opencv

Commit Graph

Author	SHA1	Message	Date
Labib Asari	c4226f0457	Merge pull request #23196 from labeeb-7z:printOptionInRoiSelector Added argument to print notice in `roiSelector.cpp` Related Issue : https://github.com/opencv/opencv/issues/23175 I've added a printNotice argument to `selectROI` (and it's overload) and `selectROIs` functions. I've also updated the function declarations in `highgui.hpp`. Tested by building locally. ### 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
Alexander Smorkalov	752ac19a2f	Merge pull request #23292 from CSBVision:patch-4 Add ENABLE_DELAYLOAD option	2 years ago
Alexander Smorkalov	86fa0308fc	Merge pull request #23139 from AleksandrPanov:add_py_charuco_sample add python charuco sample	2 years ago
Alex	0d455e05c1	add py charuco sample+choriginal.jpg+camera_params	2 years ago
Alexander Smorkalov	924a65413a	Merge pull request #23357 from zihaomu:fix_winograd_error_32bit DNN : fix bug in 32 bit cpu	2 years ago
zihaomu	6bac5453d1	fix bug in 32 bit cpu	2 years ago
Alexander Smorkalov	ccbc784195	Merge pull request #23354 from zihaomu:issue_23351 DNN : fix bug in layer fusion	2 years ago
Alexander Smorkalov	bb917c8391	Merge pull request #23353 from tingboliao:4.x Fix bugs of test case failure	2 years ago
zihaomu	386be97ce2	fix bug in layer fusion	2 years ago
tingbo.liao	7d032de7e8	Fix bugs of test case failure 4 failed tests in open_test_dnn listed below: * Test_Caffe_layers.Conv_Elu/0, where GetParam() = OCV/CPU * Test_ONNX_layers.ConvResizePool1d/0, where GetParam() = OCV/CPU * Test_TensorFlow_layers.tf_reshape_nhwc/0, where GetParam() = OCV/CPU * Test_Torch_layers.net_inception_block/0, where GetParam() = OCV/CPU In winofunc_AtXA_8x8_f32 and winofunc_BtXB_8x8_f32 implementation, incorrect input parameters cause tests failure. Add four new different variables for the last four input parameters of v_transpose4x4 to fix bugs, and update related comments. Signed-off-by: tingbo.liao <tingbo.liao@starfivetech.com>	2 years ago
Alexander Smorkalov	de2f7666fb	Merge pull request #23260 from tintou:tintou/gtk-reduce-diff highgui: Reduce the difference between GTK+2 and GTK+3 version	2 years ago
Alexander Smorkalov	22a52766dc	Merge pull request #23343 from zihaomu:fix_test_onnx_conf DNN Test ONNX: Fix the logic of the test case	2 years ago
Yuantao Feng	b94e13c8ae	Merge pull request #23319 from fengyuentau:fix_zoo_issue_136 Related issue: https://github.com/opencv/opencv_zoo/issues/136 Features added: - Support operators with multiple output: ONNX Split. - Support Slice without steps. Bugs fixed: - Wrong settings in ClipByValue (Relu6). - Wrong calculation of pads in convolution layer (It is wrong generally but only fixed specifically for CANN for now). ### 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
zihaomu	ee3740af00	move global skip out of if loop, and add opencv_deny_list	2 years ago
Zihao Mu	e03e2e7f94	Merge pull request #23192 from zihaomu:clean_up_SIMD_code ### Purpose of this PR: - Move all dispatch and SIMD code of `convolution layer` into `simd.hpp` file. - Support Winograd at AVX-only machine. - Re-name the folder from `fast_conv` to `cpu_kernels`. In the future, we can put other layers of CPU optimization into it, like `GEMM` or `MatMul`. ## Performance Test Since this patch just focuses on the code style, the performance is expected as the same as before. Test with the following script: `./bin/opencv_perf_dnn '--gtest_filter=conv' --gtest_output="xml:../1-0th.xml" --perf_threads=1` ### Test on X86 platform 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\|0.98\| \|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\|0.95\| \|conv1d::Conv1D::(GFLOPS=0.000, K=[3], IN={1, 6, 10}, OCN=6, PM=VALID, BIAS, OCV/CPU)\|0.001\|0.001\|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)\|0.002\|0.002\|1.04\| \|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)\|0.002\|0.002\|0.94\| \|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)\|0.040\|0.044\|0.93\| \|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)\|0.010\|0.010\|1.00\| \|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)\|0.106\|0.103\|1.03\| \|conv3d::Conv3D::(GFLOPS=0.002, K=[3 x 1 x 4], IN={1, 14, 5, 10, 10}, OCN=14, PM=SAME, OCV/CPU)\|0.041\|0.040\|1.03\| \|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)\|0.340\|0.329\|1.03\| \|conv3d::Conv3D::(GFLOPS=0.027, K=[3 x 3 x 3], IN={1, 6, 10, 38, 