Jan Eilers | e339bf6 | 2020-11-10 18:43:23 +0000 | [diff] [blame] | 1 | // |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 2 | // Copyright © 2020, 2023 Arm Ltd and Contributors. All rights reserved. |
Jan Eilers | e339bf6 | 2020-11-10 18:43:23 +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> |
Teresa Charlin | ad1b3d7 | 2023-03-14 12:10:28 +0000 | [diff] [blame] | 16 | #include <schema_generated.h> |
Jan Eilers | e339bf6 | 2020-11-10 18:43:23 +0000 | [diff] [blame] | 17 | #include <tensorflow/lite/version.h> |
| 18 | |
| 19 | #include <doctest/doctest.h> |
| 20 | |
| 21 | namespace |
| 22 | { |
| 23 | |
| 24 | std::vector<char> CreateResizeTfLiteModel(tflite::BuiltinOperator operatorCode, |
| 25 | tflite::TensorType inputTensorType, |
| 26 | const std::vector <int32_t>& inputTensorShape, |
| 27 | const std::vector <int32_t>& sizeTensorData, |
| 28 | const std::vector <int32_t>& sizeTensorShape, |
| 29 | const std::vector <int32_t>& outputTensorShape) |
| 30 | { |
| 31 | using namespace tflite; |
| 32 | flatbuffers::FlatBufferBuilder flatBufferBuilder; |
| 33 | |
| 34 | std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 35 | buffers.push_back(CreateBuffer(flatBufferBuilder)); |
| 36 | buffers.push_back(CreateBuffer(flatBufferBuilder)); |
Jan Eilers | e339bf6 | 2020-11-10 18:43:23 +0000 | [diff] [blame] | 37 | buffers.push_back(CreateBuffer(flatBufferBuilder, |
| 38 | flatBufferBuilder.CreateVector( |
| 39 | reinterpret_cast<const uint8_t*>(sizeTensorData.data()), |
| 40 | sizeof(int32_t) * sizeTensorData.size()))); |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 41 | buffers.push_back(CreateBuffer(flatBufferBuilder)); |
Jan Eilers | e339bf6 | 2020-11-10 18:43:23 +0000 | [diff] [blame] | 42 | |
| 43 | std::array<flatbuffers::Offset<Tensor>, 3> tensors; |
| 44 | tensors[0] = CreateTensor(flatBufferBuilder, |
| 45 | flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), inputTensorShape.size()), |
| 46 | inputTensorType, |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 47 | 1, |
Jan Eilers | e339bf6 | 2020-11-10 18:43:23 +0000 | [diff] [blame] | 48 | flatBufferBuilder.CreateString("input_tensor")); |
| 49 | |
| 50 | tensors[1] = CreateTensor(flatBufferBuilder, |
| 51 | flatBufferBuilder.CreateVector<int32_t>(sizeTensorShape.data(), |
| 52 | sizeTensorShape.size()), |
| 53 | TensorType_INT32, |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 54 | 2, |
Jan Eilers | e339bf6 | 2020-11-10 18:43:23 +0000 | [diff] [blame] | 55 | flatBufferBuilder.CreateString("size_input_tensor")); |
| 56 | |
| 57 | tensors[2] = CreateTensor(flatBufferBuilder, |
| 58 | flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), |
| 59 | outputTensorShape.size()), |
| 60 | inputTensorType, |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 61 | 3, |
Jan Eilers | e339bf6 | 2020-11-10 18:43:23 +0000 | [diff] [blame] | 62 | flatBufferBuilder.CreateString("output_tensor")); |
| 63 | |
| 64 | // Create Operator |
| 65 | tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_NONE; |
| 66 | flatbuffers::Offset<void> operatorBuiltinOption = 0; |
| 67 | switch (operatorCode) |
| 68 | { |
| 69 | case BuiltinOperator_RESIZE_BILINEAR: |
| 70 | { |
| 71 | operatorBuiltinOption = CreateResizeBilinearOptions(flatBufferBuilder, false, false).Union(); |
| 72 | operatorBuiltinOptionsType = tflite::BuiltinOptions_ResizeBilinearOptions; |
| 73 | break; |
| 74 | } |
| 75 | case BuiltinOperator_RESIZE_NEAREST_NEIGHBOR: |
| 76 | { |
| 77 | operatorBuiltinOption = CreateResizeNearestNeighborOptions(flatBufferBuilder, false, false).Union(); |
| 78 | operatorBuiltinOptionsType = tflite::BuiltinOptions_ResizeNearestNeighborOptions; |
| 79 | break; |
| 80 | } |
| 81 | default: |
| 82 | break; |
| 83 | } |
| 84 | |
Keith Davis | 892fafe | 2020-11-26 17:40:35 +0000 | [diff] [blame] | 85 | const std::vector<int> operatorInputs{0, 1}; |
| 86 | const std::vector<int> operatorOutputs{2}; |
Jan Eilers | e339bf6 | 2020-11-10 18:43:23 +0000 | [diff] [blame] | 87 | flatbuffers::Offset <Operator> resizeOperator = |
| 88 | CreateOperator(flatBufferBuilder, |
| 89 | 0, |
| 90 | flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), |
| 91 | flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), |
| 92 | operatorBuiltinOptionsType, |
| 93 | operatorBuiltinOption); |
| 94 | |
Keith Davis | 892fafe | 2020-11-26 17:40:35 +0000 | [diff] [blame] | 95 | const std::vector<int> subgraphInputs{0, 1}; |
| 96 | const std::vector<int> subgraphOutputs{2}; |
Jan Eilers | e339bf6 | 2020-11-10 18:43:23 +0000 | [diff] [blame] | 97 | flatbuffers::Offset <SubGraph> subgraph = |
| 98 | CreateSubGraph(flatBufferBuilder, |
