Remove cv::dnn::Importer

modules/dnn/CMakeLists.txt

@@ -97,15 +97,3 @@ if(BUILD_PERF_TESTS)
     endif()
   endif()
 endif()
-
-# ----------------------------------------------------------------------------
-# Torch7 importer of blobs and models, produced by Torch.nn module
-# ----------------------------------------------------------------------------
-OCV_OPTION(${the_module}_BUILD_TORCH_IMPORTER "Build Torch model importer" ON)
-if(${the_module}_BUILD_TORCH_IMPORTER)
-    message(STATUS "Torch importer has been enabled. To run the tests you have to install Torch "
-      "('th' executable should be available) "
-      "and generate testdata using opencv_extra/testdata/dnn/generate_torch_models.py script.")
-    add_definitions(-DENABLE_TORCH_IMPORTER=1)
-    ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4702 /wd4127 /wd4267) #supress warnings in original torch files
-endif()

modules/dnn/include/opencv2/dnn/all_layers.hpp

@@ -58,7 +58,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
   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:
+  In partuclar, the following layers and Caffe importer were tested to reproduce <a href="http://caffe.berkeleyvision.org/tutorial/layers.html">Caffe</a> functionality:
   - Convolution
   - Deconvolution
   - Pooling

modules/dnn/include/opencv2/dnn/dnn.hpp

@@ -426,15 +426,6 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
         void forward(std::vector<std::vector<Mat> >& outputBlobs,
                      const std::vector<String>& outBlobNames);
 
-        //TODO:
-        /** @brief Optimized forward.
-         *  @warning Not implemented yet.
-         *  @details Makes forward only those layers which weren't changed after previous forward().
-         */
-        void forwardOpt(LayerId toLayer);
-        /** @overload */
-        void forwardOpt(const std::vector<LayerId> &toLayers);
-
         /**
          * @brief Compile Halide layers.
          * @param[in] scheduler Path to YAML file with scheduling directives.
@@ -609,38 +600,18 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
         Ptr<Impl> impl;
     };
 
-    /**
-     * @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_DEPRECATED CV_WRAP virtual void populateNet(Net net) = 0;
-
-        virtual ~Importer();
-    };
-
     /** @brief Reads a network model stored in <a href="https://pjreddie.com/darknet/">Darknet</a> model files.
     *  @param cfgFile      path to the .cfg file with text description of the network architecture.
     *  @param darknetModel path to the .weights file with learned network.
     *  @returns Network object that ready to do forward, throw an exception in failure cases.
-    * @details This is shortcut consisting from DarknetImporter and Net::populateNet calls.
+    *  @returns Net object.
     */
     CV_EXPORTS_W Net readNetFromDarknet(const String &cfgFile, const String &darknetModel = String());
 
-    /**
-     *  @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_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.
+    /** @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.
+      * @param caffeModel path to the .caffemodel file with learned network.
+      * @returns Net object.
       */
     CV_EXPORTS_W Net readNetFromCaffe(const String &prototxt, const String &caffeModel = String());
 
@@ -651,16 +622,21 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
       * @param lenProto length of bufferProto
       * @param bufferModel buffer containing the content of the .caffemodel file
       * @param lenModel length of bufferModel
+      * @returns Net object.
       */
     CV_EXPORTS Net readNetFromCaffe(const char *bufferProto, size_t lenProto,
                                     const char *bufferModel = NULL, size_t lenModel = 0);
 
-    /** @brief Reads a network model stored in Tensorflow model file.
-      * @details This is shortcut consisting from createTensorflowImporter and Net::populateNet calls.
+    /** @brief Reads a network model stored in <a href="https://www.tensorflow.org/">TensorFlow</a> framework's format.
+      * @param model  path to the .pb file with binary protobuf description of the network architecture
+      * @param config path to the .pbtxt file that contains text graph definition in protobuf format.
+      *               Resulting Net object is built by text graph using weights from a binary one that
+      *               let us make it more flexible.
+      * @returns Net object.
       */
     CV_EXPORTS_W Net readNetFromTensorflow(const String &model, const String &config = String());
 
-    /** @brief Reads a network model stored in Tensorflow model in memory.
+    /** @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.
       * @param bufferModel buffer containing the content of the pb file
@@ -671,27 +647,11 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
     CV_EXPORTS Net readNetFromTensorflow(const char *bufferModel, size_t lenModel,
                                          const char *bufferConfig = NULL, size_t lenConfig = 0);
 
