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/*
* 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_TENSORINFO_H
#define ARM_COMPUTE_TENSORINFO_H
#include "arm_compute/core/Coordinates.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/ITensorInfo.h"
#include "arm_compute/core/Strides.h"
#include "arm_compute/core/TensorShape.h"
#include "arm_compute/core/Types.h"
#include "ITensorInfo.h"
#include <cstddef>
#include <memory>
namespace arm_compute
{
/** Store the tensor's metadata */
class TensorInfo final : public ITensorInfo
{
public:
/** Default constructor */
TensorInfo();
/** Default destructor */
~TensorInfo() = default;
/** Allow instances of this class to be copy constructed */
TensorInfo(const ITensorInfo &info);
/** Allow instances of this class to be copy constructed */
TensorInfo(const TensorInfo &);
/** Allow instances of this class to be copied */
TensorInfo &operator=(const TensorInfo &) = default;
/** Allow instances of this class to be move constructed */
TensorInfo(TensorInfo &&) = default;
/** Allow instances of this class to be moved */
TensorInfo &operator=(TensorInfo &&) = default;
/** Construct a tensor info with a format.
*
* Can be used for automatic derivation of the shape by the function.
*
* @param[in] format Format of the tensor.
*/
TensorInfo(Format format);
/** 2D tensor constructor
*
* @param[in] width Width of the 2D tensor
* @param[in] height Height of the 2D tensor
* @param[in] format Single plane format of the tensor.
*/
TensorInfo(unsigned int width, unsigned int height, Format format);
/** Constructor
*
* @param[in] tensor_shape It specifies the size for each dimension of the tensor in number of elements.
* @param[in] format Single plane format of the tensor.
*/
TensorInfo(const TensorShape &tensor_shape, Format format);
/** Construct a tensor info with a data type and number of channels.
*
* Can be used for automatic derivation of the shape by the function.
*
* @param[in] num_channels It indicates the number of channels for each tensor element
* @param[in] data_type Data type to use for each tensor element
*/
TensorInfo(size_t num_channels, DataType data_type);
/** Constructor
*
* @param[in] tensor_shape It specifies the size for each dimension of the tensor in number of elements.
* @param[in] num_channels It indicates the number of channels for each tensor element
* @param[in] data_type Data type to use for each tensor element
*/
TensorInfo(const TensorShape &tensor_shape, size_t num_channels, DataType data_type);
/** Constructor
*
* @param[in] tensor_shape It specifies the size for each dimension of the tensor in number of elements.
* @param[in] num_channels It indicates the number of channels for each tensor element
* @param[in] data_type Data type to use for each tensor element
* @param[in] data_layout The data layout setting for the tensor data.
*/
TensorInfo(const TensorShape &tensor_shape, size_t num_channels, DataType data_type, DataLayout data_layout);
/** Constructor
*
* @param[in] tensor_shape It specifies the size for each dimension of the tensor in number of elements.
* @param[in] num_channels It indicates the number of channels for each tensor element
* @param[in] data_type Data type to use for each tensor element
* @param[in] quantization_info The quantization settings for the tensor data.
*/
TensorInfo(const TensorShape &tensor_shape,
size_t num_channels,
DataType data_type,
QuantizationInfo quantization_info);
/** Initialize the tensor info with just a format.
*
* Can be used for automatic derivation of the shape by the function.
*
* @param[in] format Single plane format of the tensor.
*/
void init(Format format);
/** Initialize the metadata structure with the given parameters
*
* @param[in] tensor_shape Size for each dimension of the tensor in number of elements.
* @param[in] format Single plane format of the tensor.
*/
void init(const TensorShape &tensor_shape, Format format);
/** Initialize the metadata structure with the given parameters
*
* @param[in] tensor_shape Size for each dimension of the tensor in number of elements.
* @param[in] format Single plane format of the tensor.
* @param[in] strides_in_bytes Stride in bytes for accessing each dimension of the tensor.