50}, OCN=6, PM=VALID, BIAS, OCV/CPU)\|0.590\|0.567\|1.04\| \|conv3d::Conv3D::(GFLOPS=0.030, K=[5 x 5 x 5], IN={1, 6, 19, 19, 19}, OCN=6, G=2, OCV/CPU)\|1.374\|1.314\|1.05\| \|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)\|3.715\|3.528\|1.05\| \|conv3d::Conv3D::(GFLOPS=0.053, K=[3 x 3 x 3], IN={1, 10, 98, 10, 10}, OCN=10, PM=SAME, OCV/CPU)\|1.181\|1.166\|1.01\| \|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)\|2.689\|2.587\|1.04\| \|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)\|4.754\|4.500\|1.06\| \|conv3d::Conv3D::(GFLOPS=0.116, K=[5 x 5 x 5], IN={1, 2, 21, 75, 100}, OCN=2, BIAS, OCV/CPU)\|9.612\|9.112\|1.05\| \|conv3d::Conv3D::(GFLOPS=1.267, K=[5 x 5 x 5], IN={1, 3, 75, 75, 100}, OCN=3, PM=SAME, BIAS, OCV/CPU)\|69.000\|64.676\|1.07\| \|conv3d::Conv3D::(GFLOPS=1.343, K=[3 x 3 x 3], IN={1, 11, 9, 150, 200}, OCN=11, PM=VALID, BIAS, OCV/CPU)\|20.248\|18.451\|1.10\| \|conv::Conv::(GFLOPS=0.177, K=[1 x 1], IN={1, 512, 26, 26}, OCN=256, OCV/CPU)\|1.395\|1.392\|1.00\| \|conv::Conv::(GFLOPS=0.177, K=[1 x 1], IN={1, 1024, 13, 13}, OCN=512, OCV/CPU)\|1.990\|1.984\|1.00\| \|conv::Conv::(GFLOPS=0.178, K=[1 x 1], IN={1, 256, 52, 52}, OCN=128, OCV/CPU)\|1.393\|1.360\|1.02\| \|conv::Conv::(GFLOPS=0.210, K=[1 x 1], IN={1, 576, 38, 50}, OCN=96, PM=SAME, BIAS, OCV/CPU)\|1.813\|1.744\|1.04\| \|conv::Conv::(GFLOPS=0.231, K=[3 x 3], IN={1, 128, 56, 56}, OCN=32, P=[1 x 1], OCV/CPU)\|1.190\|1.191\|1.00\| \|conv::Conv::(GFLOPS=0.231, K=[3 x 3], IN={1, 256, 14, 14}, OCN=256, P=[1 x 1], OCV/CPU)\|1.286\|1.284\|1.00\| \|conv::Conv::(GFLOPS=0.280, K=[1 x 1], IN={1, 576, 38, 50}, OCN=128, PM=SAME, BIAS, OCV/CPU)\|2.295\|2.279\|1.01\| \|conv::Conv::(GFLOPS=0.302, K=[3 x 3], IN={1, 64, 64, 64}, OCN=64, PM=SAME, OCV/CPU)\|1.322\|1.331\|0.99\| \|conv::Conv::(GFLOPS=0.357, K=[1 x 1], IN={1, 64, 208, 208}, OCN=64, OCV/CPU)\|3.784\|3.533\|1.07\| \|conv::Conv::(GFLOPS=0.420, K=[3 x 3], IN={1, 96, 38, 50}, OCN=128, PM=SAME, BIAS, OCV/CPU)\|1.838\|1.844\|1.00\| \|conv::Conv::(GFLOPS=0.472, K=[3 x 3], IN={1, 128, 40, 40}, OCN=128, PM=SAME, OCV/CPU)\|1.957\|1.959\|1.00\| \|conv::Conv::(GFLOPS=0.472, K=[3 x 3], IN={1, 256, 20, 20}, OCN=256, PM=SAME, OCV/CPU)\|2.596\|2.573\|1.01\| \|conv::Conv::(GFLOPS=0.472, K=[3 x 3], IN={1, 512, 10, 10}, OCN=512, PM=SAME, OCV/CPU)\|4.183\|4.083\|1.02\| \|conv::Conv::(GFLOPS=0.561, K=[3 x 3], IN={1, 128, 38, 50}, OCN=128, PM=SAME, BIAS, OCV/CPU)\|2.413\|2.406\|1.00\| \|conv::Conv::(GFLOPS=0.624, K=[3 x 3], IN={1, 128, 46, 46}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)\|2.538\|2.546\|1.00\| \|conv::Conv::(GFLOPS=0.701, K=[3 x 3], IN={1, 128, 38, 50}, OCN=160, PM=SAME, BIAS, OCV/CPU)\|2.972\|2.980\|1.00\| \|conv::Conv::(GFLOPS=0.798, K=[3 x 3], IN={1, 64, 104, 104}, OCN=64, P=[1 x 1], OCV/CPU)\|3.452\|3.464\|1.00\| \|conv::Conv::(GFLOPS=0.798, K=[3 x 3], IN={1, 128, 52, 52}, OCN=128, P=[1 x 1], OCV/CPU)\|3.082\|3.105\|0.99\| \|conv::Conv::(GFLOPS=0.798, K=[3 x 3], IN={1, 256, 26, 26}, OCN=256, P=[1 x 1], OCV/CPU)\|4.043\|3.919\|1.03\| \|conv::Conv::(GFLOPS=0.798, K=[3 x 3], IN={1, 512, 13, 13}, OCN=512, P=[1 x 1], OCV/CPU)\|5.538\|5.531\|1.00\| \|conv::Conv::(GFLOPS=0.830, K=[3 x 3], IN={1, 64, 75, 100}, OCN=96, PM=SAME, BIAS, OCV/CPU)\|3.393\|3.418\|0.99\| \|conv::Conv::(GFLOPS=0.958, K=[3 x 3], IN={1, 192, 38, 38}, OCN=192, PM=SAME, OCV/CPU)\|4.325\|4.234\|1.02\| \|conv::Conv::(GFLOPS=0.958, K=[3 x 3], IN={1, 384, 19, 19}, OCN=384, PM=SAME, OCV/CPU)\|6.009\|5.908\|1.02\| \|conv::Conv::(GFLOPS=1.022, K=[3 x 3], IN={1, 576, 19, 19}, OCN=273, PM=SAME, BIAS, OCV/CPU)\|6.557\|6.376\|1.03\| \|conv::Conv::(GFLOPS=1.112, K=[3 x 3], IN={1, 512, 10, 10}, OCN=1206, P=[1 x 1], BIAS, OCV/CPU)\|10.114\|9.472\|1.07\| \|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)\|10.373\|9.879\|1.05\| \|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)\|12.782\|11.624\|1.10\| \|conv::Conv::(GFLOPS=1.195, K=[9 x 9], IN={1, 32, 240, 320}, OCN=3, P=[4 x 4], BIAS, OCV/CPU)\|90.931\|90.552\|1.00\| \|conv::Conv::(GFLOPS=1.196, K=[3 x 3], IN={1, 384, 26, 26}, OCN=256, P=[1 x 1], OCV/CPU)\|6.091\|5.818\|1.05\| \|conv::Conv::(GFLOPS=1.210, K=[3 x 3], IN={1, 32, 256, 256}, OCN=32, PM=SAME, OCV/CPU)\|7.083\|6.643\|1.07\| \|conv::Conv::(GFLOPS=1.245, K=[3 x 3], IN={1, 64, 75, 75}, OCN=192, PM=SAME, BIAS, OCV/CPU)\|5.054\|5.059\|1.00\| \|conv::Conv::(GFLOPS=1.245, K=[3 x 3], IN={1, 96, 75, 100}, OCN=96, PM=SAME, BIAS, OCV/CPU)\|5.005\|4.931\|1.02\| \|conv::Conv::(GFLOPS=1.248, K=[3 