opencv

Commit Graph

Author	SHA1	Message	Date
Yuantao Feng	3a83a35ab0	Merge pull request #23296 from fengyuentau:fix_identifying_constant Fix identifying initializers in ONNX graph simplification #23296 Fixes https://github.com/opencv/opencv/issues/23295 ### 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
Dmitry Kurtaev	5e1d33329b	Several fixes for ONNX importer: Expand, Gather	2 years ago
HAN Liutong	a809ae4e88	Fix HAL compatibility layer and modify use cases.	2 years ago
Dmitry Kurtaev	5df6b4a756	Merge pull request #23325 from dkurt:dnn_input_info Propagate inputs info for ONNX and TFLite models ### Pull Request Readiness Checklist Needed for generic applications such as benchmarking pipelines. So OpenCV can tell about the default input shapes specified in the models. 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	6bac5453d1	fix bug in 32 bit cpu	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
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
Alexander Alekhin	9eb5e39ff3	dnn(tflite): fix wrong axis normalization	2 years ago
Alexander Alekhin	bdff0949bb	dnn(tflite): add 3rdparty flatbuffers with pre-generated schema	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
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
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
wanli	4718a4bf81	make GEMM can be supported with transA and transB in CUDA	2 years ago
zihaomu	f45a12439a	fix depth wise issue.	2 years ago
Yuantao Feng	4d918ba40b	Merge pull request #23047 from fengyuentau:layer_norm dnn: add layer normalization for vision transformers * add layer norm onnx parser, impl and tests * add onnx graph simplifier for layer norm expanded * handle the case when constants are of type Initializer * add test case for layer norm expanded with initializers * use CV_Assert & CV_CheckType in place of CV_Assert_N; use forward_fallback for OCL_FP16 * use const ref / ref in parameters of invoker::run; extract inner const if from nested loop; use size_t in place of ull * template hasBias * remove trailing whitespace * use pointer parameter with null check; move normSize division & mean_square division outside of loop; use std::max to ensure positive value before std::sqrt * refactor implementation, optimize parallel_for * disable layer norm expanded * remove the removal of layer norm optional outputs	2 years ago
Tomoaki Teshima	186c18668c	suppress warning	2 years ago
zihaomu	840b1d5c94	add depthwise add fuse	2 years ago
zihaomu	82616eec41	fix possible segmentation fault error in winograd on x86	2 years ago
zihaomu	71765858dc	fix invalid memory access	2 years ago
Alexander Alekhin	9a2a34f94e	dnn(openvino): remove undefined status	2 years ago
Alexander Alekhin	b42c11de82	pre: OpenCV 4.7.0 (version++)	2 years ago
Alexander Alekhin	a494c75bfe	pre: OpenCV 3.4.19 (version++)	2 years ago
Dmitry Kurtaev	8681686d8f	Merge pull request #22957 from dkurt:new_openvino_api Switch to new OpenVINO API after 2022.1 release * Pass Layer_Test_Convolution_DLDT.Accuracy/0 test * Pass test Test_Caffe_layers.Softmax * Failed 136 tests * Fix Concat. Failed 120 tests * Custom nGraph ops. 19 failed tests * Set and get properties from Core * Read model from buffer * Change MaxPooling layer output names. Restore reshape * Cosmetic changes * Cosmetic changes * Override getOutputsInfo * Fixes for OpenVINO < 2022.1 * Async inference for 2021.4 and less * Compile model with config * Fix serialize for 2022.1 * Asynchronous inference with 2022.1 * Handle 1d outputs * Work with model with dynamic output shape * Fixes with 1d output for old API * Control outputs by nGraph function for all OpenVINO versions * Refer inputs in PrePostProcessor by indices * Fix cycled dependency between InfEngineNgraphNode and InfEngineNgraphNet. Add InferRequest callback only for async inference. Do not capture InferRequest object. * Fix tests thresholds * Fix HETERO:GPU,CPU plugin issues with unsupported layer	2 years ago
Vincent Rabaud	ad568edd7f	Remove references to deprecated NumPy type aliases. This change replaces references to a number of deprecated NumPy type aliases (np.bool, np.int, np.float, np.complex, np.object, np.str) with their recommended replacement (bool, int, float, complex, object, str). Those types were deprecated in 1.20 and are removed in 1.24, cf https://github.com/numpy/numpy/pull/22607.	2 years ago
zihaomu	71c6339af0	remove old convolution branch, and optimize conv3d and conv1d.	2 years ago
fengyuentau	34a0897f90	add cv::flipND; support onnx slice with negative steps via cv::flipND	2 years ago
Maksim Shabunin	d35fbe6bfc	dnn: updated YOLOv4-tiny model and tests	2 years ago
Yuantao Feng	a2b3acfc6e	dnn: add the CANN backend (#22634 ) * cann backend impl v1 * cann backend impl v2: use opencv parsers to build models for cann * adjust fc according to the new transA and transB * put cann net in cann backend node and reuse forwardLayer * use fork() to create a child process and compile cann model * remove legacy code * remove debug code * fall bcak to CPU backend if there is one layer not supoorted by CANN backend * fix netInput forward	2 years ago
Alexander Alekhin	cdbb893b27	dnn: disable OpenCL code path in MatMul processing - this mode is not supported by 22828	2 years ago
Alexander Alekhin	1102b7eff8	dnn: fix gather layer implementation - support FP16 data	2 years ago
zoom	4891818114	make MatMul support 3D or 4D with broadcast	2 years ago
Zihao Mu	0a650b573b	Merge pull request #22840 from zihaomu:optimze_conv_memory_usage DNN: reduce the memory used in convolution layer * reduce the memory in winograd and disabel the test when usage memory is larger than 2gb. * remove VERY_LOG tag	2 years ago
Alexander Alekhin	d16b3b2487	dnn(test): restore openvino tests with 'Cannot get memory' message	2 years ago
Alexander Alekhin	74d0b4cc78	dnn(openvino): fix custom layers BlockingDesc	2 years ago
zihaomu	0d56524b72	gemm support transA and transB, and first input is constance.	2 years ago
fengyuentau	9fded9ca53	batched nms impl	2 years ago
fengyuentau	441624a5fb	tile impl	2 years ago
zoom	5044af69d1	let MatMul can work when both two inputs are const	2 years ago
zihaomu	5bf64e7dfe	fix the infinite loop in tf importer of 3.4 branch	2 years ago
zoom	ef2677b0a6	Make MatMul layer support 3d or 4d operation with const input	2 years ago
zoom	11d492b0b9	Let part of the operators in nary_eltwise support cuda	2 years ago
Zihao Mu	17f2b56291	remove never used code in onnximporter	2 years ago
Zihao Mu	903bf0147e	Merge pull request #22666 from zihaomu:support_onnx_qdq_model DNN: let Quant and Dequant of ONNX_importer support the Constant input. * let Quant and Dequant support the Constant input. * fix negative value of axis.	2 years ago
Zihao Mu	18fbb72f7d	fix the infinite loop in tf importer.	2 years ago
Alexander Smorkalov	22f8fb4d5c	Do not fail tests in Yolo v7 model was not found.	2 years ago
Dmitry Kurtaev	35b2cff295	Merge pull request #22656 from dkurt:halide_fixes * Fixes for Halide * Enable some Halide tests	2 years ago

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2106 Commits (0245c0cd10387f2de7c7bd076585a285d977e53c)