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Diffstat (limited to 'libs/tflite/include/tflite/TensorLogger.h')
| -rw-r--r-- | libs/tflite/include/tflite/TensorLogger.h | 168 |
1 files changed, 0 insertions, 168 deletions
diff --git a/libs/tflite/include/tflite/TensorLogger.h b/libs/tflite/include/tflite/TensorLogger.h deleted file mode 100644 index e56a76b58..000000000 --- a/libs/tflite/include/tflite/TensorLogger.h +++ /dev/null @@ -1,168 +0,0 @@ -/* - * 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/contrib/lite/interpreter.h> -#include <tensorflow/contrib/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('{"; - - size_t 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__ |