| // |
| // Copyright © 2020, 2023 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #pragma once |
| |
| #include "TestUtils.hpp" |
| |
| #include <armnn_delegate.hpp> |
| #include <DelegateTestInterpreter.hpp> |
| |
| #include <flatbuffers/flatbuffers.h> |
| #include <tensorflow/lite/interpreter.h> |
| #include <tensorflow/lite/kernels/register.h> |
| #include <tensorflow/lite/model.h> |
| #include <tensorflow/lite/version.h> |
| |
| #include <doctest/doctest.h> |
| |
| namespace |
| { |
| |
| template <typename T, typename B = float> |
| std::vector<char> CreateConv2dTfLiteModel(tflite::BuiltinOperator convolutionOperatorCode, |
| tflite::TensorType tensorType, |
| uint32_t strideX, |
| uint32_t strideY, |
| uint32_t dilationX, |
| uint32_t dilationY, |
| tflite::Padding padding, |
| tflite::ActivationFunctionType fused_activation_function, |
| const std::vector <int32_t>& inputTensorShape, |
| const std::vector <int32_t>& filterTensorShape, |
| const std::vector <int32_t>& biasTensorShape, |
| const std::vector <int32_t>& outputTensorShape, |
| const std::vector <T>& filterData, |
| const std::vector <B>& biasData, |
| const std::vector<float> biasScales = {1.0f}, |
| const std::vector<int64_t> biasOffsets = {0}, |
| const std::vector<float> filterScales = {1.0f}, |
| const std::vector<int64_t> filterOffsets = {0}, |
| float outputQuantScale = 2.0f, |
| int outputQuantOffset = 0, |
| float quantScale = 1.0f, |
| int quantOffset = 0, |
| int32_t depth_multiplier = 1, |
| int32_t filterQuantizationDim = 0) |
| { |
| using namespace tflite; |
| flatbuffers::FlatBufferBuilder flatBufferBuilder; |
| |
| std::array<flatbuffers::Offset<tflite::Buffer>, 5> buffers; |
| buffers[0] = CreateBuffer(flatBufferBuilder); |
| buffers[1] = CreateBuffer(flatBufferBuilder); |
| buffers[2] = CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(filterData.data()), |
| sizeof(T) * filterData.size())); |
| |
| buffers[3] = CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(biasData.data()), |
| sizeof(B) * biasData.size())); |
| buffers[4] = CreateBuffer(flatBufferBuilder); |
| |
| auto quantizationParameters = |
| CreateQuantizationParameters(flatBufferBuilder, |
| 0, |
| 0, |
| flatBufferBuilder.CreateVector<float>({ quantScale }), |
| flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); |
| auto outputQuantizationParameters = |
| CreateQuantizationParameters(flatBufferBuilder, |
| 0, |
| 0, |
| flatBufferBuilder.CreateVector<float>({ outputQuantScale }), |
| flatBufferBuilder.CreateVector<int64_t>({ outputQuantOffset })); |
| |
| auto filterQuantizationParameters = |
| CreateQuantizationParameters(flatBufferBuilder, |
| 0, |
| 0, |
| flatBufferBuilder.CreateVector<float>(filterScales), |
| flatBufferBuilder.CreateVector<int64_t>(filterOffsets), |
| tflite::QuantizationDetails_NONE, |
| 0, |
| filterQuantizationDim); |
| |
| auto biasQuantizationParameters = |
| CreateQuantizationParameters(flatBufferBuilder, |
| 0, |
| 0, |
| flatBufferBuilder.CreateVector<float>(biasScales), |
| flatBufferBuilder.CreateVector<int64_t>(biasOffsets)); |
| |
| std::array<flatbuffers::Offset<Tensor>, 4> tensors; |
| tensors[0] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), |
| inputTensorShape.size()), |
| tensorType, |
| 1, |
| flatBufferBuilder.CreateString("input"), |
| quantizationParameters); |
| tensors[1] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(filterTensorShape.data(), |
| filterTensorShape.size()), |
| tensorType, |
| 2, |
| flatBufferBuilder.CreateString("filter"), |
| filterQuantizationParameters); |
| |
| auto biasTensorType = ::tflite::TensorType_FLOAT32; |