x 3], IN={1, 256, 46, 46}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)\|4.951\|5.065\|0.98\| \|conv::Conv::(GFLOPS=1.258, K=[3 x 3], IN={1, 1280, 10, 10}, OCN=546, PM=SAME, BIAS, OCV/CPU)\|11.957\|11.293\|1.06\| \|conv::Conv::(GFLOPS=1.261, K=[3 x 3], IN={1, 192, 38, 50}, OCN=192, PM=SAME, BIAS, OCV/CPU)\|5.328\|5.250\|1.01\| \|conv::Conv::(GFLOPS=1.416, K=[3 x 3], IN={1, 128, 62, 82}, OCN=128, BIAS, OCV/CPU)\|5.544\|5.292\|1.05\| \|conv::Conv::(GFLOPS=1.500, K=[3 x 3], IN={1, 128, 64, 84}, OCN=128, BIAS, OCV/CPU)\|6.186\|5.893\|1.05\| \|conv::Conv::(GFLOPS=1.586, K=[3 x 3], IN={1, 128, 66, 86}, OCN=128, BIAS, OCV/CPU)\|6.153\|5.834\|1.05\| \|conv::Conv::(GFLOPS=1.595, K=[3 x 3], IN={1, 256, 26, 26}, OCN=512, P=[1 x 1], OCV/CPU)\|8.154\|8.107\|1.01\| \|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)\|12.699\|12.256\|1.04\| \|conv::Conv::(GFLOPS=1.595, K=[3 x 3], IN={1, 512, 13, 13}, OCN=1024, P=[1 x 1], OCV/CPU)\|11.355\|11.217\|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)\|19.062\|17.814\|1.07\| \|conv::Conv::(GFLOPS=1.596, K=[3 x 3], IN={1, 64, 104, 104}, OCN=128, P=[1 x 1], OCV/CPU)\|6.820\|6.531\|1.04\| \|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)\|14.502\|13.483\|1.08\| \|conv::Conv::(GFLOPS=1.596, K=[3 x 3], IN={1, 128, 52, 52}, OCN=256, P=[1 x 1], OCV/CPU)\|6.270\|6.123\|1.02\| \|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)\|13.173\|12.451\|1.06\| \|conv::Conv::(GFLOPS=1.598, K=[3 x 3], IN={1, 32, 208, 208}, OCN=64, P=[1 x 1], OCV/CPU)\|8.326\|7.652\|1.09\| \|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)\|17.605\|16.465\|1.07\| \|conv::Conv::(GFLOPS=1.659, K=[3 x 3], IN={1, 960, 10, 10}, OCN=960, PM=SAME, OCV/CPU)\|15.675\|14.771\|1.06\| \|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)\|0.420\|0.423\|0.99\| \|conv::Conv::(GFLOPS=1.660, K=[3 x 3], IN={1, 128, 75, 75}, OCN=128, PM=SAME, OCV/CPU)\|6.788\|6.491\|1.05\| \|conv::Conv::(GFLOPS=1.675, K=[3 x 3], IN={1, 128, 68, 88}, OCN=128, BIAS, OCV/CPU)\|6.456\|6.168\|1.05\| \|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)\|0.263\|0.261\|1.01\| \|conv::Conv::(GFLOPS=1.704, K=[3 x 3], IN={1, 256, 38, 38}, OCN=256, PM=SAME, OCV/CPU)\|7.690\|7.398\|1.04\| \|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)\|0.200\|0.202\|0.99\| \|conv::Conv::(GFLOPS=1.704, K=[3 x 3], IN={1, 512, 19, 19}, OCN=512, P=[1 x 1], BIAS, OCV/CPU)\|10.542\|10.464\|1.01\| \|conv::Conv::(GFLOPS=1.704, K=[3 x 3], IN={1, 512, 19, 19}, OCN=512, PM=SAME, OCV/CPU)\|10.876\|10.728\|1.01\| \|conv::Conv::(GFLOPS=1.766, K=[3 x 3], IN={1, 128, 70, 90}, OCN=128, BIAS, OCV/CPU)\|7.194\|6.768\|1.06\| \|conv::Conv::(GFLOPS=1.859, K=[3 x 3], IN={1, 128, 72, 92}, OCN=128, BIAS, OCV/CPU)\|7.099\|6.731\|1.05\| \|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)\|0.147\|0.162\|0.91\| \|conv::Conv::(GFLOPS=1.888, K=[3 x 3], IN={1, 1024, 10, 10}, OCN=1024, PM=SAME, OCV/CPU)\|18.558\|17.141\|1.08\| \|conv::Conv::(GFLOPS=1.954, K=[3 x 3], IN={1, 128, 74, 94}, OCN=128, BIAS, OCV/CPU)\|7.641\|7.219\|1.06\| \|conv::Conv::(GFLOPS=1.995, K=[9 x 9], IN={1, 3, 320, 400}, OCN=32, P=[4 x 4], BIAS, OCV/CPU)\|22.666\|20.999\|1.08\| \|conv::Conv::(GFLOPS=2.052, K=[3 x 3], IN={1, 128, 76, 96}, OCN=128, BIAS, OCV/CPU)\|8.523\|7.921\|1.08\| \|conv::Conv::(GFLOPS=2.100, K=[3 x 3], IN={1, 144, 75, 75}, OCN=144, PM=SAME, OCV/CPU)\|8.514\|8.109\|1.05\| \|conv::Conv::(GFLOPS=2.153, K=[3 x 3], IN={1, 128, 78, 98}, OCN=128, BIAS, OCV/CPU)\|8.300\|7.878\|1.05\| \|conv::Conv::(GFLOPS=2.156, K=[3 x 3], IN={1, 576, 19, 19}, OCN=576, PM=SAME, OCV/CPU)\|13.403\|13.131\|1.02\| \|conv::Conv::(GFLOPS=2.255, K=[3 x 3], IN={1, 128, 80, 100}, OCN=128, BIAS, OCV/CPU)\|8.920\|8.357\|1.07\| \|conv::Conv::(GFLOPS=2.719, K=[3 x 3], IN={1, 96, 256, 256}, OCN=96, S=[2 x 2], PM=SAME, OCV/CPU)\|28.827\|27.616\|1.04\| \|conv::Conv::(GFLOPS=3.319, K=[3 x 3], IN={1, 128, 75, 75}, OCN=256, P=[1 x 1], BIAS, OCV/CPU)\|12.895\|12.670\|1.02\| \|conv::Conv::(GFLOPS=3.321, K=[3 x 3], IN={1, 64, 150, 150}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)\|14.120\|13.078\|1.08\| \|conv::Conv::(GFLOPS=3.398, K=[7 x 7], IN={1, 128, 46, 46}, OCN=128, P=[3 x 3], BIAS, OCV/CPU)\|27.541\|27.582\|1.00\| \|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)\|32.367\|31.140\|1.04\| \|conv::Conv::(GFLOPS=3.408, K=[3 x 3], IN={1, 256, 38, 38}, OCN=512, P=[1 x 