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 // Contains the implementation of the operations. 18 19 #define LOG_TAG "Operations" 20 21 #include "Operations.h" 22 #include "CpuOperationUtils.h" 23 24 #include "tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h" 25 #include "tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h" 26 27 namespace android { 28 namespace nn { 29 30 bool reshapeGeneric(const void* inputData, const Shape& inputShape, 31 void* outputData, const Shape& outputShape) { 32 size_t count = sizeOfData(inputShape.type, inputShape.dimensions); 33 memcpy(outputData, inputData, count); 34 return true; 35 } 36 37 bool resizeBilinearFloat32(const float* inputData, const Shape& inputShape, 38 float* outputData, const Shape& outputShape) { 39 int32_t height = (int32_t) getSizeOfDimension(outputShape, 1); 40 int32_t width = (int32_t) getSizeOfDimension(outputShape, 2); 41 42 int32_t outDimData[2] = {height, width}; 43 // We have to fake a tensor here, to satisfy ResizeBilinear(). 44 Shape outDimShape; 45 outDimShape.dimensions = {1, 1, 1, 2}; 46 47 tflite::optimized_ops::ResizeBilinear( 48 inputData, convertShapeToDims(inputShape), 49 outDimData, convertShapeToDims(outDimShape), 50 outputData, convertShapeToDims(outputShape)); 51 return true; 52 } 53 54 bool depthToSpaceGeneric(const uint8_t* inputData, const Shape& inputShape, 55 int32_t blockSize, 56 uint8_t* outputData, const Shape& outputShape) { 57 if (inputShape.type == OperandType::TENSOR_FLOAT32) { 58 tflite::optimized_ops::DepthToSpace( 59 reinterpret_cast<const float*>(inputData), 60 convertShapeToDims(inputShape), 61 blockSize, 62 reinterpret_cast<float*>(outputData), 63 convertShapeToDims(outputShape)); 64 } else if (inputShape.type == OperandType::TENSOR_QUANT8_ASYMM) { 65 tflite::optimized_ops::DepthToSpace( 66 reinterpret_cast<const uint8_t*>(inputData), 67 convertShapeToDims(inputShape), 68 blockSize, 69 reinterpret_cast<uint8_t*>(outputData), 70 convertShapeToDims(outputShape)); 71 } else { 72 LOG(ERROR) << "Unsupported data type"; 73 return false; 74 } 75 return true; 76 } 77 78 bool spaceToDepthGeneric(const uint8_t* inputData, const Shape& inputShape, 79 int32_t blockSize, 80 uint8_t* outputData, const Shape& outputShape) { 81 if (inputShape.type == OperandType::TENSOR_FLOAT32) { 82 tflite::optimized_ops::SpaceToDepth( 83 reinterpret_cast<const float*>(inputData), 84 convertShapeToDims(inputShape), 85 blockSize, 86 reinterpret_cast<float*>(outputData), 87 convertShapeToDims(outputShape)); 88 } else if (inputShape.type == OperandType::TENSOR_QUANT8_ASYMM) { 89 tflite::optimized_ops::SpaceToDepth( 90 reinterpret_cast<const uint8_t*>(inputData), 91 convertShapeToDims(inputShape), 92 blockSize, 93 reinterpret_cast<uint8_t*>(outputData), 94 convertShapeToDims(outputShape)); 95 } else { 96 LOG(ERROR) << "Unsupported data type"; 97 return false; 98 } 99 return true; 100 } 101 102 bool padGeneric(const uint8_t* inputData, const Shape& inputShape, 103 const int32_t* paddings, 104 uint8_t* outputData, const Shape& outputShape) { 105 int32_t numInputDims = static_cast<int32_t>(getNumberOfDimensions(inputShape)); 106 107 std::vector<int> beforePadding; 108 std::vector<int> afterPadding; 109 // The lower level implementation expects the paddings in the reverse order. 110 for (int32_t i = numInputDims - 1; i >= 0; --i) { 111 beforePadding.push_back(paddings[i * 2]); 112 afterPadding.push_back(paddings[i * 2 + 1]); 113 } 114 115 if (inputShape.type == OperandType::TENSOR_FLOAT32) { 116 tflite::optimized_ops::Pad( 117 reinterpret_cast<const float*>(inputData), 118 convertShapeToDims(inputShape), 119 beforePadding, afterPadding, 120 reinterpret_cast<float*>(outputData), 121 convertShapeToDims(outputShape)); 122 } else if (inputShape.type == OperandType::TENSOR_QUANT8_ASYMM) { 123 tflite::optimized_ops::Pad( 124 reinterpret_cast<const uint8_t*>(inputData), 125 convertShapeToDims(inputShape), 126 beforePadding, afterPadding, 127 