src/armnnTfLiteParser/test/ParserFlatbuffersFixture.hpp - ml/armnn - Gitiles

 //
 // Copyright © 2017 Arm Ltd. All rights reserved.
 // SPDX-License-Identifier: MIT
 //

 #pragma once

 #include <boost/filesystem.hpp>
 #include <boost/assert.hpp>
 #include <boost/format.hpp>
 #include <experimental/filesystem>
 #include <armnn/IRuntime.hpp>
 #include <armnn/TypesUtils.hpp>
 #include "test/TensorHelpers.hpp"

 #include "armnnTfLiteParser/ITfLiteParser.hpp"

 #include "flatbuffers/idl.h"
 #include "flatbuffers/util.h"

 #include <schema_generated.h>
 #include <iostream>

 using armnnTfLiteParser::ITfLiteParser;
 using TensorRawPtr = const tflite::TensorT *;

 struct ParserFlatbuffersFixture
 {
     ParserFlatbuffersFixture()
             : m_Parser(ITfLiteParser::Create()), m_NetworkIdentifier(-1)
     {
         armnn::IRuntime::CreationOptions options;
         m_Runtimes.push_back(std::make_pair(armnn::IRuntime::Create(options), armnn::Compute::CpuRef));

 #if ARMCOMPUTENEON_ENABLED
         m_Runtimes.push_back(std::make_pair(armnn::IRuntime::Create(options), armnn::Compute::CpuAcc));
 #endif

 #if ARMCOMPUTECL_ENABLED
         m_Runtimes.push_back(std::make_pair(armnn::IRuntime::Create(options), armnn::Compute::GpuAcc));
 #endif
     }

     std::vector<uint8_t> m_GraphBinary;
     std::string m_JsonString;
     std::unique_ptr<ITfLiteParser, void (*)(ITfLiteParser *parser)> m_Parser;
     std::vector<std::pair<armnn::IRuntimePtr, armnn::BackendId>> m_Runtimes;
     armnn::NetworkId m_NetworkIdentifier;

     /// If the single-input-single-output overload of Setup() is called, these will store the input and output name
     /// so they don't need to be passed to the single-input-single-output overload of RunTest().
     std::string m_SingleInputName;
     std::string m_SingleOutputName;

     void Setup()
     {
         bool ok = ReadStringToBinary();
         if (!ok) {
             throw armnn::Exception("LoadNetwork failed while reading binary input");
         }

         for (auto&& runtime : m_Runtimes)
         {
             armnn::INetworkPtr network =
                     m_Parser->CreateNetworkFromBinary(m_GraphBinary);

             if (!network) {
                 throw armnn::Exception("The parser failed to create an ArmNN network");
             }

             auto optimized = Optimize(*network,
                                       { runtime.second, armnn::Compute::CpuRef },
                                       runtime.first->GetDeviceSpec());
             std::string errorMessage;

             armnn::Status ret = runtime.first->LoadNetwork(m_NetworkIdentifier,
                                                      move(optimized),
                                                      errorMessage);

             if (ret != armnn::Status::Success)
             {
                 throw armnn::Exception(
                     boost::str(
                         boost::format("The runtime failed to load the network. "
                                       "Error was: %1%. in %2% [%3%:%4%]") %
                         errorMessage %
                         __func__ %
                         __FILE__ %
                         __LINE__));
             }
         }
     }

     void SetupSingleInputSingleOutput(const std::string& inputName, const std::string& outputName)
     {
         // Store the input and output name so they don't need to be passed to the single-input-single-output RunTest().
         m_SingleInputName = inputName;
         m_SingleOutputName = outputName;
         Setup();
     }

     bool ReadStringToBinary()
     {
         const char* schemafileName = getenv("ARMNN_TF_LITE_SCHEMA_PATH");
         if (schemafileName == nullptr)
         {
             schemafileName = ARMNN_TF_LITE_SCHEMA_PATH;
         }
         std::string schemafile;

         bool ok = flatbuffers::LoadFile(schemafileName, false, &schemafile);
         BOOST_ASSERT_MSG(ok, "Couldn't load schema file " ARMNN_TF_LITE_SCHEMA_PATH);
         if (!ok)
         {
             return false;
         }

