From d2fcb9faf4301eae034b4d53ab874bfb211781c7 Mon Sep 17 00:00:00 2001
From: Yue Li <yue.li@bytedance.com>
Date: Fri, 3 Feb 2023 16:18:37 -0800
Subject: [PATCH] integrate JVET-AC0177 (EE1-1.7: Deep In-Loop Filter with
Additional Input Information)
---
source/App/DecoderApp/DecApp.cpp | 3 +
source/App/DecoderApp/DecAppCfg.cpp | 3 +
source/App/DecoderApp/DecAppCfg.h | 3 +
source/App/EncoderApp/EncApp.cpp | 6 +
source/App/EncoderApp/EncAppCfg.cpp | 4 +
source/App/EncoderApp/EncAppCfg.h | 6 +
source/Lib/CommonLib/CommonDef.h | 3 +
source/Lib/CommonLib/NNFilterSet1.cpp | 137 ++++++++++-
source/Lib/CommonLib/NNFilterSet1.h | 7 +
source/Lib/CommonLib/NNInference.h | 222 ++++++++++++++++++
source/Lib/CommonLib/Slice.h | 7 +
source/Lib/CommonLib/TypeDef.h | 11 +-
source/Lib/DecoderLib/DecLib.cpp | 4 +
source/Lib/DecoderLib/DecLib.h | 15 +-
source/Lib/DecoderLib/VLCReader.cpp | 3 +
source/Lib/EncoderLib/EncCfg.h | 14 ++
source/Lib/EncoderLib/EncGOP.cpp | 4 +
source/Lib/EncoderLib/EncLib.cpp | 3 +
source/Lib/EncoderLib/VLCWriter.cpp | 3 +
.../AdditionalLumaInter/Conversion/convert.py | 27 +++
.../AdditionalLumaInter/Conversion/net.py | 99 ++++++++
.../AdditionalLumaInter/Training/dataset.py | 117 +++++++++
.../AdditionalLumaInter/Training/main.py | 142 +++++++++++
.../AdditionalLumaInter/Training/net.py | 99 ++++++++
.../AdditionalLumaInter/Training/train.sh | 3 +
.../AdditionalLumaInter/instruction.pdf | Bin 0 -> 53251 bytes
26 files changed, 928 insertions(+), 17 deletions(-)
create mode 100755 training/training_scripts/Nn_Filtering_Set_1/Scripts/AdditionalLumaInter/Conversion/convert.py
create mode 100644 training/training_scripts/Nn_Filtering_Set_1/Scripts/AdditionalLumaInter/Conversion/net.py
create mode 100644 training/training_scripts/Nn_Filtering_Set_1/Scripts/AdditionalLumaInter/Training/dataset.py
create mode 100755 training/training_scripts/Nn_Filtering_Set_1/Scripts/AdditionalLumaInter/Training/main.py
create mode 100644 training/training_scripts/Nn_Filtering_Set_1/Scripts/AdditionalLumaInter/Training/net.py
create mode 100755 training/training_scripts/Nn_Filtering_Set_1/Scripts/AdditionalLumaInter/Training/train.sh
create mode 100644 training/training_scripts/Nn_Filtering_Set_1/Scripts/AdditionalLumaInter/instruction.pdf
diff --git a/source/App/DecoderApp/DecApp.cpp b/source/App/DecoderApp/DecApp.cpp
index d3296a34f7..d1031389e4 100644
--- a/source/App/DecoderApp/DecApp.cpp
+++ b/source/App/DecoderApp/DecApp.cpp
@@ -620,6 +620,9 @@ void DecApp::xCreateDecLib()
m_cDecLib.setNnlfSet1InterChromaModelName (m_nnlfSet1InterChromaModelName);
m_cDecLib.setNnlfSet1IntraLumaModelName (m_nnlfSet1IntraLumaModelName);
m_cDecLib.setNnlfSet1IntraChromaModelName (m_nnlfSet1IntraChromaModelName);
+#if JVET_AC0177_MULTI_FRAME
+ m_cDecLib.setNnlfSet1AlternativeInterLumaModelName (m_nnlfSet1AlternativeInterLumaModelName);
+#endif
#endif
if (!m_outputDecodedSEIMessagesFilename.empty())
diff --git a/source/App/DecoderApp/DecAppCfg.cpp b/source/App/DecoderApp/DecAppCfg.cpp
index 027d4a3e58..8202fbc76c 100644
--- a/source/App/DecoderApp/DecAppCfg.cpp
+++ b/source/App/DecoderApp/DecAppCfg.cpp
@@ -90,6 +90,9 @@ bool DecAppCfg::parseCfg( int argc, char* argv[] )
( "NnlfSet1InterChromaModel", m_nnlfSet1InterChromaModelName, string("models/NnlfSet1_ChromaCNNFilter_InterSlice_int16.sadl"), "NnlfSet1 inter chroma model name")
( "NnlfSet1IntraLumaModel", m_nnlfSet1IntraLumaModelName, string("models/NnlfSet1_LumaCNNFilter_IntraSlice_int16.sadl"), "NnlfSet1 intra luma model name")
( "NnlfSet1IntraChromaModel", m_nnlfSet1IntraChromaModelName, string("models/NnlfSet1_ChromaCNNFilter_IntraSlice_int16.sadl"), "NnlfSet1 intra chroma model name")
+#if JVET_AC0177_MULTI_FRAME
+ ( "NnlfSet1AlternativeInterLumaModel", m_nnlfSet1AlternativeInterLumaModelName, string("models/NnlfSet1_LumaCNNFilter_InterSlice_MultiframePrior_Tid345_int16.sadl"), "NnlfSet1 alternative inter luma model name")
+#endif
#endif
("OplFile,-opl", m_oplFilename , string(""), "opl-file name without extension for conformance testing\n")
diff --git a/source/App/DecoderApp/DecAppCfg.h b/source/App/DecoderApp/DecAppCfg.h
index 7dbb765f40..e8a5dad494 100644
--- a/source/App/DecoderApp/DecAppCfg.h
+++ b/source/App/DecoderApp/DecAppCfg.h
@@ -72,6 +72,9 @@ protected:
std::string m_nnlfSet1InterChromaModelName; ///<inter chroma nnlf set1 model
std::string m_nnlfSet1IntraLumaModelName; ///<intra luma nnlf set1 model
std::string m_nnlfSet1IntraChromaModelName; ///<inra chroma nnlf set1 model
+#if JVET_AC0177_MULTI_FRAME
+ std::string m_nnlfSet1AlternativeInterLumaModelName; ///<alternative inter luma nnlf set1 model
+#endif
#endif
int m_iSkipFrame; ///< counter for frames prior to the random access point to skip
diff --git a/source/App/EncoderApp/EncApp.cpp b/source/App/EncoderApp/EncApp.cpp
index 03f3e3cca1..d39de5428f 100644
--- a/source/App/EncoderApp/EncApp.cpp
+++ b/source/App/EncoderApp/EncApp.cpp
@@ -248,6 +248,9 @@ void EncApp::xInitLibCfg()
m_cEncLib.setNnlfSet1InterChromaModelName (m_nnlfSet1InterChromaModelName);
m_cEncLib.setNnlfSet1IntraLumaModelName (m_nnlfSet1IntraLumaModelName);
m_cEncLib.setNnlfSet1IntraChromaModelName (m_nnlfSet1IntraChromaModelName);
+#if JVET_AC0177_MULTI_FRAME
+ m_cEncLib.setNnlfSet1AlternativeInterLumaModelName (m_nnlfSet1AlternativeInterLumaModelName);
+#endif
#endif
#if JVET_AB0068_RD
m_cEncLib.setUseEncNnlfOpt (m_encNnlfOpt);
@@ -1087,6 +1090,9 @@ void EncApp::xInitLibCfg()
m_cEncLib.setNnlfSet1InferSizeBase (m_nnlfSet1InferSizeBase);
m_cEncLib.setNnlfSet1InferSizeExtension (m_nnlfSet1InferSizeExtension);
m_cEncLib.setNnlfSet1MaxNumParams (m_nnlfSet1MaxNumParams);
+#if JVET_AC0177_MULTI_FRAME
+ m_cEncLib.setNnlfSet1UseMultiframe (m_nnlfSet1Multiframe);
+#endif
#endif
m_cEncLib.setLmcs ( m_lmcsEnabled );
m_cEncLib.setReshapeSignalType ( m_reshapeSignalType );
diff --git a/source/App/EncoderApp/EncAppCfg.cpp b/source/App/EncoderApp/EncAppCfg.cpp
index 8ba5c3cf29..629b53b137 100644
--- a/source/App/EncoderApp/EncAppCfg.cpp
+++ b/source/App/EncoderApp/EncAppCfg.cpp
@@ -1443,6 +1443,10 @@ bool EncAppCfg::parseCfg( int argc, char* argv[] )
( "NnlfSet1InterChromaModel", m_nnlfSet1InterChromaModelName, string("models/NnlfSet1_ChromaCNNFilter_InterSlice_int16.sadl"), "NnlfSet1 inter chroma model name")
( "NnlfSet1IntraLumaModel", m_nnlfSet1IntraLumaModelName, string("models/NnlfSet1_LumaCNNFilter_IntraSlice_int16.sadl"), "NnlfSet1 intra luma model name")
( "NnlfSet1IntraChromaModel", m_nnlfSet1IntraChromaModelName, string("models/NnlfSet1_ChromaCNNFilter_IntraSlice_int16.sadl"), "NnlfSet1 intra chroma model name")
+#if JVET_AC0177_MULTI_FRAME
+ ( "NnlfSet1AlternativeInterLumaModel", m_nnlfSet1AlternativeInterLumaModelName, string("models/NnlfSet1_LumaCNNFilter_InterSlice_MultiframePrior_Tid345_int16.sadl"), "NnlfSet1 alternative inter luma model name")
+ ( "NnlfSet1Multiframe", m_nnlfSet1Multiframe, false, "Input multiple frames in NN-based loop filter set 1" )
