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/*
* Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
*
* 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
*
* http://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.
*/
/**
* @file TensorLogger.h
* @brief This file contains TensorLogger class
* @ingroup COM_AI_RUNTIME
*/
#ifndef __NNFW_TFLITE_TENSOR_LOGGER_H__
#define __NNFW_TFLITE_TENSOR_LOGGER_H__
#include "misc/tensor/IndexIterator.h"
#include "tflite/TensorView.h"
#include <tensorflow/lite/interpreter.h>
#include <tensorflow/lite/context.h>
#include <fstream>
#include <iomanip>
namespace nnfw
{
namespace tflite
{
/**
* @brief Class to write input and output value / shape into a file in python form
* @note This is a utility to write input and output value / shape into a file in python form.\n
* any python app can load this value by running the python code below:\n
* exec(open(filename).read())\n
* generated python code looks like the following: \n
* tensor_shape_gen = []\n
* tensor_value_gen = []\n\n
* tensor_shape_gen.append("{2, 1, 2}")\n
* tensor_value_gen.append([1, 2, 3, 4])\n\n
* tensor_shape_gen.append("{2}")\n
* tensor_value_gen.append([1, 2])\n\n
* tensor_shape_gen.append("{2, 1, 2}")\n
* tensor_value_gen.append([1, 4, 3, 8])\n
*/
class TensorLogger
{
private:
std::ofstream _outfile;
public:
/**
* @brief Get TensorLogger instance
* @return The TensorLogger instance
*/
static TensorLogger &instance()
{
static TensorLogger instance;
return instance;
}
/**
* @brief Save the tensor details to file from interpreter
* @param[in] path The file path to save
* @param[in] interp The TfLite interpreter
*/
void save(const std::string &path, ::tflite::Interpreter &interp)
{
open(path);
int log_index = 0;
for (const auto id : interp.inputs())
{
_outfile << "# input tensors" << std::endl;
printTensor(interp, id, log_index++);
}
for (const auto id : interp.outputs())
{
_outfile << "# output tensors" << std::endl;
printTensor(interp, id, log_index++);
}
close();
}
private:
void open(const std::string &path)
{
if (!_outfile.is_open())
_outfile.open(path, std::ios_base::out);
_outfile << "# ------ file: " << path << " ------" << std::endl
<< "tensor_shape_gen = []" << std::endl
<< "tensor_value_gen = []" << std::endl
<< std::endl;
}
void printTensor(::tflite::Interpreter &interp, const int id, const int log_index)
{
const TfLiteTensor *tensor = interp.tensor(id);
_outfile << "# tensor name: " << tensor->name << std::endl;
_outfile << "# tflite::interpreter.tensor(" << id << ") -> "
"tensor_value_gen["
<< log_index << "]" << std::endl;
if (tensor->type == kTfLiteInt32)
{
printTensorShape(tensor);
printTensorValue<int32_t>(tensor, tensor->data.i32);
}
else if (interp.tensor(id)->type == kTfLiteUInt8)
{
printTensorShape(tensor);
printTensorValue<uint8_t>(tensor, tensor->data.uint8);
}
else if (tensor->type == kTfLiteFloat32)
{
printTensorShape(tensor);
printTensorValue<float>(tensor, tensor->data.f);
}
}
void printTensorShape(const TfLiteTensor *tensor)
{
_outfile << "tensor_shape_gen.append('{";
int r = 0;
for (; r < tensor->dims->size - 1; r++)
{
_outfile << tensor->dims->data[r] << ", ";
}
_outfile << tensor->dims->data[r];
_outfile << "}')" << std::endl;
}
template <typename T> void printTensorValue(const TfLiteTensor *tensor, T *tensor_data_ptr)
{
_outfile << "tensor_value_gen.append([";
_outfile << std::fixed << std::setprecision(10);
const T *end = reinterpret_cast<const T *>(tensor->data.raw_const + tensor->bytes);
for (T *ptr = tensor_data_ptr; ptr < end; ptr++)
_outfile << *ptr << ", ";
_outfile << "])" << std::endl << std::endl;
}
void close()
{
_outfile << "# --------- tensor shape and value defined above ---------" << std::endl;
_outfile.close();
}
};
} // namespace tflite
} // namespace nnfw
#endif // __NNFW_TFLITE_TENSOR_LOGGER_H__
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