reinterpret_cast<uint8_t*>(outputData), 128 convertShapeToDims(outputShape)); 129 } else { 130 LOG(ERROR) << "Unsupported data type"; 131 return false; 132 } 133 return true; 134 } 135 136 bool batchToSpaceGeneric(const uint8_t* inputData, const Shape& inputShape, 137 const int32_t* blockSize, 138 uint8_t* outputData, const Shape& outputShape) { 139 // Needed by low level implementation, but not really used. 140 tflite::Dims<4> blockSizeDim; 141 if (inputShape.type == OperandType::TENSOR_FLOAT32) { 142 tflite::optimized_ops::BatchToSpaceND( 143 reinterpret_cast<const float*>(inputData), 144 convertShapeToDims(inputShape), 145 blockSize, blockSizeDim, 146 reinterpret_cast<float*>(outputData), 147 convertShapeToDims(outputShape)); 148 } else if (inputShape.type == OperandType::TENSOR_QUANT8_ASYMM) { 149 tflite::optimized_ops::BatchToSpaceND( 150 reinterpret_cast<const uint8_t*>(inputData), 151 convertShapeToDims(inputShape), 152 blockSize, blockSizeDim, 153 reinterpret_cast<uint8_t*>(outputData), 154 convertShapeToDims(outputShape)); 155 } else { 156 LOG(ERROR) << "Unsupported data type"; 157 return false; 158 } 159 return true; 160 } 161 162 bool spaceToBatchGeneric(const uint8_t* inputData, const Shape& inputShape, 163 const int32_t* blockSize, 164 const int32_t* padding, const Shape& paddingShape, 165 uint8_t* outputData, const Shape& outputShape) { 166 // Needed by low level implementation, but not really used. 167 tflite::Dims<4> blockSizeDim; 168 if (inputShape.type == OperandType::TENSOR_FLOAT32) { 169 tflite::optimized_ops::SpaceToBatchND( 170 reinterpret_cast<const float*>(inputData), 171 convertShapeToDims(inputShape), 172 blockSize, blockSizeDim, 173 padding, convertShapeToDims(paddingShape), 174 reinterpret_cast<float*>(outputData), 175 convertShapeToDims(outputShape)); 176 } else if (inputShape.type == OperandType::TENSOR_QUANT8_ASYMM) { 177 tflite::optimized_ops::SpaceToBatchND( 178 reinterpret_cast<const uint8_t*>(inputData), 179 convertShapeToDims(inputShape), 180 blockSize, blockSizeDim, 181 padding, convertShapeToDims(paddingShape), 182 reinterpret_cast<uint8_t*>(outputData), 183 convertShapeToDims(outputShape)); 184 } else { 185 LOG(ERROR) << "Unsupported data type"; 186 return false; 187 } 188 return true; 189 } 190 191 bool squeezeGeneric(const void* inputData, const Shape& inputShape, 192 void* outputData, const Shape& outputShape) { 193 size_t count = sizeOfData(inputShape.type, inputShape.dimensions); 194 memcpy(outputData, inputData, count); 195 return true; 196 } 197 198 bool transposeGeneric(const uint8_t* inputData, const Shape& inputShape, 199 const int32_t* perm, const Shape& permShape, 200 uint8_t* outputData, const Shape& outputShape) { 201 // Reverse the permuted axes and convert to 4D due to the way Dims are 202 // constructed. 203 const int32_t kOutputDimensionNum = 4; 204 205 int32_t permSize = static_cast<int32_t>(getSizeOfDimension(permShape, 0)); 206 int32_t reversed_perm[kOutputDimensionNum]; 207 for (int32_t output_k = 0, input_k = permSize - 1; output_k < permSize; 208 ++output_k, --input_k) { 209 reversed_perm[output_k] = permSize - perm[input_k] - 1; 210 } 211 for (int32_t k = permSize; k < kOutputDimensionNum; ++k) { 212 reversed_perm[k] = k; 213 } 214 if (inputShape.type == OperandType::TENSOR_FLOAT32) { 215 tflite::reference_ops::Transpose( 216 reinterpret_cast<const float*>(inputData), 217 convertShapeToDims(inputShape), 218 reinterpret_cast<float*>(outputData), 219 convertShapeToDims(outputShape), 220 reversed_perm); 221 } else if (inputShape.type == OperandType::TENSOR_QUANT8_ASYMM) { 222 tflite::reference_ops::Transpose( 223 reinterpret_cast<const uint8_t*>(inputData), 224 convertShapeToDims(inputShape), 225 reinterpret_cast<uint8_t*>(outputData), 226 convertShapeToDims(outputShape), 227 reversed_perm); 228 } else { 229 LOG(ERROR) << "Unsupported data type"; 230 return false; 231 } 232 return true; 233 } 234 } // namespace nn 235 } // namespace android 236
