mirror/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2025-06-07 09:25:45 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity

Add readNet* functions which parse models from byte arrays

Browse Source
This commit is contained in:
Dmitry Kurtaev 2018-07-04 18:15:31 +03:00
parent 61d8719b8d
commit d57e5406f0
6 changed files with 150 additions and 41 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

52
modules/dnn/include/opencv2/dnn/dnn.hpp
View File

@ -644,13 +644,23 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
*/
CV_EXPORTS_W Net readNetFromDarknet(const String &cfgFile, const String &darknetModel = String());
/** @brief Reads a network model stored in <a href="https://pjreddie.com/darknet/">Darknet</a> model files.
* @param cfgFile file node to the .cfg file with text description of the network architecture.
* @param darknetModel file node to the .weights file with learned network.
* @returns Network object that ready to do forward, throw an exception in failure cases.
* @returns Net object.
*/
CV_EXPORTS_W Net readNetFromDarknet(const FileNode &cfgFile, const FileNode &darknetModel = FileNode());
/** @brief Reads a network model stored in <a href="https://pjreddie.com/darknet/">Darknet</a> model files.
* @param bufferCfg A buffer contains a content of .cfg file with text description of the network architecture.
* @param bufferModel A buffer contains a content of .weights file with learned network.
* @returns Net object.
*/
CV_EXPORTS_W Net readNetFromDarknet(const std::vector<char>& bufferCfg,
const std::vector<char>& bufferModel = std::vector<char>());
/** @brief Reads a network model stored in <a href="https://pjreddie.com/darknet/">Darknet</a> model files.
* @param bufferCfg A buffer contains a content of .cfg file with text description of the network architecture.
* @param lenCfg Number of bytes to read from bufferCfg
* @param bufferModel A buffer contains a content of .weights file with learned network.
* @param lenModel Number of bytes to read from bufferModel
* @returns Net object.
*/
CV_EXPORTS Net readNetFromDarknet(const char *bufferCfg, size_t lenCfg,
const char *bufferModel = NULL, size_t lenModel = 0);
/** @brief Reads a network model stored in <a href="http://caffe.berkeleyvision.org">Caffe</a> framework's format.
* @param prototxt path to the .prototxt file with text description of the network architecture.
@ -659,6 +669,14 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
*/
CV_EXPORTS_W Net readNetFromCaffe(const String &prototxt, const String &caffeModel = String());
/** @brief Reads a network model stored in Caffe model in memory.
* @param bufferProto buffer containing the content of the .prototxt file
* @param bufferModel buffer containing the content of the .caffemodel file
* @returns Net object.
*/
CV_EXPORTS_W Net readNetFromCaffe(const std::vector<char>& bufferProto,
const std::vector<char>& bufferModel = std::vector<char>());
/** @brief Reads a network model stored in Caffe model in memory.
* @details This is an overloaded member function, provided for convenience.
* It differs from the above function only in what argument(s) it accepts.
@ -680,6 +698,14 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
*/
CV_EXPORTS_W Net readNetFromTensorflow(const String &model, const String &config = String());
/** @brief Reads a network model stored in <a href="https://www.tensorflow.org/">TensorFlow</a> framework's format.
* @param bufferModel buffer containing the content of the pb file
* @param bufferConfig buffer containing the content of the pbtxt file
* @returns Net object.
*/
CV_EXPORTS_W Net readNetFromTensorflow(const std::vector<char>& bufferModel,
const std::vector<char>& bufferConfig = std::vector<char>());
/** @brief Reads a network model stored in <a href="https://www.tensorflow.org/">TensorFlow</a> framework's format.
* @details This is an overloaded member function, provided for convenience.
* It differs from the above function only in what argument(s) it accepts.
@ -743,6 +769,18 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
*/
CV_EXPORTS_W Net readNet(const String& model, const String& config = "", const String& framework = "");
/**
* @brief Read deep learning network represented in one of the supported formats.
* @details This is an overloaded member function, provided for convenience.
* It differs from the above function only in what argument(s) it accepts.
* @param[in] framework Name of origin framework.
* @param[in] bufferModel A buffer with a content of binary file with weights
* @param[in] bufferConfig A buffer with a content of text file contains network configuration.
* @returns Net object.
*/
CV_EXPORTS_W Net readNet(const String& framework, const std::vector<char>& bufferModel,
const std::vector<char>& bufferConfig = std::vector<char>());
/** @brief Loads blob which was serialized as torch.Tensor object of Torch7 framework.
* @warning This function has the same limitations as readNetFromTorch().
*/

