arm_compute/core/ITensorInfo.h - ml/ComputeLibrary - Gitiles

 /*
  * 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/misc/Utility.h"

 #include "support/ICloneable.h"

 #include <cstddef>

 namespace arm_compute
 {
 class QuantizationInfo;
 // Note: Any changes to the fields of the class below that have setters should be mirrored
 // (if possible) in the auto_init_if_empty function in AutoConfiguration.h

 /** Store the tensor's metadata */
 class ITensorInfo : public misc::ICloneable<ITensorInfo>
 {
 public:
     using TensorDimsState = std::vector<int>;
     /** 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 <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 */
	/*
	* 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/misc/Utility.h"

	#include "support/ICloneable.h"

	#include <cstddef>

	namespace arm_compute
	{
	class QuantizationInfo;
	// Note: Any changes to the fields of the class below that have setters should be mirrored
	// (if possible) in the auto_init_if_empty function in AutoConfiguration.h

	/** Store the tensor's metadata */
	class ITensorInfo : public misc::ICloneable<ITensorInfo>
	{
	public:
	using TensorDimsState = std::vector<int>;
	/** 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 <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 /