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Author SHA1 Message Date
Alexander Smorkalov
22a52766dc
Merge pull request #23343 from zihaomu:fix_test_onnx_conf 
DNN Test ONNX: Fix the logic of the test case
 2023-03-13 21:48:41 +03:00
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
 2023-03-13 21:46:33 +03:00
zihaomu
ee3740af00 move global skip out of if loop, and add opencv_deny_list 2023-03-13 22:16:51 +08:00
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
```
 2023-03-10 11:59:49 +03:00
Alexander Alekhin
9eb5e39ff3 dnn(tflite): fix wrong axis normalization 2023-02-21 21:20:37 +00:00
Alexander Alekhin
bdff0949bb dnn(tflite): add 3rdparty flatbuffers with pre-generated schema 2023-02-21 16:06:19 +00:00
Zihao Mu
20dac7ea48
Merge pull request #23255 from zihaomu:fused_cuda_naryeltwise 
DNN: fuse conv+naryEletwise on CUDA backend.
 2023-02-17 10:18:13 +00:00
Alexander Alekhin
58d8a2702a Merge pull request #23243 from WanliZhong:accelerate_palm_det 2023-02-14 16:25:02 +00:00
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
 2023-02-13 14:00:20 +00:00
Yuantao Feng
c2b7c1f13b
Merge pull request #23219 from fengyuentau:add_gelu 
Add GELU layer for vision transformers

* add gelu and gelu approximation

* drop setKernelParams
 2023-02-10 18:03:29 +00:00
wanli
c8f5e228fc release MUL and ADD operator on CUDA 2023-02-10 19:33:59 +08:00
Alexander Alekhin
96a45e842e
Merge pull request #23061 from WanliZhong:gemm_cuda 
DNN: make GEMM can be supported with transA and transB in CUDA
 2023-02-09 00:06:32 +03:00
wanli
4718a4bf81 make GEMM can be supported with transA and transB in CUDA 2023-01-31 15:14:17 +08:00
Alexander Alekhin
f33598f55e Merge branch 4.x 2023-01-28 17:31:32 +00:00
Alexander Alekhin
cd44aa0bb1 Merge pull request #23162 from zihaomu:issue_23151 2023-01-28 13:00:43 +00:00
zihaomu
f45a12439a fix depth wise issue. 2023-01-28 11:41:00 +08:00
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
 2023-01-27 16:35:59 +03:00
Alexander Alekhin
8ffc06ff72 Merge pull request #23173 from tomoaki0705:fix_warning_master 2023-01-23 15:33:16 +00:00
Tomoaki Teshima
186c18668c suppress warning 2023-01-23 22:47:43 +09:00
Alexander Alekhin
18cbfa4a4f Merge remote-tracking branch 'upstream/3.4' into merge-3.4 2023-01-23 00:11:12 +00:00
Alexander Alekhin
a42d879925 Merge branch 4.x 2023-01-18 22:03:42 +00:00
Alexander Alekhin
3d5e3a910f Merge pull request #23096 from zihaomu:issue_23074 2023-01-12 00:51:04 +00:00
zihaomu
840b1d5c94 add depthwise add fuse 2023-01-11 08:42:51 +08:00
Alexander Alekhin
593a376566 Merge branch 4.x 2023-01-09 11:08:02 +00:00
zihaomu
82616eec41 fix possible segmentation fault error in winograd on x86 2023-01-09 13:40:04 +08:00
Alexander Alekhin
9627ab9462 Merge pull request #23050 from zihaomu:fix_memory 2022-12-28 10:04:25 +00:00
zihaomu
71765858dc fix invalid memory access 2022-12-28 17:16:11 +08:00
Alexander Alekhin
9a2a34f94e dnn(openvino): remove undefined status 2022-12-28 06:55:00 +00:00
Alexander Alekhin
fc27a343e9 Merge pull request #22905 from zihaomu:clean_up_conv3d_1d 2022-12-26 17:39:18 +00:00
Alexander Alekhin
b42c11de82 pre: OpenCV 4.7.0 (version++) 2022-12-25 17:00:22 +00:00
Alexander Alekhin
a494c75bfe pre: OpenCV 3.4.19 (version++) 2022-12-25 16:59:47 +00:00
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
 2022-12-23 16:58:41 +00:00
Alexander Smorkalov
9012e6dd9b
Merge pull request #22965 from vrabaud:numpy_fix 
Remove references to deprecated NumPy type aliases.
