opencv

Commit Graph

Author	SHA1	Message	Date
Yuantao Feng	e3858cc5a3	Merge pull request #25147 from fengyuentau:dnn/elementwise_layers/speedup * added v_erf and implemented gelu acceleration via vectorization * remove anonymous v_erf and use v_erf from intrin_math * enable perf for ov and cuda backend	10 months ago
Abduragim Shtanchaev	efbc9f0b66	Merge pull request #25861 from Abdurrahheem:ash/torch-attention-export-fix-4x Merge pull request #25861 from Abdurrahheem:ash/torch-attention-export-fix-4x Support for Unflatten operation requred by Attention layer - 4.x #25861 ### Pull Request Readiness Checklist All test data and models for PR are located [#1190](https://github.com/opencv/opencv_extra/pull/1190) This PR fixes issue reised when importing batched vanilla `Attention` layer from `PyTorch` via ONNX. Currently batched version of `Attention` layer in PyTorch [has unflatten operation inside](`e3b3431c42/torch/nn/functional.py (L5500C17-L5500C31)`). `unflatten` operation causes issue in `reshape` layer (see the Reshape_2 in the graph below) due to incorrect output of `slice` layer. This PR particularly fixes `slice` and `concat` layers to handle `unflatten` operation. <img width="673" alt="image" src="https://github.com/opencv/opencv/assets/44877829/5b612b31-657a-47f1-83a4-0ac35a950abd"> 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	10 months ago
Alexander Smorkalov	25fb55601b	Fixed narrowing conversion warning with MSVC compiler.	10 months ago
Abduragim Shtanchaev	a8d1373919	Merge pull request #25794 from Abdurrahheem:ash/yolov10-support Add sample support of YOLOv9 and YOLOv10 in OpenCV #25794 This PR adds sample support of [`YOLOv9`](https://github.com/WongKinYiu/yolov9) and [`YOLOv10`](https://github.com/THU-MIG/yolov10/tree/main)) in OpenCV. Models for this test are located in this [PR](https://github.com/opencv/opencv_extra/pull/1186). Running YOLOv10 using OpenCV. 1. In oder to run `YOLOv10` one needs to cut off postporcessing with dynamic shapes from torch and then convert it to ONNX. If someone is looking for ready solution, there is [this forked branch](https://github.com/Abdurrahheem/yolov10/tree/ash/opencv-export) from official YOLOv10. Particularty follow this proceduce. ```bash git clone git@github.com:Abdurrahheem/yolov10.git conda create -n yolov10 python=3.9 conda activate yolov10 pip install -r requirements.txt python export_opencv.py --model=<model-name> --imgsz=<input-img-size> ``` By default `model="yolov10s"` and `imgsz=(480,640)`. This will generate file `yolov10s.onnx`, which can be use for inference in OpenCV 2. For inference part on OpenCV. one can use `yolo_detector.cpp` [sample](https://github.com/opencv/opencv/blob/4.x/samples/dnn/yolo_detector.cpp). If you have followed above exporting procedure, then you can use following command to run the model. ``` bash build opencv from source cd build ./bin/example_dnn_yolo_detector --model=<path-to-yolov10s.onnx-file> --yolo=yolov10 --width=640 --height=480 --input=<path-to-image> --scale=0.003921568627 --padvalue=114 ``` If you do not specify `--input` argument, OpenCV will grab first camera that is avaliable on your platform. For more deatils on how to run the `yolo_detector.cpp` file see this [guide](https://docs.opencv.org/4.x/da/d9d/tutorial_dnn_yolo.html#autotoc_md443) Running YOLOv9 using OpenCV 1. Export model following [official guide](https://github.com/WongKinYiu/yolov9)of the YOLOv9 repository. Particularly you can do following for converting. ```bash git clone https://github.com/WongKinYiu/yolov9.git cd yolov9 conda create -n yolov9 python=3.9 conda activate yolov9 pip install -r requirements.txt wget https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-t-converted.pt python export.py --weights=./yolov9-t-converted.pt --include=onnx --img-size=(480,640) ``` This will generate <yolov9-t-converted.onnx> file. 2. Inference on OpenCV. ```bash build opencv from source cd build ./bin/example_dnn_yolo_detector --model=<path-to-yolov9-t-converted.onnx> --yolo=yolov9 --width=640 --height=480 --scale=0.003921568627 --padvalue=114 --path=<path-to-image> ``` ### 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	10 months ago
alexlyulkov	6af0394cd2	Merge pull request #25458 from alexlyulkov:al/dnn-openvino-int-support Added int support for OpenVINO dnn backend #25458 Modified dnn OpenVINO integration to support type inference and int operations. Added OpenVINO support to Cast, CumSum, Expand, Gather, GatherElements, Scatter, ScatterND, Tile layers. I tried to add Reduce layer, but looks like OpenVINO uses float values inside Reduce operation so it can't pass our int tests. OpenVINO uses int32 precision for int64 operations, so I've modified input values for int64 tests when backend is OpenVINO. OpenVINO has a strange behavior with custom layers and int64 values. After model compilation OpenVINO may change types, so the model can have different output type. That's why these tests were disabled: - Test_ArgMax_Int.random/0, where GetParam() = (4, NGRAPH/CPU) - Test_ArgMax_Int.random/6, where GetParam() = (11, NGRAPH/CPU) - Test_Reduce_Int.random/6, where GetParam() = (11, NGRAPH/CPU) - Test_Reduce_Int.two_axes/6, where GetParam() = (11, NGRAPH/CPU) Also these tests were temporary disabled, they didn't work on both 4.x and 5.x branches: - Test_Caffe_layers.layer_prelu_fc/0, where GetParam() = NGRAPH/CPU - Test_ONNX_layers.LSTM_Activations/0, where GetParam() = NGRAPH/CPU - Test_ONNX_layers.Quantized_Convolution/0, where GetParam() = NGRAPH/CPU - Test_ONNX_layers.Quantized_Eltwise_Scalar/0, where GetParam() = NGRAPH/CPU - Test_TFLite.EfficientDet_int8/0, where GetParam() = NGRAPH/CPU ### 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. - [x] The feature is well documented and sample code can be built with the project CMake	11 months ago
alexlyulkov	72ad06bcf3	Merge pull request #25492 from alexlyulkov:al/range-fixed-5.x Fixed ONNX Range layer to support any input type #25492 Fixed ONNX Range layer to support any input type Extra PR: https://github.com/opencv/opencv_extra/pull/1173 Fixes #25363 ### 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	1 year ago
alexlyulkov	f9dd20eb07	Merge pull request #25414 from alexlyulkov:al/range-fixed 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)) ### 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. - [x] The feature is well documented and sample code can be built with the project CMake	1 year ago
alexlyulkov	5144766380	Merge pull request #25277 from alexlyulkov:al/dnn-int-tests 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 ### 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. - [x] The feature is well documented and sample code can be built with the project CMake	1 year ago
Yuantao Feng	55d7e3f8cc	Merge pull request #1165 from fengyuentau:gold_yolo [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. ### 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	1 year ago
Yuantao Feng	b758897c29	Merge pull request #25271 from fengyuentau:matmul_bias 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 \| ### 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	1 year ago
Dmitry Kurtaev	cfa42e4338	Einsum OpenVINO backend	1 year ago
Dmitry Kurtaev	0b6c9a2123	Merge pull request #25181 from dkurt:release_conv_weights Release convolution weightsMat after usage #25181 ### Pull Request Readiness Checklist 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 \| 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	1 year ago
alexlyulkov	85cc02f4de	Allowed int64 constants in ONNX parser (#25148 ) * Removed automatic int64 to int32 conversion in ONNX parser * Fixed wrong rebase code * added tests, minor fixes * fixed Cast layer * Fixed Cast layer for fp16 backend * Fixed Cast layer for fp16 backend * Fixed Cast layer for fp16 backend * Allowed uint32, int64, uint64 types in OpenCL * Fixed Cast layer for fp16 backend * Use randu in test_int --------- Co-authored-by: Alexander Lyulkov <alexander.lyulkov@opencv.ai>	1 year ago
