diff --git a/Makefile b/Makefile index 278c3110a8e450baf74e8f0ab3665ae7092309e4..0ed4aa783c49260e5a9ddd3549ed2ae8e71121e6 100644 --- a/Makefile +++ b/Makefile @@ -18,7 +18,7 @@ BUILD_SCRIPT := $(CURDIR)/cmake/CMakeBuild/bin/cmake.py TARGETS := CommonLib DecoderAnalyserApp DecoderAnalyserLib DecoderApp DecoderLib TARGETS += EncoderApp EncoderLib Utilities SEIRemovalApp StreamMergeApp -SADL_HASH := "984728cf209873d4b0641a5a312015604b47085c" # hard coded because of windows $(shell git submodule status | grep sadl | cut -d' ' -f2) +SADL_HASH := "v10" # hard coded because of windows $(shell git submodule status | grep sadl | cut -d' ' -f2) ifeq ($(OS),Windows_NT) ifneq ($(MSYSTEM),) diff --git a/cfg/encoder_lowdelay_nnvc.cfg b/cfg/encoder_lowdelay_nnvc.cfg index 8dfda0b67a18929fccd17253faad16da3c534981..588e4e379d431b38b08c4c070d164cdec197a6f8 100644 --- a/cfg/encoder_lowdelay_nnvc.cfg +++ b/cfg/encoder_lowdelay_nnvc.cfg @@ -17,14 +17,14 @@ GOPSize : 8 # GOP Size (number of B slice = GOPS IntraQPOffset : -1 LambdaFromQpEnable : 1 # see JCTVC-X0038 for suitable parameters for IntraQPOffset, QPoffset, QPOffsetModelOff, QPOffsetModelScale when enabled # Type POC QPoffset QPOffsetModelOff QPOffsetModelScale CbQPoffset CrQPoffset QPfactor tcOffsetDiv2 betaOffsetDiv2 CbTcOffsetDiv2 CbBetaOffsetDiv2 CrTcOffsetDiv2 CrBetaOffsetDiv2 temporal_id #ref_pics_active_L0 #ref_pics_L0 reference_pictures_L0 #ref_pics_active_L1 #ref_pics_L1 reference_pictures_L1 -Frame1: B 1 5 -6.5 0.2590 0 0 1.0 0 0 0 0 0 0 0 4 4 1 9 17 25 4 4 1 9 17 25 -Frame2: B 2 4 -6.5 0.2590 0 0 1.0 0 0 0 0 0 0 0 4 4 1 2 10 18 4 4 1 2 10 18 -Frame3: B 3 5 -6.5 0.2590 0 0 1.0 0 0 0 0 0 0 0 4 4 1 3 11 19 4 4 1 3 11 19 -Frame4: B 4 4 -6.5 0.2590 0 0 1.0 0 0 0 0 0 0 0 4 4 1 4 12 20 4 4 1 4 12 20 -Frame5: B 5 5 -6.5 0.2590 0 0 1.0 0 0 0 0 0 0 0 4 4 1 5 13 21 4 4 1 5 13 21 -Frame6: B 6 4 -6.5 0.2590 0 0 1.0 0 0 0 0 0 0 0 4 4 1 6 14 22 4 4 1 6 14 22 -Frame7: B 7 5 -6.5 0.2590 0 0 1.0 0 0 0 0 0 0 0 4 4 1 7 15 23 4 4 1 7 15 23 -Frame8: B 8 1 0.0 0.0 0 0 1.0 0 0 0 0 0 0 0 4 4 1 8 16 24 4 4 1 8 16 24 +Frame1: B 1 6 -6.5 0.2450 0 0 1.0 0 0 0 0 0 0 0 4 4 1 9 17 25 4 4 1 3 5 33 +Frame2: B 2 4 -6.5 0.2590 0 0 1.0 0 0 0 0 0 0 0 4 4 1 2 10 18 4 4 1 2 4 26 +Frame3: B 3 6 -6.5 0.2450 0 0 1.0 0 0 0 0 0 0 0 4 4 1 3 11 19 4 4 1 3 5 27 +Frame4: B 4 4 -6.5 0.2590 0 0 1.0 0 0 0 0 0 0 0 4 4 1 4 12 20 4 4 1 2 4 28 +Frame5: B 5 6 -6.5 0.2450 0 0 1.0 0 0 0 0 0 0 0 4 4 1 5 13 21 4 4 1 3 5 29 +Frame6: B 6 4 -6.5 0.2590 0 0 1.0 0 0 0 0 0 0 0 4 4 1 6 14 22 4 4 1 2 6 30 +Frame7: B 7 6 -6.5 0.2450 0 0 1.0 0 0 0 0 0 0 0 4 4 1 7 15 23 4 4 1 3 7 31 +Frame8: B 8 1 0.0 0.0 0 0 1.0 0 0 0 0 0 0 0 4 4 1 8 16 24 4 4 1 2 4 32 #=========== Motion Search ============= FastSearch : 1 # 0:Full search 1:TZ search diff --git a/cfg/encoder_lowdelay_vtm.cfg b/cfg/encoder_lowdelay_vtm.cfg index 4d14a1e45ec219c0795471b61f866a10e44e39ab..4e9e1e2b1fb0e2ac998fc3a7bb470d9823cb3dbd 100644 --- a/cfg/encoder_lowdelay_vtm.cfg +++ b/cfg/encoder_lowdelay_vtm.cfg @@ -17,14 +17,14 @@ GOPSize : 8 # GOP Size (number of B slice = GOPS IntraQPOffset : -1 LambdaFromQpEnable : 1 # see JCTVC-X0038 for suitable parameters for IntraQPOffset, QPoffset, QPOffsetModelOff, QPOffsetModelScale when enabled # Type POC QPoffset QPOffsetModelOff QPOffsetModelScale CbQPoffset CrQPoffset QPfactor tcOffsetDiv2 betaOffsetDiv2 CbTcOffsetDiv2 CbBetaOffsetDiv2 CrTcOffsetDiv2 CrBetaOffsetDiv2 temporal_id #ref_pics_active_L0 #ref_pics_L0 reference_pictures_L0 #ref_pics_active_L1 #ref_pics_L1 reference_pictures_L1 -Frame1: B 1 5 -6.5 0.2590 0 0 1.0 0 0 0 0 0 0 0 4 4 1 9 17 25 4 4 1 9 17 25 -Frame2: B 2 4 -6.5 0.2590 0 0 1.0 0 0 0 0 0 0 0 4 4 1 2 10 18 4 4 1 2 10 18 -Frame3: B 3 5 -6.5 0.2590 0 0 1.0 0 0 0 0 0 0 0 4 4 1 3 11 19 4 4 1 3 11 19 -Frame4: B 4 4 -6.5 0.2590 0 0 1.0 0 0 0 0 0 0 0 4 4 1 4 12 20 4 4 1 4 12 20 -Frame5: B 5 5 -6.5 0.2590 0 0 1.0 0 0 0 0 0 0 0 4 4 1 5 13 21 4 4 1 5 13 21 -Frame6: B 6 4 -6.5 0.2590 0 0 1.0 0 0 0 0 0 0 0 4 4 1 6 14 22 4 4 1 6 14 22 -Frame7: B 7 5 -6.5 0.2590 0 0 1.0 0 0 0 0 0 0 0 4 4 1 7 15 23 4 4 1 7 15 23 -Frame8: B 8 1 0.0 0.0 0 0 1.0 0 0 0 0 0 0 0 4 4 1 8 16 24 4 4 1 8 16 24 +Frame1: B 1 6 -6.5 0.2450 0 0 1.0 0 0 0 0 0 0 0 4 4 1 9 17 25 4 4 1 3 5 33 +Frame2: B 2 4 -6.5 0.2590 0 0 1.0 0 0 0 0 0 0 0 4 4 1 2 10 18 4 4 1 2 4 26 +Frame3: B 3 6 -6.5 0.2450 0 0 1.0 0 0 0 0 0 0 0 4 4 1 3 11 19 4 4 1 3 5 27 +Frame4: B 4 4 -6.5 0.2590 0 0 1.0 0 0 0 0 0 0 0 4 4 1 4 12 20 4 4 1 2 4 28 +Frame5: B 5 6 -6.5 0.2450 0 0 1.0 0 0 0 0 0 0 0 4 4 1 5 13 21 4 4 1 3 5 29 +Frame6: B 6 4 -6.5 0.2590 0 0 1.0 0 0 0 0 0 0 0 4 4 1 6 14 22 4 4 1 2 6 30 +Frame7: B 7 6 -6.5 0.2450 0 0 1.0 0 0 0 0 0 0 0 4 4 1 7 15 23 4 4 1 3 7 31 +Frame8: B 8 1 0.0 0.0 0 0 1.0 0 0 0 0 0 0 0 4 4 1 8 16 24 4 4 1 2 4 32 #=========== Motion Search ============= FastSearch : 1 # 0:Full search 1:TZ search diff --git a/cfg/encoder_randomaccess_nnvc.cfg b/cfg/encoder_randomaccess_nnvc.cfg index cac5e183abf589993670880efc8f76528daaf926..87555ad9b86f761c7092916b8740460bb98f9fe0 100644 --- a/cfg/encoder_randomaccess_nnvc.cfg +++ b/cfg/encoder_randomaccess_nnvc.cfg @@ -17,38 +17,39 @@ GOPSize : 32 # GOP Size (number of B slice = GOPS IntraQPOffset : -3 LambdaFromQpEnable : 1 # see JCTVC-X0038 for suitable parameters for IntraQPOffset, QPoffset, QPOffsetModelOff, QPOffsetModelScale when enabled # Type POC QPoffset QPOffsetModelOff QPOffsetModelScale CbQPoffset CrQPoffset QPfactor tcOffsetDiv2 betaOffsetDiv2 CbTcOffsetDiv2 CbBetaOffsetDiv2 CrTcOffsetDiv2 CrBetaOffsetDiv2 temporal_id #ref_pics_active_L0 #ref_pics_L0 reference_pictures_L0 #ref_pics_active_L1 #ref_pics_L1 reference_pictures_L1 -Frame1 : B 32 -1 0.0 0.0 0 0 1.0 0 0 0 0 0 0 0 2 3 32 48 64 2 2 32 48 -Frame2 : B 16 0 -4.9309 0.2265 0 0 1.0 0 0 0 0 0 0 1 2 2 16 32 2 2 -16 16 -Frame3 : B 8 0 -4.5000 0.2353 0 0 1.0 0 0 0 0 0 0 2 2 2 8 24 2 2 -8 -24 -Frame4 : B 4 3 -5.4095 0.2571 0 0 1.0 0 0 0 0 0 0 3 2 2 4 20 2 3 -4 -12 -28 -Frame5 : B 2 5 -4.4895 0.1947 0 0 1.0 0 0 0 0 0 0 4 2 2 2 18 2 4 -2 -6 -14 -30 -Frame6 : B 1 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 2 1 -1 2 5 -1 -3 -7 -15 -31 -Frame7 : B 3 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 2 1 3 2 4 -1 -5 -13 -29 -Frame8 : B 6 5 -4.4895 0.1947 0 0 1.0 0 0 0 0 0 0 4 2 2 2 6 2 3 -2 -10 -26 -Frame9 : B 5 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 2 1 5 2 4 -1 -3 -11 -27 -Frame10 : B 7 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 3 1 3 7 2 3 -1 -9 -25 -Frame11 : B 12 3 -5.4095 0.2571 0 0 1.0 0 0 0 0 0 0 3 2 2 4 12 2 2 -4 -20 -Frame12 : B 10 5 -4.4895 0.1947 0 0 1.0 0 0 0 0 0 0 4 2 2 2 10 2 3 -2 -6 -22 -Frame13 : B 9 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 2 1 9 2 4 -1 -3 -7 -23 -Frame14 : B 11 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 3 1 3 11 2 3 -1 -5 -21 -Frame15 : B 14 5 -4.4895 0.1947 0 0 1.0 0 0 0 0 0 0 4 2 3 2 6 14 2 2 -2 -18 -Frame16 : B 13 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 3 1 5 13 2 3 -1 -3 -19 -Frame17 : B 15 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 4 1 3 7 15 2 2 -1 -17 -Frame18 : B 24 0 -4.5000 0.2353 0 0 1.0 0 0 0 0 0 0 2 2 2 8 24 2 2 -8 8 -Frame19 : B 20 3 -5.4095 0.2571 0 0 1.0 0 0 0 0 0 0 3 2 2 4 20 2 2 -4 -12 -Frame20 : B 18 5 -4.4895 0.1947 0 0 1.0 0 0 0 0 0 0 4 2 2 2 18 2 3 -2 -6 -14 -Frame21 : B 17 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 2 1 17 2 4 -1 -3 -7 -15 -Frame22 : B 19 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 3 1 3 19 2 3 -1 -5 -13 -Frame23 : B 22 5 -4.4895 0.1947 0 0 1.0 0 0 0 0 0 0 4 2 3 2 6 22 2 2 -2 -10 -Frame24 : B 21 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 3 1 5 21 2 3 -1 -3 -11 -Frame25 : B 23 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 4 1 3 7 23 2 2 -1 -9 -Frame26 : B 28 3 -5.4095 0.2571 0 0 1.0 0 0 0 0 0 0 3 2 3 4 12 28 2 2 -4 4 -Frame27 : B 26 5 -4.4895 0.1947 0 0 1.0 0 0 0 0 0 0 4 2 3 2 10 26 2 2 -2 -6 -Frame28 : B 25 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 3 1 9 25 2 3 -1 -3 -7 -Frame29 : B 27 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 4 1 3 11 27 2 2 -1 -5 -Frame30 : B 30 5 -4.4895 0.1947 0 0 1.0 0 0 0 0 0 0 4 2 3 2 14 30 2 2 -2 2 -Frame31 : B 29 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 3 1 13 29 2 2 -1 -3 -Frame32 : B 31 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 4 1 3 15 31 2 2 -1 1 +Frame1 : B 32 -1 0.0 0.0 0 0 1.0 0 0 0 0 0 0 0 2 5 32 64 48 40 36 1 2 32 48 +Frame2 : B 16 0 -4.9309 0.2265 0 0 1.0 0 0 0 0 0 0 1 3 5 16 32 48 24 20 1 1 -16 +Frame3 : B 8 1 -4.5000 0.1900 0 0 1.0 0 0 0 0 0 0 2 4 5 8 24 16 40 12 2 2 -8 -24 +Frame4 : B 4 3 -5.4095 0.2571 0 0 1.0 0 0 0 0 0 0 3 3 3 4 8 20 3 3 -4 -12 -28 +Frame5 : B 2 5 -4.4895 0.1947 0 0 1.0 0 0 0 0 0 0 4 3 3 2 6 18 4 4 -2 -6 -14 -30 +Frame6 : B 1 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 1 1 1 2 5 -1 -3 -7 -15 -31 +Frame7 : B 3 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 2 1 3 2 4 -1 -5 -13 -29 +Frame8 : B 6 5 -4.4895 0.1947 0 0 1.0 0 0 0 0 0 0 4 3 3 2 4 6 3 3 -2 -10 -26 +Frame9 : B 5 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 2 1 5 2 4 -1 -3 -11 -27 +Frame10 : B 7 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 3 1 3 7 2 3 -1 -9 -25 +Frame11 : B 12 3 -5.4095 0.2571 0 0 1.0 0 0 0 0 0 0 3 3 4 4 8 12 6 2 2 -4 -20 +Frame12 : B 10 5 -4.4895 0.1947 0 0 1.0 0 0 0 0 0 0 4 4 4 2 4 6 10 3 3 -2 -6 -22 +Frame13 : B 9 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 3 1 5 9 2 4 -1 -3 -7 -23 +Frame14 : B 11 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 3 1 3 11 2 3 -1 -5 -21 +Frame15 : B 14 5 -4.4895 0.1947 0 0 1.0 0 0 0 0 0 0 4 4 4 2 4 6 14 2 2 -2 -18 +Frame16 : B 13 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 3 1 5 13 2 3 -1 -3 -19 +Frame17 : B 15 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 4 1 3 7 15 2 2 -1 -17 +Frame18 : B 24 1 -4.5000 0.1900 0 0 1.0 0 0 0 0 0 0 2 3 3 8 16 24 1 1 -8 +Frame19 : B 20 3 -5.4095 0.2571 0 0 1.0 0 0 0 0 0 0 3 3 3 4 12 20 2 2 -4 -12 +Frame20 : B 18 5 -4.4895 0.1947 0 0 1.0 0 0 0 0 0 0 4 3 3 2 10 18 3 3 -2 -6 -14 +Frame21 : B 17 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 3 1 9 17 2 4 -1 -3 -7 -15 +Frame22 : B 19 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 3 1 3 19 2 3 -1 -5 -13 +Frame23 : B 22 5 -4.4895 0.1947 0 0 1.0 0 0 0 0 0 0 4 3 3 2 6 22 3 3 -2 -10 4 +Frame24 : B 21 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 3 1 5 21 2 3 -1 -3 -11 +Frame25 : B 23 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 4 1 3 7 23 2 2 -1 -9 +Frame26 : B 28 3 -5.4095 0.2571 0 0 1.0 0 0 0 0 0 0 3 4 4 4 8 12 28 1 1 -4 +Frame27 : B 26 5 -4.4895 0.1947 0 0 1.0 0 0 0 0 0 0 4 4 4 2 6 10 26 2 2 -2 -6 +Frame28 : B 25 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 4 1 5 9 25 2 3 -1 -3 -7 +Frame29 : B 27 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 4 1 3 11 27 2 2 -1 -5 +Frame30 : B 30 5 -4.4895 0.1947 0 0 1.0 0 0 0 0 0 0 4 4 4 2 6 14 30 1 1 -2 +Frame31 : B 29 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 4 1 5 13 29 2 2 -1 -3 +Frame32 : B 31 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 5 1 3 7 15 31 1 1 -1 + #=========== Motion Search ============= FastSearch : 1 # 0:Full search 1:TZ search diff --git a/cfg/encoder_randomaccess_vtm.cfg b/cfg/encoder_randomaccess_vtm.cfg index 7bfdff270739e848d2c7f73757d19f7a43278234..bdcff3b6145cfc10f4e96752f76c85bcd7ebd04b 100644 --- a/cfg/encoder_randomaccess_vtm.cfg +++ b/cfg/encoder_randomaccess_vtm.cfg @@ -17,38 +17,38 @@ GOPSize : 32 # GOP Size (number of B slice = GOPS IntraQPOffset : -3 LambdaFromQpEnable : 1 # see JCTVC-X0038 for suitable parameters for IntraQPOffset, QPoffset, QPOffsetModelOff, QPOffsetModelScale when enabled # Type POC QPoffset QPOffsetModelOff QPOffsetModelScale CbQPoffset CrQPoffset QPfactor tcOffsetDiv2 betaOffsetDiv2 CbTcOffsetDiv2 CbBetaOffsetDiv2 CrTcOffsetDiv2 CrBetaOffsetDiv2 temporal_id #ref_pics_active_L0 #ref_pics_L0 reference_pictures_L0 #ref_pics_active_L1 #ref_pics_L1 reference_pictures_L1 -Frame1 : B 32 -1 0.0 0.0 0 0 1.0 0 0 0 0 0 0 0 2 3 32 48 64 2 2 32 48 -Frame2 : B 16 0 -4.9309 0.2265 0 0 1.0 0 0 0 0 0 0 1 2 2 16 32 2 2 -16 16 -Frame3 : B 8 0 -4.5000 0.2353 0 0 1.0 0 0 0 0 0 0 2 2 2 8 24 2 2 -8 -24 -Frame4 : B 4 3 -5.4095 0.2571 0 0 1.0 0 0 0 0 0 0 3 2 2 4 20 2 3 -4 -12 -28 -Frame5 : B 2 5 -4.4895 0.1947 0 0 1.0 0 0 0 0 0 0 4 2 2 2 18 2 4 -2 -6 -14 -30 -Frame6 : B 1 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 2 1 -1 2 5 -1 -3 -7 -15 -31 -Frame7 : B 3 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 2 1 3 2 4 -1 -5 -13 -29 -Frame8 : B 6 5 -4.4895 0.1947 0 0 1.0 0 0 0 0 0 0 4 2 2 2 6 2 3 -2 -10 -26 -Frame9 : B 5 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 2 1 5 2 4 -1 -3 -11 -27 -Frame10 : B 7 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 3 1 3 7 2 3 -1 -9 -25 -Frame11 : B 12 3 -5.4095 0.2571 0 0 1.0 0 0 0 0 0 0 3 2 2 4 12 2 2 -4 -20 -Frame12 : B 10 5 -4.4895 0.1947 0 0 1.0 0 0 0 0 0 0 4 2 2 2 10 2 3 -2 -6 -22 -Frame13 : B 9 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 2 1 9 2 4 -1 -3 -7 -23 -Frame14 : B 11 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 3 1 3 11 2 3 -1 -5 -21 -Frame15 : B 14 5 -4.4895 0.1947 0 0 1.0 0 0 0 0 0 0 4 2 3 2 6 14 2 2 -2 -18 -Frame16 : B 13 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 3 1 5 13 2 3 -1 -3 -19 -Frame17 : B 15 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 4 1 3 7 15 2 2 -1 -17 -Frame18 : B 24 0 -4.5000 0.2353 0 0 1.0 0 0 0 0 0 0 2 2 2 8 24 2 2 -8 8 -Frame19 : B 20 3 -5.4095 0.2571 0 0 1.0 0 0 0 0 0 0 3 2 2 4 20 2 2 -4 -12 -Frame20 : B 18 5 -4.4895 0.1947 0 0 1.0 0 0 0 0 0 0 4 2 2 2 18 2 3 -2 -6 -14 -Frame21 : B 17 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 2 1 17 2 4 -1 -3 -7 -15 -Frame22 : B 19 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 3 1 3 19 2 3 -1 -5 -13 -Frame23 : B 22 5 -4.4895 0.1947 0 0 1.0 0 0 0 0 0 0 4 2 3 2 6 22 2 2 -2 -10 -Frame24 : B 21 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 3 1 5 21 2 3 -1 -3 -11 -Frame25 : B 23 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 4 1 3 7 23 2 2 -1 -9 -Frame26 : B 28 3 -5.4095 0.2571 0 0 1.0 0 0 0 0 0 0 3 2 3 4 12 28 2 2 -4 4 -Frame27 : B 26 5 -4.4895 0.1947 0 0 1.0 0 0 0 0 0 0 4 2 3 2 10 26 2 2 -2 -6 -Frame28 : B 25 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 3 1 9 25 2 3 -1 -3 -7 -Frame29 : B 27 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 4 1 3 11 27 2 2 -1 -5 -Frame30 : B 30 5 -4.4895 0.1947 0 0 1.0 0 0 0 0 0 0 4 2 3 2 14 30 2 2 -2 2 -Frame31 : B 29 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 3 1 13 29 2 2 -1 -3 -Frame32 : B 31 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 4 1 3 15 31 2 2 -1 1 +Frame1 : B 32 -1 0.0 0.0 0 0 1.0 0 0 0 0 0 0 0 2 5 32 64 48 40 36 1 2 32 48 +Frame2 : B 16 0 -4.9309 0.2265 0 0 1.0 0 0 0 0 0 0 1 3 5 16 32 48 24 20 1 1 -16 +Frame3 : B 8 1 -4.5000 0.1900 0 0 1.0 0 0 0 0 0 0 2 4 5 8 24 16 40 12 2 2 -8 -24 +Frame4 : B 4 3 -5.4095 0.2571 0 0 1.0 0 0 0 0 0 0 3 3 3 4 8 20 3 3 -4 -12 -28 +Frame5 : B 2 5 -4.4895 0.1947 0 0 1.0 0 0 0 0 0 0 4 3 3 2 6 18 4 4 -2 -6 -14 -30 +Frame6 : B 1 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 1 1 1 2 5 -1 -3 -7 -15 -31 +Frame7 : B 3 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 2 1 3 2 4 -1 -5 -13 -29 +Frame8 : B 6 5 -4.4895 0.1947 0 0 1.0 0 0 0 0 0 0 4 3 3 2 4 6 3 3 -2 -10 -26 +Frame9 : B 5 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 2 1 5 2 4 -1 -3 -11 -27 +Frame10 : B 7 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 3 1 3 7 2 3 -1 -9 -25 +Frame11 : B 12 3 -5.4095 0.2571 0 0 1.0 0 0 0 0 0 0 3 3 4 4 8 12 6 2 2 -4 -20 +Frame12 : B 10 5 -4.4895 0.1947 0 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+Frame25 : B 23 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 4 1 3 7 23 2 2 -1 -9 +Frame26 : B 28 3 -5.4095 0.2571 0 0 1.0 0 0 0 0 0 0 3 4 4 4 8 12 28 1 1 -4 +Frame27 : B 26 5 -4.4895 0.1947 0 0 1.0 0 0 0 0 0 0 4 4 4 2 6 10 26 2 2 -2 -6 +Frame28 : B 25 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 4 1 5 9 25 2 3 -1 -3 -7 +Frame29 : B 27 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 4 1 3 11 27 2 2 -1 -5 +Frame30 : B 30 5 -4.4895 0.1947 0 0 1.0 0 0 0 0 0 0 4 4 4 2 6 14 30 1 1 -2 +Frame31 : B 29 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 4 1 5 13 29 2 2 -1 -3 +Frame32 : B 31 6 -5.4429 0.2429 0 0 1.0 0 0 0 0 0 0 5 2 5 1 3 7 15 31 1 1 -1 #=========== Motion Search ============= FastSearch : 1 # 0:Full search 1:TZ search diff --git a/cfg/nn-based/nnsr/nnsr.cfg b/cfg/nn-based/nnsr/nnsr.cfg index 6bb31b81cf38e5277582a52e2fc459deec38f2d2..a24e836744872c68f5b36cc517a1cf371c486bfe 100644 --- a/cfg/nn-based/nnsr/nnsr.cfg +++ b/cfg/nn-based/nnsr/nnsr.cfg @@ -1 +1,3 @@ +RPR : 1 NnsrOption : 1 +NnsrModelName : 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984728cf209873d4b0641a5a312015604b47085c +Subproject commit 5ceeaa74f750a3956ca373f2ca5ae90cc4481cc5 diff --git a/source/App/DecoderApp/DecApp.cpp b/source/App/DecoderApp/DecApp.cpp index 0cd85e0539678f8a70d6c394f73dbc96807231f0..79be9930cf9bec81e11caa4395df4c58b6691b2c 100644 --- a/source/App/DecoderApp/DecApp.cpp +++ b/source/App/DecoderApp/DecApp.cpp @@ -749,6 +749,9 @@ void DecApp::xCreateDecLib() #endif #if JVET_AH0096_CONTENT_ADAPTIVE_LOP m_cDecLib.setNnfuOutputFileStem(m_nnfuOutputFileStem); +#if JVET_AI0111_CONTENT_ADAPTIVE_LOP3 || JVET_AI0157_CONTENT_ADAPTIVE_VLOP + m_cDecLib.setNnfilterArch(m_nnFilterArch); +#endif #endif #endif @@ -1010,6 +1013,13 @@ void DecApp::xWriteOutput( PicList* pcListPic, uint32_t tId ) ChromaFormat chromaFormatIDC = sps->getChromaFormatIdc(); if( m_upscaledOutput ) { +#if JVET_AI0074_MULTIRATIO_RPR_NNSR + m_cVideoIOYuvReconFile[pcPic->layerId].writeUpscaledPicture( + *sps, *pcPic->cs->pps, pcPic->getRecoBuf(), m_outputColourSpaceConvert, m_packedYUVMode, + pcPic->cs->slice->getSliceQp(), pcPic->cs->pps->getPicInitQPMinus26() + 26, + pcPic->getNNInputSr(*sps, *pcPic->cs->pps), m_nnModel[NnModel::SUPER_RESOLUTION], m_upscaledOutput, + NUM_CHROMA_FORMAT, m_bClipOutputVideoToRec709Range); +#else #if JVET_AG0130_UNIFIED_SR m_cVideoIOYuvReconFile[pcPic->layerId].writeUpscaledPicture( *sps, *pcPic->cs->pps, pcPic->getRecoBuf(), m_outputColourSpaceConvert, m_packedYUVMode , pcPic->getPredBufCustom() @@ -1031,6 +1041,7 @@ void DecApp::xWriteOutput( PicList* pcListPic, uint32_t tId ) #endif , m_upscaledOutput, NUM_CHROMA_FORMAT, m_bClipOutputVideoToRec709Range ); +#endif #endif } else @@ -1146,6 +1157,13 @@ void DecApp::xWriteOutput( PicList* pcListPic, uint32_t tId ) ChromaFormat chromaFormatIDC = sps->getChromaFormatIdc(); if( m_upscaledOutput ) { +#if JVET_AI0074_MULTIRATIO_RPR_NNSR + m_cVideoIOYuvReconPostFile[pcPic->layerId].writeUpscaledPicture( + *sps, *pcPic->cs->pps, pcPic->getNnPostFilteredBuf(), m_outputColourSpaceConvert, m_packedYUVMode, + pcPic->cs->slice->getSliceQp(), pcPic->cs->pps->getPicInitQPMinus26() + 26, + pcPic->getNNInputSr(*sps, *pcPic->cs->pps), m_nnModel[NnModel::SUPER_RESOLUTION], m_upscaledOutput, + NUM_CHROMA_FORMAT, m_bClipOutputVideoToRec709Range); +#else #if JVET_AG0130_UNIFIED_SR m_cVideoIOYuvReconPostFile[pcPic->layerId].writeUpscaledPicture( *sps, *pcPic->cs->pps, pcPic->getNnPostFilteredBuf(), m_outputColourSpaceConvert, m_packedYUVMode, pcPic->getPredBufCustom(), @@ -1165,6 +1183,7 @@ void DecApp::xWriteOutput( PicList* pcListPic, uint32_t tId ) pcPic->cs->slice->getSliceType(), #endif m_upscaledOutput, NUM_CHROMA_FORMAT, m_bClipOutputVideoToRec709Range ); +#endif #endif } else @@ -1330,6 +1349,13 @@ void DecApp::xFlushOutput( PicList* pcListPic, const int layerId ) ChromaFormat chromaFormatIDC = sps->getChromaFormatIdc(); if( m_upscaledOutput ) { +#if JVET_AI0074_MULTIRATIO_RPR_NNSR + m_cVideoIOYuvReconFile[pcPic->layerId].writeUpscaledPicture( + *sps, *pcPic->cs->pps, pcPic->getRecoBuf(), m_outputColourSpaceConvert, m_packedYUVMode, + pcPic->cs->slice->getSliceQp(), pcPic->cs->pps->getPicInitQPMinus26() + 26, + pcPic->getNNInputSr(*sps, *pcPic->cs->pps), m_nnModel[NnModel::SUPER_RESOLUTION], m_upscaledOutput, + NUM_CHROMA_FORMAT, m_bClipOutputVideoToRec709Range); +#else #if JVET_AG0130_UNIFIED_SR m_cVideoIOYuvReconFile[pcPic->layerId].writeUpscaledPicture( *sps, *pcPic->cs->pps, pcPic->getRecoBuf(), m_outputColourSpaceConvert, m_packedYUVMode , pcPic->getPredBufCustom() @@ -1351,6 +1377,7 @@ void DecApp::xFlushOutput( PicList* pcListPic, const int layerId ) #endif , m_upscaledOutput, NUM_CHROMA_FORMAT, m_bClipOutputVideoToRec709Range ); +#endif #endif } else @@ -1466,6 +1493,13 @@ void DecApp::xFlushOutput( PicList* pcListPic, const int layerId ) ChromaFormat chromaFormatIDC = sps->getChromaFormatIdc(); if( m_upscaledOutput ) { +#if JVET_AI0074_MULTIRATIO_RPR_NNSR + m_cVideoIOYuvReconPostFile[pcPic->layerId].writeUpscaledPicture( + *sps, *pcPic->cs->pps, pcPic->getNnPostFilteredBuf(), m_outputColourSpaceConvert, m_packedYUVMode, + pcPic->cs->slice->getSliceQp(), pcPic->cs->pps->getPicInitQPMinus26() + 26, + pcPic->getNNInputSr(*sps, *pcPic->cs->pps), m_nnModel[NnModel::SUPER_RESOLUTION], m_upscaledOutput, + NUM_CHROMA_FORMAT, m_bClipOutputVideoToRec709Range); +#else #if JVET_AG0130_UNIFIED_SR m_cVideoIOYuvReconPostFile[pcPic->layerId].writeUpscaledPicture( *sps, *pcPic->cs->pps, pcPic->getNnPostFilteredBuf(), m_outputColourSpaceConvert, m_packedYUVMode, pcPic->getPredBufCustom(), @@ -1485,6 +1519,7 @@ void DecApp::xFlushOutput( PicList* pcListPic, const int layerId ) pcPic->cs->slice->getSliceType(), #endif m_upscaledOutput, NUM_CHROMA_FORMAT, m_bClipOutputVideoToRec709Range ); +#endif #endif } else diff --git a/source/App/DecoderApp/DecAppCfg.cpp b/source/App/DecoderApp/DecAppCfg.cpp index c01ab0b189befbe12ba428bdf0dc9bc9a08d6b26..757ccc9aee5a8c76981f1aa7fbf05812eb7482b6 100644 --- a/source/App/DecoderApp/DecAppCfg.cpp +++ b/source/App/DecoderApp/DecAppCfg.cpp @@ -95,6 +95,9 @@ bool DecAppCfg::parseCfg( int argc, char* argv[] ) #endif #if JVET_AH0096_CONTENT_ADAPTIVE_LOP ("NnfuOutputFileStem", m_nnfuOutputFileStem, string("/tmp/sequence_qp"), "Output dir for NNR bitstream and reconstructed sadl model\n") +#if JVET_AI0111_CONTENT_ADAPTIVE_LOP3 || JVET_AI0157_CONTENT_ADAPTIVE_VLOP + ("NnlfArch", m_nnFilterArch, string("lop3"), "Architecture of NN filter, i.e. lop2, lop3, vlop\n") +#endif #endif #endif diff --git a/source/App/DecoderApp/DecAppCfg.h b/source/App/DecoderApp/DecAppCfg.h index 477b67242907fecef5c3cd93dbc4ef6c86723432..46abfe18dc993c918028f32d8443c542718d16b7 100644 --- a/source/App/DecoderApp/DecAppCfg.h +++ b/source/App/DecoderApp/DecAppCfg.h @@ -74,6 +74,9 @@ protected: #endif #if JVET_AH0096_CONTENT_ADAPTIVE_LOP std::string m_nnfuOutputFileStem; ///< Directory to save the NNR bitstream and the reconstructed model +#if JVET_AI0111_CONTENT_ADAPTIVE_LOP3 || JVET_AI0157_CONTENT_ADAPTIVE_VLOP + std::string m_nnFilterArch; ///< Architecture of NNfilter, i.e. lop2, lop3 +#endif #endif #endif diff --git a/source/App/EncoderApp/EncApp.cpp b/source/App/EncoderApp/EncApp.cpp index fdd5164a9461bc78f7c0bb827d7692fc157dabb5..9fb74835dc57cbfdb3bcead68a3aa4f41fe94133 100644 --- a/source/App/EncoderApp/EncApp.cpp +++ b/source/App/EncoderApp/EncApp.cpp @@ -256,6 +256,9 @@ void EncApp::xInitLibCfg() m_cEncLib.setNnsrQpOffset (m_nnsrQpOffset); m_cEncLib.setNnsrQpOffsetOverridebyQp (m_sNnsrQpOffsetOverrideByQp); #endif +#if JVET_AI0074_MULTIRATIO_RPR_NNSR + m_cEncLib.setNnsrQpOffset2(m_nnsrQpOffset2); +#endif m_cEncLib.setProfile ( m_profile); m_cEncLib.setLevel ( m_levelTier, m_level); @@ -281,6 +284,10 @@ void EncApp::xInitLibCfg() m_cEncLib.setSourceHeight ( m_iSourceHeight ); m_cEncLib.setConformanceWindow ( m_confWinLeft / SPS::getWinUnitX( m_InputChromaFormatIDC ), m_confWinRight / SPS::getWinUnitX( m_InputChromaFormatIDC ), m_confWinTop / SPS::getWinUnitY( m_InputChromaFormatIDC ), m_confWinBottom / SPS::getWinUnitY( m_InputChromaFormatIDC ) ); m_cEncLib.setScalingRatio ( m_scalingRatioHor, m_scalingRatioVer ); +#if JVET_AI0074_MULTIRATIO_RPR_NNSR + m_cEncLib.setScalingRatio2(m_scalingRatioHor2, m_scalingRatioVer2); + m_cEncLib.setSingleScalingRatio(m_singleScalingRatio); +#endif m_cEncLib.setRprEnabled (m_rprEnabledFlag); m_cEncLib.setResChangeInClvsEnabled ( m_resChangeInClvsEnabled ); m_cEncLib.setSwitchPocPeriod ( m_switchPocPeriod ); @@ -1306,6 +1313,9 @@ void EncApp::xInitLibCfg() #if JVET_AB0149_SEARCH_PNN && JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS m_cEncLib.setAreCostsWritten(m_areCostsWritten); #endif +#if JVET_AI0130_COMB_NN_INTRA + m_cEncLib.setIsNnIspRestricted(m_isNnIspRestricted); +#endif #endif #if JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS m_cEncLib.setPtrOfstreamQuadtree(m_ofstreamQuadtree); @@ -1687,6 +1697,13 @@ void EncApp::xWriteOutput( int iNumEncoded, std::list<PelUnitBuf*>& recBufList ) #else const PPS& pps = *m_cEncLib.getPPS( ( sps.getMaxPicWidthInLumaSamples() != pcPicYuvRec->get( COMPONENT_Y ).width || sps.getMaxPicHeightInLumaSamples() != pcPicYuvRec->get( COMPONENT_Y ).height ) ? ENC_PPS_ID_RPR : 0 ); #endif +#if JVET_AI0074_MULTIRATIO_RPR_NNSR + m_cVideoIOYuvReconFile.writeUpscaledPicture( + sps, pps, *pcPicYuvRec, ipCSC, m_packedYUVMode, tempPic->cs->slice->getSliceQp(), + tempPic->cs->pps->getPicInitQPMinus26() + 26, tempPic->getNNInputSr(sps, *tempPic->cs->pps), + m_cEncLib.getNnModel(NnModel::SUPER_RESOLUTION), m_cEncLib.getUpscaledOutput(), NUM_CHROMA_FORMAT, + m_bClipOutputVideoToRec709Range); +#else #if JVET_AG0130_UNIFIED_SR m_cVideoIOYuvReconFile.writeUpscaledPicture( sps, pps, *pcPicYuvRec, ipCSC, m_packedYUVMode , tempPic->getPredBufCustom() @@ -1708,6 +1725,7 @@ void EncApp::xWriteOutput( int iNumEncoded, std::list<PelUnitBuf*>& recBufList ) #endif , m_cEncLib.getUpscaledOutput(), NUM_CHROMA_FORMAT, m_bClipOutputVideoToRec709Range ); +#endif #endif } else diff --git a/source/App/EncoderApp/EncAppCfg.cpp b/source/App/EncoderApp/EncAppCfg.cpp index a8b18ba57232352210a9888d1e9edb6751f7ad40..3cc0ae54e6a885c4f2b4fbd34d5afb7278e26da6 100644 --- a/source/App/EncoderApp/EncAppCfg.cpp +++ b/source/App/EncoderApp/EncAppCfg.cpp @@ -95,6 +95,9 @@ EncAppCfg::EncAppCfg() #if JVET_AB0149_SEARCH_PNN && JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS , m_areCostsWritten(false) #endif +#if JVET_AI0130_COMB_NN_INTRA +, m_isNnIspRestricted(false) +#endif #endif #if JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS , m_pathToQuadtreeRecords("") @@ -1526,7 +1529,7 @@ bool EncAppCfg::parseCfg( int argc, char* argv[] ) ( "RdoCnnlfInterLumaModelNNFilter1", m_nnModel[NnModel::RDO_CNN_INTER_LUMA_FILTER1], string("models/RdNnlfSet1_LumaCNNFilter_InterSlice_int16.sadl"), "NnlfSet1 inter luma model name for RDO") ( "RdoCnnlfIntraLumaModelNNFilter1", m_nnModel[NnModel::RDO_CNN_INTRA_LUMA_FILTER1], string("models/RdNnlfSet1_LumaCNNFilter_IntraSlice_int16.sadl"), "NnlfSet1 intra luma model name for RDO") #endif - ( "RPR", m_rprEnabledFlag, true, "Reference Sample Resolution" ) + ( "RPR", m_rprEnabledFlag, false, "Reference Sample Resolution" ) #if JVET_AG0130_UNIFIED_SR || JVET_AC0196_NNSR ( "NnsrOption", m_nnsrOption, false, "NN-based super resolution option") ( "NnsrModelName", m_nnModel[NnModel::SUPER_RESOLUTION], string("models/super_resolution/NNVC_SR_int16.sadl"), "unified nnsr model name. if set to default_rpr, use rpr instead of NN") @@ -1538,6 +1541,11 @@ bool EncAppCfg::parseCfg( int argc, char* argv[] ) ( "FractionNumFrames", m_fractionOfFrames, 1.0, "Encode a fraction of the specified in FramesToBeEncoded frames" ) ( "SwitchPocPeriod", m_switchPocPeriod, 0, "Switch POC period for RPR" ) ( "UpscaledOutput", m_upscaledOutput, 0, "Output upscaled (2), decoded but in full resolution buffer (1) or decoded cropped (0, default) picture for RPR" ) +#if JVET_AI0074_MULTIRATIO_RPR_NNSR + ("NnsrQpOffset2", m_nnsrQpOffset2, 4, "Base QP offset for Rate matching") // 4 + ("ScalingRatioHor2", m_scalingRatioHor2, 1.5, "Second scaling ratio in hor direction") + ("ScalingRatioVer2", m_scalingRatioVer2, 1.5, "Second scaling ratio in ver direction") +#endif ( "MaxLayers", m_maxLayers, 1, "Max number of layers" ) #if JVET_S0163_ON_TARGETOLS_SUBLAYERS ( "EnableOperatingPointInformation", m_OPIEnabled, false, "Enables writing of Operating Point Information (OPI)" ) @@ -1931,10 +1939,16 @@ bool EncAppCfg::parseCfg( int argc, char* argv[] ) m_nnsrOption = false; m_scalingRatioHor = 1.0; m_scalingRatioVer = 1.0; +#if JVET_AI0074_MULTIRATIO_RPR_NNSR + m_scalingRatioHor2 = 1.0; + m_scalingRatioVer2 = 1.0; +#endif } - CHECK(m_nnsrOption && m_nnModel[NnModel::SUPER_RESOLUTION] != "default_rpr" && - (m_scalingRatioHor != 2.0 || m_scalingRatioVer != 2.0), "NNSR does not support this specified scaling ratio."); +#if !JVET_AI0074_MULTIRATIO_RPR_NNSR + CHECK(m_nnsrOption && m_nnModel[NnModel::SUPER_RESOLUTION] != "default_rpr" && (m_scalingRatioHor != 2.0 || m_scalingRatioVer != 2.0), "NNSR does not support this specified scaling ratio."); +#endif } + m_upscaledOutput = (m_nnsrOption && (m_upscaledOutput != 0)) ? 2 : 0; #else m_nnsrOption = (m_nnsrOption && m_iSourceWidth >= NNSR_ENABLING_WIDTH && m_iSourceHeight >= NNSR_ENABLING_HEIGHT) ? true : false; m_scalingRatioHor = m_nnsrOption ? 2.0 : 1.0; @@ -1942,15 +1956,31 @@ bool EncAppCfg::parseCfg( int argc, char* argv[] ) m_upscaledOutput = (m_nnsrOption && (m_upscaledOutput != 0)) ? 2 : 0; #endif #endif +#if JVET_AI0074_MULTIRATIO_RPR_NNSR + m_resChangeInClvsEnabled = + m_scalingRatioHor != 1.0 || m_scalingRatioVer != 1.0 || m_scalingRatioHor2 != 1.0 || m_scalingRatioVer2 != 1.0; +#else m_resChangeInClvsEnabled = m_scalingRatioHor != 1.0 || m_scalingRatioVer != 1.0; +#endif #if JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS m_rprEnabledFlag = m_scalingRatioHor != 1.0 || m_scalingRatioVer != 1.0; #endif m_resChangeInClvsEnabled = m_resChangeInClvsEnabled && m_rprEnabledFlag; +#if JVET_AI0074_MULTIRATIO_RPR_NNSR + m_singleScalingRatio = true; + if ((m_scalingRatioHor != m_scalingRatioHor2) || (m_scalingRatioVer != m_scalingRatioVer2)) + { + m_singleScalingRatio = false; + } +#endif #if JVET_AG0130_UNIFIED_SR if(m_resChangeInClvsEnabled || m_nnsrOption) { +#if JVET_AI0074_MULTIRATIO_RPR_NNSR + if (m_singleScalingRatio) + { +#endif if(m_iQP < 32) m_resChangeInClvsEnabled = false; @@ -1972,8 +2002,14 @@ bool EncAppCfg::parseCfg( int argc, char* argv[] ) } m_nnsrQpOffset = qpOffsets[m_iQP]; } +#if JVET_AI0074_MULTIRATIO_RPR_NNSR + } +#endif } #endif +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + m_isNnIspRestricted = m_iIntraPeriod == 1; +#endif #if JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS if (m_resChangeInClvsEnabled) @@ -1984,9 +2020,17 @@ bool EncAppCfg::parseCfg( int argc, char* argv[] ) msg(ERROR, "`m_scalingRatioHor` does not contain an integer."); return false; } - if (integerPart != 2 && integerPart != 4 && integerPart != 8) + if (integerPart != 2 +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + && integerPart != 3 +#endif + && integerPart != 4 && integerPart != 8) { +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + msg(ERROR, "`m_scalingRatioHor` does not belong to {2, 3, 4, 8}."); +#else msg(ERROR, "`m_scalingRatioHor` does not belong to {2, 4, 8}."); +#endif return false; } if (m_iSourceWidth % (static_cast<int>(integerPart) << 3) != 0) @@ -1999,9 +2043,17 @@ bool EncAppCfg::parseCfg( int argc, char* argv[] ) msg(ERROR, "`m_scalingRatioVer` does not contain an integer."); return false; } - if (integerPart != 2 && integerPart != 4 && integerPart != 8) + if (integerPart != 2 +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + && integerPart != 3 +#endif + && integerPart != 4 && integerPart != 8) { +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + msg(ERROR, "`m_scalingRatioVer` does not belong to {2, 3, 4, 8}."); +#else msg(ERROR, "`m_scalingRatioVer` does not belong to {2, 4, 8}."); +#endif return false; } if (m_iSourceHeight % (static_cast<int>(integerPart) << 3) != 0) @@ -4991,6 +5043,9 @@ msg(VERBOSE, "Nnpf:%d ", m_nnpfcSEIEnabled && m_nnpfaSEIEnabled ? 1 : 0); msg(VERBOSE, "MIP:%d ", m_MIP); #if JVET_AB0149_INTRA_PRED msg(VERBOSE, "NnIntraPred:%d ", m_nnIntraPred); +#if JVET_AI0130_COMB_NN_INTRA + msg(VERBOSE, "IsNnIspRestricted:%d ", m_isNnIspRestricted); +#endif #endif msg(VERBOSE, "EncDbOpt:%d ", m_encDbOpt); msg( VERBOSE, "\nFAST TOOL CFG: " ); @@ -5020,6 +5075,12 @@ msg(VERBOSE, "Nnpf:%d ", m_nnpfcSEIEnabled && m_nnpfaSEIEnabled ? 1 : 0); if (m_resChangeInClvsEnabled) { +#if JVET_AI0074_MULTIRATIO_RPR_NNSR + if (!m_singleScalingRatio) + msg(VERBOSE, "RPR: 1st (%1.2lfx, %1.2lfx) 2nd (%1.2lfx, %1.2lfx) |%d ", m_scalingRatioHor, m_scalingRatioVer, + m_scalingRatioHor2, m_scalingRatioVer2, m_switchPocPeriod); + else +#endif msg( VERBOSE, "RPR:(%1.2lfx, %1.2lfx)|%d ", m_scalingRatioHor, m_scalingRatioVer, m_switchPocPeriod ); } else diff --git a/source/App/EncoderApp/EncAppCfg.h b/source/App/EncoderApp/EncAppCfg.h index 518bd9e199935190bf36b32dd51ab627270447f8..d87693e12b522300e758a6536cbed0aa36d56fd1 100644 --- a/source/App/EncoderApp/EncAppCfg.h +++ b/source/App/EncoderApp/EncAppCfg.h @@ -832,6 +832,12 @@ protected: bool m_rprEnabledFlag; double m_scalingRatioHor; double m_scalingRatioVer; +#if JVET_AI0074_MULTIRATIO_RPR_NNSR + double m_scalingRatioHor2; + double m_scalingRatioVer2; + int m_nnsrQpOffset2; + bool m_singleScalingRatio; +#endif bool m_resChangeInClvsEnabled; double m_fractionOfFrames; ///< encode a fraction of the frames as specified in FramesToBeEncoded int m_switchPocPeriod; @@ -876,6 +882,9 @@ protected: #if JVET_AB0149_SEARCH_PNN && JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS bool m_areCostsWritten; #endif +#if JVET_AI0130_COMB_NN_INTRA + bool m_isNnIspRestricted; +#endif #endif #if JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS std::string m_pathToQuadtreeRecords; diff --git a/source/Lib/CommonLib/CodingStructure.h b/source/Lib/CommonLib/CodingStructure.h index a037ecec76a9a48bdbaddd7d1a7a7400033da997..9be812b338e91e08f4b8a8e1bd5ec1d6be6a45a1 100644 --- a/source/Lib/CommonLib/CodingStructure.h +++ b/source/Lib/CommonLib/CodingStructure.h @@ -288,6 +288,9 @@ public: bool firstColorSpaceTestOnly; bool resetIBCBuffer; +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + int getNumTUs() const { return m_numTUs; } +#endif MotionBuf getMotionBuf( const Area& _area ); MotionBuf getMotionBuf( const UnitArea& _area ) { return getMotionBuf( _area.Y() ); } MotionBuf getMotionBuf() { return getMotionBuf( area.Y() ); } diff --git a/source/Lib/CommonLib/CommonDef.h b/source/Lib/CommonLib/CommonDef.h index b93f9c13d342c41eaadb9ce2e4c20a7c288e3332..6c6cc9365a4ac10e1b61d37b482d0c1425c612bb 100644 --- a/source/Lib/CommonLib/CommonDef.h +++ b/source/Lib/CommonLib/CommonDef.h @@ -553,6 +553,9 @@ static const int PLT_FAST_RATIO = 100; static const int EPBIN_WEIGHT_FACTOR = 4; #endif static const int ENC_PPS_ID_RPR = 3; +#if JVET_AI0074_MULTIRATIO_RPR_NNSR +static const int ENC_PPS_ID_RPR2 = 5; +#endif static const int SCALE_RATIO_BITS = 14; static const int MAX_SCALING_RATIO = 2; // max downsampling ratio for RPR static const std::pair<int, int> SCALE_1X = std::pair<int, int>( 1 << SCALE_RATIO_BITS, 1 << SCALE_RATIO_BITS ); // scale ratio 1x diff --git a/source/Lib/CommonLib/Contexts.cpp b/source/Lib/CommonLib/Contexts.cpp index 4dc76d8fdf8f087bea6943be8d91d1bdf72c8f19..3ceb53ca10f4f1bbac23267543f80f034f16ca69 100644 --- a/source/Lib/CommonLib/Contexts.cpp +++ b/source/Lib/CommonLib/Contexts.cpp @@ -670,10 +670,17 @@ const CtxSet ContextSetCfg::SaoTypeIdx = ContextSetCfg::addCtxSet const CtxSet ContextSetCfg::LFNSTIdx = ContextSetCfg::addCtxSet ({ +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + { 52, 37, 27, 37 }, + { 37, 45, 27, 45 }, + { 28, 52, 42, 52 }, + { 9, 9, 10, 9 } +#else { 52, 37, 27, }, { 37, 45, 27, }, { 28, 52, 42, }, { 9, 9, 10, }, +#endif }); @@ -727,18 +734,32 @@ const CtxSet ContextSetCfg::TransformSkipFlag = ContextSetCfg::addCtxSet const CtxSet ContextSetCfg::MTSIdx = ContextSetCfg::addCtxSet ({ +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + { 45, 25, 27, 0, 25, 46 }, + { 45, 40, 27, 0, 40, 46 }, + { 29, 0, 28, 0, 25, 46 }, + { 8, 0, 9, 0, DWS, DWS } +#else { 45, 25, 27, 0, }, { 45, 40, 27, 0, }, { 29, 0, 28, 0, }, { 8, 0, 9, 0, }, +#endif }); const CtxSet ContextSetCfg::ISPMode = ContextSetCfg::addCtxSet ({ +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + { 33, 43, 33 }, + { 33, 36, 33 }, + { 33, 43, 33 }, + { 9, 2, 9 } +#else { 33, 43, }, { 33, 36, }, { 33, 43, }, { 9, 2, }, +#endif }); const CtxSet ContextSetCfg::SbtFlag = ContextSetCfg::addCtxSet diff --git a/source/Lib/CommonLib/IntraPrediction.cpp b/source/Lib/CommonLib/IntraPrediction.cpp index c3026c0774dfcdbb5067659f55942a64cdcdcd06..84bd318b781b266a5cda8f70e75714318abdffb2 100644 --- a/source/Lib/CommonLib/IntraPrediction.cpp +++ b/source/Lib/CommonLib/IntraPrediction.cpp @@ -253,6 +253,12 @@ void IntraPrediction::setReferenceArrayLengths( const CompArea &area ) void IntraPrediction::predIntraAng( const ComponentID compId, PelBuf &piPred, const PredictionUnit &pu #if JVET_AB0149_INTRA_PRED , const bool& contextFlag +#if JVET_AI0130_COMB_NN_INTRA + , uint32_t& idxRepresentationPnn +#if JVET_AB0149_TM_INF_PNN + , uint32_t* const ptrIdxPredictedLfnstPnn +#endif +#endif #endif ) { @@ -281,7 +287,16 @@ void IntraPrediction::predIntraAng( const ComponentID compId, PelBuf &piPred, co case(DC_IDX): xPredIntraDc(srcBuf, piPred, channelType, false); break; case(BDPCM_IDX): xPredIntraBDPCM(srcBuf, piPred, isLuma(compID) ? pu.cu->bdpcmMode : pu.cu->bdpcmModeChroma, clpRng); break; #if JVET_AB0149_INTRA_PRED - case(PNN_IDX): xPredIntraPnn(contextFlag,piPred,srcBuf,sps.getBitDepth(channelType),compID,*pu.cu); + case(PNN_IDX): xPredIntraPnn(contextFlag,piPred,srcBuf,sps.getBitDepth(channelType),compID +#if JVET_AI0130_COMB_NN_INTRA + , idxRepresentationPnn +#if JVET_AB0149_TM_INF_PNN + , ptrIdxPredictedLfnstPnn +#endif +#else + , *pu.cu +#endif + ); break; #endif default: xPredIntraAng(srcBuf, piPred, channelType, clpRng); break; @@ -338,7 +353,16 @@ void IntraPrediction::predIntraChromaLM(const ComponentID compID, PelBuf &piPred } #if JVET_AB0149_INTRA_PRED -void IntraPrediction::xPredIntraPnn(const bool& contextFlag,PelBuf& pDst,const CPelBuf& srcBuf,const int& bitDepth,const ComponentID& compID,CodingUnit& cu) +void IntraPrediction::xPredIntraPnn(const bool& contextFlag,PelBuf& pDst,const CPelBuf& srcBuf,const int& bitDepth,const ComponentID& compID +#if JVET_AI0130_COMB_NN_INTRA + , uint32_t& idxRepresentationPnn +#if JVET_AB0149_TM_INF_PNN + , uint32_t* const ptrIdxPredictedLfnstPnn +#endif +#else + , CodingUnit& cu +#endif + ) { const SizeNn pairInit(pDst.height,pDst.width); transformations_context_prediction transfos; @@ -389,7 +413,11 @@ void IntraPrediction::xPredIntraPnn(const bool& contextFlag,PelBuf& pDst,const C CHECK(vector_argminmaxs.size() != NUM_LFNST_NUM_PER_SET - 1,"`vector_argmins.size()` is not equal to " + std::to_string(NUM_LFNST_NUM_PER_SET - 1) + "."); for (uint32_t i = 0; i < NUM_LFNST_NUM_PER_SET - 1; i++) { +#if JVET_AI0130_COMB_NN_INTRA + ptrIdxPredictedLfnstPnn[i] = vector_argminmaxs[i]; +#else cu.arrayIdxPredictedLfnstPnn[compID][i] = vector_argminmaxs[i]; +#endif } vector_argminmaxs.clear(); #endif @@ -400,12 +428,22 @@ void IntraPrediction::xPredIntraPnn(const bool& contextFlag,PelBuf& pDst,const C CHECK(vector_argminmaxs.size() != 1,"`vector_argmins.size()` is not equal to 1."); if (transfos.is_transposed) { - error_code = changeAdaptively(vector_argminmaxs[0],cu.idxRepresentationPnn); + error_code = changeAdaptively(vector_argminmaxs[0], +#if JVET_AI0130_COMB_NN_INTRA + idxRepresentationPnn +#else + cu.idxRepresentationPnn +#endif + ); CHECK(error_code < 0,"Error in `changeAdaptively`."); } else { +#if JVET_AI0130_COMB_NN_INTRA + idxRepresentationPnn = vector_argminmaxs[0]; +#else cu.idxRepresentationPnn = vector_argminmaxs[0]; +#endif } } #if INTERNAL_FLOAT @@ -427,12 +465,20 @@ void IntraPrediction::xPredIntraPnn(const bool& contextFlag,PelBuf& pDst,const C #if JVET_AB0149_TM_INF_PNN for (uint32_t i = 0; i < NUM_LFNST_NUM_PER_SET - 1; i++) { +#if JVET_AI0130_COMB_NN_INTRA + ptrIdxPredictedLfnstPnn[i] = 0; +#else cu.arrayIdxPredictedLfnstPnn[compID][i] = 0; +#endif } #endif if (isLuma(compID)) { +#if JVET_AI0130_COMB_NN_INTRA + idxRepresentationPnn = PLANAR_IDX; +#else cu.idxRepresentationPnn = PLANAR_IDX; +#endif } } } @@ -923,6 +969,9 @@ void IntraPrediction::geneIntrainterPred(const CodingUnit &cu) #if JVET_AB0149_INTRA_PRED bool is_context_extracted = false; bool contextFlag = false; +#if JVET_AI0130_COMB_NN_INTRA + uint32_t idxRepresentationPnn{MAX_INT}; +#endif #endif initIntraPatternChType(cu, pu->Y() #if JVET_AB0149_INTRA_PRED @@ -936,6 +985,12 @@ void IntraPrediction::geneIntrainterPred(const CodingUnit &cu) predIntraAng(COMPONENT_Y,cu.cs->getPredBuf(*pu).Y(),*pu #if JVET_AB0149_INTRA_PRED , contextFlag +#if JVET_AI0130_COMB_NN_INTRA + , idxRepresentationPnn +#if JVET_AB0149_TM_INF_PNN + , nullptr +#endif +#endif #endif ); int maxCompID = 1; @@ -956,6 +1011,12 @@ void IntraPrediction::geneIntrainterPred(const CodingUnit &cu) predIntraAng(COMPONENT_Cb,cu.cs->getPredBuf(*pu).Cb(),*pu #if JVET_AB0149_INTRA_PRED , contextFlag +#if JVET_AI0130_COMB_NN_INTRA + , idxRepresentationPnn +#if JVET_AB0149_TM_INF_PNN + , nullptr +#endif +#endif #endif ); @@ -971,6 +1032,12 @@ void IntraPrediction::geneIntrainterPred(const CodingUnit &cu) predIntraAng(COMPONENT_Cr,cu.cs->getPredBuf(*pu).Cr(),*pu #if JVET_AB0149_INTRA_PRED , contextFlag +#if JVET_AI0130_COMB_NN_INTRA + , idxRepresentationPnn +#if JVET_AB0149_TM_INF_PNN + , nullptr +#endif +#endif #endif ); } @@ -1066,13 +1133,23 @@ int get_width_height_masks_nn_in_pixels_from_neighboring_flags(const bool* const #if JVET_AB0149_INTRA_PRED || JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS void IntraPrediction::collectContext(const CompArea& area,const CodingUnit& cu,const int unitHeight,const int unitWidth #if JVET_AB0149_INTRA_PRED - , const Pel* const piRoiOrigin,const int iPicStride,const Pel* const refBufUnfiltered,const int bitdepth,bool& contextFlag,const bool is_context_extracted + ,const Pel* const piRoiOrigin,const int iPicStride +#if JVET_AI0130_COMB_NN_INTRA + ,const int16_t* const piSrcCb,const int& strideSrcCb +#endif + ,const Pel* const refBufUnfiltered,const int bitdepth,bool& contextFlag,const bool is_context_extracted +#if JVET_AI0130_COMB_NN_INTRA + ,const bool isNotTbFirst +#endif #endif #if JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS , const bool& isComputedHeightWidthMasks,int& heightMaskLeft,int& widthMaskAbove #endif ) { +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + CHECK(isNotTbFirst && !cu.ispMode,"`isNotTbFirst` is true and `cu.ispMode` is equal to 0."); +#endif const ChannelType chType = toChannelType(area.compID); const int tuHeight = static_cast<int>(area.height); const int tuWidth = static_cast<int>(area.width); @@ -1092,10 +1169,37 @@ void IntraPrediction::collectContext(const CompArea& area,const CodingUnit& cu,c bool arrayNeighborFlags[4*MAX_NUM_PART_IDXS_IN_CTU_WIDTH + 1]; memset(arrayNeighborFlags,0,totalUnits); arrayNeighborFlags[totalLeftUnits] = isAboveLeftAvailable(cu,chType,posLT); +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + if (isNotTbFirst && cu.ispMode == HOR_INTRA_SUBPARTITIONS) + { + for (int i{totalLeftUnits + 1}; i < totalLeftUnits + 1 + numAboveUnits; i++) + { + arrayNeighborFlags[i] = true; + } + } + else + { + isAboveAvailable(cu,chType,posLT,numAboveUnits,unitWidth,arrayNeighborFlags + totalLeftUnits + 1); + isAboveRightAvailable(cu,chType,posRT,numAboveRightUnits,unitWidth,arrayNeighborFlags + totalLeftUnits + 1 + numAboveUnits); + } + if (isNotTbFirst && cu.ispMode == VER_INTRA_SUBPARTITIONS) + { + for (int i{numLeftBelowUnits}; i < totalLeftUnits; i++) + { + arrayNeighborFlags[i] = true; + } + } + else + { + isLeftAvailable(cu,chType,posLT,numLeftUnits,unitHeight,arrayNeighborFlags + totalLeftUnits - 1); + isBelowLeftAvailable(cu,chType,posLB,numLeftBelowUnits,unitHeight,arrayNeighborFlags + totalLeftUnits - 1 - numLeftUnits); + } +#else isAboveAvailable(cu,chType,posLT,numAboveUnits,unitWidth,arrayNeighborFlags + totalLeftUnits + 1); isAboveRightAvailable(cu,chType,posRT,numAboveRightUnits,unitWidth,arrayNeighborFlags + totalLeftUnits + 1 + numAboveUnits); isLeftAvailable(cu, chType,posLT,numLeftUnits,unitHeight,arrayNeighborFlags + totalLeftUnits - 1); isBelowLeftAvailable(cu,chType,posLB,numLeftBelowUnits,unitHeight,arrayNeighborFlags + totalLeftUnits - 1 - numLeftUnits); +#endif int error_code{0}; #if JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS if (isComputedHeightWidthMasks) @@ -1160,7 +1264,19 @@ void IntraPrediction::collectContext(const CompArea& area,const CodingUnit& cu,c it->quantizer = input_quantizer_tf2cpp; } #endif - error_code = extract_pre_cin_optionally(piRoiOrigin,piPortionAbove,piPortionLeft,&arrayNeighborFlags[0],NULL,unitWidth,unitHeight,totalAboveUnits,totalLeftUnits,nbLinesAbove,nbLinesLeft,transfos.factor_downsampling_vertical,transfos.factor_downsampling_horizontal,iPicStride,-1,-1,scaleQuantizer,maskValue,transfos.is_transposed,is_fully_connected,m_mean_context); + error_code = extract_pre_cin_optionally(piRoiOrigin, +#if JVET_AI0130_COMB_NN_INTRA + piSrcCb, +#endif + piPortionAbove,piPortionLeft,&arrayNeighborFlags[0],NULL,unitWidth,unitHeight,totalAboveUnits,totalLeftUnits,nbLinesAbove,nbLinesLeft,transfos.factor_downsampling_vertical,transfos.factor_downsampling_horizontal,iPicStride, +#if JVET_AI0130_COMB_NN_INTRA + strideSrcCb, +#endif + -1,-1,scaleQuantizer,maskValue,transfos.is_transposed,is_fully_connected, +#if JVET_AI0130_COMB_NN_INTRA + isNotTbFirst && cu.ispMode == HOR_INTRA_SUBPARTITIONS,isNotTbFirst && cu.ispMode == VER_INTRA_SUBPARTITIONS, +#endif + m_mean_context); CHECK(error_code < 0,"Error in `extract_pre_cin_optionally`."); } } @@ -1195,14 +1311,28 @@ void IntraPrediction::initIntraPatternChType(const CodingUnit &cu, const CompAre // ----- Step 1: unfiltered reference samples ----- xFillReferenceSamples( cs.picture->getRecoBuf( area ), refBufUnfiltered, area, cu +#if !(JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA) #if JVET_AB0149_INTRA_PRED , contextFlag , is_context_extracted #endif #if JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS , isComputedHeightWidthMasks +#endif #endif ); +#if (JVET_AB0149_INTRA_PRED || JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS) && JVET_AI0130_COMB_NN_INTRA + const int unitHeight = isLuma(area.compID) && area.height <= 2 && cu.ispMode ? area.height : (cs.pcv)->minCUHeight >> ((cs.pcv)->noChroma2x2 && area.width == 4 ? 0 : getComponentScaleY(area.compID, (cs.sps)->getChromaFormatIdc())); + const int unitWidth = isLuma(area.compID) && area.width <= 2 && cu.ispMode ? area.width : (cs.pcv)->minCUWidth >> ((cs.pcv)->noChroma2x2 && area.width == 4 ? 0 : getComponentScaleX(area.compID, (cs.sps)->getChromaFormatIdc())); + collectContext(area,cu,unitHeight,unitWidth +#if JVET_AB0149_INTRA_PRED + ,cs.picture->getRecoBuf(area).buf,cs.picture->getRecoBuf(area).stride,NULL,-1,refBufUnfiltered,(cs.sps)->getBitDepth(toChannelType(area.compID)),contextFlag,is_context_extracted,false +#endif +#if JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS + , isComputedHeightWidthMasks,m_heightML,m_widthMA +#endif + ); +#endif // ----- Step 2: filtered reference samples ----- if( m_ipaParam.refFilterFlag || forceRefFilterFlag ) { @@ -1211,6 +1341,9 @@ void IntraPrediction::initIntraPatternChType(const CodingUnit &cu, const CompAre } void IntraPrediction::initIntraPatternChTypeISP(const CodingUnit& cu, const CompArea& area, PelBuf& recBuf, +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + bool& contextFlag,const bool& is_context_extracted, +#endif #if JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS const bool& isComputedHeightWidthMasks, #endif @@ -1226,6 +1359,10 @@ void IntraPrediction::initIntraPatternChTypeISP(const CodingUnit& cu, const Comp const Position posLT = area; bool isLeftAvail = (cs.getCURestricted(posLT.offset(-1, 0), cu, CHANNEL_TYPE_LUMA) != NULL) && cs.isDecomp(posLT.offset(-1, 0), CHANNEL_TYPE_LUMA); bool isAboveAvail = (cs.getCURestricted(posLT.offset(0, -1), cu, CHANNEL_TYPE_LUMA) != NULL) && cs.isDecomp(posLT.offset(0, -1), CHANNEL_TYPE_LUMA); +#if (JVET_AB0149_INTRA_PRED || JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS) && JVET_AI0130_COMB_NN_INTRA + const int unitHeight = area.height <= 2 ? area.height : (cs.pcv)->minCUHeight >> ((cs.pcv)->noChroma2x2 && area.width == 4 ? 0 : getComponentScaleY(area.compID, (cs.sps)->getChromaFormatIdc())); + const int unitWidth = area.width <= 2 ? area.width : (cs.pcv)->minCUWidth >> ((cs.pcv)->noChroma2x2 && area.width == 4 ? 0 : getComponentScaleX(area.compID, (cs.sps)->getChromaFormatIdc())); +#endif // ----- Step 1: unfiltered reference samples ----- if (cu.blocks[area.compID].x == area.x && cu.blocks[area.compID].y == area.y) { @@ -1241,18 +1378,30 @@ void IntraPrediction::initIntraPatternChTypeISP(const CodingUnit& cu, const Comp m_leftRefLength = cu.Y().height + area.height; m_topRefLength = cu.Y().width << 1; } -#if JVET_AB0149_INTRA_PRED +#if JVET_AB0149_INTRA_PRED && !JVET_AI0130_COMB_NN_INTRA bool contextFlag = false; #endif xFillReferenceSamples(cs.picture->getRecoBuf(cu.Y()), refBufUnfiltered, cu.Y(), cu +#if !(JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA) #if JVET_AB0149_INTRA_PRED , contextFlag , false #endif #if JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS , isComputedHeightWidthMasks +#endif #endif ); +#if (JVET_AB0149_INTRA_PRED || JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS) && JVET_AI0130_COMB_NN_INTRA + collectContext(area,cu,unitHeight,unitWidth +#if JVET_AB0149_INTRA_PRED + ,cs.picture->getRecoBuf(area).buf,cs.picture->getRecoBuf(area).stride,NULL,-1,refBufUnfiltered,(cs.sps)->getBitDepth(CHANNEL_TYPE_LUMA),contextFlag,is_context_extracted,false +#endif +#if JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS + , isComputedHeightWidthMasks,m_heightML,m_widthMA +#endif + ); +#endif // After having retrieved all the CU reference samples, the number of reference samples is now adjusted for the current subpartition m_topRefLength = cu.blocks[area.compID].width + area.width; @@ -1329,6 +1478,16 @@ void IntraPrediction::initIntraPatternChTypeISP(const CodingUnit& cu, const Comp dst++; } } +#if (JVET_AB0149_INTRA_PRED || JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS) && JVET_AI0130_COMB_NN_INTRA + collectContext(area,cu,unitHeight,unitWidth +#if JVET_AB0149_INTRA_PRED + ,cs.picture->getRecoBuf(area).buf,cs.picture->getRecoBuf(area).stride,recBuf.buf,recBuf.stride,m_refBuffer[area.compID][PRED_BUF_UNFILTERED],(cs.sps)->getBitDepth(CHANNEL_TYPE_LUMA),contextFlag,is_context_extracted,true +#endif +#if JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS + , isComputedHeightWidthMasks,m_heightML,m_widthMA +#endif + ); +#endif } // ----- Step 2: filtered reference samples ----- if (m_ipaParam.refFilterFlag || forceRefFilterFlag) @@ -1341,12 +1500,14 @@ void IntraPrediction::initIntraPatternChTypeISP(const CodingUnit& cu, const Comp void IntraPrediction::xFillReferenceSamples( const CPelBuf &recoBuf, Pel* refBufUnfiltered, const CompArea &area, const CodingUnit &cu +#if !(JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA) #if JVET_AB0149_INTRA_PRED , bool& contextFlag , const bool& is_context_extracted #endif #if JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS , const bool& isComputedHeightWidthMasks +#endif #endif ) { @@ -1596,7 +1757,7 @@ void IntraPrediction::xFillReferenceSamples( const CPelBuf &recoBuf, Pel* refBuf currUnit++; } } -#if JVET_AB0149_INTRA_PRED || JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS +#if (JVET_AB0149_INTRA_PRED || JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS) && !JVET_AI0130_COMB_NN_INTRA collectContext(area,cu,unitHeight,unitWidth #if JVET_AB0149_INTRA_PRED ,srcBuf,srcStride,refBufUnfiltered,sps.getBitDepth(chType),contextFlag,is_context_extracted diff --git a/source/Lib/CommonLib/IntraPrediction.h b/source/Lib/CommonLib/IntraPrediction.h index a023449cc0aafe1c570029dd16dca72e120f4175..64d8ef5f1d4dc1c68368e16a6ea8f8ff3625f3a3 100644 --- a/source/Lib/CommonLib/IntraPrediction.h +++ b/source/Lib/CommonLib/IntraPrediction.h @@ -129,7 +129,16 @@ protected: int m_topRefLength; int m_leftRefLength; #if JVET_AB0149_INTRA_PRED - void xPredIntraPnn(const bool& contextFlag,PelBuf& pDst,const CPelBuf& srcBuf,const int& bitDepth,const ComponentID& compID,CodingUnit& cu); + void xPredIntraPnn(const bool& contextFlag,PelBuf& pDst,const CPelBuf& srcBuf,const int& bitDepth,const ComponentID& compID +#if JVET_AI0130_COMB_NN_INTRA + , uint32_t& idxRepresentationPnn +#if JVET_AB0149_TM_INF_PNN + , uint32_t* const ptrIdxPredictedLfnstPnn +#endif +#else + , CodingUnit& cu +#endif + ); int changeAdaptively(const uint32_t idxIn,uint32_t& idxInT); #endif @@ -150,7 +159,14 @@ protected: #if JVET_AB0149_INTRA_PRED || JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS void collectContext(const CompArea& area,const CodingUnit& cu,const int unitHeight,const int unitWidth #if JVET_AB0149_INTRA_PRED - ,const Pel* const piRoiOrigin,const int iPicStride,const Pel* const refBufUnfiltered,const int bitdepth,bool& contextFlag,const bool is_context_extracted + ,const Pel* const piRoiOrigin,const int iPicStride +#if JVET_AI0130_COMB_NN_INTRA + ,const int16_t* const piSrcCb,const int& strideSrcCb +#endif + ,const Pel* const refBufUnfiltered, const int bitdepth,bool& contextFlag,const bool is_context_extracted +#if JVET_AI0130_COMB_NN_INTRA + , const bool isNotTbFirst +#endif #endif #if JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS , const bool& isComputedHeightWidthMasks,int& heightMaskLeft,int& widthMaskAbove @@ -159,12 +175,14 @@ void collectContext(const CompArea& area,const CodingUnit& cu,const int unitHeig #endif void xFillReferenceSamples ( const CPelBuf &recoBuf, Pel* refBufUnfiltered, const CompArea &area, const CodingUnit &cu +#if !(JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA) #if JVET_AB0149_INTRA_PRED , bool& contextFlag , const bool& is_context_extracted #endif #if JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS , const bool& isComputedHeightWidthMasks +#endif #endif ); void xFilterReferenceSamples(const Pel *refBufUnfiltered, Pel *refBufFiltered, const CompArea &area, const SPS &sps, @@ -193,6 +211,12 @@ public: void predIntraAng ( const ComponentID compId, PelBuf &piPred, const PredictionUnit &pu #if JVET_AB0149_INTRA_PRED , const bool& contextFlag +#if JVET_AI0130_COMB_NN_INTRA + , uint32_t& idxRepresentationPnn +#if JVET_AB0149_TM_INF_PNN + , uint32_t* const ptrIdxPredictedLfnstPnn +#endif +#endif #endif ); Pel *getPredictorPtr(const ComponentID compId) @@ -213,6 +237,9 @@ public: #endif const bool forceRefFilterFlag = false); void initIntraPatternChTypeISP (const CodingUnit& cu, const CompArea& area, PelBuf& piReco, +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + bool& contextFlag,const bool& is_context_extracted, +#endif #if JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS const bool& isComputedHeightWidthMasks, #endif diff --git a/source/Lib/CommonLib/NNFilterUnified.h b/source/Lib/CommonLib/NNFilterUnified.h index 18a9862777e75ca520c231e58b7c0443cd9b99d8..e1177a693559771f147ee18b0a45b13d1d7c4411 100644 --- a/source/Lib/CommonLib/NNFilterUnified.h +++ b/source/Lib/CommonLib/NNFilterUnified.h @@ -165,10 +165,6 @@ private: void resizeInputs(int width, int height); #endif -#if JVET_AH0096_CONTENT_ADAPTIVE_LOP - int m_modelLoadedIdx = -1; - bool m_changeModel = false; -#endif std::unique_ptr<sadl::Model<TypeSadlLFUnified>> m_model; std::vector<sadl::Tensor<TypeSadlLFUnified>> m_inputs; #if NN_HOP_UNIFIED_TEMPORAL_FILTERING diff --git a/source/Lib/CommonLib/NNSRUnified.cpp b/source/Lib/CommonLib/NNSRUnified.cpp index 4860ff61e9d64e900f4235190a354b2ef7be1e14..5c56a7eead6477a8d744799607c58c2f83925a0f 100644 --- a/source/Lib/CommonLib/NNSRUnified.cpp +++ b/source/Lib/CommonLib/NNSRUnified.cpp @@ -61,7 +61,288 @@ void NNSRUnified::initCNNModel(std::string modelFilePath) m_initFlag = true; } +#if JVET_AI0074_MULTIRATIO_RPR_NNSR +struct Input +{ + enum + { + RprRec = 0, + QPbase, + QPSlice, + SR, + nbInputs + }; +}; + +template<typename T> +static void fillInputFromBuf(UnitArea inferArea, sadl::Tensor<T> &input, CPelUnitBuf buf, bool luma, bool chroma, + double scale, int shift) +{ + CPelBuf 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 constexpr (!std::is_same_v<T, float>) + { + for (int yy = 0; yy < ver; yy++) + { + for (int xx = 0; xx < hor; xx++) + { + if (luma && !chroma) + { + input(0, yy, xx, 0) = bufY.at(xx, yy) << shift; + } + else if (!luma && chroma) + { + input(0, yy, xx, 0) = bufCb.at(xx, yy) << shift; + input(0, yy, xx, 1) = bufCr.at(xx, yy) << shift; + } + else if (luma && chroma) + { + input(0, yy, xx, 0) = bufY.at(xx, yy) << shift; + input(0, yy, xx, 1) = bufCb.at(xx >> 1, yy >> 1) << shift; + input(0, yy, xx, 2) = bufCr.at(xx >> 1, yy >> 1) << shift; + } + } + } + } + else + { + for (int yy = 0; yy < ver; yy++) + { + for (int xx = 0; xx < hor; xx++) + { + if (luma && !chroma) + { + input(0, yy, xx, 0) = bufY.at(xx, yy) / scale; + } + else if (!luma && chroma) + { + input(0, yy, xx, 0) = bufCb.at(xx, yy) / scale; + input(0, yy, xx, 1) = bufCr.at(xx, yy) / scale; + } + else if (luma && chroma) + { + input(0, yy, xx, 0) = bufY.at(xx, yy) / scale; + input(0, yy, xx, 1) = bufCb.at(xx >> 1, yy >> 1) / scale; + input(0, yy, xx, 2) = bufCr.at(xx >> 1, yy >> 1) / scale; + } + } + } + } +} + +template<typename T> +static void fillInputFromConstant(UnitArea inferArea, sadl::Tensor<T> &input, int c, bool luma, double scale, int shift) +{ + int hor, ver; + if (luma) + { + hor = inferArea.lwidth(); + ver = inferArea.lheight(); + } + else + { + hor = inferArea.lwidth() >> 1; + ver = inferArea.lheight() >> 1; + } + if constexpr (!std::is_same_v<T, float>) + { + for (int yy = 0; yy < ver; yy++) + { + for (int xx = 0; xx < hor; xx++) + { + input(0, yy, xx, 0) = round(c / scale * std::pow(2, shift)); + } + } + } + else + { + for (int yy = 0; yy < ver; yy++) + { + for (int xx = 0; xx < hor; xx++) + { + input(0, yy, xx, 0) = c / scale; + } + } + } +} + +template<typename T> +static void extractOutputs(sadl::Model<T> &m, PelUnitBuf bufDst, const CPelUnitBuf rprRecBuf, UnitArea inferArea, + int extLeft, int extRight, int extTop, int extBottom) +{ + auto output = m.result(0); + const int width = bufDst.Y().width; + const int height = bufDst.Y().height; + PelBuf &bufDstY = bufDst.get(COMPONENT_Y); + CPelBuf bufRecY = rprRecBuf.get(COMPONENT_Y); + + for (int c = 0; c < 4; ++c) // unshuffle on C++ side + { + for (int y = 0; y < height / 2; ++y) + { + for (int x = 0; x < width / 2; ++x) + { + int yy = (y * 2) + c / 2; + int xx = (x * 2) + c % 2; + if (xx < extLeft || yy < extTop || xx >= width - extRight || yy >= height - extBottom) + { + continue; + } + int out; + if constexpr (std::is_same<TypeSadlSr, float>::value) + { + out = round((output(0, y, x, c) + (float) bufRecY.at(xx, yy) / 1024) * 1024); + } + else + { + out = (output(0, y, x, c) + (bufRecY.at(xx, yy) << NNSRUnified::in_left_shift) + NNSRUnified::out_offset) + >> NNSRUnified::out_right_shift; + } + + bufDstY.at(xx, yy) = Pel(Clip3<int>(0, 1023, out)); + } + } + } + + PelBuf &bufDstCb = bufDst.get(COMPONENT_Cb); + PelBuf &bufDstCr = bufDst.get(COMPONENT_Cr); + CPelBuf bufRecCb = rprRecBuf.get(COMPONENT_Cb); + CPelBuf bufRecCr = rprRecBuf.get(COMPONENT_Cr); + + for (int y = 0; y < height / 2; ++y) + { + for (int x = 0; x < width / 2; ++x) + { + if (x < extLeft / 2 || y < extTop / 2 || x >= width / 2 - extRight / 2 || y >= height / 2 - extBottom / 2) + { + continue; + } + + int outCb; + int outCr; + if constexpr (std::is_same<TypeSadlSr, float>::value) + { + outCb = round((output(0, y, x, 4) + (float) bufRecCb.at(x, y) / 1024) * 1024); + outCr = round((output(0, y, x, 5) + (float) bufRecCr.at(x, y) / 1024) * 1024); + } + else + { + outCb = (output(0, y, x, 4) + (bufRecCb.at(x, y) << NNSRUnified::in_left_shift) + NNSRUnified::out_offset) + >> NNSRUnified::out_right_shift; + outCr = (output(0, y, x, 5) + (bufRecCr.at(x, y) << NNSRUnified::in_left_shift) + NNSRUnified::out_offset) + >> NNSRUnified::out_right_shift; + } + + bufDstCb.at(x, y) = Pel(Clip3<int>(0, 1023, outCb)); + bufDstCr.at(x, y) = Pel(Clip3<int>(0, 1023, outCr)); + } + } +} + +void NNSRUnified::CNNSRProcess(const CPelUnitBuf &rprRecBuf, PelUnitBuf &cnnBuf, const int actualHeight, + const int actualWidth, const int sliceQP, const int baseQP, const int sr) +{ + int patchSize = 256; + int padSize = 8; + + int iputLumaWidth = rprRecBuf.get(COMPONENT_Y).width; + int iputLumaHeight = rprRecBuf.get(COMPONENT_Y).height; + + int nbPatchsWidth = ceil((double) iputLumaWidth / patchSize); + int nbPatchsheight = ceil((double) iputLumaHeight / patchSize); + + std::vector<sadl::Tensor<TypeSadlSr>> m_Inputs; + m_Inputs = m_SRModel->getInputsTemplate(); + + const double inputScaleRec = 1024; + const double inputScaleQp = 64; + const double inputScaleSr = 2; + + int sizeW = 0; + int sizeH = 0; + for (int y = 0; y < nbPatchsheight; y++) + { + for (int x = 0; x < nbPatchsWidth; x++) + { + // obtain input + int patchTL_y = y * patchSize; + int patchTL_x = x * patchSize; + int patchBR_y = (patchTL_y + patchSize) > iputLumaHeight ? iputLumaHeight - 1 : patchTL_y + patchSize - 1; + int patchBR_x = (patchTL_x + patchSize) > iputLumaWidth ? iputLumaWidth - 1 : patchTL_x + patchSize - 1; + + int padPatchTL_y = (patchTL_y - padSize) < 0 ? 