 2022-12-23 15:34:02 +03:00
Alexander Smorkalov
4930516652
Merge pull request #22898 from fengyuentau:slice_neg_steps 
dnn: support ONNX Slice with negative steps by adding and using cv::flipND
 2022-12-23 14:15:06 +03:00
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.
 2022-12-23 13:53:49 +03:00
Alexander Alekhin
1f41d06f9a Merge pull request #23008 from mshabunin:fix-yolov4-tiny-hash 2022-12-23 10:14:25 +00:00
zihaomu
71c6339af0 remove old convolution branch, and optimize conv3d and conv1d. 2022-12-23 16:50:28 +08:00
fengyuentau
34a0897f90 add cv::flipND; support onnx slice with negative steps via cv::flipND 2022-12-23 16:39:53 +08:00
Maksim Shabunin
d35fbe6bfc dnn: updated YOLOv4-tiny model and tests 2022-12-22 15:49:21 +03:00
Alexander Alekhin
6b4f3e5fab Merge pull request #22993 from alalek:fixup_21738 2022-12-21 19:50:51 +00:00
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
 2022-12-21 09:04:41 +03:00
Alexander Alekhin
cdbb893b27 dnn: disable OpenCL code path in MatMul processing 
- this mode is not supported by 22828
 2022-12-20 09:46:48 +00:00
Alexander Alekhin
1102b7eff8 dnn: fix gather layer implementation 
- support FP16 data
 2022-12-20 06:09:34 +00:00
zoom
4891818114 make MatMul support 3D or 4D with broadcast 2022-12-15 10:36:08 +08:00
Alexander Alekhin
8ba44e7d55 Merge pull request #22882 from zihaomu:gemm_first_const 2022-12-08 14:18:33 +00:00
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
 2022-12-08 12:57:13 +00:00
Alexander Alekhin
b16f76eede Merge remote-tracking branch 'upstream/3.4' into merge-3.4 2022-12-03 12:39:41 +00:00
Alexander Alekhin
d16b3b2487 dnn(test): restore openvino tests with 'Cannot get memory' message 2022-12-03 01:34:48 +00:00
Alexander Alekhin
74d0b4cc78 dnn(openvino): fix custom layers BlockingDesc 2022-12-03 01:34:10 +00:00
Alexander Smorkalov
e14ca39fd7
Merge pull request #22857 from fengyuentau:batched_nms 
dnn: add batched nms
 2022-11-30 12:37:49 +03:00
Alexander Smorkalov
421ba8730a
Merge pull request #22809 from fengyuentau:tile 
dnn: support ONNX Tile
 2022-11-29 14:42:28 +03:00
zihaomu
0d56524b72 gemm support transA and transB, and first input is constance. 2022-11-29 17:13:36 +08:00
fengyuentau
9fded9ca53 batched nms impl 2022-11-29 15:32:34 +08:00
fengyuentau
441624a5fb tile impl 2022-11-29 11:15:38 +08:00
zoom
5044af69d1 let MatMul can work when both two inputs are const 2022-11-27 17:32:41 +08:00
Alexander Smorkalov
6ca205a029
Merge pull request #22478 from WanliZhong:nary_eltwise_cuda 
DNN: Let part of the operators in nary_eltwise support CUDA
 2022-11-22 16:15:50 +03:00
zihaomu
5bf64e7dfe fix the infinite loop in tf importer of 3.4 branch 2022-11-15 11:42:10 +08:00
zoom
ef2677b0a6 Make MatMul layer support 3d or 4d operation with const input 2022-11-10 11:41:44 +08:00
zoom
11d492b0b9 Let part of the operators in nary_eltwise support cuda 2022-11-02 14:08:21 +08:00
Zihao Mu
17f2b56291 remove never used code in onnximporter 2022-11-02 10:45:16 +08:00
Alexander Alekhin
ee9137f176 Merge pull request #22725 from zihaomu:fix_infinit_loop_in_tf 2022-10-31 17:03:03 +00:00
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.
