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| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 Jaivardhan Bhola
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84c2360c27 |
Merge pull request #29675 from jaivardhan-bhola:generalized-tokenizer
Generalize tokenizer loading to support method-based family dispatch in DNN - #29675 Companion PR: https://github.com/opencv/opencv_extra/pull/1402 ### Changes Added ALBERT and BERT support end-to-end, with `samples/dnn/albert_inference.py `and `samples/dnn/bert_inference.py `as validation samples, plus expanded coverage in modules/dnn/test/test_tokenizer.cpp. Required changes to `cv::dnn::dnn.hpp`, `graph_fusion_attention.cpp`, and `unicode.cpp/unicode.hpp` to support Unigram and WordPiece tokenizers. To back ALBERT/BERT, generalized `cv::dnn::Tokenizer `from a single BPE implementation into a method-dispatched frontend, adding `core_wordpiece.cpp/hpp` (WordPiece) and `core_unigram.cpp/hpp` (Unigram) as new backends. `tokenizer.cpp` now routes by method across BPE, Gemma,, SentencePiece, Unigram, and WordPiece behind one shared interface. Tested against the following samples and the output matches to old tokenizer: ``` gpt2_inference.py qwen_inference.py gemma3_inference.py ``` GPT2: ``` Preparing GPT-2 model... Inferencing GPT-2 model... Hello, I'm a language model, not a programming language. I'm a language model. I'm a language model. I'm a language model. I'm a language model. I'm a ``` Gemma3: ``` Preparing Gemma3 model... Prompt: <start_of_turn>user What is OpenCV?<end_of_turn> <start_of_turn>model Inferencing Gemma3 model... Response: Okay, let's break down what OpenCV is. **What is OpenCV?** OpenCV (Open Source Computer Vision Library) is a powerful and ``` Qwen2.5: ``` Preparing Qwen2.5 model... Prompt: <|im_start|>user What is OpenCV?<|im_end|> <|im_start|>assistant Inferencing Qwen2.5 model... Response: OpenCV is a set of computer vision libraries in C++ designed to be used for image and video processing. It provides a wide range of tools and functions for ``` ### Tokenizer References Byte-level BPE: [tokenizers/src/pre_tokenizers/byte_level.rs](https://github.com/huggingface/tokenizers/blob/main/tokenizers/src/pre_tokenizers/byte_level.rs) (This defines the byte-level mapping rules, which is used in conjunction with the [BPE model](https://www.google.com/search?q=https://github.com/huggingface/tokenizers/blob/main/tokenizers/src/models/bpe/mod.rs)) SentencePiece BPE (Metaspace): [tokenizers/src/pre_tokenizers/metaspace.rs](https://www.google.com/search?q=https://github.com/huggingface/tokenizers/blob/main/tokenizers/src/pre_tokenizers/metaspace.rs) (This defines the rule for replacing whitespace with the U+2581 _ character and handling byte fallback) Unigram: [tokenizers/src/models/unigram/mod.rs](https://www.google.com/search?q=https://github.com/huggingface/tokenizers/blob/main/tokenizers/src/models/unigram/mod.rs) (This contains the core logic for the Unigram lattice scoring and probabilistic tokenization rules) WordPiece: [tokenizers/src/models/wordpiece/mod.rs](https://www.google.com/search?q=https://github.com/huggingface/tokenizers/blob/main/tokenizers/src/models/wordpiece/mod.rs) (This explicitly cites Schuster & Nakajima in the code comments and implements the greedy longest-match rule with the ## prefix) ### Pull Request Readiness Checklist - [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 code under GPL or another license incompatible with OpenCV. - [x] The PR is proposed to the proper branch (`5.x`). - [x] There is a reference to the original bug report and related work. - [x] There is accuracy test and test data in `opencv_extra`, same branch name (`generalized-tokenizer`) — `bert/`, `t5/` fixtures back the new C++ tests. - [x] The feature is documented and sample code builds with project CMake. |
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 Abhishek Gola
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b2b4f34820 |
Merge pull request #29834 from abhishek-gola:dnn-fp8-support
FP8 model support in DNN - #29834 ONNX coverage after this PR: 76.6% co-authored by: @SavyaSanchi-Sharma ### 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 |
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 Savya Sanchi Sharma
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61127d3812 |
Merge pull request #29656 from SavyaSanchi-Sharma:backendAgnostic
Added Backend Agnostic fusion in DNN - #29656 # What This Adds A way for any layer, on any backend, to absorb trailing per-element math — with the pass knowing nothing about either layer and holding no list of op types. - **Description**: `LayerMath` (one layer's math) → `AdjacencyGraph` (hash-consed chain) - **Contract**: two `bool` virtuals on `Layer` - **CPU implementations**: three, behind that contract Pointwise fusion is the first consumer, not the point. ## The Problem ```cpp ActivationLayer* activ = dynamic_cast<ActivationLayer*>(layer_ptr); Conv2Layer* conv = getLayer<Conv2Layer>(newprog, conv_layer_idx); if (conv) conv->fuseActivation(layer); ``` Guest type, host type, method name all hardcoded in the pass. `Clip` never fused (fails the cast), `Gemm` never fused (no virtual), no 2-op chain fused at all, and no backend could fuse anything without a CPU-specific method. ## Results | Model | 5.x | this PR | speedup | |-----------------------|----------|----------|---------| | MPHand | 2.34 ms | 1.03 ms | 2.27 | | EfficientNet | 9.82 ms | 5.00 ms | 1.96 | | MPPose | 5.14 ms | 2.81 ms | 1.83 | | MobileNet_SSD_v1_ONNX | 12.58 ms | 8.38 ms | 1.50 | | BlazeFace | 0.95 ms | 0.77 ms | 1.23 | | DenseNet_121 | 20.56 ms | 19.08 ms | 1.08 | | MobileNetv2_ONNX | 2.07 ms | 1.99 ms | 1.04 | | MobileViT_XS | 6.33 ms | 6.11 ms | 1.04 | | YuNet_320 | 1.33 ms | 1.27 ms | 1.04 | | PPOCRv3 | 43.05 ms | 42.15 ms | 1.02 | | BERT | 9.78 ms | 9.68 ms | 1.01 | | BEiT_Base_Patch16_224 | 27.48 ms | 27.08 ms | 1.01 | | DeiT_Tiny_Patch16_224 | 4.74 ms | 4.72 ms | 1.01 | | MPPalm | 1.88 ms | 1.85 ms | 1.01 | | SSD | 50.19 ms | 49.82 ms | 1.01 | | YOLOv4_tiny | 7.10 ms | 7.03 ms | 1.01 | ### 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. - [ ] The feature is well documented and sample code can be built with the project CMake |
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Abhishek Gola
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67d6fdd4bc |
Merge pull request #29931 from abhishek-gola:onnx_conformance_remaining_fixes
Merge pull request #29931 from abhishek-gola:onnx_conformance_remaining_fixes fix ONNX auto_pad, PRelu broadcasting and LSTM peepholes - #29931 closes: https://github.com/opencv/opencv/issues/21078 Upated ONNX coverage: 77.8% ### 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 |
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Abhishek Gola
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c7dd924be3 |
Merge pull request #29594 from abhishek-gola:bitcast_matmul_dft_layers
Added Bitcast layer & extended MatMul and DFT layers support - #29594 ### 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 |
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Abhishek Gola
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71a601ea0e |
Merge pull request #29783 from abhishek-gola:extended_onnx_coverage
Added GridSample BiCubic, Dropout support - #29783 Updated ONNX coverage after this PR: 74.8% ### 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 |
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Abhishek Gola
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8e3e271d86 |
Merge pull request #29624 from abhishek-gola:linear_flex_attention_layers
Linear and Flex attention layers support - #29624 ### 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 |