| if (tensorType == ::tflite::TensorType_INT8 || tensorType == ::tflite::TensorType_UINT8) |
| { |
| biasTensorType = ::tflite::TensorType_INT32; |
| } |
| tensors[2] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(biasTensorShape.data(), biasTensorShape.size()), |
| biasTensorType, |
| 3, |
| flatBufferBuilder.CreateString("bias"), |
| biasQuantizationParameters); |
| tensors[3] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), |
| outputTensorShape.size()), |
| tensorType, |
| 4, |
| flatBufferBuilder.CreateString("output"), |
| outputQuantizationParameters); |
| |
| flatbuffers::Offset<void> operatorBuiltinOptions; |
| tflite::BuiltinOptions operatorBuiltinOptionsType; |
| |
| if(convolutionOperatorCode == tflite::BuiltinOperator_DEPTHWISE_CONV_2D) |
| { |
| operatorBuiltinOptionsType = tflite::BuiltinOptions_DepthwiseConv2DOptions; |
| operatorBuiltinOptions = CreateDepthwiseConv2DOptions(flatBufferBuilder, |
| padding, |
| strideX, |
| strideY, |
| depth_multiplier, |
| fused_activation_function, |
| dilationX, |
| dilationY).Union(); |
| } |
| if(convolutionOperatorCode == tflite::BuiltinOperator_CONV_2D) |
| { |
| operatorBuiltinOptionsType = tflite::BuiltinOptions_Conv2DOptions; |
| operatorBuiltinOptions = CreateConv2DOptions(flatBufferBuilder, |
| padding, |
| strideX, |
| strideY, |
| fused_activation_function, |
| dilationX, |
| dilationY).Union(); |
| } |
| |
| // create operator |
| const std::vector<int> operatorInputs{0, 1, 2}; |
| const std::vector<int> operatorOutputs{3}; |
| flatbuffers::Offset <Operator> convolutionOperator = |
| CreateOperator(flatBufferBuilder, |
| 0, |
| flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), |
| flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), |
| operatorBuiltinOptionsType, |
| operatorBuiltinOptions); |
| |
| const std::vector<int> subgraphInputs{0, 1, 2}; |
| const std::vector<int> subgraphOutputs{3}; |
| flatbuffers::Offset <SubGraph> subgraph = |
| CreateSubGraph(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(tensors.data(), tensors.size()), |
| flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()), |
| flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()), |
| flatBufferBuilder.CreateVector(&convolutionOperator, 1)); |
| |
| flatbuffers::Offset <flatbuffers::String> modelDescription = |
| flatBufferBuilder.CreateString("ArmnnDelegate: Convolution2d Operator Model"); |
| flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, convolutionOperatorCode); |
| |
| flatbuffers::Offset <Model> flatbufferModel = |
| CreateModel(flatBufferBuilder, |
| TFLITE_SCHEMA_VERSION, |
| flatBufferBuilder.CreateVector(&operatorCode, 1), |
| flatBufferBuilder.CreateVector(&subgraph, 1), |
| modelDescription, |
| flatBufferBuilder.CreateVector(buffers.data(), buffers.size())); |
| |
| flatBufferBuilder.Finish(flatbufferModel, armnnDelegate::FILE_IDENTIFIER); |
| |
| return std::vector<char>(flatBufferBuilder.GetBufferPointer(), |
| flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); |
| } |
| |
| template <typename T, typename B = float> |
| void ConvolutionTest(tflite::BuiltinOperator convolutionOperatorCode, |
| tflite::TensorType tensorType, |
| uint32_t strideX, |
| uint32_t strideY, |
| uint32_t dilationX, |
| uint32_t dilationY, |
| tflite::Padding padding, |
| tflite::ActivationFunctionType fused_activation_function, |
| std::vector<int32_t>& inputShape, |
| std::vector<int32_t>& filterShape, |
| std::vector<int32_t>& outputShape, |
| std::vector<T>& inputValues, |
| std::vector<T>& filterValues, |
| std::vector<T>& expectedOutputValues, |
| const std::vector<int32_t>& biasShape = {}, |
| const std::vector<B>& biasValues = {}, |
| const std::vector<float> biasScales = {1.0f}, |
