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Commit 6ca1a511 authored by amy.zhuang's avatar amy.zhuang
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Merge branch 'master' into ayzhuang/in-place-concat

parents 749a1c6b 9debc0bc
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.ci/onnx/jenkins/prepare_environment.sh
View file @ 6ca1a511
......@@ -68,7 +68,7 @@ function build_ngraph() {
make install || return 1
cd "${ngraph_directory}/ngraph/python"
if [ ! -d ./pybind11 ]; then
git clone --recursive -b allow-nonconstructible-holders https://github.com/jagerman/pybind11.git
git clone --recursive https://github.com/pybind/pybind11.git
fi
export PYBIND_HEADERS_PATH="${ngraph_directory}/ngraph/python/pybind11"
export NGRAPH_CPP_BUILD_PATH="${ngraph_directory}/ngraph_dist"
......
.ci/travis/ubuntu/Dockerfile
View file @ 6ca1a511
......@@ -39,7 +39,7 @@ RUN make install
# Prepare nGraph Python API
WORKDIR /root/ngraph/python
RUN git clone --recursive -b allow-nonconstructible-holders https://github.com/jagerman/pybind11.git
RUN git clone --recursive https://github.com/pybind/pybind11.git
ENV NGRAPH_CPP_BUILD_PATH /root/ngraph_dist
ENV LD_LIBRARY_PATH /root/ngraph_dist/lib
ENV PYBIND_HEADERS_PATH /root/ngraph/python/pybind11
......
CMakeLists.txt
View file @ 6ca1a511
......@@ -196,27 +196,10 @@ if(WIN32)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -D_CRT_SECURE_NO_WARNINGS")
endif()
include(unit_test_control)
set(UNIT_TEST_CONFIG_LIST "" CACHE INTERNAL "")
if (NGRAPH_INTERPRETER_ENABLE)
unit_test_control(BACKEND INTERPRETER MANIFEST src/ngraph/runtime/interpreter/unit_test.manifest)
endif()
# Set true if CPU backend is built by default
if (NGRAPH_CPU_ENABLE)
unit_test_control(BACKEND CPU MANIFEST src/ngraph/runtime/cpu/unit_test.manifest)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DNGRAPH_CPU_ENABLE")
endif()
if (NGRAPH_INTELGPU_ENABLE)
unit_test_control(BACKEND INTELGPU MANIFEST src/ngraph/runtime/intelgpu/unit_test.manifest)
endif()
if (NGRAPH_GPU_ENABLE)
unit_test_control(BACKEND GPU MANIFEST src/ngraph/runtime/gpu/unit_test.manifest)
endif()
if (NOT DEFINED NGRAPH_TBB_ENABLE)
set(NGRAPH_TBB_ENABLE ${NGRAPH_CPU_ENABLE})
endif()
......
README.md
View file @ 6ca1a511
# nGraph Library [![Build Status][build-status-badge]][build-status]
Welcome to the open-source repository for the Intel® nGraph™ Library. Our code
Welcome to the open-source repository for the **Intel® nGraph Library**. Our code
base provides a Compiler and runtime suite of tools (APIs) designed to give
developers maximum flexibility for their software design, allowing them to
create or customize a scalable solution using any framework while also avoiding
......@@ -11,7 +10,9 @@ backends, and it will be able to run on any backends we support in the future
with minimal disruption to your model. With nGraph, you can co-evolve your
software and hardware's capabilities to stay at the forefront of your industry.
The nGraph Compiler is Intel's graph compiler for Artificial Neural Networks.
![nGraph ecosystem][ngraph-ecosystem]
The **nGraph Compiler** is Intel's graph compiler for Artificial Neural Networks.
Documentation in this repo describes how you can program any framework
to run training and inference computations on a variety of Backends including
Intel® Architecture Processors (CPUs), Intel® Nervana™ Neural Network Processors
......@@ -24,18 +25,29 @@ whatever scenario you need.
nGraph provides both a C++ API for framework developers and a Python API which
can run inference on models imported from ONNX.
![nGraph ecosystem][ngraph-ecosystem]
See the [Release Notes] for recent changes.
|Framework | bridge available? | ONNX support? |
|------------|-------------------|----------------|
| neon | yes | yes |
| Framework | bridge available? | ONNX support? |
|----------------|-------------------|----------------|
| TensorFlow* | yes | yes |
| MXNet* | yes | yes |
| TensorFlow*| yes | yes |
| PyTorch* | not yet | yes |
| Chainer* | not yet | yes |
| CNTK* | not yet | yes |
| Caffe2* | not yet | yes |
| PaddlePaddle | yes | yes |
| PyTorch* | no | yes |
| Chainer* | no | yes |
| CNTK* | no | yes |
| Caffe2* | no | yes |
| Backend | current support | future support |
|-----------------------------------------------|-------------------|----------------|
| Intel® Architecture CPU | yes | yes |
| Intel® Nervana™ Neural Network Processor (NNP)| yes | yes |
| Intel [Movidius™ Myriad™ 2] VPUs | coming soon | yes |
| Intel® Architecture GPUs | via PlaidML | yes |
| AMD* GPUs | via PlaidML | yes |
| NVIDIA* GPUs | via PlaidML | some |
| Field Programmable Gate Arrays (FPGA) | no | yes |
## Documentation
......@@ -72,12 +84,13 @@ to improve the Library:
[install]: http://ngraph.nervanasys.com/docs/latest/buildlb.html
[framework integration guides]: http://ngraph.nervanasys.com/docs/latest/framework-integration-guides.html
[release notes]: http://ngraph.nervanasys.com/docs/latest/project/release-notes.html
[Github issues]: https://github.com/NervanaSystems/ngraph/issues
[contrib guide]: http://ngraph.nervanasys.com/docs/latest/project/code-contributor-README.html
[pull request]: https://github.com/NervanaSystems/ngraph/pulls
[how to import]: http://ngraph.nervanasys.com/docs/latest/howto/import.html
[ngraph-ecosystem]: doc/sphinx/source/graphics/ngraph-ecosystem.png "nGraph Ecosystem"
[ngraph-ecosystem]: doc/sphinx/source/graphics/599px-Intel-ngraph-ecosystem.png "nGraph Ecosystem"
[build-status]: https://travis-ci.org/NervanaSystems/ngraph/branches
[build-status-badge]: https://travis-ci.org/NervanaSystems/ngraph.svg?branch=master
[develop-without-lockin]: doc/sphinx/source/graphics/develop-without-lockin.png "Develop on any part of the stack wtihout lockin"
[Movidius]:https://www.movidius.com/solutions/vision-processing-unit
[Movidius™ Myriad™ 2]:https://www.movidius.com/solutions/vision-processing-unit
cmake/Modules/unit_test_control.cmake deleted 100644 → 0
View file @ 749a1c6b
# ******************************************************************************
# Copyright 2017-2018 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# 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.
