class ov::op::util::RNNCellBase¶
Overview¶
Base class for all recurrent network cells. More…
#include <rnn_cell_base.hpp>
class RNNCellBase: public ov::op::Op
{
public:
// fields
BWDCMP_RTTI_DECLARATION;
// construction
RNNCellBase(
const OutputVector& args,
std::size_t hidden_size,
float clip,
const std::vector<std::string>& activations,
const std::vector<float>& activations_alpha,
const std::vector<float>& activations_beta
);
RNNCellBase();
// methods
OPENVINO_OP("RNNCellBase", "util");
void validate_input_rank_dimension(const std::vector<PartialShape>& input);
virtual bool visit_attributes(AttributeVisitor& visitor);
std::size_t get_hidden_size() const;
float get_clip() const;
const std::vector<std::string>& get_activations() const;
const std::vector<float>& get_activations_alpha() const;
const std::vector<float>& get_activations_beta() const;
};
// direct descendants
class GRUSequenceIE;
class LSTMSequenceIE;
class RNNSequenceIE;
class LSTMCell;
class RNNCell;
class GRUCell;
class LSTMCell;
class GRUSequence;
class LSTMSequence;
class RNNSequence;Inherited Members¶
public:
// typedefs
typedef DiscreteTypeInfo type_info_t;
typedef std::map<std::string, Any> RTMap;
// methods
virtual void validate_and_infer_types();
void constructor_validate_and_infer_types();
virtual bool visit_attributes(AttributeVisitor&);
virtual const ov::op::AutoBroadcastSpec& get_autob() const;
virtual bool has_evaluate() const;
virtual bool evaluate(
const ov::HostTensorVector& output_values,
const ov::HostTensorVector& input_values
) const;
virtual bool evaluate(
const ov::HostTensorVector& output_values,
const ov::HostTensorVector& input_values,
const EvaluationContext& evaluationContext
) const;
virtual bool evaluate_lower(const ov::HostTensorVector& output_values) const;
virtual bool evaluate_upper(const ov::HostTensorVector& output_values) const;
virtual bool evaluate(
ov::TensorVector& output_values,
const ov::TensorVector& input_values
) const;
virtual bool evaluate(
ov::TensorVector& output_values,
const ov::TensorVector& input_values,
const ov::EvaluationContext& evaluationContext
) const;
virtual bool evaluate_lower(ov::TensorVector& output_values) const;
virtual bool evaluate_upper(ov::TensorVector& output_values) const;
virtual bool evaluate_label(TensorLabelVector& output_labels) const;
virtual bool constant_fold(
OutputVector& output_values,
const OutputVector& inputs_values
);
virtual OutputVector decompose_op() const;
virtual const type_info_t& get_type_info() const = 0;
const char \* get_type_name() const;
void set_arguments(const NodeVector& arguments);
void set_arguments(const OutputVector& arguments);
void set_argument(size_t position, const Output<Node>& argument);
void set_output_type(
size_t i,
const element::Type& element_type,
const PartialShape& pshape
);
void set_output_size(size_t output_size);
void invalidate_values();
virtual void revalidate_and_infer_types();
virtual std::string description() const;
const std::string& get_name() const;
void set_friendly_name(const std::string& name);
const std::string& get_friendly_name() const;
virtual bool is_dynamic() const;
size_t get_instance_id() const;
virtual std::ostream& write_description(std::ostream& os, uint32_t depth = 0) const;
const std::vector<std::shared_ptr<Node>>& get_control_dependencies() const;
const std::vector<Node \*>& get_control_dependents() const;
void add_control_dependency(std::shared_ptr<Node> node);
void remove_control_dependency(std::shared_ptr<Node> node);
void clear_control_dependencies();
void clear_control_dependents();
void add_node_control_dependencies(std::shared_ptr<Node> source_node);
void add_node_control_dependents(std::shared_ptr<Node> source_node);
void transfer_control_dependents(std::shared_ptr<Node> replacement);
size_t get_output_size() const;
const element::Type& get_output_element_type(size_t i) const;