| const std::vector<int64_t> biasOffsets = {0}, |
| const std::vector<float> filterScales = {1.0f}, |
| const std::vector<int64_t> filterOffsets = {0}, |
| float outputQuantScale = 2.0f, |
| int outputQuantOffset = 0, |
| float quantScale = 1.0f, |
| int quantOffset = 0, |
| int32_t depth_multiplier = 1, |
| int32_t filterQuantizationDim = 3, |
| const std::vector<armnn::BackendId>& backends = {}) |
| { |
| using namespace delegateTestInterpreter; |
| |
| std::vector<char> modelBuffer; |
| modelBuffer = CreateConv2dTfLiteModel(convolutionOperatorCode, |
| tensorType, |
| strideX, |
| strideY, |
| dilationX, |
| dilationY, |
| padding, |
| fused_activation_function, |
| inputShape, |
| filterShape, |
| biasShape, |
| outputShape, |
| filterValues, |
| biasValues, |
| biasScales, |
| biasOffsets, |
| filterScales, |
| filterOffsets, |
| outputQuantScale, |
| outputQuantOffset, |
| quantScale, |
| quantOffset, |
| depth_multiplier, |
| filterQuantizationDim); |
| |
| // Setup interpreter with just TFLite Runtime. |
| auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer); |
| CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk); |
| CHECK(tfLiteInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk); |
| CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk); |
| std::vector<T> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<T>(0); |
| std::vector<int32_t> tfLiteOutputShape = tfLiteInterpreter.GetOutputShape(0); |
| |
| // Setup interpreter with Arm NN Delegate applied. |
| auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, CaptureAvailableBackends(backends)); |
| CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk); |
| CHECK(armnnInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk); |
| CHECK(armnnInterpreter.Invoke() == kTfLiteOk); |
| std::vector<T> armnnOutputValues = armnnInterpreter.GetOutputResult<T>(0); |
| std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(0); |
| |
| armnnDelegate::CompareOutputData<T>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues); |
| armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, outputShape); |
| |
| tfLiteInterpreter.Cleanup(); |
| armnnInterpreter.Cleanup(); |
| } |
| |
| // Conv3d is only correctly supported for external delegates from TF Lite v2.6, as there was a breaking bug in v2.5. |
| #if defined(ARMNN_POST_TFLITE_2_5) |
| template <typename T, typename B = float> |
| std::vector<char> CreateConv3dTfLiteModel(tflite::BuiltinOperator convolutionOperatorCode, |
| tflite::TensorType tensorType, |
| std::vector<uint32_t> strides, |
| std::vector<uint32_t> dilation, |
| tflite::Padding padding, |
| tflite::ActivationFunctionType fused_activation_function, |
| const std::vector<int32_t>& inputTensorShape, |
| const std::vector<int32_t>& filterTensorShape, |
| const std::vector<int32_t>& biasTensorShape, |
| const std::vector<int32_t>& outputTensorShape, |
| const std::vector<T>& filterData, |
| const std::vector<B>& biasData, |
| const std::vector<float> biasScales = {1.0f}, |
| const std::vector<int64_t> biasOffsets = {0}, |
| const std::vector<float> filterScales = {1.0f}, |
| const std::vector<int64_t> filterOffsets = {0}, |
| float outputQuantScale = 2.0f, |
| int outputQuantOffset = 0, |
| float quantScale = 1.0f, |
| int quantOffset = 0, |
| int32_t depth_multiplier = 1, |
| int32_t filterQuantizationDim = 0) |
| { |
| using namespace tflite; |
| flatbuffers::FlatBufferBuilder flatBufferBuilder; |
| |
| std::array<flatbuffers::Offset<tflite::Buffer>, 3> buffers; |
| buffers[0] = CreateBuffer(flatBufferBuilder); |
| buffers[1] = CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(filterData.data()), |
