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models/intra/graph_output_4_16_float.sadl filter=lfs diff=lfs merge=lfs -text
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models/intra/graph_output_4_8_int16.sadl filter=lfs diff=lfs merge=lfs -text
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models/intra/graph_output_8_16_int16.sadl filter=lfs diff=lfs merge=lfs -text
models/intra/graph_output_8_8_int16.sadl filter=lfs diff=lfs merge=lfs -text
models/intra/graph_output_16_16_float.sadl filter=lfs diff=lfs merge=lfs -text
models/NnlfSet0_model_float.sadl filter=lfs diff=lfs merge=lfs -text
models/NnlfSet0_model_int16.sadl filter=lfs diff=lfs merge=lfs -text
models/RDO_I_y_model_int16.sadl filter=lfs diff=lfs merge=lfs -text
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models/post_filter/float/nnr_bitstreams_float/F_ArenaOfValor_42.nnr filter=lfs diff=lfs merge=lfs -text
models/post_filter/int16/nnr_bitstreams_int16/F_BBDrillText_27.nnr filter=lfs diff=lfs merge=lfs -text
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models/post_filter/float/nnr_bitstreams_float/C_BasketballDrill_22.nnr filter=lfs diff=lfs merge=lfs -text
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models/post_filter/int16/nnr_bitstreams_int16/C_BQMall_32.nnr filter=lfs diff=lfs merge=lfs -text
models/post_filter/int16/nnr_bitstreams_int16/F_BBDrillText_42.nnr filter=lfs diff=lfs merge=lfs -text
models/post_filter/float/nnr_bitstreams_float/F_BBDrillText_42.nnr filter=lfs diff=lfs merge=lfs -text
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models/post_filter/int16/nnr_bitstreams_int16/C_RaceHorses_big_22.nnr filter=lfs diff=lfs merge=lfs -text
models/post_filter/float/nnr_bitstreams_float/A2_CatRobot_37.nnr filter=lfs diff=lfs merge=lfs -text
models/post_filter/float/nnr_bitstreams_float/C_BQMall_42.nnr filter=lfs diff=lfs merge=lfs -text
models/post_filter/float/nnr_bitstreams_float/C_PartyScene_42.nnr filter=lfs diff=lfs merge=lfs -text
models/post_filter/float/nnr_bitstreams_float/B_RitualDance_27.nnr filter=lfs diff=lfs merge=lfs -text
models/post_filter/float/nnr_bitstreams_float/D_RaceHorses_s_32.nnr filter=lfs diff=lfs merge=lfs -text
models/post_filter/float/nnr_bitstreams_float/F_SlideShow_22.nnr filter=lfs diff=lfs merge=lfs -text
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models/post_filter/float/nnr_bitstreams_float/A2_ParkRunning_32.nnr filter=lfs diff=lfs merge=lfs -text
models/post_filter/int16/nnr_bitstreams_int16/B_Cactus_27.nnr filter=lfs diff=lfs merge=lfs -text
models/post_filter/float/nnr_bitstreams_float/D_BQSquare_32.nnr filter=lfs diff=lfs merge=lfs -text
models/post_filter/int16/nnr_bitstreams_int16/A1_Tango_42.nnr filter=lfs diff=lfs merge=lfs -text
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models/post_filter/int16/nnr_bitstreams_int16/B_MarketPlace_37.nnr filter=lfs diff=lfs merge=lfs -text
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models/post_filter/float/nnr_bitstreams_float/C_BasketballDrill_42.nnr filter=lfs diff=lfs merge=lfs -text
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models/post_filter/int16/nnr_bitstreams_int16/F_SlideShow_42.nnr filter=lfs diff=lfs merge=lfs -text
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models/post_filter/float/nnr_bitstreams_float/D_BlowingBubbles_32.nnr filter=lfs diff=lfs merge=lfs -text
models/post_filter/float/nnr_bitstreams_float/D_RaceHorses_s_27.nnr filter=lfs diff=lfs merge=lfs -text
models/post_filter/float/nnr_bitstreams_float/D_BQSquare_22.nnr filter=lfs diff=lfs merge=lfs -text
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models/post_filter/float/nnr_bitstreams_float/A1_CampfireParty_42.nnr filter=lfs diff=lfs merge=lfs -text
models/post_filter/float/nnr_bitstreams_float/A1_FoodMarket_22.nnr filter=lfs diff=lfs merge=lfs -text
models/post_filter/int16/nnr_bitstreams_int16/D_BlowingBubbles_27.nnr filter=lfs diff=lfs merge=lfs -text
models/post_filter/float/nnr_bitstreams_float/A1_FoodMarket_42.nnr filter=lfs diff=lfs merge=lfs -text
models/post_filter/float/nnr_bitstreams_float/D_RaceHorses_s_42.nnr filter=lfs diff=lfs merge=lfs -text
