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/*
* Copyright (C) 2017 The Android Open Source Project
*
* 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 ANDROID_FRAMEWORKS_ML_NN_COMMON_OPERATIONS_H
#define ANDROID_FRAMEWORKS_ML_NN_COMMON_OPERATIONS_H
#include "operations/BidirectionalSequenceLSTM.h"
#include "operations/Cast.h"
#include "operations/EmbeddingLookup.h"
#include "operations/ExpandDims.h"
#include "operations/HashtableLookup.h"
#include "operations/LSHProjection.h"
#include "operations/LSTM.h"
#include "operations/MaximumMinimum.h"
#include "operations/Multinomial.h"
#include "operations/Pow.h"
#include "operations/QuantizedLSTM.h"
#include "operations/RNN.h"
#include "operations/SVDF.h"
#include "operations/Tile.h"
#include "operations/TopK_V2.h"
#include <stddef.h>
#include <cstdint>
#include <vector>
namespace android {
namespace nn {
struct Shape;
bool floorFloat16(const _Float16* inputData, _Float16* outputData, const Shape& shape);
bool floorFloat32(const float* inputData, float* outputData, const Shape& shape);
bool depthwiseConvFloat16(const _Float16* inputData, const Shape& inputShape,
const _Float16* filterData, const Shape& filterShape,
const _Float16* biasData, const Shape& biasShape, int32_t paddingLeft,
int32_t paddingRight, int32_t paddingTop, int32_t paddingBottom,
int32_t strideWidth, int32_t strideHeight, int32_t dilationWidthFactor,
int32_t dilationHeightFactor, int32_t depthMultiplier, int32_t activation,
_Float16* outputData, const Shape& outputShape);
bool depthwiseConvFloat32(const float* inputData, const Shape& inputShape, const float* filterData,
const Shape& filterShape, const float* biasData, const Shape& biasShape,
int32_t paddingLeft, int32_t paddingRight, int32_t paddingTop,
int32_t paddingBottom, int32_t strideWidth, int32_t strideHeight,
int32_t dilationWidthFactor, int32_t dilationHeightFactor,
int32_t depthMultiplier, int32_t activation, float* outputData,
const Shape& outputShape);
bool depthwiseConvQuant8(const uint8_t* inputData, const Shape& inputShape,
const uint8_t* filterData, const Shape& filterShape,
const int32_t* biasData, const Shape& biasShape, int32_t paddingLeft,
int32_t paddingRight, int32_t paddingTop, int32_t paddingBottom,
int32_t strideWidth, int32_t strideHeight, int32_t dilationWidthFactor,
int32_t dilationHeightFactor, int32_t depthMultiplier, int32_t activation,
uint8_t* outputData, const Shape& outputShape);
bool depthwiseConvQuant8PerChannel(const uint8_t* inputData, const Shape& inputShape,
const int8_t* filterData, const Shape& filterShape,
const float* filterScales, const int32_t* biasData,
const Shape& biasShape, int32_t paddingLeft,
int32_t paddingRight, int32_t paddingTop, int32_t paddingBottom,
int32_t strideWidth, int32_t strideHeight,
int32_t dilationWidthFactor, int32_t dilationHeightFactor,
int32_t depthMultiplier, int32_t activation, uint8_t* outputData,
const Shape& outputShape);
bool localResponseNormFloat16(const _Float16* inputData, const Shape& inputShape, int32_t radius,
float bias, float alpha, float beta, int32_t axis,
_Float16* outputData, const Shape& outputShape);
bool localResponseNormFloat32(const float* inputData, const Shape& inputShape, int32_t radius,
float bias, float alpha, float beta, int32_t axis, float* outputData,
const Shape& outputShape);
bool copyData(const void* inputData, const Shape& inputShape, void* outputData,
const Shape& outputShape);
template <typename T>
bool depthToSpaceGeneric(const T* inputData, const Shape& inputShape, int32_t blockSize,
T* outputData, const Shape& outputShape);
template <typename T>
bool spaceToDepthGeneric(const T* inputData, const Shape& inputShape, int32_t blockSize,
T* outputData, const Shape& outputShape);
template <typename T>
bool padGeneric(const T* inputData, const Shape& inputShape, const int32_t* paddings, T pad_value,
T* outputData, const Shape& outputShape);
