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464 lines
16 KiB
C++
464 lines
16 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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//
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// Copyright (C) 2013, OpenCV Foundation, 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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// * Redistribution's 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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// * Redistribution's 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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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software 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 the Intel Corporation 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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#include <opencv2/core/ocl.hpp>
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#include <opencv2/ts/ocl_test.hpp>
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namespace cvtest
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{
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using namespace cv;
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using namespace cv::dnn;
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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_Caffe, memory_read)
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{
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const string proto = findDataFile("dnn/bvlc_googlenet.prototxt", false);
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const string model = findDataFile("dnn/bvlc_googlenet.caffemodel", false);
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string dataProto;
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ASSERT_TRUE(readFileInMemory(proto, dataProto));
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string dataModel;
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ASSERT_TRUE(readFileInMemory(model, dataModel));
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Net net = readNetFromCaffe(dataProto.c_str(), dataProto.size());
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ASSERT_FALSE(net.empty());
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Net net2 = readNetFromCaffe(dataProto.c_str(), dataProto.size(),
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dataModel.c_str(), dataModel.size());
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ASSERT_FALSE(net2.empty());
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}
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TEST(Test_Caffe, read_gtsrb)
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{
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Net net = readNetFromCaffe(_tf("gtsrb.prototxt"));
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ASSERT_FALSE(net.empty());
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}
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TEST(Test_Caffe, read_googlenet)
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{
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Net net = readNetFromCaffe(_tf("bvlc_googlenet.prototxt"));
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ASSERT_FALSE(net.empty());
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}
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typedef testing::TestWithParam<tuple<bool> > Reproducibility_AlexNet;
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TEST_P(Reproducibility_AlexNet, Accuracy)
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{
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bool readFromMemory = get<0>(GetParam());
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Net net;
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{
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const string proto = findDataFile("dnn/bvlc_alexnet.prototxt", false);
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const string model = findDataFile("dnn/bvlc_alexnet.caffemodel", false);
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if (readFromMemory)
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{
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string dataProto;
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ASSERT_TRUE(readFileInMemory(proto, dataProto));
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string dataModel;
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ASSERT_TRUE(readFileInMemory(model, dataModel));
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net = readNetFromCaffe(dataProto.c_str(), dataProto.size(),
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dataModel.c_str(), dataModel.size());
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}
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else
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net = readNetFromCaffe(proto, model);
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ASSERT_FALSE(net.empty());
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}
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Mat sample = imread(_tf("grace_hopper_227.png"));
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ASSERT_TRUE(!sample.empty());
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net.setInput(blobFromImage(sample, 1.0f, Size(227, 227), Scalar(), false), "data");
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Mat out = net.forward("prob");
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Mat ref = blobFromNPY(_tf("caffe_alexnet_prob.npy"));
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normAssert(ref, out);
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}
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INSTANTIATE_TEST_CASE_P(Test_Caffe, Reproducibility_AlexNet, testing::Values(true, false));
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typedef testing::TestWithParam<tuple<bool> > Reproducibility_OCL_AlexNet;
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OCL_TEST_P(Reproducibility_OCL_AlexNet, Accuracy)
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{
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bool readFromMemory = get<0>(GetParam());
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Net net;
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{
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const string proto = findDataFile("dnn/bvlc_alexnet.prototxt", false);
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const string model = findDataFile("dnn/bvlc_alexnet.caffemodel", false);
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if (readFromMemory)
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{
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string dataProto;
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ASSERT_TRUE(readFileInMemory(proto, dataProto));
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string dataModel;
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ASSERT_TRUE(readFileInMemory(model, dataModel));
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net = readNetFromCaffe(dataProto.c_str(), dataProto.size(),
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dataModel.c_str(), dataModel.size());
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}
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else
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net = readNetFromCaffe(proto, model);
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ASSERT_FALSE(net.empty());
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}
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net.setPreferableBackend(DNN_BACKEND_DEFAULT);
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net.setPreferableTarget(DNN_TARGET_OPENCL);
