Sadik Armagan | 788e2c6 | 2021-02-10 16:26:44 +0000 | [diff] [blame] | 1 | // |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame^] | 2 | // Copyright © 2021, 2023 Arm Ltd and Contributors. All rights reserved. |
Sadik Armagan | 788e2c6 | 2021-02-10 16:26:44 +0000 | [diff] [blame] | 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 | std::vector<char> CreateRoundTfLiteModel(tflite::BuiltinOperator roundOperatorCode, |
| 24 | tflite::TensorType tensorType, |
| 25 | const std::vector <int32_t>& tensorShape, |
| 26 | float quantScale = 1.0f, |
| 27 | int quantOffset = 0) |
| 28 | { |
| 29 | using namespace tflite; |
| 30 | flatbuffers::FlatBufferBuilder flatBufferBuilder; |
| 31 | |
| 32 | std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame^] | 33 | buffers.push_back(CreateBuffer(flatBufferBuilder)); |
| 34 | buffers.push_back(CreateBuffer(flatBufferBuilder)); |
| 35 | buffers.push_back(CreateBuffer(flatBufferBuilder)); |
Sadik Armagan | 788e2c6 | 2021-02-10 16:26:44 +0000 | [diff] [blame] | 36 | |
| 37 | auto quantizationParameters = |
| 38 | CreateQuantizationParameters(flatBufferBuilder, |
| 39 | 0, |
| 40 | 0, |
| 41 | flatBufferBuilder.CreateVector<float>({quantScale}), |
| 42 | flatBufferBuilder.CreateVector<int64_t>({quantOffset})); |
| 43 | |
| 44 | std::array<flatbuffers::Offset<Tensor>, 2> tensors; |
| 45 | tensors[0] = CreateTensor(flatBufferBuilder, |
| 46 | flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), |
| 47 | tensorShape.size()), |
| 48 | tensorType, |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame^] | 49 | 1, |
Sadik Armagan | 788e2c6 | 2021-02-10 16:26:44 +0000 | [diff] [blame] | 50 | flatBufferBuilder.CreateString("input"), |
| 51 | quantizationParameters); |
| 52 | tensors[1] = CreateTensor(flatBufferBuilder, |
| 53 | flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), |
| 54 | tensorShape.size()), |
| 55 | tensorType, |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame^] | 56 | 2, |
Sadik Armagan | 788e2c6 | 2021-02-10 16:26:44 +0000 | [diff] [blame] | 57 | flatBufferBuilder.CreateString("output"), |
| 58 | quantizationParameters); |
| 59 | |
| 60 | const std::vector<int32_t> operatorInputs({0}); |
| 61 | const std::vector<int32_t> operatorOutputs({1}); |
| 62 | |
| 63 | flatbuffers::Offset<Operator> roundOperator; |
| 64 | flatbuffers::Offset<flatbuffers::String> modelDescription; |
| 65 | flatbuffers::Offset<OperatorCode> operatorCode; |
| 66 | |
| 67 | switch (roundOperatorCode) |
| 68 | { |
| 69 | case tflite::BuiltinOperator_FLOOR: |
| 70 | default: |
| 71 | roundOperator = |
| 72 | CreateOperator(flatBufferBuilder, |
| 73 | 0, |
| 74 | flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), |
| 75 | flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size())); |
| 76 | modelDescription = flatBufferBuilder.CreateString("ArmnnDelegate: Floor Operator Model"); |
| 77 | operatorCode = CreateOperatorCode(flatBufferBuilder, tflite::BuiltinOperator_FLOOR); |
| 78 | break; |
| 79 | } |
| 80 | const std::vector<int32_t> subgraphInputs({0}); |
| 81 | const std::vector<int32_t> subgraphOutputs({1}); |
| 82 | flatbuffers::Offset<SubGraph> subgraph = |
| 83 | CreateSubGraph(flatBufferBuilder, |
| 84 | flatBufferBuilder.CreateVector(tensors.data(), tensors.size()), |
