Sadik Armagan | 29b49cf | 2021-02-22 18:09:07 +0000 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2021 Arm Ltd and Contributors. All rights reserved. |
| 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | |
| 6 | #pragma once |
| 7 | |
| 8 | #include "TestUtils.hpp" |
| 9 | |
| 10 | #include <armnn_delegate.hpp> |
| 11 | |
| 12 | #include <flatbuffers/flatbuffers.h> |
| 13 | #include <tensorflow/lite/interpreter.h> |
| 14 | #include <tensorflow/lite/kernels/register.h> |
| 15 | #include <tensorflow/lite/model.h> |
| 16 | #include <tensorflow/lite/schema/schema_generated.h> |
| 17 | #include <tensorflow/lite/version.h> |
| 18 | |
| 19 | #include <doctest/doctest.h> |
| 20 | |
| 21 | namespace |
| 22 | { |
| 23 | |
| 24 | template <typename T> |
| 25 | std::vector<char> CreateFillTfLiteModel(tflite::BuiltinOperator fillOperatorCode, |
| 26 | tflite::TensorType tensorType, |
| 27 | const std::vector<int32_t>& inputShape, |
| 28 | const std::vector <int32_t>& tensorShape, |
| 29 | const std::vector<T> fillValue) |
| 30 | { |
| 31 | using namespace tflite; |
| 32 | flatbuffers::FlatBufferBuilder flatBufferBuilder; |
| 33 | |
| 34 | std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; |
| 35 | buffers.push_back( |
| 36 | CreateBuffer(flatBufferBuilder, |
| 37 | flatBufferBuilder.CreateVector({}))); |
| 38 | buffers.push_back( |
| 39 | CreateBuffer(flatBufferBuilder, |
| 40 | flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(tensorShape.data()), |
| 41 | sizeof(int32_t) * tensorShape.size()))); |
| 42 | buffers.push_back( |
| 43 | CreateBuffer(flatBufferBuilder, |
| 44 | flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(fillValue.data()), |
| 45 | sizeof(T) * fillValue.size()))); |
| 46 | |
| 47 | std::array<flatbuffers::Offset<Tensor>, 3> tensors; |
| 48 | tensors[0] = CreateTensor(flatBufferBuilder, |
| 49 | flatBufferBuilder.CreateVector<int32_t>(inputShape.data(), |
| 50 | inputShape.size()), |
| 51 | tflite::TensorType_INT32, |
| 52 | 1, |
| 53 | flatBufferBuilder.CreateString("dims")); |
| 54 | |
| 55 | std::vector<int32_t> fillShape = {}; |
| 56 | tensors[1] = CreateTensor(flatBufferBuilder, |
| 57 | flatBufferBuilder.CreateVector<int32_t>(fillShape.data(), |
| 58 | fillShape.size()), |
| 59 | tensorType, |
| 60 | 2, |
| 61 | flatBufferBuilder.CreateString("value")); |
| 62 | |
| 63 | tensors[2] = CreateTensor(flatBufferBuilder, |
| 64 | flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), |
| 65 | tensorShape.size()), |
| 66 | tensorType, |
| 67 | 0, |
| 68 | flatBufferBuilder.CreateString("output")); |
| 69 | |
| 70 | tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_FillOptions; |
| 71 | flatbuffers::Offset<void> operatorBuiltinOptions = CreateFillOptions(flatBufferBuilder).Union(); |
| 72 | |
| 73 | // create operator |
| 74 | const std::vector<int> operatorInputs{ {0, 1} }; |
| 75 | const std::vector<int> operatorOutputs{ 2 }; |
| 76 | flatbuffers::Offset <Operator> fillOperator = |
| 77 | CreateOperator(flatBufferBuilder, |
| 78 | 0, |
| 79 | flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), |
| 80 | flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), |
| 81 | operatorBuiltinOptionsType, |
| 82 | operatorBuiltinOptions); |
| 83 | |
| 84 | const std::vector<int> subgraphInputs{ {0, 1} }; |
| 85 | const std::vector<int> subgraphOutputs{ 2 }; |
| 86 | flatbuffers::Offset <SubGraph> subgraph = |
