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/*
* SPDX-FileCopyrightText: Copyright 2022-2023 Arm Limited and/or its affiliates <open-source-office@arm.com>
*
* SPDX-License-Identifier: Apache-2.0
*
* Licensed under the Apache License, Version 2.0 (the License); you may
* not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an AS IS BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#pragma once
#include "tensorflow/lite/schema/schema_generated.h"
#include <stdlib.h>
#include <string>
namespace InferenceProcess {
template <typename T, typename U>
class Array {
public:
Array() = delete;
Array(T *const data, U &size, size_t capacity) : _data{data}, _size{size}, _capacity{capacity} {}
auto size() const {
return _size;
}
auto capacity() const {
return _capacity;
}
void push_back(const T &data) {
_data[_size++] = data;
}
private:
T *const _data;
U &_size;
const size_t _capacity{};
};
template <typename T, typename U>
Array<T, U> makeArray(T *const data, U &size, size_t capacity) {
return Array<T, U>{data, size, capacity};
}
class InferenceParser {
public:
const tflite::Model *getModel(const void *buffer, size_t size) {
// Verify buffer
flatbuffers::Verifier base_verifier(reinterpret_cast<const uint8_t *>(buffer), size);
if (!tflite::VerifyModelBuffer(base_verifier)) {
printf("Warning: the model is not valid\n");
return nullptr;
}
// Create model handle
const tflite::Model *model = tflite::GetModel(buffer);
if (model->subgraphs() == nullptr) {
printf("Warning: nullptr subgraph\n");
return nullptr;
}
return model;
}
template <typename T, typename U, size_t S>
bool parseModel(const void *buffer, size_t size, char (&description)[S], T &&ifmDims, U &&ofmDims) {
const tflite::Model *model = getModel(buffer, size);
if (model == nullptr) {
return true;
}
// Depending on the src string, strncpy may not add a null-terminator
// so one is manually added at the end.
strncpy(description, model->description()->c_str(), S - 1);
description[S - 1] = '\0';
// Get input dimensions for first subgraph
auto *subgraph = *model->subgraphs()->begin();
bool failed = getSubGraphDims(subgraph, subgraph->inputs(), ifmDims);
if (failed) {
return true;
}
// Get output dimensions for last subgraph
subgraph = *model->subgraphs()->rbegin();
failed = getSubGraphDims(subgraph, subgraph->outputs(), ofmDims);
if (failed) {
return true;
}
return false;
}
private:
bool getShapeSize(const flatbuffers::Vector<int32_t> *shape, size_t &size) {
size = 1;
if (shape == nullptr) {
printf("Warning: nullptr shape size.\n");
return true;
}
if (shape->size() == 0) {
printf("Warning: shape zero size.\n");
return true;
}
for (auto it = shape->begin(); it != shape->end(); ++it) {
size *= *it;
}
return false;
}
bool getTensorTypeSize(const enum tflite::TensorType type, size_t &size) {
switch (type) {
case tflite::TensorType::TensorType_UINT8:
case tflite::TensorType::TensorType_INT8:
size = 1;
break;
case tflite::TensorType::TensorType_INT16:
size = 2;
break;
case tflite::TensorType::TensorType_INT32:
case tflite::TensorType::TensorType_FLOAT32:
size = 4;
break;
default:
printf("Warning: Unsupported tensor type\n");
return true;
}
return false;
}
template <typename T>
bool getSubGraphDims(const tflite::SubGraph *subgraph, const flatbuffers::Vector<int32_t> *tensorMap, T &dims) {
if (subgraph == nullptr || tensorMap == nullptr) {
printf("Warning: nullptr subgraph or tensormap.\n");
return true;
}
if ((dims.capacity() - dims.size()) < tensorMap->size()) {
printf("Warning: tensormap size is larger than dimension capacity.\n");
return true;
}
for (auto index = tensorMap->begin(); index != tensorMap->end(); ++index) {
auto tensor = subgraph->tensors()->Get(*index);
size_t size;
size_t tensorSize;
bool failed = getShapeSize(tensor->shape(), size);
if (failed) {
return true;
}
failed = getTensorTypeSize(tensor->type(), tensorSize);
if (failed) {
return true;
}
size *= tensorSize;
if (size > 0) {
dims.push_back(size);
}
}
return false;
}
};
} // namespace InferenceProcess
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