# ******************************************************************************
function(UNIT_TEST_CONTROL)
set(options)
set(oneValueArgs BACKEND MANIFEST)
set(multiValueArgs)
cmake_parse_arguments(UNIT_TEST_CONTROL "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
if (UNIT_TEST_CONTROL_MANIFEST)
get_filename_component(UNIT_TEST_CONTROL_MANIFEST ${UNIT_TEST_CONTROL_MANIFEST} ABSOLUTE)
set(CONFIG_STRING "${UNIT_TEST_CONTROL_BACKEND}@${UNIT_TEST_CONTROL_MANIFEST}")
else()
set(CONFIG_STRING "${UNIT_TEST_CONTROL_BACKEND}@")
endif()
set(UNIT_TEST_CONFIG_LIST "${UNIT_TEST_CONFIG_LIST};${CONFIG_STRING}" CACHE INTERNAL "")
endfunction()
doc/sphinx/source/conf.py
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......@@ -68,9 +68,9 @@ author = 'Intel Corporation'
# built documents.
#
# The short X.Y version.
version = '0.8'
version = '0.9'
# The full version, including alpha/beta/rc tags.
release = '0.8.0'
release = '0.9.0'
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
......
doc/sphinx/source/index.rst
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......@@ -30,8 +30,8 @@ software engineers, and others with the means to make their work :ref:`portable`
:abbr:`Machine Learning (ML)` hardware available today: optimized Deep Learning
computation devices.
.. figure:: graphics/ngraph-ecosystem.png
:width: 650px
.. figure:: graphics/599px-Intel-ngraph-ecosystem.png
:width: 599px
.. _portable:
......@@ -66,16 +66,15 @@ Python-based API. See the `ngraph onnx companion tool`_ to get started.
.. csv-table::
:header: "Framework", "Bridge Code Available?", "ONNX Support?"
:header: "Framework", "Bridge Available?", "ONNX Support?"
:widths: 27, 10, 10
TensorFlow, Yes, Yes
MXNet, Yes, Yes
PaddlePaddle, Coming Soon, Yes
neon, none needed, Yes
PyTorch, Coming Soon, Yes
CNTK, Not yet, Yes
Other, Not yet, Doable
PyTorch, No, Yes
CNTK, No, Yes
Other, Custom, Custom
.. _deployable:
......@@ -104,14 +103,16 @@ model to run on a variety of backends:
.. csv-table::
:header: "Backend", "Current nGraph support", "Future nGraph support"
:header: "Backend", "Current support", "Future nGraph support"
:widths: 35, 10, 10
Intel® Architecture Processors (CPUs), Yes, Yes
Intel® Nervana™ Neural Network Processor™ (NNPs), Yes, Yes
NVIDIA\* CUDA (GPUs), Yes, Some
Intel® Nervana™ Neural Network Processor (NNPs), Yes, Yes
AMD\* GPUs, via PlaidML, Yes
NVIDIA\* GPUs, via PlaidML, Some
Intel® Architecture GPUs, Yes, Yes
:abbr:`Field Programmable Gate Arrays (FPGA)` (FPGAs), Coming soon, Yes
`Movidius`_, Not yet, Yes
Intel Movidius™ Myriad™ 2 (VPU), Coming soon, Yes
Other, Not yet, Ask
The value we're offering to the developer community is empowerment: we are
......@@ -119,12 +120,11 @@ confident that Intel® Architecture already provides the best computational
resources available for the breadth of ML/DL tasks. We welcome ideas and
`contributions`_ from the community.
Further project details can be found on our :doc:`project/about` page, or see
our :doc:`buildlb` guide for how to get started.
.. note:: The library code is under active development as we're continually
.. note:: The Library code is under active development as we're continually
adding support for more kinds of DL models and ops, framework compiler
optimizations, and backends.
......@@ -152,7 +152,6 @@ Contents
project/index.rst
Indices and tables
==================
......@@ -164,4 +163,3 @@ Indices and tables
.. _ngraph onnx companion tool: https://github.com/NervanaSystems/ngraph-onnx
.. _Movidius: https://www.movidius.com/
.. _contributions: https://github.com/NervanaSystems/ngraph#how-to-contribute
\ No newline at end of file
doc/sphinx/source/project/about.rst
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......@@ -5,7 +5,7 @@ Overview
Welcome to the documentation site for |InG|, an open-source C++ Compiler,
Library, and runtime suite for running training and inference on
Library, and runtime suite for Deep Learning frameworks running training and inference on
:abbr:`Deep Neural Network (DNN)` models. nGraph is framework-neutral and can be
targeted for programming and deploying :abbr:`Deep Learning (DL)` applications
on the most modern compute and edge devices.
......@@ -22,8 +22,8 @@ Features
Develop without lock-in
~~~~~~~~~~~~~~~~~~~~~~~
.. figure:: ../graphics/develop-without-lockin.png
:width: 650px
.. figure:: ../graphics/599px-Intel-ngraph-ecosystem.png
:width: 599px
Being able to increase training performance or reduce inference latency by simply
......@@ -34,47 +34,6 @@ developers working with nGraph. Our commitment to bake flexibility into our
ecosystem ensures developers' freedom to design user-facing APIs for various
hardware deployments directly into their frameworks.
.. figure:: ../graphics/ngraph-ecosystem.png
:width: 585px
nGraph currently supports :doc:`three popular <../framework-integration-guides>`
frameworks for :abbr:`Deep Learning (DL)` models through what we call
a :term:`bridge` that can be integrated during the framework's build time.
For developers working with other frameworks (even those not listed above),
we've created a :doc:`How to Guide <../howto/index>` so you can learn how to
create custom bridge code that can be used to
:doc:`compile and run <../howto/execute>` a training model.
