Davide Grohmann | 37fd8a3 | 2022-04-07 15:02:12 +0200 | [diff] [blame] | 1 | /* |
Jonny Svärd | 8788ab3 | 2023-04-27 18:05:34 +0200 | [diff] [blame] | 2 | * SPDX-FileCopyrightText: Copyright 2022-2023 Arm Limited and/or its affiliates <open-source-office@arm.com> |
Davide Grohmann | 37fd8a3 | 2022-04-07 15:02:12 +0200 | [diff] [blame] | 3 | * |
| 4 | * SPDX-License-Identifier: Apache-2.0 |
| 5 | * |
| 6 | * Licensed under the Apache License, Version 2.0 (the License); you may |
| 7 | * not use this file except in compliance with the License. |
| 8 | * You may obtain a copy of the License at |
| 9 | * |
| 10 | * www.apache.org/licenses/LICENSE-2.0 |
| 11 | * |
| 12 | * Unless required by applicable law or agreed to in writing, software |
| 13 | * distributed under the License is distributed on an AS IS BASIS, WITHOUT |
| 14 | * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| 15 | * See the License for the specific language governing permissions and |
| 16 | * limitations under the License. |
| 17 | */ |
| 18 | |
| 19 | #pragma once |
| 20 | |
| 21 | #include "tensorflow/lite/schema/schema_generated.h" |
| 22 | |
| 23 | #include <stdlib.h> |
| 24 | #include <string> |
| 25 | |
| 26 | namespace InferenceProcess { |
| 27 | |
| 28 | template <typename T, typename U> |
| 29 | class Array { |
| 30 | public: |
| 31 | Array() = delete; |
| 32 | Array(T *const data, U &size, size_t capacity) : _data{data}, _size{size}, _capacity{capacity} {} |
| 33 | |
| 34 | auto size() const { |
| 35 | return _size; |
| 36 | } |
| 37 | |
| 38 | auto capacity() const { |
| 39 | return _capacity; |
| 40 | } |
| 41 | |
| 42 | void push_back(const T &data) { |
| 43 | _data[_size++] = data; |
| 44 | } |
| 45 | |
| 46 | private: |
| 47 | T *const _data; |
| 48 | U &_size; |
| 49 | const size_t _capacity{}; |
| 50 | }; |
| 51 | |
| 52 | template <typename T, typename U> |
| 53 | Array<T, U> makeArray(T *const data, U &size, size_t capacity) { |
| 54 | return Array<T, U>{data, size, capacity}; |
| 55 | } |
| 56 | |
| 57 | class InferenceParser { |
| 58 | public: |
Davide Grohmann | 30b17b9 | 2022-06-14 15:17:18 +0200 | [diff] [blame] | 59 | const tflite::Model *getModel(const void *buffer, size_t size) { |
| 60 | // Verify buffer |
| 61 | flatbuffers::Verifier base_verifier(reinterpret_cast<const uint8_t *>(buffer), size); |
| 62 | if (!tflite::VerifyModelBuffer(base_verifier)) { |
| 63 | printf("Warning: the model is not valid\n"); |
| 64 | return nullptr; |
| 65 | } |
| 66 | |
Davide Grohmann | 37fd8a3 | 2022-04-07 15:02:12 +0200 | [diff] [blame] | 67 | // Create model handle |
| 68 | const tflite::Model *model = tflite::GetModel(buffer); |
| 69 | if (model->subgraphs() == nullptr) { |
| 70 | printf("Warning: nullptr subgraph\n"); |
Davide Grohmann | 30b17b9 | 2022-06-14 15:17:18 +0200 | [diff] [blame] | 71 | return nullptr; |
Davide Grohmann | 37fd8a3 | 2022-04-07 15:02:12 +0200 | [diff] [blame] | 72 | } |
| 73 | |
Davide Grohmann | 30b17b9 | 2022-06-14 15:17:18 +0200 | [diff] [blame] | 74 | return model; |
| 75 | } |
| 76 | |
| 77 | template <typename T, typename U, size_t S> |
| 78 | bool parseModel(const void *buffer, size_t size, char (&description)[S], T &&ifmDims, U &&ofmDims) { |
| 79 | const tflite::Model *model = getModel(buffer, size); |
| 80 | if (model == nullptr) { |
| 81 | return true; |
| 82 | } |
Mikael Olsson | 74c514a | 2023-08-07 17:42:18 +0200 | [diff] [blame^] | 83 | |