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Abhishek Gola
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8b7dc43c22 |
Merge pull request #29785 from abhishek-gola:image_decoder_layer
Add Image Decoder ONNX Layer - #29785 ### 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 |
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 Alexander Smorkalov
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73b7cce609 |
Merge pull request #29595 from abhishek-gola:handle_reinitialization
Replace global finalizeLayers flag with per-layer re-initialization |
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Abhishek Gola
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89e18be549 |
Merge pull request #29642 from abhishek-gola:kv_cache_engine
Dynamic KV-cache support - #29642 The core idea is: reserveKVCache() API to pre-allocate memory for attention caches upfront, which eliminates allocation overhead during token decoding. For LLM inference, simply call reserveKVCache(prompt_len + max_new_tokens) before the prefill stage so the decode loop runs without page allocations, significantly reducing per-token latency for models like Gemma3 and Qwen. Speedups after this PR on AMD Ryzen 9 9950X 16-Core Processor device: Qwen2.5-0.5B-Instruct, fp32, CPU, tok/s: ``` Tokens Before After Speedup 64 12.49 23.72 1.90× 128 10.37 23.14 2.23× 256 7.20 22.40 3.11× 512 4.25 21.03 4.95× ``` Gemma 3 1B-it, fp32, CPU, 512 tokens : ``` Tokens Before After Speedup 64 6.99 11.84 1.69× 128 5.84 11.72 2.01× 256 4.17 11.50 2.76× 512 2.47 11.15 4.51× ``` ### 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 |
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Abhishek Gola
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cfe81c9388 | separated weight and shape re-initialization | ||
 Prasad Ayush Kumar
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4b5add36de |
Merge pull request #29666 from Prasadayus:more_onnx-coverage
Add MatMulNBits layer and extend onnx coverage - #29666 Requires:https://github.com/opencv/opencv_extra/pull/1401 ### 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 |
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Abhishek Gola
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3ef693c48a | warning fix | ||
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Abhishek Gola
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58c28e1e82 | wrapper-free GpuMatND forward path | ||
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Abhishek Gola
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059a93339c | code refactoring | ||
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Abhishek Gola
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12fb9a6c24 | using gpuMat instead of backend wrappers | ||
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Abhishek Gola
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c48e3f7f3e | changed OpData to LayerInfo | ||
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Abhishek Gola
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cab931296f | cleanup | ||
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Abhishek Gola
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72ffdfc170 | cuda support and Layer Split + per-op executors | ||
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Abhishek Gola
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51f7547bf1 | refactoring | ||
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Abhishek Gola
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57bcf14e78 | moved to engine_opencv | ||
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Abhishek Gola
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2c67582020 | slope fix | ||
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Abhishek Gola
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49b8f7c03f | engine classic removed | ||
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Abhishek Gola
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fe482bd575 |
Merge pull request #29577 from abhishek-gola:scan_layer
Add Scan layer to the new engine #29577 ### 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 |
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Abhishek Gola
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b83e561526 |
Merge pull request #29579 from abhishek-gola:cumprod_causalconv_layers
Added Cumprod and Causalconv layers in new dnn engine #29579 ### 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 |
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  Vadim PisarevskyandClaude Opus 4.8
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f968fb969f |
Broadcasting element-wise engine for cv::Mat (+ cv::texpr) (#29426)
* experimental new arithmetics; work-in-progress * continue working on new-gen arithmetic expressions * * improved performance of the new add on small arrays * added sub * extended tests * fixed potential bug when adding multi-channel array and a single-channel scalar * improved const handling * * added copyMask * added mul/dev (without scale so far) * * accelerated mul * addedd scale to mul and div * * done substantial refactoring; however a few more rounds of refactoring are ahead. * added min, max, absdiff, addweighted. * improved performance of the new arithmetic functions, but some of them are still slow, e.g. operations with mask have some bugs (that affect speed, not accuracy). * * further (significantly) accelerated several functions, especially on small arrays: mul, binary ops with mask * further polished the new arithmetic engine * started integration of the new element-wise arithmetic engine into core * big step forward. We now use the new engine inside cv::add, subtract, multiply, divide, absdiff, min and max. * big progress: * added bitwise operations * fixed and accelerated compare * ported regression tests to test new broadcasting behaviour of arithmetic functions * lot's of improvements in compare, divide, addWeighted! * lot's of small and big performance improvements in the new arithmetics * * some more optimizations; parsing texpr-expressions is now faster as well * port new_arithm to Linux/x86: dispatch guards, scalar-Mat compat fallback, dnn shape-contract fixes Core: - arithm.simd.hpp: CV_CPU_OPTIMIZATION_DECLARATIONS_ONLY guards (the file is included once per dispatched mode on x86), vx_load_expand instead of the 128-bit v_load_expand, VTraits::vlanes() instead of ::nlanes - arithm.cpp/precomp.hpp: compat fallback for scalar-like Mat operands (1x1, 1xcn/cnx1, 4x1 CV_64F - java/python tuples, operator-(Mat, Matx)): treated as a per-channel scalar ONLY when the shapes are not broadcast-compatible, so every valid numpy-style broadcast keeps its meaning and calls that would otherwise throw get the 4.x semantics DNN (fallout of the stricter shape semantics, found by the new engine): - dict.hpp: DictValue relied on fresh AutoBuffer having size()==fixed_size; allocate explicitly - batch_norm: weights_/bias_ are 1-D [n] now; 0/1-D forward runs on exact-shape 1-D views - net_impl2: extend the post-forward sanity check to non-temp outputs - a layer that reallocates its preallocated output tensor now fails loudly instead of silently detaching the result from the graph - LSTM/LSTM2 batchwise (layout=1): getMemoryShapes now matches what forward() writes (ONNX: Y=(batch,seq,dirs,hid), Yh/Yc=(batch,dirs,hid)); forward assembles seq-major results in a local buffer and transposes INTO the preallocated outputs in place Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * u8/s8 multiply: exact integer SIMD path on non-FP16 builds (3-10x vs 5.x) The unit-scale branch of vecBinaryKernel already supported a separate work-vector type Wvec1 (used by the ARM f16 build and by u16/s16 everywhere), but on x86 the u8/s8 same-type multiply still went through the f32 hub. Route it through v_uint16/v_int16: products of 8-bit values fit exactly (255^2 < 2^16), the saturating pack on store gives bit-exact results at half the vector traffic. Also fix a latent kernel bug this exposed: the unit-scale branch stepped by Wvec's lane count while loading/storing Wvec1 vectors. All previous Wvec1 instantiations had equal lane counts, but u8's v_uint16 has 2x the lanes of v_float32 - the pairs overlapped (50% redundant work) and the tail backoff could write VECSZ bytes past the row end. The branch now derives its step, offsets and tail condition from Wvec1 itself. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * vecBinaryKernel: constexpr Op::useScalar instead of a runtime-only scale check Every binary op functor now declares whether it consumes the scale scalar (params[0]): true only for mul and the two div variants. Ops that ignore it (add/sub/min/max/absdiff) take the fast 2-arg branch unconditionally - the 'scalar == 1' check used to fail for them (their params[0] is 0), sending them through the preproc branch, and the condition now folds at compile time. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * restore cv::hal::mul8u as a wrapper over the element-wise engine The symbol is still declared in core/hal/hal.hpp and called directly by external code (the G-API fluid backend in opencv_contrib), but its implementation went away with the old arithm kernels. Forward it to getMulFunc(CV_8U, CV_8U) - with scale==1 it lands on the new exact integer SIMD path. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * silence every new-arithm warning reported by CI (ARM64/Mac) and gcc 15 - arithm.cpp: bitwise_op_ocl and the actualScalarDepth/coerceTypes helpers are consumed only by the OpenCL paths - guard them with HAVE_OPENCL; haveScalar in cv::compare is read only inside CV_OCL_RUN - CV_UNUSED for OpenCL-less builds - arithm_expr.hpp: declare getBitwiseFunc/getNotFunc/getAddWeightedFunc next to the other per-op entry points (-Wmissing-prototypes in arithm.dispatch.cpp) - arithm.simd.hpp: define CV_SIMD_16F to 0 when FP16 SIMD is absent (-Wundef); {}-init the expandScalar staging buffers (-Wmaybe-uninitialized: they are fully written before use, but the compiler cannot prove it with runtime vector widths); rename the compare kernel's lambda parameter (-Wshadow) - arithm_expr.cpp: rename the exec tile-lambda's hot-field locals that shadowed TExpr members and outer locals (-Wshadow) - test_new_arithm_extensive.cpp: rename the name-generator lambdas' parameter shadowing the INSTANTIATE macro's own (-Wshadow), drop an unused variable Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * restore 4.x scalar semantics for the bindings' 4x1 CV_64F Scalar columns The python/java bindings materialize numbers and tuples as a 2-D 4x1 CV_64F Mat - or UMat, when the call carries UMat arguments. Three CI-reported python failures came from those pseudo-scalars reaching the engine as arrays: - absdiff(int_arr, 0): the (4,1) column is broadcast-COMPATIBLE with a 1-D array, so numpy semantics silently won - an outer-product f64 result instead of the int per-channel-scalar one; - subtract(u8 4x8x4, (40,)): same, by the rows==4 coincidence; - multiply(UMat, 2., dst=UMat): the scalar arrives as a UMAT, which the scalar detection did not recognize at all. isScalarArg now treats the exact bindings shape - 2-D 4x1 CV_64F single-channel Mat/UMat against a <=4-channel array - as a scalar UNCONDITIONALLY (a 1-D [4] array has dims==1 and still broadcasts). One exception, decided in arithm_op: when the partner is itself a tiny scalar-shaped array, both are honest data and ride the broadcast (compare(Mat 4x1, Mat 1x1) - issue #8999 - stays elementwise). Small-array discipline, this all runs per engine call: the probes read Mat/UMat fields directly (rows == 4 alone rejects almost everything, no _InputArray getter dispatch), and a UMAT scalar's 32 bytes are copied into a caller-stack buffer - no heap, no getMat mapping. Measured: no latency change on 4x4/16x16 element-wise calls. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * cv::texpr: std::string_view -> const std::string& in the public API string_view in an exported signature breaks some CUDA toolchain builds, and for short expression strings the difference is immaterial (SSO, parsed once). The parser internals keep string_view - the argument converts implicitly. Also drop the now-unused <string_view> include from cvstd.hpp, so the header does not reach every nvcc TU. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * compare boundary-rewrite: fixed 4-slot kind/bound arrays instead of AutoBuffers A CONST operand is capped at 4 channels (addConst), so the per-channel kind/bound staging needs no dynamic buffers - plain int[4]/double[4], with a CV_Assert on the contract. This is also what gcc's -Wmaybe-uninitialized was flagging (it could not see the AutoBuffer's inline storage get filled). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * element-wise engine: unary math kernels (sqrt/exp/log/sin/cos/tanh/erf/relu) + select New dispatched pair math.simd.hpp / math.dispatch.cpp - the unary/ternary sibling of arithm.simd.hpp: - vecUnaryKernel: T -> T over f16/bf16/f32/f64 on top of the intrin_math primitives (v_exp/v_log/v_sin/v_cos/v_sqrt/v_erf/v_max). f32/f64 compute natively, f16/bf16 ride the f32 hub inside the kernel (vx_load_pair_as / v_store_pair_as) - no materialized casts. Continuity collapse + the halide right-edge backoff, suppressed in-place (it would re-apply Op to already-written values). tanh = (e^2x-1)/(e^2x+1) with the input clamped to +/-10 (f32) / +/-20 (f64) - unclamped saturation hits inf/inf = NaN. erf has no f64 SIMD primitive: std::erf per lane. - selectKernel(mask, x, y): 1-byte mask expanded to lane width and tested against zero in the INTEGER domain (immune to DAZ/FTZ), branches of any depth by element size, broadcast branches supported. - emitUnary: math over a float input is T -> T now (f16 in -> f16 out, native kernel when input and result depths match); integer inputs still compute in the float domain and land in f32. - emitTernary/select: literal branches are typed via typedConstFrom (an OP_CAST of a depth-less flex const crashed); a non-1-byte mask is normalized by an explicit "mask != 0" compare, never a value cast. texpr already parsed the function names - they now execute. Tests: per-depth accuracy of all 8 ops against the double std:: reference, integer input, in-place, select over 4 depths / const branch / float mask. Perf vs the classic kernels (1920x1080 f32, 16 threads, AVX2): exp 3.7x, log 2.6x, sqrt 5.7x faster; polarToCart expressed as (r*cos(a), r*sin(a)) 4.0x. Accuracy improves too (max rel err vs f64 reference): exp 8.1e-8 vs 2.1e-7, log 8.0e-8 vs 1.5e-7. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * engine: OP_COPY_MASK folded into OP_SELECT; selectKernel moved to arithm.simd.hpp copyMask(dst, mask, src) is select(mask, src, dst) - one masking primitive instead of two. The compiler emits the masked-op tail as addInsn(OP_SELECT, mask, r, out, out): the output slot rides as both arg2 and the result, so unmasked elements are preserved by reading them back through the b-branch. OP_COPY_MASK, copyMaskKernel and getCopyMaskFunc are gone. selectKernel (moved from math.simd.hpp to arithm.simd.hpp) inherits every copyMaskKernel optimization: - the interleaved multichannel fast path (2..4 channels under a per-pixel mask: expand the mask once per VECSZ rows, v_store_interleave across lanes); - the per-row scalar path with the row-skip when the selected source row IS dst (the "leave the output untouched" half of copyMask); - plus the select-specific ones: branch broadcasts (stepx == 0) and the right-edge tail backoff under dst-aliases-a-branch - safe because re-running select over already-blended elements is idempotent; only dst == mask keeps the backoff off (the store would rewrite mask bytes before the re-read). Masked-add perf is on par with the old copyMask (1280x720, 1 thread: 8UC3 204 -> 198 us, 32FC3 1395 -> 1344, 8UC1/32FC1 within noise). Full core suite 24117 green. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * engine: dedicated vectorized pow kernel (moved from arithm to math.simd.hpp) pow was the last scalar-only binary op (scalarBinaryKernel + std::pow, f32/f64 only). The new powKernel keeps exact std::pow semantics and is T x T -> T over all four float depths (f16/bf16 via the f32 hub): - scalar exponent (the dominant call shape - texpr literals ride as 0-dim broadcast consts) is dispatched PER ROW to the special cases: y==2 -> x*x, y==3 -> x*x*x, y==0.5 -> v_sqrt, y==1 -> copy, y==0 -> fill 1; - everything else - including a per-element exponent array - runs the general vectorized exp(y * log(x)) path, valid for x > 0; a vector pair containing any x <= 0 lane falls back to scalar std::pow for that pair (v_check_any), which preserves every std::pow subtlety: signed results for integer y on negative bases, NaN for fractional y, the x == 0 family; - no right-edge tail backoff: pow is not idempotent, in-place calls finish rows in the scalar tail. Perf vs the classic cv::pow (1920x1080 f32, 16 threads, AVX2): p=2 1.4x (classic special-cases it too), p=3 6.1x, p=0.5 6.3x, fractional p 4.5x with slightly better accuracy (6.1e-7 vs 7.4e-7 max rel err). Tests: exponent sweep 2/3/0.5/1/0/2.5/-1.5 vs the double std::pow reference on f32/f64, negative bases (exact signed cubes, NaN for fractional), array exponent. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * powKernel: halide right-edge tail backoff in every SIMD loop Same shape as vecBinaryKernel: the final partial vector re-processes [width - VECSZ*2, width) instead of finishing scalar, suppressed when dst aliases an input (pow is not idempotent - the overlap region must be recomputed from an untouched source, which the no-alias case guarantees). Modest measured win (~2% on ROI rows for the special-cased exponents; the general path tail was already cheap - modern libm powf is fast), no regressions; mainly aligns the kernel with the house style, where every SIMD loop ends vector-wide. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * fix MSVC 2019 C2975: function-local constexpr as a template argument inside a lambda MSVC 2019 loses the constexpr-ness of function-local constants (LOCAL_OPS, MAX_DIMS, ...) when they are used as template arguments inside a lambda body (AutoBuffer<Slice, LOCAL_OPS> / std::array<int, MAX_DIMS> in the parallel bodies of BroadcastOp::run and TExpr::exec). Hoist them to namespace scope - no behavior change. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * ocl_arithm_op: route 16U multiply to the CPU engine on Apple OpenCL The Apple OpenCL driver miscompiles the 16U multiply kernel: products near the top of the u16 range come back wrapped instead of saturated (CPU vs GPU NORM_INF up to 65535 in OCL_Arithm/Mul.Mat CV_16U cases). The same arithm.cl kernel is correct on Intel NEO and NVIDIA drivers - verified not to reproduce on Linux/Intel iGPU - so gate the decline to __APPLE__ only; the CPU engine computes 16u multiply exactly (integer SIMD path). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * engine: neg/abs as compositions, clamp kernel, ** and ?: operators, abs(a-b) peephole - OP_NEG and OP_ABS need no kernels: neg = sub(0, a), abs = absdiff(a, 0) - including the engine absdiff auto-type rule (signed |a| lands in the UNSIGNED type of the same width: |SHRT_MIN| fits u16 exactly instead of saturating; NB the public cv::absdiff auto depth keeps the source type for 4.x compatibility - values agree, the depth rule is the engine own). - peephole: abs(x - y) rewrites to absdiff(x, y) ALWAYS. On integers the literal semantics differ (the subtract saturates first: u8 gives max(x-y, 0)), but whoever writes abs(a - b) means absdiff - we deliberately hand out the useful semantics instead of the saturation artifact. The just- emitted OP_SUB is retired via the moveToOutput manoeuvre, so the program shrinks to the single absdiff instruction. abs(x), abs(x - 0) and absdiff(x, 0) all give one result. - OP_CLAMP kernel (arithm.simd.hpp): v_min(v_max(x, lo), hi) over u8/s8/u16/s16/u32/s32/f32 (+f64 with 64-bit SIMD), scalar f16/bf16/64-bit ints; lo/hi may broadcast (the common clamp(img, a, b) shape) or be full arrays; the tail backoff stays on under dst-aliases-x (clamp is idempotent). emitTernary types literal bounds via typedConstFrom (same flex-const crash select had) and keeps the auto result type pinned to x. - parser: "a ** b" == pow(a, b), precedence above * /, RIGHT-associative (a ** 2 ** 3 == a ** 8); "cond ? a : b" == select(cond, a, b), precedence below everything, right-associative chains (f1 ? a : f2 ? b : c) work without parentheses. parseTernary() is the expression entry point now. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * cv::exp/log/sqrt on the engine via math_op; hal functions wrapped as engine kernels math_op is the master function of the unary math family (the arithm_op analogue): same-shape same-type output over f16/bf16/f32/f64 (classic exp/log accepted f32/f64 only - the half floats are new), two tiers: - small (<= 100000 elements) and continuous: call the kernel DIRECTLY over the flattened data - no TExpr, no broadcastOp, no parallel_for setup; - everything else: the usual single-instruction program via compile()/exec() (parallelism for large arrays, real steps for ROIs). getMathFunc routes OP_EXP/OP_LOG at f32/f64 through the full cv::hal stack - an external vendor HAL (CALL_HAL), IPP, or the built-in table kernels, whichever is installed - by wrapping hal::exp32f/exp64f/log32f/log64f as engine kernels with the function pointer in TKernel::userdata, the same mechanism castKernel uses for core BinaryFuncs. The engine adds tiling and parallelism on top, so every tier gets the best available scalar-span implementation. v_exp/v_log remain for f16/bf16 (the f32 hub) and the ops hal has no entry points for. v_log_default_32f: the degree-8 polynomial is evaluated by Estrin pairing (4 dependent levels) instead of an 8-FMA Horner chain (~5% on the f16 hub path). An exp64 Taylor-without-division rewrite was tried and benched SLOWER than the Cephes Pade scheme (the evaluation is FMA-throughput-bound, and vdivpd pipelines well enough) - reverted; a table-based reduction is the only way further there. cv::exp f32 (16 threads, AVX2+IPP build), old -> new: 640 elements 0.16 -> 0.15 us, 16k 2.79 -> 2.49, 640x480 47 -> 20, 1920x1080 452 -> 60 us (the old CPU loops were single-threaded); f64 exp 1080p 1295 -> 161 us. No size regresses; small arrays now run at installed-HAL speed. