DNN(ONNX): Enabled several OpenCL conformance tests #26053
The tests also work in 5.x
### 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
- [ ] 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
Einsum buffer allocation fix#26059
This PR fixed buffer allocation issue in Einsum layer that causes segmentation fault on 32bit platforms. Related issue #26008
### 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
Split Javascript white-list to support contrib modules #25986
Single whitelist converted to several per-module json files. They are concatenated automatically and can be overriden by user config.
Related to https://github.com/opencv/opencv/pull/25656
### 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
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
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
### 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
[GSoC] dnn: Blockwise quantization support #25644
This PR introduces blockwise quantization in DNN allowing the parsing of ONNX models quantized in blockwise style. In particular it modifies the `Quantize` and `Dequantize` operations. The related PR opencv/opencv_extra#1181 contains the test data.
Additional notes:
- The original quantization issue has been fixed. Previously, for 1D scale and zero-point, the operation applied was $y = int8(x/s - z)$ instead of $y = int8(x/s + z)$. Note that the operation was already correctly implemented when the scale and zero-point were scalars. The previous implementation failed the ONNX test cases, but now all have passed successfully. [Reference](https://github.com/onnx/onnx/blob/main/docs/Operators.md#QuantizeLinear)
- the function `block_repeat` broadcasts scale and zero-point to the input shape. It repeats all the elements of a given axis n times. This function generalizes the behavior of `repeat` from the core module which is defined just for 2 axis assuming `Mat` has 2 dimensions. If appropriate and useful, you might consider moving `block_repeat` to the core module.
- Now, the scale and zero-point can be taken as layer inputs. This increases the ONNX layers' coverage and enables us to run the ONNX test cases (previously disabled) being fully compliant with ONNX standards. Since they are now supported, I have enabled the test cases for: `test_dequantizelinear`, `test_dequantizelinear_axis`, `test_dequantizelinear_blocked`, `test_quantizelinear`, `test_quantizelinear_axis`, `test_quantizelinear_blocked` just in CPU backend. All of them pass successfully.
### 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
dnn: optimize activations with v_exp #25881
Merge with https://github.com/opencv/opencv_extra/pull/1191.
This PR optimizes the following activations:
- [x] Swish
- [x] Mish
- [x] Elu
- [x] Celu
- [x] Selu
- [x] HardSwish
### Performance (Updated on 2024-07-18)
#### AmLogic A311D2 (ARM Cortex A73 + A53)
```
Geometric mean (ms)
Name of Test activations activations.patch activations.patch
vs
activations
(x-factor)
Celu::Layer_Elementwise::OCV/CPU 115.859 27.930 4.15
Elu::Layer_Elementwise::OCV/CPU 27.846 27.003 1.03
Gelu::Layer_Elementwise::OCV/CPU 0.657 0.602 1.09
HardSwish::Layer_Elementwise::OCV/CPU 31.885 6.781 4.70
Mish::Layer_Elementwise::OCV/CPU 35.729 32.089 1.11
Selu::Layer_Elementwise::OCV/CPU 61.955 27.850 2.22
Swish::Layer_Elementwise::OCV/CPU 30.819 26.688 1.15
```
#### Apple M1
```
Geometric mean (ms)
Name of Test activations activations.patch activations.patch
vs
activations
(x-factor)
Celu::Layer_Elementwise::OCV/CPU 16.184 2.118 7.64
Celu::Layer_Elementwise::OCV/CPU_FP16 16.280 2.123 7.67
Elu::Layer_Elementwise::OCV/CPU 9.123 1.878 4.86
Elu::Layer_Elementwise::OCV/CPU_FP16 9.085 1.897 4.79
Gelu::Layer_Elementwise::OCV/CPU 0.089 0.081 1.11
Gelu::Layer_Elementwise::OCV/CPU_FP16 0.086 0.074 1.17
HardSwish::Layer_Elementwise::OCV/CPU 1.560 1.555 1.00
HardSwish::Layer_Elementwise::OCV/CPU_FP16 1.536 1.523 1.01
Mish::Layer_Elementwise::OCV/CPU 6.077 2.476 2.45
Mish::Layer_Elementwise::OCV/CPU_FP16 5.990 2.496 2.40
Selu::Layer_Elementwise::OCV/CPU 11.351 1.976 5.74
Selu::Layer_Elementwise::OCV/CPU_FP16 11.533 1.985 5.81
Swish::Layer_Elementwise::OCV/CPU 4.687 1.890 2.48
Swish::Layer_Elementwise::OCV/CPU_FP16 4.715 1.873 2.52
```
#### Intel i7-12700K
```
Geometric mean (ms)
Name of Test activations activations.patch activations.patch
vs
activations
(x-factor)
Celu::Layer_Elementwise::OCV/CPU 17.106 3.560 4.81
Elu::Layer_Elementwise::OCV/CPU 5.064 3.478 1.46
Gelu::Layer_Elementwise::OCV/CPU 0.036 0.035 1.04
HardSwish::Layer_Elementwise::OCV/CPU 2.914 2.893 1.01
Mish::Layer_Elementwise::OCV/CPU 3.820 3.529 1.08
Selu::Layer_Elementwise::OCV/CPU 10.799 3.593 3.01
Swish::Layer_Elementwise::OCV/CPU 3.651 3.473 1.05
```
### 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
Upgrade RISC-V Vector intrinsic and cleanup the obsolete RVV backend. #25883
This patch upgrade RISC-V Vector intrinsic from `v0.10` to `v0.12`/`v1.0`:
- Update cmake check and options;
- Upgrade RVV implement for Universal Intrinsic;
- Upgrade RVV optimized DNN kernel.
