| /* |
| * Copyright (c) 2016-2021 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 <cstddef> |
| |
| namespace arm_compute |
| { |
| /** Store the tensor's metadata */ |
| class ITensorInfo : public misc::ICloneable<ITensorInfo> |
| { |
| public: |
| using TensorDimsState = std::vector<int>; |
| /** 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; |
| /** 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; |
| |
| /** 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 <typename... Infos> |
| static std::pair<TensorShape, ValidRegion> 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<TensorShape, ValidRegion>(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 */ |