ret;
+ return LayerTestResult<int8_t, 2>(actualOutput,
+ outputVector,
+ outputHandle->GetShape(),
+ outputStateInfo.GetShape());
}
// QLSTM: Projection, LayerNorm
@@ -1988,8 +1917,8 @@
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
const armnn::ITensorHandleFactory& tensorHandleFactory,
- const boost::multi_array<int8_t, 2>& input,
- const boost::multi_array<int8_t, 2>& outputExpected)
+ const std::vector<int8_t>& input,
+ const std::vector<int8_t>& outputExpected)
{
IgnoreUnused(memoryManager);
unsigned int numBatches = 2;
@@ -2051,26 +1980,21 @@
outputScale,
outputOffset);
- LayerTestResult<int8_t, 2> ret(outputStateInfo);
-
// Input tensors
std::vector<int8_t> inputVector;
inputVector.assign(input.data(), input.data() + (numBatches * inputSize));
- auto inputTensor = MakeTensor<int8_t, 2>(inputInfo, inputVector);
std::vector<int16_t> cellStateInVector = {0, 0, 0, 0, 0, 0, 0, 0};
- auto cellStateInTensor = MakeTensor<int16_t, 2>(cellStateInfo, cellStateInVector);
std::vector<int8_t> outputStateInVector = {0, 0, 0, 0, 0, 0};
- auto outputStateInTensor = MakeTensor<int8_t, 2>(outputStateInfo, outputStateInVector);
// Output tensors
std::vector<int16_t> cellStateOutVector = {-14650, 8939, 5771, 6715, -11843, 7847, 1508, 12939};
- auto cellStateOutTensor = MakeTensor<int16_t, 2>(cellStateInfo, cellStateOutVector);
std::vector<int8_t> outputVector;
outputVector.assign(outputExpected.data(), outputExpected.data() + (numBatches * outputSize));
- ret.outputExpected = MakeTensor<int8_t, 2>(outputStateInfo, outputVector);
+
+ std::vector<int8_t> actualOutput(outputStateInfo.GetNumElements());
// Create tensor handles
std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputInfo);
@@ -2118,36 +2042,31 @@
0);
// Weights and bias tensor data
- auto inputToInputWeights = MakeTensor<int8_t, 2>(inputWeightsInfo,
- {64, 77, 89, -102, -115, 13, 25, 38, -51, 64, -102, 89, -77, 64, -51, -64, -51, -38, -25, -13});
- auto inputToForgetWeights = MakeTensor<int8_t, 2>(inputWeightsInfo,
- {-77, -13, 38, 25, 115, -64, -25, -51, 38, -102, -51, 38, -64, -51, -77, 38, -51, -77, -64, -64});
- auto inputToCellWeights = MakeTensor<int8_t, 2>(inputWeightsInfo,
- {-51, -38, -25, -13, -64, 64, -25, -38, -25, -77, 77, -13, -51, -38, -89, 89, -115, -64, 102, 77});
- auto inputToOutputWeights = MakeTensor<int8_t, 2>(inputWeightsInfo,
- {-102, -51, -25, -115, -13, -89, 38, -38, -102, -25, 77, -25, 51, -89, -38, -64, 13, 64, -77, -51});
+ std::vector<int8_t> inputToInputWeights =
+ {64, 77, 89, -102, -115, 13, 25, 38, -51, 64, -102, 89, -77, 64, -51, -64, -51, -38, -25, -13};
+ std::vector<int8_t> inputToForgetWeights =
+ {-77, -13, 38, 25, 115, -64, -25, -51, 38, -102, -51, 38, -64, -51, -77, 38, -51, -77, -64, -64};
+ std::vector<int8_t> inputToCellWeights =
+ {-51, -38, -25, -13, -64, 64, -25, -38, -25, -77, 77, -13, -51, -38, -89, 89, -115, -64, 102, 77};
+ std::vector<int8_t> inputToOutputWeights =
+ {-102, -51, -25, -115, -13, -89, 38, -38, -102, -25, 77, -25, 51, -89, -38, -64, 13, 64, -77, -51};
- auto recurrentToInputWeights = MakeTensor<int8_t, 2>(recurrentWeightsInfo,
- {-25, -38, 51, 13, -64, 115, -25, -38, -89, 6, -25, -77});
- auto recurrentToForgetWeights = MakeTensor<int8_t, 2>(recurrentWeightsInfo,
- {-64, -38, -64, -25, 77, 51, 115, 38, -13, 25, 64, 25});
- auto recurrentToCellWeights = MakeTensor<int8_t, 2>(recurrentWeightsInfo,
- {-38, 25, 13, -38, 102, -10, -25, 38, 102, -77, -13, 25});
- auto recurrentToOutputWeights = MakeTensor<int8_t, 2>(recurrentWeightsInfo,
- {38, -13, 13, -25, -64, -89, -25, -77, -13, -51, -89, -25});
+ std::vector<int8_t> recurrentToInputWeights = {-25, -38, 51, 13, -64, 115, -25, -38, -89, 6, -25, -77};
+ std::vector<int8_t> recurrentToForgetWeights = {-64, -38, -64, -25, 77, 51, 115, 38, -13, 25, 64, 25};
+ std::vector<int8_t> recurrentToCellWeights = {-38, 25, 13, -38, 102, -10, -25, 38, 102, -77, -13, 25};
+ std::vector<int8_t> recurrentToOutputWeights = {38, -13, 13, -25, -64, -89, -25, -77, -13, -51, -89, -25};
- auto inputGateBias = MakeTensor<int32_t, 1>(biasInfo, {644245, 3221226, 4724464, 8160438});
- auto forgetGateBias = MakeTensor<int32_t, 1>(biasInfo, {2147484, -6442451, -4294968, 2147484});
- auto cellBias = MakeTensor<int32_t, 1>(biasInfo, {-1073742, 15461883, 5368709, 1717987});
- auto outputGateBias = MakeTensor<int32_t, 1>(biasInfo, {1073742, -214748, 4294968, 2147484});
+ std::vector<int32_t> inputGateBias = {644245, 3221226, 4724464, 8160438};
+ std::vector<int32_t> forgetGateBias = {2147484, -6442451, -4294968, 2147484};
+ std::vector<int32_t> cellBias = {-1073742, 15461883, 5368709, 1717987};
+ std::vector<int32_t> outputGateBias = {1073742, -214748, 4294968, 2147484};
- auto inputLayerNormWeights = MakeTensor<int16_t, 1>(layerNormWeightsInfo, {3277, 6553, 9830, 16384});
