Fixing of reshape layer to pass Torch test

modules/dnn/src/blob.cpp

@@ -364,9 +364,15 @@ BlobShape computeShapeByReshapeMask(const BlobShape &srcShape, const BlobShape &
 {
     if (srcRange == Range::all())
         srcRange = Range(0, srcShape.dims());
+    else
+    {
+        int sz = srcRange.size();
+        srcRange.start = srcShape.canonicalAxis(srcRange.start);
+        srcRange.end =  (srcRange.end == INT_MAX) ? srcShape.dims() : srcRange.start + sz;
+    }
 
     CV_Assert(0 <= srcRange.start && srcRange.start <= srcRange.end && srcRange.end <= srcShape.dims());
-    Shape dstShape(srcShape.dims() - srcRange.size() + maskShape.dims(), nullptr);
+    BlobShape dstShape(srcShape.dims() - srcRange.size() + maskShape.dims(), (const int*)NULL);
 
     std::copy(srcShape.ptr(), srcShape.ptr() + srcRange.start, dstShape.ptr());
     std::copy(srcShape.ptr() + srcRange.end, srcShape.ptr() + srcShape.dims(), dstShape.ptr() + srcRange.start + maskShape.dims());

modules/dnn/src/caffe/layer_loaders.cpp

 #include "../precomp.hpp"
 #include "layer_loaders.hpp"
 #include <opencv2/dnn/shape_utils.hpp>
+#include <climits>
 
 namespace cv
 {
@@ -180,13 +181,13 @@ Ptr<Layer> createLayerFromCaffe<ReshapeLayer>(LayerParams &params)
     int axis = params.get<int>("axis", 0);
     int numAxes = params.get<int>("num_axes", -1);
     CV_Assert(numAxes >= -1);
-    Range applyingRange = (numAxes == -1) ? Range::all() : Range(axis, axis + numAxes);
+    Range applyingRange = (numAxes == -1) ? Range(axis, INT_MAX) : Range(axis, axis + numAxes);
 
     Shape newShape;
     if (params.has("dim"))
     {
         const DictValue &paramShape = params.get("dim");
-        newShape = Shape(paramShape.size(), nullptr);
+        newShape = Shape::all(paramShape.size());
         for (int i = 0; i < paramShape.size(); i++)
             newShape[i] = paramShape.get<int>(i);
     }
@@ -290,4 +291,4 @@ template Ptr<Layer> createLayerFromCaffe<BNLLLayer>(LayerParams&);
 template Ptr<Layer> createLayerFromCaffe<PowerLayer>(LayerParams&);
 
 }
-}
\ No newline at end of file
+}

modules/dnn/src/layers/fully_connected_layer.cpp

@@ -89,10 +89,12 @@ void FullyConnectedLayerImpl::allocate(const std::vector<Blob*> &input, std::vec
 
 void FullyConnectedLayerImpl::forward(std::vector<Blob*> &input, std::vector<Blob> &output)
 {
-    if (!useOpenCL)
-        forward_<Mat>(input, output);
-    else
+    #ifdef HAVE_OPENCL
+    if (useOpenCL)
         forward_<UMat>(input, output);
+    else
+    #endif
+        forward_<Mat>(input, output);
 }
 
 template<typename XMat>

modules/dnn/src/layers/lrn_layer.cpp

@@ -47,7 +47,6 @@
 #include <opencv2/core/ocl.hpp>
 #include <opencv2/dnn/shape_utils.hpp>
 #include <algorithm>
-#include <type_traits>
 
 namespace cv
 {
@@ -205,6 +204,21 @@ void LRNLayerImpl::spatialNormalization(Blob &src, Blob &dst)
         spatialNormalization_<UMat>(src, dst);
 }
 
