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opencv/modules/dnn/CMakeLists.txt

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CMake
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dnn: move module from opencv_contrib https://github.com/opencv/opencv_contrib/tree/e6f63c7a38ca40c5dc33e38736e3027e3528d6cb/modules/dnn
2017-06-26 18:35:51 +08:00
if(WINRT)
ocv_module_disable(dnn)
endif()
set(the_description "Deep neural network module. It allows to load models from different frameworks and to make forward pass")
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 14:39:43 +08:00
ocv_add_dispatched_file_force_all("layers/layers_common" AVX AVX2 AVX512_SKX RVV LASX)
Merge pull request #25230 from hanliutong/rvv-conv Optimize int8 layers in DNN modules by using RISC-V Vector intrinsic. #25230 This patch optimize 3 functions in the int8 layer by using RVV Native Intrinsic. This patch was tested on QEMU using VLEN=128 and VLEN=256 on `./bin/opencv_test_dnn --gtest_filter="*Int8*"`; On the real device (k230, VLEN=128), `EfficientDet_int8` in `opencv_perf_dnn` showed a performance improvement of 1.46x. | Name of Test | Original | optimized | Speed-up | | ------------------------------------------ | -------- | ---------- | -------- | | EfficientDet_int8::DNNTestNetwork::OCV/CPU | 2843.467 | 1947.013 | 1.46 | ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [ ] I agree to contribute to the project under Apache 2 License. - [ ] 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 - [ ] 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
2024-03-31 21:47:06 +08:00
ocv_add_dispatched_file_force_all("int8layers/layers_common" AVX2 AVX512_SKX RVV LASX)
DNN: add the Winograd fp16 support (#23654) * add Winograd FP16 implementation * fixed dispatching of FP16 code paths in dnn; use dynamic dispatcher only when NEON_FP16 is enabled in the build and the feature is present in the host CPU at runtime * fixed some warnings * hopefully fixed winograd on x64 (and maybe other platforms) --------- Co-authored-by: Vadim Pisarevsky <vadim.pisarevsky@gmail.com>
2023-11-20 18:45:37 +08:00
ocv_add_dispatched_file_force_all("layers/cpu_kernels/conv_block" AVX AVX2 NEON NEON_FP16)
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 16:59:49 +08:00
ocv_add_dispatched_file_force_all("layers/cpu_kernels/conv_depthwise" AVX AVX2 RVV LASX)
dnn: use dispatcher for Winograd
2024-09-17 18:28:51 +08:00
ocv_add_dispatched_file("layers/cpu_kernels/conv_winograd_f63" AVX AVX2 NEON NEON_FP16)
dnn: add gemm_layer in place of fully_connected_layer for onnx models (#23897) * first commit * turned C from input to constant; force C constant in impl; better handling 0d/1d cases * integrate with gemm from ficus nn * fix const inputs * adjust threshold for int8 tryQuantize * adjust threshold for int8 quantized 2 * support batched gemm and matmul; tune threshold for rcnn_ilsvrc13; update googlenet * add gemm perf against innerproduct * add perf tests for innerproduct with bias * fix perf * add memset * renamings for next step * add dedicated perf gemm * add innerproduct in perf_gemm * remove gemm and innerproduct perf tests from perf_layer * add perf cases for vit sizes; prepack constants * remove batched gemm; fix wrong trans; optimize KC * remove prepacking for const A; several fixes for const B prepacking * add todos and gemm expression * add optimized branch for avx/avx2 * trigger build * update macros and signature * update signature * fix macro * fix bugs for neon aarch64 & x64 * add backends: cuda, cann, inf_ngraph and vkcom * fix cuda backend * test commit for cuda * test cuda backend * remove debug message from cuda backend * use cpu dispatcher * fix neon macro undef in dispatcher * fix dispatcher * fix inner kernel for neon aarch64 * fix compiling issue on armv7; try fixing accuracy issue on other platforms * broadcast C with beta multiplied; improve func namings * fix bug for avx and avx2 * put all platform-specific kernels in dispatcher * fix typos * attempt to fix compile issues on x64 * run old gemm when neon, avx, avx2 are all not available; add kernel for armv7 neon * fix typo * quick fix: add macros for pack4 * quick fix: use vmlaq_f32 for armv7 * quick fix for missing macro of fast gemm pack f32 4 * disable conformance tests when optimized branches are not supported * disable perf tests when optimized branches are not supported * decouple cv_try_neon and cv_neon_aarch64 * drop googlenet_2023; add fastGemmBatched * fix step in fastGemmBatched * cpu: fix initialization ofb; gpu: support batch * quick followup fix for cuda * add default kernels * quick followup fix to avoid macro redef * optmized kernels for lasx * resolve mis-alignment; remove comments * tune performance for x64 platform * tune performance for neon aarch64 * tune for armv7 * comment time consuming tests * quick follow-up fix
2023-09-20 05:53:34 +08:00
ocv_add_dispatched_file_force_all("layers/cpu_kernels/fast_gemm_kernels" AVX AVX2 NEON LASX)
dnn: cleanup dispatched code, fix SIMD128 types
2017-07-13 23:42:36 +08:00
Merge pull request #17165 from komakai:objc-binding Objc binding * Initial work on Objective-C wrapper * Objective-C generator script; update manually generated wrappers * Add Mat tests * Core Tests * Imgproc wrapper generation and tests * Fixes for Imgcodecs wrapper * Miscellaneous fixes. Swift build support * Objective-C wrapper build/install * Add Swift wrappers for videoio/objdetect/feature2d * Framework build;iOS support * Fix toArray functions;Use enum types whenever possible * Use enum types where possible;prepare test build * Update test * Add test runner scripts for iOS and macOS * Add test scripts and samples * Build fixes * Fix build (cmake 3.17.x compatibility) * Fix warnings * Fix enum name conflicting handling * Add support for document generation with Jazzy * Swift/Native fast accessor functions * Add Objective-C wrapper for calib3d, dnn, ml, photo and video modules * Remove IntOut/FloatOut/DoubleOut classes * Fix iOS default test platform value * Fix samples * Revert default framework name to opencv2 * Add converter util functions * Fix failing test * Fix whitespace * Add handling for deprecated methods;fix warnings;define __OPENCV_BUILD * Suppress cmake warnings * Reduce severity of "jazzy not found" log message * Fix incorrect #include of compatibility header in ios.h * Use explicit returns in subscript/get implementation * Reduce minimum required cmake version to 3.15 for Objective-C/Swift binding
2020-06-09 02:32:53 +08:00
ocv_add_module(dnn opencv_core opencv_imgproc WRAP python java objc js)
dnn: make OpenCL DNN code optional
2018-02-28 18:58:55 +08:00
dnn: plugin support for OpenVINO
2022-03-18 14:19:30 +08:00
include(${CMAKE_CURRENT_LIST_DIR}/cmake/plugin.cmake)
temporarily disabled OpenCL use in DNN module on Mac (#11828) * temporarily disabled OpenCL use in DNN module on Mac, since some of the tests fail * disable OpenCL in DNN on Mac at CMake level, not source level (thanks to alalek for the advice)
2018-06-26 14:35:18 +08:00
ocv_option(OPENCV_DNN_OPENCL "Build with OpenCL support" HAVE_OPENCL AND NOT APPLE)
dnn: make OpenCL DNN code optional
2018-02-28 18:58:55 +08:00
if(OPENCV_DNN_OPENCL AND HAVE_OPENCL)
dnn: plugin support for OpenVINO
2022-03-18 14:19:30 +08:00
ocv_target_compile_definitions(${the_module} PRIVATE "CV_OCL4DNN=1")
dnn: make OpenCL DNN code optional
2018-02-28 18:58:55 +08:00
endif()
