opencv

Commit Graph

Author	SHA1	Message	Date
Alexander Smorkalov	51702ffd92	pre: OpenCV 4.8.0 (version++)	2 years ago
Dmitry Kurtaev	433c364456	Merge pull request #23724 from dkurt:java_without_ant Build Java without ANT #23724 ### Pull Request Readiness Checklist Enables a path of building Java bindings without ANT * Able to build OpenCV JAR and Docs without ANT ``` -- Java: -- ant: NO -- JNI: /usr/lib/jvm/default-java/include /usr/lib/jvm/default-java/include/linux /usr/lib/jvm/default-java/include -- Java wrappers: YES -- Java tests: NO ``` * Possible to build OpenCV JAR without ANT but tests still require ANT Merge with: https://github.com/opencv/opencv_contrib/pull/3502 Notes: - Use `OPENCV_JAVA_IGNORE_ANT=1` to force "Java" flow for building Java bindings - Java tests still require Apache ANT - JAR doesn't include `.java` source code files. 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
Dmitry Kurtaev	924c01dbec	Replace CV_Assert_N	2 years ago
Wang Kai	fc2d933224	removing unreachable code and fixing a typo	2 years ago
Dmitry Kurtaev	6909fffde1	Consider half pixel mode in ONNX resize	2 years ago
Dmitry Kurtaev	f9d7f47e28	Change Scalar assignment in Python from single value	2 years ago
Wang Kai	4622f1e89b	fixing typo of a variable name in dnn::runFastConv	2 years ago
Zihao Mu	eec8a20c33	Merge pull request #23763 from zihaomu:add_runtime_check DNN: fix bug for X86 Winograd #23763 Address https://github.com/opencv/opencv/issues/23760 The patch aims to add a runtime check for X86 platform without AVX(2). ### 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
unknown	5f8e43da85	checktype in blobFromImages and blobFromImagesWithParams	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
Olivier Hotel	0442c6fa81	Addition of normalize_axis to ONNXImporter::parseSqueeze to support negative values for the axes attribut. Negative values are part of the ONNX optset>=11. Signed-off-by: Olivier Hotel <olivier.hotel@orange.com>	2 years ago
Abduragim Shtanchaev	ecd2e8ff47	added index that check all inputs of nodes that match	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
zihaomu	4384e77bd1	when stride ==0, it should be bug	2 years ago
Peter Rekdal Khan-Sunde	04970490ec	Build fix /build/build_cuda/3p/opencv/linux-x64/ubuntu22.04/Debug/modules/dnn/src/layers/cpu_kernels/convolution.cpp: In function 'void cv::dnn::packData8(char&, float&, int&, int&, int&, const int, int, int, int)': /build/build_cuda/3p/opencv/linux-x64/ubuntu22.04/Debug/modules/dnn/src/layers/cpu_kernels/convolution.cpp:448:43: error: 'CONV_NR' was not declared in this scope; did you mean 'CONV_3D'? 448 \| vx_store(inpbufC_FP32 + kCONV_NR, vx_load(inptrInC + k1)); \| ^~~~~~~ \| CONV_3D	2 years ago
Zihao Mu	5025f29378	speed up vulkan dnn, and support ios and apple m1 chip. (#23349 )	2 years ago
Dmitry Kurtaev	af14780526	Fix Region layer with OpenVINO in case of different width/height	2 years ago
Abduragim Shtanchaev	2b9d2c726a	add assert to check if layer input size is not empty	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
Yuantao Feng	eefee8574a	dnn: refactor reduce (#23613 ) * initial impl * remove reduce in8; fix reduce importer * fix bugs and add log sum exp * remove unnecessary header and fix indentation	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
zihaomu	91b6c8507a	remove flag of convolution	2 years ago
