| /* |
| * 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 "android/log.h" |
| |
| #include <stddef.h> |
| |
| #include <algorithm> |
| #include <cmath> |
| #include <cstdint> |
| #include <vector> |
| |
| namespace android { |
| namespace nn { |
| |
| struct Shape; |
| |
| enum PaddingScheme { |
| kPaddingUnknown = 0, |
| kPaddingSame = 1, |
| kPaddingValid = 2, |
| }; |
| |
| enum ActivationFn { |
| kActivationNone = 0, |
| kActivationRelu, |
| kActivationRelu1, |
| kActivationRelu6, |
| kActivationTanh, |
| kActivationSignBit, |
| kActivationSigmoid, |
| }; |
| |
| class ActivationFunctor { |
| public: |
| explicit ActivationFunctor(ActivationFn act) : act_(act) {} |
| |
| float operator()(float a) const { |
| switch (act_) { |
| case kActivationNone: |
| return a; |
| case kActivationRelu: |
| return a < 0.f ? 0.f : a; |
| case kActivationRelu6: |
| return std::max(0.f, std::min(a, 6.f)); |
| case kActivationTanh: |
| return std::tanh(a); |
| case kActivationSigmoid: |
| return 1.0f / (1.0f + std::exp(-a)); |
| default: |
| __android_log_print(ANDROID_LOG_ERROR, "NN API", |
| "Invalid enum value for activation function: 0x%0X", |
| act_); |
| exit(1); |
| } |
| } |
| |
| private: |
| ActivationFn act_; |
| }; |
| |
| bool addPrepare(const Shape& in1, const Shape& in2, Shape* out1); |
| bool addFloat32(const float* in1, const float* in2, |
| int32_t activation, |
| float* out, const Shape& shape); |
| |
| bool mulPrepare(const Shape& in1, const Shape& in2, Shape* out1); |
| bool mulFloat32(const float* in1, const float* in2, |
| int32_t activation, |
| float* out, const Shape& shape); |
| |
| bool floorPrepare(const Shape& input, Shape* output); |
| bool floorFloat32(const float* inputData, |
| float* outputData, |
| const Shape& shape); |
| |
| bool dequantizePrepare(const Shape& input, Shape* output); |
| bool dequantizeQuant8ToFloat32(const uint8_t* inputData, |
| float* outputData, |
| const Shape& shape); |
| |
| bool depthwiseConvPrepare(const Shape& input, |
| const Shape& filter, |
| const Shape& bias, |
| int32_t padding, |
| int32_t stride_width, int32_t stride_height, |
| Shape* output); |
| bool depthwiseConvFloat32(const float* inputData, const Shape& inputShape, |
| const float* filterData, const Shape& filterShape, |
| const float* biasData, const Shape& biasShape, |
| int32_t padding, 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, int32_t stride_width, int32_t stride_height, |
| int32_t depth_multiplier, int32_t activation, |
| uint8_t* outputData, const Shape& outputShape); |
| |
| bool convPrepare(const Shape& input, |
| const Shape& filter, |
| const Shape& bias, |
| int32_t padding, |
| int32_t stride_width, int32_t stride_height, |
| Shape* output); |
| bool convFloat32(const float* inputData, const Shape& inputShape, |
| const float* filterData, const Shape& filterShape, |
| const float* biasData, const Shape& biasShape, |
| int32_t padding, 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, int32_t stride_width, int32_t stride_height, |
| int32_t activation, |
| uint8_t* outputData, const Shape& outputShape); |
| |
| bool genericPoolingPrepare(const Shape& input, |
| int32_t padding, |
| int32_t stride_width, int32_t stride_height, |
| int32_t filter_width, int32_t filter_height, |
| Shape* output); |
| bool averagePoolFloat32(const float* inputData, const Shape& inputShape, |
| int32_t padding, 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, 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, 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, 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, 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 genericActivationPrepare(const Shape& input, Shape* output); |
| 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 fullyConnectedPrepare(const Shape& input, |
| const Shape& weights, |
| const Shape& bias, |
| Shape* output); |
| 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 concatenationPrepare(const std::vector<Shape>& inputShapes, |
| int32_t axis, |
| Shape* output); |
| bool concatenationFloat32(const std::vector<const float*>& inputDataPtrs, |
| const std::vector<Shape>& inputShapes, |
| int32_t axis, int32_t activation, |
| float* outputData, const Shape& outputShape); |
| bool concatenationQuant8(const std::vector<const uint8_t*>& inputDataPtrs, |
| const std::vector<Shape>& inputShapes, |
| int32_t axis, int32_t activation, |
| uint8_t* outputData, const Shape& outputShape); |
| |
| bool genericNormalizationPrepare(const Shape& input, Shape* output); |
| 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 reshapePrepare(const Shape& input, |
| const int32_t* targetDims, |
| const int32_t targetDimsSize, |
| Shape* output); |
| bool reshapeGeneric(const void* inputData, const Shape& inputShape, |
| void* outputData, const Shape& outputShape); |
| |
| bool resizeBilinearPrepare(const Shape& input, |
| int32_t height, |
| int32_t width, |
| Shape* output); |
| bool resizeBilinearFloat32(const float* inputData, |
| const Shape& inputShape, |
| float* outputData, |
| const Shape& outputShape); |
| |
| bool depthToSpacePrepare(const Shape& input, |
| int32_t blockSize, |
| Shape* output); |
| bool depthToSpaceGeneric(const uint8_t* inputData, const Shape& inputShape, |
| int32_t blockSize, |
| uint8_t* outputData, const Shape& outputShape); |
| |
| bool spaceToDepthPrepare(const Shape& input, |
| int32_t blockSize, |
| Shape* output); |
| bool spaceToDepthGeneric(const uint8_t* inputData, const Shape& inputShape, |
| int32_t blockSize, |
| uint8_t* outputData, const Shape& outputShape); |
| |
| } // namespace nn |
| } // namespace android |
| |
| #endif // ANDROID_ML_NN_COMMON_OPERATIONS_H |
