dnn: add shared fastNorm kernel for mvn, instance norm and layer norm #24409
Relates https://github.com/opencv/opencv/pull/24378#issuecomment-1756906570
TODO:
- [x] add fastNorm
- [x] refactor layer norm with fastNorm
- [x] refactor mvn with fastNorm
- [ ] add onnx mvn in importer (in a new PR?)
- [ ] refactor instance norm with fastNorm (in another PR https://github.com/opencv/opencv/pull/24378, need to merge this one first though)
### Pull Request Readiness Checklist
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Ellipses supported added for Einsum Layer #24322
This PR added addresses issues not covered in #24037. Namely these are:
Test case for this patch is in this PR [#1106](https://github.com/opencv/opencv_extra/pull/1106) in opencv extra
Added:
- [x] Broadcasting reduction "...ii ->...I"
- [x] Add lazy shape deduction. "...ij, ...jk->...ik"
Features to add:
- [ ] Add implicit output computation support. "bij,bjk ->" (output subscripts should be "bik")
- [ ] Add support for CUDA backend
- [ ] BatchWiseMultiply optimize
- [ ] Performance test
### Pull Request Readiness Checklist
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Native ONNX to Inference Engine backend #21066Resolves#21052
### Pull Request Readiness Checklist
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Supporting protobuf v22 and later(with abseil-cpp/C++17) #24372
fix https://github.com/opencv/opencv/issues/24369
related https://github.com/opencv/opencv/issues/23791
1. This patch supports external protobuf v22 and later, it required abseil-cpp and c++17.
Even if the built-in protobuf is upgraded to v22 or later,
the dependency on abseil-cpp and the requirement for C++17 will continue.
2. Some test for caffe required patched protobuf, so this patch disable them.
This patch is tested by following libraries.
- Protobuf: /usr/local/lib/libprotobuf.so (4.24.4)
- abseil-cpp: YES (20230125)
### Pull Request Readiness Checklist
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GSoC Add ONNX Support for GatherElements #24092
Merge with: https://github.com/opencv/opencv_extra/pull/1082
Adds support to the ONNX operator GatherElements [operator docs](https://github.com/onnx/onnx/blob/main/docs/Operators.md#GatherElements)
Added tests to opencv_extra at pull request https://github.com/opencv/opencv_extra/pull/1082
### Pull Request Readiness Checklist
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Fixed CumSum layer inplace flag #24367
When exclusive is false:
dst[i] = dst[i-1] + src[i]
When exclusive is true:
dst[i] = dst[i-1] + src[i-1]
So CumSum layer can be inplace only when exclusive flag is false.
* remove Conformance from test names
* integrate neon optimization into default
* quick fix: define CV_NEON_AARCH64 0 for non NEON platforms
* remove var batch that leads to memory leak
* put neon code back to fast_gemm_kernels.simd
* reorganize code to reduce duplicate code
Fixed CumSum dnn layer #24353Fixes#20110
The algorithm had several errors, so I rewrote it.
Also the layer didn't work with non constant axis tensor. Fixed it.
Enabled CumSum layer tests from ONNX conformance.
OpenVINO backend for INT8 models #23987
### Pull Request Readiness Checklist
TODO:
- [x] DetectionOutput layer (https://github.com/opencv/opencv/pull/24069)
- [x] Less FP32 fallbacks (i.e. Sigmoid, eltwise sum)
- [x] Accuracy, performance tests (https://github.com/opencv/opencv/pull/24039)
- [x] Single layer tests (convolution)
- [x] ~~Fixes for OpenVINO 2022.1 (https://pullrequest.opencv.org/buildbot/builders/precommit_custom_linux/builds/100334)~~
Performace results for object detection model `coco_efficientdet_lite0_v1_1.0_quant_2021_09_06.tflite`:
| backend | performance (median time) |
|---|---|
| OpenCV | 77.42ms |
| OpenVINO 2023.0 | 10.90ms |
CPU: `11th Gen Intel(R) Core(TM) i5-1135G7 @ 2.40GHz`
Serialized model per-layer stats (note that Convolution should use `*_I8` primitives if they are quantized correctly): https://gist.github.com/dkurt/7772bbf1907035441bb5454f19f0feef
---
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dnn: merge tests from test_halide_layers to test_backends #24283
Context: https://github.com/opencv/opencv/pull/24231#pullrequestreview-1628649980
### Pull Request Readiness Checklist
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Add Support for Einsum Layer #24037
### This PR adding support for [Einsum Layer](https://pytorch.org/docs/stable/generated/torch.einsum.html) (in progress).
