diff options
Diffstat (limited to 'src/backends/backendsCommon/test')
| -rw-r--r-- | src/backends/backendsCommon/test/LayerTests.cpp | 242 | ||||
| -rw-r--r-- | src/backends/backendsCommon/test/LayerTests.hpp | 364 |
2 files changed, 465 insertions, 141 deletions
diff --git a/src/backends/backendsCommon/test/LayerTests.cpp b/src/backends/backendsCommon/test/LayerTests.cpp index 6754106c49..2201499b3a 100644 --- a/src/backends/backendsCommon/test/LayerTests.cpp +++ b/src/backends/backendsCommon/test/LayerTests.cpp @@ -2641,6 +2641,103 @@ LayerTestResult<float,4> AdditionTest( return ret; } +LayerTestResult<float, 5> Addition5dTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) +{ + unsigned int depth = 2; + unsigned int batchSize = 2; + unsigned int channels = 2; + unsigned int height = 2; + unsigned int width = 3; + + armnn::TensorInfo inputTensorInfo1, inputTensorInfo2; + armnn::TensorInfo outputTensorInfo; + + unsigned int shape[] = {depth, batchSize, channels, height, width}; + + inputTensorInfo1 = armnn::TensorInfo(5, shape, armnn::DataType::Float32); + inputTensorInfo2 = armnn::TensorInfo(5, shape, armnn::DataType::Float32); + outputTensorInfo = armnn::TensorInfo(5, shape, armnn::DataType::Float32); + + + auto input1 = MakeTensor<float, 5>(inputTensorInfo1, std::vector<float>( + { + 2.6f, 4.0f, 4.4f, 2.7f, 4.6f, 2.8f, + 2.3f, 1.9f, 3.4f, 2.9f, 2.2f, 4.5f, + + 2.8f, 1.9f, 2.3f, 2.6f, 4.7f, 3.5f, + 0.4f, 1.5f, 2.1f, 0.7f, 5.0f, 1.1f, + + + 1.0f, 2.7f, 0.0f, 0.6f, 0.8f, 0.9f, + 1.0f, 2.6f, 0.4f, 3.8f, 0.4f, 0.8f, + + 0.5f, 4.3f, 3.1f, 4.4f, 0.7f, 1.4f, + 0.4f, 4.4f, 0.7f, 0.6f, 4.7f, 1.2f, + + })); + + auto input2 = MakeTensor<float, 5>(inputTensorInfo2, std::vector<float>( + { + 4.4f, 3.0f, 1.0f, 0.0f, 3.9f, 3.1f, + 1.7f, 2.9f, 1.3f, 0.4f, 0.4f, 4.3f, + + 4.5f, 0.2f, 2.2f, 4.1f, 3.9f, 3.0f, + 0.1f, 2.5f, 4.1f, 4.6f, 1.5f, 0.0f, + + + 0.5f, 4.9f, 2.5f, 1.5f, 3.4f, 4.5f, + 2.0f, 3.0f, 4.9f, 1.6f, 2.4f, 3.4f, + + 3.6f, 1.8f, 1.3f, 2.6f, 2.1f, 4.8f, + 2.0f, 4.3f, 4.0f, 0.2f, 0.6f, 4.4f, + })); + + LayerTestResult<float, 5> ret(outputTensorInfo); + ret.outputExpected = MakeTensor<float, 5>(outputTensorInfo, std::vector<float>( + { + 7.0f, 7.0f, 5.4f, 2.7f, 8.5f, 5.9f, + 4.0f, 4.8f, 4.7f, 3.3f, 2.6f, 8.8f, + + 7.3f, 2.1f, 4.5f, 6.7f, 8.6f, 6.5f, + 0.5f, 4.0f, 6.2f, 5.3f, 6.5f, 1.1f, + + + 1.5f, 7.6f, 2.5f, 2.1f, 4.2f, 5.4f, + 3.0f, 5.6f, 5.3f, 5.4f, 2.8f, 4.2f, + + 4.1f, 6.1f, 4.4f, 7.0f, 2.8f, 6.2f, + 2.4f, 8.7f, 4.7f, 0.8f, 5.3f, 5.6f, + })); + + std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); + std::unique_ptr<armnn::ITensorHandle> inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2); + std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::AdditionQueueDescriptor data; + armnn::WorkloadInfo info; + AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); + AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); + AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); + + std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateAddition(data, info); + + inputHandle1->Allocate(); + inputHandle2->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0][0]); + CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0][0][0]); + + workload->PostAllocationConfigure(); + workload->Execute(); + + CopyDataFromITensorHandle(&ret.output[0][0][0][0][0], outputHandle.get()); + + return ret; +} + template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 4> AdditionBroadcastTestImpl( armnn::IWorkloadFactory& workloadFactory, @@ -4103,25 +4200,25 @@ LayerTestResult<int16_t, 4> MinimumBroadcast1DVectorInt16Test( } namespace { -LayerTestResult<float,4> MultiplicationTestHelper( +template<std::size_t NumDims> +LayerTestResult<float,NumDims> MultiplicationTestHelper( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const unsigned int shape0[4], + const unsigned int shape0[NumDims], const std::vector<float> & values0, - const unsigned int shape1[4], + const unsigned int shape1[NumDims], const std::vector<float> & values1, - const unsigned int outShape[4], + const unsigned int outShape[NumDims], const std::vector<float> & outValues) { - const uint32_t dimensionCount = 4; - armnn::TensorInfo inputTensorInfo0{dimensionCount, shape0, armnn::DataType::Float32}; - armnn::TensorInfo inputTensorInfo1{dimensionCount, shape1, armnn::DataType::Float32}; - armnn::TensorInfo outputTensorInfo{dimensionCount, outShape, armnn::DataType::Float32}; + armnn::TensorInfo inputTensorInfo0{NumDims, shape0, armnn::DataType::Float32}; + armnn::TensorInfo inputTensorInfo1{NumDims, shape1, armnn::DataType::Float32}; + armnn::TensorInfo outputTensorInfo{NumDims, outShape, armnn::DataType::Float32}; - auto input0 = MakeTensor<float, 4>(inputTensorInfo0, values0); - auto input1 = MakeTensor<float, 4>(inputTensorInfo1, values1); + auto input0 = MakeTensor<float, NumDims>(inputTensorInfo0, values0); + auto input1 = MakeTensor<float, NumDims>(inputTensorInfo1, values1); - LayerTestResult<float,4> ret(outputTensorInfo); + LayerTestResult<float,NumDims> ret(outputTensorInfo); std::unique_ptr<armnn::ITensorHandle> inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0); std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); @@ -4139,15 +4236,15 @@ LayerTestResult<float,4> MultiplicationTestHelper( inputHandle1->Allocate(); outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle0.get(), &input0[0][0][0][0]); - CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); + CopyDataToITensorHandle(inputHandle0.get(), input0.origin()); + CopyDataToITensorHandle(inputHandle1.get(), input1.origin()); workload->PostAllocationConfigure(); workload->Execute(); - CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); + CopyDataFromITensorHandle(ret.output.origin(), outputHandle.get()); - ret.outputExpected = MakeTensor<float, 4>(outputTensorInfo, outValues); + ret.outputExpected = MakeTensor<float, NumDims>(outputTensorInfo, outValues); return ret; } } // anonymous namespace @@ -4176,14 +4273,81 @@ LayerTestResult<float,4> MultiplicationTest( 2, 2, 2, 2, 6, 6, 6, 6, 12, 12, 12, 12, 20, 20, 20, 20 }); - return MultiplicationTestHelper(workloadFactory, - memoryManager, - shape, - input0, - shape, - input1, - shape, - output); + return MultiplicationTestHelper<4>(workloadFactory, + memoryManager, + shape, + input0, + shape, + input1, + shape, + output); +} + +LayerTestResult<float,5> Multiplication5dTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) +{ + const unsigned int width = 3; + const unsigned int height = 2; + const unsigned int channelCount = 2; + const unsigned int batchSize = 2; + const unsigned int depth = 2; + + unsigned int shape[] = { depth, batchSize, channelCount, height, width }; + + std::vector<float> input0({ + 1.80f, 0.20f, 2.30f, 1.30f, 2.10f, 1.00f, + 2.60f, 0.60f, 2.10f, 2.30f, 2.30f, 2.00f, + + 2.50f, 1.00f, 2.90f, 3.10f, 1.50f, 2.40f, + 2.80f, 1.10f, 1.00f, 3.20f, 1.00f, 2.30f, + + + 0.30f, 2.20f, 1.00f, 0.20f, 1.60f, 1.40f, + 0.80f, 3.20f, 0.10f, 0.10f, 3.10f, 2.10f, + + 1.50f, 2.40f, 1.40f, 0.70f, 2.40f, 1.40f, + 1.60f, 1.20f, 1.90f, 0.80f, 0.00f, 0.10f, + }); + + std::vector<float> input1({ + 0.70f, 1.00f, 2.90f, 2.20f, 3.10f, 