* Move Convolution and ConvolutionBackpropData ref impls into separate files.
* Add convolution unit tests.
* New convolution reference implementation.
* Remove unused convolution ref impl argument.
* Fix style.
* Revert "Remove unused convolution ref impl argument."
This reverts commit 739065d0d0.
* WA for arm-plugin: additional include with ConvolutionBackpropData.
* Style format in Convolution SLT CPU instantiation.
* Add 1D Convolution SLT CPU tests.
* Add Convolution Serialization SLT.
* Update source banners with 2021 date.
* Specification review.
* Readability improvement in padding detection.
* Refactoring regarding Tensor usage.
* Iteration over tensor slices made more readable.
* Code refactored to use only one convolution implementation.
3D convolution is used to compute also in 1D & 2D case (parameters,
inputs and filters shapes are adjusted accordingly).
* Removed Tensor abstraction.
* Name unnamed namespace as convolution_details.
* Refactoring: replaced std::next + negative index with std::prev.
* Specification refactoring.
* Revert "Name unnamed namespace as convolution_details."
This reverts commit cea526ec49.
* Added new convolution() overload.
* Fix legacy convolution() overload (needed for kmb-plugin).
* Reduced number of template type arguments in convolution ref impl.
* Added 'output' section in Convolution spec.
* Remove floating round type configuration.
* regionyolo do_softmax attribute
* add serialization single layer tests for normalizel2 and reshape
* add prelu sslt, change letter size in op name to align with MO
* add shufflechanels sslt, add workaround to serialize the op with proper opset number
* add broadcast sslt, change attribute string representations to lowercase
* add pad sslt, change attribute string representations to lowercase
* Unify sslt name prefixes
* add prelu name translation for serialization
* change expected type of regionyolo do_softmax attribute to bool
* transform autobcast type attr to lowercase, add unit test, add special opset mapping in serialization
* style fix
* fix indentation
* fix indentation 2
* Possibility of different opset assignment for different op versions
* Update header dates in modified files
* Match special opset to type_info_t instead of a string
* Adjust the comment to match the code
* Release mo dev guide refactoring (#3266)
* Updated MO extension guide
* Minor change and adding svg images
* Added additional information about operation extractors. Fixed links and markdown issues
* Added missing file with information about Caffe Python layers and image for MO transformations dependencies graph
* Added section with common graph transformations attributes and diagram with anchor transformations. Added list of available front phase transformations
* Added description of front-phase transformations except the scope-defined and points defined. Removed legacy document and examples for such transformations.
* Added sections about node name pattern defined front phase transformations. Copy-pasted the old one for the points defined front transformation
* Added description of the rest of front transformations and and all middle and back phase transformations
* Refactored Legacy_Mode_for_Caffe_Custom_Layers and updated the Customize_Model_Optimizer with information about extractors order
* Added TOC for the MO Dev guide document and updated SVG images with PNG ones
* Fixed broken link. Removed redundant image
* Fixed broken links
* Added information about attributes 'run_not_recursively', 'force_clean_up' and 'force_shape_inference' of the transformation
* Code review comments
* Added a section about `Port`s
* Extended Ports description with examples
* Added information about Connections
* Updated MO README.md and removed a lot of redundant and misleading information
* Updates to the Customize_Model_Optimizer.md
* More updates to the Customize_Model_Optimizer.md
* Final updates for the Customize_Model_Optimizer.md
* Fixed some broken links
* More fixed links
* Refactored Custom Layers Guide: removed legacy and incorrect text, added up-to-date.
