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Merge pull request #24298 from WanliZhong:extend_perf_net_test
Extend performance test models #24298 **Merged With https://github.com/opencv/opencv_extra/pull/1095** This PR aims to extend the performance tests. - **YOLOv5** for object detection - **YOLOv8** for object detection - **EfficientNet** for classification Models from OpenCV Zoo: - **YOLOX** for object detection - **YuNet** for face detection - **SFace** for face recognization - **MPPalm** for palm detection - **MPHand** for hand landmark - **MPPose** for pose estimation - **ViTTrack** for object tracking - **PPOCRv3** for text detection - **CRNN** for text recognization - **PPHumanSeg** for human segmentation If other models should be added, **please leave some comments**. Thanks! Build opencv with script: ```shell -DBUILD_opencv_python2=OFF -DBUILD_opencv_python3=OFF -DBUILD_opencv_gapi=OFF -DINSTALL_PYTHON_EXAMPLES=OFF -DINSTALL_C_EXAMPLES=OFF -DBUILD_DOCS=OFF -DBUILD_EXAMPLES=OFF -DBUILD_ZLIB=OFF -DWITH_FFMPEG=OFF ``` Performance Test on **Apple M2 CPU** ```shell MacOS 14.0 8 threads ``` **1 thread:** | Name of Test | 4.5.5-1th | 4.6.0-1th | 4.7.0-1th | 4.8.0-1th | 4.8.1-1th | |--------------|:---------:|:---------:|:---------:|:---------:|:---------:| | CRNN | 76.244 | 76.611 | 62.534 | 57.678 | 57.238 | | EfficientNet | --- | --- | 109.224 | 130.753 | 109.076 | | MPHand | --- | --- | 19.289 | 22.727 | 27.593 | | MPPalm | 47.150 | 47.061 | 41.064 | 65.598 | 40.109 | | MPPose | --- | --- | 26.592 | 32.022 | 26.956 | | PPHumanSeg | 41.672 | 41.790 | 27.819 | 27.212 | 30.461 | | PPOCRv3 | --- | --- | 140.371 | 187.922 | 170.026 | | SFace | 43.830 | 43.834 | 27.575 | 30.653 | 26.387 | | ViTTrack | --- | --- | --- | 14.617 | 15.028 | | YOLOX | 1060.507 | 1061.361 | 495.816 | 533.309 | 549.713 | | YOLOv5 | --- | --- | --- | 191.350 | 193.261 | | YOLOv8 | --- | --- | 198.893 | 218.733 | 223.142 | | YuNet | 27.084 | 27.095 | 26.238 | 30.512 | 34.439 | | MobileNet_SSD_Caffe | 44.742 | 44.565 | 33.005 | 29.421 | 29.286 | | MobileNet_SSD_v1_TensorFlow | 49.352 | 49.274 | 35.163 | 32.134 | 31.904 | | MobileNet_SSD_v2_TensorFlow | 83.537 | 83.379 | 56.403 | 42.947 | 42.148 | | ResNet_50 | 148.872 | 148.817 | 77.331 | 67.682 | 67.760 | **n threads:** | Name of Test | 4.5.5-nth | 4.6.0-nth | 4.7.0-nth | 4.8.0-nth | 4.8.1-nth | |--------------|:---------:|:---------:|:---------:|:---------:|:---------:| | CRNN | 44.262 | 44.408 | 41.540 | 40.731 | 41.151 | | EfficientNet | --- | --- | 28.683 | 42.676 | 38.204 | | MPHand | --- | --- | 6.738 | 13.126 | 8.155 | | MPPalm | 16.613 | 16.588 | 12.477 | 31.370 | 17.048 | | MPPose | --- | --- | 12.985 | 19.700 | 16.537 | | PPHumanSeg | 14.993 | 15.133 | 13.438 | 15.269 | 15.252 | | PPOCRv3 | --- | --- | 63.752 | 85.469 | 76.190 | | SFace | 10.685 | 10.822 | 8.127 | 8.318 | 7.934 | | ViTTrack | --- | --- | --- | 10.079 | 9.579 | | YOLOX | 417.358 | 422.977 | 230.036 | 234.662 | 228.555 | | YOLOv5 | --- | --- | --- | 74.249 | 75.480 | | YOLOv8 | --- | --- | 63.762 | 88.770 | 70.927 | | YuNet | 8.589 | 8.731 | 11.269 | 16.466 | 14.513 | | MobileNet_SSD_Caffe | 12.575 | 12.636 | 11.529 | 12.114 | 12.236 | | MobileNet_SSD_v1_TensorFlow | 13.922 | 14.160 | 13.078 | 12.124 | 13.298 | | MobileNet_SSD_v2_TensorFlow | 25.096 | 24.836 | 22.823 | 20.238 | 20.319 | | ResNet_50 | 41.561 | 41.296 | 29.092 | 30.412 | 29.339 | Performance Test on [Intel Core i7-12700K](https://www.intel.com/content/www/us/en/products/sku/134594/intel-core-i712700k-processor-25m-cache-up-to-5-00-ghz/specifications.html) ```shell Ubuntu 22.04.2 LTS 8 Performance-cores (3.60 GHz, turbo up to 4.90 GHz) 4 Efficient-cores (2.70 GHz, turbo up to 3.80 GHz) 20 threads ``` **1 thread:** | Name of Test | 4.5.5-1th | 4.6.0-1th | 4.7.0-1th | 4.8.0-1th | 4.8.1-1th | |--------------|:---------:|:---------:|:---------:|:---------:|:---------:| | CRNN | 16.752 | 16.851 | 16.840 | 16.625 | 16.663 | | EfficientNet | --- | --- | 61.107 | 76.037 | 53.890 | | MPHand | --- | --- | 8.906 | 9.969 | 8.403 | | MPPalm | 24.243 | 24.638 | 18.104 | 35.140 | 18.387 | | MPPose | --- | --- | 12.322 | 16.515 | 12.355 | | PPHumanSeg | 15.249 | 15.303 | 10.203 | 10.298 | 10.353 | | PPOCRv3 | --- | --- | 87.788 | 144.253 | 90.648 | | SFace | 15.583 | 15.884 | 13.957 | 13.298 | 13.284 | | ViTTrack | --- | --- | --- | 11.760 | 11.710 | | YOLOX | 324.927 | 325.173 | 235.986 | 