Teresa Charlin | 98427a1 | 2020-11-25 18:22:57 +0000 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2020 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 | std::vector<char> CreateGatherTfLiteModel(tflite::TensorType tensorType, |
| 25 | std::vector<int32_t>& paramsShape, |
| 26 | std::vector<int32_t>& indicesShape, |
| 27 | const std::vector<int32_t>& expectedOutputShape, |
| 28 | int32_t axis, |
| 29 | float quantScale = 1.0f, |
| 30 | int quantOffset = 0) |
| 31 | { |
| 32 | using namespace tflite; |
| 33 | flatbuffers::FlatBufferBuilder flatBufferBuilder; |
| 34 | |
| 35 | std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; |
| 36 | buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); |
| 37 | |
| 38 | auto quantizationParameters = |
| 39 | CreateQuantizationParameters(flatBufferBuilder, |
| 40 | 0, |
| 41 | 0, |
| 42 | flatBufferBuilder.CreateVector<float>({quantScale}), |
| 43 | flatBufferBuilder.CreateVector<int64_t>({quantOffset})); |
| 44 | |
| 45 | std::array<flatbuffers::Offset<Tensor>, 3> tensors; |
| 46 | tensors[0] = CreateTensor(flatBufferBuilder, |
| 47 | flatBufferBuilder.CreateVector<int32_t>(paramsShape.data(), |
| 48 | paramsShape.size()), |
| 49 | tensorType, |
| 50 | 0, |
| 51 | flatBufferBuilder.CreateString("params"), |
| 52 | quantizationParameters); |
| 53 | tensors[1] = CreateTensor(flatBufferBuilder, |
| 54 | flatBufferBuilder.CreateVector<int32_t>(indicesShape.data(), |
| 55 | indicesShape.size()), |
| 56 | ::tflite::TensorType_INT32, |
| 57 | 0, |
| 58 | flatBufferBuilder.CreateString("indices"), |
| 59 | quantizationParameters); |
| 60 | tensors[2] = CreateTensor(flatBufferBuilder, |
| 61 | flatBufferBuilder.CreateVector<int32_t>(expectedOutputShape.data(), |
| 62 | expectedOutputShape.size()), |
| 63 | tensorType, |
| 64 | 0, |
| 65 | flatBufferBuilder.CreateString("output"), |
| 66 | quantizationParameters); |
| 67 | |
| 68 | |
| 69 | // create operator |
| 70 | tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_GatherOptions; |
| 71 | flatbuffers::Offset<void> operatorBuiltinOptions = CreateGatherOptions(flatBufferBuilder).Union(); |
| 72 | |
| 73 | const std::vector<int> operatorInputs{{0, 1}}; |
Finn Williams | 019840d | 2020-11-30 17:43:28 +0000 | [diff] [blame] | 74 | const std::vector<int> operatorOutputs{2}; |
Teresa Charlin | 98427a1 | 2020-11-25 18:22:57 +0000 | [diff] [blame] | 75 | flatbuffers::Offset<Operator> controlOperator = |
| 76 | CreateOperator(flatBufferBuilder, |
| 77 | 0, |
| 78 | flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), |
| 79 | operatorInputs.size()), |
| 80 | flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), |
| 81 | operatorOutputs.size()), |
| 82 | operatorBuiltinOptionsType, |
| 83 | operatorBuiltinOptions); |
| 84 | |
| 85 | const std::vector<int> subgraphInputs{{0, 1}}; |
Finn Williams | 019840d | 2020-11-30 17:43:28 +0000 | [diff] [blame] | 86 | const std::vector<int> subgraphOutputs{2}; |
Teresa Charlin | 98427a1 | 2020-11-25 18:22:57 +0000 | [diff] [blame] | 87 | flatbuffers::Offset<SubGraph> subgraph = |
| 88 | CreateSubGraph(flatBufferBuilder, |
| 89 | flatBufferBuilder.CreateVector(tensors.data(), tensors.size()), |
| 90 | flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), |
| 91 | subgraphInputs.size()), |
| 92 | flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), |
| 93 | subgraphOutputs.size()), |
| 94 | flatBufferBuilder.CreateVector(&controlOperator, 1)); |
| 95 | |
| 96 | flatbuffers::Offset<flatbuffers::String> modelDescription = |
