[FOLLOW UP] : Documentation optimizations for the new Sphinx structure #29220
### Pull Request Readiness Checklist
This PR serves as a follow-up to the new documentation system introduced in [#29206](https://github.com/opencv/opencv/pull/29206)
Co-authored by: @abhishek-gola @kirtijindal14 @Akansha-977 @Prasadayus @varun-jaiswal17
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Caffe importer cleanup #28678
Merge with: https://github.com/opencv/opencv_extra/pull/1324
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Added SDPA layer (Scaled Dot Product Attention) #29104
Merge with: https://github.com/opencv/opencv_extra/pull/1374
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dnn int8 optimization #29079
all_layers.hpp
- Add float_input flag to Conv2Int8Params and Conv2Int8Layer to let the first conv accept raw FP32 input and quantize internally.
graph_fusion_qdq.cpp :
- Fuse DQ → Sigmoid → QL into SigmoidInt8, Similarly for MAxPool.
- Fuse the input QuantizeLinear node into the first Conv2Int8.
conv2_int8_layer.cpp
- Add quantizeInterleaveBlock()
conv2_int8_kernels.simd.hpp
- Add spatial tiling to both convInt8BlockVNNI and convInt8BlockDepthwise: splits output pixels into tiles so total task count is N × ngroups × Kblk × ntiles, fully utilizing all threads even when the channel count is small.
elementwise_layers.cpp
- Widen CV_Assert to accept CV_8U in addition to CV_8S.
eltwise2_int8_layer.cpp
- Add QLinearMul support: new Mul math path for both signed and unsigned int8.
- Add numpy-style broadcast support so QLinearMul / QLinearAdd with scalar
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FP32 KV Cache #28840
OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1348
## This PR introduces basic (Paged ) KV-Cache to use on CPU
### Summary:
1. To ensure proper gemm-prepacking,
1.1. The Page Size of Key Cache is currently hardcoded as `FAST_GEMM_F32_NR`(which is 8, 12 or 16 depending on CPU architecture)
1.2. The Page Size of Values Cache is hardcoded as `FAST_GEMM_F32_PACKED_STRIDE_K`
2. there are two phases supported - prefill & generate.
2.1. prefill grows cache by `N` tokens and is allowed **only** for empty cache
2.2. generate grows cache by 1 token.
2.3. **Improtant**: it is currently not allowed to grow non-empty cache by more than one token at a time (thisbehaviour is sufficient for normal LLM querying, but should be extended if we want to implement speculative decoding)
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fix int32 overflow in shape_utils::total() for large tensors #29093
Fixes https://github.com/opencv/opencv/issues/24914
### Problem
When running inference with a ConvTranspose (deconvolution) layer on large inputs
(e.g. 4864×4864 with 30 channels), the DNN module crashes with:
OpenCV net_impl.cpp: error:
(expected: 'total(ints[i]) > 0'), where
'total(ints[i])' is -1455947776
must be greater than
'0' is 0
The root cause is `shape_utils::total()` which returns `int` (32-bit signed).
`ENGINE_CLASSIC` catches this via `CV_CheckGT` and throws.
`ENGINE_NEW` was silently bypassing the check — the overflow in `total()` itself
was never addressed.
### Changes
**`modules/dnn/include/opencv2/dnn/shape_utils.hpp`** — root fix
- Changed return type of both `total()` overloads from `int` to `size_t`
- Changed accumulator from `int elems = 1` to `size_t elems = 1`
**`modules/dnn/src/net_impl.cpp`**
- Updated `CV_CheckGT(total(...), 0)` to `CV_CheckGT(total(...), (size_t)0)`
to match the new return type
**`modules/dnn/src/net_impl2.cpp`**
- Added the same `CV_CheckGT` shape validation that `ENGINE_CLASSIC` has in
`net_impl.cpp:1333-1337` — `ENGINE_NEW` was missing this check entirely
**`modules/dnn/src/legacy_backend.hpp`**
- Removed the now-incorrect `(int)` cast in `CV_CheckEQ` — both sides are
now `size_t`
### Test
Added `Net.ShapeUtils_total_no_int32_overflow` in `modules/dnn/test/test_misc.cpp`:
- The shape [1920 × 1,478,656] is the exact im2col buffer from the bug report.
EXPECT_EQ verifies total() returns the correct size_t value 2,839,019,520.
EXPECT_LT documents that casting it to int wraps to -1,455,947,776 — the
value that caused the original crash.