1], BIAS, OCV/CPU)\|14.934\|14.910\|1.00\| \|conv::Conv::(GFLOPS=4.247, K=[3 x 3], IN={1, 480, 32, 32}, OCN=480, PM=SAME, OCV/CPU)\|18.289\|18.491\|0.99\| \|conv::Conv::(GFLOPS=4.247, K=[5 x 5], IN={1, 144, 128, 128}, OCN=144, S=[2 x 2], PM=SAME, OCV/CPU)\|37.857\|36.845\|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)\|37.402\|36.566\|1.02\| \|conv::Conv::(GFLOPS=4.993, K=[3 x 3], IN={1, 256, 46, 46}, OCN=512, P=[1 x 1], BIAS, OCV/CPU)\|19.031\|19.164\|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)\|19.019\|19.135\|0.99\| \|conv::Conv::(GFLOPS=4.994, K=[3 x 3], IN={1, 128, 92, 92}, OCN=256, P=[1 x 1], BIAS, OCV/CPU)\|20.077\|19.400\|1.03\| \|conv::Conv::(GFLOPS=4.997, K=[3 x 3], IN={1, 64, 184, 184}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)\|21.883\|21.302\|1.03\| \|conv::Conv::(GFLOPS=5.780, K=[5 x 5], IN={1, 672, 32, 32}, OCN=672, S=[2 x 2], PM=SAME, OCV/CPU)\|51.288\|49.851\|1.03\| \|conv::Conv::(GFLOPS=6.116, K=[3 x 3], IN={1, 1152, 16, 16}, OCN=1152, PM=SAME, OCV/CPU)\|27.349\|28.359\|0.96\| \|conv::Conv::(GFLOPS=6.118, K=[3 x 3], IN={1, 144, 128, 128}, OCN=144, PM=SAME, OCV/CPU)\|24.915\|25.130\|0.99\| \|conv::Conv::(GFLOPS=6.637, K=[3 x 3], IN={1, 256, 75, 75}, OCN=256, P=[1 x 1], BIAS, OCV/CPU)\|25.488\|25.899\|0.98\| \|conv::Conv::(GFLOPS=6.638, K=[3 x 3], IN={1, 128, 150, 150}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)\|27.346\|27.390\|1.00\| \|conv::Conv::(GFLOPS=6.641, K=[3 x 3], IN={1, 64, 150, 200}, OCN=192, PM=SAME, BIAS, OCV/CPU)\|28.033\|28.301\|0.99\| \|conv::Conv::(GFLOPS=6.641, K=[3 x 3], IN={1, 64, 300, 300}, OCN=64, P=[1 x 1], BIAS, OCV/CPU)\|50.216\|49.970\|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)\|29.670\|29.513\|1.01\| \|conv::Conv::(GFLOPS=8.025, K=[3 x 3], IN={1, 1024, 19, 19}, OCN=1206, P=[1 x 1], BIAS, OCV/CPU)\|50.565\|49.634\|1.02\| \|conv::Conv::(GFLOPS=9.986, K=[3 x 3], IN={1, 512, 46, 46}, OCN=512, P=[1 x 1], BIAS, OCV/CPU)\|37.900\|37.814\|1.00\| \|conv::Conv::(GFLOPS=9.987, K=[3 x 3], IN={1, 256, 92, 92}, OCN=256, P=[1 x 1], BIAS, OCV/CPU)\|41.367\|39.742\|1.04\| \|conv::Conv::(GFLOPS=9.989, K=[3 x 3], IN={1, 128, 184, 184}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)\|49.128\|50.350\|0.98\| \|conv::Conv::(GFLOPS=9.993, K=[3 x 3], IN={1, 64, 368, 368}, OCN=64, P=[1 x 1], BIAS, OCV/CPU)\|79.643\|80.645\|0.99\| \|conv::Conv::(GFLOPS=10.087, K=[3 x 3], IN={1, 576, 38, 50}, OCN=512, PM=SAME, BIAS, OCV/CPU)\|41.439\|40.895\|1.01\| \|conv::Conv::(GFLOPS=10.701, K=[3 x 3], IN={1, 512, 38, 38}, OCN=804, P=[1 x 1], BIAS, OCV/CPU)\|46.504\|46.220\|1.01\| \|conv::Conv::(GFLOPS=11.797, K=[5 x 5], IN={1, 240, 64, 64}, OCN=240, PM=SAME, OCV/CPU)\|98.086\|96.842\|1.01\| \|conv::Conv::(GFLOPS=11.797, K=[5 x 5], IN={1, 480, 32, 32}, OCN=480, PM=SAME, OCV/CPU)\|102.447\|97.299\|1.05\| \|conv::Conv::(GFLOPS=16.987, K=[5 x 5], IN={1, 1152, 16, 16}, OCN=1152, PM=SAME, OCV/CPU)\|145.047\|144.996\|1.00\| \|conv::Conv::(GFLOPS=23.122, K=[5 x 5], IN={1, 672, 32, 32}, OCN=672, PM=SAME, OCV/CPU)\|206.104\|195.543\|1.05\| ### Test on M1(ARM) platform \|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\|0.97\| \|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\|0.94\| \|conv1d::Conv1D::(GFLOPS=0.000, K=[3], IN={1, 6, 10}, OCN=6, PM=VALID, BIAS, OCV/CPU)\|0.002\|0.002\|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)\|0.003\|0.003\|1.00\| \|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)\|0.003\|0.003\|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)\|0.031\|0.031\|1.00\| \|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)\|0.009\|0.009\|1.00\| \|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\|0.066\|1.01\| \|conv3d::Conv3D::(GFLOPS=0.002, K=[3 x 1 x 4], IN={1, 14, 5, 10, 10}, OCN=14, PM=SAME, OCV/CPU)\|0.102\|0.102\|1.00\| \|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\|0.328\|1.00\| \|conv3d::Conv3D::(GFLOPS=0.027, K=[3 x 3 x 3], IN={1, 6, 10, 38, 50}, OCN=6, PM=VALID, BIAS, OCV/CPU)\|0.693\|0.747\|0.93\| \|conv3d::Conv3D::(GFLOPS=0.030, K=[5 x 5 x 5], IN={1, 6, 19, 19, 19}, OCN=6, G=2, OCV/CPU)\|1.268\|1.266\|1.00\| \|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)\|3.530\|3.581\|0.99\| \|conv3d::Conv3D::(GFLOPS=0.053, K=[3 x 3 x 3], IN={1, 10, 98, 10, 10}, OCN=10, PM=SAME, OCV/CPU)\|1.186\|1.188\|1.00\| \|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)\|2.682\|2.683\|1.00\| \|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)\|4.490\|4.501\|1.00\| \|conv3d::Conv3D::(GFLOPS=0.116, K=[5 