| 97 | flatBufferBuilder.CreateString("ArmnnDelegate: GATHER Operator Model"); |
| 98 | flatbuffers::Offset<OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, |
| 99 | BuiltinOperator_GATHER); |
| 100 | |
| 101 | flatbuffers::Offset<Model> flatbufferModel = |
| 102 | CreateModel(flatBufferBuilder, |
| 103 | TFLITE_SCHEMA_VERSION, |
| 104 | flatBufferBuilder.CreateVector(&operatorCode, 1), |
| 105 | flatBufferBuilder.CreateVector(&subgraph, 1), |
| 106 | modelDescription, |
| 107 | flatBufferBuilder.CreateVector(buffers.data(), buffers.size())); |
| 108 | |
| 109 | flatBufferBuilder.Finish(flatbufferModel); |
| 110 | |
| 111 | return std::vector<char>(flatBufferBuilder.GetBufferPointer(), |
| 112 | flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); |
| 113 | } |
| 114 | |
| 115 | template<typename T> |
| 116 | void GatherTest(tflite::TensorType tensorType, |
| 117 | std::vector<armnn::BackendId>& backends, |
| 118 | std::vector<int32_t>& paramsShape, |
| 119 | std::vector<int32_t>& indicesShape, |
| 120 | std::vector<int32_t>& expectedOutputShape, |
| 121 | int32_t axis, |
| 122 | std::vector<T>& paramsValues, |
| 123 | std::vector<int32_t>& indicesValues, |
| 124 | std::vector<T>& expectedOutputValues, |
| 125 | float quantScale = 1.0f, |
| 126 | int quantOffset = 0) |
| 127 | { |
| 128 | using namespace tflite; |
| 129 | std::vector<char> modelBuffer = CreateGatherTfLiteModel(tensorType, |
| 130 | paramsShape, |
| 131 | indicesShape, |
| 132 | expectedOutputShape, |
| 133 | axis, |
| 134 | quantScale, |
| 135 | quantOffset); |
| 136 | const Model* tfLiteModel = GetModel(modelBuffer.data()); |
| 137 | |
| 138 | // Create TfLite Interpreters |
| 139 | std::unique_ptr<Interpreter> armnnDelegate; |
| 140 | CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) |
| 141 | (&armnnDelegate) == kTfLiteOk); |
| 142 | CHECK(armnnDelegate != nullptr); |
| 143 | CHECK(armnnDelegate->AllocateTensors() == kTfLiteOk); |
| 144 | |
| 145 | std::unique_ptr<Interpreter> tfLiteDelegate; |
| 146 | CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) |
| 147 | (&tfLiteDelegate) == kTfLiteOk); |
| 148 | CHECK(tfLiteDelegate != nullptr); |
| 149 | CHECK(tfLiteDelegate->AllocateTensors() == kTfLiteOk); |
| 150 | |
| 151 | // Create the ArmNN Delegate |
| 152 | armnnDelegate::DelegateOptions delegateOptions(backends); |
| 153 | std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)> |
| 154 | theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions), |
| 155 | armnnDelegate::TfLiteArmnnDelegateDelete); |
| 156 | CHECK(theArmnnDelegate != nullptr); |
| 157 | |
| 158 | // Modify armnnDelegateInterpreter to use armnnDelegate |
| 159 | CHECK(armnnDelegate->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk); |
| 160 | |
| 161 | // Set input data |
| 162 | armnnDelegate::FillInput<T>(tfLiteDelegate, 0, paramsValues); |
| 163 | armnnDelegate::FillInput<T>(armnnDelegate, 0, paramsValues); |
| 164 | armnnDelegate::FillInput<int32_t>(tfLiteDelegate, 1, indicesValues); |
| 165 | armnnDelegate::FillInput<int32_t>(armnnDelegate, 1, indicesValues); |
| 166 | |
| 167 | // Run EnqueWorkload |
| 168 | CHECK(tfLiteDelegate->Invoke() == kTfLiteOk); |
| 169 | CHECK(armnnDelegate->Invoke() == kTfLiteOk); |
| 170 | |
| 171 | // Compare output data |
| 172 | armnnDelegate::CompareOutputData<T>(tfLiteDelegate, |
| 173 | armnnDelegate, |
| 174 | expectedOutputShape, |
| 175 | expectedOutputValues, |
| 176 | 0); |
| 177 | |
| 178 | tfLiteDelegate.reset(nullptr); |
| 179 | armnnDelegate.reset(nullptr); |
| 180 | } |
| 181 | } // anonymous namespace |