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Mat sample = imread(_tf("grace_hopper_227.png"));
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ASSERT_TRUE(!sample.empty());
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net.setInput(blobFromImage(sample, 1.0f, Size(227, 227), Scalar(), false), "data");
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Mat out = net.forward("prob");
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Mat ref = blobFromNPY(_tf("caffe_alexnet_prob.npy"));
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normAssert(ref, out);
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}
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OCL_INSTANTIATE_TEST_CASE_P(Test_Caffe, Reproducibility_OCL_AlexNet, testing::Values(true, false));
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#if !defined(_WIN32) || defined(_WIN64)
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TEST(Reproducibility_FCN, Accuracy)
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{
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Net net;
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{
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const string proto = findDataFile("dnn/fcn8s-heavy-pascal.prototxt", false);
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const string model = findDataFile("dnn/fcn8s-heavy-pascal.caffemodel", false);
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net = readNetFromCaffe(proto, model);
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ASSERT_FALSE(net.empty());
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}
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Mat sample = imread(_tf("street.png"));
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ASSERT_TRUE(!sample.empty());
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std::vector<int> layerIds;
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std::vector<size_t> weights, blobs;
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net.getMemoryConsumption(shape(1,3,227,227), layerIds, weights, blobs);
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net.setInput(blobFromImage(sample, 1.0f, Size(500, 500), Scalar(), false), "data");
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Mat out = net.forward("score");
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Mat refData = imread(_tf("caffe_fcn8s_prob.png"), IMREAD_ANYDEPTH);
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int shape[] = {1, 21, 500, 500};
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Mat ref(4, shape, CV_32FC1, refData.data);
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normAssert(ref, out);
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}
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#endif
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TEST(Reproducibility_SSD, Accuracy)
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{
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Net net;
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{
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const string proto = findDataFile("dnn/ssd_vgg16.prototxt", false);
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const string model = findDataFile("dnn/VGG_ILSVRC2016_SSD_300x300_iter_440000.caffemodel", false);
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net = readNetFromCaffe(proto, model);
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ASSERT_FALSE(net.empty());
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}
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Mat sample = imread(_tf("street.png"));
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ASSERT_TRUE(!sample.empty());
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if (sample.channels() == 4)
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cvtColor(sample, sample, COLOR_BGRA2BGR);
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Mat in_blob = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
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net.setInput(in_blob, "data");
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Mat out = net.forward("detection_out");
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Mat ref = blobFromNPY(_tf("ssd_out.npy"));
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normAssert(ref, out);
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}
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TEST(Reproducibility_MobileNet_SSD, Accuracy)
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{
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const string proto = findDataFile("dnn/MobileNetSSD_deploy.prototxt", false);
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const string model = findDataFile("dnn/MobileNetSSD_deploy.caffemodel", false);
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Net net = readNetFromCaffe(proto, model);
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Mat sample = imread(_tf("street.png"));
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Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false);
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net.setInput(inp);
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Mat out = net.forward();
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Mat ref = blobFromNPY(_tf("mobilenet_ssd_caffe_out.npy"));
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normAssert(ref, out);
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// Check that detections aren't preserved.
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inp.setTo(0.0f);
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net.setInput(inp);
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out = net.forward();
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const int numDetections = out.size[2];
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ASSERT_NE(numDetections, 0);
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for (int i = 0; i < numDetections; ++i)
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{
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float confidence = out.ptr<float>(0, 0, i)[2];
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ASSERT_EQ(confidence, 0);
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}
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}
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OCL_TEST(Reproducibility_MobileNet_SSD, Accuracy)
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{
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const string proto = findDataFile("dnn/MobileNetSSD_deploy.prototxt", false);
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const string model = findDataFile("dnn/MobileNetSSD_deploy.caffemodel", false);
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Net net = readNetFromCaffe(proto, model);
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net.setPreferableBackend(DNN_BACKEND_DEFAULT);
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net.setPreferableTarget(DNN_TARGET_OPENCL);
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Mat sample = imread(_tf("street.png"));
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Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false);
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net.setInput(inp);
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Mat out = net.forward();
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Mat ref = blobFromNPY(_tf("mobilenet_ssd_caffe_out.npy"));
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normAssert(ref, out);
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// Check that detections aren't preserved.