- auto forgetLayerNormWeights = MakeTensor<int16_t, 1>(layerNormWeightsInfo, {6553, 6553, 13107, 9830});
- auto cellLayerNormWeights = MakeTensor<int16_t, 1>(layerNormWeightsInfo, {22937, 6553, 9830, 26214});
- auto outputLayerNormWeights = MakeTensor<int16_t, 1>(layerNormWeightsInfo, {19660, 6553, 6553, 16384});
+ std::vector<int16_t> inputLayerNormWeights = {3277, 6553, 9830, 16384};
+ std::vector<int16_t> forgetLayerNormWeights = {6553, 6553, 13107, 9830};
+ std::vector<int16_t> cellLayerNormWeights = {22937, 6553, 9830, 26214};
+ std::vector<int16_t> outputLayerNormWeights = {19660, 6553, 6553, 16384};
- auto projectionWeights = MakeTensor<int8_t, 2>(projectionWeightsInfo,
- {-25, 51, 3, -51, 25, 127, 77, 20, 18, 51, -102, 51});
+ std::vector<int8_t> projectionWeights = {-25, 51, 3, -51, 25, 127, 77, 20, 18, 51, -102, 51};
// ScopedTensorHandles
armnn::ScopedTensorHandle inputToInputWeightsTensor(inputWeightsInfo);
@@ -2173,27 +2092,27 @@
armnn::ScopedTensorHandle projectionWeightsTensor(projectionWeightsInfo);
// Allocate and copy data
- AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, &inputToInputWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, &inputToForgetWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, &inputToCellWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, &inputToOutputWeights[0][0]);
+ AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, inputToInputWeights.data());
+ AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data());
+ AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data());
+ AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data());
- AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, &recurrentToInputWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, &recurrentToForgetWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, &recurrentToCellWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, &recurrentToOutputWeights[0][0]);
+ AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, recurrentToInputWeights.data());
+ AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data());
+ AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data());
+ AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data());
- AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, &inputGateBias[0]);
- AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, &forgetGateBias[0]);
- AllocateAndCopyDataToITensorHandle(&cellBiasTensor, &cellBias[0]);
- AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, &outputGateBias[0]);
+ AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, inputGateBias.data());
+ AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data());
+ AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data());
+ AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data());
- AllocateAndCopyDataToITensorHandle(&inputLayerNormWeightsTensor, &inputLayerNormWeights[0]);
- AllocateAndCopyDataToITensorHandle(&forgetLayerNormWeightsTensor, &forgetLayerNormWeights[0]);
- AllocateAndCopyDataToITensorHandle(&cellLayerNormWeightsTensor, &cellLayerNormWeights[0]);
- AllocateAndCopyDataToITensorHandle(&outputLayerNormWeightsTensor, &outputLayerNormWeights[0]);
+ AllocateAndCopyDataToITensorHandle(&inputLayerNormWeightsTensor, inputLayerNormWeights.data());
+ AllocateAndCopyDataToITensorHandle(&forgetLayerNormWeightsTensor, forgetLayerNormWeights.data());
+ AllocateAndCopyDataToITensorHandle(&cellLayerNormWeightsTensor, cellLayerNormWeights.data());
+ AllocateAndCopyDataToITensorHandle(&outputLayerNormWeightsTensor, outputLayerNormWeights.data());
- AllocateAndCopyDataToITensorHandle(&projectionWeightsTensor, &projectionWeights[0][0]);
+ AllocateAndCopyDataToITensorHandle(&projectionWeightsTensor, projectionWeights.data());
// Setup queue descriptor
data.m_InputToInputWeights = &inputToInputWeightsTensor;
@@ -2244,15 +2163,18 @@
cellStateOutHandle->Allocate();
outputHandle->Allocate();
- CopyDataToITensorHandle(inputHandle.get(), &inputTensor[0][0]);
- CopyDataToITensorHandle(outputStateInHandle.get(), &outputStateInTensor[0][0]);
- CopyDataToITensorHandle(cellStateInHandle.get(), &cellStateInTensor[0][0]);
+ CopyDataToITensorHandle(inputHandle.get(), inputVector.data());
+ CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data());
+ CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data());
workload->Execute();
- CopyDataFromITensorHandle(&ret.output[0][0], outputHandle.get());
+ CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get());
- return ret;
+ return LayerTestResult<int8_t, 2>(actualOutput,
+ outputVector,
+ outputHandle->GetShape(),
+ outputStateInfo.GetShape());
}
// QLSTM: Projection, CIFG, LayerNorm
@@ -2260,8 +2182,8 @@
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