-    /** @brief Reads a network model stored in Torch model file.
-      * @details This is shortcut consisting from createTorchImporter and Net::populateNet calls.
-      */
-    CV_EXPORTS_W Net readNetFromTorch(const String &model, bool isBinary = true);
-
     /**
-     *  @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_DEPRECATED CV_EXPORTS_W Ptr<Importer> createTensorflowImporter(const String &model);
-
-    /**
-     *  @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.
+     *  @brief Reads a network model stored in <a href="http://torch.ch">Torch7</a> framework's format.
+     *  @param model    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.
-     *
-     *  @warning Torch7 importer is experimental now, you need explicitly set CMake `opencv_dnn_BUILD_TORCH_IMPORTER` flag to compile its.
+     *  @returns Net object.
      *
      *  @note Ascii mode of Torch serializer is more preferable, because binary mode extensively use `long` type of C language,
      *  which has various bit-length on different systems.
@@ -712,10 +672,10 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
      *
      * Also some equivalents of these classes from cunn, cudnn, and fbcunn may be successfully imported.
      */
-    CV_DEPRECATED CV_EXPORTS_W Ptr<Importer> createTorchImporter(const String &filename, bool isBinary = true);
+     CV_EXPORTS_W Net readNetFromTorch(const String &model, 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().
+     *  @warning This function has the same limitations as readNetFromTorch().
      */
     CV_EXPORTS_W Mat readTorchBlob(const String &filename, bool isBinary = true);
     /** @brief Creates 4-dimensional blob from image. Optionally resizes and crops @p image from center,

modules/dnn/perf/perf_caffe.cpp

@@ -68,18 +68,18 @@ static caffe::Net<float>* initNet(std::string proto, std::string weights)
     return net;
 }
 
-PERF_TEST(GoogLeNet_caffe, CaffePerfTest)
+PERF_TEST(AlexNet_caffe, CaffePerfTest)
 {
-    caffe::Net<float>* net = initNet("dnn/bvlc_googlenet.prototxt",
-                                     "dnn/bvlc_googlenet.caffemodel");
+    caffe::Net<float>* net = initNet("dnn/bvlc_alexnet.prototxt",
+                                     "dnn/bvlc_alexnet.caffemodel");
     TEST_CYCLE() net->Forward();
     SANITY_CHECK_NOTHING();
 }
 
-PERF_TEST(AlexNet_caffe, CaffePerfTest)
+PERF_TEST(GoogLeNet_caffe, CaffePerfTest)
 {
-    caffe::Net<float>* net = initNet("dnn/bvlc_alexnet.prototxt",
-                                     "dnn/bvlc_alexnet.caffemodel");
+    caffe::Net<float>* net = initNet("dnn/bvlc_googlenet.prototxt",
+                                     "dnn/bvlc_googlenet.caffemodel");
     TEST_CYCLE() net->Forward();
     SANITY_CHECK_NOTHING();
 }
@@ -100,6 +100,14 @@ PERF_TEST(SqueezeNet_v1_1_caffe, CaffePerfTest)
     SANITY_CHECK_NOTHING();
 }
 
+PERF_TEST(MobileNet_SSD, CaffePerfTest)
+{
+    caffe::Net<float>* net = initNet("dnn/MobileNetSSD_deploy.prototxt",
+                                     "dnn/MobileNetSSD_deploy.caffemodel");
+    TEST_CYCLE() net->Forward();
+    SANITY_CHECK_NOTHING();
+}
+
 }  // namespace cvtest
 
 #endif  // HAVE_CAFFE

modules/dnn/perf/perf_net.cpp

@@ -70,7 +70,7 @@ public:
         }
         else if (framework == "tensorflow")
         {
-            net = cv::dnn::readNetFromTensorflow(weights);
+            net = cv::dnn::readNetFromTensorflow(weights, proto);
         }
         else
             CV_Error(Error::StsNotImplemented, "Unknown framework " + framework);
@@ -148,6 +148,24 @@ PERF_TEST_P_(DNNTestNetwork, SSD)
             Mat(cv::Size(300, 300), CV_32FC3), "detection_out", "caffe");
 }
 
+PERF_TEST_P_(DNNTestNetwork, OpenFace)
+{
+    processNet("dnn/openface_nn4.small2.v1.t7", "", "",
+            Mat(cv::Size(96, 96), CV_32FC3), "", "torch");
+}
+
+PERF_TEST_P_(DNNTestNetwork, MobileNet_SSD_Caffe)
+{
+    processNet("dnn/MobileNetSSD_deploy.caffemodel", "dnn/MobileNetSSD_deploy.prototxt", "",
+            Mat(cv::Size(300, 300), CV_32FC3), "detection_out", "caffe");
+}
+
+PERF_TEST_P_(DNNTestNetwork, MobileNet_SSD_TensorFlow)
+{
+    processNet("dnn/ssd_mobilenet_v1_coco.pb", "ssd_mobilenet_v1_coco.pbtxt", "",
+            Mat(cv::Size(300, 300), CV_32FC3), "", "tensorflow");
+}
+
 INSTANTIATE_TEST_CASE_P(/*nothing*/, DNNTestNetwork,
     testing::Combine(
         ::testing::Values(TEST_DNN_BACKEND),

modules/dnn/src/caffe/caffe_importer.cpp

@@ -75,7 +75,7 @@ static cv::String toString(const T &v)
     return ss.str();
 }
 
