From 91c4171421633b3ff9764bd586f43137aef0ff1a Mon Sep 17 00:00:00 2001 From: Matthew Sloyan Date: Fri, 13 Nov 2020 09:47:35 +0000 Subject: IVGCVSW-5486 TfLiteDelegate: Implement Concat and Mean operators * Implemented Concatenation & Mean operator. * Added unit tests for Concatenation & Mean operator. * Added CompareOutputData function to TestUtils.hpp. Signed-off-by: Matthew Sloyan Change-Id: I31b7b1517a9ce041c3269f69f16a419f967d0fb0 --- delegate/src/test/ControlTest.cpp | 420 ++++++++++++++++++++++++++++++++ delegate/src/test/ControlTestHelper.hpp | 344 ++++++++++++++++++++++++++ delegate/src/test/TestUtils.hpp | 33 +++ 3 files changed, 797 insertions(+) create mode 100644 delegate/src/test/ControlTest.cpp create mode 100644 delegate/src/test/ControlTestHelper.hpp (limited to 'delegate/src/test') diff --git a/delegate/src/test/ControlTest.cpp b/delegate/src/test/ControlTest.cpp new file mode 100644 index 0000000000..43491be982 --- /dev/null +++ b/delegate/src/test/ControlTest.cpp @@ -0,0 +1,420 @@ +// +// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include "ControlTestHelper.hpp" + +#include + +#include +#include + +#include + +namespace armnnDelegate +{ + +// CONCATENATION Operator +void ConcatUint8TwoInputsTest(std::vector& backends) +{ + std::vector inputShape { 2, 2 }; + std::vector expectedOutputShape { 4, 2 }; + + // Set input and output data + std::vector> inputValues; + std::vector inputValue1 { 0, 1, 2, 3 }; // Lower bounds + std::vector inputValue2 { 252, 253, 254, 255 }; // Upper bounds + inputValues.push_back(inputValue1); + inputValues.push_back(inputValue2); + + std::vector expectedOutputValues { 0, 1, 2, 3, 252, 253, 254, 255 }; + + ConcatenationTest(tflite::BuiltinOperator_CONCATENATION, + ::tflite::TensorType_UINT8, + backends, + inputShape, + expectedOutputShape, + inputValues, + expectedOutputValues); +} + +void ConcatInt16TwoInputsTest(std::vector& backends) +{ + std::vector inputShape { 2, 2 }; + std::vector expectedOutputShape { 4, 2 }; + + std::vector> inputValues; + std::vector inputValue1 { -32768, -16384, -1, 0 }; + std::vector inputValue2 { 1, 2, 16384, 32767 }; + inputValues.push_back(inputValue1); + inputValues.push_back(inputValue2); + + std::vector expectedOutputValues { -32768, -16384, -1, 0, 1, 2, 16384, 32767}; + + ConcatenationTest(tflite::BuiltinOperator_CONCATENATION, + ::tflite::TensorType_INT16, + backends, + inputShape, + expectedOutputShape, + inputValues, + expectedOutputValues); +} + +void ConcatFloat32TwoInputsTest(std::vector& backends) +{ + std::vector inputShape { 2, 2 }; + std::vector expectedOutputShape { 4, 2 }; + + std::vector> inputValues; + std::vector inputValue1 { -127.f, -126.f, -1.f, 0.f }; + std::vector inputValue2 { 1.f, 2.f, 126.f, 127.f }; + inputValues.push_back(inputValue1); + inputValues.push_back(inputValue2); + + std::vector expectedOutputValues { -127.f, -126.f, -1.f, 0.f, 1.f, 2.f, 126.f, 127.f }; + + ConcatenationTest(tflite::BuiltinOperator_CONCATENATION, + ::tflite::TensorType_FLOAT32, + backends, + inputShape, + expectedOutputShape, + inputValues, + expectedOutputValues); +} + +void ConcatThreeInputsTest(std::vector& backends) +{ + std::vector inputShape { 2, 2 }; + std::vector expectedOutputShape { 6, 2 }; + + std::vector> inputValues; + std::vector inputValue1 { 0, 1, 2, 3 }; + std::vector inputValue2 { 125, 126, 127, 128 }; + std::vector inputValue3 { 252, 253, 254, 255 }; + inputValues.push_back(inputValue1); + inputValues.push_back(inputValue2); + inputValues.push_back(inputValue3); + + std::vector expectedOutputValues { 0, 1, 2, 3, 125, 126, 127, 128, 252, 253, 254, 255 }; + + ConcatenationTest(tflite::BuiltinOperator_CONCATENATION, + ::tflite::TensorType_UINT8, + backends, + inputShape, + expectedOutputShape, + inputValues, + expectedOutputValues); +} + +void ConcatAxisTest(std::vector& backends) +{ + std::vector inputShape { 1, 2, 2 }; + std::vector expectedOutputShape { 1, 2, 4 }; + + std::vector> inputValues; + std::vector inputValue1 { 0, 1, 2, 3 }; + std::vector inputValue3 { 252, 253, 254, 255 }; + inputValues.push_back(inputValue1); + inputValues.push_back(inputValue3); + + std::vector expectedOutputValues { 0, 1, 252, 253, 2, 3, 254, 255 }; + + ConcatenationTest(tflite::BuiltinOperator_CONCATENATION, + ::tflite::TensorType_UINT8, + backends, + inputShape, + expectedOutputShape, + inputValues, + expectedOutputValues, + 2); +} + +// MEAN Operator +void MeanUint8KeepDimsTest(std::vector& backends) +{ + std::vector input0Shape { 1, 3 }; + std::vector input1Shape { 1 }; + std::vector expectedOutputShape { 1, 1 }; + + std::vector input0Values { 5, 10, 15 }; // Inputs + std::vector input1Values { 1 }; // Axis + + std::vector expectedOutputValues { 10 }; + + MeanTest(tflite::BuiltinOperator_MEAN, + ::tflite::TensorType_UINT8, + backends, + input0Shape, + input1Shape, + expectedOutputShape, + input0Values, + input1Values, + expectedOutputValues, + true); +} + +void MeanUint8Test(std::vector& backends) +{ + std::vector input0Shape { 1, 2, 2 }; + std::vector input1Shape { 1 }; + std::vector expectedOutputShape { 2, 2 }; + + std::vector input0Values { 5, 10, 15, 20 }; // Inputs + std::vector input1Values { 0 }; // Axis + + std::vector expectedOutputValues { 5, 10, 15, 20 }; + + MeanTest(tflite::BuiltinOperator_MEAN, + ::tflite::TensorType_UINT8, + backends, + input0Shape, + input1Shape, + expectedOutputShape, + input0Values, + input1Values, + expectedOutputValues, + false); +} + +void MeanFp32KeepDimsTest(std::vector& backends) +{ + std::vector input0Shape { 1, 2, 2 }; + std::vector input1Shape { 1 }; + std::vector expectedOutputShape { 1, 1, 2 }; + + std::vector input0Values { 1.0f, 1.5f, 2.0f, 2.5f }; // Inputs + std::vector input1Values { 1 }; // Axis + + std::vector expectedOutputValues { 1.5f, 2.0f }; + + MeanTest(tflite::BuiltinOperator_MEAN, + ::tflite::TensorType_FLOAT32, + backends, + input0Shape, + input1Shape, + expectedOutputShape, + input0Values, + input1Values, + expectedOutputValues, + true); +} + +void MeanFp32Test(std::vector& backends) +{ + std::vector input0Shape { 1, 2, 2, 1 }; + std::vector input1Shape { 1 }; + std::vector expectedOutputShape { 1, 2, 1 }; + + std::vector input0Values { 1.0f, 1.5f, 2.0f, 2.5f }; // Inputs + std::vector input1Values { 2 }; // Axis + + std::vector expectedOutputValues { 1.25f, 2.25f }; + + MeanTest(tflite::BuiltinOperator_MEAN, + ::tflite::TensorType_FLOAT32, + backends, + input0Shape, + input1Shape, + expectedOutputShape, + input0Values, + input1Values, + expectedOutputValues, + false); +} + +// CONCATENATION Tests. +TEST_SUITE("Concatenation_CpuAccTests") +{ + +TEST_CASE ("Concatenation_Uint8_CpuAcc_Test") +{ + std::vector backends = {armnn::Compute::CpuAcc}; + ConcatUint8TwoInputsTest(backends); +} + +TEST_CASE ("Concatenation_Int16_CpuAcc_Test") +{ + std::vector backends = {armnn::Compute::CpuAcc}; + ConcatInt16TwoInputsTest(backends); +} + +TEST_CASE ("Concatenation_Float32_CpuAcc_Test") +{ + std::vector backends = {armnn::Compute::CpuAcc}; + ConcatFloat32TwoInputsTest(backends); +} + +TEST_CASE ("Concatenation_Three_Inputs_CpuAcc_Test") +{ + std::vector backends = {armnn::Compute::CpuAcc}; + ConcatThreeInputsTest(backends); +} + +TEST_CASE ("Concatenation_Axis_CpuAcc_Test") +{ + std::vector backends = {armnn::Compute::CpuAcc}; + ConcatAxisTest(backends); +} + +} + +TEST_SUITE("Concatenation_GpuAccTests") +{ + +TEST_CASE ("Concatenation_Uint8_GpuAcc_Test") +{ + std::vector backends = {armnn::Compute::GpuAcc}; + ConcatUint8TwoInputsTest(backends); +} + +TEST_CASE ("Concatenation_Int16_GpuAcc_Test") +{ + std::vector backends = {armnn::Compute::GpuAcc}; + ConcatInt16TwoInputsTest(backends); +} + +TEST_CASE ("Concatenation_Float32_GpuAcc_Test") +{ + std::vector backends = {armnn::Compute::GpuAcc}; + ConcatFloat32TwoInputsTest(backends); +} + +TEST_CASE ("Concatenation_Three_Inputs_GpuAcc_Test") +{ + std::vector backends = {armnn::Compute::GpuAcc}; + ConcatThreeInputsTest(backends); +} + +TEST_CASE ("Concatenation_Axis_CpuRef_Test") +{ + std::vector backends = {armnn::Compute::GpuAcc}; + ConcatAxisTest(backends); +} + +} + +TEST_SUITE("Concatenation_CpuRefTests") +{ + +TEST_CASE ("Concatenation_Uint8_CpuRef_Test") +{ + std::vector backends = {armnn::Compute::CpuRef}; + ConcatUint8TwoInputsTest(backends); +} + +TEST_CASE ("Concatenation_Int16_CpuRef_Test") +{ + std::vector backends = {armnn::Compute::CpuRef}; + ConcatInt16TwoInputsTest(backends); +} + +TEST_CASE ("Concatenation_Float32_CpuRef_Test") +{ + std::vector backends = {armnn::Compute::CpuRef}; + ConcatFloat32TwoInputsTest(backends); +} + +TEST_CASE ("Concatenation_Three_Inputs_CpuRef_Test") +{ + std::vector backends = {armnn::Compute::CpuRef}; + ConcatThreeInputsTest(backends); +} + +TEST_CASE ("Concatenation_Axis_CpuRef_Test") +{ + std::vector backends = {armnn::Compute::CpuRef}; + ConcatAxisTest(backends); +} + +} + +// MEAN Tests +TEST_SUITE("Mean_CpuAccTests") +{ + +TEST_CASE ("Mean_Uint8_KeepDims_CpuAcc_Test") +{ + std::vector backends = {armnn::Compute::CpuAcc}; + MeanUint8KeepDimsTest(backends); +} + +TEST_CASE ("Mean_Uint8_CpuAcc_Test") +{ + std::vector backends = {armnn::Compute::CpuAcc}; + MeanUint8Test(backends); +} + +TEST_CASE ("Mean_Fp32_KeepDims_CpuAcc_Test") +{ + std::vector backends = {armnn::Compute::CpuAcc}; + MeanFp32KeepDimsTest(backends); +} + +TEST_CASE ("Mean_Fp32_CpuAcc_Test") +{ + std::vector backends = {armnn::Compute::CpuAcc}; + MeanFp32Test(backends); +} + +} + +TEST_SUITE("Mean_GpuAccTests") +{ + +TEST_CASE ("Mean_Uint8_KeepDims_GpuAcc_Test") +{ + std::vector backends = {armnn::Compute::GpuAcc}; + MeanUint8KeepDimsTest(backends); +} + +TEST_CASE ("Mean_Uint8_GpuAcc_Test") +{ + std::vector backends = {armnn::Compute::GpuAcc}; + MeanUint8Test(backends); +} + +TEST_CASE ("Mean_Fp32_KeepDims_GpuAcc_Test") +{ + std::vector backends = {armnn::Compute::GpuAcc}; + MeanFp32KeepDimsTest(backends); +} + +TEST_CASE ("Mean_Fp32_GpuAcc_Test") +{ + std::vector backends = {armnn::Compute::GpuAcc}; + MeanFp32Test(backends); +} + +} + +TEST_SUITE("Mean_CpuRefTests") +{ + +TEST_CASE ("Mean_Uint8_KeepDims_CpuRef_Test") +{ + std::vector backends = {armnn::Compute::CpuRef}; + MeanUint8KeepDimsTest(backends); +} + +TEST_CASE ("Mean_Uint8_CpuRef_Test") +{ + std::vector backends = {armnn::Compute::CpuRef}; + MeanUint8Test(backends); +} + +TEST_CASE ("Mean_Fp32_KeepDims_CpuRef_Test") +{ + std::vector backends = {armnn::Compute::CpuRef}; + MeanFp32KeepDimsTest(backends); +} + +TEST_CASE ("Mean_Fp32_CpuRef_Test") +{ + std::vector backends = {armnn::Compute::CpuRef}; + MeanFp32Test(backends); +} + +} + +} // namespace armnnDelegate \ No newline at end of file diff --git a/delegate/src/test/ControlTestHelper.hpp b/delegate/src/test/ControlTestHelper.hpp new file mode 100644 index 0000000000..0c9796170d --- /dev/null +++ b/delegate/src/test/ControlTestHelper.hpp @@ -0,0 +1,344 @@ +// +// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#pragma once + +#include "TestUtils.hpp" + +#include + +#include +#include +#include +#include +#include +#include + +#include + +#include + +namespace +{ + +std::vector CreateConcatTfLiteModel(tflite::BuiltinOperator