/* * Copyright (c) 2016-2023 Arm Limited. * * SPDX-License-Identifier: MIT * * Permission is hereby granted, free of charge, to any person obtaining a copy * of this software and associated documentation files (the "Software"), to * deal in the Software without restriction, including without limitation the * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or * sell copies of the Software, and to permit persons to whom the Software is * furnished to do so, subject to the following conditions: * * The above copyright notice and this permission notice shall be included in all * copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ #ifndef ARM_COMPUTE_ITENSORINFO_H #define ARM_COMPUTE_ITENSORINFO_H #include "arm_compute/core/Coordinates.h" #include "arm_compute/core/Strides.h" #include "arm_compute/core/TensorShape.h" #include "arm_compute/core/Types.h" #include "arm_compute/core/Utils.h" #include "arm_compute/core/utils/misc/Utility.h" #include "support/ICloneable.h" #include namespace arm_compute { /** Store the tensor's metadata */ class ITensorInfo : public misc::ICloneable { public: using TensorDimsState = std::vector; /** An id that uniquely identifies an ITensorInfo within some domain (e.g. a workload) */ using Id = int32_t; /** An invalid tensor id within a domain */ static constexpr Id invalid_tensor_id = 0; /** Get the value representing dynamic dimension state * * @return Value representing dynamic dimension state * */ static constexpr int32_t get_dynamic_state_value() { return _dynamic_dimension; } /** Get the value representing static dimension state * * @return Value representing static dimension state * */ static constexpr int32_t get_static_state_value() { return _static_dimension; } /** Default virtual destructor */ virtual ~ITensorInfo() = default; /** Set the data type to the specified value. * * @warning This resets the format to UNKNOWN. * * @param[in] data_type The new data type. * * @return Reference to this ITensorInfo object */ virtual ITensorInfo &set_data_type(DataType data_type) = 0; /** Set the number of channels to the specified value. * * @warning This resets the format to UNKNOWN. * * @param[in] num_channels New number of channels. * * @return Reference to this ITensorInfo object */ virtual ITensorInfo &set_num_channels(int num_channels) = 0; /** Set the format of an already initialized tensor. * * @note If the data type has already been configured (i.e. not UNKNOWN) it * must match the new format. If data type hasn't been configured it will * be based on the format. * * @param[in] format Single-plane format of the tensor. * * @return Reference to this ITensorInfo object */ virtual ITensorInfo &set_format(Format format) = 0; /** Set the shape of an already initialized tensor. * * @warning Changing the shape requires to recompute the strides and is * therefore only possible if the tensor hasn't been allocated yet. * * @param[in] shape New tensor shape. * * @return Reference to this ITensorInfo object */ virtual ITensorInfo &set_tensor_shape(const TensorShape &shape) = 0; /** Set the state for each dimension of the tensor * * This sets the state of each dimension of the shape in terms of dynamic behavior using -1 where appropriate. * The index in the state is a 1 to 1 mapping with the shape dimension index. * For example if you want to express [?, 3, 3] as a dynamic input then [-1, 3, 3] has to be set as a state * * @param[in] state Tensor dimensions state * * @return Reference to this ITensorInfo object */ virtual ITensorInfo &set_tensor_dims_state(const TensorDimsState &state) = 0; /** Set the quantization settings (scale and offset) of the tensor. * * @param[in] quantization_info QuantizationInfo containing the scale and offset * * @return Reference to this ITensorInfo object */ virtual ITensorInfo &set_quantization_info(const QuantizationInfo &quantization_info) = 0; /** Set the data layout of the tensor. * * @param[in] data_layout DataLayout containing the layout data information. * * @return Reference to this ITensorInfo object */ virtual ITensorInfo &set_data_layout(const DataLayout &data_layout) = 0; /** Resets the padding settings of the tensor. * * @return Reference to this ITensorInfo object */ virtual ITensorInfo &reset_padding() = 0; /** Update the offset to the first element and the strides to automatically computed values. * * @note The padding used by this method is really conservative so that the tensor can be used for most functions. * * @return True if the strides or the offset to the first element have changed. */ virtual bool auto_padding() = 0; /** Set the lock paddings flag of the tensor. * It should be set to True, when the tensor could be mapped to camera or frame buffer. * * @return Reference to this ITensorInfo object */ virtual ITensorInfo &set_lock_paddings(bool flag) = 0; /** Get the lock paddings flag value * * @return lock paddings flag value */ virtual bool lock_paddings() const = 0; /** Update the offset to the first element, the strides and the total size. * * @note This function can only increase the offset, strides and total size. * * @param[in] padding Padding around the XY plane in number of elements. * * @return True if the strides, offset and total size have changed. */ virtual bool extend_padding(const PaddingSize &padding) = 0; /** Return the size of the requested dimension * * @param[in] index Index of the dimension * * @return Dimension of the requested dimension */ virtual size_t dimension(size_t index) const = 0; /** Return the size of the requested data layout dimension * * @param[in] dimension DataLayoutDimension of the dimension * * @return Dimension of the requested dimension */ virtual size_t dimension(DataLayoutDimension dimension) const = 0; /** The strides in bytes for accessing each dimension of the tensor * * @return Strides in bytes for each tensor dimension */ virtual const Strides &strides_in_bytes() const = 0; /** The offset from the beginning of the memory allocation to the first element