Merge pull request #20406 from MarkGHX:gsoc_2021_webnn [GSoC] OpenCV.js: Accelerate OpenCV.js DNN via WebNN * Add WebNN backend for OpenCV DNN Module Update dnn.cpp Update dnn.cpp Update dnn.cpp Update dnn.cpp Add WebNN head files into OpenCV 3rd partiy files Create webnn.hpp update cmake Complete README and add OpenCVDetectWebNN.cmake file add webnn.cpp Modify webnn.cpp Can successfully compile the codes for creating a MLContext Update webnn.cpp Update README.md Update README.md Update README.md Update README.md Update cmake files and update README.md Update OpenCVDetectWebNN.cmake and README.md Update OpenCVDetectWebNN.cmake Fix OpenCVDetectWebNN.cmake and update README.md Add source webnn_cpp.cpp and libary libwebnn_proc.so Update dnn.cpp Update dnn.cpp Update dnn.cpp Update dnn.cpp update dnn.cpp update op_webnn update op_webnn Update op_webnn.hpp update op_webnn.cpp & hpp Update op_webnn.hpp Update op_webnn update the skeleton Update op_webnn.cpp Update op_webnn Update op_webnn.cpp Update op_webnn.cpp Update op_webnn.hpp update op_webnn update op_webnn Solved the problems of released variables. Fixed the bugs in op_webnn.cpp Implement op_webnn Implement Relu by WebNN API Update dnn.cpp for better test Update elementwise_layers.cpp Implement ReLU6 Update elementwise_layers.cpp Implement SoftMax using WebNN API Implement Reshape by WebNN API Implement PermuteLayer by WebNN API Implement PoolingLayer using WebNN API Update pooling_layer.cpp Update pooling_layer.cpp Update pooling_layer.cpp Update pooling_layer.cpp Update pooling_layer.cpp Update pooling_layer.cpp Implement poolingLayer by WebNN API and add more detailed logs Update dnn.cpp Update dnn.cpp Remove redundant codes and add more logs for poolingLayer Add more logs in the pooling layer implementation Fix the indent issue and resolve the compiling issue Fix the build problems Fix the build issue FIx the build issue Update dnn.cpp Update dnn.cpp * Fix the build issue * Implement BatchNorm Layer by WebNN API * Update convolution_layer.cpp This is a temporary file for Conv2d layer implementation * Integrate some general functions into op_webnn.cpp&hpp * Update const_layer.cpp * Update convolution_layer.cpp Still have some bugs that should be fixed. * Update conv2d layer and fc layer still have some problems to be fixed. * update constLayer, conv layer, fc layer There are still some bugs to be fixed. * Fix the build issue * Update concat_layer.cpp Still have some bugs to be fixed. * Update conv2d layer, fully connected layer and const layer * Update convolution_layer.cpp * Add OpenCV.js DNN module WebNN Backend (both using webnn-polyfill and electron) * Delete bib19450.aux * Add WebNN backend for OpenCV DNN Module Update dnn.cpp Update dnn.cpp Update dnn.cpp Update dnn.cpp Add WebNN head files into OpenCV 3rd partiy files Create webnn.hpp update cmake Complete README and add OpenCVDetectWebNN.cmake file add webnn.cpp Modify webnn.cpp Can successfully compile the codes for creating a MLContext Update webnn.cpp Update README.md Update README.md Update README.md Update README.md Update cmake files and update README.md Update OpenCVDetectWebNN.cmake and README.md Update OpenCVDetectWebNN.cmake Fix OpenCVDetectWebNN.cmake and update README.md Add source webnn_cpp.cpp and libary libwebnn_proc.so Update dnn.cpp Update dnn.cpp Update dnn.cpp Update dnn.cpp update dnn.cpp update op_webnn update op_webnn Update op_webnn.hpp update op_webnn.cpp & hpp Update op_webnn.hpp Update op_webnn update the skeleton Update op_webnn.cpp Update op_webnn Update op_webnn.cpp Update op_webnn.cpp Update op_webnn.hpp update op_webnn update op_webnn Solved the problems of released variables. Fixed the bugs in op_webnn.cpp Implement op_webnn Implement Relu by WebNN API Update dnn.cpp for better test Update elementwise_layers.cpp Implement ReLU6 Update elementwise_layers.cpp Implement SoftMax using WebNN API Implement Reshape by WebNN API Implement PermuteLayer by WebNN API Implement PoolingLayer using WebNN API Update pooling_layer.cpp Update pooling_layer.cpp Update pooling_layer.cpp Update pooling_layer.cpp Update pooling_layer.cpp Update pooling_layer.cpp Implement poolingLayer by WebNN API and add more detailed logs Update dnn.cpp Update dnn.cpp Remove redundant codes and add more logs for poolingLayer Add more logs in the pooling layer implementation Fix the indent issue and resolve the compiling issue Fix the build problems Fix the build issue FIx the build issue Update dnn.cpp Update dnn.cpp * Fix the build issue * Implement BatchNorm Layer by WebNN API * Update convolution_layer.cpp This is a temporary file for Conv2d layer implementation * Integrate some general functions into op_webnn.cpp&hpp * Update const_layer.cpp * Update convolution_layer.cpp Still have some bugs that should be fixed. * Update conv2d layer and fc layer still have some problems to be fixed. * update constLayer, conv layer, fc layer There are still some bugs to be fixed. * Update conv2d layer, fully connected layer and const layer * Update convolution_layer.cpp * Add OpenCV.js DNN module WebNN Backend (both using webnn-polyfill and electron) * Update dnn.cpp * Fix Error in dnn.cpp * Resolve duplication in conditions in convolution_layer.cpp * Fixed the issues in the comments * Fix building issue * Update tutorial * Fixed comments * Address the comments * Update CMakeLists.txt * Offer more accurate perf test on native * Add better perf tests for both native and web * Modify per tests for better results * Use more latest version of Electron * Support latest WebNN Clamp op * Add definition of HAVE_WEBNN macro * Support group convolution * Implement Scale_layer using WebNN * Add Softmax option for native classification example * Fix comments * Fix comments
2021-11-24 05:15:31 +08:00
if(WITH_WEBNN AND HAVE_WEBNN)
dnn: plugin support for OpenVINO
2022-03-18 14:19:30 +08:00
ocv_target_compile_definitions(${the_module} PRIVATE "HAVE_WEBNN=1")
Merge pull request #20406 from MarkGHX:gsoc_2021_webnn [GSoC] OpenCV.js: Accelerate OpenCV.js DNN via WebNN * Add WebNN backend for OpenCV DNN Module Update dnn.cpp Update dnn.cpp Update dnn.cpp Update dnn.cpp Add WebNN head files into OpenCV 3rd partiy files Create webnn.hpp update cmake Complete README and add OpenCVDetectWebNN.cmake file add webnn.cpp Modify webnn.cpp Can successfully compile the codes for creating a MLContext Update webnn.cpp Update README.md Update README.md Update README.md Update README.md Update cmake files and update README.md Update OpenCVDetectWebNN.cmake and README.md Update OpenCVDetectWebNN.cmake Fix OpenCVDetectWebNN.cmake and update README.md Add source webnn_cpp.cpp and libary libwebnn_proc.so Update dnn.cpp Update dnn.cpp Update dnn.cpp Update dnn.cpp update dnn.cpp update op_webnn update op_webnn Update op_webnn.hpp update op_webnn.cpp & hpp Update op_webnn.hpp Update op_webnn update the skeleton Update op_webnn.cpp Update op_webnn Update op_webnn.cpp Update op_webnn.cpp Update op_webnn.hpp update op_webnn update op_webnn Solved the problems of released variables. Fixed the bugs in op_webnn.cpp Implement op_webnn Implement Relu by WebNN API Update dnn.cpp for better test Update elementwise_layers.cpp Implement ReLU6 Update elementwise_layers.cpp Implement SoftMax using WebNN API Implement Reshape by WebNN API Implement PermuteLayer by WebNN API Implement PoolingLayer using WebNN API Update pooling_layer.cpp Update pooling_layer.cpp Update pooling_layer.cpp Update pooling_layer.cpp Update pooling_layer.cpp Update pooling_layer.cpp Implement poolingLayer by WebNN API and add more detailed logs Update dnn.cpp Update dnn.cpp Remove redundant codes and add more logs for poolingLayer Add more logs in the pooling layer implementation Fix the indent issue and resolve the compiling issue Fix the build problems Fix the build issue FIx the build issue Update dnn.cpp Update dnn.cpp * Fix the build issue * Implement BatchNorm Layer by WebNN API * Update convolution_layer.cpp This is a temporary file for Conv2d layer implementation * Integrate some general functions into op_webnn.cpp&hpp * Update const_layer.cpp * Update convolution_layer.cpp Still have some bugs that should be fixed. * Update conv2d layer and fc layer still have some problems to be fixed. * update constLayer, conv layer, fc layer There are still some bugs to be fixed. * Fix the build issue * Update concat_layer.cpp Still have some bugs to be fixed. * Update conv2d layer, fully connected layer and const layer * Update convolution_layer.cpp * Add OpenCV.js DNN module WebNN Backend (both using webnn-polyfill and electron) * Delete