mirror of
https://github.com/opencv/opencv.git
synced 2025-06-12 20:42:53 +08:00
551 lines
18 KiB
C++
551 lines
18 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
|
|
//
|
|
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
|
//
|
|
// By downloading, copying, installing or using the software you agree to this license.
|
|
// If you do not agree to this license, do not download, install,
|
|
// copy or use the software.
|
|
//
|
|
//
|
|
// License Agreement
|
|
// For Open Source Computer Vision Library
|
|
//
|
|
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
|
|
// Copyright (C) 2017, Intel Corporation, all rights reserved.
|
|
// Third party copyrights are property of their respective owners.
|
|
//
|
|
// Redistribution and use in source and binary forms, with or without modification,
|
|
// are permitted provided that the following conditions are met:
|
|
//
|
|
// * Redistribution's of source code must retain the above copyright notice,
|
|
// this list of conditions and the following disclaimer.
|
|
//
|
|
// * Redistribution's in binary form must reproduce the above copyright notice,
|
|
// this list of conditions and the following disclaimer in the documentation
|
|
// and/or other materials provided with the distribution.
|
|
//
|
|
// * The name of the copyright holders may not be used to endorse or promote products
|
|
// derived from this software without specific prior written permission.
|
|
//
|
|
// This software is provided by the copyright holders and contributors "as is" and
|
|
// any express or implied warranties, including, but not limited to, the implied
|
|
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
|
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
|
// indirect, incidental, special, exemplary, or consequential damages
|
|
// (including, but not limited to, procurement of substitute goods or services;
|
|
// loss of use, data, or profits; or business interruption) however caused
|
|
// and on any theory of liability, whether in contract, strict liability,
|
|
// or tort (including negligence or otherwise) arising in any way out of
|
|
// the use of this software, even if advised of the possibility of such damage.
|
|
//
|
|
//M*/
|
|
|
|
#include "../precomp.hpp"
|
|
#include <iostream>
|
|
#include <iterator>
|
|
#include <cmath>
|
|
#include <opencv2/dnn/shape_utils.hpp>
|
|
|
|
namespace cv
|
|
{
|
|
namespace dnn
|
|
{
|
|
|
|
template<typename Dtype>
|
|
static void tanh(const Mat &src, Mat &dst)
|
|
{
|
|
MatConstIterator_<Dtype> itSrc = src.begin<Dtype>();
|
|
MatIterator_<Dtype> itDst = dst.begin<Dtype>();
|
|
|
|
for (; itSrc != src.end<Dtype>(); itSrc++, itDst++)
|
|
*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());
|
|
|
|
if (src.type() == CV_32F)
|
|
tanh<float>(src, dst);
|
|
else if (src.type() == CV_64F)
|
|
tanh<double>(src, dst);
|
|
else
|
|
CV_Error(Error::StsUnsupportedFormat, "Function supports only floating point types");
|
|
}
|
|
|
|
static void sigmoid(const Mat &src, Mat &dst)
|
|
{
|
|
cv::exp(-src, dst);
|
|
cv::pow(1 + dst, -1, dst);
|
|
}
|
|
|
|
class LSTMLayerImpl CV_FINAL : public LSTMLayer
|
|
{
|
|
int numTimeStamps, numSamples;
|
|
bool allocated;
|
|
|
|
MatShape outTailShape; //shape of single output sample
|
|
MatShape outTsShape; //shape of N output samples
|
|
|
|
bool useTimestampDim;
|
|
bool produceCellOutput;
|
|
float forgetBias, cellClip;
|
|
bool useCellClip, usePeephole;
|
|
bool reverse; // If true, go in negative direction along the time axis
|
|
|
|
public:
|
|
|
|
LSTMLayerImpl(const LayerParams& params)
|
|
: numTimeStamps(0), numSamples(0)
|
|
{
|
|
setParamsFrom(params);
|
|
|
|
if (!blobs.empty())
|
|
{
|
|
CV_Assert(blobs.size() >= 3);
|
|
|
|
blobs[2] = blobs[2].reshape(1, 1);
|
|
|
|
const Mat& Wh = blobs[0];
|
|
const Mat& Wx = blobs[1];
|
|
const Mat& bias = blobs[2];
|
|
CV_Assert(Wh.dims == 2 && Wx.dims == 2);
|
|
CV_Assert(Wh.rows == Wx.rows);
|
|
CV_Assert(Wh.rows == 4*Wh.cols);
|
|
CV_Assert(Wh.rows == (int)bias.total());
|
|
CV_Assert(Wh.type() == Wx.type() && Wx.type() == bias.type());
|
|
|
|
// Peephole weights.