| sizeof(T) * filterData.size())); |
| |
| buffers[2] = CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(biasData.data()), |
| sizeof(B) * biasData.size())); |
| |
| auto quantizationParameters = |
| CreateQuantizationParameters(flatBufferBuilder, |
| 0, |
| 0, |
| flatBufferBuilder.CreateVector<float>({ quantScale }), |
| flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); |
| auto outputQuantizationParameters = |
| CreateQuantizationParameters(flatBufferBuilder, |
| 0, |
| 0, |
| flatBufferBuilder.CreateVector<float>({ outputQuantScale }), |
| flatBufferBuilder.CreateVector<int64_t>({ outputQuantOffset })); |
| |
| auto filterQuantizationParameters = |
| CreateQuantizationParameters(flatBufferBuilder, |
| 0, |
| 0, |
| flatBufferBuilder.CreateVector<float>(filterScales), |
| flatBufferBuilder.CreateVector<int64_t>(filterOffsets), |
| tflite::QuantizationDetails_NONE, |
| 0, |
| filterQuantizationDim); |
| |
| auto biasQuantizationParameters = |
| CreateQuantizationParameters(flatBufferBuilder, |
| 0, |
| 0, |
| flatBufferBuilder.CreateVector<float>(biasScales), |
| flatBufferBuilder.CreateVector<int64_t>(biasOffsets)); |
| |
| std::array<flatbuffers::Offset<Tensor>, 4> tensors; |
| tensors[0] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), |
| inputTensorShape.size()), |
| tensorType, |
| 0, |
| flatBufferBuilder.CreateString("input"), |
| quantizationParameters); |
| tensors[1] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(filterTensorShape.data(), |
| filterTensorShape.size()), |
| tensorType, |
| 1, |
| flatBufferBuilder.CreateString("filter"), |
| filterQuantizationParameters); |
| |
| auto biasTensorType = ::tflite::TensorType_FLOAT32; |
| if (tensorType == ::tflite::TensorType_INT8 || tensorType == ::tflite::TensorType_UINT8) |
| { |
| biasTensorType = ::tflite::TensorType_INT32; |
| } |
| tensors[2] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(biasTensorShape.data(), biasTensorShape.size()), |
| biasTensorType, |
| 2, |
| flatBufferBuilder.CreateString("bias"), |
| biasQuantizationParameters); |
| tensors[3] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), |
| outputTensorShape.size()), |
| tensorType, |
| 0, |
| flatBufferBuilder.CreateString("output"), |
| outputQuantizationParameters); |
| |
| tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_Conv3DOptions; |
| flatbuffers::Offset<void> operatorBuiltinOptions = CreateConv3DOptions(flatBufferBuilder, |
| padding, |
| strides[2], // Depth |
| strides[0], // Width |
| strides[1], // Height |
| fused_activation_function, |
| dilation[2], |
| dilation[0], |
| dilation[1]).Union(); |
| |
| // Create operator |
| const std::vector<int> operatorInputs{0, 1, 2}; |
| const std::vector<int> operatorOutputs{3}; |
| flatbuffers::Offset <Operator> convolutionOperator = |
| CreateOperator(flatBufferBuilder, |
| 0, |
| flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), |
| flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), |
| operatorBuiltinOptionsType, |
| operatorBuiltinOptions); |
| |
| const std::vector<int> subgraphInputs{0, 1, 2}; |
| const std::vector<int> subgraphOutputs{3}; |
| flatbuffers::Offset <SubGraph> subgraph = |
| CreateSubGraph(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(tensors.data(), tensors.size()), |
| flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()), |
| flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()), |
| flatBufferBuilder.CreateVector(&convolutionOperator, 1)); |
| |
| flatbuffers::Offset <flatbuffers::String> modelDescription = |
| flatBufferBuilder.CreateString("ArmnnDelegate: Convolution 3d Operator Model"); |
| |
