telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2017 Arm Ltd. All rights reserved. |
David Beck | ecb56cd | 2018-09-05 12:52:57 +0100 | [diff] [blame] | 3 | // SPDX-License-Identifier: MIT |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 4 | // |
| 5 | |
| 6 | #pragma once |
| 7 | |
| 8 | #include <boost/filesystem.hpp> |
| 9 | #include <boost/assert.hpp> |
| 10 | #include <boost/format.hpp> |
| 11 | #include <experimental/filesystem> |
| 12 | #include <armnn/IRuntime.hpp> |
| 13 | #include <armnn/TypesUtils.hpp> |
| 14 | #include "test/TensorHelpers.hpp" |
| 15 | |
| 16 | #include "armnnTfLiteParser/ITfLiteParser.hpp" |
| 17 | |
| 18 | #include "flatbuffers/idl.h" |
| 19 | #include "flatbuffers/util.h" |
| 20 | |
| 21 | #include <schema_generated.h> |
| 22 | #include <iostream> |
| 23 | |
| 24 | using armnnTfLiteParser::ITfLiteParser; |
| 25 | using TensorRawPtr = const tflite::TensorT *; |
| 26 | |
| 27 | struct ParserFlatbuffersFixture |
| 28 | { |
| 29 | ParserFlatbuffersFixture() |
| 30 | : m_Parser(ITfLiteParser::Create()), m_NetworkIdentifier(-1) |
| 31 | { |
| 32 | armnn::IRuntime::CreationOptions options; |
| 33 | m_Runtimes.push_back(std::make_pair(armnn::IRuntime::Create(options), armnn::Compute::CpuRef)); |
| 34 | |
| 35 | #if ARMCOMPUTENEON_ENABLED |
| 36 | m_Runtimes.push_back(std::make_pair(armnn::IRuntime::Create(options), armnn::Compute::CpuAcc)); |
| 37 | #endif |
| 38 | |
| 39 | #if ARMCOMPUTECL_ENABLED |
| 40 | m_Runtimes.push_back(std::make_pair(armnn::IRuntime::Create(options), armnn::Compute::GpuAcc)); |
| 41 | #endif |
| 42 | } |
| 43 | |
| 44 | std::vector<uint8_t> m_GraphBinary; |
| 45 | std::string m_JsonString; |
| 46 | std::unique_ptr<ITfLiteParser, void (*)(ITfLiteParser *parser)> m_Parser; |
David Beck | f0b4845 | 2018-10-19 15:20:56 +0100 | [diff] [blame^] | 47 | std::vector<std::pair<armnn::IRuntimePtr, armnn::BackendId>> m_Runtimes; |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 48 | armnn::NetworkId m_NetworkIdentifier; |
| 49 | |
| 50 | /// If the single-input-single-output overload of Setup() is called, these will store the input and output name |
| 51 | /// so they don't need to be passed to the single-input-single-output overload of RunTest(). |
| 52 | std::string m_SingleInputName; |
| 53 | std::string m_SingleOutputName; |
| 54 | |
| 55 | void Setup() |
| 56 | { |
| 57 | bool ok = ReadStringToBinary(); |
| 58 | if (!ok) { |
| 59 | throw armnn::Exception("LoadNetwork failed while reading binary input"); |
| 60 | } |
| 61 | |
| 62 | for (auto&& runtime : m_Runtimes) |
| 63 | { |
| 64 | armnn::INetworkPtr network = |
| 65 | m_Parser->CreateNetworkFromBinary(m_GraphBinary); |
| 66 | |
| 67 | if (!network) { |
| 68 | throw armnn::Exception("The parser failed to create an ArmNN network"); |
| 69 | } |
| 70 | |
| 71 | auto optimized = Optimize(*network, |
| 72 | { runtime.second, armnn::Compute::CpuRef }, |
| 73 | runtime.first->GetDeviceSpec()); |
| 74 | std::string errorMessage; |
| 75 | |
| 76 | armnn::Status ret = runtime.first->LoadNetwork(m_NetworkIdentifier, |
| 77 | move(optimized), |
| 78 | errorMessage); |
| 79 | |
| 80 | if (ret != armnn::Status::Success) |
| 81 | { |
| 82 | throw armnn::Exception( |
| 83 | boost::str( |
| 84 | boost::format("The runtime failed to load the network. " |
| 85 | "Error was: %1%. in %2% [%3%:%4%]") % |
| 86 | errorMessage % |
| 87 | __func__ % |