2.80f, + 1.80f, 2.00f, 0.50f, 2.30f, 1.20f, 2.70f, + + 2.40f, 0.20f, 3.20f, 1.60f, 0.20f, 2.50f, + 2.30f, 0.70f, 2.70f, 1.80f, 2.90f, 2.70f, + + + 3.20f, 3.20f, 0.70f, 1.90f, 2.70f, 2.50f, + 2.40f, 0.90f, 2.30f, 1.80f, 2.50f, 2.00f, + + 1.60f, 2.20f, 1.60f, 2.00f, 0.30f, 3.20f, + 0.40f, 3.00f, 2.60f, 0.30f, 0.00f, 2.50f, + }); + + std::vector<float> output({ + 1.26f, 0.20f, 6.67f, 2.86f, 6.51f, 2.80f, + 4.68f, 1.20f, 1.05f, 5.29f, 2.76f, 5.40f, + + 6.00f, 0.20f, 9.28f, 4.96f, 0.30f, 6.00f, + 6.44f, 0.77f, 2.70f, 5.76f, 2.90f, 6.21f, + + + 0.96f, 7.04f, 0.70f, 0.38f, 4.32f, 3.50f, + 1.92f, 2.88f, 0.23f, 0.18f, 7.75f, 4.20f, + + 2.40f, 5.28f, 2.24f, 1.40f, 0.72f, 4.48f, + 0.64f, 3.60f, 4.94f, 0.24f, 0.00f, 0.25f, + }); + + return MultiplicationTestHelper<5>(workloadFactory, + memoryManager, + shape, + input0, + shape, + input1, + shape, + output); } LayerTestResult<float, 4> MultiplicationBroadcast1ElementTest( @@ -4198,14 +4362,14 @@ LayerTestResult<float, 4> MultiplicationBroadcast1ElementTest( std::vector<float> output({ 2, 4, 6, 8, 10, 12, 14, 16}); - return MultiplicationTestHelper(workloadFactory, - memoryManager, - shape0, - input0, - shape1, - input1, - shape0, - output); + return MultiplicationTestHelper<4>(workloadFactory, + memoryManager, + shape0, + input0, + shape1, + input1, + shape0, + output); } LayerTestResult<float, 4> MultiplicationBroadcast1DVectorTest( @@ -4226,14 +4390,14 @@ LayerTestResult<float, 4> MultiplicationBroadcast1DVectorTest( 7, 16, 9, 20, 11, 24, 13, 28, 15, 32, 17, 36}); - return MultiplicationTestHelper(workloadFactory, - memoryManager, - shape0, - input0, - shape1, - input1, - shape0, - output); + return MultiplicationTestHelper<4>(workloadFactory, + memoryManager, + shape0, + input0, + shape1, + input1, + shape0, + output); } LayerTestResult<float,4> CompareMultiplicationTest( diff --git a/src/backends/backendsCommon/test/LayerTests.hpp b/src/backends/backendsCommon/test/LayerTests.hpp index 0fe5d09cd4..df33aa192e 100644 --- a/src/backends/backendsCommon/test/LayerTests.hpp +++ b/src/backends/backendsCommon/test/LayerTests.hpp @@ -539,6 +539,11 @@ LayerTestResult<T, 4> SimpleReshapeTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager); +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> +LayerTestResult<T, 5> Reshape5dTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager); + LayerTestResult<float, 4> SimpleFloorTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager); @@ -712,6 +717,10 @@ LayerTestResult<float, 4> AdditionTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager); +LayerTestResult<float, 5> Addition5dTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager); + LayerTestResult<float, 4> AdditionBroadcast1ElementTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager); @@ -764,6 +773,10 @@ LayerTestResult<float, 4> MultiplicationTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager); +LayerTestResult<float, 5> Multiplication5dTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager); + LayerTestResult<float, 4> MultiplicationBroadcast1ElementTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager); @@ -2084,6 +2097,37 @@ LayerTestResult<int16_t, 4> QuantizeClampInt16Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager); +template <armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> +std::vector<T> ConvertToDataType(const std::vector<float>& input, + const armnn::TensorInfo& inputTensorInfo) +{ + std::vector<T> output(input.size()); + auto outputTensorInfo = inputTensorInfo; + outputTensorInfo.SetDataType(ArmnnType); + + std::unique_ptr<armnn::Encoder<float>> pOutputEncoder = armnn::MakeEncoder<float>(outputTensorInfo, output.data()); + armnn::Encoder<float>& rOutputEncoder = *pOutputEncoder; + + for (auto it = input.begin(); it != input.end(); ++it) + { + rOutputEncoder.Set(*it); + ++rOutputEncoder; + } + return output; +} + +// Utility method to convert a single value to the correct type +template <typename T> +T ConvertToDataType(const float& value, + const armnn::TensorInfo& tensorInfo) +{ + std::vector<T> output(1); + std::unique_ptr<armnn::Encoder<float>> pEncoder = armnn::MakeEncoder<float>(tensorInfo, output.data()); + armnn::Encoder<float>& rEncoder = *pEncoder; + rEncoder.Set(value); + return output[0]; +} + template<typename T, typename B> LayerTestResult<T, 2> SimpleFullyConnectedTestImpl( armnn::IWorkloadFactory& workloadFactory, @@ -2130,35 +2174,75 @@ LayerTestResult<T, 2> SimpleFullyConnectedTestImpl( return result; } -template <armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> -std::vector<T> ConvertToDataType(const std::vector<float>& input, - const armnn::TensorInfo& inputTensorInfo) +template<armnn::DataType ArmnnType, typename T> +LayerTestResult<T, 2> FullyConnectedTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + bool biasEnabled) { - std::vector<T> output(input.size()); - auto outputTensorInfo = inputTensorInfo; - outputTensorInfo.SetDataType(ArmnnType); + constexpr static unsigned int inputWidth = 3u; + constexpr static unsigned int inputHeight = 2u; + constexpr static unsigned int inputChannels = 1u; - std::unique_ptr<armnn::Encoder<float>> pOutputEncoder = armnn::MakeEncoder<float>(outputTensorInfo, output.data()); - armnn::Encoder<float>& rOutputEncoder = *pOutputEncoder; + constexpr static unsigned int inputSize = inputWidth * inputHeight * inputChannels; - for (auto it = input.begin(); it != input.end(); ++it) + constexpr static unsigned int outputChannels = 2u; + + armnn::TensorInfo inputTensorInfo({ 1, inputChannels, inputHeight, inputWidth }, ArmnnType); + inputTensorInfo.SetQuantizationScale(0.1f); + inputTensorInfo.SetQuantizationOffset(63); + + armnn::TensorInfo outputTensorInfo({ 1, outputChannels }, ArmnnType); + outputTensorInfo.SetQuantizationScale(5.f); + outputTensorInfo.SetQuantizationOffset(biasEnabled ? -50 : 10); + + armnn::TensorInfo weightsDesc({ outputChannels, inputSize }, ArmnnType); + weightsDesc.SetQuantizationScale(0.2f); + weightsDesc.SetQuantizationOffset(93); + + armnn::TensorInfo biasesDesc({ outputChannels }, GetBiasTypeFromWeightsType(weightsDesc.GetDataType()).value()); + biasesDesc.SetQuantizationScale(inputTensorInfo.GetQuantizationScale() * weightsDesc.GetQuantizationScale()); + biasesDesc.SetQuantizationOffset(0); + + LayerTestResult<T, 2> result(outputTensorInfo); + + auto input = MakeTensor<T, 4>(inputTensorInfo, ConvertToDataType<ArmnnType>( + { + -1.2f, 6.1f, -3.5f, + 18.8f, -5.5f, 2.9f + }, + inputTensorInfo)); + + auto weights = MakeTensor<T, 2>(weightsDesc, ConvertToDataType<ArmnnType>( + { + -8.4f, 20.0f, -10.4f, -8, 16.4f, -11.8f, + 23.4f, 10.4f, -14.0f, -3.8f, -11.8f, 11.4f + }, + weightsDesc)); + + auto bias = MakeTensor<int32_t, 1>(biasesDesc, std::vector<int32_t>{9250, 67500}); + + result = SimpleFullyConnectedTestImpl<T>( + workloadFactory, + memoryManager, + inputTensorInfo, outputTensorInfo, + weightsDesc, biasesDesc, + weights, bias, input, + biasEnabled, true + ); + + if (biasEnabled) { - rOutputEncoder.Set(*it); - ++rOutputEncoder; + result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, + ConvertToDataType<ArmnnType>({80.f, 