diff options
Diffstat (limited to 'src/armnnSerializer/test/LstmSerializationTests.cpp')
| -rw-r--r-- | src/armnnSerializer/test/LstmSerializationTests.cpp | 118 |
1 files changed, 59 insertions, 59 deletions
diff --git a/src/armnnSerializer/test/LstmSerializationTests.cpp b/src/armnnSerializer/test/LstmSerializationTests.cpp index bdc37877f7..3178bc990e 100644 --- a/src/armnnSerializer/test/LstmSerializationTests.cpp +++ b/src/armnnSerializer/test/LstmSerializationTests.cpp @@ -190,7 +190,7 @@ TEST_CASE("SerializeDeserializeLstmCifgPeepholeNoProjection") const uint32_t numUnits = 4; const uint32_t outputSize = numUnits; - armnn::TensorInfo inputWeightsInfo1({numUnits, inputSize}, armnn::DataType::Float32); + armnn::TensorInfo inputWeightsInfo1({numUnits, inputSize}, armnn::DataType::Float32, 0.0f, 0, true); std::vector<float> inputToForgetWeightsData = GenerateRandomData<float>(inputWeightsInfo1.GetNumElements()); armnn::ConstTensor inputToForgetWeights(inputWeightsInfo1, inputToForgetWeightsData); @@ -200,7 +200,7 @@ TEST_CASE("SerializeDeserializeLstmCifgPeepholeNoProjection") std::vector<float> inputToOutputWeightsData = GenerateRandomData<float>(inputWeightsInfo1.GetNumElements()); armnn::ConstTensor inputToOutputWeights(inputWeightsInfo1, inputToOutputWeightsData); - armnn::TensorInfo inputWeightsInfo2({numUnits, outputSize}, armnn::DataType::Float32); + armnn::TensorInfo inputWeightsInfo2({numUnits, outputSize}, armnn::DataType::Float32, 0.0f, 0, true); std::vector<float> recurrentToForgetWeightsData = GenerateRandomData<float>(inputWeightsInfo2.GetNumElements()); armnn::ConstTensor recurrentToForgetWeights(inputWeightsInfo2, recurrentToForgetWeightsData); @@ -210,7 +210,7 @@ TEST_CASE("SerializeDeserializeLstmCifgPeepholeNoProjection") std::vector<float> recurrentToOutputWeightsData = GenerateRandomData<float>(inputWeightsInfo2.GetNumElements()); armnn::ConstTensor recurrentToOutputWeights(inputWeightsInfo2, recurrentToOutputWeightsData); - armnn::TensorInfo inputWeightsInfo3({numUnits}, armnn::DataType::Float32); + armnn::TensorInfo inputWeightsInfo3({numUnits}, armnn::DataType::Float32, 0.0f, 0, true); std::vector<float> cellToForgetWeightsData = GenerateRandomData<float>(inputWeightsInfo3.GetNumElements()); armnn::ConstTensor cellToForgetWeights(inputWeightsInfo3, cellToForgetWeightsData); @@ -304,7 +304,7 @@ TEST_CASE("SerializeDeserializeLstmNoCifgWithPeepholeAndProjection") const uint32_t numUnits = 20; const uint32_t outputSize = 16; - armnn::TensorInfo tensorInfo20x5({numUnits, inputSize}, armnn::DataType::Float32); + armnn::TensorInfo tensorInfo20x5({numUnits, inputSize}, armnn::DataType::Float32, 0.0f, 0, true); std::vector<float> inputToInputWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements()); armnn::ConstTensor inputToInputWeights(tensorInfo20x5, inputToInputWeightsData); @@ -317,7 +317,7 @@ TEST_CASE("SerializeDeserializeLstmNoCifgWithPeepholeAndProjection") std::vector<float> inputToOutputWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements()); armnn::ConstTensor inputToOutputWeights(tensorInfo20x5, inputToOutputWeightsData); - armnn::TensorInfo tensorInfo20({numUnits}, armnn::DataType::Float32); + armnn::TensorInfo tensorInfo20({numUnits}, armnn::DataType::Float32, 0.0f, 