Additionally, nGraph Library supports the `ONNX`_ format. Developers who
already have a "trained" model can use nGraph to bypass much of the
framework-based complexity and :doc:`../howto/import` to test or run it
on targeted and efficient backends with our user-friendly ``ngraph_api``.
With nGraph, data scientists can focus on data science rather than worrying
about how to adapt models to train and run efficiently on different devices.
Be sure to add the ``-DNGRAPH_ONNX_IMPORT_ENABLE=ON`` option when running `cmake`
to build the Library.
Supported platforms
--------------------
* Intel® Architecture Processors (CPUs),
* Intel® Nervana™ Neural Network Processor™ (NNPs), and
* NVIDIA\* CUDA (GPUs).
We built the first-generation of the Intel Nervana™ NNP family of processors
last year to show that the nGraph Library can be used to train a
:abbr:`Neural Network (NN)` more quickly. The more advanced the silicon, the
more powerful a lightweight a library can be. So while we do currently support
traditional GPUs, they are not advanced silicon, and trying to scale workloads
using traditional GPU libraries is clunky and brittle with bottlenecks. Iteration
from an already-trained NN model to one that can also perform inference
computations is immensely simplified. Read more about these compute-friendly
options on the documentation for :doc:`../fusion/index`.
.. note:: The library code is under active development as we're continually
adding support for more kinds of DL models and ops, framework compiler
optimizations, and backends.
......
doc/sphinx/source/project/doc-contributor-README.rst
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......@@ -15,13 +15,13 @@
.. limitations under the License.
.. ---------------------------------------------------------------------------
nGraph™ Library docs
====================
nGraph Library docs
===================
Read this for changes affecting anything in ``ngraph/doc``
----------------------------------------------------------
For updates to the Intel® nGraph™ Library ``/doc`` repo, please submit a PR with
For updates to the Intel® nGraph Library ``/doc`` repo, please submit a PR with
any changes or ideas you'd like integrated. This helps us maintain trackability
with respect to additions or feature requests.
......@@ -127,7 +127,7 @@ To build documentation locally, run:
.. code-block:: console
$ pip3 install [-I] Sphinx==1.6.5 [--user]
$ pip3 install [-I] Sphinx==1.7.5 [--user]
$ pip3 install [-I] breathe numpy [--user]
$ cd doc/sphinx/
$ make html
......@@ -150,11 +150,16 @@ To build documentation in a python3 virtualenv, run:
Then point your browser at ``localhost:8000``.
.. note:: For docs built in a virtual env, Sphinx latest changes may break
documentation; try building with a specific version of Sphinx.
For tips on writing reStructuredText-formatted documentation, see the `sphinx`_
stable reST documentation.
.. _ngraph repo: https://github.com/NervanaSystems/ngraph-cpp/
.. _documentation repo: https://github.com/NervanaSystems/private-ngraph/tree/master/doc
.. _ngraph repo: https://github.com/NervanaSystems/ngraph/
.. _documentation repo: https://github.com/NervanaSystems/ngraph/tree/master/doc
.. _sphinx: http://www.sphinx-doc.org/en/stable/rest.html
.. _wiki: https://github.com/NervanaSystems/ngraph/wiki/
.. _breathe: https://breathe.readthedocs.io/en/latest/
......
doc/sphinx/source/project/release-notes.rst
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......@@ -3,5 +3,12 @@
Release Notes
#############
This is the |version| of release.
This is release |release|.
API Changes
===========
.. literalinclude:: ../../../../changes.md
src/ngraph/frontend/onnx_import/op/abs.hpp
View file @ 6ca1a511
......@@ -28,13 +28,17 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
inline NodeVector abs(const Node& node)
{
return {std::make_shared<ngraph::op::Abs>(node.get_ng_inputs().at(0))};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/add.hpp
View file @ 6ca1a511
......@@ -27,6 +27,8 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
inline NodeVector add(const Node& node)
{
......@@ -35,7 +37,9 @@ namespace ngraph
return {std::make_shared<ngraph::op::Add>(ng_inputs.at(0), ng_inputs.at(1))};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/and.hpp
View file @ 6ca1a511
......@@ -27,6 +27,8 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
inline NodeVector logical_and(const Node& node)
{
......@@ -35,7 +37,9 @@ namespace ngraph
return {std::make_shared<ngraph::op::And>(ng_inputs.at(0), ng_inputs.at(1))};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/average_pool.cpp
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......@@ -25,13 +25,17 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector average_pool(const Node& node)
{
return convpool::make_ng_pool<ngraph::op::AvgPool>(node);
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/average_pool.hpp
View file @ 6ca1a511
......@@ -25,6 +25,8 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
/**
* @brief Convert ONNX AveragePool operation to an nGraph node.
