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Make external cv::dnn::Importer usage is deprecated

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pull/9305/head
Dmitry Kurtaev 7 years ago
parent 37ba1d6f2d
commit bd8e6b7e14
17 changed files with 85 additions and 149 deletions
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  1. 2
      modules/dnn/include/opencv2/dnn/all_layers.hpp
  2. 24
      modules/dnn/include/opencv2/dnn/dnn.hpp
  3. 7
      modules/dnn/misc/java/test/DnnTensorFlowTest.java
  4. 17
      modules/dnn/src/caffe/caffe_importer.cpp
  5. 5
      modules/dnn/src/tensorflow/tf_importer.cpp
  6. 5
      modules/dnn/src/torch/torch_importer.cpp
  7. 31
      modules/dnn/test/test_caffe_importer.cpp
  8. 25
      modules/dnn/test/test_layers.cpp
  9. 10
      modules/dnn/test/test_tf_importer.cpp
  10. 18
      modules/dnn/test/test_torch_importer.cpp
  11. 7
      modules/java/CMakeLists.txt
  12. 6
      modules/python/python2/CMakeLists.txt
  13. 6
      modules/python/python3/CMakeLists.txt
  14. 3
      modules/python/src2/hdr_parser.py
  15. 22
      samples/dnn/fcn_semsegm.cpp
  16. 24
      samples/dnn/ssd_object_detection.cpp
  17. 22
      samples/dnn/tf_inception.cpp

2
modules/dnn/include/opencv2/dnn/all_layers.hpp
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@ -55,7 +55,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
Classes listed here, in fact, provides C++ API for creating intances of bult-in layers.
In addition to this way of layers instantiation, there is a more common factory API (see @ref dnnLayerFactory), it allows to create layers dynamically (by name) and register new ones.
You can use both API, but factory API is less convinient for native C++ programming and basically designed for use inside importers (see @ref Importer, @ref createCaffeImporter(), @ref createTorchImporter()).
You can use both API, but factory API is less convinient for native C++ programming and basically designed for use inside importers (see @ref readNetFromCaffe(), @ref readNetFromTorch(), @ref readNetFromTensorflow()).
Bult-in layers partially reproduce functionality of corresponding Caffe and Torch7 layers.
In partuclar, the following layers and Caffe @ref Importer were tested to reproduce <a href="http://caffe.berkeleyvision.org/tutorial/layers.html">Caffe</a> functionality:

24
modules/dnn/include/opencv2/dnn/dnn.hpp
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@ -598,23 +598,27 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
Ptr<Impl> impl;
};
/** @brief Small interface class for loading trained serialized models of different dnn-frameworks. */
/**
* @deprecated Deprecated as external interface. Will be for internal needs only.
* @brief Small interface class for loading trained serialized models of different dnn-frameworks. */
class CV_EXPORTS_W Importer : public Algorithm
{
public:
/** @brief Adds loaded layers into the @p net and sets connections between them. */
CV_WRAP virtual void populateNet(Net net) = 0;
CV_DEPRECATED CV_WRAP virtual void populateNet(Net net) = 0;
virtual ~Importer();
};
/** @brief Creates the importer of <a href="http://caffe.berkeleyvision.org">Caffe</a> framework network.
/**
* @deprecated Use @ref readNetFromCaffe instead.
* @brief Creates the importer of <a href="http://caffe.berkeleyvision.org">Caffe</a> framework network.
* @param prototxt path to the .prototxt file with text description of the network architecture.
* @param caffeModel path to the .caffemodel file with learned network.
* @returns Pointer to the created importer, NULL in failure cases.
*/
CV_EXPORTS_W Ptr<Importer> createCaffeImporter(const String &prototxt, const String &caffeModel = String());
CV_DEPRECATED CV_EXPORTS_W Ptr<Importer> createCaffeImporter(const String &prototxt, const String &caffeModel = String());
/** @brief Reads a network model stored in Caffe model files.
* @details This is shortcut consisting from createCaffeImporter and Net::populateNet calls.
@ -631,13 +635,17 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
*/
CV_EXPORTS_W Net readNetFromTorch(const String &model, bool isBinary = true);
/** @brief Creates the importer of <a href="http://www.tensorflow.org">TensorFlow</a> framework network.
/**
* @deprecated Use @ref readNetFromTensorflow instead.
* @brief Creates the importer of <a href="http://www.tensorflow.org">TensorFlow</a> framework network.
* @param model path to the .pb file with binary protobuf description of the network architecture.
* @returns Pointer to the created importer, NULL in failure cases.
*/
CV_EXPORTS_W Ptr<Importer> createTensorflowImporter(const String &model);
CV_DEPRECATED CV_EXPORTS_W Ptr<Importer> createTensorflowImporter(const String &model);
/** @brief Creates the importer of <a href="http://torch.ch">Torch7</a> framework network.
/**
* @deprecated Use @ref readNetFromTorch instead.
* @brief Creates the importer of <a href="http://torch.ch">Torch7</a> framework network.
* @param filename path to the file, dumped from Torch by using torch.save() function.
* @param isBinary specifies whether the network was serialized in ascii mode or binary.
* @returns Pointer to the created importer, NULL in failure cases.
@ -663,7 +671,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
*
* Also some equivalents of these classes from cunn, cudnn, and fbcunn may be successfully imported.
*/
CV_EXPORTS_W Ptr<Importer> createTorchImporter(const String &filename, bool isBinary = true);
CV_DEPRECATED CV_EXPORTS_W Ptr<Importer> createTorchImporter(const String &filename, bool isBinary = true);
/** @brief Loads blob which was serialized as torch.Tensor object of Torch7 framework.
* @warning This function has the same limitations as createTorchImporter().

