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author | Teresa Charlin <teresa.charlinreyes@arm.com> | 2023-03-14 12:10:28 +0000 |
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committer | Teresa Charlin <teresa.charlinreyes@arm.com> | 2023-03-28 11:41:55 +0100 |
commit | ad1b3d7518429e2d16a2695d9b0bbf81b6565ac9 (patch) | |
tree | a5b8e1ad68a2437f007338f0b6195ca5ed2bddc3 /delegate/src/test/ConvolutionTestHelper.hpp | |
parent | 9cb3466b677a1048b8abb24661e92c4c83fdda04 (diff) | |
download | armnn-ad1b3d7518429e2d16a2695d9b0bbf81b6565ac9.tar.gz |
IVGCVSW-7555 Restructure Delegate
* New folders created:
* common is for common code where TfLite API is not used
* classic is for existing delegate implementations
* opaque is for new opaque delegate implementation,
* tests is for shared between existing Delegate and Opaque Delegate which have test utils to work which delegate to use.
* Existing delegate is built to libarmnnDelegate.so and opaque delegate is built as libarmnnOpaqueDelegate.so
* Opaque structure is introduced but no API is added yet.
* CmakeList.txt and delegate/CMakeList.txt have been modified and 2 new CmakeList.txt added
* Rename BUILD_ARMNN_TFLITE_DELEGATE as BUILD_CLASSIC_DELEGATE
* Rename BUILD_ARMNN_TFLITE_OPAQUE_DELEGATE as BUILD_OPAQUE_DELEGATE
Signed-off-by: Teresa Charlin <teresa.charlinreyes@arm.com>
Change-Id: Ib682b9ad0ac8d8acdc4ec6d9099bb0008a9fe8ed
Diffstat (limited to 'delegate/src/test/ConvolutionTestHelper.hpp')
-rw-r--r-- | delegate/src/test/ConvolutionTestHelper.hpp | 784 |
1 files changed, 0 insertions, 784 deletions
diff --git a/delegate/src/test/ConvolutionTestHelper.hpp b/delegate/src/test/ConvolutionTestHelper.hpp deleted file mode 100644 index 70c1da6dce..0000000000 --- a/delegate/src/test/ConvolutionTestHelper.hpp +++ /dev/null @@ -1,784 +0,0 @@ -// -// Copyright © 2020, 2023 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "TestUtils.hpp" - -#include <armnn_delegate.hpp> - -#include <flatbuffers/flatbuffers.h> -#include <tensorflow/lite/interpreter.h> -#include <tensorflow/lite/kernels/register.h> -#include <tensorflow/lite/model.h> -#include <tensorflow/lite/schema/schema_generated.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); - - 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<armnn::BackendId>& backends, - 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) - -{ - using namespace tflite; - - 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); - - - const Model* tfLiteModel = GetModel(modelBuffer.data()); - // Create TfLite Interpreters - std::unique_ptr<Interpreter> armnnDelegateInterpreter; - CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) - (&armnnDelegateInterpreter) == kTfLiteOk); - CHECK(armnnDelegateInterpreter != nullptr); - CHECK(armnnDelegateInterpreter->AllocateTensors() == kTfLiteOk); - - std::unique_ptr<Interpreter> tfLiteInterpreter; - CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) - (&tfLiteInterpreter) == kTfLiteOk); - CHECK(tfLiteInterpreter != nullptr); - CHECK(tfLiteInterpreter->AllocateTensors() == kTfLiteOk); - - // Create the ArmNN Delegate - armnnDelegate::DelegateOptions delegateOptions(backends); - std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)> - theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions), - armnnDelegate::TfLiteArmnnDelegateDelete); - CHECK(theArmnnDelegate != nullptr); - // Modify armnnDelegateInterpreter to use armnnDelegate - CHECK(armnnDelegateInterpreter->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk); - - // Set input data - auto tfLiteDelegateInputId = tfLiteInterpreter->inputs()[0]; - auto tfLiteDelageInputData = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateInputId); - for (unsigned int i = 0; i < inputValues.size(); ++i) - { - tfLiteDelageInputData[i] = inputValues[i]; - } - - auto armnnDelegateInputId = armnnDelegateInterpreter->inputs()[0]; - auto armnnDelegateInputData = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateInputId); - for (unsigned int i = 0; i < inputValues.size(); ++i) - { - armnnDelegateInputData[i] = inputValues[i]; - } - // Run EnqueueWorkload - CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); - CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); - - // Compare output data - auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0]; - auto tfLiteDelagateOutputData = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateOutputId); - auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0]; - auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateOutputId); - for (size_t i = 0; i < expectedOutputValues.size(); i++) - { - CHECK(tfLiteDelagateOutputData[i] == armnnDelegateOutputData[i]); - CHECK(doctest::Approx(tfLiteDelagateOutputData[i]).epsilon(0.000001f) == expectedOutputValues[i]); - CHECK(doctest::Approx(armnnDelegateOutputData[i]).epsilon(0.000001f) == expectedOutputValues[i]); - } -} - -// 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); - - 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<armnn::BackendId>& backends, - 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) -{ - using namespace tflite; - - 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); - - const