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Unverified Commit 54132cc9 authored by Mateusz Bencer's avatar Mateusz Bencer Committed by GitHub
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[ONNX] norm builders should produce v1 ops (#4354)

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src/ngraph/builder/norm.cpp
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src/ngraph/builder/norm.hpp
View file @ 54132cc9
......@@ -34,62 +34,65 @@ namespace ngraph
MAX
};
/// \brief Calculates L-0 norm of input tensor.
///
/// \note The L-0 norm represents the cardinality of elements different
/// from zero. This actually is not a "true" norm.
///
/// \param[in] value The input tensor.
/// \param[in] reduction_axes The axes along which we calculate norm.
///
/// \return L-0 norm of value.
///
std::shared_ptr<Node> l0_norm(const Output<Node>& value, const AxisSet& reduction_axes);
namespace opset1
{
/// \brief Calculates L-0 norm of input tensor.
///
/// \note The L-0 norm represents the cardinality of elements different
/// from zero. This actually is not a "true" norm.
///
/// \param[in] value The input tensor.
/// \param[in] reduction_axes The axes along which we calculate norm.
///
/// \return L-0 norm of value. The output sub-graph is composed of v1 ops.
///
std::shared_ptr<Node> l0_norm(const Output<Node>& value, const AxisSet& reduction_axes);
/// \brief Calculates L-1 norm of a value.
///
/// \note The L-1 norm represents the sum of absolute values.
///
/// \param[in] value The input tensor.
/// \param[in] reduction_axes The axes along which we calculate norm.
/// \param[in] bias The bias added to the calculated sum.
///
/// \return L-1 norm of value.
///
std::shared_ptr<Node>
l1_norm(const Output<Node>& value, const AxisSet& reduction_axes, float bias = 0.f);
/// \brief Calculates L-1 norm of a value.
///
/// \note The L-1 norm represents the sum of absolute values.
///
/// \param[in] value The input tensor.
/// \param[in] reduction_axes The axes along which we calculate norm.
/// \param[in] bias The bias added to the calculated sum.
///
/// \return L-1 norm of value. The output sub-graph is composed of v1 ops.
///
std::shared_ptr<Node>
l1_norm(const Output<Node>& value, const AxisSet& reduction_axes, float bias = 0.f);
/// \brief Calculates L-2 norm of input tensor.
///
/// \note The L-2 norm represents the square root of sum of squares of each
/// individual element.
///
/// \param[in] value The input tensor.
/// \param[in] reduction_axes The axes along which we calculate norm.
/// \param[in] bias The bias combined with calculated sum.
/// \param[in] bias_mode The method of bias application.
/// \param[in] keep_dims The flag indicates if axes will be removed or kept.
///
/// \return L-2 norm of value.
///
std::shared_ptr<Node> l2_norm(const Output<Node>& value,
const AxisSet& reduction_axes,
float bias = 0.f,
BiasMode bias_mode = BiasMode::ADD,
bool keep_dims = false);
/// \brief Calculates L-2 norm of input tensor.
///
/// \note The L-2 norm represents the square root of sum of squares of each
/// individual element.
///
/// \param[in] value The input tensor.
/// \param[in] reduction_axes The axes along which we calculate norm.
/// \param[in] bias The bias combined with calculated sum.
/// \param[in] bias_mode The method of bias application.
/// \param[in] keep_dims The flag indicates if axes will be removed or kept.
///
/// \return L-2 norm of value. The output sub-graph is composed of v1 ops.
///
std::shared_ptr<Node> l2_norm(const Output<Node>& value,
const AxisSet& reduction_axes,
float bias = 0.f,
BiasMode bias_mode = BiasMode::ADD,
bool keep_dims = false);
/// \brief Creates node which calculates L-p norm on input tensor.
///
/// \param[in] value The input tensor.
/// \param[in] reduction_axes The axes along which we calculate norm.
/// \param[in] p_norm The p norm to calculate.
/// \param[in] bias The bias added to the calculated sum.
///
/// \return L-p norm of value.
///
std::shared_ptr<Node> lp_norm(const Output<Node>& value,
const AxisSet& reduction_axes,
std::size_t p_norm = 2,
float bias = 0.f);
/// \brief Creates node which calculates L-p norm on input tensor.