x 5 x 5], IN={1, 2, 21, 75, 100}, OCN=2, BIAS, OCV/CPU)\|8.914\|8.938\|1.00\| \|conv3d::Conv3D::(GFLOPS=1.267, K=[5 x 5 x 5], IN={1, 3, 75, 75, 100}, OCN=3, PM=SAME, BIAS, OCV/CPU)\|69.819\|69.876\|1.00\| \|conv3d::Conv3D::(GFLOPS=1.343, K=[3 x 3 x 3], IN={1, 11, 9, 150, 200}, OCN=11, PM=VALID, BIAS, OCV/CPU)\|24.058\|22.420\|1.07\| \|conv::Conv::(GFLOPS=0.177, K=[1 x 1], IN={1, 512, 26, 26}, OCN=256, OCV/CPU)\|2.240\|2.236\|1.00\| \|conv::Conv::(GFLOPS=0.177, K=[1 x 1], IN={1, 1024, 13, 13}, OCN=512, OCV/CPU)\|3.132\|3.136\|1.00\| \|conv::Conv::(GFLOPS=0.178, K=[1 x 1], IN={1, 256, 52, 52}, OCN=128, OCV/CPU)\|1.920\|1.919\|1.00\| \|conv::Conv::(GFLOPS=0.210, K=[1 x 1], IN={1, 576, 38, 50}, OCN=96, PM=SAME, BIAS, OCV/CPU)\|2.343\|2.346\|1.00\| \|conv::Conv::(GFLOPS=0.231, K=[3 x 3], IN={1, 128, 56, 56}, OCN=32, P=[1 x 1], OCV/CPU)\|1.234\|1.116\|1.11\| \|conv::Conv::(GFLOPS=0.231, K=[3 x 3], IN={1, 256, 14, 14}, OCN=256, P=[1 x 1], OCV/CPU)\|1.109\|1.121\|0.99\| \|conv::Conv::(GFLOPS=0.280, K=[1 x 1], IN={1, 576, 38, 50}, OCN=128, PM=SAME, BIAS, OCV/CPU)\|3.197\|3.084\|1.04\| \|conv::Conv::(GFLOPS=0.302, K=[3 x 3], IN={1, 64, 64, 64}, OCN=64, PM=SAME, OCV/CPU)\|1.123\|1.148\|0.98\| \|conv::Conv::(GFLOPS=0.357, K=[1 x 1], IN={1, 64, 208, 208}, OCN=64, OCV/CPU)\|4.836\|5.061\|0.96\| \|conv::Conv::(GFLOPS=0.420, K=[3 x 3], IN={1, 96, 38, 50}, OCN=128, PM=SAME, BIAS, OCV/CPU)\|1.535\|1.463\|1.05\| \|conv::Conv::(GFLOPS=0.472, K=[3 x 3], IN={1, 128, 40, 40}, OCN=128, PM=SAME, OCV/CPU)\|1.756\|1.584\|1.11\| \|conv::Conv::(GFLOPS=0.472, K=[3 x 3], IN={1, 256, 20, 20}, OCN=256, PM=SAME, OCV/CPU)\|1.821\|1.820\|1.00\| \|conv::Conv::(GFLOPS=0.472, K=[3 x 3], IN={1, 512, 10, 10}, OCN=512, PM=SAME, OCV/CPU)\|7.049\|6.672\|1.06\| \|conv::Conv::(GFLOPS=0.561, K=[3 x 3], IN={1, 128, 38, 50}, OCN=128, PM=SAME, BIAS, OCV/CPU)\|1.967\|1.922\|1.02\| \|conv::Conv::(GFLOPS=0.624, K=[3 x 3], IN={1, 128, 46, 46}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)\|1.943\|1.977\|0.98\| \|conv::Conv::(GFLOPS=0.701, K=[3 x 3], IN={1, 128, 38, 50}, OCN=160, PM=SAME, BIAS, OCV/CPU)\|2.464\|2.310\|1.07\| \|conv::Conv::(GFLOPS=0.798, K=[3 x 3], IN={1, 64, 104, 104}, OCN=64, P=[1 x 1], OCV/CPU)\|2.860\|2.904\|0.98\| \|conv::Conv::(GFLOPS=0.798, K=[3 x 3], IN={1, 128, 52, 52}, OCN=128, P=[1 x 1], OCV/CPU)\|2.428\|2.483\|0.98\| \|conv::Conv::(GFLOPS=0.798, K=[3 x 3], IN={1, 256, 26, 26}, OCN=256, P=[1 x 1], OCV/CPU)\|2.955\|2.983\|0.99\| \|conv::Conv::(GFLOPS=0.798, K=[3 x 3], IN={1, 512, 13, 13}, OCN=512, P=[1 x 1], OCV/CPU)\|4.328\|4.484\|0.97\| \|conv::Conv::(GFLOPS=0.830, K=[3 x 3], IN={1, 64, 75, 100}, OCN=96, PM=SAME, BIAS, OCV/CPU)\|2.712\|2.778\|0.98\| \|conv::Conv::(GFLOPS=0.958, K=[3 x 3], IN={1, 192, 38, 38}, OCN=192, PM=SAME, OCV/CPU)\|3.205\|3.331\|0.96\| \|conv::Conv::(GFLOPS=0.958, K=[3 x 3], IN={1, 384, 19, 19}, OCN=384, PM=SAME, OCV/CPU)\|4.193\|4.412\|0.95\| \|conv::Conv::(GFLOPS=1.022, K=[3 x 3], IN={1, 576, 19, 19}, OCN=273, PM=SAME, BIAS, OCV/CPU)\|5.026\|4.565\|1.10\| \|conv::Conv::(GFLOPS=1.112, K=[3 x 3], IN={1, 512, 10, 10}, OCN=1206, P=[1 x 1], BIAS, OCV/CPU)\|14.490\|14.213\|1.02\| \|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)\|14.886\|14.003\|1.06\| \|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)\|15.923\|15.184\|1.05\| \|conv::Conv::(GFLOPS=1.195, K=[9 x 9], IN={1, 32, 240, 320}, OCN=3, P=[4 x 4], BIAS, OCV/CPU)\|45.136\|41.696\|1.08\| \|conv::Conv::(GFLOPS=1.196, K=[3 x 3], IN={1, 384, 26, 26}, OCN=256, P=[1 x 1], OCV/CPU)\|4.995\|4.631\|1.08\| \|conv::Conv::(GFLOPS=1.210, K=[3 x 3], IN={1, 32, 256, 256}, OCN=32, PM=SAME, OCV/CPU)\|6.402\|6.261\|1.02\| \|conv::Conv::(GFLOPS=1.245, K=[3 x 3], IN={1, 64, 75, 75}, OCN=192, PM=SAME, BIAS, OCV/CPU)\|4.478\|3.965\|1.13\| \|conv::Conv::(GFLOPS=1.245, K=[3 x 3], IN={1, 96, 75, 100}, OCN=96, PM=SAME, BIAS, OCV/CPU)\|3.908\|3.978\|0.98\| \|conv::Conv::(GFLOPS=1.248, K=[3 x 3], IN={1, 256, 46, 46}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)\|4.176\|4.206\|0.99\| \|conv::Conv::(GFLOPS=1.258, K=[3 x 3], IN={1, 1280, 10, 10}, OCN=546, PM=SAME, BIAS, OCV/CPU)\|21.509\|21.136\|1.02\| \|conv::Conv::(GFLOPS=1.261, K=[3 x 3], IN={1, 192, 38, 50}, OCN=192, PM=SAME, BIAS, OCV/CPU)\|4.426\|4.082\|1.08\| \|conv::Conv::(GFLOPS=1.416, K=[3 x 3], IN={1, 128, 62, 82}, OCN=128, BIAS, OCV/CPU)\|4.098\|4.289\|0.96\| \|conv::Conv::(GFLOPS=1.500, K=[3 