253.653 | 254.472 | | YOLOv5 | --- | --- | --- | 102.163 | 102.621 | | YOLOv8 | --- | --- | 87.013 | 103.182 | 103.146 | | YuNet | 12.806 | 12.645 | 10.515 | 12.647 | 12.711 | | MobileNet_SSD_Caffe | 23.556 | 23.768 | 24.304 | 22.569 | 22.602 | | MobileNet_SSD_v1_TensorFlow | 26.136 | 26.276 | 26.854 | 24.828 | 24.961 | | MobileNet_SSD_v2_TensorFlow | 43.521 | 43.614 | 46.892 | 44.044 | 44.682 | | ResNet_50 | 73.588 | 73.501 | 75.191 | 66.893 | 65.144 | **n thread:** | Name of Test | 4.5.5-nth | 4.6.0-nth | 4.7.0-nth | 4.8.0-nth | 4.8.1-nth | |--------------|:---------:|:---------:|:---------:|:---------:|:---------:| | CRNN | 8.665 | 8.827 | 10.643 | 7.703 | 7.743 | | EfficientNet | --- | --- | 16.591 | 12.715 | 9.022 | | MPHand | --- | --- | 2.678 | 2.785 | 1.680 | | MPPalm | 5.309 | 5.319 | 3.822 | 10.568 | 4.467 | | MPPose | --- | --- | 3.644 | 6.088 | 4.608 | | PPHumanSeg | 4.756 | 4.865 | 5.084 | 5.179 | 5.148 | | PPOCRv3 | --- | --- | 32.023 | 50.591 | 32.414 | | SFace | 3.838 | 3.980 | 4.629 | 3.145 | 3.155 | | ViTTrack | --- | --- | --- | 10.335 | 10.357 | | YOLOX | 68.314 | 68.081 | 82.801 | 74.219 | 73.970 | | YOLOv5 | --- | --- | --- | 47.150 | 47.523 | | YOLOv8 | --- | --- | 32.195 | 30.359 | 30.267 | | YuNet | 2.604 | 2.644 | 2.622 | 3.278 | 3.349 | | MobileNet_SSD_Caffe | 13.005 | 5.935 | 8.586 | 4.629 | 4.713 | | MobileNet_SSD_v1_TensorFlow | 7.002 | 7.129 | 9.314 | 5.271 | 5.213 | | MobileNet_SSD_v2_TensorFlow | 11.939 | 12.111 | 22.688 | 12.038 | 12.086 | | ResNet_50 | 18.227 | 18.600 | 26.150 | 15.584 | 15.706 |
This commit is contained in:
Wanli
GitHub
+138
-39
@@ -29,10 +29,7 @@ public:
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}
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void processNet(std::string weights, std::string proto, std::string halide_scheduler,
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const Mat& input, const std::string& outputLayer = "")
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{
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randu(input, 0.0f, 1.0f);
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const std::vector<std::tuple<Mat, std::string>>& inputs, const std::string& outputLayer = ""){
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weights = findDataFile(weights, false);
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if (!proto.empty())
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proto = findDataFile(proto);
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@@ -44,7 +41,11 @@ public:
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halide_scheduler = findDataFile(std::string("dnn/halide_scheduler_") + (target == DNN_TARGET_OPENCL ? "opencl_" : "") + halide_scheduler, true);
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}
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net = readNet(proto, weights);
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net.setInput(blobFromImage(input, 1.0, Size(), Scalar(), false));
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// Set multiple inputs
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for(auto &inp: inputs){
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net.setInput(std::get<0>(inp), std::get<1>(inp));
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}
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net.setPreferableBackend(backend);
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net.setPreferableTarget(target);
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if (backend == DNN_BACKEND_HALIDE)
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@@ -52,10 +53,14 @@ public:
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net.setHalideScheduler(halide_scheduler);
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}
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MatShape netInputShape = shape(1, 3, input.rows, input.cols);
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// Calculate multiple inputs memory consumption
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std::vector<MatShape> netMatShapes;
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for(auto &inp: inputs){
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netMatShapes.push_back(shape(std::get<0>(inp)));
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}
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size_t weightsMemory = 0, blobsMemory = 0;
