| /* |
| * 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_PACKAGES_MODULES_NEURALNETWORKS_COMMON_OPERATIONS_H |
| #define ANDROID_PACKAGES_MODULES_NEURALNETWORKS_COMMON_OPERATIONS_H |
| |
| #include <stddef.h> |
| |
| #include <cstdint> |
| #include <vector> |
| |
| #include "ActivationFunctor.h" |
| |
| 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_PACKAGES_MODULES_NEURALNETWORKS_COMMON_OPERATIONS_H |