+#endif
#endif
#if JVET_AB0068_RD
( "EncNnlfOpt", m_encNnlfOpt, false, "Encoder optimization with NN-based loop filter")
diff --git a/source/App/EncoderApp/EncAppCfg.h b/source/App/EncoderApp/EncAppCfg.h
index 5d59c2ab36..ba685eced6 100644
--- a/source/App/EncoderApp/EncAppCfg.h
+++ b/source/App/EncoderApp/EncAppCfg.h
@@ -94,6 +94,9 @@ protected:
std::string m_nnlfSet1InterChromaModelName; ///<inter chroma nnlf set1 model
std::string m_nnlfSet1IntraLumaModelName; ///<intra luma nnlf set1 model
std::string m_nnlfSet1IntraChromaModelName; ///<inra chroma nnlf set1 model
+#if JVET_AC0177_MULTI_FRAME
+ std::string m_nnlfSet1AlternativeInterLumaModelName; ///<alternative inter luma nnlf set1 model
+#endif
#endif
#if JVET_AB0068_RD
std::string m_rdoCnnlfInterLumaModelName; ///<inter luma cnnlf model
@@ -733,6 +736,9 @@ protected:
unsigned m_nnlfSet1InferSizeBase;
unsigned m_nnlfSet1InferSizeExtension;
unsigned m_nnlfSet1MaxNumParams;
+#if JVET_AC0177_MULTI_FRAME
+ bool m_nnlfSet1Multiframe;
+#endif
#endif
#if JVET_AB0068_RD
diff --git a/source/Lib/CommonLib/CommonDef.h b/source/Lib/CommonLib/CommonDef.h
index a8c42e17eb..c037e9e3d5 100644
--- a/source/Lib/CommonLib/CommonDef.h
+++ b/source/Lib/CommonLib/CommonDef.h
@@ -198,6 +198,9 @@ static const int NNQPOFFSET[QP_OFFSET_NUM] = { -5, 5 };
#if NN_FILTERING_SET_1
static const int NN_INPUT_PRECISION= 13;
static const int NN_OUTPUT_PRECISION= 13;
+#if JVET_AC0177_MULTI_FRAME
+static const int MINIMUM_TID_ENABLING_TEMPORAL_INPUTS = 3; // JVET-AC0177: minimum temporal layer id enabling temporal inputs
+#endif
#endif
diff --git a/source/Lib/CommonLib/NNFilterSet1.cpp b/source/Lib/CommonLib/NNFilterSet1.cpp
index 4cac99ccf4..e2606d203c 100644
--- a/source/Lib/CommonLib/NNFilterSet1.cpp
+++ b/source/Lib/CommonLib/NNFilterSet1.cpp
@@ -80,12 +80,19 @@ void NNFilterSet1::create( const int picWidth, const int picHeight, const Chroma
}
#endif
}
+#if JVET_AC0177_MULTI_FRAME
+void NNFilterSet1::init(std::string interLuma, std::string interChroma, std::string intraLuma, std::string intraChroma, std::string alternativeInterLuma)
+#else
void NNFilterSet1::init(std::string interLuma, std::string interChroma, std::string intraLuma, std::string intraChroma)
+#endif
{
m_interLuma = interLuma;
m_interChroma = interChroma;
m_intraLuma = intraLuma;
m_intraChroma = intraChroma;
+#if JVET_AC0177_MULTI_FRAME
+ m_alternativeInterLuma = alternativeInterLuma;
+#endif
}
void NNFilterSet1::destroy()
{
@@ -108,6 +115,9 @@ struct ModelData {
vector<sadl::Tensor<T>> inputs;
int hor,ver;
bool luma,inter;
+#if JVET_AC0177_MULTI_FRAME
+ bool alternative;
+#endif
};
template<typename T>
@@ -120,12 +130,20 @@ static std::vector<ModelData<T>> initSpace() {
static std::vector<ModelData<TypeSadl>> models=initSpace<TypeSadl>();
template<typename T>
+#if JVET_AC0177_MULTI_FRAME
+static ModelData<T> &getModel(int ver, int hor, bool luma, bool inter, const string modelName, bool alternative=false)
+#else
static ModelData<T> &getModel(int ver, int hor, bool luma, bool inter, const string modelName)
+#endif
{
ModelData<T> *ptr = nullptr;
for(auto &m: models)
{
+#if JVET_AC0177_MULTI_FRAME
+ if (m.luma == luma && m.inter == inter && m.alternative == alternative)
+#else
if (m.luma == luma && m.inter == inter)
+#endif
{
ptr = &m;
break;
@@ -151,6 +169,9 @@ static ModelData<T> &getModel(int ver, int hor, bool luma, bool inter, const str
m.inter = inter;
m.ver = 0;
m.hor = 0;
+#if JVET_AC0177_MULTI_FRAME
+ m.alternative = alternative;
+#endif
}
ModelData<T> &m = *ptr;
if (m.ver != ver || m.hor != hor)
@@ -279,6 +300,60 @@ void extractOutputsLuma (Picture* pic, sadl::Model<T> &m, PelStorage& tempBuf, P
}
}
+#if JVET_AC0177_FLIP_INPUT
+template<typename T>
+void extractOutputsLuma (Picture* pic, sadl::Model<T> &m, PelStorage& tempBuf, PelStorage& tempScaledBuf, UnitArea inferArea, int extLeft, int extRight, int extTop, int extBottom, bool inter, bool flip)
+{
+#if NN_FIXED_POINT_IMPLEMENTATION
+ int log2InputScale = 10;
+ int log2OutputScale = 10;
+ int shiftInput = NN_OUTPUT_PRECISION - log2InputScale;
+ int shiftOutput = NN_OUTPUT_PRECISION - log2OutputScale;
+ int offset = (1 << shiftOutput) / 2;
+#else
+ double inputScale = 1024;
+ double outputScale = 1024;
+#endif
+ auto output = m.result(0);
+ PelBuf bufDst = tempBuf.getBuf(inferArea).get(COMPONENT_Y);
+
+#if SCALE_NN_RESIDUE
+ PelBuf bufScaledDst = tempScaledBuf.getBuf(inferArea).get(COMPONENT_Y);
+#endif
+
+ int hor = inferArea.lwidth();
+ int ver = inferArea.lheight();
+
+ PelBuf bufRec = pic->getRecBeforeDbfBuf(inferArea).get(COMPONENT_Y);
+
+ for (int c = 0; c < 4; c++) // output includes 4 sub images
+ {
+ for (int y = 0; y < ver >> 1; y++)
+ {
+ for (int x = 0; x < hor >> 1; x++)
+ {
+ int yy = (y << 1) + c / 2;
+ int xx = (x << 1) + c % 2;
+ NNInference::geometricTransform(ver, hor, yy, xx, flip);
+ if (xx < extLeft || yy < extTop || xx >= hor - extRight || yy >= ver - extBottom)
+ {
+ continue;
+ }
+#if NN_FIXED_POINT_IMPLEMENTATION
+ int out = ( output(0, y, x, c) + (bufRec.at(xx, yy) << shiftInput) + offset) >> shiftOutput;
+#else
+ int out = ( output(0, y, x, c) + bufRec.at(xx, yy) / inputScale ) * outputScale + 0.5;
+#endif
+ bufDst.at(xx, yy) = Pel(Clip3<int>( 0, 1023, out));
+
+ #if SCALE_NN_RESIDUE
+ bufScaledDst.at(xx, yy) = Pel(Clip3<int>(0, 1023 << NN_RESIDUE_ADDITIONAL_SHIFT, out * (1 << NN_RESIDUE_ADDITIONAL_SHIFT) ) );
+ #endif
+ }
+ }
+ }
+}
+#endif
template<typename T>
void extractOutputsChroma (Picture* pic, sadl::Model<T> &m, PelStorage& tempBuf, PelStorage& tempScaledBuf, UnitArea inferArea, int extLeft, int extRight, int extTop, int extBottom, bool inter)
{
@@ -353,12 +428,17 @@ void NNFilterSet1::cnnFilterLumaBlock(Picture* pic, UnitArea inferArea, int extL
if (border_to_skip>0) sadl::Tensor<float>::skip_border = true;
// get model
+#if JVET_AC0177_MULTI_FRAME
+ bool alternative = pic->slices[0]->getSPS()->getNnlfSet1MultiframeEnabledFlag() && inter && pic->slices[0]->getTLayer() >= MINIMUM_TID_ENABLING_TEMPORAL_INPUTS ? true : false;
+ // get model
+ ModelData<T> &m = getModel<T>(inferArea.lheight(), inferArea.lwidth(), true, inter, inter ? (alternative ? m_alternativeInterLuma : m_interLuma) : m_intraLuma, alternative);
+#else
ModelData<T> &m = getModel<T>(inferArea.lheight(), inferArea.lwidth(), true, inter, inter ? m_interLuma : m_intraLuma);
+#endif
sadl::Model<T> &model = m.model;
// get inputs
vector<sadl::Tensor<T>> &inputs = m.inputs;
-
int seqQp = pic->slices[0]->getPPS()->getPicInitQPMinus26() + 26;
int sliceQp = pic->slices[0]->getSliceQp();
int delta = inter ? paramIdx * 5 : paramIdx * 2;
@@ -366,20 +446,47 @@ void NNFilterSet1::cnnFilterLumaBlock(Picture* pic, UnitArea inferArea, int extL
{
delta = 5 - delta;
}
+#if JVET_AC0177_FLIP_INPUT
+ bool flip = pic->slices[0]->getSPS()->getNnlfSet1MultiframeEnabledFlag() && (paramIdx == 2) ? true : false;
+ if (flip)
+ {
+ delta = 0;
+ }
+#endif
