Small refactoring of RNN layers

modules/dnn/src/layers/recurrent_layers.cpp

@@ -44,6 +44,7 @@
 #include "op_blas.hpp"
 #include <iostream>
 #include <cmath>
+#include <opencv2/dnn/shape_utils.hpp>
 
 namespace cv
 {
@@ -60,6 +61,7 @@ static void tanh(const Mat &src, Mat &dst)
         *itDst = std::tanh(*itSrc);
 }
 
+//TODO: make utils method
 static void tanh(const Mat &src, Mat &dst)
 {
     dst.create(src.dims, (const int*)src.size, src.type());
@@ -86,9 +88,9 @@ class LSTMLayerImpl : public LSTMLayer
     int dtype;
     bool allocated;
 
-    BlobShape outTailShape;                 //shape of single output sample
-    BlobShape outTsMatShape, outTsShape;    //shape of N output samples
-    BlobShape outResShape;                  //shape of T timestamps and N output samples
+    Shape outTailShape;                 //shape of single output sample
+    Shape outTsMatShape, outTsShape;    //shape of N output samples
+    Shape outResShape;                  //shape of T timestamps and N output samples
 
     bool useTimestampDim;
     bool produceCellOutput;
@@ -101,7 +103,7 @@ public:
         useTimestampDim = true;
         produceCellOutput = false;
         allocated = false;
-        outTailShape = BlobShape::empty();
+        outTailShape = Shape::empty();
     }
 
     void setUseTimstampsDim(bool use)
@@ -120,7 +122,7 @@ public:
     {
         CV_Assert(cInternal.empty() || C.total() == cInternal.total());
         if (!cInternal.empty())
-            C.reshaped(BlobShape::like(cInternal)).matRefConst().copyTo(cInternal);
+            C.reshaped(Shape::like(cInternal)).matRefConst().copyTo(cInternal);
         else
             C.matRefConst().copyTo(cInternal);
     }
@@ -129,7 +131,7 @@ public:
     {
         CV_Assert(hInternal.empty() || H.total() == hInternal.total());
         if (!hInternal.empty())
-            H.reshaped(BlobShape::like(hInternal)).matRefConst().copyTo(hInternal);
+            H.reshaped(Shape::like(hInternal)).matRefConst().copyTo(hInternal);
         else
             H.matRefConst().copyTo(hInternal);
     }
@@ -153,7 +155,7 @@ public:
         return res;
     }
 
-    void setOutShape(const BlobShape &outTailShape_)
+    void setOutShape(const Shape &outTailShape_)
     {
         CV_Assert(!allocated || outTailShape_.total() == outTailShape.total());
         outTailShape = outTailShape_;
@@ -171,7 +173,7 @@ public:
         blobs[0] = Wh;
         blobs[1] = Wx;
         blobs[2] = bias;
-        blobs[2].reshape(BlobShape(1, (int)bias.total()));
+        blobs[2].reshape(Shape(1, (int)bias.total()));
     }
 
     void allocate(const std::vector<Blob*> &input, std::vector<Blob> &output)
@@ -186,24 +188,24 @@ public:
         if (!outTailShape.isEmpty())
             CV_Assert(outTailShape.total() == numOut);
         else
-            outTailShape = BlobShape(numOut);
+            outTailShape = Shape(numOut);
 
         if (useTimestampDim)
         {
             CV_Assert(input[0]->dims() >= 2 && (int)input[0]->total(2) == numInp);
             numTimeStamps = input[0]->size(0);
             numSamples = input[0]->size(1);
-            outResShape = BlobShape(numTimeStamps, numSamples) + outTailShape;
+            outResShape = Shape(numTimeStamps, numSamples) + outTailShape;
         }
         else
         {
             CV_Assert(input[0]->dims() >= 1 && (int)input[0]->total(1) == numInp);
             numTimeStamps = 1;
             numSamples = input[0]->size(0);
-            outResShape = BlobShape(numSamples) + outTailShape;
+            outResShape = Shape(numSamples) + outTailShape;
         }
-        outTsMatShape = BlobShape(numSamples, numOut);
-        outTsShape = BlobShape(numSamples) + outTailShape;
+        outTsMatShape = Shape(numSamples, numOut);
+        outTsShape = Shape(numSamples) + outTailShape;
 
         dtype = input[0]->type();
         CV_Assert(dtype == CV_32F || dtype == CV_64F);
@@ -246,25 +248,25 @@ public:
 
     void forward(std::vector<Blob*> &input, std::vector<Blob> &output)
     {
-        const Mat &Wh = blobs[0].matRefConst();
-        const Mat &Wx = blobs[1].matRefConst();
-        const Mat &bias = blobs[2].matRefConst();
+        const Mat &Wh = blobs[0].getRefConst<Mat>();
+        const Mat &Wx = blobs[1].getRefConst<Mat>();
+        const Mat &bias = blobs[2].getRefConst<Mat>();
 
         int numSamplesTotal = numTimeStamps*numSamples;
-        Mat xTs = input[0]->reshaped(BlobShape(numSamplesTotal, numInp)).matRefConst();
+        Mat xTs = reshaped(input[0]->getRefConst<Mat>(), Shape(numSamplesTotal, numInp));
 
