2426 Commits

Author	SHA1	Message	Date
Dmitry Kurtaev	a8d3d1f6f9	Merge pull request #23604 from dkurt:dnn_no_protobuf ... Build DNN without Protobuf DNN module can be built without Protobuf for Darknet, TFLite, OpenVINO, Torch (not PyTorch) models. ``` cmake \ -DCMAKE_BUILD_TYPE=Release \ -DBUILD_LIST=dnn \ -DWITH_PROTOBUF=OFF \ -DWITH_OPENCL=OFF 7.1M lib/libopencv_dnn.so.4.7.0 ``` ``` cmake \ -DCMAKE_BUILD_TYPE=Release \ -DBUILD_LIST=dnn \ -DWITH_OPENCL=OFF 3.9M lib/libopencv_dnn.so.4.7.0 ``` ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake	2023-05-15 12:23:18 +03:00
wanli	46991bcd62	Solve the bug of same shape broadcast with CUDA	2023-05-15 13:55:38 +08:00
Alexander Smorkalov	85b04f0b4d	Merge pull request #23557 from WanliZhong:eltwise_cpu_bug ... fix nary elementwise bug in cpu	2023-05-11 15:56:46 +03:00
Dmitry Kurtaev	676afdc494	Update FlatBuffers source code to 23.5.9	2023-05-10 14:39:36 +03:00
wanli	85cc4086c8	fix nary elementwise bug in cpu	2023-05-10 14:29:33 +08:00
Alexander Smorkalov	25c28c5da4	Merge pull request #23485 from zihaomu:add_onnx_where ... DNN: add ONNX where node support	2023-05-05 09:21:07 +03:00
zihaomu	0513741a85	add broadcast where node	2023-05-05 11:16:19 +08:00
Alexander Smorkalov	351589e5fb	Merge pull request #23491 from fengyuentau:patch_for_segment_anything ... Fixes for Segment Anything	2023-05-04 21:07:58 +03:00
Alexander Alekhin	3c76b33532	Merge pull request #22614 from zihaomu:add_std2DB_API	2023-05-01 19:37:23 +00:00
zihaomu	8be93a6de7	add scale factor to DB demo.	2023-04-30 22:03:21 +08:00
Abduragim Shtanchaev	3b1ee0549b	added test for lstm without hidden ... states initialization	2023-04-25 16:01:13 +03:00
Alexander Smorkalov	e3e1f704a4	Merge pull request #23528 from WanliZhong:issue23278 ... DNN/CUDA: make 'abcd op 1b11' broadcast eltwise operator support cuda	2023-04-24 19:31:55 +03:00
Dmitry Kurtaev	aa57833ad5	Merge pull request #23409 from dkurt:dnn_tflite_quant ... Import and inference INT8 quantized TFLite model #23409 ### Pull Request Readiness Checklist * Support quantized TFLite models * Enable fused activations (FP32, INT8) **Merge with extra**: https://github.com/opencv/opencv_extra/pull/1048  on the image, green boxes are from TFLite and red boxes from OpenCV See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake	2023-04-24 13:44:10 +03:00
Abduragim Shtanchaev	e4e774d42b	Merge pull request #23475 from Abdurrahheem:lstm_fix_initialization ... Fix ONNX parser for single-layer LSTM hidden and cell states #23475 ### Fix ONNX parser for single-layer LSTM hidden and cell states ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake This PR addresses #21118 [issue](https://github.com/opencv/opencv/issues/21118). The problem is that the ONNX parser is unable to read the hidden state and cell state for single-layer LSTMs. This PR fixes the issue by updating the parser to correctly read hidden and cell states.	2023-04-24 13:39:41 +03:00
wanli	e4360294c5	make 'abcd op 1b11' broadcast support cuda	2023-04-23 17:46:50 +08:00
Alexander Alekhin	9ab0ff6cf2	Merge pull request #23511 from zihaomu:issue_23465	2023-04-22 04:01:26 +00:00
Zihao Mu	601778e0e6	Merge pull request #22750 from zihaomu:improve_blobFromImage ... DNN: Add New API blobFromImageParam #22750 The purpose of this PR: 1. Add new API `blobFromImageParam` to extend `blobFromImage` API. It can support the different data layout (NCHW or NHWC), and letter_box. 2. ~~`blobFromImage` can output `CV_16F`~~ ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [ ] There is a reference to the original bug report and related work - [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [ ] The feature is well documented and sample code can be built with the project CMake	2023-04-21 19:10:17 +03:00
zihaomu	54e1a8709d	fix the bug, disable the fast1x1 when padding is not 0.	2023-04-21 10:55:07 +08:00
Yuantao Feng	3c1fcd5deb	Merge pull request #23401 from fengyuentau:fix_cann_layer_support ... dnn: Support more operators in CANN backend #23401 This PR adds the support of following layers: - [x] Sub - [x] PRelu - [x] DeConv - [x] Also warn users if backend is switched back to default if some of the layers are not supported. - [ ] [Dropped] LSTM: some hacks (adding layers) were introduced which makes it even harder to build the graph for CANN backend. ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake	2023-04-20 10:18:35 +03:00
Abduragim Shtanchaev	b3a2444bcf	Merge pull request #23501 from Abdurrahheem:additional_lstm_tests ... Added LSTM and GRU tests for various batch and input length sizes #23501 Added tests with various sequence length and batch sizes Test data: https://github.com/opencv/opencv_extra/pull/1057 ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [ ] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake	2023-04-20 10:11:33 +03:00
Alexander Smorkalov	aa17f881b1	Merge pull request #23482 from zihaomu:onnx_opset13_split ... DNN: support the split node of onnx opset >= 13	2023-04-14 11:59:57 +03:00
fengyuentau	4f99e5ab37	allow null constant_value in Pad and ignore it when loading	2023-04-14 16:50:16 +08:00
fengyuentau	88cacd35c5	support broadcast on axis > 1 for Expand	2023-04-14 15:52:27 +08:00
Alexander Smorkalov	136121f6ee	Merge pull request #22660 from zhouzq-thu:4.x ... Fix objectness is not assigned in dnn::region_layer	2023-04-12 09:34:58 +03:00
Alexander Smorkalov	3f02c9d5b9	Merge pull request #23310 from hanliutong:fix_hal_compatibility ... Fix HAL compatibility layer	2023-04-11 12:43:54 +03:00
zihaomu	51281f8d69	support the split node of onnx opset >= 13	2023-04-11 16:18:50 +08:00
Yuantao Feng	3a83a35ab0	Merge pull request #23296 from fengyuentau:fix_identifying_constant ... Fix identifying initializers in ONNX graph simplification #23296 Fixes https://github.com/opencv/opencv/issues/23295 ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake	2023-04-06 15:35:31 +03:00
Dmitry Kurtaev	5e1d33329b	Several fixes for ONNX importer: Expand, Gather	2023-03-27 22:15:26 +03:00
HAN Liutong	a809ae4e88	Fix HAL compatibility layer and modify use cases.	2023-03-27 21:30:47 +08:00
Dmitry Kurtaev	5df6b4a756	Merge pull request #23325 from dkurt:dnn_input_info ... Propagate inputs info for ONNX and TFLite models ### Pull Request Readiness Checklist Needed for generic applications such as benchmarking pipelines. So OpenCV can tell about the default input shapes specified in the models. See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake	2023-03-21 14:50:53 +03:00
Alexander Smorkalov	924a65413a	Merge pull request #23357 from zihaomu:fix_winograd_error_32bit ... DNN : fix bug in 32 bit cpu	2023-03-15 11:24:54 +03:00
zihaomu	6bac5453d1	fix bug in 32 bit cpu	2023-03-15 08:24:55 +08:00
Alexander Smorkalov	ccbc784195	Merge pull request #23354 from zihaomu:issue_23351 ... DNN : fix bug in layer fusion	2023-03-14 17:23:25 +03:00
zihaomu	386be97ce2	fix bug in layer fusion	2023-03-14 19:06:06 +08:00
tingbo.liao	7d032de7e8	Fix bugs of test case failure ... 4 failed tests in open_test_dnn listed below: * Test_Caffe_layers.Conv_Elu/0, where GetParam() = OCV/CPU * Test_ONNX_layers.ConvResizePool1d/0, where GetParam() = OCV/CPU * Test_TensorFlow_layers.tf_reshape_nhwc/0, where GetParam() = OCV/CPU * Test_Torch_layers.net_inception_block/0, where GetParam() = OCV/CPU In winofunc_AtXA_8x8_f32 and winofunc_BtXB_8x8_f32 implementation, incorrect input parameters cause tests failure. Add four new different variables for the last four input parameters of v_transpose4x4 to fix bugs, and update related comments. Signed-off-by: tingbo.liao <tingbo.liao@starfivetech.com>	2023-03-14 17:05:19 +08:00
Alexander Smorkalov	22a52766dc	Merge pull request #23343 from zihaomu:fix_test_onnx_conf ... DNN Test ONNX: Fix the logic of the test case	2023-03-13 21:48:41 +03:00
Yuantao Feng	b94e13c8ae	Merge pull request #23319 from fengyuentau:fix_zoo_issue_136 ... Related issue: https://github.com/opencv/opencv_zoo/issues/136 Features added: - Support operators with multiple output: ONNX Split. - Support Slice without steps. Bugs fixed: - Wrong settings in ClipByValue (Relu6). - Wrong calculation of pads in convolution layer (It is wrong generally but only fixed specifically for CANN for now). ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake	2023-03-13 21:46:33 +03:00
zihaomu	ee3740af00	move global skip out of if loop, and add opencv_deny_list	2023-03-13 22:16:51 +08:00
Zihao Mu	e03e2e7f94	Merge pull request #23192 from zihaomu:clean_up_SIMD_code ... ### Purpose of this PR: - Move all dispatch and SIMD code of `convolution layer` into `simd.hpp` file. - Support Winograd at AVX-only machine. - Re-name the folder from `fast_conv` to `cpu_kernels`. In the future, we can put other layers of CPU optimization into it, like `GEMM` or `MatMul`. ## Performance Test Since this patch just focuses on the code style, the performance is expected as the same as before. Test with the following script: `./bin/opencv_perf_dnn '--gtest_filter=*conv*' --gtest_output="xml:../1-0th.xml" --perf_threads=1` ### Test on X86 platform Min (ms) |Name of Test|4.x | patch | 4.x vs patch (x-factor)| |---|:-:|:-:|:-:| |conv1d::Conv1D::(GFLOPS=0.000, K=[3], IN={1, 2, 19}, OCN=2, G=2, S=2, P=(1, 1), BIAS, OCV/CPU)|0.001|0.001|0.98| |conv1d::Conv1D::(GFLOPS=0.000, K=[3], IN={1, 2, 25}, OCN=2, G=2, P=(2, 2), PM=SAME, OCV/CPU)|0.001|0.001|0.95| |conv1d::Conv1D::(GFLOPS=0.000, K=[3], IN={1, 6, 10}, OCN=6, PM=VALID, BIAS, OCV/CPU)|0.001|0.001|0.97| |conv3d::Conv3D::(GFLOPS=0.000, K=[1 x 1 x 1], IN={1, 4, 9, 10, 10}, OCN=4, S=[1 x 1 x 2], P=(1, 1) x (1, 1) x (1, 1), PM=VALID, OCV/CPU)|0.002|0.002|1.04| |conv3d::Conv3D::(GFLOPS=0.000, K=[1 x 1 x 1], IN={1, 8, 1, 10, 10}, OCN=8, G=8, P=(1, 1) x (1, 1) x (1, 1), BIAS, OCV/CPU)|0.002|0.002|0.94| |conv3d::Conv3D::(GFLOPS=0.000, K=[3 x 3 x 3], IN={1, 2, 19, 19, 19}, OCN=2, G=2, S=[2 x 2 x 2], P=(1, 1) x (1, 1) x (1, 1), BIAS, OCV/CPU)|0.040|0.044|0.93| |conv3d::Conv3D::(GFLOPS=0.000, K=[3 x 4 x 2], IN={1, 4, 8, 10, 10}, OCN=4, G=4, S=[1 x 2 x 1], BIAS, OCV/CPU)|0.010|0.010|1.00| |conv3d::Conv3D::(GFLOPS=0.001, K=[3 x 3 x 3], IN={1, 2, 25, 19, 19}, OCN=2, G=2, S=[1 x 2 x 2], P=(2, 2) x (2, 2) x (2, 2), PM=SAME, OCV/CPU)|0.106|0.103|1.03| |conv3d::Conv3D::(GFLOPS=0.002, K=[3 x 1 x 4], IN={1, 14, 5, 10, 10}, OCN=14, PM=SAME, OCV/CPU)|0.041|0.040|1.03| |conv3d::Conv3D::(GFLOPS=0.006, K=[5 x 5 x 5], IN={1, 4, 50, 19, 19}, OCN=4, S=[2 x 2 x 2], P=(1, 1) x (1, 1) x (1, 1), PM=VALID, OCV/CPU)|0.340|0.329|1.03| |conv3d::Conv3D::(GFLOPS=0.027, K=[3 x 3 x 3], IN={1, 6, 10, 38, 50}, OCN=6, PM=VALID, BIAS, OCV/CPU)|0.590|0.567|1.04| |conv3d::Conv3D::(GFLOPS=0.030, K=[5 x 5 x 5], IN={1, 6, 19, 19, 19}, OCN=6, G=2, OCV/CPU)|1.374|1.314|1.05| |conv3d::Conv3D::(GFLOPS=0.045, K=[7 x 7 x 7], IN={1, 2, 38, 38, 38}, OCN=2, S=[1 x 2 x 1], OCV/CPU)|3.715|3.528|1.05| |conv3d::Conv3D::(GFLOPS=0.053, K=[3 x 3 x 3], IN={1, 10, 98, 10, 10}, OCN=10, PM=SAME, OCV/CPU)|1.181|1.166|1.01| |conv3d::Conv3D::(GFLOPS=0.071, K=[7 x 7 x 7], IN={1, 6, 15, 19, 19}, OCN=6, S=[2 x 1 x 1], P=(3, 3) x (3, 3) x (3, 3), PM=SAME, BIAS, OCV/CPU)|2.689|2.587|1.04| |conv3d::Conv3D::(GFLOPS=0.093, K=[5 x 5 x 5], IN={1, 4, 40, 75, 75}, OCN=4, S=[2 x 2 x 2], OCV/CPU)|4.754|4.500|1.06| |conv3d::Conv3D::(GFLOPS=0.116, K=[5 x 5 x 5], IN={1, 2, 21, 75, 100}, OCN=2, BIAS, OCV/CPU)|9.612|9.112|1.05| |conv3d::Conv3D::(GFLOPS=1.267, K=[5 x 5 x 5], IN={1, 3, 75, 75, 100}, OCN=3, PM=SAME, BIAS, OCV/CPU)|69.000|64.676|1.07| |conv3d::Conv3D::(GFLOPS=1.343, K=[3 x 3 x 3], IN={1, 11, 9, 150, 