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Diffstat (limited to 'delegate/src/test/SoftmaxTestHelper.hpp')
| -rw-r--r-- | delegate/src/test/SoftmaxTestHelper.hpp | 170 |
1 files changed, 170 insertions, 0 deletions
diff --git a/delegate/src/test/SoftmaxTestHelper.hpp b/delegate/src/test/SoftmaxTestHelper.hpp new file mode 100644 index 0000000000..0474561a93 --- /dev/null +++ b/delegate/src/test/SoftmaxTestHelper.hpp @@ -0,0 +1,170 @@ +// +// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#pragma once + +#include <armnn_delegate.hpp> +#include <armnnUtils/FloatingPointComparison.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 +{ +std::vector<char> CreateSoftmaxTfLiteModel(tflite::BuiltinOperator softmaxOperatorCode, + tflite::TensorType tensorType, + const std::vector <int32_t>& tensorShape, + float beta) +{ + using namespace tflite; + flatbuffers::FlatBufferBuilder flatBufferBuilder; + + std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; + buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + + std::array<flatbuffers::Offset<Tensor>, 2> tensors; + tensors[0] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), + tensorShape.size()), + tensorType, + 0); + tensors[1] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), + tensorShape.size()), + tensorType, + 0); + + const std::vector<int32_t> operatorInputs({0}); + const std::vector<int32_t> operatorOutputs({1}); + + flatbuffers::Offset<Operator> softmaxOperator; + flatbuffers::Offset<flatbuffers::String> modelDescription; + flatbuffers::Offset<OperatorCode> operatorCode; + + switch (softmaxOperatorCode) + { + case tflite::BuiltinOperator_SOFTMAX: + softmaxOperator = + CreateOperator(flatBufferBuilder, + 0, + flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), + flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), + BuiltinOptions_SoftmaxOptions, + CreateSoftmaxOptions(flatBufferBuilder, beta).Union()); + modelDescription = flatBufferBuilder.CreateString("ArmnnDelegate: Softmax Operator Model"); + operatorCode = CreateOperatorCode(flatBufferBuilder, + tflite::BuiltinOperator_SOFTMAX); + break; + case tflite::BuiltinOperator_LOG_SOFTMAX: + softmaxOperator = + CreateOperator(flatBufferBuilder, + 0, + flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), + flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), + BuiltinOptions_LogSoftmaxOptions, + CreateLogSoftmaxOptions(flatBufferBuilder).Union()); + flatBufferBuilder.CreateString("ArmnnDelegate: Log-Softmax Operator Model"); + operatorCode = CreateOperatorCode(flatBufferBuilder, + tflite::BuiltinOperator_LOG_SOFTMAX); + break; + default: + break; + } + const std::vector<int32_t> subgraphInputs({0}); + const std::vector<int32_t> subgraphOutputs({1}); + 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(&softmaxOperator, 1)); + 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()); +} + +void SoftmaxTest(tflite::BuiltinOperator softmaxOperatorCode, + tflite::TensorType tensorType, + std::vector<armnn::BackendId>& backends, + std::vector<int32_t>& shape, + std::vector<float>& inputValues, + std::vector<float>& expectedOutputValues, + float beta = 0) +{ + using namespace tflite; + std::vector<char> modelBuffer = CreateSoftmaxTfLiteModel(softmaxOperatorCode, + tensorType, + shape, + beta); + + 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 tfLiteInterpreterInputData = tfLiteInterpreter->typed_tensor<float>(tfLiteDelegateInputId); + for (unsigned int i = 0; i < inputValues.size(); ++i) + { + tfLiteInterpreterInputData[i] = inputValues[i]; + } + + auto armnnDelegateInputId = armnnDelegateInterpreter->inputs()[0]; + auto armnnDelegateInputData = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateInputId); + for (unsigned int i = 0; i < inputValues.size(); ++i) + { + armnnDelegateInputData[i] = inputValues[i]; + } + // Run EnqueWorkload + CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); + CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); + + // Compare output data + auto tfLiteInterpreterOutputId = tfLiteInterpreter->outputs()[0]; + auto tfLiteInterpreterOutputData = tfLiteInterpreter->typed_tensor<float>(tfLiteInterpreterOutputId); + auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0]; + auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateOutputId); + + for (size_t i = 0; i < inputValues.size(); ++i) + { + CHECK(armnnUtils::within_percentage_tolerance(expectedOutputValues[i], armnnDelegateOutputData[i], 1e-5)); + CHECK(armnnUtils::within_percentage_tolerance(tfLiteInterpreterOutputData[i], + armnnDelegateOutputData[i], 1e-5)); + } +} + +} // anonymous namespace |