         // parse schema first, so we can use it to parse the data after
         flatbuffers::Parser parser;

         ok &= parser.Parse(schemafile.c_str());
         BOOST_ASSERT_MSG(ok, "Failed to parse schema file");

         ok &= parser.Parse(m_JsonString.c_str());
         BOOST_ASSERT_MSG(ok, "Failed to parse json input");

         if (!ok)
         {
             return false;
         }

         {
             const uint8_t * bufferPtr = parser.builder_.GetBufferPointer();
             size_t size = static_cast<size_t>(parser.builder_.GetSize());
             m_GraphBinary.assign(bufferPtr, bufferPtr+size);
         }
         return ok;
     }

     /// Executes the network with the given input tensor and checks the result against the given output tensor.
     /// This overload assumes the network has a single input and a single output.
     template <std::size_t NumOutputDimensions, typename DataType>
     void RunTest(size_t subgraphId,
          const std::vector<DataType>& inputData,
          const std::vector<DataType>& expectedOutputData);

     /// Executes the network with the given input tensors and checks the results against the given output tensors.
     /// This overload supports multiple inputs and multiple outputs, identified by name.
     template <std::size_t NumOutputDimensions, typename DataType>
     void RunTest(size_t subgraphId,
                  const std::map<std::string, std::vector<DataType>>& inputData,
                  const std::map<std::string, std::vector<DataType>>& expectedOutputData);

     void CheckTensors(const TensorRawPtr& tensors, size_t shapeSize, const std::vector<int32_t>& shape,
                       tflite::TensorType tensorType, uint32_t buffer, const std::string& name,
                       const std::vector<float>& min, const std::vector<float>& max,
                       const std::vector<float>& scale, const std::vector<int64_t>& zeroPoint)
     {
         BOOST_CHECK(tensors);
         BOOST_CHECK_EQUAL(shapeSize, tensors->shape.size());
         BOOST_CHECK_EQUAL_COLLECTIONS(shape.begin(), shape.end(), tensors->shape.begin(), tensors->shape.end());
         BOOST_CHECK_EQUAL(tensorType, tensors->type);
         BOOST_CHECK_EQUAL(buffer, tensors->buffer);
         BOOST_CHECK_EQUAL(name, tensors->name);
         BOOST_CHECK(tensors->quantization);
         BOOST_CHECK_EQUAL_COLLECTIONS(min.begin(), min.end(), tensors->quantization.get()->min.begin(),
                                       tensors->quantization.get()->min.end());
         BOOST_CHECK_EQUAL_COLLECTIONS(max.begin(), max.end(), tensors->quantization.get()->max.begin(),
                                       tensors->quantization.get()->max.end());
         BOOST_CHECK_EQUAL_COLLECTIONS(scale.begin(), scale.end(), tensors->quantization.get()->scale.begin(),
                                       tensors->quantization.get()->scale.end());
         BOOST_CHECK_EQUAL_COLLECTIONS(zeroPoint.begin(), zeroPoint.end(),
                                       tensors->quantization.get()->zero_point.begin(),
                                       tensors->quantization.get()->zero_point.end());
     }
 };

 template <std::size_t NumOutputDimensions, typename DataType>
 void ParserFlatbuffersFixture::RunTest(size_t subgraphId,
                                        const std::vector<DataType>& inputData,
                                        const std::vector<DataType>& expectedOutputData)
 {
     RunTest<NumOutputDimensions, DataType>(subgraphId,
                                            { { m_SingleInputName, inputData } },
                                            { { m_SingleOutputName, expectedOutputData } });
 }

 template <std::size_t NumOutputDimensions, typename DataType>
 void
 ParserFlatbuffersFixture::RunTest(size_t subgraphId,
                                   const std::map<std::string, std::vector<DataType>>& inputData,
                                   const std::map<std::string, std::vector<DataType>>& expectedOutputData)
 {
     for (auto&& runtime : m_Runtimes)
     {
         using BindingPointInfo = std::pair<armnn::LayerBindingId, armnn::TensorInfo>;