bib19450.aux * Add WebNN backend for OpenCV DNN Module Update dnn.cpp Update dnn.cpp Update dnn.cpp Update dnn.cpp Add WebNN head files into OpenCV 3rd partiy files Create webnn.hpp update cmake Complete README and add OpenCVDetectWebNN.cmake file add webnn.cpp Modify webnn.cpp Can successfully compile the codes for creating a MLContext Update webnn.cpp Update README.md Update README.md Update README.md Update README.md Update cmake files and update README.md Update OpenCVDetectWebNN.cmake and README.md Update OpenCVDetectWebNN.cmake Fix OpenCVDetectWebNN.cmake and update README.md Add source webnn_cpp.cpp and libary libwebnn_proc.so Update dnn.cpp Update dnn.cpp Update dnn.cpp Update dnn.cpp update dnn.cpp update op_webnn update op_webnn Update op_webnn.hpp update op_webnn.cpp & hpp Update op_webnn.hpp Update op_webnn update the skeleton Update op_webnn.cpp Update op_webnn Update op_webnn.cpp Update op_webnn.cpp Update op_webnn.hpp update op_webnn update op_webnn Solved the problems of released variables. Fixed the bugs in op_webnn.cpp Implement op_webnn Implement Relu by WebNN API Update dnn.cpp for better test Update elementwise_layers.cpp Implement ReLU6 Update elementwise_layers.cpp Implement SoftMax using WebNN API Implement Reshape by WebNN API Implement PermuteLayer by WebNN API Implement PoolingLayer using WebNN API Update pooling_layer.cpp Update pooling_layer.cpp Update pooling_layer.cpp Update pooling_layer.cpp Update pooling_layer.cpp Update pooling_layer.cpp Implement poolingLayer by WebNN API and add more detailed logs Update dnn.cpp Update dnn.cpp Remove redundant codes and add more logs for poolingLayer Add more logs in the pooling layer implementation Fix the indent issue and resolve the compiling issue Fix the build problems Fix the build issue FIx the build issue Update dnn.cpp Update dnn.cpp * Fix the build issue * Implement BatchNorm Layer by WebNN API * Update convolution_layer.cpp This is a temporary file for Conv2d layer implementation * Integrate some general functions into op_webnn.cpp&hpp * Update const_layer.cpp * Update convolution_layer.cpp Still have some bugs that should be fixed. * Update conv2d layer and fc layer still have some problems to be fixed. * update constLayer, conv layer, fc layer There are still some bugs to be fixed. * Update conv2d layer, fully connected layer and const layer * Update convolution_layer.cpp * Add OpenCV.js DNN module WebNN Backend (both using webnn-polyfill and electron) * Update dnn.cpp * Fix Error in dnn.cpp * Resolve duplication in conditions in convolution_layer.cpp * Fixed the issues in the comments * Fix building issue * Update tutorial * Fixed comments * Address the comments * Update CMakeLists.txt * Offer more accurate perf test on native * Add better perf tests for both native and web * Modify per tests for better results * Use more latest version of Electron * Support latest WebNN Clamp op * Add definition of HAVE_WEBNN macro * Support group convolution * Implement Scale_layer using WebNN * Add Softmax option for native classification example * Fix comments * Fix comments
2021-11-24 05:15:31 +08:00
endif()
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>
2022-04-01 05:42:11 +08:00
if(HAVE_TIMVX)
dnn: plugin support for OpenVINO
2022-03-18 14:19:30 +08:00
ocv_target_compile_definitions(${the_module} PRIVATE "HAVE_TIMVX=1")
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>
2022-04-01 05:42:11 +08:00
endif()
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 14:04:41 +08:00
if(HAVE_CANN)
ocv_target_compile_definitions(${the_module} PRIVATE "HAVE_CANN=1")
endif()
Merge pull request #17499 from cyyever:fix_CUDA11 Fix cuda11 * use cudnn_version.h to detect version when it is available * remove nppi from CUDA11 * use ocv_list_filterout * dnn(cuda): temporary disable CUDNN 8.0
2020-06-28 04:34:44 +08:00
ocv_option(OPENCV_DNN_CUDA "Build with CUDA support"
HAVE_CUDA
AND HAVE_CUBLAS
AND HAVE_CUDNN
)
Merge pull request #14660 from YashasSamaga:dnn-cuda-build add cuDNN dependency and setup build for cuda4dnn (#14660) * update cmake for cuda4dnn - Adds FindCUDNN - Adds new options: * WITH_CUDA * OPENCV_DNN_CUDA - Adds CUDA4DNN preprocessor symbol for the DNN module * FIX: append EXCLUDE_CUDA instead of overwrite * remove cuDNN dependency for user apps * fix unused variable warning
2019-06-02 19:47:15 +08:00
error if cuda4dnn depends are not resolved
2020-07-09 20:31:33 +08:00
if(OPENCV_DNN_CUDA)
if(HAVE_CUDA AND HAVE_CUBLAS AND HAVE_CUDNN)
dnn: plugin support for OpenVINO
2022-03-18 14:19:30 +08:00
ocv_target_compile_definitions(${the_module} PRIVATE "CV_CUDA4DNN=1")
error if cuda4dnn depends are not resolved
2020-07-09 20:31:33 +08:00
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()
Merge pull request #14660 from YashasSamaga:dnn-cuda-build add cuDNN dependency and setup build for cuda4dnn (#14660) * update cmake for cuda4dnn - Adds FindCUDNN - Adds new options: * WITH_CUDA * OPENCV_DNN_CUDA - Adds CUDA4DNN preprocessor symbol for the DNN module * FIX: append EXCLUDE_CUDA instead of overwrite * remove cuDNN dependency for user apps * fix unused variable warning
2019-06-02 19:47:15 +08:00
endif()
dnn: plugin support for OpenVINO
2022-03-18 14:19:30 +08:00
Merge pull request #14660 from YashasSamaga:dnn-cuda-build add cuDNN dependency and setup build for cuda4dnn (#14660) * update cmake for cuda4dnn - Adds FindCUDNN - Adds new options: * WITH_CUDA * OPENCV_DNN_CUDA - Adds CUDA4DNN preprocessor symbol for the DNN module * FIX: append EXCLUDE_CUDA instead of overwrite * remove cuDNN dependency for user apps * fix unused variable warning
2019-06-02 19:47:15 +08:00
ocv_cmake_hook_append(INIT_MODULE_SOURCES_opencv_dnn "${CMAKE_CURRENT_LIST_DIR}/cmake/hooks/INIT_MODULE_SOURCES_opencv_dnn.cmake")
dnn: plugin support for OpenVINO
2022-03-18 14:19:30 +08:00
dnn: move module from opencv_contrib https://github.com/opencv/opencv_contrib/tree/e6f63c7a38ca40c5dc33e38736e3027e3528d6cb/modules/dnn
2017-06-26 18:35:51 +08:00
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
)
build: eliminate C4309 warning from protobuf files with MSVS2017
2021-11-30 12:27:39 +08:00
if(MSVC_VERSION LESS 1920) # MSVS 2015/2017, .pb.cc generated files
Merge pull request #20998 from alalek:update_protobuf_3.19.1 3rdparty(protobuf): upgrade 3.5.2 => 3.19.1 * 3rdparty(protobuf): upgrade 3.5.2 => 3.19.1 * dnn: update protobuf files (3.19.1) * 3rdparty(protobuf): re-apply OpenCV patch for custom fields (3.19.1) * protobuf: suppress new build warnings * protobuf: remove unused files
2021-11-10 20:03:45 +08:00
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()
dnn: move module from opencv_contrib https://github.com/opencv/opencv_contrib/tree/e6f63c7a38ca40c5dc33e38736e3027e3528d6cb/modules/dnn
2017-06-26 18:35:51 +08:00
else()
ocv_warnings_disable(CMAKE_CXX_FLAGS -Wno-deprecated -Wmissing-prototypes -Wmissing-declarations -Wshadow
dnn: reduce set of ignored warnings
2018-11-15 04:25:23 +08:00
-Wunused-parameter -Wsign-compare
dnn: move module from opencv_contrib https://github.com/opencv/opencv_contrib/tree/e6f63c7a38ca40c5dc33e38736e3027e3528d6cb/modules/dnn
2017-06-26 18:35:51 +08:00
)
endif()
suppress noisy warning * add -Wno-psabi when using GCC 6 * add -Wundef for CUDA 10 * add -Wdeprecated-declarations when using GCC 7 * add -Wstrict-aliasing and -Wtautological-compare for GCC 7 * replace cudaThreadSynchronize with cudaDeviceSynchronize
2019-08-08 20:49:32 +08:00
if(HAVE_CUDA)
ocv_warnings_disable(CMAKE_CXX_FLAGS -Wundef)
endif()
dnn: reduce set of ignored warnings
2018-11-15 04:25:23 +08:00
if(NOT HAVE_CXX11)
ocv_warnings_disable(CMAKE_CXX_FLAGS -Wno-undef) # LANG_CXX11 from protobuf files
endif()
dnn: move module from opencv_contrib https://github.com/opencv/opencv_contrib/tree/e6f63c7a38ca40c5dc33e38736e3027e3528d6cb/modules/dnn
2017-06-26 18:35:51 +08:00
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()
dnn: allow to use external protobuf "custom layers" feature will not work properly in these builds.
