Moved operations G-L to ov namespace (#7344)
* Moved ngraph::Node to ov namespace * Fixed code style * Fixed VPU * Fixed GNA * Fixed tests * Added aliases for backward compatibility * Fix clDNN * Try to fix build * Fixed comment * Renamed RTTI macros * Moved op utils to ov namespace * Fixed ngraph library build * Fixed unit-tests * Changed src folder * Fixed recurrent_sequence * Changed low latency * Fixed serialize * Fixed ieFuncTests * Try to fix windows * Remove custom operator<< from tests * Fixed build * Moved operations from A to ov namespace * Moved operations from B and C to ov namespace * Moved operations D-F to ov namespace * Update ngraph/core/src/op/embeddingbag_offsets_sum.cpp Co-authored-by: Katarzyna Mitrus <katarzyna.mitrus@intel.com> * Update ngraph/core/src/op/embeddingbag_packedsum.cpp Co-authored-by: Katarzyna Mitrus <katarzyna.mitrus@intel.com> * Fixed RTTI * Moved operations G-L to ov namespace * Fixed RTTI Co-authored-by: Ilya Lavrenov <ilya.lavrenov@intel.com> Co-authored-by: Katarzyna Mitrus <katarzyna.mitrus@intel.com>
This commit is contained in:
Ilya Churaev
GitHub
parent
35fef3deea
commit
e3aed9854b
@ -18,11 +18,18 @@
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#include "ngraph/function.hpp"
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#include "ngraph/function.hpp"
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#include "ngraph/node.hpp"
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#include "ngraph/node.hpp"
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namespace ov {
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namespace op {
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namespace v0 {
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class Parameter;
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}
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} // namespace op
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} // namespace ov
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namespace ngraph {
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namespace ngraph {
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namespace op {
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namespace op {
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namespace v0 {
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namespace v0 {
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class Parameter;
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using ov::op::v0::Parameter;
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}
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}
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} // namespace op
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} // namespace op
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@ -38,6 +38,13 @@
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#include "ngraph/variant.hpp"
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#include "ngraph/variant.hpp"
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#include "openvino/core/node.hpp"
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#include "openvino/core/node.hpp"
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namespace ov {
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namespace op {
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namespace v0 {
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class Result;
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}
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} // namespace op
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} // namespace ov
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namespace ngraph {
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namespace ngraph {
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using ov::Node;
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using ov::Node;
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@ -52,7 +59,7 @@ using HostTensorVector = std::vector<HostTensorPtr>;
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namespace op {
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namespace op {
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namespace v0 {
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namespace v0 {
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class Result;
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using ov::op::v0::Result;
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}
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}
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} // namespace op
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} // namespace op
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@ -5,76 +5,18 @@
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#pragma once
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#pragma once
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#include "ngraph/op/util/gather_base.hpp"
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#include "ngraph/op/util/gather_base.hpp"
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#include "openvino/op/gather.hpp"
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namespace ngraph {
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namespace ngraph {
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namespace op {
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namespace op {
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namespace v1 {
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namespace v1 {
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/// \brief Gather slices from axis of data according to indices
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using ov::op::v1::Gather;
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class NGRAPH_API Gather : public op::util::GatherBase {
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public:
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NGRAPH_RTTI_DECLARATION;
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static const int64_t AXIS_NOT_SET_VALUE = std::numeric_limits<int64_t>::max();
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Gather() = default;
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/// \param data The tensor from which slices are gathered
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/// \param indices Tensor with indexes to gather
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/// \param axis The tensor is a dimension index to gather data from
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Gather(const Output<Node>& params, const Output<Node>& indices, const Output<Node>& axis);
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bool visit_attributes(AttributeVisitor& visitor) override;
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int64_t get_axis() const override;
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std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
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};
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} // namespace v1
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} // namespace v1
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namespace v7 {
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namespace v7 {
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/// \brief Gather slices from axis of data according to indices
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using ov::op::v7::Gather;
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class NGRAPH_API Gather : public op::util::GatherBase {
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public:
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NGRAPH_RTTI_DECLARATION;
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Gather() = default;
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/// \param data The tensor from which slices are gathered
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/// \param indices Tensor with indexes to gather
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/// \param axis The tensor is a dimension index to gather data from
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/// \param batch_dims The number of batch dimension in data and indices tensors.
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/// If batch_dims = 0 Gather v7 is identical to Gather v1.
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Gather(const Output<Node>& data,
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const Output<Node>& indices,
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const Output<Node>& axis,
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const int64_t batch_dims = 0);
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bool visit_attributes(AttributeVisitor& visitor) override;
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void validate_and_infer_types() override;
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int64_t get_batch_dims() const;
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std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
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};
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} // namespace v7
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} // namespace v7
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namespace v8 {
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namespace v8 {
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/// \brief Gather slices from axis of data according to indices. Negative indices
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using ov::op::v8::Gather;
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/// are supported and indicate reverse indexing from the end
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class NGRAPH_API Gather : public op::util::GatherBase {
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public:
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NGRAPH_RTTI_DECLARATION;
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Gather() = default;
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/// \param data The tensor from which slices are gathered
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/// \param indices Tensor with indexes to gather
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/// \param axis The tensor is a dimension index to gather data from
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/// \param batch_dims The number of batch dimension in data and indices tensors.
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Gather(const Output<Node>& data,
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const Output<Node>& indices,
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const Output<Node>& axis,
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const int64_t batch_dims = 0);
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bool visit_attributes(AttributeVisitor& visitor) override;
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void validate_and_infer_types() override;
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int64_t get_batch_dims() const;
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std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
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};
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} // namespace v8
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} // namespace v8
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} // namespace op
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} // namespace op
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} // namespace ngraph
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} // namespace ngraph
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@ -5,35 +5,12 @@
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#pragma once
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#pragma once
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#include "ngraph/op/op.hpp"
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#include "ngraph/op/op.hpp"
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#include "openvino/op/gather_elements.hpp"
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namespace ngraph {
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namespace ngraph {
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namespace op {
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namespace op {
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namespace v6 {
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namespace v6 {
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/// \brief GatherElements operation
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using ov::op::v6::GatherElements;
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///
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class NGRAPH_API GatherElements : public Op {
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public:
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NGRAPH_RTTI_DECLARATION;
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GatherElements() = default;
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/// \brief Constructs a GatherElements operation.
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///
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/// \param data Node producing data that are gathered
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/// \param indices Node producing indices by which the operation gathers elements
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/// \param axis specifies axis along which indices are specified
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GatherElements(const Output<Node>& data, const Output<Node>& indices, const int64_t axis);
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void validate_and_infer_types() override;
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bool visit_attributes(AttributeVisitor& visitor) override;
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std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
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int64_t get_axis() const {
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return m_axis;
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}
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private:
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int64_t m_axis;
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};
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} // namespace v6
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} // namespace v6
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} // namespace op
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} // namespace op
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} // namespace ngraph
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} // namespace ngraph
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@ -5,36 +5,12 @@
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#pragma once
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#pragma once
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#include "ngraph/op/op.hpp"
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#include "ngraph/op/op.hpp"
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#include "openvino/op/gather_nd.hpp"
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namespace ngraph {
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namespace ngraph {
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namespace op {
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namespace op {
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namespace v5 {
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namespace v5 {
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/// \brief GatherND operation
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using ov::op::v5::GatherND;
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///
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class NGRAPH_API GatherND : public Op {
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public:
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NGRAPH_RTTI_DECLARATION;
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GatherND() = default;
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/// \brief Constructs a GatherND operation.
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///
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/// \param data Node producing data that are gathered
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/// \param indices Node producing indices by which the operation gathers elements
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/// or slices from data
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/// \param batch_dims Specifies a number of batch dimensions
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GatherND(const Output<Node>& data, const Output<Node>& indices, const size_t batch_dims = 0);
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void validate_and_infer_types() override;
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bool visit_attributes(AttributeVisitor& visitor) override;
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virtual std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
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size_t get_batch_dims() const {
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return m_batch_dims;
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}
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private:
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size_t m_batch_dims;
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};
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} // namespace v5
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} // namespace v5
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} // namespace op
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} // namespace op
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} // namespace ngraph
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} // namespace ngraph
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@ -5,34 +5,12 @@
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#pragma once
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#pragma once
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#include "ngraph/op/op.hpp"
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#include "ngraph/op/op.hpp"
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#include "openvino/op/gather_tree.hpp"
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namespace ngraph {
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namespace ngraph {
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namespace op {
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namespace op {
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namespace v1 {
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namespace v1 {
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/// \brief Generates the complete beams from the ids per each step and the parent beam
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using ov::op::v1::GatherTree;
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/// ids.
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class NGRAPH_API GatherTree : public Op {
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public:
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NGRAPH_RTTI_DECLARATION;
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GatherTree() = default;
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/// \param step_ids Tensor of shape [MAX_TIME, BATCH_SIZE, BEAM_WIDTH] with
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/// indices from per each step
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/// \param parent_idx Tensor of shape [MAX_TIME, BATCH_SIZE, BEAM_WIDTH] with
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/// parent beam indices
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/// \param max_seq_len Tensor of shape [BATCH_SIZE] with maximum lengths for each
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/// sequence in the batch
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/// \param end_token Tensor of shape [MAX_TIME, BATCH_SIZE, BEAM_WIDTH]
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GatherTree(const Output<Node>& step_ids,
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const Output<Node>& parent_idx,
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const Output<Node>& max_seq_len,
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const Output<Node>& end_token);
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bool visit_attributes(AttributeVisitor& visitor) override;
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void validate_and_infer_types() override;
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virtual std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
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};
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} // namespace v1
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} // namespace v1
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} // namespace op
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} // namespace op
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} // namespace ngraph
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} // namespace ngraph
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#include "ngraph/node.hpp"
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#include "ngraph/node.hpp"
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#include "ngraph/op/op.hpp"
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#include "ngraph/op/op.hpp"
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#include "ngraph/op/util/unary_elementwise_arithmetic.hpp"
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#include "ngraph/op/util/unary_elementwise_arithmetic.hpp"
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#include "openvino/op/gelu.hpp"
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namespace ngraph {
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namespace ngraph {
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namespace op {
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namespace op {
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namespace v0 {
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namespace v0 {
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/// \brief Gaussian Error Linear Unit
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using ov::op::v0::Gelu;
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/// f(x) = 0.5 * x * (1 + erf( x / sqrt(2) )
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class NGRAPH_API Gelu : public Op {
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public:
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NGRAPH_RTTI_DECLARATION;
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Gelu();
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/// \brief Constructs a Gelu operation.
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///
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/// \param data Input tensor
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Gelu(const Output<Node>& data);
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bool visit_attributes(AttributeVisitor& visitor) override;
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void validate_and_infer_types() override;
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std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
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};
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} // namespace v0
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} // namespace v0
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using v0::Gelu;
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using v0::Gelu;
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/// \brief Specifies the approximation to calculate Gelu
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using ov::op::GeluApproximationMode;
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enum class GeluApproximationMode { TANH, ERF };
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NGRAPH_API std::ostream& operator<<(std::ostream& s, const GeluApproximationMode& type);
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namespace v7 {
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namespace v7 {
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/// \brief Gaussian Error Linear Unit
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using ov::op::v7::Gelu;
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/// f(x) = 0.5 * x * (1 + erf( x / sqrt(2) ) for "approximation" = "erf"
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/// f(x) = 0.5 * x * (1 + tanh([sqrt(2 / pi)] * [x + 0.044715^3]) for "approximation" =
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/// "tanh"
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class NGRAPH_API Gelu : public util::UnaryElementwiseArithmetic {
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public:
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NGRAPH_RTTI_DECLARATION;
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Gelu() = default;
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/// \brief Constructs a Gelu operation.
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///
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/// \param data Input tensor
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/// \param mode Approximation mode
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Gelu(const Output<Node>& data, GeluApproximationMode mode = GeluApproximationMode::ERF);
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bool visit_attributes(AttributeVisitor& visitor) override;
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void validate_and_infer_types() override;
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bool evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const override;
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bool has_evaluate() const override;
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std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
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GeluApproximationMode get_approximation_mode() const;
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private:
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GeluApproximationMode m_approximation_mode = GeluApproximationMode::ERF;
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};
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} // namespace v7
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} // namespace v7
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} // namespace op
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} // namespace op
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} // namespace ngraph
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} // namespace ngraph
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namespace ov {
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template <>
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class NGRAPH_API AttributeAdapter<ngraph::op::GeluApproximationMode>
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: public EnumAttributeAdapterBase<ngraph::op::GeluApproximationMode> {
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public:
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AttributeAdapter(ngraph::op::GeluApproximationMode& value)
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: EnumAttributeAdapterBase<ngraph::op::GeluApproximationMode>(value) {}
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static constexpr DiscreteTypeInfo type_info{"AttributeAdapter<op::GeluApproximationMode>", 0};
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const DiscreteTypeInfo& get_type_info() const override {
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return type_info;
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}
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};
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} // namespace ov
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#pragma once
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#pragma once
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#include "ngraph/op/util/binary_elementwise_comparison.hpp"
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#include "ngraph/op/util/binary_elementwise_comparison.hpp"
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#include "openvino/op/greater.hpp"
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namespace ngraph {
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namespace ngraph {
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namespace op {
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namespace op {
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namespace v1 {
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namespace v1 {
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/// \brief Elementwise greater-than operation.
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using ov::op::v1::Greater;
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class NGRAPH_API Greater : public util::BinaryElementwiseComparison {
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public:
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NGRAPH_RTTI_DECLARATION;
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/// \brief Constructs a greater-than operation.
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Greater() : util::BinaryElementwiseComparison(AutoBroadcastSpec::NUMPY) {}
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/// \brief Constructs a greater-than operation.
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///
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/// \param arg0 Node that produces the first input tensor.
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/// \param arg1 Node that produces the second input tensor.
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/// \param auto_broadcast Auto broadcast specification
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Greater(const Output<Node>& arg0,
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const Output<Node>& arg1,
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const AutoBroadcastSpec& auto_broadcast = AutoBroadcastSpec(AutoBroadcastType::NUMPY));
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virtual std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
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bool evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const override;
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bool has_evaluate() const override;
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};
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|
||||||
} // namespace v1
|
} // namespace v1
|
||||||
} // namespace op
|
} // namespace op
|
||||||
} // namespace ngraph
|
} // namespace ngraph
|
||||||
|
|||||||
@ -5,29 +5,12 @@
|
|||||||
#pragma once
|
#pragma once
|
||||||
|
|
||||||
#include "ngraph/op/util/binary_elementwise_comparison.hpp"
|
#include "ngraph/op/util/binary_elementwise_comparison.hpp"
|
||||||
|
#include "openvino/op/greater_eq.hpp"
|
||||||
|
|
||||||
namespace ngraph {
|
namespace ngraph {
|
||||||
namespace op {
|
namespace op {
|
||||||
namespace v1 {
|
namespace v1 {
|
||||||
/// \brief Elementwise greater-than-or-equal operation.
|
using ov::op::v1::GreaterEqual;
|
||||||
class NGRAPH_API GreaterEqual : public util::BinaryElementwiseComparison {
|
|
||||||
public:
|
|
||||||
NGRAPH_RTTI_DECLARATION;
|
|
||||||
/// \brief Constructs a greater-than-or-equal operation.
|
|
||||||
GreaterEqual() : util::BinaryElementwiseComparison(AutoBroadcastSpec::NUMPY) {}
|
|
||||||
/// \brief Constructs a greater-than-or-equal operation.
|
|
||||||
///
|
|
||||||
/// \param arg0 Node that produces the first input tensor.
|
|
||||||
/// \param arg1 Node that produces the second input tensor.
|
|
||||||
/// \param auto_broadcast Auto broadcast specification
|
|
||||||
GreaterEqual(const Output<Node>& arg0,
|
|
||||||
const Output<Node>& arg1,
|
|
||||||
const AutoBroadcastSpec& auto_broadcast = AutoBroadcastSpec(AutoBroadcastType::NUMPY));
|
|
||||||
|
|
||||||
virtual std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
|
||||||
bool evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const override;
|
|
||||||
bool has_evaluate() const override;
|
|
||||||
};
|
|
||||||
} // namespace v1
|
} // namespace v1
|
||||||
} // namespace op
|
} // namespace op
|
||||||
} // namespace ngraph
|
} // namespace ngraph
|
||||||
|
|||||||
@ -7,35 +7,12 @@
|
|||||||
#include <memory>
|
#include <memory>
|
||||||
|
|
||||||
#include "ngraph/op/op.hpp"
|
#include "ngraph/op/op.hpp"
|
||||||
|
#include "openvino/op/grn.hpp"
|
||||||
|
|
||||||
namespace ngraph {
|
namespace ngraph {
|
||||||
namespace op {
|
namespace op {
|
||||||
namespace v0 {
|
namespace v0 {
|
||||||
/// \brief Global Response Normalization with L2 norm (across channels only).
|
using ov::op::v0::GRN;
|
||||||
///
|
|
||||||
class NGRAPH_API GRN : public Op {
|
|
||||||
public:
|
|
||||||
NGRAPH_RTTI_DECLARATION;
|
|
||||||
|
|
||||||
GRN() = default;
|
|
||||||
/// \brief Constructs a GRN operation.
|
|
||||||
///
|
|
||||||
/// \param data - Node producing the input tensor
|
|
||||||
/// \param bias - The bias added to the variance.
|
|
||||||
///
|
|
||||||
GRN(const Output<Node>& data, float bias);
|
|
||||||
|
|
||||||
void validate_and_infer_types() override;
|
|
||||||
bool visit_attributes(AttributeVisitor& visitor) override;
|
|
||||||
virtual std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
|
||||||
|
|
||||||
float get_bias() const {
|
|
||||||
return m_bias;
|
|
||||||
}
|
|
||||||
|
|
||||||
protected:
|
|
||||||
float m_bias = 1.0f;
|
|
||||||
};
|
|
||||||
} // namespace v0
|
} // namespace v0
|
||||||
using v0::GRN;
|
using v0::GRN;
|
||||||
} // namespace op
|
} // namespace op
|
||||||
|
|||||||
@ -7,268 +7,13 @@
|
|||||||
#include "ngraph/op/convolution.hpp"
|
#include "ngraph/op/convolution.hpp"
|
||||||
#include "ngraph/op/op.hpp"
|
#include "ngraph/op/op.hpp"
|
||||||
#include "ngraph/op/util/attr_types.hpp"
|
#include "ngraph/op/util/attr_types.hpp"
|
||||||
|
#include "openvino/op/group_conv.hpp"
|
||||||
|
|
||||||
namespace ngraph {
|
namespace ngraph {
|
||||||
namespace op {
|
namespace op {
|
||||||
namespace v1 {
|
namespace v1 {
|
||||||
/// \brief Batched convolution operation, with optional window dilation and stride.
|
using ov::op::v1::GroupConvolution;
|
||||||
class NGRAPH_API GroupConvolution : public Op {
|
using ov::op::v1::GroupConvolutionBackpropData;
|
||||||
public:
|
|
||||||
NGRAPH_RTTI_DECLARATION;
|
|
||||||
|
|
||||||
/// \brief Constructs a batched convolution operation.
|
|
||||||
GroupConvolution() = default;
|
|
||||||
/// \brief Constructs a batched convolution operation.
|
|
||||||
///
|
|
||||||
/// \param data_batch The node producing the input data batch tensor.<br>
|
|
||||||
/// `[N, C_IN, D1, ... Df]`
|
|
||||||
/// \param filters The node producing the filters tensor.<br>
|
|
||||||
/// `[GROUPS, FC_OUT, FC_IN, F1, ... Ff]`
|
|
||||||
/// \param strides The strides.<br>
|
|
||||||
/// `[f]`
|
|
||||||
/// \param dilations The dilations.<br>
|
|
||||||
/// `[f]`
|
|
||||||
/// \param pads_begin The beginning of padding shape.<br>
|
|
||||||
/// `[f]`
|
|
||||||
/// \param pads_end The end of padding shape.<br>
|
|
||||||
/// `[f]`
|
|
||||||
/// \param auto_pad The pad type for automatically computing padding sizes.<br>
|
|
||||||
/// `[f]`
|
|
||||||
///
|
|
||||||
/// Output `[N, FC_OUT * GROUPS, R1, ... Rf]`
|
|
||||||
///
|
|
||||||
GroupConvolution(const Output<Node>& data_batch,
|
|
||||||
const Output<Node>& filters,
|
|
||||||
const Strides& strides,
|
|
||||||
const CoordinateDiff& pads_begin,
|
|
||||||
const CoordinateDiff& pads_end,
|
|
||||||
const Strides& dilations,
|
|
||||||
const PadType& auto_pad = PadType::EXPLICIT);
|
|
||||||
|
|
||||||
bool visit_attributes(AttributeVisitor& visitor) override;
|
|
||||||
void validate_and_infer_types() override;
|
|
||||||
|
|
||||||
virtual std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
|
||||||
/// \return The strides.
|
|
||||||
const Strides& get_strides() const {
|
|
||||||
return m_strides;
|
|
||||||
}
|
|
||||||
void set_strides(const Strides& strides) {
|
|
||||||
m_strides = strides;
|
|
||||||
}
|
|
||||||
/// \return The dilations.
|
|
||||||
const Strides& get_dilations() const {
|
|
||||||
return m_dilations;
|
|
||||||
}
|
|
||||||
void set_dilations(const Strides& dilations) {
|
|
||||||
m_dilations = dilations;
|
|
||||||
}
|
|
||||||
/// \return The padding-below sizes (possibly negative).
|
|
||||||
const CoordinateDiff& get_pads_begin() const {
|
|
||||||
return m_pads_begin;
|
|
||||||
}
|
|
||||||
void set_pads_begin(const CoordinateDiff& pads_begin) {
|
|
||||||
m_pads_begin = pads_begin;
|
|
||||||
}
|
|
||||||
/// \return The padding-above sizes (possibly negative).
|
|
||||||
const CoordinateDiff& get_pads_end() const {
|
|
||||||
return m_pads_end;
|
|
||||||
}
|
|
||||||
void set_adding_above(const CoordinateDiff& pads_end) {
|
|
||||||
m_pads_end = pads_end;
|
|
||||||
}
|
|
||||||
/// \return The pad type for convolution.
|
|
||||||
const PadType& get_auto_pad() const {
|
|
||||||
return m_auto_pad;
|
|
||||||
}
|
|
||||||
void set_auto_pad(const PadType& auto_pad) {
|
|
||||||
m_auto_pad = auto_pad;
|
|
||||||
}
|
|
||||||
/// \return The default value for Convolution.
|
|
||||||
NGRAPH_SUPPRESS_DEPRECATED_START
|
|
||||||
virtual std::shared_ptr<Node> get_default_value() const override;
|
|
||||||
NGRAPH_SUPPRESS_DEPRECATED_END
|
|
||||||
|
|
||||||
protected:
|
|
||||||
Strides m_strides;
|
|
||||||
Strides m_dilations;
|
|
||||||
CoordinateDiff m_pads_begin;
|
|
||||||
CoordinateDiff m_pads_end;
|
|
||||||
PadType m_auto_pad;
|
|
||||||
};
|
|
||||||
|
|
||||||
/// \brief Data batch backprop for batched convolution operation.
|
|
||||||
class NGRAPH_API GroupConvolutionBackpropData : public Op {
|
|
||||||
public:
|
|
||||||
NGRAPH_RTTI_DECLARATION;
|
|
||||||
|
|
||||||
/// \brief Constructs a batched-convolution data batch-backprop operation.
|
|
||||||
GroupConvolutionBackpropData();
|
|
||||||
// clang-format off
|
|
||||||
//
|
|
||||||
// \brief Constructs a batched-convolution data batch-backprop operation.
|
|
||||||
//
|
|
||||||
// \param data The node producing data from forward-prop. Shape: [N,
|
|
||||||
// C_INPUT * GROUPS, X1, ..., XD].
|
|
||||||
// \param filter The node producing the filter from forward-prop. Shape:
|
|
||||||
// [GROUPS, C_INPUT, C_OUTPUT, K_D, ..., K_1]
|
|
||||||
// \param output_shape The shape of the data batch from forward-prop. It's size
|
|
||||||
// should be equal to number of data spatial dimensions.
|
|
||||||
// \param strides The strides from forward-prop.
|
|
||||||
// \param pads_begin The padding-below sizes from forward-prop.
|
|
||||||
// \param pads_end The padding-above sizes from forward-prop.
|
|
||||||
// \param dilations The dilations from forward-prop.
|
|
||||||
// \param auto_pad The pad type for automatically computing padding sizes.
|
|
||||||
// \param output_padding The output padding adds additional amount of paddings per
|
|
||||||
// each spatial axis in the output tensor.
|
|
||||||
//
|
|
||||||
// clang-format on
|
|
||||||
//
|
|
||||||
GroupConvolutionBackpropData(const Output<Node>& data,
|
|
||||||
const Output<Node>& filter,
|
|
||||||
const Output<Node>& output_shape,
|
|
||||||
const Strides& strides,
|
|
||||||
const CoordinateDiff& pads_begin,
|
|
||||||
const CoordinateDiff& pads_end,
|
|
||||||
const Strides& dilations,
|
|
||||||
const PadType& auto_pad = PadType::EXPLICIT,
|
|
||||||
const CoordinateDiff& output_padding = {});
|
|
||||||
|
|
||||||
// clang-format off
|
|
||||||
//
|
|
||||||
// \brief Constructs a batched-convolution data batch-backprop operation.
|
|
||||||
//
|
|
||||||
// \param data The node producing data from forward-prop. Shape: [N,
|
|
||||||
// C_INPUT * GROUPS, X1, ..., XD].
|
|
||||||
// \param filter The node producing the filter from forward-prop. Shape:
|
|
||||||
// [GROUPS, C_INPUT, C_OUTPUT, K_D, ..., K_1]
|
|
||||||
// \param output_shape The shape of the data batch from forward-prop. It's size
|
|
||||||
// should be equal to number of data spatial dimensions.
|
|
||||||
// \param strides The strides from forward-prop.
|
|
||||||
// \param dilations The dilations from forward-prop.
|
|
||||||
// \param auto_pad The pad type for automatically computing padding sizes.
|
|
||||||
// \param output_padding The output padding adds additional amount of paddings per
|
|
||||||
// each spatial axis in the output tensor.
|
|
||||||
//
|
|
||||||
// clang-format on
|
|
||||||
//
|
|
||||||
GroupConvolutionBackpropData(const Output<Node>& data,
|
|
||||||
const Output<Node>& filter,
|
|
||||||
const Output<Node>& output_shape,
|
|
||||||
const Strides& strides,
|
|
||||||
const Strides& dilations,
|
|
||||||
const PadType& auto_pad,
|
|
||||||
const CoordinateDiff& output_padding = {});
|
|
||||||
|
|
||||||
// clang-format off
|
|
||||||
//
|
|
||||||
// \brief Constructs a batched-convolution data batch-backprop operation.
|
|
||||||
//
|
|
||||||
// \param data The node producing data from forward-prop. Shape:
|
|
||||||
// [N, C_INPUT * GROUPS, X1, ..., XD].
|
|
||||||
// \param filter The node producing the filter from forward-prop. Shape:
|
|
||||||
// [GROUPS, C_INPUT, C_OUTPUT, K_D, ..., K_1]
|
|
||||||
// \param strides The strides from forward-prop.
|
|
||||||
// \param pads_begin The padding-below sizes from forward-prop.
|
|
||||||
// \param pads_end The padding-above sizes from forward-prop.
|
|
||||||
// \param dilations The dilations from forward-prop.
|
|
||||||
// \param auto_pad The pad type for automatically computing padding sizes.
|
|
||||||
// \param output_padding The output padding adds additional amount of paddings per
|
|
||||||
// each spatial axis in the output tensor.
|
|
||||||
//
|
|
||||||
// clang-format on
|
|
||||||
GroupConvolutionBackpropData(const Output<Node>& data,
|
|
||||||
const Output<Node>& filter,
|
|
||||||
const Strides& strides,
|
|
||||||
const CoordinateDiff& pads_begin,
|
|
||||||
const CoordinateDiff& pads_end,
|
|
||||||
const Strides& dilations,
|
|
||||||
const PadType& auto_pad = PadType::EXPLICIT,
|
|
||||||
const CoordinateDiff& output_padding = {});
|
|
||||||
///
|
|
||||||
/// \brief Calculates output spatial features size.
|
|
||||||
///
|
|
||||||
/// \param[in] input_data_shape The input data partial shape
|
|
||||||
/// \param[in] filters_shape The filters partial shape
|
|
||||||
/// \param[in] strides The strides values.
|
|
||||||
/// \param[in] dilations The dilations values.
|
|
||||||
/// \param[in] pads_begin The paddings at the beginning of axis.
|
|
||||||
/// \param[in] pads_end The paddings at the end of axis.
|
|
||||||
/// \param[in] output_padding The output padding values.
|
|
||||||
/// \param output_spatial_shape The placeholder for computed output spatial
|
|
||||||
/// partial
|
|
||||||
/// shape.
|
|
||||||
///
|
|
||||||
void infer_conv_backprop_output_spatial_shape(const std::vector<Dimension>& input_data_shape,
|
|
||||||
const std::vector<Dimension>& filters_shape,
|
|
||||||
const Strides& strides,
|
|
||||||
const Strides& dilations,
|
|
||||||
const CoordinateDiff& pads_begin,
|
|
||||||
const CoordinateDiff& pads_end,
|
|
||||||
const CoordinateDiff& output_padding,
|
|
||||||
std::vector<Dimension>& output_spatial_shape);
|
|
||||||
|
|
||||||
bool visit_attributes(AttributeVisitor& visitor) override;
|
|
||||||
virtual bool is_dynamic() const override;
|
|
||||||
void validate_and_infer_types() override;
|
|
||||||
|
|
||||||
virtual std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
|
||||||
|
|
||||||
/// \return The spatial shape of the output.
|
|
||||||
const PartialShape get_convolution_output_shape() const;
|
|
||||||
void set_output_shape(const Shape& output_shape);
|
|
||||||
/// \return The strides from the forward prop.
|
|
||||||
const Strides& get_strides() const {
|
|
||||||
return m_strides;
|
|
||||||
}
|
|
||||||
void set_strides(const Strides& strides) {
|
|
||||||
m_strides = strides;
|
|
||||||
}
|
|
||||||
/// \return The dilations from the forward prop.
|
|
||||||
const Strides& get_dilations() const {
|
|
||||||
return m_dilations;
|
|
||||||
}
|
|
||||||
void set_dilations(const Strides& dilations) {
|
|
||||||
m_dilations = dilations;
|
|
||||||
}
|
|
||||||
/// \return The number of pixels to add to the beginning along each axis.
|
|
||||||
const CoordinateDiff& get_pads_begin() const {
|
|
||||||
return m_pads_begin;
|
|
||||||
}
|
|
||||||
void set_pads_begin(const CoordinateDiff& pads_begin) {
|
|
||||||
m_pads_begin = pads_begin;
|
|
||||||
}
|
|
||||||
/// \return The number of pixels to add to the ending along each axis.
|
|
||||||
const CoordinateDiff& get_pads_end() const {
|
|
||||||
return m_pads_end;
|
|
||||||
}
|
|
||||||
void set_pads_end(const CoordinateDiff& pads_end) {
|
|
||||||
m_pads_end = pads_end;
|
|
||||||
}
|
|
||||||
/// \return The auto pad.
|
|
||||||
const PadType& get_auto_pad() const {
|
|
||||||
return m_auto_pad;
|
|
||||||
}
|
|
||||||
void set_auto_pad(const PadType& auto_pad) {
|
|
||||||
m_auto_pad = auto_pad;
|
|
||||||
}
|
|
||||||
/// \return The output padding.
|
|
||||||
const CoordinateDiff& get_output_padding() const {
|
|
||||||
return m_output_padding;
|
|
||||||
}
|
|
||||||
void set_output_padding(const CoordinateDiff& output_padding) {
|
|
||||||
m_output_padding = output_padding;
|
|
||||||
}
|
|
||||||
|
|
||||||
protected:
|
|
||||||
Strides m_strides;
|
|
||||||
Strides m_dilations;
|
|
||||||
CoordinateDiff m_pads_begin;
|
|
||||||
CoordinateDiff m_pads_end;
|
|
||||||
PadType m_auto_pad;
|
|
||||||
CoordinateDiff m_output_padding;
|
|
||||||
};
|
|
||||||
|
|
||||||
} // namespace v1
|
} // namespace v1
|
||||||
} // namespace op
|
} // namespace op
|
||||||
} // namespace ngraph
|
} // namespace ngraph
|
||||||
|
|||||||
@ -13,155 +13,12 @@
|
|||||||
#include "ngraph/op/op.hpp"
|
#include "ngraph/op/op.hpp"
|
||||||
#include "ngraph/op/util/activation_functions.hpp"
|
#include "ngraph/op/util/activation_functions.hpp"
|
||||||
#include "ngraph/op/util/rnn_cell_base.hpp"
|
#include "ngraph/op/util/rnn_cell_base.hpp"
|
||||||
|
#include "openvino/op/gru_cell.hpp"
|
||||||
|
|
||||||
namespace ngraph {
|
namespace ngraph {
|
||||||
namespace op {
|
namespace op {
|
||||||
namespace v3 {
|
namespace v3 {
|
||||||
///
|
using ov::op::v3::GRUCell;
|
||||||
/// \brief Class for GRU cell node.
|
|
||||||
///
|
|
||||||
/// \note It follows notation and equations defined as in ONNX standard:
|
|
||||||
/// https://github.com/onnx/onnx/blob/master/docs/Operators.md#GRU
|
|
||||||
///
|
|
||||||
/// Note this class represents only single *cell* and not whole GRU *layer*.
|
|
||||||
///
|
|
||||||
class NGRAPH_API GRUCell : public util::RNNCellBase {
|
|
||||||
public:
|
|
||||||
static constexpr NodeTypeInfo type_info{"GRUCell", 3};
|
|
||||||
const NodeTypeInfo& get_type_info() const override {
|
|
||||||
return type_info;
|
|
||||||
}
|
|
||||||
GRUCell();
|
|
||||||
///
|
|
||||||
/// \brief Constructs GRUCell node.
|
|
||||||
///
|
|
||||||
/// \param[in] X The input tensor with shape: [batch_size,
|
|
||||||
/// input_size].
|
|
||||||
/// \param[in] initial_hidden_state The hidden state tensor at current time step
|
|
||||||
/// with shape: [batch_size, hidden_size].
|
|
||||||
/// \param[in] W The weight tensor with shape:
|
|
||||||
/// [gates_count * hidden_size, input_size].
|
|
||||||
/// \param[in] R The recurrence weight tensor with shape:
|
|
||||||
/// [gates_count * hidden_size, hidden_size].
|
|
||||||
/// \param[in] hidden_size The number of hidden units for recurrent cell.
|
|
||||||
///
|
|
||||||
GRUCell(const Output<Node>& X,
|
|
||||||
const Output<Node>& initial_hidden_state,
|
|
||||||
const Output<Node>& W,
|
|
||||||
const Output<Node>& R,
|
|
||||||
std::size_t hidden_size);
|
|
||||||
|
|
||||||
///
|
|
||||||
/// \brief Constructs GRUCell node.
|
|
||||||
///
|
|
||||||
/// \param[in] X The input tensor with shape: [batch_size,
|
|
||||||
/// input_size].
|
|
||||||
/// \param[in] initial_hidden_state The hidden state tensor at current time step
|
|
||||||
/// with shape: [batch_size, hidden_size].
|
|
||||||
/// \param[in] W The weight tensor with shape:
|
|
||||||
/// [gates_count * hidden_size, input_size].
|
|
||||||
/// \param[in] R The recurrence weight tensor with shape:
|
|
||||||
/// [gates_count * hidden_size, hidden_size].
|
|
||||||
/// \param[in] hidden_size The number of hidden units for recurrent cell.
|
|
||||||
/// \param[in] activations The vector of activation functions used inside
|
|
||||||
/// recurrent cell.
|
|
||||||
/// \param[in] activations_alpha The vector of alpha parameters for activation
|
|
||||||
/// functions in order respective to activation
|
|
||||||
/// list.
|
|
||||||
/// \param[in] activations_beta The vector of beta parameters for activation
|
|
||||||
/// functions in order respective to activation
|
|
||||||
/// list.
|
|
||||||
/// \param[in] clip The value defining clipping range [-clip,
|
|
||||||
/// clip] on input of activation functions.
|
|
||||||
///
|
|
||||||
GRUCell(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,
|
|
||||||
const std::vector<float>& activations_alpha,
|
|
||||||
const std::vector<float>& activations_beta,
|
|
||||||
float clip,
|
|
||||||
bool linear_before_reset);
|
|
||||||
|
|
||||||
///
|
|
||||||
/// \brief Constructs GRUCell node.
|
|
||||||
///
|
|
||||||
/// \param[in] X The input tensor with shape: [batch_size,
|
|
||||||
/// input_size].
|
|
||||||
/// \param[in] initial_hidden_state The hidden state tensor at current time step
|
|
||||||
/// with shape: [batch_size, hidden_size].
|
|
||||||
/// \param[in] W The weight tensor with shape: [gates_count *
|
|
||||||
/// hidden_size, input_size].
|
|
||||||
/// \param[in] R The recurrence weight tensor with shape:
|
|
||||||
/// [gates_count * hidden_size, hidden_size].
|
|
||||||
/// \param[in] hidden_size The number of hidden units for recurrent cell.
|
|
||||||
/// \param[in] B The sum of biases (weight and recurrence) for
|
|
||||||
/// update, reset and hidden gates.
|
|
||||||
/// If linear_before_reset := true then biases for
|
|
||||||
/// hidden gates are
|
|
||||||
/// placed separately (weight and recurrence).
|
|
||||||
/// Shape: [gates_count * hidden_size] if
|
|
||||||
/// linear_before_reset := false
|
|
||||||
/// Shape: [(gates_count + 1) * hidden_size] if
|
|
||||||
/// linear_before_reset := true
|
|
||||||
/// \param[in] activations The vector of activation functions used inside
|
|
||||||
/// recurrent cell.
|
|
||||||
/// \param[in] activations_alpha The vector of alpha parameters for activation
|
|
||||||
/// functions in order respective to activation
|
|
||||||
/// list.
|
|
||||||
/// \param[in] activations_beta The vector of beta parameters for activation
|
|
||||||
/// functions in order respective to activation
|
|
||||||
/// list.
|
|
||||||
/// \param[in] clip The value defining clipping range [-clip,
|
|
||||||
/// clip] on input of activation functions.
|
|
||||||
/// \param[in] linear_before_reset Whether or not to apply the linear
|
|
||||||
/// transformation before multiplying by the
|
|
||||||
/// output of the reset gate.
|
|
||||||
///
|
|
||||||
GRUCell(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>{"sigmoid", "tanh"},
|
|
||||||
const std::vector<float>& activations_alpha = {},
|
|
||||||
const std::vector<float>& activations_beta = {},
|
|
||||||
float clip = 0.f,
|
|
||||||
bool linear_before_reset = false);
|
|
||||||
|
|
||||||
virtual void validate_and_infer_types() override;
|
|
||||||
bool visit_attributes(AttributeVisitor& visitor) override;
|
|
||||||
virtual std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
|
||||||
|
|
||||||
bool get_linear_before_reset() const {
|
|
||||||
return m_linear_before_reset;
|
|
||||||
}
|
|
||||||
|
|
||||||
private:
|
|
||||||
/// brief Add and initialize bias input to all zeros.
|
|
||||||
void add_default_bias_input();
|
|
||||||
|
|
||||||
///
|
|
||||||
/// \brief The Activation function f.