0, true); std::vector<float> inputGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements()); armnn::ConstTensor inputGateBias(tensorInfo20, inputGateBiasData); @@ -330,7 +330,7 @@ TEST_CASE("SerializeDeserializeLstmNoCifgWithPeepholeAndProjection") std::vector<float> outputGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements()); armnn::ConstTensor outputGateBias(tensorInfo20, outputGateBiasData); - armnn::TensorInfo tensorInfo20x16({numUnits, outputSize}, armnn::DataType::Float32); + armnn::TensorInfo tensorInfo20x16({numUnits, outputSize}, armnn::DataType::Float32, 0.0f, 0, true); std::vector<float> recurrentToInputWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements()); armnn::ConstTensor recurrentToInputWeights(tensorInfo20x16, recurrentToInputWeightsData); @@ -352,11 +352,11 @@ TEST_CASE("SerializeDeserializeLstmNoCifgWithPeepholeAndProjection") std::vector<float> cellToOutputWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements()); armnn::ConstTensor cellToOutputWeights(tensorInfo20, cellToOutputWeightsData); - armnn::TensorInfo tensorInfo16x20({outputSize, numUnits}, armnn::DataType::Float32); + armnn::TensorInfo tensorInfo16x20({outputSize, numUnits}, armnn::DataType::Float32, 0.0f, 0, true); std::vector<float> projectionWeightsData = GenerateRandomData<float>(tensorInfo16x20.GetNumElements()); armnn::ConstTensor projectionWeights(tensorInfo16x20, projectionWeightsData); - armnn::TensorInfo tensorInfo16({outputSize}, armnn::DataType::Float32); + armnn::TensorInfo tensorInfo16({outputSize}, armnn::DataType::Float32, 0.0f, 0, true); std::vector<float> projectionBiasData(outputSize, 0.f); armnn::ConstTensor projectionBias(tensorInfo16, projectionBiasData); @@ -451,7 +451,7 @@ TEST_CASE("SerializeDeserializeLstmNoCifgWithPeepholeWithProjectionWithLayerNorm const uint32_t numUnits = 20; const uint32_t outputSize = 16; - armnn::TensorInfo tensorInfo20x5({numUnits, inputSize}, armnn::DataType::Float32); + armnn::TensorInfo tensorInfo20x5({numUnits, inputSize}, armnn::DataType::Float32, 0.0f, 0, true); std::vector<float> inputToInputWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements()); armnn::ConstTensor inputToInputWeights(tensorInfo20x5, inputToInputWeightsData); @@ -464,7 +464,7 @@ TEST_CASE("SerializeDeserializeLstmNoCifgWithPeepholeWithProjectionWithLayerNorm std::vector<float> inputToOutputWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements()); armnn::ConstTensor inputToOutputWeights(tensorInfo20x5, inputToOutputWeightsData); - armnn::TensorInfo tensorInfo20({numUnits}, armnn::DataType::Float32); + armnn::TensorInfo tensorInfo20({numUnits}, armnn::DataType::Float32, 0.0f, 0, true); std::vector<float> inputGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements()); armnn::ConstTensor inputGateBias(tensorInfo20, inputGateBiasData); @@ -477,7 +477,7 @@ TEST_CASE("SerializeDeserializeLstmNoCifgWithPeepholeWithProjectionWithLayerNorm std::vector<float> outputGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements()); armnn::ConstTensor outputGateBias(tensorInfo20, outputGateBiasData); - armnn::TensorInfo tensorInfo20x16({numUnits, outputSize}, armnn::DataType::Float32); + armnn::TensorInfo tensorInfo20x16({numUnits, outputSize}, armnn::DataType::Float32, 