/
recurrent_layers.cpp
1166 lines (997 loc) · 41.5 KB
/
recurrent_layers.cpp
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/*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 <cmath>
#include <opencv2/dnn/shape_utils.hpp>
#ifdef HAVE_CUDA
#include "../cuda4dnn/primitives/recurrent_cells.hpp"
using namespace cv::dnn::cuda4dnn;
#endif
#include "layers_common.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);
}
typedef void (*ActivationFunction)(const Mat &src, Mat &dst);
static ActivationFunction get_activation_function(const String& activation) {
// most used activations for PyTorch and TF : Tanh, Sigmoid
// if you need to support more optional activations use std::map instead
if (activation == "Tanh")
{
return tanh;
}
else if (activation == "Sigmoid")
{
return sigmoid;
}
else
{
CV_Error(Error::StsNotImplemented,
cv::format("Activation function [%s] for layer LSTM is not supported", activation.c_str()));
}
}
class LSTMLayerImpl CV_FINAL : public LSTMLayer
{
int numTimeStamps, numSamples, numHidden;
bool allocated;
MatShape outTailShape; //shape of single output sample
MatShape outTsShape; //shape of N output samples
enum layout_t : int {
SEQ_BATCH_HID = 0,
BATCH_SEQ_HID = 1
};
bool useTimestampDim;
bool produceCellOutput;
float forgetBias, cellClip;
bool useCellClip, usePeephole;
bool reverse; // If true, go in negative direction along the time axis
bool bidirectional; // If true, produces both forward and reversed directions along time axis
layout_t layout; // If layout == BATCH_SEQ_HID, uses batch_size x seq_length x num_hidden for input and output
// else uses seq_length x batch_size x num_hidden
ActivationFunction f_activation;
ActivationFunction g_activation;
ActivationFunction h_activation;
bool isDefaultActivations{true};
#if CV_TRY_AVX
bool useAVX;
#endif
#if CV_TRY_AVX2
bool useAVX2;
#endif
// CUDA needs input blobs to be rearranged in a specific way, but some transformations
// in ONNXImporter are destructive, so we keep a copy.
std::vector<Mat> originalBlobs;
public:
LSTMLayerImpl(const LayerParams& params)
: numTimeStamps(0), numSamples(0)
#if CV_TRY_AVX
, useAVX(checkHardwareSupport(CPU_AVX))
#endif
#if CV_TRY_AVX2
, useAVX2(checkHardwareSupport(CPU_AVX2))
#endif
{
setParamsFrom(params);
if (params.get<bool>("is_onnx", false))
{
// collect copies of onnx blobs
originalBlobs.insert(originalBlobs.begin(), blobs.begin(), blobs.begin() + 3);
blobs.erase(blobs.begin(), blobs.begin() + 3);
}
bidirectional = params.get<bool>("bidirectional", false);
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];
const Mat& hInternal = blobs[3];
const Mat& cInternal = blobs[4];
CV_CheckEQ(Wh.dims, 2, "");
CV_CheckEQ(Wx.dims, 2, "");
CV_CheckEQ(Wh.rows, Wx.rows, "");
CV_CheckEQ(Wh.rows, (1 + static_cast<int>(bidirectional))*4*Wh.cols, "");
CV_CheckEQ(Wh.rows, (int)bias.total(), "");
// Only perform these checks if hInternal and cInternal are not empty matrices
// e.g. inputs are not given by a user
if(!hInternal.empty()){
CV_CheckEQ(hInternal.cols, Wh.cols, "");
}
if(!cInternal.empty()){
CV_CheckEQ(cInternal.cols, Wh.cols, "");
}
if (!hInternal.empty() && !cInternal.empty()){ //otherwise check in forward
CV_CheckEQ(hInternal.rows, cInternal.rows, "");
}
CV_Assert(Wh.type() == Wx.type() && Wx.type() == bias.type());
// Peephole weights.
