class ov::op::v0::RNNCell¶
Overview¶
Class for single RNN cell node. More…
#include <rnn_cell.hpp>
class RNNCell: public ov::op::util::RNNCellBase
{
public:
// fields
BWDCMP_RTTI_DECLARATION;
// construction
RNNCell();
RNNCell(
const Output<Node>& X,
const Output<Node>& initial_hidden_state,
const Output<Node>& W,
const Output<Node>& R,
std::size_t hidden_size,
const std::vector<std::string>& activations = std::vector<std::string>{"tanh"},
const std::vector<float>& activations_alpha = {},
const std::vector<float>& activations_beta = {},
float clip = 0.f
);
RNNCell(
const Output<Node>& X,
const Output<Node>& initial_hidden_state,
const Output<Node>& W,
const Output<Node>& R,
const Output<Node>& B,
std::size_t hidden_size,
const std::vector<std::string>& activations = std::vector<std::string>{"tanh"},
const std::vector<float>& activations_alpha = {},
const std::vector<float>& activations_beta = {},
float clip = 0.f
);
// methods
OPENVINO_OP("RNNCell", "opset1", util::RNNCellBase);
virtual void validate_and_infer_types();
virtual bool visit_attributes(AttributeVisitor& visitor);
virtual std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const;
};Inherited Members¶
public:
// typedefs
typedef DiscreteTypeInfo type_info_t;
typedef std::map<std::string, Any> RTMap;
// fields
BWDCMP_RTTI_DECLARATION;
// 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;
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;Detailed Documentation¶
Class for single RNN cell node.
It follows notation and equations defined as in ONNX standard: https://github.com/onnx/onnx/blob/master/docs/Operators.md#RNN
It calculates following equations:
Ht = f(Xt\*(Wi^T) + Ht-1\*(Ri^T) + Wbi + Rbi)
\* - Is a dot product,
f - is activation functions.
This class represents only single cell (for current time step) and not the whole RNN Sequence layer
See also:
LSTMSequence, LSTMCell, GRUCell
Construction¶
RNNCell(
const Output<Node>& X,
const Output<Node>& initial_hidden_state,
const Output<Node>& W,
const Output<Node>& R,
std::size_t hidden_size,
const std::vector<std::string>& activations = std::vector<std::string>{"tanh"},
const std::vector<float>& activations_alpha = {},
const std::vector<float>& activations_beta = {},
float clip = 0.f
)Constructs RNNCell node.
Parameters:
X |
The input tensor with shape: [batch_size, input_size]. |
initial_hidden_state |
The hidden state tensor at current time step with shape: [batch_size, hidden_size]. |
W |
The weight tensor with shape: [hidden_size, input_size]. |
R |
The recurrence weight tensor with shape: [hidden_size, hidden_size]. |
hidden_size |
The number of hidden units for recurrent cell. |
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. |
clip |
The value defining clipping range [-clip, clip] on input of activation functions. |
RNNCell(
const Output<Node>& X,
const Output<Node>& initial_hidden_state,
const Output<Node>& W,
const Output<Node>& R,
const Output<Node>& B,
std::size_t hidden_size,
const std::vector<std::string>& activations = std::vector<std::string>{"tanh"},
const std::vector<float>& activations_alpha = {},
const std::vector<float>& activations_beta = {},
float clip = 0.f
)Constructs RNNCell node.
Parameters:
X |
The input tensor with shape: [batch_size, input_size]. |
initial_hidden_state |
The hidden state tensor at current time step with shape: [batch_size, hidden_size]. |
W |
The weight tensor with shape: [hidden_size, input_size]. |
R |
The recurrence weight tensor with shape: [hidden_size, hidden_size]. |
B |
The bias tensor for input gate with shape: [hidden_size]. |
hidden_size |
The number of hidden units for recurrent cell. |
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. |
clip |
The value defining clipping range [-clip, clip] on input of activation functions. |
Methods¶
virtual void validate_and_infer_types()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.