4302e2c120
renamed logits -> bbox_deltas
updated ngraph unittests for Proposal
removed validate_and_infer_types Proposal-4
removed validate_and_infer_types Proposal-4
changed validate_and_infer_types in parent class of Proposal
removed get_output_size
successfully inferred Proposal on SSH and Faster-RCNN
added unittests for Proposal-4
added unittests for Proposal-4
added unittests for Proposal-4
returned back default namespace for Proposal
reduced number of outputs in v0::Proposal
correct conversion of Proposal-4 -> propodal_ie with 2 outputs
removed creator for proposal v0
removed converter for proposal v0
added Proposal-4 to MO
removed `for_deformable` attribute
added Proposal-4 to MO and nGraph Python API
removed typo in Proposal-4 specification
style corrections
style corrections and removed some redundant code
rename proposal Python api test
removed 'attrs' context from visitor
returned back AttrVisitor to check if passes OpenVINO ONNX pipeline
Should pass OpenVINO ONNX pipeline (returned back AttrVisitor just to check)
python api for Proposal-4 works ok
(style correction) python api for Proposal-4 works ok
parametrized proposal_ie some other corrections
removed 'attrs.' context from nGraph Python API tests for Proposal
minor corrections in replacer proposal->proposal_ie
corrected Python API OpenVINO-ONNX tests should pass
Improved workaround for AttributeVisitor for Proposal
Add additional check of im_info tensor shape to Proposal node in MKLDNNPlugin
😠 removed 4 extra spaces from test_dyn_attributes.py to match The Style
added new nGraph RTTI declarations, removed throwing exception in transformation
added new nGraph RTTI declarations, removed throwing exception in transformation, corrected exception in MKLDNNplugin
corrected im_info size checking in Proposal node of MKLDNNPlugin
OpenVINO™ Toolkit - Deep Learning Deployment Toolkit repository
This toolkit allows developers to deploy pre-trained deep learning models through a high-level C++ Inference Engine API integrated with application logic.
This open source version includes two components: namely Model Optimizer and Inference Engine, as well as CPU, GPU and heterogeneous plugins to accelerate deep learning inferencing on Intel® CPUs and Intel® Processor Graphics. It supports pre-trained models from the Open Model Zoo, along with 100+ open source and public models in popular formats such as Caffe*, TensorFlow*, MXNet* and ONNX*.
Repository components:
License
Deep Learning Deployment Toolkit is licensed under Apache License Version 2.0. By contributing to the project, you agree to the license and copyright terms therein and release your contribution under these terms.
Documentation
- OpenVINO™ Release Notes
- OpenVINO™ Inference Engine Build Instructions
- Get Started with Deep Learning Deployment Toolkit on Linux*
- Introduction to Deep Learning Deployment Toolkit
- Inference Engine Developer Guide
- Model Optimizer Developer Guide
How to Contribute
See CONTRIBUTING for contribution to the code. See CONTRIBUTING_DOCS for contribution to the documentation. Thank you!
Support
Please report questions, issues and suggestions using:
- The
openvinotag on StackOverflow* - GitHub* Issues
- Forum
* Other names and brands may be claimed as the property of others.