|
|
if (blobs.size() > 3)
|
|
{
|
|
CV_Assert(blobs.size() == 6);
|
|
const int N = Wh.cols;
|
|
for (int i = 3; i < 6; ++i)
|
|
{
|
|
CV_Assert(blobs[i].rows == N && blobs[i].cols == N);
|
|
CV_Assert(blobs[i].type() == bias.type());
|
|
}
|
|
}
|
|
}
|
|
useTimestampDim = params.get<bool>("use_timestamp_dim", true);
|
|
produceCellOutput = params.get<bool>("produce_cell_output", false);
|
|
forgetBias = params.get<float>("forget_bias", 0.0f);
|
|
cellClip = params.get<float>("cell_clip", 0.0f);
|
|
useCellClip = params.get<bool>("use_cell_clip", false);
|
|
usePeephole = params.get<bool>("use_peephole", false);
|
|
reverse = params.get<bool>("reverse", false);
|
|
|
|
allocated = false;
|
|
outTailShape.clear();
|
|
}
|
|
|
|
void setUseTimstampsDim(bool use) CV_OVERRIDE
|
|
{
|
|
CV_Assert(!allocated);
|
|
useTimestampDim = use;
|
|
}
|
|
|
|
void setProduceCellOutput(bool produce) CV_OVERRIDE
|
|
{
|
|
CV_Assert(!allocated);
|
|
produceCellOutput = produce;
|
|
}
|
|
|
|
void setOutShape(const MatShape &outTailShape_) CV_OVERRIDE
|
|
{
|
|
CV_Assert(!allocated || total(outTailShape) == total(outTailShape_));
|
|
outTailShape = outTailShape_;
|
|
}
|
|
|
|
void setWeights(const Mat &Wh, const Mat &Wx, const Mat &bias) CV_OVERRIDE
|
|
{
|
|
CV_Assert(Wh.dims == 2 && Wx.dims == 2);
|
|
CV_Assert(Wh.rows == Wx.rows);
|
|
CV_Assert(Wh.rows == 4*Wh.cols);
|
|
CV_Assert(Wh.rows == (int)bias.total());
|
|
CV_Assert(Wh.type() == Wx.type() && Wx.type() == bias.type());
|
|
|
|
blobs.resize(3);
|
|
blobs[0] = Mat(Wh.clone());
|
|
blobs[1] = Mat(Wx.clone());
|
|
blobs[2] = Mat(bias.clone()).reshape(1, 1);
|
|
}
|
|
|
|
bool getMemoryShapes(const std::vector<MatShape> &inputs,
|
|
const int requiredOutputs,
|
|
std::vector<MatShape> &outputs,
|
|
std::vector<MatShape> &internals) const CV_OVERRIDE
|
|
{
|
|
CV_Assert((!usePeephole && blobs.size() == 3) || (usePeephole && blobs.size() == 6));
|
|
CV_Assert(inputs.size() == 1);
|
|
const MatShape& inp0 = inputs[0];
|
|
|
|
const Mat &Wh = blobs[0], &Wx = blobs[1];
|
|
int _numOut = Wh.size[1];
|
|
int _numInp = Wx.size[1];
|
|
MatShape outTailShape_(outTailShape), outResShape;
|
|
|
|
if (!outTailShape_.empty())
|
|
CV_Assert(total(outTailShape_) == _numOut);
|
|
else
|
|
outTailShape_.assign(1, _numOut);
|
|
|
|
int _numSamples;
|
|
if (useTimestampDim)
|
|
{
|
|
CV_Assert(inp0.size() >= 2 && total(inp0, 2) == _numInp);
|
|
_numSamples = inp0[1];
