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Commit f6bddf08 authored by Jayaram Bobba's avatar Jayaram Bobba Committed by Scott Cyphers
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Opset1 Definition (#3813) 

* Opset1

* Added opset1.hpp

* Added more ops to opset0 and opset1

* Move opset1.hpp up and remove opset0.hpp

* Add versioning to more ops

* Revert to older pass names to keep compatibility for external components

* Fix compilation errors with codegen

* merge

* Added compile-time check for opset

* Added opset1 tbl

* Add op_version table of all ops

* Create factories from op_version_tbl

* reorg unsupported ops in int backend

* Added temporary alias for GreaterEqual

* Add missing case to interpreter enumeration

* Finish opset serializer cleanup (#3939)

* Opset-based opset conversion (#3937)

* Opset-based opset conversion

* Add other opset conversion

* Use ops.hpp

* Update opset0_tbl.hpp

* Switch interpreter to opset0 + a few extras (#3941)

* Switch interpreter, gcpu to opset0

* Remove unnused files

* Give interpreter its own opset

* style

* Fix namespace

* Fix rounding type conversion

* Work-around for bad clang3.9 bug

* Work-around
parent d2482523
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Showing with 840 additions and 181 deletions
src/ngraph/CMakeLists.txt
View file @ f6bddf08
......@@ -436,6 +436,7 @@ set (SRC
op/util/unary_elementwise_arithmetic.cpp
op/util/unary_elementwise_arithmetic.hpp
ops.hpp
opsets/opset.cpp
partial_shape.cpp
partial_shape.hpp
pass/algebraic_simplification.cpp
......
src/ngraph/factory.cpp
View file @ f6bddf08
......@@ -18,22 +18,7 @@
#include "ngraph/factory.hpp"
#include "ngraph/node.hpp"
#include "ngraph/op/abs.hpp"
#include "ngraph/op/acos.hpp"
#include "ngraph/op/add.hpp"
#include "ngraph/op/all.hpp"
#include "ngraph/op/allreduce.hpp"
#include "ngraph/op/and.hpp"
#include "ngraph/op/any.hpp"
#include "ngraph/op/argmax.hpp"
#include "ngraph/op/argmin.hpp"
#include "ngraph/op/avg_pool.hpp"
#include "ngraph/op/batch_norm.hpp"
#include "ngraph/op/broadcast.hpp"
#include "ngraph/op/broadcast_distributed.hpp"
#include "ngraph/op/ceiling.hpp"
#include "ngraph/op/concat.hpp"
#include "ngraph/op/parameter.hpp"
#include "ngraph/ops.hpp"
using namespace std;
......@@ -58,31 +43,9 @@ namespace ngraph
lock_guard<mutex> guard(init_guard);
if (registry.m_factory_map.size() == 0)
{
registry.register_factory<op::Abs>();
registry.register_factory<op::Acos>();
registry.register_factory<op::v0::Add>();
registry.register_factory<op::v1::Add>();
registry.register_factory<op::All>();
registry.register_factory<op::AllReduce>();
registry.register_factory<op::And>();
registry.register_factory<op::Any>();
registry.register_factory<op::ArgMax>();
registry.register_factory<op::ArgMin>();
registry.register_factory<op::v0::AvgPool>();
registry.register_factory<op::v0::AvgPoolBackprop>();
registry.register_factory<op::v1::AvgPool>();
registry.register_factory<op::v1::AvgPoolBackprop>();
registry.register_factory<op::BatchNormInference>();
registry.register_factory<op::BatchNormTraining>();
registry.register_factory<op::BatchNormTrainingBackprop>();
registry.register_factory<op::BroadcastDistributed>();
registry.register_factory<op::v0::Broadcast>();
registry.register_factory<op::v0::BroadcastLike>();
registry.register_factory<op::v1::Broadcast>();
registry.register_factory<op::Ceiling>();
registry.register_factory<op::Concat>();
registry.register_factory<op::v1::LogicalAnd>();
registry.register_factory<op::Parameter>();
#define NGRAPH_OP(NAME, NAMESPACE, VERSION) registry.register_factory<NAMESPACE::NAME>();
#include "ngraph/op/op_version_tbl.hpp"
#undef NGRAPH_OP
}
}
return registry;
......
src/ngraph/frontend/onnx_import/op/mod.cpp
View file @ f6bddf08
......@@ -38,7 +38,7 @@ namespace ngraph
ASSERT_IS_SUPPORTED(node, fmod == 1)
<< "Only 'fmod=1' mode is supported for mod operator.";
return {std::make_shared<ngraph::op::Mod>(dividend, divisor)};
return {std::make_shared<ngraph::op::v1::Mod>(dividend, divisor)};
}
} // namespace set_1
......
src/ngraph/frontend/onnx_import/op/selu.cpp
View file @ f6bddf08
......@@ -45,7 +45,7 @@ namespace ngraph
auto gamma_node = std::make_shared<ngraph::op::Constant>(
data->get_element_type(), data->get_shape(), std::vector<double>{gamma});
return {std::make_shared<ngraph::op::v1::Selu>(data, alpha_node, gamma_node)};
return {std::make_shared<ngraph::op::v0::Selu>(data, alpha_node, gamma_node)};
}
} // namespace set_1
......
src/ngraph/op/abs.hpp
View file @ f6bddf08
......@@ -23,6 +23,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief Elementwise absolute value operation.
///
......@@ -50,4 +52,6 @@ namespace ngraph
const NodeVector& deltas) override;
};
}
using v0::Abs;
}
}
src/ngraph/op/acos.hpp
View file @ f6bddf08
......@@ -23,6 +23,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief Elementwise inverse cosine (arccos) operation.
///
......@@ -49,4 +51,6 @@ namespace ngraph
const NodeVector& deltas) override;
};
}
using v0::Acos;
}
}
src/ngraph/op/add.hpp
View file @ f6bddf08
......@@ -54,6 +54,7 @@ namespace ngraph
const AutoBroadcastSpec& auto_broadcast = AutoBroadcastSpec());
std::shared_ptr<Node> copy_with_new_args(const NodeVector& new_args) const override;
bool visit_attributes(AttributeVisitor& visitor) override;
virtual bool is_commutative() const override { return true; }
protected:
......@@ -101,8 +102,8 @@ namespace ngraph
virtual void generate_adjoints(autodiff::Adjoints& adjoints,
const NodeVector& deltas) override;
};
} // namespace v1
} // namespace v1
using v0::Add;
} // namespace op
......
src/ngraph/op/all.hpp
View file @ f6bddf08
......@@ -21,6 +21,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief Logical "all" reduction operation.
class NGRAPH_API All : public util::LogicalReduction
......@@ -47,4 +49,6 @@ namespace ngraph
virtual std::shared_ptr<Node> get_default_value() const override;
};
}
using v0::All;
}
}
src/ngraph/op/allreduce.hpp
View file @ f6bddf08
......@@ -22,6 +22,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
class NGRAPH_API AllReduce : public Op
{
......@@ -29,7 +31,8 @@ namespace ngraph
static constexpr NodeTypeInfo type_info{"AllReduce", 0};
const NodeTypeInfo& get_type_info() const override { return type_info; }
AllReduce() = default;
AllReduce(const Output<Node>& arg, reduction::Type reduce_type = reduction::Type::SUM);
AllReduce(const Output<Node>& arg,
reduction::Type reduce_type = reduction::Type::SUM);
void validate_and_infer_types() override;
......@@ -42,4 +45,6 @@ namespace ngraph
reduction::Type m_reduce_type{reduction::Type::SUM};
};
}
using v0::AllReduce;
}
}
src/ngraph/op/argmax.hpp
View file @ f6bddf08
......@@ -21,6 +21,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief Computes minimum index along a specified axis for a given tensor
class NGRAPH_API ArgMax : public op::util::IndexReduction
......@@ -36,7 +38,9 @@ namespace ngraph
/// \param axis The axis along which to compute an index for maximum
/// \param index_element_type produce indices. Currently, only int64 or int32 are
/// supported
ArgMax(const Output<Node>& arg, size_t axis, const element::Type& index_element_type);
ArgMax(const Output<Node>& arg,
size_t axis,
const element::Type& index_element_type);
virtual std::shared_ptr<Node>
copy_with_new_args(const NodeVector& new_args) const override;
......@@ -44,4 +48,6 @@ namespace ngraph
virtual std::shared_ptr<Node> get_default_value() const override;
};
}
using v0::ArgMax;
}
}
src/ngraph/op/argmin.hpp
View file @ f6bddf08
......@@ -21,6 +21,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief Computes minimum index along a specified axis for a given tensor
class NGRAPH_API ArgMin : public op::util::IndexReduction
......@@ -37,7 +39,9 @@ namespace ngraph
/// \param axis The axis along which to compute an index for minimum
/// \param index_element_type produce indices. Currently, only int64 or int32 are
/// supported
ArgMin(const Output<Node>& arg, size_t axis, const element::Type& index_element_type);
ArgMin(const Output<Node>& arg,
size_t axis,
const element::Type& index_element_type);
virtual std::shared_ptr<Node>
copy_with_new_args(const NodeVector& new_args) const override;
......@@ -45,4 +49,6 @@ namespace ngraph
virtual std::shared_ptr<Node> get_default_value() const override;
};
}
using v0::ArgMin;
}
}
src/ngraph/op/asin.hpp
View file @ f6bddf08
......@@ -23,6 +23,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief Elementwise inverse sine (arcsin) operation.
///
......@@ -50,4 +52,6 @@ namespace ngraph
const NodeVector& deltas) override;
};
}
using v0::Asin;
}
}
src/ngraph/op/atan.hpp
View file @ f6bddf08
......@@ -23,6 +23,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief Elementwise inverse tangent (arctan) operation.
///
......@@ -51,4 +53,6 @@ namespace ngraph
const NodeVector& deltas) override;
};
}
using v0::Atan;
}
}
src/ngraph/op/batch_norm.hpp
View file @ f6bddf08
......@@ -25,6 +25,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief Batchnorm for training operation
class NGRAPH_API BatchNormTraining : public Op
......@@ -59,7 +61,8 @@ namespace ngraph
/// SHAPE DETAILS:
/// gamma: must have rank 1, with the same span as input's channel axis.
/// beta: must have rank 1, with the same span as input's channel axis.
/// input: must have rank >= 2. The second dimension represents the channel axis
/// input: must have rank >= 2. The second dimension represents the channel
/// axis
/// and must have a span of at least 1.
/// output[0]: shall have the same shape as 'input'.
/// output[1]: shall have rank 1, with the same span as input's channel axis.
......@@ -123,7 +126,8 @@ namespace ngraph
/// SHAPE DETAILS:
/// gamma: must have rank 1, with the same span as input's channel axis.
/// beta: must have rank 1, with the same span as input's channel axis.
