/*
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* Copyright (C) 2017 The Android Open Source Project
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#ifndef ANDROID_ML_NN_COMMON_OPERATIONS_H
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#define ANDROID_ML_NN_COMMON_OPERATIONS_H
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#include "operations/BidirectionalSequenceLSTM.h"
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#include "operations/Cast.h"
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#include "operations/EmbeddingLookup.h"
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#include "operations/ExpandDims.h"
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#include "operations/HashtableLookup.h"
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#include "operations/LSHProjection.h"
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#include "operations/LSTM.h"
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#include "operations/MaximumMinimum.h"
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#include "operations/Multinomial.h"
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#include "operations/Pow.h"
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#include "operations/QuantizedLSTM.h"
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#include "operations/RNN.h"
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#include "operations/SVDF.h"
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#include "operations/Tile.h"
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#include "operations/TopK_V2.h"
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#include <stddef.h>
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#include <cstdint>
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#include <vector>
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namespace android {
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namespace nn {
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struct Shape;
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bool floorFloat16(const _Float16* inputData, _Float16* outputData, const Shape& shape);
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bool floorFloat32(const float* inputData, float* outputData, const Shape& shape);
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bool depthwiseConvFloat16(const _Float16* inputData, const Shape& inputShape,
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const _Float16* filterData, const Shape& filterShape,
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const _Float16* biasData, const Shape& biasShape, int32_t paddingLeft,
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int32_t paddingRight, int32_t paddingTop, int32_t paddingBottom,
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int32_t strideWidth, int32_t strideHeight, int32_t dilationWidthFactor,
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int32_t dilationHeightFactor, int32_t depthMultiplier, int32_t activation,
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_Float16* outputData, const Shape& outputShape);
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bool depthwiseConvFloat32(const float* inputData, const Shape& inputShape, const float* filterData,
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const Shape& filterShape, const float* biasData, const Shape& biasShape,
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int32_t paddingLeft, int32_t paddingRight, int32_t paddingTop,
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int32_t paddingBottom, int32_t strideWidth, int32_t strideHeight,
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int32_t dilationWidthFactor, int32_t dilationHeightFactor,
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int32_t depthMultiplier, int32_t activation, float* outputData,
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const Shape& outputShape);
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bool depthwiseConvQuant8(const uint8_t* inputData, const Shape& inputShape,
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const uint8_t* filterData, const Shape& filterShape,
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const int32_t* biasData, const Shape& biasShape, int32_t paddingLeft,
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int32_t paddingRight, int32_t paddingTop, int32_t paddingBottom,
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int32_t strideWidth, int32_t strideHeight, int32_t dilationWidthFactor,
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int32_t dilationHeightFactor, int32_t depthMultiplier, int32_t activation,
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uint8_t* outputData, const Shape& outputShape);
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bool depthwiseConvQuant8PerChannel(const uint8_t* inputData, const Shape& inputShape,
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const int8_t* filterData, const Shape& filterShape,
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const float* filterScales, const int32_t* biasData,
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const Shape& biasShape, int32_t paddingLeft,
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int32_t paddingRight, int32_t paddingTop, int32_t paddingBottom,
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int32_t strideWidth, int32_t strideHeight,
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int32_t dilationWidthFactor, int32_t dilationHeightFactor,
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int32_t depthMultiplier, int32_t activation, uint8_t* outputData,
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const Shape& outputShape);
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bool localResponseNormFloat16(const _Float16* inputData, const Shape& inputShape, int32_t radius,
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float bias, float alpha, float beta, int32_t axis,
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_Float16* outputData, const Shape& outputShape);
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bool localResponseNormFloat32(const float* inputData, const Shape& inputShape, int32_t radius,
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float bias, float alpha, float beta, int32_t axis, float* outputData,
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const Shape& outputShape);
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bool copyData(const void* inputData, const Shape& inputShape, void* outputData,
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const Shape& outputShape);
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template <typename T>
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bool depthToSpaceGeneric(const T* inputData, const Shape& inputShape, int32_t blockSize,
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T* outputData, const Shape& outputShape);
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template <typename T>
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bool spaceToDepthGeneric(const T* inputData, const Shape& inputShape, int32_t blockSize,
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T* outputData, const Shape& outputShape);