2018-07-09 22:19:35 +08:00
if(NOT BUILD_PROTOBUF)
dnn: plugin support for OpenVINO
2022-03-18 14:19:30 +08:00
ocv_target_compile_definitions(${the_module} PRIVATE "OPENCV_DNN_EXTERNAL_PROTOBUF=1")
dnn: allow to use external protobuf "custom layers" feature will not work properly in these builds.
2018-07-09 22:19:35 +08:00
endif()
Misc. modules/ typos Found via `codespell`
2018-02-12 20:07:39 +08:00
#suppress warnings in autogenerated caffe.pb.* files
dnn: move module from opencv_contrib https://github.com/opencv/opencv_contrib/tree/e6f63c7a38ca40c5dc33e38736e3027e3528d6cb/modules/dnn
2017-06-26 18:35:51 +08:00
ocv_warnings_disable(CMAKE_CXX_FLAGS
/wd4125 /wd4267 /wd4127 /wd4244 /wd4512 /wd4702
/wd4456 /wd4510 /wd4610 /wd4800
dnn: protobuf build warnings
2018-01-16 03:49:36 +08:00
/wd4701 /wd4703 # potentially uninitialized local/pointer variable 'value' used
/wd4505 # unreferenced local function has been removed
Merge pull request #16088 from alalek:dnn_eltwise_layer_different_src_channels dnn(eltwise): fix handling of different number of channels * dnn(test): reproducer for Eltwise layer issue from PR16063 * dnn(eltwise): rework support for inputs with different channels * dnn(eltwise): get rid of finalize(), variableChannels * dnn(eltwise): update input sorting by number of channels - do not swap inputs if number of channels are same after truncation * dnn(test): skip "shortcut" with batch size 2 on MYRIAD targets
2019-12-12 01:16:58 +08:00
/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
dnn: move module from opencv_contrib https://github.com/opencv/opencv_contrib/tree/e6f63c7a38ca40c5dc33e38736e3027e3528d6cb/modules/dnn
2017-06-26 18:35:51 +08:00
-wd858 -wd2196
dnn: protobuf build warnings
2018-01-16 03:49:36 +08:00
-Winvalid-offsetof # Apple Clang (attr_value.pb.cc)
dnn: move module from opencv_contrib https://github.com/opencv/opencv_contrib/tree/e6f63c7a38ca40c5dc33e38736e3027e3528d6cb/modules/dnn
2017-06-26 18:35:51 +08:00
)
cmake: fix protobuf handling
2020-04-26 12:42:42 +08:00
set(include_dirs "")
set(libs "")
Merge pull request #23604 from dkurt:dnn_no_protobuf Build DNN without Protobuf DNN module can be built without Protobuf for Darknet, TFLite, OpenVINO, Torch (not PyTorch) models. ``` cmake \ -DCMAKE_BUILD_TYPE=Release \ -DBUILD_LIST=dnn \ -DWITH_PROTOBUF=OFF \ -DWITH_OPENCL=OFF 7.1M lib/libopencv_dnn.so.4.7.0 ``` ``` cmake \ -DCMAKE_BUILD_TYPE=Release \ -DBUILD_LIST=dnn \ -DWITH_OPENCL=OFF 3.9M lib/libopencv_dnn.so.4.7.0 ``` ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake
2023-05-15 17:23:18 +08:00
if(HAVE_PROTOBUF)
ocv_target_compile_definitions(${the_module} PRIVATE "HAVE_PROTOBUF=1")
if(PROTOBUF_UPDATE_FILES)
file(GLOB proto_files "${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()
dnn: move module from opencv_contrib https://github.com/opencv/opencv_contrib/tree/e6f63c7a38ca40c5dc33e38736e3027e3528d6cb/modules/dnn
2017-06-26 18:35:51 +08:00
endif()
dnn(tflite): add 3rdparty flatbuffers with pre-generated schema
2023-02-19 00:21:07 +08:00
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()
cmake: fix protobuf handling
2020-04-26 12:42:42 +08:00
list(APPEND include_dirs ${fw_inc})
list(APPEND libs ${Protobuf_LIBRARIES})
if(NOT BUILD_PROTOBUF)
list(APPEND include_dirs ${Protobuf_INCLUDE_DIRS})
endif()
Merge pull request #15537 from l-bat:ngraph * Support nGraph * Fix resize
2019-12-02 21:16:06 +08:00
dnn: make OpenCL DNN code optional
2018-02-28 18:58:55 +08:00
set(sources_options "")
Merge pull request #14660 from YashasSamaga:dnn-cuda-build add cuDNN dependency and setup build for cuda4dnn (#14660) * update cmake for cuda4dnn - Adds FindCUDNN - Adds new options: * WITH_CUDA * OPENCV_DNN_CUDA - Adds CUDA4DNN preprocessor symbol for the DNN module * FIX: append EXCLUDE_CUDA instead of overwrite * remove cuDNN dependency for user apps * fix unused variable warning
2019-06-02 19:47:15 +08:00
cmake: fix protobuf handling
2020-04-26 12:42:42 +08:00
list(APPEND libs ${LAPACK_LIBRARIES})
dnn: make OpenCL DNN code optional
2018-02-28 18:58:55 +08:00
if(OPENCV_DNN_OPENCL AND HAVE_OPENCL)
list(APPEND include_dirs ${OPENCL_INCLUDE_DIRS})
else()
set(sources_options EXCLUDE_OPENCL)
endif()
Update OpenCVDetectInferenceEngine.cmake
2018-03-28 21:34:37 +08:00
Merge pull request #14660 from YashasSamaga:dnn-cuda-build add cuDNN dependency and setup build for cuda4dnn (#14660) * update cmake for cuda4dnn - Adds FindCUDNN - Adds new options: * WITH_CUDA * OPENCV_DNN_CUDA - Adds CUDA4DNN preprocessor symbol for the DNN module * FIX: append EXCLUDE_CUDA instead of overwrite * remove cuDNN dependency for user apps * fix unused variable warning
2019-06-02 19:47:15 +08:00
if(OPENCV_DNN_CUDA AND HAVE_CUDA AND HAVE_CUBLAS AND HAVE_CUDNN)
list(APPEND include_dirs ${CUDA_TOOLKIT_INCLUDE} ${CUDNN_INCLUDE_DIRS})
Merge pull request #14827 from YashasSamaga:cuda4dnn-csl-low CUDA backend for the DNN module * stub cuda4dnn design * minor fixes for tests and doxygen * add csl public api directory to module headers * add low-level CSL components * add high-level CSL components * integrate csl::Tensor into backbone code * switch to CPU iff unsupported; otherwise, fail on error * add fully connected layer * add softmax layer * add activation layers * support arbitary rank TensorDescriptor * pass input wrappers to `initCUDA()` * add 1d/2d/3d-convolution * add pooling layer * reorganize and refactor code * fixes for gcc, clang and doxygen; remove cxx14/17 code * add blank_layer * add LRN layer * add rounding modes for pooling layer * split tensor.hpp into tensor.hpp and tensor_ops.hpp * add concat layer * add scale layer * add batch normalization layer * split math.cu into activations.cu and math.hpp * add eltwise layer * add flatten layer * add tensor transform api * add asymmetric padding support for convolution layer * add reshape layer * fix rebase issues * add permute layer * add padding support for concat layer * refactor and reorganize code * add normalize layer * optimize bias addition in scale layer * add prior box layer * fix and optimize normalize layer * add asymmetric padding support for pooling layer * add event API * improve pooling performance for some padding scenarios * avoid over-allocation of compute resources to kernels * improve prior box performance * enable layer fusion * add const layer * add resize layer * add slice layer * add padding layer * add deconvolution layer * fix channelwise ReLU initialization * add vector traits * add vectorized versions of relu, clipped_relu, power * add vectorized concat kernels * improve concat_with_offsets performance * vectorize scale and bias kernels * add support for multi-billion element tensors * vectorize prior box kernels * fix address alignment check * improve bias addition performance of conv/deconv/fc layers * restructure code for supporting multiple targets * add DNN_TARGET_CUDA_FP64 * add DNN_TARGET_FP16 * improve vectorization * add region layer * improve tensor API, add dynamic ranks 1. use ManagedPtr instead of a Tensor in backend wrapper 2. add new methods to tensor classes - size_range: computes the combined size of for a given axis range - tensor span/view can be constructed from a raw pointer and shape 3. the tensor classes can change their rank at runtime (previously rank was fixed at compile-time) 4. remove device code from tensor classes (as they are unused) 5. enforce strict conditions on tensor class APIs