Group Operations#
- group ov_ops_cpp_api
OpenVINO C++ API to create operations from different opsets. Such API is used to creation models from code, write transformations and traverse the model graph
-
class AUGRUCell : public ov::op::util::RNNCellBase
- #include <augru_cell.hpp>
AUGRUCell operation.
Public Functions
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual void validate_and_infer_types() override
-
class AUGRUSequence : public ov::op::util::RNNCellBase
- #include <augru_sequence.hpp>
AUGRUSequence operation.
Public Functions
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual void validate_and_infer_types() override
-
class Abs : public ov::op::util::UnaryElementwiseArithmetic
- #include <abs.hpp>
Elementwise absolute value operation.
Public Functions
-
Abs() = default
Constructs an absolute value operation.
-
Abs(const Output<Node> &arg)
Constructs an absolute value operation.
Output
[d1, ...]
- Parameters:
arg – Output that produces the input tensor.
[d1, ...]
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Abs() = default
-
class Acos : public ov::op::util::UnaryElementwiseArithmetic
- #include <acos.hpp>
Elementwise inverse cosine (arccos) operation.
Public Functions
-
Acos() = default
Constructs an arccos operation.
-
Acos(const Output<Node> &arg)
Constructs an arccos operation.
Output
[d1, ...]
- Parameters:
arg – Output that produces the input tensor.
[d1, ...]
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Acos() = default
-
class Acosh : public ov::op::util::UnaryElementwiseArithmetic
- #include <acosh.hpp>
Elementwise inverse hyperbolic cos operation.
Public Functions
-
Acosh() = default
Constructs an Acosh operation.
-
Acosh(const Output<Node> &arg)
Constructs an Acosh operation.
Output
[d1, ...]
- Parameters:
arg – Output that produces the input tensor.
[d1, ...]
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Acosh() = default
-
class AdaptiveAvgPool : public ov::op::Op
- #include <adaptive_avg_pool.hpp>
Adaptive average pooling operation.
Public Functions
-
AdaptiveAvgPool(const Output<Node> &data, const Output<Node> &output_shape)
Constructs adaptive average pooling operation.
- Parameters:
data – Input data
output_shape – 1D tensor describing output shape for spatial dimensions.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
AdaptiveAvgPool(const Output<Node> &data, const Output<Node> &output_shape)
-
class AdaptiveMaxPool : public ov::op::Op
- #include <adaptive_max_pool.hpp>
Adaptive max pooling operation.
Public Functions
-
AdaptiveMaxPool(const Output<Node> &data, const Output<Node> &output_shape, const ov::element::Type &index_element_type = ov::element::i64)
Constructs adaptive max pooling operation.
- Parameters:
data – Input data
output_shape – 1D tensor describing output shape for spatial dimensions.
index_element_type – Specifies the output tensor type for indices output
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
AdaptiveMaxPool(const Output<Node> &data, const Output<Node> &output_shape, const ov::element::Type &index_element_type = ov::element::i64)
-
class Add : public ov::op::util::BinaryElementwiseArithmetic
- #include <add.hpp>
Elementwise addition operation.
Public Functions
-
inline Add()
Constructs an uninitialized addition operation.
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Add(const Output<Node> &arg0, const Output<Node> &arg1, const AutoBroadcastSpec &auto_broadcast = AutoBroadcastSpec(AutoBroadcastType::NUMPY))
Constructs an addition operation.
Output
[d0, ...]
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
inline Add()
-
class Asin : public ov::op::util::UnaryElementwiseArithmetic
- #include <asin.hpp>
Elementwise inverse sine (arcsin) operation.
Public Functions
-
Asin() = default
Constructs an arcsin operation.
-
Asin(const Output<Node> &arg)
Constructs an arcsin operation.
Output
[d1, ...]
- Parameters:
arg – Output that produces the input tensor.
[d1, ...]