 2022-10-31 16:06:31 +00:00
Zihao Mu
18fbb72f7d fix the infinite loop in tf importer. 2022-10-31 20:10:25 +08:00
Alexander Smorkalov
22f8fb4d5c Do not fail tests in Yolo v7 model was not found. 2022-10-24 17:59:18 +03:00
Alexander Smorkalov
23edec83fb
Merge pull request #22667 from zihaomu:bug_fix_in_winograd 
DNN: bug fixed in Winograd
 2022-10-21 17:54:13 +03:00
Alexander Smorkalov
e4cd430710
Merge pull request #22653 from WanliZhong:issue22597 
DNN-TF: let StridedSlice layer support const input
 2022-10-21 17:51:00 +03:00
Dmitry Kurtaev
35b2cff295
Merge pull request #22656 from dkurt:halide_fixes 
* Fixes for Halide
* Enable some Halide tests
 2022-10-21 17:49:49 +03:00
Zihao Mu
cee8c86b6e fixed bug at winograd of SIMD128 and more robust code. 2022-10-21 19:14:54 +08:00
Alexander Smorkalov
5d292826b2
Merge pull request #22593 from zihaomu:optimize_wino 
optimize winograd futher more
 2022-10-19 13:08:32 +03:00
Alexander Smorkalov
f378f02954
Merge pull request #22652 from rogday:cuda_test_fixes 
Address CUDA-related errors
 2022-10-19 09:37:12 +03:00
Zhi-Qiang Zhou
c8561eae2d
Update region_layer.cpp 
Fix objectness (dstData[index + 4]) is not assigned if new_coords == 1.
 2022-10-19 11:17:23 +08:00
Smirnov Egor
dd14cf6a9c address CUDA-related errors and enable cuda in elementwise ops 2022-10-18 16:54:42 +03:00
Alexander Smorkalov
ec7fc5adca
Merge pull request #22529 from fengyuentau:scatter_scatternd 
DNN: supports Scatter and ScatterND from ONNX
 2022-10-17 14:57:46 +03:00
Alexander Smorkalov
02143cd0e2
Merge pull request #22531 from zihaomu:stop_rely_name 
Parsing quantized nodes does not rely on names
 2022-10-17 11:20:24 +03:00
Alexander Smorkalov
1c5dcbcac8
Merge pull request #22639 from WanliZhong:issue#22625 
DNN: Make Unsqueeze layer support negative axes
 2022-10-17 09:27:49 +03:00
fengyuentau
d24d8f2abe implementation of scatter and scatternd with conformance tests enabled 2022-10-17 11:30:32 +08:00
Alexander Alekhin
762481411d Merge remote-tracking branch 'upstream/3.4' into merge-3.4 2022-10-15 16:44:47 +00:00
zoom
d816442e4d Make Unsqueeze layer support negative axes. 2022-10-14 18:00:19 +08:00
Zihao Mu
0fa43e3aac Optimize the winograd futher more. 2022-10-14 10:15:45 +08:00
zoom
9119692bb8 let StridedSlice layer support const input 2022-10-12 11:50:44 +08:00
Alexander Smorkalov
ec26541771
Merge pull request #22577 from zihaomu:Disable_winograd_branch_in_tryquantize 
DNN: add enableWinograd API for Net
 2022-10-11 09:44:00 +03:00
Zihao Mu
d9eff7daeb parse quantized nodes does not rely on name. 2022-10-10 17:08:46 +08:00
Alexander Smorkalov
3419e64dcf
Merge pull request #22611 from zihaomu:greaterOrEqual 
DNN: support GreaterOrEqual and LessOrEqual op in ONNX
 2022-10-10 11:43:44 +03:00
Zihao Mu
1e2ceca4df add enableWinograd API for Net. 2022-10-09 09:33:07 +08:00
Alexander Alekhin
347246901e Merge pull request #21745 from alalek:dnn_plugin_openvino 2022-10-08 22:32:25 +00:00
Zihao Mu
9821fae59d add greater_or_equal and less_or_equal ONNX support 2022-10-08 15:51:40 +08:00
Alexander Alekhin
43b2bb2c25 dnn: plugin support for OpenVINO 2022-10-07 16:57:31 +00:00
Alexander Smorkalov
96844b0ca5
Merge pull request #22554 from WanliZhong:slice_axes_no_seq 
DNN: Let Slice layer support non-sequential and negative axes
 2022-10-03 10:15:55 +03:00
zoom
4557971481 enhance slice layer 
refactor the code for parsing Slice layer