int qp = inter ? seqQp - delta : sliceQp - delta;
std::vector<InputData> listInputData;
#if JVET_AC0089_COMBINE_INTRA_INTER
- InputData inputRec = { NN_INPUT_REC, 0, inputScale, NN_INPUT_PRECISION - log2InputScale, true, false };
- InputData inputPred = { NN_INPUT_PRED, 1, inputScale, NN_INPUT_PRECISION - log2InputScale, true, false };
- InputData inputBs = { NN_INPUT_BS, 2, inputScale, NN_INPUT_PRECISION - log2InputScale, true, false };
- InputData inputIpb = { NN_INPUT_IPB, 3, 1, NN_INPUT_PRECISION, true, false };
- InputData inputQp = { NN_INPUT_LOCAL_QP, 4, qpScale, NN_INPUT_PRECISION - log2QpScale, true, false };
- listInputData.push_back(inputRec);
- listInputData.push_back(inputPred);
- listInputData.push_back(inputBs);
- listInputData.push_back(inputIpb);
- listInputData.push_back(inputQp);
+#if JVET_AC0177_MULTI_FRAME
+ if (alternative)
+ {
+ InputData inputRec = {NN_INPUT_REC, 0, inputScale, NN_INPUT_PRECISION - log2InputScale, true, false};
+ InputData inputPred = {NN_INPUT_PRED, 1, inputScale, NN_INPUT_PRECISION - log2InputScale, true, false};
+ InputData inputRefList0 = {NN_INPUT_REF_LIST_0, 2, inputScale, NN_INPUT_PRECISION - log2InputScale, true, false};
+ InputData inputRefList1 = {NN_INPUT_REF_LIST_1, 3, inputScale, NN_INPUT_PRECISION - log2InputScale, true, false};
+ InputData inputQp = {NN_INPUT_LOCAL_QP, 4, qpScale, NN_INPUT_PRECISION - log2QpScale, true, false};
+ listInputData.push_back(inputRec);
+ listInputData.push_back(inputPred);
+ listInputData.push_back(inputRefList0);
+ listInputData.push_back(inputRefList1);
+ listInputData.push_back(inputQp);
+ }
+ else
+ {
+#endif
+ InputData inputRec = { NN_INPUT_REC, 0, inputScale, NN_INPUT_PRECISION - log2InputScale, true, false };
+ InputData inputPred = { NN_INPUT_PRED, 1, inputScale, NN_INPUT_PRECISION - log2InputScale, true, false };
+ InputData inputBs = { NN_INPUT_BS, 2, inputScale, NN_INPUT_PRECISION - log2InputScale, true, false };
+ InputData inputIpb = { NN_INPUT_IPB, 3, 1, NN_INPUT_PRECISION, true, false };
+ InputData inputQp = { NN_INPUT_LOCAL_QP, 4, qpScale, NN_INPUT_PRECISION - log2QpScale, true, false };
+ listInputData.push_back(inputRec);
+ listInputData.push_back(inputPred);
+ listInputData.push_back(inputBs);
+ listInputData.push_back(inputIpb);
+ listInputData.push_back(inputQp);
+#if JVET_AC0177_MULTI_FRAME
+ }
+#endif
#else
if (inter)
{
@@ -414,11 +521,19 @@ void NNFilterSet1::cnnFilterLumaBlock(Picture* pic, UnitArea inferArea, int extL
listInputData.push_back(inputQp);
}
#endif
+#if JVET_AC0177_FLIP_INPUT
+ NNInference::prepareInputs<T>(pic, inferArea, inputs, -1, qp, -1, listInputData, flip);
+#else
NNInference::prepareInputs<T>(pic, inferArea, inputs, -1, qp, -1, listInputData);
+#endif
NNInference::infer<T>(model, inputs);
// get outputs
+#if JVET_AC0177_FLIP_INPUT
+ extractOutputsLuma(pic, model, m_tempBuf[paramIdx], m_tempScaledBuf[paramIdx], inferArea, extLeft, extRight, extTop, extBottom, inter, flip);
+#else
extractOutputsLuma(pic, model, m_tempBuf[paramIdx], m_tempScaledBuf[paramIdx], inferArea, extLeft, extRight, extTop, extBottom, inter);
+#endif
}
diff --git a/source/Lib/CommonLib/NNFilterSet1.h b/source/Lib/CommonLib/NNFilterSet1.h
index b97214a683..6a6dfa4e63 100644
--- a/source/Lib/CommonLib/NNFilterSet1.h
+++ b/source/Lib/CommonLib/NNFilterSet1.h
@@ -53,6 +53,9 @@ public:
std::vector<PelStorage> m_tempBuf;
std::string m_interLuma, m_interChroma, m_intraLuma, m_intraChroma;
+#if JVET_AC0177_MULTI_FRAME
+ std::string m_alternativeInterLuma;
+#endif
#if SCALE_NN_RESIDUE
std::vector<PelStorage> m_tempScaledBuf;
#endif
@@ -63,7 +66,11 @@ public:
void scaleResidualBlock(Picture *pic, UnitArea inferArea, int paramIdx, ComponentID compID);
#endif
void create(const int picWidth, const int picHeight, const ChromaFormat format, const int nnlfSet1NumParams);
+#if JVET_AC0177_MULTI_FRAME
+ void init(std::string interLuma, std::string interChroma, std::string intraLuma, std::string intraChroma, std::string alternativeInterLuma);
+#else
void init(std::string interLuma, std::string interChroma, std::string intraLuma, std::string intraChroma);
+#endif
void destroy();
};
diff --git a/source/Lib/CommonLib/NNInference.h b/source/Lib/CommonLib/NNInference.h
index ed40b60636..84ffa13b63 100644
--- a/source/Lib/CommonLib/NNInference.h
+++ b/source/Lib/CommonLib/NNInference.h
@@ -135,6 +135,90 @@ public:
}
#endif
}
+#if JVET_AC0177_FLIP_INPUT
+ static void geometricTransform (int ver, int hor, int& y, int& x, bool flip)
+ {
+ x = flip ? hor - 1 - x : x;
+ }
+ template<typename T>
+ static void fillInputFromBuf (Picture* pic, UnitArea inferArea, sadl::Tensor<T> &input, PelUnitBuf buf, bool luma, bool chroma, double scale, int shift, bool flip)
+ {
+ PelBuf bufY, bufCb, bufCr;
+
+ if (luma)
+ {
+ bufY = buf.get(COMPONENT_Y);
+ }
+ if (chroma)
+ {
+ bufCb = buf.get(COMPONENT_Cb);
+ bufCr = buf.get(COMPONENT_Cr);
+ }
+
+ int hor, ver;
+ if (luma)
+ {
+ hor = inferArea.lwidth();
+ ver = inferArea.lheight();
+ }
+ else
+ {
+ hor = inferArea.lwidth() >> 1;
+ ver = inferArea.lheight() >> 1;
+ }
+#if NN_FIXED_POINT_IMPLEMENTATION
+ for (int y = 0; y < ver; y++)
+ {
+ for (int x = 0; x < hor; x++)
+ {
+ int yT = y;
+ int xT = x;
+ geometricTransform(ver, hor, yT, xT, flip);
+ if (luma && !chroma)
+ {
+ input(0, yT, xT, 0) = bufY.at(x, y) << shift;
+ }
+ else if (!luma && chroma)
+ {
+ input(0, yT, xT, 0) = bufCb.at(x, y) << shift;
+ input(0, yT, xT, 1) = bufCr.at(x, y) << shift;
+ }
+ else if (luma && chroma)
+ {
+ input(0, yT, xT, 0) = bufY.at(x, y) << shift;
+ input(0, yT, xT, 1) = bufCb.at(x >> 1, y >> 1) << shift;
+ input(0, yT, xT, 2) = bufCr.at(x >> 1, y >> 1) << shift;
+ }
+ }
+ }
+#else
+ for (int y = 0; y < ver; y++)
+ {
+ for (int x = 0; x < hor; x++)
+ {
+ int yT = y;
+ int xT = x;
+ geometricTransform(ver, hor, yT, xT, flip);
+ if (luma && !chroma)
+ {
+ input(0, yT, xT, 0) = bufY.at(x, y) / scale;
+ }
+ else if (!luma && chroma)
+ {
+ input(0, yT, xT, 0) = bufCb.at(x, y) / scale;
+ input(0, yT, xT, 1) = bufCr.at(x, y) / scale;
+ }
+ else if (luma && chroma)
+ {
+ input(0, yT, xT, 0) = bufY.at(x, y) / scale;
+ input(0, yT, xT, 1) = bufCb.at(x >> 1, y >> 1) / scale;
+ input(0, yT, xT, 2) = bufCr.at(x >> 1, y >> 1) / scale;
+ }
+ }
+ }
+#endif
+ }
+#endif
template<typename T>
static void fillInputFromConstant (Picture* pic, UnitArea inferArea, sadl::Tensor<T> &input, int c, bool luma, double scale, int shift)
{
@@ -242,6 +326,87 @@ public:
}
#endif
}
+#if JVET_AC0177_FLIP_INPUT
+ template<typename T>
+ static void fillInputFromBufIpb(Picture *pic, UnitArea inferArea, sadl::Tensor<T> &input, PelUnitBuf buf, bool luma,
+ bool chroma, double scale, int shift, bool flip)
+ {
+ PelBuf bufY, bufCb, bufCr;
+
+ if (luma)
+ {
+ bufY = buf.get(COMPONENT_Y);
+ }
+ if (chroma)
+ {
+ bufCb = buf.get(COMPONENT_Cb);
+ bufCr = buf.get(COMPONENT_Cr);
+ }
+
+ int hor, ver;
+ if (luma)
+ {
+ hor = inferArea.lwidth();
+ ver = inferArea.lheight();
+ }
+ else