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net.getMemoryConsumption(netInputShape, weightsMemory, blobsMemory);
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int64 flops = net.getFLOPS(netInputShape);
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net.getMemoryConsumption(netMatShapes, weightsMemory, blobsMemory);
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int64 flops = net.getFLOPS(netMatShapes);
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CV_Assert(flops > 0);
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net.forward(outputLayer); // warmup
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@@ -71,31 +76,46 @@ public:
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SANITY_CHECK_NOTHING();
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}
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void processNet(std::string weights, std::string proto, std::string halide_scheduler,
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Mat &input, const std::string& outputLayer = "")
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{
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processNet(weights, proto, halide_scheduler, {std::make_tuple(input, "")}, outputLayer);
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}
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void processNet(std::string weights, std::string proto, std::string halide_scheduler,
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Size inpSize, const std::string& outputLayer = "")
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{
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Mat input_data(inpSize, CV_32FC3);
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randu(input_data, 0.0f, 1.0f);
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Mat input = blobFromImage(input_data, 1.0, Size(), Scalar(), false);
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processNet(weights, proto, halide_scheduler, input, outputLayer);
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}
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};
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PERF_TEST_P_(DNNTestNetwork, AlexNet)
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{
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processNet("dnn/bvlc_alexnet.caffemodel", "dnn/bvlc_alexnet.prototxt",
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"alexnet.yml", Mat(cv::Size(227, 227), CV_32FC3));
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"alexnet.yml", cv::Size(227, 227));
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}
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PERF_TEST_P_(DNNTestNetwork, GoogLeNet)
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{
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processNet("dnn/bvlc_googlenet.caffemodel", "dnn/bvlc_googlenet.prototxt",
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"", Mat(cv::Size(224, 224), CV_32FC3));
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"", cv::Size(224, 224));
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}
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PERF_TEST_P_(DNNTestNetwork, ResNet_50)
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{
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processNet("dnn/ResNet-50-model.caffemodel", "dnn/ResNet-50-deploy.prototxt",
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"resnet_50.yml", Mat(cv::Size(224, 224), CV_32FC3));
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"resnet_50.yml", cv::Size(224, 224));
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}
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PERF_TEST_P_(DNNTestNetwork, SqueezeNet_v1_1)
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{
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processNet("dnn/squeezenet_v1.1.caffemodel", "dnn/squeezenet_v1.1.prototxt",
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"squeezenet_v1_1.yml", Mat(cv::Size(227, 227), CV_32FC3));
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"squeezenet_v1_1.yml", cv::Size(227, 227));
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}
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PERF_TEST_P_(DNNTestNetwork, Inception_5h)
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@@ -103,7 +123,7 @@ PERF_TEST_P_(DNNTestNetwork, Inception_5h)