add test for Slice layer
let 'begin' and 'end' resize to dims
add opset message comment
 2022-10-01 17:12:07 +08:00
Zihao Mu
15cfafb360
DNN: Remove unused code in onnx_importer.cpp 2022-09-29 10:53:43 +08:00
Voron
cbf43a54fb added opencv for openvino tutorial 2022-09-28 12:05:28 +02:00
Alexander Smorkalov
a6274647a4
Merge pull request #21738 from rogday:gather 
add Gather implementation
 2022-09-19 16:21:14 +03:00
Egor Smirnov
65f71ce2eb add Gather implementation 2022-09-19 15:06:44 +03:00
Alexander Smorkalov
6aefb8e86f
Merge pull request #22290 from fengyuentau:naive_yolov7 
Support for YOLOv7 ONNX (not simplified)
 2022-09-19 14:43:18 +03:00
fengyuentau
4aef9b1c93 dnn: support yolov7 (not simplified) 2022-09-19 18:38:03 +08:00
Alexander Smorkalov
e1e9261450
Merge pull request #22479 from scottchou007:master 
Fix issues in opencv_test_dnn from conv48 kernels without bias
 2022-09-16 09:05:55 +03:00
scottchou007
a3cb2020bc Fix issues in opencv_test_dnn from conv48 kernels using uninitialized tensors when there is no bias. 2022-09-15 13:41:27 -07:00
Alexander Alekhin
65bdb3a544 dnn: eliminate GCC12 warning in total() call 2022-09-14 11:37:00 +00:00
Alexander Smorkalov
c2c8da2517
Merge pull request #22448 from Ichini24:reshape-permutations-fix 
changed names of permutations if Reshpe is in NHWC
 2022-09-13 09:24:56 +03:00
wxsheng
4154bd0667
Add Loongson Advanced SIMD Extension support: -DCPU_BASELINE=LASX 
* Add Loongson Advanced SIMD Extension support: -DCPU_BASELINE=LASX
* Add resize.lasx.cpp for Loongson SIMD acceleration
* Add imgwarp.lasx.cpp for Loongson SIMD acceleration
* Add LASX acceleration support for dnn/conv
* Add CV_PAUSE(v) for Loongarch
* Set LASX by default on Loongarch64
* LoongArch: tune test threshold for Core/HAL.mat_decomp/15

Co-authored-by: shengwenxue <shengwenxue@loongson.cn>
 2022-09-10 09:39:43 +03:00
Alexander Alekhin
ca7f964104 dnn: use inheritance for OpenVINO net impl 2022-09-06 18:05:00 +00:00
anton
337452b4c0 changed names of permutations if Reshpe is in NHWC 2022-09-03 19:02:41 +02:00
Zihao Mu
b69b1eae8f fix bug 22450 2022-09-02 16:30:06 +08:00
Alexander Smorkalov
70fb1cd603 Merge pull request #22440 from zihaomu:fix_conv_bug 2022-08-30 07:01:05 +00:00
Alexander Smorkalov
d2c48b898c Merge pull request #22306 from zihaomu:qgemm_and_squeeze_opset13_onnximporter 2022-08-30 06:33:57 +00:00
Zihao Mu
2d837efba7 add qgemm and squeeze op13 supported on ONNXImporter 2022-08-30 09:50:29 +08:00
Alexander Smorkalov
1fd45a1b85
Merge pull request #22362 from fengyuentau:conv_asym_pad_fuse 
Remove asymmetric padding in Conv layer since it is supported in CPU backend
 2022-08-29 17:56:17 +03:00
Zihao Mu
2cd7e17b65 replace v_add with + 2022-08-29 17:15:35 +08:00
Alexander Smorkalov
2619099fe5
Merge pull request #22337 from zihaomu:load_ONNX_fp16_as_fp32 
DNN: load fp16 ONNX model as fp32
 2022-08-29 09:32:25 +03:00
fengyuentau
2959286eb5 tengine: supports conv with asymmetric padding 2022-08-29 02:51:26 +00:00
Zihao Mu
9638e34ab0 reuse WORDS_BIGENDIAN. 2022-08-27 07:42:38 +08:00
Zihao Mu
bb64db98d8
Further optimization of Conv2D, fused Conv_Add_Activation, bring latest code from ficus OpConv.fx. (#22401) 2022-08-26 12:57:25 +03:00
Zihao Mu
7eaec9dd22 load fp16 as fp32 and align fp16 and double in onnx_graph_simplifie 2022-08-26 10:04:44 +08:00
Zihao Mu
5e92bf8e41 support silu activation in darknet 2022-08-22 10:51:29 +08:00