Dmitry Kurtaev	a8d3d1f6f9	Merge pull request #23604 from dkurt:dnn_no_protobuf Build DNN without Protobuf DNN module can be built without Protobuf for Darknet, TFLite, OpenVINO, Torch (not PyTorch) models. ``` cmake \ -DCMAKE_BUILD_TYPE=Release \ -DBUILD_LIST=dnn \ -DWITH_PROTOBUF=OFF \ -DWITH_OPENCL=OFF 7.1M lib/libopencv_dnn.so.4.7.0 ``` ``` cmake \ -DCMAKE_BUILD_TYPE=Release \ -DBUILD_LIST=dnn \ -DWITH_OPENCL=OFF 3.9M lib/libopencv_dnn.so.4.7.0 ``` ### 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
wanli	46991bcd62	Solve the bug of same shape broadcast with CUDA	2 years ago
Dmitry Kurtaev	676afdc494	Update FlatBuffers source code to 23.5.9	2 years ago
wanli	85cc4086c8	fix nary elementwise bug in cpu	2 years ago
zihaomu	0513741a85	add broadcast where node	2 years ago
zihaomu	8be93a6de7	add scale factor to DB demo.	2 years ago
Abduragim Shtanchaev	3b1ee0549b	added test for lstm without hidden states initialization	2 years ago
Dmitry Kurtaev	aa57833ad5	Merge pull request #23409 from dkurt:dnn_tflite_quant Import and inference INT8 quantized TFLite model #23409 ### Pull Request Readiness Checklist * Support quantized TFLite models * Enable fused activations (FP32, INT8) Merge with extra: https://github.com/opencv/opencv_extra/pull/1048 ![res](https://user-images.githubusercontent.com/25801568/231433201-566b4bd6-ccff-462c-9e74-adbdcdf3648b.png) on the image, green boxes are from TFLite and red boxes from OpenCV 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	e4e774d42b	Merge pull request #23475 from Abdurrahheem:lstm_fix_initialization Fix ONNX parser for single-layer LSTM hidden and cell states #23475 ### Fix ONNX parser for single-layer LSTM hidden and cell states ### 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 This PR addresses #21118 [issue](https://github.com/opencv/opencv/issues/21118). The problem is that the ONNX parser is unable to read the hidden state and cell state for single-layer LSTMs. This PR fixes the issue by updating the parser to correctly read hidden and cell states.	2 years ago
wanli	e4360294c5	make 'abcd op 1b11' broadcast support cuda	2 years ago
Zihao Mu	601778e0e6	Merge pull request #22750 from zihaomu:improve_blobFromImage DNN: Add New API blobFromImageParam #22750 The purpose of this PR: 1. Add new API `blobFromImageParam` to extend `blobFromImage` API. It can support the different data layout (NCHW or NHWC), and letter_box. 2. ~~`blobFromImage` can output `CV_16F`~~ ### 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
zihaomu	54e1a8709d	fix the bug, disable the fast1x1 when padding is not 0.	2 years ago
Yuantao Feng	3c1fcd5deb	Merge pull request #23401 from fengyuentau:fix_cann_layer_support dnn: Support more operators in CANN backend #23401 This PR adds the support of following layers: - [x] Sub - [x] PRelu - [x] DeConv - [x] Also warn users if backend is switched back to default if some of the layers are not supported. - [ ] [Dropped] LSTM: some hacks (adding layers) were introduced which makes it even harder to build the graph for CANN backend. ### 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	b3a2444bcf	Merge pull request #23501 from Abdurrahheem:additional_lstm_tests Added LSTM and GRU tests for various batch and input length sizes #23501 Added tests with various sequence length and batch sizes Test data: https://github.com/opencv/opencv_extra/pull/1057 ### 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
fengyuentau	4f99e5ab37	allow null constant_value in Pad and ignore it when loading	2 years ago
fengyuentau	88cacd35c5	support broadcast on axis > 1 for Expand	2 years ago
zihaomu	51281f8d69	support the split node of onnx opset >= 13	2 years ago
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

1 2 3 4 5 ...

2092 Commits (1656e7573e043e28e2c73697ce0b6466aa0c7cc3)