const armnn::ITensorHandleFactory& tensorHandleFactory,
- const boost::multi_array<int8_t, 2>& input,
- const boost::multi_array<int8_t, 2>& outputExpected)
+ const std::vector<int8_t>& input,
+ const std::vector<int8_t>& outputExpected)
{
IgnoreUnused(memoryManager);
unsigned int numBatches = 2;
@@ -2323,26 +2245,21 @@
outputScale,
outputOffset);
- LayerTestResult<int8_t, 2> ret(outputStateInfo);
-
// Input tensors
std::vector<int8_t> inputVector;
inputVector.assign(input.data(), input.data() + (numBatches * inputSize));
- auto inputTensor = MakeTensor<int8_t, 2>(inputInfo, inputVector);
std::vector<int16_t> cellStateInVector = {0, 0, 0, 0, 0, 0, 0, 0};
- auto cellStateInTensor = MakeTensor<int16_t, 2>(cellStateInfo, cellStateInVector);
std::vector<int8_t> outputStateInVector = {0, 0, 0, 0, 0, 0};
- auto outputStateInTensor = MakeTensor<int8_t, 2>(outputStateInfo, outputStateInVector);
// Output tensors
- std::vector<int16_t> cellStateOutVector = {-14650, 8939, 5771, 6715, -11843, 7847, 1508, 12939};
- auto cellStateOutTensor = MakeTensor<int16_t, 2>(cellStateInfo, cellStateOutVector);
+ std::vector<int16_t> cellStateOutVector = {-14650, 8939, 5771, 6715, -11843, 7847, 1508, 12939};
std::vector<int8_t> outputVector;
outputVector.assign(outputExpected.data(), outputExpected.data() + (numBatches * outputSize));
- ret.outputExpected = MakeTensor<int8_t, 2>(outputStateInfo, outputVector);
+
+ std::vector<int8_t> actualOutput(outputStateInfo.GetNumElements());
// Create tensor handles
std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputInfo);
@@ -2390,30 +2307,29 @@
0);
// Weights and bias tensor data
- auto inputToForgetWeights = MakeTensor<int8_t, 2>(inputWeightsInfo,
- {-77, -13, 38, 25, 115, -64, -25, -51, 38, -102, -51, 38, -64, -51, -77, 38, -51, -77, -64, -64});
- auto inputToCellWeights = MakeTensor<int8_t, 2>(inputWeightsInfo,
- {-51, -38, -25, -13, -64, 64, -25, -38, -25, -77, 77, -13, -51, -38, -89, 89, -115, -64, 102, 77});
- auto inputToOutputWeights = MakeTensor<int8_t, 2>(inputWeightsInfo,
- {-102, -51, -25, -115, -13, -89, 38, -38, -102, -25, 77, -25, 51, -89, -38, -64, 13, 64, -77, -51});
+ std::vector<int8_t> inputToForgetWeights =
+ {-77, -13, 38, 25, 115, -64, -25, -51, 38, -102, -51, 38, -64, -51, -77, 38, -51, -77, -64, -64};
+ std::vector<int8_t> inputToCellWeights =
+ {-51, -38, -25, -13, -64, 64, -25, -38, -25, -77, 77, -13, -51, -38, -89, 89, -115, -64, 102, 77};
+ std::vector<int8_t> inputToOutputWeights =
+ {-102, -51, -25, -115, -13, -89, 38, -38, -102, -25, 77, -25, 51, -89, -38, -64, 13, 64, -77, -51};
- auto recurrentToForgetWeights = MakeTensor<int8_t, 2>(recurrentWeightsInfo,
- {-64, -38, -64, -25, 77, 51, 115, 38, -13, 25, 64, 25});
- auto recurrentToCellWeights = MakeTensor<int8_t, 2>(recurrentWeightsInfo,
- {-38, 25, 13, -38, 102, -10, -25, 38, 102, -77, -13, 25});
- auto recurrentToOutputWeights = MakeTensor<int8_t, 2>(recurrentWeightsInfo,
- {38, -13, 13, -25, -64, -89, -25, -77, -13, -51, -89, -25});
+ std::vector<int8_t> recurrentToForgetWeights =
+ {-64, -38, -64, -25, 77, 51, 115, 38, -13, 25, 64, 25};
+ std::vector<int8_t> recurrentToCellWeights =
+ {-38, 25, 13, -38, 102, -10, -25, 38, 102, -77, -13, 25};
+ std::vector<int8_t> recurrentToOutputWeights =
+ {38, -13, 13, -25, -64, -89, -25, -77, -13, -51, -89, -25};
- auto forgetGateBias = MakeTensor<int32_t, 1>(biasInfo, {2147484, -6442451, -4294968, 2147484});
- auto cellBias = MakeTensor<int32_t, 1>(biasInfo, {-1073742, 15461883, 5368709, 1717987});
- auto outputGateBias = MakeTensor<int32_t, 1>(biasInfo, {1073742, -214748, 4294968, 2147484});
+ std::vector<int32_t> forgetGateBias = {2147484, -6442451, -4294968, 2147484};
+ std::vector<int32_t> cellBias = {-1073742, 15461883, 5368709, 1717987};
+ std::vector<int32_t> outputGateBias = {1073742, -214748, 4294968, 2147484};
- auto forgetLayerNormWeights = MakeTensor<int16_t, 1>(layerNormWeightsInfo, {6553, 6553, 13107, 9830});
- auto cellLayerNormWeights = MakeTensor<int16_t, 1>(layerNormWeightsInfo, {22937, 6553, 9830, 26214});
- auto outputLayerNormWeights = MakeTensor<int16_t, 1>(layerNormWeightsInfo, {19660, 6553, 6553, 16384});
+ std::vector<int16_t> forgetLayerNormWeights = {6553, 6553, 13107, 9830};
+ std::vector<int16_t> cellLayerNormWeights = {22937, 6553, 9830, 26214};
+ std::vector<int16_t> outputLayerNormWeights = {19660, 6553, 6553, 16384};
- auto projectionWeights = MakeTensor<int8_t, 2>(projectionWeightsInfo,
- {-25, 51, 3, -51, 25, 127, 77, 20, 18, 51, -102, 51});
+ std::vector<int8_t> projectionWeights = {-25, 51, 3, -51, 25, 127, 77, 20, 18, 51, -102, 51};
// ScopedTensorHandles
armnn::ScopedTensorHandle inputToForgetWeightsTensor(inputWeightsInfo);
@@ -2435,23 +2351,23 @@
armnn::ScopedTensorHandle projectionWeightsTensor(projectionWeightsInfo);