200}, OCN=11, PM=VALID, BIAS, OCV/CPU)|20.248|18.451|1.10| |conv::Conv::(GFLOPS=0.177, K=[1 x 1], IN={1, 512, 26, 26}, OCN=256, OCV/CPU)|1.395|1.392|1.00| |conv::Conv::(GFLOPS=0.177, K=[1 x 1], IN={1, 1024, 13, 13}, OCN=512, OCV/CPU)|1.990|1.984|1.00| |conv::Conv::(GFLOPS=0.178, K=[1 x 1], IN={1, 256, 52, 52}, OCN=128, OCV/CPU)|1.393|1.360|1.02| |conv::Conv::(GFLOPS=0.210, K=[1 x 1], IN={1, 576, 38, 50}, OCN=96, PM=SAME, BIAS, OCV/CPU)|1.813|1.744|1.04| |conv::Conv::(GFLOPS=0.231, K=[3 x 3], IN={1, 128, 56, 56}, OCN=32, P=[1 x 1], OCV/CPU)|1.190|1.191|1.00| |conv::Conv::(GFLOPS=0.231, K=[3 x 3], IN={1, 256, 14, 14}, OCN=256, P=[1 x 1], OCV/CPU)|1.286|1.284|1.00| |conv::Conv::(GFLOPS=0.280, K=[1 x 1], IN={1, 576, 38, 50}, OCN=128, PM=SAME, BIAS, OCV/CPU)|2.295|2.279|1.01| |conv::Conv::(GFLOPS=0.302, K=[3 x 3], IN={1, 64, 64, 64}, OCN=64, PM=SAME, OCV/CPU)|1.322|1.331|0.99| |conv::Conv::(GFLOPS=0.357, K=[1 x 1], IN={1, 64, 208, 208}, OCN=64, OCV/CPU)|3.784|3.533|1.07| |conv::Conv::(GFLOPS=0.420, K=[3 x 3], IN={1, 96, 38, 50}, OCN=128, PM=SAME, BIAS, OCV/CPU)|1.838|1.844|1.00| |conv::Conv::(GFLOPS=0.472, K=[3 x 3], IN={1, 128, 40, 40}, OCN=128, PM=SAME, OCV/CPU)|1.957|1.959|1.00| |conv::Conv::(GFLOPS=0.472, K=[3 x 3], IN={1, 256, 20, 20}, OCN=256, PM=SAME, OCV/CPU)|2.596|2.573|1.01| |conv::Conv::(GFLOPS=0.472, K=[3 x 3], IN={1, 512, 10, 10}, OCN=512, PM=SAME, OCV/CPU)|4.183|4.083|1.02| |conv::Conv::(GFLOPS=0.561, K=[3 x 3], IN={1, 128, 38, 50}, OCN=128, PM=SAME, BIAS, OCV/CPU)|2.413|2.406|1.00| |conv::Conv::(GFLOPS=0.624, K=[3 x 3], IN={1, 128, 46, 46}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)|2.538|2.546|1.00| |conv::Conv::(GFLOPS=0.701, K=[3 x 3], IN={1, 128, 38, 50}, OCN=160, PM=SAME, BIAS, OCV/CPU)|2.972|2.980|1.00| |conv::Conv::(GFLOPS=0.798, K=[3 x 3], IN={1, 64, 104, 104}, OCN=64, P=[1 x 1], OCV/CPU)|3.452|3.464|1.00| |conv::Conv::(GFLOPS=0.798, K=[3 x 3], IN={1, 128, 52, 52}, OCN=128, P=[1 x 1], OCV/CPU)|3.082|3.105|0.99| |conv::Conv::(GFLOPS=0.798, K=[3 x 3], IN={1, 256, 26, 26}, OCN=256, P=[1 x 1], OCV/CPU)|4.043|3.919|1.03| |conv::Conv::(GFLOPS=0.798, K=[3 x 3], IN={1, 512, 13, 13}, OCN=512, P=[1 x 1], OCV/CPU)|5.538|5.531|1.00| |conv::Conv::(GFLOPS=0.830, K=[3 x 3], IN={1, 64, 75, 100}, OCN=96, PM=SAME, BIAS, OCV/CPU)|3.393|3.418|0.99| |conv::Conv::(GFLOPS=0.958, K=[3 x 3], IN={1, 192, 38, 38}, OCN=192, PM=SAME, OCV/CPU)|4.325|4.234|1.02| |conv::Conv::(GFLOPS=0.958, K=[3 x 3], IN={1, 384, 19, 19}, OCN=384, PM=SAME, OCV/CPU)|6.009|5.908|1.02| |conv::Conv::(GFLOPS=1.022, K=[3 x 3], IN={1, 576, 19, 19}, OCN=273, PM=SAME, BIAS, OCV/CPU)|6.557|6.376|1.03| |conv::Conv::(GFLOPS=1.112, K=[3 x 3], IN={1, 512, 10, 10}, OCN=1206, P=[1 x 1], BIAS, OCV/CPU)|10.114|9.472|1.07| |conv::Conv::(GFLOPS=1.181, K=[3 x 3], IN={1, 64, 160, 200}, OCN=128, S=[2 x 2], P=[1 x 1], BIAS, OCV/CPU)|10.373|9.879|1.05| |conv::Conv::(GFLOPS=1.182, K=[3 x 3], IN={1, 32, 320, 400}, OCN=64, S=[2 x 2], P=[1 x 1], BIAS, OCV/CPU)|12.782|11.624|1.10| |conv::Conv::(GFLOPS=1.195, K=[9 x 9], IN={1, 32, 240, 320}, OCN=3, P=[4 x 4], BIAS, OCV/CPU)|90.931|90.552|1.00| |conv::Conv::(GFLOPS=1.196, K=[3 x 3], IN={1, 384, 26, 26}, OCN=256, P=[1 x 1], OCV/CPU)|6.091|5.818|1.05| |conv::Conv::(GFLOPS=1.210, K=[3 x 3], IN={1, 32, 256, 256}, OCN=32, PM=SAME, OCV/CPU)|7.083|6.643|1.07| |conv::Conv::(GFLOPS=1.245, K=[3 x 3], IN={1, 64, 75, 75}, OCN=192, PM=SAME, BIAS, OCV/CPU)|5.054|5.059|1.00| |conv::Conv::(GFLOPS=1.245, K=[3 x 3], IN={1, 96, 75, 100}, OCN=96, PM=SAME, BIAS, OCV/CPU)|5.005|4.931|1.02| |conv::Conv::(GFLOPS=1.248, K=[3 x 3], IN={1, 256, 46, 46}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)|4.951|5.065|0.98| |conv::Conv::(GFLOPS=1.258, K=[3 x 3], IN={1, 1280, 10, 10}, OCN=546, PM=SAME, BIAS, OCV/CPU)|11.957|11.293|1.06| |conv::Conv::(GFLOPS=1.261, K=[3 x 3], IN={1, 192, 38, 50}, OCN=192, PM=SAME, BIAS, OCV/CPU)|5.328|5.250|1.01| |conv::Conv::(GFLOPS=1.416, K=[3 x 3], IN={1, 128, 62, 82}, OCN=128, BIAS, OCV/CPU)|5.544|5.292|1.05| |conv::Conv::(GFLOPS=1.500, K=[3 x 3], IN={1, 128, 64, 84}, OCN=128, BIAS, OCV/CPU)|6.186|5.893|1.05| |conv::Conv::(GFLOPS=1.586, K=[3 x 3], IN={1, 128, 66, 86}, OCN=128, BIAS, OCV/CPU)|6.153|5.834|1.05| |conv::Conv::(GFLOPS=1.595, K=[3 x 3], IN={1, 256, 26, 26}, OCN=512, P=[1 x 1], OCV/CPU)|8.154|8.107|1.01| |conv::Conv::(GFLOPS=1.595, K=[3 x 3], IN={1, 256, 52, 52}, OCN=512, S=[2 x 2], P=[1 x 1], OCV/CPU)|12.699|12.256|1.04| |conv::Conv::(GFLOPS=1.595, K=[3 x 3], IN={1, 512, 13, 13}, OCN=1024, P=[1 x 1], OCV/CPU)|11.355|11.217|1.01| |conv::Conv::(GFLOPS=1.595, K=[3 x 3], IN={1, 512, 26, 26}, OCN=1024, S=[2 x 2], P=[1 x 1], OCV/CPU)|19.062|17.814|1.07| |conv::Conv::(GFLOPS=1.596, K=[3 x 3], IN={1, 64, 104, 104}, OCN=128, P=[1 x 1], OCV/CPU)|6.820|6.531|1.04| |conv::Conv::(GFLOPS=1.596, K=[3 x 3], IN={1, 64, 208, 208}, OCN=128, S=[2 x 2], P=[1 x 1], OCV/CPU)|14.502|13.483|1.08| |conv::Conv::(GFLOPS=1.596, K=[3 x 3], IN={1, 128, 52, 52}, OCN=256, P=[1 x 1], OCV/CPU)|6.270|6.123|1.02| |conv::Conv::(GFLOPS=1.596, K=[3 x 3], IN={1, 128, 104, 104}, OCN=256, S=[2 x 2], P=[1 x 1], OCV/CPU)|13.173|12.451|1.06| |conv::Conv::(GFLOPS=1.598, K=[3 x 3], IN={1, 32, 208, 208}, OCN=64, P=[1 x 1], OCV/CPU)|8.326|7.652|1.09| |conv::Conv::(GFLOPS=1.598, K=[3 x 3], IN={1, 32, 416, 416}, OCN=64, S=[2 x 2], P=[1 x 1], OCV/CPU)|17.605|16.465|1.07| |conv::Conv::(GFLOPS=1.659, K=[3 x 3], IN={1, 960, 10, 10}, OCN=960, PM=SAME, OCV/CPU)|15.675|14.771|1.06| |conv::Conv::(GFLOPS=1.660, K=[3 x 3], IN={1, 128, 75, 75}, OCN=128, G=128, P=[1 x 1], BIAS, OCV/CPU)|0.420|0.423|0.99| |conv::Conv::(GFLOPS=1.660, K=[3 x 3], IN={1, 128, 75, 75}, OCN=128, PM=SAME, OCV/CPU)|6.788|6.491|1.05| |conv::Conv::(GFLOPS=1.675, K=[3 x 3], IN={1, 128, 68, 88}, OCN=128, BIAS, OCV/CPU)|6.456|6.168|1.05| |conv::Conv::(GFLOPS=1.704, K=[3 x 3], IN={1, 256, 38, 38}, OCN=256, G=256, P=[1 x 1], BIAS, OCV/CPU)|0.263|0.261|1.01| |conv::Conv::(GFLOPS=1.704, K=[3 x 3], IN={1, 256, 38, 38}, OCN=256, PM=SAME, OCV/CPU)|7.690|7.398|1.04| |conv::Conv::(GFLOPS=1.704, K=[3 x 3], IN={1, 512, 19, 19}, OCN=512, G=512, P=[1 x 1], BIAS, OCV/CPU)|0.200|0.202|0.99| |conv::Conv::(GFLOPS=1.704, K=[3 x 3], IN={1, 512, 19, 19}, OCN=512, P=[1 x 1], BIAS, OCV/CPU)|10.542|10.464|1.01| |conv::Conv::(GFLOPS=1.704, K=[3 x 3], IN={1, 512, 19, 19}, OCN=512, PM=SAME, OCV/CPU)|10.876|10.728|1.01| |conv::Conv::(GFLOPS=1.766, K=[3 x 3], IN={1, 128, 70, 90}, OCN=128, BIAS, OCV/CPU)|7.194|6.768|1.06| |conv::Conv::(GFLOPS=1.859, K=[3 x 3], IN={1, 128, 72, 92}, OCN=128, BIAS, OCV/CPU)|7.099|6.731|1.05| |conv::Conv::(GFLOPS=1.888, K=[3 x 3], IN={1, 1024, 10, 10}, OCN=1024, G=1024, P=[1 x 1], BIAS, OCV/CPU)|0.147|0.162|0.91| |conv::Conv::(GFLOPS=1.888, K=[3 x 3], IN={1, 1024, 10, 10}, OCN=1024, PM=SAME, OCV/CPU)|18.558|17.141|1.08| |conv::Conv::(GFLOPS=1.954, K=[3 x 3], IN={1, 128, 74, 94}, OCN=128, BIAS, OCV/CPU)|7.641|7.219|1.06| |conv::Conv::(GFLOPS=1.995, K=[9 x 9], IN={1, 3, 320, 400}, OCN=32, P=[4 x 4], BIAS, OCV/CPU)|22.666|20.999|1.08| |conv::Conv::(GFLOPS=2.052, K=[3 x 3], IN={1, 128, 76, 96}, OCN=128, BIAS, OCV/CPU)|8.523|7.921|1.08| |conv::Conv::(GFLOPS=2.100, K=[3 x 3], IN={1, 144, 75, 75}, OCN=144, PM=SAME, OCV/CPU)|8.514|8.109|1.05| |conv::Conv::(GFLOPS=2.153, K=[3 x 3], IN={1, 128, 78, 98}, OCN=128, BIAS, OCV/CPU)|8.300|7.878|1.05| |conv::Conv::(GFLOPS=2.156, K=[3 x 3], IN={1, 576, 19, 19}, OCN=576, PM=SAME, OCV/CPU)|13.403|13.131|1.02| |conv::Conv::(GFLOPS=2.255, K=[3 x 3], IN={1, 128, 80, 100}, OCN=128, BIAS, OCV/CPU)|8.920|8.357|1.07| |conv::Conv::(GFLOPS=2.719, K=[3 x 3], IN={1, 96, 256, 256}, OCN=96, S=[2 x 2], PM=SAME, OCV/CPU)|28.827|27.616|1.04| |conv::Conv::(GFLOPS=3.319, K=[3 x 3], IN={1, 128, 75, 75}, OCN=256, P=[1 x 1], BIAS, OCV/CPU)|12.895|12.670|1.02| |conv::Conv::(GFLOPS=3.321, K=[3 x 3], IN={1, 64, 150, 150}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)|14.120|13.078|1.08| |conv::Conv::(GFLOPS=3.398, K=[7 x 7], IN={1, 128, 46, 46}, OCN=128, P=[3 x 3], BIAS, OCV/CPU)|27.541|27.582|1.00| |conv::Conv::(GFLOPS=3.407, K=[3 x 3], IN={1, 512, 19, 19}, OCN=1024, D=[6 x 6], P=[6 x 6], BIAS, OCV/CPU)|32.367|31.140|1.04| |conv::Conv::(GFLOPS=3.408, K=[3 x 3], IN={1, 256, 38, 38}, OCN=512, P=[1 x 1], BIAS, OCV/CPU)|14.934|14.910|1.00| |conv::Conv::(GFLOPS=4.247, K=[3 x 3], IN={1, 480, 32, 32}, OCN=480, PM=SAME, OCV/CPU)|18.289|18.491|0.99| |conv::Conv::(GFLOPS=4.247, K=[5 x 5], IN={1, 144, 128, 128}, OCN=144, S=[2 x 2], PM=SAME, OCV/CPU)|37.857|36.845|1.03| |conv::Conv::(GFLOPS=4.566, K=[7 x 7], IN={1, 172, 46, 46}, OCN=128, P=[3 x 3], BIAS, OCV/CPU)|37.402|36.566|1.02| |conv::Conv::(GFLOPS=4.993, K=[3 x 3], IN={1, 256, 46, 46}, OCN=512, P=[1 x 1], BIAS, OCV/CPU)|19.031|19.164|0.99| |conv::Conv::(GFLOPS=4.993, K=[3 x 3], IN={1, 512, 46, 46}, OCN=256, P=[1 x 1], BIAS, OCV/CPU)|19.019|19.135|0.99| |conv::Conv::(GFLOPS=4.994, K=[3 x 3], IN={1, 128, 92, 92}, OCN=256, P=[1 x 1], BIAS, OCV/CPU)|20.077|19.400|1.03| |conv::Conv::(GFLOPS=4.997, K=[3 x 3], IN={1, 64, 184, 184}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)|21.883|21.302|1.03| |conv::Conv::(GFLOPS=5.780, K=[5 x 5], IN={1, 672, 32, 32}, OCN=672, S=[2 x 2], PM=SAME, OCV/CPU)|51.288|49.851|1.03| |conv::Conv::(GFLOPS=6.116, K=[3 x 3], IN={1, 1152, 16, 16}, OCN=1152, PM=SAME, OCV/CPU)|27.349|28.359|0.96| |conv::Conv::(GFLOPS=6.118, K=[3 x 3], IN={1, 144, 128, 128}, OCN=144, PM=SAME, OCV/CPU)|24.915|25.130|0.99| |conv::Conv::(GFLOPS=6.637, K=[3 x 3], IN={1, 256, 75, 75}, OCN=256, P=[1 x 1], BIAS, OCV/CPU)|25.488|25.899|0.98| |conv::Conv::(GFLOPS=6.638, K=[3 x 3], IN={1, 128, 150, 150}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)|27.346|27.390|1.00| |conv::Conv::(GFLOPS=6.641, K=[3 x 3], IN={1, 64, 150, 200}, OCN=192, PM=SAME, BIAS, OCV/CPU)|28.033|28.301|0.99| |conv::Conv::(GFLOPS=6.641, K=[3 x 3], IN={1, 64, 300, 300}, OCN=64, P=[1 x 1], BIAS, OCV/CPU)|50.216|49.970|1.00| |conv::Conv::(GFLOPS=6.814, K=[3 x 3], IN={1, 512, 38, 38}, OCN=512, P=[1 x 1], BIAS, OCV/CPU)|29.670|29.513|1.01| |conv::Conv::(GFLOPS=8.025, K=[3 x 3], IN={1, 1024, 19, 19}, OCN=1206, P=[1 x 1], BIAS, OCV/CPU)|50.565|49.634|1.02| |conv::Conv::(GFLOPS=9.986, K=[3 x 3], IN={1, 512, 46, 46}, OCN=512, P=[1 x 1], BIAS, OCV/CPU)|37.900|37.814|1.00| |conv::Conv::(GFLOPS=9.987, K=[3 x 3], IN={1, 256, 92, 92}, OCN=256, P=[1 x 1], BIAS, OCV/CPU)|41.367|39.742|1.04| |conv::Conv::(GFLOPS=9.989, K=[3 x 3], IN={1, 128, 184, 184}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)|49.128|50.350|0.98| |conv::Conv::(GFLOPS=9.993, K=[3 x 3], IN={1, 64, 368, 368}, OCN=64, P=[1 x 1], BIAS, OCV/CPU)|79.643|80.645|0.99| |conv::Conv::(GFLOPS=10.087, K=[3 x 3], IN={1, 576, 38, 50}, OCN=512, PM=SAME, BIAS, OCV/CPU)|41.439|40.895|1.01| |conv::Conv::(GFLOPS=10.701, K=[3 x 3], IN={1, 512, 38, 38}, OCN=804, P=[1 x 1], BIAS, OCV/CPU)|46.504|46.220|1.01| |conv::Conv::(GFLOPS=11.797, K=[5 x 5], IN={1, 240, 64, 64}, OCN=240, PM=SAME, OCV/CPU)|98.086|96.842|1.01| |conv::Conv::(GFLOPS=11.797, K=[5 x 5], IN={1, 480, 32, 32}, OCN=480, PM=SAME, OCV/CPU)|102.447|97.299|1.05| |conv::Conv::(GFLOPS=16.987, K=[5 x 5], IN={1, 1152, 16, 16}, OCN=1152, PM=SAME, OCV/CPU)|145.047|144.996|1.00| |conv::Conv::(GFLOPS=23.122, K=[5 x 5], IN={1, 672, 32, 32}, OCN=672, PM=SAME, OCV/CPU)|206.104|195.543|1.05| ### Test on M1(ARM) platform |Name of Test|4.x|patch|4.x vs patch (x-factor)| |---|:-:|:-:|:-:| |conv1d::Conv1D::(GFLOPS=0.000, K=[3], IN={1, 2, 19}, OCN=2, G=2, S=2, P=(1, 1), BIAS, OCV/CPU)|0.001|0.001|0.97| |conv1d::Conv1D::(GFLOPS=0.000, K=[3], IN={1, 2, 25}, OCN=2, G=2, P=(2, 