1 /* 2 * Copyright (c) 2016-2023 Arm Limited. 3 * 4 * SPDX-License-Identifier: MIT 5 * 6 * Permission is hereby granted, free of charge, to any person obtaining a copy 7 * of this software and associated documentation files (the "Software"), to 8 * deal in the Software without restriction, including without limitation the 9 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or 10 * sell copies of the Software, and to permit persons to whom the Software is 11 * furnished to do so, subject to the following conditions: 12 * 13 * The above copyright notice and this permission notice shall be included in all 14 * copies or substantial portions of the Software. 15 * 16 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 17 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 18 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 19 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 20 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 21 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 22 * SOFTWARE. 23 */ 24 #ifndef ARM_COMPUTE_ITENSORINFO_H 25 #define ARM_COMPUTE_ITENSORINFO_H 26 27 #include "arm_compute/core/Coordinates.h" 28 #include "arm_compute/core/Strides.h" 29 #include "arm_compute/core/TensorShape.h" 30 #include "arm_compute/core/Types.h" 31 #include "arm_compute/core/Utils.h" 32 #include "arm_compute/core/utils/misc/Utility.h" 33 #include "support/ICloneable.h" 34 35 #include <cstddef> 36 37 namespace arm_compute 38 { 39 /** Store the tensor's metadata */ 40 class ITensorInfo : public misc::ICloneable<ITensorInfo> 41 { 42 public: 43 using TensorDimsState = std::vector<int>; 44 /** An id that uniquely identifies an ITensorInfo within some domain (e.g. a workload) 45 */ 46 using Id = int32_t; 47 /** An invalid tensor id within a domain */ 48 static constexpr Id invalid_tensor_id = 0; 49 /** Get the value representing dynamic dimension state 50 * 51 * @return Value representing dynamic dimension state 52 * 53 */ get_dynamic_state_value()54 static constexpr int32_t get_dynamic_state_value() 55 { 56 return _dynamic_dimension; 57 } 58 /** Get the value representing static dimension state 59 * 60 * @return Value representing static dimension state 61 * 62 */ get_static_state_value()63 static constexpr int32_t get_static_state_value() 64 { 65 return _static_dimension; 66 } 67 /** Default virtual destructor */ 68 virtual ~ITensorInfo() = default; 69 /** Set the data type to the specified value. 70 * 71 * @warning This resets the format to UNKNOWN. 72 * 73 * @param[in] data_type The new data type. 74 * 75 * @return Reference to this ITensorInfo object 76 */ 77 virtual ITensorInfo &set_data_type(DataType data_type) = 0; 78 /** Set the number of channels to the specified value. 79 * 80 * @warning This resets the format to UNKNOWN. 81 * 82 * @param[in] num_channels New number of channels. 83 * 84 * @return Reference to this ITensorInfo object 85 */ 86 virtual ITensorInfo &set_num_channels(int num_channels) = 0; 87 /** Set the format of an already initialized tensor. 88 * 89 * @note If the data type has already been configured (i.e. not UNKNOWN) it 90 * must match the new format. If data type hasn't been configured it will 91 * be based on the format. 92 * 93 * @param[in] format Single-plane format of the tensor. 94 * 95 * @return Reference to this ITensorInfo object 96 */ 97 virtual ITensorInfo &set_format(Format format) = 0; 98 /** Set the shape of an already initialized tensor. 99 * 100 * @warning Changing the shape requires to recompute the strides and is 101 * therefore only possible if the tensor hasn't been allocated yet. 102 * 103 * @param[in] shape New tensor shape. 104 * 105 * @return Reference to this ITensorInfo object 106 */ 107 virtual ITensorInfo &set_tensor_shape(const TensorShape &shape) = 0; 108 /** Set the state for each dimension of the tensor 109 * 110 * This sets the state of each dimension of the shape in terms of dynamic behavior using -1 where appropriate. 111 * The index in the state is a 1 to 1 mapping with the shape dimension index. 112 * For example if you want to express [?, 3, 3] as a dynamic input then [-1, 3, 3] has to be set as a state 113 * 114 * @param[in] state Tensor dimensions state 115 * 116 * @return Reference to this ITensorInfo object 117 */ 118 virtual ITensorInfo &set_tensor_dims_state(const TensorDimsState &state) = 0; 119 /** Set the quantization settings (scale and offset) of the tensor. 120 * 121 * @param[in] quantization_info QuantizationInfo containing the scale and offset 122 * 123 * @return Reference to this ITensorInfo object 124 */ 125 virtual ITensorInfo &set_quantization_info(const QuantizationInfo &quantization_info) = 0; 126 /** Set the data layout of the tensor. 