-        BlobShape outMatShape(numSamplesTotal, numOut);
-        Mat hOutTs = output[0].reshaped(outMatShape).matRef();
-        Mat cOutTs = (produceCellOutput) ? output[1].reshaped(outMatShape).matRef() : Mat();
+        Shape outMatShape(numSamplesTotal, numOut);
+        Mat hOutTs = reshaped(output[0].getRef<Mat>(), outMatShape);
+        Mat cOutTs = (produceCellOutput) ? reshaped(output[1].getRef<Mat>(), outMatShape) : Mat();
 
         for (int ts = 0; ts < numTimeStamps; ts++)
         {
             Range curRowRange(ts*numSamples, (ts + 1)*numSamples);
             Mat xCurr = xTs.rowRange(curRowRange);
 
-            gemmCPU(xCurr, Wx, 1, gates, 0, GEMM_2_T);      // Wx * x_t
-            gemmCPU(hInternal, Wh, 1, gates, 1, GEMM_2_T);  //+Wh * h_{t-1}
-            gemmCPU(dummyOnes, bias, 1, gates, 1);          //+b
+            dnn::gemm(xCurr, Wx, 1, gates, 0, GEMM_2_T);      // Wx * x_t
+            dnn::gemm(hInternal, Wh, 1, gates, 1, GEMM_2_T);  //+Wh * h_{t-1}
+            dnn::gemm(dummyOnes, bias, 1, gates, 1);          //+b
 
             Mat getesIFO = gates.colRange(0, 3*numOut);
             Mat gateI = gates.colRange(0*numOut, 1*numOut);
@@ -394,30 +396,30 @@ public:
     void reshapeOutput(std::vector<Blob> &output)
     {
         output.resize((produceH) ? 2 : 1);
-        output[0].create(BlobShape(numTimestamps, numSamples, numO), dtype);
+        output[0].create(Shape(numTimestamps, numSamples, numO), dtype);
         if (produceH)
-            output[1].create(BlobShape(numTimestamps, numSamples, numH), dtype);
+            output[1].create(Shape(numTimestamps, numSamples, numH), dtype);
     }
 
     void forward(std::vector<Blob*> &input, std::vector<Blob> &output)
     {
-        Mat xTs = input[0]->reshaped(BlobShape(numSamplesTotal, numX)).matRefConst();
-        Mat oTs = output[0].reshaped(BlobShape(numSamplesTotal, numO)).matRef();
-        Mat hTs = (produceH) ? output[1].reshaped(BlobShape(numSamplesTotal, numH)).matRef() : Mat();
+        Mat xTs = reshaped(input[0]->getRefConst<Mat>(), Shape(numSamplesTotal, numX));
+        Mat oTs = reshaped(output[0].getRef<Mat>(), Shape(numSamplesTotal, numO));
+        Mat hTs = (produceH) ? reshaped(output[1].getRef<Mat>(), Shape(numSamplesTotal, numH)) : Mat();
 
         for (int ts = 0; ts < numTimestamps; ts++)
         {
             Range curRowRange = Range(ts * numSamples, (ts + 1) * numSamples);
             Mat xCurr = xTs.rowRange(curRowRange);
 
-            gemmCPU(hPrev, Whh, 1, hCurr, 0, GEMM_2_T); // W_{hh} * h_{prev}
-            gemmCPU(xCurr, Wxh, 1, hCurr, 1, GEMM_2_T); //+W_{xh} * x_{curr}
-            gemmCPU(dummyBiasOnes, bh, 1, hCurr, 1);    //+bh
+            dnn::gemm(hPrev, Whh, 1, hCurr, 0, GEMM_2_T); // W_{hh} * h_{prev}
+            dnn::gemm(xCurr, Wxh, 1, hCurr, 1, GEMM_2_T); //+W_{xh} * x_{curr}
+            dnn::gemm(dummyBiasOnes, bh, 1, hCurr, 1);    //+bh
             tanh(hCurr, hPrev);
 
             Mat oCurr = oTs.rowRange(curRowRange);
-            gemmCPU(hPrev, Who, 1, oCurr, 0, GEMM_2_T); // W_{ho} * h_{prev}
-            gemmCPU(dummyBiasOnes, bo, 1, oCurr, 1);    //+b_o
+            dnn::gemm(hPrev, Who, 1, oCurr, 0, GEMM_2_T); // W_{ho} * h_{prev}
+            dnn::gemm(dummyBiasOnes, bo, 1, oCurr, 1);    //+b_o
             tanh(oCurr, oCurr);
 
             if (produceH)