Matthew Sloyan | a35b40b | 2021-02-05 17:22:28 +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 | std::vector<char> CreateBatchSpaceTfLiteModel(tflite::BuiltinOperator batchSpaceOperatorCode, |
| 25 | tflite::TensorType tensorType, |
| 26 | std::vector<int32_t>& inputTensorShape, |
| 27 | std::vector <int32_t>& outputTensorShape, |
| 28 | std::vector<unsigned int>& blockData, |
| 29 | std::vector<std::pair<unsigned int, unsigned int>>& cropsPadData, |
| 30 | float quantScale = 1.0f, |
| 31 | int quantOffset = 0) |
| 32 | { |
| 33 | using namespace tflite; |
| 34 | flatbuffers::FlatBufferBuilder flatBufferBuilder; |
| 35 | |
| 36 | std::array<flatbuffers::Offset<tflite::Buffer>, 3> buffers; |
| 37 | buffers[0] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})); |
| 38 | buffers[1] = CreateBuffer(flatBufferBuilder, |
| 39 | flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(blockData.data()), |
| 40 | sizeof(int32_t) * blockData.size())); |
| 41 | buffers[2] = CreateBuffer(flatBufferBuilder, |
| 42 | flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(cropsPadData.data()), |
| 43 | sizeof(int64_t) * cropsPadData.size())); |
| 44 | |
| 45 | auto quantizationParameters = |
| 46 | CreateQuantizationParameters(flatBufferBuilder, |
| 47 | 0, |
| 48 | 0, |
| 49 | flatBufferBuilder.CreateVector<float>({ quantScale }), |
| 50 | flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); |
| 51 | |
| 52 | std::string cropsOrPadding = |
| 53 | batchSpaceOperatorCode == tflite::BuiltinOperator_BATCH_TO_SPACE_ND ? "crops" : "padding"; |
| 54 | |
| 55 | std::vector<int32_t> blockShape { 2 }; |
| 56 | std::vector<int32_t> cropsOrPaddingShape { 2, 2 }; |
| 57 | |
| 58 | std::array<flatbuffers::Offset<Tensor>, 4> tensors; |
| 59 | tensors[0] = CreateTensor(flatBufferBuilder, |
| 60 | flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), |
| 61 | inputTensorShape.size()), |
| 62 | tensorType, |
| 63 | 0, |
| 64 | flatBufferBuilder.CreateString("input"), |
| 65 | quantizationParameters); |
| 66 | |
| 67 | tensors[1] = CreateTensor(flatBufferBuilder, |
| 68 | flatBufferBuilder.CreateVector<int32_t>(blockShape.data(), |
| 69 | blockShape.size()), |
| 70 | ::tflite::TensorType_INT32, |
| 71 | 1, |
| 72 | flatBufferBuilder.CreateString("block"), |
| 73 | quantizationParameters); |
| 74 | |
| 75 | tensors[2] = CreateTensor(flatBufferBuilder, |
| 76 | flatBufferBuilder.CreateVector<int32_t>(cropsOrPaddingShape.data(), |
| 77 | cropsOrPaddingShape.size()), |
| 78 | ::tflite::TensorType_INT32, |
| 79 | 2, |
| 80 | flatBufferBuilder.CreateString(cropsOrPadding), |
| 81 | quantizationParameters); |
| 82 | |
| 83 | // Create output tensor |
| 84 | tensors[3] = CreateTensor(flatBufferBuilder, |
| 85 | flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), |
| 86 | outputTensorShape.size()), |
| 87 | tensorType, |
| 88 | 0, |
| 89 | flatBufferBuilder.CreateString("output"), |
| 90 | quantizationParameters); |
| 91 | |
| 92 | // Create operator |
| 93 | tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_NONE; |
| 94 | flatbuffers::Offset<void> operatorBuiltinOptions = 0; |
| 95 | switch (batchSpaceOperatorCode) |
| 96 | { |
| 97 | case tflite::BuiltinOperator_BATCH_TO_SPACE_ND: |
| 98 | { |
| 99 | operatorBuiltinOptionsType = tflite::BuiltinOptions_BatchToSpaceNDOptions; |
| 100 | operatorBuiltinOptions = CreateBatchToSpaceNDOptions(flatBufferBuilder).Union(); |
| 101 | break; |
| 102 | } |
| 103 | case tflite::BuiltinOperator_SPACE_TO_BATCH_ND: |
| 104 | { |
| 105 | operatorBuiltinOptionsType = tflite::BuiltinOptions_SpaceToBatchNDOptions; |
| 106 | operatorBuiltinOptions = CreateSpaceToBatchNDOptions(flatBufferBuilder).Union(); |
| 107 | break; |
| 108 | } |
| 109 | default: |
| 110 | break; |
| 111 | } |
| 112 | |
| 113 | const std::vector<int> operatorInputs{ {0, 1, 2} }; |
| 114 | const std::vector<int> operatorOutputs{ 3 }; |
| 115 | flatbuffers::Offset <Operator> batchSpaceOperator = |
| 116 | CreateOperator(flatBufferBuilder, |
| 117 | 0, |
| 118 | flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), |
| 119 | flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), |
| 120 | operatorBuiltinOptionsType, |
| 121 | operatorBuiltinOptions); |
| 122 | |