* Draft implementation of the Custom layer guide example for the MO part
* Fixed broken links using #. Change layer->operation in extensibility documents
* Updated Custom operation guide with IE part
* Fixed broken links and minor updates to the Custom Operations Guide
* Updating links
* Layer->Operation
* Moved FFTOp implementation to the template extension
* Update the CMake for template_extension to build the FFT op conditionally
* Fixed template extension compilation
* Fixed CMake for template extension
* Fixed broken snippet
* Added mri_demo script and updated documentation
* One more compilation error fix
* Added missing header for a demo file
* Added reference to OpenCV
* Fixed unit test for the template extension
* Fixed typos in the template extension
* Fixed compilation of template extension for case when ONNX importer is disabled
Co-authored-by: Alexander Zhogov <alexander.zhogov@intel.com>
* Update the spec
* add unit-tests
* add avgPool unit-tests to CMakelist
* Remove second constructor and change the first one to take default values for rounding_type and pad_type
* add type_prop test for default values
* add 5d input single layer test instances
* add type_prop tests
* Require input to be 4D or 5D
* add validation check for pads size
* Update few tests to take 5D input instead of 6D
* Update validate_and_infer_types method
* Update infer_batched_pooling_forward and try_apply_auto_padding methods
* Update auto_padding_spatial_dims_dynamic type_prop test for binary_conv, conv, deformable_conv, group_conv and max_pool
* style-apply
* add validation check for kernel size
* add xfail for avgpool python backend test
* style-apply
* remove avgpool backend test from xfail list
* Update spec
* Allow the 3D input
* Update type_prop test with 3D input
* style-apply
* Remove xfail_issue_38709
* fix typo
* Update spec
* Update outputs section in spec
* Update spec
* fix typo
* clean file
* Update detailed description and fix xml examples
* fix exclude-type typo
* fix typo in outputs section
* Initial support of GatherElements in MO and nGraph
* apply_style
* added lost extractor for GatherElements
* Corrected GatherElements::validate_and_infer_types
* updated package_BOM.txt
* Type_t added
* started to implement ngraph shape_type_infer unit-tests
* finally implemented all ngraph shape_inference unit-tests
* updated Supported_Frameworks_Layers.md
* added correct handling of dynamic shapes in nGraph, added unit-tests for dynamic cases, fixed dump typos in MO, replaced axis type from int -> int64_t
* implemented shape infer for dynamic shapes with intervals
* finalized MO implementation
* applied comment from review
* style-apply
* spec correction
* removed conflict
* fixed typos
* removed obsolete comments form type_prop
* significant corrections in validate_and_infer_types
* style-apply
* data_rank check for axis
* Config for TF 2.0 Faster R-CNN models, refactored subgraph_between_nodes to use graph API
* Added support for new type of Preprocessing block in the TF 2.0 OD API models. Various fixes to enable the Faster R-CNN ResNet 50
* Updated text comments
* Fixed sub_graph_between_nodes for TensorIteratorMerge. Added support for the TF 2.X EfficientDet models (not yet reshape-able)
* Fixed unit tests
* Fixed regression for TF 1.X OD API SSD model, enabled TF 2.0 OD API SSD models
* Code clean up
* Switched TF 2.0 OD API Faster R-CNN to preprocessor replacement type 2
* Refactored ObjectDetectionAPIPreprocessorReplacement and ObjectDetectionAPIPreprocessor2Replacement
* Fixed bug in the Div transformation to Mul when input is integer.
* Added support for the TF 2.0 OD API Mask R-CNN
* Added unit tests for Div operation. Updated incorrectly modified mask_rcnn_support_api_v1.14.json
* Updated document with list of supported configuration files for TF OD API models
* Review comments
* Added tests for control flow edges for the sub_graph_between_nodes function
* Two more tests
* Fix missed/redundant attrs for some operations
* Align auto_pad attr values in spec
* Update MO IR Reader extenders for appropriate operations
* Allign auto_pad attr values for appropriate operations
* Remove changes in extenders
* Update backend_attrs for some operations
* Changes in shape_infer functions to correct work with explicit mode
* Apply offline comments
* Add spec for CTCGreedyDecoder
* Update spec
* Fix spec according to code rewiev
* Update spec
* Update spec
* Update spec according to review
* Update spec
* Update spec
* Update spec
* Update example spec
* Fix space in spec
* Fix spec
* Fix spec according to review
* fix spec
* update spec
* Update spec
* Change format outputs in spec
* Hot fix
* Minor fixes
* Add new attribute for op in spec
* change input
* Add precision to outputs
* Fix input in spec
* Update spec
* Update CTCGreedyDecoderSeqLen_6.md
fix mistakes
* Change first input layout
* fix example
Co-authored-by: Your Name <you@example.com>
* Fixed tests compilation for Android ARM
* Small fixes
* Fixed issues CVS-44775, CVS-34206, CVS-34349
* Disabled KSO tests for Template
* Eliminated invalid subgraphs
* Enabled KSO QueryNetwork tests for Template
* Fixed other plugins as well
* Used NodeTypeInfo instead of std::string
Co-authored-by: apankratovantonp <anton.pankratov@intel.com>
* Revice DetectionOutput reference implementation
Ticket: 37433
* fix test_create_op
* fix test_dyn_attributes
* apply code format
* fix crash on Windows when variance_encoded_in_target == 1
* add more checks for DetectionOutput inputs
* Fix single layer tests
* apply code format
* fix ssd_vgg16_300 inference with batch size > 1
* update types in docs
* fix crash on windows
* apply code style
* fix python tests
* fix setting output type
* change False to false and True to true in docs
* Allow prior boxes to have different type than box logits
Some models work that way
* simplify output shape calculation
* fixes to docs
desired format
changed InferRequestInternal:
- added _deviceInputs member to store plugin desired perprocessing
targets
- added default argument to preProcessingRequired to describe plugin
specific desired preprocessing target
- SetBlob and GetBlob to deal with plugin desired preprocessing targets
(_deviceInputs)
- added addInputPreProcessingFor helper method to avoid code
duplication
changed TEMPLATE plugin to use new functionality:
- removed explicit presicion conversion (to use built-in one of
InferRequestInternal)
- _networkInputBlobs to use InferRequestInternal::_deviceInputs
changed PreprocessingPrecisionConvertTest:
- to force output precision to be same as input (and not FP32 always)
changed TEMPLATE plugin to allow U8 outputs
Anton Pankratv