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206 lines
7.6 KiB
C++
206 lines
7.6 KiB
C++
// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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//
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// Copyright (C) 2017, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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#include "test_precomp.hpp"
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namespace cvtest
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{
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#ifdef HAVE_HALIDE
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using namespace cv;
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using namespace dnn;
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static void loadNet(const std::string& weights, const std::string& proto,
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const std::string& framework, Net* net)
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{
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if (framework == "caffe")
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{
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*net = cv::dnn::readNetFromCaffe(proto, weights);
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}
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else if (framework == "torch")
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{
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*net = cv::dnn::readNetFromTorch(weights);
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}
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else if (framework == "tensorflow")
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{
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*net = cv::dnn::readNetFromTensorflow(weights);
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}
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else
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CV_Error(Error::StsNotImplemented, "Unknown framework " + framework);
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}
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static void test(const std::string& weights, const std::string& proto,
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const std::string& scheduler, int inWidth, int inHeight,
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const std::string& outputLayer, const std::string& framework,
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int targetId, double l1 = 1e-5, double lInf = 1e-4)
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{
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Mat input(inHeight, inWidth, CV_32FC3), outputDefault, outputHalide;
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randu(input, 0.0f, 1.0f);
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Net netDefault, netHalide;
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loadNet(weights, proto, framework, &netDefault);
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loadNet(weights, proto, framework, &netHalide);
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netDefault.setInput(blobFromImage(input.clone(), 1.0f, Size(), Scalar(), false));
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outputDefault = netDefault.forward(outputLayer).clone();
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netHalide.setInput(blobFromImage(input.clone(), 1.0f, Size(), Scalar(), false));
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netHalide.setPreferableBackend(DNN_BACKEND_HALIDE);
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netHalide.setPreferableTarget(targetId);
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netHalide.setHalideScheduler(scheduler);
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outputHalide = netHalide.forward(outputLayer).clone();
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normAssert(outputDefault, outputHalide, "First run", l1, lInf);
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// An extra test: change input.
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input *= 0.1f;
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netDefault.setInput(blobFromImage(input.clone(), 1.0, Size(), Scalar(), false));
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netHalide.setInput(blobFromImage(input.clone(), 1.0, Size(), Scalar(), false));
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normAssert(outputDefault, outputHalide, "Second run", l1, lInf);
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std::cout << "." << std::endl;
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// Swap backends.
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netHalide.setPreferableBackend(DNN_BACKEND_DEFAULT);
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netHalide.setPreferableTarget(DNN_TARGET_CPU);
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outputDefault = netHalide.forward(outputLayer).clone();
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netDefault.setPreferableBackend(DNN_BACKEND_HALIDE);
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netDefault.setPreferableTarget(targetId);
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netDefault.setHalideScheduler(scheduler);
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outputHalide = netDefault.forward(outputLayer).clone();
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normAssert(outputDefault, outputHalide, "Swap backends", l1, lInf);
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}
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////////////////////////////////////////////////////////////////////////////////
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// CPU target
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////////////////////////////////////////////////////////////////////////////////
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TEST(Reproducibility_MobileNetSSD_Halide, Accuracy)
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{
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test(findDataFile("dnn/MobileNetSSD_deploy.caffemodel", false),
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findDataFile("dnn/MobileNetSSD_deploy.prototxt", false),
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"", 300, 300, "detection_out", "caffe", DNN_TARGET_CPU);
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};
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// TODO: Segmentation fault from time to time.