| // If using an operator with a code greater than 127 then the enum value should be passed as the fifth |
| // parameter rather than the second like in other tests. |
| flatbuffers::Offset <OperatorCode> operatorCode = |
| CreateOperatorCode(flatBufferBuilder, 0, 0, 1, tflite::BuiltinOperator_CONV_3D); |
| |
| flatbuffers::Offset <Model> flatbufferModel = |
| CreateModel(flatBufferBuilder, |
| TFLITE_SCHEMA_VERSION, |
| flatBufferBuilder.CreateVector(&operatorCode, 1), |
| flatBufferBuilder.CreateVector(&subgraph, 1), |
| modelDescription, |
| flatBufferBuilder.CreateVector(buffers.data(), buffers.size())); |
| |
| flatBufferBuilder.Finish(flatbufferModel, armnnDelegate::FILE_IDENTIFIER); |
| |
| return std::vector<char>(flatBufferBuilder.GetBufferPointer(), |
| flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); |
| } |
| |
| template <typename T, typename B = float> |
| void Convolution3dTest(tflite::BuiltinOperator convolutionOperatorCode, |
| tflite::TensorType tensorType, |
| std::vector<uint32_t> strides, |
| std::vector<uint32_t> dilation, |
| tflite::Padding padding, |
| tflite::ActivationFunctionType fused_activation_function, |
| std::vector<int32_t>& inputShape, |
| std::vector<int32_t>& filterShape, |
| std::vector<int32_t>& outputShape, |
| std::vector<T>& inputValues, |
| std::vector<T>& filterValues, |
| std::vector<T>& expectedOutputValues, |
| const std::vector<int32_t>& biasShape = {}, |
| const std::vector<B>& biasValues = {}, |
| const std::vector<float> biasScales = {1.0f}, |
| const std::vector<int64_t> biasOffsets = {0}, |
| const std::vector<float> filterScales = {1.0f}, |
| const std::vector<int64_t> filterOffsets = {0}, |
| float outputQuantScale = 2.0f, |
| int outputQuantOffset = 0, |
| float quantScale = 1.0f, |
| int quantOffset = 0, |
| int32_t depth_multiplier = 1, |
| int32_t filterQuantizationDim = 3, |
| const std::vector<armnn::BackendId>& backends = {}) |
| { |
| using namespace delegateTestInterpreter; |
| |
| std::vector<char> modelBuffer; |
| modelBuffer = CreateConv3dTfLiteModel(convolutionOperatorCode, |
| tensorType, |
| strides, |
| dilation, |
| padding, |
| fused_activation_function, |
| inputShape, |
| filterShape, |
| biasShape, |
| outputShape, |
| filterValues, |
| biasValues, |
| biasScales, |
| biasOffsets, |
| filterScales, |
| filterOffsets, |
| outputQuantScale, |
| outputQuantOffset, |
| quantScale, |
| quantOffset, |
| depth_multiplier, |
| filterQuantizationDim); |
| |
| // Setup interpreter with just TFLite Runtime. |
| auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer); |
| CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk); |
| CHECK(tfLiteInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk); |
| CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk); |
| std::vector<T> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<T>(0); |
| std::vector<int32_t> tfLiteOutputShape = tfLiteInterpreter.GetOutputShape(0); |
| |
| // Setup interpreter with Arm NN Delegate applied. |
| auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, CaptureAvailableBackends(backends)); |
| CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk); |
| CHECK(armnnInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk); |
| CHECK(armnnInterpreter.Invoke() == kTfLiteOk); |
| std::vector<T> armnnOutputValues = armnnInterpreter.GetOutputResult<T>(0); |
| std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(0); |
| |
| armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, outputShape); |
| |
| armnnDelegate::CompareData(expectedOutputValues.data(), armnnOutputValues.data(), expectedOutputValues.size(), 1); |
