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Merge pull request #24231 from fengyuentau:halide_cleanup_5.x

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dnn: cleanup of halide backend for 5.x #24231

Merge with https://github.com/opencv/opencv_extra/pull/1092.

### 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 commit is contained in:
Yuantao Feng
2023-10-13 08:53:18 -05:00
committed by GitHub
parent 0976765c62
commit d789cb459c
59 changed files with 226 additions and 2610 deletions
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modules/dnn/perf/perf_net.cpp
+26 -81
View File
@@ -28,29 +28,17 @@ public:
target = (dnn::Target)(int)get<1>(GetParam());
}
void processNet(std::string weights, std::string proto, std::string halide_scheduler,
const Mat& input, const std::string& outputLayer = "")
void processNet(std::string weights, std::string proto, const Mat& input, const std::string& outputLayer = "")
{
randu(input, 0.0f, 1.0f);
weights = findDataFile(weights, false);
if (!proto.empty())
proto = findDataFile(proto);
if (backend == DNN_BACKEND_HALIDE)
{
if (halide_scheduler == "disabled")
throw cvtest::SkipTestException("Halide test is disabled");
if (!halide_scheduler.empty())
halide_scheduler = findDataFile(std::string("dnn/halide_scheduler_") + (target == DNN_TARGET_OPENCL ? "opencl_" : "") + halide_scheduler, true);
}
net = readNet(proto, weights);
net.setInput(blobFromImage(input, 1.0, Size(), Scalar(), false));
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
if (backend == DNN_BACKEND_HALIDE)
{
net.setHalideScheduler(halide_scheduler);
}
MatShape netInputShape = shape(1, 3, input.rows, input.cols);
size_t weightsMemory = 0, blobsMemory = 0;
@@ -76,34 +64,28 @@ public:
PERF_TEST_P_(DNNTestNetwork, AlexNet)
{
processNet("dnn/bvlc_alexnet.caffemodel", "dnn/bvlc_alexnet.prototxt",
"alexnet.yml", Mat(cv::Size(227, 227), CV_32FC3));
processNet("dnn/bvlc_alexnet.caffemodel", "dnn/bvlc_alexnet.prototxt", Mat(cv::Size(227, 227), CV_32FC3));
}
PERF_TEST_P_(DNNTestNetwork, GoogLeNet)
{
processNet("dnn/bvlc_googlenet.caffemodel", "dnn/bvlc_googlenet.prototxt",
"", Mat(cv::Size(224, 224), CV_32FC3));
processNet("dnn/bvlc_googlenet.caffemodel", "dnn/bvlc_googlenet.prototxt", Mat(cv::Size(224, 224), CV_32FC3));
}
PERF_TEST_P_(DNNTestNetwork, ResNet_50)
{
processNet("dnn/ResNet-50-model.caffemodel", "dnn/ResNet-50-deploy.prototxt",
"resnet_50.yml", Mat(cv::Size(224, 224), CV_32FC3));
processNet("dnn/ResNet-50-model.caffemodel", "dnn/ResNet-50-deploy.prototxt", Mat(cv::Size(224, 224), CV_32FC3));
}
PERF_TEST_P_(DNNTestNetwork, SqueezeNet_v1_1)
{
processNet("dnn/squeezenet_v1.1.caffemodel", "dnn/squeezenet_v1.1.prototxt",
"squeezenet_v1_1.yml", Mat(cv::Size(227, 227), CV_32FC3));
processNet("dnn/squeezenet_v1.1.caffemodel", "dnn/squeezenet_v1.1.prototxt", Mat(cv::Size(227, 227), CV_32FC3));
}
PERF_TEST_P_(DNNTestNetwork, Inception_5h)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) throw SkipTestException("");
processNet("dnn/tensorflow_inception_graph.pb", "",
"inception_5h.yml",
Mat(cv::Size(224, 224), CV_32FC3), "softmax2");
processNet("dnn/tensorflow_inception_graph.pb", "", Mat(cv::Size(224, 224), CV_32FC3), "softmax2");
}
PERF_TEST_P_(DNNTestNetwork, ENet)
@@ -115,92 +97,65 @@ PERF_TEST_P_(DNNTestNetwork, ENet)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
throw SkipTestException("");
#endif
processNet("dnn/Enet-model-best.net", "", "enet.yml",
Mat(cv::Size(512, 256), CV_32FC3));
processNet("dnn/Enet-model-best.net", "", Mat(cv::Size(512, 256), CV_32FC3));