......@@ -36,7 +38,9 @@ namespace ngraph
*/
NodeVector average_pool(const Node& node);
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/batch_norm.cpp
View file @ 6ca1a511
......@@ -27,6 +27,8 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector batch_norm(const Node& node)
{
......@@ -58,7 +60,9 @@ namespace ngraph
return {std::make_shared<ngraph::op::BatchNorm>(epsilon, scale, bias, x)};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/batch_norm.hpp
View file @ 6ca1a511
......@@ -23,9 +23,14 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector batch_norm(const Node& node);
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/cast.cpp
View file @ 6ca1a511
......@@ -29,6 +29,8 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector cast(const Node& node)
{
......@@ -50,14 +52,18 @@ namespace ngraph
case onnx::TensorProto_DataType_UINT16: elem_type = element::u16; break;
case onnx::TensorProto_DataType_UINT32: elem_type = element::u32; break;
case onnx::TensorProto_DataType_UINT64: elem_type = element::u64; break;
case onnx::TensorProto_DataType_UNDEFINED: elem_type = element::unspecified; break;
case onnx::TensorProto_DataType_UNDEFINED:
elem_type = element::unspecified;
break;
default: ASSERT_IS_SUPPORTED(node, false) << "unsupported type";
}
return {std::make_shared<ngraph::op::Convert>(data, elem_type)};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/cast.hpp
View file @ 6ca1a511
......@@ -25,9 +25,14 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector cast(const Node& node);
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/ceil.hpp
View file @ 6ca1a511
......@@ -28,13 +28,17 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
inline NodeVector ceil(const Node& node)
{
return {std::make_shared<ngraph::op::Ceiling>(node.get_ng_inputs().at(0))};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/clip.cpp
View file @ 6ca1a511
......@@ -33,6 +33,8 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector clip(const Node& node)
{
......@@ -40,24 +42,29 @@ namespace ngraph
double max_value =
node.get_attribute_value<double>("max", std::numeric_limits<double>::max());
double min_value =
node.get_attribute_value<double>("min", std::numeric_limits<double>::lowest());
double min_value = node.get_attribute_value<double>(
"min", std::numeric_limits<double>::lowest());
std::shared_ptr<ngraph::Node> max_value_node =
std::make_shared<ngraph::op::Constant>(
data->get_element_type(), ngraph::Shape{}, std::vector<double>{max_value});
std::make_shared<ngraph::op::Constant>(data->get_element_type(),
ngraph::Shape{},
std::vector<double>{max_value});
max_value_node = make_broadcast_node(max_value_node, data->get_shape());
std::shared_ptr<ngraph::Node> min_value_node =
std::make_shared<ngraph::op::Constant>(
data->get_element_type(), ngraph::Shape{}, std::vector<double>{min_value});
std::make_shared<ngraph::op::Constant>(data->get_element_type(),
ngraph::Shape{},
std::vector<double>{min_value});
min_value_node = make_broadcast_node(min_value_node, data->get_shape());
return {std::make_shared<ngraph::op::Minimum>(
max_value_node, std::make_shared<ngraph::op::Maximum>(data, min_value_node))};
max_value_node,
std::make_shared<ngraph::op::Maximum>(data, min_value_node))};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/clip.hpp
View file @ 6ca1a511
......@@ -25,9 +25,14 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector clip(const Node& node);
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/concat.cpp
View file @ 6ca1a511
......@@ -23,6 +23,8 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector concat(const Node& node)
{
......@@ -32,7 +34,9 @@ namespace ngraph
return {std::make_shared<ngraph::op::Concat>(inputs, axis)};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/concat.hpp
View file @ 6ca1a511
......@@ -25,9 +25,14 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector concat(const Node& node);
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/constant.cpp
View file @ 6ca1a511
......@@ -24,6 +24,8 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
namespace
{
......@@ -36,7 +38,8 @@ namespace ngraph
}
template <Tensor::Type>
inline std::shared_ptr<ngraph::op::Constant> make_ng_constant(const Tensor& tensor)
inline std::shared_ptr<ngraph::op::Constant>
make_ng_constant(const Tensor& tensor)
{
throw error::tensor::unsupported_data_type{tensor};
}
......@@ -114,7 +117,9 @@ namespace ngraph
return {make_constant(node.get_attribute_value<Tensor>("value"))};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/constant.hpp
View file @ 6ca1a511
......@@ -25,10 +25,14 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector constant(const Node& node);
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/conv.cpp
View file @ 6ca1a511
......@@ -36,6 +36,8 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
namespace
{
......@@ -63,7 +65,8 @@ namespace ngraph
// initial bounds for splice
std::vector<std::size_t> data_lower_bounds(data->get_shape().size());
std::vector<std::size_t> data_upper_bounds{data->get_shape()};
std::vector<std::size_t> filters_lower_bounds(filters->get_shape().size());
std::vector<std::size_t> filters_lower_bounds(
filters->get_shape().size());
std::vector<std::size_t> filters_upper_bounds{filters->get_shape()};
for (std::size_t group{0}; group < groups; ++group)
......@@ -136,7 +139,9 @@ namespace ngraph
return {std::make_shared<ngraph::op::Add>(conv_node, broadcasted_bias)};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/conv.hpp
View file @ 6ca1a511
......@@ -25,6 +25,8 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
/// \brief Performs ONNX Conv operation.
///
......@@ -34,7 +36,9 @@ namespace ngraph
/// operation.
NodeVector conv(const Node& node);
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/div.hpp
View file @ 6ca1a511
......@@ -27,6 +27,8 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
inline NodeVector div(const Node& node)
{
......@@ -35,7 +37,9 @@ namespace ngraph
return {std::make_shared<ngraph::op::Divide>(ng_inputs.at(0), ng_inputs.at(1))};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/elu.cpp
View file @ 6ca1a511
......@@ -37,27 +37,34 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector elu(const Node& node)
{
auto data = node.get_ng_inputs().at(0);
double alpha = node.get_attribute_value<double>("alpha", 1);
std::shared_ptr<ngraph::Node> alpha_node = std::make_shared<ngraph::op::Constant>(
std::shared_ptr<ngraph::Node> alpha_node =
std::make_shared<ngraph::op::Constant>(
data->get_element_type(), Shape{}, std::vector<double>{alpha});
alpha_node = make_broadcast_node(alpha_node, data->get_shape());
std::shared_ptr<ngraph::Node> zero_node = std::make_shared<ngraph::op::Constant>(
std::shared_ptr<ngraph::Node> zero_node =
std::make_shared<ngraph::op::Constant>(
data->get_element_type(), Shape{}, std::vector<double>{0});
zero_node = make_broadcast_node(zero_node, data->get_shape());
return {std::make_shared<ngraph::op::Maximum>(data, zero_node) +
alpha_node * std::make_shared<ngraph::op::Exp>(
alpha_node *
std::make_shared<ngraph::op::Exp>(
std::make_shared<ngraph::op::Minimum>(data, zero_node)) -
alpha_node};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/elu.hpp
View file @ 6ca1a511
......@@ -25,9 +25,14 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector elu(const Node& node);
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/equal.hpp
View file @ 6ca1a511
......@@ -27,6 +27,8 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
inline NodeVector equal(const Node& node)
{
......@@ -35,7 +37,9 @@ namespace ngraph
return {std::make_shared<ngraph::op::Equal>(ng_inputs.at(0), ng_inputs.at(1))};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/exp.hpp
View file @ 6ca1a511
......@@ -28,13 +28,17 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
inline NodeVector exp(const Node& node)
{
return {std::make_shared<ngraph::op::Exp>(node.get_ng_inputs().at(0))};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/flatten.cpp
View file @ 6ca1a511