* @param[in] offset_first_element_in_bytes Offset in bytes from the beginning of memory allocation to access the first element.
* @param[in] total_size_in_bytes Size in bytes of the memory allocation (including the offset to the first element).
*/
void init(const TensorShape &tensor_shape,
Format format,
const Strides &strides_in_bytes,
size_t offset_first_element_in_bytes,
size_t total_size_in_bytes);
/** Initialize the tensor info with just a format.
*
* Can be used for automatic derivation of the shape by the function.
*
* @param[in] num_channels Desired number of channels for each tensor element.
* @param[in] data_type Data type to use for each tensor element.
*/
void init(size_t num_channels, DataType data_type);
/** Initialize the metadata structure with the given parameters
*
* @param[in] tensor_shape Size for each dimension of the tensor in number of elements.
* @param[in] num_channels Desired number of channels for each tensor element.
* @param[in] data_type Data type to use for each tensor element.
*/
void init(const TensorShape &tensor_shape, size_t num_channels, DataType data_type);
/** Initialize the metadata structure with the given parameters
*
* @param[in] tensor_shape Size for each dimension of the tensor in number of elements.
* @param[in] num_channels Desired number of channels for each tensor element.
* @param[in] data_type Data type to use for each tensor element.
* @param[in] strides_in_bytes Stride in bytes for accessing each dimension of the tensor.
* @param[in] offset_first_element_in_bytes Offset in bytes from the beginning of memory allocation to access the first element.
* @param[in] total_size_in_bytes Size in bytes of the memory allocation (including the offset to the first element).
*/
void init(const TensorShape &tensor_shape,
size_t num_channels,
DataType data_type,
const Strides &strides_in_bytes,
size_t offset_first_element_in_bytes,
size_t total_size_in_bytes);
/** Initialize the metadata structure for the given tensor shape and single-plane format, (Padding is automatically calculated)
*
* @note The padding used by this method is really conservative so that the tensor can be used for most functions.
*
* @param[in] tensor_shape It specifies the size for each dimension of the tensor in number of elements
* @param[in] format Single plane format of the image.
*
* @return Total allocation size including padding in bytes.
*/
size_t init_auto_padding(const TensorShape &tensor_shape, Format format);
/** Initialize the metadata structure for the given tensor shape, number of channels and
* data type. (Padding is automatically calculated)
*
* @note The padding used by this method is really conservative so that the tensor can be used for most functions.
*
* @param[in] tensor_shape It specifies the size for each dimension of the tensor in number of elements
* @param[in] num_channels It indicates the number of channels for each tensor element
* @param[in] data_type Data type to use for each tensor element
*
* @return Total allocation size including padding in bytes.
*/
size_t init_auto_padding(const TensorShape &tensor_shape, size_t num_channels, DataType data_type);
// Inherited methods overridden:
std::unique_ptr<ITensorInfo> clone() const override;
ITensorInfo &set_data_type(DataType data_type) override;
ITensorInfo &set_num_channels(int num_channels) override;
ITensorInfo &set_format(Format format) override;
ITensorInfo &set_tensor_shape(const TensorShape &shape) override;
ITensorInfo &set_tensor_dims_state(const TensorDimsState &state) override;
ITensorInfo &set_quantization_info(const QuantizationInfo &quantization_info) override;
ITensorInfo &set_data_layout(const DataLayout &data_layout) override;
ITensorInfo &reset_padding() override;
bool auto_padding() override;
ITensorInfo &set_lock_paddings(bool flag) override;
bool lock_paddings() const override;
bool extend_padding(const PaddingSize &padding) override;