0.0f, 0, true); std::vector<float> recurrentToInputWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements()); armnn::ConstTensor recurrentToInputWeights(tensorInfo20x16, recurrentToInputWeightsData); @@ -499,11 +499,11 @@ TEST_CASE("SerializeDeserializeLstmNoCifgWithPeepholeWithProjectionWithLayerNorm std::vector<float> cellToOutputWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements()); armnn::ConstTensor cellToOutputWeights(tensorInfo20, cellToOutputWeightsData); - armnn::TensorInfo tensorInfo16x20({outputSize, numUnits}, armnn::DataType::Float32); + armnn::TensorInfo tensorInfo16x20({outputSize, numUnits}, armnn::DataType::Float32, 0.0f, 0, true); std::vector<float> projectionWeightsData = GenerateRandomData<float>(tensorInfo16x20.GetNumElements()); armnn::ConstTensor projectionWeights(tensorInfo16x20, projectionWeightsData); - armnn::TensorInfo tensorInfo16({outputSize}, armnn::DataType::Float32); + armnn::TensorInfo tensorInfo16({outputSize}, armnn::DataType::Float32, 0.0f, 0, true); std::vector<float> projectionBiasData(outputSize, 0.f); armnn::ConstTensor projectionBias(tensorInfo16, projectionBiasData); @@ -1236,7 +1236,7 @@ TEST_CASE("EnsureLstmLayersBackwardCompatibility") const uint32_t numUnits = 20u; const uint32_t outputSize = 16u; - armnn::TensorInfo tensorInfo20x5({numUnits, inputSize}, armnn::DataType::Float32); + armnn::TensorInfo tensorInfo20x5({numUnits, inputSize}, armnn::DataType::Float32, 0.0f, 0, true); std::vector<float> inputToInputWeightsData(tensorInfo20x5.GetNumElements(), 0.0f); armnn::ConstTensor inputToInputWeights(tensorInfo20x5, inputToInputWeightsData); @@ -1249,7 +1249,7 @@ TEST_CASE("EnsureLstmLayersBackwardCompatibility") std::vector<float> inputToOutputWeightsData(tensorInfo20x5.GetNumElements(), 0.0f); armnn::ConstTensor inputToOutputWeights(tensorInfo20x5, inputToOutputWeightsData); - armnn::TensorInfo tensorInfo20({numUnits}, armnn::DataType::Float32); + armnn::TensorInfo tensorInfo20({numUnits}, armnn::DataType::Float32, 0.0f, 0, true); std::vector<float> inputGateBiasData(tensorInfo20.GetNumElements(), 0.0f); armnn::ConstTensor inputGateBias(tensorInfo20, inputGateBiasData); @@ -1262,7 +1262,7 @@ TEST_CASE("EnsureLstmLayersBackwardCompatibility") std::vector<float> outputGateBiasData(tensorInfo20.GetNumElements(), 0.0f); armnn::ConstTensor outputGateBias(tensorInfo20, outputGateBiasData); - armnn::TensorInfo tensorInfo20x16({numUnits, outputSize}, armnn::DataType::Float32); + armnn::TensorInfo tensorInfo20x16({numUnits, outputSize}, armnn::DataType::Float32, 0.0f, 0, true); std::vector<float> recurrentToInputWeightsData(tensorInfo20x16.GetNumElements(), 0.0f); armnn::ConstTensor recurrentToInputWeights(tensorInfo20x16, recurrentToInputWeightsData); @@ -1284,11 +1284,11 @@ TEST_CASE("EnsureLstmLayersBackwardCompatibility") std::vector<float> cellToOutputWeightsData(tensorInfo20.GetNumElements(), 0.0f); armnn::ConstTensor cellToOutputWeights(tensorInfo20, cellToOutputWeightsData); - armnn::TensorInfo tensorInfo16x20({outputSize, numUnits}, armnn::DataType::Float32); + armnn::TensorInfo tensorInfo16x20({outputSize, numUnits}, armnn::DataType::Float32, 0.0f, 0, true); std::vector<float> projectionWeightsData(tensorInfo16x20.GetNumElements(), 0.0f); armnn::ConstTensor