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * getMathFunc: IPP tier + raw-HAL probing; the exp/log table kernels are deleted The raw cv_hal_* entry points return int for a reason: without an installed HAL they are stubs returning CV_HAL_ERROR_NOT_IMPLEMENTED. getMathFunc now selects the exp/log implementation in three tiers: 1. HAVE_IPP && ipp::useIPP(): ippsExp/ippsLn through thin int adapters (IPP is not routed through the cv_hal_ hooks, so it needs its own tier); 2. the raw cv_hal_exp32f/... hook, PROBED once with a 1-element call on the safe input 1.0 (cached in magic statics): implemented -> wrapped as an engine kernel with the function pointer in TKernel::userdata; 3. the engine own v_exp/v_log kernels. Whichever wins, the engine adds tiling and parallelism on top. The EXPTAB/LOGTAB table kernels and their tables (~790 lines in mathfuncs_core.simd.hpp + mathfuncs.cpp) are DELETED: they benched within ~15% of v_exp/v_log, not worth a second implementation. The public cv::hal::exp32f/exp64f/log32f/log64f keep their contract - CALL_HAL, then IPP, then the built-in implementation - but the built-in is now the engine vector kernel via ew::mathSpanEngine (one contiguous span, exported from math.dispatch.cpp). All unary math kernels (vec/scalar/hal wrappers, pow) also handle the vertical-broadcast tile (s0y == 0, a row expanded into a matrix): the first row is computed, the rest are memcpy of it - transcendentals cost far more than a row copy. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * texpr: hypot(x, y) binary op (alias: mag) hypot = sqrt(x^2 + y^2), NAIVE like cv::magnitude (not the overflow-safe std::hypot), computed in the float work type; kernels for the four float depths only (T x T -> T; integer inputs ride the usual f32-compute + cast). A 10-line EwHypot functor on top of vecBinaryKernel in arithm.simd.hpp - broadcast branches, continuity collapse and the tail backoff come for free. Registered in the parser as both "hypot" (the C/numpy name) and "mag" (the cv::magnitude-flavored alias). A building block for the future cartToPolar. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * texpr: atan2(y, x) binary op - radians, standard C range v_atan2 (arithm.simd.hpp, generic over the universal-intrinsic float vector): the fastAtan2 minimax polynomial from mathfuncs_core v_atan_f32 reworked to plain radians - the 180/pi factor dropped from the coefficients and the C quadrant logic instead of the [0, 360) wrap, so the result matches std::atan2 over (-pi, pi]. Measured absolute accuracy ~1.6e-4 rad. (v_atan_f32 itself is untouched - cv::phase/fastAtan2 keep their degree semantics.) EwAtan2 rides vecBinaryKernel: f16/bf16/f32 through v_atan2 (the f32 hub), f64 through exact scalar std::atan2. arg0 = y, arg1 = x, like std::atan2; float depths only, same emitBinary policy as pow/hypot. Parser name "atan2". Together with hypot this completes the cartToPolar building blocks. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * fix the RISC-V RVV build and two ARM64 warnings The new f64 kernel registrations (hypot, pow, the unary math family) gate on CV_SIMD_64F || CV_SIMD_SCALABLE_64F, but the vx_setall_as(const double*, v_float64&) helper family in arithm.simd.hpp was still CV_SIMD_64F-only - scalable platforms (RVV) have v_float64 with CV_SIMD_64F == 0, so vecBinaryKernel<double, ...> failed to instantiate there. Widen the helper gate to match (verified with a riscv64 rv64gcv cross-build of opencv_core - the engine f64 paths now vectorize on RVV instead of not compiling). cv::exp/cv::log: the depth local is consumed by CV_OCL_RUN only - CV_UNUSED for OpenCL-less builds (ARM64 -Wunused-variable). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * silence the remaining ARM64 gcc warnings - compare boundary-rewrite: {}-init the fixed kind/bound arrays (filled for every channel used below, but gcc cannot prove it across the cn <= 4 loop); - cv::exp/log: [[maybe_unused]] on the depth local (consumed by CV_OCL_RUN only), instead of the CV_UNUSED idiom; - AutoBuffer::reserve: a targeted -Wmaybe-uninitialized suppression around the live-element copy loop - only [0, sz) is read, all written before, but gcc inlining a grow-from-inline-storage chain cannot see that. An annotation for the analyzer, no behavior change. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * saturating 32-bit add/sub kernels; cv::texpr python binding; two CI warnings - v_add_sat/v_sub_sat for v_int32/v_uint32, local to arithm.simd.hpp for now (the plan is to grow them into proper universal intrinsics later): NEON single-instruction vqadd/vqsub, elsewhere the Hacker Delight bit tricks over universal intrinsics (u32 add is 2 ops: or with the wrapped-compare mask). EwAdd/EwSub overload vec() for the 32-bit lanes and getAddSubFunc routes 32S/32U T->T through vecBinaryKernel instead of the former pure scalar kernel. Semantics unchanged - the scalar int64 tail already saturated; directed boundary tests added (both rails, 0 - INT_MIN, u32 cases, a full-range random block vs an exact int64 reference). 640x480 32S add: 0.48x of 5.x -> parity (memory-bound); 1080p: 6-8x. - cv::texpr becomes CV_EXPORTS_W: python gets cv.texpr(expr, [inputs]) -> tuple of ndarrays, so `res, = cv.texpr(...)` and `mag, ang = cv.texpr(...)` unpacking both work. modules/python/test/test_expr.py covers arithmetic, the fused abs(a-b), casts, broadcasting, ?: and ** operators, math functions vs numpy, clamp, named temporaries, tuple outputs, the one-line cartToPolar and the int32 saturation cases. - warnings: {}-init the parser args array (gcc -Wmaybe-uninitialized on Ubuntu 20/22); the compare short-row block gates sizeof(T) <= 4 as constexpr so the f64 instantiation does not leave set-but-unused locals (gcc 9 -Wunused-but-set-variable). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * arithm_op: direct-kernel fast path for small continuous arrays Building and compiling the 1-instruction program plus the BroadcastOp setup costs ~40-250ns per call - negligible on big images, dominant at 127x61-class sizes where the classic 5.x functions were 1.3-2x faster. Mirror math_op two tiers in arithm_op: two same-type same-shape continuous arrays, no mask, no scalar, result depth == input depth, <= 100k elements -> call the T x T -> T kernel directly over the flattened elements (checks ordered cheapest-first). Applies to add/subtract/min/max/absdiff/multiply/addWeighted/and/or/xor; compare and divide lower to more than a single kernel (boundary rewrites, int guards) and keep the ordinary path. addWeighted falls through automatically for the 32/64-bit int types whose lowering is wide-compute + cast (getElemwiseFunc returns no direct kernel there). 127x61 vs 5.x, was -> now: add/subtract 8UC1 0.76x -> 1.3x, min/max u8 0.6x -> ~1x, addWeighted 1.0x -> 1.1-1.4x (32SC1 stays 4.5x); the one remaining laggard is add/sub 32SC1 (0.74-0.82x) - the price of the new SATURATING semantics (7-instruction AVX2 emulation vs the wrapping single add of 5.x; single-instruction on NEON). The 127x61 size is ADDED PERMANENTLY to the arithmetic/addWeighted/compare perf grids: per-call overhead regressions in these base functions must be caught by CI, not discovered by users. dst creation goes through createSameSize (whole-shape transfer including layout and future metadata, not piecemeal dims+sizes) here and in math_op. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * cv::pow rebuilt on the engine; integer-exponent and 1/sqrt(x) kernel branches Routing: p = 0/1/2 keep their early special cases (fill/copy/multiply); an INTEGER array with an INTEGER power keeps the classic iPow multiply chain - bit-exact compatibility, including its wrap-around quirks (iPow squares in int, so e.g. pow(255,4) on u8 wraps negative and saturates to 0 - somebody may rely on that). Everything else goes through the engine with the math_op two-tier scheme: small continuous arrays call powKernel directly (the exponent rides as a broadcast T scalar), the rest run the tiled parallel program. Integer arrays with fractional powers compute in the float domain and saturate back; the 32U/64-bit depths (classic iPow asserted on them) and f16/bf16 (the classic float path misread them) now just work. powKernel gets two new per-row exponent branches: - p == -0.5: 1/v_sqrt(x) (the classic path used IPP ippsInvSqrt_A21, a 21-bit approximation; ours is exact - slightly slower on small arrays, 4.5x faster at 1080p via parallelism); - any other INTEGER p (|p| <= 65536): LSB-first binary exponentiation, the same multiply chain and order as iPow, fully vectorized - a few ulp accurate vs ~2e-7 of exp(p*log x), and exact on non-positive bases (the sign falls out of the multiplies, 0^negative divides to inf) - no scalar patching. cv::pow f32 vs 5.x: p=0.5 365 -> 61 us at 1080p (6x), p=3 7.7x, p=5 7x AND faster at every size (the old scalar chain: 0.81 -> 0.47 us at 127x61), p=2.5 5.3x. The s16^5 iPow path is untouched (118.7 == 118.5 us). pow_exponents accuracy tests extended to 11 exponents x f32/f64 against the double std::pow reference. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * perf: SANITY_CHECK_NOTHING for the tests whose grids got the 127x61 size The 127x61 entry added to guard per-call overhead has no regression data in opencv_extra, so the legacy SANITY_CHECK in addWeighted/compare failed on CI (locally it passes silently without the test-data path). Accuracy of both functions is covered by the accuracy suite; the perf tests should measure time. PatchNaNs/finiteMask keep their SANITY_CHECK - their grids are untouched. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * fix a temp-buffer double release in the liveness pass ("d*d" crash) When the same temp is passed as SEVERAL arguments of its last-use instruction (e.g. the named-intermediate expression "d = {0} - {1}; d*d", where the MUL consumes slot d twice), the buffer-reuse scan pushed the temp's physical buffer onto the free list once per argument. That overflows the ntemps-sized freeBufs array (caught by the AutoBuffer range check in Debug: python test_expr.py::test_named_temporary) and, in larger programs, would hand the same physical buffer to two live temps. Release the buffer once by retiring lastUse[t] after the first hit. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * executor: recalibrate opCost to vectorized cycles + clamp the stripe count The per-element op costs fed into the parallel_for_ stripe hint were scalar-era estimates (~20x above the vectorized reality: the atan2/exp polynomials run at ~1.5 cycles/element, not 30). The hint therefore split transcendental/divide work into hundreds of ~1 us jobs, which the macOS/GCD backend dispatches poorly under sustained load, on top of the P/E-core equal-share straggler effect. Measured on M4 Max (12P+4E), sustained medians @1920x1080 f32: texpr atan2 320 -> 133 us, cv::exp 280 -> 141 us, cv::log 301 -> ~200 us, cv::pow(x,2.5) 388 -> 376 us; the PR tables' math rows improved ~1.5-2x across the board. - opCost is now in units of ~1/4 cycle/element of the SIMD kernels: cheap ops 1 (unchanged), div/sqrt/hypot/convert_scale 10 -> 3, transcendentals 30 -> 6. - the stripe hint is clamped by min(4*nthreads, max(32, 3*nthreads)): ~4 stripes/thread is plenty of granularity for element-wise work, and the ceiling is 32 pieces except on machines with many (heterogeneous) cores, where anything coarser than ~3 pieces/worker turns the slow cores into equal-share bottlenecks (measured: 32 stripes on 16 threads is the worst point of the curve - 193 us vs 137 us at 48 for atan2). getNumThreads() is clamped from below (WINRT/plugin backends may report 0). Not addressed here (needs cross-machine data, M2/M3 Ultra): streaming memory-bound ops saturate the M4 Max fabric at ~8 fat stripes and E-core participation only adds contention - a cost model cannot express that; candidate follow-up is a bytes-aware clamp or a GCD-backend-level fix. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * multiply: v_mul_sat integer kernels (full product clamped to the type) v_mul_sat(V, V) -> V for u8/s8/u16/s16/u32/s32 - the full-precision product clamped to the lane type, which is exactly cv::multiply's integer semantics at scale == 1. Local to arithm.simd.hpp for now, next to v_add_sat/v_sub_sat, to be promoted into proper universal intrinsics later. NEON: widening vmull + saturating narrow (vqmovn); other backends: the portable v_mul_expand + saturating v_pack composition for 8/16-bit lanes. 32-bit lanes have no universal widening multiply (no v_mul_expand for s32), so the 32-bit integer fast path is NEON-only for now and the other backends keep the previous f64 work-vector kernels (which measure well on x86 with IPP-free AVX2). EwMul::vec() now routes the integer lane types through v_mul_sat, and getMulFunc_ passes the NATIVE lane vector as the scale==1 fast-path type: whole-register loads/stores, the widening happens inside the multiply. Replaces both the half-register widening loads (u8/s8/u16/s16) and the scalar-equivalent f64 path for 32S/32U on NEON. M4 Max, 640x480 (the sizes where the old kernels lost to carotene): 8U 22.0 -> 13.6 us, 8S 15.0 -> 11.1, 16S 21.2 -> 17.4, 32S 84.8 -> 30.5 (parity with the classic path everywhere, 32S was 0.38x). 1920x1080: 8S 1.30x -> 1.78x, 16S 2.57x -> 2.98x, 32S 2.20x -> 4.02x vs 5.x. Correctness: exhaustive 8-bit (all 65536 pairs per sign), directed saturation corners for 16/32-bit (46341^2, INT_MIN*-1, 65536*65536, all sign combinations) against an exact int64 reference. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * NEON: make v_cvt_f64(v_int32) exact (was via f32, losing bits > 2^24) The NEON implementations of v_cvt_f64/v_cvt_f64_high for v_int32 did s32 -> f32 -> f64 (vcvt_f32_s32 + vcvt_f64_f32), silently rounding any |x| > 2^24. Every vectorized f64 work path with int32 inputs on AArch64 was affected: the engine's addWeighted/divide 32S kernels, convertTo 32S -> 64F, etc. Found via addWeighted 32SC1 on values ~1e9: max error was 32 vs the exact double reference (the classic carotene path is worse still - it computes in f32 end-to-end with f32-truncated weights, max error 96 on the same data). The exact sequence sxtl + scvtf (vmovl_s32 + vcvtq_f64_s64) is the same 2 instructions, so there is no cost. addWeighted 32SC1 on the engine now matches the exact-double reference bit-for-bit and stays at parity/1.4x vs the classic path (640x480/1920x1080). Pre-existing upstream bug (same code in 4.x) - worth a standalone backport with directed large-value tests. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * math_op: drop the temporary direct-IPP tier for exp/log Upstream moved the IPP math wrappers into the hal/ipp HAL module (cv_hal_exp32f/ log32f & co now resolve to ipp_hal_* which honor cv::ipp::useIPP via CV_HAL_CHECK_USE_IPP). The engine's single probeHalUnary(cv_hal_*) probe already picks that up uniformly, so the stopgap #ifdef HAVE_IPP ippExp/ippLog tier and its ipp::useIPP() branch in getMathFunc are now redundant - removed. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * arithm: restore cv::hal::and8u/or8u/xor8u/not8u as engine wrappers These public CV_EXPORTS entry points (core/hal/hal.hpp) lost their definitions when the bitwise ops moved to the element-wise engine, but they are still declared and called by other modules (opencv_objdetect's aruco) and external code - the link broke with undefined references to cv::hal::and8u/xor8u. Restore them as thin forwarders over the engine's byte-wise bitwise kernels (getBitwiseFunc / getNotFunc), mirroring the existing mul8u wrapper. CV_Assert guards the kernel lookup. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * blobdetector: guard empty contour when computing blob radius The new AutoBuffer leaves its tail uninitialized for trivial types, which surfaced a -Wmaybe-uninitialized in findBlobs where the median of per-point distances is read. Use a std::vector, default the radius to 0, and compute the median only for a non-empty contour - no unproven size invariant. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Fable 5 <noreply@anthropic.com> |