size_t dimension(size_t index) const override
{
return _tensor_shape[index];
}
size_t dimension(DataLayoutDimension dimension) const override
{
return get_data_layout_dimension_index(_data_layout, dimension);
}
const Strides &strides_in_bytes() const override
{
return _strides_in_bytes;
}
size_t offset_first_element_in_bytes() const override
{
return _offset_first_element_in_bytes;
}
int32_t offset_element_in_bytes(const Coordinates &pos) const override;
size_t element_size() const override
{
return data_size_from_type(_data_type) * _num_channels;
}
size_t num_dimensions() const override
{
return _tensor_shape.num_dimensions();
}
size_t num_channels() const override
{
return _num_channels;
}
const TensorShape &tensor_shape() const override
{
return _tensor_shape;
}
const TensorDimsState &tensor_dims_state() const override
{
return _dims_state;
}
DataType data_type() const override
{
return _data_type;
}
Format format() const override
{
return _format;
}
size_t total_size() const override
{
return _total_size;
}
PaddingSize padding() const override
{
return _padding;
}
bool has_padding() const override
{
return !_padding.empty();
}
bool is_resizable() const override
{
return _is_resizable;
}
bool is_dynamic() const override
{
return std::find(std::cbegin(_dims_state), std::cend(_dims_state), get_dynamic_state_value()) !=
std::cend(_dims_state);
}
bool are_values_constant() const override
{
return _are_values_constant;
}
ITensorInfo &set_is_resizable(bool is_resizable) override
{
_is_resizable = is_resizable;
return *this;
}
ValidRegion valid_region() const override
{
return _valid_region;
}
void set_valid_region(const ValidRegion &valid_region) override
{
_valid_region = valid_region;
}
QuantizationInfo quantization_info() const override
{
return _quantization_info;
}
DataLayout data_layout() const override
{
return _data_layout;
}
ITensorInfo &set_are_values_constant(bool are_values_constant) override
{
_are_values_constant = are_values_constant;
return *this;
}
ITensorInfo::Id id() const override
{
return _id;
}
ITensorInfo &set_id(ITensorInfo::Id id) override
{
_id = id;
return *this;
}
inline friend bool operator==(const TensorInfo &lhs, const TensorInfo &rhs);
private:
/** Calculates strides, offset and total size resulting from the specified padding around the XY plane.
*
* @param[in] padding Padding around the XY plane in elements.
*/
std::tuple<Strides, size_t, size_t> calculate_padding_requirements(const PaddingSize &padding);
size_t _total_size;
size_t _offset_first_element_in_bytes;
Strides _strides_in_bytes;
size_t _num_channels;
TensorShape _tensor_shape;
TensorDimsState _dims_state;
DataType _data_type;
Format _format;
bool _is_resizable;
ValidRegion _valid_region;
PaddingSize _padding;
QuantizationInfo _quantization_info;
DataLayout _data_layout;
bool _are_values_constant;
ITensorInfo::Id _id;
bool _lock_paddings;
};
/** Check whether two tensor info are equal.
*
* @param[in] lhs LHS tensor info.
* @param[in] rhs RHS tensor info.
*
* @return True if the given tensor infos are the same.
*/
inline bool operator==(const TensorInfo &lhs, const TensorInfo &rhs)
{
return (lhs._total_size == rhs._total_size) &&
(lhs._offset_first_element_in_bytes == rhs._offset_first_element_in_bytes) &&
(lhs._strides_in_bytes == rhs._strides_in_bytes) && (lhs._num_channels == rhs._num_channels) &&
(lhs._tensor_shape == rhs._tensor_shape) && (lhs._dims_state == rhs._dims_state) &&
(lhs._data_type == rhs._data_type) && (lhs._format == rhs._format) &&
(lhs._is_resizable == rhs._is_resizable) && (lhs._valid_region == rhs._valid_region) &&
(lhs._padding == rhs._padding) && (lhs._quantization_info == rhs._quantization_info) &&
(lhs._data_layout == rhs._data_layout) && (lhs._are_values_constant == rhs._are_values_constant) &&
(lhs._id == rhs._id);
}
} // namespace arm_compute
#endif /*ARM_COMPUTE_TENSORINFO_H */