|
|
||||||
///
|
|
||||||
util::ActivationFunction m_activation_f;
|
|
||||||
///
|
|
||||||
/// \brief The Activation function g.
|
|
||||||
///
|
|
||||||
util::ActivationFunction m_activation_g;
|
|
||||||
|
|
||||||
static constexpr std::size_t s_gates_count{3};
|
|
||||||
///
|
|
||||||
/// \brief Control whether or not apply the linear transformation.
|
|
||||||
///
|
|
||||||
/// \note The linear transformation may be applied when computing the output of
|
|
||||||
/// hidden gate. It's done before multiplying by the output of the reset gate.
|
|
||||||
///
|
|
||||||
bool m_linear_before_reset;
|
|
||||||
};
|
|
||||||
} // namespace v3
|
} // namespace v3
|
||||||
} // namespace op
|
} // namespace op
|
||||||
} // namespace ngraph
|
} // namespace ngraph
|
||||||
|
|||||||
@ -10,45 +10,12 @@
|
|||||||
|
|
||||||
#include "ngraph/op/op.hpp"
|
#include "ngraph/op/op.hpp"
|
||||||
#include "ngraph/op/util/rnn_cell_base.hpp"
|
#include "ngraph/op/util/rnn_cell_base.hpp"
|
||||||
|
#include "openvino/op/gru_sequence.hpp"
|
||||||
|
|
||||||
namespace ngraph {
|
namespace ngraph {
|
||||||
namespace op {
|
namespace op {
|
||||||
namespace v5 {
|
namespace v5 {
|
||||||
class NGRAPH_API GRUSequence : public util::RNNCellBase {
|
using ov::op::v5::GRUSequence;
|
||||||
public:
|
|
||||||
NGRAPH_RTTI_DECLARATION;
|
|
||||||
GRUSequence();
|
|
||||||
|
|
||||||
GRUSequence(const Output<Node>& X,
|
|
||||||
const Output<Node>& H_t,
|
|
||||||
const Output<Node>& sequence_lengths,
|
|
||||||
const Output<Node>& W,
|
|
||||||
const Output<Node>& R,
|
|
||||||
const Output<Node>& B,
|
|
||||||
size_t hidden_size,
|
|
||||||
op::RecurrentSequenceDirection direction,
|
|
||||||
const std::vector<std::string>& activations = std::vector<std::string>{"sigmoid", "tanh"},
|
|
||||||
const std::vector<float>& activations_alpha = {},
|
|
||||||
const std::vector<float>& activations_beta = {},
|
|
||||||
float clip = 0.f,
|
|
||||||
bool linear_before_reset = false);
|
|
||||||
|
|
||||||
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
|
||||||
|
|
||||||
void validate_and_infer_types() override;
|
|
||||||
|
|
||||||
bool visit_attributes(AttributeVisitor& visitor) override;
|
|
||||||
bool get_linear_before_reset() const {
|
|
||||||
return m_linear_before_reset;
|
|
||||||
}
|
|
||||||
op::RecurrentSequenceDirection get_direction() const {
|
|
||||||
return m_direction;
|
|
||||||
}
|
|
||||||
|
|
||||||
protected:
|
|
||||||
op::RecurrentSequenceDirection m_direction;
|
|
||||||
bool m_linear_before_reset;
|
|
||||||
};
|
|
||||||
} // namespace v5
|
} // namespace v5
|
||||||
} // namespace op
|
} // namespace op
|
||||||
} // namespace ngraph
|
} // namespace ngraph
|
||||||
|
|||||||
@ -6,31 +6,12 @@
|
|||||||
|
|
||||||
#include "ngraph/node.hpp"
|
#include "ngraph/node.hpp"
|
||||||
#include "ngraph/op/op.hpp"
|
#include "ngraph/op/op.hpp"
|
||||||
|
#include "openvino/op/hard_sigmoid.hpp"
|
||||||
|
|
||||||
namespace ngraph {
|
namespace ngraph {
|
||||||
namespace op {
|
namespace op {
|
||||||
namespace v0 {
|
namespace v0 {
|
||||||
/// \brief Parameterized, bounded sigmoid-like, piecewise linear
|
using ov::op::v0::HardSigmoid;
|
||||||
/// function. min(max(alpha*x + beta, 0), 1)
|
|
||||||
///
|
|
||||||
class NGRAPH_API HardSigmoid : public Op {
|
|
||||||
public:
|
|
||||||
NGRAPH_RTTI_DECLARATION;
|
|
||||||
|
|
||||||
HardSigmoid();
|
|
||||||
|
|
||||||
/// \brief Constructs a HardSigmoid operation.
|
|
||||||
///
|
|
||||||
/// \param data Input tensor.
|
|
||||||
/// \param[in] alpha A scalar value representing the alpha parameter.
|
|
||||||
/// \param[in] beta A scalar value representing the beta parameter.
|
|
||||||
///
|
|
||||||
HardSigmoid(const Output<Node>& data, const Output<Node>& alpha, const Output<Node>& beta);
|
|
||||||
|
|
||||||
bool visit_attributes(AttributeVisitor& visitor) override;
|
|
||||||
virtual void validate_and_infer_types() override;
|
|
||||||
virtual std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
|
||||||
};
|
|
||||||
} // namespace v0
|
} // namespace v0
|
||||||
using v0::HardSigmoid;
|
using v0::HardSigmoid;
|
||||||
} // namespace op
|
} // namespace op
|
||||||
|
|||||||
@ -7,30 +7,12 @@
|
|||||||
#include "ngraph/node.hpp"
|
#include "ngraph/node.hpp"
|
||||||
#include "ngraph/op/op.hpp"
|
#include "ngraph/op/op.hpp"
|
||||||
#include "ngraph/op/util/unary_elementwise_arithmetic.hpp"
|
#include "ngraph/op/util/unary_elementwise_arithmetic.hpp"
|
||||||
|
#include "openvino/op/hsigmoid.hpp"
|
||||||
|
|
||||||
namespace ngraph {
|
namespace ngraph {
|
||||||
namespace op {
|
namespace op {
|
||||||
namespace v5 {
|
namespace v5 {
|
||||||
/// \brief A HSigmoid Activation Function
|
using ov::op::v5::HSigmoid;
|
||||||
/// f(x) = min(max(x + 3, 0), 6) / 6 or
|
|
||||||
/// f(x) = min(ReLU(x + 3), 6) / 6
|
|
||||||
///
|
|
||||||
class NGRAPH_API HSigmoid : public ngraph::op::util::UnaryElementwiseArithmetic {
|
|
||||||
public:
|
|
||||||
NGRAPH_RTTI_DECLARATION;
|
|
||||||
HSigmoid() = default;
|
|
||||||
|
|
||||||
/// \brief Constructs a HSigmoid operation.
|
|
||||||
///
|
|
||||||
/// \param data Input tensor
|
|
||||||
HSigmoid(const Output<Node>& arg);
|
|
||||||
|
|
||||||
bool visit_attributes(AttributeVisitor& visitor) override;
|
|
||||||
|
|
||||||
virtual std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
|
||||||
bool evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const override;
|
|
||||||
bool has_evaluate() const override;
|
|
||||||
};
|
|
||||||
} // namespace v5
|
} // namespace v5
|
||||||
} // namespace op
|
} // namespace op
|
||||||
} // namespace ngraph
|
} // namespace ngraph
|
||||||
|
|||||||
@ -7,30 +7,12 @@
|
|||||||
#include "ngraph/node.hpp"
|
#include "ngraph/node.hpp"
|
||||||
#include "ngraph/op/op.hpp"
|
#include "ngraph/op/op.hpp"
|
||||||
#include "ngraph/op/util/unary_elementwise_arithmetic.hpp"
|
#include "ngraph/op/util/unary_elementwise_arithmetic.hpp"
|
||||||
|
#include "openvino/op/hswish.hpp"
|
||||||
|
|
||||||
namespace ngraph {
|
namespace ngraph {
|
||||||
namespace op {
|
namespace op {
|
||||||
namespace v4 {
|
namespace v4 {
|
||||||
/// \brief A HSwish Activation Function
|
using ov::op::v4::HSwish;
|
||||||
/// f(x) = x * min(max(x + 3, 0), 6) / 6 or
|
|
||||||
/// f(x) = x * min(ReLU(x + 3), 6) / 6
|
|
||||||
///
|
|
||||||
class NGRAPH_API HSwish : public ngraph::op::util::UnaryElementwiseArithmetic {
|
|
||||||
public:
|
|
||||||
NGRAPH_RTTI_DECLARATION;
|
|
||||||
HSwish() = default;
|
|
||||||
|
|
||||||
/// \brief Constructs a HSwish (hard version of Swish) operation.
|
|
||||||
///
|
|
||||||
/// \param data Input tensor
|
|
||||||
HSwish(const Output<Node>& arg);
|
|
||||||
|
|
||||||
bool visit_attributes(AttributeVisitor& visitor) override;
|
|
||||||
|
|
||||||
virtual std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
|
||||||
bool evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const override;
|
|
||||||
bool has_evaluate() const override;
|
|
||||||
};
|
|
||||||
} // namespace v4
|
} // namespace v4
|
||||||
} // namespace op
|
} // namespace op
|
||||||
} // namespace ngraph
|
} // namespace ngraph
|
||||||
|
|||||||
@ -11,33 +11,12 @@
|
|||||||
#include "ngraph/op/op.hpp"
|
#include "ngraph/op/op.hpp"
|
||||||
#include "ngraph/op/util/attr_types.hpp"
|
#include "ngraph/op/util/attr_types.hpp"
|
||||||
#include "ngraph/op/util/fft_base.hpp"
|
#include "ngraph/op/util/fft_base.hpp"
|
||||||
|
#include "openvino/op/idft.hpp"
|
||||||
|
|
||||||
namespace ngraph {
|
namespace ngraph {
|
||||||
namespace op {
|
namespace op {
|
||||||
namespace v7 {
|
namespace v7 {
|
||||||
/// \brief An operation IDFT that computes the inverse discrete Fourier transformation.
|
using ov::op::v7::IDFT;
|
||||||
class NGRAPH_API IDFT : public util::FFTBase {
|
|
||||||
public:
|
|
||||||
NGRAPH_RTTI_DECLARATION;
|
|
||||||
IDFT() = default;
|
|
||||||
|
|
||||||
/// \brief Constructs a IDFT operation. IDFT is performed for full size axes.
|
|
||||||
///
|
|
||||||
/// \param data Input data
|
|
||||||
/// \param axes Axes to perform IDFT
|
|
||||||
IDFT(const Output<Node>& data, const Output<Node>& axes);
|
|
||||||
|
|
||||||
/// \brief Constructs a IDFT operation.
|
|
||||||
///
|
|
||||||
/// \param data Input data
|
|
||||||
/// \param axes Axes to perform IDFT
|
|
||||||
/// \param signal_size Signal sizes for 'axes'
|
|
||||||
IDFT(const Output<Node>& data, const Output<Node>& axes, const Output<Node>& signal_size);
|
|
||||||
|
|
||||||
bool visit_attributes(AttributeVisitor& visitor) override;
|
|
||||||
|
|
||||||
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
|
||||||
};
|
|
||||||
} // namespace v7
|
} // namespace v7
|
||||||
} // namespace op
|
} // namespace op
|
||||||
} // namespace ngraph
|
} // namespace ngraph
|
||||||
|
|||||||
@ -9,86 +9,12 @@
|
|||||||
#include "ngraph/function.hpp"
|
#include "ngraph/function.hpp"
|
||||||
#include "ngraph/op/parameter.hpp"
|
#include "ngraph/op/parameter.hpp"
|
||||||
#include "ngraph/op/util/multi_subgraph_base.hpp"
|
#include "ngraph/op/util/multi_subgraph_base.hpp"
|
||||||
|
#include "openvino/op/if.hpp"
|
||||||
|
|
||||||
namespace ngraph {
|
namespace ngraph {
|
||||||
namespace op {
|
namespace op {
|
||||||
namespace v8 {
|
namespace v8 {
|
||||||
/// \brief If operation.
|
using ov::op::v8::If;
|
||||||
class NGRAPH_API If : public util::MultiSubGraphOp {
|
|
||||||
public:
|
|
||||||
enum BodyIndexes { THEN_BODY_INDEX = 0, ELSE_BODY_INDEX = 1 };
|
|
||||||
|
|
||||||
NGRAPH_RTTI_DECLARATION;
|
|
||||||
bool visit_attributes(AttributeVisitor& visitor) override;
|
|
||||||
|
|
||||||
/// \brief Constructs If with condition
|
|
||||||
///
|
|
||||||
/// \param execution_condition condition node.
|
|
||||||
If(const Output<Node>& execution_condition);
|
|
||||||
If();
|
|
||||||
|
|
||||||
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
|
||||||
|
|
||||||
/// \brief gets then_body as ngraph::Function.
|
|
||||||
///
|
|
||||||
/// \return then_body as ngraph::Function.
|
|
||||||
const std::shared_ptr<Function>& get_then_body() const {
|
|
||||||
return m_bodies[THEN_BODY_INDEX];
|
|
||||||
}
|
|
||||||
|
|
||||||
/// \brief gets else_body as ngraph::Function.
|
|
||||||
///
|
|
||||||
/// \return else_body as ngraph::Function.
|
|
||||||
const std::shared_ptr<Function>& get_else_body() const {
|
|
||||||
return m_bodies[ELSE_BODY_INDEX];
|
|
||||||
}
|
|
||||||
|
|
||||||
/// \brief sets new ngraph::Function as new then_body.
|
|
||||||
///
|
|
||||||
/// \param body new body for 'then' branch.
|
|
||||||
void set_then_body(const std::shared_ptr<Function>& body) {
|
|
||||||
m_bodies[THEN_BODY_INDEX] = body;
|
|
||||||
}
|
|
||||||
|
|
||||||
/// \brief sets new ngraph::Function as new else_body.
|
|
||||||
///
|
|
||||||
/// \param body new body for 'else' branch.
|
|
||||||
void set_else_body(const std::shared_ptr<Function>& body) {
|
|
||||||
m_bodies[ELSE_BODY_INDEX] = body;
|
|
||||||
}
|
|
||||||
|
|
||||||
/// \brief sets new input to the operation associated with parameters
|
|
||||||
/// of each sub-graphs
|
|
||||||
///
|
|
||||||
/// \param value input to operation
|
|
||||||
/// \param then_parameter parameter for then_body or nullptr
|
|
||||||
/// \param else_parameter parameter for else_body or nullpt
|
|
||||||
void set_input(const Output<Node>& value,
|
|
||||||
const std::shared_ptr<Parameter>& then_parameter,
|
|
||||||
const std::shared_ptr<Parameter>& else_parameter);
|
|
||||||
|
|
||||||
/// \brief sets new output from the operation associated with results
|
|
||||||
/// of each sub-graphs
|
|
||||||
///
|
|
||||||
/// \param then_result result from then_body
|
|
||||||
/// \param else_parameter result from else_body
|
|
||||||
/// \return output from operation
|
|
||||||
Output<Node> set_output(const std::shared_ptr<Result>& then_result, const std::shared_ptr<Result>& else_result);
|
|
||||||
|
|
||||||
void validate_and_infer_types() override;
|
|
||||||
bool evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const override;
|
|
||||||
|
|
||||||
bool has_evaluate() const override;
|
|
||||||
|
|
||||||
private:
|
|
||||||
using OutputMap = std::map<int64_t, std::shared_ptr<MultiSubGraphOp::OutputDescription>>;
|
|
||||||
|
|
||||||
void validate_and_infer_type_body(const std::shared_ptr<Function>& body,
|
|
||||||
const ngraph::op::util::MultiSubgraphInputDescriptionVector& input_descriptors);
|
|
||||||
|
|
||||||
OutputMap get_mapping_outputs_on_body_description(
|
|
||||||
const ngraph::op::util::MultiSubgraphOutputDescriptionVector& output_descriptors);
|
|
||||||
};
|
|
||||||
} // namespace v8
|
} // namespace v8
|
||||||
} // namespace op
|
} // namespace op
|
||||||
} // namespace ngraph
|
} // namespace ngraph
|
||||||
@ -10,358 +10,18 @@
|
|||||||
#include "ngraph/attribute_adapter.hpp"
|
#include "ngraph/attribute_adapter.hpp"
|
||||||
#include "ngraph/op/op.hpp"
|
#include "ngraph/op/op.hpp"
|
||||||
#include "ngraph/op/util/attr_types.hpp"
|
#include "ngraph/op/util/attr_types.hpp"
|
||||||
|
#include "openvino/op/interpolate.hpp"
|
||||||
|
|
||||||
namespace ngraph {
|
namespace ngraph {
|
||||||
namespace op {
|
namespace op {
|
||||||
namespace v0 {
|
namespace v0 {
|
||||||
/// \brief Structure that specifies attributes for interpolation
|
using InterpolateAttrs = ov::op::v0::Interpolate::Attributes;
|
||||||
struct InterpolateAttrs {
|
using ov::op::v0::Interpolate;
|
||||||
// specify dimension indices where interpolation is applied, and `axes` is any
|
|
||||||
// unordered list of indeces of different dimensions of input tensor. Required.
|
|
||||||
AxisSet axes;
|
|
||||||
// specifies type of interpolation
|
|
||||||
// one of `nearest`, `linear`, `cubic`, `area`. Required.
|
|
||||||
std::string mode;
|
|
||||||
// a flag that specifies whether to align corners or not.
|
|
||||||
// `true` (default) means the alignment is applied,
|
|
||||||
// `false` means the alignment isn't applied.
|
|
||||||
bool align_corners = true;
|
|
||||||
// a flag that specifies whether to perform anti-aliasing. default is `false`
|
|
||||||
bool antialias = false;
|
|
||||||
// specify the number of pixels to add to the beginning of the image being
|
|
||||||
// interpolated. This addition of pixels is done before interpolation calculation.
|
|
||||||
std::vector<size_t> pads_begin;
|
|
||||||
// specify the number of pixels to add to the end of the image being interpolated.
|
|
||||||
// This addition of pixels is done before interpolation calculation.
|
|
||||||
std::vector<size_t> pads_end;
|
|
||||||
};
|
|
||||||
|
|
||||||
/// \brief Layer which performs bilinear interpolation
|
|
||||||
class NGRAPH_API Interpolate : public Op {
|
|
||||||
public:
|
|
||||||
NGRAPH_RTTI_DECLARATION;
|
|
||||||
|
|
||||||
enum class InterpolateMode {
|
|
||||||
NEAREST,
|
|
||||||
LINEAR,
|
|
||||||
CUBIC,
|
|
||||||
AREA,
|
|
||||||
nearest NGRAPH_ENUM_DEPRECATED("Please use NEAREST instead") = NEAREST,
|
|
||||||
linear NGRAPH_ENUM_DEPRECATED("Please use LINEAR instead") = LINEAR,
|
|
||||||
cubic NGRAPH_ENUM_DEPRECATED("Please use CUBIC instead") = CUBIC,
|
|
||||||
area NGRAPH_ENUM_DEPRECATED("Please use AREA instead") = AREA
|
|
||||||
};
|
|
||||||
|
|
||||||
Interpolate() = default;
|
|
||||||
/// \brief Constructs a Interpolate operation
|
|
||||||
///
|
|
||||||
/// \param image Input image
|
|
||||||
/// \param output_shape Output shape of spatial axes
|
|
||||||
/// \param attrs Interpolation attributes
|
|
||||||
Interpolate(const Output<Node>& image, const Output<Node>& output_shape, const InterpolateAttrs& attrs);
|
|
||||||
bool visit_attributes(AttributeVisitor& visitor) override;
|
|
||||||
|
|
||||||
void validate_and_infer_types() override;
|
|
||||||
|
|
||||||
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
|
||||||
|
|
||||||
const InterpolateAttrs& get_attrs() const {
|
|
||||||
return m_attrs;
|
|
||||||
}
|
|
||||||
|
|
||||||
private:
|
|
||||||
InterpolateAttrs m_attrs;
|
|
||||||
};
|
|
||||||
} // namespace v0
|
} // namespace v0
|
||||||
|
|
||||||
namespace v4 {
|
namespace v4 {
|
||||||
class NGRAPH_API Interpolate : public Op {
|
using ov::op::v4::Interpolate;
|
||||||
public:
|
|
||||||
NGRAPH_RTTI_DECLARATION;
|
|
||||||
|
|
||||||
/// \brief Shape calculation mode
|
|
||||||
///
|
|
||||||
/// sizes - output shape for interpolated axes is calculated using input `sizes`
|
|
||||||
/// scales - output shape for interpolated axes is calculated using input `scales`
|
|
||||||
enum class ShapeCalcMode {
|
|
||||||
SIZES,
|
|
||||||
SCALES,
|
|
||||||
sizes NGRAPH_ENUM_DEPRECATED("Please use SIZES instead") = SIZES,
|
|
||||||
scales NGRAPH_ENUM_DEPRECATED("Please use SCALES instead") = SCALES
|
|
||||||
};
|
|
||||||
|
|
||||||
/// \brief Interpolation mode
|
|
||||||
///
|
|
||||||
/// nearest - nearest interpolation
|
|
||||||
/// linear - linear interpolation as in TensorFlow
|
|
||||||
/// linear_onnx - linear interpolation as in ONNX
|
|
||||||
/// cubic - cubic interpolation
|
|
||||||
enum class InterpolateMode {
|
|
||||||
NEAREST,
|
|
||||||
LINEAR,
|
|
||||||
LINEAR_ONNX,
|
|
||||||
CUBIC,
|
|
||||||
nearest NGRAPH_ENUM_DEPRECATED("Please use NEAREST instead") = NEAREST,
|
|
||||||
linear NGRAPH_ENUM_DEPRECATED("Please use LINEAR instead") = LINEAR,
|
|
||||||
linear_onnx NGRAPH_ENUM_DEPRECATED("Please use LINEAR_ONNX instead") = LINEAR_ONNX,
|
|
||||||
cubic NGRAPH_ENUM_DEPRECATED("Please use CUBIC instead") = CUBIC
|
|
||||||
};
|
|
||||||
|
|
||||||
/// \brief Mode of the calculation of the source coordinate from resized one
|
|
||||||
///
|
|
||||||
/// These modes are modes from ONNX runtime.
|
|
||||||
enum class CoordinateTransformMode {
|
|
||||||
HALF_PIXEL,
|
|
||||||
PYTORCH_HALF_PIXEL,
|
|
||||||
ASYMMETRIC,
|
|
||||||
TF_HALF_PIXEL_FOR_NN,
|
|
||||||
ALIGN_CORNERS,
|
|
||||||
half_pixel NGRAPH_ENUM_DEPRECATED("Please use HALF_PIXEL instead") = HALF_PIXEL,
|
|
||||||
pytorch_half_pixel NGRAPH_ENUM_DEPRECATED("Please use PYTORCH_HALF_PIXEL instead") = PYTORCH_HALF_PIXEL,
|
|
||||||
asymmetric NGRAPH_ENUM_DEPRECATED("Please use ASYMMETRIC instead") = ASYMMETRIC,
|
|
||||||
tf_half_pixel_for_nn NGRAPH_ENUM_DEPRECATED("Please use TF_HALF_PIXEL_FOR_NN instead") = TF_HALF_PIXEL_FOR_NN,
|
|
||||||
align_corners NGRAPH_ENUM_DEPRECATED("Please use ALIGN_CORNERS instead") = ALIGN_CORNERS
|
|
||||||
};
|
|
||||||
|
|
||||||
/// \brief Round modes for the nearest interpolation.
|
|
||||||
enum class NearestMode {
|
|
||||||
ROUND_PREFER_FLOOR,
|
|
||||||
ROUND_PREFER_CEIL,
|
|
||||||
FLOOR,
|
|
||||||
CEIL,
|
|
||||||
SIMPLE,
|
|
||||||
round_prefer_floor NGRAPH_ENUM_DEPRECATED("Please use ROUND_PREFER_FLOOR instead") = ROUND_PREFER_FLOOR,
|
|
||||||
round_prefer_ceil NGRAPH_ENUM_DEPRECATED("Please use ROUND_PREFER_CEIL instead") = ROUND_PREFER_CEIL,
|
|
||||||
floor NGRAPH_ENUM_DEPRECATED("Please use FLOOR instead") = FLOOR,
|
|
||||||
ceil NGRAPH_ENUM_DEPRECATED("Please use CEIL instead") = CEIL,
|
|
||||||
simple NGRAPH_ENUM_DEPRECATED("Please use SIMPLE instead") = SIMPLE
|
|
||||||
};
|
|
||||||
|
|
||||||
struct InterpolateAttrs {
|
|
||||||
// specifies type of interpolation
|
|
||||||
// one of `nearest`, `linear`, `linear_onnx`, `cubic` Required.
|
|
||||||
InterpolateMode mode = InterpolateMode::NEAREST;
|
|
||||||
// specifies shape calculation mode
|
|
||||||
// one of `sizes`, `scales` Required
|
|
||||||
ShapeCalcMode shape_calculation_mode = ShapeCalcMode::SIZES;
|
|
||||||
// specify the number of pixels to add to the beginning of the image being
|
|
||||||
// interpolated. This addition of pixels is done before interpolation
|
|
||||||
// calculation.
|
|
||||||
std::vector<size_t> pads_begin;
|
|
||||||
// specify the number of pixels to add to the end of the image being
|
|
||||||
// interpolated. This addition of pixels is done before interpolation
|
|
||||||
// calculation.
|
|
||||||
std::vector<size_t> pads_end;
|
|
||||||
// specifies how to transform the coordinate in the resized tensor to the
|
|
||||||
// coordinate in the original tensor. one of `half_pixel`, `pytorch_half_pixel`,
|
|
||||||
// `asymmetric`, `tf_half_pixel_for_nn`, `align_corners`
|
|
||||||
CoordinateTransformMode coordinate_transformation_mode = CoordinateTransformMode::HALF_PIXEL;
|
|
||||||
// specifies round mode when `mode == nearest` and is used only when `mode ==
|
|
||||||
// nearest`. one of `round_prefer_floor`, `round_prefer_ceil`, `floor`, `ceil`,
|
|
||||||
// `simple`
|
|
||||||
NearestMode nearest_mode = NearestMode::ROUND_PREFER_FLOOR;
|
|
||||||
// a flag that specifies whether to perform anti-aliasing. default is `false`
|
|
||||||
bool antialias = false;
|
|
||||||
// specifies the parameter *a* for cubic interpolation (see, e.g.
|
|
||||||
// [article](https://ieeexplore.ieee.org/document/1163711/)). *cube_coeff* is
|
|
||||||
// used only when `mode == cubic`
|
|
||||||
double cube_coeff = -0.75f;
|
|
||||||
|
|
||||||
InterpolateAttrs() = default;
|
|
||||||
|
|
||||||
InterpolateAttrs(InterpolateMode mode,
|
|
||||||
ShapeCalcMode shape_calculation_mode,
|
|
||||||
const std::vector<size_t>& pads_begin,
|
|
||||||
const std::vector<size_t>& pads_end,
|
|
||||||
CoordinateTransformMode coordinate_transformation_mode = CoordinateTransformMode::HALF_PIXEL,
|
|
||||||
NearestMode nearest_mode = NearestMode::ROUND_PREFER_FLOOR,
|
|
||||||
bool antialias = false,
|
|
||||||
double cube_coeff = -0.75)
|
|
||||||
: mode(mode),
|
|
||||||
shape_calculation_mode(shape_calculation_mode),
|
|
||||||
pads_begin(pads_begin),
|
|
||||||
pads_end(pads_end),
|
|
||||||
coordinate_transformation_mode(coordinate_transformation_mode),
|
|
||||||
nearest_mode(nearest_mode),
|
|
||||||
antialias(antialias),
|
|
||||||
cube_coeff(cube_coeff) {}
|
|
||||||
};
|
|
||||||
|
|
||||||
Interpolate() = default;
|
|
||||||
/// \brief Constructs a Interpolate operation without 'axes' input.
|
|
||||||
///
|
|
||||||
/// \param image Input image
|
|
||||||
/// \param output_shape Output shape of spatial axes
|
|
||||||
/// \param scales Scales of spatial axes, i.e. output_shape / input_shape
|
|
||||||
/// \param attrs Interpolation attributes
|
|
||||||
Interpolate(const Output<Node>& image,
|
|
||||||
const Output<Node>& output_shape,
|
|
||||||
const Output<Node>& scales,
|
|
||||||
const InterpolateAttrs& attrs);
|
|
||||||
|
|
||||||
/// \brief Constructs a Interpolate operation with 'axes' input.
|
|
||||||
///
|
|
||||||
/// \param image Input image
|
|
||||||
/// \param output_shape Output shape of spatial axes
|
|
||||||
/// \param scales Scales of spatial axes, i.e. output_shape / input_shape
|
|
||||||
/// \param axes Interpolation axes
|
|
||||||
/// \param attrs Interpolation attributes
|
|
||||||
Interpolate(const Output<Node>& image,
|
|
||||||
const Output<Node>& output_shape,
|
|
||||||
const Output<Node>& scales,
|
|
||||||
const Output<Node>& axes,
|
|
||||||
const InterpolateAttrs& attrs);
|
|
||||||
bool visit_attributes(AttributeVisitor& visitor) override;
|
|
||||||
|
|
||||||
void validate_and_infer_types() override;
|
|
||||||
|
|
||||||
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
|
||||||
bool evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const override;
|
|
||||||
bool has_evaluate() const override;
|
|
||||||
|
|
||||||
const InterpolateAttrs& get_attrs() const {
|
|
||||||
return m_attrs;
|
|
||||||
}
|
|
||||||
|
|
||||||
protected:
|
|
||||||
/// \return The interpolation axes.
|
|
||||||
std::vector<int64_t> get_axes() const;
|
|
||||||
|
|
||||||
private:
|
|
||||||
bool evaluate_interpolate(const HostTensorVector& outputs, const HostTensorVector& inputs) const;
|
|
||||||
InterpolateAttrs m_attrs;
|
|
||||||
|
|
||||||
/// \brief Corrects pads_begin and pads_end attributes.
|
|
||||||
///
|
|
||||||
/// \details When Interpolate-4 is a result of some transformation, it is possible
|
|
||||||
/// that pads_begin.size() != pads_end.size() or
|
|
||||||
/// pads_begin.size() != input_rank. In such case, we should correct
|
|
||||||
/// pads_begin and pads_end, using padding of pads_begin and pads_end by
|
|
||||||
/// zeros or using pads_begin[0 : input_rank], pads_end[0 : input_rank].
|
|
||||||
///
|
|
||||||
/// Padding of pads_begin is performed when pads_begin.size() < input_rank,
|
|
||||||
/// and pads_begin[0 : input_rank] is used when
|
|
||||||
/// pads_begin.size() < input_rank.
|
|
||||||
///
|
|
||||||
/// Similarly for pads_end.
|
|
||||||
void correct_pads();
|
|
||||||
|
|
||||||
/// \brief Calculates input shape after padding.
|
|
||||||
///
|
|
||||||
/// \param input_shape Shape of input data.
|
|
||||||
///
|
|
||||||
/// \return Padded input shape, i.e. input_shape + pads_begin + pads_end
|
|
||||||
PartialShape get_padded_input_shape(const PartialShape& input_shape) const;
|
|
||||||
|
|
||||||
/// \brief Infers output shape using scales.
|
|
||||||
///
|
|
||||||
/// \param output_shape[in,out] output shape
|
|
||||||
/// \param axes Interpolation axes
|
|
||||||
/// \param scales Scales for interpolated axes
|
|
||||||
/// \param padded_input_shape input shape after padding
|
|
||||||
void infer_using_scales(PartialShape& output_shape,
|
|
||||||
const std::vector<int64_t>& axes,
|
|
||||||
const std::vector<float>& scales,
|
|
||||||
const PartialShape& padded_input_shape) const;
|
|
||||||
|
|
||||||
/// \brief Infers output shape using sizes.
|
|
||||||
///
|
|
||||||
/// \param output_shape[in,out] output shape
|
|
||||||
/// \param axes Interpolation axes
|
|
||||||
/// \param sizes sizes for interpolated axes
|
|
||||||
void infer_using_shapes(PartialShape& output_shape,
|
|
||||||
const std::vector<int64_t>& axes,
|
|
||||||
const std::vector<int64_t>& sizes) const;
|
|
||||||
};
|
|
||||||
} // namespace v4
|
} // namespace v4
|
||||||
|
|
||||||
using v0::Interpolate;
|
using v0::Interpolate;
|
||||||
using v0::InterpolateAttrs;
|
using v0::InterpolateAttrs;
|
||||||
} // namespace op
|
} // namespace op
|
||||||
|
|
||||||
//---------------------------------------- v0 --------------------------------------------------
|
|
||||||
NGRAPH_API
|
|
||||||
std::ostream& operator<<(std::ostream& s, const op::v0::Interpolate::InterpolateMode& type);
|
|
||||||
|
|
||||||
//---------------------------------------- v4 --------------------------------------------------
|
|
||||||
|
|
||||||
NGRAPH_API
|
|
||||||
std::ostream& operator<<(std::ostream& s, const op::v4::Interpolate::InterpolateMode& type);
|
|
||||||
|
|
||||||
NGRAPH_API
|
|
||||||
std::ostream& operator<<(std::ostream& s, const op::v4::Interpolate::CoordinateTransformMode& type);
|
|
||||||
|
|
||||||
NGRAPH_API
|
|
||||||
std::ostream& operator<<(std::ostream& s, const op::v4::Interpolate::NearestMode& type);
|
|
||||||
|
|
||||||
NGRAPH_API
|
|
||||||
std::ostream& operator<<(std::ostream& s, const op::v4::Interpolate::ShapeCalcMode& type);
|
|
||||||
|
|
||||||
} // namespace ngraph
|
} // namespace ngraph
|
||||||
|
|
||||||
namespace ov {
|
|
||||||
|
|
||||||
template <>
|
|
||||||
class NGRAPH_API AttributeAdapter<ngraph::op::v0::Interpolate::InterpolateMode>
|
|
||||||
: public EnumAttributeAdapterBase<ngraph::op::v0::Interpolate::InterpolateMode> {
|
|
||||||
public:
|
|
||||||
AttributeAdapter(ngraph::op::v0::Interpolate::InterpolateMode& value)
|
|
||||||
: EnumAttributeAdapterBase<ngraph::op::v0::Interpolate::InterpolateMode>(value) {}
|
|
||||||
|
|
||||||
static constexpr DiscreteTypeInfo type_info{"AttributeAdapter<op::v0::Interpolate::InterpolateMode>", 0};
|
|
||||||
const DiscreteTypeInfo& get_type_info() const override {
|
|
||||||
return type_info;
|
|
||||||
}
|
|
||||||
};
|
|
||||||
template <>
|
|
||||||
class NGRAPH_API AttributeAdapter<ngraph::op::v4::Interpolate::InterpolateMode>
|
|
||||||
: public EnumAttributeAdapterBase<ngraph::op::v4::Interpolate::InterpolateMode> {
|
|
||||||
public:
|
|
||||||
AttributeAdapter(ngraph::op::v4::Interpolate::InterpolateMode& value)
|
|
||||||
: EnumAttributeAdapterBase<ngraph::op::v4::Interpolate::InterpolateMode>(value) {}
|
|
||||||
|
|
||||||
static constexpr DiscreteTypeInfo type_info{"AttributeAdapter<op::v4::Interpolate::InterpolateMode>", 4};
|
|
||||||
const DiscreteTypeInfo& get_type_info() const override {
|
|
||||||
return type_info;
|
|
||||||
}
|
|
||||||
};
|
|
||||||
|
|
||||||
template <>
|
|
||||||
class NGRAPH_API AttributeAdapter<ngraph::op::v4::Interpolate::CoordinateTransformMode>
|
|
||||||
: public EnumAttributeAdapterBase<ngraph::op::v4::Interpolate::CoordinateTransformMode> {
|
|
||||||
public:
|
|
||||||
AttributeAdapter(ngraph::op::v4::Interpolate::CoordinateTransformMode& value)
|
|
||||||
: EnumAttributeAdapterBase<ngraph::op::v4::Interpolate::CoordinateTransformMode>(value) {}
|
|
||||||
|
|
||||||
static constexpr DiscreteTypeInfo type_info{"AttributeAdapter<op::v4::Interpolate::CoordinateTransformMode>", 4};
|
|
||||||
const DiscreteTypeInfo& get_type_info() const override {
|
|
||||||
return type_info;
|
|
||||||
}
|
|
||||||
};
|
|
||||||
|
|
||||||
template <>
|
|
||||||
class NGRAPH_API AttributeAdapter<ngraph::op::v4::Interpolate::NearestMode>
|
|
||||||
: public EnumAttributeAdapterBase<ngraph::op::v4::Interpolate::NearestMode> {
|
|
||||||
public:
|
|
||||||
AttributeAdapter(ngraph::op::v4::Interpolate::NearestMode& value)
|
|
||||||
: EnumAttributeAdapterBase<ngraph::op::v4::Interpolate::NearestMode>(value) {}
|
|
||||||
|
|
||||||
static constexpr DiscreteTypeInfo type_info{"AttributeAdapter<op::v4::Interpolate::NearestMode>", 4};
|
|
||||||
const DiscreteTypeInfo& get_type_info() const override {
|
|
||||||
return type_info;
|
|
||||||
}
|
|
||||||
};
|
|
||||||
|
|
||||||
template <>
|
|
||||||
class NGRAPH_API AttributeAdapter<ngraph::op::v4::Interpolate::ShapeCalcMode>
|
|
||||||
: public EnumAttributeAdapterBase<ngraph::op::v4::Interpolate::ShapeCalcMode> {
|
|
||||||
public:
|
|
||||||
AttributeAdapter(ngraph::op::v4::Interpolate::ShapeCalcMode& value)
|
|
||||||
: EnumAttributeAdapterBase<ngraph::op::v4::Interpolate::ShapeCalcMode>(value) {}
|
|
||||||
|
|
||||||
static constexpr DiscreteTypeInfo type_info{"AttributeAdapter<op::v4::Interpolate::ShapeCalcMode>", 4};
|
|
||||||
const DiscreteTypeInfo& get_type_info() const override {
|
|
||||||
return type_info;
|
|
||||||
}
|
|
||||||
};
|
|
||||||
} // namespace ov
|
|
||||||
|
|||||||
@ -5,29 +5,12 @@
|
|||||||
#pragma once
|
#pragma once
|
||||||
|
|
||||||
#include "ngraph/op/util/binary_elementwise_comparison.hpp"
|
#include "ngraph/op/util/binary_elementwise_comparison.hpp"
|
||||||
|
#include "openvino/op/less.hpp"
|
||||||
|
|
||||||
namespace ngraph {
|
namespace ngraph {
|
||||||
namespace op {
|
namespace op {
|
||||||
namespace v1 {
|
namespace v1 {
|
||||||
/// \brief Elementwise less-than operation.
|
using ov::op::v1::Less;
|
||||||
class NGRAPH_API Less : public util::BinaryElementwiseComparison {
|
|
||||||
public:
|
|
||||||
NGRAPH_RTTI_DECLARATION;
|
|
||||||
/// \brief Constructs a less-than operation.
|
|
||||||
Less() : util::BinaryElementwiseComparison(AutoBroadcastSpec::NUMPY) {}
|
|
||||||
/// \brief Constructs a less-than operation.
|
|
||||||
///
|
|
||||||
/// \param arg0 Node that produces the first input tensor.
|
|
||||||
/// \param arg1 Node that produces the second input tensor.
