diff options
author | Sadik Armagan <sadik.armagan@arm.com> | 2020-11-27 12:40:52 +0000 |
---|---|---|
committer | TeresaARM <teresa.charlinreyes@arm.com> | 2020-11-30 15:12:22 +0000 |
commit | 34fa1bd7994af9abf52dbcc4aa808d0fa5f14aa3 (patch) | |
tree | 8ff6041b92da57cd56ccbcd3b746bdc0fb29078a /delegate/src/test | |
parent | b7fa5104715e097bf3778d2485d759a4612460cc (diff) | |
download | armnn-34fa1bd7994af9abf52dbcc4aa808d0fa5f14aa3.tar.gz |
IVGCVSW-5393 'TfLiteDelegate: Implement the split operators'
* Added SPLIT and SPLIT_V support to armnn_delegate
Signed-off-by: Sadik Armagan <sadik.armagan@arm.com>
Change-Id: I2def9b8be783b25ef17a997e521c6027553035d3
Diffstat (limited to 'delegate/src/test')
-rw-r--r-- | delegate/src/test/SplitTest.cpp | 262 | ||||
-rw-r--r-- | delegate/src/test/SplitTestHelper.hpp | 368 | ||||
-rw-r--r-- | delegate/src/test/TestUtils.hpp | 7 |
3 files changed, 634 insertions, 3 deletions
diff --git a/delegate/src/test/SplitTest.cpp b/delegate/src/test/SplitTest.cpp new file mode 100644 index 0000000000..5940516583 --- /dev/null +++ b/delegate/src/test/SplitTest.cpp @@ -0,0 +1,262 @@ +// +// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include "SplitTestHelper.hpp" + +#include <armnn_delegate.hpp> + +#include <flatbuffers/flatbuffers.h> +#include <tensorflow/lite/schema/schema_generated.h> + +#include <doctest/doctest.h> + +namespace armnnDelegate +{ + +// SPLIT Operator +void SplitUint8Test(std::vector<armnn::BackendId>& backends) +{ + std::vector<int32_t> axisShape { 1 }; + std::vector<int32_t> inputShape { 2, 2, 2, 2} ; + std::vector<int32_t> outputShape0 { 2, 2, 2, 1 }; + std::vector<int32_t> outputShape1 { 2, 2, 2, 1 }; + std::vector<std::vector<int32_t>> outputShapes{ outputShape0, outputShape1 }; + + std::vector<int32_t> axisData { 3 }; // Axis + std::vector<uint8_t> inputValues { 1, 2, 3, 4, 5, 6, 7, 8, + 9, 10, 11, 12, 13, 14, 15, 16 }; // Input + + + std::vector<uint8_t> expectedOutputValues0 { 1, 3, 5, 7, 9, 11, 13, 15 }; + std::vector<uint8_t> expectedOutputValues1 { 2, 4, 6, 8, 10, 12, 14, 16 }; + std::vector<std::vector<uint8_t>> expectedOutputValues{ expectedOutputValues0, expectedOutputValues1 }; + + int32_t numSplits = 2; + + SplitTest<uint8_t>(::tflite::TensorType_UINT8, + backends, + axisShape, + inputShape, + outputShapes, + axisData, + inputValues, + expectedOutputValues, + numSplits); +} + +void SplitFp32Test(std::vector<armnn::BackendId>& backends) +{ + std::vector<int32_t> axisShape { 1 }; + std::vector<int32_t> inputShape { 2, 2, 2, 2 }; + std::vector<int32_t> outputShape0 { 2, 1, 2, 2 }; + std::vector<int32_t> outputShape1 { 2, 1, 2, 2 }; + std::vector<std::vector<int32_t>> outputShapes{ outputShape0, outputShape1 }; + + std::vector<int32_t> axisData { 1 }; // Axis + std::vector<float> inputValues { 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, + 9.0f, 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f }; // Input + + + std::vector<float> expectedOutputValues0 { 1.0f, 2.0f, 3.0f, 4.0f, 9.0f, 10.0f, 11.0f, 12.0f }; + std::vector<float> expectedOutputValues1 { 5.0f, 6.0f, 7.0f, 8.0f, 13.0f, 14.0f, 15.0f, 16.0f }; + std::vector<std::vector<float>> expectedOutputValues{ expectedOutputValues0, expectedOutputValues1 }; + + int32_t numSplits = 2; + + SplitTest<float>(::tflite::TensorType_FLOAT32, + backends, + axisShape, + inputShape, + outputShapes, + axisData, + inputValues, + expectedOutputValues, + numSplits); +} + +// SPLIT Test Suite +TEST_SUITE("SPLIT_CpuRefTests") +{ + +TEST_CASE ("SPLIT_Uint8_CpuRef_Test") +{ + std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; + SplitUint8Test(backends); +} + +TEST_CASE ("SPLIT_Fp32_CpuRef_Test") +{ + std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; + SplitFp32Test(backends); +} + +} + +TEST_SUITE("SPLIT_CpuAccTests") +{ + +TEST_CASE ("SPLIT_Uint8_CpuAcc_Test") +{ + std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; + SplitUint8Test(backends); +} + +TEST_CASE ("SPLIT_Fp32_CpuAcc_Test") +{ + std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; + SplitFp32Test(backends); +} + +} + +TEST_SUITE("SPLIT_GpuAccTests") +{ + +TEST_CASE ("SPLIT_Uint8_GpuAcc_Test") +{ + std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; + SplitUint8Test(backends); +} + +TEST_CASE ("SPLIT_Fp32_GpuAcc_Test") +{ + std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; + SplitFp32Test(backends); +} + +} +// End of SPLIT Test Suite + +// SPLIT_V Operator +void SplitVUint8Test(std::vector<armnn::BackendId>& backends) +{ + std::vector<int32_t> axisShape { 1 }; + std::vector<int32_t> inputShape { 2, 4, 2, 2 }; + std::vector<int32_t> splitsShape { 2 }; + std::vector<int32_t> outputShape0 { 2, 3, 2, 2 }; + std::vector<int32_t> outputShape1 { 2, 1, 2, 2 }; + std::vector<std::vector<int32_t>> outputShapes{ outputShape0, outputShape1 }; + + std::vector<int32_t> axisData { 1 }; // Axis + std::vector<int32_t> splitsData { 3, 1 }; // Splits + std::vector<uint8_t> inputValues { 1, 2, 3, 4, 5, 6, 7, 8, + 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 22, 23, 24, + 25, 26, 27, 28, 29, 30, 31, 32 }; // Input + + + std::vector<uint8_t> expectedOutputValues0 { 1, 2, 3, 4, 5, 6, 7, 8, + 9, 10, 11, 12, 17, 18, 19, 20, + 21, 22, 23, 24, 25, 26, 27, 28 }; + std::vector<uint8_t> expectedOutputValues1 { 13, 14, 15, 16, 29, 30, 31, 32 }; + std::vector<std::vector<uint8_t>> expectedOutputValues{ expectedOutputValues0, expectedOutputValues1 }; + + int32_t numSplits = 2; + + SplitVTest<uint8_t>(::tflite::TensorType_UINT8, + backends, + inputShape, + splitsShape, + axisShape, + outputShapes, + inputValues, + splitsData, + axisData, + expectedOutputValues, + numSplits); +} + +void SplitVFp32Test(std::vector<armnn::BackendId>& backends) +{ + std::vector<int32_t> axisShape { 1 }; + std::vector<int32_t> inputShape { 2, 4, 2, 2 }; + std::vector<int32_t> splitsShape { 2 }; + std::vector<int32_t> outputShape0 { 2, 3, 2, 2 }; + std::vector<int32_t> outputShape1 { 2, 1, 2, 2 }; + std::vector<std::vector<int32_t>> outputShapes{ outputShape0, outputShape1 }; + + std::vector<int32_t> axisData { 1 }; // Axis + std::vector<int32_t> splitsData { 3, 1 }; // Splits + std::vector<float> inputValues { 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, + 9.0f, 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, + 17.0f, 18.0f, 19.0f, 20.0f, 21.0f, 22.0f, 23.0f, 24.0f, + 25.0f, 26.0f, 27.0f, 28.0f, 29.0f, 30.0f, 31.0f, 32.0f }; // Input + + + std::vector<float> expectedOutputValues0 { 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, + 9.0f, 10.0f, 11.0f, 12.0f, 17.0f, 18.0f, 19.0f, 20.0f, + 21.0f, 22.0f, 23.0f, 24.0f, 25.0f, 26.0f, 27.0f, 28.0f }; + std::vector<float> expectedOutputValues1 { 13.0f, 14.0f, 15.0f, 16.0f, 29.0f, 30.0f, 31.0f, 32.0f }; + std::vector<std::vector<float>> expectedOutputValues{ expectedOutputValues0, expectedOutputValues1 }; + + int32_t numSplits = 2; + + SplitVTest<float>(::tflite::TensorType_FLOAT32, + backends, + inputShape, + splitsShape, + axisShape, + outputShapes, + inputValues, + splitsData, + axisData, + expectedOutputValues, + numSplits); +} + +// SPLIT_V Test Suite +TEST_SUITE("SPLIT_V_CpuRefTests") +{ + +TEST_CASE ("SPLIT_V_Uint8_CpuRef_Test") +{ + std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; + SplitVUint8Test(backends); +} + +TEST_CASE ("SPLIT_V_Fp32_CpuRef_Test") +{ + std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; + SplitVFp32Test(backends); +} + +} + +TEST_SUITE("SPLIT_V_CpuAccTests") +{ + +TEST_CASE ("SPLIT_V_Uint8_CpuAcc_Test") +{ + std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; + SplitVUint8Test(backends); +} + +TEST_CASE ("SPLIT_V_Fp32_CpuAcc_Test") +{ + std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; + SplitVFp32Test(backends); +} + +} + +TEST_SUITE("SPLIT_V_GpuAccTests") +{ + +TEST_CASE ("SPLIT_V_Uint8_GpuAcc_Test") +{ + std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; + SplitVUint8Test(backends); +} + +TEST_CASE ("SPLIT_V_Fp32_GpuAcc_Test") +{ + std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; + SplitVFp32Test(backends); +} + +} +// End of SPLIT_V Test Suite + +} // namespace armnnDelegate
\ No newline at end of file diff --git a/delegate/src/test/SplitTestHelper.hpp b/delegate/src/test/SplitTestHelper.hpp new file mode 100644 index 0000000000..31fc7d5e46 --- /dev/null +++ b/delegate/src/test/SplitTestHelper.hpp @@ -0,0 +1,368 @@ +// +// Copyright © 2020 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> + +#include <string> + +namespace +{ + +std::vector<char> CreateSplitTfLiteModel(tflite::TensorType tensorType, + std::vector<int32_t>& axisTensorShape, + std::vector<int32_t>& inputTensorShape, + const std::vector<std::vector<int32_t>>& outputTensorShapes, + std::vector<int32_t>& axisData, + const int32_t numSplits, + float quantScale = 1.0f, + int quantOffset = 0) +{ + using namespace tflite; + flatbuffers::FlatBufferBuilder flatBufferBuilder; + + std::array<flatbuffers::Offset<tflite::Buffer>, 2> buffers; + buffers[0] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})); + buffers[1] = CreateBuffer(flatBufferBuilder, + flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(axisData.data()), + sizeof(int32_t) * axisData.size())); + + auto quantizationParameters = + CreateQuantizationParameters(flatBufferBuilder, + 0, + 0, + flatBufferBuilder.CreateVector<float>({ quantScale }), + flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); + + std::array<flatbuffers::Offset<Tensor>, 4> tensors; + tensors[0] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(axisTensorShape.data(), + axisTensorShape.size()), + ::tflite::TensorType_INT32, + 1, + flatBufferBuilder.CreateString("axis"), + quantizationParameters); + tensors[1] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), + inputTensorShape.size()), + tensorType, + 0, + flatBufferBuilder.CreateString("input"), + quantizationParameters); + + // Create output tensor + for (unsigned int i = 0; i < outputTensorShapes.size(); ++i) + { + tensors[i + 2] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(outputTensorShapes[i].data(), + outputTensorShapes[i].size()), + tensorType, + 0, + flatBufferBuilder.CreateString("output"), + quantizationParameters); + } + + // create operator. Mean uses ReducerOptions. + tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_SplitOptions; + flatbuffers::Offset<void> operatorBuiltinOptions = CreateSplitOptions(flatBufferBuilder, numSplits).Union(); + + const std::vector<int> operatorInputs{ {0, 1} }; + const std::vector<int> operatorOutputs{ {2, 3} }; + flatbuffers::Offset <Operator> controlOperator = + 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} }; + const std::vector<int> subgraphOutputs{ {2, 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(&controlOperator, 1)); + + flatbuffers::Offset <flatbuffers::String> modelDescription = + flatBufferBuilder.CreateString("ArmnnDelegate: SPLIT Operator Model"); + flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, BuiltinOperator_SPLIT); + + 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 SplitTest(tflite::TensorType