models/post_filter/int16/nnr_bitstreams_int16/C_BasketballDrill_22.nnr filter=lfs diff=lfs merge=lfs -text
models/post_filter/float/nnr_bitstreams_float/C_RaceHorses_big_42.nnr filter=lfs diff=lfs merge=lfs -text
models/post_filter/float/nnr_bitstreams_float/D_BasketBallPass_37.nnr filter=lfs diff=lfs merge=lfs -text
models/post_filter/int16/nnr_bitstreams_int16/A1_Tango_27.nnr filter=lfs diff=lfs merge=lfs -text
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models/post_filter/int16/nnr_bitstreams_int16/D_BQSquare_37.nnr filter=lfs diff=lfs merge=lfs -text
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models/post_filter/int16/nnr_bitstreams_int16/B_MarketPlace_42.nnr filter=lfs diff=lfs merge=lfs -text
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models/post_filter/int16/overfitted_models_int16/nnr_A1_FoodMarket_42.sadl filter=lfs diff=lfs merge=lfs -text
models/post_filter/int16/overfitted_models_int16/nnr_B_MarketPlace_37.sadl filter=lfs diff=lfs merge=lfs -text
models/post_filter/int16/overfitted_models_int16/nnr_F_BBDrillText_32.sadl filter=lfs diff=lfs merge=lfs -text
models/post_filter/float/overfitted_models_float/nnr_A2_CatRobot_32.sadl filter=lfs diff=lfs merge=lfs -text
models/post_filter/float/overfitted_models_float/nnr_B_BQTerrace_27.sadl filter=lfs diff=lfs merge=lfs -text
models/post_filter/float/overfitted_models_float/nnr_D_BasketBallPass_32.sadl filter=lfs diff=lfs merge=lfs -text
models/post_filter/float/overfitted_models_float/nnr_D_RaceHorses_s_37.sadl filter=lfs diff=lfs merge=lfs -text
models/post_filter/int16/overfitted_models_int16/nnr_A1_FoodMarket_27.sadl filter=lfs diff=lfs merge=lfs -text
models/post_filter/float/overfitted_models_float/nnr_A1_Tango_22.sadl filter=lfs diff=lfs merge=lfs -text
models/post_filter/float/overfitted_models_float/nnr_A2_DaylightRoad_37.sadl filter=lfs diff=lfs merge=lfs -text
models/post_filter/float/overfitted_models_float/nnr_C_BQMall_32.sadl filter=lfs diff=lfs merge=lfs -text
models/post_filter/float/overfitted_models_float/nnr_D_BasketBallPass_42.sadl filter=lfs diff=lfs merge=lfs -text
models/post_filter/float/overfitted_models_float/nnr_C_BasketballDrill_32.sadl filter=lfs diff=lfs merge=lfs -text
models/post_filter/float/overfitted_models_float/nnr_C_PartyScene_42.sadl filter=lfs diff=lfs merge=lfs -text
models/post_filter/float/overfitted_models_float/nnr_D_BlowingBubbles_37.sadl filter=lfs diff=lfs merge=lfs -text
models/post_filter/float/overfitted_models_float/nnr_D_RaceHorses_s_42.sadl filter=lfs diff=lfs merge=lfs -text
models/post_filter/int16/overfitted_models_int16/nnr_A1_Tango_37.sadl filter=lfs diff=lfs merge=lfs -text
models/post_filter/int16/overfitted_models_int16/nnr_C_BQMall_37.sadl filter=lfs diff=lfs merge=lfs -text
models/post_filter/float/overfitted_models_float/nnr_F_BBDrillText_32.sadl filter=lfs diff=lfs merge=lfs -text
models/post_filter/int16/overfitted_models_int16/nnr_C_BasketballDrill_37.sadl filter=lfs diff=lfs merge=lfs -text
models/post_filter/int16/overfitted_models_int16/nnr_D_BasketBallPass_32.sadl filter=lfs diff=lfs merge=lfs -text
models/post_filter/float/base_models_float/model2.sadl filter=lfs diff=lfs merge=lfs -text
models/post_filter/float/overfitted_models_float/nnr_A2_CatRobot_42.sadl filter=lfs diff=lfs merge=lfs -text
models/post_filter/float/overfitted_models_float/nnr_B_MarketPlace_42.sadl filter=lfs diff=lfs merge=lfs -text
models/post_filter/float/overfitted_models_float/nnr_F_SlideEditing_27.sadl filter=lfs diff=lfs merge=lfs -text
models/post_filter/int16/overfitted_models_int16/nnr_B_RitualDance_32.sadl filter=lfs diff=lfs merge=lfs -text
models/post_filter/int16/overfitted_models_int16/nnr_F_ArenaOfValor_32.sadl filter=lfs diff=lfs merge=lfs -text
models/post_filter/float/overfitted_models_float/nnr_C_BasketballDrill_37.sadl filter=lfs diff=lfs merge=lfs -text
models/post_filter/float/overfitted_models_float/nnr_D_BlowingBubbles_42.sadl filter=lfs diff=lfs merge=lfs -text
models/post_filter/float/overfitted_models_float/nnr_F_SlideShow_37.sadl filter=lfs diff=lfs merge=lfs -text