Alexander Alekhin
c25f776151 Merge branch 4.x 2022-08-21 15:27:31 +00:00
Alexander Alekhin
2ebdc04787 Merge remote-tracking branch 'upstream/3.4' into merge-3.4 2022-08-14 15:50:42 +00:00
fengyuentau
0cdff46725 tune for opencl 2022-08-14 17:47:48 +08:00
Alexander Alekhin
d0d115321d Merge pull request #22350 from alalek:rework_psabi_warning 2022-08-13 15:05:41 +00:00
Alexander Smorkalov
bb71cb200e
Merge pull request #22199 from zihaomu:bug_fix_22195 
DNN: Reduce Layer (add dynamic batch and ReduceSum support)
 2022-08-11 12:59:51 +03:00
fengyuentau
e7e814fa8c remove asymmetric padding checks 2022-08-10 19:52:44 +08:00
Alexander Alekhin
44b2f9637a Revert "suppress warning on GCC 7 and later" 
This reverts commit a630ad73cb.
 2022-08-07 15:43:10 +03:00
Alexander Smorkalov
b2b7193374
Merge pull request #22311 from zihaomu:layer_fused_optmized_mish 
DNN: add another two Mish activation to onnx_graph_simplifier
 2022-08-05 14:22:06 +03:00
Zihao Mu
0614c40b42 add more skip for very long test case in test_dnn. 2022-08-02 14:58:05 +08:00
Zihao Mu
d4640f4647 support ReduceLayer without reshape layer. 2022-08-02 10:32:31 +08:00
Zihao Mu
57545653b1 replace new mish impl with softplus 2022-07-28 13:19:06 +08:00
Zihao Mu
3c5377ca1b add another Mish graph simplifier. 2022-07-28 11:21:29 +08:00
HAN Liutong
e2bfe0ce76 Use "#if" instead of "#ifdef" for CV_SIMD128. 2022-07-21 03:23:57 +00:00
Zihao Mu
98c33c605d batchsize dynamic is set to index 0. 2022-07-20 19:02:16 +08:00
rogday
ed69bcae2d
Merge pull request #21865 from rogday:nary_eltwise_layers 
Reimplementation of Element-wise layers with broadcasting support

* init

* semi-working initial version

* add small_vector

* wip

* remove smallvec

* add nary function

* replace auto with Mat in lambda expr used in transform

* uncomment asserts

* autobuffer shape_buf & step_buf

* fix a missing bracket

* fixed a missing addLayer in parseElementWise

* solve one-dimensional broadcast

* remove pre_broadcast_transform for the case of two constants; fix missing constBlobsExtraInfo when addConstant is called

* one autobuffer for step & shape

* temporal fix for the missing original dimension information

* fix parseUnsqueeze when it gets a 1d tensor constant

* support sum/mean/min/max with only one input

* reuse old code to handle cases of two non-constant inputs

* add condition to handle div & mul of two non-constant inputs

* use || instead of or

* remove trainling spaces

* enlarge buf in binary_forward to contain other buffer

* use autobuffer in nary_forward

* generate data randomly and add more cases for perf

* add op and, or & xor

* update perf_dnn

* remove some comments

* remove legacy; add two ONNX conformance tests in filter

* move from cpu_denylist to all_denylist

* adjust parsing for inputs>=2

Co-authored-by: fengyuentau <yuantao.feng@opencv.org.cn>
 2022-07-19 06:14:05 +03:00
fengyuentau
1c7b71bf9e define data_layout as unknown for pack 2022-07-14 19:27:20 +08:00
Zihao Mu
1b8fba8e26 support ReduceSum with two input and dynamic shape batch size in ReduceLayer. 2022-07-13 13:46:16 +08:00
Zihao Mu
45fbb67aba fix scale layer can not handle 1x1 weight correctly. 2022-07-13 11:25:27 +08:00
Zihao Mu
139c443770
Merge pull request #22183 from zihaomu:fastConv_ARMv7_compatible 
DNN: ARMv7 compatible fastConv

* support armv7 on fastConv

* remove whitespace.