tensorType, + std::vector<armnn::BackendId>& backends, + std::vector<int32_t>& axisTensorShape, + std::vector<int32_t>& inputTensorShape, + std::vector<std::vector<int32_t>>& outputTensorShapes, + std::vector<int32_t>& axisData, + std::vector<T>& inputValues, + std::vector<std::vector<T>>& expectedOutputValues, + const int32_t numSplits, + float quantScale = 1.0f, + int quantOffset = 0) +{ + using namespace tflite; + std::vector<char> modelBuffer = CreateSplitTfLiteModel(tensorType, + axisTensorShape, + inputTensorShape, + outputTensorShapes, + axisData, + numSplits, + quantScale, + quantOffset); + const Model* tfLiteModel = GetModel(modelBuffer.data()); + + // Create TfLite Interpreters + std::unique_ptr<Interpreter> armnnDelegate; + CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) + (&armnnDelegate) == kTfLiteOk); + CHECK(armnnDelegate != nullptr); + CHECK(armnnDelegate->AllocateTensors() == kTfLiteOk); + + std::unique_ptr<Interpreter> tfLiteDelegate; + CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) + (&tfLiteDelegate) == kTfLiteOk); + CHECK(tfLiteDelegate != nullptr); + CHECK(tfLiteDelegate->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(armnnDelegate->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk); + + // Set input data + armnnDelegate::FillInput<T>(tfLiteDelegate, 1, inputValues); + armnnDelegate::FillInput<T>(armnnDelegate, 1, inputValues); + + // Run EnqueWorkload + CHECK(tfLiteDelegate->Invoke() == kTfLiteOk); + CHECK(armnnDelegate->Invoke() == kTfLiteOk); + + // Compare output data + for (unsigned int i = 0; i < expectedOutputValues.size(); ++i) + { + armnnDelegate::CompareOutputData<T>(tfLiteDelegate, + armnnDelegate, + outputTensorShapes[i], + expectedOutputValues[i], + i); + } + + tfLiteDelegate.reset(nullptr); + armnnDelegate.reset(nullptr); +} // End of SPLIT Test + +std::vector<char> CreateSplitVTfLiteModel(tflite::TensorType tensorType, + std::vector<int32_t>& inputTensorShape, + std::vector<int32_t>& splitsTensorShape, + std::vector<int32_t>& axisTensorShape, + const std::vector<std::vector<int32_t>>& outputTensorShapes, + std::vector<int32_t>& splitsData, + std::vector<int32_t>& axisData, + const int32_t numSplits, + 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, flatBufferBuilder.CreateVector({})); + buffers[1] = CreateBuffer(flatBufferBuilder, + flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(splitsData.data()), + sizeof(int32_t) * splitsData.size())); + buffers[2] = CreateBuffer(flatBufferBuilder, + flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(axisData.data()), + sizeof(int32_t) * axisData.size())); + + auto quantizationParameters = + CreateQuantizationParameters(flatBufferBuilder, + 0, + 0, + flatBufferBuilder.CreateVector<float>({ quantScale }), + flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); + + std::array<flatbuffers::Offset<Tensor>, 5> 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>(splitsTensorShape.data(), + splitsTensorShape.size()), + ::tflite::TensorType_INT32, + 1, + flatBufferBuilder.CreateString("splits"), + quantizationParameters); + tensors[2] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(axisTensorShape.data(), + axisTensorShape.size()), + ::tflite::TensorType_INT32, + 2, + flatBufferBuilder.CreateString("axis"), + quantizationParameters); + + // Create output tensor + for (unsigned int i = 0; i < outputTensorShapes.size(); ++i) + { + tensors[i + 3] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(outputTensorShapes[i].data(), + outputTensorShapes[i].size()), + tensorType, + 0, + flatBufferBuilder.CreateString("output"), + quantizationParameters); + } + + // create operator. Mean uses ReducerOptions. + tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_SplitVOptions; + flatbuffers::Offset<void> operatorBuiltinOptions = CreateSplitVOptions(flatBufferBuilder, numSplits).Union(); + + const