models/post_filter/float/overfitted_models_float/nnr_D_BQSquare_32.sadl filter=lfs diff=lfs merge=lfs -text
models/post_filter/int16/overfitted_models_int16/nnr_B_BasketBallDrive_27.sadl filter=lfs diff=lfs merge=lfs -text
models/post_filter/int16/overfitted_models_int16/nnr_C_BasketballDrill_32.sadl filter=lfs diff=lfs merge=lfs -text
models/super_resolution/Nnsr_ChromaCNNSR_Inter_Intra_float.sadl filter=lfs diff=lfs merge=lfs -text
models/super_resolution/Nnsr_ChromaCNNSR_Inter_Intra_int16.sadl filter=lfs diff=lfs merge=lfs -text
models/super_resolution/Nnsr_LumaCNNSR_Inter_float.sadl filter=lfs diff=lfs merge=lfs -text
models/super_resolution/Nnsr_LumaCNNSR_Inter_int16.sadl filter=lfs diff=lfs merge=lfs -text
models/super_resolution/Nnsr_LumaCNNSR_Intra_float.sadl filter=lfs diff=lfs merge=lfs -text
models/super_resolution/Nnsr_LumaCNNSR_Intra_int16.sadl filter=lfs diff=lfs merge=lfs -text
*.json filter=lfs diff=lfs merge=lfs -text
*.index filter=lfs diff=lfs merge=lfs -text
*.pb filter=lfs diff=lfs merge=lfs -text
*.data-* filter=lfs diff=lfs merge=lfs -text
.gitignore
View file @ ec39f991
......@@ -15,3 +15,8 @@ deploy
*.pyc
/out/
/doc/_minted-software-manual/
/models_intra_prediction/
.idea/
cmake-build-*/
__pycache__/
NCTM/
.gitlab-ci.yml
View file @ ec39f991
stages:
- build
variables:
GIT_SUBMODULE_STRATEGY: none
.build_template:
stage: build
script:
- make sadlclean
- make realclean
- make all
only:
refs:
- master
- VTM-11.0_nnvc
- merge_requests
variables:
- $CI_PROJECT_URL == 'https://vcgit.hhi.fraunhofer.de/jvet/VVCSoftware_VTM'
- $CI_MERGE_REQUEST_TARGET_BRANCH_NAME == 'master'
- $CI_PROJECT_URL == 'https://vcgit.hhi.fraunhofer.de/jvet-ahg-nnvc/VVCSoftware_VTM'
- $CI_MERGE_REQUEST_TARGET_BRANCH_NAME == 'VTM-11.0_nnvc'
.build_template_linux:
extends: .build_template
script:
- make sadlclean
- make realclean
- make all
- make realclean
......@@ -27,25 +32,15 @@ build_macos:
tags:
- macos
build_ubuntu1604:
build_ubuntu2004:
extends: .build_template_linux
tags:
- ubuntu1604
- ubuntu2004
build_ubuntu1804:
build_ubuntu2204:
extends: .build_template_linux
tags:
- ubuntu1804
build_ubuntu1804-gcc8:
extends: .build_template_linux
script:
- make realclean
- make all toolset=gcc-8
- make realclean
- make linuxbuild enable-tracing=true toolset=gcc-8
tags:
- ubuntu1804-gcc8
- ubuntu2204
build_vc191x:
extends: .build_template
......
.gitmodules 0 → 100644
View file @ ec39f991
[submodule "sadl"]
path = sadl
url = https://vcgit.hhi.fraunhofer.de/jvet-ahg-nnvc/sadl.git
CMakeLists.txt
View file @ ec39f991
......@@ -21,6 +21,10 @@ if( NOT CMAKE_BUILD_TYPE )
set( CMAKE_BUILD_TYPE "Release" CACHE STRING "Choose the type of build, options are: Debug Release RelWithDebInfo MinSizeRel." FORCE )
endif()
if( NOT DEFINED NNLF_BUILD_WITH_AVX512 )
set( NNLF_BUILD_WITH_AVX512 "1" CACHE STRING "Enable/Disable AVX512, options are: 0 (disable) 1 (enable)" )
endif()
if( CMAKE_SYSTEM_NAME STREQUAL "Linux" )
if( CMAKE_CXX_COMPILER_ID STREQUAL "GNU" )
set( USE_ADDRESS_SANITIZER OFF CACHE BOOL "Compiles with -sanitize=address and links to libasan" )
......@@ -36,8 +40,8 @@ if( CMAKE_COMPILER_IS_GNUCC )
set( BUILD_STATIC OFF CACHE BOOL "Build static executables" )
endif()
# set c++11
set( CMAKE_CXX_STANDARD 11 )
# set c++14
set( CMAKE_CXX_STANDARD 14 )
set( CMAKE_CXX_STANDARD_REQUIRED ON )
# compile everything position independent (even static libraries)
......@@ -94,11 +98,12 @@ endif()
#bb_enable_warnings( clang warnings-as-errors )
# enable warnings
bb_enable_warnings( msvc warnings-as-errors "/wd4996" )
bb_enable_warnings( msvc warnings-as-errors "/wd4996" "/wd4244" )
# enable sse4.1 build for all source files for gcc and clang
if( UNIX OR MINGW )
add_compile_options( "-msse4.1" )
set( CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fstrict-aliasing" )
endif()
# enable parallel build for Visual Studio
......@@ -122,6 +127,8 @@ if( CMAKE_CXX_COMPILER_ID STREQUAL "GNU" )
endif()
endif()
include_directories( "./sadl" )
# modify .lldbinit for lldb custom data formatters
if( XCODE )
set( LLDB_INSTALL_ROOT "$ENV{HOME}/.lldb.d" )
......