if (blobs.size() > 5)
{
CV_Assert(blobs.size() == 8);
const int N = Wh.cols;
for (int i = 5; i < 8; ++i)
{
CV_Assert(blobs[i].rows == N && blobs[i].cols == N);
CV_Assert(blobs[i].type() == bias.type());
}
}
}
layout = (layout_t) params.get<int>("layout", SEQ_BATCH_HID);
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);
numHidden = params.get<int>("hidden_size", 1);
CV_Assert(!reverse || !bidirectional);
// read activations
DictValue activations = params.get<DictValue>("activations", DictValue(String()));
if (activations.size() == 1) // if activations wasn't specified use default
{
f_activation = sigmoid;
g_activation = tanh;
h_activation = tanh;
isDefaultActivations = true;
} else {
CV_Assert(activations.size() == 3);
f_activation = get_activation_function(activations.getStringValue(0));
g_activation = get_activation_function(activations.getStringValue(1));
h_activation = get_activation_function(activations.getStringValue(2));
isDefaultActivations = activations.getStringValue(0) == "Sigmoid"
&& activations.getStringValue(1) == "Tanh"
&& activations.getStringValue(2) == "Tanh";
}
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 supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV
|| (backendId == DNN_BACKEND_CUDA && isDefaultActivations && !reverse && !usePeephole);
}
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() == 5) || (usePeephole && blobs.size() == 8));
CV_Assert((inputs.size() == 1 || inputs.size() == 3));
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);
if (layout == SEQ_BATCH_HID) {
_numSamples = inp0[1];
outResShape.push_back(inp0[0]);
} else {
_numSamples = inp0[0];
outResShape.push_back(inp0[1]);
}
}
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());
outResShape.back() *= (1 + static_cast<int>(bidirectional));
outputs.assign(1, outResShape);
if (produceCellOutput)
{
// the producer is ONNX, so CellState is different
if (!originalBlobs.empty())
{
int shp[] = {(1 + static_cast<int>(bidirectional)), _numSamples, numHidden};
MatShape newShape(shp, shp + sizeof(shp)/sizeof(shp[0]));
outputs.push_back(newShape);
}
else
{
outputs.push_back(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() == 5) || (usePeephole && blobs.size() == 8));
CV_Assert((input.size() == 1 || input.size() == 3));
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);
if (layout == SEQ_BATCH_HID){
numTimeStamps = inp0.size[0];
numSamples = inp0.size[1];
}else{
numTimeStamps = inp0.size[1];
numSamples = inp0.size[0];
}
}
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());
outTsShape.back() *= (1 + static_cast<int>(bidirectional));
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_16F)
{
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);
if (layout == BATCH_SEQ_HID){
//swap axis 0 and 1 input x
cv::Mat tmp;
// Since python input is 4 dimentional and C++ input 3 dimentinal
// we need to proccess each differently
if (input[0].dims == 4){
// here !!!
CV_Assert(input[0].size[3] == 1);
cv::transposeND(input[0], {1, 0, 2, 3}, tmp); //back to seq_len, batch_size, hidden_size format
}else{
cv::transposeND(input[0], {1, 0, 2}, tmp); //back to seq_len, batch_size, hidden_size format
}
input[0] = tmp;
}
Mat cOut = produceCellOutput ? output[0].clone() : Mat();
