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352f92e437
opencv/modules/dnn/CMakeLists.txt
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

314 lines
12 KiB
CMake
Raw Blame History

if(WINRT)
ocv_module_disable(dnn)
endif()
if(NOT HAVE_PROTOBUF)
ocv_module_disable(opencv_dnn)
endif()
set(the_description "Deep neural network module. It allows to load models from different frameworks and to make forward pass")
ocv_add_dispatched_file_force_all("layers/layers_common" AVX AVX2 AVX512_SKX RVV LASX)
ocv_add_dispatched_file_force_all("int8layers/layers_common" AVX2 AVX512_SKX LASX)
ocv_add_dispatched_file_force_all("layers/cpu_kernels/conv_block" AVX AVX2)
ocv_add_dispatched_file_force_all("layers/cpu_kernels/conv_depthwise" AVX AVX2 RVV LASX)
ocv_add_dispatched_file_force_all("layers/cpu_kernels/conv_winograd_f63" AVX AVX2)
ocv_add_module(dnn opencv_core opencv_imgproc WRAP python java objc js)
include(${CMAKE_CURRENT_LIST_DIR}/cmake/plugin.cmake)
ocv_option(OPENCV_DNN_OPENCL "Build with OpenCL support" HAVE_OPENCL AND NOT APPLE)
if(OPENCV_DNN_OPENCL AND HAVE_OPENCL)
ocv_target_compile_definitions(${the_module} PRIVATE "CV_OCL4DNN=1")
endif()
if(WITH_WEBNN AND HAVE_WEBNN)
ocv_target_compile_definitions(${the_module} PRIVATE "HAVE_WEBNN=1")
endif()
if(HAVE_TIMVX)
ocv_target_compile_definitions(${the_module} PRIVATE "HAVE_TIMVX=1")
endif()
if(HAVE_CANN)
ocv_target_compile_definitions(${the_module} PRIVATE "HAVE_CANN=1")
endif()
ocv_option(OPENCV_DNN_CUDA "Build with CUDA support"
HAVE_CUDA
AND HAVE_CUBLAS
AND HAVE_CUDNN
)
if(OPENCV_DNN_CUDA)
if(HAVE_CUDA AND HAVE_CUBLAS AND HAVE_CUDNN)
ocv_target_compile_definitions(${the_module} PRIVATE "CV_CUDA4DNN=1")
else()
if(NOT HAVE_CUDA)
message(SEND_ERROR "DNN: CUDA backend requires CUDA Toolkit. Please resolve dependency or disable OPENCV_DNN_CUDA=OFF")
elseif(NOT HAVE_CUBLAS)
message(SEND_ERROR "DNN: CUDA backend requires cuBLAS. Please resolve dependency or disable OPENCV_DNN_CUDA=OFF")
elseif(NOT HAVE_CUDNN)
message(SEND_ERROR "DNN: CUDA backend requires cuDNN. Please resolve dependency or disable OPENCV_DNN_CUDA=OFF")
endif()
endif()
endif()
ocv_cmake_hook_append(INIT_MODULE_SOURCES_opencv_dnn "${CMAKE_CURRENT_LIST_DIR}/cmake/hooks/INIT_MODULE_SOURCES_opencv_dnn.cmake")
if(HAVE_TENGINE)
ocv_target_compile_definitions(${the_module} PRIVATE "HAVE_TENGINE=1")
endif()
if(MSVC)
add_definitions( -D_CRT_SECURE_NO_WARNINGS=1 )
ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4244 /wd4267 /wd4018 /wd4355 /wd4800 /wd4251 /wd4996 /wd4146
/wd4305 /wd4127 /wd4100 /wd4512 /wd4125 /wd4389 /wd4510 /wd4610
/wd4702 /wd4456 /wd4457 /wd4065 /wd4310 /wd4661 /wd4506
)
if(MSVC_VERSION LESS 1920) # MSVS 2015/2017, .pb.cc generated files
ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4309) # 'static_cast': truncation of constant value
endif()
if(MSVC_VERSION LESS 1920) # <MSVS2019, .pb.cc generated files
ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4189) # local variable is initialized but not referenced
ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4592) # symbol will be dynamically initialized (implementation limitation)
endif()
else()
ocv_warnings_disable(CMAKE_CXX_FLAGS -Wno-deprecated -Wmissing-prototypes -Wmissing-declarations -Wshadow
-Wunused-parameter -Wsign-compare
)
endif()
if(HAVE_CUDA)
ocv_warnings_disable(CMAKE_CXX_FLAGS -Wundef)
endif()
if(NOT HAVE_CXX11)