x 3], IN={1, 128, 64, 84}, OCN=128, BIAS, OCV/CPU)\|4.646\|5.105\|0.91\| \|conv::Conv::(GFLOPS=1.586, K=[3 x 3], IN={1, 128, 66, 86}, OCN=128, BIAS, OCV/CPU)\|4.746\|4.724\|1.00\| \|conv::Conv::(GFLOPS=1.595, K=[3 x 3], IN={1, 256, 26, 26}, OCN=512, P=[1 x 1], OCV/CPU)\|5.614\|5.779\|0.97\| \|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)\|21.909\|20.718\|1.06\| \|conv::Conv::(GFLOPS=1.595, K=[3 x 3], IN={1, 512, 13, 13}, OCN=1024, P=[1 x 1], OCV/CPU)\|8.256\|8.290\|1.00\| \|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)\|25.196\|23.267\|1.08\| \|conv::Conv::(GFLOPS=1.596, K=[3 x 3], IN={1, 64, 104, 104}, OCN=128, P=[1 x 1], OCV/CPU)\|5.721\|5.172\|1.11\| \|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)\|20.066\|18.322\|1.10\| \|conv::Conv::(GFLOPS=1.596, K=[3 x 3], IN={1, 128, 52, 52}, OCN=256, P=[1 x 1], OCV/CPU)\|4.448\|4.542\|0.98\| \|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)\|19.193\|19.013\|1.01\| \|conv::Conv::(GFLOPS=1.598, K=[3 x 3], IN={1, 32, 208, 208}, OCN=64, P=[1 x 1], OCV/CPU)\|6.009\|5.964\|1.01\| \|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)\|20.169\|20.009\|1.01\| \|conv::Conv::(GFLOPS=1.659, K=[3 x 3], IN={1, 960, 10, 10}, OCN=960, PM=SAME, OCV/CPU)\|22.584\|23.423\|0.96\| \|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)\|0.372\|0.504\|0.74\| \|conv::Conv::(GFLOPS=1.660, K=[3 x 3], IN={1, 128, 75, 75}, OCN=128, PM=SAME, OCV/CPU)\|5.426\|5.456\|0.99\| \|conv::Conv::(GFLOPS=1.675, K=[3 x 3], IN={1, 128, 68, 88}, OCN=128, BIAS, OCV/CPU)\|4.945\|5.221\|0.95\| \|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)\|0.210\|0.261\|0.81\| \|conv::Conv::(GFLOPS=1.704, K=[3 x 3], IN={1, 256, 38, 38}, OCN=256, PM=SAME, OCV/CPU)\|5.720\|5.997\|0.95\| \|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)\|0.149\|0.161\|0.93\| \|conv::Conv::(GFLOPS=1.704, K=[3 x 3], IN={1, 512, 19, 19}, OCN=512, P=[1 x 1], BIAS, OCV/CPU)\|7.154\|7.225\|0.99\| \|conv::Conv::(GFLOPS=1.704, K=[3 x 3], IN={1, 512, 19, 19}, OCN=512, PM=SAME, OCV/CPU)\|7.184\|7.223\|0.99\| \|conv::Conv::(GFLOPS=1.766, K=[3 x 3], IN={1, 128, 70, 90}, OCN=128, BIAS, OCV/CPU)\|5.324\|5.343\|1.00\| \|conv::Conv::(GFLOPS=1.859, K=[3 x 3], IN={1, 128, 72, 92}, OCN=128, BIAS, OCV/CPU)\|5.114\|5.238\|0.98\| \|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)\|0.111\|0.121\|0.92\| \|conv::Conv::(GFLOPS=1.888, K=[3 x 3], IN={1, 1024, 10, 10}, OCN=1024, PM=SAME, OCV/CPU)\|25.907\|26.804\|0.97\| \|conv::Conv::(GFLOPS=1.954, K=[3 x 3], IN={1, 128, 74, 94}, OCN=128, BIAS, OCV/CPU)\|5.695\|5.654\|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)\|27.435\|27.566\|1.00\| \|conv::Conv::(GFLOPS=2.052, K=[3 x 3], IN={1, 128, 76, 96}, OCN=128, BIAS, OCV/CPU)\|6.944\|6.164\|1.13\| \|conv::Conv::(GFLOPS=2.100, K=[3 x 3], IN={1, 144, 75, 75}, OCN=144, PM=SAME, OCV/CPU)\|7.180\|6.717\|1.07\| \|conv::Conv::(GFLOPS=2.153, K=[3 x 3], IN={1, 128, 78, 98}, OCN=128, BIAS, OCV/CPU)\|6.817\|6.050\|1.13\| \|conv::Conv::(GFLOPS=2.156, K=[3 x 3], IN={1, 576, 19, 19}, OCN=576, PM=SAME, OCV/CPU)\|9.225\|8.660\|1.07\| \|conv::Conv::(GFLOPS=2.255, K=[3 x 3], IN={1, 128, 80, 100}, OCN=128, BIAS, OCV/CPU)\|7.496\|6.625\|1.13\| \|conv::Conv::(GFLOPS=2.719, K=[3 x 3], IN={1, 96, 256, 256}, OCN=96, S=[2 x 2], PM=SAME, OCV/CPU)\|35.520\|36.056\|0.99\| \|conv::Conv::(GFLOPS=3.319, K=[3 x 3], IN={1, 128, 75, 75}, OCN=256, P=[1 x 1], BIAS, OCV/CPU)\|9.990\|9.702\|1.03\| \|conv::Conv::(GFLOPS=3.321, K=[3 x 3], IN={1, 64, 150, 150}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)\|10.517\|10.746\|0.98\| \|conv::Conv::(GFLOPS=3.398, K=[7 x 7], IN={1, 128, 46, 46}, OCN=128, P=[3 x 3], BIAS, OCV/CPU)\|36.702\|36.731\|1.00\| \|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)\|41.035\|38.280\|1.07\| \|conv::Conv::(GFLOPS=3.408, K=[3 x 3], IN={1, 256, 38, 38}, OCN=512, P=[1 x 1], BIAS, OCV/CPU)\|10.981\|10.573\|1.04\| \|conv::Conv::(GFLOPS=4.247, K=[3 x 3], IN={1, 480, 32, 32}, OCN=480, PM=SAME, OCV/CPU)\|12.863\|12.384\|1.04\| \|conv::Conv::(GFLOPS=4.247, K=[5 x 5], IN={1, 144, 128, 128}, OCN=144, S=[2 x 2], PM=SAME, OCV/CPU)\|50.437\|54.088\|0.93\| \|conv::Conv::(GFLOPS=4.566, K=[7 x 7], IN={1, 172, 46, 46}, OCN=128, P=[3 x 3], BIAS, OCV/CPU)\|50.650\|50.635\|1.00\| \|conv::Conv::(GFLOPS=4.993, K=[3 x 3], IN={1, 256, 46, 46}, OCN=512, P=[1 x 1], BIAS, OCV/CPU)\|14.696\|14.606\|1.01\| \|conv::Conv::(GFLOPS=4.993, K=[3 