-class CaffeImporter : public Importer
+class CaffeImporter
 {
     caffe::NetParameter net;
     caffe::NetParameter netBinary;
@@ -390,21 +390,10 @@ public:
 
         dstNet.connect(addedBlobs[idx].layerId, addedBlobs[idx].outNum, layerId, inNum);
     }
-
-    ~CaffeImporter()
-    {
-
-    }
-
 };
 
 }
 
-Ptr<Importer> createCaffeImporter(const String &prototxt, const String &caffeModel)
-{
-    return Ptr<Importer>(new CaffeImporter(prototxt.c_str(), caffeModel.c_str()));
-}
-
 Net readNetFromCaffe(const String &prototxt, const String &caffeModel /*= String()*/)
 {
     CaffeImporter caffeImporter(prototxt.c_str(), caffeModel.c_str());

modules/dnn/src/darknet/darknet_importer.cpp

@@ -58,7 +58,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
 namespace
 {
 
-class DarknetImporter : public Importer
+class DarknetImporter
 {
     darknet::NetParameter net;
 
@@ -173,12 +173,6 @@ public:
 
         dstNet.connect(addedBlobs[idx].layerId, addedBlobs[idx].outNum, layerId, inNum);
     }
-
-    ~DarknetImporter()
-    {
-
-    }
-
 };
 
 }

modules/dnn/src/dnn.cpp

@@ -2294,8 +2294,6 @@ int64 Net::getPerfProfile(std::vector<double>& timings)
 
 //////////////////////////////////////////////////////////////////////////
 
-Importer::~Importer() {}
-
 Layer::Layer() { preferableTarget = DNN_TARGET_CPU; }
 
 Layer::Layer(const LayerParams &params)

modules/dnn/src/tensorflow/tf_importer.cpp

@@ -446,14 +446,13 @@ void ExcludeLayer(tensorflow::GraphDef& net, const int layer_index, const int in
         net.mutable_node()->DeleteSubrange(layer_index, 1);
 }
 
-class TFImporter : public Importer {
+class TFImporter {
 public:
     TFImporter(const char *model, const char *config = NULL);
     TFImporter(const char *dataModel, size_t lenModel,
                const char *dataConfig = NULL, size_t lenConfig = 0);
 
     void populateNet(Net dstNet);
-    ~TFImporter() {}
 
 private:
     void kernelFromTensor(const tensorflow::TensorProto &tensor, Mat &dstBlob);
@@ -1315,19 +1314,6 @@ void TFImporter::populateNet(Net dstNet)
 
 } // namespace
 
-Ptr<Importer> createTensorflowImporter(const String &model)
-{
-    return Ptr<Importer>(new TFImporter(model.c_str()));
-}
-
-#else //HAVE_PROTOBUF
-
-Ptr<Importer> createTensorflowImporter(const String&)
-{
-    CV_Error(cv::Error::StsNotImplemented, "libprotobuf required to import data from TensorFlow models");
-    return Ptr<Importer>();
-}
-
 #endif //HAVE_PROTOBUF
 
 Net readNetFromTensorflow(const String &model, const String &config)

modules/dnn/src/torch/THDiskFile.cpp

@@ -1,5 +1,4 @@
 #include "../precomp.hpp"
-#if defined(ENABLE_TORCH_IMPORTER) && ENABLE_TORCH_IMPORTER
 #include "THGeneral.h"
 #include "THDiskFile.h"
 #include "THFilePrivate.h"
@@ -517,4 +516,3 @@ THFile *THDiskFile_new(const std::string &name, const char *mode, int isQuiet)
 }
 
 }
-#endif

modules/dnn/src/torch/THFile.cpp

@@ -1,5 +1,4 @@
 #include "../precomp.hpp"
-#if defined(ENABLE_TORCH_IMPORTER) && ENABLE_TORCH_IMPORTER
 #include "THFile.h"
 #include "THFilePrivate.h"
 
@@ -119,4 +118,3 @@ IMPLEMENT_THFILE_SCALAR(Float, float)
 IMPLEMENT_THFILE_SCALAR(Double, double)
 