ocv_warnings_disable(CMAKE_CXX_FLAGS -Wno-undef) # LANG_CXX11 from protobuf files
endif()
if(APPLE_FRAMEWORK)
ocv_warnings_disable(CMAKE_CXX_FLAGS -Wshorten-64-to-32)
endif()
if(ANDROID)
add_definitions(-DDISABLE_POSIX_MEMALIGN -DTH_DISABLE_HEAP_TRACKING)
endif()
if(NOT BUILD_PROTOBUF)
ocv_target_compile_definitions(${the_module} PRIVATE "OPENCV_DNN_EXTERNAL_PROTOBUF=1")
endif()
ocv_target_compile_definitions(${the_module} PRIVATE "HAVE_PROTOBUF=1")
#suppress warnings in autogenerated caffe.pb.* files
ocv_warnings_disable(CMAKE_CXX_FLAGS
/wd4125 /wd4267 /wd4127 /wd4244 /wd4512 /wd4702
/wd4456 /wd4510 /wd4610 /wd4800
/wd4701 /wd4703 # potentially uninitialized local/pointer variable 'value' used
/wd4505 # unreferenced local function has been removed
/wd4458 # declaration of 'x' hides class member. GCC still works, MSVC bug is here: https://developercommunity.visualstudio.com/content/problem/219311/c-c4458-declaration-hides-class-member-warning-iss.html
-wd858 -wd2196
-Winvalid-offsetof # Apple Clang (attr_value.pb.cc)
)
set(include_dirs "")
set(libs "")
if(PROTOBUF_UPDATE_FILES)
file(GLOB proto_files "${CMAKE_CURRENT_LIST_DIR}/src/tensorflow/*.proto" "${CMAKE_CURRENT_LIST_DIR}/src/caffe/opencv-caffe.proto" "${CMAKE_CURRENT_LIST_DIR}/src/onnx/opencv-onnx.proto")
set(PROTOBUF_GENERATE_CPP_APPEND_PATH ON) # required for tensorflow
protobuf_generate_cpp(fw_srcs fw_hdrs ${proto_files})
else()
file(GLOB fw_srcs "${CMAKE_CURRENT_LIST_DIR}/misc/tensorflow/*.cc" "${CMAKE_CURRENT_LIST_DIR}/misc/caffe/opencv-caffe.pb.cc" "${CMAKE_CURRENT_LIST_DIR}/misc/onnx/opencv-onnx.pb.cc")
file(GLOB fw_hdrs "${CMAKE_CURRENT_LIST_DIR}/misc/tensorflow/*.h" "${CMAKE_CURRENT_LIST_DIR}/misc/caffe/opencv-caffe.pb.h" "${CMAKE_CURRENT_LIST_DIR}/misc/onnx/opencv-onnx.pb.h")
set(fw_inc "${CMAKE_CURRENT_LIST_DIR}/misc/caffe" "${CMAKE_CURRENT_LIST_DIR}/misc/tensorflow" "${CMAKE_CURRENT_LIST_DIR}/misc/onnx")
endif()
ocv_option(OPENCV_DNN_TFLITE "Build with TFLite support" (TARGET ocv.3rdparty.flatbuffers))
if(TARGET ocv.3rdparty.flatbuffers AND OPENCV_DNN_TFLITE)
if(NOT HAVE_FLATBUFFERS)
message(FATAL_ERROR "DNN: TFLite is not supported without enabled 'flatbuffers'. Check build configuration.")
endif()
list(APPEND libs ocv.3rdparty.flatbuffers)
list(APPEND fw_hdrs "${CMAKE_CURRENT_LIST_DIR}/misc/tflite/schema_generated.h")
list(APPEND fw_inc "${CMAKE_CURRENT_LIST_DIR}/misc/tflite")
# Schema is generated by this command:
#add_custom_command(
# OUTPUT "${CMAKE_CURRENT_BINARY_DIR}/schema_generated.h"
# COMMAND flatbuffers::flatc --cpp -o "${CMAKE_CURRENT_BINARY_DIR}" "${CMAKE_CURRENT_LIST_DIR}/src/tflite/schema.fbs")
endif()
list(APPEND include_dirs ${fw_inc})
list(APPEND libs ${Protobuf_LIBRARIES})
if(NOT BUILD_PROTOBUF)
list(APPEND include_dirs ${Protobuf_INCLUDE_DIRS})
endif()
set(sources_options "")
list(APPEND libs ${LAPACK_LIBRARIES})
if(OPENCV_DNN_OPENCL AND HAVE_OPENCL)
list(APPEND include_dirs ${OPENCL_INCLUDE_DIRS})
else()
set(sources_options EXCLUDE_OPENCL)
endif()
if(OPENCV_DNN_CUDA AND HAVE_CUDA AND HAVE_CUBLAS AND HAVE_CUDNN)
list(APPEND include_dirs ${CUDA_TOOLKIT_INCLUDE} ${CUDNN_INCLUDE_DIRS})
set(CC_LIST ${CUDA_ARCH_BIN})
separate_arguments(CC_LIST)
foreach(cc ${CC_LIST})
if(cc VERSION_LESS 3.0)
message(FATAL_ERROR "CUDA backend for DNN module requires CC 3.0 or higher. Please remove unsupported architectures from CUDA_ARCH_BIN option or disable OPENCV_DNN_CUDA=OFF.")