of the tensor. * This can be used to access efficiently elements in a 2D tensor * * @return The offset in bytes to access the first element of the tensor. */ virtual size_t offset_first_element_in_bytes() const = 0; /** The offset in bytes from the beginning of the memory allocation to access the element at position (x, y, z ...) * * @param[in] pos Vector with the coordinates of the element to access. * The size of this vector must be equal to the number of dimensions of the tensor * * @return Offset in bytes from the beginning of the memory allocation to access the element (x, y, z, ...) */ virtual int32_t offset_element_in_bytes(const Coordinates &pos) const = 0; /** Element size in bytes calculated as data_size() * num_channels() * * @return The size of one element in bytes */ virtual size_t element_size() const = 0; /** The number of dimensions of the tensor (rank) * * @return The number of dimensions of the tensor (rank) */ virtual size_t num_dimensions() const = 0; /** The number of channels for each tensor element * * @return The number of channels for each tensor element */ virtual size_t num_channels() const = 0; /** Size for each dimension of the tensor * * @return A vector with the size for each dimension of the tensor */ virtual const TensorShape &tensor_shape() const = 0; /** State of each dimension of the tensor shape * * @return A vector with the state for each dimension of the tensor, where -1 specifies dynamic dimension */ virtual const TensorDimsState &tensor_dims_state() const = 0; /** Data type used for each element of the tensor * * @return Tensor data type */ virtual DataType data_type() const = 0; /** Colour format of the image * * @return Colour format of the image */ virtual Format format() const = 0; /** Returns the total size of the tensor in bytes. * * @return Total size of the tensor in bytes. */ virtual size_t total_size() const = 0; /** Padding of tensor. * * @return Padding. */ virtual PaddingSize padding() const = 0; /** Checks if the tensor has been allocated with padding or not. * * @return True if padding is allocated in the tensor, otherwise false. */ virtual bool has_padding() const = 0; /** Flag indicating whether the size of the tensor can be changed. * * @return True if the tensor size can be changed. */ virtual bool is_resizable() const = 0; /** Flag indicating whether the shape of the tensor is dynamic, meaning that it can change on kernel/function execution. * * @return True if its dynamic else false */ virtual bool is_dynamic() const = 0; /** Flag indicating whether the values of the tensor are constant, meaning that they can change on kernel/function execution. * * @return True if values are constant else false */ virtual bool are_values_constant() const = 0; /** Set the flag whether the tensor size can be changed. * * @param[in] is_resizable Flag that marks the tensor if it can be changed or not. * * @return Reference to this ITensorInfo object */ virtual ITensorInfo &set_is_resizable(bool is_resizable) = 0; /** Set the flag whether the tensor values can change during kernel/function execution. * * @param[in] are_values_constant Flag that marks the tensor values if they can be changed or not. * * @return Reference to this ITensorInfo object */ virtual ITensorInfo &set_are_values_constant(bool are_values_constant) = 0; /** Valid region of the tensor. All elements in the valid region have defined values, i.e. are not undefined. * * @return The valid region. */ virtual ValidRegion valid_region() const = 0; /** Set the valid region of the tensor. * * @param[in] valid_region Valid region to set. */ virtual void set_valid_region(const ValidRegion &valid_region) = 0; /** Get the quantization settings (scale and offset) of the tensor. * * @return A QuantizationInfo containing the scale and offset. */ virtual QuantizationInfo quantization_info() const = 0; /** Get the data layout of the tensor. * * @return A DataLayout containing the layout data information. */ virtual DataLayout data_layout() const = 0; /** Get the workload tensor id of the tensor. * * @return Workload tensor id of the tensor */ virtual Id id() const = 0; /** Set the tensor id */ virtual ITensorInfo &set_id(ITensorInfo::Id id) = 0; /** Check if the tensor id is valid */ bool has_valid_id() const { return id() != invalid_tensor_id; } /** If infos are broadcast compatible tensor info's, return the broadcasted shape and the intersection of * the broadcasted valid regions of the tensors. * * Two tensor info's are broadcast compatible if their shapes are broadcast compatible. * * Two tensor shapes are broadcast compatible if for each dimension, they're equal or one of them is 1. * * If two shapes are compatible, each dimension in the broadcasted shape is the max of the original dimensions. * * @param[in] infos Tensor info's. * * @return The broadcasted shape and valid region, or an empty shape and valid region if the info's are * not broadcast compatible. */ template static std::pair broadcast_shape_and_valid_region(const Infos &... infos) { TensorShape bc_shape = TensorShape::broadcast_shape(infos.tensor_shape()...); ValidRegion bc_valid_region{ Coordinates(), bc_shape }; auto broadcast_valid_region = [&bc_valid_region](const ITensorInfo & info) { if(info.num_dimensions() != 0) { for(size_t d = 0; d < bc_valid_region.shape.num_dimensions(); ++d) { const bool is_broadcast = (info.tensor_shape()[d] == 1); const int anchor_max = std::max(bc_valid_region.anchor[d], info.valid_region().anchor[d]); const size_t valid_min = std::min(bc_valid_region.shape[d], info.valid_region().shape[d]); if(!is_broadcast || (valid_min == 0)) { bc_valid_region.anchor.set(d, anchor_max); bc_valid_region.shape.set(d, valid_min); } } } }; utility::for_each(broadcast_valid_region, infos...); return std::pair(bc_shape, bc_valid_region); } private: static constexpr int32_t _dynamic_dimension = -1; static constexpr int32_t _static_dimension = 0; }; } // namespace arm_compute #endif /*ARM_COMPUTE_TENSORINFO_H */