// Allocate and copy data
- AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, &inputToForgetWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, &inputToCellWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, &inputToOutputWeights[0][0]);
+ AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data());
+ AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data());
+ AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data());
- AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, &recurrentToForgetWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, &recurrentToCellWeights[0][0]);
- AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, &recurrentToOutputWeights[0][0]);
+ AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data());
+ AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data());
+ AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data());
- AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, &forgetGateBias[0]);
- AllocateAndCopyDataToITensorHandle(&cellBiasTensor, &cellBias[0]);
- AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, &outputGateBias[0]);
+ AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data());
+ AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data());
+ AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data());
- AllocateAndCopyDataToITensorHandle(&forgetLayerNormWeightsTensor, &forgetLayerNormWeights[0]);
- AllocateAndCopyDataToITensorHandle(&cellLayerNormWeightsTensor, &cellLayerNormWeights[0]);
- AllocateAndCopyDataToITensorHandle(&outputLayerNormWeightsTensor, &outputLayerNormWeights[0]);
+ AllocateAndCopyDataToITensorHandle(&forgetLayerNormWeightsTensor, forgetLayerNormWeights.data());
+ AllocateAndCopyDataToITensorHandle(&cellLayerNormWeightsTensor, cellLayerNormWeights.data());
+ AllocateAndCopyDataToITensorHandle(&outputLayerNormWeightsTensor, outputLayerNormWeights.data());
- AllocateAndCopyDataToITensorHandle(&projectionWeightsTensor, &projectionWeights[0][0]);
+ AllocateAndCopyDataToITensorHandle(&projectionWeightsTensor, projectionWeights.data());
// Setup queue descriptor
data.m_InputToForgetWeights = &inputToForgetWeightsTensor;
@@ -2498,15 +2414,18 @@
cellStateOutHandle->Allocate();
outputHandle->Allocate();
- CopyDataToITensorHandle(inputHandle.get(), &inputTensor[0][0]);
- CopyDataToITensorHandle(outputStateInHandle.get(), &outputStateInTensor[0][0]);
- CopyDataToITensorHandle(cellStateInHandle.get(), &cellStateInTensor[0][0]);
+ CopyDataToITensorHandle(inputHandle.get(), inputVector.data());
+ CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data());
+ CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data());
workload->Execute();
- CopyDataFromITensorHandle(&ret.output[0][0], outputHandle.get());
+ CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get());
- return ret;
+ return LayerTestResult<int8_t, 2>(actualOutput,
+ outputVector,
+ outputHandle->GetShape(),
+ outputStateInfo.GetShape());
}
@@ -2519,13 +2438,10 @@
void LstmUtilsZeroVectorTest()
{
armnn::TensorInfo inputDesc({4}, armnn::DataType::Float32);
- boost::multi_array<float, 1> input = MakeTensor<float, 1>(inputDesc, std::vector<float>(
- {2., 3., 3., 4.}));
+ std::vector<float> input = {2., 3., 3., 4.};
+ std::vector<float> expectedOutput = {0., 0., 0., 0.};
- boost::multi_array<float, 1> expectedOutput = MakeTensor<float, 1>(inputDesc, std::vector<float>(
- {0., 0., 0., 0.}));
-
- return LstmUtilsZeroVectorTestImpl<armnn::DataType::Float32>(input, 4, expectedOutput);
+ return LstmUtilsZeroVectorTestImpl<armnn::DataType::Float32>(input, 4, expectedOutput, inputDesc.GetShape());
}
void LstmUtilsMeanStddevNormalizationNoneZeroInputTest()
@@ -2533,16 +2449,16 @@
uint32_t batchSize = 2;
uint32_t vecSize = 4;
armnn::TensorInfo inputDesc({batchSize, vecSize}, armnn::DataType::Float32);
- boost::multi_array<float, 2> input = MakeTensor<float, 2>(inputDesc, std::vector<float>(
- { 0.1f, 0.2f, 0.3f, 0.4f, //batch 0
- 0.9f, 1.0f, 1.1f, 1.2f })); //batch 1
+ std::vector<float> input =
+ { 0.1f, 0.2f, 0.3f, 0.4f, //batch 0
+ 0.9f, 1.0f, 1.1f, 1.2f }; //batch 1
- boost::multi_array<float, 2> expectedOutput = MakeTensor<float, 2>(inputDesc, std::vector<float>(
- { -1.34164071f, -0.447213531f, 0.44721365f, 1.34164071f, //batch 0
- -1.34163153f, -0.447210163f, 0.447211236f, 1.3416326f })); //batch 1
+ std::vector<float> expectedOutput =
+ { -1.34164071f, -0.447213531f, 0.44721365f, 1.34164071f, //batch 0
+ -1.34163153f, -0.447210163f, 0.447211236f, 1.3416326f }; //batch 1
return LstmUtilsMeanStddevNormalizationTestImpl<armnn::DataType::Float32>(input,
- vecSize, batchSize, expectedOutput);
+ vecSize, batchSize, expectedOutput, inputDesc.GetShape());
}
void LstmUtilsMeanStddevNormalizationAllZeroInputTest()