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 Vincent Rabaud
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cbd227741f |
Merge pull request #29453 from vrabaud:persistence
Replace "static inline" by "inline" in headers #29453 This fixes #29436 I also replaced CV_INLINE which can be removed in a later PR. I can make a similar PR for 4.x if you want. ### 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 - [ ] 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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 Saravana Balaji Mohan Balaji
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e2f0876777 |
Merge pull request #29217 from MBSaravanaBalaji:feat/onnx-lppool
dnn: implement LpPool ONNX operator #29217 ## Summary Implements the `LpPool` ONNX operator (opset 1–18), which was previously unregistered and caused a parse failure. `LpPool` computes the Lp-norm pooling: `(sum(|x|^p))^(1/p)` over a sliding window. ## Changes - New `LpPoolLayer` in `modules/dnn/src/layers/lppool_layer.cpp` - Supports `kernel_shape`, `strides`, `dilations`, `pads`, `auto_pad` (NOTSET/SAME_UPPER), `ceil_mode`, and `p` (default 2) - SIMD fast paths for p=1 (abs + accumulate) and p=2 (square + accumulate + sqrt); scalar fallback for other values of p - Registered `LpPool` dispatch entry in both `onnx_importer.cpp` (classic engine) and `onnx_importer2.cpp` (new graph engine) - Added `LpPoolLayer` declaration to `modules/dnn/include/opencv2/dnn/all_layers.hpp` - Registered layer class in `modules/dnn/src/init.cpp` - Re-enabled 8 lppool conformance tests in `test_onnx_conformance.cpp` (previously in parser denylist) - `test_lppool_2d_same_lower` added to the global conformance denylist — same known SAME_LOWER padding bug that affects `averagepool` and `maxpool` ## Testing All applicable ONNX conformance tests pass: | Test | Result | |------|--------| | test_lppool_1d_default | PASSED | | test_lppool_2d_default | PASSED | | test_lppool_2d_dilations | PASSED | | test_lppool_2d_pads | PASSED | | test_lppool_2d_same_lower | SKIPPED (known SAME_LOWER padding bug, consistent with avgpool/maxpool) | | test_lppool_2d_same_upper | PASSED | | test_lppool_2d_strides | PASSED | | test_lppool_3d_default | PASSED | Tested on: macOS (x86_64/SSE4, Rosetta 2) and Linux x86_64 (AVX2/AVX-512, GCC 13.3.0), Release build OpenCV version: 5.0.0-pre ## Related Issues None --- ### 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. - [ ] The feature is well documented and sample code can be built with the project CMake |
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 Tonu Samuel
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4d839a16f0 |
dnn: SIMD for 13 transcendental activations in the 5.x engine
Port of #29377 to 5.x. Wires Log, Erf, Exp, Sin, Cos, Sinh, Cosh, Tan, Softplus, BNLL, Asinh, Acosh, Atanh into the dispatched activation_kernels registry, plus a Layer_Activation perf test. 1.4-12.3x across M4/A76/Threadripper/Xeon, correct to <=5e-7 vs scalar. |
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Savya Sanchi Sharma
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5d121b768f |
Merge pull request #29386 from SavyaSanchi-Sharma:test_debug
fixed Dynamic quantized linear layer error #29386 ### 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 |
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 Alexander Smorkalov
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563dcf5b91 | Pre-release 5.0.0 versions update. | ||
 omrope79
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04aee009aa |
Merge pull request #29220 from omrope79:doc_optimizations_v4
[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 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 |
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omrope79
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b67ad9a422 |
Merge pull request #28678 from omrope79:caffe-importer-cleanup
Caffe importer cleanup #28678 Merge with: https://github.com/opencv/opencv_extra/pull/1324 ### 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 - [x] The feature is well documented and sample code can be built with the project CMake |
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Abhishek Gola
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0908a2db6f |
Merge pull request #29104 from abhishek-gola:sdpa
Added SDPA layer (Scaled Dot Product Attention) #29104 Merge with: https://github.com/opencv/opencv_extra/pull/1374 ### 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 |
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 Varun Jaiswal
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bae8cb1915 |
Merge pull request #29079 from varun-jaiswal17:feat/dnn-int8-optimization
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 ### 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 |
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 nklskyoy
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c2594b41bf |
Merge pull request #28840 from nklskyoy:key-value-cache
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) ### 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 |
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Varun Jaiswal
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104d987ca2 |
Merge pull request #29093 from varun-jaiswal17:dnn-overflow-large-image
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. ### 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 <!-- Note!!! If you are an automated agent, we have a special process for you: add 🤖🤖🤖 to the end of the PR title. --> |
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Alexander Smorkalov
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c9147d6e1f |
Merge pull request #28912 from Prasadayus:Darknet-cleanup
Darknet cleanup |
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Alexander Smorkalov
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a7e02e5742 | Removed internal structs from DNN interface to fix Obj-C/Swift bindings. | ||
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Abhishek Gola
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873a4635c6 |
Merge pull request #28752 from abhishek-gola:net_profiling
Added net profiling support #28752 ### 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 |
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Abhishek Gola
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72e0bc2bf3 |
Merge pull request #28957 from abhishek-gola:fusion_block_layout_extension
Extend attention fusion for runtime QK scale and add MatMul to Gemm rewriter #28957 ### 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 |
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Abhishek Gola
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642a7307c4 |
Merge pull request #27560 from abhishek-gola:convTranspose_layer_add
Added fully functional convTranspose layer to new DNN engine #27560 Closes https://github.com/opencv/opencv/issues/26307 ### 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 |
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Abhishek Gola
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2760c0c08a |
Merge pull request #28931 from abhishek-gola:extended_fusions
