mirror of
https://github.com/opencv/opencv.git
synced 2024-12-14 08:59:11 +08:00
156 lines
6.0 KiB
C++
156 lines
6.0 KiB
C++
/*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)
|
|
{
|
|
const int targetId = GetParam();
|
|
if (targetId == DNN_TARGET_OPENCL_FP16)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
|
|
Net net = readNetFromCaffe(findDataFile("dnn/bvlc_googlenet.prototxt"),
|
|
findDataFile("dnn/bvlc_googlenet.caffemodel", false));
|
|
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)
|
|
{
|
|
const int targetId = GetParam();
|
|
if (targetId == DNN_TARGET_OPENCL_FP16)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
|
|
Net net = readNetFromCaffe(findDataFile("dnn/bvlc_googlenet.prototxt"),
|
|
findDataFile("dnn/bvlc_googlenet.caffemodel", false));
|
|
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)
|
|
{
|
|
const int targetId = GetParam();
|
|
if (targetId == DNN_TARGET_OPENCL_FP16)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
|
|
Net net = readNetFromCaffe(findDataFile("dnn/bvlc_googlenet.prototxt"),
|
|
findDataFile("dnn/bvlc_googlenet.caffemodel", false));
|
|
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,
|
|
testing::ValuesIn(getAvailableTargets(DNN_BACKEND_OPENCV)));
|
|
|
|
}} // namespace
|