| 88 | __FILE__ % |
| 89 | __LINE__)); |
| 90 | } |
| 91 | } |
| 92 | } |
| 93 | |
| 94 | void SetupSingleInputSingleOutput(const std::string& inputName, const std::string& outputName) |
| 95 | { |
| 96 | // Store the input and output name so they don't need to be passed to the single-input-single-output RunTest(). |
| 97 | m_SingleInputName = inputName; |
| 98 | m_SingleOutputName = outputName; |
| 99 | Setup(); |
| 100 | } |
| 101 | |
| 102 | bool ReadStringToBinary() |
| 103 | { |
| 104 | const char* schemafileName = getenv("ARMNN_TF_LITE_SCHEMA_PATH"); |
| 105 | if (schemafileName == nullptr) |
| 106 | { |
| 107 | schemafileName = ARMNN_TF_LITE_SCHEMA_PATH; |
| 108 | } |
| 109 | std::string schemafile; |
| 110 | |
| 111 | bool ok = flatbuffers::LoadFile(schemafileName, false, &schemafile); |
| 112 | BOOST_ASSERT_MSG(ok, "Couldn't load schema file " ARMNN_TF_LITE_SCHEMA_PATH); |
| 113 | if (!ok) |
| 114 | { |
| 115 | return false; |
| 116 | } |
| 117 | |
| 118 | // parse schema first, so we can use it to parse the data after |
| 119 | flatbuffers::Parser parser; |
| 120 | |
| 121 | ok &= parser.Parse(schemafile.c_str()); |
| 122 | BOOST_ASSERT_MSG(ok, "Failed to parse schema file"); |
| 123 | |
| 124 | ok &= parser.Parse(m_JsonString.c_str()); |
| 125 | BOOST_ASSERT_MSG(ok, "Failed to parse json input"); |
| 126 | |
| 127 | if (!ok) |
| 128 | { |
| 129 | return false; |
| 130 | } |
| 131 | |
| 132 | { |
| 133 | const uint8_t * bufferPtr = parser.builder_.GetBufferPointer(); |
| 134 | size_t size = static_cast<size_t>(parser.builder_.GetSize()); |
| 135 | m_GraphBinary.assign(bufferPtr, bufferPtr+size); |
| 136 | } |
| 137 | return ok; |
| 138 | } |
| 139 | |
| 140 | /// Executes the network with the given input tensor and checks the result against the given output tensor. |
| 141 | /// This overload assumes the network has a single input and a single output. |
| 142 | template <std::size_t NumOutputDimensions, typename DataType> |
| 143 | void RunTest(size_t subgraphId, |
| 144 | const std::vector<DataType>& inputData, |
| 145 | const std::vector<DataType>& expectedOutputData); |
| 146 | |
| 147 | /// Executes the network with the given input tensors and checks the results against the given output tensors. |
| 148 | /// This overload supports multiple inputs and multiple outputs, identified by name. |
| 149 | template <std::size_t NumOutputDimensions, typename DataType> |
| 150 | void RunTest(size_t subgraphId, |
| 151 | const std::map<std::string, std::vector<DataType>>& inputData, |
| 152 | const std::map<std::string, std::vector<DataType>>& expectedOutputData); |
| 153 | |
| 154 | void CheckTensors(const TensorRawPtr& tensors, size_t shapeSize, const std::vector<int32_t>& shape, |
| 155 | tflite::TensorType tensorType, uint32_t buffer, const std::string& name, |
| 156 | const std::vector<float>& min, const std::vector<float>& max, |
| 157 | const std::vector<float>& scale, const std::vector<int64_t>& zeroPoint) |
| 158 | { |
| 159 | BOOST_CHECK(tensors); |
| 160 | BOOST_CHECK_EQUAL(shapeSize, tensors->shape.size()); |
| 161 | BOOST_CHECK_EQUAL_COLLECTIONS(shape.begin(), shape.end(), tensors->shape.begin(), tensors->shape.end()); |
| 162 | BOOST_CHECK_EQUAL(tensorType, tensors->type); |
| 163 | BOOST_CHECK_EQUAL(buffer, tensors->buffer); |
| 164 | BOOST_CHECK_EQUAL(name, tensors->name); |
| 165 | BOOST_CHECK(tensors->quantization); |