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) throw SkipTestException("");
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processNet("dnn/tensorflow_inception_graph.pb", "",
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"inception_5h.yml",
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Mat(cv::Size(224, 224), CV_32FC3), "softmax2");
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cv::Size(224, 224), "softmax2");
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}
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PERF_TEST_P_(DNNTestNetwork, ENet)
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@@ -116,13 +136,13 @@ PERF_TEST_P_(DNNTestNetwork, ENet)
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throw SkipTestException("");
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#endif
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processNet("dnn/Enet-model-best.net", "", "enet.yml",
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Mat(cv::Size(512, 256), CV_32FC3));
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cv::Size(512, 256));
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}
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PERF_TEST_P_(DNNTestNetwork, SSD)
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{
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processNet("dnn/VGG_ILSVRC2016_SSD_300x300_iter_440000.caffemodel", "dnn/ssd_vgg16.prototxt", "disabled",
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Mat(cv::Size(300, 300), CV_32FC3));
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cv::Size(300, 300));
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}
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PERF_TEST_P_(DNNTestNetwork, OpenFace)
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@@ -134,7 +154,7 @@ PERF_TEST_P_(DNNTestNetwork, OpenFace)
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throw SkipTestException("");
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#endif
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processNet("dnn/openface_nn4.small2.v1.t7", "", "",
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Mat(cv::Size(96, 96), CV_32FC3));
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cv::Size(96, 96));
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}
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PERF_TEST_P_(DNNTestNetwork, MobileNet_SSD_Caffe)
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@@ -142,7 +162,7 @@ PERF_TEST_P_(DNNTestNetwork, MobileNet_SSD_Caffe)
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if (backend == DNN_BACKEND_HALIDE)
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throw SkipTestException("");
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processNet("dnn/MobileNetSSD_deploy_19e3ec3.caffemodel", "dnn/MobileNetSSD_deploy_19e3ec3.prototxt", "",
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Mat(cv::Size(300, 300), CV_32FC3));
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cv::Size(300, 300));
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}
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PERF_TEST_P_(DNNTestNetwork, MobileNet_SSD_v1_TensorFlow)
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@@ -150,7 +170,7 @@ PERF_TEST_P_(DNNTestNetwork, MobileNet_SSD_v1_TensorFlow)
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if (backend == DNN_BACKEND_HALIDE)
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throw SkipTestException("");
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processNet("dnn/ssd_mobilenet_v1_coco_2017_11_17.pb", "ssd_mobilenet_v1_coco_2017_11_17.pbtxt", "",
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Mat(cv::Size(300, 300), CV_32FC3));
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cv::Size(300, 300));
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}
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PERF_TEST_P_(DNNTestNetwork, MobileNet_SSD_v2_TensorFlow)
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@@ -158,7 +178,7 @@ PERF_TEST_P_(DNNTestNetwork, MobileNet_SSD_v2_TensorFlow)
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if (backend == DNN_BACKEND_HALIDE)
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throw SkipTestException("");
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processNet("dnn/ssd_mobilenet_v2_coco_2018_03_29.pb", "ssd_mobilenet_v2_coco_2018_03_29.pbtxt", "",
