G-API: Introduce level optimization flag for ONNXRT backend #26293
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Handling I32/I64 data types in G-API ONNX back-end #25817
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Extending G-API onnx::Params to pass arbitrary session options #25791
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Port G-API ONNXRT backend into V2 API #25662
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G-API: Support CoreML Execution Providers for ONNXRT Backend #24068
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G-API: Support CUDA & TensoRT Execution Providers for ONNXRT Backend #24059
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G-API: Support DirectML Execution Provider for ONNXRT Backend #24045
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G-API: Support OpenVINO Execution Provider for ONNXRT Backend #24024
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G-API: Integration branch for ONNX & Python-related changes #23597
# Changes overview
## 1. Expose ONNX backend's Normalization and Mean-value parameters in Python
* Since Python G-API bindings rely on `Generic` infer to express Inference, the `Generic` specialization of `onnx::Params` was extended with new methods to control normalization (`/255`) and mean-value; these methods were exposed in the Python bindings
* Found some questionable parts in the existing API which I'd like to review/discuss (see comments)
UPD:
1. Thanks to @TolyaTalamanov normalization inconsistencies have been identified with `squeezenet1.0-9` ONNX model itself; tests using these model were updated to DISABLE normalization and NOT using mean/value.
2. Questionable parts were removed and tests still pass.
### Details (taken from @TolyaTalamanov's comment):
`squeezenet1.0.*onnx` - doesn't require scaling to [0,1] and mean/std because the weights of the first convolution already scaled. ONNX documentation is broken. So the correct approach to use this models is:
1. ONNX: apply preprocessing from the documentation: https://github.com/onnx/models/blob/main/vision/classification/imagenet_preprocess.py#L8-L44 but without normalization step:
```
# DON'T DO IT:
# mean_vec = np.array([0.485, 0.456, 0.406])
# stddev_vec = np.array([0.229, 0.224, 0.225])
# norm_img_data = np.zeros(img_data.shape).astype('float32')
# for i in range(img_data.shape[0]):
# norm_img_data[i,:,:] = (img_data[i,:,:]/255 - mean_vec[i]) / stddev_vec[i]
# # add batch channel
# norm_img_data = norm_img_data.reshape(1, 3, 224, 224).astype('float32')
# return norm_img_data
# INSTEAD
return img_data.reshape(1, 3, 224, 224)
```
2. G-API: Convert image from BGR to RGB and then pass to `apply` as-is with configuring parameters:
```
net = cv.gapi.onnx.params('squeezenet', model_filename)
net.cfgNormalize('data_0', False)
```
**Note**: Results might be difference because `G-API` doesn't apply central crop but just do resize to model resolution.
---
`squeezenet1.1.*onnx` - requires scaling to [0,1] and mean/std - onnx documentation is correct.
1. ONNX: apply preprocessing from the documentation: https://github.com/onnx/models/blob/main/vision/classification/imagenet_preprocess.py#L8-L44
2. G-API: Convert image from BGR to RGB and then pass to `apply` as-is with configuring parameters:
```
net = cv.gapi.onnx.params('squeezenet', model_filename)
net.cfgNormalize('data_0', True) // default
net.cfgMeanStd('data_0', [0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
```
**Note**: Results might be difference because `G-API` doesn't apply central crop but just do resize to model resolution.
## 2. Expose Fluid & kernel package-related functionality in Python
* `cv::gapi::combine()`
* `cv::GKernelPackage::size()` (mainly for testing purposes)
* `cv::gapi::imgproc::fluid::kernels()`
Added a test for the above.
## 3. Fixed issues with Python stateful kernel handling
Fixed error message when `outMeta()` of custom python operation fails.
## 4. Fixed various issues in Python tests
1. `test_gapi_streaming.py` - fixed behavior of Desync test to avoid sporadic issues
2. `test_gapi_infer_onnx.py` - fixed model lookup (it was still using the ONNX Zoo layout but was NOT using the proper env var we use to point to one).
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
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G-API: replace GAPI_Assert() with 'false' and '0' to GAPI_Error()
* gapi: GAPI_Error() macro
* gapi: replace GAPI_Assert() with 'false' and '0' to GAPI_Error()
* build: eliminate 'unreachable code' after CV_Error() (MSVC 2015)
* build: eliminate 'unreachable code' warning for MSVS 2015/2017
- observed in constructors stubs with throwing exception
G-API: ONNX. Adding INT64-32 conversion for output.
* Added int64 to 32 conversion
* Added warning
* Added type checks for all toCV
* Added type checks for tests
* Small fixes
* Const for fixture in test
* std::tuple if retutn value for toCV
* Mistake
* Changed toCV for tests
* Added Assert
* Fix for comments
* One conversion for ONNX and IE
* Clean up
* One more fix
* Added copyFromONNX
* Removed warning
* Apply review comments
G-API: ONNX. Support for networks with three dimensional input.
* Padding without tests
* Removed padding
* Some small fixes
* Added wstring_convert
* Alignment fix, m b
* Small fixes
* Moved include from onnx.hpp
G-API: Support GFrame for ONNX infer
* Added GFrame for ONNX
* Cut test
* Removed IE from assert
* Review comments
* Added const/bbot rstrt
* View instead unique_ptr in func. sig.
* Added extractMat function, ONNXCompiled contains exMat - cv::Mat with non processed input data
* Added meta check for inferList2
G-API: ONNX. Support tensor input for CNN with dynamic input
* Added support for dynamic input tensor, refactored one input/output tests
* Added multiple input/output fixture, test for mobilenet
* Removed whitespace
* Removed mistake in inferROI
* Small fixes
* One more fix
* Code cleanup
* Code cleanup X2
* bb rstrt
* Fix review comments
* One more fix review comments
* Mistake
* G-API: Introduce ONNX backend for Inference
- Basic operations are implemented (Infer, -ROI, -List, -List2);
- Implemented automatic preprocessing for ONNX models;
- Test suite is extended with `OPENCV_GAPI_ONNX_MODEL_PATH` env for test data
(test data is an ONNX Model Zoo repo snapshot);
- Fixed kernel lookup logic in core G-API:
- Lookup NN kernels not in the default package, but in the associated
backend's aux package. Now two NN backends can work in the same graph.
- Added Infer SSD demo and a combined ONNX/IE demo;
* G-API/ONNX: Fix some of CMake issues
Co-authored-by: Pashchenkov, Maxim <maxim.pashchenkov@intel.com>
Septimiu Neaga
lamiayous
Ujjayant Kadian
Anatoliy Talamanov
Dmitry Matveev
Dmitry Matveev
Alexander Alekhin
xiong-jie-y
luzpaz
Aleksei Trutnev
Maxim Pashchenkov