2), PM=SAME, OCV/CPU)|0.001|0.001|0.94| |conv1d::Conv1D::(GFLOPS=0.000, K=[3], IN={1, 6, 10}, OCN=6, PM=VALID, BIAS, OCV/CPU)|0.002|0.002|0.92| |conv3d::Conv3D::(GFLOPS=0.000, K=[1 x 1 x 1], IN={1, 4, 9, 10, 10}, OCN=4, S=[1 x 1 x 2], P=(1, 1) x (1, 1) x (1, 1), PM=VALID, OCV/CPU)|0.003|0.003|1.00| |conv3d::Conv3D::(GFLOPS=0.000, K=[1 x 1 x 1], IN={1, 8, 1, 10, 10}, OCN=8, G=8, P=(1, 1) x (1, 1) x (1, 1), BIAS, OCV/CPU)|0.003|0.003|1.00| |conv3d::Conv3D::(GFLOPS=0.000, K=[3 x 3 x 3], IN={1, 2, 19, 19, 19}, OCN=2, G=2, S=[2 x 2 x 2], P=(1, 1) x (1, 1) x (1, 1), BIAS, OCV/CPU)|0.031|0.031|1.00| |conv3d::Conv3D::(GFLOPS=0.000, K=[3 x 4 x 2], IN={1, 4, 8, 10, 10}, OCN=4, G=4, S=[1 x 2 x 1], BIAS, OCV/CPU)|0.009|0.009|1.00| |conv3d::Conv3D::(GFLOPS=0.001, K=[3 x 3 x 3], IN={1, 2, 25, 19, 19}, OCN=2, G=2, S=[1 x 2 x 2], P=(2, 2) x (2, 2) x (2, 2), PM=SAME, OCV/CPU)|0.066|0.066|1.01| |conv3d::Conv3D::(GFLOPS=0.002, K=[3 x 1 x 4], IN={1, 14, 5, 10, 10}, OCN=14, PM=SAME, OCV/CPU)|0.102|0.102|1.00| |conv3d::Conv3D::(GFLOPS=0.006, K=[5 x 5 x 5], IN={1, 4, 50, 19, 19}, OCN=4, S=[2 x 2 x 2], P=(1, 1) x (1, 1) x (1, 1), PM=VALID, OCV/CPU)|0.328|0.328|1.00| |conv3d::Conv3D::(GFLOPS=0.027, K=[3 x 3 x 3], IN={1, 6, 10, 38, 50}, OCN=6, PM=VALID, BIAS, OCV/CPU)|0.693|0.747|0.93| |conv3d::Conv3D::(GFLOPS=0.030, K=[5 x 5 x 5], IN={1, 6, 19, 19, 19}, OCN=6, G=2, OCV/CPU)|1.268|1.266|1.00| |conv3d::Conv3D::(GFLOPS=0.045, K=[7 x 7 x 7], IN={1, 2, 38, 38, 38}, OCN=2, S=[1 x 2 x 1], OCV/CPU)|3.530|3.581|0.99| |conv3d::Conv3D::(GFLOPS=0.053, K=[3 x 3 x 3], IN={1, 10, 98, 10, 10}, OCN=10, PM=SAME, OCV/CPU)|1.186|1.188|1.00| |conv3d::Conv3D::(GFLOPS=0.071, K=[7 x 7 x 7], IN={1, 6, 15, 19, 19}, OCN=6, S=[2 x 1 x 1], P=(3, 3) x (3, 3) x (3, 3), PM=SAME, BIAS, OCV/CPU)|2.682|2.683|1.00| |conv3d::Conv3D::(GFLOPS=0.093, K=[5 x 5 x 5], IN={1, 4, 40, 75, 75}, OCN=4, S=[2 x 2 x 2], OCV/CPU)|4.490|4.501|1.00| |conv3d::Conv3D::(GFLOPS=0.116, K=[5 x 5 x 5], IN={1, 2, 21, 75, 100}, OCN=2, BIAS, OCV/CPU)|8.914|8.938|1.00| |conv3d::Conv3D::(GFLOPS=1.267, K=[5 x 5 x 5], IN={1, 3, 75, 75, 100}, OCN=3, PM=SAME, BIAS, OCV/CPU)|69.819|69.876|1.00| |conv3d::Conv3D::(GFLOPS=1.343, K=[3 x 3 x 3], IN={1, 11, 9, 150, 200}, OCN=11, PM=VALID, BIAS, OCV/CPU)|24.058|22.420|1.07| |conv::Conv::(GFLOPS=0.177, K=[1 x 1], IN={1, 512, 26, 26}, OCN=256, OCV/CPU)|2.240|2.236|1.00| |conv::Conv::(GFLOPS=0.177, K=[1 x 1], IN={1, 1024, 13, 13}, OCN=512, OCV/CPU)|3.132|3.136|1.00| |conv::Conv::(GFLOPS=0.178, K=[1 x 1], IN={1, 256, 52, 52}, OCN=128, OCV/CPU)|1.920|1.919|1.00| |conv::Conv::(GFLOPS=0.210, K=[1 x 1], IN={1, 576, 38, 50}, OCN=96, PM=SAME, BIAS, OCV/CPU)|2.343|2.346|1.00| |conv::Conv::(GFLOPS=0.231, K=[3 x 3], IN={1, 128, 56, 56}, OCN=32, P=[1 x 1], OCV/CPU)|1.234|1.116|1.11| |conv::Conv::(GFLOPS=0.231, K=[3 x 3], IN={1, 256, 14, 14}, OCN=256, P=[1 x 1], OCV/CPU)|1.109|1.121|0.99| |conv::Conv::(GFLOPS=0.280, K=[1 x 1], IN={1, 576, 38, 50}, OCN=128, PM=SAME, BIAS, OCV/CPU)|3.197|3.084|1.04| |conv::Conv::(GFLOPS=0.302, K=[3 x 3], IN={1, 64, 64, 64}, OCN=64, PM=SAME, OCV/CPU)|1.123|1.148|0.98| |conv::Conv::(GFLOPS=0.357, K=[1 x 1], IN={1, 64, 208, 208}, OCN=64, OCV/CPU)|4.836|5.061|0.96| |conv::Conv::(GFLOPS=0.420, K=[3 x 3], IN={1, 96, 38, 50}, OCN=128, PM=SAME, BIAS, OCV/CPU)|1.535|1.463|1.05| |conv::Conv::(GFLOPS=0.472, K=[3 x 3], IN={1, 128, 40, 40}, OCN=128, PM=SAME, OCV/CPU)|1.756|1.584|1.11| |conv::Conv::(GFLOPS=0.472, K=[3 x 3], IN={1, 256, 20, 20}, OCN=256, PM=SAME, OCV/CPU)|1.821|1.820|1.00| |conv::Conv::(GFLOPS=0.472, K=[3 x 3], IN={1, 512, 10, 10}, OCN=512, PM=SAME, OCV/CPU)|7.049|6.672|1.06| |conv::Conv::(GFLOPS=0.561, K=[3 x 3], IN={1, 128, 38, 50}, OCN=128, PM=SAME, BIAS, OCV/CPU)|1.967|1.922|1.02| |conv::Conv::(GFLOPS=0.624, K=[3 x 3], IN={1, 128, 46, 46}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)|1.943|1.977|0.98| |conv::Conv::(GFLOPS=0.701, K=[3 x 3], IN={1, 128, 38, 50}, OCN=160, PM=SAME, BIAS, OCV/CPU)|2.464|2.310|1.07| |conv::Conv::(GFLOPS=0.798, K=[3 x 3], IN={1, 64, 104, 104}, OCN=64, P=[1 x 1], OCV/CPU)|2.860|2.904|0.98| |conv::Conv::(GFLOPS=0.798, K=[3 x 3], IN={1, 128, 52, 52}, OCN=128, P=[1 x 1], OCV/CPU)|2.428|2.483|0.98| |conv::Conv::(GFLOPS=0.798, K=[3 x 3], IN={1, 256, 26, 26}, OCN=256, P=[1 x 1], OCV/CPU)|2.955|2.983|0.99| |conv::Conv::(GFLOPS=0.798, K=[3 x 3], IN={1, 512, 13, 13}, OCN=512, P=[1 x 1], OCV/CPU)|4.328|4.484|0.97| |conv::Conv::(GFLOPS=0.830, K=[3 x 3], IN={1, 64, 75, 100}, OCN=96, PM=SAME, BIAS, OCV/CPU)|2.712|2.778|0.98| |conv::Conv::(GFLOPS=0.958, K=[3 x 3], IN={1, 192, 38, 38}, OCN=192, PM=SAME, OCV/CPU)|3.205|3.331|0.96| |conv::Conv::(GFLOPS=0.958, K=[3 x 3], IN={1, 384, 19, 19}, OCN=384, PM=SAME, OCV/CPU)|4.193|4.412|0.95| |conv::Conv::(GFLOPS=1.022, K=[3 x 3], IN={1, 576, 19, 19}, OCN=273, PM=SAME, BIAS, OCV/CPU)|5.026|4.565|1.10| |conv::Conv::(GFLOPS=1.112, K=[3 x 3], IN={1, 512, 10, 10}, OCN=1206, P=[1 x 1], BIAS, OCV/CPU)|14.490|14.213|1.02| |conv::Conv::(GFLOPS=1.181, K=[3 x 3], IN={1, 64, 160, 200}, OCN=128, S=[2 x 2], P=[1 x 1], BIAS, OCV/CPU)|14.886|14.003|1.06| |conv::Conv::(GFLOPS=1.182, K=[3 x 3], IN={1, 32, 320, 400}, OCN=64, S=[2 x 2], P=[1 x 1], BIAS, OCV/CPU)|15.923|15.184|1.05| |conv::Conv::(GFLOPS=1.195, K=[9 x 9], IN={1, 32, 240, 320}, OCN=3, P=[4 x 4], BIAS, OCV/CPU)|45.136|41.696|1.08| |conv::Conv::(GFLOPS=1.196, K=[3 x 3], IN={1, 384, 26, 26}, OCN=256, P=[1 x 1], OCV/CPU)|4.995|4.631|1.08| |conv::Conv::(GFLOPS=1.210, K=[3 x 3], IN={1, 32, 256, 256}, OCN=32, PM=SAME, OCV/CPU)|6.402|6.261|1.02| |conv::Conv::(GFLOPS=1.245, K=[3 x 3], IN={1, 64, 75, 75}, OCN=192, PM=SAME, BIAS, OCV/CPU)|4.478|3.965|1.13| |conv::Conv::(GFLOPS=1.245, K=[3 x 3], IN={1, 96, 75, 100}, OCN=96, PM=SAME, BIAS, OCV/CPU)|3.908|3.978|0.98| |conv::Conv::(GFLOPS=1.248, K=[3 x 3], IN={1, 256, 46, 46}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)|4.176|4.206|0.99| |conv::Conv::(GFLOPS=1.258, K=[3 x 3], IN={1, 1280, 10, 10}, OCN=546, PM=SAME, BIAS, OCV/CPU)|21.509|21.136|1.02| |conv::Conv::(GFLOPS=1.261, K=[3 x 3], IN={1, 192, 38, 50}, OCN=192, PM=SAME, BIAS, OCV/CPU)|4.426|4.082|1.08| |conv::Conv::(GFLOPS=1.416, K=[3 x 3], IN={1, 128, 62, 82}, OCN=128, BIAS, OCV/CPU)|4.098|4.289|0.96| |conv::Conv::(GFLOPS=1.500, K=[3 x 3], IN={1, 128, 64, 84}, OCN=128, BIAS, OCV/CPU)|4.646|5.105|0.91| |conv::Conv::(GFLOPS=1.586, K=[3 x 3], IN={1, 128, 66, 86}, OCN=128, BIAS, OCV/CPU)|4.746|4.724|1.00| |conv::Conv::(GFLOPS=1.595, K=[3 x 3], IN={1, 256, 26, 26}, OCN=512, P=[1 x 1], OCV/CPU)|5.614|5.779|0.97| |conv::Conv::(GFLOPS=1.595, K=[3 x 3], IN={1, 256, 52, 52}, OCN=512, S=[2 x 2], P=[1 x 1], OCV/CPU)|21.909|20.718|1.06| |conv::Conv::(GFLOPS=1.595, K=[3 x 3], IN={1, 512, 13, 13}, OCN=1024, P=[1 x 1], OCV/CPU)|8.256|8.290|1.00| |conv::Conv::(GFLOPS=1.595, K=[3 x 3], IN={1, 512, 26, 26}, OCN=1024, S=[2 x 2], P=[1 x 1], OCV/CPU)|25.196|23.267|1.08| |conv::Conv::(GFLOPS=1.596, K=[3 x 3], IN={1, 64, 104, 104}, OCN=128, P=[1 x 1], OCV/CPU)|5.721|5.172|1.11| |conv::Conv::(GFLOPS=1.596, K=[3 x 3], IN={1, 64, 208, 208}, OCN=128, S=[2 x 2], P=[1 x 1], OCV/CPU)|20.066|18.322|1.10| |conv::Conv::(GFLOPS=1.596, K=[3 x 3], IN={1, 128, 52, 52}, OCN=256, P=[1 x 1], OCV/CPU)|4.448|4.542|0.98| |conv::Conv::(GFLOPS=1.596, K=[3 x 3], IN={1, 128, 104, 104}, OCN=256, S=[2 x 2], P=[1 x 1], OCV/CPU)|19.193|19.013|1.01| |conv::Conv::(GFLOPS=1.598, K=[3 x 3], IN={1, 32, 208, 208}, OCN=64, P=[1 x 1], OCV/CPU)|6.009|5.964|1.01| |conv::Conv::(GFLOPS=1.598, K=[3 x 3], IN={1, 32, 416, 416}, OCN=64, S=[2 x 2], P=[1 x 1], OCV/CPU)|20.169|20.009|1.01| |conv::Conv::(GFLOPS=1.659, K=[3 x 3], IN={1, 960, 10, 10}, OCN=960, PM=SAME, OCV/CPU)|22.584|23.423|0.96| |conv::Conv::(GFLOPS=1.660, K=[3 x 3], IN={1, 128, 75, 75}, OCN=128, G=128, P=[1 x 1], BIAS, OCV/CPU)|0.372|0.504|0.74| |conv::Conv::(GFLOPS=1.660, K=[3 x 3], IN={1, 128, 75, 75}, OCN=128, PM=SAME, OCV/CPU)|5.426|5.456|0.99| |conv::Conv::(GFLOPS=1.675, K=[3 x 3], IN={1, 128, 68, 88}, OCN=128, BIAS, OCV/CPU)|4.945|5.221|0.95| |conv::Conv::(GFLOPS=1.704, K=[3 x 3], IN={1, 256, 38, 38}, OCN=256, G=256, P=[1 x 1], BIAS, OCV/CPU)|0.210|0.261|0.81| |conv::Conv::(GFLOPS=1.704, K=[3 x 3], IN={1, 256, 38, 38}, OCN=256, PM=SAME, OCV/CPU)|5.720|5.997|0.95| |conv::Conv::(GFLOPS=1.704, K=[3 x 3], IN={1, 512, 19, 19}, OCN=512, G=512, P=[1 x 1], BIAS, OCV/CPU)|0.149|0.161|0.93| |conv::Conv::(GFLOPS=1.704, K=[3 x 3], IN={1, 512, 19, 19}, OCN=512, P=[1 x 1], BIAS, OCV/CPU)|7.154|7.225|0.99| |conv::Conv::(GFLOPS=1.704, K=[3 x 3], IN={1, 512, 19, 19}, OCN=512, PM=SAME, OCV/CPU)|7.184|7.223|0.99| |conv::Conv::(GFLOPS=1.766, K=[3 x 3], IN={1, 128, 70, 90}, OCN=128, BIAS, OCV/CPU)|5.324|5.343|1.00| |conv::Conv::(GFLOPS=1.859, K=[3 x 3], IN={1, 128, 72, 92}, OCN=128, BIAS, OCV/CPU)|5.114|5.238|0.98| |conv::Conv::(GFLOPS=1.888, K=[3 x 3], IN={1, 1024, 10, 10}, OCN=1024, G=1024, P=[1 x 1], BIAS, OCV/CPU)|0.111|0.121|0.92| |conv::Conv::(GFLOPS=1.888, K=[3 x 3], IN={1, 1024, 10, 10}, OCN=1024, PM=SAME, OCV/CPU)|25.907|26.804|0.97| |conv::Conv::(GFLOPS=1.954, K=[3 x 3], IN={1, 128, 74, 94}, OCN=128, BIAS, OCV/CPU)|5.695|5.654|1.01| |conv::Conv::(GFLOPS=1.995, K=[9 x 9], IN={1, 3, 320, 400}, OCN=32, P=[4 x 4], BIAS, OCV/CPU)|27.435|27.566|1.00| |conv::Conv::(GFLOPS=2.052, K=[3 x 3], IN={1, 128, 76, 96}, OCN=128, BIAS, OCV/CPU)|6.944|6.164|1.13| |conv::Conv::(GFLOPS=2.100, K=[3 x 3], IN={1, 144, 75, 75}, OCN=144, PM=SAME, OCV/CPU)|7.180|6.717|1.07| |conv::Conv::(GFLOPS=2.153, K=[3 x 3], IN={1, 128, 78, 98}, OCN=128, BIAS, OCV/CPU)|6.817|6.050|1.13| |conv::Conv::(GFLOPS=2.156, K=[3 x 3], IN={1, 576, 19, 19}, OCN=576, PM=SAME, OCV/CPU)|9.225|8.660|1.07| |conv::Conv::(GFLOPS=2.255, K=[3 x 3], IN={1, 128, 80, 100}, OCN=128, BIAS, OCV/CPU)|7.496|6.625|1.13| |conv::Conv::(GFLOPS=2.719, K=[3 x 3], IN={1, 96, 256, 256}, OCN=96, S=[2 x 2], PM=SAME, OCV/CPU)|35.520|36.056|0.99| |conv::Conv::(GFLOPS=3.319, K=[3 x 3], IN={1, 128, 75, 75}, OCN=256, P=[1 x 1], BIAS, OCV/CPU)|9.990|9.702|1.03| |conv::Conv::(GFLOPS=3.321, K=[3 x 3], IN={1, 64, 150, 150}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)|10.517|10.746|0.98| |conv::Conv::(GFLOPS=3.398, K=[7 x 7], IN={1, 128, 46, 46}, OCN=128, P=[3 x 3], BIAS, OCV/CPU)|36.702|36.731|1.00| |conv::Conv::(GFLOPS=3.407, K=[3 x 3], IN={1, 512, 19, 19}, OCN=1024, D=[6 x 6], P=[6 x 6], BIAS, OCV/CPU)|41.035|38.280|1.07| |conv::Conv::(GFLOPS=3.408, K=[3 x 3], IN={1, 256, 38, 38}, OCN=512, P=[1 x 1], BIAS, OCV/CPU)|10.981|10.573|1.04| |conv::Conv::(GFLOPS=4.247, K=[3 x 3], IN={1, 480, 32, 32}, OCN=480, PM=SAME, OCV/CPU)|12.863|12.384|1.04| |conv::Conv::(GFLOPS=4.247, K=[5 x 5], IN={1, 144, 128, 128}, OCN=144, S=[2 x 2], PM=SAME, OCV/CPU)|50.437|54.088|0.93| |conv::Conv::(GFLOPS=4.566, K=[7 x 7], IN={1, 172, 46, 46}, OCN=128, P=[3 x 3], BIAS, OCV/CPU)|50.650|50.635|1.00| |conv::Conv::(GFLOPS=4.993, K=[3 x 3], IN={1, 256, 46, 46}, OCN=512, P=[1 x 1], BIAS, OCV/CPU)|14.696|14.606|1.01| |conv::Conv::(GFLOPS=4.993, K=[3 x 3], IN={1, 512, 46, 46}, OCN=256, P=[1 x 1], BIAS, OCV/CPU)|16.201|15.426|1.05| |conv::Conv::(GFLOPS=4.994, K=[3 x 3], IN={1, 128, 92, 92}, OCN=256, P=[1 x 1], BIAS, OCV/CPU)|16.061|14.292|1.12| |conv::Conv::(GFLOPS=4.997, K=[3 x 3], IN={1, 64, 184, 184}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)|17.743|18.250|0.97| |conv::Conv::(GFLOPS=5.780, K=[5 x 5], IN={1, 672, 32, 32}, OCN=672, S=[2 x 2], PM=SAME, OCV/CPU)|77.909|78.165|1.00| |conv::Conv::(GFLOPS=6.116, K=[3 x 3], IN={1, 1152, 16, 16}, OCN=1152, PM=SAME, OCV/CPU)|21.579|21.879|0.99| |conv::Conv::(GFLOPS=6.118, K=[3 x 3], IN={1, 144, 128, 