 2022-07-07 13:23:08 +03:00
Tomoaki Teshima
a630ad73cb suppress warning on GCC 7 and later 2022-07-06 23:31:31 +09:00
Zihao Mu
a80fcacd90
Merge pull request #21372 from zihaomu:dnn_quantize_per_tensor 
Add per_tensor_quantize to int8 quantize

* add per_tensor_quantize to dnn int8 module.

* change api flag from perTensor to perChannel, and recognize quantize type and onnx importer.

* change the default to hpp
 2022-07-05 19:14:42 +03:00
Zihao Mu
59b870a87a
Merge pull request #21910 from zihaomu:fast_conv_ARM 
DNN: Accelerating convolution

* Fast Conv of ARM, X86 and universal intrinsics.

* improve code style.

* error fixed.

* improve the License

* optimize memory allocated and Adjust the threshold.

* change FasterRCNN_vgg16 to 2GB memory.
 2022-07-01 13:03:15 +03:00
Zihao Mu
ef94275eb6 bug fixed of GEMM node in ONNX_importer 2022-06-22 21:08:48 +08:00
Wanli
a6ca48a1c2
Merge pull request #22100 from WanliZhong:issue_22015 
Fix issue 22015, let Clip layer support 1-3 inputs

* Fix issue 22015.
Let layer Clip support 1-3 inputs.

* Resolve other problems caused by modifications

* Update onnx_importer.cpp

added extra checks to min/max handling in Clip

* Add assertions to check the size of the input

* Add test for clip with min and max initializers

* Separate test for "clip_init_min_max". Change the check method for input_size to provide a clearer message in case of problem.

* Add tests for clip with min or max initializers

* Change the implementation of getting input

Co-authored-by: Vadim Pisarevsky <vadim.pisarevsky@gmail.com>
 2022-06-22 14:21:16 +03:00
Zihao Mu
2411b825b4 bug fixed of GEMM node in ONNX_importer 2022-06-22 15:00:17 +08:00
Alexander Alekhin
583bd1a6e2 Merge remote-tracking branch 'upstream/3.4' into merge-3.4 2022-06-04 19:10:35 +00:00
Namgoo Lee
24547f40ff remove const from functions returning by value 2022-05-26 21:30:41 +09:00
Alexander Alekhin
e9187ae38c Merge pull request #22026 from alalek:update_version_3.4.18-pre 2022-05-24 20:23:28 +00:00
Alexander Alekhin
978dc76653 Merge pull request #22006 from rogday:21947_fix 2022-05-24 19:26:02 +00:00
rogday
a2ad997e97 fix vector access in TF::sortByExecutionOrder 2022-05-24 00:05:13 +03:00
Alexander Alekhin
e9428726ca pre: OpenCV 4.6.0 (version++) 2022-05-23 19:25:16 +00:00
Alexander Alekhin
400906b433 pre: OpenCV 3.4.18 (version++) 2022-05-23 19:18:02 +00:00
berak
50d7c61c01
Update darknet_importer.cpp 
make it more obvious, that this is a '404', not a 'parsing' problem
 2022-05-23 19:18:31 +02:00
Alexander Alekhin
d9bf522b27 Merge remote-tracking branch 'upstream/3.4' into merge-3.4 2022-05-23 16:06:14 +00:00
rogday
93dc0679ec
Merge pull request #21818 from rogday:revert_renaming 
* add prefixes to layer names and layer output names

* dnn: OPENCV_DNN_ONNX_USE_LEGACY_NAMES runtime parameter

Co-authored-by: Alexander Alekhin <alexander.a.alekhin@gmail.com>
 2022-05-23 14:50:42 +00:00
Alexander Alekhin
bb5462e327 Merge pull request #21991 from fengyuentau:qconv_asympad 2022-05-19 17:20:04 +00:00
fengyuentau
ff88132620 support asymmetric paddings for qconv 2022-05-16 19:01:37 +08:00
OpenCV Developers
d9a444ca1a Merge remote-tracking branch 'upstream/3.4' into merge-3.4 2022-05-14 11:23:21 +00:00
Yulv-git
15ac54d5d6 Fix some typos in modules/. 2022-04-30 13:40:07 +08:00
Zihao Mu
64ded50bbf parsing depth2space and space2depth of ONNX importer 2022-04-29 10:17:02 +08:00
OpenCV Developers
0fbd58bef9 Merge branch 4.x 2022-04-23 22:07:14 +00:00
rogday
9cd5a0a1e6
Merge pull request #21884 from rogday:cuda_cleanup 
Fix CUDA compilation issues and adjust thresholds.

