1 /* 2 * Copyright (C) 2017 The Android Open Source Project 3 * 4 * Licensed under the Apache License, Version 2.0 (the "License"); 5 * you may not use this file except in compliance with the License. 6 * You may obtain a copy of the License at 7 * 8 * http://www.apache.org/licenses/LICENSE-2.0 9 * 10 * Unless required by applicable law or agreed to in writing, software 11 * distributed under the License is distributed on an "AS IS" BASIS, 12 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 13 * See the License for the specific language governing permissions and 14 * limitations under the License. 15 */ 16 17 #ifndef ANDROID_ML_NN_COMMON_OPERATIONS_UTILS_H 18 #define ANDROID_ML_NN_COMMON_OPERATIONS_UTILS_H 19 20 #include "Utils.h" 21 22 #include <cstdint> 23 #include <vector> 24 25 // Macro to check if the input parameters for operation are valid or not. 26 #define NN_CHECK(v) \ 27 do { \ 28 if (!(v)) { \ 29 LOG(ERROR) << "NN_CHECK failed: " << #v << "'\n"; \ 30 return false; \ 31 } \ 32 } while(0); 33 34 #define NN_CHECK_EQ(actual, expected) \ 35 NN_CHECK((actual) == (expected)) 36 37 #define NN_OPS_CHECK NN_CHECK 38 39 namespace android { 40 namespace nn { 41 42 enum PaddingScheme { 43 kPaddingUnknown = 0, 44 kPaddingSame = 1, 45 kPaddingValid = 2, 46 }; 47 48 // The type and dimensions of an operand. 49 struct Shape { 50 OperandType type; 51 std::vector<uint32_t> dimensions; 52 float scale; 53 int32_t offset; 54 }; 55 56 // Verifies that the two shapes are the same. 57 bool SameShape(const Shape& in1, const Shape& in2); 58 59 // Sets out to the same shape as in. 60 bool SetShape(const Shape& in, Shape* out); 61 62 // Return the total number of elements, i.e. all the dimensions multiplied 63 // together. For a scalar, returns one. 64 uint32_t getNumberOfElements(const Shape& shape); 65 66 uint32_t getNumberOfDimensions(const Shape& shape); 67 68 uint32_t getSizeOfDimension(const Shape& shape, uint32_t dimensionIdx); 69 70 inline uint32_t computeOutSize(uint32_t imageSize, uint32_t filterSize, uint32_t stride, 71 uint32_t paddingHead, uint32_t paddingTail) { 72 return (imageSize - filterSize + stride + paddingHead + paddingTail) / stride; 73 } 74 75 __wur 76 bool QuantizeMultiplierSmallerThanOne(double double_multiplier, 77 int32_t* quantized_multiplier, 78 int32_t* right_shift); 79 80 __wur 81 bool QuantizeMultiplierGreaterThanOne(double double_multiplier, 82 int32_t* quantized_multiplier, 83 int* left_shift); 84 85 __wur 86 bool GetQuantizedConvolutionMultipler(const Shape& inputShape, 87 const Shape& filterShape, 88 const Shape& biasShape, 89 const Shape& outputShape, 90 float* multiplier); 91 92 void CalculateActivationRangeUint8(int32_t activation, 93 const Shape& outputShape, 94 int32_t* act_min, 95 int32_t* act_max); 96 97 int32_t CalculateInputRadius(int input_integer_bits, int input_left_shift); 98 99 inline void calculateExplicitPadding(int32_t in_size, int32_t stride, 100 int32_t filter_size, int32_t padding_implicit, 101 int32_t* padding_head, int32_t* padding_tail) { 102 *padding_head = 0; 103 *padding_tail = 0; 104 105 if (padding_implicit == kPaddingSame) { 106 int32_t out_size = (in_size + stride - 1) / stride; 107 int32_t tmp = (out_size - 1) * stride + filter_size; 108 if (tmp > in_size) { 109 *padding_head = (tmp - in_size) / 2; 110 *padding_tail = (tmp - in_size) - *padding_head; 111 } 112 } 113 } 114 115 inline PaddingScheme getPaddingScheme(int32_t inWidth, int32_t inHeight, 116 int32_t strideWidth, int32_t strideHeight, 117 int32_t filterWidth, int32_t filterHeight, 118 int32_t paddingLeft, int32_t