/// input: must have rank >= 2. The second dimension represents the channel axis
/// input: must have rank >= 2. The second dimension represents the channel
/// axis
/// and must have a span of at least 1.
/// mean: must have rank 1, with the same span as input's channel axis.
/// variance: must have rank 1, with the same span as input's channel axis.
......@@ -174,6 +178,8 @@ namespace ngraph
const Output<Node>& delta,
double epsilon);
bool visit_attributes(AttributeVisitor& visitor) override;
NGRAPH_DEPRECATED_DOC
NGRAPH_DEPRECATED("Use another constructor")
BatchNormTrainingBackprop(double epsilon,
......@@ -185,7 +191,6 @@ namespace ngraph
const Output<Node>& delta);
void validate_and_infer_types() override;
bool visit_attributes(AttributeVisitor& visitor) override;
double get_eps_value() const { return m_epsilon; }
void set_eps_value(double epsilon) { m_epsilon = epsilon; }
......@@ -203,4 +208,8 @@ namespace ngraph
double m_epsilon;
};
}
using v0::BatchNormInference;
using v0::BatchNormTraining;
using v0::BatchNormTrainingBackprop;
}
}
src/ngraph/op/broadcast_distributed.hpp
View file @ f6bddf08
......@@ -23,6 +23,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
class NGRAPH_API BroadcastDistributed : public Op
{
......@@ -43,4 +45,6 @@ namespace ngraph
int64_t m_root_id;
};
}
using v0::BroadcastDistributed;
}
}
src/ngraph/op/ceiling.hpp
View file @ f6bddf08
......@@ -21,6 +21,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief Elementwise ceiling operation.
class NGRAPH_API Ceiling : public util::UnaryElementwiseArithmetic
......@@ -34,9 +36,12 @@ namespace ngraph
///
/// \param arg Node that produces the input tensor.
Ceiling(const Output<Node>& arg);
bool visit_attributes(AttributeVisitor& visitor) override { return true; }
virtual std::shared_ptr<Node>
copy_with_new_args(const NodeVector& new_args) const override;
};
}
using v0::Ceiling;
}
}
src/ngraph/op/concat.hpp
View file @ f6bddf08
......@@ -23,6 +23,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief Concatenation operation.
class NGRAPH_API Concat : public Op
......@@ -43,6 +45,7 @@ namespace ngraph
/// \param args The nodes producing the input tensors.
/// \param axis The axis along which to concatenate the input tensors.
Concat(const NodeVector& args, int64_t axis);
bool visit_attributes(AttributeVisitor& visitor) override;
void validate_and_infer_types() override;
......@@ -67,4 +70,6 @@ namespace ngraph
int64_t m_concat_axis = -1;
};
}
using v0::Concat;
}
}
src/ngraph/op/convert.hpp
View file @ f6bddf08
......@@ -21,6 +21,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief Elementwise type conversion operation.
class NGRAPH_API Convert : public Op
......@@ -40,7 +42,6 @@ namespace ngraph
virtual std::shared_ptr<Node>
copy_with_new_args(const NodeVector& new_args) const override;
const element::Type& get_destination_type() const { return m_destination_type; }
void set_destination_type(const element::Type& destination_type)
{
......@@ -59,4 +60,6 @@ namespace ngraph
const NodeVector& deltas) override;
};
}
using v0::Convert;
}
}
src/ngraph/op/convolution.hpp
View file @ f6bddf08
......@@ -62,7 +62,6 @@ namespace ngraph
const Strides& dilations,
const PadType& auto_pad = PadType::EXPLICIT);
size_t get_version() const override { return 1; }
void validate_and_infer_types() override;
virtual std::shared_ptr<Node>
......@@ -121,7 +120,6 @@ namespace ngraph
const CoordinateDiff& pads_begin,
const CoordinateDiff& pads_end);
size_t get_version() const override { return 1; }
void validate_and_infer_types() override;
void generate_adjoints(autodiff::Adjoints& adjoints,
......@@ -180,7 +178,6 @@ namespace ngraph
const CoordinateDiff& pads_begin,
const CoordinateDiff& pads_end);
size_t get_version() const override { return 1; }
void validate_and_infer_types() override;
virtual std::shared_ptr<Node>
......
src/ngraph/op/cos.hpp
View file @ f6bddf08
......@@ -21,6 +21,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief Elementwise cosine operation.
class NGRAPH_API Cos : public util::UnaryElementwiseArithmetic
......@@ -43,4 +45,6 @@ namespace ngraph
const NodeVector& deltas) override;
};
}
using v0::Cos;
}
}
src/ngraph/op/cosh.hpp
View file @ f6bddf08
......@@ -21,6 +21,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief Elementwise hyperbolic cosine (cosh) operation.
class NGRAPH_API Cosh : public util::UnaryElementwiseArithmetic
......@@ -43,4 +45,6 @@ namespace ngraph
const NodeVector& deltas) override;
};
}
using v0::Cosh;
}
}
src/ngraph/op/crop_and_resize.hpp
View file @ f6bddf08
......@@ -22,7 +22,7 @@ namespace ngraph
{
namespace op
{
class CropAndResize : public Op
class NGRAPH_API CropAndResize : public Op
{
public:
enum class ResizeMethod
......@@ -32,7 +32,6 @@ namespace ngraph
nearest
};
NGRAPH_API
static constexpr NodeTypeInfo type_info{"CropAndResize", 0};
const NodeTypeInfo& get_type_info() const override { return type_info; }
/// \brief Constructs a crop and resize operation.
......
src/ngraph/op/cum_sum.hpp
View file @ f6bddf08
......@@ -22,6 +22,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief Tensor cumulative sum operation.
///
......@@ -32,9 +34,11 @@ namespace ngraph
/// | | Description |
/// | -------------------- |
/// --------------------------------------------------------------------------------------------------|
/// | `exclusive` | If set to 1 will return exclusive sum in which the top element
/// | `exclusive` | If set to 1 will return exclusive sum in which the top
/// element
/// is not included. |
/// | | In other terms, if set to 1, the j-th output element would be
/// | | In other terms, if set to 1, the j-th output element
/// would be
/// the
/// sum of the first (j-1) elements.|
/// | | Otherwise, it would be the sum of the first j elements.
......@@ -53,19 +57,21 @@ namespace ngraph
/// | | Description |
/// | ----- |
/// ------------------------------------------------------------------------------------------------|
/// | `axis`| zero dimension tensor specifying axis position along which cumulative sum must
/// | `axis`| zero dimension tensor specifying axis position along which cumulative sum
/// must
/// be performed. |
///
/// ## Output
///
/// | Description |
/// | ------------------------------------------------------------------------------------|
/// | Output tensor of the same type as `arg` with cumulative sums of the arg's elements |
/// |
/// ------------------------------------------------------------------------------------|
/// | Output tensor of the same type as `arg` with cumulative sums of the arg's elements
/// |
class CumSum : public Op
class NGRAPH_API CumSum : public Op
{
public:
NGRAPH_API
static constexpr NodeTypeInfo type_info{"CumSum", 0};
const NodeTypeInfo& get_type_info() const override { return type_info; }
/// \brief Constructs a cumulative summation operation.
......@@ -74,7 +80,8 @@ namespace ngraph
/// \brief Constructs a cumulative summation operation.
///
/// \param arg The tensor to be summed.
/// \param axis zero dimension tensor specifying axis position along which cumulative
/// \param axis zero dimension tensor specifying axis position along which
/// cumulative
/// sum must be performed
CumSum(const Output<Node>& arg,
const Output<Node>& axis,
......@@ -97,4 +104,6 @@ namespace ngraph
bool m_reverse;
};
}
using v0::CumSum;
}
}
src/ngraph/op/dot.hpp
View file @ f6bddf08
......@@ -24,7 +24,10 @@ namespace ngraph
{
namespace op
{
/// \brief Generalized dot product operation, including scalar-tensor product, matrix-vector
namespace v0
{
/// \brief Generalized dot product operation, including scalar-tensor product,
/// matrix-vector
/// product, and matrix multiplication.
class NGRAPH_API Dot : public Op
{
......@@ -43,13 +46,17 @@ namespace ngraph
size_t reduction_axes_count,
bool has_reduction_axes_count = true);
/// \brief Constructs a dot product operation with default dot-axis selection depending
/// \brief Constructs a dot product operation with default dot-axis selection
/// depending
/// on the inputs.
///
/// If `arg0` or `arg1` is a scalar, there are no dot-axes. Else, there is one dot-axis.
/// If `arg0` or `arg1` is a scalar, there are no dot-axes. Else, there is one
/// dot-axis.
///
/// (Note that in particular, this results in scalar-tensor products where one or the
/// other argument is a scalar, a matrix-vector products where `arg0` is a matrix and
/// (Note that in particular, this results in scalar-tensor products where one or
/// the
/// other argument is a scalar, a matrix-vector products where `arg0` is a matrix
/// and
/// `arg1` is a vector, and a matrix multiplication where `arg0` and `arg1` are both
/// matrices.)
///
......@@ -87,4 +94,6 @@ namespace ngraph
const NodeVector& deltas) override;
};
}
using v0::Dot;
}
}
src/ngraph/op/embedding_lookup.hpp
View file @ f6bddf08
......@@ -22,6 +22,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief Returns embeddings for given indices
class NGRAPH_API EmbeddingLookup : public Op
......@@ -38,7 +40,8 @@ namespace ngraph
///
/// \param data The input indices for tokens to be translated into embeddings
/// \param weights is a dense matrix [N,M] where each row 0..N
/// corresponds to an embedding (i.e. typically, a vector of real numbers) of length M
/// corresponds to an embedding (i.e. typically, a vector of real numbers) of length
/// M
EmbeddingLookup(const Output<Node>& data, const Output<Node>& weights)
: Op({data, weights})
{
......@@ -57,4 +60,6 @@ namespace ngraph
copy_with_new_args(const NodeVector& new_args) const override;
};
}
using v0::EmbeddingLookup;
}
}
src/ngraph/op/erf.hpp
View file @ f6bddf08
......@@ -21,6 +21,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
class NGRAPH_API Erf : public util::UnaryElementwiseArithmetic
{
......@@ -34,4 +36,6 @@ namespace ngraph
copy_with_new_args(const NodeVector& new_args) const override;
};
}
using v0::Erf;
}
}
src/ngraph/op/exp.hpp
View file @ f6bddf08
