| /* |
| * 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_ML_NN_COMMON_OPERATIONS_H |
| #define ANDROID_ML_NN_COMMON_OPERATIONS_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 addFloat16(const _Float16* in1, const Shape& shape1, const _Float16* in2, const Shape& shape2, |
| int32_t activation, _Float16* out, const Shape& shapeOut); |
| bool addFloat32(const float* in1, const Shape& shape1, const float* in2, const Shape& shape2, |
| int32_t activation, float* out, const Shape& shapeOut); |
| bool addQuant8(const uint8_t* in1, const Shape& shape1, const uint8_t* in2, const Shape& shape2, |
| int32_t activation, uint8_t* out, const Shape& shapeOut); |
| |
| bool mulFloat16(const _Float16* in1, const Shape& shape1, const _Float16* in2, const Shape& shape2, |
| int32_t activation, _Float16* out, const Shape& shapeOut); |
| bool mulFloat32(const float* in1, const Shape& shape1, const float* in2, const Shape& shape2, |
| int32_t activation, float* out, const Shape& shapeOut); |
| bool mulQuant8(const uint8_t* in1, const Shape& shape1, const uint8_t* in2, const Shape& shape2, |
| int32_t activation, uint8_t* out, const Shape& shapeOut); |
| |
| bool floorFloat32(const float* inputData, float* outputData, const Shape& shape); |
| |
| bool dequantizeQuant8ToFloat32(const uint8_t* inputData, float* outputData, const Shape& shape); |
| |
| bool quantizeFloat32ToQuant8(const float* inputData, uint8_t* outputData, const Shape& outputShape); |
| |
| 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 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 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 depthMultiplier, |
| int32_t activation, uint8_t* outputData, const Shape& outputShape); |
| |
| bool convFloat32(const float* inputData, const Shape& inputShape, const float* filterData, |
| const Shape& filterShape, const float* 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 activation, float* outputData, const Shape& outputShape); |
| bool convQuant8(const uint8_t* inputData, const Shape& inputShape, const uint8_t* filterData, |
| const Shape& filterShape, 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 activation, uint8_t* outputData, const Shape& outputShape); |
| |
| bool averagePoolFloat32(const float* inputData, const Shape& inputShape, 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 filter_width, |
| int32_t filter_height, int32_t activation, float* outputData, |
| const Shape& outputShape); |
| bool averagePoolQuant8(const uint8_t* inputData, const Shape& inputShape, 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 filter_width, |
| int32_t filter_height, int32_t activation, uint8_t* outputData, |
| const Shape& outputShape); |
| bool l2PoolFloat32(const float* inputData, const Shape& inputShape, 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 filter_width, |
| int32_t filter_height, int32_t activation, float* outputData, |
| const Shape& outputShape); |
| bool maxPoolFloat32(const float* inputData, const Shape& inputShape, 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 filter_width, |
| int32_t filter_height, int32_t activation, float* outputData, |
| const Shape& outputShape); |
| bool maxPoolQuant8(const uint8_t* inputData, const Shape& inputShape, 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 filter_width, |
| int32_t filter_height, int32_t activation, uint8_t* outputData, |
| const Shape& outputShape); |
| |
| template <typename T> |
| bool reluFloat(const T* inputData, const Shape& inputShape, T* outputData, const Shape& outputShape, |
| float reluMin = 0.f, float reluMax = std::numeric_limits<float>::max()); |
| template <typename T> |
| bool relu1Float(const T* inputData, const Shape& inputShape, T* outputData, |
| const Shape& outputShape); |
| template <typename T> |
| bool relu6Float(const T* inputData, const Shape& inputShape, T* outputData, |
| const Shape& outputShape); |
| |
| bool tanhFloat16(const _Float16* inputData, const Shape& inputShape, _Float16* outputData, |
| const Shape& outputShape); |
| bool tanhFloat32(const float* inputData, const Shape& inputShape, float* outputData, |
| const Shape& outputShape); |
| bool tanhQuant8(const uint8_t* inputData, const Shape& inputShape, uint8_t* outputData, |
| const Shape& outputShape); |
| |
| template <typename T> |
| bool logisticFloat(const T* inputData, const Shape& inputShape, T* outputData, |
| const Shape& outputShape); |
| |
| bool softmaxFloat16(const _Float16* inputData, const Shape& inputShape, const float beta, |
| int32_t axis, _Float16* outputData, const Shape& outputShape); |
| bool softmaxFloat32(const float* inputData, const Shape& inputShape, const float beta, int32_t axis, |
| float* outputData, const Shape& outputShape); |
| |
| bool reluQuant8(const uint8_t* inputData, const Shape& inputShape, uint8_t* outputData, |
| const Shape& outputShape); |
| bool relu1Quant8(const uint8_t* inputData, const Shape& inputShape, uint8_t* outputData, |
| const Shape& outputShape); |
| bool relu6Quant8(const uint8_t* inputData, const Shape& inputShape, uint8_t* outputData, |
| const Shape& outputShape); |
| bool logisticQuant8(const uint8_t* inputData, const Shape& inputShape, uint8_t* outputData, |
| const Shape& outputShape); |