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Added net profiling support #28752
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Extend attention fusion for runtime QK scale and add MatMul to Gemm rewriter #28957
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Added fully functional convTranspose layer to new DNN engine #27560
Closes https://github.com/opencv/opencv/issues/26307
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Added QLinear layer support #28811
closes: https://github.com/opencv/opencv/issues/26310
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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
Added Output Tensor Names support in new DNN engine #28637
closes: https://github.com/opencv/opencv/issues/26201
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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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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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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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AttentionOnnxAiLayer #27988
Implements https://onnx.ai/onnx/operators/onnx__Attention.html#attention-23
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Added Hannwindow, Hammingwindow & Blackmanwindow support #28075
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Added RandomNormalLike layer for fixing ViTs parsing issue #28110
closes: https://github.com/opencv/opencv/issues/27603
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RMSNorm: reference cpu impl #28104https://onnx.ai/onnx/operators/onnx__RMSNormalization.html
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Rotary position layer #28031
Implemented https://onnx.ai/onnx/operators/onnx__RotaryEmbedding.html#rotaryembedding-23
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Added affine grid layer to new DNN engine #27894
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Added DFT layer to new DNN engine #27941
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Added Onehot layer support to new DNN engine #27902
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Added center crop pad layer to new DNN engine #27892
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Added support for SCE and NLL losses #27809
This pull request adds the support for Negative Log-Likelihood loss and Softmax Cross-Entropy loss in new DNN engine.
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Extended Reduce layer support in new DNN engine #27816
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Added cast and castlike layers support in new DNN engine #27698
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Added nonmaxsuppression (NMS) layer to new DNN engine #27674
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Added fully functional resize layer to new DNN engine #27586
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Added unique layer to new DNN engine #27676
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Use MatShape instead of MatSize inside cv::Mat/cv::UMat #27757
**Merge together with https://github.com/opencv/opencv_contrib/pull/3996**
---
This PR continues cv::Mat/cv::UMat refactoring. See #26056, where `MatShape` was introduced. Now it's put inside cv::Mat/cv::UMat instead of a weird `MatSize`. MatSize is now an alias for MatShape:
**before:**
```
struct MatShape { ... };
struct MatSize { ... };
struct Mat {
...
int dims;
int rows;
int cols;
...
MatShape shape() const { ... /* constructs MatShape out of MatSize and returns it;
layout is always 'unknown', because we don't store it */ }
MatSize size; // size is not valid without the parent cv::Mat,
// because size.p may point to Mat::rows or to Mat::cols,
// depending on the dimensionality, and dims() returns Mat::dims.
MatStep step; // may allocate memory, depending on the dimensionality.
...
};
```
**after:**
```
struct MatShape { ... };
typedef MatShape MatSize; // they are now synonyms
struct Mat {
...
int dims;
int rows;
int cols;
...
MatShape shape() const { return size; } // just return the embedded shape (including the proper layout information)
MatSize size; // size is self-contained data structure that can be used without the parent cv::Mat.
// size.dims is now a copy of dims; size.p[*] contains copies of Mat::rows and Mat::cols when dims <= 2.
MatStep step; // does not allocate extra memory buffers.
...
};
```
There are several reasons to do that:
1. the main reason is to be able to store data layout (MatShape::layout) inside each cv::Mat/cv::UMat. This is necessary for the proper shape inference in DNN module. In particular, it's necessary for the next step of DNN inference optimization where we introduce block-layout-optimized convolution and other operations. Later on, we can use layout information to support non-interleaved images (e.g. RRR...GGG...BBB...) or even batches of such images in core/imgproc modules.
2. the other reason is to represent 3D/4D/5D etc. tensors as cv::Mat/cv::UMat instances more conveniently, without extra dynamic memory allocation. Before this patch we allocated some memory buffers dynamically to store shape & steps for more than 2D arrays. Now the whole cv::Mat/cv::UMat header can be stored completely on stack/in a container. Creating another copy of Mat/UMat header is now done more efficiently.
3. the third reason is to introduce the new coding pattern: `dst.create(src.size, <dst_type>);`. The pattern is suitable for most of element-wise (including cloning) and filtering operations. This pattern does not only look crisp and self-documenting, it will also automatically copy shape (including layout) from the source tensor into the destination matrix/tensor.
4. in the future we might add `colorspace` member to MatShape that will allow to distinguish RGB from BGR or NV12. `dst.create(src.size, <dst_type>);` will then copy the colorspace information as well.
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Added nonzero layer to new DNN engine #27701
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Added gridsample layer to new DNN engine #27700
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Added bitshift layer to new DNN engine #27666
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Alexander Smorkalov
omrope79
Abhishek Gola
Varun Jaiswal
nklskyoy
Alexander Smorkalov
Prasad Ayush Kumar
Jorge Velez
Vadim Pisarevsky