Re-enable & triage DISABLED tests for DNN module - #29702
Requires: https://github.com/opencv/opencv_extra/pull/1403
**co-authored by: @varun-jaiswal17**
### PR Changes:
## dnn test cleanup: re-enable stale-disabled tests, fix defect-blind tests, remove redundant coverage
### Removed (dead or unbuildable)
- `test_int8_layers.cpp` (1118 lines, removed entirely): cannot compile — `Net::quantize()`,
`getInputDetails`/`getOutputDetails` are all gone from `dnn.hpp`/`dnn/src`. Disabled in that same PR (#24980)
because on-the-fly quantization was removed — every test in this file called `net.quantize()` to calibrate and
run its own int8 conversion. Its own header comment said restore "when test models are quantized outside
OpenCV". Pre-quantized ONNX/TFLite test models already do that.
### Removed (redundant or assertion-free)
- `Tokenizer_BPE.Tokenizer_GPT2_Model`: line-for-line subset of `Tokenizer_GPT2` — same config, same input,
same roundtrip assertion.
- `Test_TensorFlow.read_inception`: printed `out.dims` and asserted nothing about the result;
`inception_accuracy` loads the same `.pb` and checks it against a reference.
- `Test_Caffe_nets` fixture + `INSTANTIATE`: registered **zero** `TEST_P` cases — dead scaffolding for Faster
R-CNN tests removed earlier.
- `Test_ONNX_nets.Squeezenet`: kernels {1×1, 3×3} and every op type already covered by dedicated layer tests.
- `Test_ONNX_nets.VGG16_bn`: single conv kernel (3×3), fully covered by dedicated layer tests; skipped by
default anyway under `mem_6gb`.
- `Test_ONNX_nets.CaffeNet`: identical op multiset, node count (24) and conv signatures to retained `Alexnet`.
- `Test_ONNX_nets.RCNN_ILSVRC13`: `Alexnet` minus `Softmax` (23 vs 24 nodes), identical conv signatures.
- `Test_ONNX_nets.Inception_v1`: same op set as retained `Googlenet` (+1 `Reshape`) — Inception v1 *is*
GoogLeNet.
### Given real assertions instead of stale expectations
- `Test_ONNX_layers.Elementwise_Sqrt`: moved `testONNXModels("sqrt")` below `#endif` — its only work line sat
inside `INF_ENGINE_VER_MAJOR_LT(2021040000)`, so without OpenVINO the body compiled to nothing and reported `[
OK ]` on all 3 backends.
- `Layer_Test_01D.Clip`: now calls `ClipLayer::create` with `"min"`/`"max"` — it set `lp.type = "Clip"` but
constructed `ReLU6Layer::create`, and `runLayer` never reads `layer->type`, so it just re-ran `ReLU6`.
- `Layer_Arg_Test`: removed the "disabled" comment, corrected the `convertTo` comment — the comment said the
test was disabled while it runs 8 cases, and the second said "convert to float" where the code converts to
`CV_64S`.
### Re-enabled as-is (stale disable reasons)
- `Test_ONNX_layers.LSTM`/`LSTM_bidirectional` (`test_onnx_importer.cpp:1551,1558`): disabled by #21522 (2022)
for poor 1-D-mat handling in the importer of that era; no longer reproduces.
- `Test_ONNX_layers.Split_sizes_0d` (`:1373`): disabled by #22652 for a Mul/0-d-tensor shape ambiguity (A×1 vs
1×A); dnn now supports real 1-D Mats, so the output matches the reference exactly.
- `DNNTestNetwork.YOLOv8n`
### Library fixes found while re-enabling
- `Test_ONNX_layers.LSTM_layout_seq`/`LSTM_layout_batch` (`test_onnx_importer.cpp:1721,1728`): `LSTM2` never
transposed `X` for ONNX `layout=1` (batch-first); fixed via `transposeND` gated on `layout==BATCH_SEQ_HID`
(`recurrent2_layers.cpp:172`). Fixture also had a leaked loop variable that made the reference a copy of the
input; rebuilt by hand since ORT itself refuses to run `layout=1`.
- `Test_Graph_Simplifier.ResizeSubgraph` (`test_graph_simplifier.cpp:61`): disabled by the block-layout PR
#28585; expectations updated for the `TransformLayout` pass that PR introduced. The test now covers 4 subgraphs rather than 6, because `GatherCastSubgraph` and `MulCastSubgraph` were removed by `0e36cafcf4` and `7669897910` (`Gather`/`Mul` -> `Cast` is no longer fused, since folding it away silently dropped the `Cast`'s dtype semantics). The dynamic-scale `Shape`/`Gather`/`Cast`/`Floor`/`Concat`/`Unsqueeze`/`Slice` chain these models use to compute Resize's scale factor therefore no longer collapses, and the `Mul` survives as `NaryEltwise`, which is why the expected layer lists grew
### Deliberately kept
- `ZFNet`: its **7×7** conv appears in no dedicated layer test, and its kernel set {7×7, 5×5, 3×3} differs from
`Alexnet`'s {11×11, 5×5, 3×3}.