}
PERF_TEST_P_(DNNTestNetwork, SSD)
{
processNet("dnn/VGG_ILSVRC2016_SSD_300x300_iter_440000.caffemodel", "dnn/ssd_vgg16.prototxt", "disabled",
Mat(cv::Size(300, 300), CV_32FC3));
processNet("dnn/VGG_ILSVRC2016_SSD_300x300_iter_440000.caffemodel", "dnn/ssd_vgg16.prototxt", Mat(cv::Size(300, 300), CV_32FC3));
}
PERF_TEST_P_(DNNTestNetwork, OpenFace)
{
if (backend == DNN_BACKEND_HALIDE)
throw SkipTestException("");
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2018050000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && (target == DNN_TARGET_MYRIAD || target == DNN_TARGET_HDDL))
throw SkipTestException("");
#endif
processNet("dnn/openface_nn4.small2.v1.t7", "", "",
Mat(cv::Size(96, 96), CV_32FC3));
processNet("dnn/openface_nn4.small2.v1.t7", "", Mat(cv::Size(96, 96), CV_32FC3));
}
PERF_TEST_P_(DNNTestNetwork, MobileNet_SSD_Caffe)
{
if (backend == DNN_BACKEND_HALIDE)
throw SkipTestException("");
processNet("dnn/MobileNetSSD_deploy_19e3ec3.caffemodel", "dnn/MobileNetSSD_deploy_19e3ec3.prototxt", "",
Mat(cv::Size(300, 300), CV_32FC3));
processNet("dnn/MobileNetSSD_deploy_19e3ec3.caffemodel", "dnn/MobileNetSSD_deploy_19e3ec3.prototxt", Mat(cv::Size(300, 300), CV_32FC3));
}
PERF_TEST_P_(DNNTestNetwork, MobileNet_SSD_v1_TensorFlow)
{
if (backend == DNN_BACKEND_HALIDE)
throw SkipTestException("");
processNet("dnn/ssd_mobilenet_v1_coco_2017_11_17.pb", "ssd_mobilenet_v1_coco_2017_11_17.pbtxt", "",
Mat(cv::Size(300, 300), CV_32FC3));
processNet("dnn/ssd_mobilenet_v1_coco_2017_11_17.pb", "ssd_mobilenet_v1_coco_2017_11_17.pbtxt", Mat(cv::Size(300, 300), CV_32FC3));
}
PERF_TEST_P_(DNNTestNetwork, MobileNet_SSD_v2_TensorFlow)
{
if (backend == DNN_BACKEND_HALIDE)
throw SkipTestException("");
processNet("dnn/ssd_mobilenet_v2_coco_2018_03_29.pb", "ssd_mobilenet_v2_coco_2018_03_29.pbtxt", "",
Mat(cv::Size(300, 300), CV_32FC3));
processNet("dnn/ssd_mobilenet_v2_coco_2018_03_29.pb", "ssd_mobilenet_v2_coco_2018_03_29.pbtxt", Mat(cv::Size(300, 300), CV_32FC3));
}
PERF_TEST_P_(DNNTestNetwork, DenseNet_121)
{
if (backend == DNN_BACKEND_HALIDE)
throw SkipTestException("");
processNet("dnn/DenseNet_121.caffemodel", "dnn/DenseNet_121.prototxt", "",
Mat(cv::Size(224, 224), CV_32FC3));
processNet("dnn/DenseNet_121.caffemodel", "dnn/DenseNet_121.prototxt", Mat(cv::Size(224, 224), CV_32FC3));
}
PERF_TEST_P_(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages)
{
if (backend == DNN_BACKEND_HALIDE ||
(backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && (target == DNN_TARGET_MYRIAD || target == DNN_TARGET_HDDL)))
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && (target == DNN_TARGET_MYRIAD || target == DNN_TARGET_HDDL))
throw SkipTestException("");
// The same .caffemodel but modified .prototxt
// See https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/src/openpose/pose/poseParameters.cpp
processNet("dnn/openpose_pose_mpi.caffemodel", "dnn/openpose_pose_mpi_faster_4_stages.prototxt", "",
Mat(cv::Size(368, 368), CV_32FC3));
processNet("dnn/openpose_pose_mpi.caffemodel", "dnn/openpose_pose_mpi_faster_4_stages.prototxt", Mat(cv::Size(368, 368), CV_32FC3));
}
PERF_TEST_P_(DNNTestNetwork, opencv_face_detector)
{
if (backend == DNN_BACKEND_HALIDE)
throw SkipTestException("");
processNet("dnn/opencv_face_detector.caffemodel", "dnn/opencv_face_detector.prototxt", "",
Mat(cv::Size(300, 300), CV_32FC3));
processNet("dnn/opencv_face_detector.caffemodel", "dnn/opencv_face_detector.prototxt", Mat(cv::Size(300, 300), CV_32FC3));
}
PERF_TEST_P_(DNNTestNetwork, Inception_v2_SSD_TensorFlow)
{