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Mat(cv::Size(300, 300), CV_32FC3));
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cv::Size(300, 300));
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}
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PERF_TEST_P_(DNNTestNetwork, DenseNet_121)
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@@ -166,7 +186,7 @@ PERF_TEST_P_(DNNTestNetwork, DenseNet_121)
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if (backend == DNN_BACKEND_HALIDE)
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throw SkipTestException("");
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processNet("dnn/DenseNet_121.caffemodel", "dnn/DenseNet_121.prototxt", "",
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Mat(cv::Size(224, 224), CV_32FC3));
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cv::Size(224, 224));
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}
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PERF_TEST_P_(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages)
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@@ -177,7 +197,7 @@ PERF_TEST_P_(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages)
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// The same .caffemodel but modified .prototxt
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// See https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/src/openpose/pose/poseParameters.cpp
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processNet("dnn/openpose_pose_mpi.caffemodel", "dnn/openpose_pose_mpi_faster_4_stages.prototxt", "",
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Mat(cv::Size(368, 368), CV_32FC3));
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cv::Size(368, 368));
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}
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PERF_TEST_P_(DNNTestNetwork, opencv_face_detector)
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@@ -185,7 +205,7 @@ PERF_TEST_P_(DNNTestNetwork, opencv_face_detector)
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if (backend == DNN_BACKEND_HALIDE)
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throw SkipTestException("");
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processNet("dnn/opencv_face_detector.caffemodel", "dnn/opencv_face_detector.prototxt", "",
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Mat(cv::Size(300, 300), CV_32FC3));
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cv::Size(300, 300));
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}
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PERF_TEST_P_(DNNTestNetwork, Inception_v2_SSD_TensorFlow)
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@@ -193,7 +213,7 @@ PERF_TEST_P_(DNNTestNetwork, Inception_v2_SSD_TensorFlow)
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if (backend == DNN_BACKEND_HALIDE)
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throw SkipTestException("");
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processNet("dnn/ssd_inception_v2_coco_2017_11_17.pb", "ssd_inception_v2_coco_2017_11_17.pbtxt", "",
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Mat(cv::Size(300, 300), CV_32FC3));
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cv::Size(300, 300));
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}
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PERF_TEST_P_(DNNTestNetwork, YOLOv3)
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@@ -213,9 +233,7 @@ PERF_TEST_P_(DNNTestNetwork, YOLOv3)
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#endif
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Mat sample = imread(findDataFile("dnn/dog416.png"));
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cvtColor(sample, sample, COLOR_BGR2RGB);
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Mat inp;
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sample.convertTo(inp, CV_32FC3, 1.0f / 255, 0);
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Mat inp = blobFromImage(sample, 1.0 / 255.0, Size(), Scalar(), true);
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processNet("dnn/yolov3.weights", "dnn/yolov3.cfg", "", inp);
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}
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@@ -233,9 +251,7 @@ PERF_TEST_P_(DNNTestNetwork, YOLOv4)