### Pull Request Readiness Checklist
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- [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.
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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
Update default YuNet model to new dynamic inputs #29107
Update the default model in `face_detect.py` and `face_detect.cpp` to
`face_detection_yunet_2026may.onnx`, which has symbolic `height`/`width` input dims.
## Changes
- `samples/dnn/face_detect.py`: update default `--face_detection_model` to `face_detection_yunet_2026may.onnx`
- `samples/dnn/face_detect.cpp`: update default `fd_model` to `face_detection_yunet_2026may.onnx`
Companion PR :
- https://github.com/opencv/opencv_zoo/pull/310
- https://github.com/opencv/opencv_extra/pull/1373
Closes : https://github.com/opencv/opencv/issues/28769
### 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
Block layout-based convolution in DNN #28585
merge together with https://github.com/opencv/opencv_extra/pull/1321
Some core parts of the new engine in DNN module have been revised substantially:
1. all tests seem to pass, except for `Test_Graph_Simplifier.ResizeSubgraph`, which has been disabled because it does not take the newly added `TransformLayoutLayer` into account. The test should be reworked perhaps.
1. convolution and related operations (maxpool/avgpool) now use so-called block layout (`DATA_LAYOUT_BLOCK`), where `NxCxHxW` tensors are represented as `NxC1xHxWxC0`, where `C1=(C + C0-1)/C0` and `C0` is a power-of-two (usually 4, 8, 16 or 32).
1. graph is now pre-processed and `TransformLayoutLayer` is inserted to convert data from NCHW or NHWC layout to the block layout or vice versa. The transformations are done in a lazy way only when they are really needed. For example, in the whole Resnet only 2 transformations are performed.
1. transformer-based models and other models that do not use convolutions will run as usual, without going to block layout.
1. there is yet another graph preprocessing stage added that embeds constant weights/scale and bias into convolution and batch norm layers.
1. 'batchnorm', 'activation' and 'adding a residual' are now fused with convolution, just like in the old engine. That brings some noticeable acceleration.
1. optimized convolution kernels have been added.
* depthwise convolution, as well as maxpool and avgpool support C0=4, 8, 16 etc. _as long as_ C0 is divisible by the number of fp32 lanes in a SIMD register of the target platform (e.g. on ARM with NEON there must be `C0 % 4 == 0`, on x64 with AVX2 `C0 % 8 == 0`).
* non-depthwise convolution only supports C0=8 for now. C0=8 seems to be a sweetspot for ARM with NEON, x64 with AVX2 or RISC-V with RVV (with 128- or 256-bit registers). For some platforms with dedicated matrix accelerators C0=16 or even C0=32 might be more efficient, but we could add the respective kernels later.
* only fp32 kernels have been added. fp16/bf16 kernels might be added a little later.
### Pull Request Readiness Checklist
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- [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
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Modified Caffe parser to support the new dnn engine #26208
Now the Caffe parser supports both the old and the new engine. It can be selected using newEngine argument in PopulateNet.
All cpu Caffe tests work fine except:
- Test_Caffe_nets.Colorization
- Test_Caffe_layers.FasterRCNN_Proposal
Both these tests doesn't work because of the bug in the new net.forward function. The function takes the name of the desired target last layer, but uses this name as the name of the desired output tensor.
Also Colorization test contains a strange model with a Silence layer in the end, so it doesn't have outputs. The old parser just ignored it. I think, the proper solution is to run this model until the (number_of_layers - 2) layer using proper net.forward arguments in the test.
### Pull Request Readiness Checklist
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- [ ] The PR is proposed to the proper branch
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New dnn engine #26056
This is the 1st PR with the new engine; CI is green and PR is ready to be merged, I think.
Merge together with https://github.com/opencv/opencv_contrib/pull/3794
---
**Known limitations:**
* [solved] OpenVINO is temporarily disabled, but is probably easy to restore (it's not a deal breaker to merge this PR, I guess)
* The new engine does not support any backends nor any targets except for the default CPU implementation. But it's possible to choose the old engine when loading a model, then all the functionality is available.
* [Caffe patch is here: #26208] The new engine only supports ONNX. When a model is constructed manually or is loaded from a file of different format (.tf, .tflite, .caffe, .darknet), the old engine is used.
* Even in the case of ONNX some layers are not supported by the new engine, such as all quantized layers (including DequantizeLinear, QuantizeLinear, QLinearConv etc.), LSTM, GRU, .... It's planned, of course, to have full support for ONNX by OpenCV 5.0 gold release. When a loaded model contains unsupported layers, we switch to the old engine automatically (at ONNX parsing time, not at `forward()` time).
* Some layers , e.g. Expat, are only partially supported by the new engine. In the case of unsupported flavours it switches to the old engine automatically (at ONNX parsing time, not at `forward()` time).