Makefile
View file @ ec39f991
......@@ -18,6 +18,7 @@ BUILD_SCRIPT := $(CURDIR)/cmake/CMakeBuild/bin/cmake.py
TARGETS := CommonLib DecoderAnalyserApp DecoderAnalyserLib DecoderApp DecoderLib
TARGETS += EncoderApp EncoderLib Utilities SEIRemovalApp StreamMergeApp
SADL_HASH := "v4rc2" # hard coded because of windows $(shell git submodule status | grep sadl | cut -d' ' -f2)
ifeq ($(OS),Windows_NT)
ifneq ($(MSYSTEM),)
......@@ -177,4 +178,10 @@ $(TARGETS_RELWITHDEBINFO_CLEAN_FIRST):
realclean:
$(RM) -rf bin build lib
sadlclean:
$(RM) -rf sadl
git clone https://vcgit.hhi.fraunhofer.de/jvet-ahg-nnvc/sadl.git sadl
cd sadl && git checkout $(SADL_HASH)
.NOTPARALLEL:
README.md
View file @ ec39f991
VTM reference software for VVC
AhG 11 reference software for NNVC
==============================
This software package is the reference software for Versatile Video Coding (VVC). The reference software includes both encoder and decoder functionality.
This software package is the reference software for AhG11. The reference software includes both encoder and decoder functionality.
Reference software is useful in aiding users of a video coding standard to establish and test conformance and interoperability, and to educate users and demonstrate the capabilities of the standard. For these purposes, this software is provided as an aid for the study and implementation of Versatile Video Coding.
......@@ -9,9 +9,28 @@ The software has been jointly developed by the ITU-T Video Coding Experts Group
A software manual, which contains usage instructions, can be found in the "doc" subdirectory of this software package.
Content
==================
The package contains the following components:
- a base codec based on VTM-11.0_nnvc (VTM-11.0 + JVET-V0056 patch)
- a data dumping feature at the decoder, activated with the macro NNVC\_DUMP\_DATA in TypeDef.h. Encoder log is also slightly changed in order to log some useful information
- a data loading feature in python, compatible with the data dumped by the decoder
- an inference feature based on SADL supporting both float and int16 model
- a common api supporting input preparation and inference of NN-based models
- two NN-based loop filter sets, activated with command line parameter --NnlfOption (0: disable NN filter; 1: Use NN-based loop filter set 0; 2: Use NN-based loop filter set 1)
- training scripts of the two NN-based loop filter sets
Build instructions
==================
Submodule
----------------------------------------------
The SADL sub module first need to be initialized the first time using, using the following commands:
```bash
git submodule init
git submodule update
```
The CMake tool is used to create platform-specific build files.
Although CMake may be able to generate 32-bit binaries, **it is generally suggested to build 64-bit binaries**. 32-bit binaries are not able to access more than 2GB of RAM, which will not be sufficient for coding larger image formats. Building in 32-bit environments is not tested and will not be supported.
......@@ -80,6 +99,12 @@ cd build
cmake .. -DCMAKE_BUILD_TYPE=Debug
```
When built in Unix-like environments, AVX512 is enabled by default for NN related code. Use the following commands to disable it (use SIMD instructions up to AVX2 instead).
```bash
cd build
cmake .. -DCMAKE_BUILD_TYPE=Release -DNNLF_BUILD_WITH_AVX512=0
```
Then type
```bash
make -j
......@@ -132,3 +157,303 @@ Install the needed toolchains:
pacman -S --needed base-devel mingw-w64-i686-toolchain mingw-w64-x86_64-toolchain git subversion mingw-w64-i686-cmake mingw-w64-x86_64-cmake
```
Data dumping
==================
The dumping of a dataset for a particular bitstream is obtained using:
```bash
DecoderAppStatic -b D_BlowingBubbles_416x240_50Hz_8bit_P420_37.bin --DumpBasename D_BlowingBubbles_416x240_50Hz_8bit_P420_37
```
The data to be dumped are controlled by macros in the file TypeDef.h (see below).
It will produce several data files and a json description of the dump.
We assume an encoder log file is also associated with each produced bitstream. The encoder log files should have necessary information (using the --Verbosity=6). The link between the decoder dump and the encoder configuration is done using the bitstream name.
The provided scripts will automatically create a json file containing all information (dumped data, original YUV, etc.) providing the correct directories.
Data format
----------------------------------------------
The data dumper use the following format for each component.
Note: a set of macros allows to enable/disable the data to be dumped/used.