This PR is currently not to be merged but only reviewed. Test cases are located in [#1090](https://github.com/opencv/opencv_extra/pull/1090)RP in OpenCV extra
**DONE**:
- [x] 2-5D GMM support added
- [x] Matrix transpose support added
- [x] Reduction type comupte 'ij->j'
- [x] 2nd shape computation - during forward
**Next PRs**:
- [ ] Broadcasting reduction "...ii ->...i"
- [ ] Add lazy shape deduction. "...ij, ...jk->...ik"
- [ ] Add implicit output computation support. "bij,bjk ->" (output subscripts should be "bik")
- [ ] Add support for CUDA backend
- [ ] BatchWiseMultiply optimize
**Later in 5.x version (requires support for 1D matrices)**:
- [ ] Add 1D vector multiplication support
- [ ] Inter product "i, i" (problems with 1D shapes)
### 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.
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* first commit
* turned C from input to constant; force C constant in impl; better handling 0d/1d cases
* integrate with gemm from ficus nn
* fix const inputs
* adjust threshold for int8 tryQuantize
* adjust threshold for int8 quantized 2
* support batched gemm and matmul; tune threshold for rcnn_ilsvrc13; update googlenet
* add gemm perf against innerproduct
* add perf tests for innerproduct with bias
* fix perf
* add memset
* renamings for next step
* add dedicated perf gemm
* add innerproduct in perf_gemm
* remove gemm and innerproduct perf tests from perf_layer
* add perf cases for vit sizes; prepack constants
* remove batched gemm; fix wrong trans; optimize KC
* remove prepacking for const A; several fixes for const B prepacking
* add todos and gemm expression
* add optimized branch for avx/avx2
* trigger build
* update macros and signature
* update signature
* fix macro
* fix bugs for neon aarch64 & x64
* add backends: cuda, cann, inf_ngraph and vkcom
* fix cuda backend
* test commit for cuda
* test cuda backend
* remove debug message from cuda backend
* use cpu dispatcher
* fix neon macro undef in dispatcher
* fix dispatcher
* fix inner kernel for neon aarch64
* fix compiling issue on armv7; try fixing accuracy issue on other platforms
* broadcast C with beta multiplied; improve func namings
* fix bug for avx and avx2
* put all platform-specific kernels in dispatcher
* fix typos
* attempt to fix compile issues on x64
* run old gemm when neon, avx, avx2 are all not available; add kernel for armv7 neon
* fix typo
* quick fix: add macros for pack4
* quick fix: use vmlaq_f32 for armv7
* quick fix for missing macro of fast gemm pack f32 4
* disable conformance tests when optimized branches are not supported
* disable perf tests when optimized branches are not supported
* decouple cv_try_neon and cv_neon_aarch64
* drop googlenet_2023; add fastGemmBatched
* fix step in fastGemmBatched
* cpu: fix initialization ofb; gpu: support batch
* quick followup fix for cuda
* add default kernels
* quick followup fix to avoid macro redef
* optmized kernels for lasx
* resolve mis-alignment; remove comments
* tune performance for x64 platform
* tune performance for neon aarch64
* tune for armv7
* comment time consuming tests
* quick follow-up fix
Use ngraph::Output in OpenVINO backend wrapper #24196
### Pull Request Readiness Checklist
resolves https://github.com/opencv/opencv/issues/24102
* Use `ngraph::Output<ngraph::Node>>` insead of `std::shared_ptr<ngraph::Node>` as a backend wrapper. It lets access to multi-output nodes: 588ddf1b18/modules/dnn/src/net_openvino.cpp (L501-L504)
* All layers can be customizable with OpenVINO >= 2022.1. nGraph reference code used for default layer implementation does not required CPU plugin also (might be tested by commenting CPU plugin at `/opt/intel/openvino/runtime/lib/intel64/plugins.xml`).
* Correct inference if only intermediate blobs requested.
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.
