Added Reshape layer and altered importer to correctly handle its params.

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

@@ -10,7 +10,7 @@ namespace dnn
 {
     struct BlobShape
     {
-        explicit BlobShape(int ndims, int fill = 1);
+        explicit BlobShape(int ndims = 4, int fill = 1);
         BlobShape(int num, int cn, int rows, int cols);
         BlobShape(int ndims, const int *sizes);
         BlobShape(const std::vector<int> &sizes);
@@ -25,15 +25,16 @@ namespace dnn
         int operator[](int axis) const;
         int &operator[](int axis);
 
+        //same as size(), but size of non-existing dimensions equal to 1
         int xsize(int axis) const;
 
+        ptrdiff_t total();
+
         const int *ptr() const;
 
         bool equal(const BlobShape &other) const;
 
     private:
-
-        BlobShape();
         cv::AutoBuffer<int,4> sz;
     };
 

modules/dnn/include/opencv2/dnn/blob.inl.hpp

@@ -79,6 +79,17 @@ inline int &BlobShape::operator[] (int axis)
     return sz[(axis < 0) ? axis + dims() : axis];
 }
 
+inline ptrdiff_t BlobShape::total()
+{
+    CV_Assert(dims() >= 1);
+
+    ptrdiff_t res = 1;
+    for (int i = 0; i < dims(); i++)
+        res *= sz[i];
+    return res;
+}
+
+
 inline const int *BlobShape::ptr() const
 {
     return sz;

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

@@ -274,7 +274,7 @@ inline bool DictValue::isReal() const
     return (type == Param::REAL || type == Param::INT);
 }
 
-int DictValue::size() const
+inline int DictValue::size() const
 {
     switch (type)
     {

modules/dnn/src/caffe_importer.cpp

@@ -37,30 +37,6 @@ namespace
                 ReadNetParamsFromBinaryFileOrDie(caffeModel, &netBinary);
         }
 
-        inline bool skipCaffeLayerParam(const FieldDescriptor *fd)
-        {
-            const std::string &name = fd->name();
-
-            if (fd->cpp_type() != FieldDescriptor::CPPTYPE_MESSAGE)
-            {
-                static const char *SKIP_FIELDS[] = { "type", "name", "top", "bottom", NULL };
-
-                for (int i = 0; SKIP_FIELDS[i]; i++)
-                {
-                    if (name == SKIP_FIELDS[i])
-                        return true;
-                }
-
-                return false;
-            }
-            else
-            {
-                static const std::string _param("_param");
-                bool endsWith_param = (name.size() >= _param.size()) && name.compare(name.size() - _param.size(), _param.size(), _param) == 0;
-                return !endsWith_param;
-            }
-        }
-
         void addParam(const Message &msg, const FieldDescriptor *field, cv::dnn::LayerParams &params)
         {
             const Reflection *refl = msg.GetReflection();
@@ -85,6 +61,12 @@ namespace
             case FieldDescriptor::CPPTYPE_UINT32:
                 SET_UP_FILED(GetUInt32, DictValue::arrayInt, ::google::protobuf::uint32);
                 break;
+            case FieldDescriptor::CPPTYPE_INT64:
+                SET_UP_FILED(GetInt32, DictValue::arrayInt, ::google::protobuf::int64);
+                break;
+            case FieldDescriptor::CPPTYPE_UINT64:
+                SET_UP_FILED(GetUInt32, DictValue::arrayInt, ::google::protobuf::uint64);
+                break;
             case FieldDescriptor::CPPTYPE_BOOL:
                 SET_UP_FILED(GetBool, DictValue::arrayInt, bool);
                 break;
@@ -121,7 +103,13 @@ namespace
             }
         }
 
-        void extractLayerParams(const Message &msg, cv::dnn::LayerParams &params)
+        inline static bool ends_with_param(const std::string &str)
+        {
+            static const std::string _param("_param");
+            return (str.size() >= _param.size()) && str.compare(str.size() - _param.size(), _param.size(), _param) == 0;
+        }
+
+        void extractLayerParams(const Message &msg, cv::dnn::LayerParams &params, bool isInternal = false)
         {
             const Descriptor *msgDesc = msg.GetDescriptor();
             const Reflection *msgRefl = msg.GetReflection();
@@ -130,19 +118,21 @@ namespace
             {
                 const FieldDescriptor *fd = msgDesc->field(fieldId);
 
+                if (!isInternal && !ends_with_param(fd->name()))
+                    continue;
+
                 bool hasData =  fd->is_required() ||
-                               (fd->is_optional() && (msgRefl->HasField(msg, fd) /*|| fd->has_default_value()*/)) ||
+                               (fd->is_optional() && msgRefl->HasField(msg, fd)) ||
                                (fd->is_repeated() && msgRefl->FieldSize(msg, fd) > 0);
-
+                if (!hasData)
-                if ( !hasData || skipCaffeLayerParam(fd) )
                     continue;
 
                 if (fd->cpp_type() == FieldDescriptor::CPPTYPE_MESSAGE)
                 {
                     if (fd->is_repeated()) //Extract only first item!
-                        extractLayerParams(msgRefl->GetRepeatedMessage(msg, fd, 0), params);
+                        extractLayerParams(msgRefl->GetRepeatedMessage(msg, fd, 0), params, true);
                     else
-                        extractLayerParams(msgRefl->GetMessage(msg, fd), params);
+                        extractLayerParams(msgRefl->GetMessage(msg, fd), params, true);
                 }
                 else
                 {

