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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "test_precomp.hpp"
#include "npy_blob.hpp"
#include <opencv2/core/ocl.hpp>
#include <opencv2/ts/ocl_test.hpp>
namespace opencv_test { namespace {
template<typename TString>
static std::string _tf(TString filename)
{
return (getOpenCVExtraDir() + "/dnn/") + filename;
}
typedef testing::TestWithParam<Target> Reproducibility_GoogLeNet;
TEST_P(Reproducibility_GoogLeNet, Batching)
{
Net net = readNetFromCaffe(findDataFile("dnn/bvlc_googlenet.prototxt", false),
findDataFile("dnn/bvlc_googlenet.caffemodel", false));
int targetId = GetParam();
net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setPreferableTarget(targetId);
if (targetId == DNN_TARGET_OPENCL)
{
// Initialize network for a single image in the batch but test with batch size=2.
Mat inp = Mat(224, 224, CV_8UC3);
randu(inp, -1, 1);
net.setInput(blobFromImage(inp));
net.forward();
}
std::vector<Mat> inpMats;
inpMats.push_back( imread(_tf("googlenet_0.png")) );
inpMats.push_back( imread(_tf("googlenet_1.png")) );
ASSERT_TRUE(!inpMats[0].empty() && !inpMats[1].empty());
net.setInput(blobFromImages(inpMats, 1.0f, Size(), Scalar(), false), "data");
Mat out = net.forward("prob");
Mat ref = blobFromNPY(_tf("googlenet_prob.npy"));
normAssert(out, ref);
}
TEST_P(Reproducibility_GoogLeNet, IntermediateBlobs)
{
Net net = readNetFromCaffe(findDataFile("dnn/bvlc_googlenet.prototxt", false),
findDataFile("dnn/bvlc_googlenet.caffemodel", false));
int targetId = GetParam();
net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setPreferableTarget(targetId);
std::vector<String> blobsNames;
blobsNames.push_back("conv1/7x7_s2");
blobsNames.push_back("conv1/relu_7x7");
blobsNames.push_back("inception_4c/1x1");
blobsNames.push_back("inception_4c/relu_1x1");
std::vector<Mat> outs;
Mat in = blobFromImage(imread(_tf("googlenet_0.png")), 1.0f, Size(), Scalar(), false);
net.setInput(in, "data");
net.forward(outs, blobsNames);
CV_Assert(outs.size() == blobsNames.size());
for (size_t i = 0; i < blobsNames.size(); i++)
{
std::string filename = blobsNames[i];
std::replace( filename.begin(), filename.end(), '/', '#');
Mat ref = blobFromNPY(_tf("googlenet_" + filename + ".npy"));
normAssert(outs[i], ref, "", 1E-4, 1E-2);
}
}
TEST_P(Reproducibility_GoogLeNet, SeveralCalls)
{
Net net = readNetFromCaffe(findDataFile("dnn/bvlc_googlenet.prototxt", false),
findDataFile("dnn/bvlc_googlenet.caffemodel", false));
int targetId = GetParam();
net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setPreferableTarget(targetId);
std::vector<Mat> inpMats;
inpMats.push_back( imread(_tf("googlenet_0.png")) );
inpMats.push_back( imread(_tf("googlenet_1.png")) );
ASSERT_TRUE(!inpMats[0].empty() && !inpMats[1].empty());
net.setInput(blobFromImages(inpMats, 1.0f, Size(), Scalar(), false), "data");
Mat out = net.forward();
Mat ref = blobFromNPY(_tf("googlenet_prob.npy"));
normAssert(out, ref);
std::vector<String> blobsNames;
blobsNames.push_back("conv1/7x7_s2");
std::vector<Mat> outs;
Mat in = blobFromImage(inpMats[0], 1.0f, Size(), Scalar(), false);
net.setInput(in, "data");
net.forward(outs, blobsNames);
CV_Assert(outs.size() == blobsNames.size());
ref = blobFromNPY(_tf("googlenet_conv1#7x7_s2.npy"));
normAssert(outs[0], ref, "", 1E-4, 1E-2);
}
INSTANTIATE_TEST_CASE_P(/**/, Reproducibility_GoogLeNet, availableDnnTargets());
}} // namespace