+ {
+ hor = inferArea.lwidth() >> 1;
+ ver = inferArea.lheight() >> 1;
+ }
+#if NN_FIXED_POINT_IMPLEMENTATION
+ for (int yy = 0; yy < ver; yy++)
+ {
+ for (int xx = 0; xx < hor; xx++)
+ {
+ int yT = yy;
+ int xT = xx;
+ geometricTransform(ver, hor, yT, xT, flip);
+ if (luma && !chroma)
+ {
+ input(0, yT, xT, 0) = ((bufY.at(xx, yy) >> 1) + ((bufY.at(xx, yy) >> 1) == 0) - 1) << (shift - 1);
+ }
+ else if (!luma && chroma)
+ {
+ input(0, yT, xT, 0) = ((bufCb.at(xx, yy) >> 1) + ((bufCb.at(xx, yy) >> 1) == 0) - 1) << (shift - 1);
+ input(0, yT, xT, 1) = ((bufCr.at(xx, yy) >> 1) + ((bufCr.at(xx, yy) >> 1) == 0) - 1) << (shift - 1);
+ }
+ else if (luma && chroma)
+ {
+ input(0, yT, xT, 0) = ((bufY.at(xx, yy) >> 1) + ((bufY.at(xx, yy) >> 1) == 0) - 1) << (shift - 1);
+ input(0, yT, xT, 1) = ((bufCb.at(xx, yy) >> 1) + ((bufCb.at(xx, yy) >> 1) == 0) - 1) << (shift - 1);
+ input(0, yT, xT, 2) = ((bufCr.at(xx, yy) >> 1) + ((bufCr.at(xx, yy) >> 1) == 0) - 1) << (shift - 1);
+ }
+ }
+ }
+#else
+ for (int yy = 0; yy < ver; yy++)
+ {
+ for (int xx = 0; xx < hor; xx++)
+ {
+ int yT = yy;
+ int xT = xx;
+ geometricTransform(ver, hor, yT, xT, flip);
+ if (luma && !chroma)
+ {
+ input(0, yT, xT, 0) = ((bufY.at(xx, yy) >> 1) + ((bufY.at(xx, yy) >> 1) == 0) - 1) / 2.0;
+ }
+ else if (!luma && chroma)
+ {
+ input(0, yT, xT, 0) = ((bufCb.at(xx, yy) >> 1) + ((bufCb.at(xx, yy) >> 1) == 0) - 1) / 2.0;
+ input(0, yT, xT, 1) = ((bufCr.at(xx, yy) >> 1) + ((bufCr.at(xx, yy) >> 1) == 0) - 1) / 2.0;
+ }
+ else if (luma && chroma)
+ {
+ input(0, yT, xT, 0) = ((bufY.at(xx, yy) >> 1) + ((bufY.at(xx, yy) >> 1) == 0) - 1) / 2.0;
+ input(0, yT, xT, 1) = ((bufCb.at(xx, yy) >> 1) + ((bufCb.at(xx, yy) >> 1) == 0) - 1) / 2.0;
+ input(0, yT, xT, 2) = ((bufCr.at(xx, yy) >> 1) + ((bufCr.at(xx, yy) >> 1) == 0) - 1) / 2.0;
+ }
+ }
+ }
+#endif
+ }
+#endif
#endif
template<typename T>
@@ -272,6 +437,14 @@ public:
case NN_INPUT_SLICE_TYPE:
fillInputFromConstant<T>(pic, inferArea, inputs[inputData.index], sliceType, inputData.luma, inputData.scale, inputData.shift);
break;
+#if JVET_AC0177_MULTI_FRAME
+ case NN_INPUT_REF_LIST_0:
+ fillInputFromBuf<T>(pic, inferArea, inputs[inputData.index], pic->slices[0]->getRefPic(REF_PIC_LIST_0, 0)->getRecoBuf(inferArea), inputData.luma, inputData.chroma, inputData.scale, inputData.shift);
+ break;
+ case NN_INPUT_REF_LIST_1:
+ fillInputFromBuf<T>(pic, inferArea, inputs[inputData.index], pic->slices[0]->getRefPic(REF_PIC_LIST_1, 0)->getRecoBuf(inferArea), inputData.luma, inputData.chroma, inputData.scale, inputData.shift);
+ break;
+#endif
#if JVET_AC0089_COMBINE_INTRA_INTER
case NN_INPUT_IPB:
fillInputFromBufIpb<T>(pic, inferArea, inputs[inputData.index], pic->getBlockPredModeBuf(inferArea), inputData.luma, inputData.chroma, inputData.scale, inputData.shift);
@@ -283,6 +456,55 @@ public:
}
}
}
+#if JVET_AC0177_FLIP_INPUT
+ template<typename T>
+ static void prepareInputs (Picture* pic, UnitArea inferArea, std::vector<sadl::Tensor<T>> &inputs, int globalQp, int localQp, int sliceType, const std::vector<InputData> &listInputData, bool flip)
+ {
+ for (auto inputData : listInputData)
+ {
+ switch (inputData.nnInputType)
+ {
+ case NN_INPUT_REC:
+ fillInputFromBuf<T>(pic, inferArea, inputs[inputData.index], pic->getRecBeforeDbfBuf(inferArea), inputData.luma, inputData.chroma, inputData.scale, inputData.shift, flip);
+ break;
+ case NN_INPUT_PRED:
+ fillInputFromBuf<T>(pic, inferArea, inputs[inputData.index], pic->getPredBufCustom(inferArea), inputData.luma, inputData.chroma, inputData.scale, inputData.shift, flip);
+ break;
+ case NN_INPUT_PARTITION:
+ fillInputFromBuf<T>(pic, inferArea, inputs[inputData.index], pic->getCuAverageBuf(inferArea), inputData.luma, inputData.chroma, inputData.scale, inputData.shift, flip);
+ break;
+ case NN_INPUT_BS:
+ fillInputFromBuf<T>(pic, inferArea, inputs[inputData.index], pic->getBsMapBuf(inferArea), inputData.luma, inputData.chroma, inputData.scale, inputData.shift, flip);
+ break;
+ case NN_INPUT_GLOBAL_QP:
+ fillInputFromConstant<T>(pic, inferArea, inputs[inputData.index], globalQp, inputData.luma, inputData.scale, inputData.shift);
+ break;
+ case NN_INPUT_LOCAL_QP:
+ fillInputFromConstant<T>(pic, inferArea, inputs[inputData.index], localQp, inputData.luma, inputData.scale, inputData.shift);
+ break;
+ case NN_INPUT_SLICE_TYPE:
+ fillInputFromConstant<T>(pic, inferArea, inputs[inputData.index], sliceType, inputData.luma, inputData.scale, inputData.shift);
+ break;
+#if JVET_AC0177_MULTI_FRAME
+ case NN_INPUT_REF_LIST_0:
+ fillInputFromBuf<T>(pic, inferArea, inputs[inputData.index], pic->slices[0]->getRefPic(REF_PIC_LIST_0, 0)->getRecoBuf(inferArea), inputData.luma, inputData.chroma, inputData.scale, inputData.shift, flip);
+ break;
+ case NN_INPUT_REF_LIST_1:
+ fillInputFromBuf<T>(pic, inferArea, inputs[inputData.index], pic->slices[0]->getRefPic(REF_PIC_LIST_1, 0)->getRecoBuf(inferArea), inputData.luma, inputData.chroma, inputData.scale, inputData.shift, flip);
+ break;
+#endif
+#if JVET_AC0089_COMBINE_INTRA_INTER
+ case NN_INPUT_IPB:
+ fillInputFromBufIpb<T>(pic, inferArea, inputs[inputData.index], pic->getBlockPredModeBuf(inferArea), inputData.luma, inputData.chroma, inputData.scale, inputData.shift, flip);
+ break;
+#endif
+ default:
+ THROW("invalid input data");
+ break;
+ }
+ }
+ }
+#endif
template<typename T>
static void infer(sadl::Model<T> &model, std::vector<sadl::Tensor<T>> &inputs);
};
diff --git a/source/Lib/CommonLib/Slice.h b/source/Lib/CommonLib/Slice.h
index 231684dbf7..20eb48bd31 100644
--- a/source/Lib/CommonLib/Slice.h
+++ b/source/Lib/CommonLib/Slice.h
@@ -1492,6 +1492,9 @@ private:
unsigned m_nnlfSet1InferSize[MAX_NUM_CNNLF_INFER_GRANULARITY];
unsigned m_nnlfSet1InferSizeExtension;
unsigned m_nnlfSet1MaxNumParams;
+#if JVET_AC0177_MULTI_FRAME
+ bool m_nnlfSet1MultiframeEnabledFlag;
+#endif
#endif
bool m_wrapAroundEnabledFlag;
unsigned m_IBCFlag;
@@ -1765,6 +1768,10 @@ public:
#if NN_FILTERING_SET_1
bool getNnlfSet1EnabledFlag() const { return m_nnlfSet1EnabledFlag; }
void setNnlfSet1EnabledFlag( bool b ) { m_nnlfSet1EnabledFlag = b; }
+#if JVET_AC0177_MULTI_FRAME
+ bool getNnlfSet1MultiframeEnabledFlag() const { return m_nnlfSet1MultiframeEnabledFlag; }
+ void setNnlfSet1MultiframeEnabledFlag( bool b ) { m_nnlfSet1MultiframeEnabledFlag = b; }
+#endif
#endif
void setJointCbCrEnabledFlag(bool bVal) { m_JointCbCrEnabledFlag = bVal; }
bool getJointCbCrEnabledFlag() const { return m_JointCbCrEnabledFlag; }
diff --git a/source/Lib/CommonLib/TypeDef.h b/source/Lib/CommonLib/TypeDef.h
index 9e968498f7..1de614434f 100644
--- a/source/Lib/CommonLib/TypeDef.h
+++ b/source/Lib/CommonLib/TypeDef.h
@@ -122,10 +122,10 @@ using TypeSadl = float;
#define BYPASS_INTER_SLICE 0 // only used for training data generation
#define JVET_AC0089_COMBINE_INTRA_INTER 1 // JVET-AC0089: EE1-1.5.3 Use combined inter/intra models. Luma model uses IPB input. Chroma model does not use IPB input.