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Asin() = default
-
class Assign : public ov::op::util::AssignBase
- #include <assign.hpp>
Assign operation sets an input value to the variable with
variable_id
Public Functions
-
Assign(const Output<Node> &new_value, const std::string &variable_id)
Constructs an Assign operation.
- Parameters:
new_value – Node that produces the input tensor.
variable_id – identifier of the variable to be updated.
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virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
inline virtual std::string get_variable_id() const override
Returns the identifier of corresponding variable.
-
Assign(const Output<Node> &new_value, const std::string &variable_id)
-
class Assign : public ov::op::util::AssignBase
- #include <assign.hpp>
Assign operation sets an input value to the variable with
variable_id
Public Functions
Constructs an Assign operation.
- Parameters:
new_value – Node that produces the input tensor.
variable – Class for storing and synchronizing element types, shapes and identifiers between pairs of Assign/ReadValue nodes.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
inline virtual std::string get_variable_id() const override
Returns the identifier of corresponding variable.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
class Atan : public ov::op::util::UnaryElementwiseArithmetic
- #include <atan.hpp>
Elementwise inverse tangent (arctan) operation.
Public Functions
-
Atan() = default
Constructs an arctan operation.
-
Atan(const Output<Node> &arg)
Constructs an arctan operation.
Output
[d1, ...]
- Parameters:
arg – Output that produces the input tensor.
[d1, ...]
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Atan() = default
-
class Atanh : public ov::op::util::UnaryElementwiseArithmetic
- #include <atanh.hpp>
Elementwise inverse hyperbolic tangent operation.
Public Functions
-
Atanh() = default
Constructs an Atanh operation.
-
Atanh(const Output<Node> &arg)
Constructs an Atanh operation.
Output
[d1, ...]
- Parameters:
arg – Output that produces the input tensor.
[d1, ...]
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Atanh() = default
-
class AvgPool : public ov::op::util::AvgPoolBase
- #include <avg_pool.hpp>
Batched average pooling operation.
Public Functions
-
AvgPool() = default
Constructs a batched average pooling operation.
-
AvgPool(const Output<Node> &arg, const Strides &strides, const Shape &pads_begin, const Shape &pads_end, const Shape &kernel, bool exclude_pad, RoundingType rounding_type = RoundingType::FLOOR, const PadType &auto_pad = PadType::EXPLICIT)
Constructs a batched average pooling operation.
- Parameters:
arg – The output producing the input data batch tensor.
[d1, dn]
strides – The strides.
[n]
pads_begin – The beginning of padding shape.
[n]
pads_end – The end of padding shape.
[n]
kernel – The kernel shape.
[n]
exclude_pad – If false then averages include padding elements, each treated as the number zero. If true, padding elements are entirely ignored when computing averages.
rounding_type – Whether to use ceiling or floor rounding type while computing output shape.
auto_pad – Padding type to use for additional padded dimensions
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
AvgPool() = default
-
class AvgPool : public ov::op::util::AvgPoolBase
- #include <avg_pool.hpp>
Batched average pooling operation.
Public Functions
-
AvgPool() = default
Constructs a batched average pooling operation.
-
AvgPool(const Output<Node> &arg, const Strides &strides, const Shape &pads_begin, const Shape &pads_end, const Shape &kernel, bool exclude_pad, RoundingType rounding_type = RoundingType::FLOOR, const PadType &auto_pad = PadType::EXPLICIT)
Constructs a batched average pooling operation.
- Parameters:
arg – The output producing the input data batch tensor.
[d1, dn]
strides – The strides.
[n]
pads_begin – The beginning of padding shape.
[n]
pads_end – The end of padding shape.
[n]
kernel – The kernel shape.
[n]
exclude_pad – If false then averages include padding elements, each treated as the number zero. If true, padding elements are entirely ignored when computing averages.
rounding_type – Whether to use ceiling or floor rounding type while computing output shape.
auto_pad – Padding type to use for additional padded dimensions
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
AvgPool() = default
-
class BatchNormInference : public ov::op::Op
- #include <batch_norm.hpp>
BatchNormInference operation.