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template <typename T>
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bool padGeneric(const T* inputData, const Shape& inputShape, const int32_t* paddings, T pad_value,
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T* outputData, const Shape& outputShape);
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template <typename T>
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bool batchToSpaceGeneric(const T* inputData, const Shape& inputShape, const int32_t* blockSize,
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T* outputData, const Shape& outputShape);
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template <typename T>
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bool spaceToBatchGeneric(const T* inputData, const Shape& inputShape, const int32_t* blockSize,
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const int32_t* padding, const Shape& paddingShape, T* outputData,
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const Shape& outputShape);
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bool meanFloat16(_Float16* inputData, const Shape& inputShape, const int32_t* axis,
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const Shape& axisShape, bool keepDims, _Float16* outputData,
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const Shape& outputShape);
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template <typename T, typename U>
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bool meanGeneric(T* inputData, const Shape& inputShape, const int32_t* axis, const Shape& axisShape,
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bool keepDims, T* outputData, const Shape& outputShape);
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bool stridedSliceGeneric(const uint8_t* inputData, const Shape& inputShape,
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const int32_t* beginData, const int32_t* endData,
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const int32_t* stridesData, int32_t beginMask, int32_t endMask,
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int32_t shrinkAxisMask, uint8_t* outputData, const Shape& outputShape);
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bool argMinMaxGeneric(const uint8_t* inputData, const Shape& inputShape, int32_t axis,
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bool isArgMin, uint8_t* outputData, const Shape& outputShape);
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bool splitFloat16(const _Float16* inputData, const Shape& inputShape, int32_t axis,
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const std::vector<_Float16*>* outputDataPtrs,
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const std::vector<Shape>& outputShapes);
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bool splitFloat32(const float* inputData, const Shape& inputShape, const int32_t axis,
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const std::vector<float*>* outputDataPtrs,
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const std::vector<Shape>& outputShapes);
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bool splitInt32(const int32_t* inputData, const Shape& inputShape, const int32_t axis,
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const std::vector<int32_t*>* outputDataPtrs,
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const std::vector<Shape>& outputShapes);
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bool splitQuant8(const uint8_t* inputData, const Shape& inputShape, const int32_t axis,
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const std::vector<uint8_t*>* outputDataPtrs,
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const std::vector<Shape>& outputShapes);
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bool groupedConvFloat16(const _Float16* inputData, const Shape& inputShape,
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const _Float16* filterData, const Shape& filterShape,
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const _Float16* biasData, const Shape& biasShape, int32_t numGroups,
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int32_t padding_left, int32_t padding_right, int32_t padding_top,
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int32_t padding_bottom, int32_t stride_width, int32_t stride_height,
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int32_t activation, _Float16* outputData, const Shape& outputShape);
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bool groupedConvFloat32(const float* inputData, const Shape& inputShape, const float* filterData,
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const Shape& filterShape, const float* biasData, const Shape& biasShape,
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int32_t numGroups, int32_t padding_left, int32_t padding_right,
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int32_t padding_top, int32_t padding_bottom, int32_t stride_width,
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int32_t stride_height, int32_t activation, float* outputData,
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const Shape& outputShape);
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bool groupedConvQuant8(const uint8_t* inputData, const Shape& inputShape, const uint8_t* filterData,
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const Shape& filterShape, const int32_t* biasData, const Shape& biasShape,
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int32_t numGroups, int32_t padding_left, int32_t padding_right,
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int32_t padding_top, int32_t padding_bottom, int32_t stride_width,
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int32_t stride_height, int32_t activation, uint8_t* outputData,
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const Shape& outputShape);
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bool groupedConvQuant8PerChannel(const uint8_t* inputData, const Shape& inputShape,
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const int8_t* filterData, const Shape& filterShape,
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const float* filterScales, const int32_t* biasData,
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const Shape& biasShape, int32_t padding_left,
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int32_t padding_right, int32_t padding_top, int32_t padding_bottom,
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int32_t stride_width, int32_t stride_height, int32_t numGroups,
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int32_t activation, uint8_t* outputData, const Shape& outputShape);
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bool channelShuffleGeneric(const uint8_t* inputData, const Shape& inputShape, int32_t numGroups,
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int32_t axis, uint8_t* outputData, const Shape& outputShape);
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} // namespace nn
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} // namespace android
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#endif // ANDROID_ML_NN_COMMON_OPERATIONS_H
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