......@@ -24,6 +24,8 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector flatten(const Node& node)
{
......@@ -37,7 +39,9 @@ namespace ngraph
return {reshape::flatten(data, axis)};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/flatten.hpp
View file @ 6ca1a511
......@@ -27,9 +27,14 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector flatten(const Node& node);
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/floor.hpp
View file @ 6ca1a511
......@@ -28,13 +28,17 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
inline NodeVector floor(const Node& node)
{
return {std::make_shared<ngraph::op::Floor>(node.get_ng_inputs().at(0))};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/gemm.cpp
View file @ 6ca1a511
......@@ -31,6 +31,8 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector gemm(const Node& node)
{
......@@ -60,13 +62,17 @@ namespace ngraph
std::shared_ptr<ngraph::Node> a_dot_b =
std::make_shared<ngraph::op::Dot>(input_a, input_b);
std::shared_ptr<ngraph::Node> alpha_node = std::make_shared<ngraph::op::Constant>(
a_dot_b->get_element_type(), ngraph::Shape{}, std::vector<double>{alpha});
std::shared_ptr<ngraph::Node> alpha_node =
std::make_shared<ngraph::op::Constant>(a_dot_b->get_element_type(),
ngraph::Shape{},
std::vector<double>{alpha});
alpha_node = make_broadcast_node(alpha_node, a_dot_b->get_shape());
a_dot_b = std::make_shared<ngraph::op::Multiply>(alpha_node, a_dot_b);
std::shared_ptr<ngraph::Node> beta_node = std::make_shared<ngraph::op::Constant>(
input_c->get_element_type(), ngraph::Shape{}, std::vector<double>{beta});
std::shared_ptr<ngraph::Node> beta_node =
std::make_shared<ngraph::op::Constant>(input_c->get_element_type(),
ngraph::Shape{},
std::vector<double>{beta});
beta_node = make_broadcast_node(beta_node, input_c->get_shape());
input_c = std::make_shared<ngraph::op::Multiply>(beta_node, input_c);
......@@ -75,7 +81,9 @@ namespace ngraph
return {std::make_shared<ngraph::op::Add>(a_dot_b, input_c)};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/gemm.hpp
View file @ 6ca1a511
......@@ -27,9 +27,14 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector gemm(const Node& node);
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/greater.hpp
View file @ 6ca1a511
......@@ -27,15 +27,20 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
inline NodeVector greater(const Node& node)
{
NodeVector ng_inputs{
numpy_style_broadcast_for_binary_operation(node.get_ng_inputs())};
return {std::make_shared<ngraph::op::Greater>(ng_inputs.at(0), ng_inputs.at(1))};
return {
std::make_shared<ngraph::op::Greater>(ng_inputs.at(0), ng_inputs.at(1))};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/hard_sigmoid.cpp
View file @ 6ca1a511
......@@ -34,6 +34,8 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector hard_sigmoid(const Node& node)
{
......@@ -42,11 +44,13 @@ namespace ngraph
double alpha = node.get_attribute_value<double>("alpha", 0.2);
double beta = node.get_attribute_value<double>("beta", 0.5);
std::shared_ptr<ngraph::Node> alpha_node = std::make_shared<ngraph::op::Constant>(
std::shared_ptr<ngraph::Node> alpha_node =
std::make_shared<ngraph::op::Constant>(
data->get_element_type(), ngraph::Shape{}, std::vector<double>{alpha});
alpha_node = make_broadcast_node(alpha_node, data->get_shape());
std::shared_ptr<ngraph::Node> beta_node = std::make_shared<ngraph::op::Constant>(
std::shared_ptr<ngraph::Node> beta_node =
std::make_shared<ngraph::op::Constant>(
data->get_element_type(), ngraph::Shape{}, std::vector<double>{beta});
beta_node = make_broadcast_node(beta_node, data->get_shape());
......@@ -54,7 +58,8 @@ namespace ngraph
data->get_element_type(), Shape{}, std::vector<double>{1});
one_node = make_broadcast_node(one_node, data->get_shape());
std::shared_ptr<ngraph::Node> zero_node = std::make_shared<ngraph::op::Constant>(
std::shared_ptr<ngraph::Node> zero_node =
std::make_shared<ngraph::op::Constant>(
data->get_element_type(), Shape{}, std::vector<double>{0});
zero_node = make_broadcast_node(zero_node, data->get_shape());
......@@ -64,7 +69,9 @@ namespace ngraph
alpha_node * data + beta_node))};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/hard_sigmoid.hpp
View file @ 6ca1a511
......@@ -25,9 +25,14 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector hard_sigmoid(const Node& node);
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/identity.hpp
View file @ 6ca1a511
......@@ -26,8 +26,15 @@ namespace ngraph
{
namespace op
{
inline NodeVector identity(const Node& node) { return {node.get_ng_inputs().at(0)}; }
} // namespace op
namespace set_1
{
inline NodeVector identity(const Node& node)
{
return {node.get_ng_inputs().at(0)};
}
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/leaky_relu.cpp
View file @ 6ca1a511
......@@ -37,6 +37,8 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector leaky_relu(const Node& node)
{
......@@ -46,13 +48,16 @@ namespace ngraph
ASSERT_VALID_ARGUMENT(node, ((alpha >= 0) && (alpha <= 1)))
<< " alpha value should be in range (0,1)";
std::shared_ptr<ngraph::Node> alpha_node = std::make_shared<ngraph::op::Constant>(
std::shared_ptr<ngraph::Node> alpha_node =
std::make_shared<ngraph::op::Constant>(
data->get_element_type(), Shape{}, std::vector<double>{alpha});
alpha_node = make_broadcast_node(alpha_node, data->get_shape());
return {std::make_shared<ngraph::op::Maximum>(data * alpha_node, data)};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/leaky_relu.hpp
View file @ 6ca1a511
......@@ -25,9 +25,14 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector leaky_relu(const Node& node);
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/less.hpp
View file @ 6ca1a511
......@@ -27,6 +27,8 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
inline NodeVector less(const Node& node)
{
......@@ -35,7 +37,9 @@ namespace ngraph
return {std::make_shared<ngraph::op::Less>(ng_inputs.at(0), ng_inputs.at(1))};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/log.hpp
View file @ 6ca1a511
......@@ -28,13 +28,17 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
inline NodeVector log(const Node& node)
{
return {std::make_shared<ngraph::op::Log>(node.get_ng_inputs().at(0))};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/log_softmax.hpp
View file @ 6ca1a511
......@@ -30,13 +30,17 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
inline NodeVector log_softmax(const Node& node)
{
return {std::make_shared<ngraph::op::Log>(softmax(node).at(0))};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/lrn.cpp
View file @ 6ca1a511
......@@ -26,6 +26,8 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector lrn(const Node& node)
{
......@@ -38,7 +40,9 @@ namespace ngraph
return {std::make_shared<ngraph::op::LRN>(data, alpha, beta, bias, size)};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/lrn.hpp
View file @ 6ca1a511
......@@ -25,9 +25,13 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector lrn(const Node& node);
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/matmul.hpp
View file @ 6ca1a511
......@@ -26,6 +26,8 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
inline NodeVector matmul(const Node& node)
{
......@@ -33,7 +35,9 @@ namespace ngraph
return {std::make_shared<ngraph::op::Dot>(ng_inputs.at(0), ng_inputs.at(1))};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/max.hpp
View file @ 6ca1a511
......@@ -27,13 +27,17 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
inline NodeVector max(const Node& node)
{
return variadic::make_ng_variadic_op<ngraph::op::Maximum>(node);
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/max_pool.cpp
View file @ 6ca1a511
......@@ -25,13 +25,17 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector max_pool(const Node& node)
{
return convpool::make_ng_pool<ngraph::op::MaxPool>(node);
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/max_pool.hpp
View file @ 6ca1a511
......@@ -25,6 +25,8 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
/**
* @brief Convert ONNX MaxPool operation to an nGraph node.