controlOperatorCode, + tflite::TensorType tensorType, + std::vector& inputTensorShape, + const std::vector & outputTensorShape, + const int32_t inputTensorNum, + int32_t axis = 0, + float quantScale = 1.0f, + int quantOffset = 0) +{ + using namespace tflite; + flatbuffers::FlatBufferBuilder flatBufferBuilder; + + std::vector> buffers; + buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + + auto quantizationParameters = + CreateQuantizationParameters(flatBufferBuilder, + 0, + 0, + flatBufferBuilder.CreateVector({ quantScale }), + flatBufferBuilder.CreateVector({ quantOffset })); + + std::vector operatorInputs{}; + const std::vector operatorOutputs{inputTensorNum}; + std::vector subgraphInputs{}; + const std::vector subgraphOutputs{inputTensorNum}; + + std::vector> tensors(inputTensorNum + 1); + for (int i = 0; i < inputTensorNum; ++i) + { + tensors[i] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector(inputTensorShape.data(), + inputTensorShape.size()), + tensorType, + 0, + flatBufferBuilder.CreateString("input" + std::to_string(i)), + quantizationParameters); + + // Add number of inputs to vector. + operatorInputs.push_back(i); + subgraphInputs.push_back(i); + } + + // Create output tensor + tensors[inputTensorNum] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector(outputTensorShape.data(), + outputTensorShape.size()), + tensorType, + 0, + flatBufferBuilder.CreateString("output"), + quantizationParameters); + + // create operator + tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_ConcatenationOptions; + flatbuffers::Offset operatorBuiltinOptions = CreateConcatenationOptions(flatBufferBuilder, axis).Union(); + + flatbuffers::Offset controlOperator = + CreateOperator(flatBufferBuilder, + 0, + flatBufferBuilder.CreateVector(operatorInputs.data(), operatorInputs.size()), + flatBufferBuilder.CreateVector(operatorOutputs.data(), operatorOutputs.size()), + operatorBuiltinOptionsType, + operatorBuiltinOptions); + + flatbuffers::Offset subgraph = + CreateSubGraph(flatBufferBuilder, + flatBufferBuilder.CreateVector(tensors.data(), tensors.size()), + flatBufferBuilder.CreateVector(subgraphInputs.data(), subgraphInputs.size()), + flatBufferBuilder.CreateVector(subgraphOutputs.data(), subgraphOutputs.size()), + flatBufferBuilder.CreateVector(&controlOperator, 1)); + + flatbuffers::Offset modelDescription = + flatBufferBuilder.CreateString("ArmnnDelegate: Concatenation Operator Model"); + flatbuffers::Offset operatorCode = CreateOperatorCode(flatBufferBuilder, controlOperatorCode); + + flatbuffers::Offset 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(flatBufferBuilder.GetBufferPointer(), + flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); +} + +std::vector CreateMeanTfLiteModel(tflite::BuiltinOperator controlOperatorCode, + tflite::TensorType tensorType, + std::vector& input0TensorShape, + std::vector& input1TensorShape, + const std::vector & outputTensorShape, + std::vector& axisData, + const bool keepDims, + float quantScale = 1.0f, + int quantOffset = 0) +{ + using namespace tflite; + flatbuffers::FlatBufferBuilder flatBufferBuilder; + + std::array, 2> buffers; + buffers[0] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})); + buffers[1] = CreateBuffer(flatBufferBuilder, + flatBufferBuilder.CreateVector(reinterpret_cast(axisData.data()), + sizeof(int32_t) * axisData.size())); + + auto quantizationParameters = + CreateQuantizationParameters(flatBufferBuilder, + 0, + 0, + flatBufferBuilder.CreateVector({ quantScale }), + flatBufferBuilder.CreateVector({ quantOffset })); + + std::array, 3> tensors; + tensors[0] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector(input0TensorShape.data(), + input0TensorShape.size()), + tensorType, + 