Fix flatten onnx axis==rank case #28812
fix ONNX Flatten layer incorrect output when axis equals input rank edge case
Resolves : https://github.com/opencv/opencv/pull/28781#discussion_r3060088247
OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1342
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Add Qwen2.5 tokenizer support for dnn #28781
OpenCV extra: https://github.com/opencv/opencv_extra/pull/1334
Extended the dnn tokenizer support to qwen2.5 tokenization.
- Add QWEN2_5 pre-tokenizer regex pattern to utils.hpp
- Generalised buildTokenizerGPT to buildTokenizerFromJson to handle gpt2/gpt4/ and qwen2.5
- Add qwen2/qwen2.5 model type support with special token handling
- Add Qwen2.5 tests
- Add end-to-end qwen_inference script for Qwen2.5 ONNX model
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Extended fusion for activation functions in new DNN engine #28750
After this fusion, we see following improvements in YOLO models:
| Model | Before (`ENGINE_NEW`) | After (`ENGINE_NEW`) | `ENGINE_ORT` | % Improvement (Before v/s After) |
| :--- | :--- | :--- | :--- | :--- |
| **YOLOv8n** | 18.89 ms| 12.06 ms| 12.15 ms| 36.16% |
| **YOLOv5n** | 17.12 ms| 9.29 ms| 9.23 ms| 45.73% |
| **YOLOX-S** | 38.78 ms| 25.56 ms| 25.16 ms| 34.09% |
Device details:
- Model name: Intel(R) Core(TM) i9-14900KS, x86, 32 Cores, ubuntu 24.04,
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Int8 block layout support #28741
After this patch we got the following speed ups on **resnet50-qdq.onnx** model.
- Inference time now: **_~6.6ms_** (inference time using onnxruntime is ~5.7ms).
- Inference time before: _**~11.5ms**_ [after QDQ PR #28595]
- Speed up: _**~42.6% or 1.74x**_
- Device details:
- Model name: Intel(R) Core(TM) i9-14900KS, x86, 32 Cores, ubuntu 24.04,
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Added OnnxRuntime GPU wrapper #28588
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GSoC 2025: Add Tokenizer Support to DNN Module #27534
merge with https://github.com/opencv/opencv_extra/pull/1276
### Summary
This pull request introduces initial support for a tokenizer module under `modules/dnn/src/tokenizer` as part of Google Summer of Code 2025 (Project: Tokenization for OpenCV DNN).
### Status
- [x] Project structure in place
- [x] Initial BPE tokenizer loading
- [x] Regex splitting (in progress)
- [x] Encoding logic for GPT-2 tokenizer (in progress)
- [ ] Documentation (to be improved)
### Goals
The goal is to support Hugging Face-compatible tokenization (e.g., GPT-2) natively in C++ to be integrated with DNN inference pipelines.
The core pipeline lives in `dnn/src/tokenizer/core_bpe.hpp` and `dnn/src/tokenizer/encoding.hpp`. For Unicode handling I’m using `dnn/src/tokenizer/unicode.hpp`, which is adapted from llama.cpp.
### Feedback
Please share early feedback on:
- General design structure
- Integration strategy with `dnn`
- Code organization or naming conventions
### Reference
Project: https://summerofcode.withgoogle.com/programs/2025/projects/79SW6eNK
Nms empty detection fix#28749
Requires opencv_extra: https://github.com/opencv/opencv_extra/pull/1330
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dnn: fix missing ONNX dispatch entries and RotaryEmbedding bugs #28714
- Wire RMSNormalization and RotaryEmbedding parsers into the ONNX dispatch map. Both parse functions were declared and implemented but never registered, causing the importer to fall through to the generic path and skip constant-folding of cos/sin caches.
- Fix NonMaxSuppression dispatch key typo ("NonMaxSuprression" -> "NonMaxSuppression") so the ONNX op name matches the registered layer class. Also fix the type string set inside the parser.
- Fix tautological self-comparison in RotaryEmbeddingLayer::getMemoryShapes (cos_cache_shape.dims == cos_cache_shape.dims -> sin_cache_shape.dims).
- Fix typo in RotaryEmbedding error message ("cos_cahe" -> "cos_cache").
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dnn: fix Resize initNgraph for two-input case #28724
## Summary
Fixes the issue #28707
When a Resize/Upsample layer has two inputs, the data tensor and a reference tensor whose **shape** defines the output spatial size, the OpenVINO/NGRAPH backend's `initNgraph()` was ignoring `nodes[1]` entirely and relying solely on the `outHeight`/`outWidth` member variables.
