Class ov::Tensor#

class Tensor#

Tensor API holding host memory It can throw exceptions safely for the application, where it is properly handled.

Subclassed by ov::RemoteTensor

Unnamed Group

void *data() const#

Provides an access to the underlying host memory.

Returns:

A host pointer to tensor memory

Unnamed Group

void *data(const element::Type &type) const#

Provides an access to the underlying host memory.

Note

The method throws an exception if specified type’s fundamental type does not match with tensor element type’s fundamental type

Parameters:

type – Optional type parameter.

Returns:

A host pointer to tensor memory

Unnamed Group

template<typename T, typename datatype = std::decay_t<T>>
inline T *data() const#

Provides an access to the underlying host memory casted to type T

Note

Throws exception if specified type does not match with tensor element type

Returns:

A host pointer to tensor memory casted to specified type T.

Public Functions

Tensor() = default#

Default constructor.

Tensor(const Tensor &other, const std::shared_ptr<void> &so)#

Copy constructor with adding new shared object.

Parameters:

  • other – Original tensor

  • so – Shared object

Tensor(const Tensor &other) = default#

Default copy constructor.

Parameters:

other – other Tensor object

Tensor &operator=(const Tensor &other) = default#

Default copy assignment operator.

Parameters:

other – other Tensor object

Returns:

reference to the current object

Tensor(Tensor &&other) = default#

Default move constructor.

Parameters:

other – other Tensor object

Tensor &operator=(Tensor &&other) = default#

Default move assignment operator.

Parameters:

other – other Tensor object

Returns:

reference to the current object

~Tensor()#

Destructor preserves unloading order of implementation object and reference to library.

Tensor(const element::Type &type, const Shape &shape, const Allocator &allocator = {})#

Constructs Tensor using element type and shape. Allocate internal host storage using default allocator.

Parameters:

  • type – Tensor element type

  • shape – Tensor shape

  • allocator – allocates memory for internal tensor storage

Tensor(const element::Type &type, const Shape &shape, void *host_ptr, const Strides &strides = {})#

Constructs Tensor using element type and shape. Wraps allocated host memory.

Note

Does not perform memory allocation internally

Parameters:

  • type – Tensor element type

  • shape – Tensor shape

  • host_ptr – Pointer to pre-allocated host memory with initialized objects

  • strides – Optional strides parameters in bytes. Strides are supposed to be computed automatically based on shape and element size

Tensor(const element::Type &type, const Shape &shape, const void *host_ptr, const Strides &strides = {})#

Constructs Tensor using element type and shape. Wraps allocated host memory as read only.

Note

Does not perform memory allocation internally

Parameters:

  • type – Tensor element type

  • shape – Tensor shape

  • host_ptr – Pointer to pre-allocated host memory with initialized objects

  • strides – Optional strides parameters in bytes. Strides are supposed to be computed automatically based on shape and element size

Tensor(const ov::Output<const ov::Node> &port, const Allocator &allocator = {})#

Constructs Tensor using port from node. Allocate internal host storage using default allocator.

Parameters:

  • port – port from node

  • allocator – allocates memory for internal tensor storage

Tensor(const ov::Output<const ov::Node> &port, void *host_ptr, const Strides &strides = {})#

Constructs Tensor using port from node. Wraps allocated host memory.

Note

Does not perform memory allocation internally

Parameters:

  • port – port from node

  • host_ptr – Pointer to pre-allocated host memory with initialized objects

  • strides – Optional strides parameters in bytes. Strides are supposed to be computed automatically based on shape and element size

Tensor(const ov::Output<const ov::Node> &port, const void *host_ptr, const Strides &strides = {})#

Constructs Tensor using port from node. Wraps allocated host memory as read only.

Note

Does not perform memory allocation internally

Parameters:

  • port – port from node

  • host_ptr – Pointer to pre-allocated host memory with initialized objects

  • strides – Optional strides parameters in bytes. Strides are supposed to be computed automatically based on shape and element size

Tensor(const Tensor &other, const Coordinate &begin, const Coordinate &end)#

Constructs region of interest (ROI) tensor form another tensor.

Note

Does not perform memory allocation internally

Note

A Number of dimensions in begin and end must match number of dimensions in other.get_shape()

Parameters:

  • other – original tensor

  • begin – start coordinate of ROI object inside of the original object.

  • end – end coordinate of ROI object inside of the original object.

void set_shape(const ov::Shape &shape)#

Set new shape for tensor, deallocate/allocate if new total size is bigger than previous one.

Note

Memory allocation may happen

Parameters:

shape – A new shape

const element::Type &get_element_type() const#
Returns:

A tensor element type

const Shape &get_shape() const#
Returns:

A tensor shape

void copy_to(ov::Tensor dst) const#

Copy tensor, destination tensor should have the same element type and shape.

Parameters:

dst – destination tensor

bool is_continuous() const#

Reports whether the tensor is continuous or not.

Returns:

true if tensor is continuous

size_t get_size() const#

Returns the total number of elements (a product of all the dims or 1 for scalar)

Returns:

The total number of elements

size_t get_byte_size() const#

Returns the size of the current Tensor in bytes.

Returns:

Tensor’s size in bytes

Strides get_strides() const#
Returns:

Tensor’s strides in bytes

bool operator!() const noexcept#

Checks if current Tensor object is not initialized.

Returns:

true if current Tensor object is not initialized, false - otherwise

explicit operator bool() const noexcept#

Checks if current Tensor object is initialized.

Returns:

true if current Tensor object is initialized, false - otherwise

template<typename T>
inline std::enable_if_t<std::is_base_of_v<Tensor, T>, bool> is() const noexcept#

Checks if the Tensor object can be cast to the type T.

Template Parameters:

T – Type to be checked. Must represent a class derived from the Tensor

Returns:

true if this object can be dynamically cast to the type const T*. Otherwise, false

template<typename T>
inline const std::enable_if_t<std::is_base_of_v<Tensor, T>, T> as() const#

Casts this Tensor object to the type T.

Template Parameters:

T – Type to cast to. Must represent a class derived from the Tensor

Returns:

T object

Public Static Functions

static void type_check(const Tensor &tensor)#

Checks openvino tensor type.

Parameters:

tensor – a tensor which type will be checked

Throws:

Exception – if type check with specified tensor is not pass