......@@ -21,6 +21,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief Elementwise natural exponential (exp) operation.
class NGRAPH_API Exp : public util::UnaryElementwiseArithmetic
......@@ -42,4 +44,6 @@ namespace ngraph
const NodeVector& deltas) override;
};
}
using v0::Exp;
}
}
src/ngraph/op/experimental/layers/ctc_greedy_decoder.hpp
View file @ f6bddf08
......@@ -21,6 +21,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
class NGRAPH_API CTCGreedyDecoder : public Op
{
......@@ -47,4 +49,6 @@ namespace ngraph
bool m_ctc_merge_repeated;
};
}
using v0::CTCGreedyDecoder;
}
}
src/ngraph/op/experimental/layers/detection_output.hpp
View file @ f6bddf08
......@@ -42,6 +42,8 @@ namespace ngraph
float objectness_score = 0;
} DetectionOutputAttrs;
namespace v0
{
/// \brief Layer which performs non-max suppression to
/// generate detection output using location and confidence predictions
class NGRAPH_API DetectionOutput : public Op
......@@ -75,4 +77,6 @@ namespace ngraph
DetectionOutputAttrs m_attrs;
};
}
using v0::DetectionOutput;
}
}
src/ngraph/op/experimental/layers/interpolate.hpp
View file @ f6bddf08
......@@ -32,6 +32,8 @@ namespace ngraph
std::vector<size_t> pads_end;
} InterpolateAttrs;
namespace v0
{
/// \brief Layer which performs bilinear interpolation
class NGRAPH_API Interpolate : public Op
{
......@@ -58,4 +60,6 @@ namespace ngraph
InterpolateAttrs m_attrs;
};
}
using v0::Interpolate;
}
}
src/ngraph/op/experimental/layers/prior_box.hpp
View file @ f6bddf08
......@@ -44,6 +44,8 @@ namespace ngraph
bool scale_all_sizes = false;
};
namespace v0
{
/// \brief Layer which generates prior boxes of specified sizes
/// normalized to input image size
class NGRAPH_API PriorBox : public Op
......@@ -71,4 +73,6 @@ namespace ngraph
PriorBoxAttrs m_attrs;
};
}
using v0::PriorBox;
}
}
src/ngraph/op/experimental/layers/prior_box_clustered.hpp
View file @ f6bddf08
......@@ -40,6 +40,8 @@ namespace ngraph
std::vector<float> variances;
};
namespace v0
{
/// \brief Layer which generates prior boxes of specified sizes
/// normalized to input image size
class NGRAPH_API PriorBoxClustered : public Op
......@@ -67,4 +69,6 @@ namespace ngraph
PriorBoxClusteredAttrs m_attrs;
};
}
using v0::PriorBoxClustered;
}
}
src/ngraph/op/experimental/layers/proposal.hpp
View file @ f6bddf08
......@@ -54,6 +54,8 @@ namespace ngraph
std::string framework;
};
namespace v0
{
class NGRAPH_API Proposal : public Op
{
public:
......@@ -81,4 +83,6 @@ namespace ngraph
ProposalAttrs m_attrs;
};
}
using v0::Proposal;
}
}
src/ngraph/op/experimental/layers/psroi_pooling.hpp
View file @ f6bddf08
......@@ -21,6 +21,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
class NGRAPH_API PSROIPooling : public Op
{
......@@ -35,8 +37,10 @@ namespace ngraph
/// \param output_dim Output channel number
/// \param group_size Number of groups to encode position-sensitive scores
/// \param spatial_scale Ratio of input feature map over input image size
/// \param spatial_bins_x Numbers of bins to divide the input feature maps over width
/// \param spatial_bins_y Numbers of bins to divide the input feature maps over height
/// \param spatial_bins_x Numbers of bins to divide the input feature maps over
/// width
/// \param spatial_bins_y Numbers of bins to divide the input feature maps over
/// height
/// \param mode Mode of pooling - Avg or Bilinear
PSROIPooling(const Output<Node>& input,
const Output<Node>& coords,
......@@ -67,4 +71,6 @@ namespace ngraph
std::string m_mode;
};
}
using v0::PSROIPooling;
}
}
src/ngraph/op/experimental/layers/region_yolo.hpp
View file @ f6bddf08
......@@ -21,6 +21,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
class NGRAPH_API RegionYolo : public Op
{
......@@ -39,7 +41,8 @@ namespace ngraph
/// \param[in] mask Mask
/// \param[in] axis Axis to begin softmax on
/// \param[in] end_axis Axis to end softmax on
/// \param[in] anchors A flattened list of pairs `[width, height]` that describes
/// \param[in] anchors A flattened list of pairs `[width, height]` that
/// describes
/// prior box sizes.
///
RegionYolo(const Output<Node>& input,
......@@ -76,4 +79,6 @@ namespace ngraph
int m_end_axis;
};
}
using v0::RegionYolo;
}
}
src/ngraph/op/experimental/layers/reorg_yolo.hpp
View file @ f6bddf08
......@@ -21,6 +21,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
class NGRAPH_API ReorgYolo : public Op
{
......@@ -44,4 +46,6 @@ namespace ngraph
Strides m_strides;
};
}
using v0::ReorgYolo;
}
}
src/ngraph/op/experimental/layers/roi_pooling.hpp
View file @ f6bddf08
......@@ -21,6 +21,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
class NGRAPH_API ROIPooling : public Op
{
......@@ -55,4 +57,6 @@ namespace ngraph
std::string m_method;
};
}
using v0::ROIPooling;
}
}
src/ngraph/op/experimental/range.hpp
View file @ f6bddf08
......@@ -22,6 +22,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief Range operation, analogous to `range()` in Python.
class NGRAPH_API Range : public Op
......@@ -40,7 +42,9 @@ namespace ngraph
/// element type, and same element type as `start` and `step`.
/// \param step The tensor producing the step value. Must be a scalar of integer
/// element type, and same element type as `start` and `stop`.
Range(const Output<Node>& start, const Output<Node>& stop, const Output<Node>& step);
Range(const Output<Node>& start,
const Output<Node>& stop,
const Output<Node>& step);
void validate_and_infer_types() override;
......@@ -48,4 +52,6 @@ namespace ngraph
copy_with_new_args(const NodeVector& new_args) const override;
};
}
using v0::Range;
}
}
src/ngraph/op/experimental/shape_of.hpp
View file @ f6bddf08
......@@ -21,6 +21,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief Operation that returns the shape of its input argument as a tensor.
class NGRAPH_API ShapeOf : public Op
......@@ -38,4 +40,6 @@ namespace ngraph
void validate_and_infer_types() override;
};
}
using v0::ShapeOf;
}
}
src/ngraph/op/experimental/tile.hpp
View file @ f6bddf08
......@@ -21,6 +21,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief Dynamic Tiling operation which repeats a tensor multiple times
/// along each dimension
......@@ -46,4 +48,6 @@ namespace ngraph
const NodeVector& deltas) override;
};
}
using v0::Tile;
}
}
src/ngraph/op/experimental/transpose.hpp
View file @ f6bddf08
......@@ -23,6 +23,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief Tensor transpose operation.
class NGRAPH_API Transpose : public Op
......@@ -50,4 +52,6 @@ namespace ngraph
const NodeVector& deltas) override;
};
}
using v0::Transpose;
}
}
src/ngraph/op/floor.hpp
View file @ f6bddf08
......@@ -21,6 +21,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief Elementwise floor operation.
class NGRAPH_API Floor : public util::UnaryElementwiseArithmetic
......@@ -39,4 +41,6 @@ namespace ngraph
copy_with_new_args(const NodeVector& new_args) const override;
};
}
using v0::Floor;
}
}
src/ngraph/op/fused/batch_mat_mul_transpose.hpp
View file @ f6bddf08
......@@ -33,10 +33,9 @@ namespace ngraph
/// For example, for `a` with shape `(batch_size, n, k)`, and `b` with
/// shape `(batch_size, k, m)`, the result of BatchMatMul will have shape
/// `(batch_size, n, m)`, and `BatchMatMulTranspose(a, b)[i] = Dot(a[i], b[i])`.
class BatchMatMulTranspose : public ngraph::op::util::FusedOp
class NGRAPH_API BatchMatMulTranspose : public ngraph::op::util::FusedOp
{
public:
NGRAPH_API
static constexpr NodeTypeInfo type_info{"BatchMatMulTranspose", 0};
const NodeTypeInfo& get_type_info() const override { return type_info; }
BatchMatMulTranspose() = default;
......
src/ngraph/op/fused/clamp.hpp
View file @ f6bddf08
......@@ -23,6 +23,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief Performs a clipping operation on all elements of the input node
///
......@@ -56,4 +58,6 @@ namespace ngraph
double m_max;
};
}
using v0::Clamp;
}
}
src/ngraph/op/fused/conv_fused.hpp
View file @ f6bddf08
......@@ -23,6 +23,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief Convolution + bias forward prop for batched convolution operation.
class NGRAPH_API ConvolutionBias : public ngraph::op::util::FusedOp
......@@ -49,8 +51,14 @@ namespace ngraph
const Output<Node>& filters,
const Output<Node>& bias);
const Strides& get_window_movement_strides() const { return m_window_movement_strides; }
const Strides& get_window_dilation_strides() const { return m_window_dilation_strides; }
const Strides& get_window_movement_strides() const
{
return m_window_movement_strides;
}
const Strides& get_window_dilation_strides() const
{
return m_window_dilation_strides;
}
const CoordinateDiff& get_padding_below() const { return m_padding_below; }
const CoordinateDiff& get_padding_above() const { return m_padding_above; }
const Strides& get_data_dilation_strides() const { return m_data_dilation_strides; }
......@@ -178,7 +186,7 @@ namespace ngraph
static constexpr NodeTypeInfo type_info{"ConvolutionBiasAdd", 0};
const NodeTypeInfo& get_type_info() const override { return type_info; }
ConvolutionBiasAdd() = default;
ConvolutionBiasAdd(const std::shared_ptr<op::ConvolutionBias>& conv,
ConvolutionBiasAdd(const std::shared_ptr<op::v0::ConvolutionBias>& conv,
const Output<Node>& sum_input,
bool with_relu = false);
......@@ -193,8 +201,14 @@ namespace ngraph
const Strides& data_dilation_strides,
bool with_relu = false);
const Strides& get_window_movement_strides() const { return m_window_movement_strides; }
const Strides& get_window_dilation_strides() const { return m_window_dilation_strides; }
const Strides& get_window_movement_strides() const
{
return m_window_movement_strides;
}
const Strides& get_window_dilation_strides() const
{
return m_window_dilation_strides;
}
const CoordinateDiff& get_padding_below() const { return m_padding_below; }
const CoordinateDiff& get_padding_above() const { return m_padding_above; }
const Strides& get_data_dilation_strides() const { return m_data_dilation_strides; }