| 99 | flatBufferBuilder.CreateVector(tensors.data(), tensors.size()), |
| 100 | flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()), |
| 101 | flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()), |
| 102 | flatBufferBuilder.CreateVector(&resizeOperator, 1)); |
| 103 | |
| 104 | flatbuffers::Offset <flatbuffers::String> modelDescription = |
| 105 | flatBufferBuilder.CreateString("ArmnnDelegate: Resize Biliniar Operator Model"); |
| 106 | flatbuffers::Offset <OperatorCode> opCode = CreateOperatorCode(flatBufferBuilder, operatorCode); |
| 107 | |
| 108 | flatbuffers::Offset <Model> flatbufferModel = |
| 109 | CreateModel(flatBufferBuilder, |
| 110 | TFLITE_SCHEMA_VERSION, |
| 111 | flatBufferBuilder.CreateVector(&opCode, 1), |
| 112 | flatBufferBuilder.CreateVector(&subgraph, 1), |
| 113 | modelDescription, |
| 114 | flatBufferBuilder.CreateVector(buffers.data(), buffers.size())); |
| 115 | |
| 116 | flatBufferBuilder.Finish(flatbufferModel); |
| 117 | |
| 118 | return std::vector<char>(flatBufferBuilder.GetBufferPointer(), |
| 119 | flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); |
| 120 | } |
| 121 | |
| 122 | void ResizeFP32TestImpl(tflite::BuiltinOperator operatorCode, |
| 123 | std::vector<armnn::BackendId>& backends, |
| 124 | std::vector<float>& input1Values, |
| 125 | std::vector<int32_t> input1Shape, |
| 126 | std::vector<int32_t> input2NewShape, |
| 127 | std::vector<int32_t> input2Shape, |
| 128 | std::vector<float>& expectedOutputValues, |
| 129 | std::vector<int32_t> expectedOutputShape) |
| 130 | { |
| 131 | using namespace tflite; |
| 132 | |
| 133 | std::vector<char> modelBuffer = CreateResizeTfLiteModel(operatorCode, |
| 134 | ::tflite::TensorType_FLOAT32, |
| 135 | input1Shape, |
| 136 | input2NewShape, |
| 137 | input2Shape, |
| 138 | expectedOutputShape); |
| 139 | |
| 140 | const Model* tfLiteModel = GetModel(modelBuffer.data()); |
| 141 | |
| 142 | // The model will be executed using tflite and using the armnn delegate so that the outputs |
| 143 | // can be compared. |
| 144 | |
| 145 | // Create TfLite Interpreter with armnn delegate |
| 146 | std::unique_ptr<Interpreter> armnnDelegateInterpreter; |
| 147 | CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) |
| 148 | (&armnnDelegateInterpreter) == kTfLiteOk); |
| 149 | CHECK(armnnDelegateInterpreter != nullptr); |
| 150 | CHECK(armnnDelegateInterpreter->AllocateTensors() == kTfLiteOk); |
| 151 | |
| 152 | // Create TfLite Interpreter without armnn delegate |
| 153 | std::unique_ptr<Interpreter> tfLiteInterpreter; |
| 154 | CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) |
| 155 | (&tfLiteInterpreter) == kTfLiteOk); |
| 156 | CHECK(tfLiteInterpreter != nullptr); |
| 157 | CHECK(tfLiteInterpreter->AllocateTensors() == kTfLiteOk); |
| 158 | |
| 159 | // Create the ArmNN Delegate |
| 160 | armnnDelegate::DelegateOptions delegateOptions(backends); |
| 161 | std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)> |
| 162 | theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions), |
| 163 | armnnDelegate::TfLiteArmnnDelegateDelete); |
| 164 | CHECK(theArmnnDelegate != nullptr); |
| 165 | // Modify armnnDelegateInterpreter to use armnnDelegate |
| 166 | CHECK(armnnDelegateInterpreter->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk); |
| 167 | |
| 168 | // Set input data for the armnn interpreter |
| 169 | armnnDelegate::FillInput(armnnDelegateInterpreter, 0, input1Values); |
| 170 | armnnDelegate::FillInput(armnnDelegateInterpreter, 1, input2NewShape); |
| 171 | |
| 172 | // Set input data for the tflite interpreter |
| 173 | armnnDelegate::FillInput(tfLiteInterpreter, 0, input1Values); |
| 174 | armnnDelegate::FillInput(tfLiteInterpreter, 1, input2NewShape); |
| 175 | |
| 176 | // Run EnqueWorkload |
| 177 | CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); |
| 178 | CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); |
| 179 | |
| 180 | // Compare output data |
| 181 | auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0]; |
| 182 | auto tfLiteDelageOutputData = tfLiteInterpreter->typed_tensor<float>(tfLiteDelegateOutputId); |
| 183 | auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0]; |
| 184 | auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateOutputId); |
| 185 | for (size_t i = 0; i < expectedOutputValues.size(); i++) |
| 186 | { |
| 187 | CHECK(expectedOutputValues[i] == doctest::Approx(armnnDelegateOutputData[i])); |
| 188 | CHECK(armnnDelegateOutputData[i] == doctest::Approx(tfLiteDelageOutputData[i])); |
| 189 | } |
| 190 | |
| 191 | armnnDelegateInterpreter.reset(nullptr); |
| 192 | } |
| 193 | |
| 194 | } // anonymous namespace |