modules/dnn/src/layers/elementwise_layers.cpp

@@ -132,7 +132,7 @@ namespace dnn
         template<typename TFloat>
         inline TFloat operator()(TFloat x)
         {
-            return log((TFloat)1 + exp(x));
+            return log((TFloat)1 + exp(-abs(x)));
         }
     };
 

modules/dnn/src/layers/reshape_layer.cpp

@@ -0,0 +1,137 @@
+#include "../precomp.hpp"
+#include "layers_common.hpp"
+
+namespace cv
+{
+namespace dnn
+{
+
+//TODO: Extend cv::Mat::reshape method
+class ReshapeLayer : public Layer
+{   
+public:
+    ReshapeLayer(LayerParams &params);
+
+    void allocate(const std::vector<Blob*> &inputs, std::vector<Blob> &outputs);
+
+    void forward(std::vector<Blob*>&, std::vector<Blob>&) {}
+
+protected:
+    BlobShape shapeDesc;
+    int inAxis, inNumAxes, autoAxisIdx;
+
+    void computeOutputShape(int startAxis, int endAxis, BlobShape &inpShape, BlobShape &outShape);
+};
+
+ReshapeLayer::ReshapeLayer(LayerParams &params)
+{
+    DictValue paramShape = params.get("dim");
+    shapeDesc = BlobShape(paramShape.size());
+    autoAxisIdx = -1;
+
+    for (int i = 0; i < paramShape.size(); i++)
+    {
+        int dim = paramShape.get<int>(i);
+        CV_Assert(dim >= -1);
+
+        if (dim == -1)
+        {
+            if (autoAxisIdx != -1)
+                CV_Error(Error::StsBadArg, "New shape contains multiple -1 dims");
+            autoAxisIdx = i;
+        }
+
+        shapeDesc[i] = dim;
+    }
+
+    inAxis = params.get<int>("axis", 0);
+    inNumAxes = params.get<int>("num_axes", -1);
+    CV_Assert(inNumAxes >= -1);
+}
+
+void ReshapeLayer::allocate(const std::vector<Blob*> &inputs, std::vector<Blob> &outputs)
+{
+    CV_Assert(inputs.size() == 1);
+    outputs.resize(1);
+
+    Blob &inpBlob = *inputs[0];
+    Blob &outBlob = outputs[0];
+    BlobShape inpShape = inpBlob.shape();
+
+    int startAxis = (inAxis >= 0) ? inAxis : inpShape.dims() + 1 + inAxis;
+    int endAxis = (inNumAxes == -1) ? inpShape.dims() : startAxis + inNumAxes;
+    CV_Assert(0 <= startAxis && startAxis <= inpShape.dims());
+    CV_Assert(0 <= endAxis && endAxis <= inpShape.dims());
+
+    int newDims = inpShape.dims() - (endAxis - startAxis) + shapeDesc.dims();
+    BlobShape outShape(newDims);
+
+    computeOutputShape(startAxis, endAxis, inpShape, outShape);
+
+    outBlob.shareFrom(inpBlob);
+    outBlob.reshape(outShape);
+}
+
+void ReshapeLayer::computeOutputShape(int startAxis, int endAxis, BlobShape &inpShape, BlobShape &outShape)
+{
+    int idx = 0;
+    for (int i = 0; i < startAxis; i++)
+        outShape[idx++] = inpShape[i];
+
+    for (int i = 0; i < shapeDesc.dims(); i++)
+    {
+        if (shapeDesc[i] == 0)
+        {
+            int inpAxisIdx = startAxis + i;
+            if (inpAxisIdx < 0 || inpShape.dims() <= inpAxisIdx)
+                CV_Error(Error::StsOutOfRange, "new shape contains a 0, but there was no corresponding bottom axis to copy");
+            outShape[idx++] = inpShape[startAxis + i];
+        }
+        else
+        {
+            outShape[idx++] = (shapeDesc[i] > 0) ? shapeDesc[i] : 1;
+        }
+    }
+
+    for (int i = endAxis; i < inpShape.dims(); i++)
+        outShape[idx++] = inpShape[i];
+
+    if (autoAxisIdx >= 0)
+    {
+        size_t total = inpShape.total();
+        size_t curTotal = 1;
+        for (int i = 0; i < outShape.dims(); i++)
+        {
+            if (i != startAxis + autoAxisIdx)
+                curTotal *= outShape[i];
+        }
+
+        CV_DbgAssert(curTotal <= total && total % curTotal == 0);
+
+        outShape[startAxis + autoAxisIdx] = (int)(total / curTotal);
+    }
+
+    if (inpShape.total() != outShape.total())
+    {
+        CV_Error(Error::StsBadArg, "Mismatch between input and output blob elements count");
+    }
+}
+
+
+Ptr<Layer> createFlattenLayer(LayerParams&)
+{
+    LayerParams params;
+    
+    int shapeDesc[] = {0, -1};
+    params.set("dim", DictValue::arrayInt(shapeDesc, 2));
+
+    return Ptr<Layer>(new ReshapeLayer(params));
+}
+
+
+REGISTER_LAYER_CLASS(Reshape, ReshapeLayer)
+REGISTER_LAYER_FUNC(Flatten, createFlattenLayer)
+
+
+}
+}