1460.f}, outputTensorInfo)); + } + else + { + result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, + ConvertToDataType<ArmnnType>({-107.04f, 110.f}, outputTensorInfo)); } - return output; -} -// Utility method to convert a single value to the correct type -template <typename T> -T ConvertToDataType(const float& value, - const armnn::TensorInfo& tensorInfo) -{ - std::vector<T> output(1); - std::unique_ptr<armnn::Encoder<float>> pEncoder = armnn::MakeEncoder<float>(tensorInfo, output.data()); - armnn::Encoder<float>& rEncoder = *pEncoder; - rEncoder.Set(value); - return output[0]; + return result; } template<armnn::DataType ArmnnType, typename T> @@ -2354,8 +2438,8 @@ LayerTestResult<T, 2> RsqrtNegativeTest( inputValues, expectedOutputValues); } -template<typename T> -LayerTestResult<T, 4> SimpleReshapeTestImpl( +template<typename T, size_t NumDims> +LayerTestResult<T, NumDims> SimpleReshapeTestImpl( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, armnn::TensorInfo inputTensorInfo, @@ -2363,10 +2447,10 @@ LayerTestResult<T, 4> SimpleReshapeTestImpl( const std::vector<T>& inputData, const std::vector<T>& outputExpectedData) { - auto input = MakeTensor<T, 4>(inputTensorInfo, inputData); + auto input = MakeTensor<T, NumDims>(inputTensorInfo, inputData); - LayerTestResult<T, 4> ret(outputTensorInfo); - ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputExpectedData); + LayerTestResult<T, NumDims> ret(outputTensorInfo); + ret.outputExpected = MakeTensor<T, NumDims>(outputTensorInfo, outputExpectedData); std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); @@ -2381,87 +2465,16 @@ LayerTestResult<T, 4> SimpleReshapeTestImpl( inputHandle->Allocate(); outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); + CopyDataToITensorHandle(inputHandle.get(), input.origin()); workload->Execute(); - CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); + CopyDataFromITensorHandle(ret.output.origin(), outputHandle.get()); return ret; } template<armnn::DataType ArmnnType, typename T> -LayerTestResult<T, 2> FullyConnectedTest( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - bool biasEnabled) -{ - constexpr static unsigned int inputWidth = 3u; - constexpr static unsigned int inputHeight = 2u; - constexpr static unsigned int inputChannels = 1u; - - constexpr static unsigned int inputSize = inputWidth * inputHeight * inputChannels; - - constexpr static unsigned int outputChannels = 2u; - - armnn::TensorInfo inputTensorInfo({ 1, inputChannels, inputHeight, inputWidth }, ArmnnType); - inputTensorInfo.SetQuantizationScale(0.1f); - inputTensorInfo.SetQuantizationOffset(63); - - armnn::TensorInfo outputTensorInfo({ 1, outputChannels }, ArmnnType); - outputTensorInfo.SetQuantizationScale(5.f); - outputTensorInfo.SetQuantizationOffset(biasEnabled ? -50 : 10); - - armnn::TensorInfo weightsDesc({ outputChannels, inputSize }, ArmnnType); - weightsDesc.SetQuantizationScale(0.2f); - weightsDesc.SetQuantizationOffset(93); - - armnn::TensorInfo biasesDesc({ outputChannels }, GetBiasTypeFromWeightsType(weightsDesc.GetDataType()).value()); - biasesDesc.SetQuantizationScale(inputTensorInfo.GetQuantizationScale() * weightsDesc.GetQuantizationScale()); - biasesDesc.SetQuantizationOffset(0); - - LayerTestResult<T, 2> result(outputTensorInfo); - - auto input = MakeTensor<T, 4>(inputTensorInfo, ConvertToDataType<ArmnnType>( - { - -1.2f, 6.1f, -3.5f, - 18.8f, -5.5f, 2.9f - }, - inputTensorInfo)); - - auto weights = MakeTensor<T, 2>(weightsDesc, ConvertToDataType<ArmnnType>( - { - -8.4f, 20.0f, -10.4f, -8, 16.4f, -11.8f, - 23.4f, 10.4f, -14.0f, -3.8f, -11.8f, 