| 87 | CreateSubGraph(flatBufferBuilder, |
| 88 | flatBufferBuilder.CreateVector(tensors.data(), tensors.size()), |
| 89 | flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()), |
| 90 | flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()), |
| 91 | flatBufferBuilder.CreateVector(&fillOperator, 1)); |
| 92 | |
| 93 | flatbuffers::Offset <flatbuffers::String> modelDescription = |
| 94 | flatBufferBuilder.CreateString("ArmnnDelegate: Fill Operator Model"); |
| 95 | flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, |
| 96 | fillOperatorCode); |
| 97 | |
| 98 | flatbuffers::Offset <Model> flatbufferModel = |
| 99 | CreateModel(flatBufferBuilder, |
| 100 | TFLITE_SCHEMA_VERSION, |
| 101 | flatBufferBuilder.CreateVector(&operatorCode, 1), |
| 102 | flatBufferBuilder.CreateVector(&subgraph, 1), |
| 103 | modelDescription, |
| 104 | flatBufferBuilder.CreateVector(buffers.data(), buffers.size())); |
| 105 | |
| 106 | flatBufferBuilder.Finish(flatbufferModel); |
| 107 | |
| 108 | return std::vector<char>(flatBufferBuilder.GetBufferPointer(), |
| 109 | flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); |
| 110 | |
| 111 | } |
| 112 | |
| 113 | template <typename T> |
| 114 | void FillTest(tflite::BuiltinOperator fillOperatorCode, |
| 115 | tflite::TensorType tensorType, |
| 116 | const std::vector<armnn::BackendId>& backends, |
| 117 | std::vector<int32_t >& inputShape, |
| 118 | std::vector<int32_t >& tensorShape, |
| 119 | std::vector<T>& expectedOutputValues, |
| 120 | T fillValue) |
| 121 | { |
| 122 | using namespace tflite; |
| 123 | std::vector<char> modelBuffer = CreateFillTfLiteModel<T>(fillOperatorCode, |
| 124 | tensorType, |
| 125 | inputShape, |
| 126 | tensorShape, |
| 127 | {fillValue}); |
| 128 | |
| 129 | const Model* tfLiteModel = GetModel(modelBuffer.data()); |
| 130 | CHECK(tfLiteModel != nullptr); |
| 131 | |
| 132 | std::unique_ptr<Interpreter> armnnDelegateInterpreter; |
| 133 | CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) |
| 134 | (&armnnDelegateInterpreter) == kTfLiteOk); |
| 135 | CHECK(armnnDelegateInterpreter != nullptr); |
| 136 | CHECK(armnnDelegateInterpreter->AllocateTensors() == kTfLiteOk); |
| 137 | |
| 138 | std::unique_ptr<Interpreter> tfLiteInterpreter; |
| 139 | CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) |
| 140 | (&tfLiteInterpreter) == kTfLiteOk); |
| 141 | CHECK(tfLiteInterpreter != nullptr); |
| 142 | CHECK(tfLiteInterpreter->AllocateTensors() == kTfLiteOk); |
| 143 | |
| 144 | // Create the ArmNN Delegate |
| 145 | armnnDelegate::DelegateOptions delegateOptions(backends); |
| 146 | std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)> |
| 147 | theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions), |
| 148 | armnnDelegate::TfLiteArmnnDelegateDelete); |
| 149 | CHECK(theArmnnDelegate != nullptr); |
| 150 | // Modify armnnDelegateInterpreter to use armnnDelegate |
| 151 | CHECK(armnnDelegateInterpreter->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk); |
| 152 | |
| 153 | // Run EnqueueWorkload |
| 154 | CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); |
| 155 | CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); |
| 156 | |
| 157 | armnnDelegate::CompareOutputData<T>(tfLiteInterpreter, armnnDelegateInterpreter, tensorShape, expectedOutputValues); |
| 158 | } |
| 159 | |
| 160 | } // anonymous namespace |