128}, OCN=144, PM=SAME, OCV/CPU)|20.424|19.589|1.04| |conv::Conv::(GFLOPS=6.637, K=[3 x 3], IN={1, 256, 75, 75}, OCN=256, P=[1 x 1], BIAS, OCV/CPU)|19.389|19.461|1.00| |conv::Conv::(GFLOPS=6.638, K=[3 x 3], IN={1, 128, 150, 150}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)|21.319|20.358|1.05| |conv::Conv::(GFLOPS=6.641, K=[3 x 3], IN={1, 64, 150, 200}, OCN=192, PM=SAME, BIAS, OCV/CPU)|22.609|21.826|1.04| |conv::Conv::(GFLOPS=6.641, K=[3 x 3], IN={1, 64, 300, 300}, OCN=64, P=[1 x 1], BIAS, OCV/CPU)|25.497|25.789|0.99| |conv::Conv::(GFLOPS=6.814, K=[3 x 3], IN={1, 512, 38, 38}, OCN=512, P=[1 x 1], BIAS, OCV/CPU)|21.966|22.108|0.99| |conv::Conv::(GFLOPS=8.025, K=[3 x 3], IN={1, 1024, 19, 19}, OCN=1206, P=[1 x 1], BIAS, OCV/CPU)|35.883|33.470|1.07| |conv::Conv::(GFLOPS=9.986, K=[3 x 3], IN={1, 512, 46, 46}, OCN=512, P=[1 x 1], BIAS, OCV/CPU)|31.041|29.314|1.06| |conv::Conv::(GFLOPS=9.987, K=[3 x 3], IN={1, 256, 92, 92}, OCN=256, P=[1 x 1], BIAS, OCV/CPU)|29.922|28.145|1.06| |conv::Conv::(GFLOPS=9.989, K=[3 x 3], IN={1, 128, 184, 184}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)|31.624|31.148|1.02| |conv::Conv::(GFLOPS=9.993, K=[3 x 3], IN={1, 64, 368, 368}, OCN=64, P=[1 x 1], BIAS, OCV/CPU)|38.564|39.164|0.98| |conv::Conv::(GFLOPS=10.087, K=[3 x 3], IN={1, 576, 38, 50}, OCN=512, PM=SAME, BIAS, OCV/CPU)|31.502|30.269|1.04| |conv::Conv::(GFLOPS=10.701, K=[3 x 3], IN={1, 512, 38, 38}, OCN=804, P=[1 x 1], BIAS, OCV/CPU)|34.248|34.589|0.99| |conv::Conv::(GFLOPS=11.797, K=[5 x 5], IN={1, 240, 64, 64}, OCN=240, PM=SAME, OCV/CPU)|130.211|134.120|0.97| |conv::Conv::(GFLOPS=11.797, K=[5 x 5], IN={1, 480, 32, 32}, OCN=480, PM=SAME, OCV/CPU)|127.490|132.874|0.96| |conv::Conv::(GFLOPS=16.987, K=[5 x 5], IN={1, 1152, 16, 16}, OCN=1152, PM=SAME, OCV/CPU)|199.834|200.081|1.00| |conv::Conv::(GFLOPS=23.122, K=[5 x 5], IN={1, 672, 32, 32}, OCN=672, PM=SAME, OCV/CPU)|247.346|247.523|1.00| ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [ ] There is a reference to the original bug report and related work - [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [ ] The feature is well documented and sample code can be built with the project CMake ``` force_builders=Linux AVX2,Custom Win build_image:Custom Win=msvs2019 CPU_BASELINE:Custom Win=AVX512_SKX ```	2023-03-10 11:59:49 +03:00
Alexander Alekhin	9eb5e39ff3	dnn(tflite): fix wrong axis normalization	2023-02-21 21:20:37 +00:00
Alexander Alekhin	bdff0949bb	dnn(tflite): add 3rdparty flatbuffers with pre-generated schema	2023-02-21 16:06:19 +00:00
Zihao Mu	20dac7ea48	Merge pull request #23255 from zihaomu:fused_cuda_naryeltwise ... DNN: fuse conv+naryEletwise on CUDA backend.	2023-02-17 10:18:13 +00:00
Alexander Alekhin	58d8a2702a	Merge pull request #23243 from WanliZhong:accelerate_palm_det	2023-02-14 16:25:02 +00:00
Dmitry Kurtaev	76350cd30f	Merge pull request #23161 from dkurt:dnn_tflite ... TFLite models importer * initial commit * Refactor TFLiteImporter * Better FlatBuffers detection * Add permute before 4D->3D reshape * Track layers layout * TFLite Convolution2DTransposeBias layer * Skip TFLite tests without FlatBuffers * Fix check of FlatBuffers in tests. Add readNetFromTFLite from buffer * TFLite Max Unpooling test * Add skip for TFLite unpooling test * Revert DW convolution workaround * Fix ObjC bindings * Better errors handling * Regenerate TFLite schema using flatc * dnn(tflite): more checks, better logging * Checks for unimplemented fusion. Fix tests	2023-02-13 14:00:20 +00:00
Yuantao Feng	c2b7c1f13b	Merge pull request #23219 from fengyuentau:add_gelu ... Add GELU layer for vision transformers * add gelu and gelu approximation * drop setKernelParams	2023-02-10 18:03:29 +00:00
wanli	c8f5e228fc	release MUL and ADD operator on CUDA	2023-02-10 19:33:59 +08:00
Alexander Alekhin	96a45e842e	Merge pull request #23061 from WanliZhong:gemm_cuda ... DNN: make GEMM can be supported with transA and transB in CUDA	2023-02-09 00:06:32 +03:00
wanli	4718a4bf81	make GEMM can be supported with transA and transB in CUDA	2023-01-31 15:14:17 +08:00
Alexander Alekhin	f33598f55e	Merge branch 4.x	2023-01-28 17:31:32 +00:00
Alexander Alekhin	cd44aa0bb1	Merge pull request #23162 from zihaomu:issue_23151	2023-01-28 13:00:43 +00:00
zihaomu	f45a12439a	fix depth wise issue.	2023-01-28 11:41:00 +08:00
Yuantao Feng	4d918ba40b	Merge pull request #23047 from fengyuentau:layer_norm ... dnn: add layer normalization for vision transformers * add layer norm onnx parser, impl and tests * add onnx graph simplifier for layer norm expanded * handle the case when constants are of type Initializer * add test case for layer norm expanded with initializers * use CV_Assert & CV_CheckType in place of CV_Assert_N; use forward_fallback for OCL_FP16 * use const ref / ref in parameters of invoker::run; extract inner const if from nested loop; use size_t in place of ull * template hasBias * remove trailing whitespace * use pointer parameter with null check; move normSize division & mean_square division outside of loop; use std::max to ensure positive value before std::sqrt * refactor implementation, optimize parallel_for * disable layer norm expanded * remove the removal of layer norm optional outputs	2023-01-27 16:35:59 +03:00
Alexander Alekhin	8ffc06ff72	Merge pull request #23173 from tomoaki0705:fix_warning_master	2023-01-23 15:33:16 +00:00
Tomoaki Teshima	186c18668c	suppress warning	2023-01-23 22:47:43 +09:00
Alexander Alekhin	18cbfa4a4f	Merge remote-tracking branch 'upstream/3.4' into merge-3.4	2023-01-23 00:11:12 +00:00
Alexander Alekhin	a42d879925	Merge branch 4.x	2023-01-18 22:03:42 +00:00
Alexander Alekhin	3d5e3a910f	Merge pull request #23096 from zihaomu:issue_23074	2023-01-12 00:51:04 +00:00
zihaomu	840b1d5c94	add depthwise add fuse	2023-01-11 08:42:51 +08:00
Alexander Alekhin	593a376566	Merge branch 4.x	2023-01-09 11:08:02 +00:00
zihaomu	82616eec41	fix possible segmentation fault error in winograd on x86	2023-01-09 13:40:04 +08:00
Alexander Alekhin	9627ab9462	Merge pull request #23050 from zihaomu:fix_memory	2022-12-28 10:04:25 +00:00
zihaomu	71765858dc	fix invalid memory access	2022-12-28 17:16:11 +08:00
Alexander Alekhin	9a2a34f94e	dnn(openvino): remove undefined status	2022-12-28 06:55:00 +00:00
Alexander Alekhin	fc27a343e9	Merge pull request #22905 from zihaomu:clean_up_conv3d_1d	2022-12-26 17:39:18 +00:00
Alexander Alekhin	b42c11de82	pre: OpenCV 4.7.0 (version++)	2022-12-25 17:00:22 +00:00
Alexander Alekhin	a494c75bfe	pre: OpenCV 3.4.19 (version++)	2022-12-25 16:59:47 +00:00
Dmitry Kurtaev	8681686d8f	Merge pull request #22957 from dkurt:new_openvino_api ... Switch to new OpenVINO API after 2022.1 release * Pass Layer_Test_Convolution_DLDT.Accuracy/0 test * Pass test Test_Caffe_layers.Softmax * Failed 136 tests * Fix Concat. Failed 120 tests * Custom nGraph ops. 19 failed tests * Set and get properties from Core * Read model from buffer * Change MaxPooling layer output names. Restore reshape * Cosmetic changes * Cosmetic changes * Override getOutputsInfo * Fixes for OpenVINO < 2022.1 * Async inference for 2021.4 and less * Compile model with config * Fix serialize for 2022.1 * Asynchronous inference with 2022.1 * Handle 1d outputs * Work with model with dynamic output shape * Fixes with 1d output for old API * Control outputs by nGraph function for all OpenVINO versions * Refer inputs in PrePostProcessor by indices * Fix cycled dependency between InfEngineNgraphNode and InfEngineNgraphNet. Add InferRequest callback only for async inference. Do not capture InferRequest object. * Fix tests thresholds * Fix HETERO:GPU,CPU plugin issues with unsupported layer	2022-12-23 16:58:41 +00:00
Alexander Smorkalov	9012e6dd9b	Merge pull request #22965 from vrabaud:numpy_fix ... Remove references to deprecated NumPy type aliases.	2022-12-23 15:34:02 +03:00
Alexander Smorkalov	4930516652	Merge pull request #22898 from fengyuentau:slice_neg_steps ... dnn: support ONNX Slice with negative steps by adding and using cv::flipND	2022-12-23 14:15:06 +03:00
Vincent Rabaud	ad568edd7f	Remove references to deprecated NumPy type aliases. ... This change replaces references to a number of deprecated NumPy type aliases (np.bool, np.int, np.float, np.complex, np.object, np.str) with their recommended replacement (bool, int, float, complex, object, str). Those types were deprecated in 1.20 and are removed in 1.24, cf https://github.com/numpy/numpy/pull/22607.	2022-12-23 13:53:49 +03:00
Alexander Alekhin	1f41d06f9a	Merge pull request #23008 from mshabunin:fix-yolov4-tiny-hash	2022-12-23 10:14:25 +00:00
zihaomu	71c6339af0	remove old convolution branch, and optimize conv3d and conv1d.	2022-12-23 16:50:28 +08:00
fengyuentau	34a0897f90	add cv::flipND; support onnx slice with negative steps via cv::flipND	2022-12-23 16:39:53 +08:00
Maksim Shabunin	d35fbe6bfc	dnn: updated YOLOv4-tiny model and tests	2022-12-22 15:49:21 +03:00
Alexander Alekhin	6b4f3e5fab	Merge pull request #22993 from alalek:fixup_21738	2022-12-21 19:50:51 +00:00
Yuantao Feng	a2b3acfc6e	dnn: add the CANN backend (#22634) ... * cann backend impl v1 * cann backend impl v2: use opencv parsers to build models for cann * adjust fc according to the new transA and transB * put cann net in cann backend node and reuse forwardLayer * use fork() to create a child process and compile cann model * remove legacy code * remove debug code * fall bcak to CPU backend if there is one layer not supoorted by CANN backend * fix netInput forward	2022-12-21 09:04:41 +03:00
Alexander Alekhin	cdbb893b27	dnn: disable OpenCL code path in MatMul processing ... - this mode is not supported by 22828	2022-12-20 09:46:48 +00:00
Alexander Alekhin	1102b7eff8	dnn: fix gather layer implementation ... - support FP16 data	2022-12-20 06:09:34 +00:00
zoom	4891818114	make MatMul support 3D or 4D with broadcast	2022-12-15 10:36:08 +08:00
Alexander Alekhin	8ba44e7d55	Merge pull request #22882 from zihaomu:gemm_first_const	2022-12-08 14:18:33 +00:00
Zihao Mu	0a650b573b	Merge pull request #22840 from zihaomu:optimze_conv_memory_usage ... DNN: reduce the memory used in convolution layer * reduce the memory in winograd and disabel the test when usage memory is larger than 2gb. * remove VERY_LOG tag	2022-12-08 12:57:13 +00:00
Alexander Alekhin	b16f76eede	Merge remote-tracking branch 'upstream/3.4' into merge-3.4	2022-12-03 12:39:41 +00:00
Alexander Alekhin	d16b3b2487	dnn(test): restore openvino tests with 'Cannot get memory' message	2022-12-03 01:34:48 +00:00
Alexander Alekhin	74d0b4cc78	dnn(openvino): fix custom layers BlockingDesc	2022-12-03 01:34:10 +00:00
Alexander Smorkalov	e14ca39fd7	Merge pull request #22857 from fengyuentau:batched_nms ... dnn: add batched nms	2022-11-30 12:37:49 +03:00
Alexander Smorkalov	421ba8730a	Merge pull request #22809 from fengyuentau:tile ... dnn: support ONNX Tile	2022-11-29 14:42:28 +03:00
zihaomu	0d56524b72	gemm support transA and transB, and first input is constance.	2022-11-29 17:13:36 +08:00
fengyuentau	9fded9ca53	batched nms impl	2022-11-29 15:32:34 +08:00
fengyuentau	441624a5fb	tile impl	2022-11-29 11:15:38 +08:00
zoom	5044af69d1	let MatMul can work when both two inputs are const	2022-11-27 17:32:41 +08:00
Alexander Smorkalov	6ca205a029	Merge pull request #22478 from WanliZhong:nary_eltwise_cuda ... DNN: Let part of the operators in nary_eltwise support CUDA	2022-11-22 16:15:50 +03:00
zihaomu	5bf64e7dfe	fix the infinite loop in tf importer of 3.4 branch	2022-11-15 11:42:10 +08:00
zoom	ef2677b0a6	Make MatMul layer support 3d or 4d operation with const input	2022-11-10 11:41:44 +08:00