@@ -2550,16 +2466,16 @@
uint32_t batchSize = 2;
uint32_t vecSize = 4;
armnn::TensorInfo inputDesc({batchSize, vecSize}, armnn::DataType::Float32);
- boost::multi_array<float, 2> input = MakeTensor<float, 2>(inputDesc, std::vector<float>(
+ std::vector<float> input =
{ 0.0f, 0.0f, 0.0f, 0.0f, //batch 0
- 0.0f, 0.0f, 0.0f, 0.0f })); //batch 1
+ 0.0f, 0.0f, 0.0f, 0.0f }; //batch 1
- boost::multi_array<float, 2> expectedOutput = MakeTensor<float, 2>(inputDesc, std::vector<float>(
+ std::vector<float> expectedOutput =
{ 0.0f, 0.0f, 0.0f, 0.0f, //batch 0
- 0.0f, 0.0f, 0.0f, 0.0f })); //batch 1
+ 0.0f, 0.0f, 0.0f, 0.0f }; //batch 1
return LstmUtilsMeanStddevNormalizationTestImpl<armnn::DataType::Float32>(input,
- vecSize, batchSize, expectedOutput);
+ vecSize, batchSize, expectedOutput, inputDesc.GetShape());
}
void LstmUtilsMeanStddevNormalizationMixedZeroInputTest()
@@ -2567,16 +2483,16 @@
uint32_t batchSize = 2;
uint32_t vecSize = 4;
armnn::TensorInfo inputDesc({batchSize, vecSize}, armnn::DataType::Float32);
- boost::multi_array<float, 2> input = MakeTensor<float, 2>(inputDesc, std::vector<float>(
- { 0.0f, 0.0f, 0.0f, 0.0f, //batch 0
- 0.1f, 0.2f, 0.3f, 0.4f })); //batch 1
+ std::vector<float> input =
+ { 0.0f, 0.0f, 0.0f, 0.0f, //batch 0
+ 0.1f, 0.2f, 0.3f, 0.4f }; //batch 1
- boost::multi_array<float, 2> expectedOutput = MakeTensor<float, 2>(inputDesc, std::vector<float>(
- { 0.0f, 0.0f, 0.0f, 0.0f, //batch 0
- -1.34164071f, -0.447213531f, 0.44721365f, 1.34164071f })); //batch 1
+ std::vector<float> expectedOutput =
+ { 0.0f, 0.0f, 0.0f, 0.0f, //batch 0
+ -1.34164071f, -0.447213531f, 0.44721365f, 1.34164071f }; //batch 1
return LstmUtilsMeanStddevNormalizationTestImpl<armnn::DataType::Float32>(input,
- vecSize, batchSize, expectedOutput);
+ vecSize, batchSize, expectedOutput, inputDesc.GetShape());
}
void LstmUtilsVectorBatchVectorCwiseProductTest()
@@ -2584,13 +2500,13 @@
uint32_t batchSize = 4;
uint32_t vecSize = 29;
armnn::TensorInfo vecDesc({vecSize}, armnn::DataType::Float32);
- boost::multi_array<float, 1> vector = MakeTensor<float, 1>(vecDesc, std::vector<float>(
+ std::vector<float> vector =
{ 1.1f, 2.2f, 3.3f, 4.4f, 5.5f, 6.6f, 7.7f, 8.8f, 9.9f, 10.1f,
11.11f, 12.12f, 13.13f, 14.14f, 15.15f, 16.16f, 17.17f, 18.18f, 19.19f, 20.2f,
- 21.21f, 22.22f, 23.23f, 24.24f, 25.25f, 26.26f, 27.27f, 28.28f, 0.0f}));
+ 21.21f, 22.22f, 23.23f, 24.24f, 25.25f, 26.26f, 27.27f, 28.28f, 0.0f};
armnn::TensorInfo batchVecDesc({batchSize, vecSize}, armnn::DataType::Float32);
- boost::multi_array<float, 2> batchVector = MakeTensor<float, 2>(batchVecDesc, std::vector<float>(
+ std::vector<float> batchVector =
{ /* batch 0 */
1.1f, 2.2f, 3.3f, 4.4f, 5.5f, 6.6f, 7.7f, 8.8f, 9.9f, 10.1f,
11.11f, 12.12f, 13.13f, 14.14f, 15.15f, 16.16f, 17.17f, 18.18f, 19.19f, 20.2f,
@@ -2606,10 +2522,10 @@
/* batch 3 */
-1.1f, 2.2f, -3.3f, 4.4f, -5.5f, 6.6f, -7.7f, 8.8f, -9.9f, 10.1f,
-11.11f, 12.12f, -13.13f, 14.14f, -15.15f, 16.16f, -17.17f, 18.18f, -19.19f, 20.2f,
- -21.21f, 22.22f, -23.23f, 24.24f, -25.25f, 26.26f, -27.27f, 28.28f, 0.0f}));
+ -21.21f, 22.22f, -23.23f, 24.24f, -25.25f, 26.26f, -27.27f, 28.28f, 0.0f};
// Expect output = input * output + output.
- boost::multi_array<float, 2> expectedOutput = MakeTensor<float, 2>(batchVecDesc, std::vector<float>(
+ std::vector<float> expectedOutput =
{ /* batch 0 */
1.210000f, 4.840000f, 10.889999f, 19.360001f, 30.250000f, 43.559998f,
59.289997f, 77.440002f, 98.009995f, 102.010010f, 123.432091f, 146.894394f,
@@ -2633,10 +2549,10 @@
-59.289997f, 77.440002f, -98.009995f, 102.010010f, -123.432091f, 146.894394f,
-172.396896f, 199.939606f, -229.522491f, 261.145599f, -294.808899f, 330.512421f,
-368.256134f, 408.040039f, -449.864075f, 493.728363f, -539.632874f, 587.577576f,
- -637.562500f, 689.587585f, -743.652954f, 799.758423f, 0.000000f}));
+ -637.562500f, 689.587585f, -743.652954f, 799.758423f, 0.000000f};
return LstmUtilsVectorBatchVectorCwiseProductTestImpl<armnn::DataType::Float32>(vector, batchVector,
- vecSize, batchSize, expectedOutput);
+ vecSize, batchSize, expectedOutput, vecDesc.GetShape());
}
void LstmUtilsVectorBatchVectorAddTest()
@@ -2644,20 +2560,23 @@
uint32_t batchSize = 2;
uint32_t vecSize = 3;
armnn::TensorInfo vecDesc({vecSize}, armnn::DataType::Float32);
- boost::multi_array<float, 1> vector = MakeTensor<float, 1>(vecDesc, std::vector<float>(
- { 0.0f, -0.5f, 1.0f}));
+ std::vector<float> vector = { 0.0f, -0.5f, 1.0f};
armnn::TensorInfo batchVecDesc({batchSize, vecSize}, armnn::DataType::Float32);
- boost::multi_array<float, 2> batchVector = MakeTensor<float, 2>(batchVecDesc, std::vector<float>(
- { 1.0f, 2.0f, 3.0f, //batch 0
- 4.0f, 5.0f, 6.0f})); //batch 1
+ std::vector<float> batchVector =
+ {
+ 1.0f, 2.0f, 3.0f, //batch 0
