## dnn: extend engine-new layer dtype coverage (control flow, Range, Hardmax, MaxUnpool, CumSum/CumProd, MaxPool, Resize2, normalization, Gemm, MatMul)
ONNX permits these dtypes on these ops, but the engine-new layers refused them at graph-construction time, so :
- valid models either failed to load outright or
- a layer quietly converted to float32 instead (Resize2)
- ran but silently lost precision above float32's 24-bit mantissa.
Companion PR (test data) : [1406](https://github.com/opencv/opencv_extra/pull/1406)
### Support added, per layer
| Layer | Types added | Gate / kernel |
|---|---|---|
| If | Bool, 16U, 16S, 32U, 32S, 64U | Gate only — the condition-reading switch already handled every depth |
| Loop | Bool, 16U, 16S, 32U, 32S, 64U | Gate only — same as If |
| Scan | Bool, 16U, 16S, 32U, 32S, 64U | Gate only — Scan never inspects element values at all |
| Range | 16S | Kernel only — gate was already an unconditional passthrough |
| Hardmax | 64F | Gate only — the `double` kernel has existed since 2024, just unreachable |
| MaxUnpool | 64F | Gate + a genuine `double` instantiation of the value-scatter routine |
| CumSum | 32U, 64U | Gate + two instantiations of the existing running-sum template (wraparound on overflow) |
| CumProd | 32U, 64U | Gate + two instantiations of the existing running-product template |
| MaxPool | 8S, 8U, 64F | Kernel only (gate was already open) — new scalar kernel for the blocked values-only path **and** the separate values+indices path, which had its own float32-only assert |
| Resize2 | 32S (nearest-neighbor only) | Gate + native `int32` gather; bilinear/cubic now reject 32S explicitly instead of silently converting to `float` |
| RMSNorm | 64F | Kernel — `fast_norm.cpp` templated on `T`, genuine `double` accumulator |
| LayerNorm | 64F | Kernel — same shared `fast_norm.cpp` path |
| LayerNorm2 | 64F | Kernel — same shared `fast_norm.cpp` path |
| InstanceNorm | 64F | Kernel — existing SIMD float32 blocked path left untouched, new scalar `double` path added beside it |
| GroupNorm | 64F | Kernel — same treatment as InstanceNorm |
| Gemm | 64F | Kernel — dedicated `cv::gemm` path, bypassing the float-only fastGemm/MLAS kernels |
| MatMul | 64F, 32S, 64S, 32U, 64U | Gate + two new paths: per-batch `cv::gemm` for 64F, and a direct 64-bit-accumulated loop for the four integer types |
Removed `test_maxpool_2d_uint8` from `opencv_all_denylist` : with 8U now supported, the test passes NORMASSERT on all backend/target combinations .
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
Add 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
Extended support for Resize and Split layers #29323
Merge with: https://github.com/opencv/opencv_extra/pull/1379
Key changes:
Resize layer:
_antialias_ (linear & cubic) :- PIL-style separable resampling with stretched filter support and edge-clamped, renormalized weights.
_axes_ (incl. reversed [3,2]) :- getOutShape, the scale override, and runtime _tf_crop_and_resize_ ROI parsing now map 2-element sizes/scales/roi by the axes order instead of assuming [2,3].
_keep_aspect_ratio_policy_ (not_larger/not_smaller) :- output size from min/max per-axis scale.
_half_pixel_symmetric_ :- new coordinate-transform mode + importer mapping.
_align_corners_ downsampling :- coordinate scale uses the unfloored scaled length (in−1)/(in·x_scale−1).
Split Layer:
_convertTo empty 1-D Mat:_ the empty-Mat branch collapsed a 1-D [0] to 2-D [1,0] via cv::Size(); now uses allowTransposed like the non-empty path.
### 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
Extend several operations to support block layout #28839
Requires opencv_extra: https://github.com/opencv/opencv_extra/pull/1347
After this PR, we observed the following improvements in the listed models:
Model | Before (ENGINE_NEW) | After (ENGINE_NEW) | ENGINE_ORT | % Improvement (Before v/s After)
-- | -- | -- | -- | --
Face_Paint | 514.68ms | 384.40ms | 394.38ms | 25.31%
BlazeFace | 1.03ms | 0.83ms | 0.66ms | 19.42%
**Device details:**
Model name: Intel(R) Core(TM) i9-14900KS, x86_64, 32 Cores, Ubuntu 22.04.5 LTS
Operations covered:
- [x] Pad
- [x] Resize
- [x] Reshape
- [x] Shape
- [x] InstanceNorm
- [x] GroupNorm
### 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
Added fully functional resize layer to new DNN engine #27586
### 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
Vincent Rabaud
Varun Jaiswal
Prasad Ayush Kumar
Abhishek Gola
Alexander Smorkalov