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inp.setTo(0.0f);
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net.setInput(inp);
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out = net.forward();
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const int numDetections = out.size[2];
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ASSERT_NE(numDetections, 0);
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for (int i = 0; i < numDetections; ++i)
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{
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float confidence = out.ptr<float>(0, 0, i)[2];
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ASSERT_EQ(confidence, 0);
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}
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}
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TEST(Reproducibility_ResNet50, Accuracy)
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{
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Net net = readNetFromCaffe(findDataFile("dnn/ResNet-50-deploy.prototxt", false),
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findDataFile("dnn/ResNet-50-model.caffemodel", false));
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Mat input = blobFromImage(imread(_tf("googlenet_0.png")), 1.0f, Size(224,224), Scalar(), false);
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ASSERT_TRUE(!input.empty());
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net.setInput(input);
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Mat out = net.forward();
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Mat ref = blobFromNPY(_tf("resnet50_prob.npy"));
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normAssert(ref, out);
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}
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OCL_TEST(Reproducibility_ResNet50, Accuracy)
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{
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Net net = readNetFromCaffe(findDataFile("dnn/ResNet-50-deploy.prototxt", false),
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findDataFile("dnn/ResNet-50-model.caffemodel", false));
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net.setPreferableBackend(DNN_BACKEND_DEFAULT);
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net.setPreferableTarget(DNN_TARGET_OPENCL);
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Mat input = blobFromImage(imread(_tf("googlenet_0.png")), 1.0f, Size(224,224), Scalar(), false);
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ASSERT_TRUE(!input.empty());
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net.setInput(input);
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Mat out = net.forward();
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Mat ref = blobFromNPY(_tf("resnet50_prob.npy"));
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normAssert(ref, out);
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UMat out_umat;
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net.forward(out_umat);
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normAssert(ref, out_umat, "out_umat");
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std::vector<UMat> out_umats;
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net.forward(out_umats);
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normAssert(ref, out_umats[0], "out_umat_vector");
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}
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TEST(Reproducibility_SqueezeNet_v1_1, Accuracy)
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{
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Net net = readNetFromCaffe(findDataFile("dnn/squeezenet_v1.1.prototxt", false),
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findDataFile("dnn/squeezenet_v1.1.caffemodel", false));
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Mat input = blobFromImage(imread(_tf("googlenet_0.png")), 1.0f, Size(227,227), Scalar(), false);
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ASSERT_TRUE(!input.empty());
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net.setInput(input);
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Mat out = net.forward();
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Mat ref = blobFromNPY(_tf("squeezenet_v1.1_prob.npy"));
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normAssert(ref, out);
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}
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OCL_TEST(Reproducibility_SqueezeNet_v1_1, Accuracy)
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{
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Net net = readNetFromCaffe(findDataFile("dnn/squeezenet_v1.1.prototxt", false),
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findDataFile("dnn/squeezenet_v1.1.caffemodel", false));
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net.setPreferableBackend(DNN_BACKEND_DEFAULT);
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net.setPreferableTarget(DNN_TARGET_OPENCL);
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Mat input = blobFromImage(imread(_tf("googlenet_0.png")), 1.0f, Size(227,227), Scalar(), false);
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ASSERT_TRUE(!input.empty());
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// Firstly set a wrong input blob and run the model to receive a wrong output.
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net.setInput(input * 2.0f);
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Mat out = net.forward();
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// Then set a correct input blob to check CPU->GPU synchronization is working well.