......@@ -36,7 +38,9 @@ namespace ngraph
*/
NodeVector max_pool(const Node& node);
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/mean.cpp
View file @ 6ca1a511
......@@ -26,6 +26,8 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector mean(const Node& node)
{
......@@ -41,7 +43,9 @@ namespace ngraph
return {sum / count};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/mean.hpp
View file @ 6ca1a511
......@@ -25,10 +25,14 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector mean(const Node& node);
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/min.hpp
View file @ 6ca1a511
......@@ -27,13 +27,17 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
inline NodeVector min(const Node& node)
{
return variadic::make_ng_variadic_op<ngraph::op::Minimum>(node);
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/mul.hpp
View file @ 6ca1a511
......@@ -27,15 +27,20 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
inline NodeVector mul(const Node& node)
{
NodeVector ng_inputs{
numpy_style_broadcast_for_binary_operation(node.get_ng_inputs())};
return {std::make_shared<ngraph::op::Multiply>(ng_inputs.at(0), ng_inputs.at(1))};
return {
std::make_shared<ngraph::op::Multiply>(ng_inputs.at(0), ng_inputs.at(1))};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/neg.hpp
View file @ 6ca1a511
......@@ -26,9 +26,13 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
inline NodeVector neg(const Node& node) { return {-node.get_ng_inputs().at(0)}; }
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/not.hpp
View file @ 6ca1a511
......@@ -27,13 +27,17 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
inline NodeVector logical_not(const Node& node)
{
return {std::make_shared<ngraph::op::Not>(node.get_ng_inputs().at(0))};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/or.hpp
View file @ 6ca1a511
......@@ -27,6 +27,8 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
inline NodeVector logical_or(const Node& node)
{
......@@ -35,7 +37,9 @@ namespace ngraph
return {std::make_shared<ngraph::op::Or>(ng_inputs.at(0), ng_inputs.at(1))};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/pow.hpp
View file @ 6ca1a511
......@@ -27,6 +27,8 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
inline NodeVector pow(const Node& node)
{
......@@ -35,7 +37,9 @@ namespace ngraph
return {std::make_shared<ngraph::op::Power>(ng_inputs.at(0), ng_inputs.at(1))};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/prelu.cpp
View file @ 6ca1a511
......@@ -36,6 +36,8 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector prelu(const Node& node)
{
......@@ -47,8 +49,8 @@ namespace ngraph
if ((slope_shape.size() == 1) && (slope_shape.at(0) != 1))
{
auto it =
std::find(std::begin(data_shape), std::end(data_shape), slope_shape.at(0));
auto it = std::find(
std::begin(data_shape), std::end(data_shape), slope_shape.at(0));
auto index = std::distance(std::begin(data_shape), it);
slope = make_broadcast_node(slope, data->get_shape(), index);
}
......@@ -61,7 +63,9 @@ namespace ngraph
return {std::make_shared<ngraph::op::Maximum>(data * slope, data)};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/prelu.hpp
View file @ 6ca1a511
......@@ -25,9 +25,14 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector prelu(const Node& node);
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/reciprocal.cpp
View file @ 6ca1a511
......@@ -30,6 +30,8 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector reciprocal(const Node& node)
{
......@@ -42,7 +44,9 @@ namespace ngraph
return {one_node / data};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/reciprocal.hpp
View file @ 6ca1a511
......@@ -25,9 +25,14 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector reciprocal(const Node& node);
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/reduce.cpp
View file @ 6ca1a511
......@@ -31,6 +31,8 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector reduce_mean(const Node& node)
{
......@@ -55,6 +57,10 @@ namespace ngraph
return {std::make_shared<ngraph::op::Divide>(sum_node, broadcasted_const_node)};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
} // namespace ngraph
src/ngraph/frontend/onnx_import/op/reduce.hpp
View file @ 6ca1a511
......@@ -38,6 +38,8 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
/// \brief Compute the log sum of the input tensor's elements along the provided axes.
