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diff --git a/delegate/src/test/ConvolutionTestHelper.hpp b/delegate/src/test/ConvolutionTestHelper.hpp
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-//
-// 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
-
-
-
-
generated by cgit v1.2.1 (git 2.23.0) at 2024-07-25 20:14:38 +0000