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throw SkipTestException("Test is disabled in OpenVINO 2020.4");
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#endif
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Mat sample = imread(findDataFile("dnn/dog416.png"));
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cvtColor(sample, sample, COLOR_BGR2RGB);
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Mat inp;
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sample.convertTo(inp, CV_32FC3, 1.0f / 255, 0);
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Mat inp = blobFromImage(sample, 1.0 / 255.0, Size(), Scalar(), true);
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processNet("dnn/yolov4.weights", "dnn/yolov4.cfg", "", inp);
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}
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@@ -248,24 +264,43 @@ PERF_TEST_P_(DNNTestNetwork, YOLOv4_tiny)
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throw SkipTestException("");
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#endif
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Mat sample = imread(findDataFile("dnn/dog416.png"));
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cvtColor(sample, sample, COLOR_BGR2RGB);
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Mat inp;
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sample.convertTo(inp, CV_32FC3, 1.0f / 255, 0);
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Mat inp = blobFromImage(sample, 1.0 / 255.0, Size(), Scalar(), true);
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processNet("dnn/yolov4-tiny-2020-12.weights", "dnn/yolov4-tiny-2020-12.cfg", "", inp);
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}
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PERF_TEST_P_(DNNTestNetwork, YOLOv5) {
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applyTestTag(CV_TEST_TAG_MEMORY_512MB);
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Mat sample = imread(findDataFile("dnn/dog416.png"));
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Mat inp = blobFromImage(sample, 1.0 / 255.0, Size(640, 640), Scalar(), true);
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processNet("", "dnn/yolov5n.onnx", "", inp);
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}
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PERF_TEST_P_(DNNTestNetwork, YOLOv8) {
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applyTestTag(CV_TEST_TAG_MEMORY_512MB);
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Mat sample = imread(findDataFile("dnn/dog416.png"));
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Mat inp = blobFromImage(sample, 1.0 / 255.0, Size(640, 640), Scalar(), true);
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processNet("", "dnn/yolov8n.onnx", "", inp);
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}
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PERF_TEST_P_(DNNTestNetwork, YOLOX) {
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applyTestTag(CV_TEST_TAG_MEMORY_512MB);
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Mat sample = imread(findDataFile("dnn/dog416.png"));
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Mat inp = blobFromImage(sample, 1.0 / 255.0, Size(640, 640), Scalar(), true);
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processNet("", "dnn/yolox_s.onnx", "", inp);
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}
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PERF_TEST_P_(DNNTestNetwork, EAST_text_detection)
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{
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if (backend == DNN_BACKEND_HALIDE)
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throw SkipTestException("");
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processNet("dnn/frozen_east_text_detection.pb", "", "", Mat(cv::Size(320, 320), CV_32FC3));
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processNet("dnn/frozen_east_text_detection.pb", "", "", cv::Size(320, 320));
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}
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PERF_TEST_P_(DNNTestNetwork, FastNeuralStyle_eccv16)
|
||||
{
|
||||
if (backend == DNN_BACKEND_HALIDE)
|
||||
throw SkipTestException("");
|
||||