const bool needYcTransform = !originalBlobs.empty(); // if the producer is onnx
const int numDirs = 1 + static_cast<int>(bidirectional);
for (int i = 0; i < numDirs; ++i)
{
Mat Wh = blobs[0];
Mat Wx = blobs[1];
Mat bias = blobs[2];
Mat h_0, c_0;
// Handle h_0 and c_0 based on input size
h_0 = (input.size() >= 2) ? input[1].reshape(1, input[1].size[0] * input[1].size[1]) : blobs[3];
c_0 = (input.size() == 3) ? input[2].reshape(1, input[2].size[0] * input[2].size[1]) : blobs[4];
// Perform checks if input size is 2 or 3
if (input.size() >= 2) {
CV_CheckEQ(h_0.cols, Wh.cols, "");
CV_CheckEQ(h_0.cols, c_0.cols, "");
CV_CheckEQ(h_0.rows, c_0.rows, "");
}
Mat pI, pF, pO;
Wh = Wh.rowRange(i * Wh.rows / numDirs, (i + 1) * Wh.rows / numDirs);
Wx = Wx.rowRange(i * Wx.rows / numDirs, (i + 1) * Wx.rows / numDirs);
bias = bias.colRange(i * bias.cols / numDirs, (i + 1) * bias.cols / numDirs);
h_0 = h_0.rowRange(i * h_0.rows / numDirs, (i + 1) * h_0.rows / numDirs);
c_0 = c_0.rowRange(i * c_0.rows / numDirs, (i + 1) * c_0.rows / numDirs);
if (usePeephole)
{
pI = blobs[5];
pF = blobs[6];
pO = blobs[7];
pI = pI.rowRange(i * pI.rows / numDirs, (i + 1) * pI.rows / numDirs);
pI = pI.colRange(i * pI.cols / numDirs, (i + 1) * pI.cols / numDirs);
pF = pF.rowRange(i * pF.rows / numDirs, (i + 1) * pF.rows / numDirs);
pF = pF.colRange(i * pF.cols / numDirs, (i + 1) * pF.cols / numDirs);
pO = pO.rowRange(i * pO.rows / numDirs, (i + 1) * pO.rows / numDirs);
pO = pO.colRange(i * pO.cols / numDirs, (i + 1) * pO.cols / numDirs);
}
int numOut = Wh.size[1];
Mat hInternal = internals[0], cInternal = internals[1],
dummyOnes = internals[2], gates = internals[3];
h_0.copyTo(hInternal);
c_0.copyTo(cInternal);
dummyOnes.setTo(1.);
int numSamplesTotal = numTimeStamps*numSamples;
Mat xTs = input[0].reshape(1, numSamplesTotal);
Mat hOutTs = output[0].reshape(1, numSamplesTotal);
hOutTs = hOutTs.colRange(i * hOutTs.cols / numDirs, (i + 1) * hOutTs.cols / numDirs);
Mat cOutTs;
if (produceCellOutput)
{
cOutTs = cOut.reshape(1, numSamplesTotal);
cOutTs = cOutTs.colRange(i * cOutTs.cols / numDirs, (i + 1) * cOutTs.cols / numDirs);
}
#if CV_TRY_AVX2 || CV_TRY_AVX
bool canUseAvx = gates.isContinuous() && bias.isContinuous()
&& Wx.depth() == CV_32F && gates.depth() == CV_32F
&& bias.depth() == CV_32F && Wx.cols >= 8;
bool canUseAvx_hInternal = hInternal.isContinuous() && gates.isContinuous() && bias.isContinuous()
&& Wh.depth() == CV_32F && hInternal.depth() == CV_32F && gates.depth() == CV_32F
&& Wh.cols >= 8;
#endif
int tsStart, tsEnd, tsInc;
if (reverse || i == 1) {
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);
#if CV_TRY_AVX2
if (useAVX2 && canUseAvx && xCurr.isContinuous())
{
for (int n = 0; n < xCurr.rows; n++) {
opt_AVX2::fastGEMM1T(
xCurr.ptr<float>(n),
Wx.ptr<float>(),
Wx.step1(),
bias.ptr<float>(),
gates.ptr<float>(n),
Wx.rows,
Wx.cols
);
}
}
else
#endif
#if CV_TRY_AVX
if (useAVX && canUseAvx && xCurr.isContinuous())
{
for (int n = 0; n < xCurr.rows; n++) {
opt_AVX::fastGEMM1T(
xCurr.ptr<float>(n),
Wx.ptr<float>(),
Wx.step1(),
bias.ptr<float>(),
gates.ptr<float>(n),
Wx.rows,
Wx.cols
);
}
}
else
#endif
{
gemm(xCurr, Wx, 1, gates, 0, gates, GEMM_2_T); // Wx * x_t
gemm(dummyOnes, bias, 1, gates, 1, gates); //+b
}
