dnn: add ONNX TopK #23279
Merge with https://github.com/opencv/opencv_extra/pull/1200
Partially fixes#22890 and #20258
To-do:
- [x] TopK forward impl
- [x] add tests
- [x] support Opset 1 & 10 if possible
- [ ] ~Support other backends~ (TopK has two outputs, which is not supported by other backends, such as openvino)
Perf:
M1 (time in millisecond)
| input shape | axis | dnn | ort |
| --------------- | ---- | ---- | ---- |
| (1000, 100) | 0 | 1.68 | 4.07 |
| (1000, 100) K5 | 0 | 1.13 | 0.12 |
| (1000, 100) | 1 | 0.96 | 0.77 |
| (100, 100, 100) | 0 | 10.00 | 31.13 |
| (100, 100, 100) | 1 | 7.33 | 9.17 |
| (100, 100, 100) | 2 | 7.52 | 9.48 |
M2 (time in milisecond)
| input shape | axis | dnn | ort |
| --------------- | ---- | ---- | ---- |
| (1000, 100) | 0 | 0.76 | 2.44 |
| (1000, 100) K5 | 0 | 0.68 | 0.07 |
| (1000, 100) | 1 | 0.41 | 0.50 |
| (100, 100, 100) | 0 | 4.83 | 17.52|
| (100, 100, 100) | 1 | 3.60 | 5.08 |
| (100, 100, 100) | 2 | 3.73 | 5.10 |
ONNXRuntime performance testing script: https://gist.github.com/fengyuentau/a119f94fd16721ec9974b8c7b0a45d4c
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* added v_erf and implemented gelu acceleration via vectorization
* remove anonymous v_erf and use v_erf from intrin_math
* enable perf for ov and cuda backend
Merge pull request #25861 from Abdurrahheem:ash/torch-attention-export-fix-4x
Support for Unflatten operation requred by Attention layer - 4.x #25861
### Pull Request Readiness Checklist
All test data and models for PR are located [#1190](https://github.com/opencv/opencv_extra/pull/1190)
This PR fixes issue reised when importing batched vanilla `Attention` layer from `PyTorch` via ONNX. Currently batched version of `Attention` layer in PyTorch [has unflatten operation inside](e3b3431c42/torch/nn/functional.py (L5500C17-L5500C31)). `unflatten` operation causes issue in `reshape` layer (see the Reshape_2 in the graph below) due to incorrect output of `slice` layer. This PR particularly fixes `slice` and `concat` layers to handle `unflatten` operation.
<img width="673" alt="image" src="https://github.com/opencv/opencv/assets/44877829/5b612b31-657a-47f1-83a4-0ac35a950abd">
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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Add sample support of YOLOv9 and YOLOv10 in OpenCV #25794
This PR adds sample support of [`YOLOv9`](https://github.com/WongKinYiu/yolov9) and [`YOLOv10`](https://github.com/THU-MIG/yolov10/tree/main)) in OpenCV. Models for this test are located in this [PR](https://github.com/opencv/opencv_extra/pull/1186).
**Running YOLOv10 using OpenCV.**
1. In oder to run `YOLOv10` one needs to cut off postporcessing with dynamic shapes from torch and then convert it to ONNX. If someone is looking for ready solution, there is [this forked branch](https://github.com/Abdurrahheem/yolov10/tree/ash/opencv-export) from official YOLOv10. Particularty follow this proceduce.
```bash
git clone git@github.com:Abdurrahheem/yolov10.git
conda create -n yolov10 python=3.9
conda activate yolov10
pip install -r requirements.txt
python export_opencv.py --model=<model-name> --imgsz=<input-img-size>
```
By default `model="yolov10s"` and `imgsz=(480,640)`. This will generate file `yolov10s.onnx`, which can be use for inference in OpenCV
2. For inference part on OpenCV. one can use `yolo_detector.cpp` [sample](https://github.com/opencv/opencv/blob/4.x/samples/dnn/yolo_detector.cpp). If you have followed above exporting procedure, then you can use following command to run the model.
``` bash
build opencv from source
cd build
./bin/example_dnn_yolo_detector --model=<path-to-yolov10s.onnx-file> --yolo=yolov10 --width=640 --height=480 --input=<path-to-image> --scale=0.003921568627 --padvalue=114
```
If you do not specify `--input` argument, OpenCV will grab first camera that is avaliable on your platform.
