* Retire Reduce, ReduceWindow, SelectAndScatter

* Remove lingering AnyAllReplacement code

* Remove apparently-now-unused macro

* Remove lingering op/reduce.hpp includes

* Remove FunctionCall

* Update Python stuff to remove FunctionCall, Reduce

* Add Any/All tests to GPU manifest

* Remove deleted .hpp #include from gpu_compiled_function.cpp

* Restore reduce_function.cpp since that is where the All/Any implementations ended up residing

* Add reduce_function.cpp back into CMakeLists.txt

* Remove #include of deleted reduce.hpp

* Re-remove AnyAllReplacement from GPU passes

* Remove deleted #includes from gpu_internal_function.cpp

* Remove git conflict stuff (whoops)

* Add newline at EOF, per review comment

* Address flake8 complaint about unused import

* update licenses for 2019

* style

* make GOE extend from util::Op

* fix build breaks

* change compile call to return Handle

* make CPU require compile() before call()

* fix unit tests to call compile() before call()

* fix failing ops

* update unit test

* revert some changes

* more fixups

* more diff cleanup

* a few more issues addressed

* more fixes

* update API

* more updates

* fix test_ops.py

* fix

* another attempt to fix

* fix unit test

* fix test error

This reverts commit c5b082c6.

* onnx: return signle nGraph function with multiple outputs
Signed-off-by: Artur Wojcik <artur.wojcik@intel.com>

* onnx: remove excessive code
Signed-off-by: Artur Wojcik <artur.wojcik@intel.com>

* Update ngraph python unit tests

* Fix batchnorm argument order, cleanup some comments, fix backprop

* Merge error

* Clean up training function, organize inference test

* BatchNormInference tests

* Training case

* Training test

* Fix autodiff BatchNorm test

* Cleanup

* Move file to doc checkout

* Update disabled test name in igpu manifest
Fix unnused variable

* Unit tests disables

* Review comments

* remove forward op

* fix bbrks

* fix pybind c-tor for max_pool_bprop

* Add new c-tor to MaxPoolBackprop that takes MaxPool as an explicit argument. Add serializer support.

* Add nvgpu support for new backward pooling c-tor, and calculate fprop when it isn't available.

* Add extra layout for 3 arg maxpool backprop.

* Formatting.

* cpu_workspace_insertion to expect 3-arg maxpool bprop

* GPU: add bprop_needs_pooling flag to primitive hash

* Update INTELGPU arguments_check for MaxPoolBackprop and GPU invocation for avg pool.

* Move ParameterVector and ResultVector to the ngraph namespace where they belong

* update python wrapper

* more python fixes

* style

* Update setup.py

* fix some new code

* onnx: enable external weights to enable Caffe2 support
Signed-off-by: Artur Wojcik <artur.wojcik@intel.com>

* onnx: update ONNX importer interface documentation
Signed-off-by: Artur Wojcik <artur.wojcik@intel.com>

* onnx: after review updates
Signed-off-by: Artur Wojcik <artur.wojcik@intel.com>

* create unique_ptr backend

* unit test cleanup

* address more code that was recently added

* change from reference to pointer when passing backend to reduce the number of lines changed.

* fix build error

* fix python wrapper

* style

* more specific treatment for unique_ptr

* split bn into bn_inference bn_training

* fix warnings

* Add GPU support for the new BN ops (#1569)

* Add GPU support and change batchnorm_globalstats test to use BNInference.

* Changed test back to using BNTraining for global stats and updated cudnn backend to account for it.

* Fix issues in merge with master.

* Formatting.

* CPU fixes

* remove 5-arg training BN for now

* more fixes

* python batchnorm changes

* fix onnx_import

* fix a call BatchNormInference c-tor

* yet another fix to BatchNormInference c-tor

* AND yet another fix to batchnorm_inference c-tor

* ops.py

* address adam's feedback

* Remove unnecessary parameter/argument.

* remove batch_norm_training_relu_with_global_stats

* remove bn_relu (training)

Add support for an environment variable `NGRAPH_ONNX_IMPORT_ENABLE` to setup.py
This variable controls whether the Python API is build with or without support for ONNX import functionality built into core nGraph.

