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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_COMPARISON_H__
#define __NNFW_CKER_COMPARISON_H__
#include "cker/Shape.h"
#include "cker/Types.h"
#include "cker/Utils.h"
namespace nnfw
{
namespace cker
{
template <typename T> inline bool EqualFn(T lhs, T rhs) { return lhs == rhs; }
template <typename T> inline bool NotEqualFn(T lhs, T rhs) { return lhs != rhs; }
template <typename T> inline bool GreaterFn(T lhs, T rhs) { return lhs > rhs; }
template <typename T> inline bool GreaterEqualFn(T lhs, T rhs) { return lhs >= rhs; }
template <typename T> inline bool LessFn(T lhs, T rhs) { return lhs < rhs; }
template <typename T> inline bool LessEqualFn(T lhs, T rhs) { return lhs <= rhs; }
template <typename T> using ComparisonFn = bool (*)(T, T);
template <typename T, ComparisonFn<T> F>
inline void ComparisonImpl(const Shape &input1_shape, const T *input1_data,
const Shape &input2_shape, const T *input2_data,
const Shape &output_shape, bool *output_data)
{
const int64_t flatsize = // number of data....
MatchingFlatSize(input1_shape, input2_shape, output_shape);
for (int64_t i = 0; i < flatsize; ++i)
{
output_data[i] = F(input1_data[i], input2_data[i]);
}
}
template <ComparisonFn<float> F>
inline void Comparison(const Shape &input1_shape, const float *input1_data,
const Shape &input2_shape, const float *input2_data,
const Shape &output_shape, bool *output_data)
{
ComparisonImpl<float, F>(input1_shape, input1_data, input2_shape, input2_data, output_shape,
output_data);
}
template <typename T, ComparisonFn<T> F>
inline void
BroadcastComparison4DSlowImpl(const Shape &unextended_input1_shape, const T *input1_data,
const Shape &unextended_input2_shape, const T *input2_data,
const Shape &unextended_output_shape, bool *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)
{
output_data[Offset(output_shape, b, y, x, c)] =
F(input1_data[SubscriptToIndex(desc1, b, y, x, c)],
input2_data[SubscriptToIndex(desc2, b, y, x, c)]);
}
}
}
}
}
template <typename T, ComparisonFn<T> F>
inline void BroadcastComparison4DSlow(const Shape &input1_shape, const T *input1_data,
const Shape &input2_shape, const T *input2_data,
const Shape &output_shape, bool *output_data)
{
BroadcastComparison4DSlowImpl<T, F>(input1_shape, input1_data, input2_shape, input2_data,
output_shape, output_data);
}
#define TFLITE_COMPARISON_OP(name) \
inline void name(const Shape &input1_shape, const float *input1_data, const Shape &input2_shape, \
const float *input2_data, const Shape &output_shape, bool *output_data) \
{ \
Comparison<name##Fn>(input1_shape, input1_data, input2_shape, input2_data, output_shape, \
output_data); \
} \
template <typename T> \
inline void name##NoScaling(const Shape &input1_shape, const T *input1_data, \
const Shape &input2_shape, const T *input2_data, \
const Shape &output_shape, bool *output_data) \
{ \
ComparisonImpl<T, name##Fn>(input1_shape, input1_data, input2_shape, input2_data, \
output_shape, output_data); \
} \
template <typename T> \
inline void Broadcast4DSlow##name##NoScaling(const Shape &input1_shape, const T *input1_data, \
const Shape &input2_shape, const T *input2_data, \
const Shape &output_shape, bool *output_data) \
{ \
BroadcastComparison4DSlowImpl<T, name##Fn>(input1_shape, input1_data, input2_shape, \
input2_data, output_shape, output_data); \
} \
template <typename T> \
inline void Broadcast4DSlow##name(const Shape &input1_shape, const T *input1_data, \
const Shape &input2_shape, const T *input2_data, \
const Shape &output_shape, bool *output_data) \
{ \
BroadcastComparison4DSlow<T, name##Fn>(input1_shape, input1_data, input2_shape, input2_data, \
output_shape, output_data); \
}
TFLITE_COMPARISON_OP(Equal);
TFLITE_COMPARISON_OP(NotEqual);
TFLITE_COMPARISON_OP(Greater);
TFLITE_COMPARISON_OP(GreaterEqual);
TFLITE_COMPARISON_OP(Less);
TFLITE_COMPARISON_OP(LessEqual);
#undef TFLITE_COMPARISON_OP
} // namespace cker
} // namespace nnfw
#endif
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