+ 4.0f, 5.0f, 6.0f //batch 1
+ };
- boost::multi_array<float, 2> expectedOutput = MakeTensor<float, 2>(batchVecDesc, std::vector<float>(
- { 1.0f, 1.5f, 4.0f,
- 4.0f, 4.5f, 7.0f}));
+ std::vector<float> expectedOutput =
+ {
+ 1.0f, 1.5f, 4.0f,
+ 4.0f, 4.5f, 7.0f
+ };
return LstmUtilsVectorBatchVectorAddTestImpl<armnn::DataType::Float32>(vector, batchVector,
- vecSize, batchSize, expectedOutput);
+ vecSize, batchSize, expectedOutput, batchVecDesc.GetShape());
}
#endif
@@ -2668,15 +2587,15 @@
const armnn::ITensorHandleFactory& tensorHandleFactory)
{
armnn::TensorInfo inputDesc({ 2, 2 }, armnn::DataType::Float32);
- boost::multi_array<float, 2> input = MakeTensor<float, 2>(inputDesc, std::vector<float>(
- { 2., 3., 3., 4. }));
+ std::vector<float> input = { 2., 3., 3., 4. };
armnn::TensorInfo outputDesc({ 2, 4 }, armnn::DataType::Float32);
- boost::multi_array<float, 2> expectedOutput = MakeTensor<float, 2>(outputDesc, std::vector<float>(
+ std::vector<float> expectedOutput =
{-0.36444446f, -0.00352185f, 0.12886585f, -0.05163646f,
- -0.42734814f, -0.00478661f, 0.13455015f, -0.03560682f}));
+ -0.42734814f, -0.00478661f, 0.13455015f, -0.03560682f};
return LstmLayerWithCifgWithPeepholeNoProjectionTestImpl<armnn::DataType::Float32>(
- workloadFactory, memoryManager, tensorHandleFactory, input, expectedOutput);
+ workloadFactory, memoryManager, tensorHandleFactory,
+ input, expectedOutput, inputDesc.GetShape(), outputDesc.GetShape());
}
LayerTestResult<float, 2> LstmLayerFloat32NoCifgWithPeepholeWithProjectionTest(
@@ -2685,19 +2604,18 @@
const armnn::ITensorHandleFactory& tensorHandleFactory)
{
armnn::TensorInfo inputDesc({ 2, 5 }, armnn::DataType::Float32);
- boost::multi_array<float, 2> input = MakeTensor<float, 2>(inputDesc, std::vector<float>(
+ std::vector<float> input =
{0.787926f, 0.151646f, 0.071352f, 0.118426f, 0.458058f,
- 0.295743f, 0.544053f, 0.690064f, 0.858138f, 0.497181f}));
+ 0.295743f, 0.544053f, 0.690064f, 0.858138f, 0.497181f};
armnn::TensorInfo outputDesc({ 2, 16 }, armnn::DataType::Float32);
- boost::multi_array<float, 2> expectedOutput = MakeTensor<float, 2>(outputDesc, std::vector<float>(
- {-0.00396806f, 0.029352f, -0.00279226f, 0.0159977f, -0.00835576f,
- -0.0211779f, 0.0283512f, -0.0114597f, 0.00907307f, -0.0244004f,
- -0.0152191f, -0.0259063f, 0.00914318f, 0.00415118f, 0.017147f,
- 0.0134203f, -0.013869f, 0.0287268f, -0.00334693f, 0.00733398f, -0.0287926f,
- -0.0186926f, 0.0193662f, -0.0115437f, 0.00422612f, -0.0345232f,
- 0.00223253f, -0.00957321f, 0.0210624f, 0.013331f, 0.0150954f,
- 0.02168f}));
+ std::vector<float> expectedOutput =
+ {-0.00396806f, 0.029352f, -0.00279226f, 0.0159977f, -0.00835576f,
+ -0.0211779f, 0.0283512f, -0.0114597f, 0.00907307f, -0.0244004f,
+ -0.0152191f, -0.0259063f, 0.00914318f, 0.00415118f, 0.017147f,
+ 0.0134203f, -0.013869f, 0.0287268f, -0.00334693f, 0.00733398f, -0.0287926f,
+ -0.0186926f, 0.0193662f, -0.0115437f, 0.00422612f, -0.0345232f,
+ 0.00223253f, -0.00957321f, 0.0210624f, 0.013331f, 0.0150954f, 0.02168f};
return LstmLayerNoCifgWithPeepholeWithProjectionTestImpl<armnn::DataType::Float32>(
workloadFactory, memoryManager, tensorHandleFactory, input, expectedOutput);
}
@@ -2708,16 +2626,16 @@
const armnn::ITensorHandleFactory& tensorHandleFactory)
{
armnn::TensorInfo inputDesc({2, 2}, armnn::DataType::Float32);
- boost::multi_array<float, 2> input = MakeTensor<float, 2>(inputDesc, std::vector<float>(
- {2., 3., 3., 4.}));
+ std::vector<float> input = {2., 3., 3., 4.};
armnn::TensorInfo outputDesc({2, 4}, armnn::DataType::Float32);
- boost::multi_array<float, 2> expectedOutput = MakeTensor<float, 2>(outputDesc, std::vector<float>(
- {{-0.02973187f, 0.1229473f, 0.20885126f, -0.15358765f,
- -0.0185422f, 0.11281417f, 0.24466537f, -0.1826292f}}));
+ std::vector<float> expectedOutput =
+ {-0.02973187f, 0.1229473f, 0.20885126f, -0.15358765f,
+ -0.0185422f, 0.11281417f, 0.24466537f, -0.1826292f};
return LstmNoCifgNoPeepholeNoProjectionTestImpl<armnn::DataType::Float32>(
- workloadFactory, memoryManager, tensorHandleFactory, input, expectedOutput);
+ workloadFactory, memoryManager, tensorHandleFactory,
+ input, expectedOutput, inputDesc.GetShape(), outputDesc.GetShape());
}
LayerTestResult<float, 2> LstmLayerFloat32NoCifgWithPeepholeWithProjectionWithLayerNormTest(
@@ -2726,14 +2644,14 @@
const armnn::ITensorHandleFactory& tensorHandleFactory)
{
armnn::TensorInfo inputDesc({ 2, 5 }, armnn::DataType::Float32);
- boost::multi_array<float, 2> input = MakeTensor<float, 2>(inputDesc, std::vector<float>(
+ std::vector<float> input =
{0.7f, 0.8f, 0.1f, 0.2f, 0.3f, //batch 0
- 0.3f, 0.2f, 0.9f, 0.8f, 0.1f})); //batch 1
+ 0.3f, 0.2f, 0.9f, 0.8f, 0.1f}; //batch 1
armnn::TensorInfo outputDesc({ 2, 3 }, armnn::DataType::Float32);
