void extractBinaryLayerParams(const caffe::LayerParameter& layer, LayerParams& layerParams)
{
const std::string &name = layer.name();
int li;
for (li = 0; li != netBinary.layer_size(); li++)
{
const caffe::LayerParameter& binLayer = netBinary.layer(li);
// Break if the layer name is the same and the blobs are not cleared
if (binLayer.name() == name && binLayer.blobs_size() != 0)
break;
}
if (li == netBinary.layer_size())
return;
caffe::LayerParameter* binLayer = netBinary.mutable_layer(li);
const int numBlobs = binLayer->blobs_size();
layerParams.blobs.resize(numBlobs);
for (int bi = 0; bi < numBlobs; bi++)
{
blobFromProto(binLayer->blobs(bi), layerParams.blobs[bi]);
}
binLayer->clear_blobs();
CV_Assert(numBlobs == binLayer->blobs().ClearedCount());
for (int bi = 0; bi < numBlobs; bi++)
{
delete binLayer->mutable_blobs()->ReleaseCleared();
}
}
Waifu2x::eWaifu2xError cNet::SetParameter(caffe::NetParameter ¶m, const std::string &process) const
{
param.mutable_state()->set_phase(caffe::TEST);
{
auto input_layer = param.mutable_layer(0);
auto mid = input_layer->mutable_input_param()->mutable_shape();
if (mid->size() != 1 || mid->Mutable(0)->dim_size() != 4)
return Waifu2x::eWaifu2xError_FailedParseModelFile;
}
for (int i = 0; i < param.layer_size(); i++)
{
caffe::LayerParameter *layer_param = param.mutable_layer(i);
const std::string& type = layer_param->type();
if (type == "Convolution")
{
if (process == "cudnn")
layer_param->mutable_convolution_param()->set_engine(caffe::ConvolutionParameter_Engine_CUDNN);
else
layer_param->mutable_convolution_param()->set_engine(caffe::ConvolutionParameter_Engine_CAFFE);
}
else if (type == "Deconvolution")
{
if (process == "cudnn")
layer_param->mutable_convolution_param()->set_engine(caffe::ConvolutionParameter_Engine_CUDNN);
else
layer_param->mutable_convolution_param()->set_engine(caffe::ConvolutionParameter_Engine_CAFFE);
}
else if (type == "ReLU")
{
if (process == "cudnn")
layer_param->mutable_relu_param()->set_engine(caffe::ReLUParameter_Engine_CUDNN);
else
layer_param->mutable_relu_param()->set_engine(caffe::ReLUParameter_Engine_CAFFE);
}
}
return Waifu2x::eWaifu2xError_OK;
}
void populateNet(Net dstNet)
{
CV_TRACE_FUNCTION();
int layersSize = net.layer_size();
layerCounter.clear();
addedBlobs.clear();
addedBlobs.reserve(layersSize + 1);
//setup input layer names
std::vector<String> netInputs(net.input_size());
{
for (int inNum = 0; inNum < net.input_size(); inNum++)
{
addedBlobs.push_back(BlobNote(net.input(inNum), 0, inNum));
netInputs[inNum] = net.input(inNum);
}
}
for (int li = 0; li < layersSize; li++)
{
const caffe::LayerParameter &layer = net.layer(li);
String name = layer.name();
String type = layer.type();
LayerParams layerParams;
extractLayerParams(layer, layerParams);
extractBinaryLayerParams(layer, layerParams);
int repetitions = layerCounter[name]++;
if (repetitions)
name += String("_") + toString(repetitions);
if (type == "Input")
{
for (int outNum = 0; outNum < layer.top_size(); outNum++)
{
addOutput(layer, 0, outNum);
addedBlobs.back().outNum = netInputs.size();
netInputs.push_back(addedBlobs.back().name);
}
continue;
}
else if (type == "BatchNorm")
{
if (!layerParams.get<bool>("use_global_stats", true))
{
CV_Assert_N(layer.bottom_size() == 1, layer.top_size() == 1);
LayerParams mvnParams;
mvnParams.set("eps", layerParams.get<float>("eps", 1e-5));
std::string mvnName = name + "/mvn";
int repetitions = layerCounter[mvnName]++;
if (repetitions)
mvnName += String("_") + toString(repetitions);
int mvnId = dstNet.addLayer(mvnName, "MVN", mvnParams);
addInput(layer.bottom(0), mvnId, 0, dstNet);
addOutput(layer, mvnId, 0);
net.mutable_layer(li)->set_bottom(0, layer.top(0));
layerParams.blobs[0].setTo(0); // mean
layerParams.blobs[1].setTo(1); // std
}
}
else if ("ConvolutionDepthwise" == type)
{
type = "Convolution";
}
int id = dstNet.addLayer(name, type, layerParams);
for (int inNum = 0; inNum < layer.bottom_size(); inNum++)
addInput(layer.bottom(inNum), id, inNum, dstNet);
for (int outNum = 0; outNum < layer.top_size(); outNum++)
addOutput(layer, id, outNum);
}
dstNet.setInputsNames(netInputs);
addedBlobs.clear();
}