-
+#define JVET_AC0177_MULTI_FRAME 1 // JVET-AC0177: EE1-1.7: Deep In-Loop Filter with Additional Input Information
+#define JVET_AC0177_FLIP_INPUT 1 // JVET-AC0177: flip input and output of NN filter model
#endif
-
#define JVET_AB0068_RD 1 // JVET-AB0068: EE1-1.6: RDO Considering Deep In-Loop Filtering
//########### place macros to be removed in next cycle below this line ###############
@@ -519,8 +519,15 @@ enum NNInputType
NN_INPUT_LOCAL_QP = 5,
NN_INPUT_SLICE_TYPE = 6,
#if JVET_AC0089_NNVC_USE_BPM_INFO
+#if JVET_AC0177_MULTI_FRAME
+ NN_INPUT_IPB = 7,
+ NN_INPUT_REF_LIST_0 = 8,
+ NN_INPUT_REF_LIST_1 = 9,
+ MAX_NUM_NN_INPUT = 10
+#else
NN_INPUT_IPB = 7,
MAX_NUM_NN_INPUT = 8
+#endif
#else
MAX_NUM_NN_INPUT = 7
#endif
diff --git a/source/Lib/DecoderLib/DecLib.cpp b/source/Lib/DecoderLib/DecLib.cpp
index 67eb276970..75f3a1decf 100644
--- a/source/Lib/DecoderLib/DecLib.cpp
+++ b/source/Lib/DecoderLib/DecLib.cpp
@@ -651,7 +651,11 @@ void DecLib::executeLoopFilters()
if (cs.sps->getNnlfSet1EnabledFlag())
{
m_pcNNFilterSet1.create(cs.pcv->lumaWidth, cs.pcv->lumaHeight, cs.pcv->chrFormat, cs.sps->getNnlfSet1MaxNumParams());
+#if JVET_AC0177_MULTI_FRAME
+ m_pcNNFilterSet1.init(getNnlfSet1InterLumaModelName(), getNnlfSet1InterChromaModelName(), getNnlfSet1IntraLumaModelName(), getNnlfSet1IntraChromaModelName(), getNnlfSet1AlternativeInterLumaModelName());
+#else
m_pcNNFilterSet1.init(getNnlfSet1InterLumaModelName(), getNnlfSet1InterChromaModelName(), getNnlfSet1IntraLumaModelName(), getNnlfSet1IntraChromaModelName());
+#endif
}
#endif
diff --git a/source/Lib/DecoderLib/DecLib.h b/source/Lib/DecoderLib/DecLib.h
index 3c4e673400..f85c4d2b8c 100644
--- a/source/Lib/DecoderLib/DecLib.h
+++ b/source/Lib/DecoderLib/DecLib.h
@@ -77,10 +77,13 @@ class DecLib
{
private:
#if NN_FILTERING_SET_1
- std::string m_nnlfSet1InterLumaModelName; ///<inter luma nnlfSet1 model
- std::string m_nnlfSet1InterChromaModelName; ///<inter chroma nnlfSet1 model
- std::string m_nnlfSet1IntraLumaModelName; ///<intra luma nnlfSet1 model
- std::string m_nnlfSet1IntraChromaModelName; ///<inra chroma nnlfSet1 model
+ std::string m_nnlfSet1InterLumaModelName; ///<inter luma nnlf set1 model
+ std::string m_nnlfSet1InterChromaModelName; ///<inter chroma nnlf set1 model
+ std::string m_nnlfSet1IntraLumaModelName; ///<intra luma nnlf set1 model
+ std::string m_nnlfSet1IntraChromaModelName; ///<inra chroma nnlf set1 model
+#if JVET_AC0177_MULTI_FRAME
+ std::string m_nnlfSet1AlternativeInterLumaModelName; ///<alternative inter luma nnlf set1 model
+#endif
#endif
int m_iMaxRefPicNum;
bool m_isFirstGeneralHrd;
@@ -252,6 +255,10 @@ public:
void setNnlfSet1InterChromaModelName(std::string s) { m_nnlfSet1InterChromaModelName = s; }
void setNnlfSet1IntraLumaModelName(std::string s) { m_nnlfSet1IntraLumaModelName = s; }
void setNnlfSet1IntraChromaModelName(std::string s) { m_nnlfSet1IntraChromaModelName = s; }
+#if JVET_AC0177_MULTI_FRAME
+ std::string getNnlfSet1AlternativeInterLumaModelName() { return m_nnlfSet1AlternativeInterLumaModelName; }
+ void setNnlfSet1AlternativeInterLumaModelName(std::string s) { m_nnlfSet1AlternativeInterLumaModelName = s; }
+#endif
#endif
void setDecodedPictureHashSEIEnabled(int enabled) { m_decodedPictureHashSEIEnabled=enabled; }
diff --git a/source/Lib/DecoderLib/VLCReader.cpp b/source/Lib/DecoderLib/VLCReader.cpp
index fbeccce480..143c495112 100644
--- a/source/Lib/DecoderLib/VLCReader.cpp
+++ b/source/Lib/DecoderLib/VLCReader.cpp
@@ -1748,6 +1748,9 @@ void HLSyntaxReader::parseSPS(SPS* pcSPS)
pcSPS->setNnlfSet1InferSizeExtension ( uiCode );
READ_UVLC( uiCode, "sps_nnlf_set1_max_num_params" );
pcSPS->setNnlfSet1MaxNumParams (uiCode );
+#if JVET_AC0177_MULTI_FRAME
+ READ_FLAG( uiCode, "sps_nnlf_set1_multi_frame_enabled_flag" ); pcSPS->setNnlfSet1MultiframeEnabledFlag ( uiCode ? true : false );
+#endif
}
#endif
diff --git a/source/Lib/EncoderLib/EncCfg.h b/source/Lib/EncoderLib/EncCfg.h
index 016d0fad20..3ff3f7d294 100644
--- a/source/Lib/EncoderLib/EncCfg.h
+++ b/source/Lib/EncoderLib/EncCfg.h
@@ -163,6 +163,9 @@ protected:
std::string m_nnlfSet1InterChromaModelName; ///<inter chroma nnlf set1 model
std::string m_nnlfSet1IntraLumaModelName; ///<intra luma nnlf set1 model
std::string m_nnlfSet1IntraChromaModelName; ///<inra chroma nnlf set1 model
+#if JVET_AC0177_MULTI_FRAME
+ std::string m_nnlfSet1AlternativeInterLumaModelName; ///<alternative inter luma nnlf set1 model
+#endif
#endif
#if JVET_AB0068_RD
bool m_encNnlfOpt;
@@ -776,6 +779,9 @@ protected:
unsigned m_nnlfSet1InferSizeBase;
unsigned m_nnlfSet1InferSizeExtension;
unsigned m_nnlfSet1MaxNumParams;
+#if JVET_AC0177_MULTI_FRAME
+ bool m_nnlfSet1Multiframe;
+#endif
#endif
#if JVET_O0756_CALCULATE_HDRMETRICS
double m_whitePointDeltaE[hdrtoolslib::NB_REF_WHITE];
@@ -842,6 +848,10 @@ public:
void setNnlfSet1InterChromaModelName(std::string s) { m_nnlfSet1InterChromaModelName = s; }
void setNnlfSet1IntraLumaModelName(std::string s) { m_nnlfSet1IntraLumaModelName = s; }
void setNnlfSet1IntraChromaModelName(std::string s) { m_nnlfSet1IntraChromaModelName = s; }
+#if JVET_AC0177_MULTI_FRAME
+ std::string getNnlfSet1AlternativeInterLumaModelName() { return m_nnlfSet1AlternativeInterLumaModelName; }
+ void setNnlfSet1AlternativeInterLumaModelName(std::string s) { m_nnlfSet1AlternativeInterLumaModelName = s; }
+#endif
#endif
#if JVET_AB0068_RD
std::string getRdoCnnlfInterLumaModelName() { return m_rdoCnnlfInterLumaModelName; }
@@ -2043,6 +2053,10 @@ public:
unsigned getNnlfSet1InferSizeExtension() const { return m_nnlfSet1InferSizeExtension; }
void setNnlfSet1MaxNumParams( unsigned s ) { m_nnlfSet1MaxNumParams = s; }
unsigned getNnlfSet1MaxNumParams() const { return m_nnlfSet1MaxNumParams; }
+#if JVET_AC0177_MULTI_FRAME
+ void setNnlfSet1UseMultiframe( bool b ) { m_nnlfSet1Multiframe = b; }
+ bool getNnlfSet1UseMultiframe() const { return m_nnlfSet1Multiframe; }
+#endif
#endif
#if JVET_O0756_CALCULATE_HDRMETRICS
void setWhitePointDeltaE( uint32_t index, double value ) { m_whitePointDeltaE[ index ] = value; }
diff --git a/source/Lib/EncoderLib/EncGOP.cpp b/source/Lib/EncoderLib/EncGOP.cpp
index 609e63118a..996fc82098 100644
--- a/source/Lib/EncoderLib/EncGOP.cpp
+++ b/source/Lib/EncoderLib/EncGOP.cpp
@@ -3106,7 +3106,11 @@ void EncGOP::compressGOP( int iPOCLast, int iNumPicRcvd, PicList& rcListPic,
#if NN_FILTERING_SET_1
if ( cs.sps->getNnlfSet1EnabledFlag() )
{
+#if JVET_AC0177_MULTI_FRAME
