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-rw-r--r--src/armnnDeserializeParser/test/ParserFlatbuffersSerializeFixture.hpp199
1 files changed, 0 insertions, 199 deletions
diff --git a/src/armnnDeserializeParser/test/ParserFlatbuffersSerializeFixture.hpp b/src/armnnDeserializeParser/test/ParserFlatbuffersSerializeFixture.hpp
deleted file mode 100644
index 5d8c377981..0000000000
--- a/src/armnnDeserializeParser/test/ParserFlatbuffersSerializeFixture.hpp
+++ /dev/null
@@ -1,199 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#pragma once
-
-#include "SchemaSerialize.hpp"
-
-#include <armnn/IRuntime.hpp>
-#include <armnnDeserializeParser/IDeserializeParser.hpp>
-
-#include <boost/assert.hpp>
-#include <boost/format.hpp>
-
-#include "TypeUtils.hpp"
-#include "test/TensorHelpers.hpp"
-
-#include "flatbuffers/idl.h"
-#include "flatbuffers/util.h"
-
-#include <Schema_generated.h>
-
-using armnnDeserializeParser::IDeserializeParser;
-using TensorRawPtr = armnn::armnnSerializer::TensorInfo*;
-
-struct ParserFlatbuffersSerializeFixture
-{
- ParserFlatbuffersSerializeFixture() :
- m_Parser(IDeserializeParser::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<IDeserializeParser, void (*)(IDeserializeParser* 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(&deserialize_schema_start, &deserialize_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(unsigned int layersId,
- 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(unsigned int layersId,
- 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,
- armnn::armnnSerializer::TensorInfo tensorType, const std::string& name,
- const float scale, const int64_t zeroPoint)
- {
- BOOST_CHECK_EQUAL(shapeSize, tensors->dimensions()->size());
- BOOST_CHECK_EQUAL_COLLECTIONS(shape.begin(), shape.end(),
- tensors->dimensions()->begin(), tensors->dimensions()->end());
- BOOST_CHECK_EQUAL(tensorType.dataType(), tensors->dataType());
- BOOST_CHECK_EQUAL(scale, tensors->quantizationScale());
- BOOST_CHECK_EQUAL(zeroPoint, tensors->quantizationOffset());
- }
-};
-
-template <std::size_t NumOutputDimensions,
- armnn::DataType ArmnnType,
- typename DataType>
-void ParserFlatbuffersSerializeFixture::RunTest(unsigned int layersId,
- const std::vector<DataType>& inputData,
- const std::vector<DataType>& expectedOutputData)
-{
- RunTest<NumOutputDimensions, ArmnnType>(layersId,
- { { m_SingleInputName, inputData } },
- { { m_SingleOutputName, expectedOutputData } });
-}
-
-template <std::size_t NumOutputDimensions,
- armnn::DataType ArmnnType,
- typename DataType>
-void ParserFlatbuffersSerializeFixture::RunTest(unsigned int layersId,
- 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(layersId, 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(layersId, 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(layersId, it.first);
- auto outputExpected = MakeTensor<DataType, NumOutputDimensions>(bindingInfo.second, it.second);
- BOOST_TEST(CompareTensors(outputExpected, outputStorage[it.first]));
- }
-}
generated by cgit v1.2.1 (git 2.23.0) at 2024-07-18 11:13:50 +0000