......@@ -217,4 +231,8 @@ namespace ngraph
bool m_with_relu;
};
}
using v0::ConvolutionBias;
using v0::ConvolutionBiasBackpropFiltersBias;
using v0::ConvolutionBiasAdd;
}
}
src/ngraph/op/fused/crossentropy.hpp
View file @ f6bddf08
......@@ -24,10 +24,9 @@ namespace ngraph
{
namespace op
{
class CrossEntropy : public ngraph::op::util::FusedOp
class NGRAPH_API CrossEntropy : public ngraph::op::util::FusedOp
{
public:
NGRAPH_API
static constexpr NodeTypeInfo type_info{"CrossEntropy", 0};
const NodeTypeInfo& get_type_info() const override { return type_info; }
CrossEntropy() = default;
......@@ -57,10 +56,9 @@ namespace ngraph
int64_t m_ignore_index;
};
class CrossEntropyBackprop : public util::FusedOp
class NGRAPH_API CrossEntropyBackprop : public util::FusedOp
{
public:
NGRAPH_API
static constexpr NodeTypeInfo type_info{"CrossEntropyBackprop", 0};
const NodeTypeInfo& get_type_info() const override { return type_info; }
CrossEntropyBackprop() = default;
......
src/ngraph/op/fused/depth_to_space.hpp
View file @ f6bddf08
......@@ -23,6 +23,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief DepthToSpace permutes data from the depth dimension of the input blob into
/// spatial dimensions.
......@@ -49,7 +51,8 @@ namespace ngraph
/// \brief Constructs a DepthToSpace operation.
///
/// \param data Node producing the input tensor
/// \param mode Specifies how the input depth dimension is split to block coordinates
/// \param mode Specifies how the input depth dimension is split to block
/// coordinates
/// \param block_size The size of the block of values to be moved
DepthToSpace(const Output<Node>& data,
const DepthToSpaceMode& mode,
......@@ -72,4 +75,6 @@ namespace ngraph
DepthToSpaceMode mode_from_string(const std::string& mode) const;
};
}
using v0::DepthToSpace;
}
}
src/ngraph/op/fused/elu.hpp
View file @ f6bddf08
......@@ -23,6 +23,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief Exponential Linear Unit
/// x < 0 => f(x) = alpha * (exp(x) - 1.)
......@@ -49,5 +51,7 @@ namespace ngraph
private:
double m_alpha;
};
}
using v0::Elu;
} // namespace op
} // namespace ngraph
src/ngraph/op/fused/fake_quantize.hpp
View file @ f6bddf08
......@@ -24,6 +24,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
///
/// \brief Class performing element-wise linear quantization.
......@@ -84,4 +86,6 @@ namespace ngraph
AutoBroadcastSpec m_auto_broadcast;
};
}
using v0::FakeQuantize;
}
}
src/ngraph/op/fused/gelu.hpp
View file @ f6bddf08
......@@ -23,6 +23,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief Gaussian Error Linear Unit
/// f(x) = 0.5 * x * (1 + erf( x / sqrt(2) )
......@@ -67,4 +69,7 @@ namespace ngraph
copy_with_new_args(const NodeVector& new_args) const override;
};
}
using v0::Gelu;
using v0::GeluBackpropFactor;
}
}
src/ngraph/op/fused/gemm.hpp
View file @ f6bddf08
......@@ -23,6 +23,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief Operator performing General Matrix multiplication.
///
......@@ -73,4 +75,6 @@ namespace ngraph
bool m_transB;
};
}
using v0::Gemm;
}
}
src/ngraph/op/fused/grn.hpp
View file @ f6bddf08
......@@ -24,6 +24,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief Global Response Normalization with L2 norm (across channels only).
///
......@@ -51,4 +53,6 @@ namespace ngraph
float m_bias = 1.0f;
};
}
using v0::GRN;
}
}
src/ngraph/op/fused/group_conv.hpp
View file @ f6bddf08
......@@ -24,6 +24,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief Group Convolution
class NGRAPH_API GroupConvolution : public ngraph::op::util::FusedOp
......@@ -52,8 +54,14 @@ namespace ngraph
const Strides& data_dilation_strides,
const PadType& pad_type = PadType::EXPLICIT);
Shape get_weights_dimensions() const;
const Strides& get_window_movement_strides() const { return m_window_movement_strides; }
const Strides& get_window_dilation_strides() const { return m_window_dilation_strides; }
const Strides& get_window_movement_strides() const
{
return m_window_movement_strides;
}
const Strides& get_window_dilation_strides() const
{
return m_window_dilation_strides;
}
const CoordinateDiff& get_padding_below() const { return m_padding_below; }
const CoordinateDiff& get_padding_above() const { return m_padding_above; }
const Strides& get_data_dilation_strides() const { return m_data_dilation_strides; }
......@@ -85,4 +93,6 @@ namespace ngraph
bool has_groups_in_filters_shape() const;
};
}
using v0::GroupConvolution;
}
}
src/ngraph/op/fused/group_conv_transpose.hpp
View file @ f6bddf08
......@@ -30,6 +30,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief Group Transpose Convolution (Deconvolution)
class NGRAPH_API GroupConvolutionTranspose : public util::FusedOp
......@@ -45,9 +47,11 @@ namespace ngraph
/// \param[in] filters The node producing filters data.
/// \param[in] strides The strides along each feature axis.
/// \param[in] dilations The dilations along each feature axis.
/// \param[in] padding_begin The padding added at the beggining of each feature axis.
/// \param[in] padding_begin The padding added at the beggining of each feature
/// axis.
/// \param[in] padding_end The padding added at the end of each feature axis.
/// \param[in] output_padding The zero-padding (adjustment) added to one side of the
/// \param[in] output_padding The zero-padding (adjustment) added to one side of
/// the
/// output.
/// \param[in] groups The number of groups the input channels and output
/// channels are divided into.
......@@ -71,7 +75,8 @@ namespace ngraph
///
/// \param[in] data The node producing input data.
/// \param[in] filters The node producing filters data.
/// \param[in] groups The number of groups the input channels and output channels
/// \param[in] groups The number of groups the input channels and output
/// channels
/// are divided into.
///
GroupConvolutionTranspose(const Output<Node>& data,
......@@ -85,7 +90,8 @@ namespace ngraph
/// \param[in] filters The node producing filters data.
/// \param[in] strides The strides along each feature axis.
/// \param[in] dilations The dilations along each feature axis.
/// \param[in] output_padding The zero-padding (adjustment) added to one side of the
/// \param[in] output_padding The zero-padding (adjustment) added to one side of
/// the
/// output.
/// \param[in] output_shape The output shape. When provided padding values are
/// automatically inferred.
......@@ -152,4 +158,6 @@ namespace ngraph
Shape m_output_shape;
};
}
using v0::GroupConvolutionTranspose;
}
}
src/ngraph/op/fused/gru_cell.hpp
View file @ f6bddf08
......@@ -29,6 +29,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
///
/// \brief Class for GRU cell node.
......@@ -53,7 +55,8 @@ namespace ngraph
/// [gates_count * hidden_size, input_size].
/// \param[in] R The recurrence weight tensor with shape:
/// [gates_count * hidden_size, hidden_size].
/// \param[in] initial_hidden_state The hidden state tensor at current time step with
/// \param[in] initial_hidden_state The hidden state tensor at current time step
/// with
/// shape: [batch_size, hidden_size].
/// \param[in] hidden_size The number of hidden units for recurrent cell.
///
......@@ -72,16 +75,20 @@ namespace ngraph
/// [gates_count * hidden_size, input_size].
/// \param[in] R The recurrence weight tensor with shape:
/// [gates_count * hidden_size, hidden_size].
/// \param[in] initial_hidden_state The hidden state tensor at current time step with
/// \param[in] initial_hidden_state The hidden state tensor at current time step
/// with
/// shape: [batch_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.
/// 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
/// 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,
......@@ -104,7 +111,8 @@ namespace ngraph
/// hidden_size, input_size].
/// \param[in] R The recurrence weight tensor with shape:
/// [gates_count * hidden_size, hidden_size].
/// \param[in] initial_hidden_state The hidden state tensor at current time step with
/// \param[in] initial_hidden_state The hidden state tensor at current time step
/// with
/// shape: [batch_size, hidden_size].
/// \param[in] hidden_size The number of hidden units for recurrent cell.
/// \param[in] B The bias tensor for input gate with shape:
......@@ -112,12 +120,16 @@ namespace ngraph
/// \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.
/// 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
/// 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
/// \param[in] linear_before_reset Whether or not to apply the linear
/// transformation
/// before multiplying by the output of the reset
/// gate.
///
......@@ -157,10 +169,13 @@ namespace ngraph
///
/// \brief Control whether or not apply the linear transformation.
///
/// \note The linear transformation may be applied when computing the output of hidden
/// \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;
};
}
using v0::GRUCell;
}
}
src/ngraph/op/fused/hard_sigmoid.hpp
View file @ f6bddf08
......@@ -23,6 +23,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief Parameterized, bounded sigmoid-like, piecewise linear
/// function. min(max(alpha*x + beta, 0), 1)
......@@ -50,4 +52,6 @@ namespace ngraph
copy_with_new_args(const NodeVector& new_args) const override;
};
}
using v0::HardSigmoid;
}
}
src/ngraph/op/fused/layer_norm.hpp
View file @ f6bddf08
......@@ -23,6 +23,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief Layer Normalization
///
......@@ -135,4 +137,7 @@ namespace ngraph
double m_epsilon{1e-5};
};
}
using v0::LayerNorm;
using v0::LayerNormBackprop;
}
}
src/ngraph/op/fused/log_softmax.hpp
View file @ f6bddf08
......@@ -23,6 +23,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief LogSoftmax operation
class NGRAPH_API LogSoftmax : public ngraph::op::util::FusedOp
......@@ -46,5 +48,7 @@ namespace ngraph
protected:
int64_t m_axis;
};
}
using v0::LogSoftmax;
} // namespace op
} // namespace ngraph