std::vector<int> operatorInputs{ {0, 1, 2} }; + const std::vector<int> operatorOutputs{ {3, 4} }; + flatbuffers::Offset <Operator> controlOperator = + 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, 4} }; + 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(&controlOperator, 1)); + + flatbuffers::Offset <flatbuffers::String> modelDescription = + flatBufferBuilder.CreateString("ArmnnDelegate: SPLIT_V Operator Model"); + flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, BuiltinOperator_SPLIT_V); + + 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 SplitVTest(tflite::TensorType tensorType, + std::vector<armnn::BackendId>& backends, + std::vector<int32_t>& inputTensorShape, + std::vector<int32_t>& splitsTensorShape, + std::vector<int32_t>& axisTensorShape, + std::vector<std::vector<int32_t>>& outputTensorShapes, + std::vector<T>& inputValues, + std::vector<int32_t>& splitsData, + std::vector<int32_t>& axisData, + std::vector<std::vector<T>>& expectedOutputValues, + const int32_t numSplits, + float quantScale = 1.0f, + int quantOffset = 0) +{ + using namespace tflite; + std::vector<char> modelBuffer = CreateSplitVTfLiteModel(tensorType, + inputTensorShape, + splitsTensorShape, + axisTensorShape, + outputTensorShapes, + splitsData, + axisData, + numSplits, + quantScale, + quantOffset); + const Model* tfLiteModel = GetModel(modelBuffer.data()); + + // Create TfLite Interpreters + std::unique_ptr<Interpreter> armnnDelegate; + CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) + (&armnnDelegate) == kTfLiteOk); + CHECK(armnnDelegate != nullptr); + CHECK(armnnDelegate->AllocateTensors() == kTfLiteOk); + + std::unique_ptr<Interpreter> tfLiteDelegate; + CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) + (&tfLiteDelegate) == kTfLiteOk); + CHECK(tfLiteDelegate != nullptr); + CHECK(tfLiteDelegate->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(armnnDelegate->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk); + + // Set input data + armnnDelegate::FillInput<T>(tfLiteDelegate, 0, inputValues); + armnnDelegate::FillInput<T>(armnnDelegate, 0, inputValues); + + // Run EnqueWorkload + CHECK(tfLiteDelegate->Invoke() == kTfLiteOk); + CHECK(armnnDelegate->Invoke() == kTfLiteOk); + + // Compare output data + for (unsigned int i = 0; i < expectedOutputValues.size(); ++i) + { + armnnDelegate::CompareOutputData<T>(tfLiteDelegate, + armnnDelegate, + outputTensorShapes[i], + expectedOutputValues[i], + i); + } + + tfLiteDelegate.reset(nullptr); + armnnDelegate.reset(nullptr); +} // End of SPLIT_V Test + +} // anonymous namespace
\ No newline at end of file diff --git a/delegate/src/test/TestUtils.hpp b/delegate/src/test/TestUtils.hpp index 57ae3ce6fe..284eaa74f5 100644 --- a/delegate/src/test/TestUtils.hpp +++ b/delegate/src/test/TestUtils.hpp @@ -51,12 +51,13 @@ template <typename T> void CompareOutputData(std::unique_ptr<tflite::Interpreter>& tfLiteInterpreter, std::unique_ptr<tflite::Interpreter>& armnnDelegateInterpreter, std::vector<int32_t>& expectedOutputShape, - std::vector<T>& expectedOutputValues) + std::vector<T>& expectedOutputValues, + unsigned int outputIndex = 0) { - auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0]; + auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[outputIndex]; auto tfLiteDelegateOutputTensor = tfLiteInterpreter->tensor(tfLiteDelegateOutputId); auto tfLiteDelegateOutputData = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateOutputId); - auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0]; + auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[outputIndex]; auto armnnDelegateOutputTensor = armnnDelegateInterpreter->tensor(armnnDelegateOutputId); auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateOutputId); |