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
Rewrite Universal Intrinsic code by using new API: Core module. #23980
The goal of this PR is to match and modify all SIMD code blocks guarded by `CV_SIMD` macro in the `opencv/modules/core` folder and rewrite them by using the new Universal Intrinsic API.
The patch is almost auto-generated by using the [rewriter](https://github.com/hanliutong/rewriter), related PR #23885.
Most of the files have been rewritten, but I marked this PR as draft because, the `CV_SIMD` macro also exists in the following files, and the reasons why they are not rewrited are:
1. ~~code design for fixed-size SIMD (v_int16x8, v_float32x4, etc.), need to manually rewrite.~~ Rewrited
- ./modules/core/src/stat.simd.hpp
- ./modules/core/src/matrix_transform.cpp
- ./modules/core/src/matmul.simd.hpp
2. Vector types are wrapped in other class/struct, that are not supported by the compiler in variable-length backends. Can not be rewrited directly.
- ./modules/core/src/mathfuncs_core.simd.hpp
```cpp
struct v_atan_f32
{
explicit v_atan_f32(const float& scale)
{
...
}
v_float32 compute(const v_float32& y, const v_float32& x)
{
...
}
...
v_float32 val90; // sizeless type can not used in a class
v_float32 val180;
v_float32 val360;
v_float32 s;
};
```
3. The API interface does not support/does not match
- ./modules/core/src/norm.cpp
Use `v_popcount`, ~~waiting for #23966~~ Fixed
- ./modules/core/src/has_non_zero.simd.hpp
Use illegal Universal Intrinsic API: For float type, there is no logical operation `|`. Further discussion needed
```cpp
/** @brief Bitwise OR
Only for integer types. */
template<typename _Tp, int n> CV_INLINE v_reg<_Tp, n> operator|(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b);
template<typename _Tp, int n> CV_INLINE v_reg<_Tp, n>& operator|=(v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b);
```
```cpp
#if CV_SIMD
typedef v_float32 v_type;
const v_type v_zero = vx_setzero_f32();
constexpr const int unrollCount = 8;
int step = v_type::nlanes * unrollCount;
int len0 = len & -step;
const float* srcSimdEnd = src+len0;
int countSIMD = static_cast<int>((srcSimdEnd-src)/step);
while(!res && countSIMD--)
{
v_type v0 = vx_load(src);
src += v_type::nlanes;
v_type v1 = vx_load(src);
src += v_type::nlanes;
....
src += v_type::nlanes;
v0 |= v1; //Illegal ?
....
//res = v_check_any(((v0 | v4) != v_zero));//beware : (NaN != 0) returns "false" since != is mapped to _CMP_NEQ_OQ and not _CMP_NEQ_UQ
res = !v_check_all(((v0 | v4) == v_zero));
}
v_cleanup();
#endif
```
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dnn: cleanup of tengine backend #24122🚀 Cleanup for OpenCV 5.0. Tengine backend is added for convolution layer speedup on ARM CPUs, but it is not maintained and the convolution layer on our default backend has reached similar performance to that of Tengine.
Tengine backend related PRs:
- https://github.com/opencv/opencv/pull/16724
- https://github.com/opencv/opencv/pull/18323
### Pull Request Readiness Checklist
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Invalid memory access fix for ONNX split layer parser #24076#24101
### Pull Request Readiness Checklist
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TFLite models on different backends (tests and improvements) #24039
### Pull Request Readiness Checklist
* MaxUnpooling with OpenVINO
* Fully connected with transposed inputs/weights with OpenVINO
* Enable backends tests for TFLite (related to https://github.com/opencv/opencv/issues/23992#issuecomment-1640691722)
* Increase existing tests thresholds
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.
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- [x] The PR is proposed to the proper branch
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Patch to opencv_extra has the same branch name.
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Resolve uncovered CUDA dnn layer #24080
### Pull Request Readiness Checklist
* Gelu activation layer on CUDA
* Try to relax GEMM from ONNX
resolves https://github.com/opencv/opencv/issues/24064
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.
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Remove legacy nGraph logic #24072
### Pull Request Readiness Checklist
TODO:
- [x] Test with OpenVINO 2021.4 (tested locally)
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.
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DetectionOutput layer on OpenVINO without limitations #24069
### Pull Request Readiness Checklist
required for https://github.com/opencv/opencv/pull/23987
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- [x] I agree to contribute to the project under Apache 2 License.
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G-API: Support CUDA & TensoRT Execution Providers for ONNXRT Backend #24059
### Pull Request Readiness Checklist
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PReLU with element-wise scales #24056
### Pull Request Readiness Checklist
resolves https://github.com/opencv/opencv/issues/24051
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- [x] I agree to contribute to the project under Apache 2 License.
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Update opencv dnn to support cann version >=6.3 #23936
1.modify the search path of "libopsproto.so" in OpenCVFindCANN.cmake
2.add the search path of "libgraph_base.so" in OpenCVFindCANN.cmake
3.automatic check Ascend socVersion,and test on Ascend310/Ascend310B/Ascend910B well
Python typing refinement for dnn_registerLayer/dnn_unregisterLayer functions #24066
This patch introduces typings generation for `dnn_registerLayer`/`dnn_unregisterLayer` manually defined in [`cv2/modules/dnn/misc/python/pyopencv_dnn.hpp`](https://github.com/opencv/opencv/blob/4.x/modules/dnn/misc/python/pyopencv_dnn.hpp)
Updates:
- Add `LayerProtocol` to `cv2/dnn/__init__.pyi`:
```python
class LayerProtocol(Protocol):
def __init__(
self, params: dict[str, DictValue],
blobs: typing.Sequence[cv2.typing.MatLike]
) -> None: ...
def getMemoryShapes(
self, inputs: typing.Sequence[typing.Sequence[int]]
) -> typing.Sequence[typing.Sequence[int]]: ...
def forward(
self, inputs: typing.Sequence[cv2.typing.MatLike]
) -> typing.Sequence[cv2.typing.MatLike]: ...
```
- Add `dnn_registerLayer` function to `cv2/__init__.pyi`:
```python
def dnn_registerLayer(layerTypeName: str,
layerClass: typing.Type[LayerProtocol]) -> None: ...
```
- Add `dnn_unregisterLayer` function to `cv2/__init__.pyi`:
```python
def dnn_unregisterLayer(layerTypeName: str) -> None: ...
```
### Pull Request Readiness Checklist
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Fix harmless ASAN error. #24042
For an empty radius, &v[0] would be accessed (though the called functions would not use it due to v.size() being 0). Also add checks for emptyness and fix the first element checks, in case we get INT_MAX to compare to.
### Pull Request Readiness Checklist
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- [x] I agree to contribute to the project under Apache 2 License.
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- [x] The PR is proposed to the proper branch
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G-API: Support DirectML Execution Provider for ONNXRT Backend #24045
### Pull Request Readiness Checklist
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- [ ] I agree to contribute to the project under Apache 2 License.
- [ ] 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
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Patch to opencv_extra has the same branch name.
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Alexander Alekhin
autoantwort
Dmitry Kurtaev
MuZihao
Maksim Shabunin
Ginkgo
DeePingXian
Alexander Smorkalov
HAN Liutong
Samuel Thibault
Alexander Lyulkov
Yuantao Feng
unknown
Laurent Berger
cudawarped
Anatoliy Talamanov
Kumataro
Vincent Rabaud
SaltFish-T
Vadim Levin
Vincent Rabaud
Alexander Smorkalov