| 84 | // Depending on the src string, strncpy may not add a null-terminator |
| 85 | // so one is manually added at the end. |
| 86 | strncpy(description, model->description()->c_str(), S - 1); |
| 87 | description[S - 1] = '\0'; |
Davide Grohmann | 37fd8a3 | 2022-04-07 15:02:12 +0200 | [diff] [blame] | 88 | |
| 89 | // Get input dimensions for first subgraph |
| 90 | auto *subgraph = *model->subgraphs()->begin(); |
| 91 | bool failed = getSubGraphDims(subgraph, subgraph->inputs(), ifmDims); |
| 92 | if (failed) { |
| 93 | return true; |
| 94 | } |
| 95 | |
| 96 | // Get output dimensions for last subgraph |
| 97 | subgraph = *model->subgraphs()->rbegin(); |
| 98 | failed = getSubGraphDims(subgraph, subgraph->outputs(), ofmDims); |
| 99 | if (failed) { |
| 100 | return true; |
| 101 | } |
| 102 | |
| 103 | return false; |
| 104 | } |
| 105 | |
| 106 | private: |
| 107 | bool getShapeSize(const flatbuffers::Vector<int32_t> *shape, size_t &size) { |
| 108 | size = 1; |
| 109 | |
| 110 | if (shape == nullptr) { |
| 111 | printf("Warning: nullptr shape size.\n"); |
| 112 | return true; |
| 113 | } |
| 114 | |
Jonny Svärd | 8788ab3 | 2023-04-27 18:05:34 +0200 | [diff] [blame] | 115 | if (shape->size() == 0) { |
| 116 | printf("Warning: shape zero size.\n"); |
Davide Grohmann | 37fd8a3 | 2022-04-07 15:02:12 +0200 | [diff] [blame] | 117 | return true; |
| 118 | } |
| 119 | |
| 120 | for (auto it = shape->begin(); it != shape->end(); ++it) { |
| 121 | size *= *it; |
| 122 | } |
| 123 | |
| 124 | return false; |
| 125 | } |
| 126 | |
| 127 | bool getTensorTypeSize(const enum tflite::TensorType type, size_t &size) { |
| 128 | switch (type) { |
| 129 | case tflite::TensorType::TensorType_UINT8: |
| 130 | case tflite::TensorType::TensorType_INT8: |
| 131 | size = 1; |
| 132 | break; |
| 133 | case tflite::TensorType::TensorType_INT16: |
| 134 | size = 2; |
| 135 | break; |
| 136 | case tflite::TensorType::TensorType_INT32: |
| 137 | case tflite::TensorType::TensorType_FLOAT32: |
| 138 | size = 4; |
| 139 | break; |
| 140 | default: |
| 141 | printf("Warning: Unsupported tensor type\n"); |
| 142 | return true; |
| 143 | } |
| 144 | |
| 145 | return false; |
| 146 | } |
| 147 | |
| 148 | template <typename T> |
| 149 | bool getSubGraphDims(const tflite::SubGraph *subgraph, const flatbuffers::Vector<int32_t> *tensorMap, T &dims) { |
| 150 | if (subgraph == nullptr || tensorMap == nullptr) { |
| 151 | printf("Warning: nullptr subgraph or tensormap.\n"); |
| 152 | return true; |
| 153 | } |
| 154 | |
| 155 | if ((dims.capacity() - dims.size()) < tensorMap->size()) { |
| 156 | printf("Warning: tensormap size is larger than dimension capacity.\n"); |
| 157 | return true; |
| 158 | } |
| 159 | |
| 160 | for (auto index = tensorMap->begin(); index != tensorMap->end(); ++index) { |
| 161 | auto tensor = subgraph->tensors()->Get(*index); |
| 162 | size_t size; |
| 163 | size_t tensorSize; |
| 164 | |
| 165 | bool failed = getShapeSize(tensor->shape(), size); |
| 166 | if (failed) { |
| 167 | return true; |
| 168 | } |
| 169 | |
| 170 | failed = getTensorTypeSize(tensor->type(), tensorSize); |
| 171 | if (failed) { |
| 172 | return true; |
| 173 | } |
| 174 | |
| 175 | size *= tensorSize; |
| 176 | |
| 177 | if (size > 0) { |
| 178 | dims.push_back(size); |
| 179 | } |
| 180 | } |
| 181 | |
| 182 | return false; |
| 183 | } |
| 184 | }; |
| 185 | |
| 186 | } // namespace InferenceProcess |