1 /* 2 * Copyright (c) 2016-2020 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 /** Default virtual destructor */ 44 virtual ~ITensorInfo() = default; 45 /** Set the data type to the specified value. 46 * 47 * @warning This resets the format to UNKNOWN. 48 * 49 * @param[in] data_type The new data type. 50 * 51 * @return Reference to this ITensorInfo object 52 */ 53 virtual ITensorInfo &set_data_type(DataType data_type) = 0; 54 /** Set the number of channels to the specified value. 55 * 56 * @warning This resets the format to UNKNOWN. 57 * 58 * @param[in] num_channels New number of channels. 59 * 60 * @return Reference to this ITensorInfo object 61 */ 62 virtual ITensorInfo &set_num_channels(int num_channels) = 0; 63 /** Set the format of an already initialized tensor. 64 * 65 * @note If the data type has already been configured (i.e. not UNKNOWN) it 66 * must match the new format. If data type hasn't been configured it will 67 * be based on the format. 68 * 69 * @param[in] format Single-plane format of the tensor. 70 * 71 * @return Reference to this ITensorInfo object 72 */ 73 virtual ITensorInfo &set_format(Format format) = 0; 74 /** Set the shape of an already initialized tensor. 75 * 76 * @warning Changing the shape requires to recompute the strides and is 77 * therefore only possible if the tensor hasn't been allocated yet. 78 * 79 * @param[in] shape New tensor shape. 80 * 81 * @return Reference to this ITensorInfo object 82 */ 83 virtual ITensorInfo &set_tensor_shape(const TensorShape &shape) = 0; 84 /** Set the quantization settings (scale and offset) of the tensor. 85 * 86 * @param[in] quantization_info QuantizationInfo containing the scale and offset 87 * 88 * @return Reference to this ITensorInfo object 89 */ 90 virtual ITensorInfo &set_quantization_info(const QuantizationInfo &quantization_info) = 0; 91 /** Set the data layout of the tensor. 92 * 93 * @param[in] data_layout DataLayout containing the layout data information. 94 * 95 * @return Reference to this ITensorInfo object 96 */ 97 virtual ITensorInfo &set_data_layout(const DataLayout &data_layout) = 0; 98 /** Resets the padding settings of the tensor. 99 * 100 * @return Reference to this ITensorInfo object 101 */ 102 virtual ITensorInfo &reset_padding() = 0; 103 /** Update the offset to the first element and the strides to automatically computed values. 104 * 105 * @note The padding used by this method is really conservative so that the tensor can be used for most functions. 106 * 107 * @return True if the strides or the offset to the first element have changed. 108 */ 109 virtual bool auto_padding() = 0; 110 /** Update the offset to the first element, the strides and the total size. 111 * 112 * @note This function can only increase the offset, strides and total size. 113 * 114 * @param[in] padding Padding around the XY plane in number of elements. 115 * 116 * @return True if the strides, offset and total size have changed. 117 */ 118 virtual bool extend_padding(const PaddingSize &padding) = 0; 119 /** Return the size of the requested dimension 120 * 121 * @param[in] index Index of the dimension 122 * 123 * @return Dimension of the requested dimension 124 */ 125 virtual size_t dimension(size_t index) const = 0; 126 /** Return the size of the requested data layout dimension 127 * 128 * @param[in] dimension DataLayoutDimension of the dimension 129 * 130 * @return Dimension of the requested dimension 131 */ 132 virtual size_t dimension(DataLayoutDimension dimension) const = 0; 133 /** The strides in bytes for accessing each dimension of the tensor 134 * 135 * @return Strides in bytes for each tensor dimension 136 */ 137 virtual const Strides &strides_in_bytes() const = 0; 138 /** The offset from the beginning of the memory allocation to the first element of the tensor. 139 * This can be used to access efficiently elements in a 2D tensor 140 * 141 * @return The offset in bytes to access the first element of the tensor. 142 */ 143 virtual size_t offset_first_element_in_bytes() const = 0; 144 /** The offset in bytes from the beginning of the memory allocation to access the element at position (x, y, z ...) 145 * 146 * @param[in] pos Vector with the coordinates of the element to access. 147 * The size of this vector must be equal to the number of dimensions of the tensor 148 * 149 * @return Offset in bytes from the beginning of the memory allocation to access the element (x, y, z, ...) 