dnn: move module from opencv_contrib

https://github.com/opencv/opencv_contrib/tree/e6f63c7a38ca40c5dc33e38736e3027e3528d6cb/modules/dnn
2026-07-29 15:23:05 +04:00 · 2017-06-26 13:35:51 +03:00
parent ef692350e0
commit 93729784bb
143 changed files with 135205 additions and 0 deletions
@@ -0,0 +1,173 @@
+// 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.
+
+namespace cvtest
+{
+
+#ifdef HAVE_HALIDE
+using namespace cv;
+using namespace dnn;
+
+static void loadNet(const std::string& weights, const std::string& proto,
+                    const std::string& framework, Net* net)
+{
+    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);
+}
+
+static void test(const std::string& weights, const std::string& proto,
+                 const std::string& scheduler, int inWidth, int inHeight,
+                 const std::string& outputLayer, const std::string& framework,
+                 int targetId, double l1 = 1e-5, double lInf = 1e-4)
+{
+    Mat input(inHeight, inWidth, CV_32FC3), outputDefault, outputHalide;
+    randu(input, 0.0f, 1.0f);
+
+    Net netDefault, netHalide;
+    loadNet(weights, proto, framework, &netDefault);
+    loadNet(weights, proto, framework, &netHalide);
+
+    netDefault.setInput(blobFromImage(input.clone(), 1.0f, Size(), Scalar(), false));
+    outputDefault = netDefault.forward(outputLayer).clone();
+
+    netHalide.setInput(blobFromImage(input.clone(), 1.0f, Size(), Scalar(), false));
+    netHalide.setPreferableBackend(DNN_BACKEND_HALIDE);
+    netHalide.setPreferableTarget(targetId);
+    netHalide.setHalideScheduler(scheduler);
+    outputHalide = netHalide.forward(outputLayer).clone();
+
+    normAssert(outputDefault, outputHalide, "First run", l1, lInf);
+
+    // An extra test: change input.
+    input *= 0.1f;
+    netDefault.setInput(blobFromImage(input.clone(), 1.0, Size(), Scalar(), false));
+    netHalide.setInput(blobFromImage(input.clone(), 1.0, Size(), Scalar(), false));
+
+    normAssert(outputDefault, outputHalide, "Second run", l1, lInf);
+
+    // Swap backends.
+    netHalide.setPreferableBackend(DNN_BACKEND_DEFAULT);
+    netHalide.setPreferableTarget(DNN_TARGET_CPU);
+    outputDefault = netHalide.forward(outputLayer).clone();
+
+    netDefault.setPreferableBackend(DNN_BACKEND_HALIDE);
+    netDefault.setPreferableTarget(targetId);
+    netDefault.setHalideScheduler(scheduler);
+    outputHalide = netDefault.forward(outputLayer).clone();
+
+    normAssert(outputDefault, outputHalide, "Swap backends", l1, lInf);
+}
+
+////////////////////////////////////////////////////////////////////////////////
+// CPU target
+////////////////////////////////////////////////////////////////////////////////
+TEST(Reproducibility_GoogLeNet_Halide, Accuracy)
+{
+    test(findDataFile("dnn/bvlc_googlenet.caffemodel", false),
+         findDataFile("dnn/bvlc_googlenet.prototxt", false),
+         "", 227, 227, "prob", "caffe", DNN_TARGET_CPU);
+};
+
+TEST(Reproducibility_AlexNet_Halide, Accuracy)
+{
+    test(findDataFile("dnn/bvlc_alexnet.caffemodel", false),
+         findDataFile("dnn/bvlc_alexnet.prototxt", false),
+         findDataFile("dnn/halide_scheduler_alexnet.yml", false),
+         227, 227, "prob", "caffe", DNN_TARGET_CPU);