127 * 128 * @param[in] data_layout DataLayout containing the layout data information. 129 * 130 * @return Reference to this ITensorInfo object 131 */ 132 virtual ITensorInfo &set_data_layout(const DataLayout &data_layout) = 0; 133 /** Resets the padding settings of the tensor. 134 * 135 * @return Reference to this ITensorInfo object 136 */ 137 virtual ITensorInfo &reset_padding() = 0; 138 /** Update the offset to the first element and the strides to automatically computed values. 139 * 140 * @note The padding used by this method is really conservative so that the tensor can be used for most functions. 141 * 142 * @return True if the strides or the offset to the first element have changed. 143 */ 144 virtual bool auto_padding() = 0; 145 /** Set the lock paddings flag of the tensor. 146 * It should be set to True, when the tensor could be mapped to camera or frame buffer. 147 * 148 * @return Reference to this ITensorInfo object 149 */ 150 virtual ITensorInfo &set_lock_paddings(bool flag) = 0; 151 /** Get the lock paddings flag value 152 * 153 * @return lock paddings flag value 154 */ 155 virtual bool lock_paddings() const = 0; 156 /** Update the offset to the first element, the strides and the total size. 157 * 158 * @note This function can only increase the offset, strides and total size. 159 * 160 * @param[in] padding Padding around the XY plane in number of elements. 161 * 162 * @return True if the strides, offset and total size have changed. 163 */ 164 virtual bool extend_padding(const PaddingSize &padding) = 0; 165 /** Return the size of the requested dimension 166 * 167 * @param[in] index Index of the dimension 168 * 169 * @return Dimension of the requested dimension 170 */ 171 virtual size_t dimension(size_t index) const = 0; 172 /** Return the size of the requested data layout dimension 173 * 174 * @param[in] dimension DataLayoutDimension of the dimension 175 * 176 * @return Dimension of the requested dimension 177 */ 178 virtual size_t dimension(DataLayoutDimension dimension) const = 0; 179 /** The strides in bytes for accessing each dimension of the tensor 180 * 181 * @return Strides in bytes for each tensor dimension 182 */ 183 virtual const Strides &strides_in_bytes() const = 0; 184 /** The offset from the beginning of the memory allocation to the first element of the tensor. 185 * This can be used to access efficiently elements in a 2D tensor 186 * 187 * @return The offset in bytes to access the first element of the tensor. 188 */ 189 virtual size_t offset_first_element_in_bytes() const = 0; 190 /** The offset in bytes from the beginning of the memory allocation to access the element at position (x, y, z ...) 191 * 192 * @param[in] pos Vector with the coordinates of the element to access. 193 * The size of this vector must be equal to the number of dimensions of the tensor 194 * 195 * @return Offset in bytes from the beginning of the memory allocation to access the element (x, y, z, ...) 196 */ 197 virtual int32_t offset_element_in_bytes(const Coordinates &pos) const = 0; 198 199 /** Element size in bytes calculated as data_size() * num_channels() 200 * 201 * @return The size of one element in bytes 202 */ 203 virtual size_t element_size() const = 0; 204 /** The number of dimensions of the tensor (rank) 205 * 206 * @return The number of dimensions of the tensor (rank) 207 */ 208 virtual size_t num_dimensions() const = 0; 209 /** The number of channels for each tensor element 210 * 211 * @return The number of channels for each tensor element 212 */ 213 virtual size_t num_channels() const = 0; 214 /** Size for each dimension of the tensor 215 * 216 * @return A vector with the size for each dimension of the tensor 217 */ 218 virtual const TensorShape &tensor_shape() const = 0; 219 /** State of each dimension of the tensor shape 220 * 221 * @return A vector with the state for each dimension of the tensor, where -1 specifies dynamic dimension 222 */ 223 virtual const TensorDimsState &tensor_dims_state() const = 0; 224 /** Data type used for each element of the tensor 225 * 226 * @return Tensor data type 227 */ 228 virtual DataType data_type() const = 0; 229 /** Colour format of the image 230 * 231 * @return Colour format of the image 232 */ 233 virtual Format format() const = 0; 234 /** Returns the total size of the tensor in bytes. 235 * 236 * @return Total size of the tensor in bytes. 237 */ 238 virtual size_t total_size() const = 0; 239 /** Padding of tensor. 240 * 241 * @return Padding. 