| armnnDelegate::CompareData(expectedOutputValues.data(), tfLiteOutputValues.data(), expectedOutputValues.size(), 1); |
| armnnDelegate::CompareData(tfLiteOutputValues.data(), armnnOutputValues.data(), expectedOutputValues.size(), 1); |
| |
| tfLiteInterpreter.Cleanup(); |
| armnnInterpreter.Cleanup(); |
| } |
| #endif |
| |
| template <typename T> |
| std::vector<char> CreateTransposeConvTfLiteModel(tflite::TensorType tensorType, |
| uint32_t strideX, |
| uint32_t strideY, |
| tflite::Padding padding, |
| const std::vector <int32_t>& transposeTensorShape, |
| const std::vector <int32_t>& filterTensorShape, |
| const std::vector <int32_t>& inputTensorShape, |
| const std::vector <int32_t>& outputTensorShape, |
| const std::vector <int32_t>& transposeData, |
| const std::vector <T>& filterData, |
| float filterScale = 1.0f, |
| int filterOffset = 0, |
| float outputQuantScale = 2.0f, |
| int outputQuantOffset = 0, |
| float quantScale = 1.0f, |
| int quantOffset = 0) |
| { |
| using namespace tflite; |
| flatbuffers::FlatBufferBuilder flatBufferBuilder; |
| |
| std::array<flatbuffers::Offset<tflite::Buffer>, 3> buffers; |
| buffers[0] = CreateBuffer(flatBufferBuilder); |
| buffers[1] = CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(transposeData.data()), |
| sizeof(int32_t) * transposeData.size())); |
| buffers[2] = CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(filterData.data()), |
| sizeof(T) * filterData.size())); |
| |
| auto quantizationParameters = |
| CreateQuantizationParameters(flatBufferBuilder, |
| 0, |
| 0, |
| flatBufferBuilder.CreateVector<float>({ quantScale }), |
| flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); |
| auto outputQuantizationParameters = |
| CreateQuantizationParameters(flatBufferBuilder, |
| 0, |
| 0, |
| flatBufferBuilder.CreateVector<float>({ outputQuantScale }), |
| flatBufferBuilder.CreateVector<int64_t>({ outputQuantOffset })); |
| auto filterQuantizationParameters = |
| CreateQuantizationParameters(flatBufferBuilder, |
| 0, |
| 0, |
| flatBufferBuilder.CreateVector<float>({ filterScale }), |
| flatBufferBuilder.CreateVector<int64_t>({ filterOffset })); |
| |
| std::array<flatbuffers::Offset<Tensor>, 4> tensors; |
| tensors[0] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(transposeTensorShape.data(), |
| transposeTensorShape.size()), |
| tflite::TensorType_INT32, |
| 1); |
| tensors[1] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(filterTensorShape.data(), |
| filterTensorShape.size()), |
| tensorType, |
| 2, |
| flatBufferBuilder.CreateString("filter"), |
| filterQuantizationParameters); |
| tensors[2] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), |
| inputTensorShape.size()), |
| tensorType, |
| 0, |
| flatBufferBuilder.CreateString("input"), |
| quantizationParameters); |
| tensors[3] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), |
| outputTensorShape.size()), |
| tensorType, |
| 0, |
| flatBufferBuilder.CreateString("output"), |
| outputQuantizationParameters); |
| |
| tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_TransposeConvOptions; |
| flatbuffers::Offset<void> operatorBuiltinOptions = |
| CreateTransposeConvOptions(flatBufferBuilder, padding, strideX, strideY).Union(); |
| |
| // create operator |
| const std::vector<int> operatorInputs{0, 1, 2}; |
| const std::vector<int> operatorOutputs{3}; |
| flatbuffers::Offset <Operator> convolutionOperator = |
| CreateOperator(flatBufferBuilder, |
| 0, |
| flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), |
| flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), |
| operatorBuiltinOptionsType, |
| operatorBuiltinOptions); |
| |
| const std::vector<int> subgraphInputs{0, 1, 2}; |