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OCL_FP16 MatMul with large batch
* Workaround FP16 MatMul with large batch
* Fix OCL reinitialization
* Higher thresholds for INT8 quantization
* Try fix gemm_buffer_NT for half (columns)
* Fix GEMM by rows
* Add batch dimension to InnerProduct layer test
* Fix Test_ONNX_conformance.Layer_Test/test_basic_conv_with_padding
* Batch 16
* Replace all vload4
* Version suffix for MobileNetSSD_deploy Caffe model
TFLite models on different backends (tests and improvements) #24039
### Pull Request Readiness Checklist
* MaxUnpooling with OpenVINO
* Fully connected with transposed inputs/weights with OpenVINO
* Enable backends tests for TFLite (related to https://github.com/opencv/opencv/issues/23992#issuecomment-1640691722)
* Increase existing tests thresholds
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.
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- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
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Patch to opencv_extra has the same branch name.
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Resolve uncovered CUDA dnn layer #24080
### Pull Request Readiness Checklist
* Gelu activation layer on CUDA
* Try to relax GEMM from ONNX
resolves https://github.com/opencv/opencv/issues/24064
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.
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Remove legacy nGraph logic #24072
### Pull Request Readiness Checklist
TODO:
- [x] Test with OpenVINO 2021.4 (tested locally)
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.
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DetectionOutput layer on OpenVINO without limitations #24069
### Pull Request Readiness Checklist
required for https://github.com/opencv/opencv/pull/23987
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PReLU with element-wise scales #24056
### Pull Request Readiness Checklist
resolves https://github.com/opencv/opencv/issues/24051
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[TFLite] Pack layer and other fixes for SSD from Keras #24004
### Pull Request Readiness Checklist
resolves https://github.com/opencv/opencv/issues/23992
**Merge with extra**: https://github.com/opencv/opencv_extra/pull/1076
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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Assertion Fix in Split Layer #23746
### Pull Request Readiness Checklist
This PR fixes issue mentioned in [#23663](https://github.com/opencv/opencv/issues/23663)
Merge with https://github.com/opencv/opencv_extra/pull/1067
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Support ONNX operator QLinearSoftmax in dnn #23655
Resolves https://github.com/opencv/opencv/issues/23636.
Merge with https://github.com/opencv/opencv_extra/pull/1064.
This PR maps the QLinearSoftmax (from com.microsoft domain) to SoftmaxInt8 in dnn along with some speed optimization.
Todo:
- [x] support QLinearSoftmax with opset = 13
- [x] add model and test data for QLinearSoftmax with opset = 13
- [x] ensure all models have dims >= 3.
- [x] add the script to generate model and test data
### Pull Request Readiness Checklist
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LSTM ONNX Layout Attribute Support #23614
### Explanation
This PR contains necessary changes to support `layout` attribute. This attributes is present in [ONNX](https://github.com/onnx/onnx/blob/main/docs/Operators.md#lstm) and [Torch](https://pytorch.org/docs/stable/generated/torch.nn.LSTM.html#lstm) (in touch it is name as `batch_first=True`) libraries. When `layout = 1` input to LSTM layer is expected to have batch dimension first -> `[batch_size, sequence_length, features]` vs `layout = 0` - default `[sequence_length, batch_size, features]`
### Test Data
Test data and data generator for PR located here [#1063](https://github.com/opencv/opencv_extra/pull/1063)
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Import and inference INT8 quantized TFLite model #23409
### Pull Request Readiness Checklist
* Support quantized TFLite models
* Enable fused activations (FP32, INT8)
**Merge with extra**: https://github.com/opencv/opencv_extra/pull/1048
on the image, green boxes are from TFLite and red boxes from OpenCV
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DNN: Add New API blobFromImageParam #22750
The purpose of this PR:
1. Add new API `blobFromImageParam` to extend `blobFromImage` API. It can support the different data layout (NCHW or NHWC), and letter_box.
2. ~~`blobFromImage` can output `CV_16F`~~
### Pull Request Readiness Checklist
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Added LSTM and GRU tests for various batch and input length sizes #23501
Added tests with various sequence length and batch sizes
Test data: https://github.com/opencv/opencv_extra/pull/1057
### Pull Request Readiness Checklist
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Propagate inputs info for ONNX and TFLite models
### Pull Request Readiness Checklist
Needed for generic applications such as benchmarking pipelines. So OpenCV can tell about the default input shapes specified in the models.
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Yuantao Feng
Abduragim Shtanchaev
andrewerf
Kumataro
Aser Atawya
alexlyulkov
Sean McBride
Dmitry Kurtaev
Alexander Smorkalov
CSBVision
Wanli
Zihao Mu
wanli
Abduragim Shtanchaev
