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/*
* Copyright (c) 2021 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_FLOOR_DIV_H__
#define __NNFW_CKER_FLOOR_DIV_H__
#include "cker/Shape.h"
#include "cker/Utils.h"
namespace nnfw
{
namespace cker
{
template <typename T>
inline void FloorDivBroadcast(const Shape &unextended_input1_shape, const T *input1_data,
const Shape &unextended_input2_shape, const T *input2_data,
const Shape &unextended_output_shape, T *output_data)
{
assert(unextended_input1_shape.DimensionsCount() <= 4);
assert(unextended_input2_shape.DimensionsCount() <= 4);
assert(unextended_output_shape.DimensionsCount() <= 4);
const Shape output_shape = Shape::ExtendedShape(4, unextended_output_shape);
NdArrayDesc<4> desc1;
NdArrayDesc<4> desc2;
NdArrayDescsForElementwiseBroadcast(unextended_input1_shape, unextended_input2_shape, &desc1,
&desc2);
for (int b = 0; b < output_shape.Dims(0); ++b)
{
for (int y = 0; y < output_shape.Dims(1); ++y)
{
for (int x = 0; x < output_shape.Dims(2); ++x)
{
for (int c = 0; c < output_shape.Dims(3); ++c)
{
auto out_idx = Offset(output_shape, b, y, x, c);
auto in1_idx = SubscriptToIndex(desc1, b, y, x, c);
auto in2_idx = SubscriptToIndex(desc2, b, y, x, c);
auto in1_val = input1_data[in1_idx];
auto in2_val = input2_data[in2_idx];
output_data[out_idx] = std::floor(
std::divides<double>()(static_cast<double>(in1_val), static_cast<double>(in2_val)));
}
}
}
}
}
template <typename T>
inline void FloorDivElementwise(const Shape &shape, const T *input1_data, const T *input2_data,
T *output_data)
{
int num_elements = shape.FlatSize();
for (int t = 0; t < num_elements; t++)
{
output_data[t] = std::floor(std::divides<double>()(static_cast<double>(input1_data[t]),
static_cast<double>(input2_data[t])));
}
}
} // namespace cker
} // namespace nnfw
#endif
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