| 85 | flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()), |
| 86 | flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()), |
| 87 | flatBufferBuilder.CreateVector(&roundOperator, 1)); |
| 88 | |
| 89 | flatbuffers::Offset<Model> flatbufferModel = |
| 90 | CreateModel(flatBufferBuilder, |
| 91 | TFLITE_SCHEMA_VERSION, |
| 92 | flatBufferBuilder.CreateVector(&operatorCode, 1), |
| 93 | flatBufferBuilder.CreateVector(&subgraph, 1), |
| 94 | modelDescription, |
| 95 | flatBufferBuilder.CreateVector(buffers.data(), buffers.size())); |
| 96 | |
| 97 | flatBufferBuilder.Finish(flatbufferModel); |
| 98 | return std::vector<char>(flatBufferBuilder.GetBufferPointer(), |
| 99 | flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); |
| 100 | } |
| 101 | |
| 102 | template<typename T> |
| 103 | void RoundTest(tflite::BuiltinOperator roundOperatorCode, |
| 104 | tflite::TensorType tensorType, |
| 105 | std::vector<armnn::BackendId>& backends, |
| 106 | std::vector<int32_t>& shape, |
| 107 | std::vector<T>& inputValues, |
| 108 | std::vector<T>& expectedOutputValues, |
| 109 | float quantScale = 1.0f, |
| 110 | int quantOffset = 0) |
| 111 | { |
| 112 | using namespace tflite; |
| 113 | std::vector<char> modelBuffer = CreateRoundTfLiteModel(roundOperatorCode, |
| 114 | tensorType, |
| 115 | shape, |
| 116 | quantScale, |
| 117 | quantOffset); |
| 118 | |
| 119 | const Model* tfLiteModel = GetModel(modelBuffer.data()); |
| 120 | |
| 121 | // Create TfLite Interpreters |
| 122 | std::unique_ptr<Interpreter> armnnDelegate; |
| 123 | CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) |
| 124 | (&armnnDelegate) == kTfLiteOk); |
| 125 | CHECK(armnnDelegate != nullptr); |
| 126 | CHECK(armnnDelegate->AllocateTensors() == kTfLiteOk); |
| 127 | |
| 128 | std::unique_ptr<Interpreter> tfLiteDelegate; |
| 129 | CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) |
| 130 | (&tfLiteDelegate) == kTfLiteOk); |
| 131 | CHECK(tfLiteDelegate != nullptr); |
| 132 | CHECK(tfLiteDelegate->AllocateTensors() == kTfLiteOk); |
| 133 | |
| 134 | // Create the ArmNN Delegate |
| 135 | armnnDelegate::DelegateOptions delegateOptions(backends); |
| 136 | std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)> |
| 137 | theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions), |
| 138 | armnnDelegate::TfLiteArmnnDelegateDelete); |
| 139 | CHECK(theArmnnDelegate != nullptr); |
| 140 | |
| 141 | // Modify armnnDelegateInterpreter to use armnnDelegate |
| 142 | CHECK(armnnDelegate->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk); |
| 143 | |
| 144 | // Set input data |
| 145 | armnnDelegate::FillInput<T>(tfLiteDelegate, 0, inputValues); |
| 146 | armnnDelegate::FillInput<T>(armnnDelegate, 0, inputValues); |
| 147 | |
| 148 | // Run EnqueWorkload |
| 149 | CHECK(tfLiteDelegate->Invoke() == kTfLiteOk); |
| 150 | CHECK(armnnDelegate->Invoke() == kTfLiteOk); |
| 151 | |
| 152 | // Compare output data |
| 153 | armnnDelegate::CompareOutputData<T>(tfLiteDelegate, |
| 154 | armnnDelegate, |
| 155 | shape, |
| 156 | expectedOutputValues, |
| 157 | 0); |
| 158 | |
| 159 | tfLiteDelegate.reset(nullptr); |
| 160 | armnnDelegate.reset(nullptr); |
| 161 | } |
| 162 | |
| 163 | } // anonymous namespace |