- basename\_rec\_before\_dbf.yuv: reconstruction before deblocking filter (macro NNVC\_USE\_REC\_BEFORE\_DBF): YUV format, 10bits
- basename\_rec\_after\_dbf.yuv: reconstruction after deblocking filter (macro NNVC\_USE\_REC\_AFTER\_DBF): YUV format, 10bits
- basename\_pred.yuv: prediction (macro NNVC\_USE\_PRED): YUV format, 10bits
- basename\_bs.yuv: boundaries strength (macro NNVC\_USE\_BS): YUV format, 10bits. Boundaries strength of the deblocking filter, normalize on 10 bits
- basename\_partition\_cu\_average.yuv: partition information as cu average (macro NNVC\_USE\_PARTITION\_AS\_CU\_AVERAGE): YUV format, 10 bits. Contains the average of each CU.
- basename\_qp.dat: QP slice (macro NNVC\_USE\_QP): int32, binary format
- basename\_slicetype.dat: slice type (macro NNVC\_USE\_SLICETYPE): int32, binary format
Json file
----------------------------------------------
During data dump, a json file is produced for each bitstream, with the name basename.json.
The format of the json file is as follows:
```json
{
"suffix_rec_before_dbf": "_rec_before_dbf.yuv",
"suffix_pred": "_pred.yuv",
"suffix_partition_cu_average": "_partition_cu_average.yuv",
"suffix_bs": "_bs.yuv",
"suffix_qp": "_qp.dat",
"suffix_slicetype": "_slicetype.dat",
[
{
"qp_base" : 32,
"bsname": "D_BlowingBubbles_416x240_50Hz_8bit_P420_37.bin",
"basename": "D_BlowingBubbles_416x240_50Hz_8bit_P420_37",
"width": 416,
"height": 240,
"data_count": 65
}
]
}
```
If a particular data is present, the corresponding suffix variable is defined.
An array of one element is described:
- the widht and height of the frames
- the number of frames dumped (data\_count)
- the concatenation of a suffix and basename gives the full filename for this data
- bsname: help to find the correspondance between the dataset and the original encoded sequence
- qp\_base: QP base to encode the sequence
Build the database
----------------------------------------------
The database building is done in 2 passes.
First all individual dataset files are concatenated into one dataset file:
```bash
training/tools/concatenate_dataset.py --input_dir_json dir1 --input_dir_json dir2 --output_json pre_dataset.json
```
It will give a file with all individual json files concatenated:
```json
{
"suffix_rec_before_dbf": "_rec_before_dbf.yuv",
"suffix_pred": "_pred.yuv",
"suffix_partition_cu_average": "_partition_cu_average.yuv",
"suffix_bs": "_bs.yuv",
"suffix_qp": "_qp.dat",
"suffix_slicetype": "_slicetype.dat",
"data": [
{
"bsname": "D_RaceHorses_416x240_30Hz_8bit_P420_37_P2.bin",
"qp_base": 37,
"basename": "D_RaceHorses_416x240_30Hz_8bit_P420_37_P2",
"width": 416,
"height": 240,
"data_count": 33,
"dirname": "db"
},
{
"bsname": "D_BlowingBubbles_416x240_50Hz_8bit_P420_32_P1.bin",
"qp_base": 32,
"basename": "D_BlowingBubbles_416x240_50Hz_8bit_P420_32_P1",
"width": 416,
"height": 240,
"data_count": 65,
"dirname": "db"
},
...]
}
```
For each dataset, the directory _dirname_ is added to the data field.
The file is then consolidated with information from the encoder log or the encoder configuration file used to encode the sequence in order to extract the YUV original file and other information:
```bash
training/tools/concatenate_dataset.py --input_json pre_dataset.json --input_dir_encoder direnc1 --input_dir_encoder direnc2 --output_json dataset.json --log_extension log
```
Note: To use the encoder configuration files, use the the correction extension (for example --log_extension cfg).
It results in a file with all information needed by the data loader.
```json
{
"suffix_rec_before_dbf": "_rec_before_dbf.yuv",
"suffix_pred": "_pred.yuv",
"suffix_partition_cu_average": "_partition_cu_average.yuv",
"suffix_bs": "_bs.yuv",
"suffix_qp": "_qp.dat",
"suffix_slicetype": "_slicetype.dat",
"data": [
{
"bsname": "D_RaceHorses_416x240_30Hz_8bit_P420_37_P2.bin",
"qp_base": 37,
"basename": "D_RaceHorses_416x240_30Hz_8bit_P420_37_P2",
"width": 416,
"height": 240,
"data_count": 33,
"dirname": "db",
"original_yuv": "/home/library01/VCL_VIDEO/JVET/Linux/ClassD/D_RaceHorses_416x240_30Hz_8bit_P420.yuv",
"original_temporal_subsample": 1,
"original_frame_skip": 32,
"original_bitdepth": 8
},
{
"bsname": "D_BlowingBubbles_416x240_50Hz_8bit_P420_32_P1.bin",
"qp_base": 32,
"basename": "D_BlowingBubbles_416x240_50Hz_8bit_P420_32_P1",
"width": 416,
"height": 240,
"data_count": 65,
"dirname": "db",
"original_yuv": "/home/library01/VCL_VIDEO/JVET/Linux/ClassD/D_BlowingBubbles_416x240_50Hz_8bit_P420.yuv",
"original_temporal_subsample": 1,
"original_frame_skip": 0,
"original_bitdepth": 8
},
...