Public Functions
-
BatchNormInference(const Output<Node> &input, const Output<Node> &gamma, const Output<Node> &beta, const Output<Node> &mean, const Output<Node> &variance, double epsilon)
- Parameters:
input – [., C, …]
gamma – gamma scaling for normalized value. [C]
beta – bias added to the scaled normalized value [C]
mean – value for mean normalization [C]
variance – value for variance normalization [C]
epsilon – Avoids divsion by 0 if input has 0 variance
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
BatchNormInference(const Output<Node> &input, const Output<Node> &gamma, const Output<Node> &beta, const Output<Node> &mean, const Output<Node> &variance, double epsilon)
-
class BatchNormInference : public ov::op::Op
- #include <batch_norm.hpp>
BatchNormInference operation.
Public Functions
-
BatchNormInference(const Output<Node> &input, const Output<Node> &gamma, const Output<Node> &beta, const Output<Node> &mean, const Output<Node> &variance, double epsilon)
- Parameters:
input – [., C, …]
gamma – gamma scaling for normalized value. [C]
beta – bias added to the scaled normalized value [C]
mean – value for mean normalization [C]
variance – value for variance normalization [C]
epsilon – Avoids divsion by 0 if input has 0 variance
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
BatchNormInference(const Output<Node> &input, const Output<Node> &gamma, const Output<Node> &beta, const Output<Node> &mean, const Output<Node> &variance, double epsilon)
-
class BatchToSpace : public ov::op::Op
- #include <batch_to_space.hpp>
BatchToSpace permutes data from the batch dimension of the data tensor into spatial dimensions.
Note
Values from the batch dimension are moved in spatial blocks dimensions.
Output node produces a tensor with shape: `[batch / (block_shape[0] * block_shape[1] * ... * block_shape[N - 1]), D_1 * block_shape[1] - crops_begin[1] - crops_end[1], D_2 * block_shape[2] - crops_begin[2] - crops_end[2], ..., D_{N - 1} * block_shape[N - 1] - crops_begin[N - 1] - crops_end[N - 1]` of the same type as `data` input.
Public Functions
-
BatchToSpace(const Output<Node> &data, const Output<Node> &block_shape, const Output<Node> &crops_begin, const Output<Node> &crops_end)
Constructs a BatchToSpace operation.
- Parameters:
data – Node producing the data tensor
block_shape – The sizes of the block of values to be moved
crops_begin – Specifies the amount to crop from the beginning along each axis of
data
inputcrops_end – Specifies the amount to crop from the ending along each axis of
data
input.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
BatchToSpace(const Output<Node> &data, const Output<Node> &block_shape, const Output<Node> &crops_begin, const Output<Node> &crops_end)
-
class BinaryConvolution : public ov::op::util::ConvolutionFwdPropBase
- #include <binary_convolution.hpp>
BinaryConvolution operation.
Public Functions
-
BinaryConvolution() = default
Constructs a binary convolution operation.
-
BinaryConvolution(const Output<Node> &data, const Output<Node> &kernel, const Strides &strides, const CoordinateDiff &pads_begin, const CoordinateDiff &pads_end, const Strides &dilations, BinaryConvolutionMode mode, float pad_value, const PadType &auto_pad = PadType::EXPLICIT)
Constructs a binary convolution operation.
Output
[N, C_OUT, R1, ... Rf]
- Parameters:
data – The node producing the input data batch tensor.
kernel – The node producing the filters tensor.
strides – The strides.
pads_begin – The beginning of padding shape.
pads_end – The end of padding shape.
dilations – The dilations.
mode – Defines how input tensor 0/1 values and weights 0/1 are interpreted.
pad_value – Floating-point value used to fill pad area.
auto_pad – The pad type for automatically computing padding sizes.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
inline const BinaryConvolutionMode &get_mode() const
- Returns:
The mode of convolution.