 } // namespace
-#endif

modules/dnn/src/torch/THFile.h

@@ -2,7 +2,6 @@
 #define TH_FILE_INC
 
 //#include "THStorage.h"
-#if defined(ENABLE_TORCH_IMPORTER) && ENABLE_TORCH_IMPORTER
 #include "opencv2/core/hal/interface.h"
 #include "THGeneral.h"
 
@@ -51,5 +50,4 @@ TH_API long THFile_position(THFile *self);
 TH_API void THFile_close(THFile *self);
 TH_API void THFile_free(THFile *self);
 } // namespace
-#endif //defined(ENABLE_TORCH_IMPORTER) && ENABLE_TORCH_IMPORTER
 #endif //TH_FILE_INC

modules/dnn/src/torch/THGeneral.cpp

@@ -1,5 +1,4 @@
 #include "../precomp.hpp"
-#if defined(ENABLE_TORCH_IMPORTER) && ENABLE_TORCH_IMPORTER
 
 #if defined(TH_DISABLE_HEAP_TRACKING)
 #elif (defined(__unix) || defined(_WIN32))
@@ -9,5 +8,3 @@
 #endif
 
 #include "THGeneral.h"
-
-#endif

modules/dnn/src/torch/torch_importer.cpp

@@ -47,15 +47,12 @@
 #include <iostream>
 #include <fstream>
 
-#if defined(ENABLE_TORCH_IMPORTER) && ENABLE_TORCH_IMPORTER
 #include "THDiskFile.h"
-#endif
 
 namespace cv {
 namespace dnn {
 CV__DNN_EXPERIMENTAL_NS_BEGIN
 
-#if defined(ENABLE_TORCH_IMPORTER) && ENABLE_TORCH_IMPORTER
 using namespace TH;
 
 //#ifdef NDEBUG
@@ -95,7 +92,7 @@ static inline bool endsWith(const String &str, const char *substr)
     return str.rfind(substr) == str.length() - strlen(substr);
 }
 
-struct TorchImporter : public ::cv::dnn::Importer
+struct TorchImporter
 {
     typedef std::map<String, std::pair<int, Mat> > TensorsMap;
     Net net;
@@ -1191,19 +1188,13 @@ struct TorchImporter : public ::cv::dnn::Importer
     }
 };
 
-Ptr<Importer> createTorchImporter(const String &filename, bool isBinary)
-{
-    return Ptr<Importer>(new TorchImporter(filename, isBinary));
-}
-
-
 Mat readTorchBlob(const String &filename, bool isBinary)
 {
-    Ptr<TorchImporter> importer(new TorchImporter(filename, isBinary));
-    importer->readObject();
-    CV_Assert(importer->tensors.size() == 1);
+    TorchImporter importer(filename, isBinary);
+    importer.readObject();
+    CV_Assert(importer.tensors.size() == 1);
 
-    return importer->tensors.begin()->second;
+    return importer.tensors.begin()->second;
 }
 
 Net readNetFromTorch(const String &model, bool isBinary)
@@ -1216,27 +1207,5 @@ Net readNetFromTorch(const String &model, bool isBinary)
     return net;
 }
 
-#else
-
-Ptr<Importer> createTorchImporter(const String&, bool)
-{
-    CV_Error(Error::StsNotImplemented, "Torch importer is disabled in current build");
-    return Ptr<Importer>();
-}
-
-Mat readTorchBlob(const String&, bool)
-{
-    CV_Error(Error::StsNotImplemented, "Torch importer is disabled in current build");
-    return Mat();
-}
-
-Net readNetFromTorch(const String &model, bool isBinary)
-{
-    CV_Error(Error::StsNotImplemented, "Torch importer is disabled in current build");
-    return Net();
-}
-
-#endif //defined(ENABLE_TORCH_IMPORTER) && ENABLE_TORCH_IMPORTER
-
 CV__DNN_EXPERIMENTAL_NS_END
 }} // namespace

modules/dnn/test/test_torch_importer.cpp

@@ -39,8 +39,6 @@
 //
 //M*/
 
-#ifdef ENABLE_TORCH_IMPORTER
-
 #include "test_precomp.hpp"
 #include "npy_blob.hpp"
 #include <opencv2/dnn/shape_utils.hpp>
@@ -316,9 +314,8 @@ OCL_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_TRUE(!net.empty());
     }
 
     net.setPreferableBackend(DNN_BACKEND_DEFAULT);
@@ -421,5 +418,3 @@ OCL_TEST(Torch_Importer, FastNeuralStyle_accuracy)
 }
 
 }
-
-#endif