template <typename T>
bool batchToSpaceGeneric(const T* inputData, const Shape& inputShape, const int32_t* blockSize,
T* outputData, const Shape& outputShape);
template <typename T>
bool spaceToBatchGeneric(const T* inputData, const Shape& inputShape, const int32_t* blockSize,
const int32_t* padding, const Shape& paddingShape, T* outputData,
const Shape& outputShape);
bool meanFloat16(_Float16* inputData, const Shape& inputShape, const int32_t* axis,
const Shape& axisShape, bool keepDims, _Float16* outputData,
const Shape& outputShape);
template <typename T, typename U>
bool meanGeneric(T* inputData, const Shape& inputShape, const int32_t* axis, const Shape& axisShape,
bool keepDims, T* outputData, const Shape& outputShape);
bool stridedSliceGeneric(const uint8_t* inputData, const Shape& inputShape,
const int32_t* beginData, const int32_t* endData,
const int32_t* stridesData, int32_t beginMask, int32_t endMask,
int32_t shrinkAxisMask, uint8_t* outputData, const Shape& outputShape);
bool argMinMaxGeneric(const uint8_t* inputData, const Shape& inputShape, int32_t axis,
bool isArgMin, uint8_t* outputData, const Shape& outputShape);
bool splitFloat16(const _Float16* inputData, const Shape& inputShape, int32_t axis,
const std::vector<_Float16*>* outputDataPtrs,
const std::vector<Shape>& outputShapes);
bool splitFloat32(const float* inputData, const Shape& inputShape, const int32_t axis,
const std::vector<float*>* outputDataPtrs,
const std::vector<Shape>& outputShapes);
bool splitInt32(const int32_t* inputData, const Shape& inputShape, const int32_t axis,
const std::vector<int32_t*>* outputDataPtrs,
const std::vector<Shape>& outputShapes);
bool splitQuant8(const uint8_t* inputData, const Shape& inputShape, const int32_t axis,
const std::vector<uint8_t*>* outputDataPtrs,
const std::vector<Shape>& outputShapes);
bool splitQuant8Signed(const int8_t* inputData, const Shape& inputShape, const int32_t axis,
const std::vector<int8_t*>* outputDataPtrs,
const std::vector<Shape>& outputShapes);
bool groupedConvFloat16(const _Float16* inputData, const Shape& inputShape,
const _Float16* filterData, const Shape& filterShape,
const _Float16* biasData, const Shape& biasShape, int32_t numGroups,
int32_t padding_left, int32_t padding_right, int32_t padding_top,
int32_t padding_bottom, int32_t stride_width, int32_t stride_height,
int32_t activation, _Float16* outputData, const Shape& outputShape);
bool groupedConvFloat32(const float* inputData, const Shape& inputShape, const float* filterData,
const Shape& filterShape, const float* biasData, const Shape& biasShape,
int32_t numGroups, int32_t padding_left, int32_t padding_right,
int32_t padding_top, int32_t padding_bottom, int32_t stride_width,
int32_t stride_height, int32_t activation, float* outputData,
const Shape& outputShape);
template <typename T>
bool groupedConvQuant8(const T* inputData, const Shape& inputShape, const T* filterData,
const Shape& filterShape, const int32_t* biasData, const Shape& biasShape,
int32_t numGroups, int32_t padding_left, int32_t padding_right,
int32_t padding_top, int32_t padding_bottom, int32_t stride_width,
int32_t stride_height, int32_t activation, T* outputData,
const Shape& outputShape);
template <typename T>
bool groupedConvQuant8PerChannel(const T* inputData, const Shape& inputShape,
const int8_t* filterData, const Shape& filterShape,
const float* filterScales, const int32_t* biasData,
const Shape& biasShape, int32_t padding_left,
int32_t padding_right, int32_t padding_top, int32_t padding_bottom,
int32_t stride_width, int32_t stride_height, int32_t numGroups,
int32_t activation, T* outputData, const Shape& outputShape);
bool channelShuffleGeneric(const uint8_t* inputData, const Shape& inputShape, int32_t numGroups,
int32_t axis, uint8_t* outputData, const Shape& outputShape);
} // namespace nn
} // namespace android
#endif // ANDROID_FRAMEWORKS_ML_NN_COMMON_OPERATIONS_H