7
modules/dnn/misc/java/test/DnnTensorFlowTest.java
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@ -51,12 +51,7 @@ public class DnnTensorFlowTest extends OpenCVTestCase {
sourceImageFile = f.toString();
if(!f.exists()) throw new Exception("Test image is missing: " + sourceImageFile);
net = new Net();
if(net.empty()) {
Importer importer = Dnn.createTensorflowImporter(modelFileName);
importer.populateNet(net);
}
net = Dnn.readNetFromTensorflow(modelFileName);
}
public void testGetLayerTypes() {

17
modules/dnn/src/caffe/caffe_importer.cpp
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@ -370,24 +370,15 @@ Ptr<Importer> createCaffeImporter(const String &prototxt, const String &caffeMod
return Ptr<Importer>(new CaffeImporter(prototxt.c_str(), caffeModel.c_str()));
}
#else //HAVE_PROTOBUF
Ptr<Importer> createCaffeImporter(const String&, const String&)
{
CV_Error(cv::Error::StsNotImplemented, "libprotobuf required to import data from Caffe models");
return Ptr<Importer>();
}
#endif //HAVE_PROTOBUF
Net readNetFromCaffe(const String &prototxt, const String &caffeModel /*= String()*/)
{
Ptr<Importer> caffeImporter = createCaffeImporter(prototxt, caffeModel);
CaffeImporter caffeImporter(prototxt.c_str(), caffeModel.c_str());
Net net;
if (caffeImporter)
caffeImporter->populateNet(net);
caffeImporter.populateNet(net);
return net;
}
#endif //HAVE_PROTOBUF
CV__DNN_EXPERIMENTAL_NS_END
}} // namespace

5
modules/dnn/src/tensorflow/tf_importer.cpp
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@ -1045,10 +1045,9 @@ Ptr<Importer> createTensorflowImporter(const String&)
Net readNetFromTensorflow(const String &model)
{
Ptr<Importer> importer = createTensorflowImporter(model);
TFImporter importer(model.c_str());
Net net;
if (importer)
importer->populateNet(net);
importer.populateNet(net);
return net;
}

5
modules/dnn/src/torch/torch_importer.cpp
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@ -1150,10 +1150,9 @@ Net readNetFromTorch(const String &model, bool isBinary)
{
CV_TRACE_FUNCTION();
Ptr<Importer> importer = createTorchImporter(model, isBinary);
TorchImporter importer(model, isBinary);
Net net;
if (importer)
importer->populateNet(net);
importer.populateNet(net);
return net;
}