paddingRight, 119 int32_t paddingTop, int32_t paddingBottom) { 120 if (paddingLeft == 0 && paddingRight == 0 && paddingTop == 0 && paddingBottom == 0) { 121 return kPaddingValid; 122 } 123 124 int32_t expectedPaddingLeft, expectedPaddingRight; 125 int32_t expectedPaddingTop, expectedPaddingBottom; 126 127 calculateExplicitPadding(inWidth, strideWidth, filterWidth, kPaddingSame, 128 &expectedPaddingLeft, &expectedPaddingRight); 129 calculateExplicitPadding(inHeight, strideHeight, filterHeight, kPaddingSame, 130 &expectedPaddingTop, &expectedPaddingBottom); 131 if (expectedPaddingLeft == paddingLeft && expectedPaddingRight == paddingRight && 132 expectedPaddingTop == paddingTop && expectedPaddingBottom == paddingBottom) { 133 return kPaddingSame; 134 } else { 135 return kPaddingUnknown; 136 } 137 } 138 139 // Preparation functions for the corresponding ops 140 bool addMulPrepare(const Shape& in1, const Shape& in2, Shape* out1); 141 142 bool floorPrepare(const Shape& input, Shape* output); 143 144 bool dequantizePrepare(const Shape& input, Shape* output); 145 146 bool depthwiseConvPrepare(const Shape& input, 147 const Shape& filter, 148 const Shape& bias, 149 int32_t padding_left, int32_t padding_right, 150 int32_t padding_top, int32_t padding_bottom, 151 int32_t stride_width, int32_t stride_height, 152 Shape* output); 153 154 bool convPrepare(const Shape& input, 155 const Shape& filter, 156 const Shape& bias, 157 int32_t padding_left, int32_t padding_right, 158 int32_t padding_top, int32_t padding_bottom, 159 int32_t stride_width, int32_t stride_height, 160 Shape* output); 161 162 bool genericPoolingPrepare(const Shape& input, 163 int32_t padding_left, int32_t padding_right, 164 int32_t padding_top, int32_t padding_bottom, 165 int32_t stride_width, int32_t stride_height, 166 int32_t filter_width, int32_t filter_height, 167 Shape* output); 168 169 bool genericActivationPrepare(const Shape& input, Shape* output); 170 171 bool fullyConnectedPrepare(const Shape& input, 172 const Shape& weights, 173 const Shape& bias, 174 Shape* output); 175 176 bool concatenationPrepare(const std::vector<Shape>& inputShapes, 177 int32_t axis, 178 Shape* output); 179 180 bool genericNormalizationPrepare(const Shape& input, Shape* output); 181 182 bool reshapePrepare(const Shape& input, 183 const int32_t* targetDims, 184 const int32_t targetDimsSize, 185 Shape* output); 186 187 bool resizeBilinearPrepare(const Shape& input, 188 int32_t height, 189 int32_t width, 190 Shape* output); 191 192 bool depthToSpacePrepare(const Shape& input, 193 int32_t blockSize, 194 Shape* output); 195 196 bool spaceToDepthPrepare(const Shape& input, 197 int32_t blockSize, 198 Shape* output); 199 200 bool embeddingLookupPrepare(const Shape &valueShape, 201 const Shape &lookupShape, 202 Shape *outputShape); 203 204 bool hashtableLookupPrepare(const Shape &lookupShape, 205 const Shape &keyShape, 206 const Shape &valueShape, 207 Shape *outputShape, 208 Shape *hitShape); 209 210 #define ANDROID_NN_MACRO_DISPATCH(macro) \ 211 switch (activation) { \ 212 case (int32_t) FusedActivationFunc::NONE: \ 213 macro(kNone); \ 214 break; \ 215 case (int32_t) FusedActivationFunc::RELU: \ 216 macro(kRelu); \ 217 break; \ 218 case (int32_t) FusedActivationFunc::RELU1: \ 219 macro(kRelu1); \ 220 break; \ 221 case (int32_t) FusedActivationFunc::RELU6: \ 222 macro(kRelu6); \ 223 break; \ 224 default: \ 225 LOG(ERROR) << "Unsupported fused activation function type"; \ 226 return false; \ 227 } 228 229 } // namespace nn 230 } // namespace android 231 232 #endif // ANDROID_ML_NN_COMMON_OPERATIONS_UTILS_H 233
