| /* |
| * 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/EmbeddingLookup.h" |
| #include "operations/ExpandDims.h" |
| #include "operations/HashtableLookup.h" |
| #include "operations/LSHProjection.h" |
| #include "operations/LSTM.h" |
| #include "operations/RNN.h" |
| #include "operations/SVDF.h" |
| #include "operations/Tile.h" |
| |
| #include <stddef.h> |
| |
| #include <cstdint> |
| #include <vector> |
| |
| namespace android { |
| namespace nn { |
| |
| struct Shape; |
| |
| 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 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 depthwiseConvFloat32(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 depth_multiplier, 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 padding_left, int32_t padding_right, |
| int32_t padding_top, int32_t padding_bottom, |
| int32_t stride_width, int32_t stride_height, |
| int32_t depth_multiplier, 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); |
| |
| bool reluFloat32(const float* inputData, const Shape& inputShape, |
| float* outputData, const Shape& outputShape); |
| bool relu1Float32(const float* inputData, const Shape& inputShape, |
| float* outputData, const Shape& outputShape); |
| bool relu6Float32(const float* inputData, const Shape& inputShape, |
| float* outputData, const Shape& outputShape); |
| bool tanhFloat32(const float* inputData, const Shape& inputShape, |
| float* outputData, const Shape& outputShape); |
| bool logisticFloat32(const float* inputData, const Shape& inputShape, |
| float* outputData, const Shape& outputShape); |
| bool softmaxFloat32(const float* inputData, const Shape& inputShape, |
| const float beta, |
| 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, |
| 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); |
| |
| bool concatenationFloat32(const std::vector<const float*>& inputDataPtrs, |
| const std::vector<Shape>& inputShapes, int32_t axis, |
| float* outputData, const Shape& outputShape); |
| bool concatenationQuant8(const std::vector<const uint8_t*>& inputDataPtrs, |
| const std::vector<Shape>& inputShapes, int32_t axis, |
| uint8_t* outputData, const Shape& outputShape); |
| |
| bool l2normFloat32(const float* inputData, const Shape& inputShape, |
| float* outputData, const Shape& outputShape); |
| bool l2normQuant8(const uint8_t* inputData, const Shape& inputShape, |
| uint8_t* outputData, const Shape& outputShape); |
| bool localResponseNormFloat32(const float* inputData, const Shape& inputShape, |
| int32_t radius, float bias, float alpha, float beta, |
| float* outputData, const Shape& outputShape); |
| |
| bool reshapeGeneric(const void* inputData, const Shape& inputShape, |
| void* outputData, const Shape& outputShape); |
| |
| bool resizeBilinearFloat32(const float* inputData, |
| const Shape& inputShape, |
| float* outputData, |
| const Shape& outputShape); |
| |
| bool depthToSpaceGeneric(const uint8_t* inputData, const Shape& inputShape, |
| int32_t blockSize, |
| uint8_t* outputData, const Shape& outputShape); |
| |
| bool spaceToDepthGeneric(const uint8_t* inputData, const Shape& inputShape, |
| int32_t blockSize, |
| uint8_t* outputData, const Shape& outputShape); |
| |
| bool padFloat32(const float* inputData, const Shape& inputShape, const int32_t* paddings, |
| float pad_value, float* outputData, const Shape& outputShape); |
| |
| bool padQuant8(const uint8_t* inputData, const Shape& inputShape, const int32_t* paddings, |
| uint8_t pad_value, uint8_t* outputData, const Shape& outputShape); |
| |
| bool batchToSpaceGeneric(const uint8_t* inputData, const Shape& inputShape, |
| const int32_t* blockSize, |
| uint8_t* outputData, const Shape& outputShape); |
| |
| bool spaceToBatchGeneric(const uint8_t* inputData, const Shape& inputShape, |
| const int32_t* blockSize, |
| const int32_t* padding, const Shape& paddingShape, |
| uint8_t* outputData, const Shape& outputShape); |
| |
| bool subFloat32(const float* in1, const Shape& shape1, |
| const float* in2, const Shape& shape2, |
| int32_t activation, |
| float* out, const Shape& shapeOut); |
| |
| bool squeezeGeneric(const void* inputData, const Shape& inputShape, |
| void* outputData, const Shape& outputShape); |
| |
| bool divFloat32(const float* in1, const Shape& shape1, |
| const float* in2, const Shape& shape2, |
| int32_t activation, |
| float* out, const Shape& shapeOut); |
| |
| bool transposeGeneric(const uint8_t* inputData, const Shape& inputShape, |
| const int32_t* perm, const Shape& permShape, |
| uint8_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 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 roiAlign(const float* inputData, const Shape& inputShape, const float* roiData, |
| const Shape& roiShape, float spatialScale, int32_t samplingRatio, float* 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, |
| 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); |
| } // namespace nn |
| } // namespace android |
| #endif // ANDROID_ML_NN_COMMON_OPERATIONS_H |