- boost::multi_array<float, 2> expectedOutput = MakeTensor<float, 2>(outputDesc, std::vector<float>(
+ std::vector<float> expectedOutput =
{ 0.0244077f, 0.128027f, -0.00170918f, //batch 0
- -0.00692428f, 0.0848741f, 0.063445f})); //batch 1
+ -0.00692428f, 0.0848741f, 0.063445f}; //batch 1
return LstmLayerNoCifgWithPeepholeWithProjectionWithLayerNormTestImpl<armnn::DataType::Float32>(
workloadFactory, memoryManager, tensorHandleFactory, input, expectedOutput);
}
@@ -2750,22 +2668,20 @@
const armnn::DataType constantDatatype = armnn::DataType::QAsymmU8;
armnn::TensorInfo inputDesc({2, 2}, datatype);
- boost::multi_array<int16_t , 2> input = MakeTensor<int16_t , 2>(
- inputDesc,
- armnnUtils::QuantizedVector<int16_t>({ 2.f, 3.f, 3.f, 4.f }, qScale, qOffset));
+ std::vector<int16_t> input = armnnUtils::QuantizedVector<int16_t>({ 2.f, 3.f, 3.f, 4.f }, qScale, qOffset);
armnn::TensorInfo outputDesc({2, 4}, datatype);
- boost::multi_array<int16_t, 2> expectedOutput = MakeTensor<int16_t, 2>(
- outputDesc,
- armnnUtils::QuantizedVector<int16_t>(
- {
- -0.02973187f, 0.12294730f, 0.20885126f, -0.15358765f,
- -0.01854220f, 0.11281417f, 0.24466537f, -0.18262920f
- },
- qScale, qOffset));
+ std::vector<int16_t> expectedOutput = armnnUtils::QuantizedVector<int16_t>(
+ {
+ -0.02973187f, 0.12294730f, 0.20885126f, -0.15358765f,
+ -0.01854220f, 0.11281417f, 0.24466537f, -0.18262920f
+ },
+ qScale, qOffset);
return LstmNoCifgNoPeepholeNoProjectionTestImpl<datatype>(
- workloadFactory, memoryManager, tensorHandleFactory, input, expectedOutput, qScale, qOffset, constantDatatype);
+ workloadFactory, memoryManager, tensorHandleFactory,
+ input, expectedOutput, inputDesc.GetShape(), outputDesc.GetShape(),
+ qScale, qOffset, constantDatatype);
}
@@ -2781,24 +2697,20 @@
const armnn::DataType constantDatatype = armnn::DataType::QAsymmU8;
armnn::TensorInfo inputDesc({ 2, 2 }, datatype);
- boost::multi_array<int16_t, 2> input =
- MakeTensor<int16_t, 2>(
- inputDesc,
- armnnUtils::QuantizedVector<int16_t>({ 2.f, 3.f, 3.f, 4.f }, qScale, qOffset));
+ std::vector<int16_t> input = armnnUtils::QuantizedVector<int16_t>({ 2.f, 3.f, 3.f, 4.f }, qScale, qOffset);
armnn::TensorInfo outputDesc({ 2, 4 }, datatype);
- boost::multi_array<int16_t, 2> expectedOutput =
- MakeTensor<int16_t, 2>(
- outputDesc,
- armnnUtils::QuantizedVector<int16_t>(
- {
- -0.36444446f, -0.00352185f, 0.12886585f, -0.05163646f,
- -0.42734814f, -0.00478661f, 0.13455015f, -0.03560682f
- },
- qScale, qOffset));
+ std::vector<int16_t> expectedOutput = armnnUtils::QuantizedVector<int16_t>(
+ {
+ -0.36444446f, -0.00352185f, 0.12886585f, -0.05163646f,
+ -0.42734814f, -0.00478661f, 0.13455015f, -0.03560682f
+ },
+ qScale, qOffset);
return LstmLayerWithCifgWithPeepholeNoProjectionTestImpl<datatype>(
- workloadFactory, memoryManager, tensorHandleFactory, input, expectedOutput, qScale, qOffset, constantDatatype);
+ workloadFactory, memoryManager, tensorHandleFactory,
+ input, expectedOutput, inputDesc.GetShape(), outputDesc.GetShape(),
+ qScale, qOffset, constantDatatype);
}
LayerTestResult<int16_t, 2> LstmLayerInt16NoCifgWithPeepholeWithProjectionTest(
@@ -2813,32 +2725,26 @@
const armnn::DataType constantDatatype = armnn::DataType::QAsymmU8;
armnn::TensorInfo inputDesc({ 2, 5 }, datatype);
- boost::multi_array<int16_t, 2> input =
- MakeTensor<int16_t, 2>(
- inputDesc,
- armnnUtils::QuantizedVector<int16_t>(
- {
- 0.787926f, 0.151646f, 0.071352f, 0.118426f, 0.458058f,
- 0.295743f, 0.544053f, 0.690064f, 0.858138f, 0.497181f
- },
- qScale, qOffset));
+ std::vector<int16_t> input = armnnUtils::QuantizedVector<int16_t>(
+ {
+ 0.787926f, 0.151646f, 0.071352f, 0.118426f, 0.458058f,
+ 0.295743f, 0.544053f, 0.690064f, 0.858138f, 0.497181f
+ },
+ qScale, qOffset);
armnn::TensorInfo outputDesc({ 2, 16 }, datatype);
- boost::multi_array<int16_t, 2> expectedOutput =
- MakeTensor<int16_t, 2>(
- outputDesc,
- armnnUtils::QuantizedVector<int16_t>(
- {
- -0.00396806f, 0.02935200f, -0.00279226f, 0.01599770f,
- -0.00835576f, -0.02117790f, 0.02835120f, -0.01145970f,
- 0.00907307f, -0.02440040f, -0.01521910f, -0.02590630f,
- 0.00914318f, 0.00415118f, 0.01714700f, 0.01342030f,
- -0.01386900f, 0.02872680f, -0.00334693f, 0.00733398f,
- -0.02879260f, -0.01869260f, 0.01936620f, -0.01154370f,
- 0.00422612f, -0.03452320f, 0.00223253f, -0.00957321f,
- 0.02106240f, 0.01333100f, 0.01509540f, 0.02168000f
- },
- qScale, qOffset));
+ std::vector<int16_t> expectedOutput = armnnUtils::QuantizedVector<int16_t>(
+ {
+ -0.00396806f, 0.02935200f, -0.00279226f, 0.01599770f,
+ -0.00835576f, -0.02117790f, 0.02835120f, -0.01145970f,
+ 0.00907307f, -0.02440040f, -0.01521910f, -0.02590630f,
+ 0.00914318f, 0.00415118f, 0.01714700f, 0.01342030f,
+ -0.01386900f, 0.02872680f, -0.00334693f, 0.00733398f,
+ -0.02879260f, -0.01869260f, 0.01936620f, -0.01154370f,