         // Setup the armnn input tensors from the given vectors.
         armnn::InputTensors inputTensors;
         for (auto&& it : inputData)
         {
             BindingPointInfo bindingInfo = m_Parser->GetNetworkInputBindingInfo(subgraphId, it.first);
             armnn::VerifyTensorInfoDataType<DataType>(bindingInfo.second);
             inputTensors.push_back({ bindingInfo.first, armnn::ConstTensor(bindingInfo.second, it.second.data()) });
         }

         // Allocate storage for the output tensors to be written to and setup the armnn output tensors.
         std::map<std::string, boost::multi_array<DataType, NumOutputDimensions>> outputStorage;
         armnn::OutputTensors outputTensors;
         for (auto&& it : expectedOutputData)
         {
             BindingPointInfo bindingInfo = m_Parser->GetNetworkOutputBindingInfo(subgraphId, it.first);
             armnn::VerifyTensorInfoDataType<DataType>(bindingInfo.second);
             outputStorage.emplace(it.first, MakeTensor<DataType, NumOutputDimensions>(bindingInfo.second));
             outputTensors.push_back(
                     { bindingInfo.first, armnn::Tensor(bindingInfo.second, outputStorage.at(it.first).data()) });
         }

         runtime.first->EnqueueWorkload(m_NetworkIdentifier, inputTensors, outputTensors);

         // Compare each output tensor to the expected values
         for (auto&& it : expectedOutputData)
         {
             BindingPointInfo bindingInfo = m_Parser->GetNetworkOutputBindingInfo(subgraphId, it.first);
             auto outputExpected = MakeTensor<DataType, NumOutputDimensions>(bindingInfo.second, it.second);
             BOOST_TEST(CompareTensors(outputExpected, outputStorage[it.first]));
         }
     }
 }
	//
	// Copyright © 2017 Arm Ltd. All rights reserved.
	// SPDX-License-Identifier: MIT
	//

	#pragma once

	#include <boost/filesystem.hpp>
	#include <boost/assert.hpp>
	#include <boost/format.hpp>
	#include <experimental/filesystem>
	#include <armnn/IRuntime.hpp>
	#include <armnn/TypesUtils.hpp>
	#include "test/TensorHelpers.hpp"

	#include "armnnTfLiteParser/ITfLiteParser.hpp"

	#include "flatbuffers/idl.h"
	#include "flatbuffers/util.h"

	#include <schema_generated.h>
	#include <iostream>

	using armnnTfLiteParser::ITfLiteParser;
	using TensorRawPtr = const tflite::TensorT *;

	struct ParserFlatbuffersFixture
	{
	ParserFlatbuffersFixture()
	: m_Parser(ITfLiteParser::Create()), m_NetworkIdentifier(-1)
	{
	armnn::IRuntime::CreationOptions options;
	m_Runtimes.push_back(std::make_pair(armnn::IRuntime::Create(options), armnn::Compute::CpuRef));

	#if ARMCOMPUTENEON_ENABLED
	m_Runtimes.push_back(std::make_pair(armnn::IRuntime::Create(options), armnn::Compute::CpuAcc));
	#endif

	#if ARMCOMPUTECL_ENABLED
	m_Runtimes.push_back(std::make_pair(armnn::IRuntime::Create(options), armnn::Compute::GpuAcc));
	#endif
	}

	std::vector<uint8_t> m_GraphBinary;
	std::string m_JsonString;
	std::unique_ptr<ITfLiteParser, void ()(ITfLiteParser parser)> m_Parser;
	std::vector<std::pair<armnn::IRuntimePtr, armnn::BackendId>> m_Runtimes;
	armnn::NetworkId m_NetworkIdentifier;