| const std::vector<int> subgraphOutputs{3}; |
| flatbuffers::Offset <SubGraph> subgraph = |
| CreateSubGraph(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(tensors.data(), tensors.size()), |
| flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()), |
| flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()), |
| flatBufferBuilder.CreateVector(&convolutionOperator, 1)); |
| |
| flatbuffers::Offset <flatbuffers::String> modelDescription = |
| flatBufferBuilder.CreateString("ArmnnDelegate: TransposeConv Operator Model"); |
| flatbuffers::Offset <OperatorCode> operatorCode = |
| CreateOperatorCode(flatBufferBuilder, tflite::BuiltinOperator_TRANSPOSE_CONV); |
| |
| flatbuffers::Offset <Model> flatbufferModel = |
| CreateModel(flatBufferBuilder, |
| TFLITE_SCHEMA_VERSION, |
| flatBufferBuilder.CreateVector(&operatorCode, 1), |
| flatBufferBuilder.CreateVector(&subgraph, 1), |
| modelDescription, |
| flatBufferBuilder.CreateVector(buffers.data(), buffers.size())); |
| |
| flatBufferBuilder.Finish(flatbufferModel, armnnDelegate::FILE_IDENTIFIER); |
| |
| return std::vector<char>(flatBufferBuilder.GetBufferPointer(), |
| flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); |
| } |
| |
| template <typename T> |
| void TransposeConvTest(tflite::TensorType tensorType, |
| uint32_t strideX, |
| uint32_t strideY, |
| tflite::Padding padding, |
| const std::vector <int32_t>& transposeTensorShape, |
| const std::vector <int32_t>& filterTensorShape, |
| const std::vector <int32_t>& inputTensorShape, |
| const std::vector <int32_t>& outputTensorShape, |
| const std::vector <int32_t>& transposeData, |
| const std::vector <T>& filterData, |
| std::vector<T>& inputValues, |
| std::vector<T>& expectedOutputValues, |
| float filterScale = 1.0f, |
| int filterOffset = 0, |
| float outputQuantScale = 1.0f, |
| int outputQuantOffset = 0, |
| float quantScale = 1.0f, |
| int quantOffset = 0, |
| const std::vector<armnn::BackendId>& backends = {}) |
| { |
| using namespace delegateTestInterpreter; |
| |
| std::vector<char> modelBuffer; |
| modelBuffer = CreateTransposeConvTfLiteModel<T>(tensorType, |
| strideX, |
| strideY, |
| padding, |
| transposeTensorShape, |
| filterTensorShape, |
| inputTensorShape, |
| outputTensorShape, |
| transposeData, |
| filterData, |
| filterScale, |
| filterOffset, |
| outputQuantScale, |
| outputQuantOffset, |
| quantScale, |
| quantOffset); |
| |
| |
| // Setup interpreter with just TFLite Runtime. |
| auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer); |
| CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk); |
| CHECK(tfLiteInterpreter.FillInputTensor<T>(inputValues, 2) == kTfLiteOk); |
| CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk); |
| std::vector<T> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<T>(0); |
| std::vector<int32_t> tfLiteOutputShape = tfLiteInterpreter.GetOutputShape(0); |
| |
| // Setup interpreter with Arm NN Delegate applied. |
| auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, CaptureAvailableBackends(backends)); |
| CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk); |
| CHECK(armnnInterpreter.FillInputTensor<T>(inputValues, 2) == kTfLiteOk); |
| CHECK(armnnInterpreter.Invoke() == kTfLiteOk); |
| std::vector<T> armnnOutputValues = armnnInterpreter.GetOutputResult<T>(0); |
| std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(0); |
| |
| armnnDelegate::CompareOutputData<T>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues); |
| armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, outputTensorShape); |
| |
| tfLiteInterpreter.Cleanup(); |
| armnnInterpreter.Cleanup(); |
| } |
| |
| } // anonymous namespace |
| |
| |
| |
| |