|
|
||||||
/// \param auto_broadcast Auto broadcast specification
|
|
||||||
Less(const Output<Node>& arg0,
|
|
||||||
const Output<Node>& arg1,
|
|
||||||
const AutoBroadcastSpec& auto_broadcast = AutoBroadcastSpec(AutoBroadcastType::NUMPY));
|
|
||||||
|
|
||||||
virtual std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
|
||||||
bool evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const override;
|
|
||||||
bool has_evaluate() const override;
|
|
||||||
};
|
|
||||||
} // namespace v1
|
} // namespace v1
|
||||||
} // namespace op
|
} // namespace op
|
||||||
} // namespace ngraph
|
} // namespace ngraph
|
||||||
|
|||||||
@ -5,30 +5,12 @@
|
|||||||
#pragma once
|
#pragma once
|
||||||
|
|
||||||
#include "ngraph/op/util/binary_elementwise_comparison.hpp"
|
#include "ngraph/op/util/binary_elementwise_comparison.hpp"
|
||||||
|
#include "openvino/op/less_eq.hpp"
|
||||||
|
|
||||||
namespace ngraph {
|
namespace ngraph {
|
||||||
namespace op {
|
namespace op {
|
||||||
namespace v1 {
|
namespace v1 {
|
||||||
/// \brief Elementwise less-than-or-equal operation.
|
using ov::op::v1::LessEqual;
|
||||||
class NGRAPH_API LessEqual : public util::BinaryElementwiseComparison {
|
|
||||||
public:
|
|
||||||
NGRAPH_RTTI_DECLARATION;
|
|
||||||
/// \brief Constructs a less-than-or-equal operation.
|
|
||||||
LessEqual() : util::BinaryElementwiseComparison(AutoBroadcastSpec::NUMPY) {}
|
|
||||||
|
|
||||||
/// \brief Constructs a less-than-or-equal operation.
|
|
||||||
///
|
|
||||||
/// \param arg0 Node that produces the first input tensor.
|
|
||||||
/// \param arg1 Node that produces the second input tensor.
|
|
||||||
/// \param auto_broadcast Auto broadcast specification
|
|
||||||
LessEqual(const Output<Node>& arg0,
|
|
||||||
const Output<Node>& arg1,
|
|
||||||
const AutoBroadcastSpec& auto_broadcast = AutoBroadcastSpec(AutoBroadcastType::NUMPY));
|
|
||||||
|
|
||||||
virtual std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
|
||||||
bool evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const override;
|
|
||||||
bool has_evaluate() const override;
|
|
||||||
};
|
|
||||||
} // namespace v1
|
} // namespace v1
|
||||||
} // namespace op
|
} // namespace op
|
||||||
} // namespace ngraph
|
} // namespace ngraph
|
||||||
|
|||||||
@ -5,26 +5,12 @@
|
|||||||
#pragma once
|
#pragma once
|
||||||
|
|
||||||
#include "ngraph/op/util/unary_elementwise_arithmetic.hpp"
|
#include "ngraph/op/util/unary_elementwise_arithmetic.hpp"
|
||||||
|
#include "openvino/op/log.hpp"
|
||||||
|
|
||||||
namespace ngraph {
|
namespace ngraph {
|
||||||
namespace op {
|
namespace op {
|
||||||
namespace v0 {
|
namespace v0 {
|
||||||
/// \brief Elementwise natural log operation.
|
using ov::op::v0::Log;
|
||||||
class NGRAPH_API Log : public util::UnaryElementwiseArithmetic {
|
|
||||||
public:
|
|
||||||
NGRAPH_RTTI_DECLARATION;
|
|
||||||
/// \brief Constructs a natural log operation.
|
|
||||||
Log() = default;
|
|
||||||
/// \brief Constructs a natural log operation.
|
|
||||||
///
|
|
||||||
/// \param arg Node that produces the input tensor.
|
|
||||||
Log(const Output<Node>& arg);
|
|
||||||
|
|
||||||
bool visit_attributes(AttributeVisitor& visitor) override;
|
|
||||||
virtual std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
|
||||||
bool evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const override;
|
|
||||||
bool has_evaluate() const override;
|
|
||||||
};
|
|
||||||
} // namespace v0
|
} // namespace v0
|
||||||
using v0::Log;
|
using v0::Log;
|
||||||
} // namespace op
|
} // namespace op
|
||||||
|
|||||||
@ -5,39 +5,12 @@
|
|||||||
#pragma once
|
#pragma once
|
||||||
|
|
||||||
#include "ngraph/op/op.hpp"
|
#include "ngraph/op/op.hpp"
|
||||||
|
#include "openvino/op/log_softmax.hpp"
|
||||||
|
|
||||||
namespace ngraph {
|
namespace ngraph {
|
||||||
namespace op {
|
namespace op {
|
||||||
namespace v5 {
|
namespace v5 {
|
||||||
class NGRAPH_API LogSoftmax : public Op {
|
using ov::op::v5::LogSoftmax;
|
||||||
public:
|
|
||||||
NGRAPH_RTTI_DECLARATION;
|
|
||||||
LogSoftmax() = default;
|
|
||||||
/// \brief Constructs a LogSoftmax operation.
|
|
||||||
///
|
|
||||||
/// \param arg Node that produces the first input tensor.<br>
|
|
||||||
/// `[d0, ...]`
|
|
||||||
/// \param axis The axis position (0-based) on which to calculate the LogSoftmax.
|
|
||||||
///
|
|
||||||
/// Output `[d0, ...]`
|
|
||||||
///
|
|
||||||
LogSoftmax(const Output<Node>& arg, const int64_t axis);
|
|
||||||
|
|
||||||
bool visit_attributes(AttributeVisitor& visitor) override;
|
|
||||||
void validate_and_infer_types() override;
|
|
||||||
|
|
||||||
virtual std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
|
||||||
|
|
||||||
int64_t get_axis() const {
|
|
||||||
return m_axis;
|
|
||||||
}
|
|
||||||
void set_axis(const int64_t axis) {
|
|
||||||
m_axis = axis;
|
|
||||||
}
|
|
||||||
|
|
||||||
private:
|
|
||||||
int64_t m_axis = 1;
|
|
||||||
};
|
|
||||||
} // namespace v5
|
} // namespace v5
|
||||||
} // namespace op
|
} // namespace op
|
||||||
} // namespace ngraph
|
} // namespace ngraph
|
||||||
|
|||||||
@ -12,84 +12,12 @@
|
|||||||
#include "ngraph/op/parameter.hpp"
|
#include "ngraph/op/parameter.hpp"
|
||||||
#include "ngraph/op/tensor_iterator.hpp"
|
#include "ngraph/op/tensor_iterator.hpp"
|
||||||
#include "ngraph/op/util/sub_graph_base.hpp"
|
#include "ngraph/op/util/sub_graph_base.hpp"
|
||||||
|
#include "openvino/op/loop.hpp"
|
||||||
|
|
||||||
namespace ngraph {
|
namespace ngraph {
|
||||||
namespace op {
|
namespace op {
|
||||||
namespace v5 {
|
namespace v5 {
|
||||||
/// \brief Iterate a body over tensors, accumulating into tensors.
|
using ov::op::v5::Loop;
|
||||||
class NGRAPH_API Loop : public op::util::SubGraphOp {
|
|
||||||
public:
|
|
||||||
/// \brief Allows to define the purpose of inputs/outputs in the body
|
|
||||||
struct SpecialBodyPorts {
|
|
||||||
SpecialBodyPorts() = default;
|
|
||||||
SpecialBodyPorts(int64_t in_current_iteration_input_idx, int64_t in_body_condition_output_idx)
|
|
||||||
: current_iteration_input_idx(in_current_iteration_input_idx),
|
|
||||||
body_condition_output_idx(in_body_condition_output_idx) {}
|
|
||||||
// -1 means the input is not provided, this input is optional
|
|
||||||
int64_t current_iteration_input_idx = -1;
|
|
||||||
// -1 means the output is not provided,
|
|
||||||
// this output is required, throw an exception if not provided
|
|
||||||
int64_t body_condition_output_idx = -1;
|
|
||||||
};
|
|
||||||
|
|
||||||
NGRAPH_RTTI_DECLARATION;
|
|
||||||
|
|
||||||
/// \brief Constructs a Loop operation.
|
|
||||||
Loop() = default;
|
|
||||||
|
|
||||||
/// \brief Constructs a Loop operation.
|
|
||||||
///
|
|
||||||
/// \param trip_count Node specifies the maximum number of iterations.
|
|
||||||
/// \param execution_condition Node determines whether to execute the first
|
|
||||||
/// iteration or not.
|
|
||||||
Loop(const Output<Node>& trip_count, const Output<Node>& execution_condition);
|
|
||||||
|
|
||||||
Output<Node> get_concatenated_slices(const Output<Node>& value,
|
|
||||||
int64_t start,
|
|
||||||
int64_t stride,
|
|
||||||
int64_t part_size,
|
|
||||||
int64_t end,
|
|
||||||
int64_t axis) override;
|
|
||||||
|
|
||||||
void set_special_body_ports(const SpecialBodyPorts& special_body_ports) {
|
|
||||||
m_special_body_ports = special_body_ports;
|
|
||||||
}
|
|
||||||
|
|
||||||
SpecialBodyPorts get_special_body_ports() const {
|
|
||||||
return m_special_body_ports;
|
|
||||||
}
|
|
||||||
void validate_and_infer_types() override;
|
|
||||||
bool visit_attributes(AttributeVisitor& visitor) override;
|
|
||||||
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
|
||||||
|
|
||||||
bool evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const override;
|
|
||||||
bool has_evaluate() const override;
|
|
||||||
|
|
||||||
protected:
|
|
||||||
Loop(const Loop&);
|
|
||||||
|
|
||||||
private:
|
|
||||||
void clone_to(Loop& dst, const OutputVector& new_args) const;
|
|
||||||
|
|
||||||
SpecialBodyPorts m_special_body_ports;
|
|
||||||
};
|
|
||||||
} // namespace v5
|
} // namespace v5
|
||||||
} // namespace op
|
} // namespace op
|
||||||
} // namespace ngraph
|
} // namespace ngraph
|
||||||
|
|
||||||
namespace ov {
|
|
||||||
|
|
||||||
template <>
|
|
||||||
class NGRAPH_API AttributeAdapter<ngraph::op::v5::Loop::SpecialBodyPorts>
|
|
||||||
: public DirectValueAccessor<ngraph::op::v5::Loop::SpecialBodyPorts> {
|
|
||||||
public:
|
|
||||||
AttributeAdapter(ngraph::op::v5::Loop::SpecialBodyPorts& value)
|
|
||||||
: DirectValueAccessor<ngraph::op::v5::Loop::SpecialBodyPorts>(value) {}
|
|
||||||
|
|
||||||
static constexpr DiscreteTypeInfo type_info{"AttributeAdapter<op::v5::Loop::SpecialBodyPorts>", 0};
|
|
||||||
const DiscreteTypeInfo& get_type_info() const override {
|
|
||||||
return type_info;
|
|
||||||
}
|
|
||||||
};
|
|
||||||
|
|
||||||
} // namespace ov
|
|
||||||
|
|||||||
@ -5,74 +5,12 @@
|
|||||||
#pragma once
|
#pragma once
|
||||||
|
|
||||||
#include "ngraph/op/op.hpp"
|
#include "ngraph/op/op.hpp"
|
||||||
|
#include "openvino/op/lrn.hpp"
|
||||||
|
|
||||||
namespace ngraph {
|
namespace ngraph {
|
||||||
namespace op {
|
namespace op {
|
||||||
namespace v0 {
|
namespace v0 {
|
||||||
// clang-format off
|
using ov::op::v0::LRN;
|
||||||
/// \brief Elementwise Local Response Normalization (LRN) operation.
|
|
||||||
///
|
|
||||||
/// ## Inputs
|
|
||||||
///
|
|
||||||
/// | | Type | Description |
|
|
||||||
/// | ----- | --------------------------------------- | ----------------------------------------------- |
|
|
||||||
/// | `arg` | \f$N[n, c, d_1,\dots,d_n]~(n \geq 0)\f$ | A tensor of any shape and numeric element type. |
|
|
||||||
///
|
|
||||||
/// ## Output
|
|
||||||
///
|
|
||||||
/// | Type | Description |
|
|
||||||
/// | ---------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
|
||||||
/// | \f$N[n, c, d_1,\dots,d_n]\f$ | The tensor \f$T\f$, where \f$T[n, c, d_1,\dots,d_n] = \frac{N[n,i,d_1,\dots,d_n]}{ (bias + alpha * (\sum_{i=max(0,(nsize-1)/2)}^{min(C, (nsize-1)/2)+1} N[n,i,d_1,\dots,d_n]^{2}) ^ {2})}\f$ |
|
|
||||||
// clang-format on
|
|
||||||
class NGRAPH_API LRN : public Op {
|
|
||||||
public:
|
|
||||||
NGRAPH_RTTI_DECLARATION;
|
|
||||||
|
|
||||||
/// \brief Constructs a LRN operation.
|
|
||||||
LRN() = default;
|
|
||||||
/// \brief Constructs a LRN operation.
|
|
||||||
///
|
|
||||||
/// \param arg Node that produces the input tensor.
|
|
||||||
LRN(const Output<Node>& arg, double alpha, double beta, double bias, size_t size);
|
|
||||||
|
|
||||||
LRN(const Output<Node>& arg, const Output<Node>& axes, double alpha, double beta, double bias, size_t size);
|
|
||||||
|
|
||||||
bool visit_attributes(AttributeVisitor& visitor) override;
|
|
||||||
virtual std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
|
||||||
void validate_and_infer_types() override;
|
|
||||||
|
|
||||||
double get_alpha() const {
|
|
||||||
return m_alpha;
|
|
||||||
}
|
|
||||||
void set_alpha(double alpha) {
|
|
||||||
m_alpha = alpha;
|
|
||||||
}
|
|
||||||
double get_beta() const {
|
|
||||||
return m_beta;
|
|
||||||
}
|
|
||||||
void set_beta(double beta) {
|
|
||||||
m_beta = beta;
|
|
||||||
}
|
|
||||||
double get_bias() const {
|
|
||||||
return m_bias;
|
|
||||||
}
|
|
||||||
void set_bias(double bias) {
|
|
||||||
m_bias = bias;
|
|
||||||
}
|
|
||||||
size_t get_nsize() const {
|
|
||||||
return m_size;
|
|
||||||
}
|
|
||||||
void set_nsize(size_t size) {
|
|
||||||
m_size = size;
|
|
||||||
}
|
|
||||||
AxisSet get_reduction_axes() const;
|
|
||||||
|
|
||||||
protected:
|
|
||||||
double m_alpha;
|
|
||||||
double m_beta;
|
|
||||||
double m_bias;
|
|
||||||
size_t m_size;
|
|
||||||
};
|
|
||||||
} // namespace v0
|
} // namespace v0
|
||||||
using v0::LRN;
|
using v0::LRN;
|
||||||
} // namespace op
|
} // namespace op
|
||||||
|
|||||||
@ -13,391 +13,18 @@
|
|||||||
#include "ngraph/op/op.hpp"
|
#include "ngraph/op/op.hpp"
|
||||||
#include "ngraph/op/util/activation_functions.hpp"
|
#include "ngraph/op/util/activation_functions.hpp"
|
||||||
#include "ngraph/op/util/rnn_cell_base.hpp"
|
#include "ngraph/op/util/rnn_cell_base.hpp"
|
||||||
|
#include "openvino/op/lstm_cell.hpp"
|
||||||
|
|
||||||
namespace ngraph {
|
namespace ngraph {
|
||||||
namespace op {
|
namespace op {
|
||||||
enum class LSTMWeightsFormat {
|
using ov::op::LSTMWeightsFormat;
|
||||||
FICO, // IE
|
|
||||||
ICOF, // PyTorch
|
|
||||||
IFCO, // DNNL, TF, MxNet
|
|
||||||
IFOC, // Caffe
|
|
||||||
IOFC, // ONNX
|
|
||||||
};
|
|
||||||
|
|
||||||
namespace v0 {
|
namespace v0 {
|
||||||
///
|
using ov::op::v0::LSTMCell;
|
||||||
/// \brief Class for single lstm cell node.
|
|
||||||
///
|
|
||||||
/// \note Following implementation supports:
|
|
||||||
/// \li \c peepholes Gers & Schmidhuber (2000)
|
|
||||||
/// https://ieeexplore.ieee.org/document/861302
|
|
||||||
/// \li Coupling input and forget gates.
|
|
||||||
///
|
|
||||||
/// \note It calculates following equations:
|
|
||||||
///
|
|
||||||
/// it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Pi (.) Ct-1 + Wbi + Rbi)
|
|
||||||
/// ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Pf (.) Ct-1 + Wbf + Rbf)
|
|
||||||
/// ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc)
|
|
||||||
/// Ct = ft (.) Ct-1 + it (.) ct
|
|
||||||
/// ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Po (.) Ct + Wbo + Rbo)
|
|
||||||
/// Ht = ot (.) h(Ct)
|
|
||||||
///
|
|
||||||
/// * - Is a dot product,
|
|
||||||
/// (.) - is a Hadamard product (element-wise),
|
|
||||||
/// f, g, h - are activation functions.
|
|
||||||
///
|
|
||||||
/// \note This class represents only single *cell* (for current time step) and not
|
|
||||||
/// the whole LSTM Sequence layer
|
|
||||||
///
|
|
||||||
/// \sa LSTMSequence, RNNCell, GRUCell
|
|
||||||
///
|
|
||||||
class NGRAPH_API LSTMCell : public util::RNNCellBase {
|
|
||||||
public:
|
|
||||||
NGRAPH_RTTI_DECLARATION;
|
|
||||||
|
|
||||||
LSTMCell();
|
|
||||||
///
|
|
||||||
/// \brief Constructs LSTMCell node.
|
|
||||||
///
|
|
||||||
/// \param[in] X The input tensor with shape: [batch_size,
|
|
||||||
/// input_size].
|
|
||||||
/// \param[in] initial_hidden_state The hidden state tensor at current time step
|
|
||||||
/// with shape: [batch_size, hidden_size].
|
|
||||||
/// \param[in] initial_cell_state The cell state tensor at current time step
|
|
||||||
/// with shape: [batch_size, hidden_size].
|
|
||||||
/// \param[in] W The gate weights tensor with shape:
|
|
||||||
/// [4*hidden_size, input_size].
|
|
||||||
/// \param[in] R The recurrence weights tensor with shape:
|
|
||||||
/// [4*hidden_size, hidden_size].
|
|
||||||
/// \param[in] hidden_size The number of hidden units for recurrent cell.
|
|
||||||
/// \param[in] weights_format The order of gates in weights tensors. The
|
|
||||||
/// default format is IFCO since it is used by
|
|
||||||
/// DNNL.
|
|
||||||
/// \param[in] activations The vector of activation functions used inside
|
|
||||||
/// recurrent cell.
|
|
||||||
/// \param[in] activations_alpha The vector of alpha parameters for activation
|
|
||||||
/// functions in order respective to activation
|
|
||||||
/// list.
|
|
||||||
/// \param[in] activations_beta The vector of beta parameters for activation
|
|
||||||
/// functions in order respective to activation
|
|
||||||
/// list.
|
|
||||||
/// \param[in] clip The value defining clipping range [-clip,
|
|
||||||
/// clip] on input of activation functions.
|
|
||||||
/// \param[in] input_forget Controls coupling input and forget gates.
|
|
||||||
///
|
|
||||||
LSTMCell(const Output<Node>& X,
|
|
||||||
const Output<Node>& initial_hidden_state,
|
|
||||||
const Output<Node>& initial_cell_state,
|
|
||||||
const Output<Node>& W,
|
|
||||||
const Output<Node>& R,
|
|
||||||
std::size_t hidden_size,
|
|
||||||
LSTMWeightsFormat weights_format = LSTMWeightsFormat::IFCO,
|
|
||||||
const std::vector<std::string>& activations = std::vector<std::string>{"sigmoid", "tanh", "tanh"},
|
|
||||||
const std::vector<float>& activations_alpha = {},
|
|
||||||
const std::vector<float>& activations_beta = {},
|
|
||||||
float clip = 0.f,
|
|
||||||
bool input_forget = false);
|
|
||||||
|
|
||||||
///
|
|
||||||
/// \brief Constructs LSTMCell node.
|
|
||||||
///
|
|
||||||
/// \param[in] X The input tensor with shape: [batch_size,
|
|
||||||
/// input_size].
|
|
||||||
/// \param[in] initial_hidden_state The hidden state tensor at current time step
|
|
||||||
/// with shape: [batch_size, hidden_size].
|
|
||||||
/// \param[in] initial_cell_state The cell state tensor at current time step
|
|
||||||
/// with shape: [batch_size, hidden_size].
|
|
||||||
/// \param[in] W The weight tensor with shape: [4*hidden_size,
|
|
||||||
/// input_size].
|
|
||||||
/// \param[in] R The recurrence weight tensor with shape:
|
|
||||||
/// [4*hidden_size, hidden_size].
|
|
||||||
/// \param[in] B The bias tensor for gates with shape:
|
|
||||||
/// [4*hidden_size].
|
|
||||||
/// \param[in] hidden_size The number of hidden units for recurrent cell.
|
|
||||||
/// \param[in] weights_format The order of gates in weights tensors. The
|
|
||||||
/// default format is IFCO since it is used by
|
|
||||||
/// DNNL.
|
|
||||||
/// \param[in] activations The vector of activation functions used inside
|
|
||||||
/// recurrent cell.
|
|
||||||
/// \param[in] activations_alpha The vector of alpha parameters for activation
|
|
||||||
/// functions in order respective to activation
|
|
||||||
/// list.
|
|
||||||
/// \param[in] activations_beta The vector of beta parameters for activation
|
|
||||||
/// functions in order respective to activation
|
|
||||||
/// list.
|
|
||||||
/// \param[in] clip The value defining clipping range [-clip,
|
|
||||||
/// clip] on input of activation functions.
|
|
||||||
/// \param[in] input_forget Controls coupling input and forget gates.
|
|
||||||
///
|
|
||||||
LSTMCell(const Output<Node>& X,
|
|
||||||
const Output<Node>& initial_hidden_state,
|
|
||||||
const Output<Node>& initial_cell_state,
|
|
||||||
const Output<Node>& W,
|
|
||||||
const Output<Node>& R,
|
|
||||||
const Output<Node>& B,
|
|
||||||
std::size_t hidden_size,
|
|
||||||
LSTMWeightsFormat weights_format = LSTMWeightsFormat::IFCO,
|
|
||||||
const std::vector<std::string>& activations = std::vector<std::string>{"sigmoid", "tanh", "tanh"},
|
|
||||||
const std::vector<float>& activations_alpha = {},
|
|
||||||
const std::vector<float>& activations_beta = {},
|
|
||||||
float clip = 0.f,
|
|
||||||
bool input_forget = false);
|
|
||||||
|
|
||||||
///
|
|
||||||
/// \brief Constructs LSTMCell node.
|
|
||||||
///
|
|
||||||
/// \param[in] X The input tensor with shape: [batch_size,
|
|
||||||
/// input_size].
|
|
||||||
/// \param[in] initial_hidden_state The hidden state tensor at current time step
|
|
||||||
/// with shape: [batch_size, hidden_size].
|
|
||||||
/// \param[in] initial_cell_state The cell state tensor at current time step
|
|
||||||
/// with shape: [batch_size, hidden_size].
|
|
||||||
/// \param[in] W The weight tensor with shape: [4*hidden_size,
|
|
||||||
/// input_size].
|
|
||||||
/// \param[in] R The recurrence weight tensor with shape:
|
|
||||||
/// [4*hidden_size, hidden_size].
|
|
||||||
/// \param[in] B The bias tensor for gates with shape:
|
|
||||||
/// [4*hidden_size].
|
|
||||||
/// \param[in] P The weight tensor for peepholes with shape:
|
|
||||||
/// [3*hidden_size] - 3 equals to only iof gates.
|
|
||||||
/// The order is: input, output, forget gates.
|
|
||||||
/// \param[in] hidden_size The number of hidden units for recurrent cell.
|
|
||||||
/// \param[in] weights_format The order of gates in weights tensors. The
|
|
||||||
/// default format is IFCO since it is used by
|
|
||||||
/// DNNL.
|
|
||||||
/// \param[in] activations The vector of activation functions used inside
|
|
||||||
/// recurrent cell.
|
|
||||||
/// \param[in] activations_alpha The vector of alpha parameters for activation
|
|
||||||
/// functions in order respective to activation
|
|
||||||
/// list.
|
|
||||||
/// \param[in] activations_beta The vector of beta parameters for activation
|
|
||||||
/// functions in order respective to activation
|
|
||||||
/// list.
|
|
||||||
/// \param[in] clip The value defining clipping range [-clip,
|
|
||||||
/// clip] on input of activation functions.
|
|
||||||
/// \param[in] input_forget Controls coupling input and forget gates.
|
|
||||||
///
|
|
||||||
LSTMCell(const Output<Node>& X,
|
|
||||||
const Output<Node>& initial_hidden_state,
|
|
||||||
const Output<Node>& initial_cell_state,
|
|
||||||
const Output<Node>& W,
|
|
||||||
const Output<Node>& R,
|
|
||||||
const Output<Node>& B,
|
|
||||||
const Output<Node>& P,
|
|
||||||
std::size_t hidden_size,
|
|
||||||
LSTMWeightsFormat weights_format = LSTMWeightsFormat::IFCO,
|
|
||||||
const std::vector<std::string>& activations = std::vector<std::string>{"sigmoid", "tanh", "tanh"},
|
|
||||||
const std::vector<float>& activations_alpha = {},
|
|
||||||
const std::vector<float>& activations_beta = {},
|
|
||||||
float clip = 0.f,
|
|
||||||
bool input_forget = false);
|
|
||||||
|
|
||||||
void validate_and_infer_types() override;
|
|
||||||
bool visit_attributes(AttributeVisitor& visitor) override;
|
|
||||||
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
|
||||||
|
|
||||||
bool get_input_forget() const {
|
|
||||||
return m_input_forget;
|
|
||||||
}
|
|
||||||
LSTMWeightsFormat get_weights_format() const {
|
|
||||||
return m_weights_format;
|
|
||||||
}
|
|
||||||
|
|
||||||
private:
|
|
||||||
///
|
|
||||||
/// \brief Creates the default bias input initialized with zeros.
|
|
||||||
///
|
|
||||||
/// \return The object of Output class.
|
|
||||||
///
|
|
||||||
Output<Node> get_default_bias_input() const;
|
|
||||||
|
|
||||||
///
|
|
||||||
/// \brief Creates the default peepholes input initialized with zeros.
|
|
||||||
///
|
|
||||||
/// \return The object of Output class.
|
|
||||||
///
|
|
||||||
Output<Node> get_default_peepholes_input() const;
|
|
||||||
///
|
|
||||||
/// \brief The Activation function f.
|
|
||||||
///
|
|
||||||
util::ActivationFunction m_activation_f;
|
|
||||||
///
|
|
||||||
/// \brief The Activation function g.
|
|
||||||
///
|
|
||||||
util::ActivationFunction m_activation_g;
|
|
||||||
///
|
|
||||||
/// \brief The Activation function h.
|
|
||||||
///
|
|
||||||
util::ActivationFunction m_activation_h;
|
|
||||||
///
|
|
||||||
/// \brief Controls whether to couple input and forget gates.
|
|
||||||
///
|
|
||||||
bool m_input_forget = false;
|
|
||||||
|
|
||||||
///
|
|
||||||
/// \brief The order of gates in weights tensors.
|
|
||||||
///
|
|
||||||
LSTMWeightsFormat m_weights_format;
|
|
||||||
|
|
||||||
static constexpr std::size_t s_gates_count{4};
|
|
||||||
static constexpr std::size_t s_peepholes_count{3};
|
|
||||||
};
|
|
||||||
} // namespace v0
|
} // namespace v0
|
||||||
|
|
||||||
namespace v4 {
|
namespace v4 {
|
||||||
///
|
using ov::op::v4::LSTMCell;
|
||||||
/// \brief Class for single lstm cell node.
|
|
||||||
///
|
|
||||||
/// \note Following implementation supports:
|
|
||||||
/// \li \c peepholes Gers & Schmidhuber (2000)
|
|
||||||
/// https://ieeexplore.ieee.org/document/861302
|
|
||||||
/// \li Coupling input and forget gates.
|
|
||||||
///
|
|
||||||
/// \note It calculates following equations:
|
|
||||||
///
|
|
||||||
/// it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Wbi + Rbi)
|
|
||||||
/// ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Wbf + Rbf)
|
|
||||||
/// ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc)
|
|
||||||
/// Ct = ft (.) Ct-1 + it (.) ct
|
|
||||||
/// ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Wbo + Rbo)
|
|
||||||
/// Ht = ot (.) h(Ct)
|
|
||||||
///
|
|
||||||
/// * - Is a dot product,
|
|
||||||
/// (.) - is a Hadamard product (element-wise),
|
|
||||||
/// f, g, h - are activation functions.
|
|
||||||
///
|
|
||||||
/// \note This class represents only single *cell* (for current time step) and not
|
|
||||||
/// the whole LSTM Sequence layer
|
|
||||||
///
|
|
||||||
/// \sa LSTMSequence, RNNCell, GRUCell
|
|
||||||
///
|
|
||||||
class NGRAPH_API LSTMCell : public util::RNNCellBase {
|
|
||||||
public:
|
|
||||||
NGRAPH_RTTI_DECLARATION;
|
|
||||||
|
|
||||||
LSTMCell();
|
|
||||||
///
|
|
||||||
/// \brief Constructs LSTMCell node.
|
|
||||||
///
|
|
||||||
/// \param[in] X The input tensor with shape: [batch_size,
|
|
||||||
/// input_size].
|
|
||||||
/// \param[in] initial_hidden_state The hidden state tensor at current time step
|
|
||||||
/// with shape: [batch_size, hidden_size].
|
|
||||||
/// \param[in] initial_cell_state The cell state tensor at current time step
|
|
||||||
/// with shape: [batch_size, hidden_size].
|
|
||||||
/// \param[in] W The gate weights tensor with shape:
|
|
||||||
/// [4*hidden_size, input_size].
|
|
||||||
/// \param[in] R The recurrence weights tensor with shape:
|
|
||||||
/// [4*hidden_size, hidden_size].
|
|
||||||
/// \param[in] hidden_size The number of hidden units for recurrent cell.
|
|
||||||
/// \param[in] activations The vector of activation functions used inside
|
|
||||||
/// recurrent cell.
|
|
||||||
/// \param[in] activations_alpha The vector of alpha parameters for activation
|
|
||||||
/// functions in order respective to activation
|
|
||||||
/// list.
|
|
||||||
/// \param[in] activations_beta The vector of beta parameters for activation
|
|
||||||
/// functions in order respective to activation
|
|
||||||
/// list.
|
|
||||||
/// \param[in] clip The value defining clipping range [-clip,
|
|
||||||
/// clip] on input of activation functions.
|
|
||||||
LSTMCell(const Output<Node>& X,
|
|
||||||
const Output<Node>& initial_hidden_state,
|
|
||||||
const Output<Node>& initial_cell_state,
|
|
||||||
const Output<Node>& W,
|
|
||||||
const Output<Node>& R,
|
|
||||||
std::size_t hidden_size,
|
|
||||||
const std::vector<std::string>& activations = std::vector<std::string>{"sigmoid", "tanh", "tanh"},
|
|
||||||
const std::vector<float>& activations_alpha = {},
|
|
||||||
const std::vector<float>& activations_beta = {},
|
|
||||||
float clip = 0.f);
|
|
||||||
|
|
||||||
///
|
|
||||||
/// \brief Constructs LSTMCell node.
|
|
||||||
///
|
|
||||||
/// \param[in] X The input tensor with shape: [batch_size,
|
|
||||||
/// input_size].
|
|
||||||
/// \param[in] initial_hidden_state The hidden state tensor at current time step
|
|
||||||
/// with shape: [batch_size, hidden_size].
|
|
||||||
/// \param[in] initial_cell_state The cell state tensor at current time step
|
|
||||||
/// with shape: [batch_size, hidden_size].
|
|
||||||
/// \param[in] W The weight tensor with shape: [4*hidden_size,
|
|
||||||
/// input_size].
|
|
||||||
/// \param[in] R The recurrence weight tensor with shape:
|
|
||||||
/// [4*hidden_size, hidden_size].
|
|
||||||
/// \param[in] B The bias tensor for gates with shape:
|
|
||||||
/// [4*hidden_size].
|
|
||||||
/// \param[in] hidden_size The number of hidden units for recurrent cell.
|
|
||||||
/// \param[in] activations The vector of activation functions used inside
|
|
||||||
/// recurrent cell.
|
|
||||||
/// \param[in] activations_alpha The vector of alpha parameters for activation
|
|
||||||
/// functions in order respective to activation
|
|
||||||
/// list.
|
|
||||||
/// \param[in] activations_beta The vector of beta parameters for activation
|
|
||||||
/// functions in order respective to activation
|
|
||||||
/// list.
|
|
||||||
/// \param[in] clip The value defining clipping range [-clip,
|
|
||||||
/// clip] on input of activation functions.
|
|
||||||
///
|
|
||||||
LSTMCell(const Output<Node>& X,
|
|
||||||
const Output<Node>& initial_hidden_state,
|
|
||||||
const Output<Node>& initial_cell_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>{"sigmoid", "tanh", "tanh"},
|
|
||||||
const std::vector<float>& activations_alpha = {},
|
|
||||||
const std::vector<float>& activations_beta = {},
|
|
||||||
float clip = 0.f);
|
|
||||||
|
|
||||||
void validate_and_infer_types() override;
|
|
||||||
|
|
||||||
bool visit_attributes(AttributeVisitor& visitor) override;
|
|
||||||
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
|
||||||
|
|
||||||
private:
|
|
||||||
///
|
|
||||||
/// \brief Creates the default bias input initialized with zeros.
|
|
||||||
///
|
|
||||||
/// \return The object of Output class.
|
|
||||||
///
|
|
||||||
Output<Node> get_default_bias_input() const;
|
|
||||||
|
|
||||||
///
|
|
||||||
/// \brief The Activation function f.
|
|
||||||
///
|
|
||||||
util::ActivationFunction m_activation_f;
|
|
||||||
///
|
|
||||||
/// \brief The Activation function g.
|
|
||||||
///
|
|
||||||
util::ActivationFunction m_activation_g;
|
|
||||||
///
|
|
||||||
/// \brief The Activation function h.
|
|
||||||
///
|
|
||||||
util::ActivationFunction m_activation_h;
|
|
||||||
|
|
||||||
static constexpr std::size_t s_gates_count{4};
|
|
||||||
};
|
|
||||||
} // namespace v4
|
} // namespace v4
|
||||||
} // namespace op
|
} // namespace op
|
||||||
|
|
||||||
NGRAPH_API
|
|
||||||
std::ostream& operator<<(std::ostream& s, const op::LSTMWeightsFormat& type);
|
|
||||||
} // namespace ngraph
|
} // namespace ngraph
|
||||||
|
|
||||||
namespace ov {
|
|
||||||
|
|
||||||
template <>
|
|
||||||
class NGRAPH_API AttributeAdapter<ngraph::op::LSTMWeightsFormat>
|
|
||||||
: public EnumAttributeAdapterBase<ngraph::op::LSTMWeightsFormat> {
|
|
||||||
public:
|
|
||||||
AttributeAdapter(ngraph::op::LSTMWeightsFormat& value)
|
|
||||||
: EnumAttributeAdapterBase<ngraph::op::LSTMWeightsFormat>(value) {}
|
|
||||||
|
|
||||||
static constexpr DiscreteTypeInfo type_info{"AttributeAdapter<op::LSTMWeightsFormat>", 1};
|
|
||||||
const DiscreteTypeInfo& get_type_info() const override {
|
|
||||||
return type_info;
|
|
||||||
}
|
|
||||||
};
|
|
||||||
|
|
||||||
} // namespace ov
|
|
||||||
|
|||||||
@ -15,184 +15,16 @@
|
|||||||
#include "ngraph/op/lstm_cell.hpp"
|
#include "ngraph/op/lstm_cell.hpp"
|
||||||
#include "ngraph/op/util/attr_types.hpp"
|
#include "ngraph/op/util/attr_types.hpp"
|
||||||
#include "ngraph/op/util/rnn_cell_base.hpp"
|
#include "ngraph/op/util/rnn_cell_base.hpp"
|
||||||
|
#include "openvino/op/lstm_sequence.hpp"
|
||||||
|
|
||||||
namespace ngraph {
|
namespace ngraph {
|
||||||
namespace op {
|
namespace op {
|
||||||
namespace v0 {
|
namespace v0 {
|
||||||
|
using ov::op::v0::LSTMSequence;
|
||||||
///
|
|
||||||
/// \brief Class for lstm sequence node.
|
|
||||||
///
|
|
||||||
/// \note It follows notation and equations defined as in ONNX standard:
|
|
||||||
/// https://github.com/onnx/onnx/blob/master/docs/Operators.md#LSTM
|
|
||||||
///
|
|
||||||
/// \sa LSTMCell, RNNCell, GRUCell
|
|
||||||
///
|
|
||||||
///
|
|
||||||
class NGRAPH_API LSTMSequence : public Op {
|
|
||||||
public:
|
|
||||||
NGRAPH_RTTI_DECLARATION;
|
|
||||||
LSTMSequence();
|
|
||||||
|
|
||||||
using direction = RecurrentSequenceDirection;
|
|
||||||
|
|
||||||
size_t get_default_output_index() const override {
|
|
||||||
return no_default_index();
|
|
||||||
}
|
|
||||||
explicit LSTMSequence(const Output<Node>& X,
|
|
||||||
const Output<Node>& initial_hidden_state,
|
|
||||||
const Output<Node>& initial_cell_state,
|
|
||||||
const Output<Node>& sequence_lengths,
|
|
||||||
const Output<Node>& W,
|
|
||||||
const Output<Node>& R,
|
|
||||||
const Output<Node>& B,
|
|
||||||
const Output<Node>& P,
|
|
||||||
const std::int64_t hidden_size,
|
|
||||||
const direction lstm_direction,
|
|
||||||
LSTMWeightsFormat weights_format = LSTMWeightsFormat::IFCO,
|
|
||||||
const std::vector<float> activations_alpha = {},
|
|
||||||
const std::vector<float> activations_beta = {},
|
|
||||||
const std::vector<std::string> activations = {"sigmoid", "tanh", "tanh"},
|
|
||||||
const float clip_threshold = 0,
|
|
||||||
const bool input_forget = false);
|
|
||||||
|
|
||||||
explicit LSTMSequence(const Output<Node>& X,
|
|
||||||
const Output<Node>& initial_hidden_state,
|
|
||||||
const Output<Node>& initial_cell_state,
|
|
||||||
const Output<Node>& sequence_lengths,
|
|
||||||
const Output<Node>& W,
|
|
||||||
const Output<Node>& R,
|
|
||||||
const Output<Node>& B,
|
|
||||||
const std::int64_t hidden_size,
|
|
||||||
const direction lstm_direction,
|
|
||||||
LSTMWeightsFormat weights_format = LSTMWeightsFormat::IFCO,
|
|
||||||
const std::vector<float>& activations_alpha = {},
|
|
||||||
const std::vector<float>& activations_beta = {},
|
|
||||||
const std::vector<std::string>& activations = {"sigmoid", "tanh", "tanh"},
|
|
||||||
const float clip_threshold = 0,
|
|
||||||
const bool input_forget = false);
|
|
||||||
|
|
||||||
virtual void validate_and_infer_types() override;
|
|
||||||
bool visit_attributes(AttributeVisitor& visitor) override;
|
|
||||||
|
|
||||||
virtual std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
|
||||||
|
|
||||||
std::vector<float> get_activations_alpha() const {
|
|
||||||
return m_activations_alpha;
|
|
||||||
}
|
|
||||||
std::vector<float> get_activations_beta() const {
|
|
||||||
return m_activations_beta;
|
|
||||||
}
|
|
||||||
std::vector<std::string> get_activations() const {
|
|
||||||
return m_activations;
|
|
||||||
}
|
|
||||||
float get_clip_threshold() const {
|
|
||||||
return m_clip_threshold;
|
|
||||||
}
|
|
||||||
direction get_direction() const {
|
|
||||||
return m_direction;
|
|
||||||
}
|
|
||||||
std::int64_t get_hidden_size() const {
|
|
||||||
return m_hidden_size;
|
|
||||||
}
|
|
||||||
bool get_input_forget() const {
|
|
||||||
return m_input_forget;
|
|
||||||
}
|
|
||||||
LSTMWeightsFormat get_weights_format() const {
|
|
||||||
return m_weights_format;
|
|
||||||
}
|
|
||||||
|
|
||||||
private:
|
|
||||||
///
|
|
||||||
/// \brief Gets the masked value according to sequence length in a batch.