endif()
endforeach()
unset(CC_LIST)
else()
set(sources_options ${sources_options} EXCLUDE_CUDA)
endif()
if(HAVE_TENGINE)
list(APPEND include_dirs ${TENGINE_INCLUDE_DIRS})
list(APPEND libs -Wl,--whole-archive ${TENGINE_LIBRARIES} -Wl,--no-whole-archive)
endif()
if(HAVE_TIMVX)
list(APPEND include_dirs ${TIMVX_INCLUDE_DIR})
list(APPEND libs -Wl,--whole-archive ${TIMVX_LIBRARY} -Wl,--no-whole-archive)
endif()
if(HAVE_CANN)
list(APPEND include_dirs ${CANN_INCLUDE_DIRS})
list(APPEND libs -Wl,--whole-archive ${CANN_LIBRARIES} -Wl,--no-whole-archive)
endif()
set(webnn_srcs "")
if(NOT EMSCRIPTEN)
if(HAVE_WEBNN)
list(APPEND include_dirs ${WEBNN_HEADER_DIRS})
list(APPEND include_dirs ${WEBNN_INCLUDE_DIRS})
list(APPEND libs -Wl,--whole-archive ${WEBNN_LIBRARIES} -Wl,--no-whole-archive)
list(APPEND webnn_srcs $ENV{WEBNN_NATIVE_DIR}/gen/src/webnn/webnn_cpp.cpp)
endif()
endif()
ocv_module_include_directories(${include_dirs})
if(CMAKE_CXX_COMPILER_ID STREQUAL "GNU")
ocv_append_source_files_cxx_compiler_options(fw_srcs "-Wno-suggest-override") # GCC
ocv_append_source_files_cxx_compiler_options(fw_srcs "-Wno-array-bounds") # GCC 9.3.0 (Ubuntu 20.04)
elseif(CMAKE_CXX_COMPILER_ID STREQUAL "Clang")
ocv_append_source_files_cxx_compiler_options(fw_srcs "-Wno-inconsistent-missing-override") # Clang
endif()
set(dnn_runtime_libs "")
file(GLOB_RECURSE dnn_srcs
"${CMAKE_CURRENT_LIST_DIR}/src/*.cpp"
)
file(GLOB_RECURSE dnn_int_hdrs
"${CMAKE_CURRENT_LIST_DIR}/src/*.hpp"
"${CMAKE_CURRENT_LIST_DIR}/src/*.h"
)
set(dnn_plugin_srcs ${dnn_srcs} ${dnn_int_hdrs})
ocv_list_filterout_ex(dnn_plugin_srcs
"/src/dnn.cpp$|/src/dnn_utils.cpp$|/src/dnn_utils.cpp$|/src/dnn_read.cpp$|/src/registry.cpp$|/src/backend.cpp$"
# importers
"/src/(caffe|darknet|onnx|tensorflow|torch)/"
# executors
"/src/(cuda|cuda4dnn|ocl4dnn|vkcom|webnn)/"
)
ocv_option(OPENCV_DNN_OPENVINO "Build with OpenVINO support (2021.4+)" (TARGET ocv.3rdparty.openvino))
if(TARGET ocv.3rdparty.openvino AND OPENCV_DNN_OPENVINO)
if(NOT HAVE_OPENVINO AND NOT HAVE_NGRAPH)
message(FATAL_ERROR "DNN: Inference Engine is not supported without enabled 'nGraph'. Check build configuration.")