projectionWeights(tensorInfo16x20, projectionWeightsData); - armnn::TensorInfo tensorInfo16({outputSize}, armnn::DataType::Float32); + armnn::TensorInfo tensorInfo16({outputSize}, armnn::DataType::Float32, 0.0f, 0, true); std::vector<float> projectionBiasData(outputSize, 0.0f); armnn::ConstTensor projectionBias(tensorInfo16, projectionBiasData); @@ -1454,7 +1454,7 @@ TEST_CASE("SerializeDeserializeQuantizedLstm") armnn::TensorInfo inputToInputWeightsInfo(inputToInputWeightsShape, armnn::DataType::QAsymmU8, weightsScale, - weightsOffset); + weightsOffset, true); armnn::ConstTensor inputToInputWeights(inputToInputWeightsInfo, inputToInputWeightsData); armnn::TensorShape inputToForgetWeightsShape = {4, 2}; @@ -1462,7 +1462,7 @@ TEST_CASE("SerializeDeserializeQuantizedLstm") armnn::TensorInfo inputToForgetWeightsInfo(inputToForgetWeightsShape, armnn::DataType::QAsymmU8, weightsScale, - weightsOffset); + weightsOffset, true); armnn::ConstTensor inputToForgetWeights(inputToForgetWeightsInfo, inputToForgetWeightsData); armnn::TensorShape inputToCellWeightsShape = {4, 2}; @@ -1470,7 +1470,7 @@ TEST_CASE("SerializeDeserializeQuantizedLstm") armnn::TensorInfo inputToCellWeightsInfo(inputToCellWeightsShape, armnn::DataType::QAsymmU8, weightsScale, - weightsOffset); + weightsOffset, true); armnn::ConstTensor inputToCellWeights(inputToCellWeightsInfo, inputToCellWeightsData); armnn::TensorShape inputToOutputWeightsShape = {4, 2}; @@ -1478,7 +1478,7 @@ TEST_CASE("SerializeDeserializeQuantizedLstm") armnn::TensorInfo inputToOutputWeightsInfo(inputToOutputWeightsShape, armnn::DataType::QAsymmU8, weightsScale, - weightsOffset); + weightsOffset, true); armnn::ConstTensor inputToOutputWeights(inputToOutputWeightsInfo, inputToOutputWeightsData); // The shape of recurrent weight data is {outputSize, outputSize} = {4, 4} @@ -1487,7 +1487,7 @@ TEST_CASE("SerializeDeserializeQuantizedLstm") armnn::TensorInfo recurrentToInputWeightsInfo(recurrentToInputWeightsShape, armnn::DataType::QAsymmU8, weightsScale, - weightsOffset); + weightsOffset, true); armnn::ConstTensor recurrentToInputWeights(recurrentToInputWeightsInfo, recurrentToInputWeightsData); armnn::TensorShape recurrentToForgetWeightsShape = {4, 4}; @@ -1495,7 +1495,7 @@ TEST_CASE("SerializeDeserializeQuantizedLstm") armnn::TensorInfo recurrentToForgetWeightsInfo(recurrentToForgetWeightsShape, armnn::DataType::QAsymmU8, weightsScale, - weightsOffset); + weightsOffset, true); armnn::ConstTensor recurrentToForgetWeights(recurrentToForgetWeightsInfo, recurrentToForgetWeightsData); armnn::TensorShape recurrentToCellWeightsShape = {4, 4}; @@ -1503,7 +1503,7 @@ TEST_CASE("SerializeDeserializeQuantizedLstm") armnn::TensorInfo recurrentToCellWeightsInfo(recurrentToCellWeightsShape, armnn::DataType::QAsymmU8, weightsScale, - weightsOffset); + weightsOffset, true); armnn::ConstTensor recurrentToCellWeights(recurrentToCellWeightsInfo, recurrentToCellWeightsData); armnn::TensorShape recurrentToOutputWeightsShape = {4, 4}; @@ -1511,7 +1511,7 @@ TEST_CASE("SerializeDeserializeQuantizedLstm") armnn::TensorInfo recurrentToOutputWeightsInfo(recurrentToOutputWeightsShape, armnn::DataType::QAsymmU8, weightsScale, - weightsOffset); + weightsOffset, true); armnn::ConstTensor