#if CV_TRY_AVX2
if (useAVX2 && canUseAvx_hInternal)
{
for (int n = 0; n < hInternal.rows; n++) {
opt_AVX2::fastGEMM1T(
hInternal.ptr<float>(n),
Wh.ptr<float>(),
Wh.step1(),
gates.ptr<float>(n),
gates.ptr<float>(n),
Wh.rows,
Wh.cols
);
}
}
else
#endif
#if CV_TRY_AVX
if (useAVX && canUseAvx_hInternal)
{
for (int n = 0; n < hInternal.rows; n++) {
opt_AVX::fastGEMM1T(
hInternal.ptr<float>(n),
Wh.ptr<float>(),
Wh.step1(),
gates.ptr<float>(n),
gates.ptr<float>(n),
Wh.rows,
Wh.cols
);
}
}
else
#endif
{
gemm(hInternal, Wh, 1, gates, 1, gates, GEMM_2_T); //+Wh * h_{t-1}
}
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, pI, 1, gateI, 1, gateI);
gemm(cInternal, pF, 1, gateF, 1, gateF);
f_activation(gatesIF, gatesIF);
}
else
{
Mat gatesIFO = gates.colRange(0, 3*numOut);
f_activation(gatesIFO, gatesIFO);
}
g_activation(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, pO, 1, gateO, 1, gateO);
f_activation(gateO, gateO);
}
//compute h_t
h_activation(cInternal, hInternal);
multiply(gateO, hInternal, hInternal);
//save results in output blobs
hInternal.copyTo(hOutTs.rowRange(curRowRange));
if (produceCellOutput)
cInternal.copyTo(cOutTs.rowRange(curRowRange));
}
}
// transpose to match batch first output
if (layout == BATCH_SEQ_HID){
cv::Mat tmp;
cv::transposeND(output[0], {1, 0, 2}, tmp);
output[0] = tmp;
}
if (needYcTransform && produceCellOutput)
{
fixCellState(cOut, numDirs);
}
if (produceCellOutput)
{
cOut.copyTo(output[1]);
}
}
void fixCellState(Mat& cOut, int numDirs)
{
// seq, batch, dirs, hidden
int shp[] = {0, numSamples, numDirs, numHidden};
cOut = cOut.reshape(1, sizeof(shp)/sizeof(shp[0]), shp);
// permute to {0, 2, 1, 3};
cv::Mat newCellState;
// transpose to match batch first output
if (layout == BATCH_SEQ_HID){
cv::transposeND(cOut, {2, 0, 1, 3}, newCellState);
}
else{
cv::transposeND(cOut, {0, 2, 1, 3}, newCellState);
}
cOut = newCellState;
if (numDirs == 1)
{
// Slice: Yh = Y[-1, :, :, :]
Range ranges[] = {cv::Range(cOut.size[0] - 1, cOut.size[0]), cv::Range::all(), cv::Range::all(), cv::Range::all()};
cOut = cOut(ranges);
// Reshape: 1x1xBxH -> 1xBxH
int shp[] = {1, numSamples, numHidden};
cOut = cOut.reshape(1, sizeof(shp)/sizeof(shp[0]), shp);
}
else
{
// Slice: SxDxBxH -> last sequence, first direction
Range ranges1[] = {cv::Range(cOut.size[0] - 1, cOut.size[0]), cv::Range(0, 1), cv::Range::all(), cv::Range::all()};
Mat part1 = cOut(ranges1);
// Slice: SxDxBxH -> first sequence, last direction
Range ranges2[] = {cv::Range(0, 1), cv::Range(cOut.size[1] - 1, cOut.size[1]), cv::Range::all(), cv::Range::all()};
Mat part2 = cOut(ranges2);
int shp[] = {1, part1.size[2] * part1.size[3]};
part1 = part1.reshape(1, sizeof(shp)/sizeof(shp[0]), shp);
part2 = part2.reshape(1, sizeof(shp)/sizeof(shp[0]), shp);
vconcat(part1, part2, cOut);
// Reshape: 1x2xBxH -> 2xBxH
int finalShape[] = {2, numSamples, numHidden};
cOut = cOut.reshape(1, sizeof(finalShape)/sizeof(finalShape[0]), finalShape);
}
}
#ifdef HAVE_CUDA
Ptr<BackendNode> initCUDA(void *context_, const std::vector<Ptr<BackendWrapper>> &inputs,
const std::vector<Ptr<BackendWrapper>> &outputs) override
{
const int numDirs = 1 + static_cast<int>(bidirectional);
auto toIFCO = [numDirs] (Mat& in) {
int first = in.size[0];
