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/*
* Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved
* Copyright 2017 The TensorFlow Authors. 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.
*/
#ifndef __NNFW_CKER_ONEHOT_H__
#define __NNFW_CKER_ONEHOT_H__
#include "cker/Shape.h"
namespace nnfw
{
namespace cker
{
template <typename T, typename TI>
void OneHot(const int32_t depth, const T on_value, const T off_value, int32_t axis,
const Shape &indices_shape, const TI *indices_data, const Shape &, T *output_data)
{
// prefix_dim_size == # of elements before the axis
// depth == # of elements per axis
// suffix_dim_size == # of elements after the axis
int prefix_dim_size = 1;
for (int i = 0; i < axis; ++i)
{
prefix_dim_size *= indices_shape.Dims(i);
}
const int suffix_dim_size = indices_shape.FlatSize() / prefix_dim_size;
// View the indices as a matrix of size:
// prefix_dim_size x suffix_dim_size
// View the output as a matrix of size:
// prefix_dim_size x depth x suffix_dim_size
// Then the output is:
// output(i, j, k) == (indices(i, k) == j) ? on : off
for (int i = 0; i < prefix_dim_size; ++i)
{
for (int j = 0; j < depth; ++j)
{
for (int k = 0; k < suffix_dim_size; ++k, ++output_data)
{
*output_data =
static_cast<int>(indices_data[i * suffix_dim_size + k]) == j ? on_value : off_value;
}
}
}
}
} // namespace cker
} // namespace nnfw
#endif // __NNFW_CKER_ONEHOT_H__
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