diff options
Diffstat (limited to 'delegate/src/test/ElementwiseBinaryTestHelper.hpp')
| -rw-r--r-- | delegate/src/test/ElementwiseBinaryTestHelper.hpp | 96 |
1 files changed, 44 insertions, 52 deletions
diff --git a/delegate/src/test/ElementwiseBinaryTestHelper.hpp b/delegate/src/test/ElementwiseBinaryTestHelper.hpp index 0c096d85c3..13b336e91e 100644 --- a/delegate/src/test/ElementwiseBinaryTestHelper.hpp +++ b/delegate/src/test/ElementwiseBinaryTestHelper.hpp @@ -5,6 +5,8 @@ #pragma once +#include "TestUtils.hpp" + #include <armnn_delegate.hpp> #include <flatbuffers/flatbuffers.h> @@ -19,12 +21,15 @@ namespace { +template <typename T> std::vector<char> CreateElementwiseBinaryTfLiteModel(tflite::BuiltinOperator binaryOperatorCode, tflite::ActivationFunctionType activationType, tflite::TensorType tensorType, const std::vector <int32_t>& input0TensorShape, const std::vector <int32_t>& input1TensorShape, const std::vector <int32_t>& outputTensorShape, + std::vector<T>& input1Values, + bool constantInput = false, float quantScale = 1.0f, int quantOffset = 0) { @@ -33,6 +38,18 @@ std::vector<char> CreateElementwiseBinaryTfLiteModel(tflite::BuiltinOperator bin std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + if (constantInput) + { + buffers.push_back( + CreateBuffer(flatBufferBuilder, + flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(input1Values.data()), + sizeof(T) * input1Values.size()))); + } + else + { + buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + } + buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); auto quantizationParameters = CreateQuantizationParameters(flatBufferBuilder, @@ -54,14 +71,14 @@ std::vector<char> CreateElementwiseBinaryTfLiteModel(tflite::BuiltinOperator bin flatBufferBuilder.CreateVector<int32_t>(input1TensorShape.data(), input1TensorShape.size()), tensorType, - 0, + 1, flatBufferBuilder.CreateString("input_1"), quantizationParameters); tensors[2] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), outputTensorShape.size()), tensorType, - 0, + 2, flatBufferBuilder.CreateString("output"), quantizationParameters); @@ -158,27 +175,30 @@ void ElementwiseBinaryTest(tflite::BuiltinOperator binaryOperatorCode, std::vector<T>& input1Values, std::vector<T>& expectedOutputValues, float quantScale = 1.0f, - int quantOffset = 0) + int quantOffset = 0, + bool constantInput = false) { using namespace tflite; - std::vector<char> modelBuffer = CreateElementwiseBinaryTfLiteModel(binaryOperatorCode, - activationType, - tensorType, - input0Shape, - input1Shape, - outputShape, - quantScale, - quantOffset); + std::vector<char> modelBuffer = CreateElementwiseBinaryTfLiteModel<T>(binaryOperatorCode, + activationType, + tensorType, + input0Shape, + input1Shape, + outputShape, + input1Values, + constantInput, + quantScale, + quantOffset); const Model* tfLiteModel = GetModel(modelBuffer.data()); // Create TfLite Interpreters - std::unique_ptr<Interpreter> armnnDelegateInterpreter; + 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; + std::unique_ptr <Interpreter> tfLiteInterpreter; CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) (&tfLiteInterpreter) == kTfLiteOk); CHECK(tfLiteInterpreter != nullptr); @@ -187,57 +207,29 @@ void ElementwiseBinaryTest(tflite::BuiltinOperator binaryOperatorCode, // Create the ArmNN Delegate armnnDelegate::DelegateOptions delegateOptions(backends); std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)> - theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions), - 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 tfLiteDelegateInput0Id = tfLiteInterpreter->inputs()[0]; - auto tfLiteDelageInput0Data = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateInput0Id); - for (unsigned int i = 0; i < input0Values.size(); ++i) - { - tfLiteDelageInput0Data[i] = input0Values[i]; - } - - auto tfLiteDelegateInput1Id = tfLiteInterpreter->inputs()[1]; - auto tfLiteDelageInput1Data = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateInput1Id); - for (unsigned int i = 0; i < input1Values.size(); ++i) - { - tfLiteDelageInput1Data[i] = input1Values[i]; - } - - auto armnnDelegateInput0Id = armnnDelegateInterpreter->inputs()[0]; - auto armnnDelegateInput0Data = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateInput0Id); - for (unsigned int i = 0; i < input0Values.size(); ++i) + armnnDelegate::FillInput<T>(tfLiteInterpreter, 0, input0Values); + armnnDelegate::FillInput<T>(armnnDelegateInterpreter, 0, input0Values); + if (!constantInput) { - armnnDelegateInput0Data[i] = input0Values[i]; + armnnDelegate::FillInput<T>(tfLiteInterpreter, 1, input1Values); + armnnDelegate::FillInput<T>(armnnDelegateInterpreter, 1, input1Values); } - - auto armnnDelegateInput1Id = armnnDelegateInterpreter->inputs()[1]; - auto armnnDelegateInput1Data = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateInput1Id); - for (unsigned int i = 0; i < input1Values.size(); ++i) - { - armnnDelegateInput1Data[i] = input1Values[i]; - } - // Run EnqueWorkload CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); // Compare output data - auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0]; - auto tfLiteDelageOutputData = 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(expectedOutputValues[i] == armnnDelegateOutputData[i]); - CHECK(tfLiteDelageOutputData[i] == expectedOutputValues[i]); - CHECK(tfLiteDelageOutputData[i] == armnnDelegateOutputData[i]); - } - + armnnDelegate::CompareOutputData<T>(tfLiteInterpreter, + armnnDelegateInterpreter, + outputShape, + expectedOutputValues); armnnDelegateInterpreter.reset(nullptr); } |