|
|
||||||
///
|
|
||||||
/// \note Zeros out values or sets them to default value for inputs with
|
|
||||||
/// sequence length shorter than currently procssed time step.
|
|
||||||
///
|
|
||||||
/// \param[in] data The input value.
|
|
||||||
/// \param[in] time_step The current time step denoting sequence length.
|
|
||||||
/// \param[in] batch_axis The batch axis index of data tensor.
|
|
||||||
/// \param[in] default_value The default value for masked elements.
|
|
||||||
///
|
|
||||||
/// \return The masked value.
|
|
||||||
///
|
|
||||||
std::shared_ptr<Node> get_masked_node(const Output<Node>& data,
|
|
||||||
std::int32_t time_step,
|
|
||||||
std::size_t batch_axis = 0,
|
|
||||||
const Output<Node>& default_value = Output<Node>()) const;
|
|
||||||
|
|
||||||
OutputVector lstm_pass(bool is_reverse = false) const;
|
|
||||||
|
|
||||||
// Split(bi-directional) and squeeze input data to remove 'num_direction' dimension.
|
|
||||||
std::shared_ptr<Node> prepare_input(Output<Node> node, bool is_reverse, size_t num_direction_axis = 0) const;
|
|
||||||
|
|
||||||
std::vector<float> m_activations_alpha;
|
|
||||||
std::vector<float> m_activations_beta;
|
|
||||||
std::vector<std::string> m_activations;
|
|
||||||
float m_clip_threshold;
|
|
||||||
direction m_direction;
|
|
||||||
std::int64_t m_hidden_size;
|
|
||||||
bool m_input_forget;
|
|
||||||
LSTMWeightsFormat m_weights_format;
|
|
||||||
};
|
|
||||||
} // namespace v0
|
} // namespace v0
|
||||||
|
|
||||||
namespace v5 {
|
namespace v5 {
|
||||||
///
|
using ov::op::v5::LSTMSequence;
|
||||||
/// \brief Class for lstm sequence node.
|
|
||||||
///
|
|
||||||
/// \note It follows notation and equations defined as in ONNX standard:
|
|
||||||
/// https://github.com/onnx/onnx/blob/master/docs/Operators.md#LSTM
|
|
||||||
///
|
|
||||||
/// \sa LSTMCell, RNNCell, GRUCell
|
|
||||||
///
|
|
||||||
///
|
|
||||||
class NGRAPH_API LSTMSequence : public util::RNNCellBase {
|
|
||||||
public:
|
|
||||||
NGRAPH_RTTI_DECLARATION;
|
|
||||||
LSTMSequence() = default;
|
|
||||||
|
|
||||||
using direction = RecurrentSequenceDirection;
|
|
||||||
|
|
||||||
size_t get_default_output_index() const override {
|
|
||||||
return no_default_index();
|
|
||||||
}
|
|
||||||
explicit LSTMSequence(const Output<Node>& X,
|
|
||||||
const Output<Node>& initial_hidden_state,
|
|
||||||
const Output<Node>& initial_cell_state,
|
|
||||||
const Output<Node>& sequence_lengths,
|
|
||||||
const Output<Node>& W,
|
|
||||||
const Output<Node>& R,
|
|
||||||
const Output<Node>& B,
|
|
||||||
const std::int64_t hidden_size,
|
|
||||||
const direction lstm_direction,
|
|
||||||
const std::vector<float>& activations_alpha = {},
|
|
||||||
const std::vector<float>& activations_beta = {},
|
|
||||||
const std::vector<std::string>& activations = {"sigmoid", "tanh", "tanh"},
|
|
||||||
const float clip = 0.f)
|
|
||||||
: RNNCellBase({X, initial_hidden_state, initial_cell_state, sequence_lengths, W, R, B},
|
|
||||||
hidden_size,
|
|
||||||
clip,
|
|
||||||
activations,
|
|
||||||
activations_alpha,
|
|
||||||
activations_beta),
|
|
||||||
m_direction(lstm_direction) {
|
|
||||||
constructor_validate_and_infer_types();
|
|
||||||
}
|
|
||||||
|
|
||||||
void validate_and_infer_types() override;
|
|
||||||
bool visit_attributes(AttributeVisitor& visitor) override;
|
|
||||||
|
|
||||||
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
|
||||||
|
|
||||||
direction get_direction() const {
|
|
||||||
return m_direction;
|
|
||||||
}
|
|
||||||
|
|
||||||
private:
|
|
||||||
direction m_direction;
|
|
||||||
};
|
|
||||||
} // namespace v5
|
} // namespace v5
|
||||||
} // namespace op
|
} // namespace op
|
||||||
|
|
||||||
} // namespace ngraph
|
} // namespace ngraph
|
||||||
|
|||||||
@ -5,78 +5,14 @@
|
|||||||
#pragma once
|
#pragma once
|
||||||
|
|
||||||
#include "ngraph/op/op.hpp"
|
#include "ngraph/op/op.hpp"
|
||||||
|
#include "openvino/op/parameter.hpp"
|
||||||
|
|
||||||
namespace ngraph {
|
namespace ngraph {
|
||||||
namespace op {
|
namespace op {
|
||||||
namespace v0 {
|
namespace v0 {
|
||||||
/// \brief A function parameter.
|
using ov::op::v0::Parameter;
|
||||||
///
|
|
||||||
/// Parameters are nodes that represent the arguments that will be passed to
|
|
||||||
/// user-defined functions. Function creation requires a sequence of parameters.
|
|
||||||
/// Basic graph operations do not need parameters attached to a function.
|
|
||||||
class NGRAPH_API Parameter : public op::Op {
|
|
||||||
public:
|
|
||||||
NGRAPH_RTTI_DECLARATION;
|
|
||||||
/// \brief Constructions a tensor-typed parameter node.
|
|
||||||
Parameter() = default;
|
|
||||||
/// \brief Constructions a tensor-typed parameter node.
|
|
||||||
///
|
|
||||||
/// \param element_type The element type of the parameter.
|
|
||||||
/// \param pshape The partial shape of the parameter.
|
|
||||||
Parameter(const ngraph::element::Type& element_type, const PartialShape& pshape);
|
|
||||||
|
|
||||||
bool visit_attributes(AttributeVisitor& visitor) override;
|
|
||||||
|
|
||||||
void validate_and_infer_types() override;
|
|
||||||
|
|
||||||
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
|
||||||
|
|
||||||
bool is_relevant_to_shapes() const;
|
|
||||||
void set_is_relevant_to_shapes(bool is_relevant);
|
|
||||||
|
|
||||||
const PartialShape& get_partial_shape() const {
|
|
||||||
return m_partial_shape;
|
|
||||||
}
|
|
||||||
PartialShape& get_partial_shape() {
|
|
||||||
return m_partial_shape;
|
|
||||||
}
|
|
||||||
void set_partial_shape(const PartialShape& partial_shape) {
|
|
||||||
m_partial_shape = partial_shape;
|
|
||||||
}
|
|
||||||
const element::Type& get_element_type() const {
|
|
||||||
return m_element_type;
|
|
||||||
}
|
|
||||||
void set_element_type(const element::Type& element_type) {
|
|
||||||
m_element_type = element_type;
|
|
||||||
}
|
|
||||||
|
|
||||||
protected:
|
|
||||||
PartialShape m_partial_shape;
|
|
||||||
element::Type m_element_type;
|
|
||||||
bool m_is_relevant_to_shapes{false};
|
|
||||||
};
|
|
||||||
} // namespace v0
|
} // namespace v0
|
||||||
using v0::Parameter;
|
using v0::Parameter;
|
||||||
} // namespace op
|
} // namespace op
|
||||||
using ParameterVector = std::vector<std::shared_ptr<op::Parameter>>;
|
using ParameterVector = std::vector<std::shared_ptr<op::Parameter>>;
|
||||||
} // namespace ngraph
|
} // namespace ngraph
|
||||||
|
|
||||||
namespace ov {
|
|
||||||
|
|
||||||
template <>
|
|
||||||
class NGRAPH_API AttributeAdapter<ngraph::ParameterVector> : public VisitorAdapter {
|
|
||||||
public:
|
|
||||||
AttributeAdapter(ngraph::ParameterVector& ref);
|
|
||||||
|
|
||||||
bool visit_attributes(AttributeVisitor& visitor) override;
|
|
||||||
|
|
||||||
static constexpr DiscreteTypeInfo type_info{"AttributeAdapter<ParameterVector>", 0};
|
|
||||||
const DiscreteTypeInfo& get_type_info() const override {
|
|
||||||
return type_info;
|
|
||||||
}
|
|
||||||
|
|
||||||
protected:
|
|
||||||
ngraph::ParameterVector& m_ref;
|
|
||||||
};
|
|
||||||
|
|
||||||
} // namespace ov
|
|
||||||
|
|||||||
@ -7,62 +7,14 @@
|
|||||||
#include <memory>
|
#include <memory>
|
||||||
|
|
||||||
#include "ngraph/op/op.hpp"
|
#include "ngraph/op/op.hpp"
|
||||||
|
#include "openvino/op/result.hpp"
|
||||||
|
|
||||||
namespace ngraph {
|
namespace ngraph {
|
||||||
namespace op {
|
namespace op {
|
||||||
namespace v0 {
|
namespace v0 {
|
||||||
class NGRAPH_API Result : public Op {
|
using ov::op::v0::Result;
|
||||||
public:
|
|
||||||
NGRAPH_RTTI_DECLARATION;
|
|
||||||
|
|
||||||
/// \brief Allows a value to be used as a function result.
|
|
||||||
Result() = default;
|
|
||||||
/// \brief Allows a value to be used as a function result.
|
|
||||||
///
|
|
||||||
/// \param arg Node that produces the input tensor.
|
|
||||||
Result(const Output<Node>& arg, bool needs_default_layout = false);
|
|
||||||
|
|
||||||
bool visit_attributes(AttributeVisitor& visitor) override;
|
|
||||||
void validate_and_infer_types() override;
|
|
||||||
|
|
||||||
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
|
||||||
|
|
||||||
void set_needs_default_layout(bool val) {
|
|
||||||
m_needs_default_layout = val;
|
|
||||||
}
|
|
||||||
bool needs_default_layout() const {
|
|
||||||
return m_needs_default_layout;
|
|
||||||
}
|
|
||||||
bool evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const override;
|
|
||||||
bool has_evaluate() const override;
|
|
||||||
bool constant_fold(OutputVector& output_values, const OutputVector& inputs_values) override;
|
|
||||||
|
|
||||||
private:
|
|
||||||
bool m_needs_default_layout{false};
|
|
||||||
};
|
|
||||||
} // namespace v0
|
} // namespace v0
|
||||||
|
|
||||||
using v0::Result;
|
using v0::Result;
|
||||||
} // namespace op
|
} // namespace op
|
||||||
using ResultVector = std::vector<std::shared_ptr<op::Result>>;
|
using ResultVector = std::vector<std::shared_ptr<op::Result>>;
|
||||||
} // namespace ngraph
|
} // namespace ngraph
|
||||||
|
|
||||||
namespace ov {
|
|
||||||
|
|
||||||
template <>
|
|
||||||
class NGRAPH_API AttributeAdapter<ngraph::ResultVector> : public VisitorAdapter {
|
|
||||||
public:
|
|
||||||
AttributeAdapter(ngraph::ResultVector& ref);
|
|
||||||
|
|
||||||
bool visit_attributes(AttributeVisitor& visitor) override;
|
|
||||||
|
|
||||||
static constexpr DiscreteTypeInfo type_info{"AttributeAdapter<ResultVector>", 0};
|
|
||||||
const DiscreteTypeInfo& get_type_info() const override {
|
|
||||||
return type_info;
|
|
||||||
}
|
|
||||||
|
|
||||||
protected:
|
|
||||||
ngraph::ResultVector& m_ref;
|
|
||||||
};
|
|
||||||
|
|
||||||
} // namespace ov
|
|
||||||
|
|||||||
@ -9,35 +9,12 @@
|
|||||||
#include "ngraph/function.hpp"
|
#include "ngraph/function.hpp"
|
||||||
#include "ngraph/op/parameter.hpp"
|
#include "ngraph/op/parameter.hpp"
|
||||||
#include "ngraph/op/util/sub_graph_base.hpp"
|
#include "ngraph/op/util/sub_graph_base.hpp"
|
||||||
|
#include "openvino/op/tensor_iterator.hpp"
|
||||||
|
|
||||||
namespace ngraph {
|
namespace ngraph {
|
||||||
namespace op {
|
namespace op {
|
||||||
namespace v0 {
|
namespace v0 {
|
||||||
/// \brief Iterate a body over tensors, accumulating into tensors.
|
using ov::op::v0::TensorIterator;
|
||||||
class NGRAPH_API TensorIterator : public op::util::SubGraphOp {
|
|
||||||
public:
|
|
||||||
NGRAPH_RTTI_DECLARATION;
|
|
||||||
|
|
||||||
bool visit_attributes(AttributeVisitor& visitor) override;
|
|
||||||
|
|
||||||
TensorIterator() = default;
|
|
||||||
explicit TensorIterator(const OutputVector& values);
|
|
||||||
|
|
||||||
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
|
||||||
/// \return the body of the iteration
|
|
||||||
std::shared_ptr<Function> get_body() const {
|
|
||||||
return m_bodies[0];
|
|
||||||
}
|
|
||||||
/// \param body set the body of the iteration
|
|
||||||
void set_body(const std::shared_ptr<Function>& body) {
|
|
||||||
set_function(body);
|
|
||||||
}
|
|
||||||
void validate_and_infer_types() override;
|
|
||||||
void revalidate_and_infer_types_for_body_ops();
|
|
||||||
|
|
||||||
private:
|
|
||||||
void try_to_set_num_iterations_if_no_slice_inputs();
|
|
||||||
};
|
|
||||||
} // namespace v0
|
} // namespace v0
|
||||||
using v0::TensorIterator;
|
using v0::TensorIterator;
|
||||||
} // namespace op
|
} // namespace op
|
||||||
|
|||||||
@ -42,19 +42,15 @@ namespace runtime {
|
|||||||
class HostTensor;
|
class HostTensor;
|
||||||
} // namespace runtime
|
} // namespace runtime
|
||||||
|
|
||||||
namespace op {
|
|
||||||
|
|
||||||
namespace v0 {
|
|
||||||
class Result;
|
|
||||||
} // namespace v0
|
|
||||||
} // namespace op
|
|
||||||
|
|
||||||
} // namespace ngraph
|
} // namespace ngraph
|
||||||
|
|
||||||
namespace ov {
|
namespace ov {
|
||||||
namespace op {
|
namespace op {
|
||||||
|
namespace v0 {
|
||||||
|
class Result;
|
||||||
|
} // namespace v0
|
||||||
struct AutoBroadcastSpec;
|
struct AutoBroadcastSpec;
|
||||||
}
|
} // namespace op
|
||||||
namespace pass {
|
namespace pass {
|
||||||
namespace pattern {
|
namespace pattern {
|
||||||
class Matcher;
|
class Matcher;
|
||||||
@ -76,7 +72,7 @@ class Node;
|
|||||||
/// environment) for evaluating ngraph::function.
|
/// environment) for evaluating ngraph::function.
|
||||||
using EvaluationContext = std::map<std::string, std::shared_ptr<Variant>>;
|
using EvaluationContext = std::map<std::string, std::shared_ptr<Variant>>;
|
||||||
|
|
||||||
using ResultVector = std::vector<std::shared_ptr<ngraph::op::v0::Result>>;
|
using ResultVector = std::vector<std::shared_ptr<ov::op::v0::Result>>;
|
||||||
|
|
||||||
OPENVINO_API
|
OPENVINO_API
|
||||||
std::string node_validation_failure_loc_string(const Node* node);
|
std::string node_validation_failure_loc_string(const Node* node);
|
||||||
|
|||||||
80
ngraph/core/include/openvino/op/gather.hpp
Normal file
80
ngraph/core/include/openvino/op/gather.hpp
Normal file
@ -0,0 +1,80 @@
|
|||||||
|
// Copyright (C) 2018-2021 Intel Corporation
|
||||||
|
// SPDX-License-Identifier: Apache-2.0
|
||||||
|
//
|
||||||
|
|
||||||
|
#pragma once
|
||||||
|
|
||||||
|
#include "openvino/op/util/gather_base.hpp"
|
||||||
|
|
||||||
|
namespace ov {
|
||||||
|
namespace op {
|
||||||
|
namespace v1 {
|
||||||
|
/// \brief Gather slices from axis of data according to indices
|
||||||
|
class OPENVINO_API Gather : public op::util::GatherBase {
|
||||||
|
public:
|
||||||
|
OPENVINO_RTTI_DECLARATION;
|
||||||
|
static const int64_t AXIS_NOT_SET_VALUE = std::numeric_limits<int64_t>::max();
|
||||||
|
Gather() = default;
|
||||||
|
/// \param data The tensor from which slices are gathered
|
||||||
|
/// \param indices Tensor with indexes to gather
|
||||||
|
/// \param axis The tensor is a dimension index to gather data from
|
||||||
|
Gather(const Output<Node>& params, const Output<Node>& indices, const Output<Node>& axis);
|
||||||
|
|
||||||
|
bool visit_attributes(AttributeVisitor& visitor) override;
|
||||||
|
int64_t get_axis() const override;
|
||||||
|
|
||||||
|
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
||||||
|
};
|
||||||
|
} // namespace v1
|
||||||
|
|
||||||
|
namespace v7 {
|
||||||
|
/// \brief Gather slices from axis of data according to indices
|
||||||
|
class OPENVINO_API Gather : public op::util::GatherBase {
|
||||||
|
public:
|
||||||
|
OPENVINO_RTTI_DECLARATION;
|
||||||
|
Gather() = default;
|
||||||
|
|
||||||
|
/// \param data The tensor from which slices are gathered
|
||||||
|
/// \param indices Tensor with indexes to gather
|
||||||
|
/// \param axis The tensor is a dimension index to gather data from
|
||||||
|
/// \param batch_dims The number of batch dimension in data and indices tensors.
|
||||||
|
/// If batch_dims = 0 Gather v7 is identical to Gather v1.
|
||||||
|
Gather(const Output<Node>& data,
|
||||||
|
const Output<Node>& indices,
|
||||||
|
const Output<Node>& axis,
|
||||||
|
const int64_t batch_dims = 0);
|
||||||
|
|
||||||
|
bool visit_attributes(AttributeVisitor& visitor) override;
|
||||||
|
void validate_and_infer_types() override;
|
||||||
|
int64_t get_batch_dims() const;
|
||||||
|
|
||||||
|
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
||||||
|
};
|
||||||
|
} // namespace v7
|
||||||
|
|
||||||
|
namespace v8 {
|
||||||
|
/// \brief Gather slices from axis of data according to indices. Negative indices
|
||||||
|
/// are supported and indicate reverse indexing from the end
|
||||||
|
class OPENVINO_API Gather : public op::util::GatherBase {
|
||||||
|
public:
|
||||||
|
OPENVINO_RTTI_DECLARATION;
|
||||||
|
Gather() = default;
|
||||||
|
|
||||||
|
/// \param data The tensor from which slices are gathered
|
||||||
|
/// \param indices Tensor with indexes to gather
|
||||||
|
/// \param axis The tensor is a dimension index to gather data from
|
||||||
|
/// \param batch_dims The number of batch dimension in data and indices tensors.
|
||||||
|
Gather(const Output<Node>& data,
|
||||||
|
const Output<Node>& indices,
|
||||||
|
const Output<Node>& axis,
|
||||||
|
const int64_t batch_dims = 0);
|
||||||
|
|
||||||
|
bool visit_attributes(AttributeVisitor& visitor) override;
|
||||||
|
void validate_and_infer_types() override;
|
||||||
|
int64_t get_batch_dims() const;
|
||||||
|
|
||||||
|
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
||||||
|
};
|
||||||
|
} // namespace v8
|
||||||
|
} // namespace op
|
||||||
|
} // namespace ov
|
||||||
39
ngraph/core/include/openvino/op/gather_elements.hpp
Normal file
39
ngraph/core/include/openvino/op/gather_elements.hpp
Normal file
@ -0,0 +1,39 @@
|
|||||||
|
// Copyright (C) 2018-2021 Intel Corporation
|
||||||
|
// SPDX-License-Identifier: Apache-2.0
|
||||||
|
//
|
||||||
|
|
||||||
|
#pragma once
|
||||||
|
|
||||||
|
#include "openvino/op/op.hpp"
|
||||||
|
|
||||||
|
namespace ov {
|
||||||
|
namespace op {
|
||||||
|
namespace v6 {
|
||||||
|
/// \brief GatherElements operation
|
||||||
|
///
|
||||||
|
class OPENVINO_API GatherElements : public Op {
|
||||||
|
public:
|
||||||
|
OPENVINO_RTTI_DECLARATION;
|
||||||
|
GatherElements() = default;
|
||||||
|
|
||||||
|
/// \brief Constructs a GatherElements operation.
|
||||||
|
///
|
||||||
|
/// \param data Node producing data that are gathered
|
||||||
|
/// \param indices Node producing indices by which the operation gathers elements
|
||||||
|
/// \param axis specifies axis along which indices are specified
|
||||||
|
GatherElements(const Output<Node>& data, const Output<Node>& indices, const int64_t axis);
|
||||||
|
|
||||||
|
void validate_and_infer_types() override;
|
||||||
|
bool visit_attributes(AttributeVisitor& visitor) override;
|
||||||
|
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
||||||
|
|
||||||
|
int64_t get_axis() const {
|
||||||
|
return m_axis;
|
||||||
|
}
|
||||||
|
|
||||||
|
private:
|
||||||
|
int64_t m_axis;
|
||||||
|
};
|
||||||
|
} // namespace v6
|
||||||
|
} // namespace op
|
||||||
|
} // namespace ov
|
||||||
40
ngraph/core/include/openvino/op/gather_nd.hpp
Normal file
40
ngraph/core/include/openvino/op/gather_nd.hpp
Normal file
@ -0,0 +1,40 @@
|
|||||||
|
// Copyright (C) 2018-2021 Intel Corporation
|
||||||
|
// SPDX-License-Identifier: Apache-2.0
|
||||||
|
//
|
||||||
|
|
||||||
|
#pragma once
|
||||||
|
|
||||||
|
#include "openvino/op/op.hpp"
|
||||||
|
|
||||||
|
namespace ov {
|
||||||
|
namespace op {
|
||||||
|
namespace v5 {
|
||||||
|
/// \brief GatherND operation
|
||||||
|
///
|
||||||
|
class OPENVINO_API GatherND : public Op {
|
||||||
|
public:
|
||||||
|
OPENVINO_RTTI_DECLARATION;
|
||||||
|
GatherND() = default;
|
||||||
|
|
||||||
|
/// \brief Constructs a GatherND operation.
|
||||||
|
///
|
||||||
|
/// \param data Node producing data that are gathered
|
||||||
|
/// \param indices Node producing indices by which the operation gathers elements
|
||||||
|
/// or slices from data
|
||||||
|
/// \param batch_dims Specifies a number of batch dimensions
|
||||||
|
GatherND(const Output<Node>& data, const Output<Node>& indices, const size_t batch_dims = 0);
|
||||||
|
|
||||||
|
void validate_and_infer_types() override;
|
||||||
|
bool visit_attributes(AttributeVisitor& visitor) override;
|
||||||
|
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
||||||
|
|
||||||
|
size_t get_batch_dims() const {
|
||||||
|
return m_batch_dims;
|
||||||
|
}
|
||||||
|
|
||||||
|
private:
|
||||||
|
size_t m_batch_dims;
|
||||||
|
};
|
||||||
|
} // namespace v5
|
||||||
|
} // namespace op
|
||||||
|
} // namespace ov
|
||||||
38
ngraph/core/include/openvino/op/gather_tree.hpp
Normal file
38
ngraph/core/include/openvino/op/gather_tree.hpp
Normal file
@ -0,0 +1,38 @@
|
|||||||
|
// Copyright (C) 2018-2021 Intel Corporation
|
||||||
|
// SPDX-License-Identifier: Apache-2.0
|
||||||
|
//
|
||||||
|
|
||||||
|
#pragma once
|
||||||
|
|
||||||
|
#include "ngraph/op/op.hpp"
|
||||||
|
|
||||||
|
namespace ov {
|
||||||
|
namespace op {
|
||||||
|
namespace v1 {
|
||||||
|
/// \brief Generates the complete beams from the ids per each step and the parent beam
|
||||||
|
/// ids.
|
||||||
|
class OPENVINO_API GatherTree : public Op {
|
||||||
|
public:
|
||||||
|
OPENVINO_RTTI_DECLARATION;
|
||||||
|
|
||||||
|
GatherTree() = default;
|
||||||
|
/// \param step_ids Tensor of shape [MAX_TIME, BATCH_SIZE, BEAM_WIDTH] with
|
||||||
|
/// indices from per each step
|
||||||
|
/// \param parent_idx Tensor of shape [MAX_TIME, BATCH_SIZE, BEAM_WIDTH] with
|
||||||
|
/// parent beam indices
|
||||||
|
/// \param max_seq_len Tensor of shape [BATCH_SIZE] with maximum lengths for each
|
||||||
|
/// sequence in the batch
|
||||||
|
/// \param end_token Tensor of shape [MAX_TIME, BATCH_SIZE, BEAM_WIDTH]
|
||||||
|
GatherTree(const Output<Node>& step_ids,
|
||||||
|
const Output<Node>& parent_idx,
|
||||||
|
const Output<Node>& max_seq_len,
|
||||||
|
const Output<Node>& end_token);
|
||||||
|
|
||||||
|
bool visit_attributes(AttributeVisitor& visitor) override;
|
||||||
|
void validate_and_infer_types() override;
|
||||||
|
|
||||||
|
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
||||||
|
};
|
||||||
|
} // namespace v1
|
||||||
|
} // namespace op
|
||||||
|
} // namespace ov
|
||||||
81
ngraph/core/include/openvino/op/gelu.hpp
Normal file
81
ngraph/core/include/openvino/op/gelu.hpp
Normal file
@ -0,0 +1,81 @@
|
|||||||
|
// Copyright (C) 2018-2021 Intel Corporation
|
||||||
|
// SPDX-License-Identifier: Apache-2.0
|
||||||
|
//
|
||||||
|
|
||||||
|
#pragma once
|
||||||
|
|
||||||
|
#include "openvino/op/op.hpp"
|
||||||
|
#include "openvino/op/util/unary_elementwise_arithmetic.hpp"
|
||||||
|
|
||||||
|
namespace ov {
|
||||||
|
namespace op {
|
||||||
|
namespace v0 {
|
||||||
|
/// \brief Gaussian Error Linear Unit
|
||||||
|
/// f(x) = 0.5 * x * (1 + erf( x / sqrt(2) )
|
||||||
|
class OPENVINO_API Gelu : public Op {
|
||||||
|
public:
|
||||||
|
OPENVINO_RTTI_DECLARATION;
|
||||||
|
|
||||||
|
Gelu();
|
||||||
|
/// \brief Constructs a Gelu operation.
|
||||||
|
///
|
||||||
|
/// \param data Input tensor
|
||||||
|
Gelu(const Output<Node>& data);
|
||||||
|
|
||||||
|
bool visit_attributes(AttributeVisitor& visitor) override;
|
||||||
|
|
||||||
|
void validate_and_infer_types() override;
|
||||||
|
|
||||||
|
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
||||||
|
};
|
||||||
|
} // namespace v0
|
||||||
|
|
||||||
|
/// \brief Specifies the approximation to calculate Gelu
|
||||||
|
enum class GeluApproximationMode { TANH, ERF };
|
||||||
|
OPENVINO_API std::ostream& operator<<(std::ostream& s, const GeluApproximationMode& type);
|
||||||
|
|
||||||
|
namespace v7 {
|
||||||
|
/// \brief Gaussian Error Linear Unit
|
||||||
|
/// f(x) = 0.5 * x * (1 + erf( x / sqrt(2) ) for "approximation" = "erf"
|
||||||
|
/// f(x) = 0.5 * x * (1 + tanh([sqrt(2 / pi)] * [x + 0.044715^3]) for "approximation" =
|
||||||
|
/// "tanh"
|
||||||
|
class OPENVINO_API Gelu : public util::UnaryElementwiseArithmetic {
|
||||||
|
public:
|
||||||
|
OPENVINO_RTTI_DECLARATION;
|
||||||
|
|
||||||
|
Gelu() = default;
|
||||||
|
/// \brief Constructs a Gelu operation.
|
||||||
|
///
|
||||||
|
/// \param data Input tensor
|
||||||
|
/// \param mode Approximation mode
|
||||||
|
Gelu(const Output<Node>& data, GeluApproximationMode mode = GeluApproximationMode::ERF);
|
||||||
|
|
||||||
|
bool visit_attributes(AttributeVisitor& visitor) override;
|
||||||
|
|
||||||
|
void validate_and_infer_types() override;
|
||||||
|
|
||||||
|
bool evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const override;
|
||||||
|
bool has_evaluate() const override;
|
||||||
|
|
||||||
|
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
||||||
|
|
||||||
|
GeluApproximationMode get_approximation_mode() const;
|
||||||
|
|
||||||
|
private:
|
||||||
|
GeluApproximationMode m_approximation_mode = GeluApproximationMode::ERF;
|
||||||
|
};
|
||||||
|
} // namespace v7
|
||||||
|
} // namespace op
|
||||||
|
|
||||||
|
template <>
|
||||||
|
class OPENVINO_API AttributeAdapter<op::GeluApproximationMode>
|
||||||
|
: public EnumAttributeAdapterBase<op::GeluApproximationMode> {
|
||||||
|
public:
|
||||||
|
AttributeAdapter(op::GeluApproximationMode& value) : EnumAttributeAdapterBase<op::GeluApproximationMode>(value) {}
|
||||||
|
|
||||||
|
static constexpr DiscreteTypeInfo type_info{"AttributeAdapter<op::GeluApproximationMode>", 0};
|
||||||
|
const DiscreteTypeInfo& get_type_info() const override {
|
||||||
|
return type_info;
|
||||||
|
}
|
||||||
|
};
|
||||||
|
} // namespace ov
|
||||||
33
ngraph/core/include/openvino/op/greater.hpp
Normal file
33
ngraph/core/include/openvino/op/greater.hpp
Normal file
@ -0,0 +1,33 @@
|
|||||||
|
// Copyright (C) 2018-2021 Intel Corporation
|
||||||
|
// SPDX-License-Identifier: Apache-2.0
|
||||||
|
//
|
||||||
|
|
||||||
|
#pragma once
|
||||||
|
|
||||||
|
#include "openvino/op/util/binary_elementwise_comparison.hpp"
|
||||||
|
|
||||||
|
namespace ov {
|
||||||
|
namespace op {
|
||||||
|
namespace v1 {
|
||||||
|
/// \brief Elementwise greater-than operation.
|
||||||
|
class OPENVINO_API Greater : public util::BinaryElementwiseComparison {
|
||||||
|
public:
|
||||||
|
OPENVINO_RTTI_DECLARATION;
|
||||||
|
/// \brief Constructs a greater-than operation.
|
||||||
|
Greater() : util::BinaryElementwiseComparison(AutoBroadcastSpec::NUMPY) {}
|
||||||
|
/// \brief Constructs a greater-than operation.
|
||||||
|
///
|
||||||
|
/// \param arg0 Node that produces the first input tensor.
|
||||||
|
/// \param arg1 Node that produces the second input tensor.
|
||||||
|
/// \param auto_broadcast Auto broadcast specification
|
||||||
|
Greater(const Output<Node>& arg0,
|
||||||
|
const Output<Node>& arg1,
|
||||||
|
const AutoBroadcastSpec& auto_broadcast = AutoBroadcastSpec(AutoBroadcastType::NUMPY));
|
||||||
|
|
||||||
|
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
||||||
|
bool evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const override;
|
||||||
|
bool has_evaluate() const override;
|
||||||
|
};
|
||||||
|
} // namespace v1
|
||||||
|
} // namespace op
|
||||||
|
} // namespace ov
|
||||||
33
ngraph/core/include/openvino/op/greater_eq.hpp
Normal file
33
ngraph/core/include/openvino/op/greater_eq.hpp
Normal file
@ -0,0 +1,33 @@
|
|||||||
|
// Copyright (C) 2018-2021 Intel Corporation
|
||||||
|
// SPDX-License-Identifier: Apache-2.0
|
||||||
|
//
|
||||||
|
|
||||||
|
#pragma once
|
||||||
|
|
||||||
|
#include "openvino/op/util/binary_elementwise_comparison.hpp"
|
||||||
|
|
||||||
|
namespace ov {
|
||||||
|
namespace op {
|
||||||
|
namespace v1 {
|
||||||
|
/// \brief Elementwise greater-than-or-equal operation.
|
||||||
|
class OPENVINO_API GreaterEqual : public util::BinaryElementwiseComparison {
|
||||||
|
public:
|
||||||
|
OPENVINO_RTTI_DECLARATION;
|
||||||
|
/// \brief Constructs a greater-than-or-equal operation.
|
||||||
|
GreaterEqual() : util::BinaryElementwiseComparison(AutoBroadcastSpec::NUMPY) {}
|
||||||
|
/// \brief Constructs a greater-than-or-equal operation.
|
||||||
|
///
|
||||||
|
/// \param arg0 Node that produces the first input tensor.
|
||||||
|
/// \param arg1 Node that produces the second input tensor.
|
||||||
|
/// \param auto_broadcast Auto broadcast specification
|
||||||
|
GreaterEqual(const Output<Node>& arg0,
|
||||||
|
const Output<Node>& arg1,
|
||||||
|
const AutoBroadcastSpec& auto_broadcast = AutoBroadcastSpec(AutoBroadcastType::NUMPY));
|
||||||
|
|
||||||
|
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
||||||
|
bool evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const override;
|
||||||
|
bool has_evaluate() const override;
|
||||||
|
};
|
||||||
|
} // namespace v1
|
||||||
|
} // namespace op
|
||||||
|
} // namespace ov
|
||||||
41
ngraph/core/include/openvino/op/grn.hpp
Normal file
41
ngraph/core/include/openvino/op/grn.hpp
Normal file
@ -0,0 +1,41 @@
|
|||||||
|
// Copyright (C) 2018-2021 Intel Corporation
|
||||||
|
// SPDX-License-Identifier: Apache-2.0
|
||||||
|
//
|
||||||
|
|
||||||
|
#pragma once
|
||||||
|
|
||||||
|
#include <memory>
|
||||||
|
|
||||||
|
#include "openvino/op/op.hpp"
|
||||||
|
|
||||||
|
namespace ov {
|
||||||
|
namespace op {
|
||||||
|
namespace v0 {
|
||||||
|
/// \brief Global Response Normalization with L2 norm (across channels only).
|
||||||
|
///
|
||||||
|
class OPENVINO_API GRN : public Op {
|
||||||
|
public:
|
||||||
|
OPENVINO_RTTI_DECLARATION;
|
||||||
|
|
||||||
|
GRN() = default;
|
||||||
|
/// \brief Constructs a GRN operation.
|
||||||
|
///
|
||||||
|
/// \param data - Node producing the input tensor
|
||||||
|
/// \param bias - The bias added to the variance.
|
||||||
|
///
|
||||||
|
GRN(const Output<Node>& data, float bias);
|
||||||
|
|
||||||
|
void validate_and_infer_types() override;
|
||||||
|
bool visit_attributes(AttributeVisitor& visitor) override;
|
||||||
|
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
||||||
|
|
||||||
|
float get_bias() const {
|
||||||
|
return m_bias;
|
||||||
|
}
|
||||||
|
|
||||||
|
protected:
|
||||||
|
float m_bias = 1.0f;
|
||||||
|
};
|
||||||
|
} // namespace v0
|
||||||
|
} // namespace op
|
||||||
|
} // namespace ov
|
||||||
273
ngraph/core/include/openvino/op/group_conv.hpp
Normal file
273
ngraph/core/include/openvino/op/group_conv.hpp
Normal file
@ -0,0 +1,273 @@
|
|||||||
|
// Copyright (C) 2018-2021 Intel Corporation
|
||||||
|
// SPDX-License-Identifier: Apache-2.0
|
||||||
|
//
|
||||||
|
|
||||||
|
#pragma once
|
||||||
|
|
||||||
|
#include "openvino/op/convolution.hpp"
|
||||||
|
#include "openvino/op/op.hpp"
|
||||||
|
#include "openvino/op/util/attr_types.hpp"
|
||||||
|
|
||||||
|
namespace ov {
|
||||||
|
namespace op {
|
||||||
|
namespace v1 {
|
||||||
|
/// \brief Batched convolution operation, with optional window dilation and stride.
|
||||||
|
class OPENVINO_API GroupConvolution : public Op {
|
||||||
|
public:
|
||||||
|
OPENVINO_RTTI_DECLARATION;
|
||||||
|
|
||||||
|
/// \brief Constructs a batched convolution operation.
|
||||||
|
GroupConvolution() = default;
|
||||||
|
/// \brief Constructs a batched convolution operation.
|
||||||
|
///
|
||||||
|
/// \param data_batch The node producing the input data batch tensor.<br>
|
||||||
|
/// `[N, C_IN, D1, ... Df]`
|
||||||
|
/// \param filters The node producing the filters tensor.<br>
|
||||||
|
/// `[GROUPS, FC_OUT, FC_IN, F1, ... Ff]`
|
||||||
|
/// \param strides The strides.<br>
|
||||||
|
/// `[f]`
|
||||||
|
/// \param dilations The dilations.<br>
|
||||||
|
/// `[f]`
|
||||||
|
/// \param pads_begin The beginning of padding shape.<br>
|
||||||
|
/// `[f]`
|
||||||
|
/// \param pads_end The end of padding shape.<br>
|
||||||
|
/// `[f]`
|
||||||
|
/// \param auto_pad The pad type for automatically computing padding sizes.<br>
|
||||||
|
/// `[f]`
|
||||||
|
///
|
||||||
|
/// Output `[N, FC_OUT * GROUPS, R1, ... Rf]`
|
||||||
|
///
|
||||||
|
GroupConvolution(const Output<Node>& data_batch,
|
||||||
|
const Output<Node>& filters,
|
||||||
|
const Strides& strides,
|
||||||
|
const CoordinateDiff& pads_begin,
|
||||||
|
const CoordinateDiff& pads_end,
|
||||||
|
const Strides& dilations,
|
||||||
|
const PadType& auto_pad = PadType::EXPLICIT);
|
||||||
|
|
||||||
|
bool visit_attributes(AttributeVisitor& visitor) override;
|
||||||
|
void validate_and_infer_types() override;
|
||||||
|
|
||||||
|
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
||||||
|
/// \return The strides.