alexlyulkov	1d1faaabef	Merge pull request #24411 from alexlyulkov:al/dnn-type-inference Added int32, int64 support and type inference to dnn #24411 Added a type inference to dnn similar to the shape inference, added int32 and int64 support. - Added getTypes method for layers that calculates layer outputs types and internals types from inputs types (Similar to getMemoryShapes). By default outputs and internals types = input[0] type - Added type inference pipeline similar to shape inference pipeline. LayersShapes struct (that is used in shape inference pipeline) now contains both shapes and types - All layers output blobs are now allocated using the calculated types from the type inference. - Inputs and constants with int32 and int64 types are not automatically converted into float32 now. - Added int32 and int64 support for all the layers with indexing and for all the layers required in tests. Added int32 and int64 support for CUDA: - Added host<->device data moving for int32 and int64 - Added int32 and int64 support for several layers (just slightly modified CUDA C++ templates) Passed all the accuracy tests on CPU, OCL, OCL_FP16, CUDA, CUDA_FP16. (except RAFT model) CURRENT PROBLEMS: - ONNX parser always converts int64 constants and layers attributes to int32, so some models with int64 constants doesn't work (e.g. RAFT). The solution is to disable int64->int32 conversion and fix attributes reading in a lot of ONNX layers parsers (https://github.com/opencv/opencv/issues/25102) - I didn't add type inference and int support to VULCAN, so it doesn't work at all now. - Some layers don't support int yet, so some unknown models may not work. CURRENT WORKAROUNDS: - CPU arg_layer indides are implemented in int32 followed by a int32->int64 conversion (the master branch has the same workaround with int32->float conversion) - CPU and OCL pooling_layer indices are implemented in float followed by a float->int64 conversion - CPU gather_layer indices are implemented in int32, so int64 indices are converted to int32 (the master branch has the same workaround with float->int32 conversion) DISABLED TESTS: - RAFT model REMOVED TESTS: - Greater_input_dtype_int64 (because it doesn't fit ONNX rules, the whole test is just comparing float tensor with int constant) TODO IN NEXT PULL REQUESTS: - Add int64 support for ONNX parser - Add int support for more layers - Add int support for OCL (currently int layers just run on CPU) - Add int tests - Add int support for other backends	1 year ago
Abduragim	0e6b7f1656	fix 1D handling issue in inner product	1 year ago
Abduragim	d30bf1bc3c	added test for yolo nas	1 year ago
Yuantao Feng	7fb336322d	Merge pull request #24808 from fengyuentau:fix_layernorm dnn: no layer norm fusion if axes.back() is not the axis of last dimension #24808 Merge with https://github.com/opencv/opencv_extra/pull/1137 Resolves https://github.com/opencv/opencv/issues/24797 ### 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. - [ ] The feature is well documented and sample code can be built with the project CMake	1 year ago
Abduragim	6c28d7140a	1d support for einsum	1 year ago
Abduragim Shtanchaev	3b26e183cb	changed weights of yolov7	1 year ago
Yuantao Feng	f978c99523	Merge pull request #24753 from fengyuentau:einsum_importer dnn onnx: support constaint inputs in einsum importer #24753 Merge with https://github.com/opencv/opencv_extra/pull/1132. Resolves https://github.com/opencv/opencv/issues/24697 Credits to @LaurentBerger. --- This is a workaround. I suggest to get input shapes and calculate the output shapes in `getMemoryShapes` so as to keep the best compatibility. It is not always robust getting shapes during the importer stage and we should avoid that as much as possible. ### 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	1 year ago
Alexander Alekhin	f49b26182b	dnn(test): skip very long debug tests, reduce test time	1 year ago
Alexander Alekhin	f8502d45f9	dnn(test): skip tests on 32-bit Windows	1 year ago
Alexander Smorkalov	53cd921ab4	Increate Vit_b test threshold a bit for CUDA FP16.	1 year ago
Alexander Alekhin	c9bb92d58b	dnn(test): tune FP16 test tolerance	1 year ago
Yuantao Feng	0521a3a384	Merge pull request #24476 from fengyuentau:attention_layer dnn: add attention layer #24476 Resolves #24609 Merge with: https://github.com/opencv/opencv_extra/pull/1128. Attention operator spec from onnxruntime: https://github.com/microsoft/onnxruntime/blob/v1.16.1/docs/ContribOperators.md#com.microsoft.Attention. TODO: - [x] benchmark (before this PR vs. with this PR vs. ORT). - [x] Layer fusion: Take care Slice with end=INT64_MAX. - [x] Layer fusion: match more potential attention (VIT) patterns. - [x] Single-head attention is supported. - [x] Test AttentionSubgraph fusion. - [x] Add acc tests for VIT_B_32 and VitTrack - [x] Add perf tests for VIT_B_32 and VitTrack ## Benchmarks Platform: Macbook Air M1. ### Attention Subgraph Input scale: [1, 197, 768]. \| \| mean (ms) \| median (ms) \| min (ms) \| \| ---------------------- \| --------- \| ----------- \| -------- \| \| w/ Attention (this PR) \| 3.75 \| 3.68 \| 3.22 \| \| w/o Attention \| 9.06 \| 9.01 \| 8.24 \| \| ORT (python) \| 4.32 \| 2.63 \| 2.50 \| ### ViTs All data in millisecond (ms). \| ViTs \| With Attention \| Without Attention \| ORT \| \| -------- \| -------------- \| ----------------- \| ------ \| \| vit_b_16 \| 302.77 \| 365.35 \| 109.70 \| \| vit_b_32 \| 89.92 \| 116.22 \| 30.36 \| \| vit_l_16 \| 1593.32 \| 1730.74 \| 419.92 \| \| vit_l_32 \| 468.11 \| 577.41 \| 134.12 \| \| VitTrack \| 3.80 \| 3.87 \| 2.25 \| ### 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	1 year ago
Abduragim Shtanchaev	d3dd2e463c	Merge pull request #24611 from Abdurrahheem:ash/add_yolov6_test Add test for YoloX Yolo v6 and Yolo v8 #24611 This PR adds test for YOLOv6 model (which was absent before) The onnx weights for the test are located in this PR [ #1126](https://github.com/opencv/opencv_extra/pull/1126) ### 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. - [x] The feature is well documented and sample code can be built with the project CMake	1 year ago
skycat8	848dd12a1f	Merge pull request #24553 from skycat8:yolov5 Add yolov5n to tests #24553 ### 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	1 year ago
zihaomu	b913e73d04	DNN: add the Winograd fp16 support (#23654 ) * 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>	1 year ago
Yuantao Feng	a478757483	Merge pull request #24544 from fengyuentau:layernorm_conformance dnn test: move layer norm tests into conformance tests #24544 Merge with https://github.com/opencv/opencv_extra/pull/1122 ## Motivation Some ONNX operators, such as `LayerNormalization`, `BatchNormalization` and so on, produce outputs for training (mean, stdev). So they have reference outputs of conformance tests for those training outputs as well. However, when it comes to inference, we do not need and produce those outputs for training here in dnn. Hence, output size does not match if we use dnn to infer those conformance models. This has become the barrier if we want to test these operators using their conformance tests. <!-- \| Operator \| Inference needed \| Outputs (required - total) \| Optional outputs for training? \| \| ----------------------- \| ----------------------------------- \| -------------------------- \| ------------------------------ \| \| BatchNormalization \| Yes \| 1 - 3 \| Yes \| \| Dropout \| Maybe, can be eliminated via fusion \| 1 - 2 \| Yes \| \| GRU \| Yes \| 0 - 2 \| No \| \| LSTM \| Yes \| 0 - 3 \| No \| \| LayerNormalization \| Yes \| 1 - 3 \| Yes \| \| MaxPool \| Yes \| 1 - 2 \| Yes \| \| RNN \| Yes \| 0 - 2 \| No \| \| SoftmaxCrossEntropyLoss \| No \| 1 - 2 \| -- \| --> I checked all ONNX operators with optional outputs. Turns out there are only `BatchNormalization`, `Dropout`, `LayerNormalization` and `MaxPool` has optional outputs for training. All except `LayerNormalization` have models set for training mode and eval mode. Blame ONNX for that. ## Solution In this pull request, we remove graph outputs if the graph looks like the following: ``` [X] [Scale] [Bias] [X] [Scale] [Bias] \ \| / this patch \ \| / LayerNormalization -----------> LayerNormalization / \| \ \| [Y] [Mean] [Stdev] [Y] ``` We can update conformance tests and turn on some cases as well if extending to more layers. Notes: 1. This workaround does not solve expanded function operators if they are fused into a single operator, such as `$onnx/onnx/backend/test/data/node/test_layer_normalization_2d_axis1_expanded`, but they can be run without fusion. Note that either dnn or onnxruntime does not fuse those expanded function operators. ### 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	1 year ago
fengyuentau	031846f2e1	remove filter	1 year ago
Dmitry Kurtaev	fa56623458	Merge pull request #24463 from dkurt:dnn_shared_nodes_fusion DNN graph fusion with shared nodes #24463 ### Pull Request Readiness Checklist For now, nodes from matched pattern are removed during the matching process so if nodes are used in similar subgraph, they cannot be found. required for https://github.com/opencv/opencv/pull/24397 Merge with extra: https://github.com/opencv/opencv_extra/pull/1115 A part from [model_name ](https://github.com/onnx/models/blob/main/vision/object_detection_segmentation/fcn/model/fcn-resnet101-11.onnx) with two Resize subgraphs with shared nodes: ![image](https://github.com/opencv/opencv/assets/25801568/611d89d9-12fb-4add-9218-13b10d2c086a) 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	1 year ago
