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/*
* Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#ifndef __ARM_COMPUTE_GRAPH_UTILS_H__
#define __ARM_COMPUTE_GRAPH_UTILS_H__
#include "arm_compute/core/PixelValue.h"
#include "arm_compute/graph/Graph.h"
#include "arm_compute/graph/ITensorAccessor.h"
#include "arm_compute/graph/Types.h"
#include <array>
#include <random>
#include <string>
#include <vector>
namespace arm_compute
{
namespace graph_utils
{
/** Preprocessor interface **/
class IPreprocessor
{
public:
/** Default destructor. */
virtual ~IPreprocessor() = default;
/** Preprocess the given tensor.
*
* @param[in] tensor Tensor to preprocess.
*/
virtual void preprocess(ITensor &tensor) = 0;
};
/** Caffe preproccessor */
class CaffePreproccessor : public IPreprocessor
{
public:
/** Default Constructor
*
* @param mean Mean array in RGB ordering
* @param bgr Boolean specifying if the preprocessing should assume BGR format
*/
CaffePreproccessor(std::array<float, 3> mean = std::array<float, 3> { { 0, 0, 0 } }, bool bgr = true);
void preprocess(ITensor &tensor) override;
private:
std::array<float, 3> _mean;
bool _bgr;
};
/** TF preproccessor */
class TFPreproccessor : public IPreprocessor
{
public:
void preprocess(ITensor &tensor) override;
};
/** PPM writer class */
class PPMWriter : public graph::ITensorAccessor
{
public:
/** Constructor
*
* @param[in] name PPM file name
* @param[in] maximum Maximum elements to access
*/
PPMWriter(std::string name, unsigned int maximum = 1);
/** Allows instances to move constructed */
PPMWriter(PPMWriter &&) = default;
// Inherited methods overriden:
bool access_tensor(ITensor &tensor) override;
private:
const std::string _name;
unsigned int _iterator;
unsigned int _maximum;
};
/** Dummy accessor class */
class DummyAccessor final : public graph::ITensorAccessor
{
public:
/** Constructor
*
* @param[in] maximum Maximum elements to write
*/
DummyAccessor(unsigned int maximum = 1);
/** Allows instances to move constructed */
DummyAccessor(DummyAccessor &&) = default;
// Inherited methods overriden:
bool access_tensor(ITensor &tensor) override;
private:
unsigned int _iterator;
unsigned int _maximum;
};
/** PPM accessor class */
class PPMAccessor final : public graph::ITensorAccessor
{
public:
/** Constructor
*
* @param[in] ppm_path Path to PPM file
* @param[in] bgr (Optional) Fill the first plane with blue channel (default = false)
* @param[in] preprocessor (Optional) PPM pre-processing object
*/
PPMAccessor(std::string ppm_path, bool bgr = true, std::unique_ptr<IPreprocessor> preprocessor = nullptr);
/** Allow instances of this class to be move constructed */
PPMAccessor(PPMAccessor &&) = default;
// Inherited methods overriden:
bool access_tensor(ITensor &tensor) override;
private:
const std::string _ppm_path;
const bool _bgr;
std::unique_ptr<IPreprocessor> _preprocessor;
};
/** Result accessor class */
class TopNPredictionsAccessor final : public graph::ITensorAccessor
{
public:
/** Constructor
*
* @param[in] labels_path Path to labels text file.
* @param[in] top_n (Optional) Number of output classes to print
* @param[out] output_stream (Optional) Output stream
*/
TopNPredictionsAccessor(const std::string &labels_path, size_t top_n = 5, std::ostream &output_stream = std::cout);
/** Allow instances of this class to be move constructed */
TopNPredictionsAccessor(TopNPredictionsAccessor &&) = default;
/** Prevent instances of this class from being copied (As this class contains pointers) */
TopNPredictionsAccessor(const TopNPredictionsAccessor &) = delete;
/** Prevent instances of this class from being copied (As this class contains pointers) */
TopNPredictionsAccessor &operator=(const TopNPredictionsAccessor &) = delete;
// Inherited methods overriden:
bool access_tensor(ITensor &tensor) override;
private:
template <typename T>
void access_predictions_tensor(ITensor &tensor);
std::vector<std::string> _labels;
std::ostream &_output_stream;
size_t _top_n;
};
/** Random accessor class */
class RandomAccessor final : public graph::ITensorAccessor
{
public:
/** Constructor
*
* @param[in] lower Lower bound value.
* @param[in] upper Upper bound value.