to improve debugging ability * fix parametric relu activation * add squeeze/unsqueeze tensor API * add reorg layer * optimize permute and enable 2d permute * enable 1d and 2d slice * add split layer * add shuffle channel layer * allow tensors of different ranks in reshape primitive * patch SliceOp to allow Crop Layer * allow extra shape inputs in reshape layer * use `std::move_backward` instead of `std::move` for insert in resizable_static_array * improve workspace management * add spatial LRN * add nms (cpu) to region layer * add max pooling with argmax ( and a fix to limits.hpp) * add max unpooling layer * rename DNN_TARGET_CUDA_FP32 to DNN_TARGET_CUDA * update supportBackend to be more rigorous * remove stray include from preventing non-cuda build * include op_cuda.hpp outside condition #if * refactoring, fixes and many optimizations * drop DNN_TARGET_CUDA_FP64 * fix gcc errors * increase max. tensor rank limit to six * add Interp layer * drop custom layers; use BackendNode * vectorize activation kernels * fixes for gcc * remove wrong assertion * fix broken assertion in unpooling primitive * fix build errors in non-CUDA build * completely remove workspace from public API * fix permute layer * enable accuracy and perf. tests for DNN_TARGET_CUDA * add asynchronous forward * vectorize eltwise ops * vectorize fill kernel * fixes for gcc * remove CSL headers from public API * remove csl header source group from cmake * update min. cudnn version in cmake * add numerically stable FP32 log1pexp * refactor code * add FP16 specialization to cudnn based tensor addition * vectorize scale1 and bias1 + minor refactoring * fix doxygen build * fix invalid alignment assertion * clear backend wrappers before allocateLayers * ignore memory lock failures * do not allocate internal blobs * integrate NVTX * add numerically stable half precision log1pexp * fix indentation, following coding style, improve docs * remove accidental modification of IE code * Revert "add asynchronous forward" This reverts commit 1154b9da9da07e9b52f8a81bdcea48cf31c56f70. * [cmake] throw error for unsupported CC versions * fix rebase issues * add more docs, refactor code, fix bugs * minor refactoring and fixes * resolve warnings/errors from clang * remove haveCUDA() checks from supportBackend() * remove NVTX integration * changes based on review comments * avoid exception when no CUDA device is present * add color code for CUDA in Net::dump
2019-10-21 19:28:00 +08:00
set(CC_LIST ${CUDA_ARCH_BIN})
separate_arguments(CC_LIST)
foreach(cc ${CC_LIST})
Merge pull request #16218 from JulienMaille:cuda-dnn-for-older-gpus Enable cuda4dnn on hardware without support for __half * Enable cuda4dnn on hardware without support for half (ie. compute capability < 5.3) Update CMakeLists.txt Lowered minimum CC to 3.0 * UPD: added ifdef on new copy kernel * added fp16 support detection at runtime * Clarified #if condition on atomicAdd definition * More explicit CMake error message
2020-01-15 23:28:37 +08:00
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.")
Merge pull request #14827 from YashasSamaga:cuda4dnn-csl-low CUDA backend for the DNN module * stub cuda4dnn design * minor fixes for tests and doxygen * add csl public api directory to module headers * add low-level CSL components * add high-level CSL components * integrate csl::Tensor into backbone code * switch to CPU iff unsupported; otherwise, fail on error * add fully connected layer * add softmax layer * add activation layers * support arbitary rank TensorDescriptor * pass input wrappers to `initCUDA()` * add 1d/2d/3d-convolution * add pooling layer * reorganize and refactor code * fixes for gcc, clang and doxygen; remove cxx14/17 code * add blank_layer * add LRN layer * add rounding modes for pooling layer * split tensor.hpp into tensor.hpp and tensor_ops.hpp * add concat layer * add scale layer * add batch normalization layer * split math.cu into activations.cu and math.hpp * add eltwise layer * add flatten layer * add tensor transform api * add asymmetric padding support for convolution layer * add reshape layer * fix rebase issues * add permute layer * add padding support for concat layer * refactor and reorganize code * add normalize layer * optimize bias addition in scale layer * add prior box layer * fix and optimize normalize layer * add asymmetric padding support for pooling layer * add event API * improve pooling performance for some padding scenarios * avoid over-allocation of compute resources to kernels * improve prior box performance * enable layer fusion * add const layer * add resize layer * add slice layer * add padding layer * add deconvolution layer * fix channelwise ReLU initialization * add vector traits * add vectorized versions of relu, clipped_relu, power * add vectorized concat kernels * improve concat_with_offsets performance * vectorize scale and bias kernels * add support for multi-billion element tensors * vectorize prior box kernels * fix address alignment check * improve bias addition performance of conv/deconv/fc layers * restructure code for supporting multiple targets * add DNN_TARGET_CUDA_FP64 * add DNN_TARGET_FP16 * improve vectorization * add region layer * improve tensor API, add dynamic ranks 1. use ManagedPtr instead of a Tensor in backend wrapper 2. add new methods to tensor classes - size_range: computes the combined size of for a given axis range - tensor span/view can be constructed from a raw pointer and shape 3. the tensor classes can change their rank at runtime (previously rank was fixed at compile-time) 4. remove device code from tensor classes (as they are unused) 5. enforce strict conditions on tensor class APIs to improve debugging ability * fix parametric relu activation * add squeeze/unsqueeze tensor API * add reorg layer * optimize permute and enable 2d permute * enable 1d and 2d slice * add split layer * add shuffle channel layer * allow tensors of different ranks in reshape primitive * patch SliceOp to allow Crop Layer * allow extra shape inputs in reshape layer * use `std::move_backward` instead of `std::move` for insert in resizable_static_array * improve workspace management * add spatial LRN * add nms (cpu) to region layer * add max pooling with argmax ( and a fix to limits.hpp) * add max unpooling layer * rename DNN_TARGET_CUDA_FP32 to DNN_TARGET_CUDA * update supportBackend to be more rigorous * remove stray include from preventing non-cuda build * include op_cuda.hpp outside condition #if * refactoring, fixes and many optimizations * drop DNN_TARGET_CUDA_FP64 * fix gcc errors * increase max. tensor rank limit to six * add Interp layer * drop custom layers; use BackendNode * vectorize activation kernels * fixes for gcc * remove wrong assertion * fix broken assertion in unpooling primitive * fix build errors in non-CUDA build * completely remove workspace from public API * fix permute layer * enable accuracy and perf. tests for DNN_TARGET_CUDA * add asynchronous forward * vectorize eltwise ops * vectorize fill kernel * fixes for gcc * remove CSL headers from public API * remove csl header source group from cmake * update min. cudnn version in cmake * add numerically stable FP32 log1pexp * refactor code * add FP16 specialization to cudnn based tensor addition * vectorize scale1 and bias1 + minor refactoring * fix doxygen build * fix invalid alignment assertion * clear backend wrappers before allocateLayers * ignore memory lock failures * do not allocate internal blobs * integrate NVTX * add numerically stable half precision log1pexp * fix indentation, following coding style, improve docs * remove accidental modification of IE code * Revert "add asynchronous forward" This reverts commit 1154b9da9da07e9b52f8a81bdcea48cf31c56f70. * [cmake] throw error for unsupported CC versions * fix rebase issues * add more docs, refactor code, fix bugs * minor refactoring and fixes * resolve warnings/errors from clang * remove haveCUDA() checks from supportBackend() * remove NVTX integration * changes based on review comments * avoid exception when no CUDA device is present * add color code for CUDA in Net::dump
