Update Python API to allow more flexibility for setting providers and provider options.
The providers argument (InferenceSession/TrainingSession constructors, InferenceSession.set_providers()) now also accepts a tuple of (name, options dict).
Fix get_available_providers() API (and the corresponding function in the C API) to return the providers in default priority order. Now it can be used as a starting point for the providers argument and maintain the default priority order.
Convert some usages of the deprecated global configuration functions to use EP-specific options instead.
Update some EP-specific option parsing to fail on unknown options.
Other clean up.
Provide a tool to convert Loop to Scan for Nuphar performance
Fix Nuphar CI pipeline failures.
Co-authored-by: liqun <liqun@OrtTrainingDev4.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
* merge master, keep postprocess status commit
* download float16.py everytime
* using variables to reference eps
* adding ACL EP to ep perf tool
* accuracy with absolute tolerance configurable
* add acl to dict + remove commented line
* Fix issue: https://github.com/microsoft/onnxruntime/issues/6094
Root cause: we didn't expose the OrtMemoryInfo for TRT, so it will cause issue if user want use IObinding for Tensorrt.
Short term fix, add the OrtMemoryInfo for TRT. Long term should unify the allocator for CUDA and TRT
* Enabling fasterrcnn variant and vehicle detector
* changes for 2021_2 branch
* yolov3_pytorch commit
* fixed braces in basic_backend.cc
* ci information added
* faster rcnn variant and vehicle detector changes were made in 2021.1 and not in 2021.2
* some changes to support unit tests
* disable some tests which are failing
* fix myriad tests for vehicle detector
* Did some cleanup
*cleaned up comments
*Disabled Add_Broadcast_0x1 and Add_Broadcast_1x0
tests on MYRIAD_FP16 backend due to a bug
*cleaned up capability_2021_2.cc file
*Removed extra conditions which were added
for some validation in backend_utils
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* yolov3 pytorch workaround to ensure that the output names are matched
* gemmoptest fixed on myriad
* Fixed MYRIADX CPP Test Failures
*Expand,GatherND,Range,Round op's
are only supported in model
*where op with float input data
types are not supported and fixed
*Scatter and ScatterElements op's with
negative axis are fixed
*Reshape op with 0 dim value are not
supported and fixed
*Disabled InstanceNorm_2 test on MYRIADX
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* make changes to yolov3 pytorch
* Fixed python unit tests
*Fixed failing python tests on vpu,
GPU and CPU
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fixes POW op failures on GPU_FP16
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Clean up capability_2021_2.cc
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Updated docx for MultiThreading option
*Added extra info on setting the num_of_threads
option using the API and it's actual usage
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* fixed slice and removed extra prints
* Disabled failing python tests
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Minor changes added in capabilty_2021_2
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* made changes to slice to avoid failures
* Disabling FP16 support for GPU_FP32
->Inferencing an FP16 model on GPU_FP32
leads to accuracy mismatches. so, we would
rather use GPU_FP16 to infer an FP16 model
on GPU Device
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Updated docx for Inferencing a FP16 Model
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* fix for mask rcnn
* Script for installing openvino from source
* Updated with openvino 2021.2 online installation
* code comment fixes
fixed accuracy mismatch for div
* Update OpenvinoEP-ExecutionProvider.md
updated for 2021.2 branch
* Update README.md
updated dockerfile documentation
* Update BUILD.md
build.md update documentation
* permissiong change of install_openvino.sh
* made changes to align with microsoft onnxruntime changes
* Updated with ov 2021.2.200
Co-authored-by: suryasidd <surya.siddharth.pemmaraju@intel.com>
Co-authored-by: sfatimar <sahar.fatima@intel/com>
Co-authored-by: MaajidKhan <n.maajidkhan@gmail.com>
Co-authored-by: mohdansx <mohdx.ansari@intel.com>
* Implement conversion from ortvalue to Itensor for string tensors and comparing sequence of maps of strings to floats
* PR comments
* Add ability to skip gpu tests according to adapter description
* spacing
* spacing
* spacing
* Initial implementation of generating calibration dynamic range table
* Initialize validation support for Quantization
* Initialize validation support for Quantization (cont.)
* Improve validation support for Quantization
* Improve validation support for Quantization
* Rewrite/Refine for calibration and validation
* Rewrite/Refine for calibration and validation (cont.)
* Refine code
* Refine code
* Add data reader for BERT
* Add flatbuffers to serialize calibration table
* Refine code and add BERT evaluation
* Refine the code
* minor modification
* Add preprocess/postprocess of vision team yolov3 and refine the code
* Update annotation
* Make bbox cooridates more accurate
* Fix bug
* Add support of batch processing
* Batch processing for model zoo yolov3
* Add batch inference for evaluation
* Refine the code
* Add README
* Add comments
* Refine the code for PR
* Remove batch support checking in data_reader and refine the code
* Refine the code for PR
* Refine the code for PR review
Co-authored-by: Olivia Jain <oljain@microsoft.com>
Going forward, a single unifed docker image will be published in
MCR. The hardware accelerator target choice will have to be made
in the application using OpenVINO EP's runtime config options.
* Create a helper for generating unique ids that can be used by an EP that creates compiled nodes and needs ids to be deterministic for a model when used in multiple sessions.
Added to IExecutionProvider as this can potentially be used by all compiling EPs and is more robust than a simplistic counter (although EP implementer is free to choose either approach).
* Restructure the helper so it can be called across the EP bridge.
Add ability to call id generation helper from EP bridge
- convert DNNL EP to use helper to validate
Address issue where a new Model may be loaded into the same address as a previous one.
- hash the bytes in the Graph instance (1728 bytes currently) to use as the key to the full hash for the model
Add lock around id generation to ensure no issues if multiple sessions partitions graphs at exactly the same time.
- Extremely unlikely but would be hard to debug and the locking cost is not an issue as it's only incurred during graph partitioning and not execution.