Extended graph fusion for transformer models #28931 ### Pull Request Readiness Checklist Performance numbers on Intel(R) Core(TM) i9-14900KS: ``` AlexNet::DNNTestNetwork::OCV/CPU 7.143 6.871 1.04 AlexNet::DNNTestNetwork::OCV/OCL 7.471 7.047 1.06 AlexNet::DNNTestNetwork::OCV/OCL_FP16 7.157 6.907 1.04 BERT::DNNTestNetwork::OCV/CPU 9.454 9.154 1.03 BERT::DNNTestNetwork::OCV/OCL 9.657 9.138 1.06 BERT::DNNTestNetwork::OCV/OCL_FP16 9.590 9.271 1.03 CRNN::DNNTestNetwork::OCV/CPU 18.194 17.020 1.07 CRNN::DNNTestNetwork::OCV/OCL 17.855 17.433 1.02 CRNN::DNNTestNetwork::OCV/OCL_FP16 17.629 17.277 1.02 DenseNet_121::DNNTestNetwork::OCV/CPU 24.735 23.863 1.04 DenseNet_121::DNNTestNetwork::OCV/OCL 24.991 25.140 0.99 DenseNet_121::DNNTestNetwork::OCV/OCL_FP16 24.598 24.019 1.02 EAST_text_detection::DNNTestNetwork::OCV/CPU 26.929 27.084 0.99 EAST_text_detection::DNNTestNetwork::OCV/OCL 26.863 27.531 0.98 EAST_text_detection::DNNTestNetwork::OCV/OCL_FP16 26.884 27.082 0.99 EfficientDet::DNNTestNetwork::OCV/CPU 62.667 62.716 1.00 EfficientDet_int8::DNNTestNetwork::OCV/CPU 34.446 34.796 0.99 EfficientNet::DNNTestNetwork::OCV/CPU 11.875 11.786 1.01 EfficientNet::DNNTestNetwork::OCV/OCL 12.434 11.806 1.05 EfficientNet::DNNTestNetwork::OCV/OCL_FP16 11.886 11.899 1.00 FastNeuralStyle_eccv16::DNNTestNetwork::OCV/CPU 15.104 14.327 1.05 FastNeuralStyle_eccv16::DNNTestNetwork::OCV/OCL 15.071 14.670 1.03 FastNeuralStyle_eccv16::DNNTestNetwork::OCV/OCL_FP16 15.262 14.072 1.08 GoogLeNet::DNNTestNetwork::OCV/CPU 5.030 5.031 1.00 GoogLeNet::DNNTestNetwork::OCV/OCL 5.075 5.017 1.01 GoogLeNet::DNNTestNetwork::OCV/OCL_FP16 5.053 5.055 1.00 Inception_5h::DNNTestNetwork::OCV/CPU 6.777 6.430 1.05 Inception_5h::DNNTestNetwork::OCV/OCL 7.309 6.683 1.09 Inception_5h::DNNTestNetwork::OCV/OCL_FP16 6.624 6.549 1.01 Inception_v2_Faster_RCNN::DNNTestNetwork::OCV/CPU 86.676 82.918 1.05 Inception_v2_Faster_RCNN::DNNTestNetwork::OCV/OCL 85.460 84.873 1.01 Inception_v2_SSD_TensorFlow::DNNTestNetwork::OCV/CPU 16.007 15.723 1.02 Inception_v2_SSD_TensorFlow::DNNTestNetwork::OCV/OCL 15.974 16.015 1.00 Inception_v2_SSD_TensorFlow::DNNTestNetwork::OCV/OCL_FP16 16.219 15.593 1.04 MPHand::DNNTestNetwork::OCV/CPU 3.273 3.313 0.99 MPHand::DNNTestNetwork::OCV/OCL 3.466 3.318 1.04 MPHand::DNNTestNetwork::OCV/OCL_FP16 3.314 3.258 1.02 MPPalm::DNNTestNetwork::OCV/CPU 2.839 1.973 1.44 MPPalm::DNNTestNetwork::OCV/OCL 2.773 2.132 1.30 MPPalm::DNNTestNetwork::OCV/OCL_FP16 2.783 2.133 1.30 MPPose::DNNTestNetwork::OCV/CPU 7.644 8.711 0.88 MPPose::DNNTestNetwork::OCV/OCL 7.555 7.210 1.05 MPPose::DNNTestNetwork::OCV/OCL_FP16 7.564 7.559 1.00 MobileNet_SSD_Caffe::DNNTestNetwork::OCV/CPU 9.040 8.813 1.03 MobileNet_SSD_Caffe::DNNTestNetwork::OCV/OCL 8.692 9.481 0.92 MobileNet_SSD_Caffe::DNNTestNetwork::OCV/OCL_FP16 8.792 8.884 0.99 MobileNet_SSD_v1_TensorFlow::DNNTestNetwork::OCV/CPU 7.893 7.735 1.02 MobileNet_SSD_v1_TensorFlow::DNNTestNetwork::OCV/OCL 8.233 7.816 1.05 MobileNet_SSD_v1_TensorFlow::DNNTestNetwork::OCV/OCL_FP16 7.949 7.817 1.02 MobileNet_SSD_v2_TensorFlow::DNNTestNetwork::OCV/CPU 14.373 15.282 0.94 MobileNet_SSD_v2_TensorFlow::DNNTestNetwork::OCV/OCL 14.218 15.788 0.90 MobileNet_SSD_v2_TensorFlow::DNNTestNetwork::OCV/OCL_FP16 14.473 14.853 0.97 MobileNetv2_ONNX::DNNTestNetwork::OCV/CPU 1.507 1.440 1.05 MobileNetv2_ONNX::DNNTestNetwork::OCV/OCL 1.529 1.394 1.10 MobileNetv2_ONNX::DNNTestNetwork::OCV/OCL_FP16 1.448 1.433 1.01 PPHumanSeg::DNNTestNetwork::OCV/CPU 2.302 1.919 1.20 PPHumanSeg::DNNTestNetwork::OCV/OCL 2.097 1.865 1.12 PPHumanSeg::DNNTestNetwork::OCV/OCL_FP16 2.129 1.908 1.12 PPOCRv3::DNNTestNetwork::OCV/CPU 20.612 20.858 0.99 PPOCRv3::DNNTestNetwork::OCV/OCL 21.672 21.444 1.01 PPOCRv3::DNNTestNetwork::OCV/OCL_FP16 22.660 20.857 1.09 ResNet50_QDQ_ONNX::DNNTestNetwork::OCV/CPU 6.443 6.718 0.96 ResNet50_QDQ_ONNX::DNNTestNetwork::OCV/OCL 6.842 6.823 1.00 ResNet50_QDQ_ONNX::DNNTestNetwork::OCV/OCL_FP16 6.895 6.579 1.05 ResNet_50_v1_ONNX::DNNTestNetwork::OCV/CPU 7.669 7.629 1.01 ResNet_50_v1_ONNX::DNNTestNetwork::OCV/OCL 7.514 7.473 1.01 ResNet_50_v1_ONNX::DNNTestNetwork::OCV/OCL_FP16 7.598 7.513 1.01 SFace::DNNTestNetwork::OCV/CPU 3.427 3.334 1.03 SFace::DNNTestNetwork::OCV/OCL 3.420 3.337 1.02 SFace::DNNTestNetwork::OCV/OCL_FP16 3.497 3.375 1.04 SSD::DNNTestNetwork::OCV/CPU 83.165 83.476 1.00 SSD::DNNTestNetwork::OCV/OCL 83.531 84.121 0.99 SSD::DNNTestNetwork::OCV/OCL_FP16 82.447 83.234 0.99 SqueezeNet_v1_1::DNNTestNetwork::OCV/CPU 1.334 1.308 1.02 SqueezeNet_v1_1::DNNTestNetwork::OCV/OCL 1.279 1.313 0.97 SqueezeNet_v1_1::DNNTestNetwork::OCV/OCL_FP16 1.429 1.290 1.11 VIT_Base_Patch16_224::DNNTestNetwork::OCV/CPU 71.211 75.013 0.95 VIT_Base_Patch16_224::DNNTestNetwork::OCV/OCL 71.988 71.218 1.01 VIT_Base_Patch16_224::DNNTestNetwork::OCV/OCL_FP16 73.700 82.285 0.90 VitTrack::DNNTestNetwork::OCV/CPU 4.096 2.091 1.96 VitTrack::DNNTestNetwork::OCV/OCL 3.830 2.056 1.86 VitTrack::DNNTestNetwork::OCV/OCL_FP16 3.796 2.110 1.80 YOLOX::DNNTestNetwork::OCV/CPU 24.346 23.079 1.05 YOLOX::DNNTestNetwork::OCV/OCL 24.442 23.459 1.04 YOLOX::DNNTestNetwork::OCV/OCL_FP16 23.947 23.327 1.03 YOLOv3::DNNTestNetwork::OCV/CPU 59.140 59.140 1.00 YOLOv3::DNNTestNetwork::OCV/OCL 46.725 45.292 1.03 YOLOv3::DNNTestNetwork::OCV/OCL_FP16 46.939 45.382 1.03 YOLOv4::DNNTestNetwork::OCV/CPU 70.062 68.145 1.03 YOLOv4::DNNTestNetwork::OCV/OCL 219.570 214.823 1.02 YOLOv4::DNNTestNetwork::OCV/OCL_FP16 219.276 213.472 1.03 YOLOv4_tiny::DNNTestNetwork::OCV/CPU 8.362 8.221 1.02 YOLOv4_tiny::DNNTestNetwork::OCV/OCL 8.373 8.095 1.03 YOLOv4_tiny::DNNTestNetwork::OCV/OCL_FP16 8.830 8.088 1.09 YOLOv5::DNNTestNetwork::OCV/CPU 9.237 8.484 1.09 YOLOv5::DNNTestNetwork::OCV/OCL 8.776 8.537 1.03 YOLOv5::DNNTestNetwork::OCV/OCL_FP16 8.747 8.536 1.02 YOLOv8::DNNTestNetwork::OCV/CPU 11.019 11.463 0.96 YOLOv8::DNNTestNetwork::OCV/OCL 11.204 10.766 1.04 YOLOv8::DNNTestNetwork::OCV/OCL_FP16 11.355 10.764 1.05 YuNet::DNNTestNetwork::OCV/CPU 3.371 3.466 0.97 YuNet::DNNTestNetwork::OCV/OCL 3.396 3.105 1.09 YuNet::DNNTestNetwork::OCV/OCL_FP16 3.150 3.130 1.01 ``` 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 |
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 Prasad Ayush Kumar
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5af1571073 | Darknet cleanup | ||
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Abhishek Gola
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c83b86eb57 |
Merge pull request #28811 from abhishek-gola:qlinear_support
Added QLinear layer support #28811 closes: https://github.com/opencv/opencv/issues/26310 ### 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 |
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Abhishek Gola
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c44d03d8e1 | added eyelike layer support | ||
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Abhishek Gola
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de851d24a5 |
Merge pull request #28121 from abhishek-gola:loop_layer_add
Add Loop layer to new DNN engine #28121 Addition of loop layer in 5.x for issue: https://github.com/opencv/opencv/issues/26179 and https://github.com/opencv/opencv/issues/26141 and https://github.com/opencv/opencv/issues/25200 OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1335 ### 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 |
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Abhishek Gola
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3d77645a3a |
Merge pull request #28750 from abhishek-gola:activation_fusion
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, ### 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 |
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Abhishek Gola
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53d9a67cf3 |
Merge pull request #28741 from abhishek-gola:int8_block_layout
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, ### 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 |
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Abhishek Gola
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2ec6a6bb65 |
Merge pull request #28588 from abhishek-gola:ORT_GPU_wrapper
Added OnnxRuntime GPU wrapper #28588 ### 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 |