///
......@@ -71,7 +73,8 @@ namespace ngraph
inline NodeVector reduce_log_sum_exp(const Node& node)
{
auto exp_node = std::make_shared<ngraph::op::Exp>(node.get_ng_inputs().at(0));
auto sum_node = reduction::make_ng_reduction_op<ngraph::op::Sum>(node, exp_node);
auto sum_node =
reduction::make_ng_reduction_op<ngraph::op::Sum>(node, exp_node);
return {std::make_shared<ngraph::op::Log>(sum_node)};
}
......@@ -108,7 +111,8 @@ namespace ngraph
NodeVector ng_inputs{node.get_ng_inputs()};
auto square_node =
std::make_shared<ngraph::op::Multiply>(ng_inputs.at(0), ng_inputs.at(0));
auto sum_node = reduction::make_ng_reduction_op<ngraph::op::Sum>(node, square_node);
auto sum_node =
reduction::make_ng_reduction_op<ngraph::op::Sum>(node, square_node);
return {std::make_shared<ngraph::op::Sqrt>(sum_node)};
}
......@@ -212,6 +216,10 @@ namespace ngraph
return {reduction::make_ng_reduction_op<ngraph::op::Sum>(node, square_node)};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
} // namespace ngraph
src/ngraph/frontend/onnx_import/op/relu.hpp
View file @ 6ca1a511
......@@ -26,6 +26,8 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
inline NodeVector relu(const Node& node)
{
......@@ -33,7 +35,9 @@ namespace ngraph
return {std::make_shared<ngraph::op::Relu>(ng_inputs.at(0))};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/reshape.cpp
View file @ 6ca1a511
......@@ -32,6 +32,8 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector reshape(const Node& node)
{
......@@ -39,7 +41,8 @@ namespace ngraph
auto data = ng_inputs.at(0);
auto data_shape = data->get_shape();
auto output_shape = node.get_attribute_value<std::vector<std::size_t>>("shape", {});
auto output_shape =
node.get_attribute_value<std::vector<std::size_t>>("shape", {});
// If no shape argument (opset >= 5) and there is second input.
if (output_shape.empty() && ng_inputs.size() == 2)
......@@ -58,14 +61,17 @@ namespace ngraph
return {data};
}
output_shape = reshape::infer_dimensions(node.get_name(), data_shape, output_shape);
output_shape =
reshape::infer_dimensions(node.get_name(), data_shape, output_shape);
return {std::make_shared<ngraph::op::Reshape>(
data,
reshape::get_default_axis_vector(data_shape.size()),
Shape{output_shape})};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/reshape.hpp
View file @ 6ca1a511
......@@ -25,6 +25,8 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
///
/// \brief Reshape the input tensor similar to numpy.reshape.
......@@ -35,7 +37,9 @@ namespace ngraph
///
NodeVector reshape(const Node& node);
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/selu.cpp
View file @ 6ca1a511
......@@ -40,33 +40,42 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector selu(const Node& node)
{
auto data = node.get_ng_inputs().at(0);
double alpha = node.get_attribute_value<double>("alpha", 1.67326319217681884765625);
double gamma = node.get_attribute_value<double>("gamma", 1.05070102214813232421875);
double alpha =
node.get_attribute_value<double>("alpha", 1.67326319217681884765625);
double gamma =
node.get_attribute_value<double>("gamma", 1.05070102214813232421875);
std::shared_ptr<ngraph::Node> alpha_node = std::make_shared<ngraph::op::Constant>(
std::shared_ptr<ngraph::Node> alpha_node =
std::make_shared<ngraph::op::Constant>(
data->get_element_type(), ngraph::Shape{}, std::vector<double>{alpha});
alpha_node = make_broadcast_node(alpha_node, data->get_shape());
std::shared_ptr<ngraph::Node> gamma_node = std::make_shared<ngraph::op::Constant>(
std::shared_ptr<ngraph::Node> gamma_node =
std::make_shared<ngraph::op::Constant>(
data->get_element_type(), ngraph::Shape{}, std::vector<double>{gamma});
gamma_node = make_broadcast_node(gamma_node, data->get_shape());
std::shared_ptr<ngraph::Node> zero_node = std::make_shared<ngraph::op::Constant>(
std::shared_ptr<ngraph::Node> zero_node =
std::make_shared<ngraph::op::Constant>(
data->get_element_type(), ngraph::Shape{}, std::vector<double>{0});
zero_node = make_broadcast_node(zero_node, data->get_shape());
return {gamma_node *
(std::make_shared<ngraph::op::Maximum>(data, zero_node) +
return {gamma_node * (std::make_shared<ngraph::op::Maximum>(data, zero_node) +
alpha_node * std::make_shared<ngraph::op::Exp>(
std::make_shared<ngraph::op::Minimum>(data, zero_node)) -
std::make_shared<ngraph::op::Minimum>(
data, zero_node)) -
alpha_node)};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/selu.hpp
View file @ 6ca1a511
......@@ -25,9 +25,14 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector selu(const Node& node);
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/shape.cpp
View file @ 6ca1a511
......@@ -29,6 +29,8 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector shape(const Node& node)
{
......@@ -39,7 +41,9 @@ namespace ngraph
ngraph::element::i64, Shape{data_shape.size()}, data_shape)};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/shape.hpp
View file @ 6ca1a511
......@@ -25,9 +25,14 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector shape(const Node& node);
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/sigmoid.hpp
View file @ 6ca1a511
......@@ -28,13 +28,17 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
inline NodeVector sigmoid(const Node& node)
{
return {std::make_shared<ngraph::op::Sigmoid>(node.get_ng_inputs().at(0))};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/slice.cpp
View file @ 6ca1a511
......@@ -34,6 +34,8 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector slice(const Node& node)
{
......@@ -54,13 +56,16 @@ namespace ngraph
size_t axis = axes.at(idx);
lower_bounds.at(axis) =
get_valid_array_idx(starts.at(idx), data_shape.at(axis));
upper_bounds.at(axis) = get_valid_array_idx(ends.at(idx), data_shape.at(axis));
upper_bounds.at(axis) =
get_valid_array_idx(ends.at(idx), data_shape.at(axis));
}
return {std::make_shared<ngraph::op::Slice>(data, lower_bounds, upper_bounds)};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/slice.hpp
View file @ 6ca1a511
......@@ -25,9 +25,14 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector slice(const Node& node);
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/softmax.cpp
View file @ 6ca1a511
......@@ -26,6 +26,8 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector softmax(const Node& node)
{
......@@ -50,7 +52,9 @@ namespace ngraph
return {std::make_shared<ngraph::op::Softmax>(data, axes)};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/softmax.hpp
View file @ 6ca1a511
......@@ -25,9 +25,14 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector softmax(const Node& node);
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/softplus.cpp
View file @ 6ca1a511