recurrentToOutputWeights(recurrentToOutputWeightsInfo, recurrentToOutputWeightsData); // The shape of bias data is {outputSize} = {4} @@ -1520,7 +1520,7 @@ TEST_CASE("SerializeDeserializeQuantizedLstm") armnn::TensorInfo inputGateBiasInfo(inputGateBiasShape, armnn::DataType::Signed32, biasScale, - biasOffset); + biasOffset, true); armnn::ConstTensor inputGateBias(inputGateBiasInfo, inputGateBiasData); armnn::TensorShape forgetGateBiasShape = {4}; @@ -1528,7 +1528,7 @@ TEST_CASE("SerializeDeserializeQuantizedLstm") armnn::TensorInfo forgetGateBiasInfo(forgetGateBiasShape, armnn::DataType::Signed32, biasScale, - biasOffset); + biasOffset, true); armnn::ConstTensor forgetGateBias(forgetGateBiasInfo, forgetGateBiasData); armnn::TensorShape cellBiasShape = {4}; @@ -1536,7 +1536,7 @@ TEST_CASE("SerializeDeserializeQuantizedLstm") armnn::TensorInfo cellBiasInfo(cellBiasShape, armnn::DataType::Signed32, biasScale, - biasOffset); + biasOffset, true); armnn::ConstTensor cellBias(cellBiasInfo, cellBiasData); armnn::TensorShape outputGateBiasShape = {4}; @@ -1544,7 +1544,7 @@ TEST_CASE("SerializeDeserializeQuantizedLstm") armnn::TensorInfo outputGateBiasInfo(outputGateBiasShape, armnn::DataType::Signed32, biasScale, - biasOffset); + biasOffset, true); armnn::ConstTensor outputGateBias(outputGateBiasInfo, outputGateBiasData); armnn::QuantizedLstmInputParams params; @@ -1655,14 +1655,14 @@ TEST_CASE("SerializeDeserializeQLstmBasic") armnn::TensorInfo inputWeightsInfo({numUnits, inputSize}, armnn::DataType::QSymmS8, weightsScale, - weightsOffset); + weightsOffset, true); armnn::TensorInfo recurrentWeightsInfo({numUnits, outputSize}, armnn::DataType::QSymmS8, weightsScale, - weightsOffset); + weightsOffset, true); - armnn::TensorInfo biasInfo({numUnits}, armnn::DataType::Signed32, biasScale, biasOffset); + armnn::TensorInfo biasInfo({numUnits}, armnn::DataType::Signed32, biasScale, biasOffset, true); std::vector<int8_t> inputToForgetWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements()); std::vector<int8_t> inputToCellWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements()); @@ -1816,22 +1816,22 @@ TEST_CASE("SerializeDeserializeQLstmCifgLayerNorm") armnn::TensorInfo inputWeightsInfo({numUnits, inputSize}, armnn::DataType::QSymmS8, weightsScale, - weightsOffset); + weightsOffset, true); armnn::TensorInfo recurrentWeightsInfo({numUnits, outputSize}, armnn::DataType::QSymmS8, weightsScale, - weightsOffset); + weightsOffset, true); armnn::TensorInfo biasInfo({numUnits}, armnn::DataType::Signed32, biasScale, - biasOffset); + biasOffset, true); armnn::TensorInfo layerNormWeightsInfo({numUnits}, armnn::DataType::QSymmS16, layerNormScale, - layerNormOffset); + layerNormOffset, true); // Mandatory params std::vector<int8_t> inputToForgetWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements()); @@ -2003,32 +2003,32 @@ TEST_CASE("SerializeDeserializeQLstmAdvanced") armnn::TensorInfo inputWeightsInfo({numUnits, inputSize}, armnn::DataType::QSymmS8, weightsScale, - weightsOffset); + weightsOffset, true); armnn::TensorInfo recurrentWeightsInfo({numUnits, outputSize}, armnn::DataType::QSymmS8, weightsScale, - weightsOffset); + weightsOffset, true); armnn::TensorInfo biasInfo({numUnits}, armnn::DataType::Signed32, biasScale, - biasOffset); + biasOffset, true); armnn::TensorInfo