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Moved operations from A to ov namespace
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@ilyachur
ilyachur committed Sep 2, 2021
1 parent 47f2271 commit 5d646ac
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Showing 52 changed files with 1,647 additions and 1,313 deletions.
29 changes: 2 additions & 27 deletions ngraph/core/include/ngraph/op/abs.hpp
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#include <memory>

#include "ngraph/op/util/unary_elementwise_arithmetic.hpp"
#include "openvino/op/abs.hpp"

namespace ngraph {
namespace op {
namespace v0 {
/// \brief Elementwise absolute value operation.
///
class NGRAPH_API Abs : public util::UnaryElementwiseArithmetic {
public:
static constexpr NodeTypeInfo type_info{"Abs", 0};
const NodeTypeInfo& get_type_info() const override {
return type_info;
}
/// \brief Constructs an absolute value operation.
Abs() = default;
bool visit_attributes(AttributeVisitor&) override {
return true;
}
/// \brief Constructs an absolute value operation.
///
/// \param arg Output that produces the input tensor.<br>
/// `[d1, ...]`
///
/// Output `[d1, ...]`
///
Abs(const Output<Node>& arg);

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;
};
using ov::op::v0::Abs;
} // namespace v0
using v0::Abs;
} // namespace op
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27 changes: 2 additions & 25 deletions ngraph/core/include/ngraph/op/acos.hpp
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#include <memory>

#include "ngraph/op/util/unary_elementwise_arithmetic.hpp"
#include "openvino/op/acos.hpp"

namespace ngraph {
namespace op {
namespace v0 {
/// \brief Elementwise inverse cosine (arccos) operation.
///
class NGRAPH_API Acos : public util::UnaryElementwiseArithmetic {
public:
static constexpr NodeTypeInfo type_info{"Acos", 0};
const NodeTypeInfo& get_type_info() const override {
return type_info;
}
/// \brief Constructs an arccos operation.
Acos() = default;
/// \brief Constructs an arccos operation.
///
/// \param arg Output that produces the input tensor.<br>
/// `[d1, ...]`
///
/// Output `[d1, ...]`
///
Acos(const Output<Node>& arg);
bool visit_attributes(AttributeVisitor&) override {
return true;
}
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;
};
using ov::op::v0::Acos;
} // namespace v0
using v0::Acos;
} // namespace op
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26 changes: 2 additions & 24 deletions ngraph/core/include/ngraph/op/acosh.hpp
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#include <memory>

#include "ngraph/op/util/unary_elementwise_arithmetic.hpp"
#include "openvino/op/acosh.hpp"

namespace ngraph {
namespace op {
namespace v3 {
/// \brief Elementwise inverse hyperbolic cos operation.
///
class NGRAPH_API Acosh : public util::UnaryElementwiseArithmetic {
public:
NGRAPH_RTTI_DECLARATION;

/// \brief Constructs an Acosh operation.
Acosh() = default;
/// \brief Constructs an Acosh operation.
///
/// \param arg Output that produces the input tensor.<br>
/// `[d1, ...]`
///
/// Output `[d1, ...]`
///
Acosh(const Output<Node>& arg);

virtual std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
bool visit_attributes(AttributeVisitor&) override {
return true;
}
bool evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const override;
bool has_evaluate() const override;
};
using ov::op::v3::Acosh;
} // namespace v3
using v3::Acosh;
} // namespace op
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25 changes: 2 additions & 23 deletions ngraph/core/include/ngraph/op/adaptive_avg_pool.hpp
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#include "ngraph/op/op.hpp"
#include "ngraph/op/util/attr_types.hpp"
#include "openvino/op/adaptive_avg_pool.hpp"

namespace ngraph {
namespace op {
namespace v8 {
/// \brief Adaptive average pooling operation.
///
class NGRAPH_API AdaptiveAvgPool : public Op {
public:
NGRAPH_RTTI_DECLARATION;

AdaptiveAvgPool() = default;

///
/// \brief Constructs adaptive average pooling operation.
///
/// \param data Input data
///
/// \param output_shape 1D tensor describing output shape for spatial
/// dimensions.
///
AdaptiveAvgPool(const Output<Node>& data, const Output<Node>& output_shape);

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;
};
using ov::op::v8::AdaptiveAvgPool;
} // namespace v8
} // namespace op
} // namespace ngraph
37 changes: 2 additions & 35 deletions ngraph/core/include/ngraph/op/adaptive_max_pool.hpp
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#include "ngraph/op/op.hpp"
#include "ngraph/op/util/attr_types.hpp"
#include "openvino/op/adaptive_max_pool.hpp"

namespace ngraph {
namespace op {
namespace v8 {
/// \brief Adaptive max pooling operation.
///
class NGRAPH_API AdaptiveMaxPool : public Op {
public:
NGRAPH_RTTI_DECLARATION;

AdaptiveMaxPool() = default;