Abduragim Shtanchaev	a3b3a589f9	Merge pull request #24322 from Abdurrahheem:ash/dev_einsum_ellips Ellipses supported added for Einsum Layer #24322 This PR added addresses issues not covered in #24037. Namely these are: Test case for this patch is in this PR [#1106](https://github.com/opencv/opencv_extra/pull/1106) in opencv extra Added: - [x] Broadcasting reduction "...ii ->...I" - [x] Add lazy shape deduction. "...ij, ...jk->...ik" Features to add: - [ ] Add implicit output computation support. "bij,bjk ->" (output subscripts should be "bik") - [ ] Add support for CUDA backend - [ ] BatchWiseMultiply optimize - [ ] Performance test ### 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. - [x] The feature is well documented and sample code can be built with the project CMake	2 years ago
Aser Atawya	240b245105	Merge pull request #24092 from Aser-Abdelfatah:GSoC_Support_GatherElements_ONNX GSoC Add ONNX Support for GatherElements #24092 Merge with: https://github.com/opencv/opencv_extra/pull/1082 Adds support to the ONNX operator GatherElements [operator docs](https://github.com/onnx/onnx/blob/main/docs/Operators.md#GatherElements) Added tests to opencv_extra at pull request https://github.com/opencv/opencv_extra/pull/1082 ### 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	2 years ago
alexlyulkov	014e8485b5	Merge pull request #24367 from alexlyulkov:al/fixed-cumsum-inplace-flag Fixed CumSum layer inplace flag #24367 When exclusive is false: dst[i] = dst[i-1] + src[i] When exclusive is true: dst[i] = dst[i-1] + src[i-1] So CumSum layer can be inplace only when exclusive flag is false.	2 years ago
Yuantao Feng	0507043a55	Merge pull request #24386 from fengyuentau:fix_dtype_nary_eltwise dnn: fix inconsistent input dtype for nary eltwise layers #24386 Resolves https://github.com/opencv/opencv/issues/24385 Merge with https://github.com/opencv/opencv_extra/pull/1107 Relates https://github.com/opencv/opencv/pull/24092#discussion_r1353964405 ### 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	2 years ago
Yuantao Feng	590f150d5e	dnn: hotfixes for fast gemm (#24315 ) * remove Conformance from test names * integrate neon optimization into default * quick fix: define CV_NEON_AARCH64 0 for non NEON platforms * remove var batch that leads to memory leak * put neon code back to fast_gemm_kernels.simd * reorganize code to reduce duplicate code	2 years ago
Dmitry Kurtaev	2c92eb3175	Enable more tests for OpenVINO 2023.0	2 years ago
Dmitry Kurtaev	c7ec0d599a	Merge pull request #23987 from dkurt:openvino_int8_backend OpenVINO backend for INT8 models #23987 ### Pull Request Readiness Checklist TODO: - [x] DetectionOutput layer (https://github.com/opencv/opencv/pull/24069) - [x] Less FP32 fallbacks (i.e. Sigmoid, eltwise sum) - [x] Accuracy, performance tests (https://github.com/opencv/opencv/pull/24039) - [x] Single layer tests (convolution) - [x] ~~Fixes for OpenVINO 2022.1 (https://pullrequest.opencv.org/buildbot/builders/precommit_custom_linux/builds/100334)~~ Performace results for object detection model `coco_efficientdet_lite0_v1_1.0_quant_2021_09_06.tflite`: \| backend \| performance (median time) \| \|---\|---\| \| OpenCV \| 77.42ms \| \| OpenVINO 2023.0 \| 10.90ms \| CPU: `11th Gen Intel(R) Core(TM) i5-1135G7 @ 2.40GHz` Serialized model per-layer stats (note that Convolution should use `*_I8` primitives if they are quantized correctly): https://gist.github.com/dkurt/7772bbf1907035441bb5454f19f0feef --- 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	2 years ago
fengyuentau	7fa0493ca0	init commit	2 years ago
Yuantao Feng	bb171a0c05	dnn: expand refactor with cv::broadcast for onnx models (#24295 ) * add expand impl with cv::broadcast * remove expandMid * deduce shape from -1 * add constant folding * handle input constant; handle input constant 1d * add expand conformance tests; add checks to disallow shape of neg values; add early copy for unchanged total elements * fix ExpandSubgraph * dummy commit to trigger build * dummy commit to trigger build 1 * remove conformance from test names	2 years ago
Abduragim Shtanchaev	865e7cacca	Merge pull request #24037 from Abdurrahheem:ash/dev_einsum Add Support for Einsum Layer #24037 ### This PR adding support for [Einsum Layer](https://pytorch.org/docs/stable/generated/torch.einsum.html) (in progress). This PR is currently not to be merged but only reviewed. Test cases are located in [#1090](https://github.com/opencv/opencv_extra/pull/1090)RP in OpenCV extra DONE: - [x] 2-5D GMM support added - [x] Matrix transpose support added - [x] Reduction type comupte 'ij->j' - [x] 2nd shape computation - during forward Next PRs: - [ ] Broadcasting reduction "...ii ->...i" - [ ] Add lazy shape deduction. "...ij, ...jk->...ik" - [ ] Add implicit output computation support. "bij,bjk ->" (output subscripts should be "bik") - [ ] Add support for CUDA backend - [ ] BatchWiseMultiply optimize Later in 5.x version (requires support for 1D matrices): - [ ] Add 1D vector multiplication support - [ ] Inter product "i, i" (problems with 1D shapes) ### 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. - [x] The feature is well documented and sample code can be built with the project CMake	2 years ago
Vadim Pisarevsky	416bf3253d	attempt to add 0d/1d mat support to OpenCV (#23473 ) * attempt to add 0d/1d mat support to OpenCV * revised the patch; now 1D mat is treated as 1xN 2D mat rather than Nx1. * a step towards 'green' tests * another little step towards 'green' tests * calib test failures seem to be fixed now * more fixes _core & _dnn * another step towards green ci; even 0D mat's (a.k.a. scalars) are now partly supported! * * fixed strange bug in aruco/charuco detector, not sure why it did not work * also fixed a few remaining failures (hopefully) in dnn & core * disabled failing GAPI tests - too complex to dig into this compiler pipeline * hopefully fixed java tests * trying to fix some more tests * quick followup fix * continue to fix test failures and warnings * quick followup fix * trying to fix some more tests * partly fixed support for 0D/scalar UMat's * use updated parseReduce() from upstream * trying to fix the remaining test failures * fixed [ch]aruco tests in Python * still trying to fix tests * revert "fix" in dnn's CUDA tensor * trying to fix dnn+CUDA test failures * fixed 1D umat creation * hopefully fixed remaining cuda test failures * removed training whitespaces	2 years ago
Yuantao Feng	8a96e34e33	dnn: add gemm_layer in place of fully_connected_layer for onnx models (#23897 ) * first commit * turned C from input to constant; force C constant in impl; better handling 0d/1d cases * integrate with gemm from ficus nn * fix const inputs * adjust threshold for int8 tryQuantize * adjust threshold for int8 quantized 2 * support batched gemm and matmul; tune threshold for rcnn_ilsvrc13; update googlenet * add gemm perf against innerproduct * add perf tests for innerproduct with bias * fix perf * add memset * renamings for next step * add dedicated perf gemm * add innerproduct in perf_gemm * remove gemm and innerproduct perf tests from perf_layer * add perf cases for vit sizes; prepack constants * remove batched gemm; fix wrong trans; optimize KC * remove prepacking for const A; several fixes for const B prepacking * add todos and gemm expression * add optimized branch for avx/avx2 * trigger build * update macros and signature * update signature * fix macro * fix bugs for neon aarch64 & x64 * add backends: cuda, cann, inf_ngraph and vkcom * fix cuda backend * test commit for cuda * test cuda backend * remove debug message from cuda backend * use cpu dispatcher * fix neon macro undef in dispatcher * fix dispatcher * fix inner kernel for neon aarch64 * fix compiling issue on armv7; try fixing accuracy issue on other platforms * broadcast C with beta multiplied; improve func namings * fix bug for avx and avx2 * put all platform-specific kernels in dispatcher * fix typos * attempt to fix compile issues on x64 * run old gemm when neon, avx, avx2 are all not available; add kernel for armv7 neon * fix typo * quick fix: add macros for pack4 * quick fix: use vmlaq_f32 for armv7 * quick fix for missing macro of fast gemm pack f32 4 * disable conformance tests when optimized branches are not supported * disable perf tests when optimized branches are not supported * decouple cv_try_neon and cv_neon_aarch64 * drop googlenet_2023; add fastGemmBatched * fix step in fastGemmBatched * cpu: fix initialization ofb; gpu: support batch * quick followup fix for cuda * add default kernels * quick followup fix to avoid macro redef * optmized kernels for lasx * resolve mis-alignment; remove comments * tune performance for x64 platform * tune performance for neon aarch64 * tune for armv7 * comment time consuming tests * quick follow-up fix	2 years ago
