| // |
| // 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], 0.1)); |
| CHECK(armnnUtils::within_percentage_tolerance(tfLiteInterpreterOutputData[i], |
| armnnDelegateOutputData[i], 0.1)); |
| } |
| } |
| |
| |
| /// Convenience function to run softmax and log-softmax test cases |
| /// \param operatorCode tflite::BuiltinOperator_SOFTMAX or tflite::BuiltinOperator_LOG_SOFTMAX |
| /// \param backends armnn backends to target |
| /// \param beta multiplicative parameter to the softmax function |
| /// \param expectedOutput to be checked against transformed input |
| void SoftmaxTestCase(tflite::BuiltinOperator operatorCode, |
| std::vector<armnn::BackendId> backends, float beta, std::vector<float> expectedOutput) { |
| std::vector<float> input = { |
| 1.0, 2.5, 3.0, 4.5, 5.0, |
| -1.0, -2.5, -3.0, -4.5, -5.0}; |
| std::vector<int32_t> shape = {2, 5}; |
| |
| SoftmaxTest(operatorCode, |
| tflite::TensorType_FLOAT32, |
| backends, |
| shape, |
| input, |
| expectedOutput, |
| beta); |
| } |
| |
| } // anonymous namespace |