-
inline float get_pad_value() const
- Returns:
The pad value.
-
BinaryConvolution() = default
-
class BitwiseAnd : public ov::op::util::BinaryElementwiseBitwise
- #include <bitwise_and.hpp>
Elementwise bitwise AND operation.
Public Functions
-
BitwiseAnd() = default
Constructs a bitwise AND operation.
-
BitwiseAnd(const Output<Node> &arg0, const Output<Node> &arg1, const AutoBroadcastSpec &auto_broadcast = AutoBroadcastSpec(AutoBroadcastType::NUMPY))
Constructs a bitwise AND operation.
Output
[d0, ...]
-
BitwiseAnd() = default
-
class BitwiseLeftShift : public ov::op::util::BinaryElementwiseBitwise
- #include <bitwise_left_shift.hpp>
Elementwise bitwise BitwiseLeftShift operation.
Public Functions
-
BitwiseLeftShift() = default
Constructs a bitwise BitwiseLeftShift operation.
-
BitwiseLeftShift(const Output<Node> &arg0, const Output<Node> &arg1, const AutoBroadcastSpec &auto_broadcast = AutoBroadcastSpec(AutoBroadcastType::NUMPY))
Constructs a bitwise BitwiseLeftShift operation.
Output
[d0, ...]
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
BitwiseLeftShift() = default
-
class BitwiseNot : public ov::op::Op
- #include <bitwise_not.hpp>
Elementwise bitwise negation operation.
Public Functions
-
BitwiseNot() = default
Constructs a bitwise negation operation.
-
BitwiseNot(const Output<Node> &arg)
Constructs a bitwise negation operation.
- Parameters:
arg – Node that produces the input tensor.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
BitwiseNot() = default
-
class BitwiseOr : public ov::op::util::BinaryElementwiseBitwise
- #include <bitwise_or.hpp>
Elementwise bitwise OR operation.
Public Functions
-
BitwiseOr() = default
Constructs a bitwise OR operation.
-
BitwiseOr(const Output<Node> &arg0, const Output<Node> &arg1, const AutoBroadcastSpec &auto_broadcast = AutoBroadcastSpec(AutoBroadcastType::NUMPY))
Constructs a bitwise OR operation.
Output
[d0, ...]
-
BitwiseOr() = default
-
class BitwiseRightShift : public ov::op::util::BinaryElementwiseBitwise
- #include <bitwise_right_shift.hpp>
Elementwise bitwise BitwiseRightShift operation.
Public Functions
-
BitwiseRightShift() = default
Constructs a bitwise BitwiseRightShift operation.
-
BitwiseRightShift(const Output<Node> &arg0, const Output<Node> &arg1, const AutoBroadcastSpec &auto_broadcast = AutoBroadcastSpec(AutoBroadcastType::NUMPY))
Constructs a bitwise BitwiseRightShift operation.
Output
[d0, ...]
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
BitwiseRightShift() = default
-
class BitwiseXor : public ov::op::util::BinaryElementwiseBitwise
- #include <bitwise_xor.hpp>
Elementwise bitwise XOR operation.
Public Functions
-
BitwiseXor() = default
Constructs a bitwise XOR operation.
-
BitwiseXor(const Output<Node> &arg0, const Output<Node> &arg1, const AutoBroadcastSpec &auto_broadcast = AutoBroadcastSpec(AutoBroadcastType::NUMPY))
Constructs a bitwise XOR operation.
Output
[d0, ...]
-
BitwiseXor() = default
-
class Broadcast : public ov::op::util::BroadcastBase
- #include <broadcast.hpp>
Operation which “adds” axes to an input tensor, replicating elements from the input as needed along the new axes.
Public Functions
-
Broadcast() = default
Constructs a broadcast operation.
-
Broadcast(const Output<Node> &arg, const Output<Node> &target_shape, const Output<Node> &axes_mapping, const BroadcastModeSpec &broadcast_spec = BroadcastType::EXPLICIT)
Constructs a broadcast operation.