|
||||||
|
const Strides& get_strides() const {
|
||||||
|
return m_strides;
|
||||||
|
}
|
||||||
|
void set_strides(const Strides& strides) {
|
||||||
|
m_strides = strides;
|
||||||
|
}
|
||||||
|
/// \return The dilations.
|
||||||
|
const Strides& get_dilations() const {
|
||||||
|
return m_dilations;
|
||||||
|
}
|
||||||
|
void set_dilations(const Strides& dilations) {
|
||||||
|
m_dilations = dilations;
|
||||||
|
}
|
||||||
|
/// \return The padding-below sizes (possibly negative).
|
||||||
|
const CoordinateDiff& get_pads_begin() const {
|
||||||
|
return m_pads_begin;
|
||||||
|
}
|
||||||
|
void set_pads_begin(const CoordinateDiff& pads_begin) {
|
||||||
|
m_pads_begin = pads_begin;
|
||||||
|
}
|
||||||
|
/// \return The padding-above sizes (possibly negative).
|
||||||
|
const CoordinateDiff& get_pads_end() const {
|
||||||
|
return m_pads_end;
|
||||||
|
}
|
||||||
|
void set_adding_above(const CoordinateDiff& pads_end) {
|
||||||
|
m_pads_end = pads_end;
|
||||||
|
}
|
||||||
|
/// \return The pad type for convolution.
|
||||||
|
const PadType& get_auto_pad() const {
|
||||||
|
return m_auto_pad;
|
||||||
|
}
|
||||||
|
void set_auto_pad(const PadType& auto_pad) {
|
||||||
|
m_auto_pad = auto_pad;
|
||||||
|
}
|
||||||
|
/// \return The default value for Convolution.
|
||||||
|
OPENVINO_SUPPRESS_DEPRECATED_START
|
||||||
|
std::shared_ptr<Node> get_default_value() const override;
|
||||||
|
OPENVINO_SUPPRESS_DEPRECATED_END
|
||||||
|
|
||||||
|
protected:
|
||||||
|
Strides m_strides;
|
||||||
|
Strides m_dilations;
|
||||||
|
CoordinateDiff m_pads_begin;
|
||||||
|
CoordinateDiff m_pads_end;
|
||||||
|
PadType m_auto_pad;
|
||||||
|
};
|
||||||
|
|
||||||
|
/// \brief Data batch backprop for batched convolution operation.
|
||||||
|
class OPENVINO_API GroupConvolutionBackpropData : public Op {
|
||||||
|
public:
|
||||||
|
OPENVINO_RTTI_DECLARATION;
|
||||||
|
|
||||||
|
/// \brief Constructs a batched-convolution data batch-backprop operation.
|
||||||
|
GroupConvolutionBackpropData();
|
||||||
|
// clang-format off
|
||||||
|
//
|
||||||
|
// \brief Constructs a batched-convolution data batch-backprop operation.
|
||||||
|
//
|
||||||
|
// \param data The node producing data from forward-prop. Shape: [N,
|
||||||
|
// C_INPUT * GROUPS, X1, ..., XD].
|
||||||
|
// \param filter The node producing the filter from forward-prop. Shape:
|
||||||
|
// [GROUPS, C_INPUT, C_OUTPUT, K_D, ..., K_1]
|
||||||
|
// \param output_shape The shape of the data batch from forward-prop. It's size
|
||||||
|
// should be equal to number of data spatial dimensions.
|
||||||
|
// \param strides The strides from forward-prop.
|
||||||
|
// \param pads_begin The padding-below sizes from forward-prop.
|
||||||
|
// \param pads_end The padding-above sizes from forward-prop.
|
||||||
|
// \param dilations The dilations from forward-prop.
|
||||||
|
// \param auto_pad The pad type for automatically computing padding sizes.
|
||||||
|
// \param output_padding The output padding adds additional amount of paddings per
|
||||||
|
// each spatial axis in the output tensor.
|
||||||
|
//
|
||||||
|
// clang-format on
|
||||||
|
//
|
||||||
|
GroupConvolutionBackpropData(const Output<Node>& data,
|
||||||
|
const Output<Node>& filter,
|
||||||
|
const Output<Node>& output_shape,
|
||||||
|
const Strides& strides,
|
||||||
|
const CoordinateDiff& pads_begin,
|
||||||
|
const CoordinateDiff& pads_end,
|
||||||
|
const Strides& dilations,
|
||||||
|
const PadType& auto_pad = PadType::EXPLICIT,
|
||||||
|
const CoordinateDiff& output_padding = {});
|
||||||
|
|
||||||
|
// clang-format off
|
||||||
|
//
|
||||||
|
// \brief Constructs a batched-convolution data batch-backprop operation.
|
||||||
|
//
|
||||||
|
// \param data The node producing data from forward-prop. Shape: [N,
|
||||||
|
// C_INPUT * GROUPS, X1, ..., XD].
|
||||||
|
// \param filter The node producing the filter from forward-prop. Shape:
|
||||||
|
// [GROUPS, C_INPUT, C_OUTPUT, K_D, ..., K_1]
|
||||||
|
// \param output_shape The shape of the data batch from forward-prop. It's size
|
||||||
|
// should be equal to number of data spatial dimensions.
|
||||||
|
// \param strides The strides from forward-prop.
|
||||||
|
// \param dilations The dilations from forward-prop.
|
||||||
|
// \param auto_pad The pad type for automatically computing padding sizes.
|
||||||
|
// \param output_padding The output padding adds additional amount of paddings per
|
||||||
|
// each spatial axis in the output tensor.
|
||||||
|
//
|
||||||
|
// clang-format on
|
||||||
|
//
|
||||||
|
GroupConvolutionBackpropData(const Output<Node>& data,
|
||||||
|
const Output<Node>& filter,
|
||||||
|
const Output<Node>& output_shape,
|
||||||
|
const Strides& strides,
|
||||||
|
const Strides& dilations,
|
||||||
|
const PadType& auto_pad,
|
||||||
|
const CoordinateDiff& output_padding = {});
|
||||||
|
|
||||||
|
// clang-format off
|
||||||
|
//
|
||||||
|
// \brief Constructs a batched-convolution data batch-backprop operation.
|
||||||
|
//
|
||||||
|
// \param data The node producing data from forward-prop. Shape:
|
||||||
|
// [N, C_INPUT * GROUPS, X1, ..., XD].
|
||||||
|
// \param filter The node producing the filter from forward-prop. Shape:
|
||||||
|
// [GROUPS, C_INPUT, C_OUTPUT, K_D, ..., K_1]
|
||||||
|
// \param strides The strides from forward-prop.
|
||||||
|
// \param pads_begin The padding-below sizes from forward-prop.
|
||||||
|
// \param pads_end The padding-above sizes from forward-prop.
|
||||||
|
// \param dilations The dilations from forward-prop.
|
||||||
|
// \param auto_pad The pad type for automatically computing padding sizes.
|
||||||
|
// \param output_padding The output padding adds additional amount of paddings per
|
||||||
|
// each spatial axis in the output tensor.
|
||||||
|
//
|
||||||
|
// clang-format on
|
||||||
|
GroupConvolutionBackpropData(const Output<Node>& data,
|
||||||
|
const Output<Node>& filter,
|
||||||
|
const Strides& strides,
|
||||||
|
const CoordinateDiff& pads_begin,
|
||||||
|
const CoordinateDiff& pads_end,
|
||||||
|
const Strides& dilations,
|
||||||
|
const PadType& auto_pad = PadType::EXPLICIT,
|
||||||
|
const CoordinateDiff& output_padding = {});
|
||||||
|
///
|
||||||
|
/// \brief Calculates output spatial features size.
|
||||||
|
///
|
||||||
|
/// \param[in] input_data_shape The input data partial shape
|
||||||
|
/// \param[in] filters_shape The filters partial shape
|
||||||
|
/// \param[in] strides The strides values.
|
||||||
|
/// \param[in] dilations The dilations values.
|
||||||
|
/// \param[in] pads_begin The paddings at the beginning of axis.
|
||||||
|
/// \param[in] pads_end The paddings at the end of axis.
|
||||||
|
/// \param[in] output_padding The output padding values.
|
||||||
|
/// \param output_spatial_shape The placeholder for computed output spatial
|
||||||
|
/// partial
|
||||||
|
/// shape.
|
||||||
|
///
|
||||||
|
void infer_conv_backprop_output_spatial_shape(const std::vector<Dimension>& input_data_shape,
|
||||||
|
const std::vector<Dimension>& filters_shape,
|
||||||
|
const Strides& strides,
|
||||||
|
const Strides& dilations,
|
||||||
|
const CoordinateDiff& pads_begin,
|
||||||
|
const CoordinateDiff& pads_end,
|
||||||
|
const CoordinateDiff& output_padding,
|
||||||
|
std::vector<Dimension>& output_spatial_shape);
|
||||||
|
|
||||||
|
bool visit_attributes(AttributeVisitor& visitor) override;
|
||||||
|
bool is_dynamic() const override;
|
||||||
|
void validate_and_infer_types() override;
|
||||||
|
|
||||||
|
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
||||||
|
|
||||||
|
/// \return The spatial shape of the output.
|
||||||
|
const PartialShape get_convolution_output_shape() const;
|
||||||
|
void set_output_shape(const ngraph::Shape& output_shape);
|
||||||
|
/// \return The strides from the forward prop.
|
||||||
|
const Strides& get_strides() const {
|
||||||
|
return m_strides;
|
||||||
|
}
|
||||||
|
void set_strides(const Strides& strides) {
|
||||||
|
m_strides = strides;
|
||||||
|
}
|
||||||
|
/// \return The dilations from the forward prop.
|
||||||
|
const Strides& get_dilations() const {
|
||||||
|
return m_dilations;
|
||||||
|
}
|
||||||
|
void set_dilations(const Strides& dilations) {
|
||||||
|
m_dilations = dilations;
|
||||||
|
}
|
||||||
|
/// \return The number of pixels to add to the beginning along each axis.
|
||||||
|
const CoordinateDiff& get_pads_begin() const {
|
||||||
|
return m_pads_begin;
|
||||||
|
}
|
||||||
|
void set_pads_begin(const CoordinateDiff& pads_begin) {
|
||||||
|
m_pads_begin = pads_begin;
|
||||||
|
}
|
||||||
|
/// \return The number of pixels to add to the ending along each axis.
|
||||||
|
const CoordinateDiff& get_pads_end() const {
|
||||||
|
return m_pads_end;
|
||||||
|
}
|
||||||
|
void set_pads_end(const CoordinateDiff& pads_end) {
|
||||||
|
m_pads_end = pads_end;
|
||||||
|
}
|
||||||
|
/// \return The auto pad.
|
||||||
|
const PadType& get_auto_pad() const {
|
||||||
|
return m_auto_pad;
|
||||||
|
}
|
||||||
|
void set_auto_pad(const PadType& auto_pad) {
|
||||||
|
m_auto_pad = auto_pad;
|
||||||
|
}
|
||||||
|
/// \return The output padding.
|
||||||
|
const CoordinateDiff& get_output_padding() const {
|
||||||
|
return m_output_padding;
|
||||||
|
}
|
||||||
|
void set_output_padding(const CoordinateDiff& output_padding) {
|
||||||
|
m_output_padding = output_padding;
|
||||||
|
}
|
||||||
|
|
||||||
|
protected:
|
||||||
|
Strides m_strides;
|
||||||
|
Strides m_dilations;
|
||||||
|
CoordinateDiff m_pads_begin;
|
||||||
|
CoordinateDiff m_pads_end;
|
||||||
|
PadType m_auto_pad;
|
||||||
|
CoordinateDiff m_output_padding;
|
||||||
|
};
|
||||||
|
} // namespace v1
|
||||||
|
} // namespace op
|
||||||
|
} // namespace ov
|
||||||
160
ngraph/core/include/openvino/op/gru_cell.hpp
Normal file
160
ngraph/core/include/openvino/op/gru_cell.hpp
Normal file
@ -0,0 +1,160 @@
|
|||||||
|
// Copyright (C) 2018-2021 Intel Corporation
|
||||||
|
// SPDX-License-Identifier: Apache-2.0
|
||||||
|
//
|
||||||
|
|
||||||
|
#pragma once
|
||||||
|
|
||||||
|
#include <cstddef>
|
||||||
|
#include <memory>
|
||||||
|
#include <string>
|
||||||
|
#include <vector>
|
||||||
|
|
||||||
|
#include "openvino/op/op.hpp"
|
||||||
|
#include "openvino/op/util/activation_functions.hpp"
|
||||||
|
#include "openvino/op/util/rnn_cell_base.hpp"
|
||||||
|
|
||||||
|
namespace ov {
|
||||||
|
namespace op {
|
||||||
|
namespace v3 {
|
||||||
|
///
|
||||||
|
/// \brief Class for GRU cell node.
|
||||||
|
///
|
||||||
|
/// \note Note this class represents only single *cell* and not whole GRU *layer*.
|
||||||
|
///
|
||||||
|
class OPENVINO_API GRUCell : public util::RNNCellBase {
|
||||||
|
public:
|
||||||
|
OPENVINO_RTTI_DECLARATION;
|
||||||
|
GRUCell();
|
||||||
|
///
|
||||||
|
/// \brief Constructs GRUCell node.
|
||||||
|
///
|
||||||
|
/// \param[in] X The input tensor with shape: [batch_size,
|
||||||
|
/// input_size].
|
||||||
|
/// \param[in] initial_hidden_state The hidden state tensor at current time step
|
||||||
|
/// with shape: [batch_size, hidden_size].
|
||||||
|
/// \param[in] W The weight tensor with shape:
|
||||||
|
/// [gates_count * hidden_size, input_size].
|
||||||
|
/// \param[in] R The recurrence weight tensor with shape:
|
||||||
|
/// [gates_count * hidden_size, hidden_size].
|
||||||
|
/// \param[in] hidden_size The number of hidden units for recurrent cell.
|
||||||
|
///
|
||||||
|
GRUCell(const Output<Node>& X,
|
||||||
|
const Output<Node>& initial_hidden_state,
|
||||||
|
const Output<Node>& W,
|
||||||
|
const Output<Node>& R,
|
||||||
|
std::size_t hidden_size);
|
||||||
|
|
||||||
|
///
|
||||||
|
/// \brief Constructs GRUCell node.
|
||||||
|
///
|
||||||
|
/// \param[in] X The input tensor with shape: [batch_size,
|
||||||
|
/// input_size].
|
||||||
|
/// \param[in] initial_hidden_state The hidden state tensor at current time step
|
||||||
|
/// with shape: [batch_size, hidden_size].
|
||||||
|
/// \param[in] W The weight tensor with shape:
|
||||||
|
/// [gates_count * hidden_size, input_size].
|
||||||
|
/// \param[in] R The recurrence weight tensor with shape:
|
||||||
|
/// [gates_count * hidden_size, hidden_size].
|
||||||
|
/// \param[in] hidden_size The number of hidden units for recurrent cell.
|
||||||
|
/// \param[in] activations The vector of activation functions used inside
|
||||||
|
/// recurrent cell.
|
||||||
|
/// \param[in] activations_alpha The vector of alpha parameters for activation
|
||||||
|
/// functions in order respective to activation
|
||||||
|
/// list.
|
||||||
|
/// \param[in] activations_beta The vector of beta parameters for activation
|
||||||
|
/// functions in order respective to activation
|
||||||
|
/// list.
|
||||||
|
/// \param[in] clip The value defining clipping range [-clip,
|
||||||
|
/// clip] on input of activation functions.
|
||||||
|
///
|
||||||
|
GRUCell(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,
|
||||||
|
const std::vector<float>& activations_alpha,
|
||||||
|
const std::vector<float>& activations_beta,
|
||||||
|
float clip,
|
||||||
|
bool linear_before_reset);
|
||||||
|
|
||||||
|
///
|
||||||
|
/// \brief Constructs GRUCell node.
|
||||||
|
///
|
||||||
|
/// \param[in] X The input tensor with shape: [batch_size,
|
||||||
|
/// input_size].
|
||||||
|
/// \param[in] initial_hidden_state The hidden state tensor at current time step
|
||||||
|
/// with shape: [batch_size, hidden_size].
|
||||||
|
/// \param[in] W The weight tensor with shape: [gates_count *
|
||||||
|
/// hidden_size, input_size].
|
||||||
|
/// \param[in] R The recurrence weight tensor with shape:
|
||||||
|
/// [gates_count * hidden_size, hidden_size].
|
||||||
|
/// \param[in] hidden_size The number of hidden units for recurrent cell.
|
||||||
|
/// \param[in] B The sum of biases (weight and recurrence) for
|
||||||
|
/// update, reset and hidden gates.
|
||||||
|
/// If linear_before_reset := true then biases for
|
||||||
|
/// hidden gates are
|
||||||
|
/// placed separately (weight and recurrence).
|
||||||
|
/// Shape: [gates_count * hidden_size] if
|
||||||
|
/// linear_before_reset := false
|
||||||
|
/// Shape: [(gates_count + 1) * hidden_size] if
|
||||||
|
/// linear_before_reset := true
|
||||||
|
/// \param[in] activations The vector of activation functions used inside
|
||||||
|
/// recurrent cell.
|
||||||
|
/// \param[in] activations_alpha The vector of alpha parameters for activation
|
||||||
|
/// functions in order respective to activation
|
||||||
|
/// list.
|
||||||
|
/// \param[in] activations_beta The vector of beta parameters for activation
|
||||||
|
/// functions in order respective to activation
|
||||||
|
/// list.
|
||||||
|
/// \param[in] clip The value defining clipping range [-clip,
|
||||||
|
/// clip] on input of activation functions.
|
||||||
|
/// \param[in] linear_before_reset Whether or not to apply the linear
|
||||||
|
/// transformation before multiplying by the
|
||||||
|
/// output of the reset gate.
|
||||||
|
///
|
||||||
|
GRUCell(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>{"sigmoid", "tanh"},
|
||||||
|
const std::vector<float>& activations_alpha = {},
|
||||||
|
const std::vector<float>& activations_beta = {},
|
||||||
|
float clip = 0.f,
|
||||||
|
bool linear_before_reset = false);
|
||||||
|
|
||||||
|
void validate_and_infer_types() override;
|
||||||
|
bool visit_attributes(AttributeVisitor& visitor) override;
|
||||||
|
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
||||||
|
|
||||||
|
bool get_linear_before_reset() const {
|
||||||
|
return m_linear_before_reset;
|
||||||
|
}
|
||||||
|
|
||||||
|
private:
|
||||||
|
/// brief Add and initialize bias input to all zeros.
|
||||||
|
void add_default_bias_input();
|
||||||
|
|
||||||
|
///
|
||||||
|
/// \brief The Activation function f.
|
||||||
|
///
|
||||||
|
util::ActivationFunction m_activation_f;
|
||||||
|
///
|
||||||
|
/// \brief The Activation function g.
|
||||||
|
///
|
||||||
|
util::ActivationFunction m_activation_g;
|
||||||
|
|
||||||
|
static constexpr std::size_t s_gates_count{3};
|
||||||
|
///
|
||||||
|
/// \brief Control whether or not apply the linear transformation.
|
||||||
|
///
|
||||||
|
/// \note The linear transformation may be applied when computing the output of
|
||||||
|
/// hidden gate. It's done before multiplying by the output of the reset gate.
|
||||||
|
///
|
||||||
|
bool m_linear_before_reset;
|
||||||
|
};
|
||||||
|
} // namespace v3
|
||||||
|
} // namespace op
|
||||||
|
} // namespace ov
|
||||||
54
ngraph/core/include/openvino/op/gru_sequence.hpp
Normal file
54
ngraph/core/include/openvino/op/gru_sequence.hpp
Normal file
@ -0,0 +1,54 @@
|
|||||||
|
// Copyright (C) 2018-2021 Intel Corporation
|
||||||
|
// SPDX-License-Identifier: Apache-2.0
|
||||||
|
//
|
||||||
|
|
||||||
|
#pragma once
|
||||||
|
|
||||||
|
#include <memory>
|
||||||
|
#include <string>
|
||||||
|
#include <vector>
|
||||||
|
|
||||||
|
#include "openvino/op/op.hpp"
|
||||||
|
#include "openvino/op/util/rnn_cell_base.hpp"
|
||||||
|
|
||||||
|
namespace ov {
|
||||||
|
namespace op {
|
||||||
|
namespace v5 {
|
||||||
|
class OPENVINO_API GRUSequence : public util::RNNCellBase {
|
||||||
|
public:
|
||||||
|
OPENVINO_RTTI_DECLARATION;
|
||||||
|
GRUSequence();
|
||||||
|
|
||||||
|
GRUSequence(const Output<Node>& X,
|
||||||
|
const Output<Node>& H_t,
|
||||||
|
const Output<Node>& sequence_lengths,
|
||||||
|
const Output<Node>& W,
|
||||||
|
const Output<Node>& R,
|
||||||
|
const Output<Node>& B,
|
||||||
|
size_t hidden_size,
|
||||||
|
op::RecurrentSequenceDirection direction,
|
||||||
|
const std::vector<std::string>& activations = std::vector<std::string>{"sigmoid", "tanh"},
|
||||||
|
const std::vector<float>& activations_alpha = {},
|
||||||
|
const std::vector<float>& activations_beta = {},
|
||||||
|
float clip = 0.f,
|
||||||
|
bool linear_before_reset = false);
|
||||||
|
|
||||||
|
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
||||||
|
|
||||||
|
void validate_and_infer_types() override;
|
||||||
|
|
||||||
|
bool visit_attributes(AttributeVisitor& visitor) override;
|
||||||
|
bool get_linear_before_reset() const {
|
||||||
|
return m_linear_before_reset;
|
||||||
|
}
|
||||||
|
op::RecurrentSequenceDirection get_direction() const {
|
||||||
|
return m_direction;
|
||||||
|
}
|
||||||
|
|
||||||
|
protected:
|
||||||
|
op::RecurrentSequenceDirection m_direction;
|
||||||
|
bool m_linear_before_reset;
|
||||||
|
};
|
||||||
|
} // namespace v5
|
||||||
|
} // namespace op
|
||||||
|
} // namespace ov
|
||||||
35
ngraph/core/include/openvino/op/hard_sigmoid.hpp
Normal file
35
ngraph/core/include/openvino/op/hard_sigmoid.hpp
Normal file
@ -0,0 +1,35 @@
|
|||||||
|
// Copyright (C) 2018-2021 Intel Corporation
|
||||||
|
// SPDX-License-Identifier: Apache-2.0
|
||||||
|
//
|
||||||
|
|
||||||
|
#pragma once
|
||||||
|
|
||||||
|
#include "openvino/op/op.hpp"
|
||||||
|
|
||||||
|
namespace ov {
|
||||||
|
namespace op {
|
||||||
|
namespace v0 {
|
||||||
|
/// \brief Parameterized, bounded sigmoid-like, piecewise linear
|
||||||
|
/// function. min(max(alpha*x + beta, 0), 1)
|
||||||
|
///
|
||||||
|
class OPENVINO_API HardSigmoid : public Op {
|
||||||
|
public:
|
||||||
|
OPENVINO_RTTI_DECLARATION;
|
||||||
|
|
||||||
|
HardSigmoid();
|
||||||
|
|
||||||
|
/// \brief Constructs a HardSigmoid operation.
|
||||||
|
///
|
||||||
|
/// \param data Input tensor.
|
||||||
|
/// \param[in] alpha A scalar value representing the alpha parameter.
|
||||||
|
/// \param[in] beta A scalar value representing the beta parameter.
|
||||||
|
///
|
||||||
|
HardSigmoid(const Output<Node>& data, const Output<Node>& alpha, const Output<Node>& beta);
|
||||||
|
|
||||||
|
bool visit_attributes(AttributeVisitor& visitor) override;
|
||||||
|
void validate_and_infer_types() override;
|
||||||
|
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
||||||
|
};
|
||||||
|
} // namespace v0
|
||||||
|
} // namespace op
|
||||||
|
} // namespace ov
|
||||||
35
ngraph/core/include/openvino/op/hsigmoid.hpp
Normal file
35
ngraph/core/include/openvino/op/hsigmoid.hpp
Normal file
@ -0,0 +1,35 @@
|
|||||||
|
// Copyright (C) 2018-2021 Intel Corporation
|
||||||
|
// SPDX-License-Identifier: Apache-2.0
|
||||||
|
//
|
||||||
|
|
||||||
|
#pragma once
|
||||||
|
|
||||||
|
#include "openvino/op/op.hpp"
|
||||||
|
#include "openvino/op/util/unary_elementwise_arithmetic.hpp"
|
||||||
|
|
||||||
|
namespace ov {
|
||||||
|
namespace op {
|
||||||
|
namespace v5 {
|
||||||
|
/// \brief A HSigmoid Activation Function
|
||||||
|
/// f(x) = min(max(x + 3, 0), 6) / 6 or
|
||||||
|
/// f(x) = min(ReLU(x + 3), 6) / 6
|
||||||
|
///
|
||||||
|
class OPENVINO_API HSigmoid : public util::UnaryElementwiseArithmetic {
|
||||||
|
public:
|
||||||
|
OPENVINO_RTTI_DECLARATION;
|
||||||
|
HSigmoid() = default;
|
||||||
|
|
||||||
|
/// \brief Constructs a HSigmoid operation.
|
||||||
|
///
|
||||||
|
/// \param data Input tensor
|
||||||
|
HSigmoid(const Output<Node>& arg);
|
||||||
|
|
||||||
|
bool visit_attributes(AttributeVisitor& visitor) override;
|
||||||
|
|
||||||
|
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
||||||
|
bool evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const override;
|
||||||
|
bool has_evaluate() const override;
|
||||||
|
};
|
||||||
|
} // namespace v5
|
||||||
|
} // namespace op
|
||||||
|
} // namespace ov
|
||||||
35
ngraph/core/include/openvino/op/hswish.hpp
Normal file
35
ngraph/core/include/openvino/op/hswish.hpp
Normal file
@ -0,0 +1,35 @@
|
|||||||
|
// Copyright (C) 2018-2021 Intel Corporation
|
||||||
|
// SPDX-License-Identifier: Apache-2.0
|
||||||
|
//
|
||||||
|
|
||||||
|
#pragma once
|
||||||
|
|
||||||
|
#include "openvino/op/op.hpp"
|
||||||
|
#include "openvino/op/util/unary_elementwise_arithmetic.hpp"
|
||||||
|
|
||||||
|
namespace ov {
|
||||||
|
namespace op {
|
||||||
|
namespace v4 {
|
||||||
|
/// \brief A HSwish Activation Function
|
||||||
|
/// f(x) = x * min(max(x + 3, 0), 6) / 6 or
|
||||||
|
/// f(x) = x * min(ReLU(x + 3), 6) / 6
|
||||||
|
///
|
||||||
|
class OPENVINO_API HSwish : public util::UnaryElementwiseArithmetic {
|
||||||
|
public:
|
||||||
|
OPENVINO_RTTI_DECLARATION;
|
||||||
|
HSwish() = default;
|
||||||
|
|
||||||
|
/// \brief Constructs a HSwish (hard version of Swish) operation.
|
||||||
|
///
|
||||||
|
/// \param data Input tensor
|
||||||
|
HSwish(const Output<Node>& arg);
|
||||||
|
|
||||||
|
bool visit_attributes(AttributeVisitor& visitor) override;
|
||||||
|
|
||||||
|
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
||||||
|
bool evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const override;
|
||||||
|
bool has_evaluate() const override;
|
||||||
|
};
|
||||||
|
} // namespace v4
|
||||||
|
} // namespace op
|
||||||
|
} // namespace ov
|
||||||
41
ngraph/core/include/openvino/op/idft.hpp
Normal file
41
ngraph/core/include/openvino/op/idft.hpp
Normal file
@ -0,0 +1,41 @@
|
|||||||
|
// Copyright (C) 2018-2021 Intel Corporation
|
||||||
|
// SPDX-License-Identifier: Apache-2.0
|
||||||
|
//
|
||||||
|
|
||||||
|
#pragma once
|
||||||
|
|
||||||
|
#include <cstddef>
|
||||||
|
#include <vector>
|
||||||
|
|
||||||
|
#include "openvino/op/op.hpp"
|
||||||
|
#include "openvino/op/util/fft_base.hpp"
|
||||||
|
|
||||||
|
namespace ov {
|
||||||
|
namespace op {
|
||||||
|
namespace v7 {
|
||||||
|
/// \brief An operation IDFT that computes the inverse discrete Fourier transformation.
|
||||||
|
class OPENVINO_API IDFT : public util::FFTBase {
|
||||||
|
public:
|
||||||
|
OPENVINO_RTTI_DECLARATION;
|
||||||
|
IDFT() = default;
|
||||||
|
|
||||||
|
/// \brief Constructs a IDFT operation. IDFT is performed for full size axes.
|
||||||
|
///
|
||||||
|
/// \param data Input data
|
||||||
|
/// \param axes Axes to perform IDFT
|
||||||
|
IDFT(const Output<Node>& data, const Output<Node>& axes);
|
||||||
|
|
||||||
|
/// \brief Constructs a IDFT operation.
|
||||||
|
///
|
||||||
|
/// \param data Input data
|
||||||
|
/// \param axes Axes to perform IDFT
|
||||||
|
/// \param signal_size Signal sizes for 'axes'
|
||||||
|
IDFT(const Output<Node>& data, const Output<Node>& axes, const Output<Node>& signal_size);
|
||||||
|
|
||||||
|
bool visit_attributes(AttributeVisitor& visitor) override;
|
||||||
|
|
||||||
|
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
||||||
|
};
|
||||||
|
} // namespace v7
|
||||||
|
} // namespace op
|
||||||
|
} // namespace ov
|
||||||
94
ngraph/core/include/openvino/op/if.hpp
Normal file
94
ngraph/core/include/openvino/op/if.hpp
Normal file
@ -0,0 +1,94 @@
|
|||||||
|
// Copyright (C) 2018-2021 Intel Corporation
|
||||||
|
// SPDX-License-Identifier: Apache-2.0
|
||||||
|
//
|
||||||
|
|
||||||
|
#pragma once
|
||||||
|
|
||||||
|
#include <vector>
|
||||||
|
|
||||||
|
#include "openvino/core/function.hpp"
|
||||||
|
#include "openvino/op/parameter.hpp"
|
||||||
|
#include "openvino/op/util/multi_subgraph_base.hpp"
|
||||||
|
|
||||||
|
namespace ov {
|
||||||
|
namespace op {
|
||||||
|
namespace v8 {
|
||||||
|
/// \brief If operation.
|
||||||
|
class OPENVINO_API If : public util::MultiSubGraphOp {
|
||||||
|
public:
|
||||||
|
enum BodyIndexes { THEN_BODY_INDEX = 0, ELSE_BODY_INDEX = 1 };
|
||||||
|
|
||||||
|
OPENVINO_RTTI_DECLARATION;
|
||||||
|
bool visit_attributes(AttributeVisitor& visitor) override;
|
||||||
|
|
||||||
|
/// \brief Constructs If with condition
|
||||||
|
///
|
||||||
|
/// \param execution_condition condition node.
|
||||||
|
If(const Output<Node>& execution_condition);
|
||||||
|
If();
|
||||||
|
|
||||||
|
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
||||||
|
|
||||||
|
/// \brief gets then_body as ngraph::Function.
|
||||||
|
///
|
||||||
|
/// \return then_body as ngraph::Function.
|
||||||
|
const std::shared_ptr<Function>& get_then_body() const {
|
||||||
|
return m_bodies[THEN_BODY_INDEX];
|
||||||
|
}
|
||||||
|
|
||||||
|
/// \brief gets else_body as ngraph::Function.
|
||||||
|
///
|
||||||
|
/// \return else_body as ngraph::Function.
|
||||||
|
const std::shared_ptr<Function>& get_else_body() const {
|
||||||
|
return m_bodies[ELSE_BODY_INDEX];
|
||||||
|
}
|
||||||
|
|
||||||
|
/// \brief sets new ngraph::Function as new then_body.
|
||||||
|
///
|
||||||
|
/// \param body new body for 'then' branch.
|
||||||
|
void set_then_body(const std::shared_ptr<Function>& body) {
|
||||||
|
m_bodies[THEN_BODY_INDEX] = body;
|
||||||
|
}
|
||||||
|
|
||||||
|
/// \brief sets new ngraph::Function as new else_body.
|
||||||
|
///
|
||||||
|
/// \param body new body for 'else' branch.
|
||||||
|
void set_else_body(const std::shared_ptr<Function>& body) {
|
||||||
|
m_bodies[ELSE_BODY_INDEX] = body;
|
||||||
|
}
|
||||||
|
|
||||||
|
/// \brief sets new input to the operation associated with parameters
|
||||||
|
/// of each sub-graphs
|
||||||
|
///
|
||||||
|
/// \param value input to operation
|
||||||
|
/// \param then_parameter parameter for then_body or nullptr
|
||||||
|
/// \param else_parameter parameter for else_body or nullpt
|
||||||
|
void set_input(const Output<Node>& value,
|
||||||
|
const std::shared_ptr<v0::Parameter>& then_parameter,
|
||||||
|
const std::shared_ptr<v0::Parameter>& else_parameter);
|
||||||
|
|
||||||
|
/// \brief sets new output from the operation associated with results
|
||||||
|
/// of each sub-graphs
|
||||||
|
///
|
||||||
|
/// \param then_result result from then_body
|
||||||
|
/// \param else_parameter result from else_body
|
||||||
|
/// \return output from operation
|
||||||
|
Output<Node> set_output(const std::shared_ptr<v0::Result>& then_result,
|
||||||
|
const std::shared_ptr<v0::Result>& else_result);
|
||||||
|
|
||||||
|
void validate_and_infer_types() override;
|
||||||
|
bool evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const override;
|
||||||
|
|
||||||
|
bool has_evaluate() const override;
|
||||||
|
|
||||||
|
private:
|
||||||
|
using OutputMap = std::map<int64_t, std::shared_ptr<MultiSubGraphOp::OutputDescription>>;
|
||||||
|
|
||||||
|
void validate_and_infer_type_body(const std::shared_ptr<Function>& body,
|
||||||
|
const MultiSubgraphInputDescriptionVector& input_descriptors);
|
||||||
|
|
||||||
|
OutputMap get_mapping_outputs_on_body_description(const MultiSubgraphOutputDescriptionVector& output_descriptors);
|
||||||
|
};
|
||||||
|
} // namespace v8
|
||||||
|
} // namespace op
|
||||||
|
} // namespace ov
|
||||||
360
ngraph/core/include/openvino/op/interpolate.hpp
Normal file
360
ngraph/core/include/openvino/op/interpolate.hpp
Normal file
@ -0,0 +1,360 @@
|
|||||||
|
// Copyright (C) 2018-2021 Intel Corporation
|
||||||
|
// SPDX-License-Identifier: Apache-2.0
|
||||||
|
//
|
||||||
|
|
||||||
|
#pragma once
|
||||||
|
|
||||||
|
#include <cstdint>
|
||||||
|
#include <vector>
|
||||||
|
|
||||||
|
#include "openvino/core/attribute_adapter.hpp"
|
||||||
|
#include "openvino/op/op.hpp"
|
||||||
|
#include "openvino/op/util/attr_types.hpp"
|
||||||
|
|
||||||
|
namespace ov {
|
||||||
|
namespace op {
|
||||||
|
namespace v0 {
|
||||||
|
|
||||||
|
/// \brief Layer which performs bilinear interpolation
|
||||||
|
class OPENVINO_API Interpolate : public Op {
|
||||||
|
public:
|
||||||
|
OPENVINO_RTTI_DECLARATION;
|
||||||
|
/// \brief Structure that specifies attributes for interpolation
|
||||||
|
struct Attributes {
|
||||||
|
// specify dimension indices where interpolation is applied, and `axes` is any
|
||||||
|
// unordered list of indeces of different dimensions of input tensor. Required.
|
||||||
|
AxisSet axes;
|
||||||
|
// specifies type of interpolation
|
||||||
|
// one of `nearest`, `linear`, `cubic`, `area`. Required.
|
||||||
|
std::string mode;
|
||||||
|
// a flag that specifies whether to align corners or not.
|
||||||
|
// `true` (default) means the alignment is applied,
|
||||||
|
// `false` means the alignment isn't applied.
|
||||||
|
bool align_corners = true;
|
||||||
|
// a flag that specifies whether to perform anti-aliasing. default is `false`
|
||||||
|
bool antialias = false;
|
||||||
|
// specify the number of pixels to add to the beginning of the image being
|
||||||
|
// interpolated. This addition of pixels is done before interpolation calculation.
|
||||||
|
std::vector<size_t> pads_begin;
|
||||||
|
// specify the number of pixels to add to the end of the image being interpolated.
|
||||||
|
// This addition of pixels is done before interpolation calculation.
|
||||||
|
std::vector<size_t> pads_end;
|
||||||
|
};
|
||||||
|
|
||||||
|
enum class InterpolateMode {
|
||||||
|
NEAREST,
|
||||||
|
LINEAR,
|
||||||
|
CUBIC,
|
||||||
|
AREA,
|
||||||
|
nearest OPENVINO_ENUM_DEPRECATED("Please use NEAREST instead") = NEAREST,
|
||||||
|
linear OPENVINO_ENUM_DEPRECATED("Please use LINEAR instead") = LINEAR,
|
||||||
|
cubic OPENVINO_ENUM_DEPRECATED("Please use CUBIC instead") = CUBIC,
|
||||||
|
area OPENVINO_ENUM_DEPRECATED("Please use AREA instead") = AREA
|
||||||
|
};
|
||||||
|
|
||||||
|
Interpolate() = default;
|
||||||
|
/// \brief Constructs a Interpolate operation
|
||||||
|
///
|
||||||
|
/// \param image Input image
|
||||||
|
/// \param output_shape Output shape of spatial axes
|
||||||
|
/// \param attrs Interpolation attributes
|
||||||
|
Interpolate(const Output<Node>& image, const Output<Node>& output_shape, const Attributes& attrs);
|
||||||
|
bool visit_attributes(AttributeVisitor& visitor) override;
|
||||||
|
|
||||||
|
void validate_and_infer_types() override;
|
||||||
|
|
||||||
|
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
||||||
|
|
||||||
|
const Attributes& get_attrs() const {
|
||||||
|
return m_attrs;
|
||||||
|
}
|
||||||
|
|
||||||
|
private:
|
||||||
|
Attributes m_attrs;
|
||||||
|
};
|
||||||
|
} // namespace v0
|
||||||
|
|
||||||
|
namespace v4 {
|
||||||
|
class OPENVINO_API Interpolate : public Op {
|
||||||
|
public:
|
||||||
|
OPENVINO_RTTI_DECLARATION;
|
||||||
|
|
||||||
|
/// \brief Shape calculation mode
|
||||||
|
///
|
||||||
|
/// sizes - output shape for interpolated axes is calculated using input `sizes`
|
||||||
|
/// scales - output shape for interpolated axes is calculated using input `scales`
|
||||||
|
enum class ShapeCalcMode {
|
||||||
|
SIZES,
|
||||||
|
SCALES,
|
||||||
|
sizes OPENVINO_ENUM_DEPRECATED("Please use SIZES instead") = SIZES,
|
||||||
|
scales OPENVINO_ENUM_DEPRECATED("Please use SCALES instead") = SCALES
|
||||||
|
};
|
||||||
|
|
||||||
|
/// \brief Interpolation mode
|
||||||
|
///
|
||||||
|
/// nearest - nearest interpolation
|
||||||
|
/// linear - linear interpolation as in TensorFlow
|
||||||
|
/// linear_onnx - linear interpolation as in ONNX
|
||||||
|
/// cubic - cubic interpolation
|
||||||
|
enum class InterpolateMode {
|
||||||
|
NEAREST,
|
||||||
|
LINEAR,
|
||||||
|
LINEAR_ONNX,
|
||||||
|
CUBIC,
|
||||||
|
nearest OPENVINO_ENUM_DEPRECATED("Please use NEAREST instead") = NEAREST,
|
||||||
|
linear OPENVINO_ENUM_DEPRECATED("Please use LINEAR instead") = LINEAR,
|
||||||
|
linear_onnx OPENVINO_ENUM_DEPRECATED("Please use LINEAR_ONNX instead") = LINEAR_ONNX,
|
||||||
|
cubic OPENVINO_ENUM_DEPRECATED("Please use CUBIC instead") = CUBIC
|
||||||
|
};
|
||||||
|
|
||||||
|
/// \brief Mode of the calculation of the source coordinate from resized one
|
||||||
|
///
|
||||||
|
/// These modes are modes from ONNX runtime.