2019-10-21 19:28:00 +08:00
endif()
endforeach()
unset(CC_LIST)
build: first class cuda support
2022-11-29 14:25:26 +08:00
if(ENABLE_CUDA_FIRST_CLASS_LANGUAGE)
list(APPEND libs ${CUDNN_LIBRARIES} CUDA::cublas${CUDA_LIB_EXT})
if(NOT CUDA_VERSION VERSION_LESS 10.1)
list(APPEND libs CUDA::cublasLt${CUDA_LIB_EXT})
endif()
endif()
Merge pull request #14660 from YashasSamaga:dnn-cuda-build add cuDNN dependency and setup build for cuda4dnn (#14660) * update cmake for cuda4dnn - Adds FindCUDNN - Adds new options: * WITH_CUDA * OPENCV_DNN_CUDA - Adds CUDA4DNN preprocessor symbol for the DNN module * FIX: append EXCLUDE_CUDA instead of overwrite * remove cuDNN dependency for user apps * fix unused variable warning
2019-06-02 19:47:15 +08:00
else()
set(sources_options ${sources_options} EXCLUDE_CUDA)
endif()
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>
2022-04-01 05:42:11 +08:00
if(HAVE_TIMVX)
list(APPEND include_dirs ${TIMVX_INCLUDE_DIR})
list(APPEND libs -Wl,--whole-archive ${TIMVX_LIBRARY} -Wl,--no-whole-archive)
endif()
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 14:04:41 +08:00
if(HAVE_CANN)
list(APPEND include_dirs ${CANN_INCLUDE_DIRS})
list(APPEND libs -Wl,--whole-archive ${CANN_LIBRARIES} -Wl,--no-whole-archive)
endif()
Merge pull request #20406 from MarkGHX:gsoc_2021_webnn [GSoC] OpenCV.js: Accelerate OpenCV.js DNN via WebNN * Add WebNN backend for OpenCV DNN Module Update dnn.cpp Update dnn.cpp Update dnn.cpp Update dnn.cpp Add WebNN head files into OpenCV 3rd partiy files Create webnn.hpp update cmake Complete README and add OpenCVDetectWebNN.cmake file add webnn.cpp Modify webnn.cpp Can successfully compile the codes for creating a MLContext Update webnn.cpp Update README.md Update README.md Update README.md Update README.md Update cmake files and update README.md Update OpenCVDetectWebNN.cmake and README.md Update OpenCVDetectWebNN.cmake Fix OpenCVDetectWebNN.cmake and update README.md Add source webnn_cpp.cpp and libary libwebnn_proc.so Update dnn.cpp Update dnn.cpp Update dnn.cpp Update dnn.cpp update dnn.cpp update op_webnn update op_webnn Update op_webnn.hpp update op_webnn.cpp & hpp Update op_webnn.hpp Update op_webnn update the skeleton Update op_webnn.cpp Update op_webnn Update op_webnn.cpp Update op_webnn.cpp Update op_webnn.hpp update op_webnn update op_webnn Solved the problems of released variables. Fixed the bugs in op_webnn.cpp Implement op_webnn Implement Relu by WebNN API Update dnn.cpp for better test Update elementwise_layers.cpp Implement ReLU6 Update elementwise_layers.cpp Implement SoftMax using WebNN API Implement Reshape by WebNN API Implement PermuteLayer by WebNN API Implement PoolingLayer using WebNN API Update pooling_layer.cpp Update pooling_layer.cpp Update pooling_layer.cpp Update pooling_layer.cpp Update pooling_layer.cpp Update pooling_layer.cpp Implement poolingLayer by WebNN API and add more detailed logs Update dnn.cpp Update dnn.cpp Remove redundant codes and add more logs for poolingLayer Add more logs in the pooling layer implementation Fix the indent issue and resolve the compiling issue Fix the build problems Fix the build issue FIx the build issue Update dnn.cpp Update dnn.cpp * Fix the build issue * Implement BatchNorm Layer by WebNN API * Update convolution_layer.cpp This is a temporary file for Conv2d layer implementation * Integrate some general functions into op_webnn.cpp&hpp * Update const_layer.cpp * Update convolution_layer.cpp Still have some bugs that should be fixed. * Update conv2d layer and fc layer still have some problems to be fixed. * update constLayer, conv layer, fc layer There are still some bugs to be fixed. * Fix the build issue * Update concat_layer.cpp Still have some bugs to be fixed. * Update conv2d layer, fully connected layer and const layer * Update convolution_layer.cpp * Add OpenCV.js DNN module WebNN Backend (both using webnn-polyfill and electron) * Delete bib19450.aux * Add WebNN backend for OpenCV DNN Module Update dnn.cpp Update dnn.cpp Update dnn.cpp Update dnn.cpp Add WebNN head files into OpenCV 3rd partiy files Create webnn.hpp update cmake Complete README and add OpenCVDetectWebNN.cmake file add webnn.cpp Modify webnn.cpp Can successfully compile the codes for creating a MLContext Update webnn.cpp Update README.md Update README.md Update README.md Update README.md Update cmake files and update README.md Update OpenCVDetectWebNN.cmake and README.md Update OpenCVDetectWebNN.cmake Fix OpenCVDetectWebNN.cmake and update README.md Add source webnn_cpp.cpp and libary libwebnn_proc.so Update dnn.cpp Update dnn.cpp Update dnn.cpp Update dnn.cpp update dnn.cpp update op_webnn update op_webnn Update op_webnn.hpp update op_webnn.cpp & hpp Update op_webnn.hpp Update op_webnn update the skeleton Update op_webnn.cpp Update op_webnn Update op_webnn.cpp Update op_webnn.cpp Update op_webnn.hpp update op_webnn update op_webnn Solved the problems of released variables. Fixed the bugs in op_webnn.cpp Implement op_webnn Implement Relu by WebNN API Update dnn.cpp for better test Update elementwise_layers.cpp Implement ReLU6 Update elementwise_layers.cpp Implement SoftMax using WebNN API Implement Reshape by WebNN API Implement PermuteLayer by WebNN API Implement PoolingLayer using WebNN API Update pooling_layer.cpp Update pooling_layer.cpp Update pooling_layer.cpp Update pooling_layer.cpp Update pooling_layer.cpp Update pooling_layer.cpp Implement poolingLayer by WebNN API and add more detailed logs Update dnn.cpp Update dnn.cpp Remove redundant codes and add more logs for poolingLayer Add more logs in the pooling layer implementation Fix the indent issue and resolve the compiling issue Fix the build problems Fix the build issue FIx the build issue Update dnn.cpp Update dnn.cpp * Fix the build issue * Implement BatchNorm Layer by WebNN API * Update convolution_layer.cpp This is a temporary file for Conv2d layer implementation * Integrate some general functions into op_webnn.cpp&hpp * Update const_layer.cpp * Update convolution_layer.cpp Still have some bugs that should be fixed. * Update conv2d layer and fc layer still have some problems to be fixed. * update constLayer, conv layer, fc layer There are still some bugs to be fixed. * Update conv2d layer, fully connected layer and const layer * Update convolution_layer.cpp * Add OpenCV.js DNN module WebNN Backend (both using webnn-polyfill and electron) * Update dnn.cpp * Fix Error in dnn.cpp * Resolve duplication in conditions in convolution_layer.cpp * Fixed the issues in the comments * Fix building issue * Update tutorial * Fixed comments * Address the comments * Update CMakeLists.txt * Offer more accurate perf test on native * Add better perf tests for both native and web * Modify per tests for better results * Use more latest version of Electron * Support latest WebNN Clamp op * Add definition of HAVE_WEBNN macro * Support group convolution * Implement Scale_layer using WebNN * Add Softmax option for native classification example * Fix comments * Fix comments