src/ngraph/op/fused/lstm_cell.hpp
View file @ f6bddf08
......@@ -40,6 +40,8 @@ namespace ngraph
IOFC, // ONNX
};
namespace v0
{
///
/// \brief Class for single lstm cell node.
///
......@@ -61,7 +63,8 @@ namespace ngraph
/// (.) - 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
/// \note This class represents only single *cell* (for current time step) and not
/// the
/// whole LSTM Sequence layer
///
/// \sa LSTMSequence, RNNCell, GRUCell
......@@ -77,9 +80,11 @@ namespace ngraph
///
/// \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
/// \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
/// \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,
......@@ -87,15 +92,19 @@ namespace ngraph
/// \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
/// \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.
/// 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
/// 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.
///
......@@ -118,9 +127,11 @@ namespace ngraph
///
/// \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
/// \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
/// \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].
......@@ -129,15 +140,19 @@ namespace ngraph
/// \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
/// \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.
/// 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
/// 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.
///
......@@ -161,9 +176,11 @@ namespace ngraph
///
/// \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
/// \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
/// \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].
......@@ -175,15 +192,19 @@ namespace ngraph
/// [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
/// \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.
/// 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
/// 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.
///
......@@ -260,5 +281,7 @@ namespace ngraph
static constexpr std::size_t s_gates_count{4};
static constexpr std::size_t s_peepholes_count{3};
};
}
using v0::LSTMCell;
} // namespace op
} // namespace ngraph
src/ngraph/op/fused/lstm_sequence.hpp
View file @ f6bddf08
......@@ -30,6 +30,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
///
/// \brief Class for lstm sequence node.
......@@ -72,8 +74,14 @@ namespace ngraph
"tanh"},
const float clip_threshold = 0,
const bool input_forget = false)
: FusedOp(
{X, initial_hidden_state, initial_cell_state, sequence_lengths, W, R, B, P})
: FusedOp({X,
initial_hidden_state,
initial_cell_state,
sequence_lengths,
W,
R,
B,
P})
, m_activations_alpha(activations_alpha)
, m_activations_beta(activations_beta)
, m_activations(activations)
......@@ -103,7 +111,8 @@ namespace ngraph
"tanh"},
const float clip_threshold = 0,
const bool input_forget = false)
: LSTMSequence(X,
: LSTMSequence(
X,
initial_hidden_state,
initial_cell_state,
sequence_lengths,
......@@ -173,5 +182,7 @@ namespace ngraph
bool m_input_forget;
LSTMWeightsFormat m_weights_format;
};
}
using v0::LSTMSequence;
} // namespace op
} // namespace ngraph
src/ngraph/op/fused/matmul.hpp
View file @ f6bddf08
......@@ -23,6 +23,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief Operator performing Matrix Multiplication.
class NGRAPH_API MatMul : public ngraph::op::util::FusedOp
......@@ -55,5 +57,7 @@ namespace ngraph
bool m_transpose_a;
bool m_transpose_b;
};
}
using v0::MatMul;
} // namespace op
} // namespace ngraph
src/ngraph/op/fused/mod.cpp
View file @ f6bddf08
......@@ -25,15 +25,17 @@
using namespace std;
using namespace ngraph;
constexpr NodeTypeInfo op::Mod::type_info;
constexpr NodeTypeInfo op::v1::Mod::type_info;
op::Mod::Mod(const Output<Node>& A, const Output<Node>& B, const AutoBroadcastSpec& auto_broadcast)
op::v1::Mod::Mod(const Output<Node>& A,
const Output<Node>& B,
const AutoBroadcastSpec& auto_broadcast)
: FusedOp({A, B})
, m_auto_broadcast(auto_broadcast)
{
}
NodeVector op::Mod::decompose_op() const
NodeVector op::v1::Mod::decompose_op() const
{
const auto dividend = make_shared<op::Abs>(input_value(0));
const auto dividend_sign = make_shared<op::Sign>(input_value(0));
......@@ -53,7 +55,7 @@ NodeVector op::Mod::decompose_op() const
return {make_shared<op::v1::Multiply>(dividend_sign, mod, m_auto_broadcast)};
}
shared_ptr<Node> op::Mod::copy_with_new_args(const NodeVector& new_args) const
shared_ptr<Node> op::v1::Mod::copy_with_new_args(const NodeVector& new_args) const
{
return make_shared<Mod>(new_args.at(0), new_args.at(1), m_auto_broadcast);
}
src/ngraph/op/fused/mod.hpp
View file @ f6bddf08
......@@ -23,15 +23,17 @@
namespace ngraph
{
namespace op
{
namespace v1
{
/// \brief Mod returns an element-wise division reminder with two given tensors applying
/// multi-directional broadcast rules.
class Mod : public ngraph::op::util::FusedOp
class NGRAPH_API Mod : public ngraph::op::util::FusedOp
{
public:
NGRAPH_API
static constexpr NodeTypeInfo type_info{"Mod", 0};
const NodeTypeInfo& get_type_info() const override { return type_info; }
Mod() = default;
/// \brief Constructs a Mod node.
///
/// \param A - Dividend tensor
......@@ -51,4 +53,10 @@ namespace ngraph
AutoBroadcastSpec m_auto_broadcast;
};
}
namespace v0
{
using v1::Mod;
}
}
}
src/ngraph/op/fused/mvn.hpp
View file @ f6bddf08
......@@ -23,6 +23,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief Operator performing Mean Variance Normalization
///
......@@ -37,7 +39,8 @@ namespace ngraph
/// \param data Input tensor with data
/// \param normalize_variance flag that denotes whether to perform variance
/// normalization.
/// \param across_channels flag that denotes if mean values are shared across channels.
/// \param across_channels flag that denotes if mean values are shared across
/// channels.
/// \param eps the number to be added to the variance to avoid division by zero when
/// normalizing the value
///
......@@ -77,5 +80,7 @@ namespace ngraph
bool m_normalize_variance;
AxisSet m_reduction_axes;
};
}
using v0::MVN;
} // namespace op
} // namespace ngraph
src/ngraph/op/fused/normalize_l2.hpp
View file @ f6bddf08
......@@ -25,6 +25,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief Normalization input tensor with L2 norm.
///
......@@ -41,7 +43,8 @@ namespace ngraph
/// \param axes - Node indicating axes along which reduction is
/// calculated
/// \param eps - The epsilon added to L2 norm.
/// \param eps_mode - Specifies how eps is combined with L2 value calculated
/// \param eps_mode - Specifies how eps is combined with L2 value
/// calculated
/// before division
///
NormalizeL2(const Output<Node>& data,
......@@ -63,4 +66,6 @@ namespace ngraph
EpsMode m_eps_mode;
};
}
using v0::NormalizeL2;
}
}
src/ngraph/op/fused/partial_slice.hpp
View file @ f6bddf08
......@@ -23,6 +23,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief pdpd slice op
///
......@@ -104,4 +106,7 @@ namespace ngraph
std::vector<int64_t> m_upper_bounds;
};
}
using v0::PartialSlice;
using v0::PartialSliceBackprop;
}
}
src/ngraph/op/fused/prelu.hpp
View file @ f6bddf08
......@@ -23,6 +23,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief Parametrized Relu
/// x < 0 => f(x) = x * slope
......@@ -46,4 +48,6 @@ namespace ngraph
copy_with_new_args(const NodeVector& new_args) const override;
};
}
using v0::PRelu;
}
}
src/ngraph/op/fused/rnn_cell.hpp
View file @ f6bddf08
......@@ -29,6 +29,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
///
/// \brief Class for single RNN cell node.
......@@ -43,7 +45,8 @@ namespace ngraph
/// * - Is a dot product,
/// f - is activation functions.
///
/// \note This class represents only single *cell* (for current time step) and not the
/// \note This class represents only single *cell* (for current time step) and not
/// the
/// whole LSTM Sequence layer
///
/// \sa LSTMSequence, LSTMCell, GRUCell
......@@ -59,7 +62,8 @@ namespace ngraph
///
/// \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
/// \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: [hidden_size,
/// input_size].
......@@ -69,13 +73,17 @@ namespace ngraph
/// \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.
/// 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
/// functions in order respective to activation
/// list.
/// \param[in] clip The value defining clipping range [-clip,
/// clip] on
/// input of activation functions.
///
RNNCell(const Output<Node>& X,
RNNCell(
const Output<Node>& X,
const Output<Node>& initial_hidden_state,
const Output<Node>& W,
const Output<Node>& R,
......@@ -90,7 +98,8 @@ namespace ngraph
///
/// \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
/// \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: [hidden_size,
/// input_size].
......@@ -102,13 +111,17 @@ namespace ngraph
/// \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.
/// 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
/// functions in order respective to activation
/// list.
/// \param[in] clip The value defining clipping range [-clip,
/// clip] on
/// input of activation functions.
///
RNNCell(const Output<Node>& X,
RNNCell(
const Output<Node>& X,
const Output<Node>& initial_hidden_state,
const Output<Node>& W,
const Output<Node>& R,
......@@ -139,5 +152,7 @@ namespace ngraph
static constexpr std::size_t s_gates_count{1};
};
}
using v0::RNNCell;
} // namespace op
} // namespace ngraph
src/ngraph/op/fused/scale_shift.hpp
View file @ f6bddf08
......@@ -23,6 +23,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief Operator performing Scale Shift transformation.
///
......@@ -49,4 +51,6 @@ namespace ngraph
copy_with_new_args(const NodeVector& new_args) const override;
};
}
using v0::ScaleShift;
}
}
src/ngraph/op/fused/selu.cpp
View file @ f6bddf08
......@@ -26,15 +26,15 @@
using namespace std;
using namespace ngraph;
constexpr NodeTypeInfo op::v1::Selu::type_info;