0, + flatBufferBuilder.CreateString("input"), + quantizationParameters); + + tensors[1] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector(input1TensorShape.data(), + input1TensorShape.size()), + ::tflite::TensorType_INT32, + 1, + flatBufferBuilder.CreateString("axis"), + quantizationParameters); + + // Create output tensor + tensors[2] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector(outputTensorShape.data(), + outputTensorShape.size()), + tensorType, + 0, + flatBufferBuilder.CreateString("output"), + quantizationParameters); + + // create operator. Mean uses ReducerOptions. + tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_ReducerOptions; + flatbuffers::Offset operatorBuiltinOptions = CreateReducerOptions(flatBufferBuilder, keepDims).Union(); + + const std::vector operatorInputs{ {0, 1} }; + const std::vector operatorOutputs{ 2 }; + flatbuffers::Offset controlOperator = + CreateOperator(flatBufferBuilder, + 0, + flatBufferBuilder.CreateVector(operatorInputs.data(), operatorInputs.size()), + flatBufferBuilder.CreateVector(operatorOutputs.data(), operatorOutputs.size()), + operatorBuiltinOptionsType, + operatorBuiltinOptions); + + const std::vector subgraphInputs{ {0, 1} }; + const std::vector subgraphOutputs{ 2 }; + flatbuffers::Offset subgraph = + CreateSubGraph(flatBufferBuilder, + flatBufferBuilder.CreateVector(tensors.data(), tensors.size()), + flatBufferBuilder.CreateVector(subgraphInputs.data(), subgraphInputs.size()), + flatBufferBuilder.CreateVector(subgraphOutputs.data(), subgraphOutputs.size()), + flatBufferBuilder.CreateVector(&controlOperator, 1)); + + flatbuffers::Offset modelDescription = + flatBufferBuilder.CreateString("ArmnnDelegate: Mean Operator Model"); + flatbuffers::Offset operatorCode = CreateOperatorCode(flatBufferBuilder, controlOperatorCode); + + flatbuffers::Offset 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(flatBufferBuilder.GetBufferPointer(), + flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); +} + +template +void ConcatenationTest(tflite::BuiltinOperator controlOperatorCode, + tflite::TensorType tensorType, + std::vector& backends, + std::vector& inputShapes, + std::vector& expectedOutputShape, + std::vector>& inputValues, + std::vector& expectedOutputValues, + int32_t axis = 0, + float quantScale = 1.0f, + int quantOffset = 0) +{ + using namespace tflite; + std::vector modelBuffer = CreateConcatTfLiteModel(controlOperatorCode, + tensorType, + inputShapes, + expectedOutputShape, + inputValues.size(), + axis, + quantScale, + quantOffset); + + const Model* tfLiteModel = GetModel(modelBuffer.data()); + + // Create TfLite Interpreters + std::unique_ptr armnnDelegateInterpreter; + CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) + (&armnnDelegateInterpreter) == kTfLiteOk); + CHECK(armnnDelegateInterpreter != nullptr); + CHECK(armnnDelegateInterpreter->AllocateTensors() == kTfLiteOk); + + std::unique_ptr 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 + theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions), + armnnDelegate::TfLiteArmnnDelegateDelete); + CHECK(theArmnnDelegate != nullptr); + + // Modify armnnDelegateInterpreter to use armnnDelegate + CHECK(armnnDelegateInterpreter->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk); + + // Set input data for all input tensors. + for (unsigned int i = 0; i < inputValues.size(); ++i) + { + // Get single input tensor and assign to interpreters. + auto inputTensorValues = inputValues[i]; + armnnDelegate::FillInput(tfLiteInterpreter, i, inputTensorValues); + armnnDelegate::FillInput(armnnDelegateInterpreter, i, inputTensorValues); + } + + // Run EnqueWorkload + CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); + CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); + + // Compare output data + armnnDelegate::CompareOutputData(tfLiteInterpreter, + armnnDelegateInterpreter, + expectedOutputShape, + expectedOutputValues); + + armnnDelegateInterpreter.reset(nullptr); +} + +template +void MeanTest(tflite::BuiltinOperator controlOperatorCode, + tflite::TensorType tensorType, + std::vector& backends, + std::vector& input0Shape, + std::vector& input1Shape, + std::vector& expectedOutputShape, + std::vector& input0Values, + std::vector& input1Values, + std::vector& expectedOutputValues, + const bool keepDims, + float quantScale = 1.0f, + int quantOffset = 0) +{ + using namespace tflite; + std::vector modelBuffer = CreateMeanTfLiteModel(controlOperatorCode, + tensorType, + input0Shape, + input1Shape, + expectedOutputShape, + input1Values, + keepDims, + quantScale, + quantOffset); + + const Model* tfLiteModel = GetModel(modelBuffer.data()); + + // Create TfLite Interpreters + std::unique_ptr armnnDelegateInterpreter; + CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) + (&armnnDelegateInterpreter) == kTfLiteOk); + CHECK(armnnDelegateInterpreter != nullptr); + CHECK(armnnDelegateInterpreter->AllocateTensors() == kTfLiteOk); + + std::unique_ptr 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 + 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(tfLiteInterpreter, 0, input0Values); + armnnDelegate::FillInput(armnnDelegateInterpreter, 0, input0Values); + + // Run EnqueWorkload + CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); + CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); + + // Compare output data + armnnDelegate::CompareOutputData(tfLiteInterpreter, + armnnDelegateInterpreter, + expectedOutputShape, + expectedOutputValues); + + armnnDelegateInterpreter.reset(nullptr); +} + +} // anonymous namespace \ No newline at end of file diff --git a/delegate/src/test/TestUtils.hpp b/delegate/src/test/TestUtils.hpp index 162d62f3bb..9bbab8f62b 100644 --- a/delegate/src/test/TestUtils.hpp +++ b/delegate/src/test/TestUtils.hpp @@ -7,6 +7,8 @@ #include +#include + namespace armnnDelegate { @@ -23,4 +25,35 @@ void FillInput(std::unique_ptr& interpreter, int inputIndex } } +// Can be used to compare the output tensor shape and values +// from armnnDelegateInterpreter and tfLiteInterpreter. +// Example usage can be found in ControlTestHelper.hpp +template +void CompareOutputData(std::unique_ptr& tfLiteInterpreter, + std::unique_ptr& armnnDelegateInterpreter, + std::vector& expectedOutputShape, + std::vector& expectedOutputValues) +{ + auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0]; + auto tfLiteDelegateOutputTensor = tfLiteInterpreter->tensor(tfLiteDelegateOutputId); + auto tfLiteDelageOutputData = tfLiteInterpreter->typed_tensor(tfLiteDelegateOutputId); + auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0]; + auto armnnDelegateOutputTensor = armnnDelegateInterpreter->tensor(armnnDelegateOutputId); + auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor(armnnDelegateOutputId); + + for (size_t i = 0; i < expectedOutputShape.size(); i++) + { + CHECK(expectedOutputShape[i] == armnnDelegateOutputTensor->dims->data[i]); + CHECK(tfLiteDelegateOutputTensor->dims->data[i] == expectedOutputShape[i]); + CHECK(tfLiteDelegateOutputTensor->dims->data[i] == armnnDelegateOutputTensor->dims->data[i]); + } + + for (size_t i = 0; i < expectedOutputValues.size(); i++) + { + CHECK(expectedOutputValues[i] == armnnDelegateOutputData[i]); + CHECK(tfLiteDelageOutputData[i] == expectedOutputValues[i]); + CHECK(tfLiteDelageOutputData[i] == armnnDelegateOutputData[i]); + } +} + } // namespace armnnDelegate -- cgit v1.2.1