0 : patchTL_y - padSize; + int padPatchTL_x = (patchTL_x - padSize) < 0 ? 0 : patchTL_x - padSize; + int padPatchBR_y = (patchBR_y + padSize + 1) > iputLumaHeight ? patchBR_y : patchBR_y + padSize; + int padPatchBR_x = (patchBR_x + padSize + 1) > iputLumaWidth ? patchBR_x : patchBR_x + padSize; + + int extLeft = patchTL_x - padPatchTL_x; + int extRight = padPatchBR_x - patchBR_x; + int extTop = patchTL_y - padPatchTL_y; + int extBottom = padPatchBR_y - patchBR_y; + + int actualInputPatchSizeH = padPatchBR_y - padPatchTL_y + 1; + int actualInputPatchSizeW = padPatchBR_x - padPatchTL_x + 1; + + if (sizeW != actualInputPatchSizeW || sizeH != actualInputPatchSizeH) + { + sizeW = actualInputPatchSizeW; + sizeH = actualInputPatchSizeH; + m_Inputs[Input::RprRec].resize(sadl::Dimensions({ 1, sizeH, sizeW, 3 })); + m_Inputs[Input::QPbase].resize(sadl::Dimensions({ 1, sizeH, sizeW, 1 })); + m_Inputs[Input::QPSlice].resize(sadl::Dimensions({ 1, sizeH, sizeW, 1 })); + m_Inputs[Input::SR].resize(sadl::Dimensions({ 1, sizeH, sizeW, 1 })); + } + + const UnitArea inferArea(CHROMA_420, Area(padPatchTL_x, padPatchTL_y, sizeW, sizeH)); + + if (!m_SRModel->init(m_Inputs)) + { + std::cerr << "[ERROR] issue during initialization for init" << std::endl; + exit(-1); + } + + fillInputFromBuf<TypeSadlSr>(inferArea, m_Inputs[Input::RprRec], rprRecBuf.subBuf(inferArea), true, true, + inputScaleRec, NNSRUnified::in_left_shift); + fillInputFromConstant<TypeSadlSr>(inferArea, m_Inputs[Input::QPbase], baseQP, true, inputScaleQp, + NNSRUnified::sadl_quantizers_in); + fillInputFromConstant<TypeSadlSr>(inferArea, m_Inputs[Input::QPSlice], sliceQP, true, inputScaleQp, + NNSRUnified::sadl_quantizers_in); + fillInputFromConstant<TypeSadlSr>(inferArea, m_Inputs[Input::SR], sr, true, inputScaleSr, + NNSRUnified::sadl_quantizers_in); + if (!m_SRModel->apply(m_Inputs)) + { + std::cerr << "[ERROR] issue during inference" << std::endl; + exit(-1); + } + + extractOutputs<TypeSadlSr>(*m_SRModel, cnnBuf.subBuf(inferArea), rprRecBuf.subBuf(inferArea), inferArea, extLeft, + extRight, extTop, extBottom); + } + } +} +#else void NNSRUnified::CNNSRProcess(const CPelUnitBuf &recBuf, const PelUnitBuf &predBuf, const PelUnitBuf &rprBuf, const PelUnitBuf &bsBuf, const PelUnitBuf &bpmBuf, PelUnitBuf &cnnBuf, const int actualHeight, const int actualWidth, const int sliceQP, const int baseQP, const SliceType sliceType) @@ -210,4 +491,6 @@ void NNSRUnified::CNNSRProcess(const CPelUnitBuf &recBuf, const PelUnitBuf &pred } } +#endif + diff --git a/source/Lib/CommonLib/NNSRUnified.h b/source/Lib/CommonLib/NNSRUnified.h index 691abbb26cf8cf8112c3603e95e73c618a2bbe04..83d1ef02b0bb3ab26893f87cbec1056183887f6f 100644 --- a/source/Lib/CommonLib/NNSRUnified.h +++ b/source/Lib/CommonLib/NNSRUnified.h @@ -58,7 +58,20 @@ public: std::unique_ptr<sadl::Model<TypeSadlSr>> m_SRModel; void initCNNModel(std::string modelFilePath); +#if JVET_AI0074_MULTIRATIO_RPR_NNSR + static constexpr int org_quantizers_in = 10; + static constexpr int org_quantizers_out = 10; + static constexpr int sadl_quantizers_in = 11; + static constexpr int sadl_quantizers_out = 11; + static constexpr int in_left_shift = sadl_quantizers_in - org_quantizers_in; + static constexpr int out_right_shift = sadl_quantizers_out - org_quantizers_out; + static constexpr int out_offset = (1 << out_right_shift) / 2; + + void CNNSRProcess(const CPelUnitBuf &rprRecBuf, PelUnitBuf &cnnBuf, const int actualHeight, const int actualWidth, + const int sliceQP, const int baseQP, const int sr); +#else void CNNSRProcess(const CPelUnitBuf &recBuf, const PelUnitBuf &predBuf, const PelUnitBuf &rprBuf, const PelUnitBuf &bsBuf, const PelUnitBuf &bpmBuf, PelUnitBuf &cnnBuf, const int actualHeight, const int actualWidth, const int baseQP, const int sliceQP, const SliceType sliceType); +#endif }; #endif diff --git a/source/Lib/CommonLib/Picture.cpp b/source/Lib/CommonLib/Picture.cpp index e844a609c992a367cd432b459a06d01a7f538933..de8ac1e8488048012252a16fa65acb0b5213619e 100644 --- a/source/Lib/CommonLib/Picture.cpp +++ b/source/Lib/CommonLib/Picture.cpp @@ -474,7 +474,112 @@ const CPelBuf Picture::getNnPostFilteredBuf(const ComponentID compID) PelUnitBuf Picture::getNnPostFilteredBuf() { return M_BUFS(0, PIC_NN_POST_FILTERED); } const CPelUnitBuf Picture::getNnPostFilteredBuf() const { return M_BUFS(0, PIC_NN_POST_FILTERED); } #endif +#if JVET_AI0074_MULTIRATIO_RPR_NNSR +int Picture::getNNInputSr(const SPS &sps, const PPS &pps) +{ + static Window afterScaleWindowFullResolution; + + // decoder does not have information about upscaled picture scaling and conformance windows, store this information + // when full resolution picutre is encountered + if (sps.getMaxPicWidthInLumaSamples() == pps.getPicWidthInLumaSamples() + && sps.getMaxPicHeightInLumaSamples() == pps.getPicHeightInLumaSamples()) + { + afterScaleWindowFullResolution = pps.getScalingWindow(); + afterScaleWindowFullResolution = pps.getConformanceWindow(); + } + + int curPicWidth = sps.getMaxPicWidthInLumaSamples() + - SPS::getWinUnitX(sps.getChromaFormatIdc()) + * (afterScaleWindowFullResolution.getWindowLeftOffset() + + afterScaleWindowFullResolution.getWindowRightOffset()); + int curPicHeight = sps.getMaxPicHeightInLumaSamples() + - SPS::getWinUnitY(sps.getChromaFormatIdc()) + * (afterScaleWindowFullResolution.getWindowTopOffset() + + afterScaleWindowFullResolution.getWindowBottomOffset()); + + const Window &beforeScalingWindow = pps.getScalingWindow(); + int refPicWidth = pps.getPicWidthInLumaSamples() + - SPS::getWinUnitX(sps.getChromaFormatIdc()) + * (beforeScalingWindow.getWindowLeftOffset() + beforeScalingWindow.getWindowRightOffset()); + int refPicHeight = pps.getPicHeightInLumaSamples() + - SPS::getWinUnitY(sps.getChromaFormatIdc()) + * (beforeScalingWindow.getWindowTopOffset() + beforeScalingWindow.getWindowBottomOffset()); + + int xScale = ((curPicWidth << SCALE_RATIO_BITS) + (refPicWidth >> 1)) / refPicWidth; + int yScale = ((curPicHeight << SCALE_RATIO_BITS) + (refPicHeight >> 1)) / refPicHeight; + + int verFilter = 0; + int horFilter = 0; + + if (xScale > (15 << SCALE_RATIO_BITS) / 4) + { + horFilter = 7; + } + else if (xScale > (20 << SCALE_RATIO_BITS) / 7) + { + horFilter = 6; + } + else if (xScale > (5 << SCALE_RATIO_BITS) / 2) + { + horFilter = 5; + } + else if (xScale > (2 << SCALE_RATIO_BITS)) + { + horFilter = 4; + } + else if (xScale > (5 << SCALE_RATIO_BITS) / 3) + { + horFilter = 3; + } + else if (xScale > (5 << SCALE_RATIO_BITS) / 4) + { + horFilter = 2; + } + else if (xScale > (20 << SCALE_RATIO_BITS) / 19) + { + horFilter = 1; + } + + if (yScale > (15 << SCALE_RATIO_BITS) / 4) + { + verFilter = 7; + } + else if (yScale > (20 << SCALE_RATIO_BITS) / 7) + { + verFilter = 6; + } + else if (yScale > (5 << SCALE_RATIO_BITS) / 2) + { + verFilter = 5; + } + else if (yScale > (2 << SCALE_RATIO_BITS)) + { + verFilter = 4; + } + else if (yScale > (5 << SCALE_RATIO_BITS) / 3) + { + verFilter = 3; + } + else if (yScale > (5 << SCALE_RATIO_BITS) / 4) + { + verFilter = 2; + } + else if (yScale > (20 << SCALE_RATIO_BITS) / 19) + { + verFilter = 1; + } + if (horFilter == 3 && verFilter == 3) + { + return 1; // 2.0x + } + else if (horFilter == 2 && verFilter == 2) + { + return 2; // 1.5x + } + return 0; +} +#endif void Picture::finalInit( const VPS* vps, const SPS& sps, const PPS& pps, PicHeader *picHeader, APS** alfApss, APS* lmcsAps, APS* scalingListAps ) { for( auto &sei : SEIs ) @@ -1081,16 +1186,16 @@ void Picture::rescalePicture( const std::pair<int, int> scalingRatio, isLuma( compID ) ? 1 : horCollocatedChromaFlag, isLuma( compID ) ? 1 : verCollocatedChromaFlag ); } } - -#if JVET_AG0130_UNIFIED_SR -void Picture::rescalePictureCNN(const CPelUnitBuf& recBuf, const PelUnitBuf& predBuf, const PelUnitBuf& rprBuf, const PelUnitBuf &bsBuf, const PelUnitBuf &bpmBuf, PelUnitBuf& cnnBuf, int sliceQP, int baseQP, SliceType sliceType, std::string modelPath) +#if JVET_AI0074_MULTIRATIO_RPR_NNSR +void Picture::rescalePictureCNN(const PelUnitBuf &rprRecBuf, PelUnitBuf &cnnBuf, int sliceQP, int baseQP, int sr, + std::string modelPath) { - int actualHeight = rprBuf.get(COMPONENT_Y).height; - int actualWidth = rprBuf.get(COMPONENT_Y).width; + int actualHeight = rprRecBuf.get(COMPONENT_Y).height; + int actualWidth = rprRecBuf.get(COMPONENT_Y).width; static NNSRUnified m_CNNSR; m_CNNSR.initCNNModel(modelPath); - m_CNNSR.CNNSRProcess(recBuf, predBuf, rprBuf, bsBuf, bpmBuf, cnnBuf, actualHeight, actualWidth, sliceQP, baseQP, sliceType); + m_CNNSR.CNNSRProcess(rprRecBuf, cnnBuf, actualHeight, actualWidth, sliceQP, baseQP, sr); } #endif diff --git a/source/Lib/CommonLib/Picture.h b/source/Lib/CommonLib/Picture.h index d9631c17952bda64564d590fb5f15ed04e1dfa71..3e9105dd361df81db801c3b31f900384f134df63 100644 --- a/source/Lib/CommonLib/Picture.h +++ b/source/Lib/CommonLib/Picture.h @@ -202,7 +202,9 @@ const CPelBuf getBsMapBuf(const CompArea &blk) const; const CPelBuf getBlockPredModeBuf(const CompArea &blk) const; void dumpPicBpmInfo(); #endif - +#if JVET_AI0074_MULTIRATIO_RPR_NNSR + int getNNInputSr(const SPS &sps, const PPS &pps); +#endif PelBuf getRecoBuf(const ComponentID compID, bool wrap=false); const CPelBuf getRecoBuf(const ComponentID compID, bool wrap=false) const; PelBuf getRecoBuf(const CompArea &blk, bool wrap=false); @@ -266,6 +268,10 @@ const CPelBuf getBsMapBuf(const CompArea &blk) const; const PelUnitBuf& afterScaling, const Window& scalingWindowAfter, const ChromaFormat chromaFormatIDC, const BitDepths& bitDepths, const bool useLumaFilter, const bool downsampling, const bool horCollocatedChromaFlag, const bool verCollocatedChromaFlag ); +#if JVET_AI0074_MULTIRATIO_RPR_NNSR + static void rescalePictureCNN(const PelUnitBuf &rprRecBuf, PelUnitBuf &cnnBuf, int sliceQP, int baseQP, int sr, + std::string modelPath); +#endif #if JVET_AG0130_UNIFIED_SR static void rescalePictureCNN(const CPelUnitBuf& recBuf, const PelUnitBuf& predBuf, const PelUnitBuf& rprBuf, const PelUnitBuf &bsBuf, const PelUnitBuf &bpmBuf, PelUnitBuf& cnnBuf, int sliceQP, int baseQP, SliceType sliceType, std::string modelPath); #elif JVET_AC0196_NNSR @@ -438,7 +444,11 @@ public: #endif #if JVET_AG0130_UNIFIED_SR const PPS& pps = *slice.getPPS(); +#if JVET_AI0074_MULTIRATIO_RPR_NNSR + if (pps.getPPSId() >= ENC_PPS_ID_RPR) +#else if(pps.getPPSId() == ENC_PPS_ID_RPR) +#endif { m_picprm.nb_blocks_height = (pps.getPicHeightInLumaSamples() + m_picprm.block_size - 1) / m_picprm.block_size; m_picprm.nb_blocks_width = (pps.getPicWidthInLumaSamples() + m_picprm.block_size - 1) / m_picprm.block_size; diff --git a/source/Lib/CommonLib/Slice.cpp b/source/Lib/CommonLib/Slice.cpp index 7b8e92a400f904697821cdb5f9612fc049991229..adc67bfee2a4edea9461cb28294e84ab4dd8709c 100644 --- a/source/Lib/CommonLib/Slice.cpp +++ b/source/Lib/CommonLib/Slice.cpp @@ -2924,6 +2924,9 @@ SPS::SPS() , m_scalingMatrixDesignatedColourSpaceFlag( true ) #if JVET_AB0149_INTRA_PRED , m_nnIntraPredEnabledFlag(false) +#if JVET_AI0130_COMB_NN_INTRA +, m_isNnIspRestricted(false) +#endif #endif , m_disableScalingMatrixForLfnstBlks( true) { diff --git a/source/Lib/CommonLib/Slice.h b/source/Lib/CommonLib/Slice.h index bdeef36ced11ed31f4abe66e14377d61ed0945ab..145d83098b5dfb73322b08907b5c94b52498b3e6 100644 --- a/source/Lib/CommonLib/Slice.h +++ b/source/Lib/CommonLib/Slice.h @@ -1581,6 +1581,9 @@ private: bool m_scalingMatrixDesignatedColourSpaceFlag; #if JVET_AB0149_INTRA_PRED bool m_nnIntraPredEnabledFlag; +#if JVET_AI0130_COMB_NN_INTRA + bool m_isNnIspRestricted; +#endif #endif bool m_disableScalingMatrixForLfnstBlks; @@ -1650,6 +1653,10 @@ public: #if JVET_AB0149_INTRA_PRED bool getNnIntraPredEnabledFlag() const { return m_nnIntraPredEnabledFlag; } void setNnIntraPredEnabledFlag(bool b) { m_nnIntraPredEnabledFlag = b; } +#if JVET_AI0130_COMB_NN_INTRA + bool getIsNnIspRestricted() const { return m_isNnIspRestricted; } + void setIsNnIspRestricted(bool b) { m_isNnIspRestricted = b; } +#endif #endif bool getDisableScalingMatrixForLfnstBlks() const { return m_disableScalingMatrixForLfnstBlks; } void setDisableScalingMatrixForLfnstBlks(bool flag) { m_disableScalingMatrixForLfnstBlks = flag; } diff --git a/source/Lib/CommonLib/TrQuant.cpp b/source/Lib/CommonLib/TrQuant.cpp index 10f8b8b4b3413f5c8648afcbb64fc3cfb3103480..a5aa15fe91e0317413caec86b769be4021e4217a 100644 --- a/source/Lib/CommonLib/TrQuant.cpp +++ b/source/Lib/CommonLib/TrQuant.cpp @@ -380,6 +380,19 @@ void TrQuant::xInvLfnst( const TransformUnit &tu, const ComponentID compID ) #if JVET_AB0149_INTRA_PRED else if (intraMode == PNN_IDX) { +#if JVET_AI0130_COMB_NN_INTRA +#if JVET_AB0149_SEARCH_PNN || JVET_AB0149_TM_INF_PNN +#if JVET_AB0149_SEARCH_PNN + const uint32_t idxLfnstPnn{isLuma(compID) && (tu.cu)->ispMode ? 0 : (tu.cu)->idxLfnstPnn}; +#else + const uint32_t& idxLfnstPnn{isLuma(compID) ? ((tu.cu)->ispMode ? tu.arrayIndicesPredictedLfnstLumaPnn[(tu.cu)->lfnstIdx - 1] : (tu.cu)->arrayIdxPredictedLfnstPnn[COMPONENT_Y][(tu.cu)->lfnstIdx - 1]) : (tu.cu)->arrayIdxPredictedLfnstPnn[tu.jointCbCr ? COMPONENT_Cb : compID][(tu.cu)->lfnstIdx - 1]}; +#endif + CHECK(idxLfnstPnn > MAX_INT, "`idxLfnstPnn` is strictly larger than `MAX_INT`."); + idxLfnstPnnUsed = static_cast<int>(idxLfnstPnn); +#else + intraMode = PLANAR_IDX; +#endif +#else #if JVET_AB0149_SEARCH_PNN CHECK(tu.cu->idxLfnstPnn > MAX_INT,"`tu.cu->idxLfnstPnn` is strictly larger than `MAX_INT`."); idxLfnstPnnUsed = static_cast<int>(tu.cu->idxLfnstPnn); @@ -390,6 +403,7 @@ void TrQuant::xInvLfnst( const TransformUnit &tu, const ComponentID compID ) idxLfnstPnnUsed = static_cast<int>(idxLfnstPnn); #else intraMode = PLANAR_IDX; +#endif #endif } #endif @@ -545,6 +559,19 @@ void TrQuant::xFwdLfnst( const TransformUnit &tu, const ComponentID compID, cons #if JVET_AB0149_INTRA_PRED else if (intraMode == PNN_IDX) { +#if JVET_AI0130_COMB_NN_INTRA +#if JVET_AB0149_SEARCH_PNN || JVET_AB0149_TM_INF_PNN +#if JVET_AB0149_SEARCH_PNN + const uint32_t idxLfnstPnn{isLuma(compID) && (tu.cu)->ispMode ? 0 : (tu.cu)->idxLfnstPnn}; +#else + const uint32_t& idxLfnstPnn{isLuma(compID) ? ((tu.cu)->ispMode ? tu.arrayIndicesPredictedLfnstLumaPnn[(tu.cu)->lfnstIdx - 1] : (tu.cu)->arrayIdxPredictedLfnstPnn[COMPONENT_Y][(tu.cu)->lfnstIdx - 1]) : (tu.cu)->arrayIdxPredictedLfnstPnn[tu.jointCbCr ? COMPONENT_Cb : compID][(tu.cu)->lfnstIdx - 1]}; +#endif + CHECK(idxLfnstPnn > MAX_INT, "`idxLfnstPnn` is strictly larger than `MAX_INT`."); + idxLfnstPnnUsed = static_cast<int>(idxLfnstPnn); +#else + intraMode = PLANAR_IDX; +#endif +#else #if JVET_AB0149_SEARCH_PNN CHECK(tu.cu->idxLfnstPnn > MAX_INT,"`tu.cu->idxLfnstPnn` is strictly larger than `MAX_INT`."); idxLfnstPnnUsed = static_cast<int>(tu.cu->idxLfnstPnn); @@ -555,6 +582,7 @@ void TrQuant::xFwdLfnst( const TransformUnit &tu, const ComponentID compID, cons idxLfnstPnnUsed = static_cast<int>(idxLfnstPnn); #else intraMode = PLANAR_IDX; +#endif #endif } #endif diff --git a/source/Lib/CommonLib/TypeDef.h b/source/Lib/CommonLib/TypeDef.h index e188db088638e6c5a378acfdc0ad0d586c553234..9a7a6ffd92dcd5ba3c10f4a1b0b2fef8ade3364c 100644 --- a/source/Lib/CommonLib/TypeDef.h +++ b/source/Lib/CommonLib/TypeDef.h @@ -65,6 +65,10 @@ using TypeSadlLFUnified=int16_t; #define JVET_AH0096_CONTENT_ADAPTIVE_LOP 1 // JVET-AH0096: Content-adaptive loop-filter #define NN_LF_UNIFIED_ID 1 // unified the model switch using sps ID #define JVET_AH0205_AH0189_WIDE_ATTEN 1 // JVET-AH0205: Attention for HOP + AH0189 wide activation +#if JVET_AH0096_CONTENT_ADAPTIVE_LOP +#define JVET_AI0111_CONTENT_ADAPTIVE_LOP3 1 // JVET-AI0111: Content-adaptive loop-filter LOP3 +#define JVET_AI0157_CONTENT_ADAPTIVE_VLOP 1 // JVET-AI0157: Content-adaptive loop-filter VLOP +#endif #endif @@ -95,6 +99,7 @@ using TypeSadlIntra = int16_t; #define INTERNAL_FLOAT 1 using TypeSadlIntra = float; #endif +#define JVET_AI0130_COMB_NN_INTRA 1 #endif @@ -148,8 +153,9 @@ using TypeSadlIntra = float; #endif // NN super resolution +#define JVET_AI0074_MULTIRATIO_RPR_NNSR 1 // JVET-AI0074: EE1-4.1: Multiple scaling ratios coding for NNVC with NNSR #define JVET_AG0130_UNIFIED_SR 1 -#if JVET_AG0130_UNIFIED_SR +#if JVET_AG0130_UNIFIED_SR || JVET_AI0074_MULTIRATIO_RPR_NNSR #define JVET_AH0099_RPR 1 // JVET_AH0099: EE1-4.1.2 scaling factor s=1.5 #define ADAPTIVE_RPR 1 #define SR_FIXED_POINT_IMPLEMENTATION 1 @@ -164,8 +170,8 @@ using TypeSadlSr = float; #if JVET_AC0196_NNSR #define ADAPTIVE_RPR 0 // JVET-AC0196: GOP Level Adaptive Resampling #endif -#if (JVET_AG0130_UNIFIED_SR || JVET_AC0196_NNSR) && SR_DATA_DUMP && JVET_AB0149_INTRA_PRED && JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS -#error "'JVET_AG0130_UNIFIED_SR' is equal to 1 and 'SR_DATA_DUMP' is equal to 1 and `JVET_AB0149_INTRA_PRED` is equal to 1 and `JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS` is equal to 1." +#if (JVET_AG0130_UNIFIED_SR || JVET_AC0196_NNSR || JVET_AI0074_MULTIRATIO_RPR_NNSR) && SR_DATA_DUMP && JVET_AB0149_INTRA_PRED && JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS +#error "'JVET_AG0130_UNIFIED_SR' is equal to 1 and 'SR_DATA_DUMP' is equal to 1 and `JVET_AB0149_INTRA_PRED` is equal to 1 and `JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS` is equal to 1 and 'JVET_AI0074_MULTIRATIO_RPR_NNSR' is equal to 1." #endif // NN post-filtering @@ -1631,7 +1637,7 @@ enum NnModel FILTER_SET_1_ALT_INTER_LUMA, ///< alternative inter luma nnlf set1 model #endif #endif -#if JVET_AG0130_UNIFIED_SR +#if JVET_AG0130_UNIFIED_SR || JVET_AI0074_MULTIRATIO_RPR_NNSR SUPER_RESOLUTION, ///< nnsr model #endif #if NN_POST_FILTERING diff --git a/source/Lib/CommonLib/Unit.cpp b/source/Lib/CommonLib/Unit.cpp index 1009a1a0ebd5e05f87eb953c773d46266c539aa0..dc6d110d95c6c07b3fee21373d986c791ace30b1 100644 --- a/source/Lib/CommonLib/Unit.cpp +++ b/source/Lib/CommonLib/Unit.cpp @@ -893,6 +893,17 @@ void TransformUnit::initData() noResidual = false; jointCbCr = 0; m_chromaResScaleInv = 0; +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA +#if JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS + ssdLumaPnn = MAX_UINT64; + satdLumaPnn = MAX_UINT64; + std::fill(arrayMeasuresDiscrepanciesLumaPnn.begin(),arrayMeasuresDiscrepanciesLumaPnn.end(),MAX_DOUBLE); +#endif +#if JVET_AB0149_TM_INF_PNN + std::fill(arrayIndicesPredictedLfnstLumaPnn.begin(),arrayIndicesPredictedLfnstLumaPnn.end(),MAX_INT); +#endif + idxRepresentationPnn = MAX_INT; +#endif } void TransformUnit::init(TCoeff **coeffs, Pel **pcmbuf, bool **runType) { @@ -934,6 +945,17 @@ TransformUnit& TransformUnit::operator=(const TransformUnit& other) depth = other.depth; noResidual = other.noResidual; jointCbCr = other.jointCbCr; +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA +#if JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS + ssdLumaPnn = other.ssdLumaPnn; + satdLumaPnn = other.satdLumaPnn; + arrayMeasuresDiscrepanciesLumaPnn = other.arrayMeasuresDiscrepanciesLumaPnn; +#endif +#if JVET_AB0149_TM_INF_PNN + arrayIndicesPredictedLfnstLumaPnn = other.arrayIndicesPredictedLfnstLumaPnn; +#endif + idxRepresentationPnn = other.idxRepresentationPnn; +#endif return *this; } @@ -957,6 +979,20 @@ void TransformUnit::copyComponentFrom(const TransformUnit& other, const Componen mtsIdx[i] = other.mtsIdx[i]; noResidual = other.noResidual; jointCbCr = isChroma( i ) ? other.jointCbCr : jointCbCr; +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + if (isLuma(i)) + { +#if JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS + ssdLumaPnn = other.ssdLumaPnn; + satdLumaPnn = other.satdLumaPnn; + arrayMeasuresDiscrepanciesLumaPnn = other.arrayMeasuresDiscrepanciesLumaPnn; +#endif + idxRepresentationPnn = other.idxRepresentationPnn; +#if JVET_AB0149_TM_INF_PNN + arrayIndicesPredictedLfnstLumaPnn = other.arrayIndicesPredictedLfnstLumaPnn; +#endif + } +#endif } CoeffBuf TransformUnit::getCoeffs(const ComponentID id) { return CoeffBuf(m_coeffs[id], blocks[id]); } diff --git a/source/Lib/CommonLib/Unit.h b/source/Lib/CommonLib/Unit.h index 23730ddf93f59c8a764b4f6dd1712e7e5dd76475..f475e942f4facb351bdcc76250658bc6736a2493 100644 --- a/source/Lib/CommonLib/Unit.h +++ b/source/Lib/CommonLib/Unit.h @@ -329,6 +329,9 @@ struct CodingUnit : public UnitArea uint32_t arrayIdxPredictedLfnstPnn[MAX_NUM_COMPONENT][NUM_LFNST_NUM_PER_SET - 1]; #endif uint32_t idxRepresentationPnn; +#if JVET_AI0130_COMB_NN_INTRA + Pel arrayMemPred[256]; +#endif #endif uint8_t BcwIdx; int refIdxBi[2]; @@ -530,6 +533,17 @@ struct TransformUnit : public UnitArea bool cacheUsed; #endif +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA +#if JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS + uint64_t ssdLumaPnn; + uint64_t satdLumaPnn; + std::array<double, 2> arrayMeasuresDiscrepanciesLumaPnn; +#endif +#if JVET_AB0149_TM_INF_PNN + std::array<uint32_t, NUM_LFNST_NUM_PER_SET - 1> arrayIndicesPredictedLfnstLumaPnn; +#endif + uint32_t idxRepresentationPnn; +#endif private: TCoeff *m_coeffs[ MAX_NUM_TBLOCKS ]; Pel *m_pcmbuf[ MAX_NUM_TBLOCKS ]; diff --git a/source/Lib/CommonLib/UnitTools.cpp b/source/Lib/CommonLib/UnitTools.cpp index 7ba6a676a10abb10dfc099eab3a738faca0654d0..32a72587dae4d1a5d49595460901ea2470f11457 100644 --- a/source/Lib/CommonLib/UnitTools.cpp +++ b/source/Lib/CommonLib/UnitTools.cpp @@ -540,6 +540,17 @@ bool CU::allLumaCBFsAreZero(const CodingUnit& cu) } #if JVET_AB0149_INTRA_PRED +#if JVET_AI0130_COMB_NN_INTRA +uint32_t getIntraDirLumaSubstitutePnn(const PredictionUnit& pu) +{ + uint32_t indexModeLuma{PU::getCoLocatedIntraLumaMode(pu)}; + if (indexModeLuma == PNN_IDX) + { + indexModeLuma = PU::getCoLocatedIdxRepresentationPnn(pu); + } + return indexModeLuma; +} +#else uint32_t getIntraDirLumaSubstitutePnn(const uint32_t& indexModeLuma,const uint32_t& heightChroma,const uint32_t& widthChroma) { uint32_t indexSubstitution(indexModeLuma); @@ -553,6 +564,14 @@ uint32_t getIntraDirLumaSubstitutePnn(const uint32_t& indexModeLuma,const uint32 } return indexSubstitution; } +#endif + +#if JVET_AI0130_COMB_NN_INTRA +bool isPnnLimited(const uint32_t height, const uint32_t width) +{ + return (height == 4 && width == 4) || (height == 8 && width == 4) || (height == 4 && width == 8); +} +#endif bool decideShapeHandledPnn(const uint32_t& height,const uint32_t& width) { @@ -751,6 +770,45 @@ cTUTraverser CU::traverseTUs( const CodingUnit& cu ) return cTUTraverser( cu.firstTU, cu.lastTU->next ); } +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA +uint32_t getIdxRepresentationPnnNeighbor(const PredictionUnit& pu,CodingUnit* const ptrCuNeighbor,const Position& pos) +{ + CHECK(!ptrCuNeighbor, "`ptrCuNeighbor` is NULL."); + uint32_t intraDir{MAX_UINT}; + if (ptrCuNeighbor->ispMode) + { + TransformUnit tuDumb(ptrCuNeighbor->chromaFormat,pu.block(COMPONENT_Y)); + tuDumb.cu = ptrCuNeighbor; + tuDumb.cs = ptrCuNeighbor->cs; + tuDumb.idx = (tuDumb.cs)->getNumTUs() + 1; + const TransformUnit* const tuNeighbor{(pu.cs)->getTURestricted(pos,tuDumb,CHANNEL_TYPE_LUMA)}; + CHECK(!tuNeighbor, "`tuNeighbor` is NULL."); + intraDir = tuNeighbor->idxRepresentationPnn; + } + else + { + intraDir = ptrCuNeighbor->idxRepresentationPnn; + } + CHECK(intraDir < 0 || intraDir >= NUM_LUMA_MODE, "`intraDir` does not belong to [|0, " + std::to_string(NUM_LUMA_MODE - 1) + "|]."); + return intraDir; +} + +uint32_t PU::getCoLocatedIdxRepresentationPnn(const PredictionUnit& pu) +{ + const PredictionUnit& lumaPu{getCoLocatedLumaPU(pu)}; + CHECK(PU::getIntraDirLuma(lumaPu) != PNN_IDX, "The selected intra prediction mode of the colocated luminance CB is not the PNN mode."); + if ((lumaPu.cu)->ispMode) + { + const TransformUnit& lumaTu{getCoLocatedLumaTu(pu)}; + return lumaTu.idxRepresentationPnn; + } + else + { + return (lumaPu.cu)->idxRepresentationPnn; + } +} +#endif + // PU tools #if JVET_AB0149_INTRA_PRED @@ -768,8 +826,12 @@ int PU::getIntraMPMs(const PredictionUnit& pu,unsigned* const mpm,const bool& is leftIntraDir = PU::getIntraDirLuma( *puLeft ); if (leftIntraDir == PNN_IDX) { +#if JVET_AI0130_COMB_NN_INTRA + leftIntraDir = getIdxRepresentationPnnNeighbor(pu,puLeft->cu,area.bottomLeft().offset(-1, 0)); +#else leftIntraDir = (*puLeft->cu).idxRepresentationPnn; CHECK(leftIntraDir < 0 || leftIntraDir >= NUM_LUMA_MODE,"`leftIntraDir` does not belong to [|0, " + std::to_string(NUM_LUMA_MODE - 1) + "|]."); +#endif } } const PredictionUnit* const puAbove = pu.cs->getPURestricted(posRT.offset(0, -1), pu, channelType); @@ -778,8 +840,12 @@ int PU::getIntraMPMs(const PredictionUnit& pu,unsigned* const mpm,const bool& is aboveIntraDir = PU::getIntraDirLuma(*puAbove); if (aboveIntraDir == PNN_IDX) { +#if JVET_AI0130_COMB_NN_INTRA + aboveIntraDir = getIdxRepresentationPnnNeighbor(pu,puAbove->cu,area.topRight().offset(0, -1)); +#else aboveIntraDir = (*puAbove->cu).idxRepresentationPnn; CHECK(aboveIntraDir < 0 || aboveIntraDir >= NUM_LUMA_MODE,"`aboveIntraDir` does not belong to [|0, " + std::to_string(NUM_LUMA_MODE - 1) + "|]."); +#endif } } return fillListMPMs(mpm,leftIntraDir,aboveIntraDir,pu.multiRefIdx,is_shape_handled_pnn); @@ -942,7 +1008,11 @@ void PU::getIntraChromaCandModes( const PredictionUnit &pu, unsigned modeList[NU return; } #if JVET_AB0149_INTRA_PRED +#if JVET_AI0130_COMB_NN_INTRA + const uint32_t lumaMode{getIntraDirLumaSubstitutePnn(pu)}; +#else const uint32_t lumaMode(getIntraDirLumaSubstitutePnn(getCoLocatedIntraLumaMode(pu),pu.Cb().height,pu.Cb().width)); +#endif #else const uint32_t lumaMode = getCoLocatedIntraLumaMode(pu); #endif @@ -992,7 +1062,11 @@ uint32_t PU::getFinalIntraMode( const PredictionUnit &pu, const ChannelType &chT if( uiIntraMode == DM_CHROMA_IDX && !isLuma( chType ) ) { #if JVET_AB0149_INTRA_PRED +#if JVET_AI0130_COMB_NN_INTRA + uiIntraMode = getIntraDirLumaSubstitutePnn(pu); +#else uiIntraMode = getIntraDirLumaSubstitutePnn(getCoLocatedIntraLumaMode(pu),pu.Cb().height,pu.Cb().width); +#endif #else uiIntraMode = getCoLocatedIntraLumaMode(pu); #endif @@ -1015,6 +1089,15 @@ const PredictionUnit &PU::getCoLocatedLumaPU(const PredictionUnit &pu) return lumaPU; } +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA +const TransformUnit& PU::getCoLocatedLumaTu(const PredictionUnit& pu) +{ + const Position topLeftPos(pu.blocks[pu.chType].lumaPos()); + const Position refPos(topLeftPos.offset(pu.blocks[pu.chType].lumaSize().width >> 1,pu.blocks[pu.chType].lumaSize().height >> 1)); + return pu.cu->isSepTree() ? *pu.cs->picture->cs->getTU(refPos, CHANNEL_TYPE_LUMA) : *pu.cs->getTU(topLeftPos, CHANNEL_TYPE_LUMA); +} +#endif + uint32_t PU::getCoLocatedIntraLumaMode(const PredictionUnit &pu) { return PU::getIntraDirLuma(PU::getCoLocatedLumaPU(pu)); @@ -1024,11 +1107,27 @@ uint32_t PU::getCoLocatedIntraLumaMode(const PredictionUnit &pu) uint32_t PU::getCoLocatedIdxLfnstPnn(const PredictionUnit& pu,const uint32_t& lfnstIdx) { CHECK(lfnstIdx != 1 && lfnstIdx != 2,"`lfnstIdx` does not belong to {1, 2}."); +#if JVET_AI0130_COMB_NN_INTRA + const PredictionUnit& lumaPU{getCoLocatedLumaPU(pu)}; +#else Position topLeftPos(pu.blocks[pu.chType].lumaPos()); Position refPos(topLeftPos.offset(pu.blocks[pu.chType].lumaSize().width >> 1,pu.blocks[pu.chType].lumaSize().height >> 1)); const PredictionUnit& lumaPU(pu.cu->isSepTree() ? *pu.cs->picture->cs->getPU(refPos, CHANNEL_TYPE_LUMA) : *pu.cs->getPU(topLeftPos, CHANNEL_TYPE_LUMA)); +#endif CHECK(PU::getIntraDirLuma(lumaPU) != PNN_IDX,"The selected intra prediction mode of the colocated luminance CB is not the PNN mode."); +#if JVET_AI0130_COMB_NN_INTRA + if ((lumaPU.cu)->ispMode) + { + const TransformUnit& lumaTu{getCoLocatedLumaTu(pu)}; + return lumaTu.arrayIndicesPredictedLfnstLumaPnn[lfnstIdx - 1]; + } + else + { + return (lumaPU.cu)->arrayIdxPredictedLfnstPnn[COMPONENT_Y][lfnstIdx - 1]; + } +#else return (lumaPU.cu)->arrayIdxPredictedLfnstPnn[COMPONENT_Y][lfnstIdx - 1]; +#endif } #endif diff --git a/source/Lib/CommonLib/UnitTools.h b/source/Lib/CommonLib/UnitTools.h index 508a8191f27e6da2a57f469e926c7b619e90fcd7..cbd444cdb9b8d1cb3cd82c8d5ce9092fc81d9f00 100644 --- a/source/Lib/CommonLib/UnitTools.h +++ b/source/Lib/CommonLib/UnitTools.h @@ -135,6 +135,10 @@ namespace CU // PU tools namespace PU { +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + uint32_t getCoLocatedIdxRepresentationPnn(const PredictionUnit& pu); + const TransformUnit& getCoLocatedLumaTu(const PredictionUnit& pu); +#endif int getLMSymbolList(const PredictionUnit &pu, int *modeList); int getIntraMPMs(const PredictionUnit &pu, unsigned *mpm #if JVET_AB0149_INTRA_PRED @@ -267,7 +271,12 @@ uint32_t updateCandList(T uiMode, double uiCost, static_vector<T, N>& candModeLi } #if JVET_AB0149_INTRA_PRED +#if JVET_AI0130_COMB_NN_INTRA +uint32_t getIntraDirLumaSubstitutePnn(const PredictionUnit& pu); +bool isPnnLimited(const uint32_t height, const uint32_t width); +#else uint32_t getIntraDirLumaSubstitutePnn(const uint32_t& indexModeLuma,const uint32_t& heightChroma,const uint32_t& widthChroma); +#endif bool decideShapeHandledPnn(const uint32_t& height,const uint32_t& width); int fillListMPMs(unsigned* const mpm,const int& leftIntraDir,const int& aboveIntraDir,const int& multiRefIdx,const bool& is_shape_handled_pnn); int fillListMPMsWithoutInitialization(unsigned* const mpm,const int& leftIntraDir,const int& aboveIntraDir,const int& multiRefIdx,const bool& is_shape_handled_pnn); diff --git a/source/Lib/CommonLib/intra_pred_downsampling_interpolation.cpp b/source/Lib/CommonLib/intra_pred_downsampling_interpolation.cpp index e01007feef004e5c0dae237af49c62207e9fa298..42a953044d362add726ce6eb01cea312bbad45dc 100644 --- a/source/Lib/CommonLib/intra_pred_downsampling_interpolation.cpp +++ b/source/Lib/CommonLib/intra_pred_downsampling_interpolation.cpp @@ -34,6 +34,32 @@ #include "intra_pred_downsampling_interpolation.h" #if JVET_AB0149_INTRA_PRED +#if JVET_AI0130_COMB_NN_INTRA +int are_splits_horizontal_vertical_allowed(const std::vector<std::pair<unsigned int, unsigned int>>& vector_pairs_height_width,const unsigned int& height_unsplit,const unsigned int& width_unsplit,const bool& is_nn_isp_restricted,const bool& is_septree,bool& is_split_horizontal_allowed,bool& is_split_vertical_allowed) +{ + is_split_horizontal_allowed = false; + is_split_vertical_allowed = false; + if (!is_septree || height_unsplit < 4 || width_unsplit < 4 || (height_unsplit != 4 && width_unsplit != 4) || (is_nn_isp_restricted ? height_unsplit <= 16 && width_unsplit <= 16 : false)) + { + return 0; + } + transformations_context_prediction transfos; + const unsigned int height_divided_4{height_unsplit/4}; + const unsigned int width_divided_4{width_unsplit/4}; + if (height_divided_4 >= 4) + { + const std::pair<unsigned int, unsigned int> pair_height_width_before_tr(height_divided_4, width_unsplit); + is_split_horizontal_allowed = find_equivalent_p(vector_pairs_height_width,pair_height_width_before_tr,transfos); + } + if (width_divided_4 >= 4) + { + const std::pair<unsigned int, unsigned int> pair_height_width_before_tr(height_unsplit, width_divided_4); + is_split_vertical_allowed = find_equivalent_p(vector_pairs_height_width,pair_height_width_before_tr,transfos); + } + return 0; +} +#endif + bool find_equivalent_p(const std::vector<SizeNn>& vector_pairs_height_width,const SizeNn& pair_height_width_before_tr,transformations_context_prediction& transfos) { auto it = std::find(vector_pairs_height_width.begin(),vector_pairs_height_width.end(),pair_height_width_before_tr); diff --git a/source/Lib/CommonLib/intra_pred_downsampling_interpolation.h b/source/Lib/CommonLib/intra_pred_downsampling_interpolation.h index 2eace88b1953eff86c2521391213d63de4530e43..11b146499b994cc8e1042915cb9cd4b54ce0cb48 100644 --- a/source/Lib/CommonLib/intra_pred_downsampling_interpolation.h +++ b/source/Lib/CommonLib/intra_pred_downsampling_interpolation.h @@ -75,6 +75,9 @@ int redPrimary(const T* ptr_top_left_rectangle,const int factor_downsampling_ver } return 0; } +#if JVET_AI0130_COMB_NN_INTRA +int are_splits_horizontal_vertical_allowed(const std::vector<std::pair<unsigned int, unsigned int>>& vector_pairs_height_width,const unsigned int& height_unsplit,const unsigned int& width_unsplit,const bool& is_nn_isp_restricted,const bool& is_septree,bool& is_split_horizontal_allowed,bool& is_split_vertical_allowed); +#endif bool find_equivalent_p(const std::vector<SizeNn>& vector_pairs_height_width,const SizeNn& pair_height_width_before_tr,transformations_context_prediction& transfos); template <typename T> int ulSecondary(const T* const ptr_src,T* const ptr_dst,const T* const ptr_boundary,const int size_dim_src_upsampled,const int size_dim_src_orth,const int step_src_upsampled,const int step_src_orth,const int step_dst_upsampled,const int step_dst_orth,const int step_boundary,const int factor_upsampling) diff --git a/source/Lib/CommonLib/intra_pred_extraction_context.h b/source/Lib/CommonLib/intra_pred_extraction_context.h index 42dca202a27c576c09223a02bb8e3edc1d9a7f32..f1c3285cf3dbe3b184c765d36308e40ac8097b30 100644 --- a/source/Lib/CommonLib/intra_pred_extraction_context.h +++ b/source/Lib/CommonLib/intra_pred_extraction_context.h @@ -218,8 +218,35 @@ int initialize_call(DSMALL* const piPortionAbove,DSMALL* const piPortionLeft,con } template <typename T, typename DSMALL> -int extract_call_def(const T* const piRoiOrigin,DSMALL* const piPortionAbove,DSMALL* const piPortionLeft,const bool* const pbNeighborFlags,const int& unitWidth,const int& unitHeight,const int& iAboveUnits,const int& iLeftUnits,const int& nbLinesAbove,const int& nbLinesLeft,const int& factorDownsamplingVertical,const int& factorDownsamplingHorizontal,const int& iPicStride,const int& limitUnmaskingWidth,const int& limitUnmaskingHeight,const DSMALL& maskValue,const bool& isTransposed,const bool& isFlatteningTransposedScanning,T& meanContext) +int extract_call_def(const T* const piRoiOrigin, +#if JVET_AI0130_COMB_NN_INTRA + const T* const piSrcCb, +#endif + DSMALL* const piPortionAbove,DSMALL* const piPortionLeft,const bool* const pbNeighborFlags,const int& unitWidth,const int& unitHeight,const int& iAboveUnits,const int& iLeftUnits,const int& nbLinesAbove,const int& nbLinesLeft,const int& factorDownsamplingVertical,const int& factorDownsamplingHorizontal,const int& iPicStride, +#if JVET_AI0130_COMB_NN_INTRA + const int& strideSrcCb, +#endif + const int& limitUnmaskingWidth,const int& limitUnmaskingHeight,const DSMALL& maskValue,const bool& isTransposed,const bool& isFlatteningTransposedScanning, +#if JVET_AI0130_COMB_NN_INTRA + const bool& isPortionAboveSrcCb,const bool& isPortionLeftSrcCb, +#endif + T& meanContext) { +#if JVET_AI0130_COMB_NN_INTRA + if (!isPortionAboveSrcCb && !isPortionLeftSrcCb) + { + if (piSrcCb) + { + std::cerr << "`isPortionAboveSrcCb` is false and `isPortionLeftSrcCb` is false and `piSrcCb` is not NULL." << std::endl; + return -1; + } + if (strideSrcCb != -1) + { + std::cerr << "`isPortionAboveSrcCb` is false and `isPortionLeftSrcCb` is false and `strideSrcCb` is not equal to -1." << std::endl; + return -1; + } + } +#endif const int nbLinesAboveDownsampled = nbLinesAbove/factorDownsamplingVertical, nbLinesLeftDownsampled = nbLinesLeft/factorDownsamplingHorizontal, sizeCtxHeightDownsampled = (iLeftUnits*unitHeight)/factorDownsamplingVertical, sizeCtxWidthDownsampled = (iAboveUnits*unitWidth)/factorDownsamplingHorizontal, nbLinesAboveTr = isTransposed ? nbLinesLeftDownsampled : nbLinesAboveDownsampled, nbLinesLeftTr = isTransposed ? nbLinesAboveDownsampled : nbLinesLeftDownsampled, sizeCtxHeightTr = isTransposed ? sizeCtxWidthDownsampled : sizeCtxHeightDownsampled, sizeCtxWidthTr = isTransposed ? sizeCtxHeightDownsampled : sizeCtxWidthDownsampled, contextWidth = nbLinesLeftTr + sizeCtxWidthTr; int error_code = initialize_call(piPortionAbove,piPortionLeft,nbLinesAboveTr*contextWidth,sizeCtxHeightTr*nbLinesLeftTr,maskValue); if (error_code < 0){return -1;} @@ -229,11 +256,35 @@ int extract_call_def(const T* const piRoiOrigin,DSMALL* const piPortionAbove,DSM if (error_code < 0){return -1;} DSMALL* piPortion{isTransposed ? piPortionLeft : (isFlatteningTransposedScanning ? piPortionAbove + nbLinesAboveTr*nbLinesLeftTr : piPortionAbove + nbLinesLeftTr)}; compute_steps_0(isTransposed,isFlatteningTransposedScanning,nbLinesAboveTr,nbLinesLeftTr,contextWidth,stepFast,stepOrth); - error_code = extract_c0_def(piRoiOrigin,piPortion,pbNeighborFlags,unitWidth,iAboveUnits,nbLinesAbove,factorDownsamplingVertical,factorDownsamplingHorizontal,stepFast,stepOrth,iPicStride,limitUnmaskingWidth,sumContext,counterInSumContext); + error_code = extract_c0_def( +#if JVET_AI0130_COMB_NN_INTRA + isPortionAboveSrcCb ? piSrcCb : piRoiOrigin, +#else + piRoiOrigin, +#endif + piPortion,pbNeighborFlags,unitWidth,iAboveUnits,nbLinesAbove,factorDownsamplingVertical,factorDownsamplingHorizontal,stepFast,stepOrth, +#if JVET_AI0130_COMB_NN_INTRA + isPortionAboveSrcCb ? strideSrcCb : iPicStride, +#else + iPicStride, +#endif + limitUnmaskingWidth,sumContext,counterInSumContext); if (error_code < 0){return -1;} piPortion = isTransposed ? (isFlatteningTransposedScanning ? piPortionAbove + nbLinesAboveTr*nbLinesLeftTr : piPortionAbove + nbLinesLeftTr) : piPortionLeft; compute_steps_1(isTransposed,isFlatteningTransposedScanning,nbLinesAboveTr,nbLinesLeftTr,contextWidth,stepFast,stepOrth); - error_code = extract_c1_def(piRoiOrigin,piPortion,pbNeighborFlags,unitHeight,iLeftUnits,nbLinesLeft,factorDownsamplingVertical,factorDownsamplingHorizontal,stepFast,stepOrth,iPicStride,limitUnmaskingHeight,sumContext,counterInSumContext); + error_code = extract_c1_def( +#if JVET_AI0130_COMB_NN_INTRA + isPortionLeftSrcCb ? piSrcCb : piRoiOrigin, +#else + piRoiOrigin, +#endif + piPortion,pbNeighborFlags,unitHeight,iLeftUnits,nbLinesLeft,factorDownsamplingVertical,factorDownsamplingHorizontal,stepFast,stepOrth, +#if JVET_AI0130_COMB_NN_INTRA + isPortionLeftSrcCb ? strideSrcCb : iPicStride, +#else + iPicStride, +#endif + limitUnmaskingHeight,sumContext,counterInSumContext); if (error_code < 0){return -1;} meanContext = static_cast<T>(divide_integers_closest(sumContext,counterInSumContext)); return 0; @@ -347,19 +398,67 @@ int pre_cin_all_def(DSMALL* const piPortionAbove,DSMALL* const piPortionLeft,con } template <typename T, typename DSMALL> -int extract_pre_cin(const T* const piRoiOrigin,DSMALL* const piPortionAbove,DSMALL* const piPortionLeft,const bool* const pbNeighborFlags,const int& unitWidth,const int& unitHeight,const int& iAboveUnits,const int& iLeftUnits,const int& nbLinesAbove,const int& nbLinesLeft,const int& factorDownsamplingVertical,const int& factorDownsamplingHorizontal,const int& iPicStride,const int& limitUnmaskingWidth,const int& limitUnmaskingHeight,const DSMALL& scaleQuantizer,const DSMALL& maskValue,const bool& isTransposed,const bool& isFlatteningTransposedScanning,T& meanContext) +int extract_pre_cin(const T* const piRoiOrigin, +#if JVET_AI0130_COMB_NN_INTRA + const T* const piSrcCb, +#endif + DSMALL* const piPortionAbove,DSMALL* const piPortionLeft,const bool* const pbNeighborFlags,const int& unitWidth,const int& unitHeight,const int& iAboveUnits,const int& iLeftUnits,const int& nbLinesAbove,const int& nbLinesLeft,const int& factorDownsamplingVertical,const int& factorDownsamplingHorizontal,const int& iPicStride, +#if JVET_AI0130_COMB_NN_INTRA + const int& strideSrcCb, +#endif + const int& limitUnmaskingWidth,const int& limitUnmaskingHeight,const DSMALL& scaleQuantizer,const DSMALL& maskValue,const bool& isTransposed,const bool& isFlatteningTransposedScanning, +#if JVET_AI0130_COMB_NN_INTRA + const bool& isPortionAboveSrcCb,const bool& isPortionLeftSrcCb, +#endif + T& meanContext) { - extract_call_def(piRoiOrigin,piPortionAbove,piPortionLeft,pbNeighborFlags,unitWidth,unitHeight,iAboveUnits,iLeftUnits,nbLinesAbove,nbLinesLeft,factorDownsamplingVertical,factorDownsamplingHorizontal,iPicStride,limitUnmaskingWidth,limitUnmaskingHeight,maskValue,isTransposed,isFlatteningTransposedScanning,meanContext); + extract_call_def(piRoiOrigin, +#if JVET_AI0130_COMB_NN_INTRA + piSrcCb, +#endif + piPortionAbove,piPortionLeft,pbNeighborFlags,unitWidth,unitHeight,iAboveUnits,iLeftUnits,nbLinesAbove,nbLinesLeft,factorDownsamplingVertical,factorDownsamplingHorizontal,iPicStride, +#if JVET_AI0130_COMB_NN_INTRA + strideSrcCb, +#endif + limitUnmaskingWidth,limitUnmaskingHeight,maskValue,isTransposed,isFlatteningTransposedScanning, +#if JVET_AI0130_COMB_NN_INTRA + isPortionAboveSrcCb,isPortionLeftSrcCb, +#endif + meanContext); pre_cin_all_def(piPortionAbove,piPortionLeft,pbNeighborFlags,unitWidth/factorDownsamplingHorizontal,unitHeight/factorDownsamplingVertical,iAboveUnits,iLeftUnits,nbLinesAbove/factorDownsamplingVertical,nbLinesLeft/factorDownsamplingHorizontal,limitUnmaskingWidth == -1 ? -1 : limitUnmaskingWidth/factorDownsamplingHorizontal,limitUnmaskingHeight == -1 ? -1 : limitUnmaskingHeight/factorDownsamplingVertical,scaleQuantizer,isTransposed,isFlatteningTransposedScanning,static_cast<DSMALL>(meanContext)); return 0; } template <typename T, typename DSMALL> -int extract_pre_cin_optionally(const T* const piRoiOrigin,DSMALL* const piPortionAbove,DSMALL* const piPortionLeft,const bool* const pbNeighborFlags,bool* const pbNeighborFlagsMerged,int unitWidth,int unitHeight,int iAboveUnits,int iLeftUnits,const int& nbLinesAbove,const int& nbLinesLeft,const int& factorDownsamplingVertical,const int& factorDownsamplingHorizontal,const int& iPicStride,const int& limitUnmaskingWidth,const int& limitUnmaskingHeight,const DSMALL& scaleQuantizer,const DSMALL& maskValue,const bool& isTransposed,const bool& isFlatteningTransposedScanning,T& meanContext) +int extract_pre_cin_optionally(const T* const piRoiOrigin, +#if JVET_AI0130_COMB_NN_INTRA + const T* const piSrcCb, +#endif + DSMALL* const piPortionAbove,DSMALL* const piPortionLeft,const bool* const pbNeighborFlags,bool* const pbNeighborFlagsMerged,int unitWidth,int unitHeight,int iAboveUnits,int iLeftUnits,const int& nbLinesAbove,const int& nbLinesLeft,const int& factorDownsamplingVertical,const int& factorDownsamplingHorizontal,const int& iPicStride, +#if JVET_AI0130_COMB_NN_INTRA + const int& strideSrcCb, +#endif + const int& limitUnmaskingWidth,const int& limitUnmaskingHeight,const DSMALL& scaleQuantizer,const DSMALL& maskValue,const bool& isTransposed,const bool& isFlatteningTransposedScanning, +#if JVET_AI0130_COMB_NN_INTRA + const bool& isPortionAboveSrcCb,const bool& isPortionLeftSrcCb, +#endif + T& meanContext) { bool isMerged = false; int error_code = translate_into_new(pbNeighborFlags,pbNeighborFlagsMerged,factorDownsamplingVertical,factorDownsamplingHorizontal,unitWidth,unitHeight,iAboveUnits,iLeftUnits,isMerged); - error_code = extract_pre_cin(piRoiOrigin,piPortionAbove,piPortionLeft,(isMerged ? pbNeighborFlagsMerged : pbNeighborFlags) + iLeftUnits,unitWidth,unitHeight,iAboveUnits,iLeftUnits,nbLinesAbove,nbLinesLeft,factorDownsamplingVertical,factorDownsamplingHorizontal,iPicStride,limitUnmaskingWidth,limitUnmaskingHeight,scaleQuantizer,maskValue,isTransposed,isFlatteningTransposedScanning,meanContext); + error_code = extract_pre_cin(piRoiOrigin, +#if JVET_AI0130_COMB_NN_INTRA + piSrcCb, +#endif + piPortionAbove,piPortionLeft,(isMerged ? pbNeighborFlagsMerged : pbNeighborFlags) + iLeftUnits,unitWidth,unitHeight,iAboveUnits,iLeftUnits,nbLinesAbove,nbLinesLeft,factorDownsamplingVertical,factorDownsamplingHorizontal,iPicStride, +#if JVET_AI0130_COMB_NN_INTRA + strideSrcCb, +#endif + limitUnmaskingWidth,limitUnmaskingHeight,scaleQuantizer,maskValue,isTransposed,isFlatteningTransposedScanning, +#if JVET_AI0130_COMB_NN_INTRA + isPortionAboveSrcCb,isPortionLeftSrcCb, +#endif + meanContext); if (error_code < 0) {return -1;} return 0; } diff --git a/source/Lib/DecoderLib/CABACReader.cpp b/source/Lib/DecoderLib/CABACReader.cpp index 48002da9b389ad87848712093d8fc7fa45ee4cf9..dbd80485a15e29c896a4bea0311a8624f6615524 100644 --- a/source/Lib/DecoderLib/CABACReader.cpp +++ b/source/Lib/DecoderLib/CABACReader.cpp @@ -1555,6 +1555,9 @@ void CABACReader::intra_luma_pred_modes(CodingUnit& cu) if (m_BinDecoder.decodeBin(Ctx::PnnLuminanceFlag(ctxId))) { pu->intraDir[CHANNEL_TYPE_LUMA] = PNN_IDX; +#if JVET_AI0130_COMB_NN_INTRA + isp_pnn(cu); +#endif return; } } @@ -1787,10 +1790,16 @@ void CABACReader::intra_chroma_pred_mode(PredictionUnit& pu) const bool is_shape_handled_pnn( decideShapeHandledPnn(pu.Cb().height,pu.Cb().width) ); +#if !JVET_AI0130_COMB_NN_INTRA const bool is_dm_pnn( getIntraDirLumaSubstitutePnn(PU::getCoLocatedIntraLumaMode(pu),pu.Cb().height,pu.Cb().width) == PNN_IDX ); - if (is_shape_handled_pnn && !is_dm_pnn) +#endif + if (is_shape_handled_pnn +#if !JVET_AI0130_COMB_NN_INTRA + && !is_dm_pnn +#endif + ) { if (m_BinDecoder.decodeBin(Ctx::PnnChrominanceFlag(0))) { @@ -3310,6 +3319,27 @@ void CABACReader::mts_idx( CodingUnit& cu, CUCtx& cuCtx ) { ctxIdx = 1; mtsIdx = MTS_DST7_DST7; // mtsIdx = 2 -- 4 +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + if (cu.isSepTree() && PU::getFinalIntraMode(*cu.firstPU, CHANNEL_TYPE_LUMA) == PNN_IDX) + { + if (!cu.cs->sps->getUseLFNST() || !isPnnLimited(cu.lheight(), cu.lwidth())) + { + symbol = m_BinDecoder.decodeBin(Ctx::MTSIdx(4)); + if (symbol) + { + mtsIdx = MTS_DCT8_DCT8; + symbol = m_BinDecoder.decodeBin(Ctx::MTSIdx(5)); + if (symbol) + { + symbol = m_BinDecoder.decodeBinEP(); + mtsIdx = symbol ? MTS_DST7_DCT8 : MTS_DCT8_DST7; + } + } + } + } + else + { +#endif for( int i = 0; i < 3; i++, ctxIdx++ ) { symbol = m_BinDecoder.decodeBin( Ctx::MTSIdx(ctxIdx)); @@ -3320,6 +3350,9 @@ void CABACReader::mts_idx( CodingUnit& cu, CUCtx& cuCtx ) break; } } +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + } +#endif } } @@ -3348,6 +3381,45 @@ void CABACReader::isp_mode( CodingUnit& cu ) DTRACE( g_trace_ctx, D_SYNTAX, "intra_subPartitions() etype=%d pos=(%d,%d) ispIdx=%d\n", cu.chType, cu.blocks[cu.chType].x, cu.blocks[cu.chType].y, (int)cu.ispMode ); } +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA +void CABACReader::isp_pnn(CodingUnit& cu) +{ + if (!(cu.cs)->sps->getUseISP() || cu.bdpcmMode || !CU::canUseISP(cu, getFirstComponentOfChannel(cu.chType)) || cu.colorTransform) + { + cu.ispMode = NOT_INTRA_SUBPARTITIONS; + } + else + { + bool is_split_horizontal_allowed{false}; + bool is_split_vertical_allowed{false}; + const int error_code{are_splits_horizontal_vertical_allowed(g_vectorPairsHeightWidthPnn,cu.lheight(),cu.lwidth(),cu.cs->sps->getIsNnIspRestricted(),cu.isSepTree(),is_split_horizontal_allowed,is_split_vertical_allowed)}; + CHECK(error_code < 0, "Error in `are_splits_horizontal_vertical_allowed`."); + if (!is_split_horizontal_allowed && !is_split_vertical_allowed) + { + cu.ispMode = NOT_INTRA_SUBPARTITIONS; + } + else + { + if (m_BinDecoder.decodeBin(Ctx::ISPMode(2))) + { + if (is_split_horizontal_allowed && is_split_vertical_allowed) + { + cu.ispMode = 1 + m_BinDecoder.decodeBin(Ctx::ISPMode(1)); + } + else + { + cu.ispMode = is_split_horizontal_allowed ? HOR_INTRA_SUBPARTITIONS : VER_INTRA_SUBPARTITIONS; + } + } + else + { + cu.ispMode = NOT_INTRA_SUBPARTITIONS; + } + } + } +} +#endif + void CABACReader::residual_lfnst_mode( CodingUnit& cu, CUCtx& cuCtx ) { int chIdx = cu.isSepTree() && cu.chType == CHANNEL_TYPE_CHROMA ? 1 : 0; @@ -3359,6 +3431,13 @@ void CABACReader::residual_lfnst_mode( CodingUnit& cu, CUCtx& cuCtx ) { return; } +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + const uint32_t intraMode = PU::getFinalIntraMode(*cu.firstPU,cu.chType); + if (cu.cs->sps->getIsNnIspRestricted() && intraMode == PNN_IDX && cu.ispMode) + { + return; + } +#endif RExt__DECODER_DEBUG_BIT_STATISTICS_CREATE_SET( STATS__CABAC_BITS__LFNST ); @@ -3406,15 +3485,37 @@ void CABACReader::residual_lfnst_mode( CodingUnit& cu, CUCtx& cuCtx ) } unsigned cctx = 0; +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + if (cu.isSepTree()) + { + if (intraMode == PNN_IDX) + { + cctx = 3; + } + else + { + cctx = 1; + } + } +#else if ( cu.isSepTree() ) cctx++; +#endif uint32_t idxLFNST = m_BinDecoder.decodeBin( Ctx::LFNSTIdx( cctx ) ); if( idxLFNST ) { idxLFNST += m_BinDecoder.decodeBin(Ctx::LFNSTIdx(2)); #if JVET_AB0149_INTRA_PRED && JVET_AB0149_SEARCH_PNN +#if !JVET_AI0130_COMB_NN_INTRA const uint32_t intraMode = PU::getFinalIntraMode(*cu.firstPU,cu.chType); +#endif if (intraMode == PNN_IDX) { +#if JVET_AI0130_COMB_NN_INTRA + if (!cu.ispMode) + { +#else + CHECK(cu.ispMode, "`cu.ispMode` is not equal to 0."); +#endif uint32_t maxSymbol = DEPTH_LFNST_GROUP; #if JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS if (m_areCostsWritten) @@ -3423,6 +3524,9 @@ void CABACReader::residual_lfnst_mode( CodingUnit& cu, CUCtx& cuCtx ) } #endif xReadTruncBinCode(cu.idxLfnstPnn, maxSymbol); +#if JVET_AI0130_COMB_NN_INTRA + } +#endif } #endif } diff --git a/source/Lib/DecoderLib/CABACReader.h b/source/Lib/DecoderLib/CABACReader.h index 78a9f9117a69a0178e7b2e585e9290ae94570c87..42825280d82f563a4356fd01cc03ef95d2ea2cd1 100644 --- a/source/Lib/DecoderLib/CABACReader.h +++ b/source/Lib/DecoderLib/CABACReader.h @@ -145,6 +145,9 @@ public: void mts_idx ( CodingUnit& cu, CUCtx& cuCtx ); void residual_lfnst_mode ( CodingUnit& cu, CUCtx& cuCtx ); void isp_mode ( CodingUnit& cu ); +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + void isp_pnn(CodingUnit& cu); +#endif int last_sig_coeff ( CoeffCodingContext& cctx, TransformUnit& tu, ComponentID compID ); void residual_coding_subblock ( CoeffCodingContext& cctx, TCoeff* coeff, const int stateTransTable, int& state ); void residual_codingTS ( TransformUnit& tu, ComponentID compID ); diff --git a/source/Lib/DecoderLib/DecCu.cpp b/source/Lib/DecoderLib/DecCu.cpp index 422cb0539ae579d32a4255d60cc2310c4aefe423..559a91bba01e2780464e5e6dd4a09ac25d46bd71 100644 --- a/source/Lib/DecoderLib/DecCu.cpp +++ b/source/Lib/DecoderLib/DecCu.cpp @@ -240,6 +240,9 @@ int convert_dim_mask_from_cb_to_tb_isp(const int& coord_top_left_cb,const int& c #endif void DecCu::xIntraRecBlk( TransformUnit& tu, const ComponentID compID +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + , const bool isEncoderCall, const int subTuCounter +#endif ) { if( !tu.blocks[ compID ].valid() ) @@ -273,6 +276,30 @@ void DecCu::xIntraRecBlk( TransformUnit& tu, const ComponentID compID #if JVET_AB0149_INTRA_PRED bool contextFlag = false; const bool is_context_extracted = uiChFinalMode == PNN_IDX; +#if JVET_AI0130_COMB_NN_INTRA + const bool isLoadedIsp{(tu.cu)->ispMode && (!subTuCounter || !(tu.cu)->lfnstIdx)}; + if (isLuma(compID) && isEncoderCall && is_context_extracted && (isLoadedIsp || (!(tu.cu)->ispMode && area.height*area.width <= 256))) + { + if (isLoadedIsp) + { + piPred.copyFrom(CPelBuf((tu.cu)->arrayMemPred + subTuCounter*area.area(), area)); + if (!subTuCounter) + { + m_pcIntraPred->initIntraPatternChTypeISP(*tu.cu,(tu.cu)->Y(),pReco,contextFlag,false +#if JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS + , isComputedHeightWidthMasks +#endif + ); + } + } + else + { + piPred.copyFrom(CPelBuf((tu.cu)->arrayMemPred, area)); + } + } + else + { +#endif #endif //===== init availability pattern ===== @@ -292,6 +319,9 @@ void DecCu::xIntraRecBlk( TransformUnit& tu, const ComponentID compID { CU::adjustPredArea(areaPredReg); m_pcIntraPred->initIntraPatternChTypeISP(*tu.cu, areaPredReg, pReco +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + , contextFlag,is_context_extracted +#endif #if JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS , isComputedHeightWidthMasks #endif @@ -301,6 +331,9 @@ void DecCu::xIntraRecBlk( TransformUnit& tu, const ComponentID compID else { m_pcIntraPred->initIntraPatternChTypeISP(*tu.cu, area, pReco +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + , contextFlag,is_context_extracted +#endif #if JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS , isComputedHeightWidthMasks #endif @@ -413,6 +446,12 @@ void DecCu::xIntraRecBlk( TransformUnit& tu, const ComponentID compID m_pcIntraPred->predIntraAng(compID, piPredReg, pu #if JVET_AB0149_INTRA_PRED , contextFlag +#if JVET_AI0130_COMB_NN_INTRA + , tu.idxRepresentationPnn +#if JVET_AB0149_TM_INF_PNN + , tu.arrayIndicesPredictedLfnstLumaPnn.data() +#endif +#endif #endif ); } @@ -421,10 +460,19 @@ void DecCu::xIntraRecBlk( TransformUnit& tu, const ComponentID compID m_pcIntraPred->predIntraAng(compID, piPred, pu #if JVET_AB0149_INTRA_PRED , contextFlag +#if JVET_AI0130_COMB_NN_INTRA + , isLuma(compID) && (tu.cu)->ispMode ? tu.idxRepresentationPnn : (tu.cu)->idxRepresentationPnn +#if JVET_AB0149_TM_INF_PNN + , isLuma(compID) && (tu.cu)->ispMode ? tu.arrayIndicesPredictedLfnstLumaPnn.data() : &(tu.cu)->arrayIdxPredictedLfnstPnn[compID][0] +#endif +#endif #endif ); } } +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + } +#endif #if JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS if (isComputedHeightWidthMasks) { @@ -583,6 +631,9 @@ void DecCu::xIntraRecACTBlk(TransformUnit& tu) PelBuf piPred = cs.getPredBuf(area); #if JVET_AB0149_INTRA_PRED bool contextFlag = false; +#if JVET_AI0130_COMB_NN_INTRA + uint32_t idxRepresentationPnn{MAX_INT}; +#endif #endif m_pcIntraPred->initIntraPatternChType(*tu.cu, area #if JVET_AB0149_INTRA_PRED @@ -603,6 +654,12 @@ void DecCu::xIntraRecACTBlk(TransformUnit& tu) m_pcIntraPred->predIntraAng(compID, piPred, pu #if JVET_AB0149_INTRA_PRED , contextFlag +#if JVET_AI0130_COMB_NN_INTRA + , idxRepresentationPnn +#if JVET_AB0149_TM_INF_PNN + , nullptr +#endif +#endif #endif ); } @@ -744,6 +801,9 @@ void DecCu::xReconIntraQT( CodingUnit &cu } #endif xIntraRecQT( cu, ChannelType( chType ) +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + , isEncoderCall +#endif ); } } @@ -822,8 +882,11 @@ void DecCu::xReconPLT(CodingUnit &cu, ComponentID compBegin, uint32_t numComp) const CompArea &area = cu.blocks[compID]; PelBuf picReco = cu.cs->getRecoBuf(area); cu.cs->picture->getRecoBuf(area).copyFrom(picReco); + // JVET-AI0230 save the reconstruction samples as prediction samples in the palette reconstruction function + cu.cs->getPredBuf(cu).bufs[compID].copyFrom(picReco); cu.cs->setDecomp(area); } + cu.cs->getPredBufCustom(cu).copyFrom(cu.cs->getPredBuf(cu));// JVET-AI0230 } /** Function for deriving reconstructed PU/CU chroma samples with QTree structure @@ -838,13 +901,22 @@ void DecCu::xReconPLT(CodingUnit &cu, ComponentID compBegin, uint32_t numComp) void DecCu::xIntraRecQT(CodingUnit &cu, const ChannelType chType +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + , const bool isEncoderCall +#endif ) { +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + int subTuCounter{0}; +#endif for( auto &currTU : CU::traverseTUs( cu ) ) { if( isLuma( chType ) ) { xIntraRecBlk( currTU, COMPONENT_Y +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + , isEncoderCall, subTuCounter +#endif ); } else @@ -854,9 +926,15 @@ DecCu::xIntraRecQT(CodingUnit &cu, const ChannelType chType for( uint32_t compID = COMPONENT_Cb; compID < numValidComp; compID++ ) { xIntraRecBlk( currTU, ComponentID( compID ) +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + , isEncoderCall, 0 +#endif ); } } +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + subTuCounter++; +#endif } } diff --git a/source/Lib/DecoderLib/DecCu.h b/source/Lib/DecoderLib/DecCu.h index ad87ae825d4131ed21188b1a93ceed90cfe5ab60..56aa8c1c03727d9c7cb44b4b9b0e9f4b2002ac73 100644 --- a/source/Lib/DecoderLib/DecCu.h +++ b/source/Lib/DecoderLib/DecCu.h @@ -83,6 +83,9 @@ public: /// reconstruct Ctu information protected: void xIntraRecQT ( CodingUnit& cu, const ChannelType chType +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + , const bool isEncoderCall +#endif ); void xIntraRecACTQT(CodingUnit& cu); @@ -96,6 +99,9 @@ protected: void xFillPCMBuffer ( CodingUnit& cu ); void xIntraRecBlk ( TransformUnit& tu, const ComponentID compID +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + , const bool isEncoderCall, const int subTuCounter +#endif ); void xIntraRecACTBlk(TransformUnit& tu); void xDecodeInterTU ( TransformUnit& tu, const ComponentID compID ); diff --git a/source/Lib/DecoderLib/DecLib.cpp b/source/Lib/DecoderLib/DecLib.cpp index cc133528cae51cd98ff3ed2eeb012b6cb7a2c1cb..95ac4844ff3be4e067c0d1e9d349282d95b3028e 100644 --- a/source/Lib/DecoderLib/DecLib.cpp +++ b/source/Lib/DecoderLib/DecLib.cpp @@ -822,7 +822,11 @@ void DecLib::executeLoopFilters() string recoModelFileName = recoModelStream.str(); std::ostringstream cmdStream; +#if JVET_AI0111_CONTENT_ADAPTIVE_LOP3 || JVET_AI0157_CONTENT_ADAPTIVE_VLOP + cmdStream << "cd $WU_CODE && python wu_decoding.py --arch " << m_nnFilterArch << " --base_model " << m_nnModel[NnModel::LOP_UNIFIED_FILTER] << " --nnr_bitstream " << payloadFileName << " --reco_model " << recoModelFileName; +#else cmdStream << "cd $WU_CODE && python wu_decoding.py --arch lop2 --base_model " << m_nnModel[NnModel::LOP_UNIFIED_FILTER] << " --nnr_bitstream " << payloadFileName << " --reco_model " << recoModelFileName; +#endif string cmd = cmdStream.str(); char buffer[128]; diff --git a/source/Lib/DecoderLib/DecLib.h b/source/Lib/DecoderLib/DecLib.h index f9addd106cd52fdc2e327b98f7196f72a9c5e301..b628763e674bcb30f59daa5a4f611cadc4e6655a 100644 --- a/source/Lib/DecoderLib/DecLib.h +++ b/source/Lib/DecoderLib/DecLib.h @@ -147,7 +147,7 @@ private: #if NN_LF_FORCE_USE int m_nnlfDebugOption; #endif -#if JVET_AH0080_TRANS_INPUT +#if JVET_AH0080_TRANS_INPUT && !NN_LF_UNIFIED_ID bool m_nnlfTransInput; #endif #if JVET_AH0096_CONTENT_ADAPTIVE_LOP @@ -155,6 +155,9 @@ private: std::map<int, bool> m_nnfuIpUsesNnfu; int m_nnfuIntraPeriod = 0; std::string m_nnfuOutputFileStem; +#if JVET_AI0111_CONTENT_ADAPTIVE_LOP3 || JVET_AI0157_CONTENT_ADAPTIVE_VLOP + std::string m_nnFilterArch; +#endif #endif #endif #if NN_FILTERING_SET_0 @@ -297,6 +300,11 @@ public: #if JVET_AH0096_CONTENT_ADAPTIVE_LOP std::string getNnfuOutputFileStem() { return m_nnfuOutputFileStem; } void setNnfuOutputFileStem(std::string stem) { m_nnfuOutputFileStem = stem; } +#if JVET_AI0111_CONTENT_ADAPTIVE_LOP3 || JVET_AI0157_CONTENT_ADAPTIVE_VLOP + std::string getNnfilterArch() { return m_nnFilterArch; } + void setNnfilterArch(std::string arch) { m_nnFilterArch = arch; } + +#endif #endif #endif diff --git a/source/Lib/DecoderLib/VLCReader.cpp b/source/Lib/DecoderLib/VLCReader.cpp index 2484f0054e7b9ce318500fddef01f6f0c9bbbc73..8825494a3c74f1bb1aeaa257208ccf33bc2543a2 100644 --- a/source/Lib/DecoderLib/VLCReader.cpp +++ b/source/Lib/DecoderLib/VLCReader.cpp @@ -2050,6 +2050,9 @@ void HLSyntaxReader::parseSPS(SPS* pcSPS) READ_FLAG(uiCode, "sps_mip_enabled_flag"); pcSPS->setUseMIP( uiCode != 0 ); #if JVET_AB0149_INTRA_PRED READ_FLAG(uiCode, "sps_nn_intra_pred_enabled_flag"); pcSPS->setNnIntraPredEnabledFlag( uiCode != 0 ); +#if JVET_AI0130_COMB_NN_INTRA + READ_FLAG(uiCode, "sps_nn_isp_restriction_flag"); pcSPS->setIsNnIspRestricted( uiCode != 0 ); +#endif #endif if( pcSPS->getChromaFormatIdc() != CHROMA_400) { diff --git a/source/Lib/EncoderLib/CABACWriter.cpp b/source/Lib/EncoderLib/CABACWriter.cpp index 7b8af096290d127a458b79474399e87f5af04acf..68943d2bafb1c92ace20cc1e29999ce35550edfe 100644 --- a/source/Lib/EncoderLib/CABACWriter.cpp +++ b/source/Lib/EncoderLib/CABACWriter.cpp @@ -1076,6 +1076,9 @@ void CABACWriter::intra_luma_pred_modes( const CodingUnit& cu ) if (pu->intraDir[CHANNEL_TYPE_LUMA] == PNN_IDX) { m_BinEncoder.encodeBin(1,Ctx::PnnLuminanceFlag(ctxId)); +#if JVET_AI0130_COMB_NN_INTRA + isp_pnn(cu); +#endif return; } else @@ -1231,6 +1234,9 @@ void CABACWriter::intra_luma_pred_mode( const PredictionUnit& pu ) if (ipred_mode == PNN_IDX) { m_BinEncoder.encodeBin(1, Ctx::PnnLuminanceFlag(ctxId)); +#if JVET_AI0130_COMB_NN_INTRA + isp_pnn(*pu.cu); +#endif return; } else @@ -1410,11 +1416,17 @@ void CABACWriter::intra_chroma_pred_mode(const PredictionUnit& pu) const bool is_shape_handled_pnn( decideShapeHandledPnn(pu.Cb().height,pu.Cb().width) ); +#if !JVET_AI0130_COMB_NN_INTRA const bool is_dm_pnn( getIntraDirLumaSubstitutePnn( PU::getCoLocatedIntraLumaMode(pu), pu.Cb().height, pu.Cb().width) == PNN_IDX ); - if (is_shape_handled_pnn && !is_dm_pnn) +#endif + if (is_shape_handled_pnn +#if !JVET_AI0130_COMB_NN_INTRA + && !is_dm_pnn +#endif + ) { if (pu.intraDir[CHANNEL_TYPE_CHROMA] == PNN_IDX) { @@ -2887,6 +2899,31 @@ void CABACWriter::mts_idx( const CodingUnit& cu, CUCtx* cuCtx ) if( symbol ) { +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + if (cu.isSepTree() && PU::getFinalIntraMode(*cu.firstPU, CHANNEL_TYPE_LUMA) == PNN_IDX) + { + if (cu.cs->sps->getUseLFNST() && isPnnLimited(cu.lheight(), cu.lwidth())) + { + CHECK(mtsIdx != MTS_DST7_DST7, "`cu.cs->sps->getUseLFNST()` is true and `isPnnLimited(cu.lheight(), cu.lwidth())` returns true and `mtsIdx` is not equal to `MTS_DST7_DST7`."); + } + else + { + symbol = mtsIdx > MTS_DST7_DST7 ? 