|
|
outResShape.push_back(inp0[0]);
|
|
}
|
|
else
|
|
{
|
|
CV_Assert(inp0.size() >= 2 && total(inp0, 1) == _numInp);
|
|
_numSamples = inp0[0];
|
|
}
|
|
|
|
outResShape.push_back(_numSamples);
|
|
outResShape.insert(outResShape.end(), outTailShape_.begin(), outTailShape_.end());
|
|
|
|
size_t noutputs = produceCellOutput ? 2 : 1;
|
|
outputs.assign(noutputs, outResShape);
|
|
|
|
internals.assign(1, shape(_numSamples, _numOut)); // hInternal
|
|
internals.push_back(shape(_numSamples, _numOut)); // cInternal
|
|
internals.push_back(shape(_numSamples, 1)); // dummyOnes
|
|
internals.push_back(shape(_numSamples, 4*_numOut)); // gates
|
|
|
|
return false;
|
|
}
|
|
|
|
void finalize(InputArrayOfArrays inputs_arr, OutputArrayOfArrays) CV_OVERRIDE
|
|
{
|
|
std::vector<Mat> input;
|
|
inputs_arr.getMatVector(input);
|
|
|
|
CV_Assert((!usePeephole && blobs.size() == 3) || (usePeephole && blobs.size() == 6));
|
|
CV_Assert(input.size() == 1);
|
|
const Mat& inp0 = input[0];
|
|
|
|
Mat &Wh = blobs[0], &Wx = blobs[1];
|
|
int numOut = Wh.size[1];
|
|
int numInp = Wx.size[1];
|
|
|
|
if (!outTailShape.empty())
|
|
CV_Assert(total(outTailShape) == numOut);
|
|
else
|
|
outTailShape.assign(1, numOut);
|
|
|
|
if (useTimestampDim)
|
|
{
|
|
CV_Assert(inp0.dims >= 2 && (int)inp0.total(2) == numInp);
|
|
numTimeStamps = inp0.size[0];
|
|
numSamples = inp0.size[1];
|
|
}
|
|
else
|
|
{
|
|
CV_Assert(inp0.dims >= 2 && (int)inp0.total(1) == numInp);
|
|
numTimeStamps = 1;
|
|
numSamples = inp0.size[0];
|
|
}
|
|
|
|
outTsShape.clear();
|
|
outTsShape.push_back(numSamples);
|
|
outTsShape.insert(outTsShape.end(), outTailShape.begin(), outTailShape.end());
|
|
|
|
allocated = true;
|
|
}
|
|
|
|
void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE
|
|
{
|
|
CV_TRACE_FUNCTION();
|
|
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
|
|
|
|
if (inputs_arr.depth() == CV_16S)
|
|
{
|
|
forward_fallback(inputs_arr, outputs_arr, internals_arr);
|
|
return;
|
|
}
|
|
|
|
std::vector<Mat> input, output, internals;
|
|
inputs_arr.getMatVector(input);
|
|
outputs_arr.getMatVector(output);
|
|
internals_arr.getMatVector(internals);
|
|
|
|
const Mat &Wh = blobs[0];
|
|
const Mat &Wx = blobs[1];
|
|
const Mat &bias = blobs[2];
|
|
|
|
int numOut = Wh.size[1];
|
|
|
|
Mat hInternal = internals[0], cInternal = internals[1],
|
|
dummyOnes = internals[2], gates = internals[3];
|
|
hInternal.setTo(0.);
|
|