31
modules/dnn/test/test_caffe_importer.cpp
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@ -57,22 +57,14 @@ static std::string _tf(TString filename)
TEST(Test_Caffe, read_gtsrb)
{
Net net;
{
Ptr<Importer> importer = createCaffeImporter(_tf("gtsrb.prototxt"), "");
ASSERT_TRUE(importer != NULL);
importer->populateNet(net);
}
Net net = readNetFromCaffe(_tf("gtsrb.prototxt"));
ASSERT_FALSE(net.empty());
}
TEST(Test_Caffe, read_googlenet)
{
Net net;
{
Ptr<Importer> importer = createCaffeImporter(_tf("bvlc_googlenet.prototxt"), "");
ASSERT_TRUE(importer != NULL);
importer->populateNet(net);
}
Net net = readNetFromCaffe(_tf("bvlc_googlenet.prototxt"));
ASSERT_FALSE(net.empty());
}
TEST(Reproducibility_AlexNet, Accuracy)
@ -81,9 +73,8 @@ TEST(Reproducibility_AlexNet, Accuracy)
{
const string proto = findDataFile("dnn/bvlc_alexnet.prototxt", false);
const string model = findDataFile("dnn/bvlc_alexnet.caffemodel", false);
Ptr<Importer> importer = createCaffeImporter(proto, model);
ASSERT_TRUE(importer != NULL);
importer->populateNet(net);
net = readNetFromCaffe(proto, model);
ASSERT_FALSE(net.empty());
}
Mat sample = imread(_tf("grace_hopper_227.png"));
@ -107,9 +98,8 @@ TEST(Reproducibility_FCN, Accuracy)
{
const string proto = findDataFile("dnn/fcn8s-heavy-pascal.prototxt", false);
const string model = findDataFile("dnn/fcn8s-heavy-pascal.caffemodel", false);
Ptr<Importer> importer = createCaffeImporter(proto, model);
ASSERT_TRUE(importer != NULL);
importer->populateNet(net);
net = readNetFromCaffe(proto, model);
ASSERT_FALSE(net.empty());
}
Mat sample = imread(_tf("street.png"));
@ -136,9 +126,8 @@ TEST(Reproducibility_SSD, Accuracy)
{
const string proto = findDataFile("dnn/ssd_vgg16.prototxt", false);
const string model = findDataFile("dnn/VGG_ILSVRC2016_SSD_300x300_iter_440000.caffemodel", false);
Ptr<Importer> importer = createCaffeImporter(proto, model);
ASSERT_TRUE(importer != NULL);
importer->populateNet(net);
net = readNetFromCaffe(proto, model);
ASSERT_FALSE(net.empty());
}
Mat sample = imread(_tf("street.png"));

25
modules/dnn/test/test_layers.cpp
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@ -108,12 +108,8 @@ void testLayerUsingCaffeModels(String basename, bool useCaffeModel = false, bool
cv::setNumThreads(cv::getNumberOfCPUs());
Net net;
{
Ptr<Importer> importer = createCaffeImporter(prototxt, (useCaffeModel) ? caffemodel : String());
ASSERT_TRUE(importer != NULL);
importer->populateNet(net);
}
Net net = readNetFromCaffe(prototxt, (useCaffeModel) ? caffemodel : String());
ASSERT_FALSE(net.empty());
Mat inp = blobFromNPY(inpfile);
Mat ref = blobFromNPY(outfile);
@ -252,12 +248,8 @@ TEST(Layer_Test_Concat, Accuracy)
static void test_Reshape_Split_Slice_layers()
{
Net net;
{
Ptr<Importer> importer = createCaffeImporter(_tf("reshape_and_slice_routines.prototxt"));
ASSERT_TRUE(importer != NULL);
importer->populateNet(net);
}
Net net = readNetFromCaffe(_tf("reshape_and_slice_routines.prototxt"));
ASSERT_FALSE(net.empty());
Mat input(6, 12, CV_32F);
RNG rng(0);
@ -276,12 +268,9 @@ TEST(Layer_Test_Reshape_Split_Slice, Accuracy)
TEST(Layer_Conv_Elu, Accuracy)
{
Net net;
{
Ptr<Importer> importer = createTensorflowImporter(_tf("layer_elu_model.pb"));
ASSERT_TRUE(importer != NULL);
importer->populateNet(net);
}
Net net = readNetFromTensorflow(_tf("layer_elu_model.pb"));
ASSERT_FALSE(net.empty());
Mat inp = blobFromNPY(_tf("layer_elu_in.npy"));
Mat ref = blobFromNPY(_tf("layer_elu_out.npy"));