x 3], IN={1, 512, 46, 46}, OCN=256, P=[1 x 1], BIAS, OCV/CPU)\|16.201\|15.426\|1.05\| \|conv::Conv::(GFLOPS=4.994, K=[3 x 3], IN={1, 128, 92, 92}, OCN=256, P=[1 x 1], BIAS, OCV/CPU)\|16.061\|14.292\|1.12\| \|conv::Conv::(GFLOPS=4.997, K=[3 x 3], IN={1, 64, 184, 184}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)\|17.743\|18.250\|0.97\| \|conv::Conv::(GFLOPS=5.780, K=[5 x 5], IN={1, 672, 32, 32}, OCN=672, S=[2 x 2], PM=SAME, OCV/CPU)\|77.909\|78.165\|1.00\| \|conv::Conv::(GFLOPS=6.116, K=[3 x 3], IN={1, 1152, 16, 16}, OCN=1152, PM=SAME, OCV/CPU)\|21.579\|21.879\|0.99\| \|conv::Conv::(GFLOPS=6.118, K=[3 x 3], IN={1, 144, 128, 128}, OCN=144, PM=SAME, OCV/CPU)\|20.424\|19.589\|1.04\| \|conv::Conv::(GFLOPS=6.637, K=[3 x 3], IN={1, 256, 75, 75}, OCN=256, P=[1 x 1], BIAS, OCV/CPU)\|19.389\|19.461\|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)\|21.319\|20.358\|1.05\| \|conv::Conv::(GFLOPS=6.641, K=[3 x 3], IN={1, 64, 150, 200}, OCN=192, PM=SAME, BIAS, OCV/CPU)\|22.609\|21.826\|1.04\| \|conv::Conv::(GFLOPS=6.641, K=[3 x 3], IN={1, 64, 300, 300}, OCN=64, P=[1 x 1], BIAS, OCV/CPU)\|25.497\|25.789\|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)\|21.966\|22.108\|0.99\| \|conv::Conv::(GFLOPS=8.025, K=[3 x 3], IN={1, 1024, 19, 19}, OCN=1206, P=[1 x 1], BIAS, OCV/CPU)\|35.883\|33.470\|1.07\| \|conv::Conv::(GFLOPS=9.986, K=[3 x 3], IN={1, 512, 46, 46}, OCN=512, P=[1 x 1], BIAS, OCV/CPU)\|31.041\|29.314\|1.06\| \|conv::Conv::(GFLOPS=9.987, K=[3 x 3], IN={1, 256, 92, 92}, OCN=256, P=[1 x 1], BIAS, OCV/CPU)\|29.922\|28.145\|1.06\| \|conv::Conv::(GFLOPS=9.989, K=[3 x 3], IN={1, 128, 184, 184}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)\|31.624\|31.148\|1.02\| \|conv::Conv::(GFLOPS=9.993, K=[3 x 3], IN={1, 64, 368, 368}, OCN=64, P=[1 x 1], BIAS, OCV/CPU)\|38.564\|39.164\|0.98\| \|conv::Conv::(GFLOPS=10.087, K=[3 x 3], IN={1, 576, 38, 50}, OCN=512, PM=SAME, BIAS, OCV/CPU)\|31.502\|30.269\|1.04\| \|conv::Conv::(GFLOPS=10.701, K=[3 x 3], IN={1, 512, 38, 38}, OCN=804, P=[1 x 1], BIAS, OCV/CPU)\|34.248\|34.589\|0.99\| \|conv::Conv::(GFLOPS=11.797, K=[5 x 5], IN={1, 240, 64, 64}, OCN=240, PM=SAME, OCV/CPU)\|130.211\|134.120\|0.97\| \|conv::Conv::(GFLOPS=11.797, K=[5 x 5], IN={1, 480, 32, 32}, OCN=480, PM=SAME, OCV/CPU)\|127.490\|132.874\|0.96\| \|conv::Conv::(GFLOPS=16.987, K=[5 x 5], IN={1, 1152, 16, 16}, OCN=1152, PM=SAME, OCV/CPU)\|199.834\|200.081\|1.00\| \|conv::Conv::(GFLOPS=23.122, K=[5 x 5], IN={1, 672, 32, 32}, OCN=672, PM=SAME, OCV/CPU)\|247.346\|247.523\|1.00\| ### 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 ``` force_builders=Linux AVX2,Custom Win build_image:Custom Win=msvs2019 CPU_BASELINE:Custom Win=AVX512_SKX ```	2 years ago
Alexey Shtern	c6e5f60525	Merge pull request #23301 from shtern:fix_quaternion Fixed strict type in slerp and spline; Fixed nlerp usage condition Fixes #23293 The PR is fixing the issue described in [Issue #23293 ](https://github.com/opencv/opencv/issues/23293) - [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
Alexander Smorkalov	29cc675375	Merge pull request #23268 from VadimLevin:dev/vlevin/bindings-io-arg-modifiers-fix fix: remove extra '/O' modifier for '/IO' arguments	2 years ago
Alexander Alekhin	0052d46b8e	Merge pull request #23237 from hzcyf:feature/orbbec_femto_mega_support	2 years ago
Corentin Noël	a035608100	highgui: Reduce the difference between GTK+2 and GTK+3 version Make the GTK+3 API the default one by wrapping the missing GTK+2 functions in defines Make sure to always guard with GTK_VERSION2 or GTK_VERSION3 to allow future addition of Gtk4	2 years ago
CSBVision	a5f04f9e17	Add ENABLE_DELAYLOAD option	2 years ago
Alexander Alekhin	b3c9842a98	Merge pull request #23287 from alalek:dnn_tflite_fix_illegal_access	2 years ago
Alexander Alekhin	9eb5e39ff3	dnn(tflite): fix wrong axis normalization	2 years ago
Alexander Alekhin	5a227352b4	Merge pull request #23274 from alalek:dnn_flatbuffers_builtin	2 years ago
Alexander Alekhin	bdff0949bb	dnn(tflite): add 3rdparty flatbuffers with pre-generated schema	2 years ago
Alexander Alekhin	4262127854	Merge pull request #23246 from mshabunin:rvv07-support	2 years ago
Vadim Pisarevsky	ca48e217f1	fixed another SIFT constructor (#23272 )	2 years ago
Vadim Pisarevsky	f48939c2d7	temporarily set "enable_precise_upscale=false" by default to avoid sporadic failures in regression tests (#23270 )	2 years ago