- Cleanup the obsolete RVV backend (`intrin_rvv.hpp`) and compatable header file.
With this patch, RVV backend require Clang 17+ or GCC 14+ (which means `__riscv_v_intrinsic >= 12000`, see https://godbolt.org/z/es7ncETE3)
This patch is test with Clang 17.0.6 (require extra `-DWITH_PNG=OFF` due to ICE), Clang 18.1.8 and GCC 14.1.0 on QEMU and k230 (with `--gtest_filter="*hal_*"`).
### 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
- [ ] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
code clean #25931
Align code and remove redundant CMake code
### 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
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
* 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
- [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
python: attempts to fix 3d mat parsing problem for dnn #25810
Fixes https://github.com/opencv/opencv/issues/25762https://github.com/opencv/opencv/issues/23242
Relates https://github.com/opencv/opencv/issues/25763https://github.com/opencv/opencv/issues/19091
Although `cv.Mat` has already been introduced to workaround this problem, people do not know it and it kind of leads to confusion with `numpy.array`. This patch adds a "switch" to turn off the auto multichannel feature when the API is from cv::dnn::Net (more specifically, `setInput`) and the parameter is of type `Mat`. This patch only leads to changes of three places in `pyopencv_generated_types_content.h`:
```.diff
static PyObject* pyopencv_cv_dnn_dnn_Net_setInput(PyObject* self, PyObject* py_args, PyObject* kw)
{
...
- pyopencv_to_safe(pyobj_blob, blob, ArgInfo("blob", 0)) &&
+ pyopencv_to_safe(pyobj_blob, blob, ArgInfo("blob", 8)) &&
...
}
// I guess we also need to change this as one-channel blob is expected for param
static PyObject* pyopencv_cv_dnn_dnn_Net_setParam(PyObject* self, PyObject* py_args, PyObject* kw)
{
...
- pyopencv_to_safe(pyobj_blob, blob, ArgInfo("blob", 0)) )
+ pyopencv_to_safe(pyobj_blob, blob, ArgInfo("blob", 8)) )
...
- pyopencv_to_safe(pyobj_blob, blob, ArgInfo("blob", 0)) )
+ pyopencv_to_safe(pyobj_blob, blob, ArgInfo("blob", 8)) )
...
}
```
Others are unchanged, e.g. `dnn_SegmentationModel` and stuff like that.