242 */ 243 virtual PaddingSize padding() const = 0; 244 /** Checks if the tensor has been allocated with padding or not. 245 * 246 * @return True if padding is allocated in the tensor, otherwise false. 247 */ 248 virtual bool has_padding() const = 0; 249 /** Flag indicating whether the size of the tensor can be changed. 250 * 251 * @return True if the tensor size can be changed. 252 */ 253 virtual bool is_resizable() const = 0; 254 /** Flag indicating whether the shape of the tensor is dynamic, meaning that it can change on kernel/function execution. 255 * 256 * @return True if its dynamic else false 257 */ 258 virtual bool is_dynamic() const = 0; 259 /** Flag indicating whether the values of the tensor are constant, meaning that they can change on kernel/function execution. 260 * 261 * @return True if values are constant else false 262 */ 263 virtual bool are_values_constant() const = 0; 264 /** Set the flag whether the tensor size can be changed. 265 * 266 * @param[in] is_resizable Flag that marks the tensor if it can be changed or not. 267 * 268 * @return Reference to this ITensorInfo object 269 */ 270 virtual ITensorInfo &set_is_resizable(bool is_resizable) = 0; 271 /** Set the flag whether the tensor values can change during kernel/function execution. 272 * 273 * @param[in] are_values_constant Flag that marks the tensor values if they can be changed or not. 274 * 275 * @return Reference to this ITensorInfo object 276 */ 277 virtual ITensorInfo &set_are_values_constant(bool are_values_constant) = 0; 278 /** Valid region of the tensor. All elements in the valid region have defined values, i.e. are not undefined. 279 * 280 * @return The valid region. 281 */ 282 virtual ValidRegion valid_region() const = 0; 283 /** Set the valid region of the tensor. 284 * 285 * @param[in] valid_region Valid region to set. 286 */ 287 virtual void set_valid_region(const ValidRegion &valid_region) = 0; 288 289 /** Get the quantization settings (scale and offset) of the tensor. 290 * 291 * @return A QuantizationInfo containing the scale and offset. 292 */ 293 virtual QuantizationInfo quantization_info() const = 0; 294 /** Get the data layout of the tensor. 295 * 296 * @return A DataLayout containing the layout data information. 297 */ 298 virtual DataLayout data_layout() const = 0; 299 /** Get the workload tensor id of the tensor. 300 * 301 * @return Workload tensor id of the tensor 302 */ 303 virtual Id id() const = 0; 304 /** Set the tensor id 305 */ 306 virtual ITensorInfo &set_id(ITensorInfo::Id id) = 0; 307 /** Check if the tensor id is valid 308 */ has_valid_id()309 bool has_valid_id() const 310 { 311 return id() != invalid_tensor_id; 312 } 313 /** If infos are broadcast compatible tensor info's, return the broadcasted shape and the intersection of 314 * the broadcasted valid regions of the tensors. 315 * 316 * Two tensor info's are broadcast compatible if their shapes are broadcast compatible. 317 * 318 * Two tensor shapes are broadcast compatible if for each dimension, they're equal or one of them is 1. 319 * 320 * If two shapes are compatible, each dimension in the broadcasted shape is the max of the original dimensions. 321 * 322 * @param[in] infos Tensor info's. 323 * 324 * @return The broadcasted shape and valid region, or an empty shape and valid region if the info's are 325 * not broadcast compatible. 326 */ 327 template <typename... Infos> broadcast_shape_and_valid_region(const Infos &...infos)328 static std::pair<TensorShape, ValidRegion> broadcast_shape_and_valid_region(const Infos &... infos) 329 { 330 TensorShape bc_shape = TensorShape::broadcast_shape(infos.tensor_shape()...); 331 ValidRegion bc_valid_region{ Coordinates(), bc_shape }; 332 333 auto broadcast_valid_region = [&bc_valid_region](const ITensorInfo & info) 334 { 335 if(info.num_dimensions() != 0) 336 { 337 for(size_t d = 0; d < bc_valid_region.shape.num_dimensions(); ++d) 338 { 339 const bool is_broadcast = (info.tensor_shape()[d] == 1); 340 341 const int anchor_max = std::max(bc_valid_region.anchor[d], info.valid_region().anchor[d]); 342 const size_t valid_min = std::min(bc_valid_region.shape[d], info.valid_region().shape[d]); 343 344 if(!is_broadcast || (valid_min == 0)) 345 { 346 bc_valid_region.anchor.set(d, anchor_max); 347 bc_valid_region.shape.set(d, valid_min); 348 } 349 } 350 } 351 }; 352 353 utility::for_each(broadcast_valid_region, infos...); 354 355 return std::pair<TensorShape, ValidRegion>(bc_shape, bc_valid_region); 356 } 357 358 private: 359 static constexpr int32_t _dynamic_dimension = -1; 360 static constexpr int32_t _static_dimension = 0; 361 }; 362 } // namespace arm_compute 363 #endif /*ARM_COMPUTE_TENSORINFO_H */ 364