Model* tfLiteModel = GetModel(modelBuffer.data()); - - // Create TfLite Interpreters - std::unique_ptr<Interpreter> armnnDelegateInterpreter; - CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) - (&armnnDelegateInterpreter) == kTfLiteOk); - CHECK(armnnDelegateInterpreter != nullptr); - CHECK(armnnDelegateInterpreter->AllocateTensors() == kTfLiteOk); - - std::unique_ptr<Interpreter> tfLiteInterpreter; - CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) - (&tfLiteInterpreter) == kTfLiteOk); - CHECK(tfLiteInterpreter != nullptr); - CHECK(tfLiteInterpreter->AllocateTensors() == kTfLiteOk); - - // Create the ArmNN Delegate - armnnDelegate::DelegateOptions delegateOptions(backends); - std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)> - theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions), - armnnDelegate::TfLiteArmnnDelegateDelete); - CHECK(theArmnnDelegate != nullptr); - - // Modify armnnDelegateInterpreter to use armnnDelegate - CHECK(armnnDelegateInterpreter->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk); - - // Set input data - armnnDelegate::FillInput<T>(tfLiteInterpreter, 0, inputValues); - armnnDelegate::FillInput<T>(armnnDelegateInterpreter, 0, inputValues); - - // Run EnqueueWorkload - CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); - CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); - - // Compare output data - auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0]; - auto tfLiteDelagateOutputData = tfLiteInterpreter->typed_tensor<float>(tfLiteDelegateOutputId); - auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0]; - auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateOutputId); - - armnnDelegate::CompareData(expectedOutputValues.data(), armnnDelegateOutputData, expectedOutputValues.size(), 1); - armnnDelegate::CompareData(expectedOutputValues.data(), tfLiteDelagateOutputData, expectedOutputValues.size(), 1); - armnnDelegate::CompareData(tfLiteDelagateOutputData, armnnDelegateOutputData, expectedOutputValues.size(), 1); -} -#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); - - return std::vector<char>(flatBufferBuilder.GetBufferPointer(), - flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); -} - -template <typename T> -void TransposeConvTest(std::vector<armnn::BackendId>& backends, - 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) -{ - using namespace tflite; - - std::vector<char> modelBuffer; - modelBuffer = CreateTransposeConvTfLiteModel<T>(tensorType, - strideX, - strideY, - padding, - transposeTensorShape, - filterTensorShape, - inputTensorShape, - outputTensorShape, - transposeData, - filterData, - filterScale, - filterOffset, - outputQuantScale, - outputQuantOffset, - quantScale, - quantOffset); - - - const Model* tfLiteModel = GetModel(modelBuffer.data()); - // Create TfLite Interpreters - std::unique_ptr<Interpreter> armnnDelegateInterpreter; - CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) - (&armnnDelegateInterpreter) == kTfLiteOk); - CHECK(armnnDelegateInterpreter != nullptr); - CHECK(armnnDelegateInterpreter->AllocateTensors() == kTfLiteOk); - - std::unique_ptr<Interpreter> tfLiteInterpreter; - CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) - (&tfLiteInterpreter) == kTfLiteOk); - CHECK(tfLiteInterpreter != nullptr); - CHECK(tfLiteInterpreter->AllocateTensors() == kTfLiteOk); - - // Create the ArmNN Delegate - armnnDelegate::DelegateOptions delegateOptions(backends); - std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)> - theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions), - armnnDelegate::TfLiteArmnnDelegateDelete); - CHECK(theArmnnDelegate != nullptr); - // Modify armnnDelegateInterpreter to use armnnDelegate - CHECK(armnnDelegateInterpreter->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk); - - // Set input data - auto tfLiteDelegateInputId = tfLiteInterpreter->inputs()[2]; - auto tfLiteDelageInputData = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateInputId); - for (unsigned int i = 0; i < inputValues.size(); ++i) - { - tfLiteDelageInputData[i] = inputValues[i]; - } - - auto armnnDelegateInputId = armnnDelegateInterpreter->inputs()[2]; - auto armnnDelegateInputData = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateInputId); - for (unsigned int i = 0; i < inputValues.size(); ++i) - { - armnnDelegateInputData[i] = inputValues[i]; - } - // Run EnqueueWorkload - CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); - CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); - - // Compare output data - auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0]; - auto tfLiteDelagateOutputData = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateOutputId); - auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0]; - auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateOutputId); - for (size_t i = 0; i < expectedOutputValues.size(); i++) - { - CHECK(armnnDelegateOutputData[i] == expectedOutputValues[i]); - CHECK(tfLiteDelagateOutputData[i] == expectedOutputValues[i]); - CHECK(tfLiteDelagateOutputData[i] == armnnDelegateOutputData[i]); - } -} - -} // anonymous namespace - - - - |