Dmitry Kurtaev	4b8aeb1129	Merge pull request #24039 from dkurt:tflite_test_backends 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. - [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	2 years ago
Dmitry Kurtaev	96f23e3da1	Merge pull request #24080 from dkurt:dnn_cuda_layers 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. - [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	2 years ago
Dmitry Kurtaev	6909fffde1	Consider half pixel mode in ONNX resize	2 years ago
Abduragim Shtanchaev	6b53fe8f7b	Merge pull request #23746 from Abdurrahheem:ash/graph_simplifier Assertion Fix in Split Layer #23746 ### Pull Request Readiness Checklist This PR fixes issue mentioned in [#23663](https://github.com/opencv/opencv/issues/23663) Merge with https://github.com/opencv/opencv_extra/pull/1067 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	2 years ago
Yuantao Feng	f07b01cc34	Merge pull request #23655 from fengyuentau:qlinearsoftmax Support ONNX operator QLinearSoftmax in dnn #23655 Resolves https://github.com/opencv/opencv/issues/23636. Merge with https://github.com/opencv/opencv_extra/pull/1064. This PR maps the QLinearSoftmax (from com.microsoft domain) to SoftmaxInt8 in dnn along with some speed optimization. Todo: - [x] support QLinearSoftmax with opset = 13 - [x] add model and test data for QLinearSoftmax with opset = 13 - [x] ensure all models have dims >= 3. - [x] add the script to generate model and test data ### 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	2 years ago
Abduragim Shtanchaev	d2143bcd44	Merge pull request #23614 from Abdurrahheem:lstm_layout_attribute LSTM ONNX Layout Attribute Support #23614 ### Explanation This PR contains necessary changes to support `layout` attribute. This attributes is present in [ONNX](https://github.com/onnx/onnx/blob/main/docs/Operators.md#lstm) and [Torch](https://pytorch.org/docs/stable/generated/torch.nn.LSTM.html#lstm) (in touch it is name as `batch_first=True`) libraries. When `layout = 1` input to LSTM layer is expected to have batch dimension first -> `[batch_size, sequence_length, features]` vs `layout = 0` - default `[sequence_length, batch_size, features]` ### Test Data Test data and data generator for PR located here [#1063](https://github.com/opencv/opencv_extra/pull/1063) ### 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. - [x] The feature is well documented and sample code can be built with the project CMake	2 years ago
Zihao Mu	5229312ad2	Merge pull request #22275 from zihaomu:fp16_support_conv DNN: FP16 support on Convolution 2D #22275 ## FP16 support on ARM platform This PR proposes to support FP16 backend in Convolution. For now, we only support FP16 at ARM aarch64. In addition to adding fp16, I also added `seperateIm2col` optimization in this patch. ## How to use FP16 to speed up convolution? ``` Net net = readNet(modelPath); net.setPreferableTarget(DNN_TARGET_CPU_FP16); net.setInput(blob); Mat output = net.forward(); ``` ### TODO List \| Task \| Status \| Remarks \| \|:-------:\|:--------:\|:------------:\| \| Convolution 2D FP16 \| ✔️ \| Done \| \| Winograd FP16 \| Because the current modification has reached 2k lines, winograd fp16 will be completed in the next PR. \| \| \| Accuracy Test \| ✔️ \| Done \| \| Performance Test \| ✔️ \| Done \| \| Compiler bug \| ✔️ \| Done \| ### Speed Test for FP 16. Test on M1 chip, 4 threads. \| Model Name \| FP32 (Conv+Wino) \| Conv(FP16) + Wino(FP 32) \| \|:-------:\|:--------:\|:------------:\| \| ReseNet 50 \| 26.0 ms \| 18.05 ms (25% speed up)\| \| MobileNet V2 \| 4.17 ms \| 3.09 ms (29% speed up) \| ### Speed Test for `seperateIm2col` trick on X86. Test on AMD 5600x, 12 threads. \| Model Name \| 4.x \| Patch \| \|:-------:\|:--------:\|:------------:\| \| MobileNet V2 \| 5.6 ms \| 3.0 ms (46% speed up) \| ### Performance Test #### Performance Test of X86 platform: AMD 5600X, with `-perf_threas=1` \|Name of Test\|4.x\|patch\|patch vs 4.x (x-factor)\| \|---\|:-:\|:-:\|:-:\| \|Name of Test\|4.x 0\|fp16pr final\|fp16pr final vs 4.x 0 (x-factor)\| \|---\|:-:\|:-:\|:-:\| \|conv1d::Conv1D::(GFLOPS=0.000, K=[3], IN={1, 2, 19}, OCN=2, G=2, S=2, P=(1, 1), BIAS, OCV/CPU)\|0.001\|0.001\|1.00\| \|conv1d::Conv1D::(GFLOPS=0.000, K=[3], IN={1, 2, 25}, OCN=2, G=2, P=(2, 2), PM=SAME, OCV/CPU)\|0.001\|0.001\|1.03\| \|conv1d::Conv1D::(GFLOPS=0.000, K=[3], IN={1, 6, 10}, OCN=6, PM=VALID, BIAS, OCV/CPU)\|0.001\|0.001\|0.92\| \|conv3d::Conv3D::(GFLOPS=0.000, K=[1 x 1 x 1], IN={1, 4, 9, 10, 10}, OCN=4, S=[1 x 1 x 2], P=(1, 1) x (1, 1) x (1, 1), PM=VALID, OCV/CPU)\|0.002\|0.003\|0.95\| \|conv3d::Conv3D::(GFLOPS=0.000, K=[1 x 1 x 1], IN={1, 8, 1, 10, 10}, OCN=8, G=8, P=(1, 1) x (1, 1) x (1, 1), BIAS, OCV/CPU)\|0.006\|0.006\|1.00\| \|conv3d::Conv3D::(GFLOPS=0.000, K=[3 x 3 x 3], IN={1, 2, 19, 19, 19}, OCN=2, G=2, S=[2 x 2 x 2], P=(1, 1) x (1, 1) x (1, 1), BIAS, OCV/CPU)\|0.045\|0.033\|1.39\| \|conv3d::Conv3D::(GFLOPS=0.000, K=[3 x 4 x 2], IN={1, 4, 8, 10, 10}, OCN=4, G=4, S=[1 x 2 x 1], BIAS, OCV/CPU)\|0.011\|0.009\|1.17\| \|conv3d::Conv3D::(GFLOPS=0.001, K=[3 x 3 x 3], IN={1, 2, 25, 19, 19}, OCN=2, G=2, S=[1 x 2 x 2], P=(2, 2) x (2, 2) x (2, 2), PM=SAME, OCV/CPU)\|0.109\|0.078\|1.39\| \|conv3d::Conv3D::(GFLOPS=0.002, K=[3 x 1 x 4], IN={1, 14, 5, 10, 10}, OCN=14, PM=SAME, OCV/CPU)\|0.040\|0.042\|0.94\| \|conv3d::Conv3D::(GFLOPS=0.006, K=[5 x 5 x 5], IN={1, 4, 50, 19, 19}, OCN=4, S=[2 x 2 x 2], P=(1, 1) x (1, 1) x (1, 1), PM=VALID, OCV/CPU)\|0.326\|0.342\|0.95\| \|conv3d::Conv3D::(GFLOPS=0.027, K=[3 x 3 x 3], IN={1, 6, 10, 38, 50}, OCN=6, PM=VALID, BIAS, OCV/CPU)\|0.580\|0.589\|0.99\| \|conv3d::Conv3D::(GFLOPS=0.030, K=[5 x 5 x 5], IN={1, 6, 19, 19, 19}, OCN=6, G=2, OCV/CPU)\|1.293\|1.382\|0.94\| \|conv3d::Conv3D::(GFLOPS=0.045, K=[7 x 7 x 7], IN={1, 2, 38, 38, 38}, OCN=2, S=[1 x 2 x 1], OCV/CPU)\|3.590\|3.710\|0.97\| \|conv3d::Conv3D::(GFLOPS=0.053, K=[3 x 3 x 3], IN={1, 10, 98, 10, 10}, OCN=10, PM=SAME, OCV/CPU)\|1.120\|1.191\|0.94\| \|conv3d::Conv3D::(GFLOPS=0.071, K=[7 x 7 x 7], IN={1, 6, 15, 19, 19}, OCN=6, S=[2 x 1 x 1], P=(3, 3) x (3, 3) x (3, 3), PM=SAME, BIAS, OCV/CPU)\|2.576\|2.872\|0.90\| \|conv3d::Conv3D::(GFLOPS=0.093, K=[5 x 5 x 5], IN={1, 4, 40, 75, 75}, OCN=4, S=[2 x 2 x 2], OCV/CPU)\|4.599\|4.670\|0.98\| \|conv3d::Conv3D::(GFLOPS=0.116, K=[5 x 5 x 5], IN={1, 2, 21, 75, 100}, OCN=2, BIAS, OCV/CPU)\|9.230\|9.582\|0.96\| \|conv3d::Conv3D::(GFLOPS=1.267, K=[5 x 5 x 5], IN={1, 3, 75, 75, 100}, OCN=3, PM=SAME, BIAS, OCV/CPU)\|65.946\|69.381\|0.95\| \|conv3d::Conv3D::(GFLOPS=1.343, K=[3 x 3 x 3], IN={1, 11, 9, 150, 200}, OCN=11, PM=VALID, BIAS, OCV/CPU)\|18.915\|19.289\|0.98\| \|conv::Conv::(GFLOPS=0.177, K=[1 x 1], IN={1, 512, 26, 26}, OCN=256, OCV/CPU)\|1.404\|1.457\|0.96\| \|conv::Conv::(GFLOPS=0.177, K=[1 x 1], IN={1, 1024, 13, 13}, OCN=512, OCV/CPU)\|2.060\|1.501\|1.37\| \|conv::Conv::(GFLOPS=0.178, K=[1 x 1], IN={1, 256, 52, 52}, OCN=128, OCV/CPU)\|1.409\|1.464\|0.96\| \|conv::Conv::(GFLOPS=0.210, K=[1 x 1], IN={1, 576, 38, 50}, OCN=96, PM=SAME, BIAS, OCV/CPU)\|1.793\|1.838\|0.98\| \|conv::Conv::(GFLOPS=0.231, K=[3 x 3], IN={1, 128, 56, 56}, OCN=32, P=[1 x 1], OCV/CPU)\|1.207\|1.199\|1.01\| \|conv::Conv::(GFLOPS=0.231, K=[3 x 3], IN={1, 256, 14, 14}, OCN=256, P=[1 x 1], OCV/CPU)\|1.277\|1.275\|1.00\| \|conv::Conv::(GFLOPS=0.280, K=[1 x 1], IN={1, 576, 38, 50}, OCN=128, PM=SAME, BIAS, OCV/CPU)\|2.319\|2.370\|0.98\| \|conv::Conv::(GFLOPS=0.302, K=[3 x 3], IN={1, 64, 64, 64}, OCN=64, PM=SAME, OCV/CPU)\|1.351\|1.346\|1.00\| \|conv::Conv::(GFLOPS=0.357, K=[1 x 1], IN={1, 64, 208, 208}, OCN=64, OCV/CPU)\|3.520\|3.612\|0.97\| \|conv::Conv::(GFLOPS=0.420, K=[3 x 3], IN={1, 96, 38, 50}, OCN=128, PM=SAME, BIAS, OCV/CPU)\|1.876\|1.880\|1.00\| \|conv::Conv::(GFLOPS=0.472, K=[3 x 3], IN={1, 128, 40, 40}, OCN=128, PM=SAME, OCV/CPU)\|1.981\|1.995\|0.99\| \|conv::Conv::(GFLOPS=0.472, K=[3 x 3], IN={1, 256, 20, 20}, OCN=256, PM=SAME, OCV/CPU)\|2.620\|2.627\|1.00\| \|conv::Conv::(GFLOPS=0.472, K=[3 x 3], IN={1, 512, 10, 10}, OCN=512, PM=SAME, OCV/CPU)\|4.202\|4.123\|1.02\| \|conv::Conv::(GFLOPS=0.561, K=[3 x 3], IN={1, 128, 38, 50}, OCN=128, PM=SAME, BIAS, OCV/CPU)\|2.429\|2.445\|0.99\| \|conv::Conv::(GFLOPS=0.624, K=[3 x 3], IN={1, 128, 46, 46}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)\|2.591\|2.576\|1.01\| \|conv::Conv::(GFLOPS=0.701, K=[3 x 3], IN={1, 128, 38, 50}, OCN=160, PM=SAME, BIAS, OCV/CPU)\|3.005\|2.998\|1.00\| \|conv::Conv::(GFLOPS=0.798, K=[3 x 3], IN={1, 64, 104, 104}, OCN=64, P=[1 x 1], OCV/CPU)\|3.515\|3.532\|1.00\| \|conv::Conv::(GFLOPS=0.798, K=[3 x 3], IN={1, 128, 52, 52}, OCN=128, P=[1 x 1], OCV/CPU)\|3.115\|3.134\|0.99\| \|conv::Conv::(GFLOPS=0.798, K=[3 x 3], IN={1, 256, 26, 26}, OCN=256, P=[1 x 1], OCV/CPU)\|3.937\|3.899\|1.01\| \|conv::Conv::(GFLOPS=0.798, K=[3 x 3], IN={1, 512, 13, 13}, OCN=512, P=[1 x 1], OCV/CPU)\|5.533\|5.471\|1.01\| \|conv::Conv::(GFLOPS=0.830, K=[3 x 3], IN={1, 64, 75, 