zoom	11d492b0b9	Let part of the operators in nary_eltwise support cuda	2022-11-02 14:08:21 +08:00
Zihao Mu	17f2b56291	remove never used code in onnximporter	2022-11-02 10:45:16 +08:00
Alexander Alekhin	ee9137f176	Merge pull request #22725 from zihaomu:fix_infinit_loop_in_tf	2022-10-31 17:03:03 +00:00
Zihao Mu	903bf0147e	Merge pull request #22666 from zihaomu:support_onnx_qdq_model ... DNN: let Quant and Dequant of ONNX_importer support the Constant input. * let Quant and Dequant support the Constant input. * fix negative value of axis.	2022-10-31 16:06:31 +00:00
Zihao Mu	18fbb72f7d	fix the infinite loop in tf importer.	2022-10-31 20:10:25 +08:00
Alexander Smorkalov	22f8fb4d5c	Do not fail tests in Yolo v7 model was not found.	2022-10-24 17:59:18 +03:00
Alexander Smorkalov	23edec83fb	Merge pull request #22667 from zihaomu:bug_fix_in_winograd ... DNN: bug fixed in Winograd	2022-10-21 17:54:13 +03:00
Alexander Smorkalov	e4cd430710	Merge pull request #22653 from WanliZhong:issue22597 ... DNN-TF: let StridedSlice layer support const input	2022-10-21 17:51:00 +03:00
Dmitry Kurtaev	35b2cff295	Merge pull request #22656 from dkurt:halide_fixes ... * Fixes for Halide * Enable some Halide tests	2022-10-21 17:49:49 +03:00
Zihao Mu	cee8c86b6e	fixed bug at winograd of SIMD128 and more robust code.	2022-10-21 19:14:54 +08:00
Alexander Smorkalov	5d292826b2	Merge pull request #22593 from zihaomu:optimize_wino ... optimize winograd futher more	2022-10-19 13:08:32 +03:00
Alexander Smorkalov	f378f02954	Merge pull request #22652 from rogday:cuda_test_fixes ... Address CUDA-related errors	2022-10-19 09:37:12 +03:00
Zhi-Qiang Zhou	c8561eae2d	Update region_layer.cpp ... Fix objectness (dstData[index + 4]) is not assigned if new_coords == 1.	2022-10-19 11:17:23 +08:00
Smirnov Egor	dd14cf6a9c	address CUDA-related errors and enable cuda in elementwise ops	2022-10-18 16:54:42 +03:00
Alexander Smorkalov	ec7fc5adca	Merge pull request #22529 from fengyuentau:scatter_scatternd ... DNN: supports Scatter and ScatterND from ONNX	2022-10-17 14:57:46 +03:00
Alexander Smorkalov	02143cd0e2	Merge pull request #22531 from zihaomu:stop_rely_name ... Parsing quantized nodes does not rely on names	2022-10-17 11:20:24 +03:00
Alexander Smorkalov	1c5dcbcac8	Merge pull request #22639 from WanliZhong:issue#22625 ... DNN: Make Unsqueeze layer support negative axes	2022-10-17 09:27:49 +03:00
fengyuentau	d24d8f2abe	implementation of scatter and scatternd with conformance tests enabled	2022-10-17 11:30:32 +08:00
Alexander Alekhin	762481411d	Merge remote-tracking branch 'upstream/3.4' into merge-3.4	2022-10-15 16:44:47 +00:00
zoom	d816442e4d	Make Unsqueeze layer support negative axes.	2022-10-14 18:00:19 +08:00
Zihao Mu	0fa43e3aac	Optimize the winograd futher more.	2022-10-14 10:15:45 +08:00
zoom	9119692bb8	let StridedSlice layer support const input	2022-10-12 11:50:44 +08:00
Alexander Smorkalov	ec26541771	Merge pull request #22577 from zihaomu:Disable_winograd_branch_in_tryquantize ... DNN: add enableWinograd API for Net	2022-10-11 09:44:00 +03:00
Zihao Mu	d9eff7daeb	parse quantized nodes does not rely on name.	2022-10-10 17:08:46 +08:00
Alexander Smorkalov	3419e64dcf	Merge pull request #22611 from zihaomu:greaterOrEqual ... DNN: support GreaterOrEqual and LessOrEqual op in ONNX	2022-10-10 11:43:44 +03:00
Zihao Mu	1e2ceca4df	add enableWinograd API for Net.	2022-10-09 09:33:07 +08:00
Alexander Alekhin	347246901e	Merge pull request #21745 from alalek:dnn_plugin_openvino	2022-10-08 22:32:25 +00:00
Zihao Mu	9821fae59d	add greater_or_equal and less_or_equal ONNX support	2022-10-08 15:51:40 +08:00
Alexander Alekhin	43b2bb2c25	dnn: plugin support for OpenVINO	2022-10-07 16:57:31 +00:00
Alexander Smorkalov	96844b0ca5	Merge pull request #22554 from WanliZhong:slice_axes_no_seq ... DNN: Let Slice layer support non-sequential and negative axes	2022-10-03 10:15:55 +03:00
zoom	4557971481	enhance slice layer ... refactor the code for parsing Slice layer add test for Slice layer let 'begin' and 'end' resize to dims add opset message comment	2022-10-01 17:12:07 +08:00
Zihao Mu	15cfafb360	DNN: Remove unused code in onnx_importer.cpp	2022-09-29 10:53:43 +08:00
Voron	cbf43a54fb	added opencv for openvino tutorial	2022-09-28 12:05:28 +02:00
Alexander Smorkalov	a6274647a4	Merge pull request #21738 from rogday:gather ... add Gather implementation	2022-09-19 16:21:14 +03:00
Egor Smirnov	65f71ce2eb	add Gather implementation	2022-09-19 15:06:44 +03:00
Alexander Smorkalov	6aefb8e86f	Merge pull request #22290 from fengyuentau:naive_yolov7 ... Support for YOLOv7 ONNX (not simplified)	2022-09-19 14:43:18 +03:00
fengyuentau	4aef9b1c93	dnn: support yolov7 (not simplified)	2022-09-19 18:38:03 +08:00
Alexander Smorkalov	e1e9261450	Merge pull request #22479 from scottchou007:master ... Fix issues in opencv_test_dnn from conv48 kernels without bias	2022-09-16 09:05:55 +03:00
scottchou007	a3cb2020bc	Fix issues in opencv_test_dnn from conv48 kernels using uninitialized tensors when there is no bias.	2022-09-15 13:41:27 -07:00
Alexander Alekhin	65bdb3a544	dnn: eliminate GCC12 warning in total() call	2022-09-14 11:37:00 +00:00
Alexander Smorkalov	c2c8da2517	Merge pull request #22448 from Ichini24:reshape-permutations-fix ... changed names of permutations if Reshpe is in NHWC	2022-09-13 09:24:56 +03:00
wxsheng	4154bd0667	Add Loongson Advanced SIMD Extension support: -DCPU_BASELINE=LASX ... * Add Loongson Advanced SIMD Extension support: -DCPU_BASELINE=LASX * Add resize.lasx.cpp for Loongson SIMD acceleration * Add imgwarp.lasx.cpp for Loongson SIMD acceleration * Add LASX acceleration support for dnn/conv * Add CV_PAUSE(v) for Loongarch * Set LASX by default on Loongarch64 * LoongArch: tune test threshold for Core/HAL.mat_decomp/15 Co-authored-by: shengwenxue <shengwenxue@loongson.cn>	2022-09-10 09:39:43 +03:00
Alexander Alekhin	ca7f964104	dnn: use inheritance for OpenVINO net impl	2022-09-06 18:05:00 +00:00
anton	337452b4c0	changed names of permutations if Reshpe is in NHWC	2022-09-03 19:02:41 +02:00
Zihao Mu	b69b1eae8f	fix bug 22450	2022-09-02 16:30:06 +08:00
Alexander Smorkalov	70fb1cd603	Merge pull request #22440 from zihaomu:fix_conv_bug	2022-08-30 07:01:05 +00:00
Alexander Smorkalov	d2c48b898c	Merge pull request #22306 from zihaomu:qgemm_and_squeeze_opset13_onnximporter	2022-08-30 06:33:57 +00:00
Zihao Mu	2d837efba7	add qgemm and squeeze op13 supported on ONNXImporter	2022-08-30 09:50:29 +08:00
Alexander Smorkalov	1fd45a1b85	Merge pull request #22362 from fengyuentau:conv_asym_pad_fuse ... Remove asymmetric padding in Conv layer since it is supported in CPU backend	2022-08-29 17:56:17 +03:00
Zihao Mu	2cd7e17b65	replace v_add with +	2022-08-29 17:15:35 +08:00
Alexander Smorkalov	2619099fe5	Merge pull request #22337 from zihaomu:load_ONNX_fp16_as_fp32 ... DNN: load fp16 ONNX model as fp32	2022-08-29 09:32:25 +03:00
fengyuentau	2959286eb5	tengine: supports conv with asymmetric padding	2022-08-29 02:51:26 +00:00
Zihao Mu	9638e34ab0	reuse WORDS_BIGENDIAN.	2022-08-27 07:42:38 +08:00
Zihao Mu	bb64db98d8	Further optimization of Conv2D, fused Conv_Add_Activation, bring latest code from ficus OpConv.fx. (#22401)	2022-08-26 12:57:25 +03:00
Zihao Mu	7eaec9dd22	load fp16 as fp32 and align fp16 and double in onnx_graph_simplifie	2022-08-26 10:04:44 +08:00
Zihao Mu	5e92bf8e41	support silu activation in darknet	2022-08-22 10:51:29 +08:00
Alexander Alekhin	c25f776151	Merge branch 4.x	2022-08-21 15:27:31 +00:00
Alexander Alekhin	2ebdc04787	Merge remote-tracking branch 'upstream/3.4' into merge-3.4	2022-08-14 15:50:42 +00:00
fengyuentau	0cdff46725	tune for opencl	2022-08-14 17:47:48 +08:00
Alexander Alekhin	d0d115321d	Merge pull request #22350 from alalek:rework_psabi_warning	2022-08-13 15:05:41 +00:00
Alexander Smorkalov	bb71cb200e	Merge pull request #22199 from zihaomu:bug_fix_22195 ... DNN: Reduce Layer (add dynamic batch and ReduceSum support)	2022-08-11 12:59:51 +03:00
fengyuentau	e7e814fa8c	remove asymmetric padding checks	2022-08-10 19:52:44 +08:00
Alexander Alekhin	44b2f9637a	Revert "suppress warning on GCC 7 and later" ... This reverts commit a630ad73cb.	2022-08-07 15:43:10 +03:00
Alexander Smorkalov	b2b7193374	Merge pull request #22311 from zihaomu:layer_fused_optmized_mish ... DNN: add another two Mish activation to onnx_graph_simplifier	2022-08-05 14:22:06 +03:00
Zihao Mu	0614c40b42	add more skip for very long test case in test_dnn.	2022-08-02 14:58:05 +08:00
Zihao Mu	d4640f4647	support ReduceLayer without reshape layer.	2022-08-02 10:32:31 +08:00
Zihao Mu	57545653b1	replace new mish impl with softplus	2022-07-28 13:19:06 +08:00
Zihao Mu	3c5377ca1b	add another Mish graph simplifier.	2022-07-28 11:21:29 +08:00
HAN Liutong	e2bfe0ce76	Use "#if" instead of "#ifdef" for CV_SIMD128.	2022-07-21 03:23:57 +00:00
Zihao Mu	98c33c605d	batchsize dynamic is set to index 0.	2022-07-20 19:02:16 +08:00
rogday	ed69bcae2d	Merge pull request #21865 from rogday:nary_eltwise_layers ... Reimplementation of Element-wise layers with broadcasting support * init * semi-working initial version * add small_vector * wip * remove smallvec * add nary function * replace auto with Mat in lambda expr used in transform * uncomment asserts * autobuffer shape_buf & step_buf * fix a missing bracket * fixed a missing addLayer in parseElementWise * solve one-dimensional broadcast * remove pre_broadcast_transform for the case of two constants; fix missing constBlobsExtraInfo when addConstant is called * one autobuffer for step & shape * temporal fix for the missing original dimension information * fix parseUnsqueeze when it gets a 1d tensor constant * support sum/mean/min/max with only one input * reuse old code to handle cases of two non-constant inputs * add condition to handle div & mul of two non-constant inputs * use || instead of or * remove trainling spaces * enlarge buf in binary_forward to contain other buffer * use autobuffer in nary_forward * generate data randomly and add more cases for perf * add op and, or & xor * update perf_dnn * remove some comments * remove legacy; add two ONNX conformance tests in filter * move from cpu_denylist to all_denylist * adjust parsing for inputs>=2 Co-authored-by: fengyuentau <yuantao.feng@opencv.org.cn>	2022-07-19 06:14:05 +03:00
fengyuentau	1c7b71bf9e	define data_layout as unknown for pack	2022-07-14 19:27:20 +08:00
Zihao Mu	1b8fba8e26	support ReduceSum with two input and dynamic shape batch size in ReduceLayer.	2022-07-13 13:46:16 +08:00
Zihao Mu	45fbb67aba	fix scale layer can not handle 1x1 weight correctly.	2022-07-13 11:25:27 +08:00
Zihao Mu	139c443770	Merge pull request #22183 from zihaomu:fastConv_ARMv7_compatible ... DNN: ARMv7 compatible fastConv * support armv7 on fastConv * remove whitespace.	2022-07-07 13:23:08 +03:00
Tomoaki Teshima	a630ad73cb	suppress warning on GCC 7 and later	2022-07-06 23:31:31 +09:00
Zihao Mu	a80fcacd90	Merge pull request #21372 from zihaomu:dnn_quantize_per_tensor ... Add per_tensor_quantize to int8 quantize * add per_tensor_quantize to dnn int8 module. * change api flag from perTensor to perChannel, and recognize quantize type and onnx importer. * change the default to hpp	2022-07-05 19:14:42 +03:00
Zihao Mu	59b870a87a	Merge pull request #21910 from zihaomu:fast_conv_ARM ... DNN: Accelerating convolution * Fast Conv of ARM, X86 and universal intrinsics. * improve code style. * error fixed. * improve the License * optimize memory allocated and Adjust the threshold. * change FasterRCNN_vgg16 to 2GB memory.	2022-07-01 13:03:15 +03:00
Zihao Mu	ef94275eb6	bug fixed of GEMM node in ONNX_importer	2022-06-22 21:08:48 +08:00