2021-11-24 05:15:31 +08:00
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 $ENV{WEBNN_NATIVE_DIR}/gen/src/webnn/webnn_cpp.cpp)
endif()
endif()
dnn: make OpenCL DNN code optional
2018-02-28 18:58:55 +08:00
ocv_module_include_directories(${include_dirs})
cmake: disable checks for protobuf generated files
2018-03-20 18:26:13 +08:00
if(CMAKE_CXX_COMPILER_ID STREQUAL "GNU")
ocv_append_source_files_cxx_compiler_options(fw_srcs "-Wno-suggest-override") # GCC
eliminate build warnings
2020-03-27 03:05:07 +08:00
ocv_append_source_files_cxx_compiler_options(fw_srcs "-Wno-array-bounds") # GCC 9.3.0 (Ubuntu 20.04)
cmake: disable checks for protobuf generated files
2018-03-20 18:26:13 +08:00
elseif(CMAKE_CXX_COMPILER_ID STREQUAL "Clang")
ocv_append_source_files_cxx_compiler_options(fw_srcs "-Wno-inconsistent-missing-override") # Clang
endif()
Merge pull request #15537 from l-bat:ngraph * Support nGraph * Fix resize
2019-12-02 21:16:06 +08:00
set(dnn_runtime_libs "")
dnn: drop legacy Inference Engine NN builder API
2022-02-09 22:25:08 +08:00
dnn: plugin support for OpenVINO
2022-03-18 14:19:30 +08:00
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} ${dnn_int_hdrs})
ocv_list_filterout_ex(dnn_plugin_srcs
Merge pull request #25931 from zihaomu:clean_code code clean #25931 Align code and remove redundant CMake code ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [ ] There is a reference to the original bug report and related work - [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [ ] The feature is well documented and sample code can be built with the project CMake
2024-07-18 22:18:37 +08:00
"/src/dnn.cpp$|/src/dnn_utils.cpp$|/src/dnn_read.cpp$|/src/registry.cpp$|/src/backend.cpp$"
dnn: plugin support for OpenVINO
2022-03-18 14:19:30 +08:00
# importers
"/src/(caffe|darknet|onnx|tensorflow|torch)/"
# executors
"/src/(cuda|cuda4dnn|ocl4dnn|vkcom|webnn)/"
)
dnn: drop legacy Inference Engine NN builder API
2022-02-09 22:25:08 +08:00
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()
dnn: plugin support for OpenVINO
2022-03-18 14:19:30 +08:00
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()
Merge pull request #15537 from l-bat:ngraph * Support nGraph * Fix resize
2019-12-02 21:16:06 +08:00
endif()
cmake: revise OPENCV_DNN_BACKEND_DEFAULT integration - disable message on default value
2023-09-10 21:11:01 +08:00
set(OPENCV_DNN_BACKEND_DEFAULT "" CACHE STRING "Default backend used by the DNN module (DNN_BACKEND_OPENCV if empty)")
if(OPENCV_DNN_BACKEND_DEFAULT)
ocv_append_source_file_compile_definitions("${CMAKE_CURRENT_LIST_DIR}/src/dnn_params.cpp" "OPENCV_DNN_BACKEND_DEFAULT=${OPENCV_DNN_BACKEND_DEFAULT}")
endif()
dnn(tflite): add 3rdparty flatbuffers with pre-generated schema
2023-02-19 00:21:07 +08:00
ocv_install_used_external_targets(${libs} ${dnn_runtime_libs})
Merge pull request #20406 from MarkGHX:gsoc_2021_webnn [GSoC] OpenCV.js: Accelerate OpenCV.js DNN via WebNN * Add WebNN backend for OpenCV DNN Module Update dnn.cpp Update dnn.cpp Update dnn.cpp Update dnn.cpp Add WebNN head files into OpenCV 3rd partiy files Create webnn.hpp update cmake Complete README and add OpenCVDetectWebNN.cmake file add webnn.cpp Modify webnn.cpp Can successfully compile the codes for creating a MLContext Update webnn.cpp Update README.md Update README.md Update README.md Update README.md Update cmake files and update README.md Update OpenCVDetectWebNN.cmake and README.md Update OpenCVDetectWebNN.cmake Fix OpenCVDetectWebNN.cmake and update README.md Add source webnn_cpp.cpp and libary libwebnn_proc.so Update dnn.cpp Update dnn.cpp Update dnn.cpp Update dnn.cpp update dnn.cpp update op_webnn update op_webnn Update op_webnn.hpp update op_webnn.cpp & hpp Update op_webnn.hpp Update op_webnn update the skeleton Update op_webnn.cpp Update op_webnn Update op_webnn.cpp Update op_webnn.cpp Update op_webnn.hpp update op_webnn update op_webnn Solved the problems of released variables. Fixed the bugs in op_webnn.cpp Implement op_webnn Implement Relu by WebNN API Update dnn.cpp for better test Update elementwise_layers.cpp Implement ReLU6 Update elementwise_layers.cpp Implement SoftMax using WebNN API Implement Reshape by WebNN API Implement PermuteLayer by WebNN API Implement PoolingLayer using WebNN API Update pooling_layer.cpp Update pooling_layer.cpp Update pooling_layer.cpp Update pooling_layer.cpp Update pooling_layer.cpp Update pooling_layer.cpp Implement poolingLayer by WebNN API and add more detailed logs Update dnn.cpp Update dnn.cpp Remove redundant codes and add more logs for poolingLayer Add more logs in the pooling layer implementation Fix the indent issue and resolve the compiling issue Fix the build problems Fix the build issue FIx the build issue Update dnn.cpp Update dnn.cpp * Fix the build issue * Implement BatchNorm Layer by WebNN API * Update convolution_layer.cpp This is a temporary file for Conv2d layer implementation * Integrate some general functions into op_webnn.cpp&hpp * Update const_layer.cpp * Update convolution_layer.cpp Still have some bugs that should be fixed. * Update conv2d layer and fc layer still have some problems to be fixed. * update constLayer, conv layer, fc layer There are still some bugs to be fixed. * Fix the build issue * Update concat_layer.cpp Still have some bugs to be fixed. * Update conv2d layer, fully connected layer and const layer * Update convolution_layer.cpp * Add OpenCV.js DNN module WebNN Backend (both using webnn-polyfill and electron) * Delete bib19450.aux * Add WebNN backend for OpenCV DNN Module Update dnn.cpp Update dnn.cpp Update dnn.cpp Update dnn.cpp Add WebNN head files into OpenCV 3rd partiy files Create webnn.hpp update cmake Complete README and add OpenCVDetectWebNN.cmake file add webnn.cpp Modify webnn.cpp Can successfully compile the codes for creating a MLContext Update webnn.cpp Update README.md Update README.md Update README.md Update README.md Update cmake files and update README.md Update OpenCVDetectWebNN.cmake and README.md Update OpenCVDetectWebNN.cmake Fix OpenCVDetectWebNN.cmake and update README.md Add source webnn_cpp.cpp and libary libwebnn_proc.so Update dnn.cpp Update dnn.cpp Update dnn.cpp Update dnn.cpp update dnn.cpp update op_webnn update op_webnn Update op_webnn.hpp update op_webnn.cpp & hpp Update op_webnn.hpp Update op_webnn update the skeleton Update op_webnn.cpp Update op_webnn Update op_webnn.cpp Update op_webnn.cpp Update op_webnn.hpp update op_webnn update op_webnn Solved the problems of released variables. Fixed the bugs in op_webnn.cpp Implement op_webnn Implement Relu by WebNN API Update dnn.cpp for better test Update elementwise_layers.cpp Implement ReLU6 Update elementwise_layers.cpp Implement SoftMax using WebNN API Implement Reshape by WebNN API Implement PermuteLayer by WebNN API Implement PoolingLayer using WebNN API Update pooling_layer.cpp Update pooling_layer.cpp Update pooling_layer.cpp Update pooling_layer.cpp Update pooling_layer.cpp Update pooling_layer.cpp Implement poolingLayer by WebNN API and add more detailed logs Update dnn.cpp Update dnn.cpp Remove redundant codes and add more logs for poolingLayer Add more logs in the pooling