processNet("dnn/fast_neural_style_eccv16_starry_night.t7", "", "", Mat(cv::Size(320, 240), CV_32FC3));
|
||||
processNet("dnn/fast_neural_style_eccv16_starry_night.t7", "", "", cv::Size(320, 240));
|
||||
}
|
||||
|
||||
PERF_TEST_P_(DNNTestNetwork, Inception_v2_Faster_RCNN)
|
||||
@@ -288,7 +323,7 @@ PERF_TEST_P_(DNNTestNetwork, Inception_v2_Faster_RCNN)
|
||||
throw SkipTestException("");
|
||||
processNet("dnn/faster_rcnn_inception_v2_coco_2018_01_28.pb",
|
||||
"dnn/faster_rcnn_inception_v2_coco_2018_01_28.pbtxt", "",
|
||||
Mat(cv::Size(800, 600), CV_32FC3));
|
||||
cv::Size(800, 600));
|
||||
}
|
||||
|
||||
PERF_TEST_P_(DNNTestNetwork, EfficientDet)
|
||||
@@ -296,12 +331,76 @@ PERF_TEST_P_(DNNTestNetwork, EfficientDet)
|
||||
if (backend == DNN_BACKEND_HALIDE || target != DNN_TARGET_CPU)
|
||||
throw SkipTestException("");
|
||||
Mat sample = imread(findDataFile("dnn/dog416.png"));
|
||||
resize(sample, sample, Size(512, 512));
|
||||
Mat inp;
|
||||
sample.convertTo(inp, CV_32FC3, 1.0/255);
|
||||
Mat inp = blobFromImage(sample, 1.0 / 255.0, Size(512, 512), Scalar(), true);
|
||||
processNet("dnn/efficientdet-d0.pb", "dnn/efficientdet-d0.pbtxt", "", inp);
|
||||
}
|
||||
|
||||
PERF_TEST_P_(DNNTestNetwork, EfficientNet)
|
||||
{
|
||||
Mat sample = imread(findDataFile("dnn/dog416.png"));
|
||||
Mat inp = blobFromImage(sample, 1.0 / 255.0, Size(224, 224), Scalar(), true);
|
||||
transposeND(inp, {0, 2, 3, 1}, inp);
|
||||
processNet("", "dnn/efficientnet-lite4.onnx", "", inp);
|
||||
}
|
||||
|
||||
PERF_TEST_P_(DNNTestNetwork, YuNet) {
|
||||
processNet("", "dnn/onnx/models/yunet-202303.onnx", "", cv::Size(640, 640));
|
||||
}
|
||||
|
||||
PERF_TEST_P_(DNNTestNetwork, SFace) {
|
||||
processNet("", "dnn/face_recognition_sface_2021dec.onnx", "", cv::Size(112, 112));
|
||||
}
|
||||
|
||||
PERF_TEST_P_(DNNTestNetwork, MPPalm) {
|
||||
Mat inp(cv::Size(192, 192), CV_32FC3);
|
||||
randu(inp, 0.0f, 1.0f);
|
||||
inp = blobFromImage(inp, 1.0, Size(), Scalar(), false);
|
||||
transposeND(inp, {0, 2, 3, 1}, inp);
|
||||
processNet("", "dnn/palm_detection_mediapipe_2023feb.onnx", "", inp);
|
||||
}
|
||||
|
||||
PERF_TEST_P_(DNNTestNetwork, MPHand) {
|
||||
Mat inp(cv::Size(224, 224), CV_32FC3);
|
||||
randu(inp, 0.0f, 1.0f);
|
||||
inp = blobFromImage(inp, 1.0, Size(), Scalar(), false);
|
||||
transposeND(inp, {0, 2, 3, 1}, inp);
|
||||
processNet("", "dnn/handpose_estimation_mediapipe_2023feb.onnx", "", inp);
|
||||
}
|
||||
|
||||
PERF_TEST_P_(DNNTestNetwork, MPPose) {
|
||||
Mat inp(cv::Size(256, 256), CV_32FC3);
|
||||
randu(inp, 0.0f, 1.0f);
|
||||
inp = blobFromImage(inp, 1.0, Size(), Scalar(), false);
|
||||
transposeND(inp, {0, 2, 3, 1}, inp);
|
||||
processNet("", "dnn/pose_estimation_mediapipe_2023mar.onnx", "", inp);
|
||||
}
|
||||
|
||||
PERF_TEST_P_(DNNTestNetwork, PPOCRv3) {
|
||||
applyTestTag(CV_TEST_TAG_MEMORY_512MB);
|
||||
processNet("", "dnn/onnx/models/PP_OCRv3_DB_text_det.onnx", "", cv::Size(736, 736));
|
||||
}
|
||||
|
||||
PERF_TEST_P_(DNNTestNetwork, PPHumanSeg) {
|
||||
processNet("", "dnn/human_segmentation_pphumanseg_2023mar.onnx", "", cv::Size(192, 192));
|
||||
}
|
||||
|
||||
PERF_TEST_P_(DNNTestNetwork, CRNN) {
|
||||
Mat inp(cv::Size(100, 32), CV_32FC1);
|
||||
randu(inp, 0.0f, 1.0f);
|
||||
inp = blobFromImage(inp, 1.0, Size(), Scalar(), false);
|
||||
processNet("", "dnn/text_recognition_CRNN_EN_2021sep.onnx", "", inp);
|
||||
}
|
||||
|
||||
PERF_TEST_P_(DNNTestNetwork, ViTTrack) {
|
||||
Mat inp1(cv::Size(128, 128), CV_32FC3);
|
||||
Mat inp2(cv::Size(256, 256), CV_32FC3);
|
||||
randu(inp1, 0.0f, 1.0f);
|
||||
randu(inp2, 0.0f, 1.0f);
|
||||
inp1 = blobFromImage(inp1, 1.0, Size(), Scalar(), false);
|
||||
inp2 = blobFromImage(inp2, 1.0, Size(), Scalar(), false);
|
||||
processNet("", "dnn/onnx/models/vitTracker.onnx", "", {std::make_tuple(inp1, "template"), std::make_tuple(inp2, "search")});
|
||||
}
|
||||
|
||||
|
||||
PERF_TEST_P_(DNNTestNetwork, EfficientDet_int8)
|
||||
{
|
||||
@@ -310,7 +409,7 @@ PERF_TEST_P_(DNNTestNetwork, EfficientDet_int8)
|
||||
throw SkipTestException("");
|
||||
}
|
||||
Mat inp = imread(findDataFile("dnn/dog416.png"));
|
||||
resize(inp, inp, Size(320, 320));
|
||||
inp = blobFromImage(inp, 1.0 / 255.0, Size(320, 320), Scalar(), true);
|
||||
processNet("", "dnn/tflite/coco_efficientdet_lite0_v1_1.0_quant_2021_09_06.tflite", "", inp);
|
||||
}
|
||||
|
||||
|
||||
Reference in New Issue
Block a user