11.4f - }, - weightsDesc)); - - auto bias = MakeTensor<int32_t, 1>(biasesDesc, std::vector<int32_t>{9250, 67500}); - - result = SimpleFullyConnectedTestImpl<T>( - workloadFactory, - memoryManager, - inputTensorInfo, outputTensorInfo, - weightsDesc, biasesDesc, - weights, bias, input, - biasEnabled, true - ); - - if (biasEnabled) - { - result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, - ConvertToDataType<ArmnnType>({80.f, 1460.f}, outputTensorInfo)); - } - else - { - result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, - ConvertToDataType<ArmnnType>({-107.04f, 110.f}, outputTensorInfo)); - } - - return result; -} - -template<armnn::DataType ArmnnType, typename T> LayerTestResult<T, 4> SimpleReshapeTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) @@ -2509,7 +2522,69 @@ LayerTestResult<T, 4> SimpleReshapeTest( }, outputTensorInfo); - return SimpleReshapeTestImpl<T>( + return SimpleReshapeTestImpl<T, 4>( + workloadFactory, memoryManager, inputTensorInfo, outputTensorInfo, input, outputExpected); +} + +template<armnn::DataType ArmnnType, typename T> +LayerTestResult<T, 5> Reshape5dTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) +{ + armnn::TensorInfo inputTensorInfo; + armnn::TensorInfo outputTensorInfo; + + unsigned int inputShape[] = { 2, 2, 8, 1, 1 }; + unsigned int outputShape[] = { 2, 2, 2, 2, 2 }; + + inputTensorInfo = armnn::TensorInfo(5, inputShape, ArmnnType); + inputTensorInfo.SetQuantizationScale(1.0f); + outputTensorInfo = armnn::TensorInfo(5, outputShape, ArmnnType); + outputTensorInfo.SetQuantizationScale(1.0f); + + auto input = ConvertToDataType<ArmnnType>( + { + 0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, + 8.0f, 9.0f, 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, + + 16.0f, 17.0f, 18.0f, 19.0f, 20.0f, 21.0f, 22.0f, 23.0f, + 24.0f, 25.0f, 26.0f, 27.0f, 28.0f, 29.0f, 30.0f, 31.0f, + }, + inputTensorInfo); + + auto outputExpected = ConvertToDataType<ArmnnType>( + { + 0.0f, 1.0f, + 2.0f, 3.0f, + + 4.0f, 5.0f, + 6.0f, 7.0f, + + + 8.0f, 9.0f, + 10.0f, 11.0f, + + 12.0f, 13.0f, + 14.0f, 15.0f, + + + + 16.0f, 17.0f, + 18.0f, 19.0f, + + 20.0f, 21.0f, + 22.0f, 23.0f, + + + 24.0f, 25.0f, + 26.0f, 27.0f, + + 28.0f, 29.0f, + 30.0f, 31.0f, + }, + outputTensorInfo); + + return SimpleReshapeTestImpl<T, 5>( workloadFactory, memoryManager, inputTensorInfo, outputTensorInfo, input, outputExpected); } @@ -4746,3 +4821,88 @@ LayerTestResult<T, 3> Stack3dOutput1Axis3InputTest( outputExpectedData ); } + +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> +LayerTestResult<T, 5> Stack5dOutputTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) +{ + armnn::TensorInfo inputTensorInfo ({ 2, 2, 2, 3 }, ArmnnType); + armnn::TensorInfo outputTensorInfo({ 2, 2, 2, 2, 3 }, ArmnnType); + + std::vector<std::vector<T>> inputData; + + inputData.push_back( + { + 1, 2, 3, + 4, 5, 6, + + 7, 8, 9, + 10, 11, 12, + + + 13, 14, 15, + 16, 17, 18, + + 19, 20, 21, + 22, 23, 24 + }); + + inputData.push_back( + { + 25, 26, 27, + 28, 29, 30, + + 31, 32, 33, + 34, 35, 36, + + + 37, 38, 39, + 40, 41, 42, + + 43, 44, 45, + 46, 47, 48 + }); + + std::vector<T> outputExpectedData = + { + 1, 2, 3, + 4, 5, 6, + + 7, 8, 9, + 10, 11, 12, + + + 25, 26, 27, + 28, 29, 30, + + 31, 32, 33, + 34, 35, 36, + + + + 13, 14, 15, + 16, 17, 18, + + 19, 20, 21, + 22, 23, 24, + + + 37, 38, 39, + 40, 41, 42, + + 43, 44, 45, + 46, 47, 48 + + }; + + return StackTestHelper<ArmnnType, T, 5>( + workloadFactory, + memoryManager, + inputTensorInfo, + outputTensorInfo, + 1U, + inputData, + outputExpectedData + ); +} |