+ m_pcNNFilterSet1.init(m_pcEncLib->getNnlfSet1InterLumaModelName(), m_pcEncLib->getNnlfSet1InterChromaModelName(), m_pcEncLib->getNnlfSet1IntraLumaModelName(), m_pcEncLib->getNnlfSet1IntraChromaModelName(), m_pcEncLib->getNnlfSet1AlternativeInterLumaModelName());
+#else
m_pcNNFilterSet1.init(m_pcEncLib->getNnlfSet1InterLumaModelName(), m_pcEncLib->getNnlfSet1InterChromaModelName(), m_pcEncLib->getNnlfSet1IntraLumaModelName(), m_pcEncLib->getNnlfSet1IntraChromaModelName());
+#endif
m_pcNNFilterSet1.initCABACEstimator( m_pcEncLib->getCABACEncoder(), m_pcEncLib->getCtxCache(), pcSlice );
m_pcNNFilterSet1.cnnFilterEncoder(pcPic, pcSlice->getLambdas());
}
diff --git a/source/Lib/EncoderLib/EncLib.cpp b/source/Lib/EncoderLib/EncLib.cpp
index 2421b93953..6523da53b8 100644
--- a/source/Lib/EncoderLib/EncLib.cpp
+++ b/source/Lib/EncoderLib/EncLib.cpp
@@ -1456,6 +1456,9 @@ void EncLib::xInitSPS( SPS& sps )
{
sps.setNnlfSet1MaxNumParams(m_nnlfSet1MaxNumParams);
}
+#if JVET_AC0177_MULTI_FRAME
+ sps.setNnlfSet1MultiframeEnabledFlag(m_nnlfSet1Multiframe);
+#endif
}
#endif
diff --git a/source/Lib/EncoderLib/VLCWriter.cpp b/source/Lib/EncoderLib/VLCWriter.cpp
index 10dba722dd..aad5d30b34 100644
--- a/source/Lib/EncoderLib/VLCWriter.cpp
+++ b/source/Lib/EncoderLib/VLCWriter.cpp
@@ -1022,6 +1022,9 @@ void HLSWriter::codeSPS( const SPS* pcSPS )
WRITE_UVLC( pcSPS->getNnlfSet1InferSize(CNNLF_INFER_GRANULARITY_BASE), "sps_nnlf_set1_infer_size_base" );
WRITE_UVLC( pcSPS->getNnlfSet1InferSizeExtension(), "sps_nnlf_set1_infer_size_extension" );
WRITE_UVLC( pcSPS->getNnlfSet1MaxNumParams(), "sps_nnlf_set1_max_num_params" );
+#if JVET_AC0177_MULTI_FRAME
+ WRITE_FLAG( pcSPS->getNnlfSet1MultiframeEnabledFlag(), "sps_nnlf_set1_multi_frame_enabled_flag" );
+#endif
}
#endif
diff --git a/training/training_scripts/Nn_Filtering_Set_1/Scripts/AdditionalLumaInter/Conversion/convert.py b/training/training_scripts/Nn_Filtering_Set_1/Scripts/AdditionalLumaInter/Conversion/convert.py
new file mode 100755
index 0000000000..941e42578f
--- /dev/null
+++ b/training/training_scripts/Nn_Filtering_Set_1/Scripts/AdditionalLumaInter/Conversion/convert.py
@@ -0,0 +1,27 @@
+import torch
+import torch.nn as nn
+from net import ConditionalNet
+import numpy as np
+import os
+
+# input
+yuv = np.ones((1, 1, 32, 32), dtype=np.float32)
+pred = np.ones((1, 1, 32, 32), dtype=np.float32)
+forw = np.ones((1, 1, 32, 32), dtype=np.float32)
+bacw = np.ones((1, 1, 32, 32), dtype=np.float32)
+qp = np.ones((1, 1, 32, 32), dtype=np.float32)
+
+# model
+# model = nn.DataParallel(ConditionalNet(96, 8)) # if model is trained on multiple GPUs
+model = ConditionalNet(96, 8) # if model is trained with single GPU
+state = torch.load('50.ckpt', map_location=torch.device('cpu'))
+model.load_state_dict(state)
+
+dummy_input = (torch.from_numpy(yuv), torch.from_numpy(pred), torch.from_numpy(forw), torch.from_numpy(bacw), torch.from_numpy(qp))
+torch.onnx.export(model.module, dummy_input, "NnlfSet1_LumaCNNFilter_InterSlice_MultiframePrior_Tid345_int16.onnx")
+
+
+
+
+
+
diff --git a/training/training_scripts/Nn_Filtering_Set_1/Scripts/AdditionalLumaInter/Conversion/net.py b/training/training_scripts/Nn_Filtering_Set_1/Scripts/AdditionalLumaInter/Conversion/net.py
new file mode 100644
index 0000000000..f859b403e4
--- /dev/null
+++ b/training/training_scripts/Nn_Filtering_Set_1/Scripts/AdditionalLumaInter/Conversion/net.py
@@ -0,0 +1,99 @@
+import torch
+import torch.nn as nn
+
+
+def conv3x3(in_channels, out_channels, stride=1, padding=1):
+ return nn.Conv2d(in_channels, out_channels, kernel_size=3,
+ stride=stride, padding=padding)
+
+
+def conv1x1(in_channels, out_channels, stride=1, padding=0):
+ return nn.Conv2d(in_channels, out_channels, kernel_size=1,
+ stride=stride, padding=padding)
+
+
+# Conv3x3 + PReLU
+class conv3x3_f(nn.Module):
+ def __init__(self, in_channels, out_channels, stride=1):
+ super(conv3x3_f, self).__init__()
+ self.conv = conv3x3(in_channels, out_channels, stride)
+ self.relu = nn.PReLU()
+
+ def forward(self, x):
+ x = self.conv(x)
+ x = self.relu(x)
+ return x
+
+
+# Conv1x1 + PReLU
+class conv1x1_f(nn.Module):
+ def __init__(self, in_channels, out_channels, stride=1):
+ super(conv1x1_f, self).__init__()
+ self.conv = conv1x1(in_channels, out_channels, stride)
+ self.relu = nn.PReLU()
+
+ def forward(self, x):
+ x = self.conv(x)
+ x = self.relu(x)
+ return x
+
+
+# Residual Block
+class ResidualBlock(nn.Module):
+ def __init__(self, in_channels, out_channels):
+ super(ResidualBlock, self).__init__()
+ self.conv1 = conv3x3(in_channels, out_channels)
+ self.relu = nn.PReLU()
+ self.conv2 = conv3x3(out_channels, out_channels)
+
+ def forward(self, x):
+ out = self.conv1(x)
+ out = self.relu(out)
+ out = self.conv2(out)
+ return out
+
+
+class ConditionalNet(nn.Module):
+ def __init__(self, f, rbn):
+ super(ConditionalNet, self).__init__()
+ self.rbn = rbn
+ self.convRec = conv3x3_f(1, f)
+ self.convPred = conv3x3_f(1, f)
+ self.convTemp = conv3x3_f(2, f)
+ self.convQp = conv3x3_f(1, f)
+ self.fuse = conv1x1_f(4 * f, f)
+ self.transitionH = conv3x3_f(f, f, 2)
+ self.backbone = nn.ModuleList([ResidualBlock(f, f)])
+ for _ in range(self.rbn - 1):
+ self.backbone.append(ResidualBlock(f, f))
+ self.last_layer = nn.Sequential(
+ nn.Conv2d(
+ in_channels=f,
+ out_channels=f,
+ kernel_size=3,
+ stride=1,
+ padding=1),
+ nn.PReLU(),
+ nn.Conv2d(
+ in_channels=f,
+ out_channels=4,
+ kernel_size=3,
+ stride=1,
+ padding=1),
+ # nn.PixelShuffle(2)
+ )
+
+ def forward(self, rec, pred, forw, bacw, qp):
+ rec_f = self.convRec(rec)
+ pred_f = self.convPred(pred)
+ temp_f = self.convTemp(torch.cat((forw, bacw), 1))
+ qp_f = self.convQp(qp)
+ xh = torch.cat((rec_f, pred_f, temp_f, qp_f), 1)
+ xh = self.fuse(xh)
+ x = self.transitionH(xh)
+ for i in range(self.rbn):
+ x = self.backbone[i](x) + x
+ # output
+ x = self.last_layer(x)
+ # x = x + rec
+ return x
diff --git a/training/training_scripts/Nn_Filtering_Set_1/Scripts/AdditionalLumaInter/Training/dataset.py b/training/training_scripts/Nn_Filtering_Set_1/Scripts/AdditionalLumaInter/Training/dataset.py
new file mode 100644
index 0000000000..972f36d414
--- /dev/null
+++ b/training/training_scripts/Nn_Filtering_Set_1/Scripts/AdditionalLumaInter/Training/dataset.py
@@ -0,0 +1,117 @@
+from __future__ import print_function, division
+import torch
+import numpy as np
+from torch.utils.data import Dataset, DataLoader
+from torchvision import transforms, utils
+import json
+import matplotlib.pyplot as plt
+import torchvision.transforms.functional as F
+
+
+class CnnlfDataset(Dataset):
+ """MultiType Tree partition prediction dataset."""