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net.setInput(input);
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out = net.forward();
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Mat ref = blobFromNPY(_tf("squeezenet_v1.1_prob.npy"));
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normAssert(ref, out);
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}
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TEST(Reproducibility_AlexNet_fp16, Accuracy)
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{
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const float l1 = 1e-5;
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const float lInf = 3e-3;
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const string proto = findDataFile("dnn/bvlc_alexnet.prototxt", false);
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const string model = findDataFile("dnn/bvlc_alexnet.caffemodel", false);
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shrinkCaffeModel(model, "bvlc_alexnet.caffemodel_fp16");
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Net net = readNetFromCaffe(proto, "bvlc_alexnet.caffemodel_fp16");
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Mat sample = imread(findDataFile("dnn/grace_hopper_227.png", false));
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net.setInput(blobFromImage(sample, 1.0f, Size(227, 227), Scalar(), false));
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Mat out = net.forward();
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Mat ref = blobFromNPY(findDataFile("dnn/caffe_alexnet_prob.npy", false));
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normAssert(ref, out, "", l1, lInf);
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}
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TEST(Reproducibility_GoogLeNet_fp16, Accuracy)
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{
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const float l1 = 1e-5;
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const float lInf = 3e-3;
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const string proto = findDataFile("dnn/bvlc_googlenet.prototxt", false);
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const string model = findDataFile("dnn/bvlc_googlenet.caffemodel", false);
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shrinkCaffeModel(model, "bvlc_googlenet.caffemodel_fp16");
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Net net = readNetFromCaffe(proto, "bvlc_googlenet.caffemodel_fp16");
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std::vector<Mat> inpMats;
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inpMats.push_back( imread(_tf("googlenet_0.png")) );
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inpMats.push_back( imread(_tf("googlenet_1.png")) );
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ASSERT_TRUE(!inpMats[0].empty() && !inpMats[1].empty());
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net.setInput(blobFromImages(inpMats, 1.0f, Size(), Scalar(), false), "data");
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Mat out = net.forward("prob");
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Mat ref = blobFromNPY(_tf("googlenet_prob.npy"));
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normAssert(out, ref, "", l1, lInf);
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}
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// https://github.com/richzhang/colorization
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TEST(Reproducibility_Colorization, Accuracy)
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{
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const float l1 = 1e-5;
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const float lInf = 3e-3;
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Mat inp = blobFromNPY(_tf("colorization_inp.npy"));
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Mat ref = blobFromNPY(_tf("colorization_out.npy"));
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Mat kernel = blobFromNPY(_tf("colorization_pts_in_hull.npy"));
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const string proto = findDataFile("dnn/colorization_deploy_v2.prototxt", false);
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const string model = findDataFile("dnn/colorization_release_v2.caffemodel", false);
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Net net = readNetFromCaffe(proto, model);
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net.getLayer(net.getLayerId("class8_ab"))->blobs.push_back(kernel);
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net.getLayer(net.getLayerId("conv8_313_rh"))->blobs.push_back(Mat(1, 313, CV_32F, 2.606));
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net.setInput(inp);
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Mat out = net.forward();
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normAssert(out, ref, "", l1, lInf);
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}
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TEST(Reproducibility_DenseNet_121, Accuracy)
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{
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const string proto = findDataFile("dnn/DenseNet_121.prototxt", false);
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const string model = findDataFile("dnn/DenseNet_121.caffemodel", false);
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Mat inp = imread(_tf("dog416.png"));
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inp = blobFromImage(inp, 1.0 / 255, Size(224, 224));
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Mat ref = blobFromNPY(_tf("densenet_121_output.npy"));
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Net net = readNetFromCaffe(proto, model);
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net.setInput(inp);
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Mat out = net.forward();
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normAssert(out, ref);
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}
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TEST(Test_Caffe, multiple_inputs)
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{
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const string proto = findDataFile("dnn/layers/net_input.prototxt", false);
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Net net = readNetFromCaffe(proto);
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Mat first_image(10, 11, CV_32FC3);
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Mat second_image(10, 11, CV_32FC3);
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randu(first_image, -1, 1);
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randu(second_image, -1, 1);
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first_image = blobFromImage(first_image);
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second_image = blobFromImage(second_image);
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Mat first_image_blue_green = slice(first_image, Range::all(), Range(0, 2), Range::all(), Range::all());
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Mat first_image_red = slice(first_image, Range::all(), Range(2, 3), Range::all(), Range::all());
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Mat second_image_blue_green = slice(second_image, Range::all(), Range(0, 2), Range::all(), Range::all());
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Mat second_image_red = slice(second_image, Range::all(), Range(2, 3), Range::all(), Range::all());
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net.setInput(first_image_blue_green, "old_style_input_blue_green");
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net.setInput(first_image_red, "different_name_for_red");
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net.setInput(second_image_blue_green, "input_layer_blue_green");
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net.setInput(second_image_red, "old_style_input_red");
|
|
Mat out = net.forward();
|
|
|
|
normAssert(out, first_image + second_image);
|
|
}
|
|
|
|
}
|