* 'Concat' graph optimization is disabled. The optimization eliminates Concat layer and instead makes the layers that generate tensors to be concatenated to write the outputs to the final destination. Of course, it's only possible when `axis=0` or `axis=N=1`. The optimization is not compatible with dynamic shapes since we need to know in advance where to store the tensors. Because some of the layer implementations have been modified to become more compatible with the new engine, the feature appears to be broken even when the old engine is used.
* Some `dnn::Net` API is not available with the new engine. Also, shape inference may return false if some of the output or intermediate tensors' shapes cannot be inferred without running the model. Probably this can be fixed by a dummy run of the model with zero inputs.
* Some overloads of `dnn::Net::getFLOPs()` and `dnn::Net::getMemoryConsumption()` are not exposed any longer in wrapper generators; but the most useful overloads are exposed (and checked by Java tests).
* [in progress] A few Einsum tests related to empty shapes have been disabled due to crashes in the tests and in Einsum implementations. The code and the tests need to be repaired.
* OpenCL implementation of Deconvolution is disabled. It's very bad and very slow anyway; need to be completely revised.
* Deconvolution3D test is now skipped, because it was only supported by CUDA and OpenVINO backends, both of which are not supported by the new engine.
* Some tests, such as FastNeuralStyle, checked that the in the case of CUDA backend there is no fallback to CPU. Currently all layers in the new engine are processed on CPU, so there are many fallbacks. The checks, therefore, have been temporarily disabled.
---
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
Change opencv_face_detector related tests and samples from caffe to onnx #25463
Part of https://github.com/opencv/opencv/issues/25314
This PR aims to change the tests related to opencv_face_detector from caffe framework to onnx. Tests in `test_int8_layer.cpp` and `test_caffe_importer.cpp` will be removed in https://github.com/opencv/opencv/pull/25323
### Pull Request Readiness Checklist
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- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
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* add Winograd FP16 implementation
* fixed dispatching of FP16 code paths in dnn; use dynamic dispatcher only when NEON_FP16 is enabled in the build and the feature is present in the host CPU at runtime
* fixed some warnings
* hopefully fixed winograd on x64 (and maybe other platforms)
---------
Co-authored-by: Vadim Pisarevsky <vadim.pisarevsky@gmail.com>
Remove torch (old torch7) from dnn in 5.x #24294
Merge with https://github.com/opencv/opencv_extra/pull/1097
Completely removed torch (old torch7) from dnn:
- removed modules/dnn/src/torch directory that contained torch7 model parser
- removed readNetFromTorch() and readTorchBlob() public functions
- removed torch7 references from comments and help texts
- replaced links to t7 models by links to similar onnx models in js_style_transfer turtorial (similar to https://github.com/opencv/opencv/pull/24245/files)
dnn: cleanup of halide backend for 5.x #24231
Merge with https://github.com/opencv/opencv_extra/pull/1092.
### Pull Request Readiness Checklist
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- [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
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Patch to opencv_extra has the same branch name.
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dnn: merge tests from test_halide_layers to test_backends #24283
Context: https://github.com/opencv/opencv/pull/24231#pullrequestreview-1628649980
### Pull Request Readiness Checklist
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- [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
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OCL_FP16 MatMul with large batch
* Workaround FP16 MatMul with large batch
* Fix OCL reinitialization
* Higher thresholds for INT8 quantization
* Try fix gemm_buffer_NT for half (columns)
* Fix GEMM by rows
* Add batch dimension to InnerProduct layer test
* Fix Test_ONNX_conformance.Layer_Test/test_basic_conv_with_padding
* Batch 16
* Replace all vload4
* Version suffix for MobileNetSSD_deploy Caffe model
Switch to new OpenVINO API after 2022.1 release
* Pass Layer_Test_Convolution_DLDT.Accuracy/0 test
* Pass test Test_Caffe_layers.Softmax
* Failed 136 tests
* Fix Concat. Failed 120 tests
* Custom nGraph ops. 19 failed tests
* Set and get properties from Core
* Read model from buffer
* Change MaxPooling layer output names. Restore reshape
* Cosmetic changes
* Cosmetic changes
* Override getOutputsInfo
* Fixes for OpenVINO < 2022.1
* Async inference for 2021.4 and less
* Compile model with config
* Fix serialize for 2022.1
* Asynchronous inference with 2022.1
* Handle 1d outputs
* Work with model with dynamic output shape
* Fixes with 1d output for old API
* Control outputs by nGraph function for all OpenVINO versions
* Refer inputs in PrePostProcessor by indices
* Fix cycled dependency between InfEngineNgraphNode and InfEngineNgraphNet.
Add InferRequest callback only for async inference. Do not capture InferRequest object.
* Fix tests thresholds
* Fix HETERO:GPU,CPU plugin issues with unsupported layer
DNN: reduce the memory used in convolution layer
* reduce the memory in winograd and disabel the test when usage memory is larger than 2gb.
* remove VERY_LOG tag
DNN: Accelerating convolution
* Fast Conv of ARM, X86 and universal intrinsics.
* improve code style.
* error fixed.
* improve the License
* optimize memory allocated and Adjust the threshold.
* change FasterRCNN_vgg16 to 2GB memory.