Wanli	a6ca48a1c2	Merge pull request #22100 from WanliZhong:issue_22015 ... Fix issue 22015, let Clip layer support 1-3 inputs * Fix issue 22015. Let layer Clip support 1-3 inputs. * Resolve other problems caused by modifications * Update onnx_importer.cpp added extra checks to min/max handling in Clip * Add assertions to check the size of the input * Add test for clip with min and max initializers * Separate test for "clip_init_min_max". Change the check method for input_size to provide a clearer message in case of problem. * Add tests for clip with min or max initializers * Change the implementation of getting input Co-authored-by: Vadim Pisarevsky <vadim.pisarevsky@gmail.com>	2022-06-22 14:21:16 +03:00
Zihao Mu	2411b825b4	bug fixed of GEMM node in ONNX_importer	2022-06-22 15:00:17 +08:00
Alexander Alekhin	583bd1a6e2	Merge remote-tracking branch 'upstream/3.4' into merge-3.4	2022-06-04 19:10:35 +00:00
Namgoo Lee	24547f40ff	remove const from functions returning by value	2022-05-26 21:30:41 +09:00
Alexander Alekhin	e9187ae38c	Merge pull request #22026 from alalek:update_version_3.4.18-pre	2022-05-24 20:23:28 +00:00
Alexander Alekhin	978dc76653	Merge pull request #22006 from rogday:21947_fix	2022-05-24 19:26:02 +00:00
rogday	a2ad997e97	fix vector access in TF::sortByExecutionOrder	2022-05-24 00:05:13 +03:00
Alexander Alekhin	e9428726ca	pre: OpenCV 4.6.0 (version++)	2022-05-23 19:25:16 +00:00
Alexander Alekhin	400906b433	pre: OpenCV 3.4.18 (version++)	2022-05-23 19:18:02 +00:00
berak	50d7c61c01	Update darknet_importer.cpp ... make it more obvious, that this is a '404', not a 'parsing' problem	2022-05-23 19:18:31 +02:00
Alexander Alekhin	d9bf522b27	Merge remote-tracking branch 'upstream/3.4' into merge-3.4	2022-05-23 16:06:14 +00:00
rogday	93dc0679ec	Merge pull request #21818 from rogday:revert_renaming ... * add prefixes to layer names and layer output names * dnn: OPENCV_DNN_ONNX_USE_LEGACY_NAMES runtime parameter Co-authored-by: Alexander Alekhin <alexander.a.alekhin@gmail.com>	2022-05-23 14:50:42 +00:00
Alexander Alekhin	bb5462e327	Merge pull request #21991 from fengyuentau:qconv_asympad	2022-05-19 17:20:04 +00:00
fengyuentau	ff88132620	support asymmetric paddings for qconv	2022-05-16 19:01:37 +08:00
OpenCV Developers	d9a444ca1a	Merge remote-tracking branch 'upstream/3.4' into merge-3.4	2022-05-14 11:23:21 +00:00
Yulv-git	15ac54d5d6	Fix some typos in modules/.	2022-04-30 13:40:07 +08:00
Zihao Mu	64ded50bbf	parsing depth2space and space2depth of ONNX importer	2022-04-29 10:17:02 +08:00
OpenCV Developers	0fbd58bef9	Merge branch 4.x	2022-04-23 22:07:14 +00:00
rogday	9cd5a0a1e6	Merge pull request #21884 from rogday:cuda_cleanup ... Fix CUDA compilation issues and adjust thresholds. * Fix CUDA compilation issues and adjust thresholds. * add conformance tests to denylist	2022-04-19 16:40:25 +00:00
OpenCV Developers	2985739b8c	Merge remote-tracking branch 'upstream/3.4' into merge-3.4	2022-04-16 14:41:15 +00:00
rogday	a2b84e9897	add assert to tf graph simplifier to address security concerns	2022-04-13 22:50:27 +03:00
OpenCV Pushbot	66f3c2673c	Merge pull request #21831 from zihaomu:sign_layer_onnx ... DNN: Add sign, shrink and reciprocal for onnx_impoter	2022-04-13 17:08:30 +00:00
OpenCV Pushbot	03c9648f2e	Merge pull request #21854 from opencv-pushbot:dnn_test_update_checks_face_detector_4.x	2022-04-12 17:20:22 +00:00
OpenCV Developers	e3a55af336	dnn(test): update opencv_face_detector checks ... original commit: be4a432bea	2022-04-11 20:27:06 +00:00
OpenCV Developers	be4a432bea	dnn(test): update opencv_face_detector checks	2022-04-11 20:26:25 +00:00
zihaomu	e36948cfbc	add ONNX OP sign, shrink and reciprocal	2022-04-07 15:32:12 +08:00
Alexander Alekhin	08d44f588f	dnn(test): update OpenVINO tests 2022.1.0 (OpenCV 4.x)	2022-04-05 14:13:38 +00:00
Alexander Alekhin	13a995cc1d	Merge remote-tracking branch 'upstream/3.4' into merge-3.4	2022-04-02 19:45:44 +00:00
Alexander Alekhin	4d927e73f1	dnn(test): update OpenVINO tests 2022.1.0	2022-04-02 17:42:53 +00:00
Alexander Alekhin	a233982931	Merge pull request #20938 from JulieBar:lstm_cuda2	2022-04-01 22:10:08 +00:00
Zihao Mu	7b582b71ba	Merge pull request #21036 from fengyuentau:timvx_backend_support ... dnn: TIM-VX NPU backend support * Add TimVX NPU backend for DNN module. * use official branch from tim-vx repo; fix detecting viv sdk Co-authored-by: fytao <yuantao.feng@outlook.com>	2022-03-31 21:42:11 +00:00
Smirnov Egor	abebbf04b1	Add CUDA support for LSTM. ... Co-authored-by: Julia Bareeva <jbareeva@gmail.com>	2022-03-31 16:38:22 +03:00
Alexander Alekhin	5e434073d4	Merge pull request #21796 from alalek:dnn_reduce_fixup_21601	2022-03-30 22:26:28 +00:00
Alexander Alekhin	6f5cf8c15f	dnn: fix ReduceLayer implementation, update OpenVINO tests	2022-03-30 20:03:41 +00:00
Alexander Alekhin	b687bc807a	dnn(test): update OpenVINO tests 2021.4.2	2022-03-30 18:58:35 +00:00
Alexander Alekhin	1339ebaa84	Merge remote-tracking branch 'upstream/3.4' into merge-3.4	2022-03-26 16:00:28 +00:00
Alexander Alekhin	c9b90884da	Merge pull request #21601 from zihaomu:add_reduceLayer	2022-03-26 10:20:10 +00:00
luz paz	8e8e4bbabc	dnn: fix various dnn related typos ... Fixes source comments and documentation related to dnn code.	2022-03-23 18:12:12 -04:00
Alexander Alekhin	4c79318694	dnn: fix index access	2022-03-19 06:54:07 +00:00
Zihao Mu	b6b5c27cec	Support for some reduce layers for onnx	2022-03-18 10:19:13 +08:00
Alexander Alekhin	685797f403	Merge pull request #21662 from alalek:dnn_split	2022-03-17 16:09:17 +00:00
rogday	93353aea70	Merge pull request #21522 from rogday:lstm ... Fix LSTM support in ONNX * fix LSTM and add peephole support * disable old tests * turn lambdas into functions * more hacks for c++98 * add assertions * slice fixes * backport of cuda-related fixes * address review comments	2022-03-15 09:14:05 +03:00
Alexander Alekhin	5bf3c1df24	Merge pull request #21715 from ilyachur:change_type_info_creation	2022-03-14 09:18:58 +00:00
Ilya Churaev	419918076e	Changed call of NodeTypeInfo constructor	2022-03-14 10:55:33 +03:00
Alexander Alekhin	a120adde63	dnn: add dnn.cpp file with information about git commits history	2022-03-08 19:22:47 +00:00
Alexander Alekhin	a80af177b6	dnn: split dnn.cpp code ... base commit: 19926e2979 original dnn.cpp content: 19926e2979/modules/dnn/src/dnn.cpp	2022-03-08 19:22:46 +00:00
Tsukasa Sugiura	8db7d435b9	Merge pull request #21692 from UnaNancyOwen:add_softmax ... * add apply softmax option to ClassificationModel * remove default arguments of ClassificationModel::setSoftMax() * fix build for python * fix docs warning for setSoftMax() * add impl for ClassficationModel() * fix failed build for docs by trailing whitespace * move to implement classify() to ClassificationModel_Impl * move to implement softmax() to ClassificationModel_Impl * remove softmax from public method in ClassificationModel	2022-03-07 20:26:15 +00:00
Alexander Alekhin	901e0ddfe4	Merge remote-tracking branch 'upstream/3.4' into merge-3.4	2022-03-05 19:46:28 +00:00
Alexander Alekhin	5cc27fd3b5	Merge pull request #21542 from rogday:split_expand	2022-02-28 22:38:24 +00:00
Egor Smirnov	375fe81311	fix slice and expand	2022-02-28 17:18:07 +03:00
Alexander Alekhin	899b4d1452	Merge branch 4.x	2022-02-22 19:55:26 +00:00
Yuantao Feng	f77c3574af	Merge pull request #21607 from fengyuentau:fix_FaceDetectorYN_dynamic_shape ... Use YuNet of fixed input shape to fix not-supported-dynamic-zero-shape for FaceDetectorYN * use yunet with input of fixed shape * update yunet used in face recognition regression	2022-02-21 13:49:07 +00:00
Maksim Shabunin	a251474144	Update filters in ONNX tests	2022-02-15 11:56:28 +03:00
Maksim Shabunin	45cbf70265	Update filters in ONNX tests	2022-02-14 17:16:49 +03:00
Alexander Alekhin	19926e2979	Merge remote-tracking branch 'upstream/3.4' into merge-3.4	2022-02-11 17:32:37 +00:00
Alexander Alekhin	effce0573b	dnn: drop legacy Inference Engine NN builder API	2022-02-10 11:55:24 +00:00
Alexander Alekhin	57d3002ee1	Merge remote-tracking branch 'upstream/3.4' into merge-3.4	2022-02-06 16:10:43 +00:00
Alexander Alekhin	a00a0dbfcd	Merge pull request #21564 from alalek:dnn_fix_openvino_outputs	2022-02-06 16:06:23 +00:00
Alexander Alekhin	b41d2c5c14	Merge pull request #21569 from alalek:fixup_18031	2022-02-06 16:04:38 +00:00
Alexander Alekhin	1da48beeec	dnn(ngraph): fix output names	2022-02-06 13:08:53 +00:00
Alexander Alekhin	b57ff73086	dnn(ngraph): fix outputs handling, drop 'unconnected' logic	2022-02-06 13:08:53 +00:00
Alexander Alekhin	67978b5746	dnn(ngraph): add debuging messages	2022-02-06 13:08:53 +00:00
Alexander Alekhin	062f305d1a	dnn: don't fuse 'outputs' with OpenVINO backend	2022-02-06 13:08:53 +00:00
Alexander Alekhin	1f70d4e2a5	dnn(test): re-enable ONNX split tests for OpenVINO	2022-02-06 10:36:15 +00:00
Alexander Alekhin	aa5bc20c83	dnn(ngraph): fixup get_output_as_single_output_node() replacement patch	2022-02-06 10:35:59 +00:00
Maksim Shabunin	d1e76a34a0	3.4: Use modern OpenVINO package interface ... original commit: 437af37b13	2022-02-02 09:04:03 +00:00
Maksim Shabunin	437af37b13	Use modern OpenVINO package interface	2022-02-01 16:52:17 +00:00
Alexander Alekhin	870c8d3c4e	dnn(test): fix int8 tolerances	2022-01-31 12:54:01 +00:00
Alexander Alekhin	d573472a86	Merge remote-tracking branch 'upstream/3.4' into merge-3.4	2022-01-31 12:53:45 +00:00
Alexander Alekhin	a7e6a1059c	dnn(test): fix outputs handling in ONNX conformance ... - ONNX output is 1 tensor per defined output instead of N tensors from outputs of "output" layer	2022-01-29 23:29:51 +00:00
Alexander Alekhin	85719a0a5d	dnn: support outputs registration under new names ... - fixed ONNX importer	2022-01-29 23:29:51 +00:00
Alexander Alekhin	dc35633aa4	Merge pull request #21521 from alalek:dnn_ignore_denormals	2022-01-28 15:31:44 +00:00
Zihao Mu	9e3ba487fa	Merge pull request #21518 from zihaomu:resize_onnx_opset13 ... Add resize layer compatible with ONNX opset13 version	2022-01-28 17:55:01 +03:00
Alexander Alekhin	9188ce68aa	Merge pull request #21490 from rogday:optional_outputs	2022-01-26 15:18:07 +00:00
Alexander Alekhin	70b0274c8e	dnn: apply hint to ignore denormals processing	2022-01-26 11:28:35 +00:00
Alexander Alekhin	b796ededae	Merge pull request #21437 from alalek:dnn_api_explicit_const_4.x	2022-01-21 20:19:50 +00:00
Alexander Alekhin	eb7b45d26b	dnn: fix API - explicit ctors, const methods	2022-01-21 12:38:51 +00:00
Smirnov Egor	17b2d92a3d	add optional outputs support and fix graph links	2022-01-21 12:31:46 +03:00
Alexander Alekhin	6ffa2b01e1	Merge pull request #21357 from rogday:model_diag	2022-01-18 15:50:11 +00:00
rogday	0fe7420638	fix model diagnostic tool	2022-01-18 01:22:22 +03:00
Alexander Alekhin	b304730225	dnn: fix API - explicit ctors, const methods	2022-01-17 21:45:29 +00:00
Maksim Shabunin	d5f73f89d8	Fixed issues found by static analysis	2022-01-13 14:51:25 +03:00
Alexander Alekhin	aebb65e983	Merge remote-tracking branch 'upstream/3.4' into merge-3.4	2022-01-12 13:26:10 +00:00
Alexander Alekhin	80d9f624d0	dnn: don't use aligned load without alignment checks ... - weights are unaligned in dasiamprn sample (comes from numpy)	2022-01-12 05:11:18 +00:00
Alexander Alekhin	a0d5277e0d	Merge branch 4.x	2021-12-30 21:43:45 +00:00
Alexander Alekhin	76fb3652fc	dnn(ocl): fix fp16 kernel compilation	2021-12-29 19:58:25 +00:00
Alexander Alekhin	9699e2b483	dnn(onnx): handle non-default ONNX domains ... - re-enable quantized models tests	2021-12-25 01:38:52 +00:00