* @param[in] seed (Optional) Seed used to initialise the random number generator.
*/
RandomAccessor(PixelValue lower, PixelValue upper, const std::random_device::result_type seed = 0);
/** Allows instances to move constructed */
RandomAccessor(RandomAccessor &&) = default;
// Inherited methods overriden:
bool access_tensor(ITensor &tensor) override;
private:
template <typename T, typename D>
void fill(ITensor &tensor, D &&distribution);
PixelValue _lower;
PixelValue _upper;
std::random_device::result_type _seed;
};
/** Numpy Binary loader class*/
class NumPyBinLoader final : public graph::ITensorAccessor
{
public:
/** Default Constructor
*
* @param filename Binary file name
*/
NumPyBinLoader(std::string filename);
/** Allows instances to move constructed */
NumPyBinLoader(NumPyBinLoader &&) = default;
// Inherited methods overriden:
bool access_tensor(ITensor &tensor) override;
private:
const std::string _filename;
};
/** Generates appropriate random accessor
*
* @param[in] lower Lower random values bound
* @param[in] upper Upper random values bound
* @param[in] seed Random generator seed
*
* @return A ramdom accessor
*/
inline std::unique_ptr<graph::ITensorAccessor> get_random_accessor(PixelValue lower, PixelValue upper, const std::random_device::result_type seed = 0)
{
return arm_compute::support::cpp14::make_unique<RandomAccessor>(lower, upper, seed);
}
/** Generates appropriate weights accessor according to the specified path
*
* @note If path is empty will generate a DummyAccessor else will generate a NumPyBinLoader
*
* @param[in] path Path to the data files
* @param[in] data_file Relative path to the data files from path
*
* @return An appropriate tensor accessor
*/
inline std::unique_ptr<graph::ITensorAccessor> get_weights_accessor(const std::string &path, const std::string &data_file)
{
if(path.empty())
{
return arm_compute::support::cpp14::make_unique<DummyAccessor>();
}
else
{
return arm_compute::support::cpp14::make_unique<NumPyBinLoader>(path + data_file);
}
}
/** Generates appropriate input accessor according to the specified ppm_path
*
* @note If ppm_path is empty will generate a DummyAccessor else will generate a PPMAccessor
*
* @param[in] ppm_path Path to PPM file
* @param[in] preprocessor Preproccessor object
* @param[in] bgr (Optional) Fill the first plane with blue channel (default = true)
*
* @return An appropriate tensor accessor
*/
inline std::unique_ptr<graph::ITensorAccessor> get_input_accessor(const std::string &ppm_path,
std::unique_ptr<IPreprocessor> preprocessor = nullptr,
bool bgr = true)
{
if(ppm_path.empty())
{
return arm_compute::support::cpp14::make_unique<DummyAccessor>();
}
else
{
return arm_compute::support::cpp14::make_unique<PPMAccessor>(ppm_path, bgr, std::move(preprocessor));
}
}
/** Generates appropriate output accessor according to the specified labels_path
*
* @note If labels_path is empty will generate a DummyAccessor else will generate a TopNPredictionsAccessor
*
* @param[in] labels_path Path to labels text file
* @param[in] top_n (Optional) Number of output classes to print
* @param[out] output_stream (Optional) Output stream
*
* @return An appropriate tensor accessor
*/
inline std::unique_ptr<graph::ITensorAccessor> get_output_accessor(const std::string &labels_path, size_t top_n = 5, std::ostream &output_stream = std::cout)
{
if(labels_path.empty())
{
return arm_compute::support::cpp14::make_unique<DummyAccessor>(0);
}
else
{
return arm_compute::support::cpp14::make_unique<TopNPredictionsAccessor>(labels_path, top_n, output_stream);
}
}
/** Utility function to return the TargetHint
*
* @param[in] target Integer value which expresses the selected target. Must be 0 for NEON or 1 for OpenCL or 2 (OpenCL with Tuner)
*
* @return the TargetHint
*/
inline graph::Target set_target_hint(int target)
{
ARM_COMPUTE_ERROR_ON_MSG(target > 3, "Invalid target. Target must be 0 (NEON), 1 (OpenCL), 2 (OpenCL + Tuner), 3 (GLES)");
if((target == 1 || target == 2))
{
return graph::Target::CL;
}
else if(target == 3)
{
return graph::Target::GC;
}
else
{
return graph::Target::NEON;
}
}
} // namespace graph_utils
} // namespace arm_compute
#endif /* __ARM_COMPUTE_GRAPH_UTILS_H__ */