int rest = in.total() / first / 4;
// every weight blob contains weights for Input, Output, Forget and Cell gates
Mat m = in.reshape(1, {first, 4, rest});
Mat outputGate = m.col(1);
Mat forgetGate = m.col(2);
Mat cellGate = m.col(3);
// IOFC -> IFOC
std::swap_ranges(outputGate.begin<float>(), outputGate.end<float>(), forgetGate.begin<float>());
std::swap(outputGate, forgetGate);
// IFOC -> IFCO
std::swap_ranges(outputGate.begin<float>(), outputGate.end<float>(), cellGate.begin<float>());
in = in.reshape(1, numDirs);
};
Mat& b = originalBlobs[2];
// B is a concatenation of biases for Wh and Wx
b = b.reshape(1, originalBlobs[2].size[0]*2);
for (auto& m : originalBlobs)
{
toIFCO(m);
}
b = b.reshape(1, static_cast<int>(b.total()));
Mat ordered_weights;
// Wx_f, Wh_f, [Wx_b, Wh_b,] b
for (int i = 0; i < numDirs; ++i)
{
for (size_t j = 0; j < 2; ++j) // Wx, Wh
{
Mat oneDirection = originalBlobs[j].row(i);
ordered_weights.push_back(oneDirection.reshape(1, static_cast<int>(oneDirection.total())));
}
}
ordered_weights.push_back(b);
// Pass hidden states as is
Mat h0 = blobs[3];
Mat c0 = blobs[4];
CV_Assert(!inputs.empty());
auto input_wrapper = inputs[0].dynamicCast<CUDABackendWrapper>();
auto input_shape = input_wrapper->getShape();
RNNConfiguration config
{
input_shape[0], // seqLength;
1, // numLayers;
numHidden, // hiddenSize;
input_shape[2], // inputSize;
input_shape[1], // miniBatch;
bidirectional
};
auto *context = reinterpret_cast<cuda4dnn::csl::CSLContext *>(context_);
return make_cuda_node<cuda4dnn::LSTMOp>(preferableTarget, std::move(context->stream),
std::move(context->cudnn_handle),
ordered_weights, h0, c0,
config);
}
#endif
};
Ptr<LSTMLayer> LSTMLayer::create(const LayerParams& params)
{
return Ptr<LSTMLayer>(new LSTMLayerImpl(params));
}
int LSTMLayer::inputNameToIndex(String inputName)
{
if (toLowerCase(inputName) == "x")
return 0;
return -1;
}
int LSTMLayer::outputNameToIndex(const String& outputName)
{
if (toLowerCase(outputName) == "h")
return 0;
else if (toLowerCase(outputName) == "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_16F)
{
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));
}
class GRULayerImpl CV_FINAL : public GRULayer
{
int numTimeStamps, numSamples;
bool allocated;
MatShape outTailShape; //shape of single output sample
MatShape outTsShape; //shape of N output samples
bool bidirectional; // If true, produces both forward and reversed directions along time axis
public:
GRULayerImpl(const LayerParams& params) : numTimeStamps(0), numSamples(0)
{
setParamsFrom(params);
bidirectional = params.get<bool>("bidirectional", false);
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];
const Mat& hInternal = blobs[3];
CV_CheckEQ(Wh.dims, 2, "");
CV_CheckEQ(Wx.dims, 2, "");
CV_CheckEQ(Wh.rows, Wx.rows, "");
CV_CheckEQ(Wh.rows, (1 + static_cast<int>(bidirectional)) * 3 * Wh.cols, "");
CV_CheckEQ(Wh.rows * 2, (int)bias.total(), "");
CV_CheckEQ(hInternal.cols, Wh.cols, "");
CV_CheckTypeEQ(Wh.type(), Wx.type(), "");
CV_CheckTypeEQ(Wx.type(), bias.type(), "");
}
allocated = false;
outTailShape.clear();
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
const int requiredOutputs,
std::vector<MatShape> &outputs,
std::vector<MatShape> &internals) const CV_OVERRIDE
{