+};
+
+TEST(Reproducibility_ResNet_50_Halide, Accuracy)
+{
+    test(findDataFile("dnn/ResNet-50-model.caffemodel", false),
+         findDataFile("dnn/ResNet-50-deploy.prototxt", false),
+         findDataFile("dnn/halide_scheduler_resnet_50.yml", false),
+         224, 224, "prob", "caffe", DNN_TARGET_CPU);
+};
+
+TEST(Reproducibility_SqueezeNet_v1_1_Halide, Accuracy)
+{
+    test(findDataFile("dnn/squeezenet_v1_1.caffemodel", false),
+         findDataFile("dnn/squeezenet_v1_1.prototxt", false),
+         findDataFile("dnn/halide_scheduler_squeezenet_v1_1.yml", false),
+         227, 227, "prob", "caffe", DNN_TARGET_CPU);
+};
+
+TEST(Reproducibility_Inception_5h_Halide, Accuracy)
+{
+    test(findDataFile("dnn/tensorflow_inception_graph.pb", false), "",
+         findDataFile("dnn/halide_scheduler_inception_5h.yml", false),
+         224, 224, "softmax2", "tensorflow", DNN_TARGET_CPU);
+};
+
+TEST(Reproducibility_ENet_Halide, Accuracy)
+{
+    test(findDataFile("dnn/Enet-model-best.net", false), "",
+         findDataFile("dnn/halide_scheduler_enet.yml", false),
+         512, 512, "l367_Deconvolution", "torch", DNN_TARGET_CPU, 2e-5, 0.15);
+};
+////////////////////////////////////////////////////////////////////////////////
+// OpenCL target
+////////////////////////////////////////////////////////////////////////////////
+TEST(Reproducibility_GoogLeNet_Halide_opencl, Accuracy)
+{
+    test(findDataFile("dnn/bvlc_googlenet.caffemodel", false),
+         findDataFile("dnn/bvlc_googlenet.prototxt", false),
+         "", 227, 227, "prob", "caffe", DNN_TARGET_OPENCL);
+};
+
+TEST(Reproducibility_AlexNet_Halide_opencl, Accuracy)
+{
+    test(findDataFile("dnn/bvlc_alexnet.caffemodel", false),
+         findDataFile("dnn/bvlc_alexnet.prototxt", false),
+         findDataFile("dnn/halide_scheduler_opencl_alexnet.yml", false),
+         227, 227, "prob", "caffe", DNN_TARGET_OPENCL);
+};
+
+TEST(Reproducibility_ResNet_50_Halide_opencl, Accuracy)
+{
+    test(findDataFile("dnn/ResNet-50-model.caffemodel", false),
+         findDataFile("dnn/ResNet-50-deploy.prototxt", false),
+         findDataFile("dnn/halide_scheduler_opencl_resnet_50.yml", false),
+         224, 224, "prob", "caffe", DNN_TARGET_OPENCL);
+};
+
+TEST(Reproducibility_SqueezeNet_v1_1_Halide_opencl, Accuracy)
+{
+    test(findDataFile("dnn/squeezenet_v1_1.caffemodel", false),
+         findDataFile("dnn/squeezenet_v1_1.prototxt", false),
+         findDataFile("dnn/halide_scheduler_opencl_squeezenet_v1_1.yml", false),
+         227, 227, "prob", "caffe", DNN_TARGET_OPENCL);
+};
+
+TEST(Reproducibility_Inception_5h_Halide_opencl, Accuracy)
+{
+    test(findDataFile("dnn/tensorflow_inception_graph.pb", false), "",
+         findDataFile("dnn/halide_scheduler_opencl_inception_5h.yml", false),
+         224, 224, "softmax2", "tensorflow", DNN_TARGET_OPENCL);
+};
+
+TEST(Reproducibility_ENet_Halide_opencl, Accuracy)
+{
+    test(findDataFile("dnn/Enet-model-best.net", false), "",
+         findDataFile("dnn/halide_scheduler_opencl_enet.yml", false),
+         512, 512, "l367_Deconvolution", "torch", DNN_TARGET_OPENCL, 2e-5, 0.14);
+};
+#endif  // HAVE_HALIDE
+
+}  // namespace cvtest