dnn(perf): update perf tests

2026-07-30 07:43:03 +04:00 · 2017-09-22 15:15:57 +03:00
parent b8af7c5f86
commit 78788e1efb
4 changed files with 162 additions and 201 deletions
@@ -0,0 +1,149 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2017, Intel Corporation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+
+#include "perf_precomp.hpp"
+#include "opencv2/core/ocl.hpp"
+
+#include "opencv2/dnn/shape_utils.hpp"
+
+namespace
+{
+
+#ifdef HAVE_HALIDE
+#define TEST_DNN_BACKEND DNN_BACKEND_DEFAULT, DNN_BACKEND_HALIDE
+#else
+#define TEST_DNN_BACKEND DNN_BACKEND_DEFAULT
+#endif
+#define TEST_DNN_TARGET DNN_TARGET_CPU, DNN_TARGET_OPENCL
+
+CV_ENUM(DNNBackend, DNN_BACKEND_DEFAULT, DNN_BACKEND_HALIDE)
+CV_ENUM(DNNTarget, DNN_TARGET_CPU, DNN_TARGET_OPENCL)
+
+class DNNTestNetwork : public ::perf::TestBaseWithParam< tuple<DNNBackend, DNNTarget> >
+{
+public:
+    dnn::Backend backend;
+    dnn::Target target;
+
+    dnn::Net net;
+
+    void processNet(std::string weights, std::string proto, std::string halide_scheduler,
+                        int inWidth, int inHeight, const std::string& outputLayer,
+                        const std::string& framework)
+    {
+        backend = (dnn::Backend)(int)get<0>(GetParam());
+        target = (dnn::Target)(int)get<1>(GetParam());
+
+        if (backend == DNN_BACKEND_DEFAULT && target == DNN_TARGET_OPENCL)
+        {
+#if 0 //defined(HAVE_OPENCL)
+            if (!cv::ocl::useOpenCL())
+#endif
+            {
+                throw ::SkipTestException("OpenCL is not available/disabled in OpenCV");
+            }
+        }
+
+        Mat input(inHeight, inWidth, CV_32FC3);
+        randu(input, 0.0f, 1.0f);
+
+
+        weights = findDataFile(weights, false);
+        if (!proto.empty())
+            proto = findDataFile(proto, false);
+        if (!halide_scheduler.empty() && backend == DNN_BACKEND_HALIDE)
+            halide_scheduler = findDataFile(std::string("dnn/halide_scheduler_") + (target == DNN_TARGET_OPENCL ? "opencl_" : "") + halide_scheduler, true);
+        if (framework == "caffe")
+        {
+            net = cv::dnn::readNetFromCaffe(proto, weights);
+        }
+        else if (framework == "torch")
+        {
+            net = cv::dnn::readNetFromTorch(weights);
+        }
+        else if (framework == "tensorflow")
+        {
+            net = cv::dnn::readNetFromTensorflow(weights);
+        }
+        else
+            CV_Error(Error::StsNotImplemented, "Unknown framework " + framework);
+
+        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, inHeight, inWidth);
+        size_t weightsMemory = 0, blobsMemory = 0;
+        net.getMemoryConsumption(netInputShape, weightsMemory, blobsMemory);
+        int64 flops = net.getFLOPS(netInputShape);
+
+        net.forward(outputLayer); // warmup
+
+        std::cout << "Memory consumption:" << std::endl;
+        std::cout << "    Weights(parameters): " << divUp(weightsMemory, 1u<<20) << " Mb" << std::endl;
+        std::cout << "    Blobs: " << divUp(blobsMemory, 1u<<20) << " Mb" << std::endl;
+        std::cout << "Calculation complexity: " << flops * 1e-9 << " GFlops" << std::endl;
+
+        PERF_SAMPLE_BEGIN()
+            net.forward();
+        PERF_SAMPLE_END()
+
+        SANITY_CHECK_NOTHING();
+    }
+};
+
+
+PERF_TEST_P_(DNNTestNetwork, AlexNet)
+{
+    processNet("dnn/bvlc_alexnet.caffemodel", "dnn/bvlc_alexnet.prototxt",
+            "alexnet.yml", 227, 227, "prob", "caffe");
+}
+
+PERF_TEST_P_(DNNTestNetwork, GoogLeNet)
+{
+    processNet("dnn/bvlc_googlenet.caffemodel", "dnn/bvlc_googlenet.prototxt",
+            "", 224, 224, "prob", "caffe");
+}
+
+PERF_TEST_P_(DNNTestNetwork, ResNet50)
+{
+    processNet("dnn/ResNet-50-model.caffemodel", "dnn/ResNet-50-deploy.prototxt",
+            "resnet_50.yml", 224, 224, "prob", "caffe");
+}
+
+PERF_TEST_P_(DNNTestNetwork, SqueezeNet_v1_1)
+{
+    processNet("dnn/squeezenet_v1.1.caffemodel", "dnn/squeezenet_v1.1.prototxt",
+            "squeezenet_v1_1.yml", 227, 227, "prob", "caffe");
+}
+
+PERF_TEST_P_(DNNTestNetwork, Inception_5h)
+{
+    processNet("dnn/tensorflow_inception_graph.pb", "",
+            "inception_5h.yml",
+            224, 224, "softmax2", "tensorflow");
+}
+
+PERF_TEST_P_(DNNTestNetwork, ENet)
+{
+    processNet("dnn/Enet-model-best.net", "", "enet.yml",
+            512, 256, "l367_Deconvolution", "torch");
+}
+
+
+INSTANTIATE_TEST_CASE_P(/*nothing*/, DNNTestNetwork,
+    testing::Combine(
+        ::testing::Values(TEST_DNN_BACKEND),
+        DNNTarget::all()
+    )
+);
+
+} // namespace