### 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
dnn: parallelize nary elementwise forward implementation & enable related conformance tests #25630
This PR introduces the following changes:
- [x] Parallelize binary forward impl
- [x] Parallelize ternary forward impl (Where)
- [x] Parallelize nary (Operator that can take >=1 operands)
- [x] Enable conformance tests if workable
## Performance
### i7-12700K, RAM 64GB, Ubuntu 22.04
```
Geometric mean (ms)
Name of Test opencv opencv opencv
perf perf perf
core.x64.0606 core.x64.0606 core.x64.0606
vs
opencv
perf
core.x64.0606
(x-factor)
NCHW_C_sum::Layer_NaryEltwise::OCV/CPU 16.116 11.161 1.44
NCHW_NCHW_add::Layer_NaryEltwise::OCV/CPU 17.469 11.446 1.53
NCHW_NCHW_div::Layer_NaryEltwise::OCV/CPU 17.531 11.469 1.53
NCHW_NCHW_equal::Layer_NaryEltwise::OCV/CPU 28.653 13.682 2.09
NCHW_NCHW_greater::Layer_NaryEltwise::OCV/CPU 21.899 13.422 1.63
NCHW_NCHW_less::Layer_NaryEltwise::OCV/CPU 21.738 13.185 1.65
NCHW_NCHW_max::Layer_NaryEltwise::OCV/CPU 16.172 11.473 1.41
NCHW_NCHW_mean::Layer_NaryEltwise::OCV/CPU 16.309 11.565 1.41
NCHW_NCHW_min::Layer_NaryEltwise::OCV/CPU 16.166 11.454 1.41
NCHW_NCHW_mul::Layer_NaryEltwise::OCV/CPU 16.157 11.443 1.41
NCHW_NCHW_pow::Layer_NaryEltwise::OCV/CPU 163.459 15.234 10.73
NCHW_NCHW_ref_div::Layer_NaryEltwise::OCV/CPU 10.880 10.868 1.00
NCHW_NCHW_ref_max::Layer_NaryEltwise::OCV/CPU 10.947 11.058 0.99
NCHW_NCHW_ref_min::Layer_NaryEltwise::OCV/CPU 10.948 10.910 1.00
NCHW_NCHW_ref_mul::Layer_NaryEltwise::OCV/CPU 10.874 10.871 1.00
NCHW_NCHW_ref_sum::Layer_NaryEltwise::OCV/CPU 10.971 10.920 1.00
NCHW_NCHW_sub::Layer_NaryEltwise::OCV/CPU 17.546 11.462 1.53
NCHW_NCHW_sum::Layer_NaryEltwise::OCV/CPU 16.175 11.475 1.41
NHWC_C::Layer_NaryEltwise::OCV/CPU 11.339 11.333 1.00
NHWC_H::Layer_NaryEltwise::OCV/CPU 16.154 11.102 1.46
```
### Apple M1, RAM 16GB, macOS 14.4.1
```
Geometric mean (ms)
Name of Test opencv opencv opencv
perf perf perf
core.m1.0606 core.m1.0606.patch core.m1.0606.patch
vs
opencv
perf
core.m1.0606
(x-factor)
NCHW_C_sum::Layer_NaryEltwise::OCV/CPU 28.418 3.768 7.54
NCHW_NCHW_add::Layer_NaryEltwise::OCV/CPU 6.942 5.679 1.22
NCHW_NCHW_div::Layer_NaryEltwise::OCV/CPU 5.822 5.653 1.03
NCHW_NCHW_equal::Layer_NaryEltwise::OCV/CPU 5.751 5.628 1.02
NCHW_NCHW_greater::Layer_NaryEltwise::OCV/CPU 5.797 5.599 1.04
NCHW_NCHW_less::Layer_NaryEltwise::OCV/CPU 7.272 5.578 1.30
NCHW_NCHW_max::Layer_NaryEltwise::OCV/CPU 5.777 5.562 1.04
NCHW_NCHW_mean::Layer_NaryEltwise::OCV/CPU 5.819 5.559 1.05
NCHW_NCHW_min::Layer_NaryEltwise::OCV/CPU 5.830 5.574 1.05
NCHW_NCHW_mul::Layer_NaryEltwise::OCV/CPU 5.759 5.567 1.03
NCHW_NCHW_pow::Layer_NaryEltwise::OCV/CPU 342.260 74.655 4.58
NCHW_NCHW_ref_div::Layer_NaryEltwise::OCV/CPU 8.338 8.280 1.01
NCHW_NCHW_ref_max::Layer_NaryEltwise::OCV/CPU 8.359 8.309 1.01
NCHW_NCHW_ref_min::Layer_NaryEltwise::OCV/CPU 8.412 8.295 1.01
NCHW_NCHW_ref_mul::Layer_NaryEltwise::OCV/CPU 8.380 8.297 1.01
NCHW_NCHW_ref_sum::Layer_NaryEltwise::OCV/CPU 8.356 8.323 1.00
NCHW_NCHW_sub::Layer_NaryEltwise::OCV/CPU 6.818 5.561 1.23
NCHW_NCHW_sum::Layer_NaryEltwise::OCV/CPU 5.805 5.570 1.04
NHWC_C::Layer_NaryEltwise::OCV/CPU 3.834 4.817 0.80
NHWC_H::Layer_NaryEltwise::OCV/CPU 28.402 3.771 7.53
```
### 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
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
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
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
Add support for v_exp (exponential) #24941
This PR aims to implement `v_exp(v_float16 x)`, `v_exp(v_float32 x)` and `v_exp(v_float64 x)`.