150 */ 151 virtual int32_t offset_element_in_bytes(const Coordinates &pos) const = 0; 152 153 /** Element size in bytes calculated as data_size() * num_channels() 154 * 155 * @return The size of one element in bytes 156 */ 157 virtual size_t element_size() const = 0; 158 /** The number of dimensions of the tensor (rank) 159 * 160 * @return The number of dimensions of the tensor (rank) 161 */ 162 virtual size_t num_dimensions() const = 0; 163 /** The number of channels for each tensor element 164 * 165 * @return The number of channels for each tensor element 166 */ 167 virtual size_t num_channels() const = 0; 168 /** Size for each dimension of the tensor 169 * 170 * @return A vector with the size for each dimension of the tensor 171 */ 172 virtual const TensorShape &tensor_shape() const = 0; 173 /** Data type used for each element of the tensor 174 * 175 * @return Tensor data type 176 */ 177 virtual DataType data_type() const = 0; 178 /** Colour format of the image 179 * 180 * @return Colour format of the image 181 */ 182 virtual Format format() const = 0; 183 /** Returns the total size of the tensor in bytes. 184 * 185 * @return Total size of the tensor in bytes. 186 */ 187 virtual size_t total_size() const = 0; 188 /** Padding of tensor. 189 * 190 * @return Padding. 191 */ 192 virtual PaddingSize padding() const = 0; 193 /** Checks if the tensor has been allocated with padding or not. 194 * 195 * @return True if padding is allocated in the tensor, otherwise false. 196 */ 197 virtual bool has_padding() const = 0; 198 /** Flag indicating whether the size of the tensor can be changed. 199 * 200 * @return True if the tensor size can be changed. 201 */ 202 virtual bool is_resizable() const = 0; 203 /** Flag indicating whether the shape of the tensor is dynamic, meaning that it can change on kernel/function execution. 204 * 205 * @return True if its dynamic else false 206 */ 207 virtual bool is_dynamic() const = 0; 208 /** Set the flag whether the tensor size can be changed. 209 * 210 * @param[in] is_resizable Flag that marks the tensor if it can be changed or not. 211 * 212 * @return Reference to this ITensorInfo object 213 */ 214 virtual ITensorInfo &set_is_resizable(bool is_resizable) = 0; 215 /** Set the flag whether the tensor size is dynamic. 216 * 217 * @param[in] is_dynamic Flag that marks the tensor if it's dynamic. 218 * 219 * @return Reference to this ITensorInfo object 220 */ 221 virtual ITensorInfo &set_is_dynamic(bool is_dynamic) = 0; 222 /** Valid region of the tensor. All elements in the valid region have defined values, i.e. are not undefined. 223 * 224 * @return The valid region. 225 */ 226 virtual ValidRegion valid_region() const = 0; 227 /** Set the valid region of the tensor. 228 * 229 * @param[in] valid_region Valid region to set. 230 */ 231 virtual void set_valid_region(const ValidRegion &valid_region) = 0; 232 233 /** Get the quantization settings (scale and offset) of the tensor. 234 * 235 * @return A QuantizationInfo containing the scale and offset. 236 */ 237 virtual QuantizationInfo quantization_info() const = 0; 238 /** Get the data layout of the tensor. 239 * 240 * @return A DataLayout containing the layout data information. 241 */ 242 virtual DataLayout data_layout() const = 0; 243 244 /** If infos are broadcast compatible tensor info's, return the broadcasted shape and the intersection of 245 * the broadcasted valid regions of the tensors. 246 * 247 * Two tensor info's are broadcast compatible if their shapes are broadcast compatible. 248 * 249 * Two tensor shapes are broadcast compatible if for each dimension, they're equal or one of them is 1. 250 * 251 * If two shapes are compatible, each dimension in the broadcasted shape is the max of the original dimensions. 252 * 253 * @param[in] infos Tensor info's. 254 * 255 * @return The broadcasted shape and valid region, or an empty shape and valid region if the info's are 256 * not broadcast compatible. 257 */ 258 template <typename... Infos> broadcast_shape_and_valid_region(const Infos &...infos)259 static std::pair<TensorShape, ValidRegion> broadcast_shape_and_valid_region(const Infos &... infos) 260 { 261 TensorShape bc_shape = TensorShape::broadcast_shape(infos.tensor_shape()...); 262 ValidRegion bc_valid_region{ Coordinates(), bc_shape }; 263 264 auto broadcast_valid_region = [&bc_valid_region](const ITensorInfo & info) 265 { 266 if(info.num_dimensions() != 0) 267 { 268 for(size_t d = 0; d < bc_valid_region.shape.num_dimensions(); ++d) 269 { 270 const bool is_broadcast = (info.tensor_shape()[d] == 1); 271 272 const int anchor_max = std::max(bc_valid_region.anchor[d], info.valid_region().anchor[d]); 273 const size_t valid_min = std::min(bc_valid_region.shape[d], info.valid_region().shape[d]); 274 275 if(!is_broadcast || (valid_min == 0)) 276 { 277 bc_valid_region.anchor.set(d, anchor_max); 278 bc_valid_region.shape.set(d, valid_min); 279 } 280 } 281 } 282 }; 283 284 utility::for_each(broadcast_valid_region, infos...); 285 286 return std::pair<TensorShape, ValidRegion>(bc_shape, bc_valid_region); 287 } 288 }; 289 } // namespace arm_compute 290 #endif /*ARM_COMPUTE_TENSORINFO_H */ 291