peepholeWeightsInfo({numUnits}, armnn::DataType::QSymmS16, weightsScale, - weightsOffset); + weightsOffset, true); armnn::TensorInfo layerNormWeightsInfo({numUnits}, armnn::DataType::QSymmS16, layerNormScale, - layerNormOffset); + layerNormOffset, true); armnn::TensorInfo projectionWeightsInfo({outputSize, numUnits}, armnn::DataType::QSymmS8, weightsScale, - weightsOffset); + weightsOffset, true); // Mandatory params std::vector<int8_t> inputToForgetWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements()); @@ -2213,7 +2213,7 @@ TEST_CASE("SerializeDeserializeUnidirectionalSequenceLstmCifgPeepholeNoProjectio const uint32_t numUnits = 4; const uint32_t outputSize = numUnits; - armnn::TensorInfo inputWeightsInfo1({numUnits, inputSize}, armnn::DataType::Float32); + armnn::TensorInfo inputWeightsInfo1({numUnits, inputSize}, armnn::DataType::Float32, 0.0f, 0, true); std::vector<float> inputToForgetWeightsData = GenerateRandomData<float>(inputWeightsInfo1.GetNumElements()); armnn::ConstTensor inputToForgetWeights(inputWeightsInfo1, inputToForgetWeightsData); @@ -2223,7 +2223,7 @@ TEST_CASE("SerializeDeserializeUnidirectionalSequenceLstmCifgPeepholeNoProjectio std::vector<float> inputToOutputWeightsData = GenerateRandomData<float>(inputWeightsInfo1.GetNumElements()); armnn::ConstTensor inputToOutputWeights(inputWeightsInfo1, inputToOutputWeightsData); - armnn::TensorInfo inputWeightsInfo2({numUnits, outputSize}, armnn::DataType::Float32); + armnn::TensorInfo inputWeightsInfo2({numUnits, outputSize}, armnn::DataType::Float32, 0.0f, 0, true); std::vector<float> recurrentToForgetWeightsData = GenerateRandomData<float>(inputWeightsInfo2.GetNumElements()); armnn::ConstTensor recurrentToForgetWeights(inputWeightsInfo2, recurrentToForgetWeightsData); @@ -2233,7 +2233,7 @@ TEST_CASE("SerializeDeserializeUnidirectionalSequenceLstmCifgPeepholeNoProjectio std::vector<float> recurrentToOutputWeightsData = GenerateRandomData<float>(inputWeightsInfo2.GetNumElements()); armnn::ConstTensor recurrentToOutputWeights(inputWeightsInfo2, recurrentToOutputWeightsData); - armnn::TensorInfo inputWeightsInfo3({numUnits}, armnn::DataType::Float32); + armnn::TensorInfo inputWeightsInfo3({numUnits}, armnn::DataType::Float32, 0.0f, 0, true); std::vector<float> cellToForgetWeightsData = GenerateRandomData<float>(inputWeightsInfo3.GetNumElements()); armnn::ConstTensor cellToForgetWeights(inputWeightsInfo3, cellToForgetWeightsData); @@ -2318,7 +2318,7 @@ TEST_CASE("SerializeDeserializeUnidirectionalSequenceLstmNoCifgWithPeepholeAndPr const uint32_t numUnits = 20; const uint32_t outputSize = 16; - armnn::TensorInfo tensorInfo20x5({numUnits, inputSize}, armnn::DataType::Float32); + armnn::TensorInfo tensorInfo20x5({numUnits, inputSize}, armnn::DataType::Float32, 0.0f, 0, true); std::vector<float> inputToInputWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements()); armnn::ConstTensor inputToInputWeights(tensorInfo20x5, inputToInputWeightsData); @@ -2331,7 +2331,7 @@ TEST_CASE("SerializeDeserializeUnidirectionalSequenceLstmNoCifgWithPeepholeAndPr std::vector<float> inputToOutputWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements()); armnn::ConstTensor inputToOutputWeights(tensorInfo20x5, inputToOutputWeightsData); - armnn::TensorInfo