]
}
```
The following fields are added to each data:
- original\_yuv: location of the original yuv file
- original\_bitdepth: bitdepth of the original yuv file
- original\_frame\_skip: frame to skip in original yuv file (offset from the beginning to find the data)
- original\_temporal\_subsample: correspond to TemporalSubsampleRatio in VTM (used for AI configuration)
Note: the last 2 fields are present in the encoder logs only if the NNVC encoder has been used to generate the sequence. If not present, it is assume frame\_skip=0 and temporal\_subsample=1.
Data loading
==================
The module data_loader shows an example of data loading. The basic usage is as follows (see also the example program create\_unified\_dataset.py for an example of usage):
```python
import data_loader
patch_size=128
poc_list=0
# If `generate_type` is equal to 0, the data loading
# has some specificities, e.g. normalization, for NN-based loop filter set 0.
# If `generate_type` is equal to 1, the data loading
# has some specificities, e.g. normalization, for NN-based loop filter set 1.
generate_type=1
border_size=8
dl=data_loader.DataLoader(input_json,
patch_size,
poc_list,
generate_type=generate_type)
print("Nb samples available: {}".format(dl.nb_patches()))
print("Available components: {}".format(dl.components))
# list of components to extract
comps=["org_Y", "pred_Y", "qp_base"]
p = dl.getPatchData(42,comps,border_size)
# p now contains the 42th patches in the database
```
On the fly loading
----------------------------------------------
Call getPatchData on random index to create a batch of data.
Pro: do not use more disk space because data is created on the fly from original dataset.
Cons: slower to generate a batch. The loading function should be put in an async funciton to avoid I/O latency.
Offline loading
----------------------------------------------
One can use the create\_unified\_dataset.py program to create a large dataset of already prepared patches.
Pro: faster: only open one file and perform random reading inside.
Cons: consume more disk space (float patches, duplication of the data).
Full example of data dumping and data loading
==================
The script training/example/sample_test.sh shows a full example of data dumping, dataset creation and data loading.
The loaded data are put into one binary file with all the patches inside. This file can be used to fast I/O during training.
Finally, a sample program allows to visualize the resulting dumped patches.
Common api
==================
source/Lib/CommonLib/NNInference is a common api for unifiying input preparation and inference of NN models.
The common api supports preparing input from any dumped information such as reconstruction, prediction, partitioning, boundary strength, base QP, slice QP, and slice type.
Currently, input preparation and inference of the two NN-based loop filter sets are based on the common API.
NN-based loop filter
==================
There are two NN-based loop filter sets in the common software, which are both disabled by default (--NnlfOption=0).
NN-based loop filter set 0
----------------------------------------------
To activate NN-based loop filter set 0, use --NnlfOption=1, or equivalently -c cfg/nn-based/NnlfOption\_1.cfg
To specify a model path, use e.g. --ModelPath="models/", or equivalently -mp "models/". Note that model path should be specified at both encoder and decoder.
NN-based loop filter set 1
----------------------------------------------
To activate NN-based loop filter set 1, use --NnlfOption=2, or equivalently -c cfg/nn-based/NnlfOption\_2.cfg
To specify model paths, use e.g. following command lines. Note that model paths should be specified at both encoder and decoder.
--NnlfSet1InterLumaModel="models/NnlfSet1_LumaCNNFilter_InterSlice_int16.sadl"
--NnlfSet1InterChromaModel="models/NnlfSet1_ChromaCNNFilter_InterSlice_int16.sadl"
--NnlfSet1IntraLumaModel="models/NnlfSet1_LumaCNNFilter_IntraSlice_int16.sadl"
--NnlfSet1IntraChromaModel="models/NnlfSet1_ChromaCNNFilter_IntraSlice_int16.sadl"
NN-based loop filter encoder optimization
----------------------------------------------
To activate NN-based loop filter encoder optimization, use --EncNnlfOpt=1. Note that the encoder optimization is disabled by default.
To specify model paths, use e.g. following command lines. Note that model paths should be specified at encoder.
--RdoCnnlfInterLumaModel="models/RdNnlfSet1_LumaCNNFilter_InterSlice_int16.sadl"
--RdoCnnlfIntraLumaModel="models/RdNnlfSet1_LumaCNNFilter_IntraSlice_int16.sadl"
Low-complexity version of the neural network-based intra prediction mode
------------------------------------------------------------------------
When building VTM-11-NNVC, if the macro `JVET_AB0149_INTRA_PRED` is equal to 1 (default), the low-complexity version of the NN-based
intra prediction mode can be controlled using `--NnIntraPred`. By default, the encoder and decoder parameter `PrefixAbsolutePathsToGraphsOutput`
is equal to "models/intra" as it is assumed that the VTM-11-NNVC encoder/decoder executable is run from the root directory of the
project. If the VTM-11-NNVC encoder/decoder executable is run from a directory different from the root directory of the project,
please add the following argument when running the VTM-11-NNVC encoder/decoder executable: `--PrefixAbsolutePathsToGraphsOutput=path_to_directory_models_intra`,
where `path_to_directory_models_intra` is the path to the directory "models/intra" relatively to the directory from which the
VTM-11-NNVC encoder/decoder executable is run.
