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Small refactoring of RNN layers

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pull/750/head
Vitaliy Lyudvichenko 9 years ago
parent dd9b2eb4fb
commit a62f7e1d89
1 changed files with 35 additions and 33 deletions
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  1. 68
      modules/dnn/src/layers/recurrent_layers.cpp

68
modules/dnn/src/layers/recurrent_layers.cpp
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@ -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)

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