layer implementation Fix the indent issue and resolve the compiling issue Fix the build problems Fix the build issue FIx the build issue Update dnn.cpp Update dnn.cpp * Fix the build issue * Implement BatchNorm Layer by WebNN API * Update convolution_layer.cpp This is a temporary file for Conv2d layer implementation * Integrate some general functions into op_webnn.cpp&hpp * Update const_layer.cpp * Update convolution_layer.cpp Still have some bugs that should be fixed. * Update conv2d layer and fc layer still have some problems to be fixed. * update constLayer, conv layer, fc layer There are still some bugs to be fixed. * Update conv2d layer, fully connected layer and const layer * Update convolution_layer.cpp * Add OpenCV.js DNN module WebNN Backend (both using webnn-polyfill and electron) * Update dnn.cpp * Fix Error in dnn.cpp * Resolve duplication in conditions in convolution_layer.cpp * Fixed the issues in the comments * Fix building issue * Update tutorial * Fixed comments * Address the comments * Update CMakeLists.txt * Offer more accurate perf test on native * Add better perf tests for both native and web * Modify per tests for better results * Use more latest version of Electron * Support latest WebNN Clamp op * Add definition of HAVE_WEBNN macro * Support group convolution * Implement Scale_layer using WebNN * Add Softmax option for native classification example * Fix comments * Fix comments
2021-11-24 05:15:31 +08:00
ocv_glob_module_sources(${sources_options} SOURCES ${fw_srcs} ${webnn_srcs})
Merge pull request #15537 from l-bat:ngraph * Support nGraph * Fix resize
2019-12-02 21:16:06 +08:00
ocv_create_module(${libs} ${dnn_runtime_libs})
dnn: move module from opencv_contrib https://github.com/opencv/opencv_contrib/tree/e6f63c7a38ca40c5dc33e38736e3027e3528d6cb/modules/dnn
2017-06-26 18:35:51 +08:00
ocv_add_samples()
Merge pull request #15537 from l-bat:ngraph * Support nGraph * Fix resize
2019-12-02 21:16:06 +08:00
ocv_add_accuracy_tests(${dnn_runtime_libs})
Merge pull request #13932 from l-bat:MyriadX_master_dldt * Fix precision in tests for MyriadX * Fix ONNX tests * Add output range in ONNX tests * Skip tests on Myriad OpenVINO 2018R5 * Add detect MyriadX * Add detect MyriadX on OpenVINO R5 * Skip tests on Myriad next version of OpenVINO * dnn(ie): VPU type from environment variable * dnn(test): validate VPU type * dnn(test): update DLIE test skip conditions
2019-03-29 21:42:58 +08:00
Merge pull request #24372 from Kumataro:fix24369 Supporting protobuf v22 and later(with abseil-cpp/C++17) #24372 fix https://github.com/opencv/opencv/issues/24369 related https://github.com/opencv/opencv/issues/23791 1. This patch supports external protobuf v22 and later, it required abseil-cpp and c++17. Even if the built-in protobuf is upgraded to v22 or later, the dependency on abseil-cpp and the requirement for C++17 will continue. 2. Some test for caffe required patched protobuf, so this patch disable them. This patch is tested by following libraries. - Protobuf: /usr/local/lib/libprotobuf.so (4.24.4) - abseil-cpp: YES (20230125) ### 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
2023-10-19 13:45:08 +08:00
if(NOT BUILD_PROTOBUF)
if(TARGET opencv_test_dnn)
ocv_target_compile_definitions(opencv_test_dnn PRIVATE "OPENCV_DNN_EXTERNAL_PROTOBUF=1")
endif()
endif()
Merge pull request #13932 from l-bat:MyriadX_master_dldt * Fix precision in tests for MyriadX * Fix ONNX tests * Add output range in ONNX tests * Skip tests on Myriad OpenVINO 2018R5 * Add detect MyriadX * Add detect MyriadX on OpenVINO R5 * Skip tests on Myriad next version of OpenVINO * dnn(ie): VPU type from environment variable * dnn(test): validate VPU type * dnn(test): update DLIE test skip conditions
2019-03-29 21:42:58 +08:00
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")
Use modern OpenVINO package interface
2022-01-15 05:53:19 +08:00
ocv_add_perf_tests(${dnn_runtime_libs}
cmake: fix build of dnn tests with shared common code - don't share .cpp files (PCH support is broken)
2019-03-31 16:51:09 +08:00
FILES test_common "${CMAKE_CURRENT_LIST_DIR}/test/test_common.hpp" "${CMAKE_CURRENT_LIST_DIR}/test/test_common.impl.hpp"
Merge pull request #13932 from l-bat:MyriadX_master_dldt * Fix precision in tests for MyriadX * Fix ONNX tests * Add output range in ONNX tests * Skip tests on Myriad OpenVINO 2018R5 * Add detect MyriadX * Add detect MyriadX on OpenVINO R5 * Skip tests on Myriad next version of OpenVINO * dnn(ie): VPU type from environment variable * dnn(test): validate VPU type * dnn(test): update DLIE test skip conditions
2019-03-29 21:42:58 +08:00
FILES Src ${perf_srcs}
FILES Include ${perf_hdrs}
)
dnn: move module from opencv_contrib https://github.com/opencv/opencv_contrib/tree/e6f63c7a38ca40c5dc33e38736e3027e3528d6cb/modules/dnn
2017-06-26 18:35:51 +08:00
Update CMakeLists.txt
2021-04-26 23:36:51 +08:00
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)
Added performance test for Caffe framework
2017-08-04 20:09:57 +08:00
if(BUILD_PERF_TESTS)
Update CMakeLists.txt
2021-04-26 23:36:51 +08:00
if (OPENCV_DNN_PERF_CAFFE
OR ${the_module}_PERF_CAFFE # compatibility for deprecated option
)
Added performance test for Caffe framework
2017-08-04 20:09:57 +08:00
find_package(Caffe QUIET)
if (Caffe_FOUND)
dnn: plugin support for OpenVINO
2022-03-18 14:19:30 +08:00
ocv_target_compile_definitions(opencv_perf_dnn PRIVATE "HAVE_CAFFE=1")
Added performance test for Caffe framework
2017-08-04 20:09:57 +08:00
ocv_target_link_libraries(opencv_perf_dnn caffe)
endif()
Update CMakeLists.txt
2021-04-26 23:36:51 +08:00
elseif(OPENCV_DNN_PERF_CLCAFFE
OR ${the_module}_PERF_CAFFE # compatibility for deprecated option
)
Added performance test for Caffe framework
2017-08-04 20:09:57 +08:00
find_package(Caffe QUIET)
if (Caffe_FOUND)
dnn: plugin support for OpenVINO
2022-03-18 14:19:30 +08:00
ocv_target_compile_definitions(opencv_perf_dnn PRIVATE "HAVE_CLCAFFE=1")
Added performance test for Caffe framework
2017-08-04 20:09:57 +08:00
ocv_target_link_libraries(opencv_perf_dnn caffe)
endif()
endif()
endif()
dnn: plugin support for OpenVINO
2022-03-18 14:19:30 +08:00
if(DNN_ENABLE_PLUGINS)
ocv_target_compile_definitions(${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()
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 14:04:41 +08:00
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()
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 22:00:20 +08:00
Merge pull request #24425 from fengyuentau:fix_timvx_test dnn: fix HAVE_TIMVX macro definition in dnn test #24425 ### 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
2023-10-20 19:16:51 +08:00
ocv_option(OPENCV_TEST_DNN_TIMVX "Build test with TIM-VX" (HAVE_TIMVX))
if(OPENCV_TEST_DNN_TIMVX)
if(TARGET opencv_test_dnn)
ocv_target_compile_definitions(opencv_test_dnn PRIVATE "HAVE_TIMVX=1")
endif()
endif()
dnn(tflite): add 3rdparty flatbuffers with pre-generated schema
2023-02-19 00:21:07 +08:00
ocv_option(OPENCV_TEST_DNN_TFLITE "Build test with TFLite" (OPENCV_DNN_TFLITE))
if(OPENCV_TEST_DNN_TFLITE)
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 22:00:20 +08:00
if(TARGET opencv_test_dnn)
dnn(tflite): add 3rdparty flatbuffers with pre-generated schema
2023-02-19 00:21:07 +08:00
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")
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 22:00:20 +08:00
endif()
endif()
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