cInternal.setTo(0.);
|
|
dummyOnes.setTo(1.);
|
|
|
|
int numSamplesTotal = numTimeStamps*numSamples;
|
|
Mat xTs = input[0].reshape(1, numSamplesTotal);
|
|
|
|
Mat hOutTs = output[0].reshape(1, numSamplesTotal);
|
|
Mat cOutTs = produceCellOutput ? output[1].reshape(1, numSamplesTotal) : Mat();
|
|
|
|
int tsStart, tsEnd, tsInc;
|
|
if (reverse) {
|
|
tsStart = numTimeStamps - 1;
|
|
tsEnd = -1;
|
|
tsInc = -1;
|
|
}
|
|
else {
|
|
tsStart = 0;
|
|
tsEnd = numTimeStamps;
|
|
tsInc = 1;
|
|
}
|
|
for (int ts = tsStart; ts != tsEnd; ts += tsInc)
|
|
{
|
|
Range curRowRange(ts*numSamples, (ts + 1)*numSamples);
|
|
Mat xCurr = xTs.rowRange(curRowRange);
|
|
|
|
gemm(xCurr, Wx, 1, gates, 0, gates, GEMM_2_T); // Wx * x_t
|
|
gemm(hInternal, Wh, 1, gates, 1, gates, GEMM_2_T); //+Wh * h_{t-1}
|
|
gemm(dummyOnes, bias, 1, gates, 1, gates); //+b
|
|
|
|
Mat gateI = gates.colRange(0*numOut, 1*numOut);
|
|
Mat gateF = gates.colRange(1*numOut, 2*numOut);
|
|
Mat gateO = gates.colRange(2*numOut, 3*numOut);
|
|
Mat gateG = gates.colRange(3*numOut, 4*numOut);
|
|
|
|
if (forgetBias)
|
|
add(gateF, forgetBias, gateF);
|
|
|
|
if (usePeephole)
|
|
{
|
|
Mat gatesIF = gates.colRange(0, 2*numOut);
|
|
gemm(cInternal, blobs[3], 1, gateI, 1, gateI);
|
|
gemm(cInternal, blobs[4], 1, gateF, 1, gateF);
|
|
sigmoid(gatesIF, gatesIF);
|
|
}
|
|
else
|
|
{
|
|
Mat gatesIFO = gates.colRange(0, 3*numOut);
|
|
sigmoid(gatesIFO, gatesIFO);
|
|
}
|
|
|
|
tanh(gateG, gateG);
|
|
|
|
//compute c_t
|
|
multiply(gateF, cInternal, gateF); // f_t (*) c_{t-1}
|
|
multiply(gateI, gateG, gateI); // i_t (*) g_t
|
|
add(gateF, gateI, cInternal); // c_t = f_t (*) c_{t-1} + i_t (*) g_t
|
|
|
|
if (useCellClip)
|
|
{
|
|
min(cInternal, cellClip, cInternal);
|
|
max(cInternal, -cellClip, cInternal);
|
|
}
|
|
if (usePeephole)
|
|
{
|
|
gemm(cInternal, blobs[5], 1, gateO, 1, gateO);
|
|
sigmoid(gateO, gateO);
|
|
}
|
|
|
|
//compute h_t
|
|
tanh(cInternal, hInternal);
|
|
multiply(gateO, hInternal, hInternal);
|
|
|
|
//save results in output blobs
|
|
hInternal.copyTo(hOutTs.rowRange(curRowRange));
|
|
if (produceCellOutput)
|
|
cInternal.copyTo(cOutTs.rowRange(curRowRange));
|
|
}
|
|
}
|
|
};
|
|
|
|
Ptr<LSTMLayer> LSTMLayer::create(const LayerParams& params)
|
|
{
|
|
return Ptr<LSTMLayer>(new LSTMLayerImpl(params));
|
|
}
|
|
|
|
int LSTMLayer::inputNameToIndex(String inputName)
|
|
{
|
|
if (inputName.toLowerCase() == "x")
|
|