100}, OCN=96, PM=SAME, BIAS, OCV/CPU)\|3.472\|3.464\|1.00\| \|conv::Conv::(GFLOPS=0.958, K=[3 x 3], IN={1, 192, 38, 38}, OCN=192, PM=SAME, OCV/CPU)\|4.302\|4.322\|1.00\| \|conv::Conv::(GFLOPS=0.958, K=[3 x 3], IN={1, 384, 19, 19}, OCN=384, PM=SAME, OCV/CPU)\|6.100\|6.035\|1.01\| \|conv::Conv::(GFLOPS=1.022, K=[3 x 3], IN={1, 576, 19, 19}, OCN=273, PM=SAME, BIAS, OCV/CPU)\|6.580\|6.484\|1.01\| \|conv::Conv::(GFLOPS=1.112, K=[3 x 3], IN={1, 512, 10, 10}, OCN=1206, P=[1 x 1], BIAS, OCV/CPU)\|9.741\|9.634\|1.01\| \|conv::Conv::(GFLOPS=1.181, K=[3 x 3], IN={1, 64, 160, 200}, OCN=128, S=[2 x 2], P=[1 x 1], BIAS, OCV/CPU)\|10.131\|10.156\|1.00\| \|conv::Conv::(GFLOPS=1.182, K=[3 x 3], IN={1, 32, 320, 400}, OCN=64, S=[2 x 2], P=[1 x 1], BIAS, OCV/CPU)\|12.391\|12.350\|1.00\| \|conv::Conv::(GFLOPS=1.195, K=[9 x 9], IN={1, 32, 240, 320}, OCN=3, P=[4 x 4], BIAS, OCV/CPU)\|91.074\|87.893\|1.04\| \|conv::Conv::(GFLOPS=1.196, K=[3 x 3], IN={1, 384, 26, 26}, OCN=256, P=[1 x 1], OCV/CPU)\|5.903\|5.903\|1.00\| \|conv::Conv::(GFLOPS=1.210, K=[3 x 3], IN={1, 32, 256, 256}, OCN=32, PM=SAME, OCV/CPU)\|6.890\|6.794\|1.01\| \|conv::Conv::(GFLOPS=1.245, K=[3 x 3], IN={1, 64, 75, 75}, OCN=192, PM=SAME, BIAS, OCV/CPU)\|5.160\|5.131\|1.01\| \|conv::Conv::(GFLOPS=1.245, K=[3 x 3], IN={1, 96, 75, 100}, OCN=96, PM=SAME, BIAS, OCV/CPU)\|4.970\|5.036\|0.99\| \|conv::Conv::(GFLOPS=1.248, K=[3 x 3], IN={1, 256, 46, 46}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)\|5.045\|5.015\|1.01\| \|conv::Conv::(GFLOPS=1.258, K=[3 x 3], IN={1, 1280, 10, 10}, OCN=546, PM=SAME, BIAS, OCV/CPU)\|11.583\|11.343\|1.02\| \|conv::Conv::(GFLOPS=1.261, K=[3 x 3], IN={1, 192, 38, 50}, OCN=192, PM=SAME, BIAS, OCV/CPU)\|5.348\|5.320\|1.01\| \|conv::Conv::(GFLOPS=1.416, K=[3 x 3], IN={1, 128, 62, 82}, OCN=128, BIAS, OCV/CPU)\|5.357\|5.396\|0.99\| \|conv::Conv::(GFLOPS=1.500, K=[3 x 3], IN={1, 128, 64, 84}, OCN=128, BIAS, OCV/CPU)\|6.050\|6.006\|1.01\| \|conv::Conv::(GFLOPS=1.586, K=[3 x 3], IN={1, 128, 66, 86}, OCN=128, BIAS, OCV/CPU)\|5.952\|5.953\|1.00\| \|conv::Conv::(GFLOPS=1.595, K=[3 x 3], IN={1, 256, 26, 26}, OCN=512, P=[1 x 1], OCV/CPU)\|8.014\|8.014\|1.00\| \|conv::Conv::(GFLOPS=1.595, K=[3 x 3], IN={1, 256, 52, 52}, OCN=512, S=[2 x 2], P=[1 x 1], OCV/CPU)\|12.472\|12.577\|0.99\| \|conv::Conv::(GFLOPS=1.595, K=[3 x 3], IN={1, 512, 13, 13}, OCN=1024, P=[1 x 1], OCV/CPU)\|10.803\|10.655\|1.01\| \|conv::Conv::(GFLOPS=1.595, K=[3 x 3], IN={1, 512, 26, 26}, OCN=1024, S=[2 x 2], P=[1 x 1], OCV/CPU)\|18.429\|13.405\|1.37\| \|conv::Conv::(GFLOPS=1.596, K=[3 x 3], IN={1, 64, 104, 104}, OCN=128, P=[1 x 1], OCV/CPU)\|6.659\|6.647\|1.00\| \|conv::Conv::(GFLOPS=1.596, K=[3 x 3], IN={1, 64, 208, 208}, OCN=128, S=[2 x 2], P=[1 x 1], OCV/CPU)\|14.192\|13.819\|1.03\| \|conv::Conv::(GFLOPS=1.596, K=[3 x 3], IN={1, 128, 52, 52}, OCN=256, P=[1 x 1], OCV/CPU)\|6.045\|6.068\|1.00\| \|conv::Conv::(GFLOPS=1.596, K=[3 x 3], IN={1, 128, 104, 104}, OCN=256, S=[2 x 2], P=[1 x 1], OCV/CPU)\|12.742\|12.828\|0.99\| \|conv::Conv::(GFLOPS=1.598, K=[3 x 3], IN={1, 32, 208, 208}, OCN=64, P=[1 x 1], OCV/CPU)\|8.046\|7.773\|1.04\| \|conv::Conv::(GFLOPS=1.598, K=[3 x 3], IN={1, 32, 416, 416}, OCN=64, S=[2 x 2], P=[1 x 1], OCV/CPU)\|17.440\|17.192\|1.01\| \|conv::Conv::(GFLOPS=1.659, K=[3 x 3], IN={1, 960, 10, 10}, OCN=960, PM=SAME, OCV/CPU)\|15.418\|14.972\|1.03\| \|conv::Conv::(GFLOPS=1.660, K=[3 x 3], IN={1, 128, 75, 75}, OCN=128, G=128, P=[1 x 1], BIAS, OCV/CPU)\|0.430\|0.430\|1.00\| \|conv::Conv::(GFLOPS=1.660, K=[3 x 3], IN={1, 128, 75, 75}, OCN=128, PM=SAME, OCV/CPU)\|6.692\|6.663\|1.00\| \|conv::Conv::(GFLOPS=1.675, K=[3 x 3], IN={1, 128, 68, 88}, OCN=128, BIAS, OCV/CPU)\|6.350\|6.347\|1.00\| \|conv::Conv::(GFLOPS=1.704, K=[3 x 3], IN={1, 256, 38, 38}, OCN=256, G=256, P=[1 x 1], BIAS, OCV/CPU)\|0.267\|0.265\|1.01\| \|conv::Conv::(GFLOPS=1.704, K=[3 x 3], IN={1, 256, 38, 38}, OCN=256, PM=SAME, OCV/CPU)\|7.755\|7.558\|1.03\| \|conv::Conv::(GFLOPS=1.704, K=[3 x 3], IN={1, 512, 19, 19}, OCN=512, G=512, P=[1 x 1], BIAS, OCV/CPU)\|0.203\|0.202\|1.00\| \|conv::Conv::(GFLOPS=1.704, K=[3 x 3], IN={1, 512, 19, 19}, OCN=512, P=[1 x 1], BIAS, OCV/CPU)\|10.663\|10.576\|1.01\| \|conv::Conv::(GFLOPS=1.704, K=[3 x 3], IN={1, 512, 19, 19}, OCN=512, PM=SAME, OCV/CPU)\|10.827\|10.614\|1.02\| \|conv::Conv::(GFLOPS=1.766, K=[3 x 3], IN={1, 128, 70, 90}, OCN=128, BIAS, OCV/CPU)\|7.049\|6.947\|1.01\| \|conv::Conv::(GFLOPS=1.859, K=[3 x 3], IN={1, 128, 72, 92}, OCN=128, BIAS, OCV/CPU)\|6.900\|6.901\|1.00\| \|conv::Conv::(GFLOPS=1.888, K=[3 x 3], IN={1, 1024, 10, 10}, OCN=1024, G=1024, P=[1 x 1], BIAS, OCV/CPU)\|0.165\|0.165\|1.00\| \|conv::Conv::(GFLOPS=1.888, K=[3 x 3], IN={1, 1024, 10, 10}, OCN=1024, PM=SAME, OCV/CPU)\|17.953\|17.251\|1.04\| \|conv::Conv::(GFLOPS=1.954, K=[3 x 3], IN={1, 128, 74, 94}, OCN=128, BIAS, OCV/CPU)\|7.430\|7.320\|1.01\| \|conv::Conv::(GFLOPS=1.995, K=[9 x 9], IN={1, 3, 320, 400}, OCN=32, P=[4 x 4], BIAS, OCV/CPU)\|22.187\|21.705\|1.02\| \|conv::Conv::(GFLOPS=2.052, K=[3 x 3], IN={1, 128, 76, 96}, OCN=128, BIAS, OCV/CPU)\|8.349\|8.126\|1.03\| \|conv::Conv::(GFLOPS=2.100, K=[3 x 3], IN={1, 144, 75, 75}, OCN=144, PM=SAME, OCV/CPU)\|8.273\|8.297\|1.00\| \|conv::Conv::(GFLOPS=2.153, K=[3 x 3], IN={1, 128, 78, 98}, OCN=128, BIAS, OCV/CPU)\|8.169\|8.094\|1.01\| \|conv::Conv::(GFLOPS=2.156, K=[3 x 3], IN={1, 576, 19, 19}, OCN=576, PM=SAME, OCV/CPU)\|13.602\|13.359\|1.02\| \|conv::Conv::(GFLOPS=2.255, K=[3 x 3], IN={1, 128, 80, 100}, OCN=128, BIAS, OCV/CPU)\|8.633\|8.584\|1.01\| \|conv::Conv::(GFLOPS=2.719, K=[3 x 3], IN={1, 96, 256, 256}, OCN=96, S=[2 x 2], PM=SAME, OCV/CPU)\|29.339\|28.897\|1.02\| \|conv::Conv::(GFLOPS=3.319, K=[3 x 3], IN={1, 128, 75, 75}, OCN=256, P=[1 x 1], BIAS, OCV/CPU)\|13.000\|12.920\|1.01\| \|conv::Conv::(GFLOPS=3.321, K=[3 x 3], IN={1, 64, 150, 150}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)\|14.262\|13.319\|1.07\| \|conv::Conv::(GFLOPS=3.398, K=[7 x 7], IN={1, 128, 46, 46}, OCN=128, P=[3 x 3], BIAS, OCV/CPU)\|27.453\|27.253\|1.01\| \|conv::Conv::(GFLOPS=3.407, K=[3 x 3], IN={1, 512, 19, 19}, OCN=1024, D=[6 x 6], P=[6 x 6], BIAS, OCV/CPU)\|32.052\|27.269\|1.18\| \|conv::Conv::(GFLOPS=3.408, K=[3 x 3], IN={1, 256, 38, 38}, OCN=512, P=[1 x 1], BIAS, OCV/CPU)\|15.363\|15.208\|1.01\| \|conv::Conv::(GFLOPS=4.247, K=[3 x 3], IN={1, 480, 32, 32}, OCN=480, PM=SAME, OCV/CPU)\|18.543\|18.434\|1.01\| \|conv::Conv::(GFLOPS=4.247, K=[5 x 5], IN={1, 144, 128, 128}, OCN=144, S=[2 x 2], PM=SAME, OCV/CPU)\|39.114\|37.954\|1.03\| \|conv::Conv::(GFLOPS=4.566, K=[7 x 7], IN={1, 172, 46, 46}, OCN=128, P=[3 x 3], BIAS, OCV/CPU)\|36.271\|36.972\|0.98\| \|conv::Conv::(GFLOPS=4.993, K=[3 x 3], IN={1, 256, 46, 46}, OCN=512, P=[1 x 1], BIAS, OCV/CPU)\|19.262\|19.427\|0.99\| \|conv::Conv::(GFLOPS=4.993, K=[3 x 3], IN={1, 512, 46, 46}, OCN=256, P=[1 x 1], BIAS, OCV/CPU)\|19.298\|19.349\|1.00\| \|conv::Conv::(GFLOPS=4.994, K=[3 x 3], IN={1, 128, 92, 92}, OCN=256, P=[1 x 1], BIAS, OCV/CPU)\|20.261\|19.847\|1.02\| \|conv::Conv::(GFLOPS=4.997, K=[3 x 3], IN={1, 64, 184, 184}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)\|21.867\|21.525\|1.02\| \|conv::Conv::(GFLOPS=5.780, K=[5 x 5], IN={1, 672, 32, 32}, OCN=672, S=[2 x 2], PM=SAME, OCV/CPU)\|51.756\|49.979\|1.04\| \|conv::Conv::(GFLOPS=6.116, K=[3 x 3], IN={1, 1152, 16, 16}, OCN=1152, PM=SAME, OCV/CPU)\|28.133\|27.060\|1.04\| \|conv::Conv::(GFLOPS=6.118, K=[3 x 3], IN={1, 144, 128, 128}, OCN=144, PM=SAME, OCV/CPU)\|25.035\|24.980\|1.00\| \|conv::Conv::(GFLOPS=6.637, K=[3 x 3], IN={1, 256, 75, 75}, OCN=256, P=[1 x 1], BIAS, OCV/CPU)\|25.858\|25.821\|1.00\| \|conv::Conv::(GFLOPS=6.638, K=[3 x 3], IN={1, 128, 150, 150}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)\|27.313\|27.149\|1.01\| \|conv::Conv::(GFLOPS=6.641, K=[3 x 3], IN={1, 64, 150, 200}, OCN=192, PM=SAME, BIAS, OCV/CPU)\|28.219\|28.111\|1.00\| \|conv::Conv::(GFLOPS=6.641, K=[3 x 3], IN={1, 64, 300, 300}, OCN=64, P=[1 x 1], BIAS, OCV/CPU)\|46.025\|46.674\|0.99\| \|conv::Conv::(GFLOPS=6.814, K=[3 x 3], IN={1, 512, 38, 38}, OCN=512, P=[1 x 1], BIAS, OCV/CPU)\|30.220\|29.446\|1.03\| \|conv::Conv::(GFLOPS=8.025, K=[3 x 3], IN={1, 1024, 19, 19}, OCN=1206, P=[1 x 1], BIAS, OCV/CPU)\|49.410\|48.708\|1.01\| \|conv::Conv::(GFLOPS=9.986, K=[3 x 3], IN={1, 512, 46, 46}, OCN=512, P=[1 x 1], BIAS, OCV/CPU)\|38.203\|38.001\|1.01\| \|conv::Conv::(GFLOPS=9.987, K=[3 x 3], IN={1, 256, 92, 92}, OCN=256, P=[1 x 1], BIAS, OCV/CPU)\|39.961\|39.021\|1.02\| \|conv::Conv::(GFLOPS=9.989, K=[3 x 3], IN={1, 128, 184, 184}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)\|48.685\|47.075\|1.03\| \|conv::Conv::(GFLOPS=9.993, K=[3 x 3], IN={1, 64, 368, 368}, OCN=64, P=[1 x 1], BIAS, OCV/CPU)\|75.114\|72.586\|1.03\| \|conv::Conv::(GFLOPS=10.087, K=[3 x 3], IN={1, 576, 38, 50}, OCN=512, PM=SAME, BIAS, OCV/CPU)\|41.222\|41.144\|1.00\| \|conv::Conv::(GFLOPS=10.701, K=[3 