constexpr NodeTypeInfo op::v0::Selu::type_info;
op::v1::Selu::Selu(const Output<Node>& data, const Output<Node>& alpha, const Output<Node>& lambda)
op::v0::Selu::Selu(const Output<Node>& data, const Output<Node>& alpha, const Output<Node>& lambda)
: FusedOp({data, alpha, lambda})
{
constructor_validate_and_infer_types();
}
NodeVector op::v1::Selu::decompose_op() const
NodeVector op::v0::Selu::decompose_op() const
{
const auto data = input_value(0);
const auto alpha = input_value(1);
......@@ -47,8 +47,8 @@ NodeVector op::v1::Selu::decompose_op() const
alpha)};
}
shared_ptr<Node> op::v1::Selu::copy_with_new_args(const NodeVector& new_args) const
shared_ptr<Node> op::v0::Selu::copy_with_new_args(const NodeVector& new_args) const
{
check_new_args_count(this, new_args);
return make_shared<v1::Selu>(new_args.at(0), new_args.at(1), new_args.at(2));
return make_shared<v0::Selu>(new_args.at(0), new_args.at(1), new_args.at(2));
}
src/ngraph/op/fused/selu.hpp
View file @ f6bddf08
......@@ -24,13 +24,13 @@ namespace ngraph
{
namespace op
{
namespace v1
namespace v0
{
/// \brief Performs a SELU activation function on all elements of the input node
class NGRAPH_API Selu : public ngraph::op::util::FusedOp
{
public:
static constexpr NodeTypeInfo type_info{"Selu", 1};
static constexpr NodeTypeInfo type_info{"Selu", 0};
const NodeTypeInfo& get_type_info() const override { return type_info; }
Selu() = default;
/// \brief Constructs a Selu node.
......@@ -48,6 +48,6 @@ namespace ngraph
copy_with_new_args(const NodeVector& new_args) const override;
};
}
using v1::Selu;
using v0::Selu;
} // namespace op
} // namespace ngraph
src/ngraph/op/fused/shuffle_channels.hpp
View file @ f6bddf08
......@@ -24,6 +24,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief Permutes data in the channel dimension of the input
class NGRAPH_API ShuffleChannels : public ngraph::op::util::FusedOp
......@@ -36,9 +38,11 @@ namespace ngraph
///
/// \param data - Node producing the input tensor
/// \param axis - channel dimension index in the data tensor. A negative value means
/// that the index should be calculated from the back of the input data
/// that the index should be calculated from the back of the input
/// data
/// shape.
/// \param groups - number of groups the channel dimension specified by axis should be
/// \param groups - number of groups the channel dimension specified by axis should
/// be
/// split into
ShuffleChannels(const Output<Node>& data,
const int axis = 1,
......@@ -66,4 +70,6 @@ namespace ngraph
size_t m_groups;
};
}
using v0::ShuffleChannels;
}
}
src/ngraph/op/fused/softmax_crossentropy.hpp
View file @ f6bddf08
......@@ -23,6 +23,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
class NGRAPH_API SoftmaxCrossEntropy : public ngraph::op::util::FusedOp
{
......@@ -33,9 +35,11 @@ namespace ngraph
/// \brief Softamax + CrossEntropy for numerical stabilization
/// \param arg1 Node that produces the tensor to normalize
/// \param arg2 Node that produces ground truth lables for the input
/// \param soft_label flag indicating whether to interpretate the given labels as soft
/// \param soft_label flag indicating whether to interpretate the given labels as
/// soft
/// labels
/// \param ignore_index Specifies a target value that is ignored and does not contribute
/// \param ignore_index Specifies a target value that is ignored and does not
/// contribute
/// to the input gradient Only valid if soft_label is set to False
SoftmaxCrossEntropy(const Output<Node>& arg1,
const Output<Node>& arg2,
......@@ -65,9 +69,11 @@ namespace ngraph
/// \param delta Node that produces the delta during bprop
/// \param softmax Node that produces softmax from fprop
/// \param labels Node that produces ground truth labels for input
/// \param soft_label flag indicating whether to interpretate the given labels as soft
/// \param soft_label flag indicating whether to interpretate the given labels as
/// soft
/// labels
/// \param ignore_index Specifies a target value that is ignored and does not contribute
/// \param ignore_index Specifies a target value that is ignored and does not
/// contribute
/// to the input gradient Only valid if soft_label is set to False
SoftmaxCrossEntropyBackprop(const Output<Node>& delta,
const Output<Node>& softmax,
......@@ -87,5 +93,8 @@ namespace ngraph
bool m_soft_label;
int64_t m_ignore_index;
};
}
using v0::SoftmaxCrossEntropy;
using v0::SoftmaxCrossEntropyBackprop;
} // namespace op
} // namespace ngraph
src/ngraph/op/fused/space_to_depth.hpp
View file @ f6bddf08
......@@ -23,7 +23,10 @@ namespace ngraph
{
namespace op
{
/// \brief SpaceToDepth permutes input tensor blocks of spatial data into depth dimension.
namespace v0
{
/// \brief SpaceToDepth permutes input tensor blocks of spatial data into depth
/// dimension.
///
/// \note Values from the height and width dimensions are moved to the depth dimension.
///
......@@ -70,4 +73,6 @@ namespace ngraph
SpaceToDepthMode mode_from_string(const std::string& mode) const;
};
}
using v0::SpaceToDepth;
}
}
src/ngraph/op/fused/split.hpp
View file @ f6bddf08
......@@ -25,6 +25,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief Splits the input tensor into a list of smaller tensors ("pieces")
class NGRAPH_API Split : public ngraph::op::util::FusedOp
......@@ -49,7 +51,8 @@ namespace ngraph
/// \param axis - indicates an axis along which the input tensor should be split.
/// Negative values mean counting from the back of the input tensor's
/// shape.
/// \param splits - a list of lengths that the input tensor should be split to. Use this
/// \param splits - a list of lengths that the input tensor should be split to. Use
/// this
/// constructor to split the input tensor to variable length chunks.
Split(const Output<Node>& data, const int axis, const std::vector<size_t>& splits);
......@@ -65,7 +68,8 @@ namespace ngraph
private:
/// \brief Adjusts the axis for negative values
///
/// \note Negative values mean that the API consumer wants to point the axis location
/// \note Negative values mean that the API consumer wants to point the axis
/// location
/// from the back of the tensor. This is similar to the way NumPy works.
///
/// \param axis - original axis value; negative values are accepted
......@@ -82,4 +86,6 @@ namespace ngraph
std::vector<size_t> m_splits;
};
}
using v0::Split;
}
}
src/ngraph/op/fused/squared_difference.hpp
View file @ f6bddf08
......@@ -23,6 +23,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief Calculates an element-wise squared difference between two tensors
///
......@@ -38,7 +40,8 @@ namespace ngraph
/// \param x1 First input tensor
/// \param x2 Second input tensor
/// \param auto_broadcast Auto broadcast specification
SquaredDifference(const Output<Node>& x1,
SquaredDifference(
const Output<Node>& x1,
const Output<Node>& x2,
const AutoBroadcastSpec& auto_broadcast = AutoBroadcastType::NUMPY);
......@@ -56,5 +59,7 @@ namespace ngraph
private:
AutoBroadcastSpec m_autobroadcast;
};
}
using v0::SquaredDifference;
} // namespace op
} // namespace ngraph
src/ngraph/op/fused/squeeze.hpp
View file @ f6bddf08
......@@ -26,6 +26,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
class NGRAPH_API Squeeze : public ngraph::op::util::FusedOp
{
......@@ -41,4 +43,6 @@ namespace ngraph
copy_with_new_args(const NodeVector& new_args) const override;
};
}
using v0::Squeeze;
}
}
src/ngraph/op/fused/unsqueeze.hpp
View file @ f6bddf08
......@@ -26,6 +26,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
class NGRAPH_API Unsqueeze : public ngraph::op::util::FusedOp
{
......@@ -42,4 +44,6 @@ namespace ngraph
copy_with_new_args(const NodeVector& new_args) const override;
};
}
using v0::Unsqueeze;
}
}
src/ngraph/op/gather_nd.hpp
View file @ f6bddf08
......@@ -21,6 +21,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief Gather slices from params with shapes given by indices
class NGRAPH_API GatherND : public Op
......@@ -49,4 +51,6 @@ namespace ngraph
copy_with_new_args(const NodeVector& new_args) const override;
};
}
using v0::GatherND;
}
}
src/ngraph/op/get_output_element.hpp
View file @ f6bddf08
......@@ -24,6 +24,8 @@ namespace ngraph
{
NodeVector get_output_elements(const std::shared_ptr<Node>& mon);
namespace v0
{
/// \brief Operation to get an output from a node.
class NGRAPH_API GetOutputElement : public Op
{
......@@ -54,6 +56,8 @@ namespace ngraph
size_t m_n;
};
}
using v0::GetOutputElement;
}
inline std::shared_ptr<Node> get_output_element(const Output<Node>& output,
bool for_get_output_element = false)
......
src/ngraph/op/greater_eq.cpp
View file @ f6bddf08
......@@ -39,9 +39,9 @@ shared_ptr<Node> op::v0::GreaterEq::copy_with_new_args(const NodeVector& new_arg
//---------------------------------- v1 ----------------------------------------
constexpr NodeTypeInfo op::v1::GreaterEq::type_info;
constexpr NodeTypeInfo op::v1::GreaterEqual::type_info;
op::v1::GreaterEq::GreaterEq(const Output<Node>& arg0,
op::v1::GreaterEqual::GreaterEqual(const Output<Node>& arg0,
const Output<Node>& arg1,
const AutoBroadcastSpec& auto_broadcast)
: BinaryElementwiseComparison(arg0, arg1, auto_broadcast)
......@@ -49,8 +49,8 @@ op::v1::GreaterEq::GreaterEq(const Output<Node>& arg0,
constructor_validate_and_infer_types();
}
shared_ptr<Node> op::v1::GreaterEq::copy_with_new_args(const NodeVector& new_args) const
shared_ptr<Node> op::v1::GreaterEqual::copy_with_new_args(const NodeVector& new_args) const
{
check_new_args_count(this, new_args);
return make_shared<op::v1::GreaterEq>(new_args.at(0), new_args.at(1), this->get_autob());
return make_shared<op::v1::GreaterEqual>(new_args.at(0), new_args.at(1), this->get_autob());
}
src/ngraph/op/greater_eq.hpp
View file @ f6bddf08
......@@ -49,19 +49,19 @@ namespace ngraph
namespace v1
{
/// \brief Elementwise greater-than-or-equal operation.
class NGRAPH_API GreaterEq : public util::BinaryElementwiseComparison
