| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #pragma once |
| |
| #include "Schema.hpp" |
| #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 "TypeUtils.hpp" |
| #include "armnnTfLiteParser/ITfLiteParser.hpp" |
| |
| #include <backendsCommon/BackendRegistry.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_Runtime(armnn::IRuntime::Create(armnn::IRuntime::CreationOptions())), |
| m_NetworkIdentifier(-1) |
| { |
| } |
| |
| std::vector<uint8_t> m_GraphBinary; |
| std::string m_JsonString; |
| std::unique_ptr<ITfLiteParser, void (*)(ITfLiteParser *parser)> m_Parser; |
| armnn::IRuntimePtr m_Runtime; |
| 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"); |
| } |
| |
| 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, { armnn::Compute::CpuRef }, |
| m_Runtime->GetDeviceSpec()); |
| std::string errorMessage; |
| |
| armnn::Status ret = m_Runtime->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() |
| { |
| std::string schemafile(&tflite_schema_start, &tflite_schema_end); |
| |
| // parse schema first, so we can use it to parse the data after |
| flatbuffers::Parser parser; |
| |
| bool 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, |
| armnn::DataType ArmnnType, |
| typename DataType = armnn::ResolveType<ArmnnType>> |
| 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, |
| armnn::DataType ArmnnType, |
| typename DataType = armnn::ResolveType<ArmnnType>> |
| 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, |
| armnn::DataType ArmnnType, |
| typename DataType> |
| void ParserFlatbuffersFixture::RunTest(size_t subgraphId, |
| const std::vector<DataType>& inputData, |
| const std::vector<DataType>& expectedOutputData) |
| { |
| RunTest<NumOutputDimensions, ArmnnType>(subgraphId, |
| { { m_SingleInputName, inputData } }, |
| { { m_SingleOutputName, expectedOutputData } }); |
| } |
| |
| template <std::size_t NumOutputDimensions, |
| armnn::DataType ArmnnType, |
| 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) |
| { |
| 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<ArmnnType>(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<ArmnnType>(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()) }); |
| } |
| |
| m_Runtime->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])); |
| } |
| } |