diff options
Diffstat (limited to 'delegate/src/test')
-rw-r--r-- | delegate/src/test/QuantizationTest.cpp | 429 | ||||
-rw-r--r-- | delegate/src/test/QuantizationTestHelper.hpp | 197 |
2 files changed, 626 insertions, 0 deletions
diff --git a/delegate/src/test/QuantizationTest.cpp b/delegate/src/test/QuantizationTest.cpp new file mode 100644 index 0000000000..5466d47f48 --- /dev/null +++ b/delegate/src/test/QuantizationTest.cpp @@ -0,0 +1,429 @@ +// +// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include "QuantizationTestHelper.hpp" + +#include <armnn_delegate.hpp> + +#include <flatbuffers/flatbuffers.h> +#include <tensorflow/lite/schema/schema_generated.h> + +#include <doctest/doctest.h> + +namespace armnnDelegate +{ + +// Dequantize operator test functions. +void DequantizeUint8Test(std::vector<armnn::BackendId>& backends) +{ + std::vector<int32_t> inputShape { 2, 4 }; + std::vector<int32_t> outputShape { 2, 4 }; + + // Set input and output data + std::vector<uint8_t> inputValues + { + 0, 1, 2, 3, // Lower bounds + 252, 253, 254, 255 // Upper bounds + }; + std::vector<float> expectedOutputValues + { + 0.f, 1.f, 2.f, 3.f, + 252.f, 253.f, 254.f, 255.f + }; + + QuantizationTest<uint8_t, float>(tflite::BuiltinOperator_DEQUANTIZE, + ::tflite::TensorType_UINT8, + ::tflite::TensorType_FLOAT32, + backends, + inputShape, + outputShape, + inputValues, + expectedOutputValues); +} + +void DequantizeInt8Test(std::vector<armnn::BackendId>& backends) +{ + std::vector<int32_t> inputShape { 2, 4 }; + std::vector<int32_t> outputShape { 2, 4 }; + + std::vector<int8_t> inputValues + { + -1, 0, 1, 2, + -128, -127, 126, 127 + }; + std::vector<float> expectedOutputValues + { + -1.f, 0.f, 1.f, 2.f, + -128.f, -127.f, 126.f, 127.f + }; + + QuantizationTest<int8_t , float>(tflite::BuiltinOperator_DEQUANTIZE, + ::tflite::TensorType_INT8, + ::tflite::TensorType_FLOAT32, + backends, + inputShape, + outputShape, + inputValues, + expectedOutputValues); +} + +void DequantizeInt16Test(std::vector<armnn::BackendId>& backends) +{ + std::vector<int32_t> inputShape { 2, 5 }; + std::vector<int32_t> outputShape { 2, 5 }; + + std::vector<int16_t> inputValues + { + -1, 0, 1, 2, + -32768, -16384, 16384, 32767 + }; + std::vector<float> expectedOutputValues + { + -1.f, 0.f, 1.f, 2.f, + -32768.f, -16384.f, 16384.f, 32767.f + }; + + QuantizationTest<int16_t, float>(tflite::BuiltinOperator_DEQUANTIZE, + ::tflite::TensorType_INT16, + ::tflite::TensorType_FLOAT32, + backends, + inputShape, + outputShape, + inputValues, + expectedOutputValues); +} + +// Quantize operator test functions. +void QuantizeFloat32Uint8Test(std::vector<armnn::BackendId>& backends) +{ + std::vector<int32_t> inputShape { 2, 4 }; + std::vector<int32_t> outputShape { 2, 4 }; + + // Set input and output data + std::vector<float> inputValues + { + -1.f, 0.f, 1.f, 2.f, // Lower bounds + 252.f, 253.f, 255.f, 256.f // Upper bounds + }; + std::vector<uint8_t> expectedOutputValues + { + 0, 0, 1, 2, + 252, 253, 255, 255 + }; + + QuantizationTest<float, uint8_t>(tflite::BuiltinOperator_QUANTIZE, + ::tflite::TensorType_FLOAT32, + ::tflite::TensorType_UINT8, + backends, + inputShape, + outputShape, + inputValues, + expectedOutputValues); +} + +void QuantizeFloat32Int8Test(std::vector<armnn::BackendId>& backends) +{ + std::vector<int32_t> inputShape { 2, 4 }; + std::vector<int32_t> outputShape { 2, 4 }; + + std::vector<float> inputValues + { + -1.f, 0.f, 1.f, 2.f, + -128.5f, -127.f, 126.f, 127.5f + }; + std::vector<int8_t> expectedOutputValues + { + -1, 0, 1, 2, + -128, -127, 126, 127 + }; + + QuantizationTest<float, int8_t>(tflite::BuiltinOperator_QUANTIZE, + ::tflite::TensorType_FLOAT32, + ::tflite::TensorType_INT8, + backends, + inputShape, + outputShape, + inputValues, + expectedOutputValues); +} + +void QuantizeFloat32Int16Test(std::vector<armnn::BackendId>& backends) +{ + std::vector<int32_t> inputShape { 2, 4 }; + std::vector<int32_t> outputShape { 2, 4 }; + + std::vector<float> inputValues + { + -1.f, 0.f, 1.f, 2.f, + -32768.5f, -16384.f, 16384.f, 32767.5f + }; + std::vector<int16_t> expectedOutputValues + { + -1, 0, 1, 2, + -32768, -16384, 16384, 32767 + }; + + QuantizationTest<float, int16_t>(tflite::BuiltinOperator_QUANTIZE, + ::tflite::TensorType_FLOAT32, + ::tflite::TensorType_INT16, + backends, + inputShape, + outputShape, + inputValues, + expectedOutputValues); +} + +void QuantizeInt16Int16Test(std::vector<armnn::BackendId>& backends) +{ + std::vector<int32_t> inputShape { 2, 4 }; + std::vector<int32_t> outputShape { 2, 4 }; + + std::vector<int16_t> inputValues + { + -1, 0, 1, 2, + -32768, -16384, 16384, 32767 + }; + std::vector<int16_t> expectedOutputValues + { + -1, 0, 1, 2, + -32768, -16384, 16384, 32767 + }; + + QuantizationTest<int16_t, int16_t>(tflite::BuiltinOperator_QUANTIZE, + ::tflite::TensorType_INT16, + ::tflite::TensorType_INT16, + backends, + inputShape, + outputShape, + inputValues, + expectedOutputValues); +} + +void QuantizeInt16Int8Test(std::vector<armnn::BackendId>& backends) +{ + std::vector<int32_t> inputShape { 2, 4 }; + std::vector<int32_t> outputShape { 2, 4 }; + + std::vector<int16_t> inputValues + { + -1, 0, 1, 2, + -32768, -16384, 16384, 32767 + }; + std::vector<int8_t> expectedOutputValues + { + -1, 0, 1, 2, + -128, -128, 127, 127 + }; + + QuantizationTest<int16_t, int8_t>(tflite::BuiltinOperator_QUANTIZE, + ::tflite::TensorType_INT16, + ::tflite::TensorType_INT8, + backends, + inputShape, + outputShape, + inputValues, + expectedOutputValues); +} + +void QuantizeInt8Uint8Test(std::vector<armnn::BackendId>& backends) +{ + std::vector<int32_t> inputShape { 2, 4 }; + std::vector<int32_t> outputShape { 2, 4 }; + + std::vector<int8_t> inputValues + { + -1, 0, 1, 2, + -128, -127, 126, 127 + }; + std::vector<uint8_t> expectedOutputValues + { + 0, 0, 1, 2, + 0, 0, 126, 127 + }; + + QuantizationTest<int8_t, uint8_t>(tflite::BuiltinOperator_QUANTIZE, + ::tflite::TensorType_INT8, + ::tflite::TensorType_UINT8, + backends, + inputShape, + outputShape, + inputValues, + expectedOutputValues); +} + +void QuantizeUint8Int8Test(std::vector<armnn::BackendId>& backends) +{ + std::vector<int32_t> inputShape { 2, 4 }; + std::vector<int32_t> outputShape { 2, 4 }; + + std::vector<uint8_t> inputValues + { + 0, 1, 2, 3, + 126, 127, 254, 255 + }; + std::vector<int8_t> expectedOutputValues + { + 0, 1, 2, 3, + 126, 127, 127, 127 + }; + + QuantizationTest<uint8_t, int8_t>(tflite::BuiltinOperator_QUANTIZE, + ::tflite::TensorType_UINT8, + ::tflite::TensorType_INT8, + backends, + inputShape, + outputShape, + inputValues, + expectedOutputValues); +} + +TEST_SUITE("QuantizationTests") +{ + +// Dequantize Operator Tests +TEST_CASE ("DEQUANTIZE_UINT8_GpuAcc_Test") +{ + std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc, + armnn::Compute::CpuRef }; + DequantizeUint8Test(backends); +} + +TEST_CASE ("DEQUANTIZE_UINT8_CpuAcc_Test") +{ + std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc, + armnn::Compute::CpuRef }; + DequantizeUint8Test(backends); +} + +TEST_CASE ("DEQUANTIZE_INT8_GpuAcc_Test") +{ + std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc, + armnn::Compute::CpuRef }; + DequantizeInt8Test(backends); +} + +TEST_CASE ("DEQUANTIZE_INT8_CpuAcc_Test") +{ + std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc, + armnn::Compute::CpuRef }; + DequantizeInt8Test(backends); +} + +TEST_CASE ("DEQUANTIZE_INT16_GpuAcc_Test") +{ + std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc, + armnn::Compute::CpuRef }; + DequantizeInt16Test(backends); +} + +TEST_CASE ("DEQUANTIZE_INT16_CpuAcc_Test") +{ + std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc, + armnn::Compute::CpuRef }; + DequantizeInt16Test(backends); +} + +// Quantize Operator Tests +TEST_CASE ("QUANTIZE_FLOAT32_UINT8_GpuAcc_Test") +{ + std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc, + armnn::Compute::CpuRef }; + QuantizeFloat32Uint8Test(backends); +} + +TEST_CASE ("QUANTIZE_FLOAT32_UINT8_CpuAcc_Test") +{ + std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc, + armnn::Compute::CpuRef }; + QuantizeFloat32Uint8Test(backends); +} + +TEST_CASE ("QUANTIZE_FLOAT32_INT8_GpuAcc_Test") +{ + std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc, + armnn::Compute::CpuRef }; + QuantizeFloat32Int8Test(backends); +} + +TEST_CASE ("QUANTIZE_FLOAT32_INT8_CpuAcc_Test") +{ + std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc, + armnn::Compute::CpuRef }; + QuantizeFloat32Int8Test(backends); +} + +TEST_CASE ("QUANTIZE_FLOAT32_INT16_GpuAcc_Test") +{ + std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc, + armnn::Compute::CpuRef }; + QuantizeFloat32Int16Test(backends); +} + +TEST_CASE ("QUANTIZE_FLOAT32_INT16_CpuAcc_Test") +{ + std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc, + armnn::Compute::CpuRef }; + QuantizeFloat32Int16Test(backends); +} + +TEST_CASE ("QUANTIZE_INT16_INT16_GpuAcc_Test") +{ + std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc, + armnn::Compute::CpuRef }; + QuantizeInt16Int16Test(backends); +} + +TEST_CASE ("QUANTIZE_INT16_INT16_CpuAcc_Test") +{ + std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc, + armnn::Compute::CpuRef }; + QuantizeInt16Int16Test(backends); +} + +TEST_CASE ("QUANTIZE_INT16_INT8_GpuAcc_Test") +{ + std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc, + armnn::Compute::CpuRef }; + QuantizeInt16Int8Test(backends); +} + +TEST_CASE ("QUANTIZE_INT16_INT8_CpuAcc_Test") +{ + std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc, + armnn::Compute::CpuRef }; + QuantizeInt16Int8Test(backends); +} + +TEST_CASE ("QUANTIZE_INT8_UINT8_GpuAcc_Test") +{ + std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc, + armnn::Compute::CpuRef }; + QuantizeInt8Uint8Test(backends); +} + +TEST_CASE ("QUANTIZE_INT8_UINT8_CpuAcc_Test") +{ + std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc, + armnn::Compute::CpuRef }; + QuantizeInt8Uint8Test(backends); +} + +TEST_CASE ("QUANTIZE_UINT8_INT8_GpuAcc_Test") +{ + std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc, + armnn::Compute::CpuRef }; + QuantizeUint8Int8Test(backends); +} + +TEST_CASE ("QUANTIZE_UINT8_INT8_CpuAcc_Test") +{ + std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc, + armnn::Compute::CpuRef }; + QuantizeUint8Int8Test(backends); +} + +} + +} // namespace armnnDelegate
\ No newline at end of file diff --git a/delegate/src/test/QuantizationTestHelper.hpp b/delegate/src/test/QuantizationTestHelper.hpp new file mode 100644 index 0000000000..2843e43233 --- /dev/null +++ b/delegate/src/test/QuantizationTestHelper.hpp @@ -0,0 +1,197 @@ +// +// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#pragma once + +#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> + +namespace +{ + +std::vector<char> CreateQuantizationTfLiteModel(tflite::BuiltinOperator quantizationOperatorCode, + tflite::TensorType inputTensorType, + tflite::TensorType outputTensorType, + const std::vector <int32_t>& inputTensorShape, + const std::vector <int32_t>& outputTensorShape, + float quantScale = 1.0f, + int quantOffset = 0) +{ + using namespace tflite; + flatbuffers::FlatBufferBuilder flatBufferBuilder; + + std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; + buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + + auto quantizationParameters = + CreateQuantizationParameters(flatBufferBuilder, + 0, + 0, + flatBufferBuilder.CreateVector<float>({ quantScale }), + flatBufferBuilder.CreateVector<int64_t>({ quantOffset }), + QuantizationDetails_CustomQuantization); + + std::array<flatbuffers::Offset<Tensor>, 2> tensors; + tensors[0] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), + inputTensorShape.size()), + inputTensorType, + 0, + flatBufferBuilder.CreateString("input"), + quantizationParameters); + tensors[1] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), + outputTensorShape.size()), + outputTensorType, + 0, + flatBufferBuilder.CreateString("output"), + quantizationParameters); + + // create operator + tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_NONE; + flatbuffers::Offset<void> operatorBuiltinOptions = 0; + switch (quantizationOperatorCode) + { + case BuiltinOperator_QUANTIZE: + { + operatorBuiltinOptionsType = BuiltinOptions_QuantizeOptions; + operatorBuiltinOptions = CreateQuantizeOptions(flatBufferBuilder).Union(); + break; + } + case BuiltinOperator_DEQUANTIZE: + { + operatorBuiltinOptionsType = BuiltinOptions_DequantizeOptions; + operatorBuiltinOptions = CreateDequantizeOptions(flatBufferBuilder).Union(); + break; + } + default: + break; + } + + const std::vector<int32_t> operatorInputs{ {0} }; + const std::vector<int32_t> operatorOutputs{{1}}; + flatbuffers::Offset <Operator> quantizationOperator = + 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} }; + const std::vector<int> 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(&quantizationOperator, 1)); + + flatbuffers::Offset <flatbuffers::String> modelDescription = + flatBufferBuilder.CreateString("ArmnnDelegate: Quantization Operator Model"); + flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, quantizationOperatorCode); + + 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 InputT, typename OutputT> +void QuantizationTest(tflite::BuiltinOperator quantizeOperatorCode, + tflite::TensorType inputTensorType, + tflite::TensorType outputTensorType, + std::vector<armnn::BackendId>& backends, + std::vector<int32_t>& inputShape, + std::vector<int32_t>& outputShape, + std::vector<InputT>& inputValues, + std::vector<OutputT>& expectedOutputValues, + float quantScale = 1.0f, + int quantOffset = 0) +{ + using namespace tflite; + std::vector<char> modelBuffer = CreateQuantizationTfLiteModel(quantizeOperatorCode, + inputTensorType, + outputTensorType, + inputShape, + outputShape, + quantScale, + quantOffset); + + 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 tfLiteDelageInputData = tfLiteInterpreter->typed_tensor<InputT>(tfLiteDelegateInputId); + for (unsigned int i = 0; i < inputValues.size(); ++i) + { + tfLiteDelageInputData[i] = inputValues[i]; + } + + auto armnnDelegateInputId = armnnDelegateInterpreter->inputs()[0]; + auto armnnDelegateInputData = armnnDelegateInterpreter->typed_tensor<InputT>(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 tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0]; + auto tfLiteDelageOutputData = tfLiteInterpreter->typed_tensor<OutputT>(tfLiteDelegateOutputId); + auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0]; + auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<OutputT>(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]); + } +} + +} // anonymous namespace
\ No newline at end of file |