### 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
- [ ] 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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dnn: add DepthToSpace and SpaceToDepth #25779
We are working on updating WeChat QRCode module. One of the new models is a fully convolutional model and hence it should be able to run with different input shapes. However, it has an operator `DepthToSpace`, which is parsed as a subgraph of `Reshape -> Permute -> Reshape` with a fixed shape getting during parsing. The subgraph itself is not a problem, but the true problem is the subgraph with a fixed input and output shape regardless input changes. This does not allow the model to run with different input shapes.
Solution is to add a dedicated layer for DepthtoSpace and SpaceToDepth.
Backend support:
- [x] CPU
- [x] CUDA
- [x] OpenCL
- [x] OpenVINO
- [x] CANN
- [x] TIMVX
- ~Vulkan~ (missing fundamental tools, like permutation and reshape)
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Suppress build warnings for GCC14 #25686Close#25674
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Support Global_Pool_2D ops in .tflite model #25613
### Pull Request Readiness Checklist
**Merge with extra**: https://github.com/opencv/opencv_extra/pull/1180
This PR adds support for `GlobalAveragePooling2D` and `GlobalMaxPool2D` on the TFlite backend. When the k`eep_dims` option is enabled, the output is a 2D tensor, necessitating the inclusion of an additional flatten layer. Additionally, the names of these layers have been updated to match the output tensor names generated by `generate.py` from the opencv_extra repository.
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Slice layer parser fix to support empty input case #25660
This PR fixes Slice Layer's parser to handle empty input cases (cases with initializer)
It fixed the issue rased in #24838
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Refactor DNN module to build with cudnn 9 #25412
A lot of APIs that are currently being used in the dnn module have been removed in cudnn 9. They were deprecated in 8.
This PR updates said code accordingly to the newer API.
Some key notes:
1) This is my first PR. I am new to openCV.
2) `opencv_test_core` tests pass
3) On a 3080, cuda 12.4(should be irrelevant since I didn't build the `opencv_modules`, gcc 11.4, WSL 2.
4) For brevity I will avoid including macro code that will allow for older versions of cudnn to build.
I was unable to get the tests working for `opencv_test_dnn` and `opencv_perf_dnn`. The errors I get are of the following:
```
OpenCV tests: Can't find required data file: dnn/onnx/conformance/node/test_reduce_prod_default_axes_keepdims_example/model.onnx in function 'findData'
" thrown in the test body.
```
So before I spend more time investigating I was hoping to get a maintainer to point me in the right direction here. I would like to run these tests and confirm things are working as intended. I may have missed some details.
### Pull Request Readiness Checklist
relevant issue
(https://github.com/opencv/opencv/issues/24983
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Current net exporter `dump` and `dumpToFile` exports the network structure (and its params) to a .dot file which works with `graphviz`. This is hard to use and not friendly to new user. What's worse, the produced picture is not looking pretty.
dnn: better net exporter that works with netron #25582
This PR introduces new exporter `dumpToPbtxt` and uses this new exporter by default with environment variable `OPENCV_DNN_NETWORK_DUMP`. It mimics the string output of a onnx model but modified with dnn-specific changes, see below for an example.
## Usage
Call `cv::dnn::Net::dumpToPbtxt`:
```cpp
TEST(DumpNet, dumpToPbtxt) {
std::string path = "/path/to/model.onnx";
auto net = readNet(path);
Mat input(std::vector<int>{1, 3, 640, 480}, CV_32F);
net.setInput(input);
net.dumpToPbtxt("yunet.pbtxt");
}
```
Set `export OPENCV_DNN_NETWORK_DUMP=1`
```cpp
TEST(DumpNet, env) {
std::string path = "/path/to/model.onnx";
auto net = readNet(path);
Mat input(std::vector<int>{1, 3, 640, 480}, CV_32F);
net.setInput(input);
net.forward();
}
```
---
Note:
- `pbtxt` is registered as one of the ONNX model suffix in netron. So you can see `module: ai.onnx` and such in the model.
- We can get the string output of an ONNX model with the following script
```python
import onnx
net = onnx.load("/path/to/model.onnx")
net_str = str(net)
file = open("/path/to/model.pbtxt", "w")
file.write(net_str)
file.close()
```
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Support Transpose op in TFlite #25297
**Merge with extra**: https://github.com/opencv/opencv_extra/pull/1168
The purpose of this PR is to introduce support for the Transpose op in TFlite format and to add a shape comparison between the output tensors and the references. In some occasional cases, the shape of the output tensor is `[1,4,1,1]`, while the shape of the reference tensor is `[1,4]`. Consequently, the norm check incorrectly reports that the test has passed, as the residual is zero.