1 : 0; + m_BinEncoder.encodeBin(symbol, Ctx::MTSIdx(4)); + if (symbol) + { + symbol = mtsIdx == MTS_DCT8_DCT8 ? 0 : 1; + m_BinEncoder.encodeBin(symbol, Ctx::MTSIdx(5)); + if (symbol) + { + m_BinEncoder.encodeBinEP(mtsIdx == MTS_DST7_DCT8 ? 1 : 0); + } + } + } + } + else + { +#endif ctxIdx = 1; for( int i = 0; i < 3; i++, ctxIdx++ ) { @@ -2898,6 +2935,9 @@ void CABACWriter::mts_idx( const CodingUnit& cu, CUCtx* cuCtx ) break; } } +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + } +#endif } } DTRACE( g_trace_ctx, D_SYNTAX, "mts_idx() etype=%d pos=(%d,%d) mtsIdx=%d\n", COMPONENT_Y, tu.cu->lx(), tu.cu->ly(), mtsIdx); @@ -2922,6 +2962,53 @@ void CABACWriter::isp_mode( const CodingUnit& cu ) DTRACE( g_trace_ctx, D_SYNTAX, "intra_subPartitions() etype=%d pos=(%d,%d) ispIdx=%d\n", cu.chType, cu.blocks[cu.chType].x, cu.blocks[cu.chType].y, (int)cu.ispMode ); } +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA +void CABACWriter::isp_pnn(const CodingUnit& cu) +{ + if (!(cu.cs)->sps->getUseISP() || cu.bdpcmMode || !CU::canUseISP(cu, getFirstComponentOfChannel(cu.chType)) || cu.colorTransform) + { + CHECK(cu.ispMode != NOT_INTRA_SUBPARTITIONS, "`cu.ispMode` is not equal to `NOT_INTRA_SUBPARTITIONS`."); + } + else + { + bool is_split_horizontal_allowed{false}; + bool is_split_vertical_allowed{false}; + const int error_code{are_splits_horizontal_vertical_allowed(g_vectorPairsHeightWidthPnn,cu.lheight(),cu.lwidth(),cu.cs->sps->getIsNnIspRestricted(),cu.isSepTree(),is_split_horizontal_allowed,is_split_vertical_allowed)}; + CHECK(error_code < 0, "Error in `are_splits_horizontal_vertical_allowed`."); + if (!is_split_horizontal_allowed && !is_split_vertical_allowed) + { + CHECK(cu.ispMode != NOT_INTRA_SUBPARTITIONS, "`cu.ispMode` is not equal to `NOT_INTRA_SUBPARTITIONS`."); + } + else + { + if (cu.ispMode == NOT_INTRA_SUBPARTITIONS) + { + m_BinEncoder.encodeBin(0,Ctx::ISPMode(2)); + } + else + { + m_BinEncoder.encodeBin(1,Ctx::ISPMode(2)); + if (is_split_horizontal_allowed && is_split_vertical_allowed) + { + m_BinEncoder.encodeBin(cu.ispMode - 1,Ctx::ISPMode(1)); + } + else + { + if (is_split_horizontal_allowed) + { + CHECK(cu.ispMode != HOR_INTRA_SUBPARTITIONS,"`is_split_horizontal_allowed` is true and `is_split_vertical_allowed` is false and `cu.ispMode` is not equal to `HOR_INTRA_SUBPARTITIONS`."); + } + else + { + CHECK(cu.ispMode != VER_INTRA_SUBPARTITIONS,"`is_split_horizontal_allowed` is false and `is_split_vertical_allowed` is true and `cu.ispMode` is not equal to `VER_INTRA_SUBPARTITIONS`."); + } + } + } + } + } +} +#endif + void CABACWriter::residual_lfnst_mode( const CodingUnit& cu, CUCtx& cuCtx ) { int chIdx = cu.isSepTree() && cu.chType == CHANNEL_TYPE_CHROMA ? 1 : 0; @@ -2933,6 +3020,13 @@ void CABACWriter::residual_lfnst_mode( const CodingUnit& cu, CUCtx& cuCtx ) { return; } +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + const uint32_t intraMode = PU::getFinalIntraMode(*cu.firstPU, cu.chType); + if (cu.cs->sps->getIsNnIspRestricted() && intraMode == PNN_IDX && cu.ispMode) + { + return; + } +#endif if( cu.cs->sps->getUseLFNST() && CU::isIntra( cu ) ) { @@ -2976,7 +3070,21 @@ void CABACWriter::residual_lfnst_mode( const CodingUnit& cu, CUCtx& cuCtx ) unsigned cctx = 0; +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + if (cu.isSepTree()) + { + if (intraMode == PNN_IDX) + { + cctx = 3; + } + else + { + cctx = 1; + } + } +#else if ( cu.isSepTree() ) cctx++; +#endif const uint32_t idxLFNST = cu.lfnstIdx; assert( idxLFNST < 3 ); @@ -2986,8 +3094,20 @@ void CABACWriter::residual_lfnst_mode( const CodingUnit& cu, CUCtx& cuCtx ) { m_BinEncoder.encodeBin( (idxLFNST - 1) ? 1 : 0, Ctx::LFNSTIdx(2)); #if JVET_AB0149_INTRA_PRED && JVET_AB0149_SEARCH_PNN +#if !JVET_AI0130_COMB_NN_INTRA const uint32_t intraMode = PU::getFinalIntraMode(*cu.firstPU, cu.chType); +#endif if (intraMode == PNN_IDX) { +#if JVET_AI0130_COMB_NN_INTRA + if (cu.ispMode) + { + CHECK(cu.idxLfnstPnn != 0, "The intra prediction mode selected to predict the current CB is the PNN mode and `cu.lfnstIdx` is not equal to 0 and `cu.ispMode` is not equal to 0 and `cu.idxLfnstPnn` is not equal to 0."); + } + else + { +#else + CHECK(cu.ispMode, "`cu.ispMode` is not equal to 0."); +#endif CHECK(cu.idxLfnstPnn < 0 || cu.idxLfnstPnn > DEPTH_LFNST_GROUP - 1, "`cu.idxLfnstPnn` does not belong to [|0, 6|]."); uint32_t maxSymbol = DEPTH_LFNST_GROUP; #if JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS @@ -2997,6 +3117,9 @@ void CABACWriter::residual_lfnst_mode( const CodingUnit& cu, CUCtx& cuCtx ) } #endif xWriteTruncBinCode(cu.idxLfnstPnn, maxSymbol); +#if JVET_AI0130_COMB_NN_INTRA + } +#endif } #endif } diff --git a/source/Lib/EncoderLib/CABACWriter.h b/source/Lib/EncoderLib/CABACWriter.h index d954e48d37a164d94e9d4110013ebd6813f69948..0022a6a0f8fdda16b47122900671caf7eed430eb 100644 --- a/source/Lib/EncoderLib/CABACWriter.h +++ b/source/Lib/EncoderLib/CABACWriter.h @@ -164,6 +164,9 @@ public: void mts_idx ( const CodingUnit& cu, CUCtx* cuCtx ); void residual_lfnst_mode ( const CodingUnit& cu, CUCtx& cuCtx ); void isp_mode ( const CodingUnit& cu ); +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + void isp_pnn(const CodingUnit& cu); +#endif void last_sig_coeff ( CoeffCodingContext& cctx, const TransformUnit& tu, ComponentID compID ); void residual_coding_subblock ( CoeffCodingContext& cctx, const TCoeff* coeff, const int stateTransTable, int& state ); void residual_codingTS ( const TransformUnit& tu, ComponentID compID ); diff --git a/source/Lib/EncoderLib/EncCfg.h b/source/Lib/EncoderLib/EncCfg.h index ff13841496f987ff12ff06d49c6116def1ff4269..f4fd855d183a03e6810bc989e58c524f89a1dfc9 100644 --- a/source/Lib/EncoderLib/EncCfg.h +++ b/source/Lib/EncoderLib/EncCfg.h @@ -169,6 +169,10 @@ protected: bool m_nnsrOption; int m_nnsrQpOffset; std::string m_sNnsrQpOffsetOverridebyQp; +#endif +#if JVET_AI0074_MULTIRATIO_RPR_NNSR + int m_nnsrQpOffset2; + std::string m_sNnsrQpOffsetOverridebyQp2; #endif Fraction m_frameRate; int m_FrameSkip; @@ -887,8 +891,15 @@ protected: #endif double m_scalingRatioHor; double m_scalingRatioVer; +#if JVET_AI0074_MULTIRATIO_RPR_NNSR + double m_scalingRatioHor2; + double m_scalingRatioVer2; +#endif bool m_rprEnabledFlag; bool m_resChangeInClvsEnabled; +#if JVET_AI0074_MULTIRATIO_RPR_NNSR + bool m_singleScalingRatio; +#endif int m_switchPocPeriod; int m_upscaledOutput; int m_numRefLayers[MAX_VPS_LAYERS]; @@ -898,6 +909,9 @@ protected: #if JVET_AB0149_SEARCH_PNN && JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS bool m_areCostsWritten; #endif +#if JVET_AI0130_COMB_NN_INTRA + bool m_isNnIspRestricted; +#endif #endif #if JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS std::ofstream* m_ptrOfstreamQuadtree; @@ -913,6 +927,9 @@ public: #if JVET_AB0149_SEARCH_PNN && JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS , m_areCostsWritten(false) #endif +#if JVET_AI0130_COMB_NN_INTRA + , m_isNnIspRestricted(false) +#endif #endif #if JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS , m_ptrOfstreamQuadtree(NULL) @@ -950,7 +967,12 @@ public: std::string getNnsrQpOffsetOverridebyQp() { return m_sNnsrQpOffsetOverridebyQp; }; void setNnsrQpOffsetOverridebyQp(std::string b) { m_sNnsrQpOffsetOverridebyQp = b; }; #endif - +#if JVET_AI0074_MULTIRATIO_RPR_NNSR + int getNnsrQpOffset2() { return m_nnsrQpOffset2; }; + void setNnsrQpOffset2(int b) { m_nnsrQpOffset2 = b; }; + std::string getNnsrQpOffsetOverridebyQp2() { return m_sNnsrQpOffsetOverridebyQp2; }; + void setNnsrQpOffsetOverridebyQp2(std::string b) { m_sNnsrQpOffsetOverridebyQp2 = b; }; +#endif void setProfile(Profile::Name profile) { m_profile = profile; } void setLevel(Level::Tier tier, Level::Name level) { m_levelTier = tier; m_level = level; } bool getFrameOnlyConstraintFlag() const { return m_frameOnlyConstraintFlag; } @@ -2341,8 +2363,15 @@ public: void setRprEnabled(bool b) { m_rprEnabledFlag = b; } bool isRprEnabled() const { return m_rprEnabledFlag; } void setScalingRatio( double hor, double ver ) { m_scalingRatioHor = hor, m_scalingRatioVer = ver; } +#if JVET_AI0074_MULTIRATIO_RPR_NNSR + void setScalingRatio2(double hor, double ver) { m_scalingRatioHor2 = hor, m_scalingRatioVer2 = ver; } +#endif void setResChangeInClvsEnabled(bool b) { m_resChangeInClvsEnabled = b; } bool isResChangeInClvsEnabled() const { return m_resChangeInClvsEnabled; } +#if JVET_AI0074_MULTIRATIO_RPR_NNSR + void setSingleScalingRatio(bool b) { m_singleScalingRatio = b; } + bool isSingleScalingRatio() const { return m_singleScalingRatio; } +#endif void setSwitchPocPeriod( int p ) { m_switchPocPeriod = p;} void setUpscaledOutput( int b ) { m_upscaledOutput = b; } int getUpscaledOutput() const { return m_upscaledOutput; } @@ -2362,6 +2391,10 @@ public: void setAreCostsWritten(const bool& areCostsWritten) {m_areCostsWritten = areCostsWritten;} bool getAreCostsWritten() const {return m_areCostsWritten;} #endif +#if JVET_AI0130_COMB_NN_INTRA + bool getIsNnIspRestricted() const { return m_isNnIspRestricted; } + void setIsNnIspRestricted(bool b) { m_isNnIspRestricted = b; } +#endif #endif #if JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS void setPtrOfstreamQuadtree(std::ofstream& ofstreamQuadtree) {m_ptrOfstreamQuadtree = &ofstreamQuadtree;} diff --git a/source/Lib/EncoderLib/EncCu.cpp b/source/Lib/EncoderLib/EncCu.cpp index d1290a6c20f7ac828c1094a8cac0a4b77192b61e..f6a18e77c64866e38d7735143412104b203d1b64 100644 --- a/source/Lib/EncoderLib/EncCu.cpp +++ b/source/Lib/EncoderLib/EncCu.cpp @@ -1763,6 +1763,15 @@ bool EncCu::xCheckRDCostIntra(CodingStructure *&tempCS, CodingStructure *&bestCS if ((*tempCS->sps).getNnIntraPredEnabledFlag()) { m_pcIntraSearch->setIsAlreadyComputedPnnRd(false,CHANNEL_TYPE_LUMA);m_pcIntraSearch->setIsAlreadyComputedPnnRd(false,CHANNEL_TYPE_CHROMA);m_pcTrQuant->setIsAlreadyComputedTrPnn(false,COMPONENT_Y);m_pcTrQuant->setIsAlreadyComputedTrPnn(false,COMPONENT_Cb);m_pcTrQuant->setIsAlreadyComputedTrPnn(false,COMPONENT_Cr); +#if JVET_AI0130_COMB_NN_INTRA + for (uint8_t i{1}; i < NUM_INTRA_SUBPARTITIONS_MODES; i++) + { + for (uint8_t j{0}; j < 4; j++) + { + m_pcIntraSearch->setIsAlreadyComputedPnnIspRd(false,i,j); + } + } +#endif #if JVET_AB0149_TM_INF_PNN m_pcIntraSearch->resetIdxPredictedLfnstPnnMemories(); #endif @@ -1838,6 +1847,9 @@ bool EncCu::xCheckRDCostIntra(CodingStructure *&tempCS, CodingStructure *&bestCS CHECK(tempCS->modeType != MODE_TYPE_ALL || partitioner.modeType != MODE_TYPE_ALL, "localtree should not be applied when adaptive color transform is enabled"); CHECK(adaptiveColorTrans && (CS::isDualITree(*tempCS) || partitioner.chType != CHANNEL_TYPE_LUMA), "adaptive color transform cannot be applied to dual-tree"); } +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + bool isPnnContinuing{false}; +#endif for( int trGrpIdx = 0; trGrpIdx < grpNumMax; trGrpIdx++ ) { @@ -1846,10 +1858,20 @@ bool EncCu::xCheckRDCostIntra(CodingStructure *&tempCS, CodingStructure *&bestCS if( ( trGrpIdx == 0 || ( !skipSecondMtsPass && considerMtsSecondPass ) ) && trGrpCheck[ trGrpIdx ] ) { +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + if (trGrpIdx > 0 && isPnnContinuing) + { + startLfnstIdx = 0; + } +#endif for( int lfnstIdx = startLfnstIdx; lfnstIdx <= endLfnstIdx; lfnstIdx++ ) { #if JVET_AB0149_INTRA_PRED && JVET_AB0149_SEARCH_PNN - int idxLfnstPnnEnd = sps.getNnIntraPredEnabledFlag() && lfnstIdx ? DEPTH_LFNST_GROUP : 1; + int idxLfnstPnnEnd = sps.getNnIntraPredEnabledFlag() +#if JVET_AI0130_COMB_NN_INTRA + && trGrpIdx == 0 +#endif + && lfnstIdx ? DEPTH_LFNST_GROUP : 1; isTriggeredHeuristicInter = false; for (uint8_t idxLfnstPnn = 0; idxLfnstPnn < idxLfnstPnnEnd; idxLfnstPnn++) { @@ -1909,6 +1931,9 @@ bool EncCu::xCheckRDCostIntra(CodingStructure *&tempCS, CodingStructure *&bestCS partitioner #if JVET_AB0149_INTRA_PRED && JVET_AB0149_SEARCH_PNN , isTriggeredHeuristicInter +#endif +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + , isPnnContinuing #endif , bestCostSoFar, mtsFlag, startMTSIdx[trGrpIdx], endMTSIdx[trGrpIdx], trGrpIdx > 0, !cu.colorTransform ? bestCS : nullptr); if (!validCandRet) @@ -1931,6 +1956,9 @@ bool EncCu::xCheckRDCostIntra(CodingStructure *&tempCS, CodingStructure *&bestCS m_modeCtrl->setISPMode(cu.ispMode); m_modeCtrl->setISPLfnstIdx(cu.lfnstIdx); m_modeCtrl->setMIPFlagISPPass(cu.mipFlag); +#if JVET_AB0149_INTRA_PRED && !JVET_AI0130_COMB_NN_INTRA + CHECK(cu.ispMode && PU::getFinalIntraMode(*cu.firstPU, CHANNEL_TYPE_LUMA) == PNN_IDX,"`cu.ispMode` is not equal to 0 and the selected intra prediction mode in luminance is the PNN mode."); +#endif m_modeCtrl->setBestISPIntraModeRelCU(cu.ispMode ? PU::getFinalIntraMode(*cu.firstPU, CHANNEL_TYPE_LUMA) : UINT8_MAX); m_modeCtrl->setBestDCT2NonISPCostRelCU(m_modeCtrl->getMtsFirstPassNoIspCost()); } @@ -2219,11 +2247,23 @@ bool EncCu::xCheckRDCostIntra(CodingStructure *&tempCS, CodingStructure *&bestCS { trGrpCheck[ trGrpIdx + 1 ] = false; +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + if (m_pcEncCfg->getUseNnIntraPred() && partitioner.isSepTree(*tempCS) && considerMtsSecondPass && decideShapeHandledPnn(partitioner.currArea().lheight(), partitioner.currArea().lwidth()) && !isPnnLimited(partitioner.currArea().lheight(), partitioner.currArea().lwidth())) + { + trGrpCheck[trGrpIdx + 1] = true; + isPnnContinuing = true; + } + else + { +#endif if( bestSelFlag[ trGrpIdx ] && considerMtsSecondPass ) { double dCostRatio = dct2Cost / trGrpBestCost[ trGrpIdx ]; trGrpCheck[ trGrpIdx + 1 ] = ( bestMtsFlag != 0 || bestLfnstIdx != 0 ) && dCostRatio < trGrpStopThreshold[ trGrpIdx ]; } +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + } +#endif } } //trGrpIdx #if JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS @@ -2245,6 +2285,29 @@ bool EncCu::xCheckRDCostIntra(CodingStructure *&tempCS, CodingStructure *&bestCS #endif if(!adaptiveColorTrans) m_modeCtrl->setBestNonDCT2Cost(bestNonDCT2Cost); +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + if (isLuma(partitioner.chType) && bestCS->cus[0]->firstPU->intraDir[CHANNEL_TYPE_LUMA] == PNN_IDX) + { + if (!bestCS->cus[0]->ispMode && partitioner.currArea().lheight()*partitioner.currArea().lwidth() <= 256) + { + PelBuf buf(bestCS->cus[0]->arrayMemPred,partitioner.currArea().Y()); + buf.copyFrom( + CPelBuf(m_pcIntraSearch->getPSharedPredPnn(COMPONENT_Y),partitioner.currArea().Y()) + ); + } + if (bestCS->cus[0]->ispMode) + { + const uint32_t size{bestCS->cus[0]->firstTU->Y().area()}; + for (int i{0}; i < (bestCS->cus[0]->lfnstIdx ? 1 : 4); i++) + { + PelBuf buf(bestCS->cus[0]->arrayMemPred + i*size,bestCS->cus[0]->firstTU->Y()); + buf.copyFrom( + CPelBuf(m_pcIntraSearch->getPSharedPredPnnIsp(bestCS->cus[0]->ispMode - 1, i),bestCS->cus[0]->firstTU->Y()) + ); + } + } + } +#endif return foundZeroRootCbf; } @@ -2799,6 +2862,9 @@ void EncCu::xCheckRDCostMerge2Nx2N( CodingStructure *&tempCS, CodingStructure *& { #if JVET_AB0149_INTRA_PRED bool contextFlag = false; +#if JVET_AI0130_COMB_NN_INTRA + uint32_t idxRepresentationPnn{MAX_INT}; +#endif #endif m_pcIntraSearch->initIntraPatternChType(*pu.cu, pu.Y() #if JVET_AB0149_INTRA_PRED @@ -2811,6 +2877,12 @@ void EncCu::xCheckRDCostMerge2Nx2N( CodingStructure *&tempCS, CodingStructure *& m_pcIntraSearch->predIntraAng(COMPONENT_Y, pu.cs->getPredBuf(pu).Y(), pu #if JVET_AB0149_INTRA_PRED , contextFlag +#if JVET_AI0130_COMB_NN_INTRA + , idxRepresentationPnn +#if JVET_AB0149_TM_INF_PNN + , nullptr +#endif +#endif #endif ); m_pcIntraSearch->switchBuffer(pu, COMPONENT_Y, pu.cs->getPredBuf(pu).Y(), m_pcIntraSearch->getPredictorPtr2(COMPONENT_Y, intraCnt)); @@ -2918,6 +2990,9 @@ void EncCu::xCheckRDCostMerge2Nx2N( CodingStructure *&tempCS, CodingStructure *& uint32_t bufIdx = 0; #if JVET_AB0149_INTRA_PRED bool contextFlag = false; +#if JVET_AI0130_COMB_NN_INTRA + uint32_t idxRepresentationPnn{MAX_INT}; +#endif #endif m_pcIntraSearch->initIntraPatternChType(*pu.cu, pu.Cb() #if JVET_AB0149_INTRA_PRED @@ -2930,6 +3005,12 @@ void EncCu::xCheckRDCostMerge2Nx2N( CodingStructure *&tempCS, CodingStructure *& m_pcIntraSearch->predIntraAng(COMPONENT_Cb, pu.cs->getPredBuf(pu).Cb(), pu #if JVET_AB0149_INTRA_PRED , contextFlag +#if JVET_AI0130_COMB_NN_INTRA + , idxRepresentationPnn +#if JVET_AB0149_TM_INF_PNN + , nullptr +#endif +#endif #endif ); m_pcIntraSearch->switchBuffer(pu, COMPONENT_Cb, pu.cs->getPredBuf(pu).Cb(), m_pcIntraSearch->getPredictorPtr2(COMPONENT_Cb, bufIdx)); @@ -2945,6 +3026,12 @@ void EncCu::xCheckRDCostMerge2Nx2N( CodingStructure *&tempCS, CodingStructure *& m_pcIntraSearch->predIntraAng(COMPONENT_Cr, pu.cs->getPredBuf(pu).Cr(), pu #if JVET_AB0149_INTRA_PRED , contextFlag +#if JVET_AI0130_COMB_NN_INTRA + , idxRepresentationPnn +#if JVET_AB0149_TM_INF_PNN + , nullptr +#endif +#endif #endif ); m_pcIntraSearch->switchBuffer(pu, COMPONENT_Cr, pu.cs->getPredBuf(pu).Cr(), m_pcIntraSearch->getPredictorPtr2(COMPONENT_Cr, bufIdx)); diff --git a/source/Lib/EncoderLib/EncGOP.cpp b/source/Lib/EncoderLib/EncGOP.cpp index 9c4deebbdf9971d4624b0fdf483ff8a8fb61f701..32e1eb35c8da12caecb8435c976441c50705ad89 100644 --- a/source/Lib/EncoderLib/EncGOP.cpp +++ b/source/Lib/EncoderLib/EncGOP.cpp @@ -3255,15 +3255,19 @@ void EncGOP::compressGOP( int iPOCLast, int iNumPicRcvd, PicList& rcListPic, #if NN_LF_FORCE_USE m_nnfilterUnified.forceIntraType(m_pcCfg->getNnlfDebugOption() == 2 ? true : false); if (m_pcCfg->getNnlfDebugOption() != 1 || pcSlice->isIntra()) // when m_nnlfOption == 1, skip NN filtering for inter slice (note m_picprm.sprm.mode = -1 by default) + { #endif #if JVET_AH0096_CONTENT_ADAPTIVE_LOP - if (m_pcCfg->getUseNnfu() && m_pcCfg->getNumNnfus() > 0 && m_newNnfu) - { - m_nnfilterUnified.init(m_pcCfg->getNnfuModelFileNames().front(), m_pcCfg->getSourceWidth(), m_pcCfg->getSourceHeight(), m_pcCfg->getChromaFormatIdc(), m_pcCfg->getNnlfUnifiedMaxNumPrms(), true, true); - m_pcCfg->getNnfuModelFileNames().pop_front(); + if (m_pcCfg->getUseNnfu() && m_pcCfg->getNumNnfus() > 0 && m_newNnfu) + { + m_nnfilterUnified.init(m_pcCfg->getNnfuModelFileNames().front(), m_pcCfg->getSourceWidth(), m_pcCfg->getSourceHeight(), m_pcCfg->getChromaFormatIdc(), m_pcCfg->getNnlfUnifiedMaxNumPrms(), true, true); + m_pcCfg->getNnfuModelFileNames().pop_front(); + } +#endif + m_nnfilterUnified.chooseParameters(*pcPic); +#if NN_LF_FORCE_USE } #endif - m_nnfilterUnified.chooseParameters(*pcPic); pcSlice->setNnlfUnifiedParameters(m_nnfilterUnified.getSliceprms()); } #endif @@ -4751,12 +4755,17 @@ void EncGOP::xCalculateAddPSNR(Picture* pcPic, PelUnitBuf cPicD, const AccessUni CU::getRprScaling( &sps, pps, pcPic, xScale, yScale ); std::pair<int, int> scalingRatio = std::pair<int, int>( xScale, yScale ); -#if JVET_AG0130_UNIFIED_SR || JVET_AC0196_NNSR +#if JVET_AG0130_UNIFIED_SR || JVET_AC0196_NNSR || JVET_AI0074_MULTIRATIO_RPR_NNSR if (m_pcCfg->getNnModel(NnModel::SUPER_RESOLUTION) =="default_rpr") { Picture::rescalePicture(scalingRatio, picC, pcPic->getScalingWindow(), upscaledRec, pps->getScalingWindow(), format, sps.getBitDepths(), false, false, sps.getHorCollocatedChromaFlag(), sps.getVerCollocatedChromaFlag()); } else { +#if JVET_AI0074_MULTIRATIO_RPR_NNSR + int sliceQP = pcPic->cs->slice->getSliceQp(); + int baseQP = pcPic->cs->pps->getPicInitQPMinus26() + 26; + int sr = pcPic->getNNInputSr(sps, *pcPic->cs->pps); +#else PelUnitBuf predBuf = pcPic->getPredBufCustom(); int sliceQP = pcPic->cs->slice->getSliceQp(); #if JVET_AG0130_UNIFIED_SR @@ -4765,7 +4774,7 @@ void EncGOP::xCalculateAddPSNR(Picture* pcPic, PelUnitBuf cPicD, const AccessUni int baseQP = pcPic->cs->pps->getPicInitQPMinus26() + 26; #endif SliceType sliceType = pcPic->cs->slice->getSliceType(); - +#endif PelStorage upscaledPic; upscaledPic.create(pic.chromaFormat, Area(Position(), upscaledOrg)); Picture::rescalePicture(scalingRatio, picC, pcPic->getScalingWindow(), upscaledPic, pps->getScalingWindow(), @@ -4776,11 +4785,16 @@ void EncGOP::xCalculateAddPSNR(Picture* pcPic, PelUnitBuf cPicD, const AccessUni && picC.get(COMPONENT_Y).height == upscaledPic.get(COMPONENT_Y).height) upscaledRec.copyFrom(upscaledPic); else +#if JVET_AI0074_MULTIRATIO_RPR_NNSR + Picture::rescalePictureCNN(upscaledPic, upscaledRec, sliceQP, baseQP, sr, + m_pcCfg->getNnModel(NnModel::SUPER_RESOLUTION)); +#else #if JVET_AG0130_UNIFIED_SR Picture::rescalePictureCNN(picC, predBuf, upscaledPic, bsBuf, bpmBuf, upscaledRec, sliceQP, baseQP, sliceType, m_pcCfg->getNnModel(NnModel::SUPER_RESOLUTION)); #elif JVET_AC0196_NNSR Picture::rescalePictureCNN(picC, predBuf, upscaledPic, upscaledRec, sliceQP, baseQP, sliceType); +#endif #endif } #else @@ -4839,7 +4853,7 @@ void EncGOP::xCalculateAddPSNR(Picture* pcPic, PelUnitBuf cPicD, const AccessUni } #endif -#if JVET_AG0130_UNIFIED_SR || JVET_AC0196_NNSR +#if JVET_AG0130_UNIFIED_SR || JVET_AC0196_NNSR || JVET_AI0074_MULTIRATIO_RPR_NNSR if (m_pcEncLib->getUseNnsr() && m_pcEncLib->isResChangeInClvsEnabled()) { const CPelBuf& upscaledOrg = (sps.getUseLmcs() || m_pcCfg->getGopBasedTemporalFilterEnabled()) ? pcPic->M_BUFS( 0, PIC_TRUE_ORIGINAL_INPUT).get( compID ) : pcPic->M_BUFS( 0, PIC_ORIGINAL_INPUT).get( compID ); @@ -4938,7 +4952,7 @@ void EncGOP::xCalculateAddPSNR(Picture* pcPic, PelUnitBuf cPicD, const AccessUni m_vRVM_RP.push_back( uibits ); //===== add PSNR ===== -#if JVET_AG0130_UNIFIED_SR || JVET_AC0196_NNSR +#if JVET_AG0130_UNIFIED_SR || JVET_AC0196_NNSR || JVET_AI0074_MULTIRATIO_RPR_NNSR m_gcAnalyzeAll.addResult(upscaledPSNR, (double) uibits, MSEyuvframe, upscaledPSNR, upscaledMsssim, upscaledMsssim, isEncodeLtRef); #else m_gcAnalyzeAll.addResult(dPSNR, (double) uibits, MSEyuvframe, upscaledPSNR, msssim, upscaledMsssim, isEncodeLtRef); @@ -4954,7 +4968,7 @@ void EncGOP::xCalculateAddPSNR(Picture* pcPic, PelUnitBuf cPicD, const AccessUni #endif if (pcSlice->isIntra()) { -#if JVET_AG0130_UNIFIED_SR || JVET_AC0196_NNSR +#if JVET_AG0130_UNIFIED_SR || JVET_AC0196_NNSR || JVET_AI0074_MULTIRATIO_RPR_NNSR m_gcAnalyzeI.addResult(upscaledPSNR, (double) uibits, MSEyuvframe, upscaledPSNR, upscaledMsssim, upscaledMsssim, isEncodeLtRef); #else m_gcAnalyzeI.addResult(dPSNR, (double)uibits, MSEyuvframe, upscaledPSNR, msssim, upscaledMsssim, isEncodeLtRef); @@ -4972,7 +4986,7 @@ void EncGOP::xCalculateAddPSNR(Picture* pcPic, PelUnitBuf cPicD, const AccessUni } if (pcSlice->isInterP()) { -#if JVET_AG0130_UNIFIED_SR || JVET_AC0196_NNSR +#if JVET_AG0130_UNIFIED_SR || JVET_AC0196_NNSR || JVET_AI0074_MULTIRATIO_RPR_NNSR m_gcAnalyzeP.addResult(upscaledPSNR, (double) uibits, MSEyuvframe, upscaledPSNR, upscaledMsssim, upscaledMsssim, isEncodeLtRef); #else m_gcAnalyzeP.addResult(dPSNR, (double)uibits, MSEyuvframe, upscaledPSNR, msssim, upscaledMsssim, isEncodeLtRef); @@ -4990,7 +5004,7 @@ void EncGOP::xCalculateAddPSNR(Picture* pcPic, PelUnitBuf cPicD, const AccessUni } if (pcSlice->isInterB()) { -#if JVET_AG0130_UNIFIED_SR || JVET_AC0196_NNSR +#if JVET_AG0130_UNIFIED_SR || JVET_AC0196_NNSR || JVET_AI0074_MULTIRATIO_RPR_NNSR m_gcAnalyzeB.addResult(upscaledPSNR, (double)uibits, MSEyuvframe, upscaledPSNR, upscaledMsssim, upscaledMsssim, isEncodeLtRef); #else m_gcAnalyzeB.addResult(dPSNR, (double) uibits, MSEyuvframe, upscaledPSNR, msssim, upscaledMsssim, isEncodeLtRef); diff --git a/source/Lib/EncoderLib/EncLib.cpp b/source/Lib/EncoderLib/EncLib.cpp index 5c145f88f2a8811727c1bcc22808f7af1052e919..ee71145aeee70772d7171f9b391c0556891e81fe 100644 --- a/source/Lib/EncoderLib/EncLib.cpp +++ b/source/Lib/EncoderLib/EncLib.cpp @@ -379,7 +379,99 @@ void EncLib::init( bool isFieldCoding, AUWriterIf* auWriterIf ) pps.setWrapAroundOffset ( 0 ); } } +#if JVET_AI0074_MULTIRATIO_RPR_NNSR + if (m_resChangeInClvsEnabled && !m_singleScalingRatio) + { + PPS &pps = *(m_ppsMap.allocatePS(ENC_PPS_ID_RPR2)); + Window &inputScalingWindow = pps0.getScalingWindow(); + int scaledWidth = int((pps0.getPicWidthInLumaSamples() + - SPS::getWinUnitX(sps0.getChromaFormatIdc()) + * (inputScalingWindow.getWindowLeftOffset() + inputScalingWindow.getWindowRightOffset())) + / m_scalingRatioHor2); + int minSizeUnit = std::max(8, 1 << sps0.getLog2MinCodingBlockSize()); + int temp = scaledWidth / minSizeUnit; + int width = (scaledWidth - (temp * minSizeUnit) > 0 ? temp + 1 : temp) * minSizeUnit; + + int scaledHeight = + int((pps0.getPicHeightInLumaSamples() + - SPS::getWinUnitY(sps0.getChromaFormatIdc()) + * (inputScalingWindow.getWindowTopOffset() + inputScalingWindow.getWindowBottomOffset())) + / m_scalingRatioVer2); + temp = scaledHeight / minSizeUnit; + int height = (scaledHeight - (temp * minSizeUnit) > 0 ? temp + 1 : temp) * minSizeUnit; + + pps.setPicWidthInLumaSamples(width); + pps.setPicHeightInLumaSamples(height); + Window conformanceWindow; + conformanceWindow.setWindow(0, (width - scaledWidth) / SPS::getWinUnitX(sps0.getChromaFormatIdc()), 0, + (height - scaledHeight) / SPS::getWinUnitY(sps0.getChromaFormatIdc())); + if (pps.getPicWidthInLumaSamples() == sps0.getMaxPicWidthInLumaSamples() + && pps.getPicHeightInLumaSamples() == sps0.getMaxPicHeightInLumaSamples()) + { + pps.setConformanceWindow(sps0.getConformanceWindow()); + pps.setConformanceWindowFlag(false); + } + else + { + pps.setConformanceWindow(conformanceWindow); + pps.setConformanceWindowFlag(pps.getConformanceWindow().getWindowEnabledFlag()); + } + + Window scalingWindow; + scalingWindow.setWindow(0, (width - scaledWidth) / SPS::getWinUnitX(sps0.getChromaFormatIdc()), 0, + (height - scaledHeight) / SPS::getWinUnitY(sps0.getChromaFormatIdc())); + pps.setScalingWindow(scalingWindow); + + // register the width/height of the current pic into reference SPS + if (!sps0.getPPSValidFlag(pps.getPPSId())) + { + sps0.setPPSValidFlag(pps.getPPSId(), true); + sps0.setScalingWindowSizeInPPS(pps.getPPSId(), scaledWidth, scaledHeight); + } + int curSeqMaxPicWidthY = sps0.getMaxPicWidthInLumaSamples(); // sps_pic_width_max_in_luma_samples + int curSeqMaxPicHeightY = sps0.getMaxPicHeightInLumaSamples(); // sps_pic_height_max_in_luma_samples + int curPicWidthY = width; // pps_pic_width_in_luma_samples + int curPicHeightY = height; // pps_pic_height_in_luma_samples + int max8MinCbSizeY = std::max((int) 8, (1 << sps0.getLog2MinCodingBlockSize())); // Max(8, MinCbSizeY) + // Warning message of potential scaling window size violation + for (int i = 0; i < 64; i++) + { + if (sps0.getPPSValidFlag(i)) + { + if ((scaledWidth * curSeqMaxPicWidthY) + < sps0.getScalingWindowSizeInPPS(i).width * (curPicWidthY - max8MinCbSizeY)) + printf("Potential violation: (curScaledWIdth * curSeqMaxPicWidthY) should be greater than or equal to " + "refScaledWidth * (curPicWidthY - max(8, MinCbSizeY)\n"); + if ((scaledHeight * curSeqMaxPicHeightY) + < sps0.getScalingWindowSizeInPPS(i).height * (curPicHeightY - max8MinCbSizeY)) + printf("Potential violation: (curScaledHeight * curSeqMaxPicHeightY) should be greater than or equal to " + "refScaledHeight * (curPicHeightY - max(8, MinCbSizeY)\n"); + } + } + + // disable picture partitioning for scaled RPR pictures (slice/tile config only provided for the original + // resolution) + m_noPicPartitionFlag = true; + + xInitPPS(pps, sps0); // will allocate memory for and initialize pps.pcv inside + + if (pps.getWrapAroundEnabledFlag()) + { + int minCbSizeY = (1 << sps0.getLog2MinCodingBlockSize()); + pps.setPicWidthMinusWrapAroundOffset( + (pps.getPicWidthInLumaSamples() / minCbSizeY) + - (m_wrapAroundOffset * pps.getPicWidthInLumaSamples() / pps0.getPicWidthInLumaSamples() / minCbSizeY)); + pps.setWrapAroundOffset(minCbSizeY + * (pps.getPicWidthInLumaSamples() / minCbSizeY - pps.getPicWidthMinusWrapAroundOffset())); + } + else + { + pps.setPicWidthMinusWrapAroundOffset(0); + pps.setWrapAroundOffset(0); + } + } +#endif #if ER_CHROMA_QP_WCG_PPS if (m_wcgChromaQpControl.isEnabled()) { @@ -764,6 +856,10 @@ bool EncLib::encodePrep( bool flush, PelStorage* pcPicYuvOrg, PelStorage* cPicYu #endif if (m_resChangeInClvsEnabled && iPOC % getGOPSize() == 0) { +#if JVET_AI0074_MULTIRATIO_RPR_NNSR + if (m_singleScalingRatio) + { +#endif double basePSNR = 45 - (m_gopBasedTemporalFilterEnabled ? 0 : 1); if (m_gopBasedTemporalFilterEnabled) //RA { @@ -815,6 +911,88 @@ bool EncLib::encodePrep( bool flush, PelStorage* pcPicYuvOrg, PelStorage* cPicYu } } } +#if JVET_AI0074_MULTIRATIO_RPR_NNSR + } + else + { + const int maxPSNR = 47; + int basePSNR = maxPSNR - (m_gopBasedTemporalFilterEnabled ? 0 : 3); + const int qpBias = 32; + const double weight = 0.5; + const int offPSNR2 = m_gopBasedTemporalFilterEnabled ? 2 : 3; // RA:2 AI:3 + if (m_gopBasedTemporalFilterEnabled) // RA + { + if (upscaledPSNR1 > 40.0 && upscaledPSNR2 > 40.0) + { + if (upscaledPSNR > 44.0) + { + ppsID = ENC_PPS_ID_RPR; + } + else + { + if (((basePSNR) - (m_iQP - qpBias) * weight) < upscaledPSNR) + { + ppsID = ENC_PPS_ID_RPR; + } + else + { + if ((basePSNR - offPSNR2 - (m_iQP - qpBias) * weight) < upscaledPSNR) + { + ppsID = ENC_PPS_ID_RPR2; + } + else + { + ppsID = 0; + } + } + } + } + else + { + if (((basePSNR) - (m_iQP - qpBias) * weight) < upscaledPSNR) + { + ppsID = ENC_PPS_ID_RPR; + } + else + { + if ((basePSNR - offPSNR2 - (m_iQP - qpBias) * weight) < upscaledPSNR) + { + ppsID = ENC_PPS_ID_RPR2; + } + else + { + ppsID = 0; + } + } + } + } + else // AI + { + if (upscaledPSNR1 > 40.0 && upscaledPSNR2 > 40.0) + { + ppsID = ENC_PPS_ID_RPR; + } + else + { + if (((basePSNR) - (m_iQP - qpBias) * weight) < upscaledPSNR) + { + ppsID = ENC_PPS_ID_RPR; + } + else + { + if ((basePSNR - offPSNR2 - (m_iQP - qpBias) * weight) < upscaledPSNR) + { + ppsID = ENC_PPS_ID_RPR2; + } + else + { + ppsID = 0; + } + } + } + } + } +#endif m_iGOPRprPpsId = ppsID; } else @@ -824,7 +1002,7 @@ bool EncLib::encodePrep( bool flush, PelStorage* pcPicYuvOrg, PelStorage* cPicYu #if JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS } #endif -#elif JVET_AG0130_UNIFIED_SR || JVET_AC0196_NNSR +#elif JVET_AG0130_UNIFIED_SR || JVET_AC0196_NNSR || JVET_AI0074_MULTIRATIO_RPR_NNSR if (m_resChangeInClvsEnabled) ppsID = ENC_PPS_ID_RPR; #else @@ -863,7 +1041,7 @@ bool EncLib::encodePrep( bool flush, PelStorage* pcPicYuvOrg, PelStorage* cPicYu const PPS *pPPS = ( ppsID < 0 ) ? m_ppsMap.getFirstPS() : m_ppsMap.getPS( ppsID ); const SPS *pSPS = m_spsMap.getPS( pPPS->getSPSId() ); -#if JVET_AG0130_UNIFIED_SR +#if JVET_AG0130_UNIFIED_SR || JVET_AI0074_MULTIRATIO_RPR_NNSR if (ppsID == ENC_PPS_ID_RPR) { PPS *tempPPS = ( ppsID < 0 ) ? m_ppsMap.getFirstPS() : m_ppsMap.getPS( ppsID ); @@ -875,7 +1053,18 @@ bool EncLib::encodePrep( bool flush, PelStorage* pcPicYuvOrg, PelStorage* cPicYu tempPPS->setPicInitQPMinus26(std::min(maxDQP, std::max(minDQP, baseQp))); } #endif +#if JVET_AI0074_MULTIRATIO_RPR_NNSR + else if (ppsID == ENC_PPS_ID_RPR2) + { + PPS *tempPPS = (ppsID < 0) ? m_ppsMap.getFirstPS() : m_ppsMap.getPS(ppsID); + const int maxDQP = 37; + const int minDQP = -26 + pSPS->getQpBDOffset(CHANNEL_TYPE_LUMA); + int baseQp = getBaseQP() - 26; + baseQp -= m_nnsrQpOffset2; + tempPPS->setPicInitQPMinus26(std::min(maxDQP, std::max(minDQP, baseQp))); + } +#endif if (m_resChangeInClvsEnabled) { pcPicCurr->M_BUFS( 0, PIC_ORIGINAL_INPUT ).getBuf( COMPONENT_Y ).copyFrom( pcPicYuvOrg->getBuf( COMPONENT_Y ) ); @@ -1470,6 +1659,13 @@ void EncLib::xInitSPS( SPS& sps ) { int maxPicWidth = std::max(m_iSourceWidth, (int)((double)m_iSourceWidth / m_scalingRatioHor + 0.5)); int maxPicHeight = std::max(m_iSourceHeight, (int)((double)m_iSourceHeight / m_scalingRatioVer + 0.5)); +#if JVET_AI0074_MULTIRATIO_RPR_NNSR + if (!m_singleScalingRatio) + { + maxPicWidth = std::max(maxPicWidth, (int) ((double) m_iSourceWidth / m_scalingRatioHor2 + 0.5)); + maxPicHeight = std::max(maxPicHeight, (int) ((double) m_iSourceHeight / m_scalingRatioVer2 + 0.5)); + } +#endif const int minCuSize = std::max(8, 1 << m_log2MinCUSize); if (maxPicWidth % minCuSize) { @@ -1551,6 +1747,9 @@ void EncLib::xInitSPS( SPS& sps ) sps.setUseMIP ( m_MIP ); #if JVET_AB0149_INTRA_PRED sps.setNnIntraPredEnabledFlag(m_nnIntraPred); +#if JVET_AI0130_COMB_NN_INTRA + sps.setIsNnIspRestricted(m_isNnIspRestricted); +#endif #endif CHECK(m_log2MinCUSize > std::min(6, floorLog2(sps.getMaxCUWidth())), "sps_log2_min_luma_coding_block_size_minus2 shall be in the range of 0 to min (4, log2_ctu_size - 2)"); CHECK(m_uiMaxMTTHierarchyDepth > 2 * (floorLog2(sps.getCTUSize()) - sps.getLog2MinCodingBlockSize()), "sps_max_mtt_hierarchy_depth_inter_slice shall be in the range 0 to 2*(ctbLog2SizeY - log2MinCUSize)"); @@ -2600,7 +2799,7 @@ int EncCfg::getQPForPicture(const uint32_t gopIndex, const Slice *pSlice) const #if ADAPTIVE_RPR if (pSlice->getPPS()->getPPSId() == ENC_PPS_ID_RPR) { -#if JVET_AG0130_UNIFIED_SR +#if JVET_AG0130_UNIFIED_SR || JVET_AI0074_MULTIRATIO_RPR_NNSR qp -= m_nnsrQpOffset; #else // adjust QP for rate matching. @@ -2626,6 +2825,36 @@ int EncCfg::getQPForPicture(const uint32_t gopIndex, const Slice *pSlice) const qp -= qpOffsets[baseQp]; #endif } +#if JVET_AI0074_MULTIRATIO_RPR_NNSR + else if (pSlice->getPPS()->getPPSId() == ENC_PPS_ID_RPR2) + { +#if JVET_AG0130_UNIFIED_SR + qp -= m_nnsrQpOffset2; +#else + // adjust QP for rate matching. + int baseQp = qp; + constexpr int maxQp = 64; + std::array<int, maxQp> qpOffsets; + qpOffsets.fill(m_nnsrQpOffset2); + + // rate adaptation for AI + if (getIntraPeriod() == 1) + { + std::istringstream iss(m_sNnsrQpOffsetOverridebyQp2); + int iqp, qpOffset; + while (iss >> iqp >> qpOffset) + { + if (iqp < 0 || iqp >= maxQp) + { + std::cerr << "Error in m_sNnsrQpOffsetOverridebyQp format" << std::endl; + } + qpOffsets[iqp] = qpOffset; + } + } + qp -= qpOffsets[baseQp]; +#endif + } +#endif #endif if(sliceType==I_SLICE) diff --git a/source/Lib/EncoderLib/EncModeCtrl.cpp b/source/Lib/EncoderLib/EncModeCtrl.cpp index 4ad5bc5f57cff41937dcb7289d1f0189423b3dc4..e14d186defc445fc5e4517e63fd217f92edbe99b 100644 --- a/source/Lib/EncoderLib/EncModeCtrl.cpp +++ b/source/Lib/EncoderLib/EncModeCtrl.cpp @@ -905,6 +905,29 @@ bool BestEncInfoCache::setFromCs( const CodingStructure& cs, const Partitioner& #endif encInfo.testMode = getCSEncMode( cs ); +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + if (isLuma(partitioner.chType) && encInfo.pu.intraDir[CHANNEL_TYPE_LUMA] == PNN_IDX) + { + if (!encInfo.cu.ispMode && partitioner.currArea().lheight()*partitioner.currArea().lwidth() <= 256) + { + PelBuf buf(encInfo.cu.arrayMemPred,partitioner.currArea().Y()); + buf.copyFrom( + CPelBuf(cs.cus.front()->arrayMemPred,partitioner.currArea().Y()) + ); + } + if (encInfo.cu.ispMode) + { + const uint32_t size{encInfo.tus[0].Y().area()}; + for (int i{0}; i < (encInfo.cu.lfnstIdx ? 1 : 4); i++) + { + PelBuf buf(encInfo.cu.arrayMemPred + i*size,encInfo.tus[0].Y()); + buf.copyFrom( + CPelBuf(cs.cus.front()->arrayMemPred + i*size,encInfo.tus[0].Y()) + ); + } + } + } +#endif return true; } @@ -990,6 +1013,29 @@ bool BestEncInfoCache::setCsFrom( CodingStructure& cs, EncTestMode& testMode, co testMode = encInfo.testMode; +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + if (isLuma(partitioner.chType) && encInfo.pu.intraDir[CHANNEL_TYPE_LUMA] == PNN_IDX) + { + if (!encInfo.cu.ispMode && partitioner.currArea().lheight()*partitioner.currArea().lwidth() <= 256) + { + PelBuf buf(cu.arrayMemPred,partitioner.currArea().Y()); + buf.copyFrom( + CPelBuf(encInfo.cu.arrayMemPred,partitioner.currArea().Y()) + ); + } + if (encInfo.cu.ispMode) + { + const uint32_t size{encInfo.tus[0].Y().area()}; + for (int i{0}; i < (encInfo.cu.lfnstIdx ? 1 : 4); i++) + { + PelBuf buf(cu.arrayMemPred + i*size,encInfo.tus[0].Y()); + buf.copyFrom( + CPelBuf(encInfo.cu.arrayMemPred + i*size,encInfo.tus[0].Y()) + ); + } + } + } +#endif return true; } diff --git a/source/Lib/EncoderLib/EncSlice.cpp b/source/Lib/EncoderLib/EncSlice.cpp index e4c088224927d2a2ecd445036339e8386f064683..ed9387a79531dcf66fef6cb02553ec4bb51a8b3f 100644 --- a/source/Lib/EncoderLib/EncSlice.cpp +++ b/source/Lib/EncoderLib/EncSlice.cpp @@ -1523,17 +1523,35 @@ void writeCharacteristicsTuInCuIntra(const TransformUnit& tu,const ChannelType& { refOfstreamQuadtree << "channel index 0; ";refOfstreamQuadtree << "block size " << tu.lwidth() << "x" << tu.lheight() << "; ";refOfstreamQuadtree << "block position (" << tu.lx() << ", " << tu.ly() << "); ";refOfstreamQuadtree << "intra prediction mode index " << uiIntraMode << "; ";refOfstreamQuadtree << "MIP flag " << cu.mipFlag << "; ";refOfstreamQuadtree << "depth of the TB relatively to its CB " << (cu.firstTU != cu.lastTU) << "; ";refOfstreamQuadtree << "flag of splitting the CB into 4 PBs 0; ";refOfstreamQuadtree << "LFNST indices (" << cu.lfnstIdx << ", "; #if JVET_AB0149_INTRA_PRED && JVET_AB0149_SEARCH_PNN +#if JVET_AI0130_COMB_NN_INTRA + if (isNnIntraPredEnabled && uiIntraMode == PNN_IDX) + { + if (cu.ispMode) + { + CHECK(cu.idxLfnstPnn != 0,"`uiIntraMode` is equal to `PNN_IDX` and `cu.ispMode` is not equal to 0 and `cu.idxLfnstPnn` is not equal to 0."); + } + if (cu.lfnstIdx) + { + refOfstreamQuadtree << cu.idxLfnstPnn; + } + } +#else if (isNnIntraPredEnabled && uiIntraMode == PNN_IDX && cu.lfnstIdx) { refOfstreamQuadtree << cu.idxLfnstPnn; } +#endif #endif refOfstreamQuadtree << "); "; uint32_t i = 0; refOfstreamQuadtree << "indices best modes SSDs regular "; for (i = 0; i < pu.arrayPairsIdxSsdLumaRegular.size(); i++) { +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + refOfstreamQuadtree << (cu.ispMode ? MAX_INT : static_cast<int>(pu.arrayPairsIdxSsdLumaRegular[i].first)); +#else refOfstreamQuadtree << static_cast<int>(pu.arrayPairsIdxSsdLumaRegular[i].first); +#endif if (i < pu.arrayPairsIdxSsdLumaRegular.size() - 1) { refOfstreamQuadtree << " | "; @@ -1543,7 +1561,11 @@ void writeCharacteristicsTuInCuIntra(const TransformUnit& tu,const ChannelType& refOfstreamQuadtree << "intra prediction SSDs regular "; for (i = 0; i < pu.arrayPairsIdxSsdLumaRegular.size(); i++) { +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + refOfstreamQuadtree << (cu.ispMode ? MAX_UINT64 : pu.arrayPairsIdxSsdLumaRegular[i].second); +#else refOfstreamQuadtree << pu.arrayPairsIdxSsdLumaRegular[i].second; +#endif if (i < pu.arrayPairsIdxSsdLumaRegular.size() - 1) { refOfstreamQuadtree << " | "; @@ -1553,13 +1575,37 @@ void writeCharacteristicsTuInCuIntra(const TransformUnit& tu,const ChannelType& #if JVET_AB0149_INTRA_PRED if (isNnIntraPredEnabled) { +#if JVET_AI0130_COMB_NN_INTRA + refOfstreamQuadtree << "intra prediction SSD PNN "; + if (cu.ispMode) + { + if (uiIntraMode == PNN_IDX) + { + refOfstreamQuadtree << tu.ssdLumaPnn; + } + else + { + refOfstreamQuadtree << MAX_UINT64; + } + } + else + { + refOfstreamQuadtree << pu.ssdLumaPnn; + } + refOfstreamQuadtree << "; "; +#else refOfstreamQuadtree << "intra prediction SSD PNN " << pu.ssdLumaPnn << "; "; +#endif } #endif refOfstreamQuadtree << "indices best modes SATDs regular "; for (i = 0; i < pu.arrayPairsIdxSatdLumaRegular.size(); i++) { +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + refOfstreamQuadtree << (cu.ispMode ? MAX_INT : static_cast<int>(pu.arrayPairsIdxSatdLumaRegular[i].first)); +#else refOfstreamQuadtree << static_cast<int>(pu.arrayPairsIdxSatdLumaRegular[i].first); +#endif if (i < pu.arrayPairsIdxSatdLumaRegular.size() - 1) { refOfstreamQuadtree << " | "; @@ -1569,7 +1615,11 @@ void writeCharacteristicsTuInCuIntra(const TransformUnit& tu,const ChannelType& refOfstreamQuadtree << "intra prediction SATDs regular "; for (i = 0; i < pu.arrayPairsIdxSatdLumaRegular.size(); i++) { +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + refOfstreamQuadtree << (cu.ispMode ? MAX_UINT64 : pu.arrayPairsIdxSatdLumaRegular[i].second); +#else refOfstreamQuadtree << pu.arrayPairsIdxSatdLumaRegular[i].second; +#endif if (i < pu.arrayPairsIdxSatdLumaRegular.size() - 1) { refOfstreamQuadtree << " | "; @@ -1579,7 +1629,44 @@ void writeCharacteristicsTuInCuIntra(const TransformUnit& tu,const ChannelType& #if JVET_AB0149_INTRA_PRED if (isNnIntraPredEnabled) { +#if JVET_AI0130_COMB_NN_INTRA + refOfstreamQuadtree << "intra prediction SATD PNN "; + if (cu.ispMode) + { + if (uiIntraMode == PNN_IDX) + { + refOfstreamQuadtree << tu.satdLumaPnn; + } + else + { + refOfstreamQuadtree << MAX_UINT64; + } + } + else + { + refOfstreamQuadtree << pu.satdLumaPnn; + } + refOfstreamQuadtree << "; "; + refOfstreamQuadtree << "measures of discrepancies PNN ("; + if (cu.ispMode) + { + if (uiIntraMode == PNN_IDX) + { + refOfstreamQuadtree << tu.arrayMeasuresDiscrepanciesLumaPnn[0] << ", " << tu.arrayMeasuresDiscrepanciesLumaPnn[1]; + } + else + { + refOfstreamQuadtree << MAX_DOUBLE << ", " << MAX_DOUBLE; + } + } + else + { + refOfstreamQuadtree << pu.arrayMeasuresLumaPnn[0] << ", " << pu.arrayMeasuresLumaPnn[1]; + } + refOfstreamQuadtree << "); "; +#else refOfstreamQuadtree << "intra prediction SATD PNN " << pu.satdLumaPnn << "; ";refOfstreamQuadtree << "measures of discrepancies PNN (" << pu.arrayMeasuresLumaPnn[0] << ", " << pu.arrayMeasuresLumaPnn[1] << "); "; +#endif #if JVET_AB0149_SEARCH_PNN if (areWrittenDone) { diff --git a/source/Lib/EncoderLib/IntraSearch.cpp b/source/Lib/EncoderLib/IntraSearch.cpp index c7b577aa5e7ca60020b31726a0cc82173d0bb169..93802a481729ca437cc0b6bcb4f80110ab81aea7 100644 --- a/source/Lib/EncoderLib/IntraSearch.cpp +++ b/source/Lib/EncoderLib/IntraSearch.cpp @@ -95,10 +95,20 @@ IntraSearch::IntraSearch() { m_isAlreadyComputedPnnRd[i] = false; } +#if JVET_AI0130_COMB_NN_INTRA + for (unsigned int i = 0; i < NUM_INTRA_SUBPARTITIONS_MODES - 1; i++) + { + for (unsigned int j = 0; j < 4; j++) + { + m_isAlreadyComputedPnnIspRd[i][j] = false; + } + } +#else for (unsigned int i = 0; i < MAX_NUM_COMPONENT; i++) { m_pSharedPredPnn[i] = nullptr; } +#endif #if JVET_AB0149_TM_INF_PNN resetIdxPredictedLfnstPnnMemories(); #endif @@ -238,7 +248,7 @@ void IntraSearch::destroy() m_statePtRDOQ[i] = nullptr; } } -#if JVET_AB0149_INTRA_PRED +#if JVET_AB0149_INTRA_PRED && !JVET_AI0130_COMB_NN_INTRA for (uint32_t i = 0; i < MAX_NUM_COMPONENT; i++) { delete[] m_pSharedPredPnn[i]; @@ -266,10 +276,38 @@ void IntraSearch::resetIdxPredictedLfnstPnnMemories() m_arrayIdxPredictedLfnstPnn[i][j] = MAX_INT; } } +#if JVET_AI0130_COMB_NN_INTRA + for (uint32_t i{0}; i < NUM_INTRA_SUBPARTITIONS_MODES - 1; i++) + { + for (uint32_t j{0}; j < 4; j++) + { + for (uint32_t k{0}; k < NUM_LFNST_NUM_PER_SET - 1; k++) + { + m_arrayIdxPredictedLfnstPnnIsp[i][j][k] = MAX_INT; + } + } + } +#endif } void IntraSearch::transferIdxPredictedLfnstPnnMemories(CodingUnit& cu,const ComponentID& compID,const bool& isFromMemoryToCu) { +#if JVET_AI0130_COMB_NN_INTRA + if (isFromMemoryToCu) + { + for (uint32_t i{0}; i < NUM_LFNST_NUM_PER_SET - 1; i++) + { + cu.arrayIdxPredictedLfnstPnn[compID][i] = m_arrayIdxPredictedLfnstPnn[compID][i]; + } + } + else + { + for (uint32_t i{0}; i < NUM_LFNST_NUM_PER_SET - 1; i++) + { + m_arrayIdxPredictedLfnstPnn[compID][i] = cu.arrayIdxPredictedLfnstPnn[compID][i]; + } + } +#else for (uint32_t i{0}; i < NUM_LFNST_NUM_PER_SET - 1; i++) { if (isFromMemoryToCu) @@ -281,11 +319,42 @@ void IntraSearch::transferIdxPredictedLfnstPnnMemories(CodingUnit& cu,const Comp m_arrayIdxPredictedLfnstPnn[compID][i] = cu.arrayIdxPredictedLfnstPnn[compID][i]; } } +#endif } + +#if JVET_AI0130_COMB_NN_INTRA +void IntraSearch::transferIdxPredictedLfnstPnnIspMemories(TransformUnit& tu,const uint8_t& ispMode,const int& subTuCounter,const bool& isFromMemoryToTu) +{ + uint32_t* const ptrIdxPredictedLfnstPnnIsp{m_arrayIdxPredictedLfnstPnnIsp[ispMode - 1][subTuCounter]}; + if (isFromMemoryToTu) + { + for (uint32_t i{0}; i < NUM_LFNST_NUM_PER_SET - 1; i++) + { + tu.arrayIndicesPredictedLfnstLumaPnn[i] = ptrIdxPredictedLfnstPnnIsp[i]; + } + } + else + { + for (uint32_t i{0}; i < NUM_LFNST_NUM_PER_SET - 1; i++) + { + ptrIdxPredictedLfnstPnnIsp[i] = tu.arrayIndicesPredictedLfnstLumaPnn[i]; + } + } +} +#endif #endif void IntraSearch::resetIdxRepresentationPnnMemories() { m_idxRepresentationPnn = MAX_INT; +#if JVET_AI0130_COMB_NN_INTRA + for (uint32_t i{0}; i < NUM_INTRA_SUBPARTITIONS_MODES - 1; i++) + { + for (uint32_t j{0}; j < 4; j++) + { + m_indicesRepresentationPnnIsp[i][j] = MAX_INT; + } + } +#endif } void IntraSearch::transferIdxRepresentationPnnMemories(CodingUnit& cu, @@ -300,6 +369,23 @@ void IntraSearch::transferIdxRepresentationPnnMemories(CodingUnit& cu, m_idxRepresentationPnn = cu.idxRepresentationPnn; } } + +#if JVET_AI0130_COMB_NN_INTRA +void IntraSearch::transferIdxRepresentationPnnIspMemories(TransformUnit& tu, + const uint8_t& ispMode, + const int& subTuCounter, + const bool& isFromMemoryToTu) +{ + if (isFromMemoryToTu) + { + tu.idxRepresentationPnn = m_indicesRepresentationPnnIsp[ispMode - 1][subTuCounter]; + } + else + { + m_indicesRepresentationPnnIsp[ispMode - 1][subTuCounter] = tu.idxRepresentationPnn; + } +} +#endif #endif #if JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS void IntraSearch::resetSsdsLumaMemories() @@ -412,7 +498,7 @@ void IntraSearch::init( EncCfg* pcEncCfg, #endif ); m_tmpStorageLCU.create(UnitArea(cform, Area(0, 0, MAX_CU_SIZE, MAX_CU_SIZE))); -#if JVET_AB0149_INTRA_PRED +#if JVET_AB0149_INTRA_PRED && !JVET_AI0130_COMB_NN_INTRA for (uint32_t i = 0; i < MAX_NUM_COMPONENT; i++) { m_pSharedPredPnn[i] = new Pel[MAX_CU_SIZE*MAX_CU_SIZE]; @@ -553,8 +639,13 @@ double IntraSearch::findInterCUCost( CodingUnit &cu ) } bool IntraSearch::estIntraPredLumaQT(CodingUnit &cu, Partitioner &partitioner, -#if JVET_AB0149_INTRA_PRED && JVET_AB0149_SEARCH_PNN +#if JVET_AB0149_INTRA_PRED +#if JVET_AB0149_SEARCH_PNN bool& isTriggeredHeuristicInter, +#endif +#if JVET_AI0130_COMB_NN_INTRA + const bool isPnnContinuing, +#endif #endif const double bestCostSoFar, bool mtsCheckRangeFlag, int mtsFirstCheckId, int mtsLastCheckId, bool moreProbMTSIdxFirst, CodingStructure *bestCS) { @@ -662,21 +753,35 @@ bool IntraSearch::estIntraPredLumaQT(CodingUnit &cu, Partitioner &partitioner, static_vector<ModeInfo, FAST_UDI_MAX_RDMODE_NUM> uiRdModeList; #if JVET_AB0149_INTRA_PRED int numModesForFullRD = 0; -#if JVET_AB0149_SEARCH_PNN - if (!cu.idxLfnstPnn) +#if JVET_AB0149_SEARCH_PNN || JVET_AI0130_COMB_NN_INTRA +#if JVET_AB0149_SEARCH_PNN && JVET_AI0130_COMB_NN_INTRA + CHECK(cu.idxLfnstPnn && isPnnContinuing, "`cu.idxLfnstPnn` is not equal to 0 and `isPnnContinuing` is true."); +#endif + if ( +#if JVET_AB0149_SEARCH_PNN && JVET_AI0130_COMB_NN_INTRA + !cu.idxLfnstPnn && !isPnnContinuing +#elif JVET_AB0149_SEARCH_PNN + !cu.idxLfnstPnn +#else + !isPnnContinuing +#endif + ) { #endif #else int numModesForFullRD = 3; #endif numModesForFullRD = g_aucIntraModeNumFast_UseMPM_2D[uiWidthBit - MIN_CU_LOG2][uiHeightBit - MIN_CU_LOG2]; -#if JVET_AB0149_INTRA_PRED && JVET_AB0149_SEARCH_PNN +#if JVET_AB0149_INTRA_PRED && (JVET_AB0149_SEARCH_PNN || JVET_AI0130_COMB_NN_INTRA) } #endif #if INTRA_FULL_SEARCH numModesForFullRD = numModesAvailable; #endif +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + const bool is_shape_handled_pnn = m_pcEncCfg->getUseNnIntraPred() ? decideShapeHandledPnn(height,width) : false; +#endif if (isSecondColorSpace) { uiRdModeList.clear(); @@ -696,9 +801,19 @@ bool IntraSearch::estIntraPredLumaQT(CodingUnit &cu, Partitioner &partitioner, { const CompArea& area = pu.Y(); #if JVET_AB0149_INTRA_PRED +#if !JVET_AI0130_COMB_NN_INTRA bool is_shape_handled_pnn = m_pcEncCfg->getUseNnIntraPred() ? decideShapeHandledPnn(area.height,area.width) : false; -#if JVET_AB0149_SEARCH_PNN - if (!cu.idxLfnstPnn) +#endif +#if JVET_AB0149_SEARCH_PNN || JVET_AI0130_COMB_NN_INTRA + if ( +#if JVET_AB0149_SEARCH_PNN && JVET_AI0130_COMB_NN_INTRA + !cu.idxLfnstPnn && !isPnnContinuing +#elif JVET_AB0149_SEARCH_PNN + !cu.idxLfnstPnn +#else + !isPnnContinuing +#endif + ) { #endif #endif @@ -709,6 +824,9 @@ bool IntraSearch::estIntraPredLumaQT(CodingUnit &cu, Partitioner &partitioner, pu.multiRefIdx = 0; #if JVET_AB0149_INTRA_PRED bool contextFlag = false, is_context_extracted = false; +#if JVET_AI0130_COMB_NN_INTRA + uint32_t idxRepresentationPnn{MAX_INT}; +#endif #endif #if JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS const bool is_written_first_test = (m_pcEncCfg->getOfstreamQuadtree()).is_open() && cu.mtsFlag == 0 && cu.lfnstIdx == 0; @@ -796,6 +914,12 @@ bool IntraSearch::estIntraPredLumaQT(CodingUnit &cu, Partitioner &partitioner, predIntraAng(COMPONENT_Y, piPred, pu #if JVET_AB0149_INTRA_PRED , contextFlag +#if JVET_AI0130_COMB_NN_INTRA + , idxRepresentationPnn +#if JVET_AB0149_TM_INF_PNN + , nullptr +#endif +#endif #endif ); #if JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS @@ -840,8 +964,19 @@ bool IntraSearch::estIntraPredLumaQT(CodingUnit &cu, Partitioner &partitioner, { pu.intraDir[CHANNEL_TYPE_LUMA] = PNN_IDX; initPredIntraParams(pu,area,sps); - predIntraAng(COMPONENT_Y,piPred,pu,contextFlag); - error_code = computeSsdSatdMeasuresConstrastStructureLumaPnn(piOrg,piPred,sps.getBitDepth(CHANNEL_TYPE_LUMA)); + predIntraAng(COMPONENT_Y,piPred,pu,contextFlag +#if JVET_AI0130_COMB_NN_INTRA + , cu.idxRepresentationPnn +#if JVET_AB0149_TM_INF_PNN + , cu.arrayIdxPredictedLfnstPnn[COMPONENT_Y] +#endif +#endif + ); + error_code = computeSsdSatdMeasuresConstrastStructureLumaPnn(piOrg,piPred,sps.getBitDepth(CHANNEL_TYPE_LUMA) +#if JVET_AI0130_COMB_NN_INTRA + , COMPONENT_Y, m_ssdLumaPnn, m_satdLumaPnn, m_arrayMeasuresLumaPnn +#endif + ); CHECK(error_code < 0,"Error in `computeLumaSsdSatdMeasuresConstrastStructurePnn`."); PelBuf sharedPredPnn(m_pSharedPredPnn[COMPONENT_Y],area); CHECK(getIsAlreadyComputedPnnRd(CHANNEL_TYPE_LUMA),"The prediction of the neural network mode should not be already computed."); @@ -898,6 +1033,12 @@ bool IntraSearch::estIntraPredLumaQT(CodingUnit &cu, Partitioner &partitioner, predIntraAng(COMPONENT_Y, piPred, pu #if JVET_AB0149_INTRA_PRED , contextFlag +#if JVET_AI0130_COMB_NN_INTRA + , idxRepresentationPnn +#if JVET_AB0149_TM_INF_PNN + , nullptr +#endif +#endif #endif ); @@ -969,6 +1110,12 @@ bool IntraSearch::estIntraPredLumaQT(CodingUnit &cu, Partitioner &partitioner, predIntraAng(COMPONENT_Y, piPred, pu #if JVET_AB0149_INTRA_PRED , contextFlag +#if JVET_AI0130_COMB_NN_INTRA + , idxRepresentationPnn +#if JVET_AB0149_TM_INF_PNN + , nullptr +#endif +#endif #endif ); @@ -1275,10 +1422,14 @@ bool IntraSearch::estIntraPredLumaQT(CodingUnit &cu, Partitioner &partitioner, } } #if JVET_AB0149_INTRA_PRED -#if JVET_AB0149_SEARCH_PNN +#if JVET_AB0149_SEARCH_PNN || JVET_AI0130_COMB_NN_INTRA } #endif - if (is_shape_handled_pnn) + if (is_shape_handled_pnn +#if JVET_AI0130_COMB_NN_INTRA + && (!moreProbMTSIdxFirst || !isPnnLimited(height, width)) +#endif + ) { numModesForFullRD++; uiRdModeList.push_back( @@ -1328,10 +1479,21 @@ bool IntraSearch::estIntraPredLumaQT(CodingUnit &cu, Partitioner &partitioner, csTemp->picture = cs.picture; csBest->picture = cs.picture; TUIntraSubPartitioner subTuPartitioner( partitioner ); +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + bool is_split_horizontal_allowed{false}; + bool is_split_vertical_allowed{false}; +#endif if ( testISP ) { m_modeCtrl->setIspCost( MAX_DOUBLE ); m_modeCtrl->setMtsFirstPassNoIspCost( MAX_DOUBLE ); +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + if (is_shape_handled_pnn) + { + const int error_code{are_splits_horizontal_vertical_allowed(g_vectorPairsHeightWidthPnn,height,width,cu.cs->sps->getIsNnIspRestricted(),cu.isSepTree(),is_split_horizontal_allowed,is_split_vertical_allowed)}; + CHECK(error_code < 0, "Error in `are_splits_horizontal_vertical_allowed`."); + } +#endif } int bestLfnstIdx = cu.lfnstIdx; @@ -1362,12 +1524,20 @@ bool IntraSearch::estIntraPredLumaQT(CodingUnit &cu, Partitioner &partitioner, { m_modeCtrl->setBestPredModeDCT2(uiBestPUMode.modeId); } - if (!xSortISPCandList(bestCurrentCost, csBest->cost, uiBestPUMode)) + if (!xSortISPCandList(bestCurrentCost, csBest->cost, uiBestPUMode +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + , is_split_horizontal_allowed, is_split_vertical_allowed +#endif + )) { break; } } - xGetNextISPMode(uiRdModeList[mode], (mode > 0 ? &uiRdModeList[mode - 1] : nullptr), Size(width, height)); + xGetNextISPMode(uiRdModeList[mode], (mode > 0 ? &uiRdModeList[mode - 1] : nullptr), Size(width, height) +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + , is_split_horizontal_allowed, is_split_vertical_allowed, cu.cs->sps->getIsNnIspRestricted() +#endif + ); if (uiRdModeList[mode].ispMod == INTRA_SUBPARTITIONS_RESERVED) { continue; @@ -1396,13 +1566,20 @@ bool IntraSearch::estIntraPredLumaQT(CodingUnit &cu, Partitioner &partitioner, cs.initSubStructure( *csTemp, partitioner.chType, cs.area, true ); bool tmpValidReturn = false; +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + bool splitCbfLuma{false}; +#endif if( cu.ispMode ) { if ( m_pcEncCfg->getUseFastISP() ) { m_modeCtrl->setISPWasTested(true); } - tmpValidReturn = xIntraCodingLumaISP(*csTemp, subTuPartitioner, bestCurrentCost); + tmpValidReturn = xIntraCodingLumaISP(*csTemp, subTuPartitioner +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + , splitCbfLuma +#endif + , bestCurrentCost); if (csTemp->tus.size() == 0) { // no TUs were coded @@ -1410,7 +1587,13 @@ bool IntraSearch::estIntraPredLumaQT(CodingUnit &cu, Partitioner &partitioner, continue; } // we save the data for future tests - m_ispTestedModes[m_curIspLfnstIdx].setModeResults((ISPType)cu.ispMode, (int)uiOrgMode.modeId, (int)csTemp->tus.size(), csTemp->cus[0]->firstTU->cbf[COMPONENT_Y] ? csTemp->cost : MAX_DOUBLE, csBest->cost); + m_ispTestedModes[m_curIspLfnstIdx].setModeResults((ISPType)cu.ispMode, (int)uiOrgMode.modeId, (int)csTemp->tus.size() +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + , (m_pcEncCfg->getUseNnIntraPred() ? splitCbfLuma : csTemp->cus[0]->firstTU->cbf[COMPONENT_Y]) ? csTemp->cost : MAX_DOUBLE +#else + , csTemp->cus[0]->firstTU->cbf[COMPONENT_Y] ? csTemp->cost : MAX_DOUBLE +#endif + , csBest->cost); csTemp->cost = !tmpValidReturn ? MAX_DOUBLE : csTemp->cost; } else @@ -1436,7 +1619,13 @@ bool IntraSearch::estIntraPredLumaQT(CodingUnit &cu, Partitioner &partitioner, m_regIntraRDListWithCosts.push_back( ModeInfoWithCost( cu.mipFlag, pu.mipTransposedFlag, pu.multiRefIdx, cu.ispMode, uiOrgMode.modeId, csTemp->cost ) ); } - if( cu.ispMode && !csTemp->cus[0]->firstTU->cbf[COMPONENT_Y] ) + if( cu.ispMode +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + && !(m_pcEncCfg->getUseNnIntraPred() ? splitCbfLuma : csTemp->cus[0]->firstTU->cbf[COMPONENT_Y]) +#else + && !csTemp->cus[0]->firstTU->cbf[COMPONENT_Y] +#endif + ) { csTemp->cost = MAX_DOUBLE; csTemp->costDbOffset = 0; @@ -1449,7 +1638,11 @@ bool IntraSearch::estIntraPredLumaQT(CodingUnit &cu, Partitioner &partitioner, m_modeCostStore[lfnstIdx][mode] = tmpValidReturn ? csTemp->cost : (MAX_DOUBLE / 2.0); //(MAX_DOUBLE / 2.0) ?? } #if JVET_AB0149_INTRA_PRED - if (uiOrgMode.modeId == PNN_IDX) + if (uiOrgMode.modeId == PNN_IDX +#if JVET_AI0130_COMB_NN_INTRA + && !cu.ispMode +#endif + ) { #if JVET_AB0149_TM_INF_PNN transferIdxPredictedLfnstPnnMemories(cu,COMPONENT_Y,true); @@ -1473,7 +1666,11 @@ bool IntraSearch::estIntraPredLumaQT(CodingUnit &cu, Partitioner &partitioner, #if JVET_AB0149_INTRA_PRED && JVET_AB0149_SEARCH_PNN && JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS if (m_pcEncCfg->getAreCostsWritten()) { - if (uiOrgMode.modeId == PNN_IDX && cu.lfnstIdx) + if (uiOrgMode.modeId == PNN_IDX && cu.lfnstIdx +#if JVET_AI0130_COMB_NN_INTRA + && !cu.ispMode +#endif + ) { m_arrayCostsLfnstPnn[cu.lfnstIdx - 1][cu.idxLfnstPnn] = csTemp->cost; } @@ -1613,11 +1810,31 @@ void IntraSearch::computeSsdsSatdsLumaRegular(const PelBuf& piOrg,const PelBuf& } #if JVET_AB0149_INTRA_PRED -int IntraSearch::computeSsdSatdMeasuresConstrastStructureLumaPnn(const PelBuf& piOrg,const PelBuf& piPred,const int& bitDepth) +int IntraSearch::computeSsdSatdMeasuresConstrastStructureLumaPnn(const PelBuf& piOrg,const PelBuf& piPred,const int& bitDepth +#if JVET_AI0130_COMB_NN_INTRA + , const ComponentID& compID, uint64_t& ssdPnn, uint64_t& satdPnn, double* const ptrMeasuresDiscrepanciesPnn +#endif + ) { - m_ssdLumaPnn = m_pcRdCost->getDistPart(piOrg,piPred,bitDepth,COMPONENT_Y,DF_SSE); - m_satdLumaPnn = m_pcRdCost->getDistPart(piOrg,piPred,bitDepth,COMPONENT_Y,DF_HAD); - const int error_code = compare_contrast_structure_globally(piOrg.buf,piPred.buf,piOrg.height,piOrg.width,piOrg.stride,piPred.stride,(1 << bitDepth) - 1,&m_arrayMeasuresLumaPnn[0]); +#if JVET_AI0130_COMB_NN_INTRA + ssdPnn +#else + m_ssdLumaPnn +#endif + = m_pcRdCost->getDistPart(piOrg,piPred,bitDepth,COMPONENT_Y,DF_SSE); +#if JVET_AI0130_COMB_NN_INTRA + satdPnn +#else + m_satdLumaPnn +#endif + = m_pcRdCost->getDistPart(piOrg,piPred,bitDepth,COMPONENT_Y,DF_HAD); + const int error_code = compare_contrast_structure_globally(piOrg.buf,piPred.buf,piOrg.height,piOrg.width,piOrg.stride,piPred.stride,(1 << bitDepth) - 1, +#if JVET_AI0130_COMB_NN_INTRA + ptrMeasuresDiscrepanciesPnn +#else + &m_arrayMeasuresLumaPnn[0] +#endif + ); if (error_code < 0) { return -1; @@ -1649,11 +1866,15 @@ IntraSearch::estIntraPredChromaQT( CodingUnit &cu, Partitioner &partitioner, con auto &pu = *cu.firstPU; #if JVET_AB0149_INTRA_PRED bool is_shape_handled_pnn = false; +#if !JVET_AI0130_COMB_NN_INTRA bool is_dm_pnn = false; +#endif if ((cs.sps)->getNnIntraPredEnabledFlag()) { is_shape_handled_pnn = decideShapeHandledPnn(pu.Cb().height, pu.Cb().width); +#if !JVET_AI0130_COMB_NN_INTRA is_dm_pnn = getIntraDirLumaSubstitutePnn(PU::getCoLocatedIntraLumaMode(pu), pu.Cb().height, pu.Cb().width) == PNN_IDX; +#endif #if JVET_AB0149_SEARCH_PNN if (cu.isSepTree() && cu.idxLfnstPnn) { @@ -1764,6 +1985,9 @@ IntraSearch::estIntraPredChromaQT( CodingUnit &cu, Partitioner &partitioner, con #if JVET_AB0149_INTRA_PRED bool contextFlagCb = false; bool contextFlagCr = false; +#if JVET_AI0130_COMB_NN_INTRA + uint32_t idxRepresentationPnn{MAX_INT}; +#endif #endif initIntraPatternChType(cu, pu.Cb() #if JVET_AB0149_INTRA_PRED @@ -1821,6 +2045,12 @@ IntraSearch::estIntraPredChromaQT( CodingUnit &cu, Partitioner &partitioner, con predIntraAng(COMPONENT_Cb, predCb, pu #if JVET_AB0149_INTRA_PRED , contextFlagCb +#if JVET_AI0130_COMB_NN_INTRA + , idxRepresentationPnn +#if JVET_AB0149_TM_INF_PNN + , nullptr +#endif +#endif #endif ); } @@ -1844,6 +2074,12 @@ IntraSearch::estIntraPredChromaQT( CodingUnit &cu, Partitioner &partitioner, con predIntraAng(COMPONENT_Cr, predCr, pu #if JVET_AB0149_INTRA_PRED , contextFlagCr +#if JVET_AI0130_COMB_NN_INTRA + , idxRepresentationPnn +#if JVET_AB0149_TM_INF_PNN + , nullptr +#endif +#endif #endif ); } @@ -1913,7 +2149,11 @@ IntraSearch::estIntraPredChromaQT( CodingUnit &cu, Partitioner &partitioner, con #endif int32_t numModesForFullRD(NUM_CHROMA_MODE); #if JVET_AB0149_INTRA_PRED - if (is_shape_handled_pnn && !is_dm_pnn) + if (is_shape_handled_pnn +#if !JVET_AI0130_COMB_NN_INTRA + && !is_dm_pnn +#endif + ) { numModesForFullRD++; } @@ -2230,7 +2470,7 @@ void IntraSearch::PLTSearch(CodingStructure &cs, Partitioner& partitioner, Compo } } - cs.getPredBuf().fill(0); + //cs.getPredBuf().fill(0); // JVET-AI0230 cs.getResiBuf().fill(0); cs.getOrgResiBuf().fill(0); @@ -2273,6 +2513,8 @@ void IntraSearch::PLTSearch(CodingStructure &cs, Partitioner& partitioner, Compo const CompArea &area = cu.blocks[compBegin]; cs.setDecomp(area); cs.picture->getRecoBuf(area).copyFrom(cs.getRecoBuf(area)); + // JVET-AI0230 save the reconstruction samples as prediction samples in the palette search function + cs.getPredBuf().copyFrom(cs.getRecoBuf()); } void IntraSearch::calcPixelPredRD(CodingStructure& cs, Partitioner& partitioner, Pel* orgBuf, Pel* paPixelValue, Pel* paRecoValue, ComponentID compBegin, uint32_t numComp) { @@ -3578,7 +3820,11 @@ uint64_t IntraSearch::xGetIntraFracBitsQTChroma(TransformUnit& currTU, const Com return fracBits; } -void IntraSearch::xIntraCodingTUBlock(TransformUnit &tu, const ComponentID &compID, Distortion& ruiDist, const int &default0Save1Load2, uint32_t* numSig, std::vector<TrMode>* trModes, const bool loadTr) +void IntraSearch::xIntraCodingTUBlock(TransformUnit &tu, const ComponentID &compID, Distortion& ruiDist, +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + const int subTuCounter, +#endif + const int &default0Save1Load2, uint32_t* numSig, std::vector<TrMode>* trModes, const bool loadTr) { if (!tu.blocks[compID].valid()) { @@ -3612,13 +3858,36 @@ void IntraSearch::xIntraCodingTUBlock(TransformUnit &tu, const ComponentID &comp #if JVET_AB0149_INTRA_PRED int default0Save1Load2Switch(default0Save1Load2); PelBuf& sharedPredSwitch(sharedPredTS); +#if !JVET_AI0130_COMB_NN_INTRA PelBuf sharedPredPnn(m_pSharedPredPnn[compID],area); +#endif const bool is_context_extracted = uiChFinalMode == PNN_IDX; if (is_context_extracted) { +#if JVET_AI0130_COMB_NN_INTRA + if ((tu.cu)->ispMode) + { + CHECK(default0Save1Load2Switch != 0,"`is_context_extracted` is true and `(tu.cu)->ispMode` is not equal to 0 and `default0Save1Load2Switch` is not equal to 0."); + if (!subTuCounter || !(tu.cu)->lfnstIdx) + { + default0Save1Load2Switch = !subTuCounter && getIsAlreadyComputedPnnIspRd((tu.cu)->ispMode,subTuCounter) ? 2 : 1; + sharedPredSwitch = PelBuf(m_pSharedPredPnnIsp[(tu.cu)->ispMode - 1][subTuCounter],area); + setIsAlreadyComputedPnnIspRd(true,(tu.cu)->ispMode,subTuCounter); + } + } + else + { +#endif default0Save1Load2Switch = getIsAlreadyComputedPnnRd(CHANNEL_TYPE_LUMA) ? 2 : 1; +#if JVET_AI0130_COMB_NN_INTRA + sharedPredSwitch = PelBuf(m_pSharedPredPnn[compID],area); +#else sharedPredSwitch = sharedPredPnn; +#endif setIsAlreadyComputedPnnRd(true, CHANNEL_TYPE_LUMA); +#if JVET_AI0130_COMB_NN_INTRA + } +#endif } if (default0Save1Load2Switch != 2) { @@ -3638,6 +3907,9 @@ void IntraSearch::xIntraCodingTUBlock(TransformUnit &tu, const ComponentID &comp { CU::adjustPredArea(areaPredReg); initIntraPatternChTypeISP(*tu.cu, areaPredReg, piReco +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + , contextFlag, is_context_extracted +#endif #if JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS , false #endif @@ -3647,6 +3919,9 @@ void IntraSearch::xIntraCodingTUBlock(TransformUnit &tu, const ComponentID &comp else { initIntraPatternChTypeISP(*tu.cu, area, piReco +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + , contextFlag, is_context_extracted +#endif #if JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS , false #endif @@ -3688,6 +3963,12 @@ void IntraSearch::xIntraCodingTUBlock(TransformUnit &tu, const ComponentID &comp predIntraAng(compID, piPredReg, pu #if JVET_AB0149_INTRA_PRED , contextFlag +#if JVET_AI0130_COMB_NN_INTRA + , tu.idxRepresentationPnn +#if JVET_AB0149_TM_INF_PNN + , tu.arrayIndicesPredictedLfnstLumaPnn.data() +#endif +#endif #endif ); } @@ -3697,9 +3978,22 @@ void IntraSearch::xIntraCodingTUBlock(TransformUnit &tu, const ComponentID &comp predIntraAng(compID, piPred, pu #if JVET_AB0149_INTRA_PRED , contextFlag +#if JVET_AI0130_COMB_NN_INTRA + , (tu.cu)->ispMode ? tu.idxRepresentationPnn : (tu.cu)->idxRepresentationPnn +#if JVET_AB0149_TM_INF_PNN + , (tu.cu)->ispMode ? tu.arrayIndicesPredictedLfnstLumaPnn.data() : &(tu.cu)->arrayIdxPredictedLfnstPnn[compID][0] +#endif +#endif #endif ); } +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA && JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS + if (m_pcEncCfg->getOfstreamQuadtree().is_open() && is_context_extracted) + { + const int error_code{computeSsdSatdMeasuresConstrastStructureLumaPnn(piOrg,piPred,bitDepth,COMPONENT_Y,(tu.cu)->ispMode ? tu.ssdLumaPnn : m_ssdLumaPnn,(tu.cu)->ispMode ? tu.satdLumaPnn : m_satdLumaPnn,(tu.cu)->ispMode ? tu.arrayMeasuresDiscrepanciesLumaPnn.data() : m_arrayMeasuresLumaPnn)}; + CHECK(error_code < 0, "Error in `computeSsdSatdMeasuresConstrastStructurePnn`."); + } +#endif } } #if JVET_AB0149_INTRA_PRED @@ -3708,10 +4002,24 @@ void IntraSearch::xIntraCodingTUBlock(TransformUnit &tu, const ComponentID &comp sharedPredSwitch.copyFrom(piPred); if (is_context_extracted) { +#if JVET_AI0130_COMB_NN_INTRA + if ((tu.cu)->ispMode) + { +#if JVET_AB0149_TM_INF_PNN + transferIdxPredictedLfnstPnnIspMemories(tu,(tu.cu)->ispMode,subTuCounter,false); +#endif + transferIdxRepresentationPnnIspMemories(tu,(tu.cu)->ispMode,subTuCounter,false); + } + else + { +#endif #if JVET_AB0149_TM_INF_PNN transferIdxPredictedLfnstPnnMemories(*tu.cu,compID,false); #endif transferIdxRepresentationPnnMemories(*tu.cu, false); +#if JVET_AI0130_COMB_NN_INTRA + } +#endif } } } @@ -3720,10 +4028,32 @@ void IntraSearch::xIntraCodingTUBlock(TransformUnit &tu, const ComponentID &comp piPred.copyFrom(sharedPredSwitch); if (is_context_extracted) { +#if JVET_AI0130_COMB_NN_INTRA + if ((tu.cu)->ispMode) + { + bool contextFlag{false}; + + // The decoded reference samples around the current luma CB must be extracted. + initIntraPatternChTypeISP(*tu.cu,(tu.cu)->Y(),piReco,contextFlag,false +#if JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS + , false +#endif + ); +#if JVET_AB0149_TM_INF_PNN + transferIdxPredictedLfnstPnnIspMemories(tu,(tu.cu)->ispMode,subTuCounter,true); +#endif + transferIdxRepresentationPnnIspMemories(tu,(tu.cu)->ispMode,subTuCounter,true); + } + else + { +#endif #if JVET_AB0149_TM_INF_PNN transferIdxPredictedLfnstPnnMemories(*tu.cu,compID,true); #endif transferIdxRepresentationPnnMemories(*tu.cu, true); +#if JVET_AI0130_COMB_NN_INTRA + } +#endif } } #else @@ -3816,6 +4146,9 @@ void IntraSearch::xIntraCodingTUBlock(TransformUnit &tu, const ComponentID &comp m_pcTrQuant->transformNxN(tu, compID, cQP, trModes, m_pcEncCfg->getMTSIntraMaxCand() #if JVET_AB0149_INTRA_PRED , uiChFinalMode == PNN_IDX +#if JVET_AI0130_COMB_NN_INTRA + && !(tu.cu)->ispMode +#endif #endif ); tu.mtsIdx[compID] = trModes->at(0).first; @@ -3825,6 +4158,9 @@ void IntraSearch::xIntraCodingTUBlock(TransformUnit &tu, const ComponentID &comp m_pcTrQuant->transformNxN(tu, compID, cQP, uiAbsSum, m_CABACEstimator->getCtx() #if JVET_AB0149_INTRA_PRED , uiChFinalMode == PNN_IDX && tu.mtsIdx[compID] == MTS_DCT2_DCT2 +#if JVET_AI0130_COMB_NN_INTRA + && !(tu.cu)->ispMode +#endif #endif , loadTr); } @@ -4159,12 +4495,20 @@ void IntraSearch::xIntraCodingACTTUBlock(TransformUnit &tu, const ComponentID &c } } -bool IntraSearch::xIntraCodingLumaISP(CodingStructure& cs, Partitioner& partitioner, const double bestCostSoFar) +bool IntraSearch::xIntraCodingLumaISP(CodingStructure& cs, Partitioner& partitioner +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + , bool& splitCbfLuma +#endif + , const double bestCostSoFar) { int subTuCounter = 0; const CodingUnit& cu = *cs.getCU(partitioner.currArea().lumaPos(), partitioner.chType); bool earlySkipISP = false; +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + splitCbfLuma = false; +#else bool splitCbfLuma = false; +#endif const PartSplit ispType = CU::getISPType(cu, COMPONENT_Y); cs.cost = 0; @@ -4186,7 +4530,11 @@ bool IntraSearch::xIntraCodingLumaISP(CodingStructure& cs, Partitioner& partitio tu.depth = partitioner.currTrDepth; // Encode TU - xIntraCodingTUBlock(tu, COMPONENT_Y, singleDistTmpLuma, 0, &numSig); + xIntraCodingTUBlock(tu, COMPONENT_Y, singleDistTmpLuma, +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + subTuCounter, +#endif + 0, &numSig); if (singleDistTmpLuma == MAX_INT) // all zero CBF skip { earlySkipISP = true; @@ -4508,7 +4856,11 @@ bool IntraSearch::xRecurIntraCodingLumaQT( CodingStructure &cs, Partitioner &par if( !cu.mtsFlag && checkTransformSkip ) { - xIntraCodingTUBlock( tu, COMPONENT_Y, singleDistTmpLuma, default0Save1Load2, &numSig, modeId == 0 ? &trModes : nullptr, true ); + xIntraCodingTUBlock( tu, COMPONENT_Y, singleDistTmpLuma, +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + 0, +#endif + default0Save1Load2, &numSig, modeId == 0 ? &trModes : nullptr, true ); if( modeId == 0 ) { for( int i = 0; i < 2; i++ ) @@ -4522,14 +4874,22 @@ bool IntraSearch::xRecurIntraCodingLumaQT( CodingStructure &cs, Partitioner &par } else { - xIntraCodingTUBlock( tu, COMPONENT_Y, singleDistTmpLuma, default0Save1Load2, &numSig ); + xIntraCodingTUBlock( tu, COMPONENT_Y, singleDistTmpLuma, +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + 0, +#endif + default0Save1Load2, &numSig ); } } else { if( nNumTransformCands > 1 ) { - xIntraCodingTUBlock( tu, COMPONENT_Y, singleDistTmpLuma, default0Save1Load2, &numSig, modeId == 0 ? &trModes : nullptr, true ); + xIntraCodingTUBlock( tu, COMPONENT_Y, singleDistTmpLuma, +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + 0, +#endif + default0Save1Load2, &numSig, modeId == 0 ? &trModes : nullptr, true ); if( modeId == 0 ) { for( int i = 0; i < nNumTransformCands; i++ ) @@ -4543,7 +4903,11 @@ bool IntraSearch::xRecurIntraCodingLumaQT( CodingStructure &cs, Partitioner &par } else { - xIntraCodingTUBlock( tu, COMPONENT_Y, singleDistTmpLuma, default0Save1Load2, &numSig ); + xIntraCodingTUBlock( tu, COMPONENT_Y, singleDistTmpLuma, +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + 0, +#endif + default0Save1Load2, &numSig ); } } @@ -4858,6 +5222,9 @@ bool IntraSearch::xRecurIntraCodingACTQT(CodingStructure &cs, Partitioner &parti PelBuf piResi = resiBuf.bufs[compID]; #if JVET_AB0149_INTRA_PRED bool contextFlag = false; +#if JVET_AI0130_COMB_NN_INTRA + uint32_t idxRepresentationPnn{MAX_INT}; +#endif #endif initIntraPatternChType(*tu.cu, area #if JVET_AB0149_INTRA_PRED @@ -4877,6 +5244,12 @@ bool IntraSearch::xRecurIntraCodingACTQT(CodingStructure &cs, Partitioner &parti predIntraAng(compID, piPred, pu #if JVET_AB0149_INTRA_PRED , contextFlag +#if JVET_AI0130_COMB_NN_INTRA + , idxRepresentationPnn +#if JVET_AB0149_TM_INF_PNN + , nullptr +#endif +#endif #endif ); } @@ -5700,6 +6073,12 @@ ChromaCbfs IntraSearch::xRecurIntraChromaCodingQT( CodingStructure &cs, Partitio pu #if JVET_AB0149_INTRA_PRED , contextFlagCb +#if JVET_AI0130_COMB_NN_INTRA + , (currTU.cu)->idxRepresentationPnn +#if JVET_AB0149_TM_INF_PNN + , &(currTU.cu)->arrayIdxPredictedLfnstPnn[COMPONENT_Cb][0] +#endif +#endif #endif ); } @@ -5728,6 +6107,12 @@ ChromaCbfs IntraSearch::xRecurIntraChromaCodingQT( CodingStructure &cs, Partitio pu #if JVET_AB0149_INTRA_PRED , contextFlagCr +#if JVET_AI0130_COMB_NN_INTRA + , (currTU.cu)->idxRepresentationPnn +#if JVET_AB0149_TM_INF_PNN + , &(currTU.cu)->arrayIdxPredictedLfnstPnn[COMPONENT_Cr][0] +#endif +#endif #endif ); } @@ -5864,12 +6249,20 @@ ChromaCbfs IntraSearch::xRecurIntraChromaCodingQT( CodingStructure &cs, Partitio if (nNumTransformCands > 1) { - xIntraCodingTUBlock(currTU, compID, singleDistCTmp, default0Save1Load2, nullptr, + xIntraCodingTUBlock(currTU, compID, singleDistCTmp, +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + 0, +#endif + default0Save1Load2, nullptr, modeId == 0 ? &trModes : nullptr, true); } else { - xIntraCodingTUBlock(currTU, compID, singleDistCTmp, default0Save1Load2); + xIntraCodingTUBlock(currTU, compID, singleDistCTmp, +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + 0, +#endif + default0Save1Load2); } if (((currTU.mtsIdx[compID] == MTS_SKIP && !currTU.cu->bdpcmModeChroma) @@ -6027,11 +6420,19 @@ ChromaCbfs IntraSearch::xRecurIntraChromaCodingQT( CodingStructure &cs, Partitio resiCr.copyFrom(orgResiCr[cbfMask]); if (numTransformCands > 1) { - xIntraCodingTUBlock(currTU, COMPONENT_Cb, distTmp, 0, nullptr, modeId == 0 ? &trModes : nullptr, true); + xIntraCodingTUBlock(currTU, COMPONENT_Cb, distTmp, +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + 0, +#endif + 0, nullptr, modeId == 0 ? &trModes : nullptr, true); } else { - xIntraCodingTUBlock(currTU, COMPONENT_Cb, distTmp, 0); + xIntraCodingTUBlock(currTU, COMPONENT_Cb, distTmp, +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + 0, +#endif + 0); } double costTmp = std::numeric_limits<double>::max(); if (distTmp < std::numeric_limits<Distortion>::max()) @@ -6339,7 +6740,11 @@ void IntraSearch::reduceHadCandList(static_vector<T, N>& candModeList, static_ve } // It decides which modes from the ISP lists can be full RD tested -void IntraSearch::xGetNextISPMode(ModeInfo& modeInfo, const ModeInfo* lastMode, const Size cuSize) +void IntraSearch::xGetNextISPMode(ModeInfo& modeInfo, const ModeInfo* lastMode, const Size cuSize +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + , const bool& is_split_horizontal_allowed, const bool& is_split_vertical_allowed, const bool& is_nn_isp_restricted +#endif + ) { static_vector<ModeInfo, FAST_UDI_MAX_RDMODE_NUM>* rdModeLists[2] = { &m_ispCandListHor, &m_ispCandListVer }; @@ -6368,7 +6773,11 @@ void IntraSearch::xGetNextISPMode(ModeInfo& modeInfo, const ModeInfo* lastMode, } else { - xFinishISPModes(); + xFinishISPModes( +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + is_split_horizontal_allowed,is_split_vertical_allowed,is_nn_isp_restricted +#endif + ); return; // no more modes will be tested } @@ -6412,9 +6821,16 @@ void IntraSearch::xGetNextISPMode(ModeInfo& modeInfo, const ModeInfo* lastMode, const int thresholdSplit1ForAllLFNSTs = maxNumSubPartitions - 1; int mode1 = ispTestedModes.getTestedIntraMode((ISPType)nextISPcandSplitType, 0); - mode1 = mode1 == DC_IDX ? -1 : mode1; + mode1 = mode1 == DC_IDX +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + || mode1 == PNN_IDX +#endif + ? -1 : mode1; int numSubPartsBestMode1 = mode1 != -1 ? ispTestedModes.getNumCompletedSubParts((ISPType)nextISPcandSplitType, mode1) : -1; int mode2 = ispTestedModes.getTestedIntraMode((ISPType)nextISPcandSplitType, 1); +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + CHECK(mode2 == PNN_IDX, "`mode2` is equal to `PNN_IDX`."); +#endif mode2 = mode2 == DC_IDX ? -1 : mode2; int numSubPartsBestMode2 = mode2 != -1 ? ispTestedModes.getNumCompletedSubParts((ISPType)nextISPcandSplitType, mode2) : -1; @@ -6495,12 +6911,19 @@ void IntraSearch::xGetNextISPMode(ModeInfo& modeInfo, const ModeInfo* lastMode, if (ispTestedModes.candIndexInList[nextISPcandSplitType - 1] < rdModeLists[nextISPcandSplitType - 1]->size()) { ModeInfo candidate = rdModeLists[nextISPcandSplitType - 1]->at(ispTestedModes.candIndexInList[nextISPcandSplitType - 1]); +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + const bool isCandidatePnn{candidate.modeId == PNN_IDX}; +#endif ispTestedModes.candIndexInList[nextISPcandSplitType - 1]++; // extra modes are only tested if ISP has won so far if (ispTestedModes.candIndexInList[nextISPcandSplitType - 1] > ispTestedModes.numOrigModesToTest) { - if (ispTestedModes.bestSplitSoFar != candidate.ispMod || ispTestedModes.bestModeSoFar == PLANAR_IDX) + if (ispTestedModes.bestSplitSoFar != candidate.ispMod || ispTestedModes.bestModeSoFar == PLANAR_IDX +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + || ispTestedModes.bestModeSoFar == PNN_IDX +#endif + ) { ispTestedModes.splitIsFinished[nextISPcandSplitType - 1] = true; return; @@ -6512,6 +6935,9 @@ void IntraSearch::xGetNextISPMode(ModeInfo& modeInfo, const ModeInfo* lastMode, // we look for a reference mode that has already been tested within the window and decide to test the new one according to the reference mode costs if (maxNumSubPartitions > 2 && (curIspLfnstIdx > 0 || (candidate.modeId >= DC_IDX && ispTestedModes.numTestedModes[nextISPcandSplitType - 1] >= 2))) { +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + CHECK(curIspLfnstIdx == 0 && isCandidatePnn, "`curIspLfnstIdx` is equal to 0 and `candidate.modeId` is equal to `PNN_IDX`."); +#endif int refLfnstIdx = -1; const int angWindowSize = 5; int numSubPartsLeftMode, numSubPartsRightMode, numSubPartsRefMode, leftIntraMode = -1, rightIntraMode = -1; @@ -6584,6 +7010,10 @@ void IntraSearch::xFindAlreadyTestedNearbyIntraModes(int lfnstIdx, int currentIn return; } } +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + if (currentIntraMode != PNN_IDX) + { +#endif //The mode has not been checked for another lfnstIdx value, so now we look for a similar mode within a window using the same lfnstIdx for (int k = 1; k <= windowSize; k++) @@ -6602,10 +7032,17 @@ void IntraSearch::xFindAlreadyTestedNearbyIntraModes(int lfnstIdx, int currentIn break; } } +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + } +#endif } //It prepares the list of potential intra modes candidates that will be tested using RD costs -bool IntraSearch::xSortISPCandList(double bestCostSoFar, double bestNonISPCost, ModeInfo bestNonISPMode) +bool IntraSearch::xSortISPCandList(double bestCostSoFar, double bestNonISPCost, ModeInfo bestNonISPMode +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + , const bool& is_split_horizontal_allowed, const bool& is_split_vertical_allowed +#endif + ) { int bestISPModeInRelCU = -1; m_modeCtrl->setStopNonDCT2Transforms(false); @@ -6625,7 +7062,11 @@ bool IntraSearch::xSortISPCandList(double bestCostSoFar, double bestNonISPCost, } return false; } - if (!updateISPStatusFromRelCU(bestNonISPCost, bestNonISPMode, bestISPModeInRelCU)) + if (!updateISPStatusFromRelCU(bestNonISPCost, bestNonISPMode +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + , is_split_horizontal_allowed, is_split_vertical_allowed +#endif + , bestISPModeInRelCU)) { return false; } @@ -6663,7 +7104,17 @@ bool IntraSearch::xSortISPCandList(double bestCostSoFar, double bestNonISPCost, auto* destListPtr = &m_ispCandListHor; //List creation - if (m_pcEncCfg->getUseFastISP() && bestISPModeInRelCU != -1) //RelCU intra mode +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + if (is_split_horizontal_allowed) + { + destListPtr->push_back(ModeInfo(refMode.mipFlg,refMode.mipTrFlg,refMode.mRefId,refMode.ispMod,PNN_IDX)); + } +#endif + if (m_pcEncCfg->getUseFastISP() && bestISPModeInRelCU != -1 +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + && bestISPModeInRelCU != PNN_IDX +#endif + ) //RelCU intra mode { destListPtr->push_back( ModeInfo(refMode.mipFlg, refMode.mipTrFlg, refMode.mRefId, refMode.ispMod, bestISPModeInRelCU)); @@ -6676,6 +7127,9 @@ bool IntraSearch::xSortISPCandList(double bestCostSoFar, double bestNonISPCost, destListPtr->push_back(ModeInfo(refMode.mipFlg, refMode.mipTrFlg, refMode.mRefId, refMode.ispMod, mode1)); modeIsInList[mode1] = true; } +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + CHECK(mode2 == PNN_IDX, "`mode2` is equal to `PNN_IDX`."); +#endif // Best angle in regular intra if (mode2 != -1 && !modeIsInList[mode2]) { @@ -6687,6 +7141,9 @@ bool IntraSearch::xSortISPCandList(double bestCostSoFar, double bestNonISPCost, for (int remModeIdx = 0; remModeIdx < m_regIntraRDListWithCosts.size(); remModeIdx++) { int currentMode = m_regIntraRDListWithCosts.at(remModeIdx).modeId; +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + CHECK(currentMode == PNN_IDX, "`currentMode` is equal to `PNN_IDX`."); +#endif if (currentMode != mode1 && currentMode != mode2 && !modeIsInList[currentMode]) { if (currentMode > DC_IDX) @@ -6718,6 +7175,9 @@ bool IntraSearch::xSortISPCandList(double bestCostSoFar, double bestNonISPCost, for (int k = 0; k < origHadList.size(); k++) { +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + CHECK(origHadList.at(k).modeId == PNN_IDX, "`origHadList.at(k).modeId` is equal to `PNN_IDX`."); +#endif if (newModesAdded == addedModesFromHadList) { break; @@ -6728,14 +7188,66 @@ bool IntraSearch::xSortISPCandList(double bestCostSoFar, double bestNonISPCost, newModesAdded++; } } +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + if (!is_split_horizontal_allowed && is_split_vertical_allowed && newModesAdded > 0) + { + for (int i = 0; i < NUM_LFNST_NUM_PER_SET; i++) + { + m_ispTestedModes[i].numOrigModesToTest++; + } + } +#endif - if (m_pcEncCfg->getUseFastISP() && bestISPModeInRelCU != -1) + if (m_pcEncCfg->getUseFastISP() && bestISPModeInRelCU != -1 +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + && bestISPModeInRelCU != PNN_IDX +#endif + ) { +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + destListPtr->resize(is_split_horizontal_allowed ? 2 : 1); +#else destListPtr->resize(1); +#endif } // Copy modes to other split-type list +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + if (is_split_vertical_allowed) + { + if (is_split_horizontal_allowed) + { + m_ispCandListVer = m_ispCandListHor; + } + else + { + m_ispCandListVer.push_back(ModeInfo(refMode.mipFlg,refMode.mipTrFlg,refMode.mRefId,refMode.ispMod,PNN_IDX)); + for (int i{0}; i < m_ispCandListHor.size(); i++) + { + m_ispCandListVer.push_back(m_ispCandListHor[i]); + } + } + } + else + { + if (is_split_horizontal_allowed) + { + for (int i{0}; i < m_ispCandListHor.size(); i++) + { + if (m_ispCandListHor[i].modeId != PNN_IDX) + { + m_ispCandListVer.push_back(m_ispCandListHor[i]); + } + } + } + else + { +#endif m_ispCandListVer = m_ispCandListHor; +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + } + } +#endif for (int i = 0; i < m_ispCandListVer.size(); i++) { m_ispCandListVer[i].ispMod = VER_INTRA_SUBPARTITIONS; @@ -6752,13 +7264,32 @@ bool IntraSearch::xSortISPCandList(double bestCostSoFar, double bestNonISPCost, return true; } -void IntraSearch::xSortISPCandListLFNST() +void IntraSearch::xSortISPCandListLFNST( +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + const bool& is_split_horizontal_allowed,const bool& is_split_vertical_allowed,const bool& is_nn_isp_restricted +#endif + ) { //It resorts the list of intra mode candidates for lfnstIdx > 0 by checking the RD costs for lfnstIdx = 0 ISPTestedModesInfo& ispTestedModesRef = m_ispTestedModes[0]; for (int splitIdx = 0; splitIdx < NUM_INTRA_SUBPARTITIONS_MODES - 1; splitIdx++) { ISPType ispMode = splitIdx ? VER_INTRA_SUBPARTITIONS : HOR_INTRA_SUBPARTITIONS; +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + if (is_nn_isp_restricted) + { + if (ispMode == HOR_INTRA_SUBPARTITIONS && is_split_horizontal_allowed) + { + CHECK(m_ispCandListHor.at(0).modeId != PNN_IDX, "`ispMode == HOR_INTRA_SUBPARTITIONS` is equal to `HOR_INTRA_SUBPARTITIONS` and `is_split_horizontal_allowed` is true and `m_ispCandListHor.at(0).modId` is not equal to `PNN_IDX`."); + m_ispCandListHor.pop_front(); + } + if (ispMode == VER_INTRA_SUBPARTITIONS && is_split_vertical_allowed) + { + CHECK(m_ispCandListVer.at(0).modeId != PNN_IDX, "`ispMode == VER_INTRA_SUBPARTITIONS` is equal to `HOR_INTRA_SUBPARTITIONS` and `is_split_vertical_allowed` is true and `m_ispCandListVer.at(0).modId` is not equal to `PNN_IDX`."); + m_ispCandListVer.pop_front(); + } + } +#endif if (!m_ispTestedModes[m_curIspLfnstIdx].splitIsFinished[splitIdx] && ispTestedModesRef.testedModes[splitIdx].size() > 1) { auto& candList = ispMode == HOR_INTRA_SUBPARTITIONS ? m_ispCandListHor : m_ispCandListVer; @@ -6799,7 +7330,11 @@ void IntraSearch::xSortISPCandListLFNST() } } -bool IntraSearch::updateISPStatusFromRelCU( double bestNonISPCostCurrCu, ModeInfo bestNonISPModeCurrCu, int& bestISPModeInRelCU ) +bool IntraSearch::updateISPStatusFromRelCU( double bestNonISPCostCurrCu, ModeInfo bestNonISPModeCurrCu, +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + const bool& is_split_horizontal_allowed,const bool& is_split_vertical_allowed, +#endif + int& bestISPModeInRelCU ) { //It compares the data of a related CU with the current CU to cancel or reduce the ISP tests bestISPModeInRelCU = -1; @@ -6811,7 +7346,11 @@ bool IntraSearch::updateISPStatusFromRelCU( double bestNonISPCostCurrCu, ModeInf bool bestModeCurrCuIsMip = bestNonISPModeCurrCu.mipFlg; int relatedCuIntraMode = m_modeCtrl->getIspPredModeValRelCU() >> 9; bool isSameTypeOfMode = (bestModeRelCuIsMip && bestModeCurrCuIsMip) || (!bestModeRelCuIsMip && !bestModeCurrCuIsMip); - bool bothModesAreAngular = bestNonISPModeCurrCu.modeId > DC_IDX && relatedCuIntraMode > DC_IDX; + bool bothModesAreAngular = bestNonISPModeCurrCu.modeId > DC_IDX && relatedCuIntraMode > DC_IDX +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + && bestNonISPModeCurrCu.modeId != PNN_IDX && relatedCuIntraMode != PNN_IDX +#endif + ; bool modesAreComparable = isSameTypeOfMode && (bestModeCurrCuIsMip || bestNonISPModeCurrCu.modeId == relatedCuIntraMode || (bothModesAreAngular && abs(relatedCuIntraMode - (int)bestNonISPModeCurrCu.modeId) <= 5)); int status = m_modeCtrl->getIspPredModeValRelCU(); @@ -6868,7 +7407,11 @@ bool IntraSearch::updateISPStatusFromRelCU( double bestNonISPCostCurrCu, ModeInf return true; } -void IntraSearch::xFinishISPModes() +void IntraSearch::xFinishISPModes( +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + const bool& is_split_horizontal_allowed,const bool& is_split_vertical_allowed,const bool& is_nn_isp_restricted +#endif + ) { //Continue to the next lfnst index m_curIspLfnstIdx++; @@ -6886,7 +7429,11 @@ void IntraSearch::xFinishISPModes() if (canTestLFNST) { //Construct the intra modes candidates list for the lfnst > 0 cases - xSortISPCandListLFNST(); + xSortISPCandListLFNST( +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + is_split_horizontal_allowed,is_split_vertical_allowed,is_nn_isp_restricted +#endif + ); } } } diff --git a/source/Lib/EncoderLib/IntraSearch.h b/source/Lib/EncoderLib/IntraSearch.h index e3a6e85b03550dbeeeaac8f8e510a9dace353631..ed4c3dc320c47ca4ab28e5810159706385c2b8ec 100644 --- a/source/Lib/EncoderLib/IntraSearch.h +++ b/source/Lib/EncoderLib/IntraSearch.h @@ -192,12 +192,26 @@ private: EncModeCtrl *m_modeCtrl; Pel* m_pSharedPredTransformSkip[MAX_NUM_TBLOCKS]; #if JVET_AB0149_INTRA_PRED +#if JVET_AI0130_COMB_NN_INTRA + Pel m_pSharedPredPnn[MAX_NUM_COMPONENT][MAX_CU_SIZE*MAX_CU_SIZE]; +#else Pel* m_pSharedPredPnn[MAX_NUM_COMPONENT]; +#endif bool m_isAlreadyComputedPnnRd[MAX_NUM_CHANNEL_TYPE]; +#if JVET_AI0130_COMB_NN_INTRA + Pel m_pSharedPredPnnIsp[NUM_INTRA_SUBPARTITIONS_MODES - 1][4][256]; + bool m_isAlreadyComputedPnnIspRd[NUM_INTRA_SUBPARTITIONS_MODES - 1][4]; +#endif #if JVET_AB0149_TM_INF_PNN uint32_t m_arrayIdxPredictedLfnstPnn[MAX_NUM_COMPONENT][NUM_LFNST_NUM_PER_SET - 1]; +#if JVET_AI0130_COMB_NN_INTRA + uint32_t m_arrayIdxPredictedLfnstPnnIsp[NUM_INTRA_SUBPARTITIONS_MODES - 1][4][NUM_LFNST_NUM_PER_SET - 1]; +#endif #endif uint32_t m_idxRepresentationPnn; +#if JVET_AI0130_COMB_NN_INTRA + uint32_t m_indicesRepresentationPnnIsp[NUM_INTRA_SUBPARTITIONS_MODES - 1][4]; +#endif #endif #if JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS std::array<std::pair<int8_t, uint64_t>, NB_INTRA_MODES_STATS> m_arrayPairsIdxSsdLumaRegular; @@ -268,8 +282,20 @@ private: }; struct ISPTestedModesInfo { - ISPTestedModeInfo intraMode[NUM_LUMA_MODE][2]; - bool modeHasBeenTested[NUM_LUMA_MODE][2]; + ISPTestedModeInfo intraMode[ +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + PNN_IDX + 1 +#else + NUM_LUMA_MODE +#endif + ][2]; + bool modeHasBeenTested[ +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + PNN_IDX + 1 +#else + NUM_LUMA_MODE +#endif + ][2]; int numTotalParts[2]; static_vector<int, FAST_UDI_MAX_RDMODE_NUM> testedModes[2]; int bestModeSoFar; @@ -307,7 +333,11 @@ private: { const unsigned st = splitType - 1; CHECK(st < 0 || st > 1, "The split type is invalid!"); - CHECK(iModeIdx < 0 || iModeIdx >(NUM_LUMA_MODE - 1), "The modeIdx is invalid"); +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + CHECK((iModeIdx < 0 || iModeIdx > NUM_LUMA_MODE - 1) && iModeIdx != PNN_IDX, "`iModeIdx` does not belong to [|0, " + std::to_string(NUM_LUMA_MODE - 1) + "|] U {" + std::to_string(PNN_IDX) + "}."); +#else + CHECK(iModeIdx < 0 || iModeIdx > NUM_LUMA_MODE - 1, "The modeIdx is invalid"); +#endif return modeHasBeenTested[iModeIdx][st] ? intraMode[iModeIdx][st].numCompSubParts : -1; } @@ -394,7 +424,11 @@ private: #if JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS void computeSsdsSatdsLumaRegular(const PelBuf& piOrg,const PelBuf& piPred,const uint32_t& uiMode,const int& bitDepth); #if JVET_AB0149_INTRA_PRED - int computeSsdSatdMeasuresConstrastStructureLumaPnn(const PelBuf& piOrg,const PelBuf& piPred,const int& bitDepth); + int computeSsdSatdMeasuresConstrastStructureLumaPnn(const PelBuf& piOrg,const PelBuf& piPred,const int& bitDepth +#if JVET_AI0130_COMB_NN_INTRA + , const ComponentID& compID, uint64_t& ssdPnn, uint64_t& satdPnn, double* const ptrMeasuresDiscrepanciesPnn +#endif + ); #endif #endif protected: @@ -463,12 +497,30 @@ public: { m_isAlreadyComputedPnnRd[chType] = isAlreadyComputedPnnRd; } +#if JVET_AI0130_COMB_NN_INTRA + bool getIsAlreadyComputedPnnIspRd(const uint8_t& ispMode, const int& subTuCounter) const + { + return m_isAlreadyComputedPnnIspRd[ispMode - 1][subTuCounter]; + } + void setIsAlreadyComputedPnnIspRd(const bool& isAlreadyComputedPnnIspRd, const uint8_t& ispMode, const int& subTuCounter) + { + m_isAlreadyComputedPnnIspRd[ispMode - 1][subTuCounter] = isAlreadyComputedPnnIspRd; + } + const Pel* getPSharedPredPnn(const ComponentID& compID) { return m_pSharedPredPnn[compID]; } + const Pel* getPSharedPredPnnIsp(const int& splitIdx, const int& subTuCounter) { return m_pSharedPredPnnIsp[splitIdx][subTuCounter]; } +#endif #if JVET_AB0149_TM_INF_PNN void resetIdxPredictedLfnstPnnMemories(); void transferIdxPredictedLfnstPnnMemories(CodingUnit& cu,const ComponentID& compID,const bool& isFromMemoryToCu); +#if JVET_AI0130_COMB_NN_INTRA + void transferIdxPredictedLfnstPnnIspMemories(TransformUnit& tu,const uint8_t& ispMode,const int& subTuCounter,const bool& isFromMemoryToTu); +#endif #endif void resetIdxRepresentationPnnMemories(); void transferIdxRepresentationPnnMemories(CodingUnit& cu, const bool& isFromMemoryToCu); +#if JVET_AI0130_COMB_NN_INTRA + void transferIdxRepresentationPnnIspMemories(TransformUnit& tu,const uint8_t& ispMode,const int& subTuCounter,const bool& isFromMemoryToTu); +#endif void setIsModeIsEnableUpdated(const bool& value) {m_isModeIsEnableUpdated = value;} #endif #if JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS @@ -489,8 +541,13 @@ public: public: bool estIntraPredLumaQT(CodingUnit &cu, Partitioner& pm -#if JVET_AB0149_INTRA_PRED && JVET_AB0149_SEARCH_PNN +#if JVET_AB0149_INTRA_PRED +#if JVET_AB0149_SEARCH_PNN , bool& isTriggeredHeuristicInter +#endif +#if JVET_AI0130_COMB_NN_INTRA + , const bool isPnnContinuing +#endif #endif , const double bestCostSoFar=MAX_DOUBLE, bool mtsCheckRangeFlag=false, int mtsFirstCheckId=0, int mtsLastCheckId=0, bool moreProbMTSIdxFirst=false, CodingStructure* bestCS=NULL); @@ -527,13 +584,21 @@ protected: uint64_t xGetIntraFracBitsQTChroma(TransformUnit& tu, const ComponentID &compID); void xEncCoeffQT ( CodingStructure &cs, Partitioner& pm, const ComponentID compID, const int subTuIdx = -1, const PartSplit ispType = TU_NO_ISP, CUCtx * cuCtx = nullptr ); - void xIntraCodingTUBlock (TransformUnit &tu, const ComponentID &compID, Distortion& ruiDist, const int &default0Save1Load2 = 0, uint32_t* numSig = nullptr, std::vector<TrMode>* trModes=nullptr, const bool loadTr=false ); + void xIntraCodingTUBlock (TransformUnit &tu, const ComponentID &compID, Distortion& ruiDist, +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + const int subTuCounter, +#endif + const int &default0Save1Load2 = 0, uint32_t* numSig = nullptr, std::vector<TrMode>* trModes=nullptr, const bool loadTr=false ); void xIntraCodingACTTUBlock(TransformUnit &tu, const ComponentID &compID, Distortion& ruiDist, std::vector<TrMode>* trModes = nullptr, const bool loadTr = false); ChromaCbfs xRecurIntraChromaCodingQT( CodingStructure &cs, Partitioner& pm, const double bestCostSoFar = MAX_DOUBLE, const PartSplit ispType = TU_NO_ISP ); bool xRecurIntraCodingLumaQT ( CodingStructure &cs, Partitioner& pm, const double bestCostSoFar = MAX_DOUBLE, const int subTuIdx = -1, const PartSplit ispType = TU_NO_ISP, const bool ispIsCurrentWinner = false, bool mtsCheckRangeFlag = false, int mtsFirstCheckId = 0, int mtsLastCheckId = 0, bool moreProbMTSIdxFirst = false ); bool xRecurIntraCodingACTQT(CodingStructure &cs, Partitioner& pm, bool mtsCheckRangeFlag = false, int mtsFirstCheckId = 0, int mtsLastCheckId = 0, bool moreProbMTSIdxFirst = false); - bool xIntraCodingLumaISP ( CodingStructure& cs, Partitioner& pm, const double bestCostSoFar = MAX_DOUBLE ); + bool xIntraCodingLumaISP ( CodingStructure& cs, Partitioner& pm +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + , bool& splitCbfLuma +#endif + , const double bestCostSoFar = MAX_DOUBLE ); template<typename T, size_t N> void reduceHadCandList(static_vector<T, N>& candModeList, static_vector<double, N>& candCostList, int& numModesForFullRD, const double thresholdHadCost, const double* mipHadCost, const PredictionUnit &pu, const bool fastMip); @@ -547,12 +612,32 @@ protected: void initTBCTable (int bitDepth); uint32_t getTruncBinBits (uint32_t symbol, uint32_t maxSymbol); uint32_t getEpExGolombNumBins (uint32_t symbol, uint32_t count); - void xGetNextISPMode ( ModeInfo& modeInfo, const ModeInfo* lastMode, const Size cuSize ); - bool xSortISPCandList ( double bestCostSoFar, double bestNonISPCost, ModeInfo bestNonISPMode ); - void xSortISPCandListLFNST ( ); + void xGetNextISPMode ( ModeInfo& modeInfo, const ModeInfo* lastMode, const Size cuSize +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + , const bool& is_split_horizontal_allowed, const bool& is_split_vertical_allowed, const bool& is_nn_isp_restricted +#endif + ); + bool xSortISPCandList ( double bestCostSoFar, double bestNonISPCost, ModeInfo bestNonISPMode +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + , const bool& is_split_horizontal_allowed, const bool& is_split_vertical_allowed +#endif + ); + void xSortISPCandListLFNST ( +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + const bool& is_split_horizontal_allowed,const bool& is_split_vertical_allowed,const bool& is_nn_isp_restricted +#endif + ); void xFindAlreadyTestedNearbyIntraModes ( int currentLfnstIdx, int currentIntraMode, int* refLfnstIdx, int* leftIntraMode, int* rightIntraMode, ISPType ispOption, int windowSize ); - bool updateISPStatusFromRelCU ( double bestNonISPCostCurrCu, ModeInfo bestNonISPModeCurrCu, int& bestISPModeInRelCU ); - void xFinishISPModes ( ); + bool updateISPStatusFromRelCU ( double bestNonISPCostCurrCu, ModeInfo bestNonISPModeCurrCu, +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + const bool& is_split_horizontal_allowed,const bool& is_split_vertical_allowed, +#endif + int& bestISPModeInRelCU ); + void xFinishISPModes ( +#if JVET_AB0149_INTRA_PRED && JVET_AI0130_COMB_NN_INTRA + const bool& is_split_horizontal_allowed,const bool& is_split_vertical_allowed,const bool& is_nn_isp_restricted +#endif + ); };// END CLASS DEFINITION EncSearch //! \} diff --git a/source/Lib/EncoderLib/VLCWriter.cpp b/source/Lib/EncoderLib/VLCWriter.cpp index f48e77182fc691afa150cef6133eb6e8f77dc96f..507c93c24382ce71bb5c7f47a44fa77772992bd6 100644 --- a/source/Lib/EncoderLib/VLCWriter.cpp +++ b/source/Lib/EncoderLib/VLCWriter.cpp @@ -1205,6 +1205,9 @@ void HLSWriter::codeSPS( const SPS* pcSPS ) WRITE_FLAG( pcSPS->getUseMIP() ? 1 : 0, "sps_mip_enabled_flag"); #if JVET_AB0149_INTRA_PRED WRITE_FLAG(pcSPS->getNnIntraPredEnabledFlag() ? 1 : 0, "sps_nn_intra_pred_enabled_flag"); +#if JVET_AI0130_COMB_NN_INTRA + WRITE_FLAG(pcSPS->getIsNnIspRestricted() ? 1 : 0, "sps_nn_isp_restriction_flag"); +#endif #endif if( pcSPS->getChromaFormatIdc() != CHROMA_400) { diff --git a/source/Lib/Utilities/VideoIOYuv.cpp b/source/Lib/Utilities/VideoIOYuv.cpp index 53e2cabe8fc5081eede84f6820e098013340e587..4b2898bdd84e3a07664056c413637c910c711f2c 100644 --- a/source/Lib/Utilities/VideoIOYuv.cpp +++ b/source/Lib/Utilities/VideoIOYuv.cpp @@ -1496,7 +1496,12 @@ void VideoIOYuv::ColourSpaceConvert(const CPelUnitBuf &src, PelUnitBuf &dest, co break; } } - +#if JVET_AI0074_MULTIRATIO_RPR_NNSR +bool VideoIOYuv::writeUpscaledPicture(const SPS &sps, const PPS &pps, const CPelUnitBuf &pic, + const InputColourSpaceConversion ipCSC, const bool bPackedYUVOutputMode, + int sliceQP, int baseQP, int sr, string srModelPath, int outputChoice, + ChromaFormat format, const bool bClipToRec709) +#else #if JVET_AG0130_UNIFIED_SR bool VideoIOYuv::writeUpscaledPicture( const SPS& sps, const PPS& pps, const CPelUnitBuf& pic, const InputColourSpaceConversion ipCSC, const bool bPackedYUVOutputMode, const PelUnitBuf& predBuf, const PelUnitBuf& bsBuf, const PelUnitBuf& bpmBuf, int sliceQP, int baseQP, SliceType sliceType, string srModelPath, @@ -1510,6 +1515,7 @@ bool VideoIOYuv::writeUpscaledPicture( const SPS& sps, const PPS& pps, const CPe int outputChoice, ChromaFormat format, const bool bClipToRec709 ) #endif +#endif { ChromaFormat chromaFormatIDC = sps.getChromaFormatIdc(); bool ret = false; @@ -1548,7 +1554,7 @@ bool VideoIOYuv::writeUpscaledPicture( const SPS& sps, const PPS& pps, const CPe int xScale = ( ( refPicWidth << SCALE_RATIO_BITS ) + ( curPicWidth >> 1 ) ) / curPicWidth; int yScale = ( ( refPicHeight << SCALE_RATIO_BITS ) + ( curPicHeight >> 1 ) ) / curPicHeight; -#if JVET_AG0130_UNIFIED_SR || JVET_AC0196_NNSR +#if JVET_AG0130_UNIFIED_SR || JVET_AC0196_NNSR || JVET_AI0074_MULTIRATIO_RPR_NNSR if (srModelPath=="default_rpr") { Picture::rescalePicture(std::pair<int, int>(xScale, yScale), pic, pps.getScalingWindow(), upscaledPic, afterScaleWindowFullResolution, chromaFormatIDC, sps.getBitDepths(), false, false, @@ -1560,11 +1566,15 @@ bool VideoIOYuv::writeUpscaledPicture( const SPS& sps, const PPS& pps, const CPe Picture::rescalePicture(std::pair<int, int>(xScale, yScale), pic, pps.getScalingWindow(), upscaledRPR, afterScaleWindowFullResolution, chromaFormatIDC, sps.getBitDepths(), false, false, sps.getHorCollocatedChromaFlag(), sps.getVerCollocatedChromaFlag()); +#if JVET_AI0074_MULTIRATIO_RPR_NNSR + Picture::rescalePictureCNN(upscaledRPR, upscaledPic, sliceQP, baseQP, sr, srModelPath); +#else #if JVET_AG0130_UNIFIED_SR Picture::rescalePictureCNN(pic, predBuf, upscaledRPR, bsBuf, bpmBuf, upscaledPic, sliceQP, baseQP, sliceType, srModelPath); #elif JVET_AC0196_NNSR Picture::rescalePictureCNN(pic, predBuf, upscaledRPR, upscaledPic, sliceQP, baseQP, sliceType); +#endif #endif } #else diff --git a/source/Lib/Utilities/VideoIOYuv.h b/source/Lib/Utilities/VideoIOYuv.h index bc5ddd0401d5619630bc5cef5b0f9e4494df702b..ecb011c4bd33efb7ee41c2003348f8b62bb9d19c 100644 --- a/source/Lib/Utilities/VideoIOYuv.h +++ b/source/Lib/Utilities/VideoIOYuv.h @@ -119,7 +119,10 @@ public: void setBitdepthShift( int ch, int bd ) { m_bitdepthShift[ch] = bd; } int getBitdepthShift( int ch ) { return m_bitdepthShift[ch]; } int getFileBitdepth( int ch ) { return m_fileBitdepth[ch]; } - +#if JVET_AI0074_MULTIRATIO_RPR_NNSR + bool writeUpscaledPicture(const SPS& sps, const PPS& pps, const CPelUnitBuf& pic, const InputColourSpaceConversion ipCSC, const bool bPackedYUVOutputMode, + int sliceQP, int baseQP, int sr, string srModelPath, +#else #if JVET_AG0130_UNIFIED_SR bool writeUpscaledPicture( const SPS& sps, const PPS& pps, const CPelUnitBuf& pic, const InputColourSpaceConversion ipCSC, const bool bPackedYUVOutputMode, const PelUnitBuf& predBuf, const PelUnitBuf& bsBuf, const PelUnitBuf& bpmBuf, int sliceQP, int baseQP, SliceType sliceType, string srModelPath, @@ -128,6 +131,7 @@ public: #if JVET_AC0196_NNSR const PelUnitBuf& predBuf, int sliceQP, int baseQP, SliceType sliceType, #endif +#endif #endif int outputChoice = 0, ChromaFormat format = NUM_CHROMA_FORMAT, const bool bClipToRec709 = false ); ///< write one upsaled YUV frame diff --git a/training/data_loader/data_loader.py b/training/data_loader/data_loader.py index 9577fc92db9c0e17c4c46b46459bad35f6130f54..93e1f6aaaa30f9a2ca1f43d962ccd4da3e31c5d0 100644 --- a/training/data_loader/data_loader.py +++ b/training/data_loader/data_loader.py @@ -110,10 +110,10 @@ def readDataInt16( data_h, w ) data = data[:, srcc[2] : srcc[3]] - t[dstc[0] : dstc[1], dstc[2] : dstc[3]] = data * norm + t[dstc[0] : dstc[1], dstc[2] : dstc[3]] = data if scale: t = t.repeat(2, axis=0).repeat(2, axis=1) - return t.astype("int16") + return t.astype("int16") * norm def readOne(patch_size, border_size, norm, fn, off, ctype): @@ -169,9 +169,7 @@ def hasLuma(comp): class DataLoader: components = [] database = None # contains the whole database - patch_info = ( - None - ) # contains address of each patch in the database: dataset index, frame index in the dataset, patch_index in the frame + patch_info = None # contains address of each patch in the database: dataset index, frame index in the dataset, patch_index in the frame suffix = {} # suffix for each file # patch_size in luma sample diff --git a/training/training_scripts/NN_Adaptive_Filtering/README.md b/training/training_scripts/NN_Adaptive_Filtering/README.md index 685419a81fc1480cfacd9e747eb016de064ae300..968a2e7cca0a93b1fc1b1f724b4cd3611c5151bb 100644 --- a/training/training_scripts/NN_Adaptive_Filtering/README.md +++ b/training/training_scripts/NN_Adaptive_Filtering/README.md @@ -1,8 +1,8 @@ -# Torch Over-fitting LOP2 +# NN Adaptive Filtering ## Instructions -Content-adaptive LOP2 via NN overfitting on each intra-period +Content-adaptive LOP2/LOP3/VLOP via NN overfitting on each intra-period This document explains how to conduct the training and generate the models for inference. The steps are: @@ -31,13 +31,13 @@ To get access to the NCTM repository, please follow these steps: The environment can be created with venv or anaconda: ### venv setup -`create_env.sh` can be used to create the virtual environment, called `lop2_overf` +`create_env.sh` can be used to create the virtual environment, called `adaptive_filter` Start using with: ```shell -source ${HOME}/lop2_overf/bin/activate -export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:${HOME}/lop2_overf/lib +source ${HOME}/adaptive_filter/bin/activate +export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:${HOME}/adaptive_filter/lib export PYTHONPATH=${PYTHONPATH}:${PWD} ``` @@ -45,8 +45,8 @@ export PYTHONPATH=${PYTHONPATH}:${PWD} ```shell conda env create -f environment.yml -conda activate lop2_overf -export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:${HOME}/anaconda3/envs/lop2_overf/lib +conda activate adaptive_filter +export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:${HOME}/anaconda3/envs/adaptive_filter/lib export PYTHONPATH=${PYTHONPATH}:${PWD} ``` @@ -76,7 +76,7 @@ Mandatory updates based on your system: * `deco_path`: abs path to decoded data (will be created) * `output_path`: abs path to save the output files from the overfitting (will be created) -The working directory is `<repo_path>/training/training_scripts/EE1-1.4`. +The working directory is `<repo_path>/training/training_scripts/NN_Adaptive_Filtering`. The overfitting data is organised in a specific way. Create the directory structure with the following command: @@ -122,8 +122,9 @@ Copy the JVET RA NNVC CTC mandatory sequences into the corresponding directory u ### 2.2. Video encoding and decoding -Use [NNVC-8.0rc3](https://vcgit.hhi.fraunhofer.de/jvet-ahg-nnvc/VVCSoftware_VTM/-/tree/NNVC-8.0rc3/) and enable both -the NN intra tool and the NN LOP2.0 loop-filter. +* For LOP2, use [NNVC-8.0rc3](https://vcgit.hhi.fraunhofer.de/jvet-ahg-nnvc/VVCSoftware_VTM/-/tree/NNVC-8.0rc3/) and enable both the NN intra tool and the NN LOP2 loop-filter. +* For LOP3, use [NNVC-9.1rc](https://vcgit.hhi.fraunhofer.de/jvet-ahg-nnvc/VVCSoftware_VTM/-/blob/NNVC-9.1rc/) and enable both the NN intra tool and the NN LOP3 loop-filter +* For VLOP, use [NNVC-9.1rc](https://vcgit.hhi.fraunhofer.de/jvet-ahg-nnvc/VVCSoftware_VTM/-/blob/NNVC-9.1rc/) and enable both the NN intra tool and the NN VLOP loop-filter **NOTE**: The decoded data should follow the file structure shown earlier. The output files can be re-organised after the decoding process takes place. @@ -140,15 +141,18 @@ Check the file names in [resources/config.json](resources/config.json) under `nn ## 3. SADL model to Torch model -The torch model for training is generated from the original LOP2 in Int16 SADL format by using the following command: +The torch model for training is generated from the original NN loop-filter in Int16 SADL format. +The same command will also create two JSON files: one with the model quantisers and one that maps the parameters of +the original NN loop-filter model to the model that contains the multiplier parameters. + +### 3.1. LOP2 model + +Run the command: ```shell -python conversion/sadl2torch.py +python conversion/sadl2torch.py lop2 ``` -The same command will also create two JSON files: one with the model quantisers and one that maps the parameters of -the original LOP2 model to the model that contains the multiplier parameters. - The three output files will be located under the `resources` directory as follows: @@ -159,9 +163,46 @@ resources └── nnlf_lop2_params_to_new_model.json ``` +### 3.2. LOP3 model + +Run the command: + +```shell +python conversion/sadl2torch.py lop3 +``` + +The three output files will be located under the `resources` directory as follows: + + +```shell +resources +├── nnlf_lop3_model.pt +├── nnlf_lop3_quantizers.json +└── nnlf_lop3_params_to_new_model.json +``` + +### 3.3. VLOP model + +Run the command: + +```shell +python conversion/sadl2torch.py vlop +``` + + +The three output files will be located under the `resources` directory as follows: + +```shell +resources +├── nnlf_vlop_model.pt +├── nnlf_vlop_quantizers.json +└── nnlf_vlop_params_to_new_model.json +``` + ## 4. Overfitting pipeline Overfitting pipeline includes following steps: + 1. Trainings 2. Weight-update compression 3. Conversion from Torch model to SADL model @@ -176,15 +217,16 @@ Here the weight-update is computed and compressed, generating an NNR/NNC bitstre ### 4.3 Convert Torch models to SADL The Torch model will be converted into ONNX first, converted into SADL float32 model and then quantised. -The sample script below launches in a single python process the overfitting pipeline in the same class: +The sample script below launches in a single python process the overfitting pipeline for lop2 in the same class +(replace `<model_arch>` with lop2, lop3 or vlop): ```shell -CUDA_VISIBLE_DEVICES=2 python launch_pipeline.py -vs A1_CampfireParty A1_FoodMarket A1_Tango--qps 22 27 32 37 42 -CUDA_VISIBLE_DEVICES=3 python launch_pipeline.py -vs A2_CatRobot A2_DaylightRoad A2_ParkRunning --qps 22 27 32 37 42 -CUDA_VISIBLE_DEVICES=4 python launch_pipeline.py -vs B_BasketBallDrive B_BQTerrace B_Cactus B_MarketPlace B_RitualDance --qps 22 27 32 37 42 -CUDA_VISIBLE_DEVICES=0 python launch_pipeline.py -vs C_BasketballDrill C_PartyScene C_BQMall C_RaceHorses_big --qps 22 27 32 37 42 -CUDA_VISIBLE_DEVICES=1 python launch_pipeline.py -vs D_BasketBallPass D_BQSquare D_BlowingBubbles D_RaceHorses_s --qps 22 27 32 37 42 -CUDA_VISIBLE_DEVICES=5 python launch_pipeline.py -vs F_ArenaOfValor F_BBDrillText F_SlideEditing F_SlideShow --qps 22 27 32 37 42 +CUDA_VISIBLE_DEVICES=2 python launch_pipeline.py --arch <model_arch> -vs A1_CampfireParty A1_FoodMarket A1_Tango--qps 22 27 32 37 42 +CUDA_VISIBLE_DEVICES=3 python launch_pipeline.py --arch <model_arch> -vs A2_CatRobot A2_DaylightRoad A2_ParkRunning --qps 22 27 32 37 42 +CUDA_VISIBLE_DEVICES=4 python launch_pipeline.py --arch <model_arch> -vs B_BasketBallDrive B_BQTerrace B_Cactus B_MarketPlace B_RitualDance --qps 22 27 32 37 42 +CUDA_VISIBLE_DEVICES=0 python launch_pipeline.py --arch <model_arch> -vs C_BasketballDrill C_PartyScene C_BQMall C_RaceHorses_big --qps 22 27 32 37 42 +CUDA_VISIBLE_DEVICES=1 python launch_pipeline.py --arch <model_arch> -vs D_BasketBallPass D_BQSquare D_BlowingBubbles D_RaceHorses_s --qps 22 27 32 37 42 +CUDA_VISIBLE_DEVICES=5 python launch_pipeline.py --arch <model_arch> -vs F_ArenaOfValor F_BBDrillText F_SlideEditing F_SlideShow --qps 22 27 32 37 42 ``` The overfitting results will be in `<output_path>/overfittings`. @@ -251,6 +293,7 @@ scripts to (1) merge the final segment bitstreams and (2) call the decoder. The script is tailored for the proponents environment, but it can be tuned to work with others. Sample script: +* `<model_path>` and `<model_arch>` refer to the path to the SADL int16 model of the model architecture (lop2, lop3 or vlop): ```shell python3 segment_on_off.py \ @@ -260,7 +303,8 @@ python3 segment_on_off.py \ --pass2 /pass2_results \ --output_dir /segment_on_off \ --intra_models_dir /src/models/intra \ ---lop2_model /src/models/nnlf_lop2_model_int16.sadl \ +--model_path <model_path> \ +--model_arch <model_arch> --parcat_bin /src/bin/parcatStatic \ --decoder_bin /src/bin/DecoderAppStatic ``` @@ -281,19 +325,19 @@ export WU_CODE=$PWD export PYTHONPATH=${PYTHONPATH}:${PWD} ``` -2. Activate pyton environment with +2. Activate python environment with ```shell -source ${HOME}/lop2_overf/bin/activate -export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:${HOME}/lop2_overf/lib +source ${HOME}/adaptive_filter/bin/activate +export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:${HOME}/adaptive_filter/lib ``` or ```shell -conda activate lop2_overf -export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:${HOME}/anaconda3/envs/lop2_overf/lib +conda activate adaptive_filter +export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:${HOME}/anaconda3/envs/adaptive_filter/lib ``` 3. Disable python warnings: @@ -305,8 +349,20 @@ export PYTHONWARNINGS="ignore" 4. Build sadl sample and copy the `naive_quantization` binary to the `$WU_CODE` directory -Sample decoder script: +Sample decoder script using overfitted LOP2: ```shell -./DecoderAppStatic -b D_BlowingBubbles_22.bin --NnfuOutputFileStem=D_BlowingBubbles_22 +./DecoderAppStatic -b D_BlowingBubbles_22.bin --NnlfArch=lop2 --NnfuOutputFileStem=D_BlowingBubbles_22 ``` + +Sample decoder script using overfitted VLOP: + +```shell +./DecoderAppStatic -b D_BlowingBubbles_22.bin --NnlfArch=vlop --NnfuOutputFileStem=D_BlowingBubbles_22 +``` + +Sample decoder script using overfitted LOP3: + +```shell +./DecoderAppStatic -b D_BlowingBubbles_22.bin --NnlfArch=lop3 --NnfuOutputFileStem=D_BlowingBubbles_22 +``` \ No newline at end of file diff --git a/training/training_scripts/NN_Adaptive_Filtering/conversion/onnx2sadl_modified.py b/training/training_scripts/NN_Adaptive_Filtering/conversion/onnx2sadl_modified.py index f023336cf27074882cf7df6c1ed042c11c4593e9..d1ab8f7de23d4c8098d48607d41e24d37685f504 100644 --- a/training/training_scripts/NN_Adaptive_Filtering/conversion/onnx2sadl_modified.py +++ b/training/training_scripts/NN_Adaptive_Filtering/conversion/onnx2sadl_modified.py @@ -1396,8 +1396,6 @@ def dump_onnx(graph, my_inputs, my_outputs, output_filename, verbose=False): # my_input=[my_name, my_name..] # outputs=[my_name, ...] # print(graph) - input_q = 11 - relu_q = 11 mul_q = 13 map_name_to_idx = dict() @@ -1502,8 +1500,8 @@ def dump_onnx(graph, my_inputs, my_outputs, output_filename, verbose=False): if ".multiplier" in orig_name: QUANTIZERS[quantizer_idx] = mul_q - elif "PRelu" in name: - QUANTIZERS[quantizer_idx] = relu_q + elif "PRelu" in orig_name: + QUANTIZERS[quantizer_idx] = base_quantizers[orig_name] else: QUANTIZERS[quantizer_idx] = base_quantizers.get(orig_name, 0) @@ -1600,7 +1598,7 @@ def dump_onnx(graph, my_inputs, my_outputs, output_filename, verbose=False): f.write(struct.pack("i", int(0))) if "Equal" not in name: - QUANTIZERS[quantizer_idx] = input_q + QUANTIZERS[quantizer_idx] = base_quantizers[name] else: QUANTIZERS[quantizer_idx] = 0 diff --git a/training/training_scripts/NN_Adaptive_Filtering/conversion/sadl2torch.py b/training/training_scripts/NN_Adaptive_Filtering/conversion/sadl2torch.py index 32f58f7004b8a84bb2f515f22c3d6d486b66f78f..2b3ab4695d02a6bf88ca67ce8e2c30258c828e4d 100644 --- a/training/training_scripts/NN_Adaptive_Filtering/conversion/sadl2torch.py +++ b/training/training_scripts/NN_Adaptive_Filtering/conversion/sadl2torch.py @@ -40,10 +40,17 @@ from tempfile import NamedTemporaryFile from typing import Dict import numpy as np +import onnx import torch from conversion.onnx2sadl_modified import DTYPE_SADL, OPTYPE -from models import load_torch_model, model_lop2, model_lop2_with_multiplier +from models import ( + load_torch_model, + model_lop2, + model_lop2_with_multiplier, + model_lop3, + model_lop3_with_multiplier, +) from util.file_system import read_json_file, write_json_file # used to load tensor in SADL, the max dimension of tensor is 6 @@ -325,6 +332,8 @@ def load_internal_placeholder(sadl_file): if verbose: print(f" - q: {quantizer}") + return quantizer + def load_internal_slice(sadl_file): start_h = int.from_bytes(sadl_file.read(4), byteorder="little") @@ -418,11 +427,15 @@ def read_sadl_file(sadl_model_path): print(f"[INFO] id: {layer_id} op {OPTYPE(layer_op).name}") layer_name = load_prefix(sadl_file) out = load_internal(sadl_file, OPTYPE(layer_op)) + if OPTYPE(layer_op) == OPTYPE.Placeholder: + base_model_quantizers[layer_name] = out if OPTYPE(layer_op) == OPTYPE.Const: if ( ".weight" in layer_name or ".bias" in layer_name or "PRelu" in layer_name + or layer_name.isnumeric() + and out[-1] != 0 ): const_layers[layer_name] = {} const_layers[layer_name]["dimension"] = out[0] @@ -451,29 +464,53 @@ def map_sadl_param_name_to_torch(layer_data: Dict): layer_data.pop(k, None) -def create_model(orig_sadl_model, orig_model_config): +def create_model(orig_sadl_model, orig_model_config, arch): layer_data, base_model_quantizers = read_sadl_file(orig_sadl_model) map_sadl_param_name_to_torch(layer_data) - torch_model = load_torch_model(orig_model_config, model_lop2) - for name, _ in torch_model.SADL_model.state_dict().items(): - dimension = layer_data[name]["dimension"] - weights = torch.Tensor(layer_data[name]["data"]) - # in SADL, dim format of conv weights is WHC_inC_out, convert to C_outC_intWH here - if len(dimension) == 4: - weights = weights.reshape(dimension) - weights = weights.permute(3, 2, 0, 1) - torch_model.SADL_model.state_dict()[name][:] = weights - - return torch_model, base_model_quantizers - - -def map_params(orig_model_config) -> Dict[str, str]: - orig_model = load_torch_model(orig_model_config, model_lop2) - orig_params = orig_model.state_dict() - - new_model = load_torch_model(orig_model_config, model_lop2_with_multiplier) - new_params = new_model.state_dict() + if arch in ["lop2"]: + torch_model = load_torch_model(orig_model_config, model_lop2) + for name in torch_model.SADL_model.state_dict().keys(): + dimension = layer_data[name]["dimension"] + weights = torch.Tensor(layer_data[name]["data"]) + # in SADL, dim format of conv weights is WHC_inC_out, convert to C_outC_intWH here + if len(dimension) == 4: + weights = weights.reshape(dimension) + weights = weights.permute(3, 2, 0, 1) + torch_model.SADL_model.state_dict()[name][:] = weights + return torch_model, base_model_quantizers + + if arch in ["lop3"]: + torch_model = load_torch_model(orig_model_config, model_lop3) + + for layer_param, torch_param in zip( + layer_data, torch_model.SADL_model.state_dict().keys() + ): + dimension = layer_data[layer_param]["dimension"] + weights = torch.Tensor(layer_data[layer_param]["data"]) + # in SADL, dim format of conv weights is WHC_inC_out, convert to C_outC_intWH here + if len(dimension) == 4: + weights = weights.reshape(dimension) + weights = weights.permute(3, 2, 0, 1) + torch_model.SADL_model.state_dict()[torch_param][:] = weights + if layer_param.isnumeric(): + base_model_quantizers[ + f"onnx::PRelu_{layer_param}" + ] = base_model_quantizers[layer_param] + return torch_model, base_model_quantizers + + +def map_params(orig_model_config, base_model_file, arch) -> Dict[str, str]: + if arch in ["lop2", "vlop"]: + orig_model = load_torch_model(orig_model_config, model_lop2) + new_model = load_torch_model(orig_model_config, model_lop2_with_multiplier) + elif arch in ["lop3"]: + orig_model = load_torch_model(orig_model_config, model_lop3) + new_model = load_torch_model(orig_model_config, model_lop3_with_multiplier) + + orig_model.load_state_dict(torch.load(base_model_file)) + orig_params = orig_model.SADL_model.state_dict() + new_params = new_model.SADL_model.state_dict() param_map = {} @@ -483,37 +520,69 @@ def map_params(orig_model_config) -> Dict[str, str]: if "multiplier" not in new_param: tmp_params.append(new_param) - for (orig_param, new_param) in zip(orig_params.keys(), tmp_params): - tmp_orig_param = orig_param.replace("SADL_model.", "") - tmp_new_param = new_param.replace("SADL_model.", "") - param_map[tmp_new_param] = tmp_orig_param + for orig_param, new_param in zip(orig_params.keys(), tmp_params): + param_map[new_param] = orig_param + + # update PReLU value, otherwise, only 1 PReLU layer in onnx model + for name in new_model.SADL_model.state_dict().keys(): + if "post_activation" in name or "prelu" in name: + orig_name = param_map[name] + weights = orig_model.SADL_model.state_dict()[orig_name] + new_model.SADL_model.state_dict()[name][:] = weights + + # map name of PReLU + with NamedTemporaryFile() as tmp_base_onnx, NamedTemporaryFile() as tmp_new_onnx: + orig_model.SADL_model.to_onnx(tmp_base_onnx.name) + new_model.SADL_model.to_onnx(tmp_new_onnx.name) + map_prelu_name_from_onnx(tmp_base_onnx.name, tmp_new_onnx.name, param_map) return param_map +def get_prelu_names(onnx_file): + onnx_model = onnx.load(onnx_file) + names = [] + for node in onnx_model.graph.initializer: + if "PRelu" in node.name: + names.append(node.name) + return names + + +def map_prelu_name_from_onnx(base_onnx_file, new_onnx_file, param_map): + prelu_names_orig = get_prelu_names(base_onnx_file) + prelu_names_new = get_prelu_names(new_onnx_file) + + for orig_name, new_name in zip(prelu_names_orig, prelu_names_new): + param_map[new_name] = orig_name + + if __name__ == "__main__": config = read_json_file("resources/config.json") + arch = sys.argv[1] - if "lop2" != config["architecture"]: - print(f"{config['architecture']} not supported") + if arch not in config["architecture"]: + print(f"{arch} not supported") sys.exit(-1) - model_config = read_json_file("models/models.json")[config["architecture"]] + model_config = read_json_file("models/models.json")[arch] + config = config[arch] orig_sadl_model_i = config["sadl2torch"]["base_model_sadl_i"] - output_path = config["sadl2torch"]["base_model_torch"] + torch_model_path = config["sadl2torch"]["base_model_torch"] verbose = 0 # dequantized model - torch_model_dq, base_quantizers = create_model(orig_sadl_model_i, model_config) - torch.save(torch_model_dq.state_dict(), output_path) + torch_model_dq, base_quantizers = create_model( + orig_sadl_model_i, model_config, arch + ) + torch.save(torch_model_dq.state_dict(), torch_model_path) # save quantizers of base model base_quantizers_file = config["sadl2torch"]["base_model_quantizers"] write_json_file(base_quantizers, base_quantizers_file) - org_to_new_params = map_params(model_config) + org_to_new_params = map_params(model_config, torch_model_path, arch) write_json_file( org_to_new_params, Path(config["sadl2torch"]["base_model_params_to_new"]) ) @@ -523,17 +592,26 @@ if __name__ == "__main__": with NamedTemporaryFile() as tmp_onnx, NamedTemporaryFile() as tmp_sadl_f, NamedTemporaryFile() as tmp_sadl_i, NamedTemporaryFile() as tmp_quantizers: torch_model_dq.SADL_model.to_onnx(tmp_onnx.name) - os.system( - f"python conversion/onnx2sadl_modified.py --input {tmp_onnx.name} --output {tmp_sadl_f.name} --out_quant {tmp_quantizers.name} --base_model_quantizers {base_quantizers_file}" + exit_code = os.system( + f"python conversion/onnx2sadl_modified.py --input {tmp_onnx.name} --output {tmp_sadl_f.name} --out_quant {tmp_quantizers.name} --base_quantizers {base_quantizers_file}" ) - os.system( + if exit_code: + print("Conversion onnx to sadl failed") + sys.exit(-1) + + exit_code = os.system( f"tail -n 1 {tmp_quantizers.name} | ./naive_quantization {tmp_sadl_f.name} {tmp_sadl_i.name}" ) + if exit_code: + print("Quantisation failed") + sys.exit(-1) # check equivalence of base int model and requantized dequantized base model src_md5 = md5file(orig_sadl_model_i) dst_md5 = md5file(tmp_sadl_i.name) - assert ( - src_md5 == dst_md5 - ), "Something went wrong in new custom SADL model reader" + if src_md5 == dst_md5: + print( + "checksums of base model and re-quantised model are different, param names might be different\n" + "check with sadl debug_model further" + ) diff --git a/training/training_scripts/NN_Adaptive_Filtering/conversion/torch2sadl.py b/training/training_scripts/NN_Adaptive_Filtering/conversion/torch2sadl.py index a46251c8f9f937fa9918a9be3bae5d7687355532..cac00dfa86bd68316bed9978b1e13871b94eca0e 100644 --- a/training/training_scripts/NN_Adaptive_Filtering/conversion/torch2sadl.py +++ b/training/training_scripts/NN_Adaptive_Filtering/conversion/torch2sadl.py @@ -39,18 +39,25 @@ from typing import Union import torch -from models import load_torch_model, model_lop2_with_multiplier +from models import ( + load_torch_model, + model_lop2_with_multiplier, + model_lop3_with_multiplier, +) from util.file_system import create_directory, read_json_file def torch_to_onnx( + arch: str, torch_model_file: Union[Path, str], out_onnx_path: Union[Path, str], model_config: Union[Path, str], - model_module: torch.nn.Module = model_lop2_with_multiplier, ): # Load model - model = load_torch_model(model_config, model_module) + if arch in ["lop2"]: + model = load_torch_model(model_config, model_lop2_with_multiplier) + elif arch in ["lop3"]: + model = load_torch_model(model_config, model_lop3_with_multiplier) model.load_state_dict(torch.load(torch_model_file)) # Torch to Onnx diff --git a/training/training_scripts/NN_Adaptive_Filtering/create_env.sh b/training/training_scripts/NN_Adaptive_Filtering/create_env.sh index 218d0e8581b9a4c2dd252579ed4cf2f2efb5de49..b1e8d9b846d298e9257d328511ec56ddef173439 100755 --- a/training/training_scripts/NN_Adaptive_Filtering/create_env.sh +++ b/training/training_scripts/NN_Adaptive_Filtering/create_env.sh @@ -1,7 +1,7 @@ #!