- Parameters:
arg – The input tensor to be broadcast.
target_shape – The shape of the output tensor.
axes_mapping – The axis positions (0-based) in the result that correspond to input axes. ‘Arg’ tensor is broadcast along the remaining axes. E.g., Input Shape - [3, 4], Target Shape - [3, 5, 4, 4] axes_mapping - [0, 2] => Broadcast along axes 1 and 3. axes_mapping - [0, 3] => Broadcast along axes 1 and 2.
broadcast_spec – Broadcast specification to use for determining broadcast axes. ‘axes_mapping’ should not be provided if mode other than explicit (none) is used.
-
Broadcast(const Output<Node> &arg, const Output<Node> &target_shape, const BroadcastModeSpec &broadcast_spec = BroadcastType::NUMPY)
Constructs a broadcast operation.
- Parameters:
arg – The input tensor to be broadcast.
target_shape – The shape of the output tensor.
broadcast_spec – Broadcast specification to use for determining broadcast axes
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual std::pair<bool, AxisSet> get_broadcast_axes() const override
- Returns:
true and the AxisSet if broadcast axes can be fully determined.
-
virtual bool evaluate(ov::TensorVector &outputs, const ov::TensorVector &inputs) const override
Evaluates the op on input_values putting results in output_values.
- Parameters:
output_values – Tensors for the outputs to compute. One for each result
input_values – Tensors for the inputs. One for each inputs.
- Returns:
true if successful
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Broadcast() = default
-
class Broadcast : public ov::op::util::BroadcastBase
- #include <broadcast.hpp>
Operation which “adds” axes to an input tensor, replicating elements from the input as needed along the new axes.
Public Functions
-
Broadcast() = default
Constructs a broadcast operation.
-
Broadcast(const Output<Node> &arg, const Output<Node> &target_shape, const Output<Node> &axes_mapping, const AutoBroadcastSpec &broadcast_spec = AutoBroadcastSpec())
Constructs a broadcast operation.
- Parameters:
arg – The input tensor to be broadcast.
target_shape – The shape of the output tensor.
axes_mapping – The axis positions (0-based) in the result that correspond to input axes. ‘Arg’ tensor is broadcast along the remaining axes. E.g., Input Shape - [3, 4], Target Shape - [3, 5, 4, 4] axes_mapping - [0, 2] => Broadcast along axes 1 and 3. axes_mapping - [0, 3] => Broadcast along axes 1 and 2.
broadcast_spec – Broadcast specification to use for determining broadcast axes. ‘axes_mapping’ is ignored if broadcast_spec is not NONE
-
Broadcast(const Output<Node> &arg, const Output<Node> &target_shape, const AutoBroadcastSpec &broadcast_spec = AutoBroadcastSpec(AutoBroadcastType::NUMPY))
Constructs a broadcast operation.
- Parameters:
arg – The input tensor to be broadcast.
target_shape – The shape of the output tensor.
broadcast_spec – Broadcast specification to use for determining broadcast axes
-
inline const AutoBroadcastSpec &get_broadcast_spec() const
- Returns:
Broadcast Specification.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool evaluate(ov::TensorVector &outputs, const ov::TensorVector &inputs) const override
Evaluates the op on input_values putting results in output_values.
- Parameters:
output_values – Tensors for the outputs to compute. One for each result
input_values – Tensors for the inputs. One for each inputs.
- Returns:
true if successful
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Broadcast() = default
-
class Bucketize : public ov::op::Op
- #include <bucketize.hpp>
Operation that bucketizes the input based on boundaries.
Public Functions
-
Bucketize(const Output<Node> &data, const Output<Node> &buckets, const element::Type output_type = element::i64, const bool with_right_bound = true)
Constructs a Bucketize node.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
Bucketize(const Output<Node> &data, const Output<Node> &buckets, const element::Type output_type = element::i64, const bool with_right_bound = true)
-
class Ceiling : public ov::op::util::UnaryElementwiseArithmetic
- #include <ceiling.hpp>
Elementwise ceiling operation.