+//TODO: fix cv::boxFilter with BORDER_ISOLATED flag in CPU mode
+template<>
+void LRNLayerImpl::sqrBoxFilter_<Mat>(const Mat &src, Mat &dst)
+{
+    Mat bufMat = buf.getRef<Mat>();
+    src.copyTo(bufMat);
+    cv::sqrBoxFilter(bufMat, dst, dst.depth(), Size(size, size), Point(-1, -1), false, BORDER_CONSTANT);
+}
+
+template<>
+void LRNLayerImpl::sqrBoxFilter_<UMat>(const UMat &src, UMat &dst)
+{
+    cv::sqrBoxFilter(src, dst, dst.depth(), Size(size, size), Point(-1, -1), false, BORDER_CONSTANT | BORDER_ISOLATED);
+}
+
 template<typename XMat>
 void LRNLayerImpl::spatialNormalization_(Blob &srcBlob, Blob &dstBlob)
 {
@@ -221,17 +235,7 @@ void LRNLayerImpl::spatialNormalization_(Blob &srcBlob, Blob &dstBlob)
             XMat src = getPlane(srcMat, n, cn);
             XMat dst = getPlane(dstMat, n, cn);
 
-            if (std::is_same<XMat, UMat>::value)
-            {
-                cv::sqrBoxFilter(src, dst, dst.depth(), Size(size, size), Point(-1, -1), false, BORDER_CONSTANT | BORDER_ISOLATED);
-            }
-            else
-            {
-                //TODO: fix cv::boxFilter with BORDER_ISOLATED flag in CPU mode
-                Mat bufMat = buf.getRef<Mat>();
-                src.copyTo(bufMat);
-                cv::sqrBoxFilter(bufMat, dst, dst.depth(), Size(size, size), Point(-1, -1), false, BORDER_CONSTANT);
-            }
+            sqrBoxFilter_(src, dst);
 
             dst.convertTo(dst, dst.type(), alpha/(size*size), 1);
             cv::pow(dst, beta, dst);

modules/dnn/src/layers/lrn_layer.hpp

@@ -62,6 +62,8 @@ class LRNLayerImpl : public LRNLayer
     void spatialNormalization(Blob &src, Blob &dst);
     template<typename XMat>
     void spatialNormalization_(Blob &src, Blob &dst);
+    template<typename XMat>
+    void sqrBoxFilter_(const XMat &src, XMat &dst);
 
 public:
 

modules/dnn/src/torch/torch_importer.cpp

@@ -52,6 +52,12 @@ namespace dnn {
 #if defined(ENABLE_TORCH_IMPORTER) && ENABLE_TORCH_IMPORTER
 #include "THDiskFile.h"
 
+#ifdef NDEBUG
+static bool dbgPrint = false;
+#else
+static bool dbgPrint = true;
+#endif
+
 enum LuaType
 {
     TYPE_NIL      = 0,
@@ -290,7 +296,8 @@ struct TorchImporter : public ::cv::dnn::Importer
             }
 
             String key = readString();
-            std::cout << i << "th key: " << key << "\n";
+            if (dbgPrint)
+                std::cout << i << "th key: " << key << "\n";
 
             fpos = THFile_position(file);
             int vtype = readInt();
@@ -334,13 +341,16 @@ struct TorchImporter : public ::cv::dnn::Importer
         }
 
         //Debug output
-        std::cout << "scalarParams:\n";
-        std::cout << scalarParams;
+        if (dbgPrint)
+        {
+            std::cout << "scalarParams:\n";
+            std::cout << scalarParams;
 
-        std::cout << "#" << tensorParams.size() << " tensorParams:\n";
-        std::map<String,Blob>::const_iterator it;
-        for (it = tensorParams.begin(); it != tensorParams.end(); it++)
-            std::cout << it->first << ": Tensor " << it->second.shape() << "\n";
+            std::cout << "#" << tensorParams.size() << " tensorParams:\n";
+            std::map<String,Blob>::const_iterator it;
+            for (it = tensorParams.begin(); it != tensorParams.end(); it++)
+                std::cout << it->first << ": Tensor " << it->second.shape() << "\n";
+        }
     }
 
     void readTorchTensor(int indexTensor, int typeTensor)
@@ -435,7 +445,9 @@ struct TorchImporter : public ::cv::dnn::Importer
 
         String className = readTorchClassName();
         String nnName;
-        std::cout << "Class: " << className << std::endl;
+
+        if (dbgPrint)
+            std::cout << "Class: " << className << std::endl;
 
         int type;
         if ( (type = parseTensorType(className)) >= 0 ) //is Tensor