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> |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 11 | #include <DelegateTestInterpreter.hpp> |
Sadik Armagan | 788e2c6 | 2021-02-10 16:26:44 +0000 | [diff] [blame] | 12 | |
| 13 | #include <flatbuffers/flatbuffers.h> |
Sadik Armagan | 788e2c6 | 2021-02-10 16:26:44 +0000 | [diff] [blame] | 14 | #include <tensorflow/lite/kernels/register.h> |
Sadik Armagan | 788e2c6 | 2021-02-10 16:26:44 +0000 | [diff] [blame] | 15 | #include <tensorflow/lite/version.h> |
| 16 | |
| 17 | #include <doctest/doctest.h> |
| 18 | |
| 19 | namespace |
| 20 | { |
| 21 | std::vector<char> CreateRoundTfLiteModel(tflite::BuiltinOperator roundOperatorCode, |
| 22 | tflite::TensorType tensorType, |
| 23 | const std::vector <int32_t>& tensorShape, |
| 24 | float quantScale = 1.0f, |
| 25 | int quantOffset = 0) |
| 26 | { |
| 27 | using namespace tflite; |
| 28 | flatbuffers::FlatBufferBuilder flatBufferBuilder; |
| 29 | |
| 30 | std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 31 | buffers.push_back(CreateBuffer(flatBufferBuilder)); |
| 32 | buffers.push_back(CreateBuffer(flatBufferBuilder)); |
| 33 | buffers.push_back(CreateBuffer(flatBufferBuilder)); |
Sadik Armagan | 788e2c6 | 2021-02-10 16:26:44 +0000 | [diff] [blame] | 34 | |
| 35 | auto quantizationParameters = |
| 36 | CreateQuantizationParameters(flatBufferBuilder, |
| 37 | 0, |
| 38 | 0, |
| 39 | flatBufferBuilder.CreateVector<float>({quantScale}), |
| 40 | flatBufferBuilder.CreateVector<int64_t>({quantOffset})); |
| 41 | |
| 42 | std::array<flatbuffers::Offset<Tensor>, 2> tensors; |
| 43 | tensors[0] = CreateTensor(flatBufferBuilder, |
| 44 | flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), |
| 45 | tensorShape.size()), |
| 46 | tensorType, |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 47 | 1, |
Sadik Armagan | 788e2c6 | 2021-02-10 16:26:44 +0000 | [diff] [blame] | 48 | flatBufferBuilder.CreateString("input"), |
| 49 | quantizationParameters); |
| 50 | tensors[1] = CreateTensor(flatBufferBuilder, |
| 51 | flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), |
| 52 | tensorShape.size()), |
| 53 | tensorType, |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 54 | 2, |
Sadik Armagan | 788e2c6 | 2021-02-10 16:26:44 +0000 | [diff] [blame] | 55 | flatBufferBuilder.CreateString("output"), |
| 56 | quantizationParameters); |
| 57 | |
| 58 | const std::vector<int32_t> operatorInputs({0}); |
| 59 | const std::vector<int32_t> operatorOutputs({1}); |
| 60 | |
| 61 | flatbuffers::Offset<Operator> roundOperator; |
| 62 | flatbuffers::Offset<flatbuffers::String> modelDescription; |
| 63 | flatbuffers::Offset<OperatorCode> operatorCode; |
| 64 | |
| 65 | switch (roundOperatorCode) |
| 66 | { |
| 67 | case tflite::BuiltinOperator_FLOOR: |
| 68 | default: |
| 69 | roundOperator = |
| 70 | CreateOperator(flatBufferBuilder, |
| 71 | 0, |
| 72 | flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), |
| 73 | flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size())); |
| 74 | modelDescription = flatBufferBuilder.CreateString("ArmnnDelegate: Floor Operator Model"); |
| 75 | operatorCode = CreateOperatorCode(flatBufferBuilder, tflite::BuiltinOperator_FLOOR); |
| 76 | break; |
| 77 | } |
| 78 | const std::vector<int32_t> subgraphInputs({0}); |
| 79 | const std::vector<int32_t> subgraphOutputs({1}); |
| 80 | flatbuffers::Offset<SubGraph> subgraph = |
| 81 | CreateSubGraph(flatBufferBuilder, |
| 82 | flatBufferBuilder.CreateVector(tensors.data(), tensors.size()), |