return 0;
|
|
return -1;
|
|
}
|
|
|
|
int LSTMLayer::outputNameToIndex(const String& outputName)
|
|
{
|
|
if (outputName.toLowerCase() == "h")
|
|
return 0;
|
|
else if (outputName.toLowerCase() == "c")
|
|
return 1;
|
|
return -1;
|
|
}
|
|
|
|
|
|
class RNNLayerImpl : public RNNLayer
|
|
{
|
|
int numX, numH, numO;
|
|
int numSamples, numTimestamps, numSamplesTotal;
|
|
int dtype;
|
|
Mat Whh, Wxh, bh;
|
|
Mat Who, bo;
|
|
bool produceH;
|
|
|
|
public:
|
|
|
|
RNNLayerImpl(const LayerParams& params)
|
|
: numX(0), numH(0), numO(0), numSamples(0), numTimestamps(0), numSamplesTotal(0), dtype(0)
|
|
{
|
|
setParamsFrom(params);
|
|
type = "RNN";
|
|
produceH = false;
|
|
}
|
|
|
|
void setProduceHiddenOutput(bool produce = false) CV_OVERRIDE
|
|
{
|
|
produceH = produce;
|
|
}
|
|
|
|
void setWeights(const Mat &W_xh, const Mat &b_h, const Mat &W_hh, const Mat &W_ho, const Mat &b_o) CV_OVERRIDE
|
|
{
|
|
CV_Assert(W_hh.dims == 2 && W_xh.dims == 2);
|
|
CV_Assert(W_hh.size[0] == W_xh.size[0] && W_hh.size[0] == W_hh.size[1] && (int)b_h.total() == W_xh.size[0]);
|
|
CV_Assert(W_ho.size[0] == (int)b_o.total());
|
|
CV_Assert(W_ho.size[1] == W_hh.size[1]);
|
|
|
|
blobs.resize(5);
|
|
blobs[0] = Mat(W_xh.clone());
|
|
blobs[1] = Mat(b_h.clone());
|
|
blobs[2] = Mat(W_hh.clone());
|
|
blobs[3] = Mat(W_ho.clone());
|
|
blobs[4] = Mat(b_o.clone());
|
|
}
|
|
|
|
bool getMemoryShapes(const std::vector<MatShape> &inputs,
|
|
const int requiredOutputs,
|
|
std::vector<MatShape> &outputs,
|
|
std::vector<MatShape> &internals) const CV_OVERRIDE
|
|
{
|
|
CV_Assert(inputs.size() >= 1 && inputs.size() <= 2);
|
|
|
|
Mat Who_ = blobs[3];
|
|
Mat Wxh_ = blobs[0];
|
|
|
|
int numTimestamps_ = inputs[0][0];
|
|
int numSamples_ = inputs[0][1];
|
|
|
|
int numO_ = Who_.rows;
|
|
int numH_ = Wxh_.rows;
|
|
|
|
outputs.clear();
|
|
int dims[] = {numTimestamps_, numSamples_, numO_};
|
|
outputs.push_back(shape(dims, 3));
|
|
dims[2] = numH_;
|
|
if (produceH)
|
|
outputs.push_back(shape(dims, 3));
|
|
|
|
internals.assign(2, shape(numSamples_, numH_));
|
|
internals.push_back(shape(numSamples_, 1));
|
|
|
|
return false;
|
|
}
|
|
|
|
void finalize(InputArrayOfArrays inputs_arr, OutputArrayOfArrays) CV_OVERRIDE
|
|
{
|
|
std::vector<Mat> input, outputs;
|
|
inputs_arr.getMatVector(input);
|
|
|
|
CV_Assert(input.size() >= 1 && input.size() <= 2);
|
|
|
|
Wxh = blobs[0];
|
|
bh = blobs[1];
|
|
Whh = blobs[2];