///
/// \param[in] value The input tensor.
/// \param[in] reduction_axes The axes along which we calculate norm.
/// \param[in] p_norm The p norm to calculate.
/// \param[in] bias The bias added to the calculated sum.
///
/// \return L-p norm of value. The output sub-graph is composed of v1 ops.
///
std::shared_ptr<Node> lp_norm(const Output<Node>& value,
const AxisSet& reduction_axes,
std::size_t p_norm = 2,
float bias = 0.f);
}
} // namespace builder
} // namespace ngraph
src/ngraph/frontend/onnx_import/op/lp_norm.cpp
View file @ 54132cc9
......@@ -55,7 +55,7 @@ namespace ngraph
<< "Invalid `p` attribute value: " << p_norm
<< "Only normalization of 1st or 2nd order is supported.";
std::shared_ptr<ngraph::Node> norm = ngraph::builder::lp_norm(
std::shared_ptr<ngraph::Node> norm = ngraph::builder::opset1::lp_norm(
data, AxisSet{normalize_axis}, static_cast<std::size_t>(p_norm));
const auto target_shape = default_opset::Constant::create(
......
src/ngraph/frontend/onnx_import/op/lp_pool.cpp
View file @ 54132cc9
......@@ -55,7 +55,7 @@ namespace ngraph
AxisSet reduction_axes{
common::get_monotonic_range<std::size_t>(orig_shape.size(), 2)};
slice = ngraph::builder::lp_norm(
slice = ngraph::builder::opset1::lp_norm(
slice, reduction_axes, static_cast<std::size_t>(p_norm));
// output shape is all ones except N channel
......
src/ngraph/frontend/onnx_import/op/reduce.hpp
View file @ 54132cc9
......@@ -97,7 +97,7 @@ namespace ngraph
///
inline NodeVector reduce_l1(const Node& node)
{
auto l1_norm_reduction = std::bind(ngraph::builder::l1_norm,
auto l1_norm_reduction = std::bind(ngraph::builder::opset1::l1_norm,
std::placeholders::_1,
std::placeholders::_2,
0.f);
......@@ -119,7 +119,7 @@ namespace ngraph
///
inline NodeVector reduce_l2(const Node& node)
{
auto l2_norm_reduction = std::bind(ngraph::builder::l2_norm,
auto l2_norm_reduction = std::bind(ngraph::builder::opset1::l2_norm,
std::placeholders::_1,
std::placeholders::_2,
0.f,
......
src/ngraph/op/fused/grn.cpp
View file @ 54132cc9
......@@ -68,7 +68,7 @@ NodeVector op::GRN::decompose_op() const
}
// Calculate l2 norm across channels.
shared_ptr<Node> norm = builder::l2_norm(data, AxisSet{1}, m_bias);
shared_ptr<Node> norm = builder::opset1::l2_norm(data, AxisSet{1}, m_bias);
// Get back reduced axis.
norm = std::make_shared<Broadcast>(norm, data.get_shape(), AxisSet{1});
data = data / norm;
......
src/ngraph/op/fused/normalize_l2.cpp
View file @ 54132cc9
......@@ -96,7 +96,8 @@ NodeVector op::NormalizeL2::decompose_op() const
// Calculate l2 norm across axes determined by axes input
auto builder_bias_mode =
(m_eps_mode == EpsMode::MAX) ? builder::BiasMode::MAX : builder::BiasMode::ADD;
Output<Node> norm = builder::l2_norm(data, reduction_axes, m_eps, builder_bias_mode, true);
Output<Node> norm =
builder::opset1::l2_norm(data, reduction_axes, m_eps, builder_bias_mode, true);
data = make_shared<op::Divide>(data, norm, AutoBroadcastSpec(AutoBroadcastType::NUMPY));
......
test/provenance.cpp
View file @ 54132cc9
......@@ -389,7 +389,7 @@ TEST(provenance, builder)
{
auto p1 = make_shared<op::Parameter>(element::i32, PartialShape{2, 3, 4});
p1->add_provenance_tag("P1");
auto norm = builder::lp_norm(p1, {0}, 1, 0);
auto norm = builder::opset1::lp_norm(p1, {0}, 1, 0);
norm->add_provenance_tag("norm");
for (auto node : topological_sort(NodeVector{norm}))
{
......
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