/bin/bash -python3 -m venv ${HOME}/lop2_overf -source ${HOME}/lop2_overf/bin/activate +python3 -m venv ${HOME}/adaptive_filter +source ${HOME}/adaptive_filter/bin/activate # Torch dependencies pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu113 diff --git a/training/training_scripts/NN_Adaptive_Filtering/environment.yml b/training/training_scripts/NN_Adaptive_Filtering/environment.yml index 569d237f7fd837a9e1d19a689a6b22814ec45b6e..c6563ab8387adeed496cb07bf00829ea7f53816b 100644 --- a/training/training_scripts/NN_Adaptive_Filtering/environment.yml +++ b/training/training_scripts/NN_Adaptive_Filtering/environment.yml @@ -1,4 +1,4 @@ -name: lop2_overf +name: adaptive_filter channels: - pytorch - defaults @@ -160,4 +160,4 @@ dependencies: - werkzeug==3.0.1 - wrapt==1.16.0 - zipp==3.17.0 -prefix: ~/anaconda3/envs/lop2_overf +prefix: ~/anaconda3/envs/adaptive_filter diff --git a/training/training_scripts/NN_Adaptive_Filtering/launch_pipeline.py b/training/training_scripts/NN_Adaptive_Filtering/launch_pipeline.py index 1539e459f214b23962fef37ef2b0e10791b81d0a..5772a35f764ad597c0744f6f2483e8dbbf070e58 100644 --- a/training/training_scripts/NN_Adaptive_Filtering/launch_pipeline.py +++ b/training/training_scripts/NN_Adaptive_Filtering/launch_pipeline.py @@ -44,6 +44,7 @@ def parse_arguments(): parser = argparse.ArgumentParser( description="Launching whole pipeline of overfittings, including training, nnr compression and conversion to SADL" ) + parser.add_argument("--arch", type=str, required=True, help="Model architecture") parser.add_argument( "-vs", "--video_sequences", @@ -67,6 +68,8 @@ def launch_pipeline(): num_param = min( num_layer_config[seq_name][qp], config["training"]["num_layers"] ) + # if num_param == maximum number of param to overfit, so no need to select layers + select_layers = num_param < config["training"]["num_layers"] for part in range(num_part): min_poc = part * intra_period @@ -82,13 +85,16 @@ def launch_pipeline(): f"python {script_to_execute} --max_epochs={epochs} " f"--deco_dir_train={dataset_deco_path} --orig_dir_train={dataset_orig_path} --prop_file_train={data_cfg_file} " f"--seq_qp_train={qp} --seq_name={seq_name} --slice_type=* --base_nn_params={base_model_path} " - f"--output_dir={curr_overfitting_dir} --arch={config['architecture']} " + f"--output_dir={curr_overfitting_dir} --arch={args.arch} " f"--stop_patience {stop_patience} --lr_patience {lr_patience} --lr={lr} " - f"--min_poc {min_poc} --max_poc {max_poc} --part_idx {part} --select_layer --num_param {num_param} " + f"--min_poc {min_poc} --max_poc {max_poc} --part_idx {part} --num_param {num_param} " f"--nnr_models_dir {nnr_models_dir} --onnx_models_dir {onnx_models_dir} --sadl_float_dir {sadl_float_dir} --sadl_int_dir {sadl_int_dir} --quantizers_dir {quantizers_dir} " f"--base_quantizers {base_quantizers} --map_orig_params_to_new {map_orig_params_to_new} " ) + if select_layers: + command = command + "--select_layer " + if start_epoch: nn_params = ( curr_overfitting_dir @@ -115,11 +121,11 @@ if __name__ == "__main__": config = read_json_file("resources/config.json") - if "lop2" != config["architecture"]: - print(f"{config['architecture']} architecture not supported") + if args.arch not in config["architecture"]: + print(f"{args.arch} architecture not supported") sys.exit(-1) - model_config = "models/models.json" + config = config[args.arch] dataset_orig_path = config["dataset"]["orig_path"] dataset_deco_path = config["dataset"]["deco_path"] @@ -158,7 +164,6 @@ if __name__ == "__main__": stop_patience = 50 lr_patience = 100 weight_decay = 0 - total_terms = 100 start_epoch = 0 launch_pipeline() diff --git a/training/training_scripts/NN_Adaptive_Filtering/models/model_lop2.py b/training/training_scripts/NN_Adaptive_Filtering/models/model_lop2.py index 47e781fba82674b101bc0991be341be92d2423f1..66e9519045496d6db42075a18dfabc66acc36e6d 100644 --- a/training/training_scripts/NN_Adaptive_Filtering/models/model_lop2.py +++ b/training/training_scripts/NN_Adaptive_Filtering/models/model_lop2.py @@ -224,6 +224,8 @@ class SADLNet(nn.Sequential): C21: int = 16, output_channels_y: int = 4, output_channels_uv: int = 2, + Conv2_is_separable: bool = False, + Conv2_hidden_separable_channels: int = None, ): """ Args: @@ -245,7 +247,14 @@ class SADLNet(nn.Sequential): ] ), Conv(sum(self.input_features), D6, kernel_size=1), - Conv(D6, C + C, kernel_size=3, stride=2), + Conv( + D6, + C + C, + kernel_size=3, + is_separable=Conv2_is_separable, + hidden_separable_channels=Conv2_hidden_separable_channels, + stride=2, + ), SplitLumaChromaBlocks( N_Y, N_UV, C, C1_Y, C1_UV, C21, output_channels_y, output_channels_uv ), @@ -301,6 +310,8 @@ class Net(nn.Module): C1_Y: int = 64, C1_UV: int = 48, C21: int = 16, + Conv2_is_separable: bool = False, + Conv2_hidden_separable_channels: int = None, ): super(Net, self).__init__() assert len(input_channels) == len( @@ -325,6 +336,8 @@ class Net(nn.Module): C21, 4, 2, + Conv2_is_separable, + Conv2_hidden_separable_channels, ) self.chroma_upsampler = nn.Upsample(scale_factor=2, mode="nearest") diff --git a/training/training_scripts/NN_Adaptive_Filtering/models/model_lop2_with_multiplier.py b/training/training_scripts/NN_Adaptive_Filtering/models/model_lop2_with_multiplier.py index 0c6c5414d32c07599c21b5c6a572c1b75875c54b..e9d5bed6c7dcf733260e12bdd4a9e66598973e2e 100644 --- a/training/training_scripts/NN_Adaptive_Filtering/models/model_lop2_with_multiplier.py +++ b/training/training_scripts/NN_Adaptive_Filtering/models/model_lop2_with_multiplier.py @@ -313,6 +313,8 @@ class SADLNet(nn.Module): C21: int = 16, output_channels_y: int = 4, output_channels_uv: int = 2, + Conv2_is_separable: bool = False, + Conv2_hidden_separable_channels: int = None, ): """ Args: @@ -334,7 +336,14 @@ class SADLNet(nn.Module): ] ) self.conv1 = Conv(sum(self.input_features), D6, kernel_size=1) - self.conv2 = Conv(D6, C + C, kernel_size=3, stride=2) + self.conv2 = Conv( + D6, + C + C, + kernel_size=3, + is_separable=Conv2_is_separable, + hidden_separable_channels=Conv2_hidden_separable_channels, + stride=2, + ) self.split = SplitLumaChromaBlocks( N_Y, N_UV, C, C1_Y, C1_UV, C21, output_channels_y, output_channels_uv ) @@ -406,6 +415,8 @@ class Net(nn.Module): C1_Y: int = 64, C1_UV: int = 48, C21: int = 16, + Conv2_is_separable: bool = False, + Conv2_hidden_separable_channels: int = None, ): super(Net, self).__init__() assert len(input_channels) == len( @@ -430,6 +441,8 @@ class Net(nn.Module): C21, 4, 2, + Conv2_is_separable, + Conv2_hidden_separable_channels, ) self.chroma_upsampler = nn.Upsample(scale_factor=2, mode="nearest") diff --git a/training/training_scripts/NN_Adaptive_Filtering/models/model_lop3.py b/training/training_scripts/NN_Adaptive_Filtering/models/model_lop3.py new file mode 100644 index 0000000000000000000000000000000000000000..beb53880b1d321f23b4607ed19d51ee93d7b486a --- /dev/null +++ b/training/training_scripts/NN_Adaptive_Filtering/models/model_lop3.py @@ -0,0 +1,541 @@ +""" +/* The copyright in this software is being made available under the BSD +* License, included below. This software may be subject to other third party +* and contributor rights, including patent rights, and no such rights are +* granted under this license. +* +* Copyright (c) 2010-2024, ITU/ISO/IEC +* All rights reserved. +* +* Redistribution and use in source and binary forms, with or without +* modification, are permitted provided that the following conditions are met: +* +* * Redistributions of source code must retain the above copyright notice, +* this list of conditions and the following disclaimer. +* * Redistributions 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. +* * Neither the name of the ITU/ISO/IEC nor the names of its contributors may +* 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 COPYRIGHT HOLDER 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. +""" + +from typing import Dict, Iterable, List, Optional, Tuple, Type, Union + +import torch +from torch import nn +from torch.nn import functional as F + + +class FastDCT: + def __init__(self, dct_size: int, device: str): + super().__init__() + self.dct_size = dct_size + self.device = device + + def blockDCT_ps(self, xx): + if self.dct_size == 2: + yy = self.blockDCT2x2_ps2(xx) + else: + raise ValueError("DCT size unknown", self.dct_size) + return yy + + def iblockDCT_ps(self, xx): + if self.dct_size == 2: + yy = self.iblockDCT2x2_ps2(xx) + else: + raise ValueError("DCT size unknown", self.dct_size) + return yy + + def blockDCT_ps_halfsize(self, xx): + if self.dct_size == 2: + yy = xx + else: + raise ValueError("DCT size unknown", self.dct_size) + return yy + + def iblockDCT_per_chUV(self, xx): + dct_ch = self.dct_size * self.dct_size + ch_n = xx.shape[1] // dct_ch + yy = torch.Tensor(0).to(self.device) + for i in range(ch_n): + yi = self.iblockDCT_ps(xx[:, i * dct_ch : (i + 1) * dct_ch, :, :]) + yy = torch.cat((yy, yi), dim=1) + return yy + + def blockDCT2x2_ps2(self, xx): + pix_unshuff = nn.PixelUnshuffle(self.dct_size) + yy = pix_unshuff(xx) + rec_dct_reshape = ( + torch.zeros((1, 1, 1, 1), dtype=torch.float32) + .to(self.device) + .expand(yy.shape) + .clone() + ) + + a = yy[:, 0, :, :] + b = yy[:, 1, :, :] + c = yy[:, 2, :, :] + d = yy[:, 3, :, :] + t1 = a + b + t2 = c + d + t3 = a - b + t4 = c - d + c1 = t1 + t2 + c2 = t3 + t4 + c3 = t1 - t2 + c4 = t3 - t4 + rec_dct_reshape[:, 0, :, :] = c1 + rec_dct_reshape[:, 1, :, :] = c2 + rec_dct_reshape[:, 2, :, :] = c3 + rec_dct_reshape[:, 3, :, :] = c4 + + return rec_dct_reshape / 4 + + def iblockDCT2x2_ps2(self, yy): + xx = ( + torch.zeros((1, 1, 1, 1), dtype=torch.float32) + .to(self.device) + .expand(yy.shape) + .clone() + ) + + c1 = yy[:, 0, :, :] + c2 = yy[:, 1, :, :] + c3 = yy[:, 2, :, :] + c4 = yy[:, 3, :, :] + t1 = c1 + c2 + t2 = c3 + c4 + t3 = c1 - c2 + t4 = c3 - c4 + a = t1 + t2 + b = t3 + t4 + c = t1 - t2 + d = t3 - t4 + xx[:, 0, :, :] = a + xx[:, 1, :, :] = b + xx[:, 2, :, :] = c + xx[:, 3, :, :] = d + + pix_shuff = nn.PixelShuffle(self.dct_size) + xx = pix_shuff(xx) + return xx + + +class Conv(nn.Sequential): + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: Union[int, Tuple[int, int]], + stride: Union[int, Tuple[int, int]] = 1, + padding: Optional[Union[int, Tuple[int, int]]] = None, + is_separable: bool = False, + is_horizontal: bool = True, + hidden_separable_channels: Optional[int] = None, + post_activation: Optional[Type] = nn.PReLU, + **kwargs, + ): + """ + Args: + in_channels: the number of input channels + out_channels: the number of output channels + kernel_size: the convolution's kernel size + stride: the convolution's stride(s) + padding: the convolution's padding + is_separable: whether to implement convolution separably + hidden_separable_channels: If is_separable, the number of hidden channels between convolutions. If None, use out_channels + post_activation: activation function to use after convolution. If None, no activation after convolution + **kwargs: additional kwargs to pass to nn.Conv2d + """ + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = ( + (kernel_size, kernel_size) if isinstance(kernel_size, int) else kernel_size + ) + self.stride = (stride, stride) if isinstance(stride, int) else stride + if padding is not None: + self.padding = (padding, padding) if isinstance(padding, int) else padding + else: + self.padding = tuple([k // 2 for k in self.kernel_size]) + self.is_separable = is_separable + self.is_horizontal = is_horizontal + self.post_activation = post_activation + + if self.is_separable: + self.hidden_separable_channels = hidden_separable_channels or out_channels + if self.is_horizontal: + modules = [ + nn.Conv2d( + self.in_channels, + self.hidden_separable_channels, + (self.kernel_size[0], 1), + (self.stride[0], 1), + (self.padding[0], 0), + groups=self.hidden_separable_channels, + **kwargs, + ) + ] + else: + modules = [ + nn.Conv2d( + self.hidden_separable_channels, + self.out_channels, + (1, self.kernel_size[1]), + (1, self.stride[1]), + (0, self.padding[1]), + groups=self.hidden_separable_channels, + **kwargs, + ) + ] + else: + modules = [ + nn.Conv2d( + self.in_channels, + self.out_channels, + self.kernel_size, + self.stride, + self.padding, + **kwargs, + ) + ] + + if self.post_activation is not None: + modules.append(self.post_activation()) + + super(Conv, self).__init__(*modules) + + +class MultiBranchModule(nn.Module): + """A module representing multple, parallel branches. If the input is a list, each element in the list is fed into the corresponding branch, + otherwise the input is fed into every branch. The outputs of each branch are then merged. + """ + + def __init__(self, *branch_modules, merge_dimension: int = -3): + """ + Args: + branch_modules: modules to run in parallel + merge_dimension: the dimension to merge outputs from each branch + """ + super().__init__() + self.branches = nn.ModuleList(branch_modules) + self.merge_dimension = merge_dimension + + def forward(self, args: Union[torch.Tensor, List[torch.Tensor]]) -> torch.Tensor: + inputs = args if isinstance(args, list) else len(self.branches) * [args] + branch_outputs = [branch(input) for branch, input in zip(self.branches, inputs)] + return torch.cat(branch_outputs, dim=self.merge_dimension) + + +class NewResBlock_separate_prelu(nn.Sequential): + def __init__(self, C: int = 64, C1: int = 160, C21: int = 32): + super().__init__() + self.prelu = nn.PReLU() + self.conv1_11 = Conv(C1, C, kernel_size=1, post_activation=None) + self.conv2_13 = Conv( + C, + C, + kernel_size=3, + post_activation=None, + is_separable=True, + is_horizontal=True, + hidden_separable_channels=C21, + ) + self.conv3_31 = Conv( + C, + C, + kernel_size=3, + post_activation=None, + is_separable=True, + is_horizontal=False, + hidden_separable_channels=C21, + ) + self.conv4_11 = Conv(C, C1, kernel_size=1, post_activation=None) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + temp = x + x1 = self.prelu(x) + x2 = self.conv1_11(x1) + x3 = self.conv2_13(x2) + x4 = self.conv3_31(x3) + x5 = self.conv4_11(x4) + return x5 + temp + + +class SplitLumaChromaBlocks(nn.Sequential): + def __init__( + self, + N_Y: int = 12, + N_UV: int = 6, + C: int = 16, + C1_Y: int = 64, + C1_UV: int = 48, + C21: int = 16, + output_channels_y: int = 4, + output_channels_uv: int = 2, + ): + super().__init__() + + self.split_y_path = nn.Sequential( + Conv(C, C1_Y, kernel_size=1, post_activation=None), + *[NewResBlock_separate_prelu(C, C1_Y, C21) for _ in range(N_Y)], + Conv(C1_Y, C, kernel_size=1, post_activation=None), + Conv( + C, + C, + kernel_size=3, + is_separable=True, + is_horizontal=True, + hidden_separable_channels=C21, + post_activation=None, + ), + Conv( + C, + C, + kernel_size=3, + is_separable=True, + is_horizontal=False, + hidden_separable_channels=C21, + post_activation=None, + ), + Conv(C, C, kernel_size=1), + Conv(C, output_channels_y, kernel_size=3, post_activation=None), + ) + + self.split_uv_path = nn.Sequential( + Conv(C, C1_UV, kernel_size=1, post_activation=None), + *[NewResBlock_separate_prelu(C, C1_UV, C21) for _ in range(N_UV)], + Conv(C1_UV, C, kernel_size=1, post_activation=None), + Conv( + C, + C, + kernel_size=3, + is_separable=True, + is_horizontal=True, + hidden_separable_channels=C21, + post_activation=None, + ), + Conv( + C, + C, + kernel_size=3, + is_separable=True, + is_horizontal=False, + hidden_separable_channels=C21, + post_activation=None, + ), + Conv(C, C, kernel_size=1), + Conv(C, output_channels_uv, kernel_size=3, post_activation=None), + ) + + self.Cy = C + self.Cuv = C + + def forward(self, x: torch.Tensor) -> torch.Tensor: + split_y_input = x[:, : self.Cy, :, :] + split_uv_input = x[:, self.Cy : self.Cy + self.Cuv, :, :] + + y_output = self.split_y_path.forward(split_y_input) + uv_output = self.split_uv_path.forward(split_uv_input) + return torch.cat((y_output, uv_output), dim=1) + + +class SADLNet(nn.Sequential): + """The network used during SADL inference""" + + def __init__( + self, + input_channels: Iterable[int] = [3, 3, 3, 1, 1, 1], + input_kernels: Iterable[int] = [3, 3, 3, 1, 1, 3], + D1: int = 12, + D2: int = 8, + D3: int = 4, + D4: int = 2, + D5: int = 2, + D6: int = 24, + N_Y: int = 12, + N_UV: int = 6, + C: int = 16, + C1_Y: int = 64, + C1_UV: int = 48, + C21: int = 16, + output_channels_y: int = 4, + output_channels_uv: int = 2, + ): + """ + Args: + input_channels: the number of channels expected for each input + input_kernels: the kernel size for each input convolution + output_channels: the number of output channels + """ + self.input_channels = input_channels + self.input_kernels = input_kernels + self.input_features = [D1, D2, D3, D4, D4, D5] + + super(SADLNet, self).__init__( + MultiBranchModule( + *[ + Conv(c, d, kernel_size=k, post_activation=None) + for c, d, k in zip( + self.input_channels, self.input_features, self.input_kernels + ) + ] + ), + Conv(sum(self.input_features), D6, kernel_size=1), + Conv( + D6, + D6, + kernel_size=3, + stride=2, + post_activation=None, + is_separable=True, + is_horizontal=True, + hidden_separable_channels=D6, + ), + Conv( + D6, + D6, + kernel_size=3, + stride=2, + post_activation=None, + is_separable=True, + is_horizontal=False, + hidden_separable_channels=D6, + ), + Conv(D6, C + C, kernel_size=1), + SplitLumaChromaBlocks( + N_Y, N_UV, C, C1_Y, C1_UV, C21, output_channels_y, output_channels_uv + ), + ) + + def get_example_inputs( + self, patch_size: Union[int, Tuple[int, int]] = 144, batch_size: int = 1 + ): + patch_size = ( + (patch_size, patch_size) if isinstance(patch_size, int) else patch_size + ) + return [ + torch.rand( + batch_size, conv.in_channels, *patch_size, device=conv[0].weight.device + ) + for conv in self[0].branches + ] + + def to_onnx( + self, + filename: str, + patch_size: int = 144, + batch_size: int = 1, + opset: int = 10, + **kwargs, + ) -> None: + mode = self.training + self.eval() + torch.onnx.export( + self, + self.get_example_inputs(patch_size, batch_size), + filename, + opset_version=opset, + **kwargs, + ) + self.train(mode) + + +class Net(nn.Module): + """Wrapper for SADL model that implements input pre- and post-processing for training.""" + + def __init__( + self, + input_channels: Iterable[Iterable[str]] = [ + ["rec_before_dbf_Y", "rec_before_dbf_U", "rec_before_dbf_V"], + ["pred_Y", "pred_U", "pred_V"], + ["bs_Y", "bs_U", "bs_V"], + ["qp_base"], + ["qp_slice"], + ["ipb_Y"], + ], + input_kernels: Iterable[int] = [3, 3, 1, 1, 1, 1], + D1: int = 16, + D2: int = 8, + D3: int = 4, + D4: int = 2, + D5: int = 2, + D6: int = 64, + N_Y: int = 14, + N_UV: int = 4, + C: int = 32, + C1_Y: int = 144, + C1_UV: int = 128, + C21: int = 32, + dct_ch: int = 4, + path: str = None, + ): + super(Net, self).__init__() + assert len(input_channels) == len( + input_kernels + ), "[ERROR] input size and kernels size not equal" + self.input_channels = input_channels + sizes = [ + dct_ch + dct_ch // 2, + dct_ch + dct_ch // 2, + dct_ch + dct_ch // 2, + 1, + 1, + dct_ch, + ] + self.SADL_model = SADLNet( + sizes, + input_kernels, + D1, + D2, + D3, + D4, + D5, + D6, + N_Y, + N_UV, + C, + C1_Y, + C1_UV, + C21, + 4 * dct_ch, + 2 * dct_ch, + ) + self.chroma_upsampler = nn.Upsample(scale_factor=2, mode="nearest") + self.dct_ch = dct_ch + + def preprocess_args( + self, batch: Dict[str, torch.Tensor] + ) -> Dict[str, torch.Tensor]: + return [ + torch.cat([batch[name] for name in input_], dim=1) + for input_ in self.input_channels + ] + + def postprocess_outputs( + self, batch: Dict[str, torch.Tensor], out: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + Y_res, UV_res = out.split([4 * self.dct_ch, 2 * self.dct_ch], dim=1) + return ( + F.pixel_shuffle(Y_res, 2), + UV_res, + ) + + def forward( + self, batch: Dict[str, torch.Tensor] + ) -> Tuple[torch.Tensor, torch.Tensor]: + args = self.preprocess_args(batch) + out = self.SADL_model(args) + return self.postprocess_outputs(batch, out) diff --git a/training/training_scripts/NN_Adaptive_Filtering/models/model_lop3_with_multiplier.py b/training/training_scripts/NN_Adaptive_Filtering/models/model_lop3_with_multiplier.py new file mode 100644 index 0000000000000000000000000000000000000000..5df004077d9ad60864d8ce69061e4e771438676d --- /dev/null +++ b/training/training_scripts/NN_Adaptive_Filtering/models/model_lop3_with_multiplier.py @@ -0,0 +1,539 @@ +""" +/* The copyright in this software is being made available under the BSD +* License, included below. This software may be subject to other third party +* and contributor rights, including patent rights, and no such rights are +* granted under this license. +* +* Copyright (c) 2010-2024, ITU/ISO/IEC +* All rights reserved. +* +* Redistribution and use in source and binary forms, with or without +* modification, are permitted provided that the following conditions are met: +* +* * Redistributions of source code must retain the above copyright notice, +* this list of conditions and the following disclaimer. +* * Redistributions 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. +* * Neither the name of the ITU/ISO/IEC nor the names of its contributors may +* 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 COPYRIGHT HOLDER 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. +""" + +from typing import Dict, Iterable, List, Optional, Tuple, Type, Union + +import torch +from torch import nn +from torch.nn import functional as F + +from models.model_lop2_with_multiplier import Multiplier +from models.model_lop3 import FastDCT +from util import DEVICE + + +class Conv(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: Union[int, Tuple[int, int]], + stride: Union[int, Tuple[int, int]] = 1, + padding: Optional[Union[int, Tuple[int, int]]] = None, + is_separable: bool = False, + is_horizontal: bool = True, + hidden_separable_channels: Optional[int] = None, + post_activation: Optional[Type] = nn.PReLU, + mul: bool = False, + **kwargs, + ): + """ + Args: + in_channels: the number of input channels + out_channels: the number of output channels + kernel_size: the convolution's kernel size + stride: the convolution's stride(s) + padding: the convolution's padding + is_separable: whether to implement convolution separably + hidden_separable_channels: If is_separable, the number of hidden channels between convolutions. If None, use out_channels + post_activation: activation function to use after convolution. If None, no activation after convolution + **kwargs: additional kwargs to pass to nn.Conv2d + """ + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = ( + (kernel_size, kernel_size) if isinstance(kernel_size, int) else kernel_size + ) + self.stride = (stride, stride) if isinstance(stride, int) else stride + if padding is not None: + self.padding = (padding, padding) if isinstance(padding, int) else padding + else: + self.padding = tuple([k // 2 for k in self.kernel_size]) + self.is_separable = is_separable + self.is_horizontal = is_horizontal + self.mul = mul + + if self.is_separable: + self.hidden_separable_channels = hidden_separable_channels or out_channels + if self.is_horizontal: + self.sep_conv1 = nn.Conv2d( + self.in_channels, + self.hidden_separable_channels, + (self.kernel_size[0], 1), + (self.stride[0], 1), + (self.padding[0], 0), + groups=self.hidden_separable_channels, + **kwargs, + ) + else: + self.sep_conv1 = nn.Conv2d( + self.hidden_separable_channels, + self.out_channels, + (1, self.kernel_size[1]), + (1, self.stride[1]), + (0, self.padding[1]), + groups=self.hidden_separable_channels, + **kwargs, + ) + if self.mul: + self.sep_mul1 = Multiplier(self.hidden_separable_channels) + else: + self.nonsep_conv = nn.Conv2d( + self.in_channels, + self.out_channels, + self.kernel_size, + self.stride, + self.padding, + **kwargs, + ) + if self.mul: + self.nonsep_mul = Multiplier(self.out_channels) + + self.post_activation = None if post_activation is None else post_activation() + + def forward( + self, x: torch.Tensor, activate_multiplier: torch.Tensor = None + ) -> torch.Tensor: + if self.is_separable: + y = self.sep_conv1(x) + if self.mul and activate_multiplier is not None: + m = self.sep_mul1(y) + y = torch.where(activate_multiplier, m, y) + else: + y = self.nonsep_conv(x) + if self.mul and activate_multiplier is not None: + m = self.nonsep_mul(y) + y = torch.where(activate_multiplier, m, y) + if self.post_activation is not None: + y = self.post_activation(y) + + return y + + +class MultiBranchModule(nn.Module): + """A module representing multple, parallel branches. If the input is a list, each element in the list is fed into the corresponding branch, + otherwise the input is fed into every branch. The outputs of each branch are then merged. + """ + + def __init__(self, *branch_modules, merge_dimension: int = -3): + """ + Args: + branch_modules: modules to run in parallel + merge_dimension: the dimension to merge outputs from each branch + """ + super().__init__() + self.branches = nn.ModuleList(branch_modules) + self.merge_dimension = merge_dimension + + def forward(self, args: Union[torch.Tensor, List[torch.Tensor]]) -> torch.Tensor: + inputs = args if isinstance(args, list) else len(self.branches) * [args] + branch_outputs = [branch(input) for branch, input in zip(self.branches, inputs)] + return torch.cat(branch_outputs, dim=self.merge_dimension) + + +class NewResBlock_separate_prelu(nn.Module): + def __init__(self, C: int = 64, C1: int = 160, C21: int = 32, mul: bool = False): + super().__init__() + self.prelu = nn.PReLU() + self.conv1_11 = Conv(C1, C, kernel_size=1, post_activation=None, mul=mul) + self.conv2_13 = Conv( + C, + C, + kernel_size=3, + post_activation=None, + is_separable=True, + is_horizontal=True, + hidden_separable_channels=C21, + mul=mul, + ) + self.conv3_31 = Conv( + C, + C, + kernel_size=3, + post_activation=None, + is_separable=True, + is_horizontal=False, + hidden_separable_channels=C21, + mul=mul, + ) + self.conv4_11 = Conv(C, C1, kernel_size=1, post_activation=None, mul=mul) + + def forward( + self, x: torch.Tensor, activate_multiplier: torch.Tensor + ) -> torch.Tensor: + temp = x + x1 = self.prelu(x) + x2 = self.conv1_11(x1, activate_multiplier) + x3 = self.conv2_13(x2, activate_multiplier) + x4 = self.conv3_31(x3, activate_multiplier) + x5 = self.conv4_11(x4, activate_multiplier) + return x5 + temp + + +class SplitLumaChromaBlocks(nn.Module): + def __init__( + self, + N_Y: int = 12, + N_UV: int = 6, + C: int = 16, + C1_Y: int = 64, + C1_UV: int = 48, + C21: int = 16, + output_channels_y: int = 4, + output_channels_uv: int = 2, + ): + super().__init__() + + self.split_y_path = nn.ModuleList( + m + for m in [ + Conv(C, C1_Y, kernel_size=1, post_activation=None, mul=True), + *[NewResBlock_separate_prelu(C, C1_Y, C21, True) for _ in range(N_Y)], + Conv(C1_Y, C, kernel_size=1, post_activation=None, mul=True), + Conv( + C, + C, + kernel_size=3, + is_separable=True, + is_horizontal=True, + hidden_separable_channels=C21, + post_activation=None, + mul=True, + ), + Conv( + C, + C, + kernel_size=3, + is_separable=True, + is_horizontal=False, + hidden_separable_channels=C21, + post_activation=None, + mul=True, + ), + Conv(C, C, kernel_size=1, mul=True), + Conv( + C, output_channels_y, kernel_size=3, post_activation=None, mul=True + ), + ] + ) + + self.split_uv_path = nn.ModuleList( + m + for m in [ + Conv(C, C1_UV, kernel_size=1, post_activation=None, mul=True), + *[NewResBlock_separate_prelu(C, C1_UV, C21, True) for _ in range(N_UV)], + Conv(C1_UV, C, kernel_size=1, post_activation=None, mul=True), + Conv( + C, + C, + kernel_size=3, + is_separable=True, + is_horizontal=True, + hidden_separable_channels=C21, + post_activation=None, + mul=True, + ), + Conv( + C, + C, + kernel_size=3, + is_separable=True, + is_horizontal=False, + hidden_separable_channels=C21, + post_activation=None, + mul=True, + ), + Conv(C, C, kernel_size=1, mul=True), + Conv( + C, output_channels_uv, kernel_size=3, post_activation=None, mul=True + ), + ] + ) + + self.Cy = C + self.Cuv = C + + def forward( + self, x: torch.Tensor, activate_multiplier: torch.Tensor = None + ) -> torch.Tensor: + split_y_input = x[:, : self.Cy, :, :] + split_uv_input = x[:, self.Cy : self.Cy + self.Cuv, :, :] + + y_output = split_y_input + for m in self.split_y_path: + y_output = m(y_output, activate_multiplier) + + uv_output = split_uv_input + for m in self.split_uv_path: + uv_output = m(uv_output, activate_multiplier) + + return torch.cat((y_output, uv_output), dim=1) + + +class SADLNet(nn.Module): + """The network used during SADL inference""" + + def __init__( + self, + input_channels: Iterable[int] = [3, 3, 3, 1, 1, 1], + input_kernels: Iterable[int] = [3, 3, 3, 1, 1, 3], + D1: int = 12, + D2: int = 8, + D3: int = 4, + D4: int = 2, + D5: int = 2, + D6: int = 24, + N_Y: int = 12, + N_UV: int = 6, + C: int = 16, + C1_Y: int = 64, + C1_UV: int = 48, + C21: int = 16, + output_channels_y: int = 4, + output_channels_uv: int = 2, + ): + """ + Args: + input_channels: the number of channels expected for each input + input_kernels: the kernel size for each input convolution + output_channels: the number of output channels + """ + super().__init__() + self.input_channels = input_channels + self.input_kernels = input_kernels + self.input_features = [D1, D2, D3, D4, D4, D5] + + self.multi_branch = MultiBranchModule( + *[ + Conv(c, d, kernel_size=k, post_activation=None) + for c, d, k in zip( + self.input_channels, self.input_features, self.input_kernels + ) + ] + ) + self.conv1 = Conv(sum(self.input_features), D6, kernel_size=1) + self.conv2 = Conv( + D6, + D6, + kernel_size=3, + stride=2, + post_activation=None, + is_separable=True, + is_horizontal=True, + hidden_separable_channels=D6, + ) + self.conv3 = Conv( + D6, + D6, + kernel_size=3, + stride=2, + post_activation=None, + is_separable=True, + is_horizontal=False, + hidden_separable_channels=D6, + ) + self.conv4 = Conv(D6, C + C, kernel_size=1) + self.split = SplitLumaChromaBlocks( + N_Y, N_UV, C, C1_Y, C1_UV, C21, output_channels_y, output_channels_uv + ) + + def forward(self, inputs: torch.Tensor) -> torch.Tensor: + y = self.multi_branch(inputs[:-2]) + y = self.conv1(y) + y = self.conv2(y) + y = self.conv3(y) + y = self.conv4(y) + activate_multipliers = torch.eq(inputs[-2], inputs[-1]) + y = self.split(y, activate_multipliers) + return y + + def get_example_inputs( + self, patch_size: Union[int, Tuple[int, int]] = 144, batch_size: int = 1 + ): + patch_size = ( + (patch_size, patch_size) if isinstance(patch_size, int) else patch_size + ) + x = [ + torch.rand( + batch_size, + conv.in_channels, + *patch_size, + device=conv.nonsep_conv.weight.device, + ) + for conv in self.multi_branch.branches + ] + x.append(torch.ones(1)) + x.append(torch.ones(1)) + return x + + def to_onnx( + self, filename: str, patch_size: int = 144, batch_size: int = 1, **kwargs + ) -> None: + mode = self.training + self.eval() + torch.onnx.export( + self, + self.get_example_inputs(patch_size, batch_size), + filename, + **kwargs, + ) + self.train(mode) + + +class Net(nn.Module): + """Wrapper for SADL model that implements input pre- and post-processing for training.""" + + def __init__( + self, + input_channels: Iterable[Iterable[str]] = [ + ["rec_before_dbf_Y", "rec_before_dbf_U", "rec_before_dbf_V"], + ["pred_Y", "pred_U", "pred_V"], + ["bs_Y", "bs_U", "bs_V"], + ["qp_base"], + ["qp_slice"], + ["ipb_Y"], + ], + input_kernels: Iterable[int] = [3, 3, 1, 1, 1, 1], + D1: int = 16, + D2: int = 8, + D3: int = 4, + D4: int = 2, + D5: int = 2, + D6: int = 64, + N_Y: int = 14, + N_UV: int = 4, + C: int = 32, + C1_Y: int = 144, + C1_UV: int = 128, + C21: int = 32, + dct_ch: int = 4, + ): + super(Net, self).__init__() + assert len(input_channels) == len( + input_kernels + ), "[ERROR] input size and kernels size not equal" + self.input_channels = input_channels + sizes = [ + dct_ch + dct_ch // 2, + dct_ch + dct_ch // 2, + dct_ch + dct_ch // 2, + 1, + 1, + dct_ch, + ] + self.SADL_model = SADLNet( + sizes, + input_kernels, + D1, + D2, + D3, + D4, + D5, + D6, + N_Y, + N_UV, + C, + C1_Y, + C1_UV, + C21, + 4 * dct_ch, + 2 * dct_ch, + ) + self.chroma_upsampler = nn.Upsample(scale_factor=2, mode="nearest") + self.dct_ch = dct_ch + self.dct_size = 2 + self.fast_dct = FastDCT(self.dct_size, DEVICE) + + def preprocess_args(self, batch: Dict[str, torch.Tensor]) -> List[torch.Tensor]: + for name, data in batch.items(): + if "U" in name or "V" in name: + batch[name] = self.chroma_upsampler(batch[name]) + + sample_dct = {} + for name, tensor in batch.items(): + if "qp" in name: + yy = tensor[:, 0, 0, 0] + dim = tensor.shape + yy = ( + yy.unsqueeze(1) + .unsqueeze(1) + .unsqueeze(1) + .expand( + (dim[0], 1, dim[2] // self.dct_size, dim[3] // self.dct_size) + ) + ) + elif "_Y" in name: + yy = self.fast_dct.blockDCT_ps(tensor) + elif "_U" in name or "_V" in name: + yy = self.fast_dct.blockDCT_ps_halfsize(tensor[:, :, ::2, ::2]) + else: + raise ValueError("Unknown input name", name, " in applyDCT2().") + sample_dct[name] = yy + return [ + torch.cat([sample_dct[name] for name in input_], dim=1) + for input_ in self.input_channels + ] + + def postprocess_outputs( + self, batch: Dict[str, torch.Tensor], out: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + Y_res, UV_res = out.split([4 * self.dct_ch, 2 * self.dct_ch], dim=1) + Y_res, UV_res = ( + F.pixel_shuffle(Y_res, 2), + UV_res, + ) + + Y_res = self.fast_dct.iblockDCT_ps(Y_res) + UV_res = self.fast_dct.iblockDCT_per_chUV(UV_res) + + return ( + batch["rec_before_dbf_Y"] + Y_res, + torch.cat((batch["rec_before_dbf_U"], batch["rec_before_dbf_V"]), dim=1)[ + ..., ::2, ::2 + ] + + UV_res, + ) + + def forward( + self, + batch: Dict[str, torch.Tensor], + equal_left_side: torch.Tensor, + equal_right_side: torch.Tensor, + ) -> Tuple[torch.Tensor, torch.Tensor]: + args = self.preprocess_args(batch) + args.append(equal_left_side) + args.append(equal_right_side) + out = self.SADL_model(args) + return self.postprocess_outputs(batch, out) diff --git a/training/training_scripts/NN_Adaptive_Filtering/models/model_lop2_nnr.py b/training/training_scripts/NN_Adaptive_Filtering/models/model_nnr.py similarity index 95% rename from training/training_scripts/NN_Adaptive_Filtering/models/model_lop2_nnr.py rename to training/training_scripts/NN_Adaptive_Filtering/models/model_nnr.py index aafefb2f50b7d7d00d25446fa12d0f03c8c9e3b5..913b0e316c1b3122bf8df0334fdb5d238be2f84c 100644 --- a/training/training_scripts/NN_Adaptive_Filtering/models/model_lop2_nnr.py +++ b/training/training_scripts/NN_Adaptive_Filtering/models/model_nnr.py @@ -42,7 +42,11 @@ import nnc_core import torch import config -from models import load_torch_model, model_lop2_with_multiplier +from models import ( + load_torch_model, + model_lop2_with_multiplier, + model_lop3_with_multiplier, +) from util import Colour from util.metrics import compute_loss_psnr, mse_loss @@ -65,9 +69,16 @@ def shorten_multiplier_name(in_var: str) -> str: class PytorchModel(nnc_core.nnr_model.NNRModel): def __init__( - self, model_config: Union[Path, Dict], block_size: int, border_size: int + self, + arch: str, + model_config: Union[Path, Dict], + block_size: int, + border_size: int, ) -> None: - self.model = load_torch_model(model_config, model_lop2_with_multiplier) + if arch in ["lop2", "vlop"]: + self.model = load_torch_model(model_config, model_lop2_with_multiplier) + elif arch in ["lop3"]: + self.model = load_torch_model(model_config, model_lop3_with_multiplier) self.__model_info = None self.__model_parameters = None self.dataset = None diff --git a/training/training_scripts/NN_Adaptive_Filtering/models/models.json b/training/training_scripts/NN_Adaptive_Filtering/models/models.json index 8afb5299ce24929cad0e3b09a975203456feecb4..8015ddbf57560359130b5beab50a6c6898b0a35c 100755 --- a/training/training_scripts/NN_Adaptive_Filtering/models/models.json +++ b/training/training_scripts/NN_Adaptive_Filtering/models/models.json @@ -45,7 +45,108 @@ "C" : 16, "C1_Y" : 64, "C1_UV" : 32, - "C21" : 16 + "C21" : 16, + "Conv2_is_separable": 0, + "Conv2_hidden_separable_channels": "None" + } + }, + "vlop": + { + "model": + { + "class" : "Net", + "input_channels" : [ + [ + "rec_before_dbf_Y", + "rec_before_dbf_U", + "rec_before_dbf_V" + ], + [ + "pred_Y", + "pred_U", + "pred_V" + ], + [ + "bs_Y", + "bs_U", + "bs_V" + ], + [ "qp_base" ], + [ "qp_slice" ], + [ "ipb_Y" ] + ], + "input_kernels" : [ + 3, + 3, + 1, + 1, + 1, + 1 + ], + "D1" : 4, + "D2" : 2, + "D3" : 1, + "D4" : 1, + "D5" : 1, + "D6" : 16, + "N_Y" : 8, + "N_UV" : 1, + "C" : 16, + "C1_Y" : 32, + "C1_UV" : 16, + "C21" : 16, + "Conv2_is_separable": 1, + "Conv2_hidden_separable_channels": 16 + } + }, + "lop3": + { + "model" : + { + "class" : "Net", + "input_channels" : + [ + [ + "rec_before_dbf_Y", + "rec_before_dbf_U", + "rec_before_dbf_V" + ], + [ + "pred_Y", + "pred_U", + "pred_V" + ], + [ + "bs_Y", + "bs_U", + "bs_V" + ], + [ "qp_base" ], + [ "qp_slice" ], + [ "ipb_Y" ] + ], + "input_kernels" : + [ + 3, + 3, + 1, + 1, + 1, + 1 + ], + "D1": 16, + "D2": 8, + "D3": 4, + "D4": 2, + "D5": 2, + "D6": 64, + "N_Y": 14, + "N_UV": 4, + "C": 32, + "C1_Y": 144, + "C1_UV": 128, + "C21": 32, + "dct_ch": 4 } } } diff --git a/training/training_scripts/NN_Adaptive_Filtering/overfitting_pipeline.py b/training/training_scripts/NN_Adaptive_Filtering/overfitting_pipeline.py index 293dd18380130f04d081c44aaa35d288a66c5f30..d3f2093b1bb0d4c9579a6d0666e48cc0221c7750 100644 --- a/training/training_scripts/NN_Adaptive_Filtering/overfitting_pipeline.py +++ b/training/training_scripts/NN_Adaptive_Filtering/overfitting_pipeline.py @@ -275,7 +275,7 @@ def run_pipeline( assert block_size % 2 == 0, "The block size must be multiple of 2" assert border_size % 2 == 0, "The border size must be multiple of 2" - if "lop2" != arch: + if arch not in ["lop2", "lop3", "vlop"]: print(f"{arch} architecture not supported") sys.exit(-1) @@ -350,6 +350,7 @@ def run_pipeline( print("Size of training data: ", num_patches) nn_filter = NnFilter( + arch, model_config, lr, lr_patience, @@ -371,6 +372,7 @@ def run_pipeline( # NNR Compression compress_one_model_and_2sadl( + arch, output_dir, base_nn_params, model_config, diff --git a/training/training_scripts/NN_Adaptive_Filtering/resources/config.json b/training/training_scripts/NN_Adaptive_Filtering/resources/config.json index a65c6bdefbe5a37ec270a8b652e241f07c2dbd33..d81c184ebdef2bd903812c13b864a6c51029368f 100644 --- a/training/training_scripts/NN_Adaptive_Filtering/resources/config.json +++ b/training/training_scripts/NN_Adaptive_Filtering/resources/config.json @@ -1,36 +1,108 @@ { - "architecture": "lop2", - "sadl2torch": { - "base_model_sadl_i": "/src/models/nnlf_lop2_model_int16.sadl", - "base_model_torch": "resources/nnlf_lop2_model.pt", - "base_model_quantizers": "resources/nnlf_lop2_quantizers.json", - "base_model_params_to_new": "resources/nnlf_lop2_params_to_new_model.json" + "architecture": ["lop2", "lop3", "vlop"], + "lop2": { + "sadl2torch": { + "base_model_sadl_i": "/src/models/nnlf_lop2_model_int16.sadl", + "base_model_torch": "resources/nnlf_lop2_model.pt", + "base_model_quantizers": "resources/nnlf_lop2_quantizers.json", + "base_model_params_to_new": "resources/nnlf_lop2_params_to_new_model.json" + }, + "decoder_bin": "/path/to/decoder/bin", + "intra_filters": "/path/to/intra/filters", + "dataset": { + "orig_path": "/path/to/dataset/orig", + "deco_path": "/path/to/dataset/deco", + "bit-depth": 10, + "data_cfg": "resources/datasets/jvet.json" + }, + "nnvc": { + "log": "log_dec.txt", + "bs": "bs.yuv", + "rec": "rec_before_dbf.yuv", + "pred": "pred.yuv", + "ipb": "bpm.yuv" + }, + "training": { + "output_path": "/path/to/output", + "num_models": 1, + "batch-size": 64, + "block-size": 128, + "pad-size": 8, + "min-tid": 0, + "num_layers": 106, + "slice-type": "*", + "input-keys": ["rec_before_dbf_Y", "rec_before_dbf_U", "rec_before_dbf_V", "pred_Y", "pred_U", "pred_V", "bs_Y", "bs_U", "bs_V", "ipb_Y"], + "label-keys": ["orig_Y", "orig_U", "orig_V", "mask_Y", "mask_U", "mask_V"] + } }, - "decoder_bin": "/path/to/decoder/bin", - "intra_filters": "/path/to/intra/filters", - "dataset": { - "orig_path": "/path/to/dataset/orig", - "deco_path": "/path/to/dataset/deco", - "bit-depth": 10, - "data_cfg": "resources/datasets/jvet.json" + "lop3": { + "sadl2torch": { + "base_model_sadl_i": "/src/models/nnlf_lop3_model_int16.sadl", + "base_model_torch": "resources/nnlf_lop3_model.pt", + "base_model_quantizers": "resources/nnlf_lop3_quantizers.json", + "base_model_params_to_new": "resources/nnlf_lop3_params_to_new_model.json" + }, + "decoder_bin": "/path/to/decoder/bin", + "intra_filters": "/path/to/intra/filters", + "dataset": { + "orig_path": "/path/to/dataset/orig", + "deco_path": "/path/to/dataset/deco", + "bit-depth": 10, + "data_cfg": "resources/datasets/jvet.json" + }, + "nnvc": { + "log": "log_dec.txt", + "bs": "bs.yuv", + "rec": "rec_before_dbf.yuv", + "pred": "pred.yuv", + "ipb": "bpm.yuv" + }, + "training": { + "output_path": "/path/to/output", + "num_models": 1, + "batch-size": 64, + "block-size": 128, + "pad-size": 8, + "min-tid": 0, + "num_layers": 84, + "slice-type": "*", + "input-keys": ["rec_before_dbf_Y", "rec_before_dbf_U", "rec_before_dbf_V", "pred_Y", "pred_U", "pred_V", "bs_Y", "bs_U", "bs_V", "ipb_Y"], + "label-keys": ["orig_Y", "orig_U", "orig_V", "mask_Y", "mask_U", "mask_V"] + } }, - "nnvc": { - "log": "log_dec.txt", - "bs": "bs.yuv", - "rec": "rec_before_dbf.yuv", - "pred": "pred.yuv", - "ipb": "bpm.yuv" - }, - "training": { - "output_path": "/path/to/output", - "num_models": 1, - "batch-size": 64, - "block-size": 128, - "pad-size": 8, - "min-tid": 0, - "num_layers": 106, - "slice-type": "*", - "input-keys": ["rec_before_dbf_Y", "rec_before_dbf_U", "rec_before_dbf_V", "pred_Y", "pred_U", "pred_V", "bs_Y", "bs_U", "bs_V", "ipb_Y"], - "label-keys": ["orig_Y", "orig_U", "orig_V", "mask_Y", "mask_U", "mask_V"] - } + "vlop": { + "sadl2torch": { + "base_model_sadl_i": "resources/nnlf_vlop_model_int16.sadl", + "base_model_torch": "resources/nnlf_vlop_model.pt", + "base_model_quantizers": "resources/nnlf_vlop_quantizers.json", + "base_model_params_to_new": "resources/nnlf_vlop_params_to_new_model.json" + }, + "decoder_bin": "/path/to/decoder/bin", + "intra_filters": "/path/to/intra/filters", + "dataset": { + "orig_path": "/path/to/dataset/orig", + "deco_path": "/path/to/dataset/deco", + "bit-depth": 10, + "data_cfg": "resources/datasets/jvet.json" + }, + "nnvc": { + "log": "log_dec.txt", + "bs": "bs.yuv", + "rec": "rec_before_dbf.yuv", + "pred": "pred.yuv", + "ipb": "bpm.yuv" + }, + "training": { + "output_path": "/path/to/output", + "num_models": 1, + "batch-size": 64, + "block-size": 128, + "pad-size": 8, + "min-tid": 0, + "num_layers": 53, + "slice-type": "*", + "input-keys": ["rec_before_dbf_Y", "rec_before_dbf_U", "rec_before_dbf_V", "pred_Y", "pred_U", "pred_V", "bs_Y", "bs_U", "bs_V", "ipb_Y"], + "label-keys": ["orig_Y", "orig_U", "orig_V", "mask_Y", "mask_U", "mask_V"] + } + } } diff --git a/training/training_scripts/NN_Adaptive_Filtering/segment_on_off.py b/training/training_scripts/NN_Adaptive_Filtering/segment_on_off.py index 686a3c27c706b58ff1964c707f3f40eea0d27bb4..2a4fd56635121bd0e677794c22a7a05dc4d9bc14 100644 --- a/training/training_scripts/NN_Adaptive_Filtering/segment_on_off.py +++ b/training/training_scripts/NN_Adaptive_Filtering/segment_on_off.py @@ -193,7 +193,7 @@ def create_parcat_script(df: pd.DataFrame) -> None: output_stem = output_dir / f"{curr_seq}_{curr_qp}" sequence_stream.write( - f"$DECODER -b {output_bitstream} -o /dev/null --PrefixAbsolutePathsToGraphsOutput={intra_models_dir} --NnlfModelName={lop2_model} --NnfuOutputFileStem={output_stem} --NnlfTransInput=0 > {log_file}" + f"$DECODER -b {output_bitstream} -o /dev/null --PrefixAbsolutePathsToGraphsOutput={intra_models_dir} --NnlfModelName={model_path} --NnfuOutputFileStem={output_stem} --NnlfArch={arch} > {log_file}" ) # Adding cluster params and calling script to decode single merged bitstream @@ -291,7 +291,10 @@ def parse_arguments(): help="Path to NN intra pred models dir", ) parser.add_argument( - "--lop2_model", type=str, required=True, help="Path to LOP2 model" + "--arch", type=str, required=True, help="Architecture of SADL model (lowercase)" + ) + parser.add_argument( + "--model_path", type=str, required=True, help="Path to SADL model" ) parser.add_argument( "--output_dir", type=str, required=True, help="Output directory" @@ -312,7 +315,8 @@ if __name__ == "__main__": decoder_bin = check_file(args.decoder_bin) intra_models_dir = check_directory(args.intra_models_dir) - lop2_model = check_file(args.lop2_model) + model_path = check_file(args.model_path) + arch = args.arch output_dir = create_directory(args.output_dir) diff --git a/training/training_scripts/NN_Adaptive_Filtering/trainer/nn_filter.py b/training/training_scripts/NN_Adaptive_Filtering/trainer/nn_filter.py index 34f2455bd721a8214b65c54bc848a38a88a34fc7..fb54f660aec00955448bf6c718becc0812adbe27 100644 --- a/training/training_scripts/NN_Adaptive_Filtering/trainer/nn_filter.py +++ b/training/training_scripts/NN_Adaptive_Filtering/trainer/nn_filter.py @@ -39,8 +39,11 @@ import numpy as np import torch from torch import Tensor, nn -import models.model_lop2_with_multiplier as model_with_multiplier -from models import load_torch_model +from models import ( + load_torch_model, + model_lop2_with_multiplier, + model_lop3_with_multiplier, +) from util import DEVICE, Colour from util.file_system import check_file from util.logger import Logger @@ -50,6 +53,7 @@ from util.metrics import LOSS_OP, compute_loss_psnr, zero_out_negative class NnFilter: def __init__( self, + arch: str, model_config: Dict, lr: float, lr_patience: int, @@ -75,12 +79,20 @@ class NnFilter: self._y_border_size = border_size self._uv_border_size = border_size // 2 - self._model = nn.DataParallel( - load_torch_model(model_config, model_with_multiplier) - ).to(DEVICE) - self._base_model = nn.DataParallel( - load_torch_model(model_config, model_with_multiplier) - ).to(DEVICE) + if arch in ["lop2"]: + self._model = nn.DataParallel( + load_torch_model(model_config, model_lop2_with_multiplier) + ).to(DEVICE) + self._base_model = nn.DataParallel( + load_torch_model(model_config, model_lop2_with_multiplier) + ).to(DEVICE) + elif arch in ["lop3"]: + self._model = nn.DataParallel( + load_torch_model(model_config, model_lop3_with_multiplier) + ).to(DEVICE) + self._base_model = nn.DataParallel( + load_torch_model(model_config, model_lop3_with_multiplier) + ).to(DEVICE) self._logger = Logger(output_dir, stop_patience) self._trainable_parameter_names = self._get_trainable_parameter_names(nn_params) @@ -98,8 +110,11 @@ class NnFilter: self._load_models(base_nn_params, nn_params, optimiser_params, scheduler_params) def _get_trainable_parameter_names(self, nn_params): + param_names = [] + # try to load from txt file, stored after online selection - param_names = self._logger.load_param_names() + if nn_params: + param_names = self._logger.load_param_names() # if not store parameter names before, then param can be overfitted when multipliers are different from initialized values if len(param_names) == 0 and nn_params: @@ -241,7 +256,8 @@ class NnFilter: var_names_luma = [] var_names_chroma = [] for (org_name, org_param), (new_name, new_param) in zip( - self._base_model.named_parameters(), self._model.named_parameters() + self._base_model.module.named_parameters(), + self._model.module.named_parameters(), ): assert org_name == new_name, "The models have different variables" num_ele = torch.numel(org_param) @@ -269,7 +285,7 @@ class NnFilter: # reset model self._model.module.load_state_dict(self._base_model.module.state_dict()) self._trainable_parameters = [] - for name, param in self._model.named_parameters(): + for name, param in self._model.module.named_parameters(): # multiplier if name in var_names: self._trainable_parameters.append(param) @@ -392,8 +408,8 @@ class NnFilter: ): print(f"Starting at epoch {start_epoch}...") self._model.train(True) - if self._select_layer: - select_epoch = max(1, int(max_epochs * 0.1)) if self._select_layer else -1 + + select_epoch = max(1, int(max_epochs * 0.1)) if self._select_layer else -1 for epoch in range(start_epoch, max_epochs): if self._select_layer and epoch == select_epoch: diff --git a/training/training_scripts/NN_Adaptive_Filtering/util/logger.py b/training/training_scripts/NN_Adaptive_Filtering/util/logger.py index aab5c5e9ddc5980ca0a916b90874f6b4f7f96cee..a7086c9ec64a26145b7517ad2a31f38863fa28ab 100644 --- a/training/training_scripts/NN_Adaptive_Filtering/util/logger.py +++ b/training/training_scripts/NN_Adaptive_Filtering/util/logger.py @@ -195,7 +195,7 @@ class Logger: param_names = [] try: with open(self._checkpoint_dir / "param_names.txt", "r") as file: - param_names = file.readlines() + param_names = file.read().splitlines() except Exception: print("No files with names of trainable parameters") diff --git a/training/training_scripts/NN_Adaptive_Filtering/wu_decoding.py b/training/training_scripts/NN_Adaptive_Filtering/wu_decoding.py index 09c03f6c740281bc390a0a40ee62ce8ad609c974..752c20dcf3f73f44c28b016d90c6ba5d62c31dca 100644 --- a/training/training_scripts/NN_Adaptive_Filtering/wu_decoding.py +++ b/training/training_scripts/NN_Adaptive_Filtering/wu_decoding.py @@ -64,24 +64,26 @@ def parse_arguments() -> argparse.Namespace: def decode_weight_udpate_and_2sadl(): - if "lop2" != args.arch: + if args.arch not in ["lop2", "lop3", "vlop"]: print(f"{args.arch} not supported") - base_model_config = read_json_file("models/models.json")["lop2"] + base_model_config = read_json_file("models/models.json")[args.arch] nnr_bitstream_file = Path(args.nnr_bitstream) with NamedTemporaryFile() as dq_torch_file, NamedTemporaryFile() as reco_torch_file, NamedTemporaryFile() as onnx_file, NamedTemporaryFile() as sadl_f_file, NamedTemporaryFile() as quantiser_file, NamedTemporaryFile() as base_quantizers_file, NamedTemporaryFile() as map_orig_params_to_new_file: dq_model, base_model_quantizers = create_model( - args.base_model, base_model_config + args.base_model, base_model_config, args.arch ) torch.save(dq_model.state_dict(), dq_torch_file.name) write_json_file(base_model_quantizers, base_quantizers_file.name) base_model, base_model_params = get_model_data_info( - dq_torch_file.name, base_model_config, False + args.arch, dq_torch_file.name, base_model_config, False ) - map_orig_params_to_new = map_params(base_model_config) + map_orig_params_to_new = map_params( + base_model_config, dq_torch_file.name, args.arch + ) write_json_file(map_orig_params_to_new, map_orig_params_to_new_file.name) # Decode @@ -105,7 +107,9 @@ def decode_weight_udpate_and_2sadl(): base_model.restore_and_save(restored_params, reco_torch_file.name) # Torch to Onnx - torch_to_onnx(reco_torch_file.name, onnx_file.name, base_model_config) + torch_to_onnx( + args.arch, reco_torch_file.name, onnx_file.name, base_model_config + ) # onnx to SADL float command = ( diff --git a/training/training_scripts/NN_Adaptive_Filtering/wu_encoding.py b/training/training_scripts/NN_Adaptive_Filtering/wu_encoding.py index b107ef1695205ad2f2ee4788f9547d978f885806..04b233cfd580d8f8638f28a39213d96758fb2c3b 100644 --- a/training/training_scripts/NN_Adaptive_Filtering/wu_encoding.py +++ b/training/training_scripts/NN_Adaptive_Filtering/wu_encoding.py @@ -42,7 +42,7 @@ from framework.mpeg_applications.utils import icnn_tools import config from conversion.torch2sadl import onnx_to_sadl, torch_to_onnx -from models.model_lop2_nnr import PytorchModel +from models.model_nnr import PytorchModel from util.regex import get_regex_groups_from_org_zip_file @@ -70,6 +70,7 @@ def create_enc_info() -> Dict: def get_model_data_info( + arch: str, model_path: Union[Path, str], model_config: Dict, is_overf_model: bool, @@ -77,6 +78,7 @@ def get_model_data_info( pad_size: int = 0, ) -> Tuple[PytorchModel, Dict]: model = PytorchModel( + arch, model_config, block_size, pad_size, @@ -87,6 +89,7 @@ def get_model_data_info( def compress_one_model_and_2sadl( + arch: str, overfitted_model_dir: Path, base_model_file: Path, model_config: Dict, @@ -134,10 +137,10 @@ def compress_one_model_and_2sadl( print("-----------------------------") _, overfitted_model_params = get_model_data_info( - overfitted_model_file, model_config, True, block_size, pad_size + arch, overfitted_model_file, model_config, True, block_size, pad_size ) base_model, base_model_params = get_model_data_info( - base_model_file, model_config, False, block_size, pad_size + arch, base_model_file, model_config, False, block_size, pad_size ) approx_data = base_model.init_approx_data( @@ -240,7 +243,7 @@ def compress_one_model_and_2sadl( # Torch to Onnx out_onnx_path = onnx_models_dir / saved_model_path.stem - torch_to_onnx(saved_model_path, out_onnx_path, model_config) + torch_to_onnx(arch, saved_model_path, out_onnx_path, model_config) # Onnx to SADL and quantized by using base model's quantizers onnx_to_sadl( diff --git a/training/training_scripts/NN_Filtering/HOP4/model/model.json b/training/training_scripts/NN_Filtering/HOP4/model/model.json index 5fdf80cf93c6b9f539bb9e4945327f45500e4d3d..3a7c03048b9c4a9f199a5331fee902a480ab5b38 100644 --- a/training/training_scripts/NN_Filtering/HOP4/model/model.json +++ b/training/training_scripts/NN_Filtering/HOP4/model/model.json @@ -35,7 +35,7 @@ "D4" : 16, "D5" : 16, "D6" : 48, - "N" : 24, + "N" : 23, "C" : 64, "C1" : 192, "C21" : 32, diff --git a/training/training_scripts/NN_Filtering/HOP4/model/model.py b/training/training_scripts/NN_Filtering/HOP4/model/model.py index 76c7b698f55684eb7c4bb689eca46b98b22927a1..f85ae5fece793a8e4850c05473640476c5344c9b 100644 --- a/training/training_scripts/NN_Filtering/HOP4/model/model.py +++ b/training/training_scripts/NN_Filtering/HOP4/model/model.py @@ -170,15 +170,15 @@ class Attn(nn.Module): self.conv1_1 = nn.Conv2d(c, c, kernel_size=1, bias=False) self.conv1_2 = nn.Conv2d(c, c, kernel_size=1, bias=False) self.conv1_3 = nn.Conv2d(c, c, kernel_size=1, bias=False) - + group = 8 self.conv3_1 = nn.Conv2d( - c, c, kernel_size=(3, 3), padding=(1, 1), groups=c, bias=False + c, c, kernel_size=(3, 3), padding=(1, 1), groups=group, bias=False ) self.conv3_2 = nn.Conv2d( - c, c, kernel_size=(3, 3), padding=(1, 1), groups=c, bias=False + c, c, kernel_size=(3, 3), padding=(1, 1), groups=group, bias=False ) self.conv3_3 = nn.Conv2d( - c, c, kernel_size=(3, 3), padding=(1, 1), groups=c, bias=False + c, c, kernel_size=(3, 3), padding=(1, 1), groups=group, bias=False ) self.conv2 = nn.Conv2d(c, c, kernel_size=1, bias=False) @@ -203,7 +203,7 @@ class Attn(nn.Module): class TFBlock(nn.Module): - def __init__(self, to_train, c=64, n_h=2, ext=2.66): + def __init__(self, to_train, c=64, n_h=2): super(TFBlock, self).