......@@ -32,6 +32,8 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector softplus(const Node& node)
{
......@@ -41,11 +43,13 @@ namespace ngraph
data->get_element_type(), Shape{}, std::vector<double>{1});
one_node = make_broadcast_node(one_node, data->get_shape());
return {std::make_shared<ngraph::op::Log>(std::make_shared<ngraph::op::Exp>(data) +
one_node)};
return {std::make_shared<ngraph::op::Log>(
std::make_shared<ngraph::op::Exp>(data) + one_node)};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/softplus.hpp
View file @ 6ca1a511
......@@ -25,9 +25,14 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector softplus(const Node& node);
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/softsign.cpp
View file @ 6ca1a511
......@@ -32,6 +32,8 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector softsign(const Node& node)
{
......@@ -44,7 +46,9 @@ namespace ngraph
return {data / (std::make_shared<ngraph::op::Abs>(data) + one_node)};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/softsign.hpp
View file @ 6ca1a511
......@@ -25,9 +25,14 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector softsign(const Node& node);
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/split.cpp
View file @ 6ca1a511
......@@ -79,6 +79,8 @@ namespace ngraph
} // namespace error
namespace op
{
namespace set_1
{
namespace detail
{
......@@ -105,7 +107,8 @@ namespace ngraph
upper_bounds.at(axis) =
get_valid_array_index(ends.at(index), node->get_shape().at(axis));
}
return std::make_shared<ngraph::op::Slice>(node, lower_bounds, upper_bounds);
return std::make_shared<ngraph::op::Slice>(
node, lower_bounds, upper_bounds);
}
} // namespace detail
......@@ -145,14 +148,16 @@ namespace ngraph
for (const auto& length_part : length_parts)
{
std::size_t end_index{start_index + length_part};
outputs.push_back(
detail::make_ng_slice(input, {axis_to_split}, {start_index}, {end_index}));
outputs.push_back(detail::make_ng_slice(
input, {axis_to_split}, {start_index}, {end_index}));
start_index = end_index;
}
return outputs;
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/split.hpp
View file @ 6ca1a511
......@@ -25,9 +25,14 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector split(const Node& node);
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/sqrt.hpp
View file @ 6ca1a511
......@@ -28,13 +28,17 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
inline NodeVector sqrt(const Node& node)
{
return {std::make_shared<ngraph::op::Sqrt>(node.get_ng_inputs().at(0))};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/squeeze.cpp
View file @ 6ca1a511
......@@ -29,6 +29,8 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector squeeze(const Node& node)
{
......@@ -60,7 +62,9 @@ namespace ngraph
return {std::make_shared<ngraph::op::Reshape>(data, input_order, data_shape)};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/squeeze.hpp
View file @ 6ca1a511
......@@ -25,9 +25,14 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector squeeze(const Node& node);
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/sub.hpp
View file @ 6ca1a511
......@@ -27,15 +27,20 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
inline NodeVector sub(const Node& node)
{
NodeVector ng_inputs{
numpy_style_broadcast_for_binary_operation(node.get_ng_inputs())};
return {std::make_shared<ngraph::op::Subtract>(ng_inputs.at(0), ng_inputs.at(1))};
return {
std::make_shared<ngraph::op::Subtract>(ng_inputs.at(0), ng_inputs.at(1))};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/sum.hpp
View file @ 6ca1a511
......@@ -27,13 +27,17 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
inline NodeVector sum(const Node& node)
{
return variadic::make_ng_variadic_op<ngraph::op::Add>(node);
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/tanh.hpp
View file @ 6ca1a511
......@@ -28,13 +28,17 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
inline NodeVector tanh(const Node& node)
{
return {std::make_shared<ngraph::op::Tanh>(node.get_ng_inputs().at(0))};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/thresholded_relu.cpp
View file @ 6ca1a511
......@@ -35,13 +35,16 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector thresholded_relu(const Node& node)
{
auto data = node.get_ng_inputs().at(0);
double alpha = node.get_attribute_value<double>("alpha", 1.0);
std::shared_ptr<ngraph::Node> alpha_node = std::make_shared<ngraph::op::Constant>(
std::shared_ptr<ngraph::Node> alpha_node =
std::make_shared<ngraph::op::Constant>(
data->get_element_type(), ngraph::Shape{}, std::vector<double>{alpha});
alpha_node = make_broadcast_node(alpha_node, data->get_shape());
......@@ -51,7 +54,9 @@ namespace ngraph
return {data * data_map};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/thresholded_relu.hpp
View file @ 6ca1a511
......@@ -25,9 +25,14 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector thresholded_relu(const Node& node);
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/transpose.cpp
View file @ 6ca1a511
......@@ -27,18 +27,23 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector transpose(const Node& node)
{
std::shared_ptr<ngraph::Node> data = node.get_ng_inputs().at(0);
auto permute_axes = node.get_attribute_value<std::vector<std::size_t>>("perm", {});
auto permute_axes =
node.get_attribute_value<std::vector<std::size_t>>("perm", {});
return {(permute_axes.empty()) ? reshape::transpose(data)
: reshape::reorder_axes(data, permute_axes)};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/transpose.hpp
View file @ 6ca1a511
......@@ -25,9 +25,14 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector transpose(const Node& node);
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/unsqueeze.cpp
View file @ 6ca1a511
......@@ -27,6 +27,8 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector unsqueeze(const Node& node)
{
......@@ -52,7 +54,9 @@ namespace ngraph
return {std::make_shared<ngraph::op::Reshape>(data, input_order, data_shape)};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/unsqueeze.hpp
View file @ 6ca1a511
......@@ -25,9 +25,14 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
NodeVector unsqueeze(const Node& node);
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
src/ngraph/frontend/onnx_import/op/xor.hpp
View file @ 6ca1a511
......@@ -29,6 +29,8 @@ namespace ngraph
namespace onnx_import
{
namespace op
{
namespace set_1
{
inline NodeVector logical_xor(const Node& node)
{
......@@ -43,7 +45,9 @@ namespace ngraph
std::make_shared<ngraph::op::And>(not_left, right))};
}
} // namespace op
} // namespace set_1
} //namespace op
} // namespace onnx_import
......
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