James Ward | f89964e | 2020-11-09 11:57:47 +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 <armnn_delegate.hpp> |
| 9 | |
| 10 | #include <flatbuffers/flatbuffers.h> |
| 11 | #include <tensorflow/lite/interpreter.h> |
| 12 | #include <tensorflow/lite/kernels/register.h> |
| 13 | #include <tensorflow/lite/model.h> |
| 14 | #include <tensorflow/lite/schema/schema_generated.h> |
| 15 | #include <tensorflow/lite/version.h> |
| 16 | |
| 17 | #include <doctest/doctest.h> |
| 18 | |
| 19 | namespace |
| 20 | { |
| 21 | std::vector<char> CreateTransposeTfLiteModel(tflite::TensorType tensorType, |
| 22 | const std::vector <int32_t>& input0TensorShape, |
| 23 | const std::vector <int32_t>& inputPermVecShape, |
| 24 | const std::vector <int32_t>& outputTensorShape, |
| 25 | const std::vector<int32_t>& inputPermVec) |
| 26 | { |
| 27 | using namespace tflite; |
| 28 | flatbuffers::FlatBufferBuilder flatBufferBuilder; |
| 29 | std::array<flatbuffers::Offset<tflite::Buffer>, 2> buffers; |
| 30 | buffers[0] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})); |
| 31 | buffers[1] = CreateBuffer(flatBufferBuilder, |
| 32 | flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(inputPermVec.data()), |
| 33 | sizeof(int32_t) * inputPermVec.size())); |
| 34 | std::array<flatbuffers::Offset<Tensor>, 3> tensors; |
| 35 | tensors[0] = CreateTensor(flatBufferBuilder, |
| 36 | flatBufferBuilder.CreateVector<int32_t>(input0TensorShape.data(), |
| 37 | input0TensorShape.size()), |
| 38 | tensorType, 0); |
| 39 | tensors[1] = CreateTensor(flatBufferBuilder, |
| 40 | flatBufferBuilder.CreateVector<int32_t>(inputPermVecShape.data(), |
| 41 | inputPermVecShape.size()), |
| 42 | tflite::TensorType_INT32, 1, |
| 43 | flatBufferBuilder.CreateString("permutation_vector")); |
| 44 | tensors[2] = CreateTensor(flatBufferBuilder, |
| 45 | flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), |
| 46 | outputTensorShape.size()), |
| 47 | tensorType); |
Keith Davis | 892fafe | 2020-11-26 17:40:35 +0000 | [diff] [blame] | 48 | const std::vector<int32_t> operatorInputs{0, 1}; |
| 49 | const std::vector<int32_t> operatorOutputs{2}; |
James Ward | f89964e | 2020-11-09 11:57:47 +0000 | [diff] [blame] | 50 | flatbuffers::Offset <Operator> transposeOperator = |
| 51 | CreateOperator(flatBufferBuilder, |
| 52 | 0, |
| 53 | flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), |
| 54 | flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), |
| 55 | BuiltinOptions_TransposeOptions, |
| 56 | CreateTransposeOptions(flatBufferBuilder).Union()); |
Keith Davis | 892fafe | 2020-11-26 17:40:35 +0000 | [diff] [blame] | 57 | const std::vector<int> subgraphInputs{0, 1}; |
| 58 | const std::vector<int> subgraphOutputs{2}; |
James Ward | f89964e | 2020-11-09 11:57:47 +0000 | [diff] [blame] | 59 | flatbuffers::Offset <SubGraph> subgraph = |
| 60 | CreateSubGraph(flatBufferBuilder, |
| 61 | flatBufferBuilder.CreateVector(tensors.data(), tensors.size()), |
| 62 | flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()), |
| 63 | flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()), |
| 64 | flatBufferBuilder.CreateVector(&transposeOperator, 1)); |
| 65 | flatbuffers::Offset <flatbuffers::String> modelDescription = |
| 66 | flatBufferBuilder.CreateString("ArmnnDelegate: Transpose Operator Model"); |
| 67 | flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, |
| 68 | tflite::BuiltinOperator_TRANSPOSE); |
| 69 | flatbuffers::Offset <Model> flatbufferModel = |
| 70 | CreateModel(flatBufferBuilder, |
| 71 | TFLITE_SCHEMA_VERSION, |
| 72 | flatBufferBuilder.CreateVector(&operatorCode, 1), |
| 73 | flatBufferBuilder.CreateVector(&subgraph, 1), |
| 74 | modelDescription, |
| 75 | flatBufferBuilder.CreateVector(buffers.data(), buffers.size())); |
| 76 | flatBufferBuilder.Finish(flatbufferModel); |
| 77 | return std::vector<char>(flatBufferBuilder.GetBufferPointer(), |
| 78 | flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); |
| 79 | } |
| 80 | |
| 81 | void TransposeFP32Test(std::vector<armnn::BackendId>& backends) |
| 82 | { |
| 83 | using namespace tflite; |
| 84 | |
| 85 | // set test input data |
| 86 | std::vector<int32_t> input0Shape {4, 2, 3}; |
| 87 | std::vector<int32_t> inputPermVecShape {3}; |
| 88 | std::vector<int32_t> outputShape {2, 3, 4}; |