Alexander Alekhin	217fea9667	Merge remote-tracking branch 'upstream/3.4' into merge-3.4	2021-12-24 16:48:07 +00:00
Alexander Alekhin	cdd4354256	Merge pull request #21336 from alalek:dnn_pooling_check_array_indexes	2021-12-24 08:35:11 +00:00
Alexander Alekhin	6385511e88	dnn: add checks in pooling layer implementation ... - to avoid out of buffer access	2021-12-24 00:15:30 +00:00
Alexander Alekhin	ed4becf007	dnn(onnx): debug dump of inputs/outputs/initializers in importer	2021-12-23 21:11:40 +00:00
Alexander Alekhin	f5589445b9	Merge pull request #21322 from alalek:dnn_catch_errors	2021-12-23 20:09:22 +00:00
Alexander Alekhin	011ed380aa	Merge pull request #21323 from alalek:dnn_do_not_rebuilt_network_in_setInput	2021-12-23 15:45:28 +00:00
Alexander Alekhin	88a18c8b6a	dnn(onnx): emit error in Shape for dynamic input	2021-12-23 15:42:59 +00:00
Alexander Alekhin	51e65db715	dnn(onnx): fix Resize inputs handling	2021-12-23 15:42:59 +00:00
Alexander Alekhin	cc02fcd889	dnn: improve debug messages, add ONNX opset version	2021-12-23 15:42:59 +00:00
Alexander Alekhin	c408157a4d	dnn: do not try to rebuilt network during setInput() ... - this doesn't make sense in case of multiple inputs	2021-12-23 02:40:33 +00:00
Alexander Alekhin	6e299b582a	dnn(test): decompose 'DynamicAxes' test	2021-12-23 00:47:27 +00:00
Alexander Alekhin	9777fbacf6	Merge remote-tracking branch 'upstream/3.4' into merge-3.4	2021-12-22 15:57:02 +00:00
Alexander Alekhin	c78a8dfd2d	fix 4.x links	2021-12-22 13:24:30 +00:00
Alexander Alekhin	b1a57c4cb2	fix 3.4 links	2021-12-22 12:38:21 +00:00
rogday	0a178a687a	fix const/x in Div	2021-12-20 19:53:37 +03:00
Alexander Alekhin	80492d663e	Merge remote-tracking branch 'upstream/3.4' into merge-3.4	2021-12-18 16:19:06 +00:00
Alexander Alekhin	172c539a5a	Merge pull request #21281 from alalek:update_version_4.5.5-pre	2021-12-18 13:46:38 +00:00
Alexander Alekhin	a079acc0d9	Merge pull request #21280 from alalek:update_version_3.4.17-pre	2021-12-18 13:46:29 +00:00
Smirnov Egor	71a22e45b0	add celu, hardsigmoid, selu, thresholdedrelu layers	2021-12-18 03:19:54 +03:00
Alexander Alekhin	04ee99f1a3	Merge pull request #21282 from alalek:dnn_test_vulkan_skip	2021-12-17 19:39:35 +00:00
Smirnov Egor	1bd382c1d0	Add acos, acosh, asin, asinh, atan, atanh, cos, cosh, erf, hardswish, sin, sinh, softplus, softsign, tan layers	2021-12-17 18:19:40 +03:00
Alexander Alekhin	249c508126	dnn(test): skip failed tests on Vulkan backend	2021-12-17 14:01:04 +00:00
Smirnov Egor	fec2c7e715	fix Flatten layer	2021-12-17 16:29:56 +03:00
Alexander Alekhin	07dca8cc03	pre: OpenCV 4.5.5 (version++)	2021-12-17 10:12:11 +00:00
Alexander Alekhin	60c093f086	pre: OpenCV 3.4.17 (version++)	2021-12-17 10:05:52 +00:00
Alexander Alekhin	622b9d9276	Merge pull request #21267 from mshabunin:fix-kw-2021-12	2021-12-16 18:51:47 +00:00
Gruhuang	b4bb98ea60	Merge pull request #21268 from pccvlab:tf_Arg ... add argmax and argmin parsing for tensorflow * add argmax and argmin for tf * remove whitespace * remove whitespace * remove static_cast Signed-off-by: Crayon-new <1349159541@qq.com>	2021-12-16 17:06:02 +00:00
Maksim Shabunin	792b7e0629	(3.4) Fixed several issues found by static analysis ... original commit: a079c2eb7c	2021-12-16 17:02:58 +00:00
Maksim Shabunin	a079c2eb7c	Fixed several issues found by static analysis	2021-12-16 19:21:25 +03:00
Alexander Alekhin	6d677bbd63	dnn(test): update ONNX conformance filters (4.x)	2021-12-16 12:09:31 +00:00
Alexander Alekhin	299f9837b7	Merge remote-tracking branch 'upstream/3.4' into merge-3.4	2021-12-15 16:38:56 +00:00
Alexander Alekhin	f3ba88c87c	dnn(test): update ONNX conformance filters	2021-12-15 12:53:53 +00:00
Smirnov Egor	e97c7e042b	fix max_unpool missing attributes, add default value of keepdims in reducemean/max/sum, add support for keepdims=true in full reduction branch, add new padding type to Pad	2021-12-14 22:09:27 +03:00
rogday	4827fe86bb	Merge pull request #21088 from rogday:onnx_tests ... Onnx conformance tests * Add ONNX conformance tests * dnn(test): add filters for ONNX conformance tests * add filter lists for OCV backend * address review comments * move test_clip_inbounds to all_denylist * address clip issue * avoid empty lists Co-authored-by: Alexander Alekhin <alexander.a.alekhin@gmail.com>	2021-12-14 16:58:06 +00:00
cqn2219076254	252ce0b581	add square layer	2021-12-13 21:43:13 +08:00
Alexander Alekhin	6e50e4b9ee	Merge pull request #21161 from rogday:elu_alpha_4x	2021-12-10 16:04:01 +00:00
HAN Liutong	1599f9f0c0	Merge pull request #21086 from hanliutong:rvv-dnn ... Further optimize DNN for RISC-V Vector. * Optimize DNN on RVV by using vsetvl. * Rename vl. * Update fastConv by using setvl instead of mask. * Fix fastDepthwiseConv	2021-12-10 16:03:22 +00:00
Gruhuang	17bc8565f6	Merge pull request #21154 from pccvlab:MatMul_with_two_inputs ... Add BatchMatMul layer support for tf_importer * two inputs * support batch_matmul * refactor: remove useless code * refactor: decrease nesting	2021-12-10 14:44:27 +03:00
Smirnov Egor	e608adea60	add ArgMax and ArgMin layers	2021-12-06 20:49:54 +03:00
Alexander Alekhin	73318fd514	Merge pull request #21167 from alalek:dnn_test_reenable_ov_2021_4	2021-12-04 22:12:18 +00:00
HAN Liutong	4935b14539	Merge pull request #21012 from hanliutong:rvv_clang ... Update RVV backend for using Clang. * Update cmake file of clang. * Modify the RVV optimization on DNN to adapt to clang. * Modify intrin_rvv: Disable some existing types. * Modify intrin_rvv: Reinterpret instead of load&cast. * Modify intrin_rvv: Update load&store without cast. * Modify intrin_rvv: Rename vfredsum to fredosum. * Modify intrin_rvv: Rewrite Check all/any by using vpopc. * Modify intrin_rvv: Use reinterpret instead of c-style casting. * Remove all macros which is not used in v_reinterpret * Rename vpopc to vcpop according to spec.	2021-12-03 15:13:24 +00:00
Alexander Alekhin	0835611d3a	dnn(test): re-enable tests which works with OpenVINO 2021.4.x	2021-12-03 14:01:30 +00:00
Alexander Alekhin	8b4fa2605e	Merge remote-tracking branch 'upstream/3.4' into merge-3.4	2021-12-03 12:32:49 +00:00
Alexander Alekhin	35ff9af6ce	Merge pull request #21162 from rogday:softmax_simplification	2021-12-02 17:14:48 +00:00
Alexander Alekhin	dad2b9aac8	Merge pull request #21160 from rogday:elu_alpha	2021-12-02 17:13:57 +00:00
rogday	1613d30544	Merge pull request #21159 from rogday:ceil_mode ... fix ceil_mode for Average/MaxPooling * fix ceil_mode * add a comment	2021-12-02 20:11:11 +03:00
Alexander Alekhin	b9d0dc60b0	Merge pull request #21173 from alalek:3.4_dnn_test_reenable_ov_2021_4	2021-12-02 16:33:15 +00:00
Alexander Alekhin	d206350738	Merge pull request #21172 from alalek:dnn_test_drop_non_cpu_int8	2021-12-02 13:50:04 +00:00
Alexander Alekhin	bd396e1fd5	dnn(test): re-enable tests which works with OpenVINO 2021.4.x (3.4)	2021-12-02 11:30:45 +00:00
Alexander Alekhin	f55c9ed1ba	dnn(test): drop non OCV/CPU cases for Int8 ... - zero code coverage and up to x3-x8 tests slowdown - implementation executes OCV/CPU in all cases - wrong skip conditions	2021-12-02 06:27:10 +00:00
Alexander Alekhin	5da69c0b9a	Merge pull request #21164 from rogday:sum_identity	2021-12-01 22:49:02 +00:00
Alexander Alekhin	a806e8cc58	Merge pull request #21163 from rogday:transpose_default	2021-12-01 22:47:57 +00:00
Alexander Alekhin	d9e7c1626a	Merge pull request #21153 from alalek:build_warnings_msvs2017	2021-12-01 12:49:28 +00:00
Smirnov Egor	33e97e994d	add sum of 1 input	2021-11-30 15:42:20 +03:00
Smirnov Egor	11e6848bb9	add default order to transpose	2021-11-30 15:34:34 +03:00
Smirnov Egor	829410729c	add new (Log)SoftMax simplification passes	2021-11-30 15:20:52 +03:00
Smirnov Egor	4995aecd62	add alpha parameter to ELU	2021-11-30 14:43:18 +03:00
Smirnov Egor	0e2a3686c0	add alpha parameter to ELU layer	2021-11-30 12:20:35 +03:00
Alexander Alekhin	66b2140892	build: eliminate C4309 warning from protobuf files with MSVS2017	2021-11-30 04:27:39 +00:00
Alexander Alekhin	0d2857a242	Merge pull request #21152 from rogday:fix_defaults	2021-11-29 22:39:27 +00:00
Alexander Alekhin	17d99e6266	Merge pull request #21142 from alalek:dnn_two_inputs_ocl_fp16_3.4	2021-11-29 21:44:59 +00:00
Andrew Ryrie	ea7d4be3f8	Merge pull request #20658 from smbz:lstm_optimisation ... * dnn: LSTM optimisation This uses the AVX-optimised fastGEMM1T for matrix multiplications where available, instead of the standard cv::gemm. fastGEMM1T is already used by the fully-connected layer. This commit involves two minor modifications: - Use unaligned access. I don't believe this involves any performance hit in on modern CPUs (Nehalem and Bulldozer onwards) in the case where the address is actually aligned. - Allow for weight matrices where the number of columns is not a multiple of 8. I have not enabled AVX-512 as I don't have an AVX-512 CPU to test on. * Fix warning about initialisation order * Remove C++11 syntax * Fix build when AVX(2) is not available In this case the CV_TRY_X macros are defined to 0, rather than being undefined. * Minor changes as requested: - Don't check hardware support for AVX(2) when dispatch is disabled for these - Add braces * Fix out-of-bounds access in fully connected layer The old tail handling in fastGEMM1T implicitly rounded vecsize up to the next multiple of 8, and the fully connected layer implements padding up to the next multiple of 8 to cope with this. The new tail handling does not round the vecsize upwards like this but it does require that the vecsize is at least 8. To adapt to the new tail handling, the fully connected layer now rounds vecsize itself at the same time as adding the padding(which makes more sense anyway). This also means that the fully connected layer always passes a vecsize of at least 8 to fastGEMM1T, which fixes the out-of-bounds access problems. * Improve tail mask handling - Use static array for generating tail masks (as requested) - Apply tail mask to the weights as well as the input vectors to prevent spurious propagation of NaNs/Infs * Revert whitespace change * Improve readability of conditions for using AVX * dnn(lstm): minor coding style changes, replaced left aligned load	2021-11-29 21:43:00 +00:00
Smirnov Egor	05db8784ae	fix Clip, LeakyReLU, LRN, Split defaults	2021-11-29 20:20:34 +03:00
Supernovae	b594ed99b8	Merge pull request #20933 from shubham-shahh:master ... Improved overall readability of the code * grid_nms.cu: minor fix-ups * Update grid_stride_range.hpp * Update tf_importer.cpp	2021-11-28 12:54:29 +00:00
Alexander Alekhin	58b06222ff	dnn(DataLayer): fix CPU/OpenCL code paths for FP16 handling	2021-11-28 07:44:05 +00:00
Alexander Alekhin	58dc397930	dnn(test): add two_inputs test with FP32/U8 data types ... - remove similar test from IE scope under HAVE_INF_ENGINE	2021-11-28 07:44:04 +00:00
yuki takehara	a6277370ca	Merge pull request #21107 from take1014:remove_assert_21038 ... resolves #21038 * remove C assert * revert C header * fix several points in review * fix test_ds.cpp	2021-11-27 18:34:52 +00:00
Alexander Alekhin	31b2d6be75	dnn(test): update InferenceEngine tests (4.x)	2021-11-27 18:16:52 +00:00
Alexander Alekhin	57ee14d62d	Merge remote-tracking branch 'upstream/3.4' into merge-3.4	2021-11-27 16:50:55 +00:00
Alexander Alekhin	985aa0423d	dnn(test): update InferenceEngine tests	2021-11-26 18:46:26 +00:00
Hanxi Guo	1fcf7ba5bc	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	2021-11-23 21:15:31 +00:00
Alexander Alekhin	394e640909	Merge remote-tracking branch 'upstream/3.4' into merge-3.4	2021-11-13 15:11:30 +00:00