For more deatils on how to run the `yolo_detector.cpp` file see this [guide](https://docs.opencv.org/4.x/da/d9d/tutorial_dnn_yolo.html#autotoc_md443)
**Running YOLOv9 using OpenCV**
1. Export model following [official guide](https://github.com/WongKinYiu/yolov9)of the YOLOv9 repository. Particularly you can do following for converting.
```bash
git clone https://github.com/WongKinYiu/yolov9.git
cd yolov9
conda create -n yolov9 python=3.9
conda activate yolov9
pip install -r requirements.txt
wget https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-t-converted.pt
python export.py --weights=./yolov9-t-converted.pt --include=onnx --img-size=(480,640)
```
This will generate <yolov9-t-converted.onnx> file.
2. Inference on OpenCV.
```bash
build opencv from source
cd build
./bin/example_dnn_yolo_detector --model=<path-to-yolov9-t-converted.onnx> --yolo=yolov9 --width=640 --height=480 --scale=0.003921568627 --padvalue=114 --path=<path-to-image>
```
### Pull Request Readiness Checklist
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Fixed ONNX range layer #25414
Partially address https://github.com/opencv/opencv/issues/25363
Fixed ONNX range layer. It should support any input type.
Added tests (extra [PR](https://github.com/opencv/opencv_extra/pull/1170))
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[BugFix] dnn (ONNX): Foce dropping constant inputs in parseClip if they are shared #25319
Resolves https://github.com/opencv/opencv/issues/25278
Merge with https://github.com/opencv/opencv_extra/pull/1165
In Gold-YOLO ,`Div` has a constant input `B=6` which is then parsed into a `Const` layer in the ONNX importer, but `Clip` also has the shared constant input `max=6` which is already a `Const` layer and then connected to `Elementwise` layer. This should not happen because in the `forward()` of `Elementwise` layer, the legacy code goes through and apply activation to each input. More details on https://github.com/opencv/opencv/issues/25278#issuecomment-2032199630.
### Pull Request Readiness Checklist
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Merge with https://github.com/opencv/opencv_extra/pull/1158
Todo:
- [x] Fix Attention pattern recognition.
- [x] Handle other backends.
Benchmark:
"VIT_B_32 OCV/CPU", M1, results in milliseconds.
| Model | 4.x | This PR |
| - | - | - |
| VIT_B_32 OCV/CPU | 87.66 | **83.83** |
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Release convolution weightsMat after usage #25181
### Pull Request Readiness Checklist
related (but not resolved): https://github.com/opencv/opencv/issues/24134
Minor memory footprint improvement. Also, adds a test for VmHWM.
RAM top memory usage (-230MB)
| YOLOv3 (237MB file) | 4.x | PR |
|---------------------|---------|---------|
| no winograd | 808 MB | 581 MB |
| winograd | 1985 MB | 1750 MB |
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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dnn: no layer norm fusion if axes.back() is not the axis of last dimension #24808
Merge with https://github.com/opencv/opencv_extra/pull/1137
Resolves https://github.com/opencv/opencv/issues/24797
### Pull Request Readiness Checklist
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dnn onnx: support constaint inputs in einsum importer #24753
Merge with https://github.com/opencv/opencv_extra/pull/1132.
Resolves https://github.com/opencv/opencv/issues/24697
Credits to @LaurentBerger.
---
This is a workaround. I suggest to get input shapes and calculate the output shapes in `getMemoryShapes` so as to keep the best compatibility. It is not always robust getting shapes during the importer stage and we should avoid that as much as possible.
### Pull Request Readiness Checklist
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dnn: add attention layer #24476Resolves#24609
Merge with: https://github.com/opencv/opencv_extra/pull/1128.
Attention operator spec from onnxruntime: https://github.com/microsoft/onnxruntime/blob/v1.16.1/docs/ContribOperators.md#com.microsoft.Attention.
TODO:
- [x] benchmark (before this PR vs. with this PR vs. ORT).
- [x] Layer fusion: Take care Slice with end=INT64_MAX.
- [x] Layer fusion: match more potential attention (VIT) patterns.
- [x] Single-head attention is supported.
- [x] Test AttentionSubgraph fusion.