x 3], IN={1, 512, 38, 38}, OCN=804, P=[1 x 1], BIAS, OCV/CPU)\|46.220\|46.353\|1.00\| \|conv::Conv::(GFLOPS=11.797, K=[5 x 5], IN={1, 240, 64, 64}, OCN=240, PM=SAME, OCV/CPU)\|98.201\|98.771\|0.99\| \|conv::Conv::(GFLOPS=11.797, K=[5 x 5], IN={1, 480, 32, 32}, OCN=480, PM=SAME, OCV/CPU)\|100.106\|96.971\|1.03\| \|conv::Conv::(GFLOPS=16.987, K=[5 x 5], IN={1, 1152, 16, 16}, OCN=1152, PM=SAME, OCV/CPU)\|146.977\|140.445\|1.05\| \|conv::Conv::(GFLOPS=23.122, K=[5 x 5], IN={1, 672, 32, 32}, OCN=672, PM=SAME, OCV/CPU)\|198.618\|194.665\|1.02\| #### Performance Test of ARM platform: apple M1, with `-perf_threas=1` Min (ms) \|Name of Test\|4.x\|patch\|4.x vs patch (x-factor)\| \|---\|:-:\|:-:\|:-:\| \|conv1d::Conv1D::(GFLOPS=0.000, K=[3], IN={1, 2, 19}, OCN=2, G=2, S=2, P=(1, 1), BIAS, OCV/CPU)\|0.001\|0.001\|1.07\| \|conv1d::Conv1D::(GFLOPS=0.000, K=[3], IN={1, 2, 25}, OCN=2, G=2, P=(2, 2), PM=SAME, OCV/CPU)\|0.001\|0.001\|1.10\| \|conv1d::Conv1D::(GFLOPS=0.000, K=[3], IN={1, 6, 10}, OCN=6, PM=VALID, BIAS, OCV/CPU)\|0.002\|0.002\|0.97\| \|conv3d::Conv3D::(GFLOPS=0.000, K=[1 x 1 x 1], IN={1, 4, 9, 10, 10}, OCN=4, S=[1 x 1 x 2], P=(1, 1) x (1, 1) x (1, 1), PM=VALID, OCV/CPU)\|0.003\|0.003\|0.84\| \|conv3d::Conv3D::(GFLOPS=0.000, K=[1 x 1 x 1], IN={1, 8, 1, 10, 10}, OCN=8, G=8, P=(1, 1) x (1, 1) x (1, 1), BIAS, OCV/CPU)\|0.009\|0.009\|1.00\| \|conv3d::Conv3D::(GFLOPS=0.000, K=[3 x 3 x 3], IN={1, 2, 19, 19, 19}, OCN=2, G=2, S=[2 x 2 x 2], P=(1, 1) x (1, 1) x (1, 1), BIAS, OCV/CPU)\|0.027\|0.030\|0.90\| \|conv3d::Conv3D::(GFLOPS=0.000, K=[3 x 4 x 2], IN={1, 4, 8, 10, 10}, OCN=4, G=4, S=[1 x 2 x 1], BIAS, OCV/CPU)\|0.008\|0.007\|1.07\| \|conv3d::Conv3D::(GFLOPS=0.001, K=[3 x 3 x 3], IN={1, 2, 25, 19, 19}, OCN=2, G=2, S=[1 x 2 x 2], P=(2, 2) x (2, 2) x (2, 2), PM=SAME, OCV/CPU)\|0.066\|0.072\|0.91\| \|conv3d::Conv3D::(GFLOPS=0.002, K=[3 x 1 x 4], IN={1, 14, 5, 10, 10}, OCN=14, PM=SAME, OCV/CPU)\|0.090\|0.054\|1.68\| \|conv3d::Conv3D::(GFLOPS=0.006, K=[5 x 5 x 5], IN={1, 4, 50, 19, 19}, OCN=4, S=[2 x 2 x 2], P=(1, 1) x (1, 1) x (1, 1), PM=VALID, OCV/CPU)\|0.328\|0.409\|0.80\| \|conv3d::Conv3D::(GFLOPS=0.027, K=[3 x 3 x 3], IN={1, 6, 10, 38, 50}, OCN=6, PM=VALID, BIAS, OCV/CPU)\|0.659\|0.697\|0.95\| \|conv3d::Conv3D::(GFLOPS=0.030, K=[5 x 5 x 5], IN={1, 6, 19, 19, 19}, OCN=6, G=2, OCV/CPU)\|1.266\|1.403\|0.90\| \|conv3d::Conv3D::(GFLOPS=0.045, K=[7 x 7 x 7], IN={1, 2, 38, 38, 38}, OCN=2, S=[1 x 2 x 1], OCV/CPU)\|3.550\|4.145\|0.86\| \|conv3d::Conv3D::(GFLOPS=0.053, K=[3 x 3 x 3], IN={1, 10, 98, 10, 10}, OCN=10, PM=SAME, OCV/CPU)\|1.188\|1.375\|0.86\| \|conv3d::Conv3D::(GFLOPS=0.071, K=[7 x 7 x 7], IN={1, 6, 15, 19, 19}, OCN=6, S=[2 x 1 x 1], P=(3, 3) x (3, 3) x (3, 3), PM=SAME, BIAS, OCV/CPU)\|2.683\|3.236\|0.83\| \|conv3d::Conv3D::(GFLOPS=0.093, K=[5 x 5 x 5], IN={1, 4, 40, 75, 75}, OCN=4, S=[2 x 2 x 2], OCV/CPU)\|4.491\|5.501\|0.82\| \|conv3d::Conv3D::(GFLOPS=0.116, K=[5 x 5 x 5], IN={1, 2, 21, 75, 100}, OCN=2, BIAS, OCV/CPU)\|8.916\|10.181\|0.88\| \|conv3d::Conv3D::(GFLOPS=1.267, K=[5 x 5 x 5], IN={1, 3, 75, 75, 100}, OCN=3, PM=SAME, BIAS, OCV/CPU)\|69.995\|72.296\|0.97\| \|conv3d::Conv3D::(GFLOPS=1.343, K=[3 x 3 x 3], IN={1, 11, 9, 150, 200}, OCN=11, PM=VALID, BIAS, OCV/CPU)\|22.531\|23.139\|0.97\| \|conv::Conv::(GFLOPS=0.177, K=[1 x 1], IN={1, 512, 26, 26}, OCN=256, OCV/CPU)\|2.239\|1.933\|1.16\| \|conv::Conv::(GFLOPS=0.177, K=[1 x 1], IN={1, 512, 26, 26}, OCN=256, OCV/CPU_FP16)\|-\|1.010\|-\| \|conv::Conv::(GFLOPS=0.177, K=[1 x 1], IN={1, 1024, 13, 13}, OCN=512, OCV/CPU)\|3.134\|2.068\|1.52\| \|conv::Conv::(GFLOPS=0.177, K=[1 x 1], IN={1, 1024, 13, 13}, OCN=512, OCV/CPU_FP16)\|-\|1.062\|-\| \|conv::Conv::(GFLOPS=0.178, K=[1 x 1], IN={1, 256, 52, 52}, OCN=128, OCV/CPU)\|1.918\|1.920\|1.00\| \|conv::Conv::(GFLOPS=0.178, K=[1 x 1], IN={1, 256, 52, 52}, OCN=128, OCV/CPU_FP16)\|-\|1.014\|-\| \|conv::Conv::(GFLOPS=0.210, K=[1 x 1], IN={1, 576, 38, 50}, OCN=96, PM=SAME, BIAS, OCV/CPU)\|2.340\|2.352\|0.99\| \|conv::Conv::(GFLOPS=0.210, K=[1 x 1], IN={1, 576, 38, 50}, OCN=96, PM=SAME, BIAS, OCV/CPU_FP16)\|-\|1.247\|-\| \|conv::Conv::(GFLOPS=0.231, K=[3 x 3], IN={1, 128, 56, 56}, OCN=32, P=[1 x 1], OCV/CPU)\|1.116\|1.111\|1.00\| \|conv::Conv::(GFLOPS=0.231, K=[3 x 3], IN={1, 128, 56, 56}, OCN=32, P=[1 x 1], OCV/CPU_FP16)\|-\|1.114\|-\| \|conv::Conv::(GFLOPS=0.231, K=[3 x 3], IN={1, 256, 14, 14}, OCN=256, P=[1 x 1], OCV/CPU)\|1.116\|1.112\|1.00\| \|conv::Conv::(GFLOPS=0.231, K=[3 x 3], IN={1, 256, 14, 14}, OCN=256, P=[1 x 1], OCV/CPU_FP16)\|-\|1.113\|-\| \|conv::Conv::(GFLOPS=0.280, K=[1 x 1], IN={1, 576, 38, 50}, OCN=128, PM=SAME, BIAS, OCV/CPU)\|3.067\|3.085\|0.99\| \|conv::Conv::(GFLOPS=0.280, K=[1 x 1], IN={1, 576, 38, 50}, OCN=128, PM=SAME, BIAS, OCV/CPU_FP16)\|-\|1.622\|-\| \|conv::Conv::(GFLOPS=0.302, K=[3 x 3], IN={1, 64, 64, 64}, OCN=64, PM=SAME, OCV/CPU)\|1.153\|1.187\|0.97\| \|conv::Conv::(GFLOPS=0.302, K=[3 x 3], IN={1, 64, 64, 64}, OCN=64, PM=SAME, OCV/CPU_FP16)\|-\|1.150\|-\| \|conv::Conv::(GFLOPS=0.357, K=[1 x 1], IN={1, 64, 208, 208}, OCN=64, OCV/CPU)\|4.804\|4.849\|0.99\| \|conv::Conv::(GFLOPS=0.357, K=[1 x 1], IN={1, 64, 208, 208}, OCN=64, OCV/CPU_FP16)\|-\|2.922\|-\| \|conv::Conv::(GFLOPS=0.420, K=[3 x 3], IN={1, 96, 38, 50}, OCN=128, PM=SAME, BIAS, OCV/CPU)\|1.463\|1.469\|1.00\| \|conv::Conv::(GFLOPS=0.420, K=[3 x 3], IN={1, 96, 38, 50}, OCN=128, PM=SAME, BIAS, OCV/CPU_FP16)\|-\|1.459\|-\| \|conv::Conv::(GFLOPS=0.472, K=[3 x 3], IN={1, 128, 40, 40}, OCN=128, PM=SAME, OCV/CPU)\|1.577\|1.580\|1.00\| \|conv::Conv::(GFLOPS=0.472, K=[3 x 3], IN={1, 128, 40, 40}, OCN=128, PM=SAME, OCV/CPU_FP16)\|-\|1.580\|-\| \|conv::Conv::(GFLOPS=0.472, K=[3 x 3], IN={1, 256, 20, 20}, OCN=256, PM=SAME, OCV/CPU)\|1.826\|1.818\|1.00\| \|conv::Conv::(GFLOPS=0.472, K=[3 x 3], IN={1, 256, 20, 20}, OCN=256, PM=SAME, OCV/CPU_FP16)\|-\|1.817\|-\| \|conv::Conv::(GFLOPS=0.472, K=[3 x 3], IN={1, 512, 10, 10}, OCN=512, PM=SAME, OCV/CPU)\|6.541\|5.081\|1.29\| \|conv::Conv::(GFLOPS=0.472, K=[3 x 3], IN={1, 512, 10, 10}, OCN=512, PM=SAME, OCV/CPU_FP16)\|-\|2.809\|-\| \|conv::Conv::(GFLOPS=0.561, K=[3 x 3], IN={1, 128, 38, 50}, OCN=128, PM=SAME, BIAS, OCV/CPU)\|1.912\|1.919\|1.00\| \|conv::Conv::(GFLOPS=0.561, K=[3 x 3], IN={1, 128, 38, 50}, OCN=128, PM=SAME, BIAS, OCV/CPU_FP16)\|-\|1.919\|-\| \|conv::Conv::(GFLOPS=0.624, K=[3 x 3], IN={1, 128, 46, 46}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)\|1.961\|1.971\|0.99\| \|conv::Conv::(GFLOPS=0.624, K=[3 x 3], IN={1, 128, 46, 46}, OCN=128, P=[1 x 1], BIAS, OCV/CPU_FP16)\|-\|1.961\|-\| \|conv::Conv::(GFLOPS=0.701, K=[3 x 3], IN={1, 128, 38, 50}, OCN=160, PM=SAME, BIAS, OCV/CPU)\|2.317\|2.329\|0.99\| \|conv::Conv::(GFLOPS=0.701, K=[3 x 3], IN={1, 128, 38, 50}, OCN=160, PM=SAME, BIAS, OCV/CPU_FP16)\|-\|2.322\|-\| \|conv::Conv::(GFLOPS=0.798, K=[3 x 3], IN={1, 64, 104, 104}, OCN=64, P=[1 x 1], OCV/CPU)\|2.920\|2.947\|0.99\| \|conv::Conv::(GFLOPS=0.798, K=[3 x 3], IN={1, 64, 104, 104}, OCN=64, P=[1 x 1], OCV/CPU_FP16)\|-\|2.924\|-\| \|conv::Conv::(GFLOPS=0.798, K=[3 x 3], IN={1, 128, 52, 52}, OCN=128, P=[1 x 1], OCV/CPU)\|2.467\|2.466\|1.00\| \|conv::Conv::(GFLOPS=0.798, K=[3 x 3], IN={1, 128, 52, 52}, OCN=128, P=[1 x 1], OCV/CPU_FP16)\|-\|2.496\|-\| \|conv::Conv::(GFLOPS=0.798, K=[3 x 3], IN={1, 256, 26, 26}, OCN=256, P=[1 x 1], OCV/CPU)\|3.028\|2.997\|1.01\| \|conv::Conv::(GFLOPS=0.798, K=[3 x 3], IN={1, 256, 26, 26}, OCN=256, P=[1 x 1], OCV/CPU_FP16)\|-\|2.986\|-\| \|conv::Conv::(GFLOPS=0.798, K=[3 x 3], IN={1, 512, 13, 13}, OCN=512, P=[1 x 1], OCV/CPU)\|4.353\|4.355\|1.00\| \|conv::Conv::(GFLOPS=0.798, K=[3 x 3], IN={1, 512, 13, 13}, OCN=512, P=[1 x 1], OCV/CPU_FP16)\|-\|4.355\|-\| \|conv::Conv::(GFLOPS=0.830, K=[3 x 3], IN={1, 64, 75, 100}, OCN=96, PM=SAME, BIAS, OCV/CPU)\|2.762\|2.793\|0.99\| \|conv::Conv::(GFLOPS=0.830, K=[3 x 3], IN={1, 64, 75, 100}, OCN=96, PM=SAME, BIAS, OCV/CPU_FP16)\|-\|2.797\|-\| \|conv::Conv::(GFLOPS=0.958, K=[3 x 3], IN={1, 192, 38, 38}, OCN=192, PM=SAME, OCV/CPU)\|3.428\|3.226\|1.06\| \|conv::Conv::(GFLOPS=0.958, K=[3 x 3], IN={1, 192, 38, 38}, OCN=192, PM=SAME, OCV/CPU_FP16)\|-\|3.223\|-\| \|conv::Conv::(GFLOPS=0.958, K=[3 x 3], IN={1, 384, 19, 19}, OCN=384, PM=SAME, OCV/CPU)\|3.967\|3.957\|1.00\| \|conv::Conv::(GFLOPS=0.958, K=[3 x 3], IN={1, 384, 19, 19}, OCN=384, PM=SAME, OCV/CPU_FP16)\|-\|3.960\|-\| \|conv::Conv::(GFLOPS=1.022, K=[3 x 3], IN={1, 576, 19, 19}, OCN=273, PM=SAME, BIAS, OCV/CPU)\|4.806\|4.387\|1.10\| \|conv::Conv::(GFLOPS=1.022, K=[3 x 3], IN={1, 576, 19, 19}, OCN=273, PM=SAME, BIAS, OCV/CPU_FP16)\|-\|4.366\|-\| \|conv::Conv::(GFLOPS=1.112, K=[3 x 3], IN={1, 512, 10, 10}, OCN=1206, P=[1 x 1], BIAS, OCV/CPU)\|14.509\|11.756\|1.23\| \|conv::Conv::(GFLOPS=1.112, K=[3 x 3], IN={1, 512, 10, 10}, OCN=1206, P=[1 x 1], BIAS, OCV/CPU_FP16)\|-\|6.510\|-\| \|conv::Conv::(GFLOPS=1.181, K=[3 x 