Alexander Alekhin	8041ab8a61	Merge pull request #21025 from alalek:issue_21004 ... * dnn(ocl4dnn): fix LRN layer accuracy problems - FP16 intermediate computation is not accurate and may provide NaN values * dnn(test): update tolerance for FP16	2021-11-12 01:54:07 +03:00
Alexander Alekhin	d934bb15b0	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 12:03:45 +00:00
ZaKiiiiiiiii	98b6ce353c	Merge pull request #20904 from Crayon-new:fix_bug_in_maxLayer ... fix bug: wrong output dimension when "keep_dims" is false in pooling layer. * fix bug in max layer * code align * delete permute layer and add test case * add name assert * check other cases * remove c++11 features * style:add "const" remove assert * style:sanitize file names	2021-11-09 19:24:04 +03:00
Alexander Alekhin	7842181b47	Merge remote-tracking branch 'upstream/3.4' into merge-3.4	2021-11-05 09:27:46 +00:00
Alexander Alekhin	562f2375c5	dnn(test): skip tests with high memory usage ... - 32-bit configuration may fail due to memory fragmentation	2021-11-04 13:26:33 +00:00
Alexander Alekhin	edf533c83e	Merge pull request #21007 from alalek:cmake_dnn_fix_wrong_tengine_order	2021-11-04 12:28:27 +00:00
Alexander Alekhin	c1d61c88e9	dnn(cmake): don't hijack OpenCL options with Tengine	2021-11-04 09:59:19 +00:00
Alexander Alekhin	d484939c02	Merge pull request #20999 from alalek:dnn_replace_deprecated_calls ... dnn(protobuf): replace deprecated calls * dnn: replace deprecated ByteSize() => ByteSizeLong() * dnn: replace deprecated calls, use GetRepeatedFieldRef	2021-11-03 15:59:36 +00:00
Alexander Alekhin	fce4a19d0d	5.x: cleanup compatibility code (2021-10)	2021-10-20 17:40:04 +00:00
Alexander Alekhin	ec10f2e72b	Merge pull request #20877 from rogday:simple_layers	2021-10-20 17:00:38 +00:00
rogday	b3f966e2ca	Merge pull request #20883 from rogday:eltwise_refactoring ... * backport elementwise_layers refactor * keep NULL	2021-10-19 13:29:22 +00:00
Alexander Alekhin	1926e919be	dnn(int8): fix using of incorrect UMat constructor	2021-10-18 04:46:00 +00:00
Alexander Alekhin	7ba26ada12	Merge branch 4.x	2021-10-15 21:53:39 +00:00
Alexander Alekhin	31c40fa4cc	Merge remote-tracking branch 'upstream/3.4' into merge-3.4	2021-10-15 13:35:03 +00:00
Smirnov Egor	1feb3838b5	add Ceil, Floor, Log, Round, Sqrt, Not, Equal, Less, Greater	2021-10-15 16:02:46 +03:00
Alexander Alekhin	53d6c9b9c0	Merge pull request #20860 from rogday:sum_fix	2021-10-12 15:36:32 +00:00
Smirnov Egor	238dbffb48	change asserts for Sum	2021-10-11 20:59:44 +03:00
Smirnov Egor	a9d7b6eab7	fix const - input and remove unimplemented function	2021-10-11 18:58:10 +03:00
Alexander Alekhin	4672dbda2a	Merge pull request #20818 from rogday:yolov4x_mish_cuda	2021-10-08 19:12:43 +00:00
Smirnov Egor	9c84749e2c	backport YOLOv4x-mish new_coords CUDA implementation	2021-10-08 14:14:49 +03:00
Alexander Alekhin	cca4c47781	Merge remote-tracking branch 'upstream/3.4' into merge-3.4	2021-10-08 11:05:45 +00:00
Alexander Alekhin	81e7988eb9	Merge pull request #20840 from alalek:dnn_ocl_cleanup_code	2021-10-08 05:07:51 +00:00
Alexander Alekhin	8c2dd5fb9a	dnn(ocl4dnn): cleanup dead code, improve logging	2021-10-08 00:39:40 +00:00
Alexander Alekhin	724e04e979	dnn(ocl4dnn): add extra checks to convolution layer ... - prevent running code over unsupported/non-tested configurations - prevent integer div by zero	2021-10-07 23:18:32 +00:00
Alexander Alekhin	03a08435e2	Merge remote-tracking branch 'upstream/3.4' into merge-3.4	2021-10-07 04:27:22 +00:00
Alexander Alekhin	1a29ea1038	Merge pull request #20829 from alalek:dnn_ocl_skip_int8_tests	2021-10-06 22:46:21 +00:00
Alexander Alekhin	94e92cd6c0	dnn(ocl): skip int8 tests due to memory access issues	2021-10-06 21:27:18 +00:00
Alexander Alekhin	822d468232	Merge pull request #20813 from rogday:soft_nms	2021-10-06 20:20:34 +00:00
Smirnov Egor	2221dcc9f2	add SoftNMS implementation	2021-10-06 21:31:45 +03:00
Oliver Kuckertz	a3d7811f24	Merge pull request #20725 from mologie:fix-dnn-tf-on-arm ... * dnn: fix unaligned memory access crash on armv7 The getTensorContent function would return a Mat pointing to some member of a Protobuf-encoded message. Protobuf does not make any alignment guarantees, which results in a crash on armv7 when loading models while bit 2 is set in /proc/cpu/alignment (or the relevant kernel feature for alignment compatibility is disabled). Any read attempt from the previously unaligned data member would send SIGBUS. As workaround, this commit makes an aligned copy via existing clone functionality in getTensorContent. The unsafe copy=false option is removed. Unfortunately, a rather crude hack in PReLUSubgraph in fact writes(!) to the Protobuf message. We limit ourselves to fixing the alignment issues in this commit, and add getTensorContentRefUnaligned to cover the write case with a safe memcpy. A FIXME marks the issue. * dnn: reduce amount of .clone() calls * dnn: update FIXME comment Co-authored-by: Alexander Alekhin <alexander.a.alekhin@gmail.com>	2021-10-06 16:41:05 +00:00
Alexander Alekhin	646924fce8	dnn(pytest/test_input_3d): reload model between switching targets	2021-10-05 23:23:08 +00:00
HAN Liutong	e5fb50476c	Merge pull request #20521 from hanliutong:dev-rvv-multiVLEN ... Make the implementation of optimization in DNN adjustable to different vector sizes with RVV intrinsics. * Update fastGEMM for multi VLEN. * Update fastGEMM1T for multi VLEN. * Update fastDepthwiseConv for multi VLEN. * Update fastConv for multi VLEN. * Replace malloc with cv::AutoBuffer.	2021-10-05 15:35:00 +00:00
Alexander Alekhin	3e6f27522b	pre: OpenCV 4.5.4 (version++)	2021-10-04 22:35:47 +00:00
Alexander Alekhin	1b70f94282	Merge pull request #20782 from YashasSamaga:cuda4dnn-eltwise-broadcast	2021-10-04 22:35:00 +00:00
Alexander Alekhin	ebef84e9ea	pre: OpenCV 3.4.16 (version++)	2021-10-04 20:47:07 +00:00
Jebastin Nadar	cce78cc5e2	Merge pull request #20535 from SamFC10:onnx-q ... dnn : int8 quantized layers support in onnx importer * added quantized layers support in onnx importer * added more cases in eltwise node, some more checks * added tests for quantized nodes * relax thresholds for failed tests, address review comments * refactoring based on review comments * added support for unsupported cases and pre-quantized resnet50 test * relax thresholds due to int8 resize layer	2021-10-04 18:07:38 +00:00
Zihao Mu	9085b933d8	Merge pull request #20702 from zihaomu:tf_expand_dim_layer ... Add ExpandDims layer of tf_importer.cpp * Add ExpandDims to tf_importer. * add -1 expand test case. * Support different dimensions of input. * Compatible with 5-dimensional NDHWC data * Code align * support 3-dim input. * 3-dim bug fixed. * fixing error of code format.	2021-10-04 16:37:38 +00:00
YashasSamaga	505dde09de	support broadcasting in eltwise ops	2021-10-04 12:38:45 +05:30
SamFC10	87ebf2e50b	fix illegal memory access in int8 convolution	2021-10-03 15:16:01 +05:30
Alexander Alekhin	37c3f0d8a0	Merge remote-tracking branch 'upstream/3.4' into merge-3.4	2021-10-02 17:57:18 +00:00
Alexander Alekhin	f977d10a19	dnn(ocl): fix conv DWCONV workgroup	2021-10-01 18:52:07 +00:00
Alexander Alekhin	846317ef37	dnn(ocl): fix conv BASIC workgroup	2021-09-29 14:55:46 +00:00
Alexander Alekhin	24fcb7f813	Merge remote-tracking branch 'upstream/3.4' into merge-3.4	2021-09-25 17:50:00 +00:00
rogday	38b9ec7a18	Merge pull request #20682 from rogday:min ... * Add Min layer to CPU, OpenCL, Halide, Inference Engine, NGraph and CUDA * fix indentation * add min to fusion and halide tests; fix doc	2021-09-22 15:17:37 +03:00
SamFC10	9c5d7716e2	fix for unsqueeze opset version 13	2021-09-17 17:40:57 +05:30
Alexander Alekhin	46fd26e366	Merge pull request #20699 from alalek:dnn_perf_update_convolution_tests	2021-09-16 17:11:32 +00:00
rogday	c410d7a97d	Merge pull request #20671 from rogday:yolov4x-mish ... Add support for YOLOv4x-mish * backport to 3.4 for supporting yolov4x-mish * add YOLOv4x-mish test * address review comments Co-authored-by: Guo Xu <guoxu@1school.com.cn>	2021-09-14 17:49:49 +00:00
YashasSamaga	50462dcdc6	fix effrank assert to allow input effrank <= output effrank	2021-09-13 20:44:33 +05:30
Alexander Alekhin	6e66a9222a	dnn(onnx): fix format specifier	2021-09-11 22:26:52 +00:00
Alexander Alekhin	c3ac834526	Merge remote-tracking branch 'upstream/3.4' into merge-3.4	2021-09-11 21:27:26 +00:00
Zihao Mu	51b03b87e6	BiasAdd could load Const from second place.	2021-09-11 15:34:41 +00:00
Alexander Alekhin	1aacb9bb15	dnn(perf): update convolution tests	2021-09-10 13:11:02 +00:00
Alexander Alekhin	6ace801418	Merge pull request #20661 from alalek:dnn_ocl_fix_gemm_like_kernel	2021-09-10 11:58:52 +00:00
rogday	d31b93b513	Merge pull request #20674 from rogday:prelu_slope ... Fix PReLU negative slope access pattern * fix prelu negative slope access pattern * change begin() to ptr()	2021-09-10 11:07:16 +00:00
rogday	4807cd8a6e	Merge pull request #20605 from rogday:split_slice_shenanigans ... Add Normalize subgraph, fix Slice, Mul and Expand * Add Normalize subgraph, support for starts<0 and axis<0 in Slice, Mul broadcasting in the middle and fix Expand's unsqueeze * remove todos * remove range-based for loop * address review comments * change >> to > > in template * fix indexation * fix expand that does nothing	2021-09-09 14:41:40 +03:00
Alexander Alekhin	35e824c287	dnn(ocl): fix out of bound access in GEMM-like kernels ... - dropped usage of CreateSubBuffer() - buffers lifetime management issue - fixed elementwise offset - avoid out of bounds read access	2021-09-06 18:17:21 +00:00
Alexander Alekhin	5578ad5e14	dnn(ocl): fix automatic globalsize adjusting ... - if kernel code doesn't support that	2021-09-06 03:11:29 +00:00
Alexander Alekhin	0a43b23275	Merge pull request #20651 from alalek:issue_18361	2021-09-04 18:22:12 +00:00
Alexander Alekhin	7967683296	Merge pull request #20648 from alalek:issue_20615	2021-09-04 18:21:58 +00:00
Alexander Alekhin	5b2c016834	dnn(ocl): avoid out of buffer access in copyWeightsSwizzled	2021-09-04 15:45:59 +00:00
Alexander Alekhin	407adc7061	dnn(ocl): fix buffer offsets in IDLF kernel ... - drop CreateSubBuffer - fix FUSED_CONV_ELTWISE mode	2021-09-04 15:28:35 +00:00
rogday	d0e612dc36	Merge pull request #20647 from rogday:resize_concat_optimization ... Fix resize+concat optimization * fix resize+concat optimization * add comment and fix indentation	2021-09-03 12:32:29 +00:00
Alexander Alekhin	5aa7435d25	Merge remote-tracking branch 'upstream/3.4' into merge-3.4	2021-09-02 15:24:04 +00:00
Alexander Alekhin	060a76dc3e	Merge pull request #20573 from rogday:onnx_scale_fix	2021-09-01 14:09:17 +00:00
WJJ1995	edc442afdb	Merge pull request #20511 from wjj19950828:add_humanseg_support_0806 ... * support PPSeg model for dnn module * fixed README for CI * add test case * fixed bug * deal with comments * rm dnn_model_runner * update test case * fixed bug for testcase * update testcase	2021-09-01 10:10:05 +00:00
Alexander Alekhin	ae6fabc6fe	dnn(ocl): drop CL_KERNEL_PREFERRED_WORK_GROUP_SIZE_MULTIPLE check ... - it is a hint and it should not block kernel execution	2021-08-30 20:40:14 +00:00
Alexander Alekhin	4c05a697fa	Merge remote-tracking branch 'upstream/3.4' into merge-3.4	2021-08-28 21:30:28 +00:00
Vincent Rabaud	38d0063c36	Do not use deprecated ReleaseCleared in protobuf library. ... This is to make code work with protobuf arenas for memory management (ReleaseCleared is incompatible). The cleaning of the memory is also simpler.	2021-08-26 15:36:22 +02:00
Alexander Alekhin	6fbfc58602	Merge remote-tracking branch 'upstream/3.4' into merge-3.4	2021-08-21 17:25:18 +00:00