10
modules/dnn/test/test_tf_importer.cpp
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@ -29,9 +29,8 @@ TEST(Test_TensorFlow, read_inception)
Net net;
{
const string model = findDataFile("dnn/tensorflow_inception_graph.pb", false);
Ptr<Importer> importer = createTensorflowImporter(model);
ASSERT_TRUE(importer != NULL);
importer->populateNet(net);
net = readNetFromTensorflow(model);
ASSERT_FALSE(net.empty());
}
Mat sample = imread(_tf("grace_hopper_227.png"));
@ -53,9 +52,8 @@ TEST(Test_TensorFlow, inception_accuracy)
Net net;
{
const string model = findDataFile("dnn/tensorflow_inception_graph.pb", false);
Ptr<Importer> importer = createTensorflowImporter(model);
ASSERT_TRUE(importer != NULL);
importer->populateNet(net);
net = readNetFromTensorflow(model);
ASSERT_FALSE(net.empty());
}
Mat sample = imread(_tf("grace_hopper_227.png"));

18
modules/dnn/test/test_torch_importer.cpp
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@ -66,11 +66,8 @@ static std::string _tf(TStr filename, bool inTorchDir = true)
TEST(Torch_Importer, simple_read)
{
Net net;
Ptr<Importer> importer;
ASSERT_NO_THROW( importer = createTorchImporter(_tf("net_simple_net.txt"), false) );
ASSERT_TRUE( importer != NULL );
importer->populateNet(net);
ASSERT_NO_THROW(net = readNetFromTorch(_tf("net_simple_net.txt"), false));
ASSERT_FALSE(net.empty());
}
static void runTorchNet(String prefix, String outLayerName = "",
@ -78,10 +75,8 @@ static void runTorchNet(String prefix, String outLayerName = "",
{
String suffix = (isBinary) ? ".dat" : ".txt";
Net net;
Ptr<Importer> importer = createTorchImporter(_tf(prefix + "_net" + suffix), isBinary);
ASSERT_TRUE(importer != NULL);
importer->populateNet(net);
Net net = readNetFromTorch(_tf(prefix + "_net" + suffix), isBinary);
ASSERT_FALSE(net.empty());
Mat inp, outRef;
ASSERT_NO_THROW( inp = readTorchBlob(_tf(prefix + "_input" + suffix), isBinary) );
@ -200,9 +195,8 @@ TEST(Torch_Importer, ENet_accuracy)
Net net;
{
const string model = findDataFile("dnn/Enet-model-best.net", false);
Ptr<Importer> importer = createTorchImporter(model, true);
ASSERT_TRUE(importer != NULL);
importer->populateNet(net);
net = readNetFromTorch(model, true);
ASSERT_FALSE(net.empty());
}
Mat sample = imread(_tf("street.png", false));

7
modules/java/CMakeLists.txt
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@ -413,7 +413,12 @@ endif(ANDROID)
# workarounding lack of `__attribute__ ((visibility("default")))` in jni_md.h/JNIEXPORT
string(REPLACE "-fvisibility=hidden" "" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
ocv_warnings_disable(CMAKE_CXX_FLAGS -Wunused-const-variable -Wundef)
if(MSVC)
ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4996)
else()
ocv_warnings_disable(CMAKE_CXX_FLAGS -Wunused-const-variable -Wundef -Wdeprecated-declarations)
endif()
ocv_add_library(${the_module} SHARED ${handwritten_h_sources} ${handwritten_cpp_sources} ${generated_cpp_sources}
${copied_files}

6
modules/python/python2/CMakeLists.txt
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@ -13,3 +13,9 @@ include(../common.cmake)
unset(MODULE_NAME)
unset(MODULE_INSTALL_SUBDIR)
if(MSVC)
ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4996)
else()
ocv_warnings_disable(CMAKE_CXX_FLAGS -Wdeprecated-declarations)
endif()

6
modules/python/python3/CMakeLists.txt
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@ -12,3 +12,9 @@ include(../common.cmake)
unset(MODULE_NAME)
unset(MODULE_INSTALL_SUBDIR)
if(MSVC)
ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4996)
else()
ocv_warnings_disable(CMAKE_CXX_FLAGS -Wdeprecated-declarations)
endif()

