correct shuffling + disable it since it is done on the fly by dataloader

training/tools/create_unified_dataset.py

@@ -126,7 +126,7 @@ parser.add_argument(
     type=int,
     help="nb patches to extract, nb_patches=-1 means extracting all patches",
 )
-parser.add_argument("--random", type=int, default=1, help="whether to sample randomly")
+parser.add_argument("--random", type=int, default=0, help="whether to sample randomly")
 
 parser.add_argument(
     "--json_config",
@@ -184,7 +184,7 @@ with open(output_bin, "wb") as file:
     for i in range(args.nb_patches):
         if i % n == 0:
             print(f"{i//n} %")
-        p = dl.getPatchDataInt16(i, comps, args.border_size)
+        p = dl.getPatchDataInt16(L[i], comps, args.border_size)
         p.tofile(file)
 
 print("[INFO] compute md5sum")

training/training_scripts/NN_Filtering_HOP/readme.md

+# High Operating Point model training
 ## Overview
 First edit the file ``training_scripts/NN_Filtering_HOP/config.json`` to adapt all the paths.
 All key with the name ``path`` should be edited to fit your particular environement.
+Additionally, you should also edit the variable ``vtm_xx`` to point to the VTM binaries and configuration files, the ``sadl_path`` to point to the sadl repository.
+Other keys like filenames can be let as is, except for debugging purpose.
+
+ Once the paths are setup, 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.
 
+
 ## I- Model Stage I
 ### A- Data extraction for intra from vanilla VTM
 #### 1. div2k conversion
@@ -16,6 +22,17 @@ Convert div2k  (4:4:4 RGB -> YUV420 10 bits):
 dataset files are placed in the target directory (as set in the config.json ["stage1"]["yuv"]["path"]), a json file named ["stage1"]["yuv"]["dataset_filename"] is updated with the new data.
 
 #### 2. prepare script for encoding/decoding of the dataset
+Please note that a VTM without NN tools is used. NNVC-5.0 or NNVC-4.0 tags can be used to generate the binaries and cfg file. The configuration file is the vanilla VTM one (see config.json).
+The macro for data dump should be:
+```
+// which data are used for inference/dump
+#define NNVC_USE_REC_BEFORE_DBF         1 // reconstruction before DBF
+#define NNVC_USE_PRED                   1 // prediction
+#define NNVC_USE_BS                     1 // BS of DBF
+#define NNVC_USE_QP                     1 // QP slice
+#define JVET_AC0089_NNVC_USE_BPM_INFO   1 // JVET-AC0089: dump Block Prediction Mode
+```
+Other macros can be set to 0.
 
 Extract cfg files and encoding/decoding script:
 ```sh
@@ -55,15 +72,16 @@ It will generate a unique dataset in ["stage1"]["encdec"]["path"] from all indiv
 
 ```sh
 python3 tools/create_unified_dataset.py --json_config training_scripts/NN_Filtering_HOP/config.json \
-  --random 1 --nb_patches -1 --patch_size 128 --border_size 8 --input_dataset stage1/encdec   \
+  --nb_patches -1 --patch_size 128 --border_size 8 --input_dataset stage1/encdec   \
   --components org_Y,org_U,org_V,pred_Y,pred_U,pred_V,rec_before_dbf_Y,rec_before_dbf_U,rec_before_dbf_V,bs_Y,bs_U,bs_V,qp_base,qp_slice,ipb_Y \
   --output_location stage1/dataset
 python3 tools/create_unified_dataset.py --json_config training_scripts/NN_Filtering_HOP/config.json \
-  --random 1 --nb_patches -1 --patch_size 128 --border_size 8 --input_dataset stage1/encdec_valid   \
+  --nb_patches -1 --patch_size 128 --border_size 8 --input_dataset stage1/encdec_valid   \
   --components org_Y,org_U,org_V,pred_Y,pred_U,pred_V,rec_before_dbf_Y,rec_before_dbf_U,rec_before_dbf_V,bs_Y,bs_U,bs_V,qp_base,qp_slice,ipb_Y \
   --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
@@ -79,7 +97,8 @@ If you need to adapt the settings of your device for training, please edit the f
 When ready, simply run:
 
 ```sh
-python3 training_scripts/NN_Filtering_HOP/training/main.py training_scripts/NN_Filtering_HOP/config.json \
+python3 training_scripts/NN_Filtering_HOP/training/main.py  \
+  training_scripts/NN_Filtering_HOP/config.json \
   training_scripts/NN_Filtering_HOP/model/model.json  \
   training_scripts/NN_Filtering_HOP/training/cfg/training_default.json \
   training_scripts/NN_Filtering_HOP/training/cfg/stage1.json