| 166 | BOOST_CHECK_EQUAL_COLLECTIONS(min.begin(), min.end(), tensors->quantization.get()->min.begin(), |
| 167 | tensors->quantization.get()->min.end()); |
| 168 | BOOST_CHECK_EQUAL_COLLECTIONS(max.begin(), max.end(), tensors->quantization.get()->max.begin(), |
| 169 | tensors->quantization.get()->max.end()); |
| 170 | BOOST_CHECK_EQUAL_COLLECTIONS(scale.begin(), scale.end(), tensors->quantization.get()->scale.begin(), |
| 171 | tensors->quantization.get()->scale.end()); |
| 172 | BOOST_CHECK_EQUAL_COLLECTIONS(zeroPoint.begin(), zeroPoint.end(), |
| 173 | tensors->quantization.get()->zero_point.begin(), |
| 174 | tensors->quantization.get()->zero_point.end()); |
| 175 | } |
| 176 | }; |
| 177 | |
| 178 | template <std::size_t NumOutputDimensions, typename DataType> |
| 179 | void ParserFlatbuffersFixture::RunTest(size_t subgraphId, |
| 180 | const std::vector<DataType>& inputData, |
| 181 | const std::vector<DataType>& expectedOutputData) |
| 182 | { |
| 183 | RunTest<NumOutputDimensions, DataType>(subgraphId, |
| 184 | { { m_SingleInputName, inputData } }, |
| 185 | { { m_SingleOutputName, expectedOutputData } }); |
| 186 | } |
| 187 | |
| 188 | template <std::size_t NumOutputDimensions, typename DataType> |
| 189 | void |
| 190 | ParserFlatbuffersFixture::RunTest(size_t subgraphId, |
| 191 | const std::map<std::string, std::vector<DataType>>& inputData, |
| 192 | const std::map<std::string, std::vector<DataType>>& expectedOutputData) |
| 193 | { |
| 194 | for (auto&& runtime : m_Runtimes) |
| 195 | { |
| 196 | using BindingPointInfo = std::pair<armnn::LayerBindingId, armnn::TensorInfo>; |
| 197 | |
| 198 | // Setup the armnn input tensors from the given vectors. |
| 199 | armnn::InputTensors inputTensors; |
| 200 | for (auto&& it : inputData) |
| 201 | { |
| 202 | BindingPointInfo bindingInfo = m_Parser->GetNetworkInputBindingInfo(subgraphId, it.first); |
| 203 | armnn::VerifyTensorInfoDataType<DataType>(bindingInfo.second); |
| 204 | inputTensors.push_back({ bindingInfo.first, armnn::ConstTensor(bindingInfo.second, it.second.data()) }); |
| 205 | } |
| 206 | |
| 207 | // Allocate storage for the output tensors to be written to and setup the armnn output tensors. |
| 208 | std::map<std::string, boost::multi_array<DataType, NumOutputDimensions>> outputStorage; |
| 209 | armnn::OutputTensors outputTensors; |
| 210 | for (auto&& it : expectedOutputData) |
| 211 | { |
| 212 | BindingPointInfo bindingInfo = m_Parser->GetNetworkOutputBindingInfo(subgraphId, it.first); |
| 213 | armnn::VerifyTensorInfoDataType<DataType>(bindingInfo.second); |
| 214 | outputStorage.emplace(it.first, MakeTensor<DataType, NumOutputDimensions>(bindingInfo.second)); |
| 215 | outputTensors.push_back( |
| 216 | { bindingInfo.first, armnn::Tensor(bindingInfo.second, outputStorage.at(it.first).data()) }); |
| 217 | } |
| 218 | |
| 219 | runtime.first->EnqueueWorkload(m_NetworkIdentifier, inputTensors, outputTensors); |
| 220 | |
| 221 | // Compare each output tensor to the expected values |
| 222 | for (auto&& it : expectedOutputData) |
| 223 | { |
| 224 | BindingPointInfo bindingInfo = m_Parser->GetNetworkOutputBindingInfo(subgraphId, it.first); |
| 225 | auto outputExpected = MakeTensor<DataType, NumOutputDimensions>(bindingInfo.second, it.second); |
| 226 | BOOST_TEST(CompareTensors(outputExpected, outputStorage[it.first])); |
| 227 | } |
| 228 | } |
| 229 | } |