- [x] Add acc tests for VIT_B_32 and VitTrack
- [x] Add perf tests for VIT_B_32 and VitTrack
## Benchmarks
Platform: Macbook Air M1.
### Attention Subgraph
Input scale: [1, 197, 768].
| | mean (ms) | median (ms) | min (ms) |
| ---------------------- | --------- | ----------- | -------- |
| w/ Attention (this PR) | 3.75 | 3.68 | 3.22 |
| w/o Attention | 9.06 | 9.01 | 8.24 |
| ORT (python) | 4.32 | 2.63 | 2.50 |
### ViTs
All data in millisecond (ms).
| ViTs | With Attention | Without Attention | ORT |
| -------- | -------------- | ----------------- | ------ |
| vit_b_16 | 302.77 | 365.35 | 109.70 |
| vit_b_32 | 89.92 | 116.22 | 30.36 |
| vit_l_16 | 1593.32 | 1730.74 | 419.92 |
| vit_l_32 | 468.11 | 577.41 | 134.12 |
| VitTrack | 3.80 | 3.87 | 2.25 |
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Add test for YoloX Yolo v6 and Yolo v8 #24611
This PR adds test for YOLOv6 model (which was absent before)
The onnx weights for the test are located in this PR [ #1126](https://github.com/opencv/opencv_extra/pull/1126)
### Pull Request Readiness Checklist
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Add yolov5n to tests #24553
### Pull Request Readiness Checklist
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* add Winograd FP16 implementation
* fixed dispatching of FP16 code paths in dnn; use dynamic dispatcher only when NEON_FP16 is enabled in the build and the feature is present in the host CPU at runtime
* fixed some warnings
* hopefully fixed winograd on x64 (and maybe other platforms)
---------
Co-authored-by: Vadim Pisarevsky <vadim.pisarevsky@gmail.com>
dnn test: move layer norm tests into conformance tests #24544
Merge with https://github.com/opencv/opencv_extra/pull/1122
## Motivation
Some ONNX operators, such as `LayerNormalization`, `BatchNormalization` and so on, produce outputs for training (mean, stdev). So they have reference outputs of conformance tests for those training outputs as well. However, when it comes to inference, we do not need and produce those outputs for training here in dnn. Hence, output size does not match if we use dnn to infer those conformance models. This has become the barrier if we want to test these operators using their conformance tests.
<!--
| Operator | Inference needed | Outputs (required - total) | Optional outputs for training? |
| ----------------------- | ----------------------------------- | -------------------------- | ------------------------------ |
| BatchNormalization | Yes | 1 - 3 | Yes |
| Dropout | Maybe, can be eliminated via fusion | 1 - 2 | Yes |
| GRU | Yes | 0 - 2 | No |
| LSTM | Yes | 0 - 3 | No |
| LayerNormalization | Yes | 1 - 3 | Yes |
| MaxPool | Yes | 1 - 2 | Yes |
| RNN | Yes | 0 - 2 | No |
| SoftmaxCrossEntropyLoss | No | 1 - 2 | -- |
-->
**I checked all ONNX operators with optional outputs. Turns out there are only `BatchNormalization`, `Dropout`, `LayerNormalization` and `MaxPool` has optional outputs for training. All except `LayerNormalization` have models set for training mode and eval mode. Blame ONNX for that.**
## Solution
In this pull request, we remove graph outputs if the graph looks like the following:
```
[X] [Scale] [Bias] [X] [Scale] [Bias]
\ | / this patch \ | /
LayerNormalization -----------> LayerNormalization
/ | \ |
[Y] [Mean] [Stdev] [Y]
```
We can update conformance tests and turn on some cases as well if extending to more layers.
Notes:
1. This workaround does not solve expanded function operators if they are fused into a single operator, such as `$onnx/onnx/backend/test/data/node/test_layer_normalization_2d_axis1_expanded`, but they can be run without fusion. Note that either dnn or onnxruntime does not fuse those expanded function operators.
### 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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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
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
---
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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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
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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
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.
- [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.
- [x] The feature is well documented and sample code can be built with the project CMake
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.
- [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.
- [x] The feature is well documented and sample code can be built with the project CMake
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
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.
- [x] The feature is well documented and sample code can be built with the project CMake
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
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.
- [x] The feature is well documented and sample code can be built with the project CMake
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)
### 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
- [ ] 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.
- [x] The feature is well documented and sample code can be built with the project CMake