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// TEST(Reproducibility_SSD_Halide, Accuracy)
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// {
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// test(findDataFile("dnn/VGG_ILSVRC2016_SSD_300x300_iter_440000.caffemodel", false),
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// findDataFile("dnn/ssd_vgg16.prototxt", false),
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// "", 300, 300, "detection_out", "caffe", DNN_TARGET_CPU);
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// };
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TEST(Reproducibility_GoogLeNet_Halide, Accuracy)
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{
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test(findDataFile("dnn/bvlc_googlenet.caffemodel", false),
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findDataFile("dnn/bvlc_googlenet.prototxt", false),
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"", 224, 224, "prob", "caffe", DNN_TARGET_CPU);
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};
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TEST(Reproducibility_AlexNet_Halide, Accuracy)
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{
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test(findDataFile("dnn/bvlc_alexnet.caffemodel", false),
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findDataFile("dnn/bvlc_alexnet.prototxt", false),
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findDataFile("dnn/halide_scheduler_alexnet.yml", false),
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227, 227, "prob", "caffe", DNN_TARGET_CPU);
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};
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TEST(Reproducibility_ResNet_50_Halide, Accuracy)
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{
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test(findDataFile("dnn/ResNet-50-model.caffemodel", false),
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findDataFile("dnn/ResNet-50-deploy.prototxt", false),
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findDataFile("dnn/halide_scheduler_resnet_50.yml", false),
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224, 224, "prob", "caffe", DNN_TARGET_CPU);
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};
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TEST(Reproducibility_SqueezeNet_v1_1_Halide, Accuracy)
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{
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test(findDataFile("dnn/squeezenet_v1.1.caffemodel", false),
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findDataFile("dnn/squeezenet_v1.1.prototxt", false),
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findDataFile("dnn/halide_scheduler_squeezenet_v1_1.yml", false),
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227, 227, "prob", "caffe", DNN_TARGET_CPU);
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};
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TEST(Reproducibility_Inception_5h_Halide, Accuracy)
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{
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test(findDataFile("dnn/tensorflow_inception_graph.pb", false), "",
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findDataFile("dnn/halide_scheduler_inception_5h.yml", false),
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224, 224, "softmax2", "tensorflow", DNN_TARGET_CPU);
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};
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TEST(Reproducibility_ENet_Halide, Accuracy)
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{
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test(findDataFile("dnn/Enet-model-best.net", false), "",
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findDataFile("dnn/halide_scheduler_enet.yml", false),
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512, 512, "l367_Deconvolution", "torch", DNN_TARGET_CPU, 2e-5, 0.15);
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};
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////////////////////////////////////////////////////////////////////////////////
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// OpenCL target
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////////////////////////////////////////////////////////////////////////////////
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TEST(Reproducibility_MobileNetSSD_Halide_opencl, Accuracy)
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{
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test(findDataFile("dnn/MobileNetSSD_deploy.caffemodel", false),
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findDataFile("dnn/MobileNetSSD_deploy.prototxt", false),
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"", 300, 300, "detection_out", "caffe", DNN_TARGET_OPENCL);
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};
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TEST(Reproducibility_SSD_Halide_opencl, Accuracy)
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{
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test(findDataFile("dnn/VGG_ILSVRC2016_SSD_300x300_iter_440000.caffemodel", false),
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findDataFile("dnn/ssd_vgg16.prototxt", false),
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"", 300, 300, "detection_out", "caffe", DNN_TARGET_OPENCL);
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};
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TEST(Reproducibility_GoogLeNet_Halide_opencl, Accuracy)
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{
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test(findDataFile("dnn/bvlc_googlenet.caffemodel", false),
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findDataFile("dnn/bvlc_googlenet.prototxt", false),
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"", 227, 227, "prob", "caffe", DNN_TARGET_OPENCL);
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};
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TEST(Reproducibility_AlexNet_Halide_opencl, Accuracy)
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{
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test(findDataFile("dnn/bvlc_alexnet.caffemodel", false),
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findDataFile("dnn/bvlc_alexnet.prototxt", false),
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findDataFile("dnn/halide_scheduler_opencl_alexnet.yml", false),
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227, 227, "prob", "caffe", DNN_TARGET_OPENCL);
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};
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TEST(Reproducibility_ResNet_50_Halide_opencl, Accuracy)
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{
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test(findDataFile("dnn/ResNet-50-model.caffemodel", false),
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findDataFile("dnn/ResNet-50-deploy.prototxt", false),
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findDataFile("dnn/halide_scheduler_opencl_resnet_50.yml", false),
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224, 224, "prob", "caffe", DNN_TARGET_OPENCL);
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};
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TEST(Reproducibility_SqueezeNet_v1_1_Halide_opencl, Accuracy)
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{
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test(findDataFile("dnn/squeezenet_v1.1.caffemodel", false),
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findDataFile("dnn/squeezenet_v1.1.prototxt", false),
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findDataFile("dnn/halide_scheduler_opencl_squeezenet_v1_1.yml", false),
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227, 227, "prob", "caffe", DNN_TARGET_OPENCL);
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};
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TEST(Reproducibility_Inception_5h_Halide_opencl, Accuracy)
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{
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test(findDataFile("dnn/tensorflow_inception_graph.pb", false), "",
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findDataFile("dnn/halide_scheduler_opencl_inception_5h.yml", false),
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224, 224, "softmax2", "tensorflow", DNN_TARGET_OPENCL);
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};
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TEST(Reproducibility_ENet_Halide_opencl, Accuracy)
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{
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test(findDataFile("dnn/Enet-model-best.net", false), "",
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findDataFile("dnn/halide_scheduler_opencl_enet.yml", false),
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512, 512, "l367_Deconvolution", "torch", DNN_TARGET_OPENCL, 2e-5, 0.14);
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};
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#endif // HAVE_HALIDE
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} // namespace cvtest
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