class NGRAPH_API GreaterEqual : public util::BinaryElementwiseComparison
{
public:
static constexpr NodeTypeInfo type_info{"GreaterEq", 1};
const NodeTypeInfo& get_type_info() const override { return type_info; }
/// \brief Constructs a greater-than-or-equal operation.
GreaterEq() = default;
GreaterEqual() = default;
/// \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
GreaterEq(const Output<Node>& arg0,
GreaterEqual(const Output<Node>& arg0,
const Output<Node>& arg1,
const AutoBroadcastSpec& auto_broadcast =
AutoBroadcastSpec(AutoBroadcastType::NUMPY));
......@@ -70,6 +70,9 @@ namespace ngraph
copy_with_new_args(const NodeVector& new_args) const override;
size_t get_version() const override { return 1; }
};
// DO NOT USE. Will be removed once users switch to GreaterEqual
using GreaterEq = GreaterEqual;
} // namespace v1
using v0::GreaterEq;
......
src/ngraph/op/less_eq.hpp
View file @ f6bddf08
......@@ -58,7 +58,6 @@ namespace ngraph
const NodeTypeInfo& get_type_info() const override { return type_info; }
/// \brief Constructs a less-than-or-equal operation.
LessEq() = default;
/// \brief Constructs a less-than-or-equal operation.
///
/// \param arg0 Node that produces the first input tensor.
......
src/ngraph/op/log.hpp
View file @ f6bddf08
......@@ -21,6 +21,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief Elementwise natural log operation.
class NGRAPH_API Log : public util::UnaryElementwiseArithmetic
......@@ -42,4 +44,6 @@ namespace ngraph
const NodeVector& deltas) override;
};
}
using v0::Log;
}
}
src/ngraph/op/lrn.hpp
View file @ f6bddf08
......@@ -21,6 +21,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
// clang-format off
/// \brief Elementwise Local Response Normalization (LRN) operation.
......@@ -80,4 +82,6 @@ namespace ngraph
size_t m_size;
};
}
using v0::LRN;
}
}
src/ngraph/op/negative.hpp
View file @ f6bddf08
......@@ -21,6 +21,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief Elementwise negative operation.
class NGRAPH_API Negative : public util::UnaryElementwiseArithmetic
......@@ -43,6 +45,8 @@ namespace ngraph
const NodeVector& deltas) override;
};
}
using v0::Negative;
}
NGRAPH_API
std::shared_ptr<Node> operator-(const Output<Node>& arg0);
}
src/ngraph/op/op_version_tbl.hpp 0 → 100644
View file @ f6bddf08
//*****************************************************************************
// Copyright 2017-2019 Intel Corporation
//
// Licensed under the Apache License, Version 2.0 (the "License", 0);
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//*****************************************************************************
// This collection contains one entry for each op. If an op is added it must be
// added to this list.
//
// In order to use this list you want to define a macro named exactly NGRAPH_OP
// When you are done you should undef the macro
// As an example if you wanted to make a list of all op names as strings you could do this:
#ifndef NGRAPH_OP
#warning "NGRAPH_OP not defined"
#define NGRAPH_OP(NAME, NAMESPACE, VERSION)
#endif
NGRAPH_OP(Abs, ngraph::op::v0, 0)
NGRAPH_OP(Acos, ngraph::op::v0, 0)
NGRAPH_OP(Add, ngraph::op::v0, 0)
NGRAPH_OP(Add, ngraph::op::v1, 1)
NGRAPH_OP(All, ngraph::op::v0, 0)
NGRAPH_OP(AllReduce, ngraph::op::v0, 0)
NGRAPH_OP(And, ngraph::op::v0, 0)
NGRAPH_OP(Any, ngraph::op, 0)
NGRAPH_OP(ArgMax, ngraph::op::v0, 0)
NGRAPH_OP(ArgMin, ngraph::op::v0, 0)
NGRAPH_OP(Asin, ngraph::op::v0, 0)
NGRAPH_OP(Atan, ngraph::op, 0)
NGRAPH_OP(Atan2, ngraph::op, 0)
NGRAPH_OP(AvgPool, ngraph::op::v0, 0)
NGRAPH_OP(AvgPool, ngraph::op::v1, 1)
NGRAPH_OP(AvgPoolBackprop, ngraph::op::v0, 0)
NGRAPH_OP(AvgPoolBackprop, ngraph::op::v1, 1)
NGRAPH_OP(BatchMatMul, ngraph::op, 0)
NGRAPH_OP(BatchMatMulTranspose, ngraph::op, 0)
NGRAPH_OP(BatchNormInference, ngraph::op, 0)
NGRAPH_OP(BatchNormTraining, ngraph::op, 0)
NGRAPH_OP(BatchNormTrainingBackprop, ngraph::op, 0)
NGRAPH_OP(BinaryConvolution, ngraph::op::v1, 1)
NGRAPH_OP(Broadcast, ngraph::op::v0, 0)
NGRAPH_OP(Broadcast, ngraph::op::v1, 1)
NGRAPH_OP(BroadcastDistributed, ngraph::op::v0, 0)
NGRAPH_OP(BroadcastLike, ngraph::op::v0, 0)
NGRAPH_OP(CTCGreedyDecoder, ngraph::op::v0, 0)
NGRAPH_OP(Ceiling, ngraph::op::v0, 0)
NGRAPH_OP(Clamp, ngraph::op::v0, 0)
NGRAPH_OP(CompiledKernel, ngraph::op, 0)
NGRAPH_OP(Concat, ngraph::op::v0, 0)
NGRAPH_OP(Constant, ngraph::op, 0)
NGRAPH_OP(Convert, ngraph::op, 0)
NGRAPH_OP(Convolution, ngraph::op::v0, 0)
NGRAPH_OP(Convolution, ngraph::op::v1, 1)
NGRAPH_OP(ConvolutionBackpropData, ngraph::op::v0, 0)
NGRAPH_OP(ConvolutionBackpropData, ngraph::op::v1, 1)
NGRAPH_OP(ConvolutionBackpropFilters, ngraph::op::v0, 0)
NGRAPH_OP(ConvolutionBackpropFilters, ngraph::op::v1, 1)
NGRAPH_OP(ConvolutionBias, ngraph::op::v0, 0)
NGRAPH_OP(ConvolutionBiasAdd, ngraph::op::v0, 0)
NGRAPH_OP(ConvolutionBiasBackpropFiltersBias, ngraph::op::v0, 0)
NGRAPH_OP(Cos, ngraph::op, 0)
NGRAPH_OP(Cosh, ngraph::op, 0)
NGRAPH_OP(CropAndResize, ngraph::op, 0)
NGRAPH_OP(CrossEntropy, ngraph::op, 0)
NGRAPH_OP(CrossEntropyBackprop, ngraph::op, 0)
NGRAPH_OP(CumSum, ngraph::op::v0, 0)
NGRAPH_OP(DepthToSpace, ngraph::op::v0, 1)
NGRAPH_OP(Dequantize, ngraph::op, 0)
NGRAPH_OP(DetectionOutput, ngraph::op::v0, 0)
NGRAPH_OP(Divide, ngraph::op::v0, 0)
NGRAPH_OP(Divide, ngraph::op::v1, 1)
NGRAPH_OP(Dot, ngraph::op::v0, 0)
NGRAPH_OP(DynBroadcast, ngraph::op, 0)
NGRAPH_OP(DynPad, ngraph::op, 0)
NGRAPH_OP(DynReplaceSlice, ngraph::op, 0)
NGRAPH_OP(DynReshape, ngraph::op::v0, 0)
NGRAPH_OP(DynSlice, ngraph::op, 0)
NGRAPH_OP(Elu, ngraph::op::v0, 0)
NGRAPH_OP(EmbeddingLookup, ngraph::op::v0, 0)
NGRAPH_OP(Equal, ngraph::op::v0, 0)
NGRAPH_OP(Equal, ngraph::op::v1, 1)
NGRAPH_OP(Erf, ngraph::op::v0, 0)
NGRAPH_OP(Exp, ngraph::op::v0, 0)
NGRAPH_OP(FakeQuantize, ngraph::op::v0, 0)
NGRAPH_OP(Floor, ngraph::op::v0, 0)
NGRAPH_OP(FloorMod, ngraph::op::v1, 1)
NGRAPH_OP(GRN, ngraph::op::v0, 0)
NGRAPH_OP(GRUCell, ngraph::op::v0, 0)
NGRAPH_OP(Gather, ngraph::op::v0, 0)
NGRAPH_OP(Gather, ngraph::op::v1, 1)
NGRAPH_OP(GatherND, ngraph::op::v0, 0)
NGRAPH_OP(Gelu, ngraph::op::v0, 0)
NGRAPH_OP(GeluBackpropFactor, ngraph::op::v0, 0)
NGRAPH_OP(Gemm, ngraph::op::v0, 0)
NGRAPH_OP(GenerateMask, ngraph::op::v0, 0)
NGRAPH_OP(GenerateMask, ngraph::op::v1, 1)
NGRAPH_OP(GetOutputElement, ngraph::op::v0, 0)
NGRAPH_OP(Greater, ngraph::op::v0, 0)
NGRAPH_OP(Greater, ngraph::op::v1, 1)
NGRAPH_OP(GreaterEq, ngraph::op::v0, 0)
NGRAPH_OP(GreaterEqual, ngraph::op::v1, 1)
NGRAPH_OP(GroupConvolution, ngraph::op::v0, 0)
NGRAPH_OP(GroupConvolutionTranspose, ngraph::op::v0, 0)
NGRAPH_OP(HardSigmoid, ngraph::op::v0, 0)
NGRAPH_OP(Interpolate, ngraph::op::v0, 0)
NGRAPH_OP(LRN, ngraph::op::v0, 0)
NGRAPH_OP(LSTMCell, ngraph::op::v0, 0)
NGRAPH_OP(LSTMSequence, ngraph::op::v0, 0)
NGRAPH_OP(LayerNorm, ngraph::op::v0, 0)
NGRAPH_OP(LayerNormBackprop, ngraph::op::v0, 0)
NGRAPH_OP(Less, ngraph::op::v0, 0)
NGRAPH_OP(Less, ngraph::op::v1, 1)
NGRAPH_OP(LessEq, ngraph::op::v0, 0)
NGRAPH_OP(LessEqual, ngraph::op::v1, 1)
NGRAPH_OP(Log, ngraph::op, 0)
NGRAPH_OP(LogSoftmax, ngraph::op::v0, 0)
NGRAPH_OP(LogicalAnd, ngraph::op::v1, 1)
NGRAPH_OP(LogicalNot, ngraph::op::v1, 1)
NGRAPH_OP(LogicalOr, ngraph::op::v1, 1)
NGRAPH_OP(LogicalXor, ngraph::op::v1, 1)
NGRAPH_OP(MVN, ngraph::op::v0, 0)
NGRAPH_OP(MatMul, ngraph::op::v0, 0)
NGRAPH_OP(Max, ngraph::op::v0, 0)
NGRAPH_OP(MaxPool, ngraph::op::v0, 0)
NGRAPH_OP(MaxPool, ngraph::op::v1, 1)
NGRAPH_OP(MaxPoolBackprop, ngraph::op::v0, 0)
NGRAPH_OP(MaxPoolBackprop, ngraph::op::v1, 1)
NGRAPH_OP(Maximum, ngraph::op::v0, 0)
NGRAPH_OP(Maximum, ngraph::op::v1, 1)
NGRAPH_OP(Min, ngraph::op::v0, 0)
NGRAPH_OP(Minimum, ngraph::op::v0, 0)
NGRAPH_OP(Minimum, ngraph::op::v1, 1)
NGRAPH_OP(Mod, ngraph::op::v1, 1)
NGRAPH_OP(Multiply, ngraph::op::v0, 0)
NGRAPH_OP(Multiply, ngraph::op::v1, 1)
NGRAPH_OP(Negative, ngraph::op, 0)
NGRAPH_OP(NormalizeL2, ngraph::op::v0, 0)
NGRAPH_OP(Not, ngraph::op::v0, 0)
NGRAPH_OP(NotEqual, ngraph::op::v0, 0)
NGRAPH_OP(NotEqual, ngraph::op::v1, 1)
NGRAPH_OP(OneHot, ngraph::op::v0, 0)
NGRAPH_OP(Or, ngraph::op::v0, 0)
NGRAPH_OP(PRelu, ngraph::op::v0, 0)
NGRAPH_OP(PSROIPooling, ngraph::op::v0, 0)
NGRAPH_OP(Pad, ngraph::op::v0, 0)
NGRAPH_OP(Pad, ngraph::op::v1, 1)
NGRAPH_OP(Parameter, ngraph::op, 0)
NGRAPH_OP(PartialSlice, ngraph::op::v0, 0)
NGRAPH_OP(PartialSliceBackprop, ngraph::op::v0, 0)
NGRAPH_OP(Passthrough, ngraph::op, 0)
NGRAPH_OP(Power, ngraph::op::v0, 0)
NGRAPH_OP(Power, ngraph::op::v1, 1)
NGRAPH_OP(PriorBox, ngraph::op::v0, 0)
NGRAPH_OP(PriorBoxClustered, ngraph::op::v0, 0)
NGRAPH_OP(Product, ngraph::op, 0)
NGRAPH_OP(Proposal, ngraph::op::v0, 0)
NGRAPH_OP(Quantize, ngraph::op::v0, 0)
NGRAPH_OP(QuantizedConvolution, ngraph::op::v0, 0)
NGRAPH_OP(QuantizedConvolutionBias, ngraph::op, 0)
NGRAPH_OP(QuantizedConvolutionBiasAdd, ngraph::op, 0)
NGRAPH_OP(QuantizedConvolutionBiasSignedAdd, ngraph::op, 0)
NGRAPH_OP(QuantizedConvolutionRelu, ngraph::op, 0)
NGRAPH_OP(QuantizedDot, ngraph::op::v0, 0)
NGRAPH_OP(QuantizedDotBias, ngraph::op, 0)
NGRAPH_OP(RNNCell, ngraph::op::v0, 0)
NGRAPH_OP(ROIPooling, ngraph::op::v0, 0)
NGRAPH_OP(RandomUniform, ngraph::op, 0)
NGRAPH_OP(Range, ngraph::op, 0)
NGRAPH_OP(Reciprocal, ngraph::op, 0)
NGRAPH_OP(Recv, ngraph::op::v0, 0)
NGRAPH_OP(ReduceMax, ngraph::op::v1, 1)
NGRAPH_OP(ReduceMean, ngraph::op::v1, 1)
NGRAPH_OP(ReduceMin, ngraph::op::v1, 1)
NGRAPH_OP(ReduceProd, ngraph::op::v1, 1)
NGRAPH_OP(ReduceSum, ngraph::op::v1, 1)
NGRAPH_OP(RegionYolo, ngraph::op::v0, 0)
NGRAPH_OP(Relu, ngraph::op::v0, 0)
NGRAPH_OP(ReluBackprop, ngraph::op::v0, 0)
NGRAPH_OP(ReorgYolo, ngraph::op::v0, 0)
NGRAPH_OP(ReplaceSlice, ngraph::op::v0, 0)
NGRAPH_OP(Reshape, ngraph::op::v0, 0)
NGRAPH_OP(Reshape, ngraph::op::v1, 1)
NGRAPH_OP(Result, ngraph::op, 0)
NGRAPH_OP(Reverse, ngraph::op::v0, 0)
NGRAPH_OP(Reverse, ngraph::op::v1, 1)
NGRAPH_OP(ReverseSequence, ngraph::op::v0, 0)
NGRAPH_OP(ScalarConstantLikeBase, ngraph::op, 0)
NGRAPH_OP(ScaleShift, ngraph::op::v0, 0)