Public Functions
-
Ceiling() = default
Constructs a ceiling operation.
-
Ceiling(const Output<Node> &arg)
Constructs a ceiling operation.
- Parameters:
arg – Node that produces the input tensor.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Ceiling() = default
-
class Clamp : public ov::op::util::UnaryElementwiseArithmetic
- #include <clamp.hpp>
Performs a clipping operation on all elements of the input node.
All input values that are outside of the <min;max> range are set to ‘min’ or ‘max’ depending on which side of the <min;max> range they are. The values that fall into this range remain unchanged.
Public Functions
-
Clamp(const Output<Node> &data, const double min, const double max)
Constructs a Clamp node.
- Parameters:
data – - Node producing the input tensor
min – - the lower bound of the <min;max> range
max – - the upper bound of the <min;max> range
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Clamp(const Output<Node> &data, const double min, const double max)
-
class Col2Im : public ov::op::Op
- #include <col2im.hpp>
Operator combining sliding blocks into an image tensor.
Public Functions
-
Col2Im(const Output<Node> &data, const Output<Node> &output_size, const Output<Node> &kernel_size, const Strides &strides = Strides{1, 1}, const Strides &dilations = Strides{1, 1}, const Shape &pads_begin = Shape{0, 0}, const Shape &pads_end = Shape{0, 0})
Constructs a Col2Im operation.
- Parameters:
data – Input tensor with data
output_size – Shape of the spatial dimensions of the output image
kernel_size – Size of the sliding blocks
strides – Stride in the sliding blocks in the input spatial dimensions
dilations – Local stride of the elements
pads_begin – Paddings at the beginning of each spatial axis, if undefined no padding is applied
pads_end – Paddings at the end of each spatial axis, if undefined no padding is applied
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
Col2Im(const Output<Node> &data, const Output<Node> &output_size, const Output<Node> &kernel_size, const Strides &strides = Strides{1, 1}, const Strides &dilations = Strides{1, 1}, const Shape &pads_begin = Shape{0, 0}, const Shape &pads_end = Shape{0, 0})
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class Concat : public ov::op::Op
- #include <concat.hpp>
Concatenation operation.
Public Functions
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Concat() = default
Constructs a concatenation operation.
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Concat(const OutputVector &args, int64_t axis)
Constructs a concatenation operation.
- Parameters:
args – The outputs producing the input tensors.
axis – The axis along which to concatenate the input tensors.
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Concat(const NodeVector &args, int64_t axis)
Constructs a concatenation operation.
- Parameters:
args – The nodes producing the input tensors.
axis – The axis along which to concatenate the input tensors.
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virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
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inline int64_t get_concatenation_axis() const
- Returns:
The concatenation axis.
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inline int64_t get_axis() const
- Returns:
The concatenation axis.
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virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
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Concat() = default
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class Constant : public ov::op::Op
- #include <constant.hpp>
Class for constants.
Public Functions
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Constant(const ov::Tensor &tensor)
Initialize a constant from ov::Tensor.
- Parameters:
tensor – The ov::Tensor with data
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template<typename T>
inline Constant(const element::Type &type, const Shape &shape, const std::vector<T> &values) Constructs a tensor constant.
- Parameters:
type – The element type of the tensor constant.
shape – The shape of the tensor constant.
values – A vector of literals for initializing the tensor constant. The size of values must match the size of the shape.
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template<class T, class = typename std::enable_if<std::is_fundamental<T>::value>::type>
inline Constant(const element::Type &type, const Shape &shape, T value) Constructs a uniform tensor constant.
- Parameters:
type – The element type of the tensor constant.
shape – The shape of the tensor constant.
value – A scalar for initializing the uniform tensor constant. The value is broadcast to the specified shape.