These variables are set by `finalize()` from the pre-computed output blob dimensions. However, when the output shape is determined dynamically at runtime from the second input, `finalize()` sets them from the live tensor, but the OpenVINO backend calls `initNgraph()` to build a static compiled graph. If the member variables are 0 at that point, the compiled `Interpolate` node gets hardcoded with `{0, 0}` output dimensions, causing CV_Assert failure: {N,C,0,0} vs {N,C,H2,W2}
Added conv 1x1 and conv 3x3 block layout support#28691
After this patch we get the following speed ups on resnet50.onnx:
Inference time after: ~7.6ms (inference time using onnxruntime: ~6.67ms)
Inference time before: ~14ms
Speed up: ~46% or 1.84x
Device details:
Model name: Intel(R) Core(TM) i9-14900KS
Cores: 32
RAM: 128 GB
Architecture: x86
OS: Ubuntu 24.04
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Added Output Tensor Names support in new DNN engine #28637
closes: https://github.com/opencv/opencv/issues/26201
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Fix kernel shape handling in ONNX importer2 #28646
Requires opencv_extra: https://github.com/opencv/opencv_extra/pull/1323
closes: https://github.com/opencv/opencv/issues/28321
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Added getFLOPS support in new DNN engine #28634
closes: https://github.com/opencv/opencv/issues/26199
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Block layout-based convolution in DNN #28585
merge together with https://github.com/opencv/opencv_extra/pull/1321
Some core parts of the new engine in DNN module have been revised substantially:
1. all tests seem to pass, except for `Test_Graph_Simplifier.ResizeSubgraph`, which has been disabled because it does not take the newly added `TransformLayoutLayer` into account. The test should be reworked perhaps.
1. convolution and related operations (maxpool/avgpool) now use so-called block layout (`DATA_LAYOUT_BLOCK`), where `NxCxHxW` tensors are represented as `NxC1xHxWxC0`, where `C1=(C + C0-1)/C0` and `C0` is a power-of-two (usually 4, 8, 16 or 32).
1. graph is now pre-processed and `TransformLayoutLayer` is inserted to convert data from NCHW or NHWC layout to the block layout or vice versa. The transformations are done in a lazy way only when they are really needed. For example, in the whole Resnet only 2 transformations are performed.
1. transformer-based models and other models that do not use convolutions will run as usual, without going to block layout.
1. there is yet another graph preprocessing stage added that embeds constant weights/scale and bias into convolution and batch norm layers.
1. 'batchnorm', 'activation' and 'adding a residual' are now fused with convolution, just like in the old engine. That brings some noticeable acceleration.
1. optimized convolution kernels have been added.
* depthwise convolution, as well as maxpool and avgpool support C0=4, 8, 16 etc. _as long as_ C0 is divisible by the number of fp32 lanes in a SIMD register of the target platform (e.g. on ARM with NEON there must be `C0 % 4 == 0`, on x64 with AVX2 `C0 % 8 == 0`).
* non-depthwise convolution only supports C0=8 for now. C0=8 seems to be a sweetspot for ARM with NEON, x64 with AVX2 or RISC-V with RVV (with 128- or 256-bit registers). For some platforms with dedicated matrix accelerators C0=16 or even C0=32 might be more efficient, but we could add the respective kernels later.
* only fp32 kernels have been added. fp16/bf16 kernels might be added a little later.
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CPU Kernels for fp32 KV Cache #28524
The kernels are:
- pagedAttnQKGemmKernel
- pagedAttnAVGemmKernel
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Fix gather cast axis #28633
Closes: https://github.com/opencv/opencv/issues/23231
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DNN: Fix OpenVINO 2026 build failure due to ov::Tensor::data() const change #28592
Fixes: https://github.com/opencv/opencv/issues/28586
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### PR Description
OpenVINO 2026 changed ov::Tensor::data() to return `const void*`
instead of `void*`. This causes a build error in
modules/dnn/src/op_inf_engine.cpp when constructing a cv::Mat
wrapper over Tensor memory.
Tensor memory for input/output blobs remains mutable, but the API
now enforces const-correct access. This patch casts away const with
an explicit comment to preserve existing zero-copy semantics and
restore compatibility with OpenVINO 2026.
Preserves existing zero-copy semantics of the OpenVINO backend without altering runtime behavior.
Tested by building OpenCV 4.x against OpenVINO 2026.0.0 on Ubuntu 24.04.
* added empty set support
* accept unnamed dynamic dims
* Fix for LSTM test failure
* removed converToND
* openvino failing test fix
* ARM CI issue fix
* ARM issue fix
Added ONNX Runtime as an optional wrapper #28444
This PR adds ONNXRuntime (ORT) as an _optional_ wrapper, which can be enabled by adding **WITH_ONNXRUNTIME** flag in CMake command.