NN-based super resolution
------------------------------------------------------------------------
To activate NN-based super resolution, use --NnsrOption=1. And to successfully inference the codec, the decoder should output decoded YUVs.
For example, the decoder commands should contain "-o /dev/null".
Content-adaptive post-filter
------------------------------------------------------------------------
To activate the content-adaptive post-filter use one of the two following config file:
[cfg/nn-based/nnpf_int16.cfg](cfg/nn-based/nnpf_int16.cfg)
`--SEINNPostFilterCharacteristicsPayloadFilename4` should be replaced with the absolute path of the
corresponding NNR bitstream:
[models/post_filter/int16/nnr_bitstreams_int16](models/post_filter/int16/nnr_bitstreams_int16)
For both encoding and decoding, the models are specified with `--NnpfModelPath`. For each test, 3 models are
pre-trained models and one is the over-fitted model.
The following example applies for BlowingBubbles with QP 42:
```shell
MODEL0=models/post_filter/int16/base_models_int16/model0.sadl
MODEL1=models/post_filter/int16/base_models_int16/model1.sadl
MODEL2=models/post_filter/int16/base_models_int16/model2.sadl
MODEL3=models/post_filter/int16/overfitted_models_int16/nnr_D_BlowingBubbles_42.sadl
--NnpfModelPath=${MODEL0},${MODEL1},${MODEL2},${MODEL3}
```
[overfitted_models.json](models/post_filter/overfitted_models.json) indicates which model was over-fitted.
The NNPFC SEI messages are to be signalled **once** for the whole video sequence (poc = 0).
The bitrate of the SEI messages is computed in the same fashion as the video bitstream. Therefore,
if split encoding is considered, the overhead of the SEI messages needs to be readjusted to consider the
length of the whole video sequence (value reported in the encoder log).
Finally, the post-filtered reconstruction can be saved at the decoder side by adding the parameters
`--ReconFile=reco.yuv --NnpfReconFile=recoPost.yuv`. **Note** Both parameters must be provided.
cfg/encoder_lowdelay_P_vtm.cfg
View file @ ec39f991
......@@ -133,12 +133,15 @@ AMaxBT : 1
FastMIP : 0
FastLocalDualTreeMode : 2
NnIntraPred : 0
# Encoder optimization tools
AffineAmvrEncOpt : 0
MmvdDisNum : 6
ALFStrength : 1.0
ALFAllowPredefinedFilters : 1
CCALFStrength : 1.0
EncDbOpt : 1 # apply deblocking in RDO
### DO NOT ADD ANYTHING BELOW THIS LINE ###
### DO NOT DELETE THE EMPTY LINE BELOW ###
......
cfg/encoder_lowdelay_vtm.cfg
View file @ ec39f991
......@@ -137,12 +137,15 @@ AMaxBT : 1
FastMIP : 0
FastLocalDualTreeMode : 2
NnIntraPred : 0
# Encoder optimization tools
AffineAmvrEncOpt : 0
MmvdDisNum : 6
ALFStrength : 1.0
ALFAllowPredefinedFilters : 1
CCALFStrength : 1.0
EncDbOpt : 1 # apply deblocking in RDO
### DO NOT ADD ANYTHING BELOW THIS LINE ###
### DO NOT DELETE THE EMPTY LINE BELOW ###
......
cfg/encoder_randomaccess_vtm.cfg
View file @ ec39f991
......@@ -173,6 +173,7 @@ TemporalFilter : 1 # Enable/disable GOP Based Temporal
TemporalFilterFutureReference : 1 # Enable/disable reading future frames
TemporalFilterStrengthFrame8 : 0.95 # Enable filter at every 8th frame with given strength
TemporalFilterStrengthFrame16 : 1.5 # Enable filter at every 16th frame with given strength, longer intervals has higher priority
EncDbOpt : 1 # apply deblocking in RDO
### DO NOT ADD ANYTHING BELOW THIS LINE ###
### DO NOT DELETE THE EMPTY LINE BELOW ###
......
cfg/encoder_randomaccess_vtm_gop16.cfg
View file @ ec39f991
......@@ -157,6 +157,7 @@ TemporalFilter : 1 # Enable/disable GOP Based Temporal
TemporalFilterFutureReference : 1 # Enable/disable reading future frames
TemporalFilterStrengthFrame8 : 0.95 # Enable filter at every 8th frame with given strength
TemporalFilterStrengthFrame16 : 1.5 # Enable filter at every 16th frame with given strength, longer intervals has higher priority
EncDbOpt : 1 # apply deblocking in RDO
### DO NOT ADD ANYTHING BELOW THIS LINE ###
### DO NOT DELETE THE EMPTY LINE BELOW ###
......