///
/// \brief Constructs adaptive max pooling operation.
///
/// \param data Input data
///
/// \param output_shape 1D tensor describing output shape for spatial
/// dimensions.
///
/// \param index_element_type Specifies the output tensor type for indices
/// output
///
AdaptiveMaxPool(const Output<Node>& data,
const Output<Node>& output_shape,
const ngraph::element::Type& index_element_type = ngraph::element::i64);

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;

element::Type get_index_element_type() const {
return m_index_element_type;
}

protected:
ngraph::element::Type m_index_element_type = ngraph::element::i64;
};
using ov::op::v8::AdaptiveMaxPool;
} // namespace v8
} // namespace op
} // namespace ngraph
33 changes: 2 additions & 31 deletions ngraph/core/include/ngraph/op/add.hpp
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#include <memory>

#include "ngraph/op/util/binary_elementwise_arithmetic.hpp"
#include "openvino/op/add.hpp"

namespace ngraph {
namespace op {
namespace v1 {
/// \brief Elementwise addition operation.
///
class NGRAPH_API Add : public util::BinaryElementwiseArithmetic {
public:
NGRAPH_RTTI_DECLARATION;

/// \brief Constructs an uninitialized addition operation
Add() : util::BinaryElementwiseArithmetic(AutoBroadcastSpec::NUMPY) {}

/// \brief Constructs an addition operation.
///
/// \param arg0 Output that produces the first input tensor.<br>
/// `[d0, ...]`
/// \param arg1 Output that produces the second input tensor.<br>
/// `[d0, ...]`
/// \param auto_broadcast Auto broadcast specification. Default is Numpy-style
/// implicit broadcasting.
///
/// Output `[d0, ...]`
///
Add(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 visit_attributes(AttributeVisitor& visitor) override;

bool evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const override;
bool has_evaluate() const override;
};
using ov::op::v1::Add;
} // namespace v1
} // namespace op
} // namespace ngraph
29 changes: 2 additions & 27 deletions ngraph/core/include/ngraph/op/and.hpp
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#include <memory>

#include "ngraph/op/util/binary_elementwise_logical.hpp"
#include "openvino/op/logical_and.hpp"

namespace ngraph {
namespace op {
namespace v1 {
/// \brief Elementwise logical-and operation.
///
class NGRAPH_API LogicalAnd : public util::BinaryElementwiseLogical {
public:
NGRAPH_RTTI_DECLARATION;
/// \brief Constructs a logical-and operation.
LogicalAnd() = default;

/// \brief Constructs a logical-and operation.
///
/// \param arg0 Output that produces the first input tensor.<br>
/// `[d0, ...]`
/// \param arg1 Output that produces the second input tensor.<br>
/// `[d0, ...]`
/// \param auto_broadcast Auto broadcast specification
///
/// Output `[d0, ...]`
///
LogicalAnd(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 visit_attributes(AttributeVisitor& visitor) override;
bool evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const override;
bool has_evaluate() const override;
};
using ov::op::v1::LogicalAnd;
} // namespace v1
} // namespace op
} // namespace ngraph
28 changes: 2 additions & 26 deletions ngraph/core/include/ngraph/op/asin.hpp
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#include <memory>

#include "ngraph/op/util/unary_elementwise_arithmetic.hpp"
#include "openvino/op/asin.hpp"

namespace ngraph {
namespace op {
namespace v0 {
/// \brief Elementwise inverse sine (arcsin) operation.
///
class NGRAPH_API Asin : public util::UnaryElementwiseArithmetic {
public:
static constexpr NodeTypeInfo type_info{"Asin", 0};
const NodeTypeInfo& get_type_info() const override {
return type_info;
}
/// \brief Constructs an arcsin operation.
Asin() = default;
/// \brief Constructs an arcsin operation.
///
/// \param arg Output that produces the input tensor.<br>
/// `[d1, ...]`
///
/// Output `[d1, ...]`
///
Asin(const Output<Node>& arg);

virtual std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
bool visit_attributes(AttributeVisitor&) override {
return true;
}
bool evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const override;
bool has_evaluate() const override;
};
using ov::op::v0::Asin;
} // namespace v0
using v0::Asin;
} // namespace op
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26 changes: 2 additions & 24 deletions ngraph/core/include/ngraph/op/asinh.hpp
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#include <memory>

#include "ngraph/op/util/unary_elementwise_arithmetic.hpp"
#include "openvino/op/asinh.hpp"

namespace ngraph {
namespace op {
namespace v3 {
/// \brief Elementwise inverse hyperbolic sin operation.
///
class NGRAPH_API Asinh : public util::UnaryElementwiseArithmetic {
public:
NGRAPH_RTTI_DECLARATION;

/// \brief Constructs an Asinh operation.
Asinh() = default;
/// \brief Constructs an Asinh operation.
///
/// \param arg Output that produces the input tensor.<br>
/// `[d1, ...]`
///
/// Output `[d1, ...]`
///
Asinh(const Output<Node>& arg);

virtual std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
bool visit_attributes(AttributeVisitor&) override {
return true;
}
bool evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const override;
bool has_evaluate() const override;
};
using ov::op::v3::Asinh;
} // namespace v3
using v3::Asinh;
} // namespace op
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