0/1D test for BatchNorm layer #25420
This PR introduces support for 0/1D inputs in `BatchNorm` layer.
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Comments for parser denylist #25465
Relates to https://github.com/opencv/opencv/issues/21078
This PR is designed to figure out why the test in `test_onnx_conformance_layer_parser_denylist.inl.hpp` fails. Currently, conformance tests do not pass for the following reasons:
1. BOOL, INT(8, 16) types are not supported **(MAJOR)**
2. Some layers can not be created due to various reasons **(MAJOR)**
3. Shape mismatches while creating layers **(MAJOR)**
4. Some layers are expected to support dynamic parameter initialization **(MAJOR)**
5. Some layers are expected to receive weight as inputs (no idea why that is needed) **(MAJOR)**
6. Other unknown reasons
**(MAJOR)** - These are the most frequently encountered reasons for test failure.
The style of comments is not consistent everywhere. Let's keep this PR without merging, just for our reference.
A couple of tests are commented on since they have passed on the MacOS platform.
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Conformance test denylist reduce #25442
Comment out all passing tests in `test_onnx_conformance_layer_filter_opencv_all_denylist.inl.hpp` file.
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Fixed ONNX range layer #25414
Partially address https://github.com/opencv/opencv/issues/25363
Fixed ONNX range layer. It should support any input type.
Added tests (extra [PR](https://github.com/opencv/opencv_extra/pull/1170))
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Fully connected 0D test. #25208
This PR introduces parametrized `0/1D` input support test for `Fullyconnected` layer.
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Rename remaining float16_t for future proof #25387
Resolves comment: https://github.com/opencv/opencv/pull/25217#discussion_r1547733187.
`std::float16_t` and `std::bfloat16_t` are introduced since c++23: https://en.cppreference.com/w/cpp/types/floating-point.
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Added int support to padding layer #25241
Added int32 and int64 support to padding layer (CPU and CUDA).
ONNX parser doesn't convert non-zero padding value to float now.
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1D Scatter Layer Test #25071
This PR introduces parametrized test for `Scatter` layer to test its functionality for 1D arrays
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Added int tests for CumSum, Scatter, Tile and ReduceSum dnn layers #25277
Fixed bug in tile layer.
Fixed bug in reduce layer by reimplementing the layer.
Fixed types filter in Scatter and ScatterND layers
PR for extra: https://github.com/opencv/opencv_extra/pull/1161
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Concat Layer 0/1D test #25224
This PR introduces parametrized `0/1D` input support test for `Concat` layer.
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[BugFix] dnn (ONNX): Foce dropping constant inputs in parseClip if they are shared #25319
Resolves https://github.com/opencv/opencv/issues/25278
Merge with https://github.com/opencv/opencv_extra/pull/1165
In Gold-YOLO ,`Div` has a constant input `B=6` which is then parsed into a `Const` layer in the ONNX importer, but `Clip` also has the shared constant input `max=6` which is already a `Const` layer and then connected to `Elementwise` layer. This should not happen because in the `forward()` of `Elementwise` layer, the legacy code goes through and apply activation to each input. More details on https://github.com/opencv/opencv/issues/25278#issuecomment-2032199630.
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Ownership check in TFLite importer #25312
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resolves https://github.com/opencv/opencv/issues/25310
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Optimize int8 layers in DNN modules by using RISC-V Vector intrinsic. #25230
This patch optimize 3 functions in the int8 layer by using RVV Native Intrinsic.
This patch was tested on QEMU using VLEN=128 and VLEN=256 on `./bin/opencv_test_dnn --gtest_filter="*Int8*"`;
On the real device (k230, VLEN=128), `EfficientDet_int8` in `opencv_perf_dnn` showed a performance improvement of 1.46x.
| Name of Test | Original | optimized | Speed-up |
| ------------------------------------------ | -------- | ---------- | -------- |
| EfficientDet_int8::DNNTestNetwork::OCV/CPU | 2843.467 | 1947.013 | 1.46 |
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Merge with https://github.com/opencv/opencv_extra/pull/1158
Todo:
- [x] Fix Attention pattern recognition.
- [x] Handle other backends.
Benchmark:
"VIT_B_32 OCV/CPU", M1, results in milliseconds.
| Model | 4.x | This PR |
| - | - | - |
| VIT_B_32 OCV/CPU | 87.66 | **83.83** |
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0D test for split layer #25205
This PR introduces parametrized `0/1D` input support test for `Split` layer.
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dnn: avoid const layer forwarding in layer norm layer and attention layer #25238
While profiling ViTs with dnn, I found `ConstLayer` can take a proportion of the inference time, which is weird. This comes from the data copy during the inference of `ConstLayer`. There is a chance that we can improve the efficiency of data copying but the easiest and most convenient way is to avoid `ConstLayer`. This PR change the way how we handle constants in layer normalization layer and attention layer, which is storing in the layer blobs instead of making constant layers for them.
Checklists:
- [x] Backend compatibility in layer normalization layer.
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Allowed int types in Tile and Reduce layers #25218
Allowed any Mat type in Tile layer.
Allowed int64 type in Reduce layer.
ONNX tests with int32 and int64 inputs will be added later in a separate PR
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dnn (CANN): Fix incorrect shape of 1d bias in Gemm #25166
Gemm layer was refactored some time ago. Users found that the mobilenet example in https://github.com/opencv/opencv/wiki/Huawei-CANN-Backend does not work because of incorrect shape set for 1d bias in Gemm. This PR resolves this issue.
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Release convolution weightsMat after usage #25181
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related (but not resolved): https://github.com/opencv/opencv/issues/24134
Minor memory footprint improvement. Also, adds a test for VmHWM.
RAM top memory usage (-230MB)
| YOLOv3 (237MB file) | 4.x | PR |
|---------------------|---------|---------|
| no winograd | 808 MB | 581 MB |
| winograd | 1985 MB | 1750 MB |
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