opencv/modules/dnn/test/test_caffe_importer.cpp

238 lines
8.0 KiB
C++

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#include "test_precomp.hpp"
#include "npy_blob.hpp"
#include <opencv2/dnn/shape_utils.hpp>
namespace cvtest
{
using namespace cv;
using namespace cv::dnn;
template<typename TString>
static std::string _tf(TString filename)
{
return (getOpenCVExtraDir() + "/dnn/") + filename;
}
TEST(Test_Caffe, read_gtsrb)
{
Net net = readNetFromCaffe(_tf("gtsrb.prototxt"));
ASSERT_FALSE(net.empty());
}
TEST(Test_Caffe, read_googlenet)
{
Net net = readNetFromCaffe(_tf("bvlc_googlenet.prototxt"));
ASSERT_FALSE(net.empty());
}
TEST(Reproducibility_AlexNet, Accuracy)
{
Net net;
{
const string proto = findDataFile("dnn/bvlc_alexnet.prototxt", false);
const string model = findDataFile("dnn/bvlc_alexnet.caffemodel", false);
net = readNetFromCaffe(proto, model);
ASSERT_FALSE(net.empty());
}
Mat sample = imread(_tf("grace_hopper_227.png"));
ASSERT_TRUE(!sample.empty());
net.setInput(blobFromImage(sample, 1.0f, Size(227, 227), Scalar(), false), "data");
Mat out = net.forward("prob");
Mat ref = blobFromNPY(_tf("caffe_alexnet_prob.npy"));
normAssert(ref, out);
}
#if !defined(_WIN32) || defined(_WIN64)
TEST(Reproducibility_FCN, Accuracy)
{
Net net;
{
const string proto = findDataFile("dnn/fcn8s-heavy-pascal.prototxt", false);
const string model = findDataFile("dnn/fcn8s-heavy-pascal.caffemodel", false);
net = readNetFromCaffe(proto, model);
ASSERT_FALSE(net.empty());
}
Mat sample = imread(_tf("street.png"));
ASSERT_TRUE(!sample.empty());
std::vector<int> layerIds;
std::vector<size_t> weights, blobs;
net.getMemoryConsumption(shape(1,3,227,227), layerIds, weights, blobs);
net.setInput(blobFromImage(sample, 1.0f, Size(500, 500), Scalar(), false), "data");
Mat out = net.forward("score");
Mat refData = imread(_tf("caffe_fcn8s_prob.png"), IMREAD_ANYDEPTH);
int shape[] = {1, 21, 500, 500};
Mat ref(4, shape, CV_32FC1, refData.data);
normAssert(ref, out);
}
#endif
TEST(Reproducibility_SSD, Accuracy)
{
Net net;
{
const string proto = findDataFile("dnn/ssd_vgg16.prototxt", false);
const string model = findDataFile("dnn/VGG_ILSVRC2016_SSD_300x300_iter_440000.caffemodel", false);
net = readNetFromCaffe(proto, model);
ASSERT_FALSE(net.empty());
}
Mat sample = imread(_tf("street.png"));
ASSERT_TRUE(!sample.empty());
if (sample.channels() == 4)
cvtColor(sample, sample, COLOR_BGRA2BGR);
Mat in_blob = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
net.setInput(in_blob, "data");
Mat out = net.forward("detection_out");
Mat ref = blobFromNPY(_tf("ssd_out.npy"));
normAssert(ref, out);
}
TEST(Reproducibility_ResNet50, Accuracy)
{
Net net = readNetFromCaffe(findDataFile("dnn/ResNet-50-deploy.prototxt", false),
findDataFile("dnn/ResNet-50-model.caffemodel", false));
Mat input = blobFromImage(imread(_tf("googlenet_0.png")), 1.0f, Size(224,224), Scalar(), false);
ASSERT_TRUE(!input.empty());
net.setInput(input);
Mat out = net.forward();
Mat ref = blobFromNPY(_tf("resnet50_prob.npy"));
normAssert(ref, out);
}
TEST(Reproducibility_SqueezeNet_v1_1, Accuracy)
{
Net net = readNetFromCaffe(findDataFile("dnn/squeezenet_v1.1.prototxt", false),
findDataFile("dnn/squeezenet_v1.1.caffemodel", false));
Mat input = blobFromImage(imread(_tf("googlenet_0.png")), 1.0f, Size(227,227), Scalar(), false);
ASSERT_TRUE(!input.empty());
net.setInput(input);
Mat out = net.forward();
Mat ref = blobFromNPY(_tf("squeezenet_v1.1_prob.npy"));
normAssert(ref, out);
}
TEST(Reproducibility_AlexNet_fp16, Accuracy)
{
const float l1 = 1e-5;
const float lInf = 3e-3;
const string proto = findDataFile("dnn/bvlc_alexnet.prototxt", false);
const string model = findDataFile("dnn/bvlc_alexnet.caffemodel", false);
shrinkCaffeModel(model, "bvlc_alexnet.caffemodel_fp16");
Net net = readNetFromCaffe(proto, "bvlc_alexnet.caffemodel_fp16");
Mat sample = imread(findDataFile("dnn/grace_hopper_227.png", false));
net.setInput(blobFromImage(sample, 1.0f, Size(227, 227), Scalar(), false));
Mat out = net.forward();
Mat ref = blobFromNPY(findDataFile("dnn/caffe_alexnet_prob.npy", false));
normAssert(ref, out, "", l1, lInf);
}
TEST(Reproducibility_GoogLeNet_fp16, Accuracy)
{
const float l1 = 1e-5;
const float lInf = 3e-3;
const string proto = findDataFile("dnn/bvlc_googlenet.prototxt", false);
const string model = findDataFile("dnn/bvlc_googlenet.caffemodel", false);
shrinkCaffeModel(model, "bvlc_googlenet.caffemodel_fp16");
Net net = readNetFromCaffe(proto, "bvlc_googlenet.caffemodel_fp16");
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, "", l1, lInf);
}
// https://github.com/richzhang/colorization
TEST(Reproducibility_Colorization, Accuracy)
{
const float l1 = 1e-5;
const float lInf = 3e-3;
Mat inp = blobFromNPY(_tf("colorization_inp.npy"));
Mat ref = blobFromNPY(_tf("colorization_out.npy"));
Mat kernel = blobFromNPY(_tf("colorization_pts_in_hull.npy"));
const string proto = findDataFile("dnn/colorization_deploy_v2.prototxt", false);
const string model = findDataFile("dnn/colorization_release_v2.caffemodel", false);
Net net = readNetFromCaffe(proto, model);
net.getLayer(net.getLayerId("class8_ab"))->blobs.push_back(kernel);
net.getLayer(net.getLayerId("conv8_313_rh"))->blobs.push_back(Mat(1, 313, CV_32F, 2.606));
net.setInput(inp);
Mat out = net.forward();
normAssert(out, ref, "", l1, lInf);
}
}