__init__() self.attention = Attn(to_train, c, n_h) @@ -288,7 +288,7 @@ class ResidualBlock_TF(nn.Sequential): index=index, groups=2, ), - TFBlock(to_train, C, 2, 2.66), + TFBlock(to_train, C, 2), ) def forward(self, x: torch.Tensor) -> torch.Tensor: @@ -373,11 +373,12 @@ class SADLNet(nn.Sequential): patch_size = ( (patch_size, patch_size) if isinstance(patch_size, int) else patch_size ) + global model_for_export return [ torch.rand( batch_size, conv.in_channels, *patch_size, device=conv[0].weight.device ) - for conv in self[0].branches + for conv in model_for_export[0].branches ] def to_onnx( diff --git a/training/training_scripts/NN_Filtering/HOP4/quantize/quantize_bits_per_layer.txt b/training/training_scripts/NN_Filtering/HOP4/quantize/quantize_bits_per_layer.txt index 0c1f2d7587d921ae2f0886c675f34dc7051ed6df..51b36bec76787216dc2065e7fc6052dbba48b6a8 100644 --- a/training/training_scripts/NN_Filtering/HOP4/quantize/quantize_bits_per_layer.txt +++ b/training/training_scripts/NN_Filtering/HOP4/quantize/quantize_bits_per_layer.txt @@ -1 +1 @@ -"0 11 1 11 2 11 3 11 4 11 5 11 6 11 7 0 8 11 10 11 12 11 13 0 14 11 16 11 18 11 19 0 20 11 22 11 24 11 25 0 26 11 28 11 30 11 31 0 32 11 34 11 36 11 37 0 38 11 40 11 42 0 44 11 45 0 46 11 48 11 50 11 51 0 52 11 54 11 56 11 57 0 58 11 60 11 62 11 63 0 64 11 66 11 67 0 68 11 70 11 72 0 74 11 75 0 76 11 78 11 79 0 80 11 82 11 83 0 84 11 87 11 88 0 89 11 91 11 93 11 94 0 95 11 97 11 98 0 99 11 101 11 103 0 105 11 106 0 107 11 109 11 110 0 111 11 113 11 114 0 115 11 118 11 119 0 120 11 122 11 124 11 125 0 126 11 128 11 129 0 130 11 132 11 134 0 136 11 137 0 138 11 140 11 141 0 142 11 144 11 145 0 146 11 149 11 150 0 151 11 153 11 155 11 156 0 157 11 159 11 160 0 161 11 163 11 165 0 167 11 168 0 169 11 171 11 172 0 173 11 175 11 176 0 177 11 180 11 181 0 182 11 184 11 186 11 187 0 188 11 190 11 191 0 192 11 194 11 196 0 198 11 199 0 200 11 202 11 203 0 204 11 206 11 207 0 208 11 211 11 212 0 213 11 215 11 217 11 218 0 219 11 221 11 222 0 223 11 225 11 227 0 229 11 230 0 231 11 233 11 234 0 235 11 237 11 238 0 239 11 242 11 243 0 244 11 246 11 248 11 249 0 250 11 252 11 253 0 254 11 256 11 258 0 260 11 261 0 262 11 264 11 265 0 266 11 268 11 269 0 270 11 273 11 274 0 275 11 277 11 279 11 280 0 281 11 283 11 284 0 285 11 287 11 289 0 291 11 292 0 293 11 295 11 296 0 297 11 299 11 300 0 301 11 303 11 304 0 305 11 306 0 307 11 308 0 309 11 310 0 311 11 312 0 313 11 314 0 316 0 318 0 320 0 322 0 324 0 326 0 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2 + ], + "dataloader": { + "batch_size": 128 + }, + "optimizer": { + "lr": 0.0004 + }, + "lr_scheduler": { + "milestones": [ + 120, + 123, + 126 + ] + }, + "dct_size": 2 + } + }, + + + "stage2": { + "encdec_bvi": { + "vtm_option": "--NnlfDebugOption=1 --NnlfOption=1 --NnlfModelName=[stage1/conversion/full_path_filename]", + "vtm_dec_option": "--NnlfModelName=[stage1/conversion/full_path_filename]" + }, + "encdec_bvi_valid": { + "vtm_option": "--NnlfDebugOption=1 --NnlfOption=1 --NnlfModelName=[stage1/conversion/full_path_filename]", + "vtm_dec_option": "--NnlfModelName=[stage1/conversion/full_path_filename]" + }, + "encdec_tvd": { + "vtm_option": "--NnlfDebugOption=1 --NnlfOption=1 --NnlfModelName=[stage1/conversion/full_path_filename]", + "vtm_dec_option": "--NnlfModelName=[stage1/conversion/full_path_filename]" + }, + "encdec_tvd_valid": { + "vtm_option": "--NnlfDebugOption=1 --NnlfOption=1 --NnlfModelName=[stage1/conversion/full_path_filename]", + "vtm_dec_option": "--NnlfModelName=[stage1/conversion/full_path_filename]" + }, + "training": { + "mse_epoch": 58, + "max_epochs": 60, + "component_loss_weightings": [ + 6, + 2 + ], + "dataloader": { + "batch_size": 128 + }, + "optimizer": { + "lr": 0.0004 + }, + "lr_scheduler": { + "milestones": [ + 55, + 57, + 58 + ] + }, + "dct_size": 2 + } + }, + + + "stage3": { + "encdec_bvi": { + "vtm_option": "--NnlfOption=1 --NnlfModelName=[stage2/quantize/full_path_filename]", + "vtm_dec_option": "--NnlfModelName=[stage2/quantize/full_path_filename]" + }, + "encdec_bvi_valid": { + "vtm_option": "--NnlfOption=1 --NnlfModelName=[stage2/quantize/full_path_filename]", + "vtm_dec_option": "--NnlfModelName=[stage2/quantize/full_path_filename]" + }, + "encdec_tvd": { + "vtm_option": "--NnlfOption=1 --NnlfModelName=[stage2/quantize/full_path_filename]", + "vtm_dec_option": "--NnlfModelName=[stage2/quantize/full_path_filename]" + }, + "encdec_tvd_valid": { + "vtm_option": "--NnlfOption=1 --NnlfModelName=[stage2/quantize/full_path_filename]", + "vtm_dec_option": "--NnlfModelName=[stage2/quantize/full_path_filename]" + }, + "training": { + "mse_epoch": 58, + "max_epochs": 60, + "component_loss_weightings": [ + 12, + 2 + ], + "dataloader": { + "batch_size": 64 + }, + "optimizer": { + "lr": 0.0004 + }, + "lr_scheduler": { + "milestones": [ + 41, + 51, + 55 + ] + }, + "dct_size": 2 + } + } + + +} diff --git a/training/training_scripts/NN_Filtering/VLOP2/model/model.json b/training/training_scripts/NN_Filtering/VLOP2/model/model.json new file mode 100644 index 0000000000000000000000000000000000000000..a739fdfc9e0c17af0a5928df6117c0f5f4f691c0 --- /dev/null +++ b/training/training_scripts/NN_Filtering/VLOP2/model/model.json @@ -0,0 +1,47 @@ +{ "model" : { + "path": "../../VLOP2/model/model.py", + "class" : "Net", + "input_channels" : [ + [ + "rec_before_dbf_Y", + "rec_before_dbf_U", + "rec_before_dbf_V" + ], + [ + "pred_Y", + "pred_U", + "pred_V" + ], + [ + "bs_Y", + "bs_U", + "bs_V" + ], + [ "qp_base" ], + [ "qp_slice" ], + [ "ipb_Y" ] + ], + "input_kernels" : [ + 3, + 3, + 1, + 1, + 1, + 1 + ], + "D1": 8, + "D2": 4, + "D3": 2, + "D4": 1, + "D5": 1, + "D6": 64, + "N_Y": 6, + "N_UV": 1, + "C_Y": 32, + "C_UV": 16, + "C1_Y": 96, + "C1_UV": 32, + "C21_Y": 32, + "C21_UV": 16, + "dct_ch": 4 + } } diff --git a/training/training_scripts/NN_Filtering/VLOP2/model/model.py b/training/training_scripts/NN_Filtering/VLOP2/model/model.py new file mode 100644 index 0000000000000000000000000000000000000000..16e24713f9349a8d613f9817829b914db4f8d89c --- /dev/null +++ b/training/training_scripts/NN_Filtering/VLOP2/model/model.py @@ -0,0 +1,378 @@ +""" +/* The copyright in this software is being made available under the BSD +* License, included below. This software may be subject to other third party +* and contributor rights, including patent rights, and no such rights are +* granted under this license. +* +* Copyright (c) 2010-2024, ITU/ISO/IEC +* All rights reserved. +* +* Redistribution and use in source and binary forms, with or without +* modification, are permitted provided that the following conditions are met: +* +* * Redistributions of source code must retain the above copyright notice, +* this list of conditions and the following disclaimer. +* * Redistributions 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. +* * Neither the name of the ITU/ISO/IEC nor the names of its contributors may +* 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 COPYRIGHT HOLDER 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. +""" + +from typing import Union, Tuple, Optional, Type, Iterable, List, Dict + +import torch +from torch import nn +from torch.nn import functional as F + + +class Conv(nn.Sequential): + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: Union[int, Tuple[int, int]], + stride: Union[int, Tuple[int, int]] = 1, + padding: Optional[Union[int, Tuple[int, int]]] = None, + is_separable: bool = False, + is_horizontal: bool = True, + hidden_separable_channels: Optional[int] = None, + post_activation: Optional[Type] = nn.PReLU, + **kwargs, + ): + """ + Args: + in_channels: the number of input channels + out_channels: the number of output channels + kernel_size: the convolution's kernel size + stride: the convolution's stride(s) + padding: the convolution's padding + is_separable: whether to implement convolution separably + hidden_separable_channels: If is_separable, the number of hidden channels between convolutions. If None, use out_channels + post_activation: activation function to use after convolution. If None, no activation after convolution + **kwargs: additional kwargs to pass to nn.Conv2d + """ + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = ( + (kernel_size, kernel_size) if isinstance(kernel_size, int) else kernel_size + ) + self.stride = (stride, stride) if isinstance(stride, int) else stride + if padding is not None: + self.padding = (padding, padding) if isinstance(padding, int) else padding + else: + self.padding = tuple([k // 2 for k in self.kernel_size]) + self.is_separable = is_separable + self.is_horizontal = is_horizontal + self.post_activation = post_activation + + if self.is_separable: + self.hidden_separable_channels = hidden_separable_channels or out_channels + if self.is_horizontal: + modules = [ + nn.Conv2d( + self.in_channels, + self.hidden_separable_channels, + (self.kernel_size[0], 1), + (self.stride[0], 1), + (self.padding[0], 0), + groups=self.hidden_separable_channels, + **kwargs, + ) + ] + else: + modules = [ + nn.Conv2d( + self.hidden_separable_channels, + self.out_channels, + (1, self.kernel_size[1]), + (1, self.stride[1]), + (0, self.padding[1]), + groups=self.hidden_separable_channels, + **kwargs, + ) + ] + else: + modules = [ + nn.Conv2d( + self.in_channels, + self.out_channels, + self.kernel_size, + self.stride, + self.padding, + **kwargs, + ) + ] + + if self.post_activation is not None: + modules.append(self.post_activation()) + + super(Conv, self).__init__(*modules) + + +class MultiBranchModule(nn.Module): + """A module representing multple, parallel branches. If the input is a list, each element in the list is fed into the corresponding branch, + otherwise the input is fed into every branch. The outputs of each branch are then merged.""" + + def __init__(self, *branch_modules, merge_dimension: int = -3): + """ + Args: + branch_modules: modules to run in parallel + merge_dimension: the dimension to merge outputs from each branch + """ + super().__init__() + self.branches = nn.ModuleList(branch_modules) + self.merge_dimension = merge_dimension + + def forward(self, args: Union[torch.Tensor, List[torch.Tensor]]) -> torch.Tensor: + inputs = args if isinstance(args, list) else len(self.branches) * [args] + branch_outputs = [branch(input) for branch, input in zip(self.branches, inputs)] + return torch.cat(branch_outputs, dim=self.merge_dimension) + + +class NewResBlock_separate_prelu(nn.Sequential): + def __init__(self, C: int = 64, C1: int = 160, C21: int = 32): + super().__init__() + self.prelu = nn.PReLU() + self.conv1_11 = Conv(C1, C, kernel_size=1, post_activation=None) + self.conv2_13 = Conv(C, C, kernel_size=3, post_activation=None, is_separable=True, is_horizontal=True, hidden_separable_channels=C21) + self.conv3_31 = Conv(C, C, kernel_size=3, post_activation=None, is_separable=True, is_horizontal=False, hidden_separable_channels=C21) + self.conv4_11 = Conv(C, C1, kernel_size=1, post_activation=None) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + temp = x + x1 = self.prelu(x) + x2 = self.conv1_11(x1) + x3 = self.conv2_13(x2) + x4 = self.conv3_31(x3) + x5 = self.conv4_11(x4) + return x5 + temp + + +class SplitLumaChromaBlocks(nn.Sequential): + def __init__( + self, + N_Y: int = 12, + N_UV: int = 6, + C_Y: int = 16, + C_UV: int = 16, + C1_Y: int = 64, + C1_UV: int = 48, + C21_Y: int = 16, + C21_UV: int = 16, + output_channels_y: int = 4, + output_channels_uv: int = 2, + ): + super().__init__() + + self.split_y_path = nn.Sequential( + Conv(C_Y, C1_Y, kernel_size=1, post_activation=None), + *[NewResBlock_separate_prelu(C_Y, C1_Y, C21_Y) for _ in range(N_Y)], + Conv(C1_Y, C_Y, kernel_size=1, post_activation=None), + Conv(C_Y, C_Y, kernel_size=3, is_separable=True, is_horizontal=True, hidden_separable_channels=C21_Y, post_activation=None), + Conv(C_Y, C_Y, kernel_size=3, is_separable=True, is_horizontal=False, hidden_separable_channels=C21_Y, post_activation=None), + Conv(C_Y, C_Y, kernel_size=1), + Conv(C_Y, output_channels_y, kernel_size=3, post_activation=None) + ) + + self.split_uv_path = nn.Sequential( + Conv(C_UV, C1_UV, kernel_size=1, post_activation=None), + *[NewResBlock_separate_prelu(C_UV, C1_UV, C21_UV) for _ in range(N_UV)], + Conv(C1_UV, C_UV, kernel_size=1, post_activation=None), + Conv(C_UV, C_UV, kernel_size=3, is_separable=True, is_horizontal=True, hidden_separable_channels=C21_UV, post_activation=None), + Conv(C_UV, C_UV, kernel_size=3, is_separable=True, is_horizontal=False, hidden_separable_channels=C21_UV, post_activation=None), + Conv(C_UV, C_UV, kernel_size=1), + Conv(C_UV, output_channels_uv, kernel_size=3, post_activation=None) + ) + + self.Cy = C_Y + self.Cuv = C_UV + + def forward(self, x: torch.Tensor) -> torch.Tensor: + split_y_input = x[:, : self.Cy, :, :] + split_uv_input = x[:, self.Cy : self.Cy + self.Cuv, :, :] + + y_output = self.split_y_path.forward(split_y_input) + uv_output = self.split_uv_path.forward(split_uv_input) + return torch.cat((y_output, uv_output), dim=1) + + +class SADLNet(nn.Sequential): + """The network used during SADL inference""" + + def __init__( + self, + input_channels: Iterable[int] = [3, 3, 3, 1, 1, 1], + input_kernels: Iterable[int] = [3, 3, 3, 1, 1, 3], + D1: int = 12, + D2: int = 8, + D3: int = 4, + D4: int = 2, + D5: int = 2, + D6: int = 24, + N_Y: int = 12, + N_UV: int = 6, + C_Y: int = 16, + C_UV: int = 16, + C1_Y: int = 64, + C1_UV: int = 48, + C21_Y: int = 16, + C21_UV: int = 16, + output_channels_y: int = 4, + output_channels_uv: int = 2, + ): + """ + Args: + input_channels: the number of channels expected for each input + input_kernels: the kernel size for each input convolution + output_channels: the number of output channels + """ + self.input_channels = input_channels + self.input_kernels = input_kernels + self.input_features = [D1, D2, D3, D4, D4, D5] + + super(SADLNet, self).__init__( + MultiBranchModule( + *[ + Conv(c, d, kernel_size=k, post_activation=None) + for c, d, k in zip( + self.input_channels, self.input_features, self.input_kernels + ) + ] + ), + Conv(sum(self.input_features), D6, kernel_size=1), + Conv(D6, D6, kernel_size=3, stride=2, post_activation=None, is_separable=True, is_horizontal=True, hidden_separable_channels=D6), + Conv(D6, D6, kernel_size=3, stride=2, post_activation=None, is_separable=True, is_horizontal=False, hidden_separable_channels=D6), + Conv(D6, C_Y + C_UV, kernel_size=1), + SplitLumaChromaBlocks(N_Y, N_UV, C_Y, C_UV, C1_Y, C1_UV, C21_Y, C21_UV, output_channels_y, output_channels_uv), + ) + + def get_example_inputs( + self, patch_size: Union[int, Tuple[int, int]] = 144, batch_size: int = 1 + ): + patch_size = ( + (patch_size, patch_size) if isinstance(patch_size, int) else patch_size + ) + return [ + torch.rand( + batch_size, conv.in_channels, *patch_size, device=conv[0].weight.device + ) + for conv in self[0].branches + ] + + def to_onnx( + self, + filename: str, + patch_size: int = 144, + batch_size: int = 1, + opset: int = 10, + **kwargs, + ) -> None: + mode = self.training + self.eval() + torch.onnx.export( + self, + self.get_example_inputs(patch_size, batch_size), + filename, + opset_version=opset, + **kwargs, + ) + self.train(mode) + + +class Net(nn.Module): + """Wrapper for SADL model that implements input pre- and post-processing for training.""" + + def __init__( + self, + input_channels: Iterable[Iterable[str]] = [ + ["rec_before_dbf_Y", "rec_before_dbf_U", "rec_before_dbf_V"], + ["pred_Y", "pred_U", "pred_V"], + ["bs_Y", "bs_U", "bs_V"], + ["qp_base"], + ["qp_slice"], + ["ipb_Y"], + ], + input_kernels: Iterable[int] = [3, 3, 1, 1, 1, 1], + D1: int = 16, + D2: int = 8, + D3: int = 4, + D4: int = 2, + D5: int = 2, + D6: int = 64, + N_Y: int = 14, + N_UV: int = 4, + C_Y: int = 32, + C_UV: int = 32, + C1_Y: int = 144, + C1_UV: int = 128, + C21_Y: int = 32, + C21_UV: int = 32, + dct_ch: int = 4, + path: str = None, + ): + super(Net, self).__init__() + assert len(input_channels) == len( + input_kernels + ), "[ERROR] input size and kernels size not equal" + self.input_channels = input_channels + sizes = [dct_ch + dct_ch // 2, dct_ch + dct_ch // 2, dct_ch + dct_ch // 2, 1, 1, dct_ch] + self.SADL_model = SADLNet( + sizes, + input_kernels, + D1, + D2, + D3, + D4, + D5, + D6, + N_Y, + N_UV, + C_Y, + C_UV, + C1_Y, + C1_UV, + C21_Y, + C21_UV, + 4 * dct_ch, + 2 * dct_ch + ) + self.chroma_upsampler = nn.Upsample(scale_factor=2, mode="nearest") + self.dct_ch = dct_ch + + def preprocess_args( + self, batch: Dict[str, torch.Tensor] + ) -> Dict[str, torch.Tensor]: + return [ + torch.cat([batch[name] for name in input_], dim=1) + for input_ in self.input_channels + ] + + def postprocess_outputs( + self, batch: Dict[str, torch.Tensor], out: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + Y_res, UV_res = out.split([4 * self.dct_ch, 2 * self.dct_ch], dim=1) + return ( + F.pixel_shuffle(Y_res, 2), + UV_res, + ) + + def forward( + self, batch: Dict[str, torch.Tensor] + ) -> Tuple[torch.Tensor, torch.Tensor]: + args = self.preprocess_args(batch) + out = self.SADL_model(args) + return self.postprocess_outputs(batch, out) diff --git a/training/training_scripts/NN_Filtering/VLOP2/paths.json b/training/training_scripts/NN_Filtering/VLOP2/paths.json new file mode 100644 index 0000000000000000000000000000000000000000..c3129325bceb6de7e9f15fcaff299d4a4c571ac4 --- /dev/null +++ b/training/training_scripts/NN_Filtering/VLOP2/paths.json @@ -0,0 +1,200 @@ +{ + "binaries": { + "sadl_path": "/path/to/src/sadl" + }, + + "model" : { + "path": "/path/to/src/training/training_scripts/NN_Filtering/VLOP2/model/model.py" + }, + + "dataset": { + "div2k_train": { + "//": "dataset of png to convert", + "path": "/path/to/DIV2K/DIV2K_train_HR" + }, + "div2k_valid": { + "path": "/path/to/DIV2K/DIV2K_valid_HR" + }, + "bvi": { + "//": "dataset of yuv", + "path": "/path/to/bviorg", + "dataset_file": "/path/to/src/training/training_scripts/NN_Filtering/common/datasets/bvi.json" + }, + "bvi_valid": { + "//": "dataset of yuv", + "path": "/path/to/bviorg", + "dataset_file": "/path/to/src/training/training_scripts/NN_Filtering/common/datasets/bvi_valid.json" + }, + "tvd": { + "//": "dataset of yuv", + "path": "/path/to/tvdorg", + "dataset_file": "/path/to/src/training/training_scripts/NN_Filtering/common/datasets/tvd.json" + }, + "tvd_valid": { + "//": "dataset of yuv", + "path": "/path/to/tvdorg", + "dataset_file": "/path/to/src/training/training_scripts/NN_Filtering/common/datasets/tvd_valid.json" + } + }, + + + "stage1": { + "yuv": { + "//": "path to store yuv files dataset", + "path": "/path/to/stage1/yuv" + }, + "yuv_valid": { + "//": "path to store yuv files dataset", + "path": "/path/to/stage1/yuv" + }, + "encdec": { + "//": "path to store the shell script and all the generated files by the encoder/decoder", + "path": "/path/to/stage1/encdec", + "vtm_enc": "/path/to/src/bin/EncoderAppStatic", + "vtm_dec": "/path/to/src/bin/DecoderAppStatic", + "vtm_cfg": "/path/to/src/cfg/encoder_intra_vtm.cfg" + }, + "encdec_valid": { + "//": "path to store the shell script and all the generated files by the encoder/decoder", + "path": "/path/to/stage1/encdec", + "vtm_enc": "/path/to/src/bin/EncoderAppStatic", + "vtm_dec": "/path/to/src/bin/DecoderAppStatic", + "vtm_cfg": "/path/to/src/cfg/encoder_intra_vtm.cfg" + }, + "dataset": { + "//": "path to store the full dataset which will be used by the training", + "path": "/path/to/stage1/dataset" + }, + "dataset_valid": { + "//": "path to store the full dataset which will be used by the training", + "path": "/path/to/stage1/dataset" + }, + "training": { + "path": "/path/to/stage1/train" + }, + "conversion": { + "//": "full path to output the model. input model is taken in training/ckpt_dir", + "full_path_filename": "/path/to/stage1/train/model_float.sadl" + } + }, + + + "stage2": { + "yuv_tvd": { + "//": "path to store yuv files dataset", + "path": "/path/to/stage2/yuv" + }, + "yuv_tvd_valid": { + "//": "path to store yuv files dataset", + "path": "/path/to/stage2/yuv" + }, + "yuv_bvi": { + "//": "path to store yuv files dataset", + "path": "/path/to/stage2/yuv" + }, + "yuv_bvi_valid": { + "//": "path to store yuv files dataset", + "path": "/path/to/stage2/yuv" + }, + "encdec_bvi": { + "//": "path to store the shell script and all the generated files by the encoder/decoder", + "path": "/path/to/stage2/encdec", + "vtm_enc": "/path/to/src/bin/EncoderAppStatic", + "vtm_dec": "/path/to/src/bin/DecoderAppStatic", + "vtm_cfg": "/path/to/src/cfg/encoder_randomaccess_vtm.cfg" + }, + "encdec_bvi_valid": { + "//": "path to store the shell script and all the generated files by the encoder/decoder", + "path": "/path/to/stage2/encdec", + "vtm_enc": "/path/to/src/bin/EncoderAppStatic", + "vtm_dec": "/path/to/src/bin/DecoderAppStatic", + "vtm_cfg": "/path/to/src/cfg/encoder_randomaccess_vtm.cfg" + }, + "encdec_tvd": { + "//": "path to store the shell script and all the generated files by the encoder/decoder", + "path": "/path/to/stage2/encdec", + "vtm_enc": "/path/to/src/bin/EncoderAppStatic", + "vtm_dec": "/path/to/src/bin/DecoderAppStatic", + "vtm_cfg": "/path/to/src/cfg/encoder_randomaccess_vtm.cfg" + }, + "encdec_tvd_valid": { + "//": "path to store the shell script and all the generated files by the encoder/decoder", + "path": "/path/to/stage2/encdec", + "vtm_enc": "/path/to/src/bin/EncoderAppStatic", + "vtm_dec": "/path/to/src/bin/DecoderAppStatic", + "vtm_cfg": "/path/to/src/cfg/encoder_randomaccess_vtm.cfg" + }, + "dataset": { + "//": "path to store the full dataset which will be used by the training", + "path": "/path/to/stage2/dataset" + }, + "dataset_valid": { + "//": "path to store the full dataset which will be used by the training", + "path": "/path/to/stage2/dataset" + }, + "training": { + "path": "/path/to/stage2/train" + }, + "conversion": { + "//": "full path to output the model. input model is taken in training/ckpt_dir", + "full_path_filename": "/path/to/stage2/train/model_float.sadl" + }, + "quantize": { + "//": "full path to output the quantized model", + "full_path_filename": "/path/to/stage2/train/model_int16.sadl" + } + }, + + + "stage3": { + "encdec_bvi": { + "//": "path to store the shell script and all the generated files by the encoder/decoder", + "path": "/path/to/stage3/encdec", + "vtm_enc": "/path/to/src/bin/EncoderAppStatic", + "vtm_dec": "/path/to/src/bin/DecoderAppStatic", + "vtm_cfg": "/path/to/src/cfg/encoder_randomaccess_vtm.cfg" + }, + "encdec_bvi_valid": { + "//": "path to store the shell script and all the generated files by the encoder/decoder", + "path": "/path/to/stage3/encdec", + "vtm_enc": "/path/to/src/bin/EncoderAppStatic", + "vtm_dec": "/path/to/src/bin/DecoderAppStatic", + "vtm_cfg": "/path/to/src/cfg/encoder_randomaccess_vtm.cfg" + }, + "encdec_tvd": { + "//": "path to store the shell script and all the generated files by the encoder/decoder", + "path": "/path/to/stage3/encdec", + "vtm_enc": "/path/to/src/bin/EncoderAppStatic", + "vtm_dec": "/path/to/src/bin/DecoderAppStatic", + "vtm_cfg": "/path/to/src/cfg/encoder_randomaccess_vtm.cfg" + }, + "encdec_tvd_valid": { + "//": "path to store the shell script and all the generated files by the encoder/decoder", + "path": "/path/to/stage3/encdec", + "vtm_enc": "/path/to/src/bin/EncoderAppStatic", + "vtm_dec": "/path/to/src/bin/DecoderAppStatic", + "vtm_cfg": "/path/to/src/cfg/encoder_randomaccess_vtm.cfg" + }, + "dataset": { + "//": "path to store the full dataset which will be used by the training", + "path": "/path/to/stage3/dataset" + }, + "dataset_valid": { + "//": "path to store the full dataset which will be used by the training", + "path": "/path/to/stage3/dataset" + }, + "training": { + "path": "/path/to/stage3/train" + }, + "conversion": { + "//": "full path to output the model. input model is taken in training/ckpt_dir", + "full_path_filename": "/path/to/stage3/train/model_float.sadl" + }, + "quantize": { + "//": "full path to output the quantized model", + "full_path_filename": "/path/to/stage3/train/model_int16.sadl" + } + } + +} + diff --git a/training/training_scripts/NN_Filtering/VLOP2/quantize/quantize.py b/training/training_scripts/NN_Filtering/VLOP2/quantize/quantize.py new file mode 100644 index 0000000000000000000000000000000000000000..5fe92490425eb650974639130da02d6b8c77e1af --- /dev/null +++ b/training/training_scripts/NN_Filtering/VLOP2/quantize/quantize.py @@ -0,0 +1,198 @@ +""" +/* The copyright in this software is being made available under the BSD +* License, included below. This software may be subject to other third party +* and contributor rights, including patent rights, and no such rights are +* granted under this license. +* +* Copyright (c) 2010-2024, ITU/ISO/IEC +* All rights reserved. +* +* Redistribution and use in source and binary forms, with or without +* modification, are permitted provided that the following conditions are met: +* +* * Redistributions of source code must retain the above copyright notice, +* this list of conditions and the following disclaimer. +* * Redistributions 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. +* * Neither the name of the ITU/ISO/IEC nor the names of its contributors may +* 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 COPYRIGHT HOLDER 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. +""" + +# Build SADL first to get sadl/sample_test/naive_quantization. +# Example usage: +# python quantize.py -c my_config.json --stage 3 +# or +# python quantize.py -c my_config.json --stage 3 --float_model float.sadl \ +# --int_model int.sadl + +import os +import argparse +import json + + +def parse_arguments(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--config", "-c", type=str, help="config file" + ) + parser.add_argument( + "--stage", + type=int, + choices=range(1, 4), + help="if specified use a model from a particular stage [1-3]", + ) + parser.add_argument( + "--float_model", + type=str, + ) + parser.add_argument( + "--int_model", + type=str, + ) + args = parser.parse_args() + return vars(args) + + +def qfactor_gen(content): + output_q_str = '' + linenumber = 0 + q0 = '13' + q_prev = q0 + for line in content: + linenumber += 1 + if line[:11] == '[INFO] id: ': + layer_str = line.split(' ') + layer_id = layer_str[2] + layer_name = layer_str[-1][:-1] + + if layer_name == 'Placeholder': + qq = q0 + elif layer_name == 'Const': + next_layer = content[linenumber + 5] + layer_str2 = next_layer.split(' ') + next_layer_name = layer_str2[-1][:-1] + if next_layer_name == 'Concat': + qq = '0' + else: + node_name_str0 = content[linenumber] + if 'split_y_path.' in node_name_str0: + node_name_str = node_name_str0.split('split_y_path') + node_module_str = node_name_str[1].split('.')[1] + node_module_int = int(node_module_str) + if node_module_int <= 2 : + qq = '11' + elif node_module_int > 2 and node_module_int <= 4: + qq = '10' + elif node_module_int > 4 and node_module_int <= 6: + qq = '9' + elif node_module_int == 7: + qq = '11' + else : + qq = q0 + print(layer_id, node_name_str0[:-1], node_module_str, qq) + elif 'branches' in node_name_str0 or \ + 'split_uv_path.2' in node_name_str0 or \ + 'split_uv_path.3' in node_name_str0 or \ + 'split_uv_path.4' in node_name_str0 or \ + 'split_uv_path.5' in node_name_str0 or \ + 'split_uv_path.6' in node_name_str0 : + qq = q0 + print(layer_id, node_name_str0[:-1], qq) + elif 'split_uv_path.0' in node_name_str0: + qq = '12' + print(layer_id, node_name_str0[:-1], qq) + elif 'split_uv' in node_name_str0 : + qq = '11' + print(layer_id, node_name_str0[:-1], qq) + elif next_layer_name == 'PReLU': + qq = q_prev + print(layer_id, 'PReLU', qq) + else: + # fuse layers + qq = '12' + print(layer_id, node_name_str0[:-1], qq) + + elif layer_name == 'Conv2D': + qq = '0' + elif layer_name == 'Mul': + qq = '0' + elif layer_name == 'BiasAdd' or \ + layer_name == 'LeakyRelu' or \ + layer_name == 'Concat' or \ + layer_name == 'Shape' or \ + layer_name == 'Expand' or \ + layer_name == 'Add' or \ + layer_name == 'Transpose' or \ + layer_name == 'Reshape' or \ + layer_name == 'PReLU' or \ + layer_name == 'Slice' : + continue + else: + print('Undecided layer name', layer_name) + continue + q_prev = qq + output_q_str = output_q_str + layer_id + ' ' + qq + ' ' + return output_q_str + + +if __name__ == "__main__": + args = parse_arguments() + json_config = args["config"] + stage = args["stage"] + print(args) + + try: + with open(json_config) as file: + config = json.load(file) + except Exception: + quit("[ERROR] unable to open json config") + + main_dir = config['binaries']['sadl_path'] + sadl_build_dir = os.path.join(main_dir, 'sample_test') + debug_cpp_dir = os.path.join(sadl_build_dir, 'debug_model') + if args["float_model"]: + float_sadl_dir = args["float_model"] + else: + float_sadl_dir = config[f"stage{stage}"]['conversion']['full_path_filename'] + if args["int_model"]: + int_sadl_dir = args["int_model"] + else: + int_sadl_dir = config[f"stage{stage}"]['quantize']['full_path_filename'] + debug_log_dir = float_sadl_dir[:-5] + '_debug.txt' + + print(f"[INFO] SADL build dir {sadl_build_dir}") + print(f"[INFO] input model {float_sadl_dir}") + + # debug float SADL model and get the log + cmd_debug = [debug_cpp_dir, float_sadl_dir, '>', debug_log_dir] + cmd_debug = " ".join(cmd_debug) + os.system(cmd_debug) + + # read layer info and set quantizer + file = open(debug_log_dir) + content = file.readlines() + output_q_str = qfactor_gen(content) + file.close() + + # apply quantization + naive_quantizer_cpp_dir = os.path.join(sadl_build_dir, 'naive_quantization') + cmd_quantize = ["echo '", output_q_str , " ' | ", + naive_quantizer_cpp_dir, + float_sadl_dir, + int_sadl_dir, ";"] + cmd_quantize = " ".join(cmd_quantize) + os.system(cmd_quantize) diff --git a/training/training_scripts/NN_Filtering/VLOP2/readme.md b/training/training_scripts/NN_Filtering/VLOP2/readme.md new file mode 100644 index 0000000000000000000000000000000000000000..5d103a3283a7dd497e9c519bbdffe41cab6f966b --- /dev/null +++ b/training/training_scripts/NN_Filtering/VLOP2/readme.md @@ -0,0 +1,24 @@ +# Training Stage 3 +Use the json files in VLOP2 +``` +python3 src/training/tools/create_config.py src/training/training_scripts/NN_Filtering/common/common_config.json src/training/training_scripts/NN_Filtering/VLOP2/config.json src/training/training_scripts/NN_Filtering/VLOP2/model/model.json src/training/training_scripts/NN_Filtering/VLOP2/paths.json > my_config.json + +python3 training_scripts/NN_Filtering/common/training/main.py --json_config my_config.json --stage 3 +``` + +Converting to SADL model +``` +python3 training_scripts/NN_Filtering/common/convert/to_sadl.py --json_config my_config.json --input_model stage3/training --output_model stage3/conversion/full_path_filename +``` + +Quantization of the model +``` +python3 training_scripts/NN_Filtering/VLOP2/quantize/quantize.py -c my_config.json --stage 3 +``` +or specify the input float model and output int16 model +``` +python3 training_scripts/NN_Filtering/VLOP2/quantize/quantize.py -c my_config.json --stage 3 --float_model float.sadl --int_model int.sadl +``` + +# Inference +Inference follows the NNVC LOP3 configuration. diff --git a/training/training_scripts/NN_Intra_Prediction/CMakeLists.txt b/training/training_scripts/NN_Intra_Prediction/CMakeLists.txt index 6db962e78478ea16b23f05fe9e8168bad523f5dd..c7d4a71644f482e924bb3abd8358e0e7b516b736 100644 --- a/training/training_scripts/NN_Intra_Prediction/CMakeLists.txt +++ b/training/training_scripts/NN_Intra_Prediction/CMakeLists.txt @@ -145,10 +145,18 @@ if(USE_SEARCH) message(FATAL_ERROR "When `USE_SEARCH` is 1, `USE_TM_INF` has to be 0.") endif() set(TAG_SUFFIX "/search") +elseif(USE_SEARCH_PARTIAL) + if(USE_TM_INF) + message(FATAL_ERROR "When `USE_SEARCH_PARTIAL` is 1, `USE_TM_INF` has to be 0.") + endif() + set(TAG_SUFFIX "/search_partial") elseif(USE_TM_INF) if(USE_SEARCH) message(FATAL_ERROR "When `USE_TM_INF` is 1, `USE_SEARCH` has to be 0.") endif() + if(USE_SEARCH_PARTIAL) + message(FATAL_ERROR "When `USE_TM_INF` is 1, `USE_SEARCH_PARTIAL` has to be 0.") + endif() set(TAG_SUFFIX "/tm_inf") else() set(TAG_SUFFIX "/regular") diff --git a/training/training_scripts/NN_Intra_Prediction/DRYRUN.md b/training/training_scripts/NN_Intra_Prediction/DRYRUN.md index 43dfb4c5aeebcdea0161806c34248506d0c4ed31..b65f18dfde0ab797ac19871e0e54ac6307471fc1 100644 --- a/training/training_scripts/NN_Intra_Prediction/DRYRUN.md +++ b/training/training_scripts/NN_Intra_Prediction/DRYRUN.md @@ -1,4 +1,4 @@ -# ADAPTATIONS OF README.md TO CARRY OUT A DRY-RUN +# Adaptations of README.md to carry out a dry-run ## Definition diff --git a/training/training_scripts/NN_Intra_Prediction/RAWDATALARGE.md b/training/training_scripts/NN_Intra_Prediction/RAWDATALARGE.md index da63a319ff5e5cda1ba9429dab85ca40c4775736..a19cb3171f2d14c0d122547b0302536fde64bac4 100644 --- a/training/training_scripts/NN_Intra_Prediction/RAWDATALARGE.md +++ b/training/training_scripts/NN_Intra_Prediction/RAWDATALARGE.md @@ -1,4 +1,4 @@ -# ADAPTATIONS OF README.md TO USE THE LARGE AMOUNT OF RAW DATA INSTEAD OF THE SMALL AMOUNT OF RAW DATA +# Adaptations of README.md to use the large amount of raw data instead of the small amount of raw data ## Adaptation of I diff --git a/training/training_scripts/NN_Intra_Prediction/README.md b/training/training_scripts/NN_Intra_Prediction/README.md index 63748312a32cfb4fe84a0b256095b1a395f5f6fe..ff614d79344501bf0fd0e4ef4cfacd1d1d797d50 100644 --- a/training/training_scripts/NN_Intra_Prediction/README.md +++ b/training/training_scripts/NN_Intra_Prediction/README.md @@ -1,13 +1,13 @@ # Training of the neural network-based intra prediction mode -The documentation "training_workflow.pdf" summarizes the training workflow. The contribution -JVET-AB0149 summarizes the integration of the neural network-based intra prediction mode -into VTM-11-NNVC. +The documentation "training_workflow.pdf" summarizes the training workflow. The contributions +JVET-AB0149 and JVET-AD0212 summarize the integration of the neural network-based intra prediction +mode into VTM-11-NNVC. ## Requirements 1. 900 GB of available storage space for storing a portion of the AHG11 training CTC coming from ftp://jvet@ftp.ient.rwth-aachen.de. -2. 321 GB of available storage space for storing a portion of the AHG11 training CTC coming from UGC. +2. 444 GB of available storage space for storing a portion of the AHG11 training CTC coming from UGC. 3. 2 TB of available storage space for storing the training sets at the training cycle of given index. 4. No specific requirements regarding the GPUs. Small GPUs with 8GB of GRAM are enough. @@ -22,17 +22,20 @@ git submodule init git submodule update ``` -The first setup consists in building three different versions of VTM-11-NNVC. The first version, +The first setup consists in building four different versions of VTM-11-NNVC. The first version, stored in the directory at "training/training_scripts/NN_Intra_Prediction/bin/regular", corresponds -to VTM-11-NNVC without Filter-Set, without the neural network-based intra prediction mode. It -is used to generate the training sets at the training cycle of index 0. The second version, stored -in the directory at "training/training_scripts/NN_Intra_Prediction/bin/search", corresponds to -VTM-11-NNVC without Filter-Set, with the neural network-based intra prediction mode in floats. -It is used to generate the training sets at the training cycles of indices 1, 2, and 3. The third -version, stored in the directory at "training/training_scripts/NN_Intra_Prediction/bin/tm_inf", -corresponds to VTM-11-NNVC with Filter-Sets in floats, with the neural network-based intra prediction -mode in floats. It is used for the final rate-distortion tests following the completion of the -training cycle of index 3. +to VTM-11-NNVC without the neural network-based intra prediction mode. It is used to generate +the training sets at the training cycle of index 0. The second version, stored in the directory +at "training/training_scripts/NN_Intra_Prediction/bin/search", corresponds to VTM-11-NNVC with +the neural network-based intra prediction mode in floats. It is used to generate the training +sets at the training cycles of indices 1, 2, and 3. The third version, stored in the directory +at "training/training_scripts/NN_Intra_Prediction/bin/tm_inf", corresponds to VTM-11-NNVC with +the neural network-based intra prediction mode in floats. It is used for the final rate-distortion +tests following the completion of the training cycle of index 3. The fourth version, stored in +the directory at "training/training_scripts/NN_Intra_Prediction/bin/search_partial", corresponds +to VTM-11-NNVC with the neural network-based intra prediction mode in 16-bit signed integer. It +is used to generate the training sets at the training cycle of index 2 in case a partial re-training +of the neural network-based intra prediction is targeted, c.f. "TRAININGFROMCYCLE2.md". ```sh cd training/training_scripts/NN_Intra_Prediction @@ -119,9 +122,36 @@ chmod 755 run_setups_vtm_tm_inf.sh ./run_setups_vtm_tm_inf.sh ``` -### I.4. Virtual environment for the neural network training via Tensorflow +### I.4. Building the fourth version of VTM-11-NNVC -#### I.4.1. Generation of the virtual environment +In "../../../source/Lib/CommonLib/TypeDef.h", please set the seven macros as follows. + +```sh +#define JVET_AB0149_INTRA_PRED 1 +#if JVET_AB0149_INTRA_PRED +#define JVET_AB0149_ACTIVATE_WRITER_BLOCK_STATS 1 +#define JVET_AB0149_SEARCH_PNN 1 +#define JVET_AB0149_TM_INF_PNN 0 +#define JVET_AB0149_FIXED_POINT 1 +#define SPARSE_SUPPORT 1 // for SADL + +// ... + +#define JVET_AC0196_NNSR 0 // JVET-AC0196: EE1-2.2: GOP Level Adaptive Resampling with CNN-based Super Resolution + +#endif +``` + +Then, + +```sh +chmod 755 run_setups_vtm_search_partial.sh +./run_setups_vtm_search_partial.sh +``` + +### I.5. Virtual environment for the neural network training via Tensorflow + +#### I.5.1. Generation of the virtual environment If you want a virtual environment including Tensorflow and all the other needed packages, please, in "run_setups_venv.sh", edit the variable `EXE_PYTHON` if needed. Then, @@ -131,7 +161,7 @@ chmod 755 run_setups_venv.sh ./run_setups_venv.sh ``` -#### I.4.2. GPU capability +#### I.5.2. GPU capability This virtual environment contains "tensorflow-gpu", which is Tensorflow package handling GPU capabilities. Throughout the training of the neural network-based intra prediction mode, **only** the steps involving @@ -154,7 +184,7 @@ The raw data involved in the training of the neural network-based intra predicti 3. BVI-DVC : 800 YUV video sequences. 4. TVD : 86 YUV video sequences. 5. A portion of the AHG11 training CTC coming from ftp://jvet@ftp.ient.rwth-aachen.de : 100 YUV video sequences. -6. A portion of the AHG11 training CTC coming from UGC : 97 YUV video sequences. +6. A portion of the AHG11 training CTC coming from UGC : 130 YUV video sequences. In the following subsections, these six datasets are detailed. @@ -188,7 +218,7 @@ The video sequences in the portion of the AHG11 training CTC coming from ftp://j involved in the training of the neural network-based intra prediction mode are summarized in the file at "data_training/ctcs_ahg11/rwth.csv". The file at "data_training/ctcs_ahg11/rwth.csv" contains all the URLs starting with ftp://jvet@ftp.ient.rwth-aachen.de inside the official -AHG11 training CTC excel https://vcgit.hhi.fraunhofer.de/jvet-ahg-nnvc/nnvc-ctc excluding "ftp://jvet@ftp.ient.rwth-aachen.de/ahg/verificationtests/candidates/sdr/hd/Meridian_1920x1080_30fps_8bit_420pf.zip" +AHG11 training CTC excel https://vcgit.hhi.fraunhofer.de/jvet-ahg-nnvc/nnvc-ctc excluding ftp://jvet@ftp.ient.rwth-aachen.de/ahg/verificationtests/candidates/sdr/hd/Meridian_1920x1080_30fps_8bit_420pf.zip and ftp://jvet@ftp.ient.rwth-aachen.de/ce/JVET-K1031_Deblocking/RedKayak/red_kayak_1920x1080_30p_420.zip. Indeed, the first URL leads to unfound file ".zip" and the second URL leads a file ".zip" containing a video sequence whose name can hardly be handled. @@ -201,7 +231,7 @@ one of the two following options may be chosen. ##### II.1.2.1. Automatically downloading via a provided BASH script -Please, set the six variables in the head of the file at "data_training/run_download_rwth_aachen.sh" +Please, set the five variables in the head of the file at "data_training/run_download_rwth_aachen.sh" with the help of the associated documentation. Then, ```sh @@ -227,7 +257,7 @@ directory storing the 100 YUV video sequences (or symlinks to them). The video sequences in the portion of the AHG11 training CTC coming from UGC involved in the training of the neural network-based intra prediction mode are summarized in the file at "data_training/ctcs_ahg11/ugc.csv". The file at "data_training/ctcs_ahg11/ugc.csv" -contains only 97 URLs out of the 159 URLs related to UGC inside the official AHG11 +contains only 130 URLs out of the 159 URLs related to UGC inside the official AHG11 training CTC excel https://vcgit.hhi.fraunhofer.de/jvet-ahg-nnvc/nnvc-ctc. To get the video sequences of the portion of the AHG11 training CTC coming from UGC, @@ -249,10 +279,10 @@ chmod 755 run_download_ugc.sh "data_training/run_download_ugc.sh" contains many checks. Notably, at last, "data_training/run_download_ugc.sh" checks that the number -of successfully downloaded files is equal to 97. In our case, "data_training/run_download_ugc.sh" +of successfully downloaded files is equal to 130. In our case, "data_training/run_download_ugc.sh" toke about 3 hours to complete. -Please, set the two variables in the head of the file at "data_training/run_conversion_mkv_to_yuv_ugc.sh" +Please, set the three variables in the head of the file at "data_training/run_conversion_mkv_to_yuv_ugc.sh" with the help of the associated documentation. Then, ```sh @@ -265,14 +295,14 @@ cd ../ Notably, at last, "data_training/run_conversion_mkv_to_yuv_ugc.sh" checks that the number of conversions from ".mkv" to ".yuv" 4:2:0 10-bit is equal to 8, the number of conversions from ".mkv" to ".yuv" 4:2:0 8-bit is equal -to 89, and the number of conversions from ".mkv" to ".yuv" 4:2:2 is equal +to 122, and the number of conversions from ".mkv" to ".yuv" 4:2:2 is equal to 0. ##### II.1.3.2. Re-using the video sequences already existing in your storage space Using the "KEY NOTE" at the beginning of Section II.1., the file at "data_training/ctcs_ahg11/ugc.csv", and the video sequences already in your storage space, you can prepare the dedicated -directory storing the 97 YUV video sequences (or symlinks to them). +directory storing the 130 YUV video sequences (or symlinks to them). #### II.1.4. Automatically checking the validity of the collected raw data in your storage space @@ -320,14 +350,14 @@ The path to each directory storing raw data (RGB images or YUV video sequences) in the head of "dispatching_paths_to_raw.py". Indeed, the path to each directory storing raw data is not passed as argument to "dispatching_paths_to_raw.py" as the users that previously reproduced the training of the neural network-based intra prediction mode prefer hard-coded paths. Thus, in -the head of "dispatching_paths_to_raw.py", please set the following six variables. +the head of "dispatching_paths_to_raw.py", please set the following seven variables. * `PATH_TO_DIRECTORY_DIV2K_TRAINING` : path to the directory storing the DIV2K training dataset : 800 RGB images. * `PATH_TO_DIRECTORY_DIV2K_VALIDATION` : path to the directory storing the DIV2K validation dataset : 100 RGB images. * `PATH_TO_DIRECTORY_BVIDVC` : path to the directory storing BVI-DVC : 800 YUV video sequences. * `PATH_TO_DIRECTORY_TVD` : path to the directory storing TVD : 86 YUV video sequences. * `PATH_TO_DIRECTORY_RWTH_AACHEN` : path to the directory storing the portion of the AHG11 training CTC coming from ftp://jvet@ftp.ient.rwth-aachen.de : 100 YUV video sequences. This path must be identical to the value you set for `PATH_TO_DIRECTORY_RWTH_AACHEN` in "data_training/run_download_rwth_aachen.sh" in Section II.1.2.1. -* `PATH_TO_DIRECTORY_UGC`: path to the directory storing the portion of the AHG11 training CTC coming from UGC : 97 YUV video sequences. This path must be identical to the value you set for `PATH_TO_DIRECTORY_UGC_YUVS` in "data_training/run_conversion_mkv_to_yuv_ugc.sh" in Section II.1.3.1. +* `PATH_TO_DIRECTORY_UGC`: path to the directory storing the portion of the AHG11 training CTC coming from UGC : 130 YUV video sequences. This path must be identical to the value you set for `PATH_TO_DIRECTORY_UGC_YUVS` in "data_training/run_conversion_mkv_to_yuv_ugc.sh" in Section II.1.3.1. * `NB_JOBS` : all the raw data are divided into `NB_JOBS` subsets, each subset will be later processed by a different grid job in "generating_data_intra.py" to generate a different portion of training sets. Then, @@ -401,7 +431,7 @@ where the BASH script "run_generating_data_intra.sh" created by the user must be ```sh #!/bin/bash -# If the virtual environment in I.4 is needed. +# If the virtual environment in I.5 is needed. source venv_reproducing_training/bin/activate python generating_data_intra.py path_to_directory_training_sets 0, ${SLURM_ARRAY_TASK_ID} @@ -424,7 +454,7 @@ where the BASH script "run_generating_data_intra.sh" created by the user must be ```sh #!/bin/bash -# If the virtual environment in I.4 is needed. +# If the virtual environment in I.5 is needed. source venv_reproducing_training/bin/activate python generating_data_intra.py path_to_directory_training_sets 0, ${LSB_JOBINDEX} @@ -532,7 +562,7 @@ where the BASH script "run_generating_data_intra.sh" created by the user must be ```sh #!/bin/bash -# If the virtual environment in I.4 is needed. +# If the virtual environment in I.5 is needed. source venv_reproducing_training/bin/activate python generating_data_intra.py path_to_directory_training_sets 1,sparsity_0 ${SLURM_ARRAY_TASK_ID} @@ -555,7 +585,7 @@ where the BASH script "run_generating_data_intra.sh" created by the user must be ```sh #!/bin/bash -# If the virtual environment in I.4 is needed. +# If the virtual environment in I.5 is needed. source venv_reproducing_training/bin/activate python generating_data_intra.py path_to_directory_training_sets 1,sparsity_0 ${LSB_JOBINDEX} @@ -629,7 +659,7 @@ where the BASH script "run_generating_data_intra.sh" created by the user must be ```sh #!/bin/bash -# If the virtual environment in I.4 is needed. +# If the virtual environment in I.5 is needed. source venv_reproducing_training/bin/activate python generating_data_intra.py path_to_directory_training_sets 2,sparsity_0 ${SLURM_ARRAY_TASK_ID} @@ -652,7 +682,7 @@ where the BASH script "run_generating_data_intra.sh" created by the user must be ```sh #!/bin/bash -# If the virtual environment in I.4 is needed. +# If the virtual environment in I.5 is needed. source venv_reproducing_training/bin/activate python generating_data_intra.py path_to_directory_training_sets 2,sparsity_0 ${LSB_JOBINDEX} @@ -751,7 +781,7 @@ where the BASH script "run_generating_data_intra.sh" created by the user must be ```sh #!/bin/bash -# If the virtual environment in I.4 is needed. +# If the virtual environment in I.5 is needed. source venv_reproducing_training/bin/activate python generating_data_intra.py path_to_directory_training_sets 3,sparsity_1 ${SLURM_ARRAY_TASK_ID} --are_costs_parsed @@ -774,7 +804,7 @@ where the BASH script "run_generating_data_intra.sh" created by the user must be ```sh #!/bin/bash -# If the virtual environment in I.4 is needed. +# If the virtual environment in I.5 is needed. source venv_reproducing_training/bin/activate python generating_data_intra.py path_to_directory_training_sets 3,sparsity_1 ${LSB_JOBINDEX} --are_costs_parsed @@ -833,4 +863,29 @@ the following arguments must be specified: `--PrefixAbsolutePathsToGraphsOutput= Here, it is assumed that the two executables are run from the root directory of the VTM-11-NNVC project. Besides, `--NnIntraPred=1` and `--NnlfOption=0` must be passed on to the VTM-11-NNVC encoder executable. +### VIII. Quantization of the neural networks + +For instance, let us say that the 7 neural networks in floats resulting from VI.2 must be quantized in 16-bit signed +integer. If the executable of the SADL naive quantizer is located at "../../../sadl/sample/build/naive_quantization" +relatively to the current directory and the output graphs of the 7 neural networks in 16-bit signed integer must be +stored in the directory at "../../../models_intra_prediction" relatively to the current directory, the quantization +in 16-bit signed integer can be run as + +```sh +python quantizing_nn_intra.py ../../../sadl/sample/build/naive_quantization results/freezing_graph_pnn/target_4_4/partitioning_3_reconstruction_vtm_ttm/sparsity_1/float/graph_output.pb ../../../models_intra_prediction/graph_output_4_4_int16.sadl "0 7 1 14 2 0 3 18 5 11 6 1 7 18 9 17 10 -1 11 10 13 0 15 14 16 -1 17 0 19 17 20 -1 21 10" +python quantizing_nn_intra.py ../../../sadl/sample/build/naive_quantization results/freezing_graph_pnn/target_4_8/partitioning_3_reconstruction_vtm_ttm/sparsity_1/float/graph_output.pb ../../../models_intra_prediction/graph_output_4_8_int16.sadl "0 7 1 14 2 0 3 18 5 11 6 2 7 18 9 17 10 -2 11 10 13 0 15 14 16 -2 17 0 19 18 20 -2 21 9" +python quantizing_nn_intra.py ../../../sadl/sample/build/naive_quantization results/freezing_graph_pnn/target_4_16/partitioning_3_reconstruction_vtm_ttm/sparsity_1/float/graph_output.pb ../../../models_intra_prediction/graph_output_4_16_int16.sadl "0 7 1 14 2 0 3 18 5 13 6 1 7 18 9 17 10 -1 11 10 13 0 15 14 16 -1 17 0 19 18 20 -1 21 9" +python quantizing_nn_intra.py ../../../sadl/sample/build/naive_quantization results/freezing_graph_pnn/target_4_32/partitioning_3_reconstruction_vtm_ttm/sparsity_1/float/graph_output.pb ../../../models_intra_prediction/graph_output_4_32_int16.sadl "0 7 1 13 2 1 3 18 5 12 6 1 7 18 9 16 10 -2 11 10 13 0 15 15 16 -2 17 0 19 17 20 -2 21 9" +python quantizing_nn_intra.py ../../../sadl/sample/build/naive_quantization results/freezing_graph_pnn/target_8_8/partitioning_3_reconstruction_vtm_ttm/sparsity_1/float/graph_output.pb ../../../models_intra_prediction/graph_output_8_8_int16.sadl "0 7 1 14 2 1 3 18 5 12 6 1 7 18 9 16 10 -2 11 9 13 0 15 15 16 -1 17 0 19 18 20 -2 21 8" +python quantizing_nn_intra.py ../../../sadl/sample/build/naive_quantization results/freezing_graph_pnn/target_8_16/partitioning_3_reconstruction_vtm_ttm/sparsity_1/float/graph_output.pb ../../../models_intra_prediction/graph_output_8_16_int16.sadl "0 7 1 13 2 1 3 18 5 12 6 1 7 18 9 15 10 -2 11 9 13 0 15 14 16 -2 17 0 19 18 20 -2 21 9" +python quantizing_nn_intra.py ../../../sadl/sample/build/naive_quantization results/freezing_graph_pnn/target_16_16/partitioning_3_reconstruction_vtm_ttm/sparsity_1/float/graph_output.pb ../../../models_intra_prediction/graph_output_16_16_int16.sadl "0 7 1 13 2 1 3 18 5 13 6 1 7 18 9 13 10 0 11 18 13 14 14 0 15 10 17 0 19 14 20 -3 21 0 23 17 24 -3 25 10" +``` + +--- +**NOTE** + +The quantizers provided in the above example were found during the cross-check of the training of JVET-AI0130. + +--- + diff --git a/training/training_scripts/NN_Intra_Prediction/SEPARATECPUGPU.md b/training/training_scripts/NN_Intra_Prediction/SEPARATECPUGPU.md index 485b240e9dfa3c5601e974a0666a729ad6576abd..8aee1b01bab8230ec350f119552cf21afab36f75 100644 --- a/training/training_scripts/NN_Intra_Prediction/SEPARATECPUGPU.md +++ b/training/training_scripts/NN_Intra_Prediction/SEPARATECPUGPU.md @@ -1,4 +1,4 @@ -# ADAPTATIONS OF README.md WHEN USING TWO SEPARATE CPU/GPU GRIDS +# Adaptations of README.md when using two separate CPU/GPU grids ## Setups diff --git a/training/training_scripts/NN_Intra_Prediction/TRAININGFROMCYCLE2.md b/training/training_scripts/NN_Intra_Prediction/TRAININGFROMCYCLE2.md new file mode 100644 index 0000000000000000000000000000000000000000..cdea5daff8880d93498ccc2532666ca3ec076721 --- /dev/null +++ b/training/training_scripts/NN_Intra_Prediction/TRAININGFROMCYCLE2.md @@ -0,0 +1,31 @@ +# Adaptations of README.md to train the neural network-based intra prediction mode from the training cycle of index 2 + +To train the neural network-based intra prediction mode from the training cycle of index +2 instead of training it from scratch, please follow the steps below. + +## Adaptation of I and II + +Please, execute I and II as described in "README.md". + +## Adaptation of III and IV + +Please, skip III and IV in "REAMDE.md". + +## Adaptation of V + +V must be run as described in "README.md" but adding "--is_partial" to the first two Python +commands. That is +1. In V.1. + ```sh + python generating_data_intra.py path_to_directory_training_sets 2,sparsity_0 index_job --is_partial + ``` +2. In V.2.1. + ```sh + $ python training_nn_intra.py path_to_directory_training_sets height,width partitioning_2_reconstruction_vtm sparsity_0 nb_jobs --is_partial + ``` + +## Adaptation of VI and VII + +Please, execute VI and VII as described in "README.md". + + diff --git a/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/LiveMusic_1080P-6bbe.cfg b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Animation_1080P-3dbf.cfg similarity index 83% rename from training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/LiveMusic_1080P-6bbe.cfg rename to training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Animation_1080P-3dbf.cfg index 1d338d514cab4f0e67fa036aa685341bc784daf2..f0990c4831aa61dab42700f0bcf9e0e2f982f9c9 100644 --- a/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/LiveMusic_1080P-6bbe.cfg +++ b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Animation_1080P-3dbf.cfg @@ -1,5 +1,5 @@ #======== File I/O =============== -InputFile : LiveMusic_1080P-6bbe.yuv +InputFile : Animation_1080P-3dbf.yuv InputBitDepth : 8 InputChromaFormat : 420 FrameRate : 24 diff --git a/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Animation_720P-13b7.cfg b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Animation_720P-13b7.cfg new file mode 100644 index 0000000000000000000000000000000000000000..ecc63a8ac3ddff4ae98b5227a951f29db05ec43d --- /dev/null +++ b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Animation_720P-13b7.cfg @@ -0,0 +1,10 @@ +#======== File I/O =============== +InputFile : Animation_720P-13b7.yuv +InputBitDepth : 8 +InputChromaFormat : 420 +FrameRate : 30 +FrameSkip : 0 +SourceWidth : 1280 +SourceHeight : 720 +FramesToBeEncoded : 598 +Level : 5.2 diff --git a/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Animation_720P-431c.cfg b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Animation_720P-431c.cfg new file mode 100644 index 0000000000000000000000000000000000000000..d4ddb6e3007d2dd9259c9e6562518af6c9a332e2 --- /dev/null +++ b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Animation_720P-431c.cfg @@ -0,0 +1,10 @@ +#======== File I/O =============== +InputFile : Animation_720P-431c.yuv +InputBitDepth : 8 +InputChromaFormat : 420 +FrameRate : 25 +FrameSkip : 0 +SourceWidth : 1280 +SourceHeight : 720 +FramesToBeEncoded : 498 +Level : 5.2 diff --git a/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Animation_720P-620f.cfg b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Animation_720P-620f.cfg new file mode 100644 index 0000000000000000000000000000000000000000..22b65b14e8a45d627286b590aac08dd9832f441e --- /dev/null +++ b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Animation_720P-620f.cfg @@ -0,0 +1,10 @@ +#======== File I/O =============== +InputFile : Animation_720P-620f.yuv +InputBitDepth : 8 +InputChromaFormat : 420 +FrameRate : 25 +FrameSkip : 0 +SourceWidth : 1280 +SourceHeight : 720 +FramesToBeEncoded : 500 +Level : 5.2 diff --git a/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Animation_720P-6372.cfg b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Animation_720P-6372.cfg new file mode 100644 index 0000000000000000000000000000000000000000..d00089036841080d59851bae1e57e8981045b1f2 --- /dev/null +++ b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Animation_720P-6372.cfg @@ -0,0 +1,10 @@ +#======== File I/O =============== +InputFile : Animation_720P-6372.yuv +InputBitDepth : 8 +InputChromaFormat : 420 +FrameRate : 30 +FrameSkip : 0 +SourceWidth : 1280 +SourceHeight : 720 +FramesToBeEncoded : 600 +Level : 5.2 diff --git a/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/TelevisionClip_1080P-4c24.cfg b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_1080P-12d4.cfg similarity index 62% rename from training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/TelevisionClip_1080P-4c24.cfg rename to training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_1080P-12d4.cfg index 4a82129018bb53efa53c9cfb53e39acc77bb18cd..5a4ec2d3ec3c9acb76599c45d1b38a7a954a8f26 100644 --- a/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/TelevisionClip_1080P-4c24.cfg +++ b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_1080P-12d4.cfg @@ -1,10 +1,10 @@ #======== File I/O =============== -InputFile : TelevisionClip_1080P-4c24.yuv +InputFile : Gaming_1080P-12d4.yuv InputBitDepth : 8 InputChromaFormat : 420 -FrameRate : 23.98 +FrameRate : 30 FrameSkip : 0 SourceWidth : 1920 SourceHeight : 1080 -FramesToBeEncoded : 478 +FramesToBeEncoded : 600 Level : 5.2 diff --git a/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_1080P-13e3.cfg b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_1080P-13e3.cfg new file mode 100644 index 0000000000000000000000000000000000000000..c9d0a5a5b70a9c7ca87dcf090b373509e84ffb8e --- /dev/null +++ b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_1080P-13e3.cfg @@ -0,0 +1,10 @@ +#======== File I/O =============== +InputFile : Gaming_1080P-13e3.yuv +InputBitDepth : 8 +InputChromaFormat : 420 +FrameRate : 59.94 +FrameSkip : 0 +SourceWidth : 1920 +SourceHeight : 1080 +FramesToBeEncoded : 1198 +Level : 5.2 diff --git a/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_1080P-2e97.cfg b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_1080P-2e97.cfg new file mode 100644 index 0000000000000000000000000000000000000000..bdf534ac5995f0cbac64ac24831eddf20f62dc34 --- /dev/null +++ b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_1080P-2e97.cfg @@ -0,0 +1,10 @@ +#======== File I/O =============== +InputFile : Gaming_1080P-2e97.yuv +InputBitDepth : 8 +InputChromaFormat : 420 +FrameRate : 60 +FrameSkip : 0 +SourceWidth : 1920 +SourceHeight : 1080 +FramesToBeEncoded : 1198 +Level : 5.2 diff --git a/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/LyricVideo_1080P-0625.cfg b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_1080P-45af.cfg similarity index 72% rename from training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/LyricVideo_1080P-0625.cfg rename to training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_1080P-45af.cfg index 17d8ee6ae2fb30378e713a16ccbc4be39db84aa2..5048890c2eaf9ed633641f29369de14ba99fe0bd 100644 --- a/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/LyricVideo_1080P-0625.cfg +++ b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_1080P-45af.cfg @@ -1,10 +1,10 @@ #======== File I/O =============== -InputFile : LyricVideo_1080P-0625.yuv +InputFile : Gaming_1080P-45af.yuv InputBitDepth : 8 InputChromaFormat : 420 FrameRate : 29.97 FrameSkip : 0 SourceWidth : 1920 SourceHeight : 1080 -FramesToBeEncoded : 598 +FramesToBeEncoded : 600 Level : 5.2 diff --git a/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_1080P-4865.cfg b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_1080P-4865.cfg new file mode 100644 index 0000000000000000000000000000000000000000..d74ce46ed9fc98f63c3b918ba1f0517456f14bc2 --- /dev/null +++ b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_1080P-4865.cfg @@ -0,0 +1,10 @@ +#======== File I/O =============== +InputFile : Gaming_1080P-4865.yuv +InputBitDepth : 8 +InputChromaFormat : 420 +FrameRate : 59.94 +FrameSkip : 0 +SourceWidth : 1920 +SourceHeight : 1080 +FramesToBeEncoded : 1198 +Level : 5.2 diff --git a/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_1080P-51fc.cfg b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_1080P-51fc.cfg new file mode 100644 index 0000000000000000000000000000000000000000..16696c818b7ebe2a03c306c13dfaa1aa0e0ba706 --- /dev/null +++ b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_1080P-51fc.cfg @@ -0,0 +1,10 @@ +#======== File I/O =============== +InputFile : Gaming_1080P-51fc.yuv +InputBitDepth : 8 +InputChromaFormat : 420 +FrameRate : 60 +FrameSkip : 0 +SourceWidth : 1920 +SourceHeight : 1080 +FramesToBeEncoded : 1200 +Level : 5.2 diff --git a/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_1080P-564e.cfg b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_1080P-564e.cfg new file mode 100644 index 0000000000000000000000000000000000000000..10f50afe58f3922e512b900c25326f798ca502a0 --- /dev/null +++ b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_1080P-564e.cfg @@ -0,0 +1,10 @@ +#======== File I/O =============== +InputFile : Gaming_1080P-564e.yuv +InputBitDepth : 8 +InputChromaFormat : 420 +FrameRate : 60 +FrameSkip : 0 +SourceWidth : 1920 +SourceHeight : 1080 +FramesToBeEncoded : 1198 +Level : 5.2 diff --git a/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_1080P-57ca.cfg b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_1080P-57ca.cfg new file mode 100644 index 0000000000000000000000000000000000000000..3e6af3caeeff93f26307207b7c12aea682fb7598 --- /dev/null +++ b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_1080P-57ca.cfg @@ -0,0 +1,10 @@ +#======== File I/O =============== +InputFile : Gaming_1080P-57ca.yuv +InputBitDepth : 8 +InputChromaFormat : 420 +FrameRate : 51.74 +FrameSkip : 0 +SourceWidth : 1920 +SourceHeight : 1080 +FramesToBeEncoded : 986 +Level : 5.2 diff --git a/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_1080P-6578.cfg b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_1080P-6578.cfg new file mode 100644 index 0000000000000000000000000000000000000000..2a55aeb028d54321472ba14ca6f00428d998d809 --- /dev/null +++ b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_1080P-6578.cfg @@ -0,0 +1,10 @@ +#======== File I/O =============== +InputFile : Gaming_1080P-6578.yuv +InputBitDepth : 8 +InputChromaFormat : 420 +FrameRate : 60 +FrameSkip : 0 +SourceWidth : 1920 +SourceHeight : 1080 +FramesToBeEncoded : 1198 +Level : 5.2 diff --git a/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_1080P-6dc6.cfg b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_1080P-6dc6.cfg new file mode 100644 index 0000000000000000000000000000000000000000..28d43602ec6d435530b6c4261bc5e4a93df2ad43 --- /dev/null +++ b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_1080P-6dc6.cfg @@ -0,0 +1,10 @@ +#======== File I/O =============== +InputFile : Gaming_1080P-6dc6.yuv +InputBitDepth : 8 +InputChromaFormat : 420 +FrameRate : 30 +FrameSkip : 0 +SourceWidth : 1920 +SourceHeight : 1080 +FramesToBeEncoded : 600 +Level : 5.2 diff --git a/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_1080P-71a5.cfg b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_1080P-71a5.cfg new file mode 100644 index 0000000000000000000000000000000000000000..d1ca08b966833a2330a4d7276b0bcc47d315a62e --- /dev/null +++ b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_1080P-71a5.cfg @@ -0,0 +1,10 @@ +#======== File I/O =============== +InputFile : Gaming_1080P-71a5.yuv +InputBitDepth : 8 +InputChromaFormat : 420 +FrameRate : 39 +FrameSkip : 0 +SourceWidth : 1920 +SourceHeight : 1080 +FramesToBeEncoded : 562 +Level : 5.2 diff --git a/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_1080P-72c8.cfg b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_1080P-72c8.cfg new file mode 100644 index 0000000000000000000000000000000000000000..e9dfe9528c222f2aa1ec56da47900e87154eaa03 --- /dev/null +++ b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_1080P-72c8.cfg @@ -0,0 +1,10 @@ +#======== File I/O =============== +InputFile : Gaming_1080P-72c8.yuv +InputBitDepth : 8 +InputChromaFormat : 420 +FrameRate : 30 +FrameSkip : 0 +SourceWidth : 1920 +SourceHeight : 1080 +FramesToBeEncoded : 600 +Level : 5.2 diff --git a/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Vlog_2160P-342a.cfg b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_2160P-2cb1.cfg similarity index 76% rename from training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Vlog_2160P-342a.cfg rename to training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_2160P-2cb1.cfg index d39b66ba0da750fc8c83ae49f207d62fc94af539..b06cdab39864621d00b364a3368e75a964f57276 100644 --- a/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Vlog_2160P-342a.cfg +++ b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_2160P-2cb1.cfg @@ -1,8 +1,8 @@ #======== File I/O =============== -InputFile : Vlog_2160P-342a.yuv +InputFile : Gaming_2160P-2cb1.yuv InputBitDepth : 8 InputChromaFormat : 420 -FrameRate : 60 +FrameRate : 59.94 FrameSkip : 0 SourceWidth : 3840 SourceHeight : 2160 diff --git a/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_2160P-2dc4.cfg b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_2160P-2dc4.cfg new file mode 100644 index 0000000000000000000000000000000000000000..42c5536df171a25c8b01a4fb6a1c59e5487cd591 --- /dev/null +++ b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_2160P-2dc4.cfg @@ -0,0 +1,10 @@ +#======== File I/O =============== +InputFile : Gaming_2160P-2dc4.yuv +InputBitDepth : 8 +InputChromaFormat : 420 +FrameRate : 59.94 +FrameSkip : 0 +SourceWidth : 3840 +SourceHeight : 2160 +FramesToBeEncoded : 1198 +Level : 5.2 diff --git a/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Sports_2160P-2eda.cfg b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_2160P-348d.cfg similarity index 84% rename from training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Sports_2160P-2eda.cfg rename to training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_2160P-348d.cfg index 424e287b7de63164a7efdbe7d05ee0a5bca2cc17..b27207b416d21eed79b8fe59852aadc226aaeb7a 100644 --- a/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Sports_2160P-2eda.cfg +++ b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_2160P-348d.cfg @@ -1,5 +1,5 @@ #======== File I/O =============== -InputFile : Sports_2160P-2eda.yuv +InputFile : Gaming_2160P-348d.yuv InputBitDepth : 8 InputChromaFormat : 420 FrameRate : 29.97 diff --git a/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_2160P-34a1.cfg b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_2160P-34a1.cfg new file mode 100644 index 0000000000000000000000000000000000000000..2a0a261a2fc62cbb9ecee1e67de4c60543434b96 --- /dev/null +++ b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_2160P-34a1.cfg @@ -0,0 +1,10 @@ +#======== File I/O =============== +InputFile : Gaming_2160P-34a1.yuv +InputBitDepth : 8 +InputChromaFormat : 420 +FrameRate : 60 +FrameSkip : 0 +SourceWidth : 3840 +SourceHeight : 2160 +FramesToBeEncoded : 1200 +Level : 5.2 diff --git a/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Sports_2160P-2568.cfg b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_2160P-387f.cfg similarity index 84% rename from training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Sports_2160P-2568.cfg rename to training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_2160P-387f.cfg index 51ab89a85996d6dfd1f5987cfb75837f1cb4c8d1..584b48745293aae5e1b0fe8a6ad5a79c428e353a 100644 --- a/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Sports_2160P-2568.cfg +++ b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_2160P-387f.cfg @@ -1,5 +1,5 @@ #======== File I/O =============== -InputFile : Sports_2160P-2568.yuv +InputFile : Gaming_2160P-387f.yuv InputBitDepth : 8 InputChromaFormat : 420 FrameRate : 29.97 diff --git a/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_2160P-416f.cfg b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_2160P-416f.cfg new file mode 100644 index 0000000000000000000000000000000000000000..3000c55c6dc9a633216c6f580a9628895141dfb7 --- /dev/null +++ b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_2160P-416f.cfg @@ -0,0 +1,10 @@ +#======== File I/O =============== +InputFile : Gaming_2160P-416f.yuv +InputBitDepth : 8 +InputChromaFormat : 420 +FrameRate : 29.97 +FrameSkip : 0 +SourceWidth : 3840 +SourceHeight : 2160 +FramesToBeEncoded : 600 +Level : 5.2 diff --git a/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_2160P-5c53.cfg b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_2160P-5c53.cfg new file mode 100644 index 0000000000000000000000000000000000000000..9b5878ed91baa9ba2a0ccccfe45a7494ca1a0993 --- /dev/null +++ b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_2160P-5c53.cfg @@ -0,0 +1,10 @@ +#======== File I/O =============== +InputFile : Gaming_2160P-5c53.yuv +InputBitDepth : 8 +InputChromaFormat : 420 +FrameRate : 29.97 +FrameSkip : 0 +SourceWidth : 3840 +SourceHeight : 2160 +FramesToBeEncoded : 600 +Level : 5.2 diff --git a/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_2160P-6bb6.cfg b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_2160P-6bb6.cfg new file mode 100644 index 0000000000000000000000000000000000000000..311cce405dc65989a85895f99c23a5f6b8eb6b1c --- /dev/null +++ b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_2160P-6bb6.cfg @@ -0,0 +1,10 @@ +#======== File I/O =============== +InputFile : Gaming_2160P-6bb6.yuv +InputBitDepth : 8 +InputChromaFormat : 420 +FrameRate : 60 +FrameSkip : 0 +SourceWidth : 3840 +SourceHeight : 2160 +FramesToBeEncoded : 1200 +Level : 5.2 diff --git a/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_2160P-6cd8.cfg b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_2160P-6cd8.cfg new file mode 100644 index 0000000000000000000000000000000000000000..8936c227d15c7121818891d843ab80d46e6d6338 --- /dev/null +++ b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_2160P-6cd8.cfg @@ -0,0 +1,10 @@ +#======== File I/O =============== +InputFile : Gaming_2160P-6cd8.yuv +InputBitDepth : 8 +InputChromaFormat : 420 +FrameRate : 50 +FrameSkip : 0 +SourceWidth : 3840 +SourceHeight : 2160 +FramesToBeEncoded : 998 +Level : 5.2 diff --git a/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_720P-2dbe.cfg b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_720P-2dbe.cfg new file mode 100644 index 0000000000000000000000000000000000000000..8f829c938d4aedbe8a6376c1915bb17b5ea0094f --- /dev/null +++ b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_720P-2dbe.cfg @@ -0,0 +1,10 @@ +#======== File I/O =============== +InputFile : Gaming_720P-2dbe.yuv +InputBitDepth : 8 +InputChromaFormat : 420 +FrameRate : 29.97 +FrameSkip : 0 +SourceWidth : 1280 +SourceHeight : 720 +FramesToBeEncoded : 598 +Level : 5.2 diff --git a/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_720P-64b0.cfg b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_720P-64b0.cfg new file mode 100644 index 0000000000000000000000000000000000000000..f7e70dfe4b11a18f71e822c5eb042bd1737e61c8 --- /dev/null +++ b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/Gaming_720P-64b0.cfg @@ -0,0 +1,10 @@ +#======== File I/O =============== +InputFile : Gaming_720P-64b0.yuv +InputBitDepth : 8 +InputChromaFormat : 420 +FrameRate : 29.97 +FrameSkip : 0 +SourceWidth : 1280 +SourceHeight : 720 +FramesToBeEncoded : 600 +Level : 5.2 diff --git a/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/VerticalVideo_1080P-2f00.cfg b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/VerticalVideo_1080P-2f00.cfg new file mode 100644 index 0000000000000000000000000000000000000000..69fbfe89d5546636eea98e53529d75e501001b08 --- /dev/null +++ b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/VerticalVideo_1080P-2f00.cfg @@ -0,0 +1,10 @@ +#======== File I/O =============== +InputFile : VerticalVideo_1080P-2f00.yuv +InputBitDepth : 8 +InputChromaFormat : 420 +FrameRate : 30 +FrameSkip : 0 +SourceWidth : 1080 +SourceHeight : 1920 +FramesToBeEncoded : 600 +Level : 5.2 diff --git a/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/VerticalVideo_1080P-3709.cfg b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/VerticalVideo_1080P-3709.cfg new file mode 100644 index 0000000000000000000000000000000000000000..d01fed7e090bcfbc0d38363b5bc5d3bdf0328d21 --- /dev/null +++ b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/VerticalVideo_1080P-3709.cfg @@ -0,0 +1,10 @@ +#======== File I/O =============== +InputFile : VerticalVideo_1080P-3709.yuv +InputBitDepth : 8 +InputChromaFormat : 420 +FrameRate : 30 +FrameSkip : 0 +SourceWidth : 1080 +SourceHeight : 1920 +FramesToBeEncoded : 600 +Level : 5.2 diff --git a/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/VerticalVideo_1080P-3a9b.cfg b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/VerticalVideo_1080P-3a9b.cfg new file mode 100644 index 0000000000000000000000000000000000000000..b50c4ec19bef0684fe947dcb60eb3eeff6ced239 --- /dev/null +++ b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/VerticalVideo_1080P-3a9b.cfg @@ -0,0 +1,10 @@ +#======== File I/O =============== +InputFile : VerticalVideo_1080P-3a9b.yuv +InputBitDepth : 8 +InputChromaFormat : 420 +FrameRate : 78.07 +FrameSkip : 0 +SourceWidth : 1080 +SourceHeight : 1920 +FramesToBeEncoded : 958 +Level : 5.2 diff --git a/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/VerticalVideo_1080P-3d96.cfg b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/VerticalVideo_1080P-3d96.cfg new file mode 100644 index 0000000000000000000000000000000000000000..9070afdcb448cad0d072f1e2ed0e29fc29662c91 --- /dev/null +++ b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/VerticalVideo_1080P-3d96.cfg @@ -0,0 +1,10 @@ +#======== File I/O =============== +InputFile : VerticalVideo_1080P-3d96.yuv +InputBitDepth : 8 +InputChromaFormat : 420 +FrameRate : 29.97 +FrameSkip : 0 +SourceWidth : 1080 +SourceHeight : 1920 +FramesToBeEncoded : 300 +Level : 5.2 diff --git a/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/VerticalVideo_1080P-4925.cfg b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/VerticalVideo_1080P-4925.cfg new file mode 100644 index 0000000000000000000000000000000000000000..1099293110abdb1d3483738a4baf571c6f051632 --- /dev/null +++ b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/VerticalVideo_1080P-4925.cfg @@ -0,0 +1,10 @@ +#======== File I/O =============== +InputFile : VerticalVideo_1080P-4925.yuv +InputBitDepth : 8 +InputChromaFormat : 420 +FrameRate : 30 +FrameSkip : 0 +SourceWidth : 1080 +SourceHeight : 1920 +FramesToBeEncoded : 600 +Level : 5.2 diff --git a/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/VerticalVideo_360P-2fa3.cfg b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/VerticalVideo_360P-2fa3.cfg new file mode 100644 index 0000000000000000000000000000000000000000..c2b9197c93f68ab47a39d06761b8361fb8a4cffd --- /dev/null +++ b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/VerticalVideo_360P-2fa3.cfg @@ -0,0 +1,10 @@ +#======== File I/O =============== +InputFile : VerticalVideo_360P-2fa3.yuv +InputBitDepth : 8 +InputChromaFormat : 420 +FrameRate : 28.31 +FrameSkip : 0 +SourceWidth : 540 +SourceHeight : 720 +FramesToBeEncoded : 580 +Level : 5.2 diff --git a/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/VerticalVideo_360P-634f.cfg b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/VerticalVideo_360P-634f.cfg new file mode 100644 index 0000000000000000000000000000000000000000..a6ceb59964bb9a4b3214492e31066b419576b50e --- /dev/null +++ b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/VerticalVideo_360P-634f.cfg @@ -0,0 +1,10 @@ +#======== File I/O =============== +InputFile : VerticalVideo_360P-634f.yuv +InputBitDepth : 8 +InputChromaFormat : 420 +FrameRate : 30.25 +FrameSkip : 0 +SourceWidth : 432 +SourceHeight : 768 +FramesToBeEncoded : 604 +Level : 5.2 diff --git a/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/VerticalVideo_480P-3a6a.cfg b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/VerticalVideo_480P-3a6a.cfg new file mode 100644 index 0000000000000000000000000000000000000000..f98e5d68841ad52470f0bd5de2a866a78df72f03 --- /dev/null +++ b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/VerticalVideo_480P-3a6a.cfg @@ -0,0 +1,10 @@ +#======== File I/O =============== +InputFile : VerticalVideo_480P-3a6a.yuv +InputBitDepth : 8 +InputChromaFormat : 420 +FrameRate : 160 +FrameSkip : 0 +SourceWidth : 480 +SourceHeight : 854 +FramesToBeEncoded : 2818 +Level : 5.2 diff --git a/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/VerticalVideo_720P-0f61.cfg b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/VerticalVideo_720P-0f61.cfg new file mode 100644 index 0000000000000000000000000000000000000000..58c4e16816d407f1eb62a60329ec357e23b8bdb4 --- /dev/null +++ b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/VerticalVideo_720P-0f61.cfg @@ -0,0 +1,10 @@ +#======== File I/O =============== +InputFile : VerticalVideo_720P-0f61.yuv +InputBitDepth : 8 +InputChromaFormat : 420 +FrameRate : 60 +FrameSkip : 0 +SourceWidth : 740 +SourceHeight : 1324 +FramesToBeEncoded : 1200 +Level : 5.2 diff --git a/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/VerticalVideo_720P-4ca7.cfg b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/VerticalVideo_720P-4ca7.cfg new file mode 100644 index 0000000000000000000000000000000000000000..c827449aaeba03a4b6a41ab518f9931b54d74b1c --- /dev/null +++ b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/VerticalVideo_720P-4ca7.cfg @@ -0,0 +1,10 @@ +#======== File I/O =============== +InputFile : VerticalVideo_720P-4ca7.yuv +InputBitDepth : 8 +InputChromaFormat : 420 +FrameRate : 60 +FrameSkip : 0 +SourceWidth : 748 +SourceHeight : 1332 +FramesToBeEncoded : 1200 +Level : 5.2 diff --git a/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/VerticalVideo_720P-7c1d.cfg b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/VerticalVideo_720P-7c1d.cfg new file mode 100644 index 0000000000000000000000000000000000000000..b1261d774d4f94b2ab3aa686b72242f1f7b24a40 --- /dev/null +++ b/training/training_scripts/NN_Intra_Prediction/cfgs_training/ugc/VerticalVideo_720P-7c1d.cfg @@ -0,0 +1,10 @@ +#======== File I/O =============== +InputFile : VerticalVideo_720P-7c1d.yuv +InputBitDepth : 8 +InputChromaFormat : 420 +FrameRate : 50.71 +FrameSkip : 0 +SourceWidth : 720 +SourceHeight : 1280 +FramesToBeEncoded : 656 +Level : 5.2 diff --git a/training/training_scripts/NN_Intra_Prediction/data_training/checking_md5s_ahg11_ctc_training.py b/training/training_scripts/NN_Intra_Prediction/data_training/checking_md5s_ahg11_ctc_training.py index 52b2f8a4ea11b5f93d96f2ce82ec10343d0d146b..9e388bd5ad6319c09e23b439e29b3a1508c538ef 100644 --- a/training/training_scripts/NN_Intra_Prediction/data_training/checking_md5s_ahg11_ctc_training.py +++ b/training/training_scripts/NN_Intra_Prediction/data_training/checking_md5s_ahg11_ctc_training.py @@ -176,7 +176,7 @@ if __name__ == '__main__': nb_yus_expected_bvidvc = 764 nb_yuvs_expected_tvd = 86 nb_yuvs_expected_rwth = 100 - nb_yuvs_expected_ugc = 97 + nb_yuvs_expected_ugc = 130 check_md5s(args.path_to_directory_yuvs_bvidvc, os.path.join(path_to_directory_ctc_training_ahg11, "bvidvc.csv"), diff --git a/training/training_scripts/NN_Intra_Prediction/data_training/ctcs_ahg11/ugc.csv b/training/training_scripts/NN_Intra_Prediction/data_training/ctcs_ahg11/ugc.csv index f2c0bb67aeef06eaf4cbd6cc9ca033439d00623c..e92b82427ef38d673eefe6e07a32029f9c42b210 100644 --- a/training/training_scripts/NN_Intra_Prediction/data_training/ctcs_ahg11/ugc.csv +++ b/training/training_scripts/NN_Intra_Prediction/data_training/ctcs_ahg11/ugc.csv @@ -1,7 +1,33 @@ URL,Sequence,md5sum +gs://ugc-dataset/original_videos/Animation/720P/Animation_720P-431c.mkv,Animation_720P-431c.mkv,6abb64967aa5f6a4d4b1c19171c03456 +gs://ugc-dataset/original_videos/Animation/720P/Animation_720P-620f.mkv,Animation_720P-620f.mkv,b28787f2dae6289e009478d46e1fb6ed +gs://ugc-dataset/original_videos/Animation/720P/Animation_720P-13b7.mkv,Animation_720P-13b7.mkv,2d33570047fa7a75b833b3c78d915c7b +gs://ugc-dataset/original_videos/Animation/720P/Animation_720P-6372.mkv,Animation_720P-6372.mkv,855dabb719ab5428d5b2810b6d8d6b22 gs://ugc-dataset/original_videos/CoverSong/1080P/CoverSong_1080P-0f3a.mkv,CoverSong_1080P-0f3a.mkv,7641c0c677c3741a757a8fade78ad541 +gs://ugc-dataset/original_videos/Animation/1080P/Animation_1080P-3dbf.mkv,Animation_1080P-3dbf.mkv,75c631957973f77c000547348c3ca61f gs://ugc-dataset/original_videos/CoverSong/1080P/CoverSong_1080P-3409.mkv,CoverSong_1080P-3409.mkv,981c8c134bf8a3b819609b23ec4fd58b gs://ugc-dataset/original_videos/CoverSong/1080P/CoverSong_1080P-033d.mkv,CoverSong_1080P-033d.mkv,0a23c08f6dc112e6ff30c1d6d7b7845a +gs://ugc-dataset/original_videos/Gaming/1080P/Gaming_1080P-12d4.mkv,Gaming_1080P-12d4.mkv,8ef0325e7575027a7f6d2ee804fd1eb4 +gs://ugc-dataset/original_videos/Gaming/1080P/Gaming_1080P-45af.mkv,Gaming_1080P-45af.mkv,116686ccf4e83f8508933a1265ec7e23 +gs://ugc-dataset/original_videos/Gaming/1080P/Gaming_1080P-6dc6.mkv,Gaming_1080P-6dc6.mkv,7ad92173ad484a5494b40790422d944e +gs://ugc-dataset/original_videos/Gaming/1080P/Gaming_1080P-71a5.mkv,Gaming_1080P-71a5.mkv,e363a9c723a0e0d4cdfdd00ee8f9ef1b +gs://ugc-dataset/original_videos/Gaming/1080P/Gaming_1080P-2e97.mkv,Gaming_1080P-2e97.mkv,56bc475cad8c310662a93380575b7bd5 +gs://ugc-dataset/original_videos/Gaming/1080P/Gaming_1080P-72c8.mkv,Gaming_1080P-72c8.mkv,e8a3727e1f4e373c9db10ab030ceaae0 +gs://ugc-dataset/original_videos/Gaming/1080P/Gaming_1080P-13e3.mkv,Gaming_1080P-13e3.mkv,d26b1e49a2c277d751a4c2e94676bb9b +gs://ugc-dataset/original_videos/Gaming/1080P/Gaming_1080P-57ca.mkv,Gaming_1080P-57ca.mkv,9dc6fdd7b58cffbd1f063460376b5405 +gs://ugc-dataset/original_videos/Gaming/1080P/Gaming_1080P-51fc.mkv,Gaming_1080P-51fc.mkv,eccd7c676b1ec276b2d549364ef07d06 +gs://ugc-dataset/original_videos/Gaming/1080P/Gaming_1080P-4865.mkv,Gaming_1080P-4865.mkv,98d295aac668b175f490059fbaf5d2c1 +gs://ugc-dataset/original_videos/Gaming/1080P/Gaming_1080P-564e.mkv,Gaming_1080P-564e.mkv,d67ed5e7aa2eed40c11f52a3df4912bd +gs://ugc-dataset/original_videos/Gaming/1080P/Gaming_1080P-6578.mkv,Gaming_1080P-6578.mkv,52ff48c68856a6466f69a9b13bac62c8 +gs://ugc-dataset/original_videos/Gaming/2160P/Gaming_2160P-348d.mkv,Gaming_2160P-348d.mkv,2f5aa05c749aa66ccd608baa1b232d5a +gs://ugc-dataset/original_videos/Gaming/2160P/Gaming_2160P-387f.mkv,Gaming_2160P-387f.mkv,612f1027297533fb00ae891bcab5d5e0 +gs://ugc-dataset/original_videos/Gaming/2160P/Gaming_2160P-416f.mkv,Gaming_2160P-416f.mkv,4875ea77ddf39e907bd89d9ce604bc2c +gs://ugc-dataset/original_videos/Gaming/2160P/Gaming_2160P-2dc4.mkv,Gaming_2160P-2dc4.mkv,a8c07972692d214b6c73382a19d2dc3c +gs://ugc-dataset/original_videos/Gaming/2160P/Gaming_2160P-2cb1.mkv,Gaming_2160P-2cb1.mkv,bab969c6df565be6ff5c48de7145ff5a +gs://ugc-dataset/original_videos/Gaming/720P/Gaming_720P-2dbe.mkv,Gaming_720P-2dbe.mkv,dbeefad4996277135a30286350112a81 +gs://ugc-dataset/original_videos/Gaming/720P/Gaming_720P-64b0.mkv,Gaming_720P-64b0.mkv,8502d7626f8319a89dc6317466a3f7bc +gs://ugc-dataset/original_videos/Gaming/2160P/Gaming_2160P-5c53.mkv,Gaming_2160P-5c53.mkv,89f5a7067fafd2564da7ad39413e33ff +gs://ugc-dataset/original_videos/Gaming/2160P/Gaming_2160P-34a1.mkv,Gaming_2160P-34a1.mkv,b4f0fabe296804eca3d523d4b4184a2f gs://ugc-dataset/original_videos/HDR/1080P/HDR_1080P-2d32.mkv,HDR_1080P-2d32.mkv,5bc4980955a4eaf889f51ecc29e33281 gs://ugc-dataset/original_videos/HDR/1080P/HDR_1080P-49d6.mkv,HDR_1080P-49d6.mkv,e929a7b0fa4f010d8e95924a92f75fa1 gs://ugc-dataset/original_videos/HDR/1080P/HDR_1080P-7825.mkv,HDR_1080P-7825.mkv,f644bbc0a973fa80c222fb9c3dccdba6 @@ -11,12 +37,12 @@ gs://ugc-dataset/original_videos/HDR/1080P/HDR_1080P-601b.mkv,HDR_1080P-601b.mkv gs://ugc-dataset/original_videos/Lecture/1080P/Lecture_1080P-0c8a.mkv,Lecture_1080P-0c8a.mkv,8440720c69649f79f08e9b85f479126b gs://ugc-dataset/original_videos/LiveMusic/720P/LiveMusic_720P-58db.mkv,LiveMusic_720P-58db.mkv,1a298adf75be5be1411365c8ce44b5d3 gs://ugc-dataset/original_videos/LiveMusic/1080P/LiveMusic_1080P-14af.mkv,LiveMusic_1080P-14af.mkv,72c8f40ff6d783934fd2a92c1037d98b +gs://ugc-dataset/original_videos/Gaming/2160P/Gaming_2160P-6bb6.mkv,Gaming_2160P-6bb6.mkv,e6799da7318819bacde66fd3346dbf0c gs://ugc-dataset/original_videos/LiveMusic/1080P/LiveMusic_1080P-157b.mkv,LiveMusic_1080P-157b.mkv,072bf94b96d70f90b25dbb3b0f3ed6df -gs://ugc-dataset/original_videos/LiveMusic/1080P/LiveMusic_1080P-6bbe.mkv,LiveMusic_1080P-6bbe.mkv,14749a9f5afb74f5d8189f4ac882e4e9 +gs://ugc-dataset/original_videos/Gaming/2160P/Gaming_2160P-6cd8.mkv,Gaming_2160P-6cd8.mkv,d6c702bb04b2139e951a1af486c4f5e8 gs://ugc-dataset/original_videos/LiveMusic/720P/LiveMusic_720P-66df.mkv,LiveMusic_720P-66df.mkv,73c6c773f54c80ca08c56443d325eadc gs://ugc-dataset/original_videos/LyricVideo/720P/LyricVideo_720P-74a0.mkv,LyricVideo_720P-74a0.mkv,022916ab51094f7e4184f528414fab87 gs://ugc-dataset/original_videos/LyricVideo/720P/LyricVideo_720P-59ed.mkv,LyricVideo_720P-59ed.mkv,2da8b15216966970d97b6bc10ef33013 -gs://ugc-dataset/original_videos/LyricVideo/1080P/LyricVideo_1080P-0625.mkv,LyricVideo_1080P-0625.mkv,407c51a3d6dcdc115f43c4802f7eb528 gs://ugc-dataset/original_videos/LyricVideo/1080P/LyricVideo_1080P-16b6.mkv,LyricVideo_1080P-16b6.mkv,56f7388b606001b5ad7d9a8b57922a60 gs://ugc-dataset/original_videos/MusicVideo/1080P/MusicVideo_1080P-16e6.mkv,MusicVideo_1080P-16e6.mkv,919664561f07500fdf5d6e193dc9849d gs://ugc-dataset/original_videos/LyricVideo/1080P/LyricVideo_1080P-725e.mkv,LyricVideo_1080P-725e.mkv,4472409110a16aba2fe7f536f696c129 @@ -55,10 +81,8 @@ gs://ugc-dataset/original_videos/Sports/2160P/Sports_2160P-1733.mkv,Sports_2160P gs://ugc-dataset/original_videos/Sports/2160P/Sports_2160P-1b70.mkv,Sports_2160P-1b70.mkv,4d9f92e1e877c2400bf4d26efbc2a243 gs://ugc-dataset/original_videos/Sports/2160P/Sports_2160P-324e.mkv,Sports_2160P-324e.mkv,b2b6b8c979d2a76d7c7e31a5c7d7c999 gs://ugc-dataset/original_videos/Sports/2160P/Sports_2160P-210c.mkv,Sports_2160P-210c.mkv,bdf11a76170fd4be4db2d2aa73f21ba1 -gs://ugc-dataset/original_videos/Sports/2160P/Sports_2160P-2568.mkv,Sports_2160P-2568.mkv,41f41f0e320b58e4ea4a1ba63bc141ba gs://ugc-dataset/original_videos/Sports/2160P/Sports_2160P-279f.mkv,Sports_2160P-279f.mkv,09fa967ca7d106093704397759855960 gs://ugc-dataset/original_videos/Sports/2160P/Sports_2160P-2e1d.mkv,Sports_2160P-2e1d.mkv,d48357b2b28cb0de4d15e8c7e0307b61 -gs://ugc-dataset/original_videos/Sports/2160P/Sports_2160P-2eda.mkv,Sports_2160P-2eda.mkv,93098e10f56fca5a71fb1902679fe864 gs://ugc-dataset/original_videos/Sports/720P/Sports_720P-3338.mkv,Sports_720P-3338.mkv,6bb421b67c9b2ddd471755a79370713c gs://ugc-dataset/original_videos/Sports/2160P/Sports_2160P-2626.mkv,Sports_2160P-2626.mkv,b7fc6a3739f7cff23449f577d80125ae gs://ugc-dataset/original_videos/Sports/2160P/Sports_2160P-349c.mkv,Sports_2160P-349c.mkv,ff9a42932226df88dcdde73fd791665b @@ -67,7 +91,6 @@ gs://ugc-dataset/original_videos/Sports/720P/Sports_720P-5ae1.mkv,Sports_720P-5a gs://ugc-dataset/original_videos/TelevisionClip/1080P/TelevisionClip_1080P-39e3.mkv,TelevisionClip_1080P-39e3.mkv,d24179fc768a58d906c86f447ff6b4b4 gs://ugc-dataset/original_videos/Sports/720P/Sports_720P-5bfd.mkv,Sports_720P-5bfd.mkv,63d41511573e6661aed33510ab6c3e40 gs://ugc-dataset/original_videos/TelevisionClip/1080P/TelevisionClip_1080P-3758.mkv,TelevisionClip_1080P-3758.mkv,ccd37b39360502d5354ffcde8ee9d536 -gs://ugc-dataset/original_videos/TelevisionClip/1080P/TelevisionClip_1080P-4c24.mkv,TelevisionClip_1080P-4c24.mkv,fe4f2aebd6ef8e121854bc3da795c61d gs://ugc-dataset/original_videos/TelevisionClip/1080P/TelevisionClip_1080P-6026.mkv,TelevisionClip_1080P-6026.mkv,f26dff232dbd4d6afb59d54eae25893a gs://ugc-dataset/original_videos/TelevisionClip/1080P/TelevisionClip_1080P-5e68.mkv,TelevisionClip_1080P-5e68.mkv,616b93c17f1934cf8804d42035a68b61 gs://ugc-dataset/original_videos/TelevisionClip/720P/TelevisionClip_720P-19de.mkv,TelevisionClip_720P-19de.mkv,df7a91f8c9b2c938830e3a516f9ab7c3 @@ -81,8 +104,19 @@ gs://ugc-dataset/original_videos/Sports/2160P/Sports_2160P-7bbe.mkv,Sports_2160P gs://ugc-dataset/original_videos/Sports/2160P/Sports_2160P-69b9.mkv,Sports_2160P-69b9.mkv,f100f736f4f317ab24e0dadc6651e06a gs://ugc-dataset/original_videos/Sports/2160P/Sports_2160P-6b28.mkv,Sports_2160P-6b28.mkv,1b42158d94548ad55bf71b14f3f642c8 gs://ugc-dataset/original_videos/Sports/2160P/Sports_2160P-7a11.mkv,Sports_2160P-7a11.mkv,56bb61810b8e860dac22adafcc8c287d +gs://ugc-dataset/original_videos/VerticalVideo/360P/VerticalVideo_360P-2fa3.mkv,VerticalVideo_360P-2fa3.mkv,a77a75810866b23388a43b9841459015 +gs://ugc-dataset/original_videos/VerticalVideo/360P/VerticalVideo_360P-634f.mkv,VerticalVideo_360P-634f.mkv,9a8227dffcf6330cfe42a8a635036838 +gs://ugc-dataset/original_videos/VerticalVideo/1080P/VerticalVideo_1080P-3d96.mkv,VerticalVideo_1080P-3d96.mkv,5eb532977244f8be48591bf06592579a +gs://ugc-dataset/original_videos/VerticalVideo/1080P/VerticalVideo_1080P-2f00.mkv,VerticalVideo_1080P-2f00.mkv,59e3d331bf9f8233823a980f8247e7a4 +gs://ugc-dataset/original_videos/VerticalVideo/1080P/VerticalVideo_1080P-3709.mkv,VerticalVideo_1080P-3709.mkv,604b0733da5f584abdb29b538171e57a +gs://ugc-dataset/original_videos/VerticalVideo/720P/VerticalVideo_720P-7c1d.mkv,VerticalVideo_720P-7c1d.mkv,e4a845bcd975944ef3c8102b568d469f +gs://ugc-dataset/original_videos/VerticalVideo/1080P/VerticalVideo_1080P-4925.mkv,VerticalVideo_1080P-4925.mkv,d4aa87a24533aab59237b436e2599622 +gs://ugc-dataset/original_videos/VerticalVideo/480P/VerticalVideo_480P-3a6a.mkv,VerticalVideo_480P-3a6a.mkv,0ec120369c57ed060c69fba3a9955e74 +gs://ugc-dataset/original_videos/VerticalVideo/1080P/VerticalVideo_1080P-3a9b.mkv,VerticalVideo_1080P-3a9b.mkv,61353d4745555392c6b33e01ea9353af gs://ugc-dataset/original_videos/Vlog/1080P/Vlog_1080P-18da.mkv,Vlog_1080P-18da.mkv,192d483312300ad8b83359f54677e7e8 +gs://ugc-dataset/original_videos/VerticalVideo/720P/VerticalVideo_720P-0f61.mkv,VerticalVideo_720P-0f61.mkv,e64050af01c01e074db3a972b4c308c7 gs://ugc-dataset/original_videos/Vlog/1080P/Vlog_1080P-2394.mkv,Vlog_1080P-2394.mkv,c61d9dd7ec20d3f1b52fc4ad17ee1e3c +gs://ugc-dataset/original_videos/VerticalVideo/720P/VerticalVideo_720P-4ca7.mkv,VerticalVideo_720P-4ca7.mkv,4f87169a2fe5e6b3ab1dcfb0c7d59f07 gs://ugc-dataset/original_videos/Vlog/1080P/Vlog_1080P-2cda.mkv,Vlog_1080P-2cda.mkv,5cd3018c3e96bdd933b73f34d9325aff gs://ugc-dataset/original_videos/Vlog/720P/Vlog_720P-033a.mkv,Vlog_720P-033a.mkv,d21c5b0290c89dfb90f74ed0c0fa3eaa gs://ugc-dataset/original_videos/Vlog/720P/Vlog_720P-155f.mkv,Vlog_720P-155f.mkv,54c3dc15d9c4b87fff6ca743ae63ae3f @@ -95,4 +129,3 @@ gs://ugc-dataset/original_videos/Vlog/2160P/Vlog_2160P-4419.mkv,Vlog_2160P-4419. gs://ugc-dataset/original_videos/Vlog/2160P/Vlog_2160P-059c.mkv,Vlog_2160P-059c.mkv,c9804205d7bf34a37a52029adfbc8972 gs://ugc-dataset/original_videos/Vlog/2160P/Vlog_2160P-3019.mkv,Vlog_2160P-3019.mkv,854b163e0e7c9902c56d6aa68b3e6467 gs://ugc-dataset/original_videos/Vlog/2160P/Vlog_2160P-4362.mkv,Vlog_2160P-4362.mkv,11dcf073365162ae3472a0cb3119c27f -gs://ugc-dataset/original_videos/Vlog/2160P/Vlog_2160P-342a.mkv,Vlog_2160P-342a.mkv,e735dc91d8b9980650dc44f4fb8ae0a8 diff --git a/training/training_scripts/NN_Intra_Prediction/data_training/run_conversion_mkv_to_yuv_ugc.sh b/training/training_scripts/NN_Intra_Prediction/data_training/run_conversion_mkv_to_yuv_ugc.sh index 756ee3f11667798fd1ae826dd0367641bb7b4ca3..0a441ed366c74daf4811406f9b3985c15b976865 100644 --- a/training/training_scripts/NN_Intra_Prediction/data_training/run_conversion_mkv_to_yuv_ugc.sh +++ b/training/training_scripts/NN_Intra_Prediction/data_training/run_conversion_mkv_to_yuv_ugc.sh @@ -2,7 +2,7 @@ -# Please, set the two variables below. This is the only thing you must do. +# Please, set the three variables below. This is the only thing you must do. # ------------------------------------------------------------------------ # This is the path to the directory where the downloaded video sequences ".mkv" are stored. @@ -11,6 +11,9 @@ PATH_TO_DIRECTORY_UGC_MKVS=""; # This is the path to the directory where the video sequences ".yuv" will be stored. PATH_TO_DIRECTORY_UGC_YUVS=""; +# This is the path to the executable of FFMPEG. +PATH_TO_EXE_FFMPEG=""; + # ------------------------------------------------------------------------ @@ -57,7 +60,7 @@ for PATH_WITH_EXTENSION in $(find . -type f -name "*.mkv"); do echo "[INFO] For the video sequence at ${PATH_WITH_EXTENSION}, 10-bit conversion is run"; # This is the FFMPEG conversion to 10-bit. - ffmpeg -i "${PATH_WITH_EXTENSION}" -pix_fmt yuv420p10le "${PATH_TO_YUV}" 2>> ${PATH_TO_LOG}; + ${PATH_TO_EXE_FFMPEG} -i "${PATH_WITH_EXTENSION}" -pix_fmt yuv420p10le "${PATH_TO_YUV}" 2>> ${PATH_TO_LOG}; ((NB_10_BIT++)); # It is known that 3 files ".mkv" contains video sequences in 4:2:2. @@ -68,7 +71,7 @@ for PATH_WITH_EXTENSION in $(find . -type f -name "*.mkv"); do echo "[INFO] For the video sequence at ${PATH_WITH_EXTENSION}, 8-bit conversion is run"; # This is the FFMPEG conversion to 8-bit. - ffmpeg -i "${PATH_WITH_EXTENSION}" -pix_fmt yuv420p "${PATH_TO_YUV}" 2>> ${PATH_TO_LOG}; + ${PATH_TO_EXE_FFMPEG} -i "${PATH_WITH_EXTENSION}" -pix_fmt yuv420p "${PATH_TO_YUV}" 2>> ${PATH_TO_LOG}; ((NB_8_BIT++)); fi done @@ -88,9 +91,9 @@ echo "[ERROR] The number of video sequences in 4:2:2 is not equal to 0"; exit 1; fi echo ""; -if [ ${NB_8_BIT} -eq 89 ]; then -echo "[INFO] As expected, the number of 8-bit conversions is equal to 89"; +if [ ${NB_8_BIT} -eq 122 ]; then +echo "[INFO] As expected, the number of 8-bit conversions is equal to 122"; else -echo "[ERROR] The number of 8-bit conversions is not equal to 89"; +echo "[ERROR] The number of 8-bit conversions is not equal to 122"; exit 1; fi diff --git a/training/training_scripts/NN_Intra_Prediction/data_training/run_download_ugc.sh b/training/training_scripts/NN_Intra_Prediction/data_training/run_download_ugc.sh index 97911c023511af4ee74c510655094fa92503357c..2141b335a023be9261d440780438208dbd9dc673 100644 --- a/training/training_scripts/NN_Intra_Prediction/data_training/run_download_ugc.sh +++ b/training/training_scripts/NN_Intra_Prediction/data_training/run_download_ugc.sh @@ -48,12 +48,11 @@ done echo ""; -# The expected number of downloaded video sequences is 97. -# Indeed, only a portion of the video sequences in the -# AHG11 CTC related to UGC is downloaded. -if [ ${NB_URLS} -eq 97 ]; then -echo "[INFO] As expected, the number of UGC video sequences successfully downloaded is equal to 97"; +# Only a portion of the video sequences in the AHG11 +# CTC related to UGC is downloaded. +if [ ${NB_URLS} -eq 130 ]; then +echo "[INFO] As expected, the number of UGC video sequences successfully downloaded is equal to 130"; else -echo "[ERROR] The number of UGC video sequences successfully downloaded is not equal to 97"; +echo "[ERROR] The number of UGC video sequences successfully downloaded is not equal to 130"; exit 1; fi diff --git a/training/training_scripts/NN_Intra_Prediction/dispatching_paths_to_raw.py b/training/training_scripts/NN_Intra_Prediction/dispatching_paths_to_raw.py index 49c632866a091ea68d7d794de7ab6c7c152471ba..ad4206822072569108f13819839b173aa807c46d 100644 --- a/training/training_scripts/NN_Intra_Prediction/dispatching_paths_to_raw.py +++ b/training/training_scripts/NN_Intra_Prediction/dispatching_paths_to_raw.py @@ -58,7 +58,7 @@ PATH_TO_DIRECTORY_TVD = "" # Path to the directory storing the portion of the AHG11 CTC coming from ftp://jvet@ftp.ient.rwth-aachen.de : 100 YUV video sequences. PATH_TO_DIRECTORY_RWTH_AACHEN = "" -# Path to the directory storing the portion of the AHG11 CTC coming from UGC : 97 YUV video sequences. +# Path to the directory storing the portion of the AHG11 CTC coming from UGC : 132 YUV video sequences. PATH_TO_DIRECTORY_UGC = "" # All the raw data are divided into `NB_JOBS` subsets, each subset will be @@ -102,11 +102,11 @@ if __name__ == "__main__": ) args = parser.parse_args() if constants.IS_DATA_RAW_LARGE: - step_fr_skipped_base = 8 + nb_ext_per_seq = 16 else: - step_fr_skipped_base = 4 + nb_ext_per_seq = 32 if args.is_dry_run: - print('Dry-run is activated.') + print("Dry-run is activated.") if PATH_TO_DIRECTORY_ILSVRC2012_TRAINING: raise ValueError( "`PATH_TO_DIRECTORY_ILSVRC2012_TRAINING` is not empty. `PATH_TO_DIRECTORY_ILSVRC2012_TRAINING` must be empty in dry-run." @@ -136,7 +136,7 @@ if __name__ == "__main__": "`PATH_TO_DIRECTORY_UGC` is not empty. `PATH_TO_DIRECTORY_UGC` must be empty in dry-run." ) else: - print('Dry-run is deactivated.') + print("Dry-run is deactivated.") if not PATH_TO_DIRECTORY_DIV2K_TRAINING: raise ValueError( "`PATH_TO_DIRECTORY_DIV2K_TRAINING` is empty. Please set it to the path to the directory storing the DIV2K training dataset : 800 RGB images." @@ -261,28 +261,28 @@ if __name__ == "__main__": for line_0 in file: if re.search(r"(?<=InputFile)\s*:", line_0) is not None: if list_oks[0]: - raise ValueError('`list_oks[0]` is True.') + raise ValueError("`list_oks[0]` is True.") nm_data_on = "".join(line_0.split(":")[1].split()) list_oks[0] = True elif ( re.search(r"(?<=FramesToBeEncoded)\s*:", line_0) is not None ): if list_oks[1]: - raise ValueError('`list_oks[1]` is True.') + raise ValueError("`list_oks[1]` is True.") nb_acc = int(line_0.split(":")[1]) list_oks[1] = True elif ( re.search(r"(?<=SourceWidth)\s*:", line_0) is not None ): if list_oks[2]: - raise ValueError('`list_oks[2]` is True.') + raise ValueError("`list_oks[2]` is True.") width_video = int(line_0.split(":")[1]) list_oks[2] = True elif ( re.search(r"(?<=SourceHeight)\s*:", line_0) is not None ): if list_oks[3]: - raise ValueError('`list_oks[3]` is True.') + raise ValueError("`list_oks[3]` is True.") height_video = int(line_0.split(":")[1]) list_oks[3] = True if nm_data_on is None: @@ -294,7 +294,7 @@ if __name__ == "__main__": if height_video is None: raise RuntimeError("`height_video` is None.") if not all(list_oks): - raise RuntimeError('A boolean in `list_oks` is not True.') + raise RuntimeError("A boolean in `list_oks` is not True.") del list_oks if height_video % 8 == 0 and width_video % 8 == 0: nb_pbfe_found = 0 @@ -337,31 +337,32 @@ if __name__ == "__main__": for line_in in file_2: create_indef(line_in, list_in, list_out, file_0, file_1) if height_video % 8 != 0: - raise ValueError('`height_video` is not divisible by 8.') + raise ValueError("`height_video` is not divisible by 8.") if width_video % 8 != 0: - raise ValueError('`width_video` is not divisible by 8.') - if nb_acc > 256: - step_fr_skipped = 2 * step_fr_skipped_base + raise ValueError("`width_video` is not divisible by 8.") + if height_video * width_video < (1 << 21): + nb_ext_per_seq_used = 2 * nb_ext_per_seq else: - step_fr_skipped = step_fr_skipped_base + nb_ext_per_seq_used = nb_ext_per_seq + if nb_ext_per_seq_used > nb_acc: + raise ValueError("`nb_ext_per_seq_used` is not smaller than `nb_acc`.") + if nb_ext_per_seq_used < 1: + raise ValueError("`nb_ext_per_seq_used` is not larger than 1.") + step_fr_skipped = nb_acc // nb_ext_per_seq_used step_fr_skipped_doubled = 2 * step_fr_skipped for nb_fixed in range(0, nb_acc, step_fr_skipped): - if not constants.IS_DATA_RAW_LARGE and random.randint(0, 1) == 0: - tuple_rpr_scaling_ratios = (None, 2) - else: - tuple_rpr_scaling_ratios = (None,) + tuple_rpr_scaling_ratios = (None,) + tuple_are_orv = (False, True) if nb_fixed % step_fr_skipped_doubled == 0: - tuple_are_orv = (False, True) tuple_are_orh = (False,) else: - tuple_are_orv = (False,) - tuple_are_orh = (False, True) + tuple_are_orh = (True,) for data_ordered_0 in tuple_are_orv: for data_ordered_1 in tuple_are_orh: for rpr_scaling_ratio in tuple_rpr_scaling_ratios: if rpr_scaling_ratio is not None: if height_video % (rpr_scaling_ratio << 3) != 0 or width_video % (rpr_scaling_ratio << 3) != 0: - continue + raise ValueError("`height_video` is not divisible by 8*`rpr_scaling_ratio` or `width_video` is not divisible by 8*`rpr_scaling_ratio`.") store_for_next.append( { "pbfe": pbfe, @@ -392,7 +393,7 @@ if __name__ == "__main__": collecting_inout(psingle, list_temporary, (".JPEG", ".jpg", ".png")) if len(list_temporary) > 1300000: print( - '[INFO] For the dataset of RGB images in the directory at "{}", the number of collected RGB images is limited to 1300000.'.format( + "[INFO] For the dataset of RGB images in the directory at \"{}\", the number of collected RGB images is limited to 1300000.".format( psingle ) ) @@ -451,21 +452,21 @@ if __name__ == "__main__": path_to_subdir_out, "list_dicts_characteristics_own.pkl" ) if os.path.isfile(path_to_file_out): - print('[INFO] The file at "{}" is overwritten.'.format(path_to_file_out)) + print("[INFO] The file at \"{}\" is overwritten.".format(path_to_file_out)) is_file_overwitten = True os.remove(path_to_file_out) with open(path_to_file_out, "xb") as file: pickle.dump(list_prev[i], file, protocol=2) if is_file_overwitten: print( - '[WARNING] The dispatching of paths to raw data was probably run two times in a row, without removing the files written at the first run to the directory at "{}".'.format( + "[WARNING] The dispatching of paths to raw data was probably run two times in a row, without removing the files written at the first run to the directory at \"{}\".".format( descrip_all_1 ) ) print( - '[WARNING] Please make sure that the same arguments were used for the two runs of the script "dispatching_paths_to_raw.py".' + "[WARNING] Please make sure that the same arguments were used for the two runs of the script \"dispatching_paths_to_raw.py\"." ) print( - '[WARNING] If different arguments were used for the two runs of the script "dispatching_paths_to_raw.py", two different dispatching are mixed up.' + "[WARNING] If different arguments were used for the two runs of the script \"dispatching_paths_to_raw.py\", two different dispatching are mixed up." ) print("The dispatching of paths to raw data succeeded.") diff --git a/training/training_scripts/NN_Intra_Prediction/generating_data_intra.py b/training/training_scripts/NN_Intra_Prediction/generating_data_intra.py index ae6bd749abffb9bebd4e83ad5f5e718d21141b34..3ebf41021db830c8d099eb020e0ed8cee9e5b5c8 100644 --- a/training/training_scripts/NN_Intra_Prediction/generating_data_intra.py +++ b/training/training_scripts/NN_Intra_Prediction/generating_data_intra.py @@ -96,11 +96,31 @@ if sys.platform in ("win32", "cygwin"): "search", "DecoderApp.exe", ) + PATH_TO_EXE_ENCODER_VTM_NN_PARTIAL = os.path.join( + "bin/", + "vs{}".format(VERSION_VS_MAJOR), + "msvc-{0}.{1}".format(VERSION_VS_MINOR_0, VERSION_VS_MINOR_1), + "x86_64", + "release", + "search_partial", + "EncoderApp.exe", + ) + PATH_TO_EXE_DECODER_VTM_NN_PARTIAL = os.path.join( + "bin/", + "vs{}".format(VERSION_VS_MAJOR), + "msvc-{0}.{1}".format(VERSION_VS_MINOR_0, VERSION_VS_MINOR_1), + "x86_64", + "release", + "search_partial", + "DecoderApp.exe", + ) else: PATH_TO_EXE_ENCODER_VTM_REGULAR = "bin/regular/EncoderAppStatic" PATH_TO_EXE_DECODER_VTM_REGULAR = "bin/regular/DecoderAppStatic" PATH_TO_EXE_ENCODER_VTM_NN = "bin/search/EncoderAppStatic" PATH_TO_EXE_DECODER_VTM_NN = "bin/search/DecoderAppStatic" + PATH_TO_EXE_ENCODER_VTM_NN_PARTIAL = "bin/search_partial/EncoderAppStatic" + PATH_TO_EXE_DECODER_VTM_NN_PARTIAL = "bin/search_partial/DecoderAppStatic" DICT_DESC_FULL = { (4, 4): 8, @@ -128,11 +148,11 @@ def getting_specialized(input_in): list_0 = input_in.split(",") if len(list_0) != 2: raise argparse.ArgumentTypeError( - '"{}" does not contain two strings separated by a comma.'.format(input_in) + "\"{}\" does not contain two strings separated by a comma.".format(input_in) ) if not list_0[0]: raise argparse.ArgumentTypeError( - 'In "{}", the first string on the left side of the comma is empty.'.format( + "In \"{}\", the first string on the left side of the comma is empty.".format( input_in ) ) @@ -143,13 +163,13 @@ def getting_specialized(input_in): tuple_0 = (int(list_0[0]),) except ValueError: raise argparse.ArgumentTypeError( - 'In "{}", either the string on the left side of the comma or the string on the right side of the comma cannot be converted into an integer.'.format( + "In \"{}\", either the string on the left side of the comma or the string on the right side of the comma cannot be converted into an integer.".format( input_in ) ) if tuple_0[0] < 0: raise argparse.ArgumentTypeError( - 'In "{}", the string on the left side of the comma is not a positive integer.'.format( + "In \"{}\", the string on the left side of the comma is not a positive integer.".format( input_in ) ) @@ -325,7 +345,7 @@ class GeneratorDataPart(object): "[WARNING] The same training set (at the same training cycle) is being re-written to the same directory once more. If it is expected, please remove the existing files to regenerate the same training set." ) raise OSError( - 'The file at "{}" already exists.'.format(itemm["path_to_tfrecord"]) + "The file at \"{}\" already exists.".format(itemm["path_to_tfrecord"]) ) itemm["record_writer"] = tf.io.TFRecordWriter(itemm["path_to_tfrecord"]) @@ -457,50 +477,50 @@ class GeneratorDataPart(object): rpr_scaling_ratio = dict_input["rpr_scaling_ratio"] if os.path.isfile(dict_picked["path_to_bitstream"]): raise OSError( - 'The file at "{}" already exists. This file is a temporary file. It is supposed to be automatically suppressed after the decoding completion. Did the VTM-11-NNVC encoder/decoder crash? Please remove this file manually.'.format( + "The file at \"{}\" already exists. This file is a temporary file. It is supposed to be automatically suppressed after the decoding completion. Did the VTM-11-NNVC encoder/decoder crash? Please remove this file manually.".format( dict_picked["path_to_bitstream"] ) ) if os.path.isfile(dict_picked["path_provided_0"]): raise OSError( - 'The file at "{}" already exists. This file is a temporary file. It is supposed to be automatically suppressed after the decoding completion. Did the VTM-11-NNVC encoder/decoder crash? Please remove this file manually.'.format( + "The file at \"{}\" already exists. This file is a temporary file. It is supposed to be automatically suppressed after the decoding completion. Did the VTM-11-NNVC encoder/decoder crash? Please remove this file manually.".format( dict_picked["path_provided_0"] ) ) if os.path.isfile(dict_picked["path_provided_1"]): raise OSError( - 'The file at "{}" already exists. This file is a temporary file. It is supposed to be automatically suppressed after the decoding completion. Did the VTM-11-NNVC encoder/decoder crash? Please remove this file manually.'.format( + "The file at \"{}\" already exists. This file is a temporary file. It is supposed to be automatically suppressed after the decoding completion. Did the VTM-11-NNVC encoder/decoder crash? Please remove this file manually.".format( dict_picked["path_provided_1"] ) ) if os.path.isfile(dict_picked["path_provided_2"]): raise OSError( - 'The file at "{}" already exists. This file is a temporary file. It is supposed to be automatically suppressed after the decoding completion. Did the VTM-11-NNVC encoder/decoder crash? Please remove this file manually.'.format( + "The file at \"{}\" already exists. This file is a temporary file. It is supposed to be automatically suppressed after the decoding completion. Did the VTM-11-NNVC encoder/decoder crash? Please remove this file manually.".format( dict_picked["path_provided_2"] ) ) if os.path.isfile(dict_picked["tuple_reference_writing"][0]): raise OSError( - 'The file at "{}" already exists. This file is a temporary file. It is supposed to be automatically suppressed after the decoding completion. Did the VTM-11-NNVC encoder/decoder crash? Please remove this file manually.'.format( + "The file at \"{}\" already exists. This file is a temporary file. It is supposed to be automatically suppressed after the decoding completion. Did the VTM-11-NNVC encoder/decoder crash? Please remove this file manually.".format( dict_picked["tuple_reference_writing"][0] ) ) if os.path.isfile(dict_picked["tuple_reference_writing"][1]): raise OSError( - 'The file at "{}" already exists. This file is a temporary file. It is supposed to be automatically suppressed after the decoding completion. Did the VTM-11-NNVC encoder/decoder crash? Please remove this file manually.'.format( + "The file at \"{}\" already exists. This file is a temporary file. It is supposed to be automatically suppressed after the decoding completion. Did the VTM-11-NNVC encoder/decoder crash? Please remove this file manually.".format( dict_picked["tuple_reference_writing"][1] ) ) if os.path.isfile(dict_picked["tuple_reference_writing"][2]): raise OSError( - 'The file at "{}" already exists. This file is a temporary file. It is supposed to be automatically suppressed after the decoding completion. Did the VTM-11-NNVC encoder/decoder crash? Please remove this file manually.'.format( + "The file at \"{}\" already exists. This file is a temporary file. It is supposed to be automatically suppressed after the decoding completion. Did the VTM-11-NNVC encoder/decoder crash? Please remove this file manually.".format( dict_picked["tuple_reference_writing"][2] ) ) if "data_add_basic" in dict_input: if os.path.isfile(dict_chr_own["pbfe"]): raise OSError( - 'The file at "{}" already exists. This file is a temporary file. It is supposed to be automatically suppressed after the decoding completion. Did the VTM-11-NNVC encoder/decoder crash? Please remove this file manually.'.format( + "The file at \"{}\" already exists. This file is a temporary file. It is supposed to be automatically suppressed after the decoding completion. Did the VTM-11-NNVC encoder/decoder crash? Please remove this file manually.".format( dict_chr_own["pbfe"] ) ) @@ -539,6 +559,7 @@ class GeneratorDataPart(object): "--FrameSkip={}".format(nb_fixed), "--QP={}".format(qp_used), "--LoopFilterDisable=1", + "--ISP=0", "--SAO=0", "--ALF=0", "--CCALF=0", @@ -608,9 +629,9 @@ class GeneratorDataPart(object): ) ) if rpr_scaling_ratio is not None: - if rpr_scaling_ratio != 2: + if rpr_scaling_ratio != 2 and rpr_scaling_ratio != 3: raise ValueError( - "`rpr_scaling_ratio` is not None and `rpr_scaling_ratio` is not equal to 2." + "`rpr_scaling_ratio` is not None and `rpr_scaling_ratio` does not belong to {2, 3}." ) if height_video % 8 * rpr_scaling_ratio != 0: raise ValueError( @@ -1342,12 +1363,22 @@ if __name__ == "__main__": ) parser.add_argument( "--is_dry_run", - help='if given, "dry-run" is activated. "dry-run" enables to run quickly the entire training of the neural network-based intra prediction mode on very little data, for the purpose of forecasting grid issues', + help="if given, \"dry-run\" is activated. \"dry-run\" enables to run quickly the entire training of the neural network-based intra prediction mode on very little data, for the purpose of forecasting grid issues", + action="store_true", + default=False, + ) + parser.add_argument( + "--is_partial", + help="if given, the partial re-training of the neural network-based intra prediction mode from the training cycle of index 2 is done", action="store_true", default=False, ) args = parser.parse_args() + if args.is_dry_run and args.is_partial: + raise ValueError("`args.is_dry_run` is True and `args.is_partial` is True.") index_cycle = args.tuple_index_cycle_tag_sparsity[0] + if args.is_partial and index_cycle != 2: + raise ValueError("`args.is_partial` is True and `index_cycle` is not equal to 2.") if not index_cycle: if len(args.tuple_index_cycle_tag_sparsity) != 1: raise ValueError( @@ -1360,30 +1391,48 @@ if __name__ == "__main__": path_to_directory_temp = os.path.join("results/temporary/", tag_suffix_job) create_dconcurrently(path_to_directory_temp) if index_cycle: - path_to_exe_encoder = PATH_TO_EXE_ENCODER_VTM_NN - path_to_exe_decoder = PATH_TO_EXE_DECODER_VTM_NN + if args.is_partial: + path_to_exe_encoder = PATH_TO_EXE_ENCODER_VTM_NN_PARTIAL + path_to_exe_decoder = PATH_TO_EXE_DECODER_VTM_NN_PARTIAL + else: + path_to_exe_encoder = PATH_TO_EXE_ENCODER_VTM_NN + path_to_exe_decoder = PATH_TO_EXE_DECODER_VTM_NN else: path_to_exe_encoder = PATH_TO_EXE_ENCODER_VTM_REGULAR path_to_exe_decoder = PATH_TO_EXE_DECODER_VTM_REGULAR if index_cycle: - tag_sf = os.path.join( - "partitioning_{}_reconstruction_vtm".format(index_cycle - 1), - args.tuple_index_cycle_tag_sparsity[1], - "float", - "graph_output.pb", - ) - dfout = "" - for i, pair_hw in enumerate( - ((4, 4), (4, 8), (4, 16), (4, 32), (8, 8), (8, 16), (16, 16)) - ): - dfout += "{0},{1},".format(pair_hw[0], pair_hw[1]) - dfout += os.path.join( - "results/freezing_graph_pnn/", - "target_{0}_{1}".format(pair_hw[0], pair_hw[1]), - tag_sf, + if args.is_partial: + dfout = "" + for i, pair_hw in enumerate( + ((4, 4), (4, 8), (4, 16), (4, 32), (8, 8), (8, 16), (16, 16)) + ): + dfout += "{0},{1},".format(pair_hw[0], pair_hw[1]) + dfout += os.path.join( + PATH_TO_DIRECTORY_ROOT_PROJECT, + "models/intra/", + "graph_output_{0}_{1}_int16.sadl".format(pair_hw[0], pair_hw[1]), + ) + if i != 6: + dfout += ";" + else: + tag_sf = os.path.join( + "partitioning_{}_reconstruction_vtm".format(index_cycle - 1), + args.tuple_index_cycle_tag_sparsity[1], + "float", + "graph_output.pb", ) - if i != 6: - dfout += ";" + dfout = "" + for i, pair_hw in enumerate( + ((4, 4), (4, 8), (4, 16), (4, 32), (8, 8), (8, 16), (16, 16)) + ): + dfout += "{0},{1},".format(pair_hw[0], pair_hw[1]) + dfout += os.path.join( + "results/freezing_graph_pnn/", + "target_{0}_{1}".format(pair_hw[0], pair_hw[1]), + tag_sf, + ) + if i != 6: + dfout += ";" else: dfout = "" dict_picked = { @@ -1393,7 +1442,9 @@ if __name__ == "__main__": "path_to_cfg": os.path.join( PATH_TO_DIRECTORY_ROOT_PROJECT, "cfg/encoder_intra_nnvc.cfg" ), - "path_to_bitstream": os.path.join(path_to_directory_temp, "bitstream.bin"), + "path_to_bitstream": os.path.join( + path_to_directory_temp, "bitstream.bin" + ), "path_to_exe_encoder": path_to_exe_encoder, "path_to_exe_decoder": path_to_exe_decoder, "descr_stacked_up": dfout, diff --git a/training/training_scripts/NN_Intra_Prediction/quantize/quantize.py b/training/training_scripts/NN_Intra_Prediction/quantize/quantize.py deleted file mode 100644 index a7ab20fd3aea845dd17f475c237644b8b05a0a2a..0000000000000000000000000000000000000000 --- a/training/training_scripts/NN_Intra_Prediction/quantize/quantize.py +++ /dev/null @@ -1,119 +0,0 @@ -""" -/* The copyright in this software is being made available under the BSD -* License, included below. This software may be subject to other third party -* and contributor rights, including patent rights, and no such rights are -* granted under this license. -* -* Copyright (c) 2010-2023, ITU/ISO/IEC -* All rights reserved. -* -* Redistribution and use in source and binary forms, with or without -* modification, are permitted provided that the following conditions are met: -* -* * Redistributions of source code must retain the above copyright notice, -* this list of conditions and the following disclaimer. -* * Redistributions 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. -* * Neither the name of the ITU/ISO/IEC nor the names of its contributors may -* 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 COPYRIGHT HOLDER 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. -""" -import argparse -import os -import subprocess - -DICT_QUANTIZERS = { - ( - 4, - 4, - ): "0 7 1 14 2 0 3 18 5 12 6 2 7 18 9 18 10 -2 11 10 13 0 15 13 16 -1 17 0 19 17 20 -2 21 9", - ( - 4, - 8, - ): "0 7 1 14 2 0 3 18 5 12 6 2 7 18 9 17 10 -2 11 10 13 0 15 13 16 -2 17 0 19 18 20 -2 21 9", - ( - 4, - 16, - ): "0 7 1 14 2 0 3 18 5 12 6 2 7 18 9 17 10 -2 11 10 13 0 15 13 16 -2 17 0 19 18 20 -2 21 9", - ( - 4, - 32, - ): "0 7 1 14 2 0 3 18 5 12 6 2 7 18 9 15 10 -2 11 10 13 0 15 14 16 -2 17 0 19 18 20 -2 21 9", - ( - 8, - 8, - ): "0 7 1 13 2 1 3 18 5 13 6 1 7 18 9 17 10 -2 11 10 13 0 15 14 16 -2 17 0 19 18 20 -2 21 9", - ( - 8, - 16, - ): "0 7 1 13 2 1 3 18 5 12 6 1 7 18 9 15 10 -2 11 9 13 0 15 14 16 -2 17 0 19 18 20 -2 21 9", - ( - 16, - 16, - ): "0 7 1 13 2 1 3 18 5 12 6 1 7 18 9 13 10 0 11 18 13 15 14 -2 15 10 17 0 19 13 20 -2 21 0 23 17 24 -2 25 9", -} - -if __name__ == "__main__": - parser = argparse.ArgumentParser( - prog="Quantize SADL model in float into SADL model in 16-bit signed integer" - ) - parser.add_argument( - "path_to_input", - type=str, - help="Path to the SADL model in float to be quantized", - ) - parser.add_argument( - "path_to_output", - type=str, - help="Path to the SADL model in 16-bit signed integer resulting from the quantization", - ) - parser.add_argument( - "path_to_exe_quantization_naive", - type=str, - help="Path to SADL naive quantizer executable", - ) - parser.add_argument( - "height", - type=int, - help="Height of the blocks predicted by the neural network model at `args.path_to_input`", - ) - parser.add_argument( - "width", - type=int, - help="Width of the blocks predicted by the neural network model at `args.path_to_input`", - ) - args = parser.parse_args() - - if not os.path.isfile(args.path_to_exe_quantization_naive): - raise OSError( - "The executable at `args.path_to_exe_quantization_naive` does not exist." - ) - else: - if (args.height, args.width) in DICT_QUANTIZERS: - p = subprocess.Popen( - [ - args.path_to_exe_quantization_naive, - args.path_to_input, - args.path_to_output, - ], - stdin=subprocess.PIPE, - ) - p.communicate(input=DICT_QUANTIZERS[(args.height, args.width)]) - print("[INFO] Output model at {}".format(args.path_to_output)) - else: - raise KeyError( - "`(args.height, args.width)` is not a key of `DICT_QUANTIZERS`." - ) diff --git a/training/training_scripts/NN_Intra_Prediction/quantizing_nn_intra.py b/training/training_scripts/NN_Intra_Prediction/quantizing_nn_intra.py new file mode 100644 index 0000000000000000000000000000000000000000..a4b62cdbe5b1df79c705979448153ddf23b5f718 --- /dev/null +++ b/training/training_scripts/NN_Intra_Prediction/quantizing_nn_intra.py @@ -0,0 +1,62 @@ +""" +The copyright in this software is being made available under the BSD +License, included below. This software may be subject to other third party +and contributor rights, including patent rights, and no such rights are +granted under this license. + +Copyright (c) 2010-2022, ITU/ISO/IEC +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + + Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + Redistributions 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. + Neither the name of the ITU/ISO/IEC nor the names of its contributors may + 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 COPYRIGHT HOLDER 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. +""" + +import argparse +import subprocess + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + description="Quantizes a neural network for intra prediction." + ) + parser.add_argument( + "path_to_exe_quantization", + help="path to the executable of the SADL naive quantizer", + ) + parser.add_argument( + "path_to_graph_input_float", + help="path to the input binary file : neural network graph in floats", + ) + parser.add_argument( + "path_to_graph_output_int16", + help="path to the output binary file : neural network graph in 16-bit signed integer", + ) + parser.add_argument( + "str_quantizers", + help="string in which each layer ID is paired with its quantizer. Two successive pairs are separated by a comma. In a pair, the layer ID and its quantizer are separated by a comma", + ) + args = parser.parse_args() + process = subprocess.Popen([args.path_to_exe_quantization, args.path_to_graph_input_float, args.path_to_graph_output_int16], stdin=subprocess.PIPE) + process.communicate(input=args.str_quantizers.encode()) + print("[INFO] The output binary file containing the neural network graph in 16-bit signed integer is written at {}.".format(args.path_to_graph_output_int16)) diff --git a/training/training_scripts/NN_Intra_Prediction/run_setups_vtm_search_partial.sh b/training/training_scripts/NN_Intra_Prediction/run_setups_vtm_search_partial.sh new file mode 100644 index 0000000000000000000000000000000000000000..4cfb8da82bd9e1daced5c8a179476608cdc40ceb --- /dev/null +++ b/training/training_scripts/NN_Intra_Prediction/run_setups_vtm_search_partial.sh @@ -0,0 +1,11 @@ +#!