	/// If the single-input-single-output overload of Setup() is called, these will store the input and output name
	/// so they don't need to be passed to the single-input-single-output overload of RunTest().
	std::string m_SingleInputName;
	std::string m_SingleOutputName;

	void Setup()
	{
	bool ok = ReadStringToBinary();
	if (!ok) {
	throw armnn::Exception("LoadNetwork failed while reading binary input");
	}

	for (auto&& runtime : m_Runtimes)
	{
	armnn::INetworkPtr network =
	m_Parser->CreateNetworkFromBinary(m_GraphBinary);

	if (!network) {
	throw armnn::Exception("The parser failed to create an ArmNN network");
	}

	auto optimized = Optimize(*network,
	{ runtime.second, armnn::Compute::CpuRef },
	runtime.first->GetDeviceSpec());
	std::string errorMessage;

	armnn::Status ret = runtime.first->LoadNetwork(m_NetworkIdentifier,
	move(optimized),
	errorMessage);

	if (ret != armnn::Status::Success)
	{
	throw armnn::Exception(
	boost::str(
	boost::format("The runtime failed to load the network. "
	"Error was: %1%. in %2% [%3%:%4%]") %
	errorMessage %
	__func__ %
	__FILE__ %
	__LINE__));
	}
	}
	}

	void SetupSingleInputSingleOutput(const std::string& inputName, const std::string& outputName)
	{
	// Store the input and output name so they don't need to be passed to the single-input-single-output RunTest().
	m_SingleInputName = inputName;
	m_SingleOutputName = outputName;
	Setup();
	}

	bool ReadStringToBinary()
	{
	const char* schemafileName = getenv("ARMNN_TF_LITE_SCHEMA_PATH");
	if (schemafileName == nullptr)
	{
	schemafileName = ARMNN_TF_LITE_SCHEMA_PATH;
	}
	std::string schemafile;

	bool ok = flatbuffers::LoadFile(schemafileName, false, &schemafile);
	BOOST_ASSERT_MSG(ok, "Couldn't load schema file " ARMNN_TF_LITE_SCHEMA_PATH);
	if (!ok)
	{
	return false;
	}

	// parse schema first, so we can use it to parse the data after
	flatbuffers::Parser parser;

	ok &= parser.Parse(schemafile.c_str());
	BOOST_ASSERT_MSG(ok, "Failed to parse schema file");

	ok &= parser.Parse(m_JsonString.c_str());
	BOOST_ASSERT_MSG(ok, "Failed to parse json input");

	if (!ok)
	{
	return false;
	}

	{
	const uint8_t * bufferPtr = parser.builder_.GetBufferPointer();
	size_t size = static_cast<size_t>(parser.builder_.GetSize());
	m_GraphBinary.assign(bufferPtr, bufferPtr+size);
	}
	return ok;
	}

	/// Executes the network with the given input tensor and checks the result against the given output tensor.
	/// This overload assumes the network has a single input and a single output.
	template <std::size_t NumOutputDimensions, typename DataType>
	void RunTest(size_t subgraphId,
	const std::vector<DataType>& inputData,
	const std::vector<DataType>& expectedOutputData);

	/// Executes the network with the given input tensors and checks the results against the given output tensors.
	/// This overload supports multiple inputs and multiple outputs, identified by name.
	template <std::size_t NumOutputDimensions, typename DataType>
	void RunTest(size_t subgraphId,
	const std::map<std::string, std::vector<DataType>>& inputData,
	const std::map<std::string, std::vector<DataType>>& expectedOutputData);