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Constant(const element::Type &type, const Shape &shape, const std::vector<std::string> &values)
Constructs a tensor constant This constructor is mainly to support deserialization of constants.
- Parameters:
type – The element type of the tensor constant.
shape – The shape of the tensor constant.
values – A list of string values to use as the constant data.
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Constant(const element::Type &type, const Shape &shape, const void *data)
Constructs a tensor constant with the supplied data.
- Parameters:
type – The element type of the tensor constant.
shape – The shape of the tensor constant.
data – A void* to constant data.
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virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
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virtual bool evaluate(ov::TensorVector &outputs, const ov::TensorVector &inputs) const override
Evaluates the op on input_values putting results in output_values.
- Parameters:
output_values – Tensors for the outputs to compute. One for each result
input_values – Tensors for the inputs. One for each inputs.
- Returns:
true if successful
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virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
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Shape get_shape_val() const
Returns the value of the constant node as a Shape object Can only be used on element::i64 nodes and interprets negative values as zeros.
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Strides get_strides_val() const
Returns the value of the constant node as a Strides object Can only be used on element::i64 nodes and interprets negative values as zeros.
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Coordinate get_coordinate_val() const
Returns the value of the constant node as a Coordinate object Can only be used on element::i64 nodes and interprets negative values as zeros.
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CoordinateDiff get_coordinate_diff_val() const
Returns the value of the constant node as a CoordinateDiff object Can only be used on element::i64 nodes.
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AxisVector get_axis_vector_val() const
Returns the value of the constant node as an AxisVector object Can only be used on element::i64 nodes and interprets negative values as zeros.
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AxisSet get_axis_set_val() const
Returns the value of the constant node as an AxisSet object Can only be used on element::i64 nodes and interprets negative values as zeros. Repeated values are allowed.
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size_t get_byte_size() const
Return data size in bytes.
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std::vector<std::string> get_value_strings() const
- Returns:
The initialization literals for the tensor constant.
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template<typename T>
inline std::vector<T> cast_vector(int64_t num_elements = -1) const Return the Constant’s value as a vector cast to type T.
- Template Parameters:
T – Type to which data vector’s entries will be cast.
- Parameters:
num_elements – (Optional) Number of elements to cast. In default case returns all elements
- Returns:
Constant’s data vector.
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const Tensor get_tensor_view() const
Get view on constant data as tensor.
- Returns:
OV::Tensor with constant data.
Public Static Functions
Wrapper around constructing a shared_ptr of a Constant.
- Parameters:
type – The element type of the tensor constant.
shape – The shape of the tensor constant.
values – A vector of values to use as the constant data.
Wrapper around constructing a shared_ptr of a Constant.
- Parameters:
type – The element type of the tensor constant.
shape – The shape of the tensor constant.
values – An initializer_list of values to use as the constant data.
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static inline std::shared_ptr<Constant> create(const element::Type &type, const Shape &shape, const void *memory)
Wrapper around constructing a shared_ptr of a Constant.
- Parameters:
type – The element type of the tensor constant.
shape – The shape of the tensor constant.
memory – An continues memory chunk which contains the constant data.
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Constant(const ov::Tensor &tensor)
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class Convert : public ov::op::Op
- #include <convert.hpp>
Elementwise type conversion operation.
Public Functions
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Convert() = default
Constructs a conversion operation.
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Convert(const Output<Node> &arg, const ov::element::Type &destination_type)
Constructs a conversion operation.
- Parameters:
arg – Node that produces the input tensor.
destination_type – Element type for the output tensor.
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virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
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virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
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Convert() = default
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class ConvertLike : public ov::op::Op
- #include <convert_like.hpp>
Elementwise type conversion operation.
Public Functions
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ConvertLike() = default
Constructs a conversion operation.
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virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
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ConvertLike() = default
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class AUGRUCell : public ov::op::util::RNNCellBase