/bin/bash + +cd build + +# Step 3: VTM-11-NNVC without Filter-Set, with the neural network-based intra prediction mode in 16-bit signed integer is built (for writing training data). +mkdir search_partial +cd search_partial +cmake ../../ -DNNLF_BUILD_WITH_AVX512=0 -DCMAKE_CXX_FLAGS_RELEASE="-O3 -DNDEBUG" -DUSE_SEARCH_PARTIAL=1 +make -j 8 +cd ../.. + diff --git a/training/training_scripts/NN_Intra_Prediction/training_nn_intra.py b/training/training_scripts/NN_Intra_Prediction/training_nn_intra.py index 0c3ccd915d657a7099ea8ac1ecf5d60f6ac1ce70..3f7eca71e9597c36a9719ad26c577d119cd09700 100644 --- a/training/training_scripts/NN_Intra_Prediction/training_nn_intra.py +++ b/training/training_scripts/NN_Intra_Prediction/training_nn_intra.py @@ -58,9 +58,9 @@ IS_LOSS_VISUALIZED = False if IS_LOSS_VISUALIZED: import matplotlib try: - matplotlib.use('Qt5Agg') + matplotlib.use("Qt5Agg") except ImportError: - matplotlib.use('Agg') + matplotlib.use("Agg") import matplotlib.pyplot as plt @@ -80,7 +80,7 @@ def create_apply_w_to_dense(nb_iters_training, frequency, are_weights_mmf_sparse # the ratio of non-zero weights is 50% at the end of the training. if layer.name == "dense_p": fs_done = 0.5 - elif layer.name == 'dense_mmf': + elif layer.name == "dense_mmf": if are_weights_mmf_sparser: fs_done = 0.9 else: @@ -300,7 +300,7 @@ def create_compute_component_tm_statically(batch_size, updown_single): def create_compute_components_mm_statically( - batch_size, height_target, width_target, model + batch_size, height_target, width_target, model, is_obj_changed ): @tf.function( input_signature=( @@ -340,54 +340,60 @@ def create_compute_components_mm_statically( for layer in model.layers: if layer.trainable_weights: cumul += tf.reduce_sum(tf.abs(layer.trainable_weights[0])) - return ( - tf.reduce_mean( - tf.reduce_sum( - tf.abs( - tf.matmul( - tf.matmul( - tf.constant( - numpy.tile( - numpy.reshape( - scipy.linalg.hadamard( - intermediate.shape[-2], - dtype=dtype_numpy, - ), - tuple( - [1] * (len(intermediate.shape) - 2) - + [ - intermediate.shape[-2], - intermediate.shape[-2], - ] - ), - ), - tuple(intermediate.shape[0:-2] + [1, 1]), - ) - ), - intermediate, + coeffs_transf = tf.matmul( + tf.matmul( + tf.constant( + numpy.tile( + numpy.reshape( + scipy.linalg.hadamard( + intermediate.shape[-2], + dtype=dtype_numpy, ), - tf.constant( - numpy.tile( - numpy.reshape( - scipy.linalg.hadamard( - intermediate.shape[-1], dtype=dtype_numpy - ), - tuple( - [1] * (len(intermediate.shape) - 2) - + [ - intermediate.shape[-1], - intermediate.shape[-1], - ] - ), - ), - tuple(intermediate.shape[0:-2] + [1, 1]), - ) + tuple( + [1] * (len(intermediate.shape) - 2) + + [ + intermediate.shape[-2], + intermediate.shape[-2], + ] ), - ) + ), + tuple(intermediate.shape[0:-2] + [1, 1]), + ) + ), + intermediate, + ), + tf.constant( + numpy.tile( + numpy.reshape( + scipy.linalg.hadamard( + intermediate.shape[-1], dtype=dtype_numpy + ), + tuple( + [1] * (len(intermediate.shape) - 2) + + [ + intermediate.shape[-1], + intermediate.shape[-1], + ] + ), ), - axis=(len(intermediate.shape) - 2, len(intermediate.shape) - 1), + tuple(intermediate.shape[0:-2] + [1, 1]), ) ), + ) + satds_c = tf.reduce_sum( + tf.abs(coeffs_transf), + axis=(len(intermediate.shape) - 2, len(intermediate.shape) - 1), + ) + if is_obj_changed: + sads_c = tf.reduce_sum( + tf.abs(intermediate), + axis=(len(intermediate.shape) - 2, len(intermediate.shape) - 1), + ) + comp_pred_indv = satds_c - 0.75 * tf.abs(coeffs_transf[..., 0, 0]) + sads_c + else: + comp_pred_indv = satds_c + return ( + tf.reduce_mean(comp_pred_indv), 5.0e-4 * tf.reduce_mean( glob_at_last( @@ -515,7 +521,7 @@ if __name__ == "__main__": ) parser.add_argument( "nb_files_expected_training", - help='expected number of files ".tfrecord" collected from the directory at `args.path_to_directory_sets_training`', + help="expected number of files \".tfrecord\" collected from the directory at `args.path_to_directory_sets_training`", type=int, ) parser.add_argument( @@ -533,7 +539,7 @@ if __name__ == "__main__": ) parser.add_argument( "--is_last_cycle", - help='is it the last training cycle? If true, the final graph is stored in the directory at "../../models_intra_prediction" for the final rate-distortion tests', + help="is it the last training cycle? If true, the final graph is stored in the directory at \"../../models_intra_prediction\" for the final rate-distortion tests", action="store_true", default=False, ) @@ -543,9 +549,17 @@ if __name__ == "__main__": action="store_true", default=False, ) + parser.add_argument( + "--is_partial", + help="if given, the partial re-training of the neural network-based intra prediction mode from the training cycle of index 2 is done", + action="store_true", + default=False, + ) args = parser.parse_args() + if args.is_dry_run and args.is_partial: + raise ValueError("`args.is_dry_run` is True and `args.is_partial` is True.") if args.is_dry_run and args.nb_iters_snapshot > 200: - raise ValueError('If dry-run is activated, `args.nb_iters_snapshot` must be smaller than 200. This enables to save at least one neural network model before reaching 400 training iterations.') + raise ValueError("If dry-run is activated, `args.nb_iters_snapshot` must be smaller than 200. This enables to save at least one neural network model before reaching 400 training iterations.") is_tm_used = args.tag_training_set.endswith("_ttm") if args.tag_sparsity == "sparsity_0": is_sparsity_applied = False @@ -553,7 +567,7 @@ if __name__ == "__main__": is_sparsity_applied = not is_tm_used else: raise ValueError( - "`args.tag_sparsity` is equal to neither 'sparsity_0' nor 'sparsity_1'." + "`args.tag_sparsity` is equal to neither \"sparsity_0\" nor \"sparsity_1\"." ) tag_shape_target = "target_{0}_{1}".format( args.tuple_height_width_target[0], args.tuple_height_width_target[1] @@ -574,6 +588,8 @@ if __name__ == "__main__": if sre_match is None: raise RuntimeError("Error.") index_cycle = int(sre_match.group(0)) + if args.is_partial and (index_cycle != 2 or args.tag_sparsity != "sparsity_0"): + raise ValueError("`args.is_partial` is True and `index_cycle` is not equal to 2 or `args.tag_sparsity` is not equal to \"sparsity_0\".") if sys.version_info[0] < 3: try: os.makedirs(path_to_directory_saving) @@ -601,7 +617,7 @@ if __name__ == "__main__": "sparsity_0", ) else: - if index_cycle: + if index_cycle and not args.is_partial: if is_tm_used: path_to_pb_repres = os.path.join( path_to_directory_top_tfrecords_training, "pb_repres.npy" @@ -676,15 +692,15 @@ if __name__ == "__main__": activation=tf.keras.layers.LeakyReLU(alpha=0.0625, name="leaky_relu"), use_bias=False, kernel_initializer=tf.keras.initializers.RandomNormal(stddev=stddev), - name='dense_{}'.format(i) + name="dense_{}".format(i) )(node_rp) del stddev del nb_layers_tfc # Names of the three layers of the fully-connected neural network returning the three outputs: - # 'dense_p': fully-connected layer returning the prediction of the current block - # 'dense_mmf': fully-connected layer returning the unscaled log-probability for each of the 67 VVC intra prediction modes - # 'dense_tmf': fully-connected layer returning the unscaled log-probability for each LFNST group index. + # "dense_p": fully-connected layer returning the prediction of the current block + # "dense_mmf": fully-connected layer returning the unscaled log-probability for each of the 67 VVC intra prediction modes + # "dense_tmf": fully-connected layer returning the unscaled log-probability for each LFNST group index. model = tf.keras.Model( inputs=node_ip, outputs=( @@ -757,7 +773,7 @@ if __name__ == "__main__": ) ) else: - if index_cycle == 0: + if index_cycle == 0 or args.is_partial: nb_iters_training = 3200000 optimizer = tf.optimizers.Adam( learning_rate=tf.optimizers.schedules.PiecewiseConstantDecay( @@ -825,20 +841,21 @@ if __name__ == "__main__": ) ) bs_fixed = 4096 + if len(list_grouping) == 1: + [upstream] = list_grouping + else: + upstream = tf.data.Dataset.from_tensor_slices(list_grouping).shuffle(len(list_grouping), reshuffle_each_iteration=True) td_all = ( - tf.data.TFRecordDataset( - tf.data.Dataset.from_tensor_slices(list_grouping) - .shuffle(len(list_grouping), reshuffle_each_iteration=True) - ) - .shuffle(64 * bs_fixed, reshuffle_each_iteration=True) + tf.data.TFRecordDataset(upstream, num_parallel_reads=1) .repeat() + .shuffle(64 * bs_fixed, reshuffle_each_iteration=True) .batch(bs_fixed, num_parallel_calls=tf.data.AUTOTUNE, deterministic=False) .map( lambda serialized_e : parse_example_df(serialized_e, bs_fixed, nb_lines_above, nb_lines_left, size_ctx_height, size_ctx_width, args.tuple_height_width_target[0], args.tuple_height_width_target[1], is_tm_used, index_cycle != 0), num_parallel_calls=tf.data.AUTOTUNE, deterministic=False ) - .prefetch(tf.data.AUTOTUNE) + .prefetch(8) ) del list_grouping i_td_all = iter(td_all) @@ -871,6 +888,7 @@ if __name__ == "__main__": args.tuple_height_width_target[0], args.tuple_height_width_target[1], model, + index_cycle >= 2 ) compute_grads_mm_statically = create_compute_grads_mm_statically( bs_fixed, @@ -894,7 +912,7 @@ if __name__ == "__main__": t_start = time.time() for i in range(nb_iters_remaining): if args.is_dry_run and i == 400: - print('As dry-run is activated, the training stops at 400 training iterations.') + print("As dry-run is activated, the training stops at 400 training iterations.") break ( node_pa_flattened, @@ -1025,14 +1043,14 @@ if __name__ == "__main__": ) print("The writing of the neural network graph in SADL format succeeded.") if IS_LOSS_VISUALIZED: - path_to_directory_loss = os.path.join('visualization', tag_shape_target, args.tag_training_set, args.tag_sparsity) + path_to_directory_loss = os.path.join("visualization", tag_shape_target, args.tag_training_set, args.tag_sparsity) if not os.path.isdir(path_to_directory_loss): os.makedirs(path_to_directory_loss) iters_int32 = numpy.array(list_iters, dtype=numpy.int32) losses_collected_float64 = numpy.array(list_losses, dtype=numpy.float64) plt.plot(iters_int32, losses_collected_float64)[0] - plt.title('Training loss over iterations') - plt.xlabel('iteration') - plt.ylabel('loss') - plt.savefig(os.path.join(path_to_directory_loss, 'loss.png')) + plt.title("Training loss over iterations") + plt.xlabel("iteration") + plt.ylabel("loss") + plt.savefig(os.path.join(path_to_directory_loss, "loss.png")) plt.clf() diff --git a/training/training_scripts/NN_SR_Multiratio/common_config.json b/training/training_scripts/NN_SR_Multiratio/common_config.json new file mode 100644 index 0000000000000000000000000000000000000000..9bc41fa408b72ee11e6538fb05426623ddd0c3b8 --- /dev/null +++ b/training/training_scripts/NN_SR_Multiratio/common_config.json @@ -0,0 +1,333 @@ +{ + "verbose": 2, + "binaries": { + "ffmpeg": "ffmpeg" + }, + + + "dataset": { + "div2k_train": { + "//": "dataset of png to convert", + "start": 1, + "count": 800 + }, + "div2k_valid": { + "start": 801, + "count": 100 + }, + "bvi": { + "//": "dataset of yuv" + }, + "bvi_valid": { + "//": "dataset of yuv" + }, + "tvd": { + "//": "dataset of yuv" + }, + "tvd_valid": { + "//": "dataset of yuv" + } + }, + + + "stage1": { + "yuv": { + "dataset_file": "dataset_nnsr.json" + }, + "yuv_valid": { + "dataset_file": "dataset_valid_nnsr.json" + }, + "encdec": { + "dump_dir": "dump", + "enc_dir": "enc", + "dec_dir": "dec", + "cfg_dir": "cfg", + "qps": "19 22 24 27 29 32 34 37 39 42", + "dataset_file": "dataset.json", + "encode_script": "encode_decode_dataset.sh" + }, + "encdec_valid": { + "dump_dir": "dump_valid", + "enc_dir": "enc_valid", + "dec_dir": "dec_valid", + "cfg_dir": "cfg_valid", + "qps": "19 22 24 27 29 32 34 37 39 42", + "dataset_file": "dataset_valid.json", + "encode_script": "encode_decode_dataset_valid.sh" + }, + "dataset": { + "dataset_file": "div2k-2x.json" + }, + "dataset_valid": { + "dataset_file": "div2k_valid-2x.json" + } + }, + + + "stage2": { + "yuv_tvd": { + "dataset_file": "dataset_tvd_nnsr.json" + }, + "yuv_tvd_valid": { + "dataset_file": "dataset_tvd_valid_nnsr.json" + }, + "yuv_bvi": { + "dataset_file": "dataset_bvi_nnsr.json" + }, + "yuv_bvi_valid": { + "dataset_file": "dataset_bvi_valid_nnsr.json" + }, + "encdec_bvi": { + "dump_dir": "dump_bvi", + "enc_dir": "enc_bvi", + "dec_dir": "dec_bvi", + "cfg_dir": "cfg_bvi", + "qps": "22 27 32 37 42", + "dataset_file": "dataset_bvi.json", + "encode_script": "encode_decode_dataset_bvi.sh" + }, + "encdec_bvi_valid": { + "dump_dir": "dump_bvi_valid", + "enc_dir": "enc_bvi_valid", + "dec_dir": "dec_bvi_valid", + "cfg_dir": "cfg_bvi_valid", + "qps": "22 27 32 37 42", + "dataset_file": "dataset_bvi_valid.json", + "encode_script": "encode_decode_dataset_bvi_valid.sh" + }, + "encdec_tvd": { + "dump_dir": "dump_tvd", + "enc_dir": "enc_tvd", + "dec_dir": "dec_tvd", + "cfg_dir": "cfg_tvd", + "qps": "22 27 32 37 42", + "vtm_model": "stage1/conversion", + "dataset_file": "dataset_tvd.json", + "encode_script": "encode_decode_dataset_tvd.sh" + }, + "encdec_tvd_valid": { + "dump_dir": "dump_tvd_valid", + "enc_dir": "enc_tvd_valid", + "dec_dir": "dec_tvd_valid", + "cfg_dir": "cfg_tvd_valid", + "qps": "22 27 32 37 42", + "vtm_model": "stage1/conversion", + "dataset_file": "dataset_tvd_valid.json", + "encode_script": "encode_decode_dataset_tvd_valid.sh" + }, + "dataset": { + "dataset_files": { + "bvi": { + "dataset_file": "bvi-2x.json", + "augs": [ + "vflip", + "hflip", + "transpose" + ], + "aug_prob": 0.2 + }, + "tvd": { + "dataset_file": "tvd-2x.json", + "augs": [ + "vflip", + "hflip", + "transpose" + ], + "aug_prob": 0.2 + } + } + }, + "dataset_valid": { + "dataset_files": { + "bvi": { + "dataset_file": "bvi_valid-2x.json", + "augs": [], + "aug_prob": 0.2 + }, + "tvd": { + "dataset_file": "tvd_valid-2x.json", + "augs": [], + "aug_prob": 0.2 + } + } + } + }, + + + "stage3": { + "dataset": { + "dataset_files": { + "div2k-2x": { + "dataset_file": "div2k-2x.json", + "augs": [], + "aug_prob": 0.0 + }, + "bvi-2x": { + "dataset_file": "bvi-2x.json", + "augs": [ + "vflip", + "hflip", + "transpose" + ], + "aug_prob": 0.2 + }, + "tvd-2x": { + "dataset_file": "tvd-2x.json", + "augs": [ + "vflip", + "hflip", + "transpose" + ], + "aug_prob": 0.2 + }, + "div2k-1.5x": { + "dataset_file": "div2k-1.5x.json", + "augs": [], + "aug_prob": 0.0 + }, + "bvi-1.5x": { + "dataset_file": "bvi-1.5x.json", + "augs": [ + "vflip", + "hflip", + "transpose" + ], + "aug_prob": 0.2 + }, + "tvd-1.5x": { + "dataset_file": "tvd-1.5x.json", + "augs": [ + "vflip", + "hflip", + "transpose" + ], + "aug_prob": 0.2 + } + } + }, + "dataset_valid": { + "dataset_files": { + "div2k-2x": { + "dataset_file": "div2k_valid-2x.json", + "augs": [], + "aug_prob": 0.0 + }, + "bvi-2x": { + "dataset_file": "bvi_valid-2x.json", + "augs": [], + "aug_prob": 0.2 + }, + "tvd-2x": { + "dataset_file": "tvd_valid-2x.json", + "augs": [], + "aug_prob": 0.2 + }, + "div2k-1.5x": { + "dataset_file": "div2k_valid-1.5x.json", + "augs": [], + "aug_prob": 0.0 + }, + "bvi-1.5x": { + "dataset_file": "bvi_valid-1.5x.json", + "augs": [], + "aug_prob": 0.2 + }, + "tvd-1.5x": { + "dataset_file": "tvd_valid-1.5x.json", + "augs": [], + "aug_prob": 0.2 + } + } + }, + "training": { + "ckpt_dir": "ckpt", + "ckpt_reload": "last.ckpt", + "model_onnx_filename": "last.onnx", + "seed": 42, + "device": "cuda", + "validation_interval": 1, + "component_loss_weightings": [ + 12, + 2 + ], + "dataset_config": { + "class": "FilterDataset", + "quantizer": { + "org_Y": 1024, + "org_U": 1024, + "org_V": 1024, + "pred_Y": 1024, + "pred_U": 1024, + "pred_V": 1024, + "rec_before_dbf_Y": 1024, + "rec_before_dbf_U": 1024, + "rec_before_dbf_V": 1024, + "bs_Y": 1024, + "bs_U": 1024, + "bs_V": 1024, + "qp_base": 64, + "qp_slice": 64, + "ipb_Y": 2, + "scaling_ratio" : 2, + "rec_Y": 1024, + "rec_U": 1024, + "rec_V": 1024 + } + }, + "dataloader": { + "num_workers": 16, + "shuffle": true, + "pin_memory": true, + "drop_last": false + }, + "optimizer": { + "class": "Adam", + "betas": [ + 0.9, + 0.999 + ], + "eps": 1e-08, + "weight_decay": 1e-08, + "amsgrad": false + }, + "lr_scheduler": { + "class": "MultiStepLR", + "gamma": 0.1, + "last_epoch": -1, + "verbose": false + }, + "model_ckpt": { + "//": "checkpoint training state regularly", + "save_last": true, + "export_last": true, + "every_n_epochs": 1 + }, + "loggers": { + "PrintLogger": { + "log_train_interval": 1000, + "out_filename": "logs/logs.txt", + "log_val_metrics": true + }, + "ProgressLogger": { + "out_filename": "logs/progress.txt", + "log_train_epochs": true, + "log_val_epochs": true, + "log_train_iterations": false, + "log_stage_ends": false + }, + "TensorboardLogger": { + "log_dir": "tensorboard", + "log_train_interval": 1000, + "log_train_progress": true, + "log_val_metrics": true + } + } + }, + "conversion": { + "//": "path to output the model. input model is taken in training/ckpt_dir" + }, + "quantize": { + "//": "path to output the integerized model" + } + } +} diff --git a/training/training_scripts/NN_SR_Multiratio/dataset_to_nnsr.py b/training/training_scripts/NN_SR_Multiratio/dataset_to_nnsr.py new file mode 100644 index 0000000000000000000000000000000000000000..283606cbe22a1fea6b32d24dfb4f7daf51335088 --- /dev/null +++ b/training/training_scripts/NN_SR_Multiratio/dataset_to_nnsr.py @@ -0,0 +1,63 @@ +import argparse +import json +import re +import os + + +def getKey(prm, s): + L = s.split("/") + p = prm + for k in L: + p = p[k] + return p + + +def getFullFilename(path, filename): + if os.path.basename(filename) == filename: + return os.path.join(path, filename) + else: + return filename + + +def remove_files_from_json(filename, pattern): + with open(filename, 'r') as f: + data = json.load(f) + print(filename + ':', len(data)) + + to_remove = [filename for filename in data.keys() if pattern.match(filename)] + for file in to_remove: + data.pop(file, None) + + with open(filename, 'w') as f: + json.dump(data, f, indent=1) + print(filename + ':', len(data)) + + +parser = argparse.ArgumentParser() +parser.add_argument( + "--json_config", + action="store", + type=str, + help="global configuration file" +) +parser.add_argument( + "--input_dataset", + action="store", + type=str, + help="dataset to process: expressed as name from config.json. Eg: dataset_src/div2k_train, dataset_src/bvi", + required=True, +) + +args = parser.parse_args() +try: + with open(args.json_config) as file: + prm = json.load(file) +except Exception: + quit("[ERROR] unable to open json config") + +key_input = getKey(prm, args.input_dataset) +pattern = re.compile(r'^.*_downscale\.yuv$|^.*(?:960x544|480x272).*\.yuv$') + +if "dataset_file" in key_input: + dsfile = getFullFilename(key_input["path"], key_input["dataset_file"]) + remove_files_from_json(dsfile, pattern) diff --git a/training/training_scripts/NN_SR_Multiratio/lop_config.json b/training/training_scripts/NN_SR_Multiratio/lop_config.json new file mode 100644 index 0000000000000000000000000000000000000000..5903f885c602243f14e71d75601d201e6cb877a2 --- /dev/null +++ b/training/training_scripts/NN_SR_Multiratio/lop_config.json @@ -0,0 +1,57 @@ +{ + "stage1": { + "encdec": { + "vtm_option": "--NnsrModelName=default_rpr --ScalingRatioHor=2 --ScalingRatioVer=2 --FractionNumFrames=1 --UpscaledOutput=2", + "vtm_dec_option": "--NnsrModelName=default_rpr --UpscaledOutput=2" + }, + "encdec_valid": { + "vtm_option": "--NnsrModelName=default_rpr --ScalingRatioHor=2 --ScalingRatioVer=2 --FractionNumFrames=1 --UpscaledOutput=2", + "vtm_dec_option": "--NnsrModelName=default_rpr --UpscaledOutput=2" + } + }, + + + "stage2": { + "encdec_bvi": { + "vtm_option": "--NnsrModelName=default_rpr --ScalingRatioHor=2 --ScalingRatioVer=2 --FractionNumFrames=1 --UpscaledOutput=2", + "vtm_dec_option": "--UpscaledOutput=2" + }, + "encdec_bvi_valid": { + "vtm_option": "--NnsrModelName=default_rpr --ScalingRatioHor=2 --ScalingRatioVer=2 --FractionNumFrames=1 --UpscaledOutput=2", + "vtm_dec_option": "--NnsrModelName=default_rpr --UpscaledOutput=2" + }, + "encdec_tvd": { + "vtm_option": "--NnsrModelName=default_rpr --ScalingRatioHor=2 --ScalingRatioVer=2 --FractionNumFrames=1 --UpscaledOutput=2", + "vtm_dec_option": "--NnsrModelName=default_rpr --UpscaledOutput=2" + }, + "encdec_tvd_valid": { + "vtm_option": "--NnsrModelName=default_rpr --ScalingRatioHor=2 --ScalingRatioVer=2 --FractionNumFrames=1 --UpscaledOutput=2", + "vtm_dec_option": "--NnsrModelName=default_rpr --UpscaledOutput=2" + } + }, + + + "stage3": { + "training": { + "mse_epoch": 58, + "max_epochs": 60, + "component_loss_weightings": [ + 12, + 2 + ], + "dataloader": { + "batch_size": 128 + }, + "optimizer": { + "lr": 0.0004 + }, + "lr_scheduler": { + "milestones": [ + 55, + 57, + 58 + ] + } + } + } +} diff --git a/training/training_scripts/NN_SR_Multiratio/model/model.json b/training/training_scripts/NN_SR_Multiratio/model/model.json new file mode 100644 index 0000000000000000000000000000000000000000..efc631db241b01e8d3d3cdf77fc1a84ea44a4b48 --- /dev/null +++ b/training/training_scripts/NN_SR_Multiratio/model/model.json @@ -0,0 +1,31 @@ +{ "model" : { + "path": "../../LOP/model/model.py", + "class" : "Net", + "input_channels" : [ + [ + "rec_Y", + "rec_U", + "rec_V" + ], + [ "qp_base" ], + [ "qp_slice" ], + [ "scaling_ratio" ] + ], + "input_kernels" : [ + 3, + 1, + 1, + 1 + ], + "D1" : 16, + "D4" : 8, + "D5": 8, + "D6" : 8, + "D7" : 16, + "N_Y" : 8, + "N_UV" : 4, + "C" : 16, + "C1_Y" : 32, + "C1_UV" : 16, + "C21" : 16 + } } diff --git a/training/training_scripts/NN_SR_Multiratio/model/model.py b/training/training_scripts/NN_SR_Multiratio/model/model.py new file mode 100644 index 0000000000000000000000000000000000000000..2af61f1cf41cb5f7bdc9f9f9c21a60c0eb4c0225 --- /dev/null +++ b/training/training_scripts/NN_SR_Multiratio/model/model.py @@ -0,0 +1,339 @@ +""" +/* The copyright in this software is being made available under the BSD +* License, included below. This software may be subject to other third party +* and contributor rights, including patent rights, and no such rights are +* granted under this license. +* +* Copyright (c) 2010-2022, ITU/ISO/IEC +* All rights reserved. +* +* Redistribution and use in source and binary forms, with or without +* modification, are permitted provided that the following conditions are met: +* +* * Redistributions of source code must retain the above copyright notice, +* this list of conditions and the following disclaimer. +* * Redistributions 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. +* * Neither the name of the ITU/ISO/IEC nor the names of its contributors may +* 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 COPYRIGHT HOLDER 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. +""" + +from typing import Union, Tuple, Optional, Type, Iterable, List, Dict + +import torch +from torch import nn +from torch.nn import functional as F + + +class Conv(nn.Sequential): + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: Union[int, Tuple[int, int]], + stride: Union[int, Tuple[int, int]] = 1, + padding: Optional[Union[int, Tuple[int, int]]] = None, + is_separable: bool = False, + hidden_separable_channels: Optional[int] = None, + post_activation: Optional[Type] = nn.PReLU, + **kwargs, + ): + """ + Args: + in_channels: the number of input channels + out_channels: the number of output channels + kernel_size: the convolution's kernel size + stride: the convolution's stride(s) + padding: the convolution's padding + is_separable: whether to implement convolution separably + hidden_separable_channels: If is_separable, the number of hidden channels between convolutions. If None, use out_channels + post_activation: activation function to use after convolution. If None, no activation after convolution + **kwargs: additional kwargs to pass to nn.Conv2d + """ + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = ( + (kernel_size, kernel_size) if isinstance(kernel_size, int) else kernel_size + ) + self.stride = (stride, stride) if isinstance(stride, int) else stride + if padding is not None: + self.padding = (padding, padding) if isinstance(padding, int) else padding + else: + self.padding = tuple([k // 2 for k in self.kernel_size]) + self.is_separable = is_separable + self.post_activation = post_activation + + if self.is_separable: + self.hidden_separable_channels = hidden_separable_channels or out_channels + modules = [ + nn.Conv2d( + self.in_channels, + self.hidden_separable_channels, + (self.kernel_size[0], 1), + (self.stride[0], 1), + (self.padding[0], 0), + groups=self.hidden_separable_channels, + **kwargs, + ), + nn.Conv2d( + self.hidden_separable_channels, + self.out_channels, + (1, self.kernel_size[1]), + (1, self.stride[1]), + (0, self.padding[1]), + groups=self.hidden_separable_channels, + **kwargs, + ), + nn.Conv2d( + self.out_channels, + self.out_channels, + (1, 1), + (1, 1), + (0, 0), + **kwargs, + ), + ] + else: + modules = [ + nn.Conv2d( + self.in_channels, + self.out_channels, + self.kernel_size, + self.stride, + self.padding, + **kwargs, + ) + ] + + if self.post_activation is not None: + modules.append(self.post_activation()) + + super(Conv, self).__init__(*modules) + + +class MultiBranchModule(nn.Module): + """A module representing multple, parallel branches. If the input is a list, each element in the list is fed into the corresponding branch, + otherwise the input is fed into every branch. The outputs of each branch are then merged.""" + + def __init__(self, *branch_modules, merge_dimension: int = -3): + """ + Args: + branch_modules: modules to run in parallel + merge_dimension: the dimension to merge outputs from each branch + """ + super().__init__() + self.branches = nn.ModuleList(branch_modules) + self.merge_dimension = merge_dimension + + def forward(self, args: Union[torch.Tensor, List[torch.Tensor]]) -> torch.Tensor: + inputs = args if isinstance(args, list) else len(self.branches) * [args] + branch_outputs = [branch(input) for branch, input in zip(self.branches, inputs)] + return torch.cat(branch_outputs, dim=self.merge_dimension) + + +class ResidualBlock(nn.Sequential): + def __init__( + self, C: int = 64, C1: int = 160, C21: int = 32 + ): + super(ResidualBlock, self).__init__( + Conv(C, C1, kernel_size=1), + Conv(C1, C, kernel_size=1, post_activation=None), + Conv( + C, + C, + kernel_size=3, + post_activation=None, + is_separable=True, + hidden_separable_channels=C21, + ), + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return x + super(ResidualBlock, self).forward(x) + + +class SplitLumaChromaBlocks(nn.Sequential): + def __init__( + self, + N_Y: int = 8, + N_UV: int = 4, + C: int = 16, + C1_Y: int = 32, + C1_UV: int = 16, + C21: int = 16, + output_channels_y: int = 4, + output_channels_uv: int = 2, + path: str = None, + ): + super().__init__() + + self.split_y_path = nn.Sequential( + *[ResidualBlock(C, C1_Y, C21) for _ in range(N_Y)], + Conv(C, C, kernel_size=3, is_separable=True, hidden_separable_channels=C21), + Conv(C, output_channels_y, kernel_size=3, post_activation=None) + ) + + self.split_uv_path = nn.Sequential( + *[ResidualBlock(C, C1_UV, C21) for _ in range(N_UV)], + Conv(C, C, kernel_size=3, is_separable=True, hidden_separable_channels=C21), + Conv(C, output_channels_uv, kernel_size=3, post_activation=None) + ) + + self.Cy = C + self.Cuv = C + + def forward(self, x: torch.Tensor) -> torch.Tensor: + split_y_input = x[:, : self.Cy, :, :] + split_uv_input = x[:, self.Cy : self.Cy + self.Cuv, :, :] + + y_output = self.split_y_path.forward(split_y_input) + uv_output = self.split_uv_path.forward(split_uv_input) + return torch.cat((y_output, uv_output), dim=1) + + +class SADLNet(nn.Sequential): + """The network used during SADL inference""" + + def __init__( + self, + input_channels: Iterable[int] = [3, 1, 1, 1], + input_kernels: Iterable[int] = [3, 1, 1, 1], + D1: int = 16, + D4: int = 8, + D5: int = 8, + D6: int = 8, + D7: int = 16, + N_Y: int = 8, + N_UV: int = 4, + C: int = 16, + C1_Y: int = 32, + C1_UV: int = 16, + C21: int = 16, + output_channels_y: int = 4, + output_channels_uv: int = 2, + path: str = None, + ): + """ + Args: + input_channels: the number of channels expected for each input + input_kernels: the kernel size for each input convolution + output_channels: the number of output channels + """ + self.input_channels = input_channels + self.input_kernels = input_kernels + self.input_features = [D1, D4, D5, D6] + + super(SADLNet, self).__init__( + MultiBranchModule( + *[ + Conv(c, d, kernel_size=k, stride=2, post_activation=None) + for c, d, k in zip( + self.input_channels, self.input_features, self.input_kernels + ) + ] + ), + Conv(sum(self.input_features), D7, kernel_size=1), + Conv(D7, C + C, kernel_size=3, is_separable=True, hidden_separable_channels=D7), + SplitLumaChromaBlocks(N_Y, N_UV, C, C1_Y, C1_UV, C21, output_channels_y, output_channels_uv), + ) + + def get_example_inputs( + self, patch_size: Union[int, Tuple[int, int]] = 144, batch_size: int = 1 + ): + patch_size = ( + (patch_size, patch_size) if isinstance(patch_size, int) else patch_size + ) + return [ + torch.rand( + batch_size, conv.in_channels, *patch_size, device=conv[0].weight.device + ) + for conv in self[0].branches + ] + + def to_onnx( + self, filename: str, patch_size: int = 144, batch_size: int = 1, **kwargs + ) -> None: + mode = self.training + self.eval() + torch.onnx.export( + self, self.get_example_inputs(patch_size, batch_size), filename, **kwargs + ) + self.train(mode) + + +class Net(nn.Module): + """Wrapper for SADL model that implements input pre- and post-processing for training.""" + + def __init__( + self, + input_channels: Iterable[Iterable[str]] = [ + ["rec_Y", "rec_U", "rec_V"], + ["qp_base"], + ["qp_slice"], + ["scaling_ratio"] + ], + input_kernels: Iterable[int] = [3, 1, 1, 1], + D1: int = 4, + D4: int = 1, + D5: int = 1, + D6: int = 1, + D7: int = 16, + N_Y: int = 8, + N_UV: int = 4, + C: int = 16, + C1_Y: int = 32, + C1_UV: int = 16, + C21: int = 16, + path: str = None, + ): + super(Net, self).__init__() + assert len(input_channels) == len( + input_kernels + ), "[ERROR] input size and kernels size not equal" + self.input_channels = input_channels + sizes = [len(a) for a in input_channels] + self.SADL_model = SADLNet( + sizes, input_kernels, D1, D4, D5, D6, D7, N_Y, N_UV, C, C1_Y, C1_UV, C21, 4, 2 + ) + self.chroma_upsampler = nn.Upsample(scale_factor=2, mode="nearest") + + def preprocess_args( + self, batch: Dict[str, torch.Tensor] + ) -> Dict[str, torch.Tensor]: + return [ + torch.cat([batch[name] for name in input_], dim=1) + for input_ in self.input_channels + ] + + def postprocess_outputs( + self, batch: Dict[str, torch.Tensor], out: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + Y_res, UV_res = out.split([4, 2], dim=1) + return ( + batch["rec_Y"] + F.pixel_shuffle(Y_res, 2), + torch.cat((batch["rec_U"], batch["rec_V"]), dim=1)[ + ..., ::2, ::2 + ] + + UV_res, + ) + + def forward( + self, batch: Dict[str, torch.Tensor] + ) -> Tuple[torch.Tensor, torch.Tensor]: + args = self.preprocess_args(batch) + out = self.SADL_model(args) + return self.postprocess_outputs(batch, out) diff --git a/training/training_scripts/NN_SR_Multiratio/paths.json b/training/training_scripts/NN_SR_Multiratio/paths.json new file mode 100644 index 0000000000000000000000000000000000000000..b7e0074bca569b63020d4a87167ccc9c31ee90e9 --- /dev/null +++ b/training/training_scripts/NN_SR_Multiratio/paths.json @@ -0,0 +1,154 @@ +{ + "binaries": { + "sadl_path": "/path/to/src/sadl" + }, + + "model" : { + "path": "/path/to/src/training/training_scripts/NN_SR_Multiratio/model/model.py" + }, + + "dataset": { + "div2k_train": { + "//": "dataset of png to convert", + "path": "/path/to/DIV2K/DIV2K_train_HR" + }, + "div2k_valid": { + "path": "/path/to/DIV2K/DIV2K_valid_HR" + }, + "bvi": { + "//": "dataset of yuv", + "path": "/path/to/bviorg", + "dataset_file": "/path/to/src/training/training_scripts/NN_Filtering/common/datasets/bvi.json" + }, + "bvi_valid": { + "//": "dataset of yuv", + "path": "/path/to/bviorg", + "dataset_file": "/path/to/src/training/training_scripts/NN_Filtering/common/datasets/bvi_valid.json" + }, + "tvd": { + "//": "dataset of yuv", + "path": "/path/to/tvdorg", + "dataset_file": "/path/to/src/training/training_scripts/NN_Filtering/common/datasets/tvd.json" + }, + "tvd_valid": { + "//": "dataset of yuv", + "path": "/path/to/tvdorg", + "dataset_file": "/path/to/src/training/training_scripts/NN_Filtering/common/datasets/tvd_valid.json" + } + }, + + + "stage1": { + "yuv": { + "//": "path to store yuv files dataset", + "path": "/path/to/stage1/yuv" + }, + "yuv_valid": { + "//": "path to store yuv files dataset", + "path": "/path/to/stage1/yuv" + }, + "encdec": { + "//": "path to store the shell script and all the generated files by the encoder/decoder", + "path": "/path/to/stage1/encdec", + "vtm_enc": "/path/to/src/bin/EncoderAppStatic", + "vtm_dec": "/path/to/src/bin/DecoderAppStatic", + "vtm_cfg": "/path/to/src/cfg/encoder_intra_nnvc.cfg -c /path/to/src/cfg/nn-based/nnsr.cfg" + }, + "encdec_valid": { + "//": "path to store the shell script and all the generated files by the encoder/decoder", + "path": "/path/to/stage1/encdec", + "vtm_enc": "/path/to/src/bin/EncoderAppStatic", + "vtm_dec": "/path/to/src/bin/DecoderAppStatic", + "vtm_cfg": "/path/to/src/cfg/encoder_intra_nnvc.cfg -c /path/to/src/cfg/nn-based/nnsr.cfg" + }, + "dataset": { + "//": "path to store the full dataset which will be used by the training", + "path": "/path/to/stage1/dataset" + }, + "dataset_valid": { + "//": "path to store the full dataset which will be used by the training", + "path": "/path/to/stage1/dataset" + } + }, + + + "stage2": { + "yuv_tvd": { + "//": "path to store yuv files dataset", + "path": "/path/to/stage2/yuv" + }, + "yuv_tvd_valid": { + "//": "path to store yuv files dataset", + "path": "/path/to/stage2/yuv" + }, + "yuv_bvi": { + "//": "path to store yuv files dataset", + "path": "/path/to/stage2/yuv" + }, + "yuv_bvi_valid": { + "//": "path to store yuv files dataset", + "path": "/path/to/stage2/yuv" + }, + "encdec_bvi": { + "//": "path to store the shell script and all the generated files by the encoder/decoder", + "path": "/path/to/stage2/encdec", + "vtm_enc": "/path/to/src/bin/EncoderAppStatic", + "vtm_dec": "/path/to/src/bin/DecoderAppStatic", + "vtm_cfg": "/path/to/src/cfg/encoder_randomaccess_nnvc.cfg -c /path/to/src/cfg/nn-based/nnsr.cfg" + }, + "encdec_bvi_valid": { + "//": "path to store the shell script and all the generated files by the encoder/decoder", + "path": "/path/to/stage2/encdec", + "vtm_enc": "/path/to/src/bin/EncoderAppStatic", + "vtm_dec": "/path/to/src/bin/DecoderAppStatic", + "vtm_cfg": "/path/to/src/cfg/encoder_randomaccess_nnvc.cfg -c /path/to/src/cfg/nn-based/nnsr.cfg" + }, + "encdec_tvd": { + "//": "path to store the shell script and all the generated files by the encoder/decoder", + "path": "/path/to/stage2/encdec", + "vtm_enc": "/path/to/src/bin/EncoderAppStatic", + "vtm_dec": "/path/to/src/bin/DecoderAppStatic", + "vtm_cfg": "/path/to/src/cfg/encoder_randomaccess_nnvc.cfg -c /path/to/src/cfg/nn-based/nnsr.cfg" + }, + "encdec_tvd_valid": { + "//": "path to store the shell script and all the generated files by the encoder/decoder", + "path": "/path/to/stage2/encdec", + "vtm_enc": "/path/to/src/bin/EncoderAppStatic", + "vtm_dec": "/path/to/src/bin/DecoderAppStatic", + "vtm_cfg": "/path/to/src/cfg/encoder_randomaccess_nnvc.cfg -c /path/to/src/cfg/nn-based/nnsr.cfg" + }, + "dataset": { + "//": "path to store the full dataset which will be used by the training", + "path": "/path/to/stage2/dataset" + }, + "dataset_valid": { + "//": "path to store the full dataset which will be used by the training", + "path": "/path/to/stage2/dataset" + } + }, + + + "stage3": { + "dataset": { + "//": "path to store the full dataset which will be used by the training", + "path": "/path/to/stage3/dataset" + }, + "dataset_valid": { + "//": "path to store the full dataset which will be used by the training", + "path": "/path/to/stage3/dataset" + }, + "training": { + "path": "/path/to/stage3/train" + }, + "conversion": { + "//": "full path to output the model. input model is taken in training/ckpt_dir", + "full_path_filename": "/path/to/stage3/train/model_float.sadl" + }, + "quantize": { + "//": "full path to output the quantized model", + "full_path_filename": "/path/to/stage3/train/model_int16.sadl" + } + } + +} + diff --git a/training/training_scripts/NN_SR_Multiratio/quantize/quantize.py b/training/training_scripts/NN_SR_Multiratio/quantize/quantize.py new file mode 100644 index 0000000000000000000000000000000000000000..9ba6cdb6521af3ada7cbdfab113af3fa992a5d06 --- /dev/null +++ b/training/training_scripts/NN_SR_Multiratio/quantize/quantize.py @@ -0,0 +1,156 @@ +""" +/* The copyright in this software is being made available under the BSD +* License, included below. This software may be subject to other third party +* and contributor rights, including patent rights, and no such rights are +* granted under this license. +* +* Copyright (c) 2010-2023, ITU/ISO/IEC +* All rights reserved. +* +* Redistribution and use in source and binary forms, with or without +* modification, are permitted provided that the following conditions are met: +* +* * Redistributions of source code must retain the above copyright notice, +* this list of conditions and the following disclaimer. +* * Redistributions 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. +* * Neither the name of the ITU/ISO/IEC nor the names of its contributors may +* 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 COPYRIGHT HOLDER 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. +""" +import argparse +import json +import os +import subprocess + + +def parse_arguments(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--json_config", action="store", type=str, help="global configuration file" + ) + parser.add_argument( + "--input_model", + type=str, + help="SADL float model name or training section name in the config.json", + required=True, + ) + parser.add_argument( + "--output_model", + type=str, + help="sadl int16 model name or conversion section name in the config.json", + required=True, + ) + parser.add_argument( + "--sadl_quantize", + type=str, + help="full path to sadl naive_quantizer", + required=False, + ) + args = parser.parse_args() + return vars(args) + + +def qfactor_gen(content): + word = "[INFO] id: " + stop_word = "[INFO] == end model loading ==" + + q = 11 # input + groups = 1 + + string = "" + i = 0 + q_wt = 11 + + for idx, line in enumerate(content): + i += 1 + if line.find(word) != -1: + line_split = line.split(" ") + id = line_split[2] + name = line_split[5] + if name == "Placeholder\n": + string = string + id + " " + str(q) + " " + elif name == "Conv2D\n": + if groups >= int(1): + q_upd = q_wt + groups = 0 + if int(q) > 9: + string = string[0:-3] + str(q_upd) + " " + id + " 0 " + else: + string = string[0:-2] + str(q_upd) + " " + id + " 0 " + else: + string = string + id + " 0 " + elif ( + name == "LeakyRelu\n" + or name == "Concat\n" + or name == "Add\n" + or name == "PReLU\n" + ): + id = id + elif name == "BiasAdd\n": + q_upd = 11 + if int(q) > 9: + string = string[0:-3] + str(q_upd) + " " + else: + string = string[0:-2] + str(q_upd) + " " + else: + if (name == "Const\n") and content[idx + 6].find("Concat") != -1: + q_upd = 0 + else: + q_upd = q + string = string + id + " " + str(q_upd) + " " + if name != "Const\n": + print("Entered here for wrong name", name) + elif line.find(stop_word) != -1: + break + else: + grp_line = content[idx + 6] + if grp_line.find("groups:") != -1: + groups = int(grp_line.split(" ")[-1][:-1]) + return string + + +if __name__ == "__main__": + args = parse_arguments() + json_config = args["json_config"] + infile = args["input_model"] + debug_text = os.path.join( + os.path.dirname(args["output_model"]), "sadl_float_debug_dump.txt" + ) + outfile = args["output_model"] + sadl_quantize = args["sadl_quantize"] + + try: + with open(json_config) as file: + prm = json.load(file) + except Exception: + quit("[ERROR] unable to open json config") + debug_model = os.path.join(prm["binaries"]["sadl_path"], "sample", "debug_model") + + debug_txt_content = open(debug_text, "w") + process = subprocess.Popen([debug_model, infile], stdout=debug_txt_content) + process.wait() + + debug_txt_content = open(debug_text, "r") + q_log_string = qfactor_gen(debug_txt_content.readlines()) + os.remove(debug_text) + sadl_quantize = os.path.join( + prm["binaries"]["sadl_path"], "sample", "naive_quantization" + ) + + p = subprocess.Popen([sadl_quantize, infile, outfile], stdin=subprocess.PIPE) + p.communicate(input=q_log_string.encode()) + print(f"[INFO] output model {outfile}") diff --git a/training/training_scripts/NN_SR_Multiratio/readme.md b/training/training_scripts/NN_SR_Multiratio/readme.md new file mode 100644 index 0000000000000000000000000000000000000000..63c3f46426f5eee5ceda78d4c6d87d4fc579b586 --- /dev/null +++ b/training/training_scripts/NN_SR_Multiratio/readme.md @@ -0,0 +1,311 @@ +# Super Resolution Model Training + +## Overview + +### Notes +The following training stages should be performed using specified NNVC version in order to reproduce current models. Note that some options or performance results might differ when using the latest NNVC version. + +### Preparation of the directory +At minima, paths should first be set. The file ``training/training_scripts/NN_SR_EE1-4.2/paths.json`` should be copy and edited to match your environment. +All keys with the name ``path`` should be edited to fit your particular environement. +Additionally, you should also edit the ``sadl_path`` to point to the sadl repository. + +Assuming that all data are on the same storage, then the following directory structure can be used: +``` + - src [ contains the NNVC repository] + - DIV2K [ contains the original div2k dataset] + - bviorg [ contains the original BVI dataset ] + - tvdorg [ contains the original TVD dataset ] + + - stage1 [ will be created by the scripts] + - stage2 [ will be created by the scripts] + - stage3 [ will be created by the scripts] +``` + +To create ``src`` the following commands can be used: +```sh +git clone https://vcgit.hhi.fraunhofer.de/jvet-ahg-nnvc/VVCSoftware_VTM.git src; +cd src; +git checkout VTM-11.0_nnvc +git submodule init +git submodule update +mkdir build +cd build +cmake -DCMAKE_BUILD_TYPE=Release .. +make -j +cd ../.. +``` + +To create the DIV2K directory: +```sh +mkdir DIV2K; +cd DIV2K; +wget http://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_train_HR.zip +unzip DIV2K_train_HR.zip +wget http://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_valid_HR.zip +unzip DIV2K_valid_HR.zip +cd .. +``` + +To create the bviorg directory: +```sh +mkdir bviorg; +cd bviorg; +wget https://data.bris.ac.uk/datasets/tar/3h0hduxrq4awq2ffvhabjzbzi1.zip +unzip 3h0hduxrq4awq2ffvhabjzbzi1.zip +cd .. +``` + +To create the tvdorg directory: +```sh +mkdir tvdorg; +# download TVD dataset from https://multimedia.tencent.com/resources/tvd +``` + + +With this file structure, the ``paths.json`` is simply edited by just replacing ``/path/to`` by the absolute path of the root directory of the experiment. +If your datasets are on different storages, just edit the relevant lines in ``paths.json``. + +### Creation of the consolidated configuration file +The main configuration file is ``common_config.json``. +The specific parameters are in ``lop_config.json``. +The variables specific to your environment is ``paths.json``. +The model is in ``model/model.json``. +A unique configuration is create by merging all the different files using the following command: +```sh +cp src/training_scripts/NN_SR_EE1-4.2/paths.json . +# edit paths.json +python3 src/training/tools/create_config.py src/training/training_scripts/NN_SR_EE1-4.2/common_config.json src/training/training_scripts/NN_SR_EE1-4.2/lop_config.json src/training/training_scripts/NN_SR_EE1-4.2/model/model.json src/training/training_scripts/NN_SR_EE1-4.2/paths.json > my_config.json +``` + +Then this file will be used in command line below, you should be able to run the process just by copy/pasting all lines of shell below. + +Other keys should not be edited except for testing reasons. + + +#### Dataset preparation +div2k conversion +Convert div2k (4:4:4 RGB -> YUV420 10 bits): + +```sh + python3 src/training/tools/convert_dataset.py --json_config my_config.json --input_dataset dataset/div2k_train --augmentation --output_location stage1/yuv + python3 src/training/tools/convert_dataset.py --json_config my_config.json --input_dataset dataset/div2k_valid --output_location stage1/yuv_valid +``` + +bvi/tvd conversion +Convert BVI-DVC 64 frames sequence into 65 frames sequence, and create the TVD dataset file (YUV are still taken from source). + +```sh + python3 src/training/tools/convert_dataset.py --json_config my_config.json --input_dataset dataset/bvi --output_location stage2/yuv_bvi --extend 65 + python3 src/training/tools/convert_dataset.py --json_config my_config.json --input_dataset dataset/bvi_valid --output_location stage2/yuv_bvi_valid --extend 65 + python3 src/training/tools/convert_dataset.py --json_config my_config.json --input_dataset dataset/tvd --output_location stage2/yuv_tvd + python3 src/training/tools/convert_dataset.py --json_config my_config.json --input_dataset dataset/tvd_valid --output_location stage2/yuv_tvd_valid +``` +dataset files are placed in the target directory (as set in the config.json ["stage2"]["yuv"]["path"]), a json file named ["stage2"]["yuv"]["dataset_filename"] is updated with the new data. + +** Note ** The size requirement is: about 130 GB for the extended BVI dataset. + +Remove some YUV files from the JSON files of div2k and bvi. +```sh + python3 src/training/training_scripts/NN_SR_EE1-4.2/dataset_to_nnsr.py --json_config my_config.json --input_dataset stage1/yuv + python3 src/training/training_scripts/NN_SR_EE1-4.2/dataset_to_nnsr.py --json_config my_config.json --input_dataset stage2/yuv_bvi + python3 src/training/training_scripts/NN_SR_EE1-4.2/dataset_to_nnsr.py --json_config my_config.json --input_dataset stage2/yuv_bvi_valid +``` + +#### Dataset generation +** Note ** Duplicate stage â… and â…¡, generate dataset for scaling ratios of 2x and 1.5x. +** Note ** Please remember to modify the values of scaling ratio and the names of the json files in your ``my_config.json``, when switching to different scaling rations. + +## I- Stage I +#### 1. Prepare scripts for encoding/decoding of the dataset +The macro for data dump should be: + +// which data are used for inference/dump +#define JVET_AG0129_UNIFIED_SR 0 +#define SR_DATA_DUMP 1 // only used for dumping training data for super resolution +#if SR_DATA_DUMP +#define NNVC_USE_REC_RPR 1 // reconstruction after RPR +#define NNVC_USE_SCALING_RATIO 1 // Scaling ratio of RPR (2x (1), 1.5x (2)) +#define NNVC_USE_REC_AFTER_ALF 0 // reconstruction after ALF +#define NNVC_USE_REC_AFTER_UPSAMPLING 0 // reconstruction after upsampling +#endif + +Extract cfg files and encoding/decoding script: +```sh + python3 src/training/tools/dataset_to_encoding_script.py --json_config my_config.json --input_dataset stage1/yuv --output_location stage1/encdec + python3 src/training/tools/dataset_to_encoding_script.py --json_config my_config.json --input_dataset stage1/yuv_valid --output_location stage1/encdec_valid +``` +It will generate the cfg files for the dataset and a shell script to encode and decode all sequences in the dataset in the directory ["stage1"]["encdec"]["path"]. + +#### 2. Encode/decode the sequences: + +Loop on all sequences to encode: +```sh +cd stage1/encdec; +N1=24000; +for((i=0;i<N1;i++)); do + ./encode_decode_dataset.sh $i; +done +N2=1000; +for((i=0;i<N2;i++)); do + ./encode_decode_dataset_valid.sh $i; +done +``` +or you can use the script to encode on your cluster. N is the number of sequences (run ./encode_decode_dataset.sh to get the value N). +The script will perform the following step for each sequence: + +1. encodes the yuv and produces an encoding log +2. decodes the bitstream, produces a decoding log and dump the data in ["stage1"]["encdec"]["path"]/["dump_dir"] + +** Note ** The size requirement is: about GB for the dumped data. + +#### 3. Create a consolidated dataset + +```sh +python3 src/training/tools/concatenate_dataset.py --json_config my_config.json --input_dir_json stage1/encdec --output_json stage1/encdec +python3 src/training/tools/concatenate_dataset.py --json_config my_config.json --input_dir_json stage1/encdec_valid --output_json stage1/encdec_valid +``` +It will generate a unique dataset in ["stage1"]["encdec"]["path"] from all individual datasets in ["stage1"]["encdec"]["path"]/["dump_dir"] and encoder logs in ["stage1"]["encdec"]["enc_dir"]. + + +#### 4a. Create an offline dataset with all batches + +```sh +python3 src/training/tools/create_unified_dataset.py --json_config my_config.json \ + --nb_patches -1 --patch_size 128 --border_size 8 --input_dataset stage1/encdec \ + --components org_Y,org_U,org_V,rec_Y,rec_U,rec_V,qp_base,qp_slice,scaling_ratio \ + --output_location stage1/dataset +python3 src/training/tools/create_unified_dataset.py --json_config my_config.json \ + --nb_patches -1 --patch_size 128 --border_size 8 --input_dataset stage1/encdec_valid \ + --components org_Y,org_U,org_V,rec_Y,rec_U,rec_V,qp_base,qp_slice,scaling_ratio \ + --output_location stage1/dataset_valid +``` +It will generate a unique dataset of patches ready for training in ["stage1"]["dataset"]["path"] from the dataset in ["stage1"]["encdec"]["path"]. + +**Note:** the directories in encdec can now be deleted if there is no need to regenerate an offline dataset. + +The dataset can be visualize using +```sh +python3 src/training/tools/example/display_patches_dataset.py stage1/dataset/dataset.json +``` + +** Note ** The size requirement is: about 1.3TB for the training set and about 55GB for the validation set. + + +#### 4b. Create a dataset for on-the-fly batch extraction +TODO + + +## II- Stage â…¡ +#### 1. Prepare scripts for encoding/decoding of the dataset +The macro for data dump should be: +#define JVET_AG0129_UNIFIED_SR 0 +#define SR_DATA_DUMP 1 // only used for dumping training data for super resolution +#if SR_DATA_DUMP +#define NNVC_USE_REC_RPR 1 // reconstruction after RPR +#define NNVC_USE_SCALING_RATIO 1 // Scaling ratio of RPR (2x (1), 1.5x (2)) +#define NNVC_USE_REC_AFTER_ALF 0 // reconstruction after ALF +#define NNVC_USE_REC_AFTER_UPSAMPLING 0 // reconstruction after upsampling +#endif + + +Extract cfg files and encoding/decoding script: +```sh +python3 src/training/tools/dataset_to_encoding_script.py --json_config my_config.json --input_dataset stage2/yuv_tvd --output_location stage2/encdec_tvd +python3 src/training/tools/dataset_to_encoding_script.py --json_config my_config.json --input_dataset stage2/yuv_tvd_valid --output_location stage2/encdec_tvd_valid +python3 src/training/tools/dataset_to_encoding_script.py --json_config my_config.json --input_dataset stage2/yuv_bvi --output_location stage2/encdec_bvi +python3 src/training/tools/dataset_to_encoding_script.py --json_config my_config.json --input_dataset stage2/yuv_bvi_valid --output_location stage2/encdec_bvi_valid +``` +It will generate the cfg files for the dataset and a shell script to encode and decode all sequences in the dataset in the directory ["stage2"]["encdec"]["path"]. + +#### 2. Encode/decode the sequences: + +Loop on all sequences to encode, for example: +```sh +cd stage2/encdec; +for((i=0;i<90;i++)); do + ./encode_decode_dataset_tvd.sh $i; +done +for((i=0;i<10;i++)); do + ./encode_decode_dataset_tvd_valid.sh $i; +done +for((i=0;i<1065;i++)); do + ./encode_decode_dataset_bvi.sh $i; +done +for((i=0;i<35;i++)); do + ./encode_decode_dataset_bvi_valid.sh $i; +done +``` +or you can use the script to encode on your cluster. N is the number of sequences (run ./encode_decode_dataset.sh to get the value N). + +** Note ** The size requirement is: about TB for the dumped data. + +#### 3. Create consolidated datasets + +```sh +LIST="bvi bvi_valid tvd tvd_valid"; +for DS in $LIST; do + python3 src/training/tools/concatenate_dataset.py --json_config my_config.json --input_dir_json stage2/encdec_${DS} --input_dir_dataset_json stage2/yuv_${DS} --output_json stage2/encdec_${DS} +done +``` +It will generate a unique dataset for each dataset in ["stage2"]["encdec"]["path"] from all individual datasets in ["stage2"]["encdec_xxx"]["path"]/["dump_dir"] and encoder logs in ["stage2"]["encdec_xxx"]["enc_dir"]. + +** Note ** The size requirement is: about 774GB for the training set and about 48GB for the validation set. + +#### 4a. Create offline datasets with all batches + +```sh +LIST="bvi tvd"; +for DS in $LIST; do + python3 src/training/tools/create_unified_dataset.py --json_config my_config.json \ + --nb_patches -1 --patch_size 128 --border_size 8 --input_dataset stage2/encdec_${DS} \ + --components org_Y,org_U,org_V,rec_Y,rec_U,rec_V,qp_base,qp_slice,scaling_ratio \ + --output_location stage2/dataset/dataset_files/${DS} + + python3 src/training/tools/create_unified_dataset.py --json_config my_config.json \ + --nb_patches -1 --patch_size 128 --border_size 8 --input_dataset stage2/encdec_${DS}_valid \ + --components org_Y,org_U,org_V,rec_Y,rec_U,rec_V,qp_base,qp_slice,scaling_ratio \ + --output_location stage2/dataset_valid/dataset_files/${DS} +done +``` + +The div2k dataset is also reused. The json files are copied into the stage2/dataset directory: +``` +cp stage1/dataset/*.json stage2/dataset/; +``` + +**Note:** the directories in encdec can now be deleted if there is no need to regenerate an offline dataset. + +#### 4b. create a dataset for on-the-fly batch extraction +TODO + + + +## III- Training stage +If you need to adapt the settings of your device for training, please edit the file ``training_default.json`` (default parameters) or ``stage3.json`` (parameters specific to stage2). You can also change the loggers verbosity in these files. +When ready, simply run: + +```sh +python3 src/training/training_scripts/NN_Filtering/common/training/main.py --json_config my_config.json --stage 3 +``` + +#### 2. Convert model +The last ONNX model is converted into float SADL format. +```sh +python3 src/training/training_scripts/NN_Filtering/common/convert/to_sadl.py --json_config my_config.json \ + --input_model stage3/training --output_model stage3/conversion/full_path_filename +``` + +#### 3. Integerized model +The float model is integerized into int16 SADL format using a naive quantization (all quantizers to 2^11). +First the ``naive_quantization`` software should be build. Please refer to SADL documentation to build the software. +```sh +python3 src/training/training_scripts/NN_Filtering/LOP/quantize/quantize.py --json_config my_config.json --input_model stage3/conversion/full_path_filename --output_model stage3/quantize/full_path_filename + +./src/sadl/sample/debug_model stage3/train/model_float.sadl >stage3/train/sadl_float_debug_dump.txt +python3 quantize-1.py --json_config my_config.json --input_model stage3/train/model_float.sadl --output_model stage3/train/EE1-4.2_int16.sadl +``` + + + +