* Fix CUDA compilation issues and adjust thresholds.

* add conformance tests to denylist
 2022-04-19 16:40:25 +00:00
OpenCV Developers
2985739b8c Merge remote-tracking branch 'upstream/3.4' into merge-3.4 2022-04-16 14:41:15 +00:00
rogday
a2b84e9897 add assert to tf graph simplifier to address security concerns 2022-04-13 22:50:27 +03:00
OpenCV Pushbot
66f3c2673c
Merge pull request #21831 from zihaomu:sign_layer_onnx 
DNN: Add sign, shrink and reciprocal for onnx_impoter
 2022-04-13 17:08:30 +00:00
OpenCV Pushbot
03c9648f2e
Merge pull request #21854 from opencv-pushbot:dnn_test_update_checks_face_detector_4.x 2022-04-12 17:20:22 +00:00
OpenCV Developers
e3a55af336 dnn(test): update opencv_face_detector checks 
original commit: be4a432bea
 2022-04-11 20:27:06 +00:00
OpenCV Developers
be4a432bea dnn(test): update opencv_face_detector checks 2022-04-11 20:26:25 +00:00
zihaomu
e36948cfbc add ONNX OP sign, shrink and reciprocal 2022-04-07 15:32:12 +08:00
Alexander Alekhin
08d44f588f dnn(test): update OpenVINO tests 2022.1.0 (OpenCV 4.x) 2022-04-05 14:13:38 +00:00
Alexander Alekhin
13a995cc1d Merge remote-tracking branch 'upstream/3.4' into merge-3.4 2022-04-02 19:45:44 +00:00
Alexander Alekhin
4d927e73f1 dnn(test): update OpenVINO tests 2022.1.0 2022-04-02 17:42:53 +00:00
Alexander Alekhin
a233982931 Merge pull request #20938 from JulieBar:lstm_cuda2 2022-04-01 22:10:08 +00:00
Zihao Mu
7b582b71ba
Merge pull request #21036 from fengyuentau:timvx_backend_support 
dnn: TIM-VX NPU backend support

* Add TimVX NPU backend for DNN module.

* use official branch from tim-vx repo; fix detecting viv sdk

Co-authored-by: fytao <yuantao.feng@outlook.com>
 2022-03-31 21:42:11 +00:00
Smirnov Egor
abebbf04b1 Add CUDA support for LSTM. 
Co-authored-by: Julia Bareeva <jbareeva@gmail.com>
 2022-03-31 16:38:22 +03:00
Alexander Alekhin
5e434073d4 Merge pull request #21796 from alalek:dnn_reduce_fixup_21601 2022-03-30 22:26:28 +00:00
Alexander Alekhin
6f5cf8c15f dnn: fix ReduceLayer implementation, update OpenVINO tests 2022-03-30 20:03:41 +00:00
Alexander Alekhin
b687bc807a dnn(test): update OpenVINO tests 2021.4.2 2022-03-30 18:58:35 +00:00
Alexander Alekhin
1339ebaa84 Merge remote-tracking branch 'upstream/3.4' into merge-3.4 2022-03-26 16:00:28 +00:00
Alexander Alekhin
c9b90884da Merge pull request #21601 from zihaomu:add_reduceLayer 2022-03-26 10:20:10 +00:00
luz paz
8e8e4bbabc dnn: fix various dnn related typos 
Fixes source comments and documentation related to dnn code.