|
|
Who = blobs[3];
|
|
bo = blobs[4];
|
|
|
|
numH = Wxh.rows;
|
|
numX = Wxh.cols;
|
|
numO = Who.rows;
|
|
|
|
const Mat& inp0 = input[0];
|
|
|
|
CV_Assert(inp0.dims >= 2);
|
|
CV_Assert(inp0.total(2) == numX);
|
|
dtype = CV_32F;
|
|
CV_Assert(inp0.type() == dtype);
|
|
numTimestamps = inp0.size[0];
|
|
numSamples = inp0.size[1];
|
|
numSamplesTotal = numTimestamps * numSamples;
|
|
|
|
bh = bh.reshape(1, 1); //is 1 x numH Mat
|
|
bo = bo.reshape(1, 1); //is 1 x numO Mat
|
|
}
|
|
|
|
void reshapeOutput(std::vector<Mat> &output)
|
|
{
|
|
output.resize(produceH ? 2 : 1);
|
|
int sz0[] = { numTimestamps, numSamples, numO };
|
|
output[0].create(3, sz0, dtype);
|
|
if (produceH)
|
|
{
|
|
int sz1[] = { numTimestamps, numSamples, numH };
|
|
output[1].create(3, sz1, dtype);
|
|
}
|
|
}
|
|
|
|
void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE
|
|
{
|
|
CV_TRACE_FUNCTION();
|
|
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
|
|
|
|
if (inputs_arr.depth() == CV_16S)
|
|
{
|
|
forward_fallback(inputs_arr, outputs_arr, internals_arr);
|
|
return;
|
|
}
|
|
|
|
std::vector<Mat> input, output, internals;
|
|
inputs_arr.getMatVector(input);
|
|
outputs_arr.getMatVector(output);
|
|
internals_arr.getMatVector(internals);
|
|
|
|
Mat xTs = input[0].reshape(1, numSamplesTotal);
|
|
Mat oTs = output[0].reshape(1, numSamplesTotal);
|
|
Mat hTs = produceH ? output[1].reshape(1, numSamplesTotal) : Mat();
|
|
Mat hCurr = internals[0];
|
|
Mat hPrev = internals[1];
|
|
Mat dummyBiasOnes = internals[2];
|
|
|
|
hPrev.setTo(0.);
|
|
dummyBiasOnes.setTo(1.);
|
|
|
|
for (int ts = 0; ts < numTimestamps; ts++)
|
|
{
|
|
Range curRowRange = Range(ts * numSamples, (ts + 1) * numSamples);
|
|
Mat xCurr = xTs.rowRange(curRowRange);
|
|
|
|
gemm(hPrev, Whh, 1, hCurr, 0, hCurr, GEMM_2_T); // W_{hh} * h_{prev}
|
|
gemm(xCurr, Wxh, 1, hCurr, 1, hCurr, GEMM_2_T); //+W_{xh} * x_{curr}
|
|
gemm(dummyBiasOnes, bh, 1, hCurr, 1, hCurr); //+bh
|
|
tanh(hCurr, hPrev);
|
|
|
|
Mat oCurr = oTs.rowRange(curRowRange);
|
|
gemm(hPrev, Who, 1, oCurr, 0, oCurr, GEMM_2_T); // W_{ho} * h_{prev}
|
|
gemm(dummyBiasOnes, bo, 1, oCurr, 1, oCurr); //+b_o
|
|
tanh(oCurr, oCurr);
|
|
|
|
if (produceH)
|
|
hPrev.copyTo(hTs.rowRange(curRowRange));
|
|
}
|
|
}
|
|
};
|
|
|
|
CV_EXPORTS_W Ptr<RNNLayer> RNNLayer::create(const LayerParams& params)
|
|
{
|
|
return Ptr<RNNLayer>(new RNNLayerImpl(params));
|
|
}
|
|
|
|
}
|
|
}
|