6
modules/dnn/src/caffe/caffe_importer.cpp
View File

@ -453,6 +453,12 @@ Net readNetFromCaffe(const char *bufferProto, size_t lenProto,
return net;
}
Net readNetFromCaffe(const std::vector<char>& bufferProto, const std::vector<char>& bufferModel)
{
return readNetFromCaffe(&bufferProto[0], bufferProto.size(),
bufferModel.empty() ? NULL : &bufferModel[0], bufferModel.size());
}
#endif //HAVE_PROTOBUF
CV__DNN_EXPERIMENTAL_NS_END

76
modules/dnn/src/darknet/darknet_importer.cpp
View File

@ -181,45 +181,71 @@ public:
}
};
static Net readNetFromDarknet(std::istream &cfgFile, std::istream &darknetModel)
{
Net net;
DarknetImporter darknetImporter(cfgFile, darknetModel);
darknetImporter.populateNet(net);
return net;
}
static Net readNetFromDarknet(std::istream &cfgFile)
{
Net net;
DarknetImporter darknetImporter(cfgFile);
darknetImporter.populateNet(net);
return net;
}
}
Net readNetFromDarknet(const String &cfgFile, const String &darknetModel /*= String()*/)
{
Net net;
std::ifstream cfgStream(cfgFile.c_str());
if(!cfgStream.is_open()) {
if (!cfgStream.is_open())
{
CV_Error(cv::Error::StsParseError, "Failed to parse NetParameter file: " + std::string(cfgFile));
return net;
}
DarknetImporter darknetImporter;
if (darknetModel != String()) {
std::ifstream darknetModelStream(darknetModel.c_str());
if(!darknetModelStream.is_open()){
if (darknetModel != String())
{
std::ifstream darknetModelStream(darknetModel.c_str(), std::ios::binary);
if (!darknetModelStream.is_open())
{
CV_Error(cv::Error::StsParseError, "Failed to parse NetParameter file: " + std::string(darknetModel));
return net;
}
darknetImporter = DarknetImporter(cfgStream, darknetModelStream);
} else {
darknetImporter = DarknetImporter(cfgStream);
return readNetFromDarknet(cfgStream, darknetModelStream);
}
darknetImporter.populateNet(net);
return net;
else
return readNetFromDarknet(cfgStream);
}
Net readNetFromDarknet(const FileNode &cfgFile, const FileNode &darknetModel /*= FileNode()*/)
struct BufferStream : public std::streambuf
{
DarknetImporter darknetImporter;
if(darknetModel.empty()){
std::istringstream cfgStream((std::string)cfgFile);
darknetImporter = DarknetImporter(cfgStream);
}else{
std::istringstream cfgStream((std::string)cfgFile);
std::istringstream darknetModelStream((std::string)darknetModel);
darknetImporter = DarknetImporter(cfgStream, darknetModelStream);
BufferStream(const char* s, std::size_t n)
{
char* ptr = const_cast<char*>(s);
setg(ptr, ptr, ptr + n);
}
Net net;
darknetImporter.populateNet(net);
return net;
};
Net readNetFromDarknet(const char *bufferCfg, size_t lenCfg, const char *bufferModel, size_t lenModel)
{
BufferStream cfgBufferStream(bufferCfg, lenCfg);
std::istream cfgStream(&cfgBufferStream);
if (lenModel)
{
BufferStream weightsBufferStream(bufferModel, lenModel);
std::istream weightsStream(&weightsBufferStream);
return readNetFromDarknet(cfgStream, weightsStream);
}
else
return readNetFromDarknet(cfgStream);
}
Net readNetFromDarknet(const std::vector<char>& bufferCfg, const std::vector<char>& bufferModel)
{
return readNetFromDarknet(&bufferCfg[0], bufferCfg.size(),
bufferModel.empty() ? NULL : &bufferModel[0], bufferModel.size());
}
CV__DNN_EXPERIMENTAL_NS_END