3
modules/python/src2/hdr_parser.py
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@ -410,7 +410,8 @@ class CppHeaderParser(object):
# note that we do not strip "static" prefix, which does matter;
# it means class methods, not instance methods
decl_str = self.batch_replace(decl_str, [("virtual", ""), ("static inline", ""), ("inline", ""),\
("CV_EXPORTS_W", ""), ("CV_EXPORTS", ""), ("CV_CDECL", ""), ("CV_WRAP ", " "), ("CV_INLINE", "")]).strip()
("CV_EXPORTS_W", ""), ("CV_EXPORTS", ""), ("CV_CDECL", ""), ("CV_WRAP ", " "), ("CV_INLINE", ""),
("CV_DEPRECATED", "")]).strip()
static_method = False
context = top[0]

22
samples/dnn/fcn_semsegm.cpp
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@ -91,19 +91,11 @@ int main(int argc, char **argv)
vector<cv::Vec3b> colors = readColors();
//! [Create the importer of Caffe model]
Ptr<dnn::Importer> importer;
try //Try to import Caffe GoogleNet model
{
importer = dnn::createCaffeImporter(modelTxt, modelBin);
}
catch (const cv::Exception &err) //Importer can throw errors, we will catch them
{
cerr << err.msg << endl;
}
//! [Create the importer of Caffe model]
//! [Initialize network]
dnn::Net net = readNetFromCaffe(modelTxt, modelBin);
//! [Initialize network]
if (!importer)
if (net.empty())
{
cerr << "Can't load network by using the following files: " << endl;
cerr << "prototxt: " << modelTxt << endl;
@ -113,12 +105,6 @@ int main(int argc, char **argv)
exit(-1);
}
//! [Initialize network]
dnn::Net net;
importer->populateNet(net);
importer.release(); //We don't need importer anymore
//! [Initialize network]
//! [Prepare blob]
Mat img = imread(imageFile);
if (img.empty())

24
samples/dnn/ssd_object_detection.cpp
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@ -65,21 +65,11 @@ int main(int argc, char** argv)
String modelConfiguration = parser.get<string>("proto");
String modelBinary = parser.get<string>("model");
//! [Create the importer of Caffe model]
Ptr<dnn::Importer> importer;
// Import Caffe SSD model
try
{
importer = dnn::createCaffeImporter(modelConfiguration, modelBinary);
}
catch (const cv::Exception &err) //Importer can throw errors, we will catch them
{
cerr << err.msg << endl;
}
//! [Create the importer of Caffe model]
//! [Initialize network]
dnn::Net net = readNetFromCaffe(modelConfiguration, modelBinary);
//! [Initialize network]
if (!importer)
if (net.empty())
{
cerr << "Can't load network by using the following files: " << endl;
cerr << "prototxt: " << modelConfiguration << endl;
@ -89,12 +79,6 @@ int main(int argc, char** argv)
exit(-1);
}
//! [Initialize network]
dnn::Net net;
importer->populateNet(net);
importer.release(); //We don't need importer anymore
//! [Initialize network]
cv::Mat frame = cv::imread(parser.get<string>("image"), -1);
if (frame.channels() == 4)

22
samples/dnn/tf_inception.cpp
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@ -59,31 +59,17 @@ int main(int argc, char **argv)
String classNamesFile = parser.get<String>("c_names");
String resultFile = parser.get<String>("result");
//! [Create the importer of TensorFlow model]
Ptr<dnn::Importer> importer;
try //Try to import TensorFlow AlexNet model
{
importer = dnn::createTensorflowImporter(modelFile);
}
catch (const cv::Exception &err) //Importer can throw errors, we will catch them
{
std::cerr << err.msg << std::endl;
}
//! [Create the importer of Caffe model]
//! [Initialize network]
dnn::Net net = readNetFromTensorflow(modelFile);
//! [Initialize network]
if (!importer)
if (net.empty())
{
std::cerr << "Can't load network by using the mode file: " << std::endl;
std::cerr << modelFile << std::endl;
exit(-1);
}
//! [Initialize network]
dnn::Net net;
importer->populateNet(net);
importer.release(); //We don't need importer anymore
//! [Initialize network]
//! [Prepare blob]
Mat img = imread(imageFile);
if (img.empty())

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