+
+ def __init__(self, data, transform=None):
+ """
+ Args:
+ blk_size: block size.
+ data: dataset bin file. There should be a json description file in the same folder named
+ data.json
+ transform (callable, optional): Optional transform to be applied
+ on a sample.
+ """
+ self.transform = transform
+
+ with open(data+'.json') as file:
+ description = json.loads(file.read())
+
+ self.data_file = data
+ self.patch_size = description['patch_size']
+ self.border_size = description['border_size']
+ self.components = dict(zip(description['components'], range(len(description['components']))))
+ self.nb_comp = len(self.components)
+ self.len = description['nb_patches']
+ self.block_size = self.patch_size + 2 * self.border_size
+ self.block_volume = self.block_size * self.block_size * self.nb_comp
+
+ def __len__(self):
+ return self.len
+
+ def __getitem__(self, idx):
+ if torch.is_tensor(idx):
+ idx = idx.tolist()
+ with open(self.data_file) as file:
+ block = np.fromfile(file, dtype='float32', count=self.block_volume, offset=self.block_volume * idx * 4).\
+ reshape((self.block_size, self.block_size, self.nb_comp))
+
+ org = block[:, :, self.components['org_Y']]
+ rec = block[:, :, self.components['rec_before_dbf_Y']]
+ pred = block[:, :, self.components['pred_Y']]
+ forw = block[:, :, self.components['ref_list_0_Y']]
+ bacw = block[:, :, self.components['ref_list_1_Y']]
+ qp = block[:, :, self.components['qp_base']]
+ training_sample = {'org': org, 'rec': rec, 'pred': pred, 'forw': forw, 'bacw': bacw, 'qp': qp}
+
+ if self.transform:
+ training_sample = self.transform(training_sample)
+
+ return training_sample
+
+
+class ToTensor(object):
+ """Convert ndarrays in sample to Tensors."""
+
+ def __call__(self, sample):
+ org, rec, pred, forw, bacw, qp = sample['org'], sample['rec'], sample['pred'], sample['forw'], sample['bacw'], sample['qp']
+
+ # swap color axis because
+ # numpy image: H x W x C
+ # torch image: C X H X W
+ org = org[np.newaxis, :, :]
+ rec = rec[np.newaxis, :, :]
+ pred = pred[np.newaxis, :, :]
+ forw = forw[np.newaxis, :, :]
+ bacw = bacw[np.newaxis, :, :]
+ qp = qp[np.newaxis, :, :]
+ return {'org': torch.from_numpy(org),
+ 'rec': torch.from_numpy(rec),
+ 'pred': torch.from_numpy(pred),
+ 'forw': torch.from_numpy(forw),
+ 'bacw': torch.from_numpy(bacw),
+ 'qp': torch.from_numpy(qp)}
+
+
+def preprocess(sample, device):
+ org, rec, pred = sample['org'].to(device), sample['rec'].to(device), sample['pred'].to(device)
+ forw, bacw, qp = sample['forw'].to(device), sample['bacw'].to(device), sample['qp'].to(device)
+
+ if torch.rand(1) < 0.5:
+ org[qp[:, 0, 0, 0] > 30.0/64.0, :, :, :] = F.hflip(org[qp[:, 0, 0, 0] > 30.0/64.0, :, :, :])
+ rec[qp[:, 0, 0, 0] > 30.0/64.0, :, :, :] = F.hflip(rec[qp[:, 0, 0, 0] > 30.0/64.0, :, :, :])
+ pred[qp[:, 0, 0, 0] > 30.0/64.0, :, :, :] = F.hflip(pred[qp[:, 0, 0, 0] > 30.0/64.0, :, :, :])
+ forw[qp[:, 0, 0, 0] > 30.0/64.0, :, :, :] = F.hflip(forw[qp[:, 0, 0, 0] > 30.0/64.0, :, :, :])
+ bacw[qp[:, 0, 0, 0] > 30.0/64.0, :, :, :] = F.hflip(bacw[qp[:, 0, 0, 0] > 30.0/64.0, :, :, :])
+ if torch.rand(1) < 0.5:
+ org[qp[:, 0, 0, 0] > 30.0/64.0, :, :, :] = F.vflip(org[qp[:, 0, 0, 0] > 30.0/64.0, :, :, :])
+ rec[qp[:, 0, 0, 0] > 30.0/64.0, :, :, :] = F.vflip(rec[qp[:, 0, 0, 0] > 30.0/64.0, :, :, :])
+ pred[qp[:, 0, 0, 0] > 30.0/64.0, :, :, :] = F.vflip(pred[qp[:, 0, 0, 0] > 30.0/64.0, :, :, :])
+ forw[qp[:, 0, 0, 0] > 30.0/64.0, :, :, :] = F.vflip(forw[qp[:, 0, 0, 0] > 30.0/64.0, :, :, :])
+ bacw[qp[:, 0, 0, 0] > 30.0/64.0, :, :, :] = F.vflip(bacw[qp[:, 0, 0, 0] > 30.0/64.0, :, :, :])
+
+ return {'org': org, 'rec': rec, 'pred': pred, 'forw': forw, 'bacw': bacw, 'qp': qp}
+
+
+def display_batch(batch):
+ fig, ax = plt.subplots(nrows=8, ncols=8)
+ i = 0
+ for row in ax:
+ for col in row:
+ col.imshow(batch[i, 0, :, :], cmap='gray', vmin=0., vmax=1.)
+ i += 1
+ plt.show()
+
+
+def get_loader(data, batch_size, shuffle, num_workers=4):
+ dataset = CnnlfDataset(data=data, transform=transforms.Compose([ToTensor()]))
+ data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers)
+ return data_loader
diff --git a/training/training_scripts/Nn_Filtering_Set_1/Scripts/AdditionalLumaInter/Training/main.py b/training/training_scripts/Nn_Filtering_Set_1/Scripts/AdditionalLumaInter/Training/main.py
new file mode 100755
index 0000000000..e5c6704d40
--- /dev/null
+++ b/training/training_scripts/Nn_Filtering_Set_1/Scripts/AdditionalLumaInter/Training/main.py
@@ -0,0 +1,142 @@
+import argparse
+import torch
+import torch.nn as nn
+from net import ConditionalNet
+from dataset import get_loader, display_batch, preprocess
+from math import log10
+
+
+# learning policy
+def adjust_learning_rate(optimizer, decay_rate):
+ """Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
+ print('update learing rate')
+ for param_group in optimizer.param_groups:
+ param_group['lr'] *= decay_rate
+
+
+def train(opt):
+ # Device configuration
+ if torch.cuda.is_available():
+ device = torch.device('cuda')
+ else:
+ device = torch.device('cpu')
+ # load training and validation data
+ train_loader = get_loader(data=opt.train_data, batch_size=opt.train_batch_size, shuffle=True,
+ num_workers=opt.num_workers)
+ if opt.validation_data:
+ validation_loader = get_loader(data=opt.validation_data, batch_size=opt.validation_batch_size, shuffle=False,
+ num_workers=opt.num_workers)
+ # Construct network
+ CnnlfNet = ConditionalNet(opt.feature_maps, opt.rbn)
+
+ if torch.cuda.device_count() > 1:
+ print("Let's use", torch.cuda.device_count(), "GPUs!")
