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/*
* Copyright (c) 2019-2020 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.
*/
#include "arm_compute/core/CPP/kernels/CPPTopKVKernel.h"
#include "arm_compute/core/Coordinates.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/Utils.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/core/Window.h"
#include "arm_compute/core/utils/misc/Traits.h"
namespace arm_compute
{
namespace
{
template <typename T,
typename std::enable_if<utils::traits::is_floating_point<T>::value, int>::type = 0>
inline bool greater_than(T a, T b)
{
const T epsilon = std::numeric_limits<T>::epsilon();
return (a - b > epsilon);
}
template < typename T,
typename std::enable_if < !utils::traits::is_floating_point<T>::value, int >::type = 0 >
inline bool greater_than(T a, T b)
{
return (a > b);
}
Status validate_arguments(const ITensorInfo *predictions, const ITensorInfo *targets, ITensorInfo *output, const unsigned int k)
{
ARM_COMPUTE_UNUSED(k);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(predictions, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::S32, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(targets, 1, DataType::U32);
ARM_COMPUTE_RETURN_ERROR_ON(predictions->num_dimensions() > 2);
ARM_COMPUTE_RETURN_ERROR_ON(targets->num_dimensions() > 1);
ARM_COMPUTE_RETURN_ERROR_ON(targets->dimension(0) != predictions->dimension(1));
// Validate configured output
if(output->total_size() != 0)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), targets->tensor_shape());
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8);
}
return Status{};
}
} // namespace
template <typename T>
void CPPTopKVKernel::run_topkv()
{
for(unsigned int i = 0; i < _batch_size; ++i)
{
const auto target_class_id = *reinterpret_cast<uint32_t *>(_targets->ptr_to_element(Coordinates{ i }));
const auto predicted_value = *reinterpret_cast<T *>(_predictions->ptr_to_element(Coordinates{ target_class_id, i }));
// The variable rank indicates how many values there are before the target_class_id
unsigned int rank = 0;
for(unsigned int j = 0; (j < _num_classes) && (rank < _k); ++j)
{
const auto current_prediction = *reinterpret_cast<T *>(_predictions->ptr_to_element(Coordinates{ j, i }));
if(greater_than(current_prediction, predicted_value))
{
rank++;
}
}
*(_output->ptr_to_element(Coordinates{ i })) = static_cast<uint8_t>(rank < _k);
}
}
CPPTopKVKernel::CPPTopKVKernel()
: _predictions(nullptr), _targets(nullptr), _output(nullptr), _k(), _batch_size(), _num_classes()
{
}
void CPPTopKVKernel::configure(const ITensor *predictions, const ITensor *targets, ITensor *output, const unsigned int k)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(predictions, targets, output);
// Perform validation step
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(predictions->info(), targets->info(), output->info(), k));
auto_init_if_empty(*output->info(), targets->info()->tensor_shape(), 1, DataType::U8);
_predictions = predictions;
_targets = targets;
_output = output;
_k = k;
_batch_size = predictions->info()->dimension(1);
_num_classes = predictions->info()->dimension(0);
ICPPKernel::configure(Window()); // Default 1 iteration window
}
Status CPPTopKVKernel::validate(const ITensorInfo *predictions, const ITensorInfo *targets, ITensorInfo *output, const unsigned int k)
{
ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(predictions, targets, output, k));
return Status{};
}
bool CPPTopKVKernel::is_parallelisable() const
{
return false;
}
void CPPTopKVKernel::run(const Window &window, const ThreadInfo &info)
{
ARM_COMPUTE_UNUSED(window, info);
switch(_predictions->info()->data_type())
{
case DataType::F32:
run_topkv<float>();
break;
case DataType::F16:
run_topkv<half>();
break;
case DataType::S32:
run_topkv<int>();
break;
case DataType::QASYMM8:
run_topkv<uint8_t>();
break;
case DataType::QASYMM8_SIGNED:
run_topkv<int8_t>();
break;
default:
ARM_COMPUTE_ERROR("Not supported");
}
}
} // namespace arm_compute
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