|
||||||
|
enum class CoordinateTransformMode {
|
||||||
|
HALF_PIXEL,
|
||||||
|
PYTORCH_HALF_PIXEL,
|
||||||
|
ASYMMETRIC,
|
||||||
|
TF_HALF_PIXEL_FOR_NN,
|
||||||
|
ALIGN_CORNERS,
|
||||||
|
half_pixel OPENVINO_ENUM_DEPRECATED("Please use HALF_PIXEL instead") = HALF_PIXEL,
|
||||||
|
pytorch_half_pixel OPENVINO_ENUM_DEPRECATED("Please use PYTORCH_HALF_PIXEL instead") = PYTORCH_HALF_PIXEL,
|
||||||
|
asymmetric OPENVINO_ENUM_DEPRECATED("Please use ASYMMETRIC instead") = ASYMMETRIC,
|
||||||
|
tf_half_pixel_for_nn OPENVINO_ENUM_DEPRECATED("Please use TF_HALF_PIXEL_FOR_NN instead") = TF_HALF_PIXEL_FOR_NN,
|
||||||
|
align_corners OPENVINO_ENUM_DEPRECATED("Please use ALIGN_CORNERS instead") = ALIGN_CORNERS
|
||||||
|
};
|
||||||
|
|
||||||
|
/// \brief Round modes for the nearest interpolation.
|
||||||
|
enum class NearestMode {
|
||||||
|
ROUND_PREFER_FLOOR,
|
||||||
|
ROUND_PREFER_CEIL,
|
||||||
|
FLOOR,
|
||||||
|
CEIL,
|
||||||
|
SIMPLE,
|
||||||
|
round_prefer_floor OPENVINO_ENUM_DEPRECATED("Please use ROUND_PREFER_FLOOR instead") = ROUND_PREFER_FLOOR,
|
||||||
|
round_prefer_ceil OPENVINO_ENUM_DEPRECATED("Please use ROUND_PREFER_CEIL instead") = ROUND_PREFER_CEIL,
|
||||||
|
floor OPENVINO_ENUM_DEPRECATED("Please use FLOOR instead") = FLOOR,
|
||||||
|
ceil OPENVINO_ENUM_DEPRECATED("Please use CEIL instead") = CEIL,
|
||||||
|
simple OPENVINO_ENUM_DEPRECATED("Please use SIMPLE instead") = SIMPLE
|
||||||
|
};
|
||||||
|
|
||||||
|
struct InterpolateAttrs {
|
||||||
|
// specifies type of interpolation
|
||||||
|
// one of `nearest`, `linear`, `linear_onnx`, `cubic` Required.
|
||||||
|
InterpolateMode mode = InterpolateMode::NEAREST;
|
||||||
|
// specifies shape calculation mode
|
||||||
|
// one of `sizes`, `scales` Required
|
||||||
|
ShapeCalcMode shape_calculation_mode = ShapeCalcMode::SIZES;
|
||||||
|
// specify the number of pixels to add to the beginning of the image being
|
||||||
|
// interpolated. This addition of pixels is done before interpolation
|
||||||
|
// calculation.
|
||||||
|
std::vector<size_t> pads_begin;
|
||||||
|
// specify the number of pixels to add to the end of the image being
|
||||||
|
// interpolated. This addition of pixels is done before interpolation
|
||||||
|
// calculation.
|
||||||
|
std::vector<size_t> pads_end;
|
||||||
|
// specifies how to transform the coordinate in the resized tensor to the
|
||||||
|
// coordinate in the original tensor. one of `half_pixel`, `pytorch_half_pixel`,
|
||||||
|
// `asymmetric`, `tf_half_pixel_for_nn`, `align_corners`
|
||||||
|
CoordinateTransformMode coordinate_transformation_mode = CoordinateTransformMode::HALF_PIXEL;
|
||||||
|
// specifies round mode when `mode == nearest` and is used only when `mode ==
|
||||||
|
// nearest`. one of `round_prefer_floor`, `round_prefer_ceil`, `floor`, `ceil`,
|
||||||
|
// `simple`
|
||||||
|
NearestMode nearest_mode = NearestMode::ROUND_PREFER_FLOOR;
|
||||||
|
// a flag that specifies whether to perform anti-aliasing. default is `false`
|
||||||
|
bool antialias = false;
|
||||||
|
// specifies the parameter *a* for cubic interpolation (see, e.g.
|
||||||
|
// [article](https://ieeexplore.ieee.org/document/1163711/)). *cube_coeff* is
|
||||||
|
// used only when `mode == cubic`
|
||||||
|
double cube_coeff = -0.75f;
|
||||||
|
|
||||||
|
InterpolateAttrs() = default;
|
||||||
|
|
||||||
|
InterpolateAttrs(InterpolateMode mode,
|
||||||
|
ShapeCalcMode shape_calculation_mode,
|
||||||
|
const std::vector<size_t>& pads_begin,
|
||||||
|
const std::vector<size_t>& pads_end,
|
||||||
|
CoordinateTransformMode coordinate_transformation_mode = CoordinateTransformMode::HALF_PIXEL,
|
||||||
|
NearestMode nearest_mode = NearestMode::ROUND_PREFER_FLOOR,
|
||||||
|
bool antialias = false,
|
||||||
|
double cube_coeff = -0.75)
|
||||||
|
: mode(mode),
|
||||||
|
shape_calculation_mode(shape_calculation_mode),
|
||||||
|
pads_begin(pads_begin),
|
||||||
|
pads_end(pads_end),
|
||||||
|
coordinate_transformation_mode(coordinate_transformation_mode),
|
||||||
|
nearest_mode(nearest_mode),
|
||||||
|
antialias(antialias),
|
||||||
|
cube_coeff(cube_coeff) {}
|
||||||
|
};
|
||||||
|
|
||||||
|
Interpolate() = default;
|
||||||
|
/// \brief Constructs a Interpolate operation without 'axes' input.
|
||||||
|
///
|
||||||
|
/// \param image Input image
|
||||||
|
/// \param output_shape Output shape of spatial axes
|
||||||
|
/// \param scales Scales of spatial axes, i.e. output_shape / input_shape
|
||||||
|
/// \param attrs Interpolation attributes
|
||||||
|
Interpolate(const Output<Node>& image,
|
||||||
|
const Output<Node>& output_shape,
|
||||||
|
const Output<Node>& scales,
|
||||||
|
const InterpolateAttrs& attrs);
|
||||||
|
|
||||||
|
/// \brief Constructs a Interpolate operation with 'axes' input.
|
||||||
|
///
|
||||||
|
/// \param image Input image
|
||||||
|
/// \param output_shape Output shape of spatial axes
|
||||||
|
/// \param scales Scales of spatial axes, i.e. output_shape / input_shape
|
||||||
|
/// \param axes Interpolation axes
|
||||||
|
/// \param attrs Interpolation attributes
|
||||||
|
Interpolate(const Output<Node>& image,
|
||||||
|
const Output<Node>& output_shape,
|
||||||
|
const Output<Node>& scales,
|
||||||
|
const Output<Node>& axes,
|
||||||
|
const InterpolateAttrs& attrs);
|
||||||
|
bool visit_attributes(AttributeVisitor& visitor) override;
|
||||||
|
|
||||||
|
void validate_and_infer_types() override;
|
||||||
|
|
||||||
|
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
||||||
|
bool evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const override;
|
||||||
|
bool has_evaluate() const override;
|
||||||
|
|
||||||
|
const InterpolateAttrs& get_attrs() const {
|
||||||
|
return m_attrs;
|
||||||
|
}
|
||||||
|
|
||||||
|
protected:
|
||||||
|
/// \return The interpolation axes.
|
||||||
|
std::vector<int64_t> get_axes() const;
|
||||||
|
|
||||||
|
private:
|
||||||
|
bool evaluate_interpolate(const HostTensorVector& outputs, const HostTensorVector& inputs) const;
|
||||||
|
InterpolateAttrs m_attrs;
|
||||||
|
|
||||||
|
/// \brief Corrects pads_begin and pads_end attributes.
|
||||||
|
///
|
||||||
|
/// \details When Interpolate-4 is a result of some transformation, it is possible
|
||||||
|
/// that pads_begin.size() != pads_end.size() or
|
||||||
|
/// pads_begin.size() != input_rank. In such case, we should correct
|
||||||
|
/// pads_begin and pads_end, using padding of pads_begin and pads_end by
|
||||||
|
/// zeros or using pads_begin[0 : input_rank], pads_end[0 : input_rank].
|
||||||
|
///
|
||||||
|
/// Padding of pads_begin is performed when pads_begin.size() < input_rank,
|
||||||
|
/// and pads_begin[0 : input_rank] is used when
|
||||||
|
/// pads_begin.size() < input_rank.
|
||||||
|
///
|
||||||
|
/// Similarly for pads_end.
|
||||||
|
void correct_pads();
|
||||||
|
|
||||||
|
/// \brief Calculates input shape after padding.
|
||||||
|
///
|
||||||
|
/// \param input_shape Shape of input data.
|
||||||
|
///
|
||||||
|
/// \return Padded input shape, i.e. input_shape + pads_begin + pads_end
|
||||||
|
PartialShape get_padded_input_shape(const PartialShape& input_shape) const;
|
||||||
|
|
||||||
|
/// \brief Infers output shape using scales.
|
||||||
|
///
|
||||||
|
/// \param output_shape[in,out] output shape
|
||||||
|
/// \param axes Interpolation axes
|
||||||
|
/// \param scales Scales for interpolated axes
|
||||||
|
/// \param padded_input_shape input shape after padding
|
||||||
|
void infer_using_scales(PartialShape& output_shape,
|
||||||
|
const std::vector<int64_t>& axes,
|
||||||
|
const std::vector<float>& scales,
|
||||||
|
const PartialShape& padded_input_shape) const;
|
||||||
|
|
||||||
|
/// \brief Infers output shape using sizes.
|
||||||
|
///
|
||||||
|
/// \param output_shape[in,out] output shape
|
||||||
|
/// \param axes Interpolation axes
|
||||||
|
/// \param sizes sizes for interpolated axes
|
||||||
|
void infer_using_shapes(PartialShape& output_shape,
|
||||||
|
const std::vector<int64_t>& axes,
|
||||||
|
const std::vector<int64_t>& sizes) const;
|
||||||
|
};
|
||||||
|
} // namespace v4
|
||||||
|
} // namespace op
|
||||||
|
|
||||||
|
//---------------------------------------- v0 --------------------------------------------------
|
||||||
|
OPENVINO_API
|
||||||
|
std::ostream& operator<<(std::ostream& s, const op::v0::Interpolate::InterpolateMode& type);
|
||||||
|
|
||||||
|
//---------------------------------------- v4 --------------------------------------------------
|
||||||
|
|
||||||
|
OPENVINO_API
|
||||||
|
std::ostream& operator<<(std::ostream& s, const op::v4::Interpolate::InterpolateMode& type);
|
||||||
|
|
||||||
|
OPENVINO_API
|
||||||
|
std::ostream& operator<<(std::ostream& s, const op::v4::Interpolate::CoordinateTransformMode& type);
|
||||||
|
|
||||||
|
OPENVINO_API
|
||||||
|
std::ostream& operator<<(std::ostream& s, const op::v4::Interpolate::NearestMode& type);
|
||||||
|
|
||||||
|
OPENVINO_API
|
||||||
|
std::ostream& operator<<(std::ostream& s, const op::v4::Interpolate::ShapeCalcMode& type);
|
||||||
|
|
||||||
|
template <>
|
||||||
|
class OPENVINO_API AttributeAdapter<op::v0::Interpolate::InterpolateMode>
|
||||||
|
: public EnumAttributeAdapterBase<op::v0::Interpolate::InterpolateMode> {
|
||||||
|
public:
|
||||||
|
AttributeAdapter(op::v0::Interpolate::InterpolateMode& value)
|
||||||
|
: EnumAttributeAdapterBase<op::v0::Interpolate::InterpolateMode>(value) {}
|
||||||
|
|
||||||
|
static constexpr DiscreteTypeInfo type_info{"AttributeAdapter<op::v0::Interpolate::InterpolateMode>", 0};
|
||||||
|
const DiscreteTypeInfo& get_type_info() const override {
|
||||||
|
return type_info;
|
||||||
|
}
|
||||||
|
};
|
||||||
|
template <>
|
||||||
|
class OPENVINO_API AttributeAdapter<op::v4::Interpolate::InterpolateMode>
|
||||||
|
: public EnumAttributeAdapterBase<op::v4::Interpolate::InterpolateMode> {
|
||||||
|
public:
|
||||||
|
AttributeAdapter(op::v4::Interpolate::InterpolateMode& value)
|
||||||
|
: EnumAttributeAdapterBase<op::v4::Interpolate::InterpolateMode>(value) {}
|
||||||
|
|
||||||
|
static constexpr DiscreteTypeInfo type_info{"AttributeAdapter<op::v4::Interpolate::InterpolateMode>", 4};
|
||||||
|
const DiscreteTypeInfo& get_type_info() const override {
|
||||||
|
return type_info;
|
||||||
|
}
|
||||||
|
};
|
||||||
|
|
||||||
|
template <>
|
||||||
|
class OPENVINO_API AttributeAdapter<op::v4::Interpolate::CoordinateTransformMode>
|
||||||
|
: public EnumAttributeAdapterBase<op::v4::Interpolate::CoordinateTransformMode> {
|
||||||
|
public:
|
||||||
|
AttributeAdapter(op::v4::Interpolate::CoordinateTransformMode& value)
|
||||||
|
: EnumAttributeAdapterBase<op::v4::Interpolate::CoordinateTransformMode>(value) {}
|
||||||
|
|
||||||
|
static constexpr DiscreteTypeInfo type_info{"AttributeAdapter<op::v4::Interpolate::CoordinateTransformMode>", 4};
|
||||||
|
const DiscreteTypeInfo& get_type_info() const override {
|
||||||
|
return type_info;
|
||||||
|
}
|
||||||
|
};
|
||||||
|
|
||||||
|
template <>
|
||||||
|
class OPENVINO_API AttributeAdapter<op::v4::Interpolate::NearestMode>
|
||||||
|
: public EnumAttributeAdapterBase<op::v4::Interpolate::NearestMode> {
|
||||||
|
public:
|
||||||
|
AttributeAdapter(op::v4::Interpolate::NearestMode& value)
|
||||||
|
: EnumAttributeAdapterBase<op::v4::Interpolate::NearestMode>(value) {}
|
||||||
|
|
||||||
|
static constexpr DiscreteTypeInfo type_info{"AttributeAdapter<op::v4::Interpolate::NearestMode>", 4};
|
||||||
|
const DiscreteTypeInfo& get_type_info() const override {
|
||||||
|
return type_info;
|
||||||
|
}
|
||||||
|
};
|
||||||
|
|
||||||
|
template <>
|
||||||
|
class OPENVINO_API AttributeAdapter<op::v4::Interpolate::ShapeCalcMode>
|
||||||
|
: public EnumAttributeAdapterBase<op::v4::Interpolate::ShapeCalcMode> {
|
||||||
|
public:
|
||||||
|
AttributeAdapter(op::v4::Interpolate::ShapeCalcMode& value)
|
||||||
|
: EnumAttributeAdapterBase<op::v4::Interpolate::ShapeCalcMode>(value) {}
|
||||||
|
|
||||||
|
static constexpr DiscreteTypeInfo type_info{"AttributeAdapter<op::v4::Interpolate::ShapeCalcMode>", 4};
|
||||||
|
const DiscreteTypeInfo& get_type_info() const override {
|
||||||
|
return type_info;
|
||||||
|
}
|
||||||
|
};
|
||||||
|
} // namespace ov
|
||||||
33
ngraph/core/include/openvino/op/less.hpp
Normal file
33
ngraph/core/include/openvino/op/less.hpp
Normal file
@ -0,0 +1,33 @@
|
|||||||
|
// Copyright (C) 2018-2021 Intel Corporation
|
||||||
|
// SPDX-License-Identifier: Apache-2.0
|
||||||
|
//
|
||||||
|
|
||||||
|
#pragma once
|
||||||
|
|
||||||
|
#include "openvino/op/util/binary_elementwise_comparison.hpp"
|
||||||
|
|
||||||
|
namespace ov {
|
||||||
|
namespace op {
|
||||||
|
namespace v1 {
|
||||||
|
/// \brief Elementwise less-than operation.
|
||||||
|
class OPENVINO_API Less : public util::BinaryElementwiseComparison {
|
||||||
|
public:
|
||||||
|
OPENVINO_RTTI_DECLARATION;
|
||||||
|
/// \brief Constructs a less-than operation.
|
||||||
|
Less() : util::BinaryElementwiseComparison(AutoBroadcastSpec::NUMPY) {}
|
||||||
|
/// \brief Constructs a less-than operation.
|
||||||
|
///
|
||||||
|
/// \param arg0 Node that produces the first input tensor.
|
||||||
|
/// \param arg1 Node that produces the second input tensor.
|
||||||
|
/// \param auto_broadcast Auto broadcast specification
|
||||||
|
Less(const Output<Node>& arg0,
|
||||||
|
const Output<Node>& arg1,
|
||||||
|
const AutoBroadcastSpec& auto_broadcast = AutoBroadcastSpec(AutoBroadcastType::NUMPY));
|
||||||
|
|
||||||
|
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
||||||
|
bool evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const override;
|
||||||
|
bool has_evaluate() const override;
|
||||||
|
};
|
||||||
|
} // namespace v1
|
||||||
|
} // namespace op
|
||||||
|
} // namespace ov
|
||||||
34
ngraph/core/include/openvino/op/less_eq.hpp
Normal file
34
ngraph/core/include/openvino/op/less_eq.hpp
Normal file
@ -0,0 +1,34 @@
|
|||||||
|
// Copyright (C) 2018-2021 Intel Corporation
|
||||||
|
// SPDX-License-Identifier: Apache-2.0
|
||||||
|
//
|
||||||
|
|
||||||
|
#pragma once
|
||||||
|
|
||||||
|
#include "openvino/op/util/binary_elementwise_comparison.hpp"
|
||||||
|
|
||||||
|
namespace ov {
|
||||||
|
namespace op {
|
||||||
|
namespace v1 {
|
||||||
|
/// \brief Elementwise less-than-or-equal operation.
|
||||||
|
class OPENVINO_API LessEqual : public util::BinaryElementwiseComparison {
|
||||||
|
public:
|
||||||
|
OPENVINO_RTTI_DECLARATION;
|
||||||
|
/// \brief Constructs a less-than-or-equal operation.
|
||||||
|
LessEqual() : util::BinaryElementwiseComparison(AutoBroadcastSpec::NUMPY) {}
|
||||||
|
|
||||||
|
/// \brief Constructs a less-than-or-equal operation.
|
||||||
|
///
|
||||||
|
/// \param arg0 Node that produces the first input tensor.
|
||||||
|
/// \param arg1 Node that produces the second input tensor.
|
||||||
|
/// \param auto_broadcast Auto broadcast specification
|
||||||
|
LessEqual(const Output<Node>& arg0,
|
||||||
|
const Output<Node>& arg1,
|
||||||
|
const AutoBroadcastSpec& auto_broadcast = AutoBroadcastSpec(AutoBroadcastType::NUMPY));
|
||||||
|
|
||||||
|
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
||||||
|
bool evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const override;
|
||||||
|
bool has_evaluate() const override;
|
||||||
|
};
|
||||||
|
} // namespace v1
|
||||||
|
} // namespace op
|
||||||
|
} // namespace ov
|
||||||
30
ngraph/core/include/openvino/op/log.hpp
Normal file
30
ngraph/core/include/openvino/op/log.hpp
Normal file
@ -0,0 +1,30 @@
|
|||||||
|
// Copyright (C) 2018-2021 Intel Corporation
|
||||||
|
// SPDX-License-Identifier: Apache-2.0
|
||||||
|
//
|
||||||
|
|
||||||
|
#pragma once
|
||||||
|
|
||||||
|
#include "ngraph/op/util/unary_elementwise_arithmetic.hpp"
|
||||||
|
|
||||||
|
namespace ov {
|
||||||
|
namespace op {
|
||||||
|
namespace v0 {
|
||||||
|
/// \brief Elementwise natural log operation.
|
||||||
|
class OPENVINO_API Log : public util::UnaryElementwiseArithmetic {
|
||||||
|
public:
|
||||||
|
OPENVINO_RTTI_DECLARATION;
|
||||||
|
/// \brief Constructs a natural log operation.
|
||||||
|
Log() = default;
|
||||||
|
/// \brief Constructs a natural log operation.
|
||||||
|
///
|
||||||
|
/// \param arg Node that produces the input tensor.
|
||||||
|
Log(const Output<Node>& arg);
|
||||||
|
|
||||||
|
bool visit_attributes(AttributeVisitor& visitor) override;
|
||||||
|
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
||||||
|
bool evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const override;
|
||||||
|
bool has_evaluate() const override;
|
||||||
|
};
|
||||||
|
} // namespace v0
|
||||||
|
} // namespace op
|
||||||
|
} // namespace ov
|
||||||
43
ngraph/core/include/openvino/op/log_softmax.hpp
Normal file
43
ngraph/core/include/openvino/op/log_softmax.hpp
Normal file
@ -0,0 +1,43 @@
|
|||||||
|
// Copyright (C) 2018-2021 Intel Corporation
|
||||||
|
// SPDX-License-Identifier: Apache-2.0
|
||||||
|
//
|
||||||
|
|
||||||
|
#pragma once
|
||||||
|
|
||||||
|
#include "ngraph/op/op.hpp"
|
||||||
|
|
||||||
|
namespace ov {
|
||||||
|
namespace op {
|
||||||
|
namespace v5 {
|
||||||
|
class OPENVINO_API LogSoftmax : public Op {
|
||||||
|
public:
|
||||||
|
OPENVINO_RTTI_DECLARATION;
|
||||||
|
LogSoftmax() = default;
|
||||||
|
/// \brief Constructs a LogSoftmax operation.
|
||||||
|
///
|
||||||
|
/// \param arg Node that produces the first input tensor.<br>
|
||||||
|
/// `[d0, ...]`
|
||||||
|
/// \param axis The axis position (0-based) on which to calculate the LogSoftmax.
|
||||||
|
///
|
||||||
|
/// Output `[d0, ...]`
|
||||||
|
///
|
||||||
|
LogSoftmax(const Output<Node>& arg, const int64_t axis);
|
||||||
|
|
||||||
|
bool visit_attributes(AttributeVisitor& visitor) override;
|
||||||
|
void validate_and_infer_types() override;
|
||||||
|
|
||||||
|
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
||||||
|
|
||||||
|
int64_t get_axis() const {
|
||||||
|
return m_axis;
|
||||||
|
}
|
||||||
|
void set_axis(const int64_t axis) {
|
||||||
|
m_axis = axis;
|
||||||
|
}
|
||||||
|
|
||||||
|
private:
|
||||||
|
int64_t m_axis = 1;
|
||||||
|
};
|
||||||
|
} // namespace v5
|
||||||
|
} // namespace op
|
||||||
|
} // namespace ov
|
||||||
90
ngraph/core/include/openvino/op/loop.hpp
Normal file
90
ngraph/core/include/openvino/op/loop.hpp
Normal file
@ -0,0 +1,90 @@
|
|||||||
|
// Copyright (C) 2018-2021 Intel Corporation
|
||||||
|
// SPDX-License-Identifier: Apache-2.0
|
||||||
|
//
|
||||||
|
|
||||||
|
#pragma once
|
||||||
|
|
||||||
|
#include <vector>
|
||||||
|
|
||||||
|
#include "openvino/core/function.hpp"
|
||||||
|
#include "openvino/op/constant.hpp"
|
||||||
|
#include "openvino/op/parameter.hpp"
|
||||||
|
#include "openvino/op/util/sub_graph_base.hpp"
|
||||||
|
|
||||||
|
namespace ov {
|
||||||
|
namespace op {
|
||||||
|
namespace v5 {
|
||||||
|
/// \brief Iterate a body over tensors, accumulating into tensors.
|
||||||
|
class NGRAPH_API Loop : public op::util::SubGraphOp {
|
||||||
|
public:
|
||||||
|
/// \brief Allows to define the purpose of inputs/outputs in the body
|
||||||
|
struct SpecialBodyPorts {
|
||||||
|
SpecialBodyPorts() = default;
|
||||||
|
SpecialBodyPorts(int64_t in_current_iteration_input_idx, int64_t in_body_condition_output_idx)
|
||||||
|
: current_iteration_input_idx(in_current_iteration_input_idx),
|
||||||
|
body_condition_output_idx(in_body_condition_output_idx) {}
|
||||||
|
// -1 means the input is not provided, this input is optional
|
||||||
|
int64_t current_iteration_input_idx = -1;
|
||||||
|
// -1 means the output is not provided,
|
||||||
|
// this output is required, throw an exception if not provided
|
||||||
|
int64_t body_condition_output_idx = -1;
|
||||||
|
};
|
||||||
|
|
||||||
|
NGRAPH_RTTI_DECLARATION;
|
||||||
|
|
||||||
|
/// \brief Constructs a Loop operation.
|
||||||
|
Loop() = default;
|
||||||
|
|
||||||
|
/// \brief Constructs a Loop operation.
|
||||||
|
///
|
||||||
|
/// \param trip_count Node specifies the maximum number of iterations.
|
||||||
|
/// \param execution_condition Node determines whether to execute the first
|
||||||
|
/// iteration or not.
|
||||||
|
Loop(const Output<Node>& trip_count, const Output<Node>& execution_condition);
|
||||||
|
|
||||||
|
Output<Node> get_concatenated_slices(const Output<Node>& value,
|
||||||
|
int64_t start,
|
||||||
|
int64_t stride,
|
||||||
|
int64_t part_size,
|
||||||
|
int64_t end,
|
||||||
|
int64_t axis) override;
|
||||||
|
|
||||||
|
void set_special_body_ports(const SpecialBodyPorts& special_body_ports) {
|
||||||
|
m_special_body_ports = special_body_ports;
|
||||||
|
}
|
||||||
|
|
||||||
|
SpecialBodyPorts get_special_body_ports() const {
|
||||||
|
return m_special_body_ports;
|
||||||
|
}
|
||||||
|
void validate_and_infer_types() override;
|
||||||
|
bool visit_attributes(AttributeVisitor& visitor) override;
|
||||||
|
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
||||||
|
|
||||||
|
bool evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const override;
|
||||||
|
bool has_evaluate() const override;
|
||||||
|
|
||||||
|
protected:
|
||||||
|
Loop(const Loop&);
|
||||||
|
|
||||||
|
private:
|
||||||
|
void clone_to(Loop& dst, const OutputVector& new_args) const;
|
||||||
|
|
||||||
|
SpecialBodyPorts m_special_body_ports;
|
||||||
|
};
|
||||||
|
} // namespace v5
|
||||||
|
} // namespace op
|
||||||
|
|
||||||
|
template <>
|
||||||
|
class NGRAPH_API AttributeAdapter<op::v5::Loop::SpecialBodyPorts>
|
||||||
|
: public DirectValueAccessor<op::v5::Loop::SpecialBodyPorts> {
|
||||||
|
public:
|
||||||
|
AttributeAdapter(op::v5::Loop::SpecialBodyPorts& value)
|
||||||
|
: DirectValueAccessor<op::v5::Loop::SpecialBodyPorts>(value) {}
|
||||||
|
|
||||||
|
static constexpr DiscreteTypeInfo type_info{"AttributeAdapter<op::v5::Loop::SpecialBodyPorts>", 0};
|
||||||
|
const DiscreteTypeInfo& get_type_info() const override {
|
||||||
|
return type_info;
|
||||||
|
}
|
||||||
|
};
|
||||||
|
|
||||||
|
} // namespace ov
|
||||||
78
ngraph/core/include/openvino/op/lrn.hpp
Normal file
78
ngraph/core/include/openvino/op/lrn.hpp
Normal file
@ -0,0 +1,78 @@
|
|||||||
|
// Copyright (C) 2018-2021 Intel Corporation
|
||||||
|
// SPDX-License-Identifier: Apache-2.0
|
||||||
|
//
|
||||||
|
|
||||||
|
#pragma once
|
||||||
|
|
||||||
|
#include "ngraph/op/op.hpp"
|
||||||
|
|
||||||
|
namespace ov {
|
||||||
|
namespace op {
|
||||||
|
namespace v0 {
|
||||||
|
// clang-format off
|
||||||
|
/// \brief Elementwise Local Response Normalization (LRN) operation.
|
||||||
|
///
|
||||||
|
/// ## Inputs
|
||||||
|
///
|
||||||
|
/// | | Type | Description |
|
||||||
|
/// | ----- | --------------------------------------- | ----------------------------------------------- |
|
||||||
|
/// | `arg` | \f$N[n, c, d_1,\dots,d_n]~(n \geq 0)\f$ | A tensor of any shape and numeric element type. |
|
||||||
|
///
|
||||||
|
/// ## Output
|
||||||
|
///
|
||||||
|
/// | Type | Description |
|
||||||
|
/// | ---------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||||
|
/// | \f$N[n, c, d_1,\dots,d_n]\f$ | The tensor \f$T\f$, where \f$T[n, c, d_1,\dots,d_n] = \frac{N[n,i,d_1,\dots,d_n]}{ (bias + alpha * (\sum_{i=max(0,(nsize-1)/2)}^{min(C, (nsize-1)/2)+1} N[n,i,d_1,\dots,d_n]^{2}) ^ {2})}\f$ |
|
||||||
|
// clang-format on
|
||||||
|
class NGRAPH_API LRN : public Op {
|
||||||
|
public:
|
||||||
|
NGRAPH_RTTI_DECLARATION;
|
||||||
|
|
||||||
|
/// \brief Constructs a LRN operation.
|
||||||
|
LRN() = default;
|
||||||
|
/// \brief Constructs a LRN operation.
|
||||||
|
///
|
||||||
|
/// \param arg Node that produces the input tensor.
|
||||||
|
LRN(const Output<Node>& arg, double alpha, double beta, double bias, size_t size);
|
||||||
|
|
||||||
|
LRN(const Output<Node>& arg, const Output<Node>& axes, double alpha, double beta, double bias, size_t size);
|
||||||
|
|
||||||
|
bool visit_attributes(AttributeVisitor& visitor) override;
|
||||||
|
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
||||||
|
void validate_and_infer_types() override;
|
||||||
|
|
||||||
|
double get_alpha() const {
|
||||||
|
return m_alpha;
|
||||||
|
}
|
||||||
|
void set_alpha(double alpha) {
|
||||||
|
m_alpha = alpha;
|
||||||
|
}
|
||||||
|
double get_beta() const {
|
||||||
|
return m_beta;
|
||||||
|
}
|
||||||
|
void set_beta(double beta) {
|
||||||
|
m_beta = beta;
|
||||||
|
}
|
||||||
|
double get_bias() const {
|
||||||
|
return m_bias;
|
||||||
|
}
|
||||||
|
void set_bias(double bias) {
|
||||||
|
m_bias = bias;
|
||||||
|
}
|
||||||
|
size_t get_nsize() const {
|
||||||
|
return m_size;
|
||||||
|
}
|
||||||
|
void set_nsize(size_t size) {
|
||||||
|
m_size = size;
|
||||||
|
}
|
||||||
|
AxisSet get_reduction_axes() const;
|
||||||
|
|
||||||
|
protected:
|
||||||
|
double m_alpha;
|
||||||
|
double m_beta;
|
||||||
|
double m_bias;
|
||||||
|
size_t m_size;
|
||||||
|
};
|
||||||
|
} // namespace v0
|
||||||
|
} // namespace op
|
||||||
|
} // namespace ov
|
||||||
397
ngraph/core/include/openvino/op/lstm_cell.hpp
Normal file
397
ngraph/core/include/openvino/op/lstm_cell.hpp
Normal file
@ -0,0 +1,397 @@
|
|||||||
|
// Copyright (C) 2018-2021 Intel Corporation
|
||||||
|
// SPDX-License-Identifier: Apache-2.0
|
||||||
|
//
|
||||||
|
|
||||||
|
#pragma once
|
||||||
|
|
||||||
|
#include <cstddef>
|
||||||
|
#include <memory>
|
||||||
|
#include <string>
|
||||||
|
#include <vector>
|
||||||
|
|
||||||
|
#include "openvino/op/op.hpp"
|
||||||
|
#include "openvino/op/util/activation_functions.hpp"
|
||||||
|
#include "openvino/op/util/rnn_cell_base.hpp"
|
||||||
|
|
||||||
|
namespace ov {
|
||||||
|
namespace op {
|
||||||
|
enum class LSTMWeightsFormat {
|
||||||
|
FICO, // IE
|
||||||
|
ICOF, // PyTorch
|
||||||
|
IFCO, // DNNL, TF, MxNet
|
||||||
|
IFOC, // Caffe
|
||||||
|
IOFC, // ONNX
|
||||||
|
};
|
||||||
|
|
||||||
|
namespace v0 {
|
||||||
|
///
|
||||||
|
/// \brief Class for single lstm cell node.
|
||||||
|
///
|
||||||
|
/// \note Following implementation supports:
|
||||||
|
/// \li \c peepholes Gers & Schmidhuber (2000)
|
||||||
|
/// https://ieeexplore.ieee.org/document/861302
|
||||||
|
/// \li Coupling input and forget gates.
|
||||||
|
///
|
||||||
|
/// \note It calculates following equations:
|
||||||
|
///
|
||||||
|
/// it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Pi (.) Ct-1 + Wbi + Rbi)
|
||||||
|
/// ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Pf (.) Ct-1 + Wbf + Rbf)
|
||||||
|
/// ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc)
|
||||||
|
/// Ct = ft (.) Ct-1 + it (.) ct
|
||||||
|
/// ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Po (.) Ct + Wbo + Rbo)
|
||||||
|
/// Ht = ot (.) h(Ct)
|
||||||
|
///
|
||||||
|
/// * - Is a dot product,
|
||||||
|
/// (.) - is a Hadamard product (element-wise),
|
||||||
|
/// f, g, h - are activation functions.
|
||||||
|
///
|
||||||
|
/// \note This class represents only single *cell* (for current time step) and not
|
||||||
|
/// the whole LSTM Sequence layer
|
||||||
|
///
|
||||||
|
/// \sa LSTMSequence, RNNCell, GRUCell
|
||||||
|
///
|
||||||
|
class OPENVINO_API LSTMCell : public util::RNNCellBase {
|
||||||
|
public:
|
||||||
|
OPENVINO_RTTI_DECLARATION;
|
||||||
|
|
||||||
|
LSTMCell();
|
||||||
|
///
|
||||||
|
/// \brief Constructs LSTMCell node.
|
||||||
|
///
|
||||||
|
/// \param[in] X The input tensor with shape: [batch_size,
|
||||||
|
/// input_size].
|
||||||
|
/// \param[in] initial_hidden_state The hidden state tensor at current time step
|
||||||
|
/// with shape: [batch_size, hidden_size].
|
||||||
|
/// \param[in] initial_cell_state The cell state tensor at current time step
|
||||||
|
/// with shape: [batch_size, hidden_size].
|
||||||
|
/// \param[in] W The gate weights tensor with shape:
|
||||||
|
/// [4*hidden_size, input_size].
|
||||||
|
/// \param[in] R The recurrence weights tensor with shape:
|
||||||
|
/// [4*hidden_size, hidden_size].
|
||||||
|
/// \param[in] hidden_size The number of hidden units for recurrent cell.
|
||||||
|
/// \param[in] weights_format The order of gates in weights tensors. The
|
||||||
|
/// default format is IFCO since it is used by
|
||||||
|
/// DNNL.
|
||||||
|
/// \param[in] activations The vector of activation functions used inside
|
||||||
|
/// recurrent cell.
|
||||||
|
/// \param[in] activations_alpha The vector of alpha parameters for activation
|
||||||
|
/// functions in order respective to activation
|
||||||
|
/// list.
|
||||||
|
/// \param[in] activations_beta The vector of beta parameters for activation
|
||||||
|
/// functions in order respective to activation
|
||||||
|
/// list.
|
||||||
|
/// \param[in] clip The value defining clipping range [-clip,
|
||||||
|
/// clip] on input of activation functions.
|
||||||
|
/// \param[in] input_forget Controls coupling input and forget gates.
|
||||||
|
///
|
||||||
|
LSTMCell(const Output<Node>& X,
|
||||||
|
const Output<Node>& initial_hidden_state,
|
||||||
|
const Output<Node>& initial_cell_state,
|
||||||
|
const Output<Node>& W,
|
||||||
|
const Output<Node>& R,
|
||||||
|
std::size_t hidden_size,
|
||||||
|
LSTMWeightsFormat weights_format = LSTMWeightsFormat::IFCO,
|
||||||
|
const std::vector<std::string>& activations = std::vector<std::string>{"sigmoid", "tanh", "tanh"},
|
||||||
|
const std::vector<float>& activations_alpha = {},
|
||||||
|
const std::vector<float>& activations_beta = {},
|
||||||
|
float clip = 0.f,
|
||||||
|
bool input_forget = false);
|
||||||
|
|
||||||
|
///
|
||||||
|
/// \brief Constructs LSTMCell node.
|
||||||
|
///
|
||||||
|
/// \param[in] X The input tensor with shape: [batch_size,
|
||||||
|
/// input_size].
|
||||||
|
/// \param[in] initial_hidden_state The hidden state tensor at current time step
|
||||||
|
/// with shape: [batch_size, hidden_size].
|
||||||
|
/// \param[in] initial_cell_state The cell state tensor at current time step
|
||||||
|
/// with shape: [batch_size, hidden_size].
|
||||||
|
/// \param[in] W The weight tensor with shape: [4*hidden_size,
|
||||||
|
/// input_size].
|
||||||
|
/// \param[in] R The recurrence weight tensor with shape:
|
||||||
|
/// [4*hidden_size, hidden_size].
|
||||||
|
/// \param[in] B The bias tensor for gates with shape:
|
||||||
|
/// [4*hidden_size].
|
||||||
|
/// \param[in] hidden_size The number of hidden units for recurrent cell.
|
||||||
|
/// \param[in] weights_format The order of gates in weights tensors. The
|
||||||
|
/// default format is IFCO since it is used by
|
||||||
|
/// DNNL.
|
||||||
|
/// \param[in] activations The vector of activation functions used inside
|
||||||
|
/// recurrent cell.
|
||||||
|
/// \param[in] activations_alpha The vector of alpha parameters for activation
|
||||||
|
/// functions in order respective to activation
|
||||||
|
/// list.
|
||||||
|
/// \param[in] activations_beta The vector of beta parameters for activation
|
||||||
|
/// functions in order respective to activation
|
||||||
|
/// list.
|
||||||
|
/// \param[in] clip The value defining clipping range [-clip,
|
||||||
|
/// clip] on input of activation functions.
|
||||||
|
/// \param[in] input_forget Controls coupling input and forget gates.