NGRAPH_OP(ScatterAdd, ngraph::op::v0, 0)
NGRAPH_OP(ScatterNDAdd, ngraph::op::v0, 0)
NGRAPH_OP(Select, ngraph::op::v0, 0)
NGRAPH_OP(Selu, ngraph::op::v0, 0)
NGRAPH_OP(Send, ngraph::op::v0, 0)
NGRAPH_OP(ShapeOf, ngraph::op::v0, 0)
NGRAPH_OP(ShuffleChannels, ngraph::op::v0, 0)
NGRAPH_OP(Sigmoid, ngraph::op::v0, 0)
NGRAPH_OP(SigmoidBackprop, ngraph::op::v0, 0)
NGRAPH_OP(Sign, ngraph::op::v0, 0)
NGRAPH_OP(Sin, ngraph::op::v0, 0)
NGRAPH_OP(Sinh, ngraph::op::v0, 0)
NGRAPH_OP(Slice, ngraph::op::v0, 0)
NGRAPH_OP(Softmax, ngraph::op::v0, 0)
NGRAPH_OP(Softmax, ngraph::op::v1, 1)
NGRAPH_OP(SoftmaxCrossEntropy, ngraph::op::v0, 0)
NGRAPH_OP(SoftmaxCrossEntropyBackprop, ngraph::op::v0, 0)
NGRAPH_OP(SpaceToDepth, ngraph::op::v0, 0)
NGRAPH_OP(Split, ngraph::op::v0, 0)
NGRAPH_OP(Sqrt, ngraph::op, 0)
NGRAPH_OP(SquaredDifference, ngraph::op::v0, 0)
NGRAPH_OP(Squeeze, ngraph::op::v0, 0)
NGRAPH_OP(StopGradient, ngraph::op::v0, 0)
NGRAPH_OP(StridedSlice, ngraph::op::v1, 1)
NGRAPH_OP(Subtract, ngraph::op::v0, 0)
NGRAPH_OP(Sum, ngraph::op::v0, 0)
NGRAPH_OP(Tan, ngraph::op::v0, 0)
NGRAPH_OP(Tanh, ngraph::op::v0, 0)
NGRAPH_OP(TensorIterator, ngraph::op::v0, 0)
NGRAPH_OP(Tile, ngraph::op::v0, 0)
NGRAPH_OP(TopK, ngraph::op::v0, 0)
NGRAPH_OP(TopK, ngraph::op::v1, 1)
NGRAPH_OP(Transpose, ngraph::op::v0, 0)
NGRAPH_OP(Unsqueeze, ngraph::op::v0, 0)
NGRAPH_OP(VariadicSplit, ngraph::op::v1, 1)
NGRAPH_OP(Xor, ngraph::op::v0, 0)
src/ngraph/op/passthrough.hpp
View file @ f6bddf08
......@@ -25,6 +25,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// An op directly representing backend-specific code.
///
......@@ -33,9 +35,11 @@ namespace ngraph
/// operation language in certain modes.
class Passthrough;
}
using v0::Passthrough;
}
}
class NGRAPH_API ngraph::op::Passthrough final : public Op
class NGRAPH_API ngraph::op::v0::Passthrough final : public Op
{
public:
static constexpr NodeTypeInfo type_info{"Passthrough", 0};
......
src/ngraph/op/quantize.hpp
View file @ f6bddf08
......@@ -23,9 +23,12 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief Quantize operation
/// Maps real input (r) to quantized output (q) using scale (s), zero point (z) and
/// Maps real input (r) to quantized output (q) using scale (s), zero point (z)
/// and
/// round mode: q = ROUND(r / s) + o
class NGRAPH_API Quantize : public ngraph::op::Op
{
......@@ -110,4 +113,6 @@ namespace ngraph
RoundMode m_round_mode;
};
}
using v0::Quantize;
}
}
src/ngraph/op/quantized_convolution.hpp
View file @ f6bddf08
......@@ -22,6 +22,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
class NGRAPH_API QuantizedConvolution : public Op
{
......@@ -67,8 +69,14 @@ namespace ngraph
QuantizedConvolution() = default;
const Strides& get_window_movement_strides() const { return m_window_movement_strides; }
const Strides& get_window_dilation_strides() const { return m_window_dilation_strides; }
const Strides& get_window_movement_strides() const
{
return m_window_movement_strides;
}
const Strides& get_window_dilation_strides() const
{
return m_window_dilation_strides;
}
const CoordinateDiff& get_padding_below() const { return m_padding_below; }
const CoordinateDiff& get_padding_above() const { return m_padding_above; }
const Strides& get_data_dilation_strides() const { return m_data_dilation_strides; }
......@@ -97,4 +105,6 @@ namespace ngraph
ngraph::AxisSet m_output_axes;
};
}
using v0::QuantizedConvolution;
}
}
src/ngraph/op/quantized_dot.hpp
View file @ f6bddf08
......@@ -21,6 +21,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
class NGRAPH_API QuantizedDot : public Op
{
......@@ -83,4 +85,6 @@ namespace ngraph
ngraph::AxisSet m_output_axes;
};
}
using v0::QuantizedDot;
}
}
src/ngraph/op/recv.hpp
View file @ f6bddf08
......@@ -23,6 +23,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
class NGRAPH_API Recv : public Op
{
......@@ -47,4 +49,6 @@ namespace ngraph
int m_src_id;
};
}
using v0::Recv;
}
}
src/ngraph/op/reduce_prod.hpp
View file @ f6bddf08
......@@ -30,7 +30,7 @@ namespace ngraph
class NGRAPH_API ReduceProd : public util::ArithmeticReductionKeepDims
{
public:
static constexpr NodeTypeInfo type_info{"Product", 1};
static constexpr NodeTypeInfo type_info{"ReduceProd", 1};
const NodeTypeInfo& get_type_info() const override { return type_info; }
/// \brief Constructs a product reduction operation.
ReduceProd() = default;
......
src/ngraph/op/reduce_sum.hpp
View file @ f6bddf08
......@@ -77,7 +77,7 @@ namespace ngraph
class NGRAPH_API ReduceSum : public util::ArithmeticReductionKeepDims
{
public:
static constexpr NodeTypeInfo type_info{"Sum", 1};
static constexpr NodeTypeInfo type_info{"ReduceSum", 1};
const NodeTypeInfo& get_type_info() const override { return type_info; }
/// \brief Constructs a summation operation.
ReduceSum() = default;
......
src/ngraph/op/relu.hpp
View file @ f6bddf08
......@@ -26,6 +26,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief Elementwise Relu operation.
///
......@@ -61,10 +63,14 @@ namespace ngraph
/// \brief Constructs a ReluBackprop operation.
///
/// \param arg Node that produces the relu forward input tensor.
ReluBackprop(std::shared_ptr<ngraph::Node> arg, std::shared_ptr<ngraph::Node> delta);
ReluBackprop(std::shared_ptr<ngraph::Node> arg,
std::shared_ptr<ngraph::Node> delta);
virtual std::shared_ptr<Node>
copy_with_new_args(const NodeVector& new_args) const override;
};
}
using v0::Relu;
using v0::ReluBackprop;
}
}
src/ngraph/op/replace_slice.hpp
View file @ f6bddf08
......@@ -23,6 +23,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
// clang-format off
/// \brief Takes two input tensors of identical rank, with the second tensor no larger than
......@@ -62,7 +64,8 @@ namespace ngraph
/// \param arg1 The tensor to write into `arg0`.
/// \param lower_bounds The axiswise lower bounds of the slice (inclusive).
/// \param upper_bounds The axiswise upper bounds of the slice (exclusive).
/// \param strides The slicing strides; for example, strides of `{n,m}` means to take
/// \param strides The slicing strides; for example, strides of `{n,m}` means to
/// take
/// every nth row and every mth column of `arg0` as part of the
/// slice to be replaced.
ReplaceSlice(const Output<Node>& arg0,
......@@ -89,7 +92,10 @@ namespace ngraph
/// \return The inclusive lower-bound coordinates.
const Coordinate& get_lower_bounds() const { return m_lower_bounds; }
void set_lower_bounds(const Coordinate& lower_bounds) { m_lower_bounds = lower_bounds; }
void set_lower_bounds(const Coordinate& lower_bounds)
{
m_lower_bounds = lower_bounds;
}
/// \return The exclusive upper-bound coordinates.
const Coordinate& get_upper_bounds() const { return m_upper_bounds; }
void set_uppper_bounds(const Coordinate& upper_bounds)
......@@ -108,4 +114,6 @@ namespace ngraph
Strides m_strides;
};
}
using v0::ReplaceSlice;
}
}
src/ngraph/op/reshape.hpp
View file @ f6bddf08
......@@ -23,6 +23,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
// clang-format off
/// \brief Tensor reshape operation.
......@@ -73,7 +75,8 @@ namespace ngraph
///
/// \param arg The tensor to be reshaped.
/// \param input_order The order in which to iterate over input axes. This must be a
/// permutation of the sequence \f$(0,\dots,n-1)\f$ where \f$n\f$ is
/// permutation of the sequence \f$(0,\dots,n-1)\f$ where \f$n\f$
/// is
/// the rank of the input tensor.
/// \param output_shape The output shape. If the input shape is
/// \f$(a_0,\dots,a_{k-1})\f$ then the output shape must
......@@ -104,6 +107,7 @@ namespace ngraph
Shape m_output_shape;
bool m_is_transpose{false};
};
}
namespace v1
{
......@@ -115,7 +119,7 @@ namespace ngraph
class NGRAPH_API Reshape : public Op
{
public:
static constexpr NodeTypeInfo type_info{"DynReshape", 1};
static constexpr NodeTypeInfo type_info{"Reshape", 1};
const NodeTypeInfo& get_type_info() const override { return type_info; }
Reshape() = default;
/// \brief Constructs a dynamic reshape operation. This operation does not perform
......@@ -150,5 +154,6 @@ namespace ngraph
bool m_zero_flag;
};
}
using v0::Reshape;
}
}
src/ngraph/op/reverse_sequence.hpp
View file @ f6bddf08
......@@ -21,6 +21,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
class NGRAPH_API ReverseSequence : public Op
{
......@@ -54,4 +56,6 @@ namespace ngraph
size_t m_seq_axis{0};
};
}
using v0::ReverseSequence;
}
}
src/ngraph/op/scatter_add.hpp
View file @ f6bddf08
......@@ -21,6 +21,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief Add updates to slices from inputs addressed by indices
class NGRAPH_API ScatterAdd : public Op
......@@ -52,4 +54,6 @@ namespace ngraph
copy_with_new_args(const NodeVector& new_args) const override;
};
}
using v0::ScatterAdd;
}
}
src/ngraph/op/scatter_nd_add.hpp
View file @ f6bddf08
......@@ -21,6 +21,8 @@
namespace ngraph
{
namespace op
{
namespace v0
{
/// \brief Add updates to slices from inputs addressed by indices
class NGRAPH_API ScatterNDAdd : public Op
......@@ -52,4 +54,6 @@ namespace ngraph
copy_with_new_args(const NodeVector& new_args) const override;
};
}
using v0::ScatterNDAdd;
}
}
src/ngraph/op/select.hpp
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src/ngraph/ops.hpp
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src/ngraph/opsets/opset.cpp 0 → 100644
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src/ngraph/pass/constant_folding_binary.cpp
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src/ngraph/pass/opset1_upgrade.hpp
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src/ngraph/runtime/cpu/cpu_emitter.hpp
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src/ngraph/runtime/interpreter/opset_int_tbl.hpp 0 → 100644
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src/ngraph/serializer.cpp
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test/CMakeLists.txt
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test/opset_pass/poolings_opset_pass.cpp
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