3], IN={1, 64, 160, 200}, OCN=128, S=[2 x 2], P=[1 x 1], BIAS, OCV/CPU)\|13.718\|13.287\|1.03\| \|conv::Conv::(GFLOPS=1.181, K=[3 x 3], IN={1, 64, 160, 200}, OCN=128, S=[2 x 2], P=[1 x 1], BIAS, OCV/CPU_FP16)\|-\|7.190\|-\| \|conv::Conv::(GFLOPS=1.182, K=[3 x 3], IN={1, 32, 320, 400}, OCN=64, S=[2 x 2], P=[1 x 1], BIAS, OCV/CPU)\|15.133\|14.853\|1.02\| \|conv::Conv::(GFLOPS=1.182, K=[3 x 3], IN={1, 32, 320, 400}, OCN=64, S=[2 x 2], P=[1 x 1], BIAS, OCV/CPU_FP16)\|-\|8.671\|-\| \|conv::Conv::(GFLOPS=1.195, K=[9 x 9], IN={1, 32, 240, 320}, OCN=3, P=[4 x 4], BIAS, OCV/CPU)\|41.928\|43.328\|0.97\| \|conv::Conv::(GFLOPS=1.195, K=[9 x 9], IN={1, 32, 240, 320}, OCN=3, P=[4 x 4], BIAS, OCV/CPU_FP16)\|-\|38.072\|-\| \|conv::Conv::(GFLOPS=1.196, K=[3 x 3], IN={1, 384, 26, 26}, OCN=256, P=[1 x 1], OCV/CPU)\|4.409\|4.428\|1.00\| \|conv::Conv::(GFLOPS=1.196, K=[3 x 3], IN={1, 384, 26, 26}, OCN=256, P=[1 x 1], OCV/CPU_FP16)\|-\|4.427\|-\| \|conv::Conv::(GFLOPS=1.210, K=[3 x 3], IN={1, 32, 256, 256}, OCN=32, PM=SAME, OCV/CPU)\|6.144\|5.363\|1.15\| \|conv::Conv::(GFLOPS=1.210, K=[3 x 3], IN={1, 32, 256, 256}, OCN=32, PM=SAME, OCV/CPU_FP16)\|-\|5.368\|-\| \|conv::Conv::(GFLOPS=1.245, K=[3 x 3], IN={1, 64, 75, 75}, OCN=192, PM=SAME, BIAS, OCV/CPU)\|3.926\|3.932\|1.00\| \|conv::Conv::(GFLOPS=1.245, K=[3 x 3], IN={1, 64, 75, 75}, OCN=192, PM=SAME, BIAS, OCV/CPU_FP16)\|-\|3.938\|-\| \|conv::Conv::(GFLOPS=1.245, K=[3 x 3], IN={1, 96, 75, 100}, OCN=96, PM=SAME, BIAS, OCV/CPU)\|3.920\|3.915\|1.00\| \|conv::Conv::(GFLOPS=1.245, K=[3 x 3], IN={1, 96, 75, 100}, OCN=96, PM=SAME, BIAS, OCV/CPU_FP16)\|-\|3.950\|-\| \|conv::Conv::(GFLOPS=1.248, K=[3 x 3], IN={1, 256, 46, 46}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)\|3.767\|3.764\|1.00\| \|conv::Conv::(GFLOPS=1.248, K=[3 x 3], IN={1, 256, 46, 46}, OCN=128, P=[1 x 1], BIAS, OCV/CPU_FP16)\|-\|3.762\|-\| \|conv::Conv::(GFLOPS=1.258, K=[3 x 3], IN={1, 1280, 10, 10}, OCN=546, PM=SAME, BIAS, OCV/CPU)\|19.959\|13.875\|1.44\| \|conv::Conv::(GFLOPS=1.258, K=[3 x 3], IN={1, 1280, 10, 10}, OCN=546, PM=SAME, BIAS, OCV/CPU_FP16)\|-\|7.781\|-\| \|conv::Conv::(GFLOPS=1.261, K=[3 x 3], IN={1, 192, 38, 50}, OCN=192, PM=SAME, BIAS, OCV/CPU)\|3.951\|3.955\|1.00\| \|conv::Conv::(GFLOPS=1.261, K=[3 x 3], IN={1, 192, 38, 50}, OCN=192, PM=SAME, BIAS, OCV/CPU_FP16)\|-\|3.969\|-\| \|conv::Conv::(GFLOPS=1.416, K=[3 x 3], IN={1, 128, 62, 82}, OCN=128, BIAS, OCV/CPU)\|4.050\|4.034\|1.00\| \|conv::Conv::(GFLOPS=1.416, K=[3 x 3], IN={1, 128, 62, 82}, OCN=128, BIAS, OCV/CPU_FP16)\|-\|4.093\|-\| \|conv::Conv::(GFLOPS=1.500, K=[3 x 3], IN={1, 128, 64, 84}, OCN=128, BIAS, OCV/CPU)\|4.923\|4.506\|1.09\| \|conv::Conv::(GFLOPS=1.500, K=[3 x 3], IN={1, 128, 64, 84}, OCN=128, BIAS, OCV/CPU_FP16)\|-\|4.509\|-\| \|conv::Conv::(GFLOPS=1.586, K=[3 x 3], IN={1, 128, 66, 86}, OCN=128, BIAS, OCV/CPU)\|4.759\|4.476\|1.06\| \|conv::Conv::(GFLOPS=1.586, K=[3 x 3], IN={1, 128, 66, 86}, OCN=128, BIAS, OCV/CPU_FP16)\|-\|4.447\|-\| \|conv::Conv::(GFLOPS=1.595, K=[3 x 3], IN={1, 256, 26, 26}, OCN=512, P=[1 x 1], OCV/CPU)\|6.079\|5.628\|1.08\| \|conv::Conv::(GFLOPS=1.595, K=[3 x 3], IN={1, 256, 26, 26}, OCN=512, P=[1 x 1], OCV/CPU_FP16)\|-\|5.625\|-\| \|conv::Conv::(GFLOPS=1.595, K=[3 x 3], IN={1, 256, 52, 52}, OCN=512, S=[2 x 2], P=[1 x 1], OCV/CPU)\|19.843\|17.523\|1.13\| \|conv::Conv::(GFLOPS=1.595, K=[3 x 3], IN={1, 256, 52, 52}, OCN=512, S=[2 x 2], P=[1 x 1], OCV/CPU_FP16)\|-\|8.917\|-\| \|conv::Conv::(GFLOPS=1.595, K=[3 x 3], IN={1, 512, 13, 13}, OCN=1024, P=[1 x 1], OCV/CPU)\|8.334\|8.247\|1.01\| \|conv::Conv::(GFLOPS=1.595, K=[3 x 3], IN={1, 512, 13, 13}, OCN=1024, P=[1 x 1], OCV/CPU_FP16)\|-\|8.246\|-\| \|conv::Conv::(GFLOPS=1.595, K=[3 x 3], IN={1, 512, 26, 26}, OCN=1024, S=[2 x 2], P=[1 x 1], OCV/CPU)\|23.164\|18.199\|1.27\| \|conv::Conv::(GFLOPS=1.595, K=[3 x 3], IN={1, 512, 26, 26}, OCN=1024, S=[2 x 2], P=[1 x 1], OCV/CPU_FP16)\|-\|9.305\|-\| \|conv::Conv::(GFLOPS=1.596, K=[3 x 3], IN={1, 64, 104, 104}, OCN=128, P=[1 x 1], OCV/CPU)\|5.184\|5.178\|1.00\| \|conv::Conv::(GFLOPS=1.596, K=[3 x 3], IN={1, 64, 104, 104}, OCN=128, P=[1 x 1], OCV/CPU_FP16)\|-\|5.149\|-\| \|conv::Conv::(GFLOPS=1.596, K=[3 x 3], IN={1, 64, 208, 208}, OCN=128, S=[2 x 2], P=[1 x 1], OCV/CPU)\|17.990\|18.103\|0.99\| \|conv::Conv::(GFLOPS=1.596, K=[3 x 3], IN={1, 64, 208, 208}, OCN=128, S=[2 x 2], P=[1 x 1], OCV/CPU_FP16)\|-\|9.777\|-\| \|conv::Conv::(GFLOPS=1.596, K=[3 x 3], IN={1, 128, 52, 52}, OCN=256, P=[1 x 1], OCV/CPU)\|4.831\|4.522\|1.07\| \|conv::Conv::(GFLOPS=1.596, K=[3 x 3], IN={1, 128, 52, 52}, OCN=256, P=[1 x 1], OCV/CPU_FP16)\|-\|4.523\|-\| \|conv::Conv::(GFLOPS=1.596, K=[3 x 3], IN={1, 128, 104, 104}, OCN=256, S=[2 x 2], P=[1 x 1], OCV/CPU)\|17.328\|17.319\|1.00\| \|conv::Conv::(GFLOPS=1.596, K=[3 x 3], IN={1, 128, 104, 104}, OCN=256, S=[2 x 2], P=[1 x 1], OCV/CPU_FP16)\|-\|8.948\|-\| \|conv::Conv::(GFLOPS=1.598, K=[3 x 3], IN={1, 32, 208, 208}, OCN=64, P=[1 x 1], OCV/CPU)\|5.944\|5.961\|1.00\| \|conv::Conv::(GFLOPS=1.598, K=[3 x 3], IN={1, 32, 208, 208}, OCN=64, P=[1 x 1], OCV/CPU_FP16)\|-\|5.936\|-\| \|conv::Conv::(GFLOPS=1.598, K=[3 x 3], IN={1, 32, 416, 416}, OCN=64, S=[2 x 2], P=[1 x 1], OCV/CPU)\|19.811\|20.064\|0.99\| \|conv::Conv::(GFLOPS=1.598, K=[3 x 3], IN={1, 32, 416, 416}, OCN=64, S=[2 x 2], P=[1 x 1], OCV/CPU_FP16)\|-\|11.705\|-\| \|conv::Conv::(GFLOPS=1.659, K=[3 x 3], IN={1, 960, 10, 10}, OCN=960, PM=SAME, OCV/CPU)\|22.398\|17.686\|1.27\| \|conv::Conv::(GFLOPS=1.659, K=[3 x 3], IN={1, 960, 10, 10}, OCN=960, PM=SAME, OCV/CPU_FP16)\|-\|9.859\|-\| \|conv::Conv::(GFLOPS=1.660, K=[3 x 3], IN={1, 128, 75, 75}, OCN=128, G=128, P=[1 x 1], BIAS, OCV/CPU)\|0.416\|0.416\|1.00\| \|conv::Conv::(GFLOPS=1.660, K=[3 x 3], IN={1, 128, 75, 75}, OCN=128, G=128, P=[1 x 1], BIAS, OCV/CPU_FP16)\|-\|0.417\|-\| \|conv::Conv::(GFLOPS=1.660, K=[3 x 3], IN={1, 128, 75, 75}, OCN=128, PM=SAME, OCV/CPU)\|5.356\|5.110\|1.05\| \|conv::Conv::(GFLOPS=1.660, K=[3 x 3], IN={1, 128, 75, 75}, OCN=128, PM=SAME, OCV/CPU_FP16)\|-\|5.114\|-\| \|conv::Conv::(GFLOPS=1.675, K=[3 x 3], IN={1, 128, 68, 88}, OCN=128, BIAS, OCV/CPU)\|5.092\|4.748\|1.07\| \|conv::Conv::(GFLOPS=1.675, K=[3 x 3], IN={1, 128, 68, 88}, OCN=128, BIAS, OCV/CPU_FP16)\|-\|4.754\|-\| \|conv::Conv::(GFLOPS=1.704, K=[3 x 3], IN={1, 256, 38, 38}, OCN=256, G=256, P=[1 x 1], BIAS, OCV/CPU)\|0.260\|0.229\|1.13\| \|conv::Conv::(GFLOPS=1.704, K=[3 x 3], IN={1, 256, 38, 38}, OCN=256, G=256, P=[1 x 1], BIAS, OCV/CPU_FP16)\|-\|0.229\|-\| \|conv::Conv::(GFLOPS=1.704, K=[3 x 3], IN={1, 256, 38, 38}, OCN=256, PM=SAME, OCV/CPU)\|5.872\|5.460\|1.08\| \|conv::Conv::(GFLOPS=1.704, K=[3 x 3], IN={1, 256, 38, 38}, OCN=256, PM=SAME, OCV/CPU_FP16)\|-\|5.460\|-\| \|conv::Conv::(GFLOPS=1.704, K=[3 x 3], IN={1, 512, 19, 19}, OCN=512, G=512, P=[1 x 1], BIAS, OCV/CPU)\|0.161\|0.161\|1.00\| \|conv::Conv::(GFLOPS=1.704, K=[3 x 3], IN={1, 512, 19, 19}, OCN=512, G=512, P=[1 x 1], BIAS, OCV/CPU_FP16)\|-\|0.161\|-\| \|conv::Conv::(GFLOPS=1.704, K=[3 x 3], IN={1, 512, 19, 19}, OCN=512, P=[1 x 1], BIAS, OCV/CPU)\|7.176\|7.175\|1.00\| \|conv::Conv::(GFLOPS=1.704, K=[3 x 3], IN={1, 512, 19, 19}, OCN=512, P=[1 x 1], BIAS, OCV/CPU_FP16)\|-\|7.162\|-\| \|conv::Conv::(GFLOPS=1.704, K=[3 x 3], IN={1, 512, 19, 19}, OCN=512, PM=SAME, OCV/CPU)\|7.174\|7.185\|1.00\| \|conv::Conv::(GFLOPS=1.704, K=[3 x 3], IN={1, 512, 19, 19}, OCN=512, PM=SAME, OCV/CPU_FP16)\|-\|7.157\|-\| \|conv::Conv::(GFLOPS=1.766, K=[3 x 3], IN={1, 128, 70, 90}, OCN=128, BIAS, OCV/CPU)\|5.400\|5.180\|1.04\| \|conv::Conv::(GFLOPS=1.766, K=[3 x 3], IN={1, 128, 70, 90}, OCN=128, BIAS, OCV/CPU_FP16)\|-\|5.201\|-\| \|conv::Conv::(GFLOPS=1.859, K=[3 x 3], IN={1, 128, 72, 92}, OCN=128, BIAS, OCV/CPU)\|5.330\|5.188\|1.03\| \|conv::Conv::(GFLOPS=1.859, K=[3 x 3], IN={1, 128, 72, 92}, OCN=128, BIAS, OCV/CPU_FP16)\|-\|5.177\|-\| \|conv::Conv::(GFLOPS=1.888, K=[3 x 3], IN={1, 1024, 10, 10}, OCN=1024, G=1024, P=[1 x 1], BIAS, OCV/CPU)\|0.115\|0.115\|1.00\| \|conv::Conv::(GFLOPS=1.888, K=[3 x 3], IN={1, 1024, 10, 10}, OCN=1024, G=1024, P=[1 x 1], BIAS, OCV/CPU_FP16)\|-\|0.115\|-\| \|conv::Conv::(GFLOPS=1.888, K=[3 x 3], IN={1, 1024, 10, 10}, OCN=1024, PM=SAME, OCV/CPU)\|26.156\|20.222\|1.29\| \|conv::Conv::(GFLOPS=1.888, K=[3 x 3], IN={1, 1024, 10, 10}, OCN=1024, PM=SAME, OCV/CPU_FP16)\|-\|11.203\|-\| \|conv::Conv::(GFLOPS=1.954, K=[3 x 3], IN={1, 128, 74, 94}, OCN=128, BIAS, OCV/CPU)\|5.627\|5.543\|1.02\| \|conv::Conv::(GFLOPS=1.954, K=[3 x 3], IN={1, 128, 74, 94}, OCN=128, BIAS, OCV/CPU_FP16)\|-\|5.506\|-\| \|conv::Conv::(GFLOPS=1.995, K=[9 x 9], IN={1, 3, 320, 400}, OCN=32, P=[4 x 4], BIAS, OCV/CPU)\|27.925\|27.741\|1.01\| \|conv::Conv::(GFLOPS=1.995, K=[9 x 9], IN={1, 3, 320, 400}, OCN=32, P=[4 x 4], BIAS, OCV/CPU_FP16)\|-\|17.217\|-\| \|conv::Conv::(GFLOPS=2.052, K=[3 x 3], IN={1, 128, 76, 96}, OCN=128, BIAS, OCV/CPU)\|6.359\|6.062\|1.05\| \|conv::Conv::(GFLOPS=2.052, K=[3 