const element::Type& get_element_type() const;
const Shape& get_output_shape(size_t i) const;
const PartialShape& get_output_partial_shape(size_t i) const;
Output<const Node> get_default_output() const;
Output<Node> get_default_output();
virtual size_t get_default_output_index() const;
size_t no_default_index() const;
const Shape& get_shape() const;
descriptor::Tensor& get_output_tensor(size_t i) const;
descriptor::Tensor& get_input_tensor(size_t i) const;
const std::string& get_output_tensor_name(size_t i) const;
std::set<Input<Node>> get_output_target_inputs(size_t i) const;
size_t get_input_size() const;
const element::Type& get_input_element_type(size_t i) const;
const Shape& get_input_shape(size_t i) const;
const PartialShape& get_input_partial_shape(size_t i) const;
const std::string& get_input_tensor_name(size_t i) const;
Node \* get_input_node_ptr(size_t index) const;
std::shared_ptr<Node> get_input_node_shared_ptr(size_t index) const;
Output<Node> get_input_source_output(size_t i) const;
virtual std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& inputs) const = 0;
std::shared_ptr<Node> copy_with_new_inputs(const OutputVector& new_args) const;
std::shared_ptr<Node> copy_with_new_inputs(
const OutputVector& inputs,
const std::vector<std::shared_ptr<Node>>& control_dependencies
) const;
bool has_same_type(std::shared_ptr<const Node> node) const;
RTMap& get_rt_info();
const RTMap& get_rt_info() const;
NodeVector get_users(bool check_is_used = false) const;
virtual size_t get_version() const;
virtual std::shared_ptr<Node> get_default_value() const;
bool operator < (const Node& other) const;
std::vector<Input<Node>> inputs();
std::vector<Input<const Node>> inputs() const;
std::vector<Output<Node>> input_values() const;
std::vector<Output<Node>> outputs();
std::vector<Output<const Node>> outputs() const;
Input<Node> input(size_t input_index);
Input<const Node> input(size_t input_index) const;
Output<Node> input_value(size_t input_index) const;
Output<Node> output(size_t output_index);
Output<const Node> output(size_t output_index) const;
OPENVINO_SUPPRESS_DEPRECATED_START void set_op_annotations(std::shared_ptr<ngraph::op::util::OpAnnotations> op_annotations);
std::shared_ptr<ngraph::op::util::OpAnnotations> get_op_annotations() const;
virtual OPENVINO_SUPPRESS_DEPRECATED_END bool match_value(
ov::pass::pattern::Matcher \* matcher,
const Output<Node>& pattern_value,
const Output<Node>& graph_value
);
virtual bool match_node(
ov::pass::pattern::Matcher \* matcher,
const Output<Node>& graph_value
);
static _OPENVINO_HIDDEN_METHODconst ::ov::Node::type_info_t& get_type_info_static();
virtual const ::ov::Node::type_info_t& get_type_info() const;Detailed Documentation¶
Base class for all recurrent network cells.
It holds all common attributes.
Construction¶
RNNCellBase(
const OutputVector& args,
std::size_t hidden_size,
float clip,
const std::vector<std::string>& activations,
const std::vector<float>& activations_alpha,
const std::vector<float>& activations_beta
)Constructs a RNNCellBase class.
Parameters:
hidden_size |
The number of hidden units for recurrent cell. |
clip |
The value defining clipping range [-clip, clip] on input of activation functions. |
activations |
The vector of activation functions used inside recurrent cell. |
activations_alpha |
The vector of alpha parameters for activation functions in order respective to activation list. |
activations_beta |
The vector of beta parameters for activation functions in order respective to activation list. |
Methods¶
void validate_input_rank_dimension(const std::vector<PartialShape>& input)Validates static rank and dimension for provided input parameters. Additionally input_size dimension is checked for X and W inputs.
Parameters:
input |
Vector with RNN-Cell op inputs in following order: X, initial_hidden_state, W, R and B. |