	void CheckTensors(const TensorRawPtr& tensors, size_t shapeSize, const std::vector<int32_t>& shape,
	tflite::TensorType tensorType, uint32_t buffer, const std::string& name,
	const std::vector<float>& min, const std::vector<float>& max,
	const std::vector<float>& scale, const std::vector<int64_t>& zeroPoint)
	{
	BOOST_CHECK(tensors);
	BOOST_CHECK_EQUAL(shapeSize, tensors->shape.size());
	BOOST_CHECK_EQUAL_COLLECTIONS(shape.begin(), shape.end(), tensors->shape.begin(), tensors->shape.end());
	BOOST_CHECK_EQUAL(tensorType, tensors->type);
	BOOST_CHECK_EQUAL(buffer, tensors->buffer);
	BOOST_CHECK_EQUAL(name, tensors->name);
	BOOST_CHECK(tensors->quantization);
	BOOST_CHECK_EQUAL_COLLECTIONS(min.begin(), min.end(), tensors->quantization.get()->min.begin(),
	tensors->quantization.get()->min.end());
	BOOST_CHECK_EQUAL_COLLECTIONS(max.begin(), max.end(), tensors->quantization.get()->max.begin(),
	tensors->quantization.get()->max.end());
	BOOST_CHECK_EQUAL_COLLECTIONS(scale.begin(), scale.end(), tensors->quantization.get()->scale.begin(),
	tensors->quantization.get()->scale.end());
	BOOST_CHECK_EQUAL_COLLECTIONS(zeroPoint.begin(), zeroPoint.end(),
	tensors->quantization.get()->zero_point.begin(),
	tensors->quantization.get()->zero_point.end());
	}
	};

	template <std::size_t NumOutputDimensions, typename DataType>
	void ParserFlatbuffersFixture::RunTest(size_t subgraphId,
	const std::vector<DataType>& inputData,
	const std::vector<DataType>& expectedOutputData)
	{
	RunTest<NumOutputDimensions, DataType>(subgraphId,
	{ { m_SingleInputName, inputData } },
	{ { m_SingleOutputName, expectedOutputData } });
	}

	template <std::size_t NumOutputDimensions, typename DataType>
	void
	ParserFlatbuffersFixture::RunTest(size_t subgraphId,
	const std::map<std::string, std::vector<DataType>>& inputData,
	const std::map<std::string, std::vector<DataType>>& expectedOutputData)
	{
	for (auto&& runtime : m_Runtimes)
	{
	using BindingPointInfo = std::pair<armnn::LayerBindingId, armnn::TensorInfo>;

	// Setup the armnn input tensors from the given vectors.
	armnn::InputTensors inputTensors;
	for (auto&& it : inputData)
	{
	BindingPointInfo bindingInfo = m_Parser->GetNetworkInputBindingInfo(subgraphId, it.first);
	armnn::VerifyTensorInfoDataType<DataType>(bindingInfo.second);
	inputTensors.push_back({ bindingInfo.first, armnn::ConstTensor(bindingInfo.second, it.second.data()) });
	}

	// Allocate storage for the output tensors to be written to and setup the armnn output tensors.
	std::map<std::string, boost::multi_array<DataType, NumOutputDimensions>> outputStorage;
	armnn::OutputTensors outputTensors;
	for (auto&& it : expectedOutputData)
	{
	BindingPointInfo bindingInfo = m_Parser->GetNetworkOutputBindingInfo(subgraphId, it.first);
	armnn::VerifyTensorInfoDataType<DataType>(bindingInfo.second);
	outputStorage.emplace(it.first, MakeTensor<DataType, NumOutputDimensions>(bindingInfo.second));
	outputTensors.push_back(
	{ bindingInfo.first, armnn::Tensor(bindingInfo.second, outputStorage.at(it.first).data()) });
	}

	runtime.first->EnqueueWorkload(m_NetworkIdentifier, inputTensors, outputTensors);

	// Compare each output tensor to the expected values
	for (auto&& it : expectedOutputData)
	{
	BindingPointInfo bindingInfo = m_Parser->GetNetworkOutputBindingInfo(subgraphId, it.first);
	auto outputExpected = MakeTensor<DataType, NumOutputDimensions>(bindingInfo.second, it.second);
	BOOST_TEST(CompareTensors(outputExpected, outputStorage[it.first]));
	}
	}
	}