tensorInfo20({numUnits}, armnn::DataType::Float32); + armnn::TensorInfo tensorInfo20({numUnits}, armnn::DataType::Float32, 0.0f, 0, true); std::vector<float> inputGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements()); armnn::ConstTensor inputGateBias(tensorInfo20, inputGateBiasData); @@ -2344,7 +2344,7 @@ TEST_CASE("SerializeDeserializeUnidirectionalSequenceLstmNoCifgWithPeepholeAndPr std::vector<float> outputGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements()); armnn::ConstTensor outputGateBias(tensorInfo20, outputGateBiasData); - armnn::TensorInfo tensorInfo20x16({numUnits, outputSize}, armnn::DataType::Float32); + armnn::TensorInfo tensorInfo20x16({numUnits, outputSize}, armnn::DataType::Float32, 0.0f, 0, true); std::vector<float> recurrentToInputWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements()); armnn::ConstTensor recurrentToInputWeights(tensorInfo20x16, recurrentToInputWeightsData); @@ -2366,11 +2366,11 @@ TEST_CASE("SerializeDeserializeUnidirectionalSequenceLstmNoCifgWithPeepholeAndPr std::vector<float> cellToOutputWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements()); armnn::ConstTensor cellToOutputWeights(tensorInfo20, cellToOutputWeightsData); - armnn::TensorInfo tensorInfo16x20({outputSize, numUnits}, armnn::DataType::Float32); + armnn::TensorInfo tensorInfo16x20({outputSize, numUnits}, armnn::DataType::Float32, 0.0f, 0, true); std::vector<float> projectionWeightsData = GenerateRandomData<float>(tensorInfo16x20.GetNumElements()); armnn::ConstTensor projectionWeights(tensorInfo16x20, projectionWeightsData); - armnn::TensorInfo tensorInfo16({outputSize}, armnn::DataType::Float32); + armnn::TensorInfo tensorInfo16({outputSize}, armnn::DataType::Float32, 0.0f, 0, true); std::vector<float> projectionBiasData(outputSize, 0.f); armnn::ConstTensor projectionBias(tensorInfo16, projectionBiasData); @@ -2456,7 +2456,7 @@ TEST_CASE("SerializeDeserializeUnidirectionalSequenceLstmNoCifgWithPeepholeWithP const uint32_t numUnits = 20; const uint32_t outputSize = 16; - armnn::TensorInfo tensorInfo20x5({numUnits, inputSize}, armnn::DataType::Float32); + armnn::TensorInfo tensorInfo20x5({numUnits, inputSize}, armnn::DataType::Float32, 0.0f, 0, true); std::vector<float> inputToInputWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements()); armnn::ConstTensor inputToInputWeights(tensorInfo20x5, inputToInputWeightsData); @@ -2469,7 +2469,7 @@ TEST_CASE("SerializeDeserializeUnidirectionalSequenceLstmNoCifgWithPeepholeWithP std::vector<float> inputToOutputWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements()); armnn::ConstTensor inputToOutputWeights(tensorInfo20x5, inputToOutputWeightsData); - armnn::TensorInfo tensorInfo20({numUnits}, armnn::DataType::Float32); + armnn::TensorInfo tensorInfo20({numUnits}, armnn::DataType::Float32, 0.0f, 0, true); std::vector<float> inputGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements()); armnn::ConstTensor inputGateBias(tensorInfo20, inputGateBiasData); @@ -2482,7 +2482,7 @@ TEST_CASE("SerializeDeserializeUnidirectionalSequenceLstmNoCifgWithPeepholeWithP std::vector<float> outputGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements()); armnn::ConstTensor