17
modules/dnn/src/dnn.cpp
View File

@ -3047,6 +3047,23 @@ Net readNet(const String& _model, const String& _config, const String& _framewor
model + (config.empty() ? "" : ", " + config));
}
Net readNet(const String& _framework, const std::vector<char>& bufferModel,
const std::vector<char>& bufferConfig)
{
String framework = _framework.toLowerCase();
if (framework == "caffe")
return readNetFromCaffe(bufferConfig, bufferModel);
else if (framework == "tensorflow")
return readNetFromTensorflow(bufferModel, bufferConfig);
else if (framework == "darknet")
return readNetFromDarknet(bufferConfig, bufferModel);
else if (framework == "torch")
CV_Error(Error::StsNotImplemented, "Reading Torch models from buffers");
else if (framework == "dldt")
CV_Error(Error::StsNotImplemented, "Reading Intel's Model Optimizer models from buffers");
CV_Error(Error::StsError, "Cannot determine an origin framework with a name " + framework);
}
Net readNetFromModelOptimizer(const String &xml, const String &bin)
{
return Net::readFromModelOptimizer(xml, bin);

6
modules/dnn/src/tensorflow/tf_importer.cpp
View File

@ -1856,5 +1856,11 @@ Net readNetFromTensorflow(const char* bufferModel, size_t lenModel,
return net;
}
Net readNetFromTensorflow(const std::vector<char>& bufferModel, const std::vector<char>& bufferConfig)
{
return readNetFromCaffe(&bufferModel[0], bufferModel.size(),
bufferConfig.empty() ? NULL : &bufferConfig[0], bufferConfig.size());
}
CV__DNN_EXPERIMENTAL_NS_END
}} // namespace

34
modules/dnn/test/test_darknet_importer.cpp
View File

@ -65,16 +65,32 @@ TEST(Test_Darknet, read_yolo_voc)
ASSERT_FALSE(net.empty());
}
TEST(Test_Darknet, read_filestorage_yolo_voc)
TEST(Test_Darknet, read_yolo_voc_stream)
{
std::ifstream ifile(_tf("yolo-voc.cfg").c_str());
std::stringstream buffer;
buffer << " " << ifile.rdbuf(); // FIXME: FileStorage drops first character.
FileStorage ofs(".xml", FileStorage::WRITE | FileStorage::MEMORY);
ofs.write("cfgFile", buffer.str());
FileStorage ifs(ofs.releaseAndGetString(), FileStorage::READ | FileStorage::MEMORY | FileStorage::FORMAT_XML);
Net net = readNetFromDarknet(ifs["cfgFile"]);
ASSERT_FALSE(net.empty());
Mat ref;
Mat sample = imread(_tf("dog416.png"));
Mat inp = blobFromImage(sample, 1.0/255, Size(416, 416), Scalar(), true, false);
const std::string cfgFile = findDataFile("dnn/yolo-voc.cfg", false);
const std::string weightsFile = findDataFile("dnn/yolo-voc.weights", false);
// Import by paths.
{
Net net = readNetFromDarknet(cfgFile, weightsFile);
net.setInput(inp);
net.setPreferableBackend(DNN_BACKEND_OPENCV);
ref = net.forward();
}
// Import from bytes array.
{
std::string cfg, weights;
readFileInMemory(cfgFile, cfg);
readFileInMemory(weightsFile, weights);
Net net = readNetFromDarknet(&cfg[0], cfg.size(), &weights[0], weights.size());
net.setInput(inp);
net.setPreferableBackend(DNN_BACKEND_OPENCV);
Mat out = net.forward();
normAssert(ref, out);
}
}
class Test_Darknet_layers : public DNNTestLayer