| bool softmaxQuant8(const uint8_t* inputData, const Shape& inputShape, const float beta, |
| int32_t axis, uint8_t* outputData, const Shape& outputShape); |
| |
| bool fullyConnectedFloat32(const float* inputData, const Shape& inputShape, const float* weights, |
| const Shape& weightsShape, const float* biasData, const Shape& biasShape, |
| int32_t activation, float* outputData, const Shape& outputShape); |
| bool fullyConnectedQuant8(const uint8_t* inputData, const Shape& inputShape, const uint8_t* weights, |
| const Shape& weightsShape, const int32_t* biasData, |
| const Shape& biasShape, int32_t activation, uint8_t* outputData, |
| const Shape& outputShape); |
| |
| template <typename T> |
| bool concatenation(const std::vector<const T*>& inputDataPtrs, |
| const std::vector<Shape>& inputShapes, int32_t axis, T* outputData, |
| const Shape& outputShape); |
| |
| bool l2normFloat32(const float* inputData, const Shape& inputShape, int32_t axis, float* 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); |
| |
| bool resizeBilinearFloat16(const _Float16* inputData, const Shape& inputShape, _Float16* outputData, |
| const Shape& outputShape); |
| bool resizeBilinearFloat32(const float* inputData, const Shape& inputShape, float* 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 subFloat16(const _Float16* in1, const Shape& shape1, const _Float16* in2, const Shape& shape2, |
| int32_t activation, _Float16* out, const Shape& shapeOut); |
| |
| bool subFloat32(const float* in1, const Shape& shape1, const float* in2, const Shape& shape2, |
| int32_t activation, float* out, const Shape& shapeOut); |
| |
| bool subQuant8(const uint8_t* in1, const Shape& shape1, const uint8_t* in2, const Shape& shape2, |
| int32_t activation, uint8_t* out, const Shape& shapeOut); |
| |
| bool divFloat16(const _Float16* in1, const Shape& shape1, const _Float16* in2, const Shape& shape2, |
| int32_t activation, _Float16* out, const Shape& shapeOut); |
| bool divFloat32(const float* in1, const Shape& shape1, const float* in2, const Shape& shape2, |
| int32_t activation, float* out, const Shape& shapeOut); |
| |
| template <typename T> |
| bool transposeGeneric(const T* inputData, const Shape& inputShape, const int32_t* perm, |
| const Shape& permShape, T* outputData, const Shape& outputShape); |
| |
| bool meanGeneric(const uint8_t* inputData, const Shape& inputShape, const int32_t* axis, |
| const Shape& axisShape, bool keepDims, uint8_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 roiAlignFloat32(const float* inputData, const Shape& inputShape, const float* roiData, |
| const Shape& roiShape, float spatialScale, int32_t samplingRatio, |
| float* outputData, const Shape& outputShape); |
| |
| bool roiAlignQuant8(const uint8_t* inputData, const Shape& inputShape, const float* roiData, |
| const Shape& roiShape, float spatialScale, int32_t samplingRatio, |
| uint8_t* outputData, const Shape& outputShape); |
| |
| bool roiPoolingGeneric(const uint8_t* inputData, const Shape& inputShape, const uint8_t* roiData, |
| const Shape& roiShape, float spatialScale, uint8_t* outputData, |
| const Shape& outputShape); |
| |
| bool heatmapMaxKeypoint(const float* heatmap, const Shape& heatmapShape, const float* boxes, |
| const Shape& boxesShape, float* 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); |
| |
| bool groupedConvQuant8(const uint8_t* inputData, const Shape& inputShape, const uint8_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, uint8_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); |
| |
| bool transposeConvFloat32(const float* inputData, const Shape& inputShape, const float* filterData, |
| const Shape& filterShape, const float* 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 activation, float* outputData, const Shape& outputShape); |
| |
| bool transposeConvQuant8(const uint8_t* inputData, const Shape& inputShape, |
| const uint8_t* filterData, const Shape& filterShape, |
| 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 activation, |
| uint8_t* outputData, const Shape& outputShape); |
| |
| bool pReluGeneric(const uint8_t* inputData, const Shape& inputShape, const uint8_t* alphaData, |
| const Shape& alphaShape, uint8_t* outputData, const Shape& outputShape); |
| |
| bool axisAlignedBBoxTransform(const float* roiData, const Shape& roiShape, |
| const float* bboxDeltasData, const Shape& bboxDeltasShape, |
| const float* imageInfoData, const Shape& imageInfoDataShape, |
| const float* weightsData, const Shape& weightsDataShape, |
| bool applyScale, float* outputData, const Shape& outputShape, |
| int32_t* batchSplitData, const Shape& batchSplitShape); |
| |
| bool rotatedBBoxTransform(const float* roiData, const Shape& roiShape, const float* bboxDeltasData, |
| const Shape& bboxDeltasShape, const float* imageInfoData, |
| const Shape& imageInfoDataShape, const float* weightsData, |
| const Shape& weightsDataShape, bool applyScale, bool angleBoundOn, |
| int32_t angleBoundLow, int32_t angleBoundHigh, float clipAngleThreshold, |
| float* outputData, const Shape& outputShape, int32_t* batchSplitData, |
| const Shape& batchSplitShape); |
| } // namespace nn |
| } // namespace android |
| #endif // ANDROID_ML_NN_COMMON_OPERATIONS_H |
