// 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);
}
void expectNoFallbacks(Net& net)
{
// Check if all the layers are supported with current backend and target.
// Some layers might be fused so their timings equal to zero.
std::vector<double> timings;
net.getPerfProfile(timings);
std::vector<String> names = net.getLayerNames();
CV_Assert(names.size() == timings.size());
for (int i = 0; i < names.size(); ++i)
{
Ptr<dnn::Layer> l = net.getLayer(net.getLayerId(names[i]));
bool fused = !timings[i];
if ((!l->supportBackend(backend) || l->preferableTarget != target) && !fused)
CV_Error(Error::StsNotImplemented, "Layer [" + l->name + "] of type [" +
l->type + "] is expected to has backend implementation");
}
}