outputGateBias(tensorInfo20, outputGateBiasData); - armnn::TensorInfo tensorInfo20x16({numUnits, outputSize}, armnn::DataType::Float32); + armnn::TensorInfo tensorInfo20x16({numUnits, outputSize}, armnn::DataType::Float32, 0.0f, 0, true); std::vector<float> recurrentToInputWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements()); armnn::ConstTensor recurrentToInputWeights(tensorInfo20x16, recurrentToInputWeightsData); @@ -2504,11 +2504,11 @@ TEST_CASE("SerializeDeserializeUnidirectionalSequenceLstmNoCifgWithPeepholeWithP std::vector<float> cellToOutputWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements()); armnn::ConstTensor cellToOutputWeights(tensorInfo20, cellToOutputWeightsData); - armnn::TensorInfo tensorInfo16x20({outputSize, numUnits}, armnn::DataType::Float32); + armnn::TensorInfo tensorInfo16x20({outputSize, numUnits}, armnn::DataType::Float32, 0.0f, 0, true); std::vector<float> projectionWeightsData = GenerateRandomData<float>(tensorInfo16x20.GetNumElements()); armnn::ConstTensor projectionWeights(tensorInfo16x20, projectionWeightsData); - armnn::TensorInfo tensorInfo16({outputSize}, armnn::DataType::Float32); + armnn::TensorInfo tensorInfo16({outputSize}, armnn::DataType::Float32, 0.0f, 0, true); std::vector<float> projectionBiasData(outputSize, 0.f); armnn::ConstTensor projectionBias(tensorInfo16, projectionBiasData); @@ -2611,7 +2611,7 @@ TEST_CASE("SerializeDeserializeUnidirectionalSequenceLstmCifgPeepholeNoProjectio const uint32_t numUnits = 4; const uint32_t outputSize = numUnits; - armnn::TensorInfo inputWeightsInfo1({numUnits, inputSize}, armnn::DataType::Float32); + armnn::TensorInfo inputWeightsInfo1({numUnits, inputSize}, armnn::DataType::Float32, 0.0f, 0, true); std::vector<float> inputToForgetWeightsData = GenerateRandomData<float>(inputWeightsInfo1.GetNumElements()); armnn::ConstTensor inputToForgetWeights(inputWeightsInfo1, inputToForgetWeightsData); @@ -2621,7 +2621,7 @@ TEST_CASE("SerializeDeserializeUnidirectionalSequenceLstmCifgPeepholeNoProjectio std::vector<float> inputToOutputWeightsData = GenerateRandomData<float>(inputWeightsInfo1.GetNumElements()); armnn::ConstTensor inputToOutputWeights(inputWeightsInfo1, inputToOutputWeightsData); - armnn::TensorInfo inputWeightsInfo2({numUnits, outputSize}, armnn::DataType::Float32); + armnn::TensorInfo inputWeightsInfo2({numUnits, outputSize}, armnn::DataType::Float32, 0.0f, 0, true); std::vector<float> recurrentToForgetWeightsData = GenerateRandomData<float>(inputWeightsInfo2.GetNumElements()); armnn::ConstTensor recurrentToForgetWeights(inputWeightsInfo2, recurrentToForgetWeightsData); @@ -2631,7 +2631,7 @@ TEST_CASE("SerializeDeserializeUnidirectionalSequenceLstmCifgPeepholeNoProjectio std::vector<float> recurrentToOutputWeightsData = GenerateRandomData<float>(inputWeightsInfo2.GetNumElements()); armnn::ConstTensor recurrentToOutputWeights(inputWeightsInfo2, recurrentToOutputWeightsData); - armnn::TensorInfo inputWeightsInfo3({numUnits}, armnn::DataType::Float32); + armnn::TensorInfo inputWeightsInfo3({numUnits}, armnn::DataType::Float32, 0.0f, 0, true); std::vector<float> cellToForgetWeightsData = GenerateRandomData<float>(inputWeightsInfo3.GetNumElements()); armnn::ConstTensor cellToForgetWeights(inputWeightsInfo3, cellToForgetWeightsData); |