本文整理汇总了C++中Net::setInputsNames方法的典型用法代码示例。如果您正苦于以下问题：C++ Net::setInputsNames方法的具体用法？C++ Net::setInputsNames怎么用？C++ Net::setInputsNames使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类Net的用法示例。
在下文中一共展示了Net::setInputsNames方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。
示例1: populateNet
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);
extractBinaryLayerParms(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;
}
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();
}示例2: populateNet
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();
}示例3: populateNet
void ONNXImporter::populateNet(Net dstNet)
{
CV_Assert(model_proto.has_graph());
opencv_onnx::GraphProto graph_proto = model_proto.graph();
std::map<std::string, Mat> constBlobs = getGraphTensors(graph_proto);
// List of internal blobs shapes.
std::map<std::string, MatShape> outShapes;
// Add all the inputs shapes. It includes as constant blobs as network's inputs shapes.
for (int i = 0; i < graph_proto.input_size(); ++i)
{
opencv_onnx::ValueInfoProto valueInfoProto = graph_proto.input(i);
CV_Assert(valueInfoProto.has_type());
opencv_onnx::TypeProto typeProto = valueInfoProto.type();
CV_Assert(typeProto.has_tensor_type());
opencv_onnx::TypeProto::Tensor tensor = typeProto.tensor_type();
CV_Assert(tensor.has_shape());
opencv_onnx::TensorShapeProto tensorShape = tensor.shape();
MatShape inpShape(tensorShape.dim_size());
for (int j = 0; j < inpShape.size(); ++j)
{
inpShape[j] = tensorShape.dim(j).dim_value();
}
outShapes[valueInfoProto.name()] = inpShape;
}
std::string framework_name;
if (model_proto.has_producer_name()) {
framework_name = model_proto.producer_name();
}
// create map with network inputs (without const blobs)
std::map<std::string, LayerInfo> layer_id;
std::map<std::string, LayerInfo>::iterator layerId;
std::map<std::string, MatShape>::iterator shapeIt;
// fill map: push layer name, layer id and output id
std::vector<String> netInputs;
for (int j = 0; j < graph_proto.input_size(); j++)
{
const std::string& name = graph_proto.input(j).name();
if (constBlobs.find(name) == constBlobs.end()) {
netInputs.push_back(name);
layer_id.insert(std::make_pair(name, LayerInfo(0, netInputs.size() - 1)));
}
}
dstNet.setInputsNames(netInputs);
int layersSize = graph_proto.node_size();
LayerParams layerParams;
opencv_onnx::NodeProto node_proto;
for(int li = 0; li < layersSize; li++)
{
node_proto = graph_proto.node(li);
layerParams = getLayerParams(node_proto);
CV_Assert(node_proto.output_size() >= 1);
layerParams.name = node_proto.output(0);
std::string layer_type = node_proto.op_type();
layerParams.type = layer_type;
if (layer_type == "MaxPool")
{
layerParams.type = "Pooling";
layerParams.set("pool", "MAX");
layerParams.set("ceil_mode", isCeilMode(layerParams));
}
else if (layer_type == "AveragePool")
{
layerParams.type = "Pooling";
layerParams.set("pool", "AVE");
layerParams.set("ceil_mode", isCeilMode(layerParams));
layerParams.set("ave_pool_padded_area", framework_name == "pytorch");
}
else if (layer_type == "GlobalAveragePool")
{
layerParams.type = "Pooling";
layerParams.set("pool", "AVE");
layerParams.set("global_pooling", true);
}
else if (layer_type == "Add" || layer_type == "Sum")
{
if (layer_id.find(node_proto.input(1)) == layer_id.end())
{
Mat blob = getBlob(node_proto, constBlobs, 1);
blob = blob.reshape(1, 1);
if (blob.total() == 1) {
layerParams.type = "Power";
layerParams.set("shift", blob.at<float>(0));
}
else {
layerParams.type = "Scale";
layerParams.set("bias_term", true);
layerParams.blobs.push_back(blob);
}
}
else {
layerParams.type = "Eltwise";
}
//.........这里部分代码省略.........