x 3], IN={1, 128, 76, 96}, OCN=128, BIAS, OCV/CPU_FP16)\|-\|6.048\|-\| \|conv::Conv::(GFLOPS=2.100, K=[3 x 3], IN={1, 144, 75, 75}, OCN=144, PM=SAME, OCV/CPU)\|6.559\|6.322\|1.04\| \|conv::Conv::(GFLOPS=2.100, K=[3 x 3], IN={1, 144, 75, 75}, OCN=144, PM=SAME, OCV/CPU_FP16)\|-\|6.280\|-\| \|conv::Conv::(GFLOPS=2.153, K=[3 x 3], IN={1, 128, 78, 98}, OCN=128, BIAS, OCV/CPU)\|6.412\|6.200\|1.03\| \|conv::Conv::(GFLOPS=2.153, K=[3 x 3], IN={1, 128, 78, 98}, OCN=128, BIAS, OCV/CPU_FP16)\|-\|6.197\|-\| \|conv::Conv::(GFLOPS=2.156, K=[3 x 3], IN={1, 576, 19, 19}, OCN=576, PM=SAME, OCV/CPU)\|9.167\|8.624\|1.06\| \|conv::Conv::(GFLOPS=2.156, K=[3 x 3], IN={1, 576, 19, 19}, OCN=576, PM=SAME, OCV/CPU_FP16)\|-\|8.626\|-\| \|conv::Conv::(GFLOPS=2.255, K=[3 x 3], IN={1, 128, 80, 100}, OCN=128, BIAS, OCV/CPU)\|6.755\|6.491\|1.04\| \|conv::Conv::(GFLOPS=2.255, K=[3 x 3], IN={1, 128, 80, 100}, OCN=128, BIAS, OCV/CPU_FP16)\|-\|6.520\|-\| \|conv::Conv::(GFLOPS=2.719, K=[3 x 3], IN={1, 96, 256, 256}, OCN=96, S=[2 x 2], PM=SAME, OCV/CPU)\|35.664\|34.752\|1.03\| \|conv::Conv::(GFLOPS=2.719, K=[3 x 3], IN={1, 96, 256, 256}, OCN=96, S=[2 x 2], PM=SAME, OCV/CPU_FP16)\|-\|20.260\|-\| \|conv::Conv::(GFLOPS=3.319, K=[3 x 3], IN={1, 128, 75, 75}, OCN=256, P=[1 x 1], BIAS, OCV/CPU)\|9.514\|9.414\|1.01\| \|conv::Conv::(GFLOPS=3.319, K=[3 x 3], IN={1, 128, 75, 75}, OCN=256, P=[1 x 1], BIAS, OCV/CPU_FP16)\|-\|9.462\|-\| \|conv::Conv::(GFLOPS=3.321, K=[3 x 3], IN={1, 64, 150, 150}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)\|10.631\|9.963\|1.07\| \|conv::Conv::(GFLOPS=3.321, K=[3 x 3], IN={1, 64, 150, 150}, OCN=128, P=[1 x 1], BIAS, OCV/CPU_FP16)\|-\|9.935\|-\| \|conv::Conv::(GFLOPS=3.398, K=[7 x 7], IN={1, 128, 46, 46}, OCN=128, P=[3 x 3], BIAS, OCV/CPU)\|37.465\|36.798\|1.02\| \|conv::Conv::(GFLOPS=3.398, K=[7 x 7], IN={1, 128, 46, 46}, OCN=128, P=[3 x 3], BIAS, OCV/CPU_FP16)\|-\|19.569\|-\| \|conv::Conv::(GFLOPS=3.407, K=[3 x 3], IN={1, 512, 19, 19}, OCN=1024, D=[6 x 6], P=[6 x 6], BIAS, OCV/CPU)\|38.157\|36.157\|1.06\| \|conv::Conv::(GFLOPS=3.407, K=[3 x 3], IN={1, 512, 19, 19}, OCN=1024, D=[6 x 6], P=[6 x 6], BIAS, OCV/CPU_FP16)\|-\|18.902\|-\| \|conv::Conv::(GFLOPS=3.408, K=[3 x 3], IN={1, 256, 38, 38}, OCN=512, P=[1 x 1], BIAS, OCV/CPU)\|10.356\|10.401\|1.00\| \|conv::Conv::(GFLOPS=3.408, K=[3 x 3], IN={1, 256, 38, 38}, OCN=512, P=[1 x 1], BIAS, OCV/CPU_FP16)\|-\|10.360\|-\| \|conv::Conv::(GFLOPS=4.247, K=[3 x 3], IN={1, 480, 32, 32}, OCN=480, PM=SAME, OCV/CPU)\|12.641\|12.150\|1.04\| \|conv::Conv::(GFLOPS=4.247, K=[3 x 3], IN={1, 480, 32, 32}, OCN=480, PM=SAME, OCV/CPU_FP16)\|-\|12.162\|-\| \|conv::Conv::(GFLOPS=4.247, K=[5 x 5], IN={1, 144, 128, 128}, OCN=144, S=[2 x 2], PM=SAME, OCV/CPU)\|50.545\|50.505\|1.00\| \|conv::Conv::(GFLOPS=4.247, K=[5 x 5], IN={1, 144, 128, 128}, OCN=144, S=[2 x 2], PM=SAME, OCV/CPU_FP16)\|-\|27.950\|-\| \|conv::Conv::(GFLOPS=4.566, K=[7 x 7], IN={1, 172, 46, 46}, OCN=128, P=[3 x 3], BIAS, OCV/CPU)\|54.233\|49.603\|1.09\| \|conv::Conv::(GFLOPS=4.566, K=[7 x 7], IN={1, 172, 46, 46}, OCN=128, P=[3 x 3], BIAS, OCV/CPU_FP16)\|-\|26.515\|-\| \|conv::Conv::(GFLOPS=4.993, K=[3 x 3], IN={1, 256, 46, 46}, OCN=512, P=[1 x 1], BIAS, OCV/CPU)\|13.779\|12.968\|1.06\| \|conv::Conv::(GFLOPS=4.993, K=[3 x 3], IN={1, 256, 46, 46}, OCN=512, P=[1 x 1], BIAS, OCV/CPU_FP16)\|-\|12.984\|-\| \|conv::Conv::(GFLOPS=4.993, K=[3 x 3], IN={1, 512, 46, 46}, OCN=256, P=[1 x 1], BIAS, OCV/CPU)\|15.809\|15.329\|1.03\| \|conv::Conv::(GFLOPS=4.993, K=[3 x 3], IN={1, 512, 46, 46}, OCN=256, P=[1 x 1], BIAS, OCV/CPU_FP16)\|-\|15.433\|-\| \|conv::Conv::(GFLOPS=4.994, K=[3 x 3], IN={1, 128, 92, 92}, OCN=256, P=[1 x 1], BIAS, OCV/CPU)\|14.563\|14.527\|1.00\| \|conv::Conv::(GFLOPS=4.994, K=[3 x 3], IN={1, 128, 92, 92}, OCN=256, P=[1 x 1], BIAS, OCV/CPU_FP16)\|-\|14.480\|-\| \|conv::Conv::(GFLOPS=4.997, K=[3 x 3], IN={1, 64, 184, 184}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)\|16.714\|16.484\|1.01\| \|conv::Conv::(GFLOPS=4.997, K=[3 x 3], IN={1, 64, 184, 184}, OCN=128, P=[1 x 1], BIAS, OCV/CPU_FP16)\|-\|16.362\|-\| \|conv::Conv::(GFLOPS=5.780, K=[5 x 5], IN={1, 672, 32, 32}, OCN=672, S=[2 x 2], PM=SAME, OCV/CPU)\|77.832\|65.729\|1.18\| \|conv::Conv::(GFLOPS=5.780, K=[5 x 5], IN={1, 672, 32, 32}, OCN=672, S=[2 x 2], PM=SAME, OCV/CPU_FP16)\|-\|32.065\|-\| \|conv::Conv::(GFLOPS=6.116, K=[3 x 3], IN={1, 1152, 16, 16}, OCN=1152, PM=SAME, OCV/CPU)\|21.903\|20.386\|1.07\| \|conv::Conv::(GFLOPS=6.116, K=[3 x 3], IN={1, 1152, 16, 16}, OCN=1152, PM=SAME, OCV/CPU_FP16)\|-\|20.416\|-\| \|conv::Conv::(GFLOPS=6.118, K=[3 x 3], IN={1, 144, 128, 128}, OCN=144, PM=SAME, OCV/CPU)\|20.405\|18.148\|1.12\| \|conv::Conv::(GFLOPS=6.118, K=[3 x 3], IN={1, 144, 128, 128}, OCN=144, PM=SAME, OCV/CPU_FP16)\|-\|18.128\|-\| \|conv::Conv::(GFLOPS=6.637, K=[3 x 3], IN={1, 256, 75, 75}, OCN=256, P=[1 x 1], BIAS, OCV/CPU)\|20.334\|18.521\|1.10\| \|conv::Conv::(GFLOPS=6.637, K=[3 x 3], IN={1, 256, 75, 75}, OCN=256, P=[1 x 1], BIAS, OCV/CPU_FP16)\|-\|18.495\|-\| \|conv::Conv::(GFLOPS=6.638, K=[3 x 3], IN={1, 128, 150, 150}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)\|21.527\|19.584\|1.10\| \|conv::Conv::(GFLOPS=6.638, K=[3 x 3], IN={1, 128, 150, 150}, OCN=128, P=[1 x 1], BIAS, OCV/CPU_FP16)\|-\|19.630\|-\| \|conv::Conv::(GFLOPS=6.641, K=[3 x 3], IN={1, 64, 150, 200}, OCN=192, PM=SAME, BIAS, OCV/CPU)\|22.715\|20.057\|1.13\| \|conv::Conv::(GFLOPS=6.641, K=[3 x 3], IN={1, 64, 150, 200}, OCN=192, PM=SAME, BIAS, OCV/CPU_FP16)\|-\|20.068\|-\| \|conv::Conv::(GFLOPS=6.641, K=[3 x 3], IN={1, 64, 300, 300}, OCN=64, P=[1 x 1], BIAS, OCV/CPU)\|26.228\|24.992\|1.05\| \|conv::Conv::(GFLOPS=6.641, K=[3 x 3], IN={1, 64, 300, 300}, OCN=64, P=[1 x 1], BIAS, OCV/CPU_FP16)\|-\|24.957\|-\| \|conv::Conv::(GFLOPS=6.814, K=[3 x 3], IN={1, 512, 38, 38}, OCN=512, P=[1 x 1], BIAS, OCV/CPU)\|21.524\|21.581\|1.00\| \|conv::Conv::(GFLOPS=6.814, K=[3 x 3], IN={1, 512, 38, 38}, OCN=512, P=[1 x 1], BIAS, OCV/CPU_FP16)\|-\|21.782\|-\| \|conv::Conv::(GFLOPS=8.025, K=[3 x 3], IN={1, 1024, 19, 19}, OCN=1206, P=[1 x 1], BIAS, OCV/CPU)\|34.094\|31.964\|1.07\| \|conv::Conv::(GFLOPS=8.025, K=[3 x 3], IN={1, 1024, 19, 19}, OCN=1206, P=[1 x 1], BIAS, OCV/CPU_FP16)\|-\|31.925\|-\| \|conv::Conv::(GFLOPS=9.986, K=[3 x 3], IN={1, 512, 46, 46}, OCN=512, P=[1 x 1], BIAS, OCV/CPU)\|28.677\|27.813\|1.03\| \|conv::Conv::(GFLOPS=9.986, K=[3 x 3], IN={1, 512, 46, 46}, OCN=512, P=[1 x 1], BIAS, OCV/CPU_FP16)\|-\|27.808\|-\| \|conv::Conv::(GFLOPS=9.987, K=[3 x 3], IN={1, 256, 92, 92}, OCN=256, P=[1 x 1], BIAS, OCV/CPU)\|31.274\|27.892\|1.12\| \|conv::Conv::(GFLOPS=9.987, K=[3 x 3], IN={1, 256, 92, 92}, OCN=256, P=[1 x 1], BIAS, OCV/CPU_FP16)\|-\|27.910\|-\| \|conv::Conv::(GFLOPS=9.989, K=[3 x 3], IN={1, 128, 184, 184}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)\|30.533\|30.007\|1.02\| \|conv::Conv::(GFLOPS=9.989, K=[3 x 3], IN={1, 128, 184, 184}, OCN=128, P=[1 x 1], BIAS, OCV/CPU_FP16)\|-\|30.089\|-\| \|conv::Conv::(GFLOPS=9.993, K=[3 x 3], IN={1, 64, 368, 368}, OCN=64, P=[1 x 1], BIAS, OCV/CPU)\|39.837\|38.312\|1.04\| \|conv::Conv::(GFLOPS=9.993, K=[3 x 3], IN={1, 64, 368, 368}, OCN=64, P=[1 x 1], BIAS, OCV/CPU_FP16)\|-\|38.477\|-\| \|conv::Conv::(GFLOPS=10.087, K=[3 x 3], IN={1, 576, 38, 50}, OCN=512, PM=SAME, BIAS, OCV/CPU)\|32.480\|29.237\|1.11\| \|conv::Conv::(GFLOPS=10.087, K=[3 x 3], IN={1, 576, 38, 50}, OCN=512, PM=SAME, BIAS, OCV/CPU_FP16)\|-\|29.452\|-\| \|conv::Conv::(GFLOPS=10.701, K=[3 x 3], IN={1, 512, 38, 38}, OCN=804, P=[1 x 1], BIAS, OCV/CPU)\|33.544\|32.832\|1.02\| \|conv::Conv::(GFLOPS=10.701, K=[3 x 3], IN={1, 512, 38, 38}, OCN=804, P=[1 x 1], BIAS, OCV/CPU_FP16)\|-\|32.784\|-\| \|conv::Conv::(GFLOPS=11.797, K=[5 x 5], IN={1, 240, 64, 64}, OCN=240, PM=SAME, OCV/CPU)\|134.481\|130.678\|1.03\| \|conv::Conv::(GFLOPS=11.797, K=[5 x 5], IN={1, 240, 64, 64}, OCN=240, PM=SAME, OCV/CPU_FP16)\|-\|70.134\|-\| \|conv::Conv::(GFLOPS=11.797, K=[5 x 5], IN={1, 480, 32, 32}, OCN=480, PM=SAME, OCV/CPU)\|127.930\|126.530\|1.01\| \|conv::Conv::(GFLOPS=11.797, K=[5 x 5], IN={1, 480, 32, 32}, OCN=480, PM=SAME, OCV/CPU_FP16)\|-\|65.261\|-\| \|conv::Conv::(GFLOPS=16.987, K=[5 x 5], IN={1, 1152, 16, 16}, OCN=1152, PM=SAME, OCV/CPU)\|201.346\|187.007\|1.08\| \|conv::Conv::(GFLOPS=16.987, K=[5 x 5], IN={1, 1152, 16, 16}, OCN=1152, PM=SAME, OCV/CPU_FP16)\|-\|91.525\|-\| \|conv::Conv::(GFLOPS=23.122, K=[5 x 5], IN={1, 672, 32, 32}, OCN=672, PM=SAME, OCV/CPU)\|252.038\|245.587\|1.03\| \|conv::Conv::(GFLOPS=23.122, K=[5 x 5], IN={1, 672, 32, 32}, OCN=672, PM=SAME, OCV/CPU_FP16)\|-\|125.477\|-\| ### 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 - [ ] 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	2 years ago

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324 Commits (fc9208cff5c7a64a6d667427b01f1f2d98bf9cf0)