endif()
if("openvino" IN_LIST DNN_PLUGIN_LIST OR DNN_PLUGIN_LIST STREQUAL "all")
# plugin doesn't support PCH, separate directory scope is necessary
# opencv_world requires absolute path
add_subdirectory("${CMAKE_CURRENT_LIST_DIR}/misc/plugin/openvino" "${CMAKE_CURRENT_BINARY_DIR}/dnn_plugin_openvino")
elseif(NOT OPENCV_DNN_BUILTIN_BACKEND)
list(APPEND dnn_runtime_libs ocv.3rdparty.openvino)
endif()
endif()
ocv_install_used_external_targets(${libs} ${dnn_runtime_libs})
ocv_glob_module_sources(${sources_options} SOURCES ${fw_srcs} ${webnn_srcs})
ocv_create_module(${libs} ${dnn_runtime_libs})
ocv_add_samples()
ocv_add_accuracy_tests(${dnn_runtime_libs})
set(perf_path "${CMAKE_CURRENT_LIST_DIR}/perf")
file(GLOB_RECURSE perf_srcs "${perf_path}/*.cpp")
file(GLOB_RECURSE perf_hdrs "${perf_path}/*.hpp" "${perf_path}/*.h")
ocv_add_perf_tests(${dnn_runtime_libs}
FILES test_common "${CMAKE_CURRENT_LIST_DIR}/test/test_common.hpp" "${CMAKE_CURRENT_LIST_DIR}/test/test_common.impl.hpp"
FILES Src ${perf_srcs}
FILES Include ${perf_hdrs}
)
ocv_option(OPENCV_DNN_PERF_CAFFE "Add performance tests of Caffe framework" OFF)
ocv_option(OPENCV_DNN_PERF_CLCAFFE "Add performance tests of clCaffe framework" OFF)
if(BUILD_PERF_TESTS)
if (OPENCV_DNN_PERF_CAFFE
OR ${the_module}_PERF_CAFFE # compatibility for deprecated option
)
find_package(Caffe QUIET)
if (Caffe_FOUND)
ocv_target_compile_definitions(opencv_perf_dnn PRIVATE "HAVE_CAFFE=1")
ocv_target_link_libraries(opencv_perf_dnn caffe)
endif()
elseif(OPENCV_DNN_PERF_CLCAFFE
OR ${the_module}_PERF_CAFFE # compatibility for deprecated option
)
find_package(Caffe QUIET)
if (Caffe_FOUND)
ocv_target_compile_definitions(opencv_perf_dnn PRIVATE "HAVE_CLCAFFE=1")
ocv_target_link_libraries(opencv_perf_dnn caffe)
endif()
endif()
endif()
if(DNN_ENABLE_PLUGINS)
ocv_target_compile_definitions(${the_module} PRIVATE ENABLE_PLUGINS)
if(TARGET opencv_test_dnn)
ocv_target_compile_definitions(opencv_test_dnn PRIVATE ENABLE_PLUGINS)
endif()
if(OPENCV_DEBUG_POSTFIX)
ocv_append_source_file_compile_definitions("${CMAKE_CURRENT_LIST_DIR}/src/backend.cpp" "DEBUG_POSTFIX=${OPENCV_DEBUG_POSTFIX}")
endif()
endif()
ocv_option(OPENCV_TEST_DNN_OPENVINO "Build test with OpenVINO code" (TARGET ocv.3rdparty.openvino))
if(TARGET ocv.3rdparty.openvino AND OPENCV_TEST_DNN_OPENVINO)
if(TARGET opencv_test_dnn)
ocv_target_link_libraries(opencv_test_dnn ocv.3rdparty.openvino)
endif()
endif()
ocv_option(OPENCV_TEST_DNN_CANN "Build test with CANN" (TARGET ocv.3rdparty.cann))
if(TARGET ocv.3rdparty.cann AND OPENCV_TEST_DNN_CANN)
if(TARGET opencv_test_dnn)
ocv_target_link_libraries(opencv_test_dnn ocv.3rdparty.cann)
endif()
endif()
ocv_option(OPENCV_TEST_DNN_TFLITE "Build test with TFLite" (OPENCV_DNN_TFLITE))
if(OPENCV_TEST_DNN_TFLITE)
if(TARGET opencv_test_dnn)
ocv_target_compile_definitions(opencv_test_dnn PRIVATE "OPENCV_TEST_DNN_TFLITE=1")
endif()
if(TARGET opencv_perf_dnn)
ocv_target_compile_definitions(opencv_perf_dnn PRIVATE "OPENCV_TEST_DNN_TFLITE=1")
endif()
endif()
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