cfg/nn-based/NnlfOption_1.cfg 0 → 100644
View file @ ec39f991
NnlfOption : 1 # NN-based loop filter, 0: disable NN filter; 1: Use NN-based loop filter set 0; 2: Use NN-based loop filter set 1
cfg/nn-based/NnlfOption_2.cfg 0 → 100644
View file @ ec39f991
NnlfOption : 2 # NN-based loop filter, 0: disable NN filter; 1: Use NN-based loop filter set 0; 2: Use NN-based loop filter set 1
NnlfSet1InferSizeBase : 128 # Base inference size of NN-based loop filter set 1
NnlfSet1InferSizeExtension : 8 # Extension of inference size of NN-based loop filter set 1
NnlfSet1MaxNumParams : 3 # Number of conditional parameters of NN-based loop filter set 1
\ No newline at end of file
cfg/nn-based/nnpf_float.cfg 0 → 100644
View file @ ec39f991
Nnpf: 1
SEINNPostFilterActivationEnabled: 1
SEINNPostFilterCharacteristicsEnabled: 1
SEINNPostFilterCharacteristicsNumFilters: 5
SEINNPostFilterCharacteristicsId0: 0
SEINNPostFilterCharacteristicsModeIdc0: 0
SEINNPostFilterCharacteristicsId1: 1
SEINNPostFilterCharacteristicsModeIdc1: 0
SEINNPostFilterCharacteristicsId2: 2
SEINNPostFilterCharacteristicsModeIdc2: 0
SEINNPostFilterCharacteristicsId3: 3
SEINNPostFilterCharacteristicsModeIdc3: 0
SEINNPostFilterCharacteristicsId4: 3
SEINNPostFilterCharacteristicsModeIdc4: 1
SEINNPostFilterCharacteristicsPurpose4: 1
SEINNPostFilterCharacteristicsComponentLastFlag4: 1
SEINNPostFilterCharacteristicsInpSampleIdc4: 1
SEINNPostFilterCharacteristicsInpOrderIdc4: 3
SEINNPostFilterCharacteristicsOutSampleIdc4: 1
SEINNPostFilterCharacteristicsOutOrderIdc4: 3
SEINNPostFilterCharacteristicsConstantPatchSizeFlag4: 1
SEINNPostFilterCharacteristicsPatchWidthMinus14: 127
SEINNPostFilterCharacteristicsPatchHeightMinus14: 127
SEINNPostFilterCharacteristicsOverlap4: 4
SEINNPostFilterCharacteristicsPaddingType4: 0
SEINNPostFilterCharacteristicsComplexityIdc4: 0
SEINNPostFilterCharacteristicsPayloadFilename4: test.nnr
cfg/nn-based/nnpf_int16.cfg 0 → 100644
View file @ ec39f991
Nnpf: 1
SEINNPostFilterActivationEnabled: 1
SEINNPostFilterCharacteristicsEnabled: 1
SEINNPostFilterCharacteristicsNumFilters: 5
SEINNPostFilterCharacteristicsId0: 0
SEINNPostFilterCharacteristicsModeIdc0: 0
SEINNPostFilterCharacteristicsId1: 1
SEINNPostFilterCharacteristicsModeIdc1: 0
SEINNPostFilterCharacteristicsId2: 2
SEINNPostFilterCharacteristicsModeIdc2: 0
SEINNPostFilterCharacteristicsId3: 3
SEINNPostFilterCharacteristicsModeIdc3: 0
SEINNPostFilterCharacteristicsId4: 3
SEINNPostFilterCharacteristicsModeIdc4: 1
SEINNPostFilterCharacteristicsPurpose4: 1
SEINNPostFilterCharacteristicsComponentLastFlag4: 1
SEINNPostFilterCharacteristicsInpSampleIdc4: 4
SEINNPostFilterCharacteristicsInpTensorBitDepthMinus84: 7
SEINNPostFilterCharacteristicsInpOrderIdc4: 3
SEINNPostFilterCharacteristicsOutSampleIdc4: 4
SEINNPostFilterCharacteristicsOutTensorBitDepthMinus84: 7
SEINNPostFilterCharacteristicsOutOrderIdc4: 3
SEINNPostFilterCharacteristicsConstantPatchSizeFlag4: 1
SEINNPostFilterCharacteristicsPatchWidthMinus14: 127
SEINNPostFilterCharacteristicsPatchHeightMinus14: 127
SEINNPostFilterCharacteristicsOverlap4: 4
SEINNPostFilterCharacteristicsPaddingType4: 0
SEINNPostFilterCharacteristicsComplexityIdc4: 0
SEINNPostFilterCharacteristicsPayloadFilename4: test.nnr
cmake/CMakeBuild/bin/cmake.py
View file @ ec39f991
#!/usr/bin/python
#!/usr/bin/python3
#
# cmake.py
#
......
cmake/CMakeBuild/bin/cmake_docutil.py
View file @ ec39f991
#!/usr/bin/python
#!/usr/bin/python3
#
# cmake_docutil.py
#
......
cmake/CMakeBuild/bin/cmakebuild_update.py
View file @ ec39f991
#!/usr/bin/python
#!/usr/bin/python3
#
# cmakebuild_update.py
#
......
models/NnlfSet0_model_float.sadl 0 → 100644 LFS
View file @ ec39f991
File added
models/NnlfSet0_model_int16.sadl 0 → 100644 LFS
View file @ ec39f991
File added