Below is a Python script for generating testing data. The generated data can be integrated into the repo `opencv_extra`.
```python
import numpy as np
import tensorflow as tf
PREFIX_TFL = '/path/to/opencv_extra/testdata/dnn/tflite/'
def generator(input_tensor, model, saved_name):
# convert keras model to .tflite format
converter = tf.lite.TFLiteConverter.from_keras_model(model)
#converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.optimizations = [None]
tflite_model = converter.convert()
with open(f'{PREFIX_TFL}/{saved_name}.tflite', 'wb') as f:
f.write(tflite_model)
# save the input tensor to .npy
if input_tensor.ndim == 4:
opencv_tensor = np.transpose(input_tensor, (0,3,1,2))
else:
opencv_tensor = input_tensor
opencv_tensor = np.copy(opencv_tensor, order='C').astype(np.float32)
np.save(f'{PREFIX_TFL}/{saved_name}_inp.npy', opencv_tensor)
# generate output tenosr and save it to .npy
mat_out = model(input_tensor).numpy()
mat_out = np.copy(mat_out, order='C').astype(np.float32)
if mat_out.ndim == 4:
mat_out = np.transpose(mat_out, (0,3,1,2))
interpreter = tf.lite.Interpreter(model_content=tflite_model)
out_name = interpreter.get_output_details()[0]['name']
np.save(f'{PREFIX_TFL}/{saved_name}_out_{out_name}.npy', mat_out)
def build_transpose():
model_name = "keras_permute"
mat_in = np.array([[[1,2,3], [4,5,6]]], dtype=np.float32)
model = tf.keras.Sequential()
model.add(tf.keras.Input(shape=(2,3)))
model.add(tf.keras.layers.Permute((2,1)))
model.summary()
generator(mat_in, model, model_name)
if __name__ == '__main__':
build_transpose()
```
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Remove dnn::layer::allocate in doc #25591
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Fixed OpenVINO gemm layer #25518
Fixed OpenVINO gemm layer
The problem was that our layer didn't properly handle all the possible gemm options in OpenVINO mode
Fixes#25472
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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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Rename remaining float16_t for future proof #25387
Resolves comment: https://github.com/opencv/opencv/pull/25217#discussion_r1547733187.
`std::float16_t` and `std::bfloat16_t` are introduced since c++23: https://en.cppreference.com/w/cpp/types/floating-point.
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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.
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Ownership check in TFLite importer #25312
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resolves https://github.com/opencv/opencv/issues/25310
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Optimize int8 layers in DNN modules by using RISC-V Vector intrinsic. #25230
This patch optimize 3 functions in the int8 layer by using RVV Native Intrinsic.
This patch was tested on QEMU using VLEN=128 and VLEN=256 on `./bin/opencv_test_dnn --gtest_filter="*Int8*"`;
On the real device (k230, VLEN=128), `EfficientDet_int8` in `opencv_perf_dnn` showed a performance improvement of 1.46x.
| Name of Test | Original | optimized | Speed-up |
| ------------------------------------------ | -------- | ---------- | -------- |
| EfficientDet_int8::DNNTestNetwork::OCV/CPU | 2843.467 | 1947.013 | 1.46 |
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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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dnn: avoid const layer forwarding in layer norm layer and attention layer #25238
While profiling ViTs with dnn, I found `ConstLayer` can take a proportion of the inference time, which is weird. This comes from the data copy during the inference of `ConstLayer`. There is a chance that we can improve the efficiency of data copying but the easiest and most convenient way is to avoid `ConstLayer`. This PR change the way how we handle constants in layer normalization layer and attention layer, which is storing in the layer blobs instead of making constant layers for them.
Checklists:
- [x] Backend compatibility in layer normalization layer.
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Maksim Shabunin
Alexander Smorkalov
alexlyulkov
Abduragim Shtanchaev
Alexander Smorkalov
Yuantao Feng
Yuantao Feng
ericmariasis
Aven
Daniele Affinita
HAN Liutong
zihaomu
Aliaksei Urbanski
Wanli
Dmitry Kurtaev
Kumataro
CNOCycle
Danial Javady
unknown
Laurent Berger
Alexander Lyulkov
ecchen
Susumu IINO