* rename files

* rename runtime TensorView to Tensor

* rename HostTensorView to HostTensor

* remove get_tensor method

* remove get_element_count as it is redundant to get_size

* fix a few issues with examples and python wrapper

* rename get_size to get_element_count and add get_size_in_bytes method

* Implement TopK.

* Update python wrappers for TopK, ArgMin and ArgMax.

* Address some reviewer comments.

* Add type property check tests for TopK.
Set correct TopK behavior for K==0.

* TopK: Add 1d and 3d unit tests.

* Address more reviewer comments.

* Apply code style.

* Validate/infer types as a virtual function
BroadcastLike, ScalarConstantLike
Set output shape separately from output types
Remove some obsolete tests
Replace *Like ops with fixed type versions

* Review comments

* Remove TensorViewType

* Remove PrimaryTensorView

* Reviewer comments

* Style fix.

* Another try at GPU

* GPU

* Simplify class hierarchy

* More of previous.

* Better errors

* Remove unneeded classes

* Merge

* Update Python bindings

* clang-format

* Remove redundant files

* Remove commented out lines

* clang-format

* Group includes

* Missing tests

* Merge error

* Expose onnx_import C++ interface in Python API.

* Pretty printing.

* Add Computation interface using Ngraph Function objects plus UT.

* Apply code format.

* Remove unnecessary stream open mode.

- Code formatting.

* Fix onnx_import submodule visibility.

- Folder restructurization.

* Fix some small errors.

- Wrong function type annotations.
- Class doc.
- Code formatting.
- Class inheritance from object.

* Use modified Runtime class interface.

* Add model for test_onnx_import.

* Revert back to old API.

- Use of Function object in Computation class.

* Use of previous verions API.

* Small refactoring

* Code cleanup

* use line comments instead of multiline comments for license header

* update more

* update new files

* more header updates

* style

* normalize comments

* address review comments

* [Py] Wrapper for LRN

* Add missing header

* Add default param values, docs and some unit tests

* Fixes

* clang-format

* [Py] Add __repr__ to Strides and CoordDiff

* Apply clang-format

* Repr fix

* Apply clang-format

* Enable retrieving data from Constant in python.

* Test on wide value range.

* [Py] Use get_friendly_name for object names

* Move runtime::Manager functionality into runtime::Backend

* Remove unused files

* remove obsolete function

* remove obsolete

* change to use new Backend API

* rename parameter

* remove deprecated

* remove all legacy Backend API usage

remove deprecated files

* pull in changes from master

* fix GPU calls

* disable tests in convolution generator

* update per PR comments. Enable performance counter feature.

* update per PR comments

* fix build error

* fix conditionally compiled test :(

* Enable BatchNorm op

* Enable function call op

* Enable get output element op

- Enable Padding op
- Supress multiline comment warning
- improve tox configuration

* Update avg_pool signature to be consistent with ngraph AvgPool.

- Change parameters order and use more appropriate paramter names.
- Add docstring.

* Single file with test for pooling operations.

- Add test for avg_pool for 2D case.

* Code refactoring.

- Rename parameters to be more verbose.
- Change function return type, to enable general usage.

* Add UT for ceil, ceiling and abs.

* Update docstrings and type annotations.

* Add UT for broadcast operation.

* Add UT for concat operation.

- Minor change: add optional node name function parameter.

* Code formatting.

* UT for constant and convert operations.

- Move broadcast test to test_basic.py file.

* Update function signature.

- Update to be consistent with nGraph object API.

* Review fix.

- Update type annotations.
- Update docstring.
- Change local variables names to be consistent.

* Refactoring - put some commonly used functions into util.py.

* Update convolution operation signature.

- Add docstring
- Update to be consistent with respective nGraph object API.
- Formatting in UT.

* Fix import statements under py27.

* Correct quotation marks.

* Revert changes: use AxisSet as a return type.

* Review fix.

- Update parameter names to be consistent with respective parameters of nGraph object constructors.

* Review fix

- Set seed for random number generation to be repeatable.
- Use numpy.allclose.

* Change serialize routine

* Change serialize routine call for onnx

* Run clang-format on constant.cpp

* Update function type annotation.

* rename directories to be consistent
* rename reference namespace to match directory

* add python dir to check-code-format and re-run apply-code-format on it

* 0644 -> 0755