| 83 | flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()), |
| 84 | flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()), |
| 85 | flatBufferBuilder.CreateVector(&roundOperator, 1)); |
| 86 | |
| 87 | flatbuffers::Offset<Model> flatbufferModel = |
| 88 | CreateModel(flatBufferBuilder, |
| 89 | TFLITE_SCHEMA_VERSION, |
| 90 | flatBufferBuilder.CreateVector(&operatorCode, 1), |
| 91 | flatBufferBuilder.CreateVector(&subgraph, 1), |
| 92 | modelDescription, |
| 93 | flatBufferBuilder.CreateVector(buffers.data(), buffers.size())); |
| 94 | |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 95 | flatBufferBuilder.Finish(flatbufferModel, armnnDelegate::FILE_IDENTIFIER); |
Sadik Armagan | 788e2c6 | 2021-02-10 16:26:44 +0000 | [diff] [blame] | 96 | return std::vector<char>(flatBufferBuilder.GetBufferPointer(), |
| 97 | flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); |
| 98 | } |
| 99 | |
| 100 | template<typename T> |
| 101 | void RoundTest(tflite::BuiltinOperator roundOperatorCode, |
| 102 | tflite::TensorType tensorType, |
| 103 | std::vector<armnn::BackendId>& backends, |
| 104 | std::vector<int32_t>& shape, |
| 105 | std::vector<T>& inputValues, |
| 106 | std::vector<T>& expectedOutputValues, |
| 107 | float quantScale = 1.0f, |
| 108 | int quantOffset = 0) |
| 109 | { |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 110 | using namespace delegateTestInterpreter; |
Sadik Armagan | 788e2c6 | 2021-02-10 16:26:44 +0000 | [diff] [blame] | 111 | std::vector<char> modelBuffer = CreateRoundTfLiteModel(roundOperatorCode, |
| 112 | tensorType, |
| 113 | shape, |
| 114 | quantScale, |
| 115 | quantOffset); |
| 116 | |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 117 | // Setup interpreter with just TFLite Runtime. |
| 118 | auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer); |
| 119 | CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk); |
| 120 | CHECK(tfLiteInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk); |
| 121 | CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk); |
| 122 | std::vector<T> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<T>(0); |
| 123 | std::vector<int32_t> tfLiteOutputShape = tfLiteInterpreter.GetOutputShape(0); |
Sadik Armagan | 788e2c6 | 2021-02-10 16:26:44 +0000 | [diff] [blame] | 124 | |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 125 | // Setup interpreter with Arm NN Delegate applied. |
| 126 | auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, backends); |
| 127 | CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk); |
| 128 | CHECK(armnnInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk); |
| 129 | CHECK(armnnInterpreter.Invoke() == kTfLiteOk); |
| 130 | std::vector<T> armnnOutputValues = armnnInterpreter.GetOutputResult<T>(0); |
| 131 | std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(0); |
Sadik Armagan | 788e2c6 | 2021-02-10 16:26:44 +0000 | [diff] [blame] | 132 | |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 133 | armnnDelegate::CompareOutputData<T>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues); |
| 134 | armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, shape); |
Sadik Armagan | 788e2c6 | 2021-02-10 16:26:44 +0000 | [diff] [blame] | 135 | |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 136 | tfLiteInterpreter.Cleanup(); |
| 137 | armnnInterpreter.Cleanup(); |
Sadik Armagan | 788e2c6 | 2021-02-10 16:26:44 +0000 | [diff] [blame] | 138 | } |
| 139 | |
| 140 | } // anonymous namespace |