if (backend == DNN_BACKEND_HALIDE)
throw SkipTestException("");
processNet("dnn/ssd_inception_v2_coco_2017_11_17.pb", "ssd_inception_v2_coco_2017_11_17.pbtxt", "",
Mat(cv::Size(300, 300), CV_32FC3));
processNet("dnn/ssd_inception_v2_coco_2017_11_17.pb", "ssd_inception_v2_coco_2017_11_17.pbtxt", Mat(cv::Size(300, 300), CV_32FC3));
}
PERF_TEST_P_(DNNTestNetwork, YOLOv3)
{
applyTestTag(CV_TEST_TAG_MEMORY_2GB);
if (backend == DNN_BACKEND_HALIDE)
throw SkipTestException("");
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2020040000) // nGraph compilation failure
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL)
throw SkipTestException("Test is disabled in OpenVINO 2020.4");
@@ -216,14 +171,12 @@ PERF_TEST_P_(DNNTestNetwork, YOLOv3)
cvtColor(sample, sample, COLOR_BGR2RGB);
Mat inp;
sample.convertTo(inp, CV_32FC3, 1.0f / 255, 0);
processNet("dnn/yolov3.weights", "dnn/yolov3.cfg", "", inp);
processNet("dnn/yolov3.weights", "dnn/yolov3.cfg", inp);
}
PERF_TEST_P_(DNNTestNetwork, YOLOv4)
{
applyTestTag(CV_TEST_TAG_MEMORY_2GB);
if (backend == DNN_BACKEND_HALIDE)
throw SkipTestException("");
if (target == DNN_TARGET_MYRIAD) // not enough resources
throw SkipTestException("");
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2020040000) // nGraph compilation failure
@@ -236,13 +189,11 @@ PERF_TEST_P_(DNNTestNetwork, YOLOv4)
cvtColor(sample, sample, COLOR_BGR2RGB);
Mat inp;
sample.convertTo(inp, CV_32FC3, 1.0f / 255, 0);
processNet("dnn/yolov4.weights", "dnn/yolov4.cfg", "", inp);
processNet("dnn/yolov4.weights", "dnn/yolov4.cfg", inp);
}
PERF_TEST_P_(DNNTestNetwork, YOLOv4_tiny)
{
if (backend == DNN_BACKEND_HALIDE)
throw SkipTestException("");
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2021010000) // nGraph compilation failure
if (target == DNN_TARGET_MYRIAD)
throw SkipTestException("");
@@ -251,21 +202,17 @@ PERF_TEST_P_(DNNTestNetwork, YOLOv4_tiny)
cvtColor(sample, sample, COLOR_BGR2RGB);
Mat inp;
sample.convertTo(inp, CV_32FC3, 1.0f / 255, 0);
processNet("dnn/yolov4-tiny-2020-12.weights", "dnn/yolov4-tiny-2020-12.cfg", "", inp);
processNet("dnn/yolov4-tiny-2020-12.weights", "dnn/yolov4-tiny-2020-12.cfg", inp);
}
PERF_TEST_P_(DNNTestNetwork, EAST_text_detection)
{
if (backend == DNN_BACKEND_HALIDE)
throw SkipTestException("");
processNet("dnn/frozen_east_text_detection.pb", "", "", Mat(cv::Size(320, 320), CV_32FC3));
processNet("dnn/frozen_east_text_detection.pb", "", Mat(cv::Size(320, 320), CV_32FC3));
}
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", "", Mat(cv::Size(320, 240), CV_32FC3));
}
PERF_TEST_P_(DNNTestNetwork, Inception_v2_Faster_RCNN)
@@ -282,24 +229,22 @@ PERF_TEST_P_(DNNTestNetwork, Inception_v2_Faster_RCNN)
if (target == DNN_TARGET_MYRIAD)
throw SkipTestException("Test is disabled in OpenVINO 2021.1+ / MYRIAD");
#endif
if (backend == DNN_BACKEND_HALIDE ||
(backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU) ||
if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU) ||
(backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16))
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));
"dnn/faster_rcnn_inception_v2_coco_2018_01_28.pbtxt", Mat(cv::Size(800, 600), CV_32FC3));
}
PERF_TEST_P_(DNNTestNetwork, EfficientDet)
{
if (backend == DNN_BACKEND_HALIDE || target != DNN_TARGET_CPU)
if (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);
processNet("dnn/efficientdet-d0.pb", "dnn/efficientdet-d0.pbtxt", "", inp);
processNet("dnn/efficientdet-d0.pb", "dnn/efficientdet-d0.pbtxt", inp);
}
INSTANTIATE_TEST_CASE_P(/*nothing*/, DNNTestNetwork, dnnBackendsAndTargets());