| 89 | |
| 90 | std::vector<float> input0Values = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, |
| 91 | 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23}; |
| 92 | std::vector<int32_t> inputPermVec = {2, 0, 1}; |
| 93 | std::vector<float> expectedOutputValues = {0, 3, 6, 9, 12, 15, 18, 21, 1, 4, 7, 10, |
| 94 | 13, 16, 19, 22, 2, 5, 8, 11, 14, 17, 20, 23}; |
| 95 | |
| 96 | // create model |
| 97 | std::vector<char> modelBuffer = CreateTransposeTfLiteModel(::tflite::TensorType_FLOAT32, |
| 98 | input0Shape, |
| 99 | inputPermVecShape, |
| 100 | outputShape, |
| 101 | inputPermVec); |
| 102 | |
| 103 | const Model* tfLiteModel = GetModel(modelBuffer.data()); |
| 104 | // Create TfLite Interpreters |
| 105 | std::unique_ptr<Interpreter> armnnDelegateInterpreter; |
| 106 | CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) |
| 107 | (&armnnDelegateInterpreter) == kTfLiteOk); |
| 108 | CHECK(armnnDelegateInterpreter != nullptr); |
| 109 | CHECK(armnnDelegateInterpreter->AllocateTensors() == kTfLiteOk); |
| 110 | |
| 111 | std::unique_ptr<Interpreter> tfLiteInterpreter; |
| 112 | CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) |
| 113 | (&tfLiteInterpreter) == kTfLiteOk); |
| 114 | CHECK(tfLiteInterpreter != nullptr); |
| 115 | CHECK(tfLiteInterpreter->AllocateTensors() == kTfLiteOk); |
| 116 | |
| 117 | // Create the ArmNN Delegate |
| 118 | armnnDelegate::DelegateOptions delegateOptions(backends); |
| 119 | std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)> |
| 120 | theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions), |
| 121 | armnnDelegate::TfLiteArmnnDelegateDelete); |
| 122 | CHECK(theArmnnDelegate != nullptr); |
| 123 | // Modify armnnDelegateInterpreter to use armnnDelegate |
| 124 | CHECK(armnnDelegateInterpreter->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk); |
| 125 | |
| 126 | // Set input data for tflite |
| 127 | auto tfLiteInterpreterInput0Id = tfLiteInterpreter->inputs()[0]; |
| 128 | auto tfLiteInterpreterInput0Data = tfLiteInterpreter->typed_tensor<float>(tfLiteInterpreterInput0Id); |
| 129 | for (unsigned int i = 0; i < input0Values.size(); ++i) |
| 130 | { |
| 131 | tfLiteInterpreterInput0Data[i] = input0Values[i]; |
| 132 | } |
| 133 | |
| 134 | auto tfLiteInterpreterInput1Id = tfLiteInterpreter->inputs()[1]; |
| 135 | auto tfLiteInterpreterInput1Data = tfLiteInterpreter->typed_tensor<int32_t>(tfLiteInterpreterInput1Id); |
| 136 | for (unsigned int i = 0; i < inputPermVec.size(); ++i) |
| 137 | { |
| 138 | tfLiteInterpreterInput1Data[i] = inputPermVec[i]; |
| 139 | } |
| 140 | |
| 141 | //Set input data for armnn delegate |
| 142 | auto armnnDelegateInput0Id = armnnDelegateInterpreter->inputs()[0]; |
| 143 | auto armnnDelegateInput0Data = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateInput0Id); |
| 144 | for (unsigned int i = 0; i < input0Values.size(); ++i) |
| 145 | { |
| 146 | armnnDelegateInput0Data[i] = input0Values[i]; |
| 147 | } |
| 148 | |
| 149 | auto armnnDelegateInput1Id = armnnDelegateInterpreter->inputs()[1]; |
| 150 | auto armnnDelegateInput1Data = armnnDelegateInterpreter->typed_tensor<int32_t>(armnnDelegateInput1Id); |
| 151 | for (unsigned int i = 0; i < inputPermVec.size(); ++i) |
| 152 | { |
| 153 | armnnDelegateInput1Data[i] = inputPermVec[i]; |
| 154 | } |
| 155 | |
| 156 | // Run EnqueWorkload |
| 157 | CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); |
| 158 | CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); |
| 159 | |
| 160 | // Compare output data |
| 161 | auto tfLiteInterpreterOutputId = tfLiteInterpreter->outputs()[0]; |
| 162 | auto tfLiteInterpreterOutputData = tfLiteInterpreter->typed_tensor<float>(tfLiteInterpreterOutputId); |
| 163 | auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0]; |
| 164 | auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateOutputId); |
| 165 | for (size_t i = 0; i < expectedOutputValues.size(); ++i) |
| 166 | { |
| 167 | CHECK(expectedOutputValues[i] == armnnDelegateOutputData[i]); |
| 168 | CHECK(tfLiteInterpreterOutputData[i] == expectedOutputValues[i]); |
| 169 | CHECK(tfLiteInterpreterOutputData[i] == armnnDelegateOutputData[i]); |
| 170 | } |
| 171 | |
| 172 | armnnDelegateInterpreter.reset(nullptr); |
| 173 | } |
| 174 | } |