+ 0.00422612f, -0.03452320f, 0.00223253f, -0.00957321f,
+ 0.02106240f, 0.01333100f, 0.01509540f, 0.02168000f
+ },
+ qScale, qOffset);
return LstmLayerNoCifgWithPeepholeWithProjectionTestImpl<datatype>(
workloadFactory, memoryManager, tensorHandleFactory, input, expectedOutput, qScale, qOffset, constantDatatype);
@@ -2855,23 +2761,20 @@
const armnn::DataType datatype = armnn::DataType::QSymmS16; // datatype & constants set to QSymm16
armnn::TensorInfo inputDesc({2, 2}, datatype);
- boost::multi_array<int16_t , 2> input =
- MakeTensor<int16_t , 2>(inputDesc,
- armnnUtils::QuantizedVector<int16_t>({ 2.f, 3.f, 3.f, 4.f }, qScale, qOffset));
+ std::vector<int16_t> input = armnnUtils::QuantizedVector<int16_t>({ 2.f, 3.f, 3.f, 4.f }, qScale, qOffset);
armnn::TensorInfo outputDesc({2, 4}, datatype);
- boost::multi_array<int16_t, 2> expectedOutput =
- MakeTensor<int16_t, 2>(
- outputDesc,
- armnnUtils::QuantizedVector<int16_t>(
- {
- -0.02973187f, 0.12294730f, 0.20885126f, -0.15358765f,
- -0.01854220f, 0.11281417f, 0.24466537f, -0.18262920f
- },
- qScale, qOffset));
+ std::vector<int16_t> expectedOutput = armnnUtils::QuantizedVector<int16_t>(
+ {
+ -0.02973187f, 0.12294730f, 0.20885126f, -0.15358765f,
+ -0.01854220f, 0.11281417f, 0.24466537f, -0.18262920f
+ },
+ qScale, qOffset);
return LstmNoCifgNoPeepholeNoProjectionTestImpl<datatype>(
- workloadFactory, memoryManager, tensorHandleFactory, input, expectedOutput, qScale, qOffset, datatype);
+ workloadFactory, memoryManager, tensorHandleFactory,
+ input, expectedOutput, inputDesc.GetShape(), outputDesc.GetShape(),
+ qScale, qOffset, datatype);
}
//
@@ -2884,14 +2787,13 @@
const armnn::ITensorHandleFactory& tensorHandleFactory)
{
armnn::TensorInfo inputDesc({2, 2}, armnn::DataType::QAsymmU8);
- boost::multi_array<uint8_t, 2> input = MakeTensor<uint8_t, 2>(inputDesc, std::vector<uint8_t>(
- {166, 179, 50, 150}));
+ std::vector<uint8_t> input = {166, 179, 50, 150};
armnn::TensorInfo outputDesc({2, 4}, armnn::DataType::QAsymmU8);
- boost::multi_array<uint8_t, 2> expectedOutput = MakeTensor<uint8_t, 2>(outputDesc, std::vector<uint8_t>(
- {140, 151, 146, 112, 136, 156, 142, 112 }));
+ std::vector<uint8_t> expectedOutput = {140, 151, 146, 112, 136, 156, 142, 112 };
- return QuantizedLstmTestImpl(workloadFactory, memoryManager, tensorHandleFactory, input, expectedOutput);
+ return QuantizedLstmTestImpl(workloadFactory, memoryManager, tensorHandleFactory,
+ input, expectedOutput, inputDesc.GetShape(), outputDesc.GetShape());
}
// QLSTM
@@ -2901,12 +2803,10 @@
const armnn::ITensorHandleFactory& tensorHandleFactory)
{
armnn::TensorInfo inputDesc({2, 5}, armnn::DataType::QAsymmS8);
- boost::multi_array<int8_t, 2> input = MakeTensor<int8_t, 2>(inputDesc, std::vector<int8_t>(
- {90, 102, 13, 26, 38, 102, 13, 26, 51, 64}));
+ std::vector<int8_t> input = {90, 102, 13, 26, 38, 102, 13, 26, 51, 64};
armnn::TensorInfo outputDesc({2, 4}, armnn::DataType::QAsymmS8);
- boost::multi_array<int8_t, 2> expectedOutput = MakeTensor<int8_t, 2>(outputDesc, std::vector<int8_t>(
- {-15, 21, 14, 20, -15, 15, 5, 27}));
+ std::vector<int8_t> expectedOutput = {-15, 21, 14, 20, -15, 15, 5, 27};
return QLstmTestImpl(workloadFactory, memoryManager, tensorHandleFactory, input, expectedOutput);
}
@@ -2917,12 +2817,10 @@
const armnn::ITensorHandleFactory& tensorHandleFactory)
{
armnn::TensorInfo inputDesc({2, 5}, armnn::DataType::QAsymmS8);
- boost::multi_array<int8_t, 2> input = MakeTensor<int8_t, 2>(inputDesc, std::vector<int8_t>(
- {90, 102, 13, 26, 38, 102, 13, 26, 51, 64}));
+ std::vector<int8_t> input = {90, 102, 13, 26, 38, 102, 13, 26, 51, 64};
armnn::TensorInfo outputDesc({2, 3}, armnn::DataType::QAsymmS8);
- boost::multi_array<int8_t, 2> expectedOutput = MakeTensor<int8_t, 2>(outputDesc, std::vector<int8_t>(
- {127, 127, -108, -67, 127, 127}));
+ std::vector<int8_t> expectedOutput = {127, 127, -108, -67, 127, 127};
return QLstmTestImpl1(workloadFactory, memoryManager, tensorHandleFactory, input, expectedOutput);
}
@@ -2933,12 +2831,10 @@
const armnn::ITensorHandleFactory& tensorHandleFactory)
{
armnn::TensorInfo inputDesc({2, 5}, armnn::DataType::QAsymmS8);
- boost::multi_array<int8_t, 2> input = MakeTensor<int8_t, 2>(inputDesc, std::vector<int8_t>(
- {90, 102, 13, 26, 38, 102, 13, 26, 51, 64}));
+ std::vector<int8_t> input = {90, 102, 13, 26, 38, 102, 13, 26, 51, 64};
armnn::TensorInfo outputDesc({2, 3}, armnn::DataType::QAsymmS8);
- boost::multi_array<int8_t, 2> expectedOutput = MakeTensor<int8_t, 2>(outputDesc, std::vector<int8_t>(
- {127, 127, 127, -128, 127, 127}));
+ std::vector<int8_t> expectedOutput = {127, 127, 127, -128, 127, 127};
return QLstmTestImpl2(workloadFactory, memoryManager, tensorHandleFactory, input, expectedOutput);
}
\ No newline at end of file