|
||||||
|
///
|
||||||
|
LSTMCell(const Output<Node>& X,
|
||||||
|
const Output<Node>& initial_hidden_state,
|
||||||
|
const Output<Node>& initial_cell_state,
|
||||||
|
const Output<Node>& W,
|
||||||
|
const Output<Node>& R,
|
||||||
|
const Output<Node>& B,
|
||||||
|
std::size_t hidden_size,
|
||||||
|
LSTMWeightsFormat weights_format = LSTMWeightsFormat::IFCO,
|
||||||
|
const std::vector<std::string>& activations = std::vector<std::string>{"sigmoid", "tanh", "tanh"},
|
||||||
|
const std::vector<float>& activations_alpha = {},
|
||||||
|
const std::vector<float>& activations_beta = {},
|
||||||
|
float clip = 0.f,
|
||||||
|
bool input_forget = false);
|
||||||
|
|
||||||
|
///
|
||||||
|
/// \brief Constructs LSTMCell node.
|
||||||
|
///
|
||||||
|
/// \param[in] X The input tensor with shape: [batch_size,
|
||||||
|
/// input_size].
|
||||||
|
/// \param[in] initial_hidden_state The hidden state tensor at current time step
|
||||||
|
/// with shape: [batch_size, hidden_size].
|
||||||
|
/// \param[in] initial_cell_state The cell state tensor at current time step
|
||||||
|
/// with shape: [batch_size, hidden_size].
|
||||||
|
/// \param[in] W The weight tensor with shape: [4*hidden_size,
|
||||||
|
/// input_size].
|
||||||
|
/// \param[in] R The recurrence weight tensor with shape:
|
||||||
|
/// [4*hidden_size, hidden_size].
|
||||||
|
/// \param[in] B The bias tensor for gates with shape:
|
||||||
|
/// [4*hidden_size].
|
||||||
|
/// \param[in] P The weight tensor for peepholes with shape:
|
||||||
|
/// [3*hidden_size] - 3 equals to only iof gates.
|
||||||
|
/// The order is: input, output, forget gates.
|
||||||
|
/// \param[in] hidden_size The number of hidden units for recurrent cell.
|
||||||
|
/// \param[in] weights_format The order of gates in weights tensors. The
|
||||||
|
/// default format is IFCO since it is used by
|
||||||
|
/// DNNL.
|
||||||
|
/// \param[in] activations The vector of activation functions used inside
|
||||||
|
/// recurrent cell.
|
||||||
|
/// \param[in] activations_alpha The vector of alpha parameters for activation
|
||||||
|
/// functions in order respective to activation
|
||||||
|
/// list.
|
||||||
|
/// \param[in] activations_beta The vector of beta parameters for activation
|
||||||
|
/// functions in order respective to activation
|
||||||
|
/// list.
|
||||||
|
/// \param[in] clip The value defining clipping range [-clip,
|
||||||
|
/// clip] on input of activation functions.
|
||||||
|
/// \param[in] input_forget Controls coupling input and forget gates.
|
||||||
|
///
|
||||||
|
LSTMCell(const Output<Node>& X,
|
||||||
|
const Output<Node>& initial_hidden_state,
|
||||||
|
const Output<Node>& initial_cell_state,
|
||||||
|
const Output<Node>& W,
|
||||||
|
const Output<Node>& R,
|
||||||
|
const Output<Node>& B,
|
||||||
|
const Output<Node>& P,
|
||||||
|
std::size_t hidden_size,
|
||||||
|
LSTMWeightsFormat weights_format = LSTMWeightsFormat::IFCO,
|
||||||
|
const std::vector<std::string>& activations = std::vector<std::string>{"sigmoid", "tanh", "tanh"},
|
||||||
|
const std::vector<float>& activations_alpha = {},
|
||||||
|
const std::vector<float>& activations_beta = {},
|
||||||
|
float clip = 0.f,
|
||||||
|
bool input_forget = false);
|
||||||
|
|
||||||
|
void validate_and_infer_types() override;
|
||||||
|
bool visit_attributes(AttributeVisitor& visitor) override;
|
||||||
|
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
||||||
|
|
||||||
|
bool get_input_forget() const {
|
||||||
|
return m_input_forget;
|
||||||
|
}
|
||||||
|
LSTMWeightsFormat get_weights_format() const {
|
||||||
|
return m_weights_format;
|
||||||
|
}
|
||||||
|
|
||||||
|
private:
|
||||||
|
///
|
||||||
|
/// \brief Creates the default bias input initialized with zeros.
|
||||||
|
///
|
||||||
|
/// \return The object of Output class.
|
||||||
|
///
|
||||||
|
Output<Node> get_default_bias_input() const;
|
||||||
|
|
||||||
|
///
|
||||||
|
/// \brief Creates the default peepholes input initialized with zeros.
|
||||||
|
///
|
||||||
|
/// \return The object of Output class.
|
||||||
|
///
|
||||||
|
Output<Node> get_default_peepholes_input() const;
|
||||||
|
///
|
||||||
|
/// \brief The Activation function f.
|
||||||
|
///
|
||||||
|
util::ActivationFunction m_activation_f;
|
||||||
|
///
|
||||||
|
/// \brief The Activation function g.
|
||||||
|
///
|
||||||
|
util::ActivationFunction m_activation_g;
|
||||||
|
///
|
||||||
|
/// \brief The Activation function h.
|
||||||
|
///
|
||||||
|
util::ActivationFunction m_activation_h;
|
||||||
|
///
|
||||||
|
/// \brief Controls whether to couple input and forget gates.
|
||||||
|
///
|
||||||
|
bool m_input_forget = false;
|
||||||
|
|
||||||
|
///
|
||||||
|
/// \brief The order of gates in weights tensors.
|
||||||
|
///
|
||||||
|
LSTMWeightsFormat m_weights_format;
|
||||||
|
|
||||||
|
static constexpr std::size_t s_gates_count{4};
|
||||||
|
static constexpr std::size_t s_peepholes_count{3};
|
||||||
|
};
|
||||||
|
} // namespace v0
|
||||||
|
|
||||||
|
namespace v4 {
|
||||||
|
///
|
||||||
|
/// \brief Class for single lstm cell node.
|
||||||
|
///
|
||||||
|
/// \note Following implementation supports:
|
||||||
|
/// \li \c peepholes Gers & Schmidhuber (2000)
|
||||||
|
/// https://ieeexplore.ieee.org/document/861302
|
||||||
|
/// \li Coupling input and forget gates.
|
||||||
|
///
|
||||||
|
/// \note It calculates following equations:
|
||||||
|
///
|
||||||
|
/// it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Wbi + Rbi)
|
||||||
|
/// ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Wbf + Rbf)
|
||||||
|
/// ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc)
|
||||||
|
/// Ct = ft (.) Ct-1 + it (.) ct
|
||||||
|
/// ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Wbo + Rbo)
|
||||||
|
/// Ht = ot (.) h(Ct)
|
||||||
|
///
|
||||||
|
/// * - Is a dot product,
|
||||||
|
/// (.) - is a Hadamard product (element-wise),
|
||||||
|
/// f, g, h - are activation functions.
|
||||||
|
///
|
||||||
|
/// \note This class represents only single *cell* (for current time step) and not
|
||||||
|
/// the whole LSTM Sequence layer
|
||||||
|
///
|
||||||
|
/// \sa LSTMSequence, RNNCell, GRUCell
|
||||||
|
///
|
||||||
|
class OPENVINO_API LSTMCell : public util::RNNCellBase {
|
||||||
|
public:
|
||||||
|
OPENVINO_RTTI_DECLARATION;
|
||||||
|
|
||||||
|
LSTMCell();
|
||||||
|
///
|
||||||
|
/// \brief Constructs LSTMCell node.
|
||||||
|
///
|
||||||
|
/// \param[in] X The input tensor with shape: [batch_size,
|
||||||
|
/// input_size].
|
||||||
|
/// \param[in] initial_hidden_state The hidden state tensor at current time step
|
||||||
|
/// with shape: [batch_size, hidden_size].
|
||||||
|
/// \param[in] initial_cell_state The cell state tensor at current time step
|
||||||
|
/// with shape: [batch_size, hidden_size].
|
||||||
|
/// \param[in] W The gate weights tensor with shape:
|
||||||
|
/// [4*hidden_size, input_size].
|
||||||
|
/// \param[in] R The recurrence weights tensor with shape:
|
||||||
|
/// [4*hidden_size, hidden_size].
|
||||||
|
/// \param[in] hidden_size The number of hidden units for recurrent cell.
|
||||||
|
/// \param[in] activations The vector of activation functions used inside
|
||||||
|
/// recurrent cell.
|
||||||
|
/// \param[in] activations_alpha The vector of alpha parameters for activation
|
||||||
|
/// functions in order respective to activation
|
||||||
|
/// list.
|
||||||
|
/// \param[in] activations_beta The vector of beta parameters for activation
|
||||||
|
/// functions in order respective to activation
|
||||||
|
/// list.
|
||||||
|
/// \param[in] clip The value defining clipping range [-clip,
|
||||||
|
/// clip] on input of activation functions.
|
||||||
|
LSTMCell(const Output<Node>& X,
|
||||||
|
const Output<Node>& initial_hidden_state,
|
||||||
|
const Output<Node>& initial_cell_state,
|
||||||
|
const Output<Node>& W,
|
||||||
|
const Output<Node>& R,
|
||||||
|
std::size_t hidden_size,
|
||||||
|
const std::vector<std::string>& activations = std::vector<std::string>{"sigmoid", "tanh", "tanh"},
|
||||||
|
const std::vector<float>& activations_alpha = {},
|
||||||
|
const std::vector<float>& activations_beta = {},
|
||||||
|
float clip = 0.f);
|
||||||
|
|
||||||
|
///
|
||||||
|
/// \brief Constructs LSTMCell node.
|
||||||
|
///
|
||||||
|
/// \param[in] X The input tensor with shape: [batch_size,
|
||||||
|
/// input_size].
|
||||||
|
/// \param[in] initial_hidden_state The hidden state tensor at current time step
|
||||||
|
/// with shape: [batch_size, hidden_size].
|
||||||
|
/// \param[in] initial_cell_state The cell state tensor at current time step
|
||||||
|
/// with shape: [batch_size, hidden_size].
|
||||||
|
/// \param[in] W The weight tensor with shape: [4*hidden_size,
|
||||||
|
/// input_size].
|
||||||
|
/// \param[in] R The recurrence weight tensor with shape:
|
||||||
|
/// [4*hidden_size, hidden_size].
|
||||||
|
/// \param[in] B The bias tensor for gates with shape:
|
||||||
|
/// [4*hidden_size].
|
||||||
|
/// \param[in] hidden_size The number of hidden units for recurrent cell.
|
||||||
|
/// \param[in] activations The vector of activation functions used inside
|
||||||
|
/// recurrent cell.
|
||||||
|
/// \param[in] activations_alpha The vector of alpha parameters for activation
|
||||||
|
/// functions in order respective to activation
|
||||||
|
/// list.
|
||||||
|
/// \param[in] activations_beta The vector of beta parameters for activation
|
||||||
|
/// functions in order respective to activation
|
||||||
|
/// list.
|
||||||
|
/// \param[in] clip The value defining clipping range [-clip,
|
||||||
|
/// clip] on input of activation functions.
|
||||||
|
///
|
||||||
|
LSTMCell(const Output<Node>& X,
|
||||||
|
const Output<Node>& initial_hidden_state,
|
||||||
|
const Output<Node>& initial_cell_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>{"sigmoid", "tanh", "tanh"},
|
||||||
|
const std::vector<float>& activations_alpha = {},
|
||||||
|
const std::vector<float>& activations_beta = {},
|
||||||
|
float clip = 0.f);
|
||||||
|
|
||||||
|
void validate_and_infer_types() override;
|
||||||
|
|
||||||
|
bool visit_attributes(AttributeVisitor& visitor) override;
|
||||||
|
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
||||||
|
|
||||||
|
private:
|
||||||
|
///
|
||||||
|
/// \brief Creates the default bias input initialized with zeros.
|
||||||
|
///
|
||||||
|
/// \return The object of Output class.
|
||||||
|
///
|
||||||
|
Output<Node> get_default_bias_input() const;
|
||||||
|
|
||||||
|
///
|
||||||
|
/// \brief The Activation function f.
|
||||||
|
///
|
||||||
|
util::ActivationFunction m_activation_f;
|
||||||
|
///
|
||||||
|
/// \brief The Activation function g.
|
||||||
|
///
|
||||||
|
util::ActivationFunction m_activation_g;
|
||||||
|
///
|
||||||
|
/// \brief The Activation function h.
|
||||||
|
///
|
||||||
|
util::ActivationFunction m_activation_h;
|
||||||
|
|
||||||
|
static constexpr std::size_t s_gates_count{4};
|
||||||
|
};
|
||||||
|
} // namespace v4
|
||||||
|
} // namespace op
|
||||||
|
|
||||||
|
OPENVINO_API
|
||||||
|
std::ostream& operator<<(std::ostream& s, const op::LSTMWeightsFormat& type);
|
||||||
|
|
||||||
|
template <>
|
||||||
|
class OPENVINO_API AttributeAdapter<op::LSTMWeightsFormat> : public EnumAttributeAdapterBase<op::LSTMWeightsFormat> {
|
||||||
|
public:
|
||||||
|
AttributeAdapter(op::LSTMWeightsFormat& value) : EnumAttributeAdapterBase<op::LSTMWeightsFormat>(value) {}
|
||||||
|
|
||||||
|
static constexpr DiscreteTypeInfo type_info{"AttributeAdapter<op::LSTMWeightsFormat>", 1};
|
||||||
|
const DiscreteTypeInfo& get_type_info() const override {
|
||||||
|
return type_info;
|
||||||
|
}
|
||||||
|
};
|
||||||
|
|
||||||
|
} // namespace ov
|
||||||
196
ngraph/core/include/openvino/op/lstm_sequence.hpp
Normal file
196
ngraph/core/include/openvino/op/lstm_sequence.hpp
Normal file
@ -0,0 +1,196 @@
|
|||||||
|
// Copyright (C) 2018-2021 Intel Corporation
|
||||||
|
// SPDX-License-Identifier: Apache-2.0
|
||||||
|
//
|
||||||
|
|
||||||
|
#pragma once
|
||||||
|
|
||||||
|
#include <cstddef>
|
||||||
|
#include <cstdint>
|
||||||
|
#include <memory>
|
||||||
|
#include <string>
|
||||||
|
#include <vector>
|
||||||
|
|
||||||
|
#include "openvino/op/constant.hpp"
|
||||||
|
#include "openvino/op/lstm_cell.hpp"
|
||||||
|
#include "openvino/op/util/attr_types.hpp"
|
||||||
|
#include "openvino/op/util/rnn_cell_base.hpp"
|
||||||
|
|
||||||
|
namespace ov {
|
||||||
|
namespace op {
|
||||||
|
namespace v0 {
|
||||||
|
|
||||||
|
///
|
||||||
|
/// \brief Class for lstm sequence node.
|
||||||
|
///
|
||||||
|
/// \note It follows notation and equations defined as in ONNX standard:
|
||||||
|
/// https://github.com/onnx/onnx/blob/master/docs/Operators.md#LSTM
|
||||||
|
///
|
||||||
|
/// \sa LSTMCell, RNNCell, GRUCell
|
||||||
|
///
|
||||||
|
///
|
||||||
|
class NGRAPH_API LSTMSequence : public Op {
|
||||||
|
public:
|
||||||
|
NGRAPH_RTTI_DECLARATION;
|
||||||
|
LSTMSequence();
|
||||||
|
|
||||||
|
using direction = RecurrentSequenceDirection;
|
||||||
|
|
||||||
|
size_t get_default_output_index() const override {
|
||||||
|
return no_default_index();
|
||||||
|
}
|
||||||
|
explicit LSTMSequence(const Output<Node>& X,
|
||||||
|
const Output<Node>& initial_hidden_state,
|
||||||
|
const Output<Node>& initial_cell_state,
|
||||||
|
const Output<Node>& sequence_lengths,
|
||||||
|
const Output<Node>& W,
|
||||||
|
const Output<Node>& R,
|
||||||
|
const Output<Node>& B,
|
||||||
|
const Output<Node>& P,
|
||||||
|
const std::int64_t hidden_size,
|
||||||
|
const direction lstm_direction,
|
||||||
|
LSTMWeightsFormat weights_format = LSTMWeightsFormat::IFCO,
|
||||||
|
const std::vector<float> activations_alpha = {},
|
||||||
|
const std::vector<float> activations_beta = {},
|
||||||
|
const std::vector<std::string> activations = {"sigmoid", "tanh", "tanh"},
|
||||||
|
const float clip_threshold = 0,
|
||||||
|
const bool input_forget = false);
|
||||||
|
|
||||||
|
explicit LSTMSequence(const Output<Node>& X,
|
||||||
|
const Output<Node>& initial_hidden_state,
|
||||||
|
const Output<Node>& initial_cell_state,
|
||||||
|
const Output<Node>& sequence_lengths,
|
||||||
|
const Output<Node>& W,
|
||||||
|
const Output<Node>& R,
|
||||||
|
const Output<Node>& B,
|
||||||
|
const std::int64_t hidden_size,
|
||||||
|
const direction lstm_direction,
|
||||||
|
LSTMWeightsFormat weights_format = LSTMWeightsFormat::IFCO,
|
||||||
|
const std::vector<float>& activations_alpha = {},
|
||||||
|
const std::vector<float>& activations_beta = {},
|
||||||
|
const std::vector<std::string>& activations = {"sigmoid", "tanh", "tanh"},
|
||||||
|
const float clip_threshold = 0,
|
||||||
|
const bool input_forget = false);
|
||||||
|
|
||||||
|
void validate_and_infer_types() override;
|
||||||
|
bool visit_attributes(AttributeVisitor& visitor) override;
|
||||||
|
|
||||||
|
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
||||||
|
|
||||||
|
std::vector<float> get_activations_alpha() const {
|
||||||
|
return m_activations_alpha;
|
||||||
|
}
|
||||||
|
std::vector<float> get_activations_beta() const {
|
||||||
|
return m_activations_beta;
|
||||||
|
}
|
||||||
|
std::vector<std::string> get_activations() const {
|
||||||
|
return m_activations;
|
||||||
|
}
|
||||||
|
float get_clip_threshold() const {
|
||||||
|
return m_clip_threshold;
|
||||||
|
}
|
||||||
|
direction get_direction() const {
|
||||||
|
return m_direction;
|
||||||
|
}
|
||||||
|
std::int64_t get_hidden_size() const {
|
||||||
|
return m_hidden_size;
|
||||||
|
}
|
||||||
|
bool get_input_forget() const {
|
||||||
|
return m_input_forget;
|
||||||
|
}
|
||||||
|
LSTMWeightsFormat get_weights_format() const {
|
||||||
|
return m_weights_format;
|
||||||
|
}
|
||||||
|
|
||||||
|
private:
|
||||||
|
///
|
||||||
|
/// \brief Gets the masked value according to sequence length in a batch.
|
||||||
|
///
|
||||||
|
/// \note Zeros out values or sets them to default value for inputs with
|
||||||
|
/// sequence length shorter than currently procssed time step.
|
||||||
|
///
|
||||||
|
/// \param[in] data The input value.
|
||||||
|
/// \param[in] time_step The current time step denoting sequence length.
|
||||||
|
/// \param[in] batch_axis The batch axis index of data tensor.
|
||||||
|
/// \param[in] default_value The default value for masked elements.
|
||||||
|
///
|
||||||
|
/// \return The masked value.
|
||||||
|
///
|
||||||
|
std::shared_ptr<Node> get_masked_node(const Output<Node>& data,
|
||||||
|
std::int32_t time_step,
|
||||||
|
std::size_t batch_axis = 0,
|
||||||
|
const Output<Node>& default_value = Output<Node>()) const;
|
||||||
|
|
||||||
|
OutputVector lstm_pass(bool is_reverse = false) const;
|
||||||
|
|
||||||
|
// Split(bi-directional) and squeeze input data to remove 'num_direction' dimension.
|
||||||
|
std::shared_ptr<Node> prepare_input(Output<Node> node, bool is_reverse, size_t num_direction_axis = 0) const;
|
||||||
|
|
||||||
|
std::vector<float> m_activations_alpha;
|
||||||
|
std::vector<float> m_activations_beta;
|
||||||
|
std::vector<std::string> m_activations;
|
||||||
|
float m_clip_threshold;
|
||||||
|
direction m_direction;
|
||||||
|
std::int64_t m_hidden_size;
|
||||||
|
bool m_input_forget;
|
||||||
|
LSTMWeightsFormat m_weights_format;
|
||||||
|
};
|
||||||
|
} // namespace v0
|
||||||
|
|
||||||
|
namespace v5 {
|
||||||
|
///
|
||||||
|
/// \brief Class for lstm sequence node.
|
||||||
|
///
|
||||||
|
/// \note It follows notation and equations defined as in ONNX standard:
|
||||||
|
/// https://github.com/onnx/onnx/blob/master/docs/Operators.md#LSTM
|
||||||
|
///
|
||||||
|
/// \sa LSTMCell, RNNCell, GRUCell
|
||||||
|
///
|
||||||
|
///
|
||||||
|
class NGRAPH_API LSTMSequence : public util::RNNCellBase {
|
||||||
|
public:
|
||||||
|
NGRAPH_RTTI_DECLARATION;
|
||||||
|
LSTMSequence() = default;
|
||||||
|
|
||||||
|
using direction = RecurrentSequenceDirection;
|
||||||
|
|
||||||
|
size_t get_default_output_index() const override {
|
||||||
|
return no_default_index();
|
||||||
|
}
|
||||||
|
explicit LSTMSequence(const Output<Node>& X,
|
||||||
|
const Output<Node>& initial_hidden_state,
|
||||||
|
const Output<Node>& initial_cell_state,
|
||||||
|
const Output<Node>& sequence_lengths,
|
||||||
|
const Output<Node>& W,
|
||||||
|
const Output<Node>& R,
|
||||||
|
const Output<Node>& B,
|
||||||
|
const std::int64_t hidden_size,
|
||||||
|
const direction lstm_direction,
|
||||||
|
const std::vector<float>& activations_alpha = {},
|
||||||
|
const std::vector<float>& activations_beta = {},
|
||||||
|
const std::vector<std::string>& activations = {"sigmoid", "tanh", "tanh"},
|
||||||
|
const float clip = 0.f)
|
||||||
|
: RNNCellBase({X, initial_hidden_state, initial_cell_state, sequence_lengths, W, R, B},
|
||||||
|
hidden_size,
|
||||||
|
clip,
|
||||||
|
activations,
|
||||||
|
activations_alpha,
|
||||||
|
activations_beta),
|
||||||
|
m_direction(lstm_direction) {
|
||||||
|
constructor_validate_and_infer_types();
|
||||||
|
}
|
||||||
|
|
||||||
|
void validate_and_infer_types() override;
|
||||||
|
bool visit_attributes(AttributeVisitor& visitor) override;
|
||||||
|
|
||||||
|
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
||||||
|
|
||||||
|
direction get_direction() const {
|
||||||
|
return m_direction;
|
||||||
|
}
|
||||||
|
|
||||||
|
private:
|
||||||
|
direction m_direction;
|
||||||
|
};
|
||||||
|
} // namespace v5
|
||||||
|
} // namespace op
|
||||||
|
} // namespace ov
|
||||||
78
ngraph/core/include/openvino/op/parameter.hpp
Normal file
78
ngraph/core/include/openvino/op/parameter.hpp
Normal file
@ -0,0 +1,78 @@
|
|||||||
|
// Copyright (C) 2018-2021 Intel Corporation
|
||||||
|
// SPDX-License-Identifier: Apache-2.0
|
||||||
|
//
|
||||||
|
|
||||||
|
#pragma once
|
||||||
|
|
||||||
|
#include "openvino/op/op.hpp"
|
||||||
|
|
||||||
|
namespace ov {
|
||||||
|
namespace op {
|
||||||
|
namespace v0 {
|
||||||
|
/// \brief A function parameter.
|
||||||
|
///
|
||||||
|
/// Parameters are nodes that represent the arguments that will be passed to
|
||||||
|
/// user-defined functions. Function creation requires a sequence of parameters.
|
||||||
|
/// Basic graph operations do not need parameters attached to a function.
|
||||||
|
class OPENVINO_API Parameter : public op::Op {
|
||||||
|
public:
|
||||||
|
OPENVINO_RTTI_DECLARATION;
|
||||||
|
/// \brief Constructions a tensor-typed parameter node.
|
||||||
|
Parameter() = default;
|
||||||
|
/// \brief Constructions a tensor-typed parameter node.
|
||||||
|
///
|
||||||
|
/// \param element_type The element type of the parameter.
|
||||||
|
/// \param pshape The partial shape of the parameter.
|
||||||
|
Parameter(const ngraph::element::Type& element_type, const PartialShape& pshape);
|
||||||
|
|
||||||
|
bool visit_attributes(AttributeVisitor& visitor) override;
|
||||||
|
|
||||||
|
void validate_and_infer_types() override;
|
||||||
|
|
||||||
|
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
||||||
|
|
||||||
|
bool is_relevant_to_shapes() const;
|
||||||
|
void set_is_relevant_to_shapes(bool is_relevant);
|
||||||
|
|
||||||
|
const PartialShape& get_partial_shape() const {
|
||||||
|
return m_partial_shape;
|
||||||
|
}
|
||||||
|
PartialShape& get_partial_shape() {
|
||||||
|
return m_partial_shape;
|
||||||
|
}
|
||||||
|
void set_partial_shape(const PartialShape& partial_shape) {
|
||||||
|
m_partial_shape = partial_shape;
|
||||||
|
}
|
||||||
|
const element::Type& get_element_type() const {
|
||||||
|
return m_element_type;
|
||||||
|
}
|
||||||
|
void set_element_type(const element::Type& element_type) {
|
||||||
|
m_element_type = element_type;
|
||||||
|
}
|
||||||
|
|
||||||
|
protected:
|
||||||
|
PartialShape m_partial_shape;
|
||||||
|
element::Type m_element_type;
|
||||||
|
bool m_is_relevant_to_shapes{false};
|
||||||
|
};
|
||||||
|
} // namespace v0
|
||||||
|
} // namespace op
|
||||||
|
using ParameterVector = std::vector<std::shared_ptr<op::v0::Parameter>>;
|
||||||
|
|
||||||
|
template <>
|
||||||
|
class OPENVINO_API AttributeAdapter<ParameterVector> : public VisitorAdapter {
|
||||||
|
public:
|
||||||
|
AttributeAdapter(ParameterVector& ref);
|
||||||
|
|
||||||
|
bool visit_attributes(AttributeVisitor& visitor) override;
|
||||||
|
|
||||||
|
static constexpr DiscreteTypeInfo type_info{"AttributeAdapter<ParameterVector>", 0};
|
||||||
|
const DiscreteTypeInfo& get_type_info() const override {
|
||||||
|
return type_info;
|
||||||
|
}
|
||||||
|
|
||||||
|
protected:
|
||||||
|
ParameterVector& m_ref;
|
||||||
|
};
|
||||||
|
|
||||||
|
} // namespace ov
|
||||||
61
ngraph/core/include/openvino/op/result.hpp
Normal file
61
ngraph/core/include/openvino/op/result.hpp
Normal file
@ -0,0 +1,61 @@
|
|||||||
|
// Copyright (C) 2018-2021 Intel Corporation
|
||||||
|
// SPDX-License-Identifier: Apache-2.0
|
||||||
|
//
|
||||||
|
|
||||||
|
#pragma once
|
||||||
|
|
||||||
|
#include "openvino/op/op.hpp"
|
||||||
|
|
||||||
|
namespace ov {
|
||||||
|
namespace op {
|
||||||
|
namespace v0 {
|
||||||
|
class OPENVINO_API Result : public Op {
|
||||||
|
public:
|
||||||
|
OPENVINO_RTTI_DECLARATION;
|
||||||
|
|
||||||
|
/// \brief Allows a value to be used as a function result.
|
||||||
|
Result() = default;
|
||||||
|
/// \brief Allows a value to be used as a function result.
|
||||||
|
///
|
||||||
|
/// \param arg Node that produces the input tensor.
|
||||||
|
Result(const Output<Node>& arg, bool needs_default_layout = false);
|
||||||
|
|
||||||
|
bool visit_attributes(AttributeVisitor& visitor) override;
|
||||||
|
void validate_and_infer_types() override;
|
||||||
|
|
||||||
|
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
||||||
|
|
||||||
|
void set_needs_default_layout(bool val) {
|
||||||
|
m_needs_default_layout = val;
|
||||||
|
}
|
||||||
|
bool needs_default_layout() const {
|
||||||
|
return m_needs_default_layout;
|
||||||
|
}
|
||||||
|
bool evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const override;
|
||||||
|
bool has_evaluate() const override;
|
||||||
|
bool constant_fold(OutputVector& output_values, const OutputVector& inputs_values) override;
|
||||||
|
|
||||||
|
private:
|
||||||
|
bool m_needs_default_layout{false};
|
||||||
|
};
|
||||||
|
} // namespace v0
|
||||||
|
} // namespace op
|
||||||
|
using ResultVector = std::vector<std::shared_ptr<op::v0::Result>>;
|
||||||
|
|
||||||
|
template <>
|
||||||
|
class OPENVINO_API AttributeAdapter<ResultVector> : public VisitorAdapter {
|
||||||
|
public:
|
||||||
|
AttributeAdapter(ResultVector& ref);
|
||||||
|
|
||||||
|
bool visit_attributes(AttributeVisitor& visitor) override;
|
||||||
|
|
||||||
|
static constexpr DiscreteTypeInfo type_info{"AttributeAdapter<ResultVector>", 0};
|
||||||
|
const DiscreteTypeInfo& get_type_info() const override {
|
||||||
|
return type_info;
|
||||||
|
}
|
||||||
|
|
||||||
|
protected:
|
||||||
|
ResultVector& m_ref;
|
||||||
|
};
|
||||||
|
|
||||||
|
} // namespace ov
|
||||||
43
ngraph/core/include/openvino/op/tensor_iterator.hpp
Normal file
43
ngraph/core/include/openvino/op/tensor_iterator.hpp
Normal file
@ -0,0 +1,43 @@
|
|||||||
|
// Copyright (C) 2018-2021 Intel Corporation
|
||||||
|
// SPDX-License-Identifier: Apache-2.0
|
||||||
|
//
|
||||||
|
|
||||||
|
#pragma once
|
||||||
|
|
||||||
|
#include <vector>
|
||||||
|
|
||||||
|
#include "openvino/core/function.hpp"
|
||||||
|
#include "openvino/op/parameter.hpp"
|
||||||
|
#include "openvino/op/util/sub_graph_base.hpp"
|
||||||
|
|
||||||
|
namespace ov {
|
||||||
|
namespace op {
|
||||||
|
namespace v0 {
|
||||||
|
/// \brief Iterate a body over tensors, accumulating into tensors.