+ # dim = 0 [30, xxx] -> [10, ...], [10, ...], [10, ...] on 3 GPUs
+ CnnlfNet = nn.DataParallel(CnnlfNet)
+ CnnlfNet.to(device)
+
+ if opt.pretrained_model:
+ CnnlfNet.load_state_dict(torch.load(opt.pretrained_model, map_location=device))
+
+ optimizer = torch.optim.Adam(CnnlfNet.parameters(), lr=opt.lr, weight_decay=opt.weight_decay)
+
+ # Loss and optimizer
+ criterion = nn.MSELoss()
+ if opt.MSE == 1:
+ print('Loss function: MSE')
+ criterionB = nn.MSELoss()
+ else:
+ print('Loss function: SAD')
+ criterionB = nn.L1Loss()
+
+ # Train the model
+ step_per_epoch = len(train_loader)
+ for epoch in range(0, opt.epoches):
+ print('Epoch {}'.format(epoch))
+ for i, sample_t in enumerate(train_loader):
+ global_step = epoch * step_per_epoch + i + 1
+ if global_step == opt.decay_epoch1 * step_per_epoch or global_step == opt.decay_epoch2 * step_per_epoch:
+ adjust_learning_rate(optimizer, opt.decay_rate)
+ if global_step == opt.mse_epoch * step_per_epoch:
+ criterionB = nn.MSELoss()
+ adjust_learning_rate(optimizer, opt.decay_rate)
+ sample_t = preprocess(sample_t, device)
+ org, rec, pred = sample_t['org'], sample_t['rec'], sample_t['pred']
+ forw, bacw, qp = sample_t['forw'], sample_t['bacw'], sample_t['qp']
+ # Forward pass
+ outputs = CnnlfNet(rec, pred, forw, bacw, qp)
+ lossB = criterionB(outputs, org)
+ loss = criterion(outputs, org)
+
+ # Backward and optimize
+ optimizer.zero_grad()
+ lossB.backward()
+ optimizer.step()
+ if global_step % opt.loss_interval == 0:
+ print('Global Step {}, Loss: {:.4f}'.format(global_step, loss.item()))
+ print('PSNR of the model on a batch: {}'.format(10 * log10(1 / loss.item())))
+ if opt.validation_data and global_step % opt.validation_interval == 0:
+ # validate the model
+ with torch.no_grad():
+ avg_psnr_ref = 0
+ avg_psnr_val = 0
+ _len = 0
+ for sample_v in validation_loader:
+ sample_v = preprocess(sample_v, device)
+ org, rec, pred = sample_v['org'], sample_v['rec'], sample_v['pred']
+ forw, bacw, qp = sample_v['forw'], sample_v['bacw'], sample_v['qp']
+ qp = sample_v['qp'].to(device)
+ outputs = CnnlfNet(rec, pred, forw, bacw, qp)
+ loss_ref = criterion(rec, org)
+ loss_val = criterion(outputs, org)
+ if loss_ref.item() == 0:
+ continue
+ _len += 1
+ avg_psnr_ref += 10 * log10(1 / loss_ref.item())
+ avg_psnr_val += 10 * log10(1 / loss_val.item())
+ print('PSNR of the anchor on validation: {}'.format(avg_psnr_ref / _len))
+ print('PSNR of the model on validation: {}'.format(avg_psnr_val / _len))
+ if (epoch + 1) % opt.checkpoint_interval == 0:
+ # Save the model checkpoint
+ torch.save(CnnlfNet.state_dict(), str(epoch+1) + '.ckpt')
+
+
+if __name__ == '__main__':
+ # parse arguments
+ parser = argparse.ArgumentParser()
+
+ # training/validation data
+ parser.add_argument('--num_workers', type=int, default=4, help='number of workers')
+ parser.add_argument('--train_batch_size', type=int, default=64, help='train batch size')
+ parser.add_argument('--validation_batch_size', type=int, default=64, help='validation batch size')
+ parser.add_argument('--train_data', type=str, default='luma_data.bin', help='training data')
+ parser.add_argument('--validation_data', type=str, default='', help='validation data')
+
+ # network
+ parser.add_argument('--feature_maps', type=int, default=96, help='number of feature maps')
+ parser.add_argument('--rbn', type=int, default=8, help='number of residual blocks')
+
+ # loss
+ parser.add_argument('--weight_decay', type=float, default=1e-8, help='weight decay')
+ parser.add_argument('--MSE', type=int, default=0, help='loss function, default=SAD')
+
+ # optimizer configurations
+ parser.add_argument('--lr', type=float, default=1e-4, help='learning rate, default=0.0001')
+ parser.add_argument('--decay_epoch1', type=int, default=20, help='first milestone to decay lr')
+ parser.add_argument('--decay_epoch2', type=int, default=30, help='second milestone to decay lr')
+ parser.add_argument('--mse_epoch', type=int, default=40, help='switch to the mse loss and decrease learning rate at mse_epoch')
+ parser.add_argument('--decay_rate', type=float, default=0.1, help='the multiplier to decay lr')
+ parser.add_argument('--epoches', type=int, default=50, help='number of epoches to train for')
+
+ # dump configurations
+ parser.add_argument('--loss_interval', type=int, default=1000, help='number of iteration to print loss')
+ parser.add_argument('--validation_interval', type=int, default=10000, help='number of iteration to validate')
+ parser.add_argument('--checkpoint_interval', type=int, default=10, help='number of epochs to save checkpoint')
+
+ # finetune
+ parser.add_argument('--pretrained_model', type=str, default="", help='pretrained model')
+
+ options = parser.parse_args()
+ print(options)
+ train(options)
+
diff --git a/training/training_scripts/Nn_Filtering_Set_1/Scripts/AdditionalLumaInter/Training/net.py b/training/training_scripts/Nn_Filtering_Set_1/Scripts/AdditionalLumaInter/Training/net.py
new file mode 100644
index 0000000000..7dd8089a75
--- /dev/null
+++ b/training/training_scripts/Nn_Filtering_Set_1/Scripts/AdditionalLumaInter/Training/net.py
@@ -0,0 +1,99 @@
+import torch
+import torch.nn as nn
+
+
+def conv3x3(in_channels, out_channels, stride=1, padding=1):
+ return nn.Conv2d(in_channels, out_channels, kernel_size=3,
+ stride=stride, padding=padding)
+
+
+def conv1x1(in_channels, out_channels, stride=1, padding=0):
+ return nn.Conv2d(in_channels, out_channels, kernel_size=1,
+ stride=stride, padding=padding)
+
+
+# Conv3x3 + PReLU
+class conv3x3_f(nn.Module):
+ def __init__(self, in_channels, out_channels, stride=1):
+ super(conv3x3_f, self).__init__()
+ self.conv = conv3x3(in_channels, out_channels, stride)
+ self.relu = nn.PReLU()
+
+ def forward(self, x):
+ x = self.conv(x)
+ x = self.relu(x)
+ return x
+
+
+# Conv1x1 + PReLU
+class conv1x1_f(nn.Module):
+ def __init__(self, in_channels, out_channels, stride=1):
+ super(conv1x1_f, self).__init__()
+ self.conv = conv1x1(in_channels, out_channels, stride)
+ self.relu = nn.PReLU()
+
+ def forward(self, x):
+ x = self.conv(x)
+ x = self.relu(x)
+ return x
+
+
+# Residual Block
+class ResidualBlock(nn.Module):
+ def __init__(self, in_channels, out_channels):
+ super(ResidualBlock, self).__init__()
+ self.conv1 = conv3x3(in_channels, out_channels)
+ self.relu = nn.PReLU()
+ self.conv2 = conv3x3(out_channels, out_channels)
+
+ def forward(self, x):
+ out = self.conv1(x)
+ out = self.relu(out)
+ out = self.conv2(out)
+ return out
+
+
+class ConditionalNet(nn.Module):
+ def __init__(self, f, rbn):
+ super(ConditionalNet, self).__init__()
+ self.rbn = rbn
+ self.convRec = conv3x3_f(1, f)
+ self.convPred = conv3x3_f(1, f)
+ self.convTemp = conv3x3_f(2, f)
+ self.convQp = conv3x3_f(1, f)
+ self.fuse = conv1x1_f(4 * f, f)
+ self.transitionH = conv3x3_f(f, f, 2)
+ self.backbone = nn.ModuleList([ResidualBlock(f, f)])
+ for _ in range(self.rbn - 1):
+ self.backbone.append(ResidualBlock(f, f))
+ self.last_layer = nn.Sequential(
+ nn.Conv2d(
+ in_channels=f,
+ out_channels=f,
+ kernel_size=3,
+ stride=1,
+ padding=1),
+ nn.PReLU(),
+ nn.Conv2d(
+ in_channels=f,
+ out_channels=4,
+ kernel_size=3,
+ stride=1,
+ padding=1),
+ nn.PixelShuffle(2)
+ )
+
+ def forward(self, rec, pred, forw, bacw, qp):
+ rec_f = self.convRec(rec)
+ pred_f = self.convPred(pred)
+ temp_f = self.convTemp(torch.cat((forw, bacw), 1))
+ qp_f = self.convQp(qp)
+ xh = torch.cat((rec_f, pred_f, temp_f, qp_f), 1)
+ xh = self.fuse(xh)
+ x = self.transitionH(xh)
+ for i in range(self.rbn):
+ x = self.backbone[i](x) + x
+ # output
+ x = self.last_layer(x)
+ x = x + rec
+ return x
diff --git a/training/training_scripts/Nn_Filtering_Set_1/Scripts/AdditionalLumaInter/Training/train.sh b/training/training_scripts/Nn_Filtering_Set_1/Scripts/AdditionalLumaInter/Training/train.sh
new file mode 100755
index 0000000000..ae190efa0a
--- /dev/null
+++ b/training/training_scripts/Nn_Filtering_Set_1/Scripts/AdditionalLumaInter/Training/train.sh
@@ -0,0 +1,3 @@
+#!/bin/bash
+python3 main.py
+
diff --git a/training/training_scripts/Nn_Filtering_Set_1/Scripts/AdditionalLumaInter/instruction.pdf b/training/training_scripts/Nn_Filtering_Set_1/Scripts/AdditionalLumaInter/instruction.pdf
new file mode 100644
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GitLab