Using ORT wrapper the inference time for _resnet50.onnx model_ has come to _**~7ms**_ from _**~14ms**_.
Also, we are able to run models like `ssd_mobilenet_v1.onnx`.
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[BUG FIX] Incorrect shape assert bug fix in 5.x #28575
Closes: https://github.com/opencv/opencv/issues/28563
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Added GRU layer in new DNN engine #28558
Closes: https://github.com/opencv/opencv/issues/26309 and https://github.com/opencv/opencv/issues/21078 [for GRU layer]
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DNN: Fix Squeeze to remove all size-1 dims when axes is empty #28425Fixes#28424
OpenCV Extra: [opencv/opencv_extra#1308](https://github.com/opencv/opencv_extra/pull/1308)
This PR fixes the ONNX Squeeze operator to correctly remove all size-1 dimensions when `axes` is not provided, conforming to the ONNX specification.
### Details
Per [ONNX Squeeze specification](https://onnx.ai/onnx/operators/onnx__Squeeze.html):
> 'If axes is not provided, all the single dimensions will be removed from the shape.'
Previously, OpenCV DNN would not remove any dimensions in this case, causing shape mismatch errors with models like LaMa (inpainting).
### Example
```python
# Input: [1, 1, 2, 4]
# Squeeze with no axes attribute
# Before: [1, 1, 2, 4] ✗ (No change)
# After: [2, 4] ✓ (matches ONNX Runtime)
```
### Tests
Added `testONNXModels("squeeze_no_axes")` which validates this behavior with new test data.
opencv_extra_pr=opencv/opencv_extra#1324
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Added RoiAlign layer support in new DNN engine #28453
Fixes `Unsupported Operation: RoiAlign` issue in https://github.com/opencv/opencv/issues/20258 and https://github.com/opencv/opencv/issues/22099, model parsing is successful now.
Merge with: https://github.com/opencv/opencv_extra/pull/1310
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optimize(dnn): parallelize Slice layer implementation (4.x) #28511
### Summary
Backport of PR #28447 to 4.x branch.
### Description
This PR optimizes the SliceLayer implementation for strided inputs (where step > 1). The original implementation used a recursive element-wise copy (getSliceRecursive) for any strided slice, which was extremely inefficient.
This PR introduces:
- **Parallelization**: Uses `cv::parallel_for_` to parallelize the outermost dimension of the slice operation.
- **Memcpy Optimization**: Automatically detects "pseudo-contiguous" blocks in strided slices (e.g., slicing an outer dimension but keeping inner dimensions intact) and uses `std::memcpy` instead of scalar loops.
- **Refactoring**: Replaces the recursive function with a dedicated `ParallelSlice` loop body.
### Impact
Significant performance improvement for strided slice operations (common in detection heads, strided sampling, etc.).
### Benchmark Results
Tested on CPU with 20 threads.
| Test Case | Baseline (ms) | Optimized (ms) | Speedup |
| :--- | :--- | :--- | :--- |
| Strided Axis 0 [::2, ...] | 1.10 | 0.02 | **~55x** |
| Strided Axis 2 [..., ::2] | 1.15 | 0.11 | **~10.5x** |
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optimize(dnn): optimize Slice layer for strided inputs #28447
## Description
This PR optimizes the `SliceLayer` implementation for strided inputs (where `step > 1`). The original implementation used a recursive element-wise copy (`getSliceRecursive`) for any strided slice, which was extremely inefficient.
This PR introduces:
1. **Parallelization**: Uses `cv::parallel_for_` to parallelize the outermost dimension of the slice operation.
2. **Memcpy Optimization**: Automatically detects "pseudo-contiguous" blocks in strided slices (e.g., slicing an outer dimension but keeping inner dimensions intact) and uses `std::memcpy` instead of scalar loops.
3. **Refactoring**: Replaces the recursive function with a dedicated `ParallelSlice` loop body.
## Impact
Significant performance improvement for strided slice operations (common in detection heads, strided sampling, etc.).
**Benchmark Results:**
| Test Case | Before (ms) | After (ms) | Speedup |
| :--- | :--- | :--- | :--- |
| **Strided Axis 0** `[::2, ...]` | 1.10 | **0.02** | **~55x** |
| **Strided Axis 2** `[..., ::2]` | 1.10 | **0.06** | **~18x** |
| **Contiguous** (Baseline) | 0.10 | 0.10 | 1.0x (Unchanged) |
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- [ ] The feature is well documented and sample code can be built with the project CMake
dnn: improve robustness of MVN layer#28308
This change adds small defensive improvements to the MVN layer implementation:
->Guard against zero-sized OpenCL kernel launches
->Add input validation in finalize()
->Use int64 for FLOPS computation to avoid overflow
No functional or performance changes are intended.
### 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
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