Maksim Shabunin	903ec0ec60	RISC-V: support RVV 0.7 in mainline RVV intrinsics	2 years ago
Zihao Mu	20dac7ea48	Merge pull request #23255 from zihaomu:fused_cuda_naryeltwise DNN: fuse conv+naryEletwise on CUDA backend.	2 years ago
Vadim Levin	642942a72f	fix: remove extra '/O' modifier for '/IO' arguments	2 years ago
Vaclav Vavra	923dbcc58f	different interpolation by double image (#23124 ) * different interpolation by double image * fixing scaling mapping * fixing a test * added an option to enable previous interpolation * added doxygen entries for the new parameter * ASSERT_TRUE -> ASSERT_EQ * changed log message when using old upscale mode	2 years ago
Anatoliy Talamanov	6c235c8edb	Merge pull request #23211 from TolyaTalamanov:at/pipeline-modeling-tool-perf-alignment [G-API] Pipeline modeling tool: Refactor calculating performance statistics * Add warmup execution * Align perf metrics * Add busy wait mode for source * Small fix for late frames * pl_fn to src_fn * Change show statistics * Correct warm-up iteration * Properly calculate drop frames * Enable frame dropping for streaming mode * Enable frame dropping for streaming mode * Fix comments to review * Fix typos * Cosmetic	2 years ago
Alexander Alekhin	58d8a2702a	Merge pull request #23243 from WanliZhong:accelerate_palm_det	2 years ago
Dmitry Kurtaev	76350cd30f	Merge pull request #23161 from dkurt:dnn_tflite TFLite models importer * initial commit * Refactor TFLiteImporter * Better FlatBuffers detection * Add permute before 4D->3D reshape * Track layers layout * TFLite Convolution2DTransposeBias layer * Skip TFLite tests without FlatBuffers * Fix check of FlatBuffers in tests. Add readNetFromTFLite from buffer * TFLite Max Unpooling test * Add skip for TFLite unpooling test * Revert DW convolution workaround * Fix ObjC bindings * Better errors handling * Regenerate TFLite schema using flatc * dnn(tflite): more checks, better logging * Checks for unimplemented fusion. Fix tests	2 years ago
Alexander Alekhin	47293f28cf	Merge remote-tracking branch 'upstream/3.4' into merge-3.4	2 years ago
hzcyf	325fe7e663	add support for Orbbec Femto Mega RGB-D camera	2 years ago
Yannis Guyon	56102737d7	Merge pull request #23131 from y-guyon:align_ptr_intrin_sse Fix misaligned-pointer-use in intrin_sse.hpp * Fix misaligned-pointer-use in intrin_sse.hpp * Use _mm_loadu_si32() instead of memcpy() * Use CV_DECL_ALIGNED instead of _mm_loadu_si32()	2 years ago
Yuantao Feng	c2b7c1f13b	Merge pull request #23219 from fengyuentau:add_gelu Add GELU layer for vision transformers * add gelu and gelu approximation * drop setKernelParams	2 years ago
wanli	c8f5e228fc	release MUL and ADD operator on CUDA	2 years ago
Alexander Alekhin	d3c6b85638	Merge pull request #23209 from CSBVision:patch-3	2 years ago
Alexander Alekhin	96a45e842e	Merge pull request #23061 from WanliZhong:gemm_cuda DNN: make GEMM can be supported with transA and transB in CUDA	2 years ago
Ibai Gorordo	c280cd7290	Merge pull request #23210 from ibaiGorordo:rect_nfa_bugfix Fix rect_nfa (lsd) * Fix missing log_gamma in nfa() Comparing the nfa function with the function in the binomial_nfa repository (https://github.com/rafael-grompone-von-gioi/binomial_nfa/blob/main/C99/log_binomial_nfa.c#L152), the first log_gamma call is missing. * Fix rect_nfa pixel index * Replace std::rotate * Rename tmp to v_tmp * Replace auto and std::min_element * Change slope equality check to int * Fix left limit check	2 years ago
Alexander Alekhin	44290af516	Merge pull request #23224 from VadimLevin:dev/vlevin/cxx-named-arguments	2 years ago
Alexander Alekhin	649841e6bf	Merge pull request #23225 from mshabunin:fix-clang-warnings	2 years ago
Alexander Alekhin	606c80344a	Merge pull request #23222 from siilats:patch-1	2 years ago
CSBVision	6ea22535e2	Fixes #23187 (part 1) Supports delay-loading for Ninja generators and Python bindings.	2 years ago
Maksim Shabunin	e4acd74e87	Fix some clang 14 warnings	2 years ago
Vadim Levin	b07031b594	feat: named arguments handling in Python interface	2 years ago
Alexander Alekhin	0981814473	Merge pull request #23214 from asmorkalov:as/xcode_warning_supression3.4	2 years ago
Alexander Smorkalov	3d635cb4a7	Warning supression fix for XCode 13.1 and newer. Backport #23203	2 years ago

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32722 Commits (c4226f0457112ed6fc598d4b7456d130a1097085) All Branches Search

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