| 123 | const std::vector<int> subgraphInputs{ {0, 1, 2} }; |
| 124 | const std::vector<int> subgraphOutputs{ 3 }; |
| 125 | flatbuffers::Offset <SubGraph> subgraph = |
| 126 | CreateSubGraph(flatBufferBuilder, |
| 127 | flatBufferBuilder.CreateVector(tensors.data(), tensors.size()), |
| 128 | flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()), |
| 129 | flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()), |
| 130 | flatBufferBuilder.CreateVector(&batchSpaceOperator, 1)); |
| 131 | |
| 132 | flatbuffers::Offset <flatbuffers::String> modelDescription = |
| 133 | flatBufferBuilder.CreateString("ArmnnDelegate: BatchSpace Operator Model"); |
| 134 | flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, batchSpaceOperatorCode); |
| 135 | |
| 136 | flatbuffers::Offset <Model> flatbufferModel = |
| 137 | CreateModel(flatBufferBuilder, |
| 138 | TFLITE_SCHEMA_VERSION, |
| 139 | flatBufferBuilder.CreateVector(&operatorCode, 1), |
| 140 | flatBufferBuilder.CreateVector(&subgraph, 1), |
| 141 | modelDescription, |
| 142 | flatBufferBuilder.CreateVector(buffers.data(), buffers.size())); |
| 143 | |
| 144 | flatBufferBuilder.Finish(flatbufferModel); |
| 145 | |
| 146 | return std::vector<char>(flatBufferBuilder.GetBufferPointer(), |
| 147 | flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); |
| 148 | } |
| 149 | |
| 150 | template <typename T> |
| 151 | void BatchSpaceTest(tflite::BuiltinOperator controlOperatorCode, |
| 152 | tflite::TensorType tensorType, |
| 153 | std::vector<armnn::BackendId>& backends, |
| 154 | std::vector<int32_t>& inputShape, |
| 155 | std::vector<int32_t>& expectedOutputShape, |
| 156 | std::vector<T>& inputValues, |
| 157 | std::vector<unsigned int>& blockShapeValues, |
| 158 | std::vector<std::pair<unsigned int, unsigned int>>& cropsPaddingValues, |
| 159 | std::vector<T>& expectedOutputValues, |
| 160 | float quantScale = 1.0f, |
| 161 | int quantOffset = 0) |
| 162 | { |
| 163 | using namespace tflite; |
| 164 | std::vector<char> modelBuffer = CreateBatchSpaceTfLiteModel(controlOperatorCode, |
| 165 | tensorType, |
| 166 | inputShape, |
| 167 | expectedOutputShape, |
| 168 | blockShapeValues, |
| 169 | cropsPaddingValues, |
| 170 | quantScale, |
| 171 | quantOffset); |
| 172 | |
| 173 | const Model* tfLiteModel = GetModel(modelBuffer.data()); |
| 174 | |
| 175 | // Create TfLite Interpreters |
| 176 | std::unique_ptr<Interpreter> armnnDelegateInterpreter; |
| 177 | CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) |
| 178 | (&armnnDelegateInterpreter) == kTfLiteOk); |
| 179 | CHECK(armnnDelegateInterpreter != nullptr); |
| 180 | CHECK(armnnDelegateInterpreter->AllocateTensors() == kTfLiteOk); |
| 181 | |
| 182 | std::unique_ptr<Interpreter> tfLiteInterpreter; |
| 183 | CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) |
| 184 | (&tfLiteInterpreter) == kTfLiteOk); |
| 185 | CHECK(tfLiteInterpreter != nullptr); |
| 186 | CHECK(tfLiteInterpreter->AllocateTensors() == kTfLiteOk); |
| 187 | |
| 188 | // Create the ArmNN Delegate |
| 189 | armnnDelegate::DelegateOptions delegateOptions(backends); |
| 190 | std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)> |
| 191 | theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions), |
| 192 | armnnDelegate::TfLiteArmnnDelegateDelete); |
| 193 | CHECK(theArmnnDelegate != nullptr); |
| 194 | |
| 195 | // Modify armnnDelegateInterpreter to use armnnDelegate |
| 196 | CHECK(armnnDelegateInterpreter->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk); |
| 197 | |
| 198 | // Set input data |
| 199 | armnnDelegate::FillInput<T>(tfLiteInterpreter, 0, inputValues); |
| 200 | armnnDelegate::FillInput<T>(armnnDelegateInterpreter, 0, inputValues); |
| 201 | |
| 202 | // Run EnqueWorkload |
| 203 | CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); |
| 204 | CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); |
| 205 | |
| 206 | // Compare output data |
| 207 | armnnDelegate::CompareOutputData<T>(tfLiteInterpreter, |
| 208 | armnnDelegateInterpreter, |
| 209 | expectedOutputShape, |
| 210 | expectedOutputValues); |
| 211 | |
| 212 | armnnDelegateInterpreter.reset(nullptr); |
| 213 | tfLiteInterpreter.reset(nullptr); |
| 214 | } |
| 215 | |
| 216 | } // anonymous namespace |