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225 lines
8.8 KiB
C++
225 lines
8.8 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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// (3-clause BSD License)
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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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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistributions of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistributions in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * Neither the names of the copyright holders nor the names of the contributors
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// may be used to endorse or promote products derived from this software
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// without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall copyright holders or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "test_precomp.hpp"
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#include "npy_blob.hpp"
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#include <opencv2/dnn/shape_utils.hpp>
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namespace opencv_test { namespace {
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template<typename TString>
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static std::string _tf(TString filename)
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{
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return (getOpenCVExtraDir() + "/dnn/") + filename;
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}
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TEST(Test_Darknet, read_tiny_yolo_voc)
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{
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Net net = readNetFromDarknet(_tf("tiny-yolo-voc.cfg"));
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ASSERT_FALSE(net.empty());
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}
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TEST(Test_Darknet, read_yolo_voc)
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{
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Net net = readNetFromDarknet(_tf("yolo-voc.cfg"));
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ASSERT_FALSE(net.empty());
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}
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class Test_Darknet_layers : public DNNTestLayer
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{
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public:
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void testDarknetLayer(const std::string& name, bool hasWeights = false)
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{
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std::string cfg = findDataFile("dnn/darknet/" + name + ".cfg", false);
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std::string model = "";
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if (hasWeights)
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model = findDataFile("dnn/darknet/" + name + ".weights", false);
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Mat inp = blobFromNPY(findDataFile("dnn/darknet/" + name + "_in.npy", false));
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Mat ref = blobFromNPY(findDataFile("dnn/darknet/" + name + "_out.npy", false));
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checkBackend(&inp, &ref);
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Net net = readNet(cfg, model);
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net.setPreferableBackend(backend);
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net.setPreferableTarget(target);
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net.setInput(inp);
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Mat out = net.forward();
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normAssert(out, ref, "", default_l1, default_lInf);
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}
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};
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class Test_Darknet_nets : public DNNTestLayer
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{
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public:
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// Test object detection network from Darknet framework.
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void testDarknetModel(const std::string& cfg, const std::string& weights,
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const std::vector<cv::String>& outNames,
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const std::vector<int>& refClassIds,
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const std::vector<float>& refConfidences,
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const std::vector<Rect2d>& refBoxes,
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double scoreDiff, double iouDiff, float confThreshold = 0.24)
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{
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checkBackend();
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Mat sample = imread(_tf("dog416.png"));
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Mat inp = blobFromImage(sample, 1.0/255, Size(416, 416), Scalar(), true, false);
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Net net = readNet(findDataFile("dnn/" + cfg, false),
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findDataFile("dnn/" + weights, false));
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net.setPreferableBackend(backend);
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net.setPreferableTarget(target);
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net.setInput(inp);
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std::vector<Mat> outs;
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net.forward(outs, outNames);
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std::vector<int> classIds;
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std::vector<float> confidences;
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std::vector<Rect2d> boxes;
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for (int i = 0; i < outs.size(); ++i)
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{
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Mat& out = outs[i];
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for (int j = 0; j < out.rows; ++j)
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{
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Mat scores = out.row(j).colRange(5, out.cols);
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double confidence;
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Point maxLoc;
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minMaxLoc(scores, 0, &confidence, 0, &maxLoc);
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float* detection = out.ptr<float>(j);
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double centerX = detection[0];
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double centerY = detection[1];
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double width = detection[2];
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double height = detection[3];
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boxes.push_back(Rect2d(centerX - 0.5 * width, centerY - 0.5 * height,
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width, height));
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confidences.push_back(confidence);
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classIds.push_back(maxLoc.x);
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}
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}
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normAssertDetections(refClassIds, refConfidences, refBoxes, classIds,
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confidences, boxes, "", confThreshold, scoreDiff, iouDiff);
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}
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};
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TEST_P(Test_Darknet_nets, YoloVoc)
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{
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std::vector<cv::String> outNames(1, "detection_out");
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std::vector<int> classIds(3);
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std::vector<float> confidences(3);
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std::vector<Rect2d> boxes(3);
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classIds[0] = 6; confidences[0] = 0.750469f; boxes[0] = Rect2d(0.577374, 0.127391, 0.325575, 0.173418); // a car
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classIds[1] = 1; confidences[1] = 0.780879f; boxes[1] = Rect2d(0.270762, 0.264102, 0.461713, 0.48131); // a bicycle
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classIds[2] = 11; confidences[2] = 0.901615f; boxes[2] = Rect2d(0.1386, 0.338509, 0.282737, 0.60028); // a dog
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double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 1e-2 : 8e-5;
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double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.013 : 3e-5;
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testDarknetModel("yolo-voc.cfg", "yolo-voc.weights", outNames,
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classIds, confidences, boxes, scoreDiff, iouDiff);
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}
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TEST_P(Test_Darknet_nets, TinyYoloVoc)
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{
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std::vector<cv::String> outNames(1, "detection_out");
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std::vector<int> classIds(2);
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std::vector<float> confidences(2);
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std::vector<Rect2d> boxes(2);
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classIds[0] = 6; confidences[0] = 0.761967f; boxes[0] = Rect2d(0.579042, 0.159161, 0.31544, 0.160779); // a car
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classIds[1] = 11; confidences[1] = 0.780595f; boxes[1] = Rect2d(0.129696, 0.386467, 0.315579, 0.534527); // a dog
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double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 8e-3 : 8e-5;
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double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 8e-3 : 3e-5;
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testDarknetModel("tiny-yolo-voc.cfg", "tiny-yolo-voc.weights", outNames,
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classIds, confidences, boxes, scoreDiff, iouDiff);
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}
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TEST_P(Test_Darknet_nets, YOLOv3)
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{
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std::vector<cv::String> outNames(3);
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outNames[0] = "yolo_82";
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outNames[1] = "yolo_94";
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outNames[2] = "yolo_106";
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std::vector<int> classIds(3);
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std::vector<float> confidences(3);
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std::vector<Rect2d> boxes(3);
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classIds[0] = 7; confidences[0] = 0.952983f; boxes[0] = Rect2d(0.614622, 0.150257, 0.286747, 0.138994); // a truck
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classIds[1] = 1; confidences[1] = 0.987908f; boxes[1] = Rect2d(0.150913, 0.221933, 0.591342, 0.524327); // a bicycle
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classIds[2] = 16; confidences[2] = 0.998836f; boxes[2] = Rect2d(0.160024, 0.389964, 0.257861, 0.553752); // a dog (COCO)
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double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 4e-3 : 8e-5;
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double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.011 : 3e-5;
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testDarknetModel("yolov3.cfg", "yolov3.weights", outNames,
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classIds, confidences, boxes, scoreDiff, iouDiff);
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}
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INSTANTIATE_TEST_CASE_P(/**/, Test_Darknet_nets, dnnBackendsAndTargets());
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TEST_P(Test_Darknet_layers, shortcut)
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{
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if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_CPU)
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throw SkipTestException("");
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testDarknetLayer("shortcut");
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}
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TEST_P(Test_Darknet_layers, upsample)
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{
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testDarknetLayer("upsample");
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}
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TEST_P(Test_Darknet_layers, avgpool_softmax)
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{
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testDarknetLayer("avgpool_softmax");
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}
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TEST_P(Test_Darknet_layers, region)
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{
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testDarknetLayer("region");
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}
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TEST_P(Test_Darknet_layers, reorg)
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{
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testDarknetLayer("reorg");
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}
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INSTANTIATE_TEST_CASE_P(/**/, Test_Darknet_layers, dnnBackendsAndTargets());
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}} // namespace
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