|
||||||
|
class NGRAPH_API TensorIterator : public op::util::SubGraphOp {
|
||||||
|
public:
|
||||||
|
NGRAPH_RTTI_DECLARATION;
|
||||||
|
|
||||||
|
bool visit_attributes(AttributeVisitor& visitor) override;
|
||||||
|
|
||||||
|
TensorIterator() = default;
|
||||||
|
explicit TensorIterator(const OutputVector& values);
|
||||||
|
|
||||||
|
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
|
||||||
|
/// \return the body of the iteration
|
||||||
|
std::shared_ptr<Function> get_body() const {
|
||||||
|
return m_bodies[0];
|
||||||
|
}
|
||||||
|
/// \param body set the body of the iteration
|
||||||
|
void set_body(const std::shared_ptr<Function>& body) {
|
||||||
|
set_function(body);
|
||||||
|
}
|
||||||
|
void validate_and_infer_types() override;
|
||||||
|
void revalidate_and_infer_types_for_body_ops();
|
||||||
|
|
||||||
|
private:
|
||||||
|
void try_to_set_num_iterations_if_no_slice_inputs();
|
||||||
|
};
|
||||||
|
} // namespace v0
|
||||||
|
} // namespace op
|
||||||
|
} // namespace ov
|
||||||
@ -12,7 +12,7 @@
|
|||||||
using namespace std;
|
using namespace std;
|
||||||
using namespace ngraph;
|
using namespace ngraph;
|
||||||
|
|
||||||
NGRAPH_RTTI_DEFINITION(op::v1::Gather, "Gather", 1, op::util::GatherBase);
|
OPENVINO_RTTI_DEFINITION(op::v1::Gather, "Gather", 1, op::util::GatherBase);
|
||||||
|
|
||||||
op::v1::Gather::Gather(const Output<Node>& params, const Output<Node>& indices, const Output<Node>& axes)
|
op::v1::Gather::Gather(const Output<Node>& params, const Output<Node>& indices, const Output<Node>& axes)
|
||||||
: GatherBase(params, indices, axes) {
|
: GatherBase(params, indices, axes) {
|
||||||
@ -36,7 +36,7 @@ shared_ptr<Node> op::v1::Gather::clone_with_new_inputs(const OutputVector& new_a
|
|||||||
return make_shared<v1::Gather>(new_args.at(0), new_args.at(1), new_args.at(2));
|
return make_shared<v1::Gather>(new_args.at(0), new_args.at(1), new_args.at(2));
|
||||||
}
|
}
|
||||||
|
|
||||||
NGRAPH_RTTI_DEFINITION(op::v7::Gather, "Gather", 7, op::util::GatherBase);
|
OPENVINO_RTTI_DEFINITION(op::v7::Gather, "Gather", 7, op::util::GatherBase);
|
||||||
|
|
||||||
op::v7::Gather::Gather(const Output<Node>& data,
|
op::v7::Gather::Gather(const Output<Node>& data,
|
||||||
const Output<Node>& indices,
|
const Output<Node>& indices,
|
||||||
@ -78,7 +78,7 @@ shared_ptr<Node> op::v7::Gather::clone_with_new_inputs(const OutputVector& new_a
|
|||||||
return make_shared<v7::Gather>(new_args.at(0), new_args.at(1), new_args.at(2), m_batch_dims);
|
return make_shared<v7::Gather>(new_args.at(0), new_args.at(1), new_args.at(2), m_batch_dims);
|
||||||
}
|
}
|
||||||
|
|
||||||
NGRAPH_RTTI_DEFINITION(op::v8::Gather, "Gather", 8, op::util::GatherBase);
|
OPENVINO_RTTI_DEFINITION(op::v8::Gather, "Gather", 8, op::util::GatherBase);
|
||||||
|
|
||||||
op::v8::Gather::Gather(const Output<Node>& data,
|
op::v8::Gather::Gather(const Output<Node>& data,
|
||||||
const Output<Node>& indices,
|
const Output<Node>& indices,
|
||||||
|
|||||||
@ -12,7 +12,7 @@ using namespace ngraph;
|
|||||||
|
|
||||||
// ------------------------------ V6 ------------------------------
|
// ------------------------------ V6 ------------------------------
|
||||||
|
|
||||||
NGRAPH_RTTI_DEFINITION(op::v6::GatherElements, "GatherElements", 6);
|
OPENVINO_RTTI_DEFINITION(op::v6::GatherElements, "GatherElements", 6);
|
||||||
|
|
||||||
op::v6::GatherElements::GatherElements(const Output<Node>& data, const Output<Node>& indices, const int64_t axis)
|
op::v6::GatherElements::GatherElements(const Output<Node>& data, const Output<Node>& indices, const int64_t axis)
|
||||||
: Op({data, indices}),
|
: Op({data, indices}),
|
||||||
|
|||||||
@ -12,7 +12,7 @@ using namespace ngraph;
|
|||||||
|
|
||||||
// ------------------------------ V5 ------------------------------
|
// ------------------------------ V5 ------------------------------
|
||||||
|
|
||||||
NGRAPH_RTTI_DEFINITION(op::v5::GatherND, "GatherND", 5);
|
OPENVINO_RTTI_DEFINITION(op::v5::GatherND, "GatherND", 5);
|
||||||
|
|
||||||
op::v5::GatherND::GatherND(const Output<Node>& data, const Output<Node>& indices, const size_t batch_dims)
|
op::v5::GatherND::GatherND(const Output<Node>& data, const Output<Node>& indices, const size_t batch_dims)
|
||||||
: Op({data, indices}),
|
: Op({data, indices}),
|
||||||
|
|||||||
@ -10,7 +10,7 @@
|
|||||||
using namespace std;
|
using namespace std;
|
||||||
using namespace ngraph;
|
using namespace ngraph;
|
||||||
|
|
||||||
NGRAPH_RTTI_DEFINITION(op::v1::GatherTree, "GatherTree", 1);
|
OPENVINO_RTTI_DEFINITION(op::v1::GatherTree, "GatherTree", 1);
|
||||||
|
|
||||||
op::v1::GatherTree::GatherTree(const Output<Node>& step_ids,
|
op::v1::GatherTree::GatherTree(const Output<Node>& step_ids,
|
||||||
const Output<Node>& parent_idx,
|
const Output<Node>& parent_idx,
|
||||||
|
|||||||
@ -14,7 +14,7 @@ using namespace std;
|
|||||||
using namespace ngraph;
|
using namespace ngraph;
|
||||||
|
|
||||||
// ------------------------------ V0 ------------------------------
|
// ------------------------------ V0 ------------------------------
|
||||||
NGRAPH_RTTI_DEFINITION(op::v0::Gelu, "Gelu", 0);
|
OPENVINO_RTTI_DEFINITION(op::v0::Gelu, "Gelu", 0);
|
||||||
|
|
||||||
op::v0::Gelu::Gelu() : Op() {}
|
op::v0::Gelu::Gelu() : Op() {}
|
||||||
|
|
||||||
@ -60,13 +60,13 @@ NGRAPH_API EnumNames<ngraph::op::GeluApproximationMode>& EnumNames<ngraph::op::G
|
|||||||
return enum_names;
|
return enum_names;
|
||||||
}
|
}
|
||||||
|
|
||||||
constexpr DiscreteTypeInfo AttributeAdapter<ngraph::op::GeluApproximationMode>::type_info;
|
|
||||||
} // namespace ov
|
|
||||||
std::ostream& op::operator<<(std::ostream& s, const op::GeluApproximationMode& type) {
|
std::ostream& op::operator<<(std::ostream& s, const op::GeluApproximationMode& type) {
|
||||||
return s << as_string(type);
|
return s << as_string(type);
|
||||||
}
|
}
|
||||||
|
|
||||||
NGRAPH_RTTI_DEFINITION(op::v7::Gelu, "Gelu", 7);
|
constexpr DiscreteTypeInfo AttributeAdapter<ngraph::op::GeluApproximationMode>::type_info;
|
||||||
|
} // namespace ov
|
||||||
|
OPENVINO_RTTI_DEFINITION(op::v7::Gelu, "Gelu", 7);
|
||||||
|
|
||||||
op::v7::Gelu::Gelu(const Output<Node>& data, GeluApproximationMode mode)
|
op::v7::Gelu::Gelu(const Output<Node>& data, GeluApproximationMode mode)
|
||||||
: UnaryElementwiseArithmetic(data),
|
: UnaryElementwiseArithmetic(data),
|
||||||
|
|||||||
@ -50,7 +50,7 @@ bool evaluate_greater(const HostTensorPtr& arg0,
|
|||||||
|
|
||||||
//-------------------------------------- v1 ------------------------------------
|
//-------------------------------------- v1 ------------------------------------
|
||||||
|
|
||||||
NGRAPH_RTTI_DEFINITION(op::v1::Greater, "Greater", 1, op::util::BinaryElementwiseComparison);
|
OPENVINO_RTTI_DEFINITION(op::v1::Greater, "Greater", 1, op::util::BinaryElementwiseComparison);
|
||||||
|
|
||||||
op::v1::Greater::Greater(const Output<Node>& arg0, const Output<Node>& arg1, const AutoBroadcastSpec& auto_broadcast)
|
op::v1::Greater::Greater(const Output<Node>& arg0, const Output<Node>& arg1, const AutoBroadcastSpec& auto_broadcast)
|
||||||
: BinaryElementwiseComparison(arg0, arg1, auto_broadcast) {
|
: BinaryElementwiseComparison(arg0, arg1, auto_broadcast) {
|
||||||
|
|||||||
@ -51,7 +51,7 @@ bool evaluate_greater_equal(const HostTensorPtr& arg0,
|
|||||||
|
|
||||||
//---------------------------------- v1 ----------------------------------------
|
//---------------------------------- v1 ----------------------------------------
|
||||||
|
|
||||||
NGRAPH_RTTI_DEFINITION(op::v1::GreaterEqual, "GreaterEqual", 1, op::util::BinaryElementwiseComparison);
|
OPENVINO_RTTI_DEFINITION(op::v1::GreaterEqual, "GreaterEqual", 1, op::util::BinaryElementwiseComparison);
|
||||||
|
|
||||||
op::v1::GreaterEqual::GreaterEqual(const Output<Node>& arg0,
|
op::v1::GreaterEqual::GreaterEqual(const Output<Node>& arg0,
|
||||||
const Output<Node>& arg1,
|
const Output<Node>& arg1,
|
||||||
|
|||||||
@ -16,7 +16,7 @@
|
|||||||
using namespace std;
|
using namespace std;
|
||||||
using namespace ngraph;
|
using namespace ngraph;
|
||||||
|
|
||||||
NGRAPH_RTTI_DEFINITION(op::v0::GRN, "GRN", 0);
|
OPENVINO_RTTI_DEFINITION(op::v0::GRN, "GRN", 0);
|
||||||
|
|
||||||
op::v0::GRN::GRN(const Output<Node>& data, float bias) : Op({data}), m_bias(bias) {
|
op::v0::GRN::GRN(const Output<Node>& data, float bias) : Op({data}), m_bias(bias) {
|
||||||
constructor_validate_and_infer_types();
|
constructor_validate_and_infer_types();
|
||||||
|
|||||||
@ -22,10 +22,10 @@ using namespace ngraph;
|
|||||||
// v1::GroupConvolution
|
// v1::GroupConvolution
|
||||||
//------------------------------------------------------------------------------
|
//------------------------------------------------------------------------------
|
||||||
|
|
||||||
NGRAPH_RTTI_DEFINITION(op::v1::GroupConvolution, "GroupConvolution", 1);
|
OPENVINO_RTTI_DEFINITION(op::v1::GroupConvolution, "GroupConvolution", 1);
|
||||||
|
|
||||||
shared_ptr<Node> op::v1::GroupConvolution::get_default_value() const {
|
shared_ptr<Node> op::v1::GroupConvolution::get_default_value() const {
|
||||||
return op::Constant::create(get_element_type(), get_shape(), {0});
|
return op::v0::Constant::create(get_element_type(), get_shape(), {0});
|
||||||
}
|
}
|
||||||
|
|
||||||
op::v1::GroupConvolution::GroupConvolution(const Output<Node>& data_batch,
|
op::v1::GroupConvolution::GroupConvolution(const Output<Node>& data_batch,
|
||||||
@ -249,7 +249,7 @@ shared_ptr<Node> op::v1::GroupConvolution::clone_with_new_inputs(const OutputVec
|
|||||||
// v1::GroupConvolutionBackpropData
|
// v1::GroupConvolutionBackpropData
|
||||||
//------------------------------------------------------------------------------
|
//------------------------------------------------------------------------------
|
||||||
|
|
||||||
NGRAPH_RTTI_DEFINITION(op::v1::GroupConvolutionBackpropData, "GroupConvolutionBackpropData", 1);
|
OPENVINO_RTTI_DEFINITION(op::v1::GroupConvolutionBackpropData, "GroupConvolutionBackpropData", 1);
|
||||||
|
|
||||||
op::v1::GroupConvolutionBackpropData::GroupConvolutionBackpropData()
|
op::v1::GroupConvolutionBackpropData::GroupConvolutionBackpropData()
|
||||||
: Op(),
|
: Op(),
|
||||||
@ -371,7 +371,7 @@ const PartialShape op::v1::GroupConvolutionBackpropData::get_convolution_output_
|
|||||||
|
|
||||||
void op::v1::GroupConvolutionBackpropData::set_output_shape(const Shape& shape) {
|
void op::v1::GroupConvolutionBackpropData::set_output_shape(const Shape& shape) {
|
||||||
this->input(2).replace_source_output(
|
this->input(2).replace_source_output(
|
||||||
op::Constant::create(this->get_input_element_type(2), Shape{shape.size()}, shape)->output(0));
|
op::v0::Constant::create(this->get_input_element_type(2), Shape{shape.size()}, shape)->output(0));
|
||||||
}
|
}
|
||||||
|
|
||||||
void op::v1::GroupConvolutionBackpropData::infer_conv_backprop_output_spatial_shape(
|
void op::v1::GroupConvolutionBackpropData::infer_conv_backprop_output_spatial_shape(
|
||||||
@ -393,7 +393,7 @@ void op::v1::GroupConvolutionBackpropData::infer_conv_backprop_output_spatial_sh
|
|||||||
int64_t val = strides[i] * (input_data_shape[i].get_length() - 1) +
|
int64_t val = strides[i] * (input_data_shape[i].get_length() - 1) +
|
||||||
dilations[i] * (filters_shape[i].get_length() - 1) + 1 - pads_begin[i] - pads_end[i] +
|
dilations[i] * (filters_shape[i].get_length() - 1) + 1 - pads_begin[i] - pads_end[i] +
|
||||||
output_padding[i];
|
output_padding[i];
|
||||||
output_spatial_shape.push_back(val);
|
output_spatial_shape.emplace_back(val);
|
||||||
} else {
|
} else {
|
||||||
output_spatial_shape.push_back(Dimension::dynamic());
|
output_spatial_shape.push_back(Dimension::dynamic());
|
||||||
}
|
}
|
||||||
|
|||||||
@ -14,7 +14,7 @@
|
|||||||
using namespace std;
|
using namespace std;
|
||||||
using namespace ngraph;
|
using namespace ngraph;
|
||||||
|
|
||||||
constexpr NodeTypeInfo op::v3::GRUCell::type_info;
|
OPENVINO_RTTI_DEFINITION(op::v3::GRUCell, "GRUCell", 1, op::util::RNNCellBase);
|
||||||
|
|
||||||
op::v3::GRUCell::GRUCell() : m_linear_before_reset(false) {
|
op::v3::GRUCell::GRUCell() : m_linear_before_reset(false) {
|
||||||
m_activations = {"sigmoid", "tanh"};
|
m_activations = {"sigmoid", "tanh"};
|
||||||
@ -172,9 +172,9 @@ void op::v3::GRUCell::validate_and_infer_types() {
|
|||||||
|
|
||||||
void op::v3::GRUCell::add_default_bias_input() {
|
void op::v3::GRUCell::add_default_bias_input() {
|
||||||
Output<Node> B =
|
Output<Node> B =
|
||||||
op::Constant::create(get_input_element_type(0),
|
op::v0::Constant::create(get_input_element_type(0),
|
||||||
Shape{(s_gates_count + m_linear_before_reset) * get_hidden_size()},
|
Shape{(s_gates_count + m_linear_before_reset) * get_hidden_size()},
|
||||||
vector<float>((s_gates_count + m_linear_before_reset) * get_hidden_size(), 0.f));
|
vector<float>((s_gates_count + m_linear_before_reset) * get_hidden_size(), 0.f));
|
||||||
set_argument(4, B);
|
set_argument(4, B);
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|||||||
@ -15,7 +15,7 @@
|
|||||||
using namespace std;
|
using namespace std;
|
||||||
using namespace ngraph;
|
using namespace ngraph;
|
||||||
|
|
||||||
NGRAPH_RTTI_DEFINITION(op::v5::GRUSequence, "GRUSequence", 5);
|
OPENVINO_RTTI_DEFINITION(op::v5::GRUSequence, "GRUSequence", 5);
|
||||||
|
|
||||||
op::v5::GRUSequence::GRUSequence()
|
op::v5::GRUSequence::GRUSequence()
|
||||||
: m_direction(op::RecurrentSequenceDirection::FORWARD),
|
: m_direction(op::RecurrentSequenceDirection::FORWARD),
|
||||||
|
|||||||
@ -12,7 +12,7 @@
|
|||||||
using namespace std;
|
using namespace std;
|
||||||
using namespace ngraph;
|
using namespace ngraph;
|
||||||
|
|
||||||
NGRAPH_RTTI_DEFINITION(op::v0::HardSigmoid, "HardSigmoid", 0);
|
OPENVINO_RTTI_DEFINITION(op::v0::HardSigmoid, "HardSigmoid", 0);
|
||||||
|
|
||||||
op::v0::HardSigmoid::HardSigmoid() : Op() {}
|
op::v0::HardSigmoid::HardSigmoid() : Op() {}
|
||||||
|
|
||||||
@ -34,7 +34,7 @@ void op::v0::HardSigmoid::validate_and_infer_types() {
|
|||||||
if (alpha_pshape.is_static()) {
|
if (alpha_pshape.is_static()) {
|
||||||
const auto alpha_shape = alpha_pshape.to_shape();
|
const auto alpha_shape = alpha_pshape.to_shape();
|
||||||
NODE_VALIDATION_CHECK(this,
|
NODE_VALIDATION_CHECK(this,
|
||||||
is_scalar(alpha_shape),
|
ngraph::is_scalar(alpha_shape),
|
||||||
"A scalar is expected for the 'alpha' input. Got: ",
|
"A scalar is expected for the 'alpha' input. Got: ",
|
||||||
alpha_shape);
|
alpha_shape);
|
||||||
}
|
}
|
||||||
@ -42,7 +42,7 @@ void op::v0::HardSigmoid::validate_and_infer_types() {
|
|||||||
if (beta_pshape.is_static()) {
|
if (beta_pshape.is_static()) {
|
||||||
const auto beta_shape = beta_pshape.to_shape();
|
const auto beta_shape = beta_pshape.to_shape();
|
||||||
NODE_VALIDATION_CHECK(this,
|
NODE_VALIDATION_CHECK(this,
|
||||||
is_scalar(beta_shape),
|
ngraph::is_scalar(beta_shape),
|
||||||
"A scalar is expected for the 'beta' input. Got: ",
|
"A scalar is expected for the 'beta' input. Got: ",
|
||||||
beta_shape);
|
beta_shape);
|
||||||
}
|
}
|
||||||
|
|||||||
@ -15,7 +15,7 @@
|
|||||||
using namespace std;
|
using namespace std;
|
||||||
using namespace ngraph;
|
using namespace ngraph;
|
||||||
|
|
||||||
NGRAPH_RTTI_DEFINITION(op::v5::HSigmoid, "HSigmoid", 5);
|
OPENVINO_RTTI_DEFINITION(op::v5::HSigmoid, "HSigmoid", 5, op::util::UnaryElementwiseArithmetic);
|
||||||
|
|
||||||
op::v5::HSigmoid::HSigmoid(const Output<Node>& arg) : UnaryElementwiseArithmetic(arg) {
|
op::v5::HSigmoid::HSigmoid(const Output<Node>& arg) : UnaryElementwiseArithmetic(arg) {
|
||||||
constructor_validate_and_infer_types();
|
constructor_validate_and_infer_types();
|
||||||
|
|||||||
@ -14,7 +14,7 @@
|
|||||||
using namespace std;
|
using namespace std;
|
||||||
using namespace ngraph;
|
using namespace ngraph;
|
||||||
|
|
||||||
NGRAPH_RTTI_DEFINITION(op::v4::HSwish, "HSwish", 4);
|
OPENVINO_RTTI_DEFINITION(op::v4::HSwish, "HSwish", 4, op::util::UnaryElementwiseArithmetic);
|
||||||
|
|
||||||
op::v4::HSwish::HSwish(const Output<Node>& arg) : UnaryElementwiseArithmetic(arg) {
|
op::v4::HSwish::HSwish(const Output<Node>& arg) : UnaryElementwiseArithmetic(arg) {
|
||||||
constructor_validate_and_infer_types();
|
constructor_validate_and_infer_types();
|
||||||
|
|||||||
@ -18,7 +18,7 @@
|
|||||||
|
|
||||||
using namespace ngraph;
|
using namespace ngraph;
|
||||||
|
|
||||||
NGRAPH_RTTI_DEFINITION(op::v7::IDFT, "IDFT", 7, util::FFTBase);
|
OPENVINO_RTTI_DEFINITION(op::v7::IDFT, "IDFT", 7, util::FFTBase);
|
||||||
|
|
||||||
op::v7::IDFT::IDFT(const Output<Node>& data, const Output<Node>& axes) : FFTBase(data, axes) {
|
op::v7::IDFT::IDFT(const Output<Node>& data, const Output<Node>& axes) : FFTBase(data, axes) {
|
||||||
constructor_validate_and_infer_types();
|
constructor_validate_and_infer_types();
|
||||||
|
|||||||
@ -18,7 +18,7 @@
|
|||||||
using namespace std;
|
using namespace std;
|
||||||
using namespace ngraph;
|
using namespace ngraph;
|
||||||
|
|
||||||
NGRAPH_RTTI_DEFINITION(ngraph::op::v8::If, "If", 8, MultiSubGraphOp);
|
OPENVINO_RTTI_DEFINITION(ngraph::op::v8::If, "If", 8, MultiSubGraphOp);
|
||||||
|
|
||||||
op::v8::If::If() : MultiSubGraphOp(2) {}
|
op::v8::If::If() : MultiSubGraphOp(2) {}
|
||||||
|
|
||||||
@ -49,11 +49,11 @@ static ngraph::PartialShape resolve_shape(const ngraph::PartialShape& then_pshap
|
|||||||
if ((*then_it).is_dynamic() || (*else_it).is_dynamic()) {
|
if ((*then_it).is_dynamic() || (*else_it).is_dynamic()) {
|
||||||
new_dims.push_back(Dimension::dynamic());
|
new_dims.push_back(Dimension::dynamic());
|
||||||
} else if (*then_it == *else_it) {
|
} else if (*then_it == *else_it) {
|
||||||
new_dims.push_back(Dimension(*then_it));
|
new_dims.emplace_back(*then_it);
|
||||||
} else {
|
} else {
|
||||||
auto dim_min = std::min((*then_it).get_min_length(), (*else_it).get_min_length());
|
auto dim_min = std::min((*then_it).get_min_length(), (*else_it).get_min_length());
|
||||||
auto dim_max = std::max((*then_it).get_min_length(), (*else_it).get_min_length());
|
auto dim_max = std::max((*then_it).get_min_length(), (*else_it).get_min_length());
|
||||||
new_dims.push_back(Dimension(dim_min, dim_max));
|
new_dims.emplace_back(dim_min, dim_max);
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
@ -125,7 +125,7 @@ void op::v8::If::validate_and_infer_types() {
|
|||||||
// shape and type inference for outputs from If operations
|
// shape and type inference for outputs from If operations
|
||||||
for (const auto& output_descr : m_output_descriptions[cond_index]) {
|
for (const auto& output_descr : m_output_descriptions[cond_index]) {
|
||||||
auto body_value = body->get_results().at(output_descr->m_body_value_index)->input_value(0);
|
auto body_value = body->get_results().at(output_descr->m_body_value_index)->input_value(0);
|
||||||
auto body_value_partial_shape = body_value.get_partial_shape();
|
const auto& body_value_partial_shape = body_value.get_partial_shape();
|
||||||
set_output_type(output_descr->m_output_index, body_value.get_element_type(), body_value_partial_shape);
|
set_output_type(output_descr->m_output_index, body_value.get_element_type(), body_value_partial_shape);
|
||||||
}
|
}
|
||||||
} else // condition is non constant
|
} else // condition is non constant
|
||||||
@ -236,8 +236,8 @@ bool op::v8::If::has_evaluate() const {
|
|||||||
}
|
}
|
||||||
|
|
||||||
void op::v8::If::set_input(const Output<Node>& value,
|
void op::v8::If::set_input(const Output<Node>& value,
|
||||||
const std::shared_ptr<Parameter>& then_parameter,
|
const std::shared_ptr<v0::Parameter>& then_parameter,
|
||||||
const std::shared_ptr<Parameter>& else_parameter) {
|
const std::shared_ptr<v0::Parameter>& else_parameter) {
|
||||||
NGRAPH_CHECK(then_parameter != nullptr || else_parameter != nullptr,
|
NGRAPH_CHECK(then_parameter != nullptr || else_parameter != nullptr,
|
||||||
"Missing parameters! Both parameters are nullptr!");
|
"Missing parameters! Both parameters are nullptr!");
|
||||||
auto then_param_index = m_bodies[THEN_BODY_INDEX]->get_parameter_index(then_parameter);
|
auto then_param_index = m_bodies[THEN_BODY_INDEX]->get_parameter_index(then_parameter);
|
||||||
@ -253,8 +253,8 @@ void op::v8::If::set_input(const Output<Node>& value,
|
|||||||
set_invariant_inputs(value, {then_parameter, else_parameter});
|
set_invariant_inputs(value, {then_parameter, else_parameter});
|
||||||
}
|
}
|
||||||
|
|
||||||
Output<Node> op::v8::If::set_output(const std::shared_ptr<Result>& then_result,
|
Output<Node> op::v8::If::set_output(const std::shared_ptr<v0::Result>& then_result,
|
||||||
const std::shared_ptr<Result>& else_result) {
|
const std::shared_ptr<v0::Result>& else_result) {
|
||||||
NGRAPH_CHECK(then_result != nullptr, "Incorrect result in \"then_body\"! Result cant be \'nullptr\'");
|
NGRAPH_CHECK(then_result != nullptr, "Incorrect result in \"then_body\"! Result cant be \'nullptr\'");
|
||||||
NGRAPH_CHECK(else_result != nullptr, "Incorrect result in \"else_body\"! Result cant be \'nullptr\'");
|
NGRAPH_CHECK(else_result != nullptr, "Incorrect result in \"else_body\"! Result cant be \'nullptr\'");
|
||||||
auto then_result_id = m_bodies[THEN_BODY_INDEX]->get_result_index(then_result);
|
auto then_result_id = m_bodies[THEN_BODY_INDEX]->get_result_index(then_result);
|
||||||
|
|||||||
@ -17,11 +17,9 @@
|
|||||||
using namespace std;
|
using namespace std;
|
||||||
using namespace ngraph;
|
using namespace ngraph;
|
||||||
|
|
||||||
NGRAPH_RTTI_DEFINITION(op::v0::Interpolate, "Interpolate", 0);
|
OPENVINO_RTTI_DEFINITION(op::v0::Interpolate, "Interpolate", 0);
|
||||||
|
|
||||||
op::v0::Interpolate::Interpolate(const Output<Node>& image,
|
op::v0::Interpolate::Interpolate(const Output<Node>& image, const Output<Node>& output_shape, const Attributes& attrs)
|
||||||
const Output<Node>& output_shape,
|
|
||||||
const op::v0::InterpolateAttrs& attrs)
|
|
||||||
: Op({image, output_shape}),
|
: Op({image, output_shape}),
|
||||||
m_attrs(attrs) {
|
m_attrs(attrs) {
|
||||||
constructor_validate_and_infer_types();
|
constructor_validate_and_infer_types();
|
||||||
@ -69,7 +67,7 @@ shared_ptr<Node> op::v0::Interpolate::clone_with_new_inputs(const OutputVector&
|
|||||||
return make_shared<op::v0::Interpolate>(new_args.at(0), new_args.at(1), m_attrs);
|
return make_shared<op::v0::Interpolate>(new_args.at(0), new_args.at(1), m_attrs);
|
||||||
}
|
}
|
||||||
|
|
||||||
std::ostream& ngraph::operator<<(std::ostream& s, const op::v0::Interpolate::InterpolateMode& type) {
|
std::ostream& ov::operator<<(std::ostream& s, const op::v0::Interpolate::InterpolateMode& type) {
|
||||||
return s << as_string(type);
|
return s << as_string(type);
|
||||||
}
|
}
|
||||||
|
|
||||||
@ -92,7 +90,7 @@ constexpr DiscreteTypeInfo AttributeAdapter<ngraph::op::v0::Interpolate::Interpo
|
|||||||
|
|
||||||
// Interpolate v4
|
// Interpolate v4
|
||||||
|
|
||||||
NGRAPH_RTTI_DEFINITION(op::v4::Interpolate, "Interpolate", 4);
|
OPENVINO_RTTI_DEFINITION(op::v4::Interpolate, "Interpolate", 4);
|
||||||
|
|
||||||
op::v4::Interpolate::Interpolate(const Output<Node>& image,
|
op::v4::Interpolate::Interpolate(const Output<Node>& image,
|
||||||
const Output<Node>& output_shape,
|
const Output<Node>& output_shape,
|
||||||
@ -481,22 +479,6 @@ bool op::v4::Interpolate::has_evaluate() const {
|
|||||||
return false;
|
return false;
|
||||||
}
|
}
|
||||||
|
|
||||||
std::ostream& ngraph::operator<<(std::ostream& s, const op::v4::Interpolate::InterpolateMode& type) {
|
|
||||||
return s << as_string(type);
|
|
||||||
}
|
|
||||||
|
|
||||||
std::ostream& ngraph::operator<<(std::ostream& s, const op::v4::Interpolate::ShapeCalcMode& type) {
|
|
||||||
return s << as_string(type);
|
|
||||||
}
|
|
||||||
|
|
||||||
std::ostream& ngraph::operator<<(std::ostream& s, const op::v4::Interpolate::CoordinateTransformMode& type) {
|
|
||||||
return s << as_string(type);
|
|
||||||
}
|
|
||||||
|
|
||||||
std::ostream& ngraph::operator<<(std::ostream& s, const op::v4::Interpolate::NearestMode& type) {
|
|
||||||
return s << as_string(type);
|
|
||||||
}
|
|
||||||
|
|
||||||
namespace ov {
|
namespace ov {
|
||||||
template <>
|
template <>
|
||||||
NGRAPH_API EnumNames<ngraph::op::v4::Interpolate::InterpolateMode>&
|
NGRAPH_API EnumNames<ngraph::op::v4::Interpolate::InterpolateMode>&
|
||||||
@ -553,4 +535,20 @@ EnumNames<ngraph::op::v4::Interpolate::NearestMode>::get() {
|
|||||||
}
|
}
|
||||||
|
|
||||||
constexpr DiscreteTypeInfo AttributeAdapter<ngraph::op::v4::Interpolate::NearestMode>::type_info;
|
constexpr DiscreteTypeInfo AttributeAdapter<ngraph::op::v4::Interpolate::NearestMode>::type_info;
|
||||||
|
|
||||||
|
std::ostream& operator<<(std::ostream& s, const op::v4::Interpolate::InterpolateMode& type) {
|
||||||
|
return s << as_string(type);
|
||||||
|
}
|
||||||
|
|
||||||
|
std::ostream& operator<<(std::ostream& s, const op::v4::Interpolate::ShapeCalcMode& type) {
|
||||||
|
return s << as_string(type);
|
||||||
|
}
|
||||||
|
|
||||||
|
std::ostream& operator<<(std::ostream& s, const op::v4::Interpolate::CoordinateTransformMode& type) {
|
||||||
|
return s << as_string(type);
|
||||||
|
}
|
||||||
|
|
||||||
|
std::ostream& operator<<(std::ostream& s, const op::v4::Interpolate::NearestMode& type) {
|
||||||
|
return s << as_string(type);
|
||||||
|
}
|
||||||
} // namespace ov
|
} // namespace ov
|
||||||
|
|||||||
@ -50,7 +50,7 @@ bool evaluate_less(const HostTensorPtr& arg0,
|
|||||||
|
|
||||||
// ----------------------------- v1 --------------------------------------------
|
// ----------------------------- v1 --------------------------------------------
|
||||||
|
|
||||||
NGRAPH_RTTI_DEFINITION(op::v1::Less, "Less", 1, op::util::BinaryElementwiseComparison);
|
OPENVINO_RTTI_DEFINITION(op::v1::Less, "Less", 1, op::util::BinaryElementwiseComparison);
|
||||||
|
|
||||||
op::v1::Less::Less(const Output<Node>& arg0, const Output<Node>& arg1, const AutoBroadcastSpec& auto_broadcast)
|
op::v1::Less::Less(const Output<Node>& arg0, const Output<Node>& arg1, const AutoBroadcastSpec& auto_broadcast)
|
||||||
: BinaryElementwiseComparison(arg0, arg1, auto_broadcast) {
|
: BinaryElementwiseComparison(arg0, arg1, auto_broadcast) {
|
||||||
|
|||||||
@ -13,7 +13,7 @@ using namespace ngraph;
|
|||||||
|
|
||||||
// ---------------------------------- v1 ---------------------------------------
|
// ---------------------------------- v1 ---------------------------------------
|
||||||
|
|
||||||
NGRAPH_RTTI_DEFINITION(op::v1::LessEqual, "LessEqual", 1, op::util::BinaryElementwiseComparison);
|
OPENVINO_RTTI_DEFINITION(op::v1::LessEqual, "LessEqual", 1, op::util::BinaryElementwiseComparison);
|
||||||
|
|
||||||
op::v1::LessEqual::LessEqual(const Output<Node>& arg0,
|
op::v1::LessEqual::LessEqual(const Output<Node>& arg0,
|
||||||
const Output<Node>& arg1,
|
const Output<Node>& arg1,
|
||||||
|
|||||||
@ -12,7 +12,7 @@
|
|||||||
using namespace std;
|
using namespace std;
|
||||||
using namespace ngraph;
|
using namespace ngraph;
|
||||||
|
|
||||||
NGRAPH_RTTI_DEFINITION(op::v0::Log, "Log", 0);
|
OPENVINO_RTTI_DEFINITION(op::v0::Log, "Log", 0, op::util::UnaryElementwiseArithmetic);
|
||||||
|
|
||||||
op::Log::Log(const Output<Node>& arg) : UnaryElementwiseArithmetic(arg) {
|
op::Log::Log(const Output<Node>& arg) : UnaryElementwiseArithmetic(arg) {
|
||||||
constructor_validate_and_infer_types();
|
constructor_validate_and_infer_types();
|
||||||
|
|||||||
@ -10,7 +10,7 @@
|
|||||||
using namespace std;
|
using namespace std;
|
||||||
using namespace ngraph;
|
using namespace ngraph;
|
||||||
|
|
||||||
NGRAPH_RTTI_DEFINITION(op::v5::LogSoftmax, "LogSoftmax", 5);
|
OPENVINO_RTTI_DEFINITION(op::v5::LogSoftmax, "LogSoftmax", 5);
|
||||||
|
|
||||||
op::v5::LogSoftmax::LogSoftmax(const Output<Node>& arg, const int64_t axis) : Op({arg}), m_axis(axis) {
|
op::v5::LogSoftmax::LogSoftmax(const Output<Node>& arg, const int64_t axis) : Op({arg}), m_axis(axis) {
|
||||||
constructor_validate_and_infer_types();
|
constructor_validate_and_infer_types();
|
||||||
|
|||||||
@ -15,7 +15,7 @@
|
|||||||
using namespace std;
|
using namespace std;
|
||||||
using namespace ngraph;
|
using namespace ngraph;
|
||||||
|
|
||||||
NGRAPH_RTTI_DEFINITION(op::v5::Loop, "Loop", 5);
|
OPENVINO_RTTI_DEFINITION(op::v5::Loop, "Loop", 5, op::util::SubGraphOp);
|
||||||
|
|
||||||
op::v5::Loop::Loop(const Output<Node>& trip_count, const Output<Node>& execution_condition) : SubGraphOp() {
|
op::v5::Loop::Loop(const Output<Node>& trip_count, const Output<Node>& execution_condition) : SubGraphOp() {
|
||||||
set_argument(0, trip_count);
|
set_argument(0, trip_count);
|
||||||
@ -178,7 +178,7 @@ void op::v5::Loop::validate_and_infer_types() {
|
|||||||
|
|
||||||
body_parameter->set_partial_shape(input_partial_shape);
|
body_parameter->set_partial_shape(input_partial_shape);
|
||||||
} else if (auto invariant_input_description =
|
} else if (auto invariant_input_description =
|
||||||
ov::as_type_ptr<TensorIterator::InvariantInputDescription>(input_description)) {
|
ov::as_type_ptr<v0::TensorIterator::InvariantInputDescription>(input_description)) {
|
||||||
auto body_parameter = m_bodies[0]->get_parameters().at(invariant_input_description->m_body_parameter_index);
|
auto body_parameter = m_bodies[0]->get_parameters().at(invariant_input_description->m_body_parameter_index);
|
||||||
|
|
||||||
auto body_param_partial_shape = body_parameter->get_partial_shape();
|
auto body_param_partial_shape = body_parameter->get_partial_shape();
|
||||||
@ -198,7 +198,7 @@ void op::v5::Loop::validate_and_infer_types() {
|
|||||||
auto body_value = m_bodies[0]->get_results().at(output_description->m_body_value_index)->input_value(0);
|
auto body_value = m_bodies[0]->get_results().at(output_description->m_body_value_index)->input_value(0);
|
||||||
|
|
||||||
if (auto concat_output_description =
|
if (auto concat_output_description =
|
||||||
ov::as_type_ptr<TensorIterator::ConcatOutputDescription>(output_description)) {
|
ov::as_type_ptr<v0::TensorIterator::ConcatOutputDescription>(output_description)) {
|
||||||
const auto& body_value_partial_shape = body_value.get_partial_shape();
|
const auto& body_value_partial_shape = body_value.get_partial_shape();
|
||||||
auto out_shape = body_value_partial_shape;
|
auto out_shape = body_value_partial_shape;
|
||||||
if (zero_number_of_iter) {
|
if (zero_number_of_iter) {
|
||||||
@ -220,7 +220,7 @@ void op::v5::Loop::validate_and_infer_types() {
|
|||||||
}
|
}
|
||||||
|
|
||||||
else if (auto body_output_description =
|
else if (auto body_output_description =
|
||||||
ov::as_type_ptr<TensorIterator::BodyOutputDescription>(output_description)) {
|
ov::as_type_ptr<v0::TensorIterator::BodyOutputDescription>(output_description)) {
|
||||||
const PartialShape& ps = body_value.get_partial_shape();
|
const PartialShape& ps = body_value.get_partial_shape();
|
||||||
if (ps.is_dynamic()) {
|
if (ps.is_dynamic()) {
|
||||||
set_output_type(index, body_value.get_element_type(), ps);
|
set_output_type(index, body_value.get_element_type(), ps);
|
||||||
|
|||||||
@ -14,10 +14,10 @@
|
|||||||
using namespace std;
|
using namespace std;
|
||||||
using namespace ngraph;
|
using namespace ngraph;
|
||||||
|
|
||||||
NGRAPH_RTTI_DEFINITION(op::LRN, "LRN", 0);
|
OPENVINO_RTTI_DEFINITION(op::v0::LRN, "LRN", 0);
|
||||||
|
|
||||||
op::LRN::LRN(const Output<Node>& arg, double alpha, double beta, double bias, size_t size)
|
op::LRN::LRN(const Output<Node>& arg, double alpha, double beta, double bias, size_t size)
|
||||||
: LRN(arg, op::Constant::create(element::i64, Shape{1}, {1}), alpha, beta, bias, size) {
|
: LRN(arg, op::v0::Constant::create(element::i64, Shape{1}, {1}), alpha, beta, bias, size) {
|
||||||
add_provenance_group_member(input_value(1).get_node_shared_ptr());
|
add_provenance_group_member(input_value(1).get_node_shared_ptr());
|
||||||
}
|
}
|
||||||
|
|
||||||
@ -102,5 +102,5 @@ bool ngraph::op::v0::LRN::visit_attributes(AttributeVisitor& visitor) {
|
|||||||
shared_ptr<Node> op::LRN::clone_with_new_inputs(const OutputVector& new_args) const {
|
shared_ptr<Node> op::LRN::clone_with_new_inputs(const OutputVector& new_args) const {
|
||||||
NGRAPH_OP_SCOPE(v0_LRN_clone_with_new_inputs);
|
NGRAPH_OP_SCOPE(v0_LRN_clone_with_new_inputs);
|
||||||
check_new_args_count(this, new_args);
|
check_new_args_count(this, new_args);
|
||||||
return make_shared<op::LRN>(new_args.at(0), new_args.at(1), m_alpha, m_beta, m_bias, m_size);
|
return make_shared<op::v0::LRN>(new_args.at(0), new_args.at(1), m_alpha, m_beta, m_bias, m_size);
|
||||||
}
|
}
|
||||||
|
|||||||
@ -17,8 +17,8 @@
|
|||||||
using namespace std;
|
using namespace std;
|
||||||
using namespace ngraph;
|
using namespace ngraph;
|
||||||
|
|
||||||
NGRAPH_RTTI_DEFINITION(op::v0::LSTMCell, "LSTMCell", 0, op::util::RNNCellBase);
|
OPENVINO_RTTI_DEFINITION(op::v0::LSTMCell, "LSTMCell", 0, op::util::RNNCellBase);
|
||||||
NGRAPH_RTTI_DEFINITION(op::v4::LSTMCell, "LSTMCell", 4, op::util::RNNCellBase);
|
OPENVINO_RTTI_DEFINITION(op::v4::LSTMCell, "LSTMCell", 4, op::util::RNNCellBase);
|
||||||
|
|
||||||
op::v0::LSTMCell::LSTMCell() : m_input_forget(false), m_weights_format(LSTMWeightsFormat::IFCO) {
|
op::v0::LSTMCell::LSTMCell() : m_input_forget(false), m_weights_format(LSTMWeightsFormat::IFCO) {
|
||||||
m_activations = {"sigmoid", "tanh", "tanh"};
|
m_activations = {"sigmoid", "tanh", "tanh"};
|
||||||
@ -273,14 +273,15 @@ void op::v0::LSTMCell::validate_and_infer_types() {
|
|||||||
}
|
}
|
||||||
|
|
||||||
Output<Node> op::v0::LSTMCell::get_default_bias_input() const {
|
Output<Node> op::v0::LSTMCell::get_default_bias_input() const {
|
||||||
return Output<Node>{
|
return Output<Node>{op::v0::Constant::create(get_input_element_type(0),
|
||||||
op::Constant::create(get_input_element_type(0), Shape{s_gates_count * get_hidden_size()}, vector<float>{0.f})};
|
Shape{s_gates_count * get_hidden_size()},
|
||||||
|
vector<float>{0.f})};
|
||||||
}
|
}
|
||||||
|
|
||||||
Output<Node> op::v0::LSTMCell::get_default_peepholes_input() const {
|
Output<Node> op::v0::LSTMCell::get_default_peepholes_input() const {
|
||||||
return Output<Node>{op::Constant::create(get_input_element_type(0),
|
return Output<Node>{op::v0::Constant::create(get_input_element_type(0),
|
||||||
Shape{s_peepholes_count * get_hidden_size()},
|
Shape{s_peepholes_count * get_hidden_size()},
|
||||||
vector<float>{0.f})};
|
vector<float>{0.f})};
|
||||||
}
|
}
|
||||||
|
|
||||||
shared_ptr<Node> op::v0::LSTMCell::clone_with_new_inputs(const OutputVector& new_args) const {
|
shared_ptr<Node> op::v0::LSTMCell::clone_with_new_inputs(const OutputVector& new_args) const {
|
||||||
@ -511,8 +512,9 @@ void op::v4::LSTMCell::validate_and_infer_types() {
|
|||||||
}
|
}
|
||||||
|
|
||||||
Output<Node> op::v4::LSTMCell::get_default_bias_input() const {
|
Output<Node> op::v4::LSTMCell::get_default_bias_input() const {
|
||||||
return Output<Node>{
|
return Output<Node>{op::v0::Constant::create(get_input_element_type(0),
|
||||||
op::Constant::create(get_input_element_type(0), Shape{s_gates_count * get_hidden_size()}, vector<float>{0.f})};
|
Shape{s_gates_count * get_hidden_size()},
|
||||||
|
vector<float>{0.f})};
|
||||||
}
|
}
|
||||||
|
|
||||||
shared_ptr<Node> op::v4::LSTMCell::clone_with_new_inputs(const OutputVector& new_args) const {
|
shared_ptr<Node> op::v4::LSTMCell::clone_with_new_inputs(const OutputVector& new_args) const {
|
||||||
|
|||||||
@ -16,8 +16,8 @@
|
|||||||
using namespace ngraph;
|
using namespace ngraph;
|
||||||
using namespace std;
|
using namespace std;
|
||||||
|
|
||||||
NGRAPH_RTTI_DEFINITION(op::v0::LSTMSequence, "LSTMSequence", 0);
|
OPENVINO_RTTI_DEFINITION(op::v0::LSTMSequence, "LSTMSequence", 0);
|
||||||
NGRAPH_RTTI_DEFINITION(op::v5::LSTMSequence, "LSTMSequence", 5);
|
OPENVINO_RTTI_DEFINITION(op::v5::LSTMSequence, "LSTMSequence", 5);
|
||||||
|
|
||||||
op::v0::LSTMSequence::LSTMSequence()
|
op::v0::LSTMSequence::LSTMSequence()
|
||||||
: Op(),
|
: Op(),
|
||||||
|
|||||||
@ -12,7 +12,7 @@
|
|||||||
using namespace std;
|
using namespace std;
|
||||||
using namespace ngraph;
|
using namespace ngraph;
|
||||||
|
|
||||||
NGRAPH_RTTI_DEFINITION(op::v0::Parameter, "Parameter", 0);
|
OPENVINO_RTTI_DEFINITION(op::v0::Parameter, "Parameter", 0);
|
||||||
|
|
||||||
op::Parameter::Parameter(const element::Type& element_type, const PartialShape& pshape)
|
op::Parameter::Parameter(const element::Type& element_type, const PartialShape& pshape)
|
||||||
: m_partial_shape(pshape),
|
: m_partial_shape(pshape),
|
||||||
|
|||||||
@ -15,7 +15,7 @@
|
|||||||
using namespace std;
|
using namespace std;
|
||||||
using namespace ngraph;
|
using namespace ngraph;
|
||||||
|
|
||||||
NGRAPH_RTTI_DEFINITION(op::v0::Result, "Result", 0);
|
OPENVINO_RTTI_DEFINITION(op::v0::Result, "Result", 0);
|
||||||
|
|
||||||
op::Result::Result(const Output<Node>& arg, bool needs_default_layout)
|
op::Result::Result(const Output<Node>& arg, bool needs_default_layout)
|
||||||
: Op({arg}),
|
: Op({arg}),
|
||||||
|
|||||||
@ -12,7 +12,7 @@
|
|||||||
using namespace std;
|
using namespace std;
|
||||||
using namespace ngraph;
|
using namespace ngraph;
|
||||||
|
|
||||||
NGRAPH_RTTI_DEFINITION(op::v0::TensorIterator, "TensorIterator", 0, op::util::SubGraphOp);
|
OPENVINO_RTTI_DEFINITION(op::v0::TensorIterator, "TensorIterator", 0, op::util::SubGraphOp);
|
||||||
|
|
||||||
op::v0::TensorIterator::TensorIterator(const OutputVector& values) : op::util::SubGraphOp(values) {}
|
op::v0::TensorIterator::TensorIterator(const OutputVector& values) : op::util::SubGraphOp(values) {}
|
||||||
|
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user