 2022-03-23 18:12:12 -04:00
Alexander Alekhin
4c79318694 dnn: fix index access 2022-03-19 06:54:07 +00:00
Zihao Mu
b6b5c27cec Support for some reduce layers for onnx 2022-03-18 10:19:13 +08:00
Alexander Alekhin
685797f403 Merge pull request #21662 from alalek:dnn_split 2022-03-17 16:09:17 +00:00
rogday
93353aea70
Merge pull request #21522 from rogday:lstm 
Fix LSTM support in ONNX

* fix LSTM and add peephole support

* disable old tests

* turn lambdas into functions

* more hacks for  c++98

* add assertions

* slice fixes

* backport of cuda-related fixes

* address review comments
 2022-03-15 09:14:05 +03:00
Alexander Alekhin
5bf3c1df24 Merge pull request #21715 from ilyachur:change_type_info_creation 2022-03-14 09:18:58 +00:00
Ilya Churaev
419918076e Changed call of NodeTypeInfo constructor 2022-03-14 10:55:33 +03:00
Alexander Alekhin
a120adde63 dnn: add dnn.cpp file with information about git commits history 2022-03-08 19:22:47 +00:00
Alexander Alekhin
a80af177b6 dnn: split dnn.cpp code 
base commit: 19926e2979
original dnn.cpp content: 19926e2979/modules/dnn/src/dnn.cpp
 2022-03-08 19:22:46 +00:00
Tsukasa Sugiura
8db7d435b9
Merge pull request #21692 from UnaNancyOwen:add_softmax 
* add apply softmax option to ClassificationModel

* remove default arguments of ClassificationModel::setSoftMax()

* fix build for python

* fix docs warning for setSoftMax()

* add impl for ClassficationModel()

* fix failed build for docs by trailing whitespace

* move to implement classify() to ClassificationModel_Impl

* move to implement softmax() to ClassificationModel_Impl

* remove softmax from public method in ClassificationModel
 2022-03-07 20:26:15 +00:00
Alexander Alekhin
901e0ddfe4 Merge remote-tracking branch 'upstream/3.4' into merge-3.4 2022-03-05 19:46:28 +00:00
Alexander Alekhin
5cc27fd3b5 Merge pull request #21542 from rogday:split_expand 2022-02-28 22:38:24 +00:00
Egor Smirnov
375fe81311 fix slice and expand 2022-02-28 17:18:07 +03:00
Alexander Alekhin
899b4d1452 Merge branch 4.x 2022-02-22 19:55:26 +00:00
Yuantao Feng
f77c3574af
Merge pull request #21607 from fengyuentau:fix_FaceDetectorYN_dynamic_shape 
Use YuNet of fixed input shape to fix not-supported-dynamic-zero-shape for FaceDetectorYN

* use yunet with input of fixed shape

* update yunet used in face recognition regression
 2022-02-21 13:49:07 +00:00
Maksim Shabunin
a251474144 Update filters in ONNX tests 2022-02-15 11:56:28 +03:00
Maksim Shabunin
45cbf70265 Update filters in ONNX tests 2022-02-14 17:16:49 +03:00
Alexander Alekhin
19926e2979 Merge remote-tracking branch 'upstream/3.4' into merge-3.4 2022-02-11 17:32:37 +00:00
Alexander Alekhin
effce0573b dnn: drop legacy Inference Engine NN builder API 2022-02-10 11:55:24 +00:00
Alexander Alekhin
57d3002ee1 Merge remote-tracking branch 'upstream/3.4' into merge-3.4 2022-02-06 16:10:43 +00:00
Alexander Alekhin
a00a0dbfcd Merge pull request #21564 from alalek:dnn_fix_openvino_outputs 2022-02-06 16:06:23 +00:00
Alexander Alekhin
b41d2c5c14 Merge pull request #21569 from alalek:fixup_18031 2022-02-06 16:04:38 +00:00
Alexander Alekhin
1da48beeec dnn(ngraph): fix output names 2022-02-06 13:08:53 +00:00
Alexander Alekhin
b57ff73086 dnn(ngraph): fix outputs handling, drop 'unconnected' logic 2022-02-06 13:08:53 +00:00
Alexander Alekhin